--- a
+++ b/analytics/p14_Sleep_Glucose.ipynb
@@ -0,0 +1,2291 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": 3,
+   "id": "edbac203-6c5d-4491-a386-4fcff5466456",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
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+       "    <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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>P10</td>\n",
+       "      <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",
+       "      <td>device</td>\n",
+       "    </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",
+       "      <td>...</td>\n",
+       "      <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>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",
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+       "      <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>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": 3,
+     "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": 4,
+   "id": "35371d2e-8419-44bd-83dd-6ecdfb18b717",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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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>2709</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
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+       "    <tr>\n",
+       "      <th>2710</th>\n",
+       "      <td>P14</td>\n",
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+       "      <td>3420</td>\n",
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+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2711</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
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+       "      <td>7980</td>\n",
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+       "      <td>16320</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2712</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
+       "      <td>2023-12-22T07:36:00</td>\n",
+       "      <td>28440</td>\n",
+       "      <td>7980</td>\n",
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+       "      <td>16320</td>\n",
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+       "    <tr>\n",
+       "      <th>2713</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <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",
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+       "      <td>16380</td>\n",
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+       "    </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",
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+       "    <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",
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+       "      <td>4140</td>\n",
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+       "      <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",
+       "      <td>4140</td>\n",
+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1087 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "2709             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "2710             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "2711             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "2712             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "2713             P14            2023-12-21  2023-12-21T23:42:00   \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",
+       "2709  2023-12-22T07:36:00         28440                    7980   \n",
+       "2710  2023-12-22T07:36:00         28440                    7980   \n",
+       "2711  2023-12-22T07:36:00         28440                    7980   \n",
+       "2712  2023-12-22T07:36:00         28440                    7980   \n",
+       "2713  2023-12-22T07:36:00         28440                    7980   \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",
+       "2709                     3420                     16320  device  \n",
+       "2710                     3420                     16320  device  \n",
+       "2711                     3420                     16320  device  \n",
+       "2712                     3420                     16320  device  \n",
+       "2713                     3420                     16320  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",
+       "[1087 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select records for one user\n",
+    "p_df = df[df['User First Name'] == 'P14']\n",
+    "p_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>97</td>\n",
+       "      <td>2023-12-23 05:26:54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>97</td>\n",
+       "      <td>2023-12-23 05:41:54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>99</td>\n",
+       "      <td>2023-12-23 05:56:54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>98</td>\n",
+       "      <td>2023-12-23 06:11:54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>96</td>\n",
+       "      <td>2023-12-23 06:26:54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10901</th>\n",
+       "      <td>101</td>\n",
+       "      <td>2023-12-28 23:55:01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10902</th>\n",
+       "      <td>103</td>\n",
+       "      <td>2023-12-28 23:56:03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10903</th>\n",
+       "      <td>103</td>\n",
+       "      <td>2023-12-28 23:57:03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10904</th>\n",
+       "      <td>103</td>\n",
+       "      <td>2023-12-28 23:58:04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10905</th>\n",
+       "      <td>103</td>\n",
+       "      <td>2023-12-28 23:59:04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10906 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       glucose  recorded_timestamp\n",
+       "0           97 2023-12-23 05:26:54\n",
+       "1           97 2023-12-23 05:41:54\n",
+       "2           99 2023-12-23 05:56:54\n",
+       "3           98 2023-12-23 06:11:54\n",
+       "4           96 2023-12-23 06:26:54\n",
+       "...        ...                 ...\n",
+       "10901      101 2023-12-28 23:55:01\n",
+       "10902      103 2023-12-28 23:56:03\n",
+       "10903      103 2023-12-28 23:57:03\n",
+       "10904      103 2023-12-28 23:58:04\n",
+       "10905      103 2023-12-28 23:59:04\n",
+       "\n",
+       "[10906 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load glucose dataset\n",
+    "glucose_df = pd.read_csv('../data/P14/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": 6,
+   "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>101.981221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-22</th>\n",
+       "      <td>119.200472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-23</th>\n",
+       "      <td>104.473684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-24</th>\n",
+       "      <td>92.869198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-25</th>\n",
+       "      <td>99.764344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-26</th>\n",
+       "      <td>91.634812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-27</th>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-28</th>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-29</th>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-30</th>\n",
+       "      <td>98.975789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-31</th>\n",
+       "      <td>99.834302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-01</th>\n",
+       "      <td>106.065302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-02</th>\n",
+       "      <td>114.381102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-03</th>\n",
+       "      <td>110.680769</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       glucose\n",
+       "recorded_timestamp            \n",
+       "2023-12-21          101.981221\n",
+       "2023-12-22          119.200472\n",
+       "2023-12-23          104.473684\n",
+       "2023-12-24           92.869198\n",
+       "2023-12-25           99.764344\n",
+       "2023-12-26           91.634812\n",
+       "2023-12-27           94.319737\n",
+       "2023-12-28           96.112903\n",
+       "2023-12-29           87.202888\n",
+       "2023-12-30           98.975789\n",
+       "2023-12-31           99.834302\n",
+       "2024-01-01          106.065302\n",
+       "2024-01-02          114.381102\n",
+       "2024-01-03          110.680769"
+      ]
+     },
+     "execution_count": 6,
+     "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": 7,
+   "id": "1e3e11f6-4464-4935-a567-644258e92c4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\bjoer\\AppData\\Local\\Temp\\ipykernel_6936\\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>2709</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
+       "      <td>2023-12-22T07:36:00</td>\n",
+       "      <td>28440</td>\n",
+       "      <td>7980</td>\n",
+       "      <td>3420</td>\n",
+       "      <td>16320</td>\n",
+       "      <td>device</td>\n",
+       "      <td>101.981221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2710</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
+       "      <td>2023-12-22T07:36:00</td>\n",
+       "      <td>28440</td>\n",
+       "      <td>7980</td>\n",
+       "      <td>3420</td>\n",
+       "      <td>16320</td>\n",
+       "      <td>device</td>\n",
+       "      <td>101.981221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2711</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
+       "      <td>2023-12-22T07:36:00</td>\n",
+       "      <td>28440</td>\n",
+       "      <td>7980</td>\n",
+       "      <td>3420</td>\n",
+       "      <td>16320</td>\n",
+       "      <td>device</td>\n",
+       "      <td>101.981221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2712</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
+       "      <td>2023-12-22T07:36:00</td>\n",
+       "      <td>28440</td>\n",
+       "      <td>7980</td>\n",
+       "      <td>3420</td>\n",
+       "      <td>16320</td>\n",
+       "      <td>device</td>\n",
+       "      <td>101.981221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2713</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
+       "      <td>2023-12-22T07:36:00</td>\n",
+       "      <td>28440</td>\n",
+       "      <td>7980</td>\n",
+       "      <td>3420</td>\n",
+       "      <td>16320</td>\n",
+       "      <td>device</td>\n",
+       "      <td>101.981221</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>3727</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-03</td>\n",
+       "      <td>2024-01-03T23:42:00</td>\n",
+       "      <td>2024-01-04T07:16:00</td>\n",
+       "      <td>27240</td>\n",
+       "      <td>6360</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>17520</td>\n",
+       "      <td>device</td>\n",
+       "      <td>110.680769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3728</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-03</td>\n",
+       "      <td>2024-01-03T23:42:00</td>\n",
+       "      <td>2024-01-04T07:16:00</td>\n",
+       "      <td>27240</td>\n",
+       "      <td>6360</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>17520</td>\n",
+       "      <td>device</td>\n",
+       "      <td>110.680769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3729</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-03</td>\n",
+       "      <td>2024-01-03T23:42:00</td>\n",
+       "      <td>2024-01-04T07:16:00</td>\n",
+       "      <td>27240</td>\n",
+       "      <td>6360</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>17520</td>\n",
+       "      <td>device</td>\n",
+       "      <td>110.680769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3730</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-03</td>\n",
+       "      <td>2024-01-03T23:42:00</td>\n",
+       "      <td>2024-01-04T07:16:00</td>\n",
+       "      <td>27240</td>\n",
+       "      <td>6360</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>17520</td>\n",
+       "      <td>device</td>\n",
+       "      <td>110.680769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3731</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-03</td>\n",
+       "      <td>2024-01-03T23:42:00</td>\n",
+       "      <td>2024-01-04T07:16:00</td>\n",
+       "      <td>27240</td>\n",
+       "      <td>6360</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>17520</td>\n",
+       "      <td>device</td>\n",
+       "      <td>110.680769</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1023 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "2709             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "2710             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "2711             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "2712             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "2713             P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "...              ...                   ...                  ...   \n",
+       "3727             P14            2024-01-03  2024-01-03T23:42:00   \n",
+       "3728             P14            2024-01-03  2024-01-03T23:42:00   \n",
+       "3729             P14            2024-01-03  2024-01-03T23:42:00   \n",
+       "3730             P14            2024-01-03  2024-01-03T23:42:00   \n",
+       "3731             P14            2024-01-03  2024-01-03T23:42:00   \n",
+       "\n",
+       "         End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "2709  2023-12-22T07:36:00         28440                    7980   \n",
+       "2710  2023-12-22T07:36:00         28440                    7980   \n",
+       "2711  2023-12-22T07:36:00         28440                    7980   \n",
+       "2712  2023-12-22T07:36:00         28440                    7980   \n",
+       "2713  2023-12-22T07:36:00         28440                    7980   \n",
+       "...                   ...           ...                     ...   \n",
+       "3727  2024-01-04T07:16:00         27240                    6360   \n",
+       "3728  2024-01-04T07:16:00         27240                    6360   \n",
+       "3729  2024-01-04T07:16:00         27240                    6360   \n",
+       "3730  2024-01-04T07:16:00         27240                    6360   \n",
+       "3731  2024-01-04T07:16:00         27240                    6360   \n",
+       "\n",
+       "      Deep Sleep Duration (s)  Light Sleep Duration (s)  Source     glucose  \n",
+       "2709                     3420                     16320  device  101.981221  \n",
+       "2710                     3420                     16320  device  101.981221  \n",
+       "2711                     3420                     16320  device  101.981221  \n",
+       "2712                     3420                     16320  device  101.981221  \n",
+       "2713                     3420                     16320  device  101.981221  \n",
+       "...                       ...                       ...     ...         ...  \n",
+       "3727                     3360                     17520  device  110.680769  \n",
+       "3728                     3360                     17520  device  110.680769  \n",
+       "3729                     3360                     17520  device  110.680769  \n",
+       "3730                     3360                     17520  device  110.680769  \n",
+       "3731                     3360                     17520  device  110.680769  \n",
+       "\n",
+       "[1023 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "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": 8,
+   "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>P14</td>\n",
+       "      <td>2023-12-21</td>\n",
+       "      <td>2023-12-21T23:42:00</td>\n",
+       "      <td>2023-12-22T07:36:00</td>\n",
+       "      <td>28440</td>\n",
+       "      <td>7980</td>\n",
+       "      <td>3420</td>\n",
+       "      <td>16320</td>\n",
+       "      <td>device</td>\n",
+       "      <td>101.981221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-22</td>\n",
+       "      <td>2023-12-22T22:50:00</td>\n",
+       "      <td>2023-12-23T06:16:00</td>\n",
+       "      <td>26760</td>\n",
+       "      <td>5460</td>\n",
+       "      <td>5940</td>\n",
+       "      <td>15240</td>\n",
+       "      <td>device</td>\n",
+       "      <td>119.200472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-24</td>\n",
+       "      <td>2023-12-24T00:05:00</td>\n",
+       "      <td>2023-12-24T09:45:00</td>\n",
+       "      <td>34800</td>\n",
+       "      <td>10080</td>\n",
+       "      <td>5760</td>\n",
+       "      <td>18780</td>\n",
+       "      <td>device</td>\n",
+       "      <td>92.869198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-25</td>\n",
+       "      <td>2023-12-25T00:10:00</td>\n",
+       "      <td>2023-12-25T08:52:00</td>\n",
+       "      <td>31320</td>\n",
+       "      <td>5640</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>20580</td>\n",
+       "      <td>device</td>\n",
+       "      <td>99.764344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-26</td>\n",
+       "      <td>2023-12-26T00:03:00</td>\n",
+       "      <td>2023-12-26T09:58:00</td>\n",
+       "      <td>35700</td>\n",
+       "      <td>8400</td>\n",
+       "      <td>5040</td>\n",
+       "      <td>22020</td>\n",
+       "      <td>device</td>\n",
+       "      <td>91.634812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-26</td>\n",
+       "      <td>2023-12-26T22:48:00</td>\n",
+       "      <td>2023-12-27T07:03:55</td>\n",
+       "      <td>29755</td>\n",
+       "      <td>3955</td>\n",
+       "      <td>6780</td>\n",
+       "      <td>18720</td>\n",
+       "      <td>device</td>\n",
+       "      <td>91.634812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T02:06:00</td>\n",
+       "      <td>2023-12-27T07:03:55</td>\n",
+       "      <td>17875</td>\n",
+       "      <td>3955</td>\n",
+       "      <td>1740</td>\n",
+       "      <td>12060</td>\n",
+       "      <td>device</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T06:43:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>26280</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11100</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T08:16:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>20700</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8820</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T08:43:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>19080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7440</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T09:43:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>15480</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6720</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T10:43:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>11880</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5100</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T11:43:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>8280</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2100</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T12:43:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>4680</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2100</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T13:36:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T13:46:00</td>\n",
+       "      <td>2023-12-27T14:01:00</td>\n",
+       "      <td>900</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>600</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T14:00:00</td>\n",
+       "      <td>2023-12-27T22:38:00</td>\n",
+       "      <td>31080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7680</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T15:43:00</td>\n",
+       "      <td>2023-12-27T22:38:00</td>\n",
+       "      <td>24900</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7260</td>\n",
+       "      <td>server</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T23:05:00</td>\n",
+       "      <td>2023-12-28T08:08:00</td>\n",
+       "      <td>32580</td>\n",
+       "      <td>8280</td>\n",
+       "      <td>6780</td>\n",
+       "      <td>17520</td>\n",
+       "      <td>device</td>\n",
+       "      <td>94.319737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T01:44:00</td>\n",
+       "      <td>2023-12-28T08:08:00</td>\n",
+       "      <td>23040</td>\n",
+       "      <td>8280</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>12780</td>\n",
+       "      <td>device</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T04:42:00</td>\n",
+       "      <td>2023-12-28T08:08:00</td>\n",
+       "      <td>12360</td>\n",
+       "      <td>6900</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5460</td>\n",
+       "      <td>device</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T05:43:00</td>\n",
+       "      <td>2023-12-28T08:08:00</td>\n",
+       "      <td>8700</td>\n",
+       "      <td>3780</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4920</td>\n",
+       "      <td>device</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T07:05:00</td>\n",
+       "      <td>2023-12-28T08:08:00</td>\n",
+       "      <td>3780</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2100</td>\n",
+       "      <td>device</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T07:45:00</td>\n",
+       "      <td>2023-12-28T08:08:00</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>780</td>\n",
+       "      <td>device</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T14:00:00</td>\n",
+       "      <td>2023-12-28T23:29:00</td>\n",
+       "      <td>34140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4200</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T08:35:00</td>\n",
+       "      <td>2023-12-28T14:01:00</td>\n",
+       "      <td>19560</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T09:30:00</td>\n",
+       "      <td>2023-12-28T14:01:00</td>\n",
+       "      <td>16260</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2160</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T09:38:00</td>\n",
+       "      <td>2023-12-28T14:01:00</td>\n",
+       "      <td>15780</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T10:35:00</td>\n",
+       "      <td>2023-12-28T14:01:00</td>\n",
+       "      <td>12360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T11:02:00</td>\n",
+       "      <td>2023-12-28T14:01:00</td>\n",
+       "      <td>10740</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T12:35:00</td>\n",
+       "      <td>2023-12-28T14:01:00</td>\n",
+       "      <td>5160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1320</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T13:35:00</td>\n",
+       "      <td>2023-12-28T14:01:00</td>\n",
+       "      <td>1560</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T14:00:00</td>\n",
+       "      <td>2023-12-29T09:28:00</td>\n",
+       "      <td>70080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12840</td>\n",
+       "      <td>22200</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T14:35:00</td>\n",
+       "      <td>2023-12-29T09:28:00</td>\n",
+       "      <td>67980</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12840</td>\n",
+       "      <td>21540</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T15:35:00</td>\n",
+       "      <td>2023-12-29T09:28:00</td>\n",
+       "      <td>64380</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12840</td>\n",
+       "      <td>21360</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T16:35:00</td>\n",
+       "      <td>2023-12-29T09:28:00</td>\n",
+       "      <td>60780</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12840</td>\n",
+       "      <td>21360</td>\n",
+       "      <td>server</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>2023-12-28T23:51:00</td>\n",
+       "      <td>2023-12-29T08:55:00</td>\n",
+       "      <td>32640</td>\n",
+       "      <td>9300</td>\n",
+       "      <td>5160</td>\n",
+       "      <td>16620</td>\n",
+       "      <td>device</td>\n",
+       "      <td>96.112903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T00:56:00</td>\n",
+       "      <td>2023-12-29T08:55:00</td>\n",
+       "      <td>28740</td>\n",
+       "      <td>9300</td>\n",
+       "      <td>2160</td>\n",
+       "      <td>15720</td>\n",
+       "      <td>device</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:38:00</td>\n",
+       "      <td>2023-12-29T08:55:00</td>\n",
+       "      <td>26220</td>\n",
+       "      <td>7680</td>\n",
+       "      <td>2160</td>\n",
+       "      <td>14820</td>\n",
+       "      <td>device</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T02:43:00</td>\n",
+       "      <td>2023-12-29T08:55:00</td>\n",
+       "      <td>22320</td>\n",
+       "      <td>7680</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>11640</td>\n",
+       "      <td>device</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T04:38:00</td>\n",
+       "      <td>2023-12-29T08:55:00</td>\n",
+       "      <td>15420</td>\n",
+       "      <td>6540</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7320</td>\n",
+       "      <td>device</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T05:41:00</td>\n",
+       "      <td>2023-12-29T08:55:00</td>\n",
+       "      <td>11640</td>\n",
+       "      <td>5460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4620</td>\n",
+       "      <td>device</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T06:43:00</td>\n",
+       "      <td>2023-12-29T08:55:00</td>\n",
+       "      <td>7920</td>\n",
+       "      <td>3660</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>device</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T07:36:00</td>\n",
+       "      <td>2023-12-29T08:55:00</td>\n",
+       "      <td>4740</td>\n",
+       "      <td>2640</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>device</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T08:35:00</td>\n",
+       "      <td>2023-12-29T14:01:00</td>\n",
+       "      <td>19560</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4080</td>\n",
+       "      <td>server</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T14:00:00</td>\n",
+       "      <td>2023-12-29T18:34:00</td>\n",
+       "      <td>16440</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>server</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T09:11:00</td>\n",
+       "      <td>2023-12-29T14:01:00</td>\n",
+       "      <td>17400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>server</td>\n",
+       "      <td>87.202888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-30</td>\n",
+       "      <td>2023-12-30T00:18:00</td>\n",
+       "      <td>2023-12-30T09:02:00</td>\n",
+       "      <td>31440</td>\n",
+       "      <td>6900</td>\n",
+       "      <td>7320</td>\n",
+       "      <td>16800</td>\n",
+       "      <td>device</td>\n",
+       "      <td>98.975789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "      <td>2023-12-31T01:01:00</td>\n",
+       "      <td>2023-12-31T09:13:00</td>\n",
+       "      <td>29520</td>\n",
+       "      <td>5160</td>\n",
+       "      <td>5460</td>\n",
+       "      <td>18000</td>\n",
+       "      <td>device</td>\n",
+       "      <td>99.834302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>2024-01-01T02:37:00</td>\n",
+       "      <td>2024-01-01T09:56:00</td>\n",
+       "      <td>26340</td>\n",
+       "      <td>6300</td>\n",
+       "      <td>6240</td>\n",
+       "      <td>12960</td>\n",
+       "      <td>device</td>\n",
+       "      <td>106.065302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>2024-01-01T23:59:00</td>\n",
+       "      <td>2024-01-02T08:10:00</td>\n",
+       "      <td>29460</td>\n",
+       "      <td>9600</td>\n",
+       "      <td>6960</td>\n",
+       "      <td>12900</td>\n",
+       "      <td>device</td>\n",
+       "      <td>106.065302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-02</td>\n",
+       "      <td>2024-01-02T23:43:00</td>\n",
+       "      <td>2024-01-03T07:31:00</td>\n",
+       "      <td>28080</td>\n",
+       "      <td>4260</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>18960</td>\n",
+       "      <td>device</td>\n",
+       "      <td>114.381102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-03</td>\n",
+       "      <td>2024-01-03T23:42:00</td>\n",
+       "      <td>2024-01-04T07:16:00</td>\n",
+       "      <td>27240</td>\n",
+       "      <td>6360</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>17520</td>\n",
+       "      <td>device</td>\n",
+       "      <td>110.680769</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "0              P14            2023-12-21  2023-12-21T23:42:00   \n",
+       "1              P14            2023-12-22  2023-12-22T22:50:00   \n",
+       "2              P14            2023-12-24  2023-12-24T00:05:00   \n",
+       "3              P14            2023-12-25  2023-12-25T00:10:00   \n",
+       "4              P14            2023-12-26  2023-12-26T00:03:00   \n",
+       "5              P14            2023-12-26  2023-12-26T22:48:00   \n",
+       "6              P14            2023-12-27  2023-12-27T02:06:00   \n",
+       "7              P14            2023-12-27  2023-12-27T06:43:00   \n",
+       "8              P14            2023-12-27  2023-12-27T08:16:00   \n",
+       "9              P14            2023-12-27  2023-12-27T08:43:00   \n",
+       "10             P14            2023-12-27  2023-12-27T09:43:00   \n",
+       "11             P14            2023-12-27  2023-12-27T10:43:00   \n",
+       "12             P14            2023-12-27  2023-12-27T11:43:00   \n",
+       "13             P14            2023-12-27  2023-12-27T12:43:00   \n",
+       "14             P14            2023-12-27  2023-12-27T13:36:00   \n",
+       "15             P14            2023-12-27  2023-12-27T13:46:00   \n",
+       "16             P14            2023-12-27  2023-12-27T14:00:00   \n",
+       "17             P14            2023-12-27  2023-12-27T15:43:00   \n",
+       "18             P14            2023-12-27  2023-12-27T23:05:00   \n",
+       "19             P14            2023-12-28  2023-12-28T01:44:00   \n",
+       "20             P14            2023-12-28  2023-12-28T04:42:00   \n",
+       "21             P14            2023-12-28  2023-12-28T05:43:00   \n",
+       "22             P14            2023-12-28  2023-12-28T07:05:00   \n",
+       "23             P14            2023-12-28  2023-12-28T07:45:00   \n",
+       "24             P14            2023-12-28  2023-12-28T14:00:00   \n",
+       "25             P14            2023-12-28  2023-12-28T08:35:00   \n",
+       "26             P14            2023-12-28  2023-12-28T09:30:00   \n",
+       "27             P14            2023-12-28  2023-12-28T09:38:00   \n",
+       "28             P14            2023-12-28  2023-12-28T10:35:00   \n",
+       "29             P14            2023-12-28  2023-12-28T11:02:00   \n",
+       "30             P14            2023-12-28  2023-12-28T12:35:00   \n",
+       "31             P14            2023-12-28  2023-12-28T13:35:00   \n",
+       "32             P14            2023-12-28  2023-12-28T14:00:00   \n",
+       "33             P14            2023-12-28  2023-12-28T14:35:00   \n",
+       "34             P14            2023-12-28  2023-12-28T15:35:00   \n",
+       "35             P14            2023-12-28  2023-12-28T16:35:00   \n",
+       "36             P14            2023-12-28  2023-12-28T23:51:00   \n",
+       "37             P14            2023-12-29  2023-12-29T00:56:00   \n",
+       "38             P14            2023-12-29  2023-12-29T01:38:00   \n",
+       "39             P14            2023-12-29  2023-12-29T02:43:00   \n",
+       "40             P14            2023-12-29  2023-12-29T04:38:00   \n",
+       "41             P14            2023-12-29  2023-12-29T05:41:00   \n",
+       "42             P14            2023-12-29  2023-12-29T06:43:00   \n",
+       "43             P14            2023-12-29  2023-12-29T07:36:00   \n",
+       "44             P14            2023-12-29  2023-12-29T08:35:00   \n",
+       "45             P14            2023-12-29  2023-12-29T14:00:00   \n",
+       "46             P14            2023-12-29  2023-12-29T09:11:00   \n",
+       "47             P14            2023-12-30  2023-12-30T00:18:00   \n",
+       "48             P14            2023-12-31  2023-12-31T01:01:00   \n",
+       "49             P14            2024-01-01  2024-01-01T02:37:00   \n",
+       "50             P14            2024-01-01  2024-01-01T23:59:00   \n",
+       "51             P14            2024-01-02  2024-01-02T23:43:00   \n",
+       "52             P14            2024-01-03  2024-01-03T23:42:00   \n",
+       "\n",
+       "       End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "0   2023-12-22T07:36:00         28440                    7980   \n",
+       "1   2023-12-23T06:16:00         26760                    5460   \n",
+       "2   2023-12-24T09:45:00         34800                   10080   \n",
+       "3   2023-12-25T08:52:00         31320                    5640   \n",
+       "4   2023-12-26T09:58:00         35700                    8400   \n",
+       "5   2023-12-27T07:03:55         29755                    3955   \n",
+       "6   2023-12-27T07:03:55         17875                    3955   \n",
+       "7   2023-12-27T14:01:00         26280                       0   \n",
+       "8   2023-12-27T14:01:00         20700                       0   \n",
+       "9   2023-12-27T14:01:00         19080                       0   \n",
+       "10  2023-12-27T14:01:00         15480                       0   \n",
+       "11  2023-12-27T14:01:00         11880                       0   \n",
+       "12  2023-12-27T14:01:00          8280                       0   \n",
+       "13  2023-12-27T14:01:00          4680                       0   \n",
+       "14  2023-12-27T14:01:00          1500                       0   \n",
+       "15  2023-12-27T14:01:00           900                       0   \n",
+       "16  2023-12-27T22:38:00         31080                       0   \n",
+       "17  2023-12-27T22:38:00         24900                       0   \n",
+       "18  2023-12-28T08:08:00         32580                    8280   \n",
+       "19  2023-12-28T08:08:00         23040                    8280   \n",
+       "20  2023-12-28T08:08:00         12360                    6900   \n",
+       "21  2023-12-28T08:08:00          8700                    3780   \n",
+       "22  2023-12-28T08:08:00          3780                    1680   \n",
+       "23  2023-12-28T08:08:00          1380                     600   \n",
+       "24  2023-12-28T23:29:00         34140                       0   \n",
+       "25  2023-12-28T14:01:00         19560                       0   \n",
+       "26  2023-12-28T14:01:00         16260                       0   \n",
+       "27  2023-12-28T14:01:00         15780                       0   \n",
+       "28  2023-12-28T14:01:00         12360                       0   \n",
+       "29  2023-12-28T14:01:00         10740                       0   \n",
+       "30  2023-12-28T14:01:00          5160                       0   \n",
+       "31  2023-12-28T14:01:00          1560                       0   \n",
+       "32  2023-12-29T09:28:00         70080                       0   \n",
+       "33  2023-12-29T09:28:00         67980                       0   \n",
+       "34  2023-12-29T09:28:00         64380                       0   \n",
+       "35  2023-12-29T09:28:00         60780                       0   \n",
+       "36  2023-12-29T08:55:00         32640                    9300   \n",
+       "37  2023-12-29T08:55:00         28740                    9300   \n",
+       "38  2023-12-29T08:55:00         26220                    7680   \n",
+       "39  2023-12-29T08:55:00         22320                    7680   \n",
+       "40  2023-12-29T08:55:00         15420                    6540   \n",
+       "41  2023-12-29T08:55:00         11640                    5460   \n",
+       "42  2023-12-29T08:55:00          7920                    3660   \n",
+       "43  2023-12-29T08:55:00          4740                    2640   \n",
+       "44  2023-12-29T14:01:00         19560                       0   \n",
+       "45  2023-12-29T18:34:00         16440                       0   \n",
+       "46  2023-12-29T14:01:00         17400                       0   \n",
+       "47  2023-12-30T09:02:00         31440                    6900   \n",
+       "48  2023-12-31T09:13:00         29520                    5160   \n",
+       "49  2024-01-01T09:56:00         26340                    6300   \n",
+       "50  2024-01-02T08:10:00         29460                    9600   \n",
+       "51  2024-01-03T07:31:00         28080                    4260   \n",
+       "52  2024-01-04T07:16:00         27240                    6360   \n",
+       "\n",
+       "    Deep Sleep Duration (s)  Light Sleep Duration (s)  Source     glucose  \n",
+       "0                      3420                     16320  device  101.981221  \n",
+       "1                      5940                     15240  device  119.200472  \n",
+       "2                      5760                     18780  device   92.869198  \n",
+       "3                      4380                     20580  device   99.764344  \n",
+       "4                      5040                     22020  device   91.634812  \n",
+       "5                      6780                     18720  device   91.634812  \n",
+       "6                      1740                     12060  device   94.319737  \n",
+       "7                         0                     11100  server   94.319737  \n",
+       "8                         0                      8820  server   94.319737  \n",
+       "9                         0                      7440  server   94.319737  \n",
+       "10                        0                      6720  server   94.319737  \n",
+       "11                        0                      5100  server   94.319737  \n",
+       "12                        0                      2100  server   94.319737  \n",
+       "13                        0                      2100  server   94.319737  \n",
+       "14                        0                      1020  server   94.319737  \n",
+       "15                        0                       600  server   94.319737  \n",
+       "16                        0                      7680  server   94.319737  \n",
+       "17                        0                      7260  server   94.319737  \n",
+       "18                     6780                     17520  device   94.319737  \n",
+       "19                     1980                     12780  device   96.112903  \n",
+       "20                        0                      5460  device   96.112903  \n",
+       "21                        0                      4920  device   96.112903  \n",
+       "22                        0                      2100  device   96.112903  \n",
+       "23                        0                       780  device   96.112903  \n",
+       "24                        0                      4200  server   96.112903  \n",
+       "25                        0                      3000  server   96.112903  \n",
+       "26                        0                      2160  server   96.112903  \n",
+       "27                        0                      1920  server   96.112903  \n",
+       "28                        0                      1680  server   96.112903  \n",
+       "29                        0                      1500  server   96.112903  \n",
+       "30                        0                      1320  server   96.112903  \n",
+       "31                        0                       120  server   96.112903  \n",
+       "32                    12840                     22200  server   96.112903  \n",
+       "33                    12840                     21540  server   96.112903  \n",
+       "34                    12840                     21360  server   96.112903  \n",
+       "35                    12840                     21360  server   96.112903  \n",
+       "36                     5160                     16620  device   96.112903  \n",
+       "37                     2160                     15720  device   87.202888  \n",
+       "38                     2160                     14820  device   87.202888  \n",
+       "39                     1440                     11640  device   87.202888  \n",
+       "40                        0                      7320  device   87.202888  \n",
+       "41                        0                      4620  device   87.202888  \n",
+       "42                        0                      3900  device   87.202888  \n",
+       "43                        0                      1980  device   87.202888  \n",
+       "44                        0                      4080  server   87.202888  \n",
+       "45                        0                      2400  server   87.202888  \n",
+       "46                        0                      2400  server   87.202888  \n",
+       "47                     7320                     16800  device   98.975789  \n",
+       "48                     5460                     18000  device   99.834302  \n",
+       "49                     6240                     12960  device  106.065302  \n",
+       "50                     6960                     12900  device  106.065302  \n",
+       "51                     4500                     18960  device  114.381102  \n",
+       "52                     3360                     17520  device  110.680769  "
+      ]
+     },
+     "execution_count": 8,
+     "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": 9,
+   "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": 10,
+   "id": "a5756452-f907-462b-bd02-562e417cad67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "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": 11,
+   "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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",
+      "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": 12,
+   "id": "5437e036-0e99-4119-9d3d-751c71cb7d3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: 0.183445287305828\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": 13,
+   "id": "aff8c3f7-ae66-4aa2-9c43-193bf1adf86f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: 0.32586590582363195\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": 14,
+   "id": "2a595082-8fd5-4d01-895b-e7663a5e298c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: 0.14114249285921698\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MkRERkaNpyue3w655yszMRE5ODqKjo43HVCoVBg4ciKSkJABAUlISPD09jYkTAERHR0MqleLQoUPGNkOGDDEmTgAQExODtLQ0XL9+3dim9uvUtKl5HWpZvF3lkMsa/9F2kknh7SpvtA0REVF9HDZ5ysnJAQD4+/ubHPf39zeey8nJgZ+fn8n5du3awdvb26RNfX3Ufo2G2tScr09lZSU0Go3JgxxDL7USIb5ucJLVX5LASSZBiK8beqk5QkhEROI5bPLk6FasWAGVSmV8dOrUyd4h0Z+kUglih4XBX+lslkA5ySTwVzojdlgY6z0REVGTOGzyFBAQAADIzc01OZ6bm2s8FxAQgLy8PJPz1dXVKCwsNGlTXx+1X6OhNjXn67No0SIUFxcbH5cuXRL7FsmGokLbI25sBPoHecHPQwEvVyf4eSjQP8gLcWMjWOeJiIiazGGLZAYHByMgIAC7du1C3759ARgWdR06dAjPPvssACAyMhJFRUVITk5G//79AQCJiYnQ6/UYOHCgsc3ixYuh1Wrh5OQEAEhISEC3bt3g5eVlbLNr1y7Mnj3b+PoJCQmIjIxsMD6FQgGFQmHtt01WFBXaHoNCfFhhnIiIrMquyVNpaSkyMjKMX2dmZuL48ePw9vZG586dMXv2bCxbtgxhYWEIDg7GkiVLoFarjTvyevTogXvuuQfTp0/H2rVrodVqMXPmTIwbNw5qtRoA8Pjjj+O1117D1KlTsXDhQpw6dQpr1qzBu+++a3zd559/HkOHDsU777yDUaNGYfPmzTh69KhJOQNqmaRSCcI7quwdBhERtSaCHe3evVsAYPaYNGmSIAiCoNfrhSVLlgj+/v6CQqEQhg8fLqSlpZn0ce3aNWH8+PGCu7u7oFQqhSlTpgglJSUmbU6cOCHceeedgkKhEDp06CCsXLnSLJavvvpKuO222wS5XC706tVL2L59u6j3UlxcLAAQiouLxV0EIiIispumfH47TJ2nlo51noiIiFqeVlXniYiIiMgRMXkiIiIiEsFhd9sREZFl9HqBu0qJmhGTJyKiFuxgRgHiE9NxIb8MVTo95DIpQnzdEDssjPXMiGyE03ZERC3UwYwCLPgmBclZ15FXUomici3ySiqRnHUdC75JwcGMAnuHSNQqMXkiImqB9HoB8YnpyNVUQKsz3TSt1QnI1VQgPjEdej03VBNZG5MnIqIWKDVbgwv5ZWaJUw2tTsCF/DKkZvOm5UTWxuSJiKgFKiyvQpVO32gbrU6PwvKqZoqIqO1g8kRE1AJ5u8ohlzX+K9xJJoW3q7yZIiJqO5g8ERG1QL3USoT4usFJVn9JAieZBCG+buil5h0PiKyNyRMRUQsklUoQOywM/kpnswTKSSaBv9IZscPCWO+JyAaYPBERtVBRoe0RNzYC/YO84OehgJerE/w8FOgf5IW4sRGs80RkIyyS6eBYObjt4fecxIgKbY9BIT78mSFqRkyeHBgrB7c9/J5TU0ilEoR3VNk7DKI2QyIIAiuoWYFGo4FKpUJxcTGUyltfoFlTObhuAbyatQwckm99+D0nImp+Tfn85ponB8TKwW0Pv+dERC0HkycHxMrBbQ+/50RELQeTJwfEysFtD7/nREQtB5MnB8TKwW0Pv+dERC0HkycHxMrBbQ+/50RELQeTJwfEysFtD7/nREQtB0sVWIm1SxUApjV/tDo9nFjzp9Xj95yIqHk15fObyZOV2CJ5Alhtui3i95yIqPk05fObFcYdHCsHtz38nhMROTaueSIiIiISgckTERERkQhMnoiIiIhEYPJEREREJAKTJyIiIiIRmDwRERERicDkiYiIiEgEJk9EREREIjB5IiIiIhKByRMRERGRCEyeiIiIiERg8kREREQkApMnIiIiIhGYPBERERGJwOSJiIiISAQmT0REREQiMHkiIiIiEoHJExEREZEI7ewdAFF99HoBqdkaFJZXwdtVjl5qJaRSib3DIiIiYvJEjudgRgHiE9NxIb8MVTo95DIpQnzdEDssDFGh7e0dHhERtXGctiOHcjCjAAu+SUFy1nXklVSiqFyLvJJKJGddx4JvUnAwo8DeIRIRURvH5Ikchl4vID4xHbmaCmh1gsk5rU5ArqYC8Ynp0OuFBnogIiKyPU7bkcNIzdbgQn6ZWeJUQ6sTcCG/DKnZGoR3VDVzdAbNsRaL672IiBwbkydyGIXlVajS6Rtto9XpUVhe1UwRmWqOtVhc70VE5Pg4bUcOw9tVDrms8R9JJ5kU3q7yZoroL82xFovrvYiIWgYmT+QweqmVCPF1g5Os/ikqJ5kEIb5u6KVWNmtczbEWi+u9iIhaDiZP5DCkUglih4XBX+lslkA5ySTwVzojdlhYs6//EbMWy5Ffg4iIrIPJEzmUqND2iBsbgf5BXvDzUMDL1Ql+Hgr0D/JC3NgIu6z7aY61WI6+3ouIiP7CBePkcKJC22NQiI/D7DhrjrVYjrzei4iITDF5IocklUrsVo6grpq1WNfLq+qdVrPGWqzmeA0iIrIOTtsR3URzrMVy1PVeRERkTiIIArfvWIFGo4FKpUJxcTGUSo4OtEa1azBpdXo42bjOk61eg4iI/tKUz2+HTp50Oh1effVV/Pe//0VOTg7UajUmT56Ml19+GRKJ4S9wQRCwdOlS/Otf/0JRUREGDx6Mjz/+GGFhYcZ+CgsLMWvWLGzduhVSqRRjx47FmjVr4O7ubmyTkpKCGTNm4MiRI/D19cWsWbOwYMECi2Nl8tQ2sMJ4y8JrSUQ305TPb4de8/TWW2/h448/xsaNG9GrVy8cPXoUU6ZMgUqlQmxsLAAgLi4O8fHx2LhxI4KDg7FkyRLExMTg9OnTcHZ2BgBMmDABV69eRUJCArRaLaZMmYKnnnoKmzZtAmC4cCNGjEB0dDTWrl2LkydP4sknn4Snpyeeeuopu71/cjzNsRbLkdZ7tWSs1k5EtuLQI0/33Xcf/P398Z///Md4bOzYsXBxccF///tfCIIAtVqNF154AfPmzQMAFBcXw9/fHxs2bMC4ceNw5swZ9OzZE0eOHMGAAQMAAD/99BPuvfdeXL58GWq1Gh9//DEWL16MnJwcyOWG3Uwvvvgivv/+e5w9e9aiWDnyROQ4aqq11y06WrN+zF5lL4jI8TTl89uhF4xHRUVh165dOHfuHADgxIkTOHDgAEaOHAkAyMzMRE5ODqKjo43PUalUGDhwIJKSkgAASUlJ8PT0NCZOABAdHQ2pVIpDhw4Z2wwZMsSYOAFATEwM0tLScP369Xpjq6yshEajMXkQkf2xWjsR2ZpDT9u9+OKL0Gg06N69O2QyGXQ6Hd58801MmDABAJCTkwMA8Pf3N3mev7+/8VxOTg78/PxMzrdr1w7e3t4mbYKDg836qDnn5eVlFtuKFSvw2muvWeFdEpE1ianWzulRImoKhx55+uqrr/D5559j06ZNOHbsGDZu3Ii3334bGzdutHdoWLRoEYqLi42PS5cu2TskIgKrtROR7Tn0yNP8+fPx4osvYty4cQCA8PBwZGVlYcWKFZg0aRICAgIAALm5uQgMDDQ+Lzc3F3379gUABAQEIC8vz6Tf6upqFBYWGp8fEBCA3NxckzY1X9e0qUuhUEChUNz6myQiq2K1diKyNYceeSovL4dUahqiTCaDXm/4qzI4OBgBAQHYtWuX8bxGo8GhQ4cQGRkJAIiMjERRURGSk5ONbRITE6HX6zFw4EBjm3379kGr1RrbJCQkoFu3bvVO2RGR46qp1l632GgNVmsnolvl0MnT/fffjzfffBPbt2/HH3/8ge+++w6rV6/GQw89BACQSCSYPXs2li1bhh9++AEnT57ExIkToVarMXr0aABAjx49cM8992D69Ok4fPgwfv31V8ycORPjxo2DWq0GADz++OOQy+WYOnUqUlNT8eWXX2LNmjWYO3euvd46ETURq7UTka05dKmCkpISLFmyBN999x3y8vKgVqsxfvx4vPLKK8adcTVFMtetW4eioiLceeed+Oijj3DbbbcZ+yksLMTMmTNNimTGx8c3WCSzffv2mDVrFhYuXGhxrCxVQORYWK2diCzR6iqMtyS2Sp6qq/XYmnIVV4rK0cHTFfdHBKJdO4ceMCRyGKwwTkQ30+oqjLd1/9p3Hu8nZqC0shqCAEgkwNIfTmHWsFBMH9LV3uEROTxWayciW2Dy5KD+te88Vv6UBl2tQn6CAGgqqrHypzQAYAJFRERkB5z/cUDV1Xq8n5hhkjjVptMLeD8xA9XVjdeyISIiIutj8uSAtqZcRWlldaNtSiursTXlajNFRERERDWYPDmgK0XluNkyfkEwtCMiIqLmxeTJAXXwdIXkJhuCJBJDOyIiImpeTJ4c0P0RgXBXNL6W313RDvdHBDbahoiIiKyPyZMDatdOilnDQiFroB6NTCrBrGGhrPdERERkByxV4KBqyhDUrfPkrmjHOk9ERER2xArjVsIK40RERC0PK4y3Qu3aSfFQvw72DoOIiIj+xCEMIiIiIhGYPBERERGJwOSJiIiISARRa57OnDmDzZs3Y//+/cjKykJ5eTl8fX1x++23IyYmBmPHjoVCobBVrERERER2Z9Fuu2PHjmHBggU4cOAABg8ejDvuuANqtRouLi4oLCzEqVOnsH//fmg0GixYsACzZ89uc0mUrXbbERERke3YbLfd2LFjMX/+fHz99dfw9PRssF1SUhLWrFmDd955By+99JJFARARERG1JBaNPGm1Wjg5OVncqdj2rQFHnoiIiFqepnx+W7Rg/GaJUFFRkaj2RERERC2V6N12b731Fr788kvj148++ih8fHzQoUMHnDhxwqrBERERETka0cnT2rVr0alTJwBAQkICEhIS8OOPP2LkyJGYP3++1QMkIiIiciSib8+Sk5NjTJ62bduGRx99FCNGjECXLl0wcOBAqwdIRERE5EhEjzx5eXnh0qVLAICffvoJ0dHRAABBEKDT6awbHREREZGDET3yNGbMGDz++OMICwvDtWvXMHLkSADA77//jtDQUKsHSERERORIRCdP7777Lrp06YJLly4hLi4O7u7uAICrV6/iueees3qARERERI7EojpPdHOs80TWotcLSM3WoLC8Ct6ucvRSKyGVSuwdlomWECMRkSVsVmH8t99+w6BBgyzqsLy8HJmZmejVq5dF7YnoLwczChCfmI4L+WWo0ukhl0kR4uuG2GFhiAptb+/wALSMGImIbMmiBeNPPPEEYmJisGXLFpSVldXb5vTp03jppZfQtWtXJCcnWzVIorbgYEYBFnyTguSs68grqURRuRZ5JZVIzrqOBd+k4GBGgb1DbBExEhHZmkUjT6dPn8bHH3+Ml19+GY8//jhuu+02qNVqODs74/r16zh79ixKS0vx0EMP4eeff0Z4eLit4yZqVfR6AfGJ6cjVVECrM51J1+oE5GoqEJ+YjkEhPnabHmsJMRIRNQeLkicnJyfExsYiNjYWR48exYEDB5CVlYUbN26gT58+mDNnDu6++254e3vbOl6iVik1W4ML+WVmSUkNrU7AhfwypGZrEN5R1czRGbSEGImImoPo3XYDBgzAgAEDbBELUZtVWF6FKp2+0TZanR6F5VXNFJG5lhAjEVFzEF0kk4isz9tVDrms8X+OTjIpvF3lzRSRuZYQIxFRc2DyROQAeqmVCPF1g5Os/rVCTjIJQnzd0EttvzIYLSFGIqLmwOSJyAFIpRLEDguDv9LZLDlxkkngr3RG7LAwuy7EbgkxEhE1BxbJtBIWySRrqF1DSavTw8kBayi1hBiJiCzVlM9vJk9WwuSJrKUlVO9uCTESEVnCZhXG69q1axd27dqFvLw86PWmu28+/fTTpnRJRH+SSiUOv9W/JcRIRGQropOn1157Da+//joGDBiAwMBASCT8a9OW+Bc+ERGRYxGdPK1duxYbNmzAE088YYt4qBbeQ4yIiMjxiN5tV1VVhaioKFvEQrXwHmJERESOSXTyNG3aNGzatMkWsdCfLL2HmF7Ptf5ERETNTfS0XUVFBdatW4dffvkFERERcHJyMjm/evVqqwXXVvEeYkRERI5LdPKUkpKCvn37AgBOnTplco6Lx62D9xAjIiJyXKKTp927d9siDqqF9xAjIhKHO5OpOTWpzlONy5cvAwA6duxolWDIoOYeYtfLq+qduuM9xIiI/sKdydTcRC8Y1+v1eP3116FSqRAUFISgoCB4enrijTfeMCuYSU3De4gRkRh6vYCTl4ux91w+Tl4ublObSbgzmexB9MjT4sWL8Z///AcrV67E4MGDAQAHDhzAq6++ioqKCrz55ptWD7Itigptj7ixEbyHGBE1qi2Puli6M3lQiA//2CSrEn1vO7VajbVr1+KBBx4wOf6///0Pzz33HK5cuWLVAFsKW93bjvP4RNSQmlGXuslDzQh13NiIVp1AnbxcjKkbjyCvpLLBNn4eCvxn0t+4M5ka1Cz3tissLET37t3Njnfv3h2FhYViu6Ob4D3EiKg+HHXhzmSyH9Frnvr06YMPPvjA7PgHH3yAPn36WCUoIiJqnJh6cK0VdyaTvYgeeYqLi8OoUaPwyy+/IDIyEgCQlJSES5cuYceOHVYPkIiIzHHUhTuTyX5EjzwNHToU586dw0MPPYSioiIUFRVhzJgxSEtLw1133WWLGImIqA6OunBnMtlPk+o8qdVq7qojIrIjjroYcGcy2YNFyVNKSgp69+4NqVSKlJSURttGRERYJTAiImpYzahLY7vt2sqoS1RoewwK8eHOZGo2FpUqkEqlyMnJgZ+fH6RSKSQSCep7mkQigU6ns0mgjs5WpQqIiBpTu84TR12IxLNZqYLMzEz4+voa/5+IiBwDR12Imp9FC8aDgoIgkRj+IWZlZaFDhw7GW7PUPDp06ICsrCyrB3jlyhX885//hI+PD1xcXBAeHo6jR48azwuCgFdeeQWBgYFwcXFBdHQ00tPTTfooLCzEhAkToFQq4enpialTp6K0tNSkTUpKCu666y44OzujU6dOiIuLs/p7ISKyhZp6cENv80V4RxUTJyIbE73b7u677663GGZxcTHuvvtuqwRV4/r16xg8eDCcnJzw448/4vTp03jnnXfg5eVlbBMXF4f4+HisXbsWhw4dgpubG2JiYlBRUWFsM2HCBKSmpiIhIQHbtm3Dvn378NRTTxnPazQajBgxAkFBQUhOTsaqVavw6quvYt26dVZ9P0RERNQKCCJJJBIhLy/P7HhaWprg4eEhtrtGLVy4ULjzzjsbPK/X64WAgABh1apVxmNFRUWCQqEQvvjiC0EQBOH06dMCAOHIkSPGNj/++KMgkUiEK1euCIIgCB999JHg5eUlVFZWmrx2t27dLI61uLhYACAUFxdb/BwiIiKyr6Z8fltcqmDMmDEADIvCJ0+eDIVCYTyn0+mQkpKCqKgoqyZ2P/zwA2JiYvDII49g79696NChA5577jlMnz4dgGH9VU5ODqKjo43PUalUGDhwIJKSkjBu3DgkJSXB09MTAwYMMLaJjo6GVCrFoUOH8NBDDyEpKQlDhgyBXP5XPZSYmBi89dZbuH79uslIV43KykpUVv51PyWNpvVW8SUiIqK/WDxtp1KpoFKpIAgCPDw8jF+rVCoEBATgqaeewn//+1+rBnfhwgV8/PHHCAsLw86dO/Hss88iNjYWGzduBADk5OQAAPz9/U2e5+/vbzxXs0uwtnbt2sHb29ukTX191H6NulasWGFyDTp16nSL75aIiIhaAotHntavXw8A6NKlC+bNmwc3NzebBVVDr9djwIABWL58OQDg9ttvx6lTp7B27VpMmjTJ5q/fmEWLFmHu3LnGrzUaDRMoIiKiNkD0gvGlS5c2S+IEAIGBgejZs6fJsR49euDixYsAgICAAABAbm6uSZvc3FzjuYCAAOTl5Zmcr66uRmFhoUmb+vqo/Rp1KRQKKJVKkwcRERG1fqKTJwD4+uuv8eijj2LQoEHo16+fycOaBg8ejLS0NJNj586dQ1BQEAAgODgYAQEB2LVrl/G8RqPBoUOHjDctjoyMRFFREZKTk41tEhMTodfrMXDgQGObffv2QavVGtskJCSgW7du9a53IiIiorZLdPIUHx+PKVOmwN/fH7///jvuuOMO+Pj44MKFCxg5cqRVg5szZw5+++03LF++HBkZGdi0aRPWrVuHGTNmADAsXp89ezaWLVuGH374ASdPnsTEiROhVqsxevRoAIaRqnvuuQfTp0/H4cOH8euvv2LmzJkYN24c1Go1AODxxx+HXC7H1KlTkZqaii+//BJr1qwxmZYjIiIiAiC+VEG3bt2ETZs2CYIgCO7u7sL58+cFQRCEJUuWCDNmzBDb3U1t3bpV6N27t6BQKITu3bsL69atMzmv1+uFJUuWCP7+/oJCoRCGDx8upKWlmbS5du2aMH78eMHd3V1QKpXClClThJKSEpM2J06cEO68805BoVAIHTp0EFauXCkqTpYqICIianma8vlt0b3tanN1dcWZM2cQFBQEPz8/JCQkoE+fPkhPT8egQYNw7do122R5Do73tiMiImp5mvL5LXraLiAgwFhhvHPnzvjtt98AGGouiczDiKgN0OsFnLxcjL3n8nHycjH0ev6eIKKWzeJSBTWGDRuGH374AbfffjumTJmCOXPm4Ouvv8bRo0eNhTSJiADgYEYB4hPTcSG/DFU6PeQyKUJ83RA7LAxRoe3tHR4RUZOInrbT6/XQ6/Vo186Qd23evBkHDx5EWFgYnn76aZMq3W0Jp+2ITB3MKMCCb1KQq6mAVvfXrxknmQT+SmfEjY1gAkVEdteUz29RyVN1dTWWL1+OJ598Eh07dmxyoK0Rkyeiv+j1Ah7/929IzrpukjjVcJJJ0D/IC5umDYJUKrFDhEREBjZf89SuXTvExcWhurq6SQESUduQmq3BhfyyehMnANDqBFzIL0NqNu8JSUQtj+gF48OHD8fevXttEQsRtRKF5VWo0ukbbaPV6VFYXtVMERERWY/oBeMjR47Eiy++iJMnT6J///5mt2p54IEHrBYcGaY/UrM1KCyvgrerHL3USk5zkMPzdpVDLmv8bzMnmRTerm1zjSQRieNon4Wik6fnnnsOALB69WqzcxKJBDqd7tajIgDcqUQtVy+1EiG+brheXtXgmqcQXzf0UnN9IBE1zhE/C0VP29XstqvvwcTJemp2KiVnXUdeSSWKyrXIK6lEctZ1LPgmBQczCuwdIlGDpFIJYoeFwV/pDCeZ6V+HNbvtYoeFcRSViBrlqJ+FTboxMNmWXi8gPjHdbIs3YFhom6upQHxiOosNkkOLCm2PuLER6B/kBT8PBbxcneDnoUD/IC+WKSCim3Lkz0LR03avv/56o+dfeeWVJgdDBmJ2KoV3VDVzdESWiwptj0EhPg61VoGIWgZH/iwUnTx99913Jl9rtVpkZmaiXbt26Nq1K5MnK+BOJWpNpFIJk3wiEs2RPwtFJ0+///672TGNRoPJkyfjoYceskpQbR13KhERUVvnyJ+FVlnzpFQq8dprr2HJkiXW6K7Nq9mpVHehbQ3uVCIiotbOkT8LrbZgvLi4GMXFxdbqrk3jTiUiImrrHPmzUPS0XXx8vMnXgiDg6tWr+OyzzzBy5EirBdbW1exUqqltodXp4eQAtS2IiIiai6N+Foq6MTAABAcHm3wtlUrh6+uLYcOGYdGiRfDw8LBqgC2FrW4M7GhVVYmagj/HRHQrbPk7pCmf36JHnjIzM0UHRk3HnUrU0jlidWAialkc7bOwSWueBEFAQUEBrl27Zu14iKgVcdTqwEREt0JU8pSTk4OJEyfCy8sL/v7+8PPzg5eXF5588knk5ubaKkYiaoEcuTowEdGtsHjaTqPRICoqCqWlpZgyZQq6d+8OQRBw+vRpfPHFFzhw4ACOHTsGd3d3W8ZLRC2EI1cHJiK6FRYnT2vWrIFMJkNqaip8fX1Nzr388ssYPHgw4uPj8dJLL1k9SCJqeRy5OjAR0a2weNpu+/bteOmll8wSJwDw8/PDokWLsHXrVqsGR0QtlyNXByYiuhUWJ0/nzp1DVFRUg+ejoqKQlpZmlaCIqOVz5OrARES3wuLkSaPRwNPTs8Hznp6e0Gg01oiJiFoBR64OTER0KyxOngRBgFTacHOJRAKR9TaJqJWrqQ7cP8gLfh4KeLk6wc9Dgf5BXogbG8E6T0QkTmmpvSMAIGLBuCAIuO222yCR1P9XIhMnIqpPVGh7DArxYYVxIrKcTgecPw+cOGF4pKQY/pufD2g0QDvRNb6tyuJXX79+vS3jIKJWzNGqAxORAykuNiRHNQnSiRPAqVNAeXn97dPTgR49mjfGOixOniZNmmTLOIiIiKg10+uBCxf+SpBqHllZ4vo5ebLlJE9EREREFtFoDElO7dGkkyeBsjLL+5BIgLAwoE8fICLC8N8+fYBOnWwXt4WYPBEREVHT6PVAZqb52qTMTHH9KJWmSVJEBNC7N+DmZpu4bxGTJyIiIrq50lLD6FHtRCklRdwOOIkE6Nr1r1GkmkQpKMhwroVg8kRERER/EQTgjz9Mp9xOnDDsfhPDw8OQGNWecuvdG2gF98C9peSppjxBQ+ULiIiIyIGVlZmuTaoZTRJb9LpmNKl2ohQUBDRSH7Ila1Ly9J///Afvvvsu0tPTAQBhYWGYPXs2pk2bZtXgiIiIyAoEAbh40XQkKSUFyMgwnLOUm5tpghQRAYSHG0aZ2hDRydMrr7yC1atXY9asWYiMjAQAJCUlYc6cObh48SJef/11qwdJREREFiovN9RJqj3tlpJiqKckRpcupmuT+vQBgoNb7WiSGBJBZGlwX19fxMfHY/z48SbHv/jiC8yaNQsFBQVWDbCl0Gg0UKlUKC4uhlLJG50SEZGNCQJw6ZLpLrcTJwxFJMWOJvXubZok9e4NqNpGYdumfH6LHnnSarUYMGCA2fH+/fujurpabHdERER0MzduAKmp5jvdrl8X109QkPnapK5dOZokkujk6YknnsDHH3+M1atXmxxft24dJkyYYLXAiIiI2hxBAK5cMa+bdO6coaaSpVxcDGuRaidJ4eGAp6fNQm9Lmrxg/Oeff8agQYMAAIcOHcLFixcxceJEzJ0719iuboJFREREf6qoMIwm1V2bVFgorp/Onc0LTIaGAjKZbeIm8cnTqVOn0K9fPwDA+T9rPrRv3x7t27fHqVOnjO1YvoCIiAiG0aTsbNNyACdOAGlpgE5neT/Ozn+tTaqdKHl52S52qpfo5Gn37t22iIOIiKjlq6wETp82n3a7dk1cPx07mpYD6NPHMJrUjrWtHUGTvwsZGRk4f/48hgwZAhcXFwiCwNEmIiJqGwQByMkxr8J99qy40SSFAujVy/x2Jd7etoudbpno5OnatWt49NFHsXv3bkgkEqSnpyMkJARTp06Fl5cX3nnnHVvESUREZB9VVcCZM+aJUn6+uH46dDCdbuvTB7jtNo4mtUCiv2Nz5syBk5MTLl68iB49ehiPP/bYY5g7dy6TJyIiarlyc82n3M6cAcSU4pHLTUeTau7v1r697eKmZiU6efr555+xc+dOdOzY0eR4WFgYsrKyrBYYERGRzWi1him2urcryc0V109AgPmUW7dugJOTbeImhyA6eSorK4Orq6vZ8cLCQigUCqsERUREZDV5eeY73c6cMUzHWcrJCejZ0/y+bn5+NglZrxeQmq1BYXkVvF3l6KVWQirlumJHITp5uuuuu/B///d/eOONNwAYShLo9XrExcXh7rvvtnqAREREFtFqDdv/60675eSI68ff37wKd7duhum4ZnAwowDxiem4kF+GKp0ecpkUIb5uiB0WhqhQTv05AtHJU1xcHIYPH46jR4+iqqoKCxYsQGpqKgoLC/Hrr7/aIkYiIiJTBQXmxSVTU8WNJrVrB/ToYV4SwN/fdnHfxMGMAiz4JgW5mgpodX/dn+56eRUWfJOCuLERTKAcgOjkqXfv3jh37hzef/99eHh4oLS0FGPGjMGMGTMQGBhoixiJiKitqq42jCbVTZSys8X14+trXlyyRw9DqQAHodcLiE9MN0ucAECrE5CrqUB8YjoGhfhwCs/OmrQ/UqVS4eWXX7Z2LERE1JYVFppOt504YRhNqqy0vA+ZDOje3XQRd81okoPXIkzN1uBCfplZ4lRDqxNwIb8MqdkahHdUNXN0VFuTkqf9+/fjk08+wYULF7BlyxZ06NABn332GYKDg3HnnXdaO0YiImpNqquB9HTzRdyXL4vrx8fHfMqtZ0+HGk0So7C8ClW6xm/+q9XpUVguYmqSbEJ08vTNN9/giSeewIQJE3Ds2DFU/vkXQXFxMZYvX44dO3ZYPUgiImqhrl83Ly6Zmmq4Ka6lZDLDgu26i7gDAx1+NEkMb1c55DJpo22cZFJ4uzbPwnVqmOjkadmyZVi7di0mTpyIzZs3G48PHjwYy5Yts2pwRETUQuh0QEaGeaJ06ZK4fry8zKfcevY03BS3leulViLE1w3Xy6vqnbpzkkkQ4uuGXmqlHaKj2kQnT2lpaRgyZIjZcZVKhaKiImvEREREjqyoCDh50nTK7dQpoLzc8j6kUsNoUt3blXTo0KpGk8SQSiWIHRZW7247J5kE/kpnxA4L42JxB9D4+GA9AgICkJGRYXb8wIEDCAkJsUpQDVm5ciUkEglmz55tPFZRUYEZM2bAx8cH7u7uGDt2LHLrVIi9ePEiRo0aBVdXV/j5+WH+/PmorlNqf8+ePejXrx8UCgVCQ0OxYcMGm74XIiKHp9cb1iZ9/TWwZAnw4INAly6G0aEhQ4BZs4B//Qs4fLjxxMnTExg6FIiNBf79b+DIEaC0FDh9Gti8GVi0CBg1CujYsc0mTjWiQtsjbmwE+gd5wc9DAS9XJ/h5KNA/yItlChyI6JGn6dOn4/nnn8enn34KiUSC7OxsJCUlYd68eViyZIktYgQAHDlyBJ988gkiIiJMjs+ZMwfbt2/Hli1boFKpMHPmTIwZM8ZYc0qn02HUqFEICAjAwYMHcfXqVUycOBFOTk5Yvnw5ACAzMxOjRo3CM888g88//xy7du3CtGnTEBgYiJiYGJu9J2qYo1bXddS4iG6ZRvPXaFLN49QpoKzM8j4kEiAszPx2JZ06tfmkSIyo0PYYFOLD3zUOTCIIQv17IhsgCAKWL1+OFStWoPzPvzQUCgXmzZtnrDpubaWlpejXrx8++ugjLFu2DH379sV7772H4uJi+Pr6YtOmTXj44YcBAGfPnkWPHj2QlJSEQYMG4ccff8R9992H7Oxs+P9Z+Gzt2rVYuHAh8vPzIZfLsXDhQmzfvh2nTp0yvua4ceNQVFSEn376yaIYNRoNVCoViouLoVRyPvpWOGp1XUeNi0gUvR64cMF8p1tmprh+VCrzKbfevYF6bt9F5Mia8vkteuRJIpFg8eLFmD9/PjIyMlBaWoqePXvC3d1ddMCWmjFjBkaNGoXo6GiTRenJycnQarWIjo42HuvevTs6d+5sTJ6SkpIQHh5uTJwAICYmBs8++yxSU1Nx++23IykpyaSPmja1pwepeThqdV1HjYuoUSUlpqNJKSmGr0tLLe9DIgFCQ813unXuzNEkarOaVOcJAORyOXr27GnNWOq1efNmHDt2DEeOHDE7l5OTA7lcDk9PT5Pj/v7+yPnzXkY5OTkmiVPN+ZpzjbXRaDS4ceMGXFxczF67srLSWKYBMGSudGsctbquo8ZFZKTXA3/8Yb7T7cIFcf14eJjf+LZ3b8CGfxwTtUQWJU9jxoyxuMNvv/22ycHUdenSJTz//PNISEiAs4NtU12xYgVee+01e4fRqjhqdV1HjYvaqNJSw+hR7UTp5EnDKJMYXbuaF5gMCjLsgiOiRlmUPKlU9vlASE5ORl5eHvr162c8ptPpsG/fPnzwwQfYuXMnqqqqUFRUZDL6lJubi4CAAACG3YGHDx826bdmN17tNnV36OXm5kKpVNY76gQAixYtwty5c41fazQadOrUqelvlhy2uq6jxkWtnCAAWVmmU24nTgDnzxvOWcrd3ZAc1R5RCg/naBLRLbAoeVq/fr2t46jX8OHDcfLkSZNjU6ZMQffu3bFw4UJ06tQJTk5O2LVrF8aOHQvAUIfq4sWLiIyMBABERkbizTffRF5eHvz8/AAACQkJUCqVxmnHyMhIs8roCQkJxj7qo1AooGihtwBwVI5aXbe54+KOvjaorMyws612kpSSYtgBJ0ZIiHmiFBzM0SQiK2vymqcae/fuRVlZGSIjI+Hl5WWNmIw8PDzQu3dvk2Nubm7w8fExHp86dSrmzp0Lb29vKJVKzJo1C5GRkRg0aBAAYMSIEejZsyeeeOIJxMXFIScnBy+//DJmzJhhTH6eeeYZfPDBB1iwYAGefPJJJCYm4quvvsL27dut+n6ocY5aXbc54+KOvlZOEICLF83XJmVkiBtNcnMzjB7VnnYLDwe405eoWVicPL311lsoLS01liMQBAEjR47Ezz//DADw8/PDrl270KtXL9tE2oB3330XUqkUY8eORWVlJWJiYvDRRx8Zz8tkMmzbtg3PPvssIiMj4ebmhkmTJuH11183tgkODsb27dsxZ84crFmzBh07dsS///1v1nhqZo5aXbe54uKOvlbmxo36R5PE3okhKMj8diUhIRxNIrIji+s89evXDwsXLsRjjz0GANiyZQsmTZqEhIQE9OjRAxMnToSrqyu++uormwbsqFjnyXpqj75odXo4Ocjoiy3j0usFPP7v35Ccdb3B0a3+QV7YNG0Qp/AcjSAAly+bjialpADnzhl2wVnK1fWv0aSaabfwcEM9JSKyGZvWecrMzDSp7r1jxw48/PDDGDx4MADg5ZdfxiOPPCIyZLqZtrj+xVGr69oyLu7oayFu3DDcUqT2lFtKCnD9urh+Onc2r8LdtSsgk9kmbiKyKouTp+rqapMF0klJSSZFJNVqNQoKCqwaXFvXlte/SKUSh0wSbBUXd/Q5GEEAsrNNp9xOnADS0sSNJjk7G+ok1U2U6tSmI6KWxeLkqWvXrti3bx9CQkJw8eJFnDt3DkOGDDGev3z5Mnx8fGwSZFvE9S9ti6PuNGwTKir+Gk2qvTbp2jVx/XTqZH67krAwjiYRtUIWJ08zZszAzJkzsX//fvz222+IjIw0qTCemJiI22+/3SZBtjWsaN32OOpOw1ZFEICcHPMpt7NnAZ3O8n4UCtPRpJrSAN7etoudiByKxcnT9OnTIZPJsHXrVgwZMgRLly41OZ+dnY0nn3zS6gG2RVz/0vY46k7DFquyEjhzxnzaTezSgg4dTGsm1YwmtbvlKi9E1IJZvNuOGmfN3XZ7z+Xj+c2/o6hc22AbL1cnvDfudgy9zfeWXosci6PuNHRoNaNJtZOks2eB6mrL+1AogF69zKtwt+c1J2rtbLrbjpoP17+0XY6609AhVFUZkqK60255eeL6CQw0v6dbt24cTSIii/G3hQPi+pe2zVF3Gjar3FzzuklnzgDahkdjzcjlQM+e5ou4fTlaS0S3hsmTA+L6F2oztFrDaFLd25XUuVH3Tfn7m5cD6N4dcHKyTdxE1KYxeXJQUaHtETc2gutfqPUoKDAdSTpxwlAioEpE7SonJ6BHD9ORpD59gD9v+k1E1ByYPDkwrn+hFqm62lBMsm7dpOxscf34+ZknSd27G6bjiIjsSHTyVFFRgffffx+7d+9GXl4e9HWq7R47dsxqwRHXv5CDu3bNfMrt9GlDqQBLtWtnSIrqTrsFBNgubiKiWyA6eZo6dSp+/vlnPPzww7jjjjsgkXAUhKjVq64G0tPNE6UrV8T10769+WhSjx6GUgFERC2E6ORp27Zt2LFjh/GGwETUyly/br42KTXVcBsTS8lkhu3/dUeTAgMB/sFFRC2c6OSpQ4cO8PDwsEUsRNScdDogI8O8btKlS+L68fY2T5J69jTcFJeIqBUSnTy98847WLhwIdauXYugoCBbxERE1lZUZLp4+8QJ4NQp4MYNy/uQSg2jSXVvV6JWczSJiNoU0cnTgAEDUFFRgZCQELi6usKpTh2VwsJCqwVHRCLpdMD58+a3K7l4UVw/Xl7mVbh79gRcXGwTNxFRCyI6eRo/fjyuXLmC5cuXw9/fnwvGieyluBg4edJ0yu3kSaC83PI+pFLDjW7rJkodO3I0iYioAaKTp4MHDyIpKQl9+vSxRTxEVJdeD1y4YL6I+48/xPWjUpknSb16Aa6uNgmbiKi1Ep08de/eHTfErJMgIstpNH+NJtUkSSdPAmVllvchkRhGk+quTerUiaNJRERWIDp5WrlyJV544QW8+eabCA8PN1vzpFTyZrVEN6XXG0aOau90O3ECyMwU149SaUiSaidKvXsDbm42CZuIiACJIAjCzZv9RSqVGp5Y5y9YQRAgkUig0+msF10LotFooFKpUFxczASSTJWUGHa21R1NKikR109oqHmByaAgjiYREd2Cpnx+ix552r17t+jAiNoEQfhrNKn2Trfz58X14+5umiBFRADh4YbjRERkd6KTp6FDh9oiDqKWpazMfDQpJcWwZkmMkBDzRdxduhh2wRERkUMSnTwBwP79+/HJJ5/gwoUL2LJlCzp06IDPPvsMwcHBuPPOO60dI5H9CIKhRlLdnW4ZGYZzlnJz+ys5qvlveDjAav1ERC2O6OTpm2++wRNPPIEJEybg2LFjqPzz7unFxcVYvnw5duzYYfUgiZpFebnhHm51b1dSXCyuny5dzNcmhYRwNImIqJUQnTwtW7YMa9euxcSJE7F582bj8cGDB2PZsmVWDY7IJgTBcP+2ulNu6emGXXCWcnU1jB7VHU1SqWwXOxER2Z3o5CktLQ1DhgwxO65SqVBUVGSNmIis58YNw2hS7QXcKSnA9evi+gkKMl+b1LUrR5OIiNog0clTQEAAMjIy0KVLF5PjBw4cQEhIiLXiIhJHEIArV8x3up07J240ycXFUCepdnHJ8HDA09NmoRMRUcsiOnmaPn06nn/+eXz66aeQSCTIzs5GUlIS5s2bhyVLltgiRiJTFRX1jyaJvSl1587mJQFCQwGZzDZxExFRqyA6eXrxxReh1+sxfPhwlJeXY8iQIVAoFJg3bx5mzZplixiprRIE4OpV89GktDRATDFWZ2fDaFLdRMnLy3axExFRqyW6wniNqqoqZGRkoLS0FD179oS7uztu3LgBFxcXa8fYIrDC+C2qrATOnDHf6VZQIK6fjh3/So5qkqWwMKBdk6pyEBFRK9csFcZryOVy9OzZEwBQWVmJ1atXIy4uDjk5OU3tktoCQQBycsyn3M6eBaqrLe9HoQB69TIdSYqIAHx8bBc7ERERRCRPlZWVePXVV5GQkAC5XI4FCxZg9OjRWL9+PRYvXgyZTIY5c+bYMlZqaaqqDKNJtROlEyeA/Hxx/ajV5nWTbruNo0lERGQXFn/6vPLKK/jkk08QHR2NgwcP4pFHHsGUKVPw22+/YfXq1XjkkUcg40Jbq9PrBaRma1BYXgVvVzl6qZWQSh3wRrC5ueajSWfOAFqt5X3I5UDPnuYlAdq3t13cREREIlmcPG3ZsgX/93//hwceeACnTp1CREQEqqurceLECUh4V3ebOJhRgPjEdFzIL0OVTg+5TIoQXzfEDgtDVKidEgqt1jDFVndtUm6uuH4CA80XcHfrBjg52SZuIiIiK7F4wbhcLkdmZiY6dOgAAHBxccHhw4cRHh5u0wBbCmsvGD+YUYAF36QgV1MBre6vb5GTTAJ/pTPixkbYPoHKzzff6Xb6tLjRJCcnw2hS3UTJz892cRMREVnIpgvGdTod5HL5X09s1w7u7u7io6Sb0usFxCemmyVOAKDVCcjVVCA+MR2DQnysM4Wn1Rq2/9e9XcnVq+L68fc3nW6LiAC6dzdMxxEREbUSFidPgiBg8uTJUCgUAICKigo888wzcHNzM2n37bffWjfCNig1W4ML+WVmiVMNrU7AhfwypGZrEN5R5H3UCgpME6QTJwwFJ6uqLO+jXTugRw/ztUn+/uJiISIiaoEsTp4mTZpk8vU///lPqwdDBoXlVajSNX5LEa1Oj8LyRhKe6mrDrUnqrk3KzhYXjK+v+U637t0NpQKIiIjaIIuTp/Xr19syDqrF21UOuazxG846yaTwdv1zOqyw0DRBqhlNqqy0/EXbtTMkRbWn3Pr0AQICAG4IICIiMmKhHAfUS61EiK8brpdXmUzdyfQ6dCnMRu9rf+Duiqvo/eu7QMoJww1xxfDxMb3xbUSEYVE3R5OIiIhuismTA5JKJYgdFoaXNx/F3fu+x205F9A9/w90K8iCc7WItUkymWH7f91F3Go1R5OIiIiaqMn3tiNTtri33cGzORgQEQS51oKEycvLdDSpTx/DaJKzs1ViISIiao2a9d52ZHtR3QMg9IkAjh41HhOkUki6dTNfm9ShA0eTiIiImgGTJwcneewxICrKmCRJevUCXFzsHRYREVGbxeTJ0c2bZ+8IiIiIqJbG98MTERERkQkmT0REREQiMHkiIiIiEoHJExEREZEITJ6IiIiIRGDyRERERCQCkyciIiIiEZg8EREREYnA5ImIiIhIBIdOnlasWIG//e1v8PDwgJ+fH0aPHo20tDSTNhUVFZgxYwZ8fHzg7u6OsWPHIjc316TNxYsXMWrUKLi6usLPzw/z589HdXW1SZs9e/agX79+UCgUCA0NxYYNG2z99oiIiKgFcujkae/evZgxYwZ+++03JCQkQKvVYsSIESgrKzO2mTNnDrZu3YotW7Zg7969yM7OxpgxY4zndTodRo0ahaqqKhw8eBAbN27Ehg0b8MorrxjbZGZmYtSoUbj77rtx/PhxzJ49G9OmTcPOnTub9f0SERGR45MIgiDYOwhL5efnw8/PD3v37sWQIUNQXFwMX19fbNq0CQ8//DAA4OzZs+jRoweSkpIwaNAg/Pjjj7jvvvuQnZ0Nf39/AMDatWuxcOFC5OfnQy6XY+HChdi+fTtOnTplfK1x48ahqKgIP/30k0WxaTQaqFQqFBcXQ6lUWv/NExERkdU15fPboUee6iouLgYAeHt7AwCSk5Oh1WoRHR1tbNO9e3d07twZSUlJAICkpCSEh4cbEycAiImJgUajQWpqqrFN7T5q2tT0UZ/KykpoNBqTBxEREbV+LSZ50uv1mD17NgYPHozevXsDAHJyciCXy+Hp6WnS1t/fHzk5OcY2tROnmvM15xpro9FocOPGjXrjWbFiBVQqlfHRqVOnW36PRERE5PhaTPI0Y8YMnDp1Cps3b7Z3KACARYsWobi42Pi4dOmSvUMiIiKiZtDO3gFYYubMmdi2bRv27duHjh07Go8HBASgqqoKRUVFJqNPubm5CAgIMLY5fPiwSX81u/Fqt6m7Qy83NxdKpRIuLi71xqRQKKBQKG75vREREVHL4tAjT4IgYObMmfjuu++QmJiI4OBgk/P9+/eHk5MTdu3aZTyWlpaGixcvIjIyEgAQGRmJkydPIi8vz9gmISEBSqUSPXv2NLap3UdNm5o+iIiIiGo49G675557Dps2bcL//vc/dOvWzXhcpVIZR4SeffZZ7NixAxs2bIBSqcSsWbMAAAcPHgRgKFXQt29fqNVqxMXFIScnB0888QSmTZuG5cuXAzCUKujduzdmzJiBJ598EomJiYiNjcX27dsRExNjUazcbUdERNTyNOXz26GTJ4lEUu/x9evXY/LkyQAMRTJfeOEFfPHFF6isrERMTAw++ugj45QcAGRlZeHZZ5/Fnj174ObmhkmTJmHlypVo1+6vWcs9e/Zgzpw5OH36NDp27IglS5YYX8MSTJ6IiIhanlaXPLUkTJ7IWvR6AanZGhSWV8HbVY5eaiWk0vr/kCAiolvTlM/vFrFgnKitOJhRgPjEdFzIL0OVTg+5TIoQXzfEDgtDVGh7e4dHRERw8AXjRG3JwYwCLPgmBclZ15FXUomici3ySiqRnHUdC75JwcGMAnuHSEREYPJE5BD0egHxienI1VRAqzOdSdfqBORqKhCfmA69nrPsRET2xuSJyAGkZmtwIb/MLHGqodUJuJBfhtRs3gaIiMjemDwROYDC8ipU6fSNttHq9Cgsr2qmiIiIqCFMnogcgLerHHJZ4/8cnWRSeLvKmykiIiJqCJMnIgfQS61EiK8bnGT1lyRwkkkQ4uuGXmqWwSAisjcmT0QOQCqVIHZYGPyVzmYJlJNMAn+lM2KHhbHeExGRA2DyROQgokLbI25sBPoHecHPQwEvVyf4eSjQP8gLcWMjWOeJiMhBsEgmkQOJCm2PQSE+rDBOROTAmDwRORipVILwjip7h0FERA3gtB0RERGRCEyeiIiIiERg8kREREQkApMnIiIiIhGYPBERERGJwOSJiIiISAQmT0REREQiMHkiIiIiEoHJExEREZEITJ6IiIiIRGDyRERERCQCkyciIiIiEZg8EREREYnA5ImIiIhIBCZPRERERCIweSIiIiISgckTERERkQhMnoiIiIhEYPJEREREJAKTJyIiIiIRmDwRERERicDkiYiIiEgEJk9EREREIjB5IiIiIhKhnb0DICJTer2A1GwNCsur4O0qRy+1ElKpxN5hERHRn5g8ETmQgxkFiE9Mx4X8MlTp9JDLpAjxdUPssDBEhba3d3hERARO2xE5jIMZBVjwTQqSs64jr6QSReVa5JVUIjnrOhZ8k4KDGQX2DpGIiMDkicgh6PUC4hPTkaupgFYnmJzT6gTkaioQn5gOvV5ooAciImouTJ6IHEBqtgYX8svMEqcaWp2AC/llSM3WNHNkRERUF5MnIgdQWF6FKp2+0TZanR6F5VXNFBERETWEyRORA/B2lUMua/yfo5NMCm9XeTNFREREDWHyROQAeqmVCPF1g5Os/pIETjIJQnzd0EutbObIiIioLiZPRA5AKpUgdlgY/JXOZgmUk0wCf6UzYoeFsd4TEZEDYPJE5CCiQtsjbmwE+gd5wc9DAS9XJ/h5KNA/yAtxYyNY54mIyEGwSCaRA4kKbY9BIT6sME5E5MCYPBE5GKlUgvCOKnuHQUREDeC0HREREZEITJ6IiIiIRGDyRERERCQCkyciIiIiEZg8EREREYnA5ImIiIhIBCZPRERERCIweSIiIiISgUUyHVxVlQ5r911AVmEZgrzd8MyQEMjlMnuH1WblFZUhZs2vKK3Uwl3hhJ3PD4afp5tVXyPnein+8d6vKK+qhqu8HRJmD0aAl7tVX4OIiJpOIgiCYO8gHMmHH36IVatWIScnB3369MH777+PO+6446bP02g0UKlUKC4uhlJpnTvfL/42BZuPXoJO/9cxmRQYN6AT3hwTYZXXIMt1W7wDlTrzfy4KmQRpb95rldcIe2k7tHrz405SIH35KKu8BhER/aUpn9+ctqvlyy+/xNy5c7F06VIcO3YMffr0QUxMDPLy8po9lsXfpuDzw6aJEwDo9MDnhy9h8bcpzR5TW9ZQ4gQAlToB3RbvuOXXaChxAgCt3nCeiIjsj8lTLatXr8b06dMxZcoU9OzZE2vXroWrqys+/fTTZo2jqkqHzUcvNdpm89FLqKrSNVNEbVteUVmDiVONSp2AvKKyJr9GzvXSBhOnGlq9oR0REdkXk6c/VVVVITk5GdHR0cZjUqkU0dHRSEpKMmtfWVkJjUZj8rCWtfsumI041aXTG9qR7cWs+dWq7erzj/cse66l7YiIyHaYPP2poKAAOp0O/v7+Jsf9/f2Rk5Nj1n7FihVQqVTGR6dOnawWS1ahZSMYlrajW1NaqbVqu/qUV1VbtR0REdkOk6cmWrRoEYqLi42PS5can2YTI8jbst1blrajW+OucLJqu/q4yi3b+GppOyIish0mT39q3749ZDIZcnNzTY7n5uYiICDArL1CoYBSqTR5WMszQ0Igu8l3RiY1tCPb2/n8YKu2q0/CbMuea2k7IiKyHSZPf5LL5ejfvz927dplPKbX67Fr1y5ERkY2cywyjBvQ+DTguAGdWO+pmfh5ukEhkzTaRiGT3FK9pwAvdzjd5F+jkxSs90RE5ACYPNUyd+5c/Otf/8LGjRtx5swZPPvssygrK8OUKVOaPZY3x0Rgwh2dzEagZFJgwh2s89Tc0t68t8EEylp1ntKXj2owgWKdJyIix8EimXV88MEHxiKZffv2RXx8PAYOHHjT59miSCbACuOOhhXGiYhal6Z8fjN5shJbJU9ERERkO6wwTkRERGRjTJ6IiIiIRGDyRERERCQCkyciIiIiEZg8EREREYnA5ImIiIhIBCZPRERERCIweSIiIiISgckTERERkQjt7B1Aa1FTqF2j0dg5EiIiIrJUzee2mBuuMHmykpKSEgBAp06d7BwJERERiVVSUgKVSmVRW97bzkr0ej2ys7Ph4eEBiURi73CajUajQadOnXDp0iXe068OXpv68brUj9elYbw29eN1aZiYayMIAkpKSqBWqyGVWraaiSNPViKVStGxY0d7h2E3SqWS/3gbwGtTP16X+vG6NIzXpn68Lg2z9NpYOuJUgwvGiYiIiERg8kREREQkApMnuiUKhQJLly6FQqGwdygOh9emfrwu9eN1aRivTf14XRpm62vDBeNEREREInDkiYiIiEgEJk9EREREIjB5IiIiIhKByRMRERGRCEyeyCIlJSWYPXs2goKC4OLigqioKBw5csR4XhAEvPLKKwgMDISLiwuio6ORnp5ux4ibz82uzeTJkyGRSEwe99xzjx0jtr59+/bh/vvvh1qthkQiwffff29y3pKfj8LCQkyYMAFKpRKenp6YOnUqSktLm/Fd2IY1rk2XLl3MfoZWrlzZjO/C+m52Xb799luMGDECPj4+kEgkOH78uFkfFRUVmDFjBnx8fODu7o6xY8ciNze3ed6ADVnj2vz97383+5l55plnmucN2Ehj10Wr1WLhwoUIDw+Hm5sb1Go1Jk6ciOzsbJM+rPV7hskTWWTatGlISEjAZ599hpMnT2LEiBGIjo7GlStXAABxcXGIj4/H2rVrcejQIbi5uSEmJgYVFRV2jtz2bnZtAOCee+7B1atXjY8vvvjCjhFbX1lZGfr06YMPP/yw3vOW/HxMmDABqampSEhIwLZt27Bv3z489dRTzfUWbMYa1wYAXn/9dZOfoVmzZjVH+DZzs+tSVlaGO++8E2+99VaDfcyZMwdbt27Fli1bsHfvXmRnZ2PMmDG2CrnZWOPaAMD06dNNfmbi4uJsEW6zaey6lJeX49ixY1iyZAmOHTuGb7/9FmlpaXjggQdM2lnt94xAdBPl5eWCTCYTtm3bZnK8X79+wuLFiwW9Xi8EBAQIq1atMp4rKioSFAqF8MUXXzR3uM3qZtdGEARh0qRJwoMPPmiH6OwDgPDdd98Zv7bk5+P06dMCAOHIkSPGNj/++KMgkUiEK1euNFvsttaUayMIghAUFCS8++67zRhp86p7XWrLzMwUAAi///67yfGioiLByclJ2LJli/HYmTNnBABCUlKSDaNtXk25NoIgCEOHDhWef/55m8ZmT41dlxqHDx8WAAhZWVmCIFj39wxHnuimqqurodPp4OzsbHLcxcUFBw4cQGZmJnJychAdHW08p1KpMHDgQCQlJTV3uM3qZtemxp49e+Dn54du3brh2WefxbVr15o7VLux5OcjKSkJnp6eGDBggLFNdHQ0pFIpDh061OwxNxcx/3ZWrlwJHx8f3H777Vi1ahWqq6ubO1yHkpycDK1Wa3Ltunfvjs6dO7f63zuW+vzzz9G+fXv07t0bixYtQnl5ub1DalbFxcWQSCTw9PQEYN3fM7wxMN2Uh4cHIiMj8cYbb6BHjx7w9/fHF198gaSkJISGhiInJwcA4O/vb/I8f39/47nW6mbXBjBM2Y0ZMwbBwcE4f/48XnrpJYwcORJJSUmQyWR2fge2Z8nPR05ODvz8/EzOt2vXDt7e3q36Z8jSfzuxsbHo168fvL29cfDgQSxatAhXr17F6tWrmzVeR5KTkwO5XG78YKzRFn7vWOLxxx9HUFAQ1Go1UlJSsHDhQqSlpeHbb7+1d2jNoqKiAgsXLsT48eONNwa25u8ZJk9kkc8++wxPPvkkOnToAJlMhn79+mH8+PFITk62d2h2d7NrM27cOGPb8PBwREREoGvXrtizZw+GDx9ur7CpBZk7d67x/yMiIiCXy/H0009jxYoVvDUH1av2Op7w8HAEBgZi+PDhOH/+PLp27WrHyGxPq9Xi0UcfhSAI+Pjjj23yGpy2I4t07doVe/fuRWlpKS5duoTDhw9Dq9UiJCQEAQEBAGC2yyU3N9d4rjVr7NrUJyQkBO3bt0dGRkYzR2oflvx8BAQEIC8vz+R8dXU1CgsLW/XPUFP/7QwcOBDV1dX4448/bBmeQwsICEBVVRWKiopMjreV3ztiDRw4EABa/e+dmsQpKysLCQkJxlEnwLq/Z5g8kShubm4IDAzE9evXsXPnTjz44IMIDg5GQEAAdu3aZWyn0Whw6NAhREZG2jHa5lXftanP5cuXce3aNQQGBjZzhPZhyc9HZGQkioqKTEYyExMTodfrjb/0W6Om/ts5fvw4pFKp2RREW9K/f384OTmZXLu0tDRcvHixTf3esVRNOYPW/HunJnFKT0/HL7/8Ah8fH5Pz1vw9w2k7ssjOnTshCAK6deuGjIwMzJ8/H927d8eUKVMgkUgwe/ZsLFu2DGFhYQgODsaSJUugVqsxevRoe4duc41dm9LSUrz22msYO3YsAgICcP78eSxYsAChoaGIiYmxd+hWU1paavIXbWZmJo4fPw5vb2907tz5pj8fPXr0wD333IPp06dj7dq10Gq1mDlzJsaNGwe1Wm2nd2Udt3ptkpKScOjQIdx9993w8PBAUlIS5syZg3/+85/w8vKy07u6dTe7LoWFhbh48aKxTk9aWhoAw+hBQEAAVCoVpk6dirlz58Lb2xtKpRKzZs1CZGQkBg0aZJf3ZC23em3Onz+PTZs24d5774WPjw9SUlIwZ84cDBkyBBEREXZ5T9bQ2HUJDAzEww8/jGPHjmHbtm3Q6XTGdUze3t6Qy+XW/T0jbnMgtVVffvmlEBISIsjlciEgIECYMWOGUFRUZDyv1+uFJUuWCP7+/oJCoRCGDx8upKWl2THi5tPYtSkvLxdGjBgh+Pr6Ck5OTkJQUJAwffp0IScnx85RW9fu3bsFAGaPSZMmCYJg2c/HtWvXhPHjxwvu7u6CUqkUpkyZIpSUlNjh3VjXrV6b5ORkYeDAgYJKpRKcnZ2FHj16CMuXLxcqKirs9I6s42bXZf369fWeX7p0qbGPGzduCM8995zg5eUluLq6Cg899JBw9epV+7whK7rVa3Px4kVhyJAhgre3t6BQKITQ0FBh/vz5QnFxsf3elBU0dl1qyjbU99i9e7exD2v9npEIgiCIS7eIiIiI2i6ueSIiIiISgckTERERkQhMnoiIiIhEYPJEREREJAKTJyIiIiIRmDwRERERicDkiYiIiEgEJk9E1Op16dIF7733nr3DIKJWgskTERERkQhMnoiIiIhEYPJERC1eSUkJJkyYADc3NwQGBuLdd9/F3//+d8yePdus7R9//AGJRGK8yzwAFBUVQSKRYM+ePcZjqampuO+++6BUKuHh4YG77roL58+fBwDo9Xq8/vrr6NixIxQKBfr27YuffvrJ+NyqqirMnDkTgYGBcHZ2RlBQEFasWGHyetOmTYOvry+USiWGDRuGEydOWP26EJFtMHkiohZv7ty5+PXXX/HDDz8gISEB+/fvx7Fjx5rc35UrVzBkyBAoFAokJiYiOTkZTz75JKqrqwEAa9aswTvvvIO3334bKSkpiImJwQMPPID09HQAQHx8PH744Qd89dVXSEtLw+eff44uXboY+3/kkUeQl5eHH3/8EcnJyejXrx+GDx+OwsLCW7oORNQ82tk7ACKiW1FSUoKNGzdi06ZNGD58OABg/fr1UKvVTe7zww8/hEqlwubNm+Hk5AQAuO2224zn3377bSxcuBDjxo0DALz11lvYvXs33nvvPXz44Ye4ePEiwsLCcOedd0IikSAoKMj43AMHDuDw4cPIy8uDQqEw9vf999/j66+/xlNPPdXkuImoeXDkiYhatAsXLkCr1eKOO+4wHlOpVOjWrVuT+zx+/DjuuusuY+JUm0ajQXZ2NgYPHmxyfPDgwThz5gwAYPLkyTh+/Di6deuG2NhY/Pzzz8Z2J06cQGlpKXx8fODu7m58ZGZmGqcFicixceSJiNoUqdTwN6MgCMZjWq3WpI2Li8stvUa/fv2QmZmJH3/8Eb/88gseffRRREdH4+uvv0ZpaSkCAwNN1lfV8PT0vKXXJaLmwZEnImrRQkJC4OTkhCNHjhiPFRcX49y5c/W29/X1BQBcvXrVeKz24nEAiIiIwP79+82SKgBQKpVQq9X49ddfTY7/+uuv6Nmzp0m7xx57DP/617/w5Zdf4ptvvkFhYSH69euHnJwctGvXDqGhoSaP9u3bi37/RNT8OPJERC2ah4cHJk2ahPnz58Pb2xt+fn5YunQppFIpJBKJWXsXFxcMGjQIK1euRHBwMPLy8vDyyy+btJk5cybef/99jBs3DosWLYJKpcJvv/2GO+64A926dcP8+fOxdOlSdO3aFX379sX69etx/PhxfP755wCA1atXIzAwELfffjukUim2bNmCgIAAeHp6Ijo6GpGRkRg9ejTi4uJw2223ITs7G9u3b8dDDz2EAQMGNMt1I6KmY/JERC3e6tWr8cwzzxhLCyxYsACXLl2Cs7Nzve0//fRTTJ06Ff3790e3bt0QFxeHESNGGM/7+PggMTER8+fPx9ChQyGTydC3b1/jOqfY2FgUFxfjhRdeQF5eHnr27IkffvgBYWFhAAwJXVxcHNLT0yGTyfC3v/0NO3bsME4Z7tixA4sXL8aUKVOQn5+PgIAADBkyBP7+/ja+UkRkDRKh9sQ/EVErUFZWhg4dOuCdd97B1KlT7R0OEbUyHHkiohbv999/x9mzZ3HHHXeguLgYr7/+OgDgwQcftHNkRNQaMXkiolbh7bffRlpaGuRyOfr374/9+/dzATYR2QSn7YiIiIhEYKkCIiIiIhGYPBERERGJwOSJiIiISAQmT0REREQiMHkiIiIiEoHJExEREZEITJ6IiIiIRGDyRERERCQCkyciIiIiEf4fuOpG57eWaXAAAAAASUVORK5CYII=",
+      "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": 15,
+   "id": "af7877e2-e9ec-452b-84f5-3ba173fb0508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: 0.31922258739828363\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()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}