notebooks/etri/0_parse_etri.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys\n",
    "sys.path.append('../..')\n",
    "\n",
    "from code_utils.path_conf import etri_path, interim_etri_path, processed_etri_path\n",
    "from code_utils.utils.dataframes import split_by_day\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>userId</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>handed</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>startDt</th>\n",
       "      <th>endDt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>user01</td>\n",
       "      <td>M</td>\n",
       "      <td>27</td>\n",
       "      <td>Left</td>\n",
       "      <td>167</td>\n",
       "      <td>76</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user02</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>Left</td>\n",
       "      <td>168</td>\n",
       "      <td>81</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user03</td>\n",
       "      <td>F</td>\n",
       "      <td>32</td>\n",
       "      <td>Right</td>\n",
       "      <td>166</td>\n",
       "      <td>62</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>user04</td>\n",
       "      <td>M</td>\n",
       "      <td>28</td>\n",
       "      <td>Right</td>\n",
       "      <td>171</td>\n",
       "      <td>70</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>user05</td>\n",
       "      <td>M</td>\n",
       "      <td>31</td>\n",
       "      <td>Right</td>\n",
       "      <td>177</td>\n",
       "      <td>75</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>user06</td>\n",
       "      <td>F</td>\n",
       "      <td>22</td>\n",
       "      <td>Right</td>\n",
       "      <td>164</td>\n",
       "      <td>70</td>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>2020-10-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>user07</td>\n",
       "      <td>M</td>\n",
       "      <td>30</td>\n",
       "      <td>Right</td>\n",
       "      <td>173</td>\n",
       "      <td>83</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>user08</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>Right</td>\n",
       "      <td>180</td>\n",
       "      <td>84</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>user09</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>Right</td>\n",
       "      <td>170</td>\n",
       "      <td>80</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>user10</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>Right</td>\n",
       "      <td>184</td>\n",
       "      <td>77</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>user11</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>Right</td>\n",
       "      <td>175</td>\n",
       "      <td>78</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>user12</td>\n",
       "      <td>F</td>\n",
       "      <td>30</td>\n",
       "      <td>Right</td>\n",
       "      <td>160</td>\n",
       "      <td>54</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>user21</td>\n",
       "      <td>F</td>\n",
       "      <td>26</td>\n",
       "      <td>Right</td>\n",
       "      <td>166</td>\n",
       "      <td>57</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>user22</td>\n",
       "      <td>F</td>\n",
       "      <td>30</td>\n",
       "      <td>Right</td>\n",
       "      <td>163</td>\n",
       "      <td>68</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>user23</td>\n",
       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "      <td>Right</td>\n",
       "      <td>177</td>\n",
       "      <td>84</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>user24</td>\n",
       "      <td>F</td>\n",
       "      <td>26</td>\n",
       "      <td>Right</td>\n",
       "      <td>165</td>\n",
       "      <td>67</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>user25</td>\n",
       "      <td>M</td>\n",
       "      <td>27</td>\n",
       "      <td>Right</td>\n",
       "      <td>182</td>\n",
       "      <td>72</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>user26</td>\n",
       "      <td>F</td>\n",
       "      <td>27</td>\n",
       "      <td>Right</td>\n",
       "      <td>160</td>\n",
       "      <td>47</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>user27</td>\n",
       "      <td>F</td>\n",
       "      <td>28</td>\n",
       "      <td>Right</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>2020-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>user28</td>\n",
       "      <td>M</td>\n",
       "      <td>31</td>\n",
       "      <td>Right</td>\n",
       "      <td>169</td>\n",
       "      <td>79</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>user29</td>\n",
       "      <td>M</td>\n",
       "      <td>29</td>\n",
       "      <td>Right</td>\n",
       "      <td>169</td>\n",
       "      <td>63</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>user30</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>Right</td>\n",
       "      <td>174</td>\n",
       "      <td>58</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>2020-09-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId gender  age handed  height  weight     startDt       endDt\n",
       "0   user01      M   27   Left     167      76  2020-08-30  2020-09-27\n",
       "1   user02      F   25   Left     168      81  2020-08-31  2020-09-27\n",
       "2   user03      F   32  Right     166      62  2020-08-29  2020-09-27\n",
       "3   user04      M   28  Right     171      70  2020-08-31  2020-09-27\n",
       "4   user05      M   31  Right     177      75  2020-08-31  2020-09-27\n",
       "5   user06      F   22  Right     164      70  2020-09-11  2020-10-08\n",
       "6   user07      M   30  Right     173      83  2020-08-31  2020-09-27\n",
       "7   user08      M   32  Right     180      84  2020-08-31  2020-09-25\n",
       "8   user09      M   20  Right     170      80  2020-08-31  2020-09-27\n",
       "9   user10      M   25  Right     184      77  2020-08-30  2020-09-27\n",
       "10  user11      M   32  Right     175      78  2020-08-30  2020-09-27\n",
       "11  user12      F   30  Right     160      54  2020-08-30  2020-09-27\n",
       "12  user21      F   26  Right     166      57  2020-08-29  2020-09-27\n",
       "13  user22      F   30  Right     163      68  2020-09-01  2020-09-27\n",
       "14  user23      M   35  Right     177      84  2020-08-31  2020-09-27\n",
       "15  user24      F   26  Right     165      67  2020-08-29  2020-09-27\n",
       "16  user25      M   27  Right     182      72  2020-08-31  2020-09-27\n",
       "17  user26      F   27  Right     160      47  2020-08-31  2020-09-26\n",
       "18  user27      F   28  Right     170      60  2020-09-04  2020-10-02\n",
       "19  user28      M   31  Right     169      79  2020-08-31  2020-09-26\n",
       "20  user29      M   29  Right     169      63  2020-08-30  2020-09-27\n",
       "21  user30      M   26  Right     174      58  2020-08-31  2020-09-27"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 615 entries, 0 to 614\n",
      "Data columns (total 23 columns):\n",
      " #   Column                            Non-Null Count  Dtype \n",
      "---  ------                            --------------  ----- \n",
      " 0   userId                            615 non-null    object\n",
      " 1   timezone                          615 non-null    object\n",
      " 2   date                              615 non-null    object\n",
      " 3   startDt                           615 non-null    int64 \n",
      " 4   endDt                             615 non-null    int64 \n",
      " 5   lastUpdate                        615 non-null    int64 \n",
      " 6   wakeupduration                    615 non-null    int64 \n",
      " 7   lightsleepduration                615 non-null    int64 \n",
      " 8   deepsleepduration                 615 non-null    int64 \n",
      " 9   wakeupcount                       615 non-null    int64 \n",
      " 10  durationtosleep                   615 non-null    int64 \n",
      " 11  remsleepduration                  615 non-null    int64 \n",
      " 12  durationtowakeup                  615 non-null    int64 \n",
      " 13  hr_average                        615 non-null    int64 \n",
      " 14  hr_min                            615 non-null    int64 \n",
      " 15  hr_max                            615 non-null    int64 \n",
      " 16  rr_average                        615 non-null    int64 \n",
      " 17  rr_min                            615 non-null    int64 \n",
      " 18  rr_max                            615 non-null    int64 \n",
      " 19  breathing_disturbances_intensity  615 non-null    int64 \n",
      " 20  snoring                           615 non-null    int64 \n",
      " 21  snoringepisodecount               615 non-null    int64 \n",
      " 22  sleep_score                       615 non-null    int64 \n",
      "dtypes: int64(20), object(3)\n",
      "memory usage: 110.6+ KB\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>timezone</th>\n",
       "      <th>date</th>\n",
       "      <th>startDt</th>\n",
       "      <th>endDt</th>\n",
       "      <th>lastUpdate</th>\n",
       "      <th>wakeupduration</th>\n",
       "      <th>lightsleepduration</th>\n",
       "      <th>deepsleepduration</th>\n",
       "      <th>wakeupcount</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_average</th>\n",
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       "      <th>hr_max</th>\n",
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       "      <th>rr_min</th>\n",
       "      <th>rr_max</th>\n",
       "      <th>breathing_disturbances_intensity</th>\n",
       "      <th>snoring</th>\n",
       "      <th>snoringepisodecount</th>\n",
       "      <th>sleep_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>user24</td>\n",
       "      <td>Asia/Seoul</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1598708760</td>\n",
       "      <td>1598740860</td>\n",
       "      <td>1598748252</td>\n",
       "      <td>3840</td>\n",
       "      <td>13800</td>\n",
       "      <td>9000</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>82</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user24</td>\n",
       "      <td>Asia/Seoul</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1598800140</td>\n",
       "      <td>1598825940</td>\n",
       "      <td>1598833333</td>\n",
       "      <td>4740</td>\n",
       "      <td>9540</td>\n",
       "      <td>6360</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>60</td>\n",
       "      <td>82</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2700</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user24</td>\n",
       "      <td>Asia/Seoul</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>1598885940</td>\n",
       "      <td>1598908800</td>\n",
       "      <td>1598916190</td>\n",
       "      <td>1920</td>\n",
       "      <td>8760</td>\n",
       "      <td>4740</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>58</td>\n",
       "      <td>94</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>user24</td>\n",
       "      <td>Asia/Seoul</td>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>1598979420</td>\n",
       "      <td>1598999580</td>\n",
       "      <td>1599006969</td>\n",
       "      <td>2760</td>\n",
       "      <td>7740</td>\n",
       "      <td>7440</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>240</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>user24</td>\n",
       "      <td>Asia/Seoul</td>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1599060780</td>\n",
       "      <td>1599085980</td>\n",
       "      <td>1599093370</td>\n",
       "      <td>3900</td>\n",
       "      <td>11640</td>\n",
       "      <td>7620</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>61</td>\n",
       "      <td>83</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId    timezone        date     startDt       endDt  lastUpdate   \n",
       "0  user24  Asia/Seoul  2020-08-30  1598708760  1598740860  1598748252  \\\n",
       "1  user24  Asia/Seoul  2020-08-31  1598800140  1598825940  1598833333   \n",
       "2  user24  Asia/Seoul  2020-09-01  1598885940  1598908800  1598916190   \n",
       "3  user24  Asia/Seoul  2020-09-02  1598979420  1598999580  1599006969   \n",
       "4  user24  Asia/Seoul  2020-09-03  1599060780  1599085980  1599093370   \n",
       "\n",
       "   wakeupduration  lightsleepduration  deepsleepduration  wakeupcount  ...   \n",
       "0            3840               13800               9000            3  ...  \\\n",
       "1            4740                9540               6360            1  ...   \n",
       "2            1920                8760               4740            1  ...   \n",
       "3            2760                7740               7440            1  ...   \n",
       "4            3900               11640               7620            1  ...   \n",
       "\n",
       "   hr_average  hr_min  hr_max  rr_average  rr_min  rr_max   \n",
       "0          72      63      82          15      10      20  \\\n",
       "1          69      60      82          15      11      20   \n",
       "2          70      58      94          15      12      23   \n",
       "3          68      60      80          15      12      19   \n",
       "4          71      61      83          15      11      25   \n",
       "\n",
       "   breathing_disturbances_intensity  snoring  snoringepisodecount  sleep_score  \n",
       "0                                10      840                    1           86  \n",
       "1                                 8     2700                    3           52  \n",
       "2                                 8        0                    0           61  \n",
       "3                                -1      240                    1           40  \n",
       "4                                 7      300                    1           55  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1230 entries, 0 to 1229\n",
      "Data columns (total 17 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   userId        1230 non-null   object \n",
      " 1   date          1230 non-null   object \n",
      " 2   amPm          1230 non-null   object \n",
      " 3   startInput    1230 non-null   object \n",
      " 4   endInput      1230 non-null   object \n",
      " 5   sleep         615 non-null    float64\n",
      " 6   sleepProblem  615 non-null    float64\n",
      " 7   dream         615 non-null    float64\n",
      " 8   amCondition   615 non-null    float64\n",
      " 9   amEmotion     615 non-null    float64\n",
      " 10  pmEmotion     615 non-null    float64\n",
      " 11  pmStress      615 non-null    float64\n",
      " 12  pmFatigue     615 non-null    float64\n",
      " 13  caffeine      282 non-null    object \n",
      " 14  cAmount(ml)   282 non-null    float64\n",
      " 15  alcohol       114 non-null    object \n",
      " 16  aAmount(ml)   113 non-null    float64\n",
      "dtypes: float64(10), object(7)\n",
      "memory usage: 163.5+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>userId</th>\n",
       "      <th>date</th>\n",
       "      <th>amPm</th>\n",
       "      <th>startInput</th>\n",
       "      <th>endInput</th>\n",
       "      <th>sleep</th>\n",
       "      <th>sleepProblem</th>\n",
       "      <th>dream</th>\n",
       "      <th>amCondition</th>\n",
       "      <th>amEmotion</th>\n",
       "      <th>pmEmotion</th>\n",
       "      <th>pmStress</th>\n",
       "      <th>pmFatigue</th>\n",
       "      <th>caffeine</th>\n",
       "      <th>cAmount(ml)</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>aAmount(ml)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>user24</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>am</td>\n",
       "      <td>2020-08-29 21:23</td>\n",
       "      <td>2020-08-29 21:23</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user24</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>pm</td>\n",
       "      <td>2020-08-29 21:23</td>\n",
       "      <td>2020-08-29 21:23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user24</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>am</td>\n",
       "      <td>2020-08-30 7:41</td>\n",
       "      <td>2020-08-30 7:41</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>user24</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>pm</td>\n",
       "      <td>2020-08-30 22:43</td>\n",
       "      <td>2020-08-30 22:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>coffee</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>user24</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>am</td>\n",
       "      <td>2020-08-31 7:23</td>\n",
       "      <td>2020-08-31 7:24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId        date amPm        startInput          endInput  sleep   \n",
       "0  user24  2020-08-29   am  2020-08-29 21:23  2020-08-29 21:23    4.0  \\\n",
       "1  user24  2020-08-29   pm  2020-08-29 21:23  2020-08-29 21:23    NaN   \n",
       "2  user24  2020-08-30   am   2020-08-30 7:41   2020-08-30 7:41    3.0   \n",
       "3  user24  2020-08-30   pm  2020-08-30 22:43  2020-08-30 22:43    NaN   \n",
       "4  user24  2020-08-31   am   2020-08-31 7:23   2020-08-31 7:24    3.0   \n",
       "\n",
       "   sleepProblem  dream  amCondition  amEmotion  pmEmotion  pmStress   \n",
       "0           0.0    3.0          3.0        4.0        NaN       NaN  \\\n",
       "1           NaN    NaN          NaN        NaN        4.0       4.0   \n",
       "2           2.0    2.0          2.0        3.0        NaN       NaN   \n",
       "3           NaN    NaN          NaN        NaN        4.0       5.0   \n",
       "4           2.0    2.0          3.0        3.0        NaN       NaN   \n",
       "\n",
       "   pmFatigue caffeine  cAmount(ml) alcohol  aAmount(ml)  \n",
       "0        NaN      NaN          NaN     NaN          NaN  \n",
       "1        2.0      NaN          NaN     NaN          NaN  \n",
       "2        NaN      NaN          NaN     NaN          NaN  \n",
       "3        2.0   coffee        200.0     NaN          NaN  \n",
       "4        NaN      NaN          NaN     NaN          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_user_info = (\n",
    "    pd.read_csv(etri_path / \"user_info_2020.csv\")\n",
    "    .sort_values(\"userId\")\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "display(df_user_info)\n",
    "df_user_sleep = pd.read_csv(etri_path / \"user_sleep_2020.csv\")\n",
    "display(df_user_sleep.info())\n",
    "display(df_user_sleep.head())\n",
    "df_user_survey = pd.read_csv(etri_path / \"user_survey_2020.csv\")\n",
    "display(df_user_survey.info())\n",
    "display(df_user_survey.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asia/Seoul'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_sleep[\"timezone\"].unique()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parsing the raw sensor data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the dtypes of the sensors\n",
    "sensor_dtype_mapping_dict = {\n",
    "    \"e4Acc\": {\"x\": \"float32\", \"y\": \"float32\", \"z\": \"float32\"},\n",
    "    \"e4Bvp\": {\"value\": \"float32\"},\n",
    "    \"e4Eda\": {\"eda\": \"float32\"},\n",
    "    \"e4Hr\": {\"hr\": \"float32\"},\n",
    "    \"e4Temp\": {\"temp\": \"float32\"},\n",
    "    \"mAcc\": {\"x\": \"float32\", \"y\": \"float32\", \"z\": \"float32\"},\n",
    "    \"mGps\": {\"lat\": \"float32\", \"long\": \"float32\", \"accuracy\": \"float32\"},\n",
    "    \"mGyr\": {k: \"float32\" for k in [\"x\", \"y\", \"z\", \"roll\", \"pitch\", \"yaw\"]},\n",
    "    \"mMag\": {k: \"float32\" for k in [\"x\", \"y\", \"z\"]},\n",
    "}\n",
    "\n",
    "# rename some sensor columns\n",
    "sensor_rename_mapping_dict = {\"e4Bvp\": {\"value\": \"bvp\"}}\n",
    "\n",
    "# the dtypes of the label csv\n",
    "label_dtype_mapping_dict = {\n",
    "    \"action\": \"category\",\n",
    "    \"actionSub\": \"category\",\n",
    "    \"actionSubOption\": \"int\",\n",
    "    # TODO\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "from pathlib import Path\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "def process_etri_sensor_csv(sensor_csv_path: Path, tz_str: str = 'Asia/Seoul') -> pd.DataFrame:\n",
    "    ts = pd.Timestamp(int(sensor_csv_path.stem), unit='s', tz=tz_str)\n",
    "    sensor_type = sensor_csv_path.parent.stem\n",
    "    sensor_dict = sensor_dtype_mapping_dict[sensor_type]\n",
    "    df_sensor = pd.read_csv(sensor_csv_path, dtype=sensor_dict)\n",
    "    df_sensor['timestamp'] = ts + pd.to_timedelta(df_sensor['timestamp'], unit='s')\n",
    "    if sensor_type in sensor_rename_mapping_dict:\n",
    "        df_sensor = df_sensor.rename(columns=sensor_rename_mapping_dict[sensor_type])\n",
    "    return df_sensor\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>eda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-31 21:44:50.250000+09:00</td>\n",
       "      <td>0.831534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-31 21:44:50.500000+09:00</td>\n",
       "      <td>1.173895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08-31 21:44:50.750000+09:00</td>\n",
       "      <td>1.613435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08-31 21:44:51+09:00</td>\n",
       "      <td>2.091441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-31 21:44:51.250000+09:00</td>\n",
       "      <td>2.579798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-08-31 21:44:51.500000+09:00</td>\n",
       "      <td>3.099109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-08-31 21:44:51.750000+09:00</td>\n",
       "      <td>3.594112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-08-31 21:44:52+09:00</td>\n",
       "      <td>3.934846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-08-31 21:44:52.250000+09:00</td>\n",
       "      <td>3.972996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-08-31 21:44:52.500000+09:00</td>\n",
       "      <td>3.970434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-08-31 21:44:52.750000+09:00</td>\n",
       "      <td>3.964027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-08-31 21:44:53+09:00</td>\n",
       "      <td>3.960184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-08-31 21:44:53.250000+09:00</td>\n",
       "      <td>3.956340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-08-31 21:44:53.500000+09:00</td>\n",
       "      <td>3.952496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-08-31 21:44:53.750000+09:00</td>\n",
       "      <td>3.964027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-08-31 21:44:54+09:00</td>\n",
       "      <td>3.975559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-08-31 21:44:54.250000+09:00</td>\n",
       "      <td>3.983246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-08-31 21:44:54.500000+09:00</td>\n",
       "      <td>3.984528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-08-31 21:44:54.750000+09:00</td>\n",
       "      <td>3.985809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-08-31 21:44:55+09:00</td>\n",
       "      <td>3.983246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-08-31 21:44:55.250000+09:00</td>\n",
       "      <td>3.976840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-08-31 21:44:55.500000+09:00</td>\n",
       "      <td>3.976840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-08-31 21:44:55.750000+09:00</td>\n",
       "      <td>3.967871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-08-31 21:44:56+09:00</td>\n",
       "      <td>3.964027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-08-31 21:44:56.250000+09:00</td>\n",
       "      <td>3.967871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-08-31 21:44:56.500000+09:00</td>\n",
       "      <td>3.962746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-08-31 21:44:56.750000+09:00</td>\n",
       "      <td>3.994777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-08-31 21:44:57+09:00</td>\n",
       "      <td>4.075497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-08-31 21:44:57.250000+09:00</td>\n",
       "      <td>4.165184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-08-31 21:44:57.500000+09:00</td>\n",
       "      <td>4.229247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020-08-31 21:44:57.750000+09:00</td>\n",
       "      <td>4.268966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020-08-31 21:44:58+09:00</td>\n",
       "      <td>4.280497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020-08-31 21:44:58.250000+09:00</td>\n",
       "      <td>4.286903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020-08-31 21:44:58.500000+09:00</td>\n",
       "      <td>4.275372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020-08-31 21:44:58.750000+09:00</td>\n",
       "      <td>4.268966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020-08-31 21:44:59+09:00</td>\n",
       "      <td>4.258716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-08-31 21:44:59.250000+09:00</td>\n",
       "      <td>4.244622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-08-31 21:44:59.500000+09:00</td>\n",
       "      <td>4.229247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-08-31 21:44:59.750000+09:00</td>\n",
       "      <td>4.212591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          timestamp       eda\n",
       "0  2020-08-31 21:44:50.250000+09:00  0.831534\n",
       "1  2020-08-31 21:44:50.500000+09:00  1.173895\n",
       "2  2020-08-31 21:44:50.750000+09:00  1.613435\n",
       "3         2020-08-31 21:44:51+09:00  2.091441\n",
       "4  2020-08-31 21:44:51.250000+09:00  2.579798\n",
       "5  2020-08-31 21:44:51.500000+09:00  3.099109\n",
       "6  2020-08-31 21:44:51.750000+09:00  3.594112\n",
       "7         2020-08-31 21:44:52+09:00  3.934846\n",
       "8  2020-08-31 21:44:52.250000+09:00  3.972996\n",
       "9  2020-08-31 21:44:52.500000+09:00  3.970434\n",
       "10 2020-08-31 21:44:52.750000+09:00  3.964027\n",
       "11        2020-08-31 21:44:53+09:00  3.960184\n",
       "12 2020-08-31 21:44:53.250000+09:00  3.956340\n",
       "13 2020-08-31 21:44:53.500000+09:00  3.952496\n",
       "14 2020-08-31 21:44:53.750000+09:00  3.964027\n",
       "15        2020-08-31 21:44:54+09:00  3.975559\n",
       "16 2020-08-31 21:44:54.250000+09:00  3.983246\n",
       "17 2020-08-31 21:44:54.500000+09:00  3.984528\n",
       "18 2020-08-31 21:44:54.750000+09:00  3.985809\n",
       "19        2020-08-31 21:44:55+09:00  3.983246\n",
       "20 2020-08-31 21:44:55.250000+09:00  3.976840\n",
       "21 2020-08-31 21:44:55.500000+09:00  3.976840\n",
       "22 2020-08-31 21:44:55.750000+09:00  3.967871\n",
       "23        2020-08-31 21:44:56+09:00  3.964027\n",
       "24 2020-08-31 21:44:56.250000+09:00  3.967871\n",
       "25 2020-08-31 21:44:56.500000+09:00  3.962746\n",
       "26 2020-08-31 21:44:56.750000+09:00  3.994777\n",
       "27        2020-08-31 21:44:57+09:00  4.075497\n",
       "28 2020-08-31 21:44:57.250000+09:00  4.165184\n",
       "29 2020-08-31 21:44:57.500000+09:00  4.229247\n",
       "30 2020-08-31 21:44:57.750000+09:00  4.268966\n",
       "31        2020-08-31 21:44:58+09:00  4.280497\n",
       "32 2020-08-31 21:44:58.250000+09:00  4.286903\n",
       "33 2020-08-31 21:44:58.500000+09:00  4.275372\n",
       "34 2020-08-31 21:44:58.750000+09:00  4.268966\n",
       "35        2020-08-31 21:44:59+09:00  4.258716\n",
       "36 2020-08-31 21:44:59.250000+09:00  4.244622\n",
       "37 2020-08-31 21:44:59.500000+09:00  4.229247\n",
       "38 2020-08-31 21:44:59.750000+09:00  4.212591"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sensor_csv_path = list(interim_etri_path.glob('*/user05/*/e4Acc/*.csv'))[0]\n",
    "sensor_csv_path = list(interim_etri_path.glob(\"*/user05/*/e4Eda/*.csv\"))[0]\n",
    "df_sensor = process_etri_sensor_csv(sensor_csv_path)\n",
    "df_sensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_id_bar = tqdm([\"user{:02d}\".format(i) for i in range(30, 31)])\n",
    "for user_id in user_id_bar:\n",
    "    user_id_bar.set_description(user_id)\n",
    "    sensor_bar = tqdm(sensor_dtype_mapping_dict.keys(), leave=True)\n",
    "    for sensor in sensor_bar:\n",
    "        sensor_bar.set_description(sensor)\n",
    "        sl = []\n",
    "        for sensor_csv_path in list( interim_etri_path.glob(f\"*/{user_id}/*/{sensor}/*.csv\")):\n",
    "            sl.append(process_etri_sensor_csv(sensor_csv_path, \"Asia/Seoul\"))\n",
    "        if not len(sl):\n",
    "            continue\n",
    "        df_sensor = pd.concat(sl, ignore_index=True).sort_values(by=\"timestamp\")\n",
    "\n",
    "        for date, df in split_by_day(df_sensor, time_col=\"timestamp\"):\n",
    "            save_dir = processed_etri_path / user_id\n",
    "            save_dir.mkdir(parents=True, exist_ok=True)\n",
    "            df.to_parquet( save_dir / f\"{sensor}_{date.strftime('%Y_%m_%d')}.parquet\", engine=\"pyarrow\",)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parsing the `labels` csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from code_utils.etri.mapping_dicts import (\n",
    "    action_option_mapping,\n",
    "    action_sub_option_mapping,\n",
    "    condition_sub1_option_mapping,\n",
    "    condition_sub2_option_mapping,\n",
    "    activity_mapping,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_label_csv_file(label_csv_path: Path) -> pd.DataFrame:\n",
    "    df_label = pd.read_csv(label_csv_path)\n",
    "\n",
    "    # fmt: off\n",
    "    df_label[\"ts\"] = pd.to_datetime(df_label[\"ts\"], unit=\"s\", utc=True).dt.tz_convert( \"Asia/Seoul\")\n",
    "    df_label[\"actionSubOption\"] = df_label.apply(\n",
    "        lambda r: action_sub_option_mapping.get(r.actionSub, {}).get(r.actionSubOption),\n",
    "        axis=1,\n",
    "    )\n",
    "    df_label[\"actionOption\"] = df_label[\"actionOption\"].astype(int).map(lambda x: action_option_mapping[x])\n",
    "    df_label[\"conditionSub1Option\"] = df_label[\"conditionSub1Option\"].map( lambda x: condition_sub1_option_mapping.get(x))\n",
    "    df_label[\"conditionSub2Option\"] = df_label[\"conditionSub2Option\"].map( lambda x: condition_sub2_option_mapping.get(x))\n",
    "    df_label[\"activity\"] = df_label[\"activity\"].map( lambda x: activity_mapping.get(x))\n",
    "    df_label[\"user\"] = label_csv_path.parent.parent.stem\n",
    "    return df_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>ts</th>\n",
       "      <th>action</th>\n",
       "      <th>actionOption</th>\n",
       "      <th>actionSub</th>\n",
       "      <th>actionSubOption</th>\n",
       "      <th>condition</th>\n",
       "      <th>conditionSub1Option</th>\n",
       "      <th>conditionSub2Option</th>\n",
       "      <th>place</th>\n",
       "      <th>emotionPositive</th>\n",
       "      <th>emotionTension</th>\n",
       "      <th>activity</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-31 08:00:00+09:00</td>\n",
       "      <td>meal</td>\n",
       "      <td>Meal</td>\n",
       "      <td>meal_amount</td>\n",
       "      <td>Light</td>\n",
       "      <td>ALONE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>home</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-31 08:01:00+09:00</td>\n",
       "      <td>meal</td>\n",
       "      <td>Meal</td>\n",
       "      <td>meal_amount</td>\n",
       "      <td>Light</td>\n",
       "      <td>ALONE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>home</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08-31 08:02:00+09:00</td>\n",
       "      <td>meal</td>\n",
       "      <td>Meal</td>\n",
       "      <td>meal_amount</td>\n",
       "      <td>Light</td>\n",
       "      <td>ALONE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>home</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08-31 08:03:00+09:00</td>\n",
       "      <td>meal</td>\n",
       "      <td>Meal</td>\n",
       "      <td>meal_amount</td>\n",
       "      <td>Light</td>\n",
       "      <td>ALONE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>home</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-31 08:04:00+09:00</td>\n",
       "      <td>meal</td>\n",
       "      <td>Meal</td>\n",
       "      <td>meal_amount</td>\n",
       "      <td>Light</td>\n",
       "      <td>ALONE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>home</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>2020-08-31 21:26:00+09:00</td>\n",
       "      <td>recreation_media</td>\n",
       "      <td>Watching TV or video</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>WITH_ONE</td>\n",
       "      <td>With family</td>\n",
       "      <td>Passive in conversation</td>\n",
       "      <td>other_indoor</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>2020-08-31 21:28:00+09:00</td>\n",
       "      <td>recreation_media</td>\n",
       "      <td>Watching TV or video</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>WITH_ONE</td>\n",
       "      <td>With family</td>\n",
       "      <td>Passive in conversation</td>\n",
       "      <td>other_indoor</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>2020-08-31 21:43:00+09:00</td>\n",
       "      <td>household</td>\n",
       "      <td>Purchasing goods and services (grocery/take-out)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>ALONE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>other_indoor</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2020-08-31 21:44:00+09:00</td>\n",
       "      <td>household</td>\n",
       "      <td>Purchasing goods and services (grocery/take-out)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>ALONE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>other_indoor</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>2020-08-31 21:46:00+09:00</td>\n",
       "      <td>household</td>\n",
       "      <td>Purchasing goods and services (grocery/take-out)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>ALONE</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>other_indoor</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Still</td>\n",
       "      <td>user05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ts            action   \n",
       "0   2020-08-31 08:00:00+09:00              meal  \\\n",
       "1   2020-08-31 08:01:00+09:00              meal   \n",
       "2   2020-08-31 08:02:00+09:00              meal   \n",
       "3   2020-08-31 08:03:00+09:00              meal   \n",
       "4   2020-08-31 08:04:00+09:00              meal   \n",
       "..                        ...               ...   \n",
       "750 2020-08-31 21:26:00+09:00  recreation_media   \n",
       "751 2020-08-31 21:28:00+09:00  recreation_media   \n",
       "752 2020-08-31 21:43:00+09:00         household   \n",
       "753 2020-08-31 21:44:00+09:00         household   \n",
       "754 2020-08-31 21:46:00+09:00         household   \n",
       "\n",
       "                                         actionOption    actionSub   \n",
       "0                                                Meal  meal_amount  \\\n",
       "1                                                Meal  meal_amount   \n",
       "2                                                Meal  meal_amount   \n",
       "3                                                Meal  meal_amount   \n",
       "4                                                Meal  meal_amount   \n",
       "..                                                ...          ...   \n",
       "750                              Watching TV or video          NaN   \n",
       "751                              Watching TV or video          NaN   \n",
       "752  Purchasing goods and services (grocery/take-out)          NaN   \n",
       "753  Purchasing goods and services (grocery/take-out)          NaN   \n",
       "754  Purchasing goods and services (grocery/take-out)          NaN   \n",
       "\n",
       "    actionSubOption condition conditionSub1Option      conditionSub2Option   \n",
       "0             Light     ALONE                None                     None  \\\n",
       "1             Light     ALONE                None                     None   \n",
       "2             Light     ALONE                None                     None   \n",
       "3             Light     ALONE                None                     None   \n",
       "4             Light     ALONE                None                     None   \n",
       "..              ...       ...                 ...                      ...   \n",
       "750            None  WITH_ONE         With family  Passive in conversation   \n",
       "751            None  WITH_ONE         With family  Passive in conversation   \n",
       "752            None     ALONE                None                     None   \n",
       "753            None     ALONE                None                     None   \n",
       "754            None     ALONE                None                     None   \n",
       "\n",
       "            place  emotionPositive  emotionTension activity    user  \n",
       "0            home                5               1    Still  user05  \n",
       "1            home                5               1    Still  user05  \n",
       "2            home                5               1    Still  user05  \n",
       "3            home                5               1    Still  user05  \n",
       "4            home                5               1    Still  user05  \n",
       "..            ...              ...             ...      ...     ...  \n",
       "750  other_indoor                5               1    Still  user05  \n",
       "751  other_indoor                5               1    Still  user05  \n",
       "752  other_indoor                5               2    Still  user05  \n",
       "753  other_indoor                5               2    Still  user05  \n",
       "754  other_indoor                5               2    Still  user05  \n",
       "\n",
       "[755 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parse the label csv\n",
    "label_csv_path  = list(interim_etri_path.glob(\"*/user05/*/*_label.csv\"))[0]\n",
    "parse_label_csv_file(label_csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7f120188cb1475f809ca1d933b20cd7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/571 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_csv_list = []\n",
    "for label_csv_file in tqdm(list(interim_etri_path.glob(\"*/*/*/*_label.csv\"))):\n",
    "    label_csv_list.append(parse_label_csv_file(label_csv_file))\n",
    "df_label_tot = pd.concat(label_csv_list, ignore_index=True).rename(\n",
    "    columns={\"ts\": \"timestamp\"}\n",
    ")\n",
    "category_cols = [\n",
    "    \"action\",\n",
    "    \"actionOption\",\n",
    "    \"actionSub\",\n",
    "    \"actionSubOption\",\n",
    "    \"condition\",\n",
    "    \"conditionSub1Option\",\n",
    "    \"conditionSub2Option\",\n",
    "    \"activity\",\n",
    "    \"user\",\n",
    "    \"place\",\n",
    "]\n",
    "for col in category_cols:\n",
    "    df_label_tot[col] = df_label_tot[col].astype(\"category\")\n",
    "\n",
    "df_label_tot.sort_values(by=['user', 'timestamp'], inplace=True)\n",
    "df_label_tot.to_parquet(processed_etri_path / \"labels.parquet\", engine=\"pyarrow\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Snippet to test timeline functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_label_tot = pd.read_parquet(processed_etri_path / \"labels.parquet\", engine=\"pyarrow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly_resampler import FigureResampler\n",
    "from plotly_resampler.aggregation import NoGapHandler, EveryNthPoint\n",
    "from itertools import cycle\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "\n",
    "user = \"user01\"\n",
    "\n",
    "fr = FigureResampler()\n",
    "df_label_usr = (\n",
    "    df_label_tot.query(f\"user == '{user}'\")\n",
    "    .drop_duplicates(subset=[\"timestamp\"])\n",
    "    .set_index(\"timestamp\")\n",
    ")\n",
    "df_label_usr = df_label_usr.reindex(\n",
    "    pd.date_range(df_label_usr.index[0], df_label_usr.index[-1], freq=\"1min\")\n",
    ")\n",
    "\n",
    "cols_to_plot = [\"activity\", \"place\", \"condition\"][-3:]\n",
    "\n",
    "for i, col in enumerate(cols_to_plot):\n",
    "    s = df_label_usr[col]\n",
    "    color_cycle = cycle(px.colors.qualitative.Plotly)\n",
    "    factor = len(cols_to_plot) - 1 - i\n",
    "    for col_idx, k in enumerate(s.cat.categories):\n",
    "        fr.add_trace(\n",
    "            go.Scatter(showlegend=False, line_width=0, mode=\"lines\", line_shape=\"hvh\"),\n",
    "            hf_x=pd.Index([df_label_usr.index[0], df_label_usr.index[-1]]),\n",
    "            hf_y=np.zeros(2) + 1.2 * factor,\n",
    "            # hf_x=df_label_usr.index,\n",
    "            # hf_y=np.zeros(len(df_label_usr)) + 1.2 * factor,\n",
    "        )\n",
    "        fr.add_trace(\n",
    "            go.Scatter(\n",
    "                name=k,\n",
    "                hoveron=\"fills\",\n",
    "                legendgroup=f\"group_{col}\",\n",
    "                legendgrouptitle=dict(text=f\"<b>{col.capitalize()}</b>:\"),\n",
    "                line_color=next(color_cycle),\n",
    "                line_shape=\"hvh\",\n",
    "                fill=\"tonexty\",\n",
    "                line_width=0,\n",
    "            ),\n",
    "            hf_x=df_label_usr.index,\n",
    "            hf_y=(s == k).astype(float) + 1.2 * factor\n",
    "        )\n",
    "\n",
    "for sat in np.unique(df_label_usr[df_label_usr.index.dayofweek == 5].index.date):\n",
    "    fr.add_vrect(\n",
    "        x0=sat,\n",
    "        x1=sat + pd.Timedelta(days=2),\n",
    "        fillcolor=\"rgba(0, 0, 0, .1)\",\n",
    "        line_width=0,\n",
    "    )\n",
    "fr.update_layout(template=\"plotly_white\", yaxis=dict(visible=False))\n",
    "fr.show(renderer='png', width=1400, height=550)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from code_utils.etri.visualization import add_etri_timeline_to_fig\n",
    "from plotly.subplots import make_subplots\n",
    "from plotly_resampler import FigureResampler\n",
    "\n",
    "user = \"user05\"\n",
    "fr = FigureResampler(make_subplots(rows=1, cols=1), default_n_shown_samples=2000)\n",
    "\n",
    "# The shaded area is the weekend\n",
    "add_etri_timeline_to_fig(fr, df_label_tot[df_label_tot.user == user], row=1, col=1)\n",
    "\n",
    "fr.update_layout(template=\"plotly_white\", height=700, yaxis=dict(visible=False))\n",
    "fr.show(renderer=\"png\", width=1300, height=550)\n",
    "# fr.show_dash(mode=\"external\", port=8050)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "data-quality-wearables-JR_qNb0v-py3.9",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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