{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "75adde04",
   "metadata": {},
   "source": [
    "# Python Programming Language for Data Analysis\n",
    "\n",
    "In our earlier workshop we learned how to import third party libraries such as `pandas` and use it to analyze data. In the process, we learned many fundamental aspects of programming such as:\n",
    "\n",
    "    - Variables and Data Types\n",
    "    - Operators\n",
    "    - Functions (User-defined functions, built-in functions, methods and third party functions)\n",
    "    - Indexing and Extracting elements from a sequence\n",
    "    \n",
    "We also learned how to use many core aspects of `pandas` library:\n",
    "\n",
    "    - How to import data in a csv file and get summary statistics for numeric and non-numeric columns\n",
    "    - How to list functions available in pandas modules and review its use by consulting documentations online\n",
    "    - How to filter rows and columns to get a desired subset of data\n",
    "    - How to create new columns with desired values\n",
    "    - How to group data based on one or multiple columns and get group-wise summary statistics\n",
    "    - How to plot data to visualize trends over time\n",
    "    \n",
    "In this workshop, we will now use this knowledge to perform end-to-end data analysis. First, we will begin by answering the questions we have already solved, so that we can practice what we know. Then we will focus on how to use this data such that we can create a simple model that can predict XXX the ridership of the 79th route. For the latter we will utilize the `sklearn` package for machine learning in Python.\n",
    "\n",
    "Let's begin by importing the libraries and data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ba3ce53",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08f33900",
   "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>route</th>\n",
       "      <th>routename</th>\n",
       "      <th>Month_Beginning</th>\n",
       "      <th>Avg_Weekday_Rides</th>\n",
       "      <th>Avg_Saturday_Rides</th>\n",
       "      <th>Avg_Sunday-Holiday_Rides</th>\n",
       "      <th>MonthTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Indiana/Hyde Park</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>6982.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Hyde Park Express</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>King Drive</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>21406.5</td>\n",
       "      <td>13210.7</td>\n",
       "      <td>8725.3</td>\n",
       "      <td>567413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>22432.2</td>\n",
       "      <td>17994.0</td>\n",
       "      <td>10662.2</td>\n",
       "      <td>618796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Jackson Park Express</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>18443.0</td>\n",
       "      <td>13088.2</td>\n",
       "      <td>7165.6</td>\n",
       "      <td>493926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  route             routename Month_Beginning  Avg_Weekday_Rides  \\\n",
       "0     1     Indiana/Hyde Park      01/01/2001             6982.6   \n",
       "1     2     Hyde Park Express      01/01/2001             1000.0   \n",
       "2     3            King Drive      01/01/2001            21406.5   \n",
       "3     4         Cottage Grove      01/01/2001            22432.2   \n",
       "4     6  Jackson Park Express      01/01/2001            18443.0   \n",
       "\n",
       "   Avg_Saturday_Rides  Avg_Sunday-Holiday_Rides  MonthTotal  \n",
       "0                 0.0                       0.0      153617  \n",
       "1                 0.0                       0.0       22001  \n",
       "2             13210.7                    8725.3      567413  \n",
       "3             17994.0                   10662.2      618796  \n",
       "4             13088.2                    7165.6      493926  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(filepath_or_buffer=\"cta-ridership-original.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3d72a1",
   "metadata": {},
   "source": [
    "Let's answer the following questions first:\n",
    "1. Identify the 10 routes with highest number of ridership in total. Create a bar plot of total ridership of these top 10 routes. To create `bar` plot simple provide argument `bar` to the parameter `kind` of the `plot` method. \n",
    "2. Which route has the highest average ridership? Is it also the most popular route on Saturdays or on Sundays and Holidays? Why is the route so popular?\n",
    "3. Group the data by year to figure out the yearly average trend of ridership over the years. Plot the yearly average of the average monthly total ridership value.\n",
    "4. Now use the above grouped data to plot the average ridership during the weekdays, saturday and sunday/holidays by year.\n",
    "5. Which routes have the highest difference in average ridership between weekdays and Saturdays?\n",
    "6. Which routes have the highest difference in average ridership between weekdays and Sundays/Holidays?\n",
    "7. Which routes have the most consistent average ridership between weekdays, Saturdays and Sundays/Holidays? i.e. are there any route that are not affected by the day of the week?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "267e63a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Identify the 10 routes with highest number of ridership in total. \n",
    "#    Create a bar plot of total ridership of these top 10 routes. \n",
    "\n",
    "routes_grouped = data[['routename','MonthTotal']].groupby('routename')   # created groupby object\n",
    "monthtotal_byroutes = routes_grouped.sum()                               # get sum for each group in groupby object  \n",
    "top10routes = monthtotal_byroutes.sort_values(by='MonthTotal', ascending=False)[:10] # sort and get the first 10 values\n",
    "\n",
    "top10routes.plot(kind='bar')   # create a bar plot of the series with top 10 values along with the index                                       \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e143b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg_Weekday_Rides\n",
      "Avg_Saturday_Rides\n",
      "Avg_Sunday-Holiday_Rides\n"
     ]
    }
   ],
   "source": [
    "# 2. Which route has the highest average ridership? \n",
    "#    Is it also the most popular route on Saturdays or on Sundays and Holidays? \n",
    "#    Why is the route so popular?\n",
    "\n",
    "\n",
    "cols = [ 'Avg_Weekday_Rides','Avg_Saturday_Rides', 'Avg_Sunday-Holiday_Rides'] # create a list of columns to use\n",
    "\n",
    "top10routes_byday = []  # initialize empty list to store results\n",
    "for i in cols:          # iterate over each item of the list\n",
    "    print(i)            # print the item\n",
    "    routes_grouped = data[['routename', i]].groupby('routename') # ceate group byobject with routename and item in the loop\n",
    "    total_byroutes = routes_grouped.sum()                        # get sum of each group\n",
    "    top10routes = total_byroutes.sort_values(by=i, ascending=False)[:10] # sort and extract the first 10 items\n",
    "    top10routes_byday.append(top10routes)                        # append the above result to the empty list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ca047b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for i in top10routes_byday: # for each item in the list that now has result\n",
    "    i.plot(kind='bar')      # create a bar plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4470c08b",
   "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>Avg_Weekday_Rides</th>\n",
       "      <th>Avg_Saturday_Rides</th>\n",
       "      <th>Avg_Sunday-Holiday_Rides</th>\n",
       "      <th>MonthTotal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Beginning_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>7289.061917</td>\n",
       "      <td>4573.756116</td>\n",
       "      <td>3036.292182</td>\n",
       "      <td>189227.727617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>7206.850279</td>\n",
       "      <td>4548.055549</td>\n",
       "      <td>2994.784191</td>\n",
       "      <td>187113.878487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>6787.361841</td>\n",
       "      <td>4260.948153</td>\n",
       "      <td>2798.121381</td>\n",
       "      <td>175961.016959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>6539.225350</td>\n",
       "      <td>4221.471787</td>\n",
       "      <td>2779.293575</td>\n",
       "      <td>170907.525701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>6677.692582</td>\n",
       "      <td>4212.167280</td>\n",
       "      <td>2876.988787</td>\n",
       "      <td>173637.042553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Avg_Weekday_Rides  Avg_Saturday_Rides  \\\n",
       "Month_Beginning_year                                          \n",
       "2001                        7289.061917         4573.756116   \n",
       "2002                        7206.850279         4548.055549   \n",
       "2003                        6787.361841         4260.948153   \n",
       "2004                        6539.225350         4221.471787   \n",
       "2005                        6677.692582         4212.167280   \n",
       "\n",
       "                      Avg_Sunday-Holiday_Rides     MonthTotal  \n",
       "Month_Beginning_year                                           \n",
       "2001                               3036.292182  189227.727617  \n",
       "2002                               2994.784191  187113.878487  \n",
       "2003                               2798.121381  175961.016959  \n",
       "2004                               2779.293575  170907.525701  \n",
       "2005                               2876.988787  173637.042553  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. Group the data by year to figure out the yearly average trend of ridership over the years. \n",
    "#    Plot the yearly average of the average monthly total ridership value.\n",
    "\n",
    "\n",
    "data['Month_Beginning'] = pd.to_datetime(data['Month_Beginning'], format='%m/%d/%Y') # convert to datetime object\n",
    "data['Month_Beginning_year'] = data['Month_Beginning'].dt.year                       # create new column with just year info\n",
    "\n",
    "yearly_groups = data.iloc[:,3:8].groupby('Month_Beginning_year').mean()              # select a subset of data and create groupby object and calculate mean of each group\n",
    "yearly_groups.head()                                                                 # show first five lines of the above dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc97a032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NLyjfusNf4vZdCswHTudw19aw/0ltzgPezegA0Mv9Xs/h7tD98BrsAbzJ4PP4rjt8JzwP0W0uXz7eUHC6S7+TkNcKXJlX8cbpD+AZ6PuAeJd+BSGOKoQ43YTm4Vt3+Gtc+ynBe1jIqOE8BdyyS9y5mUoYd/iQMt85pirqvti1qWK8m8UyvKfzjJB8hzln4A0DF+L1tMPVeyXe8HG7KtLXOx2nufO1FfifkDxd8Yad9rr2dSGHO2fk4g0L7XD/zTLcKy1Ov+fxHhIP4I2UPEmQ2zPesODLfNveV+HNrSbUpnyYY6rKOSPc9Rpwhz/At+7widWVc8d3Z4jsG+D6anRaz3ddzpWg697l+5nLG+h9hDpriDtfW/Cu849xr4aEtP1w24nV6JeBZ1D2hLSvatt8mHquqGb/mUH5euLd9/a7dvNI8P9e3X8WkucXeEZwv/tPnsc5o1W3iStsOERkBfCcqh7JKxdENCJyJ97DyxC/dTEMo/mJ81uBSEFEOuK9A5JJLbxaDMMwDH8ww/UtW/GG7n6qqtv9VsYwDMMIjw0VGoZhGFFFiwxrYhiGYbRcWtxQYXp6umZmZvqthmEYRlSxaNGi7aqa4bcetaHFGa7MzEwWLlxYc0bDMAzjECKywW8dakttljiZIiLbRGRZkOwoEZkjXrC8193KEIG020QkT0RWSdCyPiIyyuXPEy/omDh5ooi86OTzRCQzqMwkEVnjtoautGAYhmG0AGozxzWVb9fMC/AUcKuqBsKR3AKHFk28FG8JmDPw3mgPvG39ON4Lo9luC9R5FbBTVbPwXh6819XVAS9Ewxi85XzukO9G8TUMwzCOMGo0XKo6Cy8AWTD98RZMBW/l4EBcrAl48YQOqGo+XsiB0eKtfZWiqnPUc2N8Fi/aZaDMM+77y8Aprjd2Ol64hiL1VpyfyXcNqGEYhnGEUV+vwmV4SwKBt9RNYLHTbnwb0gC8eDXd3FYQRn5YGfUWcywG0qqpyzAMwziCqa/huhJv0c5FeItxBlZpD7eib7iQCgF5fcschohcIyILRWRhYWFhtYobhmEY0U29DJeqrlTV8ao6Cm9xz0AIgQK+7X0BdMdbVLPAfQ+VH1ZGROLwVpouqqaucPo8qao5qpqTkREV3pyGYRhGPamX4XLr+iFeHJz/xS3bD0zHiyKcKCK98Zww5qvqFrzIuce4+asf4a3mHSgT8Bi8CC8ktOIFVRwvIqnOKWO8kxmGYRhHMLVxh5+GFzisv4gUiMhVwEQRWY0Xw2kzXqgLVHU58BJerJ13gJ+raiAw2HV43oh5eD20QHTPp4E0EckDbgJudXUV4cWjWuC2yVqLgIX1RVX5x6x1bN9bVfgwwzAMIxJocWsV5uTkaH1eQF5buJczH/qElKQ47r1wGKcM7NQE2hmGYUQmIrJIVXP81qM22FqFjr4ZbXj9+lzS2yRy1TML+d1/vqSkLDSqtGEYhuE3ZriC6N+5La9dfzzXjOvD8/M3cvYjn7C0YJffahmGYRhBmOEKITEult+eNZDnrhpDSVkF33vsMx79MI+KypY1pGoYhhGtmOGqguOy0nnnhnGcMaQz989YxSVPzGFT0X6/1TKMqENV2VJc4rcaRgvCDFc1tEuO55GJI/jrJcNZ9c0eznzoE15ZVEBLc2gxjKZg6+5SHv0wj5Me+Ihj//gB89bt8Fslo4XQ4sKaNDYiwvkjupGTmcpNL37Br//9BR+s3MY9FwyhfXKC3+oZRkRRXlHJh6sKeXHBRj5cVUhFpTK6dwc2F5cyc8VWxvRJ81tFowVghquWdE9NZto1x/DErLX85d3VLNxQxJ8vHk5udrrfqhmG7+Rv38eLCzbxyuICCvccIKNtIteM68P3c3rQO701P3hqHrPW2HJsRuNghqsOxMYIPzsxi3HZGdzwwuf84Ol5XJXbm1tO709SfGzNFRhGC6KkrIK3l23hhQWbmJ9fRGyMcFL/DC45uicn9s8gPvbbmYhx/dL5w1sr2VJcQpd2rXzU2mgJmOGqB0O6teONX4zlj29/xdOz8/k0bzt/vXQ4Azqn1FzYMKIYVWXZ17t5ceFGXvt8M3sOlJOZlsxvzujPRSO70zElKWy5cf0y+MNbK/lk9Xa+f3SPsHkMo7aY4aonrRJimTxhCCf178gtLy/lvEc+5Tdn9OfK43sTExNuYXvDiF6K9x/kv0u+5oUFm/hqy24S42I4a2gXLjm6B2N6d8AFNK+S/p3a0iklkY/XFJrhMhqMGa4GctKAjsy4cSy3vvolv3/zKz5ctY0HLj7KhkOMFsHcdTuYNn8jby/7hrLySoZ0S+Hu84dw3lFdadcqvtb1iAhjszOYuWIrFZVKrD3cGQ3ADFcjkNYmkSd/OIoXFmxi8usrOOOvn/Dqz46jb0Ybv1UzjHpRerCCu15fwbT5G0lJiuPSo3vw/ZweDOnWrt51juuXwcuLClhasIsRPVMbUVvjSMMMVyMhIkwc3ZOjMzsw/sGPeW3JZm46rZ/fahlGnVm/fR8/e24xK7bs5toT+nLjqdmN4nw0NisdEZi1ersZLqNB2AvIjUxWxzYM7d6eT/O2+62KYdSZt77cwjmPzObrXSVMuSKHW88c0Gges6mtExjWrZ25xRsNxgxXE5CblcaSTbvYXXrQb1UMo1aUlVdy5/Tl/Oy5xWR1bMObv8zl5AGNH9pnXL8MlmzaRXGJXRtG/THD1QTkZmVQUanMXWtL3BiRz6ai/Vz8xBymfraeK4/vzUs/PZbuqclNsq9x/bxr4zMbkTAagBmuJmBkr/a0io9ltl2cRoTz3oqtnPPIbNZt28vffzCS288dREJc090WhvdoT9vEOBsuNBqEOWc0AYlxsYzp04HZa8xwGZHJwYpKHpixiidmrWNw1xQeu3wkvdJaN/l+42NjOC4rjVmrt6OqNb7/ZRjhsB5XE5Gblc667fv4epeFczAiiy3FJUx8ci5PzFrHD47pySvXHdcsRivAuH4ZfL2rhLWF+5ptn0bLwgxXEzE2OwOA2TYkYkQQs1YXcvbDs1mxZTcPXTqc358/tNnX2Rznro1Zq+3aMOqHGa4mol+nNnRsm8gnNlxoRAAVlcpf3l3FpP+bT0abRKZfn8uE4d180aVHh2T6pLe2eS6j3tRouERkiohsE5FlQbLhIjJXRJaIyEIRGe3kmSJS4uRLROTvQWVGiciXIpInIg+LG9wWkUQRedHJ54lIZlCZSSKyxm2TGvXImxgRITcrnc/W7qCy0gJPGv6xbU8pP3hqHg9/kMdFI7vz358fT1ZHf1d1Gdcvg7nrdlB6sMJXPYzopDY9rqnAGSGy+4C7VHU4cLv7HWCtqg5327VB8seBa4BstwXqvArYqapZwIPAvQAi0gG4AxgDjAbuEJGoet0+Nzudon1lrNiy229VjCOUOWt3cPbDs/l8007uu2gY9198FK0S/A/BM65fOqUHK1m4fqffqhhRSI2GS1VnAUWhYiAQw6MdsLm6OkSkC5CiqnPUi3v/LHC+S54APOO+vwyc4npjpwMzVbVIVXcCM/muAY1ocrO8IJM2XGg0N5WVyqMf5nH5U3NpmxTHf39+PN/PiZxV2Y/pk0ZCbIwNFxr1or5zXDcC94vIJuAB4LagtN4i8rmIfCwiY52sG1AQlKfAyQJpmwBUtRwoBtKC5WHKRAUdU5Lo36kts/Ps4jSaj/KKSq5+diH3z1jFOcO6Mv363IiLFZecEEdOZqo5aBj1or6G6zrgV6raA/gV8LSTbwF6quoI4CbgeRFJAcK9rBGY+KkqrboyhyEi17i5toWFhZF1IeRmp7Ng/U4byzeajWfmbOCDldv437MH8tClw2mTGJmva47rl8HKb/awdXep36oYUUZ9Ddck4FX3/d94c1Co6gFV3eG+LwLWAv3wekvdg8p359vhxQKgB4CIxOENPRYFy8OUOQxVfVJVc1Q1JyMjo56H1DTkZqdTVl7J/PzQ0VbDaHy27S7lwZmrOaFfBlfl9o7oF3zNLd6oL/U1XJuBE9z3k4E1ACKSISKx7nsfPCeMdaq6BdgjIse4+asfAa+58tPxDCHARcAHbh5sBjBeRFKdU8Z4J4sqxvTuQEJsjC3/ZDQL97z1lbdg7nmDI9poAQzs0paMtonMsjlgo47UOIYgItOAE4F0ESnA8/T7CfCQ6yGV4nkLAowDJotIOVABXKuqga7GdXgeiq2At90G3jDjP0UkD6+ndSmAqhaJyN3AApdvclBdUUNyQhwje7U3Bw2jyZmzdgevLdnML0/Oond6862EUV+8qMjpfLhym0VFNupEjYZLVSdWkTQqTN5XgFeqqGchMCSMvBS4uIoyU4ApNekY6YzNzuD+Gaso3HOAjLaJfqtjtEAOVlRy+2vL6J7aip+dlOW3OrXmhH4ZvLr4a5Z9XcxRPdr7rY4RJdjKGc1AwC3+s7XW6zKahimz81mzbS93nju42Zdwagi5h6Ii2zyXUXvMcDUDQ7q1o12reBsuNJqELcUlPPT+Gk4d2JFTBzV+8MemJK1NIkO6WlRko26Y4WoGYmOE47PSmL3GC+VgGI3J79/4iopK5Y5zB/utSr0Y1y+dxRstYrhRe8xwNRO5WRl8s7uUtYV7/VbFaEHMWl3Im19u4ecnZdGjQ9NELW5qxmUHoiJbxHCjdpjhaibGZnvzXBZc0mgsDpRXcOf05WSmJXPNuD5+q1NvRvZKpY1FRTbqgBmuZqJHh2R6pSXb+1xGo/HUJ/ms276PO8+LLoeMUOJjYzi2bxqzVhfaULpRK8xwNSO5WenMXVfEwYpKv1UxopyCnft55IM1nDG4Myf27+i3Og1mXL8MCnaWkL/doiIbNWOGqxkZm53O3gPlLNm0y29VjChn8usrEIT/d+4gv1VpFE6w5Z+MOmCGqxk5tk86MWJhToyG8eHKbby7Yiu/OCWLbu1b+a1Oo9AzLZnMtGRb/smoFWa4mpF2yfEM7d6e2TYJbdST0oMV3DF9OX0yWnN1bvQ6ZIRjXL8M5qzdwYFyi6RgVI8ZrmZmbFY6XxQU2zsrRr34+8dr2Vi0n7snDCEhrmVdvuOyMyg5WMEii4ps1EDLavlRQG52OhWVypy19s6KUTc27NjHYx+t5ZxhXTjeLSPWkji2bxrxscLHNiJh1IAZrmZmZM9UkhNi7X0uo06oKndOX058jPC/Z7cMh4xQWifGMapXKrNW27VhVI8ZrmYmIS6GMb072PtcRp2YuWIrH64q5MZT+9G5XZLf6jQZ4/pl8NWW3WzbY1GRjaoxw+UDudkZ5G/fR8HO/X6rYkQBJWUV3PX6Cvp1asMVx2f6rU6TEoiK/In1uoxqMMPlA7b8k1EXHv0wj693lTB5whDiY1v2JTuoSwrpbRJs+SejWlr2VRChZHdsQ6eURD6x4UKjBtYV7uXJWeu4YEQ3jumT5rc6TU5MjDA2O4NP1mynstKWfzLCY4bLB0SE47PS+SzPLk6jalSVO6YvJzEuhtvOGuC3Os3GuH7pFO0rY/nm3X6rYkQoZrh8Ymx2Ojv3H7SL06iSt5d9wydrtnPT+H50bNtyHTJCGRtY/smGC40qMMPlE4H3cD7Js4vT+C77DpRz9xsrGNglhR8e08tvdZqV9DaJDO6awse2bqFRBWa4fKJj2yQGdG5rDhpGWB7+YA1bikv5/fmDiWvhDhnhGNcvg8UbdrLHVpgxwlDjFSEiU0Rkm4gsC5INF5G5IrJERBaKyOigtNtEJE9EVonI6UHyUSLypUt7WETEyRNF5EUnnycimUFlJonIGrdNarSjjhBys9JZuH4nJWW2NpvxLWu27uHpT/K5eFR3RvXq4Lc6vjAuO4NyW2HGqILaPMpNBc4Ikd0H3KWqw4Hb3W9EZBBwKTDYlXlMRAIR7h4HrgGy3Rao8ypgp6pmAQ8C97q6OgB3AGOA0cAdIpJa5yOMYHKz0ymrqGT++iK/VTEiBFXl9teWk5wQy61nHjkOGaGM6pVK64RYm+cywlKj4VLVWUDonVWBFPe9HbDZfZ8AvKCqB1Q1H8gDRotIFyBFVeeoF+L0WeD8oDLPuO8vA6e43tjpwExVLVLVncBMvmtAo5oxvdNIiI2x1eKNQ0z/YjNz1u3gljMGkNYm0W91fCMhLhAV2YbSje9S38HzG4H7RWQT8ABwm5N3AzYF5Stwsm7ue6j8sDKqWg4UA2nV1PUdROQaN2S5sLAweoxAq4RYRvVKtfhcBuCtkPGHt75iaLd2XDa6p9/q+M64fhlsLNrPeouKbIRQX8N1HfArVe0B/Ap42sklTF6tRl7fMocLVZ9U1RxVzcnIyKhW8UgjNzudld/soXDPAb9VMXzmmTnr2br7AP/vnEHExoRr/kcW48wt3qiC+hquScCr7vu/8eagwOsV9QjK1x1vGLHAfQ+VH1ZGROLwhh6LqqmrRRFY/umztdbrOpLZXXqQxz9ay4n9Mxjd+8h0yAglM701PTskM8vc4o0Q6mu4NgMnuO8nA2vc9+nApc5TsDeeE8Z8Vd0C7BGRY9z81Y+A14LKBDwGLwI+cPNgM4DxIpLqnDLGO1mLYnDXdrRPjrfhwiOcp2ato7jkIDeP7++3KhHFuH7pzFm7g7LySr9VMSKIuJoyiMg04EQgXUQK8Dz9fgI85HpIpXjegqjqchF5CVgBlAM/V9WAr/d1eB6KrYC33QbeMOM/RSQPr6d1qaurSETuBha4fJNVtcW538XGCMf3TWf2mu2oKu4tAeMIYsfeAzw9O5+zh3ZhSLd2fqsTUYzLzuBfczeyaMNOju3b8tdqNGpHjYZLVSdWkTSqivz3APeEkS8EhoSRlwIXV1HXFGBKTTpGO7nZ6bz55RbWFu4lq2Nbv9UxmpnHP1pLycEKfnVaP79ViTiO7ZtGXIwwa02hGS7jEEfeK/kRSG5g+ScbLjzi2FJcwrNzN/C9kd3J6tjGb3UijrZJ8YzslWrzXMZhmOGKAHp0SCYzLdmWfzoCefj9PFSVG07J9luViOWEfhks37zbPG+NQ5jhihCOz0pn7rodHKywSegjhfXb9/HSwk1cNronPTok+61OxBJwi59tC1IbDjNcEcLY7HT2lVXw+cZdfqtyRKGqVPgUE+3B91YTHyv8/OQsX/YfLQzumkJa6wRbRcM4hBmuCOHYvunECLb8UzNz3b8WM/HJuc3e0/1qy26mf7GZHx/f+4iKtVUfYmKE3Ox0PllTaIFXDcAMV8TQrlU8w7q355M8e6psLsrKK/lo9Tbmry/igXdXNeu+//zuatokxvHTcX2adb/RyrjsDLbvLWPFFgu8apjhiijGZqfzxaZdFJdYDKLmYGnBLkoPVpLdsQ1PfLyOj1Zta5b9fr5xJ+99tZWfjutD++SEZtlntDO2n+d5a8s/GWCGK6LIzUqnUrEYRM3EvHzvffZnrhxN/05t+fVLX7Btd2mT7/eBd1eR1jqBHx/fu8n31VLo2DaJgV1SzC3eAMxwRRQjeqaSnBBr3lPNxNx1O+jXqQ1d27fib5eNYF9ZOTe+uKRJnTU+zdvOp3k7+NlJWbROrPH9fyOIcdnpLNqwk+fnbWTN1j0233UEY1dOBJEQF8MxfdLsfa5m4GBFJYs27OTCkd7az9md2jL5vCH85pWlPPZhHr9ogveqVJX7Z6yiS7skLh9jYUvqysU53XltyWZ++58vAW9eOKdXKqMyUzk6swNDu7UjKT62hlqMloAZrggjNyudD1ZuY1PRfnu3pwlZ9nUx+8sqGNPn25XYL87pzuy87Tz43mrG9Elr9FXa3/tqG0s27eJP3xtqN9h6kNWxLXNuO5kNO/azYH0RC9fvZOGGIt5f6c1NJsTGMLR7O3IyU8np1YFRvVLp0NrmEFsiZrgijECYk9l525lowQSbjPlufivYOIkI91wwhC8KdnHDC5/z1i/HktpIN77KSuWBGavond6aC0d1r7mAERYRITO9NZnprbk4x4t6tGPvARZt2MmiDTtZsL6IKbPzeeLjdQD0zWjN0ZkdyMnsQE6vVHqlJdtC1i0AM1wRRlbHNnRKSWT2GjNcTcm8/CL6ZLT+zjtUbZPi+dvEkXzv8U+5+d9f8NSknEa50b2+dDOrtu7h4YkjiI+1qeXGJK1NIuMHd2b84M4AlB6sYGlBMQs3eL2yt77cwgsLvGDq6W0SyemVyplDOzNheNiA6kYUYIYrwhARcrMyeH/lVioq1SLhNgEVlcqC/CLOOapr2PSh3dtx25kDmfzGCv7v0/Vcmdsw77+DFZX8ZeZqBnRuyzlDuzSoLqNmkuJjGd27w6HedGWlkle4lwXri1i0fidz1+3gneXfMKRbO/pm2MLG0Yg9+kUgY7PT2bX/IMs3F/utSovkqy272XOgnGP6VD2H9ePjMzl1YCf++PZXfFnQsPPw8qICNuzYzy2n9yfGHkSanZgYoV+ntlw+phd/uWQ4L1xzLIA5QUUxZrgikOMtzEmTMned957cmN5Vx3cSEe6/aBjpbRK5ftpi9pTW76Xw0oMVPPTeGkb2bM/JAzrWqw6jcemZlkzPDsl2fUUxZrgikIy2iQzo3NaeCJuIeflF9EpLpnO76tcITG2dwMMTR7CpaD+/+88yVOv+3tC/5m7gm92l3HL6AHMKiCBysy0aQzRjhitCGetetiwpq/BblRZFZaWyYH0RozNr5+p+dGYHfnVqP6Z/sZmXFm6q0772HijnsY/WkpuVbtF7I4yxWensPVDO0oJdfqti1AMzXBHKSf07UlZRycuLC/xWpUWxausedu0/yJg+tTckPzspi+P6pnHH9OWs3rqn1uWmzM6naF8ZN5/evz6qGk3IcX3TEbHh+GjFDFeEcmzfNI7rm8af313Fzn1lfqvTYph3aH6r9i8Xx8YIf71kOK0T4rj++cW16gXv3FfGP2atY/ygTgzv0b6+6hpNRLvkeIZ1a2fD8VGKGa4IRUS467zB7C0t574ZzRtyoyUzf30R3dq3qvOqJB1TkvjLJcNZvXUvk99YUWP+v89ay96ycn493npbkUpudjqfb9pVb8cbwz9qNFwiMkVEtonIsiDZiyKyxG3rRWSJk2eKSElQ2t+DyowSkS9FJE9EHhY3Uy0iia6+PBGZJyKZQWUmicgat01qzAOPBrI7teWK4zJ5YcFGG4tvBFSV+flFdeptBXNCvwyuPaEv0+Zv5I2lm6vMt213Kc98tp7zh3ejf+e29VXXaGJyszKoqFTmrivyWxWjjtSmxzUVOCNYoKqXqOpwVR0OvAK8GpS8NpCmqtcGyR8HrgGy3Rao8ypgp6pmAQ8C9wKISAfgDmAMMBq4Q0RS63Z40c8Np2aT1jqR219bbqthN5C1hXvZvrfssPUJ68qvx/djZM/23PbKl2zcsT9snr99mEd5hXLjqY2/UK/ReIzs1Z5W8bEWdTwKqdFwqeosIOwjies1fR+YVl0dItIFSFHVOer5FD8LnO+SJwDPuO8vA6e4ek8HZqpqkaruBGYSYkCPBNomxfPbswawZNMuc9RoIIEn6+re36qJ+NgYHrp0BCJw/bTFlJUf7k69qWg/0+Zv5JKje9ArrXWD9DWalsS4WMb06WBRx6OQhs5xjQW2quqaIFlvEflcRD4WkbFO1g0IvusWOFkgbROAqpYDxUBasDxMmcMQkWtEZKGILCwsbHlPTxeM6EZOr1TufXslxfttPL6+zMsvolNKIr3SGrbqfo8Oydx30TCWFhRz3zsrD0v763triBHhFydbbysayM1KZ13hPjbvKvFbFaMONNRwTeTw3tYWoKeqjgBuAp4XkRQg3JuXgXGvqtKqK3O4UPVJVc1R1ZyMjIxaKx8tiAh3TRjMzv1lPPjear/ViUpUlXnrdjC6d1qjvAh8xpAu/PCYXjw1O58PVm4FYM3WPfzn8wImHZdZ48vNRmQwNtu7X8y2XldUUW/DJSJxwPeAFwMyVT2gqjvc90XAWqAfXm8pOJZDdyAwu10A9Aiqsx3e0OQheZgyRxyDu7bjB8f04tk56/lqy26/1Yk61u/Yz7Y9B+rtmBGO3509kIFdUvj1S1/wTXEpf5m5muSEOK49oW+j7cNoWvp1akNG20Rzi48yGtLjOhVYqaqHhgBFJENEYt33PnhOGOtUdQuwR0SOcfNXPwJec8WmAwGPwYuAD9w82AxgvIikOqeM8U52xHLTaf1on5zAHa8tr9fyQ0cygfe3qltYt64kxcfyt8tGcKC8kklT5vP2sm+4Kre3BS+MIrxoDOl8mrfdnJ+iiNq4w08D5gD9RaRARK5ySZfyXaeMccBSEfkCz9HiWlUNOHZcBzwF5OH1xN528qeBNBHJwxtevBXAlbsbWOC2yUF1HZG0T07gN6f3Z/76IqZ/ccR2PuvFvPwi0tskNHoYi74Zbbh7whBWbd1D++R4rh7bsBAoRvOTm5XOjn1lfPWNjWRECzXG41LViVXIrwgjewXPPT5c/oXAkDDyUuDiKspMAabUpOORxPdzejBt/kbuefMrTh7QkbZJ8X6rFBXMzy9idO8OTbLQ7YWjurNzfxl9Mlrb+YhCcgNRx9dsZ3DXdj5rY9QGWzkjyoiJEe6aMIRtew7wyAd5fqsTFWwq2s/Xu0oa5AZfE1eP7cPJAzo1Wf1G09EpJYl+ndqYg0YUYYYrChneoz2X5PRgyux88rbVftHXhrBxx/5qV4uIZOblu/e3GnF+y2hZ5GZlMD+/iNKDFo0hGjDDFaX85oz+JCfEcuf0FU3uqPHFpl2c/9inXP/85yz7OvqiMs9bt4P2yfH062jLLxnhGZudzoHyShau3+m3KkYtMMMVpaS1SeTm0/szO287by/7psn288maQib+Yy7JCbEkxcfw3LyNTbavpmJevhd/KybGAjka4RnduwPxscIneS1vAYOWiBmuKOay0T0Z2CWF37+xgv1l5Y1e/2tLvubKqQvo2SGZV687jrOHdmX6kq/Ze6Dx99VUbCkuYWPRfkY34vtbRsujdWIcI3qm8qnNc0UFZriimLjYGCZPGMzm4lIe+3Bto9Y9ZXY+N7ywhBE9U3nxp8fSMSWJy8b0ZF9ZBdOXRM9c1zy3PuExdQgcaRyZjM1KZ/nm3RRZ/LuIxwxXlHN0ZgcuGNGNJ2etY/32fQ2uT1W5752VTH5jBacP7sSzV46mXSvPxXtkz/YM6NyW5+dvaPB+mot5+TtomxTHwC4pfqtiRDi52emoYr2uKMAMVwvgtjMHkBAXU6sAh9VRXlHJra98yWMfrWXi6J48dvkokuJjD6WLCJeN6cmyr3dHTXyweeuKODqzA7E2v2XUwLDu7UlJirPln6IAM1wtgI4pSdx4ajYfrNzGeyu21quO0oMVXPuvxby4cBO/PDmLP1wwJOzN/vwR3UiKj2Ha/Mh30ti2p5R12/c16vqERsslNkY4rm86s/O225JqEY4ZrhbCpOMyyerYhrveWF7nd1GK9x/kh0/P4/2VW7nrvMHcNL5/lStMpCTFc+6wrry2ZHPEhzyff+j9LZvfMmpHbnY6X+8qIb8Rht2NpsMMVwshPjaGu84bzKaiEp6cta7W5b4pLuX7T8xhyaZdPDJxBJOOy6yxzGVjerK/rILXItxJY966IlonxDKkq81vGbVjbGD5J5vnimjMcLUgjs9K5+yhXXj0wzw2FYUPKx/M2sK9XPj4ZxTs3M/UH4/mnGFda7Wf4T3aM7BLCs/P2xjRQyrz8ncwKrMDcbHWzI3a0bNDMt1TW/GJzXNFNHZFtzB+d/ZAYkS4582vqs23ZNMuLnr8M0oPVvDCNcdyfFZ6rfcRcNJYsWU3XxRE5koaRfvKWL11r81vGXVCRBibnc7ctTsor6j0Wx2jCsxwtTC6tm/F9Sdn8c7yb5i1OvwqAB+vLuSyf8ylTVIcL193HEO7131F7POHd6VVfCzTInQljfn5XvwtM1xGXcnNymDPgfKIfSgzzHC1SK4e25vMtGTunL6csvLDnxpfW/I1V01dQK+01rxy7XH0Tm9dr320TYrnvKO6Mv2LzeyOQCeNueuKSIqPYVj39n6rYkQZx/VNQwRzi49gzHC1QBLjYrnjvMGs276PKZ/mH5IHVsMY1SuVF396DB1Tkhq0n8vG9KTkYAWvff51Q1VudOblFzGyZyoJcdbEjbqR2jqBod3aMdvWLYxY7KpuoZzUvyOnDuzEw++vYUtxCfe61TDOGNyZZ64cTUojBDwc1r0dg7um8FyEOWkU7z/Iym92N2n8LaNlk5uVzucbd0XVupxHEma4WjC3nzOI8krl3Ec+5XG3Gsajl488bDWMhhBw0lj5zR6WbNrVKHU2BgvWF6Fq8beM+pObnU55pTJ37Q6/VTHCYIarBdMzLZnrTujL9r0H+OUp2VWuhtEQzjuqK8kJsTwfQU4a8/J3kBAXw/Ae7f1WxYhSRvVKJSk+xt7nilDMcLVwbjw1mw9vPpGbTutX5WoYDaFtUjwThnfl9aWbKS6JDCeNeflFDO/RvtF6lsaRR2JcLKN7p/HJGpvnikTMcLVwRKTenoO15bLRvSg9WMl/I8BJY0/pQZZ9Xcwx5gZvNJCxWemsLdzHluISv1UxQqjRcInIFBHZJiLLgmQvisgSt60XkSVBabeJSJ6IrBKR04Pko0TkS5f2sLjHfxFJdPXlicg8EckMKjNJRNa4bVJjHbTRuAzt3o6h3doxbb7/ThoLN+ykUmG0OWYYDSQ3sPyTucVHHLXpcU0FzggWqOolqjpcVYcDrwCvAojIIOBSYLAr85iIBMZrHgeuAbLdFqjzKmCnqmYBDwL3uro6AHcAY4DRwB0iklqvozSanICTxuKNu3zVY966IuJihJG92vuqhxH9DOjclvQ2iTbPFYHUaLhUdRZQFC7N9Zq+D0xzognAC6p6QFXzgTxgtIh0AVJUdY56j+TPAucHlXnGfX8ZOMXVezowU1WLVHUnMJMQA2pEDuce1ZXWEeCkMS9/B8O6tyM5Ic5XPYzoR0TIzUrj07ztVFZGzuseRsPnuMYCW1V1jfvdDdgUlF7gZN3c91D5YWVUtRwoBtKqqes7iMg1IrJQRBYWFtpkqh+0SYxjwohuvLF0M8X7/XHS2F9WzpcFxRbGxGg0crMz2L63jJXf7PFbFSOIhhquiXzb2wII57am1cjrW+ZwoeqTqpqjqjkZGRnVqGs0JZeN7smB8kpe/byg5sxNwKINOymvVFuf0Gg0crMCYU7sgTiSqLfhEpE44HvAi0HiAqBH0O/uwGYn7x5GflgZV2c7vKHJquoyIpQh3dpxVHf/nDTm5xcRGyPkZJrhMhqHzu2SyOrYxsKcRBgN6XGdCqxU1eDH6+nApc5TsDeeE8Z8Vd0C7BGRY9z81Y+A14LKBDwGLwI+cPNgM4DxIpLqnDLGO5kRwUwc3ZPVW/eyaMPOZt/3vHVFDOmaQptEm98yGo/crHTm5xfVObK40XTUxh1+GjAH6C8iBSJylUu6lMOHCVHV5cBLwArgHeDnqho429cBT+E5bKwF3nbyp4E0EckDbgJudXUVAXcDC9w22cmMCObco7rSJjGu2Z00Sg9WsGTTLpvfMhqdsdnpHCiv9OVhzAhPjY+mqjqxCvkVVcjvAe4JI18IDAkjLwUurqKuKcCUmnQ0IofWiXGcP6IrLy0s4PZzB9E+OaFZ9vv5xl2UVVQy2oYJjUZmTJ804mKE2Xnb6xRw1Wg6bOUMo9G5bHQvysoreXVx862kMS9/ByJwtDlmGI1Mm8Q4RvZMtReRIwgzXEajM6hrCsN7tOf5ZnTSmLeuiIGdU2jXquHhWgwjlNzsdJZtLmbnvjK/VTEww2U0EZeN7knetr0sWN/08wIHyitYvHGnhTExmozc7HRU4dO11uuKBMxwGU3COUd1oW1iHM/P29Dk+1paUMyB8koLHGk0GcO6taNtUpwNF0YIZriMJiE5IY4LRnbjrWXfNPnwyrx1XrC/0Ta/ZTQRcbExHNsnjU/WbPd9IWnDDJfRhFw2pidl5ZW8srhpV9KYl19E/05t6dC6eTwYjSOTsdnpfL2rhPU79vutyhGPGS6jyRjQOYURPZvWSeNghfd+jc1vGU1Nbra3nNxsCy7pO2a4jCblstE9WVe4j3n5TfPu+LKvi9lfVmHzW0aTk5mWTLf2rSzMSQRghstoUs4Z1pW2SXFMm980K2kEDOLRvS1Um9G0iAhjs9P5bO0Oyisq/VbniMYMl9GktEqI5cKR3Xn7y28oagInjXnrdtAnozUd2yY1et2GEUpudjp7SstZ+nWx36oc0ZjhMpqciaN7UlZRySuLGtdJo6JSWbh+pw0TGs3GcX3TEcHc4n3GDJfR5PTv3JZRvVIbPdzJis272XOgnGPMMcNoJjq0TmBw1xQzXD5jhstoFi4b3ZN12/cxx71z1RjMy/fqsh6X0ZzkZmWweONO9h4o91uVIxYzXEazcPawLrRrFc+0+Zsarc6564rolZZM53Y2v2U0H2Oz0ymv1EMvvhvNjxkuo1lIio/leyO78c6yLezYe6DB9VVWKgvWFzHGVsswmplRvVJJjIuxqMg+YobLaDYuG92TgxXKy43gpLFq6x6KSw7aMKHR7CTFxzK6dwd7n8tHLMa50Wxkd2rL0Zmp/GXmaqbN30hSfCytEmJpFe9tSQmxJAfJDksPytcqIZZZbvUCW5/Q8IOx2en84a2VfFNcakPVPmCGy2hW/t85g3h+3kZKDlZQUlZx6LO45CAlBysodbL9ZRUcKK/+Jc+eHZLp0SG5mTQ3jG8JREKenbedi0Z191mbIw8zXEazMqx7e4Z1b1+rvJWVSmn5twau1Bm0wO/e6a2bVlnDqIKBnVNIa53A7DWFZrh8wAyXEbHExAjJCXEkJ1gzNSKLmBjh+Kx0Plmznf1l5dZGm5kanTNEZIqIbBORZSHyX4jIKhFZLiL3OVmmiJSIyBK3/T0o/ygR+VJE8kTkYRERJ08UkRedfJ6IZAaVmSQia9w2qdGO2jAMo4FcPqYnO/aVcf+MVX6rcsRRG6/CqcAZwQIROQmYAAxT1cHAA0HJa1V1uNuuDZI/DlwDZLstUOdVwE5VzQIeBO51++gA3AGMAUYDd4iIraRqGEZEMKZPGj88phdTP1vPwvVNE/3ACE+NhktVZwGhZ+U64E+qesDl2VZdHSLSBUhR1TnqrfnzLHC+S54APOO+vwyc4npjpwMzVbVIVXcCMwkxoIZhGH5y65kD6Na+Fbe8vJTSgxV+q3PEUN/3uPoBY93Q3scicnRQWm8R+dzJxzpZNyD45Z0CJwukbQJQ1XKgGEgLlocpYxiG4TutE+O498Jh5G/fx5/ftSHD5qK+hisOSAWOAW4BXnK9pC1AT1UdAdwEPC8iKYCEqSOw2mpVadWVOQwRuUZEForIwsJCi05qGEbzcXxWOhNH9+Tp2fks3rjTb3WOCOpruAqAV9VjPlAJpKvqAVXdAaCqi4C1eL2zAiDYZ7Q7sDmorh4AIhIHtMMbmjwkD1PmMFT1SVXNUdWcjIyMeh6SYRhG/fjtWQPonJLELf/+woYMm4H6Gq7/AicDiEg/IAHYLiIZIhLr5H3wnDDWqeoWYI+IHON6Zj8CXnN1TQcCHoMXAR+4ebAZwHgRSXVOGeOdzDAMI6JomxTPHy8cxtrCffz1vTV+q9PiqfHlAxGZBpwIpItIAZ6n3xRginORLwMmqaqKyDhgsoiUAxXAtaoacOy4Ds9DsRXwttsAngb+KSJ5eD2tSwFUtUhE7gYWuHyTg+oyDMOIKE7ol8H3c7rz5Ky1nDmkM0f1aO+3Si0WaczAfpFATk6OLly40G81DMM4AikuOcjpD84ipVUcr/8il8S4WL9VqjUiskhVc/zWozbY6vCGYRiNRLtW8fzxe0NZvXUvj7yf57c6LRYzXIZhGI3ISQM6cuHI7jz+8VqWfV3stzotEjNchmEYjczt5wwirXUCN//7C8pqiHJg1B0zXIZhGI1Mu+R47rlgKCu/2cOjH9qQYWNjhsswDKMJOG1QJ84f3pVHP8xjxebdfqvTojDDZRiG0UTcce5g2icncMvLX3CwwoYMGwszXIZhGE1EausEfn/+YJZv3s3fP1rrtzotBjNchmEYTcgZQ7pwzrAuPPzBGlZ9s8dvdVoEZrgMwzCamLvOG0xKUjy3vPwF5TZk2GDMcBmGYTQxaW0SmTxhCEsLinnyk3V+qxP1mOEyDMNoBs4e1oUzh3TmrzPXkLfNhgwbghkuwzCMZmLyhCG0Tozl5n8vpaKyZa0T25yY4TIMw2gmMtomcud5g1myaRdPz7Yhw/pihsswDKMZOe+orpw2qBN/fnc16wr3+q1OVGKGyzAMoxkREe45fwhJ8bH85mUbMqwPZrgMwzCamY4pSdxx7iAWbtjJ1M/W+61O1GGGyzAMwwcuGNGNkwd05P4ZK1m/fZ/f6kQVZrgMwzB8QET4wwVDiY+N4X9eWUpLi0bflJjhMgzD8InO7ZL47VkDmZdfxPQvNvutTtRghsswDMNHLsnpwVHd23HPm1+xp/Sg3+pEBWa4DMMwfCQmRrhrwhC27TnAIx9Y0MnaUKPhEpEpIrJNRJaFyH8hIqtEZLmI3Bckv01E8lza6UHyUSLypUt7WETEyRNF5EUnnycimUFlJonIGrdNapQjNgzDiDCG92jPJTk9mDI735aDqgW16XFNBc4IFojIScAEYJiqDgYecPJBwKXAYFfmMRGJdcUeB64Bst0WqPMqYKeqZgEPAve6ujoAdwBjgNHAHSKSWq+jNAzDiHB+c0Z/khNiuXP6CnPUqIEaDZeqzgKKQsTXAX9S1QMuzzYnnwC8oKoHVDUfyANGi0gXIEVV56h3Rp4Fzg8q84z7/jJwiuuNnQ7MVNUiVd0JzCTEgBqGYbQU0tokcvPp/Zmdt523l33jtzoRTX3nuPoBY93Q3scicrSTdwM2BeUrcLJu7nuo/LAyqloOFANp1dRlGIbRIrlsdE8Gdknh92+sYH9Zud/qRCz1NVxxQCpwDHAL8JLrJUmYvFqNnHqWOQwRuUZEForIwsLCwpp0NwzDiEjiYmOYPGEwm4tLeezDtX6rE7HU13AVAK+qx3ygEkh38h5B+boDm528exg5wWVEJA5ohzc0WVVd30FVn1TVHFXNycjIqOchGYZh+M/RmR24YEQ3npy1zlbUqIL6Gq7/AicDiEg/IAHYDkwHLnWegr3xnDDmq+oWYI+IHON6Zj8CXnN1TQcCHoMXAR+4ebAZwHgRSXVOGeOdzDAMo0Vz25kDSIiL4a7Xl5ujRhhq4w4/DZgD9BeRAhG5CpgC9HEu8i8Ak1zvaznwErACeAf4uapWuKquA57Cc9hYC7zt5E8DaSKSB9wE3AqgqkXA3cACt012MsMwjBZNx5Qkbjw1mw9XFfL+V9tqLnCEIS3Nmufk5OjChQv9VsMwDKNBHKyo5MyHPuFAeQUzf3UCSfGxNRdqACKySFVzmnQnjYStnGEYhhGBxMfGMPm8wWwqKuHJWRYtORgzXIZhGBHKcVnpnD2sC49+mMemov1+qxMxmOEyDMOIYH531kBiRPj9myv8ViViMMNlGIYRwXRt34rrT85ixvKtfLza3lMFM1yGYRgRz9Vje5OZlsxd05dTVl7ptzq+Y4bLMAwjwkmMi+WO8wazbvs+pnya77c6vmOGyzAMIwo4qX9HThvUiYffX8OW4hK/1fEVM1yGYRhRwu3nDKK8UvnDWyv9VsVXzHAZhmFECT06JHPdCX15/YvNzFm7w291fMMMl2EYRhRx3Yl96Z7aijunL+dgxZHpqGGGyzAMI4pIio/l9nMGsWrrHp6ds8FvdXzBDJdhGEaUcdqgTpzQL4O/zlzNtj2lfqvT7JjhMgzDiDJEhDvOHURpeQX3vr3Kb3WaHTNchmEYUUifjDZcPbYPrywuYNGGIyvikxkuwzCMKOX6k7Lo0i6J219bTkVlywpRVR1muAzDMKKU1olx/O7sgSzfvJvn52/0W51mwwyXYRhGFHP20C4c2yeNB2asomhfmd/qNAtmuAzDMKIYEeGuCYPZe6Cc+2ccGY4aZrgMwzCinH6d2nLl8ZmIgGrLn+uK81sBwzAMo+H89qyBiIjfajQL1uMyDMNoARwpRgtqYbhEZIqIbBORZUGyO0XkaxFZ4raznDxTREqC5H8PKjNKRL4UkTwReVjcvywiiSLyopPPE5HMoDKTRGSN2yY16pEbhmEYUUltelxTgTPCyB9U1eFueytIvjZIfm2Q/HHgGiDbbYE6rwJ2qmoW8CBwL4CIdADuAMYAo4E7RCS19odmGIZhtERqNFyqOgto0GvZItIFSFHVOerNHD4LnO+SJwDPuO8vA6e43tjpwExVLVLVncBMwhtQwzAM4wiiIXNc14vIUjeUGNwT6i0in4vIxyIy1sm6AQVBeQqcLJC2CUBVy4FiIC1YHqbMYYjINSKyUEQWFhYWNuCQDMMwjEinvobrcaAvMBzYAvzZybcAPVV1BHAT8LyIpADhZg0DPptVpVVX5nCh6pOqmqOqORkZGbU+CMMwDCP6qJfhUtWtqlqhqpXAP/DmoFDVA6q6w31fBKwF+uH1lroHVdEd2Oy+FwA9AEQkDmiHNzR5SB6mjGEYhnGEUi/D5easAlwALHPyDBGJdd/74DlhrFPVLcAeETnGzV/9CHjNlZ8OBDwGLwI+cPNgM4DxIpLqhiLHO5lhGIZxBFPjC8giMg04EUgXkQI8T78TRWQ43tDdeuCnLvs4YLKIlAMVwLWqGnDsuA7PQ7EV8LbbAJ4G/ikieXg9rUsBVLVIRO4GFrh8k4PqqpJFixZtF5FICAuaDmz3WwlHJOkCpk9NmD5VE0m6QMvSp1djKtKUyJGwPIgfiMhCVc3xWw+ILF3A9KkJ06dqIkkXMH38wlbOMAzDMKIKM1yGYRhGVGGGq+l40m8FgogkXcD0qQnTp2oiSRcwfXzB5rgMwzCMqMJ6XIZhGEZUYYbLMAzDiCrMcNUSEekhIh+KyFcislxEbnDyDiIy04VemRm8bqOI3ObCtawSkdOdLFlE3hSRla6eP/mlS0id04ND1/ilj4gkiMiTIrLa/UcX+qzPRBeOZ6mIvCMi6U2tj4ikufx7ReRvIXWFDQ/khz5+tOXq/pugOputLddwrpq9LdegT4PbcsSgqrbVYgO6ACPd97bAamAQcB9wq5PfCtzrvg8CvgASgd54y1/FAsnASS5PAvAJcKYfugTV9z3geWCZn/+NS7sL+L37HgOk+3iu4oBtAR1c+TubQZ/WQC5wLfC3kLrmA8fireX5dl3bTmPq41NbrvK/8aktV3eu/GjLVZ2rRmnLkbL5rkC0bnhLVp0GrAK6OFkXYJX7fhtwW1D+GcCxYep5CPiJX7oAbYDZ7mKo18XeyPpsAlpHwrkC4oFCvBUFBPg7cE1T6xOU74qQm08XYGXQ74nAE37p40dbrk4XP9pyDfo0e1uupu00SVv2a7OhwnogXpTmEcA8oJN6azHiPju6bDWGZRGR9sC5wPs+6nI33ur+++urQ2Pp4/4PgLtFZLGI/FtEOvmlj6oexFuq7Eu8BZ4H4S1R1tT6VEV14YH80Ce4nvY0T1uuDj/aclVl2wd0aua2HJamaMt+YoarjohIG+AV4EZV3V1d1jCyQ+8eiLcS/jTgYVVd54cu4q03maWq/6nP/htbH7zhjO7Ap6o6EpgDPOCXPiISj3exjwC6AkvxemdNrU+d9PRRn0A9zdmWqyo/HH/aclX41ZarKt+obdlvzHDVAXfyXwGeU9VXnXiruNXy3ec2J68pLMuTwBpV/auPuhwLjBKR9XhDLP1E5CMf9dmB97QcuPn8Gxjpoz7DAVR1rXrjLS8BxzWDPlVRXXggP/QJ0JxtuSr8astV4Vdbrorh0DhtORIww1VLRETwutZfqepfgpKCw7JM4vBwLZeKSKKI9MYL8TLf1fV7vLhjN/qpi6o+rqpdVTUTb0J3taqe6KM+CryOF40A4BRghV/6AF8Dg0QkEJ30NOCrZtAnLFp9eKBm18fV1dxtOSw+tuWq9PGrLVdFo7TliMHvSbZo2fAuBsXrYi9x21lAGt64/hr32SGozO/wPNRW4byt8J6SFa/RBOq52g9dQurMpP6eWI2mD97k8SxX1/t4EbX91Odad66W4t2I0ppJn/V4YX724vW0Bjl5Dl78u7XA33Cr3/ihD/615bD/jY9tuapz5VdbrkqfBrflSNlsySfDMAwjqrChQsMwDCOqMMNlGIZhRBVmuAzDMIyowgyXYRiGEVWY4TIMwzCiCjNchmEYRlRhhsuIKEREReSfQb/jRKRQRN6oZ33tReRnQb9PrEtdIvKReKFOlrjQEtfURw9X13kicmsDyj8lIoPqW94wWgpmuIxIYx8wRERaud+n4b31X1/aAz+rKVMNXK6qw4HjgXtFJKE+lajqdFWtc8yqoPJXq2qdV19oTsTD7itGk2INzIhE3gbOdt8n4i3gChwKoPdfFwxvrogMc/I7RWSK6yGtE5FfuiJ/Avq6HtP9TtZGRF4WL7jfc25ZndrQBs+wVrh9jheROUGrf7dx8rNc3bPFC/b4hpNfIS64n4hMdWmfOX0vcvIT3TF8Rz8nz3Hf94rIPSLyhfsfOjl5X/d7gYhMFpG9VR2MiPxTRCYE/X7O9QpjReR+V8dSEfmpS28jIu+74/0yUFZEMl1v9DFgMYev+2gYjY4ZLiMSeQFv7cAkYBheGIcAdwGfq+ow4LfAs0FpA4DTgdHAHeItTnorsFZVh6vqLS7fCLy19QYBffB6UtXxnIgsxVsO6m5VrRAveuz/Aqeqt/r3QuAmp/MTeMtG5QIZVdbqxVHKBc7BM7ABaqNfa2Cuqh6Ft6zQT5z8IeAhVT2amhfgfQr4MYCItMNbdPUt4Cqg2NVxNPATt4ZjKXCBO96TgD8HGf3+wLOqOkJVN9SwX8NoEGa4jIhDVZfirTc3Ee9GGkwu8E+X7wMgzd10Ad5U1QOquh1vteyq4h/NV9UCVa3EW/stswaVLneGsidws4j0Ao7BMyyfisgSvIVOe+EZz3Wqmu/KTgtTX4D/qmqlG/4L1rU2+pUBgbm6RUF5jsVbiRy8SMBVoqofA1ki0hHvv35FVcuB8cCP3HHNw1sXLxsvrMofnBF/Dy8WWEDvDao6t7r9GUZjEee3AoZRBdPx4hediHfjDFBdTKoDQbIKqm7ftc13+E5UC0VkMTAGKAFmqurE4DwiMqI2dYXRQ6qQV6XfQf12odFaH0MY/glcDlwKXBmkyy9UdUZwRhG5Aq8HOUpVD4oXQiTJJe+r5/4No85Yj8uIVKYAk1X1yxD5LLwbLSJyIrBdqw+stwdo2xgKiUgy3jDeWmAucLyIZAXSRKQfsBLoI160WoBLGmPfdWAucKH7fmkt8k/FhSRR1eVONgO4zg21IiL9RKQ1XviSbc5onYTXwzSMZsd6XEZEoqoFePM1odwJ/J8brtrPtzGJqqpnh4h8KiLL8Jw+3qyHOs+JSAmQCExV1UVwqAcyTUQSXb7/VdXVzv3+HRHZjovB1ozcCPxLRH6Nd6zF1WVW1a0i8hXw3yDxU3hDj4vdHFYhcD7wHPC6iCzEG8Jc2biqG0btsLAmhtHIiEgbVd3rbvqP4kUHfrCZ9p0MlKiqisilwERVnVBD/i+BkaparZEzjEjBhgoNo/H5iXNsWI43vPZEM+57FLDE9Uh/Bvy6qowiciper+kRM1pGNGE9LsMAROQ/QO8Q8f+EOihEIyIyFOeJGcQBVR3jhz6G0VDMcBmGYRhRhQ0VGoZhGFGFGS7DMAwjqjDDZRiGYUQVZrgMwzCMqOL/A7FL2r6YZccTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()                   # create plotting objects\n",
    "yearly_groups['MonthTotal'].plot(ax=ax)    # plot a series of a dataframe and attach its axis to the plot object above\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True)) # set x-axis labels to integer\n",
    "ax.set_title('Monthly Total Ridership on CTA busses from 2002 to 2018', # set title for the plot\n",
    "             fontsize = 14) \n",
    "plt.show()                                 # display the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d1645cc4",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 5. Now use the above grouped data to plot the average ridership during the weekdays, saturday and \n",
    "#    sunday/holidays by year.\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "yearly_groups.iloc[:,:-1].plot(ax=ax)\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21772ec7",
   "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>route</th>\n",
       "      <th>routename</th>\n",
       "      <th>Month_Beginning</th>\n",
       "      <th>Avg_Weekday_Rides</th>\n",
       "      <th>Avg_Saturday_Rides</th>\n",
       "      <th>Avg_Sunday-Holiday_Rides</th>\n",
       "      <th>MonthTotal</th>\n",
       "      <th>Month_Beginning_year</th>\n",
       "      <th>diff_week_saturday</th>\n",
       "      <th>diff_week_sunday</th>\n",
       "      <th>diff_sat_sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Indiana/Hyde Park</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>6982.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153617</td>\n",
       "      <td>2001</td>\n",
       "      <td>6982.6</td>\n",
       "      <td>6982.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Hyde Park Express</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22001</td>\n",
       "      <td>2001</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>King Drive</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>21406.5</td>\n",
       "      <td>13210.7</td>\n",
       "      <td>8725.3</td>\n",
       "      <td>567413</td>\n",
       "      <td>2001</td>\n",
       "      <td>8195.8</td>\n",
       "      <td>12681.2</td>\n",
       "      <td>4485.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>22432.2</td>\n",
       "      <td>17994.0</td>\n",
       "      <td>10662.2</td>\n",
       "      <td>618796</td>\n",
       "      <td>2001</td>\n",
       "      <td>4438.2</td>\n",
       "      <td>11770.0</td>\n",
       "      <td>7331.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Jackson Park Express</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>18443.0</td>\n",
       "      <td>13088.2</td>\n",
       "      <td>7165.6</td>\n",
       "      <td>493926</td>\n",
       "      <td>2001</td>\n",
       "      <td>5354.8</td>\n",
       "      <td>11277.4</td>\n",
       "      <td>5922.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  route             routename Month_Beginning  Avg_Weekday_Rides  \\\n",
       "0     1     Indiana/Hyde Park      2001-01-01             6982.6   \n",
       "1     2     Hyde Park Express      2001-01-01             1000.0   \n",
       "2     3            King Drive      2001-01-01            21406.5   \n",
       "3     4         Cottage Grove      2001-01-01            22432.2   \n",
       "4     6  Jackson Park Express      2001-01-01            18443.0   \n",
       "\n",
       "   Avg_Saturday_Rides  Avg_Sunday-Holiday_Rides  MonthTotal  \\\n",
       "0                 0.0                       0.0      153617   \n",
       "1                 0.0                       0.0       22001   \n",
       "2             13210.7                    8725.3      567413   \n",
       "3             17994.0                   10662.2      618796   \n",
       "4             13088.2                    7165.6      493926   \n",
       "\n",
       "   Month_Beginning_year  diff_week_saturday  diff_week_sunday  diff_sat_sunday  \n",
       "0                  2001              6982.6            6982.6              0.0  \n",
       "1                  2001              1000.0            1000.0              0.0  \n",
       "2                  2001              8195.8           12681.2           4485.4  \n",
       "3                  2001              4438.2           11770.0           7331.8  \n",
       "4                  2001              5354.8           11277.4           5922.6  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6. Which routes have the highest difference in average ridership between weekdays and Saturdays? \n",
    "# 7. Which routes have the highest difference in average ridership between weekdays and Sundays/Holidays?\n",
    "\n",
    "\n",
    "# create new columns that store differences in ridership among day types\n",
    "data['diff_week_saturday'] = data['Avg_Weekday_Rides'] - data['Avg_Saturday_Rides']    # get difference of two columns of a dataframe\n",
    "data['diff_week_sunday'] = data['Avg_Weekday_Rides'] - data['Avg_Sunday-Holiday_Rides']\n",
    "data['diff_sat_sunday'] = data['Avg_Saturday_Rides'] - data['Avg_Sunday-Holiday_Rides']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4384b022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diff_week_saturday\n",
      "diff_week_sunday\n",
      "diff_sat_sunday\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff_week_saturday</th>\n",
       "      <th>diff_week_sunday</th>\n",
       "      <th>diff_sat_sunday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>routename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111th/King Drive</th>\n",
       "      <td>104286.6</td>\n",
       "      <td>142535.0</td>\n",
       "      <td>38248.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16th/18th</th>\n",
       "      <td>205568.8</td>\n",
       "      <td>269088.8</td>\n",
       "      <td>63520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31st</th>\n",
       "      <td>12376.0</td>\n",
       "      <td>12376.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31st/35th</th>\n",
       "      <td>159550.3</td>\n",
       "      <td>213529.3</td>\n",
       "      <td>53979.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35th</th>\n",
       "      <td>340682.7</td>\n",
       "      <td>520450.8</td>\n",
       "      <td>179768.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  diff_week_saturday  diff_week_sunday  diff_sat_sunday\n",
       "routename                                                              \n",
       "111th/King Drive            104286.6          142535.0          38248.4\n",
       "16th/18th                   205568.8          269088.8          63520.0\n",
       "31st                         12376.0           12376.0              0.0\n",
       "31st/35th                   159550.3          213529.3          53979.0\n",
       "35th                        340682.7          520450.8         179768.1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select columns to work with\n",
    "cols = [ 'diff_week_saturday','diff_week_sunday', 'diff_sat_sunday']\n",
    "\n",
    "# create grouped dataframes for each day type and store results in a list\n",
    "total_byroutes = []\n",
    "for i in cols:\n",
    "    print(i)\n",
    "    routes_grouped = data[['routename', i]].groupby('routename')\n",
    "    total_byroutes.append(routes_grouped.sum())\n",
    "    \n",
    "totaldiff_byroutes = pd.concat(total_byroutes, axis=1)    # convert a list of dataframe to single dataframe\n",
    "totaldiff_byroutes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7146fdf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function to get the top or bottom N items of a column with its index\n",
    "\n",
    "def get_n_items(diff_col, N=10, sort_asc=True):\n",
    "    if sort_asc:\n",
    "        get_n = diff_col.abs().sort_values()[:N]  # get absolute value and then sort in ascending order and get first N items\n",
    "    else:\n",
    "        get_n = diff_col.abs().sort_values(ascending=False)[:N] # get absolute value and then sort in descending order and get first N items\n",
    "        \n",
    "    return get_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "465c1638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff_week_saturday</th>\n",
       "      <th>diff_week_sunday</th>\n",
       "      <th>diff_sat_sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79th</th>\n",
       "      <td>1548851.2</td>\n",
       "      <td>3008064.0</td>\n",
       "      <td>1459212.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ashland</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2246509.6</td>\n",
       "      <td>1295046.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belmont</th>\n",
       "      <td>1462054.1</td>\n",
       "      <td>2406277.3</td>\n",
       "      <td>944223.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>1634425.2</td>\n",
       "      <td>2666510.9</td>\n",
       "      <td>1032085.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clark</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>874908.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cottage Grove</th>\n",
       "      <td>1346034.3</td>\n",
       "      <td>2344181.8</td>\n",
       "      <td>998147.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Halsted</th>\n",
       "      <td>1654565.8</td>\n",
       "      <td>2457469.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kimball-Homan</th>\n",
       "      <td>1500209.4</td>\n",
       "      <td>2267138.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King Drive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2392445.7</td>\n",
       "      <td>1122547.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LaSalle</th>\n",
       "      <td>1799238.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madison</th>\n",
       "      <td>1632773.1</td>\n",
       "      <td>2491969.9</td>\n",
       "      <td>859196.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulaski</th>\n",
       "      <td>1369288.9</td>\n",
       "      <td>2296854.7</td>\n",
       "      <td>927565.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1237324.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Express</th>\n",
       "      <td>1361169.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 diff_week_saturday  diff_week_sunday  diff_sat_sunday\n",
       "79th                      1548851.2         3008064.0        1459212.8\n",
       "Ashland                         NaN         2246509.6        1295046.1\n",
       "Belmont                   1462054.1         2406277.3         944223.2\n",
       "Chicago                   1634425.2         2666510.9        1032085.7\n",
       "Clark                           NaN               NaN         874908.1\n",
       "Cottage Grove             1346034.3         2344181.8         998147.5\n",
       "Halsted                   1654565.8         2457469.9              NaN\n",
       "Kimball-Homan             1500209.4         2267138.1              NaN\n",
       "King Drive                      NaN         2392445.7        1122547.2\n",
       "LaSalle                   1799238.0               NaN              NaN\n",
       "Madison                   1632773.1         2491969.9         859196.8\n",
       "Pulaski                   1369288.9         2296854.7         927565.8\n",
       "Western                         NaN               NaN        1237324.4\n",
       "Western Express           1361169.4               NaN              NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply the function to all columns of the dataframe with difference information and \n",
    "# get top 10 routes\n",
    "top10route_bydiff = totaldiff_byroutes.apply(get_n_items, sort_asc=False)\n",
    "top10route_bydiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "288c364e",
   "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>diff_week_saturday</th>\n",
       "      <th>diff_week_sunday</th>\n",
       "      <th>diff_sat_sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69th-Garfield Express Shuttle</th>\n",
       "      <td>2148.6</td>\n",
       "      <td>4346.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central/Sherman</th>\n",
       "      <td>926.5</td>\n",
       "      <td>1260.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cermak-Roosevelt Express</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1457.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago Manufacturing Campus</th>\n",
       "      <td>2594.1</td>\n",
       "      <td>2651.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago/Golf</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cicero Express</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clarendon/LaSalle Express</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clarendon/Michigan Express</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dan Ryan OWL Shuttle</th>\n",
       "      <td>1265.4</td>\n",
       "      <td>162.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King Drive Express</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LaSalle</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pershing Shuttle</th>\n",
       "      <td>393.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pullman Shuttle</th>\n",
       "      <td>1090.9</td>\n",
       "      <td>3754.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROAD CALL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge/Grant</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sedgwick</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sheridan/LaSalle Express</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Pulaski Limited</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Special Dest Signs</th>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Touhy Supplement</th>\n",
       "      <td>8.7</td>\n",
       "      <td>88.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U. of Chicago/Lakeview Express</th>\n",
       "      <td>2326.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                diff_week_saturday  diff_week_sunday  \\\n",
       "69th-Garfield Express Shuttle               2148.6            4346.6   \n",
       "Central/Sherman                              926.5            1260.8   \n",
       "Cermak-Roosevelt Express                       NaN            1457.2   \n",
       "Chicago Manufacturing Campus                2594.1            2651.5   \n",
       "Chicago/Golf                                   NaN               NaN   \n",
       "Cicero Express                                 NaN               NaN   \n",
       "Clarendon/LaSalle Express                      NaN               NaN   \n",
       "Clarendon/Michigan Express                     NaN               NaN   \n",
       "Dan Ryan OWL Shuttle                        1265.4             162.0   \n",
       "King Drive Express                             NaN               NaN   \n",
       "LaSalle                                        NaN               NaN   \n",
       "Pershing Shuttle                             393.0             350.0   \n",
       "Pullman Shuttle                             1090.9            3754.5   \n",
       "ROAD CALL                                      0.0               1.0   \n",
       "Ridge/Grant                                    NaN               NaN   \n",
       "Sedgwick                                       NaN               NaN   \n",
       "Sheridan/LaSalle Express                       NaN               NaN   \n",
       "South Pulaski Limited                          NaN               NaN   \n",
       "Special Dest Signs                             3.1               3.1   \n",
       "Touhy Supplement                               8.7              88.1   \n",
       "U. of Chicago/Lakeview Express              2326.4               NaN   \n",
       "\n",
       "                                diff_sat_sunday  \n",
       "69th-Garfield Express Shuttle               NaN  \n",
       "Central/Sherman                             NaN  \n",
       "Cermak-Roosevelt Express                    NaN  \n",
       "Chicago Manufacturing Campus                NaN  \n",
       "Chicago/Golf                                0.0  \n",
       "Cicero Express                              0.0  \n",
       "Clarendon/LaSalle Express                   0.0  \n",
       "Clarendon/Michigan Express                  0.0  \n",
       "Dan Ryan OWL Shuttle                        NaN  \n",
       "King Drive Express                          0.0  \n",
       "LaSalle                                     0.0  \n",
       "Pershing Shuttle                            NaN  \n",
       "Pullman Shuttle                             NaN  \n",
       "ROAD CALL                                   NaN  \n",
       "Ridge/Grant                                 0.0  \n",
       "Sedgwick                                    0.0  \n",
       "Sheridan/LaSalle Express                    0.0  \n",
       "South Pulaski Limited                       0.0  \n",
       "Special Dest Signs                          NaN  \n",
       "Touhy Supplement                            NaN  \n",
       "U. of Chicago/Lakeview Express              NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply the function to all columns of the dataframe with difference information and \n",
    "# get bottom 10 routes\n",
    "\n",
    "bottom10route_bydiff = totaldiff_byroutes.apply(get_n_items)\n",
    "bottom10route_bydiff "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "57531e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8. Which routes have the most consistent average ridership between weekdays, Saturdays and Sundays/Holidays? \n",
    "#    i.e. are there any route that are not affected by the day of the week?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86cfd81c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that this questions relies on some assumption of what is considered to be \"consistent\"\n",
    "# For out demonstration purpose any absolute difference within 5000 will be considered consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f448cac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function that returns rows from a given col that have values less than N\n",
    "def consistency(col, N=5000):\n",
    "    consistent_rows = col[col.abs()<=N].index\n",
    "    return consistent_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f70d205c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff_week_saturday</th>\n",
       "      <th>diff_week_sunday</th>\n",
       "      <th>diff_sat_sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69th-Garfield Express Shuttle</th>\n",
       "      <td>2148.6</td>\n",
       "      <td>4346.6</td>\n",
       "      <td>2198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central/Sherman</th>\n",
       "      <td>-926.5</td>\n",
       "      <td>-1260.8</td>\n",
       "      <td>-334.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cermak-Roosevelt Express</th>\n",
       "      <td>-4163.2</td>\n",
       "      <td>-1457.2</td>\n",
       "      <td>2706.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago Manufacturing Campus</th>\n",
       "      <td>2594.1</td>\n",
       "      <td>2651.5</td>\n",
       "      <td>57.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinatown/Pilsen Shuttle</th>\n",
       "      <td>-4229.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1251.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West 65th</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Cermak</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Loop/South Loop</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westchester</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Express</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-203.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               diff_week_saturday  diff_week_sunday  \\\n",
       "69th-Garfield Express Shuttle              2148.6            4346.6   \n",
       "Central/Sherman                            -926.5           -1260.8   \n",
       "Cermak-Roosevelt Express                  -4163.2           -1457.2   \n",
       "Chicago Manufacturing Campus               2594.1            2651.5   \n",
       "Chinatown/Pilsen Shuttle                  -4229.5               NaN   \n",
       "...                                           ...               ...   \n",
       "West 65th                                     NaN               NaN   \n",
       "West Cermak                                   NaN               NaN   \n",
       "West Loop/South Loop                          NaN               NaN   \n",
       "Westchester                                   NaN               NaN   \n",
       "Western Express                               NaN               NaN   \n",
       "\n",
       "                               diff_sat_sunday  \n",
       "69th-Garfield Express Shuttle           2198.0  \n",
       "Central/Sherman                         -334.3  \n",
       "Cermak-Roosevelt Express                2706.0  \n",
       "Chicago Manufacturing Campus              57.4  \n",
       "Chinatown/Pilsen Shuttle               -1251.2  \n",
       "...                                        ...  \n",
       "West 65th                                  0.0  \n",
       "West Cermak                                1.5  \n",
       "West Loop/South Loop                       0.0  \n",
       "Westchester                                0.0  \n",
       "Western Express                         -203.8  \n",
       "\n",
       "[76 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize list to store results\n",
    "consistent_routes = [] \n",
    "\n",
    "# no. of columns \n",
    "ncol = totaldiff_byroutes.shape[1] \n",
    "\n",
    "# iterate over no. of columns and columns names\n",
    "for i,j in zip(range(0,ncol) , totaldiff_byroutes.columns):\n",
    "    \n",
    "    # apply the above consistency function on all columns of the dataframe with difference values\n",
    "    consistent_index = totaldiff_byroutes.apply(consistency)\n",
    "    \n",
    "    # take result for one column at a time and append to the initialized list\n",
    "    consistent_routes.append(totaldiff_byroutes[j].loc[consistent_index[i]])\n",
    "\n",
    "# convert a list of series into dataframe    \n",
    "consistent_routes = pd.concat(consistent_routes, axis=1)\n",
    "consistent_routes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20ba562",
   "metadata": {},
   "source": [
    "### Pivot Table\n",
    "\n",
    "The popular Pivot Table feature of Excel can also be replicated in `pandas` using the `pivot_table` method. The  method takes the data to pivot, the column in the data that should be the row of the pivot table, the column(s) that will be the columns of the pivot table, the value to aggregate and the function to use for aggregation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b013eb67",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"12\" halign=\"left\">MonthTotal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Beginning_month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_Beginning_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>191682.569231</td>\n",
       "      <td>180008.114504</td>\n",
       "      <td>202056.333333</td>\n",
       "      <td>189006.106061</td>\n",
       "      <td>200182.601504</td>\n",
       "      <td>187035.924812</td>\n",
       "      <td>185020.507576</td>\n",
       "      <td>184324.962406</td>\n",
       "      <td>184282.171642</td>\n",
       "      <td>201919.833333</td>\n",
       "      <td>188562.340909</td>\n",
       "      <td>176664.303030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>186997.113636</td>\n",
       "      <td>178375.167939</td>\n",
       "      <td>187451.431818</td>\n",
       "      <td>190126.901515</td>\n",
       "      <td>195393.708955</td>\n",
       "      <td>180937.801471</td>\n",
       "      <td>184205.235294</td>\n",
       "      <td>185049.191176</td>\n",
       "      <td>193105.808824</td>\n",
       "      <td>206207.705882</td>\n",
       "      <td>181213.029412</td>\n",
       "      <td>176199.044118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>179165.701493</td>\n",
       "      <td>168556.231343</td>\n",
       "      <td>181676.970370</td>\n",
       "      <td>179275.511111</td>\n",
       "      <td>184536.043796</td>\n",
       "      <td>171917.528571</td>\n",
       "      <td>177907.066667</td>\n",
       "      <td>169338.891304</td>\n",
       "      <td>180159.774648</td>\n",
       "      <td>191909.283688</td>\n",
       "      <td>162295.535714</td>\n",
       "      <td>164920.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>163615.028777</td>\n",
       "      <td>162704.900000</td>\n",
       "      <td>181756.892857</td>\n",
       "      <td>171816.617021</td>\n",
       "      <td>171949.531469</td>\n",
       "      <td>171766.465278</td>\n",
       "      <td>166555.993007</td>\n",
       "      <td>162190.280822</td>\n",
       "      <td>179752.808219</td>\n",
       "      <td>186020.576389</td>\n",
       "      <td>171247.461538</td>\n",
       "      <td>161263.629371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>161472.569444</td>\n",
       "      <td>165635.729167</td>\n",
       "      <td>178743.951389</td>\n",
       "      <td>178096.659722</td>\n",
       "      <td>176994.794521</td>\n",
       "      <td>172665.896552</td>\n",
       "      <td>166595.317241</td>\n",
       "      <td>172662.727891</td>\n",
       "      <td>186046.816327</td>\n",
       "      <td>190225.736111</td>\n",
       "      <td>171809.524138</td>\n",
       "      <td>162478.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>159650.793103</td>\n",
       "      <td>158469.262069</td>\n",
       "      <td>181230.143836</td>\n",
       "      <td>162986.267123</td>\n",
       "      <td>177722.168919</td>\n",
       "      <td>163903.105960</td>\n",
       "      <td>158669.520270</td>\n",
       "      <td>161644.835526</td>\n",
       "      <td>169699.493421</td>\n",
       "      <td>175796.307190</td>\n",
       "      <td>167131.980000</td>\n",
       "      <td>156727.589404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>158595.183007</td>\n",
       "      <td>142559.398693</td>\n",
       "      <td>175356.618421</td>\n",
       "      <td>162704.558442</td>\n",
       "      <td>177779.316129</td>\n",
       "      <td>164675.909091</td>\n",
       "      <td>163282.642857</td>\n",
       "      <td>165791.522581</td>\n",
       "      <td>170422.019231</td>\n",
       "      <td>187522.670968</td>\n",
       "      <td>167717.136364</td>\n",
       "      <td>155644.850649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>161955.807947</td>\n",
       "      <td>161156.598684</td>\n",
       "      <td>172276.427632</td>\n",
       "      <td>184973.980392</td>\n",
       "      <td>187152.207792</td>\n",
       "      <td>178615.116883</td>\n",
       "      <td>187269.171053</td>\n",
       "      <td>182051.230263</td>\n",
       "      <td>189574.123377</td>\n",
       "      <td>202679.627451</td>\n",
       "      <td>171604.526316</td>\n",
       "      <td>159266.828947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>164051.013158</td>\n",
       "      <td>164146.125000</td>\n",
       "      <td>182435.556291</td>\n",
       "      <td>174261.921053</td>\n",
       "      <td>179606.483660</td>\n",
       "      <td>171605.857143</td>\n",
       "      <td>174963.254902</td>\n",
       "      <td>167717.474026</td>\n",
       "      <td>180726.227273</td>\n",
       "      <td>190903.726667</td>\n",
       "      <td>175525.610738</td>\n",
       "      <td>164446.718121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>168908.081081</td>\n",
       "      <td>161130.597315</td>\n",
       "      <td>192687.568345</td>\n",
       "      <td>185029.442857</td>\n",
       "      <td>181874.723404</td>\n",
       "      <td>180938.428571</td>\n",
       "      <td>178714.550000</td>\n",
       "      <td>182715.707143</td>\n",
       "      <td>189212.482270</td>\n",
       "      <td>196662.543478</td>\n",
       "      <td>179465.800000</td>\n",
       "      <td>163862.827338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>174595.215827</td>\n",
       "      <td>163577.877698</td>\n",
       "      <td>198571.268116</td>\n",
       "      <td>182876.115108</td>\n",
       "      <td>188321.935714</td>\n",
       "      <td>187221.913669</td>\n",
       "      <td>178593.742857</td>\n",
       "      <td>189062.758865</td>\n",
       "      <td>193413.929078</td>\n",
       "      <td>198528.647482</td>\n",
       "      <td>185121.028777</td>\n",
       "      <td>178975.848921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>178384.782609</td>\n",
       "      <td>185859.107914</td>\n",
       "      <td>201808.231884</td>\n",
       "      <td>185649.057971</td>\n",
       "      <td>193856.321429</td>\n",
       "      <td>184229.050000</td>\n",
       "      <td>180039.690647</td>\n",
       "      <td>189745.794326</td>\n",
       "      <td>186888.659574</td>\n",
       "      <td>205264.492857</td>\n",
       "      <td>186221.323741</td>\n",
       "      <td>168258.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>192483.614173</td>\n",
       "      <td>184558.724409</td>\n",
       "      <td>200014.779528</td>\n",
       "      <td>202087.653543</td>\n",
       "      <td>197548.592593</td>\n",
       "      <td>183551.007407</td>\n",
       "      <td>183128.437037</td>\n",
       "      <td>184120.588235</td>\n",
       "      <td>187021.789855</td>\n",
       "      <td>199629.625000</td>\n",
       "      <td>178684.570312</td>\n",
       "      <td>167303.421875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>160137.598425</td>\n",
       "      <td>168318.484375</td>\n",
       "      <td>190071.007812</td>\n",
       "      <td>183523.700787</td>\n",
       "      <td>190023.343750</td>\n",
       "      <td>172813.085938</td>\n",
       "      <td>173572.410853</td>\n",
       "      <td>170717.868217</td>\n",
       "      <td>188014.341085</td>\n",
       "      <td>198291.804688</td>\n",
       "      <td>164755.759690</td>\n",
       "      <td>169979.132812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>167105.401575</td>\n",
       "      <td>162483.218750</td>\n",
       "      <td>191411.456693</td>\n",
       "      <td>183556.196850</td>\n",
       "      <td>182984.210938</td>\n",
       "      <td>179720.359375</td>\n",
       "      <td>177210.328125</td>\n",
       "      <td>172042.445312</td>\n",
       "      <td>182426.038462</td>\n",
       "      <td>192683.992248</td>\n",
       "      <td>167170.775194</td>\n",
       "      <td>163185.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>158483.715385</td>\n",
       "      <td>165692.449612</td>\n",
       "      <td>178756.192308</td>\n",
       "      <td>165915.323077</td>\n",
       "      <td>170133.030769</td>\n",
       "      <td>166340.553846</td>\n",
       "      <td>157339.449612</td>\n",
       "      <td>164210.046154</td>\n",
       "      <td>171288.712121</td>\n",
       "      <td>176018.630769</td>\n",
       "      <td>162720.286822</td>\n",
       "      <td>150495.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>153340.905512</td>\n",
       "      <td>154858.796875</td>\n",
       "      <td>173420.598425</td>\n",
       "      <td>157605.834646</td>\n",
       "      <td>169127.325581</td>\n",
       "      <td>162655.279070</td>\n",
       "      <td>152702.403101</td>\n",
       "      <td>159874.184615</td>\n",
       "      <td>167603.223077</td>\n",
       "      <td>175312.687500</td>\n",
       "      <td>160717.687500</td>\n",
       "      <td>146000.421875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>150500.055118</td>\n",
       "      <td>144171.562500</td>\n",
       "      <td>164127.173228</td>\n",
       "      <td>159991.511811</td>\n",
       "      <td>165849.007752</td>\n",
       "      <td>153758.015504</td>\n",
       "      <td>152571.186047</td>\n",
       "      <td>154002.290076</td>\n",
       "      <td>159575.713178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MonthTotal                                \\\n",
       "Month_Beginning_month             1              2              3    \n",
       "Month_Beginning_year                                                 \n",
       "2001                   191682.569231  180008.114504  202056.333333   \n",
       "2002                   186997.113636  178375.167939  187451.431818   \n",
       "2003                   179165.701493  168556.231343  181676.970370   \n",
       "2004                   163615.028777  162704.900000  181756.892857   \n",
       "2005                   161472.569444  165635.729167  178743.951389   \n",
       "2006                   159650.793103  158469.262069  181230.143836   \n",
       "2007                   158595.183007  142559.398693  175356.618421   \n",
       "2008                   161955.807947  161156.598684  172276.427632   \n",
       "2009                   164051.013158  164146.125000  182435.556291   \n",
       "2010                   168908.081081  161130.597315  192687.568345   \n",
       "2011                   174595.215827  163577.877698  198571.268116   \n",
       "2012                   178384.782609  185859.107914  201808.231884   \n",
       "2013                   192483.614173  184558.724409  200014.779528   \n",
       "2014                   160137.598425  168318.484375  190071.007812   \n",
       "2015                   167105.401575  162483.218750  191411.456693   \n",
       "2016                   158483.715385  165692.449612  178756.192308   \n",
       "2017                   153340.905512  154858.796875  173420.598425   \n",
       "2018                   150500.055118  144171.562500  164127.173228   \n",
       "\n",
       "                                                                    \\\n",
       "Month_Beginning_month             4              5              6    \n",
       "Month_Beginning_year                                                 \n",
       "2001                   189006.106061  200182.601504  187035.924812   \n",
       "2002                   190126.901515  195393.708955  180937.801471   \n",
       "2003                   179275.511111  184536.043796  171917.528571   \n",
       "2004                   171816.617021  171949.531469  171766.465278   \n",
       "2005                   178096.659722  176994.794521  172665.896552   \n",
       "2006                   162986.267123  177722.168919  163903.105960   \n",
       "2007                   162704.558442  177779.316129  164675.909091   \n",
       "2008                   184973.980392  187152.207792  178615.116883   \n",
       "2009                   174261.921053  179606.483660  171605.857143   \n",
       "2010                   185029.442857  181874.723404  180938.428571   \n",
       "2011                   182876.115108  188321.935714  187221.913669   \n",
       "2012                   185649.057971  193856.321429  184229.050000   \n",
       "2013                   202087.653543  197548.592593  183551.007407   \n",
       "2014                   183523.700787  190023.343750  172813.085938   \n",
       "2015                   183556.196850  182984.210938  179720.359375   \n",
       "2016                   165915.323077  170133.030769  166340.553846   \n",
       "2017                   157605.834646  169127.325581  162655.279070   \n",
       "2018                   159991.511811  165849.007752  153758.015504   \n",
       "\n",
       "                                                                    \\\n",
       "Month_Beginning_month             7              8              9    \n",
       "Month_Beginning_year                                                 \n",
       "2001                   185020.507576  184324.962406  184282.171642   \n",
       "2002                   184205.235294  185049.191176  193105.808824   \n",
       "2003                   177907.066667  169338.891304  180159.774648   \n",
       "2004                   166555.993007  162190.280822  179752.808219   \n",
       "2005                   166595.317241  172662.727891  186046.816327   \n",
       "2006                   158669.520270  161644.835526  169699.493421   \n",
       "2007                   163282.642857  165791.522581  170422.019231   \n",
       "2008                   187269.171053  182051.230263  189574.123377   \n",
       "2009                   174963.254902  167717.474026  180726.227273   \n",
       "2010                   178714.550000  182715.707143  189212.482270   \n",
       "2011                   178593.742857  189062.758865  193413.929078   \n",
       "2012                   180039.690647  189745.794326  186888.659574   \n",
       "2013                   183128.437037  184120.588235  187021.789855   \n",
       "2014                   173572.410853  170717.868217  188014.341085   \n",
       "2015                   177210.328125  172042.445312  182426.038462   \n",
       "2016                   157339.449612  164210.046154  171288.712121   \n",
       "2017                   152702.403101  159874.184615  167603.223077   \n",
       "2018                   152571.186047  154002.290076  159575.713178   \n",
       "\n",
       "                                                                    \n",
       "Month_Beginning_month             10             11             12  \n",
       "Month_Beginning_year                                                \n",
       "2001                   201919.833333  188562.340909  176664.303030  \n",
       "2002                   206207.705882  181213.029412  176199.044118  \n",
       "2003                   191909.283688  162295.535714  164920.928571  \n",
       "2004                   186020.576389  171247.461538  161263.629371  \n",
       "2005                   190225.736111  171809.524138  162478.250000  \n",
       "2006                   175796.307190  167131.980000  156727.589404  \n",
       "2007                   187522.670968  167717.136364  155644.850649  \n",
       "2008                   202679.627451  171604.526316  159266.828947  \n",
       "2009                   190903.726667  175525.610738  164446.718121  \n",
       "2010                   196662.543478  179465.800000  163862.827338  \n",
       "2011                   198528.647482  185121.028777  178975.848921  \n",
       "2012                   205264.492857  186221.323741  168258.085714  \n",
       "2013                   199629.625000  178684.570312  167303.421875  \n",
       "2014                   198291.804688  164755.759690  169979.132812  \n",
       "2015                   192683.992248  167170.775194  163185.961538  \n",
       "2016                   176018.630769  162720.286822  150495.625000  \n",
       "2017                   175312.687500  160717.687500  146000.421875  \n",
       "2018                             NaN            NaN            NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Month_Beginning_month'] = data['Month_Beginning'].dt.month\n",
    "ridership_overtime = pd.pivot_table(data=data.iloc[:,1:], \n",
    "                                    index=['Month_Beginning_year'], \n",
    "                                    columns=['Month_Beginning_month'],\n",
    "                                    values=['MonthTotal'],\n",
    "                                    aggfunc=np.mean)\n",
    "ridership_overtime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f70fe9",
   "metadata": {},
   "source": [
    "Additional features are available in the `pivot_table` function such as filling-in the missing values in the data, which is set to NaN by default. Refer to the [official documentation](https://pandas.pydata.org/docs/reference/api/pandas.pivot_table.html) for usage. \n",
    "\n",
    "Now, with the above data, we can create a plot such that we can see the ridership trend of each month over the years. In order to automatically generate the names of the month we can use the `datetime` module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "220c7069",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt #move up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5a2ee60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrVvHhg0biImJoUuXLo8NrDz+QlLg/CrwHgRWruDdH86vge6/gKHF8/6xCILwEhEJYDka0NSFGQdvMG3/dfxr21a6fn8l1aZNG86fP09gYCA9e/bUdjiCIJSxmJgYIiMj6dy581PXM9epU4e6dety5MgRGjRogKWlZfkGKVQZOTk5rFixgjt37vDqq68+cdChJBIy8vgnOIoVobdJyymgUXVLPu5al671HdDVKb6SaGaoR/s69rSvYw9AVn4hp6JSCLmZQsjNZOYcucnMQxHoKiQauljg527zICE0MXj2Vy0zMzNef/119u7dS2hoKHFxcQwcOLD4DdXOLIWCHPB7U/37JiPh9BII2wDNxpT0j0UQhJeQVNWmIzZr1kw+deqUtsN4qmXBUXy55RIrx/rSqrattsPRmPtrNN555x0sLMRIpCBUJatXryYqKor33nsPAwODpx6XlpbGzJkzcXd3Z+jQoeUYoVBVZGZmsmzZMpKTkxkwYAD16tV77mtcuZvBgsBItp67Q4FKRRcvB8YFuNO0plWZDcRm5Rdy+kGFMJmLMekUqmR0FRLe9xJCXzdrmrlaP1JlfJKLFy+ydetWDAwMGDhwIDVr1nz8oKICmOYDNrXg9W3qx2QZZrcGXX0Yf7hMXpcgCJWXJEmnZVlu9qTnRAWwnA1sVp0Zh24w7cD1Kp0ABgQEcO7cOQIDA+nVq5e2wxEEoYwkJCRw5coV2rZtW2zyB2BpaUnbtm3Zv38/V69epU6dOuUUpVAVpKamsnTpUrKyshg+fDju7u4lPleWZQKvJzE/8CaB15Mw0tNhSIvqjGnthqutSZnHamqgS1tPO9p6qpd3ZD+UEIZGpjD/6E1mH45ARyHh7XwvIXS3pvkTEkJvb2/s7e1Zs2YN//zzD126dMHX1/fRZPXyNsiIhZ6///uYJEGT12D3J3A3DBwblPnrFAShahAJYDkz1NPhzba1+HZbOCE3k/Fzr/xN4J/EysqKxo0bc+bMGfz9/cX0L0GoIoKCgtDT0yvxGqyWLVty/vx5du7ciZubG/r6+hqOUKgKEhISWLp0KYWFhbz++uu4uLiU6Lz8wiK2nrvDwqBIrtzNxM7MgI+61mG4bw0sjcvv356JgS5tPO1ocy8hzFE+lBDeTGFh0E3mHFEnhA2cLdRrCN1saOZqhZmhHg4ODowfP55Nmzaxe/duYmJi6N2797+DLiGzwMoNPLo+euOGg2HfV+qWEN1/KbfXKwhC5SISQC0Y2qIGsw5H8PeB61U2AQR1FfDs2bMEBgbSu3dvbYcjCEIppaamcvHiRXx9fTE2Ni7ROTo6OvTs2ZMlS5Zw9OhROnXqpOEohcouNjaW5cuXo6Ojw+jRo0vUGiEtR8mK0NssOR5FYmY+dRzM+G1AQ15pVA0D3dK3bSgtY31dAjzsHmwAl6Ms5MyttHsVwmQWBUUy98hNFBJ4O1vg626Dn7s1PV7tj8upUA4ePEh8fDyDBw/GNjcSYk5C91/hv7ugGltD3V5wfrW6MbyeoRZerSAIFd0zE0BJkqoDSwFHQAXMk2V5miRJ1sAawBWIAgbJspx675xPgTeAIuAdWZb33Hu8KbAEMAJ2Au/KsixLkmRw7x5NgWRgsCzLUffOeR344l44P8iy/E+pX7WWGerpMKGNOz/suMzJqBSau1bNxoCWlpY0adKEM2fOPNgWXhCEyuvYsWNIkkSrVs/XZ8zV1RUfHx+OHz+Oj48PdnZVcxdkofQiIyNZtWoVxsbGjBw5Emvr4j8fbyVnszAoknWnYsgtKCLAw5bfB/oQ4FGxN1oz1tfF38MWfw/1UpBcZRFnbqcSem+X0cXHIpl3VJ0QNnC2oLlHG1KjQpk3bz79HGOoa2ABjYY/+eJNXoNLG+HKdvAeUI6vShCEyqIkDXQKgQ9kWa4H+AGTJUnyAj4BDsiy7AEcuPd77j03BKgPdANmSZJ0f/htNjAe8Lj3o9u9x98AUmVZrg38Cfxy71rWwNeAL9AC+FqSJKtSvWItyFRm8s3xb0jMSXzw2HDfmtia6vP3getajEzzAgICkCSJo0ePajsUQRBKITMzk7Nnz9KoUaMXau/SpUsX9PX12bFjR5XuhSq8uKtXr7J8+XIsLCwYM2bMU5M/WZY5FZXChGWnaDf1MKtO3KZnQyd2Twlg2Ru+tPG0q9DJ35MY6evQurYt73epw9o3W3Lh666sHOvLWx08MNTTYdmlPFZleJKYB6tv2/GPziD2Xc8kPbfg8Yu5tQOLGuppoIIgCE/wzAqgLMtxQNy9X2dKknQZcAb6AO3uHfYPcBj4v3uPr5ZlOR+IlCTpBtBCkqQowFyW5WAASZKWAq8Cu+6d8829a60HZkjqd++uwD5ZllPunbMPddK4qhSvudxdTLrI9pvb2XtrL5+2+JRe7r0w0tdhfBt3ftp5hdO3Umlas9LltSViYWFBkyZNOH36NAEBAVhZVc3XKQhVXUhICCqVitatW7/Q+SYmJnTq1Int27dz4cIFfHx8yjhCoTI7f/48mzdvxsnJiREjRjxxinFhkYo9l+KZH3iTc9FpWBjpMaldLV5v6Yq9edWa6mikr0Or2rYPNovLKyji7O00pAOHSLhzhzM53hxbs4q3C92p5WRDq1o2TG5fW73OUaGAxiPg8E+QGqXuESgIgvCQklQAH5AkyRVoDIQCDveSw/tJov29w5yB6IdOi7n3mPO9X//38UfOkWW5EEgHbIq51n/jGi9J0ilJkk4lJib+92mta1WtFet7r6e2ZW0+C/qMtw++TUJOAsN9a2Jtos/0g1W7Cujv7y+qgIJQieXm5nLy5Enq16+Pjc2Lr1tu0qQJzs7O7N27l9zc3DKMUKjMTpw4waZNm6hZsyavv/76Y8lfVn4hi4IiaTf1MJNXniE1R8l3feoT/GkHPupat8olf09iqKdDy+qG+KVs5ZV6xnTv2RsXvWyGml/DTpHF4mNRvLv6HCrVvep64+GABGdXaDVuQRAqphIngJIkmQIbgCmyLGcUd+gTHpOLefxFz/n3AVmeJ8tyM1mWm1XUtSWuFq4s7rqYj5t/TGhcKK9ueZX90Tt4w9+Vw1cTORedpu0QNcbCwoKmTZty7tw5UlJStB2OIAjP6cSJEyiVSvz9/Ut1HYVCQa9evcjJyeHgwYNlFJ1QWcmyzNGjR9m5cyd16tRh+PDhj7QWiUvP5X+7LtPyfwf4bns4juaGzBnRlIMftGNkS1eM9V+yfezOr4a8NPCbhG/zpowd+wbmhnrUSj3Fe410OHItgUXHItXHWrhA7Y5wbgWoirQatiAIFU+JEkBJkvRQJ38rZFneeO/heEmSnO497wQk3Hs8Bqj+0OkuwJ17j7s84fFHzpEkSRewAFKKuValpKPQ4TWv11j/yno8LD344tgXXCj4AwuzHKZX8bWA/v7+KBQKUQUUhEpGqVQSEhKCh4cHjo6Opb6ek5MTLVq04OTJk8TGxpZBhEJlJMsye/fu5eDBgzRs2JBBgwahp6cHwKU76by35hwBvxxi/tGbtPGwY9OkVqyf2IpuDRzRUVSu9X1lQqWC0DlQrTHU8AOgWrVqTJgwAVdXVxLDgxlpdJaz+zaydM1GTp06RUyNfigz4iFCDLYIgvCokuwCKgELgcuyLP/x0FNbgdeBn+/9vOWhx1dKkvQHUA31Zi8nZFkukiQpU5IkP9RTSEcC0/9zrWBgAHDw3u6ge4CfHtr4pQvw6Qu/2gqipnlNFndbzKorq/jr9F/oVj/DkdgeXIzxwNvFUtvhaYS5uTnNmjXjxIkTBAQElGoamSAI5ef06dPk5uYSEBBQZtds3749ly5dYvv27YwbNw7Ff7eyF6o0lUrFtm3bOHv2LM2bN6d79+6AxKErCcwPvMnxiGSM9XV4rWVNxrR2o7p1yVqOVGkRByHpGvSbr274fo+xsTHDhw8nLCyMiKjbHDp7jWuXL3Hz8oV7R7yFzbpDOHqk4ejoiIODA46OjpiZmVW6jXIEQSg70rN2Y5MkyR8IBC6ibgMB8BnqJG4tUAO4DQx8aLOWz4ExqHcQnSLL8q57jzfj3zYQu4C37yV6hsAy1OsLU4AhsizfvHfOmHv3A/hRluXFxcXbrFkz+dSpUyV9/Vp3O+M2nwd9ybnEM1jizboBf+BoUvpR9oooMzOTadOmUb9+ffr27avtcARBeIbCwkKmTZuGtbU1o0ePLtNrh4WFsX79enr06EGLFi3K9NpCxVVYWMjGjRsJDw+nTZs2tPRvw5Zzd1gQFMmNhCwczQ0Z1dqVoS1qYGGkp+1wK45lfSE+HKZcBN2nN7Q/HpHE8AUhDPK2ZlRjK+KPreRuTBR3LRqTlv7v6h1jY+NHEkJHR0dsbW3R0dF+z0RBEMqGJEmnZVlu9sTnqtp23JUtAQRQySombPmb4NSlGOvr82mL/+PV2q9WydG53bt3ExoayltvvSWqgIJQwZ05c4atW7cyYsQIateuXabXlmWZZcuWERsby1tvvYWZmVmZXl+oeJRKJWvWrCEiIgL/dh25VOjIspAokrKUeDmZM66NGz29q6GvKyrCj0i4DLP8oMMX0OajZx4+dc9VZhy6wfShjentlAGzfKHLD+Q1GUd8fDx379598CMhIYGiIvUaQR0dHezs7B4khPcTRCMjI02/QkEQNEAkgJVAem4B/lPXYl5jExlcpXW11nzT6psqVw28XwX08vKiX79+2g5HEISnUKlUzJgxAwMDA8aPH6+RAamkpCRmz56Nl5cX/fv3L/PrCxVHbm4uK1euJCYmhgLnxqyL0iO/UEX7OnaMC3CnZS2bKjnoWSa2vaveAOa9cDB59sBpQZGKQXODuRGfxc53A6i+sQ/kpcPk0EemjwIUFRWRnJz8ICG8nyBmZ2c/OMbCwuKxpNDKykr8fQlCBVdcAviSbaFVcVkY6THatxl/HzRnSr9kVl6fxatbXuWjZh/Rz6NflXmjNTMzo3nz5oSEhNCmTRtsbW21HZIgCE8QHh5OSkoKgwYN0tj7j62tLf7+/hw5coTGjRvj7u6ukfsI2pWZmcm8Rf+QkZrMYaU7cVF69GvszBv+bng4iMpvsXJS1Mlfw8ElSv4A9HQU/D2kMT2mBfLu6rOsazECne3vQPQJqOH7yLE6OjrY29tjb29Pw4YNHzyemZn5SEJ49+5drl27xv2igYGBwYPpo/d/tre3f7CRjyAIFZtIACuQMf5uLDoWxfUbDdnwyga+Pv413wR/w95be/mm5Tc4mTppO8Qy0bp1a06ePMmRI0fEqL8gVECyLBMYGIitrS1169bV6L38/f25cOECO3bsYOLEiejqio+lqkKWZXafvMqR3VvRLcrjpE49+rRvxGt+NbEzM3j2BQQ4tQgK88Bv4nOdVt3amB/7efPOqrPMdPPmHT0TOLv0sQTwaczMzDAzM8PDw+PBY0qlksTExEemkJ47dw6lUgmAJEnY2to+sq7Q0dERU1PT54pdEATNE5+0FYilsT6vt6rJrMMRTOnowYIuC1h3dR2/n/6dvlv78mGzD+nv0b/SVwNNTU1p0aIFwcHBtGnThorau1EQXlbXr18nPj6ePn36aHyHTj09PXr06MGKFSs4fvw4bdq00ej9BM1KT08nMjKSyMhILl65jio/Bx10cG3ZjS86NsVQT2wyUmKFSji5ANzbg3295z79FZ9qBF5L5M+jMQyu3xOHsE3Q7WcweLGqq76+Ps7Ozjg7Oz94TKVSkZaW9khSGB0dTVhY2INjTExMHptCamNjIzacEQQtEmsAK5iUbCX+vxyks5cD04Y0BiAmM4avj3/Nibsn8HPy49tW31LNtJqWIy2drKwspk2bRp06dRgwYIC2wxEE4R5Zllm0aBEZGRm888475fYlbe3atVy7do3JkydjZWX17BOECiE7O/tBwhcZGUlKSgoAKh19bilNsHWqzseD2+FgY63lSCuhC+tg41gYtg48u7zQJbLzC+k9PQj3vHAWFH4Kvf+Gpq+XcaCPy8nJeTB99P7PCQkJqFTqzeR1dXVxdnamZ8+e2NvbazweQXgZiU1gKpn/7brM/KM32fd+W2rZqadOqGQV66+t5/dTvwPwQbMPGOg5sFJXA/ft28exY8eYNGmS+AAQhAoiKiqKJUuW0L17d3x9SzZdrCykp6czc+ZMatasybBhwyr1e1tVlpeXx61btx4kfPHx8YC6OuTq6oqNkwsLz2VzIr6ID7vUYXL72uLv8kXIMsxvD/lZMPkElKISHxabTt9ZQRwx/hQnB3uksfvLMNCSKywsJCkp6UFCeOHCBZRKJX369KFBgwZaiUkQqjKRAFYySVn5BPxyiO4NHPljcKNHnovNiuXr418TGheKr5Mv37b6FmdT5ydfqILLzs7mr7/+wtPTk4EDB2o7HEEQgOXLlxMXF8eUKVPKfUOH48ePs3fvXgYPHky9es8/5U0oewUFBURHRxMZGcnNmze5c+cOsiyjq6tL9erVcXd3x83NDScnJ87FpDNh2RlylYX8NaQxnb0ctB1+5XU7BBZ1hZ6/Q/Oxpb7cgsCbxO2eypd6K2BSyAtNKS1rGRkZrFu3jujoaHx9fenSpYuYFioIZUjsAlrJ2JoaMMKvBguDInmnoweutiYPnnM2dWZ+5/msv76eqSen0m9LPz5o9gEDPAegkCpX7yQTExN8fX0JCgqibdu2ogooCFp2584dbty4QceOHbWym5+vry/nz59n165duLu7Y2AgNgopb0VFRcTGxj6o8EVHR1NUVIRCocDZ2ZmAgADc3NxwcXF55N/I2pPRfLE5DCdLQ1aO88VT7O5ZOiGzwNACfIaWyeXGtHbjnSuvooxZTVbQQqz7TS2T65aGubk5o0aNYt++fYSEhHDnzh0GDhyIubm5tkMThCpPVAArqITMPAJ+OURvn2pMHejzxGPuZN3hm+PfEBwXjK+jL9+0+gYXM5dyjrR0cnJy+Ouvv6hduzaDBg3SdjiC8FJbu3YtERERvPfeexgaGmolhtu3b7No0SJatWpFly4vtu5JKDmVSkV8fPyDhO/WrVsPdnV0dHTEzc0Nd3d3atSo8cSEvLBIxY87L7P4WBSta9swc1gTLI31y/tlVC1pt2GaD7R6Gzp/V6JTEnISsDOyK3a6bWJmPuf/6EMzLmH48VUMjYzLKuJSCwsLY8uWLejr6zNw4EBcXV21HZIgVHqiAlgJ2ZsZMsy3BkuDb/FOBw9q2Dz+Rl3NtBpzO89lw/UNTD01lX5b+/F+0/cZVGdQpakGGhsb4+vrS2BgIPHx8Tg4iClDgqANiYmJhIeHExAQoLXkD6BGjRo0btyYkJAQfHx8xHtCGZNlmaSkpAcJX1RUFLm5uYC6L6OPjw9ubm64urpibFx8gpCWo+StlWcJupHE6NaufN6jHro6leOzp0I7MQ+QoMX4Zx56KekS089N51jsMQbXGcznvp8/NQm0MzPAoe1YLA+/wao18xk66t0yDvzFNWjQAHt7e9asWcM///xD586dadmypVg/KggaIiqAFVh8Rh4Bvx6iX2Nnfu7fsNhj47Li+Cb4G47fOU5zx+Z82+pbqptVL6dISycnJ4dp06bh7u7O4MGDtR2OILyUNm/eTFhYGFOmTNF6366cnBymT5+OnZ0do0ePFl8CSyktLe2RnTozMzMBsLCwwM3N7cGP55l6dy0+k3FLT3EnLZcfX/VmUPPK8XlT4eVnwR9eULsjDFz81MOuplxl5rmZHIo+hIWBBY3sGnEk5gij64/mvabvPf3/jKqI9J/rcS7Xgbwh6+ha31FDL+TF5OXlsWXLFi5fvoyXlxd9+vQRU8EF4QWJCmAl5WBuyNDm1VkRepvJ7WtT3frpo7FOpk7M6TSHTTc28dvJ3+i/tT9TmkxhSN0hFb4aeL8KePToUe7evYujY8X6QBKEqi4tLY0LFy7QrFkzrSd/oH5P6Ny5M1u3buXcuXM0btxY2yFVKllZWY8kfKmpqYB63fXDCZ+VldULJdf7wuOZsvosRvq6rB7vR9OaosVDmTm3EvLTwW/SE5+OSItg1rlZ7L21FzM9MyY3msyIeiMw0TPhp9CfWHxpMUa6Rkxs9JTG8QodTH1fJyDwN3qsP0BDl344WRhp8AU9H0NDQwYNGkRwcDD79u0jPj6ewYMHiz0CBKGMiQpgBReXnkvbXw8zoJkLP/X1LtE5d7Pv8k3wNxyLPUYzh2Z81+o7qptX7NHZ3Nxc/vrrL9zc3BgyZIi2wxGEl8rOnTs5deoU77zzDpaWltoOB1CvTVu8eDHJycm89dZbz5yO+DLLzc19pDVDQkICAAYGBri6uj5I+Ozt7UtVTZVlmZmHbvD7vms0qGbBvJFNK1TyUOmpVDCjKRhZw7gDjzx1K+MWs8/PZufNnRjpGjHCawQjvUZiYWDx7+myiq+OfcWWiC283/R9RjcY/eT7pN5CnubDTFV/Ap3HsnKcHzqKildlj4qKYt26daJVhCC8IFEBrMScLIwY1NyFNSejeat9bapZPvvD1tHEkdkdZ7P5xmZ+Pfkr/bf1590m7zK07tAKWw00MjLCz8+PI0eOEBcXh5OTk7ZDEoSXQlZWFmfOnKFhw4YVJvkDUCgU9OzZk7lz53LgwAF69+6t7ZAqDKVS+Uhrhri4uAetGWrWrEnDhg1xc3PD0dGxzLbVz1EW8tH6C+y4EEefRtX4pX9DDPXElv1l6vpeSLkJ/T9/8FBsVixzz89la8RW9BR6jKo/itENRmNlaPXY6QpJwbetviW/KJ8/Tv+Boa4hQ+s+YRdRq5pI7u0YHXec3yNfZdahG7zd0UOTr+yFuLq6MmHCBNatW8f69euJiYmhc+fOolWEIJQBkQBWAhPb1WbNyWhmH47g+1dLNgImSRJ9PfrSslpLvg3+lp9P/MzeqL183/p7apjX0HDEL8bPz4+QkBAOHz7M0KFls/W1IAjFCw0NpbCwkNatW2s7lMc4Ojri5+dHcHAwjRo1onr1ij2TQdOioqI4dOgQ0dHRqFQqFAoFLi4utGnT5kFrBl3dsv9Yj03LZdw/p7h8N4NPutdlQht3sS5TE0JmgbkzePXhbvZd5l+Yz8YbG1GgYGjdobzh/Qa2RrbFXkJHocNPAT+RX5TPT6E/YahjSF+Pvo8f2OQ1TNaP4SOPu/x+QIdWtW0q5FRec3NzXn/99cdaRZiZiTYjglAaYgpoJfHpxgtsOB3L0Y/b42jxfDv0ybLMlogt/HriVwpUBbzb5F2G1RtWIauBhw8f5vDhw4wfP55q1appOxxBqNLy8vL4888/qVWrVoVtw5Kfn8+MGTMwNjZm/PjxL+XovyzLhIaGsmfPHiwsLPDy8sLd3Z3q1atrfIOME5EpTFx+GmWhir+HNqZ9XbEWSyPuhsGc1iS1+5gFxjqsu7oOFSr6e/RnrPdYHE2eb228skjJ2wffJiQuhF8CfqGbW7dHDyjMh9/rUFCzDR1uj0Klgp3vBmBhVP79P0vq4sWLbN26VbSKEIQSKm4KaMXLAIQnmtSuNipZZs6RiOc+V5IkXq39Kpv6bKKFUwt+OfkLo3eP5lbGLQ1EWjp+fn4YGhpy+PBhbYciCFXeyZMnyc/Px9/fX9uhPJWBgQHdunUjPj6ekydPajuccldQUMDmzZvZvXs3derU4c0336RLly7Url1b48nfytDbDF8QgoWRHpsmtxbJnwalBv/NHza2dI/ZxOorq+np3pPtfbfzhd8Xz538Aejr6PNX+79obN+YTwM/5dDtQ48eoGsADYegd30XM1+tSXxGHp9vukhFLgp4e3szbtw4DA0N+eeffzh+/HiFjlcQKjKRAFYS1a2N6dfEmVUnbpOQkfdC13AwcWBGhxn86P8j19OuM2DrAJaFL6NIVVTG0b44Q0NDWrZsybVr14iNjdV2OIJQZSmVSoKDg6lVq1aFr7Z7eXlRu3ZtDh48SEZGhrbDKTdpaWksWrSI8+fP0759ewYNGlQuPRoLilR8uTmMzzZdpGUtWzZNbk1te+3vDlsVpeen83foz3RLO8YSc2M61ezM1le38l3r73A2dS7VtY10jZjZcSb1bOrxwZEPOB57/NEDmrwGRUoaJu/hvc6ebL8Qx7pTMaW6p6bZ29szbtw46taty969e1m3bh35+fnaDksQKh2RAFYik9vXplAlM/fozRe+hiRJvFLrFTb32Yyvky+/nvyV0XtGE5UeVXaBlpKvry9GRkaiCigIGnT27FlycnIICAjQdijPJEkSPXr0oKioiD179mg7nHIRGRnJvHnzSElJYejQobRt2xaFQvMf2SnZSl5bGMqykFuMb+PO4lHNK/S0wMoqS5nFnPNz6L6hO/OvrCAgJ5fNbabxv4D/lek6fRM9E2Z3mo27hTvvHnqXU3cfWiLjUB+qNYGzy3izjTutatnw9dZLRCRmldn9NeF+q4jOnTtz+fJl5s+fT2JiorbDEoRKRSSAlUhNGxP6NKrGitBbJGaWbsTL3tie6R2m85P/T9xIu8GAbQP459I/FaIaeL8KeP36dWJiKvZopCBURkVFRRw/fpzq1atTs2ZNbYdTItbW1gQEBHDp0iUiIp5/KnxlIcsyISEhLF26FGNjY8aNG0edOnXK5d6X4zJ4ZUYQZ26n8ccgHz7rUa9CtgeozHIKclh4cSHdNnZj5rmZNHNowvoUJVMtmuDu1kEj97QwsGBu57k4mTox+cBkLiZe/PfJJiMhIRyduLP8ObgRhnoK3l55lvxC7X8XKI4kSbRu3ZqRI0eSm5vLvHnzCAsL03ZYglBpiASwknmrfW2UhSoWBL54FfA+SZLoXas3W/psoWW1lkw9NZXXd79OZHpkGURaOqIKKAiac/HiRdLT0wkICKhUuzn6+/tjbW3Njh07KCgo0HY4Za6goIBNmzY9WO83btw4bG2L3/WxrOwOi6P/7OMoC1WsndCSfk1cyuW+L4u8wjyWXlpK943d+evMX3jberO652r+tmlNnfS74PeUxu1lxMbIhvmd52NtaM2E/RO4knJF/USD/qBnDGf+wcHckN8G+BAel8Evu65qNJ6y4ubmxoQJE3B0dGT9+vXs3r2boqKKnbwKQkUgEsBKxt3OlFd8qrE0+BbJWWUz793O2I6/2//N/wL+R2R6JAO3DWRJ2BKtVgMNDAxo1aoVN27cIDo6WmtxCEJVo1KpCAwMxMHBAQ+Pitf7qzi6urr07NmTlJQUjh07pu1wylRaWhoLFy7kwoULD9b7aXqTFwCVSubPfdd4c/kZPB3M2Pa2P42qW2r8vi8LZZGSVVdW0XNjT3479RseVh4s676M2Z1mU9/GS936wbYO1NJM9e9hDiYOLOi6ABM9E8bvHc/NtJtgaA71+0LYRlBm08nLgddb1mTRsUgOXUnQeExl4X6rCF9fX0JCQvjnn3/IzMzUdliCUKGJBLASeqtDbfIKi1gQVHaVOkmS6OXeiy2vbqFVtVb8fvp33tj7BsoiZZnd43m1aNECY2NjUQUUhDJ05coVkpOTK131775atWpRv359AgMDSU5O1nY4ZeL+er/U1FSGDRtWbuv9svMLmbTiDNMOXKd/ExdWj/fDwVzzm8y8DApUBWy4toFem3rxU+hPuJi5sKjrIhZ0WUAj+0bqg24dh7sX1NW/cvq/6GzqzIIuC9BR6DB271iiM6Kh8WugzIRLmwH4tEc96jqa8eG68y+86Vx509XVpXv37vTv35+4uDjmzp3LrVsVb6dzQagoRAJYCdW2N6NXw2osPR5FanbZJmi2RrZMaz+Nb1t9y+n408w5P6dMr/887lcBIyIiRBVQEMqALMsEBgZibW2Nl5eXtsN5YV27dkVHR4edO3dW6m3gZVkmODiYpUuXYmJiwrhx4/D09CyXe0en5NB/9nH2ht/li571mDqwIYZ6L1+PxbJWpCpia8RWXtn0Ct8Ef4OtkS1zO81lSbclNHds/ujBIbPAyBp8hpRrjDXNazK/83wKVAWM3TuWOOuaYOMBZ5YCYKinw/ShjclWFvLBuvOoVJXn/5i3tzdjx47FwMCAJUuWEBwcXKnfIwRBU0QCWEm93aE22coiFpZhFfA+SZLo59GPV2u/yqKwRVxKvlTm9yip+1XAQ4cOPftgQRCKFRERQVxcHK1bty6XCpOmmJub06FDByIiIggPD9d2OC9EqVSyceNG9uzZQ506dRg7dmy5rfc7HpHEKzOCuJOWy5LRLRgb4F4pq8EViUpWsTtyN69ueZXPgz7HTN+MGR1msKLHClo5t3r8zzclEq7sgGajQc+o3OOtbVWbuZ3nkqnMZNz+8SQ17A/RIZB4DQAPBzO+6lWfwOtJzC+DPQfKk4ODw4PNk/bs2cP69etFqwhB+I/K+w3gJefpYEYPb0eWHI8iPUczmyF81PwjbAxt+CLoCwqKtLPhgr6+Pq1bt+bmzZtiOocglFJgYCBmZmb4+PhoO5RSa968OY6OjuzevbvSfblLTU1l0aJFXLx4kQ4dOpTbej9ZllkWHMVrC09gY2rAlrf8aeNpp/H7VmWyLHPg1gEGbBvAR0c/Qlehy5/t/mR1r9W0rd726Yn1iXmg0IHmY8s34Id42Xgxq9MsEnISGJdynFRdfTi79MHzQ1tUp3sDR37bc5Xz0Wlai/NFGBoaMnjwYDp37kx4eLhoFSEI/yESwErs7Q4eZOUXsuiYZnbtNNc35+tWX3Mj7QZzL8zVyD1Konnz5piYmIi1gIJQCrdv3+bWrVu0atUKXV1dbYdTajo6OvTq1YvMzMxKNUPg5s2bj6z3a9OmTblUY5WFKj7bFMaXWy7R1tOOTZNa4WZrovH7VlWyLHM05iiDtw9myuEpFBQV8EvAL6zvvZ5ONTuhkIr5O83LgDPL1JuvmFcrv6CfoJF9I2Z0mEF0dhwTariRcWE13BvwlSSJn/s1xN7MgHdWnyUrv1CrsT6v/7aKmD9/PpcuaW9GkyBUJCIBrMTqOZnTtb4Di45Fkp6rmQpdG5c2vFLrFRZcXEB4snamWt2vAkZGRhIVFaWVGAShsgsKCsLIyIimTZtqO5Qy4+LiQtOmTQkNDeXu3bvaDqdYsixz/Phxli1bhqmpKePHjy+39X5JWfkMXxDCqhO3mdSuFvNHNsPMUDR3fxGyLHP8znFG7BrB5AOTyVBm8EPrH9jUZxM93HugoyjBOspzK9Sbrmi49UNJtXBqwZ/t/uQ6SiaZKci5vOXBcxbGevw1pDHRKTl8taVy9tm73yrC3t6edevWsWfPHtEqQnjpiQSwknu7gweZeYX8czxKY/f4uPnHWBta8+WxL7U2FbRZs2aiCigIL+ju3btcu3YNPz8/9PX1tR1OmerYsSNGRkZs374dlUql7XCe6P56v71791K3bl3Gjh2LjY1Nudw7LDadPjOOcSEmnWlDGvFxt7qiufsLOnX3FKP3jGbCvgkk5CTwdcuv2dZ3G31q90FXUcKquqoIQmZDdT9wrjiDMQEuAfwW8AthBvq8feY38gr/3f2zhZs1b3fwYOOZWDafjdVilC/O3NycUaNG0aJFC4KDg0WrCOGlJxLASq6BswWd6tmzMCiSzDzNJGcWBhZ81fIrrqVeY/7F+Rq5x7Po6+vj7+9PVFQUkZHab1QvCJVJUFAQ+vr6tGjRQtuhlDljY2O6dOlCTEwMZ8+e1XY4j9HWej+AbefvMGDOcVSyzPo3W9GnkXO53LeqOZ94nnF7xzF6z2huZ9zm0xafsqPvDgZ4DkBP8ZyV1Ku7IO1Whan+PayTW1d+tPHjpCqb9/ZPeqQN1NsdatPc1YovNodxKzlbi1G+OF1dXXr06EG/fv1EqwjhpScSwCrgnY4epOcWsDRYc29k7aq3o5d7L+ZfmM+VlCsau09xmjVrhqmpKYcPHxbbOgtCCSUnJ3Pp0iWaN2+OkVH57zZYHnx8fKhRowb79+8nO7vifDm9v94vLS2N4cOH06ZNm3LZbVOlkpm65ypvrzpLg2oWbH3LH28XC43ft6q5lHyJSfsnMWLnCK6lXuPDZh+ys99OhtUbhr7OC1bSQ2aDRXWo26tsgy0jPVt/xtdJKQTFn+T/jv4fhSr1uj9dHQV/DWmMQoJ3Vp+joKhiVttLomHDhowdOxZ9fX3++ecf0SpCeCmJBLAKaOhiSfs6dswPvKnRRdqftPgECwML9a6gqvKfCqqnp4e/vz+3bt0SVUBBKKFjx46hUCjw8/PTdigaI0kSvXr1Ij8/n/3792s7nMfW+40bNw4PD49yuXdmXgHjl51ixqEbDG5WnRXjfLEzK5+KY1VxI/UGUw5NYcj2IZxPPM+7Td5lV79dvF7/dQx1DV/8wnHn4VYQtBgPOhV0IyZrd/rbNuGTHNh/ez9fHPuCIpV6vZyzpRE/92/I+eg0/th3TcuBlo6Dg8ODdbiiVYTwMhIJYBXxTkcP0nIKWB6iuSrg/amgV1OvsuDiAo3dpzhNmzbFzMxMVAEFoQQyMjI4d+4cjRs3xszMTNvhaJS9vT0tW7bk7Nmz3L59W2txKJVKNmzYoJX1flFJ2fSbdZxDVxP59pX6/NzfGwNd0dy9pGRZZvWV1QzcPpDQuFAm+Uxid//djPUei7GecelvEDIH9EygycjSX0uTmrzO8PjbvOvamx03d/B9yPcPPm97eDsxtEV15hyJ4NiNJC0HWjr3W0V06tRJtIoQXjoiAawiGtewoo2nHfOP3iRHqbkqYIcaHejh1oN5F+ZxNeWqxu7zNPergLdv3+bmzcrVnFYQytvx48eRZZnWrVtrO5Ry0bZtW8zNzdm+fbtWdvlLTU1l4cKFhIWF0bFjx3Jd7xd0PYk+M4+RmJXPsjEteL2Vq2ju/hxyCnL4JPATfgz9kZZOLdnZbycTG03ETL+MBk4y4yFsPTQeDkaWZXNNTanXCwwtGJuUwDjvcWy4voFfT/76IAn8qld9atmZ8t6acyRnVe6qmSRJ+Pv789prr5GTkyNaRQgvDZEAViHvdqxNcraSFSGaHf3+tMWnmOubq3cF1cJU0CZNmogqoCA8Q05ODqdPn8bb2xsrKytth1Mu9PX16d69OwkJCYSGhpbrvSMiIpg3bx7p6ekMHz6cgICAcknAZFlmUVAkIxeF4mBuwNbJ/rSqbavx+5ZU3o1UcsOTtR1GsW6m3WTYjmHsjtrNO43fYUbHGVgZlvH/mVMLoUgJvm+W7XU1Qc8IvAdB+FberjuCEfVGsPzycqafnQ6Akb4Ofw9pTFpOAR+tv1AlPofd3d1FqwjhpSISwCqkaU1rWte2Ye7Rm+QqNffGZWloyVd+X3E55TKLwxZr7D5Po6enR0BAANHR0URERJT7/QWhMggNDaWgoAB/f39th1Ku6tati6enJ4cOHSI9PV3j95NlmWPHjrF8+fJyX++XX1jEx+sv8N32cDrVc2DjpNbUsCmDqYploDApl6R/LpG0IIzkpeFkHLhdIROFXZG7GLJjCKn5qcztPJdxDccV38T9RRTkwcmF4NkNbGqV7bU1pclIKMpHuriej5t/zEDPgcy/OJ95F+YB4FXNnM961OXglQSNtqEqTxYWFowaNYrmzZsTHBzM0qVLRasIocoSCWAV825HT5Ky8ll5onRVwIL4bLJC7jz1A7tjzY50c+3G7POzuZ56vVT3ehFNmjTB3NxcVAEF4Qny8/MJDQ2lbt262NvbazucciVJEt27d0eWZXbv3q3Re91f77dv3z7q1atXruv9EjLzGDovhHWnY3inowdzRjTF1ED7G4uo8gtJ3xXJ3T9Pkx+RjkV3N4yb2JOx7xZpWyKQVRXj/bqgqICfQn/i46MfU9e6Lmt7rcXPSUMbJYWth5ykCtn64amcGoKTD5xZigR84fcFvd17M/3sdJaFLwPg9VaudKxrz087rxB+J0O78ZYRXV1devbsSb9+/YiNjRWtIoQqSySAVUwLN2v83K2ZcySCvIIXqwIWZShJXBhG2uYI8i6nPPW4T33VU0G/OPbFg62iy4uuri4BAQHExMRw48aNcr23IFR0p06dIi8v76Wr/t1nZWVFmzZtuHz5Mteva2aA6r/r/QYOHFhu6/0uxKTxyvRjXI7LZNbwJrzf2ROFlpu7yyqZ7NPx3J16mswjMRj72OH4YTPM2rpgNdAT0zYuZIfEkbLqCnKBdlsIxGXFMWr3KFZdWcVIr5Es7LoQBxMHzdxMliF4FtjXB7e2mrmHpjR+DeIvQtw5FJKC71p/R+eanfn15K+su7YOSZL4dUBDLI31eHvVGY3uP1DeGjZsyLhx4x60iggJCRGDzUKVIhLAKuidjh4kZuaz5mT0c58rF6hIXhaOnFeIjpUB6bsikZ/S78fa0JrPfT8nPDmcJZeWlDLq59e4cWMsLCxEFVAQHlJQUEBwcDBubm64uLhoOxytadWqFba2tuzcuZOCgrJdq6yt9X4AW87FMnBOMDoKifUTW9LD26lc7lscZXQmiXPOk7ruGjqWBthN8sF6UB10zNW98iRJwrKHGxY93ci9mETS4jBUedpJFo7FHmPQ9kFEpEfwZ7s/+aj5R8/fzP15RB6FhEvq6p8m/43ExUHbtnD3btld03sg6BrCGXXFT1ehyy8Bv9DGpQ3fB3/Ptoht2Jga8OfgRtxMyub77eFld+8K4H6rCA8PD3bv3i1aRQhVikgAq6CW7ja0cLVm9uEI8gtLXgWUZZnUTddRRmdiPagOlr1rUZiYS/aJp3+gdHHtQpeaXZh1bhY3Usu3Ene/ChgbG6uxUX5BqGzOnTtHVlYWAQEB2g5Fq+5P5UpNTSUwMLBMrvnwej8zM7MHXw7Ly6oTt3l39TkaVbdk61utqV9Nu83dizKVpKy7RsLMcxSm5GE10BP7iT4Y1DB/4vFmAS5YD65DflQGiXMvUJShLL9YVUXMPDeTifsnYmdsx5pea+hUs5PmbxwyG4xt1MmUJn3/PQQFwXffld01jSzBqw9cXAfKHAD0dPT4o90ftHBswRfHvmDfrX20rm3Lm21rsepENDsvxpXd/SuA+60iOnbsSHh4OMuWLRMDzkKVIBLAKkiSJN7p6MHdjDzWnoop8XlZQXfIOZOAeacaGDWwxbCeNfpuFmTsv1XsaO1nvp9hqmfKl8e+LPepoI0aNRJVQEG4p6ioiGPHjuHs7Iybm5u2w9E6Nzc3vL29OXbsGElJpetZplQqWb9+/YP1fm+88QbW1tZlFOmzRSRm8e22SwR42LLsDV9sTLXX3F0uVJF5NIa7U0+Rcy4B0zYuOH7YDJOmDkjPmIpq3Nge21H1KUzOJWHOeQqScjUeb0peCpMOTGLO+Tn0rtWbFT1WUNO8psbvS3IEXNsNzd4AvVI0kC+OkZG6sjh7NqhU6p8lSf14WWgyEvIz4PLWBw8Z6Bjwd4e/aWjbkI+PfszRmKO839kTn+qWfLLhArFpmv87LU8KhYKAgAB69OhBTEwMUVFR2g5JEEpNJIBVVOvaNjSpYcnsQzdQFj57vUXetVTSd97EqIENZh1qAPem7fR0Q5VdSObhp08ntTGy4TO/zwhLDuOfS/+U2WsoCV1dXdq0acOdO3e4du1aud5bECqaS5cukZaWVq5TEiu6rl27oqury44dO154kCglJYWFCxdy6dIlOnXqVK7r/QAKilRMWX0OQz0dpg70QV9Xex/duVdTiJ92hvSdkRi4muMwpQmWPdxQGJZ8AxpDTyvsxjVEzi8kcfZ5lDGa22nxXMI5Bm0bxKm7p/im5Tf80PoHjHTLKDl6ltC5oNCF5mM1d4+bN6Fhw39/b2wMw4dDZGTZXL9ma7B2fzAN9MFt9IyZ1WkWnlaevHfoPc4knOTvIY1QyTBl9VkKn7J0pDJr1KgRxsbGhISEaDsUQSg1kQBWUfergHfS89hwpvgqYEFSLskrr6DnYILVwDqPjODqu5hh3NiezKBYCtPynnqNrjW70rlmZ2adm8XNtPJt0N6oUSMsLS1FFVB4qalUKgIDA7Gzs8PT01Pb4VQYpqamdOzYkcjISMLCwp77/Bs3bjxY7zdixAj8/f3LPbmetv86F2PT+bmfNw7mGqokPUNBUi5JSy6RvPgSyGAzqj62oxugZ/dibSf0q5th96YPkp6CxHkXybueWqbxyrLMissrGL17NLoKXZb1WEZ/z/7l93eXmwZnl4P3ADDT0AYzoE74rlxR/1qhgLw8MDcHR8eyub4kQeMRcCtIXdF8iJm+GXM7zaWGeQ3ePvg2qUXX+bFvA05GpTL9YNXbnE1PT4+mTZty9epVUlKevkGeIFQGIgEsD5pYnF0CbT3t8KluycxDNyh4ymicKq+Q5KWXkBRgM9ILhYHOY8eYd60JSGTsjnrqvSRJ4jPfzzDWM+bLY19SpCq/Bqo6Ojq0adOGuLg4rl69Wm73FYSK5Nq1ayQmJhIQEIBCId7aH9asWTOqVavGnj17yMt7+kDWw2RZJigoiBUrVmBubs748eOpXbu2hiN93MmoFGYdvsHApi50a1D+G76o8gtJ2xVJ/J+nyY9Mx6KHGw5TmmBUt/TTX/XsjLGf5IOutQFJSy6Rcz6hDCKG7IJsPjr6ET+f+Bl/F3/W9FqDl41XmVy7xM4ug4JszTd+//NPUCrB3h7MzGDChLL/ruEzDCQd9Wv6D0tDS+Z3mY+DsQMT90+kdvU0+jVxZvrB65yIrHpJUvPmzVEoFJw8eVLboQhCqYhvCeVBE4uzS0CSJN7tWJuY1Fw2nYl97HlZJZOy+iqFSXlYD6+HrvWTR5Z1LQ0x83cm51xisVN1bI1s+cz3My4kXXjQJ6i8+Pj4YGVlxY4dO7hz50653lsQtE2WZQIDA7G0tKR+/fraDqfCUSgU9OzZk6ysLA4ePPjM4++v99u/fz9eXl6MHTu2XNf73ZeZV8B7a87hYmXM16+U79/rv20dTpF1JAbjRvbqtg5tXJDKcAqqjrkBdhN80K9hRsqqq2QGPf5Z9TxupN5gyPYh7Lu1j/eavsff7f/GwqCcN8spKoTQeerpk9Uaae4+KSnqBLBfP/jxR0hPhylTYOPGsr2PuRN4dIFzK9Wv7T9sjWyZ32U+FgYWTNg3gZFtDahhbcyU1WdJyym/jX7Kg7m5OV5eXpw5c0bsCCpUaiIB1CRNL84ugfZ17PF2tmDGoRuPzcnP2HuLvCspWPZ2x7CWZbHXMWvngsJEj7QdN4udZtnNtRsda3Rk+tnp3Ewvv6mgOjo6DB48GIVCwaJFi7h48WK53VsQtC0yMpLY2Fhat26Njs7jVXwBnJ2dad68OSdPnix2kCglJYUFCxYQHh5Op06dGDBgAPr6+uUY6b++2RrOnbRc/hzsU65N3pXRmSTOVrd10LU0xH5yI6wHeqJjppk/B4WRLnZjvDGsb0P69puk7458oen82yK2MWznMDKVmSzosoAxDcZoZy3s1R2Qflvzjd+nToXMTPj2W2jZUv1YcLBm7tXkNciKh+t7n/i0o4kj87vMx0DHgCmHJ/JpHxsSs/L5ZMPFKrc0w9fXl/z8fM6fP6/tUAThhYkEUJNu3oRhw+DhzQI8PODChXIL4f5awNspOWw+9++XnpxzCWQejsakhSMmfs+eVqQw1MW8cw2UkRnkhT99WockSXzh9wVGekZ8deyrcp0K6ujoyLhx46hWrRobNmxg//79qFRVbyG6IPxXYGAgpqamNGrUSNuhVGgdOnTA2NiYHTt2PPG94f56v4yMDIYPH66V9X737bgQx4YzMbzVvjZNa5ZP9fGRtg5p6rYOdhN90K9upvF7S3oKbIbXw8TXkczDMaSuv45cVLLEQVmk5Pvg7/ks6DO8bLxY13sdzR2bazjiYgTPAsuaUKeH5u6RmAh//w2DB0ODBlCvHlhYaC4B9OgCpg5PnAZ6X3Wz6szvMh8Zmd/Ov8+EDpbsvnSXVSeevydxRaRSyRSpZFxcXKhWrRqhoaHiO4ZQaYkEUJOcnNSLsQsKwPDe9Mrr16FDB9i5s9zC6FTPnnpO5sy8VwVUxmSSsv46+q7mWL5Sq8RfcEyaO6FrZ1Rsc3hQTwf5pMUnnE88z/LLy8vqZZSIqakpI0eOpGnTpgQFBbFq1aoSr/kRhMooJiaGyMhIWrZsiZ6eBhtaVwFGRkZ07dqV2NhYTp8+/eDxirLe77676Xl8tukiPi4WvN1R830GH2vr0LbkbR3KkqSQsHy1NmYda5BzOp7kZeGolMUPIsZmxTJy10jWXlvL6AajWdBlAXbGduUU8ZMCOg3RIeq1fwoNVuN//RVyc+Gbb9S/VyjA11dzCaCOHvgMhWt7IPPpawzdLdyZ13keuYW57E/7Dl8PHb7bfonr8Zrb6VUTcpSFnLmdyvKQW3y+6SJ9Zx2jwTd76PbXUZRFKvz8/EhOTiYiIuLZFxOECkgkgJoWHw9vvgkhITBpknozGHNz6NkTRoyAUvamKon7awEjk7LZHRpN8rJwdEz1sBlR77nWckg6EhY93ChMyiU7tPhF5j3detKuejumn51OVHpUKV/B89HV1aVXr1706NGDiIgIFixYQHJycrnGIAjlJSgoCENDQ5o1a6btUCoFb29vXF1dOXDgAFlZWeTn57Nu3Tqtr/e7T6WS+XDdeZSFKv4c3Ag9Hc1+TOdeTSH+r3ttHdwscHivKZbd3VCU45TTh0mShEXnmli+Wpu8qykkLbhIUXbBE489GnOUQdsGcTvjNtPaT+P9pu+jq9BO3A+EzAF9M/XOmZpy9y7MnKn+DlGnzr+Pt2wJFy9CRoZm7tv4NZCL1GsBi1HHug5zO88lLT+NLKuZGBvm8vaqs+QVlN+MoJKSZZn4jDwOXUlg5qEbTF55hg5TD1P/6z30m3WcLzaHsfX8HfR0FHSt78j1hCyWHr+Fl5cXpqamhIaGavslCMILkara3OxmzZrJp06d0nYYxcvPh//9T71o28oKpk+HQYPU6wM1RKWSeWVaIO8ng6ekg/1EH/SrmT73dWRZJmnBRQrisnH8qDkKo6d/2CbmJNJnSx9qW9ZmcdfF6GhyNPQpIiMjWbt2LbIsM2DAAK2O6gtCWUtISGDWrFm0bduW9u3bazucSiMxMZHZs2dTu3Zt0tLSSExMpFOnTrRq1Urr/RMXBkXy/fZwfurrzTDfGhq7T0FSLunbb5J3JQVdWyMsertjVEd7ie+T5FxMImX1FXRtDLEd442upXo5RZGqiJnnZjL/4nzqWtflj7Z/UN28upajBTLi4K8G0HwcdP9Zc/d59111AnjlCjz8mbZnD3TrBvv2QadOmrn34h6QGQdvn3nmd5Yz8Wd4c/+bWOk7cfXsCF73rce3fRpoJq4SKChScTMxm/C4dMLvZHA5LpPwuAxSsv/dqKa6tRH1HM3xqmaOl5M59ZzMcbEyevC+MHrxCU5FpXL4o3ZcPBXM4cOHeeutt7C1tdXWyxKEp5Ik6bQsy08cHRYJoDZdvAhjxsCpU/DKK+pNYqpV08itZFnmwoIL2ERkcLO1A216v3ifMGVsFgkzzmLaxgXL7m7FHrstYhufBX3Gx80/5jWv1174nqWRmprKqlWrSExMpEuXLvj5+Wn9S54glIWNGzdy+fJl3nvvPYyNX6wf28vqwIEDBAYGYmhoyMCBA6lVq5a2Q+LK3QxemXGMNh62zB/ZTCPvU6r8QjIORpMVFIukq8C8Yw1MW1Ur0509y1JeRBrJS8NRGOpgO6YBGeZ5/N/R/yP0bij9PfrzSYtPMNTVTm/Exxz4DgL/gHfOgnXxn40vLCYGatWC116DBQsefS4tTT2o/N138OWXmrn/uVWw+U0YtQNc/Z95ePCdYN468BamUnVuhb/GghH+dPLSYF/Ee9JzC7gSl0F4XIY62bubwbW7WSjvLV/R11VQx8GMek5meDmZ41XNgrpOZpgbFj+N/kZCFl3/OsqQ5tX5tLMbf/75J02aNKFnz54af02C8LxEAliRFRbCX3+p36wNDNS7er3xRplXA7OOxZK27SZbjWU2mcrsmdIGRSnWdqSsvUrO+UQcP2j21PYRoE483z74NqFxoWx4ZQM1zDU3ol2c/Px8Nm3axJUrV/Dx8aFXr15ivZRQqaWmpvL333/j6+tLt27dtB1OpVNQUEBISAj169fX6pTP+/IKinh15jGSsvLZPaUNtqYGzz7pOcgqmZyzCaTvjkSVWYBxUwcsurlqbGfPsqS8k0XS4jAKlQX8WHMhp/XD+MLvC16t/aq2Q/uXMgf+rA81W8GQFZq7z8SJsHChej+BmjUff75+ffXjmtpnQJkDv9dRb3DTb26JTjkSfYR3D01Bt8CVwjtj2P1OZxwtyiZpl2WZmNTcfxO9e0lfTGrug2NsTPTxqqau5qmTPXPcbU3QfcHp1d9svcTS4Ch2vduG8OD9hIeH8/7772NUjju8C0JJiASwMrhxA8aOhSNH1JvEzJ8P7u5lcum8G6kkLQrDsK4Nx7zNeXfNeWYOa0LPhi/eVLgwPZ/4qacw9LLBZmjdYo9NyEng1S2v4mHpweJui1FI2hlpVqlUHDlyhCNHjuDs7MyQIUMwM9P87naCoAnbt2/nzJkzTJkyBXNzc22HI5TSjzvCmR8YyaJRzehQt2wrJMroTNK2RqCMzkS/hhmWvWuVy86eZUWWZdaGrsR1pyF2BVao+tpSu0VDbYf1qNNLYNu7Ja6MvZCoKPD0VH9XmDXryceMHavuA5iUpN4YRhO2v6euBH5wBYwsS3TKnqg9fHzkYwpzatFA5z1WjvVH5zkHofMKirgen/UgyQuPUyd8mXnq3oSSBG62Jg+mbt6fxmlvZlCm1fTUbCXtph7G29mCX3tUZ968eXTp0oVWrVqV2T0EoSwUlwA+891BkqRFkiQlSJIU9tBjPpIkBUuSdFGSpG2SJJk/9NynkiTdkCTpqiRJXR96vOm9429IkvS3dO9/oyRJBpIkrbn3eKgkSa4PnfO6JEnX7/14/QVff+VQuzYcPAhz5sDJk+Dtra4MFpVu0XRhci4pK6+ga2eM9WBPevk4425nwvSD11GpXjz517UwwDTAmdzziSiji9/dy97Ynv9r/n+cSTjDqiurXviepaVQKGjfvj2DBg0iISGBefPmERtbuqbDgqANmZmZnD17lkaNGonkrwo4fiOJ+YGRjPCrUabJ3xPbOrxZPm0dykqmMpP3D7/PD1d/ZkvrExg5mWO4OZ3sU8VvRFauZBlCZoOjt7r5u6b88IM6qfv886cf07IlpKbCtWuai6Pxa1CYC2HrS3xKV9eufNf6OxTG17mgnM7Mw1eLPT45K5+g60nMOxrBlNVn6frnUep/vYfeM4L4eMMF1p6KprBIRZ9G1fixbwM2TWrFpW+7cvCDdswY1oTJ7WvTvo49DuaGZT6V2spEnymdPAi6kcTldB1q1KjBiRMnREsIoVJ5ZgVQkqQ2QBawVJblBvceOwl8KMvyEUmSxgBusix/KUmSF7AKaAFUA/YDnrIsF0mSdAJ4FwgBdgJ/y7K8S5KkSUBDWZbflCRpCNBXluXBkiRZA6eAZoAMnAaayrKcWly8lbYC+LDoaPXOoTt3gp+ferqHl9dzX0aVX0jCrPOoMpXYT26Ero16esKmszG8t+Y8c0Y0pVsDx6ecXARFBaD39GkaqvxC7v52Cl1bI+wmNCz2TVaWZSYfmMyp+FNs6L1B6wv27969y6pVq8jKyuKVV17Bx8dHq/FUFtHR0Zw9e5b27duL6qkW7du3j+PHj/PWW29hY2Oj7XCEUkjPKaDbtKMY6euw4+0AjPRLv1mWXKgi6/gdMg7cRi5UYebvjFmH6lrb2fNFXU25yvuH3yc2K5b3mr7HSK+RyMoikpdfJv96GuZdXTFr56L9Nd0RB2FZX3h1NjQappl73LgBdevCW2+pB4efJjxcPQ100SIYPVozscgyzAlQt7mYcOS5Tl19ZTU/hv5IYUZDlr0yjcbVbYhKzlZX9e78W9WLz8h/cI6jueEjm7J4VTOnprVxqZaxlFZBkYqufx0FGf7sasumDesZMmQIdesWPyNKEMpTqaeA3qvKbX8oAcwALGRZliVJqg7skWXZS5KkTwFkWf7fveP2AN8AUcAhWZbr3nt8KNBOluUJ94+RZTlYkiRd4C5gBwy5f8y9c+YCh2VZLraEVCUSQFC/wa5cqd7tKzMTvvgC/u//QL9k6zVklUzy8svkXUnGdkwDDGtbPXiusEhFpz+OYKyvy443GyEl34Ck65B07d6PG5B8A/RN4M0gsHB+6n2yQuNI23QDmxH1MGpQ/C5Y8dnx9N3SlzrWdVjYdaHWpoLel52dzdq1a7l16xatWrWiU6dOKDQ1ZaaSy8jIYP/+/Vy4cAGApk2b0rt3by1H9XLKzc3lzz//xNPTkwEDBmg7HKEUZFnm7VVn2R12l42TWtHQxbLU18y9kkL69psUJuViWNcai17u6NlWvrVJW25s4YeQHzDTN2Nq26k0cWjy4Dm5UEXK+mvknkvEtFU1LHq5l2u/wscsHwBx5+G9MNAt27WbD4wcCevXw82b4PiUgVsAlQpsbGDgQJg3TzOxAITOhV0fw4RAcHq+6bhzzy1mxvk/ILMZhfEDyS1Qfw/VVUjUtjd9sE6v3r2Ez9qkYq5TPXglnjFLTvFFj7pknNqCtbU1o0aN0nZYgvBAcQngiw4HhgGvAFuAgcD9co4z6grffTH3Hiu49+v/Pn7/nGgAWZYLJUlKB2wefvwJ5zxCkqTxwHiAGjW0s8lImZMkGD4cOndWJ4FffaV+81+4EErQ7ytj/y3ywpOx6O2OYS1LSI+5l+RdRzfpGmuMLqJKvIb0v5SH7qkDVq5g6wm12sOpRbB9Cgxb+9RNaUyaOZJ17A7puyIxrGtd7E5yDiYOfNT8I746/hVrrq5haN2hz/dnUsZMTEwYOXIku3bt4vjx4yQkJNC/f3+xkPshhYWFBAcHc/ToUVQqFQEBAQ+mHwYEBGBpaantEF86J06cQKlU4u+voXVGQrnZcu4O2y/E8VHXOqVO/h5p62BnhO3o+hhWsLYOJZFflM//Qv/Hhusb8HX05ec2P2Nr9OjgoqSrwHpQHdJN9Mg6doeiLCXWg+poZyfTxGtwYx+0+0xzyd+VK7BiBbz/fvHJH2i+Ifx93gNh75dwdhk4/fZcp05oNJq4jHQ2RC6klo0xI2p/RINqVng4mGKgW/7tol5U+zr2BHjY8vfBG/zepinHjh4iPj4eBwfN73IqCKX1ogngGOBvSZK+ArYC95uoPClLkIt5/EXPefRBWZ4HzAN1BfDpYVdC9vawahUMHaqeFurrCx98AN9+C/9NVApyITmCnNNRZAZaYGx7HdOLn8PhG1CQ/e9xBubY2XiwV9eHvQY1GNmrE5Ktp3rb6oc/wCyqw+7/g/OrnjqtRdKRsOjpRvLiS2SFxGHm//RqIcCrtV9lT9Qe/jz9J/7O/lQ30+5UUB0dHXr16oWjoyM7d+5kwYIFDB069KXv6SPLMteuXWP37t2kpqZSp04dunbtirW1Nenp6Vy4cIHAwEBRBSxnSqWSkJAQPD09cXzWF0GhQotJzeHLzWE0q2nFm21fvAWFKu9eW4dj6rYOFj3cKnRbh+JEZ0bzweEPuJxymXHe45jcaPJT+8dKCgmLXu7omOuTviuKpJxCbF6rV+JproVKJTnpaWSlppCdnkpOWirZaalkp6aSk5GGe5MWNGjf+dnTS0PngI4+NBvzvC+35L75Rv15//HHJTu+ZUv1d4T0dLCw0ExMxtZQrzdcWAOdvwO95xs4/abNFBwtDJl5biZnc00Z4PQjulroFVwakiTxZS8vuv11lKBUM/R0dQkNDeWVV17RdmiC8EwvlADKsnwF6AIgSZIncL8BSgz/VgMBXIA79x53ecLjD58Tc28KqAWQcu/xdv855/CLxFslvPIKtGkDH30Is6bCsTXw1jCw4t/pm2m3UapcSVX+hr4UjpViDpKJm3pbalsPdWXP1hNM7VFIEuknb/P1hotUVzSjg/0TRqxajIfwLbDrE3BvB+ZP7lFo6GmFQW1LMg/exqSJPQrjp7dXkCSJb1p9w6tbXuXr41+zoMsCrU8FBWjWrBm2trasXbuW+fPnM2DAADw8PLQdllYkJiaye/duIiIisLW1ZcSIEdR+qNmwhYUFTZo04cyZM6IKWM5Onz5Nbm5uhaz+ZR2/g141EwxcNfSFswopUsm8v/Y8MvDn4EbPvRsiPKGtQzMHLLpWjrYOT3Lo9iE+D/ocSZKY2XEmbVzaPPMcSZIwa1sdhak+qRuukTj3AiYDapBbmEV22kNJXVoK2Wlp9xI8dcKXn539xGsamVugq6/PjZMhxFwOo9PYSegZPGUtfE6KeoDUexCY2pXm5T/dxYuwZg189hnYlfAeLVuql5GcOKGeRaQpTV5TbwRzeTs0HPjcp7/p8yZ6Cj3+OvMXhapCfm7zM3qKytWeydPBjOG+NVl+4jbfNvLiwoULdOzYERMTE22HJgjFetE1gPayLCdIkqQAlqBem7dIkqT6wEr+3QTmAOBxbxOYk8DbQCjqTWCmy7K8U5KkyYD3Q5vA9JNledC9TWBOA/cn/p9BvQnMQ3MWH1dl1gAWFUBK5EPr8u4lecnXIS/93+NkHbCvCw51KTKrT8KJpiDpYj/RGx2b4r+IFRSpaPfbYWzNDNg8qdWTRzqTI2B2a3BrA8PWPHUqqPJOFgnTz2Lq74xlz2e3r9hwbQPfBH/DF75fMLju4GceX15SU1NZvXo1CQkJdOrUiVatnvLnUgXl5eVx5MgRQkND0dPTo127drRo0QIdncdHZdPT05k2bRpNmjShV69eWoj25VNYWMi0adOwtrZmtKY2d3hBBYk5xP9+GslAB/vJjdCzF03pizP7cAS/7L7C1IE+DGjq8uwT/iP/dgZp225SUEnbOjysUFXI9LPTWRS2CC8bL/5o9wfOpo/OJCnIz3uQwOWkpZKVlvJQcqf+YZJlSmOjDuQWZXHk7hqyC//9nNQ1MMDU0hpjSytMLC0xsbTCxNL63s///jAyt0BHVxdZpSJ4w2qCN6zCrnpNXvngcywdn9A2KehP2P8NvHkMHBto5g+oXz84cAAiI6Gk/SrT09UN4b/5Rr18RFNUKvi7EVjVhNe3vfBlll5aym+nfqND9Q5MbTsVPZ3KlQSmZCtp+9shfB11cbkbSMeOHQkICNB2WIJQujWAkiStQl2Js5UkKQb4GjC9l7gBbAQWA8iyfEmSpLVAOFAITJZl+X4fg4mok0UjYNe9HwALgWWSJN1AXfkbcu9aKZIkfQ+cvHfcd89K/iqlnBT1hiv/TfRSo0BV+O9xZk7qKl6DAfeqeNVh0Wb4ayG4xCDP+oLkSGeK8rOwf7MhOjbP/jKgp6NgcvvafLbpIkevJ9HW8wmjiza1oONXsOdT9VQPnyFPvJZ+NVOMmziQdfwOpn5OD3YcfZp+Hv3YE7WH30//jr+L/2Mf+NpiZWXFG2+8webNm9m3bx/x8fH07t27SjeNV6lUnDt3jgMHDpCdnU2TJk3o0KEDpqamTz3n4Sqgv7+/qAKWg/Pnz5OZmUmfPn20Hcpjcs4kgKRem5X8zyXsJzcqdibAyywsNp0/9l2lh7cj/Zs83/ueKqeAtO03yTmTgMJMH6tBnhg3stfuBigvSKUqIjYhip8OfsPNO1cYatmedgUtubZ+B2f/k9wpc3MeO1+SFBhbWNxL6qwwqm5Fgn4CjlFO9Kj9JopOZpjUtsPE0go9Q6PnGsiTFApaDRyGU21Pdk6fyvJPp9D9rQ+o1bTFvwcVFcCJ+erBUU0lf2fOwKZN6kSupMkfqKd9enlpfh2gQqFuCXHoB/WAtbXbC11mZP2R6Ono8VPoT0w5PIU/2v2BgY6G1lNqgLWJPu929OCHHZeZUtOFkydP0qpVqycOngpCRSEawZcHVRGk3XqwCcsjiV5O0r/H6eiDda170zXvT9n0ABsPMHxKr6+QEHjjDVJdupPduA/WvZwx9i95A3lloYp2vx3C0cKQDROfUu1SFcHiHpB4GSafALMnrz0qSs/n7tRTGNazxmZYvWfeOy4rjr5b+9LAtgHzO8+vUJU2WZY5evQohw4dwtnZmcGDB1fJfmvR0dHs2rWLO3fuUL16dbp37061ak+e6vtfogpYflQqFTNmzMDAwIDx48dXrP8rKpm7v5xAz9EEs/bVSZx/EQN3C2xHNUDSqThxVgS5yiJ6TQ8kK7+Q3e+2weo5djeUC1UkLriIMjqzUrV1uBtxnRsng8lKvV+1SyM7PZXs9FR4Qi9afSMjTCytMLawwsTKWl2xs7hXpbOyfqhaZ47iCWvGChJySFoYhiqvEJvXvDCsbVmq+NMT7rL19/+REBWBX/8htBwwVH3fsA2wfgwMXQ11upfqHk/VuzccO6au/j3vWr5x49QbxyUna64hPEB6LPzVAPzfh45flupS666t47vg72hdrTV/tf8LQ92nt6GqaJSFKrr8eQR7VQpeuRcZMGAADRpoaGBAEEqo1G0gKpMKmQBe3wcrHtqu3djm0eTu/q8ta4LO83+gZwXeJm3HLcxOrMIibD3MmKHeArqEXxKXhdziy81hLH/DF3+Pp2x+knQD5rQG9/YwdNVTr52+7xaZB25jN9EHg5rPTpjuv+F/1fIrBno+/xoCTbt8+TIbN27EwMCAIUOG4OLy/NO1KqKH2zqYmZnRuXNnvL29nzux2L59O2fOnOGdd94RVUANCgsLY/369QwaNAivF+gJqkl511JJWhSG9bC6GDe0I/vEXVI3Xsc0oGTTwV8mX28J45/gWyx7owUBHiVfMybLMqlrr5FzNuHBn3NlcDU4kF0zfkelUmFiYfmgWhcrJ3I66yKG5uYMbTaK2s5eDxI7PcPSf+kvTM8naVEYhUm5WA+uU+o/rwJlPgcWzObSkf24+jShx9sfYrSmH2QnwdtnNJNghYaq+wD/+KN6/d/zWrQI3nhD3Rew3rMHZEtlxUC4G6Zug1HKjVw2Xd/E18e/poVjC/7u8DfGepVnOvm+8HjGLz3JG1ZXcbK15I033tB2SMJLrrgEUPu7b7wMqjWGV6bDmD3w0U34+CaM2a1+rPU7UKebeqrlCyR/+TfTSNt1G8O61pj/MQFq1IDBg9XrBu7cefYFgEHNXHA0N2TagWs8dUDAtjZ0+BKu7YKL6556LbM2LijM9EjfcfPp13rIAI8B+Dn5MfXkVO5klSze8lSvXj3Gjh2Lrq4uixcv5ty5c9oOqVQKCwsJCgpixowZXLp0CX9/f9566y0aNmz4QlWl+5uRBAUFlXWowj2yLBMYGIitrW2FbDKcfToeyUgXo3rqhvQmLRwxaelEVmAs2WfitRxdxXHoagL/BN9iTGu350r+ADIPRpNzNgHzLjUrRfInyzKntm1k+1+/4FDLk4nzljNhzlL6fPc9h1ukMK/aUWw6N+f399fQtuMAnOt6YenoVCbJH4CuhQH2Exqi72JGyqorZAWX7rNFT9+ArhPfpfO4t4i+dIHlH00k/vpl8JuoueraV1+BrS28886Lnd+qlfpnTU8DBfU00Mw7cONAqS/V16MvP/r/yMn4k0w6MInsgidv1lMRdapnT6vatpzNsyE6Opo7JfwOJgjaIBLAcqAysiDaToXKpRmY2JTZdQtT8khecRldG0Osh9RBauSjnhL666+we7d6DcDCherdwIphoKvDm23dORmVSsjNYpZZ+k0Elxaw8yPIfPIXO4WBDhadXVHeziQ3LOmJxzzs/q6gAN8c/6ZESWN5c3BwYPz48VSvXp3NmzezZ88eioqKnn1iBSLLMlevXmXWrFns378fNzc3Jk+eTKdOnTAwePG1FpaWlg/WAqalpZVdwMID169fJz4+Hn9/fxSanMr1AlS5heReSsbYxw5J79/YLHu5Y+BuQerG6yijM7UYYcWQnJXPx+sv4Olgysfd6jzXuTnnE8jYdwvjJvaYtddu25ySUKmKOPTPPI4sX4Snb2sGfvEDRmbmXE6+zOBtgwmMCeSTFp8wte1UTPQ0t1OiwlgP2zcaYFjXmrQtEaTvjSrV54skSTTs1I0h3/6KnJfJqls+XEwv2XT55xYUBHv3wv/9HxSzFrtYnp7qjWDKIwH07AbGtnDmnzK5XO9avfkl4BfOJZxjwr4JZCorx3uIJEl80dOLsDwrUOgSEhLy7JMEQUsq1reJKio5+QjXrn9P+OUP+XdPnNJR5ReRvDQcuQhsXq+PwvBe9VBXFz76CM6fBx8fGDtWvQ10ZGSx1xvSogZ2ZgZMO3Dt6QcpdODVWep+g9vfe2piadzMAV0HY9J3RSEXqp75WpxNnfmg2QcExwWz8frGZx6vDcbGxrz22mu0aNGC4OBgVq5cSW5urrbDKpGkpCRWrFjBqlWrUCgUjBgxgqFDh2L9PJsKFENUATXnfvXPwsICb29vbYfzmJwLiVCowqTZo21kJB0F1sProWOmT9KycIoylE+5QtUnyzKfbrxIek4Bfw1ujKFeyafI5d/KIGXdNfRdzbHq51Gh1n4+SYEyn21//MzZXdto2rMPvab8H7r6+my8vpERO0dQoCpgcbfFDK83vFxei0JfB5sRXhg3cyDzYDRpG28gF5VukNHRzpgR1UNwdjJn78K57J03nUJlGf/7/vJLdcP3SZNe/BoKhXoKaXkkgLr60GgoXNsNWQllcslubt2Y2nYql5IuMWHfBNLz0599UgVQz8mcfs3duVJgTVhYGJmZlSN5FV4+IgEsB3Z2najl/hHx8dsIv/xxqZNAWSWTuu4qBfHZ2Ayri57tE3bc9PSEQ4dg9mx1L6AGDWDaNHhK5cpQT4c329Yi5GYKoTeTn35zWw/o8AVc3aFeBP8EkkLCsqc7RSl5ZAXHleg1DfAcQAvHFvx26jfiskp2TnnT0dGhR48e9O7dm8jISObPn09iYqK2w3qqvLw89uzZw6xZs4iOjqZr165MnDjxkZ5+ZeHhKmB6euX4kK4sbt26RXR0dIXdUS7ndDy6DsboOT9epdAx0cNmZH3kvEKSl4cjFzx7MKgqWnsqmr3h8XzUtQ5e1Uq+kVRhSh7JS8PRsTDA5jWvCt/YPScjnXXff86NUyG0GzmOdiPHUYSK74K/4+vjX9PUoSlre6+lkX2jco1L0pGw6u+BWfvqZJ+8S/KKy8gFpfgMPjEfY90C+n/xP3z7DuLigT2s/vpjMhLLJvHh0CE4fBg+/RSMS7n+rWVL9RrA8nhfbjxSvXP5+VVldslONTvxZ/s/uZJyhXF7x5GWl1Zm19akD7p4EqWohkql4vTp09oORxCeqGJ/olQhrq5v4u42hbt3N3P5ymfI8ot/Gco8eJvcsGQserhh6Gn19AMVCnjzTbh0Cdq2hSlTICBA/YHwBMNa1MDWVJ/pB28UH0DLyeDSHHZ++NTRPkNPKww8rcg4eBtVTsEzX5NCUvBtq29RySq+Df62Qk4Fva9p06a8/vrr5OXlsWDBAq5dK6ZqqgUqlYozZ84wffp0goOD8fHx4e2336Zly5YaSyLuVwEDAwM1cv2XVWBgICYmJjRp0uTZB5ezgoQclLczMWnq8NRqjr6TCVYD66C8nUnq5hsV+v+1JkQlZfPttnBautvwhn/Jt8hX5RWS9M8l5CIZ21H10TGp2C010uLvsvqrj0iIjKD3e5/QtGcfcgtzmXJoCuuurWOs91hmd5qNtWHZzDp4XpIkYdHVFcve7uRdTiZxYViJPpceo8yG00ugbi8UNm74DxlJnw+/IDXuDss+eZeo82dKF6gsq6t/Li4wfnzprgX/NoQPDS39tZ7FzhOq+8KZZc9cdvI82lVvx7T204hIi+CNvW+Qklfxu4HZmhowuoM30UUWHA8JpbCw8NknCUI5EwlgOXJzextX17eIi1vPlatfvlASmBuWRMb+2xg3scfUv4Q9pKpXhx07YNkyuHoVGjeGH36Agkc/AI30dRjfxp2gG0mcvlXMm6xCB/rMBGVOsVNBLXu4IecVknEwukRhupi58F7T9zh25xibb2wu2WvTkpo1azJ+/HisrKxYuXIlQUFBFeLLbXR0NAsWLGDr1q1YWVkxfvx4+vTpU2xPv7IgqoBl786dO0RERODn51ch+1DmnI4HBRg3ti/2OGNvW8w61iDndDxZx16eTREKi1S8t/YcugqJ3wf5oChhrz65SCZ5xWUKE3OxGVEPPbuKvQvi3RvXWPXlh+RmZjLwix/x9G1Nal4qY/eOJTA2kC/9vuTdJu+iU8rdIcuCaWtnrIfWRRmdScLcCxSl5z/fBc6vgrw09SDoPbWb+zHif39iamXNhv99TcjGNciqFxzg3btX3fbh88+hLDbEadFCvWN3eUwDBWgyEpKvQ/S/CadcUERBfDa54clkBsaSuuUGiYvCuPvbSeJ+PkHulWcndAEuAczoOIPbGbcZs3sMSbnP3l9A20a1diXZuAbKvFwuXAzTdjiC8BiRAJYzd7cp1Kw5kTt3VnP12vNVugruZpOy9ir61c2w6vuc60EkCUaMgMuX4dVX1aOMzZrBf6YnjPCribWJPtMOPKMKaFcH2n8GV7bDpSev29NzNMGkmSNZwXcoTCrZernBdQbTzKEZv578lbvZd0t0jrZYWloyZswY6tevz/79+9m4cSMFBS8wqlwGMjMz2bRpEwsXLiQjI4O+ffvyxhtvlLinX1kQVcCyFRQUhIGBAc2bN9d2KI+RVTLZZxMw9LRGx+zZvezMO9bA0MuG9J03ybueWg4Rat/MQxGcvZ3Gj329qWb5hGn6TyDLMmnbIsi/nobVq7VL3cNO0yJOn2DNd5+iq2/A0O9/w7muF7FZsYzcNZKrKVf5o90fDKozSNthPsK4oR22o+tTlJpPwuzzFCQ83mT+iVQqCJmj3tW7uu8jT1k5OTPsh9+p26oNx9YsY/PUH8jLznq+wO5X/2rWhDFjnu/cpzE3Vy//0HACqMopQBmTSY6qLRnyCFLWXSVh7nnifgol9svjxP95huSl4aTvuEnOmQRU2QXoVTNFYahD8pJL6g16ntAT8mEtq7VkVqdZ3Mm+w+jdo0nIKaMptxpioKvDhF4tSVMZsudQYIUYIBaEh4kEsJxJkkQt9w+oUWMssbHLuX79hxK9MRRlF5C0NBzJQBeb1+o9suPec7G3hzVrYNMmSEwEX1/45BO4t6GJsb4u4wLcOXotkbO3n/FFreVb4NwUdnwIWU9eC2feuSaSjkT6nqgShaeQFHzX6juK5KIKPxUUQF9fnwEDBtChQwcuXrzI4sWLy7UCdr+tw/Tp0wkLC8Pf35+3334bHx+fct8wwtLSksaNG4sqYBlITEwkPDycFi1aYFhGW+OXpfzrqagylBg3dXj2wajXBVsP9kTXzpjklVcoTK4cGyi9qLO3U/n74HX6Nnamt0/JB2Gyjt0hOyQO0zYumLRw1GCEpXd+3y62/PYDNs7VGfbDVKyruXAl5Qojdo4gJS+F+V3m07FGR22H+USGta2wm9AQuVBF4pzz5N/OePZJEQfU1S2/SU/sg6tnaEiPtz+kw+gJRJ07zYpP3yPxVvGbrz1i+3Y4eVKdBOo/e1ClxFq2VO8O/qJVSdQDE0UZ+eRHppN9Kp70vVEkr7pC/Iyz3PkumDvfhZAw4xwp66LIyB9CXpIVFBZiUNsS8841sR5SB7tJPjh96Ue1b1ri8HZjbIbXw35yI4ybqjfoSVocRlF28QOozR2bM6fTHBJyEhi9e3SFHyTu1sCJXEs38jOSuXIjStvhCMIjRCN4LZFlmevXfyA6Zgk1aoyldq1PnvqFXS5SkbQwjPzbGdhP8EG/ulnZBJGWBh9+qG4V4eGh/jkggKz8Qvx/OUhDF0uWjGpe/NSlhCswNwDqdIdBS594SMb+W2Tsv43dmw0xcLUoUWgrLq/g5xM/80PrH+hTu88LvLjyd/XqVTZs2IC+vj6DBw+menXNbtl+7do1du/eTUpKCp6ennTt2hUbm7JrM/Ii0tLS+Pvvv2natCk9e/bUaiyV2YYNG7h8+TJTpkzR+PTdF5G88jL5N9Jw+sz3uTYnKUzOJWHmORRm+thP8kFh8Py9Tyu67PxCev4dSEGRzK4pAZgblmz6bu6VFJL/uYRhPRtsRtRDKuGU0fImyzJBq5dyYvM63Bo3o9eU/0Pf0IiQuBCmHJqCmb4ZczrNoZZlLW2H+kyFybnq9YCZSqxH1MOoTjFrFJf1hfhwmHJRvetlMWKvXmb7n/8jLzubzuPfwiugffGBqFTQtClkZqpn6ZTllO8lS2D0aPVeAF5eTz1MLpIpSsujMDmPwpRcCpPyKEzJozA5l6KUvEc3cZJAx8oQXWtDdG0M0bUxQtfaEB0bI3Rzw1Es7QS9p0HTUc8MT5Zlsk/eJW1LBDqm+tiMqPfM7zjnE8/z5r43sTCwYGHXhTiblnA5jBaci0pi7eLZGFhX4+t3RWN4oXyJRvAVkCRJeHh8gYvza9y+vYCIm78/tdqVtv0m+TfTsernUXbJH4ClJSxYAPv2qdcDtmkDkydjqsxlclN7jl5LZOLC42TlF7OA2b4utPsUwrfApU1PPMS0jQsKc33Sd0SWuKI3tO5Qmtg34ZcTvxCfXTmaSdepU4exY8eip6fHkiVLOHv2rEbuk5SUxPLly1m5ciWSJDFixAiGDRum9eQPRBWwLERFRXHx4kX8/PwqZPKnyilQ9/5rZP9I8pccm0XeM0bwdW2MsB5Wl8LEHFLWXHvmtK/K6Icd4dxKyeGPQT4lTv6Ud7JIWXkFvWqm6p6uFTT5KyosYNeM3zmxeR3eHbvy6kdfom9oxM6bO5m4fyLVTKuxvPvyR5I/WZYr7EwOXRsj7Cf6oGtnRPI/l8g+/ZTPmoTLEHEQWox9ZvIH4FynHiN+noZjbQ92zfidA4tmU1RYzP+NTZvg3Dn4+uuyTf5AXQEECA5GpSyi4G42uZeSyQyMIXXzDRIXXiTut5PEfnmMu7+dImlRGGmbI8gKiaMwKRdda0NMfJ2w7FML29H1cfiwGc7ft8bp4+bYjfXGqq8HZm1cMGpgi76TCQq3ZmBXD848eUD4vyRJwrSFE/YTfUCChDnnyQqJK/bfjI+dDwu6LCBDmcHo3aOJzijZPgPa0MjVFsnWnaKUaMKiKnbFUni5iApgOVHl5qIwenwdiCyruHL1S+7cWY2b69u4u0955PmsE3GkbbyBaYAzlj3dNRdgdjZ88YW6VYSLC7K3NwsT9flf+zG4O5gzb2Qz3Gyf0rS3qBAWdoK02zD5BJjYPn75U3dJXX8d66F1MfaxK1FItzNu039rf1o4tWBGhxkVvgfWfTk5Oaxbt47IyEj8/Pzo3Llzmey+mZeXx5EjRwgNDUVPT4+2bdvSokULdHUrVhVFVAFfXGFhIXPnzkWpVDJ58mT0y3IqWBnJCrlD2uYI7N9ujP699g/K/ELmf3gYi+r6DP8o4Jn/VzOPxZK+7SZmHWtg0blmeYRdLvZeusv4ZaeZ2K4W/9etbonOKcpQkjDzLMhg/1YjdMwNNBzli8nPyWbr7z9yO+wCrQe/hm/fQUiSxD+X/mHqqak0d2zOX+3/wlz/31YXRenpxLw7hfzr17EcMACrwYPQK8d1ySWlyiskeVk4+RHpWPRww6yNy6MHbH0HLqyB98LBpOQDbaqiIgJX/cOpbRtx8qhD7/c+xczmP5+PKhU0bKhu0RQWBqX8rFDlFPxbxUtWV/AKtx+i0N4Nlc6jGwpJhrr3Knj/VvHu/1phpv/iAxHBs2DPpzAxGByeXnX8r6LsAlLWXCX/WirGje2x7Fsbhf7T/zwuJ19m/L7x6Ovos7DLQlwtXF8sXg27EXOXZfPnkGVVm9+njNB2OMJLpLgKoEgAy0Hu+fNET5pMtZ//h2lAwGPPy7KKy1c+Iy5uHe5u7+Hm9hYA+VHpJM6/iEEtS2xH1S+fUWEDA3ioqe3xGg2Z3Of/KNTR5e8J7Whf9yk7/sWHw7y2ULcnDFzy2NOySibh77Oo8gtx/KBZiaeNLQ9fzi8nf+En/5/oXav3i7wirSgqKmLv3r2Ehobi7u7OgAEDMH7Bnk4qlYrz58+zf/9+srOzady4MR07dqyQ1aH7tm3bxrlz53jnnXewsCjZtF9BvfHL/v37GTp0KHXq1NF2OE8UP+MsFMrYv9v4QaL3z84NZG1Vt6SpM8SYTu38ir2GLMukrr9Ozul4rIfXw9j78UGjyiYhM49ufwXiZGHIpkmt0S/Be5xKWUTivAsUJuRgN8HnQUJd0WQmJ7Hx529IiY2my4R3qN+2IypZxdRTU1kWvoyurl35yf8n9HX+HbBQxsQSPWECBbdvY9y8GdnBISBJmLZvj9XQoZi0aomkqDiTkORCFSlrr5J7IQlTf2cseriBBGQmI09rAvUHInf9GYpk9S6fRbK6gl0kq5vLq+7/XvXv7+/9fOfqZS7s3YOunj4+HXtg7VRN/VyRjHz2HKxZhzxoMNT1+vca96/9yM8qZBVPfr5QRWFaPnLuozN2FGb66N6NQDc5Gt03hv6b7NkYojDW0O7C2cnwex1oMQ66/e+5TpVVMpkHb5Nx4DZ6DsZYj/B6cq/je66lXmPc3nEoJAULuyzE3VKDA+Wl8NOMhWQkxtFz6FgC6lbs9b1C1SESQC0ruHuX6ImTyL96FYfPPsN6xPDHjpHlIsIvf8zdu5upVetjXCxeJ2H6ORRGuuq1Mpp6o/6vuDh47z3YsAEKC0FHh+hhY5jgM4TLSbl82KUOk9rVevII/9GpcPB7GPgP1H/1safzrqeStDDsySOsT6GSVYzePZrradfZ0mcLdsYlqx5WFGfOnGH79u1YWloyZMgQ7O2L3zL/v2JiYti1axexsbG4uLjQvXt3nJ0r7nqH+0QV8PmlpqYyc+ZMateuzZAhQ7QdzhMVxGcT/+cZLHq6Yxag/nd4MfEi82ZsxSO5GTmG6UgFCrp/UpeGTg2KvZZcqCJx3gUK4rKxm9QIfaenzDCoBGRZZvSSkwRHJLP9bX88HJ49VV9WyaSsvEzupWRsXvPCyEv7U7ifJPFWJBt//gZlbg693/8M14aNURYp+SLoC3ZF7WJEvRF81PwjFNK/yVzuxTCiJ05EVipxmTEdkxYtUMbEkrZ2LWnr11OUkoJ+zZpYDh2CZd++6FSQQSJZpd6FNTs4Tp38lffXI4UECglJR/0DhaQe+NWRkHQUoECdND/leR1Lg3tVPHWCp2NtqK6g/fCDenOZ1FT10o/ysPZ1iDwCH1wF3eevauddTSFlzVXkIhnrQXUwqv/0/x8RaRGM3TsWlaxifpf5eFp5liZyjbh6I4JVy5cRYVSXJR8NRqeCTvMWqhaRAFYAquxsYj/6mKyDB7EaPhyHTz9B+s/UPVku4lL4B8THb8MxbiSW1zpjP7kRevbl3Adq4kSYN0/dSL6wELy9yT15hv/beJGt5+/QvYEjUwf6YPLfDRyKCmFBR0iPuTcV9PE37KTFYeTfysDxo+Ylbm58K+MW/bf2p6VTS/7u8HelmQp63+3bt1mzZg0FBQX079+/RJWdzMxM9u/fz/nz5zE1NaVz5854e3ujqEAj5s8iqoDPZ+XKlURGRjJ58mQsy+tL2nNK2xlJVlAsTp+1QMdUn7S8NAZtG0TXwMl4elWnpp8FgXNuccH1AB9MeP2ZX8SKMpQkzDgLOhL2bzWu8A3Pn2ZZcBRfbrnEt6/U5/VWriU6J313JJmHY7Do6YZZQMkGxMrbrYvn2Pr7T+gbGtL3k2+wd3UnU5nJlENTOHH3BO83fZ9R9Uc98p6cefAQsR98gK61NdXnzcWg1qObwaiUSjL37CF15Spyz55FMjTEvGcPrIYNw6h+/fJ+iY+RZZmcswkUJuYCKqTQGWBqg9T89XvJluKhpKuESdq9nwsL8jmyYhERZ0/g2rgpHSzsMJg8EWnVSujfV3OfbQcOQKdOsHs3dO2qmXv8140DsLwfDFgMDfq90CUKU/NIXnGZgpgszNq6YN7FVf1n+QRR6VG8sfcNlEVK5nWeRz2beqWJvszJssyvf00nNjUXv55DGOZbdaa+CxWX2ASmAlCYmOAy/W+sR48mdcUKoidOoijr0T5BkqRDvbq/YZnrz12npeT3Plf+yR9AfDy8+aZ6S2pvb7h4EaPff2XakEZ83qMeey7dpe+sY0QlZT96no4uvDoL8tJh10dPvLRFDzfk/CIyD9wucTg1zWvyduO3ORxzmB2RO0rzyrSiRo0ajB8/HhsbG1atWkVg4NN7Av23rUPr1q0ftHWoTMkfQEBAgHrHwKAgbYdS4V25coVr167Rrl27Cpv8yUUyOWfjMaxrjY6pPipZxSdBn0CiIYZKMzwaOdGwUS2cGpridbsN7279kKj0qGKvqWOuj81rXhRlKklZcRm56MW3qteWGwmZ/LDjMm097RjZsmRf6rJP3SXzcAwmvo6Y+lfMin740YNs/N83mNnYMvSH37F3dSchJ4FRu0dxJv4MP/n/xOgGox9JWlJWrCDmrbcwqFUL19WrHkv+ABT6+lj07o3rqpW4bdqIRe/eZOzcRVT/AUQOHkza5s2o8p+zQXsZkiQJkyYOWHR1xcLpFOaqhZj3boFZgAumrZ0x9XPCpIUjJk0dMG5kj3FDO4wa2GJUzwZDTysMa1th4G6BQU1z9F3M0K9mip6DCXp2xhhVs6Lrh+/jO3QwV88EsWr7SlKbNkTSZPIH6obwCkX5NYQHcG8PFtVLvBnMk+haGWI/wQeTFo5kHokhaeFFijKVTzzW1cKVJV2XYKRrxBt73yAsqWI1X5ckic5t/bFW5PLPnhNk5mmnZ7Ag3Fe5vlFWcpKODg7/9zGO331LdnAwt4YORRkT+8gx2UfjsA8ahRUBRGb8TkzMivIPdONGmDkTGjVS70w2fDh8/jnSvHmMa+PO0jG+JGTm88qMIA5f/U8zVof60Pb/IGwDhG997NJ6DiaYNHckKySOghI2hwcYUW8EPnY+/C/0fyTlJpXu9WmBhYUFo0ePpkGDBhw4cIANGzagVD76QXbt2jVmzZrF/v37cXV1ZdKkSXTu3BkDg4q5KcSziB1BSyY/P5+dO3dib2+Pn1/xa+e0Ke96KqrMAkyaqqcxz70wl2OxxxhqNB5JgpoN1BX/ToO90Vfo0zCiE2P3jiU2K7a4y6Jf3Qyr/p7k30wnbftNjb+OsqQsVDFlzTmM9XX4bUDDEn2Jz4tII3XjDQw8LLF85SnT6bVIlmVCN61l18w/cK7rxZDvfsXc1o6b6Td5bedrxGTGMLPjzEfWZMsqFfG//kb89z9g2rYtNZf+g67ds6frG9arh9P33+Fx5DAOn32GKiOTuE8+5UbbdiRMnYoyJkaTL7V4sgwhs8DGA2qVXT9DSZJo1rsfAxu3JF+CFbWqcTVEw4NkZmbl0hD+EQoFNB4BNw+rN4h7QZKeAqt+HlgN8CT/dibx08+Sf+vJfRurm1dncbfFmOubM27vOM4lnHvh+2qCt7c3+gaGVCuIZeahCG2HI7zkRAKoBVaDBlFj/jwK4hOIGjyYnHvtAnLDk8nYewsTHyd82s7F1rYjV699RWzsau0Fq1DA4sXQo4d6auj69fh72LLtLX+crYwZveQkMw/deLSi5T8FHBvCjvchJ+WxS5p3romkqyB9V8mb5OoodPi+9ffkFebxXfB3FXZb8eLo6+vTv39/OnXqRFhY2IOm8f9t6zB8+PAK09ahtEQV8NmOHDlCRkYGvXr1KpPdYjUl53T8/7N3ltFVXF0YfubeuLtBQgIECBCD4O6uBQoUKKXFoVSg3n71lpa2UFyKtRSKu7tbnBAsLsTdbq7M92NCII1DkLZ51spKMnLmTOTe2Wfv/b7IDLXQa2zBxbiLLA9YzsD6AzGIt8Guvin6RpIAiImlPi37OuOU1ByjFBveOPIGSXlJFY5t6G2DUec65F66T87V+8/idmqEhcfvcCMui++Ge2Bjolfp8crkPFL/CEXLSg/LsW5SyeALhEat5viapZzfshG3jl0Z/uEX6BkaEZAUwIRDE1CoFazru472ddo/PEehIO6dd0lbuxbzsWOpu2QxsmoKXslNTLCYMJ76Bw/gtG4tBq1akbpuPWG9ehMzdRo5Z84gqtU1fbsAqNUKcnJuk5h0iIjIpezf/y4rVo7i3Te+gHh/rsqmSe+DNYlCgePSFYxLycXKpSH7F87n9MbVqFUVWC49KTVgCF9tvIr0DvyffCHb0McWmxmeCFoyklcGkXMhrszngDpGdVjfdz0WehZMPTYVv0S/J752TaGtrU2b1q1wkmfw1/lQolPznveUavkPU9sD+BxRhIcTM206qoQEbD75nvwQU7Ss9bGZ6oGgLUejURAUPJ3U1LO4uX2Pg/2I5zfZvDzo3RuuXoUDB6BXL/IKVby/I5h9gfH0d7fjxxGP9AUm3JBUQZsNg5fWlBou60Q0WceisJ7qga5L1fvD1t9Yz0++PzG/03z61+9fU3f3zHlgGi+Xy1EoFGhpadG1a9cX0tbhUZRJeWSfjsG0jzNy06plJmt7AcsnMTGRlStX4unpyZAhQ573dMpFnavk/rdXMGprT0F3Q0buG4mVvhWr2q9j66d+tB1an5Z9nYuPVxWq+fPzK2i0laxw/QBbIxvW9V2HhV75RtuiRiRlfQiKsAysJ7uj6/xi/61cjUjj5VWXGNXSkfkjPCo9Xp2rJHl5IJp8JTYzvNCyLF/Z8HmgLChg/6L5hPtdo/WQEXQcPQFBJuNU9CneO/setoa2LO+5HEdjx+JzVOnpxM6YSb6/PzbvvYfFaxNrLKOpTEggY+tW0rduQ52SgnbdupiPGY3p8OFomZtXayxRFFEUJpKXG05eXgR5eeHk5YWTmxdBQUEsj6q9pKTYkZtrRL1696h7S03Hj0JYuNSQV0prtz0+y5fDjBlw5Ajq7t048/ta/A/vo06TZgx6+wMMzap3f1ViwwaYOBGCg6Vs4LPi92GQfAfeCgLZky9wafJVpG29TUFoGvqe1pgPd0WmW3rcpLwkXj/yOol5iSztsZRWdq2e+No1QVZWFgsXLiRUZYtZIx+Wj2v5vKdUy7+YWhGYFxhVejpxb85DMO2HzMQcu/fao2X2cCVZrVYQFDSFtPQLNG26AHu7oc9vsunp0KULhIfDyZPQujWiKLL6XDjfH7qFq40xqya0pJ5lkZrf6flw+lt4eRO4DSwxlKZQTeKC68hMdLCZ4VVliwu1Rs2EwxOIzopm15BdWOn/c+Xjk5OT2bVrF7a2tnTv3h1j48qVA58nivBMUjbeRCxQYdTeAbPBpft7yqJWEbRsNBoN69evJzk5mdmzZz+2TcizIOdiPBl7w7CY5c7koJmEZYSxecBmcgN1OPPnbUZ/1hpLh5IWBmF+SRxedQPnAXp8njkHZ1Nn1vReg6lu+YGdJk9J0rJANAUqbGZ5o2X2YpY/ZxUo6bfwHFpygYNvdiotiPU3RJWG5N9uUBid9UIGt7kZ6eya/yVJEWF0nzQNr97S4tq2O9v4+vLXNLNsxpIeS0oE8IVRUcRMmYry/n0cfvgBk75PR1xELCwk+/hx0v/cTN716wg6Opj064f5K2PRc3cvEXCq1Xnk5UWQm1cy0MvLi0StftizLpPpY2hQHwMDFwwefDasTysfF+7cMcTFPJxdy7uQYq3DLz9/TXDwGKKiauiGCgqgYUNwcYGzZ6Fo/qHnTnF01RJ0DQ0Z9NYH1GlSdf+8KnHnDjRuLAm8TZ5cs2NXRMgu2DYRxu2Ahj1rZEhRI5J9Jpaso5FoWRtgOc6tTL2ElPwUJh+dTGx2LIu6L6K9Q/syRnv2bN++nZDQ2/ye684fUzrQtv4/v9qnlheT2gDwBUZUiyT/FkRheDq5Z37EqL0b9t9+g+yRvi+1Op/AwDdIz7hKs2Y/Y2f7HP3w4uOhY0fIyoJz58BNUto6dzeZWX9Kpay/jvGmSyNrUCthdTfIToSZV8Cg5Op/rm8i6dvuYDG6MQZeVbdHCM8IZ+S+kXSu25mfu/78wvXQ/BvJC0gibdsdSWLcQg9FRBb2H7VGple1bOXevXsJDAyszQI+gp+fH3v37mXw4MG0aNHieU+nQhIX+4NGZF27o2wK3cSCLgvo49yH/UsDSb+fy7iv2pX6PxRFkT0LA0iJzcZ1moy3L7+Jm4Ubq3qvwlC7fMsHZVIeSUsD0LLSx3qqR4VG0M+Ld/4KYE9gPNumtaOFU8XZmhKeh9V8rXsWpMXHsfO7z8jNyGDAnPdo6NMGURRZFriMFYEr6FSnEwu6LMBA++EDdp6/P7EzZoIoUnfZUgye0d9vwZ07pG/ZTNqZ3RSa5CF42iFr7YTKVkNeQRQKxaPlwwJ6eg4PAzyD+sVBn66uHYJQuqxTJpNa/37u8yEz26xkiVFf3L2vMn/+AlTKoXh4gKen5NverBk81prNokXw1lvSImq3biV2JUdFsPfnb8lKTqLL+Nfx7juo5t7fRBGsrWHwYFi7tmbGrAoqBfzUBFw6w6gNNTp0wb100jbfRlRqMB/pioF76b7TtII0Jh+dTGRmJAu7LaRT3dJezM+amJgYfvvtN0K1GpBj4szeWR1rbSFqeSrUqoC+wGQeDKcwPAuzl5pg+dpQsg4cIPrViahSU4uPkcv18fRcjZmZDzdvvkti0qHnN2EHBzh6FLS0pJLQaKm5u5OrNftmdcTeVI/X1l1l+ekwRJkWDFkG+Wlw+INSQxl426Btb0jm4UhEZdX7Euqb1Wem90yORx/nSNSRGru1WkojiiJZp2NI23IbHSdjbKZ7YtLbGbFQTe7VhCqPU9sLWJK8vDyOHTuGk5MTXl5ez3s6FaJMyEUZl0OUSxqbQjcxzm0cfZz7oCxUE3srHWd3qzIfUgVBoNMoVwrz1XDdmh+7/EhIagizTswiX1W+AJS2jQEWoxujjM8hfcfdF67fd39QPDv945jVrWGlwR9A9plY8nwTMe7h9MIFf3G3Q9n82TwKCwoY9b9vaejTBpVGxReXvmBF4AqGNRzGr91/LRH8ZR09SvTE15AZG+O8ZfNTC/5UqmyysoK4n7CbsPCfCb4xm8CMt7nVeSsJH2eRNltFaudYUsSL5Ny4jl6cIfXMX6N588W0bn2Arl1u0KH9Wby91tO40f9wrDseC4sO6Ok5lBn8kZfGp71X4jelI2+3Xca24OF8+MlaAgPb8N5779Gw4RHWrYM33pBENY2MpITayJHw1Vewdy9ERkpxVrnk5cF330mB39+CPwDrei688u0vuHj7cGr9Kg4uXoCyoKBmfqCCAG3bPlshGJA8AD1Hw60DkFuzAm56Dc2xedMbbTsD0jbdIuNAeCklYQs9C37r/RsNzBow59QcTkWfqtE5PA5169bFwcEBL70UQuIz2eH3HMWOavnPUpsBfI7kXk8kffsdjDo4YDZIKqfLOnKU+PffR8vSkrrLl6HX6KGPlkqVQ0Dga2RlBeHefAnW1r2e19QlddAuXcDeXsoEFim+5RWqmLc9iANB9xngYc+PIzwwuPAjnPkeRm+GJiX79gruZZCyJhjTfs4Yd3Es40Jlo9KomHBoArHZsewasgtL/doSippGVItk7L1H7pUE9D2tsRjZCEFLenBKWhmIOl2B3bxW5foy/Z0HWcA5c+ZgYmLyNKf+wrNnzx4CAwOZOnUqtra2z3s6FZKxP5zsi3G81vgz7KzrsK7POrTl2kQEJnNweTCD53jh6FZ+b9/Zv+5w43Qsoz5uxdXCc3xw7gPa12nPr91+RUeuU+55WadiyDoSiUlfZ0y6Vv214WlyPzOfvgvP4WJlyLZp7dCuRMQlLziZtE23pP+f0Y1fqGqFu1cucnDxAowsLXnpwy8xs7MnX5XPvDPzOBN7hikeU5jlNaukzcOGDSR+Px99Dw/qLl+GlkX5v/eqIIpq8vNji8o0I4r68qSvCwsfFQ6Soa9ftyib9yCTVx99fRdUgRFkbNlC9rHjoFJh2LEj5mPHYNSlC0JlokoaDYSfAv8/4NZ+UBfie9+Ttf7jWR8wljylIRYWufyx6VV0dW/g3nw52dndCAyEoKCHH2FhDwM/ExPJPenRbGHz5pIQJz/+CO+9B+fPQ4cO5f9cNBqu7tnOhb/+wLKuI4Pe+QgLhxqwC/nmG/jkE0hNhSf83VWLxJuwvB30+Rbazazx4UWVhowD4eReuo+OswmWY92Qm5R8bckqzGLasWmEpobyQ5cf6FXvOT4/AUFBQezcuZNwU29C8ww5NbcrRpWUktdSS3WpLQF9AVFEZZG8KghdF1OsXmte4iE6P/gGMTOmI+blU2fhLxh1eliyoFJl4x8wkezsEDzcl2Fl1f15TF/i7FnJVLZ5c6mcpaiHTRRFVp4N54fDt2hka8yqsR447RgAuckw43KpUtCU9SEoIjKxe6/q5vAAYRlhjNw3km6O3fip6081emv/dTQKNWl/hlJwOx3jro6Y9K5Xok8zPySF1N9DsRjbBAOPyuXeAdLT01m8eDE+Pj707//PFfB5UqKjo1m7di3t27end+/ez3s6FSKqNcR/ewV/nVB+qLeerYO2YmdoB8Cp30O555vEpAWdkGuVHwgV5CrZ9NllLBwMGfqON7vu7eJ/F/9HT6ee/NjlR7RkZT/0iKJI2uZb5AenYPlqM/SbPMMH1jLQaETGr72Cf3QGB97shItV+WWsAIUx2SStDELHwRDryR4I2i9OwY3fob2c2rAa+4aNGPreZxiYmJJekM6sk7O4kXKDj9t8zKjGo4qPF9VqEr+fT/rvv2PcuzcOP8xHple56ukDlMqMEsHdw4AvClF8aIejpWWGYXFfXn0MDIu+1ndCJqu4H1SZlETGtm1k/LUVVVISWg72mL88GrMRL6H1d0Xl9CgI2AQBf0JmDOiZgcfL5DYeh2ljDwwMICcHnJykmGnUqCz8/ceRm3cPT8/fsDBvV2K4nBy4caNkUBgUBI+639R31uARdwgPp3Q8fxiHhwfUr1+xwGhkkD8Hfv0RjUpF35lv49qqXfkHV4WTJ6FHDzh4EPr1e7KxqsvqHlCYCzMuFfc91jR5/kmk77yLoCfHcowbuvVLthtkF2Yz4/gMglOC+b7T9/R16ftU5lEVVCoVCxcuxMDMkh/u2TCzWwPm9Wny3OZTy7+T2gDwOZOXVUjUjVSMLHQxNtdDXwapK4MQdOXYzvRCZlA66FHev0/MjJkobt/G9uOPsHhEgkypzMI/YAI5Obfx9FiBpWWXJ5qfKIoU5GSTk55GbloqORnp5KanoWtoVCwGUC779sGwYdC1q6QO+kjv4pk7ycz+0w9BEFjXV4cWh4eDxygYtqLkvSblkbjQF8M29pgPaVitua8JXsMiv0XFPUm1PDnqrEJSNoSgjM/BbGhDjNrYlzpG1IgkLLiO3EgbmxleVR77v54FVKvVrFy5EoVCwcyZM9HRKT8D9iKQdzOFtI2hfFF3BZOGz6Kdg/QAKmpE1n9wAfuGZvSdUqQoqFZJKn9lPNyFnIvj9Kbb9H6jGa4+tmwK3cT3V79nQP0BfNvxW2RlleQhiUUlrwhElVqAzUyvMoUenhVrzoXz9YFQvh/uzujWThUeq8ooIGlJAIK2DJuZXsiNauj3nJ8Od49BvQ5gWv2MkKjRcOaPtfge2E3DVm3pP3su2rp6xOXEMe3YNO7n3md+5/n0cHroe6fJzydu7jxyTpzAYuJEbN6bh1BFW4TCwjRuhs4lNfVM8TZB0EJf36m4N8/QoEFxj56OzpMH+aJSSfapU6Rv3kzepcugrY1Jnz6Yv/wS+roxCAF/QHjRfBp0k7zqGg8AbT3++gtGj5aKWjp2LH0vfv5jKSiIw9trA6amFZe+iqLUIVEcEG65SdANgTuyJmg00v+IgcHDbOGDjKG7O5iZPRwnKyWJvT99R2L4XVoPGUGHl8cje1y7mJwcMDWFjz+GL798vDEeF9/1sG8OvH4cHJ+eIqcyIZfUP0JRpeVj2tcFo051SmSxc5W5zDwxE/8kf77u8HUJP8tnzenTpzl9+jRZLt3ZfzeXE+90wdHixRUDq+WfR20A+JyJDkll3+JAQGq67GQkx0guI8hQG7mVAUbmukUfehhbSJ8NzXQRFEVvvKdOYf7KK9h++AFCkUWAUpmBn/948vLu4emxBguL0uUkoihSkJtTIqjLSUuVAr2MNOlz0Ud5/kNjvlqAQ6NKVqU2boRXX4WXXoK//oJH3pyiUnOZstGXu0nZbGt0ipZRq2HMX9C45Mpb+u575F5NwPbtFmhbV/0FUKVR8crBV0jITWDXkF0VyszXUjnKxFxS1oWgyVNiMdatwqxL9oU4MveFYz3DE12nqgVz//Us4IULFzh27BijR4+mSZMXf7U3cNlRZHFKLo2IZYr31OLtiZFZbP/+Oj0nutG4rT1o1LC4Bbj2hv4/lhpHoxHZ9t01CnKUjP28Ldq68uLFmxGNRvBZ28/KLY9UZRSQtDgAmb4WNjO9kOk/+zKpWwlZDF58gS6NrVk1vmWFpZwahYrk5YGo0hXYzPBE27biTGGVEUXYMhZuH5S+t3OHRv2gUV9w8K7Uq05VWMihZb9w59I5vPoMpNvEychkcm6l3WL68ekUqgtZ0mMJ3jbeD89JTSVm+gwKgoOx/egjLMaPq/J0s7KCCQ6eQaEyhXpOUzE2ccfQoD56enWRyape6fEkKMLDSV+zmMyDx9EUqNA1U2LurovpS2OQtZ0AZiUD+VGjpMKWuLgSb2MPx1Mk4es3GqUyDW/vPzAxrqKdQmYmODtDp07kbdnLzZslM4WBgZD2iGWuk1PJoLCZWyGxF1dx4+RhnJp7MmDOexiYPMxubdokxXTR0Q+zluXaVnh5SS0bx45Vbe41hSIbFjQG95dg8OKneilNgYr0bXfID0lFv7kl5iMalRAsy1Pm8ebJN7macJUv2n/BMNdhT3U+5ZGTk8Mvv/xC4+aefOanTU83W5aMfbEFwWr5Z1EbAD5n1CoNOekFZKcWoDoehTw2h/v1TEnSiOSkKchJL0CR97cATAADYx2MzHXRTo5GuOOPiYM5DhNGYGJvio6uikJlPPei51Covo9e7kQUqebkpqWVCPDUSmWp+egaGGJoboGRuUXxZ+lrSwzNzTEyt0RHX5+1b03BqZkng9/9qPKb/PlnePddmDIFVqwokQXIVaiYtz2QY8GxnDb5HHudPGQzL4P+QwEFdU4hCT9eR7eBGVYTqid/fTf9LqP2jyouKavl8SgIyyD195sI2jKsJjZHp45RhcdrFCruf3sVvSYWWI6pejDzX80CZmRksHTpUlxcXBg7duzznk6lhEQHYbgsjetOdxg+/Y0SWbore8PxPRTJpB87oWekDRHnYEOR1cu4ndCwR6nx7t/LYOcCP3z6O9NmcH0AfvX7ldXBqxnnNo73Wr1XbmCliMgkeU2w9PowsVmVbWNqggKlmqFLL5CSU8iRtzphaVR+KaKoFkndGELB3XSsXmuOnmsN+rkF/gW7pkDHd0DfDG4fhpjLIGrA0AYa9ZYCwvpdQbfk/25+TjZ7fvyauFshdB43CZ+BwxAEgcv3L/PWqbcw0TFhRc8V1DerX3yOIjyCmClTUKWkUOenBRj3KP07LY/4+G3cvvMZOtpWuLsvxcSkcp/EGiUvDYK3g/9GSAhGo9ElU9GK9CAFiog4ZEZGmA4bhvmY0ejWl+45P1+Ki8aPl2z6yqOgIB5f35dRa/Jp4f0nRkaNyj/4AZ9/Dl98AX5+4O1darcoSgLbfw8Kb90CtVo6Rk8PBrY9RmubZch0TGk84EPa9WrM4cPS227eI57iBgaS20OZQeD06VLEmJ5edpT7NNk9E27uhndvl/obrWlEUSTnXByZhyPQstSXrCIeWYwpUBUw59QcLsZf5NO2n5YoeX6W7Nq1i5s3b6LfYgi/noli+7R2+DjXLmTXUjPUBoAvCNlnYsg8FIlJ73qYdC+58lhYoCInvYD0+xmkxiWSnpBMdkoKOenp5GelU5ibjlqTB5ocRE0OIL0raOmpaDAoCh1jJZFHGqLOrYuBiRlGlpaYWlthbmuNocWDIE8K8LR1q9a7cX7LRq7s3sakX1Zgbl+FcqOPPpIUzj76SFqCfARRFFl+JoxDRw+xW+czCtxGYvjyqhLHPBB9sJ7ijm59syrN8QGrglax2H8xP3f9+bk3d/8TyfVPIn37HbQs9bF6rRla5lX7G8k4EE7OhTjs3mtdZc+2/2oWcMuWLdy7d4+ZM2diXk0j62dNRkEGq377kbExfdGf7oplPbsS+//65iraunKGzy0yMT74HvhtkDIqD/p89ErbfRxbG0KYXzJj/tcGU2t9RFFk/rX5bArdxBSPKcz2nl3unHKu3Cdj1z2MutTFrJ9Ljd5vRXy9/yZrzkew7rVWdGtcsYpnxt4wci7GS6XTbUuXTj82WfdhWRuwdoPXDj401M5Lg3vH4c5huHscFJkg1wHnTtC4HzTqQ2ahLju/+x+ZSQn0nfE2TTpILQMHwg/wyYVPcDF1YXmP5dgaPhQjyrt+nZiZsxC0tHBcvgx9j6oFcBqNgtt3viQ+fgsW5h1o1mxhjZR1VvHiEHFaEnQJ3Q9qBdh7gvd4aP4SGFggiiL5/gGk//knWUeOgFKJQdu2mI8dw6mc7gx9SYtjx6BnJXZ1eXmR+PqNBqBliy0YGDiXf3BamuT517Mn7NhRrVtSKCA0tGRQmBB2j8Fu32Gin8qegClcjeyHWl16QaRePUmVtBQPKnaCgqR602dJ9BVY2xsGL4EW45/JJRXhGaT+eQtRocb8JdcSSrwKtYJ3Tr/D2dizfND6A15xKy9t+vSIj49n1apVdOvZi/fPFmBjosvuGR2Q1dpC1FID1AaAzxlRFMkOSiBr8z3EetrkNMsn55ESzOLPGWmoFIpS5+vo62NoZoGuIEcTFo1cpo9Biw7ITeugVuujVBdg3OQX5DrpxJx9i/zUh310Mi0BIzOprPRBmamRuS5GFtJnY3M9dA21ylx5z81IZ/XM12jWtSe9Js+qyo3C1KmwerWUEXz77VKHnL6dxJ3N7zOFnYR0XUOzriMfnq5Uk7DAF5mRNjYzq24OD6DUKHnlwCuk5qeyb9i+ErLltZSPKIpkn4oh62gUuvVNsRzftFoldqr0AhJ+uIZR5+o9lP/XsoC3b99m8+bN9OzZk45/by56wdCIGmaemMmY851xMq2H89slzZNz0gvY8OFF2g1rQIs+9aQH71+aQZ0W0OkdWNMLPMfA0KWlxs5JV7Dp88s4NjGn/3QpqBBFkS8ufcGOuzuY02IOb7i/Ue7c0nffI/fyfSxeboyB99O3VLhwL4VX1lxhfNt6fDW04nK/nEvxZOwJw6hjHcwG1q/w2GohirB5tNS3Nu08WJXTJ61WQvRlKRi8fQjSwkjMN2RXvBcqtBny+is4dn4JZHI2hGxgwfUFtLJrxcJuCzHRefg/mHngAPc/+BDtunVxXLUSHceqKbAWFMQTHDyTrOwg6tWbRoP67yAIzyDDlBEN/pskUZdHBF3wHgf25QeuqtRUMrbvIP2vLaji75OlbctfGaP45soI9B0q/9vKybmDn/9Y5DJ9WrTYgr5+OYukH30E338vBVzNq1gyWgnR4dkcXrqA7FhfrkX0YIffDFTqkgtwgiD9a5bi7l1o1AhWrpRSh88SUYSlraXqn9ePPrPLqrMUpP55i8LILIzaO2Da36VY0VqpVjL3zFxOxpxkrs9cXm326jOb1wPWrl1LVlYWTp2H887WYH4a6clLLes+83nU8u+jNgB8zkSevYZmfya5ynRO3P8TtSiVZWrr6mFk8aAM0xJDM3OpFNPCUvpsZoGRuTk6+g+DGUV4ODHTpqNKSMD+u28xHTBA2q5IxNdvLIWKZFwcVqDOcyUnXSovLf6cpiA3Q4FGU/J3rqUtKw4IHw0SrZ2MCTiygZtnTzJl6ToMTM0qv1m1Wmqk2LkTNmyACRNK/zwS02BlF3TVORztvJsJ3T2KA9Bc/yTS/7r9WA94AUkBjD80vtJMQi0SolpDxu4wcq8lYOBtg/lLrsVvitUhdVMoBXczsP+wNTLdqj3w/ZeygIWFhSxduhQdHR2mTp2KltaLLfW9MnAlBy/vZmnER5gNboBRe4cS+2+cjePMn7cZ81kbLBwMIeYa/NYThq2U/L5OfAnnfoKxW6FRaWEm38ORXN4dzuA3vXBsKmWH1Bo1H53/iIMRBytciRdVGpJ/C6YwJgebaR7o1DWu+R9AERl5hfRdeA4DXTkHZndCvwJD+oLbaaSsD5HKocc3rdkS1YDNsHsa9PkO2s2o8mkRZ/axb/Ua9ORqXqrjj6VODhoDKxY4NuT3wnj6OHbn2y4/FltxiKJI6po1JP/0M/o+LXFcsgT5o2okFZCWdpEbIXPQaApp2vQHbKyfsiCXskCybfD//aGgS/2uUtDXZCBoV12hVFSryTh+ht3TNtNW9zxoaWHcqyeG7dqhZWWNlrUVWlZWaFlaIvxNtCk7OwQ//1fQ1ragZYst6Or+7T0rOVnK/g0aBJs3P+FN/23eGg2Xdmzm4rYtxGe4sOHiR6TlPszUl5sBFEWwsYGBA2HduhqdU5W48Csc+xRmXgXrxs/ssqJaQ+ahSHLOx6HjZIzFK25omUpBs1Kj5IOzH3A06mili1BPg5CQELZt28bLL4/mgxMpJGQVcGpuVwx0Xuz3ilpefGoDwOdMxp040rbdprCVgIGDJUZFJZmPBnbVQZWeTuzs2eRf98Vq1iysZs5AEAQKCu7j5zcWpSodb6/fMTEpXd6h0YjkZxWS/UhA+ODrB8FiXqai2NPIs7sRV3Z+RdvhL9NhVBUFABQKGDAATp+GXbukN7+/kRd5Dd31vdmu6syFZp8z/yUP9HXkiBqRpKUBaHKU2M1tiaBdvRXk98++z4noE+wduhcHI4fKT/iPolGoSN10C8WddIy7O2LSq95je5QporJIXh6I2ZAGGLWr+s/8v5IFPH78OOfPn2fixIk4Ozs/7+lUyKX4S0w9NpVvC97FK7oB9h+1KWXNsn9JIOkJuYz7qp30N3P0E7i8Aubdk3rTVApY1VUqT/xbry+AWqnhzy+vIJcLvPxpa+RFXnpKjZK5p6WV+C/bf1muMIM6p5CkJQGgEbGZ7Y3cuOaVVEVRZNaf/hwJSWDXjA641y1dzvoAZUIuScsD0bLQw3qaZ5UXQapEVjwsaws2TWHiwUqFXh4QfPIox1YvwcrJmeHv/w8jfTmFdw7zyY0VHFKlMi4zi3kZucicO0Cjfoj1e5Kw9A8y/voLk/79sf/uW2S6lZd0i6JIdPQq7oUtwNCwAe7Nl2FoWIPZz79zP1Aq8QzaCgUZYOoE3q+A19hSgi7V4fBhyRXh8NpIvBL/ImPXLjSPejgUITc1RW5thZa1tRQcWlmhqFNAmP1mdGXWuNstQN/OFbmpqaSUOncu/PIL3LwpucY/Bdb+fI34cwsQgRWnvyM+o37FPYAgvSffvSs1GT5rcpLh5ybQZhr0+aby42uYvKBk0rffRdCWYTGmCXoNzQBJUO7j8x9zMOIgM7xmMN1z+jObk1qtZtGiRVhaWtK86yBeWn6JN3u48k6vKvSX1lJLBdQGgC8AoijWqAmwprCQhM/+R+bu3ZgMHIj9N18j09UlPz8OP/8xqFQ5tPD+A2Pj6gmqAKjVGnIzFFzaFca960no6R6lICeKKUvXoV1V76fsbOjeXTJHOnoUHvEyfIB4/AuE8z/zauH7JNt2YuX4ljhaGFAQlkHK6uDHMoBOyE1g0K5BdHXsWisIUw7qLAUp60JQJuZiPswVw1Z2lZ9UAaIokrQsEDFfhe07Lauc/fgvZAGTkpJYsWIFHh4eDB069HlPp0ISchMYtW8U1jpW/HpjHnoNzLB8xa3EMUqFmt/ePUezzg50GtVIyiYs8gQrVxj3SH9TfACs6QHNR8DwlaWuFRGUwsFlQXQc6Ypnj4f/44XqQt48+SYX4y8yv/N8+rmU7VVWGJ9D8vJAtB2MsJ7s/liZ64rY6RfLO1sDmdenMTO7lW9No84uJGlpAKJaxGaWV3FGoUYQRfhzlCSwM/0CWDaowikiF7f9yeUdm6nn4c3gdz5ER9+A7MJs3jr1FlcTrvKO91tMNG6McPcI3DmM5v5dYi+ak3tfD8teTbB+Zy6CUxuQV5x9UKlyuBn6PsnJh7Gx6Y9bk+/R0qohxdNHyU+HoG1Sti8hCOS64DZIyva5dKlyUFwRkydLAtbJyZKTkahUokpJkT6SU1ClJKNKSUH94Pvk5OL9YkEBClcNqTNVaCcIWC7SQqbURsvMDK24OLRsbdHq1RO5VVEm8dGsopUVMoMnb1fYsCqB6MPvEp/uxJrz37J2rcD4ilrsHvTqP2tD+Af8NQ6iLsE7oaD17K1wlEl5pP5xE1VyPiZ9nDHuXBdBJqDWqPns4mfsDdvLFI8pzPKaVaPPbRVx/vx5jh8/zvTp0/n6ZDxHQxI4Obcrdcz0n8n1a/l3UlEAWJtffkbU9IuITEcH++++Rad+fZJ//hllbCx1ly5B37IOLbw34es3Bv+ACbTw3oSRUfVWHuVyGSaW+vSe1AxjCz18DzanMOcGAccO02rQ0KoNYmwsmc126iSVmpw9K+lZP4LQ9QO4fZCVORvolu7G4CXnWTK2BR0aWqHnZkH2qRgMfWyr5aFlZ2jHa81fY3ngcsa6jS0haV6LlK1IWXcDTb4aq4nN0Wv05GIkgiBg3NGBtM23Kbidhr6bZeUnAebm5nh5eeHr60vHjh3/dVlAURQ5cOAAOjo69Or1YgsTPeiDUagVLHD8CvFqGgYtbUsdFxOahlqlwdnDStqQEAQZUdDp3ZIHOnhBp7lw5ntoOhiaDCix29ndEqdmFlzdF45rK1sMTKT/cR25Dr90+4Xpx6fz4bkP0ZXr0t2pe6l56DgYYT6yEWl/3iJ99z2pfLmGXmNj0vL4bE8IrZzNmdal/KBLVKpJ3XgTTa4S66keNRv8gWRSfvco9J1fpeBPrVJxbPUSQk4fL+7blmtpkZSXxPTj0wnPCOfbjt8+9D1z6YjSczYxkyehSAjHrq815hZnYcNJKWvr2lsq4W3Ys5SgT25uGEHB08nPj6Rhww9xcny9Zt/jNBqIOCMFfQ8EXew8oN+P4D4CDGouaFGrYfdu6W3qQdJT0NZG294ebfuKhXxEUUSTm4sqOZmUhGPc1l5A1re2ON/uj7jvKCqVCqWlJQWnz6BKTS2zKU9mYFAqq6hlZVUcJMofBI2WFsU2UH/n1Sl2+DmO5tT6lTSw8sPYuGXFN92uyFD+8mV4Hotv3hMgdB/cOQRNhzzzy2vbGGAz05v0HXfIOhxJYVQWFqMaI9fX4qsOX6Et02ZV0CqUGiVvt3j7mQSBLVq04PTp01y5coX3+/bkaEgCPxy+xaLRtc8wtTwdajOA/wKyjhwl/v330bK0xHHFcnRdXcnLi8TPbywaUUmLFn9iZOj62OPfOBPLsVVfIsjyeH3RKoyrY1QaHQ0dOoBSCRcuQIO/PcjE+sJvPcl2G83w2NGEJefwUX83JjSyJWmRH4at7TEfWj1z+DxlHoN2D8JK34rNAzaXazL9X6PgXjqpv4ci6MqxmtgMHYeak+EW1RoSfriGlpU+1pOrLvn+b84CBgQEsHv3bgYNGkTLlpU8kD1n5l+dzx+hf/Bjlx/xOetEYUwW9h+0QZCXfPA5+XsoYb5JTFrQCbmWDE5+LfX7zb0LhlYlB1UVwprukJ0AM66AYcmFgfSEXLZ8eZXG7ezoPr5kpjFXmcvko5O5lXaLJd2X0L5OSSGaB2QeiST7VEyZvYqPg1ojMnrVJULvZ3NoTqdyTZlFjUjallvkB6dg+Yob+s2tyjzuscmMg2XtwK45vLq/0iyXIi+Pfb98R1SQP+1GjKHdiLEIgkB4ZjjTj00nQ5HBL91+ob3Dw5+j4u5doqdMRZ2ZSd1ffsaoSxcoyISwk5LFxN2jkJ8GMi1walekKtqXJPVdboa+h0ymS/Pmv2Jh3q7m7jsjWgp8/TdBZnSRoMuoIkEXz0pPfxxOn4Zu3WD7dsnK9klISjrCjZDZmOl54Nl7H/KXx8OaNYDUa6jOyJCyh8lF2cWU5IdZxZSHH5qsrNKDCwJyC4uHAWJRkPggQJTZ2rBlwwrCIg24lL2IM2cq+JvJzZUM4T/8EL766slu+nHQqOGX5mDTBIavAUQp413is6aMbX/fR8l9oqac48vaB6KoISdQSeZFNXIjsOwrR8cSNBo1397bwl/xZxnn0I336g9DeiUUwaI+mDs/lR/L3r17CQoK4p133mHZuRiWnLrHzhntaeH0YqtG1/LiUlsC+h8gPziYmBkzEPMLqPPLLxh16kheXgS+fmMAkRbem5+oN+PitqNc2v4rxjZDGPHRWCzsq1HqExoqZQJNTKQg8O+rqsf+BxcWkv/ydt66bs6RkESGejnwsY4RiusJ2L7VEm2b6pXJ7A/fz4fnPuTrDl8zpOGzX2F80cj1TSR9x120rPWxeq15lS0bqsMDmxObN72rFVzu2bOHoKCgf1UvYF5eHkuWLMHCwoJJkyYhq4EytafFkcgjzD0zl1fcXmFe03e4/+1VScmyf0lVV1Ejsu6DC9RxNaPP5CI1wyWtwcgGJu4ve/CEYFjVTcoCjlhbaveFHfcIOB7NiPd9sHUu+bvPVGTy+pHXicqKYkWvFbS0LR1EixqR1N9vUnA7DatJ7sX9PI/L0lP3+PHIbX4e5cnwFuWr8GUejST7ZAym/Zwx7lK9MvVKEUXYNAKiLkqlnxYVv27npKWyc/4XpERH0mvyLNy79wYkUaxZJ2ehJWixrOcymlo+bAfIvXyZ2NlvIujp4rhiBfrNmpUeWKOG2GtFqqKH0SSHEuZiQLSjASYaC9ydP0XPpX+lpaKVUizo8geEn5a21e8i2TdUU9DlcZg9G377TSr/NKyBCtaEhD2EhLyD5bV8PEafQeZc/cVXjUIhBYbFZajJfwsSk1EXfS8WFhaflz1qGOduB7Hp8rus3tmNCtedWrSQyj+PH3+Mu6wBTn4NZ1+MNg2Fxo3Uwg8QMcRMexmG8pOIwA8WZvxhasLLWdl8lJqODMDQGt4OAa2afw9NTExk+fLl9OjRgxat29F1wWnqmOmza0b7Z1aKWsu/i9oA8D+C8v59YqbPQHHnDrYff4TFK6+Qm3sPX7+xyAQtWrT4s2K/ogoQNRrWvDmV3EwNhlYT6D/dgzrVKR+8elXqCaxfH86cgUd90JQFsLITFOahmX6RZZeS+OnYHVrbmPBTugy9BmZYvVrGA0oFaEQN4w+O537uffYP2/+ftYUQRZHsE9FkHY9Gt6EZluPckOk9ncpvTZ6S+99dRd/DGouRVW9eT0tLY8mSJbRq1Yp+/cru+fqnsW/fPvz8/Jg6dSp2dk/WY/k0iciMYPT+0TQ0b8j6PuspuJhE5oFwbN9uUcI0GSAxIovt86/T87WmNG5jB8m3JUn3fj9Cmwrk5M/8CKe+hpEboNnQErsK81X88b/LmFjq8dK80v2jqfmpvHbkNZLykljdazXu1mUIWxWoSFoWiCanEJtZ3mhZPF7AEBybybBlF+jT3I4lY7zLfeDK9UskfesdDHxsa7T0tBi/32HvrMp/rkBKTBQ7v/+cguxsBr3zIS5e0hP/qehTzDs7DztDO5b3XI6j8cMgNXPPHuI/+RRd53o4rlyJtkPlmdPCwlRuBEwlPcefOjkWNAqMQKZWSqWhDXtBo77g2rOU4E+F3A+SSjyLBV0cwatI0MW8XtXHeQI0GnB0hDZtJOHqGiEykrg3vbn1lgXW1n1o3uxXZLKn85oriiKa7GxUKSmkLFlC5tFjXOnWjrBINVEmK/h9k3b5J8+cKXkCZmQ8e0N4gIIsCN4qLTQ8qNIRBEAo+VmQld5G0fYS26hgX0XnCSCAOl9G2kk5ivsyDN1EzDoAMvg5YhfrY4/zkn0nPjNviezAO1LW0mMkT4MNGzaQmprKnDlz2Okfz7ztQSwa7cUQryp4MddSy9+oDQD/Q2hyc4mbO4+cU6cwHzcO2w/eJ7cgDD//cchkOrRssRl9/cdTSws6cYRjqxZj4fQKBfl29JjgRqPW1Xi4PXZMUgdt3VoShnm0+T3mmmQQ22ICDFrEyVuJzNkcwMsabSYqtbGa7I5eA7NqzTcwOZBxB8cx2X0yb7Z4s1rn/hsQ1RrSd94jzzcRgxY2mA9/PJuH6pC++x651xKw/6B1tdQZ/01ZwJiYGH777TfatWtHnz5PWQ7/CchT5vHKQck7c+ugrdgaSGXXaMmwnVW67+TK3nB8D0Uy6cdO6BlpPwzs3gkFkwqCCLUS1vSUPNpmXAEj6xK7b126z4kNofSY6EaTMszTE3MTefXwq2QXZrO2z1oaW5TuaVal5JO4JAAtMx2sp3tVW4kzv1DNwMXnyFWoOfxWJ8wMyv7bVURkkrwmGN16JlhNal7z/0+ZsVLpp70nTNhbYelnzM1g9iz4Gi1tHYa9/z9s60ul8tvubOPry1/TzLIZS3oswUJP6pcTRZGU5ctJ+XUxBm3bUvfXRcir8L+WmRVIcPAMlMo0Gjf+Cgf7EaDIhrBTUnbwzhHISwFBDk5tpWCwUV9JGOjvwXF+OgRvB7+NRYIuOo8IunStEUGX6nDpErRvD3/8UYFiZnV5/XXYtIno699zN2kxdrZDaNp0AcJTbEVIjMji7uUYTFZ/QK6JwEVdDXsDp7Dp+GDqlpfI/uMPGD9ecpf3qHrZ/r8ZUS2SdTSS7DOxaNc1wnKcG3JTXRb7L2Z18GqGNBjMF34HkBvZwaTDT2UOt27dYsuWLYwcORI3t6YMXnqe1JxCTr7btUIrmlpqKYuKAsAXty6plsdCZmhI3SWLsZg4kfQ//iBmxgz0qYO310bU6gL8/F4hPz/uscZu2qkbBqZmGJncxM7FlGNrb+J7OJIqLyL06gWbNsHFi5JXoFL5cJ9jK2g3C3zXQ9gpujexZc+sDlwwk5OAhvC/bqFRl+VqWz6e1p4MqD+ADSEbiMt5vHv+p6IpUJGyPoQ830SMezhhPrLRUw/+AIw6OIBaJOfy/Wqd16lTJ0RR5MKFC09pZs8GtVrN/v37MTExoWvXrs97OuUiiiJfXf6KsIwwvu/8PXaGdijjc1Em5GHoU1r8BSTlTrsGplLwBxC6B+q2rjj4A5Brw7AVUtBw4J2HvTtFNG5jh62LCZd2hlGYryp1uq2hLWt6r0FfS58px6YQnhle6hgtK30sxzZBmZhH2tbbiJrqLWx+dyiUsORcfhrlWW7wp0rJJ/X3m2iZ62E5zq3m/59EEfa+KWVEBi+uMBi6deEMO775FEMzC8Z8tQDb+g0RRZGlAUv58tKXdHDowJreax4Gf0ol9z/+hJRfF2M6ZAhOq1ZWKfiLi9uCr+9oBEFOy5bbpOAPQNdYKusdukzq/3z9OHR8W+ohPPYpLG0Fi1vA4Q8ln77w07D9dVjQGA7OBUQpw/nubak0uEH3Zx78gZT109aWBGBqhHv3JP/badNwav4WDeq/S0LiHm7d/rTq75NVRK3ScOdqAtvnX2f7/OsEnknkZpu3Mb4dgZWBOd2bbGHpr3nlD/BACObSpRqd1z8ZQS5g2s8Fy/FuqJLzSfrVH8XdDGZ7z2aG1wz2hO3lC8eGEH0JEm8+lTk0atQIc3NzLl++jEwm8NnAZtzPLGDV2dKve7XU8iTUBoD/QgS5HNsP3sfuiy/IvXiJqDFj0c00wdt7Ayp1Dn7+r1BQEF/tcbV0dGjRbzDRN/xpN8QUVx8bLu8O5/Sft6senI0cCcuXw4EDMGlSSVW0bh+BpSvsnQ2KbOpbG7F1Znsu1tHFJEvJbyt9KVCqqzXnt1q8hVwm5+frP1frvH8yqkwFySuCUIRlYj7CFdMn8PirLtrWBug1sSD38n1EZdUDdgsLCzw9Pbl+/TpZZQkg/EO4evUqiYmJ9O3bF90q+Kg9L7bd2cb+8P1M95peLAyS55sIcgEDD+tSx2enFZAam/NQ/TMtQurvazq4ahe0cZP+v0P3wo0dJXYJMoFOLzciL6uQ6wcjyzy9rnFdVvdeDcDko5OJzY4tdYxeI3NM+9enICSV7JPRVZsXcOpWEhsvRfFGRxc6NCxbzEWTpyRlQwgAVhObITOooLTucfHbCGEnoNcXYOFS5iGiKHJt7w4O/Pojdg0bM/rLHzC1sUWlUfHFpS9YEbiCYQ2H8Wv3X4vL3tU5OcRMnUbmzp1YzZiB/ffflTI0/ztqtYLQ0A+5dftjzM3b0LrVHkyMm5d9sEwmLeD1+FTqWXzrBvRfABYN4NpvsHEwbBwC945JFR5Tz8K081J5aw2qeVYXUYQdO6R1SdPybR6rx5dfgo4OfPABAM7OM6hXbzrx8Vu4e++bGgkCczMVXN0fwcaPLnJs7U0KcpV0etmVnq81JTUdYnrPpVFwCEa6WYSe3ElOTjkD1a8P1ta1AWAZ6Dezwma2NzJjHVLW3SD7ZAzT3KcxqfkkdmXd5pqBEVwv3dNcE8hkMlq3bk1MTAzx8fG0drGgv7sdK86EkZBZ8FSuWct/k9oA8F+M+cujcFq9CmVCApEvv4x2mApvrw0olen4+Y+jQJFQ7TE9e/VHW1cPv8O76TWpGS361OPmuXgOLg+msKD06n2ZTJ0KX38tlaC880hGQFtfWlHOjIVjnwFgrKfNzOmtSTfRokV0HmOWXSQuI7/K831gC3E06ii+ib7Vvd1/HIXxOSQvDUCVXoDVa80w9Hn2/WdGHeugyVWSF5BUrfP+6VnAzMxMTp06haurK25ubpWf8JwISQnh+6vf06FOB6Z6TAVAVGnIC0hCv5llmcFNVHAKAC4PAsDQvdJnt0FVv3C72VDHR8oAZSeW2GXrbIJbe3sCT8aQkVh21sLF1IVVvVZRoCrgjaNvkJBb+vXLqKMDBi1syDoeTf6NlEqnlJqjYN72IJrYGTO3T9l2OaJaQ+qmUFRpBViOa4qW1VPw5cqIgSMfg3Mn8Hm93MNunDrG2U3raNSuEyM+/gp9I2PyVfm8deotdtzdwRSPKXzR/gu0inrOlAkJRI19hdyrV7H/5hus35xd6WJQfn4cvn6jiL+/Fed6M/Dy/A1t7Wr09pk5QuvJMG47vBcOo/+U+j/fvQMDFjw1Nc/qEhAAERFPrvxZzK1bUoXLzJnwSN9vg/rvUrfuq8TErCM8/PEXIhMjszi2LoSNH13k2v4IrByNGTjLk1c+b4tHN0cat7HDs4cj4QpHVHZtsSuA1vV2s351etkDCoKUBawNAMtE20ofm5leGHjZkHUsitQNIUx1nYydoR0/2juiCdwCivKi6yfD29sbHR0drly5AsCH/dxQa0R+OHLrqVyvlv8mtQHgvxzDdu1w/msLMkNDoia8inguBm+vdRQWpuDvPw6FIrla4+kZGeHeow+3LpwhOy2ZdsMa0GVsY6JDUtn9sz+5mYqqDfTRRzBnDixaBN9++3C7Y2toN1NaXStShJNryWg0uim2yPBJKmTw4vNcCkut8pwnNpuInaEd86/ORyNWr4z0n0TBnXSSVwYBYD3NEz3X5yMdrdvAFG07Q3IuxFVrxfufngU8fPgwGo2G/v37v7CKbZmKTN45/Q5W+lZ83/H7YouU/NA0NHkqDMvw/gOICErFxFofM9uivt3QfdKDfHXk0OVaMHQ5KPNh/9ulSkHbDm2AlraM89vuljtEY4vGrOy1kgxFBpOPTiY1v+TrgCAImA9zRdvRmLStt1Em5JY7liiKfLAzmKx8JQtHe6GnXbq/RhRFMnaHSdn04a7o1q+pVFGJi0hVD6IGhiwptxRSrVJxeecW7Bs2ZuCb89DS0SG9IJ03jr7BubhzfNr2U2Z7PwzwCkJDiRz1Msq4OBxXrsDspeGVTiU17TzXrg8hLy8SD/eVNGjwLoLwBH1HukaSB2SzoU9dzbO67NghaZ8MrmISu1I+/xz09eG990psFgSBRq6f4mA/isioZURGLq/ykCXKPL+/TkRACs061+GVL9oyaLYn9ZpblhBOajesAbYuJoQ6j8Dpfg46cgW++7aUZT9YdEI7uHNHMoSvpRQyHTnmoxphNrQBBfcyyFx+kw+c3iZUk8s+HQ3c2P5Urqunp4eXlxc3btwgJycHRwsDXu/kwk6/OAJjMp7KNWv571EbAP4H0K1fH+e/tqDn4U78u3Mp/OMSnh6/oVAk4uc/jsLCylfKH6XlAMlWwe/gHgCad65D/xkepCfmsX3+ddLiy3/oKkYQ4OefYdw4+OQTWLHi4b7un4BlQ9gjlYJK92CKXlNLJsj0cNLTZtxvV1h3IaJKAYa+lj5vt3ib0LRQ9tzbU617/aeQez2BlPUhaJnrYT3TC53q2HTUMIIgYNSxDsqEPBT3Mqp1bqdOndBoNP+4LOCdO3cIDQ2lc+fOmJs/n8C7MjSihg/PfUhSfhI/dfkJMz2z4n15vonITHTQLWPRQKlQE3c7HRd3Kym4yIyT7AHcHuPJ2boRdP8Ubh+Q1B8fwcBEh1YDXYi6kUpkcPmvSc2tmrO0x1ISchOYcmwKmYrMEvsFbRlW490QdLVI2XgTda6yzHG2XIvh2M1E3uvbmCZ2ZffD5ZyLI/daAsbdHMsNjp8Y3/UQfgp6f1VhQH3rwhmykpNoM/xlBJmMuJw4JhyawO202/zc9WdGNR71yLzPE/XKOJDJqPfnnxh16FDhFERRJDJyOQEBr6GjY03rVruxtu5ZQzf4YrJjB3TpAlY1YeEYHAx//SUtalqXLqEWBIEmTb7G1nYwYeELiIlZX+Fw5ZV5Tvy+A51fbvRwIeZvyLVk9H6jGTIdLaJav41jWjZNLQ+xc3M5LR+PGsLXUiaCIGDU1gHrqR6ggSaHzGln3IpfLa3Iu7am1EJWTdG6dWvUajUPRA1ndG2AlZEuX+6/WeP9pLX8N6kNAP8jaJmb47R2LaZDh5KyeAl532zF3W05BQWx+PtPoLAwrcpjmVjZ0KR9Z4KOH6GgqMHA2d2KYe94o1GJ7PjRl7jb5ZSdPIpMBmvXSsqgM2bAtm3Sdm19GLJUUg08/nnx4ab9nBHUIiud7enW2IYv9t1k7ragKvUF9nPph6e1J7/6/0qusgoB6j8EURTJPBpJ+va76DYwxXqaB1qmz7/3zMDTGpmRNjnnqye+Y2FhgZeXF76+vmRnZz+l2dUshYWFHDx4ECsrK9q3L9uw/EVgTfAazsWd471W75WwU1BnF1JwJw1Db5tSNgwAMaFpqFUanD2KjNxvFXn+PU4ACNB2Oji2hUPzIKvkg6l717qY2xlwfutd1BX0kLa0bcmi7ouIyIxg+vHp5BSWLMWSm+hiOd4NdaaCtD9DEf/WoxyenMOX+27SsaEVkzqU3W+XH5JC5qEI9N2tMOn1lGwJMqLh6Cfg0gV8JpV7mEaj5srubVjXc6F+i1aEpoYy7uA40grSWN17NT2cejwccvt2YqZNQ9vJSVr4a1yxJYtKlU1w8HTCwhdga9OfVj47MDAo+2fybyE0VKrYrLHyz//9T/K5fffdcg8RBDlN3X7A2qoXd+5+RXz81lLHVFbmqaNfuZ2EiaU+PSc2JVNljJZlL+Simst/rS/7YB8fKQ1aWwZaKbpOJli90RxRqea99DdIkolsKIiCOL+ncj0rKytcXV25du0aKpUKYz1t5vZuhG9UOvuDqieyVkstZVEbAP6HkOnoYP/dt1i//TZZ+/eTPWcZzZ1/Ii8/Ev+AV1EqM6o8ls+g4SgVBQQeO1i8zaaeCS+93xJDUx32/hrA7StV6DHU1oatWyUt7ldekawiQJITbzsDrq2BiLPSodYGGLW1p9AviaW93Xirpys7/GIZtfIS8ZX0BQqCwPut3iclP4XVQaurfJ8vMqJKQ/rWO2SfjMHAx1YSp3hKHn/VRdCWYdTWnoLb6SiTKlCiK4NOnTqhVqs5f/78U5pdzXLu3DkyMjIYMGAAWlovxs//71y+f5mlAUvp59KP0Y1Hl9iX558EGjAoJ8MVGZSCjr4W9q5m0oabe8G6iZTNK0KjUbP9m08JOHKg8snI5FKvr6oQ9s0psYIu15LRcZQrmcn5BJ6MqXCY9g7t+anLT4SmhjLzxEzyVSVfA3SdTDAf7ooiLJPMAxHF2wtVGuZsCUBXW8aCkZ7Iygh6C+NySNtyG+26xliMalRmYPzEiCLsmSV9PWRJacuER7h75RLp8bG0GTaKy/cv89qR19CWafN7v9/xtvEuGk4kaeFC7n/yKYbt2lHvj9/Rtq04a5mTc4dr14eRknoSV9dPaNZsIXL5v98zdccO6cc9bFgNDObnB7t2Sf3sFhWL2shk2jRvvghLi86E3vqIhIS91S7zrArOHlZ493Iiwbwdtnl62Otf5MzBe6UPNDQET8/aALCKaFsbYNKjHnp31UzXeYV1pqYkXV321K7Xpk0bcnNzCQmRBKhG+jjiZm/C94duVVsQr5Za/k5tAPgfQxAErKZOoc7ChRSEhpL5+gLcLD8jL+9eURBYtd4rG+f61PPwxv/wPlSFhcXbTSz1GT6vJfYNTDm+7ibXD1XBJsLAAPbvhyZNpHfkq1el7d0/AYv60kNSUbO1cQ8nBF052YcjeKtnI1ZP8CE8OZdBi89zJbziPgZ3a3cG1R/Expsby1QRfBpoNCIqtQaFSk1+oZochYrMfCXpuYWk5ihIyVGgqaZkPYAmX0XKuhvk+Sdh0queZEgtf7H+nQ3b2oOWQM6Ff28WMDk5mQsXLuDh4YGLy4uZNUnMTeT9s+/jbOLM5+0+L9GfKIoiub6J6DgZo21T+sFf1IhE3kjFqZkFcrkMcpIh+mKp7F+Y71Wigvw5/9dGFHlVyLBbNoCen8PdoxCwqcQup6aWuHhace1gJLkZFfcUd3PqxnedvsM/yZ+3Tr1FobqwxH7DlrYYdaxDzsV4cq9JC1ILj98hOC6T74d7YGdaui9NlakgZUMIMkNtrCY0RSijN7BGuL4WIs5A76/BrHxvVlEUubxrC3rWFmxWH2fGiRk4GDnwe7/fqW9WHwBNYSHx771P6oqVmI0cgePyZciNjCq8fGLiAa77voRKlY231x84Ob72wvau1jQ7dkjVj/albSerz2efgbk5vPVWlQ6XyXRxd1+GiZEPISHvsm3h4mqVeVaVNkPrY9/AlCzHiWipRC6uW1z2ge3aSe+56tqAoioYd66Dtr0hg+51Rkc0ZMn9M5K/5VOgQYMGWFlZceXKFURRRC4T+HSgG3EZ+fx2PqLyAWqppQJezOXqWp46Jn37oF3HgZgZM8h87Sdcf5rFnZwlBAROxNtrA1paxpWO0WrQS2z/5hNunjuFR4+Hhtd6htoMmu3FiY2hXNkTTnZqAV3GNEJWUYBiZgZHjkCHDtCvH5w/D25uUinouv5w4gvo/yNyQ21MujmReSiCgrvp9Gpqy+6ZHZjy+3VeWXOFQZ4OaMkE1KKIRiOiFqUgTK0R0YgiBWJX1OIRxuz4hHqq6cXbNaJ0zN+Pf7BdIyLt14iIoohaFFFreLi/6HjpuhTtr1pg52JlyLi29RjRsi6m+pXLy6syCkhZF4IqOR/zkY2eXm/SEyI30sHAy4Y8vyRM+zhXSzq/U6dOBAQEcP78efr16/cUZ/n4iKLIgQMH0NHRoXfv3s97OmWi1CiZd3Ye+ap81vVZV2wNULw/LgdVYh5mwxqWeX5iVBb5WYU4uxc1St3aL4mV/M3+we/AHvSMTSjIzsLv4F7ajRhT+eRaT5HEZA5/CPW7gulDx+oOI1zZ/MUVLu66R6/XmlU4TF+XvuSr8vns4mfMPTOXn7r+hLbs4d+aaT8XlIm5pO++xx2lkuVnwnjZx5G+zUsr5GoUalLXhyAq1FhP90RuXLFdwmOTHglHP4X63aDlxDIPyS7M5nzcec6f3YNFVCznPVK4H32H/i79eb/1+5joSH2L6sxMYme/Sd7Vq1i/NQfLqVMrDOQ0GhVhYT8QHfMbpibeNHdfgp7us1cLfl6Eh0sKoD/9VAODXbkiWRp9+22VvSQSI7MIOhVDWMCr1O2QipXXMjy6/YSbd7cazTTL5VI/4F9f5yJm+6Cj9uX64cv49G1b8sB27WDpUrhxQ8oG1lIhglyG+YhGJC3155u8N5lt9B1jLy+mSbfPav5agkCbNm04cOAAsbGxODo60r6BFX2a2bL01D1GtqyLjcmLJa5Uyz+H2gDwP4y+uzsuW7cSM30G2TOW4/LlSCL4i4CA1/DyWo+WVsUryE7untg4N+D6/l24d+uF8Ih6nVxbRq9JTTGx0sP3UBQ56QX0mdwcnYpKFO3tpRLQDh2gd2+4cAHqtYc20+DKcmg6BJw7YtTegZzL8WQejEB3thkNbYzYPbMDH++6wYV7KchlAjJBQC4Tir6meJtM0MdErw8ZevswVN3EmMYIgoCWTIauloBMJiAvcXzRGEXbpc9F+2QCchnS90Xb5TIBQSi9XVY8J0rMTakWORh8n6/232TBkdsM9XZgfFtnmjqULUpRGJdDyvoQxEI1VpOaodfwxRQceYBxxzrkXU8k52oCJl0dq3zeA0VQX19fOnbsiLFx5QsSz5qgoCAiIyMZOHAgRpVkW54Xv/j+gn+SPz90/qE4W/QoudcTQUtWpvcfSOWfgkygXvOi/r/QfWDuArYPPeESw+8RG3qDLuMmEXvrJr4Hd+PdbxB6hpX8TGQyqfRxeQdJBXPczuIySFNrfbx6OeJ7KIrmnepg39CswqGGuQ4jX5XPd1e/4+PzH/Ndx++Qy6TMnSAXsBzThPuL/dHeF4G3qQGfDWpaagxRI5K25RbKhFwsJzZD2+4pCSlpNFJVgyCTDN8fCdZS8lM4GX2SkzEnuXL/Ciq1isFX6qIx1uft8fNpW6cdOvKHQWlhbBwxU6dSGB2Nw48/YDqoYlsORWEKN268SUbGFerWGY+r60fIZE8pyH1B2VFkQzm8clHUyvnsM0lFZvbsCg9TqzSE+SURdCqWxIgstHXlNG3fgKYdNxIWO4XE7Pewz7TG3LxNDUzqIUbmevSc1Ix9vxaglXaDS2t+oWXvzSXeq0sYwtcGgFVCp44Rxp3q0uAMtHd0Y0H4dlZ3+aTkz7WG8PDw4Pjx41y+fBlHR+k99KP+bvT8+Qw/HrnNjyNrf2e1PB61AeB/HG17e5w3/UHcu3PJ+Xgbdd/sTGyTMwQGvoGX19oK+0EEQcBn8HAO/vojYb5Xadiqban9bYc0wNhCjzOb77DrJz8GzvTE0KwCkZIGDaRMYJcuUhB47pxkMHznMOyZCdMvIugYYtrXmbTNt8nzS8LQxxYTPW0Wj/Gu0j0XqFoxePd1jC0OsmXAhOIHxefF6x1duBGXye+XotjlH8fmqzH41DNnfLt69Gtuj45WkVT/7TTSNt1Cpq+F9XTPp/eAWoNo2xmi29CM3IvxGHeqU60y1c6dOxMYGMiFCxfo27fvU5xl9cnPz+fo0aPUqVOHFi1aPO/plMnRyKP8fvN3xjQZQz+X0llUUakhLzBZ8v4rR1wiMigV+wam6BlqS2VOEWckm5ZHghbfA7vR1tPHvUcfnNy9+P39N/E7uJf2I8dWPkkLF+j9JRx4V1LD9HmteFfLvs7cupTAua13GfGBT5m9eo8y1m0s+ap8FvotRF9Ln/+1+1+xzYWgr8UaGxlj0uEnHWMMyhgr81AEBaFpmA1ugH7jp2hQfv03iDwHg34FM0eis6I5EX2CE9EnCEoOQkTE0diRcW7j8C5wwf/QWnpMeh0vpy4lhskPvkHM9OmIhYU4rVmDYZvWFV42M9Of4BuzUCrTaeq2AHv7mmiA++excye0bAnOzk840PnzcPQo/PgjlLMAlJdVSMi5OG6ciSMvqxBTG306vexKk7b2xYIuJlbr8fUbS2DQZLy9NmBqWrX3sapSr5klPv0acHVPNwrzDnN9/QZaTXr4f4aLC9jYSAHgtGk1eu1/MyY9ncgPSeXdlEmMcfqEs37L6eIzs8avo6urS4sWLbh8+TKZmZmYmppSz9KQ1zq4sPpcOK+2d6Z5nadgT1PLv54Xq2molueCzNCQukuXYDFxIupfz2N7wY2MTF8Cg6ZW2r/XuG1HTKxtuLZvZ7nHNOtUhwEzPMhIymf7/OukxlVinurpCfv2QVQU9O8PCo1UCpoeCSe+BEDfwxodR2Myj0aiKaxe74Kelh7vtHyHW2m32BP2YthCNK9jyvwRHlz5sCefDHAjOUfBnC0BtP/+JD8dvU3cmWhSN4SgZaWHzcx/RvD3AKOOdVBnFZJfgbR/WTzqC/g4vYBqjcilsFQ+2hXM5I3XiUqtOfXXEydOkJeXx8CBA5E9hVXfJyUyM5LPLn6Gh5UH83zmlXlMfmgqYn753n9ZqfmkxuU8LP+8fRg0qhL9f9lpKdy+dA73br3QNTDExrk+DVu1w+/gHgpyq2iS3HKSpIJ59BNIjyrerK0rp8NLDUmOzib0Qjky9n/jdffXmeIxhZ13d/LDtR+KX792B8Sx7nYiQV7maCflk77jbonXtpwr98k5F4dhO3uM2jtUbd6PQ1oE4rH/EVK/A4uFLIbtGcaAXQP42fdnCtWFzPSayc7BOzkw7ADv+rxL6ilfDM3Mad6tV4lhsk+dImrCBGQ6Ojj/uanC4E8URWJjN+HrNwZB0Man5fb/bPAXGys5HtSI+uenn0qG7zNmlNr1QM1zw4cXuLovAitHo3LVPHV0LGnhvREdHUsCAieRnX2zBiZXktaDXDCu0xpBZsmVAztRPuqzWmsI/1gI2nLMhzdEP9+IWYkD+OnmOpSasi1nnpTWraX/7weWEACzujfEwkCn1hailsfmxXtyqeW5IMjl2H7wPnZffIFsSxgWp2xIT79IZqZvhefJ5HJaDhhK/O2bxN0OLfe4es0tGf5uCzRqkZ0/+hJ7qxLbiU6dJFsIf38YOhTsfaD1VLiyAiIvIAgCpgNc0GQVknOueiIjAH2c++Bt480iv0WlJOSfJ6YG2rzRqT6n3u3K+tda4VnHBMXJGMRDUdwzkBHesw6yp9WX9JTQa2SOlrU+2eerZwwPUhZQrVZX2RdQFEX8o9P5ct9N2n13gjGrL7PLL47LYakMWXqBC/eqF4SWRWxsLNevX6dNmzbY14iKRM2Sr8rn7dNvoy3TZkGXBWjLy+69zPNNRG6qg2455ZVRwZKoUrH9Q+heMKkDDg8zngGH9yNqRLz7PQwK240YgyIvF98DVVxceVAKCrB3Fo+6Vjf0scG+oSmX94SjyKvaw9Usr1mMbzqeTaGbWOy/mJi0PD7dHUIrZ3OGjWqOSa965AUkF79uFNxNJ2PPPfQam2M2sEHV5lxNVBoVV+Mv893esfS2M2e0GMOaG2sw1zPng9YfcPSlo2wdtJWpnlNxNXdFEATu371N9I1AWg4chpbOw//59M2biZ05q9jfVbdh2f2bAGp1AaGh73H7zmdYWLSndavdGBuXLn/9r7Brl/T5ics/T56E06fhww8lETOKTNuvlVbzHPt5GwbN9qpQzVNX1xZvrz+Qyw3xD3iV3NwyFDufAJlcxsh3PRF0uqDQEjn36SclD2jXDu7ehZQnf338L6Fb3wzDNnb0yOiBdp4d24PWPZXrmJub07hxY65fv45SKb0Omuhp807vRlyNSOPwjSoortdSy9+oDQBrKYH5y6NwWr0KvROFCAqB2LANlZ7TvFsv9AyNuL5vR4XHWTsZSzYR5nrsWxzI7cuVeNkMHCj5BJ48KVlEdPtEMkreMxMK89B1NkW/uSXZZ2JQZxVWPNbfeGALkVaQxurgF88WQiYT6NLAih/1TZiALnftdXlXncuYjdfp9ctZNlyMJLvg6aw21jSCTMCogwPK2BwKo6qmMvuAqmQBRVEk9H4W8w/fotMPpxi27CJ/XI7Cy9GMJWO98f20J/tmd8TaSJcJa6+y7kLEY6+YqtVq9u/fj7GxMd26dXusMZ4moijy9eWvCcsIY36n+dgblR2gqrMUFNxJx6CFbbkPpZHBKZja6GNuZwiKbLh3AtwGSQEboCwoIOj4YRq2aouZ7UMRERvn+ri2bi9lAXOquLhi5gR9vpEsX67/VrxZEAQ6vdwIRa6Sq/urpnonCALzfOYxotEIVgevZsKObxGAX172Qi4TMO7uiL67FZmHIsi+EEfqplC0rA2wGNMEQV5zIhz5qnxORp/k4/Mf03VrV14/NpkdYhZuFo34qsNXnB51mrV91vKK2ytl/p4u7/oLPSNjPHs9LN9N2/g7CV98iVHnztTbuAGtMkzHi6+fH4Ov7yjuJ+zExXk2nh5r0NY2q7H7+yeyYwc0awaNGz/BIKIoZf/q1oUpU8jLKuTagSLT9t9Kq3maV7FaQ1+/Di28f0cQ5Pj5jycvL6ryk6qBkbkuFq2HIGjVJfB+DOnnzz3c+WgfYC3VwrSfC3IjLT6Ke4VVgb+RVVi997iq0qZNG/Lz8wkODi7e9rKPI03sjPn2UGitLUQt1aY2AKylFIbt2+OyfhP6AdokpRymMK/ibJ2Onj5efQZw7/oV0uIrtlcwsdTnpXktsG9oyvH1oVw/WMnD+IQJ8PPP0jv3nLmSaEJ6BJz8CgDTvi6IapGs49V/s2xm1YzBDQbz+83ficmq2HPsWaPJU5Ky9gZ5AcmY9HGm65utuPBRDxaM9MRQR87/9obQ9tsTfLI7mDuJL7ZVAiAFGvpaj5WtLS8LGJ6cw6Ljd+n1y1n6LTrHqrPh1Lc2YsFIT65/2pNVE3wY6OGAgY4WzlaG7JzRnm6Nbfhi303e3xGEQlX9N8xr166RkJBA37590dWtoJf1ObH97nb2hu1luud02tcp35Q+zz8JRDBoYVPm/sICFbG30x+Wf949CmpFifLPkDMnKMjNoeWAoaXObzdiDIX5efge3F31ybd4FRr0gGOfQVp48WZrR2OadqpD8Ok4UuOrFlAKgsCnbT+loUEXUnT20L/jPeqaGxTvMx/ZCG07QzL3hSNoyWrMQzNTkcnesL28deotOm/pzJxTczgVc4rO1t78kpLFWa3G/Dp0J0MbDsVcr3wBp+SoCMJ9r9Ki32B09PQByPPzI/GHHzDq0YO6SxYjMyw/sEhNPcvVa0PIL4jB02M19eu/hSD8t9/uk5KklvInLv88ehQuXiTx7S85tjmMDR9VXuZZVQwMXPD22oAoKvEPGE9BQdVKn6vK2CmW3E4ejkZQcviXNWhyi8rifXxAS6s2AHwMZHpamA9vgq2yLr2Tu7AmcNVTuY6zszO2trZcvny5+JlJSy7jkwFNiUnLZ92FyKdy3Vr+vfy33xFqKRe9Ro2o12oOoraGe7/NqDRj4tVnIHItLa7v31Xp2LoGkk1Eoza2XNkbwak/bqFWa8o/4e234aOPYPVqWH0YWk2Gy8sh6hJaVvoYtbUn91oCyoTq93jNaTEHLZkWP/nWhCZ4zaBKKyBpRSCKqCwsRjfGpJsjgiCgpy1nRMu67JnVkT0zO9C3uT1br8fS+5ezvLzyEgeC7qOs6Of4HJHpyDFqY0f+zVRUaQXVOvfRLODduGRWnglj4OJzdP/pDAtP3MHCUIevhzbn6kc92DipNSNa1sVEr3TZo7GeNqvGt2R294ZsvR7LmFWXScqu+lyysrI4efIkDRs2pGnTF6+MLiQ1hO+ufEd7h/ZM8ZhS7nGiKJJ7PRGdeiZoW5ct8hQbmo5GJeLsURQAhu4DQ2twkoSeRI0Gv0N7sGvgikNjt1LnW9dzwbWNlAXMz6niAoUgSAs8Mm3YPbNEKWibwS7o6Mk5v/VulbO3gTGZBPn3wVrmw8H4Fey487BCQaYjx3J8U/SaWmL1ajO0zB9fSj0hN4E/Q//kjaNv0OWvLnx8/mOCU4IZ2nAoq3qt4szIU3wbHUbPQhGDv6l+lseVXVvR0dfHu6+k6qlKSSHurbfRruOAw/ffIWiVHVyIooaIiCUEBE5CT8+eVj67sbLq/tj39m9izx7pT+pJAkC1Us2dn7ez/eWVbL9TTyrz7FS1Ms+qYmTUGC/PdSiVmfj5j0ehSH6i8R5FWxsa9uxFoboR8VpZ3Ph6kbTDwKDWEP4J0G9qib6zgrEp/TgTcPyp+Aw/sIRISkoiMjKyeHtHVyt6utmw9NQ9krMr9k2tpZZHqTQAFARhrSAISYIg3Hhkm5cgCJcFQQgQBOG6IAitH9n3oSAI9wRBuC0IQp9HtrcUBCG4aN+vQpFRkSAIuoIg/FW0/YogCM6PnPOqIAh3iz5erbG7rqVK2PeYio7CjFSd66T/+WeFxxqamdOsSw9unj1JbkblpqhyLRk9JzbFp78zoRfuc3BpEIX5qvJP+PprmDIFvvsOblmBmWNxKahJDycEXS0yDlbfGNXGwIY33N/gRPQJrt6/Wu3za5rC2GySlgWgzlJiNak5Bl5lZ2g8Hc34aZQnlz/swQf9mhCXkc/MP/3oOP8ki47fJSmrekHWs8ConQMIAjkXq7eqnZytIMWoPkqVmo+Wb+e7Q7eQy2R8MsCNix90Z+vUdoxrWw9Lo8ozcjKZwLu9G7N0bAtC72czePEFgmIzqjSPI0eOoNFo6N+//wtnmJ2pyOTd0+9iqW/J952+r1DZtjAmG1VyfoX+kRHBKejoa2Hf0BSU+XDnKDQZCEXjhvtfI/1+PC0HDC33Z9FuxFgK8/PxO7C76jdiWgf6fieZzV9dWbxZ30iHNoPrE3srnfCAyh+IcxQq3vorADsTQ7YOW0rHOh354tIXHAg/UHyMloUeVhOaouNYPYsRURQJywhjddBqRu8fTa/tvfju6nck5yXzWvPX2DxgM8dGHOPjth/TzqEd2tfXQdQF6PutdH+VkBYfx+3L5/HsPQA9IyNElYq4d+eizsyk7qJFyMuxRFEqswgKnkZ4xC/Y2Q7Gp+V2DAzqVeve/s3s2AENG4K7e/XPLS7znHuKYy5jKLCtR8dR1S/zrComJu54ea2lsDAJ/4DxKJU1ZzQ+eYrAxquzABVnI1PIOFcU9D0whFdV8D5cS7mYjW2PXMjnzfgxLPJd+FSu4e7ujr6+PleuXCmx/aP+bhQo1fx87PZTuW4t/06qkgFcD/xdg/0H4AtRFL2Az4q+RxCEpsBooFnROcsEQXjwJLIcmAK4Fn08GPN1IF0UxYbAL8D8orEsgP8BbYDWwP8EQXixTc/KYNMmSW5aJpM+b9r0vGdUdQRBoG6TNyh0FYlb8y15jyhQlYXPwGGoVSr8D++r8vhtBten2/gmxNxKZ+dPfuSkl7OCJQiwbBmMGAFzPwT9wZAWBqe+QWagjUkPRxR30skLTEadXYioVFc5UzCh6QQcDB344doPqDXPr44+/1YaySuDELRk2Ez3QK+BWaXnWBjqMK1LA87M68aaCT40tjPhl+N3aP/9SWb96ceV8NQXRiFMbqqLvocVudcS0BRU/JCRmafkr2vRjFtzhTbfHufr4zEk69jSTCeFQzN82DOzA290qo+9qf5jzWWAhz3bp7dDLhMYueISu/0rLk29d+8eISEhdOrUCQuLp2gR8BhoRA0fn/+YxLxEfuryU4WlhSCJvwjaMvQfZPf+hqgRiQpOoV4zC+RyGYSdBGWu1P9XhO+BPRhZWuHapkO517F2cqZR2474Hdpb9SwggNdYcO0Dx7+AlIdiGM06OWBZx5AL2+6hqkT594u9IcSk5fHLy15YGRnwS9df8LHz4ePzH3Mi6kTV51KERtQQmBzIz74/M3j3YIbuGcqv/r8iF+S81eIt9g7dy56he5jTYg7NrZoX20+QGgbHPwfX3uD1SpWudXXPNrS0tGnZfwgAyYt+Je/KFew+/xy9Jk3KPCcn5zbXrg8lNfUMjVw/o2nTn5DLH+9/499IejqcOCGJv1Rn7SYxMovj624+LPNMvM1A/1955YdueHZ/vDLPqmJm2hIP95Xk50fhHzARlapmSv3NzKDX0PrcSeyEQnOPI8tOo8rOkQLAvDx4pMeslqojNzHAonkcjfPro+tfSEBSQI1fQ1tbGx8fH27dukV6+sNFgfrWRrza3pkt12IIic+s8evW8u+k0gBQFMWzwN+bwETggVO1KfBgSX8IsEUURYUoihHAPaC1IAj2gIkoipdE6Wl0IzD0kXMeKI1sB3oUZQf7AMdEUUwTRTEdOEbpQPSFZtMmKWkVFSX1jUdFSd//k4JAB4fhgAxFD31i57yFMqF8tSlz+zq4tmpH4NGDFBbkV/kaTTs4MHCmB1nJ+ez4oQKbCLkc/vgDevSAmfPBugdcWgrRVzBq54DcQo+0zbe4/80V4j69SNzHF4j/8hL3f7hG4iI/klYGkrI+hLQtt0jffY/MwxFknYpBdS2Nz03mYhatw/Fz+ymMy0GVmo86V4n4jEoqcy7fl2webAywmemFtm31VpTlMoGeTW3ZOKk1p+Z25dX2zpy9k8zLqy7Tb9E5Nl2JIlfx/Fd2jTvUQVSoJQPyv5GrULEnII43NlzD55tjvL8jmJj0PGZ2a8jRtzvz1dSRCKKGqJv+NTKXZg6m7J3VAU9HM976K4DvDoai1pQOlpVKJQcOHMDS0pIOHcoPeJ4Xa2+s5UzsGeb5zMPD2qPCY0WlWvL+a25Vbs9bYmQW+dnKh+WfN/eCnhm4dAYgKTKcmJAgWvQdhLycUsQHtHtpNIUFBfhWoTS8GEGAQYtASxf2zICiRRmZXEanUY3ITivA/1h0uacfDL7PNt9YZnRtSGsXKVjX09JjcffFNLNqxtyzczkfd77SaSjVSi7GXeSrS1/Rc1tPxh0cx+8hv2NvaM8nbT7h+IjjbBqwidfdX8fF1KX0ABqNVKUg15HupwqRR1ZyEqHnTuHeow+GZuZknzxJ6urVmI0ahdmwoWWek5Cwl2vXX0KtzqOF9yYcHV994TLUz5t9+6TEVmXlnxq1htS4HG6ejy9W8wwPSJbKPNtnMuivWdSb9RKC7rNRYrawaI9782Xk5NwmIHASKlXNWNnMmQN/XX0dkBGvlcrVL/+oFYKpAfQH9EVbdp3Xkgaz5tyKp7L42qpVKwRB4OrVkhVLb3Z3xUxfm69qbSFqqSKPu3z1FnBEEIQFSEHkA7WBOsDlR46LLdqmLPr679sfnBMDIIqiShCETMDy0e1lnPOP4OOPpQW1R8nLg+nTpVimfXtwdKzeiuSzRlfXFkvLzmR3uoHRrjxi35xDvd83IitHAMNn0HDuXr3IjZNHaVG0gl0VnJpZMmxuCw4sCWTnj770neaOY5MyMi26upKWd48e8NEh+LAu7JmBMO08NtM9UYRloClQoSlQIxao0ShU0ueibeosBcpkNWLR96ilF0pH9Pic6RALSfwtwNCSIdOTI9PTQtCVI9OTI+hqPdymJ0emW/S5vH268jJ7Q0SNSNaRSLLPxKLXxAKLMU2Q6T6ZMb2LlSGfDmzK3N6N2RMQx8ZLUXy86wbfH7zFSy3rMr5dPRpYl21c/LTRcTRGp54JORfjMWrvgEKt4fTtZPYFxXMiNJECpQZ7Uz0mtndmkKcD7nVMSzzMPugF7NChA8bllMJVB0sjXf54vQ1f7Ath5dlwbidms2i0N6b6D3sIz58/T3p6OhMmTECrkoDnWXP1/lUW+y+mn3M/xjQZU+nx+TdTEQvUGLQsu7QYIDIoBUEm4NTMElSFcPsQNBkARXYSvgd2o62rh3uPPuWO8QCr4izgPloOGIq+sUml5wBgYg/9f4Sdk6VFng5vAlCnsTkNW9rgeziKxm3tMLEsmeW6n5nPhzuD8axrypyeriX2GWobsrzncl4/8jpvnXqL5T2X08quVYlj8pR5nIs7x8nok5yLPUe2Mht9LX061ulId6fudKrTCVPdKpouX1kB0Zdg6HIwqZq34LV9OwABn0HDKYyOJv79D9Br2hTbjz8qdaxGo+Teve+JiV2PqakP7s0Xo6tb/u/1v8zOndJ7batHft0atYb0hDySorJJjs4mOTqLlJgcVEpp0c/URp+Oo1xxa2ePjo4AnsOhSRMYO/aZzt3KqhvNmv3CjRtvEhQ8FU+PNcjlj9+zCtCgAXTrY8mFiGF0cNmGf5oXdYNjcLK1lQLAMrwNa6kcwcwRy8Y3KbjVnN43W3DY+zD96ver/MRqYGJiQtOmTfHz86Nr167FYmSmBtq83asRn+0J4ejNRPo0s6tkpFr+6zzu08x04G1RFHcIgjAK+A3oCZQVyogVbOcxzymBIAhTkMpLcXJyqnjmz5Dochaps7NhTNGzWp06UiD44MPLC3ReMJs3B/uRBKeexvD72eTMXknCl19i//XXZa4yOzRqQp0mTbl+YDeevQdUmiF4FGtHY15634f9SwLZ/2sg3cY3oUm7MmTsjY3h4EHo2BH+uA8jkuDUN8h7f11uz1x5iEoNGoUUDN5LvMO3Z75iQJ3+DKo7QAoSFWopmFQ8CCqlz5rcfJQFD/eV/ZdZEkFHXhwkPggYRYWawuhsDNvaYzaoQY3K0OvryBnd2omXWzniF53OxktRbLoSxfqLkXRsaMX4dvXo0cQGLfmz1YLSb29P5ubbrFjty7L4VLIVKiwNdRjZ0pFBng741DNHVo6QQqdOnQgMDOTChQv07VszBQE6WjK+GeaOm70Jn+8NYdjSC6x+1YcG1kakpKRw/vx53N3dqV+/fo1cr6ZIzE1k3tl5OJs483n7z6uU9cn1TUJuqotufbNyj4kMTsG+gSl6htpw9zgoMqGppP6Zk57GrQtn8ejZFz3Dqi0itB8xhjuXz3N9/y46jalGK7f7SLi5B05+DY36gLWk3d/+pYZEBqVwcUcYfac0Lz5coxF5d2sgSrWGhaO90S7j79pEx4SVvVby2uHXmHViFqt7r6aucV1Ox5zmZPRJLsVfolBTiJmuGT3q9aCHUw/a2rdFT6uaD9ypYXDiS2jUFzwrD8wBcjPSCT55lKadu2NkZEzkG1NAJqPOr4tKLbgpFMncuDGbjMxr1K37Kq4NP0QmK9vv8b9OTg4cPSoy641cbl/OJik6m+SoksGetq4caydjmnWug7WTMTb1jDGzMXi4aLd5M4SEwJYt0urtM8bWph8atx+4GTqP4Buz8HBfhkz2ZA8K77wDvXuMoIPrIVT55zixuxej23REvzYDWC6KiAjU6ekYtGhR7jFa7V/G/O5vtMidwdqje+k+uTu68ppVjG7bti0hISEEBgYWm8QDjG3txO+Xovj2YChdG1ujq/Xs/1Zr+efwuAHgq8Ccoq+3AWuKvo4FHB85ri5SeWhs0dd/3/7oObGCIGghlZSmFW3v+rdzTpc1GVEUVwGrAHx8fF6Y3LeTk1T2Wdb23bvh4sWHH9u2Sfv09KRVygcBYbt2UIHV0zPByqo72trmZNiEYTt9GqnLV6DfvDnmY8p+sGk1+CV2//AVdy6fx61j12pdy9hCj+FzW3Bo5Q1ObAglO60An/7OpR9urawkOe4OHSAkF4SlkkS9Y+uyBy4HQVuGXFsHuRG4WXninOHG/PDFtHftiZNJ1RYTRFFELNQUB4maAtXfso9F2xR/+1ygRlOoxnSAC0Yd6zy1si1BEGhZz4KW9Sz4ZEBT/roWzaYr0Uz93RcHUz1eaVuPl1s5YlUFEZXHRaMRuRqZxr7AeI4EJ7AMHZwjc+jbwo5Bng60b2BZpUDU0tKyxrOADxjXth6uNkZM3+TH0CUXWDTai5irh9HS0qJ37941dp2aQKlRMu/sPPJV+aztsxYD7bLVPB9FlalAcTcd426O5SoVZqXmkxqXS/uXiszFQ/eCjhHUlzwPA44cQKNR06L/4DLPLwvLuk40btcJ/8P7aTlgKAYmVcygCQIM/AWWtoHd02HSUZBrYWyhR4u+9bi6L4K42+nUaSz1PK45H87FsFTmv+SOi1X5JdQWehas7r2aiYcnMunIJJQaJRpRg4OhA6Maj6K7U3e8bbzRkj3m26NGDbtngJYODFxY5RKP6/t3oVGpaT10BAlffYXi1i0cV65Ap27dEsdlZPoSHDwLlSqLZk1/wc6u6r+L/wIajUh6Qq6U1YvK5qZfNl+PyUa3UMOJDaClK8fa0YhmnepgXa+MYO/vqFTw+efQvDmMHPlM7+VR7O2Hodbkc/v2p4SEvEOzZguRPe7fKNL6aTMPQy5HvUzbOr+RTRZHzHoyJGwHQlIS2NRmkx8lz8+fmClTEAsLaXD4ENoO5WT1G3TH2PJdkrOiGBndg62+mxnfemKNzqVu3bo4ODhw9epVfHx8kBV5s2rJZXwysCmvrr3KxotRTO78Yi1a1vJi8bivHvFAF6SArDtwt2j7XuBPQRB+BhyQxF6uiqKoFgQhWxCEtsAVYAKw+JFzXgUuASOAk6IoioIgHAG+fUT4pTfw4WPO97nwzTdSz9+jZaAGBvDtt+DtLX3MnCltj4+XKi8eBIQ//wzz50v7XF1LZgmbNi32Yn4myGQ62NkOITbuTxpP+5yCmzdJ+OZbdBs1wqBly1LH1/duhYVDXa7t3UGTDl2qHdhINhGenPrjFlf3RZCVWkDXVxpLghSP4uQkBYHdO0I9Ef56HQ6YweZtYPd45Q9ver/J0cijLLi+gF+7/1qlcwRBQNCVg64ceRUr3J4X1sa6zOruyrQuDTgemsTvlyP58chtFh2/S393O8a3c6aFk1mNBKOiKBIYm8m+wHj2B8WTmKVAX1tOz6a2yLT0cPdNoUe7+ujUrV4Q9zSygA9oU9+SvbM6MGWjL1//cYTO2hH079+/RgPNmmCh70L8k/yZ32k+DcwaVOmcPD/J+68i9c/IoFQAXDyspEDm1gEp+6ath1JRQODxQzRo2QZzu6qVND6g3UujuX3pHL77d9Fp7MSqn2hkAwMWwPZJcPFX6PQOAN69nAi9eJ9zW+8w6qNW3EzI5scjt+nTzJZRPo6VDCqp/67pvYbvr35PY4vG9HDqQWPzxjWzCHN5OcRchmErpVLWKpCfk03gsUM0bt8J4fxFMnfsxHL6NIy6dCk+RqXKJipqJVHRq9HTc8DLax3GRmWLwvxX0GhEMhLySI7OeljKGZONqlDK7GnpyEjNNSY0yoGPvjTG1sUEM1uDcisMyuTPP+HOHUlG9Fm+8ZZB3Tpj0agLuHvvG2S3dGnq9uNj+zsKgpQFfHX8ADpO2Iu26hSx+hPx9RqBz+XLMLh2YeEBuZcvEzNjJlrWVqjuJ5C8ZCkO335T9sEyGUKriTgd/ZY4cSl6J3JJdU/FUt+yxubzwBJi165dhIeH07Bhw+J9XRpZ062xNb+euMvwFnWqpI5dy3+TSgNAQRA2I2XirARBiEVS5pwMLCrK2BVQVH4pimKIIAhbgZuACpgpiuIDubbpSIqi+sChog+Qykd/FwThHlLmb3TRWGmCIHwFXCs67ktRFCt2JH/BeKVI9O3jj6VyUCcnKSh8pQwxOAcHqUH9QZN6QQH4+j4MCA8dgg1FUjmmptC27cOAsE0bqSryaWJvP4KY2PUkJu2nzo8/EjlyFLFz3sJlx3a0bUs+UAoyGT6Dh3N0xa9EBQfg7OFd7evJtWT0eNUNY0s9rh+IJDe9gL5T3Eurrrm5wZ5D8Fp3GBkNevfgyy8lxdDHwNrAmskek1nkt4jL9y/T1r7tY43zoqMll9G3uR19m9txLymHPy5HscM3lt0B8TRzMGFCu3oM9qyDvk71SkhEUeR2Yjb7AuPZF3if6LQ8dOQyujS2ZpCnAz3dbDDQ0UJToOL+jXRyzsdhMbp6D7GWlpZ4eHg8lSwgQF1zA/54zZtfFp0nRWnApjAtPLzV6Gm/GOU0x6OOs/HmRkY3Hk3/+v2rdI4oiuT5JqLjbIKWZfnqkFHBKZjZGmBmawAR5yAvpVj98+bZUxRkZ+FThvF7ZVjWdaJJ+85SFnDgsKpnAQGavyQJ0Zz+TiqptG2Klo6cjiNcObQyGP9TsbwfFIGFoQ7fD/eochDnYORQ5UWeKpNyF05+BY37g8fLVT7N/9BelAX5eHm0JOHNtzFs3w7rWbMA0GgKiYvbTETkEpTKNOxsh9Co0edoa7/gq001jEYjkpGYR3JUllTGGZ1NckwOKoX0iKGlI8Pa0ZimHRywqWeMtZMJuqYG2NoKjBsHbu0ruUBZKJXwxRfSau2wYTV7Q4+Jk9Mk1Oo8wiN+QS43oHGjLx974WLkSHjvPR38ksbhYfQLlprzXG31BnVPXsauNgAEIOfsWWJnv4mOkxNOa38jdd160tavx2Liq+g1alT2Sd7j0T75DdiH0C7KkwOHtjJh+PQanVezZs04duwYly9fLhEAAnw8wI0+C8/x87E7fDPsMXxPavlPIPzb1IJ8fHzE65XYFfwTEUUIDy9ZNhocLG2XySRvo0ezhC4uNS8uc/XaYERRpE3rfSju3SNy1MvouDak3u+/I/tb46JKqWTN7NexrOvEyE++fqLr3rwQz5lNtzG3N2TgLA+M/m7arK8vRcx9daGNLpwogPOFUj1tftXVSB+gUCsYsnsIBtoGbB249fFLwf5h5CpU7PKP4/dLUdxOzMZUX5uRLesyrm09nCsoqQOISMktCvriuZuUg1wm0L6BJYM8HejTzK6EqMoDMvaFkXPpPvbvt0JuWr1VytTUVJYsWULbtm3p06dyMZLqcuDAAa5fv46pd18WXUqhmYMJq8b74GD2fKX1o7KiGL1/NC6mLqzvux4dedX6gBRRWSQvD8T8JVcMW5WdHS8sUPHb3HN4dK1LhxGucHAe+G2EeWGI2gasf3cGWrq6jPtu4WM9cKbGxbD+3Rm0GjSczq+8Vr2Tc1OkUlDTOvDGCZBrI4oiexcFEB2WwTL9PFZPbkNH17KtLZ4JGjWs7Qspd2DmFTCuWhVCYX4eq2dOwqFhY9xPXkRUqXDZuQO5uTlJyYcIC/uR/PxozM3a0rDhB5iY/Psf6ESNSEZSkUBLVDZJ0Vklgz1tGVaOxsUlnNZOxpjbGZbK7O3dC0OGSIUivXo9xkTWrIHJkyUZ0YEDa+DOagZRFAkL+5Go6JU4Ob5Ow4YfPnYQOH8+fPihmmUz3kSuzIGCgWgLImOWD0f3Kdpc/BPIOnqUuHfnoufqiuNva9AyN0edkcG9Xr0x8PHBcXkFC83bX0e8c5IgliLkaTCc1ZgGdq7lH/8YnD59mtOnTzNr1iysrEq+9n2+N4SNlyI5NKczje1erAqWWp4dgiD4iqLoU9a+51vPUEuVEQRJuWv8eFi+HAIDISNDemP77DOwtZXsJcaPl46zt5c8jxYskILFghrwBbe3H0FOzk2ys0PQbdgQ+++/oyAwiIQvvywlO6ylrU2LfoOJDg4gMSLsia7btIMDA2Z5kJWaz/b5vqTE/s0mIjxcUmY7qYGgQuihBzPaQkT1jeEBdOW6vNPyHe6m32Xn3Z1PNPd/Eoa6WoxrW4/Db3Xirylt6ehqxfqLkXRdcJpX117lRGhiCZuE+Ix8Vp0NY9Di83RbcJqfj93B3ECHr4Y048pHPfj99TaM8nEsM/gDMGrvAKJIzqX71Z7rgyzgtWvXyM6uGX+sB8TFxXHt2jVatWrFW4PbsHq8D5EpeQxecoHrkc+vCCFflc/bp99GLpPzU5efqhz8QeXefwAxoWloVCLO7laShUHoPmjYE3SNiAj0JS0+Fp8KjN8rw7KOo5QFPLKfvMyM6p1saAUDf4b7gXD+F0Aqg9L2sQClhsnmFs83+ANJrTT2qqReWsXgDyDg6EEKcnOoHx6DMjGRugt/IVsWxnXfEdy4MRuZTA9Pz9/w9v7jHxn8bYpP5X/34sqVpheLevZuX0ng/La77Fzgy+q3z/Ln51c4vu4mIefiEDUibu3s6fGqG6M/bc3khZ156b2WdH65EU3a2mPpYFRmWeeOHWBuDl27PsbEFQr46iupxGbAgMcY4OkhCAINGsyjbt0JRMf8RkTEoscea8oU0NeXE5L5KlkZqdTN3kWerjlHF5z9T9sJZO7bT9zb76DfrBlO69ehZS51I8nNzLCcPJmcU6cq9kZu9TpCYRp1WiZjqjIidOuFGp+jj48Pcrm8lCUEwJwerhjr1dpC1FI+/+3lnX84JibSquaDlU21Gm7eLJkl3FVkv6WjAy1blswSVrdNzs52MHfvfkf8/e00Nm6GSe/eFEybSuqKlZIozOjRJY736NmXyzv/4vq+nQx4c94T3atTU0uGz23B/iVB7FzgS78p7jg2LbKJsLeXfhgqNexWglyAZjchZj/YvfFY1+tVrxctbVuyxH8JfV36YqLz3ym3EgSBNvUtaVPfksSsAv68Es3mq9G8vuE6dc31GeBhj19UOtciJSNaj7qmfNzfjQEe9tXKkGlZ6qPX1JLcq/cx7u6IrJrlpp07dyYoKIiLFy/WWBZQo9Gwf/9+jIyM6N69OwA9m9qya0Z73th4nTGrL/PVkOaMbv1s1YZFUeTry19zL/0ey3ouw96oar1lAJrCIu8/dytkuuW/5EcGpaBroIVdQ1OIuw7Z9yVhJYqM380taNSu4xPdR7sRY7h98RzX9u2ky7hJ1Tu56RBoPgLOzIdGfUk2asynp+7Q1VyL+tH5JEdnY+30nFa6k+9IaqVNBkrqpVVEWajA98Bu7M2t0Tl9CdPPp3JHtpwUv+Po6trh1mQ+9vbDEATpf0Oj1pB2X/KCk8lkyOQCgkxAJi/6kJX9/fPyBUxXqvj0Xhx5ag1uhnq8bGtBRlIeydEP1Dilnj1lgZTZk2vLsKprRJO2dljXM8GmnjHmdgbIHkOpuLBQygAOHQrajyOQ+ttvUu/G6tUvpFeTIAg0cv0UtTqfiMjFyOX61Ks3tdrjmJvDa6/BylWtWPNuMyLjwmhwdxv3hNEEHrqHV/+azVr9E0jfto2Ez/6HQevWOC5bisywZAWMxfhxpP/xB0kLfqLe5j/L/v9yagfWblhFrSe06dt43WyA75ULtGxTc16yRkZGNG/enICAALp3746e3sPqKHNDHd7q6coX+25yJCSRvs1rbSFqKUltAPgvQi6XSkHd3WFq0ftAUlJJcZklS+Cnn6R9Li4lA8LmzUFLS8okltW3qK1thrV1TxIS9uLa8ANkMl2sZ88uKQrziDyynqERHj364HdoL53GvIqJ9ZOpilnVNWbE+y0lm4glgXQd1xi39kViFImJMG2apPn9x0awt4ED74KWHniPq/a1BEHg/Vbv8/L+l1kVuIq5reY+0dz/qdia6PF2r0bM6t6QIyEJbLwUxcoz4TSyNWJu70YM9HCotDy0Iow71iE5JJU8vySM2lY9qIGSWcD27dvXSC/gtWvXuH//PiNGjCjxZupqa8yemR2YvdmfD3YGE3o/i08GNi3TauBpsOPuDvaG7WWa5zQ61qleEFYQkoqoUGNQgfiLRiMSdSMVp2aWkthS6F6QaUOjPiRHRRAdHEDHMa8i1yr/STpj5y50GzZA36N8M3oLh7o06diFgKMHaDVoOAamZtW6F/r/CJHnEHdP50PdH8hRqJg01YfLS25wbusdhr3b4tkHOxq1pFKqYwADfq5WsHDj5FHyMjNonhxH3vsOxNssRZ5uQIP6c3F0nIhcLi2oiBqRe75JXNkXTmZS9cvaZTIB4ZGgsETgKBOQyR8JJssMKGUVnCs8Mr6sxPmbtRXkaWtwUgl8EBJNzKIQDDJUwMNgr3Ebu+KePQv7xwv2yuLUKalKZvjwxzg5PBzefhtat37M2tFngyDIcGvyDRpNAffCfsDAwAVr6+orFs+ZA8uWCYQrJ2KsmIdByjmsLJtxca8GezdbbF3+OwugaRs3kvjtdxh27kTdX39FpqdX6hiZvj5Ws2eR8Oln5Jw4gXHPnqUHEgTwmQSH5tHqVRtCwhKRH5Sh9CxEW6/mvL7atGlDYGAg/v7+tGvXrsS+cW3r8de1GGb96ccH/ZrwekeX57YYVMuLR20P4H+MwkLw938YEF64APeLKvCMjKSA7+5dqff9AQYGsGqVFASmpp4lIPA1mjdfjK2NJEChzsoiYuRINHl5uGzfgbbtw0AvKyWZ3958A6/eA+g2cUqN3IMiX8XhlcHE3kqn1QBnWg185EUtLk6KbKe8Dm0SIOwUDF8NHo8n3/2/i/9jb9hedg/ZTT2TejUy/3862QVKjPVqxnNMFEWSlgQgFqqxfbtl+VLs5VCTvYDZ2dksWbKEOnXqMH78+DLfKFVqDd8dusVv5yNoV9+SZa+0wNzw6Rp33ky9yfiD4/Gx82FZj2XIZdXLlCavCUaVmo/dvFbl/nwTwjPZ8YMvvV5vSiMfW1jkCVaNYNx2Di9fyO1L55iybD36RmUH2flBQUSOehlBTw/HZUsxbF++4kZafBzr35lOiwFD6Dr+9WrdCwC3DsKWMSxSDcOs/+e82t6Zm+fjOfXHLWn+5fQ4PjXOL4Tj/4OXfgP3EVU+Ta1S8ttbr2JZ5y7mLdJBR06dOq/g4jwTHR1JMVAURSKDUriyN4LUuBwsHAzx6umEjr4cjVpE1Iho1CKaB59LbNMU7xPVjxxX/L2m+HuNumibpqyxHhnnkX2aR65dfG7RmKJapEAOvw40wylFRd/gAlb0MsZNJednI0tsnU0wtzcsrexcg0yZItn3JSdL7eDVomNH6c1x8GDYs+epzK8m0WiUXLkq9Si2bXOwOGNcHYYOhfPnYdW8r4m7eoF+mQJn6r6G3MKC0V91lnxB/+WkrFxF8i+/YNyrJw4//VRK2+BRRJWK8MFDAKi/dw9CWZ7HBZnwUxNo/hJnrQdTf68+95vn0Wpczfatr127luzsbGbPnl1sCfGAzDwl7+0I5EhIIt2b2LBgpCcWT/k9q5YXh4p6ACUPs3/RR8uWLcVaqo5GI4qRkaL455+iOGuWKGpri6IkLVPyo169B8erxHPnzjKeLQABAABJREFU24v+Aa+VGCf/9m0x1LuFGDHqZVGtUJTYd3DxAnHh+OFiXnZWjc1bpVKLx9eHiEumnhCPrwsRVUr1w52TJomivr4oxkWJ4roBovi5uSiG7H6s6yTnJYttNrURZ52YVUMzr+Xv5PolijHvnxXzbqU+1vk7d+4Uv/rqKzE7O/uJ5rFt2zbxyy+/FFNSUio/9nqM6PrRQbHj/BNi6P3MJ7puRWQUZIh9tvcRe2ztIablp1X7fGV6vhjzwVkx42hkhcdd3HVPXDr9pJifUyiK8QGi+D8TUby+XsxJTxN/GTtEPLZmWYXnR016Xbzdtp0YNmiwGOruIWafPl3h8QcXLxAXjhsu5qRX/57uJGSJOz8dKKr+ZyZqYv1EURRFtVoj/vXNVXHd++dFRb6y2mM+Nkm3RPFLa1Hc8or0YlpF1OpC8fKxD8VD+1zF4yfqiwGXXhNzcyNKHBMdmipu+/6auGTqCfH3Ty6Kt6/eFzXqql/jebMsMkG0PekvXk7JEtUqtfhbTJJoe9Jf/COu8v+vJ0WlEkVra1EcPbqaJ+rqlv0GqKf3VOZZkyQkHhSPn6gvxsfvfKzzT5+WbnXZz9HiTyMHiCe7tBV9R0wTl04+Iu5fdF3UVOPv+5+GRqMRExcuFG82biLGvjtX1Cir9hqSdeyYeLNxEzF927byD9ozSxS/thM1uWnitoUrxKj3T4sZ4Yk1NHOJGzduiP/73//E0NDQMvdrNBpxw8UI0fWjg2Kbb46LV8If7722ln8ewHWxnHipVgTmP44gQL16MGYMLF4sed6WRXT0g+Pl2NsNJzX1HAWKhOL9eo0a4fDdd+QHBpL4VUnVT5/BL6FSKAg8erDG5i2Xy+g+wY1WA124dTmB/UsCUeQXTX7uXEn9c+VvMGYL1PWB7a/DnSPVvo6VvhWT3SdzOuY0l+Iv1dj8/7GoVRB5XhIJ+T97Zx2eVfnG8c95Y93dyUbXgjG6u7slxEAaxZ8diIm0oqJIKiAhJSEdI7bRsQ3Gurvjrd8fh16Pke5zXbvYzhPvcw7bOed+7vjWELqNLZAYaZF7Mq5a49u1a4dKpeLUqeon2YeHh3P16lXatm2LuXnFek1DvB3Y9HpLihRqBv0YwL6riRWOqSpqjZoPT35IUl4S33f4HlMd04oHPUJ+cMXafyDm/9nVMRZ3+a/vBEEC9Xpz8cA/qJRKvHqWXRI+PzCQvFOnMJ88Gac1q9GuU4eYqdPIOXSozDEtB49ApVAQuHNrlc6nSKli+saLLJFNBAMrhL/fBGUREolA2+Ge5GUWcX5fVJXmrDYq5Z3QT/1Kh35qNBqSU/Zz5mxPciWbUKbLqVc0h6YtV6Gn5wKI3ti/F51n5+KL5GUW0XFMPUZ+6oenr02VPeTPikKVmp9iU2hjYoCfuSESqYTx9ha0MTHgk1txxBQWP9HPP3lS9PzdlVWqkJwcseDL3WRB6R0Pmp6eGPpSzYJiTxMry+4YGjQkImIparWi4gGP0K4deHnB0l8caWhpy0ULUxwm9qVO5C4ir2dx6VDME1j1s0ej0ZD89TekrfgJk6FDsPvm69K9eaVg0Lkzus2akbJsOeqyKo77TARFPsKVzXgO9iNdlkXsxotolDX3DK1Xrx5GRkacPXu21HZBEBjn78K2Ka3QkUsY8ctplh26+VBRt1r+e9QagLU8hFMZtS0ePG5rOxhQk5iw/aE+Rt27Yf7aa2T+9RcZGzfdO27p5IJLM28u7NuFsrjmHvyCINCijyudxtUnPiyT7QuCyc0oFLUB+/cXEx6VAoz+C2wawaaxEH64yp8ztsFYHAwc+DbwW5TqMizk/wLFebBpNKzuDUG/1di0gkyCgb8tRTczUSTmVXn8g7mAubm5FQ94BIVCwZ49ezAzM6N168on6Dd3MmXXtDZ4WBvyxvpgFh8MQ12DD9RVV1dxNPYob/u+TVPLplUer9FoyDufhLabMTKzsmPgslMLSI/Pw+VuhdAbO8G5NQq5IZcO7MHNyxczO/syPyN5yRJklpaYjhqJzNQUp9W/o1O/PrEzZpK9b1+p40xt7anftgOX/t1LXmZGpc9pwf5QbiRk89HQVkj7LYOUG3D0awBs3Y2p62fDhYPRZKXkV3rOahOwFOKCRaF6g4rzmzOzggk+P4wrV6ZQlJnJ7X0OWF7tjn3PKQCkxOSw54dLbP02mPT4PNoM82D05y1p0MbuiYZKPgk2J6aTVKxkhvP9jQeJILCwniMaYHZI9BOtTLh1qxj22bNnBR0LC2HxYrF09scfQ5cuMGyY6PfT0RHbjYyqXjHtGSAIEtzcZlFQGE1CQtU2VsTxYtpjSAgU2U9AotEQdPQAzfrXxzLlIgFbb5J4O+sJrPzZoVGrSfzsM9LXrMF0zBhsPvsMQVr58FlBELB6ew7KpCTS168vvZNdc7DzgqBVNLFvyukmNzHO0iXh3xs1dBYglUpp0aIFERERJCUlldmvkb0xu6e3pV9TO77/N4xxq86SnFMDJeJreSF5sZ4qtTxx5s8XNz0fZfgDmsZ6ei6YmLQgPuGvEg9xyxnT0W/blsT588k/f+Hecd++g8jPyuT68aobYBVRv5UtfaY1JTutkJ1LLqJSqOHddyE9XdRx0jGGMdvAwgP+HCV6sKqAllSLOT5zuJV5i61hVX+wvhTkJMHvveDmATCyh1NLQVX1Xeay0G9hiyCXkPMEvIAajYZCZSH5inxyi3PJLs4mqyiLjMIM0grS+PfYv6Snp+Pf2Z/UolTicuOIyYkhOjuayKxIbmfdJjwznJsZNwlNDyUkPYTrade5mnqVpKIwPhioS6emeSw7dYDR6//gePRpziScISA+gFNxpzgRe4Ljscc5GnOUw9GHORR1iH+j/mV/5H72Rezjn9v/sPv2bnaF72LHrR1sv7md1VdXs+zCMrq7dGdUvVHVuibFUdmo0grLLf4CEHklFUCUf0gOEXXsGvTnxomjFORk4927bAHsvFMBFAQFY/7mG/eKJUiNjHBa9Ru6TZoQN3sOWbt2lzq25eARqJQKAnduqdT5nLyZysoTEYxp6UTn+tbg2U0s8HRqMcSKed/+A92RSiWc2nKrUnNWm+QbojB9g/7QsPwqI/n5EVy+MoXg4GEUFMTgbjqDm7+YoYkyxvfzL8lIzGP/yqtsnh9IQngWLQe4MfaLVjTt5IhMXvVcrmeNUq1heXQyzQ31aGNq8FCbk642n9ax40RGLmvj057I56vVsG0b9OgB+mXVp1IqxSqfnp6i1dOkCZw9K5bNVijEgmJnzoj/Jta8d/9JYW7eASOj5kRELkOlKqry+GHDwM4Olh3yo/ntaG5E3oLuXWhGIDqFGez/+TKFuTV333+WaJRKEt57n8yNmzCfPBnrD95HkFT9lVjPxweDDh1I+2UlqszM0jv5ToKUEIgKoF+vERw3Oo/ieCqKpKpveJaFl5cXMpmsTC/gXQy0ZSwa3oxvhzQhOCqDXktOcOJmSo2to5YXh9oiMLWU4MEqoA4O4mZocbGoPXh3IzQ+YQs3bryLt9cmTEwezi9VZWURMXQY6oL7RWE0Gg0b3p9FcUEB4xf+iKSKhSwqQ+SVVPb8cBnfPq606OMKbduKJ3Hrlhjak5cqGjHZcTB2Ozi2qPTcGo2GSQcmcTPjJrsH7sZY27jG1//ckhwCG4ZCfioMWSWGB/4xDAasgGbVM05KI2P7TfKCk7D9XwukBpVLUs9T5JGcn0xKfgpn/z1LelQ62h20SVWnkpKfQkpBCin5KRSqSt/l1Ffo0zWuK/F68ZyzKqml9CypY1KH9b3Woy+vXpXV9C1hFFxOwfaDlki0y/5727nkAjnpRYz+rCUc+xaOzEcz6wZrPv8CiVTK2G+WlloQR6PREDl0GKr0dNz27S1RMEGdl0fMm1PIDwzEdv58TAaVNCT3/biY0IDjTFr2KwamZmWuMSOvmB5LjmOgLWP3tLbo3pUMKcyCH1uJFThfPw5yXc7vj+L09nD6TmuKU8OKw3mrjEoJv3WBzGiYchYMLEvtVlScSkTEMuLj/0Qi0cHZaTIO5iM4O2YCZ3Wh7aDx5BQ2IPRMIlItKU07OdC8qxPaei92sY2tiem8dSOa1Y1c6WFZ8j6p0WgYcek2gdl5HPGti7Oudo1+/pkz4O8P69bBmEcLQKvVsGULfPQRhIWJVT6//BI6d67RNTxL0tMDuHBxLJ4eH+HoOL7K47/+Gt57D4KbjCTAMxs7Lx96DR7NpVdmcb7pTBwbW9H7zSYvTDhyaWiKi4mb+y45+/ZhOWM65m+88VjVMQvDwojoPwCzCROwnluK5FVxPiysB3W6wpDf+PHUMtr/Uwd9K2Ocp/nX2LXcuXMnly9fZvbs2eiVtpP/CDeTcnjrj/PcTM7lzfbuzO7qiewFizaopXzKKwJTawDWUiHXroGvL7RuDfv3g0QCSmUeJ0/5Y2XVkwb1vykxpjAsjMgRI9Hx9MRp7RokWlqEBBxnz5Jv6TfnfTxalF0l8HE48OtVwi+mMPyDFpgFH4G+fR9+E8hJhN97Ql4avLJDDM+oJCHpIQzbNYwxDcYw13fuE1n/c0fECTHsU6oNozaBvZe4I/BTG1AWwVtnoYaMeUVyPkkLgzHq6oysnQUpBSn3jLu7hlxywf2fk/OTKVDez7swUBjQLbYbESYRpDunY6lriaWeJVa6VpjomCATZAiCgESQIBEkoIH44/EUpBVQp08dtHW1kQiSe30E7vcVBAEJ97+XCtKH+twdczU2myWHbiEVJMzuVpcm9qZlzvHQ94IECZKH1meua45cUj1jQF2sIuGLs+g2tsBsqGeZ/YoLlfw25wRNOjnSenAd8f9Vrkek91ds/eoTekyZRcP2pb8c5xw6ROxbU7Gd/wUmZSRbqQsKiH1rKnkBAdh89hmmw4c91J6ZmMCqWa/TvHufMqsEazQa3lx/nkMhSWyf0ppG9o8YFeGHYd1AaDUNun2BSqHmz3lnEQSBER+1QCqr4Rea4wvg8DwYuhoaljRqVap8oqN/Iyp6JWp1IXZ2I3F1nYaW3Jy42XPYG36NQhMLpAYTkUikNGpvj1d3Z/SMXvzKfGqNho6BoQAc8a2LpIyX6rjCYjqcC6GRoS5bm9Ups191mDtXjOpMTgYTkzsHNRrYt0/c2bxwARo0EMNd+vd/LjX+Hpfz50eTm3eT1q2OIpVWbAg8SHo6ODrCcJezvCl5heMN6jDs4y/RPXmGC5vPE+YxDP+B7nh1fzGrYquLioibMZPco0exevddzCeMr5F54997n+w9e3Dftxe5nV3JDnv/B4G/wuwb5Gjp8OWv7/NW9DCM+7hh2Kb0EPuqkpSUxIoVK+jSpQtt2lROLqigWMXnu6/x57kYvJ1NWTqyOfZV0POt5fmmPAOwVgewlgpp2BCWLoXJk+Hbb+F//wOZTB8rq14kJ+/B0+NjZLKHvRQ6np7YfTmfuJmzSPpiPraff4anX2tOWlkTuHMrdXz9n4geTZthnkRfT+fohhAGzuyJ0KCBuOjRo8UHvaENjNspegLXDYRXdov5gZWgnlk9BnkM4s8bfzLUcyiuxq41vv7nikubYMdbYOYm5lGa3nngCwK0nQ1bJkLIbjEMrpIUKgtLGnN3vk/NT2WISVscjuQwLvoDFJKH8y11pDpY6lliqWtJfbP6tLVvi5We1T0jz1LPkqCDQchvyJnZdSYGBgZlrELk6tWr3Ey8Sc+ePfFr6lfly1MaLW2hs2trJq8N4rO/8vi4jyvj/J2fuvZSwdVUNMWqCou/xFxPR63S4NrEHNIjIPEKdJtP8D870DcxpV7rdqWO06jVpCxZipazM8b9y/7/l+jq4rDiR+KmzyDxk0/QFBdjNva+W8bExpYG7Tpx6eBefPsNxsCspMfur6BY9l1L5L2e9UoafwDuncB7AgQsh3p9kTr50WaoB3t+uMyVo7E061JGYnN1SLou5hw2GFDC+FOrlSQkbOF2xGKKi1OwtOyOu9vb6Ou7AZDw+x+cvFVMtp4cucyX+q3t8e3lgoFpVTUKnl8OpGYTmlfIjw2cyzXq7HW0+MzDntkhMayKS+VVh9K9qFVFoxHz/7p0ecD4O3VKdGmdOAEuLrB2LYwadb/Qy0uIm/tsgoOHERO7DpcqisObmcH48fDrLz58rsnCwM+I43+sZuSn3+B+8CBZmVc4swNs3I2xq2PyRNb/pFDn5xM7dSp5Aaex+fQTTEeMqLG5LadNJXvPHlKWLcfuqy9LdvCZAGdXwIV1GLadTbOObQjcchXvfRp0G5iXm6ddWaytrXF1deXcuXP4+/sjrcTvuK6WlK8GNcHf3YL3t12h15ITLBjalK4Nyn921PLiU2sA1lIpJk2Cgwfhww+hfXsxxMbOdggJCX+RnLwXO7uS+ldGPXpQOPk6aStXotOwIabDh+HdewCHf/+ZuNDrONRrWOPr1DPSotXgOhxZF8L104k0nDtXfJrt23e/IoCJI7xyxwhc2x8m7AXLsr0kDzKt+TT2Re7j+6DvWd55eY2v/7lAoxG9HEe+AJe2MHwd6D5SgbLBADD7Ak58D/X7UaxW3DPsHvXc3f0+uSCZnOKcEh+nJdG6Z9iF1ImnUZAb883eR9novsFnqWeJodywQkPKqIMR169eJyAggG7dyhZELiwsZN++fdja2uLr61udq1Qmrhb6bJ/SilmbLvLJzmvcSMjms/4N0ZY9vRfO/OAkpGY6aFUg4Bx5ORVtPRk2bsZwZi0AqcbeRF6aT+vhY8sUfs/eu5eisDDsFiyosGKeRFsbh2VLiZ09m6T589EUF2M+aeK99paDRnDjxBHO7dxCp/EPv6xGpObx6a5rtHI3Z3Jbt7I/pNs8uHVIrMr5xklcGlvg3Micc7sj8PC1Rt+4BsIMVQpxfh1j6P39vcMajYbUtMPcuvUt+fm3MDb2onHjHzAx9gaguEBJ4PpzXDlrTIGREpnUiDFfTMDM7uUKI9doNCyJSsJZR4t+liYV9h9pY8bu5Ezmh8fT2cwIV73H/z+6dEnUcH/vPeDiRdHj988/Yu7CDz/Aq69COdpuLwsmxt6Ym3cgKupnHOxHIZOVrt9ZFqIwvJSVvMEQmwIO3LzKrfPncP7qS/KHjCK35Ycc+PUawz/wRdfwxbieqtxcYl5/g4ILF7D9+itMBgyo0vjMpHyC90UilUnoMLpeiXa5nR2mY8aQ/vvvmE0Yj47nI+8UlnXF52nw79B6JoPrDuZVz/E0vuRB+rYwLCc1rpGNQj8/PzZu3EhISAgNG1b+HatfUzua2Bsz9c/zTF4bxITWLvyvZ72n+tyq5elSG+xbS6UQBPj5Z7Ea6MiRkJEBxsbe6Om5kpBQdhEHy5kz0G/ThsQvviD/wgUadeiKjqERQbu2PbG11m9li72nCQHbwsnrNVhMZPzmkTBVM1d4ZZeYz7amL6SFV2puc11zXm/yOsdijxEQF/AEVv+MUSlg5zTR+Gs8DMZsfcj4yyrK4qdLP/FhwCe8Ye/AIEkSbf/wx3u9Nz229mDs3rHMPjqbr899zZrrawhMDCRfmY+rsSu9XXszvfl05rWex89dfmZbv22cHHGSoDFB7Bu8j3W91jFj8P+QWevhFelOH7c+tLRtibuJO0ZaRpV6OJqbm9O4cWPOnTtXbkXQI0eOkJubS58+fUoI59YEhjpyfhnrw1sd3dkYGMOolWdJyal6UYbqoEwvpCg8C31v63KvmVqtIfJqGk4NzZFIJaL8g21Tzp8MRCbXokmXHqWO0yiVpC5bjraHB0a9KiqzKCJoaeGwaBGGPXuQ/N13pP700702E2sbGrTrzOWD+8hJT713XKFSM3PTReRSCd8Pa4qkvDwZbUMY8AOkh8OhzwFoM9QDlULNmR23K7XGCjm5GBIuQp+FoC9WTM3KvsT5C6O4fPk1QEWTxivw9tqMibE3ymIVFw5Es+6DU1wMLsQg/yJqZSxthg976Yw/gJMZuVzIyWeqsxWySuQ0CYLA9/UckUsEZoREo6qBdJStW0Ei0dB/z2vQvDkEBMBXX4l54FOm/CeMv7u4uc1CqcwiOnpVlcd6ekLfvhp+lEzF7WYC5g5OnPxzLXJXV2ynvkaDoGUUZBdy8PfraF4AKQFVZibREyZScOkS9gu/r5Lxl5WSz6E11/njs7OEnE7k2ol4MpNKrzJs8dpkJAYGpCxaXPpkPhPF3OHwQ8gkMia2fo3fLLdTfCuL/PPJVT+xUvD09MTU1LTCYjCl4WKhz9Y3WzG+lQu/n4pkyIrTRKXVXKGaWp4vag3AWiqNsTFs3AhxcWI4KAjY2gwhMyuQ/PzSdZIEqRT7Bd8ht7EhbvoMyM6mWbfehAedJS3uyegKCYJAh9H1UCnUnNgeIVZ5O3ZMrPL2IBZ1YNwOUBWLnsDM6ErNP7r+aBwNHV8+WYjCbPhjOFxYB+3egUG/gOz+rvzp+NMM2jGIHy/+yOmE02Rp6+OAnO4KmNpsKp+3+pwVXVawpe8Wjg0/RvCYYA4MOcCGXhtY1HERH7T8gMlNJjOgzgBa2bfCw9QDY23jh4wUQRAwbG2PIiGPomqWHL9bETQgoHQDPT4+nnPnzuHr64u9fc3kXpSGRCLwTvd6LBvZnGvxWfRbfpIrsU++jHr+ebEMuJ5X+dIESRHZFOYqcG1iAVlxEBdEvnMPrp84QoP2ndAzKt1Iydq5i+LISCxnTK9S1TxBLsf+u+8w6teXlMVLSFm69F4V4ZaDhqFRqzn39/3NpKWHbnIpJpMvBzbG1rgSOSmu7aDFa2KYVeRJTKz1aNrZkZCABJIisiu9zlJJvArHvhErfjboT35+FFeuTiMoaBB5eeHU9fwcvxZ7sbTshlql4crRWNZ9dJqAbbcwzI3B99JCdNwy0DU0oknn0g3rF50lUUlYa8kYZlN2MZ9HsdXW4gsPB85l5fFr7GNWIoyNZevyeNqrj2J5YAO8/76o3/e//5VTDvTlxciwEZaWPYiOWYVCUXmplbvMmiWQqjbnz8O2tBn5ChkJcVw98i9mEyZg5W6OZ+QOoq+nE7z/KeluVhNlWhpRr4ynKCQEh6VLMepRub+/7NQCDq+9wYZPznIzKJkmHR0Y9r4vCBB6tvTKsFITE8xfm0zukSPkl1aLol4f0LeCINEob2vflrQGCkL0IsnYHY4q5/FlsiQSCS1atCA6Opr4+Pgqj9eWSfm0X0N+HutNdHo+vZeeZNelqs9Ty/NPrQH4FIgrLGZZVBJr41LZkZzB0fRsLmTnE5FfRFqxEuULsIN2lxYtxA3VrVtFj6Ct7UBAUq7ukNTEBIfly1Hl5hI3YybNOnVDJtciaNf2Msc8LibWevj0ciH8fAoRrYaICSHffluyo3UDGPc3FGWLnsDsim90d2UhwrPC+Svsrxpf+zMhK04sjnP7KPRbBp0+vFccoVBZyDfnvuG1f19DX0ufjX02cmjoIf7ss5Gljd/iw8gbvG7ShIEeA2lj34a6ZnUx0zETC61UA73mlkj0ZdUWhrewsCjTC6hWq9m9ezd6enp06tSpWvNXlb5N7djyRisEYMhPAey4WL3zqgwatYa888louxsjqyC3LPJyKhKJgFNDM7ixC4BLyUaoFAq8epWe16cpLib1hx/QadgQg2pUThRkMuy++grjwYNI/XEFKd9/j0ajwdjKhobtO3PlkOgFDIxM54cjtxji7UDvJraV/4Aun4Kpi5i7WpSLTy8X9Iy0OL4prPqeiruhn7omFHf9kNCwzzlztjupqUdwdZlGK//DODiMBmSEnE5gwydnOL4xDGNLXTo4h9P4+HzMJvUj6mYIXr36I9d5eXL+7hKclcfJzFzedLRCu4oe9aHWpnS3MOKr2wncyq+GJllqKsyZww233tzItGNw+1QxDnT+/AcSAf+buLnOQKXKIyrqlyqP7dABmtklszB1HG72ztjVbUDAlj9QKhXYfvUVdnHHsRdiOLfzNnGhVTcwnwaKpCSixo6jOCoKh59WYNipY4VjstMKOLI+hA0fnyHsXBKN29sz9gt/2gz1wNLJEIe6poSdSyxTx9Js7Fhk1tYkL/i+ZB+ZFniNhbB9kBmDIAjM8Z3D9zZrUBUpyNxZuUikimjevDlaWlrV8gLepXtDG/6Z0Za6NoZM+/MC7227QqFCVSPrq+X5oNYAfArcyi9i/u0E5obF8vq1KEZcuk3P4DD8z96g4amrOBy7hPvxy3gHXKPjuRAGnL/JK1duM/1GFB/djGVBRCIrY1LYnJjO/tQszmTmciO3gPjCYvKUqicqqFsas2dD9+4wcyaEhlpjbt6OhMTtaDRl3xx06opFYQouXCB7+Q807NCFGycOk5uR/sTW2bybE2Z2+hz7O4biN6eLGk+hoSU72jaFMdvFyqBr+kFuxaEYnRw74Wfjxw8XfyCr6AUXx028Cr92gYxIGL0ZvMbdawpJD2HE7hGsv7GekfVGsqnPJhqYN7g/1msc6JnDyYU1thxBLkXfz5bCkHQUqQUVDyiFsryAwcHBxMfH0717d3R1n16ls0b2xuyc1oYmDsbM2HiRr/eGoHoCGz/FkVmo0gvR86lYuDrySiq2Hsai7MCNXSjN63Px5Blcm/tgbu9Y6pjMrVtRxMVhOXNGtfNVBKkU23nzMBk5grRffyPpq6/QaDT4DRyORqPh5JZNzNx4EQdTPT7tV8U8YS19UZ4kIwoOfoqWjoxWg9xJjswm5Ew19dxOLkKVfJnIDr0IuDSI2Nh12NoOppX/YdzcZiKV6HMrOJmNn5/l0Job6OjL6TOtKV28s5GsWYjx4EHcyE5DS1ePZt17V28NzzlLopIwlUkZa1d12Q1BEPjO0xFdiYQZN6oQCpqdDZ99Bm5usHgx2xp9DMDAP4aCdW0BCwADA09srPsTE7uWoqKqeVgFAWZPyuIGDTiw4jZtR71CXkY65//ZibabK1YzZ+J+fBEGemoO/HaN/OzH917VJMWxcUSNGYsyKQmnX1di0Lp1uf1z0gs5ukE0/ELOJNCwnT1j5vnTdrjnQznEdVvakJ1aSGJ46c99iY4OFlPfouDiRXIPHSrZwesVMc/+vJhzXdesLn6N2rDBYg8FV1IpuJpackwV0dHRoVmzZly9erXcVIiKsDfRZeNrLXmzgzt/noum//JT3Ewqmcdfy4tJrQH4FGhnasDtdk240KoBR1vUZUfzOqxt7Mqy+k584WHPOy42jLE1p42pIS662ggCxBYWcyojl40J6SyITOSjW3FMvxHNK1ciGHDhFh0DQ/E6fR33E1dwPHaJhiev0urMDXoGhTHiYjivX4tkbmgM88PjWRaVxLp40ft4LD3nsb2PEolYSM3UFEaMADPTIRQVJZKeXr7AulHPnphPfpXMjZvwkGqjUqm4sHdndS9rhUhlEjqOqUdeVhFn6gwU8z++/770zg7eYqXL7DgxHDSvfJFiQRB4x/cdcopz+OnST+X2fa65dQhW3QmJmbgP6nQBQKVWserqKkbuGUl2cTY/dfmJ9/3eR1f2iNGkpQ8t3xQF4hMu19iyDPztQCKQe+rxvICBgYH3HoC5ubkcPHgQV1dXGjduXGNrrfSaDLTZ8GpLRrZw4qdj4by6JpDswpoVVc4LTkbQlqJbgf5ddmoB6fF5ovh7bgpEBxAi8yM/KxPvXgNKHaMuLCR1xU/oenujX8kS42UhSCTYfPwxpuPGkrF2HYmff46RhSUNO3Th2pED5KansHhEMwy0q1GnzLmV+DsZuBJuH8OzhQ02bkac/juc4oKqhWxrEi4Sf2MRp1vZEZ6zB1PTlrT020v9evPR0rIk8koqm78KZP/KqwD0eK0RQ9/zwda4gIT/vYd2/frIJ00g7FwAzXv0QUe//Mq0LyLXcws4kJbNqw6W6FezYISVtpwvPR0Izs5nRXQFG3AFBbBwoWj4ffopdO0KV66wlcFicbJSKvD/l3F1nYZGoyAy6scqjx3+tiO2xLNonQUO9Rri7uPHuR1bKMjJxmzcWAybNqRB0HKK8hX8u+oa6uckmqkoIoKosWNRZWXh9Psq9HxKrYIPQG5GIcf+DGX9R6e5EZBAgzZ2jJ3nT7sRnhiYlixM5NbMEpmWpMwwUACTgQPRcnMjeeEiNMpH7jmmzuDRTTQAVeL9f2rzqeyyOk6yYSYZO8JRV/E+VRotWrRApVLxuLJocqmEd3vUY83EFqTmFtFv+Sk2B8U8dcdDLTVPbRXQp4AgCOhJBfSkWthWo9CZUq0hR6UiS6kiU6EiW6kiU3nnX4XyoZ+z7nzFFBbf+15RwR+qvlSCiUyKkUyKsUyKsVz8t4mhHpPsLUrd6beyEuX1unWD+fM7MXqMKfEJWzA3b1/uZ1nOnEnh9RvkL1yC+6CeXPp3L34Dh6GlWzWtospi42ZM4/YOXDkWi+f4Odj8vkDcNbYtJazM2R9GbhRFztcNEIvE6JqUOXdds7oM9hjMxpCNDK07FDfjcqoUPo+cXwe7Z4JlPRi1GYzFfLj43HjeP/k+wUnBdHHqwif+n2CiY1L2PL6T4dRS0Qs4dHWNLE1qqIVeU0vyg5Mw7uaCRLfqt6p27dpx5cqVexVBDxw4gFKppHfv3k9dluEuWjIJXw1qTAM7Iz7beY0BP5zi13E+uFk+vmGgLlJRcCUF3SaWSLTKfxGPuCzuMrs0sYCQTWjUaoJD87BwcsGpcdNSx2T8uRFlcjJ2C76rkesnCALW772HREuLtF9/Q6NQkN15MOpD//KK/m28nEwrnqQsOn0EYfthx1SEN0/Rdrgnf30dROA/kaLeYQVoNBrSUg5z6+IU8jz1MNL3oKHnh5iatgAgLiyDM3/fJvF2FkYWOnQZXx+PFjZIJIKoMzZ9Bmg0OCxdwqGdfyHT0iozrPZFZ1lUEvpSCRMdLB5rngFWJuxOyeTbiES6WhhTV/+RUFmFAlavFu/fcXGi4Td/Pvj6cvu2KO+3YMFjLeGlRE/PBVvbIcTFbcTZaTI6OpW3kLWMdJjqsIMPIt/k6lVoM2Ica9+Zxtntm+kw7lVs539B4cBBNFIGcinEh6B/ImnR59nKIxWGhRE9cRKo1TivXYNOvZIVOwHyMosI3hfFtZNxoBGLx3n3dMGwAjkGLR0Zbs0suRWcTNthnkjlJf0ogkyG1exZxE6dRub27ZgOHfpwB5+J8OdwCNkDDQdgoWvB+MYT+Dx3Bcui3iPrnwhMB3tU+xqAuAlap04dgoKCaNOmDbIKqjVXRHtPS/bOaMvMTReZu+Uyp8PTmDegUfU26Wp5Lqj1AL4AyCQCpnIZLrraNDPSo52ZIf2sTBhjZ85UZ2ved7fj27qO/NTQhT+buvOPtyenWtbnaptGRLdvcs/7eMS3dO/j6DveR2ddLQQBYgqKOZ6ey4c349ienFnmurp0Ectt//KLNllZ/UhJOVhhsrkglWL//QJk1tbYnTxHUX4elw/tr+Er9jAtB7hhYKLNEYseqNTAkiVld3ZrD8M3QPINWD8YisoPd3ir2VvoyHRYEPgCvXloNHD4C9g5VSxLPWEvGNuj0WjYFb6LwTsHE5IewrzW81jYYWH5xh+IRrLvq3Dtb0i9VWPLNGhjj6ZYTd656oXuPegFvHr1KpcvX6Z169ZYWDzei2pNMLalM+tf9SMzX0H/H05xNPTxK8AVXElFU6xG36fi8LfIy6mY2uhhYqUHN3YSLalHakIi3r36l2rcqfPySPvlF/Rb+aPfosVjr/UugiBgOWcOFlPeJGvLVlK/XkiqbRMkYWfJTn2MoiBaejDwJ8iOhX8/wsrZiAatbLl8KIaMxPKr2mVnX+HCxbFcuvoaanURjUwn4NNiB6amLUiKzGbnkgv8vfACOWkFtB9Vl1GftqRuS9t7VUqTvphP4fXr2H39NQXacm6cPEqTzj3KLKrzIhORX8SO5ExesbPAVP54L4KCIPC1pwMGMgnTb0Tdj05Rq8XqYw0bwmuviSrlhw/DgQNwR8Jl252i0oMGPdYSXlpcXaYCEBGxrMpjX+8diy75LF6oxsLRmQbtO3Fx/26yU5LRdnXFavYszI7+jottMYF7IogJeXJpHRVRcO0a0eNeQZBIcF63tlTjLy+riBObwlj34WmuHY+jXktbRn/ekg6j61Vo/N2lrp8NRflKIssJ1zTo3BndZs1IXbYcdcEjqQweXcHY6V4xGIBxDceRY17EEbtg8gITKbz1+HmVLVu2JDc3l0OHDhEaGkpcXBzZ2dmoVNXL5bMy0mHdJD9md/Vkx8U4+i07ybX4FzwF5j9MrQH4kiN6HyXYamtR30AXPxMDulkYM9TGjFcdLJnjasPnHvYsqe/E6sZubG/uweEW9Qhu1YDmhnp8eDOWtOKywxE+/RRatYIP3h+KRlNMYmLFIZ1SExMcfliOUXoWFkg5/88OVI+GSdQgWjoy2o3wJD1VwYWRn8KKFWL+SFl4dBE9WfEXYMMwKC77hdFc15w3mr7BibgTnIwrPwT2uUBZDNtfh+PfQfMxYtirjhFZRVm8c/wd3j/5Ph6mHmzpu4UBdQZU3tPTcopYMfTUohpbqpadAdpuxuQGxKNRVS/cpF27diiVSrZs2YKpqSlt27atsfU9Li3dzNnxVmscTPWYuDqQn4+FP1ZYTV5wEjJzHbScy9f+Ky5QEn8zUwz/LMiAiOME57ijZ2xCvdale/DT161HlZGB5YwZ1V5fWQiCgNnUaRzyH0j7qCBGF6aj0cC5vzc/3sSOLcB/KgSvhluH8OvvjkxbysnNN0u9zgUFMVy9NovAoAHkZl/DMzyfloWdsW7+Ienxefyz4jJbvg4iJSaX1kPqMGaeP43a2SOV3X+MZm7bTuZff2H+2msYdupI4M6tSCQSfPoOLPF5LwM/RCcjlwi87lgzQu6WWnK+9nTkUk4BP0QniRp+3t6i9pC2NuzYIUo7dHy4mMe2beDlBa7P1vn03KKjY4e9/UgSEreSnx9ZpbHmnZryCmtYvwGSk6HV0NEgCAT8tQEA0zFj0PPxxuWfLzAx1+Lf366Rl/V0JG8eJP/CBaLHT0Cip4fz+nVou7s/3J5dzMm/brLuw9NcORaHZwtrRn/eko5j6mFkXrV8cId6pugZaRFaTl6xIAhYvT0HZXIy6evWP9wokYL3KxBx7N6mqa5Ml5leM1lisJZCIxUZ226hLn68oivu7u44OTlx+vRp/vzzT1auXMnChQuZN28e3333HStWrGD9+vX8/fffHDp0iLNnz3L9+nViYmLIyMhAoSiZoiCVCEzv7MEfk1uSV6xk4I8BrD0dWRsS+gJSawDWUipSQWBhPUeylSo+DS87D0suhz/+gJiY+sTFNSQuvuxqoA+iU7cudvO/wOV2LDlpqYSePlFTSy8V16aWuHtZEmToR6ZgJJYwLY/6fWDwSog5AxtHgaLs6nSj6o3CydCJbwO/RaGu2ZyuGqUgE9YPgsuboOOH0G85SOWivMPOQRyKOsQMrxn83v13HAwdqja3gaWY3H5pI2TWnLyHQRt7VFlF1U6Mv+sFBOjVqxdyeenC5s8KRzM9tr7pT89Gtny1N4TZmy9Vq9KaMq2A4ogs9CrQ/gOIvp6OWqURwz9D95JWICciRpRnkZWik6bKziZt1SoMOnZEt2np4aGPy0/Hwllg3ZqkUa+hdfQIrlq6XDn8L9mpj+kZ7fgBWHjCzmnoaRXQoo8r0dfTibxyP8dXocjk5s0vOX2mGykpB3BxfJ1WITo4ZhmS7fMFB367xsYvzhEXmkGLvq6M/cKfZl2ckD0SZlsYEkLiZ5+h17IlltOnkZuextUj/9KwfRcMzZ6917mmSSgqZlNiOiNszLDWrrm/q35WJvQTFCy4GcuNqaJ0EOvXi8Lu/frdq058l7g4OH0aBg+usSW8lLg4v4kgyImIWFq1gf7+zGQxRcUSVqwAIwtLmvfoy7Xjh0mJjkSQSLD78kukxfk0Td6BolDFv79dQ61SP5kTKYW8M2eJnvQqUjNTnNevQ8vJ6V5bfnYxp7bcZN0HAVw+HIOHjxWjP/Oj07j6GFlUrxCYRCrBw9eaqKtpFOaV/czX8/HBoGNH0lauRJWZ+XBj87EgkYnC8Hfo7dabOpYeLLJZhyq9kOx/H09iQxAExo8fz6xZs5g8eTIjRoygd+/etG/fnrp162JsbExeXh7h4eGcPHmSvXv3snnzZn777TeWLFnC/Pnz+frrr/nhhx9Ys2YN27Zt48CBA5w+fRr9vHhWDnSmvZM2n++4zJvrz5NV8By//9RSAuFls9p9fHw0j5v0Wst9vr6dwOKoJDY1dae9mWGZ/bZtg7Vr1zJt+me08N2FoWGDMvs+SOK337Lj1EHk1tZM+HntE83Nyssq4o9Pz2KZdov+hz5CuH1b3FUuj4t/iiXgPbrB8PViGedSOBJ9hOlHpvO/Fv9jdP3RT2D1j0lmNGwYKgre9/8Bmg6nUFnIkvNLWH9jPa7GrnzV9isamlex6uJDnxEDS5uBzyToVYrkRjXQqDUkfR+ERE+O1VvNqjVHYWEh8fHxuLk9vzmaGo2G5Ydv8f2/YTRxMOaXsT7YGFdeLiDr3yhyDkdj824LZCbl/04fXH2dyCupTPy2DZLNo/k3II5rGea89sPv6BmblOifvGQJaSt+wnX7NnTq16/qqVXIpZhMBq8IoEcjG5aNbE7GuvVEfvcNxxq40LBjN7q9Mf3xPiA2GH7rAk1Hoeq7jE3zzqFSqhn+UTMSEtcTGbUCpTIXW9vBuLnNRCdgFTmHfyXI+mduXJchlQo06eRA827O6OiXbuiosrOJGDIUTWEhrtu3ITM35+jaXzm/dycTF/+CiXXFVVlfND65GcevcSkE+NXHWbcaye2lceECvP8+aQFnaL9mG7baWvzTyRe5dtkC7suXw7RpcOMGlJHuVcsdbt36hqjolfi1+AcDA8/KD3RwoI9mJ+cUXkRHg0aZw2/TXsW+XgMGvvsJAOkbNpA07wvy3/qWM9f08enlgl+/J3/PzT1xgtip05A7OuC0ahVyK1H/tCC3mAsHorlyNBaVQo1nCxt8erlgYl0z9QZSonPY/GUg7UfVpVG7svVkC8PCiBgwELPx47Ge+87DjX+Nh/AjMCcE5KIxGpgYyMT9E/lZMR+ncFOspjRDy7Hsd6+aQq1Wk5+fT05ODrm5ueTk5Dz0/YP/lhZCqtBIKJZo42Rtho25CYaGhhgYGJT4V1dX95nl4P8XEQQhWKPRlFoFqTZ7s5Zymelsza7kTN4JjeFIi7roS0svLjFoEBw/3o/i4q84fXoL3bp9XKn5refMof6ViwRmZRD291bqDhxSk8t/CH1jbVoNcufoBiU3DJvQYMMGmDix/EHNRoKyUCyWsmWCGBoqLfkS2MGxAy1tW/LjxR/p7dq74ry5p0n8BVHgXVEIY7eBaztC00P534n/cSvzFiPrjWSW96ySFT6riokjNBkB59eIQvIGjx8WJkgEDFrbk7kznKLobLSdyg9vLA0dHZ3n2vgDcad2WmcP6toYMmvTRfouP8mSEc1o5V6x50ij1pAfnIR2HZMKjT+1WkPU1TScG5kjUeaRH3KM62neNGjfsVTjT5meTsaatRj27PFEjL+8IiUzN13EylCb+QMai+Gg48YiaGkRvnI5V48coEXvAZg4OlU8WVk4eEObWXDie6QN+tFmqBfHdv7MyZNz0AjJmJt3oI77XAwM6pIfdp4Tu1K5WvAzZMho1N4e7x7OD5WBfxSNRkP8e++jiI/Hee0aZObm5GdncengXuq1bv9SGn9pxUrWxqcx0Mq0Zoy/0FD4+GPYvBnMzDD/8H2+9WnAxJsJLI1PZ45r2ddw61Zo0KDW+KsMzs6vERv3B7cjFtOkcRWqgvr7M/v4d3RO+ZMNG2DSJEN8+w/h5J9riL1xFYf6jTAdOZKc/QeQrPkcz0k/EbQ3Ets6xjg1qLo0SGXJ/vdf4mbPQdujDk6//YbM1JTCXAUX/o3m8tFYlMUqPH2t8enlgqmNfo1+toWjAWZ2+oSeSSzXANTx9MS4f38y1q/HbMxo5A+WqfWZCNe2i/nzzUYC4GvjS2enzrwfs4D1Bt+QsTUMq6nNEWRPNmBPIpFgYGCAgUH5Bck0Gg0FBQUlDMPb8akEhMZxPT6bzKxsJMrC0kNIpdIShmFpxqKenh6SKmqK1lI1aq9uLeWiI5WwoJ4j0YXFLIgovxjHV1+ZcO1aF/ILdhAbW7kcAEEqxW/hEnTUGs6s+RVlymMUfqgEDVrbYVvHmIA2b5G/aIVYYKAifCZAj28gZLeYP6cuuft1VxYiV5HLiksrnsDKq0noPvi9F0i1YNJ+VM6tWXV1FSP2jCCzKLNseYfq0mYmKIvgTNVLjpeFnrc1gk71heFfJLo1tGH7W63R05IyauVZ+i8/ybbzsRQpyw4LLYrIQpVZhL53xcVfkm5nUZirEPP/bh7gcpoZSpWmzAqVaSt/RV1YiOXUqdU+p/KYt/s6kWl5LBzeDGO9+xsrpiOG02r8awhqDYfnzkKdV37hlgpp/y4aq/qkHZ1KQs4Y7Px+pyBTj3p1VtGs6W/IBDdObwtj3aJkruT3pG4LS0Z/1pJ2j2iAlUb6b7+Re+gQ1nPfQc/LC4ALe3eiLCrCb8DQcse+qPwam0KBWs0058fU24uJgVdfFQu87NkDH34oiri/8w69HKwZbG3KoqhErubklzo8JQWOH68N/6wscrkpTo4TSUnZT3b2lcoP9PenY/JGmjRQsGiRWEfMq2dfDEzNOP7HajQaDYJEgu2X89Go1bgH/oyZrT7/rrpObsaTyQfM2rWbuJmz0G3QAOfVq1FqGXBmRzhrPwjg/IEoXBubM/JjP7pObFjjxh+Iz/y6fjYk3s4iK6X038+7WE4T758py5Y/3ODSFsw9IOi3hw7P8p5FFjkcqB+MIjGfnGOxNbr2x0EQBPT09LCyssLd3Z2mTZvSpk0bxg0bwDdvvwaeHfgtox437bry+vTZTJ06lfHjxzN48GC6d++On58fTk5OyOVyUlNTuXLlCocPH2bnzp1s2LCBn3/+mQULFjy2fEUtFVNrANZSIf4mBoy1M+fnmBQulfEgBtDVhc6dh2JomMlXXx2msoWmtC0tad69D6naMq7MmIam+MkJygoSgQ6j66GQ63LSujvs2lW5gS3fgC6fwdWtsHNaqYajp6knQz2Hsil0E+GZ4TW88moQ+CtsHAkWHvDqQeL1jJl0YBKLghfRwaED2/pto7V9+eK4VcbCAxr0Fz+7ILNGppRoS9FvYUPB1VSUmWXnYr4seFob8s/0tszr35DcIiWzN1+i9deHWXgglKTskuefH5RUKe0/EMXfJRIBp4bmKK/s4GKmAy5NmmPh6FyiryIpmYw//sC4b98SBRVqgn1XE9kYGMMb7d1p6VZy7fZjx1KvXiMiVYXcePVVVI8haJydH8YFLxsu1lGjyonDzek7Yo58wJV9ZgT9E8m6D09z/kAsrtpnGTVBTacJzSpVGCLv7DmSFy7CsGcPTMeOBaAoP48L+3bj0aIV5g6P4bl8TslRqlgVl0qv0qQaKktKCsyaBXXqiHpCU6eKht+8eWB8v1rqFx72mMllTL8RTXEp99wdO8Rbca0BWHmcnCYik5lwO2Jx5Qf5+yMAsztd4to1+PdfkGvr4D90NAlhIdwKOgOAloMD1nPfoSjgBP4OsSgVag78drXG8wEzt2whfu5c9Ly9sVr2M0FHU1n3QQDBe6NwamjOiI9a0O3VRpjZ1rzh9yCeLaxBgNCzSeX2k9vZYTpmDFl//01haNj9BkEQvYCxgQ/p6DobOTOi3giW5K5EWU+b7MPRKJIecxPsKWCsK+fH0V7M69+QgPA0Bvx0jlvZAi4uLjRu3Bh/f3+6devG4MGDeeWVV5g6dSr/+9//+OCDD5gxYwYTJ05k2LBh9OzZExcXl2d9Oi89tQZgLZXiQzdbLLVkzAmJQVGO2Guzpq1RKm2wd/iLb76p/Pzeo8Yhl8u5kZpA0tdVGFgNzGz18e7pws06nYj8cbu4nVkZ2syEDu/BxQ3wz5xSx01pNgU9mR7fBX1Xs4uuCmo1HPgI9syBOl3RvLKHXcmBJeQdTHUeQ2etPNrOhqJs0QisIQxaibqNuQHxNTbn84y+toyx/i4cnN2edZNa0MzRhGVHbtH668NM+/MCwVHpaDQa1IVKCq6motfUEkFesQh3xOU0bD1M0JYpCA0+T55ChnfvAaX2Tfv5JzQqFRZT36rhs4Ok7EL+t+0yje2NmdWl7FykNrPeQZBKuZYWT/SkSajKq95bCmJlz5liZU9FHJ60pGVADK5KbZp3cyH8fApnd97GzgGGW8yhW/tETPy6V2puRVIycXPmoOXsjO28L+7ltVzcv4ei/Dz8Bg6r0lpfFNbEpZKlVFXe+5eQAO3bQ2IiZGWJoZ5ubrB0KYwZAzdvwuLForjsI5jKZSyo68j1vEIWRZZ8yd66FdzdoUmTxzyp/xAymSHOzq+RlnaUzKzgyg3y8gItLUbItmBjA4vuFHtu1KELZnYOnPxjDeo7O74mw4ej38qfouVf0ranFQm3sji7M6LG1p++bj0JH36EVusOJA78gA1fXSLon0gc65sx4qMW9HitEeZ2j6+rWhkMTHWw9zQl9GxihVUwLV6bjMTAgJS7F+8uzUaCTPchSQiAN5q+gYHcgMUW65FoS8nYehNNOe9ezwuCIDDW34XtU1qhryVj1MozLDl4E1U5a5fL5ZiamuLk5ESDBg3w8/PDqpT7QS01S60BWEulMJbL+NLTgau5BfwcU3ZlPkGQ4u4+CF/fEyxZksipU5WbX1tPn6Y9+pJoakjcls1kbt1WQysvHe+erphqF3HMojvFR6pQgbT9u2I+UdAq2P9+CSPQTMeMN5q+wam4UxyPPV7Dq64EikLYOhECloLPJLIGruCds59VX96hOtg2hTpdxTDQ4vJDYyqLzEQH3UYW5J1LRF30eKWxXyQEQaCthyW/vuLL0bc7ML6VC0dDkxm84jT9lp/ixO6baBRq9CoR/pmVUkBGQh6uTSzQ3DxIcLIF5lbmODf1KtG3ODaOjL+2YDJ4MFqOjjV6Tmq1hrf/ukSRQs3iEc3QKie3xdDMgiZdexFrZkz6zTCix09AmVGxPlZxcTphYfM4faYrKSn/4uI8hVb+h3Fs9zsS68awayZe7Yzw6e3C4DlN6K0zFwvTAujxVaXOQaNQEDd7Nur8fByWLUVqIHoaFEWFBP+zA5dm3li7VSw4/6JRoFLzc2wK7U0NaW5UyWIa8+bByZNioribm/hzz55w7Rr89hs4le8l7WZhzDAbU5ZGJ3Ex+/79JDMTDh0Sp62tKVE1HB3GoqVlwe3whZUboK0NXl5oB57krbdg3z7xv08ildJm5DjS42O5duwQIN6zbOfNA0FAf+O3NGhty/n9UUReqV4l5wdJXbmSuK+/J67TWxw2HE7Q/lgc6poy/ENferzeGHP7p2P4PUhdP2uyUwpIiih/c0pqYoL5a5PJPXqU/MDA+w26ptBoMFzeDIX35zDWNuaNpm9wKO0Iia2VFEfnkHv6xdkAbWhnzM5pbejfzJ5FB8MY8+tZkkuJYKnl2VFrANZSaXpbmtDLwpgFkYlE5Jcd129nOxiJRM2QodsZNQrSK6kL69WzH4JURmzjeiR++ikFly9XPKiaSOUSOkxuTq6hNedWnav8QEGAzp+A35uigXPo8xJG4Mh6I3ExcuG7wO+erixEfjqs7S8mlXf9nNPNhzJozzAORR1ievPp1ZN3qC5t50B+GpxfW2NTGrSxR1OoIj+oesLwLzrO5vp82KcBZ97rzBcDGlGgUJEblEisoGbZjXgSs8p/uEZeFl/AXJqYE3NsKylFBnj1H17qZkDqih8RBAGLN9+o8fNYdSqCEzdT+ahPA9wtK35h8xswFIlMSmK/HhTduiUagWlppfZVqQqIjFxBwOmOxMSuxdZmIP7+h3B3n4NMZihW8R2wAgoykB+ci19fN2yif4SUG9B3KeiaVOockr9fSEFwMLaff452nfuG3pVD+ynIznppvX8bE9NJKVYy3bkSu/O6uuL9csWdXOvTp8WHgZaWWOylClVb5tWxx1IuZ/qNaIruhILu2gUKRW34Z3WQSvVwcX6TjMwzpKdXcpfW3x+CgnhjYjE6OqLTFqCOrz+2HnUJ+GsDiiLxHiS3t8fq3bnknzlDI+Ei5g4GHFx9nZz06hkAGo2G+EU/EPjXNU63/YpQdQPsPEwZ9r4vPd9ojIXDk6+SWRbuza2QyiWEnq34uWQ2diwya2uSF3z/sMfQZyIo8uDKw9qnI+qOwMnQic+yF6DlaUL2vkiU1byGzwIDbRkLhzXluyFNuBiTSc8lJzgW9mTrPNRSeWoNwFqqxJeeDsgFgXdCY8oMedDTc8HEpAVDhmwhIUHDq69WLsrS0NyC+m3aEylRoba2InbadJSpj79rWBZ2jWxoqJfAZb2mJB26UPmBgiB6CrwnwMmFoqj6A8ilct72eZvI7Eg2hWyq4VWXQfpt+LULxF+gaNAvfCMv5LWDr6Ev12d97/VMbjIZqaTiEMEaw9kfnFqJnkhlzeR0ajsZoeVkSE5A/AsRCvOk0NeWMaalM3vH+tIUGSEWWvxwLJzW3xzmrT/OExiZXurfZuSVVExt9DA2lRF8/ja62hLqt+tcol9RRARZf+/AdOQI5DY1W8Hyenw23+4LpWsDa0a2qJxn0cDMnCZdehAaeg2jr+dTHBVF1LhXUCTfj0TQaFTEx2/m9JkuhN9egKlpS/z8/qF+/a/Q0X7kHGwai578q1vFDZyTi6DZGPDsVqn1ZO/bT/rq1ZiOHo1xn973jisVCgJ3bcOhfiMc6j2GnMpzikKt4YfoJHyN9GllUglPS2goeD4Q3qutDaNHQ1TVtc2M5TK+r+dIWH7hvWJkW7eCgwP4+lZ5uloAO7uRaGvbEH57UeVEvP39oagIi9iLjBsnpm6mpIgev3ajJpCbnsaFfbvvdTcZOhT91q1JW7iATn3MUSs1HPj1Kqoq5gMWFyg4/v46dl915LZbP+wa2TD0PR96T2mCpdOzM/zuoqUrw62pBTeDklApyz83iY4OltOmUnDpErmHDt1vsPcSI2cCVz30siSXypntPZvb2bcJaBoKgkDG9psvlOi6IAgM9XFk17TWWBho88qqc3y9NwTFU9SJrKV0ag3AWqqEjbacj9ztOJmZy8bEsl17traDUasjWbYsmO3bxU3gyuDTZyDK4mLShvRDlZVF7MyZT7QojP+crugWZnJkU3jVHkyCAL0XQrPRcGQ+nFryUHM7h3a0smvFj5d+JKOw4pC1xyImEH7tCgXphA5azoiIjay/sZ6R9Uayqc+mx9P2exzazYHsOFF4voYwaGOPKq2QwpBKupVfYvIvJIMAY1/14tjbHZnY2oUTYSkM/ek0fZadZHNQzD1R+aICJfFhmbg0sSA9cAe3s41o6u+FXKtkhcvU5T8gaGlhPnlyja63UKFi5qYLGOvJ+WZwkyqFIbfoNwSpVMblqFs4/vwzioQEoseOozghgdTUw5w915sbIe+hrW2Dl9dGmjb5GQN9j7InbDMTbJvBie/BwAa6z6/UOopuR5DwwQfoNG2C9btzH2q7fvwQuelpL633b3tyBrGFCqY7W1X8f5eYKOb3hd0peKGjI7rrjIygmpsKnc2NGGVrxg/RyZxMzGP/fjH8s7ZSfPWQSrVxdZlGdvYF0tKOVDzA31/89/RpZs6EoqL7z3WHBo1w8/Ll3I6/KMjNAe6Egn4xD0EioeD7z+gwui6Jt7M58/ftSq1PUaTi/L5I1sw5zNUMB8wMFQye60Wfqc2wcq66HNCTxNPPhqI8JVFXS49MeBDjAQPQcncneeEiNEqlePBuMZjkaxDzcERSJ6dOeFl5sejWMnS62lJ0M5P882Wn4Tyv1LEyZMfU1ozyc+KnY+EM//k0sRk1kyJSS/WovXXWUmXG2JnT0lifz27Fk1JceoijlWVPpFJ92rbdQs+eMHs2XLpU8dwWTi64NvfhSvBZLD/5hIKgYJK+qRlR8dLQtreinVE4aRIzLm2tQllsEN88+i0T4/f//RjO/nyvSRAE3vF5h3xFPj9erDlJhBLc2AVr+qDS1uf3Dm8x4vxXZBZlsqLLipqVd6gO7p3FXc2Ti0qVzqgOug0tkJpo/yckIcpD1P5LRtvDFKmxNk7menzQuwFn3u/MlwMbo1CpmbvlMv5fHeLbfSFcDkxErdbg0sSC83u2IRXUNBtaMryzMDSM7H/+wWzMGGQWFWsQVoWv94YQlpTLgqFNMdMvW9i7NO56Aa8fP4zC2RGnX1eSp5vIuf1duHR5Mmq1gsaNfsDHewumJpVwCUnlMPAnsGoAA36sVOinOj+fuBnTEbS0cFi8GEHr/jmoVSrO7diCtZsHzk2aV+ncXgTUGg3LopJooK9DF/MKXr5PnwZvbwgKEt1zU6bAmTPwxhuiYfgYfFrHHlttOW9diaZQra4N/3xMbG0Ho6vrdMcLWMEGqIMDODrC6dPUry+mcf7wAxTeiUhsM/IVivLzOff3X/eGyG1tsX7vPfIDA7EIO0SjdvZc/DeaiMtlR/YoilVc+DeadR8GcPrv2xik3qSTQyiDFw3Cxs2kBs665nFqYIauoZywSoSBCjIZVrNmUnz7Npnbt99vaDQEtI1KSEIIgsBc37mkF6azTncHWs5GZO6+jSrnyW2MPyl05FK+HNiYZSObE5aUS68lJ9h/7b+Z0vE8UGsA1lJlJILAd3UdyVep+fBm6S/iMpk+Vla9SE7Zw2+/5WFmBsOHQ2XkvHz7DqIgO4tYAy3Mxo8nY8MGMrdtr3hgNXF7ewSukQGcO5xcoZ5PCSRSGPgz1OsDe+dC8Op7TXVM6zDUcyh/hf3FrYxbNbtogNM/wqaxxFvX59U6TVgYsvaevEMb+zY1/3lVRRDEXMD0cLi+o2amlAoYtLKj6HYWxfHVlwV40SkKz0SVVVL7T09Lxig/J/bPbMcfk/3wdTHjp2PhrN5yA6VMIDw3lWvhWdRz1kPfoqQnJmXZUiT6+phPmlij6z0SmszqgEgmtHahvadltebw7S96Ac/s+plwrd9JmZ6DwrQI071meNmtxMqqR9WKG1nVhymnwb1jhV01Gg0Jn35K0a1w7BZ8h9zW9qH20IDjZCUl4jdo2JMtsPSM2Juaxc38IqY7W5d9fhqN6BJq3170+J05A+fOiVZC06biv9ser7iXkUzKonpOJMmKsHorgdY1rGLzX0MikePqOoPc3Oskp+yreIC/v2jgI27qJifDn3+KTZZOLjRo25EL+3aRnXo/z8t40ED027cj+fvv8fXTwsLRgEOrr5OdVvDQ1MpiFZcOxbDuw9MEbL2FQV483ucX0KWDhHofvPFc/11JpBI8fK2JuJJKYV7Fef8GnTuj26wZqcuWoy64cx20DaDJcDGHP+9hT2JDi4b0cevDuhvrKO5phKZYRebO50Bqqpr0bWrHnultcDbX5/V1wXy681q5Wre1PBlqDcBaqoWHvg4zXazZkZzJgdSsUvvY2Q5BpcpHw17WrxejgaZNq3huhwaNsXH3IGj3dixmz0SvZUuxKMyVKnroKong4kI7y2gkxUUcXXOt6vH1UjkMWQUe3WDXTLi08V7TlGZT0JPr8W3gtzUXt69Wwd530ex/j10erRisX8j1zJtPXt6hOtTrCxaecGJh5eU2KkDf1wZBS/Kf9gLmBych6EjRbVC69p8gCLRyt+CXcT4cndOBesi4LVex7efFKNUSpA3874WH3qXgylVyDx7CbMJ4pCYmNbbW1Nwi3vnrMnWtDXm3R+ULfzyKlr6aRkOlyFzXkZpyBFeX6fi6/YnBcSkx4yZSdLvmSs0/SubGjWTv3IXl9GkYPGJ1aNRqzv79F+YOTtTx9ntia3hWaDQalkQm4aqrRV8rk9I7FRTAxImit69rV9H794S0GVroGaLYa47QJ4XgnP/uJlBNYWPdF319D27fXoxGU8FLuL8/REdDfDydO0PjxtwThgdoPWwMaDSc3vLHvSGCIGD7+ecIMhnJH39E91cbolFr2L/yGiqlGqVCxaXDMaz76DQn/7qJmbUurTlC48Of4vHGUCynTHmujb+71PWzQa3UEF6J8ExBELB6ew7K5GTS162/3+A7CVTFotTUI8zwmoEgCCyNXoFRFycKrqRScO3J1Uh40jib67PlTX8mtnZldUAkg1cEEJn6/GsdvkzUGoC1VJupTlbU1dfhf2Gx5Jaye2Ns7I2enisJ8Vvo1Ak++AB+/x3++KOUyR5AEAR8+g4mMzGB8AtB2C9aiMzC4okWhTF4Zyr+534l9lZOpap5lUCmDcPWgWs7+PtNuCrudJvqmDKl6RROJ5yuGVmI4nzYPI6swJ+ZW68l7ytj8DD1ZEu/pyDvUB0kElE2I+kK3DxQM1PqytDztib/Ugqq7BcvDOZxURcqyb+adkf7r+JbuCxTgaRYw+Sh9aiTF42DfhbTb7jQ8qtDfL03hLhMcQc6ZelSpCYmmL3ySo2tVaPR8L+tl8kuVLBkZDN0KqFV+CgqVT4REcsION0JDC+RHmZOYehw3NxmYNDYF6e1a9EoFESNG0fRzZs1tva7FFy+TOKXX6Hfvh3mr79eov1W0BnSYqPxGzgM4SVMSDuansPl3AKmOVkjLe3+EhUFbdvC6tXwySdieU7TJ7cJ9e+/kLHcDgu0mBESTX5tMYnHQhCkuLnOJD8/nMTEneV3fiAPUBBg1iy4ckWU4wAwsrSiWffeXDt6iLTY6HvD5NbWWH/wPgXBwaj3baHTuPokR2bzz4rLrP/wNCc338TESo/+bzXA69qPaB/dgvXHH2E+ccITOuuax9LJEFMbvUq/P+j5+GDQsSNpK1fel7axqi8WTwtaJVbOfQAbfRteafgKeyP2Elk/A7mtPhl/h6MuUNb0qTw1tGVSPu7bgJXjfIhJL6DPspPsuPjf3dh92rx8T6tanhpaEgkL6zqSUKTgy9sJJdoFQcDWZgiZWYHk50fwySfQurWYCnKrgohIDz9/jK1tCNy5FamJCQ7Ll6HKzCRu5iw0iicgrdCkCY2cirFJC+PUXzcpqE58vVwHRv4Jji1h22QI2QPA8HrDcTFyYUHQAhSqx1h7bgqs6cOZyIMMcq/HweLke/IOjoY1q9VWozQeCsaOcHxBjXkBDVvbg1pD7pkXRxeppsi/nAJKNfo+lSumEXk5FYlEQJ1/g8IiNT5NbPn9tQ74u5nzy/Fw2n5zmM+//IO8Eycwe3USUoOa09LacDaagzeS+V+PetSzqVrhBrVaSWzcHwSc7sTtiMWYmbWmpd8+rAxe48bRQDISxBcFnbqeOK9dAwJEjXuFwpCQGlu/MiOD2JkzkVtaYv/NNyUMPI1Gw9ntmzGxtqWuf9sa+9zniSVRSdhpyxliU4pRd/CgmO938ybs3AmffvrEq7Js3QomOlJ+aOxIREExX93+790DahpLy24YGjQkImIp6vKki5o3F6u53gkDHTUKrK1h4QNygi0GDEOuo8OJPx+WADLu3x+Djh1JXrgIB9M8mnR0IPpaOkaWuvSf1Zx+r3ui/OZt8s6cwfbLLzEbNepJnOoTQxAEPP1sSLiVRXZqQcUDAMtZM1Hn5ZH2y8r7B30mQkYERBwt0X9io4mY65jz3fkFmAz2QJ1bTNY/Ty7y4WnRtYE1/8xoSz0bQ2ZsvMi7Wy6XiFCppeapNQBreSy8jfWZaG/B73GpBGWVdN/b2g4EJCQkbEUmE71/MhmMGAHlFfeUSKT49B5I4q0w4m5cQ6dBA2znzSM/KOiJFYUR5s6lw6FvKM5XcHJLNT0JWvowapNYYfCv8XDzIHKJnHd83yEyO5I/Q/6s3rypNyn6tRPfFEUx2dYKfX2rZyPvUB2kcmg9A2LPQVQlNacqQGahi049M/LOJqD5jz0o8oOTkVnpIneonKEWeTkVWw9jLv2zGTOtfNw6Dqalmzkrxnhz4t1OvN7OjUb7/iBd25AJGS78eS6aguLHv6a3knP5Ys912nlaMr6VS6XHaTQaUlIOcPZcL0JDP0JX1wlv7800abwCfX03fPsNRiqXc2br/VBr7Tp1cFm3DkFbm6hXxlNw9dpjr1+jUhH/9juoUlKxv+MdfZSoS+dJun0L3/5DkEif87/DanA2M5czWXm86WiF1oOGnUYD334L3buLVT2DgqBv3ye+HoVCtDP79YP2loZMtLdgZWwqARm1oaCPgyBIcHObRUFhNAkJW8ruqKUlGvx3DEBtbTHqd+9euHFD7KJnZEyL/kMIDzpDXMj1Bz5DwOazTxF0dEh4/wNaD3Jj1Kd+DJzjha21QPTESRRcvIT9gu8wGTTwSZ7uE8OzhZiTHXaucl5AHU9PjAcMIGP9ehTxdzYyGvQDPXMI/K1Ef325PtOaT+NiykWOKgMwaOdAXmAihbcya+oUnhn2JrpsfK0lb3V0Z3NwDO9uvfxCyV28iNQagLU8Nu+52WKnLWdOaAzFj4QtaGtbY27ejoTE7Wg0KpycYNUqCA6G994rf96GHTqja2hE4K6tABj37YPZK6+QsX49mdv/rvkTad8ecw8rvML3EHY2iejrFZd0LhUdIxizFSzrwabRcPsYbe3b0tquNT9d+on0wipKGEQFELq6GyMM1aw31GNE3RHPVt6hOjQfA/qWYtn9GsKgjT3qPCX5F/47wrKKlHyKo7LR9y6nGMcDZKXkk5GYj4llJslxCXiZJyJ4dr/Xbm+iy1TjDOonh1MwbCxKLW3e23aFll8d4qt/bhCTXr0y3cVKNTM2XkBPS8aCIU2QSCoXmpyZFUzw+WFcvvImoKFJ45/w9tqEibH3vT76JqY07daLGyePkR4fe++4losLzuvXIdXXJ3rCBAouXqzW2u+S+uMK8k6dwvrDD9FtVPrf2pntmzEwt6Bh+06P9VnPK0ujkjGTSxllZ3b/YE4ODB0K774LQ4aIxV48ypHcqEGOHIGMDFH+AeADd1ucdbSYFRJNXm0RicfC3LwDxkbNiYhcjkpVVHZHf3/xAX5nB/fNN0VD8K4wPIBXz37om5hy/I/VD73Ey62ssPnwQwouXCBj3TpMbfRRpacTNX4CRTdu4LB0CUa9ej2hM3zyGJnrYudhQujZpEobL5bTpoIgkLJsuXhApi0+L0P3QnZJ7/aAOgPwMPVgUfAidDvaIjPXIWPbTdQ1sGn3rJFJJbzTvR5zunqy42I8685UXTO0lspTawDW8tgYyKR87elAaF4hy6NLJkDb2g6hqCiR9PSTAAwYAFOnimEje/aUPa9cW4dm3ftw+3zgvXwCq3feFovCfPIJBVeu1uyJCALMnYv34eWY6Ck59kcoiqJq3lR1TWDs32DmBn+OQIg+wzu+75CvrJoshOryZn7fPpKRFgZk6puzossKPmj5wbOVd6gOcl3wfwvCD0Pc+RqZUtvNGLmtPjmn4v4zO4X5waL2n15z64o7A5GXxU2M1Mjj6MhUNGhW/yHJA41GQ/LiJchsben0zuv8M70Nm1/3p3Udc349GUH7747w2togAm6lVukaf/9vKNfis/l6UGOsjHQq7J+Xd5vLV94kOHgYBQUx1Kv7BX4t9mJp2bVUQ7dFv8FIteSc2fawxqSWg4NoBJqaEj1xEvlBQZVe84PknjhB6o8/YjxgACbDhpbaJ/bGVeJCruHbdxBSmbxan/M8czUnn0Pp2bzmYIn+Xe9maCj4+cH27bBgAWzcCDUYMlwRW7eKH9etm/izvlTKkvpORBcW80UpaQi1VB5BEHBzm01RUSJx8eUk6t8RhOfCBQAsLWHcOFi7Fu6m6Mt1dPAfMor40OvcPv+wrp1Rn94YdOlMyuLF5J05Q9TYcRRHRuKwYgWGnV78jZS6fjZkJuWTHJlTqf5yW1tMx44h6++/KQy9o5vpPQE0Kji/tkR/qUTK2z5vE5cbx5+3NmI62ANVeiHZ/748xtKUDnXoXM+Kebuvcz76Ceso/4epNQBrqRG6WhgzwMqExZFJhOUVPtRmadEJudyU+AdCS777TqwMPn48xJWT89use29kWtoE7hKLqggyGfYLv0dqYU7s9Oko06rppSuLAQOQubnQ4fyvZKcWErj7MeLr9c1h3A4wsocNQ3HPzWR43eH8FfYXYRlh5Y/VaEg4Mo9XAz5goakR7R3asW3AjudD3qG6+EwCbWM4ubDivpVAEAQM2tijTMqn6GZmjcz5PKNRa8g/n4SOpylSo8rp6EVcTsXQvIioq8E0NY5D3qT/Q+25R45SePkyFlPeRKKlhSAItHA148fR3pyY25E32rsTGJnOqF/P0n3xcTacjSK/uPyiAwHhqfxy/DYjWzjRrWH5eYpFRSmEhHzI2XM9SE8/hZvrTFr5H8befiQSiazMcXrGJjTr1puQR7yAAHI7O5zXrUVmbU305NfIO3Omgqv0MIq4OOLffgdtT09sPvm4TE/r2e2b0TUypnGnblWa/0VhaXQyBlIJE+zv6EH+/beo65eSIlZimTNH3DR7SqhU4hJ69xZVJu7S0sSAyQ6W/B6XysmMyr1011I6ZmatMDX1JzJyBSpVGd7/BwrB3GXmTFEP8Kef7ndr1LErprb2nPhjDeoHdGAFQcD200+R6OkRPX4CysREnFb+gkGbl0PTw93LEqlMQmglw0ABLCZPRmJoSMqiReIBM1dRRzd4DahK3m9b2bWijX0bfrn8C/l2GvT9bMg9GUdxzMvx+y+RCCwc1gxbY12mrD9PWm45Hulaqk2tAVhLjTHPwx59qYS3Q2NQP+AtkEi0sbbuR0rKQRQKcTdHR0fcPM7PhzFjxId7aegZGdOoYxdunDhKbrpo7MnMzHBYtgxVenrNF4WRSuGdd7A/9hcNXDVcPBRDSvRj3FQNrOCVnaIxuH4gb9q0w0BuUK4shEapYPfWkQyO2Mh1XT3mtfyYhZ2XPxN5B41CQXFMDHlnzpC5dStxCxdxaOZbZEfcrvpkOkbg95ooXp9cM4U69JpaIjGQk/MfkIQoupWJKrsYPe/Kef+KCpQk3MxEKlxGIkAzsySo2/teu0atJmXpUuROTpgMGFBivJ2JLnN71OP0e535bkgT5FIJH2y/SssvDzF/z/VSw0Mz84uZvekSrub6fNSnfplrUypzuX17MafPdCI+4S/s7UfRyv8wrq7TkEr1KnV+vn0HIdWSc3pLybxaubU1zmvXILe3I+b1N8g9cbJSc6qLi4mdMRONSoXD0iVIdEv3tCeG3yTy0nm8ew9Arl2xh/NFIzy/kF3JmUywt8BYIsCHH8LAgVCvHpw/D8/AU3PqlKg7V5r4+//cbHHX1WZmSHSpFalrqTzubrNRKNKIiSnpfQLAzg6cnB4yABs0gB49YPly0TkIIJXJaDNiLGmx0Vw/dvihKWQWFth+MQ8tNzecfl+Fnq/vkzqdp462nhyXJhbcCkpCVckKtVITE8wnv0ru0aPkBwaKB30nQU48hJWuz/i2z9vkK/NZcWkFxj1dkRpqkbE1DI3y5aiKa6wnZ8UYLzLyi5m+8QIq9X8jyudpUmsA1lJjWGrJ+aSOHeey8lgX/7Bnzs52KBpN8UNlpuvVE7WBjx6FL78se17v3gPRqNWc33t/rG7DhtjO+5z8wECSvvuuZk9k7Fiwtsb/6BJ0DOQcWR+C+nFKjRvZwSu7QMsQk01jmeI+mLMJZzkac7RE16ycOOb+0Z738q5RR9eSLQN2MaDu0Ccm7/CogZeydCnx775L5Jgx3OzYiZCmzQjv2o3o8RNI+OBDTuz7m4sJUfzz7izUhYUVf8Cj+L0Jcj04tbhG1i/IJBj421EUloEiuXr5ai8KecFJCLqyMrX/HiX6WhoqZQGpUeeoZ5GPgUcLcSPiDjn791MUEoLltKkI8rJDGHXkUob6OLJ7Whu2vOFPW09LVp2KpN13R3h1TRAnb4rhoRqNhg+2XyU1t4glI5qjp1XSg6dWK4iNXU/A6U5ERC7D3LwDLf32U9fzU7S0LKp0PfSMTWjevQ8hAcdJi4sp0S6ztMR57Vq0XF2JnTKFnCNHKpwz6auvKLx6Fbuvv0LL2bnMfme3b0ZbX59m3XqX2edFZnl0MtoSgdcM5KLLbf58ePVVOH4cHJ9NxeGtW8WNw549S7bpSSUsqe9EfKGCz8Jrq4I+DsbGXpibdyQq+hcUiuzSOz0gCH+XWbMgKUnc2L2Lh19rbOp4cuqvDSiKH/biGHbpgvs/e9Bt2rSmT+GZU7elDQU5CmKuVT7f32zsWGTW1iQv+F7cHPboLkYPBZUsBgPgbuLOYI/BbA7dTGRRNCYD6qBIzCfnWGyp/V9EGtoZM29AI07dSmPhv6HPejkvHbUGYC01ynAbM9qaGjAvPJ6EovtlPg0N62No0JCEhK0P9X/lFRg9WqwefuJE6XOaWNvg0bI1l/7dS1H+/Zd84379MHtlHBlr15G1Y0fNnYSODsycic6+XbRtISclOofLRx7zpmriJHoCJXKGHVuBm4FDCVmIM7f+YdDWnhxUZzPdqjW/Dz+Eo7HTY32spriY4uho8k6fJnPLlgoNvNSffiYvMBABAf0WLbB44w1s58/HafVqWPQtcaaGmJqaEyeoCHx7ZtVz7/TNxfyGy5sho2ZyFvT9bEAmvNTC8OoCJQXXUtFrZokgq9xtO/JKKgLXURYX4a1/A+r3u9emUSpJWboMrTrulS66IAgCPi5m/DDKi1PvdmJqxzpciM5gzG9n6broOO9vv8qeKwnM7uZJYwfjh8ZqNBqSkvdy5mwPQsM+QV+/Dj4+22jcaBl6ei6Vvg6P4tN3EHIt7Ycqgj6IzMwM59W/o123LrHTZ5B9oGwtyqydO8n8cyPmr07CsEuXMvulxkRxK/A0zXv0RVuvct7KF4nYwmL+SkxnlFyDpb+fWHnll19g5cqHYy+fImo1bNsmFh0tK+XQx1ifNxytWBefxtH0MgyXWiqFu9sslMosYmJWld7B3x9iYh7K3+jaFRo2FHP77z4WBEGg3ajx5KalcnF/OQn/LxlODc3QMZBXSVNYoqOD5bSpFFy6RM7BgyCVgdcrYt58eukRN1OaTUFHpsOioEXoNjBHt6kl2YejUSS9PILqw3wcGdnCkR+OhPPv9aRnvZyXiloDsJYaRRAEvqvriEqj4b2w2IcMBFu7IeTkXiMn58HS0LBiBbi5iZpC6WVsmPn2HURxQT6XDz0cDmH19tvotWhBwsef1Ejp93u88QYYGlJn62KcG5tzduftSmv7lIm5O7yyE7lGzTvxUUTnRPNHyB8UqYr49uj/mHzqXfSVCtY3mcXknj9VSt7hUQMveckS4ubOJXL0GG526CgaeN26Ez1hIgkfflSuged+8F/qXbqIx+HDOK9fh903X2M5fRomgwch1K/LkW0bsXJ1Z+ySnzE1MCIwPpKEn1ZU/Tq0mgqCBAKWVuMilkRqoIV+c2vyziejynsCGpHPAfmXUkCpQb+S4Z9qlZrIK8moii7gaG+MlU4e1Otzrz1r926KIyKwnDYdoRryBTbGOszpVpdT/+vE90OboiuX8ue5aPxczXi9nftDfTMyzhEUPISrV6cikchp2uRXvJpvwNjo8Xf+9YyMadbjjhfwAeHpB5GamOD0+yp0GzQgbtZsskqpPFUYGkbCx5+g5+uL5cyZ5X7mub//Qq6tg1fPfuX2e1H5KSYZ1BreHDlQrPR4/DhMnvxM1xQYCLGxpYd/PshcVxs89LSZHRJDdm0oaLUxNGyIlWVPomN+p7i4lIdyKXmAggCzZ8Ply3D4gYhPx4ZNcGnmzbntmynM+2/IdUilEjx8rIm4lEpRFYTajQcMQMvdnZSFi9AoleA1DgQpBP1ean9zXXNebfwqR2OPcjbhLCZ93ZBoS8nYehPNSxQy+UnfhjS2N2b25otEpb08xu2zptYArKXGcdHV5m1XW/alZrMnJevecRvrfgiC1kPFYAAMDcWwkaQkmDixdK1wG3cPHBs05vw/O1Ap77/kC3I59osXITUzI3b6NJRlWZBVxcQEXn8dYfNm2rfRA0Hg2J+hj19t0rIujNtBm/x82ijgp4s/MmJbX9ZF7WFEgYpN3VfT0GvSve6a4mKKo6LICwiolIGX9vMvFAQFI0gk6LdsicWUKdh++SVOa9bgfvBgmQaefks/tBwcSg0F1Gg0HPhlGcUF+fSaOge5tg69P/icYrmM49s2kXP0aNWugZEdNBsF59dBTs3s6Bm0sQOlmrxzL2clwPzgJGTWesjtK1dxMfF2NgWZN1AUZuFtGgeOfmBkC4i/U6nLf0C7QX0Mu3V9rHXpyKUM9nZg59TW/DO9Lb++4oP0juRDbt5NLl1+jfMXRlJUlEj9el/j12IPFhYdazSk2afPQOTaOpwuwwsIIDU0xPG339Bt3oz4d+aS+fff99pUubnETZ+OxNAA+4XfI8jKLj6TmZhAyKnjNOnaE13Dqgnbvwik5BWwITKRwft34ujqLJb79/N71sti2zaQyyuWGtS5EwqaWKTgk1svb0TA08DVbQYqVR5R0b+UbGzWTPQGPxIGOmoUWFnB3Vomd2k78hUK8/MI3FGOxuBziEajISc9ldsXAjm7fTO7F3/D8Q2/oy6raMEDePpZo1KqCT9fsjJ6WQgyGVazZ1EcEUHmtm3iPbteL7iwHhSlp1yMbTAWO307FgQtAD0pxn3dKY7OIff0yxMKrSOX8uNoLySCwBvrz9eITm0tUPaTrpZaHoPXHSz5OymD92/G0tbUAGO5DLncBEvLLiQm7sCjzrtIJNr3+nt7i7rCs2aJeYFTp5ac07ffYLZ9/Skhp47TsH3ne8fvFoWJGj2am+3aI2hpIcjlCDKZ+HX3e7kM5HIEmfyR43Kx7d6xO8elUgRLK4S502jUcgwXr6k4/81GXKwKxXYtsd9D4+7M9eD8lDhujtD7d97+ezxjLdXIc7L5NV0Hz7rTyNl6mvS4rSji4lDExaFMTn7YIpZIkNvYILe3R79lS+T29g9/WVuVm89VHa4eOUDklVO0GjUIqUEy6ekxWLq0wLfPQM7t3s7Fj96nxe9r0a5Tp/KTtp4BF9bBmR+g6+ePvUa5tT7aHibkBiRg2Nah0mGSLwKK5HyKY3Iw7uVaacMp4lIKqqLzmFha4qY8AfXn32vL3LYdRWwsjj//VGOGmCAINLATDaKioiRu315MfMIWpFI93N3extFxPFLpk5Eu0TMypnmPPpzbsYWWg4Zj4Vh67p7UQB+nX34hZspbJLz3PiiVGA8eTMJ771McG4vzmtXILC3L/axzO7cgkUnx6fNiClWXS2Iiv/60hsK2XZmqDRw8COUYw08LjUbM/+vcWdyXqwgvI32mOlmxNDqZ3pYmdDF/+Qz1p4GBvgc21v2JjV2Hk+NEtLWt7jc+Igh/Fx0dURj+008hJETM8wewcnGjfuv2nP9nJ8169MHQrGr5vk8DlVJBWmwMKVERpETdJiUqguSoSApz7ocTG5hbEHr6BAU5OXR7fVq5909rFyOMrXQJO5tIg9Z2lV6HQadO6DZvTuryHzDu2xeJzySxcNqNndBkWIn+2lJtZnrPZO7xuewM38mAZgMouJhM9v5IdOubIzN7OYpUOZrpsXhEMyauDuTDv6+yYGiTJ1Yb4b+C8LLpZ/n4+GiCqqn9VEvNcjknn57BYYy0MWdBPbFwQFraMS5emkijRsuxtno4m1+jEXd4//0Xzp4VNxkfbtew9h3RMhz33fISf/z5gYHkHj+ORqFEo1SiUSjQKBVw93vF3WMPt2sUCnhozP3vyclBo1KhlkgJaj6HQh1zWp6bh1z5hMIQpNJ7Bp7czq7GDTyNRoNKlUtxcToKRcadr3SK735/5/jdn4uL0lAoMhEesacMDBrgWedztn/6IwVxcXTKUeG5eRPSyryh3WXLJLHC2ayroPv4FU4LQtNJ+/0apsProt/cquIBLwiZeyPIPRGL7Xt+SA0rJ/+weu5fpEWtoXP7ujRL/hVmXAZTZ9RFRYR3647c1hbnP/+o0QeoUplDVNTPRMf8jkajwsFhDC7OU9DSMqt48GNSkJPNyqmTcG3uQ9+Z75bbV11YSOy06eSdOIFB+/bkHjuG1dy5mE+cUO64nLRUfp32Ko07daPLq1NqcvnPnoAAsseOw3vJatpL1fzas92zXtE9Ll0SnwW//FL5SNQitZpuQWFkKVQcbVEXE/mzN2RfRPLzozhztiv29qOo6/npw43vvANLl0J2tqgEf4fkZLFI6IQJYnrHXbKSE1k18w0aduhMt9emPZ0TKIP87CxSIu8beilREaTFxaK+I7kgk2th7uiMpbMrls6uWDm7YuHsgo6+Aac2rePMtk349h9Cu1Hjy/2cwD0RnNsVwbgvW2FYBUMsPziYqNFjsJw9G4tXJ8Fyb9C3gkn7S+2v0WgY888YEvIS2D1wN1p5EpIWnkfL2RCLiY1eKkNp0b9hLDl0ky8HNmaU3+PVSPgvIAhCsEaj8SmtrfauWMsTo4mhHq87WPFjTDKDrE1pZWqAmVkbtLVtSEj4q4QBKAjw++/iw374cDH66MGEf0EQ8Ok7iH0/LiLyYjCuzR/+ndbz9a35ctI3bog1rj/+GMuJg/nrm2CSZ62iwyBH0UB81GgsfsCofNDQfMj4fKC9MA+phTVyBwe07O2RWVuXG4L2IKIxl3/PiHvQcHvUkLvbrlBkotGUnpMgCHLkclO05KbI5aYY6HsSdSuM3BQdvHqMwMjUGbnclGJFGrdufc2FS8PxGjuQw4viuZpdiN6sWTj98kvlDdQ2s+DqFjj7C3Qo/6W9Muh4mCKz1CX3ZJxYLOUleOhpVBryzyejU9es0sZfZnI+GfGnkGnr0VByEWybganoFcvcuBFlUhJ233xTY9dHrS4iNu4PIiN/RKFIx9q6L+5us9HVfXoPZ11DI7x69uXs33+RGj0cCyeXMvtKdHRw+GE5cTNmknvkCIZdu2I2YXyFnxG0axsajRrffhUkor1IaDTiW/rMmax+fQY5+gbM8PF81qt6iK1bQSKBUpRKykRbImFpfSd6BYfx0a04ltUvu6JrLWWjp+eMre0Q4uI24uQ4GV1d+/uN/v6wYIEoCN+y5b3DVlaitNOaNTBvHljccfYZW9nQrFsvLuzbjXfvAZjbP/lqsmqVioyEOJLvGHl3v/Iy7qeKGJiaYensimtznzsGnxumtnZIysiNbjVsDAU52QTu2IKuoRG+fQeV+fl1/Ww4tyuCsHOJePdwqfS69by9MejYkbSVKzEZOgSZz0Q48CEkXQPrhiX6C4LAO77vMHbvWFZfW82UZlMw7ulC5o5w8s8nVzp3/EVgemcPLsRk8unOazSyN6KJg8mzXtILS60HsJYnSr5KTcdzIUgFgcO+ddGRSggPX0Bk1M+0bn0CHe2SQtFHj4oyU+PGwerVD7eplAp+nfYqpjZ2DPvkq6dyDgwYIJYojY7m9IFEzu+Pov/MZjjUq1nPhkpV9IChlkHx3e+LH/n5AaNOrS4uYzaJaMxpmSGXmSDXEo06udzsnoEnl5si17r/s1Rq8JBRELznb46u/ZXub8ygUceHc8UUiixu3vqShIQtoDDj5j+GNDqZQZ2BQ7D58IPKn/QfIyDmDMy8CtqVy28rj9yzCWRuv4Xla03QdjOueMBzzl2vpvmY+ug2qlzY1Olt5wnY9AlNO3ShS9LH0PljaDsHdV4et7p1R9vDA+fVpRcVqAoajZqkpF2E315IYWEspqb+1HGfi5FRk8eeuzoU5GTz67RJuDT1pu+s/1XYX1NcTPaBfzHs2AGJvn65ffOzMlk5dRJ1/dvQY8qsGlrxM6agAN58E9asIb//AHznfEZTI33+aOpe8dinSMOGolFRCRWPEnxzO4FFUUmsaexKd4sX/37wLCgsjCfgdGdsbQZQv/4Dz9yEBFETcOFCMXfjAa5dg0aN4Isv4IMHHgf52Vn8Nv1VnBo1o//bVXhOVGadebkPGXkpURGkxUSjVIjPSIlUhrm9A5Yubvc8e5bOrugZVf33Qq1WsWfpAsJOn6D7mzNp1KHsqsHbFgRTmKtg5Cd+Vdp0KwwLI2LAQMxeeQXraa/C9/XAayz0/r7MMXOOzuF47HF2D9yNla4VKT9fRpGcj81s70pvIL4IZOQV02eZqO26e1obTPVfnnOraWo9gLU8M/SkEr6r68iwS+EsikriPTdbbG2HEBm1gsSE7bi4vFliTIcO8NFH8Pnn0KWLuJt4F6lMjnev/hxbv4rEW2HY1HkKu9Vz58KOHfDbb/i+8Ra3zidzdEMoIz5qgUyr6hUU76JSFXDt+tvk5FxBochApSpbx04mM0HrjhGno2OPoWGjBww5M+Rapg/9LJMZIjwat1kF0mKjOfHnGtx9WtKwlIebXG5Mg/rfYG3dlxs33sejfxRJnhYYrVyPtqcHpsNK5iqUSts58FsXCF4tVgd9TPSaW5G9P5Kck3EvhQGYH5SERE+GThU2G64d+wcQ8PNQQxL35B/SN/yBKi0Ny+XLHmtNGo2G9PQT3Ar/ltzcGxgYNKBZ09WYmbV5pl5XXUMjmvfox9ntm0iJjsSyHC8ggKClhXGfyun4Bf+zA6WimBYDhtbASp8DIiNh0CDRe/PJJ/wxaQppt+KZ4fx8eQpCQuD6ddFOrQ6zXKw5kJbF26Ex+BrrY1YbClpldHTssLcfSVzcepydX0NPz1VssLUFZ2cxD/ARA7BhQ+jWTRSGf/vt+xGiekbG+PQdRMDmDcSHhWDnWa/K69Go1WQmJ5Yw9rJT7hdb0TU0wtLZlabdet0z9MwdHJHKaiY/XiKR0vOt2RTl5XLg56Xo6BtQx7dlqX3r+tlwdEMoKdE5WDlXPh9Vx9MT4wEDyFi/HrMxo5E3HAiXNkGXz8rcLJ3pPZMjMUdYdmEZX7T5AtPBHiQtOU/Kz5cx6uqEbmNLBMmLHxljqq/Fj6O9GPrTaWZsusjv433vFR+rpfLUegBreSpMvxHFtqQMDvjUpYGBLsHBIygqTsG/5cFSXxqVStELeOECnD8PHh7324ry8/llynhcmnpVaqe/RmjbFqKj4dYtYsJz2Ln4Il49nPEfUL3dco1Gw7Xrs0hK2o21dV+0tSwf8Mo97KmTyYyRSJ7ei4tKqeCPD98mJy2V8Qt+QM/YpNz+SmUelwLfJyN/N5o8LSzWCdSdu6by4bir+0DqTZh5GWTaFfevgKz9keQcjcHmbR9k5k+m8MjTQJ2vIH7+WQz8bDHpV7nfs+zUTFa+NQEL5ya8Uj8Q8tPhrTOosrO51bUbes2a4fjzT9VeU3b2ZW6Ff0tGxml0dBxwd5uDtXWfx9psqEkKcnP4deoknJs0o9/s92tkzsK8XFa+NfHp3m+eJP/+CyNHijfZ9esp7tWLlmdu4KSjxd9eHhWPf4p8+aXoQYqNBXv7ivuXxrXcAroHhdLX0oQVDV1qdH3/FYqKUgg43RFLy640avhAic+RI+HkSVET8BH274cePcRQ0HHj7h8vLizgt+mTMbW1Z/inX5e7aVRcWEBqdOQ9Iy85KoLU6CgUhaIkkyBIMLWzfyhXz9LZFX1Ts6eyGVVcWMBf8z4gJSqCwe9/jmODxiX6FOYp+P3dkzRqZ0/bYVXbsFYkJBDevQdGvXph99ZA+K0r9FkMPmXnK38f9D1rrq1hU59N1DevT2FYBpl7bqNMykdmrYdxV2d0Gpq/FCkSf5yN5v3tV5jR2YNZXZ+v0PXnhfI8gM/HU7uWl55P69hjLJMxOyQGlUaDrd0QCgoiycoKLrW/TAYbNojFxkaMgKKi+23aeno07dqTm2cDyEyqvNDqY/Huu6IBuGkTjvXMqNfShosHokmNrZ6uUXTMryQl7cLdbTaNGi7Cw+N9XFzexN5+BFaW3TE18UVfvw5aWuZP1fgDOLNtE8kR4XSd/FaFxh+ATKaPt/8SNHHDKVZA+ptFXDo+nryo6xWOBUQvYG4iXPzj8RZ+BwN/WxAEcgNe7DLY+ZdSQKVBrwr5G6c27wQU+HTtDFEB0OCO92/1GtRZWVhMr17xhfz8SK5cnUZg0EByc0Px9PgI/5YHsLHp99wYfwC6BoZ49erHzbMBpERF1MicF/ftprggH7+BlfRqP69oNPD11+Jbua0tBAVBnz5sScogvkjB9OfM+wdi/l/LltU3/gAaGugy28WG7cmZ7E7OrLG1/ZfQ1rbE0WEcSUm7yM0Nvd/g7y9a56UYgN26ienzixY9XMhaS0cX/8EjiQu5RsQFcbNeo9GQnZLMraCznN76JzsXfslvMyazbPww/vzoHQ7++iMhp44jlcpo1KEL3V6fzugvFzFt7V9MWLiCPjPm4jdgKK7NfTAwe3rGjZaOLgPf/QRjKxv+/nYeSRHhJfro6MtxaWzBzcAk1Cp1leaX29piOnYMWTt2UJhvDNaNIOi30rWy7jC5yWSMtY1ZELQAjUaDjqcp1jO8MBtZF9Qa0tbfIHnZBQpupD2+rNUzZmQLRwZ7ObD08E2OhFZebqMWkefnyV3LS42ZXMYXHvZczMlnVWwqVpY9kUr1SmgCPoijo1gU5vx5+N8jG+9ePfshSCQE79n+hFd+h169xKfZt9+CRkPrIR5o6ck4sj4EdRUFV9PSTnDr1rdYWfbE2bmasU1PiISboZzdvpmG7Tvj0aJVlca2G/IhsUd8yQpxJr9JIeeuDCAxelvFDxm3DmDvDacWg6ryorllITXSRq+JBXmBSagLH3++Z0VecBJyG33kduXnp91FrVJx8+wBpNqO1De+BWigfj+UGRmkr1mDYbdu6DYsWUCgPIqKUwkN/ZQzZ7uTmnoEF5eptPI/jKPj+IdkXJ4nvHsNQEtXj9Nb/nzsuYoLCwjeuxM3L1+sXNxqYHXPiJwcGDIE3nsPhg4Vw/bq1EGl0bA8KpnGBrp0NDN81qt8iIgI8d5fkfh7ZZjmZE0TA13eDYsltfjFvSc8S5ydJyOV6nM7Ysn9g6UIwt9FEMTI0IsXxbz+B2ncuTsmNrYc/O1HNn32P36YNIKVUyey47t5BGzeQEpkBJZOrrQaMor+b3/Iq8t+461VGxn+6dd0mvA6jTt1w8bdA7nWs78H6RkZM/j9z9HW02fbV5+QkVBSf7Kunw0FOQpibmRUeX6LyZORGBqSsnAR+EyExCsQV/rGOYCRlhFvNn2Tc4nnOBpzFABBIqDX1Arrmd6YDvVEXagibc11Un68RGFYxgtrCAqCwBcDGlHPxoiZGy8Sk152Gk0tJak1AGt5agywMqGzmRFfRSSQoJRjZdWb5OQ9KMuRVOjXD6ZPh8WLYdeu+8cNzMyp37YDV48cJD87q8zxNYZEIuYCXrkC+/ahYyCnzVAPkiOzuXosttLT5OdHcfXaDAz0Pahfv+YqMdYEiqJC9v6wEANTczqOf63K47X19Og2aToRx/QQQgcgTVVz7dY7XL7yJkVF5Qi+C4LoBcyIhGs1Y9AbtLFHU6wiL/ApeYhrGEViHorYXPR8rCv9OxJ65hSKwkycGnZCEroLzNzAuiFpv/6KOj8fyyp4/5TKXG7fXsLp0x2Ji/8DO7vhtPI/jLvbLGSy58tQeBQdAwO8evXn5rkAkiNvP9Zclw/uozAn+8X2/oWEQIsWYh7z99/Dn3/eK6+8OyWT2wVFTHeu/O/Z02LbNvHfmjAA5RKBJfWdyFaqeC+s8vfrWu4jl5vi5DSJlJT9ZGdfEQ82bVqqIPxdRo8Wq4AuXPjwcalMRodxr6IoKkKlUFDXvy2dJ01hxOffMW31ZiYtXUm/Oe/jP2QkdXxbYmz1/P1+PoiRhSWDP/gcjVrNlvkfk5ue9lC7cyNztPVlhJ6t+vNIamKCxWuTyT12jHxFHdAygMDfyh0ztO5QXIxcWBi8EIVace+4IBXQ97bGZo43poM8UOUUk7rqKik/X6YwPLPKa3se0NWSsmK0F2qNhikbzlOoqBWJrywVGoCCIKwSBCFZEISrDxzbJAjCxTtfkYIgXHyg7T1BEG4JghAqCEL3B457C4Jw5U7bUuHOX7MgCNp35rslCMJZQRBcHhjziiAIN+98vVJTJ13Ls0EQBL6p6wDAu6Ex2NoMQaXKJzllb7njvv1WlIaYMEGMNrmLb99BKIuLuLh/zxNc9QOMHAkODvDNNwB4trDGqYEZZ/6+TU56YYXDlco8Ll95A4AmTX5CJqucZ+dpceKPNWQkxNFjyky09aq3NtfmPjRo25FLAbewzp6C0TYpacmHOXO2O/Hxm8veafTsCZb14eRCUFctTKY0tBwM0XIxIvdUPBrV87u7qS5WoUwtoOh2FvmXUsg5EUfmP7fJ2HoTJAJ6zcoXJn+Qs9u3IUiMadLWCyKOQ/1+KFJSyNjwB0Z9+6Bdp07F61EXExO7loDTHYmIXIq5WXta+u2nXt3PHxaCfs7x7t0fbT39x/ICKouLCdq9HceGTbDzrF+Dq3uKbN8uGn9paWLu3+zZ4oYLYtjd0qgk6uhp08vy+SuYtG0bNG8Orq41M199A13ecbVhV0omO5Kr7ompBZwcJyCTmXA74k4eoJYW+PiUaQDq6orC8Lt3Q1jYw23u3n689esfjPrie7pOnkqzbr2wr1sfLV29J3wWTwZze0cGvfcZBTnZbP3yYwpz76eHSGUSPLytibiYQnE1olJMx4xBZm1N8pIVaBoPhWvbxPzuMpBL5MzxmUNkdiSbQzeXaBekEvRb2GDztg8m/d1RpheSuvIKKSsvUxSVXcqMzzcuFvosHNaMK3FZfLbr2rNezgtDZTyAq4EeDx7QaDTDNRpNM41G0wzYCmwDEAShATACaHhnzI+CINwtk7gCeA3wuPN1d85JQIZGo6kDLAK+uTOXGfAJ4Ae0AD4RBOHx1aJreaY46Gjxvpsth9NzOFzkhq6uCwnxZYeBglhBbONGKCwUdxRVdzZ4zB2ccPPy5cL+3SiKKjbAHhstLTGm5dgxOHsWQRBoP6ouGo2G43+GlhtGodFouH5jLnl5t2jUaOlT1UirDFGXL3Jh3y68evbDqVHTx5qrw/jX0DEw5FxcJLaGQ7D8TIJusTU3Qt7jwsWxFBRElxwkkUDb2ZB8XRSHrwEM29ijyiyi4HpqjcxXFdTFKpRpBRRF3DHsTsaR+U8E6RtDSFl5mcTvg4j7JID4jwNIXBBEyi+XSf8zhKw9t8kNiEeVW4xhOwekBpUrbx0fdoO0mFvI9Lxwkp0DtRLq9yPtl5VoFAos33qr3PEajZrEpF2cOdOdsLDP0Nf3wMd7K40bL79f9e8FQkdf9ALeCjxdbS/gtWMHyctIfzG9fyqVWD1l0CCoV08UVe3Y8aEuh9JzuJZbyFQnK6TPmXclPh4CAmrG+/cgUxytaGaox3thsaQUKyoeUMtDyGSGuDi/RlraMTIz7xTb8/cXY3ULS38GT5kiPjoXL35663xW2Lh70P/tD8hIiGP7N5+heOCa1G1pg1KhJvx8SpXnlejoYDltKgWXLpFT2BiUhXCp/M2t9g7t8bPx46dLP5FVVHqUlCCTYOBvh+07Phj3cUORlE/KikukrLpKcUxOldf5LOnawJopHdz581wMm4NK5qTWUpIKDUCNRnMcKHWr4Y4Xbxhw9zexP7BRo9EUaTSaCOAW0EIQBFvASKPRnNaIb8lrgQEPjFlz5/stQOc783YH/tVoNOkajSYD+JdHDNFaXkwm2FvgZaTHR7fi0LMaQWZWIPn55RdsqFsXfvwRjh8XtYXu4tt3MIU52Vw7eugJr/oOkyeDiYnolgSMLHRp0deNyCtp5d7Yo6JWkJKyjzp13sXcrM3TWWslKczLZd9PizGzc6DNqMd3tOsaGNJ50pskR94m1rsxhg5eGL6ThJvRFLKzr3DmbC+io1eh0TwSqtFwEJg4w4kF5Sa5VxadBuZIzXTIPVlzxWDuGXaRWeRfvmPY7Y0gfVOoaNgtDCLu0zuG3XdBpPx8x7DbfZvcU3EURWWjUWqQW+uh722NUXcXTId6YjGpEdYzvbD7uCX281pj+24LjKsgHBy8ZweCRBunRm3QurUDjBxQCDZkbtqEyaBBaDmXLYSdnn6KwKCBXLs2E6lUl6ZNf8Or+QaMjZs9/gV7hnj16oe2nj4Bf1W9uJBKqeTcjq3Y1qn72BsiT520NDFn+csvxfvV8eNiQvUDaDQalkQmYa8tZ7B1zeqZ1gTb70SCDypbY7tayCQCS+s7kadS825o7Aub+/QscXAYi5aWBeG3F4rXz98fFArRCCwFa2tx43b1akgv22n10uDcuBm9pr9Dws1Qdi36CpVS9PhZuxphZKlbrTBQAOMBA9Bydydl1VY0dr4QtKrc56QgCLzt+zZZRVmM2D2CmUdmsjBoIX+F/cWZhDPE58ajUovPYEEuxbCNPTZzfTHu6YIiNofkHy6SuuYaxfHVK3T3LJjd1ZNW7uZ89PdVrsU/hdSgF5zHLS/YFkjSaDQ37/xsD5x5oD32zjHFne8fPX53TAyARqNRCoKQBZg/eLyUMbW8wEgFge/rOtI1KJRfCtoymG9JSNiKu/vb5Y4bNw4OHhT1ATt2hHbtwL5+Q2zr1CVoz3aadO2BRFJ9Xb5KYWgobml+9ZUY0+LpSdNODtwMTOL4pjAc6pmio/+w1lBq6hHCby/E2rofTo6Tnuz6qsHh338mLyOd/l98X2NJ9Z5+rfH0a82Zvzcz6oPPUEydgfKdPfhs+INbKYu4eWs+Scl7qF/vKwwM7pRvlsqgzUzYPUsMYXRr/1hrECQCBq3syPo/e2cdZlXVxeH33unuYpikuzsECUVCRKRDMBBQQYUPERNBDASlW7oF6Q7pboYhprs7bu7vj00zwDDBDHjf57nMcGLvfe7ce/ZZe631W1uDUUdkYOr1+Nw1odGhS1ejy1DLn7d/19/dpkKXrkHkFb5jpMDIxhQjW1NMXC0xK2ePka0ZRramd7cb2ZqisDAuljyWtPg4bp48itK0Hv7VneDEfqg/mMQ5styD89CP8jwvI+Mqt4J+Izn5MOZmZahaZfJtVc9i/g49J8ytrKnXsSvH1q0gLvgWbv5PD4G9Q+DRf0lPiOPVQR+W6tyjRzh/XlpN0dEwb540APPgRFoWp9OzmFjBE5NSWD/r77+hShX5KmoqWpkzxs+D8UHRbIhL4W330mcAl2aMjCzx9RnGjZvjSUk5huP9QjBN8xYO++wzKeg2dy6MGiM4kJzO2thkrmXmMqOqD3VsX8ywz8dRsVEz2n4wjD3zZrBz1lTe+PgLFEollRq6cXp7KJkpuVg7mD9TmwpjY1w//4zI4R+TmtkJh/R5T50nKztWZnyz8ewL20dIWgiHIg89kBNoojShrE1ZvGy88LbxxsvGC69yXnhXLYvtRcg+Ekv8tPNYVHfCtp0PJm6lK23lYYyNlEzrXYdO044wdPk5tnzcHDvLoqn9+DJSWAOwN/e8fwB5zSTiCdsLes4DKBSKD5HhpXh7l67QOgN5U8Xagk+83ZgaFkc9276YxW7E3/+zpz58zpwJJ05Anz5w8SI4OSmo36UbW6ZM4ubJ41Rq8hy8a59+KsUUJk+GefNQGilp3a8y634+w/GNQbTud6+4bVZWMFeujsTGuipVKv9U6h4mb5w4wrXDB2jSvQ/u5Yq2Btirgz8i/Ool9q5ZStcZ0wnv15/ELyZSY/FfJKTs4sbN8Zw63QVf3+H4+gxBqTSFWn3g4C9w+PdCG4AAVg3cSN8TRvq+cCzrut4z6h4y9p5q2LlYYlbutjFnY3bXqFPamKK0LB7DLr+c37kFUGBsXgdfy0ugU6G2a0Tqhm9w6N0bkzJlHjg+JyecoOCpxMVtxtjYngrlv8LTsx9GRiWvqFfU1H2jC2e3/8Pxv1fRdfQ3+TpH6PWc+mcdLt6++NdtWMwjLEKWLYMPP5SqG4cPy9y/xzAtLA5nE2P6eDg9xwHmj4QEGWX/VdGUccyTIV4ubE9IZdzNKJo52OBuZnhIfBY8PXsRFj6foOApONRbj8LX97F5gAA1akCzPjn8mZLMimMpJGq0OJkYY6pU0P3CLZbV8KepQ97FzV9UarZ5nZz0dI6sXoqFjS2t3/2Qio3cOb0tlBun4qj72uOjMh6H9auvYlGnDokbT2D3ugPKMwufOk92Ld+VruW7AqDT64jPjiciI4LwjHDCM8KJzIgkPD2c07GnydHm3D3PSGGEfxVf3k5pS5PAamRfSSSzgsC0lRuePr6YGz+bAfu8cLY2Y2bfuvSad5zP115g/oD6KEvhIldpoMAGoEKhMAa6AfXu2xwJ3B9rUhaIvr29bB7b7z8n8nabdsiQ00ig1UPnHMxrLEKIecA8kIXgC3I9Bp4/I3zc2JKQyqzcToxXryU5+QhOTk++mdnYyHzAJk2kKMymTVC+QWPs3T04s+VvKjZuVvwP425usvNFi+CHH8DDAxdvG2q18eLCnnAqNXKjTAUHtNoMLl3+CKXSlBo1ZmNkVLqKkmelprBnwSzc/CsUS56Tlb0Drd/9kB0zficwPIjyP08iauRnxP0wHo+JE3B0bMaNmz8SEvIH8fHbqVrlF2xta0LTj2H31xB5BsrmWb803yjNjLFq6E7m4ShyA2/HHz1g2FlgVs6uVBp2+UGVnc3l/buxcqyGrbs7tlGzwMqVhA1HUZiY4DzknpqrWp1ESOhMoqJWolAY4eMzFB/vDzExsS3BKyhezCytpBdwbf69gDdPHSM5OpKOI/5X6v/+AKjV8MUXMGMGtGoFa9aA6+MFey5mZHMgOYNx/h5YGJU+IfDNm6UOVFHn/92PkUKqgrY5fZ3R1yNYWsPvxfhblxKUSjP8fD8m8Po4EpP249K0qaz1IMRdkSGABLWGDXEprI1N5tb7uQiNAo90W35v6MirjrYkajT0uBBEn0tBLKjuR1unl+te1LDrO+RkpHN22z9Y2NrS5O3euPvbcv1kLHXaez/zZ06hUOA66gvC+vYjOaU+zoHbICMWbNzzdb6R0ggPaw88rD1o6PHgApEQgqTcJGkcpkvjMCIjgq1WR1li9Q+vxTbmzVutML4Zxyq7zez2OoWFi630Gt7vQbTxwtq0ZI35ej4OfN2xKt9tvsrsf4MY3jr/0R//JQrjAWwLBAoh7g/t3AysVCgUU4AySLGXU0IInUKhyFAoFI2Bk8AAYPp95wwEjgPdgf1CCKFQKHYBP90n/NIeGFuI8RooZZgbKfmtkhdvnb/FRuVAvGPWP9UABKhbF377DUaMgOnT4dNPjajf6S32LphFZMBlvKrVLP7Bf/GFDLGaNk2GgwINO/kRdC6eA8uv02NcPQICvyAnJ5Q6tZdiYVG6opeFEOyeOw1tbi4dhn+OkXHxFJuv0rwVgUf/5fDqpfj/NhPnYUNJnDUb80oVcRw4kOrV/sDNrTPXr3/L6TNv4+01CP/aH2B0aLL0AvYufC03u/Y+mFdyRGllIo07C2MUL8mK4JUDe1DnZKMwqYFvNXsI3IPK7Q3SF23H6b3BGLu4oNNlEx6+iLDw+eh02ZQp8w5+fp9ibpa/h4YXnbod3uTctk0cW7eCt8Z898RjhRCc3LgOBw9PKjZu9pxGWAhiYmRdv6NH5T3p55/hKd/laWFx2BorGejp/JwG+Wz8/Tf4+8sKA8VJOUtzvvL34Ntb0ayNTaGnhyEU9Fnw8HibsPC5BAdPxbnJayhWroSICFRly7I7UYZ47k9ORyegto0lE8t78ls3B5J0xrQ/K+1EDzNT/qlTgd6Xgnj3cjAzqvjQ1e3l0fpTKBS80m8wORnpHFu7AgsbOyo1qsm/q26QGJmJyxPSEh6HZb16WL/6KkkHTmDfXofxuWXwyugiGauzhTPOFs7Uca3zyP40VRqRsWFkHEng1YBGvHq1EWfdr7PCcRsb9BseONbR3PGeQWh7zzj0tvHGzszuuSy2DGjiw9mwFH7ffZ1aZe1pXqF03u9Kkqc+9SkUilVIT5yzQqGIBL4TQixEqn0+8HQmhLiqUCjWAgGAFhgu7ik9DEUqiloAO26/ABYCyxQKxS2k56/X7baSFQrFj8Dp28eNF0L8B1KI/1s0sbdmQBknlke3p3HCOCprUjExsX/qeZ98IvMBR4+G5s2hxittOLp2BYdXLuGdbyZiYl7M4Qnly8sl6tmzZXFlW1tMzIxo1acSW6Zf5Pj+CWhM91Gxwrc4ODQu3rEUgCsH9hB87jStB36AU1mvp59QQBQKBW3fH86SUcPYPXca3cf9iOrmTeJ++RVT/3JYt2iOi3MbHOwbcvPWz4RHLCQhcQ9VGr2Jw79LIO4quD1b8fJHxmBihHl5+6K5oFKEXq/j3I7NOHiWJyfbHT+HINBkkXA8C6WlJfaDBxIZtZKQkD9RqxNxcWlPOf8vsLL6b62GmllaUq/TWxxds4zYoJtPDHUOuXCG+NAgXvtoRPHnExeEmBjo1Ut6+YKCZHH39HQZFtGz51NPv5GVy/aENEb4uGFrXPquLzVV3tdHjHjAkVRsvF/Whe0JaXxzK5IWDtaUMc+f6q4BUCpN8PMbQUDAF8TVe53oytVZczmITWFppGp1uJuaMNTLlR7ujlS0kvOx7gMYMkTqEr1ye63XydSY9bXL0/9SMEMDwsjW6elTpvSFJhcUhVJJ+yGfkpuVyb5Fs2n/4ecojRRcPxlbIAMQwPWzkQS/2ZWk6Kq4nV0sFbSL+X5lZ2aHnU9N8AFduor0AxE0PGVEw/iqmNV3JqWunjCiCE+XnsOIjAhOx51mS/CWB9qxMbV5wFvobet99//OFs5FZhwqFAp+frsGgbHpfLr6PFs/aU4Z+9IVhVXSKF42Faz69euLM2fOlPQwDDwD6VodzU9cxkIdzOoKmfh5DcjXeUlJcpXY0lKqnEdfOcK2P3/Fs3JV3vryO0zNi/nLfuYMNGggFUFH31uB27N6AUrXSTjadaF23SmlLrQoLT6WJaM/waN8BbqPm4BCWfxhYJf27mTP/Bm0++BjqjdpQWifvmiio/Fduwaz+wp9JaccJzDwK3JywikTp6WCaWuM315c7ON7Eblx8ihbpkzCu1Z/MlLK8G7j5ahO7CJkizkmY9uTUPEqOTmh2NnVp0L5MdjZ1S3pIZcYquxsFnzyHmUqVn6sF1AIwepv/0dGciLv/Tm/2LzihWLYMKmk0bSpTIb29ZWSmdWr5+v0T6+FsSU+ldNNquFsWvqub/ly6N9fppM1fk7rZiHZKl49fZ3G9lasrOlf6u7XpZmonFymnv2DA9o6RAk3LLRaOni60MPdgRYONo+UF8nJkYK0zZrJ9I37ydbpee9KCAeSM/ihfBmGeL04dUfzg0at4u+J3xJz8zqe1fqTlebGwElNURYwDDt63DjSN22iXIdoTN5fAZU6FPGIn442NZeM/RFknYkDpQLrxh7YtHqwfJFKpyIqI0rmHKbfl3eYES6VSO9TA7cwtsDLxgsfWx/a+7anrXdbjJWFu08FJWTy5oyjlHe1Zs2QxpiVwoWv4kShUJwVQuSZS2MwAA2UCrYnpDL4SigDTXbzS/P/5fu8f/+FV1+VMtNLl0r1vu3Tf6dMpcp0+/L74i8q26YNBAZCcDCYmZGZeYPTZ94mJ9mNnKAf6fZF41IVbqjX61j7w1ckhIUwcPIMbJ2fzyQrhGDdj+OIC77Ju7/PxixHReg772BkZ4fv2jUY2d7L/dDpcggO+YPwsIWYqXVUqjwBF7++z2WcLxKrvv0fmclJYNIf/5rOtIl9g8AIV+IbZaDx0mJlVYFy5Ubj7PSq4aEWOLlxLUdWL6XPxN/xKF/pkf0RVy+xdvxXvDr4I+q81qkERvgELCzyrrNmZvbY+msPE56josnJawz2dObHCmWffkIJ8NZbcPo0hIfL0qDPi4WRCYy7GcWUSl4vlfepOMjW6dmekMra2GQOp2QigEoigJ7nTtF/83lsDv37xPO/+QYmToTr16HCQ854tV7P0IAwtiWkMcrXnS983V6qe1duViZrfxhLcnQURubdePPzDnhXLdjnTRMTQ9Brr2Pro6JMz+rQ78n1lIsTbVIO6fsjyD4XJ2sLNi2DdcuyGFk9WVxJo9cQmxl7V5AmPF0ah4EpgcRmxeJp7Un/qv15q/xbWJoU/Flux+UYhq44x4AmPox/M3+LZS8LTzIAS18GuIH/JG+42NPKKo2V6le4knA13+e98gp8+60UwHN2hqotXmHHjVFEXQ/k70nfo87JLsZRA2PGSMn1FSvQaNK4dPkjjI0t8XH/ndigHK4ejire/p+Rs9s2ERV4lVcHDXluxh/IcIz2H36CXq9nz/wZmHiWoez0aagjI4n6/AuE7t4qoJGRBRXKj6VB1fmYaAWXQr7lytWRqNVJz228pZ2YW9eJvh5AuQbtUOfoKeN+mrM+ENU9BTwsqFL5Fxo13IaLc5uX6gGqMNR5vRPmNrYcX593XumJjWuxtLOneut2z3lk+SA4GNq1uxcXaWIipZBDQ/PdxOyIBJQoGFpKPStZWbBzp6xi8TyNP5C1aZvaW/PtrSgic9XPt/MXAL0QHEvJZOS1cGocvcLH18IJzVHzua8bJxpV5jfr1dSqvB+rM8efuiAxfLj8+P7556P7TJVK5lb1pae7I5NDY/n+VvRLVavR3Mqat78aj7WDI5rMf7i472KB2zLx8MChfz/SbinIPXMQUkKLbJzPirGTBY7vVMTt83pYVHMi41Aksb+cJm13KPqcPBS2b2OiNMHL1otmns3oXbk3YxqOYXqb6ezstpM/Wv+Bi4ULP5/6mXbr2zHt3DQScxILNL4ONTz4oIUfS4+H8c/50vVMVpIYDEADpYbfqlbDWKHjfzef7abv7y8fGJKSpAjZ3vMtWXPmf0TfCOTvn75DlV2MRmC7dlC7NmLyr1y9OpLc3GhqVJ9J1SY1KVvZgeMbg8hMURVf/89AYngoR1cvpXyDJlRt+epz79/e3YPmPQcQcv4M144cxLJ+fdy//YasI0eI/23yI8fberSmgaIrfuG5xMfv4MTJ14iN3fxSPRAUlLPbNmFqYYmRuTNlGi0i2nQC6XYm2O20oUnT/ZQp0/2lqedXVJhaWFK/01uEnD9DzM3rD+yLuXWd8MsXqN/prSKrhVlk5ObCH3/Anj3yBmdqCjod2NmBe/6EfOJVGlbGJNHD3aHU5rnt2CEvtTjVPx+HUqFgamUvBPB5YLjhHnObsBwVv4XE0PjENbpduMWWhFS6uNqzsU55TjSuwmg/D3wtzfH3/4xcyyyi25jLfIwn4O4OjRrBrFly3vb1hRUr7u03Vsq/xftlnZkbmcCo6xHoXqK/h5W9A92/noCRqQk3j80jMTL66Sc9BucPPkBpbU3CJRs4u7joBllATFwscexVGbeRdTGv5EDG/ghifjlF+r5w9HmVWnoMRkoj2ni3Ydkby1jWYRkN3Ruy4PIC2q9vz/fHvic4NfiZxzbm9co09HPkyw2XCIxNf+bzX0YMBqCBUoOXtTMfWp/jnMqFVTHx+T7vm2+kbPj9nA5qzvbrY4gNusHfP32DKjuriEd7G4UC/vc/gprGk5R8iEoVv8Pevr5U/+pTCZ1OcHjNjeLp+xnQaTVsnzkFMytr2n34cYl5hep06IRHxcocWDyPrNQUHHr0wKFfP5IXLyZ1w8ZHjlc2G4l/WA4N1e2wsPDhasBnXLz0Abm5BZ80X3TSExMIOn+Qat2M0DgNx8brDB63wPVrE/yrf46JtUHN8HHUea0j5ja2HFu/8oHtJzeuxdzKmlrtnn8ezRM5cwbq1ZN5xr6+8N57cOoUfPQRxMbmu5l5kQlo9ILh3m7FN9ZC8vff4OIiRb1KAh8LM74rV4ZDKZksjf7vRhtkaHWsjE6i67mbNDpxjSmhcfhamDKjijeXmlVjamVvmthbo7xvDnFyaoWdRXVC+zmgO3H4ie2vWCHDfIWQr7AwWb7yfiNQqVDwY3lPPvNxY0VMMsMCwlA/PMm/wNi7udP2w7EIoWH9hG/ITk8rUDtG9vY4DxlCZrQ5WduWg7Z0eK9N3Kxw6lsF10/rYOZvT/qeMGJ/PU36wQj0at3TG7iP2q61mdp6Klve2sJb5d9ia/BW3tz0Jp/s+4QzsWfyvVhjbKRkRu862JibMHT5OdJzNQW5tJcKgwFooFTxvl8NKokAvr8VRYI6f1/Q8PC8tx+42IxOI8YQF3yLvyd+W2xGYFwrK8L6OOB5ygLPMr3ubrd3taRhJz+CLyQQfD6hWPrOL8fXryYhNJh2H3yMpa1diY1DqTTitSEj0OTmsH/RHADcvhyDZZPGxH73Hdnnzj94goMv1HgH6zN/U7/KHCpU+JqUlBOcONmByKiVCPHyPBTkB50uh7NHvqRKr5sI6zOkhTbCKX0ilkvB1NIe+3woQf6XMbWwpEHnboReOEv0jUAAEsJDCTpzkjoduhR/znB+UavlylbjxpCWJmMjQ0JgwQKpfDVzJmzY8PR2gFSNlsVRiXRxtcffspR5N2+Tmwtbt0LXrmBUgo7r/mWcaOlgzQ9B0ZxOyyJL92wPqy8qOiE4mJzOsIAwah69wufXI0jUaPnK34MzTaqytnZ5urs7YvWYP45CocC/8peoXIyJytz2xL7GjXs0SjQ7W25/uM0x/h58U64Mm+JTGXQ5lBzdy3O/r9qsBvZlepCdlsyGSQWPVHLo1w9jJ3viTwlEwKann/AcMS1jjfOAqrh+XBtTLxvSd4YS++tpMg5HITTP9t3ysfXhmybfsLv7bobVGsbFhIsM2jWIPtv6sCt0Fzr909tztTVnZp+6hCdnM3rdxf+8p99gABooVTg7NWeY6TpydIKvb+YvVtvbO+/ttrZQrkFTOn32JXEht1g/8RtyszKLcLSQkXGNgOtfYZddhorfXJX1uO6jVlsvnMpac2j1dVRPiIUvTqJvBHLqn3VUa9WW8g1KviSFU1kvmnTvw42TR7lx8igKY2PKTp2KsYcHkZ9+iiYm5sETmn8GmmwUp+bj7TWIxo22Y2tbk+vXv+Hc+X5kZ4eUzIU8R/R6LVFRqzl2rDV624OIHC9sdQuIPfMu7oF7yEkyxXn4cJRmpfMBvzRR+7WOWNjYcvy2F/DkxrWYmFtQp0PnEh7ZbS5ckOrCEyZIScwrV+C11wrc3KKoRDJ1ej71Kb3evz17IDOzZMI/70ehUDClsjdGQOdzNyl36DI1jl6h09kbDA8I45fgGFbHJHEsJZOoXDX6F/wB8kZWLhOCoql/PIBeF4PZn5ROD3dHttetwOGGlfnUxw3PfIYMOzo0wSHaltCaUWg1j59nH7dg+7jtw71d+bViWfYnp9PnUhAZ2pfDKFcoFFRv1QATy44khIawafIEtOpn9+Apzc1xGfkFuUmmZKyaWQwjLTymZW1wHlQdl6G1MHG3Im1bMDG/nSHzeDRC+2xGvaO5I0NrD2VX91180/gb0tXpjPp3FB03dmTltZVka55sSDf0c2Rsh8rsuhrHvEPPHkr6MmFQATVQ6ggKmszUsATWK3qztIYf7Z2f7LFasUKGkNy/gGZkJNNk3n4bFi+G2Gsn2DL1Z1x8/Og+7kfMra0LPU6NJoVTp7si9BoaVF+NWYV6csV+y4N1b+JC01n/yxmqt/Tkld6Pqg8WJ5rcXJZ9+Sk6rZYBv87AzLJ0eDh0Wi0rx31BZkoS706ZjYW1Dapbtwjt2QsTH298V6xAaXFfGY/VfSH0MHx2FcxsEEIQE7OOm7d+Qq9X4+83Ei+vwSgLKRld2hBCkJC4m6CgyWRnB2Ok9yVwi6DL8Fmc3JJNbpaGRrsGotebUe7QGRQmT1ZdMyA5tWk9h1cu5vVhn7Fr9p/U7/wWLfsOKtlBaTQwaRL8+KNUtJo3DzoXzijN0uqofzyA+nZWLKvpX0QDLXoGDYJ//oG4OJniWNJE5qo5k5ZFWI6asFzV3Z/RuRruf1w1VSjwMjfF28IUHwszfMxN8bn9u7e5KTalUHI+WaPln7gU1samcCEjGyMFvOpoSw93R9o722JWCAWetEXjOOO7mnKO7+Nbe2yex/j6yrDPh3FxgfgnZH5sjEvh42th1LC2ZGUtfxxNXvx7fWpcNiu+O4F/rWQCDi6mfIMmdP7sS5TP6AYXWi3B7VtCRjz+mzahKFO42rnFjSo4lbTdYahD0zGyM8OmjRdW9dxQFKAkhk6v42DEQf66+hcXEy5iZ2ZHz0o96V25N84WeRd/F0IwfOU5dl6JZcX7jWlS7uVV/jWUgTDwQpGdHcrhE68x3vQvcpR2/Nuw8lMn0hUrZAhJeLj0CE6cKCeTUaOgWjVZc0iffJLNv0/CxceX7uMmFMoI1Ou1XLg4iNTUM9Srtxo721owfjx89x1cvvxIXa7Da29w6UAk3UbVw6Pc8wvB3LtwNhf3bKfHtz/hVbXGc+s3P8SHBrPiq8+o3OwVOgz/HICMgweJHDoMm9dfw3PKfTUUo87C/Feh3XhoNuJuGypVHIHXvyUxcS82NtWpUvlnbGyqlMTlFDkpqae5desX0tPPY2lZDn+/z9k6cT0WtnZ0G/szi0YfoZpnPK7LvqfMx92w+3hiSQ/5hUGTm8v8T94jNyMDI2Nj3p+xECt7h5Ib0JUrMHAgnDsn1T2nTwfHwudyzo2I57tb0WytW4H6dlZFMNCiR6MBNzfo1EmW8inNqPV6onI1hOeqCctREXb7Z3iOmtBcFekPeTMcTYzwMTd7wCj0sTDF29yUMmamGD+nEkEavWB/cjprY5PZnZiORgiqWZvTw92Rbm4OuJgW0cLRuXNc2PkGaQ2daPrKcUxMbB85JK8FW6VS5vF//72MfH6cDbo7MY0ProbiZ2HGmlrlcDN78Re81v18Bp1WT8V6sRxYPI/qrdvTfsgnz5ynn7H9HyI/H4v7OzVx+HFNMY226BBCoLqVSvruMNQRGRg5mmPbxhvL2q4ojAr2vbgQf4G/rvzFgYgDmChN6FyuMwOqDcDf7tHFr0yVli4zjpCeo2Xbp81xszUv7CWVSgwGoIEXjrNne3E514Kx6hEM8nTmp4oFq1u1Zw/07CknlLVrwdf+NJt/n4iTlw/dv56AhbVNgdq9efMnwiMWUqXyL5Qp011uTEqS1uc770i3432oc7WsGn8SEzNjeo5rgJFx8Udfh144y9+TvqNex660GvB+sfdXEI6uWcaJDWvo9uX3+NWR96ikBQuIn/w7LiM+xXno0HsHL+0KcVdh5GUwuXezFkIQn7CD69e/R6tNw8dnCH6+w1EqS184pE6Xg1qdjEaThEaTcvv3ZNSaZDTq2z81yajVSeTkhGFm6oaf/wg83N8m6OxpNk+eSKeRX6IwKs+eRQE0DpmJfcol/A+cRGFlEH95Fk5v2cCh5Yuo/Von2gz+qGQGodXCb7/JJ187O5gzR9ZBKAJUej2Njl/D39KMDXXKF0mbxcGePdC+vfQAvvlmSY+mcKRqtLeNwtuGYe69n5G5arT3PW4ZK6CsueldA1Eah7eNRXNT7IrAw3UlI5s1sclsiEslSaPF2cSYt90c6OHhSDVri6c38KxotWTUceLUn874+n5MOf/P8jzs4QXb776DAwdkOaeuXeVCgM1jpuYjKRkMuByCq6kxa2uVw9ui9N3nn4VLByI5vOYGvb5pSOCRjZzYsIaGb3anRZ93n6kdIQRhrzdCE59GuYPHUNq9GF4tIQS511NI3xOGJioTY2cLbNt6Y1HTpcA1lEPTQlkasJTNQZtR6VS08mrFu9Xepa5r3QcM65txGbw58yhVPWxZ9WFjTArggSztGAxAAy8c0THruXZtDDtd17E8QcmWQqxg37olHyyuX4epU6FjszNsnjIRR08v3vl6AhY2j65SPomYmI0EXBtF2bIDqFTxuwd3jhgh9a2Dg8HL64FdoZcT2TbzEg07+9Ggo1+BriW/5GZmsmTUMMysrOk36Q+MS0NcVR5oNRqWfzkCVU42706ehZmlJUIIoseMIX3zFjynT8O23e26bCGHYUkn6Pg7NHjUoNVoUrhxcyKxsRuxtCxP1SqTsLOrW2xjF0Kg1aY/asCpk6Vxp0m6z6iTxp5en5NnWwqFMSYmDpiaOGJi6oiJiQO2tjUp69kPIyP5oLbm+y9JT4znvT/ns/eva0RciqXJ7k8p+3ZZbCfuLrbrfFnRqFWc+mcddV7vXDLCSIGB8O67cPIkdO8u7xsuLkXW/PLoJEZdj2BNrXK84liwha7nwUcfwfLlkJAg692/rGj1gmiV+rZReL8HUU14rorkh0Qx7I2NZGhpHgaip5kpJo95OI5Xafg7LoV1sckEZOViqlDQ3lmGeLZ2tH3seUVGq1Zc7hpHUh0TmjY5iKlp/hamhJDVTkaNgipVZNROuXJ5H3s2LYs+l4KxNFKytlY5Kli9uN6bnEw1i/93lFptvGjSrRz7Fs7i4p4dvNJvMPU7P9tiUPa2JYR98TMuPV/B+Yc5xTTi4kEIQW5AkjQEY7OxauSOfdfyhVIsT8pJYs31NawKXEWqKpWazjUZWG0gbbzbYKSUUWWbL0bz6arzDG7mx7edqxbV5ZQaDAaggRcOrTaLI0cbY+38JkPSemNlZMTeBhUxLWB+QkaG1FPYtAkGD4YxH51l+58TcCxTlu5fT8j3A2B6+iXOnuuJrW0d6tReglL5UAhKWJictUaMgN9/f+T8XQuuEHwhgV5fN8TBvfhCsrZN+40bJ47QZ8LvuPmX3tV/gJib11n1zWhqtn2Ntu8PB0CvUhHWfwCqW7fwXbUS80qV5BPCwvaQEQufngOjvMN/EpMOEhj4NSpVLF5lB+Lv/znGxk9/r/V6LRptKhp10l2j7X6jTn3ba3e/USdE3sI+SqUFpqaOmJg4YmrigImpI6YmTpiYOGJies/QMzVxxMTECWNjmydOdHHBt1g+diSv9H+POh3eZNGow7jEnqFmyGz8ZnyLov6AfLzTBkoFOp180h03DqyspOFXxOqtWr2g+alr2BkbsbNexRIr+/I0dDrw9IRXXoE1pT9qrVhJ1+oIv20UhueoHwgvDc9Vo7nvWU0JeJqbPpBz6GBixK7EdA4kp6MTUMfGkp4ejrzpao/D88yXGzuWzLV/cHK+J97e71GhfN65gI9j717o0UP+vmaNLLWbFwGZOfS8GIRewOpa/tSweQ757TEx0KuXHFg+a3Dmh22zLpEQls6ASc0APdumTebG8cO8NnQk1Vu1zX9DQhDxRk2yo3SU+/coxg4lGNZeQIRekLYzlMxDkdi288G2zWNU/p6BHG0Om29tZknAEiIyIihrXZYB1QbQtXxXLIwt+H7zVRYfC2VGnzp0qlmmCK6i9GAwAA28kARc+5L4+G2oquxj4NVoRvu684VfwW+6ej388INM1WvcGGb+dI7DCyfg4FGG7t9MfKoRqFIncvr0myhQ0qDBP5iaPibEol8/aWmGh8NDN+CsNBWrfjiJk6c1XT+rU+AQhydx/fhhtv7xC0179KXJ272LvP3i4ODSBZzd9o/MVaxWEwBNXDyh77yDwtgY3/XrMHZ0hOs7YVVP6DoHaj/+2rTaTIKCJhMZtQxz87L4+40EheKBMMsHQy6T0WofX4vJ2NhOeujuGnWOmJg6SePOxPHu9ju/3/HaFRXbp0/m1pmTDJm9mMQINf9MPU+NK/OoU/UgNlOvgtWLEe7zn+fmTal4cvQodOkCc+cW6YPkHTbGpTA0IIxF1X15w8W+yNsvKg4fhpYt5fP0nYd+A4+iE4JYleauGM0DBmKumgS1XIjyMDOhu5sDPdwdS84rtnkzvPkmVw/0I56zNG1yEDMz12dqIihIhoIGBMgI6c8+kyV3HzkuO5ceF4LI0OlYXsOfhvaFF3d7HDpdNomT+sLevbhW7Ydi1uwia/vmmTh2L7hKlxG18ariiFaj4Z9fxxN+5SJdvhhH+fqN8t2W6u+fCP56KY5vv4HbhClFNsbniRCClHU3yD4Xj0O3Clg1LJp7pE6v40DEAf66+heXEi5hb2ZPz0o96V6hJ8OW3iAwNoPNHzejvGvpjZh4VgwGoIEXktTUM5w915MqVX7hx+T6bEtIY2+DSlQs5MT2999Sb8HODhZNPU/g5h+x9yjDO08wAvV6DefP9yc94xL16q3F1qZ6nscBcOmSrNU1YcKjxY2AgKPRHFgWSOt+lanavGhXmzJTklkyajj27h70Hv/bM6uJlRQaVS5LR3+CQDDw1xmYmMu/cc7ly4T1649FjRp4L1ooVS7nNAedGoadfLxawG1SUk8TGDj2gVIRCoURJncMt7shl44PeuXueuccMTGxf9TT+xzJSE5kwcfvUfu1TrQe+AFHVgdyaX847W6Mofy7nije3VpiYzOQT/R6WbtvzBgwM4Np0+RCUTF45vRC8Orp6+iE4N+GlR8o2F3aGDlSpj0mJDw+58vA08nS6ohTa/GxMMWopP/e8fHg5kb2H+M4UXMdnmV6U6nS98/cTGamnKc3bJBflXnz8g4RjsxV0+NCEDEqDYtr+BVpuLNOl0NS0r/EzRpMYgMz9BZyvrG7mkulPxKxiVZCTt5h/c+CVq3jr/8dwb+2C23elWGI6twc1v04joSwEN7+anz+Rdxy04h+uw7poeaU270HE0/PQo+vJBA6PYlLAlDdTMGpf1UsqhbdIqcQggsJUjDmYMRBTJQmtPPuyO5jlXAw9WTTx82xNnvxVWbhyQbgy5fxaOClwc6uHhYWvsREr2d8BU+sjJSMuh5R6NpLb78Nx45JufE3B9TBqu63pMbGsG78V2SnpeZ5zs2bE0lNO02VypOebPwB1KwJr78uH/LymByqNPWgTAV7jm24RVaaqlDXcj9CCHbPnYZWrabD8M9fGOMPwMTMnPYffUpaXCxH1y67u92iRg08Jkwg+8wZYidMRAC0+BwSb0Dg0w0fB/sGNGywjfr1N9Ck8V5atjhH61bXadH8JI0b7aBu3RXUqD6dypV+wN9/BF5l++Pm1hFHhyZYW1fCzMylRI0/gAs7tyL0grq369QFnwjHIeU6ZSpHoqj6gqtm/BcICYE2beDTT6FVK6n42b9/sRh/AHuT0gnMyuVTH7dSbfwJIR/uX3vNYPwVFitjI/wtzUre+ANwdYVy5bD8NwAPj+5ERa8mJyd/NX3vx9oa1q2TETvLl0tPcWTko8eVNTdlU93y+FmY0v9SMNsTUgs1fJ0ul/j4XVy5MoLDRxpy+cpwUlq543FYS50voqn6SzzZniacnluWmye+QKvNKlR/AMamRpSr50rQ+QQ0apkLampuwVtjvsPO1Z1/fv2RuJCg/DVmbodLzzaAnoQ/XkwPIIDCSIlT3yqYeFqTvCoQVVh60bWtUFDHtQ7TXp3Gpq6b6FK+C3vCt6Et8wvRZrP5aN169Ppnq0/4ImIwAA2UWhQKBWU8upOadhorbSTfl/fkVFoWS6OTCt12zZpw+jQ0bQoffFGbGNtvSY2LZe34r8hKTXng2OjotURGLcPb6z3c3fP5wD1mjFwJXbIkz+tq3a8yWrWeI2tvFvpa7nB53y5Czp+hZd93cSxTMNXUksSrag1qtevA2e2bib4ReHe7XedOOH34Ialr15KyciVU7QqO/nD4d/kU+RSMjMyws62FpaUfJiZ2pTYfKi80ublc2ruT8g0bY+fqTlJoEhm5xpQxCsPSTQNVSknxcgOPIoQM8axZE86ehYULYds2mfRWbF0K/giLw8vclK6upTv/5/RpiIgo+eLvBoqBJk3g+HH8fIYBCkJCpxeoGaVSloX45x+pmVS/voyefhgXUxM21ClPDRsLPrgayrrY5GfqR6dTkZCwmytXR942+oaRnHIMd7c3qVNuFs3HKKn8axSOF1V4HFDR5N0IPI4qCE9axYmTr5GQsKdA13c/lRq5o1HpCLmQcHebpa0db381HjNLKzZM+o6UmPwZ0iZth+NYIZO0rdvJvX6j0GMrKZRmRji/Ww0jW1OSllxFE//kIu8Fwc/Oj++afMfu7rv5sOaHWNlFcFE3kdfX9mBP2B50et3TG3lBMYSAGijV5KpiOXq0Bb4+Q/D3/4KeF4M4l57N4UaV8TArvLKlRiNVx6ZNgx6vX6Kp8w/YurjS49ufsLJ3IC3tPGfP9cHBviG1ai3Mf6FxIWSiYVKSlB/Nwxt3ZnsoJzcH413NCeWdujdCIMRtu0YI6fF6eJu4c6gAIX+qc5OJuTYbM0svXMr1AxTc+W4LPcBj2tA/2Med9u5cwv193NlmYmpE636V8apa9GUHVNnZLBk1HBNzc/r/Mg3j24XNhV5P5PCPyTx0CO8F87EyuwmbP4F+G6B8myIfR2nhwq5t7Fs0m14//Ipn5aocnbCOC5FOvOP4M67lFPDerpIeooG8iIiA996TNQ7atpXGn3fhxQyexpGUDLpfCOLnimV51zPvIsilhS+/lDpZ8fGPpEobeNGZNQuGD4eQEG6olxEZtYzGjXZhaVlw9euAAKnmHRYGM2bIWoIPk6XVMfByCEdSM5lUsSyDnvAd0OlUJCcfIi5+O4mJ+9DpsjAxccDFpT1urh2xt2+EMilFqtBcuwZ168rXhx/K16lTpP75Kdcb3SQz6zrOzm2pWOFbLCwKtsAj9IKlXx/D0cOKzp/UfmBfUlQEa74bg4m5Bb3H/4q149PDIXXTW3FrQRyWjV/Ba+6LpQj6MNqkHOJnX0RhrMR1WC2MbIuv9EeWOoteq2YQrN6O0jQZLxsvBlQdwJvl38TC+MWTKTbkABp4oblwcTCZmddp1vQQYblaWp8K5BVHG/6q7ldk3py//pJy5A0rX+btGt9j5+LCm2M/5+qNgRgpzWnQYCMmJvbP1ujff0t597VrZW3Ah9Bp9exbHEByTBYoFHcjwhR3fs9zG3evWaFEbkBP1NWFqLPj8an7MSZmDiiUcs8T21A8eMwD25QKbg9B/nN7vwKIupFCRoqKrp/Vwc332Upo5IeQC2fZMOk7Gr3Vk+a9+t97vzIzCevdG018An6rVmD6T2dw8INB24p8DKUBodfz1+cfYWZpRZ+JU9BnZbF6yBr0Vrb0L/M+vPYTNBle0sM0cD9CyJvJZ59JicvJk2HIkGIL93yYHhducS0rl9ONq2JeimtaCQEVK0rB5J07S3o0Boqc8+elsbRyJaq323HsWCtcXNpRvdrUQjWbkgK9e8OuXXK+/vNPmcpxP7k6PUMCQtmVmM44fw8+8XG7u0+vV5GUdJj4+B0kJO5Fp8vE2NgeV5f2uLp1xMG+0b2Q/5gYuXgTEiJdkO3b3+tECOjbF1atQv/3WiLqpRAcMg0Af79P8fIaVKDUgRP/BHFuVxgDf26Gld2DRk5s0E3Wjv8KOxdXen7/C+bWTxG8Ob+cpJ/HEH/RFu+/FmHVpMkzj6c0oY7KJGHuJYwdzXAZUgulRfHl6KXnaug8/RCZRhcoV/4M11Ku4GDmQK/KvehVuReO5i9Ozd0nGYAIIV6qV7169YSBl4vYuO1i7z5/kZh4UAghxIywOOG2/7zYEpdSpP0cPy6Eu7sQVb0uiyn93hJb19UR+w9UFRkZgQVrUKsVokIFIerVE0KvL9Kx3s/Jf9aJyT06iquH9hdbH/eTmZorlo47KhZ8cUikxGYVSx87Zk4Rv/fqLGKDbz2wXRUeLq43bCRuvdFRaPdPFeI7WyHCjhfLGEqaW2dOiMk9OoprR+TnPmLqLDHzw93i8IQF8rqTQ0t4hAYeICpKiDfekI7zV14RIijouXZ/Ni1TuO0/L2aExT3XfgvCxYvybZo7t6RHYqBY0GiEsLIS4pNPhBBC3Lz1q9i7r1zB59L70GqFGD1afn5atBAiLo+Pu1qnF0Ovhgq3/efFjzfDRXz8PnHl6ufiwMGaYu8+f3Hw3zoiIOBLkZj4r9Dp1I82EB4u524rKyEOHsx7INnZQjRqJISlpRBnz4rs7Ehx4eKHYu8+f3HiRAeRknrmma8tKTpTzBiyT5zfE5bn/tBL58XUPm+KlV+PEuqcnCc3psoSuh+9xM1GtURA1Woi+uuvhToy8pnHVJrIuZEsIsYeFvFzLwq9RlesfQVEp4lKX28XPeYeFaeiT4uP930sqi+uLuotqyd+OPaDCE17MeZf4Ix4jL1UepcIDRi4jYvzqxgb2xMdsx6AIWVdqGFtwVc3I0nV5F2HrSA0bgxnzoC1RzVSvd0wd0wj9kR5hLqA6lNGRjB6tMz/OXCgyMZ5PwlhIRxbu5wKjZpSpXmrYunjYazszOj8SW0UCtj85wWyUotOyOYOrwx4HwsbW3bN+ROd9t7f2NTLC88//0QdGkr0qqsIcyc4/OImuj+Js9s2Ye3kTIVGzdCmpHBr+zmEwojyVgfAozY4+JT0EA2A9AYsXw7Vqsnv+Z9/wv794O//XIcxPSweO2MjBpYp/SVB/v5b5nd17VrSIzFQLBgbQ4MGcPw4AD7eH2BkZEVwyB+FbtrICH79FVaskHmk9evDuXMPHYOG712C6WxxkxkRSXx2+STxCQdwdX2d2rUW0aL5SapUmYSTU8tHPXUhIVJxJi4Odu+WRSrzwsJCegadnKBLFyxSldSqOZeaNeag0aZz9mwPrgV+hUaTmu9rc/SwwsXbhhun4vLc71OjNm98OpqYm9fZMnXSA3PjI5hacsO6D2VaRPF3WlcS1m7iRrsOxI4fjyYu7/ZLO+YVHHB8pyKq4DSS11xH6IsvgrGKhy0/vVWDk8Ep7L1gxfRXp7Op6yY6+Xdi061NdN7YmZEHRnIh/kKxjaG4MRiABko9SqUZ7u5vkpCwF40mFWOlgt8re5Gk0TIhKKZI+/L0hNVrVtGyzW62bXiHiHOmrP5uLBlJiQVrsH9/cHODX34p0nECaDUadsz4HTMra9q+P/y5iJusWAG+vuDoYcncXbXISNOwZfoFVNmaIu3HwtqGtu8PIyE0mDNbNjywz6pxI9zGfUXmoSMkxNaHm7sg5lKR9l/SxIcGE3H1EnVf74yRsTHJi/4iwboiFhbglrIVqnYp6SEaAPmQ+NZb8ntetSpcvCjVPp9SnqSoCczKYUdiGu+VdcbauPSr//79N7RoIQUjDbykNGkCFy5ATg4mJvZ4e79HQsJu0tOL5l7dpw8cOSJ/b9YMVq1Sk5h0kICA/3H4SCMuX36fvqqJ9LIMYK+iA+ud/6ZipZ9xcnrl8eGZN29K4y8tDfbtkypxT8LdXdY9TEmRCYo5Obi4tKNxo114e79PTMx6jp9oR0zMhrt59E+jUiN3EsIzSI7OW120YqNmtP1gGCEXzrJz1lTEY9QqV6yA3r8PxtJKjba8Fe2Dd7MhvRtJq9cT1K49cZMmoU1IyPPc0oxlHVfs3vAj53IiaVuD8/2+FoRudcvSt5E3c/8NZueVWPzt/Pm+6ffs6r6LD2p+wJm4M/Tf0Z9+2/uxL2zfCycYYzAADbwQlPHojhBqYuM2A1DTxpIhZV1ZHpPE0ZSMIusnJfU0ISE/4OTYCu/yE5lzcDyJMams+Hos6YkFuFmam8tiV7t3y7yIIuT4uhUkhIfSfsinTy1iXxSsWCFz38PCpNPj1GVb5u2sQVJ0NttmXUKrLtqbX4WGTanYuDnH168kKTL8gX2Offpg36snSTsvkxbpAEcKl1tSmkgMD2XP/BmYmJlTo81raBMSSFyxkmTXmviWTUWhEFDFUP6hxFm7Vnr9du6UuX6HDkGFCiUylBlh8VgaKXm/rEuJ9P8sXL8OV68a1D9fepo0Aa1WhtUA3l6DMDa2Jzi46O7VdepoOHDgX36cMAYLy0ZcvPge8Qm7cHZuQ62aC2jZ4iRTG/ZmrJ8Hf8en8cHVUFSPk/cPCJDGn0olPfn1806beoTate+5IwcNAiEwNraiQvmxNGiwGUsLHwKujebc+b5kZd16anMVGrihUCq4fjL2scfUbPM6zXsNIPDovxxYMj9PI2jcOLgQWZH1AV34uuVv1PG9zLdR3zMoZwe2nTuRvHwFt9q1J+6339CmpOTRS+nFpmVZrJt7knksmox/86gNUoR827kqtcraMWrdRYITMgFwtnDmkzqfsPvt3YxtOJbEnERGHhxJYHLgU1orXRgMQAMvBDY2VbGxrkZM9Pq720b5ueNjbsro65Hk6ApfsyU3N4bLl4djbl6WatWmMnKkEfNWVmHF2R9Jik9jyZdjSU+Mf/aGP/pIFrr67bdCj/EOUYEBnN68gRqvtqdcvYZF1u6TGDUKsh9SYb4U7Mjm81WJCUpj14Kr6Ivg73A/rw4agomFJbvm/In+odU193HjsGzQgJgTVuQc2gqJT59cSzM5GensXTibpf/7lJSYKNp9+DHmVtYkzp9PirkXWkzw5SC4VgXn8iU93P8uiYnQowf07ClVTC5cgC++yFPp93kQlqNiY3wKA8o44WhS+osXb7jt0H/rrZIdh4FipnFj+fPYMQCMjW3w9fmQpORDpKYWXKhPr9eQlHSIa9fGcvhIY0LDBlO//k6Sk19l3Lj5TP7tFGU8JuPs3Bql0gyFQsEIXzcmVPBkR2Ia/S8Fk6V7aLHy4kUZ6qlQwMGDUKvWsw2qa1eYNAnWrIEff7y72ca6MvXqraVypQlkZl7j5KlOBAX9jk6X+9imLG1N8a7qyI1TsU8McWzY9R3qdezK+Z1bOLFh9QP7dDoIv71m+u4/s7kUV4213d+lltslzoR5UmbiRMpt24pN+3YkL/qLoDZtif/jD3Rpac923SWI3Rt+WNRyIX1nKFlniy+k1czYiFn96mFipGDo8nNkq++F3VqaWNKnSh+2vbWNee3mUc25WrGNozgwGIAGXhg8PN4mI/MqGRkBAFgaKfmtkhfBOSr+CCvcDUCnU3Hp8lD0ehW1as7FxESqW7ZtC5v2VmZP+I+kJ6cz//OxpMU/oxFoby+VANeskfkFhUSdm8POWVOxcXah1YD3C93ek8jKgkWLoGFDiH3MguSeU2606FGR0EuJHFx5vUhDMqzsHXh14AfE3LzO+R0PFn5XmJjgOe1PjF1diTziiGZH0YfZPg90Wi3ndmxm0YgPubRnB7Xad+C9P+dTpXkrNDExpK5aTUbDNzEyVuCVtgaqGMI/S4yNG6XX759/4KefZFGyypVLdEgzw+MxQsFHXi9GPOXff0OjRlD2xStVauBZcHGB8uXv5gEClC07AFNTZ4KCf3+meUKv15KUfOSu0Xfh4iDi4rfj7NSKmjXm0qL5KQYP+p33Br/K7t1mNGwoHXr3835ZF/6s7M2RlEx6XQgm7Y5+wOnT0Lq1jNb5918Zyl0QxoyRoeDffScr2N9GoVDi6dmbJo334ObWkdCwWZw82YGkpH8f21SlRu5kpqiIupn62GMUCgWv9BtM1ZavcmztCi7s3k5GhixpVaHCvRK5WRprOq1cS2quHdv69KBhZVlL0NTXF89ff8V/6xasXmlJ0py53GrbjoSZM9FlZhbsPXiOKJQKHN+piFl5e1L+vkHu9Wer/fgseNpb8GevOtyIz2DcxiuPfHaNlEY0KfPiqawaDEADLwzu7l1QKEzvisEAtHS0oae7IzPD4wjIzClQu0IIAq+PIyPjMtWqTsbK6kHvSrlysGV/JQKZQE5GJrM+HUt8xDManCNHSg/B778XaIz3c2j5IlLjY+kw7DNMLSwL3V5eXL0qU5k8PWU5s6ysx9fq8vaGmq3LUv8NX64djeHkpuAiHUvl5q3wr9uAI6uXkhr7YM6nsYMDZWfPRa83IXL2AfRxQUXad3ETcuEsS//3CQcWz8PVvzwDfp1Gm8FDsbCRCxCJs+egBxIsy+HpnoWJMtdQ/L0kSE6Gfv2gWzf5pTh7FsaOlWIXJUisSsPqmGR6eTjibvbssvPPm9BQ+dYZwj//I9wuCH/HGjEyssDXZxipqadITsmjovt96PVakpOPci1wHEeONuHChYHExW/DyaklNWvMoUXzU1Sr9jsuLm0xMpIlEz74QEZvZmRIB+TmzQ+22dPDkXnVfLmQkc3bF4JIOHpMrvLa2xc+hFuhgPnzZd7gwIF3Q1/vYGrqTLWqv1OnznIUSmMuXBzM5SufoFI9+izhW8sZE3OjJ4aBAiiUStoP+RSPqg3Zu3A2rzU4xIgR8hY1ciRY3n48iMn04I2V67Axy2RHvx6Qm363DbNy5Sg7dSp+m/7BslFDEqfPIKhNWxLnzUeflXceYmlBYazEqV8VTNysSFpxDXVE0aUDPUzLii581rYiG89HsfxEWLH18zwxGIAGXhhkkda2xMVtRq+/pzz5Xfky2Bkb83lgBLoCeJ8iIhcTG7sRP78RuLi0y/MYGxtYsr4iotIEtKpM5owcy40rz2AEenrKB8hFi6AQidch589wcc8O6nd6i7JVqxe4nbxQqWDlSpkGUb06zJ0LHTvKefHKFZg+/d6Ecj+db9sjDTv7UbVFGc7uDOPi/ogiG5dCoaDt+8NRGhmxe970R5LezStVpMyP35CbbELM50OKNSm8qEiOjmTjLz+wYdJ36HVa3hz9Dd3H/Yizt+/dY9Th4aRu2IBx1wGkJ2vwMz0Jjv7g9mKFmbzwbNsmvxBr1sAPP8DJk1CjRkmPCoA5EfFohWC494vh/bsT/mkwAP8jNGkC8fEPRL54evbCzMyD4OApj9yrpdF3jMDArzlytAnnLwwgLm4zjo7NqFFjFi2an6J6tam4uLS7a/Q9TLNm0vaqVEnqsvz4I9w/ZXRytWdpDT+CMrN5KziBqMpV5STnV/Ai9XcxM5NRAi4usvOoqEcOcXRoQqOGW/H3+4zExL0cP9GeiIglCHEvLNXE1IhydVwIOhf/xNz6M2eg/wBjBk8cQ0hCVd6qNYWda85x+DBMnQrz5oGPj7RNAxKrMezgUuy1gbDuXdA9KNxmXqkSXjNm4Lt+Pea1a5EwZQq32rUn6a/F6HMfH7Ja0ijNjXEeXB2ltSmJi6+iSSyYIyA/fNy6PK0ruTB+awDnw1+svMm8MBiABl4oynh0R6NJISFx/91tjibGTKzgyYWMbCaHxKJ9Bmng5ORj3Lo1CWfntvj5fvzEY5VK+ObnClToNBGFPpsV477k4K4nr9A9wOjRkJMDM2bk/5z7yMnMYNfcaTiV9aZZj34FaiMvgoJk9ErZsrK2bXS0lNmOjJS57S1ayAmkb98HJxQvLznJzpoFq1ffDknpXQn/2i4cWXuTm6eLLi7fxsmZV/oPJuLqJS7t2/Xo/i49cWnvT/rZKJJm/llk/RY1uVmZHFy6gCWjhhN57Qot+w5i4ORZlK/f6BEV18SZs1AYGZFepwMAPlm3wz+fU1Hx/zxpaTB4MHTqBM7OcOoUfPstmJQOT1uyRsvS6CTecnPAxyLvh+HSxoYNUjPjOVfIMFBS3Ck+fl8YqFJphp/fJ6SnXyQxaT9C6EhOOU7g9W9uG339iYn9BweHJtSoPosWzU9TvdofuLq8hpGReb66LVtW2nT9+smv7DvvwP1Rja3PnmDV6KHEObnw5uS5hDgWoXiSqyts2QLp6dIIfDhxnjvvwcc0argDO7s63Lg5ntNnuj2gkFqpkTuaXB0hlx5UINfpZAR6y5ay0sa2bfDJCDM+m/0Nbj5eBG6dSPQNKUbSt6/0uuv1MvR6xYlX2SqmQtA+2Pb5vTjR+7CoXg3vuXPxWbUS88qVif/lF261a0fy8hXo1eqie5+KECMbU5wHVQMEiYuuoMsonnEqlQqm9qyNm605w1acIymz6EtgPVceVyDwRX0ZCsG/3Oj1WnH4SFNx/sLgh7brxXuXg4Xb/vOi6fEA8XdsstA+pfh6dnaE+PdQPXHseHuh0aQ/0ziO7L4lJnTrJb7u9K5YMDMm/ye++aYQjo5CZGY+U39CCLHlj1/ElN5dHimOXhA0GiE2bBCifXtZUNfISIhu3YTYvVsI3TPUV83IEKJlSyGUSiFWrrzdtlorNkw+K2YN2y/CryYVeqx30Ov1Yu34r8S0gd1FWkL8o/vjr4vIdj4ioHJlkb5/f5H1WxTodFpxYfd2MfO93mJyz05i5+w/RWZK8mOPz711SwRUqSpif/lV/P3bGbH6q52y+HvksxcXNlAAdu0SomxZ+cUYN04IlaqkR/QIvwZHC7f950VARnZJDyVfREcLoVAI8eOPJT0SA88NjUYIa2shhg9/YLNOpxZHj7UShw83EYcONxR79/mL/QeqiUuXPxZxcTuEVls0n2m9Xojff5fzU/XqQgQFCSG2bBHC1FSIWrXExbBIUeXwJVHjyOWi/x5t3iw/8N27P3FS1ev1IjZ2izh0uJHYu6+cCLz+ndBo0oVepxeLvzwitsy4IISQc+306UKUKyfnbF9fIaZOFSL9vkeXzJRkseCT98WMwb1EYsSjxeR79xbCxESI2JXj5Xxy6PenXkbWqVMitG8/EVCpsrjxSiuRvGq10JfC+6EQQqjC00Xk10dE7J9nhS5XU2z9XI5MFRXGbRd9558QWt2TnzNLGp5QCF4hXoBwqWehfv364syZgitMGSj9BAVNJjRsLs2aHcbczP3udiEEOxLT+C0klmtZuVSwNGOUnzudXexRPuQ10elyOHP2HXJzI2lQfyOWls8e/nHjQjB/TxxHdq4Z2T6T+OUPj6enBB07JmNU/vxTJtnlk8Cj/7Jt2m8069mfxt16PvNY7xARAQsWyFd0tFwp/fBDmedXpkzB2szKkqGihw/DsmWyPpMqW8PG38+TlphD18/q4OZrW+Ax309afCyLRw3Hq0p13vry+0e8Zvrl/QibfQp1rg2+q1djVkKy/PcTcfUSB5bMJyEsBM/K1Wg14H2c7R3RxMaijYtHGxeLJi4ObWwc2vg4NHHxaKKjUSgUeG7eydKJl6nndZZGFktg5GWDB7A4yciQcrfz5kGVKrBkiVxmL2VkanXUPx5AY3srFtd4Mdxps2bB8OEyv7igOhsGXkDatIHUVJn8eR/xCbu4du1LHB2a4er2Bs5OrTAyKp6c9j17pGivQqNiTXYX2tZLkeVbHB25kZVLjwtBqPR6VtTyp66tVdF1PHmyjPz59lsZPv4EtNoMgoJ/JzJyOaamzlSs8DVBxypxYXckkTbNmD3flNRU6VT9/HMpPJrX80ZqXCyrvx2NQqmk9/jfsHW5Fx6emCg1rMqWFZz+8gOUV9fB2wuhRvcnjk0IQfaJEyT8OY2cCxcw8fTEedgw7N7sgqKE86AfJicwmaSlVzHzt8f53WoojIsn0HHN6XDG/H2Zj1uXZ9RrlYqlj6JAoVCcFULkWdPEYAAaeOHIzg7h+Im2lPMfja/vR4/s1wvB1oQ0JofEciM7l8pW5oz2c6eDsx1KhQIhBFevjiQufhu1ai3A2alVgccSExTMsq+/JiPThAu5k1i8ugxOTk85qUULqdF861a+wskykhNZOupjHMp40uuHX1E+o9y8Xi/LEM6ZIyNThIDXX5fVKd54o2h0LLKyZKTcoUPymblfP8hKU7Hht7Ooc3V0G1UXB/eimVjP7djMgcXz6DD8c6q2fPXBnTEX0UxtRci/fihtnfFduwbjx6nXFANCrUYTn4A2Po7kWzc4ceQAYXFRWBoZU0Nvglt8siy+q3kw/wIjI4xdXDBxc8PYzQ1jdzds2rYlUniz968Aurt8hVuz1vD6pOd2LS87c1dpGX8ygfQ0sFMa06tmLG/uHIfTzes49emFw5dfYmppUdLDzJNZ4fGMD4pme90K1LUrwgfWYqRNG7nodO1aSY/EwHPl66/h559lSLVVyX1Wg6Zu5s3P/blGFSZPVDNyrMXdtbSwHBU9LgSRqNGytIYfzRxsiqZTIeTq6l9/yQT73r2fekp6+iUCr39NRsZVYqKakH2xE6t2N8OlsheffXYvqvZJJISFsOaHL7G0tafX+F8fqBO8caPUsvppvIqxnl0h6gwM2Aw+T29YCEHW4cMkTJtO7pUrmPr44PzxcGzfeANFCZXByYusM3GkrL+BZW0XHHpUQqEsnkXTMesvseZMBAsH1qdNFbdi6aOwGAxAAy8dZ8/2QqVOoEnjvY94ge6gE4LN8an8HhrLrWwV1a0tGO3nTqXMNQQF//JYA/JZSQgLYdm4caSmG7Pl5iSWrvV8skbEli3QpQssXy6D9J+AEIINk74jMvAqA36ZhoOHZ77HFRcn551582QOvqurnIs++KBo8t0fJitLCsL8+y8sXiwVsVPjstkw+SzGJka8/b96WNkXPldJ6PWs/m4MyVERvDtlNlb2Dxl4y7uTc+ECYbsssahXD+/581AUQd6WLjMLbVws2jjppXvAcxcXhyYuDl1SElqlgiBXB0Jc7FEIQbmUTCoZW2Lm7o6xmysmbu4Yu7lh4n7b2HNzw9jJKc8JdNf8K0Rfi+Vdm+4oBu/I1yRt4Ol8uz6DOYowFHZaUIDiMYvEtsZKnEyMcTIxxtHEGCdT4zz/72hihLOJMZZGysfej4qKXJ2ehicCqGRlzrraL0Y9yMREcHeHL7+ECRNKejQGnivbtsnVwYMHZa29kmDRInj/fTKad2Cg/SY2bjFmwAApdGZ+O60wVqWhx4UgwnNVzK/mSztnuye3mV9UKmjXTuYQ//uvrIHyGPR62LoVpk7VYW+/nMHvTcHcTEVWRFe6vPsDSmX+58+owADWT/wGp7JevPPNT5jdp+DWu7fMCbxwPJmqx9pDdiK8tzff9WWFEGTu30/CtOmorl/HtHw5XD7+GJv27VEoS4e0SPqBCNJ3hWLd0hP7N4onSiJXo+Pt2ceISM5m6yct8HYqHg92YTAYgAZeOqKj13MtcAz16q7B3j7Pz/ZddEKwMS6F30NjCclR4yeCeM8uiMF1RqEsoptVYngoK78dR0qqEYuO/8TU2WXp1u0xB+v1UkXQ2FgWkX7CA+PFPdvZu2AWbQYPpfZrHZ86DiHkHDNnjhRc0GhkiaOPPpIhI6amBbq8fJOdLY3AAwekEThgAMSHpfPPlPPYOJnTbVRdzCwLb4wlRUWwbMyn+NdtQJfPv3pwZ9hx+Ot1Um3eJWb+bhz69sX9m68f25bQ69GlpDwaknn393i0sbF5SmIb2dvf9dgZu7gSptByPvwWObk5VKxVj5Z93sXWx7dARoFOq2fRqMOUc7jGq5a/wxeBoCw9q6wvImq9nknBMcyOSEAbak7aBB+0oeYobLUo7XSUqaBm5go9yRotSRotSWr5897/dSRptGgeM2+aKxX3jMOHDMR7v9/bZ29s9Eh4+tNYEpXImBuRrK9djuZF5akoZhYtkotP585BnTolPRoDz5WkJCmiNGmSXAF43syeDcOGwWuvwcaN6M0smDBBlutr0EB6xDxvr6smqbX0vhREQGYOM6r40NWtiKJHEhKk4ZeTIw1BL68HdmdlyciZqVNlYJCPD4wYAf37x3Ht4leoFQcxM/WlarUJODrkfxEw+PxpNv02AXs3D14d/BE+NWoDckGmalU5jBM7QjBZ3BbMbOD9vWDlnO/2hV5Pxu7dJEyfgTooCLPKlXH55GOsX3212BfCnjo2IUjdHETW8RjsOvph06J4Co9GJGfTcdphyjpYsmFYU8xNStccbTAADbx0aLVZHDnaGFfXjlSt8nO+zknPCmHK6Sls4C3ihSN1bCz5n587rRxtiuRmlRgRxurvx5GaqmTanokM/cyL776T6qGPsGQJvPuu9ALOmycl5t3dHzgkJTaapf/7BM9KVXn7q/FPHGNKCixdKg2/wEBZ1ujdd2X9+eddpzo7Wzo49++XHsiBAyHiWjJbZ1zEzc+WLp/Wxti08DfJk/+s48iqJXT+7EsqNm7+4M5FHSA1nDh1f5IXL8Fl5EhMfbzR3PbWaePj7v6uiY9/bEjmIx47Vze5zd0dY1dXlLeXj6OuX+PA4nnEBd/EvXxFWg/8kDIVC/fGRwQms/mPC3RwnoJ/4wrQaWqh2vuvcys7l/fPhxKoziV7kxMZczxB9eCXU4EevXjyopAQgkyd/gED8eHfkzW6B/6fpdPn2ZYScLhrEBo9YiA6P+RptDU24pVTgbiYGrOtboUSf8jKLx07ysLcwcGGFNb/JJUqyYlo06bn2+/UqTJhrnNnWZzd7J4HbdMmmapgbS29YU2byu0ZWh39LwVzMi2LyZW86FvmaTkd+eTqVRm/Wa4cHDkCVlZER0tR8Dlz5BzesCF88YUM0byTmpGVpmLd1Ll4N1+LUMbg7t6VCuXHYmqaP0Mt9NJ59i6YSVpcLJWatOCVAe9h4+jM339D9+7SIz9uwGlY0gnca8LAzWDybKHvQqcjfft2EmfMRB0Whnn16rh8+glWLVqU6D1K6AXJqwLJuZyIY69KWNYunnI5+wPjGL3uEsvea0TVMkWjd1BUGAxAAy8lAdfGEB+/g+bNjmNs/OTcAq02izNn30alSqBOvQ1sSbNmSmgsUSoNDWyt+J+fO80drAt9s0qKDGfN+K9IT1MwdcdPNHnVi2XLZB3BB1Cr5USg08lYzSFDpErCbfR63d0wx4G/zcTG6dGbvRByMXHOHFmGITdXFr/96CPo0QMsSjB9KSdHGoH79snV/3ffhZtn4ti98Cq+NZzpMKQ6SqPCeV/1Oh0rxn1OZnIS7/4+627xdABu7oUVbyM6Tydi7hGyDh2+u0thbv5Arp2J223D7s7vbu4YO+cdkvkw6YkJHF65mMCj/2Lt4EiLPu9SpXmrIgmDObL2Jlf+Dec9p96YDFwD5VoXus3/IvrIKL7/+yYLKtmhzTUm/beyKI6Zk0teXxBBhw4Khg+HDh0es3hTAHJ193kV8zAQkx8yIFM0Op40My+p4cdrRRWiVoysWAFjx0rxKRsb6Yx5StS7gZeRd9+F7dvlXPe8DIKffoJx46SVs2JFnuEvV6/KyJiwMDn9vv++3J6t0/PelRAOJGfwfbkyfFRUdTZ37IBOnTj/ykimek5m9RoFOh289Za0U5s0yfvt2TLtAinxqTR99yzhEfMxMrKgfLn/UaZMTxSPi1+/D61azenNf3Pqn3UolEqavNOHuh260LefMRs2SM98deUmWDsQqnaB7osLdPMTWi1pm7eQOGsWmshILGrXxuXTT7Bs0qTEDEGh0ZOw6DLq8AycB1XDvHzxaAJkqbRYmZUuQRx4sgFY4mUbivplKAPx3yEl5bTYu89fREWve+Jxer1eXLw0VOzdV14kJR25u12l04nFkQmi9tErwm3/efHm2RviaHJGoceVGBEuZn3QV0zu21d42IeJatWEuPVw5QZzc6nl/PDL3FwIIcSJjWvF5B4dRcDhA4+0n54uxJw5QtSuLU+xthbio4+EOH++0EMvUrKzhWjXTiphL1wot13cHyFmDNkn9i0JEPqnlOnID3EhQWJK7y5i+/TJD+7Q64WY00KIaXWFLidbZJ48KXICrwttamqR9KvOzRFH164Qf/TrJqb27SoOr1oqVDlFJyOu1+vF0q+PiS3jlgrxs48QWnWRtf3So9UKceyYSP1ivPi14mRR9rsLwm3/eeEy+ZoY1mKPuDV7t1i+MEdYGuc+8NWzMFKJbt2EcHeX//f3F+K334RIKrpKJvm/BL1eJKg0IjAzRxxNzhBb4lLE4sgE8XtIjJgZFid0RfAZLm6WLxfC0vLB25ulpdxu4D/GnDnyA/DIRFgM6PVCfPON7K9fP1mK4gkkJ98rhzRsmBDq27dalU53t7TUr8HRhZ43dDpZgaJ1+XA5b5vmihEjbpemeAqBJ2LEjCH7RNSNZJGZeVOcOdtb7N3nL06fflukpwfkewwpsTFiwy8/iMk9Ooq/Ph8qLh25KFxchKhX7/bbdHS6LA+x6+sCX6cQQuhVKpG8eo248UorEVCpsgjt209knTpVqDYLgy5bI2KmnBGR3x4VqsjCP+O9SPCEMhAlbrAV9ctgAP530Ov14uixV8WZs72eeFxwyAyxd5+/CAtbkOf+HK1OLIiIFzWPXBZu+8+Lt8/dFCdTCneTSIwMF7M/7Cf+GNhXVPQKFQ4OssbeXaKjhXjnnQefjvz8hJg7V8RdviCm9H5TbJ4y6YFJ5+JFIYYOFcLGRh5eq5acV9OfrYThcyU7W06uCoUQC26//Sc2BYkZQ/aJ4xuL5mHgyJplYnKPjiLo3EMTzNV/5GR2ZUOR9COE/MxdO3JQzB36rpjco6PYPGWSSI2LLbL275AUnSlmDNknLo/tL8TGYUXe/ktHYqIQK1YI0bevuGjXQgxhtrCpGS+cV18RbrvPi0GLrovMzAdrcS2vP0X4WCcKhUIvfKwTxfL6U4QQsuTf6tVCtGhxb01m0CAhzhhKMOabzEwhnJ3zXuPy8Snp0Rl47ly8KP/4y5YVbz96vRCjR8u+3ntPLgblA6323mktWwoRFye3a3R6MSIgTLjtPy++uRFZICMwK0uI2bOFqFhRtu/lpRe/NVorUrDL9/uhztWKOZ8eFPuXBty+TL2Ijv5b/Huovti3v4K4cWOi0GjyX1v41pkTYv7Hg8XkHh3FrC9+FbbmieKnn4R8/7Z+IefNU/Of+VofRqdSiaRly8WN5i1EQKXKImzQIJFdQivV2tRcEf3TSRH143GhScopkTGUBAYD0MBLS0jILLF3n7/IygrJc39Cwj6xd185ceXKZ0+9eWdrdWJueJyodlgagj3P3xJnUp+9YPsdkqIixOwh/cW0QX1E83ohQqkUYsoUeY8VQki3nVIpK7OCEGZmQqNUisWvtRSz335DZP/8s8i+dFMsWSJEkyb3HkYHDhTi+PH72inl5OQI8dprcvzz58vJa//ya2LGkH3iwr7wQrevUavFX58PFXOGDhS5WVn3duh0QkyvL8TsZkXyZsXcuiFWfjNaTO7RUSz53yci4urlQrf5OM7uDBUzhuwTGV/7CXF9Z7H188Ki10uX94QJQjRtKnIV5mIlvUQz4xMCI72wey9SuO07L2ofuCrOp2U9tbnHcemSEEOGCGFlJT+/jRoJsXSpELm5RXcpLwvZ2UKsXy/XtSws8jb+QC4GGfiPodXKlcthxbiYpdMJ8ckn4q4r7wnF1x/H8uVyjvX2FuLcudvN6vXi6xsRwm3/efHZtTChzedcEh0txLhxQjg6yiHVry/EqlW3PYwqlRCtWsmC9MeO5au9PYuuinkj/xUa9T2jVq1OEQHXxoq9+/zF4SPNRHz87ie08CBqVa44una5mNq3q/ilR3fRusoGcfGCRgitRogVPYT43l6I67vy3d6T0OXkiMRFf4nrTZpKQ/DDD0X25StF0vazoI7LEpHfHxMxv50W2ozSWcy+qDEYgAZeWnJyY8TefeXFrVuTH9mXmXlLHDhYU5w81Vlotflf8cnUasXMsDhR5fAl4bb/vOhzIajAD5FJUZFizpD+Ysbg3qJftxABQgwYII0i8dZbcqK6cEH+7NpV/PvLj2Jyj47i33odxWf8LhxIEiBEJYc4MXXYDZEU+2KGAubkCPH66/KOM2+eEDqdXmyffUnMGLJPXD8VU+j2o28Git97dha7501/cMf5FXI1sxATWWZKstgxa6qY3LOTmPVBX3Fx706h0+VvZflZ0Wp04tTWYDFr+H6xfsxaISZ6CqExWBtCCOnq3rBBiPffF6JMGSFAhOElvnJfKFytMmTYZuMcUWfLdeG2/7wYERAmMjVF83dKTRXizz+FqFRJfoZdXIQYO1aIsLAiaf6FJTdXiE2bhOjTR4aigxCurvJ25uaWtwFo8AD+R2nTRog6dYqnbZ1OiA8+kB+wL74o1ILfmTNClC0rFzFWrZLb9Hq9+CU4WrjtPy8+Dgh94vkXLshFWhMTudjx1ltCHD6cx5ASE4UoV05+YUKf3KYQQoRfTRIzhuwTN8/EPbIvJeW0OHGig9i7z19cuPihyM6OzOfVCpESEy1W/fCdmNyjo/i661ARcumSELkZMoVigocQ0Rfy3dbT0GVmioS588T1ho1EQKXKInz4cJETGFhk7eeH3JBUETHuiIidfk7ocotnHi9NPMkANIjAGHjhuXBxMJmZ12nW9BAKhRTu0GozOH2mGxpNKg0bbMLcvMwzt5ul1bEoKpFZ4fGkaHW85mzLKF93atg8W62XlNho1o7/Cq1KTYrbBL6Z5E+DBrJEwuTJsia8tzd8PeIqaSe+JDznNaZt+RgTE0G3Grf4yGg+r1z4E4VGDba2sop7p05SpcI5/5LNJU1uLrz9ttQCmDsXBg/SsWXaRWKD0+g4vCbeVQuntvbv8kWc2bKBd775Ce/qNeVGnQam1QEzW6jVE0wspcKZicV9vz/8U/6u1cHZHZs5uXEtOo2Gum90oXG3nphZFk8x49jgNA4sDyQ5OosK9V1oHt8Xy4oNoPvCYumv1CME3Lgh64ht3w6HDoFGg97Gjj21RjErcwBbL3kBCjp3htrDk1luFolSAb9W9Co6CfeHhrRvH8ycCZs3y22dO8Pw4dC27X9D4VKthr17pXDxP/9Aejo4Osrvds+estSbsbHU3fjwQ6kKfAdLSyl6bBCC+Q/yzTeyFERRF4TXamHwYFi2TBadHz++0F/EuDipHXPkCIwZAxMngpER/Bwcwx9hcaypVY5XHO8pu+n1sHMnTJki7w9WVnJII0ZIrbfHEhgoldu8veHo0TzU4u6h1wuWjj2Ki48tHYfVzGO/hoiIvwgOmQaAv9+neHkNQql8etklIQRLp53ixp65OFrFU6V5K1p27YT1urdBr4UP9oFd0ZVR0GVmkrxkCcmLl6DPyMCmw+u4fPwxZk98s4qOnKtJJC0PwLyiA04DqqIopCBdacagAmrgpSYufjtXrnxC7Vp/4eTUEiH0XLo0hKTkQ9SpvRQHh8cXXs0PGVodCyITmBORQJpWR0cXO0b5ulPFOv8ym6mxMawd/xUatQrnZhMYONQflerefjPjbD5v/wkCBX8HTmfwBxYMGgRubrcPyMyUT11bt8oH4thYqdLVpIk0Bjt1gmrVSv0TqEolJa63b5eKgIMGaNj4+3nSEnPo+lkd3HwLLqGsUeWy9H+fIIRg4K8zMLlT4ff8ctj8CYi8pfgfRgi4leHEv/H+pGnM8bfLoJVfOg62pnkaik/f9uR9arXgxKZgLh+MxNrejFf6VMLX6ios7QI9lkLVNwv8nrxw5ObKgtHbt8vPeXCw3F61Ksmvdmexrj+z95Tj1i0FLi7wwQfQ530ds3MiWR+XQkM7K2ZW9cHLvJgLXiIXbubMgQULZJmvihVlubF33wW70i/Q+UxotbK255o1sr5oSoq8xrfekkZfmzZgksdz5ooVUojxziLXxIkG4+8/y/btsh7IgQPQqlXRtKnRyFoOa9fCjz9KA7CIUKth5Eg5T3XoID/rE37Rk/1DIMZ6Jb8oK/FONwXLlslqE4GBsp7gp5/K+5JDftefdu+GN96Q782GDdLSfAxH/77FpX0RvPtrMyys877H5eREcePmeBIT92JlVZHKlX58aq3kO/R8J5fMG+tpU209JqYmNH29LXWCJ6C0LwuDd4J50d7YdGlpJP31FylLl6HPzcW2U0dchg3D1Ne3SPvJi8yTMaRuvIVlPTccur84JXWeFYMBaOClRq9XcfhIUxwdm1Kj+nSCgqcSGjqDihW/x6ts/yLrJ02jZV5kAvMiEsjQ6ensYs8Xfm5UtsqfIZgaF8va8WPR5OYy/9AELt66t9r1dr0ZNPLfxaoLP3P6WrUnzQFyufHcOWkMbt0KZ8/K7T4+94zBVq3gjgFUylCppLdg2zYpvT2gj4oNv51Fnauj26i6OLgXfHU4MuAKa374krpvvEnrgR/c26HXgzYHNDmgyX7MzxwSoqI5sO88ERFJODlY0KqhB76uRo8cl9e5qLNA6J5pvKG59fg34yMydY7UsPuXxu57MDU3gpxUyE6C/wWBafF4HEsNYWHy4XD7drl8npMja5i8+ip07MgZzzeZ9U8ZVq2S9mHz5tLQ6tYNruRmMTQgjMhcNV/4ujPCxw1j5fOdyFUqWWJs5kw4cUKu/vfrJ72CNWo816EUKTodHD4sjb6//5ZGrrU1vPmmNPrat3+grJoBA08mORmcnGR5hrFjC9+eSiU/iJs2yVCaL74ofJt5MG8eDB16L4jZrEUq9j+EkvVnWdjnTGYm1K0ru3/nnbwXQp7KjBnwySfwv//BL7889rDEyEzWTDhFy14VqdHqyR65hIQ9XL/xAypVDI6OLXByegUnx5ZYWvo/1tiJj5fryDUqRDGs4zzCLp3F2d2ZNuYHKVutNvRdB0YFucAno01JIXnhQpKXr0BoNNi9+Sauo0dhnG8rumCk7w0jfW84Nq29sHvNt1j7KikMBqCBl57rN8YTFbWKSpW+JzDwKzw83qFK5UnFsqqTotEyNyKB+ZEJZOv0dHW15ws/d8pbPt3gSouPZe34r4iNymbuwQlEpZanisdp3mvxAwcC32b75UHo8+eoukd0tHx43roV9uyRMVeWltCunTQGO3YED4+CXex95Or03MrOJV6tpbmDNaaFKJKmUsnJcssWOff17Z7NhslnMTYx4u3/1cPKvuBPlnsXzubinu30Hv8rZSpWydc52elpHF2zjMv7dmNmZUXTHn2p1bYDynzUAnwAnSZv4/ChbdnpuRw55sDNUDscbbNoXesy7jYxDx7v3wqajXj2N6C0o9HAsWP3vHxXr8rtfn7ys/rGG+Q0asXaLRbMmiVrXd4xqoYOhVq1QCcE08Pi+C00Fg8zE2ZV8aGhvXXJXhdyXWbmTFi5UhqrLVpIQ7BbtwI+GD5n9Ho4flwafevXQ0yMvJV07iyftV9/vWTrixp4walcWbrK78RPF5ScHPml2rlTTiDDhxfN+B6Du7sMC5UIHKbewthbReaHVdi5wYgWLQoZfCOEvIbZs+Gvv2QYwWNY/eNJjE2N6D7m6V49rTaLsPC5xMdvJzs7BABzszI4OrXAybElDg5NMTF5MOpm7Vr5Xf95kqBbmxMcWDyPjMQEqtrF0fLVelj1nF1skUbaxESS5s8nZeUqLGrXxnvRQhTFeOMUQpC68RZZp2Kx71IO66bPnipU2jEYgAZeejIyAjh1ujMAtra1qVtnJUZGxbs8naTWMicingWRiaj0erq5OfCFrzt+lk/uNy0+jikfjsVYkcXy4/+jV8OpZKrs+XPvVMp6mRAaWohB5eTIELo73sHwcLm9fv173sE6dZ5Y5FWt1xOUreJ6Vu4Dr5AcFXds04Z2Viyo5ourWcFvziqVLFi/ebOcw9/plM4/U85j42TOW1/UxdyqYG2rc7JZPGo4JqZm9P9lGsZ5FAC+g06r4cKubRxfvwp1bg6123ekyTt9sLB+fC5GYRBCcP1ELEfW30Sj0lG/gy91X/PByPjlzUEA5NPTjh3S6Nu9W+YBGRtDy5Z3jT4qVSIoWMGcObBokXQYVKkivX39+98Lq4zKVTM8IIwTaVl0dbXn10pe2Bo/o6FezCQny2uYPVtGsbq7y3y4IUOgTCl7xhACTp+WRt/atRAZKYMH3nhDPgh27Fi0KVsG/sMMGiTnpfj4ghsRWVnQpYsMJZ0/H957r2jHmAdKpfye3MG4QjaOs2+Qvc6VjDlF9IXWaGSs6aFDsH+/DHXIg3O7wzi+IYi+PzTG3i3/egQ5OZEkJR8iOfkwycnH0OkyUSiMsLWthZNjSxydWmJrUx0wurs4e/48VCiXy8mN6zizaS1GaGjWtDK1h/3+7Iujz0Dalq1Ejx6NQ58+uH/7TbH1AyB0gqTlAeQGJuPYpzKWNVyKtb/njcEANPCf4PTpt8hVxdCgwT+Ym7k/t34T1BpmhcezOCoRtRC84+bIZ75u+Fg83hBcOj+eG5vH4mAZh1ZnzJ97p5Cm8S9agQQh4MqVe8bg8eNym4cHdOyIpnNngps257peyfWsnLuGXnCOCt3t24KRAvwszKhkZU5FS3MqWZmTpdPz9c0o7IyNWFjdl3p2BX86VKulEbhpE0ybBl3bJrN1xkXc/Gzp8mltjE0LNsmEXjjL35O+o9FbPWjea0CexwSfP83BpQtJiY7Ep2YdWg/8AKey3gW+lqeRlpDDwRWBRAam4FHOjlb9KuPo8RI9WcfEQK9e0pJwdYUzZ+55+e7ckz08pGXxxhtSNcXWFp1O2oazZskFfaVS5tsMGyYjme9/TtwSn8qo6xFohWBSxbK84+ZQqnM37ohDzJwpr9HISF7b8OHS9i2poQshH+7WrpWvkBDpoXz9dWn0denyRD0KAwYKxrx5chXk5k0oX/7Zz09PlysSx47BkiUyLOA54OsrI9Xvx/Z/4Vi0SeFEi8r4PmGufyZSUqQoTHKyXJXJIxcuM0XFkq+OUv8NXxp19i9QN3q9hrT0CyQnHSIp+TAZGVcAgbGxPY6OzTAxbknnzi1wdHTj6FG5VpccFcn+X0cQFqvCxdWBNsPH4lm5auGu9wnE/fobyYsW4THhR+y7dy+2fgD0ah2JC6+gjszA5b3qmPnbF2t/zxODAWjgP4FGkwKAiUnxxo0/jniVhhnh8SyJTkQnBL3cnRjh6/ZYQYql8+O5/M/PHL/xKpGaTsUmkKDVC0JzVVyPjuf6lWtcT0gi0MSc4DJeaG6HVyiEwNfEiEr21lSysqCylTT2ylmaYZaHt/BqZg6DLocQq9IwqWJZ+pYpuIKnWi0fOv/5B/78Ezo0jWP3wqv41nCmw5DqKAuo0LVz1h8EHN5P34lTcPO/97CRFBnBwWULCL1wFgePMrzS/3386zYoNkNCr9NzcV8kp7YEozBS0PStclRr4YniOeeqFTvvvSfDlypUkA8yCQnSwmnc+J6Xr3btu1ZPQgIsXCiFVMLCpGfsww/h/felmML9ZOl0fHszihUxydS2sWR2VZ+netpLG0FB8loXLpRvT7Vq0hDs1+/5GFt31oPuePpu3pQPdm3byu9f165gb1/84zDwH+byZahZE5YulW79ZyElRa5QnDsnY6zfead4xpgHeSnaWpXV4Lj4Gu3cbFhY3a/oOrtxAxo1grJlpTKo7aPCaJv+OE96Yg79fmxSJPOWWp1EcvLR2x7CI6jVCQCEBFfEwrIlHV5viZ1dfZR6wc3J3Th4VUOGxoxqr7ShZd9BWNrZF3oMDyN0OiI+HELWqVP4LFmCZd06Rd7H/eizNcTPuYguXY3rR7UwKYQWQWnCYAAaMPAciVVpmBYWx/LoJATQx8ORET5ulClmZUKdEITnqO968gJve/WCclSo9Pe+597mplSyMKVycgKVzp+h0rbNlD96GAu1SqpW3AkVbdToiYpkKRotQ6+GcTAlgwFlnPixgmeexmJ+0GjkQ+jGjVJRrU3tSA6tvkGVph607l+5QJNcbmYmi78YiqW9A30nTkGTm8vx9Su5sHsbxqZmNHm7F3U6dMbIuPhyDBLCMziwPJCE8Ax8azrzSu+KWDuUTnGeAmNuzgOStncwMZH5qfeVKhFCCqXMmiWNELUaWreWhlCXLnnnyV3KyGbo1TCCc1R84u3KaD8PTF5g4zknB1avlmHP585J42/gQPkeVK5c9P0FBkqjb80auHZNelhbt5bft27dpC6HAQPPBZ1OymP26ydvAvklMVHmtAcESMWlLl2Kb4yPIS9F2/hmsfwSEsuG2uVp6lCEOch790pj9/XXZXjMQ/Nw4IkY9i2+RrdRdfEob190/SLTFDIzA0lKOsS/hw7j5nYWExM1SqU5Dg6NcLSuh93uhVy8ZsTZxDKYmJnTrGc/arV7o8jDQnVpaYT06IE+Oxu/9esxuSuLXjxoU3OJn3URANdhtTC2f/HnaoMBaMBACRCVq+bPsDhWxSSjAPqXceITHzfcC5E3B6AXgohc9SM5ejezc8m9z9DzNDOhspUFlW578ypZmVPBygyrvG7SN27cCxU9fFjqvjs7S69Np05S8u9OEtZ9oX46Nzd+Do5heng89W0tWVjdD7cCXp9GI5vdsEHWU2paLpgz20Op+7oPTboWrD7QzdPH2Tx5IuXqNybqegC5mRnUfPU1mvXsVyyrlnfQqnWc3hbC+T0RmFub0LJnRcrVdSnV4YrPzKVL8OuvsGqVtOyUSvmAZ2EhLYvJk2XyGzJtZ+VK+cx34YJc1B44UIq6VHmMTo9eCOZGJPBTcAzOpsZMr+JNc4eXJy5RCDh5UoaH3jGG27SRhmDnztI7V1CCgu4ZfZcuSadry5Yy3Prtt+8rL2PAwPOmXTtp0J0/n7/j4+LkFyMoSIaJvPZasQ7vWcjR6Wl+8hqOJsbsrF8Ro6K8v8+eLePgv/hC3kvvQ52r5a//HaFSI3da9S2GVaPbxMVB3bpZtG17krFjD5GccpicnFAAzFVglWpKaFJdQk4m41SmAm3fG5pv4bX8orp5k9CevTAtVw6f5ctQFrP0sCY2i/g5FzGyMcXlo1oYFVCLoLRgMAANGChBwnNU/BkWx+rYZEwUCgaWceZjH1dcTJ98YxFCEKXS3Pbm5d7N07uZrSJbd08q1MPMhMpW5lS8beRVtpS/WxdUGCM1VQp1bN0qc7iSku4JdnTqdE8xYsiQu6u4m+NTGRkYjo2RkgXV/WhQwLxAjQZ695ay879PFtRxv07A4Wiav1OBWm28CtTmlj9+4cbxw5StWp3WAz/E1bdgeRP5JfJ6CgeXB5KWkEOVZh407Va+wII2pQ4h5ALBL7/Iz4aVlfwcxMdLC8/UVFoytz8b16/Lj8iSJVL3pWZN+UzTt68sKfA44lQaPr0Wzr8pGbzhbMfkyl44mhTCIirlxMffC4cNDwcvL/kWvv9+/o21sDBpSK5Zc68yTJMmclGle/fSJz5j4D/Kt99K91la2pNvAgBRUdL4i4iQ81Hr1s9njM/AxrgUhgaEMbWyF709itid/sknMlRgwYJHxG52L7xK+NUkBv3SHCOT4hMRW71azsm//gqjR0NOTgRJyYdJjtxMctopdMYKQElOojWpIaa4uLahebdRWNk5FtkYMvbtI3L4x9h17YrHpJ+KfSFVFZxKwsIrmHpa4/x+DZQF1CIoDRgMQAMGSgFhOSqmhMaxLjYZM6WCQZ4uDPN2xcnEiFj1bUMvM5fr2dKjdyMrl8z7DD03U+P7vHkWt4VZzLArzgdjnU7G7G3dKh/687pfmJlBbi7XMnMYdCWEqFwNEyt4MsDT+dFj84FGIw2Edetg8m+CqtZXCL6QQLv3qlKxwbOL+2hUucSHhlCmYsFCSfNLbpaGYxtuce1oDLYuFrTuW4mylYtuEixR9HopC/fzz/Lz4OIiKx4PGwaOjqxoMJVxgQMIz3LEyyqFt1yPcMWvC/v2ybDOd96RhzZt+nThk92JaYwMDCdHp2d8BU/6eTi9XJ7TJ6DVSs2cGTNkFNid9274cCnU8nAIWqtW8nuyZo38swA0aCDDO995Rx5nwECpYscOGVmyf/+TDbqwMFkLNCFBntOs2fMb4zMghKDTuZtE5Ko53qgKVkWpSKzVyvzpAwdkiadXXrm7K+xqElunX+T1IdUpV8e16Pp8CCFk1MD27TJ64/4wdf3VjaTt/oDk8lVJdLUjMytADjvXGEvTmvhX6YGzc0vMzAofcpAwcyaJ02fgNvZLHAcOLHR7TyP7cgLJKwMxr+yIU7+qKIxezDnIYAAaMFCKCM5WMSU0lg1xKZgplZgoIV17z9BzNjF+IGzzzsuhpD0gMTHw0UdyMtZoHtzn6wutW5Paug1DK9bmQLaGfh5OTKxYsLxAjUamiaxdC7/+rKO88UVig9PoOLwm3lVLV9KSEIKgcwkcWnOD3EwNddp50aCjX4EVTEsVarX07P36q0wg8/WFUaOknLullCDPSyABwNFRRi+9917+vFi5Oj3jg6JZFJVINWtzZlf1paLVi5+DUVDueE8XL5bihwrFg+svSiV3a4bWri2Nvh49wL94HdwGDBSOlBR5c5g4Eb76Ku9jbt2Sxl9GBuzaBQ0bPt8xPiNn07LoeO4mn/m4Mca/8DV3HyA1VQppJSbKmPFyMh1Cr9OzeOwxPPzt6PBRjaLt8yFiY6VoVcWKcOTIQymJx2fCrq+gyceoW39O+K3N3Li0HKV1BCaWOgCsrSrdrT1ob18fpfLZwziFXk/UiBFk7D+A94L5WDVpUkRX93gyj0WTujkIq4bu2L9V/oVciDQYgAYMlEJuZuUyPzIBBTzg2XM2LcWhbkOHSinvO6F+3bvLekUHD8pXcjI6pZJfP/uKPzu+TV11Ngsre+Hh8+zhm1qtNALXrIFfJmnx1p4jLSGHriPr4Ob3qDJaSZCZksu/q24QeikRF28bWverjIv3S5Cnlpkpa2xNmSILw9WsCV9+Kd1KDyWn5SWRDtL7lNf2vLiWmcPQgDACs3IZUtaFr8p5FFhQ6GUjMxN8fKQy/MPY2cGpU/LBzICBF4YqVWQZiC1bHt0XGCiNP41Ger1q137uwysIwwLC2J6QypFGVShb1IJvt25JI9jdXZZzup2Pf2TdTS4fjGTQr82LPc1g5UoZmfPbb3IN8C5CwI7/wal58MZkaPgBQggCjx3ixOZpGNtF4VHTDKVVLEJoUSotcHBohJNjCxwdW2Jp6Zdvw0qXmUVY795o4+PxXb8OU6+CpYU8C2m7Qsk4EIFtW29s2/oUe39FjcEANGDAQNHQrZus5fbhh9IQjImRqi0g3RGXL0tD8MABtqoEn34yBuvsLBYsmk5DLw8Z8tOq1V1xkKeh1Uq18NWr4ecfVZTJPYs6R0e30XVxKEGZZqEXXD0cxbGNQQidoGEXf2q9WrbAJStKDQkJMH26jEFMSZF/qzFjpPDCYybph4sk30GhuOehehxCCP6KSmR8UDTWRkZMq+LNq06lw7gvTRTmPTZgoNQxeLA0/h4uCH/5sqxLolTKGOhq1UpujM9IZK6a5iev0cHZjtnVfIu+gwMHpBhbmzYyJcPYmITwDNb+dJpX+lSiekvPp7dRCISQ0/+OHY+GgqLXweq+cHMX9FoFlV4HQJ2TzfG/V3Nu+ybMrE1p8E4DbLyzSE4+ck9MxtwTR8cWODm1xNGhKcbGT15AVYeHE/JOD0xcXfFdvQqlVfE+BwghSFl/k+yzcdi/VR7rRkXs4S1mDAagAQMGnj86HYHnLjI4MZdwEzMmzP+TgWuXoQA5e7Rufc8gdHF5bDNaLQwYIMUmf/4hG5eMsxiZKHl7dH2sHZ5/LbjkmCwOLg8kJiiNspUdaNW3MnYuFs99HEVKSAj8/jssWgS5ubIo3JgxshTIU3icB9DHB0JDH39eolrLZ4Hh7ElK51VHG/6s4v1UYaT/KgV9jw0YKJXMny8XEW/ckHVDQSoXtW8vVYT3738h3dq/BMcwNSyOrXUrUL+AQmhP5M77NmIE/PEHQghW/3gKU3Nj3v5fvaLv7yFiY6FqVTl9Hz78UCioOgv+egMSb8Cg7VDmXt2+xIgw9i+aQ0TAZdz8pVqonac5SUmHSUo+RErKCXS6TBQKI2xt6+Dk2AI3t45YWuZdXzHr2DHC3/8Am7Zt8fxjKopijhYROj1JSwPIvZGCU7+qWFQrXWkoT+JJBuALvlxtwICBUouREZUb1GVH24a84uLAlx99zueHz5L722Tw84Nly2TSkqsrVK8uFc82bJCqo/dhbCzrBvfpA19+Z0m8VS1UWVq2TL9AbpbmMZ0XPTqtntPbQlgz8RTJMVm0GViFLiNqv9jG36VLMq6nQgXp0e3dW9ba2rAhX8YfwMiRj26ztJQpPo/j3+QMXj0dyL/JGUyo4MmKmv4G4+8JTJx4N+XyLk97jw0YKLXcyd86fvzezzZtZH2YQ4deSOMP4GNvV9xMjfn2VhT64nCufPCBvOH++SfMnYtCoaBiQzdig9NIS8h+6umFxd1dBogcPy6H8ACmVtBnDVg6wcqekBpxd5ezlw/vfPsTb3w6msyUJFZ8/QVHlm3Gya4ztWrOpWWLM9Stswof7w/R63MJDplKaurZx47DqmlTXP83mozdu0mcM6eYrvYeCiMljn2rYOJpTdKqQFShacXe53NBCPHEF7AIiAeuPLT9E+A6cBX49b7tY4Fbt/e9dt/2esDl2/umcc/7aAasub39JOB73zkDgZu3XwOfNlYhBPXq1RMGDBgoXej0evFzULRw239evHb6uojKUQmhVgtx4oQQkyYJ0b69EJaWQshIEyFq1hRixAgh/vlHiORkIYQQWq0Q/frJ3ZPGJYlZw/aLv387IzQqbbGPPyYoVaz84YSYMWSf2DX/sshKUxV7n8WGXi/EwYNCdOgg30xrayG++EKIyMgCNfftt7IZT08hFAohfHyEWL4872NVOp34/makcNt/XrQ4cU1cycgu+HX8x1i+XL63T3uPDRgo9eh08r7j4SHEhg3y9/LlhQgPL+mRFZpV0YnCbf958XdscvF0oNHIe7exsRD79on0pBwx46N94uSW4OLp7yH0eiG6dBHC3FyIwMA8DogLEOKnskLMaCRETuoju3OzssSBJfPF7706ixmDe4mLe3cIvU73wDEqVYLQaDKeMg69iPrf/0RApcoifd++wlxSvtFmqETMb6dF5PfHhDou67n0WViAM+Ix9tJTQ0AVCkVLIBNYKoSofntba2Ac0FEIoVIoFK5CiHiFQlEVWAU0BMoAe4GKQgidQqE4BYwATgDbgWlCiB0KhWIYUFMI8ZFCoegFvCWE6KlQKByBM0B9QABngXpCiJQnjdcQAmrAQOlle0Iqn1wLx0KpZEF1Xxrb31cHSq2GM2dkrsOBA3D0qAxHVCikEEDr1uhatGLQ6tdYtsaUn7+Mwzr1Kr41nOkwpHqx5N+pc7Wc2BTM5YORWNub8UqfSvjWKFh5ixJHr4fNm2U5jzulHEaMkPUZHBwK1KROJ525VavCzp1PPjYoO5ehV8O4lJnDwDJOfFfeE8sXPWfSgAEDBcPLSwpMGRlJj9++fTK//AVHLwSvn7lBokbLkUZViucel5Yma+rExMDJk/yzNYPMZBV9xzd+LkqVMTEyPbNKFemwNXpY8Dr4ICx/G3yaQd/1YPyoKE5ieCj7Fs0h8toV3MtXpM3gobiXq/BM49Dn5hLWrz/qkBB816zGrHz5gl9UPtEm5xI/6wIKIyWuw2phZPf801CehUKFgAohDgEP648NBX4WQqhuHxN/e/ubwGohhEoIEYL06jVUKBQegK0Q4vhti3Qp0PW+c5bc/n090EYhP8GvAXuEEMm3jb49wOv5umIDBgyUSt5wsWdHvYrYGRvR/cItFkUmcHcRytRUTmrjxkkBgNRUObt8/71UPZs5E6O3uvDXWisGOG3ly5/dyMy1J/RSIgdXXOdpi1nPSujlRFb9cJLLByOp0aosvb9r9GIaf2o1/PWXnLHfegvi4mDmTJlUNm5cgY0/kH+miIhHahQ/gBCClTFJtD19g4hcNYur+/FLJS+D8WfAwH8RCwu5qBcZKf+v08kSMy9J/RKlQsH4Cp5EqzTMiYh/+gkFwc5OiugoldCpE5Wq25KWkENcSHrx9PcQHh4wbRocOyZ/PoJ/K+g8DUL+ha2f5alg5eztS4/vJvHGx1+QnhDPinGfs3fBTHIyM/I9DqW5OWVnTEdhYUHE8OHo0oo/NNPY0RznQdXR52pJWHQFfY5W7tA9v3SUoqKgM3BFoIVCoTipUCj+VSgUDW5v9wQi7jsu8vY2z9u/P7z9gXOEEFogDXB6QlsGDBh4galoZc6O+hV51dGWr25GMTIwglxdHlKGZmbQogV8+630CKamwoEDGH3zFYuqTGagYilfLq5L1tlwrh2L4cTIhbJmVGbmg+3ExMgCurGx+Rpfdrqa3Quvsm3mJWFaEREAAGWBSURBVEzMjXl7dD1a9qyIqXkpLs+RFxkZUtjF31+q7pmZSSWdGzek18+i8LmLCxeCkxN06ZL3/lSNlg+vhvF5YAT1bC3Z37ASr7vYFbpfAwYMvKAEB8uEbvPbNT4tLGQeckhIyY6rCGlsb00nFzumh8UTqyomw8DfX+Zqh4RQ7rcRGJkouX4yf3NcUdC3L3TuLEs53riRxwF1+sIrY+DCcjg0Oc82FAoFVVq0ZvAfc6nboQuX9u1i0cghXN6/G5FPeWMTd3fKTpuGJjqGqFGjETpdIa4qf5h6WuPUrwraxBwS5xxGrPsQfisHmcVk8BcTBTUAjQEHoDEwGlh722uXl+9ZPGE7BTznARQKxYcKheKMQqE4k5CQ8LSxGzBgoISxNTZicQ0/vvB1Y01sMm+ev0lUrvrJJ5mbS8XQH37A6PBBFqZ1Z9Br0Yw5M5Cc8AzOqfy5OGqe9Gg1a3bPk/jtt7J67fjxT2xeCEHg8RhW/nCCoHPxNOzsR89xDXD3f8EMlvh4+PprWYhv1Cgp8LJzJ5w/D716PVLHr6AkJsI//8gyHWZ5RMGcSM2kzenr7EhM5Wt/D9bWLoeHWRHXxzJgwMCLhYeHFHtRq+U9XaWS/89naaAXhW/KlUEnBD8FRxdfJy1bwpw5mO7Zjr82jJtn4tBpn09dGIUC5s6Vf8LBg6Uj9xFajYWaveDABLi45rFtmVla0XrgB/T/+U+cPMuye+40Nv3+E+qc/AnbWNatg/s3X5N1+DAJU6cW8IryiTobAjZjfvELHE3/RB1nTPKlSogqXUGrKt6+i5iCGoCRwIbbOYanAD3gfHv7/ZUZywLRt7eXzWM795+jUCiMATtkyOnj2noEIcQ8IUR9IUR9lyfIyRswYKD0oFQoGO3nwZIafgRlq2h/5gZHU/If/mFkY8mC7WUYPFjB/3Z2IdfEiSPNhnPj08ky3+2nn6BdO1iwQP5/9mw5a5maSvXL7HuTS3piDlumXWDfkms4uFnRc1xDGnT0w8j4BQpTDA6G4cNlbYCffpLFlE+elN7TJ9TxKyjLlslazQ+Hf2r0gl+CY+h2/hamSgVb6lbkYx83lM8hN8WAAQMvAHFx8NFHMhf5o4/yHZ3xIuFjYcYHXi6sjU3hYkYxKnQOHgyjRlFx+2xUWVrCriQ9/Zwi4k4o6NGjUh30ERQK6DIdfFvApuEQeuSJ7bn4+NHz+19oPfADgs+dYuXXo0iNjcnXWBx69MChT2+SFiwkbcvWAlzNE1Bnw9V/YN278Ft5WNsfgg9iWbcMdo115GgaksqnCLuyT2upVJGvOoAKhcIX2HqfCMxHQBkhxLcKhaIisA/wBqoCK7knArMPqHBbBOY0Ujn0JFIEZroQYrtCoRgO1LhPBKabEKLHbRGYs0Dd28M4hxSBeTgf8QEMIjAGDLx43MzKZfCVEIJzVPxQ3pP3PJ3zncyu18vSSEsW6/h16EXMdWl0/Lgm3ibJcseRI7KYYF7nenlzqX4fTrq2RqFU0KSKluptfVGUKycNxReBixelsMuaNTIbf8AAGD0aKlUqti6FgBo1wNpaPsPdISxHxbCAMM6mZ9PL3ZGJFTyxMn5YIcCAAQMGXn4ytDqanLhGeUszNtYpX3wCLToduq7dWOI4kDLl7Xj9mzbF008eCCFTAPbtk1NRhbx0XHJSYGF7yIyD9/aAy9PnprDLF9j6xy8gBJ1GfolPzdpPH4tGQ/igweRcvozPyhVYVKv27Bd0B3UW3NgFAf/AzT2gyQZLZ6jSGap1BZ/mYCSjaVK3BZN5OAqXYbUw87YteJ/FQKEKwSsUilVAK6SHLw74DliGLA9RG1ADo4QQ+28fPw4YDGiBkUKIHbe31wcWAxbADuATIYRQKBTmt9urg/T89RJCBN8+ZzDw1e2hTBRC/PW0izUYgAYMvJhkaHV8ci2MnYnpdHdz4LdKXljkUyhEr4chQ2D5Ui2T3j+HhXEOXUfWwe3XMbK+nampDDkaNEh6yW7cIOFqFAdiypJg5Ixv9Bla7p+MTdbtEHKlUlbfrlhRzmj3//T2zkP27DkjBPz7L/z8s8x7tLaWK+kjR4Jn8adKnzwJjRvLt/aDD6Ty3brYFMbdjESpgF8retHVreDiMgYMGDDwMrAsOpHR1yOZX82Xzq72xddRRgaH+07miltTBn/sjVmtKsXX10NER0uNserV5bSUZ132lFBY0BZMLOD9fWDt+tR2U+Ni+efX8SRHR9Kq/3vU6dDlqUa0NimJkO7vAOC3fh3GTs9QtF2VCTd2QsAmafRpc8DK9T6jrxkoH537hV6gDk3HrBSmixTKAHzRMBiABgy8uOiF4I+wOH4LiaWGtQULa/jhZZ4/T5xeL22gNStUjB94FmsLHd3C/8LB1UJ6AufNg5gYtKvXcXpbKOf3hGNuZUzLXpUoV9cFRXIy3LwpXzduPPjzfmEZU1MoV+5Rw7BCBShTpshDLR+5yE2bpOF36hS4uspSDkOHFkrN81n58ENYsQKiogWHVGn8HhrL9axcGtpZMbOqT77/ZgYMGDDwMqMTgranr5Ol03OoYWXMi1H9OP74ddYtiaJV4HKqbZgMjo7F1tfDLF0KAwfCH3/IKSlPIs/C4o7gVhUGbgVTy6e2q87JZsfMKdw6fYJqr7Sl7QfDMTYxeeI5OVevEta3H+bVq+GzaBGKJ0XzqDKkp+/qRri1F7S5YO0GVbpIo8+7SZ5G34uCwQA0YMDAC8XuxDSGB4RholQwr5ovzR1s/t/efcdlVf0BHP8chiCouHAPcC+G4MpRkIVaasPc5qrMzDJHaVnmyJaWZWZWriw1UxtmWWlqapobt+LCPXChoCDwnN8f54EfKiDgAw/j+369npdwn3vP/d4bcfk+55zvSddxFospcPnj/Ou82W0rHsUc6PBqAwoVM1VKTh64zOrv9hMZcYPaTcvStEM1XN3TfpigtZmjcntiGBYGhw+bIgaJ3N2hWrWUew5LlMh8chgbC999BxMmwIEDpgLcsGHQu7dNqnlmRFQUlC2naT44kphHz7IvOobqbi4M9SpDu1JFcZS5fkIIkWTtpWt03HGYkVXK8lLl0ll2Hq0184evxjVsJ09eW2KKf90lWbLduU1V0JUrzRT7VJfk27cUFvSAWo9CpznpSq60xcL6RfP5b/F8ylavSfuhIylULO3kNnLpb5weNoyiXbtQ9u23b30z5qrp6dvzs0n6EmKhUBmo85hJ+io2ztVJX3KSAAohcp3D12PovcvMCxxVtRz9Knimaw6FxWJGef626CpDn9jO5WhXPl/qyxPNw/GreIYingUJ6l6TirVs8OloQoJZCC+lXsOjR28tjVa0aMqJYfXqpgpecmfOmIqd06ebHr9Jk8w4G39/GDECOnSwWTXPjLBozas/RDL72lmcq8ZQzZr4tZfETwghUtVz5xHWX4liQ5PaeBbIuqRsy7JwNv5yhKfndqNI98dNFeyuXc0c8SyutHrqlBkK6usLq1enMhQU4L8v4I8R0ORFaP1uutsP2/gvyz7/GFc3dx4b9iZlqtVIc//zEydycfoMyowZQ7HHWsGBZSbpO/w3JNyEwuWgTnuo87g16ctFRd/SSRJAIUSuFBWfwMv7jvP7hUg6WOcFpmcBca1NAdAT+y/xQpsdODpotFas2VuRx/t506NnNny6FxdnksDbE8OwMJM0Jle69K0J4apVsHy5+fT25k0IDjaJ38MPZ+0Q01RorVl2wQz13BMVg8M5FyYHleaJ0sUk8RNCiLs4fD2GBzbtp2vZEkyoWfHuB2TS1Qs3+PbNDTR2PUSDT583SyJt2GAmyU+dmmXnTTR7tplq/+mn8PLLaey4bDhsnAZtPoTGz6e7/YhjR/l5wjtEX7lEyPMvU6dFcKr76qiLnHimF9G7jlC55WXcSlyHIuVNT1+dx6FCwzyZ9CUnCaAQIteyaM1nx87z/tEz1C1UkJn1vKhUMIWF525TuTIcPw6+XhE0rXWapZurcPJiYSpXhvDwrI87Tdevm+GjtyeG61Ipk+3qCjduZG+MmMTvjwuRfBR+jt1RN6jg6MKecaUZHVKM4a9K4ieEEOk16uAppp+MYEXDmtQplHVD93/6aBvXt+ym2/e97lxQO4ufJVpD27bmM8w0h4JaEsxQ0LA/oPNcqPVIus9x/WokSye9z4m9u2jQ7kladOuFQ+KQzRuXYf/vpnrn4VUkxMQT/ndZEiwueH/5Ps7+IXk+6UtOEkAhRK634uJVBuwNx0kpptXx4v7iac8LdHAwD6PbKWWGieZIZ86YGfRLlph5f25u8MQTMHFiti6UrLXmr4tXmXj0LLuibuBdsABDvMqwcVIxPv1YcfKk6bQUQgiRPlfi4rnvv33UK1yQH/yqZtmyEHvXnWbVd/t5KnIxpRd8YR6ETk7w1FNmOkE2DQX18zOJYKr51s1oUxQm4gD0/g3KB6Sy450S4uNZPWc6oX8uxaueD4+2rIbrkd/hyGqwxINHJahrevpiY4oS3rkLBby9qfzdtzi4utrkOnODtBLA/JMGCyFytYdKFOHPwJp4FnCmy47DfHH8PGl9gFWpUsa25whly5piMXFx5pPamBgzPzCbkj+tNX9diKTVljB67TrKtYQEPq1VibWNavN4ieJ8O1vRtq0kf0IIkVFFnZ0Y5l2GtZejWH7xapadp2qAJ45ODhzw8DWfeDo6mrVw//oLLqW5lLZNlC9v8sw1a+Dzz9PYsYA7dF1g1teb1xmuHE/3ORxjI2npV5CHfRw5vmcH876Yy8XjR6DJAHhuJbyyE0LegQoNcKlWjXITPiRm927OjBqV5t8N+YkkgEKIXMPbzYXfAqrTxtODMYdPM2DvMa4npNydN3686UBLrmBBsz1HO3fOrGfx33/m37Nns/yUSYnf1jB67jpKZHwCn9SqyLpGtelctjhODoqlS+H8eXjmmSwPRwgh8qSe5UpS3c2F0YdOczOLhqK4uDnj5VuSQ5byJDz/AmzdCo88Yko4BwaauYBZnAT17g1t2pip64cPp7Fj4dLQfSHEx8LcjnDjSur7Rl+ALbNgzuMwsTr8+jK+hcPp+GhtYgt4Mi+sGodLtIPygXfMlS/84IN4DnqZq0t+5dLsb2xwhbmfDAEVQuQ6WmumHD/Pu0fOUKeQKzPreVM5hXmBc+fCyJFmLqDW0KqVqYwtDK01f1+6xsSjZwm9dp1KrgUY7FWap0oXx9nh1gdo27awfTscO2aXAqRCCJEnrLh4lR47jzC2Wjn6Vbz7guiZcXTnBX6fupNHB/ji5VvSbDx3zlRoWbbM/EKfORM8PbPk/AAnT5qhoPXrm+Uh0px6d+Qf+K4DVL4Pui8GJ+vafVERsG+JWZw9fB3oBChexRRxqfMYlPUDpbh64Ty/TBzP+fAjNO/8NI0e73jHEFutNacGvcK1FSuo+PVXFGrWLMuuPaeQOYBCiDxp5cWrvLD3GA7AtLpePJDGvMCePeHHH00BzmxcMz1H0lqz0pr4bb92nYrWxK9jCokfmDkdlSqZT3NzfA+qEELkYFpruu08wrar19nQpDbFnW3/iVpCvIXZw/+lQq1itHquXvKTw2efwWuvmaWJvvnGfDKaRWbONKNGPvsMBg68y86h8+Hn/uDbGSo2Mks2HPsXtAWKVzVr9NV5HMr4pFgNOy42hj+nTebA+jXUbHo/rfq/jLPLrfP9LNHRhHftRty5c3gv/IECOXpOyL2TOYBCiDzpwRJF+LNBDcq4ONN1x2GmHDuX6vj+oUMhOhq+/DKbg8xBtNasvHiVR7cdpPvOI0TExfFRzYqsb1ybbmVLpJj8gfkbwWKBvn2zOWAhhMhjlFK8Xa0cUQkJTDyaNUP8HZ0cqN6gFEd3XCD2Rnzyk5v1GTZvhpIloXVrGDzYzDfPAn36mFMMHw5HjtxlZ/+uEPQ67FwAvw2Fa2ehxVDo/y+8tBVajoKyvqkuheTs4sqjL79Ki269ObBhLd+PGs7VC+dv2cfB3Z0Kn09BASdffJGEqGjbXGguJD2AQohcLzohgcH7T7Dk/BXalyrKpFoVcXe8c62/kBDYvdssz+dy95Uk8gytNf9cvsaEo2fZevU65V2cGexVhk5lilHgLiWxLRazPGHFiqaimxBCiHs3/MAJvjtzkVUNa1HD3faVKc8ejWTxB1t5oFtN6t1f/s4dbtwwmdlnn5nV2+fNM2M2bezECahXDwIC4O+/7zIUVGsz5LNEdShVO9Pr3h7ZtpnfJk/A0dmZ9kNep0Ltere8H71hA8effY5CwUFUmDwZlUeXhpAeQCFEnubu6MiXdSrzZpWyLD1/hbZbDxJ+I/aO/YYONSstzJ9vhyDtQGvNP5eu0X7bIbrsOMLZ2Dgm1KzAhia16VGuxF2TP4B//jGT+KX4ixBC2M6r3mVxd3Rg9KFTWdJ+aa8ilKxYiDXfh7H+x0PE30y4dYeCBWHyZPjtN1NsrEEDU7bTxh1DFSvCxx/D6tUwbdpddlbKzO0rXSfTyR9AlYCGdBv/Ea7uhVg47k12rrh18r/7ffdR+rVXiVrxNxemfpHp8+Rm0gMohMhTVl+6Sv89xwD4ok5lgksUSXpPa/NBJ5hFarNoGSa701qz9nIUE8PPsikymnIuzgyqXJouZYvjksFPOnv0gKVLTeJcMOvWLhZCiHzni+PnGXP4NPN9q9zyrLKVmOg41v94iH3/nsHDsyDBPWpRvmYKk+CTF4h59FEzea+U7QrUaG2Ggv77L+zaBd7eNms6TTHRUfw2eQLhoVvxC3mU4F7P4WitYqa15szrbxD5889UmPIZhR96KHuCykZSBEYIka8cuxFLn11H2RcdwxtVyjKwUqmkimDffGNKVP/xR5bOfbcLrTX/XoliwtGzbLQmfi9XLk3XTCR+AFeumKUJ+/a9y3pOQgghMizWYuGBTfspoBxY2bAmTqnMw75XJ/dfYtV3+7l6IYY6zcvR9MmquLg537qT1jBlCrz6qikQM3u2ydps5PhxMxQ0MDAdQ0FtyGJJYO28b9jy649UqFOPdoNfx62Ih3kvNpZjT/fk5qFDeC34Hpfq1bMnqGwiCaAQIt+JTkhg2P4T/HT+CkHFCtOieGGqFnShkrMLD/kWoG5NB5Yvt3eUtrPusqnq+V9kNGVdnHmpUim6lyuRqcQv0dSp8OKLZhmpgAAbBiuEEAKAZRFX6LM7nPdqVKBP+ZJZdp64mwls/vUooSuOU7BIAR7oUpMq9VNYBmL3buja1fz78svwwQfgaps5itOnw3PPmWfLCy/YpMl027t2FX99ORn3osV4bNiblPKqAkDcuXMcfeopHAq64f3DAhyLFs3ewLKQJIBCiHxJa82XJyL4/MR5Im7+vxKa0hB3qgD3ebkSUMaFKm4uVLW+yhRwvmP9oJzs38vXmBh+lg1XoilTwJmXKpeie9kSuDre+8ergYGmCMz27TYIVAghxB201nQIPcz+6BtsaFwbjyxYFiK588eusvLb/Vw8GUXV+p606FIDd4/bqqLFxJgCMZMng4+PKRBTr17KDWZA4nq869eb/NLL656bzJCzh8L45aPxxERH0WbAYGo0aQ7A9e3bOd6zF24NG1Lxqy9ReWSxW0kAhRD5XmRcPIdvxHL4eix7L8Xy2aJYitaJIcEzlhuW//8edHN0oGpBkwxWcXOxfu1KVTcXCjvdWVnUXjZYh3quvxJF6QJOvFS5ND1slPiBSfoCAtK5fpMQQohM233tOg9vCaNfRU/GVEuhYqeNJSRYCF1+nM1Lw3Eq4EDTDtWo3bTsnR9+Lltm5kxERsLEiWZIyD1+QJo4FLRhQ1i+PPuGgiaKunyJJR+/y5mw/TTp0IWmT3VDOThwZdEizrz5FsX79KH08NeyN6gsIgmgEELcZvBgM93h0GGNU6k4jlyP5dCNWI5cj+HQ9ViOXI/lRMxNLMmOKVXAiSoFXajm5koVNxeqWZPEyq4uqa6hZ2v/XYli4tGzrLsSRakCTrxUqTQ9ypWgoI0Sv0QDB5rhOmfOQLEUagYIIYSwnSH7j7Pw7GX+aVSLKm7Zs07RlXPXWfXdfk4fvEL5msUI7lETD0+3W3c6f94UiPn9d3jkEZg1654LxHz9NfTrB198Af3731NTmRIfF8ffM6aye9VyqjZowiMDh1CgoBtnx47j8rx5lJvwIR7t2mV/YDYmCaAQQtwmPByqVTOJ4IQJKe8Ta7EQfuMmh6/HcPi66T08ciOWQ9djuBT3/5Lajgoqu97ea2h6DksXcLLJkNKNV0xVz7WXo/As4MRLlUrxdLmSNk/8wCwPVa6cedbPnWvz5oUQQtzmfGwc923cx/3FCjPLJ5vKZALaotmz7jQbfjyEJUHTqF0V/FpWwCH5s0VrUwls2DDw8DAFYtq0yfw5tVmX97//TFXQ7B4KamLQbP/jV1bPmU7xchV4/NW38ChRkuN9n+HGzp1U/u47Cvrc+7BXe5IEUAghUtC1q1kC6cQJ80zLiMtx8RxN6jU0SeERa4IYk2xIqfttQ0oTew+rFnShUDqGlG6OjGbC0TOssSZ+A62Jn1sWJH6J5s2D7t1NpbYHH8yy0wghhEhm8rFzvHvkDIv8q9K8WOFsPXfU5VjWfH+Aozsu4FmpMMFP18Kz4m0x7N4N3bqZrO2ll+DDDzNdIObYMTMUtHFjMxTUXlPvj+0KZemk9wFo+8oIyleoRPhTHdEWC96LFuJUMusK82Q1SQCFECIFW7aYeQgTJ5pF4m3BojWnY+OsPYYxyXoNYzkZc5Pkv3FLF3BKml9YteD/i9FUcnVhx7XrTDx6ltWXr1HS2YkXK5WiV/msTfwStWwJR4/CoUPZPz9DCCHyq5gEC8037cPDyZG/GtTEMZuzIq01h7dFsOb7A8REx1M/pBINH/XCyTnZh5UxMTBiBHz6qcng5s0zhWIy4csvzRDQadPg+edtdBGZcOXsGX6eMI5Lp07ywNPPUMe7Ose6dce1Th0qz56FKlDAfsHdA0kAhRAiFcHBJtE5cgScne++/72ISbBw9IZJCJOGlF6P5fCNW4eUOimI11AiKfErgbtj9hSgOXzYDI0dNw7efDNbTimEEMLq53OX6b/3GB/XrEi3ciXsEkNMdBz/Lj7E/vVnKFrajeAeNSlX/bbJ4H/8YQrEXLliegJfeinD3XjJh4Lu3g2VK9vsEjLs5o3rLPv8Yw5t/o+6DzxE44pVOfvqaxTt3JmyY0bbL7B7IAmgEEKk4rffoG1b+O47M+zRXi7FxZtk0Npz6FnAmW7limdb4pfozTfhvffM8JwKFbL11EIIke9prWm/7RDhMbFsaFw7XVMFssqJfZdYPdcsIF+3RTnue7IaLgWTLZFw/jz07WsepG3amAIxpUtn6Bzh4aYDsUkT+Osv+w0FBdAWC+sXzee/xfMpW70mTd1LcuObOZQZ/TbFunSxX2CZJAmgEEKkwmIxo1hcXGDbNvs+fOwtIcF8AuvnZ57nQgghst/2q9dpszWMlyuV4o2q5ewaS1xsAht/PcLOv0/gVqQA93etSRX/ZAvIa21Wdh82DAoXNkngo49m6BzTppmF4b/80lQHtbewjf+y7POPcXFzp8lNB1w2baXy7Fm4NUgxl8qx0koAZXaHECJfc3Aw8/9CQ2HlSntHY19//gmnTsEzz9g7EiGEyL/qF3HjqdLF+PJkBMdvxNo1FmcXR5o/VZ0OwxvgWqgAy6bt4o+vdnP96k2zg1JmfcAtW6BsWTOk5qWXTDnpdOrXzxQce/llM/LEwcFUBrVXFeoajZvRbdxEHJ2cWR0byVnvipx8eRBxp0/bJ6AsIAmgECLf697djFqZONHekdjXjBng6Wme30IIIeznjSplcQDeOXLG3qEAUNqrCB3faEDj9lU4ujOCeaP/Y9/6MySNJKxbFzZuhFdeMYvsNmpkqoWmg4MDtGsHsbHmQ0itzTSEfv3slwR6Vvam+7sfU65GLba5ObCncAGODxyIJQOJbU4mCaAQIt9zdTUfWP7xh5mInh+dOwdLlkDPnpBLC54JIUSeUc61AAMqlWLJ+StsuhJl73AAcHR0oMEjXnR5sxHFy7mzcs4+lnwaSmSENSlydYVJk2DZMoiIMGW2P/3UZHR38cknd267fh1GjrTtNWSEWxEPOowch3+rRzlS1J11MVc4NnIkeWH6nCSAQgiBKUXt5gYffWTvSOzj228hPl6GfwohRE4xoFIpyro489ahU1hyUNJRrIw7TwwJ4IGuNTgXfpXvx20kdMVxLIlr4LZubXr/Hn7Y9Ag+8gicPZtmm8ePZ2x7dnF0cqJl3xd4uN9ALhVx548juzky+VP7BmUDkgAKIQRQooQpZjZ3LuShYf7porUZ/tm0KdSube9ohBBCALg7OvJGlbLsuHaDxecu2zucWygHRb0HKtDt7cZUqFmMfxcdYvEHW7hw0tpb6elphpVMnQqrV4Ovb5rVxSpVytj27ObbsjUd336PhIIFWbr2L/bMmW3vkO6JJIBCCGH1yiumEuaUKfaOJHtt2AD790vvnxBC5DQdShfDr3BB3j1yhuiEhLsfkM0KFXPlkQG+hDxbl2uXYlj47mb+++Uw8XEJpkDMCy/A1q1QrpyZYD5wYIoFYsaPN6NwknN2Nttzigq169Hjw88opBz4Y+lC1s/6MtcOB5UEUAghrKpWhSeegC++gKicMeUiW8yYAYUKQadO9o5ECCFEcg5KMa5aec7ExjH1+Hl7h5MipRTVG5Sm29tNqN6oNFuXHeOH8Zs5feiK2aFOHVMgZvBg+PxzaNAAdu68pY3u3eGrr8xSREqZ6YQAjRtn77XcTdGKlejywWTKXb/Jhj9+ZelH7xIXG2PvsDJMEkAhhEhm2DC4cgVmzrR3JNnj2jVYsAA6dzZJoBBCiJylUdFCtC9VlKnHz3M65qa9w0mVayFnHupdh3Yv+xEfZ+Gnidv4Z/4Bbt6IN4vtfvyxWW/o0iVTIOaTT8xivFbdu5uF4S0WOHjQ9Aj26XPLLjmCe5UqtBs5jppnLxO2eQPfj3qNqxci7B1WhkgCKIQQyTRpAs2amUJm8fH2jibrLVgA0dEy/FMIIXKyN6uUxQK8m0OWhUhLpTol6PJWI/werMjuNaeYP3Yj4TsvmDdDQkzvX6tWpkcwlQIxFSqYAqLr1pl/c5pC9zWh6TP9aXDkDDHnzqEclL1DyhBJAIUQ4jbDhplPIX/80d6RZL0ZM8zonCZN7B2JEEKI1FQq6MLzFTxZdO4y265G2zucuyrg6kTzTtXp8FogBQo68dvUnfw13bqAvKcn/PKLKRDzzz/g4wNLl5oDz5yBBx6As2fp2dOsD/jGG3DggH2vJyXFnu5B9Ydb0+y/nTgdP2nvcDJE5dbJi6lp0KCB3rJli73DEELkYgkJphpm0aJm2oLKXR/spdvevWbt3o8+giFD7B2NEEKItETFJ3Dfxn14ubqwJKAaKpc8nBLiLWz78xhbloXj7OJI847Vqdm4jIl/3z7o2hV27IABA+DmTTMH4/nnYepUzpwxz6maNU1voKOjva/mVpabN7n6++94PPZYjvvvoZTaqrVukOJ7kgAKIcSdpk0zxcv++Qfuv9/e0WSNoUPhs8/g1CnzgawQQoicbe7piww9cIJpdSrzeOli9g4nQy6djmbVd/s5eySSSnWK80C3mhQpWRBiY8Hd3Xz6ejtXV+bPvEG3bvD++zB8ePbHnVullQDKEFAhhEhBz55QsiRMnGjvSLLGzZswZw60by/JnxBC5BZdyhanbiFX3jlympiEHFYd5S6Kl3PnyWEB3N+lBmcORzJ/3CZ2/H0Ci3MBOHECgoNvHXJTty6sWkWXLvDkkzBqFOzZY7/48xJJAIUQIgVubvDii/Drr2aNvLxmyRK4cEGKvwghRG7iqBRjqpXnZEwcX53MXZUnwSwg7xNUga5vN6Z89aKsW3iQHyds5aKlsBnnqZRZABBMtnfffaiHH+KLVj9TpIimVy+Ii7PvNeQFkgAKIUQqBgwwaxF9/LG9I7G9GTNMlbWQEHtHIoQQIiOaFytMm5IefHrsHOdjc2c2VLi4K4++6MvDz9QhMuIGP7y7mY1RlUl4fgBs3mwewK1bw7hxcPgwpZ5/gmkxfdi6FT4YljPXQ8xNZA6gEEKkoX9/mD0bjh2D0qXtHY1tnDhhFtt9800YO9be0QghhMioo9djuX/TfjqWKcbHtSrZO5x7ciPqJusWHiRs4zmKlXGjTBUPHBwVDg4KB0cHHBxAHT+GQ+g2Fu+pzS5LHQaWWUyFB6riEFgfB1cXs5+j+v8r8dikr9Ud+yjrdkdHh6Sv//9yQClyXGGXjMj3RWDi4uI4efIkMTExdopKCJHTubq6UqFCBZwTh55YHThgKoLmpWRp3Dgzl+LIEfD2tnc0QgghMuPtQ6f46kQEfzWogU9hN3uHc8+O7bnIfz8f5sa1OCwJFiwWjSXBvHSCxmLJ/pwlxYQyeZJoTRwf6e9jCtrkIPk+ATx69CiFCxemRIkSuTqTF0JkDa01Fy9e5Nq1a3inkBE9/jisXWt6ztxy+TPWYoGqVc1rxQp7RyOEECKzIuPiuW/jPmq5F2Sxf9U8/zeu1hpt0SxZounaWfNa1+MMt7yH5bffscQlYPHzx9KxM5bWj2BxLWiSx6Qk0pKUTFoSTDuWBAsJSV8ne1lu3ff2ZNRi+X97OkGTkKB5oGtNChVzsfctukVaCaBTdgdjDzExMXh5eeX5/zGEEJmjlKJEiRJERKQ8oX7YMLNm7ezZZlpCbrZypVnk/t137R2JEEKIe+Hh7MSr3mV5Pewkf1yIpI1nUXuHlKWUUihHxeNPQKeuMH5uFdr99zUNJl+G776Dr7+GV/qaJSW6dIHnnoNGjfLuYr73IN8UgZHkTwiRlrR+RzRrBo0bm2IwKS1TlJvMmAHFisETT9g7EiGEEPfq6bIlqOHmypjDp4m15K5lIe7Fp5+aefm9ekGsWzF46SWzmPx//5nk7/vvoUkT8PODyZPh0iV7h5yj5JsEUGQ9pRRPP/100vfx8fF4enrStm3bTLV35coVpk6dmvT96tWrM90WwP79+/H396d+/focPnz4lvfeTdYdEh4eTr169TJ9HpH3KGV6AQ8fNj2BudWlS/DTT9Cjh6luKoQQIndzclCMqVaO8Bs3mXnygr3DyTZFi5oPNPfuhbfftm5UynxaO306nDkDX34JLi4waBCUK2cefv/8A3ls+ltmSAIobMbd3Z3du3dz48YNAJYvX0758uUz3d7tCeC9+vnnn3nsscfYvn07VatWveW9d3P4eLj4+Hh7h5DvPfGEKZiSmxeGnzsXYmNl7T8hhMhLgksU4cHihZl07CwXbuafvxdat4Znn4UJE0zH3y0KF4Z+/cySEtu3mwff0qUQFAS1apmDzuff5SQkARQ21aZNG3777TcA5s+fT9euXZPeu3TpEo8//ji+vr40adKEnTt3AjB69Gj69u1LUFAQVapUYfLkyQCMGDGCw4cP4+/vz6uvvgpAVFQUTz31FLVq1aJ79+6kVMQoNDSUJk2a4OvryxNPPMHly5f5/fff+eSTT5g+fTrBwcG37D9ixAhu3LiBv78/3bt3ByAhIYHnnnuOunXrEhISkpTUHj58mNatWxMYGEiLFi3Yf9sK4RaLherVqyfNJbNYLFSrVo0LFy4QERFBhw4daNiwIQ0bNuTff/8FYNOmTTRt2pT69evTtGlTDhw4AMDs2bPp2LEj7dq1I0QWa7M7R0cYMgQ2bID16+0dTcZpbT4UDQw0I2KEEELkHaOrlSc6wcKEo2fsHUq2+ugjs6Ztr15g/VPtTv7+8PnncPo0fPMNlCoFr71mDuzYEf76y1RIy0+01nnqFRgYqG+3d+/eO7YJ23N3d9c7duzQHTp00Ddu3NB+fn561apV+tFHH9Vaaz1w4EA9evRorbXWf//9t/bz89Naa/3222/r++67T8fExOiIiAhdvHhxffPmTX306FFdt27dpPZXrVqlixQpok+cOKETEhJ0kyZN9Nq1a++Iw8fHR69evVprrfVbb72lBw0alHSeCRMmpBp7oqNHj2pHR0e9fft2rbXWHTt21N9++63WWusHH3xQh4WFaa21/u+//3RwcPAdbY0ePVpPmjRJa631n3/+qZ988kmttdZdu3ZNivfYsWO6Vq1aWmutIyMjdVxcnNZa6+XLlyftP2vWLF2+fHl98eLFFGMWtne33xVRUVoXK6a19T9RrrJ5s9ag9dSp9o5ECCFEVnj9wAldduV2vS/qur1DyVYrVpjn2+DBGTho716thwzRukQJc7CXl9bjxml98mSWxZndgC06lXxJegCFTfn6+hIeHs78+fN55JFHbnlv3bp1SXMEH3zwQS5evEhkZCQAjz76KC4uLpQsWZJSpUpx7ty5FNtv1KgRFSpUwMHBAX9/f8LDw295PzIykitXrvDAAw8A0KtXL9asWZPh6/D29sbf3x+AwMBAwsPDiYqKYv369XTs2BF/f3+ef/55zpy585O2vn37MmfOHABmzpxJnz59AFixYgUDBw7E39+f9u3bc/XqVa5du0ZkZCQdO3akXr16DB48mD179iS19fDDD1O8ePEMxy+yhru7qQL6009w6JC9o8mYGTOgYEFI1ikvhBAiDxnmXYbCTo6MPng6xRFSeVXLlubZ/MknZsmmdKld23QfnjplCsZUrQpvvQWVKkH79rBkCeTh6TeSAAqba9++PcOGDbtl+CeQ4i+jxMqLLi7/XzvF0dEx1Tlv6d3vXqV0HovFQtGiRQkNDU167du3745jK1asSOnSpVm5ciUbN26kTZs2gBkOumHDhqRjT506ReHChXnrrbcIDg5m9+7d/Prrr8TExCS15e7uniXXJzJv4EBwdoZJk+wdSfpdvw7z5sFTT5mJ80IIIfKe4s5ODPUqzerL1/j70jV7h5OtPvjAzNPv0weiozNwoIsLdO5sFsY9dAiGDzfzBh97DCpXhjffhKNHsyxue5EEUNhc3759GTVqFD4+Prdsv//++5k7dy5gKnqWLFmSIkWKpNpO4cKFuXYtY7/APDw8KFasGGutHwF9++23Sb2BaXF2diYuLi7NfYoUKYK3tzcLFy4ETEK7Y8eOFPd99tln6dGjB506dcLR0RGAkJAQpkyZkrRPaGgoYHotE4vlzJ49+66xCvsqU8YUEps1Cy7kkoJrixfD1atS/EUIIfK63uVLUqWgC2MOnSLOkn96AQsVMs/lI0dMDpcpVauaRXKPHzdDffz94b33zPaQEFi4EG7etGXYdiMJoLC5ChUqMGjQoDu2jx49mi1btuDr68uIESP45ptv0mynRIkSNGvWjHr16iUVgUmPb775hldffRVfX19CQ0MZNWrUXY/p168fvr6+SUVgUjN37lxmzJiBn58fdevW5ZdU1gRo3749UVFRScM/ASZPnpx0/XXq1GHatGkAvPbaa7z++us0a9aMhNy+yFw+MWSImWz+xRf2jiR9pk+HatXg/vvtHYkQQoisVMDBgberlePg9Vi+OZ1LPqW0kfvvNys+fP45rFx5Dw05O8Pjj8Nvv0F4uFlnYv9+6NTJFI559VWwFuzjzBl44AE4e9YGV5B9VF4bI9ygQQO9ZcuWW7bt27eP2rVr2ykikR9t2bKFwYMHJ/VEitwhI78rHn3UjBI5fjxnr6l38CDUqGE+1Hz9dXtHI4QQIqtprem04zC7r91gQ5PaFHV2sndI2eb6ddNxd/Mm7NwJaQw0y5iEBFMt9Ouv4ddfzfzA++8HJydYvRqefx5suHSZLSiltmqtG6T03l17AJVSM5VS55VSu5NtG62UOqWUCrW+Hkn23utKqUNKqQNKqVbJtgcqpXZZ35usrJO/lFIuSqkF1u0blVJeyY7ppZQ6aH31yuT1C5Gt3n//fTp06MB7771n71BEFho2DCIi4Ntv7R1J2mbOBAcHUyJbCCFE3qeUYky18kTGJ/DC3mOcic0bwxbTw83NrPRw4oTpqLMZR0do0wZ+/NE07uQEa9aYrkaLxQwJUspUW8sF0jMEdDbQOoXtk7TW/tbX7wBKqTpAF6Cu9ZipSilH6/5fAP2A6tZXYpvPAJe11tWAScAH1raKA28DjYFGwNtKqWIZvkIhstmIESM4duwYzZs3t3coIgsFBUFAgCkillOXD4qPNw/CRx+FcuXsHY0QQojsUqdQQcZVL8+GK1G02Lif6ScjSMhjo/5Sc9995kPar76CP//MghOUKWOG/3TtaorIgEn8unfPNQVj7poAaq3XAJfS2d5jwPda61it9VHgENBIKVUWKKK13mBdl2IO8HiyYxIngy0CWlp7B1sBy7XWl7TWl4HlpJyICiFEtlPKPGAOHDDTBHKiZcvM9AQp/iKEEPnPMxU8Wd2oFg2KuPPmwVM8sjWMXdeu2zusbDFmDNSpY55/V65kwQnKlgUPD4iLM/NAYmPNeNMyZbLgZLZ3L0VgBiqldlqHiCb2zJUHTiTb56R1W3nr17dvv+UYrXU8EAmUSKMtIYTIEZ56yiwZNHGivSNJ2fTpULo03LYkpxBCiHzCq6AL8/2q8EWdypyKiaPVljDePnSK6Pi8XXTO1RVmzza1WQYPzqKTnDsH/fvDf/+Zf3NRIZjMJoBfAFUBf+AM8JF1u0phX53G9swecwulVD+l1Bal1JaIiIg0whZCCNtxdoZXXjHTADZtsnc0tzpzxvRM9upl4hRCCJE/KaV4onQx1jWuRY9yJfjyRAT3b9rPnxci7R1almrYEEaMMIng0qVZcIIffzQlR/38zL8//pgFJ8kamUoAtdbntNYJWmsL8DVmjh6YXrqKyXatAJy2bq+QwvZbjlFKOQEemCGnqbWVUjxfaa0baK0beHp6ZuaShBAiU5591owC+eiju++bnebMMUXL+va1dyRCCCFygqLOTnxYsyK/BlSnsJMjvXYdpe+uo5yOybtFYkaNAl9feO45uJTeCW35QKYSQOucvkRPAIkVQpcAXayVPb0xxV42aa3PANeUUk2s8/t6Ar8kOyaxPt1TwErrPME/gRClVDHrENMQ67Zcq1ChQvYOIV9RSvH0008nfR8fH4+npydt27a1Y1S50/jx46lbty6+vr74+/uzcePGDLexevVq1q9fb7OYvLy8uJADVmIvXNhUf160KOfM/dbaVP9s0QJq1rR3NEIIIXKShh7uLG9Qk5FVyrLq0lVabNrP1yfyZpGYAgVMMbQLF+Cll+wdTc6RnmUg5gMbgJpKqZNKqWeAD61LOuwEgoHBAFrrPcAPwF7gD+BFrXXiIOMXgOmYwjCHgWXW7TOAEkqpQ8AQYIS1rUvAOGCz9TXWui175NKFHW+XnxcWd3d3Z/fu3dy4cQOA5cuXU768TCPNqA0bNrB06VK2bdvGzp07WbFiBRUrVrz7gbexdQJ4L+Lj423a3ssvm6UWPvnEps1m2rp1EBYmxV+EEEKkzNlB8VLl0qxuVItGHu68degUbbaGsSMPFonx94e33oJ583LVKM0slZ4qoF211mW11s5a6wpa6xla66e11j5aa1+tdXtrD1/i/uO11lW11jW11suSbd+ita5nfW+gtZcPrXWM1rqj1rqa1rqR1vpIsmNmWrdX01rPsvXFp2ncOPNX1NixNm02KiqKli1bEhAQgI+PD7/8YjpCw8PDqV27Ns899xx169YlJCQkKXEJCgoicXH7Cxcu4OXllXRMixYtCAgIICAgIOmP69WrVxMcHEy3bt3w8fHhrbfe4tNPP02KYeTIkUyePNmm15VTtWnTht+sJRrnz59P165dk97btGkTTZs2pX79+jRt2pQDBw4A0KJFC0JDQ5P2a9asGTt37szWuHOSM2fOULJkSVyspY5LlixJuXLl2Lp1Kw888ACBgYG0atWKM2fMr4GgoCBeeeUVmjZtSr169di0aRPh4eFMmzaNSZMm4e/vz9q1a4mIiKBDhw40bNiQhg0b8u+//wIwevRoevXqRUhICF5eXvz444+89tpr+Pj40Lp1a+Li4pJimzBhAo0aNaJRo0YcOnQIIM12+/XrR0hICD179rTpPSpfHrp1gxkzcsYQk+nTTc/kU0/ZOxIhhBA5WeWCLszzrcKXdStzNjaONlvCeOvgSaLyWJGY1183Szf172/W8M3vnOwdQLZ75RVI9sf9HdauvXVRry++MC8HBzOeKiX+/un+6N/V1ZWffvqJIkWKcOHCBZo0aUL79u0BOHjwIPPnz+frr7+mU6dOLF68mB49eqTaVqlSpVi+fDmurq4cPHiQrl27JiWKmzZtYvfu3Xh7exMeHs6TTz7JoEGDsFgsfP/992zKxooVH2z6gP2X9tu0zVrFazG80fC77telSxfGjh1L27Zt2blzJ3379mXt2rWmjVq1WLNmDU5OTqxYsYI33niDxYsX8+yzzzJ79mw++eQTwsLCiI2NxdfX16bxZ9aVXw9z83S0TdssUM6dou2qpvp+SEgIY8eOpUaNGjz00EN07tyZpk2b8tJLL/HLL7/g6enJggULGDlyJDNnzgQgOjqa9evXs2bNGvr27cvu3bvp378/hQoVYtiwYQB069aNwYMH07x5c44fP06rVq3Yt28fAIcPH2bVqlXs3buX++67j8WLF/Phhx/yxBNP8Ntvv/H4448DUKRIETZt2sScOXN45ZVXWLp0KYMGDUq13a1bt7Ju3ToKZsFCrUOHmnl3X35pHjT2EhkJCxfC00+Du7v94hBCCJE7KKV4rFQxgooV5t0jZ5h+8gK/RUQyvnp52ngWtXd4NuHsbIaCBgbCgAHwww9mOaf8Kv8lgHfTqBEcOWIGC1ssJvErWRKqpv4HckZorXnjjTdYs2YNDg4OnDp1inPnzgHg7e2Nv78/AIGBgYSHh6fZVlxcHAMHDiQ0NBRHR0fCwsKSXUYjvL29ATNXqkSJEmzfvp1z585Rv359SpQoYZPryel8fX0JDw9n/vz5PHJbLfzIyEh69erFwYMHUUol9Sx17NiRcePGMWHCBGbOnEnv3r3tEHnOUahQIbZu3cratWtZtWoVnTt35s0332T37t08/PDDgBlqXLbs/6cGJ/a03n///Vy9epUrKSzCs2LFCvbu3Zv0/dWrV7l27Rpgem6dnZ3x8fEhISGB1q3NEqA+Pj63/H+ReJ6uXbsy2FrnOa1227dvnyXJH5hJ5iEhMHkyDBny/7Vhs9v338ONGzL8UwghRMZ4ODvxQc2KdCpTnFcPnKDP7nBalSzC+OoVqOBawN7h3bN69cz6gK+/bhLAzp3tHZH95L8EMD09dS+8AF99ZRYRuXkTOnSAqVNtcvq5c+cSERHB1q1bcXZ2xsvLi5iYGICkIXYAjo6OSUNAnZycsFh7JRP3BZg0aRKlS5dmx44dWCwWXF1dk95zv+2j/8RerbNnz9I3m8sCpqenLiu1b9+eYcOGsXr1ai5evJi0/a233iI4OJiffvqJ8PBwgoKCAHBzc+Phhx/ml19+4YcffkjqVc0J0uqpy0qOjo4EBQURFBSEj48Pn3/+OXXr1mXDhg0p7q9u+1jt9u8BLBYLGzZsSDEhS/x/wcHBAWdn56TjHRwcbpm/l7zdxK/Tavf2/y9sbdgwkwTOnw/2+txgxgzw8THlr4UQQoiMCvRw588GNfnqZAQTj57l/k37ec2rDM9W8MTJIXd3mw0bBj//bHoBH3gg16zbbnP3shB83pWFCztGRkZSqlQpnJ2dWbVqFceOHbvrMV5eXmzduhWARYsW3dJW2bJlcXBw4Ntvv02z4MsTTzzBH3/8webNm2nVqtW9X0gu0rdvX0aNGoWPj88t2yMjI5OKwsyePfuW95599llefvllGjZsSPHixbMr1BzpwIEDHDx4MOn70NBQateuTURERFICGBcXx549e5L2WbBgAQDr1q3Dw8MDDw8PChcunNQTB2Zo6ZQpU25pN6MSz7NgwQLuu+8+m7WbWQ89ZHoCJ040lTiz286dsHmz6f3Lz0NbhBBC3BtnB8WLlUrxT6Oa3OdRiNGHT9N6axjbr+buIjFOTmZdwOvXTQXvPFj4NF0kAUxJFizsGB8fj4uLC927d2fLli00aNCAuXPnUqtWrbseO2zYML744guaNm16S9n7AQMG8M0339CkSRPCwsLS7N0oUKAAwcHBdOrUCUdHx3u+ntykQoUKDBo06I7tr732Gq+//jrNmjW7I3kODAykSJEi9OnTJ7vCzLGioqLo1asXderUwdfXl7179zJ27FgWLVrE8OHD8fPzw9/f/5YKn8WKFaNp06b079+fGTNmANCuXTt++umnpCIwkydPZsuWLfj6+lKnTh2mTZuW4dhiY2Np3Lgxn376KZMmTQKwSbuZpZT5dHHPHvjTDovWzJhhSl6nMXVYCCGESLdKBV34ztebr+t6EXEzjke2hvFG2Emu5eIiMbVqwfjxsGQJfPedvaOxD6XzWOrboEEDffuQvX379lG7dm07RWTs2LGD5557LluLryRnsVgICAhg4cKFVK9e3S4x5CanT58mKCiI/fv34+Agn5NkRFBQEBMnTqRBgwb2DiXDbPG74uZNqFLFPGBWrLBRYOkQGwvlypleSGvHqBBCCGEzV+MTeP/IGWadukDpAs68U708j3p6pDjNI6dLSICgINi1y3xomxdXCVNKbdVap/jHmPxlmw2mTZtG165deeedd+xy/r1791KtWjVatmwpyV86zJkzh8aNGzN+/HhJ/kSGFSgAgwbB33/D9u3Zd96ffzZLUEjxFyGEEFmhiJMj79aowG+B1SlRwJFn94TTc9dRTsTctHdoGeboCLNmQVwcPPdc/hsKKj2AQghhZavfFVeuQMWK8Nhj2Te8JCTELP5+5IgpXiyEEEJklXiLZvrJCD44aupkvOpdhucqeOKcy4rETJkCL71k1s/Nax+gSg+gEEJko6JFzSeK338PJ05k/fnCw81w0z59JPkTQgiR9ZwcFP0rlWJN41q0KFaIsYdP03rrAbZF2nat4qw2YAAEB8PgwZCOuox5hvypIIQQWSCx7tCnn2b9uWbNMv9KzSIhhBDZqaJrAb7x8WZmPS8uxSXw6LaDjAg7ydVcUiTGwQFmzjRDQJ95Jv8MBZUEUAghskDlytCpk1lSNDIy686TkGASwIcfhkqVsu48QgghREqUUjziWZQ1jWrxTIWSzDl1geYb97Hk/BVyw1QzLy/46CMzdz8bC4fblSSAQgiRRYYOhWvX4Ouvs+4cK1aYYaZ5be6CEEKI3KWwkyPvVK/A74E1KFPAmX57wum+8wjHbsTaO7S7eu45M5f+1VfNXPq8ThLAbOLo6Ii/v3/SKzw8PNV9g4KCuL2Qjci4QoUK2TuEPOPkyZM89thjVK9enapVqzJo0CBu3ky96tcnn3zC9et3Xyw2r/83Cgw0cws++cQsD5EVZsyAEiVMwRkhhBDC3vyLuPF7YA3GVivHf5HRBG3az2fHzhFnybm9gUqZQjCOjtC3L1gs9o4oa0kCmIK5c013sIOD+Xfu3Htvs2DBgoSGhia9vLy87qm9+Pj4e47p9sXPhUiJ1ponn3ySxx9/nIMHDxIWFkZUVBQjR45M9Zj0JoD3whb/D2SHYcPg1Cn44Qfbt33hgln+4emnwcXF9u0LIYQQmeHkoOhXsRRrG9UiqHgRxh85Q8iWA2zJwUViKlY0H9j+8w989pm9o8lakgDeZu5c6NfPVALS2vzbr59tksDbbd26lQceeIDAwEBatWrFmTNnkt777rvvaNq0KfXq1UtaPH706NH069ePkJAQevbsyezZsxk4cGDSMW3btmX16tUAvPDCCzRo0IC6devy9ttvJ+3j5eXF2LFjad68Oe+//z4BAQFJ7x08eJDAwEDbX6gdrV69mrZt2yZ9P3DgQGbPng2Ye/H2228TEBCAj48P+/fvByA6Opq+ffvSsGFD6tevzy+//GKP0HOMlStX4urqSh9rhRFHR0cmTZrEzJkziY6OZtiwYfj4+ODr68tnn33G5MmTOX36NMHBwQQHBwMwf/58fHx8qFevHsOHD7+l/aFDhxIQEEDLli2JiIgA4PDhw7Ru3ZrAwEBatGiR9N+md+/eDBkyhODg4Dvayalat4Y6dWDiRNtPLv/2W7OGkQz/FEIIkROVdy3ALB9vZtXzIjI+gXbbDvLagRNcicuZH+L27g1t28Lrr5ullfIqJ3sHkN1eeQVCQ1N//7//IPa2ocrXr5s/sFKbx+Pvbz4xSMuNGzfw9/cHwNvbmx9++IGXXnqJX375BU9PTxYsWMDIkSOZOXMmYJKQ9evXs2bNGvr27cvu3bsBkzSuW7eOggULJiUyKRk/fjzFixcnISGBli1bsnPnTnx9fQFwdXVl3bp1AKxYsYLQ0FD8/f2ZNWsWvXv3TvtCMuHsu+8Su2+/Tdt0qV2LMm+8cc/tlCxZkm3btjF16lQmTpzI9OnTGT9+PA8++CAzZ87kypUrNGrUiIceegh3d3cbRH5vli1bxtmzZ23aZpkyZWjTpk2q7+/Zs+eODwaKFClCpUqVmD59OkePHmX79u04OTlx6dIlihcvzscff8yqVasoWbIkp0+fZvjw4WzdupVixYoREhLCzz//zOOPP050dDQBAQF89NFHjB07ljFjxjBlyhT69evHtGnTqF69Ohs3bmTAgAGsXLkSgLCwMFasWIGjo6NN70NWcXAwcwGfecZMMH/oIdu0q7UZ/tmoEdSrZ5s2hRBCiKzQxrMoLYoVZsLRs3x9MoJlFyIZV608j5UqilI5Z+1ApeDLL81ztXdvWLvWDAvNa6QH8Da3J393255eyYeA/vTTTxw4cIDdu3fz8MMP4+/vzzvvvMPJkyeT9u/atSsA999/P1evXuXKlSsAtG/fnoIFC971fD/88AMBAQHUr1+fPXv2sHfv3qT3OnfunPT1s88+y6xZs0hISGDBggV069bt3i40l3nyyScBCAwMTJqX+ddff/H+++/j7+9PUFAQMTExHD9+3I5R2pfWOsVfzlpr1qxZQ//+/XFyMp8lFS9e/I79Nm/eTFBQEJ6enjg5OdG9e3fWrFkDgIODQ9LPY48ePVi3bh1RUVGsX7+ejh074u/vz/PPP39L73jHjh1zTfKXqHt3KF3a9ALayqZNsGeP9P4JIYTIHQo5OTKmenn+aFCDsi7O9N97jK47jhCew4rElCtnhoBu2ACTJtk7mqyR73oA79ZT5+WV8kKQlSuDdXSlTWitqVu3Lhs2bEjx/dv/4E78PnkvlJOTE5Zks1RjYmIAOHr0KBMnTmTz5s0UK1aM3r17J713exsdOnRgzJgxPPjggwQGBlKiRIl7v7jb2KKnLrNSu0eJXKwTpxwdHZPmlGmtWbx4MTVr1sy+QNMprZ66rFK3bl0WL158y7arV69y4sQJqlSpctdP7jJSAlophcVioWjRooSm0lWfE3piM8rFBV5+GUaOhJ07wdoZf09mzAA3N+jS5d7bEkIIIbKLb2E3lgXWYNapC7x35AxBm/YzxKsM/St6UsAhZ/RNdesGixfDm2/CI4+YqRx5Sc64yznI+PHmj6rk3NzMdluqWbMmERERSQlgXFwce/bsSXp/wYIFAKxbtw4PDw88PDzuaMPLy4vQ0FAsFgsnTpxImit49epV3N3d8fDw4Ny5cyxbtizVOFxdXWnVqhUvvPBC0hyvvKRy5crs3buX2NhYIiMj+fvvv+96TKtWrfjss8+SEpft27dndZg5WsuWLbl+/Tpz5swBTPGgoUOH0rt3b0JCQpg2bVpS8nzp0iUAChcuzLVr1wBo3Lgx//zzDxcuXCAhIYH58+fzwAMPAGCxWFi0aBEA8+bNo3nz5hQpUgRvb28WLlwImARyx44d2XrNWaF/f/O75OOP772tqCiYP9+sM1ikyL23J4QQQmQnR6V4toInaxvVomWJIrx75AwPbwlj05Uoe4cGmKGg06ZB4cJmKGguqTuXbpIA3qZ7d7Nwc+XK5j9+5crm++7dbXueAgUKsGjRIoYPH46fnx/+/v6sX78+6f1ixYrRtGlT+vfvz4wZM1Jso1mzZnh7e+Pj48OwYcOSCrr4+flRv3596tatS9++fWnWrFmasXTv3h2lFCEhIba7QDuLj4/HxcWFihUr0qlTJ3x9fenevTv169e/67FvvfUWcXFx+Pr6Uq9ePd56661siDjnUkrx008/sXDhQqpXr06NGjVwdXXl3Xff5dlnn6VSpUr4+vri5+fHvHnzAOjXrx9t2rQhODiYsmXL8t577xEcHIyfnx8BAQE8Zl2zwN3dPWmO4cqVKxk1ahQAc+fOZcaMGfj5+VG3bt08UYineHEzXHPePDh9+t7aWrjQJIEy/FMIIURuVs61ADPqeTPHx5uo+ATabz/EnFMX7B0WAKVKwdSpsHkzfPihvaOxLZWR4Vm5QYMGDfTta+jt27eP2rVr2yminG/ixIlERkYybtw4e4diMzt27OC5555L6hUVIj2y+nfFkSNQvTq89hq8917m22ne3CwBsW+f+aBKCCGEyO2i4xOYdOwcfcqXpLxrAXuHk6RLF/jxR9iyxTZTOLKLUmqr1rpBSu9JD2A+98QTTzBnzhwGDRpk71BsZtq0aXTt2pV33nnH3qEIcYsqVaBDBzOsxDpCNsP274d//zUL1UryJ4QQIq9wd3LkzarlclTyBzBlChQrBr16maWX8gJJAPO5n376iZ07d1KyZEl7h2Iz/fv3Z+/evXlqSKvIO4YOhStXwLriS4bNnGlKUvfsadOwhBBCCJGCkiXNdLDQUNvXBLEXSQCFECIbNW4MLVqY0tIZnVQeFwfffAPt2kGZMlkTnxBCCCFu9dhj8PTTJgHcts3e0dw7SQCFECKbDRtmlpu5bXWNu1q6FM6fl+IvQgghRHb79FNTGKZXr3tfH9zeJAEUQohs1rYt1KgBEyZARupwzZgBZctC69ZZF5sQQggh7lSsGEyfDrt3w5gx9o7m3kgCKIQQ2czBAYYMga1bYc2a9B1z6hQsW2bWI3JyytLwhBBCCJGCNm3MKJwPPoDcXGheEsBsopTi6aefTvo+Pj4eT09P2rZta5P2CxUqZJN28pK73ZOgoCBuXzJEpEwpxdChQ5O+nzhxIqNHj7ZLLHnlZ71nTzOxfOLE9O3/zTdgsZjqn0IIIYSwj48+gvLlzVDQGzfsHU3mSAKYTdzd3dm9ezc3rD8py5cvp3z58hlqIz6jFSOEsBEXFxd+/PFHLlzIGYuzZlZO+n+oYEEYONDM69u3L+19LRZT/TMoCKpVy5bwhBBCCJECDw/zTN6/H0qXNqN6vLxg7lx7R5Z+kgBmozZt2vDbb78BMH/+fLp27Zr03qZNm2jatCn169enadOmHDhwAIDZs2fTsWNH2rVrR0hICFFRUfTp0wcfHx98fX1ZnKyKxMiRI/Hz86NJkyacO3cuey8uh1q9evUtvawDBw5k9uzZt+wzY8YMBg8enPT9119/zZAhQ7IrxFzBycmJfv36MWnSpDveO3bsGC1btsTX15eWLVty/PhxIiMj8fLywmKxAHD9+nUqVqxIXFwchw8fpnXr1gQGBtKiRQv2798PQO/evXnhhRcIDg6mSpUq/PPPP/Tt25fatWvTu3fvW845dOhQAgICaNmyJREREQBptjtkyBCCg4MZPnx4Ft6ljBswAFxd4eOP097vn3/g8GEp/iKEEELkBOfOmekY166ZufzHjkG/frknCcx3M0nG/LqHvaev2rTNOuWK8Ha7unfdr0uXLowdO5a2bduyc+dO+vbty9q1awGoVasWa9aswcnJiRUrVvDGG28kJXcbNmxg586dFC9enOHDh+Ph4cGuXbsAuHz5MgDR0dE0adKE8ePH89prr/H111/z5ptv2vQ6M2vtD2FcOBFl0zZLVixEi041bNJWly5d8PX15cMPP8TZ2ZlZs2bx5Zdf2qRtWwsLG8e1qLt0F2VQ4UK1qVHjrbvu9+KLL+Lr68trr712y/aBAwfSs2dPevXqxcyZM3n55Zf5+eef8fPz459//iE4OJhff/2VVq1a4ezsTL9+/Zg2bRrVq1dn48aNDBgwgJUrVwLm53nlypUsWbKEdu3a8e+//zJ9+nQaNmxIaGgo/v7+REdHExAQwEcffcTYsWMZM2YMU6ZMSbPdsLAwVqxYgaOjo03v3b3y9DRz+mbOhHHjUl/aYcYM84ljhw7ZGp4QQgghUjBy5J1LOV2/brZ3726fmDIi3yWA9uTr60t4eDjz58/nkUceueW9yMhIevXqxcGDB1FKERcXl/Teww8/TPHixQFYsWIF33//fdJ7xYoVA6BAgQJJPV2BgYEsX748qy8nz3B3d+fBBx9k6dKl1K5dm7i4OHx8fOwdVo5TpEgRevbsyeTJkylYsGDS9g0bNvDjjz8C8PTTTycliJ07d2bBggUEBwfz/fffM2DAAKKioli/fj0dO3ZMOj42WS3ldu3aoZTCx8eH0qVLJ/13qFu3LuHh4fj7++Pg4EDnzp0B6NGjB08++eRd2+3YsWOOS/4SDR4MX34Jn39uksDbXblilovo08cMGxVCCCGEfR0/nrHtOU2+SwDT01OXldq3b8+wYcNYvXo1Fy9eTNr+1ltvERwczE8//UR4eDhBQUFJ77m7uyd9rbVGKXVHu87OzknbHR0dc9RcJ1v11GWGk5NT0jBEgJiYmBT3e/bZZ3n33XepVasWffr0ya7wMiw9PXVZ6ZVXXiEgICDNe5T4c9i+fXtef/11Ll26xNatW3nwwQeJjo6maNGihIaGpnisi4sLAA4ODklfJ36f2s+0UgqLxZJmu8n/H8ppatQwC8xOnQojRsDtoc6bBzExMvxTCCGEyCkqVTLDPlPanhvIHMBs1rdvX0aNGnVHD1NkZGRSUZjb56glFxISwpQpU5K+TxwCKlJWuXJl9u7dS2xsLJGRkfz9998p7te4cWNOnDjBvHnzbpmbKW5VvHhxOnXqxIwZM5K2NW3aNKlXeu7cuTRv3hww1TobNWrEoEGDaNu2LY6OjhQpUgRvb28WLlwImA80duzYkaEYLBYLixYtAmDevHk0b97cJu3a07BhcOkSpPS//owZ4O8PAQHZHZUQQgghUjJ+PLi53brNzc1szw0kAcxmFSpUYNCgQXdsf+2113j99ddp1qwZCQkJqR7/5ptvcvnyZerVq4efnx+rVq3KynBzrfj4eFxcXKhYsSKdOnXC19eX7t27U79+/VSP6dSpE82aNUsaVitSNnTo0FuqgU6ePJlZs2bh6+vLt99+y6effpr0XufOnfnuu++ShmyCSRJnzJiBn58fdevW5ZdffsnQ+d3d3dmzZw+BgYGsXLmSUaNG2aRde2raFJo0McVgkv/vv307bNtmev9S6PgXQgghhB107w5ffQWVK5vnc+XK5vvcMP8PQGmt7R2DTTVo0EDfvrbbvn37qF27tp0iEvawY8cOnnvuOTZlYJXOtm3bMnjwYFq2bJmFkYmczJ6/KxYvhqeegkWL/l/sZeBAmD4dzpwB+VxCCCGEEOmllNqqtW6Q0nvSAyjynGnTptG1a1feeeeddO1/5coVatSoQcGCBSX5E3bz+ONQpYpZYBbM4rJz58KTT0ryJ4QQQgjbyXdFYETe179/f/r375/u/YsWLUpYWFgWRiTE3Tk6wpAhptdv/XoIDzcVQKX4ixBCCCFsSXoAhRAih+jd21QBfeghM4/A0dEM/xRCCCGEsBXpARRCiBzi55/h5k1IXAY0IQGef95MMM8tE8uFEEIIkbNJD6AQQuQQI0f+P/lLdP262S6EEEIIYQuSAAohRA5x/HjGtgshhBBCZJQkgNnsp59+QinF/v37M3zss88+y969ewHw8vK6ZS02kbJ7ud+Z9cknn3D9+vVsO192cHR0xN/fn7p16+Ln58fHH3+MxWKxSyyFChWyy3mzQ6VKGdsuhBBCCJFRkgBms/nz59O8eXO+//77DB2XkJDA9OnTqVOnThZFljdl9n7fi7yYABYsWJDQ0FD27NnD8uXL+f333xkzZoy9w8qw+Ph4e4eQpvHjwc3t1m1ubma7EEIIIYQtSAKYjaKiovj333+ZMWNGUkKyevVq7r//fp544gnq1KlD//79k3pWChUqxKhRo2jcuDEbNmwgKCiI2xe5F6lL7X63bds2aZ+BAwcye/ZsAH7//Xdq1apF8+bNefnll5P2Gz16NBMnTkw6pl69eoSHhxMdHc2jjz6Kn58f9erVY8GCBUyePJnTp08THBxMcHBw9l1sNipVqhRfffUVU6ZMQWtNQkICr776Kg0bNsTX15cvv/wyad8PP/wQHx8f/Pz8GDFiBACHDx+mdevWBAYG0qJFi6Te2d69e/PCCy8QHBxMlSpV+Oeff+jbty+1a9emd+/et8QwdOhQAgICaNmyJREREXdtd8iQIQQHBzN8+PBsuEOZ1707fPUVVK5sCr9Urmy+lwIwQgghhLCV/FcFdNkIOLvLtm2W8YE27991t59//pnWrVtTo0YNihcvzrZt2wDYtGkTe/fupXLlyrRu3Zoff/yRp556iujoaOrVq8fYsWNtG282WzX7K84fO2LTNktVrkJw735p7pPa/U5JTEwMzz//PGvWrMHb25uuXbveNYY//viDcuXK8dtvvwEQGRmJh4cHH3/8MatWraJkyZIZu6h0eOvgSXZH3bBpm/UKFWRc9QoZOqZKlSpYLBbOnz/PL7/8goeHB5s3byY2NpZmzZoREhLC/v37+fnnn9m4cSNubm5cunQJgH79+jFt2jSqV6/Oxo0bGTBgACtXrgTg8uXLrFy5kiVLltCuXTv+/fdfpk+fTsOGDQkNDcXf35/o6GgCAgL46KOPGDt2LGPGjGHKlClpthsWFsaKFStwdHS06b3LCt27S8InhBBCiKwjPYDZaP78+XTp0gWALl26MH/+fAAaNWpElSpVcHR0pGvXrqxbtw4w8646dOhgt3hzu9Tud0r2799PlSpV8Pb2BkhXAujj48OKFSsYPnw4a9euxcPDwzaB5xJaawD++usv5syZg7+/P40bN+bixYscPHiQFStW0KdPH9ysYxqLFy9OVFQU69evp2PHjvj7+/P8889zJtlCd+3atUMphY+PD6VLl8bHxwcHBwfq1q1LeHg4AA4ODnTu3BmAHj16sG7duru227Fjx1yR/AkhhBBCZLX81wOYjp66rHDx4kVWrlzJ7t27UUqRkJCAUopHHnkEpdQt+yZ+7+rqmif+aL1bT11WSO1+t2/f/pbiJTExMcD/k5mUODk5pXhMjRo12Lp1K7///juvv/46ISEhjBo1KouuyMhoT11WOXLkCI6OjpQqVQqtNZ999hmtWrW6ZZ8//vjjjp9ti8VC0aJFCQ0NTbFdFxcXwCR5iV8nfp/a/D2l1F3bdXd3T+eVCSGEEELkbdIDmE0WLVpEz549OXbsGOHh4Zw4cQJvb2/WrVvHpk2bOHr0KBaLhQULFtC8eXN7h5vrpXa/Afbu3UtsbCyRkZH8/fffANSqVYsjR44k9TItWLAgqS0vL6+k4aPbtm3j6NGjAJw+fRo3Nzd69OjBsGHDkvYpXLgw165dy65LzXYRERH079+fgQMHopSiVatWfPHFF8RZF7ALCwsjOjqakJAQZs6cmVQQ59KlSxQpUgRvb28WLlwImMR7x44dGTq/xWJh0aJFAMybN4/mzZvbpF0hhBBCiPwg//UA2sn8+fOTimAk6tChA1988QX33XcfI0aMYNeuXUkFYcS9Se1+z5s3j06dOuHr60v16tWpX78+YKpcTp06ldatW1OyZEkaNWp0y3GJQxwbNmxIjRo1ANi1axevvvoqDg4OODs788UXXwBmjlubNm0oW7Ysq1atyqYrzlo3btzA39+fuLg4nJycePrppxkyZAhglicJDw8nICAArTWenp5J8y9DQ0Np0KABBQoU4JFHHuHdd99l7ty5vPDCC7zzzjvExcXRpUsX/Pz80h2Lu7s7e/bsITAwEA8Pj6Rk/V7bFUIIIYTID1RaQ99yowYNGujbK2Xu27eP2rVr2ymitK1evZqJEyeydOlSe4eS70VFRVGoUCG01rz44otUr16dwYMH2zsskY1y8u8KIYQQQoj0Ukpt1Vo3SOk9GQIqhNXXX3+dtNh5ZGQkzz//vL1DEkIIIYQQwqakB1AIIazkd4UQQggh8oJ76gFUSs1USp1XSu1O4b1hSimtlCqZbNvrSqlDSqkDSqlWybYHKqV2Wd+brKzlAZVSLkqpBdbtG5VSXsmO6aWUOmh99crgdQshhBBCCCGESCY9Q0BnA61v36iUqgg8DBxPtq0O0AWoaz1mqlIqcR2DL4B+QHXrK7HNZ4DLWutqwCTgA2tbxYG3gcZAI+BtpVSxjF3e/+W1nk4hhG3J7wghhBBC5Ad3TQC11muASym8NQl4DUj+V9NjwPda61it9VHgENBIKVUWKKK13qDNX1lzgMeTHfON9etFQEtr72ArYLnW+pLW+jKwnBQS0fRwdXXl4sWL8geeECJFWmsuXryIq6urvUMRQgghhMhSmVoGQinVHjiltd5x20LP5YH/kn1/0rotzvr17dsTjzkBoLWOV0pFAiWSb0/hmAypUKECJ0+eJCIiIjOHCyHyAVdXVypUqGDvMIQQQgghslSGE0CllBswEghJ6e0Utuk0tmf2mNtj6ocZXkqlSpXueN/Z2TlpEXAhhBBCCCGEyK8yswxEVcAb2KGUCgcqANuUUmUwvXQVk+1bATht3V4hhe0kP0Yp5QR4YIacptbWHbTWX2mtG2itG3h6embikoQQQgghhBAi78twAqi13qW1LqW19tJae2EStQCt9VlgCdDFWtnTG1PsZZPW+gxwTSnVxDq/ryfwi7XJJUBihc+ngJXWeYJ/AiFKqWLW4i8h1m1CCCGEEEIIITLhrkNAlVLzgSCgpFLqJPC21npGSvtqrfcopX4A9gLxwIta6wTr2y9gKooWBJZZXwAzgG+VUocwPX9drG1dUkqNAzZb9xurtU6pGI0QQgghhBBCiHTIcwvBK6UigGP2jiOPKAlcsHcQ+YDc56wn9zh7yH3OenKPs4fc56wn9zh7yH3Oejn1HlfWWqc4Ny7PJYDCdpRSW7TWDewdR14n9znryT3OHnKfs57c4+wh9znryT3OHnKfs15uvMeZKQIjhBBCCCGEECIXkgRQCCGEEEIIIfIJSQBFWr6ydwD5hNznrCf3OHvIfc56co+zh9znrCf3OHvIfc56ue4eyxxAIYQQQgghhMgnpAdQCCGEEEIIIfIJSQDzEaVURaXUKqXUPqXUHqXUIOv24kqp5Uqpg9Z/iyU75nWl1CGl1AGlVCvrNjel1G9Kqf3Wdt631zXlRLa6z7e1uUQptTs7ryMns+U9VkoVUEp9pZQKs/5Md7DHNeVENr7PXZVSu5RSO5VSfyilStrjmnKajN5jpVQJ6/5RSqkpt7UVaL3Hh5RSk5VSyh7XlBPZ6j7L8y91tvxZTtamPPtuY+PfGfL8S4GN73HOfPZpreWVT15AWSDA+nVhIAyoA3wIjLBuHwF8YP26DrADcAG8gcOAI+AGBFv3KQCsBdrY+/pyystW9zlZe08C84Dd9r62nPKy5T0GxgDvWL92AEra+/pyysuGvzOcgPOJ99Z6/Gh7X19OeGXiHrsDzYH+wJTb2toE3AcoYJn8Xrb9fZbnX9bf42TtybMvi++zPP+y9h7n5Gef9ADmI1rrM1rrbdavrwH7gPLAY8A31t2+AR63fv0Y8L3WOlZrfRQ4BDTSWl/XWq+ytnMT2AZUyLYLyeFsdZ8BlFKFgCHAO9l2AbmALe8x0Bd4z9qWRWudExdztQsb3mdlfblbe6WKAKez6zpysozeY611tNZ6HRCTvB2lVFmgiNZ6gzZ/aczh//9d8j1b3Wd5/qXOVvcY5NmXFlveZ+T5lyIb3uMc++yTBDCfUkp5AfWBjUBprfUZMD/0QCnrbuWBE8kOO2ndlrydokA74O+sjTh3ssF9Hgd8BFzPjnhzo3u5x9afX4BxSqltSqmFSqnS2RJ4LnMv91lrHQe8AOzCPPzqADOyJ/LcI533ODXlMfc70R2/r4Vxj/c5eTtFkedfimxwj+XZlw73cp/l+Zc+93KPc/KzTxLAfMj6ydpi4BWt9dW0dk1hW1LZWKWUEzAfmKy1PmLbKHO/e73PSil/oJrW+qesiC8vsMHPshPm0/t/tdYBwAZgos0DzeVs8LPsjHkI1gfKATuB120eaC6WgXucahMpbJMy37exwX1ObEeef6m413ssz770scHPsjz/7sIGP8s59tknCWA+Y/1hXAzM1Vr/aN18zjp8KHEY0Xnr9pNAxWSHV+DWruuvgINa60+yNOhcyEb3+T4gUCkVDqwDaiilVmd99LmDje7xRcwnzIl/aCwEArI49FzFRvfZH0Brfdg6PPEHoGnWR587ZPAep+Yktw5FvP33db5no/ucSJ5/KbDRPZZn313Y6D7L8y8NNrrH/pAzn32SAOYj1vHHM4B9WuuPk721BOhl/boX8Euy7V2UUi5KKW+gOqbIAEqpdwAP4JVsCD1XsdV91lp/obUup7X2wkwuDtNaB2XHNeR0NrzHGvgVCLLu1xLYm8Xh5xo2/J1xCqijlPK07vcwZk5FvpeJe5wi63Cka0qpJtY2e97tmPzEVvfZ2pY8/1Jgw59lefalwYb3WZ5/qbDh74uc++zTOaASjbyy54X5RaoxXdCh1tcjQAnMHIaD1n+LJztmJKaS3wGslc4wnyxrzA9xYjvP2vv6csrLVvf5tja9kEpoWXKPgcrAGmtbfwOV7H19OeVl4/vc3/o7Yyfmj44S9r6+nPDK5D0OBy4BUZievzrW7Q2A3db7PwVQ9r6+nPKy1X1Gnn9Zfo9va9MLefZl2X1Gnn/ZcY9z5LNPWYMTQgghhBBCCJHHyRBQIYQQQgghhMgnJAEUQgghhBBCiHxCEkAhhBBCCCGEyCckARRCCCGEEEKIfEISQCGEEEIIIYTIJyQBFEIIIYQQQoh8QhJAIYQQ2U4ppZVS3yb73kkpFaGUWprJ9ooqpQYk+z4oI20ppVYrpQ4opUKVUvuUUv0yE4e1rfZKqRH3cPx0pVSdzB4vhBBCpEUSQCGEEPYQDdRTShW0fv8wcOoe2isKDLjbTnfRXWvtDzQDPlBKFchMI1rrJVrr9zMbhNb6Wa313swenx2UIX9DCCFELiS/vIUQQtjLMuBR69ddgfmJbyiliiulflZK7VRK/aeU8rVuH62UmmntsTuilHrZesj7QFVrD94E67ZCSqlFSqn9Sqm5SimVzrgKYRLUBOs5Q5RSG5RS25RSC5VShazbH7G2vU4pNTmxx1Ep1VspNcX69Wzre+ut8T5l3R5kvYY74rNub2D9OkopNV4ptcN6H0pbt1e1fr9ZKTVWKRWV2sUopb5VSj2W7Pu51l5KR6XUBGsbO5VSz1vfL6SU+tt6vbsSj1VKeVl7R6cC24CK6byfQgghchBJAIUQQtjL90AXpZQr4AtsTPbeGGC71toXeAOYk+y9WkAroBHwtlLKGRgBHNZa+2utX7XuVx94BagDVMH07KVlrlJqJ3AAGKe1TlBKlQTeBB7SWgcAW4Ah1pi/BNporZsDnmm0WxZoDrTFJKqJ0hOfO/Cf1toPWAM8Z93+KfCp1rohcPou1zUd6AOglPIAmgK/A88AkdY2GgLPKaW8gRjgCev1BgMfJUueawJztNb1tdbH7nJeIYQQOZAkgEIIIexCa70T8ML0/v1+29vNgW+t+60ESliTF4DftNaxWusLwHmgdCqn2KS1Pqm1tgCh1nOlpbs14awEDFNKVQaaYBK0f5VSoUAvoDImCT2itT5qPXZ+Cu0l+llrbbEO60wea3riuwkkzmXcmmyf+4CF1q/npXVRWut/gGpKqVKYe71Yax0PhAA9rde1ESgBVAcU8K41GV4BlE8W9zGt9X9pnU8IIUTO5mTvAIQQQuRrS4CJQBAmAUmU0nBNbf03Ntm2BFJ/lqV3v1tPonWEUmob0Bi4ASzXWndNvo9Sqn562kohDpXK9tTii9Na67vskx7fAt2BLkDfZLG8pLX+M/mOSqnemB7NQK11nFIqHHC1vh2dyfMLIYTIIaQHUAghhD3NBMZqrXfdtn0NJmFBKRUEXNBaX02jnWtAYVsEpJRywwzPPAz8BzRTSlVLfE8pVQPYD1RRSnlZD+tsi3NnwH9AB+vXXdKx/2zMcFO01nus2/4EXrAOoUUpVUMp5Q54AOetyV8wpsdTCCFEHiE9gEIIIexGa30SM5/tdqOBWdZhiNcxQy/TaueiUupfpdRuTHGZ3zIRzlyl1A3ABZittd4KST1i85VSLtb93tRah1mXnfhDKXUB2JSJ892LV4DvlFJDMdcamdbOWutzSql9wM/JNk/HDCndZp3jFwE8DswFflVKbcEMTd1v29CFEELYk/r/yBIhhBBCpJdSqpDWOsqaPH0OHNRaT8qmc7sBN7TWWinVBeiqtX7sLvvvAgK01mkmi0IIIfI2GQIqhBBCZM5z1gIqezDDJr/MxnMHAqHWHtIBwNDUdlRKPYTpxftMkj8hhBDSAyiEECLfUEr9BHjftnn47YVQciOllA/WyqnJxGqtG9sjHiGEEDmTJIBCCCGEEEIIkU/IEFAhhBBCCCGEyCckARRCCCGEEEKIfEISQCGEEEIIIYTIJyQBFEIIIYQQQoh8QhJAIYQQQgghhMgn/ge9y4C9IZJApwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "months = [dt.date(2022, m, 1).strftime('%B') for m in range(1, 13)] # generate names of the months of a year\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ridership_overtime.plot(kind='line', style=['r*-','bo-'], ax=ax) # added style for some line so that we can get distinct lines\n",
    "plt.legend(months, ncol=3, loc='lower left', title='Month of the year',) # show legend in 3 columns\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "196f65cd",
   "metadata": {},
   "source": [
    "### Modelling the Data\n",
    "\n",
    "Now that we have explored the data and we can now focus on modelling the data. There are many ways to model the data and the best way really depends on the problem you are trying to solve. Let's say for example, we want to know if it is possible to use the information of the ridership on Weekdays and Saturdays to predict the ridership on Sundays and Holidays.\n",
    "\n",
    "**Note:** Obviously, ridership on Sundays and Holidays are dependent of many other factors besides ridership on Weekdays and Saturday. The modelling is for the purposed of demonstration only. \n",
    "\n",
    "The first part of modelling the data is that the data must be clean and any item in the data must be numeric. This is because machine learning models or statistical models in general do not take data that have textual values or missing values. So such data must be processed and transformed to some reasonable numeric representation before they are used in modelling. \n",
    "\n",
    "In our example, we will be using the two numeric columns and they have no missing data as identified above, we are ready to use this data. We will begin by separating the variables that will be predicted and the variables that will be use to predict. The former is commonly refered to as target (y) and the latter as features (X)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a74ef72",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['Avg_Weekday_Rides','Avg_Saturday_Rides']]\n",
    "y = data['Avg_Sunday-Holiday_Rides']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "484be1e5",
   "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>Avg_Weekday_Rides</th>\n",
       "      <th>Avg_Saturday_Rides</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6982.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21406.5</td>\n",
       "      <td>13210.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22432.2</td>\n",
       "      <td>17994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18443.0</td>\n",
       "      <td>13088.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg_Weekday_Rides  Avg_Saturday_Rides\n",
       "0             6982.6                 0.0\n",
       "1             1000.0                 0.0\n",
       "2            21406.5             13210.7\n",
       "3            22432.2             17994.0\n",
       "4            18443.0             13088.2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8404dae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.0\n",
       "1        0.0\n",
       "2     8725.3\n",
       "3    10662.2\n",
       "4     7165.6\n",
       "Name: Avg_Sunday-Holiday_Rides, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde25300",
   "metadata": {},
   "source": [
    "Next, we will split the feature data into training and test set. This is while we use data to train a machine learning model, its performance should be reported on a data that the model has never seen before. This ensure that the model is able to generalize i.e. it has not just learned the training data very well but also some patterns that can help predict future data points. This is very important if we want to use the model in the real world.\n",
    "\n",
    "Usually a 70-30 or 80-20 split is recommended. In our case we will keep 3/4 of the data for training and 1/4 for testing. let's import the `train_test_split` function availablel via the `model_selection` submodule of `sklearn` library. This function takes the feature and target data and give us the desired splits of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cdefc0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bff12f60",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = tts(X,              # feature data\n",
    "                                       y,              # target data \n",
    "                                       test_size=.25,  # size of the test set\n",
    "                                       random_state=42)# set a random number to get the exact split next time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6d1012f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22155, 2) (7386, 2) (22155,) (7386,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa6df89",
   "metadata": {},
   "source": [
    "You can see that 1/4 of the data for both features and target are now separated as the test set. \n",
    "\n",
    "Let's now import the `Linear_Regression` model from the `linear_model` submodule of `sklearn`, which we will use to fit our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fa2b0d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6b0233",
   "metadata": {},
   "source": [
    "Most of the functions in sklearn can be used in the same way:\n",
    "\n",
    "1. Create an instance of the object in use.\n",
    "2. Use `fit` or `fit_transform` method to fit or transform the data as needed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5f9a6ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55db929",
   "metadata": {},
   "source": [
    "Now that the data has been fit to the linear model, some model property information will now be available in the `model` object. One of these properties is the `score` method, which return the coefficient of determination of the prediction, also known as R squared. It is the proportion of the variation in the dependent variable that is predictable form the independent variable. We can input the training data to get this score.\n",
    "\n",
    "Other properties of interest are the intercept of the and the coefficients for the linear regression model. We can get these information with the `intercept_` and `coef_` methods respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "60703497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9806878464719238"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return the coefficient of determination of the prediction\n",
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "64b2e7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-50.08987832067169"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b3c4420a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00808142,  0.71893902])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a4b0cf",
   "metadata": {},
   "source": [
    "While the model R Squared value looks good, this value only measures the fit of training data to the model. How well will this model perform on an unseen test data is the next step of evaluation. Regression models often use the mean squared error metric to evaluate the performance on an unseen data. To calculate this we can use `mean_squared_error` function available throuhg the `metrics` submodule of `sklearn`. The function takes the model prediction on a given data and the actual target value for that dataset. Therefore, we first need to generate predictions from our model on the test set using the `predict` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7caeb4e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error as mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a85abde9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "258826.95917043873\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = model.predict(X_test)\n",
    "mse_lrmodel = mse(y_pred_test, y_test)\n",
    "print(mse_lrmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f180feb0",
   "metadata": {},
   "source": [
    "How do we know this is a good enough value?\n",
    "\n",
    "In machine learning, we usually have benchmark model against which we can test the performance. In this case we only have one model, so we can create another model and see which one performs better. \n",
    "\n",
    "We can repeat what we did earlier on another model or we can create a for-loop such that the exact same operation goes through all the models in a list. This latter is obviously better as we do not have to write the same code over and over again. It is also easier from readability perspective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e76b0eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7340a2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('LR', LinearRegression()), ('DT', DecisionTreeRegressor())]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize an empty list to add sklearn model objects\n",
    "models = []\n",
    "\n",
    "# add the sklearn model objects to the list one by one\n",
    "# while adding the model also give it a name so put the name and model in a tuple\n",
    "models.append(('LR', LinearRegression())) \n",
    "models.append(('DT', DecisionTreeRegressor())) # Ensemble method - collection of many decision trees\n",
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bf8ae28c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n",
      "DecisionTreeRegressor()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LR': 258826.95917043873, 'DT': 491242.7129623926}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = {}\n",
    "for name, model in models:\n",
    "    print(model)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred_test = model.predict(X_test)\n",
    "    mse_score = mse(y_pred_test, y_test)\n",
    "    scores[name] = mse_score\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4509c58e",
   "metadata": {},
   "source": [
    "Note that the Decision Tree Regression model has lower mean squared error than the Linear Regression model and therefore, is better.\n",
    "\n",
    "It might also be a good idea to store the fitted model, so that once you can explore more details of the model rather than just the scores. See [here](https://scikit-learn.org/stable/modules/tree.html#tree-regression) to learn more about decision tree model properties and sklearn features available to explore the model details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b15c83d",
   "metadata": {},
   "source": [
    "### Adding Categorical Features to our Model\n",
    "\n",
    "The above model does not have the information about the ridership pattern specific to a route. So, adding this information might help predict the Sunday-Holiday ridership behavior better. \n",
    "\n",
    "There are two ways we can continue further: \n",
    "1. Isolate data for each route and repeat the above modelling process on that subset.\n",
    "2. Add the routenames to features and create a model in which case we must convert the text to numeric values.\n",
    "\n",
    "Let's try the first option on one route."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b03f00c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>route</th>\n",
       "      <th>routename</th>\n",
       "      <th>Month_Beginning</th>\n",
       "      <th>Avg_Weekday_Rides</th>\n",
       "      <th>Avg_Saturday_Rides</th>\n",
       "      <th>Avg_Sunday-Holiday_Rides</th>\n",
       "      <th>MonthTotal</th>\n",
       "      <th>Month_Beginning_year</th>\n",
       "      <th>diff_week_saturday</th>\n",
       "      <th>diff_week_sunday</th>\n",
       "      <th>diff_sat_sunday</th>\n",
       "      <th>Month_Beginning_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18</td>\n",
       "      <td>16th/18th</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>1923.8</td>\n",
       "      <td>898.5</td>\n",
       "      <td>642.3</td>\n",
       "      <td>49130</td>\n",
       "      <td>2001</td>\n",
       "      <td>1025.3</td>\n",
       "      <td>1281.5</td>\n",
       "      <td>256.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>18</td>\n",
       "      <td>16th/18th</td>\n",
       "      <td>2001-02-01</td>\n",
       "      <td>2075.4</td>\n",
       "      <td>896.8</td>\n",
       "      <td>618.7</td>\n",
       "      <td>47570</td>\n",
       "      <td>2001</td>\n",
       "      <td>1178.6</td>\n",
       "      <td>1456.7</td>\n",
       "      <td>278.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>18</td>\n",
       "      <td>16th/18th</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>1717.8</td>\n",
       "      <td>979.8</td>\n",
       "      <td>710.8</td>\n",
       "      <td>45533</td>\n",
       "      <td>2001</td>\n",
       "      <td>738.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>18</td>\n",
       "      <td>16th/18th</td>\n",
       "      <td>2001-04-01</td>\n",
       "      <td>1685.9</td>\n",
       "      <td>1003.6</td>\n",
       "      <td>732.2</td>\n",
       "      <td>43079</td>\n",
       "      <td>2001</td>\n",
       "      <td>682.3</td>\n",
       "      <td>953.7</td>\n",
       "      <td>271.4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>18</td>\n",
       "      <td>16th/18th</td>\n",
       "      <td>2001-05-01</td>\n",
       "      <td>1662.6</td>\n",
       "      <td>957.6</td>\n",
       "      <td>757.3</td>\n",
       "      <td>44194</td>\n",
       "      <td>2001</td>\n",
       "      <td>705.0</td>\n",
       "      <td>905.3</td>\n",
       "      <td>200.3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    route  routename Month_Beginning  Avg_Weekday_Rides  Avg_Saturday_Rides  \\\n",
       "14     18  16th/18th      2001-01-01             1923.8               898.5   \n",
       "144    18  16th/18th      2001-02-01             2075.4               896.8   \n",
       "275    18  16th/18th      2001-03-01             1717.8               979.8   \n",
       "407    18  16th/18th      2001-04-01             1685.9              1003.6   \n",
       "539    18  16th/18th      2001-05-01             1662.6               957.6   \n",
       "\n",
       "     Avg_Sunday-Holiday_Rides  MonthTotal  Month_Beginning_year  \\\n",
       "14                      642.3       49130                  2001   \n",
       "144                     618.7       47570                  2001   \n",
       "275                     710.8       45533                  2001   \n",
       "407                     732.2       43079                  2001   \n",
       "539                     757.3       44194                  2001   \n",
       "\n",
       "     diff_week_saturday  diff_week_sunday  diff_sat_sunday  \\\n",
       "14               1025.3            1281.5            256.2   \n",
       "144              1178.6            1456.7            278.1   \n",
       "275               738.0            1007.0            269.0   \n",
       "407               682.3             953.7            271.4   \n",
       "539               705.0             905.3            200.3   \n",
       "\n",
       "     Month_Beginning_month  \n",
       "14                       1  \n",
       "144                      2  \n",
       "275                      3  \n",
       "407                      4  \n",
       "539                      5  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oneroute = data[data['routename']=='16th/18th']\n",
    "oneroute.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5573f40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(target_features_data, targetname, models):\n",
    "    \n",
    "    y = target_features_data[targetname]\n",
    "    X = target_features_data.drop(targetname, axis=1)\n",
    "    print(X.shape, y.shape)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = tts(X, y, test_size=1/4, random_state=42)\n",
    "\n",
    "    scores = {}\n",
    "    for name, model in models:\n",
    "        print(f'fitting model: {model}')\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred_test = model.predict(X_test)\n",
    "        mse_score = mse(y_pred_test, y_test)\n",
    "        scores[name] = mse_score\n",
    "        \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2204849d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(213, 2) (213,)\n",
      "fitting model: LinearRegression()\n",
      "fitting model: DecisionTreeRegressor()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LR': 27684.616857695066, 'DT': 54773.207407407404}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Avg_Sunday-Holiday_Rides', 'Avg_Weekday_Rides','Avg_Saturday_Rides']\n",
    "scores_oneroute = create_model(oneroute[cols], 'Avg_Sunday-Holiday_Rides', models)\n",
    "scores_oneroute"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40193729",
   "metadata": {},
   "source": [
    "Let's now try option 2 where we add the categorical feature routename to our model.\n",
    "\n",
    "What are the ways to convert categorical feature into numeric values? There are many. One popular method is to create a new column for each categorical variable and fill in value 1 for the specified routename column if the row belongs to that routename. This process is also called creating dummy variable.\n",
    "\n",
    "Pandas has an easy way to create dummy variables using the `get_dummies` method. Let's apply that on a toy dataset to see what transformation is taking place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ff0939d",
   "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>fruits</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>orange</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruits  quantity\n",
       "0   apple         2\n",
       "1  orange         5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create toy data set with categorica and numeric features\n",
    "test_data = pd.DataFrame( [['apple', 2], ['orange', 5]], columns=['fruits', 'quantity'])\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9fafdc42",
   "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>apple</th>\n",
       "      <th>orange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "   apple  orange\n",
       "0      1       0\n",
       "1      0       1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummy variables for a categorical column\n",
    "pd.get_dummies(test_data['fruits'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f9df3d13",
   "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>apple</th>\n",
       "      <th>orange</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apple  orange  quantity\n",
       "0      1       0         2\n",
       "1      0       1         5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine the dummy variables with remaining numeric column of the original data\n",
    "pd.concat([pd.get_dummies(test_data['fruits']), test_data['quantity']], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c393d8",
   "metadata": {},
   "source": [
    "The above transformation can also be done via `OneHotEncoder` function available through the `preprocessing` submodule of `sklearn`. This method is easier to use if you have many categorical columns. Here, we will see an example on our toy data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "94c2cc9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3104fbf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2x2 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 2 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. create an instance of the one hot encoder object\n",
    "ohe = OneHotEncoder()\n",
    "\n",
    "# 2. fit the data to the one hot encoder instance\n",
    "ohe.fit_transform(test_data['fruits'].values.reshape(-1,1)) # require the input data to be 2-dimensional"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0699ac72",
   "metadata": {},
   "source": [
    "The resulting object is a sparse matrix, which cannot be combined to a `dataframe`. Therefore, we must first convert it to a numpy `array` for which there is a `toarray` method provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "045cbac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. convert the transformed data to an numpy array \n",
    "dummy_fruits = ohe.fit_transform(test_data['fruits'].values.reshape(-1,1)).toarray()\n",
    "dummy_fruits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ce7e28",
   "metadata": {},
   "source": [
    "Numpy `array` can be easily converted to a pandas `dataframe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "50e2c584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "     0    1\n",
       "0  1.0  0.0\n",
       "1  0.0  1.0"
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     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. transform the numpy array to dataframe\n",
    "pd.DataFrame(dummy_fruits)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8360abcd",
   "metadata": {},
   "source": [
    "Note that we are missing the column names, which can make it difficult to know which category the column belongs to. This can be easily retrieved using the `categories_` method of the `OneHotEncoder` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "64255a64",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>apple</th>\n",
       "      <th>orange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "  apple orange\n",
       "0   1.0    0.0\n",
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     "execution_count": 47,
     "metadata": {},
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   ],
   "source": [
    "# while converting to dataframe add column names as well\n",
    "pd.DataFrame(dummy_fruits, columns=ohe.categories_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a8b2cb",
   "metadata": {},
   "source": [
    "Now, let's try this on routenames column of our dataset and see if adding this feature to the model helps predict better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "af6e8d6d",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>111th/King Drive</th>\n",
       "      <th>16th/18th</th>\n",
       "      <th>31st</th>\n",
       "      <th>31st/35th</th>\n",
       "      <th>35th</th>\n",
       "      <th>43rd</th>\n",
       "      <th>47th</th>\n",
       "      <th>51st</th>\n",
       "      <th>55th/Austin</th>\n",
       "      <th>55th/Narragansett</th>\n",
       "      <th>...</th>\n",
       "      <th>West 65th</th>\n",
       "      <th>West 95th</th>\n",
       "      <th>West Cermak</th>\n",
       "      <th>West Lawrence</th>\n",
       "      <th>West Loop/South Loop</th>\n",
       "      <th>Westchester</th>\n",
       "      <th>Western</th>\n",
       "      <th>Western Express</th>\n",
       "      <th>Wilson/Michigan Express</th>\n",
       "      <th>Wrigley Field Express</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 189 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  111th/King Drive 16th/18th 31st 31st/35th 35th 43rd 47th 51st 55th/Austin  \\\n",
       "0              0.0       0.0  0.0       0.0  0.0  0.0  0.0  0.0         0.0   \n",
       "1              0.0       0.0  0.0       0.0  0.0  0.0  0.0  0.0         0.0   \n",
       "2              0.0       0.0  0.0       0.0  0.0  0.0  0.0  0.0         0.0   \n",
       "3              0.0       0.0  0.0       0.0  0.0  0.0  0.0  0.0         0.0   \n",
       "4              0.0       0.0  0.0       0.0  0.0  0.0  0.0  0.0         0.0   \n",
       "\n",
       "  55th/Narragansett  ... West 65th West 95th West Cermak West Lawrence  \\\n",
       "0               0.0  ...       0.0       0.0         0.0           0.0   \n",
       "1               0.0  ...       0.0       0.0         0.0           0.0   \n",
       "2               0.0  ...       0.0       0.0         0.0           0.0   \n",
       "3               0.0  ...       0.0       0.0         0.0           0.0   \n",
       "4               0.0  ...       0.0       0.0         0.0           0.0   \n",
       "\n",
       "  West Loop/South Loop Westchester Western Western Express  \\\n",
       "0                  0.0         0.0     0.0             0.0   \n",
       "1                  0.0         0.0     0.0             0.0   \n",
       "2                  0.0         0.0     0.0             0.0   \n",
       "3                  0.0         0.0     0.0             0.0   \n",
       "4                  0.0         0.0     0.0             0.0   \n",
       "\n",
       "  Wilson/Michigan Express Wrigley Field Express  \n",
       "0                     0.0                   0.0  \n",
       "1                     0.0                   0.0  \n",
       "2                     0.0                   0.0  \n",
       "3                     0.0                   0.0  \n",
       "4                     0.0                   0.0  \n",
       "\n",
       "[5 rows x 189 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder()\n",
    "dummy_routename = ohe.fit_transform(data['routename'].values.reshape(-1,1)).toarray()\n",
    "dummy_routename = pd.DataFrame(dummy_routename, columns=ohe.categories_)\n",
    "dummy_routename.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0463d031",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29541, 191) (29541,)\n",
      "fitting model: LinearRegression()\n",
      "fitting model: DecisionTreeRegressor()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LR': 171772.43965343578, 'DT': 293345.00101623515}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Avg_Sunday-Holiday_Rides', 'Avg_Weekday_Rides','Avg_Saturday_Rides']\n",
    "target_features_data = pd.concat([data[cols], dummy_routename], axis=1)\n",
    "scores_routename = create_model(target_features_data, 'Avg_Sunday-Holiday_Rides' , models)\n",
    "scores_routename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "80366e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LR': 258826.95917043873, 'DT': 491242.7129623926}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb15e970",
   "metadata": {},
   "source": [
    "Clearly, the non-linear modelling fit suits the data and the problem better and adding the routename information helps both linear and non-linear model perform better. \n",
    "\n",
    "## Exercise 1:\n",
    "Create yet another model that has all features of the best model so far and add the additional information from the \"Month_Beginning_year\" column. Does adding this information increase predictability?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ccd48203",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29541, 209) (29541,)\n",
      "fitting model: LinearRegression()\n",
      "fitting model: DecisionTreeRegressor()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LR': 159713.08129798513, 'DT': 262424.8163688435}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder()\n",
    "dummy_year = ohe.fit_transform(data['Month_Beginning_year'].values.reshape(-1,1)).toarray()\n",
    "dummy_year = pd.DataFrame(dummy_year, columns=ohe.categories_)\n",
    "\n",
    "cols = ['Avg_Sunday-Holiday_Rides', 'Avg_Weekday_Rides','Avg_Saturday_Rides']\n",
    "target_features_data = pd.concat([data[cols], dummy_routename, dummy_year], axis=1)\n",
    "scores_routename_year = create_model(target_features_data, 'Avg_Sunday-Holiday_Rides' , models)\n",
    "scores_routename_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3c15df",
   "metadata": {},
   "source": [
    "## Exercise 2:\n",
    "Create yet another model that has all features of the best model so far and add the additional information from the \"Month_Beginning_month\" column. Does adding this information increase predictability?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8b9c7df6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29541, 221) (29541,)\n",
      "fitting model: LinearRegression()\n",
      "fitting model: DecisionTreeRegressor()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LR': 144173.77290807283, 'DT': 213950.96260628215}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe_month = OneHotEncoder()\n",
    "dummy_month = ohe_month.fit_transform(data['Month_Beginning_month'].values.reshape(-1,1)).toarray()\n",
    "dummy_month = pd.DataFrame(dummy_month, columns=ohe_month.categories_)\n",
    "\n",
    "cols = ['Avg_Sunday-Holiday_Rides', 'Avg_Weekday_Rides','Avg_Saturday_Rides']\n",
    "target_features_data = pd.concat([data[cols], dummy_routename, dummy_year, dummy_month], axis=1)\n",
    "scores_routename_yymm = create_model(target_features_data, 'Avg_Sunday-Holiday_Rides' , models)\n",
    "scores_routename_yymm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed45ddfe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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