PreprocElaad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e1c7fc7c-389f-4166-ac19-f0441e5910e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from EvGym import config\n",
    "\n",
    "save = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ec1f8b3-db89-4193-b0a5-689a47619b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_elaad = df_elaad = pd.read_csv(\"data/elaadnl_open_ev_datasets.csv\", parse_dates = [\"UTCTransactionStart\", \"UTCTransactionStop\"])\n",
    "df_elaad = df_elaad.rename(columns = config.elaad_rename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd5820f8-e9d7-4adc-a601-72c1f7601177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   session               10000 non-null  int64         \n",
      " 1   ChargePoint           10000 non-null  object        \n",
      " 2   Connector             10000 non-null  int64         \n",
      " 3   starttime_parking     10000 non-null  datetime64[ns]\n",
      " 4   endtime_parking       10000 non-null  datetime64[ns]\n",
      " 5   StartCard             10000 non-null  object        \n",
      " 6   connected_time_float  10000 non-null  float64       \n",
      " 7   charged_time_float    10000 non-null  float64       \n",
      " 8   total_energy          10000 non-null  float64       \n",
      " 9   max_power             10000 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(4), int64(2), object(2)\n",
      "memory usage: 781.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_elaad.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b3dc3c9-57ea-4b26-90c3-68f2a2b40a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session</th>\n",
       "      <th>ChargePoint</th>\n",
       "      <th>Connector</th>\n",
       "      <th>starttime_parking</th>\n",
       "      <th>endtime_parking</th>\n",
       "      <th>StartCard</th>\n",
       "      <th>connected_time_float</th>\n",
       "      <th>charged_time_float</th>\n",
       "      <th>total_energy</th>\n",
       "      <th>max_power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3491779</td>\n",
       "      <td>0abf481c2d3f5866a8fc7feaae460fd0</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-08-27 14:52:00</td>\n",
       "      <td>2019-08-27 17:58:00</td>\n",
       "      <td>0c24de2f8216313f75daf876ec7c2223e17c866462ae41...</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.10</td>\n",
       "      <td>9.86</td>\n",
       "      <td>3.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3326963</td>\n",
       "      <td>a22a6a745ff09431c3a0cef7373042ee</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-03-01 10:14:00</td>\n",
       "      <td>2019-03-01 13:13:00</td>\n",
       "      <td>fd31273615db1421e4be23b51db9f1c5c904ebed131b5d...</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.38</td>\n",
       "      <td>3.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3469263</td>\n",
       "      <td>f4f5e5fbf8297d4889e49f10942b030b</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-07-31 12:54:00</td>\n",
       "      <td>2019-07-31 13:21:00</td>\n",
       "      <td>f876668fd30216c9054a890007143b4d40d13ddd9234c7...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3429356</td>\n",
       "      <td>0f87094588f6330a84f30797f0458fc8</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-06-16 10:55:00</td>\n",
       "      <td>2019-06-16 14:35:00</td>\n",
       "      <td>06776db669a8f444bf7f81edc7fcf6c18c51bfd90eed2f...</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.65</td>\n",
       "      <td>38.77</td>\n",
       "      <td>10.813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3332751</td>\n",
       "      <td>77f3b31920754dac44d2b7400f16bca3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-07 21:21:00</td>\n",
       "      <td>2019-03-08 07:22:00</td>\n",
       "      <td>f88e7e074d5476d4e8a532ae1e1966de2d3f333f3c4453...</td>\n",
       "      <td>10.03</td>\n",
       "      <td>7.15</td>\n",
       "      <td>26.14</td>\n",
       "      <td>3.884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   session                       ChargePoint  Connector   starttime_parking  \\\n",
       "0  3491779  0abf481c2d3f5866a8fc7feaae460fd0          1 2019-08-27 14:52:00   \n",
       "1  3326963  a22a6a745ff09431c3a0cef7373042ee          2 2019-03-01 10:14:00   \n",
       "2  3469263  f4f5e5fbf8297d4889e49f10942b030b          1 2019-07-31 12:54:00   \n",
       "3  3429356  0f87094588f6330a84f30797f0458fc8          1 2019-06-16 10:55:00   \n",
       "4  3332751  77f3b31920754dac44d2b7400f16bca3          1 2019-03-07 21:21:00   \n",
       "\n",
       "      endtime_parking                                          StartCard  \\\n",
       "0 2019-08-27 17:58:00  0c24de2f8216313f75daf876ec7c2223e17c866462ae41...   \n",
       "1 2019-03-01 13:13:00  fd31273615db1421e4be23b51db9f1c5c904ebed131b5d...   \n",
       "2 2019-07-31 13:21:00  f876668fd30216c9054a890007143b4d40d13ddd9234c7...   \n",
       "3 2019-06-16 14:35:00  06776db669a8f444bf7f81edc7fcf6c18c51bfd90eed2f...   \n",
       "4 2019-03-08 07:22:00  f88e7e074d5476d4e8a532ae1e1966de2d3f333f3c4453...   \n",
       "\n",
       "   connected_time_float  charged_time_float  total_energy  max_power  \n",
       "0                  3.11                3.10          9.86      3.342  \n",
       "1                  3.00                3.00          9.38      3.440  \n",
       "2                  0.46                0.46          1.45      3.160  \n",
       "3                  3.65                3.65         38.77     10.813  \n",
       "4                 10.03                7.15         26.14      3.884  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "246064f8-986f-4939-9ee6-a5b745bd7cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_elaad[\"start_hour\"] = df_elaad[\"starttime_parking\"].dt.hour\n",
    "df_elaad[\"day_no\"] = (pd.to_datetime(df_elaad[\"starttime_parking\"].dt.date) - datetime.datetime(year = 2019, month = 1, day = 1)).dt.days\n",
    "\n",
    "# Clean vehicles that have higher charged_time than connected time\n",
    "df_elaad[\"charged_time_float\"] = np.minimum(df_elaad[\"charged_time_float\"], df_elaad[\"connected_time_float\"])\n",
    "\n",
    "df_elaad[\"energy_supplied\"] = np.minimum(config.alpha_c * config.eta_c * df_elaad[\"charged_time_float\"], config.B * config.FINAL_SOC)\n",
    "df_elaad[\"soc_arr\"] = (config.FINAL_SOC - df_elaad[\"energy_supplied\"]/ (config.B))\n",
    "\n",
    "\n",
    "df_elaad[\"charged_time\"] = np.ceil(df_elaad[\"charged_time_float\"]) \n",
    "df_elaad[\"connected_time\"] = np.ceil(df_elaad[\"connected_time_float\"])\n",
    "\n",
    "df_elaad[\"ts_arr\"] = np.floor((df_elaad[\"starttime_parking\"] - config.starttime_min).dt.total_seconds() / config.timestep)\n",
    "\n",
    "if config.timestep == 3600:\n",
    "    # This way it is consistent with connected_time\n",
    "    df_elaad[\"ts_dep\"] = df_elaad[\"ts_arr\"] + df_elaad[\"connected_time\"]\n",
    "else:\n",
    "    df_elaad[\"ts_dep\"] = np.ceil((df_elaad[\"endtime_parking\"] - config.starttime_min).dt.total_seconds() / config.timestep)\n",
    "df_elaad[\"ts_soj\"] = df_elaad[\"ts_dep\"] - df_elaad[\"ts_arr\"]\n",
    "df_elaad[\"laxity\"] = df_elaad[\"connected_time\"] - (config.FINAL_SOC - df_elaad[\"soc_arr\"]) * config.B / (config.alpha_c*config.eta_c)\n",
    "df_elaad[\"depart_hour\"] = df_elaad[\"start_hour\"] + df_elaad[\"connected_time\"] # Look how \n",
    "df_elaad[\"xi\"] = df_elaad[\"laxity\"] * config.psi * config.alpha_c\n",
    "df_elaad[\"laxity_float\"] = df_elaad[\"connected_time_float\"] - (config.FINAL_SOC - df_elaad[\"soc_arr\"]) * config.B / (config.alpha_c*config.eta_c)\n",
    "\n",
    "df_elaad = df_elaad.sort_values(by = [\"ts_arr\", \"ts_dep\"]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "287520f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excecutive decision: there is one session that starts in 2019 and ends in 2020. We drop that one\n",
    "df_elaad = df_elaad[df_elaad[\"ts_dep\"] < 175320].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "955de9cf-01d4-4d94-b5c2-d41544dbdfe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session</th>\n",
       "      <th>Connector</th>\n",
       "      <th>starttime_parking</th>\n",
       "      <th>endtime_parking</th>\n",
       "      <th>connected_time_float</th>\n",
       "      <th>charged_time_float</th>\n",
       "      <th>total_energy</th>\n",
       "      <th>max_power</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>day_no</th>\n",
       "      <th>...</th>\n",
       "      <th>soc_arr</th>\n",
       "      <th>charged_time</th>\n",
       "      <th>connected_time</th>\n",
       "      <th>ts_arr</th>\n",
       "      <th>ts_dep</th>\n",
       "      <th>ts_soj</th>\n",
       "      <th>laxity</th>\n",
       "      <th>depart_hour</th>\n",
       "      <th>xi</th>\n",
       "      <th>laxity_float</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.997000e+03</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9.997000e+03</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9.997000e+03</td>\n",
       "      <td>9.997000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.452763e+06</td>\n",
       "      <td>1.360308</td>\n",
       "      <td>2019-07-11 11:10:26.353906176</td>\n",
       "      <td>2019-07-11 16:59:39.029708800</td>\n",
       "      <td>5.820084</td>\n",
       "      <td>2.877626</td>\n",
       "      <td>13.634439</td>\n",
       "      <td>5.589205</td>\n",
       "      <td>12.411323</td>\n",
       "      <td>190.927678</td>\n",
       "      <td>...</td>\n",
       "      <td>0.612447</td>\n",
       "      <td>3.354906</td>\n",
       "      <td>6.325598</td>\n",
       "      <td>171154.675603</td>\n",
       "      <td>171161.001200</td>\n",
       "      <td>6.325598</td>\n",
       "      <td>3.672141e+00</td>\n",
       "      <td>18.736921</td>\n",
       "      <td>1.978880e+01</td>\n",
       "      <td>3.166627e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.261657e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019-01-01 00:30:00</td>\n",
       "      <td>2019-01-01 08:24:00</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166560.000000</td>\n",
       "      <td>166568.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-4.440892e-16</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-2.393152e-15</td>\n",
       "      <td>-8.881784e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.360960e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019-04-04 12:28:00</td>\n",
       "      <td>2019-04-04 16:18:00</td>\n",
       "      <td>1.510000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>4.530000</td>\n",
       "      <td>3.398000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.498375</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>168804.000000</td>\n",
       "      <td>168808.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.856117e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.452128e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019-07-10 07:15:00</td>\n",
       "      <td>2019-07-10 13:11:00</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>3.640000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.668160</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>171127.000000</td>\n",
       "      <td>171133.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.600000e-01</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.173344e+00</td>\n",
       "      <td>3.100000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.547565e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2019-10-22 05:59:00</td>\n",
       "      <td>2019-10-22 12:34:00</td>\n",
       "      <td>8.390000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>18.260000</td>\n",
       "      <td>7.277000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.801562</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>173621.000000</td>\n",
       "      <td>173629.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.801484e+00</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.587472e+01</td>\n",
       "      <td>4.410000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.634022e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2019-12-31 17:46:00</td>\n",
       "      <td>2019-12-31 21:04:00</td>\n",
       "      <td>161.170000</td>\n",
       "      <td>25.670000</td>\n",
       "      <td>93.929000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967305</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>175313.000000</td>\n",
       "      <td>175317.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>1.548015e+02</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>8.342098e+02</td>\n",
       "      <td>1.539715e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.075844e+05</td>\n",
       "      <td>0.480114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.094334</td>\n",
       "      <td>2.644101</td>\n",
       "      <td>14.283505</td>\n",
       "      <td>3.333594</td>\n",
       "      <td>4.531763</td>\n",
       "      <td>110.507278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257299</td>\n",
       "      <td>2.649885</td>\n",
       "      <td>7.091561</td>\n",
       "      <td>2652.263849</td>\n",
       "      <td>2652.549167</td>\n",
       "      <td>7.091561</td>\n",
       "      <td>6.333161e+00</td>\n",
       "      <td>9.163897</td>\n",
       "      <td>3.412877e+01</td>\n",
       "      <td>6.333746e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            session    Connector              starttime_parking  \\\n",
       "count  9.997000e+03  9997.000000                           9997   \n",
       "mean   3.452763e+06     1.360308  2019-07-11 11:10:26.353906176   \n",
       "min    3.261657e+06     1.000000            2019-01-01 00:30:00   \n",
       "25%    3.360960e+06     1.000000            2019-04-04 12:28:00   \n",
       "50%    3.452128e+06     1.000000            2019-07-10 07:15:00   \n",
       "75%    3.547565e+06     2.000000            2019-10-22 05:59:00   \n",
       "max    3.634022e+06     2.000000            2019-12-31 17:46:00   \n",
       "std    1.075844e+05     0.480114                            NaN   \n",
       "\n",
       "                     endtime_parking  connected_time_float  \\\n",
       "count                           9997           9997.000000   \n",
       "mean   2019-07-11 16:59:39.029708800              5.820084   \n",
       "min              2019-01-01 08:24:00              0.020000   \n",
       "25%              2019-04-04 16:18:00              1.510000   \n",
       "50%              2019-07-10 13:11:00              3.380000   \n",
       "75%              2019-10-22 12:34:00              8.390000   \n",
       "max              2019-12-31 21:04:00            161.170000   \n",
       "std                              NaN              7.094334   \n",
       "\n",
       "       charged_time_float  total_energy    max_power   start_hour  \\\n",
       "count         9997.000000   9997.000000  9997.000000  9997.000000   \n",
       "mean             2.877626     13.634439     5.589205    12.411323   \n",
       "min              0.020000      0.100000     0.167000     0.000000   \n",
       "25%              1.250000      4.530000     3.398000     9.000000   \n",
       "50%              2.240000      7.830000     3.640000    12.000000   \n",
       "75%              3.500000     18.260000     7.277000    16.000000   \n",
       "max             25.670000     93.929000    22.500000    23.000000   \n",
       "std              2.644101     14.283505     3.333594     4.531763   \n",
       "\n",
       "            day_no  ...      soc_arr  charged_time  connected_time  \\\n",
       "count  9997.000000  ...  9997.000000   9997.000000     9997.000000   \n",
       "mean    190.927678  ...     0.612447      3.354906        6.325598   \n",
       "min       0.000000  ...     0.000000      1.000000        1.000000   \n",
       "25%      93.000000  ...     0.498375      2.000000        2.000000   \n",
       "50%     190.000000  ...     0.668160      3.000000        4.000000   \n",
       "75%     294.000000  ...     0.801562      4.000000        9.000000   \n",
       "max     364.000000  ...     0.967305     26.000000      162.000000   \n",
       "std     110.507278  ...     0.257299      2.649885        7.091561   \n",
       "\n",
       "              ts_arr         ts_dep       ts_soj        laxity  depart_hour  \\\n",
       "count    9997.000000    9997.000000  9997.000000  9.997000e+03  9997.000000   \n",
       "mean   171154.675603  171161.001200     6.325598  3.672141e+00    18.736921   \n",
       "min    166560.000000  166568.000000     1.000000 -4.440892e-16     2.000000   \n",
       "25%    168804.000000  168808.000000     2.000000  5.300000e-01    13.000000   \n",
       "50%    171127.000000  171133.000000     4.000000  9.600000e-01    16.000000   \n",
       "75%    173621.000000  173629.000000     9.000000  4.801484e+00    21.000000   \n",
       "max    175313.000000  175317.000000   162.000000  1.548015e+02   175.000000   \n",
       "std      2652.263849    2652.549167     7.091561  6.333161e+00     9.163897   \n",
       "\n",
       "                 xi  laxity_float  \n",
       "count  9.997000e+03  9.997000e+03  \n",
       "mean   1.978880e+01  3.166627e+00  \n",
       "min   -2.393152e-15 -8.881784e-16  \n",
       "25%    2.856117e+00  0.000000e+00  \n",
       "50%    5.173344e+00  3.100000e-01  \n",
       "75%    2.587472e+01  4.410000e+00  \n",
       "max    8.342098e+02  1.539715e+02  \n",
       "std    3.412877e+01  6.333746e+00  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4de8a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "if save:\n",
    "    df_elaad.to_csv(\"data/df_elaad_preproc.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25924da",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "Can be skipped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87c2fe94-5443-4a28-aa0a-5e99eaf5704c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>connected_time</th>\n",
       "      <th>ts_soj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.325598</td>\n",
       "      <td>6.325598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.091561</td>\n",
       "      <td>7.091561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       connected_time       ts_soj\n",
       "count     9997.000000  9997.000000\n",
       "mean         6.325598     6.325598\n",
       "std          7.091561     7.091561\n",
       "min          1.000000     1.000000\n",
       "25%          2.000000     2.000000\n",
       "50%          4.000000     4.000000\n",
       "75%          9.000000     9.000000\n",
       "max        162.000000   162.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad[[\"connected_time\", \"ts_soj\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6a689de-e6f9-48f9-9229-36060f2cd43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>depart_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.736921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.163897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>175.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       depart_hour\n",
       "count  9997.000000\n",
       "mean     18.736921\n",
       "std       9.163897\n",
       "min       2.000000\n",
       "25%      13.000000\n",
       "50%      16.000000\n",
       "75%      21.000000\n",
       "max     175.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad[[\"depart_hour\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90c174c0-dbf0-42db-b74c-b6e0f67569be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9997 entries, 0 to 9996\n",
      "Data columns (total 23 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   session               9997 non-null   int64         \n",
      " 1   ChargePoint           9997 non-null   object        \n",
      " 2   Connector             9997 non-null   int64         \n",
      " 3   starttime_parking     9997 non-null   datetime64[ns]\n",
      " 4   endtime_parking       9997 non-null   datetime64[ns]\n",
      " 5   StartCard             9997 non-null   object        \n",
      " 6   connected_time_float  9997 non-null   float64       \n",
      " 7   charged_time_float    9997 non-null   float64       \n",
      " 8   total_energy          9997 non-null   float64       \n",
      " 9   max_power             9997 non-null   float64       \n",
      " 10  start_hour            9997 non-null   int32         \n",
      " 11  day_no                9997 non-null   int64         \n",
      " 12  energy_supplied       9997 non-null   float64       \n",
      " 13  soc_arr               9997 non-null   float64       \n",
      " 14  charged_time          9997 non-null   float64       \n",
      " 15  connected_time        9997 non-null   float64       \n",
      " 16  ts_arr                9997 non-null   float64       \n",
      " 17  ts_dep                9997 non-null   float64       \n",
      " 18  ts_soj                9997 non-null   float64       \n",
      " 19  laxity                9997 non-null   float64       \n",
      " 20  depart_hour           9997 non-null   float64       \n",
      " 21  xi                    9997 non-null   float64       \n",
      " 22  laxity_float          9997 non-null   float64       \n",
      "dtypes: datetime64[ns](2), float64(15), int32(1), int64(3), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_elaad.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d1b031c2-5b0d-4c05-8dfa-83158e1faea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session</th>\n",
       "      <th>Connector</th>\n",
       "      <th>starttime_parking</th>\n",
       "      <th>endtime_parking</th>\n",
       "      <th>connected_time_float</th>\n",
       "      <th>charged_time_float</th>\n",
       "      <th>total_energy</th>\n",
       "      <th>max_power</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>day_no</th>\n",
       "      <th>...</th>\n",
       "      <th>soc_arr</th>\n",
       "      <th>charged_time</th>\n",
       "      <th>connected_time</th>\n",
       "      <th>ts_arr</th>\n",
       "      <th>ts_dep</th>\n",
       "      <th>ts_soj</th>\n",
       "      <th>laxity</th>\n",
       "      <th>depart_hour</th>\n",
       "      <th>xi</th>\n",
       "      <th>laxity_float</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.997000e+03</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9.997000e+03</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9.997000e+03</td>\n",
       "      <td>9.997000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.452763e+06</td>\n",
       "      <td>1.360308</td>\n",
       "      <td>2019-07-11 11:10:26.353906176</td>\n",
       "      <td>2019-07-11 16:59:39.029708800</td>\n",
       "      <td>5.820084</td>\n",
       "      <td>2.877626</td>\n",
       "      <td>13.634439</td>\n",
       "      <td>5.589205</td>\n",
       "      <td>12.411323</td>\n",
       "      <td>190.927678</td>\n",
       "      <td>...</td>\n",
       "      <td>0.612447</td>\n",
       "      <td>3.354906</td>\n",
       "      <td>6.325598</td>\n",
       "      <td>171154.675603</td>\n",
       "      <td>171161.001200</td>\n",
       "      <td>6.325598</td>\n",
       "      <td>3.672141e+00</td>\n",
       "      <td>18.736921</td>\n",
       "      <td>1.978880e+01</td>\n",
       "      <td>3.166627e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.261657e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019-01-01 00:30:00</td>\n",
       "      <td>2019-01-01 08:24:00</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166560.000000</td>\n",
       "      <td>166568.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-4.440892e-16</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-2.393152e-15</td>\n",
       "      <td>-8.881784e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.360960e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019-04-04 12:28:00</td>\n",
       "      <td>2019-04-04 16:18:00</td>\n",
       "      <td>1.510000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>4.530000</td>\n",
       "      <td>3.398000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.498375</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>168804.000000</td>\n",
       "      <td>168808.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.856117e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.452128e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019-07-10 07:15:00</td>\n",
       "      <td>2019-07-10 13:11:00</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>3.640000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.668160</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>171127.000000</td>\n",
       "      <td>171133.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.600000e-01</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.173344e+00</td>\n",
       "      <td>3.100000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.547565e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2019-10-22 05:59:00</td>\n",
       "      <td>2019-10-22 12:34:00</td>\n",
       "      <td>8.390000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>18.260000</td>\n",
       "      <td>7.277000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.801562</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>173621.000000</td>\n",
       "      <td>173629.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.801484e+00</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.587472e+01</td>\n",
       "      <td>4.410000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.634022e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2019-12-31 17:46:00</td>\n",
       "      <td>2019-12-31 21:04:00</td>\n",
       "      <td>161.170000</td>\n",
       "      <td>25.670000</td>\n",
       "      <td>93.929000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967305</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>175313.000000</td>\n",
       "      <td>175317.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>1.548015e+02</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>8.342098e+02</td>\n",
       "      <td>1.539715e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.075844e+05</td>\n",
       "      <td>0.480114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.094334</td>\n",
       "      <td>2.644101</td>\n",
       "      <td>14.283505</td>\n",
       "      <td>3.333594</td>\n",
       "      <td>4.531763</td>\n",
       "      <td>110.507278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257299</td>\n",
       "      <td>2.649885</td>\n",
       "      <td>7.091561</td>\n",
       "      <td>2652.263849</td>\n",
       "      <td>2652.549167</td>\n",
       "      <td>7.091561</td>\n",
       "      <td>6.333161e+00</td>\n",
       "      <td>9.163897</td>\n",
       "      <td>3.412877e+01</td>\n",
       "      <td>6.333746e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            session    Connector              starttime_parking  \\\n",
       "count  9.997000e+03  9997.000000                           9997   \n",
       "mean   3.452763e+06     1.360308  2019-07-11 11:10:26.353906176   \n",
       "min    3.261657e+06     1.000000            2019-01-01 00:30:00   \n",
       "25%    3.360960e+06     1.000000            2019-04-04 12:28:00   \n",
       "50%    3.452128e+06     1.000000            2019-07-10 07:15:00   \n",
       "75%    3.547565e+06     2.000000            2019-10-22 05:59:00   \n",
       "max    3.634022e+06     2.000000            2019-12-31 17:46:00   \n",
       "std    1.075844e+05     0.480114                            NaN   \n",
       "\n",
       "                     endtime_parking  connected_time_float  \\\n",
       "count                           9997           9997.000000   \n",
       "mean   2019-07-11 16:59:39.029708800              5.820084   \n",
       "min              2019-01-01 08:24:00              0.020000   \n",
       "25%              2019-04-04 16:18:00              1.510000   \n",
       "50%              2019-07-10 13:11:00              3.380000   \n",
       "75%              2019-10-22 12:34:00              8.390000   \n",
       "max              2019-12-31 21:04:00            161.170000   \n",
       "std                              NaN              7.094334   \n",
       "\n",
       "       charged_time_float  total_energy    max_power   start_hour  \\\n",
       "count         9997.000000   9997.000000  9997.000000  9997.000000   \n",
       "mean             2.877626     13.634439     5.589205    12.411323   \n",
       "min              0.020000      0.100000     0.167000     0.000000   \n",
       "25%              1.250000      4.530000     3.398000     9.000000   \n",
       "50%              2.240000      7.830000     3.640000    12.000000   \n",
       "75%              3.500000     18.260000     7.277000    16.000000   \n",
       "max             25.670000     93.929000    22.500000    23.000000   \n",
       "std              2.644101     14.283505     3.333594     4.531763   \n",
       "\n",
       "            day_no  ...      soc_arr  charged_time  connected_time  \\\n",
       "count  9997.000000  ...  9997.000000   9997.000000     9997.000000   \n",
       "mean    190.927678  ...     0.612447      3.354906        6.325598   \n",
       "min       0.000000  ...     0.000000      1.000000        1.000000   \n",
       "25%      93.000000  ...     0.498375      2.000000        2.000000   \n",
       "50%     190.000000  ...     0.668160      3.000000        4.000000   \n",
       "75%     294.000000  ...     0.801562      4.000000        9.000000   \n",
       "max     364.000000  ...     0.967305     26.000000      162.000000   \n",
       "std     110.507278  ...     0.257299      2.649885        7.091561   \n",
       "\n",
       "              ts_arr         ts_dep       ts_soj        laxity  depart_hour  \\\n",
       "count    9997.000000    9997.000000  9997.000000  9.997000e+03  9997.000000   \n",
       "mean   171154.675603  171161.001200     6.325598  3.672141e+00    18.736921   \n",
       "min    166560.000000  166568.000000     1.000000 -4.440892e-16     2.000000   \n",
       "25%    168804.000000  168808.000000     2.000000  5.300000e-01    13.000000   \n",
       "50%    171127.000000  171133.000000     4.000000  9.600000e-01    16.000000   \n",
       "75%    173621.000000  173629.000000     9.000000  4.801484e+00    21.000000   \n",
       "max    175313.000000  175317.000000   162.000000  1.548015e+02   175.000000   \n",
       "std      2652.263849    2652.549167     7.091561  6.333161e+00     9.163897   \n",
       "\n",
       "                 xi  laxity_float  \n",
       "count  9.997000e+03  9.997000e+03  \n",
       "mean   1.978880e+01  3.166627e+00  \n",
       "min   -2.393152e-15 -8.881784e-16  \n",
       "25%    2.856117e+00  0.000000e+00  \n",
       "50%    5.173344e+00  3.100000e-01  \n",
       "75%    2.587472e+01  4.410000e+00  \n",
       "max    8.342098e+02  1.539715e+02  \n",
       "std    3.412877e+01  6.333746e+00  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1cfa8c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>laxity</th>\n",
       "      <th>laxity_float</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.997000e+03</td>\n",
       "      <td>9.997000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.672141e+00</td>\n",
       "      <td>3.166627e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.333161e+00</td>\n",
       "      <td>6.333746e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.440892e-16</td>\n",
       "      <td>-8.881784e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.600000e-01</td>\n",
       "      <td>3.100000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.801484e+00</td>\n",
       "      <td>4.410000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.548015e+02</td>\n",
       "      <td>1.539715e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             laxity  laxity_float\n",
       "count  9.997000e+03  9.997000e+03\n",
       "mean   3.672141e+00  3.166627e+00\n",
       "std    6.333161e+00  6.333746e+00\n",
       "min   -4.440892e-16 -8.881784e-16\n",
       "25%    5.300000e-01  0.000000e+00\n",
       "50%    9.600000e-01  3.100000e-01\n",
       "75%    4.801484e+00  4.410000e+00\n",
       "max    1.548015e+02  1.539715e+02"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad[[\"laxity\", \"laxity_float\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc995eb8-50b1-44f5-98b2-7e9e0a3f047e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9997"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad[\"session\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f292951-a402-4b1b-af0d-f99bec1f973a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364.875"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_max = df_elaad[\"ts_dep\"].max()\n",
    "ts_min = df_elaad[\"ts_arr\"].min()\n",
    "delta_ts = ts_max -ts_min\n",
    "delta_ts / 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b99912af-c3c2-425a-94ed-1413e10349a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-01 00:30:00\n",
      "2019-12-31 21:04:00\n"
     ]
    }
   ],
   "source": [
    "print(df_elaad[\"starttime_parking\"].min())\n",
    "print(df_elaad[\"endtime_parking\"].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3ee35da-a120-4830-8575-e26a8cf61b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session</th>\n",
       "      <th>ChargePoint</th>\n",
       "      <th>Connector</th>\n",
       "      <th>starttime_parking</th>\n",
       "      <th>endtime_parking</th>\n",
       "      <th>StartCard</th>\n",
       "      <th>connected_time_float</th>\n",
       "      <th>charged_time_float</th>\n",
       "      <th>total_energy</th>\n",
       "      <th>max_power</th>\n",
       "      <th>...</th>\n",
       "      <th>soc_arr</th>\n",
       "      <th>charged_time</th>\n",
       "      <th>connected_time</th>\n",
       "      <th>ts_arr</th>\n",
       "      <th>ts_dep</th>\n",
       "      <th>ts_soj</th>\n",
       "      <th>laxity</th>\n",
       "      <th>depart_hour</th>\n",
       "      <th>xi</th>\n",
       "      <th>laxity_float</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3261657</td>\n",
       "      <td>e62c50d1be0a2f80ec51d471f9630a4e</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:30:00</td>\n",
       "      <td>2019-01-01 08:24:00</td>\n",
       "      <td>826d337c1d84900452edfcb27a3fb11bcbd06f35da8406...</td>\n",
       "      <td>7.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.53</td>\n",
       "      <td>9.818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.835250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>166560.0</td>\n",
       "      <td>166568.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>37.722302</td>\n",
       "      <td>6.910000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3262038</td>\n",
       "      <td>9bae10789a789973cc7f05d2a96df76f</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 10:36:00</td>\n",
       "      <td>2019-01-01 12:52:00</td>\n",
       "      <td>26612ba143072faddaa884c2b04208992c5f44d97e5a24...</td>\n",
       "      <td>2.27</td>\n",
       "      <td>2.27</td>\n",
       "      <td>8.08</td>\n",
       "      <td>3.600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.664118</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166570.0</td>\n",
       "      <td>166573.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.933897</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3262129</td>\n",
       "      <td>5ab468315a1f42feb6d0a87307593352</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 11:49:00</td>\n",
       "      <td>2019-01-01 17:50:00</td>\n",
       "      <td>8478920deafe847713d1707d8f71a043c59fa066e55020...</td>\n",
       "      <td>6.02</td>\n",
       "      <td>3.75</td>\n",
       "      <td>11.56</td>\n",
       "      <td>3.400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.464688</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>166571.0</td>\n",
       "      <td>166578.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.513926</td>\n",
       "      <td>2.270000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3262170</td>\n",
       "      <td>00fa448dfcba79a12a3cea56fb48d63f</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 12:22:00</td>\n",
       "      <td>2019-01-01 14:29:00</td>\n",
       "      <td>e8e1953d3e6221f971ce3299d2fa8275cdc9e7700e2b39...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>0.87</td>\n",
       "      <td>3.84</td>\n",
       "      <td>7.973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.852768</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166572.0</td>\n",
       "      <td>166575.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.13</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.478357</td>\n",
       "      <td>1.250000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3262208</td>\n",
       "      <td>e35645c6b4adb10224ecfbdff6bc5473</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 12:51:00</td>\n",
       "      <td>2019-01-01 15:16:00</td>\n",
       "      <td>6490d53fbbe95d74592928292858f9b156d838f08d062e...</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.41</td>\n",
       "      <td>8.50</td>\n",
       "      <td>3.592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.645253</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166572.0</td>\n",
       "      <td>166575.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.179451</td>\n",
       "      <td>4.440892e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   session                       ChargePoint  Connector   starttime_parking  \\\n",
       "0  3261657  e62c50d1be0a2f80ec51d471f9630a4e          2 2019-01-01 00:30:00   \n",
       "1  3262038  9bae10789a789973cc7f05d2a96df76f          1 2019-01-01 10:36:00   \n",
       "2  3262129  5ab468315a1f42feb6d0a87307593352          1 2019-01-01 11:49:00   \n",
       "3  3262170  00fa448dfcba79a12a3cea56fb48d63f          2 2019-01-01 12:22:00   \n",
       "4  3262208  e35645c6b4adb10224ecfbdff6bc5473          1 2019-01-01 12:51:00   \n",
       "\n",
       "      endtime_parking                                          StartCard  \\\n",
       "0 2019-01-01 08:24:00  826d337c1d84900452edfcb27a3fb11bcbd06f35da8406...   \n",
       "1 2019-01-01 12:52:00  26612ba143072faddaa884c2b04208992c5f44d97e5a24...   \n",
       "2 2019-01-01 17:50:00  8478920deafe847713d1707d8f71a043c59fa066e55020...   \n",
       "3 2019-01-01 14:29:00  e8e1953d3e6221f971ce3299d2fa8275cdc9e7700e2b39...   \n",
       "4 2019-01-01 15:16:00  6490d53fbbe95d74592928292858f9b156d838f08d062e...   \n",
       "\n",
       "   connected_time_float  charged_time_float  total_energy  max_power  ...  \\\n",
       "0                  7.91                1.00          6.53      9.818  ...   \n",
       "1                  2.27                2.27          8.08      3.600  ...   \n",
       "2                  6.02                3.75         11.56      3.400  ...   \n",
       "3                  2.12                0.87          3.84      7.973  ...   \n",
       "4                  2.41                2.41          8.50      3.592  ...   \n",
       "\n",
       "    soc_arr  charged_time  connected_time    ts_arr    ts_dep  ts_soj  laxity  \\\n",
       "0  0.835250           1.0             8.0  166560.0  166568.0     8.0    7.00   \n",
       "1  0.664118           3.0             3.0  166570.0  166573.0     3.0    0.73   \n",
       "2  0.464688           4.0             7.0  166571.0  166578.0     7.0    3.25   \n",
       "3  0.852768           1.0             3.0  166572.0  166575.0     3.0    2.13   \n",
       "4  0.645253           3.0             3.0  166572.0  166575.0     3.0    0.59   \n",
       "\n",
       "   depart_hour         xi  laxity_float  \n",
       "0          8.0  37.722302  6.910000e+00  \n",
       "1         13.0   3.933897  0.000000e+00  \n",
       "2         18.0  17.513926  2.270000e+00  \n",
       "3         15.0  11.478357  1.250000e+00  \n",
       "4         15.0   3.179451  4.440892e-16  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b16576a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e4f48504-87e7-4519-9adf-af2ae7a9700d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1000.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lax_f = df_elaad[\"laxity_float\"] \n",
    "fig1 = plt.figure(figsize = (20, 5))\n",
    "ax1 = fig1.add_subplot(2, 1, 1)\n",
    "ax2 = fig1.add_subplot(2, 1, 2)\n",
    "ax1.set_yscale('log')\n",
    "ax1.hist(lax_f, bins = 100);\n",
    "\n",
    "ax2.hist(lax_f, bins = 100);\n",
    "ax2.set_ylim(0, 1000)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "17725182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbd34ab2c70>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i0GjEKWEQcHisj8Cg3k5pJSn7R6uM9pW+8Vn1VkK9HW/h0YjIRlIwi2ywlSOtq8WQMDDOzjbYPVBitt4JWM8cA9pJyoGR6jeWdWzFKfONmDjNiAIjyZzMnYFCYQuPSEQ2krqyRTbYyrmvzYzdg2WWB3XtGSyRZBkLzZix/tJloQwwWCmwq79EYNCIUwKDXf0lBisKZpHtSi1mkQ3WSjKqxRfCtlwIOThS5fxcg8xh/1CFsQOlywJ52Vh/iYnZOqN9JQqhEadOO0kZ6y9dta2IbA8KZpENtjz3dTF6YST18rXkAyPVb/reciHkwEiV6VqLejulFAVXtapFZHtRMItssOVWL3BZq/dGobxsOZxFZGdQMIus0ZWzd431X94trVaviNwKBbPIGlw54jpOnYnZ+lXBq1aviNysNY3KNrNhM/ukmT1hZo+b2bea2aiZfdbMnu5+HVmvyorkzXStReadr8/P1C97LiKyGmu9Xeq3gb9y95cBrwIeB94LPODudwAPdJ+LbEsLzYTpWhN3qBQ6s3lN15osNJPLtltuWZ+aqjExW6cZp1tUYxHJu1UHs5kNAd8JfBDA3dvuPgfcA3yku9lHgO9fWxVF8qvejgmsM62mmVGIAgILLpuZazmUM4dqMSRzFM4icl1raTEfBaaAPzSzr5jZH5hZH7DH3c93t7kA7LnWm83sXjM7bmbHp6am1lANkc21svVbayQ04oQ47cxtHacZmTvVwgvDN66cYKQYBRSjUN3dInJNawnmCLgL+IC7vwZY4opua3d3OgvkXMXd73P3Y+5+bHx8fA3VENk8V7Z+q+WINHPaaXrdmblaSUYhvHw1qEJoWohCRK5pLcF8Fjjr7g91n3+STlBfNLN9AN2vk2urokh+XDW95kCZYhQSYBzuLjZhxmUzcy1PMLJSnDqlSDPiisjVVv2bwd0vAGfM7KXdojcDJ4D7gXd2y94JfHpNNRTJkStbv53pNSukmVNvd1rMV94qNdZfop2k31jKsZ1kmlZTRK5rrfcx/xTwUTMrAqeAf0kn7D9hZu8CTgM/tMZ9iOTG9abXPDRave59yppgRERuxZqC2d2/Chy7xktvXsvniuTVaqfX1AQjInKzdJFL5BYsB2xgXLfrWkRkLTQlp8gtWtn6vdE82SIit0otZpFV0sQhIrIRFMwiq6SJQ0RkIyiYRVZJE4eIyEZQMIuskiYOEZGNoN8gIqukiUNEZCMomEVWSbdOichG0O1SImugiUNEZL2pxSwiIpIjCmYREZEcUTCLiIjkiIJZREQkRxTMIiIiOaJgFhERyREFs4iISI7oPmbpKddbZvFml1/UMo0ikndqMUvPuN4yi3P19k0tv6hlGkWkFyiYpWdcb5nFZ6YWb2r5RS3TKCK9QF3Z0jNaSUa1eHm3cyE0ZmptAow4c4phwEhfkVIUUG+nN/X+K7cTEdlKCmbJrSuvB+NOnDrF6IU1kGvNhEY7JU6dSjEkSZ0L8w1G+0pXhfDyMo0r369lGkUkbxTMkktz9TYnzs3jQJY5M7UWC82ESjHkZfsG2dVfIk6dqcUmt+/uZ6mVkmROFBpxakwtNrnrttHLPnOsv8TEbB3otJTj1GknqRahEJFcUVNBcqcZpzx+bp4o6Hx7PnF+gdl6zEhfgciMJ88tcGGhSWAw3FdiV3+JvUNlAoNGnFKMjJFq8arR1lqmUUR6gVrMkjvnZutMLbQoFALOzNapRCGVckQzyegvFRitFkmzrNPSna0Tp065ELJ3qAJAO8kI7NqfrWUaRSTvFMySK8045eRkjaU4pll3nr6wSF8xZN9whWIUMtpXolwImK23gcu7p9MsY6rWYqmZcHisj2acqjUsIj1HXdmSK+dm6yy1U6IgZHqpRTtJuLjY4JnJRS7VWpQLAc04o7/c+ZuyXAgZ6y9xZnaJLzxzianFJvuGyxTCQPcoi0hPUjBLrkwuttg7VKLWTjCD3YMV0hQWGikDxZCLc02WWjEvGh8AXhi5XQgDXrp3kH2DVebqMZm77lEWkZ6kYJZccXdKYUgAlMOQQhixd7jMYDXCzVhoxbzmtlGGq0XghUlD3KEQBhSigGIYMrvUphAarSTb2gMSEblFusYsubJnqMK5uTpJmpG5M1yN6CsGDFWLDJQjhisFAB4+PU2tmbBQj3nJ3gFKUUCSOYXQiEKjEae6R1lEetKaf2uZWWhmXzGzP+8+P2pmD5nZSTP7YzMrrr2aslPsH64w0lciCIxmnJGmGf2ViNH+IlkGrTTjK6dnSFMYqRax0Hjk7ByBdUZjx2lGnGQY0E5SxvpLW31IIiK3ZD1azO8BHgcGu89/Hfgtd/+4mf0+8C7gA+uwH9kmlq8LLzQT6u2YaiHqtGytc49TpRBycKSPSjEEN6qlkCwF94xnphocGq0yWC0SBAH7hiqcvrTEmZkGL98/eNmo7P3DFY3KFpGes6ZgNrODwPcC/xvwc2ZmwJuAH+lu8hHgf0HBvGNdOa1mXyliutYic5ivtwgsYKrZIskyilHAwZEqYRDQVwrZNzxMkjq1VsJcvc2uwTKz9TalMGByocXuwRKlKOTQaIUzMw0yh/1DFcYOaClHEelda20x/0fgF4GB7vMxYM7dk+7zs8CBNe5DetTyMovFKCQwODff4PSlJfYNVygERimKKEQBk4tNLtVaBGY8d6nOS/b0M1gpcGmhRV8lYq7eBgNzZ+9wlTSDYhQw34jZPRDibty2q8rt4/1bfcgiImu26mvMZvZ2YNLdH17l++81s+Nmdnxqamq11ZAcWx4x3WgnPHZungvzTZbaCTOLTSbmGqSe0UpSzs3Vma61qEQBjnNxocWFuc79zDjEaUZkhptxeKxCvZ3QjlOa7YR6K7ns9ikRkV63lhbztwHfZ2ZvA8p0rjH/NjBsZlG31XwQmLjWm939PuA+gGPHjvka6iE51IxTzszUmV1q8+UzM3jmVIoRWZYxtdji4HCZpycXydy4tNiiv1zAwoBqFFAtRUzM1tnVX+LwWB/FKCBzwCFN4RUHhjg5WaMZZ4Qhl90+JSLS61YdzO7+PuB9AGb2BuAX3P1Hzew/AT8IfBx4J/DptVdTeslyF3a9nfLoxBy1RsJ8o4VbQBxnDFUiFhttKoWIyIxGK8HdWCi1OThaJcsy6q2EgV19AAxXi1yYb1IIjXaSMdZf4qV7B7QAhYhsSxtxk+cv0RkIdpLONecPbsA+JMemay0accpjE3OcmqpxenqJWjMh6I6wXmylnJ9rUGsmtD0jCALcnYVmzPn5JpeWWuzqe2EAV2eBijKpO1nmWhVKRLa1dZlgxN0/B3yu+/gU8Lr1+FzpPc045cT5BZ6YmOP0bJ3AnHo7JU2dYiHi0GCFxy8sUgiNIDCO7hpgcrHBMxcXqbeL7B0qYwSUCgFRGNBOss62ZoxWiwpkEdn2NC2SrJvlLuyJ6SXaaeeacuoB1UJEXzkkTp3FVkqrHTNcLtBfKuDAbK3NYLlA7BmtuDN719Hd/QyVI62dLCI7jqbklHWzPAq7Hmc005QoMOIkJQzAM0jSTsDePj6AB0apYFyYq7PYSiiEAS/ZM8g/PzJKnGYknXuitHayiOw4CmZZtStn8JqptRksRyy2YvoKERRh/3CJ56Yb9JUjSoWQQ6NVaq2YpWbSvRbtlEIDjEIIrSSlGAYsNhLGB9ShIyI7j4JZVmW52zpzmFposNhMuTDfAINqIaAeZ4xUivSN9TPWV6aepLxo10DnunGcMNdMwJ35RsJSq021VGCkWmK+HtNfijBD81yLyI6kYJZVWe62PjdXZ76REIZGsWA8eaHGYKlAZHBpqUWtFTM+UOJb9g7z1lfu49xsnXPzTUqLTdIso1yMuFQLODxcpq8YMrPUolwIePn+IV1PFpEdScEsq9JKMgKDpy4uEqcZzTijWgwpR8Ziq83MUpvXHB7lzv1DpO6kWcazl5aYr7cZ7y/SjFMaccpYf5mX7ulnrpFQKUWM9Be5c/+wQllEdiwFs9y0lQtSXFho0GinZCnESUrm8PzMEkOVAjQTRvtKlIshfaWI5y4tMViJODNTo68YMVVrc3i0ymw9phgFeOaMRyGjfbodSkREwSw3ZfmasjvUWjHnZhtM15oMVUtML6UkqROZUQhCiqEzUi1Sa7Q5UWtx21gfg5UC9WZCWoAkSZhvBuwZLGmZRhGRKyiY5aZM11q4w/RSm2J3+cZCWGGq1qZUCKi1Y4YrEZln7B4sMVot0YwTas2UoUqB1KFSjhgoFWhHAUmSaZlGEZFrUDDLNV25jvJCI+7cyhQFFMKAUhQSBcbewYDhSoHnLi3RSjIGqwWGK0Wen67RSFIKYcB8M6YYhuweLBGFRj12joz16R5lEZFr0I2icpWVt0JViyGZw2y9zdxSTBQYAEPVAs04IwqMQhTw6ttGGKoUGCwXaMYJt+3qY6BcYKAYcWmxRV8xoBgGNNophm6FEhG5HrWY5SrTtRaZd76204xiGDBYKfDsYo1qKaJSDAkwhqsFAjOyrHNN+bvu3MszU4s044y+YsThsT5ml9rEidOIExYbMWboVigRkW9CwSxXWWgmzNdblKKISiGk1ko4P1Vnbinm4kKL4WrEobE+9g5VCLoTgSy1EmaW2rTjjAPDFSrFzrdWKQqZqbVIPeO2XX2M9et6sojIN6NglqvU2zGBBRSigFaSMjFX57mpJYphwNHxfppJytRCE/fOkoyTC03GB8r0lyOiKODsbJ1Do32UCyHlQsiugTK7B8u6piwichMUzDvUlfNcVwsRg5UCY/0lqoWIqWaLuUbMudklnpupM1AMGe4rUimGtNOMepwRBoa7EwYB00ttClHA7oEyZ2bqTC40OTRaJU6ddpIqlEVEbpIGf+1Ay4O76u2U+XqLNIX5Rkyj3SkHSLIMx5lvxERmhGFn8JY7TC02eH56icnFFkvtlEoxpBgFzNXblAshB0cqpJlruUYRkVVQi3kHuPLWp1b3HuLnpmu02xmVckS1GHJ+ocFiM+H0VI1yIeRFuwcY6y9RCAPibtDOTi5ycaFFFEKWZczV20SB0VeKaMQpAGEQcGi0qlayiMgqKJi3ueXWcTEKqRZD4tQ5eXGBUiGkFWf0lSMyh1OTizw9VWOgFDG31ObgcJkT5+YZrERYAEv1Ficm5hnqK5IlGbsGi2TASCXiUq1FYEYxMtpJpq5rEZE1UFf2Nre8ClQxCjAzilFA4rDYTKgWI7IMMneeOL/IYiOmr1BgoFKkmUJgTiEK6C8VWGpBYGDuRGFAuRBiQDvOqJYikiyjFIXquhYRWSO1mLep5e7rk5OLDFUKjPS9cJvSQCliarHFroHOrUyzS20WmjHD1QIY7B+uMtdoE6cZZ2fqHNnVz2h/kT1DRSrFiP5SROpOK05pJylHx/u1IpSIyDpRMG9DK7uvy1HA2bkGz16qc2CkzJ7BCuViyO7BMpXu7Uyz9TZp5iQZ9JcjCqHhmXNqaol2ljFYLvDi8T4KUdgZDBaAp1AIAnYPlRXKIiLrSMG8zTTjlMfOzdGMM9xhYr7OzGKLVjvj1OQiA+UC+4fL7B+t0leMaBRTjuzqx8yYq8dcmK9jGPPNhGox5FtGB3Ez6klKn8FQpcBiM6YVZwyUIs3iJSKyzhTM28hyS7nV7tzqdPz0DM1WhuEsxSnuMNJXpBGnGMZkrTNJyO7BMuViyKWFJqemakwvtRkqF3jZviGOjPdxbr7B3FJMfzEiMAjMODrex6sPjzBcLW71YYuIbCsK5m1iZUt5crHFqcl5lloZrTRlvh6TGQyUCrTSlOGgSJJmBBhHxvtoJRkAtWZMpRAy1lfkNYdH2D1UoRSF3BYFBFYnTp3xgSIv2TPAfg3wEhHZEArmbWBlS7mVpHz19CynLi1SDIzFRsJCK2b3UImiGWdnG5SjkPG4RBQYl2otTl9awukE9227+pmvtxnpK1GKOsEbBgEHR6vsH6roNigRkQ2mYO5RKycNubDQgAzOztZ5dGKB1J3haoGpxTaTS23KxQB3I8kgTjKSNGOxmTA2UOCvv3aOxGG0v8BIpUziGaMDJS7MNzgcBmBQb6eMVgtaqlFEZBMomHvQylHXgcHETINiFLDUTnBzao02faWImaWYXf1FPHOaSUYlS7l9sI/5pZiBaszpSzVaiVMqBiw2UxZbS7xy/zD95ZBW4rSSFDNj/1BZXdciIptEwdyDzs3Wmam3acUpT15c5LmpJRYbbS7VWvSVIhabCZl3Jg7pKwa0HUYrRYqFkForpRgGzHYXndgzXCLNwB0qxZDZRptKqcKLd/er21pEZAsomHtMM055fqaO4Xx9Yp4HT02zUG8Rp85SnNFfDMgyxw0GS0XqbQgD2D9cZrS/RDkKaScZjThhqL8MFrDYjDGDdpLSbKccGK6o21pEZIsomHtIM0556NQl/vNTUzx+foG5epMEJ3QjdShHxmwjphCEjFQLFCKjEaf0l8pcnG8RhQFBGUYHSzRaIe3UKRVgoFygEcdcWmizd6ise5NFRLbQqufKNrNDZvb3ZnbCzB4zs/d0y0fN7LNm9nT368j6VXfnmqu3+dsT5/nU8TOcmJhleqnO1GKLydkmFxYbtOKUgXJEtRhRKAT0lyKqxQJHxwcoGCy0YhpxxthgZ2rOQhR05ssuhRTCTlf2cH+Rt/yzfbo3WURkC62lxZwAP+/uXzazAeBhM/ss8OPAA+7+fjN7L/Be4JfWXtWdpxmnnJtr8MzFRR5+foaJmSYLjTa1dsrkQidocYgcFjwmCKAQhowPlNg9UGF8sEicOtOLLSrFkEoh4PnpBofHKgRm7B0uUy2ExHHGnoEyr719jL1Dla0+bBGRHW3Vwezu54Hz3ceLZvY4cAC4B3hDd7OPAJ9DwXxLmnHKudk6J84tcHJygZOTi1yYbzFTb1JrxNRaTmSd1Z5aKaQZGE6tGTPcF1Ewo52mnJ1ZYn4ppZ2m7OovEVpArZWw1Oy0rscGShwa7aMUBYz1l9R9LSKSA+tyjdnMjgCvAR4C9nRDG+ACsOc677kXuBfg8OHD61GNbWH5Vqjnp+t86dQlvvjsDBcW67QTqCdgdP7FdLos/Bvvgyh0ygUIowDLnGbiFCIYrJbwzGlnGYXQqLVjDu/q48W7BzTyWkQkZ9YczGbWD/wJ8DPuvmBm33jN3d3M/Frvc/f7gPsAjh07ds1tdpq5epvPPzXJUxcXefCZaU5NzrPYvDyAr/xaBCpFCENjrL/IbSP91Nopu0fKTDcSqgVjoFJkoR6z1Ez4lv2DtJPsG61kERHJlzUFs5kV6ITyR939U93ii2a2z93Pm9k+YHKtldwJ5uptHnrmEk+cX+BrZ2Z46vw8C/H1t8/otJzDAHYPlohT6C8VSM0pBEZfpcDYUJlGM6GRpAxVI+rtlFozYddAUSOvRURyatXBbJ2m8QeBx939N1e8dD/wTuD93a+fXlMNt7HlaTWnFpv83WMXeGqqxtMXFzk/36SefPP3FoBC2Lm+PFNr4Q5L7YQkdV40PsBcPeb2apG+wYhCFDA136RSiHjZ3kFefZtWhRIRyau1tJi/DfjvgK+b2Ve7Zb9MJ5A/YWbvAk4DP7SmGm5Tc/U2j5+b5+mpGg88ep7HL8yzsJTRvIn3Bt1/cdq51txqQghUPWWh0WaunhCFMNeIuX28jzAMMIfXHB7h6Hi/WsoiIjm2llHZ/0inN/Va3rzaz90JmnHKV0/P8PTFGn/61TNcmK0z07r590dAqQCL8eVlgUOGsdBqc2isgrmTpM7B0TLf/uJxtZJFRHqAZv7aZN+YvevpKb58eobTk3WWslv/nKX4hQFgpaDTrV0oBGRZRr3VotlOefW37OH1L9qlFrKISA9RMG+SZpzy7FSN46en+c9PTXFmeoknJxvcaiYHdEZpZyuehwaF0CiE3eUdE9g7WOLVh0cUyiIiPUbBvAmaccpXn5/lc49f4AvPXOLJi0s0V9FKBi4L8gLd+5lTKIZOlmWUopDbdw/wA689rK5rEZEepGDeYM045S+/NsHHH3qeE+fnL7suvFYpnXB2oJ1CsWi8bP8g/+pNd3BkV//67UhERDaNgnmDzNXb/ONTk/zd4xf5/FMXuNRY389fXn2kaBCGcGhXlX9x12G+91X7Nd+1iEgPUzBvgLl6mwdOXOBzT0xx/NnJdQ9l6NweFQADAyGvOTjCL73tTrWSRUS2AQXzOpurt/nM1yf420cv8sjENNP1jdlPAAxW4KW7h/iZ736ZQllEZJtQMK+jC/MN/vThs/zVoxM8cm5pQ/dVLsNdt43z7je/mJfuG9rQfYmIyOZRMK/R8rSap6ZqfOzBZ/nq83Ocq91gPs01qgTw3/zzQ/zEd96h68kiItuMgnkN5uptTpyb5/mZJf78kQm+/Owc9VXeBnWzXr6nzC+//RV8+x3XXE1TRER6nIJ5lTozeE1z4twsn/7qWU7PxGz02pXf/bIxfuEt36KuaxGRbUzBvEpPnJ/nr0+c58GnLnKutsHNZOAnv+Mw7/mel2smLxGRbU7BfIuaccoT5+f5vQee5p9OXmIp3dj9jVaMX37bnfzga2/b2B2JiEguKJhvwXOXavx/j5zlrx+9yKPnaxu+v9vHivyv3/9KXU8WEdlBFMw36cnz8/wff/MEXzh5aV2n1byebz06xP/+A6/W/ckiIjuMgvkGLsw3+LOvnOG+zz3NTHNz9vm2O8f5tR94tRahEBHZgRTM38SF+Qb/198/zSe+eGZVayavxk+/8Qg/95Y7N2dnIiKSOwrmb+IvvjbBpx7enFAeKcK//a9fyfe95tDG70xERHJLwXwNF+Yb/M1j5/mNv3iSDVh/4ip37q3wu//t63Q9WUREFMzLmnHKudk6jzw/x9+cOM8XnpralFB+80tG+eBPfOsm7ElERHqBgplOC/nTXz7TuQ1qYoH2Ju33X7x6D7/5jmObtDcREekFOz6YL8w3+OA/PMVfPHqBcwsbu/jEsiLwaz/wCk0aIiIiV9mxwTxXb/OJL57mw//0DOcWN3j6rhXueeVufvtHXrtp+xMRkd6yI4N5rt7m9/7uKT7+0GkWNmGykGU/cmwfv/aDd23eDkVEpOfsuGB+7lKNX/j4Vzh+dmFT9/sb6roWEZGbsGOC+cJ8gz/8/NPc909nNnx5xpX2D8Bnfva7NYuXiIjclB0RzF96dor/6U8e4clLrU3d7xtePMKH//v/clP3KSIivW3bB/Mnv3SaX/iTRzd9v5paU0REVmPbBvNzl2q856Nf4pHz9U3d78Fh489/+rvUdS0iIqsSbNQHm9ndZvakmZ00s/du1H6u5WMPPssbfuMfNj2U73nlbv7xvW9TKIuIyKptSIvZzELgd4HvBs4CXzKz+939xEbsb1kzTnnZ//xXG7mL69KoaxERWQ8b1ZX9OuCku58CMLOPA/cAGxbMWxXK+/rgYz/5X2kBChERWRcbFcwHgDMrnp8F/osN2hcA07XNHXEN8Kp9VT79njdu+n5FRGT72rBrzDdiZvea2XEzOz41NbXmz2slm7Bo8gpvf8W4QllERNbdRrWYJ4BDK54f7JZ9g7vfB9wHcOzYsTXP+VGKNu9vjN//0Vdz9z87sGn7ExGRnWOjgvlLwB1mdpROIL8D+JEN2hcAY/2ljfx4AF40FvHAv37Lhu9HRER2rg1pZrp7Avwr4K+Bx4FPuPtjG7GvZeVCyBP/7u4N+ewQeO9bXqxQFhGRDbdhE4y4+2eAz2zU519LuRDy3Pu/dzN3KSIisq62bPCXiIiIXE3BLCIikiMKZhERkRxRMIuIiOSIgllERCRHFMwiIiI5omAWERHJEQWziIhIjiiYRUREckTBLCIikiPmvuaFndZeCbMp4PQ6fuQu4NI6ft5W2i7Hsl2OA7bPseg48me7HIuO4+bc5u7jVxbmIpjXm5kdd/djW12P9bBdjmW7HAdsn2PRceTPdjkWHcfaqCtbREQkRxTMIiIiObJdg/m+ra7AOtoux7JdjgO2z7HoOPJnuxyLjmMNtuU1ZhERkV61XVvMIiIiPWnbBbOZ3W1mT5rZSTN771bX52aZ2SEz+3szO2Fmj5nZe7rlo2b2WTN7uvt1ZKvrejPMLDSzr5jZn3efHzWzh7rn5Y/NrLjVdbwZZjZsZp80syfM7HEz+9ZePCdm9rPd76tHzexjZlbulXNiZh8ys0kze3RF2TXPgXX8TveYvmZmd21dzS93neP4D93vra+Z2Z+a2fCK197XPY4nzewtW1Lp67jWsax47efNzM1sV/d5T52TbvlPdc/LY2b271eUb8o52VbBbGYh8LvAW4GXAz9sZi/f2lrdtAT4eXd/OfB64N3dur8XeMDd7wAe6D7vBe8BHl/x/NeB33L3FwOzwLu2pFa37reBv3L3lwGvonNMPXVOzOwA8NPAMXd/BRAC76B3zsmHgbuvKLveOXgrcEf3373ABzapjjfjw1x9HJ8FXuHurwSeAt4H0P3ZfwdwZ/c9v9f9/ZYXH+bqY8HMDgHfAzy/orinzomZvRG4B3iVu98J/Ea3fNPOybYKZuB1wEl3P+XubeDjdP6Dc8/dz7v7l7uPF+kEwAE69f9Id7OPAN+/JRW8BWZ2EPhe4A+6zw14E/DJ7ia9chxDwHcCHwRw97a7z9GD5wSIgIqZRUAVOE+PnBN3/zwwc0Xx9c7BPcAfeceDwLCZ7duUit7AtY7D3f/G3ZPu0weBg93H9wAfd/eWuz8LnKTz+y0XrnNOAH4L+EVg5eClnjonwE8C73f3VnebyW75pp2T7RbMB4AzK56f7Zb1FDM7ArwGeAjY4+7nuy9dAPZsVb1uwX+k88OZdZ+PAXMrfgH1ynk5CkwBf9jtlv8DM+ujx86Ju0/Q+av/eTqBPA88TG+ek2XXOwe9/DvgJ4C/7D7uueMws3uACXd/5IqXeu1YXgJ8R/cyzz+Y2Wu75Zt2HNstmHuemfUDfwL8jLsvrHzNO0Pocz2M3szeDky6+8NbXZd1EAF3AR9w99cAS1zRbd0j52SEzl/7R4H9QB/X6IbsVb1wDm7EzH6FzuWsj251XVbDzKrALwP/Zqvrsg4iYJTOJcV/DXyi2+u3abZbME8Ah1Y8P9gt6wlmVqATyh919091iy8ud/t0v05e7/058W3A95nZc3QuJbyJznXa4W43KvTOeTkLnHX3h7rPP0knqHvtnHwX8Ky7T7l7DHyKznnqxXOy7HrnoOd+B5jZjwNvB37UX7h/tdeO40V0/vB7pPuzfxD4spntpfeO5SzwqW7X+xfp9PztYhOPY7sF85eAO7qjTYt0LtTfv8V1uindv8g+CDzu7r+54qX7gXd2H78T+PRm1+1WuPv73P2gux+h8///d+7+o8DfAz/Y3Sz3xwHg7heAM2b20m7Rm4ET9Ng5odOF/Xozq3a/z5aPo+fOyQrXOwf3Az/WHQn8emB+RZd37pjZ3XQu+3yfu9dXvHQ/8A4zK5nZUToDp764FXW8Ge7+dXff7e5Huj/7Z4G7uj9DPXVOgD8D3ghgZi8BinQWsti8c+Lu2+of8DY6oxufAX5lq+tzC/X+djrdcV8Dvtr99zY612cfAJ4G/hYY3eq63sIxvQH48+7j27vfxCeB/wSUtrp+N3kMrwaOd8/LnwEjvXhOgH8LPAE8Cvw/QKlXzgnwMTrXxmM6v/Dfdb1zABidOzOeAb5OZyT6lh/DNzmOk3SuWy7/zP/+iu1/pXscTwJv3er63+hYrnj9OWBXj56TIvD/dn9Wvgy8abPPiWb+EhERyZHt1pUtIiLS0xTMIiIiOaJgFhERyREFs4iISI4omEVERHJEwSwiIpIjCmYREZEcUTCLiIjkyP8Pn1b7z919xWYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize = (8,6))\n",
    "ax1 = fig1.add_subplot(1, 1, 1)\n",
    "ax1.scatter(df_elaad[\"connected_time_float\"], df_elaad[\"laxity_float\"], alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94714cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'total_energy'}>]], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_elaad[[\"total_energy\"]].hist(bins = 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0cf0e315",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_elaad[\"soc_arr2\"] = np.minimum(np.maximum(config.FINAL_SOC - df_elaad[\"total_energy\"] / config.B, 0), config.FINAL_SOC)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0a6f1b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ORKiqvgZkjjIFvLlfQUmSJA2DI0tLkqSpZSIkSZKmlk+fl8Zc951lM/FuM0naNCZCmli93Hru7emSpA2xaUySJE0ta4SkeWim5jRrwyTp0awRkiRJU8tESNJESHJAkuuTrE5y9Azr901yZZJ1SQ7pWndYku+3r8OGF7WkcWfTmMaCnZq1IUkWACcCLwbWAJcnWV5V13YU+yFwOPDOrm2fCLwHWAoUcEW77Z3DiF3SeLNGSNIk2AdYXVU3VtUDwNnAwZ0FquqmqroGeKhr298HLqqqO9rk5yLggGEELWn8WSOkidDLWDqa13YBbu6YXwM8ZzO23aW7UJIjgSMBdtttt02LUtLEsUZIkoCqOqmqllbV0oULF446HElDYo2QNCUmvB/WLcCuHfOL2mW9bvuCrm2/0peoJE28OROhJKcALwNuq6pnzrD+BcA/AP/WLvpcVR3XxxilecHmvc1yObBHkt1pEptlwGt63PZC4G+S7NDO7w+8q/8hSppEvdQIfQr4KHDaBsr8a1W9rC8RSRPKRGdwqmpdkqNokpoFwClVtSrJccDKqlqe5NnA54EdgJcneW9VPaOq7kjyPppkCuC4qrpjJG9E0tiZMxGqqq8mWTyEWCRpVlW1AljRteyYjunLaZq9Ztr2FOCUgQYoaSL1q7P07yS5OskFSZ4xW6EkRyZZmWTl2rVr+3RoSZKkTdOPROhK4ClV9R+B/w18YbaC3pUhSZLGyWYnQlV1d1Xd206vALZMstNmRyZJkjRgm50IJfmNJGmn92n3efvm7leSJGnQerl9/iyaMTh2SrKG5pk9WwJU1ceAQ4A/S7IO+AWwrKpqYBFr4kz4+DWSpHmsl7vGDp1j/Udpbq+XJEmaKD5iQ5IkTS0fsSFNqZkGgLTZUtK0MRGSxoijU0vScJkISV1MRiRpepgISfOAyZskbRo7S0uSpKllIiRJkqaWiZAkSZpa9hHSVLEvjSSpkzVCkiRpalkjpL6yxkWSNEmsEZIkSVPLREiSJE2tOZvGkpwCvAy4raqeOcP6AB8BXgLcBxxeVVf2O1BpLjbLSZI2Vi81Qp8CDtjA+gOBPdrXkcDfbX5YkiRJgzdnIlRVXwXu2ECRg4HTqnEpsH2SnfsVoCRJ0qD0o4/QLsDNHfNr2mWPkuTIJCuTrFy7dm0fDi1JkrTphtpZuqpOqqqlVbV04cKFwzy0JEnSo/RjHKFbgF075he1y6RNZsdnSdIw9KNGaDnw+jSeC9xVVbf2Yb+SJEkD1cvt82cBLwB2SrIGeA+wJUBVfQxYQXPr/Gqa2+ffMKhgJUmS+mnORKiqDp1jfQFv7ltEkjSDJAfQjFm2APhkVR3ftX5r4DTgWcDtwKur6qYki4HrgOvbopdW1ZuGFrikseazxiQ9rLtv1k3Hv3REkTxSkgXAicCLae5MvTzJ8qq6tqPYEcCdVfW0JMuAE4BXt+tuqKo9hxmzpMngIzYkTYJ9gNVVdWNVPQCcTTOGWaeDgVPb6fOA/dqR7yVpViZCkiZBL+OVPVymqtYBdwE7tut2T/KtJP+S5PkzHcBxzqTpZCIkab67FditqvYC3g6cmeQJ3YUc50yaTiZCkiZBL+OVPVwmyRbAdsDtVXV/Vd0OUFVXADcATx94xJImgomQpElwObBHkt2TbAUsoxnDrNNy4LB2+hDg4qqqJAvbztYkeSrNA6JvHFLcksacd42pZzON9jwudxVpfquqdUmOAi6kuX3+lKpaleQ4YGVVLQdOBk5PsprmQdHL2s33BY5L8ivgIeBNVbWhB0lLmiImQpImQlWtoBnAtXPZMR3TvwReOcN2nwU+O/AAJU0km8YkSdLUMhGSJElTy0RIkiRNLfsICRhuR+iZjiVJ0ihYIyRJkqZWTzVCPTz1+XDgA/y/Ac4+WlWf7GOcGlPW7kiPNq4Pr5X0aHMmQj0+9RngnKo6agAxSpIkDUQvTWO9PPVZkiRp4vSSCPXy1GeAVyS5Jsl5SXadYb1Pd5YkSWOlX3eN/SNwVlXdn+RPgVOBF3YXqqqTgJMAli5dWn06tnpgnwVJkh6tl0Rozqc+r3+yc+uTwPs3PzSNmh2hpf7wOX3S+OolEXr4qc80CdAy4DWdBZLsXFW3trMHAdf1NUptFBMYSZJ6M2ci1ONTn9+S5CBgHc1Tnw8fYMySJEl90VMfoR6e+vwu4F39DU2SJGmwfMTGhLGvgSRJ/WMiNEZ83pckScPls8YkSdLUskZoQHqpcbFJS5Kk0TIRkqQRcJBTaTzYNCZJkqbWVNUI9dIZedzuyrJTsyRJgzNVidBMNiXRGLcqbZMlSZI2zdQnQr0YVKJhAiNJ0mhNbCLUS62MiYakSTFuzfLStJjYRGicmHBJkjSZ5k0iZDIiSZI2lrfPS5KkqdVTIpTkgCTXJ1md5OgZ1m+d5Jx2/WVJFvc9UklTbXOuQ0ne1S6/PsnvDzXwzbD46PMf8ZLUf3MmQkkWACcCBwJLgEOTLOkqdgRwZ1U9DfgwcEK/A5U0vTbnOtSWWwY8AzgA+D/t/iSppz5C+wCrq+pGgCRnAwcD13aUORg4tp0+D/hoklRV9TFWSdNrk69D7fKzq+p+4N+SrG73940hxd433lkm9V8vidAuwM0d82uA58xWpqrWJbkL2BH4aWehJEcCR7az9ya5vsc4d+re14Qw7uGa1LhhTGPP3HW7nXE/ZYChbM51aBfg0q5td+k+wKRen2b4jMbtu2Q8G2Y8G9aveGa9Pg31rrGqOgk4aWO3S7KyqpYOIKSBMu7hmtS4YXJjn9S4ZzJfrk/Gs2HGs2HTGE8vnaVvAXbtmF/ULpuxTJItgO2A2/sRoCSxedehXraVNKV6SYQuB/ZIsnuSrWg6HS7vKrMcOKydPgS42P5Bkvpoc65Dy4Fl7V1luwN7AN8cUtySxtycTWNtW/tRwIXAAuCUqlqV5DhgZVUtB04GTm87Id5Bc5Hqp42urh4Txj1ckxo3TG7sQ4l7c65DbblzaTpWrwPeXFUP9jG8cfvsjGfDjGfDpi6eWHEjSZKmlSNLS5KkqWUiJEmSptZYJUKT+iiPHuJ+e5Jrk1yT5MtJBjneSs/miruj3CuSVJKxuKWyl7iTvKo956uSnDnsGGfSw/dktySXJPlW+115ySji7JbklCS3JfnOLOuT5H+17+uaJHsPO8ZR6fU3NMDj79p+Z9Z/19/aLj82yS1JrmpfQ/suJbkpybfb465slz0xyUVJvt/+u8OQYvnNjnNwVZK7k7xtmOdnpt/PbOdjGL+lWeL5QJLvtsf8fJLt2+WLk/yi4zx9bEjxzPr5ZBCPy6mqsXjRdIC8AXgqsBVwNbCkq8yfAx9rp5cB50xI3L8HPLad/rNJibstty3wVZoB6ZZOQtw0dwV9C9ihnf/1CYn7JODP2uklwE2jjruNZV9gb+A7s6x/CXABEOC5wGWjjnlcPtMhxLAzsHc7vS3wvfa7cyzwzhGdl5uAnbqWvR84up0+GjhhRJ/Xj2kG1hva+Znp9zPb+RjGb2mWePYHtminT+iIZ/Fsv/sBxzPj59N+t68GtgZ2b39/CzY3hnGqEXp4CP2qegBYP4R+p4OBU9vp84D9kmSIMc5kzrir6pKquq+dvZRmHJNR6+V8A7yP5ofxy2EGtwG9xP2fgROr6k6AqrptyDHOpJe4C3hCO70d8KMhxjerqvoqzV1YszkYOK0alwLbJ9l5ONGNVK+/oYGpqlur6sp2+h7gOmYYNXsMdF67TwX+YAQx7AfcUFU/GOZBZ/n9zHY+Bv5bmimeqvpSVa1rZ4f6f1QP15dODz8up6r+DVj/uJzNMk6J0ExD6Hf/oB8xhD6wfgj9Ueol7k5H0GT8ozZn3G217K5VNU6Pve7lfD8deHqSrye5NMkBQ4tudr3EfSzw2iRrgBXAfxlOaJttY38D88VYve80XQX2Ai5rFx3VNnWcMqymqFYBX0pyRZrHlgA8qapubad/DDxpiPGstww4q2N+VOcHZj8f4/CdeiOP/D9q97a5/l+SPH+Iccz0+Qzk/IxTIjTvJXktsBT4wKhjmUuSXwM+BLxj1LFsgi1omsdeABwKfGJ9m/eYOxT4VFUtoqkiP739HKQNSvJ44LPA26rqbuDvgH8H7AncCnxwiOE8r6r2Bg4E3pxk386V1bRxDHXcljSDcB4EfKZdNMrz8wijOB+zSfJumrG2zmgX3QrsVlV7AW8HzkzyhNm276Ohfj7jdJGd1Ed59DR8f5IXAe8GDqrmKdijNlfc2wLPBL6S5Caa9urlGX2H6V7O9xpgeVX9qq0+/R5NYjRKvcR9BHAuQFV9A9iG5oGD425aH2ExFu87yZY0SdAZVfU5gKr6SVU9WFUPAZ+gD80HvaqqW9p/bwM+3x77J+ubeNp/h91cfSBwZVX9pI1tZOenNdv5GNl3KsnhwMuAP26TM9omqNvb6Sto+uQ8fdCxbODzGcj5GadEaFIf5TFn3En2Aj5OkwSNQ38VmCPuqrqrqnaqqsVVtZim3figqlo5mnAf1sv35As0tUEk2Ynmh3vjEGOcSS9x/5CmHwNJ/gNNIrR2qFFumuXA69s7Xp4L3NVR7T+f9fKZDlTbR/Jk4Lqq+lDH8s5+JX8IzHjH3wDieVySbddP03TC/Q6PvHYfBvzDMOLpcCgdzWKjOj8dZjsfI/kttd0H/pLmGn9fx/KFSRa000+l+YNy4NfSDXw+g3lcziB6gW/qi6Y54Hs0Wee722XH0Xw40PzH8BmaDlLfBJ466ph7jPufgZ8AV7Wv5aOOuZe4u8p+hTG4a6zH8x2aZr1rgW8Dy0Ydc49xLwG+TnNXxFXA/qOOuY3rLJrq6V/R1LYdAbwJeFPH+T6xfV/fHpfvyag+0yEf/3k0zSrXdFxfXgKc3n4W19D857HzkOJ5avv9vRpY1fE93xH4MvD99nr4xCGeo8fRtBxs17FsaOdnlt/PjOdjGL+lWeJZTdP3Zv13aP3d2a9oP8ergCuBlw8pnlk/H5qWlRuA64ED+xGDj9iQJElTa5yaxiRJkobKREiSJE0tEyFJkjS1TIQkSdLUMhGSJElTy0RIkiRNLRMhSZI0tUyEJEnS1DIRkiRJU8tESJIkTS0TIUmSNLVMhCRJ0tQyEdJIJfmrJJ/sw36OTfLpfsQkaf5L8rEk/33UcWj0TITmiSTPS/J/k9yV5I4kX0/y7I71i5KckeT2JD9P8s0kL+vaR5K8Jcl32jJrknwmyW8NKu6q+puq+pNB7R8gyXOTXNSel7Xte9p5kMeU1D9Jbkryon7us6reVFXva/f/giRrNnVfSQ5LckWSu9vr5vuTbNG/aDVIJkLzQJInAF8E/jfwRGAX4L3A/e36JwJfAx4AngHsBHwYODPJIR27+gjwVuAt7X6eDnwBeGkfYnzURWGIF4odgJOAxcBTgHuAvx/SsSXNf48F3kZzbX0OsB/wzlEGpN6ZCM0PTweoqrOq6sGq+kVVfamqrmnX/1fgXuCIqvpxu/4s4H8AH2xrgvYA3gwcWlUXV9X9VXVfVZ1RVcfPdNAkb0hyXZJ7ktyY5E871r2g/cvovyX5MfD3bfPVeUk+neRu4PDOJq0kFyQ5qusYVyf5o3b6I0lubv/quiLJ83s5OVV1QVV9pqrurqr7gI8Cv9vz2ZU0lpLskOSLbU3vne30onbdE9tr0Mvb+ccnWZ3k9e38p5L8dZLHARcAT05yb/t6cpL7kuzYcay92+Ns2R1HVf1dVf1rVT1QVbcAZ+A1ZmKYCM0P3wMeTHJqkgOT7NC1/sXAZ6vqoa7l5wK70SRS+wFrquqbG3Hc24CXAU8A3gB8OMneHet/g6Zm6SnAke2yg4HzgO1pLhadzgIOXT+TZEm77fntosuBPdt9ngl8Jsk2GxHvevsCqzZhO0nj5ddoanefQnMt+wXNHzpU1R3AG4FPJPl1mlrwq6rqtM4dVNXPgQOBH1XV49vXj4CvAK/qKPo64Oyq+lUPcXmNmSAmQvNAVd0NPA8o4BPA2iTLkzypLbITcOsMm97asX7HWcps6LjnV9UN1fgX4EtAZy3NQ8B72tqlX7TLvlFVX6iqhzqWrfd5YM8kT2nn/xj4XFXd3x7v01V1e1Wtq6oPAlsDv7kxMSf5beAY4C82ZjtJ46e9Hny2rb2+h6aW+z91rP8S8Bngy8BLgD+deU8zOhV4LUCSBTR/pJ0+10ZJ3ggsBf7nRhxLI2QiNE9U1XVVdXhVLQKeCTwZ+Nt29U+BmToH79yx/vZZysyqrX26tO2E/DOaC81OHUXWVtUvuza7eQPv4R6a2p9l7aJD6ag1SvLOtinurvZ423Udb654n0ZTBf7WqvrXXreTNJ6SPDbJx5P8oG1u/yqwfZu4rHcSzTXxU1V1+0bs/h+AJUl2p6lVv2uuGvMkfwD8f8CBVfXTjXkvGh0ToXmoqr4LfIrmxw/wz8AfJen+vF9Fk5h8j+YvpkVJlvZyjCRbA5+l+avnSVW1PbACSGcoM4U3x67PAg5N8jvANsAl7fGeD/xlG/MO7fHu6jrehuJ9Cs15eF9VzflXnaSJ8A6aWuHnVNUTaJqkoL0utAnRScBpwJ+3fwzN5FHXpfaPuHNpaoVexxy1QUkOoKmRf3lVfXvj34pGxURoHkjy75O8o6OT4K40tSmXtkU+TFN7cnKS30iyTZJDgXcDf9E2bX0f+D/AWW1H563acsuSHD3DYbeiaZpaC6xLciCwfx/ezgqa9v7jgHM6+jVtC6xrj7dFkmNo+ibNKckuwMXAR6vqY32IUdLwbdlek9a/tqC5LvwC+Fl7d+x7urb5K5ok543AB4DTumqL1vsJsGOS7bqWnwYcDhzEBhKhJC+kqb1+xUb2s9QYMBGaH+6huWXzsiQ/p0mAvkPz1xJtdfDzaGpYrqVpBns78LqqOqdjP2+h6Wh4IvAz4AbgD4F/7D5g24z1Fpq/mO4EXgMs39w30vYH+hzwIpoO0etdCPwTTe3VD4BfsoFmti5/AjwVOLbjrpB7NzdWSUO1gibpWf86lqb5/zE0zfuX0lwjAEjyLJrr3Our6kHgBJqk6FF/2LW16GcBNyb5WZInt8u/TtPX8cqq+sEGYvvvNH9srui4xlywWe9WQ5OquVoqJEmaTkkuBs6sqs0eAV/jyURIkqQZpBmd/yJg17YWXPOQTWOSJHVJcirNDRZvMwma36wRkiRJU8saIUmSNLVG9nTcnXbaqRYvXjyqw0vqsyuuuOKnVbVw1HH0g9cnaX7Z0PVpZInQ4sWLWbly5agOL6nPkmzo9uKJ4vVJml82dH2yaUySJE0tEyFJkjS1TIQkSdLUMhGSJElTy0RIkiRNLRMhSZI0tUZ2+7yk/ll89Plzlrnp+JcOIZLpM9O591xLk6OvNUJJtk9yXpLvJrkuye/0c/+SJEn91O8aoY8A/1RVhyTZCnhsn/cvSZLUN32rEUqyHbAvcDJAVT1QVT/r1/4lTbckByS5PsnqJEfPsH7fJFcmWZfkkBnWPyHJmiQfHU7EkiZBP5vGdgfWAn+f5FtJPpnkcZ0FkhyZZGWSlWvXru3joSXNZ0kWACcCBwJLgEOTLOkq9kPgcODMWXbzPuCrg4pR0mTqZyK0BbA38HdVtRfwc+ARf7VV1UlVtbSqli5cOC+ezShpOPYBVlfVjVX1AHA2cHBngaq6qaquAR7q3jjJs4AnAV8aRrCSJkc/E6E1wJqquqydP48mMZKkzbULcHPH/Jp22ZyS/BrwQeCdc5SzxlqaQn1LhKrqx8DNSX6zXbQfcG2/9i9Jm+jPgRVVtWZDhayxlqZTv+8a+y/AGe0dYzcCb+jz/iVNp1uAXTvmF7XLevE7wPOT/DnweGCrJPdW1aM6XEuaPn1NhKrqKmBpP/cpScDlwB5JdqdJgJYBr+llw6r64/XTSQ4HlpoESVrPR2xIGntVtQ44CrgQuA44t6pWJTkuyUEASZ6dZA3wSuDjSVaNLmJJk8JHbEiaCFW1AljRteyYjunLaZrMNrSPTwGfGkB4kiaUNUKSJGlqmQhJkqSpZSIkSZKmlomQJEmaWiZCkiRpapkISZKkqWUiJEmSppaJkCRJmlomQpIkaWqZCEmSpKllIiRJkqaWiZAkSZpaJkKSJGlqmQhJmghJDkhyfZLVSY6eYf2+Sa5Msi7JIR3L90zyjSSrklyT5NXDjVzSODMRkjT2kiwATgQOBJYAhyZZ0lXsh8DhwJldy+8DXl9VzwAOAP42yfYDDVjSxNiinztLchNwD/AgsK6qlvZz/5Km1j7A6qq6ESDJ2cDBwLXrC1TVTe26hzo3rKrvdUz/KMltwELgZwOPWtLY62si1Pq9qvrpAPYraXrtAtzcMb8GeM7G7iTJPsBWwA0zrDsSOBJgt91227QoJU0cm8YkTYUkOwOnA2+oqoe611fVSVW1tKqWLly4cPgBShqJfidCBXwpyRXtX1ePkOTIJCuTrFy7dm2fDy1pHrsF2LVjflG7rCdJngCcD7y7qi7tc2ySJli/E6HnVdXeNB0a35xk386V/sUlaRNdDuyRZPckWwHLgOW9bNiW/zxwWlWdN8AYJU2gviZCVXVL++9tNBeeffq5f0nTqarWAUcBFwLXAedW1aokxyU5CCDJs5OsAV4JfDzJqnbzVwH7Aocnuap97Tn8dyFpHPWts3SSxwG/VlX3tNP7A8f1a/+SNs/io89/xPxNx790RJFsmqpaAazoWnZMx/TlNE1m3dt9Gvj0wAOUNJH6edfYk4DPJ1m/3zOr6p/6uH9JkqS+6lsi1I7v8R/7tT9JkqRB8/Z5SZI0tQYxoKKkCdDdZwgmr9+QJG0ua4QkSdLUMhGSJElTy0RIkiRNLRMhSZI0tUyEJEnS1DIRkiRJU8tESJIkTS0TIUmSNLVMhCRJ0tQyEZI0EZIckOT6JKuTHD3D+n2TXJlkXZJDutYdluT77euw4UUtadyZCEkae0kWACcCBwJLgEOTLOkq9kPgcODMrm2fCLwHeA6wD/CeJDsMOmZJk8FESNIk2AdYXVU3VtUDwNnAwZ0FquqmqroGeKhr298HLqqqO6rqTuAi4IBhBC1p/JkISZoEuwA3d8yvaZf1bdskRyZZmWTl2rVrNzlQSZPFREiSgKo6qaqWVtXShQsXjjocSUPS10QoyYIk30ryxX7uV9LUuwXYtWN+Ubts0NtKmue26PP+3gpcBzyhz/uV1Fp89PmjDmEULgf2SLI7TRKzDHhNj9teCPxNRwfp/YF39T9ESZOobzVCSRYBLwU+2a99ShJAVa0DjqJJaq4Dzq2qVUmOS3IQQJJnJ1kDvBL4eJJV7bZ3AO+jSaYuB45rl0lSX2uE/hb4S2DbPu5T0hB11zbddPxLRxTJo1XVCmBF17JjOqYvp2n2mmnbU4BTBhqgpInUlxqhJC8DbquqK+Yo510ZkiRpbPSraex3gYOS3EQzvscLk3y6u5B3ZUiSpHHSl0Soqt5VVYuqajFNJ8aLq+q1/di3JEnSoDiOkCRJmlr9vn2eqvoK8JV+71eSJKnfrBGSJElTy0RIkiRNLRMhSZI0tUyEJEnS1DIRkiRJU8tESJIkTS0TIUmSNLVMhCRNhCQHJLk+yeokR8+wfusk57TrL0uyuF2+ZZJTk3w7yXVJ3jX04CWNLRMhSWMvyQLgROBAYAlwaJIlXcWOAO6sqqcBHwZOaJe/Eti6qn4LeBbwp+uTJEkyEZI0CfYBVlfVjVX1AM3DnQ/uKnMwcGo7fR6wX5IABTwuyRbAY4AHgLuHE7akcWciJGkS7ALc3DG/pl02Y5mqWgfcBexIkxT9HLgV+CHwP6vqjkEHLGkymAhJmu/2AR4EngzsDrwjyVO7CyU5MsnKJCvXrl077BgljYiJkKRJcAuwa8f8onbZjGXaZrDtgNuB1wD/VFW/qqrbgK8DS7sPUFUnVdXSqlq6cOHCAbwFSePIREjSJLgc2CPJ7km2ApYBy7vKLAcOa6cPAS6uqqJpDnshQJLHAc8FvjuUqCWNPRMhSWOv7fNzFHAhcB1wblWtSnJckoPaYicDOyZZDbwdWH+L/YnA45Osokmo/r6qrhnuO5A0rrYYdQCS1IuqWgGs6Fp2TMf0L2lule/e7t6ZlksSmAhJUt8tPvr8R8zfdPxLRxSJpLn0rWksyTZJvpnk6iSrkry3X/uWJEkahH7WCN0PvLCq7k2yJfC1JBdU1aV9PIYkSVLf9C0Rau/OuLed3bJ9Vb/2L0mS1G99vWssyYIkVwG3ARdV1WVd6x2wTJIkjY2+JkJV9WBV7Ukz2Nk+SZ7Ztd4ByyRJ0tgYyDhCVfUz4BLggEHsX5IkqR/6edfYwiTbt9OPAV6Mo7dKkqQx1s+7xnYGTk2ygCbBOreqvtjH/UuSJPVVP+8auwbYq1/7kyRJGjSfNSZJkqaWiZAkSZpaJkKSJGlqmQhJmghJDkhyfZLVSY6eYf3WSc5p11+WZHHHut9O8o32OYjfTrLNUIOXNLZMhCSNvfZu1BOBA4ElwKFJlnQVOwK4s6qeBnwYOKHddgvg08CbquoZwAuAXw0pdEljzkRI0iTYB1hdVTdW1QPA2cDBXWUOBk5tp88D9ksSYH/gmqq6GqCqbq+qB4cUt6QxZyIkaRLsAtzcMb+mXTZjmapaB9wF7Ag8HagkFya5MslfznQAn4UoTScTIUnz3RbA84A/bv/9wyT7dRfyWYjSdDIRkjQJbgF27Zhf1C6bsUzbL2g74Haa2qOvVtVPq+o+YAWw98AjljQRTIQkTYLLgT2S7J5kK2AZsLyrzHLgsHb6EODiqirgQuC3kjy2TZD+E3DtkOKWNOb6+awxSRqIqlqX5CiapGYBcEpVrUpyHLCyqpYDJwOnJ1kN3EGTLFFVdyb5EE0yVcCKqjp/JG9E0tgxEZI0EapqBU2zVueyYzqmfwm8cpZtP01zC70kPYJNY5IkaWqZCEmSpKll05g05hYfbXcWSRoUa4QkSdLUMhGSJElTq2+JUJJdk1yS5Nr2Cc9v7de+JUmSBqGffYTWAe+oqiuTbAtckeSiqnLgMkmSNJb6ViNUVbdW1ZXt9D3AdTz6oYiSJEljYyB9hJIsBvYCLuta7tOdJUnS2Oh7IpTk8cBngbdV1d2d63y6syRJGid9HUcoyZY0SdAZVfW5fu5bmgaOGSRJw9XPu8ZC89DD66rqQ/3aryRJ0qD0s2nsd4HXAS9MclX7ekkf9y9JktRX/bxr7GtVlar67aras32tmHtLSZpbkgOSXJ9kdZKjZ1i/dZJz2vWXtTdtdK7fLcm9Sd45tKAljT1HlpY09pIsAE4EDgSWAIcmWdJV7Ajgzqp6GvBh4ISu9R8CLhh0rJImi4mQpEmwD7C6qm6sqgeAs4GDu8ocDJzaTp8H7Nf2XSTJHwD/BqwaTriSJoWJkKRJsAtwc8f8Gh49YOvDZapqHXAXsGM7pMd/A967oQM4zpk0nUyEJM13xwIfrqp7N1TIcc6k6dTXcYQkaUBuAXbtmF/ULpupzJokWwDbAbcDzwEOSfJ+YHvgoSS/rKqPDjxqSWPPREjSJLgc2CPJ7jQJzzLgNV1llgOHAd8ADgEurqoCnr++QJJjgXtNgiStZyIkaexV1bokRwEXAguAU6pqVZLjgJVVtZxmQNfTk6wG7qBJliRpg0yEJE2EdlyyFV3LjumY/iXwyjn2cexAgpM0sewsLUmSppY1QpI0YDM9TPem4186gkgkdbNGSJIkTS0TIUmSNLVMhCRJ0tSyj5A0JDP1E5EkjZY1QpIkaWqZCEmSpKllIiRJkqZW3/oIJTkFeBlwW1U9s1/7lSaFfYAkafL0s0boU8ABfdyfJEnSQPWtRqiqvppkcb/2J0nzWXcNoiNNS6Mx1D5CSY5MsjLJyrVr1w7z0JIkSY8y1HGEquok4CSApUuX1jCPLc2ml7/M/et99JIcAHwEWAB8sqqO71q/NXAa8CzgduDVVXVTkhcDxwNbAQ8Af1FVFw81eEljywEVpU1gx+jhSrIAOBF4MbAGuDzJ8qq6tqPYEcCdVfW0JMuAE4BXAz8FXl5VP0ryTOBCYJfhvgNJ48pESFPFBGZi7QOsrqobAZKcDRwMdCZCBwPHttPnAR9Nkqr6VkeZVcBjkmxdVfcPPmxJ466ft8+fBbwA2CnJGuA9VXVyv/YvDYvJ0ljaBbi5Y34N8JzZylTVuiR3ATvS1Ait9wrgypmSoCRHAkcC7Lbbbv2LXNJY6+ddY4f2a1/SppgpgbEvj9ZL8gya5rL9Z1pvH0ZpOtk0pnnN2p154xZg1475Re2ymcqsSbIFsB1Np2mSLAI+D7y+qm4YfLiSJoWJkKRJcDmwR5LdaRKeZcBrusosBw4DvgEcAlxcVZVke+B84Oiq+vrwQt441mhKo2EipIllbc/0aPv8HEVzx9cC4JSqWpXkOGBlVS0HTgZOT7IauIMmWQI4CngacEySY9pl+1fVbcN9F5LGkYmQpIlQVSuAFV3LjumY/iXwyhm2+2vgrwceoKSJ5NPnJUnS1DIRkiRJU8tESJIkTS37CGki2DFakjQIJkIaul5uEzbxkSQNg01jkiRpalkjpLFgDZD0aN2/CwdYlPrPGiFJkjS1rBHSrDallmamv1it7ZEkjSsTIQH9S1ZMeiRJk8REaEqZsEiSZCI09kxYJEkanIlIhHoZd2Y+MOmRtCHTci2UhqmviVCSA4CPAAuAT1bV8f3c/yBsyu2p/dhG0saZ6/qSZGvgNOBZwO3Aq6vqpnbdu4AjgAeBt1TVhUMMXdIY61silGQBcCLwYmANcHmS5VV1bb+OMQybkrCY5EiD1eP15Qjgzqp6WpJlwAnAq5MsAZYBzwCeDPxzkqdX1YPDfReD4VhD0ubpZ43QPsDqqroRIMnZwMHAQBIhkw9pqvRyfTkYOLadPg/4aJK0y8+uqvuBf0uyut3fN4YU+1AN6trYy9AYJmGaRP1MhHYBbu6YXwM8p7NAkiOBI9vZe5Nc3+O+dwJ+utkRDp9xD5dx91lO2ODq7rifMsBQ5ry+dJapqnVJ7gJ2bJdf2rXtLt0H2Izr03rj8jkOJI45vguzlRmXcwLGMptpiWXW69NQO0tX1UnASRu7XZKVVbV0ACENlHEPl3EP16TGPZtNvT6tNy7nY1ziAGOZjbHMbFSx9PMRG7cAu3bML2qXSdLm6uX68nCZJFsA29F0mvbaJGlW/UyELgf2SLJ7kq1oOicu7+P+JU2vXq4vy4HD2ulDgIurqtrly5JsnWR3YA/gm0OKW9KY61vTWNsmfxRwIc3tradU1ao+7X6Tq6tHzLiHy7iHa2hxz3Z9SXIcsLKqlgMnA6e3naHvoEmWaMudS9Oxeh3w5gHdMTYun+O4xAHGMhtjmdlIYknzB5MkSdL06WfTmCRJ0kQxEZIkSVNrrBKhJAckuT7J6iRHz7B+6yTntOsvS7J4BGE+Sg9xvz3JtUmuSfLlJIMcb6Vnc8XdUe4VSSrJuNxiOWfcSV7VnvNVSc4cdowz6eF7sluSS5J8q/2uvGQUcXbFdEqS25J8Z5b1SfK/2vd0TZK9hx3jqPX6OxrQsXdtvzPrv+tvbZcfm+SWJFe1r6F8l5LclOTb7TFXtsuemOSiJN9v/91hCHH8Zsd7vyrJ3UneNqzzMtPvZrbzMOjf0CyxfCDJd9vjfT7J9u3yxUl+0XF+PjaEWGb9TJK8qz0v1yf5/X7G8ghVNRYvmg6QNwBPBbYCrgaWdJX5c+Bj7fQy4JwJifv3gMe20382KXG35bYFvkozIN3SSYib5q6gbwE7tPO/PiFxnwT8WTu9BLhpDOLeF9gb+M4s618CXAAEeC5w2ahjHrfPdcDH3xnYu53eFvhe+905FnjnCM7HTcBOXcveDxzdTh8NnDCCz+jHNAPqDeW8zPS7me08DPo3NEss+wNbtNMndMSyeLbf+gBjmfEzab/HVwNbA7u3v7MFg4hrnGqEHh5Cv6oeANYPod/pYODUdvo8YL8kGWKMM5kz7qq6pKrua2cvpRnHZNR6Od8A76P5ofxymMFtQC9x/2fgxKq6E6CqbhtyjDPpJe4CntBObwf8aIjxzaiqvkpzB9ZsDgZOq8alwPZJdh5OdGOh19/RQFTVrVV1ZTt9D3AdM4yaPWKd1+1TgT8Y8vH3A26oqh8M64Cz/G5mOw8D/Q3NFEtVfamq1rWzQ/s/qYfrSaeHH41TVf8GrH80Tt+NUyI00xD63T/oRwyhD6wfQn+Ueom70xE02f+ozRl3W0W7a1WN04PdejnfTweenuTrSS5N89TyUesl7mOB1yZZA6wA/stwQtssG/v9n2/G5v2n6SqwF3BZu+iotunjlGE0R7UK+FKSK9I8sgTgSVV1azv9Y+BJQ4plvWXAWR3zozgvMPt5GPV36I088v+k3dvm+X9J8vwhxTDTZzK08zJOidC8l+S1wFLgA6OOZS5Jfg34EPCOUceyCbagaR57AXAo8In1beBj7lDgU1W1iKa6/PT2c5A2KMnjgc8Cb6uqu4G/A/4dsCdwK/DBIYXyvKraGzgQeHOSfTtXVtPmMbQxW9IMvnkQ8Jl20ajOyyMM+zzMJsm7acbWOqNddCuwW1XtBbwdODPJE2bbvk9G/pmM00V2c4bQH6Wehu9P8iLg3cBB1TwFe9Tmintb4JnAV5LcRNN2vTyj7zDdy/leAyyvql+1Varfo0mMRqmXuI8AzgWoqm8A29A8hHCcTfvjK0b+/pNsSZMEnVFVnwOoqp9U1YNV9RDwCQbUpNCtqm5p/70N+Hx73J+sb+pp/x1mU/WBwJVV9ZM2rpGcl9Zs52Ek36EkhwMvA/64Tcxom6Fub6evoOmX8/RBxrGBz2Ro52WcEqHNGUJ/lOaMO8lewMdpkqBx6K8Cc8RdVXdV1U5VtbiqFtO0Ix9UVStHE+7DevmefIGmNogkO9H8kG8cYowz6SXuH9L0ZyDJf6BJhNYONcqNtxx4fXvny3OBuzqq/6fBSB8t1PaRPBm4rqo+1LG8s4/JHwIz3vXX51gel2Tb9dM0HXK/wyOv24cB/zDoWDocSkez2CjOS4fZzsPQf0Ntd4G/pLmm39exfGGSBe30U2n+gBzotXMDn8nwHo0ziB7Ym/qiaQ74Hk0W+u522XE0HxY0/zF8hqbT1DeBp4465h7j/mfgJ8BV7Wv5qGPuJe6usl9hDO4a6/F8h6ZZ71rg28CyUcfcY9xLgK/T3ClxFbD/GMR8Fk119a9oatqOAN4EvKnjXJ/Yvqdvj8t3ZNSf6xCP/TyaJpZrOq4vLwFObz+Pa2j+Q9l5CLE8tf3uXg2s6viO7wh8Gfh+ey184pDOzeNoWgy261g2lPMyy+9mxvMw6N/QLLGspul/s/47s/5u7Fe0n91VwJXAy4cQy6yfCU0ryg3A9cCBg/qu+IgNSZI0tcapaUySJGmoTIQkSdLUMhGSJElTy0RIkiRNLRMhSZI0tUyEJEnS1DIRkiRJU+v/B5wce0yZ/XVRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize = (8,6))\n",
    "ax1 = fig1.add_subplot(2,2,1)\n",
    "ax2 = fig1.add_subplot(2,2,2)\n",
    "ax3 = fig1.add_subplot(2,2,3)\n",
    "ax4 = fig1.add_subplot(2,2,4)\n",
    "df_elaad[\"laxity2\"] = df_elaad[\"connected_time\"] - (config.FINAL_SOC - df_elaad[\"soc_arr2\"]) * config.B / (config.alpha_c*config.eta_c)\n",
    "_ = ax1.hist(df_elaad[\"soc_arr\"], bins = 50, density = True)\n",
    "ax1.set_title(\"SOC arrival\")\n",
    "_ = ax2.hist(df_elaad[\"laxity\"], bins = 50, density = True)\n",
    "ax2.set_title(\"Laxity\")\n",
    "_ = ax3.hist(df_elaad[\"soc_arr2\"], bins = 50, density = True)\n",
    "ax3.set_title(\"SOC arrival 2\")\n",
    "_ = ax4.hist(df_elaad[\"laxity2\"], bins = 50, density = True)\n",
    "ax4.set_title(\"Laxity 2\")\n",
    "fig1.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "309991b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9997.000000\n",
       "mean        0.612447\n",
       "std         0.257299\n",
       "min         0.000000\n",
       "25%         0.498375\n",
       "50%         0.668160\n",
       "75%         0.801562\n",
       "max         0.967305\n",
       "Name: soc_arr, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad[\"soc_arr\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01c40be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9997.000000\n",
       "mean        5.062209\n",
       "std         6.683161\n",
       "min        -0.803340\n",
       "25%         1.186549\n",
       "50%         2.612245\n",
       "75%         7.088961\n",
       "max       158.660482\n",
       "Name: laxity2, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad[\"laxity2\"].describe()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2b387b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize = (8,12))\n",
    "ax1 = fig1.add_subplot(6,1,1)\n",
    "ax2 = fig1.add_subplot(6,1,2)\n",
    "ax3 = fig1.add_subplot(6,1,3)\n",
    "ax4 = fig1.add_subplot(6,1,4)\n",
    "ax5 = fig1.add_subplot(6,1,5)\n",
    "ax6 = fig1.add_subplot(6,1,6)\n",
    "\n",
    "ax1.scatter(df_elaad[\"connected_time_float\"], df_elaad[\"total_energy\"], alpha=0.1)\n",
    "ax1.set_xlabel(\"Connected time\")\n",
    "ax1.set_ylabel(\"Total energy\")\n",
    "ax2.scatter(df_elaad[\"connected_time_float\"], 1-df_elaad[\"soc_arr\"], alpha=0.1)\n",
    "ax2.set_xlabel(\"Connected time\")\n",
    "ax2.set_ylabel(\"SOC arrival\")\n",
    "ax3.scatter(df_elaad[\"connected_time_float\"], df_elaad[\"laxity\"], alpha=0.1)\n",
    "ax3.set_xlabel(\"Connected time float\")\n",
    "ax3.set_ylabel(\"Laxity\")\n",
    "ax4.scatter(df_elaad[\"charged_time_float\"], df_elaad[\"total_energy\"], alpha=0.1)\n",
    "ax4.set_xlabel(\"Charged time\") \n",
    "ax4.set_ylabel(\"Total energy\")\n",
    "ax5.scatter(df_elaad[\"total_energy\"], 1-df_elaad[\"soc_arr\"], alpha=0.1)\n",
    "ax5.set_xlabel(\"Total energy\")\n",
    "ax5.set_ylabel(\"SOC arrival\")\n",
    "ax6.scatter(df_elaad[\"total_energy\"], df_elaad[\"energy_supplied\"], alpha=0.1)\n",
    "ax6.set_xlabel(\"Total energy\")\n",
    "ax6.set_ylabel(\"Energy supplied\")\n",
    "fig1.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a188cef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9.997000e+03\n",
       "mean     3.672141e+00\n",
       "std      6.333161e+00\n",
       "min     -4.440892e-16\n",
       "25%      5.300000e-01\n",
       "50%      9.600000e-01\n",
       "75%      4.801484e+00\n",
       "max      1.548015e+02\n",
       "Name: laxity, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elaad[\"laxity\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53627fe8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_marlvr",
   "language": "python",
   "name": "env_marlvr"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}