supervised_learning_RUL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pickle import load\n",
    "from pickle import dump\n",
    "from sklearn.tree import export_text\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn import ensemble\n",
    "from sklearn.tree import export_text\n",
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert mat data to panda\n",
    "def load_data(battery):\n",
    "  mat = loadmat('/Users/yi-chenlin/Desktop/Final project/battery_data/' + battery + '.mat')\n",
    "  print('Total data in dataset: ', len(mat[battery][0, 0]['cycle'][0]))\n",
    "  counter = 0\n",
    "  dataset = []\n",
    "  capacity_data = []\n",
    "  \n",
    "  for i in range(len(mat[battery][0, 0]['cycle'][0])):\n",
    "    row = mat[battery][0, 0]['cycle'][0, i]\n",
    "    if row['type'][0] == 'discharge':\n",
    "      ambient_temperature = row['ambient_temperature'][0][0]\n",
    "      date_time = datetime.datetime(int(row['time'][0][0]),\n",
    "                               int(row['time'][0][1]),\n",
    "                               int(row['time'][0][2]),\n",
    "                               int(row['time'][0][3]),\n",
    "                               int(row['time'][0][4])) + datetime.timedelta(seconds=int(row['time'][0][5]))\n",
    "      data = row['data']\n",
    "      capacity = data[0][0]['Capacity'][0][0]\n",
    "      for j in range(len(data[0][0]['Voltage_measured'][0])):\n",
    "        voltage_measured = data[0][0]['Voltage_measured'][0][j]\n",
    "        current_measured = data[0][0]['Current_measured'][0][j]\n",
    "        temperature_measured = data[0][0]['Temperature_measured'][0][j]\n",
    "        current_load = data[0][0]['Current_load'][0][j]\n",
    "        voltage_load = data[0][0]['Voltage_load'][0][j]\n",
    "        time = data[0][0]['Time'][0][j]\n",
    "        dataset.append([counter + 1, ambient_temperature, date_time, capacity,\n",
    "                        voltage_measured, current_measured,\n",
    "                        temperature_measured, current_load,\n",
    "                        voltage_load, time])\n",
    "      capacity_data.append([counter + 1, ambient_temperature, date_time, capacity])\n",
    "      counter = counter + 1\n",
    "  print(dataset[0])\n",
    "  return [pd.DataFrame(data=dataset,\n",
    "                       columns=['cycle', 'ambient_temperature', 'datetime',\n",
    "                                'capacity', 'voltage_measured',\n",
    "                                'current_measured', 'temperature_measured',\n",
    "                                'current_load', 'voltage_load', 'time']),\n",
    "          pd.DataFrame(data=capacity_data,\n",
    "                       columns=['cycle', 'ambient_temperature', 'datetime',\n",
    "                                'capacity'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data in dataset:  616\n",
      "[1, 24, datetime.datetime(2008, 4, 2, 15, 25, 41), 1.8564874208181574, 4.191491807505295, -0.004901589207462691, 24.330033885570543, -0.0006, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "dataset, capacity = load_data('B0005')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cycle</th>\n",
       "      <th>ambient_temperature</th>\n",
       "      <th>datetime</th>\n",
       "      <th>capacity</th>\n",
       "      <th>voltage_measured</th>\n",
       "      <th>current_measured</th>\n",
       "      <th>temperature_measured</th>\n",
       "      <th>current_load</th>\n",
       "      <th>voltage_load</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-02 15:25:41</td>\n",
       "      <td>1.856487</td>\n",
       "      <td>4.191492</td>\n",
       "      <td>-0.004902</td>\n",
       "      <td>24.330034</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-02 15:25:41</td>\n",
       "      <td>1.856487</td>\n",
       "      <td>4.190749</td>\n",
       "      <td>-0.001478</td>\n",
       "      <td>24.325993</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>4.206</td>\n",
       "      <td>16.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-02 15:25:41</td>\n",
       "      <td>1.856487</td>\n",
       "      <td>3.974871</td>\n",
       "      <td>-2.012528</td>\n",
       "      <td>24.389085</td>\n",
       "      <td>-1.9982</td>\n",
       "      <td>3.062</td>\n",
       "      <td>35.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-02 15:25:41</td>\n",
       "      <td>1.856487</td>\n",
       "      <td>3.951717</td>\n",
       "      <td>-2.013979</td>\n",
       "      <td>24.544752</td>\n",
       "      <td>-1.9982</td>\n",
       "      <td>3.030</td>\n",
       "      <td>53.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-02 15:25:41</td>\n",
       "      <td>1.856487</td>\n",
       "      <td>3.934352</td>\n",
       "      <td>-2.011144</td>\n",
       "      <td>24.731385</td>\n",
       "      <td>-1.9982</td>\n",
       "      <td>3.011</td>\n",
       "      <td>71.922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cycle  ambient_temperature            datetime  capacity  voltage_measured  \\\n",
       "0      1                   24 2008-04-02 15:25:41  1.856487          4.191492   \n",
       "1      1                   24 2008-04-02 15:25:41  1.856487          4.190749   \n",
       "2      1                   24 2008-04-02 15:25:41  1.856487          3.974871   \n",
       "3      1                   24 2008-04-02 15:25:41  1.856487          3.951717   \n",
       "4      1                   24 2008-04-02 15:25:41  1.856487          3.934352   \n",
       "\n",
       "   current_measured  temperature_measured  current_load  voltage_load    time  \n",
       "0         -0.004902             24.330034       -0.0006         0.000   0.000  \n",
       "1         -0.001478             24.325993       -0.0006         4.206  16.781  \n",
       "2         -2.012528             24.389085       -1.9982         3.062  35.703  \n",
       "3         -2.013979             24.544752       -1.9982         3.030  53.781  \n",
       "4         -2.011144             24.731385       -1.9982         3.011  71.922  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50285 entries, 0 to 50284\n",
      "Data columns (total 10 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   cycle                 50285 non-null  int64         \n",
      " 1   ambient_temperature   50285 non-null  int8          \n",
      " 2   datetime              50285 non-null  datetime64[ns]\n",
      " 3   capacity              50285 non-null  float64       \n",
      " 4   voltage_measured      50285 non-null  float64       \n",
      " 5   current_measured      50285 non-null  float64       \n",
      " 6   temperature_measured  50285 non-null  float64       \n",
      " 7   current_load          50285 non-null  float64       \n",
      " 8   voltage_load          50285 non-null  float64       \n",
      " 9   time                  50285 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(7), int64(1), int8(1)\n",
      "memory usage: 3.5 MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cycle</th>\n",
       "      <th>ambient_temperature</th>\n",
       "      <th>capacity</th>\n",
       "      <th>voltage_measured</th>\n",
       "      <th>current_measured</th>\n",
       "      <th>temperature_measured</th>\n",
       "      <th>current_load</th>\n",
       "      <th>voltage_load</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50285.000000</td>\n",
       "      <td>50285.0</td>\n",
       "      <td>50285.000000</td>\n",
       "      <td>50285.000000</td>\n",
       "      <td>50285.000000</td>\n",
       "      <td>50285.000000</td>\n",
       "      <td>50285.000000</td>\n",
       "      <td>50285.000000</td>\n",
       "      <td>50285.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88.125942</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.560345</td>\n",
       "      <td>3.515268</td>\n",
       "      <td>-1.806032</td>\n",
       "      <td>32.816991</td>\n",
       "      <td>1.362700</td>\n",
       "      <td>2.308406</td>\n",
       "      <td>1546.208924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.699687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.182380</td>\n",
       "      <td>0.231778</td>\n",
       "      <td>0.610502</td>\n",
       "      <td>3.987515</td>\n",
       "      <td>1.313698</td>\n",
       "      <td>0.800300</td>\n",
       "      <td>906.640295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.287453</td>\n",
       "      <td>2.455679</td>\n",
       "      <td>-2.029098</td>\n",
       "      <td>23.214802</td>\n",
       "      <td>-1.998400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.386229</td>\n",
       "      <td>3.399384</td>\n",
       "      <td>-2.013415</td>\n",
       "      <td>30.019392</td>\n",
       "      <td>1.998000</td>\n",
       "      <td>2.388000</td>\n",
       "      <td>768.563000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.538237</td>\n",
       "      <td>3.511664</td>\n",
       "      <td>-2.012312</td>\n",
       "      <td>32.828944</td>\n",
       "      <td>1.998200</td>\n",
       "      <td>2.533000</td>\n",
       "      <td>1537.031000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.746871</td>\n",
       "      <td>3.660903</td>\n",
       "      <td>-2.011052</td>\n",
       "      <td>35.920887</td>\n",
       "      <td>1.998200</td>\n",
       "      <td>2.690000</td>\n",
       "      <td>2305.984000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>168.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.856487</td>\n",
       "      <td>4.222920</td>\n",
       "      <td>0.007496</td>\n",
       "      <td>41.450232</td>\n",
       "      <td>1.998400</td>\n",
       "      <td>4.238000</td>\n",
       "      <td>3690.234000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cycle  ambient_temperature      capacity  voltage_measured  \\\n",
       "count  50285.000000              50285.0  50285.000000      50285.000000   \n",
       "mean      88.125942                 24.0      1.560345          3.515268   \n",
       "std       45.699687                  0.0      0.182380          0.231778   \n",
       "min        1.000000                 24.0      1.287453          2.455679   \n",
       "25%       50.000000                 24.0      1.386229          3.399384   \n",
       "50%       88.000000                 24.0      1.538237          3.511664   \n",
       "75%      127.000000                 24.0      1.746871          3.660903   \n",
       "max      168.000000                 24.0      1.856487          4.222920   \n",
       "\n",
       "       current_measured  temperature_measured  current_load  voltage_load  \\\n",
       "count      50285.000000          50285.000000  50285.000000  50285.000000   \n",
       "mean          -1.806032             32.816991      1.362700      2.308406   \n",
       "std            0.610502              3.987515      1.313698      0.800300   \n",
       "min           -2.029098             23.214802     -1.998400      0.000000   \n",
       "25%           -2.013415             30.019392      1.998000      2.388000   \n",
       "50%           -2.012312             32.828944      1.998200      2.533000   \n",
       "75%           -2.011052             35.920887      1.998200      2.690000   \n",
       "max            0.007496             41.450232      1.998400      4.238000   \n",
       "\n",
       "               time  \n",
       "count  50285.000000  \n",
       "mean    1546.208924  \n",
       "std      906.640295  \n",
       "min        0.000000  \n",
       "25%      768.563000  \n",
       "50%     1537.031000  \n",
       "75%     2305.984000  \n",
       "max     3690.234000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.describe().to_csv(r'Statistics/rul-statistics.csv', index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14b97a3d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n42SYr9Z9xQM/PED4E+GUCSpzzWMHzhyg7AdleajuQ3x4pzOj+FU+rkL1wtWZ0X0Gb696m6G/DOXgUwcplqeYI3lERMQ9tm3bRpUqVZyOIVlUYp8fY8xaa229lDxfc6pdqEHJBrQIbsHbq96mcO7CeHl44eXhRYBPAKUDS1MmsAy5fXJz5uIZ1h9ez7pD6/jn1D8UylWIYnmKUTxPcRqVakSgX6DTLyVV9kXsw8N4JLrySYm8Jehbsy9f//U1zzd7nsK5C2doNmst/0b8S/tb2gPQrEzcr9uW7V1Gr+q9MjSLiIiIZF8q1S72fNPnaTmhJf3mJH6DkUDfQCIuRiR8H+ATcM10kWqFqrF+8Hq8PLLOf5p9Efsonqc43p7eiT7+XKPnGL9hPG+tfIu32r6VodnOXDzDucvnKJm3JAC1i9Umj08eloWrVIuIiIjrZJ3mlkW0KNuCQ08fIvJSJNGx0UTHRnM66jT/RvzL3oi97D+zn2IBcRfN1S5Wm6IBRbkYfZHDkYf5addPPDz/Yb5a9xWD6w2++ckyieuX07tepYKVuLfGvXz858cMbTQ0Q0erryynd6VUe3l40bh0Y82rFhEREZdSqXaDogFFb77TVXy9fCkTVIaH6j7ElE1TeHHpi9xb417y+uZ1U0LX2hexj1tL3JrsPs83fZ4pm6Zk+Gj19aUa4qaA/LTrJ46eO5rh01FERMS9rLUYY5yOIVmMK64x1OofmYgxhnfavsPRc0cdX4YupWJtLP+e+ZfSeZMeqQaoWKAifWr04eM/P+ZI5JEMSpd4qW4e3ByAH3f8mGE5RETE/fz8/Dhx4oTWVpZUsdZy4sQJ/Pz80nUcjVRnMreWuJV7a9zLO6vfYXC9wdeUwczoSOQRLsVcumHVj8SMajqKyZsm89aqt3i77dsZkC6uVBvMNSt91C9Rn1pFajF25VgG1BqAp4dnhmQRERH3KlmyJPv37+fYsWNOR5Esxs/Pj5Il09e5VKozoddavsasrbMYtWQU47qMczpOspJaozoxFQtUpG/Nvnzy5ycMvX0oRQKKuDse+8/sp0hAEXw8fRK2GWMY1XQU3Wd0Z8bWGbpgUUQkm/D29qZs2bJOx5AcSqU6EwoOCuaJ257grVVvkccnD3WL16V20dpUKlgJX0/fTDVXLDWlGmBUk1FM2jiJztM606R0E8rmK0vJvCWJvBTJkcgjHDl3hGIBxXiiwRMuybf/7P5ER/vvrnI3VQtV5dXQV+lRrQceRjOhREREJO1UqjOpEU1GsPHoRr5Z/w0f/flRwnaDwc/LD18vXzzN/6Yt5PLOxcK+C6laqGqG5kxtqa5QoAJjW4/lq3Vf8eEfH3Ix5uI1j3sYD2JtLI1LN6Zu8fTfiWn/mf2Uz1/+hu0exoORTUbSZ3Yfvt/+PXdXuTvd5xIREZGcS3dUzORiYmPYdXIX6w+vZ/ep3URFRyX8ibWxCftN2DCBduXbMaP7jAzN938//R/j1o8jYlhEqkfQY20shyMPs//MfvL65qVI7iJ4GA9KvVeKjhU7MuWeKenOl29sPvrW6Jvo3RyjY6Op8nEV8vjkYe2DazPVbwBERETEebqjYjbi6eFJpYKVqFSwUrL75ffPzyuhr7DpyCZqFKmRQen+t0Z1WgrplbswXn8nxgfrPsj7a95nTOsxKR4BT0zkpUhOR51O8mJPLw8vRjQewaB5g1iwcwEdKnZI87lEREQkZ9NE0mxiSIMh5PXNy8uhL2foefdF7EvRyh+p8cRtT2CM4YM1H6TrOAfOHABIdgWVvjX7EhwUzCuhr2gJJhEREUkzlepsIr9/fp647Qlmbp3JpiObMuy8+yL23XSN6tQqFViKHtV68OW6L4mIirj5E5KQ2BrV1/P29GZYo2H8fuB3fv3n1zSfS0RERHI2leps5Mpo9UvLXsqQ8527dI4TF06ka4pGUp5u+DRnL53ly3VfpvkYKSnVAANDBlIyb0leCX0lzecSERGRnE2lOhu5Mlo9a9ssNh7Z6Pbz/XvmXyDlK3+kRp1idWgR3IL317zPpZhLQNwdj85ePJviaRpXSnWJvCWS3c/Xy5dnb3+W5fuWE7o3NH3BRUREJEfShYrZzJAGQ/jg9w/oM7sPjUs1pkCuAhTwL0B+//wJX5fLV84lN17Ze3ov4J5SDfDM7c/QYUoH2kxsw5mLZ9h1cheRlyIJ8guiUoFKVC5YmV7Ve9G+fPtEn7//zH4K5iqIn9fNbzv6nzr/4bXlr/FK6Cv80u8XV78UERERyeZUqrOZ/P75+fCOD3l9+evM3DaTkxdOXrP0HsStab32wbVULlg5XedK7RrVqdW+fHvalGvD3oi9lM9fnmZlmlE0oCj7Ivax/fh25u+cz3dbvmPvkL0Uyl3ohucndeOXxPh7+/PM7c8w9JehrNm/hgYlG7j65YiIiEg2plKdDfWv1Z/+tfoDcWtBn7l4hhPnT3DiwgmORB6h16xevLHiDcZ3GZ+u8+yL2IeH8bjp9Iq08jAeLOq3KMnH/z7+N1U+rsKHf3zIyy1uXPVk/5mUl2qAwfUGM2bFGF4JfYX5985PU2YRERHJmTSnOpvzMB4E+QVxS/5bqF+iPndVuosH6zzI5I2T+efUP+k69r4z+yiRpwReHs7826xSwUp0qdyFj/74iMhLkTc8vv/MfkrmSXmpDvAJ4KmGT7Fg5wLWHlzryqgiIiKSzalU50DP3P4Mnh6evLnyzXQd58qNX5z0XKPnOBV1ii/XXrtKSFR0FMfPH0/VSDXAY/UfI59fPnrO7Mnuk7tdGVVERESyMZXqHKhE3hLcF3If367/NuEGKWmx9/Rex0v1bSVvo1mZZry75t2EVUIgZTd+SUxe37zMv3c+p6JOcfs3t7Pu0DqX5hUREZHsSaU6h3qu0XPExMbwzup30vT8mNgY9p/Z73iphrjXsv/MfqZumpqwLaXL6SWmYamGrLhvBX5efjQb10w3hREREZGbclupNsZ8Y4w5aozZnMTjgcaYH4wxG4wxW4wx97kri9yobL6y3FvjXj5f+znHzh1L9fOPnDvC5djLmaJUty/fnppFavLmqje5FHOJ3/f/zoQNE4DUj1RfUaVQFVYNWkXZoLLcOflO5m6f68rIIiIiks248wqzccBHwIQkHn8U2GqtvcsYUwj42xgz2Vp7KYn9xcWGNx7OpI2T6DO7D03LNKVoQFEK5ipIRFQER84d4XDk4f/9HRn3tzGGvL558fbwBqBMYBmHXwUYY3j29mfpO6cvQWOCuBB9AYD6JepTNqhsmo9bIm8JQu8Lpf2k9nSf0Z2ZPWbSqVInV8UWERGRbMRtpdpaG2qMCU5uFyCPMcYAAcBJINpdeeRGVQpV4blGz/H52s/55Z8bb3iSyzsXRQOKUjSgKBULVKRJ6SYYYzhz8QxnLp6hYoGKNCzV0IHkN+pZvSeL/llEbu/cNA9uTrMyzVxyg5sgvyB+7vszbSe1pdt33ZjVYxZ3VbrLBYlFREQkOzEpveVzmg4eV6p/tNZWT+SxPMA8oDKQB+hprb3p4sD16tWzYWFhro6a412MvsiRc0c4du4YQX5BFAkoQoBPgNOxMo3TUadpO7Et6w+vZ3bP2XSs2NHpSCIiIuJmxpi11tp6KdnXyQsV2wHrgeJACPCRMSZvYjsaYx40xoQZY8KOHUv9/F+5OV8vX0oHlqZu8brckv8WFerrBPkFsajfImoUqcGA7wckui62iIiI5FxOlur7gNk2zi5gD3Gj1jew1n5hra1nra1XqNCNt6MWyQhBfkF8fOfHnLxwks/DPnc6joiIiGQiTpbqfUArAGNMEaASkL5b/Im4WYOSDWhZtiXvrH6HqOgop+OIiIhIJuHOJfWmAquBSsaY/caY+40xg40xg+N3eQW43RizCVgMPGetPe6uPCKuMqLxCA5FHmL8+vFOR0mWtZafd/1MdKyu/xUREXE3t16o6A66UFGcZq2lwdcNOHbuGDse34GXhztXpky79YfXU/vz2rzR6g2GNR7mdBwREZEsJ6tcqCiSJRljGNlkJHtO72H65ulOx0nSvxH/AvD2qrd1YaWIiIibqVSLpEHHih2pXrg6r694nVgb63ScRB05dwSAExdO8PEfHzucRkREJHtTqRZJAw/jwfDGw9l6bCuTNk5yOk6ijkTGleoWwS14a9VbGq0WERFxI5VqkTTqUa0HDUs25IEfHmDR7kVOx7nB4cjDBPkF8Xqr1zlx4QSf/PmJ05FERESyLZVqkTTy8vBi/r3zqVywMl2mdWHFvhVOR7rGkXNHKJK7CA1KNqB9+fYarRYREXEjlWqRdMjnn49FfRdRKrAUHaZ0YN2hdU5HSnDk3BGKBBQB4MVmL3L8/HGNVouIiLiJSrVIOhUJKMKv/X4lyC+I9pPac+L8CacjAXFzqovkjivVV0ar31n9jtatFhERcQOVahEXKBVYih97/8jx88cZs2KM03GA/03/uOLBOg9y9NxRloUvczCViIhI9qRSLeIiNYrUoF+tfnz050ccOHPA0SxR0VGcjjqdMP0DoH359uT2zs2MrTMcTCYiIpI9qVSLuNBLzV8iJjaGl5e97GiOo+eOAlA0oGjCNn9vfzpW7MjsbbOJiY1xKpqIiEi2pFIt4kLBQcE8VPchvv7ra3ae2OlYjitrVF89/QOgW9VuHDt/jOX7ljsRS0REJNtSqRZxsVFNR+Hr5csLS19wLMOVuylePf0D4M4Kd5LLOxcztmgKiIiIiCupVIu4WJGAIgy5bQjTNk9jxb4VHD13lKPnjhIRFZFhGZIaqc7lnYs7K9zJ7O2aAiIiIuJKKtUibjC00VDy+eWjybdNKPJ2EYq8XYR8Y/Nl2MoghyMPAzeOVAN0q9KNw5GHWfnvygzJIiIikhN4OR1AJDsK8gtiYd+FhB0MS9i2eM9ihi8eTkxsDCObjnTr+Y+cO0KgbyB+Xn43PNahYgf8vPyYuXUmTcs0dWsOERGRnEKlWsRN6peoT/0S9RO+f6juQ9w39z5G/TaKGBvDC83cN+f66rspXi/AJ4A7yt/BrG2zeL/9+3gY/cJKREQkvfR/U5EM4unhybedv2VArQG8uPRFXlr6UqL7RcdGs2b/Gqy1aT7X1XdTTEy3qt04ePYgq/9dneZziIiIyP+oVItkIE8PT77u9DX3hdzH6GWjeXvV29c8Hh0bTe9ZvWn4dUMG/zg4zRcTJjdSDdCxYkd8PX0Zt35cmo4vIiIi11KpFslgnh6efHnXl/So1oOhvwzly7VfAnGFut+cfszcOpM25drwxbov6P99fy7HXE71OQ5HHk52pDqvb17ur30/4zaMI/x0eFpfioiIiMRTqRZxgKeHJxO7TuSO8nfw0I8PMWXTFAZ+P5Bpm6fxZus3WdRvEW+0eoMpm6bQfUZ3LkZfTPGxL0Zf5HTU6WvuppiY4U2G42k8eTX01fS+HBERkRxPpVrEIT6ePszsMZPGpRvTZ3YfJm+azOstX2doo6EADGs8jA/v+JC5f8+l75y+KT7ulVuUJzdSDVAyb0keqvsQ49aPY9fJXWl/ISIiIqJSLeKkXN65+KH3D3Sq1Im32rzF8CbDr3n8sfqPMbrZaGZuncnag2tTdMyk7qaYmGGNh+Ht6c0roa+kPryIiIgkUKkWcVigXyBze83lmdufSfTxJxs+SaBvIG+seCNFx0vqboqJKZanGI/e+iiTNk7i7+N/pzy0iIiIXEOlWiSTy+ubl8frP87sbbPZdmzbTfdP7m6KiXm20bP4efnxcujL6copIiKSk6lUi2QBTzR4An9vf8auHHvTfROmf6RgpBqgcO7CPF7/caZumsqWo1vSlVNERCSnUqkWyQIK5irIg3UeZNLGSTddAu9I5BHy+ubF39s/xccfevtQcvvk5qVlid+QRkRERJKnUi2SRTx9+9N4GI8bbhhzvSPnkr+bYmIK5CrAkNuGMGPrDDYe2ZiemCIiIjmSSrVIFlEyb0kG1BrAV+u+Spg3nZib3U0xKU81fIq8vnl5cemL6YkpIiKSI6lUi2QhzzV+jsuxl3lz5ZtJ7nOzuykmJZ9/Pp5q8BTfb/8+xcv3iYiISByVapEspHz+8gyoNYBP/vyEfyP+TXSfI5Gpn/5xxZAGQ8jnl4/Ry0anI6WIiEjOo1ItksWMbj4ai030osJLMZc4FXXqprcoT0qgXyDP3P4MP+74kd/3/57eqCIiIjmGSrVIFlM6sDQP13uYb9d/e8MNWxJuUZ6GOdVXPF7/cQr4F+DJn5/k/OXz6coqIiKSU6hUi2RBI5qMwN/Ln+d/e/6a7am5m2JS8vjm4aM7P2LN/jV0ntZZxVpERCQFVKpFsqDCuQvzVMOnmLF1BusOrUvYntq7KSalV/VefNv5Wxb/s5jO0zpz4fKFdB1PREQku1OpFsminm74NPn98zN88XCstUDq76aYnAEhAxKKdadpnVSsRUREkqFSLZJFBfoFMqrJKBbtXsSYFWOAq6Z/pHOk+oqri7VGrEVERJLm5XQAEUm7Jxo8wdpDaxmxZASFcxfmyLkj5PHJQy7vXC47x4CQAVgsg+YOovO0zsztNTdVt0AXERHJCVSqRbIwD+PBN52/4fj54zz444OUDSrrslHqqw0MGQjAoLmD6DK9C9/3/F7FWkRE5Cqa/iGSxfl4+jCzx0zqFqvL7lO7XTKfOjEDQwbyTedv+GX3L3Sd3pVYG+uW84iIiGRFKtUi2UCATwDz751P1UJVqVmkptvOMzBkIB/d+RE/7/6Z77Z857bziIiIZDXmyqoBWUW9evVsWFiY0zFEMqXo2Gg8jSfGGLedI9bGEvJZCBdjLrLlkS14eWgWmYiIZE/GmLXW2nop2Vcj1SLZiJeHl1sLNcTN436lxSvsOLGDiRsmuvVcIiIiWYVKtYikWqdKnahXvB4vLXuJSzGXnI4jIiLiOJVqEUk1YwyvtniVvRF7+Xrd107HERERcZxKtYikSdtb2tK4dGNeXf6qbgojIiI5nkq1iKTJldHqg2cP8mnYp07HERERcZRKtYikWbPgZrQu15qxK8dy/vJ5p+OIiIg4RqVaRNLlxWYvcvTcUT4P+9zpKCIiIo5RqRaRdGlcujEty7bkzVVvam61iIjkWCrVIpJuLzZ7kcORh/li7RdORxEREXGESrWIpFvTMk1pHtycsSvHEhUd5XQcERGRDKdSLSIu8WKzFzkUeYgv137pdBQREZEMp1ItIi7RPLg5Tcs0ZczKMRqtFhGRHEelWkRcZnSz0Rw8e5AB3w8gOjba6TgiIiIZRqVaRFymRdkWvNXmLb7b8h395vRTsRYRkRzDy+kAIpK9PHP7M8TExjBs8TA8jAcTukzA08PT6VgiIiJupVItIi73XOPniLExjFwyEoPh287f4u3p7XQsERERt1GpFhG3GNFkBAAjl4zkyLkjzOw+k0C/QIdTiYiIuIfmVIuI24xoMoJvO3/L0vClNPqmEfsi9jkdSURExC1UqkXErQaGDGRhn4X8e+ZfbvvqNjYc3uB0JBEREZdTqRYRt2tVrhWrBq3C28ObNhPbsPPETqcjiYiIuJTbSrUx5htjzFFjzOYkHh9qjFkf/2ezMSbGGJPfXXlExFnVClfjl36/YLG0ndSWg2cPOh1JRETEZdw5Uj0OaJ/Ug9bat6y1IdbaEGA4sMxae9KNeUTEYZUKVmLBvQs4du4Y7Se153TUaacjiYiIuITbSrW1NhRIaUnuDUx1VxYRyTxuLXErc3rOYfvx7XSa2okLly84HUlERCTdHJ9TbYzJRdyI9qxk9nnQGBNmjAk7duxYxoUTEbdoc0sbJnadyIp9K+g1q5fuvCgiIlme46UauAtYmdzUD2vtF9baetbaeoUKFcrAaCLiLj2r9+S/d/yXeX/P46EfHsJa63QkERGRNMsMN3/phaZ+iORIj9V/jKPnjvJK6CsUzl2YN1q/4XQkERGRNHG0VBtjAoFmQF8nc4iIc15q/hJHIo8wZuUYiucpzuO3Pe50JBERkVRzW6k2xkwFmgMFjTH7gRcBbwBr7Wfxu3UFFllrz7krh4hkbsYYPunwCQfOHuC5X5/j7ip3UyJvCadjiYiIpIrJavMY69WrZ8PCwpyOISIutufUHip9VImBIQP54q4vnI4jIiKCMWattbZeSvbNDBcqiohQNl9ZHrn1Eb7+62u2H9/udBwREZFUUakWkUxjZJOR5PbOzYjFI5yOIiIikioq1SKSaRTKXYhnGz3LnO1zWP3vaqfjiIiIpJhKtYhkKk82eJIiuYvw3K/Pae1qERHJMlSqRSRTye2Tm9HNR7N833Lm75zvdBwREZEUUakWkUzn/tr3Uy5fOV5c+qJGq0VEJEtQqRaRTMfb05tRTUax7tA6ftzxo9NxREREbkqlWkQypb41+1IuXzlGLxut0WoREcn0VKpFJFPSaLWIiGQlKtUikmlptFpERLIKlWoRybQ0Wi0iIlmFyWqjP/Xq1bNhYWFOxxCRDHI55jKVP66MtZYmZZpwOeYy0bHR3FvjXrpU7uJ0PBERycaMMWuttfVSsq9GqkUkU/P29OatNm8RY2MI3RtK2MEwFu9ZzEM/PsSFyxecjiciIgKoVItIFnB3lbvZO2Qve57Yw47HdzCn5xyOnjvKV+u+cjqaiIgIoFItIllQ0zJNaVy6MW+uepNLMZecjiMiIqJSLSJZ08gmI9l/Zj8TN0x0OoqIiIhKtYhkTe1uaUfdYnUZs3IM0bHRTscREZEcTqVaRLIkYwwjmoxg18ldzNgyw+k4IiKSw6lUi0iW1aVyF6oWqsrrK14n1sY6HUdERHIwlWoRybI8jAfDGw9n89HNjFg8QnddFBERx3g5HUBEJD3urXEvy/cuZ+zKsRw9d5Qv7voCLw/9aBMRkYyl//OISJbmYTz4rONnFMtTjJeWvcTRc0f5rvt35PLO5XQ0ERHJQTT9Q0SyPGMMo5uP5tMOn/LTrp9oPaE1EVERTscSEZEcRKVaRLKNwfUG81237/jz4J+0ndSW01GnnY4kIiI5hEq1iGQr91S9h1k9ZvHXob9oM7ENpy6ccjqSiIjkACrVIpLtdKrUidk9Z7PxyEZaT2zNyQsnnY4kIiLZnEq1iGRLHSt2ZE7POWw+upmnFz3tdBwREcnmVKpFJNu6s8KdPFjnQSZvnMy/Ef86HUdERLIxlWoRydaevv1pYm0s7615z+koIiKSjalUi0i2FhwUTK/qvfhi7ReaWy0iIm6jUi0i2d6zjZ7l3OVzfPLnJ05HERGRbEqlWkSyvZpFanJH+Tv47+//5cLlC07HERGRbEilWkRyhGGNh3Hs/DG+Xf+t01FERCQbUqkWkRyhSekmNCjZgLdXvc3F6ItOxxERkWxGpVpEcgRjDC80fYE9p/fQYUoHzl4863QkERHJRlSqRSTHuKPCHYzvMp6l4UtpNaEVx88fdzqSiIhkEyrVIpKj9K/Vnzk957Dp6CaafNuEfRH7nI4kIiLZgEq1iOQ4d1W6i0V9F3Ho7CGafNuEXSd3OR1JRESyOJVqEcmRmpRpwtKBSzl36RzNxjVj+/HtTkcSEZEsTKVaRHKskKIhLB24lJjYGJqNa8bmo5udjiQiIlmUSrWI5GjVC1dn2cBleHl40Xxcc9YdWud0JBERyYJUqkUkx6tUsBKhA0PJ7ZObVhNa8fv+352OJCIiWYxKtYgIcEv+WwgdGEoB/wK0mdiGFftWOB1JRESyEJVqEZF4ZYLKsGzgMornKU67Se1YsmeJ05FERCSLUKkWEblKibwlWDZwGeXylaPDlA4s37vc6UgiIpIFqFSLiFynSEARlvRfQpnAMnSc2pH1h9c7HUlERDI5lWoRkUQUyl2IX/r9QqBvIO0mtWPniZ1ORxIRkUxMpVpEJAmlAkvxS79fiLWxtJnYhgNnDjgdSUREMimVahGRZFQqWImFfRZy8sJJ7p93v9NxREQkk1KpFhG5ibrF6zKyyUh+3v0zfx740+k4IiKSCalUi4ikwMO3Pkw+v3y8tvw1p6OIiEgmpFItIpICeX3z8sRtTzD377lsOrLJ6TgiIpLJqFSLiKTQ47c9ToBPAK+veN3pKCIiksmoVIuIpFB+//w8euujTN88nR0ndjgdR0REMhGVahGRVHiywZP4evkyZsUYp6OIiEgmolItIpIKRQKK8GCdB5m4cSIbDm9wOo6IiGQSKtUiIqn0XOPnKJy7MM3HN2flvpVOxxERkUxApVpEJJWK5ynOqkGrKJy7MG0mtmH+jvlORxIREYepVIuIpEGZoDKsuG8F1QpXo/O0zkzYMMHpSCIi4iCVahGRNCqUuxBL+i+hRdkWDPh+AJ+FfeZ0pAxjraXDlA4s2LnA6SgiIpmC20q1MeYbY8xRY8zmZPZpboxZb4zZYoxZ5q4sIiLuksc3Dz/0/oG7Kt7Fw/Mf5v017zsdKUOcvXSWBTsX8NPOn5yOIiKSKbhzpHoc0D6pB40xQcAnQCdrbTWguxuziIi4jZ+XHzN7zOSeKvfw5M9P8sbyN5yO5HYRUREAhEeEOxtERCSTcFupttaGAieT2eVeYLa1dl/8/kfdlUVExN18PH2Y1m0a99a4lxFLRvDl2i+djuRWERfjS/XpcGeDiIhkEikq1caYAm44d0UgnzFmqTFmrTGmvxvOISKSYbw8vJjQZQKNSjXi5dCXuRRzyelIbpMwUn06HGutw2lERJyX0pHqNcaYGcaYO40xxkXn9gLqAh2AdsDzxpiKie1ojHnQGBNmjAk7duyYi04vIuJ6nh6ejGwykv1n9jN101Sn47jNlZHqyEuRnLyQ3C8lRURyhpSW6orAF0A/YKcx5vWkCnAq7Ad+ttaes9YeB0KBWontaK39wlpbz1pbr1ChQuk8rYiIe7Uv356aRWoyduVYYm2s03Hc4spINcDeiL0OJhERyRxSVKptnF+stb2BB4ABwB/GmGXGmIZpPPdcoLExxssYkwu4DdiWxmOJiGQaxhiGNRrGtuPb+OHvH1L8vNC9oTz848NZYjrFlZFq0LxqERFIxZxqY8wTxpgw4BngcaAg8DQwJYnnTAVWA5WMMfuNMfcbYwYbYwYDWGu3AQuBjcAfwFfW2iSX3xMRyUq6V+tO2aCyvLHijRSV5Fgby6MLHuWztZ+x/fj2DEiYPlePVKtUi4jEzWtOidXARKCLtXb/VdvDjDGJ3u0gflQ7Wdbat4C3UphBRCTL8PLw4pnbn+HRBY8SujeUZsHNkt3/++3fs/lo3LjC0vClVClUJSNiptnpqNN4eXiRyzuXSrWICCmfUz3KWvvK1YXaGNMdwFo71i3JRESyuPtC7qNQrkKMWTkm2f2stby87GUq5K9A8TzFWbp3acYETIeIixEE+gYSHBSsUi0iQspL9bBEtg13ZRARkezG39ufIQ2GsHDXQt5Z9U6S00Dm/T2PDUc2MKrpKFoEt2Bp+NJMP6864mIEgX5xpVoXKoqI3GT6hzHmDuBOoIQx5r9XPZQXiHZnMBGR7ODphk/z1+G/eOaXZ9gbsZf32r2Hp4dnwuPWWl4OfZlb8t3CvTXu5XLMZSZvmsy249uoWqiqg8mTFxEVP1IdGJzwjwDXrbgqIpL13Gyk+iAQBkQBa6/6M4+4taVFRCQZvl6+TO82nScbPMmHf3xI9xndOX/5fMLjC3YuYN2hdYxsMhIvDy+aBzcH4uZVZ2ZXRqrLBJXhzMUznI467XQkERFHJTtSba3dAGwwxky21mpkWkQkDTyMB++2e5cygWV48ucnKfluSaoVrkbVglVZ8e8KgoOC6VuzLwDl8pWjZN6SLA1fyiO3PuJw8qRFREVQLl85goOCgbgVQPL553M2lIiIg242/eM7a20P4C9jzA0T/Ky1Nd2WTEQkm3miwRNULVSVmVtnsu34NmZtm8WJCycY13kc3p7eQNwa1y2CW7Bw18JMPaXi6jnVEFeqaxer7WwoEREH3WxJvSfi/+7o7iAiIjlBm1va0OaWNgnfR16KJMAn4Jp9mgc3Z+LGiZl6XnXCnOr4Uq2LFUUkp0t2TrW19tBV+x2x1u611u4FjgKZc/hERCQLub5QAwnzqn/b81sGp0mZWBvLmYtnCPILIp9fPvL45NGyeiKS46V0Sb0ZQOxV38fEbxMRERcrG1SWUnlLZdr1qs9ePIvFEugbiDFGa1WLiJDyUu1lrb105Zv4r33cE0lEJGczxtCibOZdrzriYtwtygP9AgEoE1RGpVpEcryUlupjxphOV74xxnQGjrsnkoiINC/TnOPnj7P12Fano9wgIiq+VPvGlergQI1Ui4iktFQPBkYYY/YZY/4FngMecl8sEZGcLTOvV339SHVwUDARFyO0VrWI5GgpKtXW2t3W2gZAVaCKtfZ2a+0u90YTEcm5goOCKRtUlimbp2S6KSA3jFRfWQHktFYAEZGcK6Uj1RhjOgCPAE8ZY14wxrzgvlgiIjmbMYZnGz3Lqn9XsWDnAqfjXCOxkWpAU0BEJEdLUak2xnwG9AQeJ24pve5AGTfmEhHJ8e6vfT+35LuFEUtGEGtjb/6EDHL9SHWZoLj/HahUi0hOltKR6tuttf2BU9bal4CGQEX3xRIREW9Pb15p8Qobj2xk2uZpTsdJcP1IdQH/AuT2zq1SLSI5WkpL9YX4v88bY4oDl4Fi7okkIiJX9Kzek1pFavH8b89zKebSzZ+QAU5Hncbbwxt/L3+A/61VHRHubDAREQeltFT/aIwJAt4C1gHhwBQ3ZRIRkXgexoM3Wr3BP6f+4at1XzkdB4i/Rblf3I1frggOCtaFiiKSo6V09Y9XrLWnrbWziJtLXdlaqwsVRUQyQPvy7WlapikvL3uZsxfPOh2HiIsRCfOpr9BdFUUkp0vphYp+xpinjDGziRuhHmSM8XNvNBERgbjpFWNbj+XY+WN0mNLB8WIdcTEiYT71FcFBwZyKOpVwEaOISE6T0ukfE4BqwIfAR8StVz3RXaFERORaDUo2YOo9U1n17yraT27PmYtnHMsSEXXjSHXtorUBWLJniRORREQcl9JSXd1ae7+19rf4Pw8QV7JFRCSD9KjWg+ndpvPHgT9oO7GtY6PCiY1UNwtuRsFcBZmxdYYjmUREnOaVwv3WGWMaWGvXABhjbgPC3BdLREQSc0/Ve5jpMZPuM7pT9oOyFAkoQl7fvAT5BfF4/cfpWLGj2zMkNlLt5eFF18pdmbp5KhcuX8Df29/tOUREMpOUjlTXBVYZY8KNMeHAauBWY8wmY8xGt6UTEZEbdK7cmZ/7/sw9Ve6hZpGa5PfPz66Tu+g8rTPf/PWN28+f2IWKAN2rdifyUiQ/7/7Z7RlERDKblI5Ut3drChERSZUWZVvQomyLhO/PXTrHPd/dw/3z7uf4+eM82+hZt5w3JjaGMxfP3DD9A6B5cHMK+BdgxtYZdKncxS3nFxHJrFK6pN5ea+1e4m4CY6/8uWq7iIg4KLdPbub1nkfPaj157tfnePaXZ7HWuvw8Zy/FrTwS5Bd0w2Pent50qdyFH/7+gajoKJefW0QkM0vpknqdjDE7gT3AMuJu/vKTG3OJiEgq+Xj6MPnuyTxS7xHeWvUWo5eOdvk5rlwcmdj0D4ibAnL20lkW7V7k8nOLiGRmKZ1T/QrQANhhrS0LtALWuC2ViIikiaeHJx/d+RGDQgbxcujLfB72uUuPH3ExvlQnMv0DoGXZluT3z69VQEQkx0lpqb5srT0BeBhjPKy1vwH13JhLRETSyBjD53d9TocKHXhkwSN8v/17lx37ZiPV3p7edKnUhXl/z+Ni9EWXnVdEJLNLaak+bYwJAEKBycaYD4Bz7oslIiLp4eXhxfRu06lXvB69Z/Vm5b6VLjnuzUaqAbpX686Zi2c0BUREcpRkS7UxprwxphHQGTgPPAksBE4Aj7s/noiIpFVun9zMv3c+JfOWpN+cflyOuZzuY95spBqgVdlW5PPLpykgIpKj3Gyk+n3gjLX2nLU21lobba0dD8wBRrs7nIiIpE/BXAV5r9177Dm9h4kbJ6b7eCkZqfb29KZz5c7M/XuupoCISI5xs1JdxFq76fqN8duC3ZJIRERcqkOFDtQtVpfXlr+W7tHq01GngeRHqiFuFZAzF8/wyz+/pOt8IiJZxc1KdVAyj+ketCIiWYAxhhebvcg/p/5h0sZJ6TpWRFQEPp4++Hn5Jbtf63KtCfILYubWmek6n4hIVnGzUh1mjHng+o3GmP8Aa90TSUREXK1jxY7UKVaH15a/RnRsdJqPc+UW5caYZPfz8fShc6W4KSCXYi6l+XwiIlnFzUr1EOA+Y8xSY8w78X+WAfcDT7g9nYiIuMSV0erdp3ana7Q64mJEsvOpr9a9andOR53m139+TfP5RESyimRLtbX2iLX2duAl4u6iGA68ZK1taK097P54IiLiKndVvIvaRWvzauiraR6tjoiKuOl86ital2tNoG+gVgERkRwhRetUW2t/s9Z+GP9nibtDiYiI6109Wj1l05Q0HSM1I9W+Xr50qtSJ77d/rykgIpLtpfTmLyIikg10qtSJkKIhvBL6SppGq1MzUg3/mwKy+J/FqT6XiEhWolItIpKDGGN4oekL7Dq5i6mbpqb6+akZqQZoe0tb8vrm1RQQEcn2VKpFRHKYzpU7U6tIrTSNVqd2pPrqKSCuuKOjiEhmpVItIpLDeBgPXmj2AjtP7mTa5mkpfl5MbAxnL51NVamGuCkgp6JOMXVz6kfGRUSyCpVqEZEcqEvlLtQoXINXQl8hJjYmRc85c/EMAEF+Qak6V/vy7WlQsgH/mfcfFu5amNqoIiJZgkq1iEgO5GE8eLHZi+w4sSNhtPrMxTNsP749yWkaERcjAFI1pxribgTzU5+fqF64Ol2nd2XJHi0iJSLZj0q1iEgO1bVKV6oXrs7g+YMJGhNE4JhAqnxchUbfNOLcpXM37B8RFV+qUzn9A+JGtxf1W8Qt+W6h09ROrNy3Mt35RUQyE5VqEZEcysN48PGdH9OmXBv61uzL2NZjGdt6LGsPraXHzB43XMSY1pHqKwrmKsiv/X+leJ7i3DX1Lk5eOJnu1yAikll4OR1ARESc07RMU5qWaXrNtkDfQAbPH8zDPz7MF3d9gTEGSN9I9RVFA4oyq8csan1Wi7dWvsUbrd9Ie3gRkUxEI9UiInKNh+o9xKgmo/jqr694ednLCdvTO1J9RY0iNehdozcf/P4BhyMPp+tYIiKZhUq1iIjc4OUWLzMwZCCjl43mgzUfAK4Zqb7ipeYvcSnmEq+FvpbuY4mIZAaa/iEiIjcwxvBFxy84e/EsQ34eQqyN5fzl80D6R6oByucvz6Dag/h87ec8c/szlAkqk+5jiog4SSPVIiKSKG9Pb6beM5XuVbvz1KKn+Gb9N/h4+uDn5eeS4z/f9Hk8jAcvLXvJJccTEXGSSrWIiCTJ29ObKfdMoWe1nvxz6p9U3/glOaUCS/FwvYcZv2E8fx//22XHFRFxgkq1iIgky8vDi0l3T2JQyCAal27s0mMPbzIcfy9/+s7py6kLp1x6bBGRjKRSLSIiN+Xl4cXXnb9mVo9ZLj1u4dyFmXrPVDYe2UirCa04fv64S48vIpJRVKpFRMRRd1W6i7m95rLt+DZajG/BkcgjTkcSEUk1lWoREXFc+/Lt+bH3j/xz6h+ajWvGobOHnI4kIpIqKtUiIpIptCrXioV9FrL/zH5aTmipEWsRyVJUqkVEJNNoUqYJC/osYO/pvbSe2FpzrEUky1CpFhGRTKVpmab80PsHdp3cResJrTl54aTTkUREbkqlWkREMp1W5Vrxfc/v2XZ8G20mtuHE+RNORxIRSZZKtYiIZErtyrdjTs85bDm6hebjm2uOtYhkam4r1caYb4wxR40xm5N4vLkxJsIYsz7+zwvuyiIiIlnTnRXuZP698xNWBTlw5oDTkUREEuXOkepxQPub7LPcWhsS/+dlN2YREZEsqlW5Vvzc92cOnj1I03FN2Rexz+lIIiI3cFupttaGArq6RERE0q1x6cYs7r+Yo+eO8syiZ5yOIyJyA6fnVDc0xmwwxvxkjKmW1E7GmAeNMWHGmLBjx45lZD4REckkbi1xK/9X//+YuXUm245tczqOiMg1nCzV64Ay1tpawIfA90ntaK39wlpbz1pbr1ChQhmVT0REMpknGz5JLu9cvLb8NaejiIhcw7FSba09Y62NjP96AeBtjCnoVB4REcn8CuYqyMP1Hmbq5qnsOrnLJcc8EnmE//vp/zhz8YxLjiciOZNjpdoYU9QYY+K/rh+fRQuRiohIsp6+/Wl8PH14Y/kbLjnei0tf5MM/PmTqpqkuOZ6I5EzuXFJvKrAaqGSM2W+Mud8YM9gYMzh+l27AZmPMBuC/QC9rrXVXHhERyR6KBhTlgToPMGHjBMJPh6frWHtO7eHrv74GYPqW6S5IJyI5lTtX/+htrS1mrfW21pa01n5trf3MWvtZ/OMfWWurWWtrWWsbWGtXuSuLiIhkL882ehaD4c2Vb6brOC+Hvoyn8eQ/tf/D0vClHDp7yEUJRSSncXr1DxERkVQrmbck94Xcx9d/fc1ve35L0zF2nNjBhA0TeLjewzzZ8EkslplbZ7o4qYjkFCrVIiKSJb3S8hUq5K/AnVPuZMHOBal+/kvLXsLPy49hjYdRtVBVahSuoSkgIpJmKtUiIpIlFc5dmKUDl1K1UFW6TOvCrK2zUvzcLUe3MHXTVB679TGKBBQBoGe1nqz8dyX/Rvzrrsgiko2pVIuISJZVMFdBlvRfwq0lbqXHzB5M2jgpRc8bvWw0uX1yM7TR0IRtPav3BOC7Ld+5JauIZG8q1SIikqUF+gWyqO8imgc3Z8D3A5ixZUay+28+upmZW2fyxG1PUDDX/26PUD5/eeoWq8u0LdPcHVlEsiGVahERyfJy++RmXq953F7qdu6dfS8/7vgxyX1fX/46AT4BPNngyRse61W9F2EHw9h9crc744pINqRSLSIi2UJun9z82PtHQoqG0O27bvz6z6837LPjxA6mb5nOI/UeoUCuAjc83qNaD0BrVotI6qlUi4hIthHoF8jCPgupUKACnad1Zvne5dc8/vry1/H19OXp259O9PmlA0vTsGRDJm2cRKyNzYjIIpJNqFSLiEi2UiBXAX7t9yulA0tzx+Q7CN0bCsA/p/5h0sZJPFT3IQrnLpzk8x+u9zDbjm9j/o75GRVZRLIBlWoREcl2igQUYUn/JZQKLMWdk+8kdG8oY1aMwdPD85oVPxLTq3ovygSW4Y0Vb2CtzaDEIpLVqVSLiEi2VCxPMX4b8FtCsR63fhz3176f4nmKJ/s8b09vht4+lNX7V7N83/Jk9xURuUKlWkREsq2iAUUTijXAc42eS9HzBtUeRKFchXhjxRvujCci2YhKtYiIZGtFA4qy5v41rB+8njJBZVL0HH9vf4Y0GMLCXQtZf3i9ewOKSLagUi0iItleoF8gVQtVTdVzHrn1EfL45GHMijFuSiUi2YlKtYiISCKC/IJ45NZHmLF1BrtO7nI6johkcirVIiIiSRjSYAiexpPPwz53OoqIZHIq1SIiIkkoGlCUNre0Yea2mVpeT0SSpVItIiKSjG5VuhF+Opx1h9Y5HUVEMjGVahERkWR0rtwZLw8vZmyd4XQUEcnEVKpFRESSkd8/Py3LtmTmVk0BEZGkqVSLiIjcRPeq3dl9ajcbjmxwOoqIZFIq1SIiIjfRpXIXPI0nM7fOdDqKiGRSKtUiIiI3UTBXQZoHN2fG1hmaAiIiiVKpFhERSYFuVbux48QONh/d7HQUEcmEVKpFRERSoGvlrngYjySngMTExnDqwqkMTiUimYWX0wFERESygiIBRWhapimTN02mcO7CRF6KJPJSJOER4Ww5uoXtx7dzIfoCr7V8jRFNRjgdV0QymEq1iIhICvWp0YcHfniAx356DACDoXie4lQrXI0WwS3YdWoXI5eMpHRgafrW7OtwWhHJSCrVIiIiKXR/7ftpd0s7fL18CfAJwN/LH2NMwuOXYi7RflJ7Bs0dRIk8JWhRtoWDaUUkI2lOtYiISAoZYygVWIrCuQuTyzvXNYUawMfTh9k9Z1OhQAW6Tu/K1mNbHUoqIhlNpVpERMSFgvyCWHDvAvy9/blr6l1cjL7odCQRyQAq1SIiIi5WJqgME7pM4J9T//D1X187HUdEMoBKtYiIiBu0LteaxqUb8/ry14mKjnI6joi4mUq1iIiIGxhjeKn5Sxw4e4Cv1n3ldBwRcTOVahERETdpEdyCJqWb8MaKNzRaLZLNqVSLiIi4yZXR6oNnD/Ll2i+djiMibqRSLSIi4kYtyragWZlmvLHiDS5cvuB0HBFxE5VqERERNxvdfDSHIg/x9qq3nY6SYO3BtazZv8bpGCLZhkq1iIiImzUPbk7Paj15YekLfPzHx07HYdbWWTT8uiH3fHcP1lqn44hkC7pNuYiISAaY0HUCUdFRPPbTYwA8Wv9RR3J889c3PPDDA+T1zcvBswfZdXIXFQpUcCSLSHaikWoREZEM4OPpw3fdv6Nzpc489tNjvLf6PXad3MU/p/5h7+m9GXLnxXdXv8v98+6nTbk2LO6/GICl4Uvdfl6RnMBktV/71KtXz4aFhTkdQ0REJE0uxVyi+4zuzPt73jXbywaV5bcBv1EmqIxbzvvbnt9oOaEl3at2Z9Ldk/D28Kb4u8VpWbYlk++e7JZzimR1xpi11tp6KdlX0z9EREQykI+nDzO6z2DBzgVEXookJjaG85fPM2LJCFpOaMmygcsombeky8+7bO8yPIwH33T+Bh9PHyBurvfS8KVYazHGuPycIjmJSrWIiEgG8/H0oUvlLtdsq1e8Hq0ntqbF+BYsG7iM4nmKu/ScYQfDqFKwCgE+AQnbmpdpzrTN0zSvWsQFNKdaREQkE7i1xK0s7LOQw5GHaTm+JYcjD7vs2NZa1h5aS73i1/4Wu3lwc0DzqkVcQaVaREQkk2hYqiE/9fmJ/Wf202pCK46eO+qS4x48e5DDkYepW6zuNdsrFqhI0YCiLNu7zCXnEcnJVKpFREQykcalGzP/3vnsObWH1hNac/z88XQfM+xg3AX+149UG2OumVctImmnUi0iIpLJNAtuxg+9f2DnyZ20mdiGkxdOput4YQfD8DSe1Cpa68ZzlWnGgbMH2H1qd7rOIZLTqVSLiIhkQq3KteL7nt+z9dhW2k5sy+mo02k+VtihMKoWqkou71w3PKZ51SKuoVItIiKSSbUr347ZPWaz8chG2k9qz5mLZ1J9DGstaw/eeJHiFZUKVKJI7iIq1SLppFItIiKSiXWo2IEZ3Wew9tBa7ph8B2cvnk3V8/898y/Hzh9LslRrXrWIa6hUi4iIZHKdK3dm2j3T+H3/73Sc2pFzl86l+LlJXaR4tebBzTlw9gD/nPon3VlFciqVahERkSzgnqr3MPnuyazYt4IOUzoQeSkyRc8LOxiGl4cXNYvUTHKfK/OqF+5a6IqoIjmSSrWIiEgW0bN6TyZ1ncSKfStoO7EtEVERN33O2kNrqV64On5efknuU6lAJW4tfivvrH6HyzGXXRlZJMdQqRYREclCetfozXfdvyPsYBitJrTixPkTSe5rrSXsYBj1iiU99QPi5lWPbj6aPaf3MH7DeFdHFskRvJwOICIiIqlzd5W7mdNzDvd8dw9NxzWlddnWREVHERUTRbmgcoxsOhIvDy/CT4dz8sLJZOdTX3FH+TuoX6I+r4a+Sv9a/fHx9MmAVyKSfWikWkREJAvqULED8++dz6kLpxi/YTxz/57L0vCljF42mp4ze3Ip5lLCRYp1i9e9ydHiR6ubjWZvxF7Gr9dotUhqmay2fE69evVsWFiY0zFEREQypQ/WfMCQn4dwR/k7KJ+/PJ+FfcbZ4Wfx9fK96XOttTT8uiGHIg+x8/GdGq2WHM8Ys9Zae/Nf9aCRahERkWzliQZP8OVdX7Jw10I+/ONDahapmaJCDXGj1S81f4l9Efv49q9v3ZxUJHtRqRYREclm/lPnP0y6exKexpNGpRql6rltb2lLg5INeG35a+m6NbpITqNSLSIikg3dW+Nedjy+g9davZaq5xljeLvN2xyOPEznaZ2Jio5yU0KR7MVtpdoY840x5qgxZvNN9rvVGBNtjOnmriwiIiI5Ubl85QjwCUj18xqVbsSErhMI3RtKn9l9iImNcUM6kezFnSPV44D2ye1gjPEExgKL3JhDREREUqlX9V683+59Zm+bzWMLHiOrLWwgktHctk61tTbUGBN8k90eB2YBt7orh4iIiKTNEw2e4FDkIcauHEuQXxCvt3odY4zTsUQyJcdu/mKMKQF0BVqgUi0iIpIpvdHqDU5dOMWYlWM4cPYAX971ZYpXExHJSZy8o+L7wHPW2tib/avXGPMg8CBA6dKl3Z9MREREgLgLFz/r+BmlA0sz6rdR7IvYx+yes8nvn9/paCKZiltv/hI//eNHa231RB7bA1xp0wWB88CD1trvkzumbv4iIiLijKmbpjJw7kDKBpVlUb9FlA7UQJdkb1ni5i/W2rLW2mBrbTAwE3jkZoVaREREnNO7Rm9+7fcrhyMP03J8Sw6ePeh0JJFMw51L6k0FVgOVjDH7jTH3G2MGG2MGu+ucIiIi4l5NyjRhYd+FHDl3hFYTWnH03FGnI4lkCm6d/uEOmv4hIiLivNC9obSf1J4KBSrw24DfNMdasqUsMf1DREREsq6mZZoyt9dcth/fTtuJbTl14ZTTkUQcpVItIiIiadLmljbM7jGbTUc30XJCS46fP+50JBHHqFSLiIhImnWo2IF5veax/fh2mo9rzpHII05HEnGESrWIiIikS7vy7Vhw7wL2nN5Ds3HNOHDmgFvPF3kpkn0R+9x6DpHUUqkWERGRdGtRtgU/9/2Zg2cP0naSe+dYD5o7iDqf1+FSzCW3nUMktVSqRURExCUal27MvN7z2HVyF52mdeLC5QsuP8eWo1uYsXUGJy6cYMmeJS4/vkhaqVSLiIiIyzQPbs6krpNYuW8lvWf1Jjo22qXHf235awT4BBDgE8DsbbNdemyR9FCpFhEREZfqXq07H7T/gLl/z+WR+Y/gqnti7Dixg+lbpvPorY/SoUIHvt/+PTGxMS45tkh66eYvqdDz89U33Wf6Qw21v/bX/tpf+2t/7Q/sOb2HfRH7KJmnJH8O64MxJl3H3358O8fOH6NByQacjjrN1mNbCSkaQqBvYKZ4vdrfPftfvW9G081fRERExHFlg8pSIk8J9p/dz5M/P5muEeuo6CiOnjtC8TzF8fbwJr9/fjwwHDt3zIWJRdJOI9UiIiLiNtZanvr5Kd7//X2euO0J3mv3XsKIdWo8+MODTNgwgX+e+IfieYoD0HlaZ/469Bd7h+xN0zFFbiY1I9Ve7g4jIiIiOZcxhnfbvYsxhvfWvEd0bDT/veO/eJiU/7J8X8Q+xq0fxwN1Hkgo1AB3V76beX/PY+2htdQrnqLeI+I2KtUiIiLiVsYY3mn7Dp7Gk7dXv82JCycY13kcvl6+KXr+myvfBOC5xs9ds/2uSnfhaTyZvW22SrU4TnOqRURExO2MMbzZ5k3Gth7LtM3T6Di1I2cvnr3p8w6dPcRX675iQK0BlA4sfc1j+f3z06JsC2Ztm+WyFUZE0koj1SIiIpIhjDE82+hZCucuzH/m/Yfm45szsNbAhMdLB5amU6VO18yPfmvVW0THRjO8yfBEj3l35bt5ZMEjbDu+jaqFqrr5FYgkTRcqioiISIabv2M+vWb1IvJS5DXbx7QakzDN4+i5owS/H0z3at0Z32V8osc5dPYQJd4twYCQAXzT6RtdsCgupQsVRUREJFPrULEDR585yvnL5wGwWP7vp/9j2OJhFAkowsCQgby7+l2ioqMY0XhEkscplqcYzzZ6lrErx+Lj4cOnHT9N1UWQIq6iUi0iIiKO8Pf2x9/bP+H7cV3Gcez8Mf4z7z94Gk8+/vNjelTrQaWClZI9zhut3sDTePL6ite5FHuJr+76Ck8PT3fHF7mGSrWIiIhkCj6ePszuMZvm45vT//v+AIxsMvKmzzPG8GrLV/H18uXFpS9yKeYSE7tO1Ii1ZCiVahEREck08vjmYcG9C2gxvgX1itejRpEaKXqeMYYXmr2Ap/Fk1G+j6FihI71r9HZzWpH/0YWKIiIikunE2liAVI82x9pYan1Wi+jYaDY/vFnTQCRdUnOhon4vIiIiIpmOh/FI0/QND+PBC01fYPvx7czYOsMNyZxz7NwxLsdcdjqGJEGlWkRERLKVe6reQ7VC1Xh52cvExMY4HcclomOjqfxxZcasGON0FEmCSrWIiIhkKx7GgxeavcC249uyzWj1voh9nLxwkvk75zsdRZKgUi0iIiLZTreq3ahaqCqvhL6SLUard57YCUDYwbAU3d5dMp5W/xAREZFs58rc6l6zevHB7x9Qo3ANTkedJuJiBJdiLhETG0OMjaFU3lLcXeXuTH8nxp0n40p1jI1h+b7l3FnhTocTyfVUqkVERCRb6la1G1VDq/L0oqeT3W9Uk1G80vKVDEqVNjtP7CSXdy6iY6NZGr5UpToTUqkWERGRbMnTw5OFfRay4cgGgvyCCPILIq9vXnw8ffDy8MLDeDDs12G8uvxV/Lz8GNn05jeaccquU7uoWKAieXzy8Fv4b07HkUSoVIuIiEi2VSqwFKUCSyX5+GcdPyMqOopRv43Cz8uPp29PflTbKTtP7KRW0VpULViVV5e/SkRUBIF+gU7HkqvoQkURERHJsTyMB990/obuVbvzzC/P8OHvHzod6QbRsdHsOb2HCvkr0KJsC2JtLKF7Q52OJddRqRYREZEczcvDi8l3T6ZL5S7838L/47+//9fpSNcIPx1OdGw0FfJXoEHJBvh6+moKSCakUi0iIiI5nrenN9O7Tadr5a48sfAJ3lv9ntORElxZTq9CgQr4eflxe6nbVaozIZVqEREREcDH04fp3aZzT5V7eGrRU7yz6h2nIwH/W06vQv4KALQIbsGGwxs4eeGkk7HkOirVIiIiIvG8Pb2Zes/UhDnWb6580+lI7Dq5iwCfAArnLgxAi7ItsFiWhS9zOJlcTat/iIiIiFzF29ObKfdMwdPDk+d+fY5YG8uwxsMcy7Pz5E4q5K+QcIOa+iXq4+/lz9LwpXSt0tWxXHItlWoRERGR63h5eDGx60Q8jAfDFw8nJjbGsXWsd57YSd3idRO+9/H0oVHpRppXncmoVIuIiIgkwsvDiwldJuBpPBn12yh2nNxBmcAyeHl44ePpQ/eq3bkl/y1uzXA55jLhp8PpVb3XNdtbBLdg5JKRHDx7kOJ5irs1g6SMSrWIiIhIEjw9PPm287fk8s7F1399TXRsdMJj76x+h5/7/kydYnXcdv49p/cQY2MSLlK8oke1Hjz/2/O8s+od3mmXOS6ozOl0oaKIiIhIMjw9PPms42dcfv4y9kVLzAsxbH90O7m8c9FifAtW7FvhtnPvOrkLgPL5y1+zvXz+8vSp0YdPwz7lSOQRt51fUk6lWkRERCQVPIwHlQpWYsV9KygaUJS2E9vy866f3XKuq9eovt6opqO4GHORt1e97ZZzS+qoVIuIiIikQanAUiy/bzmVClbirql3sWDnApefY+fJneT1zUuhXIVueKxigYr0rt6bT8I+4ei5oy4/t6SOSrWIiIhIGhXOXZjfBvxGjSI1uOe7e1iyZ4lLj3/9cnrXG9V0FBcuX8g0N6rJyVSqRURERNIhyC+IRX0XUT5/ee6aehcr96102bF3ntiZ6NSPKyoXrEyv6r34+M+POXbumMvOK6mnUi0iIiKSTgVyFeDXfr9SMm9J7ph8B38e+DPdx7wUc4m9EXspn698svs93/R5zl8+z5gVY9J9Tkk7lWoRERERFygSUITF/RdTMFdB2k1qx4bDG9J1vD2n9hBrY5MdqQaoUqgKg2oP4t017/Lpn5+m65ySdirVIiIiIi5SMm9JlgxYQm6f3LSZ2Iatx7am+Vg7T8av/JE/+VIN8EmHT+hYsSOPLHiEr9d9neZzZjaxNhZrrdMxUkSlWkRERMSFgoOCWdJ/CZ4enrSe0DphrenUSm45vev5ePows/tM2t3Sjgd+eICJGyam6ZyZzfwd86n4UcU0v4cZSaVaRERExMUqFKjA4v6LuRx7mVYTWhF+OjxVz4+JjWHh7oXk88tHAf8CKXqOr5cvc3rOoUXZFgycO5Apm6akIXnmMmvbLI6fP07pwNJOR7kplWoRERERN6haqCq/9PuFsxfP0vTbpgkjzzdjrWXwj4NZtHsRLzR7Icnl9BLj7+3PvF7zaFK6Cf3m9GP8+vFpje+4yzGXmff3PDpV6oSPp4/TcW5KpVpERETETUKKhvDbgN+Iio6i6bimbDm6Jdn9rbU8+8uzfPXXV4xsMpIhDYak+py5fXKzoM8CWpZtyX1z7+OrdV+lMb2zfgv/jVNRp7inyj1OR0kRL6cDiIiIiGRntYrWYtnAZbSa0Ipm45oxt9dciuUpxpmLZ4iIisBi8ffyx8/Ljznb5/D26rd59NZHeaXFK2k+Zy7vXMzrNY+7v7ubB354gMsxl3n41odd+Krcb9bWWQT4BND2lrZOR0kRlWoRERERN6tSqAqh94XSakIrGn/bONl9+9bsy3/v+G+qpn0kxt/bn+97fk/3Gd15ZMEjXI69zP/d9n/pOmZGiYmNYc72OXSo0AE/Lz+n46SISrWIiIhIBiifvzxr7l/D99u/J5d3LvL65iXQLxCD4UL0BaKio/D28OaOCnfgYVwzQ9fXy5eZPWbSa2Yvnlj4BJdjLvP07U+75NjutHzfco6dP5Zlpn6ASrWIiIhIhimWp1iGT8Pw8fRherfp9J3Tl2d+eYZLMZcY3mR4hmZIrVlbZ+Hv5c8dFe5wOkqKqVSLiIiIZHPent5Mvnsy3h7ejFgygjnb51AgVwHy+OShaEBRRjYZSZGAIk7HBOJu+DJ7+2zal29PgE+A03FSTKVaREREJAfw8vBifJfxlA0qy6r9qzhx/gThp8OZ+/dcloYv5bcBv1EgV8rWxHan3/f/zsGzB7PU1A9QqRYRERHJMTw9PHml5bWriiz+ZzEdpnSg7aS2LO6/mCC/IGfCxZu1bRbeHt50rNjR0RyppXWqRURERHKwVuVaMbvnbDYd2cQdk+/g7MWzjmWx1jJ722za3NKGQL9Ax3KkhUq1iIiISA53Z4U7md5tOn8e+JMOUzoQERXhSI7dp3az5/Qe7qp4lyPnTw+VahERERGha5WuTLlnCqv3r6bZuGYcjjyc4RlC94YC0KxMsww/d3qpVIuIiIgIAD2q9eCH3j+w8+ROGn3TiN0nd2fo+UP3hlIwV0EqF6ycoed1BbddqGiM+QboCBy11lZP5PHOwCtALBANDLHWrnBXHhERERG5ufbl27Ok/xLunHInjb5pRNfKXTl98TSno05z9uJZomOjibExxMTG0O6Wdrze6vV03/3xitC9oTQt09Rlx8tI7hypHge0T+bxxUAta20IMAj4yo1ZRERERCSFbit5GyvuW0HBXAWZvX026w6t4+SFk/h5+RHkF0ShXIUI8AlgzMoxPPXzU1hr033OfyP+Zc/pPTQt3dQFryDjuW2k2lobaowJTubxyKu+zQ2k/7+GiIiIiLhElUJV2PzI5iQft9YyZOEQ3v/9fQL9AhndfHS6zrd833IAmpZRqU41Y0xX4A2gMNAhmf0eBB4EKF26dMaEExEREZEkGWN4r/17nL10lpeWvURe37wMaTCEqOgoLly+QC7vXPh7+6f4eKF7Q8nrm5eaRWq6MbX7GFcM1yd58LiR6h8Tm1N93X5NgResta1vdsx69erZsLAwFyUUERERkfSIiY2h96zezNg645rtgb6BzOwxk9blblrvAKj2STXKBJZhQZ8F7oiZJsaYtdbaeinZN1PcUTF+qkg5Y0xBa+1xp/OIiIiISMp4engy6e5JNCzZkIiLEfh7+ePv7c+X677kjsl38G3nb+lbs2+yxzh27hhbj22lX81+GZTa9Rwr1caY8sBua601xtQBfIETTuURERERkbTx8fThyYZPXrOtf63+3D39bvrN6ce/Ef8yrPGwJFf1WLEvbgG4rDqfGty7pN5UoDlQ0BizH3gR8Aaw1n4G3AP0N8ZcBi4APa0756KIiIiISIYJ8gvipz4/MWjeIEYsGcHRc0d5t927iRbr0L2h+Hn5Ua94imZaZEruXP2j900eHwuMddf5RURERMRZvl6+TOw6kUK5CvH+7++TyzsXr7V67Yb9QveF0rBkQ3w8fRxI6RqZYk61iIiIiGRPHsaD99q9x4XLF3h9xevk9snNiCYjEh6PiIpg/eH1PN/0eQdTpp9KtYiIiIi4lTGGTzt+yvno84xcMhJ/L38ev+1xvDy8WPXvKmJtbJaeTw0q1SIiIiKSATyMB992/pZzl87x1KKneOaXZygaUBQALw8vGpRs4HDC9FGpFhEREZEM4eXhxbRu05i6aSq7T+3mwJkDHDh7gNpFa5PLO5fT8dJFpVpEREREMoyPpw8DQgY4HcPlPJwOICIiIiKS1alUi4iIiIikk0q1iIiIiEg6qVSLiIiIiKSTSrWIiIiISDqpVIuIiIiIpJNKtYiIiIhIOqlUi4iIiIikk0q1iIiIiEg6qVSLiIiIiKSTSrWIiIiISDqpVIuIiIiIpJNKtYiIiIhIOqlUi4iIiIikk0q1iIiIiEg6qVSLiIiIiKSTSrWIiIiISDqpVIuIiIiIpJOx1jqdIVWMMceAvQ6dviBw3KFz5yR6nzOO3uuMofc54+i9zjh6rzOG3ueMk9h7XcZaWyglT85ypdpJxpgwa209p3Nkd3qfM47e64yh9znj6L3OOHqvM4be54yT3vda0z9ERERERNJJpVpEREREJJ1UqlPnC6cD5BB6nzOO3uuMofc54+i9zjh6rzOG3ueMk673WnOqRURERETSSSPVIiIiIiLppFKdAsaY9saYv40xu4wxw5zOk50YY0oZY34zxmw1xmwxxjwRv320MeaAMWZ9/J87nc6a1Rljwo0xm+Lfz7D4bfmNMb8YY3bG/53P6ZxZnTGm0lWf2/XGmDPGmCH6TLuGMeYbY8xRY8zmq7Yl+jk2cf4b/7N7ozGmjnPJs5Yk3ue3jDHb49/LOcaYoPjtwcaYC1d9tj9zLHgWlMR7neTPC2PM8PjP9N/GmHbOpM56knifp1/1HocbY9bHb0/TZ1rTP27CGOMJ7ADaAPuBP4He1tqtjgbLJowxxYBi1tp1xpg8wFqgC9ADiLTWvu1kvuzEGBMO1LPWHr9q25vASWvtmPh/MOaz1j7nVMbsJv7nxwHgNuA+9JlON2NMUyASmGCtrR6/LdHPcXwReRy4k7j/Bh9Ya29zKntWksT73BZYYq2NNsaMBYh/n4OBH6/sJ6mTxHs9mkR+XhhjqgJTgfpAceBXoKK1NiZDQ2dBib3P1z3+DhBhrX05rZ9pjVTfXH1gl7X2H2vtJWAa0NnhTNmGtfaQtXZd/NdngW1ACWdT5SidgfHxX48n7h804jqtgN3WWqduWJXtWGtDgZPXbU7qc9yZuP+BWmvtGiAo/h/ychOJvc/W2kXW2uj4b9cAJTM8WDaUxGc6KZ2Badbai9baPcAu4nqK3ERy77MxxhA3mDc1PedQqb65EsC/V32/H5U+t4j/l2Ft4Pf4TY/F/5rxG01LcAkLLDLGrDXGPBi/rYi19lD814eBIs5Ey7Z6ce0PaX2m3SOpz7F+frvPIOCnq74va4z5yxizzBjTxKlQ2UxiPy/0mXaPJsARa+3Oq7al+jOtUi2ZgjEmAJgFDLHWngE+BW4BQoBDwDvOpcs2Gltr6wB3AI/G/yosgY2bC6b5YC5ijPEBOgEz4jfpM50B9Dl2P2PMSCAamBy/6RBQ2lpbG3gKmGKMyetUvmxCPy8yVm+uHQBJ02dapfrmDgClrvq+ZPw2cRFjjDdxhXqytXY2gLX2iLU2xlobC3yJfr2VbtbaA/F/HwXmEPeeHrny6/D4v486lzDbuQNYZ609AvpMu1lSn2P9/HYxY8xAoCPQJ/4fMMRPRTgR//VaYDdQ0bGQ2UAyPy/0mXYxY4wXcDcw/cq2tH6mVapv7k+ggjGmbPzIUy9gnsOZso34eUxfA9uste9etf3qeY9dgc3XP1dSzhiTO/5CUIwxuYG2xL2n84AB8bsNAOY6kzBbumbkQ59pt0rqczwP6B+/CkgD4i5COpTYAeTmjDHtgWeBTtba81dtLxR/US7GmHJABeAfZ1JmD8n8vJgH9DLG+BpjyhL3Xv+R0fmymdbAdmvt/isb0vqZ9nJbxGwi/irnx4CfAU/gG2vtFodjZSeNgH7ApitL2QAjgN7GmBDifo0bDjzkRLhspAgwJ+7fMHgBU6y1C40xfwLfGWPuB/YSd6GGpFP8P1zacO3n9k19ptPPGDMVaA4UNMbsB14ExpD453gBcSt/7ALOE7cCi6RAEu/zcMAX+CX+Z8kaa+1goCnwsjHmMhALDLbWpvTCuxwvife6eWI/L6y1W4wx3wFbiZuC86hW/kiZxN5na+3X3HjtC6TxM60l9URERERE0knTP0RERERE0kmlWkREREQknVSqRURERETSSaVaRERERCSdVKpFRERERNJJpVpExA2MMUWNMdOMMbvjbw2/wBiT6htiGGOWGmPquSNjKnMMNMZ85HQOEZHMSqVaRMTF4m9qNAdYaq29xVpbl7g1fos4m8w5V26kICKSXalUi4i4XgvgsrX2sysbrLUbrLXLjTETjDFdrmw3xkw2xnQ2xngaY942xmw2xmw0xjx+/UGNMW2NMauNMeuMMTOMMQGJ7LPUGDPWGPOHMWaHMaZJ/PZrRpqNMT8aY5rHfx1pjHnLGLPFGPOrMaZ+/HH+McZ0uurwpeK37zTGvHjVsfrGn2+9Mebzq+5EFmmMeccYswFoaIwZY4zZGv/63k772ysikvmoVIuIuF51YG0Sj30NDAQwxgQCtwPzgQeBYCDEWlsTmHz1k4wxBYFRQGtrbR0gDHgqiXN4WWvrA0OIuzvbzeQGllhrqwFngVeJuyNkV+Dlq/arD9wD1AS6G2PqGWOqAD2BRtbaECAG6HPVcX+31tYCtsUfr1r863s1BblERLIM3aZcRCQDWWuXGWM+McYUIq6gzrLWRhtjWgOfWWuj4/e7/pa4DYCqwMr4W0T7AKuTOM3s+L/XElfUb+YSsDD+603ARWvtZWPMpuue/4u19gSAMWY20Ji4WyXXBf6Mz+UPHI3fPwaYFf91BBAFfG2M+RH4MQW5RESyDJVqERHX2wJ0S+bxCUBfoBdwXwqPaYgrtb1TsO/F+L9j+N/P+Wiu/e2k31VfX7bW2vivY68831oba4y5+v8TlmvZ+FzjrbXDE8kRZa2NiT9WtDGmPtCKuPfmMaBlCl6LiEiWoOkfIiKutwTwNcY8eGWDMabmlfnNwDjipmZgrd0av+0X4KErJdYYk/+6Y64BGhljysc/njuVq4mEAyHGGA9jTCnipnKkVhtjTH5jjD/QBVgJLAa6GWMKX8ltjClz/RPj538HWmsXAE8CtdJwfhGRTEsj1SIiLmattcaYrsD7xpjniJv2EM7/ivQRY8w24PurnvYVUBHYaIy5DHwJfHTVMY8ZYwYCU40xvvGbRwE7UhhrJbAH2Erc/OZ1aXhpfxA3naMkMMlaGwZgjBkFLDLGeACXgUeBvdc9Nw8w1xjjR9zodlLzwUVEsiTzv9/4iYhIRjDG5CJu7nIda22E03lERCT9NP1DRCQDxV+QuA34UIVaRCT70Ei1iIiIiEg6aaRaRERERCSdVKpFRERERNJJpVpEREREJJ1UqkVERERE0kmlWkREREQknVSqRURERETS6f8BvcaKR/V65b0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "ax.plot(dataset[['cycle']], dataset[['capacity']] , color='green', label='Battery capacity data')\n",
    "ax.plot([0.,168], [1.38, 1.38],dashes=[6, 2], label=\"EoL Threshold\")\n",
    "ax.set(xlabel='Cycle numbers', ylabel='Capacity')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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u4314gnVPdpyfMVV1K935+u/ojquvpHvc0E+msZjjWPu8s4xu4KqP0h3fV9Ddj70+/q0t51Lgb6rqi638I3SDjn0xyYN0P46+YILlnEHXlW8V3fnnynHzTwH2aNvyuVb2Nrr9ch/dKJWfY2LTjWnaquo8uu9iZ7XvKd8AXt5mX0J368c36bb1x6zdlXHUZ/t+um7Fn6DbPz+gu+VjIhOdyz/eYrmO7rvZZ4ctQOsuVevSc03qpBuK+K6q+ou5jmVjk+Q4upufRyW0kqR5ol2hu4+ue+QdcxzORi/JYtrIze2KpqTGK3CSJEkbQJJXtu5/W9E9RuAGfjZ4hiStExM4SZNK95DVh4a8/mzy1pI0bx1MN+jGt4Hd6bqs2fVpnknyuhHn0Bsnby09nl0oJUmSJKknvAInSZIkST2x6VwHMMz2229fixcvnuswJEkb2PLly79XVeOfN6QRPD9K0vwx6hy5USZwixcvZtmyZXMdhiRpA0vyrbmOoU88P0rS/DHqHGkXSkmSJEnqCRM4SZIkSeoJEzhJkiRJ6gkTOEmSJEnqCRM4SZIkSeoJEzhJkiRJ6gkTOEmSJEnqCRM4SZIkSeqJjfJB3lKfJZn1dVbVrK9TkiRJs88ETpph65pMJTERkyStt8VHXzQjy1l54kEzshxJM8sulJIkSZLUEyZwkiRJktQTJnCSJEmS1BMmcJIkSZLUEyZwkiRJktQTJnCSJE1TkpVJbkhybZJlrWy7JEuT3Nb+XdDKk+Rvk6xIcn2S5w0s5/BW/7Ykh8/V9kiS+sMETpKkdfNbVbVnVS1p00cDl1bV7sClbRrg5cDu7XUkcBJ0CR9wLPACYC/g2LGkT5KkUUzgJEmaGQcDp7f3pwOHDJSfUZ0rgW2T7AjsDyytqjVVdS+wFDhglmOWJPWMCZwkSdNXwBeTLE9yZCvboarubu+/A+zQ3u8M3DnQ9q5WNqp8LUmOTLIsybLVq1fP5DZIknpo07kOQJKkHnpxVa1K8vPA0iS3DM6sqkpSM7GiqjoZOBlgyZIlM7JMSVJ/eQVOkqRpqqpV7d97gPPo7mH7busaSfv3nlZ9FbDLQPNFrWxUuSRJI005gUuySZKvJ7mwTe+W5Ko2qtbZSTYf0e6YVufWJPvPVOCSJM2FJFsl2WbsPbAf8A3gAmBsJMnDgfPb+wuAN7TRKF8I3N+6Wl4C7JdkQRu8ZL9WJknSSNPpQvk24GbgKW36/cCHquqsJB8DjqCNrDUmyR7AocCzgJ2Af03yi1X16HpHLknS3NgBOC8JdOfRT1XVF5JcA5yT5AjgW8BrWv2LgQOBFcAPgd8HqKo1Sd4LXNPqHV9Va2ZvMyRJfTSlBC7JIuAg4ATgHenOWvsCr21VTgeOY1wCRzfy1llV9TBwR5IVdN1Mvrr+oUuSNPuq6nbgOUPKvw+8ZEh5AUeNWNapwKkzHaMk6Ylrql0oPwy8C/hpm34acF9VPdKmh46cxRRH2AJH2ZIkSZKkyUyawCV5BXBPVS3fkIFU1clVtaSqlixcuHBDrkqSJEmSemkqXSj3Bl6V5EBgS7p74D5C9yDSTdtVuFEjZznCliRJkiTNkEmvwFXVMVW1qKoW0w1I8qWqeh1wGfDqVm1wtK1BFwCHJtkiyW7A7sDVMxK5JEmSJM0z6/McuHfTDWiygu6euFMAkrwqyfEAVXUjcA5wE/AF4ChHoJQkSZKkdTOdxwhQVV8Gvtze3043ouT4OhfQXXkbmz6BbvRKSZIkSdJ6WJ8rcJIkSZKkWWQCJ0mSJEk9YQInSZIkST1hAidJkiRJPWECJ0mSJEk9YQInSZIkST1hAidJkiRJPWECJ0mSJEk9YQInSZIkST1hAidJkiRJPWECJ0mSJEk9YQInSZIkST1hAidJkiRJPWECJ0mSJEk9YQInSZIkST1hAidJkiRJPWECJ0mSJEk9YQInSZIkST2x6WQVkmwJXA5s0eqfW1XHJvl3YJtW7eeBq6vqkCHtHwVuaJP/WVWvmonAJUmSJGm+mTSBAx4G9q2qh5JsBlyR5PNV9etjFZJ8Bjh/RPsfVdWe6x+qJEmSJM1vk3ahrM5DbXKz9qqx+UmeAuwLfG5DBChJkiRJ6kzpHrgkmyS5FrgHWFpVVw3MPgS4tKoeGNF8yyTLklyZ5JAJ1nFkq7ds9erVU4tekiRJkuaRKSVwVfVo6wa5CNgrybMHZh8GfHqC5rtW1RLgtcCHkzxjxDpOrqolVbVk4cKFU4tekiRJkuaRaY1CWVX3AZcBBwAk2R7YC7hogjar2r+3A18GnrtuoUqSJEnS/DZpApdkYZJt2/snAy8DbmmzXw1cWFU/HtF2QZIt2vvtgb2Bm2YgbkmS5lS7veDrSS5s07sluSrJiiRnJ9m8lW/Rple0+YsHlnFMK781yf5ztCmSpB6ZyhW4HYHLklwPXEN3D9yFbd6hjOs+mWRJkk+0yV8BliW5ju7K3YlVZQInSXoieBtw88D0+4EPVdUzgXuBI1r5EcC9rfxDrR5J9qA7jz6LrmfLPyTZZJZilyT11KSPEaiq6xnR7bGq9hlStgx4c3v/FeBX1y9ESZI2LkkWAQcBJwDvSBK6EZlf26qcDhwHnAQc3N4DnAt8tNU/GDirqh4G7kiygu62hK/O0mZIknpoWvfASZIkAD4MvAv4aZt+GnBfVT3Spu8Cdm7vdwbuBGjz72/1Hysf0kaSpKGm8iBvSZLUJHkFcE9VLU+yzyys70jgSICnP/3pG3p10mMWHz1yjLopW3niQTMQiaRBXoGTJGl69gZelWQlcBZd18mPANsmGfthdBGwqr1fBewC0OY/Ffj+YPmQNo/xMTuSpEEmcJIkTUNVHVNVi6pqMd0gJF+qqtfRDdb16lbtcOD89v6CNk2b/6WqqlZ+aBulcjdgd+DqWdoMSVJP2YVSkqSZ8W7grCTvA74OnNLKTwE+2QYpWUOX9FFVNyY5h+7xOo8AR1XVo7MftiSpT0zgJElaR1X1ZeDL7f3tdKNIjq/zY+B3RrQ/gW4kS0kjzMS9eOD9eHrisAulJEmSJPWECZwkSZIk9YRdKCVJkrRBzFT3R0k/4xU4SZIkSeoJr8BJkiTpCc8Hk+uJwgROGmK77bbj3nvvnfX1Jpm1dS1YsIA1a9bM2vokSZK0/kzgpCHuvfdeuufsPnHNZrIoSZKkmeE9cJIkSZLUE16BkyRJkqbA++i0MfAKnCRJkiT1hFfgJEmSpFkyU8/G80re/OUVOEmSJEnqiUkTuCRbJrk6yXVJbkzynlZ+WpI7klzbXnuOaH94ktva6/AZjl+SJEmS5o2pdKF8GNi3qh5KshlwRZLPt3l/WlXnjmqYZDvgWGAJUMDyJBdU1ew/YEuSJEmSem7SK3DVeahNbtZeU31A1v7A0qpa05K2pcAB6xSpJEmSJM1zU7oHLskmSa4F7qFLyK5qs05Icn2SDyXZYkjTnYE7B6bvamXD1nFkkmVJlq1evXrqWyBJkiRJ88SURqGsqkeBPZNsC5yX5NnAMcB3gM2Bk4F3A8evayBVdXJbDkuWLJnqFT5JkiRp3vGZdPPXtEahrKr7gMuAA6rq7ta98mHgn4C9hjRZBewyML2olUmSJEmSpmkqo1AubFfeSPJk4GXALUl2bGUBDgG+MaT5JcB+SRYkWQDs18okSZIkSdM0lS6UOwKnJ9mELuE7p6ouTPKlJAuBANcCbwFIsgR4S1W9uarWJHkvcE1b1vFVtWbGt0KSJEmS5oFJE7iquh547pDyfUfUXwa8eWD6VODU9YhRkiRJksQ074GTJEmSJM0dEzhJkiRJ6gkTOEmSJEnqCRM4SZIkSeoJEzhJkiRJ6gkTOEmSJEnqCRM4SZIkSeoJEzhJkiRJ6gkTOEmSJEnqCRM4SZIkSeqJTec6AEmS+iTJlsDlwBZ059Fzq+rYJLsBZwFPA5YDr6+qnyTZAjgDeD7wfeB3q2plW9YxwBHAo8AfV9Uls709kuavxUdftN7LWHniQTMQiabDK3CSJE3Pw8C+VfUcYE/ggCQvBN4PfKiqngncS5eY0f69t5V/qNUjyR7AocCzgAOAf0iyyWxuiCSpf0zgJEmahuo81CY3a68C9gXObeWnA4e09we3adr8lyRJKz+rqh6uqjuAFcBeG34LJEl9ZgInSdI0JdkkybXAPcBS4D+A+6rqkVblLmDn9n5n4E6ANv9+um6Wj5UPaTO4riOTLEuybPXq1RtgayRJfWICJ0nSNFXVo1W1J7CI7qrZL2/AdZ1cVUuqasnChQs31GokST1hAidJ0jqqqvuAy4AXAdsmGRscbBGwqr1fBewC0OY/lW4wk8fKh7SRJGkoEzhJkqYhycIk27b3TwZeBtxMl8i9ulU7HDi/vb+gTdPmf6mqqpUfmmSLNoLl7sDVs7IRkqTe8jECkiRNz47A6W3EyCcB51TVhUluAs5K8j7g68Aprf4pwCeTrADW0I08SVXdmOQc4CbgEeCoqnp0lrdFktQzkyZwEzzv5kxgCfBfdL8Y/kFV/deQ9o8CN7TJ/6yqV81U8NKGUsc+BY576lyHsUHVsU+Z6xCkXqqq64HnDim/nSGjSFbVj4HfGbGsE4ATZjpGSdIT11SuwI097+ahJJsBVyT5PHAm8HutzqeANwMnDWn/o3ajt9Qbec8DdD2cnriSUMfNdRSSJEmajkkTuNZP/3HPu6mqi8fqJLma7uZrSZIkSdIGMqVBTMY/76aqrhqYtxnweuALI5pv2Z5fc2WSQyZYh8+5kSRJkqQJTCmBG/+8myTPHpj9D8DlVfXvI5rvWlVLgNcCH07yjBHr8Dk3kiRJkjSBaT1GYOB5NwcAJDkWWAi8Y4I2q9q/twNfZsiN35IkSZKkyU2awI143s0tSd4M7A8cVlU/HdF2QZIt2vvtgb3phkuWJEmSJE3TVEahHPW8m0eAbwFfTQLw2ao6PskS4C1V9WbgV4B/TPLT1vbEqjKBkyRJGmLx0RfNdQiSNnJTGYVy1PNuhratqmV0jxSgqr4C/Op6xihJkiRJYpr3wEmSJEmS5o4JnCRJkiT1hAmcJEmSJPWECZwkSZIk9YQJnCRJkiT1hAmcJEmSJPWECZwkSZIk9YQJnCRJkiT1hAmcJEmSJPXEpnMdgCRJkqR+Wnz0Reu9jJUnHjQDkcwfXoGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknpg0gUuyZZKrk1yX5MYk72nluyW5KsmKJGcn2XxE+2NanVuT7D/TGyBJkiRJ88VUrsA9DOxbVc8B9gQOSPJC4P3Ah6rqmcC9wBHjGybZAzgUeBZwAPAPSTaZodglSZIkaV6ZNIGrzkNtcrP2KmBf4NxWfjpwyJDmBwNnVdXDVXUHsALYa32DliRJkqT5aNOpVGpXzZYDzwT+HvgP4L6qeqRVuQvYeUjTnYErB6ZH1SPJkcCRAE9/+tOnEpa0QSWZ6xA2qAULFsx1CJIkSZqmKQ1iUlWPVtWewCK6K2i/PNOBVNXJVbWkqpYsXLhwphcvTUtVzfprtte7Zs2aOd7LUj8l2SXJZUluaveGv62Vb5dkaZLb2r8LWnmS/G27H/z6JM8bWNbhrf5tSQ6fq22SJPXHtEahrKr7gMuAFwHbJhm7grcIWDWkySpgl4HpUfUkSeqLR4B3VtUewAuBo9o930cDl1bV7sClbRrg5cDu7XUkcBJ0CR9wLPACuh9Hjx1L+iRJGmUqo1AuTLJte/9k4GXAzXSJ3KtbtcOB84c0vwA4NMkWSXajO3ldPQNxS5I0J6rq7qr6Wnv/IN05cWe6+75Pb9UG7w0/GDij3VN+Jd0PoDsC+wNLq2pNVd0LLKUb8EuSpJGmcg/cjsDp7T64JwHnVNWFSW4CzkryPuDrwCkASV4FLKmqv6yqG5OcA9xE94vlUVX16AbZEkmSZlmSxcBzgauAHarq7jbrO8AO7f3OwJ0DzcbuBx9VLknSSJMmcFV1Pd3JaXz57QwZUbKqLqC78jY2fQJwwvqFKUnSxiXJ1sBngLdX1QODAx9VVSWpGVqPg3xJkh4zrXvgJEkSJNmMLnk7s6o+24q/27pG0v69p5WPuh98SveJO8iXJGmQCZwkSdOQ7lLbKcDNVfXBgVkX0N0TDmvfG34B8IY2GuULgftbV8tLgP2SLGiDl+zXyiRJGmlKz4GTJEmP2Rt4PXBDkmtb2Z8BJwLnJDkC+BbwmjbvYuBAYAXwQ+D3AapqTZL3Ate0esdXlc/3kCRNyAROkqRpqKorgIyY/ZIh9Qs4asSyTgVOnbnoJElPdHahlCRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSe8DlwkiRJkubM4qMvmpHlrDzxoBlZzsbOK3CSJEmS1BMmcJIkSZLUEyZwkiRJktQTJnCSJEmS1BMOYiJJkjQDZmogBkmaiFfgJEmSJKknJr0Cl2QX4AxgB6CAk6vqI0nOBn6pVdsWuK+q9hzSfiXwIPAo8EhVLZmRyCVJkiRpnplKF8pHgHdW1deSbAMsT7K0qn53rEKSDwD3T7CM36qq761nrJIkSZI0r02awFXV3cDd7f2DSW4GdgZuAkgS4DXAvhswTkmSJEma96Z1D1ySxcBzgasGin8d+G5V3TaiWQFfTLI8yZETLPvIJMuSLFu9evV0wpIkSZKkeWHKo1Am2Rr4DPD2qnpgYNZhwKcnaPriqlqV5OeBpUluqarLx1eqqpOBkwGWLFlSU41LkiRpfTmCpKS+mNIVuCSb0SVvZ1bVZwfKNwV+Gzh7VNuqWtX+vQc4D9hrfQKWJEmSpPlqKqNQBjgFuLmqPjhu9kuBW6rqrhFttwKe1O6d2wrYDzh+PWOWJEkCvHImaf6ZyhW4vYHXA/smuba9DmzzDmVc98kkOyW5uE3uAFyR5DrgauCiqvrCDMUuSZIkSfPKVEahvALIiHlvHFL2beDA9v524DnrF6IkSZIkCaYxiIkkSdJMsvujJE2fCZwkSZo2ky9JmhvTeg6cJEmSJGnumMBJkiRJUk+YwEmSJElST5jASZIkSVJPOIiJJEmSpN6bicGVVp540AxEsmF5BU6SJEmSesIETpKkaUhyapJ7knxjoGy7JEuT3Nb+XdDKk+Rvk6xIcn2S5w20ObzVvy3J4XOxLZKk/jGBkyRpek4DDhhXdjRwaVXtDlzapgFeDuzeXkcCJ0GX8AHHAi8A9gKOHUv6JEmaiAmcJEnTUFWXA2vGFR8MnN7enw4cMlB+RnWuBLZNsiOwP7C0qtZU1b3AUh6fFEqS9DgmcJIkrb8dquru9v47wA7t/c7AnQP17mplo8ofJ8mRSZYlWbZ69eqZjVqS1DsmcJIkzaCqKqBmcHknV9WSqlqycOHCmVqsJKmnTOAkSVp/321dI2n/3tPKVwG7DNRb1MpGlUuSNCETOEmS1t8FwNhIkocD5w+Uv6GNRvlC4P7W1fISYL8kC9rgJfu1MkmSJuSDvCVJmoYknwb2AbZPchfdaJInAuckOQL4FvCaVv1i4EBgBfBD4PcBqmpNkvcC17R6x1fV+IFRJEl6HBM4SZKmoaoOGzHrJUPqFnDUiOWcCpw6g6FJkuYBu1BKkiRJUk9MmsAl2SXJZUluSnJjkre18uOSrEpybXsdOKL9AUluTbIiydHD6kiSJEmSJjeVLpSPAO+sqq8l2QZYnmRpm/ehqvqbUQ2TbAL8PfAyumfcXJPkgqq6aX0DlyRJkqT5ZtIrcFV1d1V9rb1/ELiZEQ8bHWIvYEVV3V5VPwHOAg5e12AlSZIkaT6b1j1wSRYDzwWuakVvTXJ9klPbMMjj7QzcOTB9F1NP/iRJkiRJA6acwCXZGvgM8PaqegA4CXgGsCdwN/CB9QkkyZFJliVZtnr16vVZlCRJkiQ9IU0pgUuyGV3ydmZVfRagqr5bVY9W1U+Bj9N1lxxvFbDLwPSiVvY4VXVyVS2pqiULFy6czjZIkiRJ0rwwlVEoA5wC3FxVHxwo33Gg2v8AvjGk+TXA7kl2S7I5cChwwfqFLEmSJEnz01RGodwbeD1wQ5JrW9mfAYcl2RMoYCXwBwBJdgI+UVUHVtUjSd4KXAJsApxaVTfO6BZIkiRJ0jwxaQJXVVcAGTLr4hH1vw0cODB98ai6kiRJkqSpm9YolJIkSZKkuTOVLpSSJEmS9IS3+OiL1nsZK088aAYiGc0rcJIkSZLUEyZwkiRJktQTJnCSJEmS1BMmcJIkSZLUEyZwkiRJktQTJnCSJEmS1BM+RkCSpHlkJobIliTNHa/ASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk84CqU0w5LMetuqWud1SpIkqT9M4KQZZjIlSZKkDcUulJIkSZLUEyZwkiRJktQTJnCSJEmS1BOTJnBJdklyWZKbktyY5G2t/K+T3JLk+iTnJdl2RPuVSW5Icm2SZTMcvyRJkiTNG1O5AvcI8M6q2gN4IXBUkj2ApcCzq+q/A98EjplgGb9VVXtW1ZL1jliSJEmS5qlJE7iquruqvtbePwjcDOxcVV+sqkdatSuBRRsuTEmSnniSHJDk1iQrkhw91/FIkjZ+07oHLsli4LnAVeNmvQn4/IhmBXwxyfIkR06w7COTLEuybPXq1dMJS5Kk3kmyCfD3wMuBPYDDWg8XSZJGmnICl2Rr4DPA26vqgYHyP6frZnnmiKYvrqrn0Z2gjkryG8MqVdXJVbWkqpYsXLhwyhsgSVJP7QWsqKrbq+onwFnAwXMckyRpIzelB3kn2YwueTuzqj47UP5G4BXAS2rE04uralX7954k59GdsC6faH3Lly//XpJvTWkLpCeO7YHvzXUQ0izbda4DmEM7A3cOTN8FvGB8pdZ7ZawHy0NJbh2Y3efjRp9jh37H3+fYod/xG/vcmbX48/4ZW9TQc+SkCVySAKcAN1fVBwfKDwDeBfxmVf1wRNutgCdV1YPt/X7A8ZOts6q8BKd5J8kyB/qRNF5VnQycPGxen48bfY4d+h1/n2OHfsdv7HOn7/EPmkoXyr2B1wP7tkcBXJvkQOCjwDbA0lb2MYAkOyW5uLXdAbgiyXXA1cBFVfWFmd8MSZJ6ZxWwy8D0olYmSdJIk16Bq6orgAyZdfGQMqrq28CB7f3twHPWJ0BJkp6grgF2T7IbXeJ2KPDauQ1JkrSxm9I9cJJmxdAuUpKemKrqkSRvBS4BNgFOraobp7mYPh83+hw79Dv+PscO/Y7f2OdO3+N/TEaMPSJJkiRJ2shM6zlwkiRJkqS5YwInSZIkST1hAifNsSSnJrknyTfmOhZJcy/JLkkuS3JTkhuTvK2V/3WSW5Jcn+S8JNuOaL8yyQ1thOhlsxo8E8Z/XJJV40a0Htb+gCS3JlmR5OiNJPazB+JemeTaEe3net9vmeTqJNe1+N/TyndLclXbp2cn2XxE+2NanVuT7L+RxH5mi+cb7Xy52Yj2jw78jS6Yzdjb+kfFf1qSOwZi23NE+8OT3NZeh28ksf/7QNzfTvK5Ee3ndN+3GDZJ8vUkF7bpjf4zvz68B06aY0l+A3gIOKOqnj3X8UiaW0l2BHasqq8l2QZYDhxC95iBL7XBT94PUFXvHtJ+JbCkqubkgbsTxP8a4KGq+psJ2m4CfBN4Gd2Dza8BDquqmzZ44IyOfXD9ST4A3F9Vj3uu7Uaw7wNsVVUPtUTnCuBtwDuAz1bVWeke+3RdVZ00ru0ewKeBvYCdgH8FfrGqHp3j2LcDPt+qfQq4fHzsrf1DVbX1bMQ6zATxvwW4sKrOnaDtdsAyYAlQdJ+751fVvRs+8tGxV9WVA3U+A5xfVWcMaT+n+77F8A66/feUqnpFknPYyD/z68MrcNIcq6rLgTVzHYekjUNV3V1VX2vvHwRuBnauqi9W1SOt2pV0Cd1GZ1T8U2y+F7Ciqm6vqp8AZwEHb5hIH2+y2NsX3dfQfenb6FTnoTa5WXsVsC8wlkCcTpdQj3cwcFZVPVxVdwAr6P4es2JU7FV1cZtXdM8U3lg/96P2/VTsDyytqjUtaVsKHLABwhxqstiTPIXuM/S52YppOpIsAg4CPtGmQw8+8+vDBE6SpI1UksXAc4Grxs16Ez+7KjFeAV9MsjzJkRswvEkNif+t6bqAnppkwZAmOwN3DkzfxdSTvxk1Yt//OvDdqrptRLM53/etK9m1wD10icB/APcNJP+j9umc7/vxsVfVVQPzNgNeD3xhRPMtkyxLcmWSQzZ4sENMEP8J7XP/oSRbDGm6Ue97uuTn0qp6YETzud73HwbeBfy0TT+Nnnzm15UJnCRJG6EkWwOfAd4++MUpyZ8DjwBnjmj64qp6HvBy4KjWTXvWDYn/JOAZwJ7A3cAH5iKuqRi174HDmPjq25zv+6p6tKr2pLtStRfwy7Mdw7oaH3uSwdsK/oGu++S/j2i+a1UtAV4LfDjJMzZstI83Iv5j6P4Gv0bXHfRx3Z43BpPs+8k+93O275O8ArinqpbP1jo3BiZwkiRtZNrVhs8AZ1bVZwfK3wi8AnhdjbiJvapWtX/vAc5jDroEDYu/qr7bviT+FPj4iLhWAbsMTC9qZbNmgn2/KfDbwNmj2m4M+34glvuAy4AXAdu2+GH0Pp3zfT9mIPYDAJIcCyyku5dvVJuxfX878GW6q6dzYjD+1i23quph4J/YSD/3Y4bs++3pYr5ogjZzue/3Bl7V7j89i67r5Efo2Wd+ukzgJEnaiLT7N04Bbq6qDw6UH0DXTehVVfXDEW23aoNvkGQrYD9gVke4nSD+HQeq/Y8RcV0D7N5GkNscOBSYtVHtRsXevBS4paruGtF2Y9j3C9NGJ03yZLrBYG6m+0L+6lbtcOD8Ic0vAA5NskWS3YDd6e45mxUjYr8lyZvp7hE7rCX/w9ouGOua2BKOvYFZGfhmIIZR8e/YykLXFXHYZ+ISYL+2HQvoPjuXzEbcLbahsbfZr6YbhOXHI9rO6b6vqmOqalFVLaY7Xnypql5HDz7z68METppjST4NfBX4pSR3JTlirmOSNKf2prvXZ9+sPeT+R4FtgKWt7GMASXZKcnFruwNwRZLr6L6IXFRVo+4Zmu34/3e6IfavB34L+J/j42/3rLyV7svrzcA5VXXjRhA7dF8O1+pGthHu+x2By9o+vobuXqYL6brtvSPJCrr7g05p8b8qyfEAbT+fQ/fl+wvAUbM8Gt+o2D9Gt2+/2v4ef9liX5LkE63trwDL2r6/DDixZmnk0inEf2aSG4AbgO2B942Pv6rWAO9t7a4Bjm9lcx07DP/cb2z7fpg+fObXmY8RkCRJkqSe8AqcJEmSJPWECZwkSZIk9YQJnCRJkiT1hAmcJEmSJPWECZwkSZIk9YQJnCRJkiT1hAmcJEmSJPXE/w9ziQIXo7vWuQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z5s3TeCqSpFFkAibthpNPPnmXyiUN3XzgQOBI4C+Ai9rTrBlTVedW1bKqWrZggb9ZLUlznQmYNE2WLFky7BAkTW4j8LnqfA14GDgI2AQsHqi3qJVNVH4vsH+S+WPKJUnaqfmTV5E0map6ZH6Gv0yXtHv+HvgPwJokzwD2Bn4AXAp8Osn7gCcDhwFfAwIcluRQugTrRODkqqoka4BX0L0XdipwSc/nIs0pS868bLf3cfu7j5+GSKTd4xMwaRqsWbOGn/3sZ6xZs2bYoUhqkqwCrgGemWRjktOAlcC/aUPTrwZObU/D1gEXATcD/wCcUVUPtXe8Xg9cAdwCXNTqArwF+PMk6+neCTuvz/OTJI0mn4BJ0+Doo48edgiSxqiqkyZY9coJ6p8DnDNO+eXA5eOU30Y3SqIkSVPmEzBJkiRJ6okJmLQbBt/9mkq5JEmS5ja7IEq7yWRLkiRJU+UTMEmSJEnqyZSfgCWZB6wFNlXVS2cuJEmSJGn6OZS99gS78gTsDXRD8EqSJEmSHoMpJWBJFgHHA5+Y2XAkSZIkafaa6hOwDwBvBh6eqEKS05OsTbJ28+bN0xGbJEmSJM0qkyZgSV4K3FNVX99Zvao6t6qWVdWyBQsWTFuAkiRJkjRbTOUJ2AuAlye5HVgNHJ3kkzMalSRJkiTNQpMmYFV1VlUtqqolwInAVVX1yhmPTJIkSZJmGX8HTJIkSZJ6MuXfAQOoqi8DX56RSCRJkiRplvMJmCRJkiT1xARMkiRJknpiAiZJkiRJPTEBkyRJkqSemIBJkmalJCuT3JPkW+Ose1OSSnJQW06SDyVZn+SmJM8dqHtqku+06dSB8ucl+Wbb5kNJ0s+ZSZJGmQmYJGm2Oh9YPrYwyWLgGOCOgeJjgcPadDrwsVb3QOBs4PnAEcDZSQ5o23wM+KOB7R51LEmSxjIBkyTNSlV1NbBlnFXvB94M1EDZCuDC6lwL7J/kEOAlwJVVtaWqtgJXAsvbuv2q6tqqKuBC4IQZPB1J0ixhAiZJmjOSrAA2VdU/jVm1ENgwsLyxle2sfOM45ZIk7dQu/RCzJEmjKsnjgb+k637Y53FPp+vWyFOe8pQ+Dy1J2gP5BEySNFc8DTgU+KcktwOLgG8k+RVgE7B4oO6iVraz8kXjlD9KVZ1bVcuqatmCBQum6VQkSaPKBEySNCdU1Ter6peraklVLaHrNvjcqroLuBQ4pY2GeCTww6q6E7gCOCbJAW3wjWOAK9q6+5Mc2UY/PAW4ZCgnJkkaKSZgkqRZKckq4BrgmUk2JjltJ9UvB24D1gN/C7wOoKq2AO8Arm/T21sZrc4n2jb/DHxxJs5DkjS7+A6YJGlWqqqTJlm/ZGC+gDMmqLcSWDlO+Vrg8N2LUpI01/gETJIkSZJ6YgImSZIkST0xAZMkSZKknpiASZIkSVJPTMAkSZIkqScmYJIkSZLUExMwSZIkSeqJCZgkSZIk9cQETJIkSZJ6YgImSZIkST0xAZMkSZKknpiASZIkSVJPTMAkSZIkqScmYJIkSZLUExMwSZIkSeqJCZgkSZIk9cQETJIkSZJ6YgImSZIkST0xAZMkzUpJVia5J8m3Bsr+S5Jbk9yU5PNJ9h9Yd1aS9Um+neQlA+XLW9n6JGcOlB+a5LpW/pkke/d2cpKkkWUCJkmarc4Hlo8puxI4vKr+D+B/A2cBJFkKnAg8q23z0STzkswDPgIcCywFTmp1Ad4DvL+qng5sBU6b2dORJM0GJmCSpFmpqq4Gtowp+x9Vta0tXgssavMrgNVV9WBVfRdYDxzRpvVVdVtV/RRYDaxIEuBo4OK2/QXACTN5PpKk2cEETJI0V70W+GKbXwhsGFi3sZVNVP4k4L6BZG57+aMkOT3J2iRrN2/ePI3hS5JGkQmYJGnOSfL/ANuAT830sarq3KpaVlXLFixYMNOHkyTt4eYPOwBJkvqU5NXAS4EXVVW14k3A4oFqi1oZE5TfC+yfZH57CjZYX5KkCfkETJI0ZyRZDrwZeHlV/Xhg1aXAiUn2SXIocBjwNeB64LA24uHedAN1XNoStzXAK9r2pwKX9HUekqTRNekTsCSPA64G9mn1L66qs2c6MGlYunfr+/XzL+ElTZckq4CjgIOSbATOphv1cB/gyvb/+rVV9R+ral2Si4Cb6bomnlFVD7X9vB64ApgHrKyqde0QbwFWJ3kncANwXm8nJ0kaWVPpgvggcHRVPZBkL+ArSb5YVdfOcGzSUDzWZCiJiZS0B6mqk8YpnjBJqqpzgHPGKb8cuHyc8tvoRkmUJGnKJk3AWjeLB9riXm3yU6YkSZIk7aIpvQPWfozyRuAe4Mqqum6cOg6zK0mSJEk7MaUErKoeqqpn043ydESSw8ep4zC7kiRJkrQTuzQKYlXdRzfq0/IZiUaSJEmSZrFJE7AkC5Ls3+b3BX4buHWG45IkSZKkWWcqoyAeAlyQZB5dwnZRVX1hZsOSJEmSpNlnKqMg3gQ8p4dYJEmSJGlW26V3wCRJkiRJj50JmCRJkiT1xARMkiRJknpiAiZJkiRJPTEBkyRJkqSemIBJkiRJUk9MwCRJkiSpJyZgkiRJktSTSX+IWRpFBx54IFu3bu39uEl6O9YBBxzAli1bejueJEmSdp8JmGalrVu3UlXDDmNG9ZnsSZIkaXrYBVGSNCslWZnkniTfGig7MMmVSb7T/h7QypPkQ0nWJ7kpyXMHtjm11f9OklMHyp+X5Jttmw/Fb0UkSVNgAiZJmq3OB5aPKTsT+FJVHQZ8qS0DHAsc1qbTgY9Bl7ABZwPPB44Azt6etLU6fzSw3dhjSZL0KCZgkqRZqaquBsa+KLkCuKDNXwCcMFB+YXWuBfZPcgjwEuDKqtpSVVuBK4Hlbd1+VXVtdf2dLxzYlyRJEzIBkyTNJQdX1Z1t/i7g4Da/ENgwUG9jK9tZ+cZxyh8lyelJ1iZZu3nz5t0/A0nSSDMBkyTNSe3J1YyP1lNV51bVsqpatmDBgpk+nCRpD2cCJkmaS+5u3Qdpf+9p5ZuAxQP1FrWynZUvGqdckqSdchh6zUp19n7wticOO4wZVWfvN+wQpFF0KXAq8O7295KB8tcnWU034MYPq+rOJFcA7xoYeOMY4Kyq2pLk/iRHAtcBpwAf7vNEJEmjyQRMs1L+6v458Ttg9bZhRyHtuZKsAo4CDkqykW40w3cDFyU5Dfge8Hut+uXAccB64MfAawBaovUO4PpW7+1VtX1gj9fRjbS4L/DFNkmStFMmYJKkWamqTppg1YvGqVvAGRPsZyWwcpzytcDhuxOjJGnu8R0wSZIkSeqJCZgkSZIk9cQETJIkSZJ6YgImSZIkST1xEA7NWkmGHcKMOuCAAyavJEmSpD2KCZhmpWEMQZ9k1g99L0mSpN1jF0RJkiRJ6okJmCRJkiT1xARMkiRJknpiAiZJkiRJPTEBkyRJkqSemIBJkiRJUk9MwCRJkiSpJyZgkiRJktQTEzBJkiRJ6okJmCRJkiT1xARMkiRJknoyf9gBSJLUtyR/BvwhUMA3gdcAhwCrgScBXwdeVVU/TbIPcCHwPOBe4Per6va2n7OA04CHgD+tqit6PhVJPVty5mXTsp/b3338tOxHo2fSJ2BJFidZk+TmJOuSvKGPwCRJmglJFgJ/CiyrqsOBecCJwHuA91fV04GtdIkV7e/WVv7+Vo8kS9t2zwKWAx9NMq/Pc5EkjZ6pdEHcBrypqpYCRwJntEZHkqRRNR/YN8l84PHAncDRwMVt/QXACW1+RVumrX9RkrTy1VX1YFV9F1gPHNFP+JKkUTVpAlZVd1bVN9r8j4BbgIUzHZgkSTOhqjYB7wXuoEu8fkjX5fC+qtrWqm3k523dQmBD23Zbq/+kwfJxtnlEktOTrE2ydvPmzdN/QpKkkbJLg3AkWQI8B7huRqKRJGmGJTmA7unVocCTgV+k60I4I6rq3KpaVlXLFixYMFOHkSSNiCknYEmeAHwWeGNV3T/Oer/h05yUhK430o7zkvZYLwa+W1Wbq+pnwOeAFwD7ty6JAIuATW1+E7AYoK1/It1gHI+Uj7ONJEnjmlIClmQvuuTrU1X1ufHq+A2f5qKJki2TMGmPdgdwZJLHt3e5XgTcDKwBXtHqnApc0uYvbcu09VdVVbXyE5Psk+RQ4DDgaz2dgyRpRE06DH1rnM4Dbqmq9818SJIkzZyqui7JxcA36AaaugE4F7gMWJ3kna3svLbJecDfJVkPbKEb+ZCqWpfkIrrkbRtwRlU91OvJSJJGzlR+B+wFwKuAbya5sZX9ZVVdPmNRSUM0XU+vdmU/3ZfpkvpSVWcDZ48pvo1xRjGsqn8FfneC/ZwDnDPtAUqSZq1JE7Cq+gpgfyrNGbuSDO0syTKpkiRJ0li7NAqiJEmSJOmxMwGTJEmSpJ6YgEmSJElST0zAJEmSJKknJmCSJEmS1BMTMEmSJEnqiQmYJEmSJPXEBEySJEmSemICJkmSJEk9MQGTJEmSpJ6YgEmSJElST0zApN2wePHiXSqXJEnS3GYCJu2GO+6441HJ1uLFi7njjjuGFJEkSZL2ZPOHHYA06ky2JEmSNFU+AZMkSZKknpiASZIkSVJPTMAkSXNOkv2TXJzk1iS3JPmNJAcmuTLJd9rfA1rdJPlQkvVJbkry3IH9nNrqfyfJqcM7I0nSqDABkyTNRR8E/qGq/i3w68AtwJnAl6rqMOBLbRngWOCwNp0OfAwgyYHA2cDzgSOAs7cnbZIkTcQETJI0pyR5IvBC4DyAqvppVd0HrAAuaNUuAE5o8yuAC6tzLbB/kkOAlwBXVtWWqtoKXAks7+1EJEkjyQRMkjTXHApsBv5bkhuSfCLJLwIHV9Wdrc5dwMFtfiGwYWD7ja1sovIdJDk9ydokazdv3jzNpyJJGjUmYJKkuWY+8FzgY1X1HOBf+Hl3QwCqqoCajoNV1blVtayqli1YsGA6dilJGmEmYJKkuWYjsLGqrmvLF9MlZHe3roW0v/e09ZuAwV9cX9TKJiqXJGlCJmCSpDmlqu4CNiR5Zit6EXAzcCmwfSTDU4FL2vylwCltNMQjgR+2ropXAMckOaANvnFMK5MkaULzhx2AJElD8CfAp5LsDdwGvIbuS8mLkpwGfA/4vVb3cuA4YD3w41aXqtqS5B3A9a3e26tqS3+nIEkaRSZgkqQ5p6puBJaNs+pF49Qt4IwJ9rMSWDmtwUmSZjW7IEqSJElST0zAJEmSJKknJmCSJEmS1BMTMEmSJEnqiQmYJEmSJPXEBEySJEmSemICJkmSJEk98XfAJEkaIUvOvGxa9nP7u4+flv1IknaNT8AkSZIkqScmYJIkSZLUExMwSZIkSeqJCZgkSZIk9cQETJIkSZJ6MmkClmRlknuSfKuPgCRJkiRptprKE7DzgeUzHIckSZIkzXqTJmBVdTWwpYdYJEmSJGlWm7Z3wJKcnmRtkrWbN2+ert1KkiRJ0qwxbQlYVZ1bVcuqatmCBQuma7eSJM2IJPOS3JDkC2350CTXJVmf5DNJ9m7l+7Tl9W39koF9nNXKv53kJUM6FUnSCHEUREnSXPUG4JaB5fcA76+qpwNbgdNa+WnA1lb+/laPJEuBE4Fn0b0r/dEk83qKXZI0okzAJElzTpJFwPHAJ9pygKOBi1uVC4AT2vyKtkxb/6JWfwWwuqoerKrvAuuBI3o5AUnSyJrKMPSrgGuAZybZmOS0ybaRJGkP9wHgzcDDbflJwH1Vta0tbwQWtvmFwAaAtv6Hrf4j5eNs8wjfkZYkDZo/WYWqOqmPQCRJ6kOSlwL3VNXXkxw108erqnOBcwGWLVtWM308SdKebdIETJKkWeYFwMuTHAc8DtgP+CCwf5L57SnXImBTq78JWAxsTDIfeCJw70D5doPbSJI0Lt8BkyTNKVV1VlUtqqoldINoXFVVfwCsAV7Rqp0KXNLmL23LtPVXVVW18hPbKImHAocBX+vpNCRJI8onYJIkdd4CrE7yTuAG4LxWfh7wd0nWA1vokjaqal2Si4CbgW3AGVX1UP9hS5JGiQmYJGnOqqovA19u87cxziiGVfWvwO9OsP05wDkzF6EkabaxC6IkSZIk9cQETJIkSZJ6YgImSZIkST3xHTBJkiTNmCVnXjbsEKQ9ik/AJEmSJKknJmCSJEmS1BMTMEmSJEnqiQmYJEmSJPXEBEySJEmSemICJkmSJEk9MQGTJEmSpJ6YgEmSJElST0zAJEmSJKknJmCSJEmS1BMTMEmSJEnqiQmYJEmSJPXEBEySJEmSemICJkmaU5IsTrImyc1J1iV5Qys/MMmVSb7T/h7QypPkQ0nWJ7kpyXMH9nVqq/+dJKcO65wkSaPDBEySNNdsA95UVUuBI4EzkiwFzgS+VFWHAV9qywDHAoe16XTgY9AlbMDZwPOBI4CztydtkiRNxARMkjSnVNWdVfWNNv8j4BZgIbACuKBVuwA4oc2vAC6szrXA/kkOAV4CXFlVW6pqK3AlsLy/M5EkjSITMEnSnJVkCfAc4Drg4Kq6s626Czi4zS8ENgxstrGVTVQuSdKETMAkSXNSkicAnwXeWFX3D66rqgJqmo5zepK1SdZu3rx5OnYpSRphJmCSpDknyV50ydenqupzrfju1rWQ9veeVr4JWDyw+aJWNlH5Dqrq3KpaVlXLFixYML0nIkkaOSZgkqQ5JUmA84Bbqup9A6suBbaPZHgqcMlA+SltNMQjgR+2ropXAMckOaANvnFMK5MkaULzhx2AJEk9ewHwKuCbSW5sZX8JvBu4KMlpwPeA32vrLgeOA9YDPwZeA1BVW5K8A7i+1Xt7VW3p5QwkSSPLBEySNKdU1VeATLD6RePUL+CMCfa1Elg5fdFJkmY7uyBKkiRJUk9MwCRJkiSpJ3ZBlCRJ0riWnHnZsEOQZh2fgEmSJElST0zAJEmSJKknJmCSJEmS1BMTMEmSJEnqiQmYJEmSJPVkSglYkuVJvp1kfZIzZzooaZQkedQkSZIkjWfSBCzJPOAjwLHAUuCkJEtnOjBpFEyUbJmESZIkaTxTeQJ2BLC+qm6rqp8Cq4EVMxuWNFqq6pFJkiRJmshUErCFwIaB5Y2tbAdJTk+yNsnazZs3T1d8kiRJkjRrTNsgHFV1blUtq6plCxYsmK7dSpIkSdKsMX8KdTYBiweWF7UySY3vfEmS9jRLzrxs2CFIGsdUnoBdDxyW5NAkewMnApfObFjSaJjonS/fBZMkSdJ4Jn0CVlXbkrweuAKYB6ysqnUzHpk0Iky2JEnSrpqOJ5S3v/v4aYhEfZtKF0Sq6nLg8hmORZIkSZJmtWkbhEOSJEmStHMmYJIkPUZJlif5dpL1Sc4cdjySpD3flLogSpKkHSWZB3wE+G2638i8PsmlVXXzcCOTpLlrFN6t8wmYJEmPzRHA+qq6rap+CqwGVgw5JknSHi4zMYJbks3A96Z9x9Ke7SDgB8MOQurZU6tqwbCDGIYkrwCWV9UftuVXAc+vqtePqXc6cHpbfCbw7d08tPea6eF1nB5ex93nNZwee+J1HLeNnJEuiHO1MdbclmRtVS0bdhyS9ixVdS5w7nTtz3vN9PA6Tg+v4+7zGk6PUbqOdkGUJOmx2QQsHlhe1MokSZqQCZgkSY/N9cBhSQ5NsjdwInDpkGOSJO3hHAVRmj7T1sVI0p6vqrYleT1wBTAPWFlV63o4tPea6eF1nB5ex93nNZweI3MdZ2QQDkmSJEnSo9kFUZIkSZJ6YgImSZIkST0xAZN2U5KVSe5J8q1hxyJpdkuyPMm3k6xPcuaw4xlF3rN3X5LFSdYkuTnJuiRvGHZMoyjJ45J8Lck/tev4V8OOaVQlmZfkhiRfGHYsU2ECJu2+84Hlww5C0uyWZB7wEeBYYClwUpKlw41qJJ2P9+zdtQ14U1UtBY4EzvC/xcfkQeDoqvp14NnA8iRHDjekkfUG4JZhBzFVJmDSbqqqq4Etw45D0qx3BLC+qm6rqp8Cq4EVQ45p5HjP3n1VdWdVfaPN/4jug+/C4UY1eqrzQFvcq02OjreLkiwCjgc+MexYpsoETJKk0bAQ2DCwvBE/9GrIkiwBngNcN+RQRlLrOncjcA9wZVV5HXfdB4A3Aw8POY4pMwGTJEnSLkvyBOCzwBur6v5hxzOKquqhqno2sAg4IsnhQw5ppCR5KXBPVX192LHsChMwSZJGwyZg8cDyolYm9S7JXnTJ16eq6nPDjmfUVdV9wBp8P3FXvQB4eZLb6bplH53kk8MNaXImYJIkjYbrgcOSHJpkb+BE4NIhx6Q5KEmA84Bbqup9w45nVCVZkGT/Nr8v8NvArUMNasRU1VlVtaiqltDdE6+qqlcOOaxJmYBJuynJKuAa4JlJNiY5bdgxSZp9qmob8HrgCrpBDy6qqnXDjWr0eM+eFi8AXkX3tOHGNh037KBG0CHAmiQ30X3BcmVVjcQw6to9qXKwFUmSJEnqg0/AJEmSJKknJmCSJEmS1BMTMEmSJEnqiQmYJEmSJPXEBEySJEmSemICJkmSJEk9MQGTJEmSpJ78//LxAtoXarYRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxpot and histogram of each feature\n",
    "for feature in ['cycle', 'ambient_temperature', 'capacity', 'voltage_measured', 'current_measured', \n",
    "                    'temperature_measured', 'current_load', 'voltage_load']:\n",
    "    # Creating an empty chart\n",
    "    fig, ((ax1, ax2)) = plt.subplots(1, 2,  figsize=(15, 4))\n",
    "\n",
    "    # Extracting the feature values\n",
    "    x = dataset[feature].values\n",
    "\n",
    "    # Boxplot\n",
    "    ax1.boxplot(x)\n",
    "    ax1.set_title( 'Boxplot for {}'.format(feature) )\n",
    "\n",
    "    # Histogram\n",
    "    ax2.hist(x, bins=20)\n",
    "    ax2.set_title( 'Histogram for {}'.format(feature) )\n",
    "\n",
    "    # Display\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 0 null values\n",
      "The dataset contains 0 duplicates\n",
      "Size of cleaned dataset:  50285\n"
     ]
    }
   ],
   "source": [
    "# Checking for null values\n",
    "features = ['cycle', 'ambient_temperature',\n",
    "                                'capacity', 'voltage_measured',\n",
    "                                'current_measured', 'temperature_measured',\n",
    "                                'current_load', 'voltage_load', 'time']\n",
    "N_null = sum(dataset[features].isnull().sum())\n",
    "print(\"The dataset contains {} null values\".format(N_null)) \n",
    "\n",
    "# Removing duplicates if there exist\n",
    "N_dupli = sum(dataset.duplicated(keep='first'))\n",
    "dataset = dataset.drop_duplicates(keep='first').reset_index(drop=True)\n",
    "print(\"The dataset contains {} duplicates\".format(N_dupli))\n",
    "\n",
    "# Number of samples in the dataset\n",
    "N = dataset.shape[0]\n",
    "print('Size of cleaned dataset: ', N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation matrix and heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>cycle</th>\n",
       "      <th>ambient_temperature</th>\n",
       "      <th>capacity</th>\n",
       "      <th>voltage_measured</th>\n",
       "      <th>current_measured</th>\n",
       "      <th>temperature_measured</th>\n",
       "      <th>current_load</th>\n",
       "      <th>voltage_load</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cycle</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.986857</td>\n",
       "      <td>-0.132877</td>\n",
       "      <td>0.127769</td>\n",
       "      <td>0.139936</td>\n",
       "      <td>0.455661</td>\n",
       "      <td>-0.177385</td>\n",
       "      <td>-0.119383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ambient_temperature</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capacity</th>\n",
       "      <td>-0.986857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.135859</td>\n",
       "      <td>-0.132162</td>\n",
       "      <td>-0.143717</td>\n",
       "      <td>-0.407819</td>\n",
       "      <td>0.181684</td>\n",
       "      <td>0.119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voltage_measured</th>\n",
       "      <td>-0.132877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.135859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.091667</td>\n",
       "      <td>-0.914580</td>\n",
       "      <td>-0.034467</td>\n",
       "      <td>0.384095</td>\n",
       "      <td>-0.817528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_measured</th>\n",
       "      <td>0.127769</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.132162</td>\n",
       "      <td>-0.091667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354628</td>\n",
       "      <td>-0.350758</td>\n",
       "      <td>-0.917754</td>\n",
       "      <td>0.416898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperature_measured</th>\n",
       "      <td>0.139936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.143717</td>\n",
       "      <td>-0.914580</td>\n",
       "      <td>0.354628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.091684</td>\n",
       "      <td>-0.601707</td>\n",
       "      <td>0.915537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_load</th>\n",
       "      <td>0.455661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.407819</td>\n",
       "      <td>-0.034467</td>\n",
       "      <td>-0.350758</td>\n",
       "      <td>-0.091684</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.289994</td>\n",
       "      <td>-0.225579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voltage_load</th>\n",
       "      <td>-0.177385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.181684</td>\n",
       "      <td>0.384095</td>\n",
       "      <td>-0.917754</td>\n",
       "      <td>-0.601707</td>\n",
       "      <td>0.289994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.628276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>-0.119383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119542</td>\n",
       "      <td>-0.817528</td>\n",
       "      <td>0.416898</td>\n",
       "      <td>0.915537</td>\n",
       "      <td>-0.225579</td>\n",
       "      <td>-0.628276</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         cycle  ambient_temperature  capacity  \\\n",
       "cycle                 1.000000                  NaN -0.986857   \n",
       "ambient_temperature        NaN                  NaN       NaN   \n",
       "capacity             -0.986857                  NaN  1.000000   \n",
       "voltage_measured     -0.132877                  NaN  0.135859   \n",
       "current_measured      0.127769                  NaN -0.132162   \n",
       "temperature_measured  0.139936                  NaN -0.143717   \n",
       "current_load          0.455661                  NaN -0.407819   \n",
       "voltage_load         -0.177385                  NaN  0.181684   \n",
       "time                 -0.119383                  NaN  0.119542   \n",
       "\n",
       "                      voltage_measured  current_measured  \\\n",
       "cycle                        -0.132877          0.127769   \n",
       "ambient_temperature                NaN               NaN   \n",
       "capacity                      0.135859         -0.132162   \n",
       "voltage_measured              1.000000         -0.091667   \n",
       "current_measured             -0.091667          1.000000   \n",
       "temperature_measured         -0.914580          0.354628   \n",
       "current_load                 -0.034467         -0.350758   \n",
       "voltage_load                  0.384095         -0.917754   \n",
       "time                         -0.817528          0.416898   \n",
       "\n",
       "                      temperature_measured  current_load  voltage_load  \\\n",
       "cycle                             0.139936      0.455661     -0.177385   \n",
       "ambient_temperature                    NaN           NaN           NaN   \n",
       "capacity                         -0.143717     -0.407819      0.181684   \n",
       "voltage_measured                 -0.914580     -0.034467      0.384095   \n",
       "current_measured                  0.354628     -0.350758     -0.917754   \n",
       "temperature_measured              1.000000     -0.091684     -0.601707   \n",
       "current_load                     -0.091684      1.000000      0.289994   \n",
       "voltage_load                     -0.601707      0.289994      1.000000   \n",
       "time                              0.915537     -0.225579     -0.628276   \n",
       "\n",
       "                          time  \n",
       "cycle                -0.119383  \n",
       "ambient_temperature        NaN  \n",
       "capacity              0.119542  \n",
       "voltage_measured     -0.817528  \n",
       "current_measured      0.416898  \n",
       "temperature_measured  0.915537  \n",
       "current_load         -0.225579  \n",
       "voltage_load         -0.628276  \n",
       "time                  1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[features].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.corr().to_csv(r'Statistics/battery-corr.csv', index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make correlation matrix to heatmap\n",
    "f, ax = plt.subplots(figsize=(20, 10))\n",
    "sns.heatmap(dataset[features].corr(), cmap='YlGnBu', vmax = .9, square = True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset[['cycle', 'ambient_temperature',\n",
    "                                 'voltage_measured',\n",
    "                                'current_measured', 'temperature_measured',\n",
    "                                'current_load', 'voltage_load', 'time']]\n",
    "y = dataset['capacity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = ensemble.RandomForestRegressor()\n",
    "single_rf = ensemble.RandomForestRegressor(n_estimators = 200, max_depth = 15)\n",
    "single_rf.fit(X, y)\n",
    "y_pred = single_rf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph feature importance\n",
    "importances = single_rf.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "feature_names = X.columns    \n",
    "f, ax = plt.subplots(figsize=(18, 7))\n",
    "plt.title(\"Feature ranking\", fontsize = 16)\n",
    "plt.bar(range(X.shape[1]), importances[indices], color=\"g\", align=\"center\")\n",
    "plt.xticks(range(X.shape[1]), feature_names)\n",
    "plt.xlim([-1, X.shape[1]])\n",
    "plt.ylabel(\"Importance\", fontsize = 15)\n",
    "plt.xlabel(\"Feature\", fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cycle is the only one revelant to capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle                   9.998857e-01\n",
      "current_load            1.143271e-04\n",
      "current_measured        1.908401e-08\n",
      "time                    8.023529e-10\n",
      "voltage_measured        6.937986e-14\n",
      "temperature_measured    6.628725e-14\n",
      "voltage_load            5.554914e-14\n",
      "ambient_temperature     0.000000e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# list feature importance\n",
    "important_features = pd.Series(data=single_rf.feature_importances_,index=X.columns)\n",
    "important_features.sort_values(ascending=False,inplace=True)\n",
    "print(important_features.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the feature importance result, it seems that cycle is the only considerable feature to predict capacity. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data and scaling data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>cycle</th>\n",
       "      <th>ambient_temperature</th>\n",
       "      <th>voltage_measured</th>\n",
       "      <th>current_measured</th>\n",
       "      <th>temperature_measured</th>\n",
       "      <th>current_load</th>\n",
       "      <th>voltage_load</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>4.191492</td>\n",
       "      <td>-0.004902</td>\n",
       "      <td>24.330034</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>4.190749</td>\n",
       "      <td>-0.001478</td>\n",
       "      <td>24.325993</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>4.206</td>\n",
       "      <td>16.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3.974871</td>\n",
       "      <td>-2.012528</td>\n",
       "      <td>24.389085</td>\n",
       "      <td>-1.9982</td>\n",
       "      <td>3.062</td>\n",
       "      <td>35.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3.951717</td>\n",
       "      <td>-2.013979</td>\n",
       "      <td>24.544752</td>\n",
       "      <td>-1.9982</td>\n",
       "      <td>3.030</td>\n",
       "      <td>53.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3.934352</td>\n",
       "      <td>-2.011144</td>\n",
       "      <td>24.731385</td>\n",
       "      <td>-1.9982</td>\n",
       "      <td>3.011</td>\n",
       "      <td>71.922</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",
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       "    <tr>\n",
       "      <th>40223</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.360799</td>\n",
       "      <td>-2.010457</td>\n",
       "      <td>36.185453</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.398</td>\n",
       "      <td>1855.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40224</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.358384</td>\n",
       "      <td>-2.012746</td>\n",
       "      <td>36.238577</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.396</td>\n",
       "      <td>1864.406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40225</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.356175</td>\n",
       "      <td>-2.012531</td>\n",
       "      <td>36.295522</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.394</td>\n",
       "      <td>1873.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40226</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.353691</td>\n",
       "      <td>-2.012193</td>\n",
       "      <td>36.352229</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.392</td>\n",
       "      <td>1883.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40227</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.351290</td>\n",
       "      <td>-2.011846</td>\n",
       "      <td>36.394356</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.389</td>\n",
       "      <td>1892.391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40228 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cycle  ambient_temperature  voltage_measured  current_measured  \\\n",
       "0          1                   24          4.191492         -0.004902   \n",
       "1          1                   24          4.190749         -0.001478   \n",
       "2          1                   24          3.974871         -2.012528   \n",
       "3          1                   24          3.951717         -2.013979   \n",
       "4          1                   24          3.934352         -2.011144   \n",
       "...      ...                  ...               ...               ...   \n",
       "40223    135                   24          3.360799         -2.010457   \n",
       "40224    135                   24          3.358384         -2.012746   \n",
       "40225    135                   24          3.356175         -2.012531   \n",
       "40226    135                   24          3.353691         -2.012193   \n",
       "40227    135                   24          3.351290         -2.011846   \n",
       "\n",
       "       temperature_measured  current_load  voltage_load      time  \n",
       "0                 24.330034       -0.0006         0.000     0.000  \n",
       "1                 24.325993       -0.0006         4.206    16.781  \n",
       "2                 24.389085       -1.9982         3.062    35.703  \n",
       "3                 24.544752       -1.9982         3.030    53.781  \n",
       "4                 24.731385       -1.9982         3.011    71.922  \n",
       "...                     ...           ...           ...       ...  \n",
       "40223             36.185453        1.9982         2.398  1855.031  \n",
       "40224             36.238577        1.9982         2.396  1864.406  \n",
       "40225             36.295522        1.9982         2.394  1873.703  \n",
       "40226             36.352229        1.9982         2.392  1883.063  \n",
       "40227             36.394356        1.9982         2.389  1892.391  \n",
       "\n",
       "[40228 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\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>cycle</th>\n",
       "      <th>ambient_temperature</th>\n",
       "      <th>voltage_measured</th>\n",
       "      <th>current_measured</th>\n",
       "      <th>temperature_measured</th>\n",
       "      <th>current_load</th>\n",
       "      <th>voltage_load</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40228</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.348997</td>\n",
       "      <td>-2.011760</td>\n",
       "      <td>36.448980</td>\n",
       "      <td>1.9980</td>\n",
       "      <td>2.387</td>\n",
       "      <td>1901.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40229</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.346386</td>\n",
       "      <td>-2.012456</td>\n",
       "      <td>36.507417</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.384</td>\n",
       "      <td>1911.219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40230</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.343825</td>\n",
       "      <td>-2.014181</td>\n",
       "      <td>36.559807</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.382</td>\n",
       "      <td>1920.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40231</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.341496</td>\n",
       "      <td>-2.011917</td>\n",
       "      <td>36.622468</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.380</td>\n",
       "      <td>1929.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40232</th>\n",
       "      <td>135</td>\n",
       "      <td>24</td>\n",
       "      <td>3.338640</td>\n",
       "      <td>-2.012436</td>\n",
       "      <td>36.671214</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>2.377</td>\n",
       "      <td>1939.359</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50280</th>\n",
       "      <td>168</td>\n",
       "      <td>24</td>\n",
       "      <td>3.579262</td>\n",
       "      <td>-0.001569</td>\n",
       "      <td>34.864823</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2781.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50281</th>\n",
       "      <td>168</td>\n",
       "      <td>24</td>\n",
       "      <td>3.581964</td>\n",
       "      <td>-0.003067</td>\n",
       "      <td>34.814770</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2791.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50282</th>\n",
       "      <td>168</td>\n",
       "      <td>24</td>\n",
       "      <td>3.584484</td>\n",
       "      <td>-0.003079</td>\n",
       "      <td>34.676258</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2800.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50283</th>\n",
       "      <td>168</td>\n",
       "      <td>24</td>\n",
       "      <td>3.587336</td>\n",
       "      <td>0.001219</td>\n",
       "      <td>34.565580</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2810.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50284</th>\n",
       "      <td>168</td>\n",
       "      <td>24</td>\n",
       "      <td>3.589937</td>\n",
       "      <td>-0.000583</td>\n",
       "      <td>34.405920</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2820.390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10057 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cycle  ambient_temperature  voltage_measured  current_measured  \\\n",
       "40228    135                   24          3.348997         -2.011760   \n",
       "40229    135                   24          3.346386         -2.012456   \n",
       "40230    135                   24          3.343825         -2.014181   \n",
       "40231    135                   24          3.341496         -2.011917   \n",
       "40232    135                   24          3.338640         -2.012436   \n",
       "...      ...                  ...               ...               ...   \n",
       "50280    168                   24          3.579262         -0.001569   \n",
       "50281    168                   24          3.581964         -0.003067   \n",
       "50282    168                   24          3.584484         -0.003079   \n",
       "50283    168                   24          3.587336          0.001219   \n",
       "50284    168                   24          3.589937         -0.000583   \n",
       "\n",
       "       temperature_measured  current_load  voltage_load      time  \n",
       "40228             36.448980        1.9980         2.387  1901.828  \n",
       "40229             36.507417        1.9982         2.384  1911.219  \n",
       "40230             36.559807        1.9982         2.382  1920.609  \n",
       "40231             36.622468        1.9982         2.380  1929.969  \n",
       "40232             36.671214        1.9982         2.377  1939.359  \n",
       "...                     ...           ...           ...       ...  \n",
       "50280             34.864823        0.0006         0.000  2781.312  \n",
       "50281             34.814770        0.0006         0.000  2791.062  \n",
       "50282             34.676258        0.0006         0.000  2800.828  \n",
       "50283             34.565580        0.0006         0.000  2810.640  \n",
       "50284             34.405920        0.0006         0.000  2820.390  \n",
       "\n",
       "[10057 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle data for later use\n",
    "dump(X_train, open(\"pickle/dataset/battery/X_train.pkl\", \"wb\"))\n",
    "dump(y_train, open(\"pickle/dataset/battery/y_train.pkl\", \"wb\"))\n",
    "dump(X_test, open(\"pickle/dataset/battery/X_test.pkl\", \"wb\"))\n",
    "dump(y_test, open(\"pickle/dataset/battery/y_test.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "X_train = load(open('pickle/dataset/battery/X_train.pkl', 'rb'))\n",
    "y_train = load(open('pickle/dataset/battery/y_train.pkl', 'rb'))\n",
    "X_test = load(open('pickle/dataset/battery/X_test.pkl', 'rb'))\n",
    "y_test = load(open('pickle/dataset/battery/y_test.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=20, epsilon=0.0001, gamma=1e-05)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr = SVR(C=20, epsilon=0.0001, gamma=0.00001, cache_size=200,\n",
    "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\n",
    "\n",
    "svr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svr.predict(X_train.append(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14ec5eac0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "ax.plot(X_train.append(X_test)['cycle'], y_train.append(y_test), color='green', label='Battery capacity data')\n",
    "ax.plot(X_train.append(X_test)['cycle'], y_pred, color='red', label='Prediction data')\n",
    "ax.plot([0.,168], [1.38, 1.38],dashes=[6, 2], label=\"EoL Threshold\")\n",
    "ax.set(xlabel='Cycle numbers', ylabel='Capacity')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data in dataset:  616\n",
      "[1, 24, datetime.datetime(2008, 4, 2, 15, 25, 41), 1.8564874208181574, 4.191491807505295, -0.004901589207462691, 24.330033885570543, -0.0006, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "dataset, _ = load_data('B0005')\n",
    "dataset['pre'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Actual fail at cycle number: 129\n",
      "The prediction fail at cycle number: 127\n",
      "The error of RUL= -2 Cycle(s)\n"
     ]
    }
   ],
   "source": [
    "#ln = len(X_train)\n",
    "pred=0\n",
    "Afil=0\n",
    "Pfil=0\n",
    "a=dataset['capacity'].values\n",
    "b=dataset['pre'].values\n",
    "j=0\n",
    "k=0\n",
    "for i in range(len(a)):\n",
    "    actual=a[i]\n",
    "    \n",
    "    if actual<=1.38:\n",
    "        j=i\n",
    "        Afil=dataset['cycle'][j]\n",
    "        break\n",
    "for i in range(len(a)):\n",
    "    pred=b[i]\n",
    "    if pred< 1.38:\n",
    "        k=i\n",
    "        Pfil=dataset['cycle'][k]\n",
    "        break\n",
    "print(\"The Actual fail at cycle number: \"+ str(Afil))\n",
    "print(\"The prediction fail at cycle number: \"+ str(Pfil))\n",
    "RULerror=Pfil-Afil\n",
    "print(\"The error of RUL= \"+ str(RULerror)+ \" Cycle(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 0.014\n",
      "Train R2 score: 0.9918552027791366\n",
      "Test RMSE: 0.048\n",
      "Test R2 score: -4.028755379916393\n"
     ]
    }
   ],
   "source": [
    "train_predictions = svr.predict(X_train)\n",
    "rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "print('Train RMSE: %.3f' % rmse)\n",
    "print(\"Train R2 score:\", sklearn.metrics.r2_score(y_train, train_predictions))\n",
    "\n",
    "test_predictions = svr.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "print(\"Test R2 score:\", sklearn.metrics.r2_score(y_test, test_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TPOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "X_train = load(open('pickle/dataset/battery/X_train.pkl', 'rb'))\n",
    "y_train = load(open('pickle/dataset/battery/y_train.pkl', 'rb'))\n",
    "X_test = load(open('pickle/dataset/battery/X_test.pkl', 'rb'))\n",
    "y_test = load(open('pickle/dataset/battery/y_test.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                               \n",
      "Generation 1 - Current best internal CV score: -0.0010987347994880538\n",
      "                                                                                \n",
      "Generation 2 - Current best internal CV score: -0.0010987347994880538\n",
      "                                                                                \n",
      "Generation 3 - Current best internal CV score: -0.0010660519615302666\n",
      "                                                                                \n",
      "Generation 4 - Current best internal CV score: -0.0010660519615302666\n",
      "                                                                                \n",
      "Generation 5 - Current best internal CV score: -0.0010660519615302666\n",
      "                                                             \n",
      "Best pipeline: LassoLarsCV(ExtraTreesRegressor(SelectFromModel(input_matrix, max_features=0.9500000000000001, n_estimators=100, threshold=0.35000000000000003), bootstrap=True, max_features=0.9500000000000001, min_samples_leaf=8, min_samples_split=7, n_estimators=100), normalize=False)\n",
      "-0.002540389030144266\n"
     ]
    }
   ],
   "source": [
    "from tpot import TPOTRegressor\n",
    "tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)\n",
    "tpot.fit(X_train, y_train)\n",
    "print(tpot.score(X_test, y_test))\n",
    "tpot.export('TPOT/tpot_battery_pipeline.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try the best TPOT result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data in dataset:  616\n",
      "[1, 24, datetime.datetime(2008, 4, 2, 15, 25, 41), 1.8564874208181574, 4.191491807505295, -0.004901589207462691, 24.330033885570543, -0.0006, 0.0, 0.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14e4a66d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LassoLarsCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "from tpot.builtins import StackingEstimator\n",
    "\n",
    "exported_pipeline = make_pipeline(\n",
    "    SelectFromModel(estimator=ExtraTreesRegressor(max_features=0.9500000000000001, n_estimators=100), threshold=0.35000000000000003),\n",
    "    StackingEstimator(estimator=ExtraTreesRegressor(bootstrap=True, max_features=0.9500000000000001, min_samples_leaf=8, min_samples_split=7, n_estimators=100)),\n",
    "    LassoLarsCV(normalize=False)\n",
    ")\n",
    "\n",
    "exported_pipeline.fit(X_train, y_train)\n",
    "results = exported_pipeline.predict(X_train.append(X_test))\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "\n",
    "dataset, _ = load_data('B0005')\n",
    "X = dataset[['cycle']]\n",
    "y = dataset['capacity']\n",
    "ax.plot(X, y, color='green', label='Battery capacity data')\n",
    "ax.plot(X, results, color='red', label='Prediction data')\n",
    "ax.plot([0.,168], [1.38, 1.38],dashes=[6, 2], label=\"EoL Threshold\")\n",
    "ax.set(xlabel='Cycle numbers', ylabel='Capacity')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.85648742, 1.85648742, 1.85648742, ..., 1.36985   , 1.36985   ,\n",
       "       1.36985   ])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Actual fail at cycle number: 129\n",
      "The prediction fail at cycle number: 129\n",
      "The error of RUL= 0 Cycle(s)\n"
     ]
    }
   ],
   "source": [
    "dataset['pre'] = results\n",
    "\n",
    "pred=0\n",
    "Afil=0\n",
    "Pfil=0\n",
    "a=dataset['capacity'].values\n",
    "b=dataset['pre'].values\n",
    "j=0\n",
    "k=0\n",
    "for i in range(len(a)):\n",
    "    actual=a[i]\n",
    "    \n",
    "    if actual<=1.38:\n",
    "        j=i\n",
    "        Afil=dataset['cycle'][j]\n",
    "        break\n",
    "for i in range(len(a)):\n",
    "    pred=b[i]\n",
    "    if pred< 1.38:\n",
    "        k=i\n",
    "        Pfil=dataset['cycle'][k]\n",
    "        break\n",
    "print(\"The Actual fail at cycle number: \"+ str(Afil))\n",
    "print(\"The prediction fail at cycle number: \"+ str(Pfil))\n",
    "RULerror=Pfil-Afil\n",
    "print(\"The error of RUL= \"+ str(RULerror)+ \" Cycle(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 0.000\n",
      "Train R2 score: 1.0\n",
      "Test RMSE: 0.050\n",
      "Test R2 score: -4.484471305807123\n"
     ]
    }
   ],
   "source": [
    "train_predictions = exported_pipeline.predict(X_train)\n",
    "rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "print('Train RMSE: %.3f' % rmse)\n",
    "print(\"Train R2 score:\", sklearn.metrics.r2_score(y_train, train_predictions))\n",
    "\n",
    "test_predictions = exported_pipeline.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "print(\"Test R2 score:\", sklearn.metrics.r2_score(y_test, test_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBRegressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "X_train = load(open('pickle/dataset/battery/X_train.pkl', 'rb'))\n",
    "y_train = load(open('pickle/dataset/battery/y_train.pkl', 'rb'))\n",
    "X_test = load(open('pickle/dataset/battery/X_test.pkl', 'rb'))\n",
    "y_test = load(open('pickle/dataset/battery/y_test.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LassoLarsCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from tpot.builtins import StackingEstimator\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# Average CV score on the training set was: -0.0008766141596485714\n",
    "exported_pipeline = make_pipeline(\n",
    "    StackingEstimator(estimator=XGBRegressor(learning_rate=0.001, max_depth=1, min_child_weight=9, n_estimators=100, n_jobs=1, objective=\"reg:squarederror\", subsample=0.45, verbosity=0)),\n",
    "    PolynomialFeatures(degree=2, include_bias=False, interaction_only=False),\n",
    "    LassoLarsCV(normalize=True)\n",
    ")\n",
    "exported_pipeline.fit(X_train, y_train)\n",
    "results = exported_pipeline.predict(X_train.append(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data in dataset:  616\n",
      "[1, 24, datetime.datetime(2008, 4, 2, 15, 25, 41), 1.8564874208181574, 4.191491807505295, -0.004901589207462691, 24.330033885570543, -0.0006, 0.0, 0.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x150c0ef40>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "\n",
    "dataset, _ = load_data('B0005')\n",
    "X = dataset[['cycle']]\n",
    "y = dataset['capacity']\n",
    "ax.plot(X, y, color='green', label='Battery capacity data')\n",
    "ax.plot(X, results, color='red', label='Prediction data')\n",
    "ax.plot([0.,168], [1.38, 1.38],dashes=[6, 2], label=\"EoL Threshold\")\n",
    "ax.set(xlabel='Cycle numbers', ylabel='Capacity')\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Actual fail at cycle number: 129\n",
      "The prediction fail at cycle number: 130\n",
      "The error of RUL= 1 Cycle(s)\n"
     ]
    }
   ],
   "source": [
    "dataset['pre'] = results\n",
    "\n",
    "pred=0\n",
    "Afil=0\n",
    "Pfil=0\n",
    "a=dataset['capacity'].values\n",
    "b=dataset['pre'].values\n",
    "j=0\n",
    "k=0\n",
    "for i in range(len(a)):\n",
    "    actual=a[i]\n",
    "    \n",
    "    if actual<=1.38:\n",
    "        j=i\n",
    "        Afil=dataset['cycle'][j]\n",
    "        break\n",
    "for i in range(len(a)):\n",
    "    pred=b[i]\n",
    "    if pred< 1.38:\n",
    "        k=i\n",
    "        Pfil=dataset['cycle'][k]\n",
    "        break\n",
    "print(\"The Actual fail at cycle number: \"+ str(Afil))\n",
    "print(\"The prediction fail at cycle number: \"+ str(Pfil))\n",
    "RULerror=Pfil-Afil\n",
    "print(\"The error of RUL= \"+ str(RULerror)+ \" Cycle(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 0.020\n",
      "Train R2 score: 0.9826512195746897\n",
      "Test RMSE: 0.052\n",
      "Test R2 score: -4.7678464149143815\n"
     ]
    }
   ],
   "source": [
    "train_predictions = exported_pipeline.predict(X_train)\n",
    "rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "print('Train RMSE: %.3f' % rmse)\n",
    "print(\"Train R2 score:\", sklearn.metrics.r2_score(y_train, train_predictions))\n",
    "\n",
    "test_predictions = exported_pipeline.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "print(\"Test R2 score:\", sklearn.metrics.r2_score(y_test, test_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train with cycle only to predict capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data in dataset:  616\n",
      "[1, 24, datetime.datetime(2008, 4, 2, 15, 25, 41), 1.8564874208181574, 4.191491807505295, -0.004901589207462691, 24.330033885570543, -0.0006, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "dataset, capacity = load_data('B0005')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>cycle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cycle\n",
       "0        1\n",
       "1        2\n",
       "2        3\n",
       "3        4\n",
       "4        5\n",
       "..     ...\n",
       "163    164\n",
       "164    165\n",
       "165    166\n",
       "166    167\n",
       "167    168\n",
       "\n",
       "[168 rows x 1 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = capacity[['cycle']]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.856487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.846327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.835349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.835263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.834646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1.293464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1.288003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>1.287453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1.309015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1.325079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     capacity\n",
       "0    1.856487\n",
       "1    1.846327\n",
       "2    1.835349\n",
       "3    1.835263\n",
       "4    1.834646\n",
       "..        ...\n",
       "163  1.293464\n",
       "164  1.288003\n",
       "165  1.287453\n",
       "166  1.309015\n",
       "167  1.325079\n",
       "\n",
       "[168 rows x 1 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = capacity[['capacity']]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle data for later use\n",
    "dump(X_train, open(\"pickle/dataset/battery/cycle_only/X_train.pkl\", \"wb\"))\n",
    "dump(y_train, open(\"pickle/dataset/battery/cycle_only/y_train.pkl\", \"wb\"))\n",
    "dump(X_test, open(\"pickle/dataset/battery/cycle_only/X_test.pkl\", \"wb\"))\n",
    "dump(y_test, open(\"pickle/dataset/battery/cycle_only/y_test.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "X_train = load(open('pickle/dataset/battery/cycle_only/X_train.pkl', 'rb'))\n",
    "y_train = load(open('pickle/dataset/battery/cycle_only/y_train.pkl', 'rb'))\n",
    "X_test = load(open('pickle/dataset/battery/cycle_only/X_test.pkl', 'rb'))\n",
    "y_test = load(open('pickle/dataset/battery/cycle_only/y_test.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py:985: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(C=20, epsilon=0.0001, gamma=1e-05)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr = SVR(C=20, epsilon=0.0001, gamma=0.00001, cache_size=200,\n",
    "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\n",
    "\n",
    "svr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data in dataset:  616\n",
      "[1, 24, datetime.datetime(2008, 4, 2, 15, 25, 41), 1.8564874208181574, 4.191491807505295, -0.004901589207462691, 24.330033885570543, -0.0006, 0.0, 0.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x158c5eee0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = svr.predict(X_train.append(X_test))\n",
    "\n",
    "_, capacity = load_data('B0005')\n",
    "X = capacity[['cycle']]\n",
    "y = capacity[['capacity']]\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "ax.plot(X, y, color='green', label='Battery capacity data')\n",
    "ax.plot(X, y_pred, color='red', label='Prediction data')\n",
    "ax.plot([0.,168], [1.38, 1.38],dashes=[6, 2], label=\"EoL Threshold\")\n",
    "ax.set(xlabel='Cycle numbers', ylabel='Capacity')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>cycle</th>\n",
       "      <th>ambient_temperature</th>\n",
       "      <th>datetime</th>\n",
       "      <th>capacity</th>\n",
       "      <th>pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-02 15:25:41</td>\n",
       "      <td>1.856487</td>\n",
       "      <td>1.856368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-02 19:43:48</td>\n",
       "      <td>1.846327</td>\n",
       "      <td>1.855144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-03 00:01:06</td>\n",
       "      <td>1.835349</td>\n",
       "      <td>1.853850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-03 04:16:37</td>\n",
       "      <td>1.835263</td>\n",
       "      <td>1.852485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-04-03 08:33:25</td>\n",
       "      <td>1.834646</td>\n",
       "      <td>1.851051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>164</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-05-26 10:44:38</td>\n",
       "      <td>1.293464</td>\n",
       "      <td>1.248398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>165</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-05-26 15:30:43</td>\n",
       "      <td>1.288003</td>\n",
       "      <td>1.245814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>166</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-05-26 20:21:04</td>\n",
       "      <td>1.287453</td>\n",
       "      <td>1.243283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>167</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-05-27 15:52:41</td>\n",
       "      <td>1.309015</td>\n",
       "      <td>1.240807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>168</td>\n",
       "      <td>24</td>\n",
       "      <td>2008-05-27 20:45:42</td>\n",
       "      <td>1.325079</td>\n",
       "      <td>1.238386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cycle  ambient_temperature            datetime  capacity       pre\n",
       "0        1                   24 2008-04-02 15:25:41  1.856487  1.856368\n",
       "1        2                   24 2008-04-02 19:43:48  1.846327  1.855144\n",
       "2        3                   24 2008-04-03 00:01:06  1.835349  1.853850\n",
       "3        4                   24 2008-04-03 04:16:37  1.835263  1.852485\n",
       "4        5                   24 2008-04-03 08:33:25  1.834646  1.851051\n",
       "..     ...                  ...                 ...       ...       ...\n",
       "163    164                   24 2008-05-26 10:44:38  1.293464  1.248398\n",
       "164    165                   24 2008-05-26 15:30:43  1.288003  1.245814\n",
       "165    166                   24 2008-05-26 20:21:04  1.287453  1.243283\n",
       "166    167                   24 2008-05-27 15:52:41  1.309015  1.240807\n",
       "167    168                   24 2008-05-27 20:45:42  1.325079  1.238386\n",
       "\n",
       "[168 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capacity['pre'] = y_pred\n",
    "capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Actual fail at cycle number: 129\n",
      "The prediction fail at cycle number: 127\n",
      "The error of RUL= -2 Cycle(s)\n"
     ]
    }
   ],
   "source": [
    "pred=0\n",
    "Afil=0\n",
    "Pfil=0\n",
    "a=capacity['capacity'].values\n",
    "b=capacity['pre'].values\n",
    "j=0\n",
    "k=0\n",
    "for i in range(len(a)):\n",
    "    actual=a[i]\n",
    "    \n",
    "    if actual<=1.38:\n",
    "        j=i\n",
    "        Afil=capacity['cycle'][j]\n",
    "        break\n",
    "for i in range(len(a)):\n",
    "    pred=b[i]\n",
    "    if pred< 1.38:\n",
    "        k=i\n",
    "        Pfil=capacity['cycle'][k]\n",
    "        break\n",
    "print(\"The Actual fail at cycle number: \"+ str(Afil))\n",
    "print(\"The prediction fail at cycle number: \"+ str(Pfil))\n",
    "RULerror=Pfil-Afil\n",
    "print(\"The error of RUL= \"+ str(RULerror)+ \" Cycle(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 0.021\n",
      "Train R2 score: 0.9826602260326737\n",
      "Test RMSE: 0.041\n",
      "Test R2 score: -2.4280651482293845\n"
     ]
    }
   ],
   "source": [
    "train_predictions = svr.predict(X_train)\n",
    "rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "print('Train RMSE: %.3f' % rmse)\n",
    "print(\"Train R2 score:\", sklearn.metrics.r2_score(y_train, train_predictions))\n",
    "\n",
    "test_predictions = svr.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "print(\"Test R2 score:\", sklearn.metrics.r2_score(y_test, test_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TPOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "X_train = load(open('pickle/dataset/battery/cycle_only/X_train.pkl', 'rb'))\n",
    "y_train = load(open('pickle/dataset/battery/cycle_only/y_train.pkl', 'rb'))\n",
    "X_test = load(open('pickle/dataset/battery/cycle_only/X_test.pkl', 'rb'))\n",
    "y_test = load(open('pickle/dataset/battery/cycle_only/y_test.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py:985: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                              \n",
      "Generation 1 - Current best internal CV score: -0.0016215186751841124\n",
      "                                                                              \n",
      "Generation 2 - Current best internal CV score: -0.0016215186751841124\n",
      "                                                                              \n",
      "Generation 3 - Current best internal CV score: -0.0016215186751841124\n",
      "                                                                              \n",
      "Generation 4 - Current best internal CV score: -0.0013627184353332035\n",
      "                                                                              \n",
      "Generation 5 - Current best internal CV score: -0.0007889327968975957\n",
      "                                                                              \n",
      "Best pipeline: SGDRegressor(RobustScaler(AdaBoostRegressor(RobustScaler(input_matrix), learning_rate=0.001, loss=linear, n_estimators=100)), alpha=0.001, eta0=0.01, fit_intercept=True, l1_ratio=0.0, learning_rate=constant, loss=huber, penalty=elasticnet, power_t=0.5)\n",
      "-0.00025963325628051656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py:985: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from tpot import TPOTRegressor\n",
    "tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)\n",
    "tpot.fit(X_train, y_train)\n",
    "print(tpot.score(X_test, y_test))\n",
    "tpot.export('TPOT/tpot_battery_cycle_only_pipeline.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try the best TPOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "X_train = load(open('pickle/dataset/battery/cycle_only/X_train.pkl', 'rb'))\n",
    "y_train = load(open('pickle/dataset/battery/cycle_only/y_train.pkl', 'rb'))\n",
    "X_test = load(open('pickle/dataset/battery/cycle_only/X_test.pkl', 'rb'))\n",
    "y_test = load(open('pickle/dataset/battery/cycle_only/y_test.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py:985: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py:985: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from tpot.builtins import StackingEstimator\n",
    "\n",
    "exported_pipeline = make_pipeline(\n",
    "    RobustScaler(),\n",
    "    StackingEstimator(estimator=AdaBoostRegressor(learning_rate=0.001, loss=\"linear\", n_estimators=100)),\n",
    "    RobustScaler(),\n",
    "    SGDRegressor(alpha=0.001, eta0=0.01, fit_intercept=True, l1_ratio=0.0, learning_rate=\"constant\", loss=\"huber\", penalty=\"elasticnet\", power_t=0.5)\n",
    ")\n",
    "\n",
    "exported_pipeline.fit(X_train, y_train)\n",
    "results = exported_pipeline.predict(X_train.append(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data in dataset:  616\n",
      "[1, 24, datetime.datetime(2008, 4, 2, 15, 25, 41), 1.8564874208181574, 4.191491807505295, -0.004901589207462691, 24.330033885570543, -0.0006, 0.0, 0.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x150cf24c0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bgx0X3Zio9gamj4+P9eTt12jnzh37n6uGDYO7d6FNG+jfH9KkcXZkIiIikd6BAwfImTOns8OQKOpFPz/GmO2WZfmE5HqVf0QmcePaLfeOHYOPPoJp0+wXHHv0gMftb0REREQk8lFSHRklSwajRsHhw9Cggb1ynTkzfP21XX8tIiIiIpGKkurILEMGu+Xerl12rXW3bpAtG0yeDKHckUlEREREwp+S6lC6cu9KxN0sb1744w9YswZSp4Z337WPLVwIUawmXkRERCQ6UlIdCl+u+5Jc/8vFrQe3IvbGT1ruzZsHgYFQpw6ULAkv2FZURERERCKOkupQqJ61OpfvXWbwhsERf3NjoF492LcPJk6EkyehdGmoWdM+JiIiIiIRTkl1KBRMXZDmeZszassoTt045Zwg3NygXTs4csTejXHDBrskpFUrOH3aOTGJiIjEcK6urnh7ewd9DRky5KXjy5YtS3Ctgvfu3Rs0T+LEicmYMSPe3t5UrFiRtWvX8tZbb4V7/K1atXqtDVpOnjxJ7ty5X3juZc8WHSmpDqVB5QdhjKHnqp7ODSR2bLvl3vHj0KUL/Pij/TLjZ5/B1avOjS0SabOwDZ8s/cTZYYiISDT3ZJvyJ189evQI9Vx58uQJmqdWrVp8/fXX7Nq1i5UrV4Z4joCAgFDfX16PkupQSpcgHV2KdWHOvjls/Wers8OBxIntlnuHD0OTJnZLvkyZ7J0Z7951dnROt+L4CqbtnkZAoP5wERGRiLdq1Sry589Pnjx5aNOmDQ8ePAjznHfu3KF+/frkyJGDpk2b8mRDvwwZMtC9e3cKFCjAzz//zPLlyylWrBgFChSgQYMG3LlzB4AePXqQK1cu8ubNy2effRY07/r16ylevDiZMmUKWrW2LIuuXbuSO3du8uTJw9y5c5+L5/79+7zzzjvkzJmTunXrcj+GtQHWNuVh0L1EdybtmMSnyz5lQ+sNGGOcHRKkT2+33OvSBXr1gt69YexYe2fGNm3sshEHsSyLOw/vcO3+NW49uEWOpDlwd3V32P1C6lHAI87dPkegFciO8zsolCb6bpEqIiL/ajRh8yvHzH3/363AXzX+6bHBuX//Pt7e3kHf9+zZk9q1a9OqVStWrVpFtmzZaNGiBePGjaNz586vnO9ldu7cyd9//03q1KkpUaIEGzdupGTJkgAkSZKEHTt2cOXKFerVq8fKlSuJEycOQ4cOZeTIkXTo0IEFCxZw8OBBjDHcuHEjaN7z58/z559/cvDgQWrVqkX9+vWZP38+u3btYvfu3Vy5coVChQpRunTpZ+IZN24csWPH5sCBA+zZs4cCBQqE6fmiGq1Uh0G8WPH4styXbDyzkXkH5jk7nGe9+abdcm/DBnvF+v337WPz5jmkDd/gDYPxGOhB/CHxyTA6A3nH5+Xz1Z+H+31C45/b/xBoBQL2irWIiIij/Lf8o1GjRhw6dIiMGTOSLVs2AFq2bMn69evDfK/ChQuTNm1aXFxc8Pb25uTJk0HnGjVqBMCWLVvYv38/JUqUwNvbm2nTpnHq1CkSJEiAp6cn7777LvPnzyd27NhB19apUwcXFxdy5crFxYsXAfjzzz9p3Lgxrq6upEiRgjJlyrBt27Zn4lm/fj3NmjUDIG/evOTNmzfMzxiVaKU6jNrkb8Pov0bTe3Vv6uSog5tLJPtIn7Tc+/136NkT6teHwoVh6FC7RV84WX58Oenip6NDoQ4k8krE/APzGbN1DJ8U+4SUcVOG231C48nLpK7GlRXHV9CrVC+nxiMiIhEjJCvLYRnvbLFixQr6vaurK/5PbQwXJ04cwP5X5EqVKjFnzpznrt+6dSurVq3il19+YezYsaxevfq5eS3thxFiWqkOI1cXVwaWH8jhq4eZuWems8N5MWOgVi3YswemTIFz56BcOahWDXbvDpdbnLh+guLpitOleBfa5G/DqCqjeBjwkGEbh4XL/GFx+qbdDeWtbG+x8fRG7j5UjbmIiESc7Nmzc/LkSY4ePQrAjBkzKFOmTITcu2jRomzcuDHo3nfv3uXw4cPcuXOHmzdvUr16dUaNGsXuV+QDpUqVYu7cuQQEBHD58mXWr19P4cKFnxlTunRpZs+eDcC+ffvYs2ePYx4qklJSHQ5qZ6+NT2ofBqwbwMOAh84OJ3iurnbLvcOH7Zca//oL8ueHZs3gxIlQT/so4BFnbp0hY8KMQceyJslKs7zNGOc7jvO3z4dD8KF36qa9Ut0mfxseBT5i/amw/5ObiIjIizypqX7y1aNHDzw9PZkyZQoNGjQgT548uLi40L59+6BratSoQdq0aUmbNi0NGjQI13iSJUvG1KlTady4MXnz5qVYsWIcPHiQ27dv89Zbb5E3b15KlizJyJEjXzpP3bp1yZs3L/ny5aN8+fIMGzaMlCmf/ZfoDz74gDt37pAzZ0769u1LwYIFw/VZIjsT1Zb1fXx8rMjY83DZ0WVUnVWV76p/xweFPnB2OCFz44ZdBjJ6NPj7wwcfwOefQ7JkrzXN8evHyfxtZn6o9QNt8rcJOn7s2jGyj81Oh0IdGF1tdDgHH3Lv/f4eCw8t5OTHJ0k0NBEfFvqQkVVe/oeHiIhEPQcOHCBnzpzODkOiqBf9/BhjtluW5ROS67VSHU4qZ65MyfQlGbhhIPcfRZEWMgkT2hvHHDlir2D/73+QOTN88QU8brcTEieu26vcT69UA2ROnJmW+VoyYfsE/rn1TzgG/npO3zxN+gTp8XL3omT6knpZUURERMKdkupwYoxhUPlBnLt9jnG+45wdzutJk8be8nzfPqhUCfr1s5Pr//0PHr66nOXEDTupzpAww3PnPi/9OQFWAIP/dMKW7o+dunmKNxK8AUClTJXYd2mf00tSREREJHqJZK0qorbSb5SmUqZKDP5zMLcf3MbNxQ03FzfiesQlTfw0pI2fllRxU3Hu9jn2XNzD3kt7OXXzFEm8kpAiTgpSxE1B9azVyZI4i3MeIEcOu+Xeli32Lo0dO9qbyAwcCA0bgsuL/w528sZJXI0r6RKke+5cxkQZae3dmu93fE/X4l15I+Ebjn6KZ1iWxembp6mWpRoAlTJXoseqHqw8vpLm+ZpHaCwiIiISfSmpDmdDKw6l8szK9F/X/5Vj47jHIUPCDGz128qlu5fwD/RnyJ9DONjxIPFjxXd8sMEpWhTWrIGlS+3kunFj+8XGoUOhYsXnhp+4cYJ0CdIF206wT+k+zNgzg8/XfM6MujMcHf0zrt6/yr1H94JWqr1TepM0dlJWHF+hpFpEJBqyLCtybMYmUUp4vGOopDqc5U+Vn8tdLxNoBRIQGIB/oD83H9zkn1v/cPbWWc7dPkfKuCnJmyIvGRNlxMXYq7+BViCbzmyi9JTS9FvTj1FVRzn3QYyxW+5VqQKzZ9svMFaqZCfVQ4bAU2/0nrh+4rl66qelS5COzkU6M3TjUD4t+in5U+WPiCcA/m2nlz5BegBcjAsVMlZg5fGV+oNXRCSa8fT05OrVqyRJkkR/vkuIWZbF1atX8fT0DNM8SqodxMW44OLqgrurO17uXqSMm5KCqYNvLeNiXCiZviTvF3yfMVvH0Dp/a/KmiAQ7Ebm42C33GjSA8ePhyy/BxwcaNbLLQrJk4cSNE1TPUv2l03Qv2Z2JOybSfWV3ljdfHkHB/7vxy9NlJ5UyVWLu33P5+/Lf5E6eO8JiERERx0qbNi1nz57l8uXLzg5FohhPT0/Spk0bpjmUVEcygyoM4pcDv/DBHx+wofWGoJVsp4sVCz7+GFq3huHDYcQImDcP/7ZtsOJcIGOh4FeqARJ6JqRP6T58suwTlh9bTuXMlSMk7P+uVANUy1oNNxc3/rf1f4x7K4q9VCoiIsFyd3cnY8aX//dIxFEiScYmTyT2SszQikPZdGYT03ZNc3Y4z4sf3265d+wYtGuH66TJHP0Was/aDrduvfTSD3w+IGPCjHRb0Y2AwIAICffUzVPEdo9NEq8kQcdSx0vN+wXf5/sd33Pk6pEIiUNERESiNyXVkVAr71YUS1uMbiu7ser4Ki7fjYT/jJUyJXz3HesWf8cf2SDPxF/tNnyjR8ODBy+8JJZbLL6q8BW7L+6m39p+bDi1gVM3TvEo4BHX71/nyNUjbDqziQOXD4RbmE96VP+3tu7z0p8Tyy0Wfdf2Dbd7iYiISMylHRUjqV0XdlH8h+Lc97c3kkkeJzmZE2XG3dUdNxc3XI2r/auL/Wvy2MkZXHEwib0SR2ic/9v6Pzou6cjlsktI+uVwWLUKMmSwa6+bNHmuDV+gFUj5aeVZd2pdsHPGco3FsY+OkSZ+mjDHV+j7QiTxSsLSZkufO/f56s8ZtGEQO97bEaEvT4qIiEjU8Do7KqqmOpLyTunN6U9Os/P8TvZd2se+S/s4dfMU/oH+PPB/QIBldxZ50mHk90O/4+HqwZjqYyI0zhM3TuDp5kmS0lVgZVVYscJuw9e8uV17PXgwVK1qdxPBfiFzRfMVHLt+jNM3T3P65mnO3T5HXI+4JIudjFhusWgyrwlfb/qab6p+E+b4Tt88Tf6UL06YuxbvyjjfcfRa3YslTZeE+V4iIiIScympjsSSxk5KpcyVqJS50ivHfrDoA8ZvH89HRT4ia5KsERCd7eSNk7yR4I1/yysqVYIKFeCnn6B3b6heHcqWtdvwFSkCgLurOzmS5iBH0hwvnHPp0aVM2D6BHiV7kDJuylDHdv/RfS7dvRTUo/q/EngmoGfJnnRd0ZW1J9dSNkPZUN9LREREYjbVVEcT/cr2I5ZrLHqv7h2h9z1x4wQZE/3nTWsXF3jnHThwAMaOhf377Q1l6teHQ4deOWevUr14GPCQ4ZuGhym2M7fOAM92/vivDoU6kCZeGnqu6hkujd9FREQkZlJSHU2kjJuSz4p/xs/7f+avs39F2H1fuvGLhwd06GB3ChkwAJYtgzffhPffh3Pngp0zS+IsNM3TlHG+47h091KoY3vSo/plSbWXuxf9y/Zny9kt/Hbot1DfS0RERGI2JdXRSJdiXUgRJwVdV3SNkFXXm343ue53/aW7KQIQNy707Wsn1x06wJQpkCUL9OoFN2688JLepXpz/9F9Rm4eGer4nvSofnrjlxdp5d2KbEmy0Wt1rwhr9SciIiLRi5LqaCRerHj0K9OPDac3sOjwIoff78SNEwDPl38EJ3lyu+XewYNQr579EmOmTPZGMn5+zwzNnjQ77+R+h7Fbx3L+9nmOXD3C8mPLmbF7BmtPruXsrbMEWoEvvd3pm6dxMS6kiffyLiJuLm4MKj+I/Zf3M3PPzJA9i4iIiMhT1FIvmnkU8Ijc43Jz+OphEsRKQCKvRCTyTPTvr56JeDP5m3Qq3AlXF9cw3WvBgQXU+6kevu18X7oFe7B27YKePWHpUkiXzt5UpnlzcLXj+vvS3+QZlweLF/+Merp50jRPUybVmvTC861+bcWqE6s488mZV4ZiWRaFvi/E5XuXOdzxMLHcYr3+84iIiEi0opZ6MZi7qzuLGi9i1t5ZXL9/nWt+17h+/zrX/a6z//J+rt6/yqSdk0jilYTm+ZqH6V6vvVL9X97esGQJrFkD3bv/uwX64MHw1lu8mfxNvq/5PadvniZjooxkTJiRFHFTcPbWWY5dO8aqE6v4YecPNM3TlHIZyz03/ZONX0LCGMOQikOoNKMS433H83HRj0P3TCIiIhIjaaU6hgm0AvGZ6MMNvxsc7HgQD1ePUM/VaXEnpu+Zzo3uN57bsfC1WRbMm2e34Tt8GEqUgKFD7V+Dcf/RfbKMyULmRJlZ12rdczFk/jYzRdIUYfbbs0McRsXpFdl9cTfHPzpOvFjxQv04IiIiEvW9zkq1aqpjGBfjwqDygzhx4wQ/7PghTHOduGF3/ghzQg325jD168O+fTB+PBw/DiVLQu3adku+F/By96JXyV5sOL2BVSdWPXMu0ArkzM0zIV6pfmJwhcFcuXeFj5Z+xKOAR6F+HBEREYlZlFTHQFWzVKVk+pJ8uf5L7j26F+p5Tt44GfrSj+C4u9st944cgUGDYO1ayJMH2rSBM8/XRrct0Ja08dPSd03fZzqeXLhzgUeBj4Ld+CU4hdIUonep3kzdNZVqs6px7f61sD6RiIiIxABKqmMgYwxflf+K83fO87+t/wvVHJZlceLGCTIkyBC+wT0RJ47dcu/4cejcGWbNgqxZoWtXuPZvohvLLRa9S/Vm89nNLD+2POj4k3Z6r7tSDTCw/ECm1J7ChtMbKDKpCAcuHwjz44iIiEj05rCk2hgz2RhzyRizL5jzCYwxvxtjdhtj/jbGtHZULPK8Um+UolqWagzZOISbfjdf+/rL9y5z79G98F+p/q8kSeyWe4cP27s0jhhht+EbMgTu2avsbfK3IX2C9PRd25dAK5B9l/bx474fgVf3qA5OK+9WrGm5hlsPblH0h6KsPbk2vJ5IREREoiGHvahojCkN3AGmW5aV+wXnewEJLMvqboxJBhwCUlqW9fBl8+pFxfCz8/xOCkwsQOXMlSmapigp4qYgsVdibj+4zaW7l7h87zKX7l4K+rp87zIGQ7xY8XBzcWP/5f389s5v1MxeM+KC3rvXXsFetAhSp4b+/aF1aybtmUq739sRzyMetx/eBiBzoszs/WAvXu5eob7d6ZunqTarGseuHePH+j9SJ0ed8HkOERERifRe50VFh3b/MMZkABYFk1T3BNIBHYAMwAogm2W9fEcPJdXhq+fKnkzaOYmr964+1w86fqz4JIudjORxkpM8TnKSxU4GwO2Ht7n14BauLq5MqzONxF6JIz7wDRvsNnybN0P27PgP+pLWZiGebl6UTF+SEulLkDlR5nB5ifLqvavUmF2Dbee2ManmJFrn1z+qiIiIxARRJamOB/wG5ADiAY0sy/rjVXMqqXYM/0B/rt67ypV7V+xkOk4yPN08nR3Wy1kW/PabvYHMgQNQtKhdFlKmTLjf6s7DO7z909ssP7ac0VVH81GRj8L9HiIiIhK5RJWWelWAXUBqwBsYa4yJ/6KBxpj3jDG+xhjfy5cvR1yEMYibixsp4qbgzeRvki5BusifUIPdhq92bdizByZNsruDlC0LNWrYx8JRXI+4/N74d2pmq0nXFV05deNUuM4vIiIiUZszk+rWwHzLdhQ4gb1q/RzLsiZaluVjWZZPsmTJIjRIiQLc3ODdd+02fMOGwaZN9m6NLVrAyZPhdhsPVw/+V93ulvLFui/CbV4RERGJ+pyZVJ8GKgAYY1IA2YHjToxHojovL7vl3vHj9q8//wzZs8Mnn8CVK+Fyi3QJ0vGBzwdM2z2Nw1cPh8ucIiIiEvU5sqXeHGAzkN0Yc9YY864xpr0xpv3jIV8CxY0xe4FVQHfLssIn85GYLVEie4vzI0egeXP49lvInBkGDoS7d8M8fc+SPYnlFot+a/uFQ7COc+XeFXwm+uB7Tu8giIiIOJrDkmrLshpblpXKsix3y7LSWpb1g2VZ4y3LGv/4/DnLsipblpXHsqzclmXNdFQsEkOlTWvXWu/bB+XLQ58+kCULjBsHj0K/BXmKuCn4uMjH/LjvR/ZcDN/a7fC0+8Jutp/fzsdLP8aRLySLiIiIdlSUmCBnTliwwK61zpoVPvwQcuWCn36yO4iEQtfiXUkQKwF91vQJ52DDz7nb5wDYdGYTvx781bnBiIiIRHNKqiXmKFYM1q2zN47x9IRGjaBwYVi16rWnSuSViM+Kf8Zvh35j/an1Dgg27M7fOQ9ApkSZ6LmqJ/6B/k6OSEREJPpSUi0xizF2y71du2DaNLh0CSpWhCpV7N0aX8PHRT4mQ8IM1P6xNlv/2eqYeMPg/O3zxHGPw8jKIzl09RA/7PjB2SGJiIhEW0qqJWZydbVb7h06BCNGwLZtdhu+996DCxdCNEW8WPFY23Itib0SU3F6RTae3ujYmF/T+TvnSR0vNbWy16Jk+pL0W9uPOw/vODssERGRaElJtcRsnp7w6adw9Ch8/DFMmWLXXX/1Fdy//8rL30j4ButarSNVvFRUmVmFtSfXOj7mEDp3+xyp4qXCGMOwisO4ePciIzePdHZYIiIi0ZKSahGAxIlh5EjYvx8qVYLeve0e17NnQ2DgSy9NGz8t61qt442Eb1Bjdg1OXD8RQUG/3Pk750kVNxUAxdIVo17OeozYPIL7j179lwURERF5PUqqRZ6WNSvMnw9r10KyZNC0qf2Co+/Lez2njJuSZc2WAdB1RdcICPTlLMvi/G27/OOJDoU6cOvBLX4//LsTIxMREYmelFSLvEiZMnad9fTpcOYMFClil4fcuhXsJWnjp6VHiR7MOzCPdSfXRWCwz7v98DZ3H90NWqkGKPNGGdLES8PMPWoJLyIiEt6UVIsEx8XF3pHxwAH44AMYM8bueb10abCXfFb8M9InSM/HSz8mIDAgAoN91vnbdju9VPH+TapdXVxpkqcJS44u4co9bV4qIiISnpRUi7xKggQwdixs2WL/vnHjYF9i9HL3YljFYey+uJvJOydHcKD/etKj+unyD4BmeZvhH+jPT3//5IywREREoi0l1SIhVbiwvVp944a9Q2MwGr7ZkJLpS9J7dW9u+t2MuPieErRS/VT5B0DeFHnJkzyPSkBERETCmZJqkddRrhxkzAiTJgU7xBjDN1W+4cq9KxT6vhBFJxWl6KSilJtWjj9P/xkhYT7Zovzp8o8nmuVtxuazmzl27ViExCIiIhITKKkWeR0uLvDuu7Bmjd3bOhgFUxfk22rfkilRJhJ6JiShZ0KOXz9OpRmVWHhwocPDPH/nPF5uXiSIleC5c41zN8ZgmLV3lsPjEBERiSmUVIu8rlat7OR68strpjsW7sjSZkuDvra/t528KfJS76d6TNoR/Ep3eDh/53zQxi//lS5BOspkKMPMPTOxLMuhcYiIiMQUSqpFXleaNFC9OkydCv7+Ib4saeykrGqxikqZKtHu93Z8teGrF457FPCIjac3Emi9fNOZlzl/+/xz9dRPa5anGUeuHWHbuW2hvoeIiIj8S0m1SGi0bQvnz8OSJa91WVyPuPzW+Dea5GlC79W96bO6zzOrxfce3aP2j7UpOaUkzRc054H/g1CF92SL8uC8nettYrnG4rtt34VqfhEREXmWkmqR0KheHVKmfOkLi8HxcPVgRt0ZtM3floEbBtJjZQ8sy+Km302qzqzK0qNLafhmQ2bvnU3lmZW5dv/aa9/j/J3zpI6bOtjzCT0T0rFwR6bvns7uC7tfe34RERF5lpJqkdBwd7drq//4A86de+3LXYwLE2pO4AOfDxi2aRgdFneg3LRybDm7hR/r/8jc+nOZVW8WW85uofgPxTl+/XiI57778C63Htx66Uo1QO9SvUnomZDPVnym2moREZEwUlItElrvvguBgdCwIZw69dqXuxgX/lf9f3xU+CPG+Y7j4JWD/Nb4Nxq+2RCAJnmasKL5Ci7dvUS1WdXwDwxZ/faTjV9eVlMNkMgrEX3L9GXl8ZUsO7bsteMXERGRfympFgmtLFlg5kzYswfy5YO5c197CmMM31T9hu9rfs/61uupmqXqM+dLv1GaqXWmcvjqYabtmhaiOZ9s/PLf3RRf5MNCH5I5UWa6rujq1G3VRUREojol1SJh0aQJ7NoFOXPCO+9Ay5Zw+fJrTWGMoW2Btvik9nnh+ZrZalI4TWEGrBsQohcXg1aqX1H+AXZ995CKQ9h3aR9Td019rbhFRETkX0qqRcIqUyZYvx769IHZsyFrVhg5Eh4+DJfpjTEMKj+IM7fOMGH7hFeOD26L8uC8nfNtiqUtRp81fbjz8E6YYhUREYmplFSLhAd3d/jiC7sUpEQJ6NIF3nwTfvsNwuElwAoZK1A2Q1kGbRjE3Yd3Xzr23O1zeLh6kNgrcYjmNsYwvPJwzt85z4hNI8Icq4iISEykpFokPOXMaXcEWbLETrRr14ZKlWDv3jBN+2S1+tLdS4zZOualY8/fsTd+edFuisEpnq449XPVZ9imYUEr3SIiIhJySqpFHKFqVdi9G8aMgZ07wdsbPvjgteutn1Y8XXGqZ63OsI3DuOF3I9hxT7Yof12DKwzmUcAj+q7pG+oYRUREYiol1SKO4u4OHTvCkSP2r99/b3cMGTEi1PXWA8sN5LrfdYb+OTTYMedunwtR54//ypI4Cx0KdWDyrsnsvRi2lXUREZGYRkm1iKMlTgyjR9slICVKwGef2fXWCxe+dr11/lT5aZa3GSO3jAx2Q5jzt8+H+CXF//q89OfEjxWfbiu7hep6ERGRmEpJtUhEyZkTFi+2v9zcoE4dKF8etm9/rWmGVBiCm4sbXVd0fe6cn78f1/2uhzqpThI7CZ+X+pylR5ey/NjyUM0hIiISEympFolo1arZXULGjoV9+8DHB1q0gDNnQnR5mvhp6FmyJ/MPzGftybXPnLtw5wIQso1fgtOxcEcyJMzAe7+/x7Frx0I9j4iISEyipFrEGdzdoUMHOHoUunWDn36CbNng88/h9u1XXt6lWBfeSPAGnZd2fmYnxHO3zwEh2/glOLHcYvFT/Z+4/fA2JSaXYPeF3aGeS0REJKZQUi3iTAkSwNChcPCgXQ4yaJC9eczEieDvH+xlXu5eDKs0jN0Xd/PDzh+Cjr/uxi/BKZSmEBtab8Dd1Z0yU8vw5+k/wzSfiIhIdKekWiQyyJAB5syBLVvsDiHvv2+34Vu6NNhLGuRqQKn0pei9undQmcbrbFH+KrmS5WJjm42kjJuSSjMqsejwojDPKSIiEl0pqRaJTIoUgQ0b4JdfwM/Prr+uUsWuwf4PYwwT3ppAoBVIuWnlOHH9BOdvn8fNxY2ksZOGSzjpE6RnQ+sN5E6emzo/1mH67unhMq+IiEh0o6RaJLIxBt5+G/bvh5EjYds2yJ8f2raF88/udpgzWU5WNl/JnYd3KDetHFvPbSVl3JS4mPD7v3ayOMlY3WI1ZTKUoeWvLflmyzfhNreIiEh0oaRaJLLy8IBPPrFfZvzoI5g+3a63/uILuHs3aFj+VPlZ0XwFNx/cZOXxlWGup36ReLHisbjJYt7O+TafLPuEPqv7hPs9REREojIl1SKRXeLEMGqUvXJdtSr062d3Cpk6FQIDASiYuiDLmy0nQawEZEqUySFhxHKLxdz6c3k3/7sM3DCQlcdXOuQ+IiIiUZGxXnNHN2fz8fGxfH19nR2GiPP8+Sd06QJbt9ovM44YYW8iA5y9dRYvNy+SxE7isNv7+fuR8385iR8rPjve24Gri6vD7iUiIuJMxpjtlmX5hGSsVqpFopqSJWHzZpg9G65fhwoVgjaUSRs/rUMTagBPN0+GVRzGnot7mLprqkPvJSIiElUoqRaJilxcoHFju7/111/brfi8vaFlSzh92uG3r5+rPsXTFefzNZ9z+8GrN6sRERGJ7pRUi0Rlnp7w2Wdw/Lj969y5dr11165w7ZrDbmuMYWTlkVy4c4FhG4c57D4iIiJRhZJqkeggUSIYNgwOH7ZXsEeMgMyZ7WP37zvklkXSFqFJniYM3zycMzfPOOQeIiIiUYWSapHoJH16mDIFdu+G4sWhe/d/O4UEBIT77QZXGAxAr9W9wn1uERGRqERJtUh0lCcP/PEHrFkDqVJB69Z2zfUff0A4dvxJnyA9nYt0ZtaeWey6sCvc5hUREYlqlFSLRGdly8Jff8FPP9nbnr/1FpQrZ7fjCyfdS3YnkVcieq7qGW5zioiIRDVKqkWiO2OgQQN785ixY+1fixSBhg3hyJEwT5/QMyG9S/Vm6dGlrD6xOhwCFhERiXqUVIvEFO7u0KEDHDtm78q4eDHkymUfu3gxTFN/WOhD0idIT7cV3Qi0AsMpYBERkahDSbVITBMvHvTvD0ePQrt2MGECZMkCX3wBjx6FakpPN0++LPcl289v55f9v4RvvCIiIlGAkmqRmCplSvjuO7scpEoVe/W6USN4+DBU0zXN05S8KfLSa1UvHgaEbg4REZGoSkm1SEyXLRv88gt88w0sWAB169ovNb4mVxdXhlYcyrHrx2g6vykP/B+Ef6wiIiKRlJJqEbF9/LFdCrJkid0l5O7d156iapaqjKoyil/2/0LNOTW58/COAwIVERGJfJRUi8i/3nvP3ihmzRqoXRv8/V97is5FOzO19lRWn1hNxekVuXbfcduli4iIRBZKqkXkWS1awPffw6pV9guNodDSuyXzGs5j14VdlJxckiNXw966T0REJDJTUi0iz2vTBt59FwYNsstBQqF2jtosa7aMS3cvUej7Qvxx+I9wDlJERCTyUFItIi82ZgzkzQvNm8OZM6GaokyGMmx/bzuZEmWi5pyafLHuC/WxFhGRaElJtYi8mJcX/Pyz3WKvUaNQ97B+I+EbbGyzkeb5mtNvbT9a/tqSgMCAcA5WRETEuRyWVBtjJhtjLhlj9gVzvqsxZtfjr33GmABjTGJHxSMioZAtG0yaBJs3Q8eOYFmhmsbL3YuptacyqPwgZu6ZSauFrZRYi4hItOLmwLmnAmOB6S86aVnW18DXAMaYmsAnlmWpTYBIZNOwIezcCUOGQObM0K1bqKYxxtCrVC8Aeq/ujcEwpfYUXF1cwzNaERERp3BYUm1Z1npjTIYQDm8MzHFULCISRoMGwYkT0L07ZMhgJ9qh1KtULyzL4vM1n+NiXPih1g9KrEVEJMpz5Ep1iBhjYgNVgY7OjkVEguHiYvevPnvWbrmXJg2UKBHq6XqX7k2gFUjftX0xxvBDrR9wMXrFQ0REoq7I8F+xmsDGl5V+GGPeM8b4GmN8L1++HIGhiUgQT0/49VdIn97ecfGnn8I0XZ8yfRhQdgBTd02l7W9t1RVERESitMiQVL/DK0o/LMuaaFmWj2VZPsmSJYugsETkOUmTwrJl9guMjRpBgwYQhr/o9i3Tl35l+jFl1xTe+/09JdYiIhJlOTWpNsYkAMoAC50Zh4i8howZYeNGGDwYfvsNcuWyW++FUv+y/elbui8/7PyBT5Z+Eo6BioiIRBxHttSbA2wGshtjzhpj3jXGtDfGtH9qWF1guWVZdx0Vh4g4gJsb9OgBO3bAG2/YLy42agRXroRquv5l+9OxUEe+3fotW85uCedgRUREHM9Yoew76yw+Pj6Wr6+vs8MQkSf8/WHYMOjfHxImhHHj4O23X3ua2w9uk31sdtLET8Nfbf/Si4siIuJ0xpjtlmX5hGSs/qslImHj5ga9esH27ZAuHdSvb28U85o7MMaLFY9hlYbhe86XqbumOiZWERERB1FSLSLhI08e2LIFunSB//0PKld+7ZcYm+ZpSvF0xemxsgc3/G44Jk4REREHUFItIuHH3R2GD4cZM+ytzQsVgl27Qny5MYYx1cZw5d4VBqwd4Lg4RUREwpmSahEJf82awYYNdr114cLQtSvcvBmiSwukKkC7Au0Ys3UM+y/vd3CgIiIi4UNJtYg4RqFCdneQFi1gxAjImhW+/x4CAl556aAKg4jrEZduK7pFQKAiIiJhp6RaRBwneXKYNAl8fSF7dnjvPahU6ZW11kljJ6VnyZ78ceQP1p5cGzGxioiIhIGSahFxvAIFYP16+OEHu9a6YEE70X6Jj4p8RNr4aem2ohtRrfWniIjEPEqqRSRiGANt2ti7MRoDJUvC1KnBDvdy9+LLcl+y7dw2ft4f+h0bRUREIoKSahGJWAUK2D2tS5aE1q1hwAAIZiW6ed7m5Emeh56revIw4GEEByoiIhJySqpFJOIlTQpLl0KrVvZOjJ98AoGBzw1zdXFlaMWhHL9+nAm+E4KOqxxEREQiGyXVIuIcbm52jfXHH8Po0fDuu3YLvv+omqUq5TOWp/OyzngN8sLtCzdcvnChwx8dnBC0iIjIi7k5OwARicFcXGDUKEiUyF6x9vODWbPs448ZY5jw1gQm+E7Axbjg5uLGzgs7Gb99PB0KdyBXslzOi19EROQxJdUi4lzGQL9+4OkJPXpA5swwcOAzQ7IkzsLXlb8O+v7KvStkGp2Jvmv68kvDXyI6YhERkeeo/ENEIodu3aBdOxg0yN7m/CWSxk5Kl2JdmHdgHr7nXt6aT0REJCIoqRaRyMEY+N//oHx5aNsW/vzzpcM/KfYJSbyS8PnqzyMoQBERkeApqRaRyMPdHX7+Gd54A+rWhePHgx0aP1Z8epbsybJjy1h3cl0EBikiIvI8JdUiErkkTgyLFkFAALz1Fty8GezQDwt9SOp4qem9urfa7ImIiFMpqRaRyCdbNpg3D44cgUaNXthqD+xdF/uU7sPGMxvps6YPgdbzva5FREQigpJqEYmcypWDceNg2TJ7c5hgtC3QljbebRi0YRANfm7A3Yd3IzBIERERm5JqEYm82raFLl1g7Fj76wXcXNyYVGsSo6qM4teDv1JicglO3zwdwYGKiEhMp6RaRCK3oUOhZk3o1AnatIGrV58bYoyhc9HO/NHkD07cOEHh7wuz8/xOJwQrIiIxlZJqEYncXF1h7lx7Y5jp0yFnTnvXxRe8mFg1S1U2v7sZD1cPykwtw5oTa5wQsIiIxERKqkUk8vPygsGDYccOyJQJmjWDqlVf2HIvV7JcbGyzkXQJ0lF1VlV+2a8dF0VExPGUVItI1JE3L2zcCGPGwObNkDu3XR7y6NEzw9IlSMeG1hvwSe1Dw58bKrEWERGHU1ItIlGLqyt07Aj799ur1T16gI8PbNjwzLDEXolZ0XwF3im96bK8Cw8DHjopYBERiQmUVItI1JQ2LcyfDwsWwLVrULo0vPMOnP6380ds99h8VeErTt88zeSdk50YrIiIRHdKqkUkaqtTBw4ehH79YOFCyJEDBgwAPz8AqmSuQrG0xRi0YRAP/B84N1YREYm2lFSLSNQXJw70728n1zVr2r/Plw/WrMEYwxflvuDsrbNM2jHJ2ZGKiEg0paRaRKKPN96w2+8tW2ZvbV6+PLRqRYV4+SiVvhRf/fkV9x/dd3aUIiISDSmpFpHop3Jl2LcPevWCWbMwOXMy8Wpxzt06x8TtE50dnYiIRENKqkUkevLygkGDYNcuyJ6dHF2HsmNuQn6a/yWX7l5ydnQiIhLNKKkWkejtzTftdnsTJ5LnfACrRl1lwIdvsu/SPmdHJiIi0YiSahGJ/lxcoF073A4d4VHB/Pxv+hVmtfZh8ZHFzo5MRESiCSXVIhJzpEhBvLWbuFe7BoMXP+BIixqM3TTa2VGJiEg0oKRaRGIWT09iz/+NR5068PEW8OjUmW4ruhFoBTo7MhERicKUVItIzOPigvu3Ywn8+CPe2wFL539Ns/nNtDmMiIiEmpJqEYmxXPr1x4ofn5/3v8mcfXOoPrs6N/1uOjssERGJgpRUi0jMlSgR5tNPyb7+bxZl/4L1p9ZTempp/rn1j7MjExGRKEZJtYjEbJ07Q6JE1Ji1lcVNFnP8+nGK/VCM/Zf3OzsyERGJQpRUi0jMliABfPYZLFpEpasJWN9qPY8CH1Ficgk2nNrg7OhERCSKUFItItKpEyRJAv36kT9Vfja/u5kUcVJQaUYllh1d5uzoREQkClBSLSISLx507w5Ll8KSJWRImIGNbTaSM1lOav9YmyVHljg7QhERieSUVIuIAHz4IeTODXXqwLx5JImdhFUtVpErWS7qzK3DH4f/cHaEIiISiSmpFhEBiBMH1q0DHx9o0AAmTCCxV2JWtVhFnuR5qDu3LkuPLnV2lCIiEkkpqRYReSJxYlixAmrUgPbt4YsvSOSZkJUtVpIjaQ7e+/09/Pz9nB2liIhEQkqqRUSeFjs2zJ8PLVtCv37QsSMJ3eMxqsooztw6w3jf8c6OUEREIiEl1SIi/+XuDlOmQLdu8N130LgxFdKUpELGCny14StuP7jt7AhFRCSSUVItIvIixsDQoTBiBPz8M1SvzpBCvbh87zLfbPnG2dGJiEgko6RaRORlPv0Upk+H9evxqdeBT2KVZfjm4Vy9d9XZkYmISCSipFpE5FWaN4fly+HWLYb3/ZO2a24xdP1gZ0clIiKRiJJqEZGQKFcO9uzBpcZbjFgOlTqN4shWtdgTERGbkmoRkZBKkgTmz+fqyEGUOBVIhmLVON2iDly86OzIRETEyZRUi4i8DmNI8kkvru3ZyoISiUk9ayGPMqaH/v3htrqCiIjEVEqqRURCIW2OQlRbcZIOX5dlQcaHMGAAVubMMGYMPHzo7PAixK0HtwgIDHB2GCIikUKIkmpjTBJHByIiEtXEixWP7zqv5K+Rn1K4LexLbsFHH0GOHDB7NgQGOjtEhwkIDCDT6EyM8x3n7FBERCKFkK5UbzHG/GyMqW6MMSG5wBgz2RhzyRiz7yVjyhpjdhlj/jbGrAthLCIikYariysjqoygedtvyVf/Cp90zol/3DjQtCkULAjLloFlOTvMcHfD7wZX719l+/ntzg5FRCRSCGlSnQ2YCDQHjhhjvjLGZHvFNVOBqsGdNMYkBL4DalmW9SbQIISxiIhEOp2KdOLnhr8wLslxcrd9wJWJ38CNG1C1KlSsCNu2OTvEcHXD7wYAx64dc24gIiKRRIiSasu2wrKsxkA7oCWw1RizzhhTLJhr1gPXXjJtE2C+ZVmnH4+/9Hqhi4hELm/nepuVLVZy7u4FWsddCQcPwujRsGcPFC4MDRvCkSPODjNcXPe7DsCx60qqRUTgNWqqjTEfG2N8gc+ATkBSoAswO5T3zgYkMsasNcZsN8a0eMn93zPG+BpjfC9fvhzK24mIOF7J9CXpUbIHiw4vYuuV3XaN9bFj0LcvLF4MuXJBx45w/bqzQw2TJyvV526f4/6j+84NRkQkEghp+cdmID5Qx7KsGpZlzbcsy9+yLF9gfCjv7QYUBGoAVYA+wZWUWJY10bIsH8uyfJIlSxbK24mIRIxOhTuRNHZS+q7pax+IHx8GDLCT63btYNw4yJYNJk+Osi8zXr//718KTtw44cRIREQih5Am1Z9blvWlZVlnnxwwxjQAsCxraCjvfRZYZlnWXcuyrgDrgXyhnEtEJNKIFyse3Yp3Y9mxZWw8vfHfEylSwHffwfbtdlL97rtQogTs2AHA35f+ZsSmEU6K+vU8WakG1VWLiEDIk+oeLzjWM4z3XgiUNMa4GWNiA0WAA2GcU0QkUuhQuAMp4qSg79q+z5/09oYNG2DqVDh+HHx8sD78kI/ntOCzFZ9FiST1SU01wPHrx50YiYhI5PDSpNoYU80YMwZIY4z59qmvqYD/K66dg102kt0Yc9YY864xpr0xpj2AZVkHgKXAHmArMMmyrGDb74mIRCWx3WPTo2QPVp9YzdqTa58f4OICLVvCoUPQqRNMmMCcXjtovguWH1se0eG+tht+N3A1rsTziKeXFUVEePVK9TnAF/ADtj/19Rt2HXSwLMtqbFlWKsuy3C3LSmtZ1g+WZY23LGv8U2O+tiwrl2VZuS3L+iZMTyIiEsm8X/B9UsdLTd81fbGC61WdMCH+o0ZQu2s6zqT0YvqvcH/G5AiNMzRu+N0gkVciMifOrKRaRIRXJNWWZe22LGsakNmyrGlPfc23LCtqv7ouIuJgXu5efF7qczac3kDzBc2D7ZIxeedkfvc6xT+/zuBwrpS0H++L//bI3df6ut91EnomJFOiTCr/EBHh1eUfPz3+7U5jzJ7/fkVAfCIiUVp7n/YMLDeQ2XtnU3JKSU7fPP3M+XuP7tF/bX9KpCvBW7nrcWjcl1z1goDaNSEStxC94XeDRJ6JyJwoMyeunyDQippdTEREwsuryj8+fvzrW0DNF3yJiMhLGGPoXbo3C99ZyNFrR/GZ6MPUXVPZeX4ndx7eYfSW0Zy/c54hFYdgjKFEoXq8/Y7B5fJVaNQIHj1y9iO80PX79kp15kSZeRDwgH9u/ePskEREnMrtZSctyzr/+LcuwHnLsvwAjDFeQAoHxyYiEm3UzF6Tv9r+RZ0f69B6Yeug4y7GhZrZalIyfUkAEnslxqVQYQYHXKXvD2ugZ08YPtxZYQfrht8N3kj4BpkSZQLsDiDpEqRzclQiIs7z0qT6KT8DxZ/6PuDxsULhHpGISDSVI2kO9n6wl4NXDnL46mEOXz3M6Zun6Vqi6zPjKmeuzIBzg+j+XhtijRwJb70FZcs6J+hgXPe7TsJYCcmcODNgb1deJkMZJ0clIuI8IU2q3SzLevjkG8uyHhpjPBwUk4hItOXu6k6eFHnIkyJPsGMqZ67Ml+u/ZOl7Fai9ej20agV79tg7M0YClmUFdf9InyA9rsY1SvTWFhFxpJBu/nLZGFPryTfGmNrAFceEJCISsxVJU4R4HvFYcn49TJ8OZ87AJ584O6wg9/3v8zDgIQk9E+Lm4sYbCd/g+A11ABGRmC2kSXV7oJcx5rQx5gzQHXjfcWGJiMRc7q7ulM9YnmXHlmEVLQo9esDkyfDbb84ODfh3i/JEnokAyJwos1aqRSTGC1FSbVnWMcuyigK5gJyWZRW3LOuoY0MTEYm5KmeuzMkbJ+2NVfr1g3z5oF07uHDB2aFx/b69TUFCz4TA46RaG8CISAwX0pVqjDE1gA+BT40xfY0xfR0XlohIzFY5c2Xg8ZblHh4wYwbcuQPVq8Pt206NLWil2steqc6UKBPX7l8LOi4iEhOFKKk2xowHGgGdAAM0AN5wYFwiIjFa5kSZyZo4Kz/s/MHeWCVPHvjlF/uFxXr14OHDV0/iINf9/rNS/bgDiHZWFJGYLKQr1cUty2oBXLcsawBQDMjmuLBERGI2YwwDyg5gx/kdTN011T5YrRpMmgQrV0Lr1hDonF0M/1tT/aRXteqqRSQmC2lSff/xr/eMMamBR0Aqx4QkIiIA7+R+hxLpStBzVU9u+t20D7ZqBV99BbNnw4cfOqUU5EU11aCVahGJ2UKaVC8yxiQEvgZ2ACeB2Q6KSUREsFerR1cdzeW7lxm4fuC/J3r0gC5dYMIEyJIFxo6N0HKQJyvVT5LqeLHikSx2Mr2sKCIxWki7f3xpWdYNy7LmYddS57AsSy8qiog4WMHUBWnt3ZrRf43m8NXD9kFj7K3Lt2yBXLmgUyfImdNevY6AkpDrfteJ4x4Hd1f3oGOZEmVSUi0iMVpIX1T0NMZ8aoyZj71C3cYY4+nY0EREBGBQhUF4unnSZXmXZ08UKQKrV8OSJRAvHjRtCgULwrJlYFkOi+eG342gVeonMifOrPIPEYnRQlr+MR14ExgDjMXuVz3DUUGJiMi/UsZNSZ/SfVh0eBHDNw1/9qQxULUq7NgBM2fCjRv29xUrwr59DonnyRblT8ucKDOnb57mYYDzupKIiDhTSJPq3JZlvWtZ1prHX+2wk2wREYkAnYt2pkGuBnRd0ZXPln9mt9l7mouLvVJ98CCMHg27dkH+/NCtm93fOhxd97v+3Ep1zqQ5CbQC8T3nG673EhGJKkKaVO8wxhR98o0xpgigPzlFRCKIu6s7c96eQ4dCHRixeQQtf23Jo4BHzw+MFQs++ggOHYIWLeDrr+16699/D7dYbvjdCGqn90SNbDXwdPNk1p5Z4XYfEZGoJKRJdUFgkzHmpDHmJLAZKGSM2WuM2eOw6EREJIiriytjqo1hUPlBzNwzk1zf5aLctHLUmlOLZvObsf7U+n8HJ00KP/wAf/4JiRJBrVrQoQPcvx/8DULo+v3nV6rjx4pPnRx1mPv3XJWAiEiMFNKkuiqQESjz+Cvj42NvATUdE5qIiPyXMYZepXoxq94ssiXJRkBgAGdunWHZsWVUmF6BSTsmPXtBiRKwbZvdgu+77+yXG/fvD1MML1qpBmiWpxlX719l6dGlYZpfRCQqCmlLvVOWZZ3C3gTGevL11HEREYlATfI04Y8mf7C+9Xp2vr+To52OUiFjBdr93o7Pln9GQGDAv4NjxbJb8C1ZAhcvgo+PXXcdEBD8DYIREBjAzQc3n1upBqicuTLJYidj5p6ZYXgyEZGoKaQt9WoZY44AJ4B12Ju/LHFgXCIi8hoSeCZgUZNFdCzUkRGbR1Dvp3rcf/SfUo+qVWH3bihfHjp3hlKl4MCB17rPrQe3AJ7r/gF23fc7ud/ht0O/BW0QIyISU4S0/ONLoChw2LKsjEAFYIvDohIRkdfm5uLGmOpjGFNtDL8f+p16P9XDz9/v2UEpU9ovLc6cab/M6O0NX34J9+6F6B7X/Z7dovy/mudtzoOAB8zbPy8MTyIiEvWENKl+ZFnWVcDFGONiWdYawMeBcYmISCh1LNyR72t+z9KjS2nwc4PnXxw0xm6/t38/1K4NfftC1qzw/ffg7//SuZ+sQL+ophrAJ7UP2ZJkY+ZelYCISMwS0qT6hjEmLrAemGWMGQ3cdVxYIiISFu8WeJdxNcax6PAi3vnlnRe330uRAn76Cdatg/Tp4b33IHdumD8/2B0Zr99/+Uq1MYbmeZuz9uRaTt88HV6PIyIS6b00qTbGZDHGlABqA/eAT4ClwFWgk+PDExGR0Grv057RVUez4OACPvzjw+AHli4NmzbBggX2JjJvvw3FisHatc8NDVqpfkFN9RNN8jQBYPbe2WEJX0QkSnnVSvU3wC3Lsu5alhVoWZa/ZVnTgAVAf0cHJyIiYfNRkY/oUqwLk3ZOYsf5HcEPNAbq1IE9e2DSJDh7FsqVg+rV7ZcbH3tVTTVApkSZKJGuBDP2zMAKZsVbRCS6eVVSncKyrL3/Pfj4WAaHRCQiIuGqT+k+JPFKQveV3V892M0N3n0XjhyBYcNgyxZ7u/NmzeDEiVfWVD/RPG9z9l/ez64Lu8L+ACIiUcCrkuqELznnFY5xiIiIgyTwTMDnpT9n5fGVLD+2PGQXeXlB165w7Bh06wbz5kH27BQfPpfk9wxxPeK+9PIGbzbAw9VDPatFJMZ4VVLta4xp99+Dxpi2wHbHhCQiIuHtA58PyJAwA91XdifQCgz5hYkSwZAhcPQotGpFkYXbOfaNhfnyS7hzJ9jLEnslpkbWGszeNxv/wJd3FBERiQ5elVR3BlobY9YaY0Y8/loHvAt87PDoREQkXMRyi8Wg8oPYdWFX6F4gTJMGJk6k+6jq/JkjDvTrB5kzw9ix8PDhCy9plrcZF+5cYPWJ1WGMXkQk8ntpUm1Z1kXLsooDA7B3UTwJDLAsq5hlWRccH56IiISXd3K/Q/6U+fl89ec88H8Qqjn2Jwnk8w457FrrnDmhUyf71zlzIPDZFfAaWWuQ0DMhM/bMCI/wRUQitRD1qbYsa41lWWMef2nJQUQkCnIxLgytOJRTN0/x3bbvQjXHDb8bdju9IkVgzRpYvBjixYMmTcDHB5YtC+pxHcstFg1zNWT+gfnceRh8qYiISHQQ0s1fREQkGqiUuRKVMlVi4IaBQZ08Xsd1v+v/ttMzBqpVgx077G3Pr1+HqlWhYkXYtg2A5vmac+/RPRYeXBh+DyEiEgkpqRYRiWGGVhzKtfvXGPrn0Ne+9obfjefb6bm42NueHzwIo0fD3r1QuDA0aUJx90xkSJhBJSAiEu0pqRYRiWHyp8pPkzxN+Oavbzh76+xrXXv9/vXgN36JFQs++shuw9enD8ybh0vOXHxzMgcrjy7n/O3zYQ9eRCSSUlItIhIDDSw3kEArkH5r+oX4Gj9/Px4EPHjlxi/EiwdffGGvWBcsSO1vlrJ+skXfEW9x+8HtMEYuIhI5KakWEYmBMibKyIc+HzJ191T+vvR3iK65fv/VW5Q/I1s2WLkSpk+nwJ14TPhiB2srZObG8QOhjFpEJPJSUi0iEkP1Lt2buB5xabagGU3mNaHM1DJkG5ONz1d/jvW4g8fTgrYo93rFSvXTjIHmzfE8fppjretQZctlPHLl5k7/XvDoUTg9iYiI8ympFhGJoZLGTsoXZb/g8NXD/PXPX1iWRZr4aRi0YRA9VvZ4LrG+7veaK9VPS5iQrD8s4K9lk1mRGeIOGExg4cJ2iYiISDSgpFpEJAb7uOjH3O11l2MfHWN96/WsbrGaD3w+YNimYQxYN+CZsUEr1a+qqX6JUhVa47bgN+o2gvunjkLBgjBoEPhrK3MRidqUVIuISBBjDGOrj6WNdxsGrBvAkD+HBJ177ZrqYNTIVgP/2m/x5ocWfrWqw+efQ4EC9mYyIiJRlJJqERF5hotxYWLNiTTJ04Seq3oyYO0ALMsKXU11MIZXGs4/sR7wccsUMH8+3L4N5cvD22/DiRNhnl9EJKIpqRYRkee4urgyrc40Wnm3ov+6/nRY3IGr968CYV+pBsieNDsdC3Vk0s5J7CmeGfbvh4EDYelSyJnTXr2+o63NRSTqMC96wzsy8/HxsXx9fZ0dhohIjGBZFj1X9WToxqHEcY+DhcXdXnfDZe7r96+TZUwW8qfMz4rmKzDGwD//QPfuMGsWpE4NQ4fauzUaEy73FBF5HcaY7ZZl+YRkrFaqRUQkWMYYhlQcwsjKI7n76G64rFI/kcgrEQPKDmDViVXMPzDfPpgmDcycCRs32kl18+ZQtSr4+YXbfUVEHMHN2QGIiEjk90mxT8iQMAOX710O13nfL/g+k3ZMoun8phhjqJeznn2ieHH46y8YNw46doTWre3VaxetBYlI5KQ/nUREJETq5qzLewXfC9c53V3dWdliJflT5af+T/UZu3XsvyddXKBDBxgyBH78EXr3Dtd7i4iEJyXVIiLiVEljJ2VVi1XUzF6TTks60X1FdwKtwH8HdOsG7dvbyfXEic4LVETkJZRUi4iI08V2j828hvN4v+D7DNs0jHa/tSMgMMA+aQyMGQM1asCHH8KyZc4NVkTkBVRTLSIikYKbixvjaowjeZzkfLn+S249vMWserPwcPUANze7BKRECWjcGHbsgAwZnB2yiEgQrVSLiEikYYzhi3JfMLzScH7Z/wu1f6zNvUf37JNx49obxQQGQv366ggiIpGKkmoREYl0uhTvwvc1v2fZ0WVUnlGZa/ev2ScyZ4YZM2D7dujUyblBiog8xWFJtTFmsjHmkjFmXzDnyxpjbhpjdj3+6uuoWEREJOppW6Atc+vPZdu5bZSYXIKTN07aJ2rWhF69YNIkmDzZqTGKiDzhyJXqqUDVV4zZYFmW9+OvLxwYi4iIREEN3mzAiuYruHDnAsV+KMaO8zvsE198ARUr2i8uHjjg3CBFRHBgUm1Z1nrgmqPmFxGRmKH0G6XZ2GYjHq4elJ5Smk1nNoGrq73zYpw40LatXWctIuJEzq6pLmaM2W2MWWKMedPJsYiISCSVK1kuNr+7maSxk9JxcUe7j3WKFDBqFGzaBN995+wQRSSGc2ZSvQN4w7KsfMAY4NfgBhpj3jPG+BpjfC9fDt8tckVEJGpIHS81A8sPZOeFnfz898/2webNoUoV6NEDTp1yboAiEqM5Lam2LOuWZVl3Hv9+MeBujEkazNiJlmX5WJblkyxZsgiNU0REIo/GuRuTJ3kePl/zOY8CHtkbw0yYYJ98/32wrFDNe+qGEnIRCRunJdXGmJTGGPP494Ufx3LVWfGIiEjk5+riylcVvuLotaNM3vm488cbb9hbmC9bBmPHvnZiPX33dDKMzsD6U+sdELGIxBSObKk3B9gMZDfGnDXGvGuMaW+Maf94SH1gnzFmN/At8I5lhXKJQUREYowaWWtQIl0JBqwb8O/GMB9+COXKwUcfQalSsHFjiOa6+/AuPVf1BOCHnT84KmQRiQEc2f2jsWVZqSzLcrcsK61lWT9YljXesqzxj8+PtSzrTcuy8lmWVdSyrE2OikVERKIPYwxDKg7h/J3zjPlrjH3QxcVeqR4/Ho4fh5IloXZt+Pvvl841YvMIzt0+R6HUhZi3fx53Ht6JgCcQkejI2d0/REREXlvJ9CWpkbUGg/8czN6Le+2D7u52XfWRIzBoEKxdC3nzQuvWcPr0c3Ocv32eYRuH8XbOtxlZZSR3H91l3v55EfsgIhJtKKkWEZEoaWSVkcR2j03JKSVZfWL1vyfixLF3XDx+HD75BObMgWzZ4LPP4Oq/r+70WdOHhwEPGVJxCCXSlSBzosxM2z3NCU8iItGBkmoREYmSsiXJxpa2W0gXPx1VZ1Zlxu4Zzw5IkgSGD4fDh6FxY7undaZM8NVX7D2+hck7J9OhUAeyJM6CMYYW+Vqw5uQadQIRkVBRUi0iIlFW+gTp+bPNn5R6oxQtfm3B4A2DXzAoPUyZAnv2QNmy0Ls3qQuW5ePdXvQp3iNoWIt8LQCYsWfG83OIiLyCkmoREYnSEnomZEnTJTTN05Req3vRe1VvXthM6s03YeFCfOeO4kC8B4xacI/EhUrDL7+AZZEhYQbKZijL9N3TX3y9iMhLKKkWEZEoz8PVg+l1p/N+wff56s+v+GTZJy9MjC3L4sObs2nSOS0P5/9sv9zYoAEUKQJbttAyX0uOXDvC5rObnfAUIhKVKakWEZFowcW4MK7GODoX6czov0bTflF7Aq3AZ8YsOLiAbee2MaDcF3jUrQ+7d9ulIefOQbFiNB21ijceeDFtl15YFJHXo6RaRESiDWMMI6uMpHep3kzcMZGm85vyMOAhAP6B/vRe3ZucSXPSPF9z+wJXV2jVCg4ehG7dcJ/9I39/G4Db5KlcvH3BeQ8iIlGOkmoREYlWjDEMLD+QYRWH8eO+H6k5pyZ3Ht5h+u7pHLxykEHlB+Hm4vbsRXHjwtChsGcPJn8B/vfrQ87UKQf37jnnIUQkyjFR7WUMHx8fy9fX19lhiIhIFDBl5xTa/t4Wn9Q+nLt9jtTxUrPl3S0YY4K/KDCQBc0KUfvHHfjnzoXHr7/brfhEJMYxxmy3LMsnJGO1Ui0iItFW6/ytWdBoAbsv7ObsrbMMqTDk5Qk1gIsL+b+bR+2mrjw6cQwKFoT16yMmYBGJspRUi4hItFYrey3WtlrLd9W/o1zGciG6JkPCDKR75z0KtA3gYbIkUKsW7N3r4EhFJCpTUi0iItFe0bRF+aDQB691Te9SvTmVxJVe3QrYW59XqwZnzjgoQhGJ6pRUi4iIvECa+Gn4wOcDRp2bx8k54+D2bTuxvn7d2aGJSCSkpFpERCQYPUr2wN3FnRF3VsCvv8Lhw1C7Nty44ezQRCSSUVItIiISjBRxU1Arey3m/j2XR6VLwowZsHmz/fLizp3ODk9EIhEl1SIiIi/RLG8zLt+7zIrjK6BRI7sTyIMHUKwYTJoEUaw1rYg4hpJqERGRl6iapSqJvRIzc89M+0CxYvYqdalS0K4dNGgAR486N0gRcTol1SIiIi/h4epBw1wN+fXgr9x+cNs+mCwZLF0KAwfCkiWQMyd06AAXtLW5SEylpFpEROQVmuVtxn3/+/x68Nd/D7q6Qu/e9ip127YwYQJkyaKSEJEYSkm1iIjIKxRPV5wMCTMwc+/M50+mSgXjxsGBA1C0qF0S0ro13LsX8YGKiNMoqRYREXkFYwzN8jRj5fGVXLgTTIlH1qywbBn06wfTp9sJ9uHDERuoiDiNkmoREZEQaJq3KYFWID/u+zHYMZaLC/Tvb9dZnztnJ9aHDkVckCLiNG7ODkBERCQqyJE0BwVTFWTSjkkk8kzE3Ud3ufvwLmdvneXg1YMcunKIS3cvMaLyCD6o8gFs3Won1TVqwJYtkDSpsx9BRBxISbWIiEgItfZuTcclHWm1sFXQsTjucciRNAcl0pfgn1v/8OHiD4ntHpuW3i1h4UIoVw7q1oWVKyFWLOcFLyIOpaRaREQkhD4o9AEVMlXA3cWduB5xieMRhzjucTDGAODn70fNOTVp81sb4njEoX6x+jBtGrzzjt0hZPp0eDxWRKIXJdUiIiIh5GJcyJE0R7DnPd08+bXRr1SZWYUm85oQ2z021Rs1giNHoE8fuw3f11/bfa5FJFrRi4oiIiLhKI5HHP5o8gd5UuSh4c8NOX/7vN3PumdPmDkTsmWDb7+FR4+cHaqIhCMl1SIiIuEsgWcC5tafy4OABwxYN8Au+fjqK9izBwoVgo8/hvz5YcMGZ4cqIuFESbWIiIgDZEmchQ98PmDSjkkcuHzAPpgrl93L+tdf4e5dKF0a3n8fbtxwZqgiEg6UVIuIiDhIn9J9iO0em56rev570BioXRv27YPPPrO3Nc+ZE37+Wdubi0RhSqpFREQcJFmcZPQo2YOFhxay4dR/Sj3ixLFfWty2DVKnhoYNoVYtOHPGOcGKSJgoqRYREXGgzkU7kyZeGj5b8RnWi1aiCxSAv/6CESNg9Wq7RGT0aAgIiPhgRSTUlFSLiIg4UGz32HxZ7ku2/rOVmXtmvniQmxt8+in8/TeULAmdO0OFCvDPPw6L69ztc5y4fsJh84vENEqqRUREHKxFvhYUTVuUNr+1Yc7eOcEPzJABFi+GqVPB1xfy5YM//gj3eFYdX0Wu/+Wiyswq4T63SEylpFpERMTBXF1cWdp0KSXSlaDp/KaM3To2+MHGQMuWsH07pE0Lb71lr2I/eBAusUzaMYmqs6ryKPARR64d4di1Y+Eyr0hMp6RaREQkAiTwTMDSZkuplb0WnZZ0osfKHqw4toJVx1ex9uRaLty58OwF2bPDli3QsSOMGgUlSsDRo6G+f6AVSPcV3Wn3ezsqZKzAulbrAFh+bHlYHktEHjMvfGkiEvPx8bF8fX2dHYaIiEio+Af6897v7zFl15RnjsePFZ8/mvxByfQln7/o11+hTRt7F8YJE6BJk9e+72+HfqP2j7V5v+D7jK0+FlfjSsbRGcmfKj8LGi0I5dOIRG/GmO2WZfmEZKxWqkVERCKQm4sbP9T6gZ3v72RD6w2sa7WOpU2XkjJuSirPqMzSo0ufv6hOHdi1y66xbtoUGjWC48df677rT60nlmssvq32LW4ubhhjqJy5MqtPrMY/0D9cnk0kJlNSLSIiEsGMMXin9KZk+pKUfqM0VbJUYUPrDWRPmp1ac2rx898/P39R+vSwdi0MGAC//w45ckCnTnDxYojuueXsFgqmLoiHq0fQsUqZKnHrwS22/rM1nJ5MJOZSUi0iIhIJJI+TnDUt11A4TWHemfcOk3dOfn6Qmxv07WvXVrdpA+PGQebM0K8f3L4d7NwPAx7ie86XommKPnO8QqYKGIzqqkXCgZJqERGRSCKhZ0KWNVtGpUyVePe3dxm5eeSLB6ZODePHw/79UL06fPGFnVx/++0Lu4TsvrCbBwEPKJau2DPHE3slplCaQqw4vsIRjyMSoyipFhERiUTieMTht8a/UT9Xfbos70LfNX1fvBMjQLZs8NNPsHUr5MkDH39sl4XMmgWBgUHDtpzdAkDRtEWfm6JSpkr8dfYvbvjdcMTjiMQYSqpFREQiGQ9XD358+0fezf8uX67/ko+XfkygFRj8BYUKwcqVsGwZJEwIzZrZ258vWQKWxeazm0kTLw1p46d97tLKmSsTYAWw5sQaxz2QSAygpFpERCQScnVx5fua3/Np0U8Zs3UMrRe2fnmXDmOgcmV705jZs+0a6+rVoXx57v+59rnSjyeKpi1KXI+4KgERCSM3ZwcgIiIiL2aMYXjl4STySkSfNX249eAWc96eg6ebZ/AXubhA48bw9tvw/fcEDOjPgrVXOLzzEOQ5aJeHPMXD1YOyGcrqZUWRMNJKtYiISCRmjOHz0p/zbdVv+fXgr7w1+y3uPLzz6gs9PKBDB5YsGUPfspDZ9yjkzg3vvQf//PPM0MqZKnPs+jGOX3+93tci8i8l1SIiIlFApyKdmFZnGmtPrqX8tPJcunspRNf9eX0XQ8q78+jwQejQAaZOhSxZoEcPuH4dsOuqARYc0M6KIqGlpFpERCSKaJGvBfMbzWffpX0U/6E4R68dfeU1W85uIX+q/HimTg+jR8OhQ1C/PgwbZncM2bGDbEmyUTZDWYZuHMqtB7ci4ElEoh8l1SIiIlFIrey1WN1yNTcf3KTYD8X46+xfwY71D/Rn27ltz276kjEjzJhht+FzdYWSJTG//MKwisO4fO8yX2/8OgKeQiT6UVItIiISxRRNW5RNbTaRIFYCSk8tTZqRaUgyLAlxv4pLnnF5OHTlEAB7L+7l3qN7L+xPjY+PnVjnzw8NG1Jo0mLeebMRIzaP4NztcxH8RCJRn5JqERGRKChrkqxsencT7xd8n2pZqtE4d2PeL/g+F+9cpPjk4mw8vTFo05fg2umRIgWsXg0tWkD//nw/6zbufo/ot6ZfBD6JSPRggt2lKZLy8fGxfH19nR2GiIhIpHT8+nGqzarGqRunyJokK5fvXuZ8l/MYY4K/yLJg+HDo3p0zWZJRvNZllnbby5vJ34y4wEUiIWPMdsuyfEIyVivVIiIi0UimRJnY1GYTPql92HdpH8XSFXt5Qg32xjFdu8LChaQ9f5etE+GHCe8Hvz26iDxHSbWIiEg0kyR2Ela2WEmvkr34rNhnIb+wZk3Mps3EjpuQQV9sZMrI5kqsRULIYUm1MWayMeaSMWbfK8YVMsb4G2PqOyoWERGRmMbTzZNBFQZRIn2J17swTx7i7dzPtdSJaNRzFlO/beOYAEWiGUeuVE8Fqr5sgDHGFRgKaG9UERGRSMIlRUpS/fU3N1MkoEHXqUz97n1nhyQS6TksqbYsaz1w7RXDOgHzgJBtCyUiIiIRwiVlKlJs2cftZPGp98lEfhjThkAr0NlhiURaTqupNsakAeoC45wVg4iIiATPNU1akm/Zy/0k8Wj18RQW1s7OresXnB2WSKTkzBcVvwG6W9ar/9prjHnPGONrjPG9fPmy4yMTERERAFzTpSf536fYX6sodX8/yvVs6Tn96zRnhyUS6TgzqfYBfjTGnATqA98ZY+q8aKBlWRMty/KxLMsnWbJkERiiiIiImESJyPPrZnbNHkmgFUD6uq04+04NuHHD2aGJRBpOS6oty8poWVYGy7IyAL8AH1qW9auz4hEREZGX8278CR77DjKlUjJS/bQYv2yZ4NdfnR2WSKTgyJZ6c4DNQHZjzFljzLvGmPbGmPaOuqeIiIg4VpqUWam98CBteuXikMt1qFsXGjaEixedHZqIU2mbchEREXlttx7cos6MGhSb8ycD/nTDLU48GDkSWra0d2gUiQa0TbmIiIg4VPxY8VnUchnbW1chdzt/zqSNB61bQ5UqcPSos8MTiXBKqkVERCRUYrvH5rfGv+FTvilv1D3NnPYlsTZvhpw5oWNHlYRIjKKkWkRERELNw9WD6XWn061Ud5qk/JM2w0rw6N3WMH48ZM4MffvCrVvhes9AK5CHAQ/DdU6RsFJSLSIiImHiYlwYUnEIY6qNYdql5VQudoT7e3ZAjRrw5Zd2cj16NDx4EC7367y0M29+9yYBgQHhMp9IeFBSLSIiIuGiY+GOzKg7g3Un11Frexfuz5wK27ZBvnzQuTPkyAEzZkBA6JPhQ1cO8d227zh67SjrTq0Lt9hFwkpJtYiIiISbpnmbMqX2FFYdX0W9n+rh550bVq6E5cshcWJo0QIKFIDFiyEUHcj6rOmDp5sncT3iMmfvHAc8gUjoKKkWERGRcNXSuyXf1/yepUeXUv+n+vj5+0GlSvaq9Zw5cPeuXRpStixs2RLieX3P+fLz/p/pUqwLdXPU5ZcDv/DAP3xKSkTCSn2qX0OjCZtfOWbu+8U0XuM1XuM1XuM1Hjh/+zyHrx0moWdCtnZvQvxY8e3x4zfB+fNw6iQ8fARJk0LGjBA79kvn33NxD3ce3qZI2qLc9LvJ3kt7yZ08N0m8kkSK59V4x4x/emxEU59qERERcbpU8VKRI2kObvrdpNy0cly6e8k+YQykTg2Fi0CGDHD9ur2KffhQsC8z3vC7znW/66RP8AauxpVEXolwd3Hn4h217ZPIQSvVIiIi4lCLjyym/k/1SRM/DcubLSdjoozPDrh8Gb76Cr77Dlxc4KOPoEcPSJQIAMuyKDypMBfvXORwp8N4unkC0OGPDkzZNYVLXS8R1yNuRD+WxABaqRYREZFIo3rW6qxssZIr965Q7IdibD7zn3/uT5YMRo2CQ4egQQP4+mvIlAmGDYP795l/YD6+53wZUHZAUEIN0DhPY+7732fhwYUR/EQiz1NSLSIiIg5XPF1xNrbZSByPOJSbVo6Ze2Y+PyhDBpg+HXbtghIloHt3rKxZ2TmwA3kS56RFvhbPzZk+QXpm75sdIc8g8jJKqkVERCRC5EqWi61tt1I0bVGaL2hOz5U9OXTlUNDX1XtX7YF588KiRbBuHZcTx2LgnIts/OYWrgt/e6YNn4tx4Z0332H5seVcuXfFSU8lYlNSLSIiIhEmSewkLG++nHYF2jFk4xBy/C9H0FeakWlYenRp0Nj7xQpRsNUDun2Ylbge8aBePSheHNb9u+lLkzxN8A/05/vt3zvjcUSCKKkWERGRCOXh6sGEtyawqsUqZtebzex6s5lVbxa5kuWizo91WHFsBQD/2/Y/zt7+h+pdJ2L27oVJk+DMGbu/dfXqsHs3eVPkpXrW6vRa3YthG4cR1RowSPSh7h8iIiISKVy9d5Xy08tz+OphZtebzbu/vUvhNIVZ2uzf1Wvu34exY+1uITdvQtOmPOj3Oa13D2DOvjl0KtyJUVVG4eri6rwHkWhD3T9EREQkykkSOwkrm68kS+Is1PupHtf9rvNVha+eHeTlBV27wvHj0K0b/PILsXLlYdb6ZPTN+QFjto7hnXnv4B/o75yHkBhLSbWIiIhEGsniJGNVi1UUTFWQ9wq8R4FUBV48MFEiGDIEjh6FVq0wY8cyoO1M/jxTmSU7f2HSjkkRG7jEeCr/EBERkUjnSX5ijAnZBQcPQu/eMH8+1+K783V5T3rPOEncuIkdGKVEdyr/EBERkSjNGBPyhBogRw6YNw82b8Y1V24G/3qbh9mzwJw5EBjouEAj0Nx9czl546Szw5BgKKkWERGR6KNoURJs2s7AHsX5J/AmNGkCPj6wfPkzPa6jmjsP79B4XmMGrBvg7FAkGEqqRUREJHoxhgZdJlPgfZjWpSJcvw5VqkDFirBtm7OjC5XDVw9jYbH82HK1DYyklFSLiIhItJM9aXba+rxH2wRrObpxEYweDXv2QOHCPKpfj9t7t3Pt/jX8/P2cHWqIHLh8AIBzt8/x9+W/nRyNvIiSahEREYmW+pXtRyzXWBSeUYpkD74k6fs3GVAGHvy2AK98PvxUKgne/VKw7uS6V0/mZAevHMRg15gvO7rMydHIi6j7h4iIiERbc/bOYdGRRSSIlYD4seITP1Z8Etzwo/j01eT9dQsPXAIZU8yFgqPmUjH/284ON1gNfm7A7gu7cXd1J238tCxrpsQ6IrxO9w8l1SIiIhIzHTuGX89ueP48n6te8E/HFuT9YgJ4ejo7sufkGZeHjAkzkjlRZsZvH8+1btfwcvdydljRnlrqiYiIiLxK5sx4/jSP25vXcSRzQvJ+PZ07GdPA1KkQEODs6IIEBAZw+OphciTNQZUsVfDz92P9qfXODkv+Q0m1iIiIxGjxipYmz/azdOtRkP0u16B1a8iXD377LVK04Tt54yQPAx6SI2kOSr9RmliusVh+bLmzw5L/UFItIiIiMV4cjzh8+eVGRn7TkPoN4NLNc1C7NpQqBRs3OjW2g1cOApAjaQ5iu8em1BulWHZMNdWRjZJqERERESCWWyxm159D6tadSNP6OlPeL4J1/DiULGkn2H87p5Xd00k1QJXMVfj78t+cvXXWKfHIiympFhEREXnMxbgwuupoBlQaRJtUf1Hvy9w8/KIfrF0LefPapSGnT0doTAevHCR5nOQk9koM2Ek1wIpjKyI0Dnk5JdUiIiIiTzHG0KtUL76v+T2//bOK0qmWcm2fL3zyCcyZA9mywWefwdWrERLPwasHg1apAXInz02quKlUAhLJKKkWEREReYG2Bdoyr+E8dl3YRYnfanGk5/vc/3s3Ae80who1CjJlgq++grt3HRrHgcsHyJHk36TaGEPlzJVZcXwFAYGRp0tJTKekWkRERCQYdXLUYXnz5Zy/fZ5sY7MRe2YO3DJOJ3f7QHbmSAi9e0PWrDBhAjx6FO73v3LvClfvX31mpRrgrWxvce3+NRYcXBDu95TQcXN2ACIiIiKRWek3SrO13VYWHV6Ef6A//oH+nLpxigLJJ9KxUn5GrfbArX17GDECBg2C+vXBmHC5939fUnyiTo465EqWi96re1M7e23cXd3D5X4SelqpFhEREXmFbEmy8WmxT+lWohu9SvViQs0JzKg7gwmx9pG3yU0uzP4ePDygYUMoUgRWrw6X+waXVLu5uDGkwhAOXz3MDzt/CJd7SdgoqRYREREJhWZ5m7Gi+Qou3L1Ivn96s2vZNHs3xgsXoEIFqFIFdu4M0z0OXjmIp5sn6ROkf+7cW9neomT6kvRf2587D++E6T4SdkqqRUREREKpTIYybHp3E7FcY1FuZkW2VMgOhw/bpSC+vlCgADRpAseOhWr+g1cOkj1JdlxdXJ87Z4xhaMWhXLx7kW+2fBPGJ5GwUlItIiIiEgY5kuZgQ+sNJPFKQqUZlVh7YQt8+ikcP26/yPjrr5AjB3TsCBcvvtbcB68cfK7042nF0xWnbo66DNs4jMt3L4fxSSQslFSLiIiIhNEbCd9gQ+sNpE+QnmqzqrHkyBJIkAAGDrRXqdu2hfHjIXNm6NcPbt165Zx+/n6cuHHipUk1wFcVvuLeo3v0WNkDy7LC65HkNSmpFhEREQkHqeKlYl2rdeRKlovaP9Zm/oH5j0+kgnHjYP9+qF4dvvjCTq6//RYePAh2vqPXjhJoBb4yqc6RNAefFf+Mybsm03FxRwKtwPB8LAkhJdUiIiIi4SRp7KSsbrGaQmkK0fDnhszcM/Pfk9mywU8/wbZt9pbnH39sl4XMnAmBzyfCwXX+eJHBFQbTrXg3vvP9jpa/tsQ/0D/cnsmZrt+/zq4Lu6LECrySahEREZFwlMAzAcuaLaNMhjK0WNCCCb4Tnh3g4wMrV8KyZZAoETRvDvnzw5Il8FTyeODyAcBu5/cqxhiGVBzCoPKDmLlnJvV/qo+fv1+4PpczzNk3h/wT8nPgygFnh/JKSqpFREREwllcj7j80eQPqmetTvs/2jN4w+BnV1uNgcqV7Q4hc+bYW51Xrw7lysFff7H7wm5G/zWavCnyEts9dojuaYyhV6lejKk2hoWHFlJtVjVu+t100BNGjNl7Z5M7eW5yJcvl7FBeSUm1iIiIiAN4unkyv9F8muZpSq/Vvfjwjw+fL8twcYF33rHrrceOhQMHoGhRTlcqRJ5r7sxrOO+179uxcEdm1ZvFxtMbKT21NOdunwunJ4pYJ2+cZOOZjTTJ3cTZoYSIkmoRERERB/Fw9WB63en0KNGD8dvHU3duXe4+vPuCgR7QoQPbN/zEoEqelD3iz+qvL5Gl+1D455/Xvm+TPE1Y3HQxx68fp9gPxYJKSaKSOXvnANA4T2MnRxIyJioUfj/Nx8fH8vX1dXYYIiIiIq9l3LZxdFzSkVRxUxHbPTa3H97m1gO7tZ6nmyeebp5cu3+NVHFTsa7GL6QbOx2++w5cXe2XGrt3t2uwX8OO8zuoPqs6jwIfsajxIoqlK+aIRwt3lmWRZ1weEnom5M82fzotDmPMdsuyfEI0Vkm1iIiISMRYfGQxE7ZPwMvNi3ge8YgXKx4uxgU/fz/8/P1wd3GnV6lepEuQzr7g5Eno29fuEJIgAfTsCZ06gZdXiO95/Ppxqsyswj+3/mFu/bnUzF7TMQ8XjnZf2I33BG++q/4dHxT6wGlxKKkWERERiU727LET6sWLIU0aGDAAWrYEN7cQXX757mVqzK7B9vPbmfDWBNoWaOvggMOm+4rujNwykvNdzpM0dlKnxaGkWkRERCQ6Wr/eLgPZsgVy5oRBg6BOHbubyCvceXiHhj83ZMnRJRRNW5SEngmJ5xGP5HGS07tUb1LFS+X4+EMg0AokwzcZyJsiL4uaLHJqLK+TVOtFRREREZGoonRp2LQJ5s+3e1rXqwfFi9vJ9ivE9YjLwncW0r1Ed7zcvLh67yr7Lu1j0o5JlJpSilM3TkXAA7zan6f/5MytMzTJEzW6fjyhlWoRERGRqMjfH6ZOhf797Q4h1avD4MH2bo2vYcvZLVSbVY14HvFY2WJliDabcaT3f3+fmXtncumzS8TxiOPUWLRSLSIiIhLdublB27Zw5AgMHWqvYHt7Q4sW9guOIVQ0bVHWtFyDn78fpaeUZu/FvQ4L+VX8A/35ef/P1MlRx+kJ9etSUi0iIiISlXl5QbducPw4dO0KP/8M2bND585w+XKIpvBO6c361utxc3Gj1JRSrDy+0rExB2PbP9u47nedOtnrOOX+YaGkWkRERCQ6SJTIXrE+csRerR4zBjJnhi+/hDt3Xnl5jqQ52NhmI+kSpKPqzKqM2zYuAoJ+1qoTqzAYymUsF+H3DiuH1VQbYyYDbwGXLMvK/YLztYEvgUDAH+hsWdYru3urplpEREQkBA4cgN69YcECSJEC+vSBdu3s3Rtf4vaD2zSe15g/jvxBx0IdaVewHbce3OLWg1vceXgH/0B/Aq1AAgIDKJq2KNmTZg+3kMtOLcutB7fY8f6OcJszLCJFSz1jTGngDjA9mKQ6LnDXsizLGJMX+MmyrByvmldJtYiIiMhr2LIFevSAdevsleuBA6FhQ3AJvmAhIDCA7iu7M2LziJdOHcc9DouaLKJshrJhDvPuw7skHpaYjwp/xNeVvw7zfOHhdZLqkHUMDwXLstYbYzK85PzT/w4RB4habUhEREREooKiRWHNGli61E6uGzeGr7+GIUOgUqUXXuLq4srwysOpma0ml+9dJkGsBCTwTEBcj7i4ubjhYly49+gejec1pvqs6ix8ZyGVMr94rpD68/SfPAx4SMVMFcM0j7M4LKkOCWNMXWAwkByo4cxYRERERKItY6BaNahSBWbPhs8/h8qVoUIFO7n2efFibJkMZV467dqWa6k0oxI159RkfqP5VM9aPdQhrjqxCncXd0qmLxnqOZzJqS8qWpa14HHJRx3s+uoXMsa8Z4zxNcb4Xg7hW6wiIiIi8h8uLtCsGRw6BN98A7t3Q6FC0KiR/YLja0oWJxmrW64md/Lc1P6xNulHpSf518mJNzge2cdmx/dcyEt2V51YRfF0xaNcK70nIkX3D8uy1gOZjDEv3NzdsqyJlmX5WJblkyxZsgiOTkRERCSaiRULPv4Yjh2Dvn3hjz8gVy744AM4f/61pkrslZiVLVbSoVAHKmSqQP1c9WlXoB0P/B9QakopZu6Z+co5rt67ys7zO6mQsUJon8jpnJZUG2OyGGNvVG+MKQDEAq46Kx4RERGRGCd+fBgwwE6u338fJk2CLFns8pCbN0M8TULPhHxT9Rum1J7CdzW+Y2SVkWxrt42iaYvSfEFzPlv+Gf6B/sFev+bkGiysKFtPDQ5Mqo0xc4DNQHZjzFljzLvGmPbGmPaPh7wN7DPG7AL+BzSyotqe6SIiIiLRQYoUMHYsHDwItWvDoEF2p5CRI8HPL1RTJouTjOXNltOpcCdGbB5Bvbn18PN/8Vwrj68knkc8CqUpFJancCqHtdRzFLXUExEREXGwHTugZ09YvhzSp4cvvrBrsV1dQzXdd9u+o8PiDlTKVIlf3/mV2O6xnzmfdUxWcibNyW+NfwuP6MPN67TUixQ11SIiIiISiRQoAMuWwapVkDw5tGoF3t6waBGEYkH2w0IfMrX2VFadWEW1WdW4/eB20LlTN05x9NrRKF1PDU5uqSciIiIikVj58rB1K/zyi707Y82aULKkvR168eKvNVVL75Z4unnSdH5Tyk0rR6M3G5EqXir2XdoHQIVMUTupVvmHiIiIiLzao0cweTL07w8XLkCtWvDVV/Dmm681zcKDC2nzWxuu3b8WdCxNvDSc+eQMj3tYRBqRYptyR1FSLSIiIuJEd+/C6NH2avWdO9Cypd1BJF26EE9hWRZ3Ht7h/J3zXLhzgdTxUpMlcRYHBh06SqpFRERExLGuXoXBg2HMGHvHxo4d7ZcbkyRxdmThRi8qioiIiIhjJUkCw4fbOzE2bgyjRtlt+AYPhnv3nB1dhFNSLSIiIiKhlz49TJkCe/ZAmTLQq5e9gczEieAf/IYv0Y2SahEREREJuzffhIULYcMGyJTJ3qHxzTftziFRrNw4NJRUi4iIiEj4KVnSTqwXLgR3d2jQAIoUgTVrnB2ZQympFhEREZHwZYzdcm/3brs05MIFu+d11aqwa5ezo3MIJdUiIiIi4hiurvZujIcP2y81btsG+fND06Zw/LizowtXSqpFRERExLE8PaFLFzh2zH6RccECyJEDOnWCS5ecHV24UFItIiIiIhEjYUIYNAiOHoU2bWDcOPulxv794fZtZ0cXJkqqRURERCRipU4N48fD/v1Qvbq9I2PmzPDtt/DggbOjCxUl1SIiIiLiHNmywU8/wdatkCcPfPwx5MwJs2ZBYKCzo3stSqpFRERExLkKFYKVK2HZMkiQAJo1gwIFYMmSKNPjWkm1iIiIiDifMVC5MmzfDrNn2zXW1avbrfhOnnR2dK+kpFpEREREIg8XF2jcGA4cgLFj4coVSJLE2VG9kpJqEREREYl8PDygQwfYswfixXN2NK+kpFpEREREIi9jnB1BiCipFhEREREJIyXVIiIiIiJhpKRaRERERCSMlFSLiIiIiISRkmoRERERkTBSUi0iIiIiEkZKqkVEREREwkhJtYiIiIhIGCmpFhEREREJIyXVIiIiIiJhpKRaRERERCSMlFSLiIiIiISRkmoRERERkTBSUi0iIiIiEkZKqkVEREREwkhJtYiIiIhIGCmpFhEREREJI2NZlrNjeC3GmMvAKSfdPilwxUn3jkn0OUccfdYRQ59zxNFnHXH0WUcMfc4R50Wf9RuWZSULycVRLql2JmOMr2VZPs6OI7rT5xxx9FlHDH3OEUefdcTRZx0x9DlHnLB+1ir/EBEREREJIyXVIiIiIiJhpKT69Ux0dgAxhD7niKPPOmLoc444+qwjjj7riKHPOeKE6bNWTbWIiIiISBhppVpEREREJIyUVIeAMaaqMeaQMeaoMaaHs+OJTowx6Ywxa4wx+40xfxtjPn58vL8x5h9jzK7HX9WdHWtUZ4w5aYzZ+/jz9H18LLExZoUx5sjjXxM5O86ozhiT/amf213GmFvGmM76mQ4fxpjJxphLxph9Tx174c+xsX37+M/uPcaYAs6LPGoJ5nP+2hhz8PFnucAYk/Dx8QzGmPtP/WyPd1rgUVAwn3Wwf14YY3o+/pk+ZIyp4pyoo55gPue5T33GJ40xux4fD9XPtMo/XsEY4wocBioBZ4FtQGPLsvY7NbBowhiTCkhlWdYOY0w8YDtQB2gI3LEsa7gz44tOjDEnAR/Lsq48dWwYcM2yrCGP/8KYyLKs7s6KMbp5/OfHP0ARoDX6mQ4zY0xp4A4w3bKs3I+PvfDn+HEi0gmojv2/wWjLsoo4K/aoJJjPuTKw2rIsf2PMUIDHn3MGYNGTcfJ6gvms+/OCPy+MMbmAOUBhIDWwEshmWVZAhAYdBb3oc/7P+RHATcuyvgjtz7RWql+tMHDUsqzjlmU9BH4Eajs5pmjDsqzzlmXtePz728ABII1zo4pRagPTHv9+GvZfaCT8VACOWZblrA2roh3LstYD1/5zOLif49rY/wG1LMvaAiR8/Bd5eYUXfc6WZS23LMv/8bdbgLQRHlg0FMzPdHBqAz9alvXAsqwTwFHsPEVe4WWfszHGYC/mzQnLPZRUv1oa4MxT359FSZ9DPP6bYX7gr8eHOj7+Z8bJKksIFxaw3Biz3Rjz3uNjKSzLOv/49xeAFM4JLdp6h2f/kNbPtGME93OsP78dpw3/b+/+QuQq7zCOfx+NRo1VEK29MNbUP1CVuI0S1BhJbRQFSU1raYK2phRUiAXrRYs1IIgX2mopVKrFpqgY/5KkhjS1TZWIBG1i0mpMUrTaCErYFC9sRbS7m6cX551mdt3Nzu6Z3UnG53MzZ985553fHF7O/uac3zkv/KHp7xmS/irpBUlzOxVUlxnueJExPTHmAr2232xqG/OYTlIdBwRJRwMrgZtt/xu4HzgV6AF2A/d2LrqucZHtWcAVwNJyKez/XNWCpR6sTSQdDiwAni5NGdOTION44km6DegHVpSm3cDJtr8C3AI8JumYTsXXJXK8mFyLGXwCZFxjOkn16N4Dpjf9fVJpizaRdBhVQr3C9ioA2722B2zvBR4kl7dqs/1eed0DrKbap72Ny+HldU/nIuw6VwBbbfdCxvQEG2kc5/jdZpKWAFcC15QfMJRShPfL8hbgLeCMjgXZBfZzvMiYbjNJU4BvAE822sY7ppNUj24zcLqkGeXM0yJgTYdj6hqljmk5sNP2z5vam+seFwKvD902WidpWrkRFEnTgMuo9uka4Lqy2nXAM52JsCsNOvORMT2hRhrHa4DvlqeAnE91E9Lu4TqI0Um6HPgRsMD2R03tJ5SbcpH0JeB04O3ORNkd9nO8WAMskjRV0gyqfb1psuPrMvOBv9t+t9Ew3jE9ZcJC7BLlLuebgD8ChwK/tb29w2F1kznAd4BtjUfZAD8BFkvqobqMuwu4oRPBdZETgdXVbximAI/ZflbSZuApSd8H3qG6USNqKj9cLmXwuP1pxnR9kh4H5gHHS3oXuB24i+HH8TqqJ3/8A/iI6gks0YIR9vOtwFRgfTmWvGz7RuBi4A5JfcBe4Ebbrd5495k3wr6eN9zxwvZ2SU8BO6hKcJbmyR+tGW4/217Op+99gXGO6TxSLyIiIiKippR/RERERETUlKQ6IiIiIqKmJNURERERETUlqY6IiIiIqClJdURERERETUmqIyImgKQvSHpC0ltlavh1ksY8IYakDZLOm4gYxxjHEkn3dTqOiIgDVZLqiIg2K5MarQY22D7V9rlUz/g9sbORdU5jIoWIiG6VpDoiov2+CvTZfqDRYPtV2y9KekTSVY12SSskfV3SoZLukfS6pNck/WBop5Iuk/SSpK2SnpZ09DDrbJB0t6RNkt6QNLe0DzrTLGmtpHll+UNJP5O0XdKfJc0u/bwtaUFT99NL+5uSbm/q69ryeX+T9Oummcg+lHSvpFeBCyTdJWlH+X73jH/3RkQceJJUR0S039nAlhHeWw4sAZB0LHAh8HvgeuAUoMf2TGBF80aSjgeWAfNtzwJeAW4Z4TOm2J4N3Ew1O9topgHP2z4L+A9wJ9WMkAuBO5rWmw18E5gJfEvSeZK+DHwbmGO7BxgArmnq9y+2zwF2lv7OKt/vzhbiiog4aGSa8oiISWT7BUm/knQCVYK60na/pPnAA7b7y3pDp8Q9HzgT2FimiD4ceGmEj1lVXrdQJeqj+S/wbFneBnxiu0/StiHbr7f9PoCkVcBFVFMlnwtsLnEdCewp6w8AK8vyB8DHwHJJa4G1LcQVEXHQSFIdEdF+24Gr9/P+I8C1wCLgey32KaqkdnEL635SXgfYd5zvZ/DVySOalvtsuyzvbWxve6+k5v8TZjCXuB62feswcXxse6D01S9pNvA1qn1zE3BJC98lIuKgkPKPiIj2ex6YKun6RoOkmY36ZuAhqtIMbO8obeuBGxpJrKTjhvT5MjBH0mnl/WljfJrILqBH0iGSplOVcozVpZKOk3QkcBWwEXgOuFrS5xtxS/ri0A1L/fexttcBPwTOGcfnR0QcsHKmOiKizWxb0kLgF5J+TFX2sIt9iXSvpJ3A75o2+w1wBvCapD7gQeC+pj7/JWkJ8LikqaV5GfBGi2FtBP4J7KCqb946jq+2iaqc4yTgUduvAEhaBvxJ0iFAH7AUeGfItp8DnpF0BNXZ7ZHqwSMiDkrad8UvIiImg6SjqGqXZ9n+oNPxREREfSn/iIiYROWGxJ3AL5NQR0R0j5ypjoiIiIioKWeqIyIiIiJqSlIdEREREVFTkuqIiIiIiJqSVEdERERE1JSkOiIiIiKipiTVERERERE1/Q/D9bkgryFwUgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "\n",
    "_, capacity = load_data('B0005')\n",
    "X = capacity[['cycle']]\n",
    "y = capacity['capacity']\n",
    "ax.plot(X, y, color='green', label='Battery capacity data')\n",
    "ax.plot(X, results, color='red', label='Prediction data')\n",
    "ax.plot([0.,168], [1.38, 1.38],dashes=[6, 2], label=\"EoL Threshold\")\n",
    "ax.set(xlabel='Cycle numbers', ylabel='Capacity')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Actual fail at cycle number: 129\n",
      "The prediction fail at cycle number: 131\n",
      "The error of RUL= 2 Cycle(s)\n"
     ]
    }
   ],
   "source": [
    "capacity['pre'] = results\n",
    "\n",
    "pred=0\n",
    "Afil=0\n",
    "Pfil=0\n",
    "a=capacity['capacity'].values\n",
    "b=capacity['pre'].values\n",
    "j=0\n",
    "k=0\n",
    "for i in range(len(a)):\n",
    "    actual=a[i]\n",
    "    \n",
    "    if actual<=1.38:\n",
    "        j=i\n",
    "        Afil=capacity['cycle'][j]\n",
    "        break\n",
    "for i in range(len(a)):\n",
    "    pred=b[i]\n",
    "    if pred< 1.38:\n",
    "        k=i\n",
    "        Pfil=capacity['cycle'][k]\n",
    "        break\n",
    "print(\"The Actual fail at cycle number: \"+ str(Afil))\n",
    "print(\"The prediction fail at cycle number: \"+ str(Pfil))\n",
    "RULerror=Pfil-Afil\n",
    "print(\"The error of RUL= \"+ str(RULerror)+ \" Cycle(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 0.023\n",
      "Train R2 score: 0.9792009985331469\n",
      "Test RMSE: 0.018\n",
      "Test R2 score: 0.3085042784051355\n"
     ]
    }
   ],
   "source": [
    "train_predictions = exported_pipeline.predict(X_train)\n",
    "rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "print('Train RMSE: %.3f' % rmse)\n",
    "print(\"Train R2 score:\", sklearn.metrics.r2_score(y_train, train_predictions))\n",
    "\n",
    "test_predictions = exported_pipeline.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "print(\"Test R2 score:\", sklearn.metrics.r2_score(y_test, test_predictions))"
   ]
  }
 ],
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