Step 3 - Model Development.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bc9c1c59-7e21-4afb-8dfc-3f6a7e25a021",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Model Development"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50dfd6b7-6f09-45dd-a6b8-ba75a91c1808",
   "metadata": {},
   "source": [
    "A Model will help us understand the exact relationship between different variables and how these variables are used to predict the result."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13413967-11d1-4ee2-9eec-94e56e15160a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d6cfeeb-7d9e-4ebb-a9eb-8b46071b9e9c",
   "metadata": {},
   "source": [
    "Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a63556b0-4a98-4133-ad82-0fe2fd9b34e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2613357-e62c-45e7-adc4-431298a8ccab",
   "metadata": {},
   "source": [
    "Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6e67fd5-9ecf-4cb0-92c8-52cc383bfe6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LNG Flow</th>\n",
       "      <th>LNG Rundown Temperature</th>\n",
       "      <th>MR IGV Position</th>\n",
       "      <th>HMR Flow</th>\n",
       "      <th>HMR/MR Ratio</th>\n",
       "      <th>Percentage Nitrogen in MR</th>\n",
       "      <th>Percentage Methane in MR</th>\n",
       "      <th>Percentage Ethane in MR</th>\n",
       "      <th>Percentage Propane in MR</th>\n",
       "      <th>Percentage Butane in MR</th>\n",
       "      <th>Ambient Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12470.894531</td>\n",
       "      <td>-145.712891</td>\n",
       "      <td>77.762802</td>\n",
       "      <td>18679.066406</td>\n",
       "      <td>5.310718</td>\n",
       "      <td>3.646484</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>43.532619</td>\n",
       "      <td>10.102832</td>\n",
       "      <td>0.125508</td>\n",
       "      <td>33.207268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12109.980469</td>\n",
       "      <td>-145.844727</td>\n",
       "      <td>77.999893</td>\n",
       "      <td>18802.667969</td>\n",
       "      <td>5.283063</td>\n",
       "      <td>3.659180</td>\n",
       "      <td>42.519924</td>\n",
       "      <td>43.567482</td>\n",
       "      <td>10.041308</td>\n",
       "      <td>0.124922</td>\n",
       "      <td>33.207268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12578.753906</td>\n",
       "      <td>-145.966797</td>\n",
       "      <td>77.957260</td>\n",
       "      <td>18743.687500</td>\n",
       "      <td>5.322514</td>\n",
       "      <td>3.625488</td>\n",
       "      <td>42.646095</td>\n",
       "      <td>43.582424</td>\n",
       "      <td>9.938086</td>\n",
       "      <td>0.124023</td>\n",
       "      <td>32.043064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11478.726562</td>\n",
       "      <td>-145.561523</td>\n",
       "      <td>72.290688</td>\n",
       "      <td>17428.503906</td>\n",
       "      <td>6.037915</td>\n",
       "      <td>3.590332</td>\n",
       "      <td>42.380470</td>\n",
       "      <td>43.839748</td>\n",
       "      <td>9.975683</td>\n",
       "      <td>0.124102</td>\n",
       "      <td>31.496241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11518.482422</td>\n",
       "      <td>-145.175781</td>\n",
       "      <td>72.255318</td>\n",
       "      <td>16420.748047</td>\n",
       "      <td>6.156987</td>\n",
       "      <td>3.558594</td>\n",
       "      <td>42.609573</td>\n",
       "      <td>43.771683</td>\n",
       "      <td>9.854004</td>\n",
       "      <td>0.124414</td>\n",
       "      <td>31.496241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11785.019531</td>\n",
       "      <td>-146.176758</td>\n",
       "      <td>75.999893</td>\n",
       "      <td>18631.525391</td>\n",
       "      <td>5.313677</td>\n",
       "      <td>3.617188</td>\n",
       "      <td>42.730762</td>\n",
       "      <td>43.580765</td>\n",
       "      <td>9.858788</td>\n",
       "      <td>0.128008</td>\n",
       "      <td>31.496241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11912.583984</td>\n",
       "      <td>-146.694336</td>\n",
       "      <td>77.086159</td>\n",
       "      <td>17979.867188</td>\n",
       "      <td>5.253815</td>\n",
       "      <td>3.439941</td>\n",
       "      <td>42.270901</td>\n",
       "      <td>44.206642</td>\n",
       "      <td>9.869726</td>\n",
       "      <td>0.122266</td>\n",
       "      <td>31.496241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12021.826172</td>\n",
       "      <td>-146.899414</td>\n",
       "      <td>73.999893</td>\n",
       "      <td>18722.001953</td>\n",
       "      <td>5.278831</td>\n",
       "      <td>3.479004</td>\n",
       "      <td>42.528225</td>\n",
       "      <td>43.768360</td>\n",
       "      <td>10.011230</td>\n",
       "      <td>0.123164</td>\n",
       "      <td>30.949419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12188.800781</td>\n",
       "      <td>-146.782227</td>\n",
       "      <td>72.028290</td>\n",
       "      <td>19286.933594</td>\n",
       "      <td>5.307084</td>\n",
       "      <td>3.486816</td>\n",
       "      <td>42.674316</td>\n",
       "      <td>43.703613</td>\n",
       "      <td>9.933984</td>\n",
       "      <td>0.118789</td>\n",
       "      <td>30.949419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12176.355469</td>\n",
       "      <td>-146.918945</td>\n",
       "      <td>72.028290</td>\n",
       "      <td>19251.404297</td>\n",
       "      <td>5.310825</td>\n",
       "      <td>3.507812</td>\n",
       "      <td>42.878517</td>\n",
       "      <td>43.594044</td>\n",
       "      <td>9.823926</td>\n",
       "      <td>0.111406</td>\n",
       "      <td>30.949419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LNG Flow  LNG Rundown Temperature  MR IGV Position     HMR Flow   \\\n",
       "0  12470.894531              -145.712891        77.762802  18679.066406   \n",
       "1  12109.980469              -145.844727        77.999893  18802.667969   \n",
       "2  12578.753906              -145.966797        77.957260  18743.687500   \n",
       "3  11478.726562              -145.561523        72.290688  17428.503906   \n",
       "4  11518.482422              -145.175781        72.255318  16420.748047   \n",
       "5  11785.019531              -146.176758        75.999893  18631.525391   \n",
       "6  11912.583984              -146.694336        77.086159  17979.867188   \n",
       "7  12021.826172              -146.899414        73.999893  18722.001953   \n",
       "8  12188.800781              -146.782227        72.028290  19286.933594   \n",
       "9  12176.355469              -146.918945        72.028290  19251.404297   \n",
       "\n",
       "   HMR/MR Ratio  Percentage Nitrogen in MR  Percentage Methane in MR  \\\n",
       "0      5.310718                   3.646484                 42.500000   \n",
       "1      5.283063                   3.659180                 42.519924   \n",
       "2      5.322514                   3.625488                 42.646095   \n",
       "3      6.037915                   3.590332                 42.380470   \n",
       "4      6.156987                   3.558594                 42.609573   \n",
       "5      5.313677                   3.617188                 42.730762   \n",
       "6      5.253815                   3.439941                 42.270901   \n",
       "7      5.278831                   3.479004                 42.528225   \n",
       "8      5.307084                   3.486816                 42.674316   \n",
       "9      5.310825                   3.507812                 42.878517   \n",
       "\n",
       "   Percentage Ethane in MR  Percentage Propane in MR  Percentage Butane in MR  \\\n",
       "0                43.532619                 10.102832                 0.125508   \n",
       "1                43.567482                 10.041308                 0.124922   \n",
       "2                43.582424                  9.938086                 0.124023   \n",
       "3                43.839748                  9.975683                 0.124102   \n",
       "4                43.771683                  9.854004                 0.124414   \n",
       "5                43.580765                  9.858788                 0.128008   \n",
       "6                44.206642                  9.869726                 0.122266   \n",
       "7                43.768360                 10.011230                 0.123164   \n",
       "8                43.703613                  9.933984                 0.118789   \n",
       "9                43.594044                  9.823926                 0.111406   \n",
       "\n",
       "   Ambient Condition  \n",
       "0          33.207268  \n",
       "1          33.207268  \n",
       "2          32.043064  \n",
       "3          31.496241  \n",
       "4          31.496241  \n",
       "5          31.496241  \n",
       "6          31.496241  \n",
       "7          30.949419  \n",
       "8          30.949419  \n",
       "9          30.949419  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"data1.csv\")\n",
    "df1.drop('Unnamed: 0', axis= 1, inplace=True)\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "103480ff-5242-4ad2-b2dc-94c009195c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LNG Flow</th>\n",
       "      <th>LNG Rundown Temperature</th>\n",
       "      <th>LNG Shell Temp Top Bundle</th>\n",
       "      <th>LMR JT Valve Valve Opening</th>\n",
       "      <th>Volumetric MR Flow to MR comp. discharge</th>\n",
       "      <th>HMR/MR Ratio</th>\n",
       "      <th>MCHE Warm Bypass Temperature</th>\n",
       "      <th>MR Vessel Level</th>\n",
       "      <th>High High Pressure Propane/Mixed Refrigerant kettle level</th>\n",
       "      <th>High Pressure Propane/Mixed Refrigerant kettle level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12470.894531</td>\n",
       "      <td>-145.712891</td>\n",
       "      <td>0.727539</td>\n",
       "      <td>53.137032</td>\n",
       "      <td>57909.148438</td>\n",
       "      <td>5.310718</td>\n",
       "      <td>-42.124023</td>\n",
       "      <td>25.312500</td>\n",
       "      <td>71.503906</td>\n",
       "      <td>72.011719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12109.980469</td>\n",
       "      <td>-145.844727</td>\n",
       "      <td>0.693359</td>\n",
       "      <td>53.687386</td>\n",
       "      <td>58373.812500</td>\n",
       "      <td>5.283063</td>\n",
       "      <td>-44.075195</td>\n",
       "      <td>26.765625</td>\n",
       "      <td>72.187500</td>\n",
       "      <td>71.443359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12578.753906</td>\n",
       "      <td>-145.966797</td>\n",
       "      <td>0.742188</td>\n",
       "      <td>53.676559</td>\n",
       "      <td>57913.492188</td>\n",
       "      <td>5.322514</td>\n",
       "      <td>-41.948242</td>\n",
       "      <td>27.669922</td>\n",
       "      <td>71.998047</td>\n",
       "      <td>71.568359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11478.726562</td>\n",
       "      <td>-145.561523</td>\n",
       "      <td>0.561523</td>\n",
       "      <td>42.709133</td>\n",
       "      <td>56186.050781</td>\n",
       "      <td>6.037915</td>\n",
       "      <td>-44.016602</td>\n",
       "      <td>30.724609</td>\n",
       "      <td>72.734375</td>\n",
       "      <td>72.949219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11518.482422</td>\n",
       "      <td>-145.175781</td>\n",
       "      <td>0.634766</td>\n",
       "      <td>38.990089</td>\n",
       "      <td>57086.832031</td>\n",
       "      <td>6.156987</td>\n",
       "      <td>-44.965820</td>\n",
       "      <td>34.058594</td>\n",
       "      <td>73.919922</td>\n",
       "      <td>72.933594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11785.019531</td>\n",
       "      <td>-146.176758</td>\n",
       "      <td>0.673828</td>\n",
       "      <td>52.073986</td>\n",
       "      <td>57377.804688</td>\n",
       "      <td>5.313677</td>\n",
       "      <td>-46.665039</td>\n",
       "      <td>27.765625</td>\n",
       "      <td>70.425781</td>\n",
       "      <td>68.732422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11912.583984</td>\n",
       "      <td>-146.694336</td>\n",
       "      <td>0.805664</td>\n",
       "      <td>51.865128</td>\n",
       "      <td>57288.046875</td>\n",
       "      <td>5.253815</td>\n",
       "      <td>-47.901367</td>\n",
       "      <td>25.185547</td>\n",
       "      <td>70.507812</td>\n",
       "      <td>72.578125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12021.826172</td>\n",
       "      <td>-146.899414</td>\n",
       "      <td>0.815430</td>\n",
       "      <td>53.788208</td>\n",
       "      <td>56305.187500</td>\n",
       "      <td>5.278831</td>\n",
       "      <td>-47.599609</td>\n",
       "      <td>25.519531</td>\n",
       "      <td>71.050781</td>\n",
       "      <td>71.318359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12188.800781</td>\n",
       "      <td>-146.782227</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>55.735157</td>\n",
       "      <td>55580.218750</td>\n",
       "      <td>5.307084</td>\n",
       "      <td>-47.546875</td>\n",
       "      <td>24.658203</td>\n",
       "      <td>70.541016</td>\n",
       "      <td>71.087891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12176.355469</td>\n",
       "      <td>-146.918945</td>\n",
       "      <td>1.000977</td>\n",
       "      <td>55.621208</td>\n",
       "      <td>55576.246094</td>\n",
       "      <td>5.310825</td>\n",
       "      <td>-47.664062</td>\n",
       "      <td>24.488281</td>\n",
       "      <td>71.675781</td>\n",
       "      <td>71.742188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LNG Flow  LNG Rundown Temperature  LNG Shell Temp Top Bundle  \\\n",
       "0  12470.894531              -145.712891                   0.727539   \n",
       "1  12109.980469              -145.844727                   0.693359   \n",
       "2  12578.753906              -145.966797                   0.742188   \n",
       "3  11478.726562              -145.561523                   0.561523   \n",
       "4  11518.482422              -145.175781                   0.634766   \n",
       "5  11785.019531              -146.176758                   0.673828   \n",
       "6  11912.583984              -146.694336                   0.805664   \n",
       "7  12021.826172              -146.899414                   0.815430   \n",
       "8  12188.800781              -146.782227                   0.937500   \n",
       "9  12176.355469              -146.918945                   1.000977   \n",
       "\n",
       "   LMR JT Valve Valve Opening  Volumetric MR Flow to MR comp. discharge  \\\n",
       "0                   53.137032                              57909.148438   \n",
       "1                   53.687386                              58373.812500   \n",
       "2                   53.676559                              57913.492188   \n",
       "3                   42.709133                              56186.050781   \n",
       "4                   38.990089                              57086.832031   \n",
       "5                   52.073986                              57377.804688   \n",
       "6                   51.865128                              57288.046875   \n",
       "7                   53.788208                              56305.187500   \n",
       "8                   55.735157                              55580.218750   \n",
       "9                   55.621208                              55576.246094   \n",
       "\n",
       "   HMR/MR Ratio  MCHE Warm Bypass Temperature  MR Vessel Level  \\\n",
       "0      5.310718                    -42.124023        25.312500   \n",
       "1      5.283063                    -44.075195        26.765625   \n",
       "2      5.322514                    -41.948242        27.669922   \n",
       "3      6.037915                    -44.016602        30.724609   \n",
       "4      6.156987                    -44.965820        34.058594   \n",
       "5      5.313677                    -46.665039        27.765625   \n",
       "6      5.253815                    -47.901367        25.185547   \n",
       "7      5.278831                    -47.599609        25.519531   \n",
       "8      5.307084                    -47.546875        24.658203   \n",
       "9      5.310825                    -47.664062        24.488281   \n",
       "\n",
       "   High High Pressure Propane/Mixed Refrigerant kettle level  \\\n",
       "0                                          71.503906           \n",
       "1                                          72.187500           \n",
       "2                                          71.998047           \n",
       "3                                          72.734375           \n",
       "4                                          73.919922           \n",
       "5                                          70.425781           \n",
       "6                                          70.507812           \n",
       "7                                          71.050781           \n",
       "8                                          70.541016           \n",
       "9                                          71.675781           \n",
       "\n",
       "   High Pressure Propane/Mixed Refrigerant kettle level  \n",
       "0                                          72.011719     \n",
       "1                                          71.443359     \n",
       "2                                          71.568359     \n",
       "3                                          72.949219     \n",
       "4                                          72.933594     \n",
       "5                                          68.732422     \n",
       "6                                          72.578125     \n",
       "7                                          71.318359     \n",
       "8                                          71.087891     \n",
       "9                                          71.742188     "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"data2.csv\")\n",
    "df2.drop('Unnamed: 0', axis= 1, inplace=True)\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b23c6a6-4c31-42e0-bc17-31aefb19036a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Using Data gotten from features selected by Intuition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0aed1ea-4655-48e0-892b-5649b932178c",
   "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>LNG Flow</th>\n",
       "      <th>LNG Rundown Temperature</th>\n",
       "      <th>MR IGV Position</th>\n",
       "      <th>HMR Flow</th>\n",
       "      <th>HMR/MR Ratio</th>\n",
       "      <th>Percentage Nitrogen in MR</th>\n",
       "      <th>Percentage Methane in MR</th>\n",
       "      <th>Percentage Ethane in MR</th>\n",
       "      <th>Percentage Propane in MR</th>\n",
       "      <th>Percentage Butane in MR</th>\n",
       "      <th>Ambient Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12470.894531</td>\n",
       "      <td>-145.712891</td>\n",
       "      <td>77.762802</td>\n",
       "      <td>18679.066406</td>\n",
       "      <td>5.310718</td>\n",
       "      <td>3.646484</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>43.532619</td>\n",
       "      <td>10.102832</td>\n",
       "      <td>0.125508</td>\n",
       "      <td>33.207268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12109.980469</td>\n",
       "      <td>-145.844727</td>\n",
       "      <td>77.999893</td>\n",
       "      <td>18802.667969</td>\n",
       "      <td>5.283063</td>\n",
       "      <td>3.659180</td>\n",
       "      <td>42.519924</td>\n",
       "      <td>43.567482</td>\n",
       "      <td>10.041308</td>\n",
       "      <td>0.124922</td>\n",
       "      <td>33.207268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12578.753906</td>\n",
       "      <td>-145.966797</td>\n",
       "      <td>77.957260</td>\n",
       "      <td>18743.687500</td>\n",
       "      <td>5.322514</td>\n",
       "      <td>3.625488</td>\n",
       "      <td>42.646095</td>\n",
       "      <td>43.582424</td>\n",
       "      <td>9.938086</td>\n",
       "      <td>0.124023</td>\n",
       "      <td>32.043064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11478.726562</td>\n",
       "      <td>-145.561523</td>\n",
       "      <td>72.290688</td>\n",
       "      <td>17428.503906</td>\n",
       "      <td>6.037915</td>\n",
       "      <td>3.590332</td>\n",
       "      <td>42.380470</td>\n",
       "      <td>43.839748</td>\n",
       "      <td>9.975683</td>\n",
       "      <td>0.124102</td>\n",
       "      <td>31.496241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11518.482422</td>\n",
       "      <td>-145.175781</td>\n",
       "      <td>72.255318</td>\n",
       "      <td>16420.748047</td>\n",
       "      <td>6.156987</td>\n",
       "      <td>3.558594</td>\n",
       "      <td>42.609573</td>\n",
       "      <td>43.771683</td>\n",
       "      <td>9.854004</td>\n",
       "      <td>0.124414</td>\n",
       "      <td>31.496241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LNG Flow  LNG Rundown Temperature  MR IGV Position     HMR Flow   \\\n",
       "0  12470.894531              -145.712891        77.762802  18679.066406   \n",
       "1  12109.980469              -145.844727        77.999893  18802.667969   \n",
       "2  12578.753906              -145.966797        77.957260  18743.687500   \n",
       "3  11478.726562              -145.561523        72.290688  17428.503906   \n",
       "4  11518.482422              -145.175781        72.255318  16420.748047   \n",
       "\n",
       "   HMR/MR Ratio  Percentage Nitrogen in MR  Percentage Methane in MR  \\\n",
       "0      5.310718                   3.646484                 42.500000   \n",
       "1      5.283063                   3.659180                 42.519924   \n",
       "2      5.322514                   3.625488                 42.646095   \n",
       "3      6.037915                   3.590332                 42.380470   \n",
       "4      6.156987                   3.558594                 42.609573   \n",
       "\n",
       "   Percentage Ethane in MR  Percentage Propane in MR  Percentage Butane in MR  \\\n",
       "0                43.532619                 10.102832                 0.125508   \n",
       "1                43.567482                 10.041308                 0.124922   \n",
       "2                43.582424                  9.938086                 0.124023   \n",
       "3                43.839748                  9.975683                 0.124102   \n",
       "4                43.771683                  9.854004                 0.124414   \n",
       "\n",
       "   Ambient Condition  \n",
       "0          33.207268  \n",
       "1          33.207268  \n",
       "2          32.043064  \n",
       "3          31.496241  \n",
       "4          31.496241  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b19da9f-3d90-4f4b-a388-e934d1ce2e0c",
   "metadata": {},
   "source": [
    "The regressor we will be making in this section will be in two phases:\n",
    "- Unioutput regressor model\n",
    "- Multioutput regressor model\n",
    "\n",
    "The **Unioutput Regressor model** will predict only the LNG Flow while the **Multioutput Regressor model** will predict the LNG Rundown temperature, then LNG Flow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afa1510-3623-475a-b477-a79d6d60ed8b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Model selection\n",
    "\n",
    "Since this is a regression problem. We will be using and testing the following regression algorithm\n",
    "- LinearRegression\n",
    "- KNeighborsRegressor\n",
    "- DecisionTreeRegressor\n",
    "- RandomForestRegressor "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2512cfd4-71f5-4c9b-af95-80e114af4fbe",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d381b02e-490f-45cb-a299-ced0ef25dab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import absolute, mean, std\n",
    "\n",
    "# import estimators\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.multioutput import RegressorChain\n",
    "\n",
    "# pipeline for preprocessing\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6bc113-e2ec-4697-8b9a-3a4fa5d55616",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d086311-44eb-4bf8-974f-fd079f876d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74a05bd6-67e5-447e-af5e-97f92787e00d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_input = df1.iloc[:, 2:].copy()\n",
    "df1_target = df1.iloc[:, 0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f48b37a-aa76-4b02-a792-c2e0ff7e7f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df1_input, df1_target, test_size = 0.25, random_state= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "886b145f-6e56-47da-9ac2-cdae0fc0ba7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MR IGV Position</th>\n",
       "      <th>HMR Flow</th>\n",
       "      <th>HMR/MR Ratio</th>\n",
       "      <th>Percentage Nitrogen in MR</th>\n",
       "      <th>Percentage Methane in MR</th>\n",
       "      <th>Percentage Ethane in MR</th>\n",
       "      <th>Percentage Propane in MR</th>\n",
       "      <th>Percentage Butane in MR</th>\n",
       "      <th>Ambient Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15227</th>\n",
       "      <td>43.049469</td>\n",
       "      <td>17529.824219</td>\n",
       "      <td>5.751136</td>\n",
       "      <td>2.927734</td>\n",
       "      <td>42.976467</td>\n",
       "      <td>43.589066</td>\n",
       "      <td>10.162305</td>\n",
       "      <td>0.302656</td>\n",
       "      <td>28.303505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15869</th>\n",
       "      <td>66.039696</td>\n",
       "      <td>18185.628906</td>\n",
       "      <td>6.736280</td>\n",
       "      <td>3.149902</td>\n",
       "      <td>42.946583</td>\n",
       "      <td>43.220509</td>\n",
       "      <td>10.297656</td>\n",
       "      <td>0.298555</td>\n",
       "      <td>30.967058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21751</th>\n",
       "      <td>66.500267</td>\n",
       "      <td>21804.941406</td>\n",
       "      <td>5.216740</td>\n",
       "      <td>3.415039</td>\n",
       "      <td>43.091019</td>\n",
       "      <td>43.157425</td>\n",
       "      <td>10.031738</td>\n",
       "      <td>0.232266</td>\n",
       "      <td>27.580288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13688</th>\n",
       "      <td>52.000031</td>\n",
       "      <td>18257.861328</td>\n",
       "      <td>5.233665</td>\n",
       "      <td>2.952148</td>\n",
       "      <td>43.215530</td>\n",
       "      <td>43.384865</td>\n",
       "      <td>10.075488</td>\n",
       "      <td>0.283437</td>\n",
       "      <td>28.762129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26010</th>\n",
       "      <td>61.081810</td>\n",
       "      <td>20127.462891</td>\n",
       "      <td>5.049800</td>\n",
       "      <td>3.092285</td>\n",
       "      <td>43.442970</td>\n",
       "      <td>43.447952</td>\n",
       "      <td>9.696777</td>\n",
       "      <td>0.259648</td>\n",
       "      <td>29.608822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MR IGV Position     HMR Flow   HMR/MR Ratio  Percentage Nitrogen in MR  \\\n",
       "15227        43.049469  17529.824219      5.751136                   2.927734   \n",
       "15869        66.039696  18185.628906      6.736280                   3.149902   \n",
       "21751        66.500267  21804.941406      5.216740                   3.415039   \n",
       "13688        52.000031  18257.861328      5.233665                   2.952148   \n",
       "26010        61.081810  20127.462891      5.049800                   3.092285   \n",
       "\n",
       "       Percentage Methane in MR  Percentage Ethane in MR  \\\n",
       "15227                 42.976467                43.589066   \n",
       "15869                 42.946583                43.220509   \n",
       "21751                 43.091019                43.157425   \n",
       "13688                 43.215530                43.384865   \n",
       "26010                 43.442970                43.447952   \n",
       "\n",
       "       Percentage Propane in MR  Percentage Butane in MR  Ambient Condition  \n",
       "15227                 10.162305                 0.302656          28.303505  \n",
       "15869                 10.297656                 0.298555          30.967058  \n",
       "21751                 10.031738                 0.232266          27.580288  \n",
       "13688                 10.075488                 0.283437          28.762129  \n",
       "26010                  9.696777                 0.259648          29.608822  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18edd7db-d29e-4bca-bad6-5ddbc54746c5",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Model building for unioutput regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0aba2619-57e4-40bb-9207-76be95b6cdd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare a dictionary of estimators\n",
    "ESTIMATORS = {\n",
    "    \"Linear Regression\": LinearRegression(),\n",
    "    \"K-neighbors\": KNeighborsRegressor(),\n",
    "    \"Decision Tree\": DecisionTreeRegressor(),\n",
    "    \"Random Forest\": RandomForestRegressor(max_depth=2, random_state=0),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0e24e887-2709-4164-a491-b67f8c83170a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare function to automate the fitting of all the estimators\n",
    "def pipe_reg(estimators, X, y, X_test, y_test):\n",
    "    # Create an empty dictionary to collect prediction values\n",
    "    y_test_predict = dict()\n",
    "    y_error = dict()\n",
    "    \n",
    "    for name, estimator in estimators.items():\n",
    "        cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "        \n",
    "        pipe = Pipeline(\n",
    "            [('scalar', StandardScaler()),\n",
    "            ('regressor', estimator)]\n",
    "        )\n",
    "        \n",
    "        # evaluate the model and collect the scores\n",
    "        n_scores = cross_val_score(pipe, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n",
    "        \n",
    "        # force the scores to be positive\n",
    "        n_scores = absolute(n_scores)\n",
    "        # summarize performance\n",
    "        print(f'{name}-->MAE: {mean(n_scores):.4f}, {std(n_scores):.4f}', end = \" \")\n",
    "        \n",
    "        # fit model\n",
    "        pipe.fit(X, y)\n",
    "        \n",
    "        # predict\n",
    "        y_test_predict[name] = pipe.predict(X_test)\n",
    "        print(f\", R2-score = {r2_score(y_test, y_test_predict[name]):.4f},\", end = \" \")\n",
    "        \n",
    "        # calculate mse\n",
    "        y_error[name] = mean_squared_error(y_test, y_test_predict[name], squared= False)\n",
    "        print(f\", MSE = {y_error[name]:.4f}\")\n",
    "        \n",
    "    return y_test_predict, y_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34718535-774d-4cc0-b146-afb3b6521c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression-->MAE: 455.4345, 8.6816 , R2-score = 0.9411, , MSE = 628.9384\n",
      "K-neighbors-->MAE: 182.3402, 5.6624 , R2-score = 0.9853, , MSE = 313.8738\n",
      "Decision Tree-->MAE: 222.5605, 6.4347 , R2-score = 0.9742, , MSE = 416.1779\n",
      "Random Forest-->MAE: 614.5097, 10.1334 , R2-score = 0.8983, , MSE = 826.8076\n"
     ]
    }
   ],
   "source": [
    "# call the function\n",
    "y_test_predict, y_error = pipe_reg(ESTIMATORS, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9005671d-5dc7-45e8-83de-5229dc807ba6",
   "metadata": {},
   "source": [
    "|Algorithm|R2-score|RMSE\n",
    "|------|------|------|\n",
    "|Linear Regressor|0.9411|628.9384|\n",
    "|K-neighbors|0.9853|313.8738|\n",
    "|Decision Tree|0.9748|411.4508|\n",
    "|Random Forest|0.8981|826.8076|"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17d3622-d42b-4baf-ad89-aaa85736fc44",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Visualize the performance of the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c2120fb-2528-4d8d-bd44-327781f069e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize = (10, 7),  constrained_layout = True)\n",
    "names = iter(y_test_predict.keys())\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        name = next(names)\n",
    "        axes[i,j].scatter(y_test, y_test_predict[f\"{name}\"])\n",
    "        axes[i, j].set_title(f\"{name}\")\n",
    "        axes[i, j].set_xlabel(\"LNG Flow\")\n",
    "        axes[i, j].set_ylabel(\"predicted LNG Flow\")\n",
    "plt.savefig(\"unioutput-intuition\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d042600-fea0-47a1-8d05-3029fd13195c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8e9f0ba8-d6e8-472c-85a2-f088b22ebe9f",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Model building for multioutput regressor\n",
    "\n",
    "Here we are going to analyze models that can predict both LNG and MR Temperature, whose results can then be used to predict the LNG Flow."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a55a978-c7f3-4b4d-8a39-8a79e91acafa",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e089db9b-cced-4b89-b906-c6d4e9424888",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_input = df1.iloc[:, 2:].copy()\n",
    "df1_target = df1.iloc[:, [0, 1]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e86d4bf0-a8c9-4622-a58e-4dd180a460fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df1_input, df1_target, test_size = 0.25, random_state= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a35925b7-fa3d-4f9c-bf68-c9ff795ed687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((24018, 9), (24018, 2))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e2a427e9-b927-4bf4-b1a2-ccab197519c1",
   "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>LNG Flow</th>\n",
       "      <th>LNG Rundown Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15227</th>\n",
       "      <td>12092.349609</td>\n",
       "      <td>-144.521484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15869</th>\n",
       "      <td>12079.558594</td>\n",
       "      <td>-146.391602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21751</th>\n",
       "      <td>13531.438477</td>\n",
       "      <td>-145.688477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13688</th>\n",
       "      <td>12504.427734</td>\n",
       "      <td>-145.170898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26010</th>\n",
       "      <td>13445.036133</td>\n",
       "      <td>-144.160156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LNG Flow  LNG Rundown Temperature\n",
       "15227  12092.349609              -144.521484\n",
       "15869  12079.558594              -146.391602\n",
       "21751  13531.438477              -145.688477\n",
       "13688  12504.427734              -145.170898\n",
       "26010  13445.036133              -144.160156"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a51a37d6-560e-43bd-bdc5-a6fffc2b60ac",
   "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>MR IGV Position</th>\n",
       "      <th>HMR Flow</th>\n",
       "      <th>HMR/MR Ratio</th>\n",
       "      <th>Percentage Nitrogen in MR</th>\n",
       "      <th>Percentage Methane in MR</th>\n",
       "      <th>Percentage Ethane in MR</th>\n",
       "      <th>Percentage Propane in MR</th>\n",
       "      <th>Percentage Butane in MR</th>\n",
       "      <th>Ambient Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15227</th>\n",
       "      <td>43.049469</td>\n",
       "      <td>17529.824219</td>\n",
       "      <td>5.751136</td>\n",
       "      <td>2.927734</td>\n",
       "      <td>42.976467</td>\n",
       "      <td>43.589066</td>\n",
       "      <td>10.162305</td>\n",
       "      <td>0.302656</td>\n",
       "      <td>28.303505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15869</th>\n",
       "      <td>66.039696</td>\n",
       "      <td>18185.628906</td>\n",
       "      <td>6.736280</td>\n",
       "      <td>3.149902</td>\n",
       "      <td>42.946583</td>\n",
       "      <td>43.220509</td>\n",
       "      <td>10.297656</td>\n",
       "      <td>0.298555</td>\n",
       "      <td>30.967058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21751</th>\n",
       "      <td>66.500267</td>\n",
       "      <td>21804.941406</td>\n",
       "      <td>5.216740</td>\n",
       "      <td>3.415039</td>\n",
       "      <td>43.091019</td>\n",
       "      <td>43.157425</td>\n",
       "      <td>10.031738</td>\n",
       "      <td>0.232266</td>\n",
       "      <td>27.580288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13688</th>\n",
       "      <td>52.000031</td>\n",
       "      <td>18257.861328</td>\n",
       "      <td>5.233665</td>\n",
       "      <td>2.952148</td>\n",
       "      <td>43.215530</td>\n",
       "      <td>43.384865</td>\n",
       "      <td>10.075488</td>\n",
       "      <td>0.283437</td>\n",
       "      <td>28.762129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26010</th>\n",
       "      <td>61.081810</td>\n",
       "      <td>20127.462891</td>\n",
       "      <td>5.049800</td>\n",
       "      <td>3.092285</td>\n",
       "      <td>43.442970</td>\n",
       "      <td>43.447952</td>\n",
       "      <td>9.696777</td>\n",
       "      <td>0.259648</td>\n",
       "      <td>29.608822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MR IGV Position     HMR Flow   HMR/MR Ratio  Percentage Nitrogen in MR  \\\n",
       "15227        43.049469  17529.824219      5.751136                   2.927734   \n",
       "15869        66.039696  18185.628906      6.736280                   3.149902   \n",
       "21751        66.500267  21804.941406      5.216740                   3.415039   \n",
       "13688        52.000031  18257.861328      5.233665                   2.952148   \n",
       "26010        61.081810  20127.462891      5.049800                   3.092285   \n",
       "\n",
       "       Percentage Methane in MR  Percentage Ethane in MR  \\\n",
       "15227                 42.976467                43.589066   \n",
       "15869                 42.946583                43.220509   \n",
       "21751                 43.091019                43.157425   \n",
       "13688                 43.215530                43.384865   \n",
       "26010                 43.442970                43.447952   \n",
       "\n",
       "       Percentage Propane in MR  Percentage Butane in MR  Ambient Condition  \n",
       "15227                 10.162305                 0.302656          28.303505  \n",
       "15869                 10.297656                 0.298555          30.967058  \n",
       "21751                 10.031738                 0.232266          27.580288  \n",
       "13688                 10.075488                 0.283437          28.762129  \n",
       "26010                  9.696777                 0.259648          29.608822  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ebd3e9f-a622-45e8-bc77-96d1ff064fcc",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### multioutput model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b87f6fa5-f4cc-4239-82a5-89e8695e930c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipe_reg_multi(estimators, X, y, X_test, y_test):\n",
    "    # Create an empty dictionary to collect prediction values\n",
    "    y_test_predict = dict()\n",
    "    y_error = dict()\n",
    "    \n",
    "    for name, estimator in estimators.items():\n",
    "        cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "        \n",
    "        wrapper = RegressorChain(estimator, order=[0, 1])\n",
    "        \n",
    "        pipe = Pipeline(\n",
    "            [('scalar', StandardScaler()),\n",
    "            ('regressor', wrapper)]\n",
    "        )\n",
    "        \n",
    "        # evaluate the model and collect the scores\n",
    "        n_scores = cross_val_score(pipe, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n",
    "        \n",
    "        # force the scores to be positive\n",
    "        n_scores = absolute(n_scores)\n",
    "        # summarize performance\n",
    "        print(f'{name}-->MAE: {mean(n_scores):.4f}, {std(n_scores):.4f}', end = \" \")\n",
    "        \n",
    "        # fit model\n",
    "        pipe.fit(X, y)\n",
    "        \n",
    "        # predict\n",
    "        y_test_predict[name] = pipe.predict(X_test)\n",
    "        print(f\", R2-score = {r2_score(y_test, y_test_predict[name]):.4f}\")\n",
    "        \n",
    "        # calculate mse\n",
    "        y_error[name] = mean_squared_error(y_test, y_test_predict[name], squared= False)\n",
    "        print(f\", MSE = {y_error[name]:.4f}\")\n",
    "        \n",
    "    return y_test_predict, y_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fdda966a-c68a-40da-9284-de81037ab1e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression-->MAE: 231.4189, 4.3738 , R2-score = 0.7685\n",
      ", MSE = 320.8516\n",
      "K-neighbors-->MAE: 91.6407, 2.8222 , R2-score = 0.9758\n",
      ", MSE = 158.7817\n",
      "Decision Tree-->MAE: 111.8177, 3.4479 , R2-score = 0.9708\n",
      ", MSE = 209.9193\n",
      "Random Forest-->MAE: 308.1008, 5.0725 , R2-score = 0.8734\n",
      ", MSE = 417.3099\n"
     ]
    }
   ],
   "source": [
    "y_test_predict, y_error = pipe_reg_multi(ESTIMATORS, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "768a475d-310a-4222-9c1c-9d772069e8fd",
   "metadata": {},
   "source": [
    "|Algorithm|R2-score|RMSE|\n",
    "|------|------|------|\n",
    "|Linear Regressor|0.7685|320.8516\n",
    "|K-neighbors|0.9758|158.7817\n",
    "|Decision Tree|0.9738|205.5621\n",
    "|Random Forest|0.8734|417.3099"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2775581e-2504-41fe-97f4-eefbdd826107",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5bb8482b-222c-4472-a70a-1c621c9ca8be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize = (10, 7),  constrained_layout = True)\n",
    "names = iter(y_test_predict.keys())\n",
    "for i in range(2):\n",
    "    \n",
    "    for j in range(2):\n",
    "        name = next(names)\n",
    "        axes[i,j].scatter(y_test.iloc[:, 1], y_test_predict[f\"{name}\"][:, 1])\n",
    "        axes[i, j].set_title(f\"{name}\")\n",
    "        axes[i, j].set_xlabel(\"LNG Rundown Temperature\")\n",
    "        axes[i, j].set_ylabel(\"predicted LNG Rundown Temperature\")\n",
    "plt.savefig(\"multi-output-intuition-Temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "35523e19-515c-4af7-8f7d-164f27fb4647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mma2MHXeFmb0y/Hwn1r7SzA4Lj/lwqEDinOsg+eJ2ZkJzlsQ34MZVA6lMgvgA2znXMubN7uWtR/bSVWC/kn17ulm8fF3dLidWYiClHD/nnGt282b3csmpM+nt6UZAT3eGkSb+rp3WJIgvcnTONZ14HdOsCaGg6fbkhyFaY/AqotfY5AsenXOuYvE43iUxYsbkSRme3TJcMI43gzQWOvoA2znXVD7Zd2/e1eWlFAJp3jmRsQxSKwXlnHO1UE2N6k/23TumbF52xrqW1T/SlMZCRx9gO+caJt9GAfkG1+0orVJQzjmXtnL3HOjrH2DhsrVNU7WpGt2ZLuYfP6Pq5/EcbOdcQ3yy717OW7J6tAxTdqOAThhcQ3qloJxzLm1Jtavz5SZnN/Nq1cF1d2bCaH54b083l5w60zeacc61pr7+gY6Zqc4nrRkS55yrhaQrbPnaFy9f1/DNvKrx4vB27lpwbOrP6wNs51zdLV6+riMH14Kycxmdc67e9u3pzrtgfN+ebj7Zdy/X3v04I2ajixdbWa2uJvoA2zlXV339Ay1R6SNtXRIPXvKmRnfDOecS5avglNWd6WLay7vzLl5sVYKaXU30AbZzrm6yC2c60ZlH7d/oLjjnXKLchY1xAo6Yugd3Pbhp/ANblICz5k6t2dVEH2A75+om38KZTnD0QXty0byZje6Gc64JVFP+rpbnLRSfDdpqcN0l8YXTDq/p++4DbOdc3XRiabp3zJ3qg2vnHFB++bt6nrdT4nN3piu1SiGFeJk+51xq+voHOHrR7UxfcAtHL7qdvv6BMfdNSNjiPLt9brs5+BW7+uDaOTeqnPJ39T5vJ5QOTbMMXzE+g+2cS0WhGRKAC266N3FBjAHDI82+eW75PnTMwY3ugnOuiZRT/q7e551//IzEHOxWd/RBe3LN+/+6rues+QBb0hXAm4Gnzeyw0LYYOAnYCjwIvMfMBiVNA/4AZL9SrTCzD4THHAl8F+gGbgX+1cxM0p7AEmAa8AhwmpltrvXrcs6NVWiG5IWXthUN2i9sbb+g7tuhO+fiCpW/a/R5s7EqW0WkVUvwNUtaXj1SRL4LnJDTdhtwmJm9Gvhf4ILYfQ+a2azw84FY+9eB9wMHh5/scy4AfmZmBwM/C7edc3WWNEMyMDjUsjt8VatTchqdc6WZf/wMujNdY9rqsfFUqeedN7uXuxYcyyOLTuTBS97EZafPGve4ZnfH/Rsb3QWgDjPYZvaLMDMdb/tJ7OYK4G2FnkPSPsDuZrYi3L4KmAf8CDgFeF049ErgTuDj1ffcOVeK7Mr0pHmOVp0FSUMn5DQ650oXnyWutIpIJVVIip036Tmz95+7ZHUFr7YxmmVioxlysN9LlOKRNV1SP/Bn4JNm9kugF9gQO2ZDaAPY28yeDL//Edi7xv11rumlWQaq0HMVqpsK0QxJO+bzlaKWGxg451pXfOBarnxrXc5dspqFy9ay8ORDCz5v0nnzPed5S1Zz7pLVSNBq8yPNMrHR0Coikj4BbAOuCU1PAlPNbDbwUeB7knYv9fnMzCB5B2ZJ50haKWnlxo3NcQnBubRlg+XA4BDGjsWG8Yoe1TzXeUtW88m+aPFiobqpXRJvPbKX3iYJdvVWyw0M0iLpCklPS/p9rG2xpPsl3SPp+5J6YvddIGm9pHWSjo+1nxDa1ktaEGufLunu0L5E0sS6vTjn2lBSzB0cGq44zud7zuxAqtUG1/VItylVw2awJb2baPHj68PAGDN7CXgp/L5K0oPAXwIDwH6xh+8X2gCekrSPmT0ZUkmeTjqnmV0OXA4wZ86cFvvfxrnSFFpsWO6ALynwZrfKLXQpbsSMG1cNcMTUPTpqa/TJkzJceFLhmaQm8l3gq8BVsbbbgAvMbJukzxGtkfm4pFcBZwCHAvsCP5X0l+ExXwPeQHR18beSlpnZfcDngEvN7DpJ3wDeR7SexjmXI+lqYby90MCllDif7xzNklJRiZ7uDLvuvFPdN+0pRUMG2JJOAD4G/F8z2xJrnwJsMrMRSQcSLWZ8yMw2SfqzpLnA3cC7gK+Ehy0DzgYWhX9/UMeX4lzTSbMMVKHHZAfZhQwNj/CrNtr9K5/eJgvq5ShzjcwpwHVhIuRhSeuB14b71pvZQwCSrgNOkfQH4FjgH8IxVwIL8QG2c+MklTld+egmblw1UHKqXaGYnXSOPbozLbkQPTNBRdNiGqkeZfquJVqEuJekDcCFRDMiOwO3Kdp4IluO7++Az0gaBrYDHzCz7KfzB9lRpu9H4QeigfVSSe8DHgVOq/Vrcq6ZpVkGKum5ytGul4p2ndjF2s/kFkhqO/E1Mr1EA+6s+FqYx3PajwJeDgya2bY8x48h6RzgHICpU6em0nHnWknSlcdr7368rEXiheJ80jl2ybTmnoPD2210o5xmHGTXo4rImXmav51w7I3AjQn3rQQOy9P+DPD6avroXDvJt1lAqXlpuZcPjzlkCteseKxtB8nVuPgtja+zWkt51sjUjKfvuU7W1z+QOJFRzuC6WJxPmt3evKX1Zq+z6rXVfCWaoYqIcy5FlZaBynf5sNrBdbtWEenpzjRdME9TvjUyROte9o8dFl8Lk6/9GaBH0k5hFjt+vHNtrdRKTtm4mySpzGk2NS33HABHL7o973nTuCLZjCpdY1RrPsB2rg3llmPq6x9IDLpZhVaSV0LQloNrAQtPPrTR3aiZpDUyROtdvifpi0SLHA8GfkP0lhwsaTrRAPoM4B/CTrt3EOVwX4evkXEdIinXGcbPshaqxNSd6eKtR/aOy8HOzlRn43x2MJ9bq3pgcIj5N6wZPW87b4XejAs1WzPxxjlXslLL9qU9s9GO1/lFa5TfK1VYI/NrYIakDWEty1eB3YjWyKwO1T8ws7XAUuA+4MfAh8xsJMxOfxhYDvwBWBqOhWjTr4+GBZEvJyE90Ll2UqiSU65CA8NLTp3JRfNmcsmpM+nt6UZEM9eXnDpz3F4ESfF7eMT49M3Rn+O82b289cheuqK1b22lZ1Km0V0Yx2ewnWtzpZbt6+QdF0vRytVCkpSzRiYcfzFwcZ72W4Fb87Q/xI5KI851hHIqOSWlbfT2dI/GmkIb0xSaAc/avGWYvv4BPn3z2pbOty7k+Re30dc/0FTx2QfYzrWp7GXDpJmN3HYfXOd32emzmipoO+cqV+kut+U8rpxKTtUsSofSUyPmX7+G4e3tG+OzFUWaKVZ7iohzbajYZUNg3GXCTt1xMUlvT7cPrp1rI5Xuclvu4+YfP4PuTNeYtqRB87zZvQVTQIoptfxqqw2us+/H5EkZMhNKS2lptjxsn8F2roXlK6t3yz1PlnQZcMRsdOFjz6QML7XhwpdK+KDaufZU6S635T6u3EpOSSkg8fi+R3cGCQa3DI95vmMOmVLSpl+tpLenm7sWHDt6O/dz7oWXtuXdGKeSvR5qyQfYzrWofCvVywm0YkeaSLvm5ZWjpzvT1LuCOeeqU+kut5U8rlDedCly43t8QJm7y2O7yZ3pz1cVq5q0mnrxAbZzLSL3W/zglq1VlVtqrQuGtdOd6SrrkqxzrjVVusttmrvj5pMvv7vY4sWh4ZG2m7nOKhaLK93rod58gO1cC8g3W+2q146VQZxz+VW6oLDahYiFJNXMbsda1QC7Tuxiy9boteWb5JlU4rbt1V4hqAcfYDvXAkopxeTKk5vn55xrb5XOfNZyxjQpv7tdy6b2TJrI2s8cy/QFt+S9f2h4e517VDs+wHauBTTb6uh20Gz5es652qt05rNWM6ZJsX3EjO5MV9tNrGRfb63TbpqBl+lzrgW0U9BpBpMnZZr+8qJzrv0lxfZsub5subqe7ubbqbAS2ddbTinDVuUz2M41qfjCl55QC7SUWqYTBLvvkslbxshBZoK48KRDG90N55wrmN+dO2s+69M/aem4Hh9At8pCxWr4ANu5JpS78GXzlmEyXSIzAYqlqJnR0kG4lrwUn3OumZQ60OzrH+Clba2VLvKOuVO54/6Nia+rFRYqVsMH2M41gdwyTVu2bhuXezc8UtqCF4O2XSBTCR9UO+fSUulW69U8T1//QFNudb7rxC5e2Jo86L9o3sw69qb5FB1gS7oa+DnwSzO7v/Zdcq5z9PUP8Omb147Z6CWNEnwjZojOqXU9eVKGwS3D9EzKYAbPDg235SXHJB6nnau9pJJ6ULx2cyXPkx2EN1tZVgFnzZ3KRfNmJqat9PZ0p/ZlpFWVMoP9beBvga9IOgjoB35hZl8q9SSSrgDeDDxtZoeFtj2BJcA04BHgNDPbLEnAl4A3AVuAd5vZ78JjzgY+GZ72IjO7MrQfCXwX6AZuBf7VzKfvXHPLtxtVmnL/AA5+xa488PQLNTlXo8QDfYerOk475wqrdKv1XAuXrS36PLX+fKhU7t4BC08+NG8O+TGHTEnly0grKzrANrM7JP0CeA1wDPAB4FCiQXCpvgt8Fbgq1rYA+JmZLZK0INz+OPBG4ODwcxTwdeCoMCC/EJhDNHZYJWmZmW0Ox7wfuJtogH0C8KMy+udc3dW7tnU7Da4FHTkjkiSlOO2cKyBpJrmcGea+/oHENTIDg0N8su9e7rh/Y9PNWgNcdvqscfE2KYc8rS8jrayUFJGfAbsCvwZ+CbzGzJ4u5yRm9gtJ03KaTwFeF36/EriTaIB9CnBVmIFeIalH0j7h2NvMbFPo123ACZLuBHY3sxWh/SpgHj7Adk3Oa1tX5h0+Yz1OGnHaOVfYBEG+NOgJKv05Fi9fV/D+Zt3+/OBX7Jo4MM63WPG8JavzHttJn3ul1MG+B9gKHAa8GjhMUhpFefc2syfD738E9g6/9wKPx47bENoKtW/I0z6OpHMkrZS0cuPGjdW/Atex+voHOHrR7UxfcAtHL7qdvv6Bsp/Da1uXp0vywXWyWsVp51yQtMawnLWHzTgzXYoNm18s63Mu6fOtkz73ig6wzew8M/s74FTgGeA7wGCanQiz1TXPmTazy81sjpnNmTJlSq1P59pUNjduYHAIY0duWbmD7GMOmUIZEx8d67LTZ/HIohN58JI3+eA6QaVxWtIVkp6W9PtY256SbpP0QPh3cmiXpC9LWi/pHklHxB5zdjj+gbBWJtt+pKR7w2O+HNbYONexWvUvYGh4hIXL1pZ8fCdsJFNMKSkiHyZaPHMk0WLEK4guQVbrKUn7mNmTIQUkezlzANg/dtx+oW2AHSkl2fY7Q/t+eY53ribKzS2LrwTPls+bPCnD8y9u65gqH5Xq7enumHy9alQRp7+Lr49xriQ93fk38Cq0y2K8ksYe3VGVo1Y1ODRMX/9ASTF5dBHksrWj79kumc7aPLyUV7sL8EXgEDP7ezP7tJndnsK5lwHZmY6zgR/E2t8VZkvmAs+GVJLlwHGSJocZleOA5eG+P0uaG2ZH3hV7LudSl5RDlu/SX3y2GxitTb15y3DT1TRtNp0221GliuK0mf0C2JTTfArRuhjCv/Ni7VdZZAWQXR9zPGF9TBhUZ9fH7ENYHxOuUl4Vey7nWs7Ckw8lk5NwnZkgFp6cf2fY3Kud7bABWLEc8lwvbduxM9rmLcMVXe1tVaWkiHweeBH4gKQPSzq83JNIupZo8c0MSRskvQ9YBLxB0gPA34fbEM1yPASsB74JfDD0YxPwWeC34ecz2QWP4Zhvhcc8iM+QuBoqlEM2LScnu96VQtpFb083l5w602evS5RGnI7x9THO5TFvdi+L3344vT3diChOLX774Ylxqh3jfzmLFAtd7e0EpaSIfAQ4B7gpNF0t6XIz+0qpJzGzMxPuen2eYw34UMLzXEF06TO3fSXR4h7nam7+8TM4b8nqxPSOeL3PTloxnU8lO0oKuGvBsbXpUJtKI07nY2YmqS7rY4DLAebMmeOXdlzTKmd773aM/+UsUkx6/e34vuRTSorIPwJHmdmnzOxTwFyinDrnOtK82b1Fc6ez39I7acV0ru5MF2cetf+4hS7FdPJ7VoU04/RTIb2DMtbHJLX7+hjXsVoplnWVUGuw3LS9Tq8kUsoAW0B8jn8ktDnXsQotasl6YnAo70rqTnHJqTO5aN5MLjl1Jr0lBlTPu65YmnHa18c4l4JWqRTV053hC28/nMtOnzUm/eUdc6eOuV1u2l6nVxIpZav07wB3S/p+uD2PaFvejhNfDey7yHWuvv4BXti6rehx+8YqYGSriHSKePWP7CXVoxfdnvc96JLYbuZ/U9WpKE6H9TGvA/aStIGoGsgiYGlYK/MocFo4/FbgTURrXbYA74FofYyk7PoYGL8+5rtAN9HaGF8f4zpCX/8AN64aaIlKUbvuvNOYeJ2WpF0eOyXGy0rIjwz1Tv8m3PylmfXXtFd1MGfOHFu5cmXJx2dXA8cT9rszXb4QqwMlDRTj8v2/ke//oXaU9Hfhf0OVk7TKzOYUOaYt4nS5sdm5ZlTK50SzEPDwohMb3Y2WVCg2J85gh9qmWY+En9H7YjMUHaHc2seuvcRrWZci36Axe/v8pWvKXvjXKnoLzFB0+mxGLXicdq7+Srma3eiFfF0SZx61P3fcv3G0n1u2bmPzlvGlAjslJ7reCqWIrCLaNCCbQpQdESj8fmAN+9V0On01bCerZOa50KBx9+6d8ga5VpaZoILlqrLKWYHvSuJx2rk6iE+yZP+4IKoade6S1Xz65rVceFJUD3vx8nUNTw2Ze+DkcTvfJl1F7JSc6HpLHGCb2fR6dqTZ7dvTnXf20r/5tb9qa5n29Q/wbzfdw5bh7cUPbkFdKm1w7dLncdq52ssdmOYbPG/eMsz8G9aA0RSbiK14aPO4Nr+KWF+FUkQ+bGZfDb8famalb0LfhuYfP8O/+XWocvPo4hVG+voH+OjS1TRBvK2YgLPmTuWaux8bt82v51A3lsdp52qv1EmW4ZHmCfRJaYh+FbF+CqWIvBf4avj9v4Ejat+d5uXf/DpTX//AmMuBxWRLiU5fcMtozlsrD64hukpz0byo5J5X0mk6Hqedq7FWTAXtUisUCGxvpZTpA697Dfg3v05Ubi7ddoPBoSi/ullXkJezu2JmgsZcpfG/gabmcdq5GkhKEW1mu2Qm0Nc/4PG6gQoNsHskvYVoM5rdJZ0av9PMbsr/MOdaU77Z2WIzF5kJAGqKnLtSCNheYHC968QuXtgaXQrt6c6w8ORDPUA3N4/TzqUo3+dAvhTRZvfC1hEuuOleIN3a1q50iXWwJX2nwOPMzN5bmy7Vh9da7RzxgNkzKYMZPDs0PCbFIWl19c47TRidkW4XvQmzMb093dy14NgG9MgVk1RrtR3jtMdm1yiFavVD820YNnlShgtPOrRgvzyu11ZFdbDN7D2165Jz9ZEbMOPl8QYGh7jgpntZ+egmrr378XFpE0PDIwwNj5SVg90KfMFu+/A47Vx6Cu13cdeCYwvuSFtvmS5x4UmHjqbtTV9wS97PqVbMH28XExrdAedqqdjq76HhEa5e8VjBnOR4keFKvWPu1CqfIT3zZvdyyakz6e3pRkQzHF4JxDnXDvr6Bzh60e1MX3ALRy+6nb7+gZIfW8p+F/OPn0F3pqvqflajt6ebxW8bWxo1qWSwlxJunFIXOTrX1MZsAiDGlZOrllHe4sBc16x4LN0OVckXKzrnGqkWFYlyr1hmr1JCaXnIpex3MW92Lysf3cTVDYjp+cqiJm2Akz3er0w2jg+wXcvr6x9g/vVrRhca1moX8mq2N2+nFBPnnKtGtQPhJIVSPLL3FxrQF0qfi38hqHcFPEHePufbACc7yO71MqoNV2ijmd2Bvc3sgXD77UD2a9xyM3uqmhNLmgEsiTUdCHwK6AHeD2wM7f9mZreGx1wAvA8YAT5iZstD+wnAl4Au4FtmtqiavrnGK2d2Y+GytXWp4tFuudiu9dU6TjtXC4UGwtUMCJNSPLID+GID+qT9LoCxA9k6fhAUWqSY7320Io9x9VNoBvvzwK+AB8LtS4AfEQXv/wN8oJoTm9k6YBaApC5gAPg+8B7gUjP7fPx4Sa8CzgAOBfYFfirpL8PdXwPeAGwAfitpmZndV03/XOOUO7tRryofkyZ2sd1oqVJNuSZPyhQ/yLWSmsZp52qhlFznSuzRncn7edAllTygz02f6+sf4Pyla6q6glmq7kxXWYvPa/U+unQUWuT4GuDK2O3nzOxfzOwfgcNS7sfrgQfN7NECx5wCXGdmL5nZw8B64LXhZ72ZPWRmW4HrwrGuRRW7zNcoL2wd4a1HNt/ltq4JpV2vzK46d22lnnHauVTUYkFeX/8AL2zdNq49MyF57Ux2IJq0MDI72VOPwTVQ9uJzX9jY3ArNYO9kY4tkvzP2e0/K/TgDuDZ2+8OS3gWsBM43s81AL7AidsyG0AbweE77USn3z9VRoct8Ry+6fVzayORJmTHl92rpxlXlbZ2eqzvTxYvbRlK5xCjgrLlTmXPAnkXrs3o+XtuqZ5x2LhXllgotJWVw8fJ1DI+MD6yZLrHd8q+h2benO+8V0/nXr+HTN6+t2+cKRJt8lbv43EuuNrdCM9jbJf1F9oaZ/R5AUi+wPa0OSJoInAxcH5q+DhxElD7yJPCFFM91jqSVklZu3Lix+ANcQyR9+xZR8DN2pI309Q9w4UmHkumqz6qToeERdslUVt2yS+KtR/amMrju6c5w6emzuGheNMNx14Jjuez0WePKR3Vnurjs9FmjNVxd26lLnHYuTeWUCs0OgPPF/rikiZktw9vzDq6zA9F8V0yHt1tdB9cAW7dtL6ukIHjJ1WZXaAZ7MXCzpPOB/tB2BFHO3+IU+/BG4HfZxTjxRTmSvgn8MNwcAPaPPW6/0EaB9jHM7HLgcoh2C0uj865ySbMS+b6V55s1jm8AAPlXiPf1D3DektVlzzj3JOTyAbw4vJ13zJ1adpmmETNuXFVeAM2V3bkrXwBNWqDjwbat1SxOSzoP+EeiP717idbH7EOUhvdyYBXwTjPbKmln4CrgSOAZ4HQzeyQ8T97F6a6zlTpbW2xBZPZzpJwY3yWN7s7YDJvGQDSor2SRp5dcbV6FdnK8WtKfgIuIFhYC/B74lJn9KMU+nEksPUTSPmb2ZLj5lnBOgGXA9yR9kWiR48HAb4jGXgdLmk40sD4D+IcU++dqoNBCRoBdMhPGBNWk4JmdtUgKMvNm93LektVl9a0708WbD98ncQC9R3eGi+bN5JoVj5U9cK9kgWRPd4bVFx5X0rEebDtLreJ0mAH/CPAqMxuStJQotr6JaBH6dZK+QTRw/nr4d7OZvVLSGcDngNOTFqebWeuuFHZ1VWghX76tzUuxPcxoxz9zmoEvTmwvBetgm9mPgR/X6uSSdiWq/vFPseb/kDSLaEz1SPY+M1sbgvx9wDbgQ9kgLenDwHKiMn1XmNnaWvXZpSNpVmLhsrW8tG17yQFzj+7iVTGSNg+I6+nO8OzQ8Ois76dvTv5faHBomGkLbmFCDTa0yefZOlVJca2phnF6J6Bb0jAwiShl71h2TGBcCSwkGmCfEn4HuAH4qiQRW5wOPCwpuzj91zXor2tDhTZ/KbRTb29PN1u2bsub6lHssY3iixPbS6E62J8q8Dgzs89We3Ize4HoUmO87Z0Jh2NmFwMX52m/Fbi12v64+kn6pl5uyb3BoWFmffonLDw5f9oE5F8Ikiv38eeWMOudVuntbM3Sv/r/fsTQ8Pi0WQ+6Lkmt4rSZDUj6PPAYMAT8hCglZNDMsqUa4gvNewmLzc1sm6RniWJ7ocXp8ddxDnAOwNSpUyvpsmtRxRYwFlrIl3R1UsBdC46NNiG7Yc2YxY+ZLhV8bCMdc8iURnfBpajQDPYLedp2JboU+HKg6gG261ylzCqXanBoOG+d7E/23cu1dz/OiBnFKtmdv3QNKx/dxB33b6xrTl58xfclp77aV4S7ctUkTkuaTDT7PB0YJFqEfkJlXSzO18d0plL2PCi0tiSpetKYSYnc/5tsxzH1zr/OriXqUv7SgXfc78UX2kliOQQz+0L2hyjwdRMtcrmOaNdF5yo2//gZ4ypeVFMHJLdO9if77uXqFY+NBrFis80jZly94rG6B9z4im9fEe7KVcM4/ffAw2a20cyGgZuAo4EeSdmJmfiC8tFF6OH+PYgWOxZanO46XKl7HmQrJT286MQxFZHyfY7EJyUWL183bpff4e3G+UvXcMwhU8Y9tha6pNF4funps3hk0YmjOeC5PAe7vRTMwZa0J/BR4CyifLsjQk1q50aVs615VnxWYmBwKJVtyAcGh5j16Z/UbWfHavX2dBfdRcy5YmoUpx8D5kqaRJQi8nqifQnuAN5GNIA/G/hBOH5ZuP3rcP/tZmaSkhanuw6S9BlRaM+D6QtuKfp5UqxyUtKESbai01uP7OWHa56s2WeGgC+cdvi4/hfKK3fto1AO9mLgVKJZkZlm9nzdeuVaRrnbmsdlB5NHL7o91XSRVuCpHy4NtYrTZna3pBuA3xEtKu8P57gFuE7SRaHt2+Eh3wb+Oyxi3ERUOaTg4nTXGQp9RhRK0zBK+zxJmpQoVlN6aHiEW+55kl133qmqz413hM2+8pWWPWvu1Lx98w1iOoMs4VKFpO3AS0RBMX6QiBbP7F777tXOnDlzbOXKlY3uRstLGhyXU1pu+oJbqp69TmMGvBa6JogvvP1wwOtTu8pJWmVmc/K0t12c9tjcXpI+I7K7y5ZSZi+7ELxUff0DfHTp6tQWohfyyKITR89ZToyv5Mqvaz5JsRkK18GubLs611EKVQPp6x8oKWBUu9ik0KYw9bDrxC62bB1h355ujjlkCnfcvzFv0PTg6dLmcdo1u0J1rHNTPIrtd1BMX/8AC5etrdvnQZd2rBwqN73P0wHbX8Ec7CSSHjMzr6XkCg6OS92VqtRZjCQvbdueyiD7HXOnjlYRKXVG/B1zp3LRvJlVnde5WvA47ZpBsXzj+EAzaba7lNzkvv4B5l+/Ztyixmp0SZx51P6Jm47NPXByaudy7aeiATbVFXxwbWT+8TMSa0YPDA4x+zM/wYwxm7jEB93Zy2TVFPwfGh5hl8wEMhNUcXDt7ekeN1DO9i1fwM9MEIvfPn7xinNNxOO0a7hy8o2ryU3OVzGkWiNmXDRvJrfc82TeDWseecarfrhklV5ebMZ0V9cA82b3MnlS8m6Km7cMMzg0PLpgZf71a0YXn2RnHNJY4Di4ZZjFbz+cSZny/5dOCuDZ0lCPLDqRy06fNaZ8ng+uXQvwOO0arpzyo9WUKq1VibtpC27JO7iu5TldeyhUReSjSXcBL6tNd1w1GrVo4sKTDi05xWN4u7Fw2Vrmze5l4bK1qc047BtK3i1evo4tJQY9hceVWlbQB9Su2Xicdq2gnPhZaaxtxMYxXlbPFVIoRWS3Avd9Ke2OuOpUUy6vWrk1rYsZHBrm6EW3p7YQJT4DXc6Mgq/cdm3A47TrGIUmkQqlK9aCl9VzxSSW6Wt37VQKqq9/gPOXrsm79Wq55Y2qlWZN66xMl8AYM9udXYTYmxNkyz1/tlapL1R0zaxQKah2006x2aUndxIJokFuPIXkqItv46nntta8L7mfO65zVVSmz7WGbNDJN7iG+ueIzT9+BuctWZ1a8ucEwemv2Z85B+xZUvpLvkUymS6x68T8mwkYcPWKx5hzwJ4eLJ1zrkklbat+7pLVnLtkNbtO7OKFrbXfw6jek1audfkAu8UVq8BR7xyxebN7WfnopsSyRuXabnDjqgHmHLBnSUGt0Na5hTa0+fTNa32A7ZxzdVTOuqFik0WVDK67BCNlzgYdc8iUss/jOpMPsFtcoaDTqByxi+bNTG2ADdEsRak1tSF5kUyhRTBJq8Sdc66VNeOOgfk2hBkYHOK8JatZ+eimvCl7aS9izM5E9/UPlHXV9Y77N6bWB9feKqkiAoCZfTH97rhyJQWdLqnk8kat4InBoao/KOq9CMa5WvM47Qpp5OL3UvsUZ8A1sZS9eMzvKVAOthLZyal5s3vL+lzw0nyuVIWKBu8WfuYA/wz0hp8PAEfUvmuuFPOPn0F3pmtMW3emiy+c1l51mnsmZbjgpnsZCNvpZj8osjW1SzFvdi/dCXWye7rTDd7O1YnH6aCvf4CjF93O9AW3cPSi28uKDe0qKW958fJ1DepR8bRGA85dsppZn/4J829YMxrz077KGE+f7C0jldJL87lSJQ6wzezTZvZpYD/gCDM738zOB44EUtt+V9Ijku6VtFrSytC2p6TbJD0Q/p0c2iXpy5LWS7pH0hGx5zk7HP+ApLPT6l+zq6Ywf6voznRhRiofFJec+moyE8ZucJeZIBaefGjV/XSu3uoVp5tddla0mi/g7ShptrWRs7ClnntwaJjhchOkyxDPpc43UTUhzz6oXprPlaOUHOy9gXjdm62hLU3HmNmfYrcXAD8zs0WSFoTbHwfeCBwcfo4Cvg4cJWlP4EKiWRwDVklaZmabU+5nWeqV+1bPTVBq8Zq6pMQqKF0Sbz2yNzGnu9ycvEKLIJ1rYfWI002r0ExtJ/9tJ6UQNnIWthEbwuQTz6XO/VzomZTh+Re3sd3GloZ965G+4ZgrXSkD7KuA30j6frg9D7iyZj2KnAK8Lvx+JXAn0QD7FOAqi4p3r5DUI2mfcOxtZrYJQNJtwAnAtTXuZ6JmzH0rV+5g+phDpnDjqoGSXlNviUFUwJlHRWX48tU4feuRvSz5zeOJj+9SnmmGInxXRteGGhGnm0YzztQ2g3xlSxs9C1vPtTCZCTC8Pf99uf9vxD8Xjl50+7iUFMMXOLryFMrBBsDMLgbeA2wOP+8xs39PsQ8G/ETSKknnhLa9zezJ8Psf2TET0wvER1sb2JFzmK99DEnnSFopaeXGjbX9Q2nG3Ldy5Lvkes2Kx0p+TfOPn0EpQ18jKsMH5E11+eGaJwtup5408+1cJ6lDnG5qSTOynZ4v24wphPNm95b02VCtzASgwARMof83/AubS0OpZfomAX82s+9ImiJpupk9nFIf/sbMBiS9ArhN0v3xO83MJKUyijKzy4HLIdotLI3nTNLqf6D5viAkvWH5XlM5K7OzmwXk2x2r2HOUszjFuTaXapyW1AN8CziM6M//vcA6YAkwDXgEOM3MNksS0dbsbwK2AO82s9+F5zkb+GR42ovMLPWZ9WacqW0WzXTFLntVtB7TItHMdf4zFft/oxlTa1zrKTqDLelCovSMC0JTBrg6rQ6Y2UD492ng+8BrgadC6gfh36fD4QPA/rGH7xfaktobptVnVMr5IrBHdybv6v1yB7/lLkzyD1DnIjWK018CfmxmhwCHA39gx/qYg4Gfhdswdn3MOUTrY4itjzmKKLZfmF20nqZmnKl1Y8WvijZasf83kqpz+eeNK0fRATbwFuBk4AUAM3uCqCxU1STtKmm37O/AccDvgWVAthLI2cAPwu/LgHeFaiJzgWdDKsly4DhJk0PwPi60NUyr/4EmfRHIveCWmSBe2Lot7+r9fO9BsUuDuSknkxNqn05Q8SDpXAdJNU5L2gP4O+Db4fm2mtkg0TqY7Az0lUS53hBbH2NmK4Ds+pjjCetjwqLz7PqY1M2b3ctdC47l4UUncteCYz02NJli5fnqpbenu+j/G/6FzaWhlAH21rCo0GB0IJyWvYH/kbQG+A1wi5n9GFgEvEHSA8Dfh9sAtwIPAeuBbwIfBAiLGz8L/Db8fCa74LFRWv0PNGlw/H8O2nPMa3rZLjuNK6U0NDzC+UvXAOPzqs+aO3Xc8+aKz55feNKhZLpyyup1iS+eNqtl3kvn6iDtOD0d2Ah8R1K/pG+F56zJ+hjX/KqtM55meuTkSZnEPQ0KKWeSy7+wuWqVkoO9VNJ/Ec1IvJ8oD+9baZzczB4iuvSY2/4M8Po87QZ8KOG5rgCuSKNf+VRSnq6Zct9yFXs982b3svLRTVyz4rHRLDYDfvfYs2O+KExfcEve5x8x44Kb7uWSU2dy14Jjx9w354A9Wbx8XeKlwvjsuZfVc64kacfpnYg2qvkXM7tb0pfYkQ4CpLs+JixwPwdg6tSOKd/dMtKoirVLZgJDSSU9yjS4Zbikcn+ZCeJlu+w0erx/drh6kpVQhUHSG4jSLgQsN7Pbat2xWpszZ46tXLmypGPzbe3anelqqRnpuHyvJ18gShoE9/Z0jw6aj150e8EgFz+2lH608vvqXK1IWmVmc4ock1qclvQXwAozmxZu/y3RAPuVwOvM7MmQAnKnmc0Ig/s7zezacPw6ovKprwvH/1NoH3NcPuXEZld7ff0DnL90Td6KTfH4XmjS5pN99ybuZVCJ3p5unghpiYWOyfahXntSuM5TKDYXncGW9Dkz+zhR7lxuW0dot00M8r2e4e02WvczOzuRlC8Xv9SXb/V+0rG5fHbauXSkHafN7I+SHpc0w8zWEV1RvC/8nE2Utpe7PubDkq4jWtD4bBiELwf+Pbaw8Th2LMR0TS47CZJUDnVgcIjpC26hZ1KGZ7cMsz3WPv/6KE1w3uxerr07eS+DcmW6VHACaPKkDP2fOm7ca2jlPSlcayolReQNRKvT496Yp61t1bLkXiO+WZfS76HhkcQdFvOlcCTNcBjRLHfS62rmNBrnWkgt4vS/ANdImki09uU9ROt2lkp6H/AocFo49laiEn3ricr0vQei9TGSsutjoAnWx7jSlbIw0WDcpiwQTdosXLaWebN7U9uvYPKkDBeedOjoZ0a+K6AXnnTomMe02wSZax2JA2xJ/0y0iPAgSffE7toN+FWtO9ZMalUTs1HfrEvdqnbEjO5MV9HasknBLstnDJyrjVrGaTNbDeS79NlU62Nc7VQ7iTQ4FA28kyZrSiHg4UUnjmsv9Qpoq+9J4VpXoWW43wNOIroEeFLs50gzO6sOfWsatSq516jdHvO9nnyylU9KqYQSr5qSTyvtYulcC/E47WomrX0bzjxq/8T7ipVuLTQsL6XSR6vvSeFaV+IMtpk9CzwbVo9vMrPnACTtLukoM7u7Xp1stFrlCjfqm3Xu69mjO8MLW7eNKbcn4JhDppSVwpE9dvqCW/IGRZ8xcC5dHqddLRVbY1NMdh+Di+bNBODaux9nxIwuiTOP2p877t9Y9Gpqtbv1+i6frlFKycH+OlG5pqzn87S1vVrkCleaepJG3nbu6/lk373jSvLduGqAOQfsWfZz+zazztWdx2mXumJrbArJdGlMPvRF82aODrSzksq8ZqUxEPbF9K5RShlgy2K1/Mxsu6RSHtc2arUQsZJv1rXK277j/o3jZp0rXQjiMwbO1V3Hx2mXX7WfX9lj59+wZsxVzq4JYredd2JwaHg0xzr7b2+e82T7MTA4NO74fPI9R6V8Mb1rhFK2QnpI0kckZcLPvxKtKO8I2QFtvq3Aq1XJbo+1yttOM12l1XexdK4FdXScdvml+vmVMw6eALz58H3oznSNDpKz/27Zui2xH/Hj8g2uuzNdXHb6LN890bW8ohvNSHoF8GXgWKI/sZ8B55rZ07XvXu2UuplB0kYqubU26yUpvzlppXWpkl5noY1inHP1UWyjmXaK077RTHoKbQTW29PNMYdM4Y77N47Obufezs4gF9tQLB8R/Y/Y29PNlq3b8pbyy+qS2G7m6Ruu5VS10UwI0Gek3qsWkTSDu3nLMH39A3UPBLXKb/a0DudaV6fHaZdfoSuQA4NDY3ZXzHc7m35YyZXM7ERQKQPz7WZVTRA514wK1cH+mJn9h6SvkKdSjpl9pKY9axKFakY3olB9rQbCvhDEudbjcdoVUuqeB0my6Yd7dGdGa1rXgi+Ad+2o0Az2H8K/HX2tbv7xMzh3yeq89zWi7FwtB8K+EMS5luNxuoMVW8BYbZk9iGaguyYUq1ZduVpfKW3EbsnOQeE62DeHf6+sX3daS6O+dftA2DkHHqc7WSkVpeITMtXMZI9sT2er81xpVgrJp1G7JTsHhVNEbqbAJkpmdnJNetRkkqpzCDw/2TnXUB6nO1dSRanzl67hvCWrx8zWzpvdO26w2Wj1WEBfqOqWD7BdrRVKEfl8+PdU4C+Aq8PtM4GnatmpZpKUBmL4N2DnXMN5nO5QSZ9N2dJ3ubO1+dILp728mxUPbS57E5kk2cohcd2ZCWzbbmNqaNdrAX2jdkt2DgrUwTazn5vZz4Gjzex0M7s5/PwD8LfVnljS/pLukHSfpLWhbiuSFkoakLQ6/Lwp9pgLJK2XtE7S8bH2E0LbekkLqu1bXFIayKRMKSXEnXOudmodp13zKiVFMXePhHmze7lrwbE8vOhE5h8/g9899mxqg2vYUZYvu//BZafP4g+ffSOL33Z4Q/ZFSHqPfFGlq4dSdvraVdKBZvYQgKTpwK4pnHsbcL6Z/U7SbsAqSbeF+y41s8/HD5b0KqIyVIcC+wI/lfSX4e6vAW8ANgC/lbTMzO5LoY/MP34GH126mtwUtC3D2/lk373jtn51zrkGqFWcdk2q1AWMSbO1+dInqpWU9tGodUNeftY1UinTsOcBd0q6U9LPgTuAc6s9sZk9aWa/C78/R7QavtBf4CnAdWb2kpk9DKwHXht+1pvZQ2a2FbguHJuKebN7ExMcr7378bRO45xz1ahJnHbNK3fH3C7lr/SRNFtbTZrE5EkZujNdY9oEHHPIlIqfsxZ8V2HXSKVsNPNjSQcDh4Sm+83spTQ7IWkaMBu4Gzga+LCkdxGVnjrfzDYTDb5XxB62gR0D8sdz2o9Ks39JV9DSvLRWS16myLn2Vo847ZpPfGY43yLGQrO1O02A4e3ln1PAhScdyspHN43ZmMaAJb95nDkH7NlUny9edcs1StEZbEmTgPnAh81sDTBV0pvT6oCklwE3Em3r+2fg68BBwCzgSeALKZ7rHEkrJa3cuHFjSY8565u/Tuv0DZENugODQxg7Fr709Q80umvOuZTUOk675lfObO1Z3/x1RYNrgLPmTmXe7F5+uObJcfcNbzcWLltb2RM712ZKSRH5DrAV+OtwewC4KI2TS8oQDa6vMbObAMzsKTMbMbPtwDeJUkCy590/9vD9QltS+zhmdrmZzTGzOVOmlHYp664HN5XxippPoTJFzrm2UZM4LalLUr+kH4bb0yXdHRaUL5E0MbTvHG6vD/dPiz1H3sXpLn3xRYx3LTg2cea20OfaZafPKniO7LqjpJ0da7njo3OtpJQB9kFm9h/AMICZbSG6SlQVSQK+DfzBzL4Ya98ndthbgN+H35cBZ4RAPh04GPgN8Fvg4BD4JxIthFxWbf/ahZcpcq4j1CROA//Kjt0iAT5HtAj9lcBm4H2h/X3A5tB+aTgud3H6CcB/ShqbvOvqqpSrl0n53EntzrnxShlgb5XUTShvKekgII3cvqOBdwLH5pTk+w9J90q6BziGaPEOZrYWWArcB/wY+FCY6d4GfBhYTvRBsDQc6/AyRc51iNTjtKT9gBOBb4XbAo4FbgiHXAnMC7+fEm4T7n99OD5pcbprkGJXLxcvX8fcAyfnvW+nLjF9wS0cveh2dp2Y/3vS5EmZqvvoXDsopUzfhUQD2v0lXUM0MH53tSc2s/8h/wzLrQUeczFwcZ72Wws9rlXUYjGilylyriPUIk5fBnwM2C3cfjkwGCY1YOxC817CYnMz2ybp2XB8ocXprkLlflbEjy+2NL/Q1c2XtkWJ2wODQ2QmiK4JGrONeqZLXHjSoWW9FufaVcEBtqQJwGSiXcLmEg2I/9XM/lSHvnWU3BXgubtwVSrf7l1eRcS59lGLOB0WSD5tZqskvS6NfhY53znAOQBTp06t9elaWrmfFeVukb5vT3dJKYTD242e7gy77ryTf7Y4l0fBAbaZbZf0MTNbCtxSpz51pEKLEasNWF6myLn2VaM4fTRwckjb2wXYHfgS0CNppzCLHV9Qnl1svkHSTsAewDOUuAjdzC4HLgeYM2dOa9Q/raP4DPQEaVyJ2EKfFQuXrS1rQ5n5x89g8fJ1DJQwyH52aJjVFx5X8nM710lKycH+qaT/F7Y23zP7U/OeNYmuOq3p8MWIzrkqpBqnzewCM9vPzKYRLVK83czOItrA5m3hsLOBH4Tfl4XbhPtvNzMjeXG6K1FuqdWk/RfyfVb09Q+UVdXjHaEE3/zjZ4zbSCYfX8vjXLJScrBPD/9+KNZmwIHpd6f57JLp4oWt6W4nm8++Pd15Zww8gDnnSlCvOP1x4DpJFwH9RJWgCP/+t6T1wCaiQTlmtlZSdnH6NsLi9JT71PSqWV9T6pbm+T4ryi3Hesf90f4QuamFe3RneGHrNoZHdgzufS2Pc4WVspPj9Hp0pFnVY3ANvhjROVe5WsZpM7sTuDP8/hB5qoCY2YvA2xMen3dxeqeodn1NKVcxkz4ryr0CGj8+N7XQdwR2rjxFB9iSdgE+CPwN0YzIL4FvhIDqUuKLEZ1zlfI43byqXV+TdHWzS2K7WcHPiqTHSpAv06TQFVNfy+NceUpJEbkKeA74Srj9D8B/kzBb4SrnAcw5VyGP002q2vU1SVc3k7ZBL+Wxbz2ylxtXDfgVU+dqqJQB9mFm9qrY7Tsk3VerDjnnnCtbx8bpZk9dqHZ9TTVXNws9ds4Bezb1++ZcqytlgP07SXPNbAWApKOAlbXtlnPOuTJ0ZJyu1f4BaUpjfU01VzeTHutXTJ2rrVIG2EcCv5L0WLg9FVgn6V7AzOzVNeudc865UnRknK7l/gFp8fU1znWmUgbYJ9S8F84556rRkXG62fYPSEpX8dli5zpPKWX6Hq1HR5xzzlWmU+N0M+0f0ArpKs65+illJ0fnnHOu6eTbcbBR1TAKpas45zpPKSkiLkGddlF3zjmXRzPlN9cyXaXZK6U458bzAXYVLj19VqO74JxzHa1Z8ptrla7iqSfOtSZPEamCBzfnnHNQu3QVTz1xrjX5DLZzzjlXpVqlqzRbpRTnXGnaZoAt6QTgS0AX8C0zW1Trcx7yiVu5/+I31fo0zjnnUlaLvOZ86SrVnift1JNa5XN7nrhzY7XFAFtSF/A14A3ABuC3kpaZWU23Cn5xxGr59M4554qoZGBXr7zmNM6Txk6Qafanns/rXCtrlxzs1wLrzewhM9sKXAec0uA+Oeecq6HswG5gcAhjx8Cur3+g4OPqldecxnnmze7lklNn0tvTjYDenm4uOXVmRQPXWr1uzxN3bry2mMEGeoHHY7c3AEflHiTpHOAcgKlTp9anZ84552qi0q3S65XXnNZ50qqUUqvX7Xnizo3XLjPYJTGzy81sjpnNmTJlSqO745xzrgqVDuyS8pfT3gGyXucpVa3602yv07lm0C4D7AFg/9jt/UKbc865MknaX9Idku6TtFbSv4b2PSXdJumB8O/k0C5JX5a0XtI9ko6IPdfZ4fgHJJ2dZj8rHdjVawfIZtppspb9abbX6VwzaJcB9m+BgyVNlzQROANY1uA+Oedcq9oGnG9mrwLmAh+S9CpgAfAzMzsY+Fm4DfBG4ODwcw7wdYgG5MCFRCl7rwUuzA7K01DpwC7NvOZmOE+j+9Nsr9O5ZtAWOdhmtk3Sh4HlRGX6rjCztWk8d5cgqVjIZb6To3OuDZnZk8CT4ffnJP2BaK3LKcDrwmFXAncCHw/tV5mZASsk9UjaJxx7m5ltApB0G3ACcG0a/aym9nS9doBslp0ms2rVn2Z7nc41WlsMsAHM7Fbg1rSf9wunzeLcJavHtb9j7lQPJs65tidpGjAbuBvYOwy+Af4I7B1+z7fQvLdAe2pafWDn9aOda09tM8CuletXPpa3/eGNz9e5J845V1+SXgbcCJxrZn+WNHqfmZmkVDYD6NQKT14/2rn21S452DVz14Obymp3zrl2IClDNLi+xsxuCs1PhdQPwr9Ph/akheYlLUDv1ApPXj/aufblA+wqFNvMwDnnWpGiqepvA38wsy/G7loGZCuBnA38INb+rlBNZC7wbEglWQ4cJ2lyWNx4XGhzeP1o59qZp4hUodhmBs4516KOBt4J3CtpdWj7N2ARsFTS+4BHgdPCfbcCbwLWA1uA9wCY2SZJnyWq9ATwmeyCx7S0cg7zvj3dDOQZTO/RneHoRbe35GtyzkV8gF0Fn2VwzrUjM/sfQAl3vz7P8QZ8KOG5rgCuSK93O7R6DvP842eM6T9AZoJ4Yes2BoeGgdZ7Tc65iKeIVGGP7kyju+Cccx2r1XOY89WPftkuOzGcUxu2lV6Tcy7iM9hVGB7Z3uguOOdcx2qHHObcMoPTF9yS97hWek3OOR9gV+WFrSPFD3LOOVcTSTnMxbZKb2bN9JpKzW9v5Tx452rFU0Scc861pPnHzyDTNTZVPNOlolulN7NKt39PWza/fWBwCGNHLnhu9axSj3Ou0/gAuwo9noPtnHONlbvVTSpb30QDx6MX3c70Bbdw9KLb6zZgzJeXfcmpM+s+I1xqfnur58E7VyueIlKFhScf2uguOOdcx1q8fB3D28eOqIe3W9UlVBtdnaQZtn8vNb+9HfLgnasFn8GuQqMDoHPOdbJaDe58VjY55zu3vdTjnOs0PsAu4h1zp5bV7pxzrj5qNbjzWdnSc8GbJWfcuWbjA+wiLpo3k3fMnUqXooU0XRLvmDuVi+bNbHDPnHOusx1zyJSy2kvls7Kl54I3S864c83Gc7BLcNG8mT6gds65JnPH/RvLai9Vvh0WO3FWttRc8GbIGXeu2fgA2znnXEuqVSpHdrDotZ2dc5VqyABb0mLgJGAr8CDwHjMblDQN+AOQXUmywsw+EB5zJPBdoBu4FfhXMzNJewJLgGnAI8BpZra5bi/GOedcQ9RyUxaflXXOVaNROdi3AYeZ2auB/wUuiN33oJnNCj8fiLV/HXg/cHD4OSG0LwB+ZmYHAz8Lt51zzrU5X2DnnGtWDRlgm9lPzGxbuLkC2K/Q8ZL2AXY3sxVmZsBVwLxw9ynAleH3K2Ptzjnn2pgvsHPONatmyMF+L1GKR9Z0Sf3An4FPmtkvgV5gQ+yYDaENYG8zezL8/kdg7xr31znnXJPwVA7nXDOq2QBb0k+Bv8hz1yfM7AfhmE8A24Brwn1PAlPN7JmQc90nqeTtEkNOduJGuZLOAc4BmDq19DrWff0DvtjFOeealMdo51yzqdkA28z+vtD9kt4NvBl4fUj7wMxeAl4Kv6+S9CDwl8AAY9NI9gttAE9J2sfMngypJE8X6NPlwOUAc+bMSRyIx/X1D3DektVkDx4YHOK8JasB38nROecardHbmjvnXD4NycGWdALwMeBkM9sSa58iqSv8fiDRYsaHQgrInyXNlSTgXcAPwsOWAWeH38+OtafiYzesIXckbqHdOedccZJOkLRO0npJqS5EX7hsbcdva+6caz6NysH+KrAzcFs0Xh4tx/d3wGckDQPbgQ+Y2abwmA+yo0zfj8IPwCJgqaT3AY8Cp6XZ0a0j+Se6k9qdc87tECZNvga8gWj9zG8lLTOz+6p97r7+AQaHhvPel+a25p6C4pwrV0MG2Gb2yoT2G4EbE+5bCRyWp/0Z4PWpdtA551xaXgusN7OHACRdR1T9qeoB9rkhXS+ftLY19xQU51wlGlUH2znnXGfoBR6P3Y5XgaqZtGphL16+zlNQnHNl8wG2c865hpJ0jqSVklZu3LgxledMa3a5VtuxO+famw+wi5jYpbLanXPOjTEA7B+7Ha8CBUQVnsxsjpnNmTJlSl07V0xSqklaKSjOufbkA+wi/uNth5M7lFZod845V9RvgYMlTZc0ETiDqPpTS/Dt2J1zlfABdhHzZvdy6emzxmzFe+nps3xxi3POlcDMtgEfBpYDfwCWmtnaNJ77kUUnltVeCd+O3TlXCYU9XjrOnDlzbOXKlY3uhnPOFSVplZnNaXQ/6sFjs3OuVRSKzT6D7ZxzzjnnXIp8gO2cc84551yKfIDtnHPOOedcinyA7ZxzzjnnXIo6dpGjpI3Ao2U+bC/gTzXoThqatW/N2i9o3r41a7+gefvWrP2CdPp2gJk1V4HoGqkwNkPz/j/QrP2C5u1bs/YLmrdvzdovaN6+1TQ2d+wAuxKSVjbrSv5m7Vuz9guat2/N2i9o3r41a7+gufvWTpr1fW7WfkHz9q1Z+wXN27dm7Rc0b99q3S9PEXHOOeeccy5FPsB2zjnnnHMuRT7ALs/lje5AAc3at2btFzRv35q1X9C8fWvWfkFz962dNOv73Kz9gubtW7P2C5q3b83aL2jevtW0X56D7ZxzzjnnXIp8Bts555xzzrkU+QC7RJJOkLRO0npJC+pwvv0l3SHpPklrJf1raN9T0m2SHgj/Tg7tkvTl0L97JB0Re66zw/EPSDo7pf51SeqX9MNwe7qku8P5l0iaGNp3DrfXh/unxZ7jgtC+TtLxKfWrR9INku6X9AdJf91E79l54b/l7yVdK2mXRrxvkq6Q9LSk38faUnuPJB0p6d7wmC9LUpV9Wxz+e94j6fuSeoq9F0l/r0nvdyX9it13viSTtFcj3rNOl/Tfusbn9PhcWb+aMj6rSWJzeI6mjM8J/Wp4bE7qW+y+xsVnM/OfIj9AF/AgcCAwEVgDvKrG59wHOCL8vhvwv8CrgP8AFoT2BcDnwu9vAn4ECJgL3B3a9wQeCv9ODr9PTqF/HwW+B/ww3F4KnBF+/wbwz+H3DwLfCL+fASwJv78qvI87A9PD+9uVQr+uBP4x/D4R6GmG9wzoBR4GumPv17sb8b4BfwccAfw+1pbaewT8Jhyr8Ng3Vtm344Cdwu+fi/Ut73tBgb/XpPe7kn6F9v2B5UR1m/dqxHvWyT+F/lvX+LwenyvrV9PFZ5ooNofnacr4nNCvhsfmpL6F9obG55oGoXb5Af4aWB67fQFwQZ378APgDcA6YJ/Qtg+wLvz+X8CZsePXhfvPBP4r1j7muAr7sh/wM+BY4Ifhf7o/xf7QRt+v8D/3X4ffdwrHKfc9jB9XRb/2IAqUymlvhvesF3g8/PHuFN634xv1vgHTGBsoU3mPwn33x9rHHFdJ33LuewtwTfg973tBwt9rof9PK+0XcANwOPAIOwJ43d+zTv1J+m/dgH54fC7er6aMzzRZbA6PHRNr0nqPqo01uf3Kua9hsTmpbzQ4PnuKSGmyf4BZG0JbXYRLULOBu4G9zezJcNcfgb2L9LEWfb8M+BiwPdx+OTBoZtvynGP0/OH+Z8PxtejXdGAj8B1Fl0e/JWlXmuA9M7MB4PPAY8CTRO/DKprjfYP03qPe8Hva/ct6L9EMQiV9K/T/adkknQIMmNmanLua7T1rZw2NzeDxuQxNGZ9bIDZDa8TnponN0Bzx2QfYTU7Sy4AbgXPN7M/x+yz6OmV17s+bgafNbFU9z1uinYguE33dzGYDLxBdThvViPcMIOTMnUL0IbMvsCtwQr37UYpGvUfFSPoEsA24pgn6Mgn4N+BTje6LaxyPz2VpyvjcSrEZmjM+N1NshuaJzz7ALs0AUS5P1n6hraYkZYiC9zVmdlNofkrSPuH+fYCni/Qx7b4fDZws6RHgOqLLkF8CeiTtlOcco+cP9+8BPFODfkH0zXKDmd0dbt9AFNAb/Z4B/D3wsJltNLNh4Cai97IZ3jdI7z0aCL+n2j9J7wbeDJwVPmAq6dszJL/f5TqI6AN5Tfhb2A/4naS/qKBfNXnPOkRDYjN4fK5As8bnZo/N0MTxuQljMzRLfK4kP6jTfoi+eT8U/oNlE/MPrfE5BVwFXJbTvpixix3+I/x+ImMT938T2vckynubHH4eBvZMqY+vY8cimusZu0Dhg+H3DzF2QcjS8PuhjF0E8RDpLKL5JTAj/L4wvF8Nf8+Ao4C1wKRwviuBf2nU+8b4HL/U3iPGLwh5U5V9OwG4D5iSc1ze94ICf69J73cl/cq57xF25PjV/T3r1J9C/61rfF6Pz5X1qeniM00Wm8NzTaMJ43OefjVFbM7Xt5z7HqEB8bmmQaidfohWnv4v0QrYT9ThfH9DdBnoHmB1+HkTUa7Sz4AHgJ/G/gcQ8LXQv3uBObHnei+wPvy8J8U+vo4dAfzA8D/h+vCHsnNo3yXcXh/uPzD2+E+E/q4jpaoJwCxgZXjf+sIfSlO8Z8CngfuB3wP/HYJP3d834FqiXMNholml96X5HgFzwmt8EPgqOYuaKujbeqLcuOzfwTeKvRck/L0mvd+V9Cvn/kfYEcDr+p51+k/Sf+san9Pjc2V9mkUTxmeaJDaH52jK+JzQr4bH5qS+5dz/CA2Iz76To3POOeeccynyHGznnHPOOedS5ANs55xzzjnnUuQDbOecc84551LkA2znnHPOOedS5ANs55xzzjnnUuQDbNdRJD2fp22hpC2SXpHvOEl7S/qepIckrZL0a0lvyfM80yQNSVod+5ko6d2Svlq7V+Wcc63P47NrJz7Adi7yJ+D83EZJIqrX+gszO9DMjiTaXGC/3GODB81sVuxna8167JxzncHjs2s5PsB2LnIFcLqkPXPajwW2mtk3sg1m9qiZfaXaE0r6qKTfh59zQ9t8SR8Jv18q6fbw+7GSrqn2nM4514I8PruW4wNs5yLPEwXxf81pPxT4XRnPc1Ds8uPXkg6SdCTwHqJteucC75c0m2gr4b8Nh80BXiYpE9p+UUY/nHOuXXh8di1np0Z3wLkm8mVgtaTPJx0QgvLfEM2avCbPIQ+a2awSzvU3wPfN7IXwvDcRBemvA0dK2h14iejDY0647yNlvBbnnGsnHp9dS/EZbOcCMxsEvgd8KNa8FjgidsyHgNcDU2rUh2HgYeDdwK+IZkyOAV4J/KEW53TOuWbn8dm1Gh9gOzfWF4F/YsfVnduBXST9c+yYSSmc55fAPEmTJO0KvCW0Ze/7f0SXHH8JfADoNzNL4bzOOdeqPD67luEDbNdpJknaEPv5aPxOM/sT8H1g53DbgHnA/5X0sKTfAFcCHy/zvO+Onxd4Gvgu8BvgbuBbZtYfjv0lsA/wazN7CniRHcHdOefalcdn1zbkX7qcc84555xLj89gO+ecc845lyIfYDvnnHPOOZciH2A755xzzjmXIh9gO+ecc845lyIfYDvnnHPOOZciH2A755xzzjmXIh9gO+ecc845lyIfYDsXI+lHks4u4bjnJR1Yjz4551wnk7RQ0tWN7odz5fABtms5kh6RNCTpOUmDkn4l6QOSqv7/2czeaGZXlnDcy8zsoWrPFxcG7dmf7eE1Zm+flea5nHOuGrE4/LykP0r6rqSXNbpf1ZD0uhB747H45jqef5okk7RT8aNds/MBtmtVJ5nZbsABwCKirXG/3dguVScM2l9mZi8DHiN6jdm2a7LHefB1zjWJk0K8mgXMBi5obHdS8UQ8FpvZSeU+gaSuWnTMtRYfYLuWZmbPmtky4HTgbEmHAUjaWdLnJT0m6SlJ35DUnX2cpFMkrZb0Z0kPSjohtN8p6R/D76+U9HNJz0r6k6QlscebpFeG3/eQdJWkjZIelfTJ7Gy6pHdL+p/Ql82SHpb0xnJeY5hV2SDp45L+CHxH0gRJC0Lfn5G0VNKescfMDTP7g5LWSHpdhW+xc84VZGZ/BJYTDbQBiMWn5yTdJ+ktsfsKxkVJ00PsfU7SbcBe8fNJOlnS2hDf7pT0V7H7HpE0X9I9kl6Q9G1Je4f0v+ck/VTS5HJfo6S/CucaDOc+OXbfdyV9XdKtkl4AjpG0r6Qbw+fCw5I+Ejv+tZJWhs+fpyR9Mdz1i/DvYJg9/+ty++mahw+wXVsws98AG4C/DU2LgL8kCvivBHqBT0EU3ICrgPlAD/B3wCN5nvazwE+AycB+wFcSTv8VYA/gQOD/Au8C3hO7/yhgHdGHxH8A35akMl/iXwB7Es3YnwP8CzAvnG9fYDPwtfD6eoFbgIvCY/4fcKOkKWWe0znnipK0H/BGYH2s+UGieLwH8Gngakn7xO4vFBe/B6wK930WGF0XI+kvgWuBc4EpwK3AzZImxp77rcAbiD4DTgJ+BPxbOH4C8BHKICkD3Ez0efAKovh7jaQZscP+AbgY2A34VTh+DdFnz+uBcyUdH479EvAlM9sdOAhYGtr/LvzbE2bPf11OP11z8QG2aydPAHuGIH0OcJ6ZbTKz54B/B84Ix70PuMLMbjOz7WY2YGb353m+YaIB7b5m9qKZ/U/uAeFS4BnABWb2nJk9AnwBeGfssEfN7JtmNgJcCewD7F3ma9sOXGhmL5nZEPAB4BNmtsHMXgIWAm8L6SPvAG41s1vD67sNWAm8qcxzOudcIX2SngMeB54GLszeYWbXm9kTIQYtAR4AXht7bN64KGkq8Brg/wvx7hdEg9Ws04FbQvweBj4PdAP/J3bMV8zsKTMbAH4J3G1m/Wb2IvB9onSWJPuGWersz2nAXOBlwCIz22pmtwM/BM6MPe4HZnaXmW0HZgJTzOwz4fiHgG+y4zNoGHilpL3M7HkzW1HwXXYtyQfYrp30ApuIZikmAauyQRL4cWgH2J9odqWYjwECfhMuCb43zzF7ARng0Vjbo6EvWX/M/mJmW8Kv5S4G2hg+HLIOAL4fe31/AEaIBu4HAG+Pf0gAf0P0Aeacc2mZF9bCvA44hFgqh6R3KUrDy8agwxib6pEUF/cFNpvZC7Fj4/F13/jtMKB9nLEx96nY70N5bheKv0+YWU/sZ2k45+PhXPE+xc/5eOz3A8gZqBPNoGcnVt5HNLt+v6TfSnpzgf64FuWLpVxbkPQaomD3P8CfiILooWEGI9fjRJflCgp5he8Pz/83wE8l/cLM4pdB/8SOme77QttUIN95q2E5tx8H3mtmd+UeKOlx4L/N7P0p98E558Yxs59L+i7RbPI8SQcQzdi+Hvi1mY1IWk00YVHMk8BkSbvGBtlT2REDnyCaIQYgXLHcn/RjbtwTwP6SJsQG2VOB/40dE4/RjwMPm9nB+Z7MzB4AzlS0VudU4AZJL2d8nHctzGewXUuTtHv49n8dcLWZ3RsC4DeBSyW9IhzXG8t/+zbwHkmvV7RYsFfSIXme++0htxCiHGcjStUYFS5vLgUulrRb+GD5KFDrmq3fCOc8IPR1iqRTwn1XAydJOl5Sl6RdFC2U3C/x2ZxzrjqXAW+QdDiwK1G83Agg6T1EM9hFmdmjRCltn5Y0MUxuxCt5LAVODPE7A5wPvESU91wrdwNbgI9JyihaNH4S0edOPr8BnlO0ML07xOHDwkQQkt4haUr4rBoMj9lO9H5tJ1rP41qcD7Bdq7o5lvv3CeCLjF1Y+HGiBTcrJP0Z+CkwA0YXRL4HuBR4Fvg50Qx0rtcAd0t6HlgG/GtC7et/AV4AHiKaQf8ecEW1L7CIL4U+/SS8DyuIFg1hZo8DpxBdktxI9B7Nx//enXM1YmYbiRaPf8rM7iNai/JrovSMmcC4q20F/ANRPNtElNd9Vew864jWmXyF6AriSUTlArem8DLyCs99EtFCzj8B/wm8K2HtTnbi5c1Ei+wfDo/5FtGCT4ATgLXhs+VLwBlmNhRSZS4G7gqpJXNr9Zpc7cnMr0g455xzzjmXFp/Rcs4555xzLkU+wHbOOeeccy5FPsB2zjnnnHMuRT7Ads4555xzLkU+wHbOOeeccy5FHbvRzF577WXTpk1rdDecc66oVatW/cnMphQ/svV5bHbOtYpCsbljB9jTpk1j5cqVje6Gc84VJenR4ke1B4/NzrlWUSg2e4qIc84555xzKfIBtnPOOeeccynyAbZzzjnnnHMpqssAW9IVkp6W9Ps8950vySTtFW5L0pclrZd0j6QjYseeLemB8HN2rP1ISfeGx3xZkurxupxzzjnnnMtVr0WO3wW+ClwVb5S0P3Ac8Fis+Y3AweHnKODrwFGS9gQuBOYABqyStMzMNodj3g/cDdwKnAD8qIavxznXpvr6B1i8fB1PDA7RMynDS8MjbBnePuaYLom5B07mkWeGGBgcoktixIzenm7mHz+D61c+xl0PbhrzmL13m8jdn3hDPV+Kc65B4nFk355ujjlkCnfcv5EnBofIdImtIzZ67N67TeRPzw8zYkaXxJlH7c9F82aOe66BwaFGvJSOMnlShgtPOpR5s3urfq66DLDN7BeSpuW561LgY8APYm2nAFeZmQErJPVI2gd4HXCbmW0CkHQbcIKkO4HdzWxFaL8KmIcPsJ1zZfpk371cs+Ixsh99m7cM5z1uxGzMAHrEokcMDA5x7pLVeR/z1HNbmbbgltFBeBoB3DnXfPr6B7jgpnsZGh4Borhw9Yod84jxwTVEsSFrxGz02IvmzRz3XK62Nm8ZZv4NawCqjtENK9Mn6RRgwMzW5GR09AKPx25vCG2F2jfkaXfOtZlCs0L7ljBw7esf4NM3rx0dOEtgBr093Ux7efe4WedaGBgc4oKb7gWqD+DOueazePm6qgfE1979OBfNm5nKc7nyDI8Yi5eva80BtqRJwL8RpYfU87znAOcATJ06tZ6nds5VqdisULGBa1//APNvWMNwbPYoTDwzMDhU18uvQ8MjqQRw51zzeSKFWJK9KpbGc7nypfG+N2oG+yBgOpCdvd4P+J2k1wIDwP6xY/cLbQNEaSLx9jtD+355jh/HzC4HLgeYM2eO5TvGOdecSpnJGRoe4dwlq1m8fN2Y2e2eSRkGtwzTTH/0/sHpXHvat6e76i/sXeHKfhrP5cq3b0931c/RkDJ9Znavmb3CzKaZ2TSitI4jzOyPwDLgXaGayFzgWTN7ElgOHCdpsqTJRLPfy8N9f5Y0N1QPeRdjc7qdc22gnAFpdnZ7YHAII8qra6bBNaQTwJ1zzWf+8TPoznRV9RxnHrV/as/lypPpEvOPn1H189SrTN+1wK+BGZI2SHpfgcNvBR4C1gPfBD4IEBY3fhb4bfj5THbBYzjmW+ExD+ILHJ1rO3t0ZxrdhdQIUgngzrnmM292L5ecOpPJk0qLWXvvNnF0xrpL4h1zp45WEck+V08bxb9mNnlShsVvO7ylqoicWeT+abHfDfhQwnFXAFfkaV8JHFZdL51zjZa7iDG+aLGdqtsbvsDRuXb3Yk55z3x6e7q5a8GxRY97advY5+rOdHHJqTNTjSN/9f/9iKES+pwmAQ8vOrGu56yXhlURcc65uHyLGOOLFpNK5rWiXk8Pca6tlbJmpDvTxfzjZxScWEh6rloslN4l01X3AXY7p8r5ANs51xQKfYhANNPRbHnUlch+qDrn2lehNSOC0YE0UHBiodBzpb1QerABkxjHHDKl7uesFx9gO+eaQtJK+YHBIc5fuqYtBte+yYxznSGp+kduSsjRi27PO7GwcNna0VntCWGn2HznKFWxWfJCfa6lO+7fWNfz1VNDqog45xxEQf/oRbczfcEtBY/L9+HSiu5acGxTDa4lXSHpaUm/j7UtlnS/pHskfV9ST+y+CyStl7RO0vGx9hNC23pJC2Lt0yXdHdqXSJpYtxfnXAPlq/6R7+pV0iz04NDwaBWkpPj3wkvb6OvPW5V4jGz6Xfb5srPkuY895pAp1HupSzuXK/UBtnOuIT7Zdy/nLVk9GvTbXZNWAfgucEJO223AYWb2auB/gQsAJL0KOAM4NDzmPyV1SeoCvga8EXgVcGY4FuBzwKVm9kpgM1CogpRzbSNb/aO3pxsRzVznW5RYziz0hJzR7+DQcN6Bcq5i6XcQDcJvXDUwJhYLOPqgPWs6UGznHGwfYDvn6q6vf4BrVjzWEQNriALtwpMPbXQ3xjGzXwCbctp+Ymbbws0V7NjI6xTgOjN7ycweJiqL+trws97MHjKzrcB1wClhX4JjgRvC468E5tXy9TjXTObN7uWuBcfy8KITE69elVPnenuegJk7UM6nlBzufINwAx55Zogvnj4rlYXZubPj7b4exQfYzrm6W7x8XccMrgG+ePqspkoNKcN72bGvQC/weOy+DaEtqf3lwGBssJ5tH0fSOZJWSlq5cWP75mQ6lyvfTHep9bOziqVZJM0Sx9sLDcKzXxSqTR8xKDqj3058kaNzri6yi2w6bdvf3p7ulvwQkfQJYBtwTa3PZWaXA5cDzJkzp5O+eznHvNm9Y2JEbslSiGZ7d95pAoND4yt9FEuzmH/8jLzPF589TlrgGH/uahdBllrzu134DLZzrubii2w6TSteApX0buDNwFlh8y+AAWD/2GH7hbak9meAHkk75bQ75wpIyt9eePKhJS2cLPX54oP6UhZlVhPL2j0dJB+fwXbOVayU0k9Q2qYLAs6aO5UbV22o+2YHtdRqs9eSTgA+BvxfM9sSu2sZ8D1JXwT2BQ4GfkP0n+5gSdOJBtBnAP9gZibpDuBtRHnZZwM/qN8rca515c5qx5USc8t5vuz9xZ47+/v861cTD9ETgN27Mzw7NDymvncl/WwnPsB2zlWk2M6LcaWUYtolM4Gb2mxw3ewkXQu8DthL0gbgQqKqITsDt0XrFFlhZh8ws7WSlgL3EaWOfMjMRsLzfBhYDnQBV5jZ2nCKjwPXSboI6Ae+XbcX51wbKjZQrva5YcfAOLt4MneQXer5O21AncsH2M61oVJnlqtRzva9peTutePAujvT3Fl4ZnZmnubEQbCZXQxcnKf9VuDWPO0PEVUZcc41uXImTVxxzR39nXNlK3VTgWolzUoPDA4xfcEtHL3o9tFztvN2uIXsUmL5Leeca7RS6mW70vkMtnNtppyZ5WoUmpWOD+xXPrqJG1d15tq2wS3jV/w751wzKqVediH1uHLaSnwG27k2U22QLFUpGyQMDY9w7d2PF13g2K7aeZcy51x7KaVedpJ6XTltJT7Adq7NVBMky5Fb+inJiHVmWeNOLEvlnGtdpZTqS+LpJeP5ANu5NlNNkCxXfCvgnu78u491qdr9v1pPl9T2u5Q559pLKfWyk9TrymkrqfkAW9IVkp6W9PtY22JJ90u6R9L3JfXE7rtA0npJ6yQdH2s/IbStl7Qg1j5d0t2hfYmkibV+Tc41s2qCZKX6+gd4Yeu2ce2ZCeLMo/YvmErS29PNZafPqlnfGuHMo/b3wbVzruXEJ03uWnBsyXGsXldOW4msxpdvJf0d8DxwlZkdFtqOA243s22SPgdgZh+X9CrgWqKyTvsCPwX+MjzV/wJvADYAvwXONLP7Ql3Wm8zsOknfANaY2deL9WvOnDm2cuXKVF+rc+2k2IKVvv4BFi5bm3fr3rjenm6OOWQKP1zzZNFj20WXxBdOOzy1QbakVWY2J5Una3Iem51rPUnbu7f7lbxCsbnmM9hm9gtgU07bT8wsO921gmgLXYBTgOvM7CUzexhYTzTYfi2w3sweMrOtRLuCnaJoF4RjgRvC468E5tXy9TjXCYotWOnrH2D+9WtKGjAPDA5x46oBFp58KEcftGeNe94cRsw6foGPc65++voHOHrR7eNKpNZL7pXTSZkJvLhthHOXrOagC27lk3331rU/zaAZcrDfC/wo/N4LPB67b0NoS2p/OTAYG6xn251zMeUG30ILVvr6Bzh/6RqGt5d+9Sv72Lse3FT84DbR6Qt8nHP10SwVPLLpJWfNncqW4e1kEyRGzLh6xWO86v/7UUdNOjS0DrakTxBtuXtNnc53DnAOwNSpU+txSucarpTduXLTQZLqW2cfW0llkGI7ObajTl7g41wnysbSfPGut8za0Llx+ZhDpnDH/RvH3c53rqHhaPZ45aObxqTn7Tqxi0zXhDFXH7sUrZW5aN5MDvnErbw4Mj6+S9Gamq157ivVluHtnLtkNecuWT3aNnlShgtPOrQt00gaNsCW9G7gzcDrbUci+ACwf+yw/UIbCe3PAD2Sdgqz2PHjxzGzy4HLIcrzS+FlONf0im08k28ALqLNYnJ1SR1b0zrXO+ZOTfxwy+rkBT7OdZq+/gHm37CG4YRBaDlbj+eLy1eveGzMc8VvJ8k95oWtI8DYGJ6dYb5mxWN54z6AGVUNrpNs3jLM/BvWAO23HXtDUkQknQB8DDjZzLbE7loGnCFpZ0nTgYOB3xAtajw4VAyZCJwBLAsD8zuAt4XHnw38oF6vw7lWUKx8Ur4BuEHe2tadWtM6V29PNxfNm8ldC47lkUUnctnps+pWGtE515w+ffPaxMF1VqmpY/nicq01KroPj1hbptPVo0zftcCvgRmSNkh6H/BVYDfgNkmrQ/UPzGwtsBS4D/gx8CEzGwmz0x8GlgN/AJaGYwE+DnxU0nqinOxv1/o1OddKkmZRJ0j09Q8kDsB9KJ1fvoFzI0ojOueay+YtpVVJKiV1rNPSy9rx9dY8RcTMzszTnDgINrOLgYvztN8K3Jqn/SGiKiPOuTzmHz9jXPkk2FHpYo/uTMeUz6tWoRzKebN7fUDtnCuqlNSxQmth2lE7ptM1QxUR51wNZWdX8+2oODQ8gkTBjWBcJLsZznlLVjekDJZzrrkl7WYbV2rqWL4dedtVpkttmU7nA2znOsC82b1sT8ifHtwyPCa9wY03eVKmKcpgOeea18KTDyUzITmKlpM6li/t7B1zp+a9nUREi7HjA/9dJ3aN+yLQJfGOuVMbsqPu5EkZFr8tvU25mklDy/Q55+on6ZLjvj3dY9Ibpi24pd5da3pmFKzE4pxz2VhQaAfccp8vjfhy0byZJR+78tFNXHv344yY0SUx98DJ/OaRzUUXbyaZPCnD4Jbhqt+LVuQDbOc6RL5c7HyXK5NK9HWyZxNy1NtxYY5zrny5NasvPX1Wyw0m+/oHWPLbx0erRY2YseLhzUzs0pgBdmYCDG8f+9h8nxsiWvhZbv3vduEpIs61qdzdG4FxlxzfemQvi5evG7PD4/9p8+3MM10qKVcyq7enO3EBTjsuzHHOladZdlKsVr4ygyPbjaHYaLo708XLdhkfP/NNyoxucNKi70e1fIDtXBvKF/CzO2gNbtnKpafPYv7xM7hx1cC4Y379UHtvZz48YiVXTcnO8OdbcNQOda4lXSHpaUm/j7XtKek2SQ+EfyeHdkn6sqT1ku6RdETsMWeH4x+QdHas/UhJ94bHfFnKs9LWuRZXaDOvesidTKl0IFtKmcGh4ZGSyxHmPq4da10X4ikizjWZ3EuNhS6tJR1baJOCF7aOjNmqNtf2Ds8PkaKc63yXNdPKrWwi3yXal+CqWNsC4GdmtkjSgnD748AbiTb/Ohg4Cvg6cJSkPYELgTlEk1arJC0zs83hmPcDdxOVWT0B+FEdXpdzdVNsM69ayrfjY6m7RdZbp6XU+QDbuSZSTrAsdGynBbI0me2YnY6/5+1Y59rMfiFpWk7zKcDrwu9XAncSDbBPAa4KO+iukNQjaZ9w7G1mtglA0m3ACZLuBHY3sxWh/SpgHj7Adm2m0ALyWkuaPf/0zWvLilf1SN/otJQ6H2A710QKXWrMDZaFju20TQrSNjQczfIvXr6uXWaqy7G3mT0Zfv8jsHf4vRd4PHbchtBWqH1DnvaaKOfKj2u8vv4BFi5bO5quNXlShhNfvQ933L+RgcEhuqTRShbxf3u6M0hRedGeSRleHB4ZkyNcKgH/56A9ue/J5ypKeSjFwOBQw6oybd4y3HQVoXLfj8wE2GbRpEaXxJlH7V9WxZNm5wNs55pIOZcak471gXV6mvlyaz2YmUmqedKQpHOAcwCmTp1a9uNb6TJ5J/pk371cs+KxgtWJNm8Z5uoVj43ejleyiP8bXz9RzcDYgLsebO/1Js0u/r1oxGz0v3+7DLJ9kaNzTaScahWddrmtUTpwcc5TIfWD8O/ToX0A2D923H6hrVD7fnnaxzGzy81sjpnNmTJlStkdbvQiM5fsk333cnWRwbVzWdfe/Xjxg1qED7CdayLlVKvopK10K9XTnWHypAwiugQ9KTM25E2elOEdc6cWfR87LKd9GZCtBHI28INY+7tCNZG5wLMhlWQ5cJykyaHiyHHA8nDfnyXNDdVD3hV7rlQ1cpGZK6ydBkyu9kYSdhxuRSWniEi6Gvg58Eszu792XXKuM2VzSIeGR0bzDQsV6M/dNaycsJR9/nZ1WZ5NHkbf3zw5unMO2JPFy9clptc0+9WCSuOzpGuJFinuJWkDUTWQRcBSSe8DHgVOC4ffCrwJWA9sAd4DYGabJH0W+G047jPZBY/AB4kqlXQTLW6syQLHRi4yc4W1c5xx6etqo0qe5eRgfxv4W+Arkg4C+oFfmNmXatIz5zpIbg7piFneSha54pUtjl50e8n51/FFQ52gWI5u9if3OGiZetcVxWczOzPhrtfnOdaADyU8zxXAFXnaVwKHFe569UrdpdTVXyfFGVe9M4/av/hBLUJWxv/4krqA1wDHAB8AhszskBr1rabmzJljK1euLOsxvkrd1UrS4Li3p5u7Fhxb0nP09Q8UrG8d1wnbocdn/wt9+cgeB4zOYsevIBxzyBTuuH9jQ//uJa0yszlFjmmL+FxJbAaPz80qm4PtXK52qCJSKDaXkyLyM2BX4NfAL4HXmNnThR/VPnyVuqulNHJI583uHVP2qpB2H1zD2L/RQjP7A4NDzL9+DYjRbYKzVxCOOWQKN64aaPq/+06Pz9CedcobpdiXlXK+zGQHTPEqItkv+L093Ux7eTe/fmhTyRtcZTeCypX9olzqJMM75k4d88V52su7WfHQ5lRm2ydPynDhSYeWvUGYay8lz2BLuhQ4EngJuAv4BfBrM2vJVSTlzpKkMcPoXJK0/v/Kl+LQ6aq5RJ302Hr/3RebwW6n+FzpDLZLR1Ka1CWnziyYRpW9v9pzFdKd6eKtR/aO+dKbe/43fPFOHnj6hZKeK1+fy7kSePArduW2j76upGNdeyoUm0uuImJm55nZ3wGnAs8A3wEGS+zAFZKelvT7WNuekm6T9ED4d3Jol6QvS1ov6R5JR8Qec3Y4/gFJZ8faj5R0b3jMl8OK9VT5KnVXS+VUD8nV1z/A0YtuZ/qCW1i8fB1vPbKX3p5u2mepSHWqmZFKemyz/d1XE5+diytW8jDNkoj5nguiL7CPLDqRy06fNRrLenu6ueTUmVw0byaXnDpzXHt2oLxla2mbziT1uZzX8dDGLSUf6zpPOSkiHyZaRHMk8AjRgpZflvjw7wJfBa6KtS0AfmZmiyQtCLc/DrwRODj8HAV8HThK0p5EK9znEF1dWiVpmZltDse8H7ibaKX7CaS8Wt1Xqbs0JF0azK0IEr8v9zHxnOA9ujO8sHXbaGrDwOAQV694jJ7uDJeePqtgZQxXXNIMdrP93VcZn50bVWwyKc3JpmLPlZT2UygdqJx+5IuN5Ty+nC/vnhbSecqpIrIL8EVglZltK+ckZvYLSdNymk8hKg8FcCVwJ9EA+xTgqrBifYWknrDZweuA27LlnyTdBpwg6U5gdzNbEdqvAuaR8gDbV6m7apVayaLYY+ILhpLyrQeHhku+zOkgM0FjcrCh8OXoJvy7rzg+OxdXbDIpzcmmpOeaINHXP1DRADTpOfMRjJ4nOwAut9xpKXwNV2cqJ0Xk88CLwAckfVjS4VWee++wEQHAH4G9w++9QLwy/YbQVqh9Q572VM2b3VvwspRzxVRyaTXpEqrb4bLTZ41eTs5MKO0Db/KkDD3dmdG/5cVvP5zFbzu87MvRzaIG8dl1qGLpatWks5VyLohmhi+46V76+vNu/Fn2c2a68scFI4qx2QFwuVf75h44uaTjfKfRzlROishHgHOAm0LT1ZIuN7OvVNsJMzNJNS9sIOkcotfA1KlTy368r1J31ajk0qqnd0S7MT47NJx3Zqk7M2FMes1wkVIExRZjlXs5ulnUMj67zlIoXa2U+ys51/lL14xLt8gOQMt93qT+JV3Re2JwqOKJjEeeKS0++xquzlROisg/AkeZ2QsAkj5HVBKq0gD+lKR9zOzJkAKSLSk1AMQrje8X2gbYkVKSbb8ztO+X5/hxzOxy4HKIVqpX2G/nKlLJpdVO36ShO9PFwpMPBWD+9WvGDKAzE8Qlp7569HahDytBu+c9ph2fXQcr9qUyzS+d82b3cl6BwW+lz5nbv6T1KPv2dFd8nlIf52u4OlPJKSJEn1Hxr3gjoa1Sy4BsJZCzgR/E2t8VqonMBZ4NqSTLgeMkTQ4VR44Dlof7/ixpbqge8q7YcznXNCq5tNppg+tMl8akbmRnm+fN7mXx28emcCx+++FjPkSTPqx6e7p5eNGJ3LXg2HYdXEP68dm5ukn6201zAFoo/lZ6nlIfl2ZajWsd5cxgfwe4W9L3w+15RNvzFiXpWqLZ570kbSCqBrIIWCrpfcCjwGnh8FuBNwHrgS3AewDMbJOkzwK/Dcd9JrvgEfggUaWSbqLFjakucHQuDcUurcZXme/RnSH9YpPNKb7pRKEZ5mKzZh2+ELni+Oxco9Xjb7dY/M09f6ZLYCSmnZXTvzTTalzrKHer9COAvwk3f2lm/TXpVR34ZgaumZSz4UJSdYtWs+vELi5+S7oLBtu1FFaJW6W3RXz22NyZGv23m+/8wLhJj8Etw20VW1x1CsXmogPsUH86UWwWuaV4EHfNJGknx1zxWd7sB0KrLYScIPjrA/fkkWeGEj/M/ANsrKQg3o7x2WOzc65VFBpgl5IisoroCm72gnV2RJ69sntg1T10rsOVslhGUNftuWth8qQMF5506LiasPNvWDPmcqzXiS2Zx2fnnGtCRQfYZja9Hh1xrpPkXo7smZRh85b8m8Zk9UzKcPSi23licIhJE7t4YWtrpYd0Z7o48dX75C3JFd/gJavSMl2dxOOzc841p6JVRMIWvNnfD61td5xrf/FNDYxotvb5F7clboYA0YKb51/cNvqYVhtc9/Z0c8TUPbhmxWNlVUbxOrGFeXx2zrnmVEqZvvfGfv/vWnXEuU6Rb1OD4e3GrhN3Gi1D19OdYdeJO8o6bRuxopuoNKPuTBeXnT6L+cfP4FcPbiprG2LwOrEl8PjsnHNNqJwyfeB1VZ0DqlvxnjQrOzg0zLND0Qr1Yw6Zwo2rduyX1EpD69yyexDt1FbsNWQmaMyXiA4qsZcWj8/OOdckShlg90h6C9Fs9+6STo3faWY35X+Yc+0pt6ReuQvyknb1AkZTRq5Z8VhLDaqzsosY47W9L7jp3pLSQl62y06jeeg93RkWnnyo518X5/HZOeeaUCkpIj8HTgbeDPwCOCn28+badc255pQvxSO7IK8U+Xb1ytVqg+suictOn0X/p44bMyjO917lIxizyPOlbdtr0c12VLP4LOk8SWsl/V7StZJ2kTRd0t2S1ktaImliOHbncHt9uH9a7HkuCO3rJB1fTZ+cc65VlFJF5D316IhzrSIpxaPUBXm5u3q12mA6n+1meWebSy0/mPseeAWR0tQqPkvqBT4CvMrMhiQtBc4g2mX3UjO7TtI3gPcBXw//bjazV0o6A/gccLqkV4XHHQrsC/xU0l+aWWut0nXOuTKVMoPtnItJWnhXzoK8ebN7uWvBsTy86ER622AhX6XvSW9Pd+IXjGKD877+AY5edDvTF9zC0Ytup69/oODxrmw7Ad2SdgImAU8CxwI3hPuvJNqSHeCUcJtw/+slKbRfZ2YvmdnDwHrgtfXpvnPONY4PsJ0rU74Uj2oW5LX6Qr5Crz3pvbrs9Fk8suhE7lpwbOIXjEKD83ylDi+46V4fZKfEzAaAzwOPEQ2snyXa1GbQzLaFwzYA2UsMvcDj4bHbwvEvj7fneYxzzrWtcquIONfxclM8qtnWO1uNpFlIUEaZarok3npkL4uXr+O8JatHK6Dccf/G0ffmrUf2jrmd+17NP37GmEWjUPwLS6E8eE8rqZ6kyUSzz9OBQeB64IQanu8c4ByAqVOn1uo0zjlXN0UH2JJ2B/Y2swfC7bcD2aml5Wb2VA3751xTmje7t+qBXG41kqZg+XOik4yYceOqgTEVVa5e8djo/QODQ9y4aoBLTp2Z+H5V8oWl2jz4dlHD+Pz3wMNmtjE8703A0URVS3YKs9T7AdlLBgPA/sCGkFKyB/BMrD0r/phRZnY5cDnAnDlz2mFZgnOuw5Uyg/154FfAA+H2JcCPiIL4/wE+UJuuOddayq2NXWqFjVpIGkRn0zKSygjm6pKKvoZSZpbL/cKSVOqwAzemqVV8fgyYK2kSMAS8HlgJ3AG8DbgOOBv4QTh+Wbj963D/7WZmkpYB35P0RaJFjgcDv6mwT8451zJKycF+DTsWrwA8Z2b/Ymb/CBxWm24511oqyQlu1Gxrb083Z82dOm5XEgHHHDKlpDKCEKVxlLrtedqvNe08+BZWk/hsZncTLVb8HXAv0WfF5cDHgY9KWk+UY/3t8JBvAy8P7R8FFoTnWQssBe4Dfgx8yCuIuHx80bJrN6UMsHcyG/Mp+s7Y7z3pdse51lRJbexGzLZmJoj5x8/gonkzxw2yDUZ3j7zk1JlMnpQZ9/js8b093Vxy6sySK6Ck/Vrnze4dPb9i/enA/OuaxWczu9DMDjGzw8zsnaESyENm9loze6WZvd3MXgrHvhhuvzLc/1DseS42s4PMbIaZ/aiaPrn25IuWXTsqJUVku6S/MLM/ApjZ72G0TqrvBuEcleUE51vcV2unv3b/0UHoHfdvTKw/fdeCY5k3u7ektJdiryF3Zrmabebj0siDbwMen13Lq3TRclqxxLlaKGWAvRi4WdL5QH9oO4Io929xNSeXdB7wj0STZ/cC7wH2IcrvezlRWah3mtlWSTsDVwFHEi2eOd3MHgnPcwHRRgcjwEfMbHk1/XKuXD2TMmN2IsxKmrnNfjDUOwf7xlUDzDlgT+bN7k0c/A8MDjF9wS2jH1h3LTg28fnyLVDMrSIS/9Crdpt5N07N4rNz9VLJBIXHEtfsStnJ8WpJfwIuItqNC+D3wKequdznO4W5dtHXP8DzL24b157pUt6c4L7+Ac6/fg0j2+tfLCE+K5S0UBAYc5kWCn9glTOT7OX10lWr+OxcPVWyaNljiWt2JdXBNrMfEy1QqcX5uyUNM3ansH8I918JLCQaYJ8Sfodo8c1Xc3cKAx4Oi2xeS7Sa3bmaX0ZcvHwdw3kGy7tO3GnMzG22D1B6GbxayPahlBSVtD+wvLxe+moYn52ri0pq4Xsscc2ulDrYnypwt5nZZys5sZkNSMruFDYE/IQydgqTFN8pbEXsqRN3CvPNDDpPvsuI5y5ZzblLVgPQ051h4cmHljWAzB2wJ80CPzs0nLcP9dIl5a3ykZ0Vyk3vqHTL8nJ4eb101So+O1dPldTC91jiml0pM9gv5GnblShl4+VARQG83juFgW9m0ImK5TkPDg0z//o1QGl5e/kG7MVqSjeq3vWIGd2ZroKzQvH0jqMX3V7SB1Y1VwQqmalyBdUkPjtXb+UuWvZY4ppd0TJ9ZvaF7A/R4LSbaDHidcCBVZx7dKcwMxsGxuwUFo7Jt1MYlewU5jpTKbOvw9ut5O3K8w2WDcbVlM4G+r7+gZI3bUlbvJReKaXsSqktXW05LS+vl64axmfnmprHEtfsSsrBlrQn0eYBZxHlRR9hZpurPLfvFOZqrlAKR1ypaRBJxxlRgI/P6gKjiwRradeJXbywdeygPzswLmdWqJTLtGksLPLyeumqUXx2rul5LHHNrJQc7MXAqUSzIzPN7Pk0Tmxmd0vK7hS2jajE1OXALcB1ki4KbfGdwv47LGLcRFQ5BDNbGyqQ3Beex3cKc6NKrTVdLG8vmxaRlFeUb1OWeqSG9PZ0c9eCY+tWW9oXFjWXWsVn55xz1ZEV2epY0nbgJaLBa/xgES2i2b123audOXPm2MqVKxvdDVcH2cFn0kx2ZoJY/PbDEweWxRYpZroExphKIrm5z2nIzfXuznSVdEk0zSoqSXna2YG+qw1Jq8xsTp72tovPHpudc60iKTZDaXWwS9lO3bmmFZ+V7esf4NM3rx3dFKaUKiKFZqJ7e7p54aVtDA6N3WRmaHgECYp8fy1Zd6aLtx7Zm7iBS5K0N2PwhUXNxeOzc841p5JysJNIeszMvN6daxmV5OwlpT8IuGvBsUxfcEve+ysdXGe6xOmv2b/swXQ+aW/GUEk5LdcYHp9du2rEFum+LbsrV1UDbMYXT3CuqaQRFIvVWy11IWUpJk/KcOFJ5dXlLqQWOdO+sKhleHx2bacRW6T7tuyuEtVeXvRa0q5pVVtSLqtY+bpjDpmSVpfzbrlejaTFm74ZQ0fw+OzaTqGrcu10Ttf6Sqki8tGku4CXpdsd59KTFBTPX1r6xjJZO+80YfS5Jk/KcOKr92HhsrWjO0KmZXi7cf7SNZy3ZPWYGfdKZuL7+gfYsnX8gN1zptuHx2fXaRpRycirJ7lKlJIisluB+76UVkeci0sjtSMp+I2YMf+GNSxctpZnh4YLPn++CiLPv7iNq1c8Vt4LKkN2e/Pstu6f+P69bN22fbRKSSmXJ5Mqn1SyNbxrah6fXUdJSsmbINHXP1CT2ObbsrtKlFJF5NP16IjrPEmD6Hz5buctWc3KRzdx0byZJT9/odzo4REbrfyRff5zl6ymN7ZJTFJpv3g5vkolba+eT+4mMlB8oWJS5ZNdd97JB9dtxOOz6zRJexuMmNUsL9qrJ7lKVLvI0bmKFFo0krQd+TUrHmPOAXuWHDxL3WQm+/zZfsy/fg0oGoTXShrPXOjypF/SdM61o2z8P3/pmtGrfVnVVEgq5ZxeRcSVwwfYriEKLRoptB15uVtyQ/5AXEgaM9SlmjwpM1qTu5xZbSh8edIvaTrn2tW82b2cl7D+pVaTCF49yZXLNylwDVFohrXQILDc4Dlvdi9fOO3wcVVAam1CCQXSuiT6P3Ucjyw6kUcWncilp88quZ/FLk8Wq3zinHOtzCskuWZXTRURAMzsi+l1x3WKQjOs84+fwXlLVuedza0keM6b3cvKRzfVdGFirlImwXNn1bOzIwuXrR23M2SmS+w6cScGh4bpksaUiMo3q+KXNDtDLeOzpB7gW8BhRBdX3gusA5YA04BHgNPMbLMkES2qfBOwBXi3mf0uPM/ZwCfD015kZldW2ifnsjwv2jW7cqqIzABeAywLt08CflOLTrn2Vyg4ZgfE16x4bMwgu9Lg2dc/wI2ryqt9XQ+9eb4sZC9D5lsACpS12YFf0uwItYzPXwJ+bGZvkzQRmAT8G/AzM1skaQGwAPg48Ebg4PBzFPB14ChJewIXAnOIBumrJC0zs81V9s11OJ9EcM1OVmJuqqRfACea2XPh9m7ALWb2dzXsX83MmTPHVq5c2ehudLRipfjS2pr26EW3p7bTYlq6M11ccmpUEaXU15j0Onp7urlrwbE17a9rLEmrzGxOgftTjc+S9gBWAwda7ENC0jrgdWb2pKR9gDvNbIak/wq/Xxs/LvtjZv8U2sccl4/HZudcqygUm8tZ5Lg3sDV2e2toc64ixWZY05qBbYbKGT3dGXbdeacxA+ncWfpiM9JeGcQVkHZ8ng5sBL4j6XBgFfCvwN5m9mQ45o+xc/QCj8cevyG0JbWPIekc4ByAqVOnVtFt55xrDuUMsK8CfiPp++H2PMBz6VzTK1QPux66M13jNnfp6x8YlwID+ctMZWfyk641+aIeR/rxeSfgCOBfzOxuSV8iSgcZZWYmKZWSO2Z2OXA5RDPYaTync841UskDbDO7WNKPgL8NTe8xs/7adMu58iTlLGc3iym3BF61sufrTUj7KDRgjs9IJ+3ImFUoLz2tFBvX/GoQnzcAG8zs7nD7BqIB9lOS9omliDwd7h8A9o89fr/QNkCUJhJvv7OKfjnnXEsotw72JODPZvYdSVMkTTezh2vRMdeZKhkU5tu05qNLVrM9dkw9B9ddEl847fCC/S6U1hGfkU7akRGSB+9QeCMfH2S3rdTis5n9UdLjkmaY2Trg9cB94edsYFH49wfhIcuAD0u6jmiR47NhEL4c+HdJk8NxxwEXVPwKnXOuRZRcB1vShUSrxbPBMQNcXc3JJfVIukHS/ZL+IOmvJe0p6TZJD4R/J4djJenLktZLukfSEbHnOTsc/0AoCeVaUHZQODA4hLFjUNjXX7gCSL5B6PaEY2tNwJlH7V90EJuU1iEYMyOdNBAXcNeCY8vaKj1e2s+1l1rEZ+BfgGsk3QPMAv6daGD9BkkPAH8fbgPcCjwErAe+CXwQwMw2AZ8Ffht+PhPanHOurZWz0cxbgJOBFwDM7Al2lIiqVLYM1CHA4cAfiC5D/szMDgZ+xo68v3gZqHOIykARKwN1FPBa4MLYbIlrIZUOCuu5yG/ypAw93RkgmqnOZcCNqwaKfinItxGMgLPmTh0zaK50MwVfENlxUo/PZrbazOaY2avNbJ6ZbTazZ8zs9WZ2sJn9fXawbJEPmdlBZjbTzFbGnucKM3tl+PlONX1yzrlWUc4Ae2so12QAknat5sShDNTfAd8GMLOtZjYInMKOxTlXEi3WIbRfFQL5CqAn5AAeD9xmZptCbdXbgBOq6ZtrjEoHhfVa5Nfb003/p45j9YXR7osPXvKmvLWsS/lSMG92L5ecOpPenm4UnvvS02dx0byZY46rdEdG3+Ws46Qan51zzlWnnAH20lDDtEfS+4GfEu3yVal4Gah+Sd8KHwo1KQMFUSkoSSslrdy4cWMVXXe1kDT4M6Ia0EmzwvkGoWlLGtRWM1M8b3Yvdy04locXnZiY7pFvIH7JqTOLpqD4VukdJ+347JxzrgrlVBH5vKQ3AH8m2jXsU2Z2W5XnrlsZqPB8XgqqieXb3TGr0CK97O3zl64Zt/14pfLVrc43qC205XtaKqkH7rucdZYaxGfnnHNVKHmALelzZvZxohSM3LZKeBkoN0Z8UJhv0JqvRnT8sectWZ1aX54dGmb1hccVPa7Qlu+N5luld44axGfn2pKXL3X1Uk6ZvjcQrVKPe2OetpJ4GSiXT3ZQOH3BLXlL6xUrb5fWhjI9kzIlBWKfKXZNItX43AnqNdDq6x9g4bK1DA4NA9FC6QtPOrSqcyX1vdB+AE8MDrFHdwYJNm8ZTuW1lSJbTnTlo5v43t2Psb3Mi4yTJ2U48dX7cMf9GxkYHKJLquhK5a4Tu3jZzl089dyODU8HBoc4d8lqzg2TM2n8t3EuS1bkf1RJ/0xUcukgohJMWbsBvzKzsyo+uTSLKE9wIlGJp/cQ5YUvBaYCjwKnmdkmSQK+SrSAcQvRRgorw/O8F/i38LQXl7JSfc6cObZy5cpih7k6yf1geOGlbaMfSHG9Pd3cteDYvB8kKx/dxNUrHkulP92ZCYDGzUyXkv/sXNokrTKzOXnaaxafG6UesTnfBk61+Pvu6x9g/vVrGM4ZVWa6xOK3Fa6VX+g58/X9rUf2cuOqgTHtmS4xMmINK1uaNYHGlU4tVzX/bVznSYrNUNoAew9gMnAJY3Okn2vleqY+wG4e+T4wMl0CY8wHU6ZL7DpxJwaHhsftzJiZEM1qlDs7Uq7sAN+5eiowwG67+FyP2Hz0otvzXu1K++876TzVnCvpOSud2XXjeZx3pSo0wC6aImJmzwLPhkWIm8zsufCku0s6KpZD7VxR+Wae89W/Hh6JPigkMIsu3T3/4o5Z7dyPkdwZolKVu4V6vhQVz+lzjeLxuTL1qhNf6PkqPVfS43xwnR7fL8CloZwyfV8Hno/dfj60OVeSpJ0aC+VNm0HXBGFW+SC6kLPmTs1bzm7ypEze43Org+R7TectWc0n++5Nva/OFeDxuQz1qhNf6PkqPVfS4/JtfOUq4/sFuDSUM8CWxfJJzGw75S2SdB0uaafGYh8MI9stbz52tXp7urlo3sy8daYvPOnQkupI53tNBlyz4rGiuzk6lyKPz2WoV534+cfPIDNhfHzLdKnicyX1/cyj9q/5fgCVKmeg0WjV/LdxLq6cAPyQpI+wY1bkg0QLE50rSaFLm5kujaaF1EP8w7RQObt8K/KPXnT7aFvS7LuFx3qqiKsTj89lqFf1n+zzpVlFpFDf5xyw55j2tKoqVaOZqojs27MLDzz9wmjbThNEpksMDW8fPZdXEXFpKbrIcfRA6RXAl4FjicYPPwPONbOnCz6wSfkix/ortLAoqWpItXq6M+MWRVYaRPMtxiyUwy3g4UUnVtBr58YqtJAm3N828dljc3rqtZjT16G4TlXVIsesEKjPSK1XruPk25RFwDGHTOGalMrrxWVnTnLP+eJwZQWjktJBkngen6sXj88un3pthOWbWjk3XtEBtqSPmdl/SPoKecYTZvaRmvTMtZRSN2VZ+egmrlnx2Oj/SAbcuGqAnkmZVDc/yH6IJOV9V5K+UWhlee5MdrPs5ujam8dnV4hvhOVc45Qyg/2H8K9fs3N55aZOZKuDAOMC+R33bxw3ChgaHmHnnSbQnekaNxgu1eRJGSZN3IknBofomZTBDM5bsjpxhrmSMkxJOY29sXKD/iHm6szjsyvIZ5eda4xS6mDfHP69svbdca2onFnipIHt4NAwPd0ZXhweKasuNUSzx9mc6nx50vmUkr6ROyt/zCFTWPKbx8dufjNBo4Np/xBz9ebxuT3zf0t9TZUcl90ufXDLcNu8X841o1JSRG6mQKqpmZ2cao9cyyln04ZCK9srWeQoolrW8UuhxQbXpaRv5JuVX/Kbx8dv95tQYbAdP/Rd8+n0+FzO1bNWUeprqvS4eJxth/fLuWZVSnnKzwNfAB4GhoBvhp/ngQdr1zXXKsrZtCGpLmwlenu6ufT0WVw0b+ZoW7E86Wyd62IfJnl3l9xujOTUmBoeMRYvXzemLWlDHa+L7Wqgo+Nzoatn1errH+DoRbczfcEtHL3o9rr9/Zb6mqo5rthjnHPVKyVF5OcAkr6QU4rkZkme9+fKX6mewvj6kYTyd4XypMspS1VOjnbusWkurHSukE6Pz7Xa8ryRM+OlvqZqjyv3GOdcecrZYGlXSQdmb0iaDuyafpdcq5k3uzfvboj5PogWL19X9YYyvQXyp9Paoa2cEnu5x9bqQ9+5AmoSnyV1SeqX9MPs80q6W9J6SUskTQztO4fb68P902LPcUFoXyfp+Gr7FFerLc9rOTNeTKmvqdrjyj3GOVeecgbY5wF3SrpT0s+BO4Bza9Ir13Lmze7lrgXH8vCiE7lrwbGJC22q3Vms2GC5nMF+IfkG6pmw61ex/tTqQ9+5AmoVn/+VHZVKAD4HXGpmrwQ2A+8L7e8DNof2S8NxSHoVUX3uQ4ETgP+UlNp+3rXa8ryRX5JLfU3VHFfsMc656pWz0cyPJR0MHBKa7jezl2rTLdduspdcy7XrxC56Jk0sa7FgGhU9kurH5mvLPVe9NndwLqsW8VnSfsCJwMXARyWJaKfIfwiHXAksJNqe/ZTwO8ANwFfD8acA14W+PCxpPfBa4NfV9C2rVnWek1LN6vEludTXVOlxXkXEufooeYAtaRLwUeAAM3u/pIMlzTCzH9aue64V5augUUp1j3y2bB1h7WfS29K3HEkD9VIG+OCbO7j6qVF8vgz4GLBbuP1yYNDMtoXbG4Ds/9S9wOMAZrZN0rPh+F5gRew5449JRS1KZDb6S3Kprynt45xz6Sl5gA18B1gF/HW4PQBcD1Q1wA6XC1cCA2b25pA7eB1RcF4FvNPMtkraGbgKOBJ4BjjdzB4Jz3EB0SXKEeAjZra8mj65yuVbHHTuktUVP1+rplX4B5qrs1Tjs6Q3A0+b2SpJ/3979x8lV1nfcfz9ZbPABiub1JSGTWJCSjknlDaBrQkntKWgBIJHIqcVom2JttJKWkERTZRTtEWJ4lFAPSACNmgMUIyrFVoOgh5plcBCAiFgJCQBskUJSvBHYgnh2z/uM8nsZGbn171zn5n5vM6Zs3eee+fe7z67+93vPPPce09OI8AqxzsfOB9g2rRpWR+uKr1JFpFm1VNgz3T3c8xsMYC77wofATarMMfvteF5YY7fLWZ2HUnhfC1Fc/zM7Nyw3Tklc/yOBL5jZr/v7o3dElCaUu9ItRl4hXMeNa1CpGZp5+f5wFvMbCFwKEl+vhroN7NxYRR7CkkhT/g6FdhuZuOAw0kGQgrtBcWv2cfdrweuBxgcHGzuLOiU6E2yiDSjnpMcXzazPsJNDcxsJpDWHL8bwvPCHL/bwyYrgUVh+azwnLD+1NI5fu6+FSjM8ZMMVLs2bD0nAfX19vDZt81m24oz2bbiTK46Z3bTJyeKdKlU87O7L3f3Ke4+nWQA4153fwfJyZN/ETY7D/hmWP5WeE5Yf6+7e2g/N1xlZAZwNPBAo3GJiLSLekawLwP+C5hqZqtIRjiWNHn8q2iDOX6SqHZt2KF1Ixxkxt5KQ9JFBsp85KoRI5GGZZGfy/kQcIuZXQ6sA24M7TcCXwknMf6cpCjH3Tea2W3A48ArwFJ9uigi3aCmAtvMDgImAGcD80huFXKhu7/Q6IFbPccvHDOqeX7tptq1YZev2VBTcQ3UddMXEaksi/xczN2/B3wvLG+hzCeE7v4b4C8rvP7jJFciERHpGjUV2O7+qpl90N1vA+5I6dgtneMXvo/o5vm1k7GuDdvoVUJEpDkZ5WcREWlCPXOwv2NmHzCzqWY2sfBo9MCa4xe/0vnW/eN7y253eF9vXTeQ+at5+vRAJGWp5mcREWlOPXOwzwlflxa1OXBUmW2boTl+ESg337pwJ8PiW533HmT8+uVXKu3mAAcBg6/X/32RlLUqP4uISA3quZPjjKyC0By/OBTfIKbcyYp7XnX6+3o57JBx+64Nu+vlV3hx156y+zPCJQ2KvEoyl1snM4qkJ8v8LCIi9avnTo6HAhcAJ5HUTfcB14XCV1qo3J0Smy1YS0esK52s+NLuPay/7LR9z2csqzzls9Ik93ou5Sci1Sk/i4jEpZ4pIjcDvwQ+F56/HfgKFUaVJRvVLpXXqFpPUiy9s2L/+N6yI9gTxvcy/uBxZedmF+8jizcLIl1I+VlEJCL1FNh/4O6zip5/18weTzugblNvgTnWpfKaKUxrGVUud2fFSlfl+02FYr14H1m9WRDpQsrPIiIRqecqIg+b2bzCEzObCwynH1Kcqt3BsNF9Ll+zgZGdu3H2F5hj7XusS+U1o3RkuqDHbMw7K760u/z86917Xj1gZLu/r3fUPqpdV1tEatbV+VlEJDb1jGCfAPzAzJ4Jz6cBm8xsA+Du/oepRxeJVk7LqDYafWR/X9VpF424ZMExo75HSEabq92uvFI85Rx2yLhR+8rqzYJIF+ra/CwiEqN6CuzTM4sicq2eljFWgVmpEC6dulGv4lHleuZDl4unktLvK6s3CyJdqGvzs4hIjOq5TN/TWQYSsyynZdRbYDZaCNdi0ZyBA/ZTbY54uXgqXbrvIDOG1o3se01WbxZEuk0352cRkRjVM4LdtVo9LaNagVmuEM5CrVNjSuMpfV3BXvdRr8/yzYKIiIhIXlRg1yC2aRlpqOXqJY1OjSmsu/i2Rw64nnbp61v1ZkFERESkVVRg16DV0zKyVuvIdDNTYxbNGeB9t65v+PUiIiIi7UoFdo06aaS11pHpZqfG6CRGERER6Ub1XAdbOkStI9OXLDiGvt6eUW31TI1p9vUiIiIi7Ugj2BFp1W3Dax1ZbnZqjE5iFBERkW6kAjsSrbxteD0nbTY7NaaTptaIiIiI1EJTRCLRytuGL5ozwBVnH8dAf9+Yt0EXERERkfppBDsSrb5tuEaWRaQSM5sK3AwcAThwvbtfbWYTgVuB6cA24G3u/qKZGXA1sBDYBSxx94fDvs4DLg27vtzdV7byexERyYNGsCNR6coatVxxY2jdCPNX3MuMZXcwf8W9DK0bSTs8EekurwAXu/ssYB6w1MxmAcuAe9z9aOCe8BzgDODo8DgfuBYgFOSXAXOBNwCXmdmEVn4jIiJ5yK3ANrOpZvZdM3vczDaa2YWhfaKZ3W1mT4avE0K7mdk1ZrbZzB41s+OL9nVe2P7JMFoShXoK30avuFGYuz2yczfO/rnbnVhkXzq0gZnL72T6sjuYufxOLh3akHdIIh3J3Z8rjEC7+y+BJ4AB4CygMAK9ElgUls8CbvbE/UC/mU0GFgB3u/vP3f1F4G7g9NZ9JyIi+chzBLujR0jqLXwbnRfdyrnbebp0aANfvf+ZfXeG3OvOV+9/RkW2SMbMbDowB1gLHOHuz4VVPyGZQgJJ8f1s0cu2h7ZK7SIiHS23OdghST8Xln9pZsUjJCeHzVYC3wM+RNEICXC/mRVGSE4mjJAAmFlhhGR1y76ZMhq5zXgj86JbPXc7L6vXPlux/fJFx7U4GpHuYGavAb4OXOTuv0imWifc3c3MUzrO+SQDJ0ybNi2NXYqI5CqKOdidOELSqsK3mbnb7aQwcl1ru4g0x8x6SYrrVe6+JjT/NAxsEL4+H9pHgKlFL58S2iq1j+Lu17v7oLsPTpo0Kd1vREQkB7kX2KUjJMXrwmh1ahWUmZ1vZsNmNrxjx460dltWqwrfbrlbYk/RyFkt7SLSuHBVkBuBJ9z9M0WrvgUUznM5D/hmUfvfhHNl5gEvhYGSu4DTzGxCmLp3WmgTEelouRbYrRwhgdaOkrSq8O2Wa1ovnju1rnYRacp84K+BU8xsfXgsBFYAbzKzJ4E3hucAdwJbgM3Al4ALAMLUvX8FHgyPfylM5xMR6WS5zcGuYYRkBQeOkPyjmd1CckLjS+7+nJndBXyi6MTG04Dlacb6ji/9kP95av//hPkzJ7Lq3SeO+ZpW3ia8G65pXZhnvXrts+x1p8eMxXOnav61SAbc/b+BSh8PnVpmeweWVtjXTcBN6UUnIhI/85zmsJrZScB9wAbg1dD8YZJ52LcB04CnSW5k8PNQkH+e5ATGXcA73X047Otd4bUAH3f3L1c7/uDgoA8PD1eNs7S4LqilyBYRSYOZPeTug3nH0Qq15mYRkbyNlZvzvIpIW4yQlCuux2oXERERke6W+0mOIiIiIiKdRAW2iIiIiEiKVGBXMX/mxLraRURERKS7qcCuYtW7TzygmNYJjiIiIiJSSW4nObYTFdMiIiIiUiuNYIuIiIiIpEgj2DUaWjfSkpvGiIiIiEh7U4Fdg6F1Iyxfs4Hde/YCMLJzN8vXbABQkS0iIiIio2iKSA2uvGvTvuK6YPeevVx516acIhIRERGRWKnArsH/7txdV7uIiIiIdC8V2DU4sr+vrnYRERER6V6ag12DSxYcM2oONkBfbw+XLDgmx6hERCQmWZ0Mf+nQBlavfZa97vSYsXjuVC5fdFwKEYtIVlRg16CQIHUVERERKSerk+EvHdrAV+9/Zt/zve77nqvIFomXCuwaLZozoIJaRETKGutk+Gb+d6xe+2zFdhXYIvFSgS0iIm0rlnsUZHUy/F73utpFJA46yVFERNpSYVrGyM7dOPunZQytG2l5LFmdDN9jVle7iMRBBbaIiLSlmO5RcMmCY+jr7RnVlsbJ8IvnTq2rXUTi0DFTRMzsdOBqoAe4wd1XpLXvWD6CrKaZOPP6Htulb0UkPjHdo6DRk+Gr5cDCPGtdRUSkvZh3wDwuM+sBfgy8CdgOPAgsdvfHK71mcHDQh4eHq+57aN0IF926vuy6bSvObCTcTJSewQ7J6MkVZydJeKwEPtZrsyx28zquSLsxs4fcfTDvOBpVzwBIrbkZYObyO9hb5l+YAVtTzM9ZDQQoB4q0t7Fyc6dMEXkDsNndt7j7y8AtwFlp7LhScQ0wfdkdaRwiFZU+Kv3Yf2ysOkcxr49ZY/p4V0SyEQZAvgCcAcwCFpvZrDT2Xa64BnBIbR52lvO8lQNFOlenFNgDQPG1jLaHtq5R6SPRF3ftqZrA8/qYNaaPd0UkM5kNgIwlrSI1yyJYOVCkc3VKgV0TMzvfzIbNbHjHjh15h5Oqes9UL07ged0KXregF+kKVQdAssjNaRWpWRbByoEinatTCuwRoPiU6imhbRR3v97dB919cNKkSS0LrhUqncHe39dbdvviBJ7V2e/V5HVcEYlLFrk5rSI1yyJYOVCkc3VKgf0gcLSZzTCzg4FzgW/lHFNLLZozwBVnH8dAfx8GDPT3ccXZx/HRtxxbNYFXem3WJ9nkdVwRaamaBkDSllaRmmURrBwo0rk64ioiAGa2ELiK5Cz1m9z942NtX8+Z6pVOZozpKiJj0aXwRNpbO19FxMzGkVzl6VSSwvpB4O3uvrHc9vXkZiifn686Z3aqOU45VETKGSs3d0yBXa96k7iISF7aucCG+gZAlJtFpF2MlZs75kYzIiISJ3e/E7gz7zhERFqlU+Zgi4iIiIhEQQW2iIiIiEiKVGCLiIiIiKSoa09yNLMdwNN1vux1wAsZhJOGWGOLNS6IN7ZY44J4Y4s1Lkgntte7e2ddvL+CBnMzxPs7EGtcEG9sscYF8cYWa1wQb2yZ5uauLbAbYWbDsZ7JH2tsscYF8cYWa1wQb2yxxgVxx9ZJYu3nWOOCeGOLNS6IN7ZY44J4Y8s6Lk0RERERERFJkQpsEREREZEUqcCuz/V5BzCGWGOLNS6IN7ZY44J4Y4s1Log7tk4Saz/HGhfEG1uscUG8scUaF8QbW6ZxaQ62iIiIiEiKNIItIiIiIpIiFdg1MrPTzWyTmW02s2UtON5UM/uumT1uZhvN7MLQPtHM7jazJ8PXCaHdzOyaEN+jZnZ80b7OC9s/aWbnpRRfj5mtM7Nvh+czzGxtOP6tZnZwaD8kPN8c1k8v2sfy0L7JzBakFFe/md1uZj8ysyfM7MSI+ux94Wf5mJmtNrND8+g3M7vJzJ43s8eK2lLrIzM7wcw2hNdcY2bWZGxXhp/no2b2DTPrr9YXlf5eK/V3I3EVrbvYzNzMXpdHn3W7Sj/rjI+p/NxYXFHmZ4skN4d9RJmfK8SVe26uFFvRuvzys7vrUeUB9ABPAUcBBwOPALMyPuZk4Piw/FvAj4FZwKeAZaF9GfDJsLwQ+E/AgHnA2tA+EdgSvk4IyxNSiO/9wNeAb4fntwHnhuXrgPeE5QuA68LyucCtYXlW6MdDgBmhf3tSiGsl8Hdh+WCgP4Y+AwaArUBfUX8tyaPfgD8FjgceK2pLrY+AB8K2Fl57RpOxnQaMC8ufLIqtbF8wxt9rpf5uJK7QPhW4i+S6za/Lo8+6+THWzzrj4yo/NxZXdPmZiHJz2E+U+blCXLnn5kqxhfZc83OmSahTHsCJwF1Fz5cDy1scwzeBNwGbgMmhbTKwKSx/EVhctP2msH4x8MWi9lHbNRjLFOAe4BTg2+GX7oWiP7R9/RV+uU8My+PCdlbah8XbNRHX4SSJ0kraY+izAeDZ8Mc7LvTbgrz6DZjO6ESZSh+FdT8qah+1XSOxlax7K7AqLJftCyr8vY71e9poXMDtwB8B29ifwFveZ936qPSzziEO5efqcUWZn4ksN4fXjso1afVRs7mmNK6Sdbnl5kqxkXN+1hSR2hT+AAu2h7aWCB9BzQHWAke4+3Nh1U+AI6rEmEXsVwEfBF4Nz38b2Onur5Q5xr7jh/Uvhe2ziGsGsAP4siUfj95gZocRQZ+5+wjwaeAZ4DmSfniIOPoN0uujgbCcdnwF7yIZQWgktrF+T+tmZmcBI+7+SMmq2Pqsk+Wam0H5uQ5R5uc2yM3QHvk5mtwMceRnFdiRM7PXAF8HLnL3XxSv8+TtlLc4njcDz7v7Q608bo3GkXxMdK27zwF+TfJx2j559BlAmDN3Fsk/mSOBw4DTWx1HLfLqo2rM7CPAK8CqCGIZD3wY+Oe8Y5H8KD/XJcr83E65GeLMzzHlZognP6vArs0IyVyegimhLVNm1kuSvFe5+5rQ/FMzmxzWTwaerxJj2rHPB95iZtuAW0g+hrwa6DezcWWOse/4Yf3hwM8yiAuSd5bb3X1teH47SULPu88A3ghsdfcd7r4HWEPSlzH0G6TXRyNhOdX4zGwJ8GbgHeEfTCOx/YzK/V2vmST/kB8JfwtTgIfN7HcbiCuTPusSueRmUH5uQKz5OfbcDBHn5whzM8SSnxuZH9RtD5J33lvCD6wwMf/YjI9pwM3AVSXtVzL6ZIdPheUzGT1x/4HQPpFk3tuE8NgKTEwpxpPZfxLNvzP6BIULwvJSRp8QcltYPpbRJ0FsIZ2TaO4DjgnLHw39lXufAXOBjcD4cLyVwD/l1W8cOMcvtT7iwBNCFjYZ2+nA48Ckku3K9gVj/L1W6u9G4ipZt439c/xa3mfd+hjrZ53xcZWfG4spuvxMZLk57Gs6EebnMnFFkZvLxVaybhs55OdMk1AnPUjOPP0xyRmwH2nB8U4i+RjoUWB9eCwkmat0D/Ak8J2iXwADvhDi2wAMFu3rXcDm8HhnijGezP4EflT4Jdwc/lAOCe2Hhuebw/qjil7/kRDvJlK6agIwGxgO/TYU/lCi6DPgY8CPgMeAr4Tk0/J+A1aTzDXcQzKq9Ldp9hEwGL7Hp4DPU3JSUwOxbSaZG1f4O7iuWl9Q4e+1Un83ElfJ+m3sT+At7bNuf1T6WWd8TOXnxmKaTYT5mUhyc9hHlPm5Qly55+ZKsZWs30YO+Vl3chQRERERSZHmYIuIiIiIpEgFtoiIiIhIilRgi4iIiIikSAW2iIiIiEiKVGCLiIiIiKRIBbZ0FTP7VZm2j5rZLjP7nXLbmdkRZvY1M9tiZg+Z2Q/N7K1l9jPdzHab2fqix8FmtsTMPp/ddyUi0v6Un6WTqMAWSbwAXFzaaGZGcr3W77v7Ue5+AsnNBaaUbhs85e6zix4vZxaxiEh3UH6WtqMCWyRxE3COmU0saT8FeNndrys0uPvT7v65Zg9oZu83s8fC46LQdomZvTcsf9bM7g3Lp5jZqmaPKSLShpSfpe2owBZJ/IokiV9Y0n4s8HAd+5lZ9PHjFyptZGYnAO8kuU3vPODdZjaH5FbCfxI2GwReY2a9oe37dcQhItIplJ+l7YzLOwCRiFwDrDezT1faICTlk0hGTf64zCZPufvsGo51EvANd/912O8akiR9LXCCmb0W+D+Sfx6DYd176/heREQ6ifKztBWNYIsE7r4T+BqwtKh5I3B80TZLgVOBSRnFsAfYCiwBfkAyYvLnwO8BT2RxTBGR2Ck/S7tRgS0y2meAv2f/pzv3Aoea2XuKthmfwnHuAxaZ2XgzOwx4a2grrPsAyUeO9wH/AKxzd0/huCIi7Ur5WdqGCmzpNuPNbHvR4/3FK939BeAbwCHhuQOLgD8zs61m9gCwEvhQncddUnxc4Hng34AHgLXADe6+Lmx7HzAZ+KG7/xT4DfuTu4hIp1J+lo5hetMlIiIiIpIejWCLiIiIiKRIBbaIiIiISIpUYIuIiIiIpEgFtoiIiIhIilRgi4iIiIikSAW2iIiIiEiKVGCLiIiIiKRIBbaIiIiISIr+HwuAqXwVMo+mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize = (10, 7),  constrained_layout = True)\n",
    "names = iter(y_test_predict.keys())\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        name = next(names)\n",
    "        axes[i,j].scatter(y_test.iloc[:, 0], y_test_predict[f\"{name}\"][:, 0])\n",
    "        axes[i, j].set_title(f\"{name}\")\n",
    "        axes[i, j].set_xlabel(\"LNG FLow\")\n",
    "        axes[i, j].set_ylabel(\"predicted LNG Flow\")\n",
    "plt.savefig(\"multi-output-intuition-LNG-Flow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dee344f6-e442-431e-bbb2-9539cbfc8ab6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "52d7c9e3-538e-46b1-9ce9-87400b5bd484",
   "metadata": {},
   "source": [
    "## Using Data gotten from sklearn selectd features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4d522ae-0288-4894-aeb0-c79fbdb0143c",
   "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>LNG Flow</th>\n",
       "      <th>LNG Rundown Temperature</th>\n",
       "      <th>LNG Shell Temp Top Bundle</th>\n",
       "      <th>LMR JT Valve Valve Opening</th>\n",
       "      <th>Volumetric MR Flow to MR comp. discharge</th>\n",
       "      <th>HMR/MR Ratio</th>\n",
       "      <th>MCHE Warm Bypass Temperature</th>\n",
       "      <th>MR Vessel Level</th>\n",
       "      <th>High High Pressure Propane/Mixed Refrigerant kettle level</th>\n",
       "      <th>High Pressure Propane/Mixed Refrigerant kettle level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12470.894531</td>\n",
       "      <td>-145.712891</td>\n",
       "      <td>0.727539</td>\n",
       "      <td>53.137032</td>\n",
       "      <td>57909.148438</td>\n",
       "      <td>5.310718</td>\n",
       "      <td>-42.124023</td>\n",
       "      <td>25.312500</td>\n",
       "      <td>71.503906</td>\n",
       "      <td>72.011719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12109.980469</td>\n",
       "      <td>-145.844727</td>\n",
       "      <td>0.693359</td>\n",
       "      <td>53.687386</td>\n",
       "      <td>58373.812500</td>\n",
       "      <td>5.283063</td>\n",
       "      <td>-44.075195</td>\n",
       "      <td>26.765625</td>\n",
       "      <td>72.187500</td>\n",
       "      <td>71.443359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12578.753906</td>\n",
       "      <td>-145.966797</td>\n",
       "      <td>0.742188</td>\n",
       "      <td>53.676559</td>\n",
       "      <td>57913.492188</td>\n",
       "      <td>5.322514</td>\n",
       "      <td>-41.948242</td>\n",
       "      <td>27.669922</td>\n",
       "      <td>71.998047</td>\n",
       "      <td>71.568359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11478.726562</td>\n",
       "      <td>-145.561523</td>\n",
       "      <td>0.561523</td>\n",
       "      <td>42.709133</td>\n",
       "      <td>56186.050781</td>\n",
       "      <td>6.037915</td>\n",
       "      <td>-44.016602</td>\n",
       "      <td>30.724609</td>\n",
       "      <td>72.734375</td>\n",
       "      <td>72.949219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11518.482422</td>\n",
       "      <td>-145.175781</td>\n",
       "      <td>0.634766</td>\n",
       "      <td>38.990089</td>\n",
       "      <td>57086.832031</td>\n",
       "      <td>6.156987</td>\n",
       "      <td>-44.965820</td>\n",
       "      <td>34.058594</td>\n",
       "      <td>73.919922</td>\n",
       "      <td>72.933594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LNG Flow  LNG Rundown Temperature  LNG Shell Temp Top Bundle  \\\n",
       "0  12470.894531              -145.712891                   0.727539   \n",
       "1  12109.980469              -145.844727                   0.693359   \n",
       "2  12578.753906              -145.966797                   0.742188   \n",
       "3  11478.726562              -145.561523                   0.561523   \n",
       "4  11518.482422              -145.175781                   0.634766   \n",
       "\n",
       "   LMR JT Valve Valve Opening  Volumetric MR Flow to MR comp. discharge  \\\n",
       "0                   53.137032                              57909.148438   \n",
       "1                   53.687386                              58373.812500   \n",
       "2                   53.676559                              57913.492188   \n",
       "3                   42.709133                              56186.050781   \n",
       "4                   38.990089                              57086.832031   \n",
       "\n",
       "   HMR/MR Ratio  MCHE Warm Bypass Temperature  MR Vessel Level  \\\n",
       "0      5.310718                    -42.124023        25.312500   \n",
       "1      5.283063                    -44.075195        26.765625   \n",
       "2      5.322514                    -41.948242        27.669922   \n",
       "3      6.037915                    -44.016602        30.724609   \n",
       "4      6.156987                    -44.965820        34.058594   \n",
       "\n",
       "   High High Pressure Propane/Mixed Refrigerant kettle level  \\\n",
       "0                                          71.503906           \n",
       "1                                          72.187500           \n",
       "2                                          71.998047           \n",
       "3                                          72.734375           \n",
       "4                                          73.919922           \n",
       "\n",
       "   High Pressure Propane/Mixed Refrigerant kettle level  \n",
       "0                                          72.011719     \n",
       "1                                          71.443359     \n",
       "2                                          71.568359     \n",
       "3                                          72.949219     \n",
       "4                                          72.933594     "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d707f2-e7f4-4c54-9a57-33ba68ee3924",
   "metadata": {},
   "source": [
    "#### Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "81f03720-36b6-40d5-930c-48a302d674f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_input = df2.iloc[:, 2:].copy()\n",
    "df2_target = df2.iloc[:, 0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fdddf221-8f37-4a46-a6cf-a1a485fb074c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df2_input, df2_target, test_size = 0.25, random_state= 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1be745a-34ce-42e2-8cc4-963383ada5f8",
   "metadata": {},
   "source": [
    "### Model building for unioutput regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "245ae407-01a7-4813-a402-06c3a0852f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression-->MAE: 806.9148, 16.6448 , R2-score = 0.8086, , MSE = 1134.2457\n",
      "K-neighbors-->MAE: 188.4875, 5.9081 , R2-score = 0.9821, , MSE = 346.5597\n",
      "Decision Tree-->MAE: 224.5621, 6.1217 , R2-score = 0.9800, , MSE = 366.9459\n",
      "Random Forest-->MAE: 674.1269, 15.8442 , R2-score = 0.8391, , MSE = 1039.9041\n"
     ]
    }
   ],
   "source": [
    "y_test_predict, y_error = pipe_reg(ESTIMATORS, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40fcfb2c-6b2a-4f29-a86d-3e4dfba963c2",
   "metadata": {},
   "source": [
    "|Algorithm|R2-score|RMSE|\n",
    "|------|------|------|\n",
    "|Linear Regressor|0.8086|1134.2457\n",
    "|K-neighbors|0.9821|346.5597\n",
    "|Decision Tree|0.9791|374.7349\n",
    "|Random Forest|0.8391|1039.9041"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd55ca1-7980-48e5-87f8-4fe27f8e4ebb",
   "metadata": {},
   "source": [
    "#### visualise modelperformance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e136644-6839-4e36-8cf2-ec5f1e04d1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize = (10, 7),  constrained_layout = True)\n",
    "names = iter(y_test_predict.keys())\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        name = next(names)\n",
    "        axes[i,j].scatter(y_test, y_test_predict[f\"{name}\"])\n",
    "        axes[i, j].set_title(f\"{name}\")\n",
    "        axes[i, j].set_xlabel(\"LNG Flow\")\n",
    "        axes[i, j].set_ylabel(\"predicted LNG Flow\")\n",
    "plt.savefig(\"unioutput-sklearn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ece44c3f-792e-4448-b56d-dfea43e6490a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa3e2bb0-2cb6-46ca-9bd4-0939b44b6684",
   "metadata": {},
   "source": [
    "### Model building for multioutput regressor\n",
    "\n",
    "Here we are going to analyze models that can predict both LNG Rundown Temperature, whose results can then be used to predict the LNG Flow."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c496ab2b-9741-4d80-8194-6ab7e7dcb737",
   "metadata": {},
   "source": [
    "#### Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "26e51292-f175-46aa-95cf-fa00e6a290fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_input = df2.iloc[:, 2:].copy()\n",
    "df2_target = df1.iloc[:, [0, 1]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dd737359-50e3-45e2-99b4-0b3228dad273",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df2_input, df2_target, test_size = 0.25, random_state= 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a78830-c022-41b3-a85b-474ee880b76e",
   "metadata": {},
   "source": [
    "#### Multioutput model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4aa52b75-0d47-4b78-9e0e-b1f17c4a21ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression-->MAE: 405.4878, 8.3535 , R2-score = 0.8357\n",
      ", MSE = 570.8415\n",
      "K-neighbors-->MAE: 94.6559, 2.9558 , R2-score = 0.9818\n",
      ", MSE = 174.6468\n",
      "Decision Tree-->MAE: 112.2803, 3.1986 , R2-score = 0.9833\n",
      ", MSE = 189.4457\n",
      "Random Forest-->MAE: 337.7967, 7.9459 , R2-score = 0.8647\n",
      ", MSE = 523.2764\n"
     ]
    }
   ],
   "source": [
    "y_test_predict, y_error = pipe_reg_multi(ESTIMATORS, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6115c4a4-9084-4ddb-a81a-4f39fab154d6",
   "metadata": {},
   "source": [
    "|Algorithm|R2-score|RMSE|\n",
    "|------|------|------|\n",
    "|Linear Regressor|0.5862|569.9988\n",
    "|K-neighbors|0.8346|175.2968\n",
    "|Decision Tree|0.8861|187.3043\n",
    "|Random Forest|0.6911|522.3893"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99841a5e-c298-4da9-8ca8-a62acda110c3",
   "metadata": {},
   "source": [
    "#### visualizs model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7dcb9ff-ccef-4250-ab17-8fb266f84be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize = (10, 7),  constrained_layout = True)\n",
    "names = iter(y_test_predict.keys())\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        name = next(names)\n",
    "        axes[i,j].scatter(y_test.iloc[:, 1], y_test_predict[f\"{name}\"][:, 1])\n",
    "        axes[i, j].set_title(f\"{name}\")\n",
    "        axes[i, j].set_xlabel(\"LNG Rundown Temperature\")\n",
    "        axes[i, j].set_ylabel(\"predicted LNG Rundown Temperature\")\n",
    "plt.savefig(\"multi-output-sklearn-LNG-Temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d091951c-96f1-4f33-a536-064dfaf42fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize = (10, 7),  constrained_layout = True)\n",
    "names = iter(y_test_predict.keys())\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        name = next(names)\n",
    "        axes[i,j].scatter(y_test.iloc[:, 0], y_test_predict[f\"{name}\"][:, 0])\n",
    "        axes[i, j].set_title(f\"{name}\")\n",
    "        axes[i, j].set_xlabel(\"LNG FLow\")\n",
    "        axes[i, j].set_ylabel(\"predicted LNG Flow\")\n",
    "plt.savefig(\"multi-output-sklearn-LNG-Flow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1dca3b5-23d1-40fb-8c7e-5bbe17fa054d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}