risk_return_analysis.ipynb

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    "# Analyzing Portfolio Risk and Return\n",
    "\n",
    "In this Challenge, you'll assume the role of a quantitative analyst for a FinTech investing platform. This platform aims to offer clients a one-stop online investment solution for their retirement portfolios that’s both inexpensive and high quality. (Think about [Wealthfront](https://www.wealthfront.com/) or [Betterment](https://www.betterment.com/)). To keep the costs low, the firm uses algorithms to build each client's portfolio. The algorithms choose from various investment styles and options.\n",
    "\n",
    "You've been tasked with evaluating four new investment options for inclusion in the client portfolios. Legendary fund and hedge-fund managers run all four selections. (People sometimes refer to these managers as **whales**, because of the large amount of money that they manage). You’ll need to determine the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and betas.\n",
    "\n",
    "## Instructions\n",
    "\n",
    "### Import the Data\n",
    "\n",
    "Use the ``risk_return_analysis.ipynb`` file to complete the following steps:\n",
    "\n",
    "1. Import the required libraries and dependencies.\n",
    "\n",
    "2. Use the `read_csv` function and the `Path` module to read the `whale_navs.csv` file into a Pandas DataFrame. Be sure to create a `DateTimeIndex`. Review the first five rows of the DataFrame by using the `head` function.\n",
    "\n",
    "3. Use the Pandas `pct_change` function together with `dropna` to create the daily returns DataFrame. Base this DataFrame on the NAV prices of the four portfolios and on the closing price of the S&P 500 Index. Review the first five rows of the daily returns DataFrame.\n",
    "\n",
    "### Analyze the Performance\n",
    "\n",
    "Analyze the data to determine if any of the portfolios outperform the broader stock market, which the S&P 500 represents. To do so, complete the following steps:\n",
    "\n",
    "1. Use the default Pandas `plot` function to visualize the daily return data of the four fund portfolios and the S&P 500. Be sure to include the `title` parameter, and adjust the figure size if necessary.\n",
    "\n",
    "2. Use the Pandas `cumprod` function to calculate the cumulative returns for the four fund portfolios and the S&P 500. Review the last five rows of the cumulative returns DataFrame by using the Pandas `tail` function.\n",
    "\n",
    "3. Use the default Pandas `plot` to visualize the cumulative return values for the four funds and the S&P 500 over time. Be sure to include the `title` parameter, and adjust the figure size if necessary.\n",
    "\n",
    "4. Answer the following question: Based on the cumulative return data and the visualization, do any of the four fund portfolios outperform the S&P 500 Index?\n",
    "\n",
    "### Analyze the Volatility\n",
    "\n",
    "Analyze the volatility of each of the four fund portfolios and of the S&P 500 Index by using box plots. To do so, complete the following steps:\n",
    "\n",
    "1. Use the Pandas `plot` function and the `kind=\"box\"` parameter to visualize the daily return data for each of the four portfolios and for the S&P 500 in a box plot. Be sure to include the `title` parameter, and adjust the figure size if necessary.\n",
    "\n",
    "2. Use the Pandas `drop` function to create a new DataFrame that contains the data for just the four fund portfolios by dropping the S&P 500 column. Visualize the daily return data for just the four fund portfolios by using another box plot. Be sure to include the `title` parameter, and adjust the figure size if necessary.\n",
    "\n",
    "    > **Hint** Save this new DataFrame&mdash;the one that contains the data for just the four fund portfolios. You’ll use it throughout the analysis.\n",
    "\n",
    "3. Answer the following question: Based on the box plot visualization of just the four fund portfolios, which fund was the most volatile (with the greatest spread) and which was the least volatile (with the smallest spread)?\n",
    "\n",
    "### Analyze the Risk\n",
    "\n",
    "Evaluate the risk profile of each portfolio by using the standard deviation and the beta. To do so, complete the following steps:\n",
    "\n",
    "1. Use the Pandas `std` function to calculate the standard deviation for each of the four portfolios and for the S&P 500. Review the standard deviation calculations, sorted from smallest to largest.\n",
    "\n",
    "2. Calculate the annualized standard deviation for each of the four portfolios and for the S&P 500. To do that, multiply the standard deviation by the square root of the number of trading days. Use 252 for that number.\n",
    "\n",
    "3. Use the daily returns DataFrame and a 21-day rolling window to plot the rolling standard deviations of the four fund portfolios and of the S&P 500 index. Be sure to include the `title` parameter, and adjust the figure size if necessary.\n",
    "\n",
    "4. Use the daily returns DataFrame and a 21-day rolling window to plot the rolling standard deviations of only the four fund portfolios. Be sure to include the `title` parameter, and adjust the figure size if necessary.\n",
    "\n",
    "5. Answer the following three questions:\n",
    "\n",
    "* Based on the annualized standard deviation, which portfolios pose more risk than the S&P 500?\n",
    "\n",
    "* Based on the rolling metrics, does the risk of each portfolio increase at the same time that the risk of the S&P 500 increases?\n",
    "\n",
    "* Based on the rolling standard deviations of only the four fund portfolios, which portfolio poses the most risk? Does this change over time?\n",
    "\n",
    "### Analyze the Risk-Return Profile\n",
    "\n",
    "To determine the overall risk of an asset or portfolio, quantitative analysts and investment managers consider not only its risk metrics but also its risk-return profile. After all, if you have two portfolios that each offer a 10% return but one has less risk, you’d probably invest in the smaller-risk portfolio. For this reason, you need to consider the Sharpe ratios for each portfolio. To do so, complete the following steps:\n",
    "\n",
    "1. Use the daily return DataFrame to calculate the annualized average return data for the four fund portfolios and for the S&P 500. Use 252 for the number of trading days. Review the annualized average returns, sorted from lowest to highest.\n",
    "\n",
    "2. Calculate the Sharpe ratios for the four fund portfolios and for the S&P 500. To do that, divide the annualized average return by the annualized standard deviation for each. Review the resulting Sharpe ratios, sorted from lowest to highest.\n",
    "\n",
    "3. Visualize the Sharpe ratios for the four funds and for the S&P 500 in a bar chart. Be sure to include the `title` parameter, and adjust the figure size if necessary.\n",
    "\n",
    "4. Answer the following question: Which of the four portfolios offers the best risk-return profile? Which offers the worst?\n",
    "\n",
    "#### Diversify the Portfolio\n",
    "\n",
    "Your analysis is nearing completion. Now, you need to evaluate how the portfolios react relative to the broader market. Based on your analysis so far, choose two portfolios that you’re most likely to recommend as investment options. To start your analysis, complete the following step:\n",
    "\n",
    "* Use the Pandas `var` function to calculate the variance of the S&P 500 by using a 60-day rolling window. Visualize the last five rows of the variance of the S&P 500.\n",
    "\n",
    "Next, for each of the two portfolios that you chose, complete the following steps:\n",
    "\n",
    "1. Using the 60-day rolling window, the daily return data, and the S&P 500 returns, calculate the covariance. Review the last five rows of the covariance of the portfolio.\n",
    "\n",
    "2. Calculate the beta of the portfolio. To do that, divide the covariance of the portfolio by the variance of the S&P 500.\n",
    "\n",
    "3. Use the Pandas `mean` function to calculate the average value of the 60-day rolling beta of the portfolio.\n",
    "\n",
    "4. Plot the 60-day rolling beta. Be sure to include the `title` parameter, and adjust the figure size if necessary.\n",
    "\n",
    "Finally, answer the following two questions:\n",
    "\n",
    "* Which of the two portfolios seem more sensitive to movements in the S&P 500?\n",
    "\n",
    "* Which of the two portfolios do you recommend for inclusion in your firm’s suite of fund offerings?\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Import the required libraries and dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the required libraries and dependencies\n",
    "# YOUR CODE HERE\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Use the `read_csv` function and the `Path` module to read the `whale_navs.csv` file into a Pandas DataFrame. Be sure to create a `DateTimeIndex`. Review the first five rows of the DataFrame by using the `head` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>SOROS FUND MANAGEMENT LLC</th>\n",
       "      <th>PAULSON &amp; CO.INC.</th>\n",
       "      <th>TIGER GLOBAL MANAGEMENT LLC</th>\n",
       "      <th>BERKSHIRE HATHAWAY INC</th>\n",
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       "      <th>2014-10-01</th>\n",
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       "      <td>59.977830</td>\n",
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       "            SOROS FUND MANAGEMENT LLC  PAULSON & CO.INC.  \\\n",
       "date                                                       \n",
       "2014-10-01                  31.950240          14.991826   \n",
       "2014-10-02                  31.936110          14.994072   \n",
       "2014-10-03                  31.969707          14.999596   \n",
       "2014-10-06                  32.048215          14.999471   \n",
       "2014-10-07                  31.964216          14.994720   \n",
       "\n",
       "            TIGER GLOBAL MANAGEMENT LLC  BERKSHIRE HATHAWAY INC  S&P 500  \n",
       "date                                                                      \n",
       "2014-10-01                    59.977830               51.948712   194.35  \n",
       "2014-10-02                    59.978626               51.957619   194.38  \n",
       "2014-10-03                    60.002264               52.022484   196.52  \n",
       "2014-10-06                    60.006244               52.036387   196.29  \n",
       "2014-10-07                    59.993735               52.005864   193.26  "
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    "# Import the data by reading in the CSV file and setting the DatetimeIndex \n",
    "# Review the first 5 rows of the DataFrame\n",
    "# YOUR CODE HERE\n",
    "whales_df = pd.read_csv(\n",
    "    Path('Resources/whale_navs.csv'), \n",
    "    index_col='date', \n",
    "    parse_dates=True, \n",
    "    infer_datetime_format=True\n",
    ")\n",
    "display(whales_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Use the Pandas `pct_change` function together with `dropna` to create the daily returns DataFrame. Base this DataFrame on the NAV prices of the four portfolios and on the closing price of the S&P 500 Index. Review the first five rows of the daily returns DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>SOROS FUND MANAGEMENT LLC</th>\n",
       "      <th>PAULSON &amp; CO.INC.</th>\n",
       "      <th>TIGER GLOBAL MANAGEMENT LLC</th>\n",
       "      <th>BERKSHIRE HATHAWAY INC</th>\n",
       "      <th>S&amp;P 500</th>\n",
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       "      <td>59.977830</td>\n",
       "      <td>51.948712</td>\n",
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       "            SOROS FUND MANAGEMENT LLC  PAULSON & CO.INC.  \\\n",
       "date                                                       \n",
       "2014-10-01                  31.950240          14.991826   \n",
       "2014-10-02                  31.936110          14.994072   \n",
       "2014-10-03                  31.969707          14.999596   \n",
       "2014-10-06                  32.048215          14.999471   \n",
       "2014-10-07                  31.964216          14.994720   \n",
       "\n",
       "            TIGER GLOBAL MANAGEMENT LLC  BERKSHIRE HATHAWAY INC  S&P 500  \n",
       "date                                                                      \n",
       "2014-10-01                    59.977830               51.948712   194.35  \n",
       "2014-10-02                    59.978626               51.957619   194.38  \n",
       "2014-10-03                    60.002264               52.022484   196.52  \n",
       "2014-10-06                    60.006244               52.036387   196.29  \n",
       "2014-10-07                    59.993735               52.005864   193.26  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Prepare for the analysis by converting the dataframe of NAVs and prices to daily returns\n",
    "# Drop any rows with all missing values\n",
    "# Review the first five rows of the daily returns DataFrame.\n",
    "# YOUR CODE HERE\n",
    "daily_returns = (whales_df - whales_df.shift()) / whales_df.shift()\n",
    "daily_returns = whales_df.pct_change().dropna()\n",
    "whales_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantitative Analysis\n",
    "\n",
    "The analysis has several components: performance, volatility, risk, risk-return profile, and portfolio diversification. You’ll analyze each component one at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Analyze the Performance\n",
    "\n",
    "Analyze the data to determine if any of the portfolios outperform the broader stock market, which the S&P 500 represents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1:  Use the default Pandas `plot` function to visualize the daily return data of the four fund portfolios and the S&P 500. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Daily Return plot'}, xlabel='date'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the daily return data of the 4 funds and the S&P 500 \n",
    "# Inclue a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "daily_returns.plot(figsize = (15,7), title = \"Daily Return plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Use the Pandas `cumprod` function to calculate the cumulative returns for the four fund portfolios and the S&P 500. Review the last five rows of the cumulative returns DataFrame by using the Pandas `tail` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>SOROS FUND MANAGEMENT LLC</th>\n",
       "      <th>PAULSON &amp; CO.INC.</th>\n",
       "      <th>TIGER GLOBAL MANAGEMENT LLC</th>\n",
       "      <th>BERKSHIRE HATHAWAY INC</th>\n",
       "      <th>S&amp;P 500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-09-04</th>\n",
       "      <td>0.987355</td>\n",
       "      <td>0.958187</td>\n",
       "      <td>1.055714</td>\n",
       "      <td>1.244856</td>\n",
       "      <td>1.762645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-08</th>\n",
       "      <td>0.985640</td>\n",
       "      <td>0.956378</td>\n",
       "      <td>1.054373</td>\n",
       "      <td>1.238608</td>\n",
       "      <td>1.714484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-09</th>\n",
       "      <td>0.986739</td>\n",
       "      <td>0.958409</td>\n",
       "      <td>1.057221</td>\n",
       "      <td>1.240858</td>\n",
       "      <td>1.748341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-10</th>\n",
       "      <td>0.985498</td>\n",
       "      <td>0.959740</td>\n",
       "      <td>1.055539</td>\n",
       "      <td>1.237883</td>\n",
       "      <td>1.717983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-11</th>\n",
       "      <td>0.985086</td>\n",
       "      <td>0.957887</td>\n",
       "      <td>1.055081</td>\n",
       "      <td>1.236625</td>\n",
       "      <td>1.718858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SOROS FUND MANAGEMENT LLC  PAULSON & CO.INC.  \\\n",
       "date                                                       \n",
       "2020-09-04                   0.987355           0.958187   \n",
       "2020-09-08                   0.985640           0.956378   \n",
       "2020-09-09                   0.986739           0.958409   \n",
       "2020-09-10                   0.985498           0.959740   \n",
       "2020-09-11                   0.985086           0.957887   \n",
       "\n",
       "            TIGER GLOBAL MANAGEMENT LLC  BERKSHIRE HATHAWAY INC   S&P 500  \n",
       "date                                                                       \n",
       "2020-09-04                     1.055714                1.244856  1.762645  \n",
       "2020-09-08                     1.054373                1.238608  1.714484  \n",
       "2020-09-09                     1.057221                1.240858  1.748341  \n",
       "2020-09-10                     1.055539                1.237883  1.717983  \n",
       "2020-09-11                     1.055081                1.236625  1.718858  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate and plot the cumulative returns of the 4 fund portfolios and the S&P 500\n",
    "# Review the last 5 rows of the cumulative returns DataFrame\n",
    "# YOUR CODE HERE\n",
    "cumulative_returns = (1 + daily_returns).cumprod()\n",
    "cumulative_returns.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Use the default Pandas `plot` to visualize the cumulative return values for the four funds and the S&P 500 over time. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Daily plot return'}, xlabel='date'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gEAgEzjEbhZuvw1JaWsoNN9xA37596d+/PwcOHFCtGi+99BKurq5qeoOvv/6a7777zqmrbeDAgQwYMIClS5fi5ubGihUreP7554mJiSE+Pp4hQ4aoKQ22b99OYmIi/fv3Z/jw4dxyyy0MGTKkSesNCQnhnXfeYdKkSSQkJPDAAw+wZMkSNJq6b+vNN9+M2Wx2aFu2bBkJCQn07t2bF198kW+//bZByxQo1p5BgwY5tC1ZsqSO22/69OksXry4zvkPP/ywuqtv7dq1hIeHEx4ervaPHj2aAwcOkJGRwQsvvEBYWBj9+vVj4MCBjBo1ihtuuEGNKbO5Vm3/aqcpkCSJRx55pI6lDmpipmz/arsfwfkzawq///47ERER6r+NGzcCcOmll6ptV111FSEhISxdupRHHnmEmJgY+vTpw7p16+rE5gkEAoHAEbOx2s2n7RgyRmosTkaSpE+AyUC2LMv9nPT7AF8CkShuw9dlWf609rjaJCYmyrXz7hw8eLDRD3OBQFCD+J0RCATnIqkH8vjh3d0EhHsy8z9Dz8g1JUnaLstyorO+pki6hcDFDfTfDRyQZXkAMBZ4Q5Ikl+YuUiAQCAQCgaApGCsUy5Te0DEsU42uQpbltUB+Q0MAL0nxd3lWj22+b0QgEAgEAoGgEcqKqvjlo30A6Fy07bwahdbYzTcPWAmcAryAGbIsW1thXoFAIBAIBAIH8tJL1dcdRUy1hn3sImAX0AVIAOZJkuQ0glaSpNskSdomSdI2Z7mUBAKBQCAQCBqiqrzG+aVz6SRuviZwI7BcVjgKHAdinQ2UZXmBLMuJsiwn2pciEQgEAoFAIGgKZYVVALi4dZxUma0hplKB8QCSJIUAMUByK8wrEAgEAoFA4EDOyRI8fFzw9DMgWxrOSHCmaFRMSZK0BNgIxEiSlCZJ0s2SJN0hSdId1UOeA0ZIkrQX+B14TJbl3LZbctui1WpJSEigX79+XHXVVWpCTbPZTGBgIHPnznUYHxUVpeZNAvjrr7+YPHkyAAsXLlTzSdnzySefEB8fT//+/enXr59aQFiWZZ5//nl69epF7969GTduHPv373e4ln1m9G+++YY5c+Y4vY9NmzYxYMAA4uPjG63/tmXLFkaPHk1MTAyxsbHccsst6n1///339O/fn9jYWOLj4+sk4rQxduxYbKkuGlvnzz//TGJiIn369CE2NpZHHnmkwfUJBAKBQGCjqsyMu48BSSNhtXYMMdWojUyW5QbTR8uyfAqY2GoramdsZWEArr32Wj744AMeeughfv31V2JiYvjqq6948cUX662L1xhpaWm88MIL7NixAx8fH0pLS9VafPPnz2fDhg3s3r0bd3d3fv31Vy6//HL279+Pq6sroGQ5379/f6NlTp544gnefvttxo0bx/Hjx+sdl5WVxVVXXcXSpUsZPnw4sizz7bffUlJSQlJSEo888gi//fYb3bt35/jx40yYMIEePXrQv3//Bq9f3zr37dvHPffcw48//khsbCxms5kFCxY05dEJBAKBQICx0oyLmxZjhaXDiKmOEbnVQRk1ahRHjx4FlCzf999/P5GRkWzatOm058zOzsbLywtPT09AKa7cvXt3AF555RXee+893N3dAZg4cSIjRoxg0aJF6vmPPPIIL774YqPXcXFxIS0tDUCd3xnz58/nhhtuYPjw4YCSNfzKK68kJCSE119/nccff1w9v3v37sydO5fXXnut0evXt85XX32VJ554gthYJaxOp9Nx1113NTqfQCAQCASg5JjSG3RodRq1Rl9703Git2rz878hc2/rzhkaD5e83KShZrOZn3/+mYsvvpiKigp+//13PvzwQwoLC1myZIkqPprLgAEDCAkJoXv37owfP55p06Zx2WWXUVxcTFlZGdHR0Q7jExMTHVx9V199Ne+//74q8uojOjqauXPn0qdPHxITnSZsBRRLUX1uwP3799dxwSUmJjJ//vzGbrPede7bt4+HH3640fMFAoFAIHCGsdKMi6sW2SpTXtz0eq1tibBM1cJW7y0xMZHIyEhuvvlmVq1axbhx43B3d2f69Ol89913asFjZ+6+hlyAWq2W1atX880339C7d28efPBBtf6fM2RZdphPq9Xy6KOP8tJLL9V7zooVKygqKuLnn39m1qxZJCUlkZOT0+Saf/Vdu742ZzRlnQKBQCAQNAerxUpZYRWefgZc3HRUlZvae0lAR7ZMNdGC1NrYx0zZWLJkCevXrycqKgqAvLw8/vzzTy688EICAgIoKChQi+nm5+c7LaxrjyRJDB06lKFDhzJhwgRuvPFGnn76aTw8PEhOTqZHjx7q2B07djBmzBiH86+//npeeumleuOmfvnlF8aPH098fDwff/wxU6ZM4aqrrmLGjBl1xsbFxbF9+3amTJnitG/btm0O8VE7duygb9++Dd5fQ+u0XW/AgAFNmkMgEAgEAhvFuZVYLTK+Ie6YKi0OOafaE2GZaoTi4mL++ecfUlNTSUlJISUlhfnz57NkyRJA2cX2xRdfAGCxWPjyyy8ZN25cvfOdOnWKHTt2qMe7du2iW7duADz66KPcd999VFRUALBmzRr++ecfZs2a5TCHXq/nwQcf5O2333Z6jYEDB7Js2TIqKysZNWoUU6dO5YUXXuCaa+ruJbjnnnv47LPP2Lx5s9r25ZdfkpmZySOPPMJLL71ESkoKACkpKbz44otNdtM5W+ejjz7Kiy++yJEjRwCwWq28+eabTZpPIBAIBOc2BZllAPiFeWDw1FNVYe4QQehCTDXC8uXLueCCCzAYDGrblClTWLlyJVVVVfznP//h6NGjDBgwgIEDB9KzZ0+uu+46dezChQuJiIhQ/5lMJh555BFiY2NJSEhg2bJlvPPOOwDce++9DBkyhPj4eGJiYnjuuedYsWIFbm5uddZ18803YzY7V+Q333wz8fHxqrsyIyOD119/nSuvvFJNeWAjJCSEpUuX8sgjjxATE0OfPn1Yt24d3t7eJCQk8Morr3DZZZcRGxvLZZddxquvvkpCQgIAkyZN4tSpUw0+v9rr7N+/P2+//TbXXHMNffr0oV+/fmRkZACwcuVKnnrqqQbnEwgEAsG5S35GtZgK9cDNUw8yVJW1v6tPkuX2UXSJiYmyLS+RjYMHD9KnT592WY9A0BkRvzMCgeBcYs3CA6QdKmDOyyM5sjWT3z4+wAWz+9BnRFibX1uSpO2yLDvd0SUsUwKBQCAQCDoFBRll+IUq6YPcPFwA+OPzg6r7r70QYkogEAgEgnOY9MMF5KSWtPcyGuXIlkyyT5TgF+YBgKuXXu2rKGlfV1/H3c0nEAgEAoGgzTBWmtm4/Bj71qYDcPcHF7TziuonJ7WE3z45AIC7t2KRcvPU241o3yB0YZkSCAQCgeAcZNP3yaqQ6uh89eJW9bXNzefqUSOm2in8W0VYpgQCgUAgOAfpCCkFmsukO+OJ6q/kctS5aNX29hZTwjIlEAgEAsE5iLnKgleAK4mTogCQO6i42ve3Umd24IRIug8IclqFw2pu3xp9QkzZkZeXR0JCAgkJCYSGhhIeHq4e24oPAyQlJTF58mSio6MZPHgw48aNY+3atYCSVyooKEg9LyEhgQMHDpCSkoKbmxsJCQn07duX2bNnYzI5D5hrbP577rmnzjn//PMPQ4cOJTY2ltjYWBYsWKD2Pf300+q9xMbGcuedd2K11vzgmc1mAgMDmTt3rsOcY8eOpXb6CntSUlKQJIn//Oc/altubi56vb7OGgcMGFAnaeicOXMIDw+nqqpKPdeWZd7GW2+9haurK0VFRQ7tq1evVu83ISGBGTNmkJqaqs7bvXt39fmPGDFCfXaSJPH777+r83z33XdIksQ333yj3nNMTIx67pVXXqk+Q3d3d7Kzs9VzPT09G/yZMRoda0ZFRUWRm5vr0Fbf+1laWsrtt99OdHQ0cXFxjB492iGxqkAgELSU/IwyvANc0bsqFh6T0dLOK6qLsdLM30uUJM+VDeSTqmznsjJCTNkREBDArl272LVrF3fccQcPPvigeqzRKI+qsrKSSy+9lNtuu41jx46xfft23nvvPZKTk9V5ZsyYoZ63a9cutfxKdHQ0u3btYu/evaSlpfHVV1/VWUNT5q9NZmYms2bN4oMPPuDQoUP8888/fPjhh/z444/qGNu9HDhwgL179/L333+rfb/++isxMTF89dVXNDfvWI8ePVi1apV6/PXXX9cpc3Pw4EGsVitr166lrMxx+6pWq+WTTz6pd/4lS5YwZMgQvvvuO7Vt37593HvvvXz22WccOnSIXbt2ce2116qZ2gFee+019flv2LBBbY+Pj1ez1wMsXbq0TmmbRYsWqefaRBZAYGAgb7zxhsPYhn5mXFxc6r2vxrjlllvw9/cnKSmJ/fv3s3DhwjpCTCAQCE6XL57cQE5qCWE9fXFxVSJ+TJUdT0yd2JenvrZY6rc+bVx+7Ewsp16EmGomixYtYvjw4Vx++eVqW79+/ZgzZ06T59BqtQwdOpT09LqBf6cz//z585kzZw6DBg0ClA/9V199lZdfrlvf0Gg0UllZiZ+fn9q2ZMkS7r//fiIjI9m0aVOT7wOUWoZ9+vRRLVjLli3j6quvdhizePFirr/+eiZOnMjKlSsd+h544AHeeustp9ncjx07RmlpKc8//7yDAHrllVd4/PHHHZJVXn755YwePbrR9Y4aNYotW7ZgMpkoLS3l6NGjakb3xrjppptYtmwZ+fn5TRp/uhw7dozNmzfz/PPPqyK+R48eXHrppW16XYFAcG5gtVgpzq0EoEsvX/QGxTJlrOwYde7sKS+qsfBbTPV/2Z/y4MAzsZx66bAB6K9seYVD+Ydadc5Y/1geG/pYi+bYv3+/KlrqY9myZfzzzz/q8caNGx36Kysr2bx5s1pGprnzOzvnhhtucGhLTExk//796vFbb73Fl19+yYkTJ7jkkktUAVFRUcHvv//Ohx9+SGFhIUuWLGH48OHNuv7MmTNZunQpoaGhaLVaunTp4lBmZtmyZfz2228cPnyYefPmObj7IiMjOf/88/niiy+47LLLHOZdsmQJ11xzDaNGjeLw4cNkZ2cTHBzM/v37eeSRRxpc06OPPsrzzz8PKMWVFy1aBChFpi+88EJ++eUXioqKuPzyyzl+/LjDuddee61awmfChAm89tprgOLWu+mmm3jnnXd45plnmvWMmsP+/ftJSEhAq9U2PlggEAgaoaLUSO7JUkK6e6M3aCkvrnGJhfbwIe2Q8gXRVNXxLFPlJTViqt/oLnX6r3tuOGVFVfgGu9fpO5MIy1QLmTp1Kv369WPatGlqW203n+2D+dixYyQkJBAQEEBkZCT9+/c/rflrI8uy04A8+zab+yk7O5uysjKWLl0KwKpVqxg3bhzu7u5Mnz6d7777Douleb9QF198Mb/99htLlixhxowZDn1bt24lKCiIbt26MX78eHbs2EFBQYHDmMcff5zXXnvNIY4LFBfczJkz0Wg0TJs2ja+//rrOtW0xS7179+b1119X2+3dfDYhZcMm/pYuXeq0+LO9m88mpGzcd999fPbZZxQXFzft4QgEAkE7s2lFMivf2cVHD6wleWcO5urYKL9Qd/QGLfpqN5+xA7r5KkqMuPu4cPcHFxAR61+n3yfIjS49fc/8wmrRYS1TLbUgtRVxcXFqMDgoAczbtm1r1FICNTFTGRkZjB07lpUrVzq48053/ri4OLZt2+Yw1/bt29VYLXv0ej0XX3wxa9euZebMmSxZsoT169ergd95eXn8+eefXHjhhY3ejw0XFxcGDx7MG2+8wf79+/nhhx/UviVLlnDo0CF1/uLiYr799ltuueUWdUzPnj1JSEhwiCHbs2cPSUlJTJgwAVDckz169ODuu+8mLi6OHTt2MGDAADVm6fXXX6e0tLRJ6x06dCj79u3Dzc2N3r17N/k+AXx9fZk1axbvv/9+s85rDnFxcezevRur1aq6+QQCgeB0KcqqKXCfm1aKd5DyBX/YlGgAXGwB6B3QzVdRYsLN6/TjT88U4i91M5k1axbr1693iP0pLy9v4Iy6hIWF8fLLL/PSSy+1yvx33303CxcuZNeuXYAiiB577DH+9a9/1RkryzIbNmwgOjqa4uJi/vnnH1JTU0lJSSElJYX58+c7xCc1lYcffphXXnmFgIAAtc1qtfL111+zZ88edf4VK1Y4nf+JJ55wsCwtWbKEp59+Wj3v1KlTpKenc+LECf71r3/xwgsvcPDgQXV8c9+Dl156iRdffLHZ9wnw0EMP8eGHHzqN82oNoqOjSUxM5L///a+6ISApKYkVK1a0yfUEAsHZTUWpiah45W+zpJEwV7vzdAZFAtTETHU8y1R5sRF3L33jA9sZIaaaiZubG6tWreKDDz6gR48eDB8+nOeff54nn3xSHbNs2TKH1Aj2u8lsXHHFFZSXl7Nu3bpmz79w4UIiIiLUfxaLhS+//JJbb72V2NhYRowYwU033eQQg/TWW2+RkJBAv379MJvN3HXXXSxfvpwLLrgAg8GgjpsyZQorV65U0xVceuml6nWuuuqqep9LXFxcnbittWvXEh4eTnh4uNo2evRoDhw4QEZGRp3z7WPFli5dytSpUx3GTJ06laVLlxIfH88777zD7NmziY2NZeTIkRw8eJBZs2apYx999FGH96B2moJLLrmEcePGOb2Xa6+9Vj3PmYUuMDCQqVOnqs+oOfTv3199ng899BBQ9/1MS0vjf//7H5mZmfTs2ZP4+HhuvfVWunSpGy8gEAgEjVFVbsbVywW9qxZjuVlNgWBLeqnu5uuAMVPFuRV4Bbq19zIaRWruVvjWIjExUa6dw+jgwYMOO7QEAkHDiN8ZgUDQEMZKMx89sJYBF3YleUcOYb18iB4YzM8f7OXqx4cQFOmljhk+LZpBE7u195JVyoqqWPjYekZe2ZOECyPbezlIkrRdluVEZ30dNmZKIBAIBAJBy/jlo32AYn1y93HhyOYsjmzOAmrce3oXW8xUx7JMnUoqBCCsAwSYN4YQUwKBQCAQnIUU5ZSTul9JexA3qgv56aVk2fXb3HySRkJv0HYYMfX1S1sJjPRCIynrCurq2d5LahQhpgQCgUAgOAv5a9FhAIIivfDwMeDuY3Do1xtqwqb1rlqMVR1jN1/2iRKyT5TQpZcvgV090Wg7fnh3x1+hQCAQCASCZpGyNxetXvmIH3V1LwBc3BwTAesMNccurroOYZmyLxlzKqkQi7ljFl+ujbBMCQQCgUBwFlFZZuLH+XsA8PJ3VWOObLv2bGjtLD56g7ZDpEYozHJMc5Od0jkSJAvLlEAgEAgEZxEl+ZVOX7u41W8/cXHVYmpjN1/m8SIs5vqLFQNkHXcUT96Brm25pFZDiKlaaLVaEhISGDBgAIMGDVJzRKWkpODm5uaQu+jzzz8HICoqivj4ePr378+YMWM4ceKEOp+nZ03g3E8//USvXr1ITU3l8OHDjB07loSEBPr06cNtt90GwF9//cXkyZMd1jRnzhy++eYbAMaOHasWFW7ourb7sP1zVvTYfl5n6wUlP5WrqytFRUVq6ZaEhARCQ0MJDw93yONU+9yFCxdyzz33OLQNGDDAoYTL1KlT+f7779XjmJgYtaYewPTp01m+fDkAOTk56PV6PvzwQwAWLFjgUL6muLiY6OjoOrX2nn76aTUh6Jw5cwgPD1dzROXm5qrZ2QGOHDnCpEmT6NmzJ3369OHqq68mKysLgUAg6AxsWH6Ur17Y6rQv5rxQdQdfbZGid9W1qWUq71Qp376ynU3fH2twXFZykcPxxFv6tdmaWhMhpmrh5ubGrl272L17Ny+99BJz585V+2zlYGz/Zs+erfb9+eef7Nmzh7FjxzqIARu///479957L6tXryYyMpL77rtPrZd38OBB7r333tNab33Xtd2H7d+///3v05p/yZIlDBkyhO+++04t3bJr1y7uuOMOdf27du3CxaXxdP8HDx7EarWydu1aysrKABgxYoQqWPPy8vD09HQoDL1x40ZGjBgBwNdff82wYcPUDOq33noraWlprFmzBoCnnnqKm266ie7duze4Dq1WyyeffFKnvbKykksvvZQ777yTo0ePcvDgQe68805ycnKa8KQEAoGg/dn5a6rD8aS7amrA6g1azq+OnwqK9HYYpzdo2zRpZ3FOBQB56fWX/SrMKufAeseEzgb3zhGNJMRUAxQXF+Pn59esc4YPH056erpD27p167j11lv58ccfiY5WaiFlZGQQERGhjomPj2/RWp1dt6UcO3aM0tJSnn/++dMqMVObxYsXc/311zNx4kS1XM7IkSNVMbVhwwYmT55MTk4Osixz/Phx3NzcCA0NBRRh98Ybb5CWlkZ6ejqSJPF///d/PPDAA2zbto3ff/+dRx99tNF1PPDAA7z11lt1ysEsXryY4cOHO2SOHzduHP36dY5vRgKB4NzFbLKwesFeh7Y73htL9/6BDm1h0T4ADLrIMQmmRishW9ou2NvmbmzI1fjj+3vqtGm0UputqTXpsJIv88UXqTp4qFXnNPSJJfTxxxscU1FRQUJCApWVlWRkZPDHH3+ofceOHSMhIUE9fu+99xg1apTD+atXr+aKK65Qj6uqqpgyZQp//fUXsbGxavuDDz7IBRdcwIgRI5g4cSI33ngjvr6+gCK+7K+Tmppax/VXm9rXtd2Hjblz5zq4xGw8+uijTi1poIiXa665hlGjRnH48GGys7MJDg6udw21r5mfn+9QfHnZsmX89ttvHD58mHnz5nHNNdcwePBg9u3bh9FoZMOGDYwZM4bk5GQOHjzIzp07GTlyJAAnT54kMzOToUOHcvXVV7Ns2TIeeugh+vfvz0UXXcT48eP5/vvvm2Qhi4yM5Pzzz+eLL75wEE779u1j8ODBjZ4vEAgEHY2UPXkc25GDh6+B6IFBuHm7qLv57PEL9eDuDy6o067RSlgtDccztYSi7IrqV/WLI1vB5T4jwji4IYMeCUF4+XWOmKkOK6baC5t7DBQX0+zZs9m3T8kga3PzOWPcuHFkZWURHBzsIE70ej0jRozg448/5p133lHbb7zxRi666CJWr17NihUr+PDDD9m9ezcAo0aNYtWqVerYOXPm1Lve+q5rfx8N8dprr3HllVeqx/ZxT0uXLuW7775Do9Ewbdo0vv76a+6+++5656p9zYULF6rxXVu3biUoKIhu3boRERHBTTfdREFBAX5+fsTFxbFjxw42bdrEv/71L5KTk9mwYQM7d+5UXXxLly7l6quvBmDmzJncfPPNam27u+++m59//rneWnvOePzxx7n88su59NJLm3yOQCAQdERkWWbHL0rM7Mz/DMXVo/mFgTVaDVZr21mmCqvFVEWJsd4xgZFeZJ8o4YLZfRh3XSySpnNYpaADi6nGLEhnguHDh5Obm9ukmJk///wTDw8P5syZw1NPPcWbb74JgEaj4auvvuLCCy/kxRdf5HG7++rSpQs33XQTN910E/369VNFW3Oo77otZc+ePSQlJTFhwgQAjEYjPXr0aFBMNcSSJUs4dOiQGuxdXFzMt99+yy233MKIESNYu3YtJSUl+Pn5MWzYMObNm8fOnTu544471POzsrJYtGgRAKdOnSIpKYlevXqh0WjQaJrnse7ZsycJCQl89dVXaltcXBx///33ad2fQCAQtBfFuZXkpJYQEet3WkIKbJapthNTZYXKpp+GxJSp0oJPkFLUuDMJKRAxUw1y6NAhLBYLAQEBTRrv5ubG22+/zeeff05+fr7a7u7uzqpVq1i0aBEff/wxoLjlTCYTAJmZmeTl5REeHn5a66zvui1hyZIlPP3006SkpJCSksKpU6dIT0932DHYVKxWK19//TV79uxR51uxYoUahzVy5Eg+/PBDBgwYAED//v3ZtGkTqampxMXFcfjwYcrKykhPT1fPnzt3LkuXLm3RPT7xxBPqLj+AWbNmsWHDBn788Ue1bfXq1ezdu9fZ6QKBQNAhSD9cAMCoGb1Pew6NVsLShmLKlhKhvtQIxkozRdnlDcZUdWSEmKqFLe4nISGBGTNm8Nlnn6HVKn5cW8yU7d+7775b5/ywsDCuueYa5s+f79Du7+/P6tWref7551mxYgW//vor/fr1Y8CAAVx00UW89tpraqD16VD7uvb3kZCQ0OzdfEuXLmXq1KkObVOnTj0tAbN27VrCw8MdxOLo0aM5cOAAGRkZjBgxguTkZIYPHw6ATqcjODiYxMRENBoNS5YsqbOW6dOntzgoPi4ujkGDBqnHbm5urFq1ivfee49evXrRt29fFi5c2GCcmEAgELQ3ybtycPd2wS/U/bTn0LZxALotHkt2con8U2V89MBask+UqKkbOhuS7OzOzgCJiYmyLZ7GxsGDB+nTp0+7rEcg6IyI3xmBQPDBPX/R+7wQLrj+9P8WbF6ZzPafU7jr/+oGp7cGC/+9nrLCKjz9DNzw0kiHvvl31Gz0CunuzZWPJbbJGlqKJEnbZVl2ujhhmRIIBAKBoBNRkl/Jnj9PIssyZqMFi9mqxhqdLpJGQpZBbqMgdNUy1cj8hdnlDfZ3VDqnc1IgEAgEgnOUdcuOcHx3Lq6eenavOQlw2oHnNmz5nKwWGW0bBH/bChbXdobV9o5VlbdtSZu2QogpgUAgEAg6ETbrzt+LDqslYFpNTFll2iJqyWq2xUw5iqeKEpPD8RUPDmyDq7c9ws0nEAgEAkEnoihXySZuX0svsKtnfcObhFarqZ7TTGlBVYvmcoYt7YLVKpN6IE8VVYVZZeqYq+YmEt67eVVHOgqNiilJkj6RJClbkqR6kyBJkjRWkqRdkiTtlyRJJOoRCAQCgaCVMRkt5J8qoyCjzKH98vsS8Ak6/Z18UGOZWvHWTj6bu75Fc9XGarGqCUGrysz88O5udv6WSkWJkYJMJUbq+ueHE9zNu6FpOjRNcfMtBOYBnzvrlCTJF3gfuFiW5VRJksQ+coFAIBAIWpHi3AqWvbAVY4USU9R7aAhHtmQB0LWvf4vn9/QzAKjipjU5uCGjTtvG5cfYuPwYgy6KRKOV8PLvHGVj6qNRy5Qsy2uBhjJBzgKWy7KcWj0+u5XW1i688MILxMXF0b9/fxISEti8eTOg+Hlvu+02+vbtS3x8PBs3bnQ4Lyoqivj4eAYMGMDEiRPJzMysM/ecOXPo3r27mvvJVnpFlmXuu+8+evbsSf/+/dmxY4d6zurVq4mJiaFnz568/PLLbXfjAoFAIOiw5KWXqkIKIHZYGIDT+nung1dAy3YDni6lBVV4+Bo6Xcbz2rTGu9Ab8JMk6S9JkrZLkjS7voGSJN0mSdI2SZK2NaVEy5lm48aNrFq1ih07drBnzx7WrFlD165dAfjnn39ISkpi//79bN68mR49etQ5/88//2T37t0kJiby4osvOr3Ga6+9xq5du9i1a5daFPjnn38mKSmJpKQkFixYwJ133gmAxWJR684dOHCAJUuWcODAgba5eYFAIBB0WCpKHQO1vQIUS46ulcSUppaYac06fTYXYvSguo6rsqIqPHwMrXat9qI1dvPpgMHAeMAN2ChJ0iZZlo/UHijL8gJgAShJO1vh2q1KRkYGgYGBGAzKGxsYGKj2ubi4kJWVhclkwt3dHXf3+v3To0ePdpodvT5WrFjB7NmzkSSJYcOGUVhYSEZGBikpKfTs2VMVbjNnzmTFihX07dv3NO9QIBAIBJ2NwqxyMo4WAnDJHfF4Bbji5u0CQHA3r1a5hk3w2JAtMrSStchUpQTKG9zq7hOsKjfj6SvEFEAakCvLchlQJknSWmAAUEdMNYd1Xx0h92RpKyyvhsCunoy6uv7aRRMnTuTZZ5+ld+/eXHjhhcyYMYMxY8YAEBISQnFxMXPmzGHRokVIUv0/ZKtWrSI+Pt5p3xNPPMGzzz7L+PHjefnllzEYDKSnp6sWMICIiAjS09OdttvcjgKBQCA4+5FlmUX/3QRAeIwvPRKC1L4pDyQQFNk6YkqqZeCyWKyt5kK0iSm9k7p7xgozLmEerXKd9qQ1ntQKYJQkSTpJktyB84CDrTDvGcfT05Pt27ezYMECgoKCmDFjBgsXLgTgyiuv5Pfff8fd3Z0HH3wQgLvuusuhKO64ceNISEiguLiYuXPn1pn/pZde4tChQ2zdupX8/HxeeeUVoG7eDQBJkuptFwgEAsG5QcaxIvX1+Vf1cuiLiPXH4N6y/FI2an+2tGYmdFOlBUkjoXepsUzZ8mJVlJowdNLixvY0egeSJC0BxgKBkiSlAf8F9ACyLH8gy/JBSZJWA3sAK/A/WZbrTaPQVBqyILUlWq2WsWPHMnbsWOLj4/nss8+YNGkSubm5xMTE8OGHHzJ9+nSeeeYZtm3bxmuvvaae++effzq4BmsTFqYEDBoMBm688UZef/11QLE4nTx5Uh2XlpZGly5dMBqNTtsFAoFAcPZjMVv57nVlQ9L0fw0mMKJ1rFDOqO3ms7Zi0ePKcjMGdx32eq1LL1+Sd+VgqrTg4n4OiClZlq9pwpjXgNcaG9fROXz4MBqNhl69FPW/a9cuunXrRlBQELIs8+effzJu3DgWLFhAbGwsV199NR4eTTdPZmRkEBYWhizLfP/99/Tr1w+Ayy+/nHnz5jFz5kw2b96Mj48PYWFhBAUFkZSUxPHjxwkPD2fp0qUsXry4Te5dIBAIBB2Lo9uVzfHhMb6EdG/bHEy1d9O1ZgB6VZkJVw+9eg2Dh46KEqPa73IuWKbOJUpLS7n33nspLCxEp9PRs2dPFixYgCRJfPvtt9x3332Ul5fj7u7OvHnzePXVV/nmm2+48sormzT/tddeS05ODrIsk5CQwAcffADApEmT+Omnn+jZsyfu7u58+umnAOh0OubNm8dFF12ExWLhpptuIi4urs3uXyAQCAQdh5zUEnQuGqY8MLDNQzxqz9+alqnSgipFTFVfIzDCC4vJqvafE26+c4nBgwezYcMGp32JiYl1+mbNmqW+TklJaXT+P/74w2m7JEnMnz/fad+kSZOYNGlSo3MLBAKB4OyiILMMvUF7RmJl28rNl3eqlMzkIs67vDtWiyKgAiM8yUyuiQVrrbiv9kTU5hMIBAKBoINhMVlJ3Z9fpxBwW1HHzWex1jOyeaz/OgmAuNHhFFfXFPTv4kFwVI3b0tVTiCmBQCAQCAStzPZfTpzR69U2frWWZerkwQIA3DxdMFYqGdwN7jpGTI1Wx3gHdO5SMiDElEAgEAgEp01ZURV7/0prNUuOjRN7cwG44aURrTpvfbTlbj5bdnWzUck3pXPROuSwstUF7Mx0uJgpWZZFLiWBoAk4y0MmEAjOLJ/P3YDVKpN9opjxNzRcncJssrDtxxTCe/vh4q4jJMr5Dr3SgiqyT5QwfGo0nn5nxmpT281nMbdcHNp2BA6eFAWA2ajMqXdxjAPTaDu/XadD3YGrqyt5eXniQ0IgaARZlsnLy8PVtfObxwWCzsa+v9P4/IkNJG3LUgXDsZ05jSa6PHWkkO2rT7Dy3V2sfGdXveMyjhUCEBHr11pLbhRNLSOGzYrUEmxz2JJ12gSazqVDSY9WoUNZpiIiIkhLS6MjFkEWCDoarq6uREREtPcyBIJzjsObMynJq+TI5ky1zVRpwVhpbnBnWkFmufraWGHGapXrFBiuqjDz6//2AxAQ7tnKK6+f2pYpmxWpJahlZAyKeJpwUxx7/jhJYNe2Sz7aXnQoMaXX6+nevXt7L0MgEAgEgjpkJhchSRKFWRUApOzNUzokQFaK9jYkpsqLq9BoJKIHB5O0NQtjudlhJ1tBZhmLn66pv6rVtZ8Fx2xquZiyj5EC8A1xZ/Q1MWr/tEcG4el/dljXzz5bm0AgEAgEbcCqebv55pVtVJY5pivoP1axEFdVmOs9N+dkCTt+SUWj19Atzh+AzSuTHcb8Um2RArjwxobjr9qa1nDzFWYrorM+wRTW0xcvIaYEAoFAIDh3qCp3LpaCIhW3lbGefoC/vjwEQNz5XQiPUWKh9q1NJ+1wgTomuJuXOl/MeaGtsubTpTXEVHZKMQDBkWefW682QkwJBAKBQNAIsiw7bOef+Z+hAPQd1UWNbWrIMlVebKTPiDDOv6oXnn6uqnBa8dZOUvbkYrVYyUsrxSvAlav+ndiGd9I0LOaWbQT7a/FhtvxwHN8Q97Oi9l5jnP13KBAIBALBabLnzzQ8fFzo0tsXi8lKzHmhdB8QSEC4J3e8NxatXkNxruLOqs9yBUrAud5Vqx7bC7OtPx6notRI9okSDB66OsHgZ4zq2C9oeWqE/WvTAQjo4tHCRXUOhGVKIBAIBIJ6WLfsCKsX7CM3rRSAmGGhRA8KBmoEkc3yYqzHMiXLMsYqCy6uNfaLC67vo74O6uZNcZ5SamXctbGtfxNN5JY3RnHjq+cDrVdOxvMsyG7eFISYEggEAsE5Q87JEnb+lkpVefNq3uWeVMRUYETddAUubjqQqBOYbsNUZQEZB8uUb4g7N7w0ElCsOMd352Jw16lCrT0wuOtx89KD1DI3n61sDEC/UeGtsbQOjxBTAoFAIKiXQxsz+HPRofZeRqtwbGc2X72wlQ3fHuWrF7c2Ot4+CeeGb4/i4WvAzculzjiNRsLVQ8/xPbl8/sSGOrFTtnxL9pYpUMqoGNyVtry0Ugwe7V/wV5IktFpNsyxTxkozmclFNcd29+8b4t6q6+uoCDElEAgEgnr5/bODHFh3qtNXpjh1tJDVH+5Tj4tzK6ksbdg6VV5idDh2ZpWy4eapJy+tlJK8Sn77eD+F2eUcWH+Kopxytq46DoCLnWXKhv1jNVe1fAdda6DRSc2yTP32yQG+fXU7pQWV7Ps7jWM7z73E2yIAXSAQCASAYmGQJAm9oe6HflmhkcObM3DzcqHvyC7tsLqWcWDdqTptpYWVDkkza5NzosThuCExZT/PiX15nNinJPTU6TVqAky9q5OPXHs11UHK0mq1GqzNCEDPq44nO7Qxs07urHMFIaYEAoFAAMBn/14PwK1vj8FqsXJ0R7bat+TZzar7pjOKKa/qQGjfEHcKs5SyLo2VTKmsFVcV0ICYqi/zuX0mcWci1R6dvmM4izQ6CYul6ZYpWyxYSUGlQ3vipVGtuawOTcd45wQCgUDQ7hgrLRgrLWSlFHMqqZDfPj5Q02cXB2PqIO6opnBsZzZ/Lz5M8q4cDO46pj86WBVFjSWmtN2zwUOxOzRkmTI0IZeSm1ddwWUvWdqzfIw9Wq2mWakRbOViSvMdxVR7Jx49k3SMd04gEAgE7UpBZpn6evlr2x0yc8eNUixRfmFKzqDy4qozu7jTRJZlVn+4j31r08k/VUb3hCBcPfWMu05JP9CQZSozuYh1y5IAuPJfiYy8smeDwdQu7vWLKe9AV65/fjgBXeqKMXvRou1AlqnmuPk01cuuHYPm2kCdwrONjvHOCQQCgaBdscX4XHpXf6wWme0/n1D7hlzanen/Gsz5V/YEoLzI6HSOjkRRTjkr3t6pHg+9rDvjZyu5nXQuykdffcV8i3Iq+PbV7QAEhHviG+JOwoWRSFL9QU0NWab0rjq8A92c9lntAr07jGVKpyErpRirtamuPuW51E4N4eLWsFvzbKJjvHMCgUAgaFdOJRXiHeRGt/gAtS2qfyB3f3ABHr4GQnv44O5jAGDD8mMOaQM6Itt+SiH9cCEAE2+JY8il3dU+fbVb6peP9nHyYH4dEbD37zT19YwnhzTpeoYGLFOmyvozo9vHUQ0Y37VJ12prCjLLKc6tVHchNo7ys2CfAV6jk9Bozx2JIQLQBQKBQEDm8WK69fVHkiRih4VyaFMmQ2oFEPsEuWFw15GZXER6UiER1QV7OyJFORXq6x4JQQ597j41uaJWvrOL0B7eTP9XTT2847tyCI/x45Lb+zVojbKnofpzxbmV9fZNf2wwGUmF9BsT0aTrnAlsQjn7RHGTxmcmK+PsxZSug1jZzhTn1t0KBAJBKyPLMp8/voGdv6a291JOm5S9uVQUG/EJVlxRo2fFcOtbownu5u0wTm/QMu2RwQBUFHdcV5/FbCX3ZCnB3by45Pb4Ou4znd7R/ZSZXMzWH2usMOXFRgK7eta7Q88ZNsuUs7iqqP6B9Z4X0MWzQwmpppJ1vJj5d/zhkKzTno4S/3WmOLfuViAQCFoZs8lKSX4lG5Yfbe+lNIsdv5zg0MYMLGYrP87fA9TsytK7aOu1tGj1iqXG0kq129qCfWvTMVVZGHp5D3oMDHI65uLb+zkcb/lBEVNmowWz0YpbA/mnnGGLmXJmyLrkjvhmzdVhaMCTm35E2aCwa81Jp/1Oc2qdxZxbdysQCAStjH3KgMoyE64doCRIY1hMVjZ+dwyo2cXXtY8ffc9vPH+UzcpjbUHttrZAlmW2/ZRC7PAwMo4W4eFrILKvf73jg7p6OW23xU81931UxacTNaXRdJBsnE1FokEhBTVpHrKOO7dMefoaWnlRHRshpgQCgaAF2Iupgowywnr6tt9iGmHLquOERftQYpcPaMcvqfiFeXD5/QObNIctqLg5eYjOBHnppWz54ThphwqoLDMRFOnVYLyTM7EkyzIV1dv73Tzr1uBrCNWVaJfRfMw1vfEJ6ny16dQ8Uw1owOwUJTt8aYHzNBnOSueczQgxJRAIBC1g/z81ZUpqF7jtSMiyrO7OCgh3zHdUkFHm7BSnaHXKJ6y1GRmyzwS2nFHGSjOFWeVExdcfpwQ1WbvtsZisaq6khsrMOMOWYT1xUhRHtmTRY2BQp8wUD7bafDRondq3Nt1pe/SgII7tyDmndvKBiJkSCASCFnF4Y6b6uqqs4cK57Yl91vLCrHJihp1edmqNrmNapg5uyACgNL8Kq0XGP6xhi5C91arfmHAAdv6WWiOmmuvmc9Vx9wcX0HtoKJPvGdBphRQolqmGqF302stfEZJ6g1bdOanRdjLXZgsRYkogEAhOk+LcCirLTCROigJg3ddJ7bugBrDPTm0xWwnt7k14jC+AuouvKWirPyQ7kpg6uj2bA9UWQlvMk3cz3GtdevkCkLwrpyZmqpmWqbMJTbX1sT43X+0cYy5uOmY+NZQbXztfTfSpznGOIMSUQCAQnCa2HU09BwdjcNdhrOiYNeu2/nicL57c6NDm4Wtgyv0DGXZFDy6/P6HJc2m0GiRJcasVZpc79GUcLeSLJzdQUdr2aROMlWaMlWasVplfPtpXp9/Dt+kxTz0HB9MzMZiS/ErKq1M+uHqcu1EwqmWqHjdfbRev3qAloIsnehfFMhUZ5895l/Vo41V2LISYEggEgtPk+O5cXD31+HfxIHZEWIfMrWM2WdRt//aEx/ghaSQGXxyFd0DTLVOguPp2/HKCRU9tchBU+/85RXFuJcd25LR43Q0hyzLLX9vORw+sVYvr9h8X4eCa8/BpfDfZhJv7MmZWDJIk0WNAEFVlZk4ezMfFTXfOxfzY05hVyVJbTNnFn7m46rjs3oR6y+ecrZy70lsgEAhaQGWZieO7cwEl/kan02Ctp9abjdKCSjx8DEitvFW+MKscD1+DQ2kSUHJBLXxsvXoc2dcf31B3XD30uLQgD5BWK2Gp9hqWFxnxDVZcajYxY6p0tNCV5Fey8btjXHB9rJrL6nQpK6pi1bzd5KUrQfM7f1OSpUb1D3TYDNCUOne9h9TEjQVFKqkSso4XO2RIPxdpTEjWLoJc++fuXESIKYFAIDgNyoqULeGhPZQs4Vq9BqtVxmqxOv0wKs6t4IsnN+IX6s41/z2vyWVKGuPEvjxWzdsNwF3/N85h3j8+P6iW+Jh87wC69vFvlZxHShC6IphMxhrhpNPbCgg7iqk/Pj9I2qECYoeFEhkXQEtY+c4u8k/V7D7c97eyq8wnyI2+I7uw9680Lr6tX32n14u9gNJ1QAvjmUTTyO2nHsh3ODZXdUz39plEiCmBQCBoJvmnylj67BYAhk5WYkO06i43GY3dF/WV7+5C76Jl4MRIQCkiW1Fiwt275dYPY6VZFVKgBJm7edXMe2RzFqC4wLq1UMTYY2/1qW2FUtbl2Facq9TJa055Fmcc25mtCqmLbu3H9tUp5J4sRdJIePoZGD2zN6Nm9DotoWpvqWuKVetsxmY5rb1rD5Sah2s+PeDQVl+W+XOJc/snRiAQCE6DnJMl6mtDdaCyLV7KUsvVd/JAPsm7cjDbWXBKC+ovfNsckrYqYim0hw8AqfvznI5r7ZxQWruYGvuUC+Zq90/tun3Fecr9Wq2nvw6z0cKvH+9Xj3sODiYs2hcAL3+Dag1sicXPli6iI8a+nUlsz9DiJMu9qapuLrXWsrJ2Zs7tnxiBQCA4DSpKFLEQc16omgDT5hqyTxlQaZd3ypZUEpRcSK2yjup0B5Pv6U9QpBdrFh5U11ZWWHMNW+qG1qK8pOa+Mo4Vqq9tQrK0sJZYrP5Mtragnl9VuRmrWWbMrBjuen8cUJPfqLVK+Nhif851N5/NMuXs/TJVOXkPhZYSYkogEAiaS3mREa1Ow/g5fVSXkO1/+3gh+9ge+9iiomq3V4vXUWxEb9BicNcT1lOxTm38Xqm5l32iGIBpjw7Go5XrpNnHyCRtyVJFo01MlRXWWKbsLXUtqednK1ticNepH/a2rOOtFdBvK4HS2hsEOhs2Q1Njlilb/F2nqz3YBggxJRAIBM1k52+pWMxWB/eGbZeavQUq365Mi61do5M4vrvlqQOsFivHd+XgF6rspBs6uTsAB9crmcBt9fd8Q9p2i7rZZFV3NRorlQ/a0sIqNd4maXuWOtbSAsvUzx/uVV7Yfb4b3BUXa2tl2w7prghSexF8LqJpwDJlrrZMjb02hujBwYDT2s7nHI2KKUmSPpEkKVuSpLpZ0RzHDZEkySJJ0pWttzyBQCDoWBzfk+u03ZZrxz6GqNIueaWtIHLPQcFknyipk0W6qWSlFFNWWMXx3bmUFlQx+JIowDG4W7bKVJSYQGp50HdDDL6kG5JGoji3gpyTJaQfLgQUy9UvH+3HVGVh3bKarPAtid2yfbDbF5IOjfYhsq8/Y66JOe157emREMSIaT0Ze21sq8zXWek1JAQATz/XOn02C2t4b7+aos5CTTVpN99CYB7weX0DJEnSAq8Av7TOsgQCgaBj8veiQ7h7uzD14UEO7S7V8TbGSjMFmWX89H970bnUfF/dVO1+69LLlyNbsijJr2xyYsOCzDI2r0jGw9fAnj/TAIiI9UOjlYjqX1PQd9SM3qxbdoSSgkoqSk24eujb1AUzbEo0B9dnUF5s5KsXtgKgc9FgNlo5tiObnoOD6TM8jN1/nAROT0xlHi/i78WH0Wg1RMb54+lX47LUu2i57L6EVrkXG7Zdl+cy/caEs3llslP3sC0mUKvXoK22xp7rux+hCWJKluW1kiRFNTLsXuBbYEhrLEogEAg6IiajhbIiI+dN6YFviGPtN3vLVPaJEgqzHEutmE1WtDqNel5hdnmTxdT+tac4ttPRNZh2SCllYy+WAsI9AMVNtX9dOsHViSjbEo1OcnAHDbigK9tXnwAUYWm1215/OgHo+/9OJ/dkKVCza1HQtkiShE6vcZoawSaINVqJkdN74uHjQo+EwDrjzjVaLCclSQoHpgIfNGHsbZIkbZMkaVtOTtuWGxAIBILWxla6xLaLzB7bTjBTlcXB1WfPuOtjVTGVmVzc5OsWZDYthsc294/z94AMJmPbFCOeeHMcY2YprjWtVuMQqJx4aZT6uiS/0jEA/TQsU/bB+i5uItP2mULSSDjRUqog1mglXD30DJsSfU6X3rHRGk/gbeAxWZYbTYEqy/ICWZYTZVlODAoSSb4EAkHnoqRBMaUY+k2VFjUQuzaBXT3VZJ1bVx13aqk5sT+P3LQShzb7FAtKLTnn66udCNSWnb216TUkhH6jwwHlQ9WWQ2vwJd3Q6WsET2meIqZqtto3T0yZjYqVz4aLQeSZPmNI4ExN2d5DrRBQDrTG00gElkqSlAJcCbwvSdIVrTCvQCAQdChs+aE8/evGkti7+UyVFqe5d/xC3BUXSnUsVUmtfFPGSjOr3tvN6g8d9/tUlpnoNSSEO+ePpd/ocAIilNxWY67p7TDOfnfhBbP7MHqGY39bUJBZru7mc/N0FHMlBZWYTVbcvZTYrZS9zoP36+PIliwHy5bItH3mkJAcdqbasMVMtdYOyrOFFospWZa7y7IcJctyFPANcJcsy9+3dF6BQCDoaJTkVyJJ4OkkMNeW6HHjd8coLzHi4qpj6sMDmXzPAHWMzR1ywew+QN1s6RXVyTCLchzzUFWWmXH11KvnB3dTLE4aJ4G/0YMUwdFzcHCLiwo3F5sb7uY3RhEZF0BJXiUWsxVXLxe6JwQ5WJmaQs7JEjQaiWv+ex43vzGKLr1822DVAmeU5FdydHt2HSurfcyUoIZGbaaSJC0BxgKBkiSlAf8F9ACyLDcaJyUQCASdFYvJyj9fJzFgfFd0LlqSd+XgE+zuNEbE3iqUcbQIF1ctXXr5qa5Be2rq+DmKqapyU52xVosVY4XZIcv38CuisVqsRA8KrjN+/Jy+DJ1cqcZwnUls9e1cPfT4BLqSlVKExWRBp9fg5qWvIx4bw1hpxtPfgH+YR1ssV9AEKstMDnULrRYZJJHYtDZN2c13TVMnk2V5TotWIxAIBB2I43ty2bc2nX1r09W2oZd1b/S8opxyfIKUYHAXN+XPbESsn9qvdVJ6BqCqosYKYLFY0Wo1VJUrba4eNX+uXT31jL+hr9Nr6120+HdpH/Fh/6Grc9FiNlopya/Cv4sHWp2mzv02hrHCgt5VxEm1J3Ktt8xqsaLRSqIeXy3ET6lAIBA44fDmTNZ8eqBOe+zwsEbPtZpltTSJwU3HFQ8OJMguTYFOLT3j+ElVkFGTTuHrF7cy+OIodTebm5djPFJHRG+3207rosFislKcU0H3AYFIGqlJlqnCrHIKs8uJig/EWGHG4CY+ptqT2sllLRZZ7N5zgvgpFQgEglpUlpkchNScV0ZitcjkpZc63cnnDHs3W3iMn0NfbcvUqaRCNFqJdcuOqGPy0sv49eP96A1agiK96N6/4+fysbdM6avjtaxWGd9gd8qKqrBaZQoyyxRXaT1uou/e2EF5sZER03pyKqmQqPiAM7J2gXNq55qyWmS0Il6qDkJeCgQCQTUVpUbWfXWEfX+nqW3jb+iDh48BL39XouKbLmgCwj3r7bPFTK16bzdlhVV898YOvn11u9rf+7wQ9bWpykL/cRFnPJj8dLAXU/YxNT7Bbuo9L356MztWp9Q7R2WpEje2YflRZU5hmWpXrLUsU1XlJhF87gQhpgQCgaCag+sz2PNHGptXHsfgruPuDy5oklvPGUFdGxBT+po/vbZs4TYm3RlfJx9TRxYUs54+T31tn1Qz63hNUlKfIHeHkiNZKfXv6vMNdcws7yxxpOAMUuv555woafCLwrmKEFMCgUBQjf0HvrGy0TzEdbDFSQH4BLvXO05nJ6ZqJ+gMj/GrE1tkP29Hwy/Ug7BopcyL3s56lnBhV/W1h68LWl2NNaN2clF77HctAuSfKm2tpQpOg9qWKZPR4jQ1yLmOEFMCgUBQzT9fJwEgSTBgfNdGRtdl5lM1Vhqf4Prr7tmLtoyjReprNy89Lq46PGvFZTnLJ9WRmPLAQK57briDay+0hw+9EoPx7+KBJElE9gtg4IRIdHpNgzX67PMahfX0YeRVvdp07YKGqW0ltZisDpZVgULHtR0LBALBGUaSFLfSLW+NPq08Tfbn1Law2FPfh1HXvv4ADJvSg16DgzG46/nnmyQCIzq2W0Wr1+ATVFc8Tryln/raO8CNEdN7cnxPrtPM2jYqio0EdvXkvMt6ENUJgu7PdmxiqqrcRM7JUiwmq0PJIIGCEFMCgUBQjZuXC1H9Ax0CqZuDfWBuQ3l46hNTNjeZi6uOsJ6+AFx+X8JpraWjonPR1EkJYUO2ypSXmIgdHiaEVAfBarGScayI5a/VbJAQlqm6CDElEAjOecxGC79/dpDyYiMG99P/s9jU4q/aetx23k6sO2cbOr0WU5XzeLT8jDJkq3xOPIfOgtUi88/Xhx3abLUlBTWIJyIQCM5JrFaZ0gKl0PC+tekc3Z4NOGYqby5SE7eM15djqc+I09s52Jlw89JTUWJ02leUrSQoDerq5bRfcOaxWuU6P6/1fRk4lxGWKYFAcM5RnFvBF09uBJSiw7b0BLe9M6ZFNe3qE0lNYeItcbh5dvws5y3F09fAqaTCOu2pB/L4+cO9AC2yDgpaF6tFrlOHT7j56iJ+YgUCwTmD1WLl2M4cjmzJUtv++PwgAN6Bru1SHNhGTydFi89GPPwMVJWbMVVZHJ63zTIIQkx1JKwWK7XD/3RCTNVB/MQKBIJzgt2/n6Q4t4I9fyrZzWOHheIV4MrWH1MAmHBTXKtdK7oJwmj8nD74h3nw84d7Kc2vqvPt/2zF009J+1BWWIVvSE0uLpuLDxDFjTsQzixTQkzVRfzECgSCsx5ZltUcUjbOm9IDT78aMRXaw6dVrnXbu2OaVLssdpgSHzXj8aGUFVW1yrU7A7aEj6UFlQ5iqiCzjKj+gcSPCW+Ru1TQulgtTmKmRGqEOggxJRAIznoObshwOL7mv+epFpILb+yLq2f9OaGai76ZNfRcPfWtev2OjodNTBXWCEjZKlNZaiIwwpPIOFHYuCNhtcjU9vMJy1RdhJgSCARnJWVFVRxcf4rNK48DEBzljd5FQ156GX529d9izgttryWek3j6G9C5aEg/XKBa56oqzMhyw4lOBe2D1WJFrlVSRitSI9RBiCmBQHBWsvevNLb/rOzS8/Q3MO2RQUgaCdkqN5hQU9C26PRa/MM8KC+qSY9QWlAJgJu3EFMdDatFdijxA6ATqRHqIMSUQCA4KzFWKIkhew0J4cI5fdDYEmqKeJx2R++qc0jcefJgAQBh0b7ttCJBfZQVVdWJmdI105V9LiDkpUAgOOuwmKwk78wmItaPiTfH1QgpQYdAb9BitBdTB/LwC/PAq1aBZ0H7s/3nE3Tp7ZjIViTtrIt4IgKB4Kzj0KYMyoqMDLqoW3svReAEvcGxpExRbmWHL+Z8LmO1ONZSFOVk6iKeiEAgOOvYtzadoEivFpWGEbQdeldHMWU1W0VW7Q6MxVwrAF28V3UQMVMCgaDTs2tNKke3Z+Mf5kFId2/y0koZfEmUCDTvoNhbpg5tyqC0oAqz0XnxY0H7YzHXskwJMVUHIaYEAkGnZ/03RwHIOl6s5pTqOfjcKM/SGdEbtJirLMhWmd8XKuV8SvMr23lVgvqwmKzqTlgQlilniCciEAg6NaUFNckf+57fBYOHDu9AV/zCPNpxVYKGsNXkS7creGy11jNY0O5YzVa0uhorrwhAr4uwTAkEgg6H1SqTtDWL4G5ebF99glNHCrlgdiwRsf7qmOwTxaz59ABxo8MBmP7YYEK7+3D+1b2QrXVLYAg6Di7VYmrFWztrGmW5ntGC9sDdx0XNBWaujmkzGxXFK9zndRFiSiAQdCj2r0vnr0WH67T/8cUhvANcGXllL4IivTi4PoOCzHL++UqpuRcU4QU0v5yL4Mxjs0zZI7RUx+K654az4L6/AcUypdNpOHcqSDYfYasTCAQdBtkqc3hTpnrsE+QGKPXrSvIqST9SyL6/0wCoKK3JoO3iphNxHJ0I+0K5wVHegFKMWtBxsP9SYqqyqok6NTphlXKGsEwJBIIOw88f7iXjWBG9zwthwo1xavvvnx/kUHVgedL2bCxmmdyTpXj5uxIQ7sGFN8XVN6WgA2Iff2Orx1e7/pug41BVbsLFTcd5l/cgqn9gey8HAHN+Ppn/fZqQJ59AHxLS3ssRYkogELQ9lWUmSvIqCYjw5M8vDlJRYsLNU8/4OX3VMbIscyqpEL1By/Arejqcb/9d2FRp4fBmxXp13uU9SJwUdQbuQNCa2FsR9dUJIIVhquNSVWHG3celw/yuGU+cIPnyKchVVWjc3ejyyivtvSQhpgQCQduz/tujqmXJngEXdqWyzExEjB/ZKSVUlZu5YHYfPP0MDuMqy0x1zjV46Igb1aXN1ixoO+x3g3kHKq7cXkPa37ogcI6x3Ize0H5yQZYdi5Mfu+jimk5Nx4iRFGJKIBC0OVnJRU7blz2/FYCLbu1H7skSAEK6e9cZN+TS7ngHupG8M4eS/Er0Bi03vXq+qLnXSbEXUx6+Bm57d4xIBNmBMZusTjcNnAlKfv+dtLvvIfrXX3CJjKzTby0ra4dV1UWIKYFA0GZYTFYKc8opK6yiW3wAA8Z3Jet4MZtXJOPiqsVYqWS9/uWjfeo5Hr6GOvMERXoRFKns1ju4/hRjr4sVQqoTY+/m0+o1YgdmJ0DbToHnhV9/A0DlwUO4REaS//kXAAQ/8jBlW7ZgTE1tl3XVRogpgUDQZuz9O03NTh4W7UPXWH+6xvrTf1wELq465t/xh8P4vud3wcW1/g/W86/qxcgre4o8N50ce8uUsEh1Dtrrd85aXl79wkLZpk1kvfgiHiNH4jvzGmSzhZy33ybj6acJ/e9/2/XvgvgpFggErU7+qTKKcyvIPlGCq6ee+LERRA+qKe/i4qp8jxt8cTeH88ZdF9voH0QhpDo/ulqWKUHHR2qnJLjmnBwAsl55lbT7HwAg9Jln0Hp64HfddQAULl1G2YYN7bI+G8IyJRAIVMoKq9i8MpnRM3ureWWaisViZcM3R9nzZ5ra5uqpJyLWj9Ezezs9Z9gV0Qy7IpqkbVkU51a0aO2CzoOLW81HjyhN0jlo6+8weR9/gqFXTzzOP5+8BR/hfcnFFCxegvH4cTzOPx9zTg6GHt0JeeIJXCKUqgdaTw8iF35K6pwbKf75ZzxHjmzbRTaAEFMCgUBl88pkDm7IIDzGj95DQ9i4/Bixw8Pw71J/nTurVaYwq5ykrVkOQgqgstRE3xGN77jrlXhu7eSqvTvpXMPgbiemhGWqwzLx5jh+/Xg/0LYWYVNGBtmvvQZAl1deJuftt8l5+20AtL6+RMyfh8ZQN5YSwGPYMHr+vgZdaGibra8piJ9igUCgYnO//LX4MNkpJez8LZWvX9ra4Dn716az5JnNbPspBYAxs2LUPp8gNyJi/dpsvR2FpmTvlmWZ4p9+InnKFSRfOhlzQcEZWFnHxP6DWScsUx2WnoNrXPNt6eYz5+Wrr0899m+Hvu4rvq9XSNnQh4cjadt3E4OwTAkEApXKcjMA5ioL37yyTXltsjZ4TsYxJe1BWE8fhk/tSVi0D6ZKCxuWH6VLb992i7U4U8hWK0cvvBBLfgE9vv8Ol6gop+OqjiSR/tDD6nHaPfcS/sbr6Nv5G3V7IyxTHRdJIyFJSkLVtjSk5v7f/zkcd/1oAYZevdAFBiLpOodMET/FAoEAs9HCd2/sIGlrltP+/evSqSo3UZBZN6dLcW4FXXr5Mu2RwYRF+yjzmZSUB26eLm236A6COTMT86kM5MpKjl18Sb3jLHm56mt9RAQV27dzdOw4Sv/+22GcKSsbS2FhWy23wyHEVMdG0ioqqrXdfJbSUkwZGVgKCyn9/XcAQp58km6LvsRz1Cj0oaGdRkiBsEwJBAJg9x8nOZVUCChJM3slhlCcV0FYtC+/fbqf9COFHNmSxamkQsZdH0u3fgG4e7lgsVjJSikmYXxXh/nKi5UixF4Brmf6Vs44pWvXNWmcpUix4PX4YSWW4mJOXKvsRKo8cgTPMWMAxRWYMnMm5sxMYnZsR+Pm1jaL7gBoNBJWqyxSI3RwNBoJKzJSK79Nx6dPx3QileBHFGtt9++/wzU2tnUvcgZpVExJkvQJMBnIlmW5n5P+a4HHqg9LgTtlWd7dqqsUCARtxskD+Wz6PpnQHt5MfXhQnWSYO345QUWJURVbf35xCICBEyPZ93c6yBDS3cfhnIETIzFWmIk57+x3YRV+/TWucXF4jDqfvI/+h2w0IrnUtciZc/MAJaBW41PzvHLnv49bfH90IcFIOh3mDKXsTtq999H1/94n7cEHMaWl0+2zhWh9fOrM21lxcdNRWWYSu/k6OBpN61um8v73P0wnlGSb2a+/gUu3bp1aSEHT3HwLgYsb6D8OjJFluT/wHLCgFdYlEAjOEJtWHAOUwHFnWcVL8itJO6QES9sHpO78NRVTleLOq10CxjvAjQk3xbVbCYq2RrZaqdi7j7KNG6ncvx/vyZMxRPcEi4Wqo0fVcWVbtpB8xVTyPv6YqiOH0fr4oA0MRB8cTPh77ypzVVaSOmcOyZdM4tiEiQBoAwIo++cfct59j9I1v1N16BCF333XLvfaVnj6NxxULOgYqG6+VtK81ooKct7/PyR3d7XNa+KE1pm8HWnUMiXL8lpJkqIa6LfPlLUJiGiFdQkEgjOEsdJCRKwfgRFeTvvtA9D7jAhjws1xbFx+lF1rTqrtXv5nvzvPnoLFS8h6/nkAdGFh+M26BkueYnk6Pm06PX5YienUKU7efgcAhZWVaDw8MPTto37D954wgXRnk0sSfjOuJvf9/yPvo4+UawQHk/3qa3hdcIFan0y2WJBNJjSunfPZT7qzP4c3ZZ4TruDOjM0y1dIIdOOJE+R//gUla9Ygl5cT+cnH6ELDsOTl4jZoUCustH1pbfvqzcDP9XVKknSbJEnbJEnallOd1VQgELQfxkozRdnlhEbX7z665j9DGX9DH4ZPiyY8xg+NRnLIE3Tx7XW8/2c1xb/8qgopAO+LLkJjMKDvUpNPK/X221UhBWBMSaFy/35c+/Z1mKvn338T9NBDSHo9AH7XXkvsnt14jh3rMCbg1lvBauXYxIso+esvZFkm9/8+4HDCQA4PTiTz2eeoPHKkje64bfDydyVxUtQ5nW+rM2ATU5oW7spNmTGTgkWLMGdl4RIdjfuQIRh6dMd9yJB2T2vQGrRaALokSeNQxNT59Y2RZXkB1W7AxMTExhOzCASCNuXwpkxkGbrFBdQ7xjvQDe9Ax0Do6EHB7F93isvvT8AvtP6EnmcjGU8+qb526dGDgNtuVY/9rruOgi+/xHxKiXsKefJJ3PrFkTLzGgA8hg13mEsfEkzgbbcSeNutmE6dQuvri6TX49a/P9G//oI+IgJJo8Hvumsp/vlnKnbsIO2OOzH07k1VtXiylpVRsHgxBYsXE/TQQ/hOn4YuoP73UyBoDjY3Hy3QUqVr1zrsUA39z5PqF4izhVaxTEmS1B/4HzBFluW81phTIBC0PYc3ZxIU6VUn5qkx/EI9uOGlkeeckDJlZ2MtKVGPo3/6EZ2/v3oc8ti/1NduiYPxv+5aXAcMwPfqqwm86y48R9X7XRN9ly5o7OJIXCIjkTTKn2hJkui28FMCbrkZl+7dVSEVcOcditWq+pt9zptvkvnMs61zswIBrROAXrZpMwBB99+HxssL135nnzW7xWJKkqRIYDlwvSzLncvOLBCcw8iyTP6pMkKjfYSrpYlkv6qUvPC66CK6vPpKnX77b9u+U6cpbZJE2LPPEHTfvS26tuTiQvAjjxD9809K8LpOh/dFFxH88EPE7ttLl+pyHOXbtzcpI7tA0CQkm5vv9KcwZ2Wh79qVwDvvJGbrFrSenq20uI5DU1IjLAHGAoGSJKUB/wX0ALIsfwA8BQQA71f/QTbLspzYVgsWCASnz8kD+RzcmMGoq3ux5880TFUW/MPObuuSKSsLnZ9fnXQF5du2oY+IQOvnh7W8HJ1f/WVvrFVVaAwGKnbuxDU+nvC336pXgEZ9/RXW8go8zhvaqvdhj/eECXjt3qXGmkiShM9lk7GWlpD5zLMYj6dg6NG9za4vOHdQf8yb8YVLlmVSr58NgNvAgRT/+COeF1zQBqvrODRlN981jfTfAtzSaisSCARtxo5fT5B2qICs40UU51YCENTV+S6+zkTp339T/Ntv+M2YAZIGt35xZL/5FuVbtlCxaxc+U6YQMvffaH19kWWZvA8+IOcdJTWBvlskphOpuA8ZQtf/fVSnDljFvv2cuPZaPMeOxZSejv8Nsxu05LnFx7fpvdpwFrTrMWoUAGXr16OPCCfnnXfQeHgQdNddZ2RNgs5J8W+/IUkSXhdeWKfP9rPeFC1lraoi5513MWWconybUo7K9n/Ys8+03oI7ICIDukBwFlCUU07+qTK6DwhqcJwt75NNSE28Ja7Z8VIdBdlopPLAAfIWfkbJ6tUAFH3zLQAu3bphPHFCHVu0YgVFK1bQ6591mE6dUoUUoCYPLN+6lZw33yJkrmOh1bING5Crqij55RcA3BM7ruHdJSICfbdIytavRzYayf/4EwACbrgBjYcHeZ98iuRqwH/WrHZeqaAtkY1Gjs+YSdA9d2MpLCL/s8+I+uZrNE6SyQKk33sfANFr1uASEe7QZ/ub0ZQam4Xffkv+J5/UafcYORJdYGBzb6NTIcSUQHAWsPTZLZhNVu54b6xDrbM1nx4g71QpVovM6Bm9yUmtCZ7u1i+AXokh7bHcFlG+cyeZTz0FkkYNxAYw9OmDx3nnkb9wIcYTJ9CFhdHju+WY8/JIvnQyAEnnj8J92DAAIj/5GFNWNrnz5mFKVzI+FSxerJS30Gop/ftvNO4eGI8fR+vriy40FEtxEYaYmDN/083AtW9fSn5eTelff6ltxy6dTOhTT5H96qsAGHr2RB8aij48/KzYli4Aa3k5+V8uIv/TT7EUKEl20+6+R+0/efvtdJ0/H427O5bSUo5PnYbXhReicavJ85Xz5puEv/mGw7xuXooAq88aa62ooPLAAazl5VTu3ae2G2JiiHjv3XPmZ0yIKYGgk5O8M0dNrPnBvX8x7IoemE1WvPxdObw5Ux33/Vs7Aeg+IJDBl0QRGN45g0DzP/mEqiQly7ihV0+8J03C77rr0Hp5Vbf1IuOJJ+ix4nu03t5ofX2J3buH9Ef/Rcnq1ZRv2kTgXXfhMWIEAL5TrwCg5PffSbv7Hsq3bqVizx5y3n5HvabW15duX34JsrXDfzAYjyWrrwNuuZm8/32MOTOTNDtXX+rsGwAIe/45fK+88oyvUdC6yCYTafc/QNm6dUj1JHEt37iJEzfModsXn1P619+YTp4k/9NPAfCaOBGNtxfFK3/AUlTkULbI3UvZVOFMSxX/9BPpDz2sHmt8fHBPTEQfHo7vldPVBLPnAkJMCQSdnBP7HbORbPpe+TANiFDEUnCUN6YqCwUZZQBE9Q8kJKpzuvbKd+6k5M+/8J48Gbf4fnhddBH6UMf6f77Tp+E7fZpDm6TXE/7mGxyqdgf63zC7ztweI0ciublR/NtvGI8ec+gLvPMOtJ6dI1A/+OGHyJn/PpEf/w+NpycBt9xC8erVZD79DK59+6KPiKDk118BKPnrLyGmOiGmrCyKV/2I33XXojEYyHjyScrWrSP06afxmzmDil27MJ44ofxM63RofHwo/uknTj38CHkLPsJapbj5vSZOxGvCBLwnXULl3r0UffMtR8ddgPfllxH29NNAjWXKYrY6rCHrtddUN7INa1ER7kOHEHTffW3/EDoYUnttoU1MTJS3VQemCQSC0+fbV7dhtUJ2SnGdvoBwD2b+5zwACrPKcfXU4+rROZPlWauqOD51GtaKCtXq1FwsRUVUHT2K++DBTvtPzLkRuaICU1YWHuedR5dXXm7psjsEstVKya+/4jFiBMbUk6RceSWG2FispaX0XPNbey9P0EwynnmGwiVLMfTqhVviYAqXLMVv9vWEPv54vefIskzS+aPUskdaPz96b6ypBidbrSQNH4GlqAiA3lu3oPXyYuevqWxYfpQB47ty/lW9lLEWC4filFxRYS88j6WwiOzq1BwR8+fhNX58m9x3eyNJ0vb6shUIy5RA0ImRZZnsEyX0v6Aro67uhauHHncfFz56YC0AscPD1LG+Ie71TdMpyP/0U4zJyUTMe++0hBSA1senXiEFoA8Lo+yff7AUFqILOnsCZiWNBu+LlXr1bv3i6HPoINlvvEHews+QrVY1OaigY2M8eRJrWRnlGzcBUJWURFVSEpKbG4G33dbguUr6jMvIX7gQAN+rrnLs12iI/Gwhx6+Yqlzr+HHc+vfHzVv58lVRYlTH2mKyAHymKVbgquRjyFVG3M8b1rKb7KQIMSUQdGLMRitWi4ybl57QHjVxDoFdPck9WUqfEWENnN15yF+0iJy338FrwgSn27dbC/fBgyj67jsANN711ys8G9CFhoLJhCUvD11QEMaTJzkx61pCn3sWL7vagO2NbLVSsWsXbv37I+lO7yPLmJaObDLi0rUrkk6HKSsbXVBgpxGR5oICKvfscaj3GP7225izs7BWVeF1wQVN2i0XdP99SG6u6IODnbp3XWNjifrmG1KuvBJzdf1cm5vPXkwZU1IACLz7bjUwvcsLL5z2/Z0NCDElEHRiqspNABjcHH+VpzwwkPJiIwb3zunSA8XqlvPGG5Tv2EnFjh1ofX3rpC1obXymTSP77Xew5OaetvWrs6APU4R28epf8J58KcmTL0OuqiLtjjvp9c869cO54KuvKPn9dzyGDEHj7Y33RRc5BCi3NUXLl5Px5H/o8uor+Fx+ebPPL/zmGzKe/A+gCMiAW28h67nn8RgzmsgPP2zt5bY6BUuXkvm0Y44mnylT8L74ombPpXFzI/j++xscY7PImrKygJo8dL3Pq4lNLFi8GI2nJ/43zmn2Gs5WhJgSCDohR7dno9FIeAcpBYhdaokpV4/OGxtlo2TNGvL+9zEAhr596Pb5521ehkLSaAh/8w2ynn8Bt4ED2/Ra7Y2hRw8Asl54geJfViNXVal9yVOnEv3jj2i9vclb8BGmtDTK/lZcxzlvvEnvzZvOyBplq5WilT8AqJYSUGJ2yjdvxn3YsAatS5UHDtQIqeBgzJmZZD33PABlf6/FdOoU+i5d2vAOWkbF7t2qkHIbPJjAO+/ENa4vWl/fNrumLigIyWAg69nn8Bw5Evdu3bj7g5rs5ca0NIpX/4L/jXPOyrIwp0vnsHEKBAKVsqIqfvloHz9/uJeV7yjpDs7GkjBF368AwHVAf8KeeeaM/eH2GDqUHitX4BrT+4xcr71wiYoi4v/eB6Bi23YAopYtJWL+PCw5uVTs3kP+4sWY0tLwvfpqon/9BV1QEJaiIjVIuTamU6fIfvttZIvFod1SWkbB0mXIVqvT8+oj/9NPKd+ypXqOUkBJDHkorh+pN91M2caNTusQyrJM8epfOD5tOgBdP/qI6NU/43XJxWjsrGpVR482az1nmrz/fYzWx4eea/8matGXeJ4/UimN1Ia1NCWNRhXWWS++BChu9mOTLqXw228pXrUKrFb8r722zdbQGRGWKYGgk7H9pxT1dUWJCb9Qd/y7nB1iypSVTd7//ocpPZ3SP/7Ad+YMdYu2oPXxGjdOfR3+5hu4DRiAuTq4+OStt6p9HqPOxyUyki6vvEzqTTdTvnOn07iqjCefpGzDRrzGj3coq5P+8EOU/b0W1759cOvfv8nrK/71V9wSEjCmplK8YiWFi5c4CLmTN9+CLiSEXn//ReF335Mxdy5R335D2dq1apb7oAfux3PU+QBEvPUWVqORwqVLyXrxJU7edjuxe3bXqdvYEbBWVlLy++/4XXMN+uDgM3ptQ2wsVYcOIZvNAKo1L+OJJwFwjYvr0Ba99kBYpgSCNiD7RDG71qSSk1pCQaaS36kkv5J9a9OxmJr37dyewuxy9v6dTs/BwZx3uVLINm50eJt+Uz1TyLLMyVtupuCLLyj94w8APDtQIPTZSuTCTwm49Va8LrkEAJ2fH6FP/1ftD33mGXWru2v/AWiDAsl4/AmsZWUO8xT98ANlGzYCULF3L5WHlez0pevXqy7C+ixazki7/wEqd+9BcnMl8M47sZaVYSkqwiUqisD77lXHmbOyyP/sMzLmzgUgZfqV5LzzLpK7O9Grfybwjjsc5tW4uOA/uybPWMbTz2DOz2/SmkrXraPkjz+RjUaKfliFKSublOuu4/CgwaoIrY/KI0coWbOGnHffI//zL6g8cKDB8caUFLBacU+sf/dpWxH+1psA6EKdV0jwHH92Fy0+HYRlSiBoRTKTi9iw/CgZRx0/NHoNCSF5Vw4WkxWNRqLv+c3/VifLMoue2qTOFxUfQJdefoRFnx27zozJyVQlHcVtwABC//sUsizj2rdvey/rrMdj2DA8hjluZ/ebORPXuH7IJhPug2pix7SeHoQ98wxpd91N3qcLsRQVETL330gaDace/Zc6LuvZ5wCI2bmD7NdrypNUHUnCs7oYc31U7N1L2fr1ai1E70suwe/qq/G77losubnogpT6k3KVkbzqAPKsl+rmA4v++Wf0IfVbdLou+JCTt91O0fLlYLXS5eWXnI4zZWUhG41Yioo5eatj+gFdWBjmjAwATv3rMSI/WlDv9Y5fPqVOW9hLL6kZ+EH5HTdn56APCcaYrCTfdamObTuTGLp3B72eom+XE/b882g8PbFWu1kB8XvpBCGmBIIWUlpQiYevAWRY8fZOzMa6lqekrVnq66KccgCsFisyoNU2zUBsK04MSl09jVZDl16+LVp7e2MtL8ecm4tLZCQla34HIPzdd9CHdL6agWcbbvH9nLbbAtdz580DQOvlpWSc1+vBZMJtwAAqdu8GoHzbNqoOHgRAHxlJwbJlBNx8U4PXTbv7HszZ2QB0/fADPMeMAZQ8STYhBRD84AP4z7mB1Dk3ou8agf9111H03fdovLzwvfqqBoUUgOfo0fjfcAP5n31GxZ499Y5LnXMjxuPHlXsNCEAfEqJalWxCSjIYKFu3DktJCcbkZNwGDECWZUzpp9B4uJP1wovqfF6XXIzfjJlkv/UmGXPn4j3pEjQGA7LZTMZ//0vRt8vxmT6N8o2b0Hh74xIV1eB9tBkmZaew6eRJNO7ueI2/gKIVKwEhppwhxJRA0AKWPreZvPSyOu0jr+xJYIQnHr4GFj+9GVCykZfkV6li66sXt1KSV8kFs/vQY2BQg666kvxKvvyP4kIZNaMXWl3n89DLskz55i24Dx2CpNFQsXs3KTNmAkowtDElBbeEBCGkOjhaPz+H49z33yf3fSWQPfjRRwm4+SYqDx7k+NRp5FZbjgJuvx0kyPvgQ5Ivu5zQZ55xsHjZsFZUqEIKlNichtD5+dFjxffqcW0LW2OEzP03kpsreR/9D2tFBRo3N4d+48mTqpAC6PLKK3gMHYI5Px+dvz+ZLynWLEPPnmQ99zypc26kcv9+NJ6euMbGUr5tmxp/BBD19VdqLJlbXByVu/eQ+dxzhD33HIXffEvRt8sB1P+7ffE5GoOhWffU2pgyM7FWVqLx9iH8vXcp+Xn1GY/h6gwIMSUQnAYWi5Uvn9xIaUFVnb6Lbu1Hz8E1f2yuf2E4rh56NBqJjx/9hz1/puEX5qGKsNULlErrgy7qRkl+JRNvrvsBsvevNPV19wFBdfo7A+WbNpF6o2KV8Bg1irJ169Q+WxLAs6V8y9mMprqgtDMMPaMBcO3TB5foaHWXYMCtt1CyZg2gZO3O//QT3Ae953CutaqKU9XlUIIffQSX7t2blIiypbjFx4PFQtH33+M7fTqSiwuy1Ur6/Q9Q8ptSaidy4afoAgMx9OwJoNaDDPuvElsmW63kzptP5f79yr2UllJeXS6t6tAhPMeNI/Duu3HrV/O77X/TzRQsXkLRN99SvGKlWknYb9Y1FCxeQtAD9+M+ZEib339jmE6mIVdUoHF1xXvCBLwnTGjvJXVIOt/XW4GgA5BzokQVUn1HhnHjq+dj8FC+m0TFBziM9Q5ww8VVh85Fi7lK2TL+9+LDdebc8csJkrZmqQHqRTkVal9lmWJyv/6F4Xj5O68K35GxVlVRdSxZPbYJKbcBA0CrJeDOO4jZuQOXbt3aa4mCJiJpNPhdey1BDz1E9K+/OPTZxAaAxl0pX+Q2cCBaT098Jk8m+LHHACjfshXZaHQ4t+CLLyj5eTVaX1/8rrsOrwvOTJCzaz/FUpT5zLMc6j+A0rVryXz6GVVIufTogft55zncW20kjQbvSZMAnCY07fLqKw5CCsAlIpyIaouebDIhG43ouoQR+tRT9Dl0sE7g/JnGljYj44knkE0mJLfO93fnTCIsUwLBaXBooxIrMfOpoQR0UfIfXT13CBUlJnQu2hbNXZxXwbqvkjh5IJ/J9w6gW1wAJXmVhHT3xjvArfEJOiDpDz1M6e/VMVHvvUvR8u9wGzCAgNtvOyt2Ip5rhP7nSfW179VXU/jVV7gPH4bObrt82HPPkvXyK0RUx1ZJOh0BN87BEN2Dk7fdzsm771EDtmVZpmjVjxj69KHbF1+cUdeWPiQY1/h4KvfuBeDkbbcD4HnheLwuvBCv8eOb9DMacPttlPzxB6FP/QfZaELj4YGhZzQaTy+0ns5Tl3hdMI5uixeRXy0kfSZf1no31kLs02YAaFw759+eM4UQU50Mq1UmaUsmPQeHoNULw2J7UVFqQmfQqkIKwDvQDe/A0/uDM+76WAqzytn5aypLn9uC1aIkIjy6PZtucQGUFlQREN75sg3LViula9eqQkofGYnXhRcKV8FZRNizzxD27DN12l1jY+m28NM67R4jRgBQsX272pb/yadUHTpE6HPP1is82pLAO+8k7a67HNrCnn0Wnb9/k+fQBwfT688/mn1t90GDcB80iKq77sIlOrrZ558pNMIy1SDi07gTUVVu4v/u+pM1Cw/y0wd7yU0rdejPPlFM6v48p+dWlBhJO1yAbK2bLVjgHGeZlQFKC6pIP1JAZJ+m/6GtjykPJHDX/42j78guapyVTUiF9vDh0IYMinLKKcmvxMu/fQNR60O2WMj//AvSH3qIyupA2/IdO8j//AsKvvyStDvuBJTcNT2+/05Yos5xJJ0O/5tvQjaZMOfmYsrOpmDJEtyHDcN3+vR2WZPXBeOI2bGdgFtuBsBz3LhmCanWwNCrV4crvBxiZ4H0PENu186KsEy1MbIsk3uylMKscvzCPAiMaJp1QbbKmE1W9AbFZWSxWPlrUU2cTer+PFL35zHhpr70SAhCo5X4+iUl4HHMrBj6jQ5XxxorzCycux6rWebi2/sRPVDsxGiMA+tPsen7Y8z67zBcPR1r3B3alEFVmZnhU5v/LXLOKyPJSS2hssxEeG8/h/in4G7eDL2sO1t+OE5oD28SLuzK6gVFLH56M1aLjGcHjZUq376drBeVrd/FP/1M4F13kvv+/zmM6fblF7gNHiyElAAAQ3RPZJOJpPNrck659uvXrmJC4+6O37XXogsOEcKhGtc+SgqEyE8+FrtsG0GIqTbEVGXhuzd2kJNaAoCbl56bXms4YZ2NrT+lsHXVcQK7eqJ30ZJxTEkCGTs8FA8fA9tXnwDgt08OENbTxyFJ5N+LD9N7aAgursrbW5BVjtWsWDtSducS1T8QjSQhaVr3g81itlKYVd4p3VH2VJaZ+PMLxcKSebyIqPjAOv06Fw2+Ie7NntvDx4BHfP0WpiGXdqdLT188/Q14B7rRfUAgx3fnAnTYwHNzVrbDcW0h5TdrFu6JiWdySYIOjs7J1npbzqb2RB8Whv/s69t7GR0G90EDidm5o07KCEFdOpZN8Szj2M5sVUiBUketssyE1WLlh/d2sW9tuoMrSbbKZCYXsWtNKltXKblNck+WqkIKlA/jHgMdt8bXzrYNSl4iG/vXpgPgE+TGiQP5LPzXen75336Kcyv49eP97P7jJJtXJteZo7lsWH6Upc9tUcun2PPzB3vZ/fvJFl+jJRgrzFgt9ZdykWWZtMMFfPxwzZb98mIjsiwz/44/2Pj9MXUeF7e2+x4SHuOHT5A7kiTRM7HmQ6fDiqm8XIdjyd2dsJdfwvuyy4he8xshTz7RTisTdFR0ATUutKAH7gdAe4bdaoKmIYRU0xCWqTZi0/fHVOvRpXf1Z83CA1SVmzl5MB83Tz2p+/NJ3Z+PJEHcKMUl98N7uzh5sOH6Ti5uOoIivRhzTW+6xQey9LktGCvMdcYdWHeKUTOUqvcFmUrG7ejBweyoXtOxHdkc26FYFGzZuQdOjFStWadD2iFl7euWHeHy+wciW2XKS4ycSiokeVcOybtyiB0eisFd38hMrY/ZaOGjB9cSNzqcwRd3cypMju3I4ZeP9jm0FedWqEk2d6w+wYm9ueSll6E7Q8H/9rv3OqqYKtu4EV1oKMEPP0zp2rV0efklJK0W3yuuaO+lCToo+ogI9bXnmDHoQkLxHDumHVckELQMYZlqBjknS8g+UdzoOLPJws7fUgmL9uGS2+OJ6h/I1Y8ryddMVRaq7MSPzaoky3IdITV6Zm+H4/Om9KDv+V2QJIl+YyLw8ncldliow5ir5ibi6WcgK0VZp8VkJSe1hIQJkXj5NRzAXJJX2WB/Q+xfl07+KcUidfJgASX5lfz6yX4WPraeX/+3Xx338wd7T/saLeHYzhxlnWvT+fzxDeSll9YZs+9vJTFm/Jhwbn17NF16+XJsR46a4wmoyXbeyi7S+rB3JdryWLUEc24uaffe51BwVjaZsJSWUfLHn5gyMx3H5+djPHECWZaRLRZki8Whv2LvXsr+XovPlCn4XDaZ8NdeRdK2LDWE4OxH6+VF6HPP4tK9Oy5RUfhOvQJdrczqAkFnQoipWsiyTEFmGWuXHcFsrPngOLI1k69e2Mp3b+xodI6SvEqsFpm40eGqS87goVhj/vziEKs/VKwfrp56Ug/k8eP7e0g77CikPHxc6tRdG3xRN1w9HK068eOUb3he/q4Mu6IHQV296NrXv3oNVv748iAWs5WwaB/cvFzU84ZdUbd4prNs3k3FFhwfOyIMgM8f38DRbYrlq5tdEsv0I4VUlpnYvy693t1yrU1hVjlrPnWs0L71xxROHshn/h1/qAK5rMhIZF9/Rl8Tg4urjphhoRRmlfP54xvqzDniNILPTwfb+23w0LVK8Hb+Z59R8ttvFCxZqrZlPv8CRxITSbvrLo6OHUfB11+rfSdmz+bYRReT+/77pN58C8mTLsWUmYlcXbfLVmDWvlirQNAU/K66iuiffxJuJMFZgXDz2fH3ksPs+ztdPfb0MzBoYjcKs8r5/TOlWKfZaKWi1Iibp0t901BepGT29fCpGeNiqPttPSDcg/TDhaTsySVljxJ3cvl9Cax8dxfnX92bgHBPLr8/geN7crGYrE4Dxn2D3Zn68EBcPV3wD1Pys3gHuFJebOT/7v5LHRfaw4eKkpqMwzHnhbJ1VQqJk7oREevPt69ux1hZ113YGLIss2rebvU48ZJuHNpQE0jaZ0QYF8zuw4IH/sZUqYjTH+fvJjO5mKBIL9y9Xdjyw3FGX9Mbnb71LBqyLGOqsuDiqmPHr4prc/jUaKIHBfHlfzY5uDlP7MsjuJs3FSVGutqlO4geFKwGotsz9toYYs4LrdPeVtz02vmYjJbGBzYByVVxFRrTTlL822/kvjePqiNHHMZk/ucpXGNiKNuwAeNRJU4s9715av/RsUoyP7dBg6g6fBjXuLj2K8YqEAgEHQAhpqpJ3pXjIKRAiTvqPSSURf/dBCjWnE3fJ1NW6CimrFYZSVKqmlstVlIP5gM4bGWXNBJ9RoRxsFponHd5dwqzK6hNRKwfd/3fONUK0bWPv8MHvDO69HI0j3s5yZLt7u2Cu3fNmj18Ddz+3hgkSaI4V1mHLTaoOZhNVlL3K/fbe2gIPkHu6vZ+AN9QxU1lcNOpYiozucYFueHboyRty6ZbvwCiB7Veyobju3P5+YO9dB8QiMVsRe+qZeDESKdjt/xwHNkqU1Vuxt27xvJncNMREeunxoIBaDSS6mo9U7h5udBa3901bsr7UfTNtxR98y0AupAQIt6fjyRJnPr3XKqOHCHl6hl1zvW+9FLQaihe+QMAFTsUK21HqB8mEAgE7YkQUyhWjL8WHcLFTUfs8FD2/JGGVq+hKKeCpG1KcHbssFDCon0BOL47h7VLD3PJ7fEY3HX8391/EdU/kItv68fy17aTfaIEN28XfIMdt84nTopSxZSXv6tqwbJhL6JagldA/YHK1z4zjLxTpQ7XsWVSt5iab/2oKquxZiVcqIgV+wBzW0C7RlfXo/znosMUZCgxSGZT84VcQ9his2xpBbr29Vfv2TvQleJcx/iwrT+mADi4QgFGTOvJhuVHyTtVRkWxERe31nG3tRfWsro7LXv+9iuSi3LfPVauoHznTrKeex50OsLffIPs19+gbN06wt94HQCfSy9FtljVjNG+M64+czcgEAjOKU4Wn+TzA59jls3cGHcjGWUZlJnKSC9N52jhUa7sdSU6jY4+AX3adZ1CTKEEgSs11TQMmdSdwqxyeiWG8PtnBykvUuKI+pzfRd1ZZbO6HNyQQd+RSi2qlD25HNqYQfYJJRWCvUvNhv12+h6DginJr4lRGnttTKt9SNtEXM/EYDVuSe0Lca+TH8lWS850GpapqnIldmbiLXEERSrV5F3tAqXdqhNeDr00ijULDzqcaxNS9vO0hLKiKsqLjQ4WOBv2bdc+O5wF9/2NxVz3fj18HIP0gyK9mPLAQJY+t7laTHXu4Gpjaiq6sDB6/fkHpuxsZKNJFVI23AcOpPvyb9XjiLffQrbWPCvPMcquq95bNqP19j4zCxcIBOccmWWZ3PTrTWSWKRtjvjnyTZ0xy5OWA3B3wt3cMaD9ikOf02JKlmU2LD/Grt9SAbjsvgRcPfVcdm+Cutvr0EblTdS7aPH0M9B3ZBgH1ivWJWOl2WFn3v51p9TXYdF1K4cbqsWUzkWD3kXLwAmR+AS50XNwcKsm0HT3duHaZ4bhFejK0W3ZDrFbztC1xDJVrty/q501ymAXJG8LwI8ZFkbMsDA+eXQdFSU1wsk3xJ3CrHLKCuuKz+by+eMbsFpkvIPqOsUM7jU/6hqNxJhZvUnemYNXgBt7/0pT+7r2de5S1VZb1vQtSB3REag6ehRDz56AUkusqTjLTC2ElEAgaE1kWWbxocUcLzpOlHcUr2x9BYD/DPsP3gZvCisLCXEPwc/VjwivCFKLU5m/az5bMrfg79q+eco69ydDC9nxywlVSCVOinIQQDYrkm1bvM5F+TAJ6uYN1WIqJ7WE6IE1YspmXek+IJALZtc1OUoaiRlPDlEDrbV6Db2GtE2Kfpv1adojg/Dv0nDhUI1WQpKaHzOVk1rCrx8raQ/st+3b7zisbW3r0stPDfwG6JEQxNHtWRTllDfr2rUxVprVmnbFOUoMmC3GDZQAfHv6jOhCnxFdkGWZoZd1588vDjFierQqmmqj0SrthjZM1tnWmPPzMR49isfw4e29FIFAcI6wI2sHb21/iyMFR4j1j2XueXOJ9o1GQkKncfx7ujZtLS9vedmh7cHBD3J1jPNQgkC3QD6+6OM2W3tz6LyfDC3EWGlm88rjRA8M4sKb+tbZSVY7BYGtRp6fnYss/UghxXk1QeS2PE0jpvWsc76NwAivVll/Uwnr6dvoGEmS0Lpomx23tOWHZMoKFVelveXHxVV5Vm5edZ9B7WSXbl56wqJ9ST2Qh2yVT8tCV5JfyeLqTQK28is6Fw0x54WScGEkGk39pXMkScLVQ88ld8Q3eA2bmLbdW2fDePIkxyZMBMB3+rR2Xo1AIGhPNmds5tP9n5IQlEBuRS4H8w4S4BbAY0MfI9wzvPEJmkByYTJW2cob299gT84eAHZk7+CqH64CwE3nxocTPqR/YH/SS9N5ZesrrE1bS7hnON9P+Z7komR8Db6EeYS1ynramnNSTFWUGvnlo/3IVpmYYaFOt+TrDVq69vXn5AFlp5otrsg+3shisqo5owBsaZPshUVnQafXNEtMleRXkrI3Tz22jwfzCnAlsKsnI6b1rHNebQuUm5cL4TF+HN6cSd6p0tMSm9kpxeraz5vSg/Ov7oW7l4v6nrUGPsHupB0q6JRuvtwFH5Hz5psA+F13HYboM5MjSyAQtB9W2cqBvAN8f/R7/kn/hyGhQyisLMTX1Zfvj34PwPr09Xi5eOGuc2dP7h62ZW5j7nlz2ZOzhyeGNa0MVFFVEf+k/0NmWSZv73gbgCnRU1hxbIU65v5B9zOu6zge/uthjhUdI9wznKKqImb/PJtBwYPYka3sDDZoDTx+3uO46lzpG9C3VZ9HW9P5PhlagaStWaRXJ8lsqCivLXgaaixT7k7ij4ZPjWbjd8fU4/qsUh0ZnV6DpYm5jJJ35ai75UK6exMQ4elwzzq9lhlPDHV67rAroknZk8uuNUqdPu8AVzWFxLLnt3L3B82r1i7LMmXVmwTGXR9LQJe2KbJss0g6C1rvyBR8/bUqpALvu5fAO+9s5xUJBIKmUlRVxM7snUR6RYIE7jp3DFoDfq6O6XBkWcZsNZNaksofqX+QXZ7NpoxNpBSnqGPSjzqm/vlo4kdEeUcR4h6CJElsz9rOnNVzePyfxwGY3Xc2Xb271ru2tWlr+fzA52zO2Fynzyak+gf2Z0DwAK7oeQWBboEsunQRu7N3M7zLcH48/iNz181lR/YOwjzC+OKSLwjxaJuwlzPBOSmmyouVYOe+o7o0mEbAPk+URqu4iZztuBs4IVIVU1q9plWDyc8UunrcfBnHiijOrXBIUplWnUcLFEvduGtjm3yd8N5+hPf2U8VUSA8fNC14Xive2kn6kUJAib9qK/zCFDFlc2t2BmSzmcz/PAWALiiIwDvu6NRpHQQCB3YugoBoiBzWvPMKUsAzFPQds9YlgMli4j8b/sOPyT867Y/yjkIjaSioLMCKlaIqx2L3bjo3unl345kRz3B++PkEuQWRU5GDm86NlcdWUmGuYFiY43MbHDKYCd0m8NuJ3wB4cv2T/O+i/6HX1DUOpJWk8fBfD1NpUUJbLo++HA+9B9N7TcdN58aig4u4pPslJAQnOJznofdgRPgIAC7tfinZ5dnE+MUwMnzkaT2njsQ5KaYqSk24eekbFQEx54WqhYEb+hCSNBKRcf6k7s+vk3ags6DVa6gsM7FvbTrRg4LUpKTfv7UDq1mm+4BANWdUqZ2gOF1xoTdoiYj1U4VU/NgIjmzJbOQsR0oLqlQhBW0bz+RTvUOwNbWILMsgy053yrUG5iwlR5r7kCFELvy0za4jEKhYzJCfDEG9Gx/bouuYYIWS54x/HQd3Jzu5zFWwZxmsvBceSQLPYNj2Cax6ELqeB9csdX5eG1NprqTKovzd9DEoG2PKTeXkV+bj7+rP+7veJ6cih5+O/0RXr67MiJmBv6s/VtlKemk627K2caTgCGarGVmWscgWPPQeDAoehLfBmwcHPejUwhPsruzevbbPtfWu7ZVRr/D8yOeZ/N1kdmTv4LP9n3FL/C2UGktZf2o9A4IG8MHuD/g2SUmdMn/8fPoF9quzk27ueXMbfQ6SJHFTv5ua9tA6AeekmEramoWnb8NFfwG1PEttZr+oKOsvntigxkldcns8v368n+FnqGZba6PTa0g7VEDaoQIyjhYy4aY4AKxm5QYrS024uOqQZZnju3MJ7OpJ7slS+o0+vWDFW94ajb0ucfXQUVVhxmqVm2ypyjha6HBs23HXFngHujFiWk+iB52+9cucm8upxx8n7Lnn0IeEkPX8CxQsWgRaLb3++hNdUOta1ozpilk/4I7bRfFhQetgroKKAvAKhbxjijjR6GDYnVCarQiV9G1w82/Q1bmrv1WotCs4/7/xcP134BflOObXJ2HLAuX1+8NgxpfK+gBOboZXu0P4YLjpF9A2LzSj0lzJ8aLjaCQNvf0U4djQF+4TxSd4Y9sb7M3dS15FHjLK39WxXccyKHgQ83bOw2itSQ+jkTSKhWfSojouPXuMFiNaSYskSWiklv/902v16LV6Xh/zOjesvoF3drzD7L6zeXvH2yw7vMxh7OeXfE5CUIKwdldzzomp0oIqTJUWCjJPfyu+V7X774aXR6olUnQuWibd2b9V1tge2HarARzZksX4OX2RrTWFiE1Vyn2e2KcEneeeLG12fJM9tQWT3lUHMpiNFtUC1hiZyUXoXDSYjVb6jGzbHR+SJNVbjqYpyEYjqbfcStWhQxSvWoX/TTdR8NVXSqfFQtmGDbhU539yi4tr0pwVe/ei8fTE0L07ltJSct58i6JVq/CZMoXQJx7HdErJe+YS3jq7cwTnGEXpsPoxCOkHHoFwfC2c3AIlmRDYG3IPO45f/3bN608vgekfQ9wVrbceWYa1r4FXGETapffIT4Z3BoCkgaBYmPOjIvpsQgrAalbWBNB9tHIvAOnblXuKcnQzGS1GCqsK8dR7sv7UenwNvgDsyt5FWmkamzM2k16qfFmRkBgdMZpZfWYxossIdQ6T1URyYTJfH/laFSKx/rGM6zoON50bKcUprE1by18n/wJgRswM8ivzGdllJFf0vAKT1YSrrmFXpIu24RyCp8ugkEHq6yMFRygzOVZOeG30awwMHtgm1+6snPViymqxgiSpH942t9SwK3ogy3KjqnriLXGUFjh3ZXn4GKBubs5Oid7g+KPw8//tcditZyu0a3sWznbqtej61WLObLTi0sRQhooSIx6+BmY8ObTe/FAdhYKvvqbqkFI0Ofu113FLSACTiYDbb6fgyy859di/1bGx+/c1yZKUcpWSe8Xrooso+eWXmmt98QWlf/yBvlpE6cLad2uxLMvkVeaRW5FLqHsoPgYfKswVuGhd6uSZKTOVUVhVSFFVEanFqezM3smpslPoNXoO5B0gvzIfX4Mv7jp3ZGTKTGWUGEvo4tmFSK9Iunp1RaPRoEGDVbZSZirDz9WPALcAAt0C6eLZhR4+yu9+VnkWHv/f3nmHyVGc+f9T3dOTZzZHhVXOkSQRhEQwGSNsTDAO2JhkHzY+bPxzOjjAxj7bOAD2GewzNvEsGzgyJgchQEignLVKm3OYPN31+6Nmk7SrtLtaSdTneebZ7TjV73R3feutt96yAoTcIaSUvZang4SdoD3ZTtyOE0+rTywdozXZSkV7Be2pdloTrcTtODPyZ1AaLKU2WosQgkQ6wZjsMbgNNw3xBmLpGONzxjMsOAyPuW8P+SGhpQJaK6B4BtStg/yJyuu07hmcdc/QZghMCa7wcDyjT0GUv62OG/cpqF3bJaSmfw7O+A/47Sx44lpwB2Hs6VC7Romyg/BiNG/+F7HHLichBMnMJy3A67YwxpwGTTugMTMAqGkT/DrjKbIsOPfnMPpURGsV/PNq5UW78JeIRBtIB+Mv5/P0srupr3iRs8rOIpaOsb5xPY9veJy2ZFuv5cn2ZJPlyeLOk++kKd7E+9Xv8+auN3lz15uMCI3AdmzidpzGeFds6QVjLuBLU760x5QnGxo38MqOV5g3bB4zCno2xk3j8PAmr29cz5qGNcwunE1xoJipeVM5Z/Q5Q12sww4hpdz7DkL8D3ABUCulnNbLdgH8FjgPiAJXSSmX7+uLjzvuOPnhhx8eVKH3l6d+/REVG5rwhSy++ot5OI7kj998AyctWTLaJDQsyB++cOygluFASNfXY+bm7jW2xUkkaPzr33Dl5hA48URcpQMz6e4rD65hw3s1fW4XAm647zTe+ccm1i6u4trfnDqg7t31S6p49a/r+OKdJxLO379pfZ/7/UraGuNc/qNB7E4YICpu/g6xjz7q9BZ1UPKTn+CbNZPW51+g6e//i11Xz/h33saVn9/nuVK1tWz/whdJ7dixx7bsz11C86KuKReM/Dxy/vUEtmPTlGhiZ9tO3qt6j6AVxJY2tmNjSxtHOjjS6fzfljaOs9ty5m9rshUDg4SdIJaOdQqMhJ0g5A4xPns8HtOD3/ITT8dZXLmYWLorH5shlNDxmB7cppu0kybXm0tropW2VM8KzDIsRoRGEElFmFEwgzxvHi3JFiKpCJZh4TJcGBi0p9pZ27CWaDraeS0SSdAK0pxo3sNOpjCx5Z6jV12Gi7STJuwOE7ACJOwEAStAVXsVaZneY//uuA13j66a/aHAV0CRv4iEk0BKidf00hBvwGN6GJ01mjklcwhYAdqT7ZiGScpOYUubtJMmLdOknTRJO0lLooWWRAvtqfZOoZqyU9REaxBCYBkWpYFSsr3ZxNIxdrTuQCJJ2SlSThI7Uo8jbWwEtgAHsIXAySzvfp3ZhsVwM0A0kEukdi056RRp06I9pwzDMDGdNK7mnfjTCVwSGk0Dd85ogqFSglaQoBUky5NFtiebhngD0VSUWDrW+Uk6SRzHoSlWT0X0wGIpB4qrpl5F2B2mJFiCy3BRE6lhYdk5ZPnywOwpuutj9dz70b08semJzi68uSVzGRYcxvzh8zlt5GlDcQn9oi3ZxkmPdXnavjzly3zn+O8MYYmGHiHEMinlcb1t2x/P1IPAvcDf+th+LjA+85kD/CHzd0ix005n+oOO6Usee2NrZwxQxfZtiPIEdBNTTiJBfI3K6C1cLtxlZZhZWUjHIbltO+maajBMfNOmYgR6j6dq/sc/aHn2OVKNDQTnziXrgguoX72cutpynGUrWXHZLAJvLGfcshpSxXkYp51M5bqlpAzJ9OeU27zmuFFEzppDeNlmqibmkh5ZwtRHl+LOyia9eSuunV2iRwSDeCdOJOvCC8m+7FIlcKKNKq7BdEO8hZrmctY0bWBXWx1F7hzaPILGdARHGGR781jSsJL0zuFMYV6f9pQS4tEUTdVRcor8CCGoaK9gWc0yqiPVCARXTLqC8pZyPC4PY7PGHlDLqmt+wP1Lz7Dm7Qq2razvkdk8lo7REGvAEAa2tImlY53xBEhAqDwmHR4G21H75PnyyPZk43P5CLlD2I6NaZg9YhAc6XSOnmlJtHR6GDymh5poDQYqxsHr8lL+xrOE316FcByWX6UehZPLl5K02oiN9zJpU9ckyz/cfi81Vi6BcQGmf7qQ8/9cz+pzzsBM2tTMHsGu684jFfTSnmqnOdHMyNBIRv/pZUp2E1LrJwV54gwfW7P/xcTLPAyrSTF3A/xrVhOvLTpjD/t5TA+mMDENE1Ooa93jr9H7eo/pwRAGIXcIn8vX+Uk5KT6q/YjmRDMJO0F9rJ6AFeDsUWczLnsc+b58trduJ+2kCblD1EZrcaSDaZg0xhsJu8MUB4rJ8eQQdAfxmB6OKzoOv9W/QR3RVJSGWAMtyRae2vwUG5s2MjVvKiNCI5DITnHY8X88HVdixUnjc/loTbZycunJjMkeg9f04rN8+Ez1W3eMmgpaQUzDRErJxqaNNCeaKfQXKlGXWWeZVmcgcWV7JVWRKiraK6iL1pFyUvgtPyk7xaisUSTsBK/vfJ3Xd76+12szhIHbcBP2hAm7w/gtP37LT9pJE3QHGREegZQSiWRH6w42NG0g7A5T6C8k3+XHatiCu24bhpQYLh+u/HEY/jzMSD2GdDCLpmNmjyBkhZT4clK0JFqojlRTG62l2B3GVxqmomEduQVTKPPldgZFp2xJrHELKQEjU2nsSDPtvjwqEi20J9tpSjQRS8cIu8M97iOfy0fAFcAwDPLjbVzQ1MKw8edhjVmAx5+H27BwtewiUv4mTL8E0T1WyHEAgZQpyIxC6xA3HXRfTleuIPftu5mQSLJ2zlexJ1+Ag8OJJSfued8l2uDuKSAdOOUmGHkSeEJQMoN8Xz63nXQbt554K450SDkpPKbn8IknaqtW5Q6X7vchIXfPnH/5vr4beJr98EwBCCFGAc/24Zn6I/CGlPKxzPIGYIGUsmpv5xxsz1RLXSsP/7jr/BffNZr/ufFecoLnM2PVH8hvUMk26085hrxrrqTxxefwvfgu/qZ4j/NEctxYsTTueM+0AVKAkJDIC5IePwLcDr7j5pD870fxRtOUF0FZDfTlY6oLQyAB/t16EFMmWH3oieYAZEdg2VjB88cLFqyS5LTBtB3qN0wbEPcL6vJtPp4M5QWCFcUGk8oFX3vJoaAVYm54bYZg+ThBMA5WGkqaJIngORQnLtjjO5tyPyCnUXl+Pn/eKp55ewo5+a1MmF/B7RX/YmX79l7LGrSCeF3erkoYMISJYZjkefNoT7VjCgMpJUknxcimKUx6/1O0X7CKvLIAUkqaEk2E3WGaorWMzB7L8OBwYukYFZsbif9TpWpoy6/ho7lPkbSTlLeU9+ptOBB8Lh/xdByJ7PScdLjti/xFVEX2elsTjkj+eI+NmXms/uvqHLaOtLj13gaiRWFevG42dZEafHGHsm0xInOnkLCTRNNRREMz1/9yPYFo1732p7MM/nWswTUvSRassFldJpi9VZ18wzgvEzer+/X/bjyG9pmjCXvCWIaFKUySdpJcXy4hdwhTmGR7ssn15jIqPIpsb3a/7KQZfKoj1exo3UFJoAS/5e/0MJnCVH93E/wHzKKvwBo1SSyn/xhOHWCvQzoB29+FeDO8fTdUrwRhwuf/F8adiYMkno53iRY7peKe0glV8b/5c3j3dxAohO9uGtiydeeDB+D5zLV/f5cSSL3xxs/hjZ/uuf62lj3XDSYVy1QgfWslROpg3s2QNw7qN8LqJyAVA28Yyk6CyRcpWz55rWpg54yCa15XoxjtdJeHzbGV7XcTfy9ue5HvvvldABZduIhJufufBudoZG+eqYEQU88CP5NSvpNZfhX4npRyr0ppsMXUk3ffReXGLgfZ6W98g6XH3kJbqIzi8p/RENhF1AtnL1fX7wBry2DbeIfWkESkBIFWg9JqSFiCukJJTb4kHJMc+5HBhAqoD4FjQGG3ZyltwGNfDjCiwMJXk8CqSFFUZZNtxwknHFLSj892GHZSFe2OycrWAEUyTVkyjRWwsYI2NZsDNFd6YVIc91Y3ie0+0vkpti6MkpV22GW5MQxBwE6xxbJoSpmM2WyRVWdixqG0ShLsJb7eVVZCOpmAqsY9tu0qncfGCZfvsX7iypvxMoYVM77BpK2/YOOob1JQ8zZm+5P8/VSDQEGS+S0xQqFhPC7iTLbcTHfSrPH5sKONOHYCx3DhODaOgKQvm1rLTcB2cKdjkIxiAXZiFhM2fINlZU+xvOgdbDOFS8Lwpukg4mzL6XqZXr/ktz3KuOys3xD0ZBMOFHHssJM6W4VelxcpJY50EEIQTSmjuE032Z5sXIYLt+mmLlpHS6KFtlQbjfFGHOkQcodI2AkS6QSmYRJPx6mL1TEhZwKjs0aT48nB6/JiOzbZXnWu2IuvYNz6awAKvnoJDY8/hys/H9/sWbT83zPkf/3rFHzzxq6C22mI1KrKIvNSS1VXk66tpf7++2l/5VV8l3+W0h/8iG3zT8duauo8NOvihYTOOotdN6jh4ROXfdint1TzCaZqhRr5NuxYlV9p44uqwqxZC6v+DhPOUSkCBtuDkozCQxfDTjXtE+f+AuZc27VdShVE3txL46xoGtywePDK5thwex50eKzO/im4PHD819TyisdVAP4/r1E5reZ/D+6f33X8Na8rj0+oeI9TDxjRRvBmKcH0x1PBToI3WwnV7gSLoOxkqFuvYtm6kz9BHR8eDsNmK6EbLlW/TfMOmHEZLLxvj6/e3rqdgBXQnikGX0w9B9y1m5i6RUq5rJd9rwWuBRg5cuSx27f37tUYCGKRJI/cdRuJ+jMBmL7q26yariq6T1+2hQJRT3VTM++/twLR2kxldiFtgWnYvgKijRW870wmLU3GFQRYUu8mhYsJ+V4mhxMsnCTweB1m5QZoqniPhvqtxMmndelyxg63GJXeoW5qbza43MrdnI6rF9uIE8DlVS+IvHGQjkGsGVJRdWMXTIJIPdgJdZPXrSe+cydbjRGIGWeyLeLm60+WIzGYUBggGk8yqiDI5+eOZsbwLGrbEoTtBKF/PIzhcdP0yKMETzuNwpv/vXPofWzlSiKLF2MEAvhmzMAzYQLv/vvdrOCkHjacE32BPFGDM2Y6L1TOIqt5Ka05cwk0PcOsza/iyUwC3fNHhvx5BVieCDRX4oTKiLeFSDVFSTW04yRsrIBDolESnpKNNXIk3uFBWpMGT5VfAcAE38uckPMHzOB0/rrxPwE4a8Yviad3YLjH8sbH3+r8umJrPZ/N65bT5MvPgD8PskaoYNNkRC0bBmxfAh8/DLOuVKOBDqICkVLitLbS8OCDxFeswFVQQGzFSoTlIrFpMwD502PkTWymvdJDzcdh0lEllMb+5BLcAVv95oYFVR+rIdqmG0qPUfdAsAiGHw/N29l693skamNgmmD39LhNWrmC5M6dbD3/AvzHH0/ZQ331wms+EcRb4N17YekD4A6p+z1vHGx+pff9TbcKMj/pRph52aErZ+06laYgVAI3vNuV56liGTzQy+jgc/8Lxp4B+QM74GUPWirg9Z+q90MHx38NatfD9ne61l18f5e9ti2GB8/r2pY1Er750R7xVP0m0gC/mgAI5XFKxeGGdyB3jPLi1axRXqfiGeDL7krz0FoJdRvUwIKiaVAyE167E97+ZeeppeXHLpyGq+IDteL/7VCiTdMrgy2mDstuvg7uv+lNUnGbiTdOYcM9SqnfcN+CPXISVTbHyA24cRmCl9fW0BRN8YMnVyEETC0NM74wRHM0yfvljUQzcT3HjMzm83PK+PM75bREk9x05gQuPb7v9Pvd2VrXzstra5g+LIthOT6y/W4+/8B7uF0G8ZTD7JHZTC4OMaYgyNrKVn7y/Loex39m9jDWVbdRluvnxTV7BmiOLQiwYGIhVx43jJVV7dzyz5WcNDaPU8bl86kpRZRk+XhvawO/fXUTm2ramOHycOKunl2ZX/zJiYTzfDiO5NGfLaZlRyZz/EV5zD9tIk2PPEJ8zRqcRBLfrJlg2zQ9+hjp2toe5zGzsnCPGYPweBCGQeTdd/cob8KdxeKTlAs9r8TDJV8toH5HjH8+pEYUXnrTJAomlfLATW+SzKSjGDXe4sSijzFjEVKr3yGxfhXpmIkQsqt/VYI71yLZYuIkEjhpg2SbiXQELr+BWToO79SpZC1ciJmdhWf8eJru+QlmYgdmOIgYOx8CubS99ja+WbOo+ekd2E2te5QfwPSbjD6jAivgwLFXQcFkZO06Wp5/BV92BI8341kSpnKpOylV8ZXOgqbtIO0uIe0Ose5vPbsbsk6eTMvidRR8+dPk3/T/kN5sWp9/nuD8BZjBXrxSdhpqVquXrE7YefTSuBUe/4IaMefNUkI9Wg9WACaeoxoWAFnD1fb8CVA4WXlfhoKKZfCnM+HUW+C0TEPoH1+FTS/DtW8oL0z2SCUQBzv55+7cN1eNZuyLb62EnLKu5Td+Bm/c1bV89Ssw4vi+j5dS5eOqWgHjzty/5/L+06ByuRLAI+bAp26HYcfs+7i+SCdgxeO8UWlw1WIlZmea23jC+hH29Mtwf/a/D/7chwjbkfxj2U6eXVnFF+aWcfbUQfQKdmOwxdT5wL+hRvPNAX4npdzn8KpDJaZWvLqTdxZt4upfzeOhH75LMNfLFf+x//HxKdvBZYjOQMJk2uHe1zbxl8XbcKQkkhFWbtMgaTtcccIIJhSFiKfUaKKWWIpTxxdwzMgcUo5D2Guxta6dhfctpjW+99FBu/Orz81k8eZ6LpxZymmTCjvXVzbHeHzpTnL8FnlBD797dROba9v3ei6/2ySatBmW7WPWyGw++riGK1u7Xq62AamFw2iIJPn70p1cFs6idJsK8Dr3uumMmV1ASzRFYzTJ6PyuilxKSXztWoRpYvj9ONEonnHjEK6u1pqTSOC0t4PjYAQCxFavJllVw6MvdJ3nUyt+RHMqwNLj1Mu2qGYpcwrLeVpc2rnPyUt+iKfbSC3htvCWFYDjINNppG0jYzFSrUnc+T7McBiRU4oVsDEilaSrtpE2SoiW73/MgzAkWaOihIbH8ZcKovV+PIU+LF8SmUwg8ker4eEnXNuzspJSecncAdWtYJjKVb97hebYqhLxhKn84Y9peeopAEy3w8jT6ql8P4cRpzZg+R3w50O4BNJJ5X1zB1R8RGulaqE2lkN7tYqTuPxRKOojf1W0UVXIoPa1/OA+yKBvKVUOn7YqyB2rukeqV6o4DsunupdO/IZqYcdbVMWSNaLLDo1bIdoAyXZ1fPbIwe+C6gvHVi3+SL3KU+QJqrKtfkKVacwCKJisrqu9BvLG9+2VqN8MHz2kzhEeprwtoQGYh6xll6psU1G46F6YerGKPWqtUJ6Sw1VEP3C6alR87WXl/fn9HJh4Hlzx2NCW66UfwpJ74VN3qN90yb1d5Yw2wfgze+7fIY7sBPxmOpx/Nxx/dd/nf+F78H5GrJz9U/Us7E5juUr1YAVg3TPwXqbr7do3oHR25mslaUdimQaxpE0ibdMYSdIQSSKAllgKQwiEgPZEmne3NPDCqiqy/W6OH5XDWxvrqW6NIwR8fcFYVuxsYcG2X/MV10uYt2wZkszwfeE4kgff3cZLa6ppi6dxuwzWVraStLsa/wUhD3cunDbooqpfYkoI8RiwAMgHaoBbAQtASvnfmdQI9wLnoFIjfGVf8VJw6MTUunereO1v6/jCHSey6K6lTDi+iFOvmDgg5161q4XVlS2cOqGA97Y0cPOiFfs8Jstn0RJL4TYNvnnGOFrjaSxT0BhJMmd0HgtnD2NLXTvZPovq1jg3PvoRW+sjzBufz0NX758IlFLyzuZ6SrN9nPGrN5k1IpuHrj4Bv9vFI+9vZ11VG/XtCVpiKR744nFk+S1+89RarBeVh+slX5J1bptUpg4LeV0UtTlcHFEV3h/CMZKWgccyaIun+d45kxiV52fRsl0EPS7u+sx0Ap4Dd3XX7mhl0U/VPbGg8WGi0+bzQWWXp+/0N77Bh3O+T8qXyxnO03hGluGfcwJWaSnu4cMxAoEeom2vtNfC/QugtYLmrT7aq7wk21xI4cFdnEPO13+AqHofIo04zTU0vvAh0VoPuZPaKfrpvSouwZt1wJmTDwSZSuHEYiTXfoCVn4srYKquy+pVsGupEijVq9Qoo5ZdytOVN16JtHRceSDCw9QL2TCVyBt3hnoh122Ajx9VAq/qY1X5duDJgrNuh9lfVMd1UL9JHRepU96FSecrO8SblcDY+QFs6H0usT3IGw8Nm+mMU0F0+78b7hAUT4cZl8LMK9T5Kz9WYq29RolIT0jFfxROhlGnQlslbH5VxYGEhymPX8ks1S0SLIBJFyghilTn2fk+rH9OxRSFS6GtRtm2dq06tjcMlxJY3XF5lZgpmaXstmOJKtuuZVCzque+woDJFyobe8JKXGYNV96HWHNGbKfU71r5kRKksSZ1v408Uf12qZgSy/EWuO4t1Y1zpPDanSoo/ZRvd3U7HX8NnP/LvR832KTiStAXTVGe3dX/gMmf7rNxIaWkLZHmo+1NnPrEMYgZl8H5v+rz9PH75uGtW0lKWFgyxatjvsf6EZdy8exhFHptWPV3zBduQTg9QyiecZ3Nfb5raUkKsnwWW+raEQiKsjxUNMVw9u0T4fwZJWyti7CuqrVz+Y6LppEbUIk///fvD3PZ2m+wU5TwQda5fPamX++n0fZOYyRJyOvCMo19znCRTDv8+KnVLN/RRGs8RU1rguKwl+rWOJOKQwzP8RFL2RQEPYwtCHLG5CL+/E45/1y+i2+cNpbvnj24AfL99kwNBodKTG1bVc9z961k4bdn83+//ZjZZ43kxIUDP+WL40je2FhLWV6AXU0xZg3PpjmWZFdTjN++sokPtqmg7xNG5ZIfcvPtMycwvqiPUSO7sb66lZKwjyz/gVfcm2raGJ7jx+fee5qCeCTJn29WsQHX3LuAnU1RYkmbiuYYp44v4M/PrEe8rFIy3JsTx+U2KQh72NEQJb3bk2yZguvnj2VbQ5T6tgRrKltwmQbHjMzm7stmEfaq6/jFS+tZU9nKGZMKmVgcZkppmL9+660+y3jND8ax6M+V5A8PcvY1ezhJDxwpYeNLqvIdeRIU7uVBjLeQfuP3mHOuROQcfCb0IWHrm/Dq7Sq2olu+J4SpkiqGS1RXYPF0JSA2vKAEUodgyC5T29Y/u3/fZwVU7NrO91Tc36hTlEcm3qJES6ItE+MxXcXONJUDQgmMYKHySJkuJd5qVisxGG2gh+Ay3V1dQak4FE9TArO7wOno0mosV9+ZmQ8Nw1LCszsd2bOjjapV7vKo2LW8cUqwOWklUp208ip5w6qrpnY9tO5Soq9uHax9ukuAGS4lsMKlKhbu7J+q8+18Dz5+TAWA744w+xZw3cmfqAKekxElNOdct3+/zeHCppfhkUu6lscsgMsfO3iP6CCQSNt4XCardrWQ7bcYkevnmRWVvLquhqTtUNkcZ3VFS+f77++eOzhBqC7C301+lGprBE79ZqZMPwa3adCw4nmu3/X/+CsXsoIJ/Fb0LRwfSJ/H8cZG3hUz2eieRnzkfDbWtrG1LsKUkjDHjcrBEIIlWxoYle+nJMvHqDw/RWEvXreJ12USSaQJel3k+N3kB93kBT04juSjnc24TYOppeEewqZi20aGPdjVRbnl+m2MLe57KpsevPMbeOVW+FFtp5c5mXb467vbuPvljUwtDXPW8DRf+XAhz824h3lnX4IhBG3xNDkBi5fX1vDrVzbSEk119tj4LJNYymZ8YZDLTxjJV08e1WeqCduRJNPOPuu5/vKJFlOt9TEe+tESxh5TwJbldZz/9RmMmnHoRyVEk2liSZu84GGS8Xg3pCP5/ddVTpvepomJtCZ48BY1oub6exdgmKrr87mVVXzj0eVMKg7xvXMncf+bW1lT2UJrPI3XMhiVF2BScYiGSJK3N9UzMtfP3DG5rNzVwvrqngkaxxcGWbixj4okkzT0gW+/RfHMPIoXlOAyDY4tyyFlO1iDOC/fUUMyqrww1SsBoboj3L3EWkkJHz+iuutaM0lGa9cqcTFloRJfuWNg2zuqCwypPDLhYUpsGNbAdi/ZaVj3f7DrQyWgjrtafefuXaSJdiW6PCElnrrHtqTi6piqFSrOpX6j8qol29WAhLGn9W6LAyVSr0ZSmW7lKdpbXNLOpao7R0rY+oYSdG4/tFapsueNhYat6nomnqPsi4CWnUqIDqJXdNBp2QW/znQ7h4fBTasHrUtyZ2OUtnga25G8vbmO5dub8LtdTCoJ8bljR3D7s2tZuauZ/KCH+RMK+PJJo7jt6TU8+VFFZ/iGZQpOGJ3L4s0NdOiPLJ9FUzTFlJIw4wqDHF+7iC82dY2G+3P6XK52vcAPUlezyRnGzdYixntaSF33NlnhHKoryil55d/wVizpUd7Xpt5F+PjLmTkiG1fmyw5Jvqp0Au7sCh95ZsqvufDS/ZuI2LmjCMOOs+nK9/n+K4185pjh/ODJnt7Ym1z/4CbXE/zTnsfNqRv6PNek4hDPf3Pefs/Reij5RIsp6Uge+PZbpBI2Lsvg6l/N60wQqenJfde/BvQupqSU/P6G3sVWczRJtt+9x/7dXwBSSv7+4U6+90/1gBkCrps/lq8vGEt9e5LX19dy+7Nr+W7zntnP11hpJqdMUqcX4nmtjmf8Sda7lejKC7hpiKjA+HOnFe81o31rPMWG6jaOH3X4xANoNJ9YnvsOLPsLfHcz+PbPA9JRXzlS+SltqeKGmqNJ3tlcz9+WbGf2yGwKgh7WVbWxoaaV1RU9B4wEPS7aE/uOV832W3hcBmPyg6yvbqUpmuK86cX88nMzcZsGZrdYWoBUaw3W3fsImD/5WyqAvDvVq9RIY9OlpufJ3r9BTIPCPccqL+HSPwGw/Yo3KZs4a9/H3aZGAJ6f+Alr5OjO1SeMzuWhq09gZ2OU/EfPIbt5NY+mT+ePWd9kYlGIf61VvR2PfG0OeUE3EwpDh6WI6qC/GdCPaIQhcDJu2OxivxZSB4kQglCel7aG+B7bdhdSHfvvvnzZ8SO5aNYwnvqogkklYWaNyAYg5LUYdfIo8kMeHv7LKubHe7a4C4YHMcrjfPBuJfOwGD0pl9n5Xh77YGenkAJ4YXU1dzy7lpPH5XHMyBy2NUSJJtMcV5ZL2nE4/3dvs7Mxxk8vns7n5xxhXXUazdHG+b+Es+5QAfx9UNEc4zcvb6SmLUFLNMnWught3YSQx2UwsTjEmspW7Mx7/oNyFVKRF3BTnOXl83NGMndMHoaAKSVhRucHaImlWFfVxsPvb6c5muS2C6dSGPJy7UMf8n55I5NLwrzwra4ZIapb4nywrZELppf0Wdlb4SIllEKl8EQmR9XkC1U3NaiYxTm9eGSKp6vP4cCNKqNR6sO/Yckk4qnr4Xvv9b5vWw3Sm0VDNEVHX8/Z5lLWpEfjNg2+MLeM/7hwCgDjCkOQVDG5F46WfP5qNb1OeX2ELJ/VGbd1JHPUe6YAFt21lNrtbfiz3Hzl56ccku88Ernv+tcwLYPr71nQ6/b2pjjCEGqC50HirdXVfPiXDXgiyvN08c2zScZtnrtvJVbAhdsyuepnaob39dWt5PjdFIW9PLuykn979KP9+g5DwMLZwzhhVC6Xn6BFlebAkVKyszFGfSTBppo23tpYj2EIrjt1DNOGHf55emJJm7Z4Cr/HhSMltq08x1vq2vG7XYzK85OyJW9urKM9kSaRViOnLFMwtTSL0ycVcuLYPLbVRxhXGMRrmbREU9S1qxjJc6eV4O42+Xg8ZVPdEmdU/r67Updtb+KNDbWsrWzl1fUqzcrM4Vlk+d3k+C3Kcv1IoL49STxlU9EUY+qwMKdNLCTLZ5EXdBP0uHpt5O0LKSVvbaqnJMvLhP2Mae2VRV9RcYKfvge2vK66bLOPnHeN8+jlGBtfUAu7ZXhva66n/Z5TKLFV9qPfpD/DTa4nOrc3XbII38Qz8FoZx4WdVnGAdxYBUsUm3rDkwLt1HUfFWgby1HLzDjUSWAiVvyx/4qCPXv1Ed/MBNNdEeeTW9yibnscF3ziCRrwcYuKRFIYhcPuG1mG55aNaXvzjakZOzeXCG2cRb0/x5++oWepHz8znvBtm7HGMlJIlWxvY1RSjqjnOzqYoAbfJX5dsJ8dvYQjB7RdNY96EfL7xyHLe3lQPwIc/OpP8wzSOTTP0RJNpVuxsQSIZkeOnLZ7m4fe38+j7PedGDHldtGUCZz89s5RR+QGWbKlHIKhti5MX9GAINbJpTEGQkiwv88YXMCLXx6i8AHVtCdKOxHYktW1x8oMeyvL8eFyqQmqLp9hWH8U0BG6XIOy1yA96EAJWV7TyyroaNta0UZrtIzegGhjN0SQvrammsjmOZQpyA27cLoOa1gTl9ZFerzfocZFIq3n1QHl3xhUGEQKCHotE2mb59qbOlDDd6RipDGqoepbP4jPHDKO+LcnflmzrDNQenuPjrCnFfH7OSMry/Gypa+el1TV8sK2B8roIlS3K+20aghnDs/jcsSO0J/lQE2uGn6uYw6qvfkijq5DfvbqJ310xmxVvPc0Jb13VY3eJQFz6V/j7l9SKa15TWfelhDsKIH+8irsMD1cDNkCNqj3j1p65xFIxlXF+5hVgedW6mjUqBvGFW2DLaypeM9akPlYAUpl7ed7NcMZ/DJ5N+IR38wFkF/k562tTyR8eHOqiHNZ4A4dHQKs/rMRN2TTlPPYGLbIKfLTUxTDM3l3sQghOGrvnwIL/vGjPUX8PXT2HF1dXcf3Dy6luiQ+omIok0rTGUyRSDn63SdDrQiBIOQ6xpE1ta4KcgIWU0BBJUtMaxxQiE3+hKpCgx4XLUDEZqvI08LgMLNOgKZpUqW0SKVpi6rOlNoItJSNz/fjdJsm0Q9qRRJM2y7Y3krIl+UEPlqnOF3C7mFCsWt2OI2mOJmmJpYmlbKaWhnEZAo9l4HGZpGxV7vyQB9uRtGVSeXgtE8sUtMbTtGbKoUbidJUr7LXwWAYtsTRu02DumFxKs30EPC6CHhMpYUNNG16XSU7AIpZ0aIunqGqJ8+6Wejwuk/ygm6DXRSzpkHYcWmMpyhuipG0H25GkMrlm8oIeGiNJ0rZDabaPtC1pjCaJJtKEfRYFIQ8FQQ8FIQ85ATe2I/FZJoaAlC35eFczOxqiuF0G4wqCtCfSvF/eSH17Yo/f2DQERWEPZ00p5vRJhQzP8TGuMMjyHU1c99Aynl6hgvbHFATI8lmMyg/QGEmSTDvkBT3sbIyyOBPf04Eh6HV4e3HYi89tsq0hwu7tXrdp4LWMvearm1wSZkJREL/HRVMkSVM0RcBjct70Yk4ck0csZfNBeSPTh2Vz0rg8jitTsUsNkSSOlBQE95ysN2U7/PDJVWxviFIY9rK9IcLKXS0k0jbXzR9DXWuC2rYES7Y28F8vqsnbJxWHKK+P4HEZFIW9/M/icv5ncTlC0HldAbfJCaNzWTCpkG+fOQGf2yR4EClWNAOAL7vz37cXv8WmijrOanmHv779Kzwrl3ICEDv9TsyVj+KuX4tweWDKRWqk65ZX4elvwUn/pvLMOamuqW3mXAcv/1j9v/5Z9fl+BSRaVbqaTS/D63cqUTXjUrX+Dz1n5ujMiQddQgrUSNkh5BPhmdIceTRUtJNbGuh8kS9/aTtLntxC3vAgl/9onzlh98nKXc18+l41OvHN7y6gOMtLdUuczbXt1LUl2FzbjhAwqThMfshDezxNWZ6f19bX0hhJsr0hQiLtkLIdkmmHpC1Jpm12NsZ6JJMbagpCHnL9bhqjyU7x0XaAyWIPBJ9lkuWzCHpdVDTFsExBXtBDRXOMZHr/7VIU9hDwuKhvS9AaTyuBaQpchqAsL0C2z8osG8RTNi2xFPlBT+d1moYgP+jGZ7loiaWob09Q15agrj3RZzkmFYcI+1RSXb/bxcwR2ZTl+plSGiZlO8RTNrYDZ00t6lOAN0aSrNjZTFmen9H5gT5HYcVTNu9sqqeiOUZ1a5zmaJIJRSFyA248LpOGSIL6tiQ7GqO0xJLMGJ7NhCLVGEzZkqZokormGLGkzaTiMGdOLiQ34MaR0BxL0tCeJMtnUZLlHfSRYFLKziTFfneX+GloT/Dq+lpOHV9AUbinKFu6rZGrH1xKypZ8/zyVkuSimcMOKv2LZnD41aLXuHnNxT3W3Zm6kpGilks97+L9UYXKFXfvcTB6Pnz5aZUbbfFv4bU7ep4sf6LyKF32sJqyZ+zparLm3ac7mv1Fldy2L37coEbieoJKhd9zrIq7m9v36MCB5BPfzac58qnf1cb/3rmU7CI/V/7n3H6fT0rJz15Yzx/f2trrdpchcKTs1VvgtQxKsnzkB91YptH58bgMcgIWE4pCBD0uIkmbSCKNlCrWxGOZBD0mkYSN22UQ9loMz/GpmBVHfZftSNoTKWwHbMfBdpQnIJG2SaQdsv1uDKGC9rN8FmGvi/yQBwHUtiUQgJUZaeQyRa+ehZZYis217UQSaYZnpjIKe13YUrKmshUBGEIQT9m4TIEjVcUY8lqEvK6MsFBCsqMcWT6LsM/V2S3VYeOO725PpNlWH6E9kSaSSBNJ2iRSNnlBN9sbouQFPfgtk+IsLzkBN0UhD65MuosOcTQQSCmJpezOa3K7DCqaYozKD+ju3kNMNKnSFYS8WkAdtty2Z/xflcwlu3gUvszobtY+DWPmd83pV/lxz4mgR82Dq3rJUZdOwp0Fe64XpprJAYnKifMubF+skgSHS/t7Rf3iE9/Npzny6RiFaR+Ad2NvCCH4/nmTOaYsh7WVrbhdBgG3yYwR2WT5VJBr2pFUtcRpaE+wtT7CrqYYF8wo6V9g6iCyv5VSls/i2LI9h6K7gGNG7meSvv2gu4gLelwHHZg9UEIKVJk6vCcdXUglWX2PJtMMHt29WJojhxLRiCzrlnB1yqd77lA6C678J/hzVC61KRf1fiKXG868DYqmw8i5cNcwtf47G7sS5UbqlIAqmjIYlzKg6LtZc0QQzvMy9pgCZn+qbN87HwBnTy3ucz4nlwmj8wOMzg9wnM5NpdFoPmHYp/0I8/U791gvpn127wd2zGE4rO+8f4CaTqg7485UcVagktIOsSfqQNDdfBqNRqPRaPYknUDeWYRjWCQvfQxfqhlWLVJT/wx0GoJUXE3B1Ndk4YcBuptPo9FoNBrNgeHyIG5rxgQ6O8OnX7KXA/pBRyqEIxQ9oZlGo9FoNBpNP9BiSqPRaDQajaYfaDGl0Wg0Go1G0w+0mNJoNBqNRqPpB1pMaTQajUaj0fQDLaY0Go1Go9Fo+oEWUxqNRqPRaDT9QIspjUaj0Wg0mn6gxZRGo9FoNBpNPxiy6WSEEHXA9iH58v6TD9QPdSGOMrRNBwdt14FH23Tg0TYdeLRNB54yKWVBbxuGTEwdyQghPuxrfh7NwaFtOjhouw482qYDj7bpwKNtemjR3XwajUaj0Wg0/UCLKY1Go9FoNJp+oMXUwXH/UBfgKETbdHDQdh14tE0HHm3TgUfb9BCiY6Y0Go1Go9Fo+oH2TGk0Go1Go9H0Ay2mNBqNRqPRaPqBFlMajUajGXSEEGKoy3C0oW16+KDFVC8IIUYNdRmORoQQxwkhCoe6HEcTQogzhRDHDnU5jjaEEFnd/tcV1sDgGuoCHIVYQ10AjUKLqW4IIY4RQrwC3C6EMIe6PEcLQoipQoh3gVuB7CEuzlGBEGK2EOIF4Elg3FCX52hBCDFHCPF/wJ+EEF8VQnikHqXTL4QQc4UQj6Deq+P1u7X/CCFOFEIsAn4phJiibTr0aDGFankKIX4IPAY8LqX8kpTS7tg2tKU7KvgW8KSU8kIp5UbQdj1YhBCmEOJ+4AHgj8CjwOTMNv089wMhxAzgPuAfwCLgdLRQ7RdCiGnAPcCzQA1wLfClzDb9DjgIMt79e4HnUdPFfAv4amabtukQoV++QKblaQHvSCn/BJ0tf5dulR48mYo/F5Cohx8hxMVCiOGAL7OsH/4DICPyXwTmSSmfAv4JnCaE8EopnSEt3JHPscBmKeVDwMuAF9jRsVHfqwfFXGC9lPIxVAMgClwphBglpZTapgfFTGCjlPIvwK+AJ4CLhBATtE2Hjk+smBJCzBdCzOm26hfAMCHEL4UQS4E7gL8KIS4ZmhIemXS3a6bijwKnAqcLIR4GrgPuBH6T2UeL1X2w+70qpXxCShnLvDQdYCPgH7ICHqH08g54DrhYCPETYBUwHPidEOJ7oO/V/aEXmy4FRgghxkopI6j7tQW4BrRN9wchxEIhxA+EEOdnVn0MHNfNpkuBD1HvVm3TIeITJ6aEECEhxBOoWJPrhBA5AJmb8iFgFnCzlPIC4C3gHCHEhKEq75HCXuwaB/6C6j55SUp5DvBDYJoQ4twhK/ARQF82zXRLi8xLcz1wBsqLor0n+8Fe7tVaVKvfBfxASjkXeBA4RQhx4lCV90igF5vmZjZtAT4A/iKEeAo4DtWF6hJCeIeksEcIQoiCjM3+HWhE2fASKWUdyiN9Y2bXZuAVwC+EKBmKsmo+gWIKSAKvAV8AKoHPdWyQUj4CXCqlfCuz6hWgAGg/1IU8AunTrsDvUd16BQBSygrgHVQrVdM3vdpUZhBCGFLKXcD7wCUd24aqsEcQe3sHrAcmATszq5YBtUDiEJfxSKOve7VdSnkL8G/Ag1LKC4HNwIxMQ0vTN2OBxVLKU6WU/w3cDHw7s+0xYJIQ4oxM934DMAzl9dMMAZ8IMSWE+FLG/ZwtpUwAf0IJpY0od+mEzH5CStnY7dBPoeJ9tJjqhf21q5SyHdWK+rIQYpYQ4gbgTGDbEBX9sOUA7lVDSukIIVzAJiAydKU+/Nlfu2b4F3Bbxst3OTAVVVlpurEPmx7b3aZSypWZGD9Qgf3vaS/qnmRsukAI4UcJ+b9l1pvA2swHVDf048BvhRDjUN5pAbgPfak1cBTPzZd5UItRo50clLs5AHxLSlmf2Wc88GUgLqW8M7POAE4BfosKPv1eprWq4eDtmll/GaobZSqqG2XNIS7+YUl/7tWMoPo10C6l/PGQXMBhygHaNSGlvCOzzoeaJLYQMIFvSinX7vkNnzz6+fwfiwqYtoFrpZRbDnHxD0v2ZVMhhCmltIUQXwA+LaW8tNuxtwATUN7Ua6SU6w79FWjgKPVMZW4+CYSACinlGcDXUf3Of+zYT0q5CaX+S4UQ4zJ9+BKoAG6VUl6khVQX/bBrQAhhSSn/F/hhxq5aSNHve9WX2fzvWkj15CDsWiJUDiS/lDIGfAX4spTyTC2kFP24Vzvu022o9+oZWkgp9mHT+3fb/SxU2g6EEMUAUsr/Ar4upTxFC6mh5ajKSJvp8rgdMIUQzwNhVCsIKWVaCPFNoFIIMV9K+WZm/ZNCiMmo4eZB4PTMy1M/7BkGyK6nAet0TI9C23Rw6KddXwCCQojTMhVT9dBcxeHFQNyrQoiO9+qbQ3MVhxcHY1NUuEm5EOJ24DNCiHOklLuklMmhuAZNT44az5QQYj6qNZSDCnC8A0ihcvCcAJ3BubcDt3U77nOo0WWvo4IidSu0GwNoV91qyqBtOjhouw48+r068ByMTTMxU19FeabCwGlSDT7RHCYcNTFTQoh5wCipEu4hhPg9KkgvBtwopTw2Ew9VCPwOFQtVnjkOKeXbQ1T0wxpt14FH23Rw0HYdeLRNB56DsOl3Ub1INwJ/k1IuH5qSa/bGUeOZQin9v4uuOYoWAyOllA+iXKk3SjWEdDhgSynLQT3s+oHfK9quA4+26eCg7TrwaJsOPAdiU0dKuV1KuUVKeZMWUocvR42YklJGpZQJmZlTD5XWoC7z/1eAyUKIZ1H5OfQNuZ9ouw482qaDg7brwKNtOvAcoE2XgU7GeyRwVAWgQ2ffsgSKgKczq9uAHwDTgHKpkkZqDgBt14FH23Rw0HYdeLRNB54DsakeZHL4c9R4prrhoCYtrgdmZBT+j1Hu0nf0A3/QaLsOPNqmg4O268CjbTrwaJseRRw1AejdEULMBd7NfP4ipfzzEBfpqEDbdeDRNh0ctF0HHm3TgUfb9OjhaBVTw4EvAndLNc2BZgDQdh14tE0HB23XgUfbdODRNj16OCrFlEaj0Wg0Gs2h4miMmdJoNBqNRqM5ZGgxpdFoNBqNRtMPtJjSaDQajUaj6QdaTGk0Go1Go9H0Ay2mNBrNEYcQ4jYhxHf2sn2hEGLKoSyTRqP55KLFlEajORpZCGgxpdFoDgk6NYJGozkiEEL8EPgSsBM1l9kyoAW4FnADm1E5e2YBz2a2tQCfzZziPqAAiALXSCnXH8LiazSaoxgtpjQazWGPEOJY4EFgDmpO0eXAf6OyRjdk9rkTqJFS3iOEeBB4Vkr5j8y2V4HrpZSbhBBzgLuklKcf+ivRaDRHI0fdRMcajeaoZB7wpJQyCiCE6JgYdlpGRGUDQeCl3Q8UQgSBk4BFQoiO1Z7BLrBGo/nkoMWURqM5UujNjf4gsFBKuUIIcRWwoJd9DKBZSjlr0Eqm0Wg+0egAdI1GcyTwFnCxEMInhAgBF2bWh4AqIYQFXNlt/7bMNqSUrUC5EOJzAEIx89AVXaPRHO3omCmNRnNE0C0AfTuwC1gLRIBbMutWASEp5VVCiJOBB4AEcAngAH8ASgALeFxKefshvwiNRnNUosWURqPRaDQaTT/Q3XwajUaj0Wg0/UCLKY1Go9FoNJp+oMWURqPRaDQaTT/QYkqj0Wg0Go2mH2gxpdFoNBqNRtMPtJjSaDQajUaj6QdaTGk0Go1Go9H0Ay2mNBqNRqPRaPrB/we62hEkt2vpwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cumulative returns using the Pandas plot function\n",
    "# Include a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "cumulative_returns.plot(figsize=(10,5), title=\"Daily plot return\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Answer the following question: Based on the cumulative return data and the visualization, do any of the four fund portfolios outperform the S&P 500 Index?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question** Based on the cumulative return data and the visualization, do any of the four fund portfolios outperform the S&P 500 Index?\n",
    "\n",
    "**Answer** # Based on the cumulative data none of the four funds outperform the S&P 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the cumulative return data and visualization none of the four fund portfolios ooutperform the S&P 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the Volatility\n",
    "\n",
    "Analyze the volatility of each of the four fund portfolios and of the S&P 500 Index by using box plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Use the Pandas `plot` function and the `kind=\"box\"` parameter to visualize the daily return data for each of the four portfolios and for the S&P 500 in a box plot. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Box Plot of Daily Returns Data'}>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the daily return data to create box plots to visualize the volatility of the 4 funds and the S&P 500 \n",
    "# Include a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "daily_returns.plot.box(figsize=(17,10),title=\"Box Plot of Daily Returns Data\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Use the Pandas `drop` function to create a new DataFrame that contains the data for just the four fund portfolios by dropping the S&P 500 column. Visualize the daily return data for just the four fund portfolios by using another box plot. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Box Plot of Daily Returns Data'}>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AwJG8AwAAAAAQcCTvAAAAAAAEHMk7AAAAAAABR/IOAAAAAEDAkbwDAAAAABBwJO8AAAAAAAQcyTsAAAAAAAFH8g4AAAAAQMCRvAMAAAAAEHAk7wAAAAAABBzJOwAAAAAAAUfyDgAAAABAwJG8AwAqJpVKKRKJKBQKKRKJKJVK+R0SAADAjFDrdwAAgJkhlUopHo+rs7NTra2tymQyikajkqT29nafowMAAKhu5pzzO4aKWbJkidu6davfYQDArBSJRJRMJtXW1jbclk6nFYvF1N3d7WNkAAAA1cHMHnLOLSk7jeQdAFAJoVBIuVxOdXV1w235fF6NjY0aHBz0MTIAAIDqMF7yzph3AEBFhMNhZTKZEW2ZTEbhcNiniAAAAGYOkncAQEXE43FFo1Gl02nl83ml02lFo1HF43G/QwMAAKh6FKwDAFREsShdLBZTNptVOBxWIpGgWB0AAEAFMOYdAAAAAIAAYMw7AAAAAABVjOQdAAAAAICAI3kHAAAAACDgSN4BAAAAAAi4iiTvZvZ2M3vczJ40s5vLTDcz+5w3/Tdm9oaSaV1m9oyZdY9a5hNmttvMHvF+3lGJWAEAAAAAqDaTTt7NLCTpC5IulXS+pHYzO3/UbJdKOtf7uVbSF0umfUXS28dY/Wedc6/zfu6bbKwAAAAAAFSjSlx5f6OkJ51zv3fO9Uv6mqTLR81zuaS7XMHPJZ1kZmdIknPux5L2VyAOAAAAAJixUqmUIpGIQqGQIpGIUqmU3yFhGlUieW+WtKvkeY/XdqzzlHO9182+y8zmlZvBzK41s61mtnXfvn3HEjcAAAAAVIVUKqV4PK5kMqlcLqdkMql4PE4CP4tUInm3Mm3uFcwz2hcl/ZGk10naK+kz5WZyzt3hnFvinFtyyimnHGWVAAAAAFB9EomEOjs71dbWprq6OrW1tamzs1OJRMLv0DBNKpG890haWPK8RdKeVzDPCM65p51zg865IUl3qtA9HwAAAABmnWw2q9bW1hFtra2tymazPkWE6VaJ5P2Xks41s7PNrF7SlZLuGTXPPZI+4FWd/3NJLzjn9o630uKYeM97JHWPNS8AAAAAzGThcFiZTGZEWyaTUTgc9ikiTLdJJ+/OuQFJ10vaIikr6evOuW1mtsLMVniz3Sfp95KeVOEq+oeKy5tZStLPJJ1nZj1mFvUmrTOzR83sN5LaJH1ksrECAAAAQDWKx+OKRqNKp9PK5/NKp9OKRqOKx+N+h4ZpYs4dbeh59ViyZInbunWr32EAAAAAQMWlUiklEglls1mFw2HF43G1t7f7HRYqyMwecs4tKTuN5B0AAAAAAP+Nl7xXYsw7AAAAAACYQiTvAAAAAAAEHMk7AAAAAAABR/IOAAAAAEDAkbwDAAAAABBwJO8AAAAAAAQcyTsAAAAAAAFH8g4AAAAAQMCRvAMAAABAFUilUopEIgqFQopEIkqlUn6HhGlE8g4AAAAAAZdKpXTDDTeot7dXktTb26sbbriBBH4WIXkHAAAAgIBbuXKlamtr1dXVpVwup66uLtXW1mrlypV+h4ZpQvIOAAAAAAHX09OjjRs3qq2tTXV1dWpra9PGjRvV09Pjd2iYJiTvAAAAAAAEHMk7AKBiKKQDAMDUaGlp0fve9z6dffbZCoVCOvvss/W+971PLS0tfoeGaULyDgCoiFQqpXg8rmQyqVwup2QyqXg8TgIPAEAFLF26VAcPHtSRI0c0NDSkI0eO6ODBg1q6dKnfoWGakLwDACoikUios7NzxFi8zs5OJRIJv0MDAKDqpdNpvfvd79aBAwckSQcOHNC73/1updNpfwPDtCF5BwBURDabVWtr64i21tZWZbNZnyICAGDmeOyxx/TII4/o/vvvV39/v+6//3498sgjeuyxx/wODdOE5B0AUBHhcFiZTGZEWyaTUTgc9ikiAABmjvr6esVisRE93GKxmOrr6/0ODdOE5B0AUBHxeFzRaFTpdFr5fF7pdFrRaFTxeNzv0AAAqHr9/f36/Oc/P+I4+/nPf179/f1+h4ZpUut3AACAmaG9vV2SFIvFlM1mFQ6HlUgkhtsBAMArd/7552vp0qUjjrNXXXWVNm/e7HdomCYk7wCAimlvbydZBwBgCsTjccXjcXV2dqq1tVWZTEbRaJTCsLMIyTsAAAAABBw93MCYdwBAxaRSKUUiEYVCIUUiEe7xDgBABbW3t6u7u1uDg4Pq7u4mcZ9luPIOAKiIVCpVtjufJL5cAAAATJI55/yOoWKWLFnitm7d6ncYADArRSIRJZNJtbW1Dbel02nFYjF1d3f7GBkAAEB1MLOHnHNLyk4jeQcAVEIoFFIul1NdXd1wWz6fV2NjowYHB32MDAAAoDqMl7wz5h0AUBHhcFiZTGZEWyaTUTgc9ikiAABmFmrLzG4k7wCAiojH44pGo0qn08rn80qn04pGo4rH436HBgBA1UulUrrhhhvU29sr55x6e3t1ww03kMDPInSbBwBUTCqVUiKRGL6FTTwep1gdAAAVsHDhQg0MDOjuu+8eLgx71VVXqba2Vrt27fI7PFQIY94BAAAAoIqZmR544AG97W1vG277/ve/r4svvlgzKaeb7RjzDgAAAABAFSN5BwAAAICAa2lp0dVXXz2itszVV1+tlpYWv0PDNCF5R6BRUROoLuyzAABMjXXr1mlgYEAdHR1qbGxUR0eHBgYGtG7dOr9DwzSp9TsAYCypVErxeFydnZ3DRTmi0agkUQALCCD2WQAApk7xWJpIJCRJTU1NuuWWWzjGziJceUdgJRIJdXZ2qq2tTXV1dWpra1NnZ+fwBxaAYGGfBQAAmDpUm0dghUIh5XI51dXVDbfl83k1NjZqcHDQx8gAlMM+CwDA1Cne572pqUk7d+7UokWL1Nvbq9tvv52r7zMI1eZRlcLhsDKZzIi2TCajcDjsU0QAxsM+CwDA1Fm5cqVqa2vV1dWlXC6nrq4u1dbWauXKlX6HhmlC8o7AisfjikajIypqRqNRxeNxv0MDUAb7LAAAU6enp0cbN24cMTxt48aN6unp8Ts0TBMK1iGwit1/YrGYstmswuGwEokE3YKAgGKfBQBgaqXTaX3kIx8ZPs6++93v9jskTCPGvAMAAABAwJ188sl6/vnndeqpp+qZZ54Zfpw3b56ee+45v8NDhTDmHQAAAACqnHNOzz777IhHzB4k7wAAAAAQcPv379eJJ56ohQsXysy0cOFCnXjiidq/f7/foWGakLwDAAAAQBWIx+Pavn27hoaGtH37dorCzjIk7wAAAABQBdavXz/iri7r16/3OyRMI6rNAwAAAEDAtbS06ODBg+ro6NDOnTu1aNEiHTlyRC0tLX6HhmnClXcAAAAACLh169apvr5ekoYL1dXX12vdunV+hoVpRPIOAAAAAAHX3t6u22+/XU1NTTIzNTU16fbbb1d7e7vfoWGacJ93AAAAAAACgPu8AwAAAECVS6VSikQiCoVCikQiSqVSfoeEaUTyDgCoGL5UANWFfRaoHqlUSjfccIN6e3slSb29vbrhhhvYb2eRiiTvZvZ2M3vczJ40s5vLTDcz+5w3/Tdm9oaSaV1m9oyZdY9aZr6Zfd/MnvAe51UiVgDA1EilUorH40omk8rlckomk4rH43ypAAKKfRaoLitXrlQ+n5f0UsG6fD6vlStX+hkWptGkk3czC0n6gqRLJZ0vqd3Mzh8126WSzvV+rpX0xZJpX5H09jKrvlnSD5xz50r6gfccABBQiURCnZ2damtrU11dndra2tTZ2alEIuF3aADKYJ8FqktPT48aGhrU1dWlvr4+dXV1qaGhQT09PX6HhmlSiSvvb5T0pHPu9865fklfk3T5qHkul3SXK/i5pJPM7AxJcs79WNL+Muu9XNJG7/eNkpZWIFYAwBTJZrNqbW0d0dba2qpsNutTRADGwz4LVJ+bbrppxAm3m266ye+QMI0qkbw3S9pV8rzHazvWeUY7zTm3V5K8x1PLzWRm15rZVjPbum/fvmMKHABQOeFwWJlMZkRbJpNROBz2KSIA42GfBarP+vXrlU6nlc/nlU6ntX79er9DwjSqrcA6rEzb6PvPTWSeV8Q5d4ekO6TCreIqsU4AwLGLx+O64oor1NTUpB07duiss85Sb2+vbr/9dr9DA1BGPB5XNBpVZ2enWltblclkFI1G6TYPBFRLS4sOHTqkjo6O4eNsLpdTS0uL36FhmlQiee+RtLDkeYukPa9gntGeNrMznHN7vS72z0w6UgDAlMrlcjpw4ICcc9q9e7caGxv9DgnAGNrb2yVJsVhM2WxW4XBYiURiuB1AsKxbt07XXXeddu/ePeI4u27dOr9DwzSpRLf5X0o618zONrN6SVdKumfUPPdI+oBXdf7PJb1Q7BI/jnskXe39frWk71QgVlQZbmEDVI+VK1eqqalJW7ZsUX9/v7Zs2aKmpiaq4AIB1t7eru7ubg0ODqq7u5vEHQi4xsZGNTc3q6amRs3NzZwkn2Umnbw75wYkXS9pi6SspK8757aZ2QozW+HNdp+k30t6UtKdkj5UXN7MUpJ+Juk8M+sxs6g3aY2kt5nZE5Le5j3HLMItbIDq0tPTo7vuumtEIZ277rqLKrgAAFRAIpHQpk2btH37dg0ODmr79u3atGkTQ11mESveI3AmWLJkidu6davfYaBCIpGIksmk2trahtvS6bRisZi6u7t9jAxAOWamBx54QG9729uG277//e/r4osv1kw61gAA4IdQKKRcLqe6urrhtnw+r8bGRg0ODvoYGSrJzB5yzi0pN60S3eaBKcEtbIDq0tLSoquvvnpEFdyrr76aQjoAAFQAd4gAyTsCiw8ooLqsW7dOAwMD6ujoUGNjozo6OjQwMEAhHQAAKqB4h4jSk+TRaFTxeNzv0DBNSN4RWHxAAdWlvb1dV1xxhfbu3auhoSHt3btXV1xxBQWwAACogPb2dl122WW69NJLVV9fr0svvVSXXXYZx9lZhOQdgdXe3q5EIqFYLKbGxkbFYjFuYQMEWCqV0r333qv7779f/f39uv/++3XvvfdSZBIAgApIpVLatGmTzjjjDNXU1OiMM87Qpk2bOM7OIhSsAwBUBEUmAQCYOgsXLtShQ4d00kknaceOHTrrrLN04MABzZ07V7t27fI7PFQIBesAAFOOIpMAAEydnp4eNTQ0qKurS319ferq6lJDQwO3ZJ1FSN4BABURDof1yU9+UpFIRKFQSJFIRJ/85CcpMgkAQIVceOGFI4aUXnjhhX6HhGlE8g4AqIi2tjatXbtWHR0dOnjwoDo6OrR27doR3egBAMArt2nTphHH2U2bNvkdEqYRY94BABURiUS0dOlSbd68WdlsVuFwePg5Y94BAJicuro6hUIhDQ0NKZ/Pq66uTjU1NRocHFQ+n/c7PFTIeGPeSd4BABURCoWUy+VUV1c33JbP59XY2KjBwUEfIwMAoPqZmcxsOGEvJvLOOc2knG62o2AdAGDKhcNhZTKZEW2ZTIYx7wAAVEBtbe3w1XZJqqmpUV1dnWpra32ODNOF5B0AUBHxeFzRaFTpdFr5fF7pdFrRaFTxeNzv0AAAqHoDAwMaGBjQmjVr1NvbqzVr1gy3YXag2zwAoGJSqZQSicTwmPd4PK729na/wwIAoOqZmdrb2/Wb3/xm+Dj72te+VqlUim7zMwhj3gEAAACgipmZTjzxRM2bN087duzQWWedpeeff14vvPACyfsMwph3AAAAAKhi8+fP14svvqhcLiczUy6X04svvqj58+f7HRqmCdUNAAAAACDg5syZo6GhITU2Nso5p8bGRp144omaM2eO36FhmnDlHQBQMalUSpFIRKFQSJFIRKlUyu+QAACYEfbs2aPPfe5zampqkpmpqalJn/vc57Rnzx6/Q8M0IXlHoJEIANUjlUrphhtuUG9vr5xz6u3t1Q033MB+CwBABYTDYX3729/Wk08+qaGhIT355JP69re/zS1ZZxGSdwRWKpVSPB5XMplULpdTMplUPB4nEQACauXKlTp06JB2794t55x2796tQ4cOaeXKlX6HBgBA1WtubtbmzZs1Z84cmZnmzJmjzZs3q7m52e/QME2oNo/AikQiSiaTamtrG25Lp9OKxWLq7u72MTIA5ZiZampqdOutt2rFihXasGGDPvaxj2loaIgquAAATFJdXZ1CoZCGhoaUz+dVV1enmpoaDQ4OKp/P+x0eKoRbxaEqhUIh5XI51dXVDbfl83k1NjZqcHDQx8gAlGNmuvbaa/WlL31puO26667THXfcQfIOAMAkmZmOP/54nXzyydq5c6cWLVqk5557TgcPHuQ4O4NwqzhUpXA4rEwmM6Itk8kwrgcIsE2bNunss89WTU2Nzj77bG3atMnvkACMg9oyQHXJ5XLavXu3hoaGtHv3buVyOb9DwjQieUdgxeNxRaNRpdNp5fN5pdNpRaNRxeNxv0MDUEZNTU3Z+8/W1HCoAYKI2jJA9cnn88Nd5Et/x+zAfd4RWO3t7ZKkWCymbDarcDisRCIx3A4gWE466SQ9//zzcs6NGOd+0kkn+RsYgLISiYQ6OzuHa8u0tbWps7NTsViMYy0ABBBj3gEAFREKhXTdddepq6tLfX19amhoUEdHh770pS9RpwIIIGrLANXFzCRJ8+bN04EDB4ZPmktizPsMwph3AMCUC4fDmj9/vs455xzV1NTonHPO0fz586lTAQQUtWWA6lNbW6sTTzxRknTiiSeqtpaO1LMJyTsAoCLa2tq0du1adXR06ODBg+ro6NDatWtH3O4RQHBQWwaoPgMDAzpy5Igk6ciRIxoYGPA5Ikwnus0DACoiEolo6dKl2rx583CdiuLz7u5uv8MDUEYqlVIikRjeZ+PxOOPdgYCqqakp2z3ezDQ0NORDRJgKdJsHAEy5bDar8847b0Tbeeedp2w261NEAI6mvb1d3d3dGhwcVHd3N4k7EGBve9vbjqkdMw/JOwCgIs4880zFYjH19vZKknp7exWLxXTmmWf6HBkAANXvscceGy5aV2Rmeuyxx3yKCNON5B0AUBGHDx/WoUOHFIvFdPDgQcViMR06dEiHDx/2OzQAAKpeT0+PnHOaN2+epELVeeecenp6fI4M04XkHQBQEfv379fKlSvV1dWl448/Xl1dXVq5cqX279/vd2gAAMwIjY2N+ta3vqX+/n5961vfUmNjo98hYRqRvAMAKmZ0IZ2ZVBQVmIlSqZQikYhCoZAikYhSqZTfIQEYx+hkneR9duHGgACAipg/f77WrFmj2tpaDQ0N6fHHH9e2bds0f/58v0MDUEYqlVI8HldnZ6daW1uVyWQUjUYlicJ1QEC9+OKLuvDCC4ef19RwLXY24b8NAKiIvr4+SRq+XU3xsdgOIFgSiYQ6OzvV1tamuro6tbW1qbOzU4lEwu/QAJRRPDleamhoSLW1XI+dLUjeEWh05wOqR29vr+rr6xUKhSRJoVBI9fX1w9XnAQRLNptVa2vriLbW1lZu7wgE1Fj3cuce77MHyTsCq9idL5lMKpfLKZlMKh6Pk8ADATZnzhxt2bJF/f392rJli+bMmeN3SADGEA6HlclkRrRlMhmFw2GfIgIwnqGhIb3+9a8fvl2cmen1r389yfssYjOpmNCSJUvc1q1b/Q4DFRKJRJRMJtXW1jbclk6nFYvF1N3d7WNkAMoxM9XW1srMlM/nVVdXJ+ecBgYGKFwHBNBYY94TiQRj3oEAMjOZmU499VQ9/fTTOu200/TMM8/IOcdxdgYxs4ecc0vKTWOABAIrm82qp6dHkUhE2WxW4XBYq1atojsfEGADAwPDVwRI2oFgKybosVhs+DhL4g4Em3NO+/btkyTt27eP4+wsQ/KOwDrzzDO1cuVK3X333cNXBK666iqdeeaZfocGYByhUEgDAwPDjwAAoHJGF4bF7MGYdwRa8QreWM8BBMvxxx+vlpYW1dTUqKWlRccff7zfIQEYA7VlAKC6kLwjsPbs2aO1a9cqFoupsbFRsVhMa9eu1Z49e/wODcAYTj/9dO3YsUNDQ0PasWOHTj/9dL9DAjAGbhUHANWF5B2BFQ6H1dLSou7ubg0ODqq7u1stLS1UwQUCqqGhQU888YTe9a53ad++fXrXu96lJ554Qg0NDX6HBqAMbhUHANWFMe8IrHg8rqVLl+rIkSPDlauPO+44bdiwwe/QAJQxODio2tpa3XPPPTrllFMkSbW1tRocHPQ5MgDlFG8VV3pXF24VBwDBxZV3BNZPf/pTHTp0SCeffLJqamp08skn69ChQ/rpT3/qd2gAyhgYGNC8efO0ePFimZkWL16sefPmUbQOCKh4PK5oNKp0Oq18Pq90Oq1oNKp4PO53aACAMkjeEVh33nmnbr31Vu3du1eDg4Pau3evbr31Vt15551+hwagDDPTe9/7Xm3fvl1DQ0Pavn273vve91JoEgio9vZ2JRKJEbVluFUcEHzF4yrH19nHZtK9AZcsWeK2bt3qdxioEDNTb2+v5syZM9x2+PBhNTU1cU9LIIBqagrng0877TQ988wzOvXUU/X0009L4nY2AABM1njJOt+NZw4ze8g5t6TcNK68I7AaGhpeNr59w4YNFL8CAqq5uVl1dXX6wx/+oKGhIf3hD39QXV2dmpub/Q4NAACg6pG8I7CWL1+uVatWaf369Tp8+LDWr1+vVatWafny5X6HBqCMw4cPa3BwUJ/5zGfU29urz3zmMxocHNThw4f9Dg0AAKDqkbwjsJLJpFasWKHVq1erqalJq1ev1ooVK5RMJv0ODUAZ+/fv12WXXTZin73sssu0f/9+v0MDMIZUKqVIJKJQKKRIJKJUKuV3SADGURyiNtZzzGzcKg6B9uY3v1npdFrZbFbnnHOO3vzmN/sdEoBx3H///crn85Kkvr4+3X///T5HBGAsqVRK8XhcnZ2dam1tVSaTUTQalSSK1gGv0OKb753S9def8yYN7t+l/HM9qju5RaH5C5X77X9M+XafWnPZlK4fE0PBOgTWWF8qqIQLBBOFdIDqEolEtHTpUm3evFnZbFbhcHj4eXd3t9/hARjFzGRmuu222/TPPWfpH1p26KMf/aiccxxnZ5DxCtZVJHk3s7dLul1SSNKXnXNrRk03b/o7JB2WdI1z7uHxljWzT0haLmmft5rVzrn7xouD5H1miUQiSiaTamtrG25Lp9OKxWJ8qQACiOQdqC41NTU666yz1NXVNXySvKOjQzt27OAOEUAAXXLJJXrggQdUU1OjoaGh4ceLL75YW7Zs8Ts8VMiUVps3s5CkL0i6VNL5ktrN7PxRs10q6Vzv51pJX5zgsp91zr3O+xk3ccfMk81m1draOqKttbVV2WzWp4gATEQoFBrxCCCY6uvrFYvF1NbWprq6OrW1tSkWi6m+vt7v0ACUsWXLFl188cXDJ8SdcyTus0wlKhy8UdKTzrnfO+f6JX1N0uWj5rlc0l2u4OeSTjKzMya4LGapcDisTCYzoi2TySgcDvsUEYCJWLdunXp7e7Vu3Tq/QwEwjv7+fq1Zs0Znn322ampqdPbZZ2vNmjXq7+/3OzQAY9iyZYuGhoZ01qrvaWhoiMR9lqlE8t4saVfJ8x6vbSLzHG3Z683sN2bWZWbzym3czK41s61mtnXfvn3lZkGVisfjikajSqfTyufzSqfTikajisfjfocGYByf+tSn1NTUpE996lN+hwJgHM3NzcOJenHYS39/v5qbR3+NAwAEQSWqzZcb5Dh6cONY84y37Bcl/ZP3/J8kfUZSx8tmdu4OSXdIhTHvEwsZ1aBYlC4Wiw0X0qFYHRB8zz///IhHAME1ulbFeLUrAAD+qsSV9x5JC0uet0jaM8F5xlzWOfe0c27QOTck6U4VutgDAAKqoaHhmNoB+Gv37t2qq6uT9FJRybq6Ou3evdvPsAAAY6hE8v5LSeea2dlmVi/pSkn3jJrnHkkfsII/l/SCc27veMt6Y+KL3iOJ8uKzTPFWcclkUrlcTslkUvF4XKlUyu/QAJQxODio2tqRHbpqa2s1ODjoU0QAxlNfX6+bb75Z27dv19DQkLZv366bb76ZgnUAEFCTTt6dcwOSrpe0RVJW0tedc9vMbIWZrfBmu0/S7yU9qcJV9A+Nt6y3zDoze9TMfiOpTdJHJhsrqksikdBVV12lWCymxsZGxWIxXXXVVUokEn6HBqCMgYEBHXfcccNX8urq6nTcccdpYGDA58gAlNPf369kMjmitkwymaRgHQAEVCXGvMu7jdt9o9o2lPzuJH14ost67X9bidhQvR577DEdPnxYnZ2dw/efjUajeuqpp/wODcAYcrncuM8BBMf555+vpUuXjqgts2zZMm3evNnv0AAAZVSi2zwwJerr63X99dePuP/s9ddfT3c+IMDy+fzwlfaBgQHl83mfIwIwlng8rjvuuEO9vb1yzqm3t1d33HEHd3UBgIAieUdg0Z0PqE7FwlfFRwDBlcvltHv3bjnntHv3bnrLAECAkbwjsM4//3wtWLBAF110kerr63XRRRdpwYIFOv/88/0ODcA45s2bN+IRQDCtXLlSoVBIzc3NMjM1NzcrFApp5cqVfocGACiD5B2B1dzcrK1bt2rFihU6cOCAVqxYoa1bt6q5udnv0ACM4+DBgyMeAQRTT0+PGhsb1dXVpb6+PnV1damxsVE9PT1+hwYAKIPkHYH1ox/9SG95y1vU1dWlk046SV1dXXrLW96iH/3oR36HBmAcpWPeAQTbjTfeOKK2zI033uh3SACAMZC8I7D6+vr0+OOP64wzzlBNTY3OOOMMPf744+rr6/M7NADjqKmpGfEIILg+85nPjKgt85nPfMbvkAAAY+CbFQKt2I0vl8sNd+sDEGxDQ0MjHgEEU0tLi/r6+tTR0aGGhgZ1dHSor69PLS0tfocGACiD5B2B1tvbq1/96lfK5/P61a9+pd7eXr9DAnAUtbW1Ix4BBNO6detUV1cnSTIzSVJdXZ3WrVvnZ1gAgDGQvCPQXve61+mjH/2ompqa9NGPflSve93r/A4JwDjq6uqGkwAzG04MAARPe3u7rrjiCu3du1dDQ0Pau3evrrjiCrW3t/sdGgCgDJJ3BNb8+fP18MMPj7hn9MMPP6z58+f7HBmAseTzeeXz+Zf9DiB4UqmUNm3apDPOOENmpjPOOEObNm1SKpXyOzQAQBkk7wisF198UZJGXMUrbQcAAK9c8T7vpbeK4z7vABBcJO8IrIGBAZnZiCvvZsbtpwAAqICenh7dddddI24Vd9ddd3GfdwAIKJJ3BJpzTqeddprMTKeddtpwIg8AACbvwQcfVCQSUSgUUiQS0YMPPuh3SACAMZC8oyqQtAMAUFnz58/X2rVr9dxzz0mSnnvuOa1du5baMgAQUCTvCLynn356xCMAAKgc59zwDwAguEjeAQAAZqH9+/dr1apVWrBggcxMCxYs0KpVq7R//36/QwMAlEHyjsCrqakZ8QgAAAAAsw3ZEAJvaGhoxCMAAJi8+fPna926dero6NDBgwfV0dGhdevWMeYdAAKK5B0AAGAWmjNnjk444QQlk0nNnTtXyWRSJ5xwgubMmeN3aACAMkjeAQAAZqE9e/ZoyZIl2rFjh5xz2rFjh5YsWaI9e/b4HRoAoAySdwAAgFnopJNOUjqd1m233abe3l7ddtttSqfTOumkk/wODQBQRq3fAQBHU1dXp3w+P/wIAAAm78UXX1Rtba1uvvlm3XTTTaqrq1Ntba1efPFFv0MDAJRB8o7AKybsJO4AAFTOwMCABgYGhp9znAWAYKPbPAAAwCx22mmnjXgEAAQTyTsAAMAs9vTTT494BAAEE8k7AKCi6uvrZWaqr6/3OxQAAIAZgzHvAICK6u/vH/EIAACAyePKOwAAAAAAAUfyDgAAAABAwJG8AwAAAAAQcIx5B4BZYvHN987YbT+15rIpXT8AAIDfSN4BYJaY6gR30d0LtWvXLjU2NiqXyw0/Lly4kOQaAABgkug2DwCoiJ07d2rhwoXK5XKSNJy479y50+fIAAAAqh/JOwLPzEY8AgiunTt3yjmns1Z9T845EncAAIAKods8XrHpGj/rnBvxOB3bposvAAAAgCAheccrNtUJrq0tPNbV1Smfzw8/Tse2AQAAACBISN4RWAsXFopfFRP24uPChQv9DAsAAABV5IJPPqAXjuT9DqPi/LyLzFQ58bg6/fp/Xux3GIFF8o7A2rlzpxYtWqRdu3YNt1H8CgAAAMfihSN5em1WiZl4QqKSKFiHQKP4FQAAAACQvAMAAAAAEHgk7wAAAAAABBzJOwAAAAAAAUfyDgAAAABAwJG8AwAAAAAQcNwqDgAAIKD8um3SVG+X23YBwLEjeQcAAAioqUxyG/+5QX19fS9rb2hoILkGgACi2zwAAMAslMvl1NDQMKKtoaFBuVzOp4gAAOMheQcAAJilcrmcnHM6a9X35JwjcQeAAKPbPAD47IJPPqAXjuT9DqPi/BqrO1VOPK5Ov/6fF/sdBgAAmKVI3mcgEoHqQCKAoheO5BlfWgVm2mcQAACoLiTvMxCJQHUgEQAAAAAwURVJ3s3s7ZJulxSS9GXn3JpR082b/g5JhyVd45x7eLxlzWy+pE2SFkt6StL7nXPPVyJeAACAV4oebtWDXm4AZpJJJ+9mFpL0BUlvk9Qj6Zdmdo9z7rGS2S6VdK7382eSvijpz46y7M2SfuCcW2NmN3vPV002XgAAgMmgh1v1mIknJADMXpWoNv9GSU86537vnOuX9DVJl4+a53JJd7mCn0s6yczOOMqyl0va6P2+UdLSCsQKAAAAAEDVqUS3+WZJu0qe96hwdf1o8zQfZdnTnHN7Jck5t9fMTi23cTO7VtK1krRo0aJX+CfMLMeHb9afbLzZ7zBwFMeHJYkrN2CfrRbssyhin60e7LeQ2GerCfvs+CqRvFuZNjfBeSay7Licc3dIukOSlixZckzLzlQHs2vozlcF6MqHIvbZ6sA+iyL22erBfguJfbaasM+OrxLd5nskLSx53iJpzwTnGW/Zp72u9fIen6lArAAAAAAAVJ1KXHn/paRzzexsSbslXSnpqlHz3CPpejP7mgrd4l/wusLvG2fZeyRdLWmN9/idCsQKAAAwaVwdqg4nHlfndwgAUDGTTt6dcwNmdr2kLSrc7q3LObfNzFZ40zdIuk+F28Q9qcKt4j443rLeqtdI+rqZRSXtlPS+ycYKAAAwWTOx++3im++dkX8XAMwkFbnPu3PuPhUS9NK2DSW/O0kfnuiyXvtzki6qRHyzEVcEgo+rAQAAAAAmqiLJO4JlJp4554oAAAAAgNmM5B0AAoDeMsFHbxnMRKlUSolEQjseyyryvbDi8bja29v9DgsAUAbJOwD4bCb2KqG3DBB8qVRKV131Uo3hbdu2DT8ngQeA4KnEreIAAABQZZYtW3ZM7QAAf3HlHQAAIKCmckhNoZ5w+fapHspDzxwAOHYk7wAAAAE1lUmurfVnuwCAV4Zu8wAAAAAABBxX3gEAAADMaNzVpTpwZ5fxkbwDAAAAmLFm4jAQ7uoyO9FtHgAAAACAgCN5BwAAAAAg4Og2j0CbO3euent7JRWq4jY1NenQoUM+RwUAAAAA04sr7wis0sS9qLe3V3PnzvUpIgAAAADwB8k7Amt04n60dgAAAACYqeg2j1fMz1tuTPW2qd4JAAAAIEhI3vGKTXWCa2v92zYAAAAABAnd5gEAAAAACDiSdwBAxbz2ta+VmWnH2nfKzPTa177W75AAAABmBJJ3AEBFvPa1r9Wjjz46ou3RRx8lgQcAAKgAxrwDwCwx1YUed4xK3IseffRRikwCAABMEsk7AMwSFJkEUE5NTY2GhoaGHwEAwUS3eQAAgFmsmLCTuANAsJG8AwAAAAAQcCTvAAAAs1R9fb3q6uokSXV1daqvr/c5IgDAWEjeAQAAZqmhoSHl83lJUj6fp+s8AAQYyTsAAMAsNTAwoLlz58rMNHfuXA0MDPgdEgBgDCTvAAAAs1RdXZ0WLFggM9OCBQuGu9ADAIKH5B0AAGCWOuGEE8Z9DgAIDpJ3AACAWcjMdNxxx2nHjh0aGhrSjh07dNxxx8nM/A4NAFAGyTsAAMAs1NLSop6eHr3pTW/Snj179KY3vUk9PT1qaWnxOzQAQBm1fgcAHE1jY6NyudzwIwAAmLxnnnlGp59+un7605/qzDPPlCSdfvrpeuaZZ3yODABQDlfeEXjFhJ3EHQCAyunr69OBAwdGtB04cEB9fX3+BAQAGBfJOwAAwCyVy+X0d3/3dzpw4ID+7u/+jhPlABBgJO8AAACz2DnnnKO6ujqdc845focCABgHyTsAAMAs9Y53vEOrV69WU1OTVq9erXe84x1+hwQAGAPJOwAAwCz1k5/8RPfff7/6+/t1//336yc/+YnfIQEAxkC1eQSemck5N/wIINjYZ4HqcPHFF+uBBx7QW9/6Vg0NDammpkZDQ0O6+OKL/Q4NAFAGV94ReMUv/yQBQHVgnwWqwzXXXKPGxkYNDQ1JkoaGhtTY2KhrrrnG38AAAGWRvCPwampqRjwCAIDJSyQSuu++++ScG/657777lEgk/A4NAFAG2RACr6mpSTU1NWpqavI7FAAAZoxsNqvW1tYRba2trcpmsz5FBAAYD8k7Ai0UCungwYMaGhrSwYMHFQqF/A4JAIAZIRwOK5PJjGjLZDIKh8M+RQQAGA/JOwKtsbFRixcvVk1NjRYvXqzGxka/QwJwFGY24hFAMMXjcUWjUaXTaeXzeaXTaUWjUcXjcb9DAwCUQfKOwJo/f74OHz6sXbt2aWhoSLt27dLhw4c1f/58v0MDMA7qVADVob29Xeeee64uuugi1dfX66KLLtK5556r9vZ2v0MDAJTBNysE1lVXXSXnnAYHByVJg4ODcs7pqquu8jkyAOPhyjtQHWKxmB588EHddttt6u3t1W233aYHH3xQsVjM79AAAGWQvCOwNm/erBNPPHFEt/kTTzxRmzdv9js0AOMYGBgY8QggmO68806tXbtWN954o+bMmaMbb7xRa9eu1Z133ul3aACAMkjeEVg9PT36xje+oe3bt2twcFDbt2/XN77xDfX09PgdGgAAVa+vr08rVqwY0bZixQr19fX5FBEAYDwk7wCAiqLbPFAdGhoatGHDhhFtGzZsUENDg08RAQDGQ/KOwGppadHVV189ogru1VdfrZaWFr9DAzCGU045RfX19ZKk+vp6nXLKKT5HBGAsy5cv16pVq7R+/XodPnxY69ev16pVq7R8+XK/QwMAlFHrdwDAWNatW6frrrtOl1xyifL5vOrq6tTY2KgvfelLfocGYAz79u1TKBSSVBjzvm/fPp8jAjCWZDIpSVq9erVuuukmNTQ0aMWKFcPtAIBg4co7Aq2xsVHNzc0yMzU3N3Ofd6AKlN4hAkCwJZNJ5XI5OeeUy+VI3AEgwEjeEViJREKbNm3S9u3bNTQ0pO3bt2vTpk1KJBJ+hwYAAAAA04rkHYGVzWbV09OjSCSiUCikSCSinp4eZbNZv0MDAAAAgGk1qeTdzOab2ffN7Anvcd4Y873dzB43syfN7OajLW9mi83siJk94v1sKLdezGxnnnmmVq5cOdylL5lMauXKlTrzzDP9Dg3AOIpj3ouPAACgMi655BLV1NRox9p3qqamRpdcconfIWEamXPulS9stk7SfufcGi8pn+ecWzVqnpCk30p6m6QeSb+U1O6ce2ys5c1ssaTvOecixxLPkiVL3NatW1/x34NgWbhwoQ4dOqSTTjpJO3fu1KJFi3TgwAHNnTtXu3bt8js8AKMUbw1XU1OjoaGh4UdJmsyxBgCAarH45nunbN1Pb/ofyj31q5e1Ny5+vU674p+mbLuS9NSay6Z0/XiJmT3knFtSdtokk/fHJf2Vc26vmZ0h6YfOufNGzfMmSZ9wzl3iPf+4JDnnPj3W8iTvkAoJwIIFC9TU1KQdO3borLPOUm9vr5599tnhhABAcIx3X3eSdwAAJoeT5LPDeMn7ZMe8n+ac2ytJ3uOpZeZpllR6mbTHazva8meb2a/M7Edm9hdjBWBm15rZVjPbyi2JZpb6+nrdfPPNIwrW3XzzzcP3kAYQTHPnzh3xCCC4UqnUiNoyqVTK75AAHEUxYedi1uxz1Pu8m9m/Szq9zKT4BLdR7lLM0U4N7ZW0yDn3nJn9qaTNZvYa59yLL1uRc3dIukMqXHmfYEyoAv39/fr0pz+tZDI5fOX90KFD6u/v9zs0AGMwMx06dEiSdOjQIZkZVwOAgEqlUorH4+rs7FRra6symYyi0agkqb293efoAIylsbFRuVxu+BGzx1GvvDvn3uqci5T5+Y6kp73u7vIenymzih5JC0uet0ja4/1ednnnXJ9z7jnv94ck/U7Sq1/Zn4hq1dzcrHw+L+mlbkL5fF7Nzc3jLQbAR845zZs3TzU1NZo3bx6JOxBgiURCnZ2damtrU11dndra2tTZ2cktWYGAq62tHfGI2WOy3ebvkXS19/vVkr5TZp5fSjrXzM42s3pJV3rLjbm8mZ3iFbqTmb1K0rmSfj/JWFGF5syZo66uLuVyOXV1dWnOnDl+hwRgDLW1taqtrdXzzz+voaEhPf/888NtAIInm82qtbV1RFtrayu3ZAUCrrSHG2aXySbvayS9zcyeUKGa/BpJMrMzzew+SXLODUi6XtIWSVlJX3fObRtveUl/Kek3ZvZrSd+UtMI5t3+SsaLK7NmzR2vXrlUsFlNjY6NisZjWrl2rPXv2HH1hANNuYGBAg4ODI24VNzg4qIGBAZ8jA1BOOBxWJpMZ0ZbJZBQOh32KCAAwnkldDvG6tl9Upn2PpHeUPL9P0n3HsPy3JH1rMrGh+oXDYbW0tKi7u3u4LZ1O86UCCKja2lo1NDTolFNO0c6dO7Vw4ULt27dPfX19focGoIx4PK5oNPqyMe90mweAYKIvIwIrHo/riiuuUFNT0/B93nt7e3X77bf7HRqAMgYGBnTyySerq6trOBFob29Xb2+v36EBKKNYlC4WiymbzSocDiuRSFCsDgACiuQdVYGiV0B1aG5u1kUXXSTnnMxMr3/96/X000/7HRaAMbS3t5OsA1WmeCcX7ugy+0x2zDswZRKJhDZt2jTiPu+bNm2iOx8QUE1NTXr44YeH7w5hZnr44YfV1NTkc2QAAMwcxYSdxH32IXlHYFEFF6guhw8fPqZ2AP5LpVKKRCIKhUKKRCJKpVJ+hwQAGAPJOwKLKrhAdXHO6bjjjtOiRYtkZlq0aJGOO+44rgwAAZVKpXTdddfpt7/9rYaGhvTb3/5W1113HQk8AAQUyTsCq1iw7uyzz1ZNTY3OPvtsXXHFFYrH436HBmAMy5YtGzHUZdmyZX6HBGAM119/vQ4fPqw1a9aot7dXa9as0eHDh3X99df7HRoAoAwK1qEqFMfQAgi2L3/5y/rud7+rp59+WqeddhrF6oAA279/v9atW6cbb7xRknTjjTdqcHBQK1eu9DkyAGOpqanR0NDQmM8xs3HlHYFVWrBucHCQgnVAwBUL0+3bt2/EIwXrgOCKRCLjPgcQLENDQwqFQpKkUChE4j7LkLwjsChYB1SXefPmac6cOSO+VMyZM0fz5s3zOTIA5dTW1mrZsmVKp9PK5/NKp9NatmyZamvpmAkEUbEn6uDg4IhHeqjOHiTvCKxwOKz3v//9amxslJmpsbFR73//+ylYBwTUnj17tGHDBr361a9WTU2NXv3qV2vDhg3as2eP36EBKGPFihV64YUX1N7ervr6erW3t+uFF17QihUr/A4NQBlz5sw5pnbMPCTvCKzm5mZt3rxZHR0dOnDggDo6OrR582Y1Nzf7HRqAMsLhsB5//PERbY8//jgn3ICASiaT+tCHPqQDBw5Ikg4cOKAPfehDSiaT/gYGoKze3l7V19ePaKuvr1dvb69PEWG6kbwjsH70ox9p2bJl+vGPf6z58+frxz/+sZYtW6Yf/ehHfocGoIy2tjatXbtWHR0dOnjwoDo6OrR27Vq1tbX5HRqAMSSTSeVyOTnnlMvlSNyBgGtqatKDDz6o/v5+Pfjgg9SVmWVsJt1/d8mSJW7r1q1+h4EKMTP19vaO6Ap0+PBhNTU1cd9oIIAikYiWLl2qzZs3K5vNKhwODz/v7u72OzwAAKqamampqUmnnHKKdu7cqUWLFmnfvn3q7e3lu/EMYmYPOeeWlJvGlXcEVkNDgzZs2DCibcOGDWpoaPApIgDjyWazOu+880a0nXfeeRSZBACgQnp7e/XUU09paGhITz31FF3mZxmSdwTW8uXLtWrVKq1fv16HDx/W+vXrtWrVKi1fvtzv0ACUceaZZyoWiw1/kejt7VUsFtOZZ57pc2QAAFS/YlX54l1cio9Um589SN4RWMlkUitWrNDq1avV1NSk1atXa8WKFYzHAwLq8OHDOnTokGKxmA4ePKhYLKZDhw7p8OHDfocGAEDVc84pFArp+eeflyQ9//zzCoVCdJmfRUjeEWgU0gGqx/79+/Wxj31MXV1dOv7449XV1aWPfexj2r9/v9+hARhDKpVSJBJRKBRSJBJRKpXyOyQA4xgcHFQoFJIkhUKh4Xu9Y3YgeQcAVMyFF16o7u5uDQ4Oqru7WxdeeKHfIQEYQyqVUjweHz5RnkwmFY/HSeABIKCoNg8AqIiFCxfq4MGDmjdv3nAV3Oeff17HH3+8du3a5Xd4AEaJRCJKJpMjbueYTqcVi8W4QwQQQMWx7WYm59zwoyS6zs8gVJsHAEy5pUuX6sUXX9SuXbs0NDSkXbt26cUXX9TSpUv9Dg1AGdlsVq2trSPaWltbuUMEEGCjE3aK1c0uJO8INMbiAdVj8+bNamxsVE1N4dBSU1OjxsZGbd682d/AAJQVDoeVyWRGtGUyGYXDYZ8iAnA0zjnNmzdPZqZ58+ZxxX2WIXlHYDEWD6guPT09OuGEE7Rlyxb19/dry5YtOuGEE9TT0+N3aADKiMfjikajSqfTyufzSqfTikajisfjfocGYBzPP/+8nHPDVecxezDmHYHFWDygupiZ1q1bp4997GPDbbfeeqtWrlzJlQEgoFKplBKJhLLZrMLhsOLxuNrb2/0OC0AZxS7yc+fO1aFDh4YfJca8zyTjjXkneUdghUIh5XI51dXVDbfl83k1NjZyWwwggMxMp59+uu6++261trYqk8noqquu0h/+8Ae+VAAAMElmppqaGg0NDQ23FZ9znJ05KFiHqsRYPKC6tLS0KJfLqaOjQ42Njero6FAul1NLS4vfoQEAMCMMDQ3ptNNOk5nptNNOG5HIY+YjeUdgMRYPqC7r1q0b0VNGkurq6rRu3TqfIgIAYGYxMz399NNyzunpp5+m2vwsU+t3AMBYimPuYrHY8Fi8RCLBWDwgoIr7ZiKRkCQ1NTXplltuYZ8FAKBCRnePp7v87ELyjkBrb2/niz8AAAAgjTnmHbMD3eYBABXB7R0BAJhaxTHvkhjzPgtRbR4AUBHc3hEAgKlTrDYfCoWUz+dVV1enwcFBqs3PMFSbBwBMuWw2q9bW1hFtra2tymazPkUEAMDMMjQ0pIaGBklSQ0MDV95nGZJ3AEBFcHtHAACm3qFDh0Y8YvYgeQcAVAS3dwQAYGoV7+9e+ojZg2rzAICK4PaOAABMrTlz5ui4446Tmem4447TnDlz1Nvb63dYmCYUrAMAAACAgCsm7AMDA8MF62pra3XkyBEK1s0gFKwDAAAAgCo2f/58HTlyZLhI3dDQkI4cOaL58+f7HBmmC8k7AAAAAFSBmpoaLViwQJK0YMEC1dSQzs0m/LcBAAAAIOD279+vd77znTpw4IAk6cCBA3rnO9+p/fv3+xsYpg3JOwAAAABUgV/84he6//771d/fr/vvv1+/+MUv/A4J04jkHYGWSqUUiUQUCoUUiUSUSqX8DgkAAACYdrW1tXr22Wd14YUXqr6+XhdeeKGeffZZ1dZyA7HZgv80AiuVSikej6uzs1Otra3KZDKKRqOSxK2nAAAAMKsMDAxIKlSdd87JzIbbMDtw5R2BlUgk1NnZqba2NtXV1amtrU2dnZ1KJBJ+hwYAAABMu1AoNHylvba2VqFQyOeIMJ1I3hFY2WxWra2tI9paW1uVzWZ9iggAAADwz9DQkNasWaPe3l6tWbNm+LZxmB1I3hFY4XBYmUxmRFsmk1E4HPYpIgAAAMA/ixYt0urVq9XU1KTVq1dr0aJFfoeEaUTyjsCKx+OKRqNKp9PK5/NKp9OKRqOKx+N+hwZgDBSZBABg6uzYsUOXXHKJ9u3bp0suuUQ7duzwOyRMIwrWIbCKRelisZiy2azC4bASiQTF6oCAosgkAABTpzjW/Z577tEpp5wyog2zgznn/I6hYpYsWeK2bt3qdxgAMCtFIhEtXbpUmzdvHj7hVnze3d3td3gAAFS1mpoanXzyyZo7d6527typRYsW6dChQ3ruuecY+z6DmNlDzrkl5aZxqgYAUBGPPfaYDh8+/LIr70899ZTfoQEAUPXOP//84ZPiktTU1KRly5YNP8fMx5h3AEBF1NfX6/rrrx9xe8frr79e9fX1focGAEDVi8fjuvvuu5VMJpXL5ZRMJnX33XdTD2oW4co7AKAi+vv7lUwm9frXv374ynsymVR/f7/foQEAUPXa29v105/+VJdeeqn6+vrU0NCg5cuXU1dmFuHKOwCgIs4//3wtW7ZMsVhMjY2NisViWrZsmc4//3y/QwMAoOqlUinde++9uv/++9Xf36/7779f9957L3d2mUUmlbyb2Xwz+76ZPeE9zhtjvreb2eNm9qSZ3VzS/j4z22ZmQ2a2ZNQyH/fmf9zMLplMnACAqUd3PgAApk4ikdAFF1ygSy+9VPX19br00kt1wQUXKJFI+B0apslkr7zfLOkHzrlzJf3Aez6CmYUkfUHSpZLOl9RuZsXLMN2S/lrSj0ctc76kKyW9RtLbJf2Ltx7MMsUreGY2fCUPQDC1t7frsssuG/Gl4rLLLqM7HwAAFbBt2zbdc889mjdvnmpqajRv3jzdc8892rZtm9+hYZpMNnm/XNJG7/eNkpaWmeeNkp50zv3eOdcv6WvecnLOZZ1zj4+x3q855/qcc9slPemtB7NILBbThg0bdMstt6i3t1e33HKLNmzYQAIPBBTd+QAAmFpNTU26++67lcvldPfdd6upqcnvkDCNJpu8n+ac2ytJ3uOpZeZplrSr5HmP1zaeCS9jZtea2VYz27pv374JB47gu/POO7V27VrdeOONmjNnjm688UatXbtWd955p9+hASgjkUios7NzRLX5zs5OuvMBAFAhc+bMGfc5ZrajJu9m9u9m1l3m5/IJbsPKtLlKLeOcu8M5t8Q5t+SUU06ZYEioBn19fVqxYsWIthUrVqivr8+niACMJ5vNqrW1dURba2urstmsTxEBADCzvOpVrxoxPO1Vr3qV3yFhGh01eXfOvdU5Fynz8x1JT5vZGZLkPT5TZhU9khaWPG+RtOcom30ly2CGaWho0IYNG0a0bdiwQQ0NDT5FBGA84XBYmUxmRFsmk1E4HPYpIgAAZo6mpib97Gc/U0dHhw4cOKCOjg797Gc/o+v8LDLZ+7zfI+lqSWu8x++UmeeXks41s7Ml7VahEN1VE1jv3Wa2XtKZks6V9ItJxooqs3z5cq1atUpS4Yr7hg0btGrVqpddjQcQDPF4XFdccYWampq0Y8cOnXXWWert7dXtt9/ud2gAAFS9efPmKZ/P64tf/KK++MUvSpLq6+s1b17ZG35hBpps8r5G0tfNLCppp6T3SZKZnSnpy865dzjnBszseklbJIUkdTnntnnzvUdSUtIpku41s0ecc5c457aZ2dclPSZpQNKHnXODk4wVVSaZTEqSVq9erZtuukkNDQ1asWLFcDuA4Mnlcjpw4ICcc9q9e7caGxv9DgkAgBlh9+7dWrBggZqamrRz504tWrRIvb292r17t9+hYZqYc0cbfl49lixZ4rZu3ep3GAAwKy1cuFCDg4P66le/qtbWVmUyGS1btkyhUEi7du06+goAAMCYGhsb9d73vlePPPKIstmswuGwXve61+mb3/ymcrmc3+GhQszsIefcknLTJlttHgAASVJPT482btw4otr8xo0b1dPT43doAABUvb6+Pm3atEkdHR06ePCgOjo6tGnTJoo5zyIk7wAAAAAQcA0NDbriiivU1dWl448/Xl1dXbriiiso5jyLkLwDACqipaVFH/jAB5ROp5XP55VOp/WBD3xALS0tfocGAEDV6+/v15YtW9Tb2ytJ6u3t1ZYtW9Tf3+9zZJguJO8AgIpYt26dBgcH1dHRoYaGBnV0dGhwcFDr1q3zOzQAAKpec3Oz8vm8JKlYtyyfz6u5udnPsDCNSN4BABXR3t6u22+/XU1NTTIzNTU16fbbb1d7e7vfoQEAMCPMmTNHXV1d6uvrU1dXl+bMmeN3SJhGVJsHAAAAgIALhUL6yle+orVr1w5Xm1+1apWuueYaDQ5yV+2ZYrxq85O9zzsAAAAAYIqFw2G1tLSou7t7uC2dTiscDvsYFaYT3eYBAAAAIODi8bii0eiIwrDRaFTxeNzv0DBNuPIOAAAAAAFXrCETi8WGu80nEglqy8wijHkHAAAAACAAxhvzTrd5AAAAAKgCsVhMjY2NMjM1NjYqFov5HRKmEck7AKBiUqmUIpGIQqGQIpGIUqmU3yEBADAjxGIxbdiwQbfccot6e3t1yy23aMOGDSTwswhj3gEAFZFKpRSPx9XZ2anW1lZlMhlFo1FJYjweAACTdOedd2rt2rW68cYbJWn4cfXq1Uomk36GhmnClXcEGlfxgOqRSCR0wQUX6NJLL1V9fb0uvfRSXXDBBUokEn6HBgBA1evr69OKFStGtK1YsUJ9fX0+RYTpRvKOwCpexUsmk8rlckomk4rH4yTwQEBt27ZN3/ve90Z05/ve976nbdu2+R0aAABVr6GhQRs2bBjRtmHDBjU0NPgUEaYbyTsCK5FIqLOzU21tbaqrq1NbW5s6Ozu5igcElJlp+fLluvHGGzVnzhzdeOONWr58uczM79AAAKh6y5cv16pVq7R+/XodPnxY69ev16pVq7R8+XK/Q8M04VZxCKxQKKRcLqe6urrhtnw+r8bGRg0ODvoYGYByzEyLFy9WV1fX8Jj3jo4OPfXUU5pJxxoAAPwSi8V05513qq+vTw0NDVq+fDnj3WcYbhWHqhQOh5XJZEa0ZTIZhcNhnyICMJ6Ghga95S1vGb6NTSwW01ve8ha68wEAUCHF4aTOueFhpZg9SN4RWPF4XNFoVOl0Wvl8Xul0WtFoVPF43O/QAJSxfPlybdq0SR0dHTp48KA6Ojq0adMmuvMBAABUALeKQ2AVby0Vi8WUzWYVDoeVSCS45RQQUMWz/6tXr9ZNN92khoYGrVixgqsCAAAAFcCVdwRae3u7uru7NTg4qO7ubhJ3IODe/OY365xzzlFNTY3OOeccvfnNb/Y7JAAAZgxuozy7ceUdAFARxds7dnZ2Dhesi0ajksSJNwAAJonjLKg2DwCoiEgkomQyqba2tuG2dDqtWCym7u5uHyMDAKD6cZydHcarNk/yDgCoCG7vCADA1OE4OztwqzgAwJTj9o4AAEwdjrMgeQcAVAS3dwQAYOpwnAUF6xBoqVRKiURi+FZx8XicghxAQHF7RwAApg7HWZC8I7CoqAlUn/b2dvZPAACAKUC3eQRWIpFQZ2en2traVFdXp7a2NnV2diqRSPgdGgAAADCtihe2ksmkcrmcksmk4vE493qfRag2j8CioiYAAABQwK3iZgeqzaMqUVETAAAAKMhms2ptbR3R1traqmw261NEmG4k7wgsKmoCAAAABVzYAgXrEFhU1AQAAAAKihe2Rhdzph7U7MGYdwAAAACoAtxGeeYbb8w7yTsAAAAAAAFAwToAAAAAAKoYyTsAAAAAAAFH8g4AAAAAVSCVSikSiSgUCikSiSiVSvkdEqYR1eYBAAAAIOBSqZTi8fjLqs1LomjdLEHBOgAAAAAIuEgkomQyqba2tuG2dDqtWCym7u5uHyNDJVFtHgAAAACqWCgUUi6XU11d3XBbPp9XY2OjBgcHfYwMlUS1eQAAAACoYuFwWJlMZkRbJpNROBz2KSJMN5J3AAAAAAi4eDyuaDSqdDqtfD6vdDqtaDSqeDzud2iYJiTvAICKoQouAABTo729XYlEQrFYTI2NjYrFYkokEhSrm0WoNg8AqAiq4AIAMLXa29s5ps5iFKwDAFQEVXABAAAmh4J1AIApl81m1draOqKttbVV2WzWp4gAHA1DXQCgepC8AwAqgiq4QHUpDnVJJpPK5XJKJpOKx+Mk8AAQUCTvAICKoAouUF0SiYQ6OzvV1tamuro6tbW1qbOzU4lEwu/QAABlMOYdAFAxqVRKiURC2WxW4XBY8XicwjpAQIVCIeVyOdXV1Q235fN5NTY2anBw0MfIAGD2Gm/MO9XmAQAVQxVcoHoUh7qUFplkqAsABBfd5gEAAGYhhroAQHWZ1JV3M5svaZOkxZKekvR+59zzZeZ7u6TbJYUkfdk5t8Zrf5+kT0gKS3qjc26r175YUlbS494qfu6cWzGZWAEAAPCSYi+ZWCw2PNQlkUjQewYAAmpSY97NbJ2k/c65NWZ2s6R5zrlVo+YJSfqtpLdJ6pH0S0ntzrnHzCwsaUjSlyR9dFTy/j3nXORY4mHMOwAAAACgWk3lfd4vl7TR+32jpKVl5nmjpCedc793zvVL+pq3nJxzWefc42WWAQAAAAAAnskm76c55/ZKkvd4apl5miXtKnne47Udzdlm9isz+5GZ/cVYM5nZtWa21cy27tu371hiBwAAAACgKhx1zLuZ/buk08tMmmg1EyvTdrS++nslLXLOPWdmfypps5m9xjn34stW5Nwdku6QCt3mJxgTAAAAAABV46jJu3PurWNNM7OnzewM59xeMztD0jNlZuuRtLDkeYukPUfZZp+kPu/3h8zsd5JeLYkB7QAAAACAWWey3ebvkXS19/vVkr5TZp5fSjrXzM42s3pJV3rLjcnMTvEK3cnMXiXpXEm/n2SsAAAAAABUpckm72skvc3MnlChmnzxFnBnmtl9kuScG5B0vaQtKtz+7evOuW3efO8xsx5Jb5J0r5lt8db7l5J+Y2a/lvRNSSucc/snGSsAAAAAAFVpUreKCxpuFQcAAAAAqFZTeas4AAAAAAAwxUjeAQAAAAAIOJJ3AAAAAKgCqVRKkUhEoVBIkUhEqVTK75AwjY56qzgAAAAAgL9SqZTi8bg6OzvV2tqqTCajaDQqSWpvb/c5OkwHCtYBAAAAQMBFIhElk0m1tbUNt6XTacViMXV3d/sYGSppvIJ1JO8AAAAAEHChUEi5XE51dXXDbfl8Xo2NjRocHPQxMlQS1eYBAAAAoIqFw2FlMpkRbZlMRuFw2KeIMN1I3gEAAAAg4OLxuKLRqNLptPL5vNLptKLRqOLxuN+hYZpQsA4AAAAAAq5YlC4WiymbzSocDiuRSFCsbhZhzDsAAAAAAAHAmHcAAAAAAKoYyTsAAAAAAAFH8g4AqJhUKqVIJKJQKKRIJKJUKuV3SAAAADMCBesAABWRSqUUj8fV2dmp1tZWZTIZRaNRSaKYDgAAwCRRsA4AUBGRSETJZFJtbW3Dbel0WrFYTN3d3T5GBgAAUB3GK1hH8g4AqIhQKKRcLqe6urrhtnw+r8bGRg0ODvoYGQAAQHWg2jwAYMqFw2FlMpkRbZlMRuFw2KeIAAAAZg6SdwBARcTjcUWjUaXTaeXzeaXTaUWjUcXjcb9DAwAAqHoUrAMAVESxKF0sFlM2m1U4HFYikaBYHQAAQAUw5h0AAAAAgABgzDsAAAAAAFWM5B0AAAAAgIAjeQcAAAAAIOBI3gEAAAAACDiSdwAAAAAAAo7kHQAAAACAgCN5BwAAAAAg4EjeAQAAAAAIOJJ3AAAAAAACjuQdAAAAAICAI3kHAAAAACDgSN4BAAAAAAg4kncAAAAAAAKO5B0AAAAAgIAjeQcAAAAAIOBI3gEAAAAACDiSdwAAAAAAAo7kHQAAAACAgCN5BwAAAAAg4EjeAQAAAAAIOJJ3AAAAAKgCqVRKkUhEoVBIkUhEqVTK75AwjWr9DgAAAAAAML5UKqV4PK7Ozk61trYqk8koGo1Kktrb232ODtPBnHN+x1AxS5YscVu3bvU7DAAAAACoqEgkomQyqba2tuG2dDqtWCym7u5uHyNDJZnZQ865JWWnkbwDAAAAQLCFQiHlcjnV1dUNt+XzeTU2NmpwcNDHyFBJ4yXvjHkHAAAAgIALh8PKZDIj2jKZjMLhsE8RYbqRvAMAAABAwMXjcUWjUaXTaeXzeaXTaUWjUcXjcb9DwzShYB0AAAAABFyxKF0sFlM2m1U4HFYikaBY3SzCmHcAAAAAAAKAMe8AAAAAAFQxkncAAAAAAAKO5B0AAAAAgICbVPJuZvPN7Ptm9oT3OG+M+d5uZo+b2ZNmdnNJ+61m9p9m9hsz+zczO6lk2se9+R83s0smEycAAAAAANVsslfeb5b0A+fcuZJ+4D0fwcxCkr4g6VJJ50tqN7PzvcnflxRxzr1W0m8lfdxb5nxJV0p6jaS3S/oXbz0AAAAAAMw6k03eL5e00ft9o6SlZeZ5o6QnnXO/d871S/qat5yccw845wa8+X4uqaVkvV9zzvU557ZLetJbDwAAAAAAs85kk/fTnHN7Jcl7PLXMPM2SdpU87/HaRuuQdP8xLiMzu9bMtprZ1n379h1j+AAAAAAABF/t0WYws3+XdHqZSfEJbsPKtI24ubyZxSUNSPrqRJcZbnTuDkl3SIX7vE8wJgAAAAAAqsZRk3fn3FvHmmZmT5vZGc65vWZ2hqRnyszWI2lhyfMWSXtK1nG1pHdKusg55yayDAAAAAAAs8lku83fI+lq7/erJX2nzDy/lHSumZ1tZvUqFKK7RypUoZe0StK7nXOHR633SjNrMLOzJZ0r6ReTjBUAAAAAgKo02eR9jaS3mdkTkt7mPZeZnWlm90mSV5DueklbJGUlfd05t81b/vOSjpf0fTN7xMw2eMtsk/R1SY9J+r+SPuycG5xkrAAAAAAAVCV7qad69VuyZInbunWr32EAAAAAAHDMzOwh59ySctMme+UdAAAAAABMMZJ3AAAAAAACjuQdAAAAAICAI3kHAAAAACDgSN4BAAAAAAg4kncAAAAAAAKO5B0AAAAAgIAjeQcAAAAAIODMOed3DBVjZvsk7fA7DkyJBZKe9TsIABPGPgtUF/ZZoLqwz85cZznnTik3YUYl75i5zGyrc26J33EAmBj2WaC6sM8C1YV9dnai2zwAAAAAAAFH8g4AAAAAQMCRvKNa3OF3AACOCfssUF3YZ4Hqwj47CzHmHQAAAACAgOPKOwAAAAAAAUfyDgAAAABAwJG8TxMzi5vZNjP7jZk9YmZ/5rXXm9k/m9nvzOwJM/uOmbWULDfozd9tZt81s5NKpr3GzB40s996y/4PMzNv2mlm9j0z+7WZPWZm940RV3H9xZ/FZnaNmX1+1Hw/NLMl3u9Pmdm3Sqa918y+4v1+jZntM7NfeTFtMbM3j7HtT5iZM7NzSto+4rUtKWl7vdd2yajlnZl9puT5R83sE6Pm+bWZpUa11ZrZLV58xb87Ps5rcnPJa7Cz+Bp7bZvN7JD3+2IzOzJq2Q+M95qZ2QdL5u03s0e939eMivmvzOx7ZV7D4f/LqPY3mtmPzexxM/tPM/uymc0p829AQI3a979R/P95799nzezTo+Z/yswWlDwffs+U26e99g7vPfcbbzuXe+1mZv/o7SO/NbO0mb1m1LbKfgaU2cafe/vho2a28Sh/85jvWzNb6sX5n966lo6xjgl9VnnPLzWzrWaW9dZ723jxIVjM7OSSz88/mNnukueHS+Y71wrHw9+Z2UPe+/kvvWnFY1bp5/b5oz7PHzOzu8ysbow4jrb+cvteq5n9wnvf/aeZXVsy7RMlf8t/mtkXzaymZPpYnwFljwcl0xdb4bj5TyVtC8wsPzpGK3/s/IoXV0PJsk+NmucjZpYzsxNHtb+95O99xMw2mdmikvVuL3n9f1ry2jkzu6hkPe/x2t5b8jc/XrLsN0tew8NmdmrJsoeO8p6pHxXziM/UkpjK/T/nmtmXvPfANit8jv3ZWP8LTI69dHz8tZk9bN73TDv697Di8e5HZnZWyfoOlfz+Disc+xaZ2Xnee+wRKxwn7vDmedl3Mu99XPq+XDKB7Zb9vjnWesvF6z0f3u+O9h4vs+zL3tOj938z+zcrOeZ6+9w/ljz/lpn9tff7KVb4TLnOe36tmW0qmfcEbz85e9Q2P2FmHy35m8f8rDGzV5vZfWb2pPd/+bqZnTb6tZupSN6ngZm9SdI7Jb3BOfdaSW+VtMubfIuk4yW92jl3rqTNkr5tNpwgHnHOvc45F5G0X9KHvXUeJ+keSWucc6+WdIGkN0v6kLfc/5L0fefcBc658yW97ANh1PqLP09N8M9aYiVf5kfZ5Jx7vff3rPH+nvAY8z4q6cqS5++V9NioedolZbzHUn2S/nr0wbXI22aNpL80s6aSSZ+SdKakP3HOvU7SX0gq/VI2+jUpTaQPSHqLt/6TJJ0xarO/G7XsXSXTXvaaOef+tTivpD2S2rznY/2/jsr7APuGpFXOufMkhSX9XxXeZ6gepft+v6QVXvvFkh6X9P6Sz4ljZoWThHFJrd7n0p9L+o03+cMqfJ5c4H2+fFrSPWbWWLKK8T4DSiUk/YNz7k8kfWKceMZ835rZBZJuk3S5c+6PJb1b0m1m9toJbL9snGYWkfR5Sf/NOReWFJH0+wmsDwHhnHuu5PNzg6TPljwfkiTvPXuvpDucc3/knPtTSTFJrypZ1aZRn9vFY9DvvHX9iaQWSe8fHcME1z96mdMl3S1phfd+bpV0nZldVjLbZ71tn+9t/7+WTJvMZ8DvVfg+UvQ+SdtGxTfWsVOSBiV1jLP+dkm/lPSekvVFJCUlXe2c+2Pv7/qqpMUly32s5PUvPeH/qEYe+6+U9OtR21xWsmxpkvOspJtKZxzvPeOc6x/n7zqaL6vwHe1c59xrJF0jqex3E1RE8fh4gaSPq3CMKhrve1ibd7z7oaR/1ChWOFGUlPR259xOSZ/TS++RsDftlRhru+N93zwWw/vdZN/jY+z/P1XhO4HM7GRJhyS9qWSxN3nzSIXPlJ/rpf32TkktZvZW7/n/ktTlnNt+lFDKftaUfOZ+0Tl3jvd/+aKkU472t80UJO/T4wxJzzrn+iTJOfesc26PFa4ofVDSR5xzg960f1UhKb2wzHp+JqnZ+/0qSf/hnHvAW+6wpOv1UpJ+hqSe4oLOud+osm6TtPpoMznn0ipUw7x2jFk2Sype7XuVpBck7StO9L6YvFeFA+HFo5KHAW/dHxlj3VdJ+t+SHlDhy76813y5pJhzLufFeNA594mj/S2er+mlkw1/LenbE1xOmuBrVgEflrTROfczSXIF33TOPT0N28bU+ImkYg+Vdkm3S9qpQsL9Sp0q6aAKB2E55w6VHExXqbCPHPamPaDCgXlZyfITfT/3q5D46CgH6/Hetx+VdEtxee/x05I+NoHtjxXnSkkJ59x/eusccM79ywTWh+qyTNLPnHP3FBucc93Oua9MdAXe8fkXeun4O9n1f1jSV5xzD3vzP6vC+7HcSdt6SY2Sni9pm8xnwBFJWXvpCv0Vkr4+ap6XHTtL/LOkj5hZ7egVm9kfSZqrQnJSmnCvUmH/zRYbnHP3OOd+PIF4fyLpjWZWZ2ZzVfgcfGQCy0lSl6QrzGz+BOd/Rby/+88k/aNzbkiSnHO/d87dO5XbxbATNHL/mIjS79OSJDP7CxUSzcucc7/zmkd/l350EnGW3e5kjbPfvVLl9v//kJe8e4/fk3SKFZytwkmIP3jT21U4adZiZs3OOSfp7yT9s/e5c5GkWycQxz+r/GfNVSp85n632OCcSzvnuo/x76xaJO/T4wFJC63Q/fRfzKx4Bv0cSTudcy+Omn+rpBFXiswspMIbvvgF4TWSHiqdx/uwmWtmJ0j6gqROK3Tfi5vZmWPEdlxJd5p/O4a/6euS3mAlXd7H8bCkPx5j2ouSdnln5tslbRo1/S2Stnt/2w8lvWPU9C9IWmajuuh5rvDWl9JLH2jF1/zgOPGWviaPmNkVJdN+oMLZyJAKSfzoeP9o1LJ/UTLtWF6zyYho1HsD1cs7cF0q6VGvx81FKhw4S9/Xr8SvJT0tabuZ/auZvcvb3gmSmkq+vBSN/lya6Pv5d5I+beN05/WM97592eddmXjGMlac7Cezw2tUOAaN54pRn9vHlU70Thr/mQo9QV7J+sstc7T380fM7BFJeyX91jn3iBdLJT4DvibpSq/3zaAKvb5KlTt2Fu1UoSfc35ZZb7u3zE8knWcvdVmfyGt0a8nr/9WSdifp3yVdosKJ/nvKLPvVkmVLk4JDKiTwNxxl25P1GkmPFC/CYFoUv6f9pwq9Hv6pZNp438OK3q7CxaOiBknfkbS0eELX81lJD5rZ/Vbomn5SybS/KN2OXn6iq5zR2x3v+2apW0dtq9RY+91Yjhu1rv81anq5/f8hSRErDC15swonIR5XoYfcm1VI7mVmCyWd7pz7hQrH3iuk4QuIW1T4Dv33E+zlMtZnzaw/dpO8TwPn3CFJf6rC1ed9kjaZ2TWSTIUD02il7cd5O9dzkuZL+n6Zecps0m1RodvenSokzr8ys3JdSkq77BS7uY253pLfB1U4c/bxMeYd/feMp3g1e6mk0ScQ2r3pxflGfJHwTnzcJenvR2zQ7L9I2uec26HCh8UbzGzeywJ7acz5Lu9DR3p5N6bSBH1QhQ+TKyQd514+zGB0d62fjFp2oq8ZUNz3t6pwEOtUobtr2rsi/i1J7/FOJEnl99sx7wXqfdF8uwo9W34r6bM2qmbEKKM/c476frbCGPoTVTj5cLcVxgafYma/HGc7E9n2WG3lsN9hmBXGbnabWWmvqdHd5o947X9UcvzdOZEebGOs/2Wz6ej762e9bq+nSmoys2KPr/E+Aybq/0p6m8qcMJ/gsfMWFXq9jP4OeaWkr3lXn7+tQvfZEeyl8bi/NW98q6e02/yyUYsVvyNcqUJCMVppt/nRvXE+J+lq76QkZo7i97Q/VuE4dpfZ8BCS8b6Hpc3sGRWGr95d0p5XoXdZtHQjXm/YsApDuv5K0s/NG4ct6Sel21H5E0tH2+543zdLfWzUtkoddb8b5ciodf1/xQlj7f+u0HN4m6Q3qNDb5/+pkMC/2fspdpm/Ui/15Bn9nf0LknZ7PXInaqzPmlmNF2OaOOcGnXM/dM79TxW6t/+NpCclnWVmo8civ0Evjfs+4u1cZ6nQfe7DXvs2SSOuZFmh2/mh4lVl59x+59zdzrm/VWEszF9OMNznJI0+WM9XYfxYqf/trXPRUdb3eknZcaZ/V4UzayN6IXhfSP5G0v9nhUIVSUmXlnm9/lmFD9zSsXntkv7YW+53KnSrKr7mi4rrcN6YcxW660/0C9DXvFhGdzWciIm+ZpOxTYWTRahupQfYmHemul3SW7339UOSTpbU5s0/er8tt8+O4HVN/4Vz7tMqHHT/xtsHe73Pk1Kln0tFR3s/XyLpB15Xw6gKVzau18t7rEjjv29f9nk3RjxjKRcn+8nsUPzCKUnyTlJfo8L+cTTFMe/nSPpzMyt3Ze2VrL/c+/lPVeb97JzLq5BsF4/f430GTIj3WfKQCl1bvzVq8ljHztLln1Sh6/pwDQAr1J84V9L3vWWv1Etf3IdfI+eNx1VhyNvcCcb7CxWuti1wzv12Yn/l8LIHVEiWPnSUWSdjm6QLrKSoIKaPKwy1WqCJjXluU+H79DaNvOI8pML7+b+Y2YhhVs65Pc65Lufc5SoM14y8gjDH2u6kHGW/eyXG2/9/qsLn0PHOuedVGNdeTN7/o2T5a7zl71FhvzjXmzbk/UxYuc8acewmeZ8OVqhWeW5J0+sk7XDO9UraKGl98cy5FapizpH0YOk6nHMvqHB1+aNWqHj7VUmt5hWA8LrSfU7SOu/5hfZSlebjJf2RClfvJuKXkt5ihaI68rq7NuilInvFmPIqdCn6h3H+9v+qQo+DO8eax7vKsUqFwlal3irp1865hc65xc65s1T4orF01PL7VUiko942a1Q48/hab7nFKnS3a/euVnRK+rzXFbJ4kmBEldmj+IkK423LXQEY10Reswr4vApXGoYr3ZrZfyv+P1GdvCtHrZIWlbyvP6yXDtQ/lNe9zHtP/zdJY57hNrMzzewNJU2vk7TD+/1WSZ8rdh/2PmdaNfKKwUTez79SoUtyo3f1499UKJJXbt8Z7317m6SPm9lir32xCuPYP1NmPS8zRpy3SlptZq/21lljZjdOZH2oKnercDwrTbyP6c4bzrm9KoxHL9d745Ws/wsqfMF9nTRcAGqtvON3Ke9q4psl/W4CnwHH4jMqFId8rmRbYx47yyyfUKEWRVG7pE8Ul3POnSmp2QqVtddJitvIwrXHeveTj+uV14xZL+k6SS8bp18JrjDEaKukTxav/nq9jC6fiu1hJDP7YxUuvjx3tHml4e+c/yDpA1ZSD8H7fvhOFYZiFr9Pvt37zl0sNHmypN2vJM6xtjtJ4+13x2QC+/9/qLAfFQtG/kaFq/CLJG0zs/NUGHLXXLJ88cLAZIz+rLlb0putpMCn93/6k0lup2qQvE+PuZI2WuF2M79RoXrsJ7xpH5eUk/RbM3tChR3nPc65l3Wpc879SoWd5krvQ+BySf9oZo+rUJH1lyp8AZYKZ6W2etv7maQvO+cm1FXVFQpE3SDpPq/L4D+rkPiWO2PWqZcfEIvjB3+rwsH2b1xJoZoxtvk15xXvKdGul3ej/5YKxSpG+4xequz6lyp0zSn9gP2xpPPN7AwVkoe9krrN7FcqJOMb9dK4v9FjkEZU//SuVt7mCkWGRhs91urvy8xT7jWbiIvMrKfkp1jp896Stm94/78rVajG/biZZVWoqD+6tgKqy19LetDrvlb0HUnv9rrx/ZOkc8zs1yokzU9K+j8l815T+v5R4Q4Lt5l36yYVhoIUx4YmVfg8edT7fPkfKlR6P6KXG+/93KnCZ9MjZrZVheI/H5X0TRt168Lx3reuMN53laTvWmGM43clrXQvjQO+z8au61E2TlfoAv0PklLetrq9+GRm7zazil0dgX+89+w7Ja0ws9+b2c9UKOz0qZLZRo95L3d7082S5tio8bMTXP/ofa94cu1O7/38UxWqL3+3ZJmPePtltwrv23/R0T8DpFHHg3Fel23OudG3bjzasXPE8ho5jv1Kvfx4/W8qfF95VIXPlru8z5v/UKErcunJwFtH/Q9GnFB3zt0/Tnfb0jHv/17mb33Wi6Xh5Yse1W9KXs/1XtuI/6cVagf8d0mnS3rSzB5V4YLF6FoCqJzh72kq9OS62r1Uc+Co38O8E3IpvdSbtdi+X4Vu+P/onXy5WIXvir9WYcz2x9xLhdmOWZntjvt9cwLG3O9eQXhH2/9/qsJw3GJR2QFJz0ja6uUHY31nn1QRvdGfNSWfuTEr3NLvMRV6Oz0zme1UEyuTIwIAAAAAgADhyjsAAAAAAAFH8g4AAAAAQMCRvAMAAAAAEHAk7wAAAAAABBzJOwAAAAAAAUfyDgAAAABAwJG8AwAAAAAQcP8/MoFz7gcAvjUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new DataFrame containing only the 4 fund portfolios by dropping the S&P 500 column from the DataFrame\n",
    "# Create box plots to reflect the return data for only the 4 fund portfolios\n",
    "# Include a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "whales_df = daily_returns.drop('S&P 500',axis=1)\n",
    "whales_df.plot.box(figsize=(17,10),title=\"Box Plot of Daily Returns Data\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Answer the following question: Based on the box plot visualization of just the four fund portfolios, which fund was the most volatile (with the greatest spread) and which was the least volatile (with the smallest spread)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question** Based on the box plot visualization of just the four fund portfolios, which fund was the most volatile (with the greatest spread) and which was the least volatile (with the smallest spread)?\n",
    "\n",
    "**Answer** # The BERKSHIRE HATHAWAY was the most volatile with the greatest spread and SOROS the least volatile."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the Risk\n",
    "\n",
    "Evaluate the risk profile of each portfolio by using the standard deviation and the beta."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Use the Pandas `std` function to calculate the standard deviation for each of the four portfolios and for the S&P 500. Review the standard deviation calculations, sorted from smallest to largest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIGER GLOBAL MANAGEMENT LLC    0.000996\n",
       "SOROS FUND MANAGEMENT LLC      0.001405\n",
       "PAULSON & CO.INC.              0.002199\n",
       "BERKSHIRE HATHAWAY INC         0.003256\n",
       "S&P 500                        0.011550\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate and sort the standard deviation for all 4 portfolios and the S&P 500\n",
    "# Review the standard deviations sorted smallest to largest\n",
    "# YOUR CODE HERE\n",
    "standard_deviation = daily_returns.std()\n",
    "standard_deviation.head()\n",
    "standard_deviation_sorted = standard_deviation.sort_values()\n",
    "standard_deviation_sorted\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Calculate the annualized standard deviation for each of the four portfolios and for the S&P 500. To do that, multiply the standard deviation by the square root of the number of trading days. Use 252 for that number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIGER GLOBAL MANAGEMENT LLC    0.015804\n",
       "SOROS FUND MANAGEMENT LLC      0.022297\n",
       "PAULSON & CO.INC.              0.034912\n",
       "BERKSHIRE HATHAWAY INC         0.051692\n",
       "S&P 500                        0.183345\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate and sort the annualized standard deviation (252 trading days) of the 4 portfolios and the S&P 500\n",
    "# Review the annual standard deviations smallest to largest\n",
    "# YOUR CODE HERE\n",
    "\n",
    "annualized_standard_deviation = standard_deviation * np.sqrt(252)\n",
    "annualized_standard_deviation.sort_values()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Use the daily returns DataFrame and a 21-day rolling window to plot the rolling standard deviations of the four fund portfolios and of the S&P 500 index. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the daily returns DataFrame and a 21-day rolling window, \n",
    "# plot the rolling standard deviation of the 4 portfolios and the S&P 500\n",
    "# Include a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "\n",
    "#whales = whales_df.plot(figsize=(17,10), title=\"21-Day Rolling window\")\n",
    "\n",
    "whales_df.rolling(window=21).std().plot()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Use the daily returns DataFrame and a 21-day rolling window to plot the rolling standard deviations of only the four fund portfolios. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Rolling 21-Day Variance of S&P 500 Returns'}, xlabel='date'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the daily return data and a 21-day rolling window, plot the rolling standard deviation of just the 4 portfolios. \n",
    "# Include a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "daily_returns.drop('S&P 500',axis=1)\n",
    "whales_21_variance = daily_returns['S&P 500'].rolling(window=21).std()\n",
    "\n",
    "whales_21_variance.plot(\n",
    "    figsize=(10, 7),\n",
    "    title='Rolling 21-Day Variance of S&P 500 Returns'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Answer the following three questions:\n",
    "\n",
    "1. Based on the annualized standard deviation, which portfolios pose more risk than the S&P 500?\n",
    "\n",
    "2. Based on the rolling metrics, does the risk of each portfolio increase at the same time that the risk of the S&P 500 increases?\n",
    "\n",
    "3. Based on the rolling standard deviations of only the four fund portfolios, which portfolio poses the most risk? Does this change over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1**  Based on the annualized standard deviation, which portfolios pose more risk than the S&P 500?\n",
    "\n",
    "**Answer 1** #\n",
    "BERKSHIRE HATHAWAY INC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2** Based on the rolling metrics, does the risk of each portfolio increase at the same time that the risk of the S&P 500 increases?\n",
    "\n",
    "**Answer 2** #No\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3** Based on the rolling standard deviations of only the four fund portfolios, which portfolio poses the most risk? Does this change over time? \n",
    "\n",
    "**Answer 3** #TIGER GLOBAL MANAGEMENT LLC    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the Risk-Return Profile\n",
    "\n",
    "To determine the overall risk of an asset or portfolio, quantitative analysts and investment managers consider not only its risk metrics but also its risk-return profile. After all, if you have two portfolios that each offer a 10% return but one has less risk, you’d probably invest in the smaller-risk portfolio. For this reason, you need to consider the Sharpe ratios for each portfolio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Use the daily return DataFrame to calculate the annualized average return data for the four fund portfolios and for the S&P 500. Use 252 for the number of trading days. Review the annualized average returns, sorted from lowest to highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIGER GLOBAL MANAGEMENT LLC    0.000996\n",
       "SOROS FUND MANAGEMENT LLC      0.001405\n",
       "PAULSON & CO.INC.              0.002199\n",
       "BERKSHIRE HATHAWAY INC         0.003256\n",
       "S&P 500                        0.011550\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the annual average return data for the for fund portfolios and the S&P 500\n",
    "# Use 252 as the number of trading days in the year\n",
    "# Review the annual average returns sorted from lowest to highest\n",
    "# YOUR CODE HERE\n",
    "year_trading_days = 252\n",
    "average_annual_return_portfolio = daily_returns.mean() * year_trading_days\n",
    "average_annual_return_portfolio\n",
    "average_annual_return_portfolio_sorted = standard_deviation.sort_values()\n",
    "average_annual_return_portfolio_sorted\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Calculate the Sharpe ratios for the four fund portfolios and for the S&P 500. To do that, divide the annualized average return by the annualized standard deviation for each. Review the resulting Sharpe ratios, sorted from lowest to highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAULSON & CO.INC.             -0.189998\n",
       "SOROS FUND MANAGEMENT LLC     -0.102290\n",
       "TIGER GLOBAL MANAGEMENT LLC    0.579002\n",
       "S&P 500                        0.589612\n",
       "BERKSHIRE HATHAWAY INC         0.717512\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the annualized Sharpe Ratios for each of the 4 portfolios and the S&P 500.\n",
    "# Review the Sharpe ratios sorted lowest to highest\n",
    "# YOUR CODE HERE\n",
    "sharpe_ratios = average_annual_return_portfolio / annualized_standard_deviation\n",
    "sharpe_ratios.sort_values()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Visualize the Sharpe ratios for the four funds and for the S&P 500 in a bar chart. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sharpe Ratios bar chart'}>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the Sharpe ratios as a bar chart\n",
    "# Include a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "sharpe_ratios.plot.bar(title=\"Sharpe Ratios bar chart\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Answer the following question: Which of the four portfolios offers the best risk-return profile? Which offers the worst?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question** Which of the four portfolios offers the best risk-return profile? Which offers the worst?\n",
    "    \n",
    "**Answer** # The best is BERKSHIRE HATHAWAY INC  and "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diversify the Portfolio\n",
    "\n",
    "Your analysis is nearing completion. Now, you need to evaluate how the portfolios react relative to the broader market. Based on your analysis so far, choose two portfolios that you’re most likely to recommend as investment options."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use the Pandas `var` function to calculate the variance of the S&P 500 by using a 60-day rolling window. Visualize the last five rows of the variance of the S&P 500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Rolling 60-Day Variance of S&P 500 Returns'}, xlabel='date'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the variance of the S&P 500 using a rolling 60-day window.\n",
    "# YOUR CODE HERE\n",
    "whales_21_variance = daily_returns['S&P 500'].rolling(window=60).var()\n",
    "\n",
    "whales_21_variance.plot(\n",
    "    figsize=(10, 7),\n",
    "    title='Rolling 60-Day Variance of S&P 500 Returns'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each of the two portfolios that you chose, complete the following steps:\n",
    "\n",
    "1. Using the 60-day rolling window, the daily return data, and the S&P 500 returns, calculate the covariance. Review the last five rows of the covariance of the portfolio.\n",
    "\n",
    "2. Calculate the beta of the portfolio. To do that, divide the covariance of the portfolio by the variance of the S&P 500.\n",
    "\n",
    "3. Use the Pandas `mean` function to calculate the average value of the 60-day rolling beta of the portfolio.\n",
    "\n",
    "4. Plot the 60-day rolling beta. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Portfolio 1 - Step 1: Using the 60-day rolling window, the daily return data, and the S&P 500 returns, calculate the covariance. Review the last five rows of the covariance of the portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-09-04    0.000103\n",
       "2020-09-08    0.000116\n",
       "2020-09-09    0.000120\n",
       "2020-09-10    0.000121\n",
       "2020-09-11    0.000120\n",
       "Name: S&P 500, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the covariance using a 60-day rolling window \n",
    "# Review the last five rows of the covariance data\n",
    "# YOUR CODE HERE\n",
    "whales_rolling_60_covariance = daily_returns['S&P 500'].rolling(window=60).cov()\n",
    "whales_rolling_60_covariance.plot(\n",
    "    figsize=(10, 7),\n",
    "    title='Rolling 60-Day Covariance'\n",
    ")\n",
    "whales_rolling_60_covariance.tail()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Portfolio 1 - Step 2: Calculate the beta of the portfolio. To do that, divide the covariance of the portfolio by the variance of the S&P 500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-09-04    1.0\n",
       "2020-09-08    1.0\n",
       "2020-09-09    1.0\n",
       "2020-09-10    1.0\n",
       "2020-09-11    1.0\n",
       "Name: S&P 500, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the beta based on the 60-day rolling covariance compared to the market (S&P 500)\n",
    "# Review the last five rows of the beta information\n",
    "# YOUR CODE HERE\n",
    "\n",
    "\n",
    "\n",
    "whales_beta = daily_returns['S&P 500'].rolling(window=60).cov() / daily_returns['S&P 500'].rolling(window=60).var()\n",
    "\n",
    "whales_beta.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Portfolio 1 - Step 3: Use the Pandas `mean` function to calculate the average value of the 60-day rolling beta of the portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the average of the 60-day rolling beta\n",
    "# YOUR CODE HERE\n",
    "market_rolling_60_covariance.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Portfolio 1 - Step 4: Plot the 60-day rolling beta. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Rolling 60-Day Beta'}, xlabel='date'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the rolling beta \n",
    "# Include a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "market_rolling_60_covariance = daily_returns['S&P 500'].rolling(window=60).cov()\n",
    "market_rolling_60_covariance.plot(\n",
    "    figsize=(10, 7),\n",
    "    title='Rolling 60-Day Beta'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Portfolio 2 - Step 1: Using the 60-day rolling window, the daily return data, and the S&P 500 returns, calculate the covariance. Review the last five rows of the covariance of the portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-09-04    0.000103\n",
       "2020-09-08    0.000116\n",
       "2020-09-09    0.000120\n",
       "2020-09-10    0.000121\n",
       "2020-09-11    0.000120\n",
       "Name: S&P 500, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the covariance using a 60-day rolling window \n",
    "# Review the last five rows of the covariance data\n",
    "# YOUR CODE HERE\n",
    "whales_60_variance = daily_returns['S&P 500'].rolling(window=60).cov()\n",
    "whales_60_variance.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Portfolio 2 - Step 2: Calculate the beta of the portfolio. To do that, divide the covariance of the portfolio by the variance of the S&P 500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-09-04    1.0\n",
       "2020-09-08    1.0\n",
       "2020-09-09    1.0\n",
       "2020-09-10    1.0\n",
       "2020-09-11    1.0\n",
       "Name: S&P 500, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the beta based on the 60-day rolling covariance compared to the market (S&P 500)\n",
    "# Review the last five rows of the beta information\n",
    "# YOUR CODE HERE\n",
    "\n",
    "whales_60_beta = daily_returns['S&P 500'].rolling(window=60).cov() / daily_returns['S&P 500'].rolling(window=60).var()\n",
    "whales_60_beta.tail()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Portfolio 2 - Step 3: Use the Pandas `mean` function to calculate the average value of the 60-day rolling beta of the portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00013534265120427927"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average of the 60-day rolling beta\n",
    "# YOUR CODE HERE\n",
    "whales_60_variance.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Portfolio 2 - Step 4: Plot the 60-day rolling beta. Be sure to include the `title` parameter, and adjust the figure size if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Rolling 60-Day Beta'}, xlabel='date'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the rolling beta \n",
    "# Include a title parameter and adjust the figure size\n",
    "# YOUR CODE HERE\n",
    "\n",
    "\n",
    "whales_60_beta.plot(\n",
    "    figsize=(10, 7),\n",
    "    title='Rolling 60-Day Beta'\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer the following two questions:\n",
    "\n",
    "1. Which of the two portfolios seem more sensitive to movements in the S&P 500?\n",
    "\n",
    "2. Which of the two portfolios do you recommend for inclusion in your firm’s suite of fund offerings?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1** Which of the two portfolios seem more sensitive to movements in the S&P 500?\n",
    "    \n",
    "**Answer 1** #TIGER GLOBAL MANAGEMENT LLC\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2** Which of the two portfolios do you recommend for inclusion in your firm’s suite of fund offerings?\n",
    "    \n",
    "**Answer 2** # S&P 500 & BERKSHIRE HATHAWAY INC "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
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