diff --git a/examples/scratch_pad/issues/issue436.py b/examples/scratch_pad/issues/issue436.py
index 5d3dc7ab..a07a63f7 100644
--- a/examples/scratch_pad/issues/issue436.py
+++ b/examples/scratch_pad/issues/issue436.py
@@ -6,17 +6,18 @@
 import mplfinance as mpf
 import sys
 
+import matplotlib
+print('backend=',matplotlib.get_backend())
+
 coin = 'BTC' 
 bot_status = 'trading'
 
 def limit():
-    timeout = 2.5
+    timeout = 0.5
     print(end='')
     rlist, _, _ = select([sys.stdin], [], [], timeout)
-    print('rlist=',rlist)
     if rlist:
         s = sys.stdin.readline().strip()
-        print('s=',s)
         if s == 'g':
             print('\033[1;34m show chart')
             chart()
@@ -41,8 +42,8 @@ def dataframe():
 
 def chart():
     df = dataframe()
-    df.index = pd.DatetimeIndex(df['timestamp'])
-    df = df.iloc[::-1]
+    #df.index = pd.DatetimeIndex(df['timestamp'])
+    #df = df.iloc[::-1]
     s = mpf.make_mpf_style(base_mpf_style='charles', gridcolor='#555555', gridstyle="--", rc={'axes.edgecolor': 'white', 'font.size': 5})
     fig, axlist = mpf.plot(df, type='candle', style=s, title= coin, ylabel = 'Price (€)', volume=True, warn_too_much_data=9999999, returnfig=True)
     mpf.show(block=True)
diff --git a/examples/scratch_pad/so75737197.ipynb b/examples/scratch_pad/so75737197.ipynb
new file mode 100644
index 00000000..473e2d3c
--- /dev/null
+++ b/examples/scratch_pad/so75737197.ipynb
@@ -0,0 +1,1100 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# Draw circle on plot with mplfinance\n",
+    "\n",
+    "## https://stackoverflow.com/questions/75737197/\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This allows multiple outputs from a single jupyter notebook cell:\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import pandas as pd\n",
+    "import mplfinance as mpf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pr451data.csv  so75737197_data.bak.csv  so75737197_data.csv  tmp.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls *.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Index</th>\n",
+       "      <th>Start Date</th>\n",
+       "      <th>Open Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>Close Price</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>End Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2023-03-12 18:30:00</td>\n",
+       "      <td>3.996</td>\n",
+       "      <td>4.038</td>\n",
+       "      <td>3.988</td>\n",
+       "      <td>4.008</td>\n",
+       "      <td>1216259.0</td>\n",
+       "      <td>2023-03-12 18:44:59.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-03-12 18:45:00</td>\n",
+       "      <td>4.008</td>\n",
+       "      <td>4.024</td>\n",
+       "      <td>3.990</td>\n",
+       "      <td>3.993</td>\n",
+       "      <td>638860.0</td>\n",
+       "      <td>2023-03-12 18:59:59.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-03-12 19:00:00</td>\n",
+       "      <td>3.993</td>\n",
+       "      <td>4.024</td>\n",
+       "      <td>3.992</td>\n",
+       "      <td>4.019</td>\n",
+       "      <td>297226.0</td>\n",
+       "      <td>2023-03-12 19:14:59.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2023-03-12 19:15:00</td>\n",
+       "      <td>4.018</td>\n",
+       "      <td>4.023</td>\n",
+       "      <td>3.973</td>\n",
+       "      <td>3.985</td>\n",
+       "      <td>1101139.0</td>\n",
+       "      <td>2023-03-12 19:29:59.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-03-12 19:30:00</td>\n",
+       "      <td>3.986</td>\n",
+       "      <td>4.003</td>\n",
+       "      <td>3.976</td>\n",
+       "      <td>3.993</td>\n",
+       "      <td>427351.0</td>\n",
+       "      <td>2023-03-12 19:44:59.999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Index           Start Date  Open Price  High Price  Low Price  Close Price  \\\n",
+       "0      0  2023-03-12 18:30:00       3.996       4.038      3.988        4.008   \n",
+       "1      1  2023-03-12 18:45:00       4.008       4.024      3.990        3.993   \n",
+       "2      2  2023-03-12 19:00:00       3.993       4.024      3.992        4.019   \n",
+       "3      3  2023-03-12 19:15:00       4.018       4.023      3.973        3.985   \n",
+       "4      4  2023-03-12 19:30:00       3.986       4.003      3.976        3.993   \n",
+       "\n",
+       "      Volume                 End Date  \n",
+       "0  1216259.0  2023-03-12 18:44:59.999  \n",
+       "1   638860.0  2023-03-12 18:59:59.999  \n",
+       "2   297226.0  2023-03-12 19:14:59.999  \n",
+       "3  1101139.0  2023-03-12 19:29:59.999  \n",
+       "4   427351.0  2023-03-12 19:44:59.999  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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>Index</th>\n",
+       "      <th>Start Date</th>\n",
+       "      <th>Open Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>Close Price</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>End Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>35</td>\n",
+       "      <td>2023-03-13 03:15:00</td>\n",
+       "      <td>4.418</td>\n",
+       "      <td>4.435</td>\n",
+       "      <td>4.393</td>\n",
+       "      <td>4.432</td>\n",
+       "      <td>354457.0</td>\n",
+       "      <td>2023-03-13 03:29:59.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>36</td>\n",
+       "      <td>2023-03-13 03:30:00</td>\n",
+       "      <td>4.433</td>\n",
+       "      <td>4.461</td>\n",
+       "      <td>4.415</td>\n",
+       "      <td>4.449</td>\n",
+       "      <td>256813.0</td>\n",
+       "      <td>2023-03-13 03:44:59.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>37</td>\n",
+       "      <td>2023-03-13 03:45:00</td>\n",
+       "      <td>4.450</td>\n",
+       "      <td>4.462</td>\n",
+       "      <td>4.435</td>\n",
+       "      <td>4.439</td>\n",
+       "      <td>226006.0</td>\n",
+       "      <td>2023-03-13 03:59:59.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>38</td>\n",
+       "      <td>2023-03-13 04:00:00</td>\n",
+       "      <td>4.437</td>\n",
+       "      <td>4.464</td>\n",
+       "      <td>4.418</td>\n",
+       "      <td>4.458</td>\n",
+       "      <td>304705.0</td>\n",
+       "      <td>2023-03-13 04:14:59.999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>39</td>\n",
+       "      <td>2023-03-13 04:15:00</td>\n",
+       "      <td>4.459</td>\n",
+       "      <td>4.465</td>\n",
+       "      <td>4.436</td>\n",
+       "      <td>4.439</td>\n",
+       "      <td>288049.0</td>\n",
+       "      <td>2023-03-13 04:29:59.999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Index           Start Date  Open Price  High Price  Low Price  \\\n",
+       "35     35  2023-03-13 03:15:00       4.418       4.435      4.393   \n",
+       "36     36  2023-03-13 03:30:00       4.433       4.461      4.415   \n",
+       "37     37  2023-03-13 03:45:00       4.450       4.462      4.435   \n",
+       "38     38  2023-03-13 04:00:00       4.437       4.464      4.418   \n",
+       "39     39  2023-03-13 04:15:00       4.459       4.465      4.436   \n",
+       "\n",
+       "    Close Price    Volume                 End Date  \n",
+       "35        4.432  354457.0  2023-03-13 03:29:59.999  \n",
+       "36        4.449  256813.0  2023-03-13 03:44:59.999  \n",
+       "37        4.439  226006.0  2023-03-13 03:59:59.999  \n",
+       "38        4.458  304705.0  2023-03-13 04:14:59.999  \n",
+       "39        4.439  288049.0  2023-03-13 04:29:59.999  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('so75737197_data.csv')\n",
+    "df.head()\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40, 5)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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>Open Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>Close Price</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Start Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-03-12 18:30:00</th>\n",
+       "      <td>3.996</td>\n",
+       "      <td>4.038</td>\n",
+       "      <td>3.988</td>\n",
+       "      <td>4.008</td>\n",
+       "      <td>1216259.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-03-12 18:45:00</th>\n",
+       "      <td>4.008</td>\n",
+       "      <td>4.024</td>\n",
+       "      <td>3.990</td>\n",
+       "      <td>3.993</td>\n",
+       "      <td>638860.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-03-12 19:00:00</th>\n",
+       "      <td>3.993</td>\n",
+       "      <td>4.024</td>\n",
+       "      <td>3.992</td>\n",
+       "      <td>4.019</td>\n",
+       "      <td>297226.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Open Price  High Price  Low Price  Close Price     Volume\n",
+       "Start Date                                                                    \n",
+       "2023-03-12 18:30:00       3.996       4.038      3.988        4.008  1216259.0\n",
+       "2023-03-12 18:45:00       4.008       4.024      3.990        3.993   638860.0\n",
+       "2023-03-12 19:00:00       3.993       4.024      3.992        4.019   297226.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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>Open Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>Close Price</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Start Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-03-13 04:00:00</th>\n",
+       "      <td>4.437</td>\n",
+       "      <td>4.464</td>\n",
+       "      <td>4.418</td>\n",
+       "      <td>4.458</td>\n",
+       "      <td>304705.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-03-13 04:15:00</th>\n",
+       "      <td>4.459</td>\n",
+       "      <td>4.465</td>\n",
+       "      <td>4.436</td>\n",
+       "      <td>4.439</td>\n",
+       "      <td>288049.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Open Price  High Price  Low Price  Close Price    Volume\n",
+       "Start Date                                                                   \n",
+       "2023-03-13 04:00:00       4.437       4.464      4.418        4.458  304705.0\n",
+       "2023-03-13 04:15:00       4.459       4.465      4.436        4.439  288049.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('so75737197_data.csv',\n",
+    "                 usecols=['Start Date','Open Price','High Price','Low Price','Close Price','Volume'],\n",
+    "                 index_col='Start Date',parse_dates=True)\n",
+    "df.shape\n",
+    "df.head(3)\n",
+    "df.tail(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mpf.plot(df,style='yahoo',type='candle',columns=['Open Price','High Price','Low Price','Close Price','Volume'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "import mplfinance as mpf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "\n",
+    "last_index_nan_value = 0\n",
+    "# Don't spend memory ram unnecesary pl0x\n",
+    "# matplotlib.use(\"Agg\")\n",
+    "\n",
+    "# def set_DateTimeIndex(df_trading_pair):\n",
+    "#     df_trading_pair = df_trading_pair.set_index('Start Date', inplace=False, parse_dates=True)\n",
+    "#     print(df_trading_pair.head())\n",
+    "#     # Rename the column names for best practices\n",
+    "#     df_trading_pair.rename(columns = { \"Open Price\" : 'Open',\n",
+    "#                                        \"High Price\" : 'High',\n",
+    "#                                        \"Low Price\" : 'Low',\n",
+    "#                                        \"Close Price\" :'Close',\n",
+    "#                               }, inplace = True)\n",
+    "#     return df_trading_pair\n",
+    "\n",
+    "# def convert_to_unix_ms(string_date):\n",
+    "#     date_format = \"%d %b '%y %H:%M\"\n",
+    "#     dt = datetime.datetime.strptime(string_date, date_format)\n",
+    "#     unix_timestamp_ms = int(dt.timestamp() * 1000)\n",
+    "#     return unix_timestamp_ms\n",
+    "\n",
+    "def plot_this(df):\n",
+    "    #global trading_pair\n",
+    "    #global start_date\n",
+    "    #global end_date\n",
+    "    \n",
+    "    #df_trading_pair_date_time_index = set_DateTimeIndex(df)\n",
+    "    df_trading_pair_date_time_index = df\n",
+    "    \n",
+    "    # Define periods\n",
+    "    k_period = 14\n",
+    "    d_period = 1\n",
+    "    smooth_window = 3\n",
+    "       \n",
+    "    stochastic = pd.DataFrame()\n",
+    "    stochastic['%K'] = ((df['Close Price'] - df['Low Price'].rolling(k_period).min()) \\\n",
+    "                        / (df['High Price'].rolling(k_period).max() - df['Low Price'].rolling(k_period).min())) * 100\n",
+    "    stochastic['%D'] = stochastic['%K'].rolling(d_period).mean()\n",
+    "    stochastic['%SD'] = stochastic['%D'].rolling(smooth_window).mean()\n",
+    "    stochastic['UL'] = 80\n",
+    "    stochastic['DL'] = 20\n",
+    "    \n",
+    "    # Get the index of the last nan value in the lower bound series    \n",
+    "    global last_index_nan_value\n",
+    "    last_index_nan_value = len(stochastic['%D']) - pd.isna(stochastic['%D'])[::-1].argmax() - 1\n",
+    "        \n",
+    "    # Store the plots of the last 120 data rows of upper and lower bounds as well as the entry and exit points\n",
+    "    plots_to_add = {\"Stochastics\":mpf.make_addplot((stochastic[['%K', '%SD', 'UL', 'DL']]), \n",
+    "                                                   ylim=[0, 100], panel=2, ylabel=\"Stochastics\", \n",
+    "                                                   y_on_right=False)}\n",
+    "    #print('stochastic=\\n',stochastic[['%K', '%SD', 'UL', 'DL']])\n",
+    "    # Plotting\n",
+    "    # Create my own `marketcolors` style:\n",
+    "    mc = mpf.make_marketcolors(up='#0ECB81',down='#F64670',inherit=True)\n",
+    "    # Create my own `MatPlotFinance` style:\n",
+    "    s  = mpf.make_mpf_style(figcolor='#162125', facecolor= \"#162125\", marketcolors=mc, y_on_right=True, \n",
+    "                            rc={'font.size':18, 'xtick.color': 'w'}, gridcolor='white', gridstyle='--', edgecolor='white')\n",
+    "\n",
+    "    circle_data = [float('nan')]*len(df)\n",
+    "    circle_data[17] = stochastic.iloc[17]['%K']\n",
+    "    circle_data[25] = stochastic.iloc[25]['%K']\n",
+    "    \n",
+    "    ap = mpf.make_addplot(circle_data,type='scatter',markersize=1200,color=\"#162125\",#facecolor=None,\n",
+    "                          alpha=0.7,edgecolors='yellow',marker='o',linewidths=6,panel=2,ylim=(0,120))\n",
+    "    # Plot it\n",
+    "    candlestick_plot, axlist = mpf.plot(df,#df_trading_pair_date_time_index,\n",
+    "                        columns=['Open Price','High Price','Low Price','Close Price','Volume'],\n",
+    "                        figsize=(20,10),\n",
+    "                        figratio=(12, 6),\n",
+    "                        panel_ratios=(5,1,1.5),\n",
+    "                        type=\"candle\",\n",
+    "                        volume=True,\n",
+    "                        style=s,\n",
+    "                        tight_layout=True,\n",
+    "                        #datetime_format = '%b %d, %H:%M:%S',\n",
+    "                        datetime_format = '%b %d, %H:%M',\n",
+    "                        ylabel = \"Price ($)\",\n",
+    "                        returnfig=True,\n",
+    "                        show_nontrading=True,\n",
+    "                        warn_too_much_data=870, # Silence the Too Much Data Plot Warning by setting a value greater than the amount of rows you want to be plotted\n",
+    "                        addplot = list(plots_to_add.values())+[ap] # Add the stochastic plot as well as the bullish entries to the main plot\n",
+    "                        )\n",
+    "    # Add Title\n",
+    "#    axlist[0].set_title(\"APEUSDT - 15m\", fontsize=60, style='italic', fontfamily='fantasy', color=\"white\")\n",
+    "    axlist[0].set_title(\"APEUSDT - 15m\", fontsize=60, style='italic', color=\"white\")  # Use `axtitle=dict()`\n",
+    "    \n",
+    "    # Set the color of the xticks, yticks and ylabel in every axes object\n",
+    "    ## Main Plot (Candlesticks)\n",
+    "    axlist[0].tick_params(axis='y', colors='white')   # ytick.color\n",
+    "    axlist[0].yaxis.label.set_color('white')          # ytick.labelcolor\n",
+    "    ## Volume Indicator\n",
+    "    axlist[2].tick_params(axis='y', colors='white')\n",
+    "    axlist[2].yaxis.label.set_color('white')\n",
+    "    ## Stochastics Indicator\n",
+    "    axlist[4].tick_params(axis='y', colors='white')\n",
+    "    axlist[4].yaxis.label.set_color('white')\n",
+    "    \n",
+    "    \n",
+    "    # Get the Volume indicator and modify its font size\n",
+    "    vol_ax = plt.gcf().axes[2]\n",
+    "    vol_ax.yaxis.label.set_size(15)\n",
+    "    \n",
+    "    # Set the x axis label\n",
+    "    axlist[0].set_xlabel('Timezone UTC')\n",
+    "    # Find the interval between the 7 custom x-tick marks\n",
+    "#     time_delta = (df[\"Start Date\"].iloc[-1]-df[\"Start Date\"].iloc[last_index_nan_value+1])/6\n",
+    "#     # Set the locations of the custom x-tick marks\n",
+    "#     tick_locations = [df[\"Start Date\"].iloc[last_index_nan_value+1] + i*time_delta for i in range(7)]\n",
+    "#     # Set the labels of the custom x-tick marks\n",
+    "#     tick_labels = [date.strftime(\"%b %d, %H:%M\") for date in tick_locations]\n",
+    "#     # Apply the custom x-tick marks and labels\n",
+    "#     axlist[0].xaxis.set_ticks(tick_locations)\n",
+    "#     axlist[0].xaxis.set_ticklabels(tick_labels)\n",
+    "\n",
+    "    # Set the y axis range \n",
+    "    ymin_value = pd.concat([df[\"Low Price\"]], axis=0).min()\n",
+    "    ymax_value = pd.concat([df[\"High Price\"]], axis=0).max()\n",
+    "    axlist[0].set_ylim([ymin_value,ymax_value])\n",
+    "    # Save the plot\n",
+    "    random_filename = \"TEST_APEUSDT\"+\".png\"\n",
+    "    mpf.show()\n",
+    "    return stochastic[['%K', '%SD', 'UL', 'DL']]\n",
+    "#    candlestick_plot.savefig(random_filename,dpi=300, bbox_inches = \"tight\")\n",
+    "    \n",
+    "    #RELEASE THE MEMORY RAM\n",
+    "#    plt.close('all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df_o = pd.read_csv('so75737197_data.csv',usecols=['Start Date','Open Price','High Price','Low Price','Close Price','Volume'])\n",
+    "#df_o.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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wiXAG9ZWTTXldxgZxBgIAAAAAAOgtnorsoFon5HUZm7TcbdOY1te9LgMAAKCoVq5apdf/84YW/Ps/ev0/b+jXvzpLA2trvS6rZDEIBgAAAAAAAD5X64QU32qQ12Vs2sdLva4AAACgqP4192Vtu/NuXZZNO/O0Ht/Ot3bdJaf7/2vuyz1eQ09hEAx4LOd3Gqelhu1/rH+5X5MqC1fXhvBuY5SDZLJZe489SMlks9elAL5EhgAAAPyL3+UAGzIE2JAhWGWz2aJs54G/3CnXdbt9/8H1Xy9gNTYMggGP+eadxhLvNkZZyGazWrx0adF+aQDKDRkCAADwL36XA2zIEGBDhuAX997/oFytPwgOOAENGbSFdvjG9qqOx/Xo409qdeMaDyrsPgbBAIBeJR6v0sIF8zRsxEglEk1elwP4DhkCAADwL36XA2zIEGBDhuAXk86c+qXf71NToysvuVDbDh+ugw87vEhV5YdBMLolGAwqGl13HeJs1lUymVQoFFIkEl63PJNVsrlZ4XBI4fC65em2tFpaW1UZiagitO5ll0qllEq1KRaNKhAMdCxvbU2pra1NsVhMgYDTsby5uUWZTEbxeFWX+pLJZmWz2fWWJxJNCgQCisWi6y0vlZ6Ulm84cro8x71pP9FT+fQUr1r7vXhVVcn21Ox0+hlRypy1PxN47fWungKBteu2Z6kceir1/eSnnwmSfLWf/Pbc+opfavZLne0cR8rh8mSe8uNz6yd+qtdx5MhH9fqII3U5LnbnmNv530N+/d3oiz2Vw+979OSfnkIVa+8Ti3atxc89leN+oqfS7anzcahceirH/VSsnjq/Hjprr6WUrV6zRhNPO1MvPvOEpp15ms781Xlel7RRgU3fBZBG7bmHFi6Y13F75P4ZkqTDD/lhl+W33nCdJOnkCSd0WX7x+edKki4+/9wuy0+ecIIk6dYbruuy/PBDfihJeuT+GV2Wj9pzD0nS/DnPdlleP2xox7uJOt/i8SrVDxvaZdn8Oc+WVE/BQLCQu65HRSLhXruf6Kl8enrtheckSa+98FzJ9lQVi3Ujkd6risV47fXCnobVfUXS2gyVS0+lvp/88jMh8vk/LP20n/zy3FbFYuv9Z2NJcxxfPbd+qVWSQp3+o6bUhUIVvnpuq2IxRSsrN33HEhHwWc6++J+Ypayiwj85C4fDOR9zO/97yK+/G5XK7xH01Dt7OuT74yRJ993157LpqRz3Ez2Vbk+dj0Pl0lM57qdi9fTcPx/teD1sqJZS19zSonmvLdDYfcd4XcqXcmrr6n3yduLy1B6ILV+9VI3ZlNflbJC7eKXipz9aNu8yaV9eKu+c+Vf6a775jOCmjz/R7hVvdfy9N+0neiqfnuJVVXrthee04x576ZNly0uyp4aLx8gZ3Felzl2ySgN+9TSvvV7WUyAQ0LvzX9GOe+ylRNO6yzj5uadS30+fXjjaNz8Toqc85Kv95KfnVpIvapXW/vtBjuOLen333C5ZJbmunCGbeV3KJvnyuRX1FoK7ZJXen7pUVVtt4XUp3ZL4eKkk+eLf6U0fL9XuFW93/L07x9ya6njHv4c+/azBl78bfbGncvh9j57801OookJvvTpXO+y+p5LNzWXRUznuJ3oq3Z5qaqo7jkMNK1aWRU/luJ+KeUZw++uh8/8x5XpG8NzZT2nrLbfU5Vdfqyuuvrbb6/WEv955u3bZeaS+st2Iom43FwyCPeaXQXB08sNel1G2/l25sy/+gSmt/QfxN1pe9boMwCweryrpzyFpnn6wP/6Dl+NDr1XqGSo3/EwoHD89t5J8Uavkr3r9VKvkr3r9VKtEvYXkLl6phWct9dW/eyV/DILz/Tc6v8sBNmQIsCFDaNc+H7N+ZrRXg+D9x4zWrTdcp3feW6gxB3+/aNvNlX+udwMAQA8IBAIaMmiQ3l34vrLZrNflAL5DhgAAAPyL3+UAGzIE2JAh+MVVv714o9+LxWIaVlenr39tuBzH0Q0331rEynLHIBgA0KvEYlHNfuwf5neaAb0VGQIAAPAvfpcDbMgQYEOG4BdHHHboJu/z3yVLdOU112nGAw8WviADBsEAAAAAekTL+fvJ7RPxuoxucVa3el0CAAAAAAAoQYf++OiNfq+trU3Lln+qjxcvLmJF+WMQDAAAAKBHuH0ivvi8SklytdLrEgAAAAAAQAl64aW5XpfQYxgEAwB6ncbGhNclAL5GhgAAAErDU5EdVOuEun1/J+2o6Zs/0YvpbeVWugWsbH3L3TaNaX29qNsECoF/DwE2ZAhWfWpqFAwGO/4eCAQkSdHKSvXbbN2b01taW5VMJoteX6lhEAygbOX6D2Iv8Q/i4kkkmvTVHXf2ugzAt8gQgP/P3n2Hx1GdbwN+pmxXb5ZkyU2Wey/YuBsbg+khoYeeACmQkJBAIPkRSP1CekgCCSWhtxCaA5jqXnHvttxkS7Ykq2vblPP9sapWXY200krPfV3r1U49xzvvzu68c84hIqLeI02yISY7I+z1PIjvhtK0I78w8vsk6mL8PURkDWOIusLH772FQVlZzaZ/+86v49t3fr3+9StvvInv/PCBDm1z4+efdLo8QgjMWLi40+t3NyaCiajP6uwP4h7BH8QRoygK5s46F6vWroNhGD1dHKKowxgiIiIiIqL+ir+HiKxhDFFvlZ01sKeL0G2YCCYioj7P/9PFEPGO0AtJwkq3GzVfXgqIyHaF1hFSRaCni0DUJpfLiVf//QxyJkxGdXVNTxeHiIiIiIgoYvh7iMgaxhB1henzzuvybWYMH9Xl2+wtmAgmIqI+T8Q7IA1sGB/CC0BKsPdcgdogUNbTRSAiIiIiIiIiIiKiPoCJYOoQjrVKRERERERERERWRMv1JV5bIiIior6CiWDqEI61SkRERABgmgL7DhyEafa+rtWJiIiIqHeLmutLvLZEreDvISJrGEMUjaZNnoQZ06chY8AAAEDh6dPYsGkzNm/d1rMF6yAmgomIeokm49j2YlJFAM6fftzTxSCiHuL1ejH/wot7uhhEREREREQRx99DRNYwhiiaDBs6BH/93WOYOH4cAECSJACAEKEbGbbv3IVvfu8+HDl6rMfK2BFMBBMR9RJnj2PbW3EMW6L+zWaz4eovXYHX/vsWNE3r6eIQERERERFFDH8PEVnDGKJokZaairdefgGpKSk4dboI777/PvJPnIQQAtlZWbh06YWYNGE8/vvSC1hy+ZUoKi7u6SK3iolgIiIiIuowh8OO3//6F3j7f//jjzYiIiIiol4imsZfBhAVZQWajxfN30NE1jCGKFrc++1vIjUlBU8+8y/84rHfNTtef/b/HsNDP/g+7rr9Vnz3m3fhwUd+1kMlbR8TwURERNRp0XKxAQj9gJ/1owHsgp2i7rg9t6cLQURERES9XrSNvxwVZQU4XjQRUT+1eME8HDp8BD/95a9bnK/rOh751f/D4oULcP55C5gIJiIior4pai42AEB+IbtgJwDRd9wSERERERF1hP+ni6Pi5meAN0ATUe+WlpaGZR982O5yO3fvxsUXXhCBEnUeE8FEFJZo+UIpVQSAX5f2dDGIiPoc0zDx2cpVMA2zp4tCREREREQUUb3991C03PwM8Abo/qq3xxBRneqqamRmtN+IIDMjA9VV1REoUecxEUxEYYmWL5T8MklE1D28Ph+uveX2ni4GERERERFRxPH3EJE1jCGKFpu3bsX55y3E4oUL8PFnn7e4zKIF83HO1ClY/slnES1buJgIJiIiIqIOs9ttuOcbd+HPf38CwaBWP509RhARERFRX/KpYzzSJFtPF6NDioTW/kLUJVr7PUR9X7T85gV6d7fbjCGKFn954h9YtGA+nv3743h72ft48513kX/iJAAga2AmvnTpJbjikothmib+8sSTPVzatjERTEREYYu2L79E1HXsdjt+8J278cTTzzT50cYeI4iIiIioL0mTbIjJbr9LyF4hv7CnS9BvtPZ7iPq+aPnNC/Tu372MIYoWm7duw3d++CP89heP4suXX4orL7ukyXxJkuD3+3HvA/+HL7Zt76FSdgwTwdQnRUuSiq2SKFrxyy8RERERERER9QeNW4dLugTvtNuwQR8F4RQ9XLKmioSGc3u6EGGKlpb30fh/S90nWnIPQCj/sPZXp3ttnEkOV08XoU3/efsdrN2wAV+99hrMmDYVA9LSAACni4qwftNmvPTa6ygoPNXDpWwfE8HUJ0VLkooJKiIiIiIiIiIiot6rpdbhnuT4HipNG6KwZXjUtLyPwv/b3pqs9EkSMj7+MWp+vhAQold3Y92aaMk9AKH8Q6+OM3fvOUYlSYIQzW/wKTx1Go/98c89UKKuw0QwEREREXWYrul48dXXoWt6TxeFiIiIiIiIiFrQm5OVXgBSgh1AKFEZLS3DAbYO78u2r1uFN995D2+89TZ27dnb08XpUkwEExEREfVCvfqH0CP/wRJpLOAMveQPISIiIiIiIiLqjF7dYvVsUdg6nDomNSUFd9x6M+649WYcPJSH1996G2++825UdP3cHiaCiYiIiHoh/hAiIiIiIiIiIiLqfkuvvApXXXE5Lrt4KUbkDseD930PP/r+vVi3cRPeeOttvPv+h6ipqenpYnaK3NMFICIiIiIiIiIiIiIiIiLqCdt27MRDj/4cE8+dixtuvwNvvbcMfr8fs2fOwO9/9Qvs2rAGT/zp91i0YD5kObpSq2wRTERERERERERERERERET9mmma+HTFSny6YiVcTicuvvACfOWKyzDn3Jm4/OKLcNlFS1FaVoa33l2GN95+B9t27OzpIreLieBeYoAagxgz2NPFaJFwGpDT/EByXE8XpUPkgB8ZzjhIakxPF6Vd/L/tPvy/7T7CaQBAVJQVYHm7E+Os+/D/tvvw/7b7RNPnFxBd5Y2msgLRVd5oKisQXeWNprICLG93isZzL4CoKG80lRWIrvJGU1mB6CpvNJUViK7yRtNvByC6zg/R+H8L8HtCd4jGY6FX//+6HD1dgjb5/H688dbbeOOtt5GSnIQrL7sUX77iMkwYOxa333wjbrvpqzh89Bje+O9b+OPfnujp4rZKShuaK3q6EP3Z+LFj8PG7b/V0MYiIiIiIiIiIiIiIiIgiavGlV2Dn7j09XYwOyxk6FF++/FLcfP11SEpKhBACmbmje7pYrWKL4B525NgxAMDEc+egOkoHmqbIcjtd+Osffotv3XsfvH5fTxeHKOowhoisYQwRWcMYIrKGMURkDWOIyBrGEJE1jCFqLMbjwfZ1q+vzZNEgKTERC+bOwYK5c5CYmNDTxekQtgjuYTExHuTt2IqcCZNRXc1EMBEREREREREREREREfVt0ZIfczocWLpkMb58xeWYN2sWVFWBJEkoLS3DW+8tw+tvvd2rxwqWe7oARBQeu92G+75zN+x2W08XhSgqMYaIrGEMEVnDGCKyhjFEZA1jiMgaxhCRNYwhiibz58zGX377G+zauA5//f1vsWj+PBimgXf/9wFuuuMuTDh3Dh569Oe9OgkMsEVwj4uWOx6o9+AxQ2QNY4jIGsYQkTWMISJrGENE1jCGiKxhDBFZwxiixnrj8TBh3Fh85YrLcPnFFyE1JQWSJEEIgfUbN+H1t97Gu+9/0GvK2lEcI5iIiIiIiIiIiIiIiIiI+qXvfvMuXHn5ZRg+bCgkSQIAHDiUhzfeehv/efsdFBSe6uESdh4TwURERERERERERERERETUL93/ve8CAIpLSvDfd5fhjbfexs7de3q2UF2EiWCiKKNrOl589XXomt7TRSGKSowhImsYQ0TWMIaIrGEMEVnDGCKyhjFEZA1jiHqr/777Hl7/79v4fNVqCNG3RtTlGME9rDf2gU5ERERERERERERERETUXZgfiwy5pwtAROFxOhz4/a9+AafD0dNFIYpKjCEiaxhDRNYwhoisYQwRWcMYIrKGMURkDWOIKPKYCCaKMqpNxQ3XXAXVxp7diTqDMURkDWOIyBrGEJE1jCEiaxhDRNYwhoisYQwRRR6jjYiIiIiIKEyqKpCdpWHoYA3ZWRpiY0zYbAKqCug6oGkSqqpl5J+w4cgxG/JP2KDrUk8Xm4iIiIiIiIj6ESaCiYiIiIiI2iUwMjeImef4kDtMw8BMDWoYv6Z0HThZYMPxkz4AuwGI7iooEREREREREREAJoKJok4wGMRjf/oLgsFgTxeFKCoxhoisYQxRf+Nympg104dFC2qQPVDv9HZUFRg8SMOQIeU4dPga/OT+Aiz/xIU161zwBzhiD1FH8TxEZA1jiMgaxhCRNYwhosiT0obm8lb0HhQT40Hejq3ImTAZ1dU1PV0cIiIiIiICEBtr4IqLqzFvjhdOR/f9ZPIHJKxc7cZby2JQVaV0236IiIiIiIiIehPmxyKDt54TRRm3y4VX/vU03C5XTxeFKCoxhoisYQxRf3DONB/+36PFWLKopsuTwIoiY8qkiVCU0E8xp0NgyaIa/PqRYkyf6uvSfRH1RTwPEVnDGCKyhjFEZA1jiCjy2DU0UZSRFRkL582FrPA+DqLOYAwRWcMYor4sNtbAzddXYMY0f7fuJyU5CZCkJtPiYk3cc1cZNmz24d8vxbN1MFEreB4isoYxRGQNY4jIGsYQUeQxEUxERERERP3ehHF+3HlbOeJizW7dj2maOJR3GEK03NJ4xjQ/Ro8M4slnErBjl7Nby0JEREREREREfRtvuyAiIiIion5t9kwvvn93abcngetUVldDmK3vKy7WxPfvLsXsmd6IlIeIiIiIiIiI+qZubxGsKApGjxwB0xTYs29fd++OqM8LBIL43gMPIRAI9nRRiKISY4jIGsYQ9TXnza/BrV+tsLSNsnIZx0/YUFMjwzAARQE8HhODsjQkJjRN+AoBVFdVo5UGwfVkGbjr9nI4HAKfrvBYKh9RX8LzEJE1jCEiaxhDRNYwhogiT0obmtvOJYi25QwdissvuQj5J07i9f++1WTerBnn4O9//B1SU1IAACcLC/GN734fm7dstbLLPiUmxoO8HVuRM2Eyqqtrero4RERERET9xpxzvbjztvKw16uukbByjRt79ztw5KgNFZWtj+cbH2dg6BANY0YFMHeWFzGe8H9+PfF0Atasd4e9HhEREREREVFvxfxYZFjuGvrqK6/Affd8G1mZGU2mx8fF4em//QVpqamQJAmSJCErMxMvPv2P+sRwOFxOJ86bPw/3fusbeObvj2Pzqs9w+vABnD58APd95+4ObSM1JRk/ffABrPn4AxzdswP7tmzE26++hBuuvqpD6w8elI3Hfv4oNq34BMf27sTuTevxyr+exsUXLgm7PkSd5Xa7seKDZXC7eTGQqDMYQ0TWMIaor5gwzo+v31Ie1jp5R2x48pkE3PODdLz8ejy27XC2mQQGgIpKBdt2OPHSa/G45wfp+MezifD5swGp4/u949ZyTBjnD6usRH0Vz0NE1jCGiKxhDBFZwxgiijzLXUPPOXcmAOC9D5Y3mX791V9BQnw8Tpw8ie/96Mfw+f34zc8ewagRufj6LTfhl7/9fVj7mTxxAl5+9qlOl3PCuLF45V/PIDkpEQBQXV2NGI8HM6dPw8zp03DJ0gtw0x3fgKZpLa6/aMF8PPX4n+o/oCqrqpCYEI+F8+Zi4by5ePn1N/Dd+x/sdPmIOkqWJYwakQtZDuPqIRHVYwwRWcMYor4gNtbAnbeVQ+7gbbE1XgnPvRSPtRtcCCuDexZNk7B2Qwyczrnw+T7AV68tg8fdfgthWQbuvK0cDzyciqqqthPPRH0dz0NE1jCGiKxhDBFZwxgiijzLLYLT0wcAAI4eP95k+oXnL4YQAr947PdYtXYdNm/Zih/+5GFIkoQFc+d0al9l5eVYuWYtHn/yn7jznntxuqioQ+vFxsbghaeeRHJSIg4cysOSy69EzoQpGDpuEh54+BEEg0GcN38efvaTlhO5g7Ky8M+//BFutxsbNn+BcxctQe7EqRg+cSp+++fHAQDXXfUVfOuOr3WqXkREREREFDk3X1+BuFiz/QUBbNnmxP0/ScPaDW5YSQI3JWHdRg8e+L80bNnm7NAacbEmbr7e2ljGRERERERERNS/WG4RnJyUhIrKyiYtaVVVxaQJ46HrBpZ/8mn99M1btkLXdQwdMjjs/azftBmjppzTZNpDP/x+h9b95tdux4C0NHh9Ptxw29dx/MQJAICmaXj2+RcRGxODh37wfdx47TX4x7P/xuEjR5us/8N7vwOPx4PTRUX46u13oLKqCgDg9Xrx2B//jLSUFNx0/bX47re+gRdeeQ0VlZVh14+IiIiIiLrfOdN8mDGtY90sv/RaHN7/yIOuSwA3VV6h4A9/TcTSJTW4/qr2f0PMmObHhs0+bPrC1S3lISLqjVRVIDtLw9DBGrKzNMTGmLDZBFQV0PVQbwtV1TLyT9hw5JgN+Sds0HW2MiIiIiIiArogESxMs1l/7uPHjIHdZsP2nbvg9fmazKusqkZMjCfs/Zhmx+7Yb8lVV14BAHj7vWX1SeDGnvr38/jON+5ETEwMvnz5ZXjsj3+un+d2uerHAP7Xiy/XJ4Eb+/Pfn8RN11+LuNhYLF2yGK+88Wany0rUHp/Pj2tuvg0+H8eJI+oMxhCRNYwhimaxsUaHW9U+83w8PlsZ/u+W9hiGgS3btsEwjNopEt5fHgO/X8JtN7ZftltuqMC+A3Z2EU39Fs9D/YHAyNwgZp7jQ+4wDQMzNahhXL3SdeBkgQ0HD9uwfqML+w/a0V039EQjxhCRNYwhImsYQ0SRZ7lr6IJTp2BTVeTm5NRPW3zeAgDAxi++aLZ8bEwMSkvLrO62w3KGDkX2wIEAgE8+X9niMl6vF+s3h8q6YM7sJvPOmTYVblfojvtPV7S8fv7Jk9h/8FBo/U52e03UUYZh4PNVqxtdPCSicDCGiKxhDFE0u+Li6g51Cf3S63HdkgSuc6aF30OfrfTgpdfj2l03LtbE5RdVd0exiKICz0N9l8tpYtGCGvzyp8X48Q/PYPECLwYPCi8JDACqCgwepGHxAi9+/MMz+OVPi7FoQQ2cjs43MOhLGENE1jCGiKxhDBFFnuUWwes2bMKwIUPw04cewHd+cD8GpA3AzddfCyEEPv58RZNlhw8bBptN7fDYvl1h1Mjc+r/3HTjY6nL79h/A4gXzMSJ3eNP1R4xoskyr6x84gJG5wzHyrPXPZrfbYLfb61/HeDxNngFA13T4AwE4HQ6otoa3KBgMIhjU4Ha5ICsNOfxAIAhN0+B2u5sMsu7z+WEYRrMW2F6vD6ZpNpteXV0DWZbhdruaTVcUBS5Xw/hlping9Xphs9ngcDTUxzRMeH2+ZvVknbquTgCwbc0qzFlyIapravpEnfri+8Q69d46xXg8WL38A8xZciFOnS7qE3U6u+ysE+vUnXWSZRlbVq1o8TwUrXXqi+8T69S8Tg6HifPmh36HSJIEWZEhhIAwTQgAoX+ArTtcWP5JHBRFghACpmlClmVIUkMZTdOEEKLD0+suciiKAlVRMGvmDKxdvwGBYLB+OgAs/yQOo0dqmDzBV1vO0PpS7fYM3YAkAectCOL9j10IBOSw3icBCYYzCVpMOmypQyDFpgKmDknzwwjUwPBVwwYdku6HpPsBzYdgTSV0bxWcKiALrb5NHY891qmn6qQoCr5Y+Xmz81A016kvvk/h1CkmxsD5C05j/lwv3O66bYc+F+s+92SlUS8IIvS5WvdZ3jBdwDBMSLIEWW6YPjjbxC03VOCGq6uxdkMc3vswEdXVSr99n+JiY+p/DxWXnOkTdeqL7xPr1HvrZFNVbFrxGWYtXtKkJ8xorlNffJ9Yp95bp7i42Prz0JnSsj5Rp774PkWqTo3zYtSN0obmCiuPc89bIrw+n9B1vf5hGIbYvnNXs2V//OjPhWEY4ql/P29pn3WPY/n5QgghfvPHP7e6zI8eflTUGTZ+UqvLPfTIz+qXGzJ2Yv30v/3zaSGEEKVlZW2W5YmnnxFCCFFyprTN5X7zxz+L9rzwymsibWiueOGV15pMr6vnpytWNpl+7/0PirShuWLv/gNNpl99060ibWiuqKysajJ97pKlYtj4Sc32O2z8JDF3ydIm0yorq0Ta0Fxx9U23Npm+d/8BkTY0V9x7/4NNpn+6YmWL9WSduq5OE2bObjYt2uvUF98n1ol1Yp1Yp75apwuuuLJZfaK9Tn3xfWKdWqrTMlFVtVB8+LFH7NozXJjmxWLn7hzxxbZBYv3GDPHhxx7x4ScDxNfvvlnccPsd4obb7xALll4i0obmigVLL6mfdsPtd4iZ550v0obmiguv+EqT6ZNnzRNpQ3PF5dfe0GT66KnniLShueKam29rMn3ouIkiY/ioJtPuuOcm8d93Y8XHn8WJL7YNEl9sGyR27s4RweCF4sOPPWLzlmwhxCVCiP+1+T59/PkKMWD8ueJrv3paPPLObvGVv68Ro378vhh8/3udfuT8aJmY/OhyseCxz8SYe58TWTc9Ju789wbx4Js7xP97f6944vNDYvJ13xNDz79BrD5YLHaeKBfHz9SIcm9QDB0/uR8fe6xTV9bp5ju/ESpXVd+pU198nzpep1VCiOvFx5/HidXrUkXo8y300LSGzz3TvET4fOeLM6VzxOGjE8SHH3vEhs2Z4mTBNFFQOF2cKZ0jCgqniw8/9oiDeWObbCf/xBTx4ccekX9iSu20G4QQq/k+sU6sE+vUqTo98PAjQggh9h881Gfq1BffJ9aJdWKdoqdOdfPShlrPGfLR8kNKG5orrCaT582Zhd88+ggGD8qGaZpYtXYdvvejh1BQeKrJcp8uewejR47A1759D5Z9sNzqbrFp5acYlJWFx/70F/z2T39pcZnvfPMuPHjf9wAAmbmjW+1y4KvXXI3f/ernAIDxM2ajqLgYAPDbX/wMN153DQoKT2Hy7HmtluVH378X3/3WNxAIBDFo9LhWl2upRfD2dasx8dw59Xcz96Y7Muqm96W7TKK9TgCQt2Nrk2Mm2uvUF98n1qn31qnx5y5bBLNOrFPnWgQf3PZFi+ehaK1TX3yfWKez6yTw8I9OYmBGsL4VmSRLEGaom9C6FsFPPJ2I9Zti6rchuqlF8Lw5s7Fy9ZpmLYLrzJxehbtuL0ejTUCSZZiGWd8y7uRJOx759UCYZmioG1tcKuxpQyDisyDiBwLxmYCt6f9JTxLCBLSGlsbQQs+6rwqS5ocKrX66CHrhryqHamqwy3poPWFG7NiLjXHDpQi4VRNuVUDWfHAqBpJjHfCoAi4lNF0x/IixAfEuBS5FwKOacKkCDmhw2wBIMjRTgi6AoCHBrxkwoMCADF1I0EwJQQPwBXWYkgJTUhE0JegmENAEvEEdUGwwJQWaGVreF9ThDRpQbA4YUKAJQDMl1Ph1+II6VLsbhiTVL19VE4BPM+CJaTiugfbjyeV2A7ISekgKanwBKDY7nK6G6SZkBIIaVJsDNoezdroMU8gI6iZUux2KzV4/HUKCaQrY7TYoqgpZliHLCkRtc3ybzRaKHVmpfV8kSJIEVVFqp4daecoSMHz4KKxe+QkCJ/YAVacgdaBO/e9zr3fXKT3dieuvKsG0KaHvEoZuANJZn4e1LXwBNPk8bNyLQ4vTJUCWQscPJAnCbPRZ3qiMGzY58czzMTD0mH71PsXFxtT/HmKLYNaJdepci+B9WzZh/IxZbBHMOrFOnWwRXHceYotg1qnuOm3OhMmorq4BdY8uSQTXSU5KRHV1Tf0FjcZUVcX0KZMBANt27ITPb30w8GhMBJ8tJsaDvB1beaBTh/GYIbKGMURkDWOIotHI3AB+/MMzbS6Td8SGn/4yBYDU5nJWKYqC8+bPw6crVrYxLpbATx8sQc5QrcW5lYF4HK3MwXPvj0OZGAo5KRuSM7b7Ct0LCM0P6EFAmKHEoQBs0OBGAC7JDw8CcEsBuGufPQjAJdX+LQXgQhAeKQC3FAwtJwUbHnLdNA0eKQiX3PL/ezQzhAQNKjShQIPS5G8dKnTIkCEgS4AEEfobAjJMyJJoNM2EjNDrxtPOXqfxPEXqsksOTZhCQiliUWLEoMQvo6haQ0l5DYq9QIlfRrFPDj3X/h0wuze2qeMmjPPjztvKOzRme3errJLx5DMJ2LHL2f7CfQS/yxFZwxgisoYxRI3xeIgMy2MEN3amtKzVebquY93GTV25uw5pfPC4XM5WD6bGd1A0ad1S+3fj+W2t33hdou7g9fow74KL4PX62l+YiJphDBFZwxiiaDTznPaP148/86C7k8BAqHXw2g0b2kgCA4CETz73IGdoOWo0D45VDsOxyhwcrczB0YphKAukhBbLqBtFs3OEoYf2pnTmZ6GADQbs0OCAFnqWNNihw4Fg6FlqNA9a/TR742mS3nSZ2nUc0BttU4PDrsGJIFxSAB744UIAdqmt/0NqTJEEFGhwSn0nyS1LAimoRIpaCcQg9EhvffmqoIRiv9wsSVz3uvHflVqoRXK/4IiBnJoDJTUHUmIW4K+CfnQjzMI9gOj6JP7smV7ccWs5Gg3h26PiYk18/+5S/OPZBKxZ7+7p4kQEv8sRWcMYIrKGMUQUeV2aCO6NThWdrv87Y0A6Dlbntbhc+oABAIDKqip4vd6G9U8XAQASExLgdDjgDwTaXP90o/0RdQfTNHGysBCm2fN3TxNFI8YQkTWMIYpGucPaTn5V10jYsDly3Sj7/S3/poBqh5yQBTkpGxvFIBxdlYESf4bFvQl44EeSKENS9WEk+Y4h2X8CSXoRElEJp2LCqUqwqwocNhkOVYJDkeBQAbsCOGQBhyLgkE3YJRMO2YBD0mGHAbmbWnoSdYdYu0Cs3cCwuPZvIAgYoVbFLSWJz04glwZkGCKKksbOOCipOZDThkNOzYEcN6DZIkrWBJjVZ2DkrYF+ZAMQ9LawofCdN78Gt361wtI2ysplHD9hQ02NDMMAFAXweEwMytKQmNC57yayDNx1ezkcDoFPVzQfmqmv4Xc5ImsYQ0TWMIaIIs9yInjqpIn49aM/xeat2/Cjhx9pc9nf/+oXGD92DO576CfYvnOX1V13yL79B+v/HjUiFwfzWk4Ejxo5AgBw4OChpusfONBkmW07dra8/ojQ+vvPWp+oq7G7BCJrGENE1jCGKKJkBbC7AUMD9ECnWqfZbAIDM9tOBK9a64amRSaRU9819Kq1MGMHQE7Khpw4KNS9c+wASI2ayZW0MpqOA0EkoxLJUiWSpQokoyr0LFUiWapCMiqQbJ5BMsqRJHvhUmovsvSeIYP7tBrhgA8OSBCwQYcNBmzQoUq82BUtHAow0GNioKf998wUQGlAQkmzJLGCEr9Unyg2RWhZs3Yds4Vpona60cK0utdGC9PqHgK16zZ6wJUIpAwDUoZBpAwHYtMg0H6LZzkmGfLEy6COvRDG8S3QD62CKC/o9P/pnHO9nUoCV9dIWLnGjb37HThy1IaKytb7QYiPMzB0iIYxowKYO8uLGE9454xbv1qBQEDq8y2D+V2OyBrGEJE1jCGiyLOcCL7ysksxdvQo/PUfT7W77Bdbt+G6q76MKy+7NGKJ4LwjR5B/8iSyBw7EefPn4t33P2i2jNvlwsxpUwEAn69e02Texs1fwOvzwe1yYeG8uS0mgrMyMzEyd3ho/VWru6EWRERERER9iCQBjlhIrjhIzvjQsyu+0eu6v5uOeyv0AKAFIHR/x541P1KTq3G8OgZO1Rd6KKHnxq1Z9+xzdHN9ZUhx6ZCTsqEkD8bWhNGwXX4RJDn0c0yFjkRUIUXKDyVzURFK6EoVtQnfxn9XIkZqJUPcmJU+o/sBTcjwCge8cMAr7PAKJ7xo/OyAF85Gz87QsnDAC1co2Ss5USNc8MlO+IQTNXDADzsEWu7zVoIJ1dRggwZV6LBLOlSh1XexrUKHTeiwSTpsQqt91mGTjPrlbUKHKumhxLLQYZeM0HowYIcOVTKaJJ9tkg4bzNpnI/So+1syoNY+KzDrE4vi7AQjpNrE49lJR8AU4qx5omG+KRq9rv3brFtHwDBDz2b9co3Wrd++1CxRarPbcfNXLsOqlZ8gyQmkOjQkS1WIlXqme0FZAlKcAilOA0Bv7K68GMCBZlND77dUP6KzUTu6c6mIRZ7IrH8ckgYib9gYlA6bAaP4CIxDq2Cc2AGIjt/YMGGcH1+/pTysUucdseHjzzzYsNnV4Rt1KioVbNuhYNsOJ17/bxxmTPNh8cKaVsdbb8kdt5ajqlruV2MGExEREVHfZjkRfO6McwB0LAH6v+Uf4be//Blmz5xhdbdhef3Nt/C9u7+FKy65GL//y9+Qf/Jkk/m33ngDYmJioOs6/vP2O03meX0+LPtgOa760uW45Ybr8NS/n0NVVXWTZb5919cBAFVV1Xh/+cfdWxkiIiIiot7M7mmU2I1vnux1xgHOuCYtXztKUh2A6oCEuA6vUwbg15taKKbsr08OF2fosCcFAM1fm2xu4VkLAHrDc33CuVlLZQlKbAqSBqQjNSUJKYmxSI11IEWpqW2tuwfJ0vrahG8osZsg8U74swUNIGhK8BtA0JAQMCQEDKBGl+HVJdToEry1jxpNanda3eu6aZp5dmIpUPuoslBqqbaRpRS62aHuuBACobRmbyXXPjoj8l0ix8R48I2X/4JvPlLbikRWIadOhyczF2mZA5EaoyKltoV8CiqQIlUiRapACmqnSRVIRHW/79pclgTk+uOyIYEdI/kxCMVYiO1Nli8TMcgbmIm8jEwcCp6LAyfLsO9gHvLP+NvsGjs21sCdt3V8TOAar4TnXorH2g0uWDm+NE3C6nVurF7nwqwZPtx0fQU87vbfc1kG7rytHA88nIqqKt5RQ0RERETRz3IiOCN9ACqrqlBe0X4XP2Xl5aisqkLGgOZj0HREfFwcFKXhi7hc+0vC5XQiKTGxfro/EGgyzu/fnnoaN1xzFQakpeGFp/+Bu+/7IXbs2g2bzYbrr/4K7r/3uwCA5195FYePHG2239/84U+4aMlipA8YgOf/+STufeBBHDl6DG6XC3d97TbcfP11AIA//PVvqKis7FTdiIiIiIh6NZuzNpHbvAUvGiV5JcXyT4yICJpOBINOVAYTgfjON6B1IoBp5i7MlndiprIPWUo5kqTdUKTI9IDU1Xx6KAFbl3gNGFLt6+Z/h14DfkNC8Ky/AwYQqN1O0Gg03azdzlnLnL190QMJRutqm7HWP1NEmAbM0/tRdXo/qrYCh2NToWSMgZwxBnLqPEhy8+hWYCAJVfWJ4VCSuAKpUiWSRDlS9EIkG2eQIlch1aHDznwgEqVqTJMOYJp8IHQlKTf0CJgKjtTYcbDUxMEKFYcqlNBzpQKvLuPm6ysQF9ux1sNbtjnxzPPxbXb/HD4Jaze4sWefA7d+tQJTJrXfo0JcrImbr6/A408mdWE5iIiIiIh6hpQ2NNfST9Qju7dD0zSMmDStQ8sf2LYZdrsdQ8ZMCHtfm1Z+ikFZWe0u98obb+I7P3ygybQJ48bilX89g+SkUMK4qqoaDocddrsdAPDZylW46Y67EAy23GXQogXz8dTjf4LbHRorpqKyEh63G6oautD18utv4Lv3Pxh2ndgnPnVGTIyHxwuRBYwhImsYQ32MYm/SWldynZXYrUv4qt3cfXKUUGBggnQYs+VdmC3vxhT5AByS3tPFaiJgACW145SeqX0+++8Sv4zSgIwarSE5q5lAT7TwJApXh89DqgPygJFQMsdASR8NydXx3gTqmFVFiDm9DUllO5HkPYpUh44Ul4lUp4kUZ+g51dXwd6yddwHUKdYkSKkmatwCXhdQ4xaocQEBO5p91Lz0Whze/8jTfEaXEli6pAbXX9WxG/j//EQiNn3RNwdX53c5ImsYQ0TWMIaoDvNjkWH5dv2SM2cwMCMDA9LScLqoqM1l0wcMQGxMDApPnba627Dt2LUb8y+8CN++8w6cf94CZGZkwOvzYcv2HXjtP//FS6+/ASFa/8H2yecrsOCiS3H3nXdg/pxZSEtLQ3lFJXbt2YPnXn4Fyz5YHsHaUH8myzIGZmTgYN5hmGbHx2UiohDGEJE1jKHoJsWkQM2ZDSkhsyHRa+9dF7mFaUD4KgBfJYS/AsJfDchKqEWy6mj5uYUWf11YIuRKJ+sTvzPkPYiL8FikAgJBG0IPOxC0Nbw+dMqGVz+IQXGjZG+VJoEJXeqrwjoP6QGYJ3fAPLkDGiRIiVlQMsZAyRgNOXlwx/YXmwZv7BJ4sQT5mh/m6QMwCvfAOLYX8DdPKDoVgZRGSeI4uwlJAhQpNJ6wDAG5tvfw0Ou6eQKSJEFxx0OJSQo93IlQFAWyVDeSr4AMEzIEJAgotX/LaD5fhgnZ1CD5KyEFKoFAFWTNG1q3cXlqyyRJgF0GsmMMDI4xoHS2t/BGUm0CKJeQUt7080hTBLxuoMYlUOMG3tnoxqGNTqgSoHdrHl3C+8tj4PdLuO3G9nu1u+WGCuw7YO9zXUTzuxyRNYwhImsYQ0SRZ7lF8JN/+gMuvehC/OWJf+BXv/tDm8s+eN/3cPddd2DZh8vxtW/dY2W3fQbveKBw8ZghsoYxRGRNRGLI7oF91i3QdrwLUXq8e/bRDynDZsI26UuQVHuP7F+YJhCogvBVQvgqIPy1z/WPUOIXAS/C7ldXsYXGDrY5mz0vuUBHcpoNft0Jv+GGX3fCp7sRMJzw6S6UVrpRWu1pklTOwBnMVnbVJ3/TpPIu//8oDUgo8TVqrRtoueXuRV+qxLRZ/lbzujt2O/DYH5O7vHxEvVWXnYccMVAyRoe6kR4wslM3xZhlJ0JJ4YI9EGXHzxovvANkBXJiNuTUnNAjZWjo86uThL8KZvFhGMV5MIvzICoK0Zl+yh2ywNA4A8PjdOTG6xgebyA3Xkduggm30n0XbDUTOFql4FBt19KNu5qu1LogM93I0iXVHWoZ/OHHHrzwanyX7run8fcQkTWMISJrGEPUGI+HyLDcIvjF11/HZRcvxbfu+BpOnCzA86+82uJyN153Db51x9cghMBLr71hdbdERERE1EfZp10FJW045PPugb7nQ+h7PwEE7xTuNNUB27SroQ6a0m27EP7qUOtdX6Pkbn2iN/SMQHX3vY+GBhgaRKA6VJ5Gs0Y5zmDKkECrq67d4MLL/43H7PQg5mYEMS9DQ05c57p61oWMPVoGCvx2lHhNlFQGUFLhRYkPoUSvT0apZsOkGfPx0YrVMAyj3W1qNqnNxr26xpa/RJ0SqIZxdBOMo5sASYacMgxy5phQYjhuQIc2ISdmQU7Mgm3MEgh/NYxTe2EW7oFxah+gtTAWraxCTh4MOXUY5NThkJOHWLo5R/gqYRQfglmX+K3smt7XAqaEfeUq9pU3vWQkQSDDbSI3PQYjh2VhRIoNOcpp5MgFGNAFN8zYZCA33kBufPPPxtNeuVFyWMXB2r9P1sjoTA8I7y/3YFRusN0xg+fN8eKNt2LhD3RtIpqIiIiIKFIsJ4JXrl6Ld9//AJcuvRD/72c/xa033oCPPv0MJ04WAACyBmZiyaKFGJmbC0mS8N4HH+LTFSstF5yIiIiI+h5l6DlQsiYCACRZgW3cRZDTR0Pb8CJEzZkeLl30kRKzYJ95E+TY1E6tL4LeFlrw1iZ26xK//krAbD+h2VO0FhKlsgEkVALJ5RImGEH83zXFkDuZT91TpmJVoR0rC+1Yd9qGGl0H0DiR3LSVoaIoMNDxhILb3XbyPKiFUVgiapkwYRYfgll8CPr2dyB5kiHXdSGdlgtJaf/SieSMgTpkOjBkOoRpwCw5ArNwL8yKwtrkbw7k5MGQFFuni2nWlNUmfUPJX1Fd0ultdYaAhAKvgoLDPqw4fBBQHVCGTIc6/OuIj4vFMKkAw6UC5MgFyJFCj8HSadgk6+eIAW4TA9wmZqc3/dCrCoaS1ntrE9d7y0J/l7WbuJXw7AvxGDkiAI+79VbTLqfArJk+fLrCY7kOREREREQ9wXIiGADuue9+QACXXnQhRo8cgVEjcpvMl6TQVZW33luGe+9/sCt2SdSvVVVV93QRiKIaY4jImm6LIVc8bJOubDZZSRkKecl90La+GWq5RR2i5M6DbcKlLSYwhGlA1JxpkuRFoyRvKOlbCRjBHih516qqliEJIK4KSCqXkFwuIaESkEWjzG8YSeDj1TJWFdqxqtCO1afsKPaHP3akrne8xfGgrLYzvdU1bKVG/U93f5cTNWdgHFoF49AqQLFDHpBbO7bwGEjuhHbXl2QFStpwKGnDLZXDrC6pb+1rFudB1JRa2l6X0wMwDq2GcWgNSgbkonz4XGzLnAPJbPhcUqFjkFSE4dJJ5EiF9Uni4VI+YqXWe2voqFi7wPQ0DdPTmn5WnvbK2FubGK5LFB8oV+EzGj7wyysUPP9yPO66vbzNfSxeUINPV7jRl8Ze5+8hImsYQ0TWMIaIIsvyGMGNzTl3Jq676suYPmUKUlNTACFQVFyCTVu24qXXXsfaDRu7ald9BvtAJyIiImqg5MyCbeLlrXaVaeRvR/CL14CgN8IliyJ2N+zTr4UycHyLs83qMwiuf66Pj78sMCLewNyMIK4c58XEWB02o3MX8M/4Jaw+Za9v9XusWkGkkgEJ8Qb+8tu2u3p95vl4fLaSLdWIIkWKz4SSOQZyxhjISYMhyV13M4ZZWdSoxe9hCF95l207UnLGxmDcRdOx+uR58OoxbSwpkIpyDAvsxbDSdRgWPIDhsQHkxuvI8nTPMAKmAI5UKdhX22o4lCRWcOO3yjBsWNs36Pz8N8nYf9DRLeUiIiIi6q+YH4uMLmkRXGf1uvVYvW59V26SiM6iKArmzjoXq9au69C4ckTUFGOIyJrujiEjby3MooOwz7gRclJ28/1nT4QzZQiCG1+GeXp/l+8/2snJQ2CbeRNkT2KL840T2xHc9ErLY1dGuUx3KPE7t3as3/TGXSqHkQT2asC6oobE754yFaKLE7/JSYk4U1rW7nJDBrff7/PR453vZpYoGvX0dzlRUQC9ogDY+zFg90BJHwk5cyyU9FGQ7O6wtmVWFNa39jWKDwP+ym4qdeTMmXQCi0ccwKU5r2PTqdn49PiFOFE9pIUlJRQjEcWOWdiQMQtCD8I49gX0TavgqjmJnDgDufE6htc+58YbGBanwxF+Jwz1ZAnIiTOQE2fg4sENrZGD+YC/XEa1R6DaA1S5Q88BO+rv+5kx3ddnEsE9HUNE0Y4xRGQNY4go8rq0RTCFj3c8ULh4zBBZwxgisiZiMSQrUMcsgTpqcautrfQDK6DtXAYYHCQVkKCOOg/quKWQ5OZXyYWhQ9v2Foy8NT1Qtu6RYDcxKz2IebXJ3+HxnbuIYELgmCnjPzvdWFVoxxclNmhm97X4VRQF582fh09XrGz3wsf1V1dg6fmtx5muA1+/OwO63ne6KyVqT6/9LifJkJOHNIwtnJDZZLYQJkR5YW3S9xDMksNAoBeVv4v8/CfFGDyo4bwsBHCofCQ+y78QW4rOgSnab49gFB2Cfmg1zJM7AdFwU48sCWR7DOTGh5LDIxN0jEoIPbu7tJlDiKYKVLuBKo/AsYCC37yUgH1lKiq16O6Sv9fGEFGUYAwRWcMYosZ4PERGN3xVJiIiIiKyyDSg73of5ql9sJ1zA+SY5GaLqCPmQx4wAsH1L0BUFPRAIXsJRwzsM26Akj6qxdlmZRGC6/8NUR7d/0dOReCctIbE74RkHXIn859VboHSBIEziQJlcUBFQOCPr3qgab0noWqzCcyd1XYX6CcKbEwCE/UWwoRZchhmyWHoO9+D5E6APGAkJLs71OVzyWFA8/V0KbuVzSYwMLPpzVmSBOQm7kdu4n6U+xPxzKql2OtbCMkV1+p26sZXFt5y6HlroR9eBwSqYQoJx6pVHKtW8fHJhta5siQwOMbAqAQdoxN1jK5NEOfEGVAs5GxtuoTESiCxUsIgCMy9MNSTw4kauUn30nvLVRyqUBHsxpuIiIiIiIg6K6xE8Mzp0wAAPr8f23fuajItXOs3be7UekRERETUf5glRxD46LewTb4S6pDpzebL8RlwLL4X+s5l0A+sANC/OruR04bDPuPGVi+o60c3Q9vyBqAHWpzf8wRcCuCxCbhVAY9qwqMKuG0CHlXAYxPI8hiYmx7EtDQNzk52CVoqAG+6idIEoDRBIHjWENQxqsCMaT6sXhdet67daeZ0H2I8bR/PB/PYLTRRbyW85TCObOjpYkRU1kANahtXmRKcZRD7PoB/1yooWROhDJ8DJWVoq8tL7gTYxl8EdcwSGPnboB9a1eL49qaQcKRKxZEqFe/nN0x3yAK5tUnh0Y2SxJkWxyDO8pjI8gSxOCtYP003gbxKBfvqxx5WsadMxfFqpcuHFyAiIiIiCkdYieD/vvwChBA4mHcY8y+8uMm0cAghMHDEmLDWIaIQ0xTYd+AgTLN/Xegm6iqMISJreiSGND+0jS/BLNgD29SvQHJ4msyWFBW2SZdDzhgDbeNLEL7yyJWtp0hyqOvsMedDkpo3dxJ6ANqW/8A4uqnLdqlKocRsXYLWo9Ylb+tem02n286e3zDd02h6Z1v1tuWMX8LqUw3j/DozdPz4h6VtrrN4YQ1Wr3MB3XzBXgiB6pqadn5DCSxe2H63WBs2ubquYERRgt/leq+hHRjX/MhRG2AaMI5vgXF8C6TELKjD50AZNAWS0vLNLZKiQh0yDeqQaTDPHIN+aDWM/G2Aqbe5r4ApYVepDbtKm2433m7Wtx6uSxKPSdIRZ+v8MaXKwMgEAyMTDFw+pOHmK68G7K8IJYe3l9rwyQkH8mssDHTcBRhDRNYwhoisYQwRRV5YYwQXHtoHADh0+AjmLlnaZFq4Moa33HVdf8M+0ImIiIjC4IqH/ZzroQwY0eJsEfRC++L10AXivsoVD/vMG6Gk5rQ42ywvQHD9cxCVp5tMHxRjYEKS1mKCti6R23i6+6wErqNnr1u3yasB64pCid9Vp+zYXaqe1QJL4Jc/LUb2wLaTBn9/KgFrN/R8q+DZM7246/byNpfJP6HiwUdS0d2JayKijrr5hnIsXtB6l/Zl5TLu+UF6yzPtHqjDZkLJmQ3Zk9juvoS/GvrhdTCOrIeoaftGn44R+OcvTyNLMRFTIyHGi/pnWXTt5+zuUhUfnnDgg3wHdpw5+3xFRERE1H8wPxYZYbUIbil5y4QuUWTZbDZc/aUr8Np/34KmtX/HNRE1xRgisqbHY8hXgeCKJ6CMmAfb+EsgKU2/zkp2N+zn3gw9Yyy0rf8BNH/ky9iN5IwxsJ9zHSRHTIvz9by10La9BRjN35v5GQH89tyqbi5hZOgmsKXEVt/i94sSG7Q2x2aU8MnnHtxyQ0Wb273p+grs3utARWX3Zb0lSUJmejoKTp1qsVVwQryBG69ru5wA8PHnHjAJTP1Rj5+HqFWxMW13uXz8RBvd2QdroO/7BPr+zyBnjIGaO7fVm74AQHLGwDbmfNjGnA+z5Cj0/C0w8rcD/spOll7C/tM2OMcFUJJU99ksIAnA7QNiaoCKgzZUH7FhdIKOwbFGp3u0GJukY2ySju9NqEGhV8aH+Q58mO/A6lP2iIwzzBgisoYxRGQNY4go8sJqEUxdj3c8ULh4zBBZwxgisqY3xZAUnwH7jK9CTshscb5ZUwptw4swSw5HuGTdQFagjr8YtpELW5wtND+0za+22RL6y0N9+Nvczl4g73l7ytRQi99CO9YV2VCtNe8Suy1Oh4m//O40nI62f/5s2ebEH/6aiO5KsiqKgvPmz8OnK1bCMIyz5grc+60yTJnU9g0MPr+Ee+4bAH8gvP8Dor6gN52HqKl7v30GUya2Pib92g0u/P2p9lv71pHiBoS6jR48HZLN0e7ywjRhFh+Ckb8VxokdQLD11skt+ebXy3DuOb5W54fOD0kAALcqMCK+affSoxN1pLk6P/5wtSbhswI7Psh34JOTDpR102c8Y4jIGsYQkTWMIWqMx0NkhNUimIiIiIiotxAVhQh8/AfYxl8MdeSCZvNlTxLsC78Ffd+n0Hd/AJhnJ92ig+RJgn3mTZCTB7c43yzND3UFXV3S5nZq9J5rPerVgBpdRo0uoUaT4NWl+r9rzvrbe9Yy1ZqM/eUKiv3WWun6AzJWrnZjyaK2f1xOmeTH0iU1eH95y62uu9PSJTXtJoEBYOVqN5PARNTrqO1cYWp270s7ROVpaFv+A23nMihDpkMdPgdybFqry0uyDGXACCgDRkBM+QrMU/tCYxEX7Ab01hPUdfS2Rw+A2mgMYa8uYdsZG7adadrKOdnRMP5wXZJ4VIKOmA6MPxxjE7h0cACXDg5AN4ENRbZQa+ETDhyt4uU7IiIiIuocy98kCw/tw+miYkyaNbdDy29a8QkyMzIwcMQYq7smIiIiov7O1KFtfxtG4d5Ql8nuhCazJUmGbfRiKANGIrjhBYiqop4pZyfJWRNhn3YNJLurxfn6gRXQdrzboSR3jdZ+IjhgoHkytj5ZKzd93ei5PrHbwnSfIcHs4vEVO+utZTGYeY4PcbFtt9i6/qpK+P0SPlvpiVDJgIXzanD9Ve232K6skvHWssgnqYmI2tNeIlXp7P08mh/GwVUwDq6GPGAE1Ny5kDNGQ5JavyFGkhUomWOhZI6F0IMwCvfAOL4FZuFewGy5oO0lsvUOnEfPBGSsOW3HmtP2hrJAYHi8gfOzArggK4Bz0rR2u5VWZWB2uobZ6RoenV6NfeUKlueHxhXeUmLjuMJERERE1GFdckuhFOb3TyncFYionmmY+GzlKphG57ucIurPGENE1vTWGDKLDsC//DHYp14FJXtSs/lyUjYc538f2vZ3YOStiXwBwyWrsE26HOrwOS3OFoEaBDe9DLNgd4c3ubfchjtXxjdteXtWS9y2x9mNflVVCv71Yjzuuaus3WVvu7ECTqfA+8u7dixeIQTOnCltND6wwNIlHUsCA8CzL8Sjurr7xjAm6u1663mIAK2dRKnHY/U9EzBP70fw9H5I7kQog6ZAGTQZcsLANteSVDvU7ElQsydBBH0wTu6Ekb8V5ukDgGgok9vddvmCnRzGUEDCwQoVBytU/G23B8kOE4uzArgwO4AFGQG42xg6uc6oBAOjEry4Z7wXRT4Zy0+ExhVeWWiH3wjvHMUYIrKGMURkDWOIKPIsjxFceGgfioqLMfHcjrUI3rZ2FVKSk5A1cqyV3fYZXdUHevy1KpTEvn3hjoiIiKhDbC5IrrjW71bUAhD+yt7bVbSshlo2K63cs6lrEL7y3lv+KJCUaMDl6tjPIL9fQlmF0i3/3bICJMYbcDo7VhafT0JpGZPARNQ7JcSbbSZ7DRM4daobujhWVEB1QrK5wmt2bJqA7ofQ/IAeRHq6DqWNXvdramSUV3Rtt/ySFBpvOMYmEKMKqHJ4l+hMAXh1GdWahGpdAq+pExERUSQZZQIVr7TTLUwbOEZwZER0kJG42FikJCehoqJjd7tTxymJEtQ0jhNGREREBAQAFLc+2wEgBgB663cnE0Bp67MdADxA7y1/71cJGZUd/a2qAnJy9/1vV0BGRUfLYgPU1ofHJCLqUdWQUd3eOLvd8hlmAvDWPjpLRolpD22qNY7uKX+g9nGmsxtwhJ4kRPgiHxEREVGbX56otwj7O+KYUSMxdvToJtOcTieu+tIVra4jSRLi42JxydILIcsydu7ZE3ZBiYiIiIiIiIiIiIiIiIioY8JOBC9dcj6+f/e3mkyLjYnBn37zq3bXlSQJQgg88fSz4e6WiIiIiIiIiIiIiIiIiIg6KOxEcGVlJU4WFNS/zho4EKZpovDUqVbXMU2Bqupq7DtwEM+/8io2bNrcudJSq4wyATbD7x9kWUZuTg4O5uXBNPmeE4WLMURkTdTGkCRBcsYCdnfL8wUgAtVAoCb0IlLs7lC5WhrPuL5M1ZErTz/k8ZhIiLd2LBsmoGkShAkIEXo7JRmw2USL400qqgJD79igw+UVMmpq2A04UZ2oPQ/1E2mpBmy21s+jpgmcOq1CRPBU24QkAaoDks0JqI6Wz7+t0bXa8YT96JaB4zuoblxhT+3YwrYwxxUWQoLk8KCo0o+qIKAzjIjCwvMQkTWMob4llJei3i7sRPA///Uc/vmv5+pfFx7ahzOlpZg+f1GXFozCY2VAboouTocDV/30Bjz41KPwB7SeLg5R1GEMEVkT3TF0BnLGANinXxtKvrbAKDkCbcOLEDWdHqmvY2xO2KZeDXXQ5BZnC285guufh1lS1r3lIJQCGDTTiztuLYcciXyrBMR4PKiuqWnzngPTBP7xbAJ2rncD6LmEA1FvE93nob7v0hvKsXhB22P1PvlBAlava+XGrIgIAqgCVDuUzHFInTgR1e4xMETHLpEJYcIszoNxfCuME9uBoJWxiTun4VuKwPgkHRdmB3BBdgDjk8K/NrSlWMUH+U58eMKBfeUKQqMNE1FreB4isoYxRBR5UtrQXEsp++/f823U1HjxxNPPdFWZ+pWYGA/ydmxFzoTJqK6u6eniEBEREfV9jhjYp18LJXNsi7OF5oe29b8wjm7slt1Lidmwn3sT5JiUFucbBbsR3PgyEOR3w0iaMM6PO28rR1xsz9+VXlkl48lnErBjl7Oni0JEFJaRuQH8+Idt30yVd8SGn/4yBb0j4SjwyEMlGDDQji2nZ2DT6VnYXzoWAh27M0iYBsxT+2Ec3wKjYBegB7q5vG0b6DFwQVYoKTw7PQhbmDc4Ha1S8GG+Ax/mO7C52IaA2RveIyIiIuqrmB+LDMv3vP/uz48zCUwUQU6HA7//1S/gdDh6uihEUYkxRGRNn4ihQDWCq59CcPNrEHqw2WzJ5oT9nOtgn3VL611Jd5IyYj4c593TYhJYmAa0bW8huPopJoF7wI5dTjzwcCo2bO7e5KssSxgzaiTkVpofr9/kxP3/l8okMFEr+sR5qA/bf9CO/JNtt6zNGaph1gxfhErUttkzfRg2RIPHVoO5WZ/ie1N/jt/MuwvXjnwWw+L3t7u+JCtQMsfAPvOrcF72KOzn3gx54ARAsUWg9M2drFHwzH43rvk4EaNfTcUdK+Px5hEnKoIdS+gOiTVw5xgv3rygDIevL8Lnl57BX+dU4BtjajAvI4BkR8/fLEXU03geIrKGMUQUeZZbBLfn/PMWYP7s2TCFiY8/X4GVq9d25+6iDu94oHDxmCGyhjFEZE1fiyEpNhX2GTdCTspucb7wVSC48WWYp9u/GNwmuxv26ddBGTiuxdlm9RkE1z8HUXrc2n6oS0yf6sMtN1R0S+tgRVWwaP48fLJiZZNxgiurZPzrxXhs+sLV5fsk6kv62nmoL1q0oAa33FDR5jI1Xgn3/yQNFZVKhErVXEK8gV8/WgSPu/XLYiW+VDzx/kIc1WdCThzY4W0LzQ/j5E4Yx7fAPH0AED2bQFUlgZkDNFyY7cfSQRqyPJ0fXuyUV8auUhW7y1TsLrNhV6mKI1UKTMHWw9Q/8DxEZA1jiBrj8RAZYY8RfLaLLliCn/7ofny+ejV++OOHm8x75KEf4eu33FT/+ms334Qnnn4Wj/76N1Z3S0RERERkmagqRuCTP0IdewHUUYshndVKU3LFwzH/LugHVkDbuQwwwh/DSE4ZCtvMGyG7E1ucb+RvQ3Dzq4Dm71QdqOtt+sKFfQfsuPyiasyf64XT0X33zvr8ElauduPt/8WgqqrnEiJERF1lzToXrv1KZZufnR63wG03VuAPf01Ez3QRLXDrVyvaTAIDgEcqwbFPVyIQWA0pNg3KoClQBk2GHJvW5nqSzQl1yHSoQ6ZDBKphnNgB4+ROiIpTEL4KtDlIfDfQhYTVp+xYfcqOX+91I2/5C/j93Zdg4YAqTE4JLymc7jaR7g5icVZDrypeDdhbbmtIEJeq2FOuwqtb7oiQiIiIiCyynAi+YNF5yBqYiQ2bNjeZPn7sGNxx680AgBMFBdA0DUMHD8Zdt9+Kjz/7HGs3dM+Ya0REREREYREm9F3vwzy1D7ZzboAck9xsEXXEfMgDRiC4/gWIioIObliCOnoR1LEXQpKbJ/iEoUHb9haMPPaY0xtVVSl44dV4vPFWLGaf68OiBTXIHtj5FlRnO3nSjvc/smPtehf8AV4oJ6K+wx+QsXK1G0sWtd2qY8okP5YuqcH7y2MiVLIGS5fUYMqk9m/AWrnaXf8ZLaqKoO/+APruDyAlZEEZNBlK9mTInpZv9KojOWKg5syCmjMrtB09AFFVArO6GKKqCKKqGGZVEURVUYRuCpOA9HH46/5E/L8v7Eh3GViSHcAFWQHMzQjC0Yl7ktw2YGqqhqmpDTfMmQI4UqVgd21yeFepDbvLVBR6ZfSO8aGJiIiI+gfLieBJE8YDAFauWddk+nVXfQUA8L/lH+Fr37oHQgj88uGf4NYbb8BXr72aiWCiTgoGg3jsT39BMNh8TEMiah9jiMiavhxDZskRBJY/BtvkK6EOPafZfDk+A47F90LftQz6/hVoszWPMxb2c26Akj6y5X1VnkZw3XNhJJWpp/gDMj753INPPndjZG4QM6b7kJujIStTgxrGryldB04U2HDkqBPHjo/Hw7/IQzDYdYllov6iL5+H+pK3lsVg5jm+drvYv/6qSvj9Ej5b6YlQyYCF82pw/VWV7S5XWSXjrWUtJ6lF+Qno5Seg73gPcsoQKNmToWRPguSMbXe7kuqAlDiwxa6mhb8KZlVtgri6uNHfJYBptLC18J0dQ6d8Cp474MZzB9zwqCbmZwZxTqqGsUk6xiZqSHZ2rvWyLAE5cQZy4gxcNiRQP/2MX8Kesqathw9UqNDZtTRFCZ6HiKxhDBFFnuUxgndvXIfY2FgMGt10vLO1n3yIIYMG4eIvX4OtO3YAANJSU7F93SrknziJcxYssrLbPoN9oBMRERH1PnLWRNinXgXJ0fKFaeP0QWgbX4LwlTdfN20E7DNugOSKa3Fd/egmaFveAHT+8I1mqiqQnaVhyCANg7I1xHhM2G2AahPQNQlBDaiukXE834ajx23IP2GDrvMiNxH1H9On+nDPXWUdWval1+Pw/nIPurelqMDSJR1LAgPAn/6eiM1bwhi3XZIhp+WGWgoPnADJ3nVjvgvThPCWQlQVN2pBXAxRXQTh7c6upgUy3CbGJuoYl6RhTKKOcUk6hsYakLvwrQoYwIEKFbtKVexp1Hq4IsgeM4iIiPoy5sciw3IiOH/fLlTX1GD01Bn10xITErBn83qUV1Q0mQ4AeTu2QFFVDBkzwcpu+wwe6BQut8uFZ/7+OG77xrfh9fl6ujhEUYcxRGRNv4ohVzzs069rtVWvCHqhffEGjPytoQmSHBprePRiSFLzC5dCD0D74j8wjm3qzlJTL9evYoioGzCGosu37yzFjGkd6+54yzYnnn0hHuUVXT9eekK8gVu/WtGh7qABYMNmJx5/MqnzO5QVyOmjQ0nhzHGQVHvnt9UOoQchqpsmiOtaEkNrHiNdEUNu1cSYBL221XAoOTw6UYPbcr+DTeVXy9hdZsPuUhW7alsPH69WINi1NPUgnoeIrGEMUWPMj0WG5a9oNV4v4mJjoaoqdD3UtdmMaVMBAF9s3dZseU1n92dEVsiKjIXz5kJWeGcsUWcwhois6Vcx5KtAcOWTUEbMg238JZCUpl+dJbsb9nNvgp45BvreT2CbehWU1GEtbsosPxnqCrqqKBIlp16sX8UQUTdgDEWXf78Uj9Ejg+12EQ2ExgweOSKA516Kx9oNLnRN62CB2TN9uPG6CnjcHWsHUVkl498vxVvbrWnALNgFs2AXNNUOOTUXctwASLGpkGLTIMemdqgb6Y6QVDukhIFAwkCcnUIX/upmYxFLRjXOPXeWpRjy6jI2l9ixuaQhwS1LAkNjDYyr7VJ6bGIoUZzhbv+9b012jInsmAAuzG7oWroqKGFPeaj1cGj8YRv2lavwG0wOU2TwPERkDWOIKPIsJ4IP5R3GlEkTsXjBfHzw8ScAgMsvuQhCCKzftLnJsi6nE3GxsTiWn291t0REREREESBgHFgB8/QB2Gd8FXJCZrMl1MHToA6e1uoW9ENroG1/GzC07iwoERFRr1NVpeDJZxLw/btLIXfgeq/HLfCNr5VjyaIafPK5B+s3uaBp4Sf4bDaBmdN9WLywBsOGdPz8a5rAk88koKqqC1sl60GYhbthFu4+q5DOUFI4JrVpgjg2FZLq6JJdS84YKM4YIGVo/TQTwOj/+wBiwfdhrzjVdCziqmIIbzk609W0KSTkVarIq1Tx9lFn/fQUp4kxtYnhcbUtiHPjdaidvP4faxeYkaZhRlrD+6qbwL5yFVtLbNh2JvS8v5zjDhMREREBXZAIXvbhckydPAm/+9UvMDxnGAakpeLyiy+CaZp4938fNFl20oTxkCQJx/NPWN0tEREREVHEiIpCBD7+A9TxF8E2cmHH1gn6ENz8KswT27u5dERERL3Xjl1O/OPZBNx1e3mH18kZqiFnaDmuv7oCq9a6sWefA0eP2drsNjoh3sCQwRrGjApg7iwvYjzhJzP/8WwCduxytr9gV9D8EKXHYZQebz7PFV+bFE6DHJsGqS5Z7EmCJFtPUgsBwJ0IxZ0IYHTTeXoQouYMRKAaCPogNF+orJoPIugDGj9rvtpl/IDur91wUyV+GSsLHVhZ2JDcdsgCI2u7lh6XqNV3MR1n79zodaoMjEsKJZpvrJ3m04FdpbYmyeEjVexWmoiIiPofy4ngZ557AV+54nKMGTUSD973PUhS6AvV08+90Kzl78UXLAm1FN7IcdGIOisQCOJ7DzyEQCDY00UhikqMISJr+nUMmTr07e/ALNwL+znXQ3IntL5o6fFQV9A1ZyJXPooK/TqGiLoAYyg6rVnvhsMhcOtXK8JaL8YjsPT8Giw9PzRmXFm5jOMnbPB6Zeg6oKqA221iUJaGxITOd0EMAM++EI81692WttFlfBUwfRVA0SEYjafLCiRPMqTY1FCCODYVUkzt3664Ltm1pNohxWd0al1RnzhunCz2N0sea0Efdmk+7CzxA4UN6wzy1HYrnaRjXGLo70Ed6Fa8JS4VmJ6mYXqjlsMVQQnbz9iwrUTFlhIbtp2xodAro2u6Iaf+guchImsYQ0SRJ6UNze3c7XaNuN1u3HnrzZg6ZTIqKyvx0aef4b/vLmuyjM1mwwf/fQNxsTG44+57sXXHDqu77RM4GDYRERFRFLK7YZt6FdTsSc1mafs/g75zGWAazdcjIiLqx2bP9OKOW8s71E10pJhmqCVwr0kCd5bNWZsUDrUklmJTG7qdtkWolbNForblcX0rY82HWKMMo+WTGGMvxFhHMca4SjHSUwmHbPlyJgDgtFeubzG89YwN28/YUBboRQcoERFRH8b8WGR0SSKYOo8HOoXL7Xbj/Tdfx9Irr4LX6+3p4hBFHcYQkTWMoaaUwdNgm3gZJGcszJoyaFv+03wMQKJGGENE1jCGot+EcX7ceVs54jrZ0rMrVVbJePKZCHYH3VOccfUJYntiJmYsuAAb9h6DcCd2SVfTkaZCxzCpEGOkYxgrH8V4KQ/jpcPwyB0fD7otR6sUbCtRsfVMqGvpnaU2eHW2GqYQnoeIrGEMUWPMj0WG5a6hiSiyZFnCqBG5kGX+CCHqDMYQkTWMoaaMY5thHP8CkicForoEAO+xpLYxhoisYQxFvx27nHjg4VTcfH0FZkzz91g51m9y4t8vxaO6OvoSoWHzV8L0VwLFeVBiPHj19V+FLrjW+Gq7mk6rTRTXtiZ2J4ZaEdtckHpT8+1aOlQcENk4ILLxljkHACDDRI5UgPH+LZjg24iJthMYmxCEvRNv75BYA0NiDVwxNAAAMEzgQIXa0HK4xIa95So0k59D/RHPQ0TWMIaIIo+JYCIiIiIiK4SAqC7u6VIQERFFjaoqBY8/mYQNm3245YaKiLYOrqyS8a8X47HpC1fE9tlrCROiuhiiuhhmYUsLSIBqh2RzAXZX6NnmgmSve65NFtc+GpZxNiwToRbHJmQcFFk46MjCm47LIPQAlP2bMKLkE0x0FGBSsobJKRpGxBsIN/egyMDoRB2jE3VcNzx080LAAHaXhloNb6vtVvpQhQLB8YaJiIiol+nSRHBG+gBMnzIFGRnpcLtckKTWv/z8/i9/7cpdExERERERERFRFNn0hQv7Dthx+UXVmD/XC6ej+3rW8PklrFztxtv/i0FVVT9oBdwlBKAHIPQA4CvvXL8nir0+QdyQLHY2TSw3SiI3m6d07tKlpDpgDpuDfcPmYE/JUTyftwbG+m3wyEFMSNbrE8OTknUMjjXC3r5DAaak6piSqgPwAQCqghJ2lIZaDW+r7Vb6RI0MMDlMREREPahLxghOSkzEb37+CJaev7jN5C8ASJIEIQQyc0db3W2fwD7QKVyKomDurHOxau06GEb4P1aI+jvGEJE1jCEiaxhDRNYwhvoup8PE7HN9WLSgBtkD9S7bbv4JFR9/7sHa9S74A72vm+NIi7oYUmy1yeJGrYwbJ449SVCyJ4fmtUMEqqEf2Qgjby1EzZn66ckOExOTNUxK0TA5WcOkFB1prq5ppV7ik0KthmtbDm87Y0OJn8dhNIu6GCLqZRhD1BjzY5FhORHsdrnw/puvI3d4DoKahj1792HyxAkIahq2bt+BtNQUDB08GABQVl6Bffv3AwCuvOEm66XvA3igExERERERERHVERiZG8SM6T7k5mjIytSghtEoVNeBEwU2HMyzYcMmF/YftIMtMvs4xQ5l0GSoObMhJ2V3aBWjcC/0vLUwC/cA4uykr0Cm28TklIZWwxOTNcTZu6bF+rEqBZuKbdhUbMPmYhv2lqkwBI9RIiLqf5gfiwzLXUPfeuMNGJE7HAfzDuMrX70ZRcXFKDy0D+Xl5bji2hsAAFmZmfjJ/T/AJUsvwKcrV+HxJ/9pueBE/VVMjAfb1qzCpNlz+eFI1AmMISJrGENE1jCGiKxhDPUHEvYfdGD/QQcAQFUFsrM0DBmkYVC2hhiPCbsNUG0CuiYhqAHVNTKO59tw9LgN+Sds0HUm1VrTJ2PICMI4sgHGkQ2QkgZBzZkNJXsSJNXe6ipKxmgoGaNhestg5K2DfmQ94K+qnSuhwKug4LiCZcedtVMEhsUZ9V1KT07RMS5Jg7MTvYwPjjUwONbAV4aFxhuu0SRsKQklhTcV2/BFsQ3lQbYa7q36ZAwRRRBjiCjyLCeCL1pyPoQQ+OVjv0NRcXGLy5woKMCd37kXfxe/w4P3fQ/bd+7CqrXrrO6aqN+KjY3p6SIQRTXGEJE1jCEiaxhDRNYwhvoXXZdw5KgdR462ntSj8PTlGBKlx6GVHoe2/W2oQ6ZDyZkNOTa11eVldyLk8RdBHXsBjJM7YRxaDbM4r/l2ISGvUkVepYr/HAl1Q61KAqMS9frupCclaxiVoEMNM4frsQnMzQhibkawftqBcqU2MWzHpmIbDlUoEGzZ3mv05RgiigTGEFFkWU4ED88ZBgD4dOXKphtWbc2W/dVv/4ArLrkYt990IxPBRERERERERERE1PWCXugHVkA/sBLygNxQt9GZ4yDJLWdpJVmBmj0JavYkmBWnoB9eC+PoJkDzt7oLXUjYVWrDrlIbnj8YmuZSBMYlNXQpPSlFQ05c+GNgjkgwMCLBwPW5of2XByRsru1KenOxDVtKbKjR2WqYiIiI2mc5EexwOFBeUYlgUKuf5g8E4PG4my17/MQJVFZVYfLE8VZ3S0RERERERERERNQGAfP0AQRPHwBc8VCHnQt12ExIrvhW15Dj02GffCXE+IthHN8C/dBaiPITHdqbz5BqW/E2tGCPt5uYmKxhcrKOKakapqUGkeIMb7zhBIfA4qwgFmeFWg0bJrCnXA21Gi4KJYePVSvgeNhERER0NsuJ4OKSEiQlJDSZdqa0FJnp6chIH4DCU6frp8uyDLfLBZfTZXW3RP2W1+vDvAsugtfr6+miEEUlxhCRNYwhImsYQ0TWMIaIrOnXMeSrgL77A+h7lkMeOB5qziwoA0a0urikOmoTx+fCPHMU+qG1ME5sAwyt1XVaUhGUsbLQgZWFjtopAkNiDUxP1UKPtFCX0nIYOVxFBsYn6RifpOPWkaH3stgnY1PtOMObimzYccaGgMnEcFfr1zFE1AUYQ0SRZzkRfLKgEAMzMpCSnISSM6UAgN179iIzPR0XLVmCp597vn7ZCxYvgqqqTZLDRBQe0zRxsrAQpmn2dFGIohJjiMgaxhCRNYwhImsYQ0TWMIYACBPmie0IntgOKTYVas5sKEPOgWRvveGKnDwE9uQhEJMuh350I4y8tRDVJZ0sgISjVSqOVql4/XBon7E2E5NTQonhabWPOHt4rYZTXSYuGhTARYMCAICgAewsDSWG61oOn/IpnSwz1WEMEVnDGCKKPMuJ4M1bt+KcqVMwY/o0LPtgOQDg7WX/w5JF5+GhH3wPDocdu/buxdhRo3Dvt78JIQQ+XbGyna0SUWtiYjzI27EVORMmo7q6pqeLQxR1GENE1jCGiKxhDBFZwxgisoYx1JSoKoa27S1oO5dBGTQZas4cyEnZrS4vOTywjVwI28iFME7th35oNczCPYCwltCo0pq2GpYgMDLBwPTUIKbWthweHh/eWMN2BZiaqmFqakML5vxqGV8U22q7r7Zhd6kKXbDVcDgYQ0TWMIaIIs9yIvh/HyzHt77+NVx1xRX1ieA333kPN1x9FWbNnIEf//C++mUlSUJRcTEe+9NfrO6WiIiIiIiIiIiIyDpDg3FkI4wjGyElZkMdPhtK9mRIqr3VVZT0kVDSR8L0lsE4vB764fWAv7JLiiMgYV+5in3lKp4/GJqW5DBrWwsHMS1Vw+QUDe4wr+xmx5jIjgngiqGhVsNeHdhWUttiuNiOzcU2lAbkLqkDERER9Q6WE8Fbtu9AxvBRzabfcPsduPfb38TlF1+EzIwMVFVV4dOVq/Dr3/8Bp4uKrO6WiIiIiIiIiIiIqEuJsnxom16Btv0dKEOmQ82ZBTk2rdXlZXci5HFLoY5ZAvPkTuh5a2AWHerycpUGZCw/4cDyE6FWw6okMCZRx/Q0DdNrk8PZMeG1THarwKx0DbPSNQBeAEBepYJNRQ3J4X3lCgC2GiYiIopWlhPBrfEHAvjV7/6AX/3uD921CyIiIiIiIiIiIqKuF/TCOLACxoGVkNNyoQ6fBTlzHCS55XF2JVmBkj0JSvYkmJWnoeethXF0I6D5u6V4upCwo9SGHaU2PL3PDQBIdxmYlqphelponOEJSRrsYQ4LnBNnICfOwLXDQ+U+USPjoxMOLM93YM0pOwImk8JERETRREobmit6uhD9GfvEp86IifHweCGygDFEZA1jiMgaxhCRNYwhImsYQxa44qEOmwl16ExI7oR2Fxd6EMbxLdDz1kCUnej+8p3FIQtMSK5NDKeEntNcnR/P2KsBKwod+OiEHR+ddKDIF2aWuY9gDBFZwxiiOsyPRUa3tQgmou4hyzIGZmTgYN5hmGbnv7wT9VeMISJrGENE1jCGiKxhDBFZwxiyyFcBffeH0Pd8BDlzLNThc6AMGNHq4pJqDyWOh82EeeZYqJXwie2AHohIcQOmhE3FdmwqrhvrWGBwjFE71nAoMTwmQYfSwWGB3TZg6aAAlg4KAKjC1hIVy0848NEJB3aWqugPXUgzhoisYQwRRV6XtggekJaG0SNHIDEhHqpqa3PZ1//7VlftNqrxjgcKF48ZImsYQ0TWMIaIrGEMEVnDGCKyhjHU9aSYVKg5s6AMPQeS3d3u8kKYEJVFMMvyYZblQ5Tmwyw/CRhaBErbnEc1MTlFrx9neFqqhgRH+JeLC70NXUivPmWHz+ibSWHGEJE1jCFqjMdDZHRJi+DRI0fgFw//BDOnT+vQ8kKIiCeCr/nyl/Dnx/5fu8tddeMtWLlmbYvzBg/Kxrfv+DoWzJ2NtLQ0VFfXYOfu3Xj+lVex7IPlXV1kIiIiIiIiIiIi6sVEdTG07W9D2/U/KNmToebMgpw8uNXlJUmGFJ8OOT4dGDI9tA3ThKg8FUoOl9YmiMsLAFPv9vLX6DJWn7Jj9alQq2EJAsPjDcxMC2JxVhDzMgJwd+AKcobbxE0jfLhphA8+HVhVaK9vLXyqn3YhTURE1BtYTgTnDB2Kt199CTEeDyRJQlDTcOZMKQyj+7+odIZhGDhTWtrq/EAw2OL0RQvm46nH/wS3O3RnX2VVFRIT4rFw3lwsnDcXL7/+Br57/4PdUmYiIiIiIiIiIiLqxQwNxtGNMI5uhJSYHWolPGgKJNXe7qqSLENKyISckAkMnQEAEKYBUVFYnxg2y/IhKgoB0+jWaghIOFih4mCFiucPuuFUBGanB7EkK4DzswIY6Gm/K1eXCizJDmJJdhBAFXacaehCevsZFaIfdCFNRETUW1hOBP/gu3cjNiYGp04X4Yc/+T988vnKXt23+8nCQkyfd15Y6wzKysI///JHuN1ubNj8Bb57/49w+MhRuN1ufOuOr+G+e76N6676Cg7mHcZf//FUN5WcqEFVVXVPF4EoqjGGiKxhDBFZwxgisoYxRGQNY6j7ibJ8aJtfhbb9HShDpkPNmQ05Li2sbUiyAikxC3JiFoBzQ9s1dIiKgobkcGk+ROUpQHTftVi/IeGTkw58ctKB+zcIjE3Ua5PCQUxO0SB3IKc7IVnHhGQd902swWmvjI9P2vHRCQdWFDrg1aMvKcwYIrKGMUQUWZbHCN65YQ2Sk5Jw9U23YvW69V1Vri5X1zX08RMnwk4EP/67x3DVly7H6aIizDl/KSqrqprMf+znj+Km669FZVUVps1diIrKyg5vm32gExERERERERER9W1yag7k9JGQE7MhJ2ZDcni6ZLtCD0KUFzTtVrrqNCAsXfLtkFSngUUDQ62FF2QG4bGFt0+/Aaw51dCF9MkadiFNRNSfMD8WGZZbBMfGxiIQDGLN+g1dUZ5ex+1y4eILlwAA/vXiy82SwADw578/iZuuvxZxsbFYumQxXnnjzUgXk/oRRVEwd9a5WLV2HQyje7sDIuqLGENE1jCGiKxhDBFZwxgisoYx1HPM4jyYxXn1ryVPEqTEbMhJ2Q3JYbsr7O1Kqh1SyhDIKUPqpwk9ALPsJEST5HAxgK5NDhf7FbyS58IreS7YZYFZtV1IL8kKIDum/VbKTgVYNDCIRQOD+H8zqrC7VMVHJxxYfsKOrWdsMEXvay3MGCKyhjFEFHmWE8FFRcVITkqEiMBdZj3hnGlT4XaFvoR9umJli8vknzyJ/QcPYWTucCyYO4eJYOpWLpcTr/77Gd4lQ9RJjCEiaxhDRNYwhoisYQwRWcMY6j1ETSlETSnME9trp0iQYpJDCeH65HAWJJsz7G1LqgNK6jAgdVjD/jQ/zLIToaRwXXK4uqSLagMETQmfFzjweYEDD24UGJ2g4/ysIM7PCmBaase6kB6bpGNsko7vTqhBiU/CRydDLYU/L7CjRpe7rKxWMIaIrGEMEUWe5UTw8k8+xW03fRXjxozGrj17u6JM3SolKQnL334Tw4cNhawoKCoqxqYtW/Diq69j7YaNzZYfNWJE/d/79h9odbv7DhzAyNzhGJk7vM392+022O32+tcxHk+TZwDQNR3+QABOhwOqreEtCgaDCAY1uF0uyErDl59AIAhN0+B2uyE3+lbl8/lhGAZiYpp2NeP1+mCaZrPp1dU1kGUZbrer2XRFUeByNXzxNE0Br9cLm80Gh6OhPqZhwuvzNasn69R1darT+JiJ9jr1xfeJdeq9dWr8udtX6nR22Vkn1qk76yTLoXVbOg9Fa5364vvEOvXeOjU+D/WVOjXGOrFO3V0nRQl1G3r2eSia69QX3yfWqffWqfF5qK/UqU+9T9UlcJheyGWha5ACEvxqDBA3EI4BwyDiBwJxGYDasJ+OkmxOKGnDoaQ1unYZ9MEoy4dUcRJq9SmgogDwlUF0QZ3ydeDv+4P48y4NWQkOLMz04bx0L+YO8CK2A11Ip7gErhvux3XD/QgawLrTdnxeFItPT7lxwmsD0DPvk00NLVPXcKhO1B97fTGeWKdeWafG56G+Uqe++D5Fqk5nf6enbpI2NFdYeYyZNkMUFReLNes3iKyRYyxtqzsfd9/3Q9FYaVmZ8PsDTaa99NrrImP4qCbr/e2fT9cv39b2n3j6GSGEECVnSttc7jd//LNozwuvvCbShuaKF155rcn03/zxzyJtaK74dMXKJtPvvf9BkTY0V+zdf6DJ9KtvulWkDc0VlZVVTabPXbJUDBs/qdl+h42fJOYuWdpkWmVllUgbmiuuvunWJtP37j8g0obminvvf7DJ9E9XrGyxnqxT19VpwszZzaZFe5364vvEOrFOrBPr1FfrdMEVVzarT7TXqS++T6wT68Q6sU59tU433/mNULmq+k6d+uL7xDqxTqxT99VJ0w2xr7BSTL72u2LINQ+JK/66Wox46H9i8P3vdclj4iMfiosee19kXvodcdevnxIVvmDX1kkLiJ/dcZF48qp0Yfx+vBAPx4X/eHyG8L/3I3HxOYPFNTfdEtH36YGHHxFCCLH/4KE236c6fenYY51YJ9aJdeqOOtXNSxva83nEvvqQ0obmdrhP54GZGS1OHzViBB7/3W9QVFyCvz/1NLZu34Hqmrab9Z8sKAwvY23R/DmzMX3qFPzvw+U4dPgwgkENsixjyqSJ+OF378H8ObMBAE//+3k8+MjP6tf77S9+hhuvuwYFhacwefa8Vrf/o+/fi+9+6xsIBIIYNHpcq8u11CJ4+7rVmHjunPr/s950R0bd9L50l0m018k0Bd5/83VcdeMt8Pq8faJOffF9Yp16b53cLjf+8+Jz+PINN6GouLhP1OnssrNOrFN31gmQsOyNV1s8D0Vrnfri+8Q69d46NT4PlZw50yfq1FhfeZ9Yp95bJ0mS8d7rrzQ7D0Vznfri+8Q69d46xXg89eehM6WlfaJOffF9CrdOQpKBmDSI+IHQ3GmhcYcTMgHZcmeQgGkA5fmQig/CLNwLf9FROB32LqqTGzmxGhale7Eww4upSX40Wr1DSv0yVpx249NTbqwqcqEiIHfr+6TICt557WV8+Yab4A/4W6hT/zr2WCfWKewWwTEx9eehsvLyPlGnvvg+RbJF8PZ1q9lVeDcLKxFccLBrun4WQmDgiDFdsq2uIEkSnv37X7F0yWIYhoHZ51+II0ePAej6RPDZYmI8yNuxlQc6ERERERERERERdQ1ZgRSfUTvWcDbkpGxI8RmQZMXSZoWvEsbp/TAL98I4vR8IettfqYMSHSbOywxgSVYACwcGEW/v8GVrAIBuApuLbfi8wI5PCxzYcUaFQAcGJyYioh7B/FhkhHWPlSRJXfKoG1uutxBC4Ke/+jWA0HhDSxadVz+vrpVu4zssWlI3v72W0ERW2Ww23HD1VbDZbD1dFKKoxBgisoYxRGQNY4jIGsYQkTWMoX7ENCDKTsA4vA7aF68h8NHv4H/zAfg//gOCX7wB/cgGmOUFEKYZ1mYlVxzUIdNhP/cmOC//GRyLvgN17AWQkgYDkrWka1lAxn+OuHDnqgSMeTUVV36YiL/vdiOvsmPJa1UGZg7Q8MDkGiy/uBS7ri7G3+ZU4KphPqQ6DUtlq8MYIrKGMUQUeWG1CM7KzOyyHZ8oKOiybXWV3ZvWIyU5CU8/9wIe/OmjAIC7br8Njzz0AABg8Ojx8AcCLa77j7/8EZdffBH27NuHhRdd1uF98o4HChePGSJrGENE1jCGiKxhDBFZwxgisoYxRM0oNsgJAyHVthqWE7MhxaVBksJvyCMCNbWthffBOL0P8Fd1WTGHxeo4PyuAC7IDmJGmQe1EO6MdZ9T61sKbi23QzPAT14whImsYQ9QYj4fICGugiN6YvO1u+w4cqP971MgR2LZjZ4vLjRoxAgCw/+ChiJSLiIiIiIiIiIiIyBJDg3nmKHDmKOrbzKqOUHfS6SOgpI+GnJjVoU1JDg/UQVOAQVMAAGbZCRin9sEs3Bvahwiv9XFjh6tUPLlXxZN7PYizmThvYBDnZwWwaGAAiY6OtXOakKxjQrKOe8Z7Ua1JWFVox6cFdnxe4MDxamtdZhMREfVWYSWC+7LBg7KRkpwEADief6J++sbNX8Dr88HtcmHhvLktJoKzMjMxMnc4AODzVasjU2AiIiIiIiIiIiKirqYHYBYfgll8CPrO/wHOOCgDRkLOGAVlwEhIDk+HNiMnZoWSyKMXQ2h+mKcPhBLDp/ZCeMs7XbxKTcZbR51466gTiiQwPVXDooEBLMgMYkKy3qFtxNgElg4KYOmgAIAqHKpQ6lsLrz1lh8/g2MJERNQ3dEkiOCbGA9MU8Hq9bS7ndrshy1KvbOL98I/uBwAYhoGPPv2sfrrX58OyD5bjqi9djltuuA5P/fs5VFVVN1n323d9HQBQVVWN95d/HLlCU79kGiY+W7kKptH5uyiJ+jPGEJE1jCEiaxhDRNYwhoisYQxRp/grYRzbBOPYJmiSBClxEJSMUVDSR0FKGtShbqQlmxNK1gQoWRMAAGbFKRin9sI8tQ9m8WHA7FgC92yGkLC+yI71RXb8YiuQ6jQwPzOI8zKDmJ8ZQIqzY62Fh8cbGB7vw9dG++A3gA2nG1oL7ytXAIQSw4whImsYQ0SRF9YYwS256IIleOrxP+HtZf/DN777/TaX/dcTf8OSRQtx613fwoeffGplt2HJHjgQ/3z8T3jxtdexcvVaHMvPBwBIkoQpEyfgvu/cjfPmzwMAPPvCi3jg/x5psv6grCx8/v678Hg8WLdxE+594EEcOXoMbpcLd33tNvzgO3dDlmU8+uvf4K//eCqssrEPdCIiIiIiIiIiIopKdg+UASMgZ4wOJYadsWFvQugBmEWHalsL74OoLumSokkQmJCs47zMUGvhaamdG1u4oEauby28stCOimAnNkJERM0wPxYZlhPBT/zp97jsoqW45ubbsGrtujaXXbxwAZ7/5xN485138a3v/cDKbsOSPXAgNq9qaOXrDwRQXV2DmBgPnA5H/fSXX38D33/wJzAMo9k2Fi2Yj6ce/xPcbjcAoKKyEh63G6qq1q/73fsfDLtsPNApXHa7Dfd84y78+e9PIBjUero4RFGHMURkDWOIyBrGEJE1jCEiaxhD1L0kSAmZUDJGQ04fBTl5CCQ5/LF3zapimKf2hRLDRYcAI9glpYuzmZiTEWotvHBgAFme8FskGiZwypaNl7dV4JN8FdvOqDAFu5Em6iieh6gx5sciw3IiePVHH2DIoGzkjJ+MQLDtk7LT4UDezq3IO3IU8y64yMpuw+J0OHD91Vdh2pRJGDt6NFKSkhAfH4dAIICCU6execsWvPT6f7Dpiy1tbmfwoGzcfecdmD9nFtLS0lBdXYNde/bguZdfwbIPlneqbDzQKVw8ZoisYQwRWcMYIrKGMURkDWOIyBrGEEWUzQk5LbchMexODHsTwtBhFufVJ4ZF5akuKpxAbrxR31p4VnoQzvBz1igNSFhRYMdnBQ58VmBHka8TGyHqR3geosZ4PESG5TGCMwakoaKyqt0kMBBqiVtRWYWM9AFWdxsWfyCAZ55/Ac88/4Kl7Rw7no/7HvpJF5WKiIiIiIiIiIiIqI/S/DBP7oR5cicAQIpLh5I+KpQUTs2BpLR/aVpSVCjpI6Gkj4QNl8OsKatNCu+FWXQQ0PydLJyEgxUqDlaoeHKvBy5FYOaAIBZmBrEwM4ARCc17jGxJkkPgS0MD+NLQAABgd6laP7bwxiIbgiZbCxMRUc+ynAgWAnC7nB1e3uV0QAhLjZCJiIiIiIiIiIiIKIqIylPQK08BBz4HVDvk1OGhxHDGaMgxKR3ahuxJhJxzLtSccyFMA2bJkYbWwuUFADp33dlnSLWteh0AYpHlMbCwtrXwvIwg4uwd2+7YJB1jk3TcPc6LGk3C6lM2fFbgwKcn7ThWbflSPBERUdgsn31OFhYiN2cYRo8cgb37D7S57JhRo+B0OnH46FGruyXqt3RNx4uvvg5d03u6KERRiTFEZA1jiMgaxhCRNYwhImsYQ9Rr6EGYhXtgFu4BtgJSTArk9NGhxHDacEiqvd1NSLICJW04lLThsE24BKa3DMbxrTCOfQFRUWCpeCdqFDx/0I3nD7qhSgJTUjWclxnEoiwNE5I6NmaxxyZwQXYQF2SHlj9SqeDTAjs+O+nAmtM2eHXZUhmJohHPQ0SRZ3mM4F898jBuueE6vP/Rx7jtG99uc9ln//5XXLD4PLzw6mv44Y8ftrLbPoN9oBMRERERERERERHVklXIqTkN3UjHp4e9CbPiFIzjX4SSwt6yLi1eitPE/IxQa+GFmUGkusywt6GZwI4zNmwssmFjsQ2bimwo9nN8YSLqX5gfiwzLtx09/e/nYZomLly8CH/9/WNITUlutkxqSjL+/sff4cLzF8E0TTz1r+es7pao33I6HPj9r34Bp8PR00UhikqMISJrGENE1jCGiKxhDBFZwxiiqGDqME/vh7b9bQQ+/H/wv/cogptfg3FiB0QHxwSW49NhG38xnJf8H+zn3QMlZzbg8FgumtPhwIMP/wrLChJw95p4jH89BYveS8IvtsRg7SkbtA7mhG0yMDVVwzfGevHsggrsuroE668owZ9nV+DGXC9GxuuQOtnNNVFvxvMQUeRZbhEMAN++8+t46AffhxACmq5jx85dOFEQ6n4jKzMTE8ePh6oqkCQJv3jsd3j8yX9aLnhfwTseKFw8ZoisYQwRWcMYIrKGMURkDWOIyBrGEEU9WYGcPARy+igo6aMhJw7s8KrCNGCe2h9qKVywC9A71sVzY+3FUIzNxNz0IBZkBnFeZgCDYsNvLVynLCBhc7ENm4rs2Fhsw7YSG3yG1OntEfUGPA9RYzweIqNLRqh//Ml/oqq6Gj/+4X2I8XgwbcpkTJ08CQAgSaGTU1V1NX7268fw/CuvdsUuiYiIiIiIiIiIiKg/MQ2YxXkwi/Og71wGOOOgpI+CkjURcvpISHLr3StLsgIlcwyUzDEQegDGyV0wjm+BeWofIDqfsG2sWpPxfr4T7+c7AQjkxBlYmBnEwswAZqUH4Q7janyiQ+D8rCDOzwolrDUT2HlGxcZie6hLaXYnTUREHdAliWAA+PeLL+O/77yHS5deiOlTJyM1NRVCCBQXF2PTlq1453/vM6NPRERERERERERERF3DXwnj6EYYRzcCDg+UrElQBk+FkjK0zdUk1QF18FSog6dCBKph5G+DcWwLzDNHgS7rkllCXqWKvEoVT+1zwyELzBgQai08I03DhCQN9jDyuDYZmJKqY0qqjrvGhKYdrVLqk8Kbiu3YX65AgK2GiYioQZclggGgsqoKL772Ol587fWu3CwRNRIMBvHYn/6CYDD87muIiDFEZBVjiMgaxhCRNYwhImsYQ9SnBWpg5K2BkbcGkicJyqApUAZNhRyf3uZqkiMG6vA5UIfPgVlTGuo6+tgWiMpTzZa1EkMBU8LKQgdWFobGRnUqApOSNZyTpuGctCCmpWpIdISXhB4Sa2BIrIGrc0JjJ5fXdSddbMOGIju7k6Zeh+chosjrkjGCqfPYBzoRERERERERERFR95DiM0OthAdNhuxO7PB6ZvlJGMe2wMjfAuEt774C1pIgkBtv4Jy0IKanhhLEw+IMS9tkd9JE1JsxPxYZcldvcGTucFx95Zfwza/fjm9+/XZcfeWXMGJ4TlfvhqjfcrtceOVfT8PtcvV0UYiiEmOIyBrGEJE1jCEiaxhDRNYwhqg/EhUF0He8i8B7P0Pgs8eh562FCLSfcJATBsI28VI4L3kY9gXfgjLsXLjikrothgQkHKhQ8cJBN76zNh7nvpWCca+l4JbP4vG33W5sLrYhGGZeuK476bvGePHMggrsuroEG75Ugr/MrsCNuV6MStAhdVlX2ETt43mIKPK6rGvoBXPn4P8e+CFGjchtcf7e/Qfw6K9/gxWr13TVLon6JVmRsXDeXMhKl9/HQdQvMIaIrGEMEVnDGCKyhjFEZA1jiPo3AbM4D2ZxHrStb0JOHwVl0FQomWMhqfY211TShkNJGw6YOl484gKyJgCHtwCG1q0lLvYreD9fwfv5TgCh7qQn1nUnnRrEtDQNSV3YnfTGIju2sjtp6kY8DxFFXpckgm+78av42U8ehCRJkCQJhmGgtKwMAJCYkABVVTFm1Ei8/OxTeOjRn+PZ51/sit0SEREREREREREREYXHNGAW7IZZsBua6oAycDyUwVMhp42AJLeRoJJVfLz3NDD5WjjHXQHj5E4Yx76AWXQQEGa3F9tvSNhQZMeGIjsADyQIDI83cE5qENPTQgninDC7k05wCCzOCmJxVhBADTQT2FWqYmNRbXfSxTYU+didNBFRtLKcCB4zahR+9pMHIcsytmzbjt/++XGsWb8ewWDobii73YbZM2fie9/+JqZNmYyf/fhBbNi0GXv27bdceCIiIiIiIiIiIiKiTtMDMI5thnFsM+CIgTJoMtRBUyEnD25zNcnmhDpkOtQh0yH8VTDyt0I/9gVE6fEIFTzUnfTBChUHK1S8eCg0LcVpYnpdYjhVw8RkDfYw8rg2GZicomNyio47x4Sm7StT8NphF9447MRpJoWJiKKK5UTwXbffClmWsfyTT3HrN74N02x651MwqOGzlauwYvUaPPv3x7Fk0Xm447Zb8N0f/sjqron6pUAgiO898BACgWBPF4UoKjGGiKxhDBFZwxgisoYxRGQNY4ioHYFqGAdXwTi4ClJMCpRBU6AMmgo5Lq3N1SRnLNTceVBz58GsKoZxfAuM419AVBVHqOANSvwy3s931ncn7ZAFJqaEksLnpIUSxOF2Jz0q0cD/Ta3GQ5Or8XmhHa/mufDBcQcCJruQpvDwPEQUeVLa0FxLo8FvWvkpBmZkYMaCxcg/ebLNZQdlZWHD5x/jREEBps87z8pu+4yYGA/ydmxFzoTJqK6u6eniEBEREREREREREVEjUmIWlEFToA6aAskV3+H1zNJ8GMe/gJ6/DfBVdF8Bw9AV3UkDobGF3z7qxKt5TnxRYgPApDARhYf5sciwPCJ3akoKKquq2k0CA8DxEydQUVmJ1JQUq7sl6rfcbjdWfLAMbre7p4tCFJUYQ0TWMIaIrGEMEVnDGCKyhjFE1Dmi7AT07e9A+uQx/OrCLOD4Zoigr9315KRs2CZdAecl/wf7/G9AGToDsDkjUOLW1XUn/eIhN767Nh6z3krB2NdScctn8fjrbjc2FdkQ6EBeOMEhcPNIH/53URlWX34G94yrQYY7/IQy9S88DxFFnuWuof1+P1wuFxRFgWG0/UGvKApcLhd8vvZPkkTUMlmWMGpELmSZd9kRdQZjiMgaxhCRNYwhImsYQ0TWMIaIrFFk4LoFE/Hje25B9cZXIWeMgTp4CuSMsZCU1i+1S5IMZcAIKANGQEz5MsyigxC+SkDzQ+j+0HPtA5of0M9+HQRgqWPPNrXVnfSF2QFMT9PaXD833sBDU6rxo8nVWFlox6t5Trx/3Amfwc8aaornIaLIs5wIPngoD1MnT8KlSy/EW+8ta3PZyy66EHabDTt27rK6WyIiIiIiIiIiIiKinmHqME/uQPDkDsDmhJI1ITSecNpwSFLrHXFKig1KxpiwdiWECWiB2qRxAELz1SaRA4Dmq00Y107X/RBa4+kNy4USyu0LmBI2FtmxsciOx3d7kBOn45ocH64a5kemx2x1PVkCFmQGsSAziMpgFd455sQrh5zYVMyuo4mIeorlRPC773+AaVMm49ePPowzpaVYtXZdi8vNmz0Lv370pxBC4J3/vW91t0REREREREREREREPU/zwziyEcaRjYAzDuqgyaGkcFJ2l2xekmTA7oJkd1najjBNQG8jkaz7a18Halsp+4CgD3mlx/DLrSp+vS0Gc9KDuDbHj4sG+eFqI7sQZxf4aq4PX8314XClgtfynHjtsAsnaxRLdSAiovBIaUNzLfUpYbfbsPztNzEyNxdCCGzeug0r16zFqVOnAQAZGemYO+tcTJs8CZIkYd+BA1hy+ZehaW13J9FfcDBsCpeiKJg761ysWruu3e7Yiag5xhCRNYwhImsYQ0TWMIaIrGEMEVkTbgxJsWlQBk2BMmgK5NjUCJSwewjND23X+zAOrkJdF9WxNhOXDfbj6hw/Zg7o2LV+UwCrT4W6jv7fcSe8OlsJ9zc8D1FjzI9FhuVEMAAMSEvDs39/HJMnTgAACNF0k5IU+kDfsm07bvvm3ThdVGR1l30GD3QiIiIiIiIiIiKivk1KGgR10FTIA8dD9iT2dHE6xSjci+CmVwB/ZZPpQ2N1XJ3jx9U5PmS10XV0Y9WahHeOOvBqngvri9h1NFF/xPxYZHRJIhgIJXsvXXohLr/kIkwcPw4pyckAgJIzZ7B95y689e4yvPfBh82SxP0dD3QKV0yMB9vWrMKk2XN5zBB1AmOIyBrGEJE1jCEiaxhDRNYwhois6bIYkiRAdUCyOQGbC5LqBGwOSDYXYHNCsjlanq7WTrfVTlcdXVe5DhL+agQ3vwKzYHfzakFgdrqGa3J8uGSQH25bx7Z5tErBq3lOvJ7nQj67ju7TeB6ixpgfiwzLYwTXqRv7l+P/EnW/2NiYni4CUVRjDBFZwxgisoYxRGQNY4jIGsYQkTVdEkNC1I7B6wdQjk43nZLkFhLEzoZn9ezXZyWSba5QQlq1d3yXzhg45nwN+qE10La/DRgN3UILSFh9yo7Vp+x4YEMsLh0cwDU5PsxKb7vr6CGxBu6fVIP7J9VgzSkbXs1z4d1jDnh1ubP/M9SL8TxEFFldlggmIiIiIiIiIiIiIqIIESYQ9EIEvQDKOp9QlhVAdZ6VIHZCsruhjjoPcnx6s1XU4bMhpw1HcP3zEOUnm82v0WW8kufCK3kuDI7RcVWOH9cM82FQbNtdR89O1zA7XcOvzpHw7jEHXstzYe1pGwS7jiYi6hTLieDCQ/twuqgYk2bN7dDym1Z8gsyMDAwcMcbqromIiIiIiIiIiIiIyArTAII1EMFQ16yNE8rGiW2wTbwM6vA5zVaT4wbAsei70Hf9D/r+z89as8GxahW/3R6D3233YOaAUNfRlw0OwGNrPXXtsQlcO9yPa4f7cbxaxmt5LryW58SxarZtIyIKR5d8akph3owjhbsCEdXzen2Yd8FF8Hp9PV0UoqjEGCKyhjFEZA1jiMgaxhCRNYwhImv6ZQwZGrQt/4FRuBf26ddCcsY2mS0pKmwTL4OcPgrBjS8BvopWNyUgYd1pO9adtuPBjSYuHhTAtTk+zMlou+voQTEm7ptYg/sm1mDd6VDX0e8cdaCGXUdHnX4ZQy1RHZBTc6Ck5UJOGw7JnQgIE8LUQzdmmAZQ+7eof914mg6YZv3rhmkGIBqWFY3XM0LzxFnbCi3XyrYa70e03Zqfeq+I3z5js9lhmjxgiDrLNE2cLCxkHBF1EmOIyBrGEJE1jCEiaxhDRNYwhois6c8xZBbugX/5Y7BPvw5Kxuhm85UBI+Bc8gMEN78G8+SOdrfn1WW8ftiF1w+7kO0x8JVhPlw73I8hsUab6507QMO5AzT88hxg2TEnXs1zYvUpO7uOjhL9NoYUG+TkIZAH5EJJy4WUmA1JVpot1tuPYmHoTRLGgc//ClFV1NPFonZE9JaZuNhYpCQnoaKiMpK7JepTYmI8yNuxFTExnp4uClFUYgwRWcMYIrKGMURkDWOIyBrGEJE1/T6G/FUIrvoHglvehDCat+KVHB44Zt8K27RrANXe4c3m1yj4w84YzPhvMi77IBEvHnSiKth2SsytAlfl+PHGknJs/nIJHphUjaGxethVosjqNzEkyZCTh0Idcz7sC74J5xW/hGPBN2EbfT7k5CEtJoGjgaSokGxOSA4PJFdcTxeHOijsFsFjRo3E2NFN7/hxOp246ktXtLqOJEmIj4vFJUsvhCzL2LlnT9gFJSIiIiIiIiIiIiKinmUcWgWz6CDsM2+EnJDZbL46bCbk1BwEN7wAUXo8jC1L2FBkx4YiOx7aKHDRID+uyfFjbkYQcht54SyPiXsn1ODeCTXYXGzDhiIb9pSp2FOm4lCFiqDZ29tZUtSTJEjxA6EMGA45LRdySg4km6OnS9X9zLZb8FPvIKUNzW19RPYWfP+eb+P7d3+rYQOSBCE6tom6Za+/7ev4fNXq8EraR9XdAZMzYTKqq2s6vZ1fqDKan3KpL5IVGbk5w3Aw7zBMo591oUHUBRhDRNYwhoisYQwRWcMYIrKGMURkDWPoLJIEyRkDyd5a604B4a+GCNYAYWUhmrLJAnF2gXi7gE0Of0NBQ0LABAKGVPsANCaHe0RfiiFJUUMt3xU7JNUOSJ3sgFeI0Di9QkCS6o5Lqbaf6NrXTaaHf+xKEJAhIMGsfQ69bvjbbJgmtTCtyToN09dU23C/v+0xvtvSVfkxalvYLYIrKytxsqCg/nXWwIEwTROFp061uo5pClRVV2PfgYN4/pVXsWHT5s6VllqVCWBoW7dFUd8hBIKH8jAYQJu3whFRyxhDRNYwhoisYQwRWcMYIrKGMURkDWOouUB16NEWSbI4+KkEBAERBIKd3IKj9lEvooNmUr2+FEPCADRf6NGPhFLCEkwAmVJ0J/P7i7ATwf/813P457+eq39deGgfzpSWYvr8RV1aMCIiIiIiIiIiIiIiIiLqfWQwERwNwk4En+23f34cNTXerigLEREREREREREREREREfVyUd6mu9+wnAj+3Z8fb3G6zWbDefPmImfYUASDQezYvQcbN39hdXfUigIAMC0MtEBRoy+No0DUExhDRNYwhoisYQwRWcMYIrKGMURkDWOoYyRFheSKBxRbywuYBoSvAkLvbEfPrZMlwK4IOGQBhwI4FAGHIqB0Q8bKEE3HHQ6YEoKGFB2X6WUZkqyG3iNFDf0tq50af7ZNQgDCgDBNwDQgwURachKKioogDB1CGIDZQ7EkSaGxfevG+D3reJUgoMKACgO22mdV0uunqTCg9JMWsQKh9JMpQl1D1/1d5GcqOBqEnQj2eDy4aMn5AIC3ly1DMNh8IOiJ48fh6b/9BZnp6U2mb9m2Hbd+49soLinpZHGpNQ/p/eMDh4AYpwt5772HJRMmozrAAdSJwsUYIrKGMURkDWOIyBrGEJE1jCEiaxhDHaQHAa0U6rilUEcuhCQ1H5BXmCb0vR9B37McEF18bbtJykICICHVaWB0oo7RCTrGJIYeIxJ0OJWu3bVuAnmVCvaVq9hTpmJPmQ17y1Tk18jokfaTkgwpNg1yQiakhEzI8ZmQEwZCcsVFviytEIYO4a+E8JZD+MpDNwn4KgBvRdPXVo8TWYGcPARyWi7cA4YgIzkWA5UypOP/t3ff4VEVexjHv9tCCi0BEnqTDoKgiCCCNBEU6dJ7b6KCIAqigh2vlSodARERURFFpfcO0hHpvQZIIdvuH2GXhISWA0nYvJ/nuQ9kz+4655rXOWd+M3POk8N0jhym+H+GmG7z7OtUxOGCSIfpjv4XdYfvu/7+2OKvPLjuuhBcpVJFvvzkQ7bv3MXsuT8lOJ41SwgzJnxDcHBmTDfMHin3SBmmfTOGZxs2SXKDRUREREREREREREQkFXM5cWz7FdfJ3fg93gpTYOZ4h01mM7aStbGEFSVm7be4I87d1+acibZw5oSFZSfSeV+zmNwUzOikhLdAbKd4Zgd5MyS94Gg1Q9HMTopmdlI//1Xv65diTOy6aGXXhdgCsefvl+0Ji+RJ5heIOdO1gm/mawXfjGGYbrYyO5UwWayYgkIgKOSm73G7XRB95VpR+FpxOPLiDT+HgzOGAIubsEAnuYLc5MyWidxZM5Izk42cgXZymI+R3fQPWUyXk/EME+dwwckoMyciLJyMMnPFfvdFWk9hN8YF2qhZbuauC8EVyj8GwI8//5Lo8d7duhISEozb7WbWj3P5duYsIiIjada4Ed06tqd0qZI8X6c2vy74w1jLRdKwy5cfnNlIIqmRMiRijDIkYowyJGKMMiRijDIkYowydHdcp/8leuEn+D36IpY8ZRIcN2fNT7pn+mPf9CPOQ+uTtW1Ot4l94Vb2hVuZF+f1DDYXxTI7KB58ffVw8cwOMvolfc/njH5uKoTaqRAaf4fVs9EmTkdZOBlp5lSUmdNRZk7F+9nCqUgzV11xinwmE6b02eKs8s2FKXMOzIHBSW5fYtwuJ+7LZ3CFH8d98TjuyIuYAjJgCsiMKSATpoDMEJAJU0BGTOZ7vLT6BiaTGVtAINkCYggzRRNqiiLMZCO7yUKYyUwoEGZyEWa6RCZTVJxPnr+v7boZ57Ui7/EIC8cjY/88EWnmWJw/z0SbcbpVvJX7zxRaoPBd/ddr3qwZlC9XlurPvcDuvfsSHP9n7UqyhISw8O/FtO/eM96xzz/+gGaNGjJv/m907/uqsZb7iPTpg9i/bTMPlS7LlSvaTkREREREREREREREfI8l/+PYyjbCZEuX6HHH4c3YN84Ge1Six1OWm9xBrnirh4sFOyiU0Yn1Hi7qvZkLdhunnRk4QzCnLaGcNmXltDuz93+nCOa0OzNR+Cfp+90xkbguxhZ8XeHHYv9+6RQ4Ez4aNAGTCdJliC0OB14vEsf+nOn6z1a/xD+Oi6xcItR0gbC4/+P630NNF8hmupSkc7vXblbkPR5p4XhE7J+no1TkvROqjyWPu14RHJYtGw6Hkz37/k1wrGjhQmTNkgW32834KVMTHP9m0lSaNWpIqRIlktZaEcFisfBUpYosX7Uap9OZ0s0ReeAoQyLGKEMixihDIsYoQyLGKEMixihDxjgPrsN19j/8KrTGnCVfguPWvGUxZ82Pfe10XGf2p0ALb8XE0QgLRyMsLDx6vZCdzuymcOb4zx4untlBWOC9fe5xsM1OsO08RTkP3Pz/m8vugGvF4WBOE6dQ7A7mDJk55crEqctOwi+ejy36Xiv+uqMuJr1xbjdEX4p9zu+FI3EPkMnPTY5AF2EBTrJnsJAzUzpK5A3BL/ocof52svtFk80aic10j58TnUSeIu+JSAvHImL/9BR3VeSVB9Vdrwjev20T0VdjKFn+iQTHmjdpxGcfvs/VmBgKl3kUuz3+bBGLxcKR3duJjIyiUJlyxlruIzTjQe6WfmdEjFGGRIxRhkSMUYZEjFGGRIxRhkSMUYbuEZMZa8naWIvVxGROuJzW7Xbh2L0Ix47fwfVgFtyz+rsontnu3V66WObY/wXc9dK8+yPSDqeiLJyKit2C+lRknL/H+flijInEnj0baHUTFuAkR6CL7IFOwgJcZA90kSPO38MCnKnmfCF2K/DTjkCORVg4fsnB8St4i7snIswcU5E32em/qcnjrmNotljIkD59osdKlyoFwL5/9ycoAgM4nU4uhl8iY4bEPy8iIiIiIiIiIiIiIj7M7cKxfQGuk3uwVWiFOSgk3mGTyYyteE0sYUWJWTsN9+UzKdTQpDsbbWb5yXQsP5kOrOkwh+TDmi0fBcMyUSKLm2K2ExQ2HYvd9piLhJoukM7kSLb2BdqggM1JgYy3LrRHO+H0tWcVRzsg9FqR18jzku+HKJeFU44MsSueTVk4bcrK8ZhAjl+M5tipCxw9fpJTF6NU5JU06a4LwWfPnSNXjhzky5uHQ4ePxDv2WNlHcLvdbNn2z00/HxQUSGRUatzjX0REREREREREREREkoPr7H9cXfgJtnJNsOZ7NMFxc0ge0tXqh33LTzj/W5MCLUyigExYshbAnLUA5iwFMGXO5V35fOja/xYkqPm6yUQEoabYonAYF679/aL3NU/BONAUk2yn4m+BvOld5E2fMls32114VyifjDRzMsrMyTh/PxUZ+3zeS3bPyuVo4Ni1/91IRWBJm+66EPzPjp3kypGDNi2aMfyjEd7XC+TPR6kSxQFYvW5dop/NnTMnfjYbBw8eSmJzRcTlcrN77z5crtQ160rkQaEMiRijDIkYowyJGKMMiRijDIkYowzdB/Zo7Gu/xXViJ7ZHm2Ky+cc7bLKmw++xZjizFydmw/cQk8q2jzWZMGXMHlv0zVow9s8bVjjf4RcRTnrC3enZ584NgOvKOdzhsc/xdV08jjvcivsKpLe5yB7gIizARVigM3aVboCLsGvbMXt+zpDKVu3G5XLHrpo+GRm7FfXJSMu1Iu/1v5+KNHM22oxbBVwRQ+76GcH16jzLuK8+x+Fw8MGnn/HHX4vImSM7b7/xOiWKFSUiIpIylZ4iIiLhf5CbNKjPVyM+4uf5C+jW95V7dhIPMu2BLiIiIiIiIiIiIiJpnSkoBFuF1liyFkj0uDsqnJh1M3Cd2pvMLYvDYsMckvd60TdLfkx+AYa+0u2IwR1+Ate1oq/74nFc4cfBHm3oewOtbkIDnIkWicMCXYQGxD7PNzjdvS0YX7xqurZi1xKv0Hsi0sypa6+djjLj0DbNaZ7qY8njrlcE/7Lgd1avW0/Fx8szeEB/Bg/o7z3mdrsZM3FSokVggAbP18XtdrN2w8akt1gkjbPZbLzYsAHfz/0p0Wdxi8itKUMixihDIsYoQyLGKEMixihDIsYoQ/eXO+I8MYu/xlq8BtYStTGZLfGOmwIyka5qD+x7FuP4Zz64bv1823vCPwPmLAW8Wz2bgnMnaNfdcoWfxHX2P1xnD+A6fxj3lTPgvverdyMdJg5etnLw8q3f529xExpwvTCcPU7R2PNzaIATf0vs84LjFnlPXivunrj299NRFqKcNy/wxs0QypBIsrjrFcEAGTKkZ+Snn1CrejXva263m+mzZjNgyFDcifxHq0D+fKxYuACTyUTF6s9w6MiRBO9JizTjQe6WfmdEjFGGRIxRhkSMUYZEjFGGRIxRhkSMUYaSjykkH35PtMacPmuix10XjxGz5lvcl07ey38qpoyh17d5zpIfc4Zshr7R7bTjOn84tuh79gCucwchJvLeNPcBpAxJXPp9SB53vSIY4PLlK7Tt2oP8+fJ6nwu8Zes/HD1+/KafcdgdtOvWE7vdriKwiIiIiIiIiIiIiIgkyn3+EFcXjsBWtiHWAhUSHDdnzkW6mq9g3/Yzzn9XJu0fYrZiDskTf5vndEHG2h19Bde5A9dX/F44mjwrl0VEbiJJhWCPg4cOc/DQ4Tt675Fjxzhy7JiRf5yIiIiIiIiIiIiIiKQFjqvY13+H68RubI81xeQXGO+wyeqHX7kmOLOXIGb9TLh65dbfly4Ic5YC11b8FsAcnAeTxVCJBNel07FF33OxK37dl88Y+j4RkXvN2H/lRCTZuZwuFi9bjsvpSummiDyQlCERY5QhEWOUIRFjlCERY5QhEWOUoZThPLoF17mD2Cq0whJaKMFxS84S+NceQMy6mbhO7vK+bkqfDXO2AteLvxnDDLXD7XTgunAkzjbPB+CqtrO9G8qQSPJL0jOC5d7RHugiIiIiIiIiIiIiIrdhMmEtWg1rqbqYzJZE3+I4tAGTxQ9z1gKY/DMY+se5r0bgOnfweuH3whFw2g19p4hcp/pY8jCndANE5O74+dno37cPfn62lG6KyANJGRIxRhkSMUYZEjFGGRIxRhkSMUYZSmFuN47di7j69+e4Lp1O9C3WfI9hyV06SUVg15WzOA6uJ2bD90T//iHR84YQs2I8jt1/4zr7n4rA94AyJJL8tCI4hWnGg9wt/c6IGKMMiRijDIkYowyJGKMMiRijDIkYowylIhY/bI/Ux/pQpSR93O1y4r5wFNe5gzivrfgl+tI9bqTcSBmSuPT7kDz0jGAREREREREREREREXlwOGOwb5yN88Qu/Mo3w5Qu/S3f7o6Jir/N8/nD4IxJpsaKiKQcFYLvUlBQED06d+T5Z58hb+7cOF0u9h84yE+/zGfC1GnY7doeQkRERERERERERETkfnMd3070H4fxe7wlluxFr78ecf560ffsf7gvnQS3NkcVkbRHheC7kDtnTubOnEbePHkAiIyMxM/Pj7KlH6Zs6YdpXL8eTVq3I/yStpCQ+8dhdzB91mwcdkdKN0XkgaQMiRijDIkYowyJGKMMiRijDIkYowylUtGXiFk2BlNIPkzpgnBfPI476mJKt0oSoQyJJD89I/gOWSwW/vplLiWKFePkqVP06T+QZStXYTKZeKFuHT59fzgZMqTnz8VLaN2p6x1/r/ZAFxERERERERERERERkbRE9bHkYU7pBjwomjVuSIlixQDo1LMPy1auAsDtdjNv/m+8NngIALWqPc1TlSqmVDMlDfBPl47/ffAe/unSpXRTRB5IypCIMcqQiDHKkIgxypCIMcqQiDHKkIgxypBI8lMh+A692KghACtWrWbD5i0Jjs/9ZT6HDh8BoGnDBsnYMklrrDYrrZo1xWrTzu4iSaEMiRijDIkYowyJGKMMiRijDIkYowyJGKMMiSQ/FYLvQIC/P48/Wg6Av5cuu+n7Fi2LPfb0U08mS7tERERERERERERERERERBKjaRd3oHChh7BYLADs3rvvpu/zHAsLDSVzpkxcDA9P8B4/Pxt+fn7en9MHBcV+Jls2798ddgfRMVfx90sXb2ZMTEwMMXY7gf4BmC3Xa/hXr8Zgd9gJDAjEbDZ5X4+KjsbpdHq/1yMyKgqXy5Xg9SsREZjNZgIDAhK8brFYCPD3977mcrmJjIrEZrWRLt3183E5XURGR+Fni3+eOqd7d04ecX9nHvRz8sV/Tzqn1HtOQYGBQGyGAJ84pxvbrnPSOd3PczKbYz+bWD/0oJ6TL/570jml3nOK2w+ZzWafOKe4fOXfk84p9Z6T+dq9+Y390IN8Tr7470nnlHrPKUP66+NQFovFJ87JF/896ZxS7zlZrbHvyZY1S7x2Psjn5Iv/nnROqfecMmRID8T2QzarzSfOyRf/PSXXOXnuj+X+MoUWKOxO6Uakds9Ur8a08WMBqFa3Hjt370n0fc/WrMGUcaMBqPrsc4kWjfv37cNrffvcv8aKiIiIiIiIiIiIiIiIPADKVKzMyVOnU7oZPksrgu9A+vTxZxnfTGT09WM3W8X55egxjJkwMd5rwZkzc+HiRWONlDQjfVAQW1evoEzFylyJiEjp5og8cJQhEWOUIRFjlCERY5QhEWOUIRFjlCERY5QhuVH6oCAVge8zFYKTWUyMnZgYe7zXrlzRf/Dk7l2JiNDvjogBypCIMcqQiDHKkIgxypCIMcqQiDHKkIgxypB46Pfg/jPf/i0S9xfxxj3Z4wr0v35Ms1lEREREREREREREREREJKWoEHwHTp6+viw9e1jYTd+XPfv1Y1rKLiIiIiIiIiIiIiIiIiIpRYXgO7Dv3/04nU4AihUpfNP3eY6dOn2ai+HhydI2SXtiYmL45IuviImJSemmiDyQlCERY5QhEWOUIRFjlCERY5QhEWOUIRFjlCGR5GcKLVDYndKNeBD89N10Kj5enuWrVtOkdbtE37N28V/kz5eXWXPm8tJrA5O5hSIiIiIiIiIiIiIiIiIisbQi+A59/+NcAJ58ogLlypROcPyFunXIny8vALPn/pScTRMRERERERERERERERERiUeF4Ds0a85cdu7ejdlsZsKor3mqUkUATCYT9eo8y6cfDAfgryVLWb5qdUo2VURERERERERERERERETSOG0NfRfy5MrFjzOmkjdPHgAiIyMxmc0E+PsDsG37Dpq0bkf4pUsp2UwRERERERERERERERERSeNUCL5LQUFB9OzSiedq1yJv7ty4XG72HzzA3J/nM2HqNOx2e0o3UURERERERERERERERETSOBWCRURERERERERERERERER8jJ4RLCIiIiIiIiIiIiIiIiLiY1QIFhEREZE0K336IPz8bCndDBERERERERF5wJjNKrFJ6qffUhERkQfUi40aUrZ06ZRuhsgDq8HzzzH09YE0b9KYDBnSp3RzREREREREkk2dZ2oRmi1bSjdD5IFVu0Z1Xu3Ti0IFC6Z0U0RuyZrSDRCRtClb1izky5uXbFmzYrVYWLRsOVFRUbhcrpRumsgD4ZPh79K6+YvMnjuPmIl2duzaldJNEnmg9HupN6/27onZbOa7H+awbOUqLl++ktLNEnmgWK1W8ubJjdPpJDr6KqdOn07pJok8MPLlyUP27GGEZM6M3eFg9br1xMTEYLfbMZlMuN3ulG6iSKqWPn0QeXPnIXOmjLjdblavW5/STRJ5oHjGFEZ+M56xEyZx5uy5lG6SyAOlb8/u9OramQzp05MlJIT3PhnBlSsRKd0skUSpECwiya5Dm1bUfaYWFR8vj8ViAWDTlq1M/342v/3xJxfDw1O4hSKpW+9uXWjd/EUAmjasj8vtYvzkqWzfqWKwyJ14581BdO3QDrfbTZ/+A1m3YSOHjx5N6WaJPFBavtiEGk9Xpe4ztbh0+TJOp4shw4bz64I/uBoTk9LNE0nVenXtTKMX6lGsSGHvdoJbt+9gxerVzPx+DvsPHEjhFoqkbm1aNKPm01V5uspT2KxWbyH485Gj2bh5C1HR0SndRJFUrd9Lvb1jCr26dMbtcjNu0mQVg0Xu0LuDB9GlfTsABr39Lhs3b1ERWFI1FYJFJFkNHTSQ7p064Ha7uRh+iStXrpA1SwjlHilDxowZsZgtzPxhDg6HI6WbKpJqHTt+HKfTyZmz58geFkqzRg1xu1xMmPqtisEit9G5XRu6dmhH+KVLtOvWk7XrN3iPWSwWnE4ngFZjidzC4IH9YwcNr2XE4XASEpyZTz94j8ioaBYs/DOFWyiSennuh1wuF3v2/Yvb7SZP7lyUKVWSMqVK0qxRQ3q+0p/lq1andFNFUqUhA1+jZ5dOuN1ursbEcOFiOIGBATz5RAXSp0/P+598yrKVq1K6mSKpWlRUFADHTpwgV44c9O7WBbfbzTeTp6gYLHIbvbt1oUv72DGFjj16s2rtOu8xs9ns3e1SYwqSmqgQLCLJZvDA/nTv1IHo6Gj6vzmE7Tt3ERkZxUMFCzDmi/9RqGABWjRtzLz5v3Hp8uWUbq5IqnXyVOzWmwv+/IsLFy/yau+eNG/SGEDFYJFbyJcnD00bNQCga5+XWbt+AyaTCZPJhMvl8haBReTmPEUsh8PBgCFDOXzkKDabjU5tW1Oz2tO8/kpfVq5eo2s5kUR079SR7p06EBUVRbtuPdm4eQsWq4XcOXPycs8elH64JPnz5uXbCeMYOGQo8379TSsbReJ46/UB9OjckasxMbzx9rvs2bsPh9PJwyVK8NagAZQpVZKXenRjxeo1euyUyC38d+AgAH8vWcrVq1fp0r4dfbp3BVAxWOQWSpcqSbuWzXG5XLTv1pM118YUANxud7y+x1MEVkFYUgMVgkUkWbR8sQm9unTGbnfQtG0HNmza7D125Ngx2nbtwfQJ4yhbpjRNGtRn4rRvU7C1Iqnbvv37OXP2HOXLleXFth3IkD6ILu3b0bxJY0wmE+OnTPMWg3XBKXJdwQL5ebhECX5Z8DvLVq7CZDJhNptxOp3kzpmTmtWf5sknKpApY0ZiYmKYMXsOm7du5cTJUynddJFUoVPbNnTv1IGrMTE0bdOe9Rs3eY+dOHmSksWLExAQQLp0fqA6sEg8WUKCea52LQA69uwTb8Xvrj17GfjW29Ss9jRtWjTj8UfL8dGwd/Dz8+O7H+YQE2NPoVaLpB5tWjTzFoGbtG4Xb0xh6z/biYiM4NP3h/PkExWo80xN5v++MAVbK5K6HTh0iMjISArky8cbbw8jS0gIjV6oR5/uXTGZTNomWuQmCj/0ELlz5eKbyVNYs34DZrMZk8mE0+kkLDSUUiWK8+QTFfCz2Th24gSLly1n9959Kd1sERWCReT+e6hAAV5s1BCAvgMGsmHT5njFKZPJxO49e9mxazcVHnuUsNBsKdlckVTv7Lnz/PvffxQqWBC3281bwz8AoEv7djRr3AjAWwxWEVjkutKlSmIymdi1Zy+AtwhcvGgRRn/+KYUKFvQ+ux6gxtNVmfvLr8yaM5elK1amVLNFUoWsWUJ47tlnAOjTbwDrN26Kdz0Xfukybtzs3rsXp9OF2WwmKCiQy5evpGSzRVKNnDly8Fi5spw6fYYduxLu3nIxPJx583/j0JEjDHj5JSpXfIJhQ97k0qXL/PTrfE3ukzStRLGitGzaBIBer/ZPMKYAsGnrNk6ePk2BfPnImT17SjVV5IGw99/9/HfwENmyZuHQkSO8P+J/OJ0umjasT+9uXQAYN2kKZ86eTeGWiqQuVZ6sBMCOXbsBvEXgYkUK89mH7/NQwQJkSJ/e+/7X+73Cux98zG8LF2qCuaQoc0o3QER832PlHuHxR8vx99Jl3mf1xL1hc7vdXLp8mc1btwFQtHBhzGYzZrP+EyVyI08u9v27n+xhoVSs8DgAwz8ewbhJUwBo1rgRndu3pWjhQgB8NeJjPhn+bso0WCQVyZwpEwD+6dIB4HQ6KVq4EHOmT6No4cKs27iJ8VOmMnvuPNZdW+n4Qt069OramacqVUyxdoukBvnz5qXi4+W5cDGcnbv3ALHXcHEnT0RERJI1SxbefK0f876bzs+zZvDatYKWiECM3U74pUtcuRKR6HG73c6GTZt5/5P/sWrNWvxsNr745EMefaSMisCSplV8/HFKlSjOL7/97p2cd2MmDh0+4r1+K5A/P4B3u04Ruc5sNuN2uzl2/ATFihShdMkSHDt+gs9GjmL23HlA7DNQu3VsT6aMGQF4763B9O/bJyWbLZIqpE8fBMCViNhrOc/E8p++m84jpR/myNFjLF62gt1793L0+HH8bDaGDhpI2xbNyZY1a0o2XdI4rQgWkfuuUoUKmEwmFi9dzrnzFxIcN5vNuFwuwsPDAbD52fQ8H5Gb8GRj+arVdGjTiuxhoQDExNgZ/vEIALp2aEezRg1xu1xkypSJOrVqAvDFqDEcPX48ZRoukgrs3L0Ht9vtvQHLkCE9bw0aSHDmTEyZMZPX33rH+16z2cwH77xF2xbNqfh4eQ4fOcrGLVuJjIxMqeaLpKjAoEAArly5gp+fzfu659nagwf0p/BDBYm+epWSJYpjtVgwm80UKVSIOrVqMuqbCfzw07wUabtIamCxWPCz2ShS6CFqVKvKrwv+SPR9brebrdu388XosQQFBVHm4VJ8NOwd2nbtzvETJ5O51SKpQ+2a1bFarSxevjzRiRSeMYXoa8/U9qzG0gQKkYQ8YwqLli2jds3q5MqZk41btnLg4CE+/eprAJo2rE+vrp1xuVzkyZ2LBs8/x/nzF5g2cxanTp9OyeaLpKjTZ2JXyefNnRuIffTHp+8PJ1PGjEyd+R1vvD0Mp9OJn5+NRx95hF7dulCjahV6de3CkWPHmPH9DynZfEnDtNxORO67vgNe5+PPvmD+H4k/o8dzc3bkWGyByoRm7Yrcjufm6+nKT2Kz2bBYLNjtdj4Y8T9Gj58IQPMmjalTqyZ2u4N6TZtz9PhxzYqXNO3UmdOYTCZaNG1M1cpP4rA7KFW8GCvXrPUWga1Wq3cwceCQt5k+azZWq5VWzZrycMkSKXwGIikn5moMAHly56JxgxfIEhJMYEAA6dMH8eUnH9G4fj2uRETw4f8+p3OvPtRr2pypM7/DcW2rtM7t2/JE+cdS+CxEUs4/O3by1+IlQOxuE7lz5rzpe10uF+s3bmLuL79yMTycHNmz81jZsoBWOEra9GLbDoydOJlFS5ff8n2eyRLKicjtnby2TW3c67NDh4/w+ajRzJrzIwB9unelwfPP4XQ6ade9p4rAkubt3B27JXTzxo0oWCA/IcEhlChejAV//sXAIW/jdDqxWq3ExNhZvW49bwx9l8XLVmCzWRk6aCC5cuZI4TOQtEqFYBG5rzzbBX42cvRNLxg9hWCXK3ZFiX86P0wmk/eznq1wAwMDyZol5H43WeSBsGnrNvbu+5d8efPgdDpxOp2YzWair15l+Mcj2LNvH3a7A4BlK1dy7MQJQLPiJW1btmIVP/06H4AenTvSrWN7QrNl46/FSwGw2Ww4HA5cLpe37xn20Sds3xn7LMeq154HpMFFSYvWrN/A5OkzAejVpTPffPUF30+dxNwZ39K0YX0uXb5Ms7YdGDthEn8uWsKmrdsYOORtRnzxFSaTiRJFi1K2TOkUPguRlON0Otl07VE4T1WqSNXKT8bbWv1GUdHR/PDTPA4fPUpIcGbqP18X0LWcpD1Wa+xmhm+//+FNxxQ8KxyjoqLive65nvP86Z8uHRkypEdEYq/tLlwMp3ixovFe/+/AQf731SgOHDqEwxE7pjD3l/ls2LQ5JZopkqqs37iJf/87QO5cOXmu9jPUrlmddH5+fDd7DhDbZ3lyA3Dk2DGmzfyOCxfDCfAPoFDBginVdEnjVAgWkfvKs10g3H7QwumMvXlzuly43W7vQLzL5SJrlhDGf/0lnwwfpmKwyDXHTpykaOHCFCtS2JsVgBHvDaNo4cJYLLHdfI2nqzLwlb5azShpVtzC7ao167gSEcFjZct6n/ubKWMGgHg3bJ48Xbp8mQsXLwLXnwekQXhJq6bP+t67nVnFCo/zaNlHyJE9jKsxMQz/aASbtm7z5s0zcP/VmHHM/2MhNpuV5o0bkc7PL8XaL5LcPHnwFKE+HzmadRs3kTlTJga+2pcnn6hw08+azWbOnb/AyLHjcTqd5M+b95aFYxFfFff67HauZy72zxvHFEZ9/imv9OqpYrAIsc84PXrsGEULFyZb1qzevgrgld49KJAvn/e1Jg1e4M3X+ukZp5Lm7d67j7XrNxAYGEi3ju0p90gZANzEjhHEHQeH2LGD5atXExFxBZvN6n28m0hyUyFYRO6puBeOd8pzs+YZWPfc6JlMJlwuF1lCgpk4eiTVqlTG5mdL9DnDIr7AarVSqGBBihctQoH8+fBPl857LG4hy/P3FatXA1C6VElv0eqzD9+jRdPGOBwOmrZpz2dfjwKgWeNGvNq7FwUL5E+msxFJfnly5aJmtad5qUc3Gr3wPOWurT50u93ewfNp381i/h8LCQoK5PHHHgUgb57c3vfFzZrVasXtdnMlIvZZdEeOHkvGsxFJGVUqVyI4c+ZEj23fuYtBb79Dlz59eeOdYbw8cBDvfPAx589f8G6TFvd6znNd+N+BgwDYHQ7vIImIL7JYLOTJlYssIcEEZ84cZ+cjF35+NtxuN4PfHc7O3XvIljUrX37y0U23TPdc29ntdiwWCzmyZydjhgxJut8SeZAkZecVTy5MN+TDYrHEG1OoU6smefPk5vLlK/ekrSKpUe6cOQkLDSU0W7abvsczQWLjlq0EZ85E2dIPxxtTaN4kdkyhx8v9mDrzOwB6d+vCq316kTlTpmQ5D5GUEBQUROlSJanyZCVKlSgeb+KQZ0zhjXfeZcOmzWQJCeGZ6tUAKFakCJBw0rjVasXldBEZGbtjxSX1P5JCrCndABHxDS/UrcOCP//CbrfHW5l4JzydpOfmzfO8U6fTSZaQYCaPGcVj5cqyZv0G2nfr6R2o14os8SUtmjametUqPP9sba7GxGC1WPhlwe/8PH8BC/78K97vu+fvnoH1AvnyAfD5xx/QrFFDHA4HjVq1Zf3GTWzastU7UzFnzuwcOHgo2c9NJDl069iehvWep8zDpbyvHTl6jJHfjGfK9Jk4nU5v3/LygEHkCAujyrWtnhu9UI816zcwbeYsb75sNht2u50c2cN49JEynDl7lq3bd6TIuYkkl/998B71n6vDiC+/Zsb3PxB+6VKC98TE2Pl1wR/en78a8TFZs2Th9JmzAPGuAz2D+VmzZAFg45YtxMTY7/dpiKSIDm1aUe2pylSvWoXw8Njs/PTrfFatW8f83xd6f/f37NvHZyNHMeDlvhR+qCBjv/yM/m8OYeWadURGRnq/z9MPce2+Z9nKld4dKkR8UZXKlVi5ei1Op/Ou7/c9/Y7Dfn31sMlkSnRMoXvfV+9520VSgyYN6vNUpYrUf74ukZFRWK0Wps78joV/L2bdho3x3uvJzNZ//gFaEBYau0ox7phCg+at2LhlK9t37SJdunQ0a9SQRx8pk+j1oYgvaNuyOc/WrEm1KpW9r82Z9wuz5vzI8lWrvY9ki4mxM+jtd/ni4w8pcW1r9Q6tW/HPjp0sWb4CwPvIQ4fDQd48uQkNzcbeff/yj8YUJIWoECwihn08/B3aNG/GxKnfMvT9D70rQO6mGByX1WrF6XQSFhrKN19/wWPlyrJ2w0aatG4XbyBfxFcMHtifXl06ewc7Ll4MJyw0G/XqPEvJ4sXBZGLBwj8TfO7f/w4QERFJ1acqkz0sjBcbNcDhcNCwZRs2bNrsfWbwe598ytFjx5g8faYmUohPGjpoIN07dcDtdnPo8BHOnT/PI6UfJk/uXAzo+xIHDh1i2YpV3hs3l8tFs3YdmTFxvPcm7+Nh72A2m5ly7RmodrudbFmzMmn0SEKzZWPWj3NZv3FTSp6myH31/ttv0aJpYwBaN38R4KbFYA+z2UyWkBBsNitPV6nMtJmzcLlc8QY+cufMSY2nqxAdHc2GTVuS41REkp2nH4LYCXsut4usWbLQoU0rOrZtzZgJkxg57hvOnjtPTIydpStWks4vHS/16EaRQg/x2YfvM3biZP5essy7ut5ut5MxQwZef/VlTCYT2zRwKD5sxHvDeOG5Onw+cjRjJ05OUjE4LovZgtvtJjRbNsaP/FJjCuLz3nytH727dfHe70dGRpEhfXq6tG9HiaLF+GL0GNau35DgcwcPHQagdq0aPFq2jLcI3LBlGzZu2QrETkD/avQ4Dh06zFdjv9FYgvikIQNfo2eXTrhcLq7GxHDy5Cny5c1DvbrPEhDgz4GDhzh6/Lh3rDt2p6R3eX/oEEoWL0ZISDAv9eiGyWRi8bLluN1uHA4HIcHBjPrsUzJnysRPv/7G0ePHU/hMJa1SIVhEDBn4Sl/aNG8GQNNGDXC5XbzzwcdJKgZ7LiYPHzlKhgzpGffV55S/dsPWuFVb3bCJT/IMHDqdTt58dzj/HTjIwUOH6dKhLS2aNqFg/nw0euF5Vq5ew6XLl+N99uixY/x74ABlSpWkTKmS8YrAnq3VIXYgcfyUaQDKkPicN1/rR/dOHbDbHXTr+wqbtmzl1OnTdGzTmn59ehESEkzxIkVYtmIVEDv73ZODlh078+n7w6nxdFXCQrPx4TtDqVG1CmfOniPGbueZGtXImT07K9espd+gwZpIIT4tc6aMAMTY7RTMn59WzW5fDHa5XOzcs5tqVSrzWt8+nDt/gd/+WOgd+MgeFsakMbGTKX76dT4//DQv2c5HJLm80rsn3Tt1ICoqit79B3Lo8BEuXbpEyxebULniEzxWrizdO3UgR/Ywxk6YzNbt27l8+Qq///U3F8PDGfDKS5QuWZK+PbrTuH49Zs+dx/ETJ7DZ/OjTvSuFChbg7yVLGTNhUkqfqsh98Ub/V2nVrCkQOxHJ7XYzbtKUuyoGe8YebDYbACdPnyZTxoyMH/mlxhTE5739xut069gel8vFe598yuGjRzlz9hxtWzSj0Qv1eLJiBfbt359oIXjH7t3sP3CQGlWrACQYU/Dkb/+BA3w2cjSgMQXxPXGLwK+8/ia79+7lwKFDvPPGIFo0bUztGtWZNG16giLuug0b6flKPz59fziPlSvL44+WY9xXnzNp2nROnT6D2WyiU9s25Mubh5Vr1jL43eEAGlOQFGEKLVBYv3UikiR1az/D/z4YTsYMGdi9dx8PFSyAy+nk21nfJ6kY/ELdOoz98jM2b92G2WKhTKmSumETn9a+dUs+ePstoqOjadq2Axs2bY533LNS2Ol0Uq9pCzZv25bgOxo8/xzvvTWYkJBg6jdvxboNG3VRKWlGl/ZteXfwG/EyFLev+ObrL3j+2dqMmzSFoe99EO+zfn427zadzZs0osqTlWhY7/l47zl//gJ/LllCv0GD1Q+Jz/L0Gf379qFXl05Mnj6TNs2bERQUyL//HWDG97NvWQyu80wt3h40kLx5cuN2uxkzYRJnz50jfVAQzZs0Jkf2MFatWcuL7TrGW5Uv4gtKlyrJhFFfkSMsjEat2ibYerNs6dLUq/ssPTp3BOCvxUsYOW486zZu8q6eD82WjU+Gv8MTj5cnQ/r0Cf4ZC/9eRMeefZQf8Un1n6vLZx++R7p06Th5+jQ5s2fn8JGjTJ4+466KwZ73dOvYnrffeJ2/ly4jOFMmyj1SRmMK4tM6t2vDsCFvEhUVRbP2nRLsYOTZ6jk6Oprqz9dP8KiowIAAunRoR98e3QgICNCYgqQ5PTp35K3XBxAREUnzDp0SjMst+HE2j5R+mAFDhjJt5qxEv8Nms/He0MFUePRRihQuFO+Y0+lk/u8L6flqf13LSYrSimARSZK8uXPT4Pm6ZMqYkZmz57Dw70U0b9KYalWfovW1FSR3Wwz2PEeudKmSWCwW1m/arBs28VklihWjZdMmAHTv+2q8Gbee3/ePP/uCqk8+SakSxSn/aFk2b9uW4IZs8bLlvPvRx+zZ9y9btv2jGzZJMypXfIJObdsA0KFHb+8Nm+fmymQy4XdtVcjpM2cI8PcnW9asOJwOjp84SUzM9Wfaf/fDj3z3w4/MnjuP/PnykitnDo4eO862f7azeds/8XIp4ms8fcbipcvo16cX+/bvp1b9hvz181wKFSxAyxdjV2ndrBi8YOGflCxejGaNG5I7Z05vwctj/h8L6fbSK7qeE5+UO1cucuXIwd9LlrJuw0Zvv+L5c/O2bez7bz+nzpzh7UEDqVntaZxOF5cuX2bn7j2YTCZOnT5N2649qFv7GcqWfphqVZ4iIMCfdRs2sn3nbiZ9Oz3ebhYivqJggfw0rPc8AQEBTPp2Bhs2baJ3t64UL1qE9q1aAtxxMdhzzOGIzUjVJythtVo1piA+rWzp0t4d+rr0eZn1Gzd5s+J5zvwnn3/JE489Rt48uSlWpEiCQnBkVBTf/zgXm83GoiVL2bQ14ZiDiK+qWe1penbpBED77j0TrITPlDEj/v7pAAgPv4TZbCYgIICrV6/icMQ+k94zwXzA4KGUKFaMShXKU75cOYKDM7Nl2z9s27GD+b8v1JiCpDgVgkUkSZ6qVJF6dZ7l6PHj/P7nXyxctJjjJ09iMsHTVZJWDLZf60QtFgvrNm7SDZv4tIqPl+fhkiX44aefWb5qNXB9AOP6YAdcDA8HIH/evPHe4xF+6RLf//iT93XdsElakM7Pj2dr1iBf3jwM++gTlixf4T3m6W9y5shOzhw5cLlc5M6Vi5+/n0nePLm5EhHBj/N+4eux3yQoai1etjzRf57JZFI/JD7v7PnzuN1uGjz/HDO+/4Emrdvzw7eT76gYPOKLrzhx4iRPVqxA7RrVcbndLF62nA2bNjN+yjQVscRnFS300LVnYlsxm83e67C49z1XrkQwdsIknA4Hw4a8Se2a1bl4KZyXBwzybmVrt9v57Y+F/PbHQj783+eYzWbsdrv3O8xms/IjPqfKk5WoXbM6Bw8f5s9Fi1m8bDmnz5zl3cFvJKkYHJenCNyoZRuNKYjPerJiBYoULsS072axbGXsY3A8GfH0IRcuXCQyKgqTyUSxwoVYsPDPBN9z4uQpPh85GofDoSKwpBmZMmakXp1nyZolC4PefpcVq9cAsRnyjCnkyB6Gv78/Fy6GkztXTr4dP46w0Gzs2bePZStX8d0PPxITY/dey+3cvZudu3d7H80Wl67lJKWpECwiSeIpXP264A8WLloMwLbtO/jf16OApBWDf/tjIYePHCXGbtcNm/i0wIAAqlauBMDa9RuIjIpK9H12u52t27dTueITWG0377J1oyZpzdWYGFatW0/+fHm9/REQr58ZNuRNSpUoTozdTtXKlbDH2ImMjCJn9uz07taFbNmyMmTYe1y+fOWOV5mI+LJDh4+wdsNGihUpTJaQYDZv20b95q2Y9910bzHYbDYzceq3REVH8+Zr/Zg1Zy7//vcfANO/n83072eTI3sYEDuo6KGBD/FVl69cASBrlhCsVov3kQOJmThtOgEBAbzR/1WaNWrIgYOH+GLUmHgFX4gtIt+YF20hKL5oz959APzy2+/eyXgrVq9h6Hsf8M6bg+IVg8dOnOzdTv1W12Xz/1jISz26cvFiOA1btNaYgvisjBkyeB9rs3rtugR9CcRef0VGRbFrzx6KFy3ifYZ2YjyrG3XfI2nJxfBwFvz5V7yJ5XH7jIGvvEz+vHmJjo6mV9cupPPzIygokBLFitKw3vPkCAvjs5GjE+TPs+Nl3DzpWk5SmgrBInLXzGYzh48epcRjT3Dh4kXgeke59Z/tSSoGe7bSqFCtpvc9umETXxUZFcX4qdPYtWcv3876PtH3eC4Yr16NASBzpkwAep6IyDV//PU3Gzdv4dTp097XPNmYOPpr6tSqycXwcHq+0p89+/Zx/MRJHin9MHWfqUWf7l1p1qghJ0+e4sP/fa4BD5FrDhw8xBPlHyNnjhycO3+B7Tt3xSsGN2vcCKfTRYliRWnasD7P1qpJjedfwG53eHMUtwDsoX5LfNWqteu4dPkyD5csQa+uXfjs2n1QYlwuV+wjCPLmpeWLTWjZtAlLlq9g6z/b471PfZKkFavXradSzdrerWo99//LV61OtBh8u5XBZrOZk6dOUeuFRpw9d05jCuLTLl2+zNdjx1GxwuPM/WV+ou/xXH9duhw7aSlTxoyAxhREIHZ3vYnTviUwIICDhw57X/f0GeO+/Jxna9Ug/NIl3nxnGP/+d4Ct/2ynTfNmPPfsM1St/CQDXunL0eMnmD33p3jfrWs5SY3MKd0AEXnweC4YPUXgG7fM9BSDlyxbjtlioXWzFxk6aABWq9X7zCwPT3ErJsaO1Wr1fr9WjoivW7ZiFeOnTL3t+65evRr7l2vXkZ4LSk+O0vn53Zf2iaR2TqczXhHYo9WLTalTqyZHjx/nhRdbsnjZck6ein3flm3/MHn6DH746WcA6tZ+hrDQ0GRtt0hqtnbDBgAqVXgciB2U375zF41atSUyMpLCDxXkld49aNqwPtFXr9L3tYHExNg12CFp1rHjJ1i6YiUQu81txcfL3/L9J0+d4s9Fizl3/jw5smenUMECydFMkVTLUwS+8f7fUwzetWcvefPkpn2rlnTt0A6LxYLb7fautgLIkCE9gHfF8OkzZzSmIGnC3F/mM+KLrwHiZeJG3jGFG3jGFPz8br5SWMSXHTp8hF179iZ4/Z03B1Gv7rMcPHyYF15swZx5v3gn7k37bhaTv53B3n3/4nA4KP9o2eRutkiSqBAsIoYlNvh3u2IwQNHChZg/ZxZv9H8VuL4VDWjliKQNZ86eu+17PFsOXomIAOI/ryRLSDAzJ0/gtZdfuq/tFHmQrFy7lhFffs2LbTuwb/9+TCZTvD7l+ImTbN+5E4AsISGYzTcfNBFJa/bs+xeXy0XBAvkBvM8v3frPduq92BKHw0GAfwBut5uZs+ewaes24NaDjyK+LPzSJX7+bQEAT5R/jGaNG5IvT55bfub3v/5m1Zp12GxW6tWpAyhDIond/9+uGAyxYwpTxo6mc7s2gLbhlLTn9JkzwK1XIEZdexRVVHQ0cH3xhWdM4etPP6F965b3v7EiD4hff/+Dv5cuo03n7uz9d7930oTneu33v/5m45atWK1WnqpUkcDAwJRsrsgdUSFYRO6bxIrBbw8aCECeXLmYOm4MBfPnp2yZ0incUpHUK51fOgD8/WP/tNls3hu2SWNGUfHx8lQs/1hKNlEkVTl46DCfjxzNgYOHbrp14OGjxwBwu13YrJoBL+Jx9uw5Ll4Mp9pTT5E5UybMZrP3mVetmzXFarVitVowmUxUqlCenl06kTlTJq0IljTt1wV/8NH/PgegWeNGdO3Qjlw5cyT6Xs8A4rYdOwCwWGKHZJQhkcQlVgzu1rE9APny5uHb8WOp+Hh5Hi37SIq2UyQ18+y+ly5d7G5icccUpowdTb06z1Kx/K13tBBJK0wmE+s3bqJjj178+99/8SaWx92RYs+//wIQfukyLu0+IQ8AFYJF5I4lZab61n+289nI0d5icKtmTflqxMfMmT6VvHlys3rtOpq373QfWiuS+iQlQ56btQD/AADsdjtZQoKZPGYU5cuVZc36DTRt2+GetlMktbrTDHm2AbxxYN0zk7d0yRIA/LloMYePHr2HLRR5sB09fpwt27eTPn16MqRP783SZx++R4c2rXA4HLw66E0iIiIpWrgwzRo1pHP7tgQFBaVwy0VShqdfGTV+gveRHx3btqZX1y4UKfRQgvdZLBYA/K492uPf/w4kZ3NFHkg3FoPbtGjGW68PYPbUyeTOlYs16zfQu9+AlG6mSLJIypiCpxDs6Xvijik8WvYR1m/aTM9X+9/Tdoo8qDxjCDEx9ng/x2U2myletAgAq9asJfom26+LpCYqBIvIHTGbzd7OL2/u3Hd18bll2z98MWoMfy1egslspv5zdcmTOxer162nadsOOJ1O76CIiK+Km6GSxYvf8e+8ZxuniMhIALJlzcKkMaN4rFxZ1m7YSJPW7ZQhSROSmqG4n3e5XOTKmYPmTRoTHR3NyjXr7kdTRVKtG6/f4v7s+fvJk6cIzpzJu2PL5x+9T/MmjXE4HDRp057vfviRxq3bcvnKFYoULkShggWIuPb4AhFf5inmAmTKmBG4vvVsTIydr8eOZ/L0mQB0aN2Sfn16U+eZWvHe53A4CM6cmaYN6+N2u72PKhBJCxLL0J1avmo1b3/wITt27SZ/3rx0aNPKO6ag+yFJK5I6LhcTEwNcf1bwjWMKDVu0VoYkTTDSD3k+73a7yZ8vLzWqVuHsuXOsWqsxBXkwqBAsInfEM3jx3luD+eKTD3mi/GN3VQzetHUbP89fgN1ux2azsn7TZpq2ae+92HRqGw3xcZ4MDR/yJqM+G0GD55+7oxstz43eydOnsFqtTBo9kvLXbtgat2qrDEmakdQMwfUicNYsIUwc/TXZw0L5+bffmTPv5/vZZJFUJe7gYaUKj2Oz2eJtb+Y5tmjZMlwuF3nz5Ob9t9+iWeNGOBwOGrZsw9r1G7zPDG7WtiMrVq/RKixJM+LeD336wfAEzwI+dfo0n375FaO+mQDAC8/V4e1BA/n8o/cpVaI4pUuVpEL5x5g7cxr58+blt4V/MveX+cl+HiIp5XYZup1lK1Yxadp0oqOj8U+XTmMKkuYYHZc7cfIUZrNZYwqSZhnph+KOKXzz1RdkzZKFBX/+xd9Llt6v5orcU9aUboCIPDiqV61Cx7atARj46su89uZb7Nu//44+W7RwIV7v9zLpg4JYt3GTLjYlTapQ/jE6tWsDwMu9enDk2DHWbdh4y88EBgYC8FjZssz7bjrlHimjGzZJs5KSIYi94cubOzeTx46ieNEirFyzllcHvektgunZjJIWeAY+hg15g0b16jF20mRGj5/ofQawx/nzFzCbzbzR/1VMJpO3CLxh02ZMJhN2ux2z2czmbdto2qY9ELvloMPhSO5TEkl2L9St470fMplMDBwylLPnznuPnz13nmEffcKJkyfp91JvsmcPo1njRjxTowaZM2XkakwM/unSsXjZCrq99Aoul8s7sCiSFtwuQ7dStHAhenfvgr+/P+s3baZRyza6H5I0JynjcunSpQOgbJnSzPtuOo+WfURjCpJmJbUfcrlchIWGMu2bMZQoVpRlK1cxaOi7GlOQB4ZWBItIAjebUbhz9x7vDPcrV67ccRH4oQIFmDlpAvnz5mX12nU0atkGh8Ohi01Jc9au38A7H3wMwMFDh+6ogOUZWH+4RHHKPVJGEykkTUtKhh4uWYIu7dsyb9YMihctwp+LFtOsXUdvhnTDJmlJqRLF6dyuLSEhwXTr2IGaT1dN8J61GzYyZcZM7/Vgo1ZtvUVgT15uLFqpCCy+5mb3Q0ePHWP23HlA7MD6zQYOx0+ZRofuvRjxxVfsP3CQqOgoTCYTa9dv4PORo2nduau3H1IRWNKSO83QjYoUeohvJ4zzjik0bNFaYwqSJiVlXM6TkWpVnuKxcmU1piBpWlL6ocIPPcTzdWrz24/f83DJEixatpyWHbvgdDrj7bokkpppRbCIeGXMkIFLly8T4O9P5LXnkgLegb+Tp07xzeSp7Ny9mznzfol37GZMJhN1a9ciR/YwNmzaHO+ZwLrYFF/i52cjR/bsREVFA3D6zBnvsbgrPcZMmMj+A//x56IlwO0zFB5+CYfDQWBgIOs3bdYNm/i0O5lJezcZyps7NyPeH8bDJUpw4cJFvpk8hbff/wiXy6UMSZq0fecuevcbwNeffszGzZtZ8OdfCd7jcrkYPX4iVouVH3/+hfUbN2mWu6QZmTNl4mJ4OAEBAURGRnpf91zLbdq6DYBNW7cy+dsZQMJ+yPPzmvUbWLN+A5O+nY6fzY+Q4GD2Hzjgfa/ZbFY/JGnOnWToRmazmfatWpI7Z07WbtioMQXxaenTB3HlSgQB/v5ERUcnOJ6UcbkrERGx3x0UpDEF8Xn58ubB5XThcDo4cfJUguN32w8VLVyIP3/+CZvNSkREJFNnfsegoe9qTEEeOKbQAoV1Ry+SxjWs9xzlH32UalUqc/FiONFXrzJ91mx27NrFrj17ARLt3O60wwsJDub5OrWZPmu2LjbFJ3Vs05oa1apS7anKhF+6hMVi4adf57N2/QbvzVli7iQLVSpXYsqYUWzbsZPGrdpq5rv4pOpVq7BqzVqir169q4LTnWSh/nN1KVm8GKvXrWfJ8hW43W5twyk+Kyw0lKioKPz8bAlmt8fN1uOPPepdUX+zPNhsNux2u4rAkia82KghFR9/jKqVK3Pu/HliYmKY9t0stm3fyc7du4HEt0C/XT8UNz/KkviyggXyc/DQ4bu+vrrT+5rsYWG0ataUz0eO1piC+KSa1Z7mkdIP82zNGlyJiOBKRASjv5nAyjVrb/m5O8nCC3XrMPbLz9i4eQv1m7dShsQnNW3YgKefepL6z9UlIjISs9nMiC++Yv7vCzl6/PhNP3cnWXi5Z3fKPVKGH376mV8W/K4xBXkgqRAsksYNHtCfXl07e3+Osdvxs9kA2PrPdn78+RfGTZoCJG3w4sbPqKMUXzN00EC6d+oAgN3u4Nz582QPC/Uen/bdLP731ShOnko4E/FWbhyw37Bps2Ycik/69P3hNHrheb4cM45R48ZzNSbmngyWx82Kp6AFGogX39Sw3nNUrPA4L9StQ0RkJFaLhdHjJ7Fk+XJ2793nfd+Nv//qU0Rg8MD+9Opy/X7I4XBgtVpxu91s37mLOfN+ZuzEyYDuZUQS8/nHH1C8aBGGffgJK9esvefXWTfmTn2X+JrXXn6Jbh3aExQUGO91p9PJ4GHvMWX6zCTlKu51X/WqVVi6YqWKwOKT3nytH727dfE+r/fCxXCCM2fC4XAw4suv+WLUmCR9b9z+JygoiIhrq+s1piAPIhWCRdIwT0cZffUqg4a+w+EjR7HZbDz/bG1aNWvqfTbWxKnf8ua7w2/6PXE7wNBs2Thz9qw6REkTXunVgwGv9CUqKoper77G/gMHuHT5Ci82asDTT1Wm4uPlAfjjr0WM/GY8m7ZsTfSGK26GChbIz6HDR3A6nQlWnmjwUXzNoH6v8FKPbgAcOXqMmbN/YNQ3E+66GHyzDCkzkha80f9V+nTv6v05MjKSwMBAXC4XP/z0M2MnTmLn7j13/b0a4JC0YOArfXm5Vw9i7HYGvzOcE6dOERQYSM1qT1P/ubrYbLFP0xo/ZSpDhr1/0++Jm5e4A4Uivu6t1wfQo3NHAP5euowx4ycmqRisDEla9e7gQXRp3w63283M2XM4fPQouXLmoGSxYpR7pAwul4uWHbuwdMXKW37Pzcblbiz6qggsvsazOMPlcjH0vQ/47+AhXC4X7Vu1pHbN6kRfvUqdhk3iTY5NzM36Id0Tia/QM4JF0qh6dZ6ld7cuxNjtNG3Tng2bNnuPLV2xkv8OHqR3t66EBGemY9vWBAUF0v/Nt3A4HAkG1j0dYreO7alVvRr/+2okq9etV0cpPq1k8eI0b9IYp9NJi45dWLt+g/fYl6PHsmjpMp6r/Qwv9+pB7ZrVCQjw5+ux37ByzdoEhSlPVrp2aEfzJo0ZPX4iP/78S4LtB1XQEl/yQt06dGrbBpfLxfETJ8iTOxetmr8IcNfF4JtlSIMc4uveeXMQXTu0w+Vy8elXIzl85Ch2u53nnn2GenWepXH9euw/cOC2heC4WXu4ZAn27NtHTIy2hRbfVrniE3Rs2xq73UHjVm3j3Q/Nm/8by1etpmG953j6qcp0bteWwIBAXh/6TqJbpnvvhzp14NEyZXhvxKccOnwk2c9JJDk1bdiAHp07elcY1qhaBa5l4W6LwcqQpEV9unelS/t2REZG0rpzN1avWw/ETgAvXaokQwcN5Inyj/Fa3z5s2rqVy5ev3PS7bjYud+P9kO6PxJf07taF7p06cCUigladunoffQNw+swZihYpRJ5cuQgNzXbbQvDN+iHdC4mvUCFYJI0q90gZAD4fOZoNmzZ7BzM8KxBHj59Iliwh3m3SmjVuhN3u4LXBb+FyuRIMfjz6SBnefuN1IPb5WYPfHc72nbuS/8REkknunDnIkzsXy1auYu36Dd5MeP7cvnMX/x08xIlTp/jo3bep8mQlXC4XF8PD+WfHzgTfV6JYMd55cxAQe0N45uxZlixfkcxnJZI88ufLS4N6zxEYGMCUGd+xYvVq+nTrSpmHSyW5GKwMSVrTtUM7unZoR1RUFM07dI438DFv/m+M+mwEDes9T+9uXfhx3i+3fDaWJ2Nd2relV9cuTP52OqPGTyAmxn7fz0MkpRTIn4+MGTIwc/acePdDntVS3/84l/8OHuT4iZO0fLEJLV9sgtPlZMDgoYk+G65yxSd4e9BAACxWC2+8PYxTp0+n1OmJ3FfFixahSYMXcLvdfD32G44eO84n771Ljaeret9zt8VgZUjSkthJRm0AaNetJ6vXrff2Qy6Xix27dvPX4iU8Uf4xsmQJwYTptt+pcTlJS+rWfoa+PbsD0L5bT9Zt2Bhv7ODKlQjSBwVhsVi4ejV2XMFqtXofGQUJV/uqHxJfZk7pBohI8suYIQPVqzwFwPETJ+Idczgc3i2hvxo9jv0HDnqPtW7+Iq/06gGQ4Ibu6PETjB4/EYDMmTImaQtCkQdJgfz5MZlMWCwW73PkIH42IiMjmTrjO/q9MRiIvdnr3bULZnNs9+vJGsDO3bt598OPAXA6HSxbuSq5TkUk2T1VsSJ1atXkyNFj/L1kKfN/X8jgd4ezdfsOcuXIQavmL9KzSyfS+fl5J1jcjjIkaUn5R8vRrlULADr1esk78AGxA38A4ydP5fSZM9isVrJnD7vtdxbIn493B79BWGg2unRoT/3nnrt/JyCSCpQuWRKIvV6D69dlcVdLbdi0mQlTpzF15ncAtGnejDdf6wck3KnlSkQEc+b9AkChggU5febM/T0BkRRiMpmoWrkyVZ6sxN5//2XlmrV8O+t7Ovd+CYAaT1ele6cOPPlEhTu6hvNQhiQtqfF0FYIzB/PZ16NYsXoNEH8swW63M/+PhURGRpI/b16KFC502+/UuJykFdnDwmhcvx7pg4J4ddCbrFyzFsA7UQ8gLCwUp9PFkaPHyJk9jC8/+YiJo76ie6eOVKrwuPf9cfsp9UPiy7QiWCQNunr1KjHXZkCVLF4MINGtzTz+O3iQ5atW065lC9q1asm6jZu8nazHqdOnGTtxMoePHuXb775PdNWwiC+JiIx9Xkhotmz4+6fjyhXHTd874/sfCAoM5N3Bb/DCc3U4dOQI74/4X4J8jB4/kRMnT/Hr73/gcrn0fFPxWdt37sLhcPDLgt/5a/ESADZs3sLgd4czfMibSV4ZrAxJWlG54hMUyJePSdOme1e+e/LheazAnn/3ExkVRWi2bJQuWTLetreJOXDwEL37DeDrTz/m1OnT/PjzL/f3JERSmJvYzBQrWgSTyXTT/mLn7j1MnTGLwIBAmjR4gR6dO7L33/3MnvtTvPdt2fYPE6ZMY/9///HV2G/i7RQj4ks8E44AfvplPstXrQZg/u8L6dKnL9989UWSVgYrQ5JW5M2dm87t2uJ2u286edVisXD58mWioqMJDAwkQ/r0t/1ejctJWuFnsxGaNeu18YSl3tfjPgO7d9fOhIVm40pEBO+//RaZM2UCoGa1p9m9dy+jx0/i+x/nxsuH+iHxZVoRLJIGmc1mTl2b1VQwf37CQkPjHffMhvLz88PPZuPkyVP8PH8BW//ZTlhoNso8XCrR7z11+jSTv52Bw+HAYrGooxSftnLNWs6eO0eRQg/xUvdu3lmHN/PTr/OZ9O0MABo3eIHHypW96fs8GVIBS3zV5m3bqFSjNsM/HgHE3rBB7MqrwcPeY+s/2+9qZXDc15Uh8XXBmTPTuW0bTCYTy1evSfR6y2q1EhERweEjRwGw2W49/9eToTnzfqZ5+048U78RTqfztn2byIPsz0WLuRIRwUMFCsQrWiVmx65d/PDTPLZu34HJZKLG01UIDAxM8L7N27bx2cjRuh8Sn2a325k3fz7N2nXk81FjgOvXcr8u+IMuffoCd7cy2NPfKEOSFhw+epRxk6aw8O/FrFm/IdH3OJ1Ozp2/wJGjx4CECzZuRuNykhYcPnqUt9//kPFTpnHm7Fnv654i8OjPP+WZGtW5dPkyH3/2BX1fe53Kteow6psJXL5yhaKFC9OxbWvKP1ouwXerHxJfpTt7kTQoKjqa3/5YCMTenPXo3JHgzJm9N1+eTq5dqxbkyZ2LhYsWs2rtOuZf+0yHNq28M6luJu6WaiK+6PSZMyxdETt798knKlC50hO3fP+Zs+f4c9FiTp85Q46wMIoWuvXWTsqQ+Lojx2IHNUwmU4JtOO+0GJwpY0Yg4ZZOoAyJ77pw8SLDPxnBL7/9zoKFfyb6Hs+q4FOnYyf+eQpWNxuIj5uhpStWajKFpAnHjp8kMjKS7GGhPFe7VqKF3biWrljJz/N/w2w2U/+5upQqUfyW71c/JL7sxMlT3pWMZrM53u/73RSD/dOlA/CuXIxLGRJf9vb7HzJgyFDg5tdnAPZr13Q3Ts5L7HFTN1KGxJdt3LKVdRs2Jnh95P8+ocHzz/HfwYM0aN6KbyZPZeGixew/cIBhH33CF6PGYDKZKFW8mPcxITejDIkvUSFYJI366Zf53mdddevYniEDX6NhvefJlDEjZUuXZlC/V+jXpxenTp/xbiU4fvJU/jt4kMCAQKxWS0o2XyTZ3OzG6sqVCOb+8isA5R4pQ4smjXmoQIFbftfiZctZvGw5JpOJ5+vUvuX3i/iK2/2OJzbD9nbFYICihQsxbfxYXurR7abfI+KrZs6ew5vvDgdunTFPQdizjafnvZ7BQ5vN5n3vjRnSwIf4iputbN+5e7d3NWPzJo3p1rG999iNufL8POqbCay69oicSo+XT/S9Ir7mdrtDJDZp6E6KwUULF+KtQQOpWvlJQNdy4rviZsiTAbfb7X3+6M12dzGbzfj5+QGxW+HG/T6Xy0WWkGA6tmlN0Tt4frDIg+xudila8Odf7Nm3jw7de7Frz17vZz07V4wcN56/ly7DYrF4x+VE0gIVgkXSqMioKKbOmMWsH+cC0KJpY77+9GMW//YLc6ZP4aUe3bh0+TIDhwxl45atAERfvUp09FVCgjOT/g6eTyLiCxK7KfPcvP29ZClvDX8fgAbPP0fPLp3IlydPot/j+cyuPXuB64PyGvAQX5fU3/FEi8FdOwOQL08evh0/lvLlyt52RZbIg+5mRSbPNmiJZcwz4BFjj4n9Myb2z7jPzs4SEsx7QwfTuP4L96PZIqlGYkUqz2DgDz/NY8b3PwAw4OWX6NapA5AwV3FXzXsesROaLVui7xXxNUndHSKxYvBTlSoC8FCBAnw3eSIdWrdMdGtOEV8SN0N32mc4nU5cLle8aziIHUfwXMfNmDSe4W+9yZNP3Hp3MpEH3d30Q78u+IM6DZuy99/9mEwm72fjPvbm1KnTAFy6dOneN1Yklbr1w6JE5IEVFhrKqdOnb/meHbt2MeLzrzh06DC9unbBz8+PHNnDCL90ib+WLGXazFksX7UaiB0syZolCyHBwQC4XRrwEN9W8fHylCxejLq1n+HSpcvs2bePOfN+Zv+BgzidTiwWC06nk4nTppMta1b6dO9KyxebYDab+GbyNHbu3g1cn61rsVhwOBze1Ve79+5NydMTue9ul6E7sWHTZoYMf59hg9+gzMOlaPliE7IEB1O7ZnVy58rFmvUb6PFyv/t8JiIpKylFphsHSzxFL5vNht1uJ0tIMNO+GUvZMqVxu93MmffzPWmrSGpSu0Z1ihUtQvWqVTh2/Dj7Dxzkz0WL2bvvX6KvXsVsNnP58hW+m/Mj2bJmoVb1arw9aCBmk4nR4ycm+D6r1YrdbicqKhq4/ogDEV91Jxm63eC8pxj8zVdfUOPpqjidTnLlzMErvXqSPSyUDZs289nXo5LpjESSl5EMea7/PIVgx7X7J4fDQZaQYCaPGUXpkiXZsu0fpsyYmTwnJJLMkpqhqOjYa7XEJvb5p0tH9uxhAGzauu3+n4RIKqFCsIgPGjbkDYoUKsTb73/oXX14M0ePH+ezkaOZ/8dCMmXKRLasWdm1Zw9nz53j8uUrwPVBwwL58pI5U0Z+W/gnh44cSY5TEUkRr/TuSbuWLQgLzeZ9rXbN6pQvV5YZs+fw06/zvdttOp1Oxk2ajM1mo3unDjRv0pjgzMHM/eVX5s3/zXtR6nA4yJwpEy1fbALAPzt2Jf+JiSSTu8nQ7azfuIm33vuAtwcNpGyZ0rRu0Yx0fn6sXreepm3ax5uYIeJL7sVkCu8W0Nd2ofAUgSePGUXZMqXZsGkzb7w97L6dg0hKGdTvFXp07oTNdm3I49qKwwbPP8fOXbt54513OXf+AhDbz3z73fdYrTaqVanMW68PICQ4mKkzvvMWe81mszc/VZ96kstXrrBz954UOTeR5HA3GbqdXxf8QbeXXmHsl5/xTI3qPPnEEwQFBepaTnya0Qx5ClwB/v4A+F/7M2uWECaOHslj5cqydsNGGrdqqwyJT7qX/RBcz1TBAgV45OGHOXT4CCuvPe5DJC1QIVjExwwf8iad2rUhMjKSvj278/nI0ezeu++2n9v77/5EX7dYLN5Bj08/GI6/vz/rNmy8180WSTWGDXmDzu3aArHPFgkPD8dstvBiowZUrPA4mEzs2rOXHbt2eS8kz547zxejxnAlIoL+L/Wmds3qlCxRjLrP1GLcpMmYTCbSpUvHe0MHUyBfPn5b+Cc//DQvhc9U5P64mwyZTKbbrnY0mUys27CRCVO/5ZPh7xAQEMD6TZs1cCg+zehkCk+2vFuhXfszW9YsTBj1tQYPxacNHtifXl06Y7c7+GLUGJwuFzarlbYtm5M3T24KFSzAw6VK0PvV19jyz3ZcLhcLFy3G7nAQERnB88/Wpne3LhQp9BCr1q1n2sxZpA8KIlPGjIz6bAS5cuRg7i+/smzlqpQ+VZH74m4ydCerqcxmMz//toAc2cMYMvA1goICWbdxk67lxGfdiwyZTCZMJpN3V5fTp0+TIUN6JqkILGnA/eiHPFuqj/5sBCHBmZn78y9s2LQ5Gc5GJHVQIVjEh7Ru9iKd2rXB5XJhtdmoXaM6ZpOZ/3098o6KwYlxOp3kzZ2biaO/pmD+/Cz48y++mTz1HrdcJHV4pXdPOrdry5WICNp36xlvduDCRYsY//WXVHy8PE0b1mfHrl3xtqC5GB7Op19+zfETJxjU7xWyZsnCC8/V4ekqlcmYIQNXY2JI5+fHomXL6drn5XjPaRTxFXeboTvZ8tbtdlOsSGH69+1NQEAA6zZu0qCH+LR7PZkC4OTJU1itViaNHsmjZR/R4KH4rAbPP0evLp2Jjo6mUcu2bN52fXBw3vwFNGvcgDrP1KJAvnyM/fIz3nhnGEtXrCQmxs7iZcs5dfo0R44eo0fnjjxTozrP1KhO57atCQwMwmazkiF9epatXEWf/gO9zw3WM4LFlyQlQ4uXrbjl5CSXy0WxIoVp16oFFouFDZs2qw8Sn3WvMuTJRVR0NNFXr5I/Xz4GD3xNRWDxeferH8qWNSvTvhlDkcKFWLRsOW+99wGAruUkzTCndANE5N4o83ApmjZqAMCU6TP55bffMVss1Kr+NK/27kWxIoXv+jsLFSzIq3168ePMaZQsXowly1fQpXdfbwFLxJfUrlGd7p064Ha7adWpKyvXrPXOvjWbzcz/fSFfjh4LQKUKj5M+fVCi3zNz9hzad+vJByP+x45du7l06RIul4sVq9fw6VcjadO5m/eGTUVg8SX3KkM3KvzQQ0wbP5b8efOyeu06GrVsg8Ph0KCH+KS4kymatG5Hxx69eeX1N+k74HU6934JwDuZAm7+/GDP6xZzbAZLlijOnOlTVQQWn1e6VEkA3h/xPzZv24b12rboJpOJHbt28dWYcXz82ZfsP3CQ3Lly8cE7Q6lUoYL38zt37+HdDz+ma5+X+XvJUs6fv0DuXLkICc7MfwcOMmHKNFp27OLNjwYOxdckJUOVKz5xy+8sUawYE0Z9RYF8+Vi9dh0NWrTWtZz4rHudIavFgn+6dLzzxutUeOxRTYoVn3evM5Q7Z06qVXmK3378njIPl2LZylW07dLdO7atazlJK7QiWMQH+PnZeKFuHR5/tBxbt+9g6szvOHjoMNHR0TRt1IBa1Z8GuOuVwenTB9GqWVNCgoOZNG06g4e9h8vl0sWm+Bw/PxvP1qpBxgwZGP7xCNZt2IjJZPL+nnsKtv8dPAhAgfz5CMkczJUrEYl+36at29i0dRtTps8kXbp0hIQEc/DQYe9xs9msDIlPudcZiqvli03InTMnazdspGnbDhr0EJ9142SKdRs2en/X406meKlHN+9kijvJEECTBi+QPihIg4fis0wmEzablTrP1AQgMjIKwLs6xDPId+bsOX75bQEXwy/y1usDKVSwAB8Pe4dWnbqyb/9+by5+WfA7i5YtJzAggBzZw/Dz82P33r3ezOlaTnzNvcrQjTsemc1mhr/1JgXz52fF6jU0b99JfZD4pHudIbPZjM1q9RbBQkKCtZpefNr96IfKPFyKkf8bQf68eQCYNedH+r0xRBmSNElL+kR8gN3uICw0FIDv58xl9959RF+9ytD3P2T2jz8leWXwlm3/0LZLD9p368mb7w73XoyqoxRfU7xoUZo3aczZc+e8W9kmNitwzfoNnDl7FoD06dPf8jtNJhNXY2K4dPkyhw4fiXdMK4HF19yPDHl8MWoMH3z6mQY9xKfFnUzx3ief3vFkitux2+0ApA8K0uCh+DS3201MjJ2zZ88Bsddhcf+MK/rqVZauWMn7n3zKgUOHyJM7F6M//5QMGdJ7J14AREZGcubsWbZt38GGTZvjTbzQtZz4mnuVoRuz4XK56PlKf376dT4tOnRWHyQ+615nyOVycTUmhhWr1wCx43NaTS++7H70QxcuXGTt+g2sXLOW1wa/xSuvv+m91lOGJK1RIVjEB7jdbga9/Q4vvfY6E6d9C4DVaiUiIoK33vvAUDF4x65dLF2x0vsMLA16iC86fuIkh48c5e8lS9my7Z+bvi8mJga3202Avz+BgQG3/M64RTBtNSO+7n5kCGJXkVwMD+fL0WM1cCg+7X5Npti4ZSsAW7fv0OChpAn79v8HQNuWzcmdM+dNr8FiYuwsW7WaCVO+5fSZM5QsXoyuHdrHW0Wi6zdJi+5FhuKyWq2cPHWKHi/3Ux8kacK9ztC0mbMY+t4H1Huxhe6HJE24lxk6fPQoQ4a/T4+X+zFz9hyNbUuapkKwiI+4fPkKs+f+BMQOnDscDkwmE5GRkUkqBnue6xiXBkPEF5nNZs6cPcuzDZswbtIU72uJuXo1hsioKBxOp3d7mrjfE1disxZFfNH9zNCNN2ga9BBfdb8mU8yZ9zPtuvbg+SbNNHgoPs1z3bV81SouXb5MzhzZqVGtKun8/G76mYiICBb+vYht23cA8Pij5fCz2ZKlvSKpzf3K0I3Xe+qDxFfdrwwdPnqUcZOmaCKF+Lz7laHIyEjOX7jg/Vlj25JWqRAs4oPizmK/k2Kwp7Mt/NBDDBn4GpkyZtTFpaQZLpcLk8nEhYsX2bl7j/e1m73Xc8zE9UKvZ/VI5kyZaPliEwIDA3VxKWmGMiRizP2aTGG7NgiycNFiDR6Kz/P0GYuXreDg4cNkCQmhfasWlCxe7JafO3LsmDd3VZ6sRKUnKtz3toqkRsqQiDHJkSFdx4kvUz8kcn+pECzi425XDC5RrChut5tiRQozY9I39OzSibYtm6d0s0WS1Z0WnNxuNxazBT+bDb90sbMSrVYrLpeLLCHBzJw0nk/fH0795+rcz+aKpDrKkEjS3a/JFJ7nA3to8FB8nclk4mJ4OK8MfIMzZ89SrEgRhr81mAL5893yc+s3bmLTlq04HI6bTsIQSQuUIRFjlCERY5QhkftHyRBJA24sBv8wd563GNy7Wxfq1KrJpDEjyZ0zJ2s3bGTUNxNSuskiqZLJZMLlduFwOHFcG2B3OBxkCQlm4uiRPFL6YTZt2cr3P/6Usg0VSaWUIZHEaTKFiHGeHO3cvYfhH3/K+QsXKVumNF+P+JhCBQve9HNmiwV/f3+sVit+t9h+UMTXKUMixihDIsYoQyL3jwrBImlE3GLwkOHve4vBdWrV5ItPPiR/3rysXruOxq3aep8hJyLxmUwm3G43Tuf17TizhAQzecwoHn+0HGvWb+CFZi1xOp2ahSiSCGVIxBhNphC5M38uWsyXY8Zy4WI45R4pw9f/+4SqlZ8kY4YM3vd4+pnQbFnJHhrK/gMH2bVnT0o1WSRVUYZEjFGGRIxRhkTuLY2wiaQhbrcbs9lMZGQk/d4YzMK/F2GxWMmQPj3rNm6iadsO3iKwtg8UScizEsvf358L4eFkypiRKWNH81i5sqzdsJEmrdt5M3SzbT1F0jJlSMQYTaYQuTMXLl7kx3m/8OmXX3Pu/HnKlCrJ8LcG07dnd0oUi33WnNvtJmeO7EwY+RUhIcGsWruWAwcPpXDLRVIHZUjEGGVIxBhlSOTeMoUWKHxn+5CJiE/JnTMnc2dOI3euXKzftJlGLdvgcDhUBBa5hcyZMrFw3o8EBgbw2uChdGnfloqPl2ftho3xVtMrQyKJU4ZEjMmcKRO/z/2BfHnz8GStZzl37jzTJ4zj0bKPKEciiUifPoinKlXio3eHki1rVpxOJ9HRV1m6ciUZ0qenSKFChIVmY+mKlbTq1BWn0+mdcCEiypCIUcqQiDHKkMi9oUKwSBpUtHAhvh0/lty5crF63XqatmmvQUOROxCcOTPz58wib+7cnDh5kty5cmngXeQuKEMixmgyhUjS5MqZg8ED+lOsSGGKFSniff3kqdMsXbmSfoMGKz8it6AMiRijDIkYowyJGKNCsEgakyljRqaNH0v5cmVZs35DvG041VGK3FrmTJn4+fuZPFQgP2azmQ2bNtOgRWtlSOQOKUMixmgyhcjdM5vNuFwuAvz9yRISQtXKT3pf+2fnTv7ZsTP20QXKj0iilCERY5QhEWOUIRHjVAgWSYP69uxOyeLF6PFyPw0aityFAH9/fpn9HSWLF2PN+g00bdNeW6qL3AVlSMQYTaYQuT+0haCIMcqQiDHKkIgxypDIrakQLJKGJNYpatBQ5O7ky5OHIa+/Rve+r6qAJZIEypBI0mkyhYiIiIiIiIjcDRWCRURE7tCNA+0aeBe5O8qQiHGaTCEiIiIiIiIid0qFYBERERERkQeAJlOIiIiIiIiIyN0wp3QDRERERERE5PZuLPqqCCwiIiIiIiIit2JN6QaICOTJlQu7w0E6Pz8OHTmS0s0ReeAoQyLGKEMiIpJSsmXNwkMFChAWGsrlK1f4Z8dOzpw9m9LNEnlgKEMixihDIsYoQyKpnwrBIimoSYP6PFWpIs8/+wzR0VdJly4dE6ZO4/c//2bztm0p3TyRVE8ZEjFGGRK5NzSZQiRpOrZpTb26z/JE+ce8r/29ZCk//PQzP/06PwVbJvJgUIZEjFGGRIxRhkQeDCoEi6SQN1/rR+9uXbw/O10ugoICealHNx4qWIDR30xg45atd/29JpMJt1uP/hbfpwyJGKMMiRinyRQiSTd00EC6d+qA2+3m/IWLnDh5kpLFi1Hj6ar4+/uza88e9uz7N6WbKZJqKUMixihDIsYoQyIPDj0jWCQFvP3G6/Tu1gWHw8Hwj0fQqddL9Hi5H5OnzwTgudrPUKd2rdt+j8lk8v69XJnSZMqYUYPvkiYoQyLGKEMixr35Wj++GvERLzZqQGBgIDY/m3cyRa9unXn0kTJJ+t64uRLxVYMH9Kd7pw7Y7Q56vtKfF15sQc16Dfjg088AePKJCpR5+OHbfk/cvBQp9BCBgYH3rc0iqYkyJGKMMiRijDIk8mBRIVgkmXVu14ZuHdsTHR1No1ZtGTluPL/9sZC/lyxl0NB3+GbyFAB6dOpIiWJFb/ldnsH2bp06MGPSePq91Esdpvg8ZUjEGGVIxDhNphBJum4d29Ora2eioqJo1KoNP/06n/0HDgDw5eixLPx7EQA5sofd9rs8eeneqSPfjh9H62ZNsdls96/xIqmAMiRijDIkYowyJPLgUSFYJBmVK1OaNs2bAdClz8us37jJe8xisQAwc/Ycjh4/jsvlIjRbttt+Z47sYbw9aCCZMmakcsWKFC9SGNBqEvFNypCIMcqQiHGaTCGSdE8/VZluHTsA0L57LzZs2hzveFBQEMHBwQBcunwZm81G9rAwAgMCvO8xm+MPYxQskJ+hgwaQJ3cu6tZ+hrA76LtEHlTKkIgxypCIMcqQyINJhWCRZPRkxSco9FBBps+azdIVK+MdczqdAOzas5cLFy5itVop83Cp237niZOnaNa+I06nk+JFi/B0lacAtJpEfJIyJGKMMiRijCZTiCRdUFAQz1SvFvs7//6HLFu5ynvMMyCYLWsWMmXIQFRUFLly5uD7qZNY8ONsJoz6ilf79ALA5XLFG0D878BBXh44CKfTSYXHHqVWjWrJe2IiyUQZEjFGGRIxRhkSeXCpECySTNKnD6Jti2aYzWaWrVyF3W5P8B6r1QrAseMn4v18KyaTiWUrVjFwyNsAPP5oOe9ApIgvUYZEjFGGRIzTZAqRpHO7XJw8fZqf5y9g8bLl3tctFgsulwuAIQNfo0jhQpjMZhrWe57cuXKSPSyUp5+qzGt9+/DFxx8CeN/v8f2PP/HZyNEAlCpePJnOSCR5KUMixihDIsYoQyIPLhWCRZLJlSsRvD70XX78+Rd+/f2PRN/jcDgAOHHyJAABAf7ArVeEeAYJV6xZw+JlK6jyZCUKFSxwL5sukiooQyLGKEMixmgyhYgxkVFRzPh+Np988RV7/93vfd0ziWLcl59T95laXLgYTq9XX6NF+06Ur1Kddl178PP8BdjtDl5s1IC+Pbsn+G63283SFSvZ++9+6tV9ljy5ciXbeYkkF2VIxBhlSMQYZUjkwXX7kQkRuWf+XrKUjZu33HaFh/3aQLyfzQbEDhC63W7MZjMulws/PxsxMfZ4xw4dPsLSlSt5rNwjREVF398TEUkhypCIMcqQSNJ5JlM0euH5JE2muFnubpxMUa1KZQoVLMCeff/e61MQSXFnz53n7LnzCV7v91Jv6tV9lgOHDtG+W894g4sLFy3m4qVL5M6Vk7JlSt90pf2GTZv5e8lSOrVrQ0wiEzVEfIEyJGKMMiRijDIk8mDSimCRZHYxPPymA4GeFVd2R2xn5xlk9zw7weVykSUkmE+GD6NT2zZA/G0Dx06YxJM1a3P46NH7eQoiKUoZEjFGGRJJur+XLOXNd4YnaTIFXH92lp+fzftezzHPZIrLV65oMoWkOTNn/8APP/1M2y492Pvvfm8uPH+u27CRv5YsxWQy8UT58oQEB8f7vOd97374MU9Uq8mp06eT9wREUpgyJGKMMiRijDIkkrppRbBIanRtbNHTCdpsNux2O1lCgpk6bgzlHimD2x3/WQqeAfozZ88ld2tFUh9lSMQYZUjkpi6Gh9/0mGfl7+0mU7z1+kC2bd/BhKnTEkym+HHez8qRpClms5njJ07Sd8DruFyueCvo4+bj5KlTAFy5csW7BaGH2+32fu7EyVPJ13iRVEAZEjFGGRIxRhkSSf20IljkHitWpHCSP+vpHD0D757VIp7B98ljRlHukTKs37SZfm8MifdZlyv+gLzIg0oZEjFGGRJJBRKZTOEpAk8dN4YXGzXg4ZLF433Es1pYRWB50N1tP+TpPzx/3rji3pONYkWKAPDXkqWEX7qU4Htut1Jf5EGhDIkYowyJGKMMifgeFYJF7qEP3x3KD99OoVb1pw19j/va6KHLFftntqxZmTRmFI+VK8vaDRtp2KI1TqcTi8VitMkiqYoyJGKMMiRinCZTiCTdveqHPDwr6fPlyUOD5+ty6fJlVq9dd0++WyQ1UoZEjFGGRIxRhkR8kwrBIvfI0EEDadeyBcGZM/NG/1ep8XTVu/4Oz6Chx/ETJ7BarUwc9RXlrw2+N27V1jv4fuM2GiIPMmVIxBhlSMQ4TaYQSbp70Q/F5Rk4zJolhAmjviI0WzZ++2Mhv/7+xz1qsUjqogyJGKMMiRijDIn4LhWCRe6BJg3q07VDO9xuN2fPnaNYkSK89fqAu+4wPatIzKbYaJYoVowfZ0zzDhpq8F18lTIkYowyJGKcJlOIJN296oficrlc5MyRne8mT6Rk8WIsX7Wa/m++5X2GnIgvUYZEjFGGRIxRhkR8mzWlGyDyoCtetAhNGryA2WxmyoyZnDp9hqYN61Ok0EO89foAAP5esvSuvtPTGdarU5uAgADWbdykQUPxWcqQiDHKkIhxNxv4gLvLjyZTSFp0P/qh0qVKUvihhxg8oD/Zw0L5a/ES2nfvpfyIT1KGRIxRhkSMUYZEfJ8KwSIGWCwWnq/zLFUrP8meffv4Ye48NmzewqVLl+nUrnWSO0y73Q5AQEAA6zdt1qCh+CxlSMQYZUjEOE2mEEm6+9EP5cyRnXnfTcff358LF8OZ9O0MBr87HJfLpfyIz1GGRIxRhkSMUYZE0gYVgkUMcDqdxMTEADD7WkcJMHHat5hM0LFt0jrMlWvW0qd7V7bv3BXvGXLqKMXXKEMixihDIsZoMoWIMfejHzp+4iS9+w/g+Wdr8/P8Bfz+19+43W7MZrPyIz5HGRIxRhkSMUYZEkkbTKEFCrtTuhEiD7pqVZ5i8bLlAFitVhwOBwCd27WhY9vWFMiXj73/7ufdDz++40HE8o+WY/PWbTgcDg0ais9ThkSMUYZEkq5vz+68/urLDP94BCPHjQdiV/R2ats6yfmpWvlJvps8ge07d/FswyYqAovPux/9UDo/P65eG5g0mUzerddFfJEyJGKMMiRijDIk4ttUCBYxwGw243K5Evwct3Pr1LYNHdu2omD+/Ozb/x/vfvgxfy1ekuC7PJ2szWbzriIBNGgoPk0ZEjFGGRK5NzSZQiRp7kc/FDeDIr5OGRIxRhkSMUYZEkkbzCndAJEHWdyOMu7Pbrfb+2y4CVOnMXHqdP47eJDCDxXkrdcHULPa0/E+V6xIYSaPGUmRQg/FG3wHNGgoPk0ZEjFGGRIxxmyOvR3yFIHNZjMOh8Obn/FTpjFhyrf8d/AgRQo9xNBBAxPkx8NqjX3qjs1mA2D9xk0qAovPux/9kAYOJS1RhkSMUYZEjFGGRNIGPSNY5D7xdJhut5sJU6cB0LFtK2+HaTaZWLhoMUUKPcSUsaPJmyc3p8+c5dVBb6Zwy0VSB2VIxBhlSOT2bjfwcbP8APFmwRcrUpjBA/rz7ocfs/ff/fG+U0VgSavUD4kYowyJGKMMiRijDIn4Dm0NLXKf3Wwrjb37/mXS9Bl069ie/HnzsnrtOpq27aDBQpEbKEMixihDIkl3qy3Rhn/0iXfgY9o3Y8mbJzczZ8/RwIfIDdQPiRijDIkYowyJGKMMiTz4VAgWSQZxO8wu7dvSrlVLHiqQn+joaPz9/eN1lNo+UCQhZUjEGGVIJOk08CFinPohEWOUIRFjlCERY5QhkQebnhEskgziPlfhm8lT+e2PhURfvYq/vz/rN21WRylyG8qQiDHKkEjS3fh8rMnfzmD/gYMUKVyIoa8PSFAEtlgsKdxikdRH/ZCIMcqQiDHKkIgxypDIg02FYJFk4pk1lS9vHho8/xz+6dKxftNmGrVso45S5A4oQyLGKEMiSaeBDxHj1A+JGKMMiRijDIkYowyJPLhUCBa5Dc+qDrPZeFyKFHqIH2dMI0/uXKxet56GLVrjcDjUUYpPU4ZEjFGGRFIHDXxIWqV+SMQYZUjEGGVIxBhlSERUCBa5BbPZjNPpJEtIMN9PncQT5R9L8ncF+PvzUo/u5MyePXblSJv2GjQUn6cMiRijDIkYp4EPkaRTPyRijDIkYowyJGKMMiQiANaUboBIauZyuciQIT2TxoyifLmyXI2J4Z+du4iIiLjr74qKjubXBb9z9tw5hn30iTpKSROUIRFjlCERY+IOfIz98nNGfPEVa9ZvSNJ3aeBD0iL1QyLGKEMixihDIsYoQyICYAotUNid0o0QSc1ef/Vl+vbszt9LlvLK629w5uy5e/K96iglrVCGRIxRhkSMyZAhPdMnfEP5cmVZtGw5Xfu8nKSBD4Bna9bgicfLa+BD0hT1QyLGKEMixihDIsYoQyKiFcEiN/B0YmazGZfLRdkypTlw6BBd+7xMZFQUJpPJ+4w4I9RRiq9ShkSMUYZE7q1eXTpTvlxZ78BHUovAAL//9Te///U3oIEP8V3qh0SMUYZEjFGGRIxRhkTkRnpGsMgNnE4nWbOE8On7w3ju2Wd4pPTDLFm2gsioKCwWyz3pKEV8mTIkYowyJGLMjc8EjjvwcebsOUwm0z3552jgQ3yV+iERY5QhEWOUIRFjlCERuZFWBIvcwGq1MmPieB4uWYKsWbJgMpk4efo0gDpKkTugDIkYowyJGOMZ+HjztX78tWQpj5R+mDk//ewd+FABV+TW1A+JGKMMiRijDIkYowyJyI20IljkBg6Hg6/GjOP8hYvUrPY0GdKn58knKmCz2XC5XCndPJFUTxkSMUYZEjHGM/DRvEljmjdupIEPkbukfkjEGGVIxBhlSMQYZUhEbqRCsE/2CwgAABB6SURBVEgiflnwO/3fGMyFi+EAFClUiPrP1fVuMSgit6YMiRijDIkknQY+RIxTPyRijDIkYowyJGKMMiQicSn5Ijex4M+/6DfoTS6Gh5M9LJSmDetT4bFHU7pZIg8MZUjEGGVIJOk08CFinPohEWOUIRFjlCERY5QhEfHQM4JFbmAymYDYrQMX/PkXLpeLL0d8RJUnKxETE4Pb7WbN+g0p3EqR1EsZEjFGGRK5Nxb8+RcA//vwPe/Ax7Hjx1m9bn0Kt0wkdVM/JGKMMiRijDIkYowyJCI3UiFY0iSz2YzL5fL+mTNHdq5evcq58xe8z46zWCw4nU7++HsRL/UfyJcjPqJmtae936EOU9IyZUjEGGVI5P7RwIfI7akfEjFGGRIxRhkSMUYZEpG7oUKwpDmeTjBDhvT06daVRx8pw2OPluPixYv8tXgps36cy9Zt/3A1JkYdpkgilCERY5QhEeM08CGSdOqHRIxRhkSMUYZEjFGGRORumUILFHandCNEkotnsDA4c2amjhvNY+XKEhUVRfTVGNIHBWGzWdm7719m/TiXKTO+IyIiwtthAtSuUZ0vR3xExgwZ+HPRYkZ+M4G16jAlDVGGRIxRhkSMS8rAB8TPz1+LlzBy3HgNfEiao35IxBhlSMQYZUjEGGVIRJJChWBJM0wmE263m0wZM/L9tEmULlmSrf9sZ9hHn3D02HEcTgfDhwzm2Vo1OHrsGHPm/cJXY79JtMP84pMPyZQxIxs3b2HAkLfZuXt3Cp+dyP2nDIkYowyJGKeBD5GkUz8kYowyJGKMMiRijDIkIkllTukGiCQXt9tNYEAAH747lNIlS7Jo2XJeaNaClWvWcujIEY4dP0H+fHkAyJ0rF62bv0jvrp0JCgrC6XRisVgA+OPvRfTpPwCAgIAAdu/dm2LnJJKclCERY5QhEWNMJhMul4tMGTPy3ZQJPFauLFv/2U6bLt2p07AJFWvU4vc//6ZI4UJ0aN2SPt26JJqfl/oPJPzSJWpVr8aQAf0pUaxYCp+ZSPJQPyRijDIkYowyJGKMMiQiSaUVwZKmNGlQny8+/oDtu3ZRr2lzYmLsAAQGBvLjjGmUKVWSjZu3cPL0aZ6pXp1z588za86Pic6eevyxR9m4eQtOp9M7I0vE1ylDIsYoQyLGBAYE8OkHw2nw/HMsWracDt17enMEsPi3nylWpAgA586fZ9rMWXw9bnyC/NSq/jRTx41h5+491HqhIS6XK0XORyS5qR8SMUYZEjFGGRIxRhkSkaSwpnQDRJKL1WrludrPcOnyZV4f8o63o/RPl47vp0ykTKmSrFqzlsat25EjexgF8+eneNEiNK5fD5PJxJdjxhEREYHVasXhcLBuw0aAeB2oiC9ThkSMUYZEjKtb+xleqFuHbTt2xCsCewY+ihUpEm/go3mTxphMpgQDH38uWkL95q3YuHkLLpdLAx+SJqgfEjFGGRIxRhkSMUYZEpGk0tbQ4lM+/+h9Xn/15USPORwOduzezep16zl4+DAAfn42Rn42gkfLPsKWbf/QsWcfAE6cPMXHn30BxG6l0axxI3p26UT69EE4HI5436uOUnyJMiRijDIkcv/c6cDH802bM2TYe/z7339kDwulcf16vNS9q3dLNKs1di7sug0bvVukqQgsvkL9kIgxypCIMcqQiDHKkIjcD1oRLD7jo2Fv06xxIwCuRETw9dhvvMc8qzxGfPEVodmyceHiRQCaNmhA3WdqcfDwYdp27U74pUvYbDbsdju79u7l7LlzRF+9SlBgEK/27snJk6eY9t2slDg9kftOGRIxRhkSMe7zj97n5KnTfPi/zxMc8wx8uHHf8cDHpDEjvQMfDqeT0eMncOVKRLzv1cCH+Ar1QyLGKEMixihDIsYoQyJyv6gQLD6hbcvmtG3RnOjoaPz9/XnztX643W5GjhsPgNvt9naYp8+c8X7uicfLAzB6/ETOnD2HxWLBbr/2nDk3mE1mVq5eQ4YMGQgJDmbG7B+S/dxEkoMyJGKMMiRinAY+RJJO/ZCIMcqQiDHKkIgxypCI3E8qBItP+O/AQe/ft+3YQemSJRk8oD9AvA7Tw2QykS1rVp58ogIAZ8+dB/BuF+hwOMibJzchIcH8uXgJy1eu5kpEBC6XC7PZjMvlSqYzE0keypCIMcqQiDEa+BAxRv2QiDHKkIgxypCIMcqQiNxPekaw+IQVq9cw7btZ+Pn58dfipd4VJIMH9KdX184J3u8ZRNy1dy8ApUuWIDhzZiB228EsIcG8O3gQMXY7p06f4dLly7hcLkwmkzpK8UnKkIgxypCIMTcOfEDC/Nw48BGaLdtNBz6AeAMfrwx8g8at2uJ0OjGbdQskvkf9kIgxypCIMcqQiDHKkIjcTxoFEZ+xcfMWzGYzjV+oxx9/L2LStzOAm3eYZrOZLdv+AaBJw/p069ie2jWq80z1asybNZNiRYrw8/wFbNy8xfuZuAOQIr5GGRIxRhkSSToNfIgYp35IxBhlSMQYZUjEGGVIRO4XU2iBwkq/+IyZk8ZT5clK9Hr1NdZu2MDAV/p6nzU3/OMR3q00PDJlzMjoz/9HtSqVvVsORl+9in+6dCxftZoWHTrjdDq9WxGK+DplSMQYZUgk6Zo1bsjnH33AocNH6N1/AI1eqEeH1i2BxPNjNpvp91JvXu3dk2MnTvDD3Hls3roNt9vNW4MG8lCB/Pzw08+89NpA5UfSDPVDIsYoQyLGKEMixihDInI/aEWw+ATPFn/z5v+G2Wym0QvPc+LkKd4f8RkzZ88BEs6eMpvNhF+6RM9X+jF5+kw2b90GwK7de5gyY6a3o7RYLOooxecpQyLGKEMixs2aM5cly1eQJ3cucufMyVdjxjJrzo9A4rPgXS4X4yZOZvGyFeTKkYOXenRj8thRjP3qcx4qkJ/lq1bz8sBB3gEREV+mfkjEGGVIxBhlSMQYZUhE7ietCJYHip+fjZgY+02P58gexvwfvidH9jC69nmZXxb8Tt7cuXmpRzdaNWsKxJ89ZbFYcDqd2Gw2LGYz+fPl5fDRY0RGRsY7LuIrlCERY5QhkfvDbDbjcrlo3qQRn334Pn8uWkzbrj0IzZaN1199mRZNGwPx8+P5TOZMmRj46suULlmCco+UYfPWbWzbsYM33xnuHfhQjsRXqB8SMUYZEjFGGRIxRhkSkZSgFcHywBgy8DUG9XuVKk9Wive6Z8aUyWTixMlTfDZyFC6Xi4oVHgfg8NGjfDl6LNNnzQbiz57ybI1ht9uJvnqV3Xv3eTtKz3ERX6EMiRijDIkY5+dnS/R1z3N7l65YyYmTp6hVvRr16jzL6TNn+Hzk6ETz43K5sFgsXAwP563h79O4VVuq1a1Hkzbtef2td1QEFp+jfkjEGGVIxBhlSMQYZUhEUopWBMsD4aNhb9O2RXPvgN7Umd+xas065s3/LcF7y5UpzbTx4wgJzky9F1uwYdNmAPLkykXfnt0TnT0l4uuUIRFjlCER44YMfA2z2cziZctZtnKV93XPyl7Pc6vatGjGh+8MZcqM73jj7XcBbjkLXs+7krRA/ZCIMcqQiDHKkIgxypCIpCStCJZUr33rlrRt0RyA8EuXOX7yJC82asiYL/7H91Mn8Xyd2uTNndv7/k1bt/H9j3MBqPtMLaxWKwBHjh3ji1Fj4s2e6tG5YzKfjUjyU4ZEjFGGRIz7aNjb9OzSiS7t2zJrykQ+GvY29Z+rC1xfDewp5u7YuYuL4Zfo0Lolj5UrC8TOgr8xP55Z8CoCi69TPyRijDIkYowyJGKMMiQiKU2FYEn1jh47zl+Ll+ByubDZrGzaspV+g95k775/eapSRb7+9BPmTJ9Cs8YNKVWiOAAzZ//A6TNneK72M2TOlNH7XZ4O89vvvgfgrdcH0Lh+vRQ5L5HkogyJGKMMiRijgQ8RY9QPiRijDIkYowyJGKMMiUhKUyFYUr2/Fi9h3KQpLF62nAzp01P3mVpkypSJp+vWo/8bQ1ixeg25c+Xi848+YNLorxk8oD8mk4n9Bw6SN09uenbpjMlk8n7fkWPH+HL0WH5Z8Dv79v/HvPkLUvDsRO4/ZUjEGGVIxBgNfIgYo35IxBhlSMQYZUjEGGVIRFKanhEsD4ynKlWka4d21Kz2NABvvDOMSdOmA9CscUMqVnicxi/Uw2q1svWf7dhsNkoUK8qK1Wvo0rsvF8PD431faLZsnDl7FrfbjcViwel0JvMZiSQvZUjEGGVIJOmeqlSRbh3bU+PpqrhcLgYPe4/J386gZdMm1KldixpVqwBw9Ngx5s1fwOy5P/HBO0Op+Hh5Ro+fyLCPPom3BXSeXLkY8vprFCtShOrPvYDD4UipUxNJNuqHRIxRhkSMUYZEjFGGRCSlqBAsD5SnKlWkS/u21KpeDYD3R/yPr8aMA8BqtVKiWFE6tG7JE4+XJ3/evACsWb+BlwcM4tCRI4l+p8lk0rPlJM1QhkSMUYZEkk4DHyLGqR8SMUYZEjFGGRIxRhkSkZSgQrA8cG7sMN/75FO+HvuN93iAvz8ZM2akT7cumEwm/ly8hCXLV6RUc0VSHWVIxBhlSCTpNPAhYpz6IRFjlCERY5QhEWOUIRFJbioEywPpxg5z2EefMOqbCQDxVoRkzJCBS5cvp1g7RVIrZUjEGGVIJOk08CFinPohEWOUIRFjlCERY5QhEUlO1pRugEhSLF+12vv3WtWrMWTgawCM+mYCTqcTq9WKw+FQRylyE8qQiDHKkEjS3ZifN1/rh8vl8g58xNjtnDp9msHD3tPAh8hNqB8SMUYZEjFGGRIxRhkSkeSkQrA8sG7VYTocDm0TKHIbypCIMcqQSNJp4EPEOPVDIsYoQyLGKEMixihDIpJcVAiWB9qtOky3243ZbMblcqVU80RSPWVIxBhlSCTpNPAhYpz6IRFjlCERY5QhEWOUIRFJDioEywMvsQ7T5XIzZsJEdZQid0AZEjFGGRJJOg18iBinfkjEGGVIxBhlSMQYZUhE7jcVgsUneDpMt9tNrerVGDpoAKfPnObHn39N4ZaJPBiUIRFjlCGRpNPAh4hx6odEjFGGRIxRhkSMUYZE5H5SIVh8xvJVq3G73QQHB2M2mZg3f0FKN0nkgaIMiRijDIkknQY+RIxTPyRijDIkYowyJGKMMiQi94sptEBhPXhLfErpUiX5Z8dO3G43FosFp9OZ0k0SeaAoQyLGKEMiSVe54hMMeKUvZpOJ+s1bKT8iSaB+SMQYZUjEGGVIxBhlSETuNRWCxWeZTCbcbv16iySVMiRijDIkkjQa+BC5N9QPiRijDIkYowyJGKMMici9okKwiIiIiIikOhr4EBERERERERExxpzSDRAREREREbmRisAiIiIiIiIiIsaoECwiIiIiIiIiIiIiIiIi4mNUCBYRERERERERERERERER8TEqBIuIiIiIiIiIiIiIiIiI+BgVgkVEREREREREREREREREfIwKwSIiIiIiIiIiIiIiIiIiPkaFYBERERERERERERERERERH6NCsIiIiIiIiIiIiIiIiIiIj1EhWERERERERERERERERETEx6gQLCIiIiIiIiIiIiIiIiLiY1QIFhERERERERERERERERHxMSoEi4iIiIiIiIiIiIiIiIj4mP8DXSg54rifhRUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 2000x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stoch = plot_this(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index,Start Date,Open Price,High Price,Low Price,Close Price,Volume,End Date\n",
+      "0,2023-03-12 18:30:00,3.996,4.038,3.988,4.008,1216259.0,2023-03-12 18:44:59.999\n",
+      "1,2023-03-12 18:45:00,4.008,4.024,3.99,3.993,638860.0,2023-03-12 18:59:59.999\n",
+      "2,2023-03-12 19:00:00,3.993,4.024,3.992,4.019,297226.0,2023-03-12 19:14:59.999\n",
+      "3,2023-03-12 19:15:00,4.018,4.023,3.973,3.985,1101139.0,2023-03-12 19:29:59.999\n",
+      "4,2023-03-12 19:30:00,3.986,4.003,3.976,3.993,427351.0,2023-03-12 19:44:59.999\n",
+      "5,2023-03-12 19:45:00,3.993,4.01,3.965,3.975,750141.0,2023-03-12 19:59:59.999\n",
+      "6,2023-03-12 20:00:00,3.976,3.998,3.967,3.988,552681.0,2023-03-12 20:14:59.999\n",
+      "7,2023-03-12 20:15:00,3.989,4.009,3.983,4.004,322794.0,2023-03-12 20:29:59.999\n",
+      "8,2023-03-12 20:30:00,4.005,4.037,4.003,4.035,682787.0,2023-03-12 20:44:59.999\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(40, 5)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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>Open Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>Close Price</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Start Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-03-12 18:30:00</th>\n",
+       "      <td>3.996</td>\n",
+       "      <td>4.038</td>\n",
+       "      <td>3.988</td>\n",
+       "      <td>4.008</td>\n",
+       "      <td>1216259.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-03-12 18:45:00</th>\n",
+       "      <td>4.008</td>\n",
+       "      <td>4.024</td>\n",
+       "      <td>3.990</td>\n",
+       "      <td>3.993</td>\n",
+       "      <td>638860.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-03-12 19:00:00</th>\n",
+       "      <td>3.993</td>\n",
+       "      <td>4.024</td>\n",
+       "      <td>3.992</td>\n",
+       "      <td>4.019</td>\n",
+       "      <td>297226.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Open Price  High Price  Low Price  Close Price     Volume\n",
+       "Start Date                                                                    \n",
+       "2023-03-12 18:30:00       3.996       4.038      3.988        4.008  1216259.0\n",
+       "2023-03-12 18:45:00       4.008       4.024      3.990        3.993   638860.0\n",
+       "2023-03-12 19:00:00       3.993       4.024      3.992        4.019   297226.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "!head 'so75737197_data.csv'\n",
+    "df = pd.read_csv('so75737197_data.csv',\n",
+    "                 usecols=['Start Date','Open Price','High Price','Low Price','Close Price','Volume'],\n",
+    "                 index_col='Start Date',parse_dates=True)\n",
+    "df.shape\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k_period = 14\n",
+    "d_period = 1\n",
+    "smooth_window = 3\n",
+    "\n",
+    "stochastic = pd.DataFrame()\n",
+    "stochastic['%K'] = ((df['Close Price'] - df['Low Price'].rolling(k_period).min()) \\\n",
+    "                    / (df['High Price'].rolling(k_period).max() - df['Low Price'].rolling(k_period).min())) * 100\n",
+    "stochastic['%D'] = stochastic['%K'].rolling(d_period).mean()\n",
+    "stochastic['%SD'] = stochastic['%D'].rolling(smooth_window).mean()\n",
+    "stochastic['UL'] = 80\n",
+    "stochastic['DL'] = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create my own `marketcolors` style:\n",
+    "mc = mpf.make_marketcolors(up='#0ECB81',down='#F64670',inherit=True)\n",
+    "# Create my own `MatPlotFinance` style:\n",
+    "s  = mpf.make_mpf_style(figcolor='#162125', facecolor= \"#162125\", marketcolors=mc, y_on_right=True, \n",
+    "                        rc={'font.size':18, 'xtick.color': 'w', \n",
+    "                            'axes.labelsize': 12,\n",
+    "                            'ytick.color': 'w', 'ytick.labelcolor': 'w', 'axes.labelcolor':'w'}, \n",
+    "                        gridcolor='white', gridstyle='--', edgecolor='white')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_stochastic_indicator_purple(df, stochastic):\n",
+    "    # Add logic here to determine where the purple circles should be.\n",
+    "\n",
+    "    # For now, for demonstration purposes, \n",
+    "    # we are just hard-coding the 17th position:\n",
+    "\n",
+    "    # We are going to display the circles using mpf.make_addplot(type='scatter')\n",
+    "    # With scatter plots, we want data where we want the circles to appear,\n",
+    "    # and NAN values in all other locations:\n",
+    "    circles = [float('nan')]*len(df)\n",
+    "    circles[17] = stochastic.iloc[17]['%K']\n",
+    "    return circles\n",
+    "\n",
+    "def get_stochastic_indicator_red(df, stochastic):\n",
+    "    # Add logic here to determine where the red circles should be.\n",
+    "\n",
+    "    # For now, for demonstration purposes, \n",
+    "    # we are just hard-coding the 25th position:\n",
+    "\n",
+    "    # We are going to display the circles using mpf.make_addplot(type='scatter')\n",
+    "    # With scatter plots, we want data where we want the circles to appear,\n",
+    "    # and NAN values in all other locations:\n",
+    "    circles = [float('nan')]*len(df)\n",
+    "    circles[25] = stochastic.iloc[25]['%K']\n",
+    "    return circles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1800x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "purple_circles = get_stochastic_indicator_purple(df, stochastic)\n",
+    "red_circles    = get_stochastic_indicator_red(df, stochastic)\n",
+    "\n",
+    "\n",
+    "# Store the plots of the last 120 data rows of upper and lower bounds as well as the entry and exit points\n",
+    "# Raising ylim to 120 so there is room for the circles that may be in the upper 90's close to 100:\n",
+    "range_markers = [float('nan')]*len(df)\n",
+    "range_markers[15] = 50\n",
+    "range_markers[19] = 50\n",
+    "range_markers[23] = 50\n",
+    "range_markers[27] = 50\n",
+    "\n",
+    "# from matplotlib import colors as mcolors\n",
+    "# cp = mcolors.to_rgba('purple',0.25)\n",
+    "# print('cp=',cp)\n",
+    "# cr = mcolors.to_rgba('red',0.25)\n",
+    "# print('cr=',cr)\n",
+    "\n",
+    "ylimits = (-10,140)\n",
+    "plots_to_add = {\"Stochastics\":mpf.make_addplot((stochastic[['%K', '%SD', 'UL', 'DL']]), \n",
+    "                                               ylim=ylimits, panel=2, ylabel=\"Stochastics\", \n",
+    "                                               y_on_right=False),\n",
+    "                # Notice, we make the marker color the same as the plot face color, so the marker appears see-though;\n",
+    "                #         then we set the marker edge colors to the color we want to see:\n",
+    "               \"Purple_Cicles\":mpf.make_addplot(purple_circles,type='scatter',markersize=9600,color='None',#\"#162125\",\n",
+    "                                                edgecolors='purple',marker='o',linewidths=10,panel=2,ylim=ylimits),\n",
+    "                                                #edgecolors=cp,marker='o',linewidths=7,panel=2,ylim=ylimits),\n",
+    "               \"Red_Cicles\":   mpf.make_addplot(red_circles,type='scatter',markersize=9600,color='None',#None,#\"#162125\",\n",
+    "                                                edgecolors='red'   ,marker='o',linewidths=10,panel=2,ylim=ylimits),\n",
+    "                                                #edgecolors=cr   ,marker='o',linewidths=7,panel=2,ylim=ylimits),\n",
+    "               \"RangeMarkers\": mpf.make_addplot(range_markers,type='scatter',markersize=250,color=\"w\",\n",
+    "                                                edgecolors='yellow',marker='^',linewidths=3,panel=2,ylim=(0,140))\n",
+    "\n",
+    "               }\n",
+    "\n",
+    "#ap = list(plots_to_add.values())\n",
+    "\n",
+    "#Plot it\n",
+    "mpf.plot(df,columns=['Open Price','High Price','Low Price','Close Price','Volume'],\n",
+    "         figsize=(18,10),panel_ratios=(5,1.25,2),type=\"candle\",volume=True,style=s,\n",
+    "         tight_layout=True,datetime_format = '%b %d, %H:%M',ylabel = \"Price ($)\",\n",
+    "         show_nontrading=True,warn_too_much_data=870, # Silence the Too Much Data Warning\n",
+    "         addplot = list(plots_to_add.values()), # Add the stochastic plot as well as the bullish entries to the main plot\n",
+    "         title=dict(title=\"APEUSDT - 15m\", fontsize=40, style='italic', weight='bold', color=\"white\"),\n",
+    "         xlabel='Timezone UTC',\n",
+    "         #savefig='apeusdt.png'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (4067800170.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[15], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    stop here\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "stop here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stoch.iloc[10:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stoch.iloc[17]\n",
+    "stoch.iloc[17]['%K']\n",
+    "df.iloc[17]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "purple_circle_ix = 17\n",
+    "red_circle_ix = 25\n",
+    "stoch.iloc[25]\n",
+    "stoch.iloc[25]['%K']\n",
+    "df.iloc[25]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circle_data = [float('nan')]*len(df)\n",
+    "circle_data[17] = stoch.iloc[17]['%K']\n",
+    "circle_data[25] = stoch.iloc[25]['%K']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Evaluate if there's a pattern\n",
+    "previous_signal = \"None\"\n",
+    "bearish_indices = []\n",
+    "bearish_entries = []\n",
+    "for i in range(last_index_nan_value+1,len(df)-3):\n",
+    "    slice_df = df.iloc[i:i+4]    \n",
+    "    if (slice_df[\"Volume\"].max() > df[\"Volume\"][slice_df.index[0]-8:slice_df.index[0]]).all():\n",
+    "        start = slice_df.index[0]\n",
+    "        end = slice_df.index[-1]\n",
+    "        # First BEARISH signal\n",
+    "        all_stochastics_up = stochastic.loc[start:end][[\"%D\", \"%SD\"]].ge(stochastic.loc[start:end][\"UL\"], axis=0).all(axis=1)                \n",
+    "        all_stochastics_down = stochastic.loc[start:end][[\"%D\", \"%SD\"]].le(stochastic.loc[start:end][\"DL\"], axis=0).all(axis=1)\n",
+    "        if all_stochastics_up.sum() >= 2 and previous_signal == \"None\":\n",
+    "\n",
+    "            previous_signal = \"bearish\"\n",
+    "            bearish_indices.append(stochastic.loc[start:end].index.values.tolist())\n",
+    "\n",
+    "    # SHORT ENTRY signal\n",
+    "    if previous_signal == \"bearish\":\n",
+    "        if df[\"Start Date\"].loc[bearish_indices[-1][-1]] < slice_df[\"Start Date\"].iat[0]:           \n",
+    "            if ((slice_df[:2][\"Close Price\"] > slice_df[:2][\"Open Price\"]).all() and (slice_df[-2:][\"Close Price\"] < slice_df[-2:][\"Open Price\"]).all()).all():\n",
+    "                start = slice_df.index[0]\n",
+    "                end = slice_df.index[-1]\n",
+    "\n",
+    "                previous_signal = \"None\"\n",
+    "                bearish_entries.append(stochastic.loc[start:end].index.values.tolist())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  mvals are the markers, i.e. in this case: 'o'\n",
+    "#  cvals are the colors.\n",
+    "# _ = _mscatter(xvals,plot_yvals,ax,mvals,s=marker_size,c=cvals,linewidths=lwids,edgecolors=evals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/scratch_pad/so75737197_data.csv b/examples/scratch_pad/so75737197_data.csv
new file mode 100644
index 00000000..35c92472
--- /dev/null
+++ b/examples/scratch_pad/so75737197_data.csv
@@ -0,0 +1,41 @@
+Index,Start Date,Open Price,High Price,Low Price,Close Price,Volume,End Date
+0,2023-03-12 18:30:00,3.996,4.038,3.988,4.008,1216259.0,2023-03-12 18:44:59.999
+1,2023-03-12 18:45:00,4.008,4.024,3.99,3.993,638860.0,2023-03-12 18:59:59.999
+2,2023-03-12 19:00:00,3.993,4.024,3.992,4.019,297226.0,2023-03-12 19:14:59.999
+3,2023-03-12 19:15:00,4.018,4.023,3.973,3.985,1101139.0,2023-03-12 19:29:59.999
+4,2023-03-12 19:30:00,3.986,4.003,3.976,3.993,427351.0,2023-03-12 19:44:59.999
+5,2023-03-12 19:45:00,3.993,4.01,3.965,3.975,750141.0,2023-03-12 19:59:59.999
+6,2023-03-12 20:00:00,3.976,3.998,3.967,3.988,552681.0,2023-03-12 20:14:59.999
+7,2023-03-12 20:15:00,3.989,4.009,3.983,4.004,322794.0,2023-03-12 20:29:59.999
+8,2023-03-12 20:30:00,4.005,4.037,4.003,4.035,682787.0,2023-03-12 20:44:59.999
+9,2023-03-12 20:45:00,4.035,4.12,4.035,4.091,2179361.0,2023-03-12 20:59:59.999
+10,2023-03-12 21:00:00,4.091,4.096,4.063,4.084,474021.0,2023-03-12 21:14:59.999
+11,2023-03-12 21:15:00,4.084,4.103,4.077,4.087,480628.0,2023-03-12 21:29:59.999
+12,2023-03-12 21:30:00,4.086,4.107,4.076,4.086,212594.0,2023-03-12 21:44:59.999
+13,2023-03-12 21:45:00,4.086,4.107,4.079,4.105,364555.0,2023-03-12 21:59:59.999
+14,2023-03-12 22:00:00,4.104,4.108,4.06,4.072,474296.0,2023-03-12 22:14:59.999
+15,2023-03-12 22:15:00,4.072,4.257,4.069,4.232,3230671.0,2023-03-12 22:29:59.999
+16,2023-03-12 22:30:00,4.232,4.247,4.208,4.241,851126.0,2023-03-12 22:44:59.999
+17,2023-03-12 22:45:00,4.241,4.276,4.218,4.254,1268534.0,2023-03-12 22:59:59.999
+18,2023-03-12 23:00:00,4.255,4.315,4.253,4.312,1469747.0,2023-03-12 23:14:59.999
+19,2023-03-12 23:15:00,4.313,4.354,4.295,4.343,1352840.0,2023-03-12 23:29:59.999
+20,2023-03-12 23:30:00,4.344,4.479,4.336,4.464,1995492.0,2023-03-12 23:44:59.999
+21,2023-03-12 23:45:00,4.463,4.532,4.412,4.517,2488653.0,2023-03-12 23:59:59.999
+22,2023-03-13 00:00:00,4.517,4.592,4.482,4.58,2140025.0,2023-03-13 00:14:59.999
+23,2023-03-13 00:15:00,4.58,4.695,4.552,4.625,1973254.0,2023-03-13 00:29:59.999
+24,2023-03-13 00:30:00,4.626,4.7,4.577,4.677,2444439.0,2023-03-13 00:44:59.999
+25,2023-03-13 00:45:00,4.677,4.678,4.584,4.595,1353901.0,2023-03-13 00:59:59.999
+26,2023-03-13 01:00:00,4.594,4.601,4.528,4.528,1181759.0,2023-03-13 01:14:59.999
+27,2023-03-13 01:15:00,4.528,4.546,4.489,4.499,785683.0,2023-03-13 01:29:59.999
+28,2023-03-13 01:30:00,4.499,4.507,4.473,4.49,634040.0,2023-03-13 01:44:59.999
+29,2023-03-13 01:45:00,4.49,4.5,4.473,4.475,361538.0,2023-03-13 01:59:59.999
+30,2023-03-13 02:00:00,4.476,4.479,4.445,4.45,507443.0,2023-03-13 02:14:59.999
+31,2023-03-13 02:15:00,4.451,4.457,4.422,4.43,514609.0,2023-03-13 02:29:59.999
+32,2023-03-13 02:30:00,4.431,4.438,4.412,4.412,283667.0,2023-03-13 02:44:59.999
+33,2023-03-13 02:45:00,4.413,4.437,4.401,4.435,443083.0,2023-03-13 02:59:59.999
+34,2023-03-13 03:00:00,4.435,4.451,4.411,4.418,304109.0,2023-03-13 03:14:59.999
+35,2023-03-13 03:15:00,4.418,4.435,4.393,4.432,354457.0,2023-03-13 03:29:59.999
+36,2023-03-13 03:30:00,4.433,4.461,4.415,4.449,256813.0,2023-03-13 03:44:59.999
+37,2023-03-13 03:45:00,4.45,4.462,4.435,4.439,226006.0,2023-03-13 03:59:59.999
+38,2023-03-13 04:00:00,4.437,4.464,4.418,4.458,304705.0,2023-03-13 04:14:59.999
+39,2023-03-13 04:15:00,4.459,4.465,4.436,4.439,288049.0,2023-03-13 04:29:59.999