diff --git a/210215.ipynb b/210215.ipynb
new file mode 100644
index 0000000..649355f
--- /dev/null
+++ b/210215.ipynb
@@ -0,0 +1,1618 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_path = Path(\"F:\\Stamatics\\machine_temp_failure_missing.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(file_path)"
+   ]
+  },
+  {
+   "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>timestamp</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>02-12-2013 21:15</td>\n",
+       "      <td>73.967322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-12-2013 21:20</td>\n",
+       "      <td>74.935882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>02-12-2013 21:25</td>\n",
+       "      <td>76.124162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>02-12-2013 21:30</td>\n",
+       "      <td>78.140707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>02-12-2013 21:35</td>\n",
+       "      <td>79.329836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22690</th>\n",
+       "      <td>19-02-2014 15:05</td>\n",
+       "      <td>98.185415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22691</th>\n",
+       "      <td>19-02-2014 15:10</td>\n",
+       "      <td>97.804168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22692</th>\n",
+       "      <td>19-02-2014 15:15</td>\n",
+       "      <td>97.135468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22693</th>\n",
+       "      <td>19-02-2014 15:20</td>\n",
+       "      <td>98.056852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22694</th>\n",
+       "      <td>19-02-2014 15:25</td>\n",
+       "      <td>96.903861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              timestamp      value\n",
+       "0      02-12-2013 21:15  73.967322\n",
+       "1      02-12-2013 21:20  74.935882\n",
+       "2      02-12-2013 21:25  76.124162\n",
+       "3      02-12-2013 21:30  78.140707\n",
+       "4      02-12-2013 21:35  79.329836\n",
+       "...                 ...        ...\n",
+       "22690  19-02-2014 15:05  98.185415\n",
+       "22691  19-02-2014 15:10  97.804168\n",
+       "22692  19-02-2014 15:15  97.135468\n",
+       "22693  19-02-2014 15:20  98.056852\n",
+       "22694  19-02-2014 15:25  96.903861\n",
+       "\n",
+       "[22695 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 22695 entries, 0 to 22694\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   timestamp  22695 non-null  object \n",
+      " 1   value      21410 non-null  float64\n",
+      "dtypes: float64(1), object(1)\n",
+      "memory usage: 354.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>21410.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>85.968188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>13.553070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.084721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>83.082867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>89.251290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>93.910748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>108.510543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              value\n",
+       "count  21410.000000\n",
+       "mean      85.968188\n",
+       "std       13.553070\n",
+       "min        2.084721\n",
+       "25%       83.082867\n",
+       "50%       89.251290\n",
+       "75%       93.910748\n",
+       "max      108.510543"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>timestamp</th>\n",
+       "      <th>temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>02-12-2013 21:15</td>\n",
+       "      <td>73.967322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-12-2013 21:20</td>\n",
+       "      <td>74.935882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>02-12-2013 21:25</td>\n",
+       "      <td>76.124162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>02-12-2013 21:30</td>\n",
+       "      <td>78.140707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>02-12-2013 21:35</td>\n",
+       "      <td>79.329836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22690</th>\n",
+       "      <td>19-02-2014 15:05</td>\n",
+       "      <td>98.185415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22691</th>\n",
+       "      <td>19-02-2014 15:10</td>\n",
+       "      <td>97.804168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22692</th>\n",
+       "      <td>19-02-2014 15:15</td>\n",
+       "      <td>97.135468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22693</th>\n",
+       "      <td>19-02-2014 15:20</td>\n",
+       "      <td>98.056852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22694</th>\n",
+       "      <td>19-02-2014 15:25</td>\n",
+       "      <td>96.903861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              timestamp  temperature\n",
+       "0      02-12-2013 21:15    73.967322\n",
+       "1      02-12-2013 21:20    74.935882\n",
+       "2      02-12-2013 21:25    76.124162\n",
+       "3      02-12-2013 21:30    78.140707\n",
+       "4      02-12-2013 21:35    79.329836\n",
+       "...                 ...          ...\n",
+       "22690  19-02-2014 15:05    98.185415\n",
+       "22691  19-02-2014 15:10    97.804168\n",
+       "22692  19-02-2014 15:15    97.135468\n",
+       "22693  19-02-2014 15:20    98.056852\n",
+       "22694  19-02-2014 15:25    96.903861\n",
+       "\n",
+       "[22695 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.rename(columns={\"value\": \"temperature\"},inplace = True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['timestamp', 'temperature'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['delta_temperature'] = df['temperature'].shift(-1) - df['temperature']\n",
+    "df = df[:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>timestamp</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>delta_temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>02-12-2013 21:15</td>\n",
+       "      <td>73.967322</td>\n",
+       "      <td>0.968560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-12-2013 21:20</td>\n",
+       "      <td>74.935882</td>\n",
+       "      <td>1.188280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>02-12-2013 21:25</td>\n",
+       "      <td>76.124162</td>\n",
+       "      <td>2.016546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>02-12-2013 21:30</td>\n",
+       "      <td>78.140707</td>\n",
+       "      <td>1.189128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>02-12-2013 21:35</td>\n",
+       "      <td>79.329836</td>\n",
+       "      <td>-0.619417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22689</th>\n",
+       "      <td>19-02-2014 15:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22690</th>\n",
+       "      <td>19-02-2014 15:05</td>\n",
+       "      <td>98.185415</td>\n",
+       "      <td>-0.381246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22691</th>\n",
+       "      <td>19-02-2014 15:10</td>\n",
+       "      <td>97.804168</td>\n",
+       "      <td>-0.668700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22692</th>\n",
+       "      <td>19-02-2014 15:15</td>\n",
+       "      <td>97.135468</td>\n",
+       "      <td>0.921384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22693</th>\n",
+       "      <td>19-02-2014 15:20</td>\n",
+       "      <td>98.056852</td>\n",
+       "      <td>-1.152991</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22694 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              timestamp  temperature  delta_temperature\n",
+       "0      02-12-2013 21:15    73.967322           0.968560\n",
+       "1      02-12-2013 21:20    74.935882           1.188280\n",
+       "2      02-12-2013 21:25    76.124162           2.016546\n",
+       "3      02-12-2013 21:30    78.140707           1.189128\n",
+       "4      02-12-2013 21:35    79.329836          -0.619417\n",
+       "...                 ...          ...                ...\n",
+       "22689  19-02-2014 15:00          NaN                NaN\n",
+       "22690  19-02-2014 15:05    98.185415          -0.381246\n",
+       "22691  19-02-2014 15:10    97.804168          -0.668700\n",
+       "22692  19-02-2014 15:15    97.135468           0.921384\n",
+       "22693  19-02-2014 15:20    98.056852          -1.152991\n",
+       "\n",
+       "[22694 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = df['timestamp']\n",
+    "y = df['temperature']\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.xlabel(\"TimeStamps\")\n",
+    "plt.ylabel(\"Temperature\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = df['timestamp']\n",
+    "y = df['delta_temperature']\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.xlabel(\"TimeStamps\")\n",
+    "plt.ylabel(\"Temperature\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "above_threshold = df[df['temperature'] > 70.00]\n",
+    "below_threshold = df[df['temperature'] <= 70.00]\n",
+    "\n",
+    "\n",
+    "plt.scatter(above_threshold['timestamp'], above_threshold['temperature'], color='red', label='Above 70.00')\n",
+    "\n",
+    "\n",
+    "plt.scatter(below_threshold['timestamp'], below_threshold['temperature'], color='blue', label='Below 70.00')\n",
+    "\n",
+    "\n",
+    "plt.xlabel('TimeStamps')\n",
+    "plt.ylabel('Temperature')\n",
+    "plt.legend()\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_dataset(file, date_col=None):\n",
+    "    '''\n",
+    "    folder: is a Path object\n",
+    "    file: the CSV filename in that Path object. \n",
+    "    date_col: specify a date_col to use as index_col \n",
+    "    \n",
+    "    returns: a pandas DataFrame with a DatetimeIndex\n",
+    "    '''\n",
+    "    df = pd.read_csv(file, \n",
+    "                     index_col=date_col, \n",
+    "                     parse_dates=[date_col])\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_dfs(df1, df2, col, title=None, xlabel=None, ylabel=None):\n",
+    "    '''\t\n",
+    "    df1: original dataframe without missing data\n",
+    "    df2: dataframe with missing data\n",
+    "    col: column name that contains missing data\n",
+    "    '''    \n",
+    "    df_missing = df2.rename(columns={col: 'missing'})\n",
+    "    \n",
+    "    columns = df_missing.loc[:, 'missing':].columns.tolist()\n",
+    "    subplots_size = len(columns)\n",
+    "    \n",
+    "    # subplots_size = df2.shape[1]\n",
+    "    fig, ax = plt.subplots(subplots_size+1, 1, sharex=True)\n",
+    "    plt.subplots_adjust(hspace=0.25)\n",
+    "    fig.suptitle = title \n",
+    "    \n",
+    "    df1[col].plot(ax=ax[0], figsize=(10, 16))\n",
+    "    ax[0].set_title('Original Dataset')\n",
+    "    ax[0].set_xlabel(xlabel)\n",
+    "    ax[0].set_ylabel(ylabel)    \n",
+    "    \n",
+    "    for i, colname in enumerate(columns):\n",
+    "        df_missing[colname].plot(ax=ax[i+1])\n",
+    "        ax[i+1].set_title(colname)\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rmse_score(df1, df2, col=None):\n",
+    "    '''\n",
+    "    df1: original dataframe without missing data\n",
+    "    df2: dataframe with missing data\n",
+    "    col: column name that contains missing data\n",
+    "\n",
+    "    returns: a list of scores\n",
+    "    '''\n",
+    "    df_missing = df2.rename(columns={col: 'missing'})\n",
+    "    columns = df_missing.loc[:, 'missing':].columns.tolist()\n",
+    "    scores = []\n",
+    "    for comp_col in columns[1:]:\n",
+    "        rmse = np.sqrt(np.mean((df1[col] - df_missing[comp_col])**2))\n",
+    "        scores.append(rmse)\n",
+    "        print(f'RMSE for {comp_col}: {rmse}')\n",
+    "    return scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>timestamp</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>delta_temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22689</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22690</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22691</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22692</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22693</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22694 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       timestamp  temperature  delta_temperature\n",
+       "0          False        False              False\n",
+       "1          False        False              False\n",
+       "2          False        False              False\n",
+       "3          False        False              False\n",
+       "4          False        False              False\n",
+       "...          ...          ...                ...\n",
+       "22689      False         True               True\n",
+       "22690      False        False              False\n",
+       "22691      False        False              False\n",
+       "22692      False        False              False\n",
+       "22693      False        False              False\n",
+       "\n",
+       "[22694 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file1 = \"F:\\Stamatics\\machine_temp_failure_missing.csv\"\n",
+    "file2 =  \"F:\\Stamatics\\machine_temp_failure_original.csv\"\n",
+    "df_missing = read_dataset(file1, 'timestamp')\n",
+    "df_original = read_dataset(file2, 'timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing.rename(columns={\"value\": \"temperature\"},inplace = True)\n",
+    "df_original.rename(columns ={\"value\":\"temperature\"},inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temperature</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:15:00</th>\n",
+       "      <td>73.967322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:20:00</th>\n",
+       "      <td>74.935882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:25:00</th>\n",
+       "      <td>76.124162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:30:00</th>\n",
+       "      <td>78.140707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:35:00</th>\n",
+       "      <td>79.329836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:05:00</th>\n",
+       "      <td>98.185415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:10:00</th>\n",
+       "      <td>97.804168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:15:00</th>\n",
+       "      <td>97.135468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:20:00</th>\n",
+       "      <td>98.056852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:25:00</th>\n",
+       "      <td>96.903861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     temperature\n",
+       "timestamp                       \n",
+       "2013-02-12 21:15:00    73.967322\n",
+       "2013-02-12 21:20:00    74.935882\n",
+       "2013-02-12 21:25:00    76.124162\n",
+       "2013-02-12 21:30:00    78.140707\n",
+       "2013-02-12 21:35:00    79.329836\n",
+       "...                          ...\n",
+       "2014-02-19 15:05:00    98.185415\n",
+       "2014-02-19 15:10:00    97.804168\n",
+       "2014-02-19 15:15:00    97.135468\n",
+       "2014-02-19 15:20:00    98.056852\n",
+       "2014-02-19 15:25:00    96.903861\n",
+       "\n",
+       "[22695 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_missing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temperature</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013-12-02 21:15:00</th>\n",
+       "      <td>73.967322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-12-02 21:20:00</th>\n",
+       "      <td>74.935882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-12-02 21:25:00</th>\n",
+       "      <td>76.124162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-12-02 21:30:00</th>\n",
+       "      <td>78.140707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-12-02 21:35:00</th>\n",
+       "      <td>79.329836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:05:00</th>\n",
+       "      <td>98.185415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:10:00</th>\n",
+       "      <td>97.804168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:15:00</th>\n",
+       "      <td>97.135468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:20:00</th>\n",
+       "      <td>98.056852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-19 15:25:00</th>\n",
+       "      <td>96.903861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     temperature\n",
+       "timestamp                       \n",
+       "2013-12-02 21:15:00    73.967322\n",
+       "2013-12-02 21:20:00    74.935882\n",
+       "2013-12-02 21:25:00    76.124162\n",
+       "2013-12-02 21:30:00    78.140707\n",
+       "2013-12-02 21:35:00    79.329836\n",
+       "...                          ...\n",
+       "2014-02-19 15:05:00    98.185415\n",
+       "2014-02-19 15:10:00    97.804168\n",
+       "2014-02-19 15:15:00    97.135468\n",
+       "2014-02-19 15:20:00    98.056852\n",
+       "2014-02-19 15:25:00    96.903861\n",
+       "\n",
+       "[22695 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_original"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "temperature    1285\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_missing.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1152 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_dfs(df_original, \n",
+    "         df_missing, \n",
+    "         'temperature',\n",
+    "         title=\"Temp vs Time\",\n",
+    "         xlabel=\"Timestamp\",\n",
+    "         ylabel=\"Temperature\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing['ffill'] = df_missing['temperature'].fillna(method='ffill')\n",
+    "df_missing['bfill'] = df_missing['temperature'].fillna(method='bfill')\n",
+    "df_missing['mean'] = df_missing['temperature'].fillna(df_missing['temperature'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE for ffill: 1.4666115658526897\n",
+      "RMSE for bfill: 5.538329974927272\n",
+      "RMSE for mean: 2.6118121108260417\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = rmse_score(df_original, \n",
+    "                    df_missing, \n",
+    "                    'temperature')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1R1x+abfdBy1xMa6XLl+tXM7djBZrcK1M7g7hPNORbVdEBIQweB0AlFJ3Ariz3+ZKAHNCKZeIhh7fcZX/LT/9A6tAVr7pDTKw8jVD8YJZY7BhX2NAyUqJaOhh5nUiigj2uOVIS5djW/+ZdnYx/SIpf8Zl2a3f0xB45Twcw+qUbqFQG/fFNAtEwxsDKyIaFL4ShNpbhB5YWeGyfeKIlAH7hmNxZndHdP6TclJsg+KnjEodnAoRUURiYEVEg+JbJ4z1+ri95eeyOa77vXjTfLxxy4ku22J0XJbGX+6COedQ8ZjRafjfdxfgF9oyOkQ0PDGwIqJB4SvNgT1smVWQ7rI9IykO0/Ndt8WGocXK3SH7D8g/flzmgBmLRDS88ApARGFxxtSRLvfd5bHy1HsY4yYLen/Xe1g/0NlPz5zkcx8HDwlCmWyBiJwxsCKisOg/AN0eovizPmCMyfely+wj+Jo0MgW3nDbRZzl23b3WAdt8jRsjouGHgRURhUWPxTUoscdZzvGWc4z1zA1zHbf9GbzeP3BLjjMHXkkn2w4fHbCNYRUR9cfAiojCor271+W+vQvQU4PVgvE5jtv+dAWaTf1zXenfaWdLt8DOQCLqw8CKiMKisF++J3t4YvIyxuqp6+bg2oVFSHazYHN/A5ag6Xc30F68SSMHpn1gTyAR9cfAiojCoqPb4nLfn0WYT5qYi1+fP9XtDL2TJubgLxfPdNzv36oVarvSyLSEAdsYWBFRfwysiCgsXtlU5XLfvqRNp1PAlRDrflyUu+Drqevm4qLj8x33+4+xMoWYosFdVyIHrxNRfwysiCgi2OOWlzYedGzLSo7zuq83/Qe4N7X3eN1/8kjvGdPtxf1uybF49eaFAAAr0y0QUT8MrIgoLC49ocDlvj1YunZhIQAgPsbz5cmfBZv7tzD1L8/e1vTkNSfg9VsW4r0fney1PHuglpUchyRthmGwCzoT0dAVUmAlIhki8pKIbBeRbSIyX0SyROR9Edml/c7Uq7JENHTkpMQj26lFyh4sxWkBUP+s5s786dXr32J1x7nHuJRvt2jyCMzIz/BZnmPWIgTlh2ypF+pau7w9hYiGoVBbrO4D8K5SagqAmQC2AbgNwAql1EQAK7T7REQuFBSWXzvHcd/ewGRvafLWGOTPeKn+u9gXc05LiLUdP8DxUc6zDF0CKvYFEpGToAMrEUkHcDKAxwFAKdWtlGoCsATAcm235QAuCK2KRDQU9ViUy1gp+217S5PFS2TlTywzIPhy3PUdULkbw2VPi6WgMC0vfeAOREQIrcWqCEAtgCdEZJOIPCYiyQBGKqUOa/tUAxjpsQQiGrZe3ljl0l1n7wockH/KDX9arPqXY28J86eh6v/Omzpgmzg9v0jLwZUQy2GqROQqlKtCDIDjADyslJoNoA39uv2Ura3d7WVMRJaKSJmIlNXW1oZQDSKKFudNH+24fc60US7Bjz95rOy+u2i8z336D163p25IT9K6Ar08Nzc1fsA2e2lWpRxld/YMXD+QiIa3UAKrgwAOKqXWafdfgi3QqhGR0QCg/T7i7slKqWVKqRKlVElubm4I1SCiaHH/ZbPx0zMnAbANIncOokz9ugK9SUuIxS/OnoK3v3+Sx336t2rNzE/H75Yci3sv0pKIeoms3M1IdA7UWjr7UjdwiBUROQs6sFJKVQM4ICKTtU2LAZQDeB3A1dq2qwG8FlINiWjIMJsEaysbAACl5dUYmebcMqR1Bfp5VfruovGYOibNy7Fc74sIrppfiFHptgzqeZmJHp974oQcj49ZlUK8h8SlRES+F9zy7lYAT4tIHIBKANfAFqy9ICLXAdgH4JIQj0FEQ8ieujYAwIGGDqRqM/SAgbMCQ+WpnLyMRPzr28dhfrHn4Ck5fuCl8fUvDgEAmtt7MMrN8jZERECIgZVSajOAEjcPLQ6lXCIauq47sQi/e7McFzstPwP0rR3oT1egP7wFaGdPG+3xMV/qWrtdUjn4MyaMiIYPTmkhokFlH0Tef5HkbYdtSTf1arHSK0DrT8QWTPVfi5CICGBgRUSDzB6PWPtNqKtr7QYwOC1WgYqLMeGcaaMA9M0Y5HI2ROQOAysiGlR9mdVdA5N5xVkA9AusRqfrNw6qu9eKEVpA5S1xKRERAysiGlSnThmB7OQ4XHtikcv2Y0bbZviF2tJ04ynFAPQNrACgTRsD1j+w4hArInLGwIqIBlVuajw2/OoMRyBlZ88dFerCxj8/awrW3r4YI4KcuWcfOxXXL1/DSxsOAgDe21odUv2IaGhjYEVEEcE+u+5oR4+PPb0zm8SRqypQb956Itb84jQAwHM3znNsL8xOctzu6mW2dSLyjIEVEUWUksKssB17Wl66Iyg7bmymY7sCcNX8cQCARZNcV4pgTyAROWNgRUQRJc7NcjLhphSQney6fmD/rkwiIoCBFRGF2U/PnIT5xdmO+/3HNoXThz9bBABQUI4lcizabMZYM9uqiGigUJe0ISIKyS2nTcQtp0103I+kgMU+QzEhxuwYA2bRhlh9VdUcrmoRUQSLnK+GRESIrCVi8jMT8ZMzJuHf3znBkV/Lnn/LnnUhkupLROHHFisiIg9EBLcutrWmmR0tVkwQSkSescWKiMgP8bG2y6VRaxAS0dDAFisiIj9cUlKAffXtuPW0CQCAmfnp+OJgM9MtEJGLkFusRMQsIptE5E3tfpGIrBORChF5XkTiQq8mEVF4JcSa8auvTUVqQiwAoCAryccziGg40qMr8AcAtjnd/zOAvyulJgBoBHCdDscgIooo3czATkRuhBRYiUg+gPMAPKbdFwCnAXhJ22U5gAtCOQYRUSQqLa8JdxWIKAKF2mL1DwA/B2D/6pYNoEkp1avdPwggz90TRWSpiJSJSFltbW2I1SAiCg9mWyAiZ0EHViLyNQBHlFIbgnm+UmqZUqpEKVWSm5vr+wlERBEkEpfeIaLwC+XKsBDA10VkL4DnYOsCvA9AhojYZxvmA6gKqYZERBHomgWF4a4CEUWgoAMrpdTtSql8pVQhgEsBrFRKXQFgFYCLtN2uBvBayLUkIoowmw80hbsKRBSBjGjL/gWAH4tIBWxjrh434BhERGG1bk8DAKCutTvMNSGiSKJLglCl1GoAq7XblQDm6FEuERERUTTh6EsioiBcf2JRuKtARBGIgRURURDaui3hrgIRRSAGVkREQfhsd124q0BEEYiBFRFREPbWt4e7CkQUgRhYEREREemEgRURERGRThhYEREFISs5LtxVIKIIxMCKiCgIV84bBwD4/mkTwlwTIookDKyIiIJgsSoAQIyZl1Ei6sMrAhFREHodgZWEuSZEFEl0WdKGiGi4uf6kImw7fBSXzxkb7qoQUQRhYEVEFISclHgsv5bLohKRK3YFEhEREekk6MBKRApEZJWIlIvIVhH5gbY9S0TeF5Fd2u9M/apLREREFLlCabHqBfATpdRUAPMA3CwiUwHcBmCFUmoigBXafSIiIqIhL+jASil1WCm1UbvdAmAbgDwASwAs13ZbDuCCEOtIREREFBV0GWMlIoUAZgNYB2CkUuqw9lA1gJF6HIOIiIgo0oUcWIlICoD/AfihUuqo82NKKQVAeXjeUhEpE5Gy2traUKtBREREFHYhBVYiEgtbUPW0UuplbXONiIzWHh8N4Ii75yqllimlSpRSJbm5uaFUg4iIiCgihDIrUAA8DmCbUupvTg+9DuBq7fbVAF4LvnpERERE0SOUBKELAVwJYIuIbNa23QHgbgAviMh1APYBuCSkGhIRERFFiaADK6XUGgCeFslaHGy5RERERNGKmdeJiIiIdMLAioiIiEgnDKyIiIiIdMLAioiIiEgnDKyIiIiIdMLAioiIiEgnDKyIiIiIdMLAioiIiEgnDKyIiIiIdMLAioiIiEgnDKyIiIiIdMLAioiIiEgnDKyIiIiIdGJIYCUiZ4vIDhGpEJHbjDgGERERUaTRPbASETOAfwI4B8BUAJeJyFS9j0NEREQUaWIMKHMOgAqlVCUAiMhzAJYAKDfgWEQ0BK35xamIM3OkAhFFHyMCqzwAB5zuHwQw14DjENEQlZ+ZFO4qEBEFJWxfCUVkqYiUiUhZbW1tuKpBREREpBsjAqsqAAVO9/O1bS6UUsuUUiVKqZLc3FwDqkFEREQ0uIwIrD4HMFFEikQkDsClAF434DhEREREEUX3MVZKqV4RuQXAewDMAP6tlNqq93GIiIiIIo0opcJdB4hILYB94a5HGOQAqNNupwNoDmNdIonzefFmOJ4zf8+NJ0P5nIV6bjyJ5nNm1DnxJdLPWbjOizeRcs4i8dx4MtjnzPncjFNKuR3HFBGB1XAlImVKqRLt9jKl1NJw1ykSOJ8XH/sNu3Pm77nx8vwhe85CPTdeyo3ac2bUOfHjuBF9zsJ1XryJlHMWiefGk8E+Z/6eGyaKiRxvhLsCUYjnLHA8Z4HjOQscz1ngeM4CF5HnjIFVhFBKReQLJJLxnAWO5yxwPGeB4zkLHM9Z4CL1nDGwCq9l4a5AhOJ58YznxjOem4F4TtzjefGM58Yzv84Nx1gRERER6YQtVkREREQ6YWBFREREpBMGVkREREQ6YWBFREREpBMGVkREREQ6YWBFREREpBMGVkREREQ6YWBFREREpBMGVkREREQ6YWBFREREpBMGVkQUNURkrIi0iog5hDJaRaRYz3oREdlxrUAiIiIinbDFioiIiEgnDKyIKOxEZK+I/ExEvhSRNhF5XERGisg7ItIiIh+ISKaIFIqIEpEY7XnfEZFKbZ89InKFtn2CiHwoIs0iUicizzsdS4nIBO32kyLyTxF5SytjnYiMd9r3TBHZoZXzkFbm9YN9fogoejCwIqJIcSGAMwBMAnA+gHcA3AEgF7Zr1feddxaRZAD3AzhHKZUKYAGAzdrDdwEoBZAJIB/AA16OeymA32r7VgD4g1Z+DoCXANwOIBvADu0YREQeMbAiokjxgFKqRilVBeBjAOuUUpuUUp0AXgEw281zrACmiUiiUuqwUmqrtr0HwDgAY5RSnUqpNV6O+4pSar1SqhfA0wBmadvPBbBVKfWy9tj9AKpD/iuJaEhjYEVEkaLG6XaHm/spzjsrpdoAfAvATQAOa915U7SHfw5AAKwXka0icq2X4zoHS+1OxxkD4IDT8RSAg/7/OUQ0HDGwIqKopZR6Tyl1BoDRALYDeFTbXq2UukEpNQbAjQAeso+rCsBh2LoRAQAiIs73iYjcYWBFRFFJG9y+RBtr1QWgFbauQYjIxSJiD4IaASj7YwF4C8B0EblAGyx/M4BR+tSeiIYqBlZEFK1MAH4M4BCABgCnAPiu9tgJANaJSCuA1wH8QClVGUjhSqk6ABcDuAdAPYCpAMpgC+KIiNxiglAiIj+IiAm2MVZXKKVWhbs+RBSZ2GJFROSBiJwlIhkiEg9b6gcBsDbM1SKiCMbAiojIs/kAdgOogy231gVKqY7wVomIIhm7AomIiIh0whYrIiIiIp0wsCIiIiLSSUy4KwAAOTk5qrCwMNzVICIiIvJpw4YNdUqpXHePRURgVVhYiLKysnBXg4iIiMgnEdnn6TF2BRIRERHphIEVERERkU4YWBERERHphIEVEUUVq1Xhb+/vRENbd7irQkQ0AAMrIooqayrqcP+KXfjlK1vCXRUiogEYWBFRVLFYbatFtHdbwlwTIqKBGFgRUVQRsf0OdjGu9Xsa0MGgjIgMwsCKiKKKaJFVMOucHmhoxyWPfIafvLhZ51oREdkwsCKiiPHU2n043NyB5o4e/HNVBQpvewuFt73lso9Ja7Hq6rUGXH5FbSsA4O0t1SHX1ZPLlq3F397faVj5RBTZIiLzOhFFPqUU6tu6kZMSb0j5VU0d+NWrX+FXr3rfr6WzF4CtSy9QuVrdC7ISA36uvz6rrMdnlfX48RmTDDsGEQ30aUUdRATzx2eHtR4+W6xE5N8ickREvnLaliUi74vILu13prZdROR+EakQkS9F5DgjK09Eg2fZR5Uo+f0H2F/fbkj5Da2e0yc4d/t9/9lNQR/DXkxaQmzQZdDgWLX9CApvewsVR1rCXRWKAu9sOYzLH1uHyx5diy0Hm8NaF3+6Ap8EcHa/bbcBWKGUmghghXYfAM4BMFH7WQrgYX2qSUThtmrHEQDAwUZjAqsdNZ4/QPc6BXO91mCHrSPosVVKKdz4VBm+qvJ8wW5o63bptlytnS9vqps7sfDuldhX3xZUvYaylzdVAQBWba8Nc02Gl7WV9Xh7y2HDyi8/dNSQHHTffXqj4/Ynu+t0Lz8QPgMrpdRHAPq3uS8BsFy7vRzABU7b/6Ns1gLIEJHROtWViIaw+BjPl6N3v9JnTNTOGtsYK5N9aqGfDjR04L2tNfjaA2s87tO/Je3BlRU+y31500FUNXXg0Y8rA6qPvzbsa8Rrm6sMKdtoK7bVAAD+8Pa2MNdkeLl02Vp8zylI0du593+Mc+/7WLfyrFaF6uZOl21tXb26lR+MYMdYjVRK2UPaagAjtdt5AA447XdQ22Zc+EtEg0KgzcYzqHyzyXOw8+d3t+O7i8brdixrgDMKe62+B8qvqXD9lny438XenafX7gcA/Hftfvz+gukB1ckfFz78KQBgyaw83cs2GvOUuXfl4+vw2e56VPzx3HBXJWjVR32/N5xZrAqHmjqwv6Ede+vbsK++HXvrbL/3NbShs8f1/dkapYGVg1JKiUjA11oRWQpbdyHGjh0bajWIKMrlpnofFH/kaCdueGqDLsfqtQR2ycpMigv4GFVNHahu7oQIMDItweM+g+FAQzsKspIG5Vhka0UBAJOXLwvB+nhXeLu59NLa1YuU+L4QpMdiRVVjR1/g5PT7YEMHui19wVNcjAnjspIwLjsZJ03MwbicZPzqVccwcDzxyV7cef6xg/r3OAs2sKoRkdFKqcNaV599MEEVgAKn/fK1bQMopZYBWAYAJSUlRn0JJiKdOBJzGvRudRfsJMWZHS0Xc/64QrdjeRvPpad5f1qBOLMJO/9wjtvHT5mUiw93Gj+G6KuqZgZWg6j4jrcxrzgLzy2dH+6qBK3HYkWsWd+MTDVOLVXT7nwPV84bh731bdjf0I6DjR2OVRUA23t/XHYyJo9MxRlTR6IwOxnjspNQmJ2MUWkJA4JW58Aq3IINrF4HcDWAu7Xfrzltv0VEngMwF0CzU5chEQ0BAQ5P8ttlj64dsC0/M9ExLsqd/t96jeJP12FKfIzbLgjnb9r9newUWCmlHMlP9eatDtHgzS8PYUxGIvIzEpGTEh9yS1D5oaOwWBWm56frVMOB1lYGng7El8FcMaC924L0RH0Dq7n9vhy9urkKhdnJmJ6XjvNnjLEFTjm2ACo3Jd6w94PRfF6RRORZAIsA5IjIQQB3whZQvSAi1wHYB+ASbfe3AZwLoAJAO4BrDKgzEYXRFY+tw967zwupDKUUrMoWsFiVQmun+zER3oIqwPZBYw+sqpo6sGFfI74+c0xIdXPHn4mIbd2Bj+tYW1nvuD3/TysxpygLc4qyMLcoCxNGpIT0wdLtlEB1Zn5G0OVEglue6ZsYEGsWjE5PRF5GIsZkJCIvMxF5GQm229q2hFizx7Kqmztx7v22wdOhvo4HW/9xfEb68mATTpqYG1IZSikcaOjA2j31Lq91ACjMTsKqny7SLXiKMUlIM4b15DOwUkpd5uGhxW72VQBuDrVSRBR5th0+6rh92l9Ww6IFRVarLUCyWG3BklLK9pjVNXhy7KeUbt2Jymko/YUPfYrqo50BBVaHmjrwxheHsPTkYq8XeH+Wzwn0bzrY2I73y20z3+JjTDihKAtrK+vx+heHAABZyXE4oTATc4qyMbcoC8eMTvM6wL+/i/71qeO2P4PvI9UDl83GxJEpONTUgaqmTlQ1duBQk+3n0911qDnaOSDwzUmJw5iMRIxJtwVetqArAXkZSbjmyc/D84fooHSrcSsG9Hfl4+sDDjyVUthX3451e+qxtrIB6yrrcUibxJGd7DpO8f/Om6pri1RKQgya2nt0Ky8UzLwewS56+FNcvaAQ5xvwDZwoUI1OF61j89JhFlvaApNJYOp32ywCEYHZ/phJbI97eOyed3cEVSerFQOWvPG3S+2NLw7hVi1FwtnTRqHHohAfY3I7FumQHzP8AuXcovS/7y7AtLx0KKWwv6Ed6/Y0YL32895WW/CVGh+D4wszHS1a0/MyEOclRcWXTkkSX9pQhdvOmaL739DQ1o2MxFhDBmnbjclIwJRRaZgyKs3t4z0WK6qbO23BVnMHqhq1AKypAxW1rfhwZy06eqJ/hmGvxYoPtBQUALC7thXjc1PCWCPbe21PXRvW7WnA2sp6rKtscMz4y0mJw9zibHy3KAvzirMxYUQKim5/2/Fcs1nf14w5groNGVhFsLJ9jSjb18jAiiLOA5fN1rW8YAMr51Y0O6XcjwOrbelyuX+rU96pHosVp//tIwDApl+dgcb2bozLTobZJFBK4YJ/fhJU/bxxDv7sebVEBOOykzEuOxmXlNjmAR1u7nAEWev3NOCeHbZzlRBrwuyCvkBr9thMJMa57wL714e7dQ+sDjd3YP6fVuKWUyfgp2dN1rVsZ8k+xtDFmm3BsKfB+UopNLX3oKqpA1VNHbjzta0BT/ePBBv2Nbp8uVn81w+x/a6zvXZ7BspXy6xSCrtr21xapI5o76vc1HjMK7a1rs4rzsb43GSvX3C+OtiMUyeP0K3uPRE0jpCBVZB6LFY8+nEllp5UjBidZ06QZ61dvZh253u456IZjg8eGr7cdeu8vKkKFx2fP2D7CX/4wGM53392s+P27LveBwBHwLB6h+9Ze+4CPF+cP3JMXi4ho9MTsWRWniMXVX1rFz7f22gLtPbW44GVu3Cfso09mp6Xbus6LM5yKSM/U/+1Ee1JHh9cVWFoYJWeGNryQyKCzOQ4ZCbHYVpeOvbXt0dl0tHS8hrEmU0uExHm/2kFNv36TN2OUXHEdUyjUgoVR1qx1qlFqq7VFkiNTIvH/PHZmFuUjXnFWSjK8R5I9ffGl4dw6+KJutX9qNM4zZMm5uhWbjAYWAXpmic+x5qKOmytOop/XqH/koh/eS+4b/BDXXWzLe/PIx/uZmBFbv30xS/Q3WvF5XP9z49X7iYwWr/XNqvL1/IbFqvCbS9vCayScG1VCyQTfHZKPM6eNgpnTxsFADja2YMN+xodLVqPr6nEvz7c7fIcI1JkNA7SeJZgcoh5E0E9Rn5TSqG0vBoLJ2RjlVOg39jeo+ts0re3uI7hOuEPH6BOW8NzdHoCTpqY42iRGpedFPBxn7l+Li5/bB0AIMbbt4kgjM1Kwv4G29JXT103V9eyA8XAKkj22RlGzdJ4cJXv5TAi2Y9f2IwRqQkDuh+UUujqtaKj24LOXgs6e5xv23+sjt8d2rauHgs6eiyO1oMWD7PIQtXQ1o3H11TiZ2fpPx6FBsdpU0bgjle2oLPHgmtPLDL8eP/5bC++ONAU8POcuyZDGaKUlhCLUyePcHSrdHRbsOlAIy5/dF3whUYQPbu6AO8Z/iPV9uoWHGjowPcWTXAJrABbC+vsggzMKsjE7LEZmFmQEXQr398/2Oly/+RJuZhXlI15xdkoyEoMOYBbMKGvJUnvWD81IXLCmcipSZTytr7ZcHXkaCde3mjLC/vuV4dtgVKvBR3dFnT1Bt8PnhhrdgxCPdJvvIw3PRYr6lu7UdvShbrWLtS2djlu17V2o7alE3Wt3ahr7XLMKpkwIgXfmD2wO2k4i6TpzN7869vH4/vPbsLv3ixHZ68F31s0IbiCtD/V22dJVVMH7n1vB06elIuPAkz0edG/PnPc1nN2VGKcGQvGD25XyF9Ld+BHp08ydBC7XoyuoT8zSANVurUGIsDiY1zHJH195hgkxZmxaX8TVu/c6WiZnDAiBbMLMjB7rC3YmjQyNeCA8pKSfNxz0Uy9/oQBvrNgnK7lRdJnMQOrEMXHGvPPjI8xhRSEhJPzmJQZ+RlIiDUhMdaMhFgz4mPNLvftt+NjzUiIMSMxzrYtIcb2uO0xE+JjTBARlxlg1c2dtkCpxRYs1TkCpm7UOW3zNAU3JT4GOSlxyEmJx4TcFMwvzsZTa/cBAHZUe8+fNBydUJiFz/rlojGaSODdWHExJjx4+Wz85MUvcM+7O9DZbcGPzpgU8LHtXYH9WwjslFL49atfQSngDxdMw0n3rAr4GHaBLgodqKqmDhxu7sDI1IEZq/XwwMoKbK9uwd+/NWtQEraG4jdvlBtavhHfPUrLq3Hc2EyMSHVdGumX5x3jWC6ppbMHXx5sxqb9jdi0vwkrth/BixsOArBlMZ+Rn24LtAoyMGtsxoCy+hODQ1C9h3J4mrgRDpH9DogCCTHG/DOjNagCgOc+3++4fb/Os8eczfvTwCVO3AVLOSnxyEm1bctNjUduSjxyUuLdvhH/u24flALmFGUaVu9oNTYrybDA6kenTxrQDQEEPzYoxmzC3y6ZhfgYE+5fWYHOIN9PbV29eEPLK9Xf21uqsWL7Efzy3GO8LhfzzA2+x3sY0dCTnhiL5o6+LxXz/7QScWYT8jMTkZ+VhILMRIzVZtMVZCZhbFYS0pOC60L6zflTcddb2/DNhz7BY1edgLHZw3f5nA93HvG9UwAONrZj66GjuN3NrE7nbtLUhFgsnJCDhVp3mz11x6b9Tdh8oAmb9jfi0Y8qHa3O+ZmJLoHWsWPcp7Mwit5Z1Uel6T9BI1gMrEJU72Nga7CC6VqIFBv3NxlW9k2njHcMzP3DN6b5FSwF4qHLj8N3n96I7GTvCwIPR3/65nR09Fhwz0UzdC/bflE/bcoIrNyuzweT2SS4+5szEB9jxrKPKoMqY8HdK13uj9AWim5u78Gdr2/FtLw0XLOwcMDzLpg1Bq9utgVknrrl3vvhyTjrH7YUD0a0WE3LS8MnFbZAODU+Br84ZwoONLbjQEM7DjR04MuDTQNac1MTYlCQmYSCLNegqyArCfmZfRnND/VbPPo7C4swcWQqvvf0Rnz9n2vw0BXHDXp3ZKS49skyXcuzJ5E981jbZIUbTy7GI9rr2dvLxjl1xwWzbTNKO3ss2HqoGZv2N2HT/iZs2Nvg+OIQ1292e7QN8k8wqPcoGAysQuRrxlCw7BdwcjU+N9lx+4q5+vbRA0CS1o1h1FiihrZubD7QiNOmjDSkfCOZTGJYC6Q9B01sv6SBr968MKQcUiaT4HdLjkVCrAmPfrwn4Oc7t/gAfR82d7+7HY3t3XjymhPcplv5x6WzHYGVJ5NHpQZcn0A4d+U8u3QepuUNXBfvaGePI9A60NDuCLx217Zh9Y7aAS3nI1LjMTYrCWX7Gh3b7K+JhRNy8PotC3H98jJc+fh63Hn+VFw5b1xQLRNHO42bcXjSxBx8vGvwloYJ1fvlNZg4IgVFObZrXyifOQmxZhw/LgvHj+tLx1Hd3InNBxqx6UATHvmw7wuIPRiLFpEUCDKwilDeMioPZzE6Z+vtr17L0bL1UDOOH6d/d+A1T6zHFweb8dVvz4r4sSiDacH4HORnJuL7iyc6Mo0DA79FB0NEcMe5xwQVWPWnFLCush7Prt+PpScXuw1WgmHEh8KM/HTHrGVP9UxLiMWxY9Jx7JiBj1utCnWtXTjQ2I79TsGXfUq7XYbTDLRx2cl4+XsL8KPnN+PXr23FtsNH8duvTwv4ejbjN6UB7R+IaXnphgVWegeETe3dWLenATedUuz2cT1eNqPSE3B2+micPW20I7C6+Ph8zCvO1qH0gT748cm6p1qINEP7r4ti/NB1z+g3pL379TEdPoTdqaxtA2BbM4/6pCfFYs0vThvwAe/vTCZfQbBe4zl6LFbc/soW5Gcm4oenuyY3DGWMihFdgVNG2+pz1rHBtY6aTIIRaQk4flwWvjE7H99fPBH3XjwTz98432W//v+j1IRYLLuyBDefOh7Prj+AKx5b60gqGQnmFGX53ilI//l0r67lrdx+BBarwplTRzm2tTstz6P3OKV3fnAS5hdn44/fnK5ruc4mjEhFYU6y7x2D9POzjUtW669h8+n95cEmTM9L1/2FaJRQL7RKKbR29aKxrQcN7d1obOtGfZvt9/vbarB+TwM+/vmpXgfdRiKjM+rGa5MRug2aPNDSZcu/FR2vwvDzt8FqZn4GTpqY45gh5c284iysrWwIqj6N7T1obO/B8mvnICnO9fIZaZeW82eMxvjcZLetUXpyN83dZBL87KwpmDwqDT9/6QssefATLLvqeMPr4g89WkHdae3qxWNr9mBUWgKqj3YiJyX0xKalW2swKi0B051aHOcXZ+OtLw+HXLY7x4xOw7NL5xlSttHsXd+R0CgRUg1E5EcAroct48sWANcAGA3gOQDZADYAuFIpZcxAJD+9veUwvvf0Rvztkpn45nHRkZuo/xf17l4rGtu70dDmFCRp9+0/je3dqG+1/W5s63FZ+sCdk+5ZFfDq5f647sQiPL7GmBYfvZMF9jenKAvPlx3AiQYHcNES4Iebv+cpMc6EH57uX0qF/gFRoC6YNQanTMoNqYzBICKDEsiMSvcczH595hgU5yTjhv+U4aKHP8NfLp6J82aMNrxO3kwxaGzbf9fuQ1N7D1753gm4/eUtGBfizMjOHgs+3FmLC4/Pc0mRcdqUvlxWvIpEpqCvMCKSB+D7AKYqpTpE5AUAlwI4F8DflVLPici/AFwH4GFdahuk3dr6R38t3Ym9dW3otih091rRY7H2/e53u6dXoctiRU+vdt/p8cFIheDcYjX9zvccLR3upCfGIis5DlnJccjPTMKM/HRkJccjKzkWmUlxyE6JQ2ZSnGOf6QaOXwBsq7Abxeh4ZHq+7YNIz8VB3TEiieBQ5O+K9bee5v+aY/62Bk8ZlYrt1S0u27KT4/Crr011u7/ReX8iiXN+sfxM7wHEtLx0vH7Libjpvxtw8zMbsaN6An4YxmSi2Snx+N6i8Xj04+BmirrT3t2LRz+qxEkTczB7rD5jM9fsqkNHj8WlGxAAxmRETloBci/UNrMYAIki0gMgCcBhAKcBuFx7fDmA3yDMgZW9e6CqqQP3r6xAnNmEuBgTYs2i/bbd79tuu50eF6ttE8SZ+/aLNZvwpM596f05X3QuKslHVpJtEdFsbTHRrGRbsJSZFBvSItDXPvk5Ys3i+JtjzSbExvTdj9Ee63tcEBtjctk/xtx3fmLNgs1BLO/hL6M/vOyn3WJQ4GNP/PrZ7nrH9Gk96bluWCTwd4xVIC2Zwb5dUhNisPKnizwuFzKETrtPS2aO8Tnr0VluajyeuWEufvXqV7jfKZlocpi6bUT0TeT5zLr9qG/rxg+cFhUO9RJSWl6N1PgYr4PIh9Nrzl+R8J016Fe1UqpKRP4CYD+ADgClsHX9NSml7M0rBwGEfc5mnray+9PXz8WC8dm6fPCcPClH93wlzrKT+/rn7zz/WF3L/v7iibh/xS4AtvXKHK10Wkud8/1eiwo69cC35/m/CK6/jP6Sa2/NMLpFaelTG7D37vMcaye2dfWircuClq4etHVZ0NrVg9YuC9q6etHa2YvWLttPW5fr7ZbOXrR1257b2tmLkenxWPWTRSEF25HEiA+OnTX+ZdXv31o1Ki0hoDXYVv7kFGw73OJ7xygUTKtJfIwZf75wBo4ZnYbfv7UNFz78KR69qiQs4zwFott7vLPHgkc+qsT84myUFOozMN5iVfhg2xGcOmUEZ4j7KZKCzFC6AjMBLAFQBKAJwIsAzg7g+UsBLAWAsWP1/wB2Zp+BZV8WRQ+zCozNzG1kev6RaX05st649USf+1utCj1WK3osCj3O3aUW5dJF2mu1Pf7Aygp8VlmP3+gcEA4Ge2Bl1Kw95yB11u9K0dbVix6Lf8dKijMjJT7G9pMQg+S4GORnJiE1IQbJ8WbsrGnF+j0N6LEoGLQgwKDzp9suL8AP+WA/UH3Vpf+jxbkpKM5NCepYkS4myG84IoJrFhZh4ohU3PzMRnz9wTX4ZxiSiZpEv0WAn1u/H7UtXbj/Utccb6GUv2FfIxraunGmjxmdw6n7OZqE0g57OoA9SqlaABCRlwEsBJAhIjFaq1U+gCp3T1ZKLQOwDABKSkoMbR6wf5bp2UVi+MvZwDNy8sTABt6aTIJ4kxnxMQD8yFvqvIK53oxemd7+4WnUMDGLU2D19ZljkGwPlOJj+t02awGT7X5SXIzPv/2RD3dj/Z4GKCNfPIPM01u2KCcZe+psqSs+ue20QamLzzFBkfSV2WDmENOenDgxB6/dvBDX/8eWTPQ350/Ft52SiTr/fw0hokuXUVevBf/6sBJzCrMwr7ivtSrUz5rSrdWIM5uiYpJEpImE8auhBFb7AcwTkSTYugIXAygDsArARbDNDLwawGuhVjJU9tYHPa97Rl9DjfxwNDo4MZLR44e6em05YtbsqsVFxxs7g/R3S6YZWv5Q4Okbef8M7YMhit82utMjUW9hTjJe+d4C/PC5zfjVa1uxrboFvzn/WMTFmFy+gBjBXvtQxyS+WHYQ1Uc7ce/FMwaUE+znu1IKpeU1WDAhG6kJ3rueh1Es79N3FhTig/IanDs9vLNOgRAShCql1gF4CcBG2FItmGBrgfoFgB+LSAVsKRce16GeIbFHsHom4TO6CdbIoNuIZIRDRaX2LTmQgbmRJgK+sIXkYj8C2nCMOwm0KzAQ0faWDLYrsL/UhFgsu6oE31s0Hs+s249vP7YO9a1dhgdWfWMpgy+ju9eKh1fvxuyxGTixXyt9KGdnR00L9je0D5gNSN4V56bg09sXY4QfueyMFtLVSSl1p1JqilJqmlLqSqVUl1KqUik1Ryk1QSl1sVIq7Cl37W8ePb9x1rUZ+2fZ3+9zdBoM6YzfvD2bPTYj3FUIWrR9OHtynFMWdU8tt7FhGJzv632zcIJt9tZwGGysZ6u32ST4+dlTcN+ls/DFwSZ8/cFPUNVvkWe92d8roYylfGXTQVQ1deD7iyd6aPUKruzSrTUQAU6famzKFzLO0L8CoG+MlZ4tNVlJoWfV9cb+fn/gcv0XvQ1X/phoYHQCUiNlJMVhwoiUqA+w/Hl5hhJY7a1v972TG766jOYW2QIrI74MRRq9WqycLZmVh5duWmB4axXg1BUY5PN7LVb8c9VuzMhPxyI346BCeQ+WlldjdkEGRqT6bnmJ9vf6UDVMAiv9x1gZ3Z3mqLMBZbMr0DMjPjAGyyUlBfjgx6eEnFk83PoHMO6WTAnH/8lXK419Jm9Gkv8pGaKVUek8puen4/VbFxpStjP7l8tgG6xe23wI+xvacetpnlqrglPV1IGvqo4akuOOBs+wCKyOHZOGey+aEfC0bG+MnnnlKN2Az48ojh0MN9RXXY8G/QP/d35wEv568UyXbeGYgOHriCXjMvG7JccauoBtpDAysB2RmoAiAxfpdRZMV6DFqvDgqgocMzoNpx/jubsumKDtg/IaAMCZU/1bOJvpFiLTsPgUyc9MwsUlBcjQsfsuzcdsjZA5Wqz0f+MMha7AJbPGGFJuNLdYDRXO/wKlbINSL+w3oD08Y6x8DF4XwVXzC42/NkQAowNbe369R6483pDyQ2lkevPLQ9hT14bvnzbBY2tVsOWXlldjwoihm/9suIjuPoMwMpkE500fje3VRw0p3/5lx4heu2jvCtx+19mGfbAOhaAz2vnz+tQrAJ40MsXvTOxszOyjR7oFb1LiYwxZIN7O/oU10FYlq1XhgZUVmDQyBWf56K4LtMGqub0HaysbcOPJxX4/J8ov5UMWLxWh0DF7b3/2N7wR7xt/F7aNVAmx5qjOxUXeOb88Pb1U9fhgH5+bjNIfneL3/tH+hURP0d5lbr98BDqk452vqlFxpBW3nDbR65ewYHoaVu6ogcWqcIaf3YAUuaL73TGEKceAe/0v5vx8oEjmKYD52VmTHbeDyfwdalJRBlZ9or3LvC/dgv/PsbVW7UJxbjLO8yMJZaAZwEu31mBEajxm5mf4/Zzo/i8MXewKDIEAhjVZOboCDSibHxD+eWXTQfRYFCxWhV5tLUT7otSO+9a+7Rarbe1E228Fi9WKHquCxdK3H/nm/Pp0/my6+dQJuPe9HQCA2CA+2JPiYtDc0RN0vdZU1AX93KEm2hf57usK9P89+f62GmyvbsHfLpnps8U80EtsZ48FH+6sxTdm53E4whDAwCoEImJ8V6AB7zF2o/nnR89/4XMfs0kQY/8xm7TfghiTSfvtept883aabj1tAh77eA+S4gPPNxbJ3yeibXZXtL+WxdEV6B+lFO5fsQvjspPw9Zn6T5z5pKIO7d2WgNMsGL3EFwWHgVWIunutqDjSCotVOX56rVZYVV8rhkXZWjksFttt2z4KVu23xWqFxQpYtFYNi1Xh0Y8rARg0K5DvRb+s/ukimE2CWLNJ+y0u92NMEvCFbdqd76G1q9egGg8N3s7pT86cjJ+cORl3vvbVINbI5tGrSgb9mJEq2r+c2V9jys/F1lftOIKth47inotm+N1aF8iX7tKtNUiNj8H84uwAnkWRioFVCBJiTKhq6sDpf/vQsGOIAS3u/Jbjn0IDcun841uzcP1/ynQvdyhx/sz21DISzGs41C7wU9xk2B6uor7FSvvtz+B1pRTuW1GB/MxEfGN2XkDl+8NiVfhgWw0WTRkR8HJI0f1fGLoYWIXgJ2dOxoIJ2TCJrbvH3oph1n5iTAKT07YYkwkmExz7ml0ec933uLveR4+FY3KGmpLCTN87DXPOAZCnBVWDiZFC/RDi95E+0T7Gys6fIVYf76rDFwea8MdvTA8ozYu/w7c27m9EfVu330lBKfIxsArBqPQEfGN2vu8dgxAfY0aPhV1GQ0164tBPHhkq+4Q/b7P49JiAYf/cu+PcKRidnojzZ45B4W1vedyfcVWfaO8KvH/lLgBAbWsXMpM9J462tVbtwpj0BFx4vH+tVQACisJLt1Yj1ixYNDnwFlEG+5EppK8dIpIhIi+JyHYR2SYi80UkS0TeF5Fd2m9+RQ+Co6majVaDLtYsmDwy1ZCy2Q3rm/0ceZtEGcxZ7N+NYy9j6cnjcb4fA5L5v+sT7V2B2V6CKWef7a7Hhn2NuGnReMTHBDZhwp9Lt1IKpeU1WDA+B6lBZOznazIyhdqeex+Ad5VSUwDMBLANwG0AViilJgJYod2nADlmrTCyGnQ77joH7/zgpHBXY9iyt0Z5e+0HMyX9tCm2dd1yU+Nt5Qf4fH6E9Yn2FqsbTx4PAEiO995pc//KXRiRGo9LSgoCKt/fs7OzphX76ttx5rHsBhxKgg6sRCQdwMkAHgcApVS3UqoJwBIAy7XdlgO4ILQqDk/2Dw6mPhp8Jm28G4WH/dTr3WIFx5eVYJ7Mbhdn4VirUU8t2szczh6Lx33W72mwLTFzyngkxAae3sMfpVurAQBnHMPAaigJ5d1RBKAWwBMisklEHhORZAAjlVKHtX2qAfAVEwR/vrUTDUV+LbkU1OD1vrlgvrhbr43dLn2ivcXq3ve2AwBe33zI4z4PrNyFnJQ4XD5nbFDH8OfaXVpeg9ljMzxO0qDoFEpgFQPgOAAPK6VmA2hDv24/ZXtluX11ichSESkTkbLa2toQqjE0HT/ONjQtNsDpt0TRzp8A5uLjA+uaAZzWh7Nfkbx87k3LSw+4/FBEW8wW7WOs7Dy1WG3Y14iPd9Vh6cnFSIwzJhntoaYObKlqxplTA0sKSpEvlE/tgwAOKqXWafdfgi3QqhGR0QCg/T7i7slKqWVKqRKlVEluLvPD9Hf/pbPx5q0nIi2IAY1E0cyfz+wJI1IwPS8d//jWLL/LPW5cJr45Ow/3XDTD576hris41OmxCHY42bOnF2QluX38gZW7kJkUiyvmjjOsDh9sqwEAjq8agoIOrJRS1QAOiIh9ZdTFAMoBvA7gam3b1QBeC6mGw1RinHnQvzXT4CnO1T/56FCxfk+DX/u9ceuJuMDPhI2AbVzQ3741C0Va4ldv44RmFmT4Xe5wFBPEItiRpPzwUQDAqu0Dv/d/ebAJq3fU4vqTin0ObvfEn7CzdGsNinOTMT43JahjUOQKNY/VrQCeFpE4AJUAroEtWHtBRK4DsA/AJSEeg2hIWXv7YqQkMIWcJwca2w0tvygnGbecOsHrTK/R6YmG1iHaRXuLVWq8556A+1dUID0xFlfND621ytsQq+b2HqytrMf1Jw0cy0fRL6Sru1JqMwB3C2gtDqVcoqFsVDoHqnpz2ZyxeKHsoGHliwh+etZk3zs6SQ2y5WKo8muCQQQ7d8ZofFZZjzlFWS7btx5qxgfbavCj0ycFlVfKztc4wVU7jqDXqtgNOERFd3suEQ05kTiVv4ULZ7swR3mL1cx82zCLCSNcu+EeXFmB1PgYfGdhYcjH8LYOYWl5NXJT4zErPyPk41DkibwrGBENa9E+lX84iI3yMVb211ivU7K0HdUteOeranxnYWHIS095ewV39liwekctzpg6kvnyhqjofncQ0ZDDwCryRfv/6MjRLgDAso8qHdseXFWB5Dgzrl1YZOixP91dh/ZuCxddHsI4cGCYGpOeAAuTj1IEivLP7GEh2vNYbdzfCMCWrwoAKo604s0vD+GmU8Z7XZQ5EJ4ur6Vba5ASH4P547N1OQ5FHgZWw9Snt3N+AUWmjm5ruKsw6KItTDGZBH/4xjScMik6cxAeMzrN5f4/V1UgIcaM60/Up7XK09h1i1Xhg201WDQ5N+BFnSl6sCuQiCLKriMtYTv2/ZfNxhu3nAgA+Pu3Zjq223NfGSUa246vmDsO+ZnuE2xGOvtswB+dPgl769rw2uYqfHveWGSnxOt2DHctVpv2N6KutRtnHsts60MZAysiiignTQxfK8jXZ47BdG3G2Ddm5zu2G71mp4WrrQ8q++D71IQY/HNVBWLNJtzgZn3IYImHNsjS8hrEmgWLJkdnSx/5h4EVEUWUhNjIuywZFfZkJtlmnzGwGlz2dBF769vwyqYqXDZnLEak6ptfrn+6BaUU3ttajfnjc7hU2RAXeVcwIhrW4iJo4fH/XDsHgPcs2qGwZ+C3ciLJoLIPvv/PZ/tgEsFNp4zX9wBuGqx2HWnFvvp2zgYcBiLnCkZEBCAughKEFmbbxlZ5S/YYCnsGc7ZYDS7nWY2XnJA/KKshlG6tBgCcwcBqyOOsQCKKKL6WAxlMozMSMCM/HT8/a4oh5e+tt62L2NzRY0j55J5zHq7vLppgyDH6N0K+X16DWQUZGJnGJa2Gusj5akhEFGFizSa8fsuJOHFijqHHOdjYYWj55Mo5eM/L0H/B7f5fDQ43d+CLg81srRom2GJFRBRmuan6TfMn/7x680JD02g4N1h9UF4DADiLiy4PC2yxIiIKk+eWzsP43GTMKsgId1WGnVkFGSGvCehJ/97s0vIaFOckY3xuivsn0JAScmAlImYR2SQib2r3i0RknYhUiMjzIqLP+gBEREPMvOJsrPjJIiTEMgv3UNLS2Yvmdtu4ueaOHny2ux5nHDsyosYPknH0aLH6AYBtTvf/DODvSqkJABoBXKfDMYiIiKLC1kNHsaPGtoLA6h1H0GtVOHMqs60PFyEFViKSD+A8AI9p9wXAaQBe0nZZDuCCUI5BREQUrUq31iAnJR6z2d07bITaYvUPAD8HYF81NRtAk1KqV7t/EEBeiMcgIiKKOp09FqzecQRnTB0Jk4ndgMNF0IGViHwNwBGl1IYgn79URMpEpKy2tjbYahAREUWUM6aOxDGj0/DZ7nq0dVtwJmcDDiuhpFtYCODrInIugAQAaQDuA5AhIjFaq1U+gCp3T1ZKLQOwDABKSkqYdpiIiIaE97X0CqXl1UiOM2PB+Oww14gGU9AtVkqp25VS+UqpQgCXAliplLoCwCoAF2m7XQ3gtZBrSUREFGXeL6/BoikjEB/DWZ/DiRF5rH4B4MciUgHbmKvHDTgGERFRRKtr7eaiy8OQLpnXlVKrAazWblcCmKNHuURERNHs1Ckjwl0FGmTMvE5ERGSQtARjsrtT5GJgRURERKQTBlZEREREOmFgRURERKQTBlZEREREOmFgRUREZIA7zp0S7ipQGDCwIiIiMkBRTkq4q0BhoEseKyIiIrIpyknGnro2ZCUbk2ph793nGVIu6YMtVkRERDrKTLIFVIqr4A5LDKyIiIh0ZBIBADCuGp4YWBEREenohpOLAQATcjnGajjiGCsiIiIdnXXsKI6DGsbYYkVERESkk6ADKxEpEJFVIlIuIltF5Afa9iwReV9Edmm/M/WrLhEREVHkCqXFqhfAT5RSUwHMA3CziEwFcBuAFUqpiQBWaPeJiIiIhrygAyul1GGl1EbtdguAbQDyACwBsFzbbTmAC0KsIxEREVFU0GWMlYgUApgNYB2AkUqpw9pD1QBG6nEMIiIiokgXcmAlIikA/gfgh0qpo86PKaUUPKTyEJGlIlImImW1tbWhVoOIiIgo7EIKrEQkFrag6mml1Mva5hoRGa09PhrAEXfPVUotU0qVKKVKcnNzQ6kGERERUUQIZVagAHgcwDal1N+cHnodwNXa7asBvBZ89YiIiIiiRygJQhcCuBLAFhHZrG27A8DdAF4QkesA7ANwSUg1JCIiIooSQQdWSqk1AMTDw4uDLZeIiIgoWjHzOhEREZFOGFgRERER6YSBFREREZFOGFgRERER6YSBFREREZFOGFgRERER6YSBFREREZFOGFgRERER6YSBFREREZFOGFgRERER6YSBFREREZFOGFgRERER6YSBFREREZFODAmsRORsEdkhIhUicpsRxyAiIiKKNLoHViJiBvBPAOcAmArgMhGZqvdxiIiIiCJNjAFlzgFQoZSqBAAReQ7AEgDlBhyLiIag55bOQ1KcOdzVICIKmBGBVR6AA073DwKYa8BxiGiImlecHe4qEBEFJWyD10VkqYiUiUhZbW1tuKpBREREpBsjAqsqAAVO9/O1bS6UUsuUUiVKqZLc3FwDqkFEREQ0uIwIrD4HMFFEikQkDsClAF434DhEREREEUX3MVZKqV4RuQXAewDMAP6tlNqq93GIiIiIIo0opcJdB4hILYB94a5HGOQAqNNupwNoDmNdIonzefFmOJ4zf8+NJ0P5nIV6bjyJ5nNm1DnxJdLPWbjOizeRcs4i8dx4MtjnzPncjFNKuR3HFBGB1XAlImVKqRLt9jKl1NJw1ykSOJ8XH/sNu3Pm77nx8vwhe85CPTdeyo3ac2bUOfHjuBF9zsJ1XryJlHMWiefGk8E+Z/6eGy5pEzneCHcFohDPWeB4zgLHcxY4nrPA8ZwFLiLPGQOrCKGUisgXSCTjOQscz1ngeM4Cx3MWOJ6zwEXqOWNgFV7Lwl2BCMXz4hnPjWc8NwPxnLjH8+IZz41nfp0bjrEiIiIi0glbrIiIiIh0wsCKiIiISCcMrIiIiIh0wsCKiIiISCcMrIiIiIh0wsCKiIiISCcMrIiIiIh0wsCKiIiISCcMrIiIiIh0wsCKiIiISCcMrIiIiIh0wsCKiKKWiEwWkc0i0iIiPxCRN0SkWUReFJErRKTUaV8lIhO020+KyO/DV3MiGqpiwl0BIqIQ/BzAKqXULBG5EsBIANlKqV7t8afDVzUiGo7YYkVE0WwcgK1Ot3c6BVVERIOOLVZEFJVEZCWAUwCcKCL3AUgEYBGRCwD8AIAFwPVKqRPDV0siGm7YYkVEUUkpdRqAjwHcopRKBvA7AM8rpVKUUo+Ht3ZENFwxsCIiIiLSCQMrIiIiIp0wsCIiIiLSCQMrIiIiIp0wsCIiIiLSiSilwl0HIiIioiGBLVZEREREOmFgRURERKQTBlZEREREOmFgRURERKSTiFgrMCcnRxUWFoa7GkREREQ+bdiwoU4plevusYgIrAoLC1FWVhbuahARERH5JCL7PD3GrkAiIiIinTCwIiIiItIJAysiIiIinTCwIqKo8355DTp7LOGuBhHRAAysiCiqbNzfiBv+U4Y/vLUt3FUhIhqAgRURRZWjHT0AgH0N7WGuCRHRQAysiCiqiAgAoKWzJ6jnN7Z1w2rl4vNEZAwGVkQUVT7eWQsA2LS/KeDn1rZ0YfZd7+MfH+zUuVZERDYMrIgoYmzY14iObtug9M/3NqDwtrcw748rXPZp6giupQoAyg8fBQDcv7Ii+Er68Oz6/fi0os6w8okoskVE5nUioiMtnbjw4U8HbK8+2ulyvzmEwCorKQ4AkJZg3KXv9pe3AAD23n2eYccgooFaOnvQa1HITI4Laz18tliJyL9F5IiIfOW0LUtE3heRXdrvTG27iMj9IlIhIl+KyHFGVp6IBs/bWw6j8La3UN/aZUj5rZ29fu1Xfuho0MfQhmchPzMp6DJocFQcacGJf16JOoNebzS0lO1twPTflGL2Xe9jT11bWOviT1fgkwDO7rftNgArlFITAazQ7gPAOQAmaj9LATysTzWJKNy+9/RGAMArm6oMKX/DvkaPjzW397VSVTV1BH2M5Z/uBQCYghgE8eaXh1Db4vlDvqvXgkuXfea4v7/e96zFzh4L7nqzHG1d/gWVw8mjH+3BwcYOvPnFoXBXZVjZX98e0pcXX1q7etFjsepe7kX/6nvvfRLmrniflxel1EcAGvptXgJguXZ7OYALnLb/R9msBZAhIqN1qisRRYBR6QmGlBsX4/lytG5PvS7HeHHDQQCAQAJ6Xn1rF255ZhNO+MMHHve5681yrK3su1SefO8qn+U+tHo3Hl+zBz//35cB1cdfda1dfgV4kej5sgMAgN+8UR7mmgwvJ9+7Cufe/7Fh5U+78z1c++TnhpUPAHvD3GIV7ECDkUqpw9rtagAjtdt5AA447XdQ23YYRBTVFk7IxicV9UhPjDWk/HgvgdXSpzboOmbJqgJLt+DPgPn/rt0fcD3uX7ELAPDWl4fxz8sDfrpPJb+3BYIc7zV0PLS6AjurW/CPS2eHuypB+3hXcC1KzR092Fffhn317dhX34a9Tr+d5abG61HNoIU8glMppUQk4KQwIrIUtu5CjB07NtRqEJHB7K08RqWASvMRsHX2WPDQKn1m81kC/CN6Lb73z8tIDKmb0khN7d3ISArvgF7Sxz3v7gCAqA6sAKDXYkWM2fXLlFIKDW3djoCpfwDV2O76BWdkWjzGZSfj1Mm5eKHsoGP7n97ZjhtPGT8of4c7wQZWNSIyWil1WOvqO6JtrwJQ4LRfvrZtAKXUMgDLAKCkpITZ+oginH3gtwqwtcdfJvHePfe7N8vxzLrAW4Xc2V7dEtD+3b2+x4Rkp8QNCKwKb3sLC8Zn45kb5gV0PL19vrcRZ0wd6XvHCJKWEIOjfk5oiDQ3P7MRE0ek4IenTwp3VYLmLvDRo0y743//AX557jHY19AXOO2ra0eL03hDEWBMeiIKc5JwzvTRKMxOwtisZBTmJGFsVhKS4vpCGOfAKtyCDaxeB3A1gLu13685bb9FRJ4DMBdAs1OXIRFFMfER+IRq2UeVXh93F1R19liQEGs2qkoOXb2+F3z2NK7j092ex4clx5nR1m38YtJGzeQ0knNQta++DaPSExAfo8//uqGtG1alkJNiTJfRW1/aPvb0Dqx6DRj07UmPRUGn0+0w4ZfvOG43d/Tg5//7EmaToCAzEeOyk3H82EyMy7YHTskoyErU7X8+mHwGViLyLIBFAHJE5CCAO2ELqF4QkesA7ANwibb72wDOBVABoB3ANQbUmYjC6L4Vu7Bo8ghdy7RYFVZuPzJge3FOMiq9DERt6+p1BFatXb042NiOKaPSdK0bAHT50WLlrR3PUwDY3tMXVF2//HPMLcrGnKIsHDsmTdfWgnnF2bqVFQ6n3LsaIkBuSjzGZCQiLyMReZmJGJOegLzMJIzJSEBeRiLSE2N9fgFo7+7FcXe9DyD6xp55mzmrtwON7Zg0MjXkcqxWhR01LVhbOfALxoc/W4QxGYmI1bllLNx8BlZKqcs8PLTYzb4KwM2hVoqIIs/+eluAs2l/E77/7CZYlIJSCharglXZLqBWpd1Wttv2x1z2czxm225VCjtrWt0es8VHGoLWrl5ka60O1z7xOdbvbQjow7KlswebDzThpIm5XvfzpyuwxUu31csbq3D5XNexpEdaOuHcq7q7tg0fbLMFl0lxZhw/LhNzi7IwpygbM/LTA26Z++0bWx23LQZ13w6Gb87Ow4IJOahq7MChpg5UNXVg2+Gj+GBbzYCANznObAu8MhP7ArAM2+0xGQkYlZaAm/67MUx/SehKy2sG7Vhn/v2joAJPq1VhW/VRrK1swLrKeqzf24Cm9oGTPx64bDbGZSfrUVUAQEZSrNvjhAMzr0ewZR/txsmTcg35Bk4UKOeZN1uqmiECmEVgEoHJJDAJXG7bHxMBYswmxMf020+0/UziMbDyljcKAGLNJhTe9pbLNqWUX92WZXsb8PcPduKTinqsvX0xUhJiYBZBYpxrAKOUwj9DHDS/7KPd+NYJBTCb+upV19LtuF0yLhMvfXcBjhztxPq9DVi/x/bzl1LbmoZxMSbMKsjQAq0sHDc2E8nx3i/fT3yy13H7wx21GJ+bEtLfEC7XnVSEY8ekD9iulEJ9W7ct2Gq0BVyHmjpR1dSOQ02d+PJgMxraul2eYxLjJl8YTSmF0vJqx/261i7DujIDYbEqbDt8FGsr67G2sgHr99Q7unHHZSfhzKkjMa84G3OLs7Hw7pWO56XovPqB2eChCoFgYBXB/vj2dvz53R3Y/cdzw10VIoxJT8ChZtvyMqt+ukjXsvsHR/5y15KkVN9Ae2f9ZwI6JxRs7erFvD/Z1iTc9YdzXLomao52oSzELpi99e14b2s1zp3el9bPOUmiSQu4RqQl4GszxuBrM8YAABrbulG2rxHr99Rj/Z4GPLR6Nx5YWQGzSTAtL90WaBVm4YTCLKQneZ5Vefc723HtiUUh/Q39tXT2YPpvSnHfpbOwZFaermU7y/Qwm1FEkJMSj5yUeMzIz3C7T0e3BYeaOxytXYeaOgxdJ9JI26tbcKChb3LEL1/Zgn99+3jDxz7212uxolwLpNZVNmD93gZHa21hdhLOnT4ac4uzMLcoG2MyEj2Wc0jnGbSDfR68YWAVJKUUNh1owuyCDEP/oYFOCx/qeixWXL+8DD88fSJmj80Md3WGlcEYZB2on774xYBtmw824Tg3r40z/v6hx3L+9eFux+3T/roaBxo6cM9FM3BJSYFf3YBH+q1n2F9hdhL+9eFunDNtlON60e50Pk0eLiGZyXE4Y+pIx4y+1q5ebHAKtJ78ZC+WfVQJEWDyyFTMK7aN0TqhMMulHCPy+vzo+c0AgB88t9nQwMpXGg5vEuPMGJ+b4tJal5IQgz++vV2Pqg2q0q01EIGj+/i9rTW44T9leOzqE3Q7hrtgp9dixVeH7IFUPT7f24hWrYu+OCcZX5sxBvO0QCqQ5MHPrt+PK+aO063ukbT0EQOrIN39znY88lElfnzGJHx/8UTdy/9gEPvSo8m++jZ8uLMWBxvbseIni8JdnWGlIwIDK3ctSVc+tg7//s4JmNtvwHZlredB8C9t6JuqbW8VeGnDQVxSUoBeq+/A6jdO45ncueHkYvzyla/w2e56LJiQAwCODycALl2E3qTEx+CUSbk4ZZJtTFhnjwWbDzQ5ug6f//wAntSW7XFmxHc/+3gwoyXqPOvTV1qPSFVaXo3jxma6DGDX+3/QfwLJ1f9ej7K9DY4vVeNzk7Fk1hjMLc7GvKIsjEgLbBWG7y0aj4dW277E+PG2CojzGKtwT0pgYBWkR7Sp4Y9+XGlIYHX9f8p0L3MwvbqpCqkJMVh8jH+5c3osVnT2WNDRY0FXj+12Z48VHT0W7bbtsU37mwB4n4EVCotVYW99W9SORzFS9yBO9Q7FqPQEXP3Eejx21Qk4cWJOyOX5ajV+v7wGb2+p9rrPhcfl4+/v78LDH+52BFaN7X3jfwJdYscuIdaMecXZjll/PRYrvqpqxvo9DfjTO32tMlE8dt3voNNf0RhYHWxsx9ZDR3H7OVMGzAy88vF1mF2QgVljMzCrIBNZycEngv2/V79yuX+oqQPfOC7P0RI6IjW05ax+fvYUR2Cl90tyTHoiB68PFYORQyfadPZY8EOtm+BrM0ajs8eKrl4LOrot6OzVAqZuC7p6+4KnQLs8vbU+eGKx2rL61rV2obalC3WtXU63bdvtSy08e8M8zB8f3VPUh6vnb5yPbz+2Dtcu/xz/+vZxOG1KaIkxe728Nls6e/Dr177C5JGp2FHjOeloQqwZ1ywsxL3v7cBXVc2YlpeOn7/Utz6gXp/1sWYTZo/NxOyxmS6BldHe/aoaZ08bNWjHC4XegdpgeF/rwTjz2FEu/9cx6Qmob+3Gg6sqHIPyC7OTtNdABmYXZGLK6NSg0hmcNDEHT103V5f6u3PqZO8zcQMVHxs5KRsYWIXI2/pmoYikqaOBsifHA4DyQ0cRH2tGYqwJCbFmpCXGIkG7nRBrRkKMGYlxJiTEaPfjzEiI6Xs8Mdbssv/pf3MdJ+McLLkGTN2oa+lCrVPg1NDW5XZGUHyMCTkp8S7jUD7YVsPAKgIkxJrQ2RNYS1lOSjyevWEervr3etz41Abcf+lsnDM98LXgdx+xzVTceuiox33+WroT1Uc78c8rjsM3H/rUa3nfnjcOD6/ejUc+qsQDl7kuR2L0h73RrY03/XcDfnT6JNx62gTHQPxI9dznB3zvFAKrAeNiS7fWYOKIFBTluKYneHbpPIzLTkZbVy+2VDVj0/4mbNrfiDUVdXhlk23Rk/gYE2bkp2NWQYYj4Bqd7nlQud0YP/YJhd49PXp3GYeCgVWIjAqsojWoAoBVO/r66VfqPHvMWcnvP/AZLOWkxiM/Mwmzx2Y4ZhDlpsZrt+OQmxqPlPgYx4Bi++y02WMzDKs3DXTZnLF4dr27zOrBBQSZyXF4+oa5uOaJz3HLs5vwVz8GoPdXr03VdzdAHgA27m/E8s/24qp549wOlre749wpAID0xFhcPncsHvu4Ej87czJyU+Md6SSMmCrunFqgtqULJb9/H/mZtqVACrISUeC4nYTR6QkhJST95nF5+PsHO7Gj5ij+cvFMl6VGIs22w54DZT0cbNR3tltjWzfW723ATacUD3jMvv5jcnyMS5ewUgqHmjuxaX+jI9ha/uk+PPrxHgDAqLQEW4vWWFuwNW1M+oA0I0bTu7cn3AsvO4vcV3+UMKq/fsKIFFQccZ/bJ9K9+aVxqxhddHy+Y6Dx6ceMcAqS7AFTHHJS45HqFCwF4t6LZuBnL32JsVlJelc96v3srMm4970dePv7J+le9unHjMCz6/fj5Em5+GhnrS5lpiXE4j/XzsF1yz/Hj17YHFQZ1z35udvtPRYrbv/fFoxMTcBPz5rstYylJ/ctBnvtwiI88ckePPpxJX521mRHd6ARl5H547PxSUVftuvTjxmJA43t2HSgEW9tOezS/W42CcZkJKAgM8n2k5WIAi3oKshMQk5KnMv7qa1f4ta/XjwTU0en4Y9vb8OeunY8etXxyM8cnu+hk+9dpWt5K7cfgcWqcOZUW1frFXPH4mlteSdPrxsRcSRHtafu6Oq1YNvhFmze34hNB5qwaX8T3vnKNjbQbBIcMzq1Xxm6/hmGS/GR120wRU5NotQug4KfGfnpURtYGWlOUZYjsLr7whm6l2//1tNjMWa0b1evBTXNXRibHX0fOvbW2TwvuWmCZc/plNhvnMRT183BlY+vD7rc5PgYPPGdObjxvxuCCthWuFlmB7Cta7ijpgWPXlWC1ISB6QB2/eEcTHRaF81uVHoCvjE7Dy+UHcA4p9eAESlbnAfE98811Wux4nBzJw40tONAYzsONHRgv3Z7xfYjA6auJ8aaHa1cBVlJLjMP77pgGkQE159UjAkjUnDrs5uw5MFP8PC3j8ecIte0D/4yojvNrjg3OagxmuFSWl6NUWkJmJ5nS5Lqz/JK7sTHmDGrIAOzCjLwHW1bXWsXNu9vwqYDjdh8oMll//4pOyJdJAWCDKwilFFdjNEu1mzsu8eqTZ8yatHan774Jd744hC2/e7sQW96140B/4Kpo20fGt+YnYf3tvalGslODr15PzHOjEevOh6T/+/dkMsCgD11bbhvxS6cM22UI79Uf94GCy89eTxe3HDQJTO6EV2BE0akYE2FbTJG/zxTMWaTo0XKnfbuXhxs7LAFXg3t2N/QoQVg7QPWfHNu3V00eQRevXkhbvhPGS5/dC1+t2TagKV8/LHAKUO33s46dhQeXr3b945B6NF5LFtnjwUf7azDRcfnO8auOb9SQn3V5KTE4/SpI3G69jq2D4UozknGhcfnh1i6e09ccwLih9jagP0xsIpQyRE8RiGczCZj35BvfGHrxvzTO9tx5rH6z3JarbWAdFusSER0BVb2KftGfDMcm53kNveMv//uOLPJ6wDt+Bj9zvUdL29BfIwJv/36sS7bS8Zl+pWhfcKIFJxxzEiXdd+MeFmH0p2dFBeDSSNT3S7Cq5RC0e1vO+73DwrH56bgle8txPef3YQ7XtmCbYeP4tfnTw1oZlq1j4SroThmtHFLhL2ysUrX8tbsqkNHj8UlgO8MssXKH/ddOgs/eG4zXrl5oWHHOFXnBdz7u3ahvisMBGNoh41OunojL7mhN2YdW2Z6LFYcaenEjuoWfLa7Hk98sgeFt72Fzp7oOicAMLsgw9DyY7RvhUadG/uiwhE+ccqrway6p5ac/EzX7sgr54/DlFGpOHe672B4yqiBwUIgPqusx23nTBmQHDGQgPOmReNd7hsxVvOq+eNw9zen674kVv9uyzg3revpibH493dOwI0nF+Optftw5ePrBqzbFy45KcHnefKm12LFgyGuKdlfaXk1UrWB6XaFBnYhL5mVh713n4f0ELLdh4u967swJ/zDLEJqFhGRHwG4HrZcX1sAXANgNIDnAGQD2ADgSqVUWN9Rn+9twMX/+gzPXD/XkZwv0nm60Cql0NLVi4bWbjS0d6OxrRv1bbbfDe3daGjtRmN7Nxra+n7sC2L2N+VX7xqSofaahYUu3Rx6Mnrmx4QRtsSgRicIjaR1rfyltJR+g1l3T1P3Y/ptt1gV3v3hyX6VOSYjEdurPeec8uWEwkxcdkLg3VvO+s8iNCKwijGbcOmc0OrpjwwPaxSaTYLbzz0GU0an4hf/24Il/1yDR68qCfui8kalEXj9i0PY39COR648Hj954QssnBBauhaLVeGDbUdw6pQRLsHrhcfl4wFtvcPou4oMD0EHViKSB+D7AKYqpTpE5AUAlwI4F8DflVLPici/AFwH4GFdahukddqYgIdW70ZrVy96LArdFgt6ehW6LFb09FrRY7GiW/tt29a3T7fFavtx2s9ozt/UL1u2Fo3tfQGUp4SFcWYTspLjkJkch+zkOORlJiErKRZZyfHISo5FZnIcspLjcPmj6wytu5FZno3+TF80eQT+9M52XDqnwNDjqChMhe3oChzEY3oKOPoHXN87dbzb/dyX6d9+I1LjcaRl4Fi7P31zui65mv733QW48OFPA6pTJHLXXejsG7PzUZSTgqX/KcM3H/oUf//WLJxlQDe7vwpzknH53LF4sUy/fFYWq8KDKyswZVQqzjhm5IAW1WBs2NeIhrZunHms6zi+cVE48WW4CXUgTwyARBHpAZAE4DCA0wBcrj2+HMBvEObAKlmbhrmmos4xmNObuBgT4s0mxMaYEGc2ITZGbL/NJsTHmILKYhso5wt3r9WKsVlJmFWQgSwtOMpMikNWShyykuIcwVRynDng1oS/vb8TcWZBrPb32f5mp/sut02Ii+l332xCjLaP/VwZmYww2KU//GX/1xoV98THmNDVa8XeunZMz0835iAGsZ+SwWxs89QV2NUvv1UgS234m4wzJ2VgYPXGLSdiwgj3gUSgr83jx/W1WkXbMisnTcxxrFLgj1kFGXjj1hOx9KkNuPGpDfjxGbZkouFquc1MitX1Pf7WlsOorGvDPy8/znHtDrX80q3ViDObHOtC2jmfsyh72QyKSPjOGnRgpZSqEpG/ANgPoANAKWxdf01KKXvf00EAxi177qeJ2oXwtnOm4MQJOVqAYAsa4rTgKU4LmGJM4teb/eWNB/HjF9wnDtRDWkLfv+bFmxboWvb1JxbhsTW2RHH3r9ila9nOTjSg29XoC4n9f281+N15/oNr3HbDKqXQ2WNFa1cv2rp60Wr/6exFW3cvWjpdt/fdtqC1swcj0xJcLu566muxGsyuQPfbR6TFo6opuESM+xv8e165m0SS3oJh1W/1s2dumOt38BFtXcPHjkkPKLACgJFpCXh+6Tzc8fIW/O39ndhR3YJ7L54RlmSiAtHtPW61KjywYhcmjkjBOTot66OUQml5DRZMyHabzoMGiqS3UChdgZkAlgAoAtAE4EUAZwfw/KUAlgLA2LHGjgOwX/COH5eJaXn6tBL0/xahtzQDBw+OdspDtOdP56LHotBj0bo5LVb0WBR67fd7vTxmUY5uVPv9XovVsZbVQ98+zrC/wShmgwMr51KvfHydIzBq67KgpbMHbd3+rZsoAqTExSA5PgYpCbbfTe3d2Li/Ce09FkOS5fWNsdK9aI88teTEhjCNzqiJCf1fMgvG52DBeP++XETb7PNg054kxJrx10tm4pjRafjTO9uwp64Ny8KQTFREv0WA391ajV1HWnHfpbNcvtCEUv6Omhbsb2jHTad47+IezC855L9Qrr6nA9ijlKoFABF5GcBCABkiEqO1WuUDcDv/VCm1DMAyACgpKTG0ecD+OaXnl3jDm+4NPCOnTMrBXdptEUFcjLid2ROsa08sQktnL9IM+KZlRL4fZ/b/q9Wg3kzn8Xktnb1IiY/BiNQEW4AUb3YESanxWtBk/0lwvZ8Yax7QKvXkJ3vwmzfK0dNrBQwY4x+OJnbPmaX7br//I/8GrUeyaOsKDGVtQxHBDScXY+JIz8lEC7ISccDPlsVg66DH69lqVbh/xS4U5yQ7Mpzbyw9F6dYaiACnTzU2NcFQFAnjV0MJrPYDmCciSbB1BS4GUAZgFYCLYJsZeDWA10KtZKjsJ1rP5nbj4yrjXhyJBje9x2qD6I1gfFeg7ff+hnZjDwTgVZ1zxdjXetM7SWE4efpG7vzBHuiaY5Fw4e0v2roC+8/KDIZzMtErHrMlE71Mm8VodKBpL10pFdK5/2BbDbZXt+CvF88cEGyG8jIrLa/G7IIMn2MHo+xlY6izjh2F/3y2D3OKQpuNqYegmymUUusAvARgI2ypFkywtUD9AsCPRaQCtpQLj+tQz5AoR4uVjoGVwU2wRl77jW71MZLRH0D2BVrvM3DsmVHitMDKqMkDfV9QDCneYabTOCZPXzCc38vxsZHRjxbKWzbaZgWGsmCzM3sy0QXjc3D7y1vw69e+Qo/F6ld3eCjsL59QrrNKKTywsgJjs5KwZNYYl8dC+XdWNXXgq6qjhiQoHsoWTsjB3rvPw9Qx4U3nAYQ4K1ApdSeAO/ttrgQwJ5Ry9WYfL6PnxetoZ49+hblh5GUl2i7ig8nX1PFIFhtj+8c+9dk+x6r3elq3pwGA8V8qvnXCWHxxcIvXfZyDu0BbrIwyPjcZG/zIvO5OtHUF6tFiZWdPJvrnd7dj2UeV2FXTOmCtQr3Zz3co19nVO2qxpaoZf75wum6BJgC8v9W2MPKZHpZLosg3LNZNsRrQYmX0t2T7N6mPfnaq7mVHW7fDYMo0qAtzMIzNSkKMSfDIR5WGHSMvI1HXD1V3/Cne+b0cF+CH2t56Y7p5z585Bi+UHQwqMWQoY5bCQe/6mk2CO849BlNGpeK2l7cYnivQXnurUjAH8UVBKYX7VuxCXkYivjHb05p6wYVtpeU1mDAiBcUGJykm4wyTwEr/9p94s7Hfku1dIEYEcNF2ER9MRgcNRjp+XBbKf3e2oakiYs0mQ1I5OPPnC5ApAlus7IJp0Yu27zpGvU++eVw+inNTcME/PzGkfLtQuwLXVNRh84Em/P6CaW4n/gT7/2xq78a6PQ248eRiv/aPttfNcDEsAquxWUm4ev44ZOu4RpSRg8sBYxe8jeLYwXAxOq7RGA56zu4Ml/6v+f9eNxfvfHUYT6/b79gWiV8O5hVn48Lj8vHD0ycG/Nyo6wo0MD/ELIPXAwX6Wu2DuY4rpXDfB7swKi0BF5d4aq0KLmhbuf0ILFbF8VVRLvqvwn44ZnQafrtkGkbruEZUsgF5gpw5BgobMJ5lKHQFTjVohfqYEPIjkT76BxknTszBH74x3WVbJAZWsWYT/nrJTBRkBZ6TKQL/HK+MPv8f/9w2BOLHZ0wypPxQWqw+q6xH2b5GfHfReMTHuG8tDfYS+355DUamxWOGn/kWmccqMg2LFisjxJpNOG/6aGyvHpidWQ9GLh8SiR9KgSj90ckYle7/EiaBiPJTMyT4E9tG+2u4P6O7V/UWbIJQfxVkJWHrb89CUpwx3bz2gCSYwOqBFRXITY3Ht07wvp5ooEV39ljw4c5afGN2nt+vhyHwHXlI4tfzCGXkgrdRdg0fYNLIVEOSjwJDozUv2vnTLabH/2l8brLbZYXCIdq6As2D0LKbHB9j2PvRfg0MtCvw870N+KyyHjeeXOx1bF8wLUmfVNShvdvCbsAhgIFVKHRcFqE/I5Ka2kXbRZyGF+fXp3OLgvP086H2Co62LzvRPMkD6GvpCTRd1v0rdiEnJQ5XzB2ne51Kt9YgNT4G84v9n1Ua3f+FoYtdgSEQwLDIytEVaEDZDKz8U3GkFb1WK3otCr1W2zqItt/KdbvVltCwx6Jgsfatp2h7TGmPGZ/0cKjw9Pp86IrjMOGX73jdx5vUhBi0dPb63jEMou09Ge1dsX1dgf6/Jzfub8THu+pw2zlTkOhHF2UgZVusCh9sq8GiKSOGxASU4Y6BVQhExMAWK/sx9C87yq+Jg+b0v32oa3lR9tkZNp5enzFmE/IyElHV1IH4ID58Ijl4ibYuaKPHWBnNMXg9gOc8sGIXMpNiceU8361Vgf47N+5vRH1bd8BJQaPtdTNcMLDSmVK2FgqL9rvXqmB1armw//S63LbCaoWj5cNiVfh0dx0AY2Z9RPIHTCR54LLZiDEJYswm7bfAbBLEmk223ybb7xiz2B43mfpu2/exP8dky/9UeNtb4f6zIp63D4tPbjsNAHD7y94zs7svN+gqAQBuOmV8aAV4EW3LTA3GGCsjOdIt+JmHdMvBZqzaUYufnjnJ7xnhgQRtpVurEWsWLJqcG8CzKFIxsAqBWYA9dW045lfvugRTujKixYpNVn45f+YY3zsF6M8XTscv/hd4UDCc+Jd5PfByQ33VGzX1H4i+VuSoH2Ol/fZ38Pr9K3chLSEGVy0oDKh8fyilUFpegwXjc5Aa4KSc6P4vDF0MrELwnYVFyEqOh9lk+wYXY7K1Tth/YgbcNrnsa3Lap//9yx9dB4DdR0PN4mNGwrZmOXni3KI6Ks19Wo1gWl1D7TYx8r0YbV92oj6wCiCPVfmho3i/vAY/PH1iQLOR/R1itbOmFfvq27HUz2zrFPkYWIVgVkGGYVmCU+Jj0NoVmQNtKXjZUbwW4WCx9zLFmsVjwKFHi5X9c29mQQbG5ybjB4sn4pR7V/v9fD1FW/d8tK9Q8OaXhwEArV29PtcHfXDVLqTEx+CaBUX+HyCA/2eptujyGccEvuhylL1shg0GVhHK0VTNiWRDCgeb+mY/R9561YNp4TmhMAvvah9izl67eWFA9TJCtL0son2M1cb9jQCApvYeFGR53m9nTQve3lKNW06dgPSkwLrp/L10l5bXYPbYDIzw0DpL0Sekd4eIZIjISyKyXUS2ich8EckSkfdFZJf2O1Ovyg4n0jcIgAbZZ7efhg3/d3q4qzFs2VtvvE1XD6aF56r5ttlcuanxQdXL2BYrAws3QLR3Bd61ZBoA+Fw/9sGVFUiKM+O6EwNorYL/r5VDTR3YUtWMM6cGlxSUX9QiU6hfO+4D8K5SagqAmQC2AbgNwAql1EQAK7T7FKC+b+2MrAbb6PREZKcE9+FLobN/ZnttsQrm8ySE9eEAg8dYRdkHZLR3BdrzcPVaPL8Ydte24o0vD+HK+eN8dhcG6/3yGgDAmccG3g1IkSvowEpE0gGcDOBxAFBKdSulmgAsAbBc2205gAtCq+Lw1PfhwsCKhhd/goygBq/3b0fw8tb6upsZoUa2DkRbws1ob7F6cGUFAKC0fGDXsN0/V1UgPsaEG04KblC5PwlCS8urMT43GeNzU4I6BkWmUFqsigDUAnhCRDaJyGMikgxgpFLqsLZPNQC3obiILBWRMhEpq62tDaEaQ9Oo9EQA0fdNlihU/rzkT54UeL4fx/pwfnzgnRFgosZQRdu7PNrHWNW3dQEAalu63D6+t64Nr20+hCvmjkNOEK3X/ryGm9t7sLaygWsDDkGhvDtiABwH4GGl1GwAbejX7adsVzC3VzGl1DKlVIlSqiQ3l0nR+lt+zQn468UzDWuCJopU/nyZWDghBwBw7UL/x75MGZUGALjxFK0FwsthBrtFhukWBteFx+UDAKaOSXP7+EOrK2A2CW40MAXCqh1HYLGqQQ/iyXihBFYHARxUSq3T7r8EW6BVIyKjAUD7fSS0Kg5PI9IScOHx+eGuBtGgq6xt82u/vXefh1+fP9XvctOTYrH37vNw2hTtg8xLw1XxIHfNRFvLdLSPsTpLayXKz0wc8NiBhna8vLEKl88ZG/RMPX/OTml5NXJT4zErPyOoY1DkCjrdglKqWkQOiMhkpdQOAIsBlGs/VwO4W/v9mi41JRoinl86D2MyBl7QyWZNhbFDAwqyElGYnYQ7v36sx30mj0o1tA79RVtgFW1jwvrzNnj94Q93wyTS17IZJG89zp09FqzeUYsLZudFXWsl+RZqHqtbATwtInEAKgFcA1sr2Asich2AfQAuCfEYREPK3OLscFchon177ji8vcXzoOJQxceYsfpnpwb0nFQ/14cLVrR9tsZG+Rgre9DT2N7jsv1QUwdeLDuAS0oKMDo9+C8/viY6fLq7Du3dloAXXaboENLVQim1GUCJm4cWh1IuEQ1faYmBJWIcDC0Gr4IQba0W5ijvCnznK9v8qj++vQ1nT+sbPP7Ih7uhFPDdRcYtuA0ApVtrkBIfg/nj+SVrKIrurx1ENOREW7eYHqLtb472wev20+28bNiRo5149vMDuPC4fORnJoV8DE8LPFusCh9sq8GiybmIjzGHfByKPAysiCiiROL4HaMDiQj8k72KxP9RIM6ZNhoA8O25Yx3bHvmoEharwvdODb21ytvZ2bS/EXWt3UyzMIQxsCKiiGKOwKtSr7c08DqItharaB9jNTrdNtvPPvuztqULT6/bhyWzxmBcdrIux/A0eL20vAaxZsGiyUwzNFRxEeZhquz/TucCz0QRIsriKphMgvnF2bj1tAnhrkpQ7C1uNUc7AQCPfVyJ7l4rbj5Vn7/H0/9TKYX3tlZj/vgcpCVE3lhC0kd0f+2goOWkxAe9GC2RkTYfaA7bsa+aPw7/+NYsAMAtTh+yBVnGpseIxq61Z5fOwwItUWu0qayz5Ur70zvb0dDWjafW7sP5M8fourSMuy+uu460Yl99O2cDDnEMrIgooswpzArbsX+3ZBoumJ0HAPjpWZMd280GNykNWMeQDDVppC1P2eIpI/D4mkp09FhcAulQefp/lm61pRFhtvWhjYEVEUWU9KTI6yIxutfc0wwyMka6ltJjyuhULP90H86dNhoTR+qbFNbd/7S0vAazCjIwMsiM7hQdGFgRUUSJj4mcy9JvtezsRo9H5HjHwWWf5fnox3vQ2tWLW/QeK+amwepQUwe+PNiMM49la9VQFzlXMCIiAHERNC3w1MkjAABWgyMfxlWDyx5YdfdacebUkThmtPvFmPX0wbYaAMCZU5lmYaiLnCsYEREiKwt5dkocAOCahUWGHkexyWpQOU8W+P7iiYYco/+/tHRrDYpzkzFhxOAu8E2Dj+kWiIg8SI6Pwd67zwt3NUhnzmv5TctL17/8fvebO3qwtrIe151kbIBOkYGBFRFRmEXi+ohD3ZPXnIApo4zrAnRusFq94wh6rYrdgMMEuwKJiMLkd0tsg+Nn5WeEtyLD0KLJIzAq3ZjZef2zc5RurUFOSjxmF2QYcjyKLCEHViJiFpFNIvKmdr9IRNaJSIWIPC8icaFXk4ho6LlqfiH23n0eMpN5mRxytCarzh4LVu84gjOmjoyo8YNkHD1arH4AYJvT/T8D+LtSagKARgDX6XAMIiKiqLC2sgHr9zYAAD7bXY+2bgvTLAwjIQVWIpIP4DwAj2n3BcBpAF7SdlkO4IJQjkFERBStSsurkRxnxoLx2eGuCg2SUFus/gHg5wCs2v1sAE1KqV7t/kEAeSEeg4iIKOpYrArvl9dg0ZQRiI8xh7s6NEiCDqxE5GsAjiilNgT5/KUiUiYiZbW1tcFWg4iIKKKcfsxITB2dhs0HGlHX2s1Fl4eZUNItLATwdRE5F0ACgDQA9wHIEJEYrdUqH0CVuycrpZYBWAYAJSUlzI5HRERDwoGGduyoaUHp1hrEmgWnThkR7irRIAq6xUopdbtSKl8pVQjgUgArlVJXAFgF4CJtt6sBvBZyLYmIiKLEjpoWAMB7W6sxrzgbaQnMUzacGJHH6hcAfiwiFbCNuXrcgGMQERFFtL317TjzWCYFHW50ybyulFoNYLV2uxLAHD3KJSIiimZnHMPxVcMNM68TEREZxKjs7hS5GFgRERER6YSBFREREZFOGFgRERER6YSBFREREZFOGFgREREZ4LoTi8JdBQoDBlZEREQGmFfMhZeHI13yWBEREZFNZlIsGtt7kJVsTMb1Lb85Ez0WrgQXqdhiRUREpKOinGQAgDIo9klNiEVWcpwxhVPIGFgRERHpSEQAAGxTGp4YWBEREenojKm2ZWxGM+v6sMQxVkRERDq68eRiXFJSwO66YYotVkRERDoSEQZVw1jQgZWIFIjIKhEpF5GtIvIDbXuWiLwvIru035n6VZeIiIgocoXSYtUL4CdKqakA5gG4WUSmArgNwAql1EQAK7T7RERERENe0IGVUuqwUmqjdrsFwDYAeQCWAFiu7bYcwAUh1pGIiIgoKugyxkpECgHMBrAOwEil1GHtoWoAI/U4BhEREVGkCzmwEpEUAP8D8EOl1FHnx5RSCh5SeYjIUhEpE5Gy2traUKtBREREFHYhBVYiEgtbUPW0UuplbXONiIzWHh8N4Ii75yqllimlSpRSJbm5uaFUg4iIiCgihDIrUAA8DmCbUupvTg+9DuBq7fbVAF4LvnpERERE0SOUBKELAVwJYIuIbNa23QHgbgAviMh1APYBuCSkGhIRERFFiaADK6XUGgDi4eHFwZZLREREFK2YeZ2IiIhIJwysiIiIiHTCwIqIiIhIJwysiIiIiHTCwIqIiIhIJwysiIiIiHTCwIqIiIhIJwysiIiIiHTCwIqIiIhIJwysiIiIiHTCwIqIiIhIJwysiIiIiHTCwIqIiIhIJwysiIiIiHRiSGAlImeLyA4RqRCR24w4BhEREVGkidG7QBExA/gngDMAHATwuYi8rpQq1/tYRDQ03f3N6UhLjA13NYiIAqZ7YAVgDoAKpVQlAIjIcwCWAGBgRUR+uXTO2HBXgYgoKEZ0BeYBOOB0/6C2zYWILBWRMhEpq62tNaAaRERERIMrbIPXlVLLlFIlSqmS3NzccFWDiIiISDdGBFZVAAqc7udr24iIiIiGNCMCq88BTBSRIhGJA3ApgNcNOA4RERFRRBGllP6FipwL4B8AzAD+rZT6g4/9awHs070ikS8HQJ12Ox1AcxjrEkmcz4s3w/Gc+XtuPBnK5yzUc+NJNJ8zo86JL5F+zsJ1XryJlHMWiefGk8E+Z87nZpxSyu04JkMCK/KPiJQppUq028uUUkvDXadI4HxefOw37M6Zv+fGy/OH7DkL9dx4KTdqz5lR58SP40b0OQvXefEmUs5ZJJ4bTwb7nPl7bph5PXK8Ee4KRCGes8DxnAWO5yxwPGeB4zkLXESeMwZWEUIpFZEvkEjGcxY4nrPA8ZwFjucscDxngYvUc8bAKryWhbsCEYrnxTOeG894bgbiOXGP58UznhvP/Do3HGNFREREpBO2WBERERHphIEVERERkU4YWBERERHphIEVERERkU4YWBERERHphIEVERERkU4YWBERERHphIEVERERkU4YWBERERHphIEVERERkU4YWBERERHphIEVEUUdEdkrIqd7eOy7IlIjIq0ikq39LtYee1JEfq/dXiQiBwez3kQ09MWEuwJERHoRkVgAfwMwTyn1hbY5JYxVIqJhhi1WRDSUjASQAGBruCtCRMMTAysiilYniEi5iDSKyBMiMgPADu2xJhFZCQAiokRkQviqSUTDCbsCiShaXQHgLABtAN4AcAmAYwHsAZChlOoNY92IaJhiixURRasHlVIHlFINAP4A4LJwV4iIiIEVEUWrA0639wEYE66KEBHZMbAiomhV4HR7LIBD4aoIEZEdAysiilY3i0i+iGQB+CWA58NdISIiBlZEFK2eAVAKoBLAbgC/D291iIgAUUqFuw5EREREQwJbrIiIiIh0wsCKiIiISCcMrIiIiIh0wsCKiIiISCcMrIiIiIh0EhFrBebk5KjCwsJwV4OIiIjIpw0bNtQppXLdPRYRgVVhYSHKysrCXQ0iIiIin0Rkn6fH2BVIREREpBMGVkREREQ6YWBFRFHnYGM7uGoEEUUiBlZEFFV21bTgxD+vwiMfVYa7KkREAzCwIqKocrCxAwDw2e76MNeEiGggBlZEFFVEbL/ZEUhEkYiBFRFFFdEiq2DGWLV09qDwtrfw2uYqvatFRASAgRURRZDmjh5YrbaA6XBzBy745yd44pM9LvtoDVYIZux6+aGjAIAfPLc5hFp6t66yHpW1rYaVT0SRLSIShBIRNbf3YObvSjF1dBqOGZ2G/208CADYfKAJ1ywscuxn1SKqNRV1AR/DZBLfO4XoW8vWAgD23n2e4cciosjjs8VKRP4tIkdE5CunbVki8r6I7NJ+Z2rbRUTuF5EKEflSRI4zsvJENHh21bTgO0+sR2ePxZDyj3b2AADKDx91BFXuvFjm+TFfEmPNAIBjx6QFXQYNjuaOHvz+zXJ091rDXRWKAnvr2lB421sovO0t1LV2hbUu/nQFPgng7H7bbgOwQik1EcAK7T4AnANgovazFMDD+lSTiMLtV699hdU7arFxf6Mh5Xsrt6m923H7rS2Hgz7GphDqvqumxWtQqZTCu1/11a25o8dnmVarwmubq2Cxcih+f/e+tx2PrdmDVzdxPNxgauvqdXm/RYufvviF4/YnQbRm68lnYKWU+ghAQ7/NSwAs124vB3CB0/b/KJu1ADJEZLROdSWiSGBQDGD1MmiqqqlDl2P86rWtAPpmFvqrpbMHZ/z9Iyy6d7XHfZZ9VImb/rvRcf/c+z72We4rm6rwg+c247GPjcnJ1WuxRm2Lz6ubDgEAfv6/L8Nck+Hl5HtWYdbv3jes/OPveh9/Ld2he7ll+/q+NH1xoFn38gMR7OD1kUop+1ezagAjtdt5AA447XdQ20ZEUU5g7PikhBizx8fOu3+NrscKdOD7kRZb10L10U6P+/zpne0u9/0JBt/RWrie+GRvYBXy04RfvoNJ//eOIWUbrbWrN9xViEifVNThxbIDvncMUn2bsa1V9W3deGBlRdDP77FYsbeuDat3HMHyT/fit29sxbVPfu6yT3FucqjVDEnIg9eVUkpEAv4OKyJLYesuxNixY0OtBhENEqM6rZLivV+OrFaFFduP6HKsQLveei2+948xCXoDLPeDbba/x1vApofuXiviYjgJfCi44rF1AICLSwrCXBPjdPZYcLCxHXvr2rG3vg376tuxr6Ed++rbcLCxw+X9mxRnxrhs10Dq/179Ct+eN26wq+0QbGBVIyKjlVKHta4++9WuCoDzfztf2zaAUmoZgGUAUFJSwgEGRBHOkZjToHdrgo8P/ufLDuD2l7focqxdRwJLh9Bj8d2dNj0/HZv2N7lsu37551h8zEhcNsf9l8fZYzMGPMcIn+6uw6LJIww/jp7SEmJwtDM6W62e+mwvCrKSou6cO7NYFcw6z6J1zj33+zfLceHx+dinBU5769sdtw81d7hcZ1ITYlCUk4wZ+Rn4+swxGJuVhMKcZIzLTkJuSjxEBIW3vaVrXUMRbGD1OoCrAdyt/X7NafstIvIcgLkAmp26DIkoigU6LilQH+8aOOB00sgU7KyxBUHugqpeixUx5sBbYgJtseryY5zS/vr2Ads+2HYEH2w74jmwKsgclMCqxuAWMSNEa1AF9I3l0zvlxmAuPN5jscJs8tw9H4wbn9rguP3Ymj14bE1fjrrs5DiMy07C3KIsjM1OQmG2LXAqzE5GRlKsIzFwNPAZWInIswAWAcgRkYMA7oQtoHpBRK4DsA/AJdrubwM4F0AFgHYA1xhQZyIKo7J9DThxYo7u5T64auC4iw4fqR2aOnqQkxIPwNZV2G2xIiFW3w8DAOjq9Z1iottLq1Z1cydGpScM2D5lVKrj9n0f7MKcoizMHpuh+98wIz9D1/IG2/TfvIe8jETkZSRijPaTl5mIvIwE5GUkITc13u/WFatV4aR7VqHbYsXnvzzd4Jrra3t1y6Adq6WzV7fXYXevFV8ebEJpeY3L9gcvn43C7GSMzU5CWkKsLseKBD4DK6XUZR4eWuxmXwXg5lArRUSRp7HNlj7gHx/sQnJcDCxKwaoUrFYFq7K1Aiml3e73mMt+yrafxemxlze6n1J/oMH7AHDnL/C/fHULnl1/IKBWgl6LFfVt3RiZNjDoceZPi1WLlxaWpU+V4YUb57t8UB3t7HGZ8faPFTuhFBBrFszMz8CcoizMKcrC8eMykRrEh857W6sdt+OjeHxVTko8zps+ClVNHahq6kTZvsYBqSxiTIJR6QmO4Csv0ykAy0jEmIwEJMXZPu7ueqtct1mmg610a43vnXRywT8/wSe3nRbUc7t6LfjiQDPWVtZj3Z56bNjXiM4e1/fQ75Yci6/NGKNHVQEAGUmxaGr3neJkMDDzegT7aGctpoxOxYhU7xd9osFQfvio4/Yf3t7mcT+TAGaTQERgFoFJbBnPTdptd48FSymFwtvewvS8dGypanZs86fbYG9dG5Z/thdPfLIXm351BjKT4zzu29EdWlLULVXN+NlLX+L+S2c56lbb0pfE8IUb52PyqFRs2NeAdXsasH5PA5Z9VImHVu+GSYBjx6RjTlEW5hZl4YTCLK91tXPudtlS1Yzi3JSQ/oZweeI7J2B6frrLtpbOHhxu7kRVYweqmjpwqKnv97o9Daj+onNAd29mUizyMhPxVdVRRKvS8r5g2egJCYEEn509Fmw+0IR1lQ1YW1mPjfsb0dVrhQgwZVQaLpszFnOLsnHTf/tekwVZSbrW1xxBXYUMrCLYVf9ej7FZSfjo56eGuypELrb85kyYHcGSFiCJBB0kBTvwdJvWNWIPqgBbK5Y/19hFf1ntuN3Q3o3LH1uHQ00d2PzrMwYEZn8JMe/Oz86ajHve3YEpo1Jx86kTAMAleUVyvBnpibE4bcpInDbFlr2mvbsXm/Y3Yd2eBqyrrMdTa/fhcW1MyuSRqY4WrTlFWT5b3P5auhNLZumb+abHYsXNT2/ED06fiGPHpPt+QpAykga21qUmxCI1IRaTRqa6eYatJbKmpcsWcPULvqLVwcZ2bD3UFxQ+tXYfrjuxyMszjNPZY8HG/Y2OQGrTgSZ0a4HU1NFp+Pa8cZirvTYzktx/CWjv0ncFh8FYrspfDKxC0NljMWQ8h7P9DQMHxA5nSik8vmYPvjZjjNsxKzQ4gumaMsLyT/cO2FbT0onR6YkDtt/52lcDttl9uKMW27QWuZ+88AU27m/EXy+ZiePHZWFPXRsqa9u81qO92/tA6++eMh47qltw73s7MHFECs48dpRL8GZyEwkmxcVg4YQcLJxgG8/W1WvBlwebsX6PrVXr5Y0H8dTafQCAwuwkLcjKxtyiLORnuv79RmTSfnzNHpSW16C0vMbQdRHTEgN/rcWYTY5uwRMKXR9b9tFu/PHt7W6fF8ne7zc+6a43y6GUwvUnFet2jEYPOaw6uu2BVD3WVjZg84EmdFusjtbUq+aNw7zibJxQmIV0N4GwO09+ugfnzdAvf7hzC3C4MbAK0ltfHsbNz2zE41eXYPExI30/IUAHGxlQubO3vh2/f2sb3vjyMF67eWG4q0NhttJNXqtLl63F09fPRX6ma1fD8s/2eSznd2+WO26/rC2h8ud3d+CFG+djg1NGZ0/+8cEur4+LCP584QzsrWvDj57fjP99b4FLQlR3gVV/8TFmnFBo6wq8+VRbq0z54aOOQKu0vAYvaOsojun3pcOIQPjudwYnOEmO0/fLqz/nOhKVbq3BxBEpLqlCfv/WNl0Dq7J+r/W/vLcDayvr8cXBJvRYFEwCTMtLx3cWFmJecRZKCrMCGnQ+YUQKKrT6t+ncYuVs86/PMKxsfzCwCtLNz9iWrvjhc5ux5bdn6V7+iX9epXuZg2l/fTtiY8Rty4G/rFaFrl4rOnss6Oy1oKPbgq+0pvCWzsgYpBhJqps78dTavQEnqRxqGtu68a1HbMFVYU4IGZi10+jrY/irqma/lqRJiDXjkStL8PUH1+D65WV44LLZjseC6cWIMZswIz8DM/IzcP1JxbBaFXYdacX6PfVYt6cBh74cGplugkmn4U00BlaNbd1Yv7cBN51SPCAH2z3vbsesggzMHpuJ3NT4kI7zf6+6pjR5+MPdmJaXjmtPLMK8omyUFAY3kcLugx+fYli+qWNGpzlanT11Pw4WBlYhio+N3tk2RlFK4eR7bYHhHedOQWePFR09FluA1KMFStpPx4BtVkcQ5W0mlq+uGV/1a2rvQV1rF2pbu1Db0oW61m7UtXbh4dW7AQArf3JK1A32ffPLQ/jnKlv9o3kWWKieuWEernx8HS555DM8c8NcTBjhfhyOv7x9DvdarLj95S3ISo5HXavvrohR6QlYdlUJLnnkM3zjoU+djhH6h73JJJg8KhWTR6XiyvmFePPLwUuYuKumBRM9jHeKNBE0FMdvK7cfgcWqcObUUY73uN2yjyodX6byMxMxe2wmZhdkYPbYDEwdk4Z4L0tF9VdztO81nBIfg7V3LEaKjxURgjV1TJqu5UXSygIMrEIUyIs2EMlxZrSFOBMpXN5zmhJsH8sQaxYkxJgRH2tGYpwJCTFmJMSakRhrRmpCDHJT45EYa0ZCrEn7re2rbbPv+8PnN7s9plIKzR09qG2xBUt1rd2os99u6XIEUXUt3ahv60KPmyVKYpyuuM9/fgC3n3uMvifGYPbUA1/99ixDLoZXPr7ObRJPI8WZTV7zQ7kzLS8dzy2djyseW4dvPbIWT103N6iL+FGtVbTRyxTuJz/diy1VzXjw8tm45ZlNfpU7qyAD9140Az94brNjWzR+2Dv7xkOf4h/fmoXTp+o/LEJvzgPAjWA1oMW4tLwao9ISMD3PdZLAuz88CYXZydh6qBmb9jdh0/4mbNjbgDe+sC1gHWc24di8NEeL1uyCDORnJvoVyJ83fbRhQRUA/PysybqWlxhBjRwMrEJkVMtAtAZVALBuT73j9le/PQsJMSbdmvOdA6trnliPutZu1LZ0eQ2WslPikJsaj5yUeEwZlea4naNtz02x3U9PjEXxHW8D0H8q8FAQasoBb+YVZ2FtZcOA7YEGVXaTR6XihRvn4YrH1uGyR9fiqevmBFyGPRnjXU7jr5wdaGjHX0t34rQpI3De9NG4Be4Dq2/OHjgbb8msvH6BlbGRVVVTB77zxHoUZCZhbFYSCrISkZ+ZpFtixqKcZNzwVBl+euZkfG/R+IjOkv3ihoOGlh/sa9aTjm4LPtxZi4uPLxgw8210eiISYs04flwWjh+X5dhe3dyJzQcaHcHWs+v3Oxb6zkmJx+yxthat2QWZmJGfjmQDAyhPRviYyRqozDB3/zljYBWieINmBcbHmPxKShiJ/uM0SFjvbzwLxmfj0922wO1ISxdyU+MxeVQqclLitYApDrmO27ZgKZBpuL889xj84e1tA74ZEnDPRTNw2l8/xONXl+he9nUnFmNtZQPmFGZh/d6BAVYwinNT8MKN83HZo2txxaPrgirjiU/2uN2ulML/vfoVRIC7LpjmNZC456IZbrdfMXcsnl63H4AxgVX/c1nb0oUN+xoHJDJNT4xFQVaiLeDKTEJ+VhIKMm338zIT3bbK919a5cWb5uPnL32Je9/bge3VLbjnwhlI1HnQebSY8qt3dS1vTUUdOnusOPNYW2vgKZNy8eHOWgCeu6lHpSfg7PTROHuabdZdj8WKHdUt2HSgCZv2N2Lz/ibHLEOTAJNGpmL22Exd6z3YsvzI7TZYGFiFaNthY5qVz50+Gq9scp+NOtIFug5bIM6bMdoRWL31/ZN0L3/CSNu4KssgrskVLYpzUwybVm9/zfSfqn3XBdPwq1c9p0nwpSArCS/caOsWbOkKfO25377h2lo1Wptt9/oXh/Dhzlr8+mtTkZcxcILGl785EzN+UwrA8+Dru5ZMcwRWRjTwOI85+fEZk/D9xRMBAM3tPTjQ2I4DDe3Y39Cu3e7A9sMt+KD8iEuLiwgwMjUBY7OSkJ+V6Gjx+teHfeN8bjplPBJizbjv0lk4ZnQa7nlvO/bUtWLZlSUY4+bchFtirNnnUkmRpHRrNVITYjC3KBsAghqgHms2YVpeOqblpePKeeMA2AbEbz7YpLVqNeLNLw+5PKcgK/L+d95EUiMpA6sINZwHH3uTaHDeMHv23k6Dur3u+2AX/v7BTuz4/dmGjc+LRvbg5ITCTJd8PceNzQi57DEZiXj+xnmY84cVIZellC0n1O/eKMfM/HRcvaDQ7X7+dK85t6Qa8aHgnMvqFi0pKWALXtOTbB+y/VmtCkdaunCgsR376/uCrgMN7fhsdz1eOVqF/t855o/P1v4GwXcXjcekkSn4wXOb8fUHP8EjVx7n0kXlrzteGbjgtl6uWViIh1bv9r1jEPReJNliVVix/QhOmzLCESg7v1RCedlkJsfh1MkjcOrkEQBs/3v7UAgA+O6iCZ6eGpKfnDHJ8PyP4cbAKkIN12Z0X/xdaDVY9iUj/vr+TiyYoP9Cw4+tsU3L7+yxMrByMj0/HR/8+BSMz012Sd6o1/9br2WhFBT+8NY2NHX04Knr5rrUr2Rc5oA8QP4yoivQORu7v93hJm3NvVHpCTihcGBA1NVrQVVjB07764eObf2XEll8zEi8evMCXL+8DJctW4ffXzANl5xQEFDdn9Fa8oxgZCvaaq2LTi8b9jWioa0bZ04d5djWo/MYLjuTSfD1mWPw+heH8PHPTzXsWnur1nJqlHOmjfK9k8HYLBKh4nTO3QLYvk21dvViV00Llv6nTPdvV4NhvMEpEOwLhVY3dxpSvn18SyQ1W0eKCSNSBoxVMiLgCOXzor61Gy9uOIgbTioeMNMw0v6nN5xcjPNnjsHGX+mXLDE+xjwgDUmMeeAfPmFEKl69eSHmFGXh5//7Er99Yyt6DQoIAjVhhDHXEKUU7l/hPVFsoEq3ViPObMIpk3Md2xLj+tpD9J4kcP9ls7H37vOicvKOaO139hbUcBoWLVb76ttw+t8+xLs/PNnwD2a9+PMNs7vXiqb2btS3df9/e/cdJ1dVPn7888zM9pLdbDZ900MaJWUhhFACgVAVbIgoAlJEEFBsfPUrduWr/iwIilFEEAVpCiplITQJJJBAKNkkZNPrZpNN2V5mzu+Pe2d3tszulHt3yj7v12uzU+7cOTl7595nTnkOBxtaqW1spbbB+jnYYD/e2EptQxu1DS0cbGjrMn7i3F//l2e+dKrjZf/EvLGuzbxx66QYVJJvDYB0c5oxxNeEP5hEG1jd/Zm5/W5zxvQRPL+uut/tetMeMIwvyeVLZ7r7rdsJ+Vm+LklI3RKue74oN5M/X3k8P35qPX9avoWN1fXceemchCdvdOuzvbzqAG9vP8QPLpzFt59Yy5lxrshhjKGispqTppR0KfMVJ03gwTfca9FT8YvrCBORLwNXY+Uofg+4EhgFPASUAKuBy4wxzi9UFYUn1+ymzW/48/Kt3HD6FNr8AVraA7T5A7SG/G7tuG9o9ftpaze0dNsm+Nttoc3rP35qXUfAVBsMlupb+xyMOyQng6F5mdaK7kU5HDOmkOK8TEryMju6WoLTyZ3m5tRdt1sFLpo9ht+/vJkvLJrs7hupiETbHRHZ2I34Wmp/dNExaT9GJBp9zaD1eT3c9qGZTB9VwP/+430uvGs5f/xseUKTiR49Zgjzxhfz1vbYum3DuWPZRkYWZnPx8WX8deV24u102FBdx/baRq47reu5aFJp52oC+gUtOcV8BRSRMcBNwExjTJOIPAxcApwH/NIY85CI3A1cBfzOkdLGKDgr5y8rtnUsWhorEXe66boLvZ7c99pWSvIyKc7LZGheJuNLcinOzezyWHFuJiX51u+i3Awy+ihj6BiW5yqryfAKmV4PGT4PGV5P532vB1/Ibet5IcPj6bNFzc0uRnH5VBJMEur2WK665vakWcg4mXUfv+OESFvBZo0u7JFM8qcfP5aTpzo/9i7VTB9Z0PHFLJLW9YvLy5hcmsfn//IWH/nta/z6ktmurLEaqfkTh/LOjkOO7W/F5gO8sbWW73xoZsfYyXhPgxVrqxGBM2cO7/J4X+d2FX+9OyHepgUfkCMibUAusAc4A7jUfv4+4LskOLA6eow1FuL8Y0Zx8tRhZHg9ZPo8ZHqFTDuYCAYWmR3Phdy3H8vwSpcgLZ5p4P3JDhm8vuGH5zq67+AARYBr7l8V0z58HukIwjI6Ai/rdnC5mXEu9NO73WIVvEgEXPp0BvOTnfLTF9n04/P63d4YQ2Orn/qWduunuZ2GlnbqWqzfobe7L3WRDqL9e0cy5iSSpWeg9wzdF5dHPgj7a2dP60jKmG4WTRsedYv3vPFDefKLC/n8X1Zz9f2r+NrZ0/jCaYlJJuoRibPdsqs7lm1kWH4WnzphnGP7rKjcy5yyoj4nXiTbuD5liTmwMsbsEpGfA9uBJqACq+vvkDEm2Ee1E+iZdhgQkWuBawHGjXPuYOz1vexWjisWTuh1pksszp45wtXAqjQ/vsU0+3L0mMKOwOpfXzyZVr/VxRn8aW03tAfs++2m2/Omy+1g92joc8HA6m/XzHft/+CWYGuG2996/AHDT59Z3yUwsoInP/XNbTS0+K3HWtsjKktoC1tTq9/1MWIDJdqWw0i2rm0YmJEJN5w+hRtOd2fKeqL5YmzRHV2Uw8OfX8DXH3uXnz6zgfV76vi/BCQTFXHuy9OqrbW8tukA/3v+jC5dxPHsfdehJt7fdYRbz53e53Zut+Cr2MTTFVgMXAhMBA4BjwDnRPp6Y8xSYClAeXm5q5ex4AfIyd4dp1dc787NC3t5SHB5zFjnM4z/8KJWqvbVM7bY+RYrt5f+CB4jbiU5Dc2mv/SVzeRl+cgP/mT7GJKTwZiibPKzfORl+Siwf+dnd24X+pq8LB8F2T6yfB6O/9Ey9te3YBz9Lp5Y4f7co4Zks8eeuXneMSN56r29A1gqFU9XeU6mlzsumc2MUQX87NkNbNnfwNLPzmPUkM40CMW5GX2u0Rgvwblz7B0vVDE0L5NL53c2EMTbCvfcWut4XpICay8mi2CVJ8Ns93i+1p4JbDHG1ACIyOPAQqBIRHx2q9VYIOHpw4OBlZNNzm4vmurmxXGEw2s0dVeUm9kleHOS2/UeDNwON7l3Ug/a+KNzk3pNtWQQ7ht5bkgLR+hSFpFUp1t1ngTn8wGT0UuKhWiICNcvmsK0EQXc/NAaPvSb5fz+snnMG28tq1KY43Jg1dEybeI6HtbsOMQrH9TwjXOmk5vpXCtxRWU1U4bn90ht0Z2ePjoFE95OdnnmeCTiaXbZDpwoIrliHZmLgUrgReDj9jaXA0/EV8T4BU94TrZ2uN0E6+ZJ2u3gxE1uByKb91vdmN8Ps/Cuk1z7v6T4BT4vM7Q7pff/TOgA3tDPYiK7RlK82qPi9TjTYr94xgj+cf1J5GV5+dTSFTy8agcA7b0sqO6kztaN+Pbzm2UbKcrN4LIF43s8F+u+DzW2snJLrbZWRekT88by/C2ncsrU0v43dlnMnw5jzErgUeAtrFQLHqyuvW8At4hIFVbKhXscKGdcOlqsHNxnQ2v0645Fw83TihszrdLFsPzkWcgzWunyZ731vBn9bhO65FO0/2+3ugqKcmKf5Zlqf7tYx1j1ZuqIAp4IJhN99F2+/69K17KLBwUD8HiOhPd3HWbZ+n1ctXBijzGN8dTOC+v34Q8YlsxKfAbxVCIiTBmeuDQeoeL62mGM+Y4xZrox5mhjzGXGmBZjzGZjzAnGmCnGmE8YYyKbguMiN1qs3BYs8+PXn+T4vrX7KbyxRamXcTjoLPsbbm6KD1zPiOCiHbrAcJe10yI4tA+51M0bbLU4cZI73eDJpLds6/EIJhO9cuEE/rR8C/vq3L1seDparGIPre5YtpGCbB+XL5wQZovY9l2xtpoRhVkc20d+MJXcBkVCjM4xVs7tM8/B/vTeBLtAxriwrlUqdwW6zevwBWMgfe/Ds1j5zcUpPyMwki9APk9oi1VoV2D/Drk0dic4oNvnUDdZMnOyxapjn14P3/nQLH76sWMd33d3wUMm1jkq6/YcoaKymisXTux1we1YrzXNbX5e/qCGs2aOiCg/mH5HTk7pfwbAykI+d1xRlwGv8XIrz1FQcPdufG7cTn6Zyty4YAyUDK/H9YkJA6H7xeJrZ0/r8XdxusXECUePtloYrj11UoJL4j6nxlj1JtoFm2PRMXg9xlalO1+oIj/Lx+fCtlbFNsZqedV+mtr8XRZdVqlnUARW8yeV8Pj1C/udYRENt/OudHwmXbh+pENXoJNBcqhUDqzSRfcWqxtOn0JVt2SqoV8OumyewD9fcV4mW28/n1OPSvzgWbe5Hdj+/doTATj3aHcCjHgGr2+sruOp9/dw+Unjw657GOsptmJtNQVZPk6cFNlCwprHKjmldp9BAmVneDlpcklHLh3HdeTecmNJD8d3OaB+9+m5zBrtzvgDbc1LvEgaQ7p0BSbJrMDBxO0vIPMnlfDPGxYyfaQ7g5E7Bq/HEFjd+WIVORlerjq575bJaHftDxieX1fN6dOHdxlDqFKP/vXiUJyb6VqQEtCuwLDOPWYU40rcGWSeDq15qS6yMVZhWqyiMKYoh5e/tii2Fw9yA3EOmV1W5Npi1x0tVlGGP5tr6vnXO7u57MTxXfKn9dh/DGfut7Yf5EBDa8cklEjo6So5aWAVD3EvLYJxIalpUCrNjlSDT+gxH9qiMCEkmA7tiop2VmBQdoaH8SV5sRRx0Ev1hYA9MXYF3vXiJjJ9Hq4+xflxdBVr95LhFRZNi7wrWc/kyUm7AuMg4FpkFdytGx8cjauiY4whYKDNH8AfMLT7rbUU2wPG+vHbt4OPd/nddRvVv3CNIU/ccDLHfb8C6JYgNMLjuSDbR12zu/nnBotUb/UOtihFMwlp24EG/rlmF5cvmEBpQf9ruUaTysEYQ0VlNSdNHkZBL7MMVWrRwCpO7rVYWb/dCII0QWhkZnz7GXsxag2IBlK4FtUhuZ0XnNAEoTkhqU9y+ug6SuaW2lQbG5bqkzw6uwIj99sXN+H1CJ8/rf/WqmgPtQ+q69l2oDHqGaU6dCE5aWAVBycPamOM1RoSMASM4UizlWvHjRNuMl9gksllC8bj9QgZHsHr8eDzCj6P4PN67N/2/Y7nPNb23pBtOraznrvgN68m+r+V9Pq6Zm/80bkcamzjjmUbOx67+pSJzBxVSG6ml2P6SKoY72E/c1RhfDtIIynfYtWxVmBk2+882Mhjb+3k0vnjIk5pEk3QVmEvunzWDF3GJh1oYBUHAXYfauLiu1+nPRDAb8BvdwEFjBUk+UN+2gOGQKC3xwPhE9W5km7B+X2mo29GsLRKtG5aPLVLUKB66usLS4bXQ2lBVpfgqzA7g3MimJYf72H/zxsWxrmH9JHqY6yCx0Kk3XW/e2kTInDdaZOj2n+kKiqrmTOuiOFR5qHTU3ly0sAqDmfPGsnew814PJDr8+H1SMePzyN47N9ej+AVq+UieDvYAuKRkG1CXvuTp9cD7gRB2nycOJ+eP04Dq36EtqiGm3kVyzEc73GvH5tOqd9iZf2OJK7ac7iJR1bt5BPlZYyOYiWMSFvDdh9q4r1dh/nGOdMj3rdKbhpYxeH8Y0dx/rGjXNn3Hcs20tDqd2XfKnFK+piirSzBa3aGV8JOt4+lO7v7K0Kve0PzMrn9o8dw7V9WR/z6wSzVx1h9UF0HQFug/8Wef//yZgLG8IUIW6uAqKLw5yqrAVgyK/puQA32k5MGVknKE+UYAJUafCnehTIQgmuk9TWJMpbr+tjiHA40tPZ4fP0PzsEj0m9SRm3p7ZTqLVYPvrEDgO0HGhleEL77bd+RZv72xnY+OncMZUPdyZ1XUbmXyaV5THZwZRCVWHGd5UWkSEQeFZH1IrJORBaIyFAReU5ENtq/i50q7GDS2VStkdVAe/S6BTx10ymJLsag1fmlIvyxH0uM8+WzjgLoMVU+O8MbUabr1A4lnJXqY6z+51yr221Mcd9de0tf2Yw/YLjh9ClRv0ckZ+7DjW2s2FzLklmxLd2jwX5yivfT8WvgGWPMdOA4YB1wK7DMGDMVWGbfV1EKfmvXuGrglU8YyszROgMsUYKNIX21WMVyQcm0g4FYFzjXa1inVG+xCo7da+8jlcr++hYeWLmNC48bHXUi2Uhr54UN1fgDhiVRZFtXyS/mwEpEhgCnAvcAGGNajTGHgAuB++zN7gMuiq+Ig1PwgxlNAjul0kEk46diuax3BmMm5N/enTBxaB+vV6k+xupvb2wH4I0ttWG3+eN/t9DSHuCGM6JvrYLIehueq6xmeEEWx40tiuk9VHKKp8VqIlAD3Csib4vIH0UkDxhhjNljb7MX0FA8BsGLiybrVoNNJPHL1BHRL84bSUtY0Kfnj4t6//FItZgt1Vus1u4+AsAH++p6fb62oZX7X9/KBceOjmnsUyR/z+Y2Py9tqOGsmSM6eihUeognsPIBc4HfGWPmAA106/YzVsje62lMRK4VkVUisqqmpiaOYqSnB66ezxUnTWBYvs4iU4NLJC1WH5s7BoCTpwyLeL/BwceRdLv4PKk9hshtqT7G6sRJJYC1EHdv/vTqFhpb/dwYY2tVJF7btJ/GVn/M46tU8orn07ET2GmMWWnffxQr0KoWkVEA9u99vb3YGLPUGFNujCkvLY180cnBYsaoQr774Vna/aAGncNNbf1uIyL89+un84fPlke839FFObz73SWdC+j20XI1orD/teAGs1RvsfrcwgkAzBrdM1P/4cY27nttK+cdM5KjYmgZhci6qivWVlOQ5WOBHeSp9BFzugVjzF4R2SEi04wxG4DFQKX9czlwu/37CUdKqlSa+OFFRzNjVGwn7MHgiTW7ItoulunvhSEL3F7aR3df+YSeY6xUp1QfYxVscfP30i9872tbqGtp54unT3Xt/f0Bw/PrqjltWmlEM1JVaok3j9WNwF9FJBPYDFyJ1Qr2sIhcBWwDLo7zPZRKK585cXyii5DUzp41kqfe2+va/guzM9h6+/lRvUYbjrtK9RarYHdzc1vXJMx1zW386dUtnDVzRNwzg/sau/729oPsr2/VbsA0FVdgZYxZA/TWFr84nv0qpQavZEyUqJNzu0r1RLcvfWCNUPnl8x9w6lGdQ1Huf30bR5rbuemM+Fqr+hvCUVFZTYZXWDRNh8Gko9T+dCil0k4sy9WogZXqXYH1ze2AlXk9qKGlnT/+dzOnTyvlmLE9x15Fy4QZxGeM4dm1e1kweViXrmmVPjSwUkollWTsZhqSoxfAUD5v8v2NohHMU3b69OEdjz2wYhsHG9u4cXH8Y6v6qp2N++rZdqBRk4KmMQ2slFJJJQnjqohmKg4m3hRvVTx6jNUidcpUK11HU6ufpa9s5pSpw5g7zplV2MJ1H1estcYPnqWBVdrSRZgHqX/feLKOG1FJSZMlJr9gq+It9vqLqSbYlVnfYnUJ/nXlNg40tHKTA61V0Pdkh4rKamaXFTGiMPzizyq1aYvVIHX0mCGOjCNQymnr9hxJ2HsXZvv41AllAJw5o7NFQRP1diUibL39fMcCkYG2+1AzAN/6x/s0t/n5/SubWTCphOMdTLPR2xfX3YeaeHfnYZbM0taqdKaBlVIqqUyIcsFbJ7373bP5yUePBWDpZfM6Hi9weZCxrgk6sErsQHnisDz+/uYOaupauHGxc1nWJcwoq+fXVQOwZKamWUhnGlgppZJKWXH0iT/dENolGcmCuvHoLVGlcs+oIVY33MfnjeXulzdx/ITiAcmAXrG2mkmleUwZnnwpRZRzNLBSSiWVZMpEHewWdCvuGVtsrVUXCLizf9W7YOb1h97czp7Dzdy0eKrjy4d1T7dwuKmNFZsPaGvVIJA8ZzCllCK5AqvrTpsMhM9JFK/gIHC/dgUOqGC976htYnZZUVSLeUeklxjtpQ37aA8YHV81CCTPGUwppUiuPFbB/FWnTHUnQ/b+uhZAuwIHWmiC05tdaK2CnoPXK9ZWU1qQxeyxRY6/l0oumm5BKaXCKMrN5L9fP52RQ9yZGt/Qaq1Vt7G6TsfdDKDQQMqNZWW6h2nNbX5e2rCPC+eM0XQig4AGVkop1Yeyoe4Ppk+mVrrB4tZzp7Nw8jBXWquALp3Hr286QEOrX5OCDhLaFaiUUglyyfHW4PhgJnA1cK47bbJrufy6x2oVlXvJy/Ry0mT3Zx6qxIs7sBIRr4i8LSL/tu9PFJGVIlIlIn8XEc2sp5RSvfjhRUfz0lcXMbooJ9FFUS7xBwzPVVazaPpwsnzeRBdHDQAnWqxuBtaF3P8/4JfGmCnAQeAqB95DKaXSjs/rYcKwxCVEVe5YsbmWN7bUArBmx0H217fqosuDSFyBlYiMBc4H/mjfF+AM4FF7k/uAi+J5D6WUUipVVaytJsMrnD59eKKLogZIvC1WvwK+DgTT25UAh4wx7fb9ncCYON9DKaWUSjnGGJ5du5cTJ5VQ6PKySCp5xBxYicgFwD5jzOoYX3+tiKwSkVU1NTWxFkMppZRKKmfOGMGs0YVU7atn64FGlszSbOuDSTwtVguBD4vIVuAhrC7AXwNFIhJM4zAW2NXbi40xS40x5caY8tJSd5LvKaWUUgPPUNfcTkWltejyWTN0fNVgEnNgZYz5H2PMWGPMBOAS4AVjzKeBF4GP25tdDjwRdymVUkqpFPH8un1sr22kYu1ejisrci3BrEpObuSx+gZwi4hUYY25useF91BKKaWS2js7D+tswEHIkczrxpiXgJfs25uBE5zYr1JKKZXKztZFlwcdzbyulFJKuWRyqa4BOdhoYKWUUkq5xK21CFXy0sBKKaWUUsohGlgppZRSSjlEAyullFJKKYdoYKWUUkq5QGcEDk4aWCmllFIu+NjcsYkugkoAR/JYKaWUUqqrITnuLLz84lcXUd/c7sq+Vfy0xUoppZRy0NxxRQD4vO6kWpg4LI9jxg5xZd8qfhpYKaWUUg4K5q4KmAQXRCWEBlZKKaWUg6aPLADc6wpUyU3HWCmllFIOuu1DM7ng2NEcNaIg0UVRCaAtVkoppZSDsnxeFkwuSXQxVILEHFiJSJmIvCgilSKyVkRuth8fKiLPichG+3exc8VVSimllEpe8bRYtQNfMcbMBE4EbhCRmcCtwDJjzFRgmX1fKaWUUirtxRxYGWP2GGPesm/XAeuAMcCFwH32ZvcBF8VZRqWUUkqplODIGCsRmQDMAVYCI4wxe+yn9gKa018ppZRSg0LcgZWI5AOPAV8yxhwJfc4YY4BeM3mIyLUiskpEVtXU1MRbDKWUUkqphIsrsBKRDKyg6q/GmMfth6tFZJT9/ChgX2+vNcYsNcaUG2PKS0tL4ymGUkoppVRSiGdWoAD3AOuMMb8IeepJ4HL79uXAE7EXTymllFIqdcSTIHQhcBnwnoissR/7JnA78LCIXAVsAy6Oq4RKKaWUUiki5sDKGPMqEG6FycWx7lcppZRSKlVp5nWllFJKKYdoYKWUUkop5RANrJRSSimlHKKBlVJKKaWUQzSwUkoppZRyiAZWSimllFIO0cBKKaWUUsohGlgppZRSSjlEAyullFJKKYdoYKWUUkop5RANrJRSSimlHKKBlVJKKaWUQzSwUkoppZRyiAZWSimllFIOcSWwEpFzRGSDiFSJyK1uvIdSSimlVLLxOb1DEfECdwFnATuBN0XkSWNMpdPvpZRKT6dPK2VEYXaii6GUUlFzPLACTgCqjDGbAUTkIeBCQAMrpVRE7r3yhEQXQSmlYuJGV+AYYEfI/Z32Y0oppZRSaS1hg9dF5FoRWSUiq2pqahJVDKWUUkopx7gRWO0CykLuj7Uf68IYs9QYU26MKS8tLXWhGEoppZRSA8uNwOpNYKqITBSRTOAS4EkX3kcppZRSKqmIMcb5nYqcB/wK8AJ/Msb8qJ/ta4Btjhck+Q0D9tu3hwCHE1iWZBJaL30ZjHUWad2Ek851Fm/dhJPKdeZWnfQn2essUfXSl2Sps2Ssm3AGus5C62a8MabX7jZXAisVGRFZZYwpt28vNcZcm+gyJYPQeulnu0FXZ5HWTR+vT9s6i7du+thvytaZW3USwfsmdZ0lql76kix1lox1E85A11mkdaOZ15PHvxJdgBSkdRY9rbPoaZ1FT+sselpn0UvKOtPAKkkYY5LyAElmWmfR0zqLntZZ9LTOoqd1Fr1krTMNrBJraaILkKS0XsLTuglP66YnrZPeab2Ep3UTXkR1o2OslFJKKaUcoi1WSimllFIO0cBKKaWUUsohGlgppZRSSjlEAyullFJKKYdoYKWUUkop5RANrJRSSimlHKKBlVJKKaWUQzSwUkoppZRyiAZWSimllFIO0cBKKaWUUsohGlgppZRSSjlEAyullFJKKYdoYKWUUkop5RANrJRSSimlHKKBlVIqaYnIVhH5moi8KyINInKPiIwQkadFpE5EnheRYnvbE0XkNRE5JCLviMiikP1cKSLr7NdsFpHPhzy3SER2ishXRGSfiOwRkSsH/n+rlEoHGlgppZLdx4CzgKOADwFPA98ESrHOYTeJyBjgP8APgaHAV4HHRKTU3sc+4AKgELgS+KWIzA15j5HAEGAMcBVwVzBgU0qpaGhgpZRKdr8xxlQbY3YB/wVWGmPeNsY0A/8A5gCfAZ4yxjxljAkYY54DVgHnARhj/mOM2WQsLwMVwCkh79EGfN8Y02aMeQqoB6YN3H9RKZUuNLBSSiW76pDbTb3czwfGA5+wuwEPicgh4GRgFICInCsiK0Sk1n7uPGBYyH4OGGPaQ+432vtVSqmo+BJdAKWUcsAO4C/GmGu6PyEiWcBjwGeBJ4wxbSLyT0AGtohKqcFAW6yUUungAeBDInK2iHhFJNselD4WyASygBqgXUTOBZYksrBKqfSlgZVSKuUZY3YAF2INaq/BasH6GuAxxtQBNwEPAweBS4EnE1RUpVSaE2NMosuglFJKKZUWtMVKKaWUUsohGlgppZRSSjlEAyullFJKKYdoYKWUUkop5RANrJRSSimlHJIUCUKHDRtmJkyYkOhiKKWUUkr1a/Xq1fuNMaW9PZcUgdWECRNYtWpVoouhlFJKKdUvEdkW7jntClRKKaWUcogGVkoppZRSDtHASimllFLKIRpYKaVSyt7DzUy49T88/d6eRBdFKaV60MBKKZVS1u05AsBDb+5IcEmUUqonDayUUinF4xEAArqAvFIqCWlgpZRKKVtq6gH478b9Ub+23R/g+r+upnL3EaeLpZRSgAZWSqkk1dzm58X1+1i7+3CXx9ftqYt5n5V7jvDUe3u56K7l8RYvrD2Hmzjc1Oba/pVSyS0pEoQqpVRTq58Ztz3Dl888ilFDsvn6Y+92PLf19vM7brf6AzG/x8HGtrj30Z8FP3mBoXmZvPXts1x7D6VU8uq3xUpE/iQi+0Tk/ZDHhorIcyKy0f5dbD8uInKHiFSJyLsiMtfNwiulBs7BhlbufnkTxqWxTTV1LQD88vkPugRV3W3Z3xDze5TkZQIwc1RhzPuIRG1Dq6v7Hwxa2wM8sWaXa8ebSi/1Le18+M5Xufju12lu8ye0LJF0Bf4ZOKfbY7cCy4wxU4Fl9n2Ac4Gp9s+1wO+cKaZSKtHO/tUr3P70el7csM+V/X9QHb6LL/TiumbHoZjfY+fBRgBEon9tmz/Q70U+9ITeFmGr2CZ7zJjq6s4XNnLzQ2t4dm11oouiUsD3/7WWd3ce5o2ttbwaw/hLJ/UbWBljXgFquz18IXCfffs+4KKQx+83lhVAkYiMcqisSqkEKs61Wnu8HneGZh5pDj8uaa1Dg82ve+AtIPrAqrnNz9RvPc0tD78Tdptn3t/L9G8/03H/Sw+t6Xe/z1dWs/j/vcwTa3ZFV6BB4OUPagC4Y9nGBJdkcJlw63+YcOt/XNv/j59a58qXs4dX7ey4/frmA47vPxqxniFHGGOC2fn2AiPs22OA0OQyO+3HlFIprrQgCwBPDK09kcjNDD/k8ztPrnXnTSNUfaQZgH+8HT4A+tojXYOu/0SQwDTY5XlzBEFYLD5x92uuXiTd9M5Oa9JC5R6dwRlqc009q7cdTHQxYrb0lc1cee+bce3DGMOB+hZWbzvI42/t5BfPfdDl+XFDc+Paf7ziHrxujDEiEnUnuIhci9VdyLhx4+IthlJqgLg15CXLF/57XvBCsudwkyPv1e6P7j/R2t5/t15dS3vU5XB7LNabW6168wcMXrciYjWgzvh/LwNdJ3SkI2MM++pa2Lq/gW0HGtlW28DWA41sO9DAtv2NXT5v3Vug/7JiG5efNGFgCxwi1sCqWkRGGWP22F19wXa9XUBZyHZj7cd6MMYsBZYClJeX6+hEpZJcLOOSopGf3ffp6PnKaq6+f5Uj77W/viWq7SOZRTh3XBFvbT/U5bFHVu1gUmk+88YXR/V+Tnt7+0HKJwxNaBmiVZDliylYTQavVe2ntCCLqSMKEl2UmBljEIc/9KFjFF/btJ/5E0vYc7iJ7QcaO4KmrQfsQOpAI00hYxZ9HqFsaC7jS3KZN66Y8SV5TBiWy/iSPMYW5zDtfzu74av2JXbcYqyB1ZPA5cDt9u8nQh7/oog8BMwHDod0GSql0oBb34L6m+3XW1AV68l/f310LUUtEbRY7T3c3OOxrz1qdfWFa13wegR/wP3vldtrG1MusErVoArg0j+uBFK7VanNb8j0ORtYPbK6cxzUpX9YSabX0+VLS6bPw3g7eFo4ZRgTSqzAaUJJHqOLsvF5UyP1Zr+BlYg8CCwChonITuA7WAHVwyJyFbANuNje/CngPKAKaASudKHMSqkEOhBla0+kvv5o+BQL4RxqbKPYTqHgppa2/gOr+j4CgUDAdCzFE2pYfibVR6z6XLaumvIJQxmSkxF7QcMYW5zYMSfxuvQPKxhdlMMY+2d0UQ5jinMYNSSb7Axv1Pv7wb8raWrz8+OPHONCad0TnNU6EKr21TNztHNpSRpa2rt8xr0e4cqTJzChJI/xJblMKMljZGF2r5+TVNNvYGWM+VSYpxb3sq0Bboi3UEqp5BNMH3DLw+8wakgOxhj8xhAwVuAQsG/7A6bX56zHsR/v+tw7MaZQqG9p7wisHlixjZ89u4F3vrPEqf9yh0i6Ao80hw+sHn1rJxeXl3V5rLnN3xFUAVx13ypEYMbIQk6YOJT5E4dy/MShDMvPiqnMoRnrgxMPUlVzm5//bqxhX11LjzF+w/KzGFOU3RF4BYOu4O3i3IwurZqPrt7JPa9uAUi5wOq5yoFLPfHI6h18Z/SsmF9f19zGqm0HWbH5ACs31/Lerq4rKNywaDK3LJkWbzE7DMnJSJoVDzTzehLbdaiJkrzMmL6RKeW0ptbO8Q6f+sOKBJakk9cj3PLwGk6cWML//tPKYRxp92B9SztPrNnFt/7xPmu/dzZ5WeFPh/EmHLz96fWcPXMkQ3I7W6O2Hujs+hxekMWvL5nDG1tqeWPrAR56czt/fm0rAJNL8zhhYgnzJw7lhIlDGV2UE9F7nn/Hqx23dx9qYuKwvLj+D4ny+PUnMXecNUattT3A3sPN7DrUxO5DTV1+b6iu48UN+2ju1rqYk+FltB14jS3O4cE3dvT2NimhIiSnlxtjoELdu3wr3/lQ5IHVkeY2Vm2tZcXmWlZuPsB7uw4TMJDhFY4bW8R1p03irhc3dWy/oY+8dbFIpskZGlglsYW3v8CpR5Vy/+dOSHRRlOrSRP/gNSfiEetkJiJ4PYJHwCNi/XjAK9Zzwe2sx7ttZz83+/vPxVSm1vYAj7+1i8ff6pwjY0xkA+2vvu9NVmy2UvRVH2nm6ff3kun1cM2pk3ps++Ab22MqX9ChxlZ+XrGBH1x0dMdjbe2dTS8TSvJYMLmEBZNLgKm0tgd4f/dhK9DaUsu/39ndUYayoTmcMKEz0BpfktvvBfZ3L21i4ZRhcf0fujPGcO/yrVxw3CiGF2Q7uu9QwfxpYI3BGVeSy7iS3rs2jTHUNrSy+1Azuw41sutQsxV4HWxi9+Em1qVw6oaDDa28sbUzpeRLH9Rw+rThCSvP4aY23txSa7VIball7W4rkMr0ephdVsQNp0/hxEklzB1XTE6m1TgQGlg5PV5KAysVsVfsJHmq02tV+zmurKjPFgblvNAx1lYAkHhv7+iZz6elPdBxIg/17Nq9Xe4HgyqAbbWN/OzZDQAUZPvYVtvIFSdNYERhNocaW3lpQ9+fw0A/A9A/u2AC972+lYvLyzhm7BAAmts7W8G6x0WZPg9zxxUzd1wx1502GX/AsG7PkY5A68UN+3jsLWsg8PCCrI6uwxMmljB1eH6PcSqvVjmfiXrZun18/9+VPLBiGy98dZHj+w8qimLMmYhQkp9FSX5WRz139+vnN/LL5z/o9blk9sL6fV0mOlx575s8ct0CjndwUkJja/ju7EONrbyxxW6R2nKAyj1HMMY6VueUFfHFM6Zy4qShzB1XHFEvyxGHu+2CS2IlA70yxahy9xHOu+O/PH/LqUwZ7vyU2kSvdZSsdh9q4tI/ruTco0fyu8/MS3RxBpXgVOnTjipNcEk6ffnvPTOhX3P/KpZ+dl6PhKOf/8vqsPsJTVh46+PvAfDezsM8cPX8iMa1/HXltr7LedZR/PvdPXz7ifd5/Asn4fEIDSGD3fv7tu31CEePGcLRY4bwuZMnYoxhU009K+1Aa+XmWv79rjUBuyg3o8fFdkyE3YfRCM7S3BzH2o2RcPoLVF5Wag6tqKjcy8jCbPYe6Zx9+om7X3d05uF7O7uOg3rm/b0dLVLr91qBVJYd9N+8eConTiphdllRTMNVVm11L8npY19Y4Nq+I6GBVYzO/81/AWvK6BvfOtPx/c/6zrOO7zMdBL9ROd0/r/q3fo9V5y8neSvqa5v2c8Wf3uSeK8opyI59hl0kA9bBSlr6f89s6HObITkZfPO86dzy8Ds8snoHnzx+HA0tnV+ePFGOlRERpgwvYMrwAj49fzzGGHYebLIDrQO8saX7KmSpK8PrbBePm+OS3NLU6uflD2r4xLwy/rKiaxD/9Ht7mDOumJFD4u+O/dXzXZcPuu6B1WRneJg3vpgvn3kUJ04q4biyIWT5YgtO1//gnI5ln5ocbjyYPrKA9Xutc9S88YlNLaKBVYyCM1MiXWg1WgOR28ZNwWU0/nbNfFraAjS1+Wlu89MccrulzW/fDtAccrul3U9Tq5/m4O+QxxrsAdSba+L/lmyMoaHVT01dC/vrW9hf18IDK7exvOoA73/vbPK1q7GLSAONRPv1JXP40t/XcNk9b3DflSd0GTDuhu88sTai88BH5ozhwTe2WwPZZ43k/z3XGYzFe60XsZInlg3N5ePzxgJ0WcrG7VjicFObK2kiwPlAyOE4bUC8WrWf5rYAS2aN6BFYfeGv1vqXIwuzmTOuyP4p5pgxQ6JuSeq+xt4j1y3g2LGxB1LdhZan3OGkuX2t3DDQ9MoRJ52x19OKkA/npX9Y2ee2ORlesjM8ZGd4ycnwkpXhJce+X5iTYT/W+XxwmnQ4xhjqW9rZX9/aESzVdPy2HusIpOpbeswgCrrrxSq+cc706P/zKuE+dNxosnwevvi3t/nUH1bwl6tOoCSGlAXtdrDU1xI+z7y/l4rKam49dzq3P72+z/2JCN+/8Ggu+M2r/OzZDV2+HLg98NatZYiCPnLXcv5weTmTS/PdfSMH1Da6OyW/v/F2sahYu5eCbB/zJ3Yd2/jw5xeQ5fPw9vaDvL3jEG9vP8TT71tjCX0eYcaowo5ga3ZZMRMimOgQ9MnyMkfHb3X3I4dTXWQl0bVYA6s4aWDV08qQQcEPXnMi2RkecjK9ZPu8IQGUhyyfJ+pvo6GB1S8qNkQcLIlASV4mw/KzKC3IYuKwPIblZ1JakMWw/KyOx8/9tdXFW+xyK4fqakRhVpecTvFaMmskSz87j8//ZTWXLF3BX6+eH/U+gsvTBBdK7u5Icxu3PfE+M0YVctXJE/sNrABmjCrk8gUTuPe1rl8Q3G5E2XWoiV9UbGDs0FzG2S1bIwuzHQvoDje1cdGdy7nj0jkJnakWiTuWbex/ozj4HY5i/QHDsvX7OGP6cDK7tcpMG1nAkJwMjisr4gr7sf31LazZfoi3dxzk7e2HeGz1Tu5/3WrlKs7NYHaZ1aI1Z1wRx5UVURhHd3k8po10dmxyYT9LYg2k5ClJikqm5sdkEXrRcHr22PCCLPbZsz9+82JVR7A0LL8zWAoGScHHhxVkMjQ3M6LpvZ+YN5ZHVu9kzrjEru2WjGaXFbFmxyGucGFx029fMJMv/u1tpg7PZ6ND63wtmjace684nqvvX8Unl8aWd2tFt66RUD99Zj3761v4w2fLyYhi6viXzprKv97d3WUWU7RjrCIROuYE4M4Xq7rM7MzwCmOKcigbmsvY4mDAlUOZfbuoW2LNvjx548lcc98qPvfnN7n1nOlce+qklBzL5IS5P4gtdUg4q7cdpLahlSUzR/Z4rrcqHpafxZkzR3DmzBGAFZht3FdnBVt2wPXSBzUdaUmmlOZ3BFupbHiheyk/oqWBVZxCT1xO+sicMfzj7V7Xr056h1xsav/Cosl871+VAGz84bmO50I575hRPLJ6J74kyomSLM6aOYI1Ow7xP+c530UaDEzGl+Q5FlgBnDRlGPd/7oQus/6icUmYgGzV1loeWLGdzy2cyHFlRVHtszA7g2+dN4Mv/X1Nx2NuLONRkt+Z/+ni8rH88KJjrAVvaxvZUdvEjoONbK9tZGdtI8/u3kttQ9f1E/OzfIwtzulo4SortoKwcUNzuwScC6eUMKYoh0e/sICvPfIuP3l6Pev31vGTjx4zKFv06/rIwB+LirVWfrXTplmzcT+7YHxHC1QkvB5h+shCpo8s5JITxgFWa+u7Ow53dCE+v666yzp+QK8pS5JZMp2yNbBKUpkpstjkQAsdIOvGgpzBrpF2lyYPPFdZzS+e+4B/33iy4+Nqllft55aH19Dudycjc3BGphutK3l2aoSyoc6nBSifMJS/XjOfD9+53JH9tbT7ufXx9xhTlMNXlhwV0z4unD26a2DlwkVhaF7nuLLvffhoa4HbkjzGl/Segb2+pZ0dtY3sqLUDroNN7KhtZOuBBl7ZWBN2POI1p1gJVXMzfdx56Rymv1DA/3vuAzbvb2DpZfMYEUNLwhNr3PtSefXJE/mjPaRg58HGpF5H0RhDRWU1C6eUdEymCV0BIdbDpjA7g5OnDuPkqcM63mfbgUYW/fyljm2+drZzy80MNhpYJalU+7YwUNwe5Pumndn4z8u3ujJw85aH11DX3E59S7vjs6gqdx+h+kgLF84e7Vry1EnD8qLq9orUwikl3PGpOZw9awT3Lt/a8fgpU4fx343xJ7c8dmxR3PsAa82937+8map99dx7xfFh6/lrZ0/rSDja28oJIsLyW89g4e0vAO4EqxNCspNHcj7Jz/IxY1QhM0b1XHjXGMP++lZ2HLQCr5sfWtPxXOhnUkS4cfFUjhpZwC1/X8OHfvMqv79sXtTdTKH7d1roRIZ/v7uH606b7Ni+3++2Hl68NlTXsb22kS8s6iyj02O4wPq7TQhZ8uhzCye6dg751xdPZt1e9zLgjy12/stZtDSwSlJujt0KBAzbaxu7fJBShZtLZ4A1yBdgTYyLAvcn2E3gZnz4o48ck3KpIkSEDx83usfj375gJkt++UqPx8cU5XT8rQA+dULZgKwBV9fcxp0vVHHBsaM4fXrXQdrl44tZtc1KenjD6VM6AqtTwyRUDU3a6UZX4MfmjuU3L1Tx8OfjT5YoIpQWWGMX544rDhtYBZ09aySPX7+Qq+9/k08uXcFPPnIMH7PTQCTavJBp/k7HKHe+UOXo/irWViMCi2d0Hmut7Z0th063TFf96Fya2vxx5X/rzzFjh4TNiu+Ea3tZkmqgpdbZN0YNLe388D+VfPO8Ga4eME6K5kRrjKGupZ2DDa0caGjlYEMrtcGfxuD9NmobWjjY2MYWO1Py5xZO5LYPzXS87AunlLC8Kvyg33jMGVfkyn6DgmNC2gPu5mwarAN7oxWuJaf7xTzL5+W5L58a0ZieJTNHUBFBNvXeNLcFKMz29bo4bTx/UldarIblOZqVO5xwdT5tZAFP3nAy1//1Lb7yyDtsqK7jG+dMT/iabiYkmjI4F1mt33uEZ9bu5brTJnP3y5s4ZWr8azNWVO5lTllRly+UV508sSPLvtN8Xg8FKToMRVyfWxu5uAIrEfkycDVggPeAK4FRwENACbAauMwY0xp2JwPgz69t5cE3dlCSl8VXu/UbBwKGVn+AVn+Atvbgb0Or309Le4A2v6G1PUCbP0Cr/XzoNwa3hJ57nnxnd9egqbGV2vpWDjZawdPBxlba/L2fIDK8wtC8TIpzMxmal8ms0TkdgdWflm9xJbCaOrzAtcDK7Xjkk+Vl/G3ldm5eHNvYmUgZtxMLpYlw1+DukwvmTxzK1BGRTd8OxFn33zp/BqUF0efF6ksyDbyN1oyRPbsOg4rzMrn/qhP44b8rWfrKZjbsreOOT81xLZloJE6Y2NnF7+TH8M4XqsjL9HLdaZN4acM+cuMczrHrUBPv7zrCred2nSwSepyn8GGT1mIOrERkDHATMNMY0yQiDwOXAOcBvzTGPCQidwNXAb9zpLQxCl7E7nyxiofe3NERJLX5A64NUo6XNySCuOnBtztuF+VmMDQ3k+K8TMYW53Lc2CKK8zIZmpfB0LwshuZlUJybSUleFsV5GeRn+Xq0jvz73c6MzNVHmsnwesjwChleD5leT9zdEm4GDW5/KwmeDAtz3G3MTc6jLvmEbd3o9vC5x4yKeJ+Rtg7NGl3I2t1dx4JcvmA8F5eXRfxeTpcpWQzLz2J/vZUuor/xWxleD9+78GimjSzktifeT3gyUTdai6v21fGf96zxWkW51mzMeE+Dz9mLhi+x0yYEpVo3/0BLhu+s8f6FfECOiLQBucAe4AzgUvv5+4DvkuDAarr9japsaA4nTykl0ytk+jxWIGH/zup2P9Pn6bqd10OGz/qd6fPwt5Xb+fNrW10rs9fT2Rz73JdPpTgvk6KcDEdmwk0uzWOTnfV5/o+X9fLegs8jHf/n0KDLZ9/urBPrvs/jIdO+/cSa3R37cZrb15/gSdetJYUyfR5a2wNc8ac3ePz6hVG/3h8wNLS2U9/cTkOLNQi+vsW6/Wi36dLpIFzAEc9hUN8S2XT43hKWfu/CoyN+n2i6HFMtsPrY3DH8/pXNUb3m0vnjmDI8ny88sJqL7lrOHZ9K/mSikbrzhSqyfV6uPnmiY/usqKxmyvB8JvURgKbYYTNoxBxYGWN2icjPge1AE1CB1fV3yBgTPHPtBMb09noRuRa4FmDcuHGxFiMiwTEAv7h4tmMzvT5z4nhXA6vRRZ196pF2cUTqY/PG8lN70dgff+QY2vx2V6fdDdrmD9AWCLkdfM5vaGsP0B4I0GrfbmkLUN/cbt33BzqWAQF46NoTHS03uN/0HQwG3frWEyz/W9sP8fR7e7oERnX27/rmdupb/B2BU+hzja39L1za0uZPm2+14VpPi3MzgdjWiwwd9N6XYItMrH776bkRr6+Yal2BvhgX3Dth4lCe+OJCrr1/NVf9+U1uPXc615ySuGSiTrSub9nfwJPv7Oaqkyd2mXEYz54PNbayckstn0+CgdgqevF0BRYDFwITgUPAI8A5kb7eGLMUWApQXl7uauNdcEyFkyevoXmZ/W8UBzcrZGbIdOpL5zsf1G7d38CrVftdSVfg9gk4eIzEOw4nnJaQ8XnBxVODMn0e8rN85GV5yc/KID/Ly7D8TMaX5FKQ7SMv00d+ts/exvqdn2U9lpfp40N3voo/YEjS3u2YhPtrhwZct3/U2TXHnOLzeiJuYU61FiufJ/aW87HFuTz6hQV89ZF3+PFT61m/p44fJyiZqBMf87terCLD6+EaB4OgF9bvwx8wLJnVM9t6qGQasJ1oyfQRiudr7ZnAFmNMDYCIPA4sBIpExGe3Wo0FEp4+vPMi6VzNu/4N08WL45Th7o5tmDAsz7VUDm7Xe/ACNxATFJ6++ZSO4Cgvy9djHbBoFedmsr++xdGZTokW7mQZehzMnxTdsklJdP7tEEeckhDxrkyQm+njrkvn8psXqvjFcx+wqVsy0QkluWw90OhEUV21o7aRf7y9i88uGN9l5l68XwAr1lYzojCLY8f0nZYgmYKJRAseO0VJsM5rPB/n7cCJIpIr1lG0GKgEXgQ+bm9zOfBEfEWMX/Ay4+RF2e1vCm61mID7STbd5HaL1Z7DzQD84N+Vrr4PWAvylg3NpTgvM+6gqov0iavCCj2GszOiqzu3jqF4PrKpln7DG2NXYCgR4abFU7n7M/PYWF3Hh37zakf+ODeSYPYm3tbd375UhVeEz5/aM8lorP+F5jY/L39Qw1kzR7iS3yxdXXvqJH71ydm95sMbaDGfzY0xK4FHgbewUi14sLr2vgHcIiJVWCkX7nGgnHExHV2Bzh2kkY6diJWbp5VU63YYSMGqaYhgLFOySZc/67fOm9HvNqHHcLYvOVYpiKf+vSn2x3NyLc1zjh7J49efRFaGh4t//zqPv7UTf5j0MU6L5wvsrkNNPLp6J588voyRQ7omLo6ndl7duJ+mNn+viy6r8DK8Hi6aMyYpvqTE9TXZGPMdY8x0Y8zRxpjLjDEtxpjNxpgTjDFTjDGfMMbENwLUAcFcj04GFE0uX3iDn/e7Lp3r+L6T4LhLWlNd7iZ10zR7koMbS84MpPzszhEK4VqGQ1ussqJssXKrm/cqe0ZYtIsyQ+oNXvc63Hc5fWQhT9xwMnPHFXHLw++w2245dls8g9fvfmkTANctCrckTmz7rqjcS0GWjxOj7OJWySO1z8ARCn4rcTKgcDvBXXCcTPmE6NbYikSqfTseSG4s7DxQfvuZufz16vkUuzyxwm2hQUa48WKhX5KyomyxinRWYLSCa6sVxDAjMxm+ZUcjw4GuwO6G5mXyl6vmc9mJ4x3fd3edk1Rie/3ew838/c0dfHze2C5LEwXF+uf0BwzL1u3j9OnDIxoekGKHzaCRuleRKIhYOZmcbLFye4Bw8IuUG58b7QoMz8kujoFWmJ3BwinxL6ORaN2DjHOP7tklErpFsowZnFBiTdhYMmtEP1v2lGqfSbfqPMPr4QcXRZ4rLFbB8sfaFXj3y5vwG8P1i6aE3SaWXb+1/SAHGlpjOoZU8hgUgdVZM0fwwY/OZebo8EsvRMvtlo2Oz6QL569UO4kPpGS5SA9m3VtU7/jUHN797pIujyVjC0/Z0Fze/97ZMbW4pFpDaYbL0xh/+rFjAWt2oBuCx08sLVb76pp58I3tfGTOGMqG9l6+WA/PirV7yfR6OC3Mwt093icp57iq9MgimAD5WT5GFGa5d2AHuy9d2H+qTe3u7qYzpnDatMhOPNFK5RardNH9+Axm+g+VrH+nWBOzptqXHbe/gFx8fBntAcM5vbRWOiFY/FjGWP3hlc20+QPccHr41qpYGGOoqKzmpCklFGQnPmWAil2KX2ITq3z8UPKy3JmR1JF5S1userhlyTTmjXc++SgkZ0vIYBN6fIa77jlxYS/I9nHP5eVx78cJqXbcxZp5PRqXzh/nWiJmj8TWFXigvoUHVmznwtljmNhPrr5oQ7YPquvZdqAxqtmAKXbYDBoaWMVD3EuLEPy8uxEEpXpgpdJbJEGGE4dwaUEWi2ckx1iWJG2ACyvVu8w9MXYF/vHVLTS3+/ttrYqlp6HCXnT5zBnpsX7iYKaBVRwEXIusOmYyurDvVO8KTHbGGFrbAzS1+jnS3MbBhlb21TWz57A7s9HSTbhr9gtfOa3jdiwtPIXZyTvyIdW+7MSzpE0yCFZ3NIutH2xo5f7XtnL+MaMiWr0i2m7Gispq5owrYnhhdv8b21LrqBk8kvdMM8h1zArUrsCEufju12kLBPAHDG1+gz8QoN1vaA8Y2v0B63cvt9Nprb5ECD0+Qw/VSaWdF7NkHWMVq1TLsJ3q9R88xqIJfu5dvoWGVj83njG1322jPcXuPtTEe7sO841zpkf1ulTrQh4sNLCKg4i41xUYfA8XvpNoHqvIeD1CVoYPn0fwejxkeAWvR8jweuzf1n2fx4PPI9aiux7B5+3cxhf8sZ+79fH3Ev3fSnpd8lh1+4Ct/OZi1u05wrN2t0k0kvkilGpxykCMsXJTx+D1CLc/3NTGvcu3cs6skUwbWRDRa6K5NjxXWQ3ElqpDJR8NrOIgwP76Fr7/r0r8gQB+Y/AHDO1+03k7YAjYv/0hP+2BAIEAtNstIn5jv86+vbmmofNNnC53ap8TB8yD157o+D7f3HqQx97a6fh+00lfAdCIwmxGFGZ3XIii22+3+1G+/o1vLY76PSOVaq3Iqd4VGKzvSLsC/7x8K3Ut7dy4OLKZgNH+NSsq9zK5NI/JpdGt/JBaR83goYFVHGaOLuS5ymoeXrWjo3XCY//2hvz4PIJHrJYMr8eDV6wTk9cjZPp8vb42GFi5cb5N5m/u6e7GM6ZoYNWP0CCjIMy4qFgCkXiP+qIc9zLap9pg8FQrb3fBrtdI4qq65jb+tHwLZ84YzqzRQxwvy+HGNlZsruXaUyc5vm+VGBpYxeG60yZz3Wnh1omKz4vrn0nJhYBV37ov1qp6Cr1mh8vnE8t3g/6CsZsXT+XXyzZGVK7BLtW7Ag/UW0vYRjLG6v7Xt3G4qS2isVWhIh2+9cKGavwBw5KZ0XcD6nfk5KSBVZJKta4BFZnsDHfynqWT4LHf1wDpWD4fOZleaOj5+AtfOY28LB8jCrP7DKzcbOlNtc97qrdYBVuqqvbV97ldQ0s7f/zvZhZNK41uce0o/p4Va6sZXpDFcWOj2L9KanF1lItIkYg8KiLrRWSdiCwQkaEi8pyIbLR/O7+K8GAQHFwZSGwxBqOffvxYll42L9HFGLSC16S+vvDHEoh867wZAJR0Szo5qTSfERFMcXczlNAlbQZW8PAZU9xzAeVQf125jYON0bdWQWSD15vb/Lz8QQ1nzRwR08xQHdaRnOL9dPwaeMYYMx04DlgH3AosM8ZMBZbZ91WUYs0MrOJ3cXkZS2a5s5SG6l8kx34sDSZDcqxuxVgvRm5ew7TFamBNsrOmt/vDH2NNrX6WvrKZk6cMY9746NoHIq2d5VX7aWz16/kmzcQcWInIEOBU4B4AY0yrMeYQcCFwn73ZfcBF8RVxcAqetzSwUoNNZ46hPraJ69u9Cfm3d6N7GQvnZutAqrU8pPoYq0325KCN++rCbvPgG9vZX9/KjWfEtiZgJOO3nquspiDLx4JJJTG9h0pO8bRYTQRqgHtF5G0R+aOI5AEjjDF77G32ApqYIwYdF5cEl0OpgRZJzBTLGnIdXYwRfKhuWTIt6v3HI9UagFI9QWjQB9W9j7FqbvNz98ubmD9xKPNjCHoiiZP9AcPz66pZNH04mb7U7lpVXcXz1/QBc4HfGWPmYA0L7dLtZ6yQvdfTmIhcKyKrRGRVTU1NHMVIT986fwZZPg+Fusq5GmQiab256uSJAIwp6nuMTKjg2KoZowqtB/oIsLIG+EKXal1rqZ7Hqj8Pr9rBvroWbl4c/diqSL29/SD761tjmg2okls8n46dwE5jzEr7/qNYgVa1iIwCsH/v6+3FxpilxphyY0x5aWlpHMVITx+dO5YNPzxXv8moQSeS7u8Mr4d7rzyef1x/UsT7nTqigMe+sIBvnT+j323zB3hdwVTrCvSmeFdguT1m6vgJPcdOtbT7+d1Lm5g3vpgFk2Prooukdioqq8nwCoum6fUv3cR81TbG7AV2iEiwzXwxUAk8CVxuP3Y58ERcJVQqzZw5YwTfv3BWoouRtB5bHVkC1dOnDY9qwVqAeeOHMiw/C4AT+7honjZ1YC92KdZgRUaqFbibsqG5ABTl9uxSfmz1LvYcbuamxVNdC3iNMTy7di8nTR4WNlebSl3xfi27EfiriGQCm4ErsYK1h0XkKmAbcHGc76FUWvnj5eWJLkJSmzm6kMff3uXa/ksLsnjhK6cxtjg37DYDvSiyzgocWNecMol/vL2LC2eP7vJ4mz/Ab1+q4riyIk6dOiyu9+ir4XXjvnq2HWjkmlM023o6iiuwMsasAXq7Sri3qJZSKq2dOAAzpCZFuSab21ItTvGlWuKtbsLNavzH27vYebCJ7314VlytVf29tsJeRPwsHV+VllL706GUSjsp1njjCG2xGlgdiTdCWpXa/QHuerGKWaMLOWP68Ljfw/QxO6KisprZZUURJaZVqUcDK6VUUknGi3ZxrrvjYFItsEqXdAtNbZ3rsT75zm62HWjkxjPiH1vV16t3H2ri3Z2HWTJLW6vSlQZWSqmk4k3CIONgY5ur+0+17AWpHli9t+swAN/+5/uAlVPqzhermD6ywLH0B+HGWD2/rhqAJTM123q60kWYB6nffnpuxKuvKzWQBnrgeDJItRarYKtiNHnEkkmb31qEtaXd+v2f9/awuaaBuy6d68jx19efs2JtNZNK85gyPLnG+SnnaGA1SJ13zKhEF0GpXm070JDQ9w8mEJ0xqpB1e44AkOnyYO1Uy2MlImy9/fxEFyNm+VmdXbuBgOHOFzYyZXg+5x7tbivS4cY2Vmw+wNU6GzCtpVgDtFIq3eVkJO773ju3LelIOvrnK4/veHxMsbstM4OwkS6hCkISwD67di8fVNdz4xlTHG0t7a1H4MUN+2gPGB1flea0xUoplVRmjCpI2HsPCRmkHjpjy+3F0CWiXN3KKUNyOv/Od7xQxaRheVxw7Og+XhGdcH/Pisq9lBZkMXtskWPvpZKPtlgppZJKMi3jVGi3bLg9HrGvqfnKeYUhgdW6PUe4/vQpjs9G7f43bW7z8/KGGs6aOWJQjiMcTJLnDKaUUrg/nika/7rxZMD9wCegcdWACu0KHDc0t0cG9rj1Eje9vukADa1+XXR5EEieM5hSSpFcWb1zMrwATCjJc/V9jE7RHVChgdX1iyaT4cIx1/1PWlG5l/wsX8wLO6vUoWOslFIqjOGF2fzpinLmjR+a6KIoB4W2in507ljH99+9wcofMDxXWc2iaaVk+byOv59KLhpYKaVUH86Yrl036SaY3uK8Y0a6NqYvtMFqzY6D7K9vZcksTQo6GGhgpZRSCXJcWRHv7DjE0WOGJLoog46bebhEunYFVqytJsMrLJpW6tp7quQRd2AlIl5gFbDLGHOBiEwEHgJKgNXAZcaY1njfRyml0s0DV53A8qoDTC7VLNzpyhjDs2v3cuKkEgqz3V1zUiUHJ9pAbwbWhdz/P+CXxpgpwEHgKgfeQyml0k5BdgbnuJztWw28FZtreWNLLQBV++rZeqBRuwEHkbgCKxEZC5wP/NG+L8AZwKP2JvcBF8XzHkoppVSqqqi0Fl0+a4aO1Rss4m2x+hXwdSBg3y8BDhlj2u37O4Excb6HUkoplZIq1u7luLIiRg7J7n9jlRZiDqxE5AJgnzFmdYyvv1ZEVonIqpqamliLoZRSSiWV2WVFjCzMZs/hJt7ZeViTgg4y8QxeXwh8WETOA7KBQuDXQJGI+OxWq7HArt5ebIxZCiwFKC8v1+x4Siml0sKIwiya2/w8b3cDnq2LLg8qMbdYGWP+xxgz1hgzAbgEeMEY82ngReDj9maXA0/EXUqllFIqRTy7tpr1e+uoqKxm0rA8nfU5yLiRGe0bwC0iUoU15uoeF95DKaWUSmqvbzrAWbNGdCQkVYODIwlCjTEvAS/ZtzcDJzixX6WUUipVtQcMS2ZqmoXBJnlWO1VKKaXSzJyyokQXQQ0wDayUUkopl3g82g042GhgpZRSSinlEA2slFJKKaUcooGVUkoppZRDNLBSSimlXFA+vjjRRVAJoIGVUkop5YLLFoxPdBFUAjiSx0oppZRSXZXkZbmy33suL6emrsWVfav4aWCllFJKOWjOuCLe3n6InEx3OoUWz9C1B5OZdgUqpZRSDgpmrgqYhBZDJYgGVkoppZSDinMzAfBpctBBSbsClVJKKQf9/BPH8dhbO5mty9kMShpYKaWUUg4qzsvk6lMmJboYKkG0K1AppZRSyiExB1YiUiYiL4pIpYisFZGb7ceHishzIrLR/q0Z0pRSSik1KMTTYtUOfMUYMxM4EbhBRGYCtwLLjDFTgWX2faWUUkqptBdzYGWM2WOMecu+XQesA8YAFwL32ZvdB1wUZxmVUkoppVKCI2OsRGQCMAdYCYwwxuyxn9oL9JrJTESuFZFVIrKqpqbGiWIopZRSSiVU3IGViOQDjwFfMsYcCX3OGGOAXlOkGWOWGmPKjTHlpaWl8RZDKaWUUirh4gqsRCQDK6j6qzHmcfvhahEZZT8/CtgXXxGVUkoppVJDPLMCBbgHWGeM+UXIU08Cl9u3LweeiL14SimllFKpI54EoQuBy4D3RGSN/dg3gduBh0XkKmAbcHFcJVRKKaWUShExB1bGmFfpXGuyu8Wx7lcppZRSKlVp5nWllFJKKYdoYKWUUkop5RANrJRSSimlHKKBlVJKKaWUQzSwUkoppZRyiAZWSimllFIO0cBKKaWUUsohGlgppZRSSjlEAyullFJKKYdoYKWUUkop5RANrJRSSimlHKKBlVJKKaWUQzSwUkoppZRyiCuBlYicIyIbRKRKRG514z2UUkoppZKN44GViHiBu4BzgZnAp0RkptPvo5RKb2OLcxJdBKWUiprPhX2eAFQZYzYDiMhDwIVApQvvpZRKQxt/dC6S6EIopVQM3OgKHAPsCLm/035MKaUikuH14PPqEFClVOpJ2JlLRK4VkVUisqqmpiZRxVBKKaWUcowbgdUuoCzk/lj7sS6MMUuNMeXGmPLS0lIXiqGUUkopNbDcCKzeBKaKyEQRyQQuAZ504X2UUkoppZKKGGOc36nIecCvAC/wJ2PMj/rZvgbY5nhBkt8wYL99ewhwOIFlSSah9dKXwVhnkdZNOOlcZ/HWTTipXGdu1Ul/kr3OElUvfUmWOkvGuglnoOsstG7GG2N67W5zJbBSkRGRVcaYcvv2UmPMtYkuUzIIrZd+tht0dRZp3fTx+rSts3jrpo/9pmyduVUnEbxvUtdZouqlL8lSZ8lYN+EMdJ1FWjc67SZ5/CvRBUhBWmfR0zqLntZZ9LTOoqd1Fr2krDMNrJKEMSYpD5BkpnUWPa2z6GmdRU/rLHpaZ9FL1jrTwCqxlia6AElK6yU8rZvwtG560jrpndZLeFo34UVUNzrGSimllFLKIdpipZRSSinlEA2sXCYiuuSZiooeMyoaeryoaOkx4y4NrNyXDyAi3kQXJJmIyIdFZHKiy5GkOo4VPQH2JCJ63upKzzG90HNMn/Qc4yI9QblALMNF5CXgjwDGGH9iS5UcRORMEXkduAcYlejyJBMROV9Engd+ISKnAhgdBAl0XCRvSXQ5koWeY8LTc0x4eo4JT0QuEpEfOLEvDaxcYB+ozfbPsSJyLgzeb9r2RSBfRP4F/K/9swIYbz8/KOsllIhMAH4E/AZYB1wrIlfbzw3a+hERn4h8A7gD+LmIzDbGBAZ764yeY7rSc0z/9BzTk33ceO16+Dlwq4icEu9+B2Vlus0+SMcCa4BbgdsAjDGBBBYrYYylHnjAGLPIGLMMeBa40H5+UNZLN5OBV40xTwD3YrVC3CgixXYgMSib640x7cAGYDpwC/B7+/FB3Tqj55iu9BwTET3HdGMfN36gCpgDXA/E3WqlgZUDROQmEbldRD4GHR/i3cBRwHJgj4hcJyJTE1nOgRZSL58AMMb83X7cAxwEdohIViLLmCgi8nERmR/y0E7gYyKSZYxpNsa8BLyGfcEcTEKOm4vth/5j18mvgOEicqm9XUbCCjnA9BzTOz3HhKfnmPDs4+YPwRY74GVjTJ0x5g9AnohcZW8XU4ykgVUc7GbELwOfBFYBPxSRK0RkKDAFeMcYsx/r4P1/wC/s1/kSVeaB0Eu9fN+ul1LouChsAc43xrQksKgDzh4X8zJW19b/BD+4xpgNwHPA/9nbCXA3UCYiIwbDOIhejpvvicgVQHHIZrcAPwMwxrQNeCEHmJ5jeqfnmPD0HNM3+5xyKfAYcJmI/A8wKWST24Bbgi15sbyHBlZxsA/E04H/NcY8CnwZmA2cBewFThGRp4Arsb5VbrZfmtbdGGHq5TjgnJBtXgN2isiHE1PKxDDG7AOewKqLPcDnQ57+HnCBiMwKGUNTB9QPeEETIMxxcyxwdsg2/wA+EJGvgjVQORFlHSh6jumdnmPC03NMvxYD/2eMeQb4CpANfDr4pDHmaTrHoBUEW0OjoYFVjEKaCFcBpwDYf6gNWB/wOVhNr28aY2YBlwCLRGRMOn8z6KNePgBmich0e7tCYD2Q9q0OQSF18xugEqgAzheRUQDGmE1YM5l+KyInA58BhgNpPz6kj+NmI9ZxMy1k8y8APxWRvcCYAS3oANJzTO/0HBOenmPCC6mbt4ELAIwxq4DXgTEisjBk828AP8E6/4yM9r00sIqQdJuFFNJEWAUUiMgx9v1XgAJgH3CdMeY79va1wEJjzK4BKvKAiKJeXgaGYOfcMcYcwRp8O2KAijrgwtWNMabNHpT9GtaJ/+aQbX6CdeK7CpgGXGWMaRqwQg8QERli//ZCRMdNgb39bOAPWM34c40x9w1gsV0VRZ0MtnNMtMfKYDrH9Fo3eo4BERlp/w52hQaPm+WAR+x0E8D7WC17o+3tpwC/Bf6JdY75TbTvrYFVP0SkXET+AtwmIcnmQsYwvAG0A0tExGeMWYs1xXeOMaZZrKmcAmDPWkkLMdRLJVbrQnnIbi4xxvx5oMo8UPqoGwkeC7b9wJPAUSIy1h4bUWyMuR/4vDHmYmPM3gEuvmtExCMihSLyb6zxHx2z+0KC0P6OmwPA9caYTxhjdg/s/8B5MdZJ2p9jHDpWIA3PMf3UzaA+xwCIyBwRWYY9uy8YUIW0WG0E1gKfFBGvMWYnVvA9wX7+MPBFY8xHYz3HaGAVhn3w3ok1vXsZVqK574pIjoh47G8DGGOqsJqkJ2NNewZoAbbaz/vTqVneqXqxt2keyLK7LYK6McYYIyJZYs3M8RtjXsH6kL+P9Y17GIAxpjVR/w+32Ce4OiATq+n9k2AF48ELQx/HzTb7+R3GmPcGuuxuibNOttrPp9U5BpypF3ubtDrHQL91M2jPMXZM+UvgfuA+Y8w1Ic95Qlqs6oD/AllYufEysCbIHAAwxtQYYzbGUxYNrMKw/wgvAovtbzw/AwzgD4mAfyAi9wCrsb45nCAiq4FarL7ttKP1El6EdfM9rPwxo+z712ENLv09cGy8H+gUMB2oAX4NfFpECoLBeD/HzbOJKvAAiLVO0vazZNN6Ca+vuhmU5xj7y0UB8LbdIoeITA4NqsTKrP43rFapb2MFVP+17zs2rCCtp+RGS0ROBGqNMR8AGGMesx8/E3gUeBP4sYj8CSvqnwzcZozZam93KeAzxhwa+NK7R+slvBjqZiohdYM1TuQk+9t3WgmtGxER+8RXBbRizV7bAlwhIk8CZQyC40brpHdaL+HFUDdTgW8PtnOM/dAtwJsichvWbOJqoF5EfoU183Ey8D/BuhCRzwF5xpg6RwtmjBn0P0AR8B+sJsL/tSsaQOzf5cB59u3vAz8GxoW83pPo/4PWS8rVjTfR/4eBrhv7uQXAr+3b12J96/4XkJ/Ox43WidZLAupmsJ5jbgLeAU7F6u77P6wZfqUDddxoV6AlD6ur4Ub7dpfFKY0xq4wxT9nbPoV10ayFHn236UbrJbx46yad8wz1Wje27Vgzuf4OfB14C6gy9qDrND5utE56p/USXrx1MyjPMcaYO4BFxphXjJUc9p9Y599GGJjjZtAGViLyWRE5TUQKjTU9eSnwMFbCtPkiMjrMS+dhLSURHECZVh9srZfwtG7Ci6JuioFSrOSWc4DrgGkiMgPSq260Tnqn9RKe1k140Zx/jTEHQ146D9jBAJ5/g90Wg4KICFayr79hJUTbhBXt3mysZSEQK0nYxVhJ9x6wHysE5mN15+wFvmI6+3RTntZLeFo34UVZN6uMMX+xHxsW8nw+kGmsHEwpT+ukd1ov4WndhBfH+TcLq7v051g5qgb0/DtoWqzEylcRnDWwyxizGCuDcy1W5AuAMWY51nTd6SIyRESyjZVozgA/NMZ8KJ0ukFov4WndhBdD3Uyz6ybPGLNfrNxLHmNMfbpcDLROeqf1Ep7WTXhxnH9z7C7AVhJ0/k37Fiuxksn9APBijXUpBD5ujLncft6D1U3zSWPMy/Zj+cAPgYXAOKxEfCmfjDCU1kt4WjfhxVk3J9GZ2DJt6kbrpHdaL+Fp3YSXDufftG6xEpHTsPKcFGNNOf0B1rpRp4vICdDR3/pd+yfofOB6YA1wTLodvFov4WndhOdA3bxDmtWN1knvtF7C07oJL13Ov+mexyoA/L+QPuk5wETgNuB3wDw7+v0ncIaITDBW7o9m4ExjZaxNR1ov4WndhKd105PWSe+0XsLTugkvLeomrVussCLfh6VzbanlWLmE/gx4ReRGO/odi5UdeyuAMeaJZPkDuUTrJTytm/C0bnrSOumd1kt4WjfhpUXdpHVgZYxpNMa0mM58HmdhJVIDuBKYIdZClg9i5QEJzkJIa1ov4WndhKd105PWSe+0XsLTugkvXeom3bsCgY7BcAZrBesn7YfrgG8CRwNbjJUXoyPB42Cg9RKe1k14Wjc9aZ30TuslPK2b8FK9btK6xSpEAMgA9gPH2hHvt4GAMebV4B9oENJ6CU/rJjytm560Tnqn9RKe1k14KV03aZ9uIUisxRpfs3/uNcbck+AiJQWtl/C0bsLTuulJ66R3Wi/had2El8p1M5gCq7HAZcAvjJU8TKH10hetm/C0bnrSOumd1kt4WjfhpXLdDJrASimllFLKbYNljJVSSimllOs0sFJKKaWUcogGVkoppZRSDtHASimllFLKIRpYKaWUUko5RAMrpdSAEpEiEbnevj1aRB518b1mi8h5bu1fKaW608BKKTXQioDrAYwxu40xH3fxvWYDGlgppQaM5rFSSg0oEXkIuBDYAGwEZhhjjhaRK4CLgDxgKvBzIBMrSWALcJ4xplZEJgN3AaVAI3CNMWa9iHwC+A7gBw4DZwJVQA6wC/gJsAX4NZANNAFXGmM2RPHeLwHvAKdhrbX6OWPMG27Uk1IqNWmLlVJqoN0KbDLGzAa+1u25o4GPAscDPwIajTFzgNeBz9rbLAVuNMbMA74K/NZ+/DbgbGPMccCHjTGt9mN/N8bMNsb8HVgPnGLv8zbgx1G+N0CuXfbrgT/FUxFKqfTjS3QBlFIqxIvGmDqgTkQOA/+yH38PazHWfOAk4BERCb4my/69HPiziDwMPB5m/0OA+0RkKmCwFnqN6L1DtnsQwBjziogUikiRMeZQbP9dpVS60cBKKZVMQtcEC4TcD2CdrzzAIbvFqAtjzHUiMh84H1gtIvN62f8PsAKoj4jIBOClKN674626v3Uf/x+l1CCjXYFKqYFWBxTE8kJjzBFgiz2eCrEcZ9+ebIxZaYy5DagBynp5ryFY460Aroit+HzSfr+TgcPGmMMx7kcplYY0sFJKDShjzAFguYi8D/wshl18GrhKRN4B1mINhAf4mYi8Z+/3NaxB5i8CM0VkjYh8Evgp8BMReZvYW+yb7dffDVwV4z6UUmlKZwUqpVSE7FmBXzXGrEp0WZRSyUlbrJRSSimlHKItVkoppZRSDtEWK6WUUkoph2hgpZRSSinlEA2slFJKKaUcooGVUkoppZRDNLBSSimllHKIBlZKKaWUUg75//AJzDRlGbkIAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 720x1152 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_dfs(df_original, df_missing, 'temperature')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.impute import SimpleImputer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing = read_dataset(file1, 'timestamp')\n",
+    "df_original = read_dataset(file2, 'timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_missing.rename(columns={\"value\": \"temperature\"},inplace = True)\n",
+    "df_original.rename(columns ={\"value\":\"temperature\"},inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "strategy = [\n",
+    "    ('Mean Strategy', 'mean'),\n",
+    "    ('Median Strategy', 'median'),\n",
+    "    ('Most Frequent Strategy', 'most_frequent')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_vals = df_missing['temperature'].values.reshape(-1,1)\n",
+    "\n",
+    "\n",
+    "for s_name, s in strategy:\n",
+    "    df_missing[s_name] = (\n",
+    "        SimpleImputer(strategy=s).fit_transform(temp_vals))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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>temperature</th>\n",
+       "      <th>Mean Strategy</th>\n",
+       "      <th>Median Strategy</th>\n",
+       "      <th>Most Frequent Strategy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:15:00</th>\n",
+       "      <td>73.967322</td>\n",
+       "      <td>73.967322</td>\n",
+       "      <td>73.967322</td>\n",
+       "      <td>73.967322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:20:00</th>\n",
+       "      <td>74.935882</td>\n",
+       "      <td>74.935882</td>\n",
+       "      <td>74.935882</td>\n",
+       "      <td>74.935882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:25:00</th>\n",
+       "      <td>76.124162</td>\n",
+       "      <td>76.124162</td>\n",
+       "      <td>76.124162</td>\n",
+       "      <td>76.124162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:30:00</th>\n",
+       "      <td>78.140707</td>\n",
+       "      <td>78.140707</td>\n",
+       "      <td>78.140707</td>\n",
+       "      <td>78.140707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-12 21:35:00</th>\n",
+       "      <td>79.329836</td>\n",
+       "      <td>79.329836</td>\n",
+       "      <td>79.329836</td>\n",
+       "      <td>79.329836</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     temperature  Mean Strategy  Median Strategy  \\\n",
+       "timestamp                                                          \n",
+       "2013-02-12 21:15:00    73.967322      73.967322        73.967322   \n",
+       "2013-02-12 21:20:00    74.935882      74.935882        74.935882   \n",
+       "2013-02-12 21:25:00    76.124162      76.124162        76.124162   \n",
+       "2013-02-12 21:30:00    78.140707      78.140707        78.140707   \n",
+       "2013-02-12 21:35:00    79.329836      79.329836        79.329836   \n",
+       "\n",
+       "                     Most Frequent Strategy  \n",
+       "timestamp                                    \n",
+       "2013-02-12 21:15:00               73.967322  \n",
+       "2013-02-12 21:20:00               74.935882  \n",
+       "2013-02-12 21:25:00               76.124162  \n",
+       "2013-02-12 21:30:00               78.140707  \n",
+       "2013-02-12 21:35:00               79.329836  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_missing.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE for Mean Strategy: 2.6118121108260417\n",
+      "RMSE for Median Strategy: 2.0176725629240635\n",
+      "RMSE for Most Frequent Strategy: 22.936820075564288\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = rmse_score(df_original, df_missing, 'temperature')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1152 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_dfs(df_original, df_missing, 'temperature')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting seaborn\n",
+      "  Downloading seaborn-0.12.2-py3-none-any.whl (293 kB)\n",
+      "     -------------------------------------- 293.3/293.3 kB 2.0 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from seaborn) (1.23.5)\n",
+      "Requirement already satisfied: pandas>=0.25 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from seaborn) (1.4.2)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from seaborn) (3.5.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.28.4)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (21.3)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.6)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from pandas>=0.25->seaborn) (2022.1)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\dell\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\\localcache\\local-packages\\python39\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n",
+      "Installing collected packages: seaborn\n",
+      "Successfully installed seaborn-0.12.2\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import scipy.stats as stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxplot(data=df_missing['temperature'])\n",
+    "plt.title('Box Plot - Temperature')\n",
+    "plt.xlabel('Temperature')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxenplot(data=df_missing['temperature'])\n",
+    "plt.title('Boxen Plot - Temperature')\n",
+    "plt.xlabel('Temperature')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "pd.plotting.lag_plot(df_missing['temperature'])\n",
+    "plt.title('Lag Plot - Temperature')\n",
+    "plt.xlabel('Temperature (t)')\n",
+    "plt.ylabel('Temperature (t+1)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def iqr_outliers(data):\n",
+    "    q1 = data.quantile(0.25)\n",
+    "    q3 = data.quantile(0.75)\n",
+    "    iqr = q3 - q1\n",
+    "    lower_bound = q1 - 1.5 * iqr\n",
+    "    upper_bound = q3 + 1.5 * iqr\n",
+    "    outliers = data[(data < lower_bound) | (data > upper_bound)]\n",
+    "    return outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outliers:\n",
+      "timestamp\n",
+      "2013-03-12 23:55:00    65.906496\n",
+      "2013-04-12 00:00:00    66.204651\n",
+      "2013-04-12 00:05:00    66.094398\n",
+      "2013-04-12 00:10:00    64.925151\n",
+      "2013-04-12 00:15:00    65.910542\n",
+      "                         ...    \n",
+      "2014-09-02 11:45:00    30.975528\n",
+      "2014-09-02 11:50:00    35.624129\n",
+      "2014-09-02 11:55:00    43.971303\n",
+      "2014-09-02 12:00:00    53.135749\n",
+      "2014-09-02 12:05:00    64.134769\n",
+      "Name: temperature, Length: 2061, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "outliers = iqr_outliers(df_missing['temperature'])\n",
+    "print('Outliers:')\n",
+    "print(outliers)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "z_scores = stats.zscore(df_missing['temperature'])\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(z_scores)\n",
+    "plt.title('Z-Score Plot - Temperature')\n",
+    "plt.xlabel('Index')\n",
+    "plt.ylabel('Z-Score')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}