--- a
+++ b/analytics/p12_Sleep_Glucose.ipynb
@@ -0,0 +1,1640 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "0ef95ab1-8c24-457c-b9b8-0eee3955d0fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "edbac203-6c5d-4491-a386-4fcff5466456",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
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+       "      <th></th>\n",
+       "      <th>User First Name</th>\n",
+       "      <th>Calendar Date (Local)</th>\n",
+       "      <th>Start Time (Local)</th>\n",
+       "      <th>End Time (Local)</th>\n",
+       "      <th>Duration (s)</th>\n",
+       "      <th>Rem Sleep Duration (s)</th>\n",
+       "      <th>Deep Sleep Duration (s)</th>\n",
+       "      <th>Light Sleep Duration (s)</th>\n",
+       "      <th>Source</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <td>2023-12-22</td>\n",
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+       "      <td>2023-12-22</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P10</td>\n",
+       "      <td>2023-12-22</td>\n",
+       "      <td>2023-12-22T01:17:00</td>\n",
+       "      <td>2023-12-22T09:03:00</td>\n",
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+       "      <td>16560</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P10</td>\n",
+       "      <td>2023-12-22</td>\n",
+       "      <td>2023-12-22T01:17:00</td>\n",
+       "      <td>2023-12-22T09:03:00</td>\n",
+       "      <td>27960</td>\n",
+       "      <td>8400</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>16560</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P10</td>\n",
+       "      <td>2023-12-22</td>\n",
+       "      <td>2023-12-22T01:17:00</td>\n",
+       "      <td>2023-12-22T09:03:00</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3791</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
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+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3792</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
+       "      <td>4140</td>\n",
+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3793</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
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+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3794</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
+       "      <td>4140</td>\n",
+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3795</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
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+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3796 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "0                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "1                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "2                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "3                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "4                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "...              ...                   ...                  ...   \n",
+       "3791             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "3792             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "3793             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "3794             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "3795             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "\n",
+       "         End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "0     2023-12-22T09:03:00         27960                    8400   \n",
+       "1     2023-12-22T09:03:00         27960                    8400   \n",
+       "2     2023-12-22T09:03:00         27960                    8400   \n",
+       "3     2023-12-22T09:03:00         27960                    8400   \n",
+       "4     2023-12-22T09:03:00         27960                    8400   \n",
+       "...                   ...           ...                     ...   \n",
+       "3791  2024-01-08T07:34:00         27180                    4140   \n",
+       "3792  2024-01-08T07:34:00         27180                    4140   \n",
+       "3793  2024-01-08T07:34:00         27180                    4140   \n",
+       "3794  2024-01-08T07:34:00         27180                    4140   \n",
+       "3795  2024-01-08T07:34:00         27180                    4140   \n",
+       "\n",
+       "      Deep Sleep Duration (s)  Light Sleep Duration (s)  Source  \n",
+       "0                        3000                     16560  device  \n",
+       "1                        3000                     16560  device  \n",
+       "2                        3000                     16560  device  \n",
+       "3                        3000                     16560  device  \n",
+       "4                        3000                     16560  device  \n",
+       "...                       ...                       ...     ...  \n",
+       "3791                     6600                     16380  device  \n",
+       "3792                     6600                     16380  device  \n",
+       "3793                     6600                     16380  device  \n",
+       "3794                     6600                     16380  device  \n",
+       "3795                     6600                     16380  device  \n",
+       "\n",
+       "[3796 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load dataset\n",
+    "df = pd.read_csv('../data/garmin/sleep.csv', sep=',')\n",
+    "\n",
+    "# Trim columns\n",
+    "df = df.loc[:, ['User First Name', 'Calendar Date (Local)', 'Start Time (Local)', 'End Time (Local)', 'Duration (s)', 'Rem Sleep Duration (s)', 'Deep Sleep Duration (s)', 'Light Sleep Duration (s)', 'Source']]\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "35371d2e-8419-44bd-83dd-6ecdfb18b717",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>User First Name</th>\n",
+       "      <th>Calendar Date (Local)</th>\n",
+       "      <th>Start Time (Local)</th>\n",
+       "      <th>End Time (Local)</th>\n",
+       "      <th>Duration (s)</th>\n",
+       "      <th>Rem Sleep Duration (s)</th>\n",
+       "      <th>Deep Sleep Duration (s)</th>\n",
+       "      <th>Light Sleep Duration (s)</th>\n",
+       "      <th>Source</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1289</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
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+       "    <tr>\n",
+       "      <th>1290</th>\n",
+       "      <td>P12</td>\n",
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+       "    <tr>\n",
+       "      <th>1291</th>\n",
+       "      <td>P12</td>\n",
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+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
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+       "      <td>172800</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1292</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
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+       "      <td>172800</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1293</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2579</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T14:00:00</td>\n",
+       "      <td>2024-01-08T12:21:00</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>11460</td>\n",
+       "      <td>server</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2580</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T14:00:00</td>\n",
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+       "    <tr>\n",
+       "      <th>2581</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T14:00:00</td>\n",
+       "      <td>2024-01-08T12:21:00</td>\n",
+       "      <td>80460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61140</td>\n",
+       "      <td>11460</td>\n",
+       "      <td>server</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2582</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T14:00:00</td>\n",
+       "      <td>2024-01-08T12:21:00</td>\n",
+       "      <td>80460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61140</td>\n",
+       "      <td>11460</td>\n",
+       "      <td>server</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2583</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T14:00:00</td>\n",
+       "      <td>2024-01-08T12:21:00</td>\n",
+       "      <td>80460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61140</td>\n",
+       "      <td>11460</td>\n",
+       "      <td>server</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1295 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "1289             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1290             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1291             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1292             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1293             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "...              ...                   ...                  ...   \n",
+       "2579             P12            2024-01-07  2024-01-07T14:00:00   \n",
+       "2580             P12            2024-01-07  2024-01-07T14:00:00   \n",
+       "2581             P12            2024-01-07  2024-01-07T14:00:00   \n",
+       "2582             P12            2024-01-07  2024-01-07T14:00:00   \n",
+       "2583             P12            2024-01-07  2024-01-07T14:00:00   \n",
+       "\n",
+       "         End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "1289  2023-12-23T08:45:00         28380                   48240   \n",
+       "1290  2023-12-23T08:45:00         28380                   48240   \n",
+       "1291  2023-12-23T08:45:00         28380                   48240   \n",
+       "1292  2023-12-23T08:45:00         28380                   48240   \n",
+       "1293  2023-12-23T08:45:00         28380                   48240   \n",
+       "...                   ...           ...                     ...   \n",
+       "2579  2024-01-08T12:21:00         80460                       0   \n",
+       "2580  2024-01-08T12:21:00         80460                       0   \n",
+       "2581  2024-01-08T12:21:00         80460                       0   \n",
+       "2582  2024-01-08T12:21:00         80460                       0   \n",
+       "2583  2024-01-08T12:21:00         80460                       0   \n",
+       "\n",
+       "      Deep Sleep Duration (s)  Light Sleep Duration (s)  Source  \n",
+       "1289                    31680                    172800  device  \n",
+       "1290                    31680                    172800  device  \n",
+       "1291                    31680                    172800  device  \n",
+       "1292                    31680                    172800  device  \n",
+       "1293                    31680                    172800  device  \n",
+       "...                       ...                       ...     ...  \n",
+       "2579                    61140                     11460  server  \n",
+       "2580                    61140                     11460  server  \n",
+       "2581                    61140                     11460  server  \n",
+       "2582                    61140                     11460  server  \n",
+       "2583                    61140                     11460  server  \n",
+       "\n",
+       "[1295 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select records for one user\n",
+    "p_df = df[df['User First Name'] == 'P12']\n",
+    "p_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2df3de7b-8cd8-4b07-802a-55eb967f339b",
+   "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>glucose</th>\n",
+       "      <th>recorded_timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>103</td>\n",
+       "      <td>2023-12-23 00:00:27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>104</td>\n",
+       "      <td>2023-12-23 00:01:29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>107</td>\n",
+       "      <td>2023-12-23 00:02:29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>112</td>\n",
+       "      <td>2023-12-23 00:03:29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>112</td>\n",
+       "      <td>2023-12-23 00:04:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18863</th>\n",
+       "      <td>98</td>\n",
+       "      <td>2023-12-28 23:55:57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18864</th>\n",
+       "      <td>98</td>\n",
+       "      <td>2023-12-28 23:56:57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18865</th>\n",
+       "      <td>98</td>\n",
+       "      <td>2023-12-28 23:57:57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18866</th>\n",
+       "      <td>98</td>\n",
+       "      <td>2023-12-28 23:58:58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18867</th>\n",
+       "      <td>98</td>\n",
+       "      <td>2023-12-28 23:59:58</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>18868 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       glucose  recorded_timestamp\n",
+       "0          103 2023-12-23 00:00:27\n",
+       "1          104 2023-12-23 00:01:29\n",
+       "2          107 2023-12-23 00:02:29\n",
+       "3          112 2023-12-23 00:03:29\n",
+       "4          112 2023-12-23 00:04:30\n",
+       "...        ...                 ...\n",
+       "18863       98 2023-12-28 23:55:57\n",
+       "18864       98 2023-12-28 23:56:57\n",
+       "18865       98 2023-12-28 23:57:57\n",
+       "18866       98 2023-12-28 23:58:58\n",
+       "18867       98 2023-12-28 23:59:58\n",
+       "\n",
+       "[18868 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load glucose dataset\n",
+    "glucose_df = pd.read_csv('../data/P12/supersapiens/merged.csv', sep=';')\n",
+    "\n",
+    "# Convert timestamp\n",
+    "glucose_df['recorded_timestamp'] = pd.to_datetime(glucose_df['recorded_timestamp'])\n",
+    "\n",
+    "glucose_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6258651b-17d4-4bdb-82c2-794b760b9dce",
+   "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>glucose</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recorded_timestamp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-12-21</th>\n",
+       "      <td>81.019064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-22</th>\n",
+       "      <td>95.214912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-23</th>\n",
+       "      <td>100.565649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-24</th>\n",
+       "      <td>103.860322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-25</th>\n",
+       "      <td>104.198041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-26</th>\n",
+       "      <td>108.802076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-27</th>\n",
+       "      <td>108.478355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-28</th>\n",
+       "      <td>105.195748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-29</th>\n",
+       "      <td>105.966184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-30</th>\n",
+       "      <td>112.118027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-31</th>\n",
+       "      <td>107.381974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-01</th>\n",
+       "      <td>106.787106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-02</th>\n",
+       "      <td>110.796813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-03</th>\n",
+       "      <td>113.972067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-04</th>\n",
+       "      <td>110.583884</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       glucose\n",
+       "recorded_timestamp            \n",
+       "2023-12-21           81.019064\n",
+       "2023-12-22           95.214912\n",
+       "2023-12-23          100.565649\n",
+       "2023-12-24          103.860322\n",
+       "2023-12-25          104.198041\n",
+       "2023-12-26          108.802076\n",
+       "2023-12-27          108.478355\n",
+       "2023-12-28          105.195748\n",
+       "2023-12-29          105.966184\n",
+       "2023-12-30          112.118027\n",
+       "2023-12-31          107.381974\n",
+       "2024-01-01          106.787106\n",
+       "2024-01-02          110.796813\n",
+       "2024-01-03          113.972067\n",
+       "2024-01-04          110.583884"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Group glucose by day\n",
+    "glucose_mean_per_day = pd.DataFrame(glucose_df.groupby(glucose_df['recorded_timestamp'].dt.date)['glucose'].mean())\n",
+    "\n",
+    "# Change index data type\n",
+    "glucose_mean_per_day.index = pd.to_datetime(glucose_mean_per_day.index)\n",
+    "\n",
+    "glucose_mean_per_day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59e96fb9-ca44-42ff-9c09-5300d4ca7aaf",
+   "metadata": {},
+   "source": [
+    "# Aggregate average glucose level of the day with sleep data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1e3e11f6-4464-4935-a567-644258e92c4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/g1/1zlhdjc106z1jjy_cn0v6lxr0000gp/T/ipykernel_19892/165701922.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  p_df['Calendar Date (Local)'] = pd.to_datetime(p_df['Calendar Date (Local)'])\n"
+     ]
+    },
+    {
+     "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>User First Name</th>\n",
+       "      <th>Calendar Date (Local)</th>\n",
+       "      <th>Start Time (Local)</th>\n",
+       "      <th>End Time (Local)</th>\n",
+       "      <th>Duration (s)</th>\n",
+       "      <th>Rem Sleep Duration (s)</th>\n",
+       "      <th>Deep Sleep Duration (s)</th>\n",
+       "      <th>Light Sleep Duration (s)</th>\n",
+       "      <th>Source</th>\n",
+       "      <th>glucose</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1289</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
+       "      <td>28380</td>\n",
+       "      <td>48240</td>\n",
+       "      <td>31680</td>\n",
+       "      <td>172800</td>\n",
+       "      <td>device</td>\n",
+       "      <td>100.565649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1290</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
+       "      <td>28380</td>\n",
+       "      <td>48240</td>\n",
+       "      <td>31680</td>\n",
+       "      <td>172800</td>\n",
+       "      <td>device</td>\n",
+       "      <td>100.565649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1291</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
+       "      <td>28380</td>\n",
+       "      <td>48240</td>\n",
+       "      <td>31680</td>\n",
+       "      <td>172800</td>\n",
+       "      <td>device</td>\n",
+       "      <td>100.565649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1292</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
+       "      <td>28380</td>\n",
+       "      <td>48240</td>\n",
+       "      <td>31680</td>\n",
+       "      <td>172800</td>\n",
+       "      <td>device</td>\n",
+       "      <td>100.565649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1293</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
+       "      <td>28380</td>\n",
+       "      <td>48240</td>\n",
+       "      <td>31680</td>\n",
+       "      <td>172800</td>\n",
+       "      <td>device</td>\n",
+       "      <td>100.565649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2484</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T14:00:00</td>\n",
+       "      <td>2024-01-05T14:01:00</td>\n",
+       "      <td>86460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53880</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>server</td>\n",
+       "      <td>110.583884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2485</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T14:00:00</td>\n",
+       "      <td>2024-01-05T14:01:00</td>\n",
+       "      <td>86460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53880</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>server</td>\n",
+       "      <td>110.583884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2486</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T14:00:00</td>\n",
+       "      <td>2024-01-05T14:01:00</td>\n",
+       "      <td>86460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53880</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>server</td>\n",
+       "      <td>110.583884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T14:00:00</td>\n",
+       "      <td>2024-01-05T14:01:00</td>\n",
+       "      <td>86460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53880</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>server</td>\n",
+       "      <td>110.583884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2488</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T14:00:00</td>\n",
+       "      <td>2024-01-05T14:01:00</td>\n",
+       "      <td>86460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53880</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>server</td>\n",
+       "      <td>110.583884</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1200 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "1289             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1290             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1291             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1292             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1293             P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "...              ...                   ...                  ...   \n",
+       "2484             P12            2024-01-04  2024-01-04T14:00:00   \n",
+       "2485             P12            2024-01-04  2024-01-04T14:00:00   \n",
+       "2486             P12            2024-01-04  2024-01-04T14:00:00   \n",
+       "2487             P12            2024-01-04  2024-01-04T14:00:00   \n",
+       "2488             P12            2024-01-04  2024-01-04T14:00:00   \n",
+       "\n",
+       "         End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "1289  2023-12-23T08:45:00         28380                   48240   \n",
+       "1290  2023-12-23T08:45:00         28380                   48240   \n",
+       "1291  2023-12-23T08:45:00         28380                   48240   \n",
+       "1292  2023-12-23T08:45:00         28380                   48240   \n",
+       "1293  2023-12-23T08:45:00         28380                   48240   \n",
+       "...                   ...           ...                     ...   \n",
+       "2484  2024-01-05T14:01:00         86460                       0   \n",
+       "2485  2024-01-05T14:01:00         86460                       0   \n",
+       "2486  2024-01-05T14:01:00         86460                       0   \n",
+       "2487  2024-01-05T14:01:00         86460                       0   \n",
+       "2488  2024-01-05T14:01:00         86460                       0   \n",
+       "\n",
+       "      Deep Sleep Duration (s)  Light Sleep Duration (s)  Source     glucose  \n",
+       "1289                    31680                    172800  device  100.565649  \n",
+       "1290                    31680                    172800  device  100.565649  \n",
+       "1291                    31680                    172800  device  100.565649  \n",
+       "1292                    31680                    172800  device  100.565649  \n",
+       "1293                    31680                    172800  device  100.565649  \n",
+       "...                       ...                       ...     ...         ...  \n",
+       "2484                    53880                      7140  server  110.583884  \n",
+       "2485                    53880                      7140  server  110.583884  \n",
+       "2486                    53880                      7140  server  110.583884  \n",
+       "2487                    53880                      7140  server  110.583884  \n",
+       "2488                    53880                      7140  server  110.583884  \n",
+       "\n",
+       "[1200 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert column type\n",
+    "glucose_mean_per_day.index = pd.to_datetime(glucose_mean_per_day.index)\n",
+    "p_df['Calendar Date (Local)'] = pd.to_datetime(p_df['Calendar Date (Local)'])\n",
+    "\n",
+    "# Join Garmin Dataset with Glucose Data\n",
+    "p_df_merged = pd.merge(p_df, glucose_mean_per_day, left_on='Calendar Date (Local)', how='inner', right_index=True)\n",
+    "\n",
+    "p_df_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "14caa6da-863f-4b44-81e0-f9d999ce5cd2",
+   "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>User First Name</th>\n",
+       "      <th>Calendar Date (Local)</th>\n",
+       "      <th>Start Time (Local)</th>\n",
+       "      <th>End Time (Local)</th>\n",
+       "      <th>Duration (s)</th>\n",
+       "      <th>Rem Sleep Duration (s)</th>\n",
+       "      <th>Deep Sleep Duration (s)</th>\n",
+       "      <th>Light Sleep Duration (s)</th>\n",
+       "      <th>Source</th>\n",
+       "      <th>glucose</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-23</td>\n",
+       "      <td>2023-12-23T00:52:00</td>\n",
+       "      <td>2023-12-23T08:45:00</td>\n",
+       "      <td>28380</td>\n",
+       "      <td>48240</td>\n",
+       "      <td>31680</td>\n",
+       "      <td>172800</td>\n",
+       "      <td>device</td>\n",
+       "      <td>100.565649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-24</td>\n",
+       "      <td>2023-12-24T00:48:00</td>\n",
+       "      <td>2023-12-24T05:14:40</td>\n",
+       "      <td>16000</td>\n",
+       "      <td>2200</td>\n",
+       "      <td>5040</td>\n",
+       "      <td>7500</td>\n",
+       "      <td>device</td>\n",
+       "      <td>103.860322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-25</td>\n",
+       "      <td>2023-12-25T00:31:00</td>\n",
+       "      <td>2023-12-25T10:21:00</td>\n",
+       "      <td>35400</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>19920</td>\n",
+       "      <td>device</td>\n",
+       "      <td>104.198041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-26</td>\n",
+       "      <td>2023-12-26T01:06:00</td>\n",
+       "      <td>2023-12-26T09:38:00</td>\n",
+       "      <td>30720</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>20100</td>\n",
+       "      <td>device</td>\n",
+       "      <td>108.802076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T00:27:00</td>\n",
+       "      <td>2023-12-27T07:52:00</td>\n",
+       "      <td>26700</td>\n",
+       "      <td>6720</td>\n",
+       "      <td>4140</td>\n",
+       "      <td>14640</td>\n",
+       "      <td>device</td>\n",
+       "      <td>108.478355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T01:13:00</td>\n",
+       "      <td>2023-12-28T07:31:00</td>\n",
+       "      <td>22680</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>6240</td>\n",
+       "      <td>12060</td>\n",
+       "      <td>device</td>\n",
+       "      <td>105.195748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:14:00</td>\n",
+       "      <td>2023-12-29T08:58:00</td>\n",
+       "      <td>27840</td>\n",
+       "      <td>6120</td>\n",
+       "      <td>5160</td>\n",
+       "      <td>14100</td>\n",
+       "      <td>device</td>\n",
+       "      <td>105.966184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-30</td>\n",
+       "      <td>2023-12-30T03:16:00</td>\n",
+       "      <td>2023-12-30T11:35:00</td>\n",
+       "      <td>29940</td>\n",
+       "      <td>5040</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>13800</td>\n",
+       "      <td>device</td>\n",
+       "      <td>112.118027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "      <td>2023-12-31T02:16:00</td>\n",
+       "      <td>2023-12-31T11:40:00</td>\n",
+       "      <td>33840</td>\n",
+       "      <td>8400</td>\n",
+       "      <td>3780</td>\n",
+       "      <td>18840</td>\n",
+       "      <td>device</td>\n",
+       "      <td>107.381974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>2024-01-01T02:20:00</td>\n",
+       "      <td>2024-01-01T09:42:00</td>\n",
+       "      <td>26520</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>9000</td>\n",
+       "      <td>30720</td>\n",
+       "      <td>device</td>\n",
+       "      <td>106.787106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-03</td>\n",
+       "      <td>2024-01-03T02:24:00</td>\n",
+       "      <td>2024-01-03T09:01:00</td>\n",
+       "      <td>23820</td>\n",
+       "      <td>3660</td>\n",
+       "      <td>5220</td>\n",
+       "      <td>13200</td>\n",
+       "      <td>device</td>\n",
+       "      <td>113.972067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>P12</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T01:07:00</td>\n",
+       "      <td>2024-01-04T06:13:00</td>\n",
+       "      <td>18360</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>17040</td>\n",
+       "      <td>31920</td>\n",
+       "      <td>device</td>\n",
+       "      <td>110.583884</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "0              P12            2023-12-23  2023-12-23T00:52:00   \n",
+       "1              P12            2023-12-24  2023-12-24T00:48:00   \n",
+       "2              P12            2023-12-25  2023-12-25T00:31:00   \n",
+       "3              P12            2023-12-26  2023-12-26T01:06:00   \n",
+       "4              P12            2023-12-27  2023-12-27T00:27:00   \n",
+       "5              P12            2023-12-28  2023-12-28T01:13:00   \n",
+       "6              P12            2023-12-29  2023-12-29T01:14:00   \n",
+       "7              P12            2023-12-30  2023-12-30T03:16:00   \n",
+       "8              P12            2023-12-31  2023-12-31T02:16:00   \n",
+       "9              P12            2024-01-01  2024-01-01T02:20:00   \n",
+       "10             P12            2024-01-03  2024-01-03T02:24:00   \n",
+       "11             P12            2024-01-04  2024-01-04T01:07:00   \n",
+       "\n",
+       "       End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "0   2023-12-23T08:45:00         28380                   48240   \n",
+       "1   2023-12-24T05:14:40         16000                    2200   \n",
+       "2   2023-12-25T10:21:00         35400                    7200   \n",
+       "3   2023-12-26T09:38:00         30720                    2400   \n",
+       "4   2023-12-27T07:52:00         26700                    6720   \n",
+       "5   2023-12-28T07:31:00         22680                    4380   \n",
+       "6   2023-12-29T08:58:00         27840                    6120   \n",
+       "7   2023-12-30T11:35:00         29940                    5040   \n",
+       "8   2023-12-31T11:40:00         33840                    8400   \n",
+       "9   2024-01-01T09:42:00         26520                    3000   \n",
+       "10  2024-01-03T09:01:00         23820                    3660   \n",
+       "11  2024-01-04T06:13:00         18360                    7200   \n",
+       "\n",
+       "    Deep Sleep Duration (s)  Light Sleep Duration (s)  Source     glucose  \n",
+       "0                     31680                    172800  device  100.565649  \n",
+       "1                      5040                      7500  device  103.860322  \n",
+       "2                      3600                     19920  device  104.198041  \n",
+       "3                      3600                     20100  device  108.802076  \n",
+       "4                      4140                     14640  device  108.478355  \n",
+       "5                      6240                     12060  device  105.195748  \n",
+       "6                      5160                     14100  device  105.966184  \n",
+       "7                      4380                     13800  device  112.118027  \n",
+       "8                      3780                     18840  device  107.381974  \n",
+       "9                      9000                     30720  device  106.787106  \n",
+       "10                     5220                     13200  device  113.972067  \n",
+       "11                    17040                     31920  device  110.583884  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Trim columns\n",
+    "#sleep_glucose_df = p_df_merged.loc[:, ['User First Name', 'Calendar Date (Local)', 'Start Time (Local)', 'End Time (Local)', 'Duration (s)', 'Rem Sleep Duration (s)', 'Deep Sleep Duration (s)', 'Light Sleep Duration (s)', 'Source']]\n",
+    "sleep_glucose_df = p_df_merged.drop_duplicates(ignore_index=True)\n",
+    "\n",
+    "sleep_glucose_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "416d1e3b-34e3-421f-8241-8a8dad214188",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['Duration (s)'], label='Sleep Duration', alpha=0.7)\n",
+    "plt.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['glucose'], label='Glucose', alpha=0.7)\n",
+    "\n",
+    "# Format the x-axis labels\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Set labels and title\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Values')\n",
+    "plt.title('Total Sleep Duration and Glucose over Time')\n",
+    "\n",
+    "# Display legend\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a5756452-f907-462b-bd02-562e417cad67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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E278hTzzxhEaNGqU5c+boySef1NmzZ/Xjjz/q+eefT/z92rVrlyTpvvvuS/b1rv+5yZ49uwoXLpxi+9LS9T/PzgBepEiRZPc7fwY97RMAZCSEbgC4DV588UVNnjxZPXr0UHR0tMLCwuRwONSqVat/veyOr6+vatSooRo1aqhMmTLq0KGDZs6cqf79+ydbPiEhQQ0bNlSfPn2SPe4MygkJCcqXL5+mTZuWbLmUwocdtmzZIh8fH0VGRkpSknDvdP3EYE7JzZh96tQp1atXT6GhoRo4cKBKliwpf39//f7773r11Vdv23JIznt0r3ftBzPXi4+PV8OGDXXixAm9+uqrKleunIKCgnTo0CG1b98+SdtTeo20VK5cOW3cuFFXrlxxu9e/UqVKaVJ/Suc2LeTLl08bNmzQ/Pnz9dNPP+mnn37S5MmT1bZt28RJ11L7e/Nv1a5dW8WLF9fXX3+tJ598Ut9//70uXLigJ554IrGM8/x+8cUXyX6Yde2IsmSu9Lj+gwe7pPSzdrOfc0/7BAAZCf+CAcBNFCtWTIsWLdKZM2fcRlqdlx8XK1YscV9KYfCbb75Ru3btNHz48MR9Fy9eTPXM0KlVvXp1SeYy55SULFlSZ8+eVYMGDW5YV8mSJbVo0SLVrVs3Vcs87d69W5Zlub0Hf/zxhyT9qwmQDhw4oOXLlys6Ojrx/XeOBl///qXmqgOnZcuW6fjx45o1a5bbutF79+5NUjal83o958/Czp07kxzbsWOH8uTJk+xSTZ7avHmz/vjjD02dOlVt27ZN3H+jy6NvplixYlq8eLHOnj3rNtqdXF+S89BDD2n16tX69ttv9fjjj99yO3LmzJnkvF6+fDnJz3TJkiW1ZcuWG9ZVrFgxbdq0SQkJCW6hM7nfXV9fXzVt2lRNmzZVQkKCXnjhBX388cd66623VKpUqVT/3qTUDsm8lyVKlHDr1969e5PU+fjjj2v06NGKi4vTjBkzVLx4cdWuXdut75L5sOBW2pMeZcY+AYAT93QDwE00btxY8fHx+uijj9z2jxw5Ug6HQw8++GDivqCgoGSDtI+PT5KRyzFjxtzy6N3SpUuTHQl13jN8o0uCH3/8ca1atUrz589PcuzUqVO6evVqYrn4+Hi98847ScpdvXo1ST///vtvffvtt4mP4+Li9Pnnn6tKlSq3fGn5iRMn1Lp1a8XHx+uNN95I3O/8A/3a+2jj4+P1ySefpLpu58jbte/j5cuXNW7cuCRlg4KCUnW5eYECBVSlShVNnTrV7f3ZsmWLFixYkGbrjCfXdsuykixx5YnGjRvr6tWrGj9+fOK++Ph4jRkzJlXP79KliyIiItSzZ8/ED1uudaOR+2uVLFkyyf3Rn3zySZLflZYtWybeVpHSazVu3FixsbGaMWNG4rGrV69qzJgxCg4OVr169SSZ2e2vlS1btsQR+kuXLklK/e9Ncho0aCBfX199+OGHbu/DpEmTdPr0aTVp0sSt/BNPPKFLly5p6tSpmjdvXpIPMWJiYhQaGqpBgwbpypUrSV7vn3/+SbEt6VVm7BMAODHSDQA30bRpU91777164403tG/fPlWuXFkLFizQd999px49eiQGQEmqVq2aFi1apBEjRqhgwYKKjIxUrVq19NBDD+mLL75QWFiYoqKitGrVKi1atEi5c+e+pTa9+OKLOn/+vB555BGVK1dOly9f1sqVKxNHxZKbNMypd+/emjNnjh566CG1b99e1apV07lz57R582Z988032rdvn/LkyaN69erp+eef1+DBg7VhwwY1atRIOXLk0K5duzRz5kyNHj1ajz76aGK9ZcqUUceOHbV27VpFRETos88+05EjRzR58uRU9emPP/7Qf/7zH1mWpbi4OG3cuFEzZ87U2bNnNWLECLf7aytUqKDatWurb9++OnHihHLlyqXp06ffMPhcr06dOsqZM6fatWun7t27y+Fw6Isvvkg2HFarVk0zZsxQr169VKNGDQUHB6tp06bJ1vvBBx/owQcfVHR0tDp27Ji4ZFhYWFiy60/finLlyqlkyZJ65ZVXdOjQIYWGhuq///1vknu7PdG0aVPVrVtXr732mvbt25e4znpq723PlSuXvv32WzVt2lSVK1dWq1atVKNGDeXIkUN//fVX4jJm19/ze71nn31WnTt3VsuWLdWwYUNt3LhR8+fPV548edzK9e7dW998840ee+wxPfPMM6pWrZpOnDihOXPmaMKECapcubKee+45ffzxx2rfvr3WrVun4sWL65tvvtGKFSs0atSoxCsnnn32WZ04cUL33XefChcurP3792vMmDGqUqVK4v3fqf29SU7evHnVt29fvf3223rggQf08MMPa+fOnRo3bpxq1Kihp556yq38nXfeqVKlSumNN97QpUuX3C4tl8z9zePHj9fTTz+tO++8U61atVLevHl14MABzZ07V3Xr1k3yIWF6lxn7BACJbv+E6QCQviW3TM6ZM2esnj17WgULFrRy5MhhlS5d2vrggw8SlyZy2rFjh3XPPfdYAQEBlqTEJY5OnjxpdejQwcqTJ48VHBxsxcTEWDt27EiyDFJqlwz76aefrGeeecYqV66cFRwcbPn6+lqlSpWyXnzxRevIkSNuZa9/DWd/+vbta5UqVcry9fW18uTJY9WpU8caNmxYkmXHPvnkE6tatWpWQECAFRISYlWsWNHq06eP9ffff7u9RpMmTaz58+dblSpVsvz8/Kxy5cpZM2fOvGE/nCQlbtmyZbPCw8OtqlWrWi+99JK1devWZJ+zZ88eq0GDBpafn58VERFhvf7669bChQuTXTKsQoUKydaxYsUKq3bt2lZAQIBVsGDBxKXXrq/j7Nmz1pNPPmmFh4dbkhKXSkpuyTDLsqxFixZZdevWtQICAqzQ0FCradOm1rZt29zKOJcM++eff9z2p7Qc2vW2bdtmNWjQwAoODrby5MljderUydq4cWOS9rRr184KCgpK8nzn61/r+PHj1tNPP22FhoZaYWFh1tNPP22tX78+VUuGOR0+fNjq3bu3FRUVZQUEBFh+fn5WiRIlrLZt2yYueXWjvsbHx1uvvvqqlSdPHiswMNCKiYmxdu/enezP8fHjx61u3bpZhQoVsnx9fa3ChQtb7dq1s44dO5ZY5siRI4m/e76+vlbFihWT9OWbb76xGjVqZOXLl8/y9fW1ihYtaj3//PPW4cOH3cp58nuTnI8++sgqV66clSNHDisiIsLq0qWLdfLkyWTLvvHGG5Ykq1SpUinWt3TpUismJsYKCwuz/P39rZIlS1rt27e3fvvtt8QyKZ3/1LiVJcM++OCDJG2UlOTfAue5X7t2rcd9AoCMxmFZqbzeCwCAFBQvXlx33HGHfvjhB283BQAAIF3hnm4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbMI93QAAAAAA2ISRbgAAAAAAbELoBgAAAADAJtm93YDM4urVq1q/fr0iIiKULRufZQAAAADI2hISEnTkyBFVrVpV2bNn3eiZdXuextavX6+aNWt6uxkAAAAAkK6sWbNGNWrU8HYzvIbQnUYiIiIkmR+oAgUKeLk1AAAAAOBdhw8fVs2aNROzUlZF6E4jzkvKCxQooMKFC3u5NQAAAACQPmT122+zdu8BAAAAALARoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAm2T3dgMA/DtNm3q7Bf/O9997uwUAACBFGfkPDf7IQDrBSDcAAAAAADbxaugeP16qVEkKDTVbdLT000+u4/XrSw6H+9a5s3sdBw5ITZpIgYFSvnxS797S1avuZZYtk+68U/Lzk0qVkqZMSdqWsWOl4sUlf3+pVi1pzZq07SsAAAAAIOvxauguXFh6/31p3Trpt9+k++6TmjWTtm51lenUSTp82LUNHeo6Fh9vAvfly9LKldLUqSZQ9+vnKrN3rylz773Shg1Sjx7Ss89K8+e7ysyYIfXqJfXvL/3+u1S5shQTIx09avMbAAAAAADI1Lwaups2lRo3lkqXlsqUkd57TwoOllavdpUJDJTy53dtoaGuYwsWSNu2Sf/5j1SlivTgg9I775hR68uXTZkJE6TISGn4cKl8ealbN+nRR6WRI131jBhhwn2HDlJUlHlOYKD02We35W0AAAAAAGRS6eae7vh4afp06dw5c5m507RpUp480h13SH37SufPu46tWiVVrChFRLj2xcRIcXGu0fJVq6QGDdxfKybG7JdMOF+3zr1MtmzmsbNMci5dMq/j3M6cubV+AwAAAAAyL6/PXr55swnZFy+aUe5vvzWjzZL05JNSsWJSwYLSpk3Sq69KO3dKs2aZ47Gx7oFbcj2Ojb1xmbg46cIF6eRJE/iTK7NjR8rtHjxYevvtW+szAAAAACBr8HroLlvW3Gt9+rT0zTdSu3bS8uUmeD/3nKtcxYpSgQLS/fdLe/ZIJUt6rcmSzKh7r16ux4cOuT4sAAAAAABASgeh29fXzCguSdWqSWvXSqNHSx9/nLRsrVrm6+7dJnTnz590lvEjR8zX/PldX537ri0TGioFBEg+PmZLroyzjuT4+ZnNKS7uxv0EACBLY61fAEAWlW7u6XZKSDD3SydnwwbztUAB8zU62lyefu0s4wsXmkDtHHWOjpYWL3avZ+FC133jvr4m7F9bJiHBPL723nIAAAAAADzl1ZHuvn3NjONFi5qJyL780qypPX++uYT8yy/N7Oa5c5t7unv2lO65x6ztLUmNGplw/fTTZimx2FjpzTelrl1do9CdO0sffST16SM984y0ZIn09dfS3LmudvTqZS5rr15dqllTGjXKTOjWocPtfkcAAAAAAJmJV0P30aNS27Zm/e2wMBOm58+XGjaU/vpLWrTIFYCLFJFatjSh2snHR/rhB6lLFzMqHRRkwvPAga4ykZEmYPfsaS5bL1xY+vRTM4O50xNPSP/8Y9b3jo01y4/Nm5d0cjUAAAAAADzh1dA9aVLKx4oUMROq3UyxYtKPP964TP360vr1Ny7TrZvZAAAAAABIK+nunm4AAAAAADILQjcAAAAAADbx+pJhAAAAAOB1GXlpQ4nlDdMxRroBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbsGQYAABAGmLVIQDAtQjdADKWjPzXLH/JAgAAZDmEbgBIrzLyBwwSHzIAAACIe7oBAAAAALANI93pXHx8vK5cueLtZiAdy5PHs/IJCdLp076Kj+czNwAAAMBuhO50yrIsxcbG6tSpU95uCtK59u09K29Z0pkz2fTVV5E6c8bXljYBAAAAMAjd6ZQzcOfLl0+BgYFyOBzebhLSKc9/NBIUFPS37rvvsObMKSrL4mcLAAAAsAuhOx2Kj49PDNy5c+f2dnOQzvn4eP6c4OC8KlHibwUEXNX58znSvlEAkIW9tSaDT4IoJkEEgLTETZ3pkPMe7sDAQC+3BJmVw+ErHx8pICDe200BAAAAMjVCdzrGJeWwj/nZ4kcMAAAAsBeXlwMAAODWNc3gl9N/z+X0AOzFSDduK4fDodmzZ3u7GenagAEDVKVKFW83AwAAAEAaYKQ7g7mdHyZ7+sHvP//8o379+mnu3Lk6cuSIcubMqcqVK6tfv36qW7euPY28RfXr19fy5cslSb6+vsqTJ4/uvPNOdejQQS1atLht7XA4HPr222/VvHnzxH2vvPKKXnzxxdvWBgAAAAD2IXQjzbRs2VKXL1/W1KlTVaJECR05ckSLFy/W8ePHvd20ZHXq1EkDBw7U1atXdfDgQX377bdq1aqV2rdvr08++eSW642Pj5fD4VC2bLd2IUlwcLCCg4Nv+fUBZBBckgsAQJbA5eVIE6dOndIvv/yiIUOG6N5771WxYsVUs2ZN9e3bVw8//HCKz/vrr7/0+OOPKzw8XLly5VKzZs20b98+tzKffvqpypcvL39/f5UrV07jxo1LPLZv3z45HA5Nnz5dderUkb+/v+64447EUewbCQwMVP78+VW4cGHVrl1bQ4YM0ccff6yJEydq0aJFkqRly5bJ4XDo1KlTic/bsGGDHA5HYjunTJmi8PBwzZkzR1FRUfLz89OBAwe0du1aNWzYUHny5FFYWJjq1aun33//PbGe4sWLS5IeeeQRORyOxMfXX16ekJCggQMHqnDhwvLz81OVKlU0b968xOMHD+5TmTIOzZ8/S08/fa8qVQpU06aVtX79qpu+BwAAAADsRehGmnCOzs6ePVuXLl1K1XOuXLmimJgYhYSE6JdfftGKFSsUHBysBx54QJcvX5YkTZs2Tf369dN7772n7du3a9CgQXrrrbc0depUt7p69+6tl19+WevXr1d0dLSaNm16SyPs7dq1U86cOTVr1iyPnnf+/HkNGTJEn376qbZu3ap8+fLpzJkzateunX799VetXr1apUuXVuPGjXXmzBlJ0tq1ayVJkydP1uHDhxMfX2/06NEaPny4hg0bpk2bNikmJkYPP/ywdu3a5VZu5Mg39Mwzr+i77zYoMrKMevVqratXr3r8HgAAAABIO4RupIns2bNrypQpmjp1qsLDw1W3bl29/vrr2rRpU4rPmTFjhhISEvTpp5+qYsWKKl++vCZPnqwDBw5o2bJlkqT+/ftr+PDhatGihSIjI9WiRQv17NlTH3/8sVtd3bp1U8uWLVW+fHmNHz9eYWFhmjRpksf9yJYtm8qUKZNktP1mrly5onHjxqlOnToqW7asAgMDdd999+mpp55SuXLlVL58eX3yySc6f/584ih83rx5JUnh4eHKnz9/4uPrDRs2TK+++qpatWqlsmXLasiQIapSpYpGjRrlVq5jx1d0771NFBlZRt27v61Dh/Zr//7dHr8HAAAAANIO93QjzbRs2VJNmjTRL7/8otWrV+unn37S0KFD9emnn6p9+/ZJym/cuFG7d+9WSEiI2/6LFy9qz549OnfunPbs2aOOHTuqU6dOicevXr2qsLAwt+dER0cnfp89e3ZVr15d27dvv6V+WJbl8Rrpvr6+qlSpktu+I0eO6M0339SyZct09OhRxcfH6/z58zpw4ECq642Li9Pff/+dZCK6unXrauPGjW77ypZ1vX7evAUkSSdOHFXJkuU86gvgNdzjDAAAMiFCN9KUv7+/GjZsqIYNG+qtt97Ss88+q/79+ycbus+ePatq1app2rRpSY7lzZtXZ8+elSRNnDhRtWrVcjvu4+NjS/vj4+O1a9cu1ahRQ5ISJ0OzLCuxzJUrV5I8LyAgIElQb9eunY4fP67Ro0erWLFi8vPzU3R0dOKl82kte/Ycid8725KQkGDLawEAAABIHS4vh62ioqJ07ty5ZI/deeed2rVrl/Lly6dSpUq5bWFhYYqIiFDBggX1559/JjkeGRnpVtfq1asTv7969arWrVun8uXLe9zeqVOn6uTJk2rZsqUk1yXghw8fTiyzYcOGVNW1YsUKde/eXY0bN1aFChXk5+enY8eOuZXJkSOH4uPjU6wjNDRUBQsW1IoVK5LUHRUVlap2AAAAAPAeRrqRJo4fP67HHntMzzzzjCpVqqSQkBD99ttvGjp0qJo1a5bsc9q0aaMPPvhAzZo1S5yde//+/Zo1a5b69OmjwoUL6+2331b37t0VFhamBx54QJcuXdJvv/2mkydPqlevXol1jR07VqVLl1b58uU1cuRInTx5Us8888wN23z+/HnFxsa6LRk2cuRIdenSRffee68kqVSpUipSpIgGDBig9957T3/88YeGDx+eqvekdOnS+uKLL1S9enXFxcWpd+/eCggIcCtTvHhxLV68WHXr1pWfn59y5syZpJ7evXurf//+KlmypKpUqaLJkydrw4YNyV4hAAAAACB9IXQjTQQHB6tWrVoaOXKk9uzZoytXrqhIkSLq1KmTXn/99WSfExgYqJ9//lmvvvqqWrRooTNnzqhQoUK6//77FRoaKkl69tlnFRgYqA8++EC9e/dWUFCQKlasqB49erjV9f777+v999/Xhg0bVKpUKc2ZM0d58uS5YZsnTpyoiRMnytfXV7lz51a1atU0Y8YMPfLII4llcuTIoa+++kpdunRRpUqVVKNGDb377rt67LHHbvqeTJo0Sc8995zuvPNOFSlSRIMGDdIrr7ziVmb48OHq1auXJk6cqEKFCiU7gVv37t11+vRpvfzyyzp69KiioqI0Z84clS5dWpIUcWGvJCnfxf0qeC7IvLfn4yRJuS8eVMFzu5LUecmK18VLR9Vl01Dp4LEkx28v7oMFAABA5uWwrr1ZFbfs4MGDKlKkiP766y8VLlz4X9V18eJF7d27V5GRkfL390+jFmZO+/btU2RkpNavX++2tnVWcm5D0lB9M5eseB3454gu953i9dBd84iHoTsjT7bl6URbGbmvEv29GfqbcXjY1zURGbivymL/LktMgngzGfn8ZqV/p6R0+bOclhkpI2OkG5nPLs9DaLry/yPYAAAAADI+JlIDAAAAAMAmjHQjQytevLi4QwIAAABAesVINwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCN7zC4XBo9uzZ3m4GAAAAANiKdbozmqZNb99rff/9LT0tNjZWgwcP1ty5c3Xw4EGFhYWpVKlSeuqpp9SuXTsFBgamcUMBAAAAIH0idCNN/fnnn6pbt67Cw8M1aNAgVaxYUX5+ftq8ebM++eQTFSpUSA8//LC3mwkAAAAAtwWXlyNNvfDCC8qePbt+++03Pf744ypfvrxKlCihZs2aae7cuWqazEj9smXL5HA4dOrUqcR9GzZskMPh0L59+xL3rVixQvXr11dgYKBy5sypmJgYnTx5UpJ06dIlde/eXfny5ZP/HXforlattHbTpsTnnjx9Wm1efll5a9VSQMWKKt2woSb/97+Jx/86fFiPv/SSwqtVU64aNdSsSxftO3gw7d8gAAAAACn7+WdzdW/BgpLDIV1/S6plSf36SQUKSAEBUoMG0q5d7mVOnJDatJFCQ6XwcKljR+ns2dvVgyQI3Ugzx48f14IFC9S1a1cFBQUlW8bhcNxS3Rs2bND999+vqKgorVq1Sr/++quaNm2q+Ph4SVKfPn303//+V1OnTtXvs2erVLFiiunYUSf+P8i/NWqUtu3erZ8+/VTbf/pJ4wcMUJ6cOSVJV65cUcwzzygkKEi/fPmlVnz1lYIDA/VAx466fPnyLbUXAAAAwC04d06qXFkaOzb540OHSh9+KE2YIP3vf1JQkBQTI1286CrTpo20dau0cKH0ww8myD/33O1pfzK4vDwrOHfu1p53/SdGN7F740ZZlqWyISFuz81Ts6Yu/n947dqmjYb07m0O/P23KeccUd6zx3waJUn795uve/dKxYtr6NChql69usaNG5dYb4UKFSRJ586d0/jx4zVlyhQ9+OCD0q5dmvjuu1p4772a9M036v3sszpw+LCqRkWpesWKkqTihQsn1jPjxx+VkJCgT997L/FDgcmDByu8enUtW7NGje66y6P3AQAAAMAtevBBsyXHsqRRo6Q335SaNTP7Pv9ciogwI+KtWknbt0vz5klr10rVq5syY8ZIjRtLw4aZEfTbjJFu2G7NN99ow3ffqUKpUrp0iyPHzpHu5OzZs0dXrlxR3bp1E/flyJFDNStV0vY9eyRJXVq31vS5c1Xl4YfVZ+hQrfz998SyG3fs0O4DBxRStaqCq1RRcJUqylWzpi5euqQ9Bw7cUnsBAAAApLG9e6XYWHNJuVNYmFSrlrRqlXm8apW5pNwZuCVTPls2MzLuBYx0I82UKlpUDodDO/fuddtfomhRSVKAv3+yz8uWzXz2Y1lW4r4rV6+6lQkICPhXbXuwXj3tX7ZMPy5bpoUrV+r+du3UtU0bDXvtNZ09f17VKlTQtOHDkzwvb65c/+p1AQAAgCzvzBkpLs712M/PbJ6KjTVfIyLc90dEuI7Fxkr58rkfz55dypXLVeY2Y6QbaSZ3zpxqWLeuPvrPf3Tu/PlUPy/v/99bffiffxL3bdi+3a1MpUqVtHjx4mSfX7JkSfn6+mrFihWJ+65cuaK1mzcrqlQp1+vkyqV2LVroP8OGadQbb+iTGTMkSXdGRWnX/v3Klzu3ShUr5raFhYSkuh8AAAAAkgqNijIj0s5t8GBvN+m2InQjTY0bMEBX4+NVvUULzZg7V9t379bOP//Uf777Tjv+/FM+2ZL+yJUqVkxFChTQgDFjtGvfPs1dulTDP/vMrUzfvn21du1avfDCC9q0aZN27Nih8ePH69ixYwoKClKXLl3Uu3dvzZs3T9t271anN9/U+YsX1fHRRyVJ/UaP1neLFmn3/v3aumuXfli6VOVLlpQktXn4YeXJmVPNunTRL2vXau9ff2nZ//6n7u+8o4Ne+jQMAAAAyCzitm2TTp92bX373lpF+fObr0eOuO8/csR1LH9+6ehR9+NXr5oZzZ1lbjMuL89ovv/e8+d4OCHav1GyaFGtnz1bgyZMUN/hw3XwyBH5+foqqmRJvdKxo1548skkz8mRI4e+GjFCXQYMUKWmTVWjYkW927OnHuvePbFMmTJltGDBAr3++uuqWbOmAgICVKtWLbVu3VqS9P777yshIUFPP/20zsTFqfodd2j+pEnKGRYmSfLNkUN9hw/XvkOHFODvr7urVdP0kSMlSYEBAfp52jS9+sEHatGtm86cO6dCERG6PzpaocHBt+FdAwAAADKxkBDXhMn/RmSkCc6LF0tVqph9cXHmXu0uXczj6Gjp1Clp3TqpWjWzb8kSKSHB3PvtBYRupLkC+fJpTL9+GtOvX4plrD/+cHtct1o1bbruA4Xry9SrV8/tEvJr+fv768MPP9SHH36Y7IcMb77wgt584YUU25M/b15NHTo0xeMAAAAAboOzZ6Xdu12P9+6VNmww92QXLSr16CG9+65UurQJ4W+9ZWYkb97clC9fXnrgAalTJ7Os2JUrUrduZmZzL8xcLhG6AWQwa9Z4uwW3rqa3GwAAAJDe/fabdO+9rse9epmv7dpJU6ZIffqYJZGfe86MaN91l1ki7NpJm6dNM0H7/vvNrOUtW5q1vb2E0A0AAAAASB/q1zfrcafE4ZAGDjRbSnLlkr78Ms2bdquYSA0AAAAAAJsQugEAAAAAsAmhOx2zbnRZBZAW+BkDAAAAbEXoTody5MghSTp//ryXW4LM6oosWVfjpTMXvN0UAAAAIFPz6kRq48ebbd8+87hCBalfP+nBB83jixell1+Wpk+XLl2SYmKkceOkiAhXHQcOmCXZli6VgoPNpHaDB0vZr+nZsmVm0rutW6UiRaQ335Tat3dvy9ix0gcfSLGxUuXK0pgxUk0vTTXs4+Oj8PBwHf3/Rd0DAwPlcDhuvcL4+DRqmZdcvOhZ+SzW30uWZ/21JJ24eFbxG/8kdAMAAAA282roLlxYev99s8SaZUlTp0rNmknr15sA3rOnNHeuNHOmFBZmZn1v0UJyLtUcHy81aWLWR1+5Ujp8WGrbVsqRQxo0yJTZu9eU6dzZzBy/eLH07LNSgQImxEvSjBkmlE+YYNZLHzXKHNu5U8qXzytvjfLnzy9JicH7X0mLOrzJ00ugs1h/L/3jYX8tS/EnzyjhiyVcXg4AAADYzKuhu2lT98fvvWdGvlevNoF80iQz0/t995njkyebtc5Xr5Zq15YWLJC2bZMWLTKj31WqSO+8I736qjRggOTra4J0ZKQ0fLipo3x56ddfpZEjXaF7xAizdnqHDubxhAkm7H/2mfTaa7fjnUjK4XCoQIECypcvn65cufLvKhs6NG0a5S3jx3tWPov1d9NTHvY3PkH657R0NYNfEQAAAABkAOlmne74eDOife6cFB0trVsnXbkiNWjgKlOunFS0qLRqlQndq1ZJFSu6X24eE2MuN9+6Vapa1ZS5tg5nmR49zPeXL5vX6tvXdTxbNvOcVats626q+fj4yMfH599VcuxY2jTGW65d6D41slp/D2bw/gIAAACZmNdD9+bNJmRfvGjuyf72WykqStqwwYxUh4e7l4+IMPddS+brtYHbedx57EZl4uKkCxekkydN4E+uzI4dKbf70iWzOZ05k5reAgAAAACyEq+H7rJlTcA+fVr65hszEdry5d5u1c0NHiy9/ba3WwEAQMawZo23W3DrvDSvKgAgk/D6kmG+vlKpUlK1aibIVq4sjR5tJke7fFk6dcq9/JEj5phkvh45kvS489iNyoSGSgEBUp48ko9P8mWcdSSnb1/zQYFz27bNo24DAAAAALIAr4fu6yUkmMu2q1Uzs5AvXuw6tnOnWSIsOto8jo42l6dfO1n1woUmUEdFucpcW4ezjLMOX1/zWteWSUgwj51lkuPnZ17HuYWE3HqfAQAAAACZk1cvL+/b16zJXbSouSf6yy/Nmtrz55slwjp2NEt55cplgu2LL5ogXLu2eX6jRiZcP/20mbA6Ntaswd21qwnFklkq7KOPpD59pGeekZYskb7+2sxO7tSrl7msvXp1szb3qFFmQjfnbOYAAAAAANwKr4buo0fNutqHD5uQXamSCdwNG5rjI0eamcRbtjSj3zEx0rhxruf7+Eg//GBmK4+OloKCTHgeONBVJjLSBOyePc1l64ULS59+6louTJKeeEL65x+pXz8T3KtUkebNSzq5GgAAAAAAnvBq6J406cbH/f2lsWPNlpJixaQff7xxPfXrS+vX37hMt25mAwAAAAAgraS7e7oBAAAAAMgsCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATbJ7uwGw35o13m7Bv1PT2w0AAAAAgFvESDcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATlgwDAMALWM4RAICsgZFuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGyS3dsNAAAkb80ab7fg36np7QYAAACkA4x0AwAAAABgE0I3AAAAAAA24fJyAAAA3DJuhQGAG2OkGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALCJV0P34MFSjRpSSIiUL5/UvLm0c6d7mfr1JYfDfevc2b3MgQNSkyZSYKCpp3dv6epV9zLLlkl33in5+UmlSklTpiRtz9ixUvHikr+/VKuWtGZNmnUVAAAAAJAFeTV0L18ude0qrV4tLVwoXbkiNWoknTvnXq5TJ+nwYdc2dKjrWHy8CdyXL0srV0pTp5pA3a+fq8zevabMvfdKGzZIPXpIzz4rzZ/vKjNjhtSrl9S/v/T771LlylJMjHT0qI1vAAAAAAAgU/Nq6J43T2rfXqpQwYTcKVPMqPW6de7lAgOl/PldW2io69iCBdK2bdJ//iNVqSI9+KD0zjtm1PryZVNmwgQpMlIaPlwqX17q1k169FFp5EhXPSNGmHDfoYMUFWWeExgoffaZzW8CAAAAAMCMqL71lglvAQFSyZIm3FmWq4xlmRHWAgVMmQYNpF27vNfmVEhX93SfPm2+5srlvn/aNClPHumOO6S+faXz513HVq2SKlaUIiJc+2JipLg4aetWV5kGDdzrjIkx+yUTztetcy+TLZt57CxzvUuXzGs4tzNnPO8vAAAAAOD/DRkijR8vffSRtH27eTx0qDRmjKvM0KHShx+aUdL//U8KCjLh7uJF77X7JrJ7uwFOCQnmsu+6dU24dnrySalYMalgQWnTJunVV81937NmmeOxse6BW3I9jo29cZm4OOnCBenkSfOhSnJlduxIvr2DB0tvv31LXQUAAAAAXG/lSqlZM3NvsGQm3PrqK9dkW5YljRolvfmmKSdJn39ugtvs2VKrVl5o9M2lm5Hurl2lLVuk6dPd9z/3nPngomJFqU0b855++620Z4932unUt68ZmXdu27Z5tz0AAAAAkKHVqSMtXiz98Yd5vHGj9Ouv5h5iyUzWFRvrfolyWJiZBTulS5TTgXQx0t2tm/TDD9LPP0uFC9+4bK1a5uvu3eYS//z5k84yfuSI+Zo/v+urc9+1ZUJDzW0APj5mS66Ms47r+fmZzSku7sbtBgAAAIAs6cwZ98B0fZhyeu01U65cORPQ4uOl994zo6+S61Lm5C5Rdh5Lh7w60m1ZJnB/+620ZIm5X/5mNmwwXwsUMF+jo6XNm91nGV+40ATqqChXmcWL3etZuNDslyRfX6laNfcyCQnmsbMMAAAAAMBzoVFRZkTauQ0enHzBr782E3p9+aVZUmrqVGnYMPM1A/PqSHfXrub9/O47s1a388OJsDAzAr1njzneuLGUO7e5p7tnT+mee6RKlUzZRo1MuH76aXNPfWysucS/a1fXhyedO5t78fv0kZ55xgT8r7+W5s51taVXL6ldO6l6dalmTXOrwLlzZjZzAAAAAMCtidu2TaGFCrl2JDfKLUm9e5vRbue92RUrSvv3m5Derp3rMuQjR1yjsM7HVarY0va04NXQPX68+Vq/vvv+yZPNUmK+vtKiRa4AXKSI1LKlCdVOPj7m0vQuXcyodFCQOR8DB7rKREaagN2zpzR6tLmE/dNPzb3iTk88If3zj5l9PjbWnLN585JeuQAAAAAA8EBIiPu6zyk5f94sI3UtHx9zGbJkgl3+/OaSZGfIjoszs5h36ZKmTU5LXg3d1y63lpwiRaTly29eT7Fi0o8/3rhM/frS+vU3LtOtm9kAAAAAALdZ06bmHu6iRaUKFUyAGzHCXK4sSQ6HWfLq3Xel0qVNCH/rLbPUVfPm3mz5DaWLidQAAAAAAFncmDEmRL/wgpm0q2BB6fnnzeXITn36mMugn3tOOnVKuusuc4myv7/Xmn0zhG4AAAAAgPeFhJh7i0eNSrmMw2HuJb72fuJ0Lt2s0w0AAAAAQGZD6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCbZvd0AAAAAAOnTmjXebsGtq+ntBgD/j5FuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGyS3dsNAABAktas8XYL/p2a3m4AAABIlxjpBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJre0TndCgrR7t3T0qPn+WvfckxbNAgAAAAAg4/M4dK9eLT35pLR/v2RZ7sccDik+Pq2aBgAAAABAxuZx6O7cWapeXZo7VypQwARtAAAAAACQlMehe9cu6ZtvpFKl7GgOAAAAAACZh8cTqdWqZe7nBgAAAAAAN+bxSPeLL0ovvyzFxkoVK0o5crgfr1QprZoGAAAAAEDG5nHobtnSfH3mGdc+h8NMqsZEagAAAAAAuHgcuvfutaMZAAAAAABkPh6H7mLF7GgGAAAAAACZj8ehW5L27JFGjZK2bzePo6Kkl16SSpZMw5YBAAAAAJDBeTx7+fz5JmSvWWMmTatUSfrf/6QKFaSFC+1oIgAAAAAAGZPHI92vvSb17Cm9/37S/a++KjVsmFZNAwAAAAAgY/N4pHv7dqljx6T7n3lG2rYtLZoEAAAAAEDm4HHozptX2rAh6f4NG6R8+f59gwAAAAAAyCw8vry8UyfpueekP/+U6tQx+1askIYMkXr1SuvmAQAAAACQcXkcut96SwoJkYYPl/r2NfsKFpQGDJC6d0/j1gEAAAAAkIF5HLodDjORWs+e0pkzZl9ISFo3CwAAAACAjO+W1ul2ImwDAAAAAJCyVIXuO++UFi+WcuaUqlY1o90p+f33tGoaAAAAAAAZW6pCd7Nmkp+f6/sbhW4AAAAAAGCkKnT37+/6fsAAm1oCAAAAAEAm4/E63SVKSMePJ91/6pQ5BgAAAAAADI9D9759Unx80v2XLkkHD6ZBiwAAAAAAyCRSPXv5nDmu7+fPl8LCXI/j481Ea5GRadk0AAAAAAAytlSH7ubNzVeHQ2rXzv1YjhxS8eLS8OFp1zAAAAAAADK6VIfuhATzNTJSWrtWypPHriYBAAAAAOBFV69Ky5ZJe/ZITz4phYRIf/8thYZKwcEeVeXxPd1796Zd4B48WKpRw7Q/Xz4zmr5zp3uZixelrl2l3LlN31q2lI4ccS9z4IDUpIkUGGjq6d3bvEfXWrbMrDfu5yeVKiVNmZK0PWPHmhF7f3+pVi1pzZq06ScAAAAAIIPYv1+qWNGsl921q/TPP2b/kCHSK694XF2qR7qvde6ctHy5CbuXL7sf69499fUsX276UKOGCcmvvy41aiRt2yYFBZkyPXtKc+dKM2ea+8i7dZNatJBWrDDH4+NN4M6fX1q5Ujp8WGrb1lzyPmiQKbN3rynTubM0bZq5//zZZ6UCBaSYGFNmxgypVy9pwgQTuEeNMsd27jRBHgAAAACQBbz0klS9urRxoxn9dXrkEalTJ4+r8zh0r18vNW4snT9vwneuXNKxY65RZk9C97x57o+nTDF1rFsn3XOPdPq0NGmS9OWX0n33mTKTJ0vly0urV0u1a0sLFpiQvmiRFBEhVakivfOO9OqrZk1xX18TpCMjXfecly8v/fqrNHKkK3SPGGHevw4dzOMJE0zY/+wz6bXXPH2XAAAAAAAZ0i+/mBFdX1/3/cWLS4cOeVydx5eX9+wpNW0qnTwpBQSY8Lt/v1StmjRsmMev7+b0afM1Vy7zdd066coVqUEDV5ly5aSiRaVVq8zjVavMyH9EhKtMTIwUFydt3eoqc20dzjLOOi5fNq91bZls2cxjZ5nrXbpkXsO5nTlza30GAAAAAKQjCQnJr5N98KC5N9pDHofuDRukl182odTHx4TPIkWkoUPN5eG3KiFB6tFDqltXuuMOsy821ny4EB7uXjYiwhxzlrk2cDuPO4/dqExcnHThghmpj49PvoyzjusNHmwud3duUVGe9BYAAAAAkC41amTuN3ZyOKSzZ6X+/c1l3x7yOHTnyGECt2QuBT9wwHwfFib99ZfHr5+oa1dpyxZp+vRbr+N26tvXjMw7t23bvN0iAAAAAMC/Nny4mUQsKsrM7P3kk65Ly4cM8bg6j+/prlrVLBlWurRUr57Ur58ZKf7iC9cItae6dZN++EH6+WepcGHX/vz5zaXfp065j3YfOWKOOctcP8u4c3bza8tcP+P5kSNmtveAADNi7+OTfBlnHdfz8zObU1xcanoKAAAAAEjXChc2k6jNmGG+nj0rdewotWljAqSHPB7pHjTIzPotSe+9J+XMKXXpYmZR/+QTz+qyLBO4v/1WWrLETHZ2rWrVzMj64sWufTt3mtH16GjzODpa2rxZOnrUVWbhQhOonZd8R0e71+Es46zD19e81rVlEhLMY2cZAAAAAEAWkT27CdlDh0rjxpnlr24hcEsehm7LMpeUO4NovnxmBvK4ODMRWeXKnr14167Sf/5jZicPCTH3T8fGmvusJXPJeseOZimvpUvNa3ToYF6/dm1TplEjE66fftp8CDF/vvTmm6Zu50h0587Sn39KffpIO3aY9+zrr82kcE69ekkTJ0pTp0rbt5sPEs6dc81mDgAAAADIAqZONUtZOfXpYy69rlPHzCLuIY9Dd6lS/+7e7WuNH2/uh65f34yeO7cZM1xlRo6UHnpIatnSLCOWP780a5bruI+PuTTdx8eE8aeeMut0DxzoKhMZad6zhQvNBwPDh0uffupaLkySnnjCzL7er59ZdmzDBvOBwvWTqwEAAAAAMrFBg1yj2qtWSR99ZEa88+RxH7lNJY/u6c6WzdzLffy4+fpvWdbNy/j7S2PHmi0lxYpJP/5443rq1zdrjN9It25mAwAAAABkUX/9ZUabJWn2bOnRR6XnnjNLbdWv73F1Ht/T/f77Uu/eZqZxAAAAAAAyleBgM9IsSQsWSA0bmu/9/V33QnvA49nL27aVzp83l2n7+ia9l/zECY/bAAAAAABA+tCwoZk4rWpV6Y8/XGtzb91qlg7zkMeh+9o1wgEAAAAAyFTGjjWzc//1l/Tf/0q5c5v969ZJrVt7XJ3HobtdO49fAwAAAACAjCE83Eyedr23376l6jwO3QcO3Ph40aK31A4AAAAAANKHU6ekSZPMetKSVKGC9MwzZl1rD3kcuosXlxyOlI/Hx3vcBgAAAAAA0offfjPrSwcESDVrmn0jRkjvvWcmVrvzTo+q8zh0X7/s1pUrZp+zDQAAAAAAZFg9e0oPPyxNnChl///IfPWqmVytRw/p5589qs7j0F25ctJ91atLBQtKH3wgtWjhaY0AAAAAAKQTv/3mHrgl832fPib8esjjdbpTUrastHZtWtUGAAAAAMhyDh2SnnrKzBgeECBVrGhCsJNlSf36SQUKmOMNGki7dqVtG0JDk5/M7K+/pJAQj6vzOHTHxblvp09LO3aYGdVLl/b49QEAAAAAkE6elOrWlXLkkH76Sdq2TRo+XMqZ01Vm6FDpww+lCROk//1PCgoy919fvJh27XjiCaljR2nGDBO0//pLmj7dXF5+O5YMCw9POpGaZUlFiph2AAAAAADgsSFDTLCcPNm1LzLS9b1lSaNGmRHfZs3Mvs8/lyIipNmzpVat0qYdw4aZ0Nu2rbmXWzIfBHTpIr3/vsfVeRy6ly51f5wtm5Q3r1SqlPsl7wAAAAAApNqcOWbU+rHHpOXLpUKFpBdekDp1Msf37pViY80l5U5hYVKtWtKqVWkXun19pdGjpcGDpT17zL6SJaXAwFuqzuOYXK/eLb0OAAAAACArOnPG3Jvs5Odntuv9+ac0frzUq5f0+utm0rDu3U0IbtfOBG7JjGxfKyLCdSwtnD5t1sLOlcvcU+504oQZaQ4N9ag6j+/pXrJE6tZNeughqWlT8x54OGM6AAAAACCLCI2KMiPSzm3w4OQLJiSYNbAHDZKqVpWee86Mck+YcHsb3KpV8vdOf/31LY2mexS6O3c2I/lffSUdPy798480bZp0773Siy96/NoAAAAAgEwubts2M3rs3Pr2Tb5ggQJSVJT7vvLlXTOJ589vvh454l7myBHXsbTwv/+ZkHu9+vXNMQ+lOnR/+625n/2zz6Rjx8wl86tXm+A9caL0ySfmEnwAAAAAABKFhJhLsp1bcpeWS2bm8p073ff98YdUrJj5PjLShOvFi13H4+JMEI6OTrv2XrrkmkDtWleuSBcueFxdqkP35Mnm0vr27d1nL8+WTXrmGalHD2nSJI9fHwAAAAAAqWdPM7I7aJC0e7f05ZdmdLdrV3Pc4TDB8913zYjv5s1mhvGCBaXmzdOuHTVrmte93oQJUrVqHleX6onUfv/dzMyekhYtpJYtPX59AAAAAPC6NWu83YJ/p6a3G5AWatQwl1j37SsNHGhGtkeNktq0cZXp00c6d87c733qlHTXXdK8eZK/f9q14913zX3VGzdK999v9i1ebCZ2W7DA4+pSHbqPHZMKF075eOHC5j5vAAAAAABuyUMPmS0lDocJ5AMH2teGunXN/dQffGAmTwsIkCpVMpd2ly7tcXWpDt2XL5v1wFOsKLspAwAAAABAhlalipk1PA14tE73W2+lvB74+fNp0RwAAAAAALzIOVt6SooW9ai6VIfue+5JOpFccmUAAAAAAMiwihd3nz38evHxHlWX6tC9bJlH9QIAAAAAkPGsX+/++MoVs2/ECOm99zyuzqPLywEAAAAAyNQqV066r3p1szTZBx+Ypbs8kOp1ugEAAAAAyLLKljXLhnmIkW4AAAAAAJzi4twfW5Z0+LA0YIC9S4YBAAAAAJDphYcnnUjNsqQiRaTp0z2ujtANAAAAAIDT0qXuj7Nlk/LmlUqVkrJ7HqFvKXSfPClNmiRt324ely8vPfOMlCvXrdQGAAAAAEA6Ua9emlbncej++Wfp4Yel0FAzgZskjRkjvfOO9P33rNUNAAAAAMhg5sxJfdmHH/aoao9Dd9eu0uOPS+PHSz4+Zl98vPTCC+bY5s2e1ggAAAAAgBc1b566cg6HCcAe8HjJsN27pZdfdgVuyXzfq5c5BgAAAABAhpKQkLrNw8At3cJI9513mnu5y5Z13799e/JriAMAAAAAkO5dvCgtWiQ99JB53LevdOmS63j27NLAgZK/v0fVehy6u3eXXnrJjGrXrm32rV4tjR0rvf++tGmTq2ylSp7WDgAAAACAF0yZIs2d6wrdH30kVaggBQSYxzt2SPnzm8u8PeBx6G7d2nzt0yf5Yw6HWcLsFi51BwAAAADAO6ZNSxp0v/xSKlHCfP+f/5jRZrtD9969nj4DAAAAAIB0bvduqWJF12N/f7NGt1PNmmb2cA95HLqLFfP4NQAAAAAASN9OnXK/h/uff9yPJyS4H08lj2cvl6QvvpDq1pUKFpT27zf7Ro2SvvvuVmoDAAAAAMDLCheWtmxJ+fimTaaMhzwO3ePHm0vYGzc2HwQ479sODzfBGwAAAACADKdxY6lfPzOL+fUuXJDefltq0sTjaj0O3WPGSBMnSm+84b5Wd/Xq0ubNHr8+AAAAAADe9/rr0okTZn3sDz4wl3J/9500dKjZd/KkKeOhW5pIrWrVpPv9/KRz5zx+fQAAAAAAvC8iQlq5UurSRXrtNbMsl2SW5mrYUBo3zpTxkMehOzJS2rAh6YRq8+ZJ5ct7/PoAAAAAAKQPkZEm3J44YWYzl6RSpaRcuW65So9Dd69eZpb0ixdN8F+zRvrqK2nwYOnTT2+5HQAAAAAApA+5cpklwtKAx6H72WelgADpzTel8+elJ580s5iPHi21apUmbQIAAAAAIFPwOHRLUps2Zjt/Xjp7VsqXL62bBQAAAABAxndL63RfvSotWmTW6w4IMPv+/tsEcAAAAAAAYHg80r1/v/TAA9KBA9KlS2YSt5AQacgQ83jCBDuaCQAAAABAxuPxSPdLL5k1uU+edI1yS9Ijj0iLF6dl0wAAAAAAyNg8Hun+5RezdJmvr/v+4sWlQ4fSqFUAAAAAAGQCHo90JyRI8fFJ9x88aC4zBwAAAAAAhscj3Y0aSaNGSZ98Yh47HGYCtf79pcaN07h1wC1Ys8bbLfh30mY1QAAAAADpgcehe/hwKSZGioqSLl4063Tv2iXlySN99ZUdTQQAAAAAIGPyOHQXLixt3ChNny5t2mRGuTt2NOt2XzuxGgAAAAAAWZ3HoVuSsmeXnnoqrZsCAAAAAEDmkqrQPWdO6it8+OFbbQoAAAAAAJlLqkJ38+apq8zhSH5mcwAAAAAAsqJUhe6EBLubAQAAAABA5uPxOt0AAAAAACB1Uh26V62SfvjBfd/nn0uRkVK+fNJzz0mXLqV18wAAAAAAyLhSHboHDpS2bnU93rzZLBXWoIH02mvS999Lgwfb0UQAAAAAADKmVIfuDRuk++93PZ4+XapVS5o4UerVS/rwQ+nrrz178Z9/lpo2lQoWNJOwzZ7tfrx9e7P/2u2BB9zLnDhh1ggPDZXCw80HAWfPupfZtEm6+27J318qUkQaOjRpW2bOlMqVM2UqVpR+/NGzvgAAAAAAcL1Uh+6TJ6WICNfj5culBx90Pa5RQ/rrL89e/Nw5qXJlaezYlMs88IB0+LBr++or9+Nt2pgR+IULzeXvP/9sLnV3iouTGjWSihWT1q2TPvhAGjBA+uQTV5mVK6XWrU1gX7/ezNbevLm0ZYtn/QEAAAAA4Fqpmr1cMoF7714zUnz5svT779Lbb7uOnzkj5cjh2Ys/+KB7cE+On5+UP3/yx7Zvl+bNk9aulapXN/vGjJEaN5aGDTMj6NOmmfZ+9pnk6ytVqGBG7UeMcIXz0aNNuO/d2zx+5x0T4j/6SJowwbM+AQAAAADglOqR7saNzb3bv/wi9e0rBQaaS7adNm2SSpZM+wYuW2YmaitbVurSRTp+3HVs1SpzSbkzcEvmHvNs2aT//c9V5p57TOB2iomRdu40o/fOMg0auL9uTIzZn5JLl8wounM7c+bf9BIAAAAAkBmlOnS/846UPbtUr565j3viRPcg+9ln5jLutPTAA2aG9MWLpSFDXJe0x8eb47GxJpBfK3t2KVcuc8xZ5trL4iXX45uVcR5PzuDBUliYa4uKurU+AgAAAAAyr1RfXp4nj7lf+vRpKThY8vFxPz5zptmfllq1cn1fsaJUqZIZTV+2zH1SN2/o29dMIOd06BDBGwAAAADgLtUj3U5hYUkDt2RGl68d+bZDiRIm/O/ebR7nzy8dPepe5upVM6O58z7w/PmlI0fcyzgf36xMSveSS+Ze89BQ1xYScmt9AgAAAABkXh6Hbm86eNDc012ggHkcHS2dOmVmJXdaskRKSDDLmTnL/PyzdOWKq8zCheYe8Zw5XWUWL3Z/rYULzX4AAAAAAG6VV0P32bNmJvENG8zjvXvN9wcOmGO9e0urV0v79plQ3KyZVKqUmeRMksqXN/d9d+okrVkjrVghdetmLksvWNCUefJJMwLfsaNZWmzGDDNb+bWXhr/0kpkFffhwaccOs6TYb7+ZugAAAAAAuFVeDd2//SZVrWo2yQThqlWlfv3MJeybNkkPPyyVKWNCc7VqZvZ0Pz9XHdOmSeXKmXu8GzeW7rrLfQ3usDBpwQIT6KtVk15+2dR/7VredepIX35pnle5svTNN9Ls2dIdd9yWtwEAAAAAkEmleiI1O9SvL1lWysfnz795HblymcB8I5UqmbB+I489ZjYAAAAAANJKhrqnGwAAAACAjITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAABIf95/X3I4pB49XPsuXpS6dpVy55aCg6WWLaUjR7zWxNQgdAMAAAAA0pe1a6WPP5YqVXLf37On9P330syZ0vLl0t9/Sy1aeKeNqUToBgAAAACkH2fPSm3aSBMnSjlzuvafPi1NmiSNGCHdd59UrZo0ebK0cqW0erX32nsThG4AAAAAQPrRtavUpInUoIH7/nXrpCtX3PeXKycVLSqtWnV72+iB7N5uAAAAAAAgEztzRoqLcz328zNbcqZPl37/3Vxefr3YWMnXVwoPd98fEWGOpVOMdAMAAAAAbBMaFSWFhbm2wYOTL/jXX9JLL0nTpkn+/re3kTZipBsAAAAAYJu4bdsUWqiQa0dKo9zr1klHj0p33unaFx8v/fyz9NFH0vz50uXL0qlT7qPdR45I+fPb0fQ0QegGAAAAANgnJEQKDb15ufvvlzZvdt/XoYO5b/vVV6UiRaQcOaTFi81SYZK0c6d04IAUHZ327U4jhG4AAAAAgPeFhEh33OG+LyjIrMnt3N+xo9Srl5QrlwnyL75oAnft2re/valE6AYAAAAAZAwjR0rZspmR7kuXpJgYadw4b7fqhgjdAAAAAID0adky98f+/tLYsWbLIJi9HAAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsIlXQ/fPP0tNm0oFC0oOhzR7tvtxy5L69ZMKFJACAqQGDaRdu9zLnDghtWkjhYZK4eFSx47S2bPuZTZtku6+W/L3l4oUkYYOTdqWmTOlcuVMmYoVpR9/TMueAgAAAACyIq+G7nPnpMqVpbFjkz8+dKj04YfShAnS//4nBQVJMTHSxYuuMm3aSFu3SgsXSj/8YIL8c8+5jsfFSY0aScWKSevWSR98IA0YIH3yiavMypVS69YmsK9fLzVvbrYtW2zoNAAAAAAgy8juzRd/8EGzJceypFGjpDfflJo1M/s+/1yKiDAj4q1aSdu3S/PmSWvXStWrmzJjxkiNG0vDhpkR9GnTpMuXpc8+k3x9pQoVpA0bpBEjXOF89GjpgQek3r3N43feMSH+o49M4AcAAAAA4Fak23u69+6VYmPNJeVOYWFSrVrSqlXm8apV5pJyZ+CWTPls2czIuLPMPfeYwO0UEyPt3CmdPOkqc+3rOMs4Xyc5ly6ZUXTndubMLXcVAAAAAJBJpdvQHRtrvkZEuO+PiHAdi42V8uVzP549u5Qrl3uZ5Oq49jVSKuM8npzBg82HAM4tKip1/QIAAAAAZB3pNnSnd337SqdPu7Zt27zdIgAAAABAepNuQ3f+/ObrkSPu+48ccR3Ln186etT9+NWrZkbza8skV8e1r5FSGefx5Pj5mRnTnVtISOr6BQAAAADIOtJt6I6MNKF38WLXvrg4c692dLR5HB0tnTplZiV3WrJESkgw9347y/z8s3TliqvMwoVS2bJSzpyuMte+jrOM83UAAAAAALgVXg3dZ8+amcQ3bDCP9+413x84YNbt7tFDevddac4cafNmqW1bMyN58+amfPnyZtbxTp2kNWukFSukbt3MzOYFC5oyTz5pJlHr2NEsLTZjhpmtvFcvVzteesnMgj58uLRjh1lS7LffTF0AAAAAANwqry4Z9ttv0r33uh47g3C7dtKUKVKfPmYt7+eeMyPad91lwrG/v+s506aZcHz//WbW8pYtzdreTmFh0oIFUteuUrVqUp48Ur9+7mt516kjffmlWZ7s9del0qXNsmR33GFj5wEAAAAAmZ5XQ3f9+mY97pQ4HNLAgWZLSa5cJjDfSKVK0i+/3LjMY4+ZDQAAAACAtJJu7+kGAAAAACCjI3QDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAALxv8GCpRg0pJETKl09q3lzaudO9zMWLUteuUu7cUnCw1LKldOSIV5qbWoRuAAAAAID3LV9uAvXq1dLChdKVK1KjRtK5c64yPXtK338vzZxpyv/9t9SihffanArZvd0AAAAAAAA0b5774ylTzIj3unXSPfdIp09LkyZJX34p3XefKTN5slS+vAnqtWvf9ianBiPdAAAAAAD7nDkjxcW5tkuXUve806fN11y5zNd168zod4MGrjLlyklFi0qrVqVtm9MQoRsAAAAAYJvQqCgpLMy1DR588yclJEg9ekh160p33GH2xcZKvr5SeLh72YgIcyyd4vJyAAAAAIBt4rZtU2ihQq4dfn43f1LXrtKWLdKvv9rXsNuE0A0AAAAAsE9IiBQamvry3bpJP/wg/fyzVLiwa3/+/NLly9KpU+6j3UeOmGPpFJeXAwAAAAC8z7JM4P72W2nJEiky0v14tWpSjhzS4sWufTt3SgcOSNHRt7etHmCkGwAAAADgfV27mpnJv/vOjI4779MOC5MCAszXjh2lXr3M5GqhodKLL5rAnU5nLpcI3QAAAACA9GD8ePO1fn33/ZMnS+3bm+9HjpSyZZNatjSzoMfESOPG3c5WeozQDQAAAADwPsu6eRl/f2nsWLNlENzTDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2CRdh+4BAySHw30rV851/OJFs3567txScLBZqu3IEfc6DhyQmjSRAgOlfPmk3r2lq1fdyyxbJt15p+TnJ5UqJU2ZYnPHAAAAAABZQroO3ZJUoYJ0+LBr+/VX17GePaXvv5dmzpSWL5f+/ltq0cJ1PD7eBO7Ll6WVK6WpU02g7tfPVWbvXlPm3nulDRukHj2kZ5+V5s+/TR0EAAAAAGRa2b3dgJvJnl3Knz/p/tOnpUmTpC+/lO67z+ybPFkqX15avVqqXVtasEDatk1atEiKiJCqVJHeeUd69VUziu7rK02YIEVGSsOHmzrKlzfBfuRIKSbmdvUSAAAAAJAZpfuR7l27pIIFpRIlpDZtzOXikrRunXTlitSggatsuXJS0aLSqlXm8apVUsWKJnA7xcRIcXHS1q2uMtfW4SzjrCMlly6ZepzbmTP/rp8AAAAAgMwnXYfuWrXM5eDz5knjx5tLwe++2wTc2FgzUh0e7v6ciAhzTDJfrw3czuPOYzcqExcnXbiQctsGD5bCwlxbVNSt9hIAAAAAkFml68vLH3zQ9X2lSiaEFysmff21FBDgvXZJUt++Uq9erseHDhG8AQAAAADu0vVI9/XCw6UyZaTdu8193pcvS6dOuZc5csR1D3j+/ElnM3c+vlmZ0NAbB3s/P1PGuYWE3GqvAAAAAACZVYYK3WfPSnv2SAUKSNWqSTlySIsXu47v3Gnu+Y6ONo+jo6XNm6WjR11lFi40Idk5Kh0d7V6Hs4yzDgAAAAAAblW6Dt2vvGKWAtu3zyz59cgjko+P1Lq1uY+6Y0dziffSpWZitQ4dTFiuXds8v1EjE66fflrauNEsA/bmm2Ztbz8/U6ZzZ+nPP6U+faQdO6Rx48zl6z17eq3bAAAAAIBMIl3f033woAnYx49LefNKd91llgPLm9ccHzlSypZNatnSzCYeE2NCs5OPj/TDD1KXLiaMBwVJ7dpJAwe6ykRGSnPnmpA9erRUuLD06acsFwYAAAAA+PfSdeiePv3Gx/39pbFjzZaSYsWkH3+8cT3160vr13vcPAAAAAAAbihdX14OAAAAAEBGRugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoTu64wdKxUvLvn7S7VqSWvWeLtFAAAAAJCFZLJQRui+xowZUq9eUv/+0u+/S5UrSzEx0tGj3m4ZAAAAAGQBmTCUEbqvMWKE1KmT1KGDFBUlTZggBQZKn33m7ZYBAAAAQBaQCUNZdm83IL24fFlat07q29e1L1s2qUEDadWqpOUvXTKb0+nTCZKkw4cP29xSz52Jv+jtJvwrBw8e9Kg8/c1YslJ/s1JfJfp7M/Q348hKfZXoL9xl5PPLz7L3ObNRwunTUmio64Cfn9mu52koyyAI3f/v2DEpPl6KiHDfHxEh7diRtPzgwdLbb1+754gkqWbNmra1McsqUsTbLbi96G/mlZX6KtHfzC4r9Tcr9VWiv8g8stq5Tcf9zXbHHe47+veXBgxIWtDTUJZBELpvUd++5lYDp6tXq2r79jUqUiRC2bJlnav2z5w5o6ioKG3btk0hISHebg7SGOc38+LcZm6c38wrq53brNbfrIRzm7k5z++WLVt0/uRJ5Y+KkrJfEz2TG+XOxAjd/y9PHsnHRzpyxH3/kSNS/vxJyye9IiK76tatYWcT06W4uDhJUqFChRR67SUjyBQ4v5kX5zZz4/xmXlnt3Ga1/mYlnNvMzXl+ixQpotAKFVL/RE9DWQaRdYZkb8LXV6pWTVq82LUvIcE8jo72XrsAAAAAIEvIpKGMke5r9OoltWsnVa8u1awpjRolnTtnJs4DAAAAANgsE4YyQvc1nnhC+ucfqV8/KTZWqlJFmjcv6X38cPHz81P//v3ll8Xuy8gqOL+ZF+c2c+P8Zl5Z7dxmtf5mJZzbzO1fnd9MGMoclmVZ3m4EAAAAAACZEfd0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdGcQgwcPVo0aNRQSEqJ8+fKpefPm2rlzp1uZixcvqmvXrsqdO7eCg4PVsmVLHblmYfmNGzeqdevWKlKkiAICAlS+fHmNHj3arY5ff/1VdevWVe7cuRUQEKBy5cpp5MiRN23frFmz1KhRI+XOnVsOh0MbNmxwO37ixAm9+OKLKlu2rAICAlS0aFF1795dp0+fvmG9y5YtU7NmzVSgQAEFBQWpSpUqmjZtWpLXrl69usLDwxPLfPHFFzdtc3rC+U35/E6ZMkUOh8Nt8/f3v2mb0wvObcrntn79+knOrcPhUJMmTW7a7vSC85vy+b1y5YoGDhyokiVLyt/fX5UrV9a8efNu2ub0IqudW2d/g4KC5OvrKz8/P/n6+qpEiRJ68803deXKlcT+hoeHK3v27AoKCpLD4dCoUaNS3d9rrVixQtmzZ1eVKlVu2l/LstSvXz8VKFBAAQEBatCggXbt2uVW5r333lOdOnUUGBio8PDwm9bptGnTJt19993y9/dXkSJFNHToULfjW7duVcuWLVW8eHG3/mYkt+vn+VoZ5fxm9L8jObcpn9t09TekhQwhJibGmjx5srVlyxZrw4YNVuPGja2iRYtaZ8+eTSzTuXNnq0iRItbixYut3377zapdu7ZVp06dxOOTJk2yunfvbi1btszas2eP9cUXX1gBAQHWmDFjEsv8/vvv1pdffmlt2bLF2rt3r/XFF19YgYGB1scff3zD9n3++efW22+/bU2cONGSZK1fv97t+ObNm60WLVpYc+bMsXbv3m0tXrzYKl26tNWyZcsb1vvee+9Zb775prVixQpr9+7d1qhRo6xs2bJZ33//fWKZpUuXWrNmzbK2bduWWMbHx8eaN29eat7adIHzm/L5nTx5shUaGmodPnw4cYuNjU3N25oucG5TPrfHjx93O69btmyxfHx8rMmTJ6finU0fOL8pn98+ffpYBQsWtObOnWvt2bPHGjdunOXv72/9/vvvqXlrvS6rnVtnf3/66SdrwIAB1l133WUVKlTImjFjhpUvXz6rb9++if0dO3as9dRTT1mlS5e2cuTIYY0cOTLV/XU6efKkVaJECatRo0ZW5cqVb3I2LOv999+3wsLCrNmzZ1sbN260Hn74YSsyMtK6cOFCYpl+/fpZI0aMsHr16mWFhYXdtE7LsqzTp09bERERVps2bawtW7ZYX331lRUQEOD2/q9Zs8Z65ZVXrK+++srKnz9/Yn8zktv18+yUkc5vRv87knOb8rlNT39DErozqKNHj1qSrOXLl1uWZVmnTp2ycuTIYc2cOTOxzPbt2y1J1qpVq1Ks54UXXrDuvffeG77WI488Yj311FOpatfevXuT/c8/OV9//bXl6+trXblyJVV1OzVu3Njq0KHDDctUrVrVevPNNz2qNz3h/LrO7+TJk1P9D3BGwLlN+Xd35MiRVkhIiNsfChkN59d1fgsUKGB99NFHbmVatGhhtWnTxqN604usdm6v7W/Pnj2t2rVrp9jfl156KcV6UurvE088Yb355ptW//79b/qHe0JCgpU/f37rgw8+SNx36tQpy8/Pz/rqq6+SlPfk/41x48ZZOXPmtC5dupS479VXX7XKli2bbPlixYplyNB9Pbt/njPq+XXKyH9Hcm5d5zY9/Q3J5eUZlPPysFy5ckmS1q1bpytXrqhBgwaJZcqVK6eiRYtq1apVN6zHWUdy1q9fr5UrV6pevXpp1HL31w4NDVX27Nk9fl5KbbYsS4sXL9bOnTt1zz33pEUzvYLz697ms2fPqlixYipSpIiaNWumrVu3pmVTbyvObcptnjRpklq1aqWgoKB/20Sv4fy62nzp0qUkl/EFBATo119/TZN23m5Z7dw6+3v27FnNmzdPJUuWTLa/Pj4+2rdv3w3rub6/kydP1p9//qn+/funqt179+5VbGys22uHhYWpVq1aN3yvU2PVqlW655575Ovrm7gvJiZGO3fu1MmTJ/9V3emZnT/PGfn8Zoa/Izm37uc2vfwN6dn/qEgXEhIS1KNHD9WtW1d33HGHJCk2Nla+vr5J7oOIiIhQbGxssvWsXLlSM2bM0Ny5c5McK1y4sP755x9dvXpVAwYM0LPPPpumfTh27JjeeecdPffccx497+uvv9batWv18ccfu+0/ffq0ChUqpEuXLsnHx0fjxo1Tw4YN07LJtw3n1/38li1bVp999pkqVaqk06dPa9iwYapTp462bt2qwoULp2m77ca5Tfq767RmzRpt2bJFkyZNSotmegXn1/38xsTEaMSIEbrnnntUsmRJLV68WLNmzVJ8fHyatvl2yGrnNiEhQTVq1EicY+G5557T3XffrZkzZybpr4+Pj86cOZNsPcn1d9euXXrttdf0yy+/pPqDHef7GRER4bb/Ru91asXGxioyMjJJvc5jOXPm/Ff1p0d2/jxn1PObWf6O5Ny6n9v09DckI90ZUNeuXbVlyxZNnz79luvYsmWLmjVrpv79+6tRo0ZJjv/yyy/67bffNGHCBI0aNUpfffWVJGnatGkKDg5O3H755RePXzsuLk5NmjRRVFSUBgwYkLi/QoUKifU++OCDSZ63dOlSdejQQRMnTlSFChXcjoWEhGjDhg1au3at3nvvPfXq1UvLli3zuG3pAefX/fxGR0erbdu2qlKliurVq6dZs2Ypb968KYa39Ixzm/R312nSpEmqWLGiatas6XG70gvOr/v5HT16tEqXLq1y5crJ19dX3bp1U4cOHZQtW8b70yOrnduuXbsqODhYixYt0pdffqm5c+cm+0GBp/2Nj4/Xk08+qbfffltlypRJ9nlp0d+U3OxnOauw6+c5I5/fzPJ3JOfWXbr6G9Lb17fDM127drUKFy5s/fnnn277Fy9ebEmyTp486ba/aNGi1ogRI9z2bd261cqXL5/1+uuvp+o133nnHatMmTKWZVlWXFyctWvXrsTt/PnzbmVvdm9ZXFycFR0dbd1///1uEyhYlmXt27cvsd6DBw+6HVu2bJkVFBR004llnDp27Gg1atQoVWXTE85v6s7vo48+arVq1SpVZdMLzm3K5/bs2bNWaGioNWrUqFT1Kz3i/KZ8fi9cuGAdPHjQSkhIsPr06WNFRUWlqn/pRVY7t8n194svvrB8fX2T7a+Pj4/VrFmzVPX35MmTliTLx8cncXM4HIn7Fi9enGx/9+zZk2wf77nnHqt79+5J+pzSfZzJ9ffpp59O0v4lS5ZYkqwTJ04kqSOj39Nt589zZji/Thnx70jOberOrbf+hiR0ZxAJCQlW165drYIFC1p//PFHkuPOSRK++eabxH07duxIMknCli1brHz58lm9e/dO9Wu//fbbVrFixVJV9kb/+Z8+fdqqXbu2Va9ePevcuXOpfv2lS5daQUFBSSbkuZEOHTpY9erVS3V5b+P8pv78Xr161SpbtqzVs2fPVL+GN3Fub35uJ0+ebPn5+VnHjh1Ldd3pBec39b+7ly9ftkqWLGn17ds31a/hTVnt3N6ov1OnTrV8fHxS7O+1E6ndqL/x8fHW5s2b3bYuXbpYZcuWtTZv3pziJIrOyZiGDRvm1re0nIzp8uXLifv69u2b6SZSux0/z5nh/DplpL8jObepP7fe/BuS0J1BdOnSxQoLC7OWLVvmNu39tZ94d+7c2SpatKi1ZMkS67fffrOio6Ot6OjoxOObN2+28ubNaz311FNudRw9ejSxzEcffWTNmTPH+uOPP6w//vjD+vTTT62QkBDrjTfeuGH7jh8/bq1fv96aO3euJcmaPn26tX79euvw4cOWZZlfsFq1alkVK1a0du/e7fb6V69eTbHeJUuWWIGBgVbfvn3dnnP8+PHEMoMGDbIWLFhg7dmzx9q2bZs1bNgwK3v27NbEiRM9fp+9hfOb8vl9++23rfnz51t79uyx1q1bZ7Vq1cry9/e3tm7d6vH77A2c25TPrdNdd91lPfHEE6l+T9MTzm/K53f16tXWf//7X2vPnj3Wzz//bN13331WZGRkktGW9CqrnVtnf9944w3r448/tpYvX26tXr3a+uKLL6yCBQtabdq0Sezv/PnzrWnTplmVKlWycuTIYb3yyiuJbblZf6+XmhmQLcssOxQeHm5999131qZNm6xmzZolWXZo//791vr16623337bCg4OttavX2+tX7/eOnPmTIr1njp1yoqIiLCefvppa8uWLdb06dOTLNl26dKlxLoKFCiQ2N9du3bdtN3pxe36eb5eRji/Gf3vSM5tyuc2Pf0NSejOICQlu127nu2FCxesF154wcqZM6cVGBhoPfLII4n/+VqW+eVIro5rP03/8MMPrQoVKliBgYFWaGioVbVqVWvcuHFWfHz8Dds3efLkZOvu37+/ZVlmRCSlPuzduzfFetu1a5fsc6799PGNN96wSpUqZfn7+1s5c+a0oqOjrenTp3vy9nod5zfl89ujRw+raNGilq+vrxUREWE1btw4w6zza1mc2xudW8tyfdq+YMGC1L6l6QrnN+Xzu2zZMqt8+fKWn5+flTt3buvpp5+2Dh065Mnb61VZ7dymVLZgwYLWoEGDrAsXLiT2NywsLMV+3ay/10vtH+4JCQnWW2+9ZUVERFh+fn7W/fffb+3cudOtTEo/l0uXLr1h3Rs3brTuuusuy8/PzypUqJD1/vvvux13Xk1ws3/P0rPb9fN8vYxwfjP635Gc25TPbXr6G9JhWZYlAAAAAACQ5jLeFKIAAAAAAGQQhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAIB1q3769HA6HHA6HcuTIoYiICDVs2FCfffaZEhISUl3PlClTFB4ebl9DAQDADRG6AQBIpx544AEdPnxY+/bt008//aR7771XL730kh566CFdvXrV280DAACpQOgGACCd8vPzU/78+VWoUCHdeeedev311/Xdd9/pp59+0pQpUyRJI0aMUMWKFRUUFKQiRYrohRde0NmzZyVJy5YtU4cOHXT69OnEUfMBAwZIki5duqRXXnlFhQoVUlBQkGrVqqVly5Z5p6MAAGRihG4AADKQ++67T5UrV9asWbMkSdmyZdOHH36orVu3aurUqVqyZIn69OkjSapTp45GjRql0NBQHT58WIcPH9Yrr7wiSerWrZtWrVql6dOna9OmTXrsscf0wAMPaNeuXV7rGwAAmZHDsizL240AAADu2rdvr1OnTmn27NlJjrVq1UqbNm3Stm3bkhz75ptv1LlzZx07dkySuae7R48eOnXqVGKZAwcOqESJEjpw4IAKFiyYuL9BgwaqWbOmBg0alOb9AQAgq8ru7QYAAADPWJYlh8MhSVq0aJEGDx6sHTt2KC4uTlevXtXFixd1/vx5BQYGJvv8zZs3Kz4+XmXKlHHbf+nSJeXOndv29gMAkJUQugEAyGC2b9+uyMhI7du3Tw899JC6dOmi9957T7ly5dKvv/6qjh076vLlyymG7rNnz8rHx0fr1q2Tj4+P27Hg4ODb0QUAALIMQjcAABnIkiVLtHnzZvXs2VPr1q1TQkKChg8frmzZzDQtX3/9tVt5X19fxcfHu+2rWrWq4uPjdfToUd199923re0AAGRFhG4AANKpS5cuKTY2VvHx8Tpy5IjmzZunwYMH66GHHlLbtm21ZcsWXblyRWPGjFHTpk21YsUKTZgwwa2O4sWL6+zZs1q8eLEqV66swMBAlSlTRm3atFHbtm01fPhwVa1aVf/8848WL16sSpUqqUmTJl7qMQAAmQ+zlwMAkE7NmzdPBQoUUPHixfXAAw9o6dKl+vDDD/Xdd9/Jx8dHlStX1ogRIzRkyBDdcccdmjZtmgYPHuxWR506ddS5c2c98cQTyps3r4YOHSpJmjx5stq2bauXX35ZZcuWVfPmzbV27VoVLVrUG10FACDTYvZyAAAAAABswkg3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgk/8DJzf6R8/0A0AAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a figure and axis\n",
+    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "# Plot 'sleepDuration' on the primary y-axis\n",
+    "ax1.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['Duration (s)'], label='Sleep Duration', alpha=0.7, color='b')\n",
+    "ax1.set_xlabel('Date')\n",
+    "ax1.set_ylabel('Sleep Duration', color='b')\n",
+    "ax1.tick_params('y', colors='b')\n",
+    "\n",
+    "# Create a secondary y-axis for 'glucose'\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['glucose'], label='Glucose', alpha=0.7, color='r')\n",
+    "ax2.set_ylabel('Glucose', color='r')\n",
+    "ax2.tick_params('y', colors='r')\n",
+    "\n",
+    "# Format the x-axis labels\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "#plt.xticks(df['Calendar Date (Local)'], rotation=90, ha='right', fontsize=8)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Set title\n",
+    "plt.title('Total Sleep Duration and Glucose over Time')\n",
+    "\n",
+    "# Display legend\n",
+    "fig.legend(loc='upper left', bbox_to_anchor=(0.1, 0.9))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "233ba1e9-9dab-4a4a-b6c3-fa18fd8691e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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dcMABpbfffnuRj5grlUqliRMnlg4++ODSaqutVqqpqSmts846pWHDhpXmzp1bt80bb7xR+sY3vlHq0qVLqU2bNqX+/fuX7r777gb7ufrqq0s77rhjaZVVVinV1NSU1l133dKPfvSj0owZMxpsN23atNKwYcNKvXr1KrVq1arUvXv30q677lq65pprPvO4zJ8/v3TmmWeW+vTpU2rVqlWpV69epeHDh5fmzJmz2O2HDBlSSlIaOHDgEvd52223lXbYYYdS+/btS+3bty9tsMEGpWHDhpVeeeWVum122mmnz/z4vCX5PB8x97vf/a7BdiNHjiwlWeSjAD/5Z//OO+8s83sCYOVUVSotxXV7AAAAQMW5Jx4AAAAKQokHAACAglDiAQAAoCCUeAAAACgIJR4AAAAKwufEJ6mtrc2UKVPSsWPHVFVVVXocAAAAmplSqZT3338/PXv2THX1ks+3K/FJpkyZkl69elV6DAAAAJq5yZMnZ80111zieiU+SceOHZN8fLA6depU4WkAAABobmbOnJlevXrV9dMlUeKTukvoO3XqpMQDAABQMZ91i7cH2wEAAEBBKPEAAABQEEo8AAAAFIR74pdSbW1t5s2bV+kxKNOqVau0aNGi0mMAAAA0CSV+KcybNy/jx49PbW1tpUdhMbp06ZLu3bt/5gMgAAAAik6J/wylUilvvfVWWrRokV69eqW62h0IK4pSqZTZs2fn7bffTpL06NGjwhMBAAAsX0r8Z/joo48ye/bs9OzZM+3atav0OJRp27ZtkuTtt9/O6quv7tJ6AABgpea08mdYsGBBkqR169YVnoQl+eSPK/Pnz6/wJAAAAMuXEr+U3G+94vLPBgAAaC6UeAAAACgIJR4AAAAKwoPtPqfDb3iiSX/e9d/dZrntu6qqKnfccUcGDx683H4GAAAA/zln4ldyU6dOzXHHHZe+ffumTZs26datW7bffvtceeWVmT17dqXHAwAAYBk4E78S++c//5ntt98+Xbp0ybnnnptNNtkkNTU1ef7553PNNddkjTXWyD777FPpMQEAAFhKzsSvxI4++ui0bNkyTz75ZA444IBsuOGGWWeddbLvvvvmj3/8Y/bee+9Fvufhhx9OVVVVpk+fXrfsmWeeSVVVVSZMmFC37LHHHsvOO++cdu3a5Qtf+EIGDRqU9957L0kyd+7cHHvssVl99dXTpk2b7LDDDnniiYW3H7z33nsZMmRIVltttbRt2zbrrbdeRo4cWbd+8uTJOeCAA9KlS5d07do1++67b4OfDQAA0Fwp8Sup//u//8t9992XYcOGpX379ovd5vN+NNszzzyTXXfdNf369cuYMWPy6KOPZu+9986CBQuSJCeffHJuu+22jBo1Kk899VT69u2bQYMG5d13302SnHrqqXnxxRdzzz335KWXXsqVV16ZVVddNcnHn/U+aNCgdOzYMX/961/z2GOPpUOHDvna176WefPmfa55AQAAVhYup19Jvf766ymVSvniF7/YYPmqq66aOXPmJEmGDRuW8847b5n3ff7552frrbfOFVdcUbdso402SpJ88MEHufLKK3PDDTdk9913T5Jce+21uf/++3P99dfnRz/6USZNmpQtttgiW2+9dZJk7bXXrtvPLbfcktra2lx33XV1f2QYOXJkunTpkocffji77bbbMs8LAACwsnAmvpl5/PHH88wzz2SjjTbK3LlzP9c+PjkTvzhvvPFG5s+fn+23375uWatWrdK/f/+89NJLSZKhQ4fm5ptvzuabb56TTz45f/vb3+q2ffbZZ/P666+nY8eO6dChQzp06JCuXbtmzpw5eeONNz7XvAAAACsLZ+JXUn379k1VVVVeeeWVBsvXWWedJEnbtm0X+33V1R//XadUKtUtmz9/foNtlvS9S2v33XfPxIkT86c//Sn3339/dt111wwbNiy/+MUvMmvWrGy11Va58cYbF/m+1VZb7T/6uQAAAEXnTPxKapVVVslXv/rVXHbZZfnggw+W+vs+KcpvvfVW3bJnnnmmwTabbrppRo8evdjvX3fdddO6des89thjdcvmz5+fJ554Iv369Wvwcw455JD8z//8Ty6++OJcc801SZItt9wyr732WlZfffX07du3wVfnzp2X+n0AAACsjJyJX4ldccUV2X777bP11lvnjDPOyKabbprq6uo88cQTefnll7PVVlst8j19+/ZNr169csYZZ+Scc87Jq6++mgsvvLDBNsOHD88mm2ySo48+OkcddVRat26dhx56KN/85jez6qqrZujQofnRj36Url27Zq211sr555+f2bNn5/DDD0+SnHbaadlqq63qLum/++67s+GGGyZJhgwZkgsuuCD77rtvzjrrrKy55pqZOHFibr/99px88slZc801l/+BAwCg2Tn8hic+e6OCuf6721R6BJYDJf5zKsL/IdZdd908/fTTOffcczN8+PD861//Sk1NTfr165eTTjopRx999CLf06pVq/z2t7/N0KFDs+mmm2abbbbJ2WefnW9+85t126y//vq57777csopp6R///5p27Zttt1223zrW99Kkvz85z9PbW1t/r//7//L+++/n6233jp//vOf84UvfCFJ0rp16wwfPjwTJkxI27Zt8+Uvfzk333xzkqRdu3Z55JFH8uMf/zj77bdf3n///ayxxhrZdddd06lTpyY4agAAACuuqlL9m5+bqZkzZ6Zz586ZMWPGIkVxzpw5GT9+fPr06ZM2bdpUaEI+jX9GAAD8p5yJp9I+rZfW5554AAAAKAglHgAAAApCiQcAAICCUOIBAACgIJT4peT5fysu/2wAAIDmwkfMfYYWLVokSebNm5e2bdtWeBoWZ/bs2Uk+/ng8AFgcT50GYGWhxH+Gli1bpl27dnnnnXfSqlWrVFe7eGFFUSqVMnv27Lz99tvp0qVL3R9cAAAAVlZK/GeoqqpKjx49Mn78+EycOLHS47AYXbp0Sffu3Ss9BgAAwHKnxC+F1q1bZ7311su8efMqPQplWrVq5Qw8AADQbCjxS6m6ujpt2rSp9BgAAAA0Y27wBgAAgIJwJh4AAIAkPs2jCJyJBwAAgIJQ4gEAAKAglHgAAAAoCCUeAAAACkKJBwAAgIJQ4gEAAKAgfMQcsFLwcSgAADQHSjzASsQfMwAAVm4upwcAAICCUOIBAACgIJR4AAAAKAglHgAAAArCg+0AAJoRD8AEKDZn4gEAAKAgnIkHAKDZcUUCUFTOxAMAAEBBKPEAAABQEC6nB4CVmEuGAWDl4kw8AAAAFIQSDwAAAAWhxAMAAEBBuCcegJWSe8EBgJWRM/EAAABQEEo8AAAAFIQSDwAAAAXhnngoOPf9AgBA86HEU0iKKwAA0By5nB4AAAAKQokHAACAglDiAQAAoCCUeAAAACgIJR4AAAAKQokHAACAglDiAQAAoCCUeAAAACgIJR4AAAAKQokHAACAglDiAQAAoCAqWuIfeeSR7L333unZs2eqqqpy5513NlhfKpVy2mmnpUePHmnbtm0GDhyY1157rcE27777boYMGZJOnTqlS5cuOfzwwzNr1qwmfBcAAADQNFpW8od/8MEH2WyzzXLYYYdlv/32W2T9+eefn0suuSSjRo1Knz59cuqpp2bQoEF58cUX06ZNmyTJkCFD8tZbb+X+++/P/Pnzc+ihh+bII4/MTTfd1NRvp0kcfsMTlR6h0V3/3W0qPQIAAEAhVLTE77777tl9990Xu65UKuXiiy/OT3/60+y7775Jkl//+tfp1q1b7rzzzhx00EF56aWXcu+99+aJJ57I1ltvnSS59NJLs8cee+QXv/hFevbsudh9z507N3Pnzq17PXPmzEZ+ZwAAAND4Vth74sePH5+pU6dm4MCBdcs6d+6cbbfdNmPGjEmSjBkzJl26dKkr8EkycODAVFdXZ+zYsUvc94gRI9K5c+e6r169ei2/NwIAAACNZIUt8VOnTk2SdOvWrcHybt261a2bOnVqVl999QbrW7Zsma5du9ZtszjDhw/PjBkz6r4mT57cyNMDAABA46vo5fSVUlNTk5qamkqPAQAAAMtkhT0T37179yTJtGnTGiyfNm1a3bru3bvn7bffbrD+o48+yrvvvlu3DQAAAKwsVtgS36dPn3Tv3j2jR4+uWzZz5syMHTs2AwYMSJIMGDAg06dPz7hx4+q2efDBB1NbW5ttt922yWcGAACA5amil9PPmjUrr7/+et3r8ePH55lnnknXrl2z1lpr5fjjj8/ZZ5+d9dZbr+4j5nr27JnBgwcnSTbccMN87Wtfy/e+971cddVVmT9/fo455pgcdNBBS3wyPQAAABRVRUv8k08+mV122aXu9YknnpgkOeSQQ3LDDTfk5JNPzgcffJAjjzwy06dPzw477JB777237jPik+TGG2/MMccck1133TXV1dXZf//9c8kllzT5ewEAAIDlraIlfuedd06pVFri+qqqqpx11lk566yzlrhN165dc9NNNy2P8QAAAGCFssLeEw8AAAA0pMQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFocQDAABAQSjxAAAAUBBKPAAAABSEEg8AAAAFsUKX+AULFuTUU09Nnz590rZt26y77rr52c9+llKpVLdNqVTKaaedlh49eqRt27YZOHBgXnvttQpODQAAAMvHCl3izzvvvFx55ZW57LLL8tJLL+W8887L+eefn0svvbRum/PPPz+XXHJJrrrqqowdOzbt27fPoEGDMmfOnApODgAAAI2vZaUH+DR/+9vfsu+++2bPPfdMkqy99tr57W9/m8cffzzJx2fhL7744vz0pz/NvvvumyT59a9/nW7duuXOO+/MQQcdVLHZAQAAoLGt0Gfit9tuu4wePTqvvvpqkuTZZ5/No48+mt133z1JMn78+EydOjUDBw6s+57OnTtn2223zZgxY5a437lz52bmzJkNvgAAAGBFt0Kfif/JT36SmTNnZoMNNkiLFi2yYMGCnHPOORkyZEiSZOrUqUmSbt26Nfi+bt261a1bnBEjRuTMM89cfoMDAADAcrBCn4m/9dZbc+ONN+amm27KU089lVGjRuUXv/hFRo0a9R/td/jw4ZkxY0bd1+TJkxtpYgAAAFh+Vugz8T/60Y/yk5/8pO7e9k022SQTJ07MiBEjcsghh6R79+5JkmnTpqVHjx513zdt2rRsvvnmS9xvTU1NampqluvsAAAA0NhW6DPxs2fPTnV1wxFbtGiR2traJEmfPn3SvXv3jB49um79zJkzM3bs2AwYMKBJZwUAAIDlbYU+E7/33nvnnHPOyVprrZWNNtooTz/9dC666KIcdthhSZKqqqocf/zxOfvss7PeeuulT58+OfXUU9OzZ88MHjy4ssMDAABAI1uhS/yll16aU089NUcffXTefvvt9OzZM9///vdz2mmn1W1z8skn54MPPsiRRx6Z6dOnZ4cddsi9996bNm3aVHByAAAAaHwrdInv2LFjLr744lx88cVL3KaqqipnnXVWzjrrrKYbDAAAACpghb4nHgAAAFhIiQcAAICCUOIBAACgIJR4AAAAKAglHgAAAApCiQcAAICCUOIBAACgIJR4AAAAKAglHgAAAApCiQcAAICCUOIBAACgIJR4AAAAKAglHgAAAApCiQcAAICCUOIBAACgIJR4AAAAKAglHgAAAApCiQcAAICCUOIBAACgIJR4AAAAKAglHgAAAApCiQcAAICCUOIBAACgIJR4AAAAKAglHgAAAApCiQcAAICCUOIBAACgIJR4AAAAKAglHgAAAAqi5dJstMUWW6SqqmqpdvjUU0/9RwMBAAAAi7dUJX7w4MHLeQwAAADgsyxViT/99NOX9xwAAADAZ/hc98RPnz491113XYYPH5533303yceX0b/55puNOhwAAACw0FKdia/vueeey8CBA9O5c+dMmDAh3/ve99K1a9fcfvvtmTRpUn79618vjzkBAACg2VvmM/Ennnhivvvd7+a1115LmzZt6pbvscceeeSRRxp1OAAAAGChZS7xTzzxRL7//e8vsnyNNdbI1KlTG2UoAAAAYFHLXOJramoyc+bMRZa/+uqrWW211RplKAAAAGBRy1zi99lnn5x11lmZP39+kqSqqiqTJk3Kj3/84+y///6NPiAAAADwsWUu8RdeeGFmzZqV1VdfPR9++GF22mmn9O3bNx07dsw555yzPGYEAAAA8jmeTt+5c+fcf//9efTRR/Pcc89l1qxZ2XLLLTNw4MDlMR8AAADw/yxziZ88eXJ69eqVHXbYITvssMPymAkAAABYjGW+nH7ttdfOTjvtlGuvvTbvvffe8pgJAAAAWIxlLvFPPvlk+vfvn7POOis9evTI4MGD8/vf/z5z585dHvMBAAAA/88yl/gtttgiF1xwQSZNmpR77rknq622Wo488sh069Ythx122PKYEQAAAMjnKPGfqKqqyi677JJrr702DzzwQPr06ZNRo0Y15mwAAABAPZ+7xP/rX//K+eefn8033zz9+/dPhw4dcvnllzfmbAAAAEA9y/x0+quvvjo33XRTHnvssWywwQYZMmRI/vCHP6R3797LYz4AAADg/1nmEn/22WfnW9/6Vi655JJsttlmy2MmAAAAYDGWucRPmjQpVVVVy2MWAAAA4FMs8z3xVVVV+etf/5rvfOc7GTBgQN58880kyW9+85s8+uijjT4gAAAA8LFlLvG33XZbBg0alLZt2+bpp5+u+3z4GTNm5Nxzz230AQEAAICPLXOJP/vss3PVVVfl2muvTatWreqWb7/99nnqqacadTgAAABgoWUu8a+88kp23HHHRZZ37tw506dPb4yZAAAAgMVY5hLfvXv3vP7664ssf/TRR7POOus0ylAAAADAopa5xH/ve9/Lcccdl7Fjx6aqqipTpkzJjTfemJNOOilDhw5dHjMCAAAA+RwfMfeTn/wktbW12XXXXTN79uzsuOOOqampyUknnZQf/OAHy2NGAAAAIJ+jxFdVVeW//uu/8qMf/Sivv/56Zs2alX79+qVNmzaZMmVKevbsuTzmBAAAgGZvmUv8J1q3bp1+/frVvX722Wez5ZZbZsGCBY0yGAAAANDQMt8TDwAAAFSGEg8AAAAFocQDAABAQSz1PfHPPffcp65/5ZVX/uNhAAAAgCVb6hK/+eabp6qqKqVSaZF1nyyvqqpq1OEAAACAhZa6xI8fP355zgEAAAB8hqUu8b17916ecwAAAACfwYPtAAAAoCCUeAAAACgIJR4AAAAKQokHAACAgvhcJf6jjz7KAw88kKuvvjrvv/9+kmTKlCmZNWtWow6XJG+++Wa+853vZJVVVknbtm2zySab5Mknn6xbXyqVctppp6VHjx5p27ZtBg4cmNdee63R5wAAAIBKW+YSP3HixGyyySbZd999M2zYsLzzzjtJkvPOOy8nnXRSow733nvvZfvtt0+rVq1yzz335MUXX8yFF16YL3zhC3XbnH/++bnkkkty1VVXZezYsWnfvn0GDRqUOXPmNOosAAAAUGlL/RFznzjuuOOy9dZb59lnn80qq6xSt/zrX/96vve97zXqcOedd1569eqVkSNH1i3r06dP3f8ulUq5+OKL89Of/jT77rtvkuTXv/51unXrljvvvDMHHXRQo84DAAAAlbTMZ+L/+te/5qc//Wlat27dYPnaa6+dN998s9EGS5L//d//zdZbb51vfvObWX311bPFFlvk2muvrVs/fvz4TJ06NQMHDqxb1rlz52y77bYZM2bMEvc7d+7czJw5s8EXAAAArOiWucTX1tZmwYIFiyz/17/+lY4dOzbKUJ/45z//mSuvvDLrrbde/vznP2fo0KE59thjM2rUqCTJ1KlTkyTdunVr8H3dunWrW7c4I0aMSOfOneu+evXq1ahzAwAAwPKwzCV+t912y8UXX1z3uqqqKrNmzcrpp5+ePfbYozFnS21tbbbccsuce+652WKLLXLkkUfme9/7Xq666qr/aL/Dhw/PjBkz6r4mT57cSBMDAADA8rPMJf7CCy/MY489ln79+mXOnDn59re/XXcp/Xnnndeow/Xo0SP9+vVrsGzDDTfMpEmTkiTdu3dPkkybNq3BNtOmTatbtzg1NTXp1KlTgy8AAABY0S3zg+3WXHPNPPvss7nlllvy7LPPZtasWTn88MMzZMiQtG3btlGH23777fPKK680WPbqq6+md+/eST5+yF337t0zevTobL755kmSmTNnZuzYsRk6dGijzgIAAACVtswlPklatmyZIUOGZMiQIY09TwMnnHBCtttuu5x77rk54IAD8vjjj+eaa67JNddck+TjS/mPP/74nH322VlvvfXSp0+fnHrqqenZs2cGDx68XGcDAACAprbMl9OPGjUqf/zjH+ten3zyyenSpUu22267TJw4sVGH22abbXLHHXfkt7/9bTbeeOP87Gc/y8UXX9zgjwcnn3xyfvCDH+TII4/MNttsk1mzZuXee+9NmzZtGnUWAAAAqLRlLvHnnntu3WXzY8aMyWWXXZbzzz8/q666ak444YRGH3CvvfbK888/nzlz5uSll15a5LPoq6qqctZZZ2Xq1KmZM2dOHnjggay//vqNPgcAAABU2jJfTj958uT07ds3SXLnnXfmG9/4Ro488shsv/322XnnnRt7PgAAAOD/WeYz8R06dMj//d//JUnuu+++fPWrX02StGnTJh9++GHjTgcAAADUWeYz8V/96ldzxBFHZIsttsirr75a99nw//jHP7L22ms39nwAAADA/7PMZ+Ivv/zyDBgwIO+8805uu+22rLLKKkmScePG5Vvf+lajDwgAAAB8bJnPxHfp0iWXXXbZIsvPPPPMRhkIAAAAWLzP9Tnx06dPz/XXX5+XXnopSbLRRhvlsMMOS+fOnRt1OAAAAGChZb6c/sknn8y6666bX/7yl3n33Xfz7rvv5qKLLsq6666bp556annMCAAAAORznIk/4YQTss8+++Taa69Ny5Yff/tHH32UI444Iscff3weeeSRRh8SAAAA+Bwl/sknn2xQ4JOkZcuWOfnkk7P11ls36nAAAADAQst8OX2nTp0yadKkRZZPnjw5HTt2bJShAAAAgEUtc4k/8MADc/jhh+eWW27J5MmTM3ny5Nx888054ogjfMQcAAAALEfLfDn9L37xi1RVVeXggw/ORx99lCRp1apVhg4dmp///OeNPiAAAADwsWUu8a1bt85///d/Z8SIEXnjjTeSJOuuu27atWvX6MMBAAAACy1ziZ8xY0YWLFiQrl27ZpNNNqlb/u6776Zly5bp1KlTow4IAAAAfGyZ74k/6KCDcvPNNy+y/NZbb81BBx3UKEMBAAAAi1rmEj927NjssssuiyzfeeedM3bs2EYZCgAAAFjUMpf4uXPn1j3Qrr758+fnww8/bJShAAAAgEUtc4nv379/rrnmmkWWX3XVVdlqq60aZSgAAABgUcv8YLuzzz47AwcOzLPPPptdd901STJ69Og88cQTue+++xp9QAAAAOBjy3wmfvvtt8+YMWPSq1ev3HrrrbnrrrvSt2/fPPfcc/nyl7+8PGYEAAAA8jnOxCfJ5ptvnhtvvLGxZwEAAAA+xTKX+EmTJn3q+rXWWutzDwMAAAAs2TKX+LXXXjtVVVVLXL9gwYL/aCAAAABg8Za5xD/99NMNXs+fPz9PP/10LrroopxzzjmNNhgAAADQ0DKX+M0222yRZVtvvXV69uyZCy64IPvtt1+jDAYAAAA0tMxPp1+SL37xi3niiScaa3cAAABAmWU+Ez9z5swGr0ulUt56662cccYZWW+99RptMAAAAKChZS7xXbp0WeTBdqVSKb169crNN9/caIMBAAAADS1ziX/ooYcavK6urs5qq62Wvn37pmXLz/Wx8wAAAMBSWObWvdNOOy2POQAAAIDPsFQl/n//93+Xeof77LPP5x4GAAAAWLKlKvGDBw9eqp1VVVVlwYIF/8k8AAAAwBIsVYmvra1d3nMAAAAAn6HRPiceAAAAWL6W+sF2H374YUaPHp299torSTJ8+PDMnTu3bn2LFi3ys5/9LG3atGn8KQEAAIClL/GjRo3KH//4x7oSf9lll2WjjTZK27ZtkyQvv/xyevbsmRNOOGH5TAoAAADN3FJfTn/jjTfmyCOPbLDspptuykMPPZSHHnooF1xwQW699dZGHxAAAAD42FKX+Ndffz2bbLJJ3es2bdqkunrht/fv3z8vvvhi404HAAAA1Fnqy+mnT5/e4B74d955p8H62traBusBAACAxrXUZ+LXXHPNvPDCC0tc/9xzz2XNNddslKEAAACARS11id9jjz1y2mmnZc6cOYus+/DDD3PmmWdmzz33bNThAAAAgIWW+nL6U045Jbfeemu++MUv5phjjsn666+fJHnllVdy2WWX5aOPPsopp5yy3AYFAACA5m6pS3y3bt3yt7/9LUOHDs1PfvKTlEqlJElVVVW++tWv5oorrki3bt2W26AAAADQ3C11iU+SPn365N577827776b119/PUnSt2/fdO3adbkMBwAAACy0TCX+E127dk3//v0bexYAAADgUyz1g+0AAACAylLiAQAAoCCUeAAAACgIJR4AAAAKQokHAACAglDiAQAAoCCUeAAAACgIJR4AAAAKQokHAACAglDiAQAAoCCUeAAAACgIJR4AAAAKQokHAACAglDiAQAAoCCUeAAAACgIJR4AAAAKQokHAACAglDiAQAAoCCUeAAAACgIJR4AAAAKQokHAACAglDiAQAAoCCUeAAAACgIJR4AAAAKQokHAACAgihUif/5z3+eqqqqHH/88XXL5syZk2HDhmWVVVZJhw4dsv/++2fatGmVGxIAAACWk8KU+CeeeCJXX311Nt100wbLTzjhhNx111353e9+l7/85S+ZMmVK9ttvvwpNCQAAAMtPIUr8rFmzMmTIkFx77bX5whe+ULd8xowZuf7663PRRRflK1/5SrbaaquMHDkyf/vb3/L3v/99ifubO3duZs6c2eALAAAAVnSFKPHDhg3LnnvumYEDBzZYPm7cuMyfP7/B8g022CBrrbVWxowZs8T9jRgxIp07d6776tWr13KbHQAAABrLCl/ib7755jz11FMZMWLEIuumTp2a1q1bp0uXLg2Wd+vWLVOnTl3iPocPH54ZM2bUfU2ePLmxxwYAAIBG17LSA3yayZMn57jjjsv999+fNm3aNNp+a2pqUlNT02j7AwAAgKawQp+JHzduXN5+++1sueWWadmyZVq2bJm//OUvueSSS9KyZct069Yt8+bNy/Tp0xt837Rp09K9e/fKDA0AAADLyQp9Jn7XXXfN888/32DZoYcemg022CA//vGP06tXr7Rq1SqjR4/O/vvvnyR55ZVXMmnSpAwYMKASIwMAAMBys0KX+I4dO2bjjTdusKx9+/ZZZZVV6pYffvjhOfHEE9O1a9d06tQpP/jBDzJgwIB86UtfqsTIAAAAsNys0CV+afzyl79MdXV19t9//8ydOzeDBg3KFVdcUemxAAAAoNEVrsQ//PDDDV63adMml19+eS6//PLKDAQAAABNZIV+sB0AAACwkBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUhBIPAAAABaHEAwAAQEEo8QAAAFAQSjwAAAAUxApd4keMGJFtttkmHTt2zOqrr57BgwfnlVdeabDNnDlzMmzYsKyyyirp0KFD9t9//0ybNq1CEwMAAMDys0KX+L/85S8ZNmxY/v73v+f+++/P/Pnzs9tuu+WDDz6o2+aEE07IXXfdld/97nf5y1/+kilTpmS//far4NQAAACwfLSs9ACf5t57723w+oYbbsjqq6+ecePGZccdd8yMGTNy/fXX56abbspXvvKVJMnIkSOz4YYb5u9//3u+9KUvLXa/c+fOzdy5c+tez5w5c/m9CQAAAGgkK/SZ+HIzZsxIknTt2jVJMm7cuMyfPz8DBw6s22aDDTbIWmutlTFjxixxPyNGjEjnzp3rvnr16rV8BwcAAIBGUJgSX1tbm+OPPz7bb799Nt544yTJ1KlT07p163Tp0qXBtt26dcvUqVOXuK/hw4dnxowZdV+TJ09enqMDAABAo1ihL6evb9iwYXnhhRfy6KOP/sf7qqmpSU1NTSNMBQAAAE2nEGfijznmmNx999156KGHsuaaa9Yt7969e+bNm5fp06c32H7atGnp3r17E08JAAAAy9cKXeJLpVKOOeaY3HHHHXnwwQfTp0+fBuu32mqrtGrVKqNHj65b9sorr2TSpEkZMGBAU48LAAAAy9UKfTn9sGHDctNNN+UPf/hDOnbsWHefe+fOndO2bdt07tw5hx9+eE488cR07do1nTp1yg9+8IMMGDBgiU+mBwAAgKJaoUv8lVdemSTZeeedGywfOXJkvvvd7yZJfvnLX6a6ujr7779/5s6dm0GDBuWKK65o4kkBAABg+VuhS3ypVPrMbdq0aZPLL788l19+eRNMBAAAAJWzQt8TDwAAACykxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWx0pT4yy+/PGuvvXbatGmTbbfdNo8//nilRwIAAIBGtVKU+FtuuSUnnnhiTj/99Dz11FPZbLPNMmjQoLz99tuVHg0AAAAazUpR4i+66KJ873vfy6GHHpp+/frlqquuSrt27fKrX/2q0qMBAABAo2lZ6QH+U/Pmzcu4ceMyfPjwumXV1dUZOHBgxowZs9jvmTt3bubOnVv3esaMGUmSmTNnLt9hG8G8D2dVeoRG93mOu+OwkGPxMcfhY47DQo7FxxyHjzkOCzkWH3McKCcTH3McKueTOUul0qduV1X6rC1WcFOmTMkaa6yRv/3tbxkwYEDd8pNPPjl/+ctfMnbs2EW+54wzzsiZZ57ZlGMCAADAZ5o8eXLWXHPNJa4v/Jn4z2P48OE58cQT617X1tbm3XffzSqrrJKqqqoKTrbimDlzZnr16pXJkyenU6dOlR6HCpMHyskE5WSC+uRhIceC+uSBcvUz0bFjx7z//vvp2bPnp35P4Uv8qquumhYtWmTatGkNlk+bNi3du3df7PfU1NSkpqamwbIuXbosrxELrVOnTv4FQx15oJxMUE4mqE8eFnIsqE8eKPdJJjp37vyZ2xb+wXatW7fOVlttldGjR9ctq62tzejRoxtcXg8AAABFV/gz8Uly4okn5pBDDsnWW2+d/v375+KLL84HH3yQQw89tNKjAQAAQKNZKUr8gQcemHfeeSennXZapk6dms033zz33ntvunXrVunRCqumpiann376Ircd0DzJA+VkgnIyQX3ysJBjQX3yQLnPk4nCP50eAAAAmovC3xMPAAAAzYUSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxDcj999/f04//fQ8+OCDSZJHHnkku+++e77yla9k5MiRFZ6OSpoyZUpOP/30DBkyJCeddFJefvnlSo9EE9t7773zm9/8Jh9++GGlR2EF8uyzz+bggw/OOuusk7Zt26Z9+/bZZJNNcuqpp2bmzJmVHo8VyBtvvJGvfOUrlR5jheBYND9+x+bTLI/fs5X4ZuJ//ud/sscee+Tuu+/OvvvumxtuuCH77rtv1lxzzfTp0ydHHXVUfv/731d6TJpIu3bt8s477yRJXnzxxfTr1y833XRT5s+fnz/+8Y/Zaqut8txzz1V4SprSH//4xxx22GHp0aNHhg4dmnHjxlV6JCrsz3/+cwYMGJDZs2dn++23T3V1dQ477LDsueeeufnmm7Pllltm6tSplR6TFcSsWbPyl7/8pdJjrBAci+bF79iUa4rfs6tKpVKpMYZlxbbFFlvk0EMPzbHHHpvRo0dn7733zjnnnJMTTjghSXLhhRfmjjvuyKOPPlrhSWkK1dXVmTp1alZfffUMHjw4tbW1uf3229OyZcvU1tZmyJAhmTVrVu66665Kj0oTqa6uzgsvvJD77rsvv/rVr/KPf/wjm2yySY444ogMGTIkX/jCFyo9Ik1siy22yPe///0cddRRST4+03TsscfmpZdeyvz587P77runV69ezjI1E5dccsmnrn/zzTfzi1/8IgsWLGiiiSrHsaA+v2NTril+z1bim4kOHTrk+eefT58+fZIkrVu3zpNPPplNN900SfLyyy9nhx12yL///e9KjkkTqf8vl7XWWis33nhjvvzlL9etf/rpp7PnnntmypQpFZySplQ/E0ny+OOP5/rrr88tt9ySefPmZfDgwTniiCNcItqMtG3bNi+99FLWXnvtJEmpVEpNTU0mTpyYHj165K9//Wv233//vP3225UdlCZRXV2dHj16pHXr1otdP2/evEydOrVZFFfHgvr8jk25pvg9u2VjDMqKr1WrVpk3b17d65qamnTo0KHBa/fCNh9VVVWpqqpK8vG/aDp37txgfZcuXfLee+9VYjRWEP3790///v3zy1/+Mrfeemuuv/76fPWrX/VLaTOyxhpr5JVXXqkr8W+88UZqa2uzyiqrJEnWXHPNzJo1q4IT0pR69+6d8847LwcccMBi1z/zzDPZaqutmniqynAsqM/v2JRrit+z3RPfTPTt27fBQxTefPPNur8YJh//crbmmmtWYjQqoFQqZf3110/Xrl0zZcqURe7Lef3119O9e/cKTceKpF27dvnud7+bv/71r3nppZcqPQ5N6OCDD84RRxyRq666KiNHjszXv/717LPPPnVnH5955pkG/x1h5bbVVlt96rMyqqqq0lwu7nQsqM/v2JRrit+znYlvJk455ZQG97R26tSpwfonn3xyiX9RZuVTfg9r3759G7z++9//nq9//etNORIVttNOOy3x0tBPrL/++k00DSuCU045JR988EF+9rOfZe7cuRk0aFD++7//u279GmuskSuvvLKCE9KUzjrrrMyePXuJ6/v165fx48c34USV41hQn9+xKdcUv2e7Jx4AAAAKwpl4AAA+l1KplNra2rRo0aLSo1TEjBkz6j5qsXv37ovc+0rzIg+UW16ZcE98M/KnP/0pRxxxRE4++eQG9+4kyXvvveep082MPFBOJihXPxPlz0SQieblo48+yk9/+tPstNNOOf3005MkF1xwQTp06JB27drlkEMOafBwr5Xdddddl379+qVr167p169fg/99/fXXV3o8mpg8UG55Z0KJbyZuuumm7LPPPpk6dWrGjBmTLbbYIjfeeGPd+nnz5uUvf/lLBSekKckD5WSCcuWZ2HLLLWWiGTvzzDNz3XXXZeutt87vf//7DB06NJdeemmuueaaXHvttRk9enQuvvjiSo/ZJC644IIcd9xx2XfffTN69Oi88MILeeGFFzJ69OgMHjw4xx13XH7xi19UekyaiDxQrkkyUaJZ2HzzzUv//d//Xff6lltuKbVv37503XXXlUqlUmnq1Kml6urqSo1HE5MHyskE5WSC+tZZZ53SXXfdVSqVSqXXXnutVF1dXbr55pvr1t9yyy2ljTfeuFLjNam11lqrdMsttyxx/c0331zq1atXE05EJckD5ZoiE+6JbyZee+217L333nWvDzjggKy22mrZZ599Mn/+fE8ib2bkgXIyQTmZoL4pU6Zks802S/Lxk5Zbt25d9zpJttlmm0ycOLFS4zWpt99+O5tssskS12+yySb597//3YQTUUnyQLmmyIQS30x06tQp06ZNa/C5lbvsskvuvvvu7LXXXvnXv/5VweloavJAOZmgnExQX+fOnTN9+vT06tUrSbLlllumY8eOdevnzp2bqqqqSo3XpLbZZpv8/Oc/z/XXX5+WLRv+Kr1gwYKcd9552WabbSo0HU1NHijXFJlQ4puJ/v3755577smXvvSlBst32mmn3HXXXdlrr70qNBmVIA+UkwnKyQT19evXL0899VTd2aXHHnuswfrnn38+6623XiVGa3KXXXZZBg0alO7du2fHHXdMt27dkiTTpk3LI488ktatW+e+++6r8JQ0FXmgXFNkwoPtmokTTjghbdq0Wey6nXfeOXfddVcOPvjgJp6KSpEHyskE5WSC+q666qrsuOOOS1w/f/78nHzyyU04UeVsuummefXVV/Ozn/0sHTt2zD//+c/885//TMeOHXP22Wfn5ZdfzsYbb1zpMWki8kC5pshEValUKjXSvAAAAMBy5Ex8M7bnnnvmrbfeqvQYrCDkgXIyQTmZoD55WMixoD55oFxjZ0KJb8YeeeSRfPjhh5UegxWEPFBOJignE9QnDws5FtQnD5Rr7Ewo8QAAAFAQSnwz1rt377Rq1arSY7CCkAfKyQTlZIL65GEhx4L65IFyjZ0JD7YDAACAgnAmvpmpra1d4vJJkyY18TRUmjxQTiYoJxPUJw+f7YMPPsgjjzxS6TFYQcgD5RojE0p8MzFz5swccMABad++fbp165bTTjstCxYsqFv/zjvvpE+fPhWckKYkD5STCcrJBPXJw9J7/fXXs8suu1R6DFYQ8kC5xshEy0aahRXcqaeemmeffTa/+c1vMn369Jx99tl56qmncvvtt6d169ZJEndWNB/yQDmZoJxMUJ88AKw43BPfTPTu3TujRo3KzjvvnCT597//nT333DNdunTJ//7v/2b69Onp2bNng7+qs/KSB8rJBOVkgvrkYaGuXbt+6voFCxZk1qxZzeJYIA8sqikyocQ3E+3atcs//vGPBpe6vf/++xk0aFDatm2b6667Ln379vUvmGZCHignE5STCeqTh4Xat2+foUOHZpNNNlns+okTJ+bMM89sFscCeWBRTZEJl9M3E2uttVZeeumlBv/x7dixY+67777stttu+frXv17B6Whq8kA5maCcTFCfPCy0+eabp1evXjnkkEMWu/7ZZ5/NmWee2cRTUSnyQLmmyIQH2zUTu+22W0aOHLnI8g4dOuTPf/5z2rRpU4GpqBR5oJxMUE4mqE8eFtpzzz0zffr0Ja7v2rVrDj744KYbiIqSB8o1RSZcTt9MvPfee5kyZUo22mijxa5///3389RTT2WnnXZq4smoBHmgnExQTiaoTx4AVhxKPAAAABSEe+JJkkybNi1XX311TjvttEqPwgpAHignE5STCeprbnmYN29e7rzzzowZMyZTp05NknTv3j3bbbdd9t1337qP3aN5kAfKLe9MOBNPko8fsLDlllt6ciZJ5IFFyQTlZIL6mlMeXn/99QwaNChTpkzJtttum27duiX5+A8ZY8eOzZprrpl77rknffv2rfCkNAV5oFxTZMKZ+Gbiueee+9T1r7zyShNNwopAHignE5STCeqTh4U++eiop59+Op06dWqwbubMmTn44IMzbNiw/PnPf67QhDQleaBcU2TCmfhmorq6OlVVVVncP+5PlldVVTWLv6AjDyxKJignE9QnDwu1a9cujz/+eDbeeOPFrn/++eez7bbbZvbs2U08GZUgD5Rrikw4E99MdO3aNeeff3523XXXxa7/xz/+kb333ruJp6JS5IFyMkE5maA+eVioS5cumTBhwhJ/QZ8wYUK6dOnStENRMfJAuabIhBLfTGy11VaZMmVKevfuvdj106dPX+xf11k5yQPlZIJyMkF98rDQEUcckYMPPjinnnpqdt111wb3u44ePTpnn312fvCDH1R4SpqKPFCuKTKhxDcTRx11VD744IMlrl9rrbUycuTIJpyISpIHyskE5WSC+uRhobPOOivt27fPBRdckB/+8IepqqpKkpRKpXTv3j0//vGPc/LJJ1d4SpqKPFCuKTLhnngAAPgcxo8f3+Djo/r06VPhiagkeaDc8spEdaPshUJ67LHHMnfu3EqPwQpCHignE5STCeqTh6RPnz4ZMGBAamtr07Nnz0qPQ4XJA+WWVyaciW/GOnXqlGeeeSbrrLNOpUdhBSAPlJMJyskE9cnDQo4F9ckD5Ro7E87EN2P+fkN98kA5maCcTFCfPCzkWFCfPFCusTOhxAMAAEBBKPHN2NVXX133kQcgD5STCcrJBPXJw0KOBfXJA+UaOxPuiQcAgP/Qww8/nG233TZt27at9CisAOSBco2ZCWfim5HrrrsuhxxySN3nuN5yyy3ZcMMNs8466+T000+v8HQ0NXmgnExQTiaoTx4+3W677ZYJEyZUegxWEPJAucbMRMtG2QsrvIsvvjg//elPM2jQoPzXf/1XpkyZkl/+8pc54YQTsmDBglx44YVZY401cuSRR1Z6VJqAPFBOJignE9QnDwttueWWi13+0UcfZf/990+bNm2SJE899VRTjkWFyAPlmiITSnwzcfXVV+eaa67Jt7/97Tz99NPp379/rrrqqhx++OFJkjXWWCNXXnlls/iPL/LAomSCcjJBffKw0PPPP5+BAwfmS1/6Ut2yUqmUZ599NrvssktWX331Ck5HU5MHyjVJJko0C23bti1NnDix7nVNTU3phRdeqHv92muvlbp06VKJ0agAeaCcTFBOJqhPHhZ69NFHS+uuu27ptNNOKy1YsKBuecuWLUv/+Mc/KjgZlSAPlGuKTLgnvplo165dPvjgg7rXq622Wjp06NBgm48++qipx6JC5IFyMkE5maA+eVho++23z7hx4/Lqq69mu+22yxtvvFHpkaggeaBcU2RCiW8mNthggzz33HN1rydPnpzevXvXvX755Zez9tprV2AyKkEeKCcTlJMJ6pOHhjp37pzf/va3+f73v58ddtgh11xzTaqqqio9FhUiD5Rb3plwT3wzcd5556V9+/ZLXD9p0qR8//vfb8KJqCR5oJxMUE4mqE8eFu/QQw/NDjvskCFDhjSbKxFYMnmg3PLKhM+JBwCA/0BtbW3ef//9dOrUyRlY5IFFNHYmlHgAAAAoCPfENyNXXHFFBg4cmAMOOCCjR49usO7f//531llnnQpNRiXIA+VkgnIyQX3ysJBjQX3yQLnlnQklvpm45JJL8qMf/SgbbLBBampqsscee2TEiBF16xcsWJCJEydWcEKakjxQTiYoJxPUJw8LORbUJw+Ua5JMNMoH1bHC69evX+nGG2+se/3YY4+VVltttdKpp55aKpVKpalTp5aqq6srNR5NTB4oJxOUkwnqk4eFHAvqkwfKNUUmPJ2+mRg/fny22267utfbbbddHnzwwQwcODDz58/P8ccfX7nhaHLyQDmZoJxMUJ88LORYUJ88UK4pMqHENxOrrrpqJk+e3OAzXDfeeOM8+OCD+cpXvpIpU6ZUbjianDxQTiYoJxPUJw8LORbUJw+Ua4pMuCe+mdhhhx1y++23L7K8X79+GT16dO65554KTEWlyAPlZIJyMkF98rCQY0F98kC5psiEM/HNxE9+8pOMGzduses22mijPPjgg7ntttuaeCoqRR4oJxOUkwnqk4eFHAvqkwfKNUUmfE48AAAAFIQz8c3M448/njFjxmTq1KlJku7du2fAgAHp379/hSejEuSBcjJBOZmgPnlYyLGgPnmg3PLMhDPxzcTbb7+d/fbbL3/729+y1lprpVu3bkmSadOmZdKkSdl+++1z2223ZfXVV6/wpDQFeaCcTFBOJqhPHhZyLKhPHijXFJnwYLtm4uijj05tbW1eeumlTJgwIWPHjs3YsWMzYcKEvPTSS6mtrc2wYcMqPSZNRB4oJxOUkwnqk4eFHAvqkwfKNUUmnIlvJjp27JhHHnkkW2yxxWLXjxs3LjvvvHPef//9Jp6MSpAHyskE5WSC+uRhIceC+uSBck2RCWfim4mamprMnDlzievff//91NTUNOFEVJI8UE4mKCcT1CcPCzkW1CcPlGuKTCjxzcSBBx6YQw45JHfccUeDUM2cOTN33HFHDj300HzrW9+q4IQ0JXmgnExQTiaoTx4WciyoTx4o1ySZKNEszJkzp3TUUUeVWrduXaquri61adOm1KZNm1J1dXWpdevWpaFDh5bmzJlT6TFpIvJAOZmgnExQnzws5FhQnzxQriky4Z74ZmbmzJkZN25cg4862GqrrdKpU6cKT0YlyAPlZIJyMkF98rCQY0F98kC55ZkJJR4AAAAKwj3xzciHH36YRx99NC+++OIi6+bMmZNf//rXFZiKSpEHyskE5WSC+uRhIceC+uSBcss9E41w2T8F8Morr5R69+5dqqqqKlVXV5d23HHH0ptvvlm3furUqaXq6uoKTkhTkgfKyQTlZIL65GEhx4L65IFyTZEJZ+KbiR//+MfZeOON8/bbb+eVV15Jx44ds8MOO2TSpEmVHo0KkAfKyQTlZIL65GEhx4L65IFyTZEJ98Q3E926dcsDDzyQTTbZJElSKpVy9NFH509/+lMeeuihtG/fPj179syCBQsqPClNQR4oJxOUkwnqk4eFHAvqkwfKNUUmnIlvJj788MO0bNmy7nVVVVWuvPLK7L333tlpp53y6quvVnA6mpo8UE4mKCcT1CcPCzkW1CcPlGuKTLT87E1YGWywwQZ58skns+GGGzZYftlllyVJ9tlnn0qMRYXIA+VkgnIyQX3ysJBjQX3yQLmmyIQz8c3E17/+9fz2t79d7LrLLrss3/rWt+LOiuZDHignE5STCeqTh4UcC+qTB8o1RSbcEw8AAAAF4Uw8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAkSb773e+mqqoqVVVVadWqVbp165avfvWr+dWvfpXa2tql3s8NN9yQLl26LL9BAaAZU+IBgDpf+9rX8tZbb2XChAm55557sssuu+S4447LXnvtlY8++qjS4wFAs6fEAwB1ampq0r1796yxxhrZcsstc8opp+QPf/hD7rnnntxwww1JkosuuiibbLJJ2rdvn169euXoo4/OrFmzkiQPP/xwDj300MyYMaPurP4ZZ5yRJJk7d25OOumkrLHGGmnfvn223XbbPPzww5V5owBQUEo8APCpvvKVr2SzzTbL7bffniSprq7OJZdckn/84x8ZNWpUHnzwwZx88slJku222y4XX3xxOnXqlLfeeitvvfVWTjrppCTJMccckzFjxuTmm2/Oc889l29+85v52te+ltdee61i7w0AiqaqVCqVKj0EAFB53/3udzN9+vTceeedi6w76KCD8txzz+XFF19cZN3vf//7HHXUUfn3v/+d5ON74o8//vhMnz69bptJkyZlnXXWyaRJk9KzZ8+65QMHDkz//v1z7rnnNvr7AYCVUctKDwAArPhKpVKqqqqSJA888EBGjBiRl19+OTNnzsxHH32UOXPmZPbs2WnXrt1iv//555/PggULsv766zdYPnfu3KyyyirLfX4AWFko8QDAZ3rppZfSp0+fTJgwIXvttVeGDh2ac845J127ds2jjz6aww8/PPPmzVtiiZ81a1ZatGiRcePGpUWLFg3WdejQoSneAgCsFJR4AOBTPfjgg3n++edzwgknZNy4camtrc2FF16Y6uqPH61z6623Nti+devWWbBgQYNlW2yxRRYsWJC33347X/7yl5tsdgBY2SjxAECduXPnZurUqVmwYEGmTZuWe++9NyNGjMhee+2Vgw8+OC+88ELmz5+fSy+9NHvvvXcee+yxXHXVVQ32sfbaa2fWrFkZPXp0Nttss7Rr1y7rr79+hgwZkoMPPjgXXnhhtthii7zzzjsZPXp0Nt100+y5554VescAUCyeTg8A1Ln33nvTo0ePrL322vna176Whx56KJdcckn+8Ic/pEWLFtlss81y0UUX5bzzzsvGG2+cG2+8MSNGjGiwj+222y5HHXVUDjzwwKy22mo5//zzkyQjR47MwQcfnB/+8If54he/mMGDB+eJJ57IWmutVYm3CgCF5On0AAAAUBDOxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFIQSDwAAAAWhxAMAAEBBKPEAAABQEEo8AAAAFMT/DyQwuQLsF86jAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "plt.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['Duration (s)'], label='Sleep Duration', alpha=0.7)\n",
+    "\n",
+    "# Format the x-axis labels\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Set labels and title\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Sleep Duration')\n",
+    "plt.title('Total Sleep Duration over Time')\n",
+    "\n",
+    "# Display legend\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Create a bar chart\n",
+    "plt.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['glucose'], label='Glucose', alpha=0.7)\n",
+    "\n",
+    "# Format the x-axis labels\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Set labels and title\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Glucose Level')\n",
+    "plt.title('Total Glucose over Time')\n",
+    "\n",
+    "# Display legend\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28a6e44e-ac75-4343-a251-c14b4d43d5b1",
+   "metadata": {},
+   "source": [
+    "# Correlation between Sleep Duration and Glucose Level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "5437e036-0e99-4119-9d3d-751c71cb7d3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: -0.0789468769870221\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "column1 = sleep_glucose_df['glucose']\n",
+    "column2 = sleep_glucose_df['Duration (s)']\n",
+    "\n",
+    "# Calculate the correlation coefficient\n",
+    "correlation_coefficient = column1.corr(column2)\n",
+    "\n",
+    "print(f'Correlation Coefficient: {correlation_coefficient}')\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "sns.scatterplot(x=column1, y=column2)\n",
+    "\n",
+    "# Add a regression line and correlation coefficient\n",
+    "sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
+    "plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb424cab-a065-408a-8f18-51bf6dff7253",
+   "metadata": {},
+   "source": [
+    "As the correlation coefficient is close to zero, it indicates that there is almost no linear relationship between the two columns. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "aff8c3f7-ae66-4aa2-9c43-193bf1adf86f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: -0.4277888111830138\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "column1 = sleep_glucose_df['glucose']\n",
+    "column2 = sleep_glucose_df['Deep Sleep Duration (s)']\n",
+    "\n",
+    "# Calculate the correlation coefficient\n",
+    "correlation_coefficient = column1.corr(column2)\n",
+    "\n",
+    "print(f'Correlation Coefficient: {correlation_coefficient}')\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "sns.scatterplot(x=column1, y=column2)\n",
+    "\n",
+    "# Add a regression line and correlation coefficient\n",
+    "sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
+    "plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6225363-e04d-4a78-99bb-21544891fcf3",
+   "metadata": {},
+   "source": [
+    "A correlation coefficient of approximately -0.43 indicates a moderate negative correlation between the two variables. The negative sign suggests that as one variable increases, the other tends to decrease, and vice versa. The magnitude of -0.43 suggests that the relationship is stronger than a weak correlation but not extremely strong."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "2a595082-8fd5-4d01-895b-e7663a5e298c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: -0.5594624692168104\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "column1 = sleep_glucose_df['glucose']\n",
+    "column2 = sleep_glucose_df['Rem Sleep Duration (s)']\n",
+    "\n",
+    "# Calculate the correlation coefficient\n",
+    "correlation_coefficient = column1.corr(column2)\n",
+    "\n",
+    "print(f'Correlation Coefficient: {correlation_coefficient}')\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "sns.scatterplot(x=column1, y=column2)\n",
+    "\n",
+    "# Add a regression line and correlation coefficient\n",
+    "sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
+    "plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "af7877e2-e9ec-452b-84f5-3ba173fb0508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: -0.5404692688567403\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "column1 = sleep_glucose_df['glucose']\n",
+    "column2 = sleep_glucose_df['Light Sleep Duration (s)']\n",
+    "\n",
+    "# Calculate the correlation coefficient\n",
+    "correlation_coefficient = column1.corr(column2)\n",
+    "\n",
+    "print(f'Correlation Coefficient: {correlation_coefficient}')\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "sns.scatterplot(x=column1, y=column2)\n",
+    "\n",
+    "# Add a regression line and correlation coefficient\n",
+    "sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
+    "plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
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