transform_data_CHPrep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: 20221212\n",
    "- sensor會重複寫入無法校正\n",
    "    - 直接使用\n",
    "\n",
    "- 篩除掉sensor NaN、mismatch後的資料直接使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.22.3\n",
      "1.5.2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "# import feather\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "print(np.__version__)\n",
    "print(pd.__version__)\n",
    "dir_data = Path('data/preprocessed_CH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/c/Users/yunghui/Documents/DORA\n",
      "data/:\n",
      "total 1514640\n",
      "-rwxrwxrwx 1 yunghui yunghui 1550988456 Nov 18 10:45 cars.7z\n",
      "drwxrwxrwx 1 yunghui yunghui        512 Dec  8 16:41 preprocessed_Andy\n",
      "drwxrwxrwx 1 yunghui yunghui        512 Dec  8 16:37 preprocessed_CH\n",
      "\n",
      "data/preprocessed_Andy:\n",
      "total 6420\n",
      "-rwxrwxrwx 1 yunghui yunghui 6573925 Dec  7 15:05 HR55AD8921_Target.csv\n",
      "\n",
      "data/preprocessed_CH:\n",
      "total 14136\n",
      "-rwxrwxrwx 1 yunghui yunghui  323994 Dec  6 13:33 df_event_KA01AF2992.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui 1354946 Dec  6 13:33 df_event_KA01AF482.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui  557290 Dec  1 16:00 df_event_KA04MW6569.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui 2166618 Dec  1 16:00 df_event_KA51AG8742.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui  615970 Dec  6 13:33 df_event_MH04KF8187.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui  307386 Dec  5 16:23 df_event_agg_KA01AF2992.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui 1245690 Dec  5 16:23 df_event_agg_KA01AF482.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui  489906 Dec  5 16:22 df_event_agg_KA04MW6569.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui 1833666 Dec  5 16:23 df_event_agg_KA51AG8742.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui  572874 Dec  5 16:23 df_event_agg_MH04KF8187.feather\n",
      "-rwxrwxrwx 1 yunghui yunghui 4981634 Dec  7 09:39 df_event_all_v1.feather\n"
     ]
    }
   ],
   "source": [
    "!pwd\n",
    "# !ls -l\n",
    "!ls -lR data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Explore with CH's preprocessed data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check out the data profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load data after filter Fierce behavior event "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('data/preprocessed_CH/df_event_KA01AF2992.feather'),\n",
       " PosixPath('data/preprocessed_CH/df_event_KA01AF482.feather'),\n",
       " PosixPath('data/preprocessed_CH/df_event_KA04MW6569.feather'),\n",
       " PosixPath('data/preprocessed_CH/df_event_KA51AG8742.feather'),\n",
       " PosixPath('data/preprocessed_CH/df_event_MH04KF8187.feather')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Load files\n",
    "# path_event_DORA = [path for path in list(dir_data.glob('*'))\n",
    "#              if path.suffix in ['.csv', '.feather'] \n",
    "#              and 'all' not in path.name\n",
    "#              and 'agg' not in path.name\n",
    "#              ]\n",
    "# path_event_DORA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'vehicle_id', 'latitude', 'longitude', 'speed', 'heading',\n",
      "       'timestamp', 'gsen_timeTick', 'gyro_timeTick', 'gsen_X', 'gyro_X',\n",
      "       'gsen_Y', 'gyro_Y', 'gsen_Z', 'gyro_Z', 'timestamp_conv', 'event_id'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20504 entries, 0 to 20503\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   id              20504 non-null  int64         \n",
      " 1   vehicle_id      20504 non-null  object        \n",
      " 2   latitude        20504 non-null  float64       \n",
      " 3   longitude       20504 non-null  float64       \n",
      " 4   speed           20504 non-null  float64       \n",
      " 5   heading         20504 non-null  float64       \n",
      " 6   timestamp       20504 non-null  int64         \n",
      " 7   gsen_timeTick   20504 non-null  object        \n",
      " 8   gyro_timeTick   20504 non-null  object        \n",
      " 9   gsen_X          20504 non-null  object        \n",
      " 10  gyro_X          20504 non-null  object        \n",
      " 11  gsen_Y          20504 non-null  object        \n",
      " 12  gyro_Y          20504 non-null  object        \n",
      " 13  gsen_Z          20504 non-null  object        \n",
      " 14  gyro_Z          20504 non-null  object        \n",
      " 15  timestamp_conv  20504 non-null  datetime64[ns]\n",
      " 16  event_id        20504 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(9)\n",
      "memory usage: 2.7+ MB\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>timestamp_conv</th>\n",
       "      <th>event_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
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       "      <td>[-0.1768442541360855, -0.3292020559310913, -0....</td>\n",
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       "      <td>[0.35891878604888916, 0.359760046005249, 0.359...</td>\n",
       "      <td>[-0.0084075927734375, -0.026397705078125, -0.0...</td>\n",
       "      <td>[0.4236612319946289, 0.5882062911987305, 0.645...</td>\n",
       "      <td>[0.024566650390625, 0.02606201171875, 0.024032...</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39079364</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094095</td>\n",
       "      <td>77.594323</td>\n",
       "      <td>4.052998</td>\n",
       "      <td>184.984238</td>\n",
       "      <td>1658901880193</td>\n",
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       "      <td>[5459092, 5459292, 5459493, 5459693, 5459892]</td>\n",
       "      <td>[-0.3817916512489319, 0.030077725648880005, 0....</td>\n",
       "      <td>[-0.0150909423828125, 0.01611328125, 0.0014190...</td>\n",
       "      <td>[0.303417444229126, -0.0026443004608154297, 0....</td>\n",
       "      <td>[-0.0308685302734375, 0.019500732421875, 0.033...</td>\n",
       "      <td>[0.7125339508056641, -1.2284603118896484, -0.1...</td>\n",
       "      <td>[0.0198822021484375, 0.017852783203125, 0.0147...</td>\n",
       "      <td>2022-07-27 06:04:40.193</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39079620</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.086250</td>\n",
       "      <td>77.593758</td>\n",
       "      <td>7.070510</td>\n",
       "      <td>185.000580</td>\n",
       "      <td>1658901973438</td>\n",
       "      <td>[5549111, 5549312, 5549512, 5549712, 5549911]</td>\n",
       "      <td>[5549111, 5549312, 5549512, 5549712, 5549911]</td>\n",
       "      <td>[0.42737051844596863, 0.10562610626220703, -0....</td>\n",
       "      <td>[0.0118560791015625, -0.0209503173828125, -0.0...</td>\n",
       "      <td>[-0.038906097412109375, 0.31329238414764404, 0...</td>\n",
       "      <td>[-0.0077667236328125, -0.0175628662109375, -0....</td>\n",
       "      <td>[-1.6793060302734375, -0.3619565963745117, 3.1...</td>\n",
       "      <td>[0.0094451904296875, 0.0131683349609375, 0.010...</td>\n",
       "      <td>2022-07-27 06:06:13.438</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         id  vehicle_id   latitude  longitude     speed     heading  \\\n",
       "0  39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "1  39079364  KA01AF2992  13.094095  77.594323  4.052998  184.984238   \n",
       "2  39079620  KA01AF2992  13.086250  77.593758  7.070510  185.000580   \n",
       "\n",
       "       timestamp                                  gsen_timeTick  \\\n",
       "0  1658901879193  [5458092, 5458294, 5458492, 5458692, 5458893]   \n",
       "1  1658901880193  [5459092, 5459293, 5459494, 5459693, 5459893]   \n",
       "2  1658901973438  [5549111, 5549312, 5549512, 5549712, 5549911]   \n",
       "\n",
       "                                   gyro_timeTick  \\\n",
       "0  [5458091, 5458294, 5458492, 5458692, 5458892]   \n",
       "1  [5459092, 5459292, 5459493, 5459693, 5459892]   \n",
       "2  [5549111, 5549312, 5549512, 5549712, 5549911]   \n",
       "\n",
       "                                              gsen_X  \\\n",
       "0  [-0.1768442541360855, -0.3292020559310913, -0....   \n",
       "1  [-0.3817916512489319, 0.030077725648880005, 0....   \n",
       "2  [0.42737051844596863, 0.10562610626220703, -0....   \n",
       "\n",
       "                                              gyro_X  \\\n",
       "0  [0.0027008056640625, -0.009552001953125, -0.01...   \n",
       "1  [-0.0150909423828125, 0.01611328125, 0.0014190...   \n",
       "2  [0.0118560791015625, -0.0209503173828125, -0.0...   \n",
       "\n",
       "                                              gsen_Y  \\\n",
       "0  [0.35891878604888916, 0.359760046005249, 0.359...   \n",
       "1  [0.303417444229126, -0.0026443004608154297, 0....   \n",
       "2  [-0.038906097412109375, 0.31329238414764404, 0...   \n",
       "\n",
       "                                              gyro_Y  \\\n",
       "0  [-0.0084075927734375, -0.026397705078125, -0.0...   \n",
       "1  [-0.0308685302734375, 0.019500732421875, 0.033...   \n",
       "2  [-0.0077667236328125, -0.0175628662109375, -0....   \n",
       "\n",
       "                                              gsen_Z  \\\n",
       "0  [0.4236612319946289, 0.5882062911987305, 0.645...   \n",
       "1  [0.7125339508056641, -1.2284603118896484, -0.1...   \n",
       "2  [-1.6793060302734375, -0.3619565963745117, 3.1...   \n",
       "\n",
       "                                              gyro_Z          timestamp_conv  \\\n",
       "0  [0.024566650390625, 0.02606201171875, 0.024032... 2022-07-27 06:04:39.193   \n",
       "1  [0.0198822021484375, 0.017852783203125, 0.0147... 2022-07-27 06:04:40.193   \n",
       "2  [0.0094451904296875, 0.0131683349609375, 0.010... 2022-07-27 06:06:13.438   \n",
       "\n",
       "   event_id  \n",
       "0         1  \n",
       "1         1  \n",
       "2         2  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Load data after filter Fierce behavior event \n",
    "# df_event = pd.concat([pd.read_feather(path_) for path_ in path_event_DORA]).reset_index(drop=True)\n",
    "# print(df_event.columns)\n",
    "# df_event.info()\n",
    "# df_event.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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/nmVlZTEfHx+NeJp6xQFjjL355pts8ODBGmW//fYbW7ZsGXNzc2N9+/ZlQqGQ9e/fn7300kssNzdXK4a8vDw2bdo0Zm5uzgwMDJiDgwObMWMGS09P16h37do1NmfOHGZpackMDAyYk5MTk0qlrK6uTl0nMTGRGRsbs6qqqmbPCSHtgceYHlZqEkJIB5s7dy7279/fbh+2Jc27cuUKnJ2d8eOPPzZ727QjPfPMM3j22Wfx3//+t7NDIU8ZWhNFCCGkVZycnDB//nzExcV1dig4cuQILl++rLV4npCOQGuiCCGEtFpT30PsDM899xzNRpJOQzNRhBBCCCEc0JooQgghhBAOaCaKEEIIIYQDSqIIIYQQQjigJIoQQrqhZ599FjweDzweDy+88EKnxBAVFaWOoWfPnp0SAyGdiZIoQkiXt3PnTvXFmsfjQSgUwtbWFnPnztXpUyV/V87OzkhKSsLSpUvVZXfv3sXHH3+Mf/7zn7C0tETv3r3xj3/8Q/0tvZbcv38f8+fPh6urK8zMzNCzZ0+IxWJ8+umnaGho0Kj76quvIikpCd7e3nodFyHdBb3igBDSbXzwwQcYOHAgHjx4gF9++QU7d+7EqVOncP78eRgaGnZ2eB3O2toas2fP1ijLysrCu+++i+effx7/7//9PwiFQnzzzTd4+eWXUVhYiPfff/+Jbd6/fx+///47nn/+eTg6OoLP5+PMmTNYsmQJfv31V+zdu1dd193dHe7u7jh27Bhyc3PbZYyEdGWURBFCuo2AgAB4eHgAABYsWAALCwusWbMGhw4dwowZMzo5uq5hxIgRuHz5MhwcHNRlr7/+OiQSCdasWYPly5fDxMSk2eP79u2LX375RaPstddeg5mZGeLj47F+/Xr069ev3eInpDuh23mEkG7r0W2k4uJidVl9fT1WrlwJd3d3mJmZwcTEBN7e3jhx4oTGsVevXgWPx8O6devw+eefY9CgQTAwMMCYMWOQnZ2t1VdKSgpcXFxgaGgIV1dXpKamYu7cuXB0dNSop1KpsGHDBowYMQKGhoawtrbGokWLcO/evRbH09DQgAsXLrTpY80DBw7USKAAgMfjITg4GHV1dbhy5Qqndh+Ns7KyknNshPzd0EwUIaTbunr1KgCgT58+6rKqqip88cUXeOWVV7Bw4UJUV1dj27Zt8Pf3x9mzZzF69GiNNvbu3Yvq6mosWrQIPB4Pa9euxbRp03DlyhX06NEDAPDDDz9g5syZGDlyJGJjY3Hv3j3Mnz8ftra2WjEtWrQIO3fuRFhYGN58802UlJQgPj4eeXl5OH36tLrNpty4cQPDhw9HaGgodu7c2ebz81e3b98GAFhYWOhUv76+HlVVVbh//z7OnTuHdevWwcHBAYMHD9ZrXIR0a537/WNCCGnZjh07GAB27NgxdufOHVZaWsr279/PLC0tmYGBASstLVXXbWxsZHV1dRrH37t3j1lbW7N58+apy0pKShgAZm5uzv788091+bfffssAsO+++05dNnLkSGZnZ8eqq6vVZSdPnmQAmIODg7rs559/ZgDYnj17NPo/cuRIk+WPexRTaGhoi+fEx8eH+fj4tFiPMcbu3r3LrKysmLe3t071GWPsq6++YgDUPw8PD/bbb781WTc0NJSZmJjo3DYhfxc0E0UI6TYkEonGtqOjI3bv3g07Ozt1mUAggEAgAPDw1lplZSVUKhU8PDyaXPw8c+ZMjZmsR7cIH932unnzJgoKCvDOO+9oPMbv4+ODkSNHoqqqSl2WkpICMzMzTJo0CRUVFepyd3d39OzZEydOnEBISEiz43N0dATT80ckVCoVZs2ahcrKSnz22Wc6Hzdx4kSkpaWhsrIS6enpyM/Ph0Kh0GtshHR3lEQRQrqNhIQEDB06FHK5HNu3b0dmZiYMDAy06u3atQuffPIJLly4oPFY/sCBA7Xq2tvba2w/SqgerWG6du0aADR5G2vw4MEaidnly5chl8thZWXVZPzl5eUtDVHvIiIicOTIEXz55ZcQi8U6H2dtbQ1ra2sAwPTp07F69WpMmjQJly9fpoXlhPz/KIkihHQbnp6e6qfzgoODMWHCBISEhODixYvqWaLdu3dj7ty5CA4OxrJly2BlZQWBQIDY2FiNBeiPPJq1ehyXGSGVSgUrKyvs2bOnyf2WlpatbrMt3n//fWzatAlxcXF49dVX29TW9OnT8e677+Lbb7/FokWL9BQhId0bJVGEkG7pUWI0ceJExMfHY8WKFQCA/fv3w8nJCQcOHACPx1PXj4mJ4dTPoyfdioqKtPY9XjZo0CAcO3YM48ePh5GREaf+9CUhIQHvvfceoqKi8NZbb7W5vfv37wMA5HJ5m9si5O+CXnFACOm2nn32WXh6emLDhg148OABgP+bWfrrTNKvv/6KrKwsTn3Y2NjA1dUVX375JWpqatTlGRkZKCgo0Kg7Y8YMKJVKrFq1SqudxsbGFl8PoI9XHABAcnIy3nzzTcyaNQvr169v1bEVFRVNzsJ98cUXAKCeCSSE0EwUIaSbW7ZsGV566SXs3LkTr732Gl544QUcOHAAU6dORWBgIEpKSrB582a4uLhoJEGtsXr1akyZMgXjx49HWFgY7t27h/j4eLi6umq06ePjg0WLFiE2NhYymQyTJ09Gjx49cPnyZaSkpODTTz/F9OnTm+1HH684OHv2LObMmQNzc3P4+flp3VocN24cnJycmj1+9+7d2Lx5M4KDg+Hk5ITq6mocPXoUaWlpCAoKgq+vL6e4CPk7oiSKENKtTZs2DYMGDcK6deuwcOFCzJ07F7dv38aWLVtw9OhRuLi4YPfu3UhJScHJkyc59REUFISvvvoK7733HlasWIEhQ4Zg586d2LVrF37//XeNups3b4a7uzu2bNmCd955B0KhEI6Ojpg9ezbGjx+vhxE/WWFhIerr63Hnzh3MmzdPa/+OHTuemERNmDABZ86cwVdffYWysjIIhUIMGzYM69evR0RERHuGTki3w2P6fp6WEEKeEqNHj4alpSXS0tI6vO9nn30WDQ0N+PbbbyESidCrV68Oj0GhUOD+/fuIiIjAd999x3mmj5DuitZEEUJICxoaGtDY2KhRdvLkSeTn5+PZZ5/tnKAAnDlzBpaWlk9891R7evfdd2FpaYl9+/Z1Sv+EdDaaiSKEkBZcvXoVEokEs2fPho2NDS5cuIDNmzfDzMwM58+fh7m5eYfHlJOTo36XlaWlZaveAaUvly5dwvXr1wEAQqGwUxNKQjoDJVGEENICuVyO8PBwnD59Gnfu3IGJiQn8/PwQFxeHQYMGdXZ4hJBOQkkUIYQQQggHtCaKEEIIIYQDesXB35RKpcLNmzdhamqq8dZmQgghhDwZYwzV1dWwsbEBn9/8fBMlUX9TN2/exIABAzo7DEIIIaTbKi0thZ2dXbP7KYn6mzI1NQXw8A+gM94fQwghhHRXVVVVGDBggPpa2hxKorqByspKSCQSNDY2orGxEZGRkVi4cOETj3l0C69Xr16URBFCCCEctLQchpKobsDU1BSZmZkwNjaGQqGAq6srpk2b1invpiGEEELIQ/R0XjcgEAhgbGwMAKirqwNjrMmvrBNCCCGk43SpJCouLg48Hg9RUVFPrJeZmYmgoCDY2NiAx+Ph4MGDTdZLSEiAo6MjDA0NMXbsWJw9e7ZN/XKhr1grKyshFothZ2eHZcuWwcLCQu+xEkIIIUR3XSaJys7OxpYtWzBq1KgW6yoUCojFYiQkJDRbJzk5GdHR0YiJiUFubi7EYjH8/f1RXl7Oud/Tp0+joaFBq7ywsBBlZWXtGmvv3r2Rn5+PkpIS7N27t9n+CCGEENJBWBdQXV3NhgwZwtLS0piPjw+LjIzU+VgALDU1Vavc09OTSaVS9bZSqWQ2NjYsNjaWU79KpZKJxWI2ffp01tjYqC6/cOECs7a2ZmvWrGnXWP9q8eLFLCUlpcl98fHxbPjw4Wzo0KEMAJPL5S3GRQghhHQnDm99r/HTN7lcrtM1tEvMREmlUgQGBkIikeilvfr6euTk5Gi0x+fzIZFIkJWVxalfPp+Pw4cPIy8vD3PmzIFKpUJxcTF8fX0RHByM5cuXt1usZWVlqK6uBvDwG16ZmZkYNmxYk+1JpVIUFhYiOzubUzyEEEII0U2nP523b98+5Obm6vWiX1FRAaVSCWtra41ya2trXLhwgXO/NjY2OH78OLy9vRESEoKsrCxIJBIkJia2a6zXrl1DeHi4ekF5REQERo4cyblPQgghhLRdpyZRpaWliIyMRFpaGgwNDbtFv/b29khKSoKPjw+cnJywbdu2dv+siqenJ2QyWbv2QQghhJDW6dTbeTk5OSgvL4ebmxuEQiGEQiEyMjKwceNGCIVCKJVKTu1aWFhAIBBoLb4uKytDv3792tRvWVkZwsPDERQUhNraWixZsoRTjLrGSgghhJCuqVOTKD8/PxQUFEAmk6l/Hh4emDVrFmQyGQQCAad2RSIR3N3dkZ6eri5TqVRIT0+Hl5cX534rKirg5+eH4cOH48CBA0hPT0dycjKWLl3KKU5dYiWEEEJI19Spt/NMTU3h6uqqUWZiYgJzc3N1eXx8PFJTUzWSjJqaGhQVFam3S0pKIJPJ0LdvX9jb2wMAoqOjERoaCg8PD3h6emLDhg1QKBQICwvTqd/HqVQqBAQEwMHBAcnJyRAKhXBxcUFaWhp8fX1ha2vb5KxUW2MlhBBCSNfU6QvLW1JRUYHi4mKNsnPnzmHixInq7ejoaABAaGgodu7cCQCYOXMm7ty5g5UrV+L27dsYPXo0jhw5orWAW1d8Ph+rV6+Gt7c3RCKRulwsFuPYsWOwtLRs8rjOiJUQQggh7Y/HGH0/5O+oqqoKZmZmkMvl9AFiQgghfyuOK37Q2L4aF6jX9nW9hnaJ90QRQgghhHQ3lEQRQgghhHBASRQhhBBCCAeURHUDlZWV8PDwwOjRo+Hq6oqtW7d2dkiEEELIU6/LP51HHr4KIjMzE8bGxlAoFHB1dcW0adNgbm7e2aERQgghTy2aieoGBAIBjI2NAQB1dXXqb+gRQgghpPN0qSQqLi4OPB4PUVFRT6yXmZmJoKAg2NjYgMfj4eDBg03WS0hIgKOjIwwNDTF27FicPXtWvS8xMRGjRo1Cr1690KtXL3h5eeHHH3/U42j0Fyvw8JaeWCyGnZ0dli1bBgsLC73HSgghhBDddZkkKjs7G1u2bMGoUaNarKtQKCAWi5GQkNBsneTkZERHRyMmJga5ubkQi8Xw9/dHeXk5AMDOzg5xcXHIycnBuXPn4OvriylTpuD3339vts3Tp0+joaFBq7ywsFDr23f6jBUAevfujfz8fJSUlGDv3r3N9kcIIYSQDsK6gOrqajZkyBCWlpbGfHx8WGRkpM7HAmCpqala5Z6enkwqlaq3lUols7GxYbGxsc221adPH/bFF180uU+pVDKxWMymT5/OGhsb1eUXLlxg1tbWbM2aNR0W6+LFi1lKSsoT+5LL5QwAk8vlLcZFCCGEdCcOb32v8dM3Xa+hXWImSiqVIjAwEBKJRC/t1dfXIycnR6M9Pp8PiUSCrKwsrfpKpRL79u2DQqFo9qO/fD4fhw8fRl5eHubMmQOVSoXi4mL4+voiODgYy5cvb7dYy8rKUF1dDQCQy+XIzMzEsGHDmmwvISEBLi4uGDNmDKd4CCGEEKKbTn86b9++fcjNzUV2drbe2qyoqIBSqdT69py1tTUuXLig3i4oKICXlxcePHiAnj17IjU1FS4uLs22a2Njg+PHj8Pb2xshISHIysqCRCJBYmJiu8Z67do1hIeHqxeUR0REYOTIkU22J5VKIZVK1a+sJ4QQQkj76NQkqrS0FJGRkUhLS4OhoWGH9z9s2DDIZDLI5XLs378foaGhyMjIeGIiZW9vj6SkJPj4+MDJyQnbtm0Dj8dr1zg9PT0hk8natQ9CCCGEtE6n3s7LyclBeXk53NzcIBQKIRQKkZGRgY0bN0IoFEKpVHJq18LCAgKBQGvxdVlZGfr166feFolEGDx4MNzd3REbGwuxWIxPP/30iW2XlZUhPDwcQUFBqK2txZIlSzjF2NpYCSGEENK1dGoS5efnh4KCAshkMvXPw8MDs2bNgkwmg0Ag4NSuSCSCu7s70tPT1WUqlQrp6enNrnl6VKeurq7Z/RUVFfDz88Pw4cNx4MABpKenIzk5GUuXLuUUZ1tiJYQQQkjn6tTbeaampnB1ddUoMzExgbm5ubo8Pj4eqampGklGTU0NioqK1NslJSWQyWTo27cv7O3tAQDR0dEIDQ2Fh4cHPD09sWHDBigUCoSFhQEA3n77bQQEBMDe3h7V1dXYu3cvTp48iaNHjzYZq0qlQkBAABwcHJCcnAyhUAgXFxekpaXB19cXtra2Tc5K6SNWQgghhHQ9nb6wvCUVFRUoLi7WKDt37hwmTpyo3o6OjgYAhIaGYufOnQCAmTNn4s6dO1i5ciVu376N0aNH48iRI+oF3OXl5ZgzZw5u3boFMzMzjBo1CkePHsWkSZOajIPP52P16tXw9vaGSCRSl4vFYhw7dgyWlpZNHqePWAkhhBDS9fAYo++H/B09ejpPLpejV69enR0OIYQQojeOK37Q2L4aF6jX9nW9hnaJ90QRQgghhHQ3lEQRQgghhHBASRQhhBBCCAeURBFCCCGEcEBJVDdQWVkJDw8PjB49Gq6urti6dWtnh0QIIYQ89br8Kw7Iw/dpZWZmwtjYGAqFAq6urpg2bRrMzc07OzRCCCHkqUUzUd2AQCCAsbExAKCurk79IWJCCCGEdJ4ulUTFxcWBx+MhKirqifUyMzMRFBQEGxsb8Hg8HDx4sMl6CQkJcHR0hKGhIcaOHYuzZ8+q98XGxmLMmDEwNTWFlZUVgoODcfHiRT2ORn+xAg9v6YnFYtjZ2WHZsmWwsLDQe6yEEEII0V2XSaKys7OxZcsWjBo1qsW6CoUCYrEYCQkJzdZJTk5GdHQ0YmJikJubC7FYDH9/f5SXlwMAMjIyIJVK8csvvyAtLQ0NDQ2YPHkyFApFs22ePn0aDQ0NWuWFhYVaHxDWZ6wA0Lt3b+Tn56OkpAR79+5ttj9CCCGEdBDWBVRXV7MhQ4awtLQ05uPjwyIjI3U+FgBLTU3VKvf09GRSqVS9rVQqmY2NDYuNjW2ynfLycgaAZWRkNLlfqVQysVjMpk+fzhobG9XlFy5cYNbW1mzNmjUdFuvixYtZSkpKk/vi4+PZ8OHD2dChQxkAJpfLW4yLEEII6U4c3vpe46dvcrlcp2tol5iJkkqlCAwMhEQi0Ut79fX1yMnJ0WiPz+dDIpEgKyuryWPkcjkAoG/fvk3u5/P5OHz4MPLy8jBnzhyoVCoUFxfD19cXwcHBWL58ebvFWlZWhurqanWcmZmZGDZsWJPtSaVSFBYWIjs7m1M8hBBCCNFNpz+dt2/fPuTm5ur1ol9RUQGlUqn1AV9ra2tcuHBBq75KpUJUVBTGjx8PV1fXZtu1sbHB8ePH4e3tjZCQEGRlZUEikSAxMbFdY7127RrCw8PVC8ojIiIwcuRIzn0SQgghpO04JVFXrlyBk5NTmzsvLS1FZGQk0tLSYGho2Ob2uJJKpTh//jxOnTrVYl17e3skJSXBx8cHTk5O2LZtG3g8XrvG5+npCZlM1q59EEIIIaR1ON3OGzx4MCZOnIjdu3fjwYMHnDvPyclBeXk53NzcIBQKIRQKkZGRgY0bN0IoFEKpVHJq18LCAgKBQGvxdVlZGfr166dR9sYbb+D777/HiRMnYGdn12LbZWVlCA8PR1BQEGpra7FkyRJOMXKJlRBCCCFdB6ckKjc3F6NGjUJ0dDT69euHRYsWaT2Srws/Pz8UFBRAJpOpfx4eHpg1axZkMhkEAgGX8CASieDu7o709HR1mUqlQnp6Ory8vAAAjDG88cYbSE1NxfHjxzFw4MAW262oqICfnx+GDx+OAwcOID09HcnJyVi6dCmnOHWNlRBCCCFdD6fbeaNHj8ann36KTz75BIcOHcLOnTsxYcIEDB06FPPmzcOrr74KS0vLFtsxNTXVWoNkYmICc3NzdXl8fDxSU1M1koyamhoUFRWpt0tKSiCTydC3b1/Y29sDAKKjoxEaGgoPDw94enpiw4YNUCgUCAsLA/DwFt7evXvx7bffwtTUFLdv3wYAmJmZwcjISCtWlUqFgIAAODg4IDk5GUKhEC4uLkhLS4Ovry9sbW2bnJXSR6yEEEII6YL08SjggwcP2Pr165mBgQHj8XjMwMCAvfrqq+zmzZutbuvxVxzExMQwBwcHjTonTpxgALR+oaGhGvU+++wzZm9vz0QiEfP09GS//PKLel9TxwNgO3bsaDa2n376id2/f1+rPDc3l5WWljZ5jD5i5ULXxzMJIYSQ7qarvOKAxxj374ecO3cO27dvx759+2BiYoLQ0FDMnz8ff/zxB95//31UVVVxus1H2q6qqgpmZmaQy+Xo1atXZ4dDCCGE6I3jih80tq/GBeq1fV2voZxu561fvx47duzAxYsX8fzzz+PLL7/E888/Dz7/4RKrgQMHYufOnXB0dOQUPCGEEEJIV8cpiUpMTMS8efMwd+5c9O/fv8k6VlZW2LZtW5uCI4QQQgjpqjglUZcvX26xjkgkQmhoKJfmCSGEEEK6PE6vONixYwdSUlK0ylNSUrBr1642B0U0VVZWwsPDA6NHj4arqyu2bt3a2SERQgghTz1OSVRsbCwsLCy0yq2srLB69eo2B0U0mZqaIjMzEzKZDL/++itWr16Nu3fvdnZYhBBCyFONUxJ1/fr1Jl9O6eDggOvXr7c5KKJJIBDA2NgYAFBXV6f+hh4hhBBCOg+nJMrKygq//fabVnl+fj7Mzc05BxMXFwcej4eoqKgn1svMzERQUBBsbGzA4/Fw8ODBJuslJCTA0dERhoaGGDt2rMbrFnRto630ESvw8JaeWCyGnZ0dli1b1uRMICGEEEI6Dqck6pVXXsGbb76JEydOQKlUQqlU4vjx44iMjMTLL7/MKZDs7Gxs2bIFo0aNarGuQqGAWCxGQkJCs3WSk5MRHR2NmJgY5ObmQiwWw9/fH+Xl5Tq38bjTp0+joaFBq7ywsFDr23f6jBUAevfujfz8fJSUlGDv3r3N9kcIIYSQDsLlTZ51dXVsxowZjMfjsR49erAePXowgUDAwsLCWF1dXavbq66uZkOGDGFpaWlabyxvCQCWmpqqVe7p6cmkUql6W6lUMhsbGxYbG6tzG3+lVCqZWCxm06dPZ42NjeryCxcuMGtra7ZmzZoOiZUxxhYvXsxSUlKe2Be9sZwQQsjfVVd5YzmnmSiRSITk5GRcuHABe/bswYEDB1BcXIzt27dDJBK1uj2pVIrAwEBIJBIu4Wipr69HTk6ORnt8Ph8SiQRZWVmc2uTz+Th8+DDy8vIwZ84cqFQqFBcXw9fXF8HBwVi+fHm7xVpWVobq6moAgFwuR2ZmJoYNG9ZkewkJCXBxccGYMWM4xUMIIYQQ3XB6T9QjQ4cOxdChQ9sUwL59+5Cbm4vs7Ow2tfNXFRUVUCqVsLa21ii3trbGhQsXOLdrY2OD48ePw9vbGyEhIcjKyoJEIkFiYmK7xnrt2jWEh4erF5RHRERg5MiRTbYnlUohlUrVr6wnhBBCSPvglEQplUrs3LkT6enpKC8vh0ql0th//PhxndopLS1FZGQk0tLSYGhoyCWUDmdvb4+kpCT4+PjAyckJ27ZtA4/Ha9c+PT09IZPJ2rUPQgghhLQOp9t5kZGRiIyMhFKphKurK8RiscZPVzk5OSgvL4ebmxuEQiGEQiEyMjKwceNGCIVCKJVKLuHBwsICAoFAa/F1WVkZ+vXrx6nNv7YRHh6OoKAg1NbWYsmSJW1qrz1jJYQQQkj74TQTtW/fPnz99dd4/vnn29S5n58fCgoKNMrCwsLg7OyMt956CwKBgFO7IpEI7u7uSE9PR3BwMABApVIhPT0db7zxBud4Kyoq4Ofnh+HDhyMlJQWXLl3Cs88+CwMDA6xbt65LxUoIIYSQ9sUpiRKJRBg8eHCbOzc1NYWrq6tGmYmJCczNzdXl8fHxSE1NRXp6urpOTU0NioqK1NslJSWQyWTo27cv7O3tAQDR0dEIDQ2Fh4cHPD09sWHDBigUCoSFhencxl+pVCoEBATAwcEBycnJEAqFcHFxQVpaGnx9fWFra9vkrJQ+YiWEEEJI18Mpifr3v/+NTz/9FPHx8e2+HqiiogLFxcUaZefOncPEiRPV29HR0QCA0NBQ7Ny5EwAwc+ZM3LlzBytXrsTt27cxevRoHDlyRL2AW5c2/orP52P16tXw9vbWeAJRLBbj2LFjsLS0bDJ+fcRKCCGEkK6Hx1jrvx8ydepUnDhxAn379sWIESPQo0cPjf0HDhzQW4CEm0dP58nlcvTq1auzwyGEEEL0xnHFDxrbV+MC9dq+rtdQTjNRvXv3xtSpUzkHRwghhBDS3XFKonbs2KHvOAghhBBCuhVOrzgAgMbGRhw7dgxbtmxRv0375s2bqKmp0VtwhBBCCCFdFaeZqGvXruG5557D9evXUVdXh0mTJsHU1BRr1qxBXV0dNm/erO84CSGEEEK6FM4v2/Tw8MC9e/dgZGSkLp86darGqwhI21VWVsLDwwOjR4+Gq6srtm7d2tkhEUIIIQQcZ6J+/vlnnDlzRutjw46Ojrhx44ZeAiMPmZqaIjMzE8bGxlAoFHB1dcW0adNgbm7e2aERQgghTzVOM1EqlarJT7L88ccfMDU1bXNQ5P8IBAIYGxsDAOrq6tQfISaEEEJI5+KURE2ePBkbNmxQb/N4PNTU1CAmJqbNn4Jpq7i4OPB4PERFRT2xXmZmJoKCgmBjYwMej4eDBw+2Szy69JOQkABHR0cYGhpi7NixOHv2rMb+yspKiMVi2NnZYdmyZbCwsGiXWAkhhBCiO05J1CeffILTp0/DxcUFDx48QEhIiPpW3po1a/Qdo86ys7OxZcsWjBo1qsW6CoUCYrEYCQkJOrV9+vRpNDQ0aJUXFhZqfTy4Nf0kJycjOjoaMTExyM3NhVgshr+/P8rLy9V1evfujfz8fJSUlGDv3r1P7I8QQgghHYNTEmVnZ4f8/Hy88847WLJkCZ555hnExcUhLy8PVlZW+o5RJzU1NZg1axa2bt2KPn36tFg/ICAAH374oU4vDVWpVJBKpQgJCdG4jXnx4kX4+vpi165dnPtZv349Fi5ciLCwMLi4uGDz5s0wNjbG9u3btepaW1tDLBbj559/bra/hIQEuLi4YMyYMS2OixBCCCHccX5PlFAoxOzZs7F27Vps2rQJCxYs0HhSr6NJpVIEBgZCIpHovW0+n4/Dhw8jLy8Pc+bMgUqlQnFxMXx9fREcHIzly5dzare+vh45OTkaMfP5fEgkEmRlZQEAysrK1O/hksvlyMzMxLBhw5ptUyqVorCwENnZ2ZxiIoQQQohuOD2d9+WXXz5x/5w5czgFw9W+ffuQm5vbromDjY0Njh8/Dm9vb4SEhCArKwsSiQSJiYmc26yoqIBSqdT60LC1tTUuXLgA4OE7ucLDw9ULyiMiIjBy5Mg2jYUQQgghbccpiYqMjNTYbmhoQG1tLUQiEYyNjTs0iSotLUVkZCTS0tJgaGjYrn3Z29sjKSkJPj4+cHJywrZt28Dj8dq1T09PT8hksnbtgxBCCCGtx+l23r179zR+NTU1uHjxIiZMmICvvvpK3zE+UU5ODsrLy+Hm5gahUAihUIiMjAxs3LgRQqGwyVcxcFVWVobw8HAEBQWhtrYWS5YsaVN7FhYWEAgEWgvFy8rK0K9fvza1TQghhJD2xXlN1OOGDBmCuLg4rVmq9ubn54eCggLIZDL1z8PDA7NmzYJMJoNAINBLPxUVFfDz88Pw4cNx4MABpKenIzk5GUuXLuXcpkgkgru7u8Zb3lUqFdLT0+Hl5aWPsAkhhBDSTjjdzmu2MaEQN2/e1GeTLTI1NYWrq6tGmYmJCczNzdXl8fHxSE1N1UhWampqUFRUpN4uKSmBTCZD3759YW9vr9GeSqVCQEAAHBwckJycDKFQCBcXF6SlpcHX1xe2trbNzkq11E90dDRCQ0Ph4eEBT09PbNiwAQqFAmFhYW0+N4QQQghpP5ySqEOHDmlsM8Zw69YtxMfHY/z48XoJTJ8qKipQXFysUXbu3DlMnDhRvR0dHQ0ACA0Nxc6dOzXq8vl8rF69Gt7e3hqfuhGLxTh27BgsLS2b7bulfmbOnIk7d+5g5cqVuH37NkaPHo0jR45oLTYnhBBCSNfCYxy+IcLna94F5PF4sLS0hK+vLz755BP0799fbwESbqqqqmBmZga5XI5evXp1djiEEEKI3jiu+EFj+2pcoF7b1/UaymkmSqVScQ6MEEIIIeTvQG8LywkhhBBCniacZqIerevRxfr167l0QQghhBDSpXFKovLy8pCXl4eGhgb1J0guXboEgUAANzc3db32fhHl06KyshISiQSNjY1obGxEZGQkFi5c2NlhEUIIIU81TklUUFAQTE1NsWvXLvXHfu/du4ewsDB4e3vj3//+t16DfNqZmpoiMzMTxsbGUCgUcHV1xbRp02Bubt7ZoRFCCCFPLU5roj755BPExsaqEygA6NOnDz788EN88skneguOPCQQCGBsbAwAqKurU39HjxBCCCGdh1MSVVVVhTt37miV37lzB9XV1ZyDiYuLA4/HQ1RUVIt1ExIS4OjoCENDQ4wdOxZnz57V2F9dXY2oqCg4ODjAyMgI48aN0/hAcUv79SUzMxNBQUGwsbEBj8fDwYMHOY2nsrISYrEYdnZ2WLZsGSwsLPQeKyGEEEJ0xymJmjp1KsLCwnDgwAH88ccf+OOPP/DNN99g/vz5mDZtGqdAsrOzsWXLFowaNarFusnJyYiOjkZMTAxyc3MhFovh7++P8vJydZ0FCxYgLS0NSUlJKCgowOTJkyGRSHDjxg2d9jfl9OnTaGho0CovLCzU+v7dIwqFAmKxGAkJCW0aT+/evZGfn4+SkhLs3bu32f4IIYQQ0kEYBwqFgi1evJgZGBgwPp/P+Hw+E4lEbPHixaympqbV7VVXV7MhQ4awtLQ05uPjwyIjI59Y39PTk0mlUvW2UqlkNjY2LDY2ljHGWG1tLRMIBOz777/XOM7NzY29++67Le5vilKpZGKxmE2fPp01Njaqyy9cuMCsra3ZmjVrWhwnAJaamtrq8Txu8eLFLCUl5Yl9yeVyBoDJ5fIW4yKEEEK6E4e3vtf46Zuu11BOM1HGxsbYtGkT7t69q35S788//8SmTZtgYmLS6vakUikCAwMhkUharFtfX4+cnByNunw+HxKJBFlZWQCAxsZGKJVKGBoaahxrZGSEU6dOtbi/KXw+H4cPH0ZeXh7mzJkDlUqF4uJi+Pr6Ijg4GMuXL2/tsHUeT1lZmfo2qVwuR2ZmpvqpyMclJCTAxcUFY8aM4RQPIYQQQnTTppdt3rp1C7du3cKQIUNgYmLCabHzvn37kJubi9jYWJ3qV1RUQKlUan1bztraGrdv3wbw8Gk2Ly8vrFq1Cjdv3oRSqcTu3buRlZWFW7dutbi/OTY2Njh+/DhOnTqFkJAQ+Pr6QiKRIDExsdXjbs14rl27Bm9vb4jFYnh7eyMiIgIjR45ssj2pVIrCwsJ2Wd9FCCGEkP/D6RUHd+/exYwZM3DixAnweDxcvnwZTk5OmD9/Pvr06aPzE3qlpaWIjIxEWlqa1qxQWyUlJWHevHmwtbVVv7/qlVdeQU5Ojk77m2Nvb4+kpCT4+PjAyckJ27Zta/f3YXl6ekImk7VrH4QQQghpHU4zUUuWLEGPHj1w/fp19aP3ADBz5kwcOXJE53ZycnJQXl4ONzc3CIVCCIVCZGRkYOPGjRAKhVAqlVrHWFhYQCAQaC2sLisrQ79+/dTbgwYNQkZGBmpqalBaWoqzZ8+ioaEBTk5OOu1vTllZGcLDwxEUFITa2losWbJE5/E2RdfxEEIIIaRr4ZRE/fTTT1izZg3s7Ow0yocMGYJr167p3I6fnx8KCgogk8nUPw8PD8yaNQsymQwCgUDrGJFIBHd3d6Snp6vLVCoV0tPT4eXlpVXfxMQE/fv3x71793D06FFMmTKlVfv/qqKiAn5+fhg+fDgOHDiA9PR0JCcnY+nSpTqPua3jIYQQQkjXwOl2nkKh0JiBeuTPP/+EgYGBzu2YmprC1dVVo8zExATm5ubq8vj4eKSmpmokGdHR0QgNDYWHhwc8PT2xYcMGKBQKhIWFqescPXoUjDEMGzYMRUVFWLZsGZydndV1Wtr/OJVKhYCAADg4OCA5ORlCoRAuLi5IS0uDr68vbG1tm5yVqqmpQVFRkXq7pKQEMpkMffv2hb29vc7jIYQQQkjXwimJ8vb2xpdffolVq1YBePiNPJVKhbVr12LixIl6DbCiogLFxcUaZTNnzsSdO3ewcuVK3L59G6NHj8aRI0c0FmfL5XK8/fbb+OOPP9C3b1+8+OKL+Oijj9CjRw+d9j+Oz+dj9erV8Pb2hkgkUpeLxWIcO3YMlpaWTR537tw5jXPy6OPNoaGh2Llzp87jIYQQQkjXwmMcHqk7f/48/Pz84ObmhuPHj+Nf//oXfv/9d/z55584ffo0Bg0a1B6xklaoqqqCmZkZ5HI5evXq1dnhEEIIIXrjuOIHje2rcYF6bV/XayinNVGurq64dOkSJkyYgClTpkChUGDatGnIy8ujBIoQQgghT4VW385raGjAc889h82bN+Pdd99tj5gIIYQQQrq8Vs9E9ejRA7/99lt7xEIIIYQQ0m1wup03e/ZsbNu2Td+xEEIIIYR0G5yezmtsbMT27dtx7NgxuLu7a30vb/369XoJjjxUWVkJiUSCxsZGNDY2IjIyEgsXLuzssAghhJCnWquSqCtXrsDR0RHnz5+Hm5sbAODSpUsaddr7EyhPI1NTU2RmZsLY2BgKhQKurq6YNm0azM3NOzs0Qggh5KnVqiRqyJAhuHXrFk6cOAHg4fuNNm7cSO8zamcCgUD9ctO6ujowxjh97JkQQggh+tOqNVGPX7h//PFHKBQKvQUTFxcHHo+HqKioFusmJCTA0dERhoaGGDt2LM6ePauxv7q6GlFRUXBwcICRkRHGjRuH7Oxs9X6lUon//Oc/GDhwIIyMjDBo0CCsWrVK78lJZmYmgoKCYGNjAx6Ph4MHD3IaT2VlJcRiMezs7LBs2TJYWFjoNU5CCCGEtA6nheWP6DPhyM7OxpYtWzBq1KgW6yYnJyM6OhoxMTHIzc2FWCyGv78/ysvL1XUWLFiAtLQ0JCUloaCgAJMnT4ZEIsGNGzcAAGvWrEFiYiLi4+Pxv//9D2vWrMHatWvx2WefNdvv6dOn0dDQoFVeWFio9QHhRxQKBcRiMRISEto0nt69eyM/Px8lJSXYu3dvs/0RQgghpGO0Koni8Xhaa570sQaqpqYGs2bNwtatW9GnT58W669fvx4LFy5EWFgYXFxcsHnzZhgbG2P79u0AgPv37+Obb77B2rVr8c9//hODBw/Ge++9h8GDByMxMREAcObMGUyZMgWBgYFwdHTE9OnTMXnyZK0ZoEdUKhWkUilCQkKgVCrV5RcvXoSvry927drV5HEBAQH48MMPMXXqVM7j+Stra2uIxWL8/PPPTbaVkJAAFxcXjBkzptn+CCGEENJ2rb6dN3fuXEybNg3Tpk3DgwcP8Nprr6m3H/1aSyqVIjAwEBKJpMW69fX1yMnJ0ajL5/MhkUiQlZUF4OHTg0qlEoaGhhrHGhkZ4dSpUwCAcePGIT09Xb0wPj8/H6dOnUJAQECT/fL5fBw+fBh5eXmYM2cOVCoViouL4evri+DgYCxfvrzV49Z1PGVlZaiurgbw8Jt/mZmZGDZsWJPtSaVSFBYWaty6JIQQQoj+tWpheWhoqMb27Nmz2xzAvn37kJubq/NFv6KiAkqlUmsxu7W1NS5cuADg4dNsXl5eWLVqFYYPHw5ra2t89dVXyMrKwuDBgwEAK1asQFVVFZydnSEQCKBUKvHRRx9h1qxZzfZtY2OD48ePw9vbGyEhIcjKyoJEIlHPbnGhy3iuXbuG8PBw9YLyiIgIjBw5knOfhBBCCGm7ViVRO3bs0GvnpaWliIyMRFpamtasUVslJSVh3rx5sLW1hUAggJubG1555RXk5OQAAL7++mvs2bMHe/fuxYgRIyCTyRAVFQUbGxutZPGv7O3tkZSUBB8fHzg5OWHbtm3t/loHT09PyGSydu2DEEIIIa3TpoXlbZWTk4Py8nK4ublBKBRCKBQiIyMDGzduhFAo1Fh79IiFhQUEAoHWwuqysjL069dPvT1o0CBkZGSgpqYGpaWlOHv2LBoaGuDk5AQAWLZsGVasWIGXX34ZI0eOxKuvvoolS5YgNjb2iTGXlZUhPDwcQUFBqK2txZIlS9p0DnQdDyGEEEK6lk5Novz8/FBQUACZTKb+eXh4YNasWZDJZBAIBFrHiEQiuLu7Iz09XV2mUqmQnp4OLy8vrfomJibo378/7t27h6NHj2LKlCkAgNraWvD5msMXCARQqVTNxltRUQE/Pz8MHz4cBw4cQHp6OpKTk7F06VKup6DV4yGEEEJI18Dpsy/6YmpqCldXV40yExMTmJubq8vj4+ORmpqqkWRER0cjNDQUHh4e8PT0xIYNG6BQKBAWFqauc/ToUTDGMGzYMBQVFWHZsmVwdnZW1wkKCsJHH30Ee3t7jBgxAnl5eVi/fj3mzZvXZKwqlQoBAQFwcHBAcnIyhEIhXFxckJaWBl9fX9ja2jY5K1VTU4OioiL1dklJCWQyGfr27Qt7e3udx0MIIYSQrqVTkyhdVFRUoLi4WKNs5syZuHPnDlauXInbt29j9OjROHLkiMbibLlcjrfffht//PEH+vbtixdffBEfffQRevToAQD47LPP8J///Aevv/46ysvLYWNjg0WLFmHlypVNxsHn87F69Wp4e3tDJBKpy8ViMY4dOwZLS8smjzt37hwmTpyo3o6OjgbwcJH+zp07dR4PIYQQQroWHqPvh/wtVVVVwczMDHK5HL169erscAghhBC9cVzxg8b21bhAvbav6zW0U9dEEUIIIYR0V5REEUIIIYRwQEkUIYQQQggHlER1A5WVlfDw8MDo0aPh6uqKrVu3dnZIhBBCyFOvyz+dRx6+CiIzMxPGxsZQKBRwdXXFtGnTYG5u3tmhEUIIIU8tmonqBgQCAYyNjQEAdXV16m/oEUIIIaTzdKkkKi4uDjweD1FRUS3WTUhIgKOjIwwNDTF27FicPXtWY391dTWioqLg4OAAIyMjjBs3TuMjx46OjuDxeFo/qVSq1zFlZmYiKCgINjY24PF4OHjwIKfxVFZWQiwWw87ODsuWLYOFhYVe4ySEEEJI63SZJCo7OxtbtmzBqFGjWqybnJyM6OhoxMTEIDc3F2KxGP7+/igvL1fXWbBgAdLS0pCUlISCggJMnjwZEokEN27cUPd369Yt9S8tLQ0A8NJLLzXb7+nTp9HQ0KBVXlhYqPXtu0cUCgXEYjESEhLaNJ7evXsjPz8fJSUl2Lt3b7P9EUIIIaRjdIkkqqamBrNmzcLWrVvRp0+fFuuvX78eCxcuRFhYGFxcXLB582YYGxtj+/btAID79+/jm2++wdq1a/HPf/4TgwcPxnvvvYfBgwcjMTERAGBpaYl+/fqpf99//z0GDRoEHx+fJvtUqVSQSqUICQnR+DDyxYsX4evri127djV5XEBAAD788ENMnTqV83j+ytraGmKxGD///HOL54kQQggh7adLJFFSqRSBgYGQSCQt1q2vr0dOTo5GXT6fD4lEgqysLABAY2MjlEolDA0NNY41MjLCqVOnmmxz9+7dmDdvHng8XpP98vl8HD58GHl5eZgzZw5UKhWKi4vh6+uL4OBgLF++vDVDbtV4ysrKUF1dDeDh52wyMzMxbNiwJttLSEiAi4sLxowZwykeQgghhOim05/O27dvH3JzczXWKz1JRUUFlEql1nflrK2tceHCBQAPn2bz8vLCqlWrMHz4cFhbW+Orr75CVlYWBg8erNXmwYMHUVlZiblz5z6xbxsbGxw/fhze3t4ICQlBVlYWJBKJenaLC13Gc+3aNYSHh6sXlEdERGDkyJFNtieVSiGVStWvrCeEEEJI++jUJKq0tBSRkZFIS0vTmjVqq6SkJMybNw+2trYQCARwc3PDK6+8gpycHK2627ZtQ0BAAGxsbFps197eHklJSfDx8YGTkxO2bdvW7OyVvnh6ekImk7VrH4QQQghpnU69nZeTk4Py8nK4ublBKBRCKBQiIyMDGzduhFAo1Fh79IiFhQUEAoHWwuqysjL069dPvT1o0CBkZGSgpqYGpaWlOHv2LBoaGuDk5KRx3LVr13Ds2DEsWLBAp5jLysoQHh6OoKAg1NbWYsmSJRxG3vrxEEIIIaRr6dQkys/PDwUFBZDJZOqfh4cHZs2aBZlMBoFAoHWMSCSCu7s70tPT1WUqlQrp6enw8vLSqm9iYoL+/fvj3r17OHr0KKZMmaKxf8eOHbCyskJgYMtfgK6oqICfnx+GDx+OAwcOID09HcnJyVi6dCmH0XMbDyGEEEK6hk69nWdqagpXV1eNMhMTE5ibm6vL4+PjkZqaqpFkREdHIzQ0FB4eHvD09MSGDRugUCgQFhamrnP06FEwxjBs2DAUFRVh2bJlcHZ21qijUqmwY8cOhIaGQih88qlQqVQICAiAg4MDkpOTIRQK4eLigrS0NPj6+sLW1rbJWamamhoUFRWpt0tKSiCTydC3b1/Y29vrPB5CCCGEdC2dvrC8JRUVFSguLtYomzlzJu7cuYOVK1fi9u3bGD16NI4cOaKxOFsul+Ptt9/GH3/8gb59++LFF1/ERx99hB49eqjrHDt2DNevX8e8efNajIPP52P16tXw9vaGSCRSl4vFYhw7dgyWlpZNHnfu3DlMnDhRvR0dHQ0ACA0Nxc6dO3UeDyGEEEK6Fh6j74f8LT16Ok8ul6NXr16dHQ4hhBCiN44rftDYvhrX8pKc1tD1Gtol3hNFCCGEENLdUBJFCCGEEMIBJVGEEEIIIRxQEkUIIYQQwgElUd1AZWUlPDw8MHr0aLi6umLr1q2dHRIhhBDy1OvyrzggD9+nlZmZCWNjYygUCri6umLatGkwNzfv7NAIIYSQpxbNRHUDAoEAxsbGAIC6ujr1h4gJIYQQ0nm6VBIVFxcHHo+HqKioFusmJCTA0dERhoaGGDt2LM6ePauxv7q6GlFRUXBwcICRkRHGjRuH7OxsjTo3btzA7NmzYW5uDiMjI4wcORLnzp3T55CQmZmJoKAg2NjYgMfj4eDBg5zGU1lZCbFYDDs7OyxbtgwWFhZ6jZMQQgghrdNlkqjs7Gxs2bIFo0aNarFucnIyoqOjERMTg9zcXIjFYvj7+6O8vFxdZ8GCBUhLS0NSUhIKCgowefJkSCQS3LhxAwBw7949jB8/Hj169MCPP/6IwsJCfPLJJ+jTp0+z/Z4+fRoNDQ1a5YWFhVofEH5EoVBALBYjISGhTePp3bs38vPzUVJSgr179zbbHyGEEEI6COsCqqur2ZAhQ1haWhrz8fFhkZGRT6zv6enJpFKpelupVDIbGxsWGxvLGGOstraWCQQC9v3332sc5+bmxt59913GGGNvvfUWmzBhgs4xKpVKJhaL2fTp01ljY6O6/MKFC8za2pqtWbOmxTYAsNTU1FaP53GLFy9mKSkpTe6Lj49nw4cPZ0OHDmUAmFwubzGu1nB463uNHyGEENLR2vtaJJfLdbqGdomZKKlUisDAQEgkkhbr1tfXIycnR6Mun8+HRCJBVlYWAKCxsRFKpRKGhoYaxxoZGeHUqVMAgEOHDsHDwwMvvfQSrKys8MwzzzzxqTc+n4/Dhw8jLy8Pc+bMgUqlQnFxMXx9fREcHIzly5dzGbpO4ykrK0N1dTWAh98EzMzMxLBhw5psTyqVorCwUOvWJSGEEEL0q9OTqH379iE3NxexsbE61a+oqIBSqdT6OK+1tTVu374N4OHTbF5eXli1ahVu3rwJpVKJ3bt3IysrC7du3QIAXLlyBYmJiRgyZAiOHj2KxYsX480338SuXbua7dvGxgbHjx/HqVOnEBISAl9fX0gkEiQmJnIcvW7juXbtGry9vSEWi+Ht7Y2IiAiMHDmSc5+EEEIIabtOfcVBaWkpIiMjkZaWpjVr1FZJSUmYN28ebG1tIRAI4ObmhldeeQU5OTkAAJVKBQ8PD6xevRoA8Mwzz+D8+fPYvHkzQkNDm23X3t4eSUlJ8PHxgZOTE7Zt2wYej6fX2B/n6ekJmUzWrn0QQgghpHU6dSYqJycH5eXlcHNzg1AohFAoREZGBjZu3AihUAilUql1jIWFBQQCgdbC6rKyMvTr10+9PWjQIGRkZKCmpgalpaU4e/YsGhoa4OTkBADo378/XFxcNNoYPnw4rl+//sSYy8rKEB4ejqCgINTW1mLJkiVch9+q8RBCCCGka+nUJMrPzw8FBQWQyWTqn4eHB2bNmgWZTAaBQKB1jEgkgru7O9LT09VlKpUK6enp8PLy0qpvYmKC/v374969ezh69CimTJkCABg/fjwuXryoUffSpUtwcHBoNt6Kigr4+flh+PDhOHDgANLT05GcnIylS5dyPQWtHg8hhBBCuoZOvZ1namoKV1dXjTITExOYm5ury+Pj45GamqqRZERHRyM0NBQeHh7w9PTEhg0boFAoEBYWpq5z9OhRMMYwbNgwFBUVYdmyZXB2dlbXWbJkCcaNG4fVq1djxowZOHv2LD7//HN8/vnnTcaqUqkQEBAABwcHJCcnQygUwsXFBWlpafD19YWtrW2Ts1I1NTUoKipSb5eUlEAmk6Fv376wt7fXeTytxf7/l3FWVVVxbqMpqrpajW19t08IIYS0pL2vRY/aYy292FrvzwW20eOvOIiJiWEODg5a9T777DNmb2/PRCIR8/T0ZL/88ovG/uTkZObk5MREIhHr168fk0qlrLKyUqPOd999x1xdXZmBgQFzdnZmn3/++RNj++mnn9j9+/e1ynNzc1lpaWmTx5w4cYIB0PqFhoa2ajytVVpa2mS/9KMf/ehHP/rRT7dfc9f2R3iM0fdD/o5UKhVu3rwJU1NTvS18r6qqwoABA1BaWopevXrppc2nHZ1T/aLzqX90TvWLzqf+tcc5ZYyhuroaNjY24PObX/lEHyD+m+Lz+bCzs2uXtnv16kX/8esZnVP9ovOpf3RO9YvOp/7p+5yamZm1WKfT3xNFCCGEENIdURJFCCGEEMIBJVFEZwYGBoiJiYGBgUFnh/K3QedUv+h86h+dU/2i86l/nXlOaWE5IYQQQggHNBNFCCGEEMIBJVGEEEIIIRxQEkUIIYQQwgElUYQQQgghHFASRQghhBDCASVRBAAQGxuLMWPGwNTUFFZWVggODsbFixdbPC4lJQXOzs4wNDTEyJEjcfjw4Q6Itnvgck63bt0Kb29v9OnTB3369IFEIsHZs2c7KOKujevf6CP79u0Dj8dDcHBw+wXZzXA9p5WVlZBKpejfvz8MDAwwdOhQ+m8f3M/nhg0bMGzYMBgZGWHAgAFYsmQJHjx40AERd32JiYkYNWqU+m3kXl5e+PHHH594TEdelyiJIgCAjIwMSKVS/PLLL0hLS0NDQwMmT54MhULR7DFnzpzBK6+8gvnz5yMvLw/BwcEIDg7G+fPnOzDyrovLOT158iReeeUVnDhxAllZWRgwYAAmT56MGzdudGDkXROX8/nI1atXsXTpUnh7e3dApN0Hl3NaX1+PSZMm4erVq9i/fz8uXryIrVu3wtbWtgMj75q4nM+9e/dixYoViImJwf/+9z9s27YNycnJeOeddzow8q7Lzs4OcXFxyMnJwblz5+Dr64spU6bg999/b7J+h1+Xnvh5YvLUKi8vZwBYRkZGs3VmzJjBAgMDNcrGjh3LFi1a1N7hdUu6nNPHNTY2MlNTU7Zr1652jKx70vV8NjY2snHjxrEvvviChYaGsilTpnRMgN2QLuc0MTGROTk5sfr6+g6MrHvS5XxKpVLm6+urURYdHc3Gjx/f3uF1W3369GFffPFFk/s6+rpEM1GkSXK5HADQt2/fZutkZWVBIpFolPn7+yMrK6tdY+uudDmnj6utrUVDQ0Orjnla6Ho+P/jgA1hZWWH+/PkdEVa3pss5PXToELy8vCCVSmFtbQ1XV1esXr0aSqWyo8LsNnQ5n+PGjUNOTo76tv2VK1dw+PBhPP/88x0SY3eiVCqxb98+KBQKeHl5NVmno69LwnZplXRrKpUKUVFRGD9+PFxdXZutd/v2bVhbW2uUWVtb4/bt2+0dYrej6zl93FtvvQUbGxut/1F42ul6Pk+dOoVt27ZBJpN1XHDdlK7n9MqVKzh+/DhmzZqFw4cPo6ioCK+//joaGhoQExPTgRF3bbqez5CQEFRUVGDChAlgjKGxsRGvvfYa3c77i4KCAnh5eeHBgwfo2bMnUlNT4eLi0mTdjr4uURJFtEilUpw/fx6nTp3q7FD+Nric07i4OOzbtw8nT56EoaFhO0bX/ehyPqurq/Hqq69i69atsLCw6MDouidd/0ZVKhWsrKzw+eefQyAQwN3dHTdu3MDHH39MSdRf6Ho+T548idWrV2PTpk0YO3YsioqKEBkZiVWrVuE///lPB0XbtQ0bNgwymQxyuRz79+9HaGgoMjIymk2kOlS73CQk3ZZUKmV2dnbsypUrLdYdMGAA++9//6tRtnLlSjZq1Kh2iq57as05feTjjz9mZmZmLDs7ux0j6550PZ95eXkMABMIBOofj8djPB6PCQQCVlRU1EERd32t+Rv95z//yfz8/DTKDh8+zACwurq69gqxW2nN+ZwwYQJbunSpRllSUhIzMjJiSqWyvULs1vz8/Fh4eHiT+zr6ukRroggAgDGGN954A6mpqTh+/DgGDhzY4jFeXl5IT0/XKEtLS2v2XvXThss5BYC1a9di1apVOHLkCDw8PNo5yu6jtefT2dkZBQUFkMlk6t+//vUvTJw4ETKZDAMGDOigyLsuLn+j48ePR1FREVQqlbrs0qVL6N+/P0QiUXuG2+VxOZ+1tbXg8zUvxQKBQN0e0aZSqVBXV9fkvg6/LrVLaka6ncWLFzMzMzN28uRJduvWLfWvtrZWXefVV19lK1asUG+fPn2aCYVCtm7dOva///2PxcTEsB49erCCgoLOGEKXw+WcxsXFMZFIxPbv369xTHV1dWcMoUvhcj4fR0/naeJyTq9fv85MTU3ZG2+8wS5evMi+//57ZmVlxT788MPOGEKXwuV8xsTEMFNTU/bVV1+xK1eusJ9++okNGjSIzZgxozOG0OWsWLGCZWRksJKSEvbbb7+xFStWMB6Px3766SfGWOdflyiJIowxxgA0+duxY4e6jo+PDwsNDdU47uuvv2ZDhw5lIpGIjRgxgv3www8dG3gXxuWcOjg4NHlMTExMh8ff1XD9G/0rSqI0cT2nZ86cYWPHjmUGBgbMycmJffTRR6yxsbFjg++CuJzPhoYG9t5777FBgwYxQ0NDNmDAAPb666+ze/fudXj8XdG8efOYg4MDE4lEzNLSkvn5+akTKMY6/7rEY4zmCwkhhBBCWovWRBFCCCGEcEBJFCGEEEIIB5REEUIIIYRwQEkUIYQQQggHlEQRQgghhHBASRQhhBBCCAeURBFCCCGEcEBJFCGEEEIIB5REEUIIIYRwQEkUIYQQQggHlEQRQgghhHDw/wGk+6kXbKRfzwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figsize =(6, 2)\n",
    "# df_event_sec = df_event.groupby(['vehicle_id', 'event_id']).id.count()\n",
    "# # df_event_sec.plot.hist(figsize=figsize, title='Sample interval : second');\n",
    "# s_interval = np.sort(df_event_sec.unique())\n",
    "# title = f'Sample interval(Sec)'\n",
    "# title += f'\\nRange : {s_interval}'\n",
    "# df_event_sec.plot.hist(figsize=figsize, bins=100, title=title, logy=True);\n",
    "\n",
    "# print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load data after aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('data/preprocessed_CH/df_event_agg_KA01AF2992.feather'),\n",
       " PosixPath('data/preprocessed_CH/df_event_agg_KA01AF482.feather'),\n",
       " PosixPath('data/preprocessed_CH/df_event_agg_KA04MW6569.feather'),\n",
       " PosixPath('data/preprocessed_CH/df_event_agg_KA51AG8742.feather'),\n",
       " PosixPath('data/preprocessed_CH/df_event_agg_MH04KF8187.feather')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Load files\n",
    "# path_event_agg_DORA = [path for path in list(dir_data.glob('*'))\n",
    "#              if path.suffix in ['.csv', '.feather'] \n",
    "#              and 'agg' in path.name\n",
    "#              ]\n",
    "# path_event_agg_DORA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['event_id', 'gsen_X', 'gsen_Y', 'gsen_Z', 'gyro_X', 'gyro_Y', 'gyro_Z',\n",
      "       'lat_std', 'long_std', 'speed_mean', 'speed_std', 'vehicle_id',\n",
      "       'gsen_X_mean', 'gsen_X_std', 'gsen_X_max', 'gsen_X_min', 'gsen_X_range',\n",
      "       'gyro_X_mean', 'gyro_X_std', 'gyro_X_max', 'gyro_X_min', 'gyro_X_range',\n",
      "       'gsen_Y_mean', 'gsen_Y_std', 'gsen_Y_max', 'gsen_Y_min', 'gsen_Y_range',\n",
      "       'gyro_Y_mean', 'gyro_Y_std', 'gyro_Y_max', 'gyro_Y_min', 'gyro_Y_range',\n",
      "       'gsen_Z_mean', 'gsen_Z_std', 'gsen_Z_max', 'gsen_Z_min', 'gsen_Z_range',\n",
      "       'gyro_Z_mean', 'gyro_Z_std', 'gyro_Z_max', 'gyro_Z_min', 'gyro_Z_range',\n",
      "       'lat_mean', 'long_mean'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>lat_std</th>\n",
       "      <th>long_std</th>\n",
       "      <th>speed_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>gsen_Z_max</th>\n",
       "      <th>gsen_Z_min</th>\n",
       "      <th>gsen_Z_range</th>\n",
       "      <th>gyro_Z_mean</th>\n",
       "      <th>gyro_Z_std</th>\n",
       "      <th>gyro_Z_max</th>\n",
       "      <th>gyro_Z_min</th>\n",
       "      <th>gyro_Z_range</th>\n",
       "      <th>lat_mean</th>\n",
       "      <th>long_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[-0.1768442541360855, -0.3292020559310913, -0....</td>\n",
       "      <td>[0.35891878604888916, 0.359760046005249, 0.359...</td>\n",
       "      <td>[0.4236612319946289, 0.5882062911987305, 0.645...</td>\n",
       "      <td>[0.0027008056640625, -0.009552001953125, -0.01...</td>\n",
       "      <td>[-0.0084075927734375, -0.026397705078125, -0.0...</td>\n",
       "      <td>[0.024566650390625, 0.02606201171875, 0.024032...</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>4.221517</td>\n",
       "      <td>...</td>\n",
       "      <td>5.481957</td>\n",
       "      <td>-2.476293</td>\n",
       "      <td>7.958250</td>\n",
       "      <td>0.020793</td>\n",
       "      <td>0.003755</td>\n",
       "      <td>0.026062</td>\n",
       "      <td>0.014755</td>\n",
       "      <td>0.011307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[0.42737051844596863, 0.10562610626220703, -0....</td>\n",
       "      <td>[-0.038906097412109375, 0.31329238414764404, 0...</td>\n",
       "      <td>[-1.6793060302734375, -0.3619565963745117, 3.1...</td>\n",
       "      <td>[0.0118560791015625, -0.0209503173828125, -0.0...</td>\n",
       "      <td>[-0.0077667236328125, -0.0175628662109375, -0....</td>\n",
       "      <td>[0.0094451904296875, 0.0131683349609375, 0.010...</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>7.078683</td>\n",
       "      <td>...</td>\n",
       "      <td>3.184722</td>\n",
       "      <td>-2.618477</td>\n",
       "      <td>5.803199</td>\n",
       "      <td>0.014061</td>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.022751</td>\n",
       "      <td>0.009445</td>\n",
       "      <td>0.013306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>[0.09482461214065552, 0.15548771619796753, 0.2...</td>\n",
       "      <td>[0.024686694145202637, 0.18659746646881104, 0....</td>\n",
       "      <td>[-0.0026922225952148438, -0.14077472686767578,...</td>\n",
       "      <td>[0.01654052734375, -0.005401611328125, -0.0039...</td>\n",
       "      <td>[-0.0142669677734375, -0.0097808837890625, 0.0...</td>\n",
       "      <td>[0.00848388671875, 0.0093231201171875, 0.01338...</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>9.010814</td>\n",
       "      <td>...</td>\n",
       "      <td>3.178970</td>\n",
       "      <td>-2.633689</td>\n",
       "      <td>5.812659</td>\n",
       "      <td>0.017439</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>0.030319</td>\n",
       "      <td>0.008484</td>\n",
       "      <td>0.021835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   event_id                                             gsen_X  \\\n",
       "0         1  [-0.1768442541360855, -0.3292020559310913, -0....   \n",
       "1         2  [0.42737051844596863, 0.10562610626220703, -0....   \n",
       "2         3  [0.09482461214065552, 0.15548771619796753, 0.2...   \n",
       "\n",
       "                                              gsen_Y  \\\n",
       "0  [0.35891878604888916, 0.359760046005249, 0.359...   \n",
       "1  [-0.038906097412109375, 0.31329238414764404, 0...   \n",
       "2  [0.024686694145202637, 0.18659746646881104, 0....   \n",
       "\n",
       "                                              gsen_Z  \\\n",
       "0  [0.4236612319946289, 0.5882062911987305, 0.645...   \n",
       "1  [-1.6793060302734375, -0.3619565963745117, 3.1...   \n",
       "2  [-0.0026922225952148438, -0.14077472686767578,...   \n",
       "\n",
       "                                              gyro_X  \\\n",
       "0  [0.0027008056640625, -0.009552001953125, -0.01...   \n",
       "1  [0.0118560791015625, -0.0209503173828125, -0.0...   \n",
       "2  [0.01654052734375, -0.005401611328125, -0.0039...   \n",
       "\n",
       "                                              gyro_Y  \\\n",
       "0  [-0.0084075927734375, -0.026397705078125, -0.0...   \n",
       "1  [-0.0077667236328125, -0.0175628662109375, -0....   \n",
       "2  [-0.0142669677734375, -0.0097808837890625, 0.0...   \n",
       "\n",
       "                                              gyro_Z   lat_std  long_std  \\\n",
       "0  [0.024566650390625, 0.02606201171875, 0.024032...  0.000020  0.000002   \n",
       "1  [0.0094451904296875, 0.0131683349609375, 0.010...  0.000052  0.000005   \n",
       "2  [0.00848388671875, 0.0093231201171875, 0.01338...  0.000042  0.000004   \n",
       "\n",
       "   speed_mean  ...  gsen_Z_max gsen_Z_min  gsen_Z_range  gyro_Z_mean  \\\n",
       "0    4.221517  ...    5.481957  -2.476293      7.958250     0.020793   \n",
       "1    7.078683  ...    3.184722  -2.618477      5.803199     0.014061   \n",
       "2    9.010814  ...    3.178970  -2.633689      5.812659     0.017439   \n",
       "\n",
       "   gyro_Z_std  gyro_Z_max  gyro_Z_min  gyro_Z_range  lat_mean  long_mean  \n",
       "0    0.003755    0.026062    0.014755      0.011307       NaN        NaN  \n",
       "1    0.003872    0.022751    0.009445      0.013306       NaN        NaN  \n",
       "2    0.006221    0.030319    0.008484      0.021835       NaN        NaN  \n",
       "\n",
       "[3 rows x 44 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Load data after aggregate\n",
    "# figsize=(6,2)\n",
    "# df_agg = pd.concat([pd.read_feather(path_) for path_ in path_event_agg_DORA]).reset_index(drop=True)\n",
    "# print(df_agg.columns)\n",
    "# # df_agg.info()\n",
    "# df_agg.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vehicle_id\n",
       "KA01AF2992    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "KA01AF482     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "KA04MW6569    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "KA51AG8742    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "MH04KF8187    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "Name: event_id, dtype: object"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_agg.groupby(['vehicle_id']).event_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_agg_timestep = df_agg.gsen_X.apply(lambda df_: len(df_))\n",
    "# s_interval = np.sort(df_agg_timestep.unique())\n",
    "# title = f'Sample interval : Timestep(5hz/s)'\n",
    "# title += f'\\nRange : {s_interval}'\n",
    "# df_agg_timestep.plot.hist(figsize=figsize, bins=100, title=title, logy=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### note\n",
    "- timestep btw 8-17\n",
    "    - maybe have enough information \n",
    "- is evenly distributed?\n",
    "    - If the data sampling point is continuous, it will not affect the feature of the data itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Missing datalog and timesteps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load All event data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load files\n",
    "\n",
    "file_ = 'df_event_all_v1'\n",
    "\n",
    "# dir_DORA = Path('DORA')\n",
    "# path_DORA = [path for path in list(dir_DORA.glob('*'))\n",
    "#              if path.suffix in ['.csv', '.feather']  \n",
    "#              and 'all' not in path.name\n",
    "#              and 'agg' not in path.name\n",
    "#              ]\n",
    "# path_DORA\n",
    "# df_event = pd.concat([pd.read_feather(path_) for path_ in path_DORA]).reset_index(drop=True)\n",
    "\n",
    "# df_event.to_feather(dir_DORA/f'{file_}.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'vehicle_id', 'latitude', 'longitude', 'speed', 'heading',\n",
      "       'timestamp', 'gsen_timeTick', 'gyro_timeTick', 'gsen_X', 'gyro_X',\n",
      "       'gsen_Y', 'gyro_Y', 'gsen_Z', 'gyro_Z', 'timestamp_conv', 'event_id'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20504 entries, 0 to 20503\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   id              20504 non-null  int64         \n",
      " 1   vehicle_id      20504 non-null  object        \n",
      " 2   latitude        20504 non-null  float64       \n",
      " 3   longitude       20504 non-null  float64       \n",
      " 4   speed           20504 non-null  float64       \n",
      " 5   heading         20504 non-null  float64       \n",
      " 6   timestamp       20504 non-null  int64         \n",
      " 7   gsen_timeTick   20504 non-null  object        \n",
      " 8   gyro_timeTick   20504 non-null  object        \n",
      " 9   gsen_X          20504 non-null  object        \n",
      " 10  gyro_X          20504 non-null  object        \n",
      " 11  gsen_Y          20504 non-null  object        \n",
      " 12  gyro_Y          20504 non-null  object        \n",
      " 13  gsen_Z          20504 non-null  object        \n",
      " 14  gyro_Z          20504 non-null  object        \n",
      " 15  timestamp_conv  20504 non-null  datetime64[ns]\n",
      " 16  event_id        20504 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(9)\n",
      "memory usage: 2.7+ MB\n"
     ]
    },
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>event_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
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       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39079364</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094095</td>\n",
       "      <td>77.594323</td>\n",
       "      <td>4.052998</td>\n",
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       "      <td>2022-07-27 06:04:40.193</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39079620</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.086250</td>\n",
       "      <td>77.593758</td>\n",
       "      <td>7.070510</td>\n",
       "      <td>185.000580</td>\n",
       "      <td>1658901973438</td>\n",
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       "      <td>2022-07-27 06:06:13.438</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>39079621</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.086186</td>\n",
       "      <td>77.593752</td>\n",
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       "      <td>2022-07-27 06:06:13.438</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39079622</td>\n",
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       "      <td>2022-07-27 06:06:13.438</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "         id  vehicle_id   latitude  longitude     speed     heading  \\\n",
       "0  39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "1  39079364  KA01AF2992  13.094095  77.594323  4.052998  184.984238   \n",
       "2  39079620  KA01AF2992  13.086250  77.593758  7.070510  185.000580   \n",
       "3  39079621  KA01AF2992  13.086186  77.593752  7.032840  184.999451   \n",
       "4  39079622  KA01AF2992  13.086123  77.593746  7.132697  185.001999   \n",
       "\n",
       "       timestamp                                  gsen_timeTick  \\\n",
       "0  1658901879193  [5458092, 5458294, 5458492, 5458692, 5458893]   \n",
       "1  1658901880193  [5459092, 5459293, 5459494, 5459693, 5459893]   \n",
       "2  1658901973438  [5549111, 5549312, 5549512, 5549712, 5549911]   \n",
       "3  1658901973438           [5550509, 5550512, 5550712, 5550912]   \n",
       "4  1658901973438  [5551115, 5551312, 5551512, 5551712, 5551912]   \n",
       "\n",
       "                                   gyro_timeTick  \\\n",
       "0  [5458091, 5458294, 5458492, 5458692, 5458892]   \n",
       "1  [5459092, 5459292, 5459493, 5459693, 5459892]   \n",
       "2  [5549111, 5549312, 5549512, 5549712, 5549911]   \n",
       "3           [5550509, 5550512, 5550712, 5550912]   \n",
       "4  [5551115, 5551312, 5551512, 5551712, 5551912]   \n",
       "\n",
       "                                              gsen_X  \\\n",
       "0  [-0.1768442541360855, -0.3292020559310913, -0....   \n",
       "1  [-0.3817916512489319, 0.030077725648880005, 0....   \n",
       "2  [0.42737051844596863, 0.10562610626220703, -0....   \n",
       "3  [0.2674969732761383, 0.2073156237602234, -0.07...   \n",
       "4  [0.1853664219379425, 0.1695137917995453, 0.095...   \n",
       "\n",
       "                                              gyro_X  \\\n",
       "0  [0.0027008056640625, -0.009552001953125, -0.01...   \n",
       "1  [-0.0150909423828125, 0.01611328125, 0.0014190...   \n",
       "2  [0.0118560791015625, -0.0209503173828125, -0.0...   \n",
       "3  [0.0033416748046875, 0.0077056884765625, -0.01...   \n",
       "4  [0.003326416015625, -0.005828857421875, -0.000...   \n",
       "\n",
       "                                              gsen_Y  \\\n",
       "0  [0.35891878604888916, 0.359760046005249, 0.359...   \n",
       "1  [0.303417444229126, -0.0026443004608154297, 0....   \n",
       "2  [-0.038906097412109375, 0.31329238414764404, 0...   \n",
       "3  [0.18249452114105225, 0.09992015361785889, -0....   \n",
       "4  [0.12484514713287354, 0.06923925876617432, -0....   \n",
       "\n",
       "                                              gyro_Y  \\\n",
       "0  [-0.0084075927734375, -0.026397705078125, -0.0...   \n",
       "1  [-0.0308685302734375, 0.019500732421875, 0.033...   \n",
       "2  [-0.0077667236328125, -0.0175628662109375, -0....   \n",
       "3  [0.055084228515625, 0.04156494140625, -0.04782...   \n",
       "4  [-0.00244140625, -0.0056304931640625, -0.00755...   \n",
       "\n",
       "                                              gsen_Z  \\\n",
       "0  [0.4236612319946289, 0.5882062911987305, 0.645...   \n",
       "1  [0.7125339508056641, -1.2284603118896484, -0.1...   \n",
       "2  [-1.6793060302734375, -0.3619565963745117, 3.1...   \n",
       "3  [-0.8850908279418945, -0.5479907989501953, 0.4...   \n",
       "4  [-0.14698505401611328, 0.18669414520263672, -0...   \n",
       "\n",
       "                                              gyro_Z          timestamp_conv  \\\n",
       "0  [0.024566650390625, 0.02606201171875, 0.024032... 2022-07-27 06:04:39.193   \n",
       "1  [0.0198822021484375, 0.017852783203125, 0.0147... 2022-07-27 06:04:40.193   \n",
       "2  [0.0094451904296875, 0.0131683349609375, 0.010... 2022-07-27 06:06:13.438   \n",
       "3  [0.01507568359375, 0.0162506103515625, 0.01370... 2022-07-27 06:06:13.438   \n",
       "4  [0.012847900390625, 0.014434814453125, 0.01103... 2022-07-27 06:06:13.438   \n",
       "\n",
       "   event_id  \n",
       "0         1  \n",
       "1         1  \n",
       "2         2  \n",
       "3         2  \n",
       "4         2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_event = pd.read_feather(dir_data/f'{file_}.feather')\n",
    "\n",
    "print(df_event.columns)\n",
    "df_event.info()\n",
    "df_event.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vehicle_id\n",
       "KA01AF2992    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "KA01AF482     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "KA04MW6569    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "KA51AG8742    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "MH04KF8187    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "Name: event_id, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check whether the \"event_id\" field of each vehicle is unique\n",
    "df_event.groupby(['vehicle_id']).event_id.unique()\n",
    "\n",
    "# Each vehicle has its own event_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- <table><tr><td bgcolor=darkblue> Should \"speed\" be included as a feature? </td></tr></table> -->\n",
    "##### <font color=lightgreen size=6> Should \"speed\" be included as a feature? </font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Count Missing data dist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_gsen = ['gsen_X', 'gsen_Y', 'gsen_Z']\n",
    "col_gyro = ['gyro_X', 'gyro_Y', 'gyro_Z']\n",
    "col_index = ['event_id', 'timestamp']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2022-08-24 09:29:04.287</td>\n",
       "      <td>723</td>\n",
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       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2022-08-24 23:21:04.974</td>\n",
       "      <td>764</td>\n",
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       "      <td>[]</td>\n",
       "      <td>2022-08-25 00:00:55.854</td>\n",
       "      <td>790</td>\n",
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       "      <th>3085</th>\n",
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       "      <td>KA01AF482</td>\n",
       "      <td>12.940225</td>\n",
       "      <td>78.259923</td>\n",
       "      <td>3.383906</td>\n",
       "      <td>80.038651</td>\n",
       "      <td>1661393258865</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>2022-08-25 02:07:38.865</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3256</th>\n",
       "      <td>46147061</td>\n",
       "      <td>KA01AF482</td>\n",
       "      <td>12.957031</td>\n",
       "      <td>78.271498</td>\n",
       "      <td>8.258431</td>\n",
       "      <td>345.018951</td>\n",
       "      <td>1661450888095</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>2022-08-25 18:08:08.095</td>\n",
       "      <td>892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id vehicle_id   latitude  longitude      speed     heading  \\\n",
       "2864  45794708  KA01AF482  13.218784  77.974100  16.275145  330.007965   \n",
       "2964  45934968  KA01AF482  13.201758  77.997731  20.164141  121.927315   \n",
       "3025  45941498  KA01AF482  13.112760  77.989698   8.755254   45.999386   \n",
       "3085  45963066  KA01AF482  12.940225  78.259923   3.383906   80.038651   \n",
       "3256  46147061  KA01AF482  12.957031  78.271498   8.258431  345.018951   \n",
       "\n",
       "          timestamp gsen_timeTick gyro_timeTick gsen_X gyro_X gsen_Y gyro_Y  \\\n",
       "2864  1661333344287            []            []     []     []     []     []   \n",
       "2964  1661383264974            []            []     []     []     []     []   \n",
       "3025  1661385655854            []            []     []     []     []     []   \n",
       "3085  1661393258865            []            []     []     []     []     []   \n",
       "3256  1661450888095            []            []     []     []     []     []   \n",
       "\n",
       "     gsen_Z gyro_Z          timestamp_conv  event_id  \n",
       "2864     []     [] 2022-08-24 09:29:04.287       723  \n",
       "2964     []     [] 2022-08-24 23:21:04.974       764  \n",
       "3025     []     [] 2022-08-25 00:00:55.854       790  \n",
       "3085     []     [] 2022-08-25 02:07:38.865       818  \n",
       "3256     []     [] 2022-08-25 18:08:08.095       892  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out missing sensor data \n",
    "mask_sensor_missing = df_event.gsen_timeTick.apply(lambda x: len(x))==0\n",
    "idx_sensor_missing = df_event[mask_sensor_missing].index.values\n",
    "df_event[mask_sensor_missing].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Identify abnormal data\n",
    "- timeTick missing \n",
    "- timeTick mismatch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
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       "   gsen_timeTick  gyro_timeTick\n",
       "0              5              5\n",
       "1              5              5\n",
       "2              5              5\n",
       "3              4              4\n",
       "4              5              5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Findout timeTick missing and mismatch data\n",
    "df_gsen_t = df_event.gsen_timeTick.apply(lambda x: len(x))\n",
    "df_gyro_t = df_event.gyro_timeTick.apply(lambda x: len(x))\n",
    "\n",
    "df_timeTick = pd.concat([df_gsen_t, df_gyro_t], axis=1)\n",
    "df_timeTick.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimeTick missing : \t\t 60\n",
      "TimeTick mismatch : \t\t180\n",
      "TimeTick missing & mismatch : \t  2\n",
      "----------------------------------------------------------------------------------------------------------------------------------\n",
      "       gsen_timeTick  gyro_timeTick\n",
      "2864               0              0\n",
      "4233               0              0\n",
      "6300               0              0\n",
      "8607               0              0\n",
      "13312              0              0\n",
      "      gsen_timeTick  gyro_timeTick\n",
      "747               5              6\n",
      "748               6              5\n",
      "750               6              5\n",
      "1544              7              6\n",
      "1573              6              5\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>timestamp_conv</th>\n",
       "      <th>event_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12375</th>\n",
       "      <td>31321237</td>\n",
       "      <td>KA51AG8742</td>\n",
       "      <td>13.110674</td>\n",
       "      <td>77.987114</td>\n",
       "      <td>4.739337</td>\n",
       "      <td>189.081512</td>\n",
       "      <td>1653119179000</td>\n",
       "      <td>[]</td>\n",
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       "      <td>[]</td>\n",
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       "      <td>[]</td>\n",
       "      <td>[0.0037994384765625, 0.006134033203125, 0.0057...</td>\n",
       "      <td>2022-05-21 07:46:19.000</td>\n",
       "      <td>1214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6303</th>\n",
       "      <td>56521548</td>\n",
       "      <td>KA01AF482</td>\n",
       "      <td>12.990292</td>\n",
       "      <td>78.206492</td>\n",
       "      <td>19.885721</td>\n",
       "      <td>77.997543</td>\n",
       "      <td>1664675817588</td>\n",
       "      <td>[41905122, 41905319]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[0.029389619827270508, 0.7395737767219543]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[-0.7267400026321411, -0.5777555108070374]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[-0.23696613311767578, 3.072925567626953]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2022-10-02 01:56:57.588</td>\n",
       "      <td>2275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  vehicle_id   latitude  longitude      speed     heading  \\\n",
       "12375  31321237  KA51AG8742  13.110674  77.987114   4.739337  189.081512   \n",
       "6303   56521548   KA01AF482  12.990292  78.206492  19.885721   77.997543   \n",
       "\n",
       "           timestamp         gsen_timeTick  \\\n",
       "12375  1653119179000                    []   \n",
       "6303   1664675817588  [41905122, 41905319]   \n",
       "\n",
       "                                           gyro_timeTick  \\\n",
       "12375  [77476109, 77476310, 77476509, 77476708, 77476...   \n",
       "6303                                                  []   \n",
       "\n",
       "                                           gsen_X  \\\n",
       "12375                                          []   \n",
       "6303   [0.029389619827270508, 0.7395737767219543]   \n",
       "\n",
       "                                                  gyro_X  \\\n",
       "12375  [0.0068206787109375, 0.0287628173828125, -0.00...   \n",
       "6303                                                  []   \n",
       "\n",
       "                                           gsen_Y  \\\n",
       "12375                                          []   \n",
       "6303   [-0.7267400026321411, -0.5777555108070374]   \n",
       "\n",
       "                                                  gyro_Y  \\\n",
       "12375  [-0.03955078125, 0.024139404296875, 0.05503845...   \n",
       "6303                                                  []   \n",
       "\n",
       "                                          gsen_Z  \\\n",
       "12375                                         []   \n",
       "6303   [-0.23696613311767578, 3.072925567626953]   \n",
       "\n",
       "                                                  gyro_Z  \\\n",
       "12375  [0.0037994384765625, 0.006134033203125, 0.0057...   \n",
       "6303                                                  []   \n",
       "\n",
       "               timestamp_conv  event_id  \n",
       "12375 2022-05-21 07:46:19.000      1214  \n",
       "6303  2022-10-02 01:56:57.588      2275  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# df_timeTick_abnormal = df_timeTick.query('gsen_timeTick==0 | gyro_timeTick==0 | gsen_timeTick != gyro_timeTick')\n",
    "\n",
    "idx_t_missing = df_timeTick.query('gsen_timeTick==0 | gyro_timeTick==0').index.values\n",
    "idx_t_mismatch = df_timeTick.query('gsen_timeTick != gyro_timeTick').index.values\n",
    "inter_ = set(idx_t_missing) & set(idx_t_mismatch)\n",
    "\n",
    "print(f'TimeTick missing : \\t\\t{len(idx_t_missing):>3d}')\n",
    "print(f'TimeTick mismatch : \\t\\t{len(idx_t_mismatch):>3d}')\n",
    "print(f'TimeTick missing & mismatch : \\t{len(inter_):>3d}')\n",
    "print('-'*130)\n",
    "print(df_timeTick.loc[idx_t_missing][::12])\n",
    "print(df_timeTick.loc[idx_t_mismatch].head(5))\n",
    "df_event.iloc[list(inter_)]\n",
    "\n",
    "# TimeTick missing : 60\n",
    "# TimeTick mismatch : 180\n",
    "# TimeTick missing & mismatch : 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[array([214952014, 214952191, 214952390, 214952591, 214952791]),\n",
       "        array([214952014, 214952191, 214952390, 214952590, 214952790, 214952995])],\n",
       "       [array([214953011, 214953191, 214953393, 214953591, 214953791, 214953991]),\n",
       "        array([214953190, 214953391, 214953591, 214953791, 214953991])],\n",
       "       [array([214953011, 214953191, 214953393, 214953591, 214953791, 214953991]),\n",
       "        array([214953190, 214953391, 214953591, 214953791, 214953991])]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_event.loc[idx_t_mismatch, ['gsen_timeTick','gyro_timeTick']]\n",
    " .iloc[:3].values\n",
    ")\n",
    "\n",
    "# Time mismatch format inconsistency. \n",
    "# Head misalignment, tail misalignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### data Time Series Misalignment\n",
    "- Time mismatch format inconsistency. \n",
    "    - Head misalignment\n",
    "    - Tail misalignment\n",
    "\n",
    "- Solution:\n",
    "    - Delete\n",
    "    - ~~Alignment~~\n",
    "        - Execution difficulties ： TimeTick btw gsen and gyro is inconsist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=lightgreen > Drop Data with timestep mismatch and missing </font>\n",
    "- by idx\n",
    "- get reseverd id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data All : 20,504\n",
      "Data that will be dropped  :    238\n",
      "Data that will be retained : 20,266\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
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       "      <td>[-0.1768442541360855, -0.3292020559310913, -0....</td>\n",
       "      <td>[0.0027008056640625, -0.009552001953125, -0.01...</td>\n",
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       "      <td>2022-07-27 06:04:39.193</td>\n",
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       "      <td>77.594323</td>\n",
       "      <td>4.052998</td>\n",
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       "      <td>2022-07-27 06:04:40.193</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39079620</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.086250</td>\n",
       "      <td>77.593758</td>\n",
       "      <td>7.070510</td>\n",
       "      <td>185.000580</td>\n",
       "      <td>1658901973438</td>\n",
       "      <td>[5549111, 5549312, 5549512, 5549712, 5549911]</td>\n",
       "      <td>[5549111, 5549312, 5549512, 5549712, 5549911]</td>\n",
       "      <td>[0.42737051844596863, 0.10562610626220703, -0....</td>\n",
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       "      <td>[0.0094451904296875, 0.0131683349609375, 0.010...</td>\n",
       "      <td>2022-07-27 06:06:13.438</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
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       "      <td>77.593752</td>\n",
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       "      <td>184.999451</td>\n",
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       "      <td>[0.2674969732761383, 0.2073156237602234, -0.07...</td>\n",
       "      <td>[0.0033416748046875, 0.0077056884765625, -0.01...</td>\n",
       "      <td>[0.18249452114105225, 0.09992015361785889, -0....</td>\n",
       "      <td>[0.055084228515625, 0.04156494140625, -0.04782...</td>\n",
       "      <td>[-0.8850908279418945, -0.5479907989501953, 0.4...</td>\n",
       "      <td>[0.01507568359375, 0.0162506103515625, 0.01370...</td>\n",
       "      <td>2022-07-27 06:06:13.438</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39079622</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.086123</td>\n",
       "      <td>77.593746</td>\n",
       "      <td>7.132697</td>\n",
       "      <td>185.001999</td>\n",
       "      <td>1658901973438</td>\n",
       "      <td>[5551115, 5551312, 5551512, 5551712, 5551912]</td>\n",
       "      <td>[5551115, 5551312, 5551512, 5551712, 5551912]</td>\n",
       "      <td>[0.1853664219379425, 0.1695137917995453, 0.095...</td>\n",
       "      <td>[0.003326416015625, -0.005828857421875, -0.000...</td>\n",
       "      <td>[0.12484514713287354, 0.06923925876617432, -0....</td>\n",
       "      <td>[-0.00244140625, -0.0056304931640625, -0.00755...</td>\n",
       "      <td>[-0.14698505401611328, 0.18669414520263672, -0...</td>\n",
       "      <td>[0.012847900390625, 0.014434814453125, 0.01103...</td>\n",
       "      <td>2022-07-27 06:06:13.438</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  vehicle_id   latitude  longitude     speed     heading  \\\n",
       "0  39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "1  39079364  KA01AF2992  13.094095  77.594323  4.052998  184.984238   \n",
       "2  39079620  KA01AF2992  13.086250  77.593758  7.070510  185.000580   \n",
       "3  39079621  KA01AF2992  13.086186  77.593752  7.032840  184.999451   \n",
       "4  39079622  KA01AF2992  13.086123  77.593746  7.132697  185.001999   \n",
       "\n",
       "       timestamp                                  gsen_timeTick  \\\n",
       "0  1658901879193  [5458092, 5458294, 5458492, 5458692, 5458893]   \n",
       "1  1658901880193  [5459092, 5459293, 5459494, 5459693, 5459893]   \n",
       "2  1658901973438  [5549111, 5549312, 5549512, 5549712, 5549911]   \n",
       "3  1658901973438           [5550509, 5550512, 5550712, 5550912]   \n",
       "4  1658901973438  [5551115, 5551312, 5551512, 5551712, 5551912]   \n",
       "\n",
       "                                   gyro_timeTick  \\\n",
       "0  [5458091, 5458294, 5458492, 5458692, 5458892]   \n",
       "1  [5459092, 5459292, 5459493, 5459693, 5459892]   \n",
       "2  [5549111, 5549312, 5549512, 5549712, 5549911]   \n",
       "3           [5550509, 5550512, 5550712, 5550912]   \n",
       "4  [5551115, 5551312, 5551512, 5551712, 5551912]   \n",
       "\n",
       "                                              gsen_X  \\\n",
       "0  [-0.1768442541360855, -0.3292020559310913, -0....   \n",
       "1  [-0.3817916512489319, 0.030077725648880005, 0....   \n",
       "2  [0.42737051844596863, 0.10562610626220703, -0....   \n",
       "3  [0.2674969732761383, 0.2073156237602234, -0.07...   \n",
       "4  [0.1853664219379425, 0.1695137917995453, 0.095...   \n",
       "\n",
       "                                              gyro_X  \\\n",
       "0  [0.0027008056640625, -0.009552001953125, -0.01...   \n",
       "1  [-0.0150909423828125, 0.01611328125, 0.0014190...   \n",
       "2  [0.0118560791015625, -0.0209503173828125, -0.0...   \n",
       "3  [0.0033416748046875, 0.0077056884765625, -0.01...   \n",
       "4  [0.003326416015625, -0.005828857421875, -0.000...   \n",
       "\n",
       "                                              gsen_Y  \\\n",
       "0  [0.35891878604888916, 0.359760046005249, 0.359...   \n",
       "1  [0.303417444229126, -0.0026443004608154297, 0....   \n",
       "2  [-0.038906097412109375, 0.31329238414764404, 0...   \n",
       "3  [0.18249452114105225, 0.09992015361785889, -0....   \n",
       "4  [0.12484514713287354, 0.06923925876617432, -0....   \n",
       "\n",
       "                                              gyro_Y  \\\n",
       "0  [-0.0084075927734375, -0.026397705078125, -0.0...   \n",
       "1  [-0.0308685302734375, 0.019500732421875, 0.033...   \n",
       "2  [-0.0077667236328125, -0.0175628662109375, -0....   \n",
       "3  [0.055084228515625, 0.04156494140625, -0.04782...   \n",
       "4  [-0.00244140625, -0.0056304931640625, -0.00755...   \n",
       "\n",
       "                                              gsen_Z  \\\n",
       "0  [0.4236612319946289, 0.5882062911987305, 0.645...   \n",
       "1  [0.7125339508056641, -1.2284603118896484, -0.1...   \n",
       "2  [-1.6793060302734375, -0.3619565963745117, 3.1...   \n",
       "3  [-0.8850908279418945, -0.5479907989501953, 0.4...   \n",
       "4  [-0.14698505401611328, 0.18669414520263672, -0...   \n",
       "\n",
       "                                              gyro_Z          timestamp_conv  \\\n",
       "0  [0.024566650390625, 0.02606201171875, 0.024032... 2022-07-27 06:04:39.193   \n",
       "1  [0.0198822021484375, 0.017852783203125, 0.0147... 2022-07-27 06:04:40.193   \n",
       "2  [0.0094451904296875, 0.0131683349609375, 0.010... 2022-07-27 06:06:13.438   \n",
       "3  [0.01507568359375, 0.0162506103515625, 0.01370... 2022-07-27 06:06:13.438   \n",
       "4  [0.012847900390625, 0.014434814453125, 0.01103... 2022-07-27 06:06:13.438   \n",
       "\n",
       "   event_id  \n",
       "0         1  \n",
       "1         1  \n",
       "2         2  \n",
       "3         2  \n",
       "4         2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Data All : {len(df_event.index.values):>6,d}')\n",
    "idx_drop = set(idx_t_missing) | set(idx_t_mismatch)\n",
    "# idx_drop.sort()\n",
    "print(f'Data that will be dropped  : {len(idx_drop):>6,d}')\n",
    "\n",
    "idx_reserved = list(set(df_event.index.values) - idx_drop) \n",
    "print(f'Data that will be retained : {len(idx_reserved):>6,d}')\n",
    "\n",
    "# Data All : 20,504\n",
    "# Data that will be dropped  :    238\n",
    "# Data that will be retained : 20,266\n",
    "\n",
    "df_event.iloc[idx_reserved].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explode sensor data frome list format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100958 entries, 0 to 20503\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   id              100958 non-null  int64         \n",
      " 1   vehicle_id      100958 non-null  object        \n",
      " 2   latitude        100958 non-null  float64       \n",
      " 3   longitude       100958 non-null  float64       \n",
      " 4   speed           100958 non-null  float64       \n",
      " 5   heading         100958 non-null  float64       \n",
      " 6   timestamp       100958 non-null  int64         \n",
      " 7   gsen_timeTick   100958 non-null  object        \n",
      " 8   gyro_timeTick   100958 non-null  object        \n",
      " 9   gsen_X          100958 non-null  object        \n",
      " 10  gyro_X          100958 non-null  object        \n",
      " 11  gsen_Y          100958 non-null  object        \n",
      " 12  gyro_Y          100958 non-null  object        \n",
      " 13  gsen_Z          100958 non-null  object        \n",
      " 14  gyro_Z          100958 non-null  object        \n",
      " 15  timestamp_conv  100958 non-null  datetime64[ns]\n",
      " 16  event_id        100958 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(9)\n",
      "memory usage: 13.9+ MB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>timestamp_conv</th>\n",
       "      <th>event_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
       "      <td>KA01AF2992</td>\n",
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       "      <td>0.358919</td>\n",
       "      <td>-0.008408</td>\n",
       "      <td>0.423661</td>\n",
       "      <td>0.024567</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458294</td>\n",
       "      <td>5458294</td>\n",
       "      <td>-0.329202</td>\n",
       "      <td>-0.009552</td>\n",
       "      <td>0.35976</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>0.588206</td>\n",
       "      <td>0.026062</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458492</td>\n",
       "      <td>5458492</td>\n",
       "      <td>-0.477587</td>\n",
       "      <td>-0.01712</td>\n",
       "      <td>0.359269</td>\n",
       "      <td>-0.044296</td>\n",
       "      <td>0.645035</td>\n",
       "      <td>0.024033</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458692</td>\n",
       "      <td>5458692</td>\n",
       "      <td>0.128577</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.172681</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>-2.081446</td>\n",
       "      <td>0.024353</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458893</td>\n",
       "      <td>5458892</td>\n",
       "      <td>-0.198447</td>\n",
       "      <td>-0.003693</td>\n",
       "      <td>0.187229</td>\n",
       "      <td>0.012802</td>\n",
       "      <td>0.825244</td>\n",
       "      <td>0.021576</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565018</td>\n",
       "      <td>27565017</td>\n",
       "      <td>2.91445</td>\n",
       "      <td>-0.035446</td>\n",
       "      <td>-1.703996</td>\n",
       "      <td>0.013611</td>\n",
       "      <td>0.351263</td>\n",
       "      <td>0.028122</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565217</td>\n",
       "      <td>27565216</td>\n",
       "      <td>-2.949032</td>\n",
       "      <td>-0.027237</td>\n",
       "      <td>0.460132</td>\n",
       "      <td>0.004761</td>\n",
       "      <td>-2.437326</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565416</td>\n",
       "      <td>27565416</td>\n",
       "      <td>1.892698</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.242738</td>\n",
       "      <td>-0.002045</td>\n",
       "      <td>-0.230687</td>\n",
       "      <td>-0.032181</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565617</td>\n",
       "      <td>27565617</td>\n",
       "      <td>-0.870719</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.033652</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>-1.473144</td>\n",
       "      <td>-0.014069</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565817</td>\n",
       "      <td>27565817</td>\n",
       "      <td>-0.845461</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>-0.006703</td>\n",
       "      <td>0.023621</td>\n",
       "      <td>-1.367128</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100958 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  vehicle_id   latitude  longitude     speed     heading  \\\n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "...         ...         ...        ...        ...       ...         ...   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "\n",
       "           timestamp gsen_timeTick gyro_timeTick    gsen_X    gyro_X  \\\n",
       "0      1658901879193       5458092       5458091 -0.176844  0.002701   \n",
       "0      1658901879193       5458294       5458294 -0.329202 -0.009552   \n",
       "0      1658901879193       5458492       5458492 -0.477587  -0.01712   \n",
       "0      1658901879193       5458692       5458692  0.128577  0.012604   \n",
       "0      1658901879193       5458893       5458892 -0.198447 -0.003693   \n",
       "...              ...           ...           ...       ...       ...   \n",
       "20503  1664759455906      27565018      27565017   2.91445 -0.035446   \n",
       "20503  1664759455906      27565217      27565216 -2.949032 -0.027237   \n",
       "20503  1664759455906      27565416      27565416  1.892698 -0.003052   \n",
       "20503  1664759455906      27565617      27565617 -0.870719 -0.003052   \n",
       "20503  1664759455906      27565817      27565817 -0.845461 -0.018707   \n",
       "\n",
       "         gsen_Y    gyro_Y    gsen_Z    gyro_Z          timestamp_conv  \\\n",
       "0      0.358919 -0.008408  0.423661  0.024567 2022-07-27 06:04:39.193   \n",
       "0       0.35976 -0.026398  0.588206  0.026062 2022-07-27 06:04:39.193   \n",
       "0      0.359269 -0.044296  0.645035  0.024033 2022-07-27 06:04:39.193   \n",
       "0      0.172681    0.0112 -2.081446  0.024353 2022-07-27 06:04:39.193   \n",
       "0      0.187229  0.012802  0.825244  0.021576 2022-07-27 06:04:39.193   \n",
       "...         ...       ...       ...       ...                     ...   \n",
       "20503 -1.703996  0.013611  0.351263  0.028122 2022-10-03 01:10:55.906   \n",
       "20503  0.460132  0.004761 -2.437326  0.020874 2022-10-03 01:10:55.906   \n",
       "20503  0.242738 -0.002045 -0.230687 -0.032181 2022-10-03 01:10:55.906   \n",
       "20503  0.033652  0.018936 -1.473144 -0.014069 2022-10-03 01:10:55.906   \n",
       "20503 -0.006703  0.023621 -1.367128   0.01767 2022-10-03 01:10:55.906   \n",
       "\n",
       "       event_id  \n",
       "0             1  \n",
       "0             1  \n",
       "0             1  \n",
       "0             1  \n",
       "0             1  \n",
       "...         ...  \n",
       "20503      1097  \n",
       "20503      1097  \n",
       "20503      1097  \n",
       "20503      1097  \n",
       "20503      1097  \n",
       "\n",
       "[100958 rows x 17 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sensor_exploded = df_event.iloc[idx_reserved].apply(pd.Series.explode)\n",
    "df_sensor_exploded.info()\n",
    "df_sensor_exploded "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100958 entries, 0 to 20503\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   id              100958 non-null  int64         \n",
      " 1   vehicle_id      100958 non-null  object        \n",
      " 2   latitude        100958 non-null  float64       \n",
      " 3   longitude       100958 non-null  float64       \n",
      " 4   speed           100958 non-null  float64       \n",
      " 5   heading         100958 non-null  float64       \n",
      " 6   timestamp       100958 non-null  int64         \n",
      " 7   gsen_timeTick   100958 non-null  int64         \n",
      " 8   gyro_timeTick   100958 non-null  int64         \n",
      " 9   gsen_X          100958 non-null  float64       \n",
      " 10  gyro_X          100958 non-null  float64       \n",
      " 11  gsen_Y          100958 non-null  float64       \n",
      " 12  gyro_Y          100958 non-null  float64       \n",
      " 13  gsen_Z          100958 non-null  float64       \n",
      " 14  gyro_Z          100958 non-null  float64       \n",
      " 15  timestamp_conv  100958 non-null  datetime64[ns]\n",
      " 16  event_id        100958 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(10), int64(5), object(1)\n",
      "memory usage: 13.9+ MB\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>timestamp_conv</th>\n",
       "      <th>event_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
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       "      <td>2022-07-27 06:04:39.193</td>\n",
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       "      <td>2022-07-27 06:04:39.193</td>\n",
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       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
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       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>39079362</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
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       "      <td>2022-07-27 06:04:39.193</td>\n",
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       "      <td>-0.198447</td>\n",
       "      <td>-0.003693</td>\n",
       "      <td>0.187229</td>\n",
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       "      <td>0.825244</td>\n",
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       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
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       "      <td>0.013611</td>\n",
       "      <td>0.351263</td>\n",
       "      <td>0.028122</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
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       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565217</td>\n",
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       "      <td>-2.949032</td>\n",
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       "      <td>0.004761</td>\n",
       "      <td>-2.437326</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565416</td>\n",
       "      <td>27565416</td>\n",
       "      <td>1.892698</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.242738</td>\n",
       "      <td>-0.002045</td>\n",
       "      <td>-0.230687</td>\n",
       "      <td>-0.032181</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
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       "      <td>27565617</td>\n",
       "      <td>-0.870719</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.033652</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>-1.473144</td>\n",
       "      <td>-0.014069</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>56817252</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565817</td>\n",
       "      <td>27565817</td>\n",
       "      <td>-0.845461</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>-0.006703</td>\n",
       "      <td>0.023621</td>\n",
       "      <td>-1.367128</td>\n",
       "      <td>0.017670</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100958 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  vehicle_id   latitude  longitude     speed     heading  \\\n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "0      39079362  KA01AF2992  13.094135  77.594326  4.390037  185.001465   \n",
       "...         ...         ...        ...        ...       ...         ...   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "20503  56817252  MH04KF8187  21.219641  72.968640  3.287662  359.055634   \n",
       "\n",
       "           timestamp  gsen_timeTick  gyro_timeTick    gsen_X    gyro_X  \\\n",
       "0      1658901879193        5458092        5458091 -0.176844  0.002701   \n",
       "0      1658901879193        5458294        5458294 -0.329202 -0.009552   \n",
       "0      1658901879193        5458492        5458492 -0.477587 -0.017120   \n",
       "0      1658901879193        5458692        5458692  0.128577  0.012604   \n",
       "0      1658901879193        5458893        5458892 -0.198447 -0.003693   \n",
       "...              ...            ...            ...       ...       ...   \n",
       "20503  1664759455906       27565018       27565017  2.914450 -0.035446   \n",
       "20503  1664759455906       27565217       27565216 -2.949032 -0.027237   \n",
       "20503  1664759455906       27565416       27565416  1.892698 -0.003052   \n",
       "20503  1664759455906       27565617       27565617 -0.870719 -0.003052   \n",
       "20503  1664759455906       27565817       27565817 -0.845461 -0.018707   \n",
       "\n",
       "         gsen_Y    gyro_Y    gsen_Z    gyro_Z          timestamp_conv  \\\n",
       "0      0.358919 -0.008408  0.423661  0.024567 2022-07-27 06:04:39.193   \n",
       "0      0.359760 -0.026398  0.588206  0.026062 2022-07-27 06:04:39.193   \n",
       "0      0.359269 -0.044296  0.645035  0.024033 2022-07-27 06:04:39.193   \n",
       "0      0.172681  0.011200 -2.081446  0.024353 2022-07-27 06:04:39.193   \n",
       "0      0.187229  0.012802  0.825244  0.021576 2022-07-27 06:04:39.193   \n",
       "...         ...       ...       ...       ...                     ...   \n",
       "20503 -1.703996  0.013611  0.351263  0.028122 2022-10-03 01:10:55.906   \n",
       "20503  0.460132  0.004761 -2.437326  0.020874 2022-10-03 01:10:55.906   \n",
       "20503  0.242738 -0.002045 -0.230687 -0.032181 2022-10-03 01:10:55.906   \n",
       "20503  0.033652  0.018936 -1.473144 -0.014069 2022-10-03 01:10:55.906   \n",
       "20503 -0.006703  0.023621 -1.367128  0.017670 2022-10-03 01:10:55.906   \n",
       "\n",
       "       event_id  \n",
       "0             1  \n",
       "0             1  \n",
       "0             1  \n",
       "0             1  \n",
       "0             1  \n",
       "...         ...  \n",
       "20503      1097  \n",
       "20503      1097  \n",
       "20503      1097  \n",
       "20503      1097  \n",
       "20503      1097  \n",
       "\n",
       "[100958 rows x 17 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sensor_exploded = df_event.iloc[idx_reserved].apply(pd.Series.explode)\n",
    "col_sensor = df_sensor_exploded.filter(regex='[XYZ]$').columns.values # ['gsen_X', 'gyro_X', 'gsen_Y', 'gyro_Y', 'gsen_Z', 'gyro_Z']\n",
    "col_sensorTimeTick = df_sensor_exploded.filter(regex='Tick$').columns.values # ['gsen_timeTick', 'gyro_timeTick']\n",
    "df_sensor_exploded[col_sensor] = df_sensor_exploded[col_sensor].astype(float) \n",
    "df_sensor_exploded[col_sensorTimeTick] = df_sensor_exploded[col_sensorTimeTick].astype(int) \n",
    "\n",
    "df_sensor_exploded.info()\n",
    "df_sensor_exploded "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check abnormal data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- df_count_timestep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize = (6,2)\n",
    "df_count_timestep = (df_sensor_exploded.groupby(['vehicle_id','event_id'])\n",
    "                     .id.count().rename('count')\n",
    "                     .reset_index()\n",
    "                     ) \n",
    "s_interval = np.sort(np.unique(df_count_timestep['count']))\n",
    "title = f'Sample interval : Timestep(5hz/s)'\n",
    "title += f'\\nAfter drop missing and mismatch'\n",
    "title += f'\\nRange : {s_interval}'\n",
    "df_count_timestep['count'].plot.hist(figsize=figsize, bins=100, title=title, logy=True);\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timestamps b<5  : 0.587%\n",
      "Timestamps b<10 : 7.485%\n"
     ]
    }
   ],
   "source": [
    "p_b5 = df_count_timestep.query('count < 5').size / len(df_count_timestep)\n",
    "p_b10 = df_count_timestep.query('count < 10').size / len(df_count_timestep)\n",
    "\n",
    "print(f'Timestamps b<5  : {p_b5*100:5.3f}%') \n",
    "print(f'Timestamps b<10 : {p_b10*100:5.3f}%') "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Note : problematic timestamps range\n",
    "- There are still some problematic data\n",
    "    - range of timestamps : 1 - 17\n",
    "    - expected value : 15(3s)\n",
    "        - Acceptable range : 5(1s) - 20(3s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      vehicle_id  event_id  count\n",
      "334   KA01AF2992       335      4\n",
      "740    KA01AF482       183      3\n",
      "1003   KA01AF482       448      4\n",
      "1598   KA01AF482      1045      4\n",
      "1652   KA01AF482      1099      4\n",
      "1739   KA01AF482      1187      3\n",
      "1778   KA01AF482      1226      3\n",
      "1819   KA01AF482      1268      2\n",
      "2111   KA01AF482      1563      2\n",
      "2181   KA01AF482      1634      4\n",
      "2220   KA01AF482      1673      4\n",
      "2249   KA01AF482      1702      3\n",
      "3833  KA51AG8742        52      4\n",
      "3865  KA51AG8742        84      4\n",
      "7419  MH04KF8187       336      4\n",
      "7607  MH04KF8187       528      1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[array([41662001, 41662196, 41662397, 41662597, 41662796, 41662996]),\n",
       "        array([41662196, 41662397, 41662597, 41662796, 41662996])],\n",
       "       [array([41663196, 41663410, 41663607, 41663796]),\n",
       "        array([41663196, 41663407, 41663607, 41663796])]], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con = 'count < 5'\n",
    "print(df_count_timestep.query(con))\n",
    "for idx, rows_ in df_count_timestep.query(con).iterrows():\n",
    "    vehicle_id_, event_id_, count_ = rows_\n",
    "    # print(f'{idx}, vehicle_id : {vehicle_id_}, event_id : {event_id_}, count : {count_}')\n",
    "    \n",
    "    if idx == 7419: break\n",
    "\n",
    "(df_event.query('vehicle_id == @vehicle_id_ and event_id == @event_id_')\n",
    " .loc[:,['gsen_timeTick','gyro_timeTick']].values \n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      vehicle_id  event_id  count\n",
      "639    KA01AF482        80     17\n",
      "1549   KA01AF482       996     16\n",
      "1664   KA01AF482      1111     16\n",
      "1665   KA01AF482      1112     16\n",
      "3149  KA04MW6569       166     16\n",
      "4026  KA51AG8742       245     16\n",
      "4542  KA51AG8742       762     16\n",
      "4608  KA51AG8742       828     17\n",
      "4803  KA51AG8742      1023     17\n",
      "5589  KA51AG8742      1811     16\n",
      "5795  KA51AG8742      2017     17\n",
      "5843  KA51AG8742      2065     16\n",
      "5886  KA51AG8742      2108     16\n",
      "5906  KA51AG8742      2128     16\n",
      "5908  KA51AG8742      2130     16\n",
      "5972  KA51AG8742      2194     16\n",
      "5996  KA51AG8742      2218     16\n",
      "6125  KA51AG8742      2347     16\n",
      "7617  MH04KF8187       538     16\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[array([43018156, 43018177, 43018383, 43018580, 43018883, 43018977]),\n",
       "        array([43018156, 43018177, 43018378, 43018578, 43018883, 43018976])],\n",
       "       [array([43018156, 43018177, 43018383, 43018580, 43018883, 43018977]),\n",
       "        array([43018156, 43018177, 43018378, 43018578, 43018883, 43018976])],\n",
       "       [array([43019178, 43019378, 43019602, 43019788, 43019979]),\n",
       "        array([43019180, 43019377, 43019602, 43019788, 43019981])]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con = 'count > 15'\n",
    "print(df_count_timestep.query(con))\n",
    "for idx, rows_ in df_count_timestep.query(con).iterrows():\n",
    "    vehicle_id_, event_id_, count_ = rows_\n",
    "    # print(f'{idx}, vehicle_id : {vehicle_id_}, event_id : {event_id_}, count : {count_}')\n",
    "    \n",
    "    if idx == 639: break\n",
    "\n",
    "(df_event.query('vehicle_id == @vehicle_id_ and event_id == @event_id_')\n",
    " .loc[:,['gsen_timeTick','gyro_timeTick']].values \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Note : <font color=red > Some of the data Timetick has duplicated </font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ~~Find all duplicates first~~\n",
    "- by timestamp\n",
    "    -  timestamp -> event_id  \n",
    "- by gsen & gyro\n",
    "    - Doesn't seem feasible\n",
    "    - numpy array can't be compared "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>id</th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>timestamp_conv</th>\n",
       "      <th>event_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>43953409</td>\n",
       "      <td>KA01AF482</td>\n",
       "      <td>13.099223</td>\n",
       "      <td>78.256380</td>\n",
       "      <td>5.029138</td>\n",
       "      <td>105.973831</td>\n",
       "      <td>1660766526084</td>\n",
       "      <td>[43018156, 43018177, 43018383, 43018580, 43018...</td>\n",
       "      <td>[43018156, 43018177, 43018378, 43018578, 43018...</td>\n",
       "      <td>[-0.835548996925354, -1.346818447113037, 0.473...</td>\n",
       "      <td>[0.0571136474609375, -0.051116943359375, 0.014...</td>\n",
       "      <td>[-0.23057964444160461, 0.1087217628955841, -0....</td>\n",
       "      <td>[0.0703887939453125, -0.1074981689453125, -0.0...</td>\n",
       "      <td>[-3.2357683181762695, -3.771421432495117, 3.44...</td>\n",
       "      <td>[0.0189666748046875, 0.0156707763671875, 0.018...</td>\n",
       "      <td>2022-08-17 20:02:06.084</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>43953413</td>\n",
       "      <td>KA01AF482</td>\n",
       "      <td>13.099223</td>\n",
       "      <td>78.256380</td>\n",
       "      <td>5.029138</td>\n",
       "      <td>105.973831</td>\n",
       "      <td>1660766527064</td>\n",
       "      <td>[43018156, 43018177, 43018383, 43018580, 43018...</td>\n",
       "      <td>[43018156, 43018177, 43018378, 43018578, 43018...</td>\n",
       "      <td>[-0.835548996925354, -1.346818447113037, 0.473...</td>\n",
       "      <td>[0.0571136474609375, -0.051116943359375, 0.014...</td>\n",
       "      <td>[-0.23057964444160461, 0.1087217628955841, -0....</td>\n",
       "      <td>[0.0703887939453125, -0.1074981689453125, -0.0...</td>\n",
       "      <td>[-3.2357683181762695, -3.771421432495117, 3.44...</td>\n",
       "      <td>[0.0189666748046875, 0.0156707763671875, 0.018...</td>\n",
       "      <td>2022-08-17 20:02:07.064</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>43953414</td>\n",
       "      <td>KA01AF482</td>\n",
       "      <td>13.099213</td>\n",
       "      <td>78.256424</td>\n",
       "      <td>5.391209</td>\n",
       "      <td>104.085785</td>\n",
       "      <td>1660766527064</td>\n",
       "      <td>[43019178, 43019378, 43019602, 43019788, 43019...</td>\n",
       "      <td>[43019180, 43019377, 43019602, 43019788, 43019...</td>\n",
       "      <td>[-0.2170635461807251, -0.9632717967033386, -1....</td>\n",
       "      <td>[0.03271484375, 0.025787353515625, 0.008117675...</td>\n",
       "      <td>[-0.46733155846595764, 0.1901770532131195, 0.3...</td>\n",
       "      <td>[0.0442962646484375, 0.0397186279296875, -0.10...</td>\n",
       "      <td>[2.988705635070801, -0.6884565353393555, -3.56...</td>\n",
       "      <td>[0.0359039306640625, 0.0359039306640625, 0.012...</td>\n",
       "      <td>2022-08-17 20:02:07.064</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id vehicle_id   latitude  longitude     speed     heading  \\\n",
       "1427  43953409  KA01AF482  13.099223  78.256380  5.029138  105.973831   \n",
       "1428  43953413  KA01AF482  13.099223  78.256380  5.029138  105.973831   \n",
       "1429  43953414  KA01AF482  13.099213  78.256424  5.391209  104.085785   \n",
       "\n",
       "          timestamp                                      gsen_timeTick  \\\n",
       "1427  1660766526084  [43018156, 43018177, 43018383, 43018580, 43018...   \n",
       "1428  1660766527064  [43018156, 43018177, 43018383, 43018580, 43018...   \n",
       "1429  1660766527064  [43019178, 43019378, 43019602, 43019788, 43019...   \n",
       "\n",
       "                                          gyro_timeTick  \\\n",
       "1427  [43018156, 43018177, 43018378, 43018578, 43018...   \n",
       "1428  [43018156, 43018177, 43018378, 43018578, 43018...   \n",
       "1429  [43019180, 43019377, 43019602, 43019788, 43019...   \n",
       "\n",
       "                                                 gsen_X  \\\n",
       "1427  [-0.835548996925354, -1.346818447113037, 0.473...   \n",
       "1428  [-0.835548996925354, -1.346818447113037, 0.473...   \n",
       "1429  [-0.2170635461807251, -0.9632717967033386, -1....   \n",
       "\n",
       "                                                 gyro_X  \\\n",
       "1427  [0.0571136474609375, -0.051116943359375, 0.014...   \n",
       "1428  [0.0571136474609375, -0.051116943359375, 0.014...   \n",
       "1429  [0.03271484375, 0.025787353515625, 0.008117675...   \n",
       "\n",
       "                                                 gsen_Y  \\\n",
       "1427  [-0.23057964444160461, 0.1087217628955841, -0....   \n",
       "1428  [-0.23057964444160461, 0.1087217628955841, -0....   \n",
       "1429  [-0.46733155846595764, 0.1901770532131195, 0.3...   \n",
       "\n",
       "                                                 gyro_Y  \\\n",
       "1427  [0.0703887939453125, -0.1074981689453125, -0.0...   \n",
       "1428  [0.0703887939453125, -0.1074981689453125, -0.0...   \n",
       "1429  [0.0442962646484375, 0.0397186279296875, -0.10...   \n",
       "\n",
       "                                                 gsen_Z  \\\n",
       "1427  [-3.2357683181762695, -3.771421432495117, 3.44...   \n",
       "1428  [-3.2357683181762695, -3.771421432495117, 3.44...   \n",
       "1429  [2.988705635070801, -0.6884565353393555, -3.56...   \n",
       "\n",
       "                                                 gyro_Z  \\\n",
       "1427  [0.0189666748046875, 0.0156707763671875, 0.018...   \n",
       "1428  [0.0189666748046875, 0.0156707763671875, 0.018...   \n",
       "1429  [0.0359039306640625, 0.0359039306640625, 0.012...   \n",
       "\n",
       "              timestamp_conv  event_id  \n",
       "1427 2022-08-17 20:02:06.084        80  \n",
       "1428 2022-08-17 20:02:07.064        80  \n",
       "1429 2022-08-17 20:02:07.064        80  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dup_ = df_event.query('vehicle_id == @vehicle_id_ and event_id == @event_id_')\n",
    "df_dup_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.835549   -1.34681845  0.47327501  0.17217231 -0.56210119 -0.56349373] \n",
      " [-0.835549   -1.34681845  0.47327501  0.17217231 -0.56210119 -0.56349373]\n",
      "[ True  True  True  True  True  True]\n",
      "1427    False\n",
      "1428    False\n",
      "1429    False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "g_1 = df_dup_.iloc[0].gsen_X\n",
    "g_2 =  df_dup_.iloc[1].gsen_X\n",
    "print(g_1, '\\n', g_2 )\t\n",
    "print(g_1 == g_2)\n",
    "print(df_dup_.duplicated(subset=['gsen_X'], keep=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Note \n",
    "- The np.ndarray values in the fields cannot be compared correctly by pd.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of timstamp duplicated : 36.69%\n",
      "Proportion of event_IDs with timestamp duplicates  : 71.68%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mask_ = df_event.duplicated(subset=['timestamp'], keep=False)\n",
    "print(f'Proportion of timstamp duplicated : {100*len(df_event[mask_]) / len(df_event):5.2f}%')\n",
    "\n",
    "df_ts_dup = df_event[mask_]\n",
    "\n",
    "df_ts_dup_c = df_ts_dup.groupby(['event_id']).timestamp.count().rename('count')\n",
    "\n",
    "n_event_id_dup = df_ts_dup_c.index.values.size\n",
    "n_event_id_total = df_event.event_id.unique().size\n",
    "print(f'Proportion of event_IDs with timestamp duplicates  : {100*n_event_id_dup/n_event_id_total:5.2f}%')\n",
    "/ df_event.event_id.unique().size\n",
    "\n",
    "\n",
    "df_ts_dup_c.plot.hist(bins=100, figsize=(4, 2), title='Dist. of Timstamp Duplicated');\n",
    "\n",
    "# "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Note: The percentage of duplicate writes is very high.\n",
    "- Cannot be restored\n",
    "- Direct use of these data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "65a95d5b",
   "metadata": {},
   "source": [
    "## Convert to the required format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### format for ts2vec\n",
    "- (sample, timestamp, deaturem)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop timestamp < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7972\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([('KA01AF2992', 1), ('KA01AF2992', 2), ('KA01AF2992', 3), ...,\n",
       "       ('MH04KF8187', 1095), ('MH04KF8187', 1096), ('MH04KF8187', 1097)],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index_stamp_reserved = df_count_timestep.query('count >= 10').set_index(['vehicle_id', 'event_id']).index.values\n",
    "print(len(index_stamp_reserved))\n",
    "index_stamp_reserved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>vehicle_id</th>\n",
       "      <th>event_id</th>\n",
       "      <th>id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>timestamp_conv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458092</td>\n",
       "      <td>5458091</td>\n",
       "      <td>-0.176844</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>0.358919</td>\n",
       "      <td>-0.008408</td>\n",
       "      <td>0.423661</td>\n",
       "      <td>0.024567</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458294</td>\n",
       "      <td>5458294</td>\n",
       "      <td>-0.329202</td>\n",
       "      <td>-0.009552</td>\n",
       "      <td>0.359760</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>0.588206</td>\n",
       "      <td>0.026062</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458492</td>\n",
       "      <td>5458492</td>\n",
       "      <td>-0.477587</td>\n",
       "      <td>-0.017120</td>\n",
       "      <td>0.359269</td>\n",
       "      <td>-0.044296</td>\n",
       "      <td>0.645035</td>\n",
       "      <td>0.024033</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458692</td>\n",
       "      <td>5458692</td>\n",
       "      <td>0.128577</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.172681</td>\n",
       "      <td>0.011200</td>\n",
       "      <td>-2.081446</td>\n",
       "      <td>0.024353</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458893</td>\n",
       "      <td>5458892</td>\n",
       "      <td>-0.198447</td>\n",
       "      <td>-0.003693</td>\n",
       "      <td>0.187229</td>\n",
       "      <td>0.012802</td>\n",
       "      <td>0.825244</td>\n",
       "      <td>0.021576</td>\n",
       "      <td>2022-07-27 06:04:39.193</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99547</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565018</td>\n",
       "      <td>27565017</td>\n",
       "      <td>2.914450</td>\n",
       "      <td>-0.035446</td>\n",
       "      <td>-1.703996</td>\n",
       "      <td>0.013611</td>\n",
       "      <td>0.351263</td>\n",
       "      <td>0.028122</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99548</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565217</td>\n",
       "      <td>27565216</td>\n",
       "      <td>-2.949032</td>\n",
       "      <td>-0.027237</td>\n",
       "      <td>0.460132</td>\n",
       "      <td>0.004761</td>\n",
       "      <td>-2.437326</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99549</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565416</td>\n",
       "      <td>27565416</td>\n",
       "      <td>1.892698</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.242738</td>\n",
       "      <td>-0.002045</td>\n",
       "      <td>-0.230687</td>\n",
       "      <td>-0.032181</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99550</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565617</td>\n",
       "      <td>27565617</td>\n",
       "      <td>-0.870719</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.033652</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>-1.473144</td>\n",
       "      <td>-0.014069</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99551</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565817</td>\n",
       "      <td>27565817</td>\n",
       "      <td>-0.845461</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>-0.006703</td>\n",
       "      <td>0.023621</td>\n",
       "      <td>-1.367128</td>\n",
       "      <td>0.017670</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99552 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       vehicle_id  event_id        id   latitude  longitude     speed  \\\n",
       "0      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "1      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "2      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "3      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "4      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "...           ...       ...       ...        ...        ...       ...   \n",
       "99547  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "99548  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "99549  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "99550  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "99551  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "\n",
       "          heading      timestamp  gsen_timeTick  gyro_timeTick    gsen_X  \\\n",
       "0      185.001465  1658901879193        5458092        5458091 -0.176844   \n",
       "1      185.001465  1658901879193        5458294        5458294 -0.329202   \n",
       "2      185.001465  1658901879193        5458492        5458492 -0.477587   \n",
       "3      185.001465  1658901879193        5458692        5458692  0.128577   \n",
       "4      185.001465  1658901879193        5458893        5458892 -0.198447   \n",
       "...           ...            ...            ...            ...       ...   \n",
       "99547  359.055634  1664759455906       27565018       27565017  2.914450   \n",
       "99548  359.055634  1664759455906       27565217       27565216 -2.949032   \n",
       "99549  359.055634  1664759455906       27565416       27565416  1.892698   \n",
       "99550  359.055634  1664759455906       27565617       27565617 -0.870719   \n",
       "99551  359.055634  1664759455906       27565817       27565817 -0.845461   \n",
       "\n",
       "         gyro_X    gsen_Y    gyro_Y    gsen_Z    gyro_Z  \\\n",
       "0      0.002701  0.358919 -0.008408  0.423661  0.024567   \n",
       "1     -0.009552  0.359760 -0.026398  0.588206  0.026062   \n",
       "2     -0.017120  0.359269 -0.044296  0.645035  0.024033   \n",
       "3      0.012604  0.172681  0.011200 -2.081446  0.024353   \n",
       "4     -0.003693  0.187229  0.012802  0.825244  0.021576   \n",
       "...         ...       ...       ...       ...       ...   \n",
       "99547 -0.035446 -1.703996  0.013611  0.351263  0.028122   \n",
       "99548 -0.027237  0.460132  0.004761 -2.437326  0.020874   \n",
       "99549 -0.003052  0.242738 -0.002045 -0.230687 -0.032181   \n",
       "99550 -0.003052  0.033652  0.018936 -1.473144 -0.014069   \n",
       "99551 -0.018707 -0.006703  0.023621 -1.367128  0.017670   \n",
       "\n",
       "               timestamp_conv  \n",
       "0     2022-07-27 06:04:39.193  \n",
       "1     2022-07-27 06:04:39.193  \n",
       "2     2022-07-27 06:04:39.193  \n",
       "3     2022-07-27 06:04:39.193  \n",
       "4     2022-07-27 06:04:39.193  \n",
       "...                       ...  \n",
       "99547 2022-10-03 01:10:55.906  \n",
       "99548 2022-10-03 01:10:55.906  \n",
       "99549 2022-10-03 01:10:55.906  \n",
       "99550 2022-10-03 01:10:55.906  \n",
       "99551 2022-10-03 01:10:55.906  \n",
       "\n",
       "[99552 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ = (df_sensor_exploded.set_index(['vehicle_id', 'event_id'])\n",
    "       .loc[index_stamp_reserved]\n",
    "       .reset_index()\n",
    "       )\n",
    "df_\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build vehicle_event_id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of sample :  7972\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>event_id</th>\n",
       "      <th>id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>timestamp_conv</th>\n",
       "      <th>v_event_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
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       "      <td>0.358919</td>\n",
       "      <td>-0.008408</td>\n",
       "      <td>0.423661</td>\n",
       "      <td>0.024567</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>KA01AF2992_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458294</td>\n",
       "      <td>5458294</td>\n",
       "      <td>-0.329202</td>\n",
       "      <td>-0.009552</td>\n",
       "      <td>0.359760</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>0.588206</td>\n",
       "      <td>0.026062</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>KA01AF2992_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458492</td>\n",
       "      <td>5458492</td>\n",
       "      <td>-0.477587</td>\n",
       "      <td>-0.017120</td>\n",
       "      <td>0.359269</td>\n",
       "      <td>-0.044296</td>\n",
       "      <td>0.645035</td>\n",
       "      <td>0.024033</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>KA01AF2992_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458692</td>\n",
       "      <td>5458692</td>\n",
       "      <td>0.128577</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.172681</td>\n",
       "      <td>0.011200</td>\n",
       "      <td>-2.081446</td>\n",
       "      <td>0.024353</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>KA01AF2992_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458893</td>\n",
       "      <td>5458892</td>\n",
       "      <td>-0.198447</td>\n",
       "      <td>-0.003693</td>\n",
       "      <td>0.187229</td>\n",
       "      <td>0.012802</td>\n",
       "      <td>0.825244</td>\n",
       "      <td>0.021576</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "      <td>KA01AF2992_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99547</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565018</td>\n",
       "      <td>27565017</td>\n",
       "      <td>2.914450</td>\n",
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       "      <td>-1.703996</td>\n",
       "      <td>0.013611</td>\n",
       "      <td>0.351263</td>\n",
       "      <td>0.028122</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>MH04KF8187_1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99548</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565217</td>\n",
       "      <td>27565216</td>\n",
       "      <td>-2.949032</td>\n",
       "      <td>-0.027237</td>\n",
       "      <td>0.460132</td>\n",
       "      <td>0.004761</td>\n",
       "      <td>-2.437326</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>MH04KF8187_1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99549</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565416</td>\n",
       "      <td>27565416</td>\n",
       "      <td>1.892698</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.242738</td>\n",
       "      <td>-0.002045</td>\n",
       "      <td>-0.230687</td>\n",
       "      <td>-0.032181</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>MH04KF8187_1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99550</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565617</td>\n",
       "      <td>27565617</td>\n",
       "      <td>-0.870719</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.033652</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>-1.473144</td>\n",
       "      <td>-0.014069</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>MH04KF8187_1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99551</th>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565817</td>\n",
       "      <td>27565817</td>\n",
       "      <td>-0.845461</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>-0.006703</td>\n",
       "      <td>0.023621</td>\n",
       "      <td>-1.367128</td>\n",
       "      <td>0.017670</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "      <td>MH04KF8187_1097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99552 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       vehicle_id  event_id        id   latitude  longitude     speed  \\\n",
       "0      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "1      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "2      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "3      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "4      KA01AF2992         1  39079362  13.094135  77.594326  4.390037   \n",
       "...           ...       ...       ...        ...        ...       ...   \n",
       "99547  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "99548  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "99549  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "99550  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "99551  MH04KF8187      1097  56817252  21.219641  72.968640  3.287662   \n",
       "\n",
       "          heading      timestamp  gsen_timeTick  gyro_timeTick    gsen_X  \\\n",
       "0      185.001465  1658901879193        5458092        5458091 -0.176844   \n",
       "1      185.001465  1658901879193        5458294        5458294 -0.329202   \n",
       "2      185.001465  1658901879193        5458492        5458492 -0.477587   \n",
       "3      185.001465  1658901879193        5458692        5458692  0.128577   \n",
       "4      185.001465  1658901879193        5458893        5458892 -0.198447   \n",
       "...           ...            ...            ...            ...       ...   \n",
       "99547  359.055634  1664759455906       27565018       27565017  2.914450   \n",
       "99548  359.055634  1664759455906       27565217       27565216 -2.949032   \n",
       "99549  359.055634  1664759455906       27565416       27565416  1.892698   \n",
       "99550  359.055634  1664759455906       27565617       27565617 -0.870719   \n",
       "99551  359.055634  1664759455906       27565817       27565817 -0.845461   \n",
       "\n",
       "         gyro_X    gsen_Y    gyro_Y    gsen_Z    gyro_Z  \\\n",
       "0      0.002701  0.358919 -0.008408  0.423661  0.024567   \n",
       "1     -0.009552  0.359760 -0.026398  0.588206  0.026062   \n",
       "2     -0.017120  0.359269 -0.044296  0.645035  0.024033   \n",
       "3      0.012604  0.172681  0.011200 -2.081446  0.024353   \n",
       "4     -0.003693  0.187229  0.012802  0.825244  0.021576   \n",
       "...         ...       ...       ...       ...       ...   \n",
       "99547 -0.035446 -1.703996  0.013611  0.351263  0.028122   \n",
       "99548 -0.027237  0.460132  0.004761 -2.437326  0.020874   \n",
       "99549 -0.003052  0.242738 -0.002045 -0.230687 -0.032181   \n",
       "99550 -0.003052  0.033652  0.018936 -1.473144 -0.014069   \n",
       "99551 -0.018707 -0.006703  0.023621 -1.367128  0.017670   \n",
       "\n",
       "               timestamp_conv       v_event_id  \n",
       "0     2022-07-27 06:04:39.193     KA01AF2992_1  \n",
       "1     2022-07-27 06:04:39.193     KA01AF2992_1  \n",
       "2     2022-07-27 06:04:39.193     KA01AF2992_1  \n",
       "3     2022-07-27 06:04:39.193     KA01AF2992_1  \n",
       "4     2022-07-27 06:04:39.193     KA01AF2992_1  \n",
       "...                       ...              ...  \n",
       "99547 2022-10-03 01:10:55.906  MH04KF8187_1097  \n",
       "99548 2022-10-03 01:10:55.906  MH04KF8187_1097  \n",
       "99549 2022-10-03 01:10:55.906  MH04KF8187_1097  \n",
       "99550 2022-10-03 01:10:55.906  MH04KF8187_1097  \n",
       "99551 2022-10-03 01:10:55.906  MH04KF8187_1097  \n",
       "\n",
       "[99552 rows x 18 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_['v_event_id'] = df_.vehicle_id + '_' + df_.event_id.astype(str)\n",
    "\n",
    "n_sample =  df_['v_event_id'].nunique()\n",
    "print('Number of sample : ', n_sample )\n",
    "\n",
    "df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_.groupby(['v_event_id']).id.count().plot.hist(bins=50, figsize=(3,2), logy=True);\n",
    "\n",
    "# all timestamp > 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>gsen_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gyro_Z</th>\n",
       "      <th>vehicle_id</th>\n",
       "      <th>event_id</th>\n",
       "      <th>id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gsen_timeTick</th>\n",
       "      <th>gyro_timeTick</th>\n",
       "      <th>timestamp_conv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_event_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>-0.176844</td>\n",
       "      <td>0.358919</td>\n",
       "      <td>0.423661</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>-0.008408</td>\n",
       "      <td>0.024567</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458092</td>\n",
       "      <td>5458091</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>-0.329202</td>\n",
       "      <td>0.359760</td>\n",
       "      <td>0.588206</td>\n",
       "      <td>-0.009552</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>0.026062</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458294</td>\n",
       "      <td>5458294</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>-0.477587</td>\n",
       "      <td>0.359269</td>\n",
       "      <td>0.645035</td>\n",
       "      <td>-0.017120</td>\n",
       "      <td>-0.044296</td>\n",
       "      <td>0.024033</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458492</td>\n",
       "      <td>5458492</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>0.128577</td>\n",
       "      <td>0.172681</td>\n",
       "      <td>-2.081446</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.011200</td>\n",
       "      <td>0.024353</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458692</td>\n",
       "      <td>5458692</td>\n",
       "      <td>2022-07-27 06:04:39.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>-0.198447</td>\n",
       "      <td>0.187229</td>\n",
       "      <td>0.825244</td>\n",
       "      <td>-0.003693</td>\n",
       "      <td>0.012802</td>\n",
       "      <td>0.021576</td>\n",
       "      <td>KA01AF2992</td>\n",
       "      <td>1</td>\n",
       "      <td>39079362</td>\n",
       "      <td>13.094135</td>\n",
       "      <td>77.594326</td>\n",
       "      <td>4.390037</td>\n",
       "      <td>185.001465</td>\n",
       "      <td>1658901879193</td>\n",
       "      <td>5458893</td>\n",
       "      <td>5458892</td>\n",
       "      <td>2022-07-27 06:04:39.193</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>2.914450</td>\n",
       "      <td>-1.703996</td>\n",
       "      <td>0.351263</td>\n",
       "      <td>-0.035446</td>\n",
       "      <td>0.013611</td>\n",
       "      <td>0.028122</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565018</td>\n",
       "      <td>27565017</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>-2.949032</td>\n",
       "      <td>0.460132</td>\n",
       "      <td>-2.437326</td>\n",
       "      <td>-0.027237</td>\n",
       "      <td>0.004761</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565217</td>\n",
       "      <td>27565216</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>1.892698</td>\n",
       "      <td>0.242738</td>\n",
       "      <td>-0.230687</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>-0.002045</td>\n",
       "      <td>-0.032181</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565416</td>\n",
       "      <td>27565416</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>-0.870719</td>\n",
       "      <td>0.033652</td>\n",
       "      <td>-1.473144</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>-0.014069</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565617</td>\n",
       "      <td>27565617</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>-0.845461</td>\n",
       "      <td>-0.006703</td>\n",
       "      <td>-1.367128</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>0.023621</td>\n",
       "      <td>0.017670</td>\n",
       "      <td>MH04KF8187</td>\n",
       "      <td>1097</td>\n",
       "      <td>56817252</td>\n",
       "      <td>21.219641</td>\n",
       "      <td>72.968640</td>\n",
       "      <td>3.287662</td>\n",
       "      <td>359.055634</td>\n",
       "      <td>1664759455906</td>\n",
       "      <td>27565817</td>\n",
       "      <td>27565817</td>\n",
       "      <td>2022-10-03 01:10:55.906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99552 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   gsen_X    gsen_Y    gsen_Z    gyro_X    gyro_Y    gyro_Z  \\\n",
       "v_event_id                                                                    \n",
       "KA01AF2992_1    -0.176844  0.358919  0.423661  0.002701 -0.008408  0.024567   \n",
       "KA01AF2992_1    -0.329202  0.359760  0.588206 -0.009552 -0.026398  0.026062   \n",
       "KA01AF2992_1    -0.477587  0.359269  0.645035 -0.017120 -0.044296  0.024033   \n",
       "KA01AF2992_1     0.128577  0.172681 -2.081446  0.012604  0.011200  0.024353   \n",
       "KA01AF2992_1    -0.198447  0.187229  0.825244 -0.003693  0.012802  0.021576   \n",
       "...                   ...       ...       ...       ...       ...       ...   \n",
       "MH04KF8187_1097  2.914450 -1.703996  0.351263 -0.035446  0.013611  0.028122   \n",
       "MH04KF8187_1097 -2.949032  0.460132 -2.437326 -0.027237  0.004761  0.020874   \n",
       "MH04KF8187_1097  1.892698  0.242738 -0.230687 -0.003052 -0.002045 -0.032181   \n",
       "MH04KF8187_1097 -0.870719  0.033652 -1.473144 -0.003052  0.018936 -0.014069   \n",
       "MH04KF8187_1097 -0.845461 -0.006703 -1.367128 -0.018707  0.023621  0.017670   \n",
       "\n",
       "                 vehicle_id  event_id        id   latitude  longitude  \\\n",
       "v_event_id                                                              \n",
       "KA01AF2992_1     KA01AF2992         1  39079362  13.094135  77.594326   \n",
       "KA01AF2992_1     KA01AF2992         1  39079362  13.094135  77.594326   \n",
       "KA01AF2992_1     KA01AF2992         1  39079362  13.094135  77.594326   \n",
       "KA01AF2992_1     KA01AF2992         1  39079362  13.094135  77.594326   \n",
       "KA01AF2992_1     KA01AF2992         1  39079362  13.094135  77.594326   \n",
       "...                     ...       ...       ...        ...        ...   \n",
       "MH04KF8187_1097  MH04KF8187      1097  56817252  21.219641  72.968640   \n",
       "MH04KF8187_1097  MH04KF8187      1097  56817252  21.219641  72.968640   \n",
       "MH04KF8187_1097  MH04KF8187      1097  56817252  21.219641  72.968640   \n",
       "MH04KF8187_1097  MH04KF8187      1097  56817252  21.219641  72.968640   \n",
       "MH04KF8187_1097  MH04KF8187      1097  56817252  21.219641  72.968640   \n",
       "\n",
       "                    speed     heading      timestamp  gsen_timeTick  \\\n",
       "v_event_id                                                            \n",
       "KA01AF2992_1     4.390037  185.001465  1658901879193        5458092   \n",
       "KA01AF2992_1     4.390037  185.001465  1658901879193        5458294   \n",
       "KA01AF2992_1     4.390037  185.001465  1658901879193        5458492   \n",
       "KA01AF2992_1     4.390037  185.001465  1658901879193        5458692   \n",
       "KA01AF2992_1     4.390037  185.001465  1658901879193        5458893   \n",
       "...                   ...         ...            ...            ...   \n",
       "MH04KF8187_1097  3.287662  359.055634  1664759455906       27565018   \n",
       "MH04KF8187_1097  3.287662  359.055634  1664759455906       27565217   \n",
       "MH04KF8187_1097  3.287662  359.055634  1664759455906       27565416   \n",
       "MH04KF8187_1097  3.287662  359.055634  1664759455906       27565617   \n",
       "MH04KF8187_1097  3.287662  359.055634  1664759455906       27565817   \n",
       "\n",
       "                 gyro_timeTick          timestamp_conv  \n",
       "v_event_id                                              \n",
       "KA01AF2992_1           5458091 2022-07-27 06:04:39.193  \n",
       "KA01AF2992_1           5458294 2022-07-27 06:04:39.193  \n",
       "KA01AF2992_1           5458492 2022-07-27 06:04:39.193  \n",
       "KA01AF2992_1           5458692 2022-07-27 06:04:39.193  \n",
       "KA01AF2992_1           5458892 2022-07-27 06:04:39.193  \n",
       "...                        ...                     ...  \n",
       "MH04KF8187_1097       27565017 2022-10-03 01:10:55.906  \n",
       "MH04KF8187_1097       27565216 2022-10-03 01:10:55.906  \n",
       "MH04KF8187_1097       27565416 2022-10-03 01:10:55.906  \n",
       "MH04KF8187_1097       27565617 2022-10-03 01:10:55.906  \n",
       "MH04KF8187_1097       27565817 2022-10-03 01:10:55.906  \n",
       "\n",
       "[99552 rows x 17 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data = ['gsen_X', 'gsen_Y', 'gsen_Z', 'gyro_X', 'gyro_Y', 'gyro_Z']\n",
    "col_meta = ['vehicle_id', 'event_id', 'id']\n",
    "col_meta += ['latitude', 'longitude', 'speed', 'heading',]\n",
    "col_meta += ['timestamp', 'gsen_timeTick', 'gyro_timeTick', 'timestamp_conv']\n",
    "\n",
    "df_sensor_meta = df_.set_index(['v_event_id'])[col_data + col_meta]\n",
    "\n",
    "df_sensor_meta "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Load \"dora_meta.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 99552 entries, KA01AF2992_1 to MH04KF8187_1097\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   gsen_X          99552 non-null  float64\n",
      " 1   gsen_Y          99552 non-null  float64\n",
      " 2   gsen_Z          99552 non-null  float64\n",
      " 3   gyro_X          99552 non-null  float64\n",
      " 4   gyro_Y          99552 non-null  float64\n",
      " 5   gyro_Z          99552 non-null  float64\n",
      " 6   vehicle_id      99552 non-null  object \n",
      " 7   event_id        99552 non-null  int64  \n",
      " 8   id              99552 non-null  int64  \n",
      " 9   latitude        99552 non-null  float64\n",
      " 10  longitude       99552 non-null  float64\n",
      " 11  speed           99552 non-null  float64\n",
      " 12  heading         99552 non-null  float64\n",
      " 13  timestamp       99552 non-null  int64  \n",
      " 14  gsen_timeTick   99552 non-null  int64  \n",
      " 15  gyro_timeTick   99552 non-null  int64  \n",
      " 16  timestamp_conv  99552 non-null  object \n",
      "dtypes: float64(10), int64(5), object(2)\n",
      "memory usage: 13.7+ MB\n"
     ]
    }
   ],
   "source": [
    "save_path = Path('ts2vec/datasets/DORA')\n",
    "file_ = f'dora_meta'\n",
    "# df_sensor_meta.to_csv(save_path/f'{file_}.csv',)\n",
    "df_sensor_meta = pd.read_csv(save_path/f'{file_}.csv', index_col=0)\n",
    "df_sensor_meta\n",
    "df_sensor_meta.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "feather does not support serializing <class 'pandas.core.indexes.base.Index'> for the index; you can .reset_index() to make the index into column(s)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [172], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_sensor_meta\u001b[39m.\u001b[39;49mto_feather(save_path\u001b[39m/\u001b[39;49m\u001b[39mf\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m{\u001b[39;49;00mfile_\u001b[39m}\u001b[39;49;00m\u001b[39m.feather\u001b[39;49m\u001b[39m'\u001b[39;49m,)\n",
      "File \u001b[0;32m~/miniconda3/envs/py310/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[39m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/miniconda3/envs/py310/lib/python3.10/site-packages/pandas/core/frame.py:2794\u001b[0m, in \u001b[0;36mDataFrame.to_feather\u001b[0;34m(self, path, **kwargs)\u001b[0m\n\u001b[1;32m   2769\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   2770\u001b[0m \u001b[39mWrite a DataFrame to the binary Feather format.\u001b[39;00m\n\u001b[1;32m   2771\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2790\u001b[0m \u001b[39msupports custom indices e.g. `to_parquet`.\u001b[39;00m\n\u001b[1;32m   2791\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   2792\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpandas\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mfeather_format\u001b[39;00m \u001b[39mimport\u001b[39;00m to_feather\n\u001b[0;32m-> 2794\u001b[0m to_feather(\u001b[39mself\u001b[39;49m, path, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/miniconda3/envs/py310/lib/python3.10/site-packages/pandas/io/feather_format.py:67\u001b[0m, in \u001b[0;36mto_feather\u001b[0;34m(df, path, storage_options, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(df\u001b[39m.\u001b[39mindex, (Int64Index, RangeIndex)):\n\u001b[1;32m     66\u001b[0m     typ \u001b[39m=\u001b[39m \u001b[39mtype\u001b[39m(df\u001b[39m.\u001b[39mindex)\n\u001b[0;32m---> 67\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m     68\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mfeather does not support serializing \u001b[39m\u001b[39m{\u001b[39;00mtyp\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     69\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mfor the index; you can .reset_index() to make the index into column(s)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     70\u001b[0m     )\n\u001b[1;32m     72\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m df\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39mequals(RangeIndex\u001b[39m.\u001b[39mfrom_range(\u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(df)))):\n\u001b[1;32m     73\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m     74\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mfeather does not support serializing a non-default index for the index; \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     75\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39myou can .reset_index() to make the index into column(s)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     76\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: feather does not support serializing <class 'pandas.core.indexes.base.Index'> for the index; you can .reset_index() to make the index into column(s)"
     ]
    }
   ],
   "source": [
    "df_sensor_meta.to_feather(save_path/f'{file_}.feather',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7972, 17, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Create empty array, fill with NaN\n",
    "dim = 6 # gsensor x, y, z and  gyro x, y, z \n",
    "t_stamp_max = df_sensor_meta.groupby(['v_event_id']).id.count().max()\n",
    "\n",
    "\n",
    "data_array = np.full((n_sample, t_stamp_max, dim), np.nan) \n",
    "print(data_array.shape)\n",
    "data_array "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fill the value according to \"v_event_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gsen_X</th>\n",
       "      <th>gsen_Y</th>\n",
       "      <th>gsen_Z</th>\n",
       "      <th>gyro_X</th>\n",
       "      <th>gyro_Y</th>\n",
       "      <th>gyro_Z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_event_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>-0.176844</td>\n",
       "      <td>0.358919</td>\n",
       "      <td>0.423661</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>-0.008408</td>\n",
       "      <td>0.024567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>-0.329202</td>\n",
       "      <td>0.359760</td>\n",
       "      <td>0.588206</td>\n",
       "      <td>-0.009552</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>0.026062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>-0.477587</td>\n",
       "      <td>0.359269</td>\n",
       "      <td>0.645035</td>\n",
       "      <td>-0.017120</td>\n",
       "      <td>-0.044296</td>\n",
       "      <td>0.024033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>0.128577</td>\n",
       "      <td>0.172681</td>\n",
       "      <td>-2.081446</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.011200</td>\n",
       "      <td>0.024353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA01AF2992_1</th>\n",
       "      <td>-0.198447</td>\n",
       "      <td>0.187229</td>\n",
       "      <td>0.825244</td>\n",
       "      <td>-0.003693</td>\n",
       "      <td>0.012802</td>\n",
       "      <td>0.021576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>2.914450</td>\n",
       "      <td>-1.703996</td>\n",
       "      <td>0.351263</td>\n",
       "      <td>-0.035446</td>\n",
       "      <td>0.013611</td>\n",
       "      <td>0.028122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>-2.949032</td>\n",
       "      <td>0.460132</td>\n",
       "      <td>-2.437326</td>\n",
       "      <td>-0.027237</td>\n",
       "      <td>0.004761</td>\n",
       "      <td>0.020874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>1.892698</td>\n",
       "      <td>0.242738</td>\n",
       "      <td>-0.230687</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>-0.002045</td>\n",
       "      <td>-0.032181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MH04KF8187_1097</th>\n",
       "      <td>-0.870719</td>\n",
       "      <td>0.033652</td>\n",
       "      <td>-1.473144</td>\n",
       "      <td>-0.003052</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>-0.014069</td>\n",
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       "      <td>-0.018707</td>\n",
       "      <td>0.023621</td>\n",
       "      <td>0.017670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99552 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   gsen_X    gsen_Y    gsen_Z    gyro_X    gyro_Y    gyro_Z\n",
       "v_event_id                                                                 \n",
       "KA01AF2992_1    -0.176844  0.358919  0.423661  0.002701 -0.008408  0.024567\n",
       "KA01AF2992_1    -0.329202  0.359760  0.588206 -0.009552 -0.026398  0.026062\n",
       "KA01AF2992_1    -0.477587  0.359269  0.645035 -0.017120 -0.044296  0.024033\n",
       "KA01AF2992_1     0.128577  0.172681 -2.081446  0.012604  0.011200  0.024353\n",
       "KA01AF2992_1    -0.198447  0.187229  0.825244 -0.003693  0.012802  0.021576\n",
       "...                   ...       ...       ...       ...       ...       ...\n",
       "MH04KF8187_1097  2.914450 -1.703996  0.351263 -0.035446  0.013611  0.028122\n",
       "MH04KF8187_1097 -2.949032  0.460132 -2.437326 -0.027237  0.004761  0.020874\n",
       "MH04KF8187_1097  1.892698  0.242738 -0.230687 -0.003052 -0.002045 -0.032181\n",
       "MH04KF8187_1097 -0.870719  0.033652 -1.473144 -0.003052  0.018936 -0.014069\n",
       "MH04KF8187_1097 -0.845461 -0.006703 -1.367128 -0.018707  0.023621  0.017670\n",
       "\n",
       "[99552 rows x 6 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill the value according to \"v_event_id\"\n",
    "col_data = ['gsen_X', 'gsen_Y', 'gsen_Z', 'gyro_X', 'gyro_Y', 'gyro_Z']\n",
    "df_temp = df_sensor_meta[col_data]\n",
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.1 s, sys: 0 ns, total: 17.1 s (15, 6)\n",
      "Wall time: 17 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for idx, v_event_id_ in enumerate(v_event_id):\n",
    "    v_ = df_temp.loc[v_event_id_ ].values \n",
    "    # Since the data is unequal in length\n",
    "    # It needs to fill the values according to the length of each data\n",
    "    tstamp_slice = v_.shape[0]\n",
    "    \n",
    "    data_array[idx][:tstamp_slice] = v_\n",
    "    \n",
    "    info = f'[{idx:5d}|{len(v_event_id):5d} ({100*idx/len(v_event_id):5.2f})%] '\n",
    "    info += f' | shape : {data_array.shape}, {v_.shape}'\n",
    "    print(info, end='\\r')\n",
    "    # if idx==100:\n",
    "    #     break "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7972, 17, 6)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dora'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_path = Path('ts2vec/datasets/DORA')\n",
    "file_ = f'dora'\n",
    "file_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-1.76844254e-01,  3.58918786e-01,  4.23661232e-01,\n",
       "          2.70080566e-03, -8.40759277e-03,  2.45666504e-02],\n",
       "        [-3.29202056e-01,  3.59760046e-01,  5.88206291e-01,\n",
       "         -9.55200195e-03, -2.63977051e-02,  2.60620117e-02],\n",
       "        [-4.77586895e-01,  3.59269023e-01,  6.45034790e-01,\n",
       "         -1.71203613e-02, -4.42962646e-02,  2.40325928e-02],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[ 4.27370518e-01, -3.89060974e-02, -1.67930603e+00,\n",
       "          1.18560791e-02, -7.76672363e-03,  9.44519043e-03],\n",
       "        [ 1.05626106e-01,  3.13292384e-01, -3.61956596e-01,\n",
       "         -2.09503174e-02, -1.75628662e-02,  1.31683350e-02],\n",
       "        [-4.55256045e-01,  3.33588481e-01,  3.18472195e+00,\n",
       "         -4.69512939e-02, -6.31561279e-02,  1.06048584e-02],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[ 9.48246121e-02,  2.46866941e-02, -2.69222260e-03,\n",
       "          1.65405273e-02, -1.42669678e-02,  8.48388672e-03],\n",
       "        [ 1.55487716e-01,  1.86597466e-01, -1.40774727e-01,\n",
       "         -5.40161133e-03, -9.78088379e-03,  9.32312012e-03],\n",
       "        [ 2.64873505e-01,  1.99272513e-01, -1.22634888e-01,\n",
       "         -3.90625000e-03,  2.68554688e-03,  1.33819580e-02],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-2.23585129e-01, -6.64658844e-03,  3.98473740e-02,\n",
       "          1.40991211e-02, -1.01470947e-02,  5.54962158e-02],\n",
       "        [-2.39692211e-01,  1.31908894e-01,  9.66405869e-02,\n",
       "          2.80456543e-02,  4.10919189e-02,  2.54516602e-02],\n",
       "        [-1.06455421e+00,  2.39203751e-01, -4.81838226e-01,\n",
       "          3.07159424e-02,  1.46789551e-02,  1.30920410e-02],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[ 3.86281013e-02,  1.62407607e-01, -1.69897079e-02,\n",
       "         -1.99890137e-03, -8.22448730e-03, -3.55834961e-02],\n",
       "        [ 7.10353851e-02, -4.72729892e-01, -5.39298534e-01,\n",
       "         -4.48150635e-02, -1.33361816e-02,  1.79901123e-02],\n",
       "        [ 5.34472942e-01, -2.60616541e-02, -7.61842728e-02,\n",
       "          7.17163086e-03,  1.46789551e-02, -5.44433594e-02],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[-4.77889538e-01,  2.22240686e-02,  4.97686386e-01,\n",
       "          2.91442871e-03, -8.65173340e-03, -1.63116455e-02],\n",
       "        [ 6.36648655e-01, -3.74569625e-01,  1.05237412e+00,\n",
       "         -1.20086670e-02,  1.77612305e-02, -4.05883789e-03],\n",
       "        [-1.40092993e+00, -7.51775384e-01,  1.64703131e-01,\n",
       "         -3.18145752e-02, -3.11279297e-03,  1.20239258e-02],\n",
       "        ...,\n",
       "        [-8.45460892e-01, -6.70291437e-03, -1.36712837e+00,\n",
       "         -1.87072754e-02,  2.36206055e-02,  1.76696777e-02],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan,\n",
       "                     nan,             nan,             nan]]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.savez_compressed(save_path/f'{file_}.npz', sensor=data_array)\n",
    "np.load(save_path/f'{file_}.npz',)['sensor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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