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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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": 2,
"id": "edbac203-6c5d-4491-a386-4fcff5466456",
"metadata": {},
"outputs": [
{
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"<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",
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"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": 2,
"metadata": {},
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}
],
"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": 3,
"id": "35371d2e-8419-44bd-83dd-6ecdfb18b717",
"metadata": {},
"outputs": [
{
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],
"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",
"672 P10 2024-01-04 2024-01-04T01:32:00 \n",
"673 P10 2024-01-04 2024-01-04T01:32:00 \n",
"674 P10 2024-01-04 2024-01-04T01:32:00 \n",
"675 P10 2024-01-04 2024-01-04T01:32:00 \n",
"676 P10 2024-01-04 2024-01-04T01:32:00 \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",
"672 2024-01-04T09:25:00 28380 8640 \n",
"673 2024-01-04T09:25:00 28380 8640 \n",
"674 2024-01-04T09:25:00 28380 8640 \n",
"675 2024-01-04T09:25:00 28380 8640 \n",
"676 2024-01-04T09:25:00 28380 8640 \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",
"672 5400 14340 device \n",
"673 5400 14340 device \n",
"674 5400 14340 device \n",
"675 5400 14340 device \n",
"676 5400 14340 device \n",
"\n",
"[677 rows x 9 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Select records for one user\n",
"p_df = df[df['User First Name'] == 'P10']\n",
"p_df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2df3de7b-8cd8-4b07-802a-55eb967f339b",
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" <td>103</td>\n",
" <td>2024-01-02 23:55:41</td>\n",
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" <tr>\n",
" <th>16195</th>\n",
" <td>101</td>\n",
" <td>2024-01-02 23:56:39</td>\n",
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" <tr>\n",
" <th>16196</th>\n",
" <td>101</td>\n",
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" <th>16197</th>\n",
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"text/plain": [
" glucose recorded_timestamp\n",
"0 109 2023-12-25 00:00:28\n",
"1 108 2023-12-25 00:01:29\n",
"2 108 2023-12-25 00:02:29\n",
"3 104 2023-12-25 00:23:35\n",
"4 105 2023-12-25 00:24:35\n",
"... ... ...\n",
"16194 103 2024-01-02 23:55:41\n",
"16195 101 2024-01-02 23:56:39\n",
"16196 101 2024-01-02 23:57:40\n",
"16197 101 2024-01-02 23:58:41\n",
"16198 101 2024-01-02 23:59:41\n",
"\n",
"[16199 rows x 2 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load glucose dataset\n",
"glucose_df = pd.read_csv('../data/P10/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": 5,
"id": "6258651b-17d4-4bdb-82c2-794b760b9dce",
"metadata": {},
"outputs": [
{
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"text/plain": [
" glucose\n",
"recorded_timestamp \n",
"2023-12-23 77.927152\n",
"2023-12-24 101.720760\n",
"2023-12-25 90.249815\n",
"2023-12-26 92.717573\n",
"2023-12-27 97.659067\n",
"2023-12-28 98.874487\n",
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"2023-12-30 98.216867\n",
"2023-12-31 94.986328\n",
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"2024-01-05 117.261656\n",
"2024-01-06 106.212245"
]
},
"execution_count": 5,
"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": 6,
"id": "1e3e11f6-4464-4935-a567-644258e92c4c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\bjoer\\AppData\\Local\\Temp\\ipykernel_21960\\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"
]
},
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"\n",
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"[581 rows x 10 columns]"
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},
"execution_count": 6,
"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": 7,
"id": "14caa6da-863f-4b44-81e0-f9d999ce5cd2",
"metadata": {},
"outputs": [
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" <tr>\n",
" <th>15</th>\n",
" <td>P10</td>\n",
" <td>2023-12-28</td>\n",
" <td>2023-12-28T00:54:00</td>\n",
" <td>2023-12-28T08:48:00</td>\n",
" <td>28440</td>\n",
" <td>6660</td>\n",
" <td>6960</td>\n",
" <td>14280</td>\n",
" <td>device</td>\n",
" <td>98.874487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>P10</td>\n",
" <td>2023-12-28</td>\n",
" <td>2023-12-28T23:58:00</td>\n",
" <td>2023-12-29T08:11:00</td>\n",
" <td>29580</td>\n",
" <td>5700</td>\n",
" <td>4380</td>\n",
" <td>18480</td>\n",
" <td>device</td>\n",
" <td>98.874487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>P10</td>\n",
" <td>2023-12-29</td>\n",
" <td>2023-12-29T23:54:00</td>\n",
" <td>2023-12-30T08:58:00</td>\n",
" <td>32640</td>\n",
" <td>9660</td>\n",
" <td>3360</td>\n",
" <td>19560</td>\n",
" <td>device</td>\n",
" <td>92.847626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>P10</td>\n",
" <td>2023-12-31</td>\n",
" <td>2023-12-31T00:40:00</td>\n",
" <td>2023-12-31T09:08:00</td>\n",
" <td>30480</td>\n",
" <td>10680</td>\n",
" <td>7620</td>\n",
" <td>12060</td>\n",
" <td>device</td>\n",
" <td>94.986328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>P10</td>\n",
" <td>2023-12-31</td>\n",
" <td>2023-12-31T14:00:00</td>\n",
" <td>2023-12-31T17:38:00</td>\n",
" <td>13080</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>720</td>\n",
" <td>server</td>\n",
" <td>94.986328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>P10</td>\n",
" <td>2023-12-31</td>\n",
" <td>2023-12-31T23:31:00</td>\n",
" <td>2024-01-01T10:01:00</td>\n",
" <td>37800</td>\n",
" <td>3960</td>\n",
" <td>3780</td>\n",
" <td>20160</td>\n",
" <td>device</td>\n",
" <td>94.986328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>P10</td>\n",
" <td>2024-01-02</td>\n",
" <td>2024-01-02T00:22:00</td>\n",
" <td>2024-01-02T09:27:00</td>\n",
" <td>32700</td>\n",
" <td>19920</td>\n",
" <td>10080</td>\n",
" <td>35280</td>\n",
" <td>device</td>\n",
" <td>101.041543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>P10</td>\n",
" <td>2024-01-02</td>\n",
" <td>2024-01-02T14:00:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>86460</td>\n",
" <td>0</td>\n",
" <td>65160</td>\n",
" <td>360</td>\n",
" <td>server</td>\n",
" <td>101.041543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:30:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>16260</td>\n",
" <td>0</td>\n",
" <td>15720</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:31:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>16200</td>\n",
" <td>0</td>\n",
" <td>15660</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:32:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>16140</td>\n",
" <td>0</td>\n",
" <td>15600</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:36:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>15900</td>\n",
" <td>0</td>\n",
" <td>15360</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:37:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>15840</td>\n",
" <td>0</td>\n",
" <td>15300</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:38:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>15780</td>\n",
" <td>0</td>\n",
" <td>15240</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:45:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>15360</td>\n",
" <td>0</td>\n",
" <td>14820</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:43:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>15480</td>\n",
" <td>0</td>\n",
" <td>14940</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:46:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>15300</td>\n",
" <td>0</td>\n",
" <td>14760</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:50:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>15060</td>\n",
" <td>0</td>\n",
" <td>14520</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:56:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>14700</td>\n",
" <td>0</td>\n",
" <td>14160</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:58:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>14580</td>\n",
" <td>0</td>\n",
" <td>14040</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T09:59:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>14520</td>\n",
" <td>0</td>\n",
" <td>13980</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T10:01:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>14400</td>\n",
" <td>0</td>\n",
" <td>13920</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T10:07:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>14040</td>\n",
" <td>0</td>\n",
" <td>13620</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T14:00:00</td>\n",
" <td>2024-01-04T01:18:00</td>\n",
" <td>40680</td>\n",
" <td>0</td>\n",
" <td>840</td>\n",
" <td>1620</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T10:17:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>13440</td>\n",
" <td>0</td>\n",
" <td>13020</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T11:43:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>8280</td>\n",
" <td>0</td>\n",
" <td>7980</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T12:43:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>4680</td>\n",
" <td>0</td>\n",
" <td>4500</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>P10</td>\n",
" <td>2024-01-03</td>\n",
" <td>2024-01-03T13:43:00</td>\n",
" <td>2024-01-03T14:01:00</td>\n",
" <td>1080</td>\n",
" <td>0</td>\n",
" <td>1020</td>\n",
" <td>0</td>\n",
" <td>server</td>\n",
" <td>106.021649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>P10</td>\n",
" <td>2024-01-04</td>\n",
" <td>2024-01-04T01:32:00</td>\n",
" <td>2024-01-04T09:25:00</td>\n",
" <td>28380</td>\n",
" <td>8640</td>\n",
" <td>5400</td>\n",
" <td>14340</td>\n",
" <td>device</td>\n",
" <td>104.166667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" User First Name Calendar Date (Local) Start Time (Local) \\\n",
"0 P10 2023-12-23 2023-12-23T14:00:00 \n",
"1 P10 2023-12-23 2023-12-23T00:29:00 \n",
"2 P10 2023-12-23 2023-12-23T14:00:00 \n",
"3 P10 2023-12-23 2023-12-23T09:21:00 \n",
"4 P10 2023-12-24 2023-12-24T01:13:00 \n",
"5 P10 2023-12-24 2023-12-24T14:00:00 \n",
"6 P10 2023-12-24 2023-12-24T09:45:00 \n",
"7 P10 2023-12-24 2023-12-24T13:39:00 \n",
"8 P10 2023-12-24 2023-12-24T14:00:00 \n",
"9 P10 2023-12-25 2023-12-25T01:07:00 \n",
"10 P10 2023-12-25 2023-12-25T14:00:00 \n",
"11 P10 2023-12-25 2023-12-25T09:45:00 \n",
"12 P10 2023-12-26 2023-12-26T01:50:00 \n",
"13 P10 2023-12-26 2023-12-26T13:33:00 \n",
"14 P10 2023-12-27 2023-12-27T01:20:00 \n",
"15 P10 2023-12-28 2023-12-28T00:54:00 \n",
"16 P10 2023-12-28 2023-12-28T23:58:00 \n",
"17 P10 2023-12-29 2023-12-29T23:54:00 \n",
"18 P10 2023-12-31 2023-12-31T00:40:00 \n",
"19 P10 2023-12-31 2023-12-31T14:00:00 \n",
"20 P10 2023-12-31 2023-12-31T23:31:00 \n",
"21 P10 2024-01-02 2024-01-02T00:22:00 \n",
"22 P10 2024-01-02 2024-01-02T14:00:00 \n",
"23 P10 2024-01-03 2024-01-03T09:30:00 \n",
"24 P10 2024-01-03 2024-01-03T09:31:00 \n",
"25 P10 2024-01-03 2024-01-03T09:32:00 \n",
"26 P10 2024-01-03 2024-01-03T09:36:00 \n",
"27 P10 2024-01-03 2024-01-03T09:37:00 \n",
"28 P10 2024-01-03 2024-01-03T09:38:00 \n",
"29 P10 2024-01-03 2024-01-03T09:45:00 \n",
"30 P10 2024-01-03 2024-01-03T09:43:00 \n",
"31 P10 2024-01-03 2024-01-03T09:46:00 \n",
"32 P10 2024-01-03 2024-01-03T09:50:00 \n",
"33 P10 2024-01-03 2024-01-03T09:56:00 \n",
"34 P10 2024-01-03 2024-01-03T09:58:00 \n",
"35 P10 2024-01-03 2024-01-03T09:59:00 \n",
"36 P10 2024-01-03 2024-01-03T10:01:00 \n",
"37 P10 2024-01-03 2024-01-03T10:07:00 \n",
"38 P10 2024-01-03 2024-01-03T14:00:00 \n",
"39 P10 2024-01-03 2024-01-03T10:17:00 \n",
"40 P10 2024-01-03 2024-01-03T11:43:00 \n",
"41 P10 2024-01-03 2024-01-03T12:43:00 \n",
"42 P10 2024-01-03 2024-01-03T13:43:00 \n",
"43 P10 2024-01-04 2024-01-04T01:32:00 \n",
"\n",
" End Time (Local) Duration (s) Rem Sleep Duration (s) \\\n",
"0 2023-12-24T00:05:00 36300 0 \n",
"1 2023-12-23T10:04:00 34500 15180 \n",
"2 2023-12-24T09:19:00 69540 0 \n",
"3 2023-12-23T10:04:00 2580 1260 \n",
"4 2023-12-24T08:56:00 27780 6000 \n",
"5 2023-12-25T00:43:00 38580 0 \n",
"6 2023-12-24T14:01:00 15360 0 \n",
"7 2023-12-24T14:01:00 1320 0 \n",
"8 2023-12-25T09:43:00 70980 0 \n",
"9 2023-12-25T09:41:00 30840 11160 \n",
"10 2023-12-26T00:35:00 38100 0 \n",
"11 2023-12-25T14:01:00 15360 0 \n",
"12 2023-12-26T09:03:00 25980 6960 \n",
"13 2023-12-26T14:01:00 1680 0 \n",
"14 2023-12-27T09:16:00 28560 8040 \n",
"15 2023-12-28T08:48:00 28440 6660 \n",
"16 2023-12-29T08:11:00 29580 5700 \n",
"17 2023-12-30T08:58:00 32640 9660 \n",
"18 2023-12-31T09:08:00 30480 10680 \n",
"19 2023-12-31T17:38:00 13080 0 \n",
"20 2024-01-01T10:01:00 37800 3960 \n",
"21 2024-01-02T09:27:00 32700 19920 \n",
"22 2024-01-03T14:01:00 86460 0 \n",
"23 2024-01-03T14:01:00 16260 0 \n",
"24 2024-01-03T14:01:00 16200 0 \n",
"25 2024-01-03T14:01:00 16140 0 \n",
"26 2024-01-03T14:01:00 15900 0 \n",
"27 2024-01-03T14:01:00 15840 0 \n",
"28 2024-01-03T14:01:00 15780 0 \n",
"29 2024-01-03T14:01:00 15360 0 \n",
"30 2024-01-03T14:01:00 15480 0 \n",
"31 2024-01-03T14:01:00 15300 0 \n",
"32 2024-01-03T14:01:00 15060 0 \n",
"33 2024-01-03T14:01:00 14700 0 \n",
"34 2024-01-03T14:01:00 14580 0 \n",
"35 2024-01-03T14:01:00 14520 0 \n",
"36 2024-01-03T14:01:00 14400 0 \n",
"37 2024-01-03T14:01:00 14040 0 \n",
"38 2024-01-04T01:18:00 40680 0 \n",
"39 2024-01-03T14:01:00 13440 0 \n",
"40 2024-01-03T14:01:00 8280 0 \n",
"41 2024-01-03T14:01:00 4680 0 \n",
"42 2024-01-03T14:01:00 1080 0 \n",
"43 2024-01-04T09:25:00 28380 8640 \n",
"\n",
" Deep Sleep Duration (s) Light Sleep Duration (s) Source glucose \n",
"0 0 4620 server 77.927152 \n",
"1 3180 12360 device 77.927152 \n",
"2 15060 14760 server 77.927152 \n",
"3 0 1260 device 77.927152 \n",
"4 5580 15060 device 101.720760 \n",
"5 16620 1560 server 101.720760 \n",
"6 0 60 server 101.720760 \n",
"7 0 0 server 101.720760 \n",
"8 30300 16380 server 101.720760 \n",
"9 6780 12900 device 90.249815 \n",
"10 6480 2340 server 90.249815 \n",
"11 2700 1320 server 90.249815 \n",
"12 6240 12780 device 92.717573 \n",
"13 0 60 server 92.717573 \n",
"14 5460 14640 device 97.659067 \n",
"15 6960 14280 device 98.874487 \n",
"16 4380 18480 device 98.874487 \n",
"17 3360 19560 device 92.847626 \n",
"18 7620 12060 device 94.986328 \n",
"19 0 720 server 94.986328 \n",
"20 3780 20160 device 94.986328 \n",
"21 10080 35280 device 101.041543 \n",
"22 65160 360 server 101.041543 \n",
"23 15720 0 server 106.021649 \n",
"24 15660 0 server 106.021649 \n",
"25 15600 0 server 106.021649 \n",
"26 15360 0 server 106.021649 \n",
"27 15300 0 server 106.021649 \n",
"28 15240 0 server 106.021649 \n",
"29 14820 0 server 106.021649 \n",
"30 14940 0 server 106.021649 \n",
"31 14760 0 server 106.021649 \n",
"32 14520 0 server 106.021649 \n",
"33 14160 0 server 106.021649 \n",
"34 14040 0 server 106.021649 \n",
"35 13980 0 server 106.021649 \n",
"36 13920 0 server 106.021649 \n",
"37 13620 0 server 106.021649 \n",
"38 840 1620 server 106.021649 \n",
"39 13020 0 server 106.021649 \n",
"40 7980 0 server 106.021649 \n",
"41 4500 0 server 106.021649 \n",
"42 1020 0 server 106.021649 \n",
"43 5400 14340 device 104.166667 "
]
},
"execution_count": 7,
"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": 8,
"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": 9,
"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": 10,
"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": 11,
"id": "5437e036-0e99-4119-9d3d-751c71cb7d3f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation Coefficient: -0.3126622525528817\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": 12,
"id": "aff8c3f7-ae66-4aa2-9c43-193bf1adf86f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation Coefficient: 0.3048148041780017\n"
]
},
{
"data": {
"image/png": 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TU6tV6NXBzdZlUCOxe46IiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhksHlounTpEv7yl7/A09MTjo6O6NWrFw4ePCitF0Jg4cKF8Pf3h6OjIyIjI5Gammq2jZycHEyePBl6vR7u7u6YOnUqCgsLzdocPnwYt99+OxwcHBAYGIglS5ZUqyU+Ph5du3aFg4MDevXqhR9//LFp3jQRERG1ODYNTbm5uRg6dCjs7e3x008/4fjx43jnnXfQrl07qc2SJUuwYsUKrF69Gvv27YOzszOioqJw48YNqc3kyZNx7NgxbN26FT/88AN27tyJ6dOnS+sNBgNGjRqFoKAgJCUlYenSpXjllVewZs0aqc2ePXswadIkTJ06FYcOHcK4ceMwbtw4HD16tHkOBhERESmbsKF58+aJ2267rdb1JpNJ+Pn5iaVLl0rL8vLyhE6nE59//rkQQojjx48LAOLAgQNSm59++kmoVCpx6dIlIYQQ77//vmjXrp0oKSkx23eXLl2k5w899JCIjo422394eLh46qmnZL2X/Px8AUDk5+fLak9ERES2Z8n3t03PNH3//fcYOHAgHnzwQfj4+KBfv3748MMPpfVpaWnIzMxEZGSktMzNzQ3h4eFITEwEACQmJsLd3R0DBw6U2kRGRkKtVmPfvn1Sm2HDhkGr1UptoqKicOrUKeTm5kptqu6nsk3lfoiIiKhts2loOnv2LD744AOEhYVhy5YtmDFjBmbNmoW1a9cCADIzMwEAvr6+Zq/z9fWV1mVmZsLHx8dsvZ2dHTw8PMza1LSNqvuorU3l+puVlJTAYDCYPYiIiKj1srPlzk0mEwYOHIg333wTANCvXz8cPXoUq1evxpQpU2xZWr1iY2Px6quv2roMIiIiaiY2PdPk7++P7t27my3r1q0b0tPTAQB+fn4AgKysLLM2WVlZ0jo/Pz9kZ2ebrS8vL0dOTo5Zm5q2UXUftbWpXH+zBQsWID8/X3pcuHBB3psmIiKiFsmmoWno0KE4deqU2bI//vgDQUFBAIDg4GD4+flh27Zt0nqDwYB9+/YhIiICABAREYG8vDwkJSVJbRISEmAymRAeHi612blzJ8rKyqQ2W7duRZcuXaQr9SIiIsz2U9mmcj830+l00Ov1Zg8iIiJqxZphYHqt9u/fL+zs7MQbb7whUlNTxWeffSacnJzEp59+KrVZvHixcHd3F9999504fPiwGDt2rAgODhbFxcVSm9GjR4t+/fqJffv2id27d4uwsDAxadIkaX1eXp7w9fUV//d//yeOHj0q1q9fL5ycnMS//vUvqc1vv/0m7OzsxNtvvy1OnDghFi1aJOzt7cWRI0dkvRdePUdERNTyWPL9bdPQJIQQGzduFD179hQ6nU507dpVrFmzxmy9yWQSL7/8svD19RU6nU7ceeed4tSpU2Ztrl27JiZNmiRcXFyEXq8Xjz/+uCgoKDBr8/vvv4vbbrtN6HQ60b59e7F48eJqtXzxxRfilltuEVqtVvTo0UNs2rRJ9vtgaCIiImp5LPn+VgkhhG3PdbUOBoMBbm5uyM/PZ1cdERFRC2HJ97fNb6NCRERE1BIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMtg0NL3yyitQqVRmj65du0rrb9y4gZkzZ8LT0xMuLi6YMGECsrKyzLaRnp6O6OhoODk5wcfHB3PmzEF5eblZm+3bt6N///7Q6XQIDQ1FXFxctVpWrVqFTp06wcHBAeHh4di/f3+TvGciIiJqmWx+pqlHjx64fPmy9Ni9e7e07vnnn8fGjRsRHx+PHTt2ICMjA+PHj5fWG41GREdHo7S0FHv27MHatWsRFxeHhQsXSm3S0tIQHR2NESNGICUlBbNnz8a0adOwZcsWqc2GDRsQExODRYsWITk5GX369EFUVBSys7Ob5yAQERGR8gkbWrRokejTp0+N6/Ly8oS9vb2Ij4+Xlp04cUIAEImJiUIIIX788UehVqtFZmam1OaDDz4Qer1elJSUCCGEmDt3rujRo4fZth9++GERFRUlPR80aJCYOXOm9NxoNIqAgAARGxsr+73k5+cLACI/P1/2a4iIiMi2LPn+tvmZptTUVAQEBCAkJASTJ09Geno6ACApKQllZWWIjIyU2nbt2hUdO3ZEYmIiACAxMRG9evWCr6+v1CYqKgoGgwHHjh2T2lTdRmWbym2UlpYiKSnJrI1arUZkZKTUpiYlJSUwGAxmDyIiImq9bBqawsPDERcXh82bN+ODDz5AWloabr/9dhQUFCAzMxNarRbu7u5mr/H19UVmZiYAIDMz0ywwVa6vXFdXG4PBgOLiYly9ehVGo7HGNpXbqElsbCzc3NykR2BgYIOOAREREbUMdrbc+ZgxY6R/9+7dG+Hh4QgKCsIXX3wBR0dHG1ZWvwULFiAmJkZ6bjAYGJyIiIhaMZt3z1Xl7u6OW265BadPn4afnx9KS0uRl5dn1iYrKwt+fn4AAD8/v2pX01U+r6+NXq+Ho6MjvLy8oNFoamxTuY2a6HQ66PV6swcRERG1XooKTYWFhThz5gz8/f0xYMAA2NvbY9u2bdL6U6dOIT09HREREQCAiIgIHDlyxOwqt61bt0Kv16N79+5Sm6rbqGxTuQ2tVosBAwaYtTGZTNi2bZvUhoiIiMimV8+98MILYvv27SItLU389ttvIjIyUnh5eYns7GwhhBBPP/206Nixo0hISBAHDx4UERERIiIiQnp9eXm56Nmzpxg1apRISUkRmzdvFt7e3mLBggVSm7NnzwonJycxZ84cceLECbFq1Sqh0WjE5s2bpTbr168XOp1OxMXFiePHj4vp06cLd3d3s6vy6sOr54iIiFoeS76/bTqm6eLFi5g0aRKuXbsGb29v3Hbbbdi7dy+8vb0BAO+99x7UajUmTJiAkpISREVF4f3335der9Fo8MMPP2DGjBmIiIiAs7MzpkyZgtdee01qExwcjE2bNuH555/H8uXL0aFDB3z00UeIioqS2jz88MO4cuUKFi5ciMzMTPTt2xebN2+uNjiciIiI2i6VEELYuojWwGAwwM3NDfn5+RzfRERE1EJY8v2tqDFNRERERErF0EREREQkg8VjmtLS0rBr1y6cP38eRUVF8Pb2Rr9+/RAREQEHB4emqJGIiIjI5mSHps8++wzLly/HwYMH4evri4CAADg6OiInJwdnzpyBg4MDJk+ejHnz5iEoKKgpayYiIiJqdrJCU79+/aDVavHYY4/hq6++qjbzdUlJCRITE7F+/XoMHDgQ77//Ph588MEmKZiIiIjIFmRdPbdlyxazS/Trcu3aNZw7dw4DBgxodHEtCa+eIyIianks+f6WdaZJbmACAE9PT3h6espuT0RERNQSWHz1XHJyMo4cOSI9/+677zBu3Di8+OKLKC0ttWpxREREREphcWh66qmn8McffwAAzp49i4kTJ8LJyQnx8fGYO3eu1QskIiIiUgKLQ9Mff/yBvn37AgDi4+MxbNgwrFu3DnFxcfjqq6+sXR8RERGRIlgcmoQQMJlMAIBffvkFd999NwAgMDAQV69etW51RERERAphcWgaOHAg/vGPf+CTTz7Bjh07EB0dDaBi0kve4JaIiIhaK4tD07Jly5CcnIxnnnkGL730EkJDQwEAX375JYYMGWL1AomIiIiUQNY8TXLcuHEDGo0G9vb21thci8N5moiIiFoeq8/TJISASqWqsw3vO0dEREStmazuuR49emD9+vX1zsOUmpqKGTNmYPHixVYpjoiIiEgpZJ1pWrlyJebNm4e//vWvuOuuuzBw4EAEBATAwcEBubm5OH78OHbv3o1jx47hmWeewYwZM5q6biIiIqJmZdGYpt27d2PDhg3YtWsXzp8/j+LiYnh5eaFfv36IiorC5MmT0a5du6asV7E4pomIiKjlseT722oDwds6hiYiIqKWx5Lvb4unHCAiIiJqixiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZZM3TdDOTyYTTp08jOzsbJpPJbN2wYcOsUhgRERGRklgcmvbu3YtHHnkE58+fx82zFahUKhiNRqsVR0RERKQUFoemp59+GgMHDsSmTZvg7+9f7z3piIiIiFoDi0NTamoqvvzyS4SGhjZFPURERESKZPFA8PDwcJw+fbopaiEiIiJSLIvPND377LN44YUXkJmZiV69esHe3t5sfe/eva1WHBEREZFSWHzvObW6+skplUoFIUSbHgjOe88RERG1PJZ8f1t8piktLa3BhRERERG1VBaHpqCgoKaog4iIiEjRGjS55ZkzZ7Bs2TKcOHECANC9e3c899xz6Ny5s1WLIyIiIlIKi6+e27JlC7p37479+/ejd+/e6N27N/bt24cePXpg69atTVEjERERkc1ZPBC8X79+iIqKwuLFi82Wz58/Hz///DOSk5OtWmBLwYHgRERELY8l398Wn2k6ceIEpk6dWm35E088gePHj1u6OSIiIqIWweLQ5O3tjZSUlGrLU1JS4OPjY42aiIiIiBTH4oHgTz75JKZPn46zZ89iyJAhAIDffvsNb731FmJiYqxeIBEREZESWDymSQiBZcuW4Z133kFGRgYAICAgAHPmzMGsWbPa7A18OaaJiIio5WnSMU0qlQrPP/88Ll68iPz8fOTn5+PixYt47rnnGhWYFi9eDJVKhdmzZ0vLbty4gZkzZ8LT0xMuLi6YMGECsrKyzF6Xnp6O6OhoODk5wcfHB3PmzEF5eblZm+3bt6N///7Q6XQIDQ1FXFxctf2vWrUKnTp1goODA8LDw7F///4GvxciIiJqfSwOTVW5urrC1dW10UUcOHAA//rXv6rdt+7555/Hxo0bER8fjx07diAjIwPjx4+X1huNRkRHR6O0tBR79uzB2rVrERcXh4ULF0pt0tLSEB0djREjRiAlJQWzZ8/GtGnTsGXLFqnNhg0bEBMTg0WLFiE5ORl9+vRBVFQUsrOzG/3eiIiIqHWQ1T3Xv39/bNu2De3atUO/fv3qPKNk6ZQDhYWF6N+/P95//3384x//QN++fbFs2TLk5+fD29sb69atwwMPPAAAOHnyJLp164bExEQMHjwYP/30E+655x5kZGTA19cXALB69WrMmzcPV65cgVarxbx587Bp0yYcPXpU2ufEiRORl5eHzZs3AwDCw8Nx66234p///CcAwGQyITAwEM8++yzmz58v632we46IiKjlsfq958aOHQudTif925rjlmbOnIno6GhERkbiH//4h7Q8KSkJZWVliIyMlJZ17doVHTt2lEJTYmIievXqJQUmAIiKisKMGTNw7Ngx9OvXD4mJiWbbqGxT2Q1YWlqKpKQkLFiwQFqvVqsRGRmJxMREq71PIiIiatlkhaZFixZJ/37llVestvP169cjOTkZBw4cqLYuMzMTWq0W7u7uZst9fX2RmZkptakamCrXV66rq43BYEBxcTFyc3NhNBprbHPy5Mlaay8pKUFJSYn03GAw1PNuiYiIqCWzeExTSEgIrl27Vm15Xl4eQkJCZG/nwoULeO655/DZZ5/BwcHB0jJsLjY2Fm5ubtIjMDDQ1iURERFRE7I4NJ07dw5Go7Ha8pKSEly8eFH2dpKSkpCdnY3+/fvDzs4OdnZ22LFjB1asWAE7Ozv4+vqitLQUeXl5Zq/LysqCn58fAMDPz6/a1XSVz+tro9fr4ejoCC8vL2g0mhrbVG6jJgsWLJCuHszPz8eFCxdkv3ciIiJqeWRPbvn9999L/96yZQvc3Nyk50ajEdu2bUNwcLDsHd955504cuSI2bLHH38cXbt2xbx58xAYGAh7e3ts27YNEyZMAACcOnUK6enpiIiIAABERETgjTfeQHZ2tjQb+datW6HX69G9e3epzY8//mi2n61bt0rb0Gq1GDBgALZt24Zx48YBqBgIvm3bNjzzzDO11q/T6aRxXkRERNQGCJlUKpVQqVRCrVZL/658aLVaccstt4iNGzfK3VyNhg8fLp577jnp+dNPPy06duwoEhISxMGDB0VERISIiIiQ1peXl4uePXuKUaNGiZSUFLF582bh7e0tFixYILU5e/ascHJyEnPmzBEnTpwQq1atEhqNRmzevFlqs379eqHT6URcXJw4fvy4mD59unB3dxeZmZmya8/PzxcARH5+fqOOARERETUfS76/ZZ9pMplMAIDg4GAcOHAAXl5eTRTj/ue9996DWq3GhAkTUFJSgqioKLz//vvSeo1Ggx9++AEzZsxAREQEnJ2dMWXKFLz22mtSm+DgYGzatAnPP/88li9fjg4dOuCjjz5CVFSU1Obhhx/GlStXsHDhQmRmZqJv377YvHlztcHhRERE1HZZfBsVqhnnaSIiImp5rD5P082uX7+OHTt2ID09HaWlpWbrZs2a1ZBNEhERESmaxaHp0KFDuPvuu1FUVITr16/Dw8MDV69ele79xtBERERErZHFUw48//zzuPfee5GbmwtHR0fs3bsX58+fx4ABA/D22283RY1ERERENmdxaEpJScELL7wAtVoNjUaDkpISBAYGYsmSJXjxxRebokYiIiIim7M4NNnb20OtrniZj48P0tPTAQBubm6c4JGIiIhaLYvHNPXr1w8HDhxAWFgYhg8fjoULF+Lq1av45JNP0LNnz6aokYiIiMjmLD7T9Oabb8Lf3x8A8MYbb6Bdu3aYMWMGrly5gjVr1li9QCIiIiIlsOhMkxACPj4+0hklHx8fbN68uUkKIyIiIlISi840CSEQGhrKsUtERETU5lgUmtRqNcLCwnDt2rWmqoeIiIhIkSwe07R48WLMmTMHR48ebYp6iIiIiBTJ4nvPtWvXDkVFRSgvL4dWq4Wjo6PZ+pycHKsW2FLw3nNEREQtT5Pee27ZsmUNrYuIiIioxbI4NE2ZMqUp6iAiIiJSNItDU+UM4LXp2LFjg4shIiIiUiqLQ1OnTp2gUqlqXW80GhtVEBEREZESWRyaDh06ZPa8rKwMhw4dwrvvvos33njDaoURERERKYnFoalPnz7Vlg0cOBABAQFYunQpxo8fb5XCiIiIiJTE4nmaatOlSxccOHDAWpsjIiIiUhSLzzQZDAaz50IIXL58Ga+88grCwsKsVhgRERGRklgcmtzd3asNBBdCIDAwEOvXr7daYURERERKYnFo+vXXX82eq9VqeHt7IzQ0FHZ2Fm+OiIiIqEWwOOUMHz68KeogIiIiUjSLQ1NCQgK+/vprnDt3DiqVCsHBwXjggQcwbNiwpqiPiIiISBEsunru6aefRmRkJD7//HNcu3YNV65cwWeffYYRI0bg2WefbaoaiYiIiGxOdmj65ptv8PHHH+M///kPrl69isTEROzduxdXrlzBhx9+iDVr1uD7779vylqJiIiIbEYlhBByGt53333o0aMHYmNja1w/b948nDx5Et99951VC2wpDAYD3NzckJ+fD71eb+tyiIiISAZLvr9ln2lKTk7G/fffX+v68ePHIykpSX6VRERERC2I7NB09epVdOjQodb1HTp0wLVr16xSFBEREZHSyA5NpaWlsLe3r3W9nZ0dSktLrVIUERERkdJYNOXAyy+/DCcnpxrXFRUVWaUgIiIiIiWSHZqGDRuGU6dO1duGiIiIqDWSHZq2b9/ehGUQERERKZtFk1sSERERtVUMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDBbN01QpNzcX//73v3HixAkAQLdu3fDEE0/Aw8PDqsURERERKYXFZ5p27tyJ4OBgrFixArm5ucjNzcXKlSsRHByMnTt3NkWNREREbYbJJHDkYj52/HEFRy7mw2QSti6J/qQSQlj00+jVqxciIiLwwQcfQKPRAACMRiP++te/Ys+ePThy5EiTFKp0ltwlmYiIqCZ7Tl/FioRUnL1yHaVGE7QaNUK8nTFrZBiGhHrZurxWyZLvb4vPNJ0+fRovvPCCFJgAQKPRICYmBqdPn7ZoWx988AF69+4NvV4PvV6PiIgI/PTTT9L6GzduYObMmfD09ISLiwsmTJiArKwss22kp6cjOjoaTk5O8PHxwZw5c1BeXm7WZvv27ejfvz90Oh1CQ0MRFxdXrZZVq1ahU6dOcHBwQHh4OPbv32/ReyEiImqMPaevYu5Xh5F0PhfZBSXIKypDdkEJks7nYu5Xh7Hn9FVbl9jmWRya+vfvL41lqurEiRPo06ePRdvq0KEDFi9ejKSkJBw8eBAjR47E2LFjcezYMQDA888/j40bNyI+Ph47duxARkYGxo8fL73eaDQiOjoapaWl2LNnD9auXYu4uDgsXLhQapOWlobo6GiMGDECKSkpmD17NqZNm4YtW7ZIbTZs2ICYmBgsWrQIycnJ6NOnD6KiopCdnW3p4SEiIrKYySSwIiEVWYYbKDOadwCVGQWyDDewIiGVXXU2ZnH33IYNGzB37lw8++yzGDx4MABg7969WLVqFRYvXoxu3bpJbXv37m1xQR4eHli6dCkeeOABeHt7Y926dXjggQcAACdPnkS3bt2QmJiIwYMH46effsI999yDjIwM+Pr6AgBWr16NefPm4cqVK9BqtZg3bx42bdqEo0ePSvuYOHEi8vLysHnzZgBAeHg4br31Vvzzn/8EAJhMJgQGBuLZZ5/F/PnzZdXN7jkiImqoIxfzMXXtAWQXlNTaxsdVh39PuRW9Org1Y2WtnyXf3xZfPTdp0iQAwNy5c2tcp1KpIISASqWC0WiUvV2j0Yj4+Hhcv34dERERSEpKQllZGSIjI6U2Xbt2RceOHaXQlJiYiF69ekmBCQCioqIwY8YMHDt2DP369UNiYqLZNirbzJ49GwBQWlqKpKQkLFiwQFqvVqsRGRmJxMTEWustKSlBScn/PtwGg0H2eyUiIqoqp6gUpUZTnW3KjCbkFJU2U0VUE4tDU1pamlULOHLkCCIiInDjxg24uLjgm2++Qffu3ZGSkgKtVgt3d3ez9r6+vsjMzAQAZGZmmgWmyvWV6+pqYzAYUFxcjNzcXBiNxhrbnDx5sta6Y2Nj8eqrrzboPRMREVXl4aSFVlP3iBl7jRoeTtpmqohqYnFoCgoKsmoBXbp0QUpKCvLz8/Hll19iypQp2LFjh1X30RQWLFiAmJgY6bnBYEBgYKANKyIiopaqR4AeId7OyC0qrTamCQDsNSqEeDujRwCHf9hSg2YE/+STTzB06FAEBATg/PnzAIBly5bhu+++s3hbWq0WoaGhGDBgAGJjY9GnTx8sX74cfn5+KC0tRV5enln7rKws+Pn5AQD8/PyqXU1X+by+Nnq9Ho6OjvDy8oJGo6mxTeU2aqLT6aSr/iofREREDaFWqzBrZBh89Q6w16jM1tlrVPDVO2DWyDCo1apatkDNweLQ9MEHHyAmJgZ333038vLypHFL7u7uWLZsWaMLMplMKCkpwYABA2Bvb49t27ZJ606dOoX09HREREQAACIiInDkyBGzq9y2bt0KvV6P7t27S22qbqOyTeU2tFotBgwYYNbGZDJh27ZtUhsiIqKmNiTUC0sm9MaAoHbwcdWhnZM9fFx1GBDUDksm9OY8TUogLNStWzfxzTffCCGEcHFxEWfOnBFCCHHkyBHh6elp0bbmz58vduzYIdLS0sThw4fF/PnzhUqlEj///LMQQoinn35adOzYUSQkJIiDBw+KiIgIERERIb2+vLxc9OzZU4waNUqkpKSIzZs3C29vb7FgwQKpzdmzZ4WTk5OYM2eOOHHihFi1apXQaDRi8+bNUpv169cLnU4n4uLixPHjx8X06dOFu7u7yMzMlP1e8vPzBQCRn59v0TEgIiKqymg0icMX8sT2U9ni8IU8YTSabF1Sq2bJ97fFocnBwUGcO3dOCGEemv744w/h4OBg0baeeOIJERQUJLRarfD29hZ33nmnFJiEEKK4uFj89a9/Fe3atRNOTk7i/vvvF5cvXzbbxrlz58SYMWOEo6Oj8PLyEi+88IIoKysza/Prr7+Kvn37Cq1WK0JCQsTHH39crZaVK1eKjh07Cq1WKwYNGiT27t1r0XthaCIiImp5LPn+tniepu7duyM2NhZjx46Fq6srfv/9d4SEhGDlypX4+OOPkZyc3BQnxBSP8zQRERG1PE06T1NMTAxmzpyJGzduQAiB/fv34/PPP0dsbCw++uijBhdNREREpGQWh6Zp06bB0dERf//731FUVIRHHnkEAQEBWL58OSZOnNgUNRIRERHZnMXdc1UVFRWhsLAQPj4+1qypRWL3HBERUctjyfd3g+ZpKi8vxy+//IJPPvkEjo6OAICMjAwUFhY2ZHNEREREimdx99z58+cxevRopKeno6SkBHfddRdcXV3x1ltvoaSkBKtXr26KOomIiIhsyuIzTc899xwGDhyI3Nxc6SwTANx///3VJpEkIiIiai0sPtO0a9cu7NmzB1qt+U0DO3XqhEuXLlmtMCIiIiIlsfhMk8lkkm6dUtXFixfh6upqlaKIiIiIlMbi0DRq1Cize8ypVCoUFhZi0aJFuPvuu61ZGxEREZFiWDzlwMWLFxEVFQUhBFJTUzFw4ECkpqbCy8sLO3fubLPTD3DKASIiopbHku/vBs3TVF5ejvXr1+Pw4cMoLCxE//79MXnyZLOB4W0NQxMREVHL06S3UQEAOzs7/OUvf2lQcUREREQtkazQ9P3338ve4H333dfgYoiIiIiUSlZoGjdunKyNqVSqGq+sIyIiImrpZIUmk8nU1HUQERERKVqD7j1HRERE1NbIDk2JiYn44YcfzJb997//RXBwMHx8fDB9+nSUlJRYvUAiIiIiJZAdml577TUcO3ZMen7kyBFMnToVkZGRmD9/PjZu3IjY2NgmKZKIiIjI1mSHppSUFNx5553S8/Xr1yM8PBwffvghYmJisGLFCnzxxRdNUiQRERGRrckOTbm5ufD19ZWe79ixA2PGjJGe33rrrbhw4YJ1qyMiIiJSCNmhydfXF2lpaQCA0tJSJCcnY/DgwdL6goIC2NvbW79CIiIiIgWQHZruvvtuzJ8/H7t27cKCBQvg5OSE22+/XVp/+PBhdO7cuUmKJCIiIrI12bdRef311zF+/HgMHz4cLi4uWLt2LbRarbT+P//5D0aNGtUkRRIRERHZmsU37M3Pz4eLiws0Go3Z8pycHLi4uJgFqbaEN+wlIiJqeZr0hr1ubm41Lvfw8LB0U0REREQtBmcEJyIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhksHggOACcOnUKK1euxIkTJwAA3bp1w7PPPosuXbpYtTgiIiIipbD4TNNXX32Fnj17IikpCX369EGfPn2QnJyMnj174quvvmqKGomIiIhszuJ5mjp37ozJkyfjtddeM1u+aNEifPrppzhz5oxVC2wpOE8TERFRy2PJ97fFZ5ouX76MRx99tNryv/zlL7h8+bKlmyMiIiJqESwOTXfccQd27dpVbfnu3bvN7kVHRERE1JpYPBD8vvvuw7x585CUlITBgwcDAPbu3Yv4+Hi8+uqr+P77783aEhEREbUGFo9pUqvlnZxSqVQwGo0NKqol4pgmIiKilqdJ7z1nMpkaXBgRERFRS9WoyS1v3LhhrTqIiIiIFM3i0GQ0GvH666+jffv2cHFxwdmzZwEAL7/8Mv79739bvUAiIiIiJbA4NL3xxhuIi4vDkiVLoNVqpeU9e/bERx99ZNXiiIiIiJTC4tD03//+F2vWrMHkyZOh0Wik5X369MHJkyetWhwRERGRUlgcmi5duoTQ0NBqy00mE8rKyizaVmxsLG699Va4urrCx8cH48aNw6lTp8za3LhxAzNnzoSnpydcXFwwYcIEZGVlmbVJT09HdHQ0nJyc4OPjgzlz5qC8vNyszfbt29G/f3/odDqEhoYiLi6uWj2rVq1Cp06d4ODggPDwcOzfv9+i90NEREStl8WhqXv37jVObvnll1+iX79+Fm1rx44dmDlzJvbu3YutW7eirKwMo0aNwvXr16U2zz//PDZu3Ij4+Hjs2LEDGRkZGD9+vLTeaDQiOjoapaWl2LNnD9auXYu4uDgsXLhQapOWlobo6GiMGDECKSkpmD17NqZNm4YtW7ZIbTZs2ICYmBgsWrQIycnJ6NOnD6KiopCdnW3ReyIiIqJWSljo22+/FW5ubmLx4sXCyclJLF26VEybNk1otVrx888/W7o5M9nZ2QKA2LFjhxBCiLy8PGFvby/i4+OlNidOnBAARGJiohBCiB9//FGo1WqRmZkptfnggw+EXq8XJSUlQggh5s6dK3r06GG2r4cfflhERUVJzwcNGiRmzpwpPTcajSIgIEDExsbKqj0/P18AEPn5+Ra+ayIiIrIVS76/LT7TNHbsWGzcuBG//PILnJ2dsXDhQpw4cQIbN27EXXfd1agAl5+fDwDw8PAAACQlJaGsrAyRkZFSm65du6Jjx45ITEwEACQmJqJXr17w9fWV2kRFRcFgMODYsWNSm6rbqGxTuY3S0lIkJSWZtVGr1YiMjJTa3KykpAQGg8HsQURERK2XxZNbAsDtt9+OrVu3WrUQk8mE2bNnY+jQoejZsycAIDMzE1qtFu7u7mZtfX19kZmZKbWpGpgq11euq6uNwWBAcXExcnNzYTQaa2xT2+D22NhYvPrqqw17s0RERNTiNGhyy7y8PHz00Ud48cUXkZOTAwBITk7GpUuXGlzIzJkzcfToUaxfv77B22hOCxYsQH5+vvS4cOGCrUsiIiKiJmTxmabDhw8jMjISbm5uOHfuHKZNmwYPDw98/fXXSE9Px3//+1+Li3jmmWfwww8/YOfOnejQoYO03M/PD6WlpcjLyzM725SVlQU/Pz+pzc1XuVVeXVe1zc1X3GVlZUGv18PR0REajQYajabGNpXbuJlOp4NOp7P4vRIREVHLZPGZppiYGDz22GNITU2Fg4ODtPzuu+/Gzp07LdqWEALPPPMMvvnmGyQkJCA4ONhs/YABA2Bvb49t27ZJy06dOoX09HREREQAACIiInDkyBGzq9y2bt0KvV6P7t27S22qbqOyTeU2tFotBgwYYNbGZDJh27ZtUhsiIiJq4ywdZa7X68Xp06eFEEK4uLiIM2fOCCGEOHfunNDpdBZta8aMGcLNzU1s375dXL58WXoUFRVJbZ5++mnRsWNHkZCQIA4ePCgiIiJERESEtL68vFz07NlTjBo1SqSkpIjNmzcLb29vsWDBAqnN2bNnhZOTk5gzZ444ceKEWLVqldBoNGLz5s1Sm/Xr1wudTifi4uLE8ePHxfTp04W7u7vZVXl14dVzRERELY8l398WhyZvb2+RnJwshDAPTT///LPo0KGDRdsCUOPj448/ltoUFxeLv/71r6Jdu3bCyclJ3H///eLy5ctm2zl37pwYM2aMcHR0FF5eXuKFF14QZWVlZm1+/fVX0bdvX6HVakVISIjZPiqtXLlSdOzYUWi1WjFo0CCxd+9e2e+FoYmIiKjlseT7WyWEEJacmZo2bRquXbuGL774Ah4eHjh8+DA0Gg3GjRuHYcOGYdmyZVY9E9ZSGAwGuLm5IT8/H3q93tblEBERkQyWfH9bPKbpnXfeQWFhIXx8fFBcXIzhw4cjNDQUrq6ueOONNxpcNBEREZGSWXz1nJubG7Zu3Yrdu3fj8OHDKCwsRP/+/atNHklERETUmljcPUc1Y/ccERFRy2PJ97dFZ5pMJhPi4uLw9ddf49y5c1CpVAgODsYDDzyA//u//4NKpWpU4URERERKJXtMkxAC9913H6ZNm4ZLly6hV69e6NGjB86fP4/HHnsM999/f1PWSURERGRTss80xcXFYefOndi2bRtGjBhhti4hIQHjxo3Df//7Xzz66KNWL5KIiKitMJkEjmUYkFNUCg8nLXoE6KFWsydHCWSPaRo1ahRGjhyJ+fPn17j+zTffxI4dO7BlyxarFthScEwTERE11p7TV7EiIRVnr1xHqdEErUaNEG9nzBoZhiGhXrYur1VqkikHDh8+jNGjR9e6fsyYMfj999/lV0lERESSPaevYu5Xh5F0PhfZBSXIKypDdkEJks7nYu5Xh7Hn9FVbl9jmyQ5NOTk58PX1rXW9r68vcnNzrVIUERFRW2IyCaxISEWW4QbKjOYdQGVGgSzDDaxISIXJxAvebUl2aDIajbCzq30IlEajQXl5uVWKIiIiakuOZRhw9sr1aoGpUplR4OyV6ziWYWjmyqgq2QPBhRB47LHHoNPpalxfUlJitaKIiIjakpyiUpQaTXW2KTOakFNU2kwVKUtpqRGrd57F+ZzrCPJwxtPDQqDVapq9DtmhacqUKfW24ZVzRERElvNw0kKrqbvzx16jhoeTtpkqUo6Xvj6M9QcvoGqmXJ7wByYODMQb43s3ay2yQ9PHH3/clHUQERG1WT0C9AjxdkZuUWmNXXT2GhVCvJ3RI6BtXZ390teH8dn+C9WWG02QljdncLL4hr3UvEwmgSMX87Hjjys4cjGfgwCJiFohtVqFWSPD4Kt3gL3GfE4me40KvnoHzBoZ1qbmayotNWL9weqBqar1By+gtNTYTBU14Ia91Hw4XwcRUdsxJNQLSyb0ln7vlxlNsG/Dv/dX7zyLeoZ5wWiqaDcrMqxZamJoUqjK+Tpuvvw0t6gUc786jCUTere5/4GIiFq7IaFeGBziyRnBAZy9Iu9KQbntrIGhSYHkztcxOMSzTf6PRETUmqnVKvTq4GbrMmwuq6DMqu2sgWOaFIjzdRARUVvn62pv1XbWwNCkQJyvg4iI2jpHe3mdYXLbWQNDkwJxvg4iImrrnHTyJq+U284aGJoUqHK+jpsvO63UVufrICKitiMjX96dRuS2swaGJgXifB1ERNTWtXd3sGo7a2BoUqjK+ToGBLWDj6sO7Zzs4eOqw4CgdpxugIiIWr0Qb2ertrMGTjmgYJyvg4iI2qoDabmy2z0S3qlpi/kTQ5PCcb4OIiJqi/KL5M2/JLedNbB7joiIiBTHV+ZYJbntrIGhiYiIiBQnsouPVdtZA0MTERERKY6X3gH1DeFVqyraNReOaSIiIiLlMJmAzEw4JR3FfSd2wi8vGx3ys9DekI0AwxXc89hylGkqbp2iaeYLoxiaiIiIqPmUlwMXLwLnzwPnzlX8t+ojPR0oLUUYgGU1vNzfcBXp7fwBADo7DfKKm28gOEMTERERWc+NGxXBp2oQqhqOLl6sOJvUQB3ys6TQ5KTVNOstxRiaiIiISL6CgtrPEp0/D2RmWmU3ol07nHPxQqqjFy66euOSmw8uuvngpE8wANvcUoyhiYiIiCoIAeTk1HyGqPLfufImnayXnx8QFGT+6NRJ+rfK1RWXT1/Fa18dRpbhBsqMQnqprW4pxtBERETUVphMQFZW7WeJzp0Drl9v/H7UaqB9e/MwVCUQoWNHwKH+q94qbym2IiEVZ69cR5nRBHuNGiHezpg1MqzZbynG0ERERNRalJcDly7VPp7o/HmgtLTx+9FqK4JPLWeJ0L49YG/f+P1AWbcUY2giIiJqKUpK/jfI+uYwdO5cRWAyGhu/Hyen6kGo6nM/v4qzSc1EKbcUY2giIiJSisLCus8SXb5snf24u1cPRVX/7ekJqHhz+JsxNBERETUHISoGUdcWis6dqxiEbQ0+PtXPElUNRvrmu+KsNWFoIiIisgYhKgZZ13aW6Pz5isv1G6umQdZVg1HHjoCjY+P3Q9UwNBEREclhNFYfZH1zOCopafx+7O2rD7KuGo46dLDaIGuyjE1D086dO7F06VIkJSXh8uXL+OabbzBu3DhpvRACixYtwocffoi8vDwMHToUH3zwAcLCwqQ2OTk5ePbZZ7Fx40ao1WpMmDABy5cvh4uLi9Tm8OHDmDlzJg4cOABvb288++yzmDt3rlkt8fHxePnll3Hu3DmEhYXhrbfewt13393kx4CIiBSipAS4cKH2s0QXLlhlkPUNex2uefrB6ZbOaNctrPqZIn//Zh1kTfLZNDRdv34dffr0wRNPPIHx48dXW79kyRKsWLECa9euRXBwMF5++WVERUXh+PHjcPhzfofJkyfj8uXL2Lp1K8rKyvD4449j+vTpWLduHQDAYDBg1KhRiIyMxOrVq3HkyBE88cQTcHd3x/Tp0wEAe/bswaRJkxAbG4t77rkH69atw7hx45CcnIyePXs23wEhIqKmc/163eOJrDXI2s1NCkKX3Xzw1TUNTjp44rxLxazWOY562Nup4at3wJIJvZt9riFqOJUQQtTfrOmpVCqzM01CCAQEBOCFF17A3/72NwBAfn4+fH19ERcXh4kTJ+LEiRPo3r07Dhw4gIEDBwIANm/ejLvvvhsXL15EQEAAPvjgA7z00kvIzMyEVltxf5r58+fj22+/xcmTJwEADz/8MK5fv44ffvhBqmfw4MHo27cvVq9eLat+g8EANzc35OfnQ88BdkREzUsIIC+v9rNE584B165ZZ1/e3nVfeeZWcWm8ySTwyEd7kXQ+12w260r2GhUGBLXDummDbTLnEFWw5PtbsWOa0tLSkJmZicjISGmZm5sbwsPDkZiYiIkTJyIxMRHu7u5SYAKAyMhIqNVq7Nu3D/fffz8SExMxbNgwKTABQFRUFN566y3k5uaiXbt2SExMRExMjNn+o6Ki8O2339ZaX0lJCUqq9F0bDAYrvGsiIqqREEB2dt2X41vj97BKBQQE1B6IOnasmMNIhmMZhj9nsa753ESZUeDsles4lmFQxBxEVD/FhqbMP2/45+vra7bc19dXWpeZmQkfHx+z9XZ2dvDw8DBrExwcXG0blevatWuHzMzMOvdTk9jYWLz66qsNeGdERFSN0VjRPVYZhG7uOktPB27caPx+7OyAwMCarzoLCqpYV+WP7MbIKSpFqdFUZ5syowk5RVaYoZuahWJDk9ItWLDA7OyUwWBAYGCgDSsiIlKw0tL/DbKu6SzRhQsVtwBpLAeHumey9vcHNJrG70cGDycttJq6B3Tba9TwcLJOSKOmp9jQ5OfnBwDIysqCv7+/tDwrKwt9+/aV2mRnZ5u9rry8HDk5OdLr/fz8kJWVZdam8nl9bSrX10Sn00Gn0zXgnRERtUJFRbVfil85yNoaQ2j1+prPEFUGI29vxcxk3SNAjxBvZ+QWldY6pinE2xk9AjgOtqVQbGgKDg6Gn58ftm3bJoUkg8GAffv2YcaMGQCAiIgI5OXlISkpCQMGDAAAJCQkwGQyITw8XGrz0ksvoaysDPZ/zmuxdetWdOnSBe3atZPabNu2DbNnz5b2v3XrVkRERDTTuyUiUrjKQda1DbS+csU6+/Hyqn08UVBQxe0/Wgi1WoVZI8Mw96vDyDLcMAtO9hoVfPUOmDUyjIPAWxCbhqbCwkKcPn1aep6WloaUlBR4eHigY8eOmD17Nv7xj38gLCxMmnIgICBAusKuW7duGD16NJ588kmsXr0aZWVleOaZZzBx4kQEBAQAAB555BG8+uqrmDp1KubNm4ejR49i+fLleO+996T9Pvfccxg+fDjeeecdREdHY/369Th48CDWrFnTrMeDiMgmhKgIPTcHoaoBKT+/8fupHGRd21mijh0BZ+fG70dBhoR6YcmE3liRkPrnoHAT7DVqhHg7Y9bIME430MLYdMqB7du3Y8SIEdWWT5kyBXFxcdLklmvWrEFeXh5uu+02vP/++7jllluktjk5OXjmmWfMJrdcsWJFrZNbenl54dlnn8W8efPM9hkfH4+///3v0uSWS5YssWhyS045QESKVTnIuq4zRcXFjd+PnV3FbNVVzxBVPVNkxUHWLY3JJHAsw4CcolJ4OGnRI0DPM0wKYcn3t2LmaWrpGJqIyGbKyswHWd8cjNLTrTfIumPH2gdZBwQ02yBraluaMnS2inmaiIjoT8XFdZ8lysgATHVf2i6Lq2vdV575+ChmkDW1HXtOX5W6N0uNJmht2L3J0EREZGv5+XXfBPamq4QbqrydBzQhwVDVFIgqB1kzFJGC7Dl9tcaB9LlFpZj71eFmvw0NQxMRUVMSouL2HTdfgl81IOXlWWdf/v5mIeiMoyfir6qRonLDeVdvGJ2cOQCZWgyTSWBFQmq1wARUzKaeZbiBFQmpGBzi2WzjwxiaiIgaw2QyH2RdUygqKmr8fjSa6oOsq/47MBCoMnec9Be6/s8vHCOAghKb/YVOZCkl3oaGoYmIqC5lZcClS7WfJUpPr2jTWDpdzYOsK4NRQEDF1WkyKPEvdFvgFWstmxJvQ8PQREQtitW/CIuLK4JPbWeKLl2yziBrF5fazxJVDrJW133LDbmU+Bd6c1PS4GFqGCXehoahiYhajAZ9ERoMNU/aWBmObrqFUoN5eNR9e4927ZptkLUS/0JvTkobPEwNo8Tb0DA0EVGLUPlFmJlfjPLKPCAExJVs/PvI7/Do6oCuJTnVg1FurnUK8POr/VL8jh0rLtdXCCX+hd5c2DXZeijxNjQMTUSkXCYTkJkJU9o5HIj7BfedTkN7Qzba52UjwJCN9vnZcC670fj9qNX1z2Tt4CCzZNuPo1HiX+jNhV2TrYvSbkPD0EREtlNeXv8g69JSqAE815j9aLUVZ4NqGkvUqRPQvr3sQdZ1Uco4GiX+hd5c2nrXZGs0JNQLg0M8bf7HCMDQRERN6caN6oOsq4ajS5cq7ovWSNftHZDh5gOP7mHw7HFL9XDk62u1Qda1Udo4GqX9hd5c2nLXZGumVqsUcWaQoYmIGq6wsPoZoqr/zsy0ym5K9W5IdfDERTcfXNL74JKbDy7++d9Lem/kOlb81fnOg31xf//2VtmnJZQ6jkZJf6E3l9bQNamELl6qGUMTEdVMCCCnhoHVVUNRTo519uXrW+cg6z0ZNzDtvwdRbqr9/uJqlQrtnOytU4+FlDyORil/octhjbDQ0rsmldLFW5u2HugYmojaKpOp4p5mNd0AtnJZYWHj96NWV4wZqulGsJVXnjk61rkJT4OAi84OecW1TyLporODp4uu1vVNieNoGs+aYaGldk0qrYu3pvqUHOiaA0MTUWtVXg5kZNTedZaeDpSUNH4/9vb/G2Rd08SN7dtXtGmEHgF6dPV3xYFzOagpm2jUQFd/V5t1uXAcTeM0RVhoaV2TSu3iraT0QNdcGJqIWqqSEuDChdrPFF28aJVB1nByqv0sUadOFfMXNfEga6V3ubSGcTS20pRhoSV1TSq5i1fpga45MTQRKVXlIOvabgJ7+bJ19uPuXvNYosr/eno220zWdVFyl4vSQ52SKTksNCcld/HyZ/Q/DE1EtiBExUzVtd3a4/x54No16+zLx6f2UNSxI+DWcn7JKbnLRcmhTsmaMiy0pEHLSu7iVUKgU8rPkqGJqCkIUX2Q9c1nigoKGr8ftRoICKh50sbKQdZOTo3fj4IouctFyaFOqZoqLLS0QctK7uK1daBT0s+SoYmoIYzGiokZaztLlJ5eMbFjY9nbV9zCo6ZA1KlTxa0/GjnImqxLyaFOiZoiLLTEQctK7uK1ZaBT2s+SoYmoJqWlFYOsa5u48cIF6wyydnSs/SxRUBDg7w9oNI3fD7VZSunWqI21w0JLHrSs1C5eWwU6Jf4sGZqobbp+ve5JGy9fruhiayw3t5qvOKv8r5eXIgZZU+ukpG6NulgzLLT0QctK7eK1RaBT4s+SoYlap7y8mscRVT6/etU6+/H2rvkMUeXD3d06+yGykNK6NepjrbCghEHLjaXULt7mDnRK/FkyNFHLUznIuq4zRQZD4/ejUtU8yLrK7T1a2yBrsh5bdospsVtDDmuEBVsPWm7tmjPQKfFnydCkcEofj9AkjMaK7rHazhKdP2+dQdZ2dhWDrGs7UxQYCGj5i5UsZ+tuMSV2azQXJV+FRpZR4s+SoUnBbP2Lt8lUHWRdUyC6cKHiFiCN5eBQ81iiyn9zkDU1ASV0iymxW6O5KPkqNLKMEn+WDE0KpYRfvA1WVFTzpI2V4SgjwzqDrF1da74Mv/K/3t4cZN0KKfnsq1K6xZTYrdGclHoVGllOaT9LhiYFUsov3lrl59c9aeOVK9bZj5dX7WeKKgdZMxS1KUo/+6qUbjEldms0N6VehUaWU9LPkqFJgWz6i1eIitBT1+098vMbvx+VqqJ7rK5B1s7Ojd8PtRot4eyrUrrFlNitYQtKvQqNLKeUnyVDkwI16S9ek6likHVdl+MXFzeobjMaTe0zWVcOstbpGr8fahMUf/b1T0rqFlNatwZRa8DQpECN+sVbVlZ9kPXNM1mXlTW+SJ2u5ivOOnWqeAQEcJA1WY1Sur3qo7RuMSV1axC1BgxNClTXL15dWQmCrl/FHTeK0OOHM8CFdPMzRhkZFWeTGsvVte6ZrH18OJ6Imo1Sur3qo8RuMaV0axC1BgxNClT5i3d+/CE8+t1q+OVlo4MhG+3zs+FdlGednXh61n4pPgdZk8IoqdurPuwWI2q9GJoUakioFxY/2A+93vgFrtcbMLt15SDr2iZudHGxes1ETUVp3V71YbcYUevE0KRgQ0K9IG7pDBw6ZL5CowE6dKj9cvzAwIqJHYlaCSV2e9WH3WJErQ9Dk8Kppk4FcnPNA1L79hW3ACFqQ9jtRdR2KWVSW5UQ1piamQwGA9zc3JCfnw+9XhldBEStkVJ+eRJR86ic1PZUZoE0qW0XP1er/bFkyfc3Q5OVMDQRERFZ157TV/Hc+kO4WliKqmFFBcDLRYvlE/s1OjhZ8v1d9+UoRERERDZgMgm8vuk4rtwUmABAALhSWIrXNx2HydR8534YmoiIiEhxjlzKx+nswjrbnM4uxJFLVri1l0wcTaxw5eUmbDx8GZfyitDe3Qn39vaHnR2zLpFSKW3MldLqofrx936FlPQ8lNdzFqncJJCSnoc+ge7NUhND001WrVqFpUuXIjMzE3369MHKlSsxaNAgm9Ty4c4zWJlwGoUl5RCiYq7JRd8fxbMjQ/HksM42qYmIalc5YPXslevSgFVbXt2ntHqofvy9/z9CZraX284a2l50rcOGDRsQExODRYsWITk5GX369EFUVBSys7ObvZYPd57B4s2nYLhRDpOo6L81CcBwoxyLN5/ChzvPNHtNRFS7PaevYu5Xh5F0PhfZBSXIKypDdkEJks7nYu5Xh7Hn9NU2XQ/Vj7/3zfULdIddPWdF7dQq9Gums0wAQ5OZd999F08++SQef/xxdO/eHatXr4aTkxP+85//NGsd5eUmrEw4DWMtpyWNJoGVCadRXm6Fe8wRUaOZTAIrElKrTbwJVNxMOMtwAysSUpttwKrS6qH68fd+db3auyHUxwW15Sa1Cgj1cUGv9s03iSxD059KS0uRlJSEyMhIaZlarUZkZCQSExOrtS8pKYHBYDB7WMvGw5dRWFJeZ5vCknJsPHzZavskooY7lmH4c8LNmr/wyowCZ69cx7EM6/2eaEn1UP34e786tVqFl6O7w8NZC42qIiRVPjQqwNNZi5ejuzfvDbCbbU8Kd/XqVRiNRvj6+pot9/X1RWZmZrX2sbGxcHNzkx6BgYFWq+VSXhHqmz1LiIp2RGR7OUWlKDXWfQagzGhCTlFpm6yH6sff+zUbEuqFFRP7YWCndnBztIeTVgM3R3sM7NTOKnM0WYoDwRtowYIFiImJkZ4bDAarBaf27k5QqVDn/0AqVUU7IrI9DycttJq6/wa116jh4aRtk/VQ/fh7v3ZKugE2zzT9ycvLCxqNBllZWWbLs7Ky4OfnV629TqeDXq83e1jLvb394aKrO8+66Oxwb29/q+2TiBquR4AeId7OsNfU/EvcXqNCiLczegQ0z90ClFYP1Y+/9+tWeQPs4bd4o1cHN5tNm8HQ9CetVosBAwZg27Zt0jKTyYRt27YhIiKiWWuxs1Pj2ZGh0NTyodCoVXh2ZGibnLeDSInUahVmjQyDr96hWlCx16jgq3fArJFhzfaLXmn1UP34e79lYPdcFTExMZgyZQoGDhyIQYMGYdmyZbh+/Toef/zxZq+lcj6Om+frcNHZtcn5OoiUbkioF5ZM6C3Ni1RmNMHehvMiKa0eqh9/7ysfb9h7k3/+85/S5JZ9+/bFihUrEB4eXu/rmuqGvZwZlqhlUdoM3Eqrh+rH3/vNy5Lvb4YmK2mq0ERERERNx5Lvb0ZXIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZeO85K6mcWN1gMNi4EiIiIpKr8ntbzg1SGJqspKCgAAAQGBho40qIiIjIUgUFBXBzc6uzDe89ZyUmkwkZGRlwdXWFStX6boZpMBgQGBiICxcu8N56DcDj1zg8fo3HY9g4PH6No+TjJ4RAQUEBAgICoFbXPWqJZ5qsRK1Wo0OHDrYuo8np9XrFfeBbEh6/xuHxazwew8bh8WscpR6/+s4wVeJAcCIiIiIZGJqIiIiIZGBoIll0Oh0WLVoEnU5n61JaJB6/xuHxazwew8bh8Wuc1nL8OBCciIiISAaeaSIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIonRaMTLL7+M4OBgODo6onPnznj99dfN7scjhMDChQvh7+8PR0dHREZGIjU11YZVK4ec4/fYY49BpVKZPUaPHm3DqpWnoKAAs2fPRlBQEBwdHTFkyBAcOHBAWs/PYN3qO378DP7Pzp07ce+99yIgIAAqlQrffvut2Xo5n7WcnBxMnjwZer0e7u7umDp1KgoLC5vxXdiWNY5hp06dqn0mFy9e3IzvwgKC6E9vvPGG8PT0FD/88INIS0sT8fHxwsXFRSxfvlxqs3jxYuHm5ia+/fZb8fvvv4v77rtPBAcHi+LiYhtWrgxyjt+UKVPE6NGjxeXLl6VHTk6ODatWnoceekh0795d7NixQ6SmpopFixYJvV4vLl68KITgZ7A+9R0/fgb/58cffxQvvfSS+PrrrwUA8c0335itl/NZGz16tOjTp4/Yu3ev2LVrlwgNDRWTJk1q5ndiO9Y4hkFBQeK1114z+0wWFhY28zuRh6GJJNHR0eKJJ54wWzZ+/HgxefJkIYQQJpNJ+Pn5iaVLl0rr8/LyhE6nE59//nmz1qpE9R0/ISq+sMaOHdvMlbUcRUVFQqPRiB9++MFsef/+/cVLL73Ez2A96jt+QvAzWJubv/DlfNaOHz8uAIgDBw5IbX766SehUqnEpUuXmq12pWjIMRSiIjS99957zVhpw7F7jiRDhgzBtm3b8McffwAAfv/9d+zevRtjxowBAKSlpSEzMxORkZHSa9zc3BAeHo7ExESb1Kwk9R2/Stu3b4ePjw+6dOmCGTNm4Nq1a7YoV5HKy8thNBrh4OBgttzR0RG7d+/mZ7Ae9R2/SvwM1k/OZy0xMRHu7u4YOHCg1CYyMhJqtRr79u1r9pqVxpL/XxcvXgxPT0/069cPS5cuRXl5eXOXKwtv2EuS+fPnw2AwoGvXrtBoNDAajXjjjTcwefJkAEBmZiYAwNfX1+x1vr6+0rq2rL7jBwCjR4/G+PHjERwcjDNnzuDFF1/EmDFjkJiYCI1GY8PqlcHV1RURERF4/fXX0a1bN/j6+uLzzz9HYmIiQkND+RmsR33HD+BnUC45n7XMzEz4+PiYrbezs4OHhwc/j5D/nTFr1iz0798fHh4e2LNnDxYsWIDLly/j3XffbdZ65WBoIskXX3yBzz77DOvWrUOPHj2QkpKC2bNnIyAgAFOmTLF1eYon5/hNnDhRat+rVy/07t0bnTt3xvbt23HnnXfaqnRF+eSTT/DEE0+gffv20Gg06N+/PyZNmoSkpCRbl9Yi1Hf8+BkkpYmJiZH+3bt3b2i1Wjz11FOIjY1V3G1X2D1Hkjlz5mD+/PmYOHEievXqhf/7v//D888/j9jYWACAn58fACArK8vsdVlZWdK6tqy+41eTkJAQeHl54fTp081YqbJ17twZO3bsQGFhIS5cuID9+/ejrKwMISEh/AzKUNfxqwk/gzWT81nz8/NDdna22fry8nLk5OTw84iGf2eEh4ejvLwc586da8ryGoShiSRFRUVQq80/EhqNBiaTCQAQHBwMPz8/bNu2TVpvMBiwb98+RERENGutSlTf8avJxYsXce3aNfj7+zd1eS2Os7Mz/P39kZubiy1btmDs2LH8DFqgpuNXE34GaybnsxYREYG8vDyzs6AJCQkwmUwIDw9v9pqVpqH/v6akpECtVlfr+lQEW49EJ+WYMmWKaN++vXTJ/Ndffy28vLzE3LlzpTaLFy8W7u7u4rvvvhOHDx8WY8eO5eXef6rv+BUUFIi//e1vIjExUaSlpYlffvlF9O/fX4SFhYkbN27YuHrl2Lx5s/jpp5/E2bNnxc8//yz69OkjwsPDRWlpqRCCn8H61HX8+Bk0V1BQIA4dOiQOHTokAIh3331XHDp0SJw/f14IIe+zNnr0aNGvXz+xb98+sXv3bhEWFtamphxo7DHcs2ePeO+990RKSoo4c+aM+PTTT4W3t7d49NFHbfm2asXQRBKDwSCee+450bFjR+Hg4CBCQkLESy+9JEpKSqQ2JpNJvPzyy8LX11fodDpx5513ilOnTtmwauWo7/gVFRWJUaNGCW9vb2Fvby+CgoLEk08+KTIzM21cubJs2LBBhISECK1WK/z8/MTMmTNFXl6etJ6fwbrVdfz4GTT366+/CgDVHlOmTBFCyPusXbt2TUyaNEm4uLgIvV4vHn/8cVFQUGCDd2MbjT2GSUlJIjw8XLi5uQkHBwfRrVs38eabbyo2xKuEqDJdMRERERHViGOaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmImr1OnXqhGXLltm6DCJq4RiaiIiIiGRgaCIiIiKSgaGJiFq8goICTJ48Gc7OzvD398d7772HO+64A7Nnz67W9ty5c1CpVEhJSZGW5eXlQaVSYfv27dKyY8eO4Z577oFer4erqytuv/12nDlzBgBgMpnw2muvoUOHDtDpdOjbty82b94svba0tBTPPPMM/P394eDggKCgIMTGxprtb9q0afD29oZer8fIkSPx+++/W/24EJF1MTQRUYsXExOD3377Dd9//z22bt2KXbt2ITk5ucHbu3TpEoYNGwadToeEhAQkJSXhiSeeQHl5OQBg+fLleOedd/D222/j8OHDiIqKwn333YfU1FQAwIoVK/D999/jiy++wKlTp/DZZ5+hU6dO0vYffPBBZGdn46effkJSUhL69++PO++8Ezk5OY06DkTUtOxsXQARUWMUFBRg7dq1WLduHe68804AwMcff4yAgIAGb3PVqlVwc3PD+vXrYW9vDwC45ZZbpPVvv/025s2bh4kTJwIA3nrrLfz6669YtmwZVq1ahfT0dISFheG2226DSqVCUFCQ9Nrdu3dj//79yM7Ohk6nk7b37bff4ssvv8T06dMbXDcRNS2eaSKiFu3s2bMoKyvDoEGDpGVubm7o0qVLg7eZkpKC22+/XQpMVRkMBmRkZGDo0KFmy4cOHYoTJ04AAB577DGkpKSgS5cumDVrFn7++Wep3e+//47CwkJ4enrCxcVFeqSlpUndf0SkTDzTRERtilpd8beiEEJaVlZWZtbG0dGxUfvo378/0tLS8NNPP+GXX37BQw89hMjISHz55ZcoLCyEv7+/2fipSu7u7o3aLxE1LZ5pIqIWLSQkBPb29jhw4IC0LD8/H3/88UeN7b29vQEAly9flpZVHRQOAL1798auXbuqhSkA0Ov1CAgIwG+//Wa2/LfffkP37t3N2j388MP48MMPsWHDBnz11VfIyclB//79kZmZCTs7O4SGhpo9vLy8LH7/RNR8eKaJiFo0V1dXTJkyBXPmzIGHhwd8fHywaNEiqNVqqFSqau0dHR0xePBgLF68GMHBwcjOzsbf//53szbPPPMMVq5ciYkTJ2LBggVwc3PD3r17MWjQIHTp0gVz5szBokWL0LlzZ/Tt2xcff/wxUlJS8NlnnwEA3n33Xfj7+6Nfv35Qq9WIj4+Hn58f3N3dERkZiYiICIwbNw5LlizBLbfcgoyMDGzatAn3338/Bg4c2CzHjYgsx9BERC3eu+++i6efflqaImDu3Lm4cOECHBwcamz/n//8B1OnTsWAAQPQpUsXLFmyBKNGjZLWe3p6IiEhAXPmzMHw4cOh0WjQt29faRzTrFmzkJ+fjxdeeAHZ2dno3r07vv/+e4SFhQGoCHJLlixBamoqNBoNbr31Vvz4449S1+CPP/6Il156CY8//jiuXLkCPz8/DBs2DL6+vk18pIioMVSiasc+EVErcP36dbRv3x7vvPMOpk6dautyiKiV4JkmImrxDh06hJMnT2LQoEHIz8/Ha6+9BgAYO3asjSsjotaEoYmIWoW3334bp06dglarxYABA7Br1y4OrCYiq2L3HBEREZEMnHKAiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEiG/wdtjBmpuxUBOAAAAABJRU5ErkJggg==",
"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": 13,
"id": "2a595082-8fd5-4d01-895b-e7663a5e298c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation Coefficient: -0.3200733059413525\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"column1 = sleep_glucose_df['glucose']\n",
"column2 = sleep_glucose_df['Rem Sleep Duration (s)']\n",
"\n",
"# Calculate the correlation coefficient\n",
"correlation_coefficient = column1.corr(column2)\n",
"\n",
"print(f'Correlation Coefficient: {correlation_coefficient}')\n",
"\n",
"# Create a scatter plot\n",
"sns.scatterplot(x=column1, y=column2)\n",
"\n",
"# Add a regression line and correlation coefficient\n",
"sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
"plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
"\n",
"# Display the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "af7877e2-e9ec-452b-84f5-3ba173fb0508",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation Coefficient: -0.3426978685703337\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
}