Tesla_Stock_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3595fc5-e716-4fb5-ba31-31e229a28096",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a71161fa-4a2c-4a66-af8e-11221b9d17fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Tesla_Stock_Price.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b6bd010-8860-477f-b4e0-bcc5afe1c73e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi_7</th>\n",
       "      <th>rsi_14</th>\n",
       "      <th>cci_7</th>\n",
       "      <th>cci_14</th>\n",
       "      <th>sma_50</th>\n",
       "      <th>ema_50</th>\n",
       "      <th>sma_100</th>\n",
       "      <th>ema_100</th>\n",
       "      <th>macd</th>\n",
       "      <th>bollinger</th>\n",
       "      <th>TrueRange</th>\n",
       "      <th>atr_7</th>\n",
       "      <th>atr_14</th>\n",
       "      <th>next_day_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>9.986667</td>\n",
       "      <td>10.165333</td>\n",
       "      <td>9.770000</td>\n",
       "      <td>10.006667</td>\n",
       "      <td>92826000</td>\n",
       "      <td>55.344071</td>\n",
       "      <td>54.440118</td>\n",
       "      <td>-37.373644</td>\n",
       "      <td>15.213422</td>\n",
       "      <td>9.682107</td>\n",
       "      <td>9.820167</td>\n",
       "      <td>10.494240</td>\n",
       "      <td>9.674284</td>\n",
       "      <td>0.169472</td>\n",
       "      <td>9.740800</td>\n",
       "      <td>0.395333</td>\n",
       "      <td>0.402641</td>\n",
       "      <td>0.447550</td>\n",
       "      <td>9.970667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.146000</td>\n",
       "      <td>9.906667</td>\n",
       "      <td>9.970667</td>\n",
       "      <td>70425000</td>\n",
       "      <td>53.742629</td>\n",
       "      <td>53.821521</td>\n",
       "      <td>-81.304471</td>\n",
       "      <td>17.481130</td>\n",
       "      <td>9.652800</td>\n",
       "      <td>9.826069</td>\n",
       "      <td>10.495693</td>\n",
       "      <td>9.680190</td>\n",
       "      <td>0.162623</td>\n",
       "      <td>9.776167</td>\n",
       "      <td>0.239333</td>\n",
       "      <td>0.379311</td>\n",
       "      <td>0.432677</td>\n",
       "      <td>9.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.026667</td>\n",
       "      <td>9.682667</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>80416500</td>\n",
       "      <td>46.328174</td>\n",
       "      <td>50.870410</td>\n",
       "      <td>-123.427544</td>\n",
       "      <td>-37.824708</td>\n",
       "      <td>9.629467</td>\n",
       "      <td>9.825047</td>\n",
       "      <td>10.496740</td>\n",
       "      <td>9.682577</td>\n",
       "      <td>0.141790</td>\n",
       "      <td>9.797900</td>\n",
       "      <td>0.344000</td>\n",
       "      <td>0.374267</td>\n",
       "      <td>0.426343</td>\n",
       "      <td>9.957333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>9.841333</td>\n",
       "      <td>10.026667</td>\n",
       "      <td>9.683333</td>\n",
       "      <td>9.957333</td>\n",
       "      <td>75511500</td>\n",
       "      <td>53.263037</td>\n",
       "      <td>53.406750</td>\n",
       "      <td>-84.784651</td>\n",
       "      <td>-20.779431</td>\n",
       "      <td>9.597747</td>\n",
       "      <td>9.830235</td>\n",
       "      <td>10.503407</td>\n",
       "      <td>9.688051</td>\n",
       "      <td>0.136402</td>\n",
       "      <td>9.837900</td>\n",
       "      <td>0.343334</td>\n",
       "      <td>0.369848</td>\n",
       "      <td>0.420414</td>\n",
       "      <td>10.085333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>9.923333</td>\n",
       "      <td>10.246667</td>\n",
       "      <td>9.917333</td>\n",
       "      <td>10.085333</td>\n",
       "      <td>92448000</td>\n",
       "      <td>58.368660</td>\n",
       "      <td>55.423026</td>\n",
       "      <td>60.799662</td>\n",
       "      <td>43.570559</td>\n",
       "      <td>9.573240</td>\n",
       "      <td>9.840239</td>\n",
       "      <td>10.511147</td>\n",
       "      <td>9.695964</td>\n",
       "      <td>0.140837</td>\n",
       "      <td>9.870167</td>\n",
       "      <td>0.329334</td>\n",
       "      <td>0.364060</td>\n",
       "      <td>0.413908</td>\n",
       "      <td>9.835333</td>\n",
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      "text/plain": [
       "         date       open       high       low      close    volume      rsi_7  \\\n",
       "0  2014-01-02   9.986667  10.165333  9.770000  10.006667  92826000  55.344071   \n",
       "1  2014-01-03  10.000000  10.146000  9.906667   9.970667  70425000  53.742629   \n",
       "2  2014-01-06  10.000000  10.026667  9.682667   9.800000  80416500  46.328174   \n",
       "3  2014-01-07   9.841333  10.026667  9.683333   9.957333  75511500  53.263037   \n",
       "4  2014-01-08   9.923333  10.246667  9.917333  10.085333  92448000  58.368660   \n",
       "\n",
       "      rsi_14       cci_7     cci_14    sma_50    ema_50    sma_100   ema_100  \\\n",
       "0  54.440118  -37.373644  15.213422  9.682107  9.820167  10.494240  9.674284   \n",
       "1  53.821521  -81.304471  17.481130  9.652800  9.826069  10.495693  9.680190   \n",
       "2  50.870410 -123.427544 -37.824708  9.629467  9.825047  10.496740  9.682577   \n",
       "3  53.406750  -84.784651 -20.779431  9.597747  9.830235  10.503407  9.688051   \n",
       "4  55.423026   60.799662  43.570559  9.573240  9.840239  10.511147  9.695964   \n",
       "\n",
       "       macd  bollinger  TrueRange     atr_7    atr_14  next_day_close  \n",
       "0  0.169472   9.740800   0.395333  0.402641  0.447550        9.970667  \n",
       "1  0.162623   9.776167   0.239333  0.379311  0.432677        9.800000  \n",
       "2  0.141790   9.797900   0.344000  0.374267  0.426343        9.957333  \n",
       "3  0.136402   9.837900   0.343334  0.369848  0.420414       10.085333  \n",
       "4  0.140837   9.870167   0.329334  0.364060  0.413908        9.835333  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1797e0f3-bacd-4350-ad7d-c2bc46d881fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2516, 20)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0cd6631f-a67f-4f26-9930-55423665d7a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2516 entries, 0 to 2515\n",
      "Data columns (total 20 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   date            2516 non-null   object \n",
      " 1   open            2516 non-null   float64\n",
      " 2   high            2516 non-null   float64\n",
      " 3   low             2516 non-null   float64\n",
      " 4   close           2516 non-null   float64\n",
      " 5   volume          2516 non-null   int64  \n",
      " 6   rsi_7           2516 non-null   float64\n",
      " 7   rsi_14          2516 non-null   float64\n",
      " 8   cci_7           2516 non-null   float64\n",
      " 9   cci_14          2516 non-null   float64\n",
      " 10  sma_50          2516 non-null   float64\n",
      " 11  ema_50          2516 non-null   float64\n",
      " 12  sma_100         2516 non-null   float64\n",
      " 13  ema_100         2516 non-null   float64\n",
      " 14  macd            2516 non-null   float64\n",
      " 15  bollinger       2516 non-null   float64\n",
      " 16  TrueRange       2516 non-null   float64\n",
      " 17  atr_7           2516 non-null   float64\n",
      " 18  atr_14          2516 non-null   float64\n",
      " 19  next_day_close  2516 non-null   float64\n",
      "dtypes: float64(18), int64(1), object(1)\n",
      "memory usage: 393.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c8ec7d3-19e0-411c-a86f-0c411bb09fae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi_7</th>\n",
       "      <th>rsi_14</th>\n",
       "      <th>cci_7</th>\n",
       "      <th>cci_14</th>\n",
       "      <th>sma_50</th>\n",
       "      <th>ema_50</th>\n",
       "      <th>sma_100</th>\n",
       "      <th>ema_100</th>\n",
       "      <th>macd</th>\n",
       "      <th>bollinger</th>\n",
       "      <th>TrueRange</th>\n",
       "      <th>atr_7</th>\n",
       "      <th>atr_14</th>\n",
       "      <th>next_day_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2.516000e+03</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "      <td>2516.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94.098510</td>\n",
       "      <td>96.172733</td>\n",
       "      <td>91.865096</td>\n",
       "      <td>94.072491</td>\n",
       "      <td>1.131986e+08</td>\n",
       "      <td>53.058382</td>\n",
       "      <td>52.862457</td>\n",
       "      <td>9.809933</td>\n",
       "      <td>13.202457</td>\n",
       "      <td>91.810735</td>\n",
       "      <td>91.806751</td>\n",
       "      <td>89.542873</td>\n",
       "      <td>89.547927</td>\n",
       "      <td>0.647230</td>\n",
       "      <td>93.162246</td>\n",
       "      <td>4.699351</td>\n",
       "      <td>4.680234</td>\n",
       "      <td>4.656461</td>\n",
       "      <td>94.167250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>108.593936</td>\n",
       "      <td>111.022486</td>\n",
       "      <td>105.911918</td>\n",
       "      <td>108.500301</td>\n",
       "      <td>7.547433e+07</td>\n",
       "      <td>18.239752</td>\n",
       "      <td>13.352063</td>\n",
       "      <td>100.975002</td>\n",
       "      <td>109.285239</td>\n",
       "      <td>106.581797</td>\n",
       "      <td>106.094893</td>\n",
       "      <td>104.809940</td>\n",
       "      <td>103.979872</td>\n",
       "      <td>6.325994</td>\n",
       "      <td>107.718086</td>\n",
       "      <td>6.581929</td>\n",
       "      <td>5.858869</td>\n",
       "      <td>5.762082</td>\n",
       "      <td>108.530957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.366667</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>9.111333</td>\n",
       "      <td>9.289333</td>\n",
       "      <td>1.062000e+07</td>\n",
       "      <td>6.395305</td>\n",
       "      <td>16.564126</td>\n",
       "      <td>-233.333333</td>\n",
       "      <td>-297.930166</td>\n",
       "      <td>9.490973</td>\n",
       "      <td>9.813725</td>\n",
       "      <td>10.494240</td>\n",
       "      <td>9.674284</td>\n",
       "      <td>-25.271333</td>\n",
       "      <td>9.740800</td>\n",
       "      <td>0.135334</td>\n",
       "      <td>0.257427</td>\n",
       "      <td>0.315794</td>\n",
       "      <td>9.289333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.763167</td>\n",
       "      <td>16.082168</td>\n",
       "      <td>15.491167</td>\n",
       "      <td>15.814167</td>\n",
       "      <td>6.643185e+07</td>\n",
       "      <td>39.859440</td>\n",
       "      <td>43.595435</td>\n",
       "      <td>-76.876737</td>\n",
       "      <td>-78.543937</td>\n",
       "      <td>15.496080</td>\n",
       "      <td>15.446697</td>\n",
       "      <td>15.356488</td>\n",
       "      <td>15.594446</td>\n",
       "      <td>-0.392822</td>\n",
       "      <td>15.733792</td>\n",
       "      <td>0.496333</td>\n",
       "      <td>0.569335</td>\n",
       "      <td>0.577166</td>\n",
       "      <td>15.822167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.801001</td>\n",
       "      <td>22.198334</td>\n",
       "      <td>21.487666</td>\n",
       "      <td>21.877667</td>\n",
       "      <td>9.320775e+07</td>\n",
       "      <td>53.226417</td>\n",
       "      <td>51.621434</td>\n",
       "      <td>19.823624</td>\n",
       "      <td>24.702835</td>\n",
       "      <td>21.563733</td>\n",
       "      <td>21.575870</td>\n",
       "      <td>21.106043</td>\n",
       "      <td>21.195890</td>\n",
       "      <td>0.098391</td>\n",
       "      <td>21.859567</td>\n",
       "      <td>0.894001</td>\n",
       "      <td>0.833601</td>\n",
       "      <td>0.805047</td>\n",
       "      <td>21.881334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>200.017505</td>\n",
       "      <td>204.525829</td>\n",
       "      <td>194.482498</td>\n",
       "      <td>200.049999</td>\n",
       "      <td>1.323710e+08</td>\n",
       "      <td>65.900330</td>\n",
       "      <td>61.937068</td>\n",
       "      <td>94.426550</td>\n",
       "      <td>99.180514</td>\n",
       "      <td>192.341650</td>\n",
       "      <td>196.465729</td>\n",
       "      <td>194.728575</td>\n",
       "      <td>194.154595</td>\n",
       "      <td>1.094057</td>\n",
       "      <td>199.300335</td>\n",
       "      <td>7.700836</td>\n",
       "      <td>9.021824</td>\n",
       "      <td>9.291728</td>\n",
       "      <td>200.844997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>411.470001</td>\n",
       "      <td>414.496674</td>\n",
       "      <td>405.666656</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>9.140820e+08</td>\n",
       "      <td>97.460910</td>\n",
       "      <td>94.197983</td>\n",
       "      <td>233.333333</td>\n",
       "      <td>350.643337</td>\n",
       "      <td>357.870532</td>\n",
       "      <td>344.030730</td>\n",
       "      <td>330.935132</td>\n",
       "      <td>321.063884</td>\n",
       "      <td>38.067930</td>\n",
       "      <td>373.152498</td>\n",
       "      <td>54.326660</td>\n",
       "      <td>24.512656</td>\n",
       "      <td>22.793229</td>\n",
       "      <td>409.970001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open         high          low        close        volume  \\\n",
       "count  2516.000000  2516.000000  2516.000000  2516.000000  2.516000e+03   \n",
       "mean     94.098510    96.172733    91.865096    94.072491  1.131986e+08   \n",
       "std     108.593936   111.022486   105.911918   108.500301  7.547433e+07   \n",
       "min       9.366667     9.800000     9.111333     9.289333  1.062000e+07   \n",
       "25%      15.763167    16.082168    15.491167    15.814167  6.643185e+07   \n",
       "50%      21.801001    22.198334    21.487666    21.877667  9.320775e+07   \n",
       "75%     200.017505   204.525829   194.482498   200.049999  1.323710e+08   \n",
       "max     411.470001   414.496674   405.666656   409.970001  9.140820e+08   \n",
       "\n",
       "             rsi_7       rsi_14        cci_7       cci_14       sma_50  \\\n",
       "count  2516.000000  2516.000000  2516.000000  2516.000000  2516.000000   \n",
       "mean     53.058382    52.862457     9.809933    13.202457    91.810735   \n",
       "std      18.239752    13.352063   100.975002   109.285239   106.581797   \n",
       "min       6.395305    16.564126  -233.333333  -297.930166     9.490973   \n",
       "25%      39.859440    43.595435   -76.876737   -78.543937    15.496080   \n",
       "50%      53.226417    51.621434    19.823624    24.702835    21.563733   \n",
       "75%      65.900330    61.937068    94.426550    99.180514   192.341650   \n",
       "max      97.460910    94.197983   233.333333   350.643337   357.870532   \n",
       "\n",
       "            ema_50      sma_100      ema_100         macd    bollinger  \\\n",
       "count  2516.000000  2516.000000  2516.000000  2516.000000  2516.000000   \n",
       "mean     91.806751    89.542873    89.547927     0.647230    93.162246   \n",
       "std     106.094893   104.809940   103.979872     6.325994   107.718086   \n",
       "min       9.813725    10.494240     9.674284   -25.271333     9.740800   \n",
       "25%      15.446697    15.356488    15.594446    -0.392822    15.733792   \n",
       "50%      21.575870    21.106043    21.195890     0.098391    21.859567   \n",
       "75%     196.465729   194.728575   194.154595     1.094057   199.300335   \n",
       "max     344.030730   330.935132   321.063884    38.067930   373.152498   \n",
       "\n",
       "         TrueRange        atr_7       atr_14  next_day_close  \n",
       "count  2516.000000  2516.000000  2516.000000     2516.000000  \n",
       "mean      4.699351     4.680234     4.656461       94.167250  \n",
       "std       6.581929     5.858869     5.762082      108.530957  \n",
       "min       0.135334     0.257427     0.315794        9.289333  \n",
       "25%       0.496333     0.569335     0.577166       15.822167  \n",
       "50%       0.894001     0.833601     0.805047       21.881334  \n",
       "75%       7.700836     9.021824     9.291728      200.844997  \n",
       "max      54.326660    24.512656    22.793229      409.970001  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97489cef-4859-42f3-af23-cd82d2100768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date              0\n",
       "open              0\n",
       "high              0\n",
       "low               0\n",
       "close             0\n",
       "volume            0\n",
       "rsi_7             0\n",
       "rsi_14            0\n",
       "cci_7             0\n",
       "cci_14            0\n",
       "sma_50            0\n",
       "ema_50            0\n",
       "sma_100           0\n",
       "ema_100           0\n",
       "macd              0\n",
       "bollinger         0\n",
       "TrueRange         0\n",
       "atr_7             0\n",
       "atr_14            0\n",
       "next_day_close    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0536f2c-f775-433a-9dd5-e4a4893a6161",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df.set_index('date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f83ba559-4b66-4b2c-b84d-7294ed26bfac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi_7</th>\n",
       "      <th>rsi_14</th>\n",
       "      <th>cci_7</th>\n",
       "      <th>cci_14</th>\n",
       "      <th>sma_50</th>\n",
       "      <th>ema_50</th>\n",
       "      <th>sma_100</th>\n",
       "      <th>ema_100</th>\n",
       "      <th>macd</th>\n",
       "      <th>bollinger</th>\n",
       "      <th>TrueRange</th>\n",
       "      <th>atr_7</th>\n",
       "      <th>atr_14</th>\n",
       "      <th>next_day_close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>9.986667</td>\n",
       "      <td>10.165333</td>\n",
       "      <td>9.770000</td>\n",
       "      <td>10.006667</td>\n",
       "      <td>92826000</td>\n",
       "      <td>55.344071</td>\n",
       "      <td>54.440118</td>\n",
       "      <td>-37.373644</td>\n",
       "      <td>15.213422</td>\n",
       "      <td>9.682107</td>\n",
       "      <td>9.820167</td>\n",
       "      <td>10.494240</td>\n",
       "      <td>9.674284</td>\n",
       "      <td>0.169472</td>\n",
       "      <td>9.740800</td>\n",
       "      <td>0.395333</td>\n",
       "      <td>0.402641</td>\n",
       "      <td>0.447550</td>\n",
       "      <td>9.970667</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.146000</td>\n",
       "      <td>9.906667</td>\n",
       "      <td>9.970667</td>\n",
       "      <td>70425000</td>\n",
       "      <td>53.742629</td>\n",
       "      <td>53.821521</td>\n",
       "      <td>-81.304471</td>\n",
       "      <td>17.481130</td>\n",
       "      <td>9.652800</td>\n",
       "      <td>9.826069</td>\n",
       "      <td>10.495693</td>\n",
       "      <td>9.680190</td>\n",
       "      <td>0.162623</td>\n",
       "      <td>9.776167</td>\n",
       "      <td>0.239333</td>\n",
       "      <td>0.379311</td>\n",
       "      <td>0.432677</td>\n",
       "      <td>9.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.026667</td>\n",
       "      <td>9.682667</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>80416500</td>\n",
       "      <td>46.328174</td>\n",
       "      <td>50.870410</td>\n",
       "      <td>-123.427544</td>\n",
       "      <td>-37.824708</td>\n",
       "      <td>9.629467</td>\n",
       "      <td>9.825047</td>\n",
       "      <td>10.496740</td>\n",
       "      <td>9.682577</td>\n",
       "      <td>0.141790</td>\n",
       "      <td>9.797900</td>\n",
       "      <td>0.344000</td>\n",
       "      <td>0.374267</td>\n",
       "      <td>0.426343</td>\n",
       "      <td>9.957333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>9.841333</td>\n",
       "      <td>10.026667</td>\n",
       "      <td>9.683333</td>\n",
       "      <td>9.957333</td>\n",
       "      <td>75511500</td>\n",
       "      <td>53.263037</td>\n",
       "      <td>53.406750</td>\n",
       "      <td>-84.784651</td>\n",
       "      <td>-20.779431</td>\n",
       "      <td>9.597747</td>\n",
       "      <td>9.830235</td>\n",
       "      <td>10.503407</td>\n",
       "      <td>9.688051</td>\n",
       "      <td>0.136402</td>\n",
       "      <td>9.837900</td>\n",
       "      <td>0.343334</td>\n",
       "      <td>0.369848</td>\n",
       "      <td>0.420414</td>\n",
       "      <td>10.085333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>9.923333</td>\n",
       "      <td>10.246667</td>\n",
       "      <td>9.917333</td>\n",
       "      <td>10.085333</td>\n",
       "      <td>92448000</td>\n",
       "      <td>58.368660</td>\n",
       "      <td>55.423026</td>\n",
       "      <td>60.799662</td>\n",
       "      <td>43.570559</td>\n",
       "      <td>9.573240</td>\n",
       "      <td>9.840239</td>\n",
       "      <td>10.511147</td>\n",
       "      <td>9.695964</td>\n",
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       "      <td>9.870167</td>\n",
       "      <td>0.329334</td>\n",
       "      <td>0.364060</td>\n",
       "      <td>0.413908</td>\n",
       "      <td>9.835333</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high       low      close    volume      rsi_7  \\\n",
       "date                                                                         \n",
       "2014-01-02   9.986667  10.165333  9.770000  10.006667  92826000  55.344071   \n",
       "2014-01-03  10.000000  10.146000  9.906667   9.970667  70425000  53.742629   \n",
       "2014-01-06  10.000000  10.026667  9.682667   9.800000  80416500  46.328174   \n",
       "2014-01-07   9.841333  10.026667  9.683333   9.957333  75511500  53.263037   \n",
       "2014-01-08   9.923333  10.246667  9.917333  10.085333  92448000  58.368660   \n",
       "\n",
       "               rsi_14       cci_7     cci_14    sma_50    ema_50    sma_100  \\\n",
       "date                                                                          \n",
       "2014-01-02  54.440118  -37.373644  15.213422  9.682107  9.820167  10.494240   \n",
       "2014-01-03  53.821521  -81.304471  17.481130  9.652800  9.826069  10.495693   \n",
       "2014-01-06  50.870410 -123.427544 -37.824708  9.629467  9.825047  10.496740   \n",
       "2014-01-07  53.406750  -84.784651 -20.779431  9.597747  9.830235  10.503407   \n",
       "2014-01-08  55.423026   60.799662  43.570559  9.573240  9.840239  10.511147   \n",
       "\n",
       "             ema_100      macd  bollinger  TrueRange     atr_7    atr_14  \\\n",
       "date                                                                       \n",
       "2014-01-02  9.674284  0.169472   9.740800   0.395333  0.402641  0.447550   \n",
       "2014-01-03  9.680190  0.162623   9.776167   0.239333  0.379311  0.432677   \n",
       "2014-01-06  9.682577  0.141790   9.797900   0.344000  0.374267  0.426343   \n",
       "2014-01-07  9.688051  0.136402   9.837900   0.343334  0.369848  0.420414   \n",
       "2014-01-08  9.695964  0.140837   9.870167   0.329334  0.364060  0.413908   \n",
       "\n",
       "            next_day_close  \n",
       "date                        \n",
       "2014-01-02        9.970667  \n",
       "2014-01-03        9.800000  \n",
       "2014-01-06        9.957333  \n",
       "2014-01-07       10.085333  \n",
       "2014-01-08        9.835333  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75aba18-13dd-4a17-9aab-6a0056c0be4f",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ddf59fbd-6029-4426-bab3-af8ab3ba2921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18100db5690>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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cJ9/4KleGVU1cy7b8xgu6yaHZmDvDY1vVXTY64v7g4rhpUo3RCc3CDzusGLGazEGxfWmwMqBSpvFxQ9UhuW45bheWqIxoeT1NKkZqs907Eep5vquu+hkjCXaxS7m16WCkGFaY3Lr5LnlhKy0/+bLy8ftamzljj21rx3Ixx7aPblYxLgqS403yYQEmQDACAAAAAADQAq9JH/tbt96qsjMqJ/GF+64m3YeGy2OzuDJMZGRsu955RVzxUNXklT1zxco1BjR2LbSZoGJkxBlQKdsYejwx8oRctxIFI5rOgPkJKkYkadwZn/r5Zsg1XuPw9QS7WFR2TePwdUm69dEhudXghm/HP++oVZmCoEmSSvf+vD0LxpzaMb5N3Ym/s67XmZZv2LMRjAAAAAAAALTANGkp9MP7f6RvPPEr+Ynr7cUm1+gGSgQjnTBS3qnn3xNX8JTN/LgU1qyll10IAhHLb/wAZTxpzB1SWY3h3Bdv+6Kq7ngUZNjZqVfFNKsYqbXmGq3O/WfXM+6kwYjVVWyoGFkzEvx++nLlu43BiBKvsRYileZHToYp2l7amaoYcR0qRjB18+N/DQAAAAAAAOY5zzQO+L3i0cslxa15pImCkdHZWhYmMVIekJe4+F028+RKuN0YjGRqLbBWHth4nycZ+dphNZ/3sW1sa9xKaxrBSLOKkZwnyRgNdaDayTf+5BUj3UVlVqxI7VsaLtPIkecGD05VjFiW/DA9ioKRJoEQ5r8d1SEVq4kZI25lkqOB5vjtBwAAAAAAaMFkA36zXvNthXNJBqkY6YjhyrBGi/FtpzpPvlluNwY0dlcQjFiFxuHpRW/yQOeJkY3Rxf52VYxIwWd5oDwy5fPNlLub4etWoSDLtrXunM9qycknS5L6RiUZI1+OfDcIMf2697lW2VVrpVW2uDS6pzHGaIc7pt7E6BufGSOYBn77AQAAAAAAWrBtJD1roduOZzlkEl/kt40kY7Rk1Ojr/+nqr67zNFIemqNVImnEGdFgT3z7qU9Z2bnFJDQLInK1YKRJ9UZXoljpmQ/52rAtHRpsGtmkTBjIWdnGx+/WBGFKwZEGK3Nf7eQZX4Ww8qrwxr9quN9UgjuXvuENWvupT0qScq6lYlV1wUj6fa51UqOV1p6r5JZUMZ76E3+OXYcZI5g6ghEAAAAAAICWpNsYHTs8EG1nvPhCdU/Z6D8v8PSN8zwtG7H0ut8ZjYwPztUikTDijKsn7LIz+Izn6uhT39DZBdU0aaWVDYORZm2tCn7w+dp7u9HH/8fXFy9MVy89Of5k22eMSFJXVRouz30w4vquusIigJ7jj9e+37sodX/vi18cbdvd3fIKwXu3bFSqeBX5XjhvpO511SpICEb2HCPVEb3jqnfoq3d8Vb7xVXKDUpH+8fhvruc1tjkEdodgBAAAAAAAoAW14etHlcr6zWObdEypHN2XnDFy7J1Gq4bTjy1RMdIRI15FPeGPaeVHPzat0GA2WLnGdeSKQQutZhUj+fBC/5rBRACXuBb8xOguZcLwxG5y7unqcqThyty3gXONF80YKfT2yuqKy36WvvoEZXp7UsdX+/okSctGjSpeRfKD4Mivey+zVvgehW/juCZu14X54bYnb9Pvt/xeF/zxAv3v/f+rshf8Qi9NVoy4tNLC1BGMAAAAAAAAtMALg5HVnqelvp8ORhJf4O9q0tWlVBlu3IlZN+ZUVAx/Hn0rl3V2MQn5/fZT93Oek9pXa6WVWbpUyuUkSd3POlyS1BWGHqW4e5vWJ8odHh/elZgxkmvbOruq0ki1A8GI7yeCkW5ZXd3RfdnlyxuOr/b3S5KWjkoVtyI/rODy7fpgJHjPjh4LfndH/ebD7DF/lN347+yfd/45ut2f+FjWWqcBU0EwAgAAAAAA0AJjgvQjE37JfK3n6fTBoBIkOWOk0OTLy+PVuR9gDcmtxInV0jWNF9Q7xbIs7XPhf6f25cOKkcySJdr3O9/W/pdfrq7Dnx7cFwYjbiYOQ/5uU7xdtivRQHE7N40ZIxPocoxGq+O7P7DNKr4b/R5lenpk5RPzfNbt33B8ZsUKSdLyUel3u74vJ3y/TF3FSG3W+quGg6vqI066JRnmn4pXibZHhzeq7JZl+SZqkSdJHsPXMQ0EIwAAAAAAAC3ww4oRK9F+5+8HhvTugSGtcOJkpOA0tufxSnN/cRmSKYeVFrmcugr53Rw9x+rmX+SKhWi7+MxnqrD/frK6eoP7wgqIZAD3lHFXH9u5S1IQjNTuy7SxXVhXVRp15r5ipOq7KoRFAHaxS1YuroKx+3objt/7gA2SpKWjRr48DYW/o6Z+lktYMZIL3ytHVIzMd6lgZOPNKrkl9ZfSx/gEI5gGghEAAAAAAIAW1IKR+u/jv3dwSCeNxMFHoUlXF3987i8uL3aOU1a2ElwIL+W7d3N0B9RVM9j5QsMhtVkktQv5th+Hbl2eq77wtmP5iVZaMwtG+l/5SnUf9pTgOarSuDP3oZ6pxpUcdrGYDkZ6ehqOz69dIykYvi7FM3/8TN17EQ5fz9YqcCyCkfku2Upr1K+q4pa0pO7PqUswgmmYHxOnAAAAAAAA5jk/bKVV/y3T0q6c/jB2oPbTFklSvtk1ulKpyU7MppHRzeqtVYx09XV4NY0sy0rfzjepaAlDjtoMm2ziOn624qo3MSOj1kork5vhjBHLkh229epypB2JC9Oz7d9v/3c9OPigvEqcLlpdXbK8OCixCo0BUnZ1LRgJK2tqh9cFI5ZdqxgJ/usxfH3eq/rx0KY/duW1ZfBhLR1N/9x8jxkjmDqCEQAAAAAAgBaYsGLENvFFudLOnB69elUUikjBxeR6VnXuLi4jUCkPqid826vdje2X5gXLSCa4SN/sgr8VDlLPhRUOdiIYsSueehLBSDaqGJnhjBHLkhW2Hcu56VZGs+2/7wzmrqwK29E5WVuWbacrRpoFI6vWSpL6w+KWWvWMydSFRLWKEY+KkT1FeXxX6vb5d/23nlZXMUIwgukgGAEAAAAAAGiB73t69U2+Vi21tfORHlWGcrJzjRdWl1V91deV+IlvPWNuVKqj6g2DEb9n/lWM1LPyjZUeVthuq3ahP10x4qg70VorrhiZ+eW+WviQd6WyN/ehXi1c9PPB608GI00DpDD4yoXXx2vvk6lrK2ZlrNT9nkXFyHxXGd2Sur2zMtjQSovh65gOghEAAAAAAIAWrLt3SC+41pdU0DYFF2ezXV7DcTlPDSOdGQ4896puKaoY8fvmZzBiWVKtAKlZK61oxkj4MUtWjHhVW90m3hHNGGlDMFILH3KuVJ2jYMTz49+lQpgjmvC1WIl5LJnlyxsea4XzWaKWY17t8ekQpdZKK5MIRowxDW3NMH9UqqMN+5aOpQOtdgcjN26+UZc+cKk+/ryPa1nXsraeG/MHwQgAAAAAAEALuocaL7655ca2RaZJdx7fJxiZa9XqmHrCGSNWX3+HVzMBy0gKLso3axFVGzSeC4OCTCoYsVIVI1FVyUxnjCgORvKuUdWfm1ZayVkSBTd4XX4+vnS57pzPyhscVGG//RoeWwuVagFSPqwcMbmu9HGZoJIrl3jfHN9RPtNkvgvmhUoYzP310LC+uyT4PV5al5X4bvtaaV328GU66zdnSZJ6cj06+/lnt+3cmF8IRgAAAAAAAFpQbfF6s/Ebv33u+/TAn2tVtxS10souWdLZxUwk8VFpVjGSCdddKIeVDokCJb9qq9c0ttKaccWIJdldQaCQdyVnroIRLw5G8rVWWrn4PVn6hjdM+Fi7kA5Gai21lC+mjwuDEY3bOmCz0UPrguclGJm/ym5ZOdfokIel9Yf42txta8l4+hivjX9fa6GIJD0+/Hjbzov5x979IQAAAAAAAHByLbbbMY3HGSpG5lzFGY9aaeWWzs9gJPlJaRqMLF0qScrVgpFEByGvYmvo+iV698+DNKCtFSNhMJJzJdfMzXyc5JD3WsWH32TuSjO19y4bPi4XVpyYumDECgfT77xtqc79lqdnPWRSlSrovO3j2/XgwIPR7YpX0WnX+Drkim697VfBh3zpaF0rrVn6+7pzjL/bCxnBCAAAAAAAQAucKV5vHj3uJPlrggcZ0ziLBLPLccejVlqFZfMzGElXjDS20oqCkUpjxUhpZ17jTxR1/J+MbN9EoYmda2zvNqUlWZasriBQCIKRDlSM1FphNXlPmqkFI3lPkjHR49XVnTouqhgJrR1IPy867/SrTtdf/uwvNVAekCSNOxWd+Ifgw/2MO4NjltYPX3fbE2C4dZUnTwzMzXwddAbBCAAAAAAAQAuMZXZ/UIL7tCNk2+GlF+M3XHTD7Kq4ZfWWgu2eFfNzgLKVCkYak7daMJKpSJZvUjNGki3b8k7cSivfpPJkquwwUMi7kjdHFSPNWmlNNRiRpA/uGIxbaRXSM0Zkpy+F2kZy2jy4G9Pn+Z4eGX5ErnG1o7RDkjTqxMFcudso4xn1l9KP8/32BM9jTjpxcan0W9AIRgAAAAAAAFpg/CZT1UN2T49Gl/Wl92WzyoQXYjO+VHJLzR6KWVJ1y1Errd4Vyzu7mIkU4ooGe5IZI5Ys9ZTTw9eT8q6UDa8NFwozbaUVV4zkXcnXHAUjfmPFSEOwMYFkMHLUWDWaNWJ1pYMVy0pfQM96opXWPDJUHZJvgg95FCSX4p9Ppc9XV+LH5eSCsNr32hM6D1eGU7c9a6gt58X8RDACAAAAAADQCr95xcje/3m+Drrxtyr1pOcZZLKZqGIk4zd+Gxmzq+zEw9f7V8/PYMQq9sfbhcbqCCuXk93TI0nqK00ejMQVIzMMRixLVlgxkpvDYKTZjJFkcDSZZDDy1JKjY0bL4f66YCRf11rLTz8vOqvWPkuShsvBz2XZ9sTnLxO3SfNsS37YNc5vUzXesJMORqzMSFvOi/mJYAQAAAAAAKAVE1SMdB16qOxCQa7SQ9czuZxkh7MhfGncHZ/1JSJWKo2pK+yEs2TNis4uZiKZeB5Is+HrUtxOq3eSYKRYjS/yZbpaaz81Gbs7CGPyrpEsX84ctBRKt9IKQ8iuFoMR25YJ30vjS2sqwRvVEDatPih1M+sbZozMI7sGHoq2/7hplySpOBqHHlnHUiH8KHoZWybsRdeuVlqj1dHUbZOpEJwtYAQjAAAAAAAALbCaVIzY/f3Krl8vSXLqg5FsRlY47Nn2pW/e9U3dvOXm2V8oJEnlofjb3l1L5+nwdRN/pnYXjPSVzITBSHdiRvRE52lZXcWIJJXd2R9C3Wz4utVixYgkKRe8buNZ8r3gd9EupN8Lq5j+HGR8qewxYHu+2HXrhdG2UfAhyFTjD33P9oyWjwS/M1Yhp9qfXOO1Jxipfc77PU+Z8HfzqO8epS2jW9pyfswvBCMAAAAAAAAtMKYxGOk65BBZ4beW6ytG7FxWysSttH7y4E/00d98dPYXCkmSOxi0Lit1WbISlRnzSuKCbrNWWlIyGJEyE7Rz6y3vPmDZnWVveYtk21px+umyirWKkeC+ufjWfLNWWlaLFSPBg8JgxLdkwmAkU0zPKLGy2dTtjCeVHSoC5ouB8q5o2w+DkayTTgNfdXPwWc8XC1Ew4vgVPT78+Iyfv7z5VknSgY6jFYnfzf+6879mfG7MPwQjAAAAAAAArWgWjBx2WLS93+r+1H25XE5WYsaIJO0o7Zi99SFlbCgYnFzpmr+Xv0yiPZuVaz4bJGqlNcnw9VrFiGdr2iHQ2k9+Qofe8QcV9t8vaqVVqM2/dkvTOudUOF7criuuGClOcHQT4ftn/CAckaSu7rpgJZt+b7K+VHIJRuaLXV78OfMVfNhtJ/13txYC2oWCTBiMPOLfp1de+kr9cdsfZ/T8le33SZKKvtEKL/5ly1jzNFjFjMzf/2UAAAAAAACYR+pbafU8/2gtf/vfRreX9qa/nW7XtdKSJOOnv7GO2VMeCQYpu8UZDiOfTcmKEctqekg8Y2TiVlo94bV9z25+jlbVqk2sQvBZzs9hKy0vMSciFw1fb31eSnbZMklSdTQrE57q4H3Ss2Xye+2Vup3xpNIcvDa0ZleirZkJf4i5uvE2XWHHteQsHSv80/yNP/x4Rs9fNsEvWMEYLU/8bmZt/m4vRAQjAAAAAAAALahvpbXq79+n3OrV8Y5M+jKLlcnICr+h/tzx4IKfZbtNW3Kh/apjwbB7v3sKVQdzLFkxMpFkKy17omAkvJ7sZ2YWjNRY4WyOXHhtuOTMQTCy475ouxYAWbnW24L1P++5kqTx7floxkiuKx1W9hxzTOp2xp+b0Aet2WXiOTO+XLm+q3xdxci+28INOxO10qoFI0/sqktRpqis4ANfMCbVSitrEYwsRAQjAAAAAAAALagPNOxiXZseO91upa+nEFWMvHAsvvjq+DO7eIfdGywPyioFFzbzfSt2c3QHTTEYKYQfHTfcV9MTthfysu251GeHgULtovRIdbwt552MVxrQgU8YHfmAr2z4tthTCEZy69dJkvyqHc0YseqGr3cdfnjqNq205pcBE4cRxniqeBV1hZ95rzsdclXvfzQKRuzwT7OlmQUYlbBi5Ol/svW6b+e1eiA4MRUjCxPBCAAAAAAAQCvqLmLXzz+wMumLZ73dBdnhAOFkC6SyxzfUZ9sDgw+ofzy4qFlYuabDq5lYaxUjSyRJvaW4MqTngH1Tx0SttDLtudRXGwSfDa9Tj1Znf8aI53n63Lc9ffR/fR30RPCzs3KtX5C2wjDH9ywZE4SUdl0rrvrh67YvVQhG5o0BK/598IynsluOWmd5y5sEnHUVI5aZ2SyQkvGU8YyOuj6nnmFbJ/4hWA/ByMJEMAIAAAAAANCKumvYVl2bHtUNvbayWdl+cNHV9uOLd1WvKsyuocqQ+sJr+YW1e01+cCcl2vVMJK4YMeoOr+F3H3Rg6pi4lVabgpFw1kjGt2T7Zm6CERP/gi0fDf5r51ufD2N3BUGl71ryFQQiDb+jklb/wz9E21lmjMwruxIzcoyCipFalZRZuTJ17Jp//LgUzuWp/W11jauZqMjXqqH4th/+OhGMLEwEIwAAAAAAAC2pqxjJp9v0WPXBSCYj2w8vuhpL+bAVV8XjG+qzwfVd/fD+H+rx4cc1Mr5D/WH3p9z6/Tq7sMlMoZVWUDESVsEcfGjqmO7wY9buVlpSMAi97M5+mOeZxpDIyk4hGCkGax7b0iV/PFivlW8c3r78b9+mJccfJSmo5OL3cX7wfE+Ddvz59Uw6GNGadOVX74tf3NBK62H3J/rCLV/QY8OPTWsNZeNp2Wh8uxauThSM3L3zbt2z855pPRc6j2AEAAAAAACgBaa+YqT+omtdMCI7IysMRoyRusITlBwuxM6GH9z3A/3z7/5Zr7z0lbrvydujVlrFvfbp8MomNtXh67WKkfx++6eO6akEr9Wv/wxOUzL0y7tS1ZvZN/Fb4fuNwUgm13owYnUVG/bZhcYZJZZlqXhoEJZlfKlMMDIvDFWHZKy4YsQ1jipuWYXa8PW6yi+7WEy00ornP33rz9/SP934T9NaQ9mvaulYfK6lYUiSsRp/r6peVW+74m065bJT9MDAA9N6PnQWwQgAAAAAAEAr6oev17X5qZ8xYmUzsgrhgHZfKoSPH3dmvy3RYnTj5huj7Ysf+4V6w7e5Z9U8Hr4+hVZaBVfRt9kzDcPXg/+adgUjmUx01TDnSlV39oMRr0n4MpUZI3Z3YzBiFRorRpLnzXjMGJkvdpV2pm67vqtydTSaMZLdqy4Y6e6WVzdjpObO7XdOaw1lrxqFIZK0JAxJmlUzjTqjKrnBH5nk3x7sOQhGAAAAAAAAWlF38U3132avC0ZkZ2Qd8nJJ0tiOQtRKi4qR2VF/8bK3Nqh8xdK5X0wb2b29MuEF4NqQdbuvP3VMLRixso0VEtN+3mzwpHNVMeI2CYmmUjFiN5knMmEwkg8CyyyttOaNgZHNqduu76paHVFX2Eorv2pt6n6rq0vFsGTErvvbvC67alprKHlVrR6KT7ZkPF5LveSsqOHq8LSeD51FMAIAAAAAANCKMNgo9+S1z7e/JSvR9kVqXjFSenRL8Jgd+bhihG+ozwqTqOjJeCa6oJpdsqRDK2oPy7Jk13dt6+9L3a4FJplM8yBgWs8bzivJuVLVc3Zz9MyZJsHIVIavW8UmFSP55kGR1R18JjK+IRiZJ3aOPpG67fqeypWRaMZIoa5KyrIsdVnBZ/T8rdu1bzX+jA6PbZnWGsq+q3W74tvLRiUZo+2jjVV+yWBkqDLUcD/mv9br0QAAAAAAABYxKxwHseXA1XrWc5/beH9Da62M8nvvrfBLx+ryaq20xoX2MMboQ9d/SLZly0vMqKhVUEiS3dfX5JF7Fjsr1V6dsSzZvb1Nj7MyrQcJu2PZwTMGFSO7b/k1U36TqhR7ChUjzdSHl5HupZLC4etuufkxmFMDo1tTtz3jqlQZ0Yowfyj292k8k0m3nwt/vHkjff3Jbfr/lvTrB/19Gs3sfnZPM2Xf0ZqBOGDNeVKxIt2xcWfDsclgZPv44LSeD51FxQgAAAAAAEArahUJdvPLKfXBiDIZrXzfGdHN9eXggt637/jKbKxuURqoDOjqx67WlY9eqUeGH4n214IRP2cH8zL2cFbiI2eK3bIm+Ay2a8aIJNlWEFTMVTDSrGJkKq20MsuWtXys1RNWjHhS1Rlr+XGYPQPj21K3Xd/VWDmuGCn29TTM5LHsMBkx0nrX0xkDQeWGY0vONKqcyr6rYjW9b+mYlM3U91GUKn5caXT7xica7sf8RzACAAAAAADQgqhVk9X84rOVsetuZ5RdHfe6L4RfiP/z2KP0pG+TgfJAtL0tcWG1Nl/ELyyQZimJwge/p3m1iCSZ+jk3M3nK8GJwzjVzMmPEd5sEI1NopZVbvVobvvEN7fWVr0jZrIpHHjnhsXZPUEVUcKQywci80Dh83dHY6KCyYfFHcUnj575WETS+Pa/td/Uq58UBxqaRqbfTqhgvCmJqlo5JObvxs5kMXoarc9tK6+4dd+ttV7xNd+24a06fd6EhGAEAAAAAAGhF7ZrbBN/Wrw2rjm739sqyrGj2wcHDblR18ujA9llb5mKyq7yr6f6ecvA+e13tG0beScmKkcmCEdntqxiJghFPcicJRjaNbFLJbZzBMFVNZ4xMsZVW71+8UP0vO0kH//YG7fP/XTjhcXZ/MLy+tyxVaaU1L+yqDEqSin6QhNijj6oyHAcOme5urTj9dEnSqjPPlBQHIzvv6dOOu/pVur87Ov41P32ldpYaW2BNpixP+fCjblYslSQtGTPKW41zaJKzaXzN7ZyaN1/+Zt365K16/6/eP6fPu9AQjAAAAAAAALTAioKRCSpGVmyItu3+fmXCi692GIwc/T/d+sj/Bhf9to5M7YIdmpsoGFk9HrzPflf7hpF3lB1/E95MFoxk21cxYofPmXckzzRvpXXfrvv08h+/XG/4vzfM+PmaBSPWNGeMZJYskV2Y+GefWRK00uoti+Hr88SusOpifyeoxChs/ZWqo0FlnWcHn4VV7/977fuj/9WK098hKR0YStL4trxecaunJaPBZ/eObXdMaQ1Vz1Ou9jHccICkoGLE+OnPyLgzru2lONw21tTbds2Eb4K/b0NzXKmy0BCMAAAAAAAAtKI2z3eiGSNL4rZZub33irbtrq5o+6gHgwt228aaX9DH1AyMbG66v1AJvknudE8SIswDaz5+liRp9T98eNLjrE600sqGwYg7cTBy5aNXSpI2jmyc+RP6jQOzC13tGyafVAtG8q7kV6u7ORpzYSBsaXZgNQgZdmZslceCYMQJPwZWNqvi054Wzw2y0lV6o08UddrVRh/+cfB5nehzOxHjxp/BpQc8RVJQMeJ7cVWRMUav/elrddZvzoofaHXmM5Q1SzryvAvFAmm0CAAAAAAAMNuCC8XWBDNGkt9Qz+8dV4/UWmklbR8faNiHqds19FjT/YXwOuamwjo9bw7XM1XL/+Zv1P/KVyq7YsWkx6WGr09WMdLOYMSOW2mNOYNNj3H99s0e8dzGc/X3dTc5cubsnh4ZK6gCy5eoGJkPRsOqjH2d4HOwy86oOh6EJV5dm8Iay2q+/5BwFvpUghFjjOTElVm59eslSQdulu5PVBUNVga1dWxr6rFZuzOfoeGxBVIR1yFUjAAAAAAAALTAqn2ZODNBK61EMJLbe+9ou/rQQw3H7izRAqUddo02H7BcDK9TmqWrmt4/n+wuFJGU6OMmmXBwePOTtbGVViZupfW7nT/VcHW44RjHb18LIdOkYsRq4+tJnde2ZfLBRfX8+Ny2QUJz42HItiFspbUjm5FTCoORXPMAZKLqvZqxsAqlFY7vRPNFvExG2VXB345nPGq06onB6LgnRp9oeKxvuUGwMseMT83DTBCMAAAAAAAAtGJ3M0byyWBkr6bH+OG38AfLg+1c2aI1UGo+xP55u4LWNkcd/pS5XM6sSX4x3vSGFSPNQoN2Vowc+BeSFM1c+NP2PzUc086KEXlzF4xIksKL7Rlnau2W0H737LxHowp+DgeFwciuTEal8qgkyc9NcAl7goqRmpHqSMtrKLmlKBjxc1llV66M7jv0nsFou1kwYqz2hoSTST9P8/8tQmsIRgAAAAAAAKbAsptfrLUTg77zG+JWWmvO+lhUTWL7lmSMhpp8+x5Tt6ucbkmWc41+eEFZqx8MhhKses6zOrGstmvWSstqUrlkZds3k8Naua8kKe8GYd7O0s6GY6Y6w2Eyxm8yfD0xn6ftssGbarsEI512ymWnSJKWjhr5P1umv7w5SCh2VoK/k36u+d/c3eQiTauckn6/5fc69gfH6peP/VIVr6J8mDn4uVwq6N61NP4FHB94JNpeNWj0ipt9FapGJac0+WLapOzG8066DfNxZoJgBAAAAAAAoAW1VlrWRMPX8/loO7dX3Epr+amn6qAbfhPdzvjSSGWwbeu6cfONenDgwbadb08y4KZb5Rz8hJEZCi6iZtesUfcxx3RiWW1nJVppKawYqc1ASGljMGIXglCi9i36HaUdDcfMdsVIcm5Pu9lhMJL1THtfB6bt1Gt8OYM5/eUvg9v5cOaHmSAYqYVb9cbDP8UP7WxeUVbzjqveoV3lXfrUjZ9Sxa1En3WTz6v7yGdHx7mKP5vOQ7+Ktr/4TVen/dLXG3/ta+f46KTP1S4lNw5giiIYmQmCEQAAAAAAgBbUrk2bCSpGTDW+SJXbK33R2srFF6yznlRy2zN8/cGBB/Wuq9+l1/3f69pyvj3NQDgUeakXfOt/bSW+gLn81FMnHM68x0kEI7WqkL3PP0/dz31u+rB2ttLqDgafd4Uf62bByKbRTW17vqYzRnLtC3oazp0NKm7yrlTxGMA+HxzyRHpORyH87OVyxabH2/nmraQKYeXHjvHBlp7XGKOSV1JXLYjJ52UXixrYP3he34vbV7n5IJh86eiYusrB35cjHjXaPja7wcg9O+/Rp377KT02EM+s8q25n2uykDChBQAAAAAAoBXhNahmLYwkqfDUpyq/337K779/wzfdk7MSnv6o0cOHtN77fjL3DdzXlvPsiTzf04A8SZa+vnWbft7bozdsdVTREknS8lP/prMLbKNkK63awOnCAQfoKd/+lh44+hlyB4MryM4Eod10ZFcEMxaWhkU528a3pe53fEe3bL2lbc/XrGJkNtm5rDxJuTAY6cn1zOnzI+AlWqitrOt8VQgrOHJd3U0faz/l2dLdv2zYnzGS5RuNOROHFb6JP2+e8VRxK1EIaLqCQMQO/26bVDAS3Jf8XwHPlraNzm57xFq7sdsTv3MEIzNDxQgAAAAAAEALategJpwxks9r/59fpg3/eX7jnYkw5SM/8mUq7QlGFnMLoKHSTpmwIuTAiqMPbBlS5bdBKPLbdYdPGGDtiZKttOpbuWW64qqKMbd9FTLZ1aslBXMfJGnr+NbU/ZtHN7ftuaTmFSOzyc4Hv8c5V6q4VIx0Sq1aZ8VQ+iK/ZYwKYQWHXZygYmTp6gnPm/WkcXfiv7PJwezj7rhu33Z7FIzUqqUyYTDie/HfWSfcHjL90T7Pkh4denzC55qpZLXWY8kqLXtuf2cWGipGAAAAAAAAdsM3fhSMZLITX06ZcP5IXUsnuzrW9LipWszByMDOByRJ/Z6nJ29YptEn4oun+z5lTaeWNSssO3HR2K77LBXzkoLPk9/GGSPZ1cF7uCz80v3W0XQwsnFkY9ueS5Ksua4YyQfvVd6jlVYnOeHQ8vo2Wt3leL5Npti8YsSaIDCRgmCk7E38d3aoMpS6/YVbv6CT6oORTPAZ8X1fxhhZliXHD6pHKn5BUvC58TLSI8OPaLZccu8lE9zjTbAfraBiBAAAAAAAYDc831M2vAaVyc98ILTlVlKtXKZrMQcjI8PBhfk+V6lQRJKeuqr5hdQ9VqJixK4L3+xiV7T9rP0n/gb9VGVXLJck9YWznreXtstJtBR6aPDRtj2XNPcVI5kwGMm50u+3/H5OnxsxJ6zcOHRjOhjpKUv58OOW7elt+li7a5JgxJfK/sSttAYrg5KkPs/Xfn7wO1UMgxG7O2irlssFf+uzntFo2JbLCf/mZrw4oPRs6bGR+yd8rpnaXkoPkf+LO3391XWeLItgZCYIRgAAAAAAAHbD8R1lw+u22TYEIznPpFq5TJdr4mCkHUHLnqQUXqhcM9DYPmrpG94w18uZVamCo/pWWt1xMFIszvyzGT1nWBmVCT9WRkZPjj8Z3f/IwGNtey5Jkj+38xIyhbykIBj57O8/O6fPjVjVGZckHbw5/fPPu3HFiNXTX/8wSZLdPXnFSNUfkzHNP1ejYbC33nV10tCAJEXD1zN9QRDT39sdnWvLyC5JcRidd+JfSmNJ94/+Tl+69UsTrmcmNg2nq7POuMzX635ndPAT1Vl5vsWCYAQAAAAAAGA3XONGFSPZQtfkB7cg50nDlZkP631kR9wOJjnEeDEohcHSO3+UDoSe8t3vqOfo53ViSbMmOWPEztRVjPTEQ8OtSdq8TVl4ruQYgy1jW6Ltx0fSMxVmGszNecVIGIzkF2/R1bzguEEwUqtMqsl68c/GX7qu6WN310rLl6uyV27+vLseliTlZLTKC/521maMZHv7JEnFMHjJetKW4SA8qfquDnvM12H3x9VThXDzm3d/c8L1zMRjw4kQMhH09JQX19/8diMYAQAAAAAA2A3Xd5UJr0HlChNfjGtVzpUGyoMzPs9wJZ6NUOt9v1iUnXH1lIyW1+VL+QMO6MyCZlF6xkh6qLzdk2gb1saB87Xh9clg5NHBJ6LtTaPpb7E7vqO7d9497YDOqq8YaeNrafp8haC6JufNbaUK0qrhvKVc3ccmGYxk+pY1fexkrbTy4c91osq8WuVHxkjLw2Ck1kor1x9WqOTz0Vp2lobDx3k6+3u+Trghnl+Sn8U/vSW3pCdL26LbyffJWIurSrDdCEYAAAAAAAB2w/GcqGIk19WOVlrSUKX5N5mnwpj4KlmyrdZiUHbHtWS8cX92WfOLqHuyZMWIVT98vX9JfF+mjRUjtWBEkhV+S/2PWx6VFFxU3lbakjr83N+fqzdd9ib9++3/3tLp7911r8adxA8wHL6+Y2Vea876mA64/OczW/9u2IlWWugcxw3+DmbqgxE/MWNkgsqQ7MoVE553iRN8niaqzPN8V3nH6PB7pRWl4NhaxUjX0qWSJDtfmzEi7SoNSpJctzEFSVYdTdS6a7oeH05XZnUlumf5FqHeTBCMAAAAAAAA7EbQSiu4CNWWVlqu0Whl5v3hfcVX5FxvYV7hfXjwYR37g2P17bu/ndpfcsbV1yQYWZBSwUhdK619nhnfyLTvUp+VqNioVY08GYYhW8a2yKsL4n70wI8ktdZO6I/b/6i//Nlf6q9/8dfxzvCCspuztfzUU5V/ylNmsvzdqlWM0Eqrs2ozRmrBcyWTC28b5d1w5kex+d/c7qOPbtiXKQQnWhI+dsSZqGLE0Tuu9PXaX2TUfX0wU6RQC0b6g1ZaVq62Fumx4Ud18k9O1k/HHm4419JqXLkx7rb3j1J9y7pkMJJzqBiZCYIRAAAAAACA3XD9eMZIboKLdFORd6WR8syDETvxrf1Kk28yLwQX3PFV7Srv0r/d+m+6Y9sd0TeyS25J/ePpb0wXDj20E0ucdUOrj4y2G4KRvr74vnZWjNiNwci2sUclSRvDYdBLq7lpnfp3m38nSXpg4AHtKO0IdobBo2/PzeVKKx/8HudcaVVh7zl5TjSKKkbCz1g5E1fy1CpGchMMWbfzefW86C+i2wff/HvZ2eBz1Le7ihHj6rg7w8/c5iAkK1aD27neICixwlZaOU+6c9fNemjooYaWX5LU7Ui58O/SUGWo8YAZ+PPOP0uSnj9e0lLP06sG4uDlhU7z2StoDcEIAAAAAADAbri+G10QyxVm3kor60llZ+ZfVV/+eNxuqLxAg5FHd8Xf+L728Wv1oWvP1Mv/9yTtrA6rP7xGuH2/p2r1Rz+qDf/19Q6tcnYNrXlOtF0fjGTCi7iSZGXbOGMkca7aReudo/fIGKPHw2HQK5zpBSOru1dH21c9elXwfGErLZOxmj6m3ayuOBhx53jwO2LjlWBWR62VVikbzvXw42qeiYIRSVr94Q9LkrqPOkqZ/n5VMsHPdWkYcgxXmwcjzSrsusI/oXZPj6Q4GMl60pCzK9quZzxL2TAY8Ux7B6Jf/uBPJUnPL5V17eNP6N074tfz0r7VEz0MLSAYAQAAAAAA2I3kjJH8JBfpJnPANVdH23lX8jXzC2jGjy/ulZyFGYxsHIqrYq56+Pe6euOvtGlss24ZfSwKRqrr9taKt52m3OoFeqEwm6gEqa8YSQQjszF8XYqDkSHbqOSWtOn+n0mSjnG3Njwub+d3e2438bm94tErgo05rhixw2Ak7y2++TzzybgzLss3yoTFX6VsEDw/42GjQvgnLd8z8d/croMP1oG/vl4bvv41SdKO/BpJ0v/7cVaFqtFAuXkFR/IzWFOsBP+1e7olSVY4YyTnSSNhMNKsYsT4luwwGJnujJEto1t02cOXNaxroLRDMkbPvyarrTcsk+/Evx9+aeZzqhYzghEAAAAAAIDdqLgVZcOLw9Mdvp7fe2/1Hro8OIcreW34lrqf+HJ9yZ15a675yLUHou1Npbuj7SeckaiVlh0OS16orGRbq/pgJPx2u5QOM2Ysca5zt+2Mth3fUbk8KEnq9Y0ydReCc5nGKhLHd3T3zrvlGz+6XfPoUFB9UqsY0ZxVjAQX24OKkYUZKs53d2y7Qx+57xvR31ZJWj8atFZ76R9MVMGRT7SLaya3enX0e5BN/F09YIvRjrHmwUj9jBwpUTHSnQ5GMp5U8YPKluwEGVotxNk+Wpp0rRN59U9erbN+c5Z+eP8PU/sdY9RXktyHixrZVNTAg93RfX6ZYGQmCEYAAAAAAAB2o1ytRBUj2a7pzxix8sFF45wnuf7MK0aSZ1iIM0Z848tR8wubo5aJKkayy5fP4ao6IBGGNMwY6YkrRuzeyS8gT0XwPEHo8cKxsqwwAHF8J2oXlFH8Tf+aZhUj/3bLv+lNl71J377728E5vPizunMsuLhrz/WMkUIcjDS7SI7Zd/4fzpeUDhsKiYqJ2qDxbG+PWrV08Mlo2zLSN+/5mh4ebByYXl+Z8ZNNm9VfCYPWWiutQtxKqyY7QZ5dCIe97xyttLzWpIoXPO6mzTel9nuSVg/Gt0efiKtn/HGCkZmY0l+ac889V895znPU19en1atX6+STT9Z9992XOua0006TZVmpf0cffXTqmEqlojPOOEMrV65UT0+PXvOa12jTpk0zfzUAAAAAAACzoOKMx8FId/fkB0/CygUtkYKLsTMPRnwr/nZ9xXX0kV9/RJ/53WdmfN75Yld5l4wmrqypBSNdq1bM0Yo6w8pMHIwUDthfPce+SEte9zr1HHN0/UNnpvZURsqHwUjVq0aVH1kThCNJzSpGvn/v9yVJX779y5LSrassKziX5YfnycxRMFIMfo9zrpHf5rkQaM3SrqWSMfrMd+P331f8N62+gqMVdqJipCfMKL72x681HJf8+2vZRvtXXdmulXo+K1drpRV/xnMTZGjFcH8hN7OKp0yiOsw3vowlrR1o3p6LipGZmdJfmuuvv17vfe97ddNNN+nqq6+W67p66UtfqrGxsdRxL3vZy7Rly5bo3+WXX566/8wzz9Sll16qSy65RDfccINGR0f1qle9Sp7HHyEAAAAAADD/VKpxMJLrnv638mvDfNsVjCTP8NDQg/rFI7/Q/9z/P6lv5O/Jto9vlyS9/A5XL7+lMSDpKwUXDLtXrZzTdc25ZCutuuDAyma1z9e/rvXnfq69rbQk1XK3gQd6VIs7HN+RGwYjGRnVP+NkM0bs8FKkMxp/q1+Wp13lXdqsoDxgroav22EwkvckX1SMdEJ/vl85V9pne7zPsRs/w8l2cVPRE+YGGatZe7dEOJcx8t34cxcFI2FVUbJipGHGiB38DcqHf3K9GVYCWolgqHaufZ9MByP9L3y6JMlUFmb7xLmS3f0hsSuuuCJ1+5vf/KZWr16t2267TS960Yui/YVCQWvXrm16jqGhIV144YX6zne+oxNOOEGS9N3vflcbNmzQNddco5NOOqnhMZVKRZVKXIY0PDw8lWUDAAAAAADMSLUSByN21/QrRuyolZaR34ZWWslvV28a3Rht37TlJh2z/hhl7Sld+pl3tpe2y/aN3vYLSfJ18yGWDn/UaN9tRt9+iR1VjPStXdXJZc6+ZBhiz01wIEmWZWRkaec9fdrwfF/3rLZV9arywmDENoqGTtc0qxiJTxj8x3noV4mdvj5xwye0spZ7tTncmXApxeBie84VFSMd4htf+bpMKl9XIeZbklVofa5Tbq+95DzxhKQ4GMmpv+G4+oqRKBixjKywXWLTVlqJ7VUf/KB2ffNCeQNDKlZr553Z7Cjbin/Xa7N4Dtqc/h3L771W0p3yywsjAO+UGdWmDQ0FPR6X1/VxvO6667R69WodfPDBOv3007Vt27bovttuu02O4+ilL31ptG/9+vU6/PDDdeONNzZ9nnPPPVdLliyJ/m3YsGEmywYAAAAAAJiSilOKLohZuUku/O5GumJk5sPXvcQ18h89/M1o+z2/fI8uuOOCGZ+/054c3qhiomV/b0l67899vfIWo2c/aLRyJNi/dN3qzixwjlipGSNzExwETxZvrgq/p+z4jrzw4nVWpvFb1ya9vqoXf6s9F1aTuMnPvuXrN0/8Rpnw9yszWbDSRslgRJYnY5q3K8Ls8YzXEIxYfvrvopvNyLJaDwP3ueibUanTCwaDZKTqN1ZWpGaMWJLvhG20siZ6PisfBCS5VDAStpTL21r5ztNVOPhQSdLh4RgTz29fMOIZTxnP6IAt6WNya9dIkvwKlU4zMe1gxBijD37wg3rhC1+oww8/PNr/8pe/XBdffLF+9atf6Ytf/KJuueUWHX/88VHFx9atW5XP57Vs2bLU+dasWaOtW7c2fa6zzjpLQ0ND0b+NGzc2PQ4AAAAAAGA2OJWRaMh0bYD6dFj5Ws/6Ns0YmeS+b9z5jRmfv9O273og+ta3JB26Mb54/dH/jV9975rF00rLmtOKkXi7txLPGKmFehkTdRKKbB5MD59+cjxum+U6weffSZzXsoITZMIZI9nM3FQ52cVgaH3twrzLAPY59+jOkYZgpJ43xc9DfsMGrfy7d0uSusKPYtltHIju1lXs+W5wmdzOxh/oZq20XjYclKl5/UHl4JLXnSxJOuZOyTJmxn/XnxiI/+B5nqt9tkuFuvcosyKokPOrVDrNxLSDkfe9733605/+pO9///up/W984xv1yle+Uocffrhe/epX6xe/+IXuv/9+/fznP5/0fMaYCdO/QqGg/v7+1D8AAAAAAIC58sCuB6JtKzfxDIXdqbWEybnSTx77rxmvy5vkm9R79e414/N32rbhx6MBytLEQ4gzUxjOvCdKVozYc1oxEr/f3eHPwfEdeWF1RTBjJP0zGa+k47qtY/EXoatecIXXaVKdkQ0fls3OVcVIEIzUhmmnKggwJx7dOTrhMPMaNzv1z7vdF1w7zleCv4+tBCOmNng9G39+mwUj/U4YEIaf4f6XvlR2d1Erh6RDN06vYiQ5l+TxXaV4jW5ZBz/R+Lti9y6RJPnOzKsOF7NpBSNnnHGG/u///k/XXnut9t5770mPXbdunZ7ylKfogQeC/wOxdu1aVatVDQwMpI7btm2b1qxZM53lAAAAAAAAzKr/23x1tD2TihE7DEbyrjTs7JrxkPTJLovt07fPjM7daa7vasvoZnWX4wuDtdZZi01yqLqVmVFn/GnrrgY/h7FqOaoYyRpFlVSxdFiXrBipfWJdq/Fib62VVjbT+jyJmbC6w2AkfN7aPAfMnZ6C3VAxsuQNr0/ddqcRBGbCL9XnomCkWSutdDByReU5kqSBYjw3uxZkJ4ORfNhKq9bxyu7uVs/zni1JOmCLmVaLxIoXBzfJXMX1ylq7q0kw0hd0YvId2r/NxJT+khpj9L73vU8//vGP9atf/Ur77bffbh+zc+dObdy4UevWrZMkHXnkkcrlcrr66vj/UGzZskV3/f/s3XmYZGV5Pv77PVttvU9Pd0/PPsMA6iAiIAluyKISgbhi1ESNxMSIKAou6M/o1xhMNAoGXLIYRXGPgho3XEEDKCDIoiwzzDD71tNbrWd7f3+cvap6qe6q6p7u+3NdXtRyqs7bM9UFvvd5nufBB3HmmWc2uHwiIiIiIiIiotbT4iMRtLm3+hEpr2d9sNF2tHx0PsuatmIkrXbM670XUsEq4EXffhFuL+1NVIycOlK7ed1x2dvauLIFEgtD2hqMxD5eWX9vuWSbsP2AQwHQXXOFfPQiKSXu3Hdn7ClvI7deBKH6b6PPoyKrESLbCcBvpSUlK0YWgCudmoqRofe/H0o6Cp8ddQ4VI13e363uf3dUnNpgxKpqnbbD8gKR8Ww0r0ikvUq0RDDiv6ynI1pjatMGAMDqkdm10qqeZ1OyoyoRJ/br5DgVGHXeThn09uSl1lv7JM1aQ9+kl156KW688UZ85StfQWdnJw4cOIADBw6gVPL+8vL5PK688krccccd2LlzJ375y1/iwgsvRH9/P17ykpcAALq7u3HJJZfgiiuuwM9+9jPce++9+Mu//EucdNJJOPfcc5v/ExIRERERERERzYPlWuHGmK0k2xo1SqS9YCTYXDtaGpnX2lxMHYzcs+vIvN57Id2y8xbsK+wDAAzGmo6k8rU/b99zn92uZS2Y5PD19gUj8T/tTDCvwTJhB620pMRTK1VtimT0qs/+/rP4zvbvxN7P2/W1UKeVVrABbLSnhb7i/y4q0gtlGIwsAGnDsKPPQu9rXgMlnUbXGVvCx+ZWMeK1mtL8ihGzTjBiO8m/b9PyAm/bb58FVLXSkhJPf8xFz4j3+5fKRQG5sc6rzhsYm7mVlumYeOl3X4p33fau8LFyJSqFKyu78MJvvRAfuuNDcOxKTXDUdcZxULq6kDr+eKSOP37ac9H0GrrE4TOf+QwA4Kyzzko8/vnPfx6vf/3roaoqHnjgAXzxi1/E2NgYVq1ahec973n4+te/js7OzvD4a665Bpqm4eKLL0apVMI555yDL3zhC1DnkAASEREREREREbXSSGkk3LSd7550sNEWbHaNlOYXXkxXMeIWD83rvRfSrsld4e0zHok2Gp1y7d6R0bcMrpqOt9JqYzAST0Yy/t5yxTFhSxcQwGPuWpxS3o2vd0X7fjL2ok///tNV7+dtgteLIIKqLLWzrxkrn1HQJgnwfh9HS2MYiFULUOupbikMidMnnYSh9/9/AAAllwuPmVsw4n0eVXPqYMRxqoavW96xTjwYSXu3DQd42uMS7/kfF4AXqInhzeFxih/EpE2JSWf6gO23B36LbWPbsG1sGz76nI966zv4YPi8re3F3jzwzUe/iQtXnRn+u2Lsgpfh+DVp9LzhrVC7urDpu9+p9/bUgIaCkeoyn2qZTAY//vGPZ3yfdDqN6667Dtddd10jpyciIiIiIiIiaruDxYNhMCKUqYOI2QiuUg/eb7Q8v4oRZ5qKkeOUXVM+t9iN+5U0wpVYd3j6Y4NNyaUs3j5LWaAZI+kgGLFNb/i6AO5zj8crrW2J46b/DQkqRmqvqu+xXAAC2b6VTVnvTIQRtezSHWD35AGcsIJX4LdTPBgJvhsBQOmI2gDaauOtC4MZI1rJ+12x7ULNMW5VgNFZKAIAzGwUyohUNrxdPQRd23hKtN6sd1zaAsZnmBslqlrNCSFgafXnVt2673b0+v+ucFavx4rL3zjte1NjFuablIiIiIiIiIjoGHG4eDgMMuQ8gxGR9jbdgvYx5Vhv+blwp6kYMdtZWdBkY/vvBQAMjnmbjVORAlBy2akPWCpiV80LsTB/r6nYvAbHDzZOfHwM2ceqZoKIqVsJCeH9Illu7eCErD/UuqunZ/6LnQWhKBCKd07DBvZNHmjLeSmiynJYERGv4FE7ogokS2k8GFG6onZsf3Grg0xxZ80xrpOsRDtvx901rw1mjADAWC75XZteEVWqKTnvNWkTkO70wYgS+/01XS9ttOxy3WM/99g3wz8fxWjP7J3l5Nj9NyQRERERERERURvEK0bcebYBFxm/lVYws2SGq4tnIusM+v2HHaN40/cdZEbrX4V8LDg6sRcAsP7Q9N1LREqFmCYcWiri7bPaWjEioj//lF8xUrYrsKVEriTx4l9tx/7f9OKpR+Of4+lmLHjPmXXmeWT8sDCdSdU81ypC9c6p28D+wsG2nZc8qltG1g/cRDr6e4+HE3MKRmKtuF56uwTcYs0xrl1/SPqpT14b3o4HI68fmUwcp8UCvKDCJWUBTgPBSMkq4c79d2K0Mjbl8UEwoqbb93uxXDT+ySIiIiIiIiIiWkYOFQ+FQYYzh373ccFGW7DZZTmVaY6emS289fQ5Do76oc2J38tha0HiSXsrwHvm9fYtN2lOokPvqAk3Rl2J93zLwdO3Tx+M6LnlsbUVb6U170E3jZ05vBW00ipYRTiQ2Hgw+rvZcEDi/mA0yDQVI8FcdsufURKX9itGhNa+v9OgYkS3gYJV/6p9aiUbJ+7xA7ETTgwfVTqj9njWHFppVc/hseqEp/k6c0cAwIiF1ULToHTk4OYLWDkpMRo7Tuvvj9YbqxhxZwi7XRn9fvzD7f+AX+z+BU7sWDfl8br/e6Gk0lMeQ3PDihEiIiIiIiIioml4rbT8odHq/NqZKBnvyuKwYmSGq4tnUtEyUFyJjBNtUmsFb7tn1cj0ocJCu2PfHTjzq2fis/d/tua51KiVCEW04VV130NdDm20AIhYINfOihE3dnV732EFK8ckClYBtpTYGOs8tTo2B0aI6T93rnS9YKSK4T8kppi30ApBxYhhAxWr/kY5tY50XWzd6f0d5P70T8LHle6oTZWtzv/zUCgLWFXftUes2ioSAICT/GxqQ0MAACufDGj01avD28KvGDEcwDWnD7vj6/jF7l8AAB7O74JwJfoman93ghBdS7OVVrMxGCEiIiIiIiIimsah4qGwldZ8gxGR8Vq8RBUj89uMVU0X13/Gwd/cXLuhNr7IMoMjpSP4/uPfDzcGP3THhwAAn77v04njynYZmfHkz5M7409Qj7p6mQzLjgcj86xaaoQrkuf60I0OClYeLiQ2HYj+jvrHEq+qeZ+Tdrj4py/YWH9QwpEOLEy9Aay0sWVQ8EepO0DFml/1FjVu4IiNrhLg6AoyT31q+LjaFQUjT9s8OO/zvPw2YNvotvC+K12M2LV/3/rateh91V8kHxv0ghFzMvm7oK2Kwtpg+DoAKOUpAhefNbGv7uPv/JaLz37Kwdad3u/PG8bGvfP7/+5R06wYaTYGI0RERERERERE04jPGLG0+W3aVgcjh/OFeb3fht0u+ieAp2wXgExuNk90LK7ZG2/9+Vvxnl+9B/905z8BSPbaj9s/9jgGx5KP5f7kjPC2iG9CxoY0L2mx1kBCad/f66Tanbi/YhIo2AWvlVYsGOkbjdYk/FZaMvZ5fP/XXGzZD1z2PQeudGHXCUa0YAh3GzeA7bL35/rCe1xUphiATa2zeZf3lz6+rgciNlxc6Y3aVK3s66p53WwMvPtd4e2NByQeOPi78P7h4uGa78sN3/g6jvvJLdBWrkw8rg15wYw5GVWuZM84IzEMXRgGXL9SSpRK067LPLq97uOnbfNe/6Lfev/s9StXwoqRFGeMNBuDESIiIiIiIiKiaRwqHoTmXwRvavPbtBVZbyM/uAr4zu3zG/hciXWZed3PXOhWtNk3lqvzggX0wJEHAADfeuxbOFQ8BFk9ZMK379AD6CwlNy2NTZvD21pvb+yZxRX+tIqMtc9qZ8WILWrbGJXsIrSKxHBs4EImlikYwrsSv1Jnfk7aBBzXgeO4+NM/uFgRax2kBMGI0b4NYNfy/lzP/KOEyWCk7XrGvS/WwlAy/FC6V4S3hT63mTN9r3tdeHv3SoH7d98W3t9f2A81Vtikr1+H9Natdd9HH0xWrDz0tGdi3ef/O/GYEAKO/2spzemrAM0ZqgT9sTfIyqjNGwDoGQYjzcZghIiIiIiIiIhoCgWrgKJdCitGHH2eFSN+MBJsdjmwElfWN8yNXvuiuyRu3BYNeyi3b1RDw3538HcoTo6H963YwOK9Rx9BJrZ32HneuVBjYYhYjldOx4dHt3HGiKwztLpoF7DqUPKx4PcDAFR4H+6SXXvl/ETWa2P09AddvP07Lq75j+iFquOdq52ttOIsmzNG2k0Jvr+0ZPih9g5Ed+b4/SgUBdqLLgIAmBpw5+HfQ0qJj931MfzVD/8qDEYcRcGm732vZmB7QPNbaQXyerbusa7/qyJdp+a5ONOd/nMWrMvwf26NrbRahsEIEREREREREdEUDha9io4O29uk0ue5aaukvTZQivQ2Bfdo/4dnfvWZOFA4MMMr6wvaDwXWj0WXQQsX8wtdWmhfYR+yVhTiHCkdCW/vndiFtL93OHLOBVj9iU9A6+0Jn5d21Q+9HCjxipE2VsnUCUZKdgGrD3mfs1K3N3Raje8F+620ynUqMCayAo50sHm397lMx+Zhi6BiJNW+DeDO5z8fADDSCdgOK0bazv96ElVVUKKjA0rGa1WVOvkZc3777q1PAgBkTOCgU8Dt+27HF//wRQDRZ9Y0tERbrGr6ULJipCSmODYMRqb/foqHwPUo/ld4yv/uDqoLjQyHrzcbgxEiIiIiIiIioikcKnqXxvea3m5Vep7tTOLzE4Le8ZPWJG7ZeQtc6TYckGix1lmK4aJwMFqf5gDlRbTZm9Wi3l7X3HMNdutRScvhUhSS7CscCIMRd806CF1PzBVx8/noTets3C9NsRkeavtaadVrd1ZxSljhF/tMrvLmMRgOoDoSm/ZLKNLbyS05XsVIuhJ9RiczXiutfFX2MXhUQhS9n6udFSM9F18crcuefmg2NZ/wN/+lmqwYEUJg3Ze+gnU33IDuiy6a8/sbnV5wt7LsfX9vH4vmewSVGe4MQaM2lKwY6VvRU/c4GQQjzvQVI9YMFSOaKwEpkSoDmi3DACe9HCvlWmxuTdqIiIiIiIiIiJaB0bI3SGHLPglAIL16eF7vFx8wbNhAxb+7/YDE+0ffj+9u/y6uOesanLv+3Fm9X6JixAVG/hgNI9dcr51RRsvMa83NYLkWivbUg+YPFg4BK4Hb9tyGHxWfwMn+3qHW4W1silgA4ubzUHt74YyOouN5z2vpuheLeN2P2sZWWtXyaaDsFIOiEDh+dYdhS1xzo4WhfQq+dI73XFAx0hfLsWwVsF0Xhdger3Al3vuNaDO5na3ShOGFc5oDOM70Q7Op+YJ5GkKt3aLObH3KvN9fZLzvvk4/2M6Xx8LngmBEiul/n7SBgcT95zyl/r8DXP87SjrTV4wUrOnDasUFrvusg8GxHrzjODdqrZVexL0Rj1GsGCEiIiIiIiIimkIwQHr4gLfp1fu8587r/YSqwvZ3Y+JzGR49NIrvbv8uAOAzv//MrN9Pi3Vlce3kNo/mSFzxyyvgShcLbaIyUffx7rx3dfSOsf2wXAuX/uxSAEDG9Pvr52onyEvTxMabb8aa669D95/P/WryY0osGFLaWTFSVZEz0glUnHKY1Mgg6HMFhvZ5n7/j9rqQUobBSO9kFOtoDmA6ViIY6SoBq2KD3NvZSkv4VUu6A8g6w+KptUQwY6ROMNIMSsarNDMs73P8wL694XPhjJEZghG1pydxP5Wd4vM5y1Zak+XpA7iNeQeDY97t07ZJqP4fkZFiK61mYzBCRERERERERDQF0/FKFzr8q94Ht2yc93ta/h6gEds/cxAlHI+OPoqvPvxV2DNssAFeq5Upn3OAuw/ejXsO3jPntTbLhOkFIylHwRvGxnFGqYxnPOLiP69z8Nc/cbFr/CDuP3x/eHwwe0Lvqg1GAEAfHEDnuedOOTB5SWvjz1wdjGiO10orCEZc3duslW4suHG9AdNBMPKU0ehzrLqA6diJedoX7UleQd/OVlpBBZfmAK7LYKTdRPA5UFoUjPgt+HS/Au2PBw6Gz739iJfGiVz3tO8hqn4HlFhbvzg5y+HrFXv6z1l6ik5bOoORpluG//YgIiIiIiIiIpody7WgWxLpirfrZVQN4p0L1e8fo8dyDymTA3mv/s3V+MYj35jxvfRp5vgGV0Q/cvSRhtfYbOMVbyjFmqKJ1/3Gwmd3HsE7fuIt/vx7JPZPHsQDhx8A4M0dWFfwNhdXD61YmAUvMvH4S2lnMFI1Y0RzAQgZBRv1hlYLoGAVwhkj8VZaXrBiQ3Wi933t/cmd4La20goqRmzAwfQb2nfuvxPnf+t83Ln/znYsbVkIgxGtNcGItrIfAJDJC0BKKIr3mdRdia0l7/tnYIqZIXED77wyvK367f2qBcEIZpoxUhWMdLguvvHEeHjfter/frdzttBywWCEiIiIiIiIiGgKpl0JN3ZtTYfS2Tn9C2Yh6w9o0GP7Zy5qryL+9d5fz/he2jRFJUGrrkdHH21ofa0QVIz8zXclDv6uBwfv7UI6tt1/uHQYj456g5EvPTIOLe9tWfXGK3SWzaD1OkR8+Hobt/Oq/siDz1Swoe3WaXvlBsGI7W1C91QFIyWr4g2Y9k3sTr5HO4MRJagYcQFbTr+h/cZb3og9+T144y1vbMfSloVgVo2itqYawli3DlJVodkCKyYA6QcjKUg4Ze/3SF0xc/iaPe208LYyQzDizlAxYjkWVh+RYYu5FY6DtbE2btKZ4ntO54yRZmMwQkREREREREQ0BbMyjl5/Y7fcvaKmrcpcCL9iREtUjNQGI+YUMw/uP/gI3v6TD+NwYSQxp6Ra8NwDh+6b61KbJqgY2bzL+/Ob3JOOXS4OjJlHsePILgDAusMApEAx25UYfKx0dbVvwYtN7HOnLmDFSBDmKWHFSP15CwWrELbS6p6MHlddoGiVE9VSTiW6Er7vkjc05XdstsJWWjbgYOFn8Sw3Iig9Uluz6S90He7qtQCANUckpPA+k7qUsMve507r75/5fWJhnZKboWJkhmDEGC/jmv908O/XO3jD2DiuHBlDTk4/kB1gxUgrtKZOiYiIiIiIiIhoCTCLI+jzr+yV/Sub86Yi8Q/vvetsjBXHd9d9+Wt++ApASBz48RieM4tgZPvEThSsAnJ6/Xkd7RBUjASMLjtxZbRpV1AoeT/visPe45UNmxOb5Gs/dT12v+nvMfied7dhxYtXO+eqVM8YCQKNIBgRmuEFXFLEXgO84nuvQEbLAABysVnTmgOU7XLdQE9/1/sw+Ia/bOr6ZxK00jIcQFjTb2hT8wXZqGhRMAIA2qbNkLt2Yu0RYOcmL2z2ghHv90jrn7liRBixYKRjiu/RWc4Y6T4YBd5vH/UCY9etDQML3VnkxovR2zMYaTpWjBARERERERERTcEqj4UVI/rg/OeLANHF/7GCCcg6rbT2VY5O8QbeCx8dfRTqLIIRCbng7bQmKhNImdEPXBk1YE5Em6G2a2LUGoNuS3Tu8q7jzT75SYn3yJ52Go7/7W/Q8/KXt2fRi8lCtdKqmTHi/R0qQXGFqoUVUOEr/LtBK614dYjmSBTNojerpEoq274WWgERm5Hy4l9y+Hq7hcGI1rpgpPfJJwDwKkZMv2LEkBJ2yQsaZtNKK/45mWnGiHSnrzxyZPT7It3kP+MOK/Nv20jTYzBCRERERERERDSFijkZVox0rh5qzpv6u4FXHxrB3wVXDKN2ivqIW0HBKiTXE2uvlVX7wh799cSvyt81OjqPBc/fhDmBruLUz7uygjFp4qqvu8Aeb4N89Wkn1xzXzmqJxSQePbS3YiR5X61qpSVUA6LqQvbqapB4yzjVBcpWoe5sHCOTmd9i50DE5jac+cA0A3uo6aSU4Qdb0eq3ZGuGzJbjAPjBiAwqRgCr4H1w9dWrZ/Eu0W+gkqtfMTLb4evxlm2u7b1I1qkYGVc7YOrtDwuXk+X5bxMiIiIiIiIiolkolAvo82ck9Kwdbup7r7Zs9PptV9w6M0YAYLQ4krj/0OFt4e20pkZX7teRimUtv3hk79wX2gTjpZFpgxFHTGBwFNi6K9qA7Hrq1jas7NjTzhkcUiS3DhUpIFwZXumvaBpcM3mMGvtMrj0s0X00el5zgIOFA/VbaWVbtzk+lXglQGGK0++Z3IMrfnlFm1a0fFScCoQfCOh6tmXnSR3nByMjgAMv/DKkhJX3ghFj3boZ30MfHkb2Gc9Ax/OeBzFFgBdWjMjpg5F4RUkYjNQZuF5WDch0+8PC5YTBCBERERERERHRFAqVInrz3i6wMdScVlrx7kSG31bFhVn30E/95mp8/sHPh/fv2vuH8HZJHpx2+LrhALrlvb9Qmn81/I92/AgX3nQhHjn6yIzHTpSOoH9CTvl82gQ6qsaszGbDcrlQlYWZL5A1ascTG3asYkSpbYEUb+921TeSH1DNAXaPPxIOcY/TUwtwdXxsbkM+nazICrzz1nfiliduaeeqloWCVQg/R1oLgxFj/XpIAWQrCEPujoqEYwYVI2tmfA+hKFj/xRuw9jOfnjmYnKGVlmpFz99370b89oeb8eajl9UcNzjcj1zXws2FWg4YjBARERERERERTaFil8MZI9rAQFPe0xL+ZrL0WroAgJT1g4v/3f9/+MQ9n0DR8sotHjochRB5N7ry3q3eVPYv6T971EsbhNKcwdKPjj6Kzz3wOViuhXfe9k7snNiJK26d+Wr68fIonrxr6mAkU0HNZrnQajfll6snPevpcBQV+Z7+tp53sDsqoxCat43YVUwOze57/esTr4lXjPRPJN9Pc4HH999aN9BT0gswYyS2yf34kMCRqgotAHj46MPtXNKyUYwFI0oLW0YJw4DZ432XrDninXBgzPtnKdcFdaph6g2SwWdphoqReDDSsbuEzvES3vb7H9Uc96w3vxaK3rrZK8RghIiIiIiIiIgo5NjJyg3bqYRXGTcrGDmoeJUnEgKaXzEi4cBwpq4KMB1vXTsmtkdrRSUasJ6OrrjuuuhCqIa3+RZUYQhRO8NkLl79/Vfj2t9di8/c95nwsScmnpjxdRMVr1VW3MabbwpvZ01At6PgRGvSoPulQstm8OTf3Y3TfvXztp5Xic0z0fyQpC8fDV8Xmo7B97wbmVNPjY5zpw7ANAd4RJTrByOxtlbt1H3OSQCAUgrYM3m45nl7itCS5qdQHocSfFa01oZiVq/32RrwRjphxZgXYuT7mvc9I/1fFTnDjBHVrq0oUfPJBNHYsAEdz3pmYgYONR+DESIiIiIiIiIiANu3/wTP+tLT8e/f+avwMa1kIeXvizYrGKko3ibd5J40gpoICRtCThOMuF4wcqicDCE0xw9WMlEwMvxP/wRh+APjS/7zojmbu0Grof984D/rPm+79c9TsIvoKHtrsc55IdZ/5ctIn3gi4G9Ypitei6bAhq98uSnrXUqUdLr9G6Wxigqty5t30JOPZowEVT3CiNYVb6VVvQUcBCJpuzY8EQvRSguA0d0BwAt79owfXJA1LEeFyng0I0lr8eda9z6nwdylFWPeP4v9LQhgpwkGAUCzpn8eAFa+7a0QQjAYaTEGI0REREREREREAD71fx9EXhG4fuy+8LHUpLeTVklpUNLNGQ7dVfAuWx7blguvrnelHQ7vBYCTyslZBxWngqJVRFEmr2gPr7zPRsGI0HWoKe/Nsn7FSFBx0ioVp4IHjzyIM796Jr7w4Bdqni+6Zli9kvrzlyD79Kd7a015wUjWlOHPsnfdidBXr27pemmW4sFItx+MFKIZI1CDYCSq9oi30nKrdh6D5/rKtVfNiwWqGIHmBZKqC+yrUzFCrVEwJ6LPUQtbaQGA8IMR3Q9fg2Ak1cQ5RuH390yttOqEgmpvLzZ+5ztYfd2/YehD/w+dL3yh94TOdoKtxGCEiIiIiIiIiAhAWtRWbGTz3iZXsbM5oQgApK0o9AhnhEgH0r++/tt79mOgqh2L6ZjYPbkbAKDaKWT8Ab9asL/cvzJxvNC9Xbq0vxE4bh2B485/zshxPcfVfXzn+E6879fvQ8ku4eP3fDzxnJQSJddCR8m737myL1pn1tsQTZtRxYjUF2iDnGrFghERBAhObPi6H4zE22Bp08yeDj7vXXVyuoWqGAmqXlQXOFSonTFCrVE0JxMt2VopqLww/FCix+9cdfIzntK0c0jF+12RzvTD17U6wcjmH/4A6ROOR9d556H34ovD2Tet/nNZ7hiMEBEREREREREByCm1m1CGf2V7Jdu8TVtLja4CjipGHEi/P1FaSmSr2rFUnAomKgUAwIAs4UTT21kOg5ULX4Lcs56FwaveAwAQ/iZdyt+Eu2/8f/HWX7x13mt3q0sAfNvGtmG8Ml73OdM1YUsXOb9ipHtwRfickvGqELKV6GpuuVCVA1QrFozAnzeiyHgw4oUlxoaN4WHxVlpK1R5xUDFimLWfo4WqGAnCHdUFDpeOAACuvuPjeOOPLoMrp9/kprmz7FI0fF1t7RZ18NnSbWD1EYmh3d7nNrV2TRNP4v9zhs+McJLf7Z3Pfz7Unp76x7KVVkuxHoeIiIiIiIiICECnEm3MutKFIhQEozncJm5QOUpUmaIF748oGNGkRLpqc810TOwd96bAZ6SDZxfLuDedDoMRtbMT6/4rmvshNG+XTo9tUt+257Z5r/3QZLHu49vHtmPSnKz7XNEqImMCqr8fmOnrDZ9TOnLeYxXACYYX8yrpRUPEK0b8YERIhEOzg2Ck/01/h8mf/Qzm448nWmlVb3cHn1dpCwDJDeKFGr4ezLdQXWC0chQA8NVHvwAA+OmOO2oOd+1c25a2lFlOBZmqgK1VVL9Vl2ED7/9q9KWoDzZ/xohssDLP2LhxyueCaiZqDVaMEBEREREREREByKnRxmywya8Ew82buEElY5vNxhENm/dJuHDg+hvFKgCtqtuK6ZiYrHi9qNLSxUDJ23wLNpqNdLKiRfhXYBvNmbkerb1mnLZn29i2cEB8taJdDNto2aqamNWidXQCADImYASb5qwYWTziFSP+5rUiEQ5fV4IqklwOw//yLwBmaKXlAv+172AYOCZO5VcPtVu8ldZ4ZTTx3PbDY7UvkAzumsG0y1ErrRYHI0rK+84xbKAvHz2urVw5xSvmwP9dkTPMGKkezm5s2DD1W7JipKUYjBARERERERERARBKFH6Mlr0N0qDtidvMuRexfbHMT7vwkRscrNtfCStG7D0GTv+Jjkw5OrBsl1G2vdkkz/ktsOWLXTh5uxsGI5lccgaK0Lwtn7JMbv1YjjW/tQvvhNcdOIyv7D2Ac3JXAvAqRqZSsAphG61yKrn5rXd2AwAypgxbaWnphZk1QXXUqRhR3FgrLS3a0A4GXMdbae3qr33L0/MmFOm9Qbx6asFaacWCkbw9CtuNUhtXynCq9mrLe1zUS3WoYYVysW2ttIJgJFupejybbdo5guHr0p2hlVZV6G1sWD/lsUqmebOtqBaDESIiIiIiIiIiALaItknGSkf8oeH+bpfW2k3bnolos3X81l6seUzDhb+NNtiOFIqoOF66cPavVQgIXHFTFIys7Klq76N6685WDfodrboivnHemlY4Dk4yTdz9+zEAwJ783ilfUbKK6PBDnkomuU61M6oYeeak9/NtXt0HWiRmqhiJbWgHV7fH27cV/Yxr7G/fHj7mmNF7VvQoBIu37Worv5WW4gIlZxymE1U+Wa4Z/rAf3TuCs+9z0etXbtH83Lv7UNsqRtSMF4BkYsFI15+d39yTzLJiRMjkd3JqmlZaK99xBbTBQay84h3zXx/VYKMyIiIiIiIiIiIAdmxjdqxwEF/+45ejmQhGM6sYZM0jwqndTOuOjfMYLxfCipFA2oqu3Feq2hAFFSMnPipwie3gC+cqcFSB0fIoBrIDc165K70AR/N/hiE3j3F4w+PjHNeB6lcDmJWJsGLEzHYmjgtbaVWAYCZ9trN5V3HTPNUbvu7KaENbiYKR4Or7dKyjWvD7g/6odMSxvNfYigpLM4BK/bk17RKvGDExiZIdBR9luxzeTt3agTftc3Hvoybw5rYvc8kpVMqxipHWblFnclkUAWQr0Xfv8Mc+1tRziFlWjFR//U81eB0AjDWrcdwvf7FwoeESx4oRIiIiIiIiIiIA8QY5Y8XD+Je7/gVGUHGht7i9kz9sXXWiXbNC7JRHiyVUnEr1q2DY3vwTfWgo8bjitziyx3S84HcSZ/7Be9/5Voykbe/1igsUDxu4EfU3F4t2tNltlceiGSO5nsRxapffSqsCZMa8bSqtjxUji0X6hOPD20KJKkaiDe3oSn8l51UDaS6g+b83wbwRNdYSyLW9TV5H1eCIhd+aDCpdNEcCkHj1918dPldyCgAARUq4+7xfyFO2S0hZG25SYxRhx1qytfZzkOvqABC10nJFC6pUwoqRZDDytYe/hvP+5zy85vuvwY7xHRAz5Ca1b8tQpFVYMUJEREREREREBMCWsXZWpSMAEM69EEZre70rrkS6AnzwK1HlhRXbtRkvFwDFgm7Vbsiqa9aEV70H4rMfAGBFKQ3ADGenzJULB4AK8XAaT9zdiZ7NBWALgKq9u6JVRKfhVYPY5TF0+Bfey85kxYjS2QUA6CpKdB321px75jPntUZqnpVvfSugauh64Qsw/rmPAqgORmorRgCvNdqkFlWMaKmUd3m2C7i29xpX1eAqCx+MBK20Mv5a9xX2hU8V7UkAQKoqCDFdEymVs3DmQ1HssPJIbXErLaMjGYw4rZhpEgQYVRUjN2+7GQcKB3CgcAC37LwFeuyjNPyv/9r8ddCsLYJvHyIiIiIiIiKihWe7UShxpHgYQDQvQVQNDZ+XOlebaw5wwW8lNh2IHou3JDpanoTpmOjL175dLnZVf8AZenLivqt7W0DzDUYcf/PPudsLOMa256DL2iuaC3YhvG2Z+XDGiNLVlThO6eoBAGzZD6i2gNbfh9QJJ8xrjdQ8Si6HwXe9E5mnPjW8wj4ejIhYCyShaYDmfRaCz24QjKgpI9yFlH7FiKuo0PxqjYUUVIxk6oyGKNreL1y66ne2XvUWNUbEKkagtDYYUfwZI2HFSCvON0UrrfhnZdwcD+fz7Pnzs9B9wYuavw6aNQYjREREREREREQAbDeqGLl/9FEAUcWIkm7t3AvVRRgeBOLByFhpHBWngr7J2temNm+ueUyuOz1xv9NqTiutemOFu8u1vWEKZqyVll0JZ4zoPd2J40TVMPbcM89k65jFKghG3PrD1wFAGN4xmapgREsZEP4ueNBKy1U1rH7xBQAAuXpNK1c+LeFXjKh1WhwFFSPxYMQVQMVmMDJvIqoYKbutbU0WfM+kLe++04oKlSlaaZlO9EU+UhpB8KNqGiuOFhqDESIiIiIiIiIiAHZsgPjvJrYBiLfSytV7SdOoLmBWNTzPxIKRCWsCplNB32TtBqKxcVPt+6WSm25bnKMA5l8xYguRmIMCAGc9WLujPFqOEhzLqYQzRvTensRxIl0djDx7Xuuj1knMGPH/yquDESXlfYiDz25QcZVKp+DqXju6PZY3iN1RVKx566VYdfXV2PLVL7d49dNQpwlGHK9iJBXbuLcV4ImJJ9qytKXMlVZYMdLqWTNKVQDrtGDYu1CCYGTq6qIf7PhBGCoaWhOrEGlOGIwQEREREREREQFwZG09RLCxq2aaVzEiUL+VllV1EXMmdlF63pqE6ZpTVIzUBiNaVTDSYXu7vvMORgCsO5x8bNVo7Y7y0eJEeNtyzLBiJFUdjFRtWOpr1s5rfdRCdSpGqgdYK2m/LVVFAlKGgVjv0Aronb0AgBsr5wAASlJASaXQ89KXQB8YaMMPUJ8wDACA7tZ+jit+Ky099jtrq8Bf//iv27O4JUzaJXT5n48nDfe09Fwim5xt5CotCEaCihF36ooRIGpDp6mtnVtFM2MwQkREREREREQEoGTbNY8FFSNpf3hvU9TpGqO6gKUlW0ilbOCd/+Pgim85qFijcO3CFBUjG2vfL2Uk7gfDz+fTSsuVLlYfAd7/1WSA1D9ee+xY2ZsxUnEq+MQT/xu2CdO7q1pp6cl16kODc14ftVYQgojYjJHqodlKxgvkciaQKwOqf1z30EA4y0M63naktUi2JYN11asYMe1JDI9IvOGb0e+m3dpxGMvGmh1ReKporf1DFdnk97ejtmC2jQiHjCQerp5HE1RbyRZXIdLMFsc3EBERERERERHRAitaVs1jG8peMpJqZjBSh+pIVM8w7x+XOP0xiTMelUgVx9Fz+Lc1w9czp54KJVPbkkXv60vc77s7AyHlvCpGHNfBy//PRUcZsNavxaoP/yMAoNff39QtiRf91sXgqMR4xStt+cYj38C4UwrnpehdySu3RdXwbW3lyjmvj1qs7vD15Ia26gcj2YpElz9mpqSloKVT4d/1X449CAAYWtHa36nZEv3HAYg2rOMsewJXfNvB5l0MRpotm48qKfTVq1t6LiWX/N6xNWOKI+cuaKVVgY3vbf9eGIjUBCP+746bWhyf/+Ws+XVDRERERERERETHIAe1O6ODJQc2VGSrhoY3m+YCblUnr2xsPy1tVqCLg+ib9Daeu190LvT1x6P/LZfWfb/cM06veSxdAfZOHJnzGm1poyfv7eqVL7wAmZNPBgD0+MHIs/4g8bqfudi0X2DPKaP459/+M36z/zcAosobPZtsHyOMWDCiiJqghBYP4f9+xIMRRU2meVrWC+myJtDpt0kqZrwNYOl4HwJ1/x4AQFdn89rTzUvaW4daWzAGaR7G4FjyMQYjzeG6XhB9cGMXnqS1dotaZLsS91tRMSL8OSlFmHjvr9+Lh48+jLef+vawReNZhSJ+mctCBP+a0dlKa6GxYoSIiIiIiIiICIAja4MRxfY2frM9XTXPzV39GSN61VDzoP8+AGQrFmwgnDHS+xevxsq3Xgah1N/aUXt6sPrfPpl4zLCBkp2ve/xs2I4VthtS0jnow8MAgHRFIFOWGD7qrb+7CNy083P48h+/jG1j28JzA4BRVd0Sb6XV/3d/O+e1URvYXj82xY0PX09uaCs5PxipAF1F7/NQCjalneTvl2jxZvhsGWu9uTZ6SUGulPwdLMKtqSSZ5MzseStaRRyRXsWI3oYh5CKX/P5uZcVIMH/nm49+MzFfZKXjBSRRtRW35Rca/waIiIiIiIiIiFBbMZIzHZgT3pXFHb3NC0YERM1jqhtVVZgnnVLzfMa0YEOg1881tDUbZjxP1/Ofj8zTnx7eT1kARO2A+dly7HIYjKjpHJRcDmqHt6m5ciKaNZI268xBCYKRaSpGRHqRVBBQfcNPBeBt7Aabv2rVbAgl6/0dPqlghhUjVlf9aqvFEoyoXV1hyLfusPfYZUfHAABFRdQEI6VU7e8vNebR0UchXH/ukNb6WRtK1fB1xWhBMOJXjATBR8kuTRuMKCpLjxYagxEiIiIiIiIiIgB2VcXIy/4v2uBPdzWvH3xnunZDWHWi8AB15pmkTQeu3Q3NBaSQs57F4ZaispNLbnEBISFlnenvs2BbxahiJOVtgOtDAwCAleMSK8e9901Xj2qRMvzZUrlk+JFonaVww3kxE/5MhPjw9eqKJSXnbXKfNmnipYe9ISNupx+MVH3uFkswAgCpE08EAGw46K0x46/VEaJm81RxJaTLTe35eHz8cah+RutqqZafT6TScGJfL/XmMs2X4n9/KbGPeTBfRJMSnX4QFFVb8TO00BiMEBERERERERGhtmLkgjuj22quecGIVmf/X4tVjIjO2uqUVMWFUfS2ccoZY9abyjIWjJzyuLcxZ8s6wxRmwbFL4WamkvI2FvU1XhuilePAQFgxknyd6kabhanqipFYMDJVWzBaJPzWP4obVYzoelUrrQ7vynzFEnjShPc5Ez29AABZ3apOXzzBSPrEEwAA6w95P1hqmvBQcwChOLDc6gSQZmvb2DZo/sfBbdOsDSv2cRO9vU1/f6F4QYeIfXSCihFDSqRlMKPHO0AwGFlw/DcOEREREREREREAu87sj0BwJXyzdZ/ntbpSXUAPulx1dtYcl65IqJa3sWYbs99Q63je82oes525BSO2GasY8QMNfc0aAMDqEYmegr/WqmDEiJ0u3TFdxQg3ChezYOM3OXy9qpWWX+3k2gJOxdt21KbYhE783S+w1Alexcj6QxK6lNMGI8EV/0Wr2I6lLUmPj8UqRtoUjJixYETv62v6+9cLRipWAUJKdJddZKorRjR+3y00BiNEREREREREtGz9bOev8KH/+zhs154+GMk2sfVKbNM1tcpriaU5sYqRjtpgRHcA+MGI08CG2sq3XlbzmDnXYMQuRTNG/Nkg+toNAIAn7Yq1Hau6kD4ejCipZNucRJUIO2ktbko0QyHY3NX1qmDEb5uV35vB+E4vBDP6vU3o4Q9/OHGsUBdfxcimQxI/emIvUlJCTBGOBJUO4+VCu5a35HgVI96fr2u0Z7ZQPBhJ9zc/GEmr3tyS+EwaZ2Iv/vaHLj75SaDjSHIGCVtpLTwGI0RERERERES0LEkpcfmtb8Y3t30BX3ngJ7BiwYhuxTb6TzqpZW2egqvmVdcLRwBA1KlO0R3Atf1gRJ/9hlp1L33VkXMORqzY8PXqipF1R6LjDNubwxAIAh9bFRBi6vSDrbQWNxFrpRVs7tYMXw/micRkB/oBALkzz8Txv4n60zmjoy1aaeOCz7FwBfpKQPdjOr74rw5O2ebWHBsEIyPFyXYucVZMx8Sbf/pmfOHBLyz0UqY0aU7iYPFg+H3nGs2f91FPPBjJDcxuRlMjUkEwEsvTbLOAc37vPTDwnS68+5tOGByzldbC479xiIiIiIiIiGhZ2jO5J7z9+90jsEW0CRrMy7BTGWz4xtebet74dejC8DbTNCdqwSLSta1lNFsCjrc+dx6zGbIVwHTmNhvBilWMaP4a9OHhusemTWDzPolsORq8LmcKPthKa3Hz/35OL5WhO96HVU8l22EpXT01L+vwgxEAULuj4MTas6fm2IUiVBWK7gePpkDPbTmkbODC39RWjQQt78ZKi69i5Lvbv4tf7f0VPn7Pxxd6KVPaMb4DANBpe3+2FaU9LdU6Yt/vXauaH4wEc5/irbTKdrLd2qnbJNYd9m6zldbCYzBCRERERERERMvS3bt+Ed5WFRXxuGBo1N+0G1w1bZXDfAktar8SXmlcZ/aC7gDwAwZnHrMZcmXAnOPQaMuphFd5BxUj2sr6G4zPeFTiIzc4+KcbnLBixFVnCkbYS2sxCypGuhwZa6lmJI5RumpbFHVPsQlt7dvX3AXOk+oHI64VfU7zdYoZgoqRo6V8O5bVkMOlwwu9hBmNVcYAAB1+BdzE3ArYGrYS0Yl6Vg00/f0VvzWcEmvBlrem/oywYmThMRghIiIiIiIiomXpoUe/E962ZQn7/UKMcwpFPGXE20STq1a3dhGaFzDE5zYoqorxZzw7cZhuA/CvsHa1xoKR1dd8IrydrQDWnFtpVcLwRjP8TUB/2Ha15/3e+2FWHwVe9RvvfM4MwYixbv2c1kVt4leMSAcIBsLoRlXFSHdtMNJbtQmtrvQqSLTBweavcR6sTm/tjhl9Tu06e9dBKDReWXzByLEwEL7iVAAAhh+yok2zZlQ1CiyMFc2fMaJnvEq/V45Gn4vxyiTcKfJezhhZeAxGiIiIiIiIiGhZmrCiVji3jnwWAKDbEm/6BnDBL7wtE23N2uafOHZFsQiCkXjFiKrAHkoGMroDqGFP/saCka7zz4exyhvonitLmPYcgxGnHFaMqH7FiEilkr1jfPE++09/yN8ZTNVf97ovfAGD770KuWeeOad1UZv4FT1ubKdXqa4Y6V6RuC8FkO3rSTy2/oYb0HH22VjzqU+1Zp1zVFqxEQBgl6PtUkcByv7HtvPiCwEAmt9GbKKy+FppFazFt6ZqZbsMAPALdHD6luZXb9QT/9yqfc0PRoysF4x0WRIdrl8NY+ZRTNU/vno+D7UfgxEiIiIiIiIiWpaCDToAMGUJAHDW/RLyQLTZm9mwrrWL0KKBvUELFqGo2DzckzhMd4CU3wHL1ZOb0bOhZr3duWwFsNy5BSOmVQ6rWrQgGBECilYbjGys1J6jZ6h+S6Xcn5yBvte+tqUty2j+gtY/0on+nkRVWze1qoLISSk1f6+pTZuw9tOfQmbrU1q00jnq7AIAOOVow9pVEIaB2dOeDgAICg8YjMyN6ZgAgI07vM9FZ0d7hq/vtofC20qm+edU/dlQriOQCYKRSj5sJVitKzdFYkJtw2CEiIiIiIiIiJalst/SJW5gLLnJ37VpQ0vXEFaMSJlopaWnk5tmmg0YQSsto/FgRMl6G4HzmTFSsSvhfAU1VrVSSefC22q3t7lsHKltj2Os2jin89IioXjbiPFgpHoejshmE/ctoz1tkppBdHmf3XjFiCuimSL66i0AvN9FAMibi6dt1V0H7sJNj92E0tjO8DFXulO/YAGVi4fRNyHRUfA/R21qKeVYtQFuM4m09x1bPJTC33xPondSYqQyzmBkETt2vp2IiIiIiIiIiJqo5JrBqIRQT9UF173HtWAzP9ZKC3VaaQlVqbkSX3cAw88zpNH4hpra4W1Y5+YxY8Q0y7EZI9H6CpkV6JncAwBwxiemXkNX95zOS4tDUDHi+sGIFKipBqm+b+qNtX1bSGqn127OLkXBiBH7VTF6vfZLuiUgpFxU1Rlv+PEbAAA9jhMGDbZrw1AbD1FbzZzYi9Uj0XegOzHZlvP2Cmfmg+ZB8YMRK6/hlIeBtxUc3Pq3R8OqBH0oBetAFMZzxsjCY8UIERERERERES1LJVm7UdZdtdeZGmrBgOjEjJFYKy3/Am+hqhBV7bJ0x/ufd2cOrbQ6vRZH2bKcRzBSit5Pj661tbXZrUft6pzTeWmREN42opX3/+5nsaso04tvY34qQbWTHWul1W1GVRdqb09427CAghX9PiwWY7HNdmuOlWGtVjYL6I/lp/bISFvOm7bKMx80D6KqPdeTdwOjxfHwvlL9q6AwGFloDEaIiIiIiIiIaFkqobbVTE8hdiUzBMQc2lY1xA85FDeaYa6oKkTVgHXNjleUNH4VvuJfDT+fGSOWGW0s6rH1ubO88lnxZzjQsUmoVduIs9hVHDhua2sW0wJat1fRFG+ldUY+usJf7ehA8O2QNoGitXhaadUTzPJYbCpOCUOjse/ZyamrzJpJmq3981Ay2ZrHxkpj3rmBmllMNb9P1Hb8GyAiIiIiIiKiZamM2p7zPfnotqlqLR8InqgYCYMPtU4rrWgGidDSDZ9H7e4B4M0YsZzkleSWY+HSn12K9//f+3G0fHTK97CtaGMxPmPEFdH20ppPf2rqNbBi5NhWdYW7nMXvRrqrp0WLaT69xw9GStHPKWy/bRgAaBpczbufNoGys/gqRuIWa8VIxa5gaDS6v+JNb2rLefte91oAQPefX9SS9xd1gpG86ZUg2hqgKFX/vmErrQXHYISIiIiIiIiIlqW6wUjsInBbtGjjKjFjpF4wotRUqug2YsPZ5zB83Z/vkat4swfiHhp5CLftuQ03b7sZL/nOS7A3v7fue8QrRhQ9HoxEG+SdZ58Nc9Wa8H76L14dvYYVI0vLFLOsM6ecEt6urnxazFJ+MOJaseHrwaB5xZuf4mjed0LGBMr24ghGpKz/FxEEI38c+SMu/8XleHz88XYua0olK6oY6b/2k0gff3xbzjtwxRVY9/n/xtCHPtSS9xcDx9U8FsyosVVAiGSFomAwsuAYjBARERERERHRslSquuA9U0luMDpK67dNglkiqls1Y0TTEsfpThScKEayl/1sqD3e4OhsWcKsmjHyxMQT4e2j5aP4weM/qPseO4qHAACuAETsz0aKqj8nf0g1AGROOilaAytGjm3VBSJTbMiv/exnope0uhVdE6W6Omoek34wYqve76Oa9oKetAWYbmtnVsyWU2dWEhBVhr36+6/Gz3b9DFf84t3tXNaUCmYprBjp3LK5becVhoHcn/4plFSqJe+vdNQGv6r/V+OooiYYUVesaMk6aPYYjBARERERERHRsmO7NqyqVkArqlrdp1Jt2NTVvbZYihsLPlS1ZtNZc7zwBJhjK60ebxMuW6diZNdjySBkwqzt+X+gcAC/nHwMAOBW7Sa5VX+OSv/K8Haqryd6PJdreN20eHQ8+9no++u/Du+LKSpGVH9WB4CalnCLWaqzNhgJKkYqireZnuv0/pk2JUx3cVSMVP8+B8YqY5BSwpbe89vHdrZxVVOT4yVkTC9gNdauXejlNI1I134va/53tqsACmIBVioNtaenPQujKTEYISIiIiIiIqJlp+JUah67fHcyEOjItubK4rigYkSRsYoRRYG0k5ud8YoRoTcejCi9/QC8VlrVswd2Pf4TAECX423cjVdqg5F/vPMfofn7elZVB5jqWRMdw4Ph7VRvT3hbtOhKbWoPoWkYfPe7ogfcqY8NKMdQxYhRJ7iT/owRx68YEX5YmjYBS9Z+hyyEqWaJ/NUP/wofuP0D4X3Havx7oxWyR7x+hcUO9Zj6fMwkmOMUF3ynSwHI/o3h4/qqoZbPr6KZMRghIiIiIiIiomWnZBVrHuvLJy+Bb1kP+HozRtzkjBFpVQUjdqxiRG98M1HtGwAAZMuAXfWz79K9Td+tFW+4+h8OHkw8f7R8FLftuQ2pYP9VTW4nrehKtvbqHB6KzhurHmhVCxtaIFNUjMQdS620lGxti7qgYsT1gxHF/13RHcCRi6OV1lQVIwBw07abwtvSXRzBiCh7XyRWSpvhyGOL1tdb85gaVowIaJueGT5uDK9q17JoGgxGiIiIiIiIiGjZKdWpiugqVD2gtWbjLr6fHMwSiQ9fVzQN0kpeBa47sYoSrfH2RKKjB4A3DLhYHkk8t8d/v6eYXjAyVo7+bG7edjOe+/XnAgB6K15QpFX9uQx0ZxP3tZVRKy0lm0Puuc9BeutWpNo0ZJnaYzbXu+trjp1WSUqdVkjBjBFZVTGi24ADs32Lm8Z0wUh3qjt2b3FsA0u/Ms1VF8d6mkXJZiGyye9C3fG+1F1FAKno86UNMRhZDJZWNEdERERERERENAul8tHE/VMfc+Hel5wx0LKKkTjFO4ci4w+pMDZuTBym28ngpFHBrAfNBUaLUUWIK11MKgpWjkmsn3SAHkCa+/GxX38djxzdjt+MfjU8drhUBqDArtrQFFVD6tVY+ywlncLaz37WO46tY5aNNZ/+FIq/vQvdf37RQi9l1qo3tQFAun4w4v/OBRUwug1I1B963m7TBSNlO17VMosSHwC/O3A3Pn33x3HVM/8Rm3uPm+fqaknb+3OTShu+X9tM6+2FVYwq8lJ+diaVZKCtDw1Vv5QWAIMRIiIiIiIiIlp2yqVRAMCwZeOUSgWv+5/aNk9Ca30rLeGHDEpsXoOiKsid/gysuvpqKJ0d2HvZW6E5Eoq/SavM4Urr+BDssVgwUrJL6CpKXPsfDjTRiee+wMUDT87ji9s/XPMe/WUXgIKSWlWxUrUetasrOm86zUBkGeo8+2x0nn32Qi+jIULXvTKYevmBv6mtGN4/DQeY1ZCVNqgXjPz3/oN4w6rBxCwlQ6ltH1jPG398CUy4+NvvvwY/+8vfNG2dAdfx/txkO4LnNlNXrIC1d294P+0X/klFhNWBAKCtYjCyGCytmiUiIiIiIiIiolkoV8YBABnXxT8fHql7jJKtHcbcdGpUMRLOGFG8DbSel74E2dNOAwBorgj71c+pYiT2molSVC2TN/NYc1hCdwBhC1z6fRdPe7h+i6DeirfAo2pX4nG1M3nfWLcuOu8xNGOCGpM944yFXkJTCSGgTNWlzp8tItLeHBKvYmRxBCP1hq/3OrVrU5TZDYs3/Z/rkDO7IKVhrvf+bnXAugRofX2J++mwYkQkwmmdrbQWBVaMEBEREREREdGyU6qMI1uWePd/Cewf7K57jNLZ2ZJzGxs3oPKHPwKI2lB12S7y/m2hRRUW8UHrKcsLJubU4iu2KVc28+HtglVA32Ty0N4xG/Wupc355z+oJocMr7z8cpQffhi9r3wlAEDr78e6L94AhdUiS9Lwv/4rRr/6VQx/9F8WeilNJxSJutNT/N8fkfHa7Rm2BBZJK614MPK07S7+6ucujNMUYE31kbMLcjKui5LSwmvpg4qROcxKWuzUFclgpLPk/dNVBNTe6HtTZ8XIosBghIiIiIiIiIiWnVJlAmf/XqJnUmBssn5liNLZUffx+VrzyU/i0Mc/gRWXXBJWjGgS6HFcuFCgqNF2TdC6BwBS/v6nqs+vYsQyoyvB81YeK6qCEc2pP4ugUukAYOK4DYOJx/XBAWy66duJx3LPeEbDa6RjQ/cFL0L3BS9a6GW0hJxiXIfqVz4pWe87QbcBiMVRMWI7UYXXe7/hran4817glORxQswuyBm0Hew0WhiMuP5Acq22feGxTutbkbj/ml/6IZAikH7yk6PjWDGyKDAYISIiIiIiIqJlp1yZhD7DPqHa0aKKkbVrsebaawAA5s6d3oOJ4euxTUm9NhhR5lAxIhQFUgBCApYdXWH+y12/QHchGYRM9eeiVbzznrB+ZcPnJzoWyCmyjoFeLzwVWe87wVhEw9cts4BTtrkYjjrkQZq1wYYUsxu+PuA42Anve2fSnESn0dzvQccPXqWebur7LgZqVSutgFBUpJ/yZHS96EVQOjqgdrShTSPNiMEIERERERERES07JTMPdYZ9zWw7qh78kEPGg5HY0HchBExFg+HaGC47kFATzzdCKoBwANuKrjD/38e/h4uCAcF+cKLXuWpetyU6/coSbcWK2gOIlgAJFfVaThlpr7pBZL15OoupYqRSyeOqb868Fll3qnwtPfZltDe/Fyf2nTjntVW799C9UKQ34V5qSy8Y0VbUD0akqkIoClZ//F/bvCKaDoevExEREREREdGyU7by0Nz6G4WrP/lJrPqnD6OrDe2CghkjkCKsGqmeIWL7Q4qF7c0+UObQSgvw2rkAgO1EFSOWY0UDgrPeRqVRFYworsQ1/+HghG1ew3xtYGBO5yda7Lovuqju48HgbJGLghEhXEg5u7ChlSpmYdrnT9gt8cEbbaw9NLsgx47NBdozuWdea6v2hQc/D80PpKWRbep7LwZq3xShsTK3MJtaixUjRERERERERLTslO3ilBUjmaedDH1wsP6TzRarGBH+0OfqVlkdnRm4R0twHeG/ZI6bbIp3pbbjOnBcB4pQMGnlw2BEdGSBQjlRMfL6JwpYcU8aA+PRY9oAW2nR0jT4vvci/ZSnQF89jD1vvjR8PAhGlEwGAGD43x2O60BTF3Z71axMH4z8443eYq/4tgP5Lgkh6gyXj7FEdB19s4MR2Gb4vSua3KJrMdD6eqd4glvwixErRoiIiIiIiIho2ThaPorX/fB1+PLYg+GVy9WUbBuvZA4rRqJ2WtXBiOpvxsKdb8WIdy7NAUp2CaOVUVRcM5pd0uUPlvb/XM4rFPFnXzVwxiPJq+KNNWvmdH6ixU7t7ETfX/0ltJXJ8C+sGPF/FzMV73HLXfg5IzNVjAR68oAjZ16vrcSCkXxzg5EnJp8IK/UUY+kNX1d7euo+rqhGexdCs8JghIiIiIiIiIiWjY/f/XH87tDvMO6a0KboLBNcFd4OYdssKbz/AVDV5HaN2t2dvD/XK9T9VlqaAxSsAg4UDgAAOkxvozLV650naKV1UqkSrikw9IF/QGrLlrmdn+gY4YyNJe4HwUgQmPTmvd+Zsm1ioZUqE7M6ztRnF4zEK0aOlo9Oc2RjHNfBrsKBsGJEMfSmvfdiofb31308p3fXfZwWVkPByEc+8hGcfvrp6OzsxMDAAF784hfjkUceSRwjpcQHP/hBDA8PI5PJ4KyzzsJDDz2UOKZSqeCyyy5Df38/crkcLrroIuzZ0+TSLCIiIiIiIiKiKvceuje8rU4RjIg2tj2pnidS7/xqT3JTTapzu841qBhRXaBoF8NgJOcHI+m+HgDR8PWT9yc3UQ+v6Efvq141p3MTHUvST3pS4n4QjAQt9nrz3uOWUzWQZwGUS5OzOq6ie+HETOxYMJI3Z/fes7Evvw+WtJHy/8jSmaVXMaIYBjZ9/3+x7os3JB4X2tL7WZeChv5Neuutt+LSSy/FnXfeiZ/85CewbRvPf/7zUShEJVsf/ehH8YlPfALXX3897rrrLgwNDeG8887D5GT0i3T55Zfjpptuwte+9jX8+te/Rj6fxwUXXADHWfjyMyIiIiIiIiJaunZP7g5v12ultfJtb23jahDOGIkTSnK7RulKBiOmnH5GwFScsGJEomhFwUgwY0TtDgZLe0HJqt3JdZRSuTmdl+hYo/X3Y/UnPh7eF377Os0PRjrKgGFJlG0LH7z9g/jvB/97QdYJAKUpgpGrDx1J3K9ogOVadY/9+a6f4/Z9twMArNgMkvwsq1FmY8fEDgBAj+0l0un00gwLUps3I3vKKYnHpM5WWotRQ5dA/OhHP0rc//znP4+BgQHcc889eM5zngMpJa699lq8733vw0tf+lIAwA033IDBwUF85Stfwd/93d9hfHwcn/vc5/ClL30J5557LgDgxhtvxNq1a/HTn/4UL3jBC5r0oxERERERERERRUZKI4n7HaXk8yvf9lb0//3ft3FFtSEIgJqwpLqVVmqOLWgsCKThVYwcLk7gQNELRoIZI0Flim4DQkqUd6UTr59M98zpvETHosypp0Z3/CoKpaMDwlAgTRd9k8B9h3+Hbz32LQDAG7a+YSGWCXOKYKTPcaE60XwgUwc+cPsHcO3zrk0cN1Yew9t+8TYAwN1/eTfsWO7azIqRneM7AQTBiIpMdmkGI4BXYSR0AWl5f/5SX7o/67FsXjNGxsfHAQB9fX0AgB07duDAgQN4/vOfHx6TSqXw3Oc+F7ff7qWO99xzDyzLShwzPDyMrVu3hsdUq1QqmJiYSPyPiIiIiIiIiKgR9x++P3E/mBMQMDZubOdyPLOoGKke6Lt+oHNOp3Jjw9cPTIzhQH4/dEvCqHg7ofqqIQCA4QCXbC+iPJq8ytns6pvTeYmORfEB7PaIF6oKIaB1ezOIevPA0fJYeIyUye+TdrGmGL5u2BKdsfDXVoGf7foZKk4lcdxIOQqMd47vRLym5HBhvGnr3DmxEwDQ5VeMLOVgBACUdFSPIPX2za2i2ZtzMCKlxDve8Q4861nPwtatWwEABw54VxoM+mVlgcHBwfC5AwcOwDAM9Pb2TnlMtY985CPo7u4O/7d27dq5LpuIiIiIiIiIlqltY9sS91dUXXdpbNjQvsX4ZlMx0vHc5yRfUydMmY1M2gs6NBfYXziIR48+jH7/z8Ay0tD6VwAA1pQd/MW2MgBAyUTnUlesmNN5iY5F8d9N++DB8Lbe6wWTfZMSldjwdVsm543cc/Ae3LLzlpatz5Uurr/3etw5ub3u8ykL6CpG9w1/ebfuvjVx3KFCFIxsG9sGG1HAU3CKaJYgGOn0W/Xlsulpjj72qanou9PVl/bPeqyaczDylre8Bffffz+++tWv1jwnRLLXpZSy5rFq0x1z1VVXYXx8PPzf7t276x5HRERERERERDSVSStqC6PbEj1Ve37GunVtXhHqVoygKizJPv3pyD372eH9uQYj3Z3e5pzqAP/98MexfWInVkx6m5SF7hUQae+qZsOR2LXXqx7pfunF4etPP2n9nM5LdKyzj0TzOrQVXsu5vkmgHKu+MB0z8ZrX/+j1uOLWK/D4+OMtWdN9h+7Dv9//7zhk5es+r5lAZzEKOQy/FOSz9/xP4rjHj0ahz5HieKJiRCo2bLc5A+afGH8CAJAzvTWpuWxT3nexiofK0BqaZkFtMqdg5LLLLsN3v/td/OIXv8CaNWvCx4eGvH9pVld+HDp0KKwiGRoagmmaGB0dnfKYaqlUCl1dXYn/ERERERERERE1omhFSUhfnS7dIrMA7U7qVIzUqyLpffWrojtzDEaUlFcxkortfHb6fySVzh6IlBecSFcgPept8va8/BXhsV3qwrQKIlooKy/3Zm8MXnVV+JjuV1b15SXKVjl8PN6iynGd8PauiV0tWdv+wn4AXmu8ehxLqVsx8sjIY4njJq1oj3aiXExUjAC1gc9cHS0fBQDoRe/CeCWXa8r7LlZqKhaGqAxGFqOGghEpJd7ylrfg29/+Nn7+859jY1XvzY0bN2JoaAg/+clPwsdM08Stt96KM888EwBw6qmnQtf1xDH79+/Hgw8+GB5DRERERERERNRsBSvqxd8/WbvJP1O3i1YQQgDV560TfMTbfNVtvzULaqe3ERmfOxBslkrDgDC8YEi6Aqrl7bbG22dlTnrqnM5LdKzqf9ObcPzdd6Pj2c8KH9MGBwB4FSP5WLVGPEAo2IW6jzfTkZJXxaK59Z/X7srh9Mei77kwQFGTM0bMvXeEt//joU8iVQAu/I2LbNl7bcVOHj8XUkrY0saTdkk4I15Aq3Z0zPt9FzM1HX2PC1aMLEoN/a1ceuml+MpXvoLvfOc76OzsDCtDuru7kclkIITA5ZdfjquvvhpbtmzBli1bcPXVVyObzeLVr351eOwll1yCK664AitWrEBfXx+uvPJKnHTSSTj33HOb/xMSEREREREREaEqGGneTOH5UwTg+BuYSv2ARl+9OrztVua20ap2BcFItFmqB5ulugGR8lrbuKZAsAI1l8PmH/8IlW3bkPuTM+Z0XqJjmdqRrGzQhlYBAHonJb56MJrXEa8YiVen7ZsYa8m6DhcPAwCy9hSB7kEDzzoY/a4r/k2hJIMO9YmfA51GeP/P73TxZ3dLKK6C7/ypQN4so3eexXTB/JU3/igqb1nqFSPKuq3Ag7d5d5b4z3qsaigY+cxnPgMAOOussxKPf/7zn8frX/96AMC73vUulEolvPnNb8bo6CjOOOMM3HLLLejs7AyPv+aaa6BpGi6++GKUSiWcc845+MIXvgB1jqWgREREREREREQzyZvR1d0rJqc5sM2EokA63mXfQq2/yakYBvre8AZYu3cjteW4OZ1H7fL2ZtbkXQDeHowerxhJBwOCvTVIISCyWRjr18NYz/kiRACgDXijBHoKwIHi3vDxeDAS/6559Mj+lqxjrDIGADhrQgeQnAPiXvgSKN+7KfFYUDEiFBOudKEIr/LMFskKtB4/P153xAWgIG/Ov2IkmFPixE6lLPWKkeEtALxgROaW9s96rGooGJFy5l6SQgh88IMfxAc/+MEpj0mn07juuutw3XXXNXJ6IiIiIiIiIqI5m6hEm5X9E4toXoaqIJh4LJSp23kNvuud8ztNjzc0+tmjFXwMOgAgbft/DoYBkUpeFu6kswvSXoxoMRNZL2CsbmFlOdHwnniLrZHyoZasw5Fe0tFpFgEkf0+VNWtrjldj6y1aRXQY3ma9VdWaL+uPTRkY8/5ZMMuYryAYsWPXxC/5ipHOaEb24Kr+BVwJTWVuTSmJiIiIiIiIiI4xhcLB8PaKOsPXF4pQYr3opwlG5kvt9jbqFDM6RyqoGNF1iHRyo1Lksi1bC9GxKvg9qR56XrajACHetm+ktLsl6wjChh5ZqnlO1KlQiAcjk7GKFquqYiRb8cLSlX67wXwTgpFgGL0b+3pTMvPsz7XIKZ3R38HAqhXTHEkLhcEIERERERERES0LlZLXkx9SYsPBRVYxEt5uXTASzBCRsQ1S3a8YKUgVIpVOHG/E2qITkUdk/IqRqmAkHiDkDzwY3p4sPNaSdQQVI5pT+11WrxpDkYDwuwGNl6NgpLqVVsYfYdQzCWi2RNFqQsWIP2PEivUuUnt75/2+i5kaqxhR+F26KDEYISIiIiIiIqJloeBXY/Tmgd4C4CySLlFCjXYLW1kxItJBMBKdw/ArRiYcBcIwEscbvT0tWwvRsUpkvEqA6mBkvBxVbpTGoyqRQ24hrJhoJsu16q4DAERH/TZVih+KjpajIUtWVbu8rD9SRAHQPwEUmjhjJOe/VfcnPgmxxGdNp5/y5PD2Um8bdqxiMEJERERERERES54rXRT9Xvo5/wLoQhqwlIbGr7aGGm+l1brTBC2AgmDkz37r4gV3e89tWdPnryO6+jz3zDNbtxiiY1R1MGK43u/MZLkYHmM6ZnjbFhKHg2q1JgrCFs2uDVPVKQabB2uOzw2xq7aHM7EcZGBMomjNPxg5VPTmrHT42VFu1cC833OxS23ahN5Xvwq55zwbqc2bF3o5VAeDESIiIiIiIiJa8kp2CVv2SnzqUzae+QfvsmlHAeQiGC4uND26LVrX4ktkgmDEu//6n0U9tTav7vUHrUd/Ht0XXNCytRAdq0QqBcAfvi4luv2AYrISzRWpxIIRANg92fw5I0EVhmbWPqdk6s8Het9NNnRLIm9G1S3xihHhSmRj7zcwDpTs+QUj45VxvOYHrwEQhdKZvqXdRisw9A//gHX/8R8Q2iII4KkGgxEiIiIiIiIiWvIK5XFc9l0HKyeAl93uhQ+LJRiJzxgRSguDkbR3Fbl0BCCT5wk2ewNKLgVj/fqWrYXoWBXf5FZd4KI7XDzrQReF2NwO0zGRqUh0F7zfsz2Te5q+jnDGSJ2KEUzRpurExwX+5hYXRStKP+LBSKYqZBkYkyhX8piPB4484C3JkUhb/vK6u+f1nkTNwGCEiIiIiIiIiJa8/NiOml78rgK4WPhgRCjxTczmzyIIz5P1gxFX1GyAwk0GJZlNS7/VDdFcCD2q8Nq8H3jBrxW89XsuirEAoeJa+H83OvjPf3PQOylbWjGiWrXPqVr0nTL8L/+ceO6EPcmB6lbsO/CkyeSbDY4BFbOI6+69Dlf96ipI2XhwW7a9cwXVIi4Eh5HTosBghIiIiIiIiIiWvOL4LpSTs8VhK8kNxAUTrxgR7jQHzk8wG0G6Ua//gJufTNzXujItWwfRsSwejPTmo6CglJgxYmGDN1YDL/idiz355leM2DIIRmrD3YGeLPrf8hbkzvxTdJ1/fuI5wwKKiRkjwF/c6uDffprH9XuOJI5dOSZRNifxH/f/B/738f/Fo6OPNrzOsuOdq8M/ZSWVXvKD1+nYwAZnRERERERERLTkFcpjqOjJx3oKQDqtwS3Xf027xCtGRCsrRlJe2OFUVPTFuuNkTj0VPa94ReJYbe0JLVsH0TFNVQFIACJRheaUo3DRdKPKi4ExYPc821HVY9te2Ve9ihGhaVj5lkvrvs6wgbIZhTipvIKX3i4BpOGc5a9TAJDejJGjlYPhsSOFMtDX2DqDipEgjC2n6w+GJ2o3VowQERERERER0ZJXMCdrgpGsicVx5XKbKkYUIyqZ+fvvezu6e/uBDV++EfqqVQAAY4M3V6TrVX/TsnUQHcuEEBD+18YHDo5GT5Si4eumHaUVKQs4NBk91yyOXYKQEpmH07VrVKbe8k1ZQNmMghqtHH3n7PplPwBA7/OGt3eVgNHJveHzNz/2Pfxwxw8bWmfUSsurrjEzDEZocWDFCBEREREREREteV4w4l8GHTfNBmK7JGaMtHI5sRZAw0e9f9pVO0Mbv/Ut2CMjMNata+FCiI5tQgGk44UMAVmO+tMF1RyAd8xIsfnBiG2X0D8+xZPTBL4pGzDNaD3pUm2Vmtapwxrxbo8VDwAp7/YPd38dP9z9dTxr9bPQacxuTkjR9qpTgooRO8dghBaHhf+3PxERERERERFRixWsAtR6xRjKwg9fj29iCqXx4cazFZ+NELCr9k+VXI6hCNFM/O8Nx4x9f8RmjNxW3BXeTpsSlt38fn2OXUFfbDSQsWlTeFvt7p7+taWoYiRVqv1iVPuH4fo/Wsk8WvP8gcKBWa9zdPedAKIZI04HB6/T4sBghIiIiIiIiIiWvIJVQMqqDR1Wf/zjEKkUhj7wDwuwKk+87Y1oYU4Tb6UVGGpdDkO0ZAn/V9a1Yr+7FW/nf8f4DhRjFSNpC3BlpelrsBwTKya9X+DJ47dC7e0Nn1MymWlf65aiRCVdJxhB1xBcv5pM1gl1GglGRnb+EgDQ4bfSQmfXrF9L1EpspUVERERERERES17BLmK4zpDi3DOegRPuuRtCW8AtkgWsGEm5qZadj2ipCmaMxCtGRMULPx4aeShRnZY2ASnrfPnMk+06YcWI7F8JBQ1UpcTmoeiVOt85igJXBWABul379IFiA8GI//0WtNISXQxGaHFgxQgRERERERERLXkFu5SYBxC3oKEIqgYlrz29dSeqE4zsw6rWnY9oiapXMaJYXpXIo6OPQouN7chWABcmmq3k2GHFiDo4hIErr4QwDPS/+e9nfnElmoeiOLXBiBAKpOaFPkadYOTe3XtrH5zCiOr9GeX83EZlMEKLBCtGiIiIiIiIiGjJK9rlKYORBRevGOnf2LLTiDp9uhzJa2aJGuVVdgk4ZvT7o1a88OMPR/6QqBjJlgG0oGLEgQwrRtKrBpF+8pNx/N131W2ZV034g+J/uOOHmESdVlpKEIzIusHI/U/smPU6w4oRPxjRe3qnOZqoffhvPyIiIiIiIiJa8iquuWiDkcSMEaO2qqOZel71F8g961nhfcV1pjmaiOoJWmlZxVioaVXgShd/GPlDomJEAWBYddKFeZKQ6PMrRrLDw965ZhGKAEC5MoHHRh/Du297dxji7H/GU6IDFAHNDzRyZYmX/drF+oNRZUlPuoTZsFwLY7H3AQCjr2dWryVqNQYjRERERERERLTkWY69aIOReMWItnJlS0+16gMfwLr/+s/wvpB1rhYnomkFrbSsfNSMp2DlsX1sByatSRhOsjorU25+AOkIYIVfMdK5bnjaY4c+9P+QO/NPYXUo/lrHcffBuyEh0Wv5LbO0aGC71rcCuu79bK/4tYtX/srFx/7bwcUT3gmlO4nZOFo6CuFKvO+rDk7wu2+lGYzQIsFghIiIiIiIiIiWPMeyoC3SDCBeMaIPDrb13Iq7SP9QiBYxUWdHVbMlrr7tSwCAwVJyekG64kLKOkPO50G6Ej1573b32umDkd6LL8a6//5vKFlvXZZVwkRlAgDQZ/rr0nSs+uePIHvGGeh/y6WwMl7Lq02xOesnmF66bDn5Wa1xpHAQJ+4BTt4Z/exqV/esXkvUapwxQkRERERERERLnqw0v5VN02ixipF2ByOsGCFqWL1gJGUDD4z9HACwoZgCUA6fy1UkbNeGrjavVV5HSUJzARcC+iwrzYSuATBhOMCPt30PANDtmAB0QNPQ8+IXo+fFLwYAlLuG0YF9idfnXBeQEpYszup8R8afgGEnA6E16wdm9VqiVmPFCBEREREREREtfabXysatnT++4IQSb6XV3k3DVR2zm0lARBGh1j6WsiQqGAUAbCglA5BcBag4laad35Uueie9L7PJdBpCm92174rurcuwgMfyTwAAUn6XL6En1yxS6ZrX9/4hhf++1sHg3llWjEzugagqlOla0Ter1xK1GoMRIiIiIiIiIlryhOVVRtja4t4K0QbbG4ykFvcfB9GiVLdiJDbD6ALrkcRz2TJwqHCoaee3XRsbDvmJw5pVs36dSPnBSKyATvhFY1KrCkayuZrX996RQUcZOPuOwqzON5LfD6WqKE3JZuofTNRm/NcfERERERERES15wvSDEb3Opd4LzBkdDW9rK1a09dzSaf5QaKKlLh6MKGmv6sqIBSNqVRjQUQb+/Lt/jn35ZGuqubJcCxsO+sHIcZtn/TpheGuNhziW9CpPzFyyjV9H39SzQMY6Z1d6N1I8WDPbKT5TiWgh8ZNIREREREREREueankBgKMvvnGr9shIeFuo7Qluei6+GACw8m1vbcv5iJaSeCstY+0QgGTYIKrCgGzZCzH+59H/acr5LcdCxu/MJbpnH6Yq6RSA5FrXml75iEx1JI7tXdk75fuUjNkNkh8pjyJlznp5RG3FYISIiIiIiIiIljxheRt5rrb4KkbsI0fafs6hD34Ax/38Z+i+8MK2n5voWBevGNGHvEqLlN+eSpOyZphRzg8xDpcON+X8plMJq1IUo3YWyFSUlBeMBAPRV1s2OhzvtlIVGqudyaAkTrdnF4zkrUmkYyEM2hT8Es0GgxEiIiIiIiIiWvIUf/PPXYQVI7Jcbvs5haJAHx5u+3mJloJEMLJ6NQAgZQKZisT/+6KDIw8lQ4Wc/yvejGDkvb96Ly64+cIwGJGNBCN+xUjQ9muVbUP6IU718HWlY/7BSNEuJYKR6nMQLSQGI0RERERERES05IlgI0/XkH3GMwAAmdNOXcAVEdExS0TBgL5+AwAgbUo8bbvEln1AedRIHJ71K0aOFOdfHfa9x7+Hkl0KgxHRQDCiZbxjg+HrKSkh/fdRq4ORXG0wUtzstQ3TrZqn6io6lUQrraBihWgxYDBCREREREREREueGgYjOlZ/8loMXvUerPm3f1vYRfnST34yAKDrz85f4JUQ0WxIO7ptrPeGn2dMQKkqpAiqLoKZHs1qpQVEA94VvfFgJFhPWkpMuFnv/apbaXV1Ju5Lw0D+1K0AZl8xUnIsrJyIjlW6uma9VqJWW3z1o0RERERERERETSa82etwdR1aby/6Xve6hV1QzJrPfAaTP/4Rul/60oVeChHNgmNGM0TUFSsBeMFIpmrQuLFpI8r3P4CUP+PoaPkopJQQIjmDpBGGYsB0TSjhjJHZt6fSsn4wYgMrxiXO/04a6iHvjRwlOf8jdeKJifsDl74ZR49uBwDoNmalKC2sOxQFIyqDEVpEWDFCREREREREREue6s8YQQObiO2iDw6g77WvhTpNT38iWjzceHuoXA4AkKl4/4vLnnIKgKhCAwCOlObXTsuRXsqrhRUjsx9ormW96hDdAl79Sxfr9rrhc5NVYYc+OAh93brwvtANKOlM+PrZKDsO1sZ+XKWqCoVoITEYISIiIiIiIqIlTw02/TRj2uOIiGbixIKBMBgxgWzFC2Ann3k21v77Z9H5ghcAANKxIGVvfu/8zu0HI4rrnUtpYKC56gcbKVuis5R8rrcrW3N89vTTwttC16Fk/OHt01SMSClx/+H7UbSK6ByXiWN7X/WqWa+VqNUYjBARERERERHRkqf6rbRgMBghovlxEVV3BcGI5gJdftggh4bR8dznQvErNOIVI7snd8/9vDKq8ND87zRVm30wIjJeMJKpAE/bkZwTctL6vprjs6efHr1W16EafsXINMHID3b8AK/5wWvwN7dcgjX+SJXJgUFs+J//Qed55816rUStxmCEiIiIiIiIiJa8sGLESC3oOojo2LfyPf8AAOi9+GVhMAIAfZPeP9UO7zElnRx2DsyvlZbjOuHtYMaIasx+hLTiByObDtY+J7Ta98nFghFpWdB0L1gWMhnSxH37sW8DAB448mA4X8Q6YSsyW58yr9kqRM3G4etEREREREREtOSFFSOp9IKug4iOfd0XXoTs6c+ANjgIIQQUQ4Fruugf94IAzZ+lITJexUjaAiAlIAQePjz3VlpBGy0gNmOkTqAxFZHNTfmc2tNT85i+enV42z5yBOoq7+dSXC+kUdTaa+4tO3ps9VF/jZtPmPUaidqFFSNEREREREREtKRJKcO2M4rBYISI5k8fGgorIJSs185qYNx/rtMPELJehYYiAd3/Dvrtrp1zPme8SiOqGJl9Ky0lUxuMrL72Gqz68D8idUL98GLwvVfB2LgRPa94BXS/FaEqAVvW76e143A0gT5b9v6Z6a9t00W00FgxQkRERERERERLWskuhT3xlXTtgGEiovnQOtKwxyrI+EPWja4gGIm+bzIVwNKAihyf83lsN+rJFVSMqHoDFSOZ5PefldXR9cIXTvuavte+Fn2vfa13rnsNOIgqRupxRTTVPWN6FTS53q5Zr5GoXVgxQkRERERERERL2mhlNLxaW0t3TH8wEVGD1M5k4JDq9ltpqSqEX9DxxkMTAADTrWCuXDeq0ggrRvQGKkZynYn7TgNtuADASHkVMKoL2G5txchYeQwTeDi8n/aDoo7e7obOQ9QODEaIiIiIiIiIaEk7WjgcVYz4G3tERM2idiYD13R3VCEhUl67rac8Fgwerz+0fDYcxwxvB2FvYxUjyVZaJa2xCjpNSwHwWoPVa6X1nK+flbgfVNB09HTWHEu00BiMEBEREREREdGSdnRyD4xgDy/HDToiai61O/m9kumJghGrsxcA0HFPFsftlXBRvwXVbDi2lzSoUmLA8t5HTxmzfr2SSwY4ZbXBYMSfMVKvldZIaQSy6mcLKkaMTlbq0eLDYISIiIiIiIiIlrSj+X2cMUJELaP29CTup2PByETv+vD2KdvdmvCgEa7rJQ0KAHjjO6A30EpLdA8k309RGzq/GgQjErCcZMXIPQfvqzk+CEbis1aIFgsGI0RERERERES0pI0UDkJ3vF1EJZ1a4NUQ0VKj9vUl7+eillVqdzRfI1cB5HxaafnD11UpIV2vNZeeaiAYyfZCxlpvSbWxGSNqrGKkYidnpdy9/97ksY6E4WdASi7ZwotoMWAwQkRERERERERL2tFSfMYIgxEiai61b2V42wUgjKi91Zo10XO5MiBRO5tjthzHD0YASL9ipJEZIwCAWNWcnEfFiGmZiefuP3hP8jSxp1kxQosRgxEiIiIiIiIiWtKOlo6GM0ZUVowQUZOpK6IWVZYGCCGi52IhSa4MiPnMGPGDEUUC8CtGlAZaaQGAkk5Hd9TGtoaDGSOqC1ScqGLEdm1sG/9j4ti05a9ZEYmgiGixYDBCREREREREREvaUXM8rBhRU+npDyYiapDavyq8LVSReE5oUUVHd0EC85oxElSMyLBiBA22w9KymfC2JRqrGNF0L1hWXKBiRsHIWHkUlSkGr9s6t59pceInk4iIiIiIiIiWrIePPow78juh+3t2GitGiKjJ1IHh6LZSFYzEWl2tmATmE4zYjpc2aK4EpKh5/9mIV4yYUkxzZC3V8L4/VQmYVil6n8Jhb122RLbsJTZhMGI0Fr4QtUuDTeiIiIiIiIiIiI4dl/70UgCIKkYYjBBRk6n9g+FtiaqwIVYx0pMHVGfuM0aCipFU7C2E0dh32nyCEcTadhXLxfB2pTIBALj6BgdrRiTMV4ziWvR7a2YbLVqkWDFCREREREREREvWodIhAAgrRvQMW2kRUXMpnZ3RHUcmnut95SvDcEQB0JefRystP1QxYm+hpBoLHkQmaqVVrg5xZnqtGgUj+fJoeNu0i1AdiQ2HAM0ROH4P8LF9I95rdH7n0uLEYISIiIiIiIiIWu5g/hBGSiMLdv5g+DpbaRFRs8WHrWtu8jl91SqccNdvgW6vpdSKiaoDGmD7FSPBYHMJJCpSZkPt7g5vl91GK0aiECZfngxvW1YJfZPJQzN+Ky03xe9cWpwYjBARERERERFRSx0tjeHcb52Dc7/+53DduW8KzpmUYTCS68i2//xEtHzYtd9xSiYDpdsLFfryc/8OjFppeVUprioSocxsGOvXR+tqMFQRarSVXCiPhbcrVgH9E9Fxe37dh4ldfmVKmhUjtDgxGCEiIiIiIiKilvrWwz8GANhiHAWr0tZzP6n3eHRGM4KR6e2e+mAiohYRaa8NlWFJSClnOLo+x/GCkbUHvPtSabDiA4CxYUN4+0+OH2jsxWo0SL1QyYe3TbuM/vHkz5Tf5wUibqx1F9FiwmCEiIiIiIiIiFpq5OiD4e2CWW7ruUtju7Da7+Al+3oSg4eJiJolMWek3vOaFypoLuDKuVWNONIrffur//UCEc1q/H3iwUh/d2MVdCIWjBwqHgoDHssuJSpG4sqdfQ2vkagdGIwQERERERERUUsp2/83vJ2vtDcYKVp5rB7xNu9ym9a29dxEtHys/cynIQwDg++9qu7ziuZVjGgO4Mi5DWB3/OHr82Fs3BDelnaD61CireQ7jvwfPnb3x2C5Ft71h/9C/4Tf3mtwFZRcLjwu37eh+l2IFoXGGskRERERERERETWoZBcAfwBvu1tpFRQFq494G3bpTZvaem4iWj6yp52GE+65G0LX6z4vdG8bVnUBy7FgqEbd46bjujYMa25tuAJqT09429q3r6HXCiEAIQEpoLjAl/7wJXzpD18CgLBiRH3dJeh64jGMff3rAIBTTtowr/UStQorRoiIiIiIiIiopQoi2sgrmKVpjmw+CwLDfist47gtbT03ES0vU4UiAKBqXhCiOoDpzwpplOOaWDk+p5eG4sPard275/B6759KVRevlf6MkY51q5F9xunh491DKxtfJFEbsGKEiIiIiIiIiFoqH9uIK7a5YsQWwOqj3oZd6vgnt/XcREQBRQ9aaUlU7Lm1xHJcGwNj86sYiXMLhcZfJCQAASW+DCnDipHu9WuRXTUIbeVK2EeOIHXccc1YKlHTsWKEiIiIiIiIiFqqsEDBiCtdqDawcsy7b2w5vm3nJiKKUwyvYkRzAdOda8WIPe+KEQBY86nrofb0YPhjH234tfUqRnJlIO3/SLm1q6F2dGDzj3+EzT/+UWLYO9FiwooRIiIiIiIiImqpghIPRto3fN12bTxpt4QCQDFcqH19bTs3EVGc0KPh65U5DlH3gpH5V4x0nnMOOs4+O9FWa9b8l3TbDg76W8sd/td6RQeUdBoAoGSzMNatm/daiVqFFSNERERERERE1FKTC1QxYrs2Tt7hbSIeeuppc9sEJCJqAiXlBQaqC5hzbKXlSgcDsYqRVR/+xzmvZ67fhyLTCQDotaOA5t92HwEAqLo65/UQtRuDESIiIiIiIiJqqXjFSKmNwYjlWtD9/cdK31DbzktEVC2oGFEdoGKbc3qPimVipT9jpPcTn0TPy1/etPXNlms6AICBiSgYWVXy+mppKTYnomMHgxEiIiIiIiIiahkpJQpKtP1QsttbMaL6e3dC09t2XiKiaoqRAuDNGHnfHe+Y03sUzApWBkPON6xp1tIaIste36xXfi8KvFXTv81ghI4hDEaIiIiIiIiIqGUqTgV2rGVLeY5XSs+F7drRgGAGI0S0gIThtdLSHGBfYS8c12n4PYoVExn/K1Tt6m7m8hpm2AKQXvLs+sGImetdyCURNYTBCBERERERERG1TN7KJ+6X21gxYjkmVD8YERqvZCaihSPSOQDejBFXOhgpjzT8HsVKBYbfHlBJp5q5vFkbeOeV4e1nHTLxl+MT2P9bLxBxlM4FWRPRXDAYISIiIiIiIqKWKViFxH3TaWPFSCwYgWa07bxERDX8YCRneVUW+/L7Gn4L17TC2yKdbs66GrTikkuQefrTAQD/dF8eVx6IpsEPbD1xQdZENBcMRoiIiIiIiIioZaorRhzXneLI5rPtctRKS2XFCBEtnKBiJOd4wcjjo3sbf5NKFCwrqYWpGAGA7OmnAwAO/q4b++/qCR8f/v/et0ArImocgxEiIiIiIiIiapm8WRWMSLtt57adCpRg+LrOGSNEtHBEugMAkHW8tPbRkV2Nv4nlBSOuWNjvtM5zzw1vT+7OAABWX3stlFxuoZZE1DAGI0RERERERETUMtUVI65sfODwXNlOJWqlxWCEiBaQyHjzNzJ+NvzEeOOttITpvdhe4AK4zElbIdSo+k885SR0vfAFC7giosaxjpSIiIiIiIiIWqZQmQCkxN/90EUxBTzxijbOGLHjw9cXru0MEZFIeTNBUn4rrf2F/Y2/RxiMiOYtbI70nANzwrvm3njpyxd4NUSNY8UIEREREREREbVMvjyKgTHgnN9LXPhbCZjltp3bcs1wxojCihEiWkBKZxcAIO1/BR6tHGr4PYTlDV9fDMGIosrwtrF6eAFXQjQ3DEaIiIiIiIiIqGUKlXEY8bEi7twrRg4XD0NKOfOBvngrLaGpcz4vEdF86cNeeKDlFUBKTNqHG34PxfJaETqLoAeQiAcjnC1CxyAGI0RERERERETUMvnKeDgAHQDgWHN6n+8//n2c/c2zcc0918z6NbYTqxjRFsFOIhEtW/rwKu+GraCjBNjIo2gVG3oPYS2eVlqKFgtGOhmM0LGHwQgRERERERERtUzBnAzDCQCQTmVO7/PRuz4KAPj8Q5+f9Wu8ihFv847BCBEtJCWdRjHtzToaGPcey1v5xt7DD0acxRaMdDAYoWMPgxEiIiIiIiIiaplDpSNhOysAEE7UV+vHO3+Ml373pXh8/PEZ38dQGg82bMcKz62obKVFRAsrn80AAFaNeaHCZKWxihE43heaqy58MILYEhiM0LGIwQgRERERERERtcwThf0QiVZa0YyRK2+9Eo+NPoa3/+LtM75PujzR8LnHSiW20iKiRWM82wEAGBrz7h8pNFYxIlzvC02KhQ9GLKM7vK1mswu4EqK5YTBCRERERERERC0hpcQTxUOJGSOyzoyRWVWMVAoNn//Xjx5AMB9Y1RmMENHCWnecN4B9xaT3xZQ3G2stKF1v+DoWQTAysuLp4W1hGAu4EqK5YTBCRERERERERC1huRYcOIlWWopr1z320dFHUbCmDj/Ssb4t1iwHuDturJUWgxEiWmBr1g0CADrL3v2CWWro9WHFiLLwwUj25FMXeglE89JwMHLbbbfhwgsvxPDwMIQQuPnmmxPPv/71r4cQIvG/P/mTP0kcU6lUcNlll6G/vx+5XA4XXXQR9uzZM68fhIiIiIiIiIgWF8v1AoxgADoAIBZqGEp0lfHLvvuycMB6PZ0y2gi8adt3Z3V+TXHDVlqqxhkjRLSw1O4uAEBH2ftOLFjlxt7A/y5dDMHIiX//BlROeArcF798oZdCNCcNByOFQgEnn3wyrr/++imPeeELX4j9+/eH//vBD36QeP7yyy/HTTfdhK997Wv49a9/jXw+jwsuuACO4zT+ExARERERERHRomT680TU2P/dl24UjKhKMqz49mPfnvK9nFjrmC/+7udTHldxKrjpsZtwuHgYuhoFI6wYIaKFpvb0AgByQcWI1VjFCBZRxYiSzeJp3/kfPOWf/3Ghl0I0Jw3/V8H555+P888/f9pjUqkUhoaG6j43Pj6Oz33uc/jSl76Ec889FwBw4403Yu3atfjpT3+KF7zgBY0uiYiIiIiIiIgWoaBiJBULRoQb3dFEclvCtbqmfK9ybB/wicIfpzzu2nuuxY1/vBEn9p2IzcrGcMaIojIYIaKFpfb0AIiCkdJcK0YWwYwRomNdS2aM/PKXv8TAwWW26wAAjJRJREFUwACOP/54vPGNb8ShQ4fC5+655x5YloXnP//54WPDw8PYunUrbr/99rrvV6lUMDExkfgfERERERERES1uYTBix1ppxWaMqDLZOSLtept9pmPil7t/mZg58nC8uEQ/jJHi0brn/NZj3/KOP/owNCHDGSMVyY1EIlpYat8AACBb8r4Ti40GIzIogePYaKL5avpv0fnnn48vf/nL+PnPf46Pf/zjuOuuu3D22WejUqkAAA4cOADDMNDb25t43eDgIA4cOFD3PT/ykY+gu7s7/N/atWubvWwiIiIiIiIiarKglZYRzz/iFSPlycTx/coIAODfbr0Kl/38Mrzr+68DAGwf2w6YEi+420XvpLeh+KNtv6l7zpIda00j7LCVlgUGI0S0sLTjnw4AyBYBw5KwSiMNvV4sohkjRMe6pgcjr3zlK/GiF70IW7duxYUXXogf/vCHePTRR/H9739/2tdJKSGmKAO76qqrMD4+Hv5v9+7dzV42ERERERERETWZ5V8NnYoNXxexihENMnF82d/s+/oTPwYA3Db+CADgDyN/wF/c5uKSn7j46Je9KpRvPvpNlO3pr7Z2XTusGHnS2r55/CRERPOn9vVB7e6GgMDwCFBueMZI0BuQFSNE89Xy36JVq1Zh/fr1eOyxxwAAQ0NDME0To6OjieMOHTqEwcHBuu+RSqXQ1dWV+B8RERERERERLW6W6bXCTkVZCGTQCgYI538Eyv4Fk7pMPrFr4gmcst17rHvU28rYXrgLb/7Zm6c8d1+6D67rhMGIzuHrRLTAhBAwNm8GAKwekajYcwtGWDFCNH8tD0ZGRkawe/durFq1CgBw6qmnQtd1/OQnPwmP2b9/Px588EGceeaZrV4OEREREREREbWJZXozQgwnPmMkCkaqA5CyUCClhF5VSbLr6CMY7Yg94L/urgN3JY6bNKPWXIPZQbgyqhgRqgoiooWW2rwJgB+MOI0FIyL4/hSsGCGar4Yvl8jn89i2bVt4f8eOHbjvvvvQ19eHvr4+fPCDH8TLXvYyrFq1Cjt37sR73/te9Pf34yUveQkAoLu7G5dccgmuuOIKrFixAn19fbjyyitx0kkn4dxzz23eT0ZEREREREREC8r0g4qUI4FgxkdVGBLnCm9ge3Vg8ujhR/GkjAD8wKQ3D4x21r7+/sP3h7d1RYfj2kh5Y06g5HJz/jmIiJrF2BRUjAB7nUpDrxV+LiJVBiNE89VwMHL33Xfjec97Xnj/He94BwDgda97HT7zmc/ggQcewBe/+EWMjY1h1apVeN7znoevf/3r6OyM/ovlmmuugaZpuPjii1EqlXDOOefgC1/4AlRevUFERERERES0ZAQVI6uOxNq+xIav23W6wZSdciIY+d5jP8O2yj6c8Wj02PCIxGhn8sVSSrzpp28K7x/KF7CmFLXKUNiWm4gWgdSmjQCA1UckzAaCEcu1cFDxj2fFCNG8NRyMnHXWWZDTXN3x4x//eMb3SKfTuO6663Ddddc1enoiIiIiIiIiOkZYVhFPfdzFqQ/GQozYjBFb1CYjZbuMjtiw9vfefjm27E3uQ6w7DDy0Ifm6kfJI4v6hySKMkrftYWuAYhhz/CmIiJonmDGyahSwrPKsX/fI0Ueg+F+FaZXfZ0TzxXiRiIiIiIiIiFrCtAp49kNVF1fGZoy4LvDnd7g4LhZ8VOwKFEgosXDk+KpgZNOB2gs2nxjfmbivqi70ktdHy0xxUDERLQ768DAkAN0BjHxx1q/7w8gfwplJPSor4Ijmi8EIEREREREREbVExSyiu1D1YKxi5Jm/B17zSxdXf9FBt+O12CpaFax9QsENH3fw3Pu9YwfGZg5G9o4/kXxAODBKFgDATHH7g4gWB6EokP40AWnNvpXWweJBKMHXp8LvNKL54m8REREREREREbVE2Syio5QMMUSsPfeTd0SP6/7DBauC139bIGUDl37f2wVce8R7ruetbwMArD4CpMzk+44UDwAANptelYgrHegVLxixWDFCRItIEIzAMmf9mryZj4IRteHpCERUhcEIEREREREREbVExSqis6qFvi2j4esrxqPHDT8wGS2NQa0qCFlzxHug59nPBDo0KAA2HPSe+9tb/hZ37r8Tu8f2AwAG/MoTCRuKZQMAHJ3bH0S0eLhBMGJbs35N/uD94YyRfMea5i+KaJnhfxkQERERERERUUtUrDI6/Rb60i/aGEcFtusFFunYxdJb9ng7fg8cui/xHh1FiR6/HVdq0yZkNgwCAI7b7x1/x/478MZb3oiH9u8BAAzaQTDiQHG8y6tdlRUjRLSIBN9JjQQjxZFwxohtcMYI0XwxGCEiIiIiIiKilihUCsgG4Uc2BQBwIbE3vxcA4MR2JS79mrdR+MMnvpt4j6CNlujrhJLLIbN+FYDauSMmJr3H6wQjkv34iWgx8YMR4dizfsm4VQorRgrO9McS0cz4XwZERERERERE1HSHiyP40sjd0QMprye+IoFJ0wsxnDqFHPtK+xL3/+neCQBA2g9EtL4VAIAz/yiRi80vsWQZqiMxNAJASkA4UPyQhBUjRLSoBMGIPftgpOBWwhkjkyaTEaL5YjBCRERERERERE33ods+jVRsz0/qfjDiAkWrCFe6cGexK6Ht1wEAnRe9DACg9vUDALqLwOU3e7uE3a6AUd6HD3zZwdZv5vCSOyQgbExKb8CJq3L7g4gWET8YUZzZBxwFaeHs+70w+LRN/S1ZFtFywv8yICIiIiIiIqKme3D0bhh++3xLA+C3s1IkULJLcGwT7iwKORzTe13mhK0AALVvIHzu5J3eJmHGtaCaRZzodejCq251MXRUIi+9Pl5S4/YHES0ewv9OaiQYMcaipLm/M930NREtN/wvAyIiIiIiIiJqqopTwRHzcaTCYERJBCNFuwjLLtVWjEiJalbBrzTp6AAAqGuOTzz//33VwbonBPRiMmW58LcuNH/PUarqPH8iIqLmUcJgxJ31a2wn+n50C4Wmr4louWEwQkRERERERERNde+hewEgDEZsTYTBiJBAySzAMvM1wcjJj9cGIwG1sxMAoPQNJh5/6k6JK78hIUvJ8KO7AGj+RqJkKy0iWkQUtbFgxHIsxHIROBOTrVgW0bLC/zIgIiIiIiIioqY6XDwMADD8zi+WLpIVIwfvR6E0UtNK633fcNE7WT8cUfxgJHXCCTA2bKh5PltM3tdsQPP3HKXGihEiWjwU/ztJcyUcd+Z2WnlzEnpsZpM7yWCEaL4YjBARERERERFRU5XsEgDAsLyQI9FKywVKVhH5OsEIALzzW/U3CZVsFgAgFAWrr/lEzfO5cvK+7iBspQVWjBDRIqLpXotAzfZaD84kXzgII/bV6DAYIZo3/pcBERERERERETVV0fLKN1JBxYgmAHghiZBAEQ4KpaN1X3vc/vrvKZRoC0OkawcPZ/29Rdfv3W84MgxGXFVr8CcgImqdIBjRHaBklWc42g9GrKiazti4oVVLI1o2GIwQERERERERUVONlvIAohkjpq5AEd6mniKBomOiUBmHMvVIkWkpmUzNY5kgGMnlAAAn7IkqRhRNn9uJiIhaQO/pAwBkKxIjxZkHqU/mDyZaafX//ZtbtTSiZYPBCBERERERERE11ZGxAwAAww9GLE2BgDfwQ5Feq618ZWzaYERfu3bK55Q6FSMZ03szp6srfOyc33uPDRkrGlo/EVErGVueCgBYOQ6MFKYPRn6x4x5ccveHoftB7951J0DtyLV6iURLHoMRIiIiIiIiImoq8+DvAUQVI5auQAg/GHGBol1CoTIBxZ36Pdb9139O+ZyoUzEStNKS3X01zxmp2iCFiGih6MOrAAArxyWOzlAx8q5b3wcgCpqlnmrp2oiWCwYjRERERERERNRUBQFkyjIRjMBvZ6VIoOSUkTcnp60YUVdMXeUhDANQ1cRjJ+zx36y7t+b4gXXHNfYDEBG1kD6wEgDQNwmMV0rTHmu7Xg+tYPi6NIyWro1ouWAwQkRERERERERN1XFgEjdc4+BVt3klIULLwu3dBABQXaBol1Gw8mHFiP329yRe7ygKlFwOva9+NQCg73WvTTwvhMDaT38Kq/7fP4SPrRnx/qn01QYq6aHNTfm5iIiaQaS8qjdFzjx8XfptCIMZI2qGFXBEzcBghIiIiIiIiIia6mm/HUvcz2jr0LlpAwBg9RGJvF1BwSqGFSNi3QaUekV4fCWXhRACg+95N9bf+CUMXHFFzTk6nvtc9LzyVUgNJNvKqHWCEbWnZ14/DxFRMwndq/rQHG/m0vS8YMTwgxGDwQhRUzAYISIiIiIiIqKmymeT90czw+h82skAgI0HJQqOiYJdDCtGVE2FFFEwYmU7AXgts7Knnea1zppCevNw4r7e35+4rw2vQsdznjPXH4WIqPn8YERxgbJTmf5Y4SXI2Yr3T6Ors6VLI1outIVeABEREREREREtLflMcqq6a6Sh9nqzP9IWsNsZx+78OJ7nV4womgo3NjLE6eqa9bmCK68Dqf4VCK6/1levxoZvfmPaYIWIqN2EP0BddYGSPUMw4leM9Oa9e+nBgRaujGj5YMUIERERERERETVVSUtOVXdTKQjNuzZTc6LHw1ZaqoocomTE6eqZ9bmErifuZ1ZGFSPrbrgBWl/frN+LiKgdRMprh6VKoGxN30ormDHS4wcjncODLV0b0XLBYISIiIiIiIiImsutCkYMIwww1HgwEmulpSpRKy30zD7MEEZVMNLXEz2ns1EGES1CsVZa1ixnjPQUvO/V7tWrWrkyomWD/4VARERERERERM1VFYz09naFwYgW67IVVIyoqgZHRK9RVqyc9amqW2ll1wzDOG4zpGVBq5o3QkS0GMRbaZnO9MFICiWUELXSyrFihKgpGIwQERERERERUXM5ybvnPX0DUK+Vlh+SKLoKEetpofXPvoe+MFLhbQlA7ejApptv9p5T1fovIiJaQMLwW2k5gOmUpz22ImwoLtBV8O5rK2cfHBPR1BiMEBEREREREVFTyWTBCNL9fVHFSJ0ZI6qqQsQqRoJB7bMRH6yudnVCKAqgsHM4ES1ifsWIAsAyi1Me5jg2XCHQm5dQALhCQOXcJKKmYDBCRERERERERE0lnGQyovX1QbpeeUi9YETRFAglFozksrM/l6yEt1ObNs1htURE7RVvAWhbU1eMjE7uARANXq/ksl74S0Tzxt8kIiIiIiIiImoaKaXX0ypG7esLNwITM0aC4euqlqwY0ZMD1acTtKQBAGMjgxEiWvyEFl2r7thTByNHRh4BAPTmve9Hu7O7tQsjWkYYjBARERERERFR04xXxqFWzRhROjog9GDGSGzIelgxokKFHT6u6rNvcCE2/El429i4cQ4rJiJqs1gw4k4zfP3Q2HYAwInj3vej29PT0mURLScMRoiIiIiIiIioaa6/7/pEVUjmlFMghAhnjKiuCIeQhBUjugoFVvga3WigYiQTtd0yNm6Y+8KJiNpEqGp427LyUx53ZMJrpTU46X9n9ve3dmFEywiDESIiIiIiIiJqml/t+VVYMbL32edg/RdvAIAwGAEA1Q9EouHrGhQRlZlojQQjWnRsihUjRHQsiM0JKdsTUx52uLAfQko86VEBAMgMDbV8aUTLBYMRIiIiIiIiImqaU/pPD4MPp7M7DETiPfU1B4CUUTCiqRAieo9GghFnbCy8ra9bN9dlExG1jRAC0p+rVHYnpzzucOkIzvudRM9Rbwu3f8PatqyPaDlgMEJERERERERETXPPzsNRKy09FT4erxjRHCA2ax2plI6jK0+Nnm9g+DqcaDaJYhgNr5eIaCEIf1fWcgtwXKfm+Z8+8VP8sLgbf3Z31JswO7iqXcsjWvJmP82MiIiIiIiIiGgGqdKOsJWW1NPRE6oKCG++iOZGbbQAr99+ofs4dOGPABqbMdL9spch/+v/Q9cLnt+M5RMRtYVQBKQDKK7EaGUU/Zlofsj+/H68/ZdvBxDNYgIAlcPXiZqGwQgRERERERERNc2A2B+20oIeVXAIISA0DdKyoDnJzT4oKqQaNbXQU7MPRtSODqz7z/+Y56qJiNpLKICE9114uHg4EYwcLR8Nb8dDZAYjRM3DVlpERERERERE1DRjas6bIQJAqWqJFcwZ0ZzqihEFMjaDpJGKESKiY5I/gF1zgU/c8wm8+OYX42DhIAAgb+XDw9R4xUh3V1uXSLSUMRghIiIiIiIioqZxFCecMRIPOwBA6FEw0lnyH9R1CMOAq0VhCIMRIlrqhKoC8CpG7tx/J7aPb8fnH/o8AGDn+M7wODU2fkTt7WvnEomWNAYjRERERERERNQ00rJw6javHOTJW4YTz4lsFgCQNoHVR7xjjPXrIFQ1EYyoDEaIaIkLKujiFSGl8jgA4MO/+bB3jJToKHvPrfn0p6B25Nq6RqKljMEIERERERERETXNqQ/a4e3B009JPKd2dQIAcmWJ1SPeY6lNmwEAUkRbFMGV1ERES5XwWw3Gg5F9++9JHNNZ9FptuRDoePaz27k8oiWPw9eJiIiIiIiIqGm6JqJdPn316sRzalc3AODpEyaetl8C0GFs2ggAkErs2k2V2xVEtLTFZy4FdpijsBwrvN/rjxopZTrCIIWImoMVI0RERERERETUFFJKlPxij/1/eiqEEInnlW4vGHnD4TyOO+wFKKlNm7zXxitGNFaMENHSpvX1AABWjsvwsYNuGfsK+wAAipT4lye80rpSV2/b10e01DEYISIiIiIiIqKmKNklKH7BiDBqe+GrnV0AAMdSUJnwrpY2/FZaHVkjPE5orBghoqUttXk9AGD9IZl4/L5D9wEA+kwHGF8BALB7OHSdqNn4XxpERERERERE1BQFMw8t6KSld9Q8r/gzRkqHDTgVryoktXEDAGDjQCcOBQdyxggRLXHGunUAgP6J5OP3HroXui3xL5+VUIp+n62+/javjmjpY8UIEREREREREdWYNCfxL7/9Fzx05KHZv6Z0BKq/j+cYmZrngxkj+X1p7wEhoOS8yhIRmzFS3YKLiGipUfsHAQAdJeCMh12865sOciWJ+w7dhw0HgM5i9D2oDTAYIWo2BiNEREREREREVOPjd38cN/7xRvzF9/9i1q8pFA5D9StGZN1gpDN5/zlnRXcUVokQ0fKhrhwCAHSWJK64ycVp2yRecoeL7ePb4VR9HWaGhhZghURLG4MRIiIiIiIiIqrx0MjsK0UCk8VDYSstoes1zyv+jJFA+hlnRHdUblEQ0fKh9q8C4FWMBDqL/nNO8tjO1QxGiJqN/9VBRERERERERDUsx2r4NYXSSLihV2+AutqdDEa6eqIKkngrLSKipU7t8ward8WCkYqfJ6fs5ED2vrXD7VoW0bLB/+ogIiIiIiIiohqW23gwki8dDVtpCa22YkTtTLbSEulUdIfBCBEtI2pPDwDAsKPHLD9P7i4kj+1aw4oRombjf3UQERERERERUY05BSOV8bCVlqLXVowoXcmKESWdDm9XhyZEREuZ0tEBiORjKf9r923fdROP6wMDbVoV0fJR+18pRERERERERLTsmY7Z8Gvy5jg6pmulVV0xkoqCkc7zzkPHuecg+7SnNXxeIqJjjRACakaBU4xCkGyl/rFKKlX/CSKaMwYjRERERERERFRjLhUjk5VJdE83fL2rO3k/1kpLaBrWXn99w+ckIjpWqVkdTjFKQzrKcpqjiaiZ2EqLiIiIiIiIiGrMJRgp2IVoxki9Vlq5bKJ1TLxihIhouVFzyUqQjgog3GQ4oq9d284lES0bDEaIiIiIiIiIqIblzKFixCpCc7xNPaXO8HUhBFQjuh+vGCEiWm7Ujmzifq4ika766l3/pS+2cUVEyweDESIiIiIiIiKqYUu74dcUnHJYMVJv+DoAKOmoZESkWTFCRMuX2tWRuJ+uRAPYAWDzT38KfWiozasiWh4YjBARERERERFRU+SdClR/+LpSZ8YIAKhGtBWhMBghomVM7epM3M9UgJTp3a7oBow1qxdgVUTLA4MRIiIiIiIiIppRwSrgu9u/iwlzYspj8tKGFlaM1A9GhBpVjCgdHXWPISJaDtSensT9tCmQ9YMRS2erQaJWql/XSkREREREREQU895fvRc/3/1zPGf1c/Cpcz9V95i8tMNWWqoxxZZDfPg6K0aIaBlT+1bUPNaT9+Y0mXqm3cshWlZYMUJERERERERE0xqvjOPnu38OALht721THpeHG7bSEnWGr3tPxGaMxG4TES03al9/zWO9ee+ftsGKEaJWYjBCRERERERERNP6z/v/c8ZjbNdGERIdZe/+2uHe+gcyDCEiAgCo/YM1j62c8CpGnBQr6ohaicEIERERERERESU4rpO4/8TEEzO+5rGjO7ByHOgqAVJV0fekE+ofyGCEiAgAoPavqnnsr/d7JSMugxGilmIwQkREREREREQJZaecuB8fuC7d+rND7jvwILbs8650zpywBcoU80OEwq0IIiIAUAdW1zzmllUAgExzxghRK/G/RoiIiIiIiIgooWSXwttSKiiNbIuelPW3Eg6M7sCWvX4wcsrTp3zv/nf9AwCg56yTmrBSIqJjl7pyuOYxq+R9x8opwmUiao76l3kQERERERER0bJVtuMVIxJW6QhgGN5d4dR9zeTkAZwcVIw87WlTvnfu3Bdjyx3PhdrT05zFEhEdo5RstuYxu+RVjAhWjBC1FIMRIiIiIiIiIkqIV4wIIXFUVWPP1t9KKE0exsaD3u3MU5867ftrvVMMZiciWuYcv5WWyDAYIWolttIiIiIiIiIiooRkxQgSwYhSPq7ua5z8Ueh+MYk+XNsehoiIaq368D8CQkDt7kw8ruRqq0mIqHkYjBARERERERFRQrxipJor6z/uFAoAAFsTELreimURES05PS9/OU645270/tXrEo+rrBghaikGI0RERERERESUUHbKUz7nyvozRlDyXmMb3GogImqEks1C7exIPKaxYoSopfhfK0RERERERESUULSLUz63Mls/GFEqJgDATql1nycioqkpHdXBSG6BVkK0PDAYISIiIiIiIqKE6hkjcSlZv82WUrYBALbBNlpERPNldLBihKiVGIwQERERERERUUK9GSOq9IaLOJAYr4zXPG9UvEoSO2W0dnFEREuQtOzEfb2qgoSImovBCBEREREREREl1FSMSIk3/MLB5Tc7OCz24FlfexZu2XlL4hDddAEATprBCBFRozKnnJK4n2LFCFFLMRghIiIiIiIiooTqYOSEvcB5vwHO/KPE2iPeYx+680OJY1TbD0b0VFvWSES0lKRPOB65d74nut/FihGiVmIwQkREREREREQJpdEdift9EzK8nfZmrMNQkpUhwvVvaJwxQkQ0F8YznhHe1nOsGCFqJQYjRERERERERJRgbvPaZCn+XJGMGT2XsrzHDEVLvEZ1/Bs6gxEiorno7umse5uImo/BCBERERERERElWK6XcmyteIlIOhGMeP88UjqCf//9v+PSn10K0zEhHL+qROOMESKiudDTUStCVowQtVbDwchtt92GCy+8EMPDwxBC4Oabb048L6XEBz/4QQwPDyOTyeCss87CQw89lDimUqngsssuQ39/P3K5HC666CLs2bNnXj8IERERERERETWHle0FADy7VMJfjk/gotFC+FwQjFRcC9ffdz1u23Mb3n3bu2MVIwxGiIjmQu3ujm53smKEqJUaDkYKhQJOPvlkXH/99XWf/+hHP4pPfOITuP7663HXXXdhaGgI5513HiYnJ8NjLr/8ctx000342te+hl//+tfI5/O44IIL4DhO3fckIiIiIiIiovYx/TZZGVfi3UfHsKVkh88FwUjcT3f9FNKfMSIMBiNERHMhDANbbv8/bLnjdgi2JSRqKW3mQ5LOP/98nH/++XWfk1Li2muvxfve9z689KUvBQDccMMNGBwcxFe+8hX83d/9HcbHx/G5z30OX/rSl3DuuecCAG688UasXbsWP/3pT/GCF7yg5n0rlQoqlUp4f2JiotFlExEREREREdEsmdILQnR/xohrR9dVpiwg67ooKslrLXX/WkfFSIGIiOZG6+tb6CUQLQtNnTGyY8cOHDhwAM9//vPDx1KpFJ773Ofi9ttvBwDcc889sCwrcczw8DC2bt0aHlPtIx/5CLq7u8P/rV27tpnLJiIiIiIiIqKYYMaIDonSiI7Rx3Lhc2kLeMe+cfz+sV3A+FPCxzU/GBE6gxEiIiJa3JoajBw4cAAAMDg4mHh8cHAwfO7AgQMwDAO9vb1THlPtqquuwvj4ePi/3bt3N3PZRERERERERBRjSi/lUFwF9/5mU+K51Ucktn6tA3t+1Ye7R3+Ip5S9Dg9hxUgq09a1EhERETWq4VZasyGESNyXUtY8Vm26Y1KpFFIpXnFCRERERERE1A6m60JA4vc7T8GTJ7YlnnvOQxKAQOFgGkpBwfs/B/y+30HZHy2iGun2L5iIiIioAU2tGBkaGgKAmsqPQ4cOhVUkQ0NDME0To6OjUx5DRERERERERAvHgotz75N41c+8UMTWDEyct7XmuP2/6YUyoeKUxyV0fz67ms62c6lEREREDWtqMLJx40YMDQ3hJz/5SfiYaZq49dZbceaZZwIATj31VOi6njhm//79ePDBB8NjiIiIiIiIiGjhWNLFOfe54f2O449DOtNRc1zhYNTdoaPsDWrXGYwQERHRItdwK618Po9t26Iy2h07duC+++5DX18f1q1bh8svvxxXX301tmzZgi1btuDqq69GNpvFq1/9agBAd3c3LrnkElxxxRVYsWIF+vr6cOWVV+Kkk07Cueee27yfjIiIiIiIiP7/9u47PIqq7eP4d7amJ4SQAoTeexGpKoIgqCh2UbF3H/tj9xGs2Htv2AV8FSsiKF06gvTeSe892+b9Y3FDTCiBFITf57pyMTtzZuY+uyfDZu4554gcFg++wGTqAGGnDcabugn3AfYJL/L/6wxRYkRERESOblVOjCxdupRTTz018Pruu+8G4Morr+Tjjz/mvvvuo7i4mFtuuYXs7Gx69+7NtGnTCA8PD+zz8ssvY7PZuOiiiyguLmbw4MF8/PHHWK3WaqiSiIiIiIiIiBwJNya2sg4jOFu1gtzdB9wnvNj/ryNYk6+LiIjI0a3KiZGBAwdimuZ+txuGwdixYxk7dux+ywQFBfH666/z+uuvV/X0IiIiIiIiIlKDtuZuZZu1fI8Re3w8hvPAPUEi9iZG7GGhNRidiIiIyJGr1jlGREREREREROTf7Y4ZdwDg3GfcLEezZlicjkrLG3Z7udfBEeGVlhMRERE5WigxIiIiIiIiIiIB2/O2Y/WaRO6dM6Tpl19gjYjAYq94C8HWIIagTp3KrQuOiqqFKEVEREQOnxIjIiIiIiIiIhLgtARTr8C/7LVYCO7WDYCIFt3KlYt78AFa/PQT9kaNyq13hIfVQpQiIiIih0+JEREREREREREJ8HrtROf7l13hERgW/60DR0jZHCO2hglEX3kl1shIwk8fWm5/a6jmGBEREZGjmxIjIiIiIiIiIgKAaZqY5BOdb/pf168f2GY4nYHl4C5dA8thJ58MtrLbC5aQA0/SLiIiIlLXlBgREREREREREQBKvaV4DTPQY6RBy+aBbYajbPL14K5liRGL00lo+8Zlr5UYERERkaOcEiMiIiIiIiIiAkBuUQYAMXt7jDgbls0fsr/ECICzWWJZOZutJkMUEREROWJKjIiIiIiIiIgIAJmZ6wFonO1PjNgbl/UEMazWwHJQh/bl9nP0GFwL0YmIiIhUDz3GISIiIiIiIiIAZObuAiA+2wQMHE3KeoI4WrYk+sorsTdtgiUoqNx+keeeS97PUwju3r02wxURERE5LEqMiIiIiIiIiAgAmQV7AIjKM4B/9BgxDOIefKDS/SxBQTT9/LOaD1BERESkGmgoLREREREREREBILMwlVP/8hHk8r+2xcTUbUAiIiIiNUCJEREREREREREBILM4g5un+AKvLRERdRiNiIiISM1QYkREREREREREAMgszSn32jCMuglEREREpAYpMSIiIiIiIiIiAGS6C+o6BBEREZEap8SIiIiIiIiIiACQ6S0OLNu6dK3DSERERERqjhIjIiIiIiIiIgJApukiN8S/HP3oo3UbjIiIiEgNUWJERERERERERPD6vORgEuTyv3aEhdVtQCIiIiI1RIkRERERERERESG7NBtME6fH/9oZrsSIiIiIHJuUGBERERERERERMvOTAr1FAGxhoXUXjIiIiEgNUmJERERERERERMjM2UZUoX/ZbXNgOBx1G5CIiIhIDVFiRERERERERETIzN9F40wTAFfDRAzDqOOIRERERGqGEiMiIiIiIiIiQkZeEo3T/cshbVrXbTAiIiIiNUiJERERERERERFhR3YSiRn+HiMNOrev42hEREREao4SIyIiIiIiIiJCUkEGjfcmRoJaq8eIiIiIHLuUGBERERERERERiksKaJjpX3a2alm3wYiIiIjUICVGRERERERERISI7CIcXvDaLNgbN67rcERERERqjBIjIiIiIiIiIkL9TDcAhQ1CMCy6XSAiIiLHLn3TERERERERERGiMz0AFMdG1HEkIiIiIjVLiRERERERERERoX6ODwBXXL06jkRERESkZikxIiIiIiIiIiLYXCYARkhYHUciIiIiUrOUGBERERERERERrF7/v5agkLoNRERERKSGKTEiIiIiIiIiIli9/h4j1mD1GBEREZFjmxIjIiIiIiIiIoLVP/c69qDwug1EREREpIYpMSIiIiIiIiJynDNNE9veobTsoUqMiIiIyLFNiRERERERERGR41yJtwT73h4jztB6dRuMiIiISA1TYkRERERERETkOJdbnIPj78RIWP26DUZERESkhikxIiIiIiIiInKcyy1Mwr53KC1ruHqMiIiIyLFNiRERERERERGR41xe7i4cbv+yIyKqTmMRERERqWlKjIiIiIiIiIgc53ILynqM2IKD6jYYERERkRqmxIiIiIiIiIjIcS4nPzkwx4g92Fm3wYiIiIjUMCVGRERERERERI5zBQWZWEz/slM9RkREROQYp8SIiIiIiIiIyHGuJD83sBwSGV6HkYiIiIjUPCVGRERERERERI5zJUWFAHgtYHM66jgaERERkZqlxIiIiIiIiIjIcc5TWASA22ZgGEYdRyMiIiJSs5QYERERERERETnOeUpK/P/adZtAREREjn36xiMiIiIiIiJyHNidt4uM4oxKt/lcLgDcNmtthiQiIiJSJ2x1HYCIiIiIiIiI1Ky8ogzO/fYMSgz48/I/2ZW/CxOTllEtATBL3QB47EqMiIiIyLFPiRERERERERGRY1zy1t8o2Tt1yEOzn2Lqrm8AaFevI3efcAdmqQcAj91eVyGKiIiI1BoNpSUiIiIiIiJyjCvBDCz/nRQBWJ+9hpum30SuxwuAx+Go9dhEREREapt6jIiIiIiIiIgc44rcReVed93iI9gFpXbICTUxPf7EieGsXxfhiYiIiNQqJUZEREREREREjnHZRTkYponFB6ElcP//+bD5/NtyQ2BKL/+AEu7IhnUYpYiIiEjtUGJERERERERE5BiXlZnOm296SY+EpV0IJEUAIosgJs/fY8QWEVFHEYqIiIjUHs0xIiIiIiIiInIQE9ZPYHHy4roO47B5dqcRkw/td8P1y0oBsJ59VmD76Sn+dUZ4eJ3EJyIiIlKblBgREREREREROYClKUt5atFTXDvt2roO5bC5CwsCyyWpTgCCO3bDFuIBwJrnvz1QL1ZzjIiIiMixT4kRERERERERkQPYU7CnrkM4Yq781Arrgpo3xQyyAuAu9I+03ap5fK3GJSIiIlIXlBgREREREREROQCLUfmfzulF6dwx4w7m75lfyxFV3e6itArrgqPr4U7oVG6dNVJzjIiIiMixT4kRERERERERkQMwDKPS9S8te4kZu2Zw42831nJEVZOft4cST8U//x1RUYRHRwdeh/TpQ0jPnrUZmoiIiEidUGJERERERERE5ACshjWw7DN9geWM4sy6CKfKtu36g+DSiuutEeHE3n0X2O3UGz2aph+PxxoZWfsBioiIiNQyW10HICIiIiIiInI027fHiNvnxmn1T15eUlR5T5KjzdbU5UQUm+XW+TCwhIUR1L49bRYswBIcVEfRiYiIiNQ+9RgREREREREROQDLPn86u7yuwHJw2pbA8pQtv9VqTIfCZ/r4aPVHfJkyj5ZJ5bd5LFYMi79e1rBQDKu1kiOIiIiIHJuUGBERERERERE5gH17jOybGGlYVMTt33vpsMPHV8u/q4PIDmzKxm95ednLbCzJpnVS+R4jziaJdRSViIiISN3TUFoiIiIiIiIiB+D1eQPLbp87sNx1bgnt1poMWGty933L6iK0A/pj8RsANEsFpwdKgh10mTOPghkzcLZuXcfRiYiIiNQdJUZEREREREREDsBdnBVY3rfHSFiuJ7CcauZT6i0NzD9yNNhZlES4z8GwZf4J44tbtsYaHk7kOefUcWQiIiIidUtDaYmIiIiIiIgcQNL6hYHlfRMjLmvZ8FRnLTLZmLWxRuNwe90UuYsOqazp87HNZuPZ8V5OWe2Ps36/U2oyPBEREZF/DSVGRERERERERA6g2LNPMsRXtuy2lSVGzl3g48+UlTUax5mTz6T/V/0PKTmSmbWJelkWYvLK1jU+pV8NRiciIiLy76HEiIiIiIiIiMgBGIYvsFzkLg0s79tjJMgNM7dNr7EYvD4vyYXJeEwPm3I2HbT8tt1/0HpP+QnXgzt1qqnwRERERP5VlBgRERERERERORCjLMGQV1wM+Ie1KrSUrfdY4M/MZbi97gq7V4ciT1kvkeyCsrlNTNPENM0K5bel/UXLlLL10ZddgMV59Mx/IiIiIlKXlBgREREREREROQCv6YG9yYeckkIAlm+bhn2fHIjNB7G5JulF2TUSQ25JQWA5q8g/nNfW3K2c+MWJvPLnK3h8Hrw+LwAl7lLWZ2+mxd7ESMm1txL78GM1EpeIiIjIv5GtrgMQEREREREROZr5Sl28+q6XtU0MMjvmADB30w80dZfvqZGYbpKcn0HD8NhqjyGjsGyyEJ/pz8i8veJtSrwlfLT6I2bunMO2vM3EOWNILcnG5vVwbpq/fIvzR2BY9FykiIiIyN/0zUhERERERETkAOJXZ5KQDYP/MlmftYo5u+fwQ9piGmX6t9tiIgBokg7J+Zk1EkNmcX5gudhTwjuLnmPq9qmBddvyNgOQWpoBhpcm6f5eLMVOJxHNm9RITCIiIiL/VuoxIiIiIiIiInIAXrNs8vVpyZOZljyZ5skmUYXgsduIGXwiGRN/o2GmSVpRVo3EkFVUlhgpdBfz6brPwQDDNDENA0yTgatM+m/00S7LgzPT/+d+XqOmGIZRIzGJiIiI/FspMSIiIiIiIiJyAD58Fdb12OIfRiu3bSeaNG0OQFQhzE9bTb/P3uWi1pdxZ5/Lqi2G+SmzAsuZJVnkGyYJmSbjPvHyUy8LVp/J+fP/Htqr7E99T9su1RaDiIiIyLFCQ2mJiIiIiIiIHIDX9FZY122rP1kSdOoQrI2aARBVYDIjZQL5vl18uOGZajt/UUkOv+35NvB6wtaXALj6dy8hpXDRPN8+SZHy2g45udriEBERETlWqMeIiIiIiIiIyAHs22PE8JmElELrJP/rdmefjq0gB/D3GKkJm9b5kyKRhSZ5IWAaBl23+Oi25eD7Nh5+Ws0EJSIiIvIvph4jIiIiIiIiIgewb48Rmxfa7TaxmJBVL4awxEbYGzYGIKIYIgsq77lRVbmluWQUZwCwI3cbrfeYvP+al2t+9WGYJrf9WH54r1KrnaZffknTzz8juHt3ABxNNb+IiIiISGWqPTEyduxYDMMo9xMfHx/YbpomY8eOpWHDhgQHBzNw4EDWrFlT3WGIiIiIiIiIVIsS3IFlu7esZ0hBdBwA1shIsmKtAHTaUZYY8XgrDsF1KLw+L6dNGMmgiUOZuWMWO3K3cv48fyLk9OUmDTP9SZh9lTZpQUiP7oSccAINn32GqIsvJvH99w7r/CIiIiLHuhrpMdKxY0eSk5MDP6tWrQpse+6553jppZd44403WLJkCfHx8QwZMoT8/PyaCEVERERERETkiJSYrsBySCnc+Is/SVEQHBVYXxzpH6k6tKRsv525GYd1vo3ZmykhA9Nwc8esO5ibvxWvtWz7IxP8CRdfxw6BdQlNyh5IdDRpQsJjY3E0aXJY5xcRERE51tVIYsRmsxEfHx/4adCgAeDvLfLKK6/w8MMPc95559GpUyc++eQTioqK+PLLL2siFBEREREREZEj4vZ6Asuv/Vo2kYjb5igrZPNnLhxlRVmTdgiTgFRi2raZABimiYmPdd4CgstyM9Tf+1xhwjnnENS1CwCRZ484rHOJiIiIHI9qJDGyadMmGjZsSPPmzbnkkkvYunUrANu2bSMlJYWhQ4cGyjqdTk455RTmz5+/3+OVlpaSl5dX7kdERERERESkNrh8ZfN52LYEBZatpWXjWRl2f48R+z6JkfWpZaMnHCrTNPl+40TGfOHh9fc8ONz+obnisivOXVLvssto9vnntJ47h8gzz6zyuURERESOV9WeGOnduzeffvopv/76K++//z4pKSn069ePzMxMUlJSAIiLiyu3T1xcXGBbZcaNG0dkZGTgJzExsbrDFhEREREREamU26x8QvUYoywLYrHZAXB4ysqmZlV9Ps3kwmTyCtPpuBNiswx6bjKxeUzq/+P5wFKHA8NqxbDbse0dpUFEREREDo2tug84fPjwwHLnzp3p27cvLVu25JNPPqFPnz4AGIZRbh/TNCus29eDDz7I3XffHXidl5en5IiIiIiIiIjUuBJXEabPAComR9qfW/b3r9XhH1bL7oWWSSZOt0lBi+Qqny/PlUdEUdnrdrtNUuoZFZ5qLHEEV/nYIiIiIuJX7YmRfwoNDaVz585s2rSJkSNHApCSkkJCQkKgTFpaWoVeJPtyOp04nc6aDlVERERERESknJz8Xdh85dc1nzYN19o1hJ92WmCdw+EfYqteAYz50kuQGz64av8jI+xPiaekXGJk8CYPBFuA8g8T5llDqnxsEREREfGrkTlG9lVaWsq6detISEigefPmxMfHM3369MB2l8vF7Nmz6devX02HIiIiIiIiIlIluXm7sHnLrwtqkkjEsGEYtrJnDZ1B/h4cvTeYBLn965pvrPr8mEXFOQz+qywT48izMuJPb4VyeVY9PCgiIiJyuKo9MfLf//6X2bNns23bNhYtWsQFF1xAXl4eV155JYZhcOedd/L0008zefJkVq9ezVVXXUVISAiXXnppdYciIiIiIiIickSy83ZjrZiXqMDp9PfgcOwz+bqzwF3182Vs57QV5Yft8hT5EzCO8LKDF9uUGBERERE5XNU+lNbu3bsZNWoUGRkZNGjQgD59+rBw4UKaNm0KwH333UdxcTG33HIL2dnZ9O7dm2nTphEeHl7doYiIiIiIiIgckZzCFKy+yidf35czKAzXP9YFF0F6UToNQg59cvT87LT9n6NFE1x/JQEQEVPvkI8pIiIiIuVVe2JkwoQJB9xuGAZjx45l7Nix1X1qERERERERkWqVW5ReYSitythCQiokRuoVmKxIXsKQlmcc8vl8Sf4J20sc0CDRSv4W/8kTnnqKnEmTAH9ipP9Nlx/yMUVERESkvBqfY0RERERERETk3yq7OLPc5OsJ48ZVWs4aHFxhXf08WJ40v0rn8xQVAOC2g9vaJLA+6vzzCO3vn5szuEcPYoYOrtJxRURERKSMEiMiIiIiIiIi+5FbmkNYsX95y5mXEnXuyErLWWKaV1gXWQST1n/H1tyth3w+T3E+AF6rQdgQf08TR6uWAERfcw2NXn+Nph+Pr0INREREROSflBgRERERERER2Y8sVz5t9vjnGEk4sft+y9niWwSW3XYHPrt/OSYP3v7zk0M+n8dVCPgTI/Wvu5b4Jx6n6Xh/IsQaFkbEkCEYDkdVqyEiIiIi+1BiRERERERERKQSbq+bxZmpJGSDCXQ6/aT9lg2KigwsF9SLxRfmBCA2x2TGlpWHfE5faQkAXpsFS3Aw9S68EFuDQ5+8XUREREQOTokRERERERERkUok5e2k9W5/b5G82Bic+yQ//skRGR5YdjWIxxMVBkBsLjh8SYd8zoKSPAB8VtvhhCwiIiIih0CJEREREREREZFK5GZupv0uf2LE7DHggGWtERGBZTMuAU/9aAAa5JoEW/MO+Zw5bv9QWj5bSFXDFREREZFDpMSIiIiIiIiISCV2ZyYFEiNtTz/lgGWtYWGB5ejEeEhoBEBsDhRYDu1Pb5e7mFyfDwCPParqAYuIiIjIIVFiRERERERERKQS+WlJNE33L0f0PvGAZY2Qsh4eiU3jsTdtCUCfDSYNUww8Ps9Bz7dt9x9YvQagxIiIiIhITVJiRERERERERKQSpSnJABQFgS06+oBlDcMILDtbtCC0dbvA6xumepm+ffpBz7dx93wce/MnpsN5GBGLiIiIyKHQbG4iIiIiIiIi+5i+YzrNIprhzs0CoNRpHGQPv0avvIJr+zZCevcmPjuX3XvXN0+Fi+bex/AWww+4/6aMNdi8e184HIcZvYiIiIgcjBIjIiIiIiIiInuty1zH3bPuBuC2/GYAuIIObbCFiGGnB5bDo6Ow3nkH3ldeJTv00M69sWA38X+PuGVXYkRERESkpmgoLREREREREZG99hTsCSzvKvQPpeV1Wg/rWM2GDgYg2AVmccJBy2/y5OPw+Cd7Dw0LPqxzioiIiMjBKTEiIiIiIiIisldBaVFgubSkGAB7UNhhHcsS6Z+XJMgNDnvhAcvmFqaT5fPRd50/MdK2a5vDOqeIiIiIHJwSIyIiIiIiIiJ7bdyylPY7TRrkmETn+5MU4Q2aH9axLKFlY2jZPQWYpv94uaW5TNowiczizMD2y3+5nOFLTeJzoDSiHnGXXnL4lRARERGRA9IcIyIiIiIiIiJ7Wbau57EvvOXWxQ4ZcVjHMpxOMEwwDZxuL/nufCIcEVw19So252xme9527ut1HwB7cvYwdr7Pf7677sQadogTk4iIiIhIlSkxIiIiIiIiIse17Tl7uPCHSzgpYRiJSckVtscOHXJYxzUMA4vThq/ES7ALMooyiHBEsDlnMwCT1n9DfEg8I1qOICEbQkrB7TRIuPiCI6qPiIiIiByYEiMiIiIiIiJyXHtlzvuUmDlMT5rAGXm+Ctvt0dGHfWxLSBC+kkJCS2BPfjpNIpoEtpX6inh+6fPM2TOHtrv9w2zZ4mMxLBr1WkRERKQm6duWiIiIiIiIHNeC0ufRa6OP+nkm9fbOK5LatjP5CU0oufy6Izq2LTIcgLBik505aeS58iqUWZS8iDMX+xMyzlYdjuh8IiIiInJw6jEiIiIiIiIixzXbjkzu/cafmMiI8K+zDxzCiXddf8THtkZFASmEF0NSfhrZ+UmVloss8v8bfdbhzWciIiIiIodOPUZERERERETkuOX2ubGllQZex+RBUWgIfW66vFqOb42uD0B4MaQUZpCasYUL53p571UPJ27wJ2PsbpPQvSFE9O9fLecVERERkf1TjxERERERERE5LuW58nhy9mOEucuvD73pdqzBwdVyDmtMPADhxSabitLJzgnjwnn+4bpuneNiZwMn5t6yptXAEh5eLecVERERkf1TjxERERERERE5Lj0zcwy/JE0juqD8+rZXX1Zt57DFNwYgJhfyilPIz88IbAvOsPHau14umbO350j9KAzDqLZzi4iIiEjllBgRERERERGR49LGXb9j95h02WYG1qX0GYxhq77BFZytWwHQONMk15VKSWZ6hTL91/nPH9qrd7WdV0RERET2T4kREREREREROS4Vmi6umu6jQR6UBjsIeuo5TnnvpWo9h7PV3sRIBqR7k/m/XbMC28L6n1CubMQ551bruUVERESkckqMiIiIiIiIyHHH43XTbq2FISv8vTUS772b5uePwOJwVOt57ImJYDVweCA2ByKK/OfLiLYQe9ddgXKWYBuhfftW67lFREREpHJKjIiIiIiIiMhxxePzMGXNF/Rb63/tPn040aOuqJFzGVYrlphQABIzTIJde8/ptGJv1iZQLqRpJIbdXiMxiIiIiEh5SoyIiIiIiIjIceXLdV/y6NIXaL3H33uj5S031eik57ZG9QFIzIDgUv86t9OCJSwsUMYSHlVj5xcRERGR8pQYERERERERkeNCVnEOHp+HRau+44ylJk4PFAdZCGnTukbPG9y8CQCN08t6jOQ5y5cJOnlEjcYgIiIiImVsdR2AiIiIiIiISE17e8aTfLZhAh57JGcsyuHiuT4AUlr2qtHeIgChHbqS+81cYvJM9sT4zxXjjAKgyccfUzB7NtFXXl2jMYiIiIhIGSVGRERERERE5Jj208IXSP/ySz6cZ7K4TTahJf4htLZ16kC/156p8fPbmncFoP1uaL/bn5CJSegIQGif3oT26V3jMYiIiIhIGSVGRERERERE5Jj18Z/j+Wjpx7y+wJ8MOXGjGdjW49EniGoYX+MxWKNjKqyLa16zw3eJiIiIyP5pjhERERERERE5JuUUZTJ+wYs8MsGLw1t+W3poBHGd29dKHNaoqArrDIejVs4tIiIiIhUpMSIiIiIiIiLHpO9+GcNDX3lJzIDS8HASPvs8sK2076k1PrfI3/6ZGLE1a07UeefVyrlFREREpCIlRkRERERqSE5JDimFKQAk5e3miZljGT35AX7esKhO4vH6vDw79xGu+eEicktz6yQGqX1fb/yaH7f8WNdhiNQ60zTZPXceTTKgKMRCh68nEdWrJxGvvkFx/1MZ+Ni9tRaLJSgIrGVDeCWO/wh7fM0P4SUiIiIildMcIyIiIiLVLKkgha1Z23ll8q0UuUsJatKXzNQFnP+HD3tLg7GZ8xneehYWS+09o1Jams9/vx5B9qZ0ogrg/pI7eevCD7EYek7maOQzfdXy2WQUZ/D4gscBaBzemO6x3Y/4mLVp0oZJJBcmc3mLkXy56FX6dr6cExr2qOuw5F/ii/lPErPbBUB6jxNwNGsGQKPTB9Po9MG1Ho/V6sPrtQJgDw+r9fOLiIiISBklRkRERESqUW5xDud+fSYllPLWZ16iC2BZyz/oucX/pPCQFSbf9Mugh/VEEoK78uLgR+jQoPkhHz+jOINP1nxCn4Q+uLweIhxh9IjrccDhYEzT5NrJ5zJ0Yhq9NvnjmJq8iMcbvM3YU289sgpLtfvmrw94YcUbDEnoyyODX8Vh9c9DYJr+z64qQ/+kpK3G7jZplAkz139/VCZG8l35FLoLiQ8te3reNE2mbJvCEwufAOCTP98nIRvG7/mdT0dMpFODdnUVrvxL5JTk8OmKSby41v97Ez/swjqOiHLjNVhCQuouDhERERHRUFoiIiIi1enNqXfQc00Jw5eaRBf41/2dFPnb+fNNLphbRFLxIh78/bVDPnZmfhJXfn0WS3/+iJfH34jv4pvwDrucxz66CYASTwkAuaW5LE9dTk5JDgDfzBtHq9+TA0kRgGF/mqz5413WpK8HoNhTzIR1XzJl0/ekFaXx3aafWJm6hjt/GMXEP9883LejRvy6aRFjZ47H4/Xg9XmZvmM6yQXJB9wnozCNb1d/ytbcrbUU5eGZvPpTxq54lQK8TE6ex0XfXA5AWlEaIyeP4qxvLsTj8xzy8VKzNnHbjz6eG+9l7S9fM/SrUazP2FFT4R+SrJIsij3FAOwp2MPI70YyYvIIMoozAmXeWf4uD8x9gKBSk/p5JuM+9vLCh16u+M3Ndb/cHkgSyfHl7899ybofuHnS9fy+fc5+yz34w+V02eDD5oPsevXoev6ZtRlqpQyLuc+y/hQXERERqUvqMSIiIiJSTfJd+WTNWsptv/rKrQ9p2xgcQdS75haS770Tn8fC+fNNWif5ePXsxZimedBeAFt3zmP0bzdwzY/Qb135m8Ix0+bycNRVrN2wlIL4hjTencLJyzx80txgRleDFikwbq4/pqTuvWjh20XJXyn02OzhkikXMuGMyXz6+0OULlxDkBs+SjAYtszHTid0LYbZrVfy2+50xpxyO40jo6vlvTqUOv/Tmsw1PD37ATK2byM2x+SWvJ3szP+F3PwCYqOb8M2FP1DgKiAqKKrcfu/NeoS5MyfTbpfJu91DmHDd79QLjqyWelSnHzf/wLj5z3HJQh8nrTbJDoOnLlnLxys/4cXlLwTKrU3bTpf4Vgc93k9rPufBla8xaYO/vVw208ejjVdx75QH+PGKr2qsHpXx+rwkFSZR7C7i2u9GYXcEc1WX2/l6xQt0Wl5EVCH8J/R+Jpz7IWt2zuGrBW9w+WIfZy8q39aH/GkytWcSK1I20T2hzX7Pl+fKo9RTyo9bf6Rrg670jOtZ01WUGrZo1zJu/+1WYvM9pAaVcMXvPmbMmc+g11dXuJb8sOwj2k/eypDl/vaTceJptTbJ+oGYhhPw1nUYIiIiIgIY5r/wcau8vDwiIyPJzc0lIiKirsMRERGR45zH5+H+7+9lfu4s/vtFCR12lW0Lvu0Wmt16W+B14Ywp7LzlnsDrrwcYDHrkGwY0a1/umBl5u7lr4uWss2bTKLwT9dK2MfzHHLpsr/jVrdgBU3sanLvAJCkaGmb9Iz4L2PbmalovmE/BFy+Q/Ma3lNrh00EWfutucO2vPoYu3//XwjGXWdmd2J4/rpx0xDcY/9g0hQcXPk62r5BQYnhh4CsMaNq10rKphak8s/gZfN5SUhfP4py50Hm7idWEl0dacLjhpik+5ncweP1s/9j94/q/yEmJvflm0zdEWZxsfvYpRuy9wb4pAeY8cA0vnH4vu9PXcuWUy7Dbgvn8vB+ICY45onodqc6fdOa/33g5cWPZ5zCji8Hszhau+N3L1wMs/NnawsPdX+OSLqce8FgeTylnvNODLhvh2mnlE3UfDbHw1CvLA0N01aQNWRt4YuET/JW2ghYpUOiExz/3kh8MT1xq5arpPvrvTfTdf7WV4hhI3GIyaraPxpmVH3NeB4NFV5/HByOeBPxJNhMTi2FhTfpmnpt+O3+6dxFZYDJyoY/tjay8Pm7lUXFjXA7ONE225GyhSUQTHFYHpd5Svt/wNd9MfoaHvvTi+Ede4daHuvL28Bdp26AR4O+F9NH9p3PB7/6CeYOH0OW5ZwgOrfuhq3ZeczWF8xdirRdFmwUL6jocERERkWNOVfIGSoyIiIiIHKHn/+8qIr9cTMMsk5Yp/nUh51+AMySY2LvurDCWvGvXLvI+eo70r35jY0PY8NhjnNm+Ez7TpFNMBzZmb+TBHy+n0/wizp3v49VzLPTYbDJkhf9rW9T99xLevDkhJ/Riw5kDILW00rhMu4Hh9u/jDrYT/cHnNO7ZBV9xIbsvGEzhltz91slrs2AE2bAU+Ccu9hkwrbtB3L0v0Ck6hhyPwckte1X5vSopzuHmZ/tz9jwfNg9YTXj1HCuTbv2DSGf5XhwLV3/JTYufps96k7gcOHOxj/CS/R97eyxM724h+6ShNI1ws2jlLMKL4bEvyt9Jvf9qKyUJ9Uk2yzJIIxoM5ekzXqxyfaqLz/Tx3zs7c/0/ehsBJNWDhtn+5XuvsdKv94M8dMplBzxeVtYWvrhlBKetqPyr/tfPX86jIx4+4rgPJLM4kzO/PYNCTxHnLPBx2ayKddtXQRD8coLBhfPKYg4ePJiIfn1xNm+ONTSIbRdfhteAu26rx/Rb5jP5z7d5ddUnZJslvDjgPV7440EabUjhpik+wva2lRI7NJj3O40jG9ZkdaUamKbJ80uf57O1n3FFhyu4t9e9vLngKf76/ktu/dFXISkC8OOJBjP7NaJfbEM2LV9Kv3UmvTeYBLkh6awzGfzCCxV3qiOl27aR9emnxNx4I/b4+IPvICIiIiJVosSIiIiIyBGauOZLftw0nYcG3E+T4GjenfpfdhQWcP2QJ+ic0BEAn8/LuMkXkD9jI1f+XnbTtyC2ESfMmnbAMeTdm1ex+ayL8Bnw/L1dyU1dRaHToCTKIGG3j2HLTPpsqPg1Lb1/Z07+cFLgdcnaVWw776LA69JGjbAlJVPy0BN07tOJlEfvxBLsIP7JN7A3bBwoZ3q9ZDx1Dxlf/lru+LH/uxPP8jnUf/BVrPWi8eXlkHzjBeT/5Z/DY9yFFpa3smCY8N6gr+nTpGwS7FnbVvDpqsk8dsqtJEbGVlrv3/94Acs9HxKfU37906McNOx3E08PvoFCTyGzdszk9dkPc/3XPjrvKP8+hD76KEXjHsd0V/7ePnJzPG03pjL697L9ikNtBMXZMbYWB9atbmKwqwHkBxtM7WXh3XO/pFlUUyIctfv98qv1X/H0oqd58X0PiRngPe0U2j/1LBt796lQNiUKJt49ku4t2zNvz2xeOu0Z5q6bzpa0rVx5yg3Ehvjf963bZ7PhiptolrZ3x7hYmr74AjsuvwKAHwaFUjxqGEGmjxHtL6FDTOdq71ExfcM33L1wLCMW+Rg9Y/9JEdMwMCr5kyT86WdpfN7Z5dZtOKUHvtRifuht0OfBp/nPov8Rn2XSfpfJqo5BRKeU8uRnFe+eb/30Gc488Zxy69xeN8vT/qRTTGdC7HXfm0Dgu8Wv8L91HxJWZGINC2Xu5Qu5+bGu3DbBjQUoja1H9GXXEDd0MBvPPwNbUdm+meFQP7/stcdmEPv9VOJaNqn1eoiIiIhI3VBiREREROQg3D43mUV5xIfVL7c+PWc7E1Z9yKSVk+m61WR7+8Y0d+fR//tc2u4xmd7dYGnveHp1u4qwgj9J+WY6F871YTWhqHlr4gedTMy5I3G2OvgcEEv6tCcsB94bZuGaab7AcFcHUvLEo3S/cFS5dXk/TyHrs0+I+9+jBHfsWKX5O0rWr6dk9WqSf/wFIyqStq+8WGFf0zTZ0LszZp7/hvP2WP/wXRv+cyMPjrwTr8/L5MVv8tiG97F6TbxWg5PNBOLC6rM2fRttmgzj8TPH8sGfbzJp1tu8+p7/OPVGX0j2Z18HzjOji8HkU6MpdeSRuMtLWDHc9b0P0wCj7wnY2/eg6bVXYo+OpnT9akpWryaox4k4mjdn2fAhhG7fA8BLIy1cMcNHTJ7/uL6GEQTf/zjOTUvJe+PzSt+HpGh45kIrKdEGrw58hUFNBx/S+3cgPtOHxTjwBMten5eeH3flork+zl3g/1recMYsIhvGkfXSw6S+9+3eOjTETE/C6ob7rrHRdpeP5qkm44dYCCuBZqkmq5vZ+fGS6SSENWDBss8IveJp7F5o+PMUIlo0wzAM1rXzD9k2tYfB1gSDAWtMvutr4GnRigmXfYvVYj1gvB6fh03Zm0gITWDyximc2XIIsaENKi37wpSbmbtyLs98XJaosISFEXnuudhs2WR8/hPFI86i03/uYvOgsve70B5Et3mzsEVWnAcm56OXSX7uPZLqwZ032ei31sed3/t/cd4+w0LfdSbdtvnfR0f/Drj+WAvAy3d35t3rJ2IYBj7Tx7Qd03hq1oMkbndR0rQh0fEd6NmgLzefcEm5z+/Bz84hxZXG25f/RkhQ+AHfGzly93xxKhnr0njg/3x818fgq1MsfPKSlyA3pHZuwykTvsWw+tto2pi7yJw4tdLjlLZoQvy4l4jv2rE2wxcRERGROqbEiIiIiMh+5JbmkpqxnndfvZZi0yR08IXc0vdKvl7+Jat3zCZu2W6apkKf9SahpTC3g0FhEAz7s/xXprkdDbwWGLjKv35bl34M+eRN7MFBhxzLohE9idhUVOm2vKaNaPfia/jWribl+XGQ7x8XKOH7H4hq2/owa3/4ihfMZPvVt5Rb985F4ST2O4s5Ob+Tk5HJuI+9RBT5h93KDwG7ByKKYXo3gz9Oj6frnJTAzf/d8U0YMutX8qb8RMoT/8Ob7a9fZjg43QSGQQLY3u90hn/0ygHjK0hKYdcg/7wbf8+pUhJmp+OMP3BE+G9o+1wu1t1+D6SnYVmzEoCcdp2I2LkOS5EXlw2WtTL4YqCFk7rey6OnjMZisbAheRlfbfqa1NIc4kJiueuEeyoM+wX+pMGy1GV0i+3GM/PG8n/bfmRA3Dk8fvJdNAitfP6S3Wmr+ejBi7jwD//7srXziZz59SeB7e7kZPKnTye0Xz923Xgh7j0lvH+6JTDkVkoUxOaCxYRVTQ2evMRCl6DmnOBpzOlPzcJrQMfVqwM3k1Oevo3sT38rN+8MwIrmBkn33ck9p95QaZxpRWnYLDbG/HIXKWuWsaMBFAcZtAnuxzcXvVvpPhd/cBIjJmfSc4u/bk2/+pLgbt0qTdr58nJIe+w2NqTZsJ8+kn6Xn1OhDIAnLZlNJw8C4NWzLYye4SO6wL8tJxSiCv3LTab8QmiLZqzp0Q5LkcF9V1s5e+j/uL77xTz3zQV8VrCBq6Z7OWOpyfZYyAoz+KuFwfrOYUSWBjH0lOtp5nPx44cv0iTdxHr5ldw84v5KY5Lqc/HHPbn1rSIa5FXc5njzfVoOHlBhfdEfM/Fk5eNyhhDUuBFGdhYh/fppThkRERGR45ASIyIiIiKVWLTmK25c8hSDlpuBG8sTT7IQVmLSKsk/j8XfN1Yr44ttgCUtvcL65H79GPTRh1WOJ/n+q8j5flHZijbtMIrzKYiOpftH72MNDQXAnZbGlrNHYBo22s6agcXprPK5qkPWwsXs/uoL7L9OA2BRWwOHG7pvrdrXSa/FQu7N99L/tqsC67K/eJ3kJ9/EMMvfzMxo2Izmr75M487tOJj0sXeSMaFsaLDgF16h2VmnH3Q/T3o6Wy8+G29SDuCfoP2RK60Mj7mKcWfcxWmfdCPdMGmQCyduNJnZxSAoNI7ezhDO6HEjA1ueRW5pLnf/dCWZGzezPQ6GLTM5Z4GPyf0sTO0Zyq8X/EJCeP0K5/5k3hPUf+RLWqZA3jlnceIzz+33hm7KDWeTPWfTAeuyo4E/sfRNfwu3/uwjNxz6LFkX2J7/5SvsfrzyRMbsTgbzLhnAU6eNpWlUQ0zT5I8983hrxVusylwNpsnlM32cvcgkIwLmtzf4obeFGTf8idNWfiJ3r8/L1U9048GvPPgMaPrTz4S1bHHA2A/VpgEn4MnY/y9q/tARnPjacwBs7d+J0kwvb59hYVYXg2vi+rN21jzu+v7A3bN+72rwXV8Lr7/j7+3y0gWh3HrLBLo19PcEO1CvLJ/pw+Pz1Mrk9seas1/uyLPvVvxsfKGhdFi0EMNmq4OoREREROTfQokRkVq2OWsbP2/7mas6ja70CVI5tlRliBoRqR4lnhIcVsdBhyU6EJ/p48YXu3DZ/3mJy9l/OY/dwAgPwbjwcizvlt1Azu/bnV4ffUHqM/eSM2kKpgvw+ShqEkOXiT9jj6r69d+9ZyfJd95I0ZZUDLudFj/8gD0urtKy3rw8ME2slQwvVNtWXDoE55+797vdUj+coNPPIrJDB5If+V+5bcXBwXRbuKDS5E7Rkj/I+2sF9vhm5H7xBSFduxD/wAOHHFfelCnsufseAMKHnEaj11475Ou1t6CQtFefJucz/9BV8zoYTDmzBa8MfYgXX76e6371Yd9n6opPBltIqQe7YgxiEtqRm7qeO7/20jQddsVAYkZZ2Z96Gey+/Hw6JTSjZWRbBjUdwPt/PMZrm77mork+LtjbWyT251+o37LZfmMsWb+ObSPPA8AVFk6jhx7EvXYVacuXYXOEYlm+vMI+KQl2Tp25MvDavWsHm08fBnvvPXufeQXLK49jpPgnol/UxuCtsyz8eNnvPPPTlcwq2E2DXLhkjo8TN5hY//GXw6qmBq5nXufy7uWHHpu98QfWPfQAp6w2KTqxMz0/nUR18RUXs7lvd7wl/s+2MKEJock7/dtC7LSbMx9rWBgAO0f0o3CTf+b6XTEwq7OF0TMPPmadz/D3wvnb+NMsLDqxO6/1uZj7Fj+FK6uAy/vcx7UnXlFuP7eriGu/PYf1Ral8fe5kmtZrWR1VPi643KU8d2N3zp9vktq4CV1uvB5KS7GEhhJ59ohArycRERERkf1RYkSkFq3dNpM7fvgP0bmwromBYUKctQHvnv0pzaMS6zo8OQxun5tXfn+KktIc2sS156ITbgjcWJu15WcemT+G69qN4qpe99RxpCLHhw2pK7jil6vpFtONJwc9xxvLPqJbbBfObTv8kI+xOXszd/96I0MnJzP4L/9Xn5xO7ai3ZzO+XC+5DSIJzS/Cfs1NtL32KizBwf79BvbGnZKHq19funz4YYWb7L6iIiwhRz5ps8/lAo+nWo5VG1I+/ojsZ57HtNkwPB5Mq5Xsrr2p174N7e65rVw9SjZuxHS5yd+xk9VL1tLy3DNp2vXgvT8Ohzstja3Dz8DZvh1N3n8/8DlWRe7E8ex57DkMH7x1YQRnnn4OkXd8tt+eRG4rTO1p0G+dWW7i53/aEw1fnGphW5xBs4Zt2Z68gctn+Oi/ruyreLt1aw+ayEl9/nkK5y8g8a03sSckBNb7SkvZdNpgfOmZ5cpnnXwS/d97r9y6rc+/ROmH7wPQZNZsgqMi2HzzjXgXLAZgbSKMvdzGmYt9jJrlw1FxLvNy3nvkDF6+/EUAluzeyEcrJtPZt4m24/6gYTYkPPsQUeeMPvBBqqjwx0/Z9eDTmB6DmKefxhlkJ/vDN2lw3yOE9O4fKFeyZhUZLzxO/oLVFY5R/9ZbCenenYLZs6l/1ZXkb9xEcOvW7LruXLzby3+YhU54+mIrSdFw0Vwfw5eZfH2KnTte/YPIvXOPrN+ziHGTb2LIL6V03Gny8+2dGHLWfzkx/kQ9UHEIdqRuImXY2UQUg/fhR+k0etTBdxIRERER2YcSIyK1ZOWOWTw54T/89ysv4SXltz17kYOzL36T8zpWHAtZjk5pRWmYpsn4ac+RMWMq3baZGCbkXH8Jt458lKXrp3DL7PvovMNkbaLB5Ct+Iz40HvD3IsnI20VocH1CHKFHFEd6kX+YngYhlU9mK3I8+WvPAl744gZu/9LDnvrw0VArJ27wsbRdEJPvXXZINxuzClJ54+5BDFvsC1yrjTv/S7ubrsU0TfD59vskcsm6dRQuWkT0pZdiODQszt9Mt5vCRYsJ7tY18GT+0cJXUoLhcGBYDr930ZKLBxL2Vyr/19/AfmobznlyAwAhbaKJOvMMclZspGjm4gMeI7NVJ/r/MJF55w8gZl12uW0bGkHbPeXLx919C9E33HbYMYN/PpLiv1aCu4ikMWPx1q9Pm4n/hz06ulw50+Vi1R3/xYiIpNMzjwd+j4pm/cSOm+4FYFZng5NWl+8hkhFWj7jrz8f78gfljvfRRbE899gsTNNHz0+74zFM7G6TL17wZ1Raz5uLLabyOVaOhLegAG9ODo7GjQ9adm379hh762K2akPzJx8juFu3Ssu61i8n870XccV3JmvBn4Slb8WT4Z/IpMQOQe695aww+l4rTSxR3NDxOla/+RzDl5iB7R8OtZAXAhkRBqedchWL131P/fCmXH7y/XRP6Hyk1T/mLFr2MxGX/ReANkuXHHXXFhERERE5+ikxItVqUfIiMotyGN5i6HH5tFtKYQovLn6FEJeNHaWZ9Gx0Ajd2uYyXfngcx7ffcfoyk8puvWxoBM9eYOWBbndzTt9rKmzfnb+byRun0CisEee1PbPmKyKVmrdrLss3zmP17pkEL99D40yTxHTosr3s0vjmmRYanjqSbfO+4+YpPoJd/vWvnGNhd8swOq0sIDUKlrW20J0QPr2ybL4At89/d8RusR80lr92LuGJP55kg2srAHFmKy7qdDU3nHB29VX4GPTn1t+4bc79NIvoyRfnvXfwHeRfY+222fw09mYGLzcJcZXfllwPmvz0G63qN6qw37I9yxi/8APC7KHUqxdL5orJXP1mDuAfHsdz7dV0/e99tVAD+bf663/X4fj6Dxa3MZgw0MJL73kptVvotmoN4E8MZU+cRGi/vuR8/haZX/6M12oh4oVXKbj7DgBaTvuVoMTGFG/axPYRlV/HXU4n8S+9QNzg06q9Dj6XC8Nmq3KCaF279uVe57ZrRbTXwLtpEzzzMo1ax7Hn/EvLlfnyFAtz+7RgYGInli37kX7rfKxpavDwRB8lTgvdVqyu8++QeXPmsuWJccTeeAONLhhZpX3du3aweciwwGuvw47V5Q683hUDczpZuGzW/ofoWtbSoGWyicWE7/pa+LNbCyZe8QVRQXX3t4zX52XZrtn8uuIDrurzAIlxXeokjo0ZG5iy7hfik3Pp+ugESm3QbfW6g+8oIiIiIvIPSoxItdmau5WR343ExCTCSMAozqZro4E8N3QsoUf4VPzRLt+Vz9MLnmD50incPdlLYgZkhMO2eIOVbQ0u/s1H2N4nj9PatqNpqxaEtmqFJzuLnE8/DxxncwJMuvFEhnYaxkXtz8PlKuSpjy+h4ezdeKwwpZeVH25ZTIjj0Ib7SM/bxeM/XM72nEzabTc4/YoxDO58fp3fcDhamaZJRnEGYY4wgm3BTF35MT/O+IDoht0Z2usspj9zNyMXlCU79uUJs2Mr8N/4yAqD6IKDn+/3rgZXf7qIUGc4yzfP4dGfbqZBpsF1102gX9NOuLwulqYuJTEskcbhjfGYHrbmbCXGFsqz/xvKqBkmy1saFARBXghM62GhT/ureG7wf6v5nTl2XPXhCRSklJAWCdnhBt1Cm1DPHo7d2YBT4zrzZ9Zm6gfVZ1TP64kOij74AaXWeb0e7p18M1nuYoZ0HI7F5qSBO59dTz1Hn5X73++n/w2iQYczGNa6F25fKZFBkeSmreWtF67mkungtsEfHQysPgLDZ7WYOgVns+a1VDP5t0r66m1yH3uN7bEw6SQL933jIyMsjJOWLqm0vCc7G9Ptxh4bS/GqVdjq18fesGFgu+lykf35xxgWH6XbNuHrcjIh9aMI69SxRnpSHImVb7+E/VX/MFtFYeF0+fknDIuBJzOToHbt8OXlseHE3gCEtI2naEMK6xrDKyOtBLng+Y+8ODxlx0uLDeGUOcvqoirVas/dd1Cw4i/C7ryLhiPOJvfDF0h+5SPwlC9XEBtDdsumJC44eJ3fubcfr1zzAZ/Ne4x5SUsp8ETy/IhXaRRRs21iQ9IS7ph8A413u2iQCy2TTZJOiuPRu2ce8bHTi9IJtYcSYj/4sIBen5f3f72P3Am/cOIGk9mdLVw810dmJAxYpMSIiIiIiFSdEiNSbT5b8RHTv3mRTjtM7B4Y9qfJslYGH5wXz1UnXsnZLc8mOrheXYd5QPku/xjR4Y7wKu33v0/Op89Ha2mReuBy2wZ354w3vyy3bseIfhRtKhs2Y0Vzg7mdDLY0DcJZWMqYL72ElPq35QVDwZcfMLh9/3LH8Pg85JTmUOAqYOaumUQ5o4g14d0vH+GSmT4aZ4DdC4vbGHw+2MmgnQ5C+gzm6hEPEhFUtboeC35bP41HFz1Eo2w3TZNMSiPsOAtNolNKaJkMGRGQXB8iCmHEYv9lb3ssNEsrf5zieg4ceS7yhwyg/RlnkXx7+Ul/Uzs0wdllAFETyn/mf/NYYOsXYyixRjDzq/u4for/s/74smbcfPNLXDPlcoJSS/FaoSAIWuWH0nZ9PnYPnLWk4uU4IwJeOM+KtUE4NncED5/zOl0T2lTPm3YYqmMC7OqSX5rPHb8+SOOfZjJqjo+8YJjZxaDPBpPgUv+kubM7+Z/Q9Vrh954OLr3pE05q2q2uQ5e9itxF3P7zA7B6Duf+VkpkISxtZRBRDE1TTRruvYwWxEbT89ffWPbKO9ijovB+/Bwhuf7P1+b1f9Y5obC1kZ3VTby8+ra30jkRki+9gUGP3lW7lZR/pdL1q9g68qJy67ZFxHPG4iO/cXy0y121hqQLLwCg3pNPEX/BeeW2m6bJjvPOwFdQRMNxT7Ft9HVg7v/hjLzTTqL3G8dmb77iv1awe9yTeFb4exJ5bAatFywhfcMWMq+5gqLTzqTnPbeSPPYhiuctxhMZSYMRp5D92fcAfD3AoOX11/H68o8YstzEa4HNfXsz6YqPaizmmUve4NXf3mHcJ+Uvkos7Wxj00VQ2ZG1gYOJAbBZblY+9ds8ibvn+BsJLbYQ3ak/rbBexXc9gYKte/LT1J6Id4fSIbE2PZqexPnMt775/HYOm5dA6ufxx9iRYOG3mmiOppoiIiIgcp5QYkWrz4jfXcNqjCyrcYFrS2uD1ERZKnAad3JHkEsqYYa/Qu3HZ8AuF7kIshoVgW9UnPj1cJZ4S3D43i7fN4t6Fj2IrcXPKGhObB2wjruSJ4QcfOiW3NJev/nyT7Hc/Z+TCsl+PkKFDKdm6Ed/m7ZQGWcg4eSgDHn+MoKiKbTDvmy/Z8/ATFdb7DP8NvH8ac30jnrn6M5rVi2N98jL+2L2SyRs/wkjOZfAKH03TTOa3t2CYcNksX2Ds6gqxh8CTlzi5deQ7DG/T56B1/TfamrOVlekrGd5iOIt2r+bDP98iypND0Kx1nDffJKK46scsCnGSeOkoHO06EnXWWZimiWEYmKZJ8n9H48rMJrTTSTg7dSNimH8oDdM02X3FORQs2YQ5ZBBNLr6EXdfdAPiH2EpMNzl/ftmHPaWXhW2nxHL65ym0SjlwPNbLrsRIT8b843e8hf5fvr/HNJ8wMoLHnll04ANUQU5BMp/98SbNmp9K38SuRDmjsFlsuLwu5uyeQ4QjAocliC6xnZi3dSpvf3EfUdYQ7r15Ai2iW1RbHAfj9Xn5ecV7LNw4j0tPfYQOsW25+rv7sS+ewj2T9z90yb48FvjvdRaGtj2Pe86s+Pu5P0XuIp5d+DTdYntwbtvzDr7DIUgtSGZLzh66xXU4pKdqjzU5OduZsuwtJq6fxbX/l1/hpti+CoKD6fTb7wTXL0vCb3xmDN6PJx3wHPkN7EQ6Hfh2F1JQz479zifpetEI9a6TQ2J6PGw4bTBmSln2PHXouQx87ek6jKp2mC4X20dfgSUqiiZvvVnp/DumzweGgWEY5Lz/PMkvlt3IN5olEtwwkdL16zDsDpqMH4+zxbHdS8uTmcmecc8S2b8fUeeOBAh8l/ibOyUFw+nEVq8eW0aejmv9TtY08X8/uP17H869PU9WNTXIuP9qbj71v9VyvZq8fgZv/fkOd/b6D8aO6az8/BuGLzXL9eoB2JAIr55vw1Fk0qTFMG7rMYJvVoynTfMhnNykP4mRzQ56rkdfPZnz3k8P1AX8SeuJJ1uweSC6wMTqgx0nxGMmpXH75Eoy2EDOkF70ff3TI6i1iIiIiByvlBiRavPsy0M5+91d+91eagOvBVw2+GCYjZiBt/HMkOtJzd7C+T9dQpC9Hr9c9DMOa81PWJtbnMOAiQOIzoe4HLhxijfwtDHAR6db8Zwxinv6XkWII5hFuxZyUpNTCHP6hwRbkbaCT1d/zG87f+OsRSaXzvJhNcHTMI5O037DsNn8k/Sa5kHH6zZNk8J5fxDUqSMZU39l1xuv4yzOxV5U9gdgy9+ms/bS4TjTPLx9hoXZnQ1ic2D4Uh/R+ZASDecs3P+vZ71nniPv1TF4k8tnApLqwfvDLIw463FG9Ti30if7U7I2sWrnPOrV707Pxl3/NTcK8135DJswiLjdxWRGgOGDi+f66LfWrPTp8Px6DryhdpwuO942cRSl7sDwhhB28gXERhtkrdmAYdpo88RTOCOrfi0xfT48KSnYEhIwDINVvbpjyy8pV8aSGI5vVz47Y2DuAcYfz22SQL1R19D64vOwhPhvlHvS09l91QiKt+QGym2Lg8JXXuHMTifx0bJX2ZO1lW6NTuPirheXxWWajPnkbEqWb2V3nBNPqyFMuuhZAHymDwODqUvf4LtFX2LfkUuHHbArxmBBO4P6sQm8cvrbvPz9jVj+TCEm18RngT0NnTTILuHiGeADnr7MwdWXfsRpLXtW+X2rKtM0ufutgfT7Pp02SSZvnmVhQXsLXbf4ePBrXyDZaG/fEveO3XjCIghpmIBrhX8MJutFo/D+NAn2/v5NONnCsEe/pG/jrvs9n9f0Bp6WffPnu3F+MJX1jQ3OvWci/Zt1OqS49xTsIdwRTpA1iBlrJ+LyuFmZspA/1syn52b/k8FLWht09sXijQimWatBdGnehTBHGL0T+pBalMrGzB2clHjiv+Z39EA2pyzn6z9eoWj9Jjb48ui9wcfAVSYhpf6kVU7jBjQ+51KM5B1kpu7C07wj8ScPonGv7lid5f8PMU2TPW++Tt6bb2OYkDl4EN4ta4nZmYLFB6UOA+ezr9L2pN4UrFpOeM++mjhdqsy9Zw9Zn35K0ZKlhPQ6gbgHH6zrkI5aRfNnkT9zLmGnnU5o7xPrOpyjnmv9X2wZecl+t69obvDJEAujelzHlQPvPKRjLk1Zxo3Tb+Kydpdx1wl3kFGcwfKU1fzw5n8YPcPHkjYGnbebxO79SpHdugMnjH+PBRNfIvb1b9nRALxWaJYC44dYKHLChfN87Iw1+LGPwbkD76VxbFMiQmLp3rAjJZ4Sbpv+MD7TTfeIBqRvXMgp728lIfvAcf6TJ8hO2vCLOfXx+/CkpmGWluBo0eKY+H9PRERERGqfEiNSbV6+txfDfizAZbdQHNsEX3gk3R+4hR0334j5jyfzs0Ph3mutnNj8UhrkLSN14Xryg6HBaaO5t+91xASXjZdcVJLLrsz1tGlY/oZfRnEGkc7IQ5qo+p8m/X4/2W/9yMlrKm/S2aHwa08LfzU3SI0x8fkMXHaDfsFNsazZSXySh1ZJ5SfdTuvQlgET/w+rverDCVSmdONGdt1/J8EdO9LoyedJf/QWMiYdwrAcBrA3rLzWrenw+puEN0v0H3PTely7knA0asjWS8+HQv+N95ldDP7vnFb8eNFXhNpD2ZK0nk9mP0Pytr8IznDRZZtJwyyT+Z3suM4YxdPD7z2kYRO2Zq7nic9voMhmp2f3YVzS8RKaRPpjKXIVku3KITYk9rA+wwNZl7yUm3+6nnNmljLsz8o/44K+A+j27FMYNhuujExC2rSu1hgOJuuTT0gbP57SnEwcJR4yTz2JPv97iE2DhuH/EP0KmzUktLQYX2IrYq+7gZiT+u/3BoCvpIRloy4maONWbF7/I5jPX+hgfUMv10zz0TzFZE5nCwMf+IJTW3QDYPPOpWy6fHRgmLBfehq0efhdWkcF89SH15IaZaV+Zin3flN576PPTrXgdMNF8w7cE+P+q63cfMErDGsxuEZvYPzfnHHUv+9T4nP8r30GbGgE7Xf7X3vrR2J9ZBzth58a2Mf0eCheuRJH8+bY6tWjaMFcUp74H6VbU/FY4KE7u/LjDRMC5QsK0/jqj+f5KWst5vYddNluEuaCnc1CCMos4vpf/e/FY6Ms5HVoTovQVrh2bODUbqMY1fcKALJLsgmzh1HsLWbst9cQO3UtUQWwO8bgjCU+ckNhe5zBwFX7ab9BUOKAIqc/UeW1QKkdtrSIxDlgFI8Muo6IoH9P75IiVyEvzhjDhpzdnNVhGJO/e567J/sq9OoqDrUQ+9q7NO4/oMrn8KSnY4mIwOJ0AuArLsZXUIAlPBxLUFB1VENEpNqZpsmW/ifgzioKrGv2+UdkrVhF3gsvB9b91RwGTppPfPj+h619af4kft76AwUFKzh/vo/1jQ18Fogogo47zArfi32YFN10IyfccSeGYZAy6weyb7r/gPF6LPDOGRb6rDfxWGHRqBNp5KhPyw9+IaLIJC6HwLx7ACUP/Y/GCbF4Z/9C9v9N2e9xc6Ki6D17VuAaLiIiIiJypJQYkSO2OzeLa7+5nrN/Xcegv0w2d2/IiK9+D2wvTEll8803EWkWEty9F9nffIfp8t84nDTAQnY43PiLD48F7rjRSlgxJLY5izfOHYdhGNz++anM9GZwUvAAXjrvZV5f8BTTtvxMCi46B/fhy4s/qBDTltS/+Hr5u6wuSKVx/Y7c2vN6NmTsIchuoVVEPHe9ewaPfuR/ItztsOAOiaT+NdfSaNipbDrnHIxiT4Vj7o9pmCRfeRuD7r+lRm/4+lwutp9xMqW7c8utt4Q78OX7ZwNPnPoLwU6T1MceIvTMS4kcMWK/xytesYz1119LUL5/ApPv+hj8NDCS+/rex/RPHuac2SaNMyvu9/7pFtY2s9O9wUAeH/U8dmvFpIbP9FHoKuDBx/px87defBbICYNpvazc/fxcdu6azV2/PUxOkIHdEc2Esz+heeThDZ2xMW0lfyyZSHJeLu27DOGs9mdw57heXPV1abk/vPdlfe8T2px8dDylano8lG7ZirN1KwyLhRUD++FMKXuEssn33xPatmrzhJimyerrL8I2b3WFbW4rXHe7lbgiH8FuK4WGyQsflu9C89aZFqymyY1Tyl/ySyOcOJpE4EktwpZegEH59u4Nc+KND8adVURIlovSuHoEFxRgFrrxAU9dYmF3s5ac3+5Sru1xFunF6Yf9ue8rpySH6dtn8uGfr3LalDTOXGpiAv/8bTSdVlr9PBVH48aHdNzVfTpgzTF59WwLf3S00DS0A11iW5E07Tuu+9WH10LgadoDSY+ABnmwurnBBVPW8Mms53ht26fYLRZOXuHj7D98xOTvf3+3w4LddWhDgAEk14NvzqjHc/dNI9QZVmF7bmkuVsPK6vSVFBS7OKFRNx6efAW5hUVcMuAxzvrHHEZ/m7NzKb9tm8P/BtxW6e/94SgozWfczHtYu3I+F8300SzV5JcTLIxY7COsBFx2A4vPxB0aRNiDT9H6rCEY9upNpIqIHO1KN2+maOlSwCSoTRuCe/h7YGaPf4OUZ98MlHv54aG8N/rVCvtvz93O+4vfpHDKFAwT+q016bRz/3/WeTo1pTA1Fc/AgQx4oiz54isuZs2A3tgK/U9KFCU0wpafjqPARWlQMKFhuXgyyve4c1lhWWuDvuvLn68wwg6nnUPPpx4PfH/edeO5FMxeD0CTKVPYefHFkJ9PWpsutPjfgyT26nbob5qIiIiIyEEoMSKHpchdxJw9c0jJTmXCjOd5eKKXmDz/tm0XnssZT+x/XO38GTPYfcutBzz+8+dZWNYmmNFtLmThzM+4bIaXuZ0szO1ocOXvPrptNQkt8Q8D9cAjM2kSGQv45w156KOziF6YxJDl/jkk/mxpsKc+dN9iklTfYGMjg8R0k1NWm+xs1JLTf/+p3Lk96enkfvd/pL34WqWxeWyQ2aoJkaHR+DKzaHzztcSdc1GlZaubOzmZzacOAiD/roc54aoLMQyD9DG3YW/SjHo3V23oDtM0yXrhEdI+/Daw7ofeBmcv8v+qe6yQXj8EOp5E3OqFWNLL7gKX2OHF8yzY2rTiw1Hf4vG6mLDgWX5e+QNxO0pwuOG6qRUvGS+ea6F5Jpwz1+d/wj3B4NeeBlntOhDi8HBB12s5t/1ZgfJT1k9hR0EmHaPiKSjO54zO/nkbSr2lXDHpHAZ8v5OBK01se+8bL2hn0DDTpGk6uB2Qd+4VtA42cLZtx9qdmbiyszllzD1H7bALSx97jtCvxgdet1u75qDDsVXGk57OmlEjcOw+hDv3QGa8Sf0GDWBVRqXbsxIi6Dt1TuBJTde2bWwZfkZge0arhvT/9hcse4cg8pWWYtjtlPy1ku2jRgXKJdWDV0Za2dUAHG4wHcGclngud/e7ngYhDapUR6/Py6vzHuXLDd9z9kIfg/8yiS7wb8u79Q5cK/8kZu5cMlu0p9ebL2KPiwsMPXYo9tx2IXnT/cmljAgC17h/KmgaRXBSLlb3Pu3dgn8csX24rJA+8Qn+euZRzlhq4rJSblg3n9WgKMpJWGYJptNKftMW2M67nJ5XXUTe9OnkfP1/NLj7LpyNGpE9+1dcjiAKNiwhLSkFh6cEW9I2rKvSsLsMih3wv6ucRNVLJCS4KQ8Ov5uMPUv58KdnaL62hIQsCCkFm8ckJdqgWapJszT4+QSDomsugaIcBna6iC3Js5i3ayEn1uvEj8u/o8gJ7nqt+WD467RtcGgJpn/y+rz8tmMaW7ZMY8+MXzn9N5MGlby3pQ4b7efMwxEa4p+fwFY9vfFERI4lnowMNgw8CYsHnr3AQsyJPXluxMeB4VHzSnO57M2TuXiGm56bK34vczsMcIDHYify3AuJ79ub0IFD9ns+186dFC5YSNjAU7DHxWGapr/3XVgYhdO+Y9cdD+1339IzTyZo/VrcLTvQ6aU3K1zXPSlJ7LzlRpw9+9Lo4YfwZGVhWK1YIyMP890REREREdk/JUakynIKU7n4izM5aWERvTaaNNnnPmqp3UH9LyeR2Lntfvc3TZPUJ54k+8sv91smK8w/+aLLBhfP8QWGxckJgaii8uW2vPEYvdt0ZefuBXw77VUun1ASuDl6MNuGX8QZLz9W6baSjRtxp6bhbN4cT2Y6qfNnYXQ7iUbdOmIPPraGXVl9/bVY584vt85nQLslS7CG+Z84N71e0l98lozxn2HscyVIjYKvbm2LJy+NoT/m0Gm7yb638UsdVhIfHUvaI/87YAwFQf7J5md2Nvh1eBNOiOpISNpaktfupEWKSccdJj4Dkh+5jYiE9kxbNI4B3++m98bKL0seKzT+5TfqNWl0OG9Jncn9czlJl14KQMFVN9PrgduP6HjpM6az4+U3iRk8mOJ33tpvOceIXjR/4j02XTIU3/p0AHyGQeydd5ITHU/jM4YQHBpcbh/TNHFtWIe32EVwl86VTroL4C0uZvN15+Fbtj2wzmMhkMxa1MZg0kAHLZo1Z33Bbp4f9CrdG/fbb6wFrgImrJrAB0tf46YpHnpvKGsDHpuBOeoqujx8H16Pl6RlK2ncs/Nh3VR3bVnHlosuhMLKJ3yNe/5p7O06Ed66NabPh+l240lJwQgOxh4bS8G0H8n56UfC+/Zk55MvY/MafDzYwlW/l2VMfAakDzmdk59/tlqGB/Gk7mbt2YOw5/o/i1Ib5IbCTydaSEw3GfSXifUQ/yef18HANCAu26RRJoT6O5exsSF8daqdQaffxJpts2hWvzV3nfYUALvyduG0Ovh61tOk/zGP0mI3Zo9+jLv8bTKzNjF/03QmrvmanjPS6Lij/P8fu9p1oWU98OYVUGyPIOqGW2k6qOpDZomIHG+S77yGnKkLAH9yZFlLB4Mbj+SOE69g6pyP6D7m/4jc5/tzXmwMwTfdQ+Nu7Qlv3BB7RHiFCeAPV+Z7b+MqKCT+rnvIevoOMv9YQoojhIjeg+n24P6TJiIiIiIitU2JEdmvv/9AMk2TObtnk1aUTovw5rz3/U2M/KowMCfB30L7NSHx3Z8OeZiTglkzKZw7i7DTz8TZqhXFKWlsefttnNOnHXTf4P89RvETYwD/3AUnbPTRaYdJ81QIckNupIOI864mrkEoOfN/p2jJarxR4TgSIjBW7MTjsFJ6xS10/8+1WIM0VrEnO5vML75k5+rVhM2aBcD2uIYMn/17hbLFq9dQOGc2afOnYizdFFifHwTh/xi6qqBBAxx3PUjX84bz42VX02rZQgByE0LwtGhNs6ZNyP59KtasUgx3+V4RBUFg81JhXovZnQwa5Jp02OV/7TPAe/3VtD33AjZP+JTMDSsJsRnEjLyOZmcNP7I3pg6YXi/rO/on7E78eDxhffpU27FzvvmWosWLaHD77VhjYsj7/hs8qWmEnnwqQV26+H/f3W7yZ/3KttRS2pw5mOB6UdV3/okTSB5TeSISYFp3g+/7WAgNi+bHa+dUWiYlcz13jL+Ik5Z4GPxX+f+SLG2b0fKDT7E1qFrPkwMx3W5Kt20jZeo0rFkp2GNCsCRvo96tY7E3PPSk2+L+HQjPLIs3KzaaBo+/RIsTOmELC622eAGKZnzL9lsfwjArv8FVUj8cS4gdX99TCV45D9/6VAobRmN1FRCc4Trk88zoYtBro8m2eIMPRlrJdFpJTPWQmG5yyRwf9fcOD7YzBp6+zEHb7R6u+s1HvcLyxymMDqP5R19Qv13VhowTERE/b0Eh60echSU5BfAPqZgdBjtiDbYkGPznJx+loT4av/8FUU2aYY2OPqzeqCIiIiIixxIlRqSCEk8J05eP54NF71AaYifKhEZ/FeN0+588HrnAR+NMMA3I6tSeDoN7UTT9Z+Jf+xRboxZHfH5Pdja7rrmSknX+m+4l3XsRcdLJhKbtJHvSN4RcfgVNH7qfxQM7Ep5Scdz9rOgw2n0ykfqtK4/FV1yMJTi40m0C2376mV0vvELw9bfS67KRByz7R/++RGfmBF57LRDy1ngaJUTiTk0n7OSTA9vStu1i6e33E3/1lfQ47/Ryx/GkpbD9tefw/DIDs7C0wnnyOjYi1BaC9a9N5dbnxjgJe/gVOg0fWOV6Hs2KliyhZNMm6o0addQO+XW4CufPJ+2jj4k45WQyXnsVe2wDSrZtw9j7q+y2wjvDLVx9xzf0SmxXbt/dqSs59/tLeeNtb7lJuTPiGtHqvfdIaHvk15+akvzT/5H+4KPY9w63Ff3VJOK6d66x8xX+/hOuXTsojoyn+Ks3KcovJT3YTujIa+h9xRXlypouF9hs+LIzyX1/HPaOffAV5JP5/VcUbU2FxFbUv/ch4nv1wJO8i40jzsBSfPCvA24b2PczXVNxKBSfczFdr76esMR/V68uEZGjkWvHDracPmy/2zNPOYkB775XixGJiIiIiBzdlBiRgEJ3IVdPuoD6K3fSd61Jr00mRU7/OPQVyobYaPDhRJp271AjsZRu20bK2Meof8P1hPUvmwjYV1KCJcg/jNXqy4ZgXbY7sK2oZQsi/nMPLU87CYsm560Vm+YsYvtrb9NqcH+cVgOzaw8a9e5xRMd07d7Dhq//D2teKkFNWtD03AuwRkUBsGXUCFzLNwNgJsbT7sdfAu1B/n3+7pVWtPAPdt36H3yF/i5HecHw5PWxfH/zTCyGhbS8FC7+5nyCM3J58R8TxReHhtD+u+8JTjy8+S5qU3ZSClnzF1M/IYao/vsfKuxo59q6kT3vPEdRgYElMx/++qvcdk8oeIJCSHz/K4I3z2P7g89h8Rq4bJDRpR19X3qX4NgYPa0sIlLNPNnZZH88Hou1hF2/z8G5YUdgm+PND2g5uP8B9hYREREROb4oMSIBH8x5hXpPvBsYoqiCEDsU+cc1ivjscxr16ll7wVXCvX0D257+H2ajTjS95GKC2u5/XhM5NpgeD65du7BGRGCrX7+uw5FqZPp84PWyrlcnjBILXw8w+PokK43DGmPPyeHGz3JokVJW3jX6WjrddgOGzValCdWl+pVs2IBr8yZKNq4n6tLROOLiym13bVhFSXIKQScOwBGi3noiIrXFk5lB2tMPYLbuRKOb7qzrcEREREREjipKjBynNqdt49Xf7yLK2YJsdw6FBTk49mzlzi/93UOSOnQh5uLLiV06jZxfZxH58CM0vPhicn/6GWtkBGEnnVTHNRCRY1H6x++S8cwr5ITCPddZKQyCa3/1MWRF2X8/9oGDaPHic1hCq3duDhEREREREREROT4oMXKc+vHdRwgb/w2xOWABXFbYGg/t9kCJ00b3v1YFypoeD4bNVmexisjxw+dysfW0/rjTCipsKz3pJLq++46GYBIRERERERERkSNSlbyB7kQdQxqE1yM+p+xDdXj9SRGA1I7dypVVUkREaovF4SDx/U9hn8uO4bTR6On/0e3995QUERERERERERGRWqW748eQPpfeQ179zjibN6N443qKpv9A7vQ/wAfNhgyr6/BE5DjmbNsex7Ovk/fMGMKiImn6xts4mjat67BEREREREREROQ4pKG0jnHevDxc27YR1LmznsoWERERERERERERkWNSVfIG6jFyjLNGRBDctWtdhyEiIiIiIiIiIiIiclRQFwIRERERERERERERETluKDEiIiIiIiIiIiIiIiLHDSVGRERERERERERERETkuKHEiIiIiIiIiIiIiIiIHDeUGBERERERERERERERkeOGEiMiIiIiIiIiIiIiInLcUGJERERERERERERERESOG0qMiIiIiIiIiIiIiIjIcUOJEREREREREREREREROW7UaWLkrbfeonnz5gQFBdGzZ0/mzp1bl+GIiIiIiIiIiIiIiMgxrs4SIxMnTuTOO+/k4YcfZvny5Zx00kkMHz6cnTt31lVIIiIiIiIiIiIiIiJyjDNM0zTr4sS9e/emR48evP3224F17du3Z+TIkYwbN+6A++bl5REZGUlubi4RERE1HaqIiIiIiIiIiIiIiBzFqpI3qJMeIy6Xi2XLljF06NBy64cOHcr8+fMrlC8tLSUvL6/cj4iIiIiIiIiIiIiISFXVSWIkIyMDr9dLXFxcufVxcXGkpKRUKD9u3DgiIyMDP4mJibUVqoiIiIiIiIiIiIiIHEPqdPJ1wzDKvTZNs8I6gAcffJDc3NzAz65du2orRBEREREREREREREROYbY6uKkMTExWK3WCr1D0tLSKvQiAXA6nTidztoKT0REREREREREREREjlF10mPE4XDQs2dPpk+fXm799OnT6devX12EJCIiIiIiIiIiIiIix4E66TECcPfddzN69GhOOOEE+vbty3vvvcfOnTu56aab6iokERERERERERERERE5xtVZYuTiiy8mMzOTxx9/nOTkZDp16sSUKVNo2rRpXYUkIiIiIiIiIiIiIiLHOMM0TbOug6iqvLw8IiMjyc3NJSIioq7DERERERERERERERGROlSVvEGdzDEiIiIiIiIiIiIiIiJSF5QYERERERERERERERGR44YSIyIiIiIiIiIiIiIictxQYkRERERERERERERERI4bSoyIiIiIiIiIiIiIiMhxQ4kRERERERERERERERE5bigxIiIiIiIiIiIiIiIixw0lRkRERERERERERERE5LihxIiIiIiIiIiIiIiIiBw3lBgREREREREREREREZHjhhIjIiIiIiIiIiIiIiJy3LDVdQCHwzRNAPLy8uo4EhERERERERERERERqWt/5wv+zh8cyL8yMZKfnw9AYmJiHUciIiIiIiIiIiIiIiJHi/z8fCIjIw9YxjAPJX1ylPH5fCQlJREeHo5hGHUdzlEjLy+PxMREdu3aRURERF2HI/8CajNSVWozcjjUbqSq1GakqtRmpKrUZqSq1GakqtRmpKrUZqSq1GYqMk2T/Px8GjZsiMVy4FlE/pU9RiwWC40bN67rMI5aERER+mWQKlGbkapSm5HDoXYjVaU2I1WlNiNVpTYjVaU2I1WlNiNVpTYjVaU2U97Beor8TZOvi4iIiIiIiIiIiIjIcUOJEREREREREREREREROW4oMXIMcTqdjBkzBqfTWdehyL+E2oxUldqMHA61G6kqtRmpKrUZqSq1GakqtRmpKrUZqSq1GakqtZkj86+cfF1ERERERERERERERORwqMeIiIiIiIiIiIiIiIgcN5QYERERERERERERERGR44YSIyIiIiIiIiIiIiIictxQYkRERERERERERERERI4bSoyIiIiIiIiIiIiIiMhxQ4mRo8y4cePo1asX4eHhxMbGMnLkSDZs2FCujGmajB07loYNGxIcHMzAgQNZs2ZNuTLvvfceAwcOJCIiAsMwyMnJ2e85S0tL6datG4ZhsGLFihqoldSk2mwzzZo1wzCMcj8PPPBATVZPakBtX2d+/vlnevfuTXBwMDExMZx33nk1VTWpIbXVZmbNmlXhGvP3z5IlS2q6mlKNavM6s3HjRs455xxiYmKIiIigf//+zJw5syarJzWgNtvMn3/+yZAhQ4iKiqJ+/frccMMNFBQU1GT1pAZUR5vJysritttuo23btoSEhNCkSRNuv/12cnNzyx0nOzub0aNHExkZSWRkJKNHjz7g31dydKrNNvPUU0/Rr18/QkJCiIqKqo3qSQ2orTazfft2rr32Wpo3b05wcDAtW7ZkzJgxuFyuWqurVI/avM6cffbZNGnShKCgIBISEhg9ejRJSUm1Uk+pPrXZZv6m+8BllBg5ysyePZtbb72VhQsXMn36dDweD0OHDqWwsDBQ5rnnnuOll17ijTfeYMmSJcTHxzNkyBDy8/MDZYqKihg2bBgPPfTQQc9533330bBhwxqpj9S82m4zjz/+OMnJyYGfRx55pMbqJjWjNtvMN998w+jRo7n66qv566+/+OOPP7j00ktrtH5S/WqrzfTr16/c9SU5OZnrrruOZs2accIJJ9R4PaX61OZ15swzz8Tj8TBjxgyWLVtGt27dOOuss0hJSanROkr1qq02k5SUxGmnnUarVq1YtGgRU6dOZc2aNVx11VU1XUWpZtXRZpKSkkhKSuKFF15g1apVfPzxx0ydOpVrr7223LkuvfRSVqxYwdSpU5k6dSorVqxg9OjRtVpfOXK12WZcLhcXXnghN998c63WUapXbbWZ9evX4/P5ePfdd1mzZg0vv/wy77zzziHdz5GjS21eZ0499VQmTZrEhg0b+Oabb9iyZQsXXHBBrdZXjlxttpm/6T7wPkw5qqWlpZmAOXv2bNM0TdPn85nx8fHmM888EyhTUlJiRkZGmu+8806F/WfOnGkCZnZ2dqXHnzJlitmuXTtzzZo1JmAuX768Jqohtagm20zTpk3Nl19+uaZClzpSU23G7XabjRo1Mj/44IMajV9qX03/3/Q3l8tlxsbGmo8//ni1xi+1r6baTHp6ugmYc+bMCazLy8szAfO3336rmcpIraipNvPuu++asbGxptfrDaxbvny5CZibNm2qmcpIrTjSNvO3SZMmmQ6Hw3S73aZpmubatWtNwFy4cGGgzIIFC0zAXL9+fQ3VRmpDTbWZfY0fP96MjIys9tilbtRGm/nbc889ZzZv3rz6gpc6UZtt5vvvvzcNwzBdLlf1VUBqXU23Gd0HLk89Ro5yf3d7io6OBmDbtm2kpKQwdOjQQBmn08kpp5zC/Pnzq3Ts1NRUrr/+ej777DNCQkKqL2ipUzXZZgCeffZZ6tevT7du3XjqqafUvfcYUFNt5s8//2TPnj1YLBa6d+9OQkICw4cPrzDsifz71PR15m8//PADGRkZepL7GFBTbaZ+/fq0b9+eTz/9lMLCQjweD++++y5xcXH07Nmzeishtaqm2kxpaSkOhwOLpezPoODgYADmzZtXHaFLHamuNpObm0tERAQ2mw2ABQsWEBkZSe/evQNl+vTpQ2Rk5BH9Hyd1r6bajBy7arPN5ObmBs4j/1611WaysrL44osv6NevH3a7vRprILWtJtuM7gNXpMTIUcw0Te6++24GDBhAp06dAALDQsTFxZUrGxcXV6UhI0zT5KqrruKmm27S8CTHkJpsMwB33HEHEyZMYObMmfznP//hlVde4ZZbbqme4KVO1GSb2bp1KwBjx47lkUce4aeffqJevXqccsopZGVlVVMNpLbV9HVmXx9++CGnn346iYmJhx+w1LmabDOGYTB9+nSWL19OeHg4QUFBvPzyy0ydOlVjuv+L1WSbGTRoECkpKTz//PO4XC6ys7MDQ5UkJydXUw2ktlVXm8nMzOSJJ57gxhtvDKxLSUkhNja2QtnY2FgN2fcvVpNtRo5NtdlmtmzZwuuvv85NN91UTdFLXaiNNnP//fcTGhpK/fr12blzJ99//30110JqU022Gd0HrpwSI0ex//znP6xcuZKvvvqqwjbDMMq9Nk2zwroDef3118nLy+PBBx884jjl6FGTbQbgrrvu4pRTTqFLly5cd911vPPOO3z44YdkZmYeUdxSd2qyzfh8PgAefvhhzj//fHr27Mn48eMxDIOvv/76yAKXOlPT15m/7d69m19//XW/46LKv0dNthnTNLnllluIjY1l7ty5LF68mHPOOYezzjpLN7n/xWqyzXTs2JFPPvmEF198kZCQEOLj42nRogVxcXFYrdYjjl3qRnW0mby8PM4880w6dOjAmDFjDniMAx1H/h1qus3Isae22kxSUhLDhg3jwgsv5Lrrrque4KVO1Eabuffee1m+fDnTpk3DarVyxRVXYJpm9VVCalVNthndB66cEiNHqdtuu40ffviBmTNn0rhx48D6+Ph4gApZwbS0tArZwwOZMWMGCxcuxOl0YrPZaNWqFQAnnHACV155ZTXUQGpbTbeZyvTp0weAzZs3H9FxpG7UdJtJSEgAoEOHDoF1TqeTFi1asHPnziMJXepIbV5nxo8fT/369Tn77LMPP2Cpc7Xxfeann35iwoQJ9O/fnx49evDWW28RHBzMJ598Uj2VkFpVG9eZSy+9lJSUFPbs2UNmZiZjx44lPT2d5s2bH3kFpNZVR5vJz89n2LBhhIWFMXny5HLDkMTHx5OamlrhvOnp6Uf8XVrqRk23GTn21FabSUpK4tRTT6Vv37689957NVATqS211WZiYmJo06YNQ4YMYcKECUyZMoWFCxfWQI2kptV0m9F94MopMXKUMU2T//znP3z77bfMmDGjwh9ozZs3Jz4+nunTpwfWuVwuZs+eTb9+/Q75PK+99hp//fUXK1asYMWKFUyZMgWAiRMn8tRTT1VPZaRW1Fabqczy5cuBshvg8u9QW22mZ8+eOJ1ONmzYEFjndrvZvn07TZs2PfKKSK2p7euMaZqMHz+eK664Qjca/qVqq80UFRUBlJsv4u/Xf/dak3+Huvg+ExcXR1hYGBMnTiQoKIghQ4YcUR2kdlVXm8nLy2Po0KE4HA5++OEHgoKCyh2nb9++5Obmsnjx4sC6RYsWkZube8TfpaV21VabkWNHbbaZPXv2MHDgQHr06MH48eMrfLeRf4e6vM783VOktLS0mmojtaG22ozuA+9Hzc3rLofj5ptvNiMjI81Zs2aZycnJgZ+ioqJAmWeeecaMjIw0v/32W3PVqlXmqFGjzISEBDMvLy9QJjk52Vy+fLn5/vvvm4A5Z84cc/ny5WZmZmal5922bZsJmMuXL6/pKko1q602M3/+fPOll14yly9fbm7dutWcOHGi2bBhQ/Pss8+u9TrLkanN68wdd9xhNmrUyPz111/N9evXm9dee60ZGxtrZmVl1Wqd5cjU9v9Nv/32mwmYa9eurbU6SvWqrTaTnp5u1q9f3zzvvPPMFStWmBs2bDD/+9//mna73VyxYkWt11sOX21eZ15//XVz2bJl5oYNG8w33njDDA4ONl999dVara8cuepoM3l5eWbv3r3Nzp07m5s3by53HI/HEzjOsGHDzC5dupgLFiwwFyxYYHbu3Nk866yzar3OcmRqs83s2LHDXL58ufnYY4+ZYWFh5vLly83ly5eb+fn5tV5vOXy11Wb27NljtmrVyhw0aJC5e/fucmXk36W22syiRYvM119/3Vy+fLm5fft2c8aMGeaAAQPMli1bmiUlJXVSdzk8tfl/0750H9hPiZGjDFDpz/jx4wNlfD6fOWbMGDM+Pt50Op3mySefbK5atarcccaMGXPQ4+xLvxD/XrXVZpYtW2b27t3bjIyMNIOCgsy2bduaY8aMMQsLC2uxtlIdavM643K5zHvuuceMjY01w8PDzdNOO81cvXp1LdVUqktt/980atQos1+/frVQM6kptdlmlixZYg4dOtSMjo42w8PDzT59+phTpkyppZpKdanNNjN69GgzOjradDgcZpcuXcxPP/20lmop1ak62szMmTP3e5xt27YFymVmZpqXXXaZGR4eboaHh5uXXXaZmZ2dXXuVlWpRm23myiuvrLTMzJkza6/CcsRqq82MHz9+v2Xk36W22szKlSvNU0891YyOjjadTqfZrFkz86abbjJ3795dyzWWI1Wb/zftS/eB/QzT1Kw8IiIiIiIiIiIiIiJyfNCghSIiIiIiIiIiIiIictxQYkRERERERERERERERI4bSoyIiIiIiIiIiIiIiMhxQ4kRERERERERERERERE5bigxIiIiIiIiIiIiIiIixw0lRkRERERERERERERE5LihxIiIiIiIiIiIiIiIiBw3lBgREREREREREREREZHjhhIjIiIiIiIiIiIiIiJy3FBiREREREREREREREREjhtKjIiIiIiIiIiIiIiIyHHj/wFuc7iLnlobqQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the stocks of Tesla  performing from year 2014 to 2023.\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(df.index, df['open'], label='Open')\n",
    "plt.plot(df.index, df['close'], label='Close')\n",
    "plt.plot(df.index, df['high'], label='High')\n",
    "plt.plot(df.index, df['low'], label='Low')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "996bbdda-fc56-4d46-bad5-94cef6067ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Correlation between the columns\n",
    "df_corr = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e39c21a6-343f-4d7c-bb28-bf4040936f71",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(20, 10))\n",
    "sns.heatmap(df_corr, annot=True, fmt='.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d07dfef-1bda-4695-869f-cb6dca14d73c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#“next_day_close” column has a 100% percent correlation with “open”, “close”, “low”, “high”, “sma_50”, and so on. \n",
    "#This means if values in those columns increase the values in the “next_day_close” column will also increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e916031d-b5db-4819-98dd-39ee61645364",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the dataset into features(Independent Variables)and labels(Dependent variables).\n",
    "X = df.drop(columns='next_day_close')\n",
    "Y = df['next_day_close']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f629e73c-d971-4b9f-9e31-3786ad3f32a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  open        high         low       close     volume  \\\n",
      "date                                                                    \n",
      "2014-01-02    9.986667   10.165333    9.770000   10.006667   92826000   \n",
      "2014-01-03   10.000000   10.146000    9.906667    9.970667   70425000   \n",
      "2014-01-06   10.000000   10.026667    9.682667    9.800000   80416500   \n",
      "2014-01-07    9.841333   10.026667    9.683333    9.957333   75511500   \n",
      "2014-01-08    9.923333   10.246667    9.917333   10.085333   92448000   \n",
      "...                ...         ...         ...         ...        ...   \n",
      "2023-12-22  256.760010  258.220001  251.369995  252.539993   93249800   \n",
      "2023-12-26  254.490005  257.970001  252.910004  256.609985   86892400   \n",
      "2023-12-27  258.350006  263.339996  257.519989  261.440002  106494400   \n",
      "2023-12-28  263.660004  265.130005  252.710007  253.179993  113619900   \n",
      "2023-12-29  255.100006  255.190002  247.429993  248.479996  100615300   \n",
      "\n",
      "                rsi_7     rsi_14       cci_7      cci_14      sma_50  \\\n",
      "date                                                                   \n",
      "2014-01-02  55.344071  54.440118  -37.373644   15.213422    9.682107   \n",
      "2014-01-03  53.742629  53.821521  -81.304471   17.481130    9.652800   \n",
      "2014-01-06  46.328174  50.870410 -123.427544  -37.824708    9.629467   \n",
      "2014-01-07  53.263037  53.406750  -84.784651  -20.779431    9.597747   \n",
      "2014-01-08  58.368660  55.423026   60.799662   43.570559    9.573240   \n",
      "...               ...        ...         ...         ...         ...   \n",
      "2023-12-22  58.296612  58.137456   50.821325   80.672033  232.553000   \n",
      "2023-12-26  63.570549  60.824035   93.909968   86.446838  232.662800   \n",
      "2023-12-27  68.998630  63.793639  171.938770  119.554558  232.813200   \n",
      "2023-12-28  53.186966  55.978816   45.772983   73.958135  232.779799   \n",
      "2023-12-29  46.164227  52.070118  -98.880167   -0.320098  232.895800   \n",
      "\n",
      "                ema_50     sma_100     ema_100      macd   bollinger  \\\n",
      "date                                                                   \n",
      "2014-01-02    9.820167   10.494240    9.674284  0.169472    9.740800   \n",
      "2014-01-03    9.826069   10.495693    9.680190  0.162623    9.776167   \n",
      "2014-01-06    9.825047   10.496740    9.682577  0.141790    9.797900   \n",
      "2014-01-07    9.830235   10.503407    9.688051  0.136402    9.837900   \n",
      "2014-01-08    9.840239   10.511147    9.695964  0.140837    9.870167   \n",
      "...                ...         ...         ...       ...         ...   \n",
      "2023-12-22  240.360582  241.505100  238.389711  5.223831  244.503500   \n",
      "2023-12-26  240.997814  241.477999  238.750509  5.495890  245.529999   \n",
      "2023-12-27  241.799468  241.553799  239.199806  6.031711  246.265999   \n",
      "2023-12-28  242.245763  241.571099  239.476641  5.723858  246.717998   \n",
      "2023-12-29  242.490243  241.558899  239.654925  5.042505  247.137998   \n",
      "\n",
      "            TrueRange     atr_7    atr_14  \n",
      "date                                       \n",
      "2014-01-02   0.395333  0.402641  0.447550  \n",
      "2014-01-03   0.239333  0.379311  0.432677  \n",
      "2014-01-06   0.344000  0.374267  0.426343  \n",
      "2014-01-07   0.343334  0.369848  0.420414  \n",
      "2014-01-08   0.329334  0.364060  0.413908  \n",
      "...               ...       ...       ...  \n",
      "2023-12-22   6.850006  8.514500  8.943707  \n",
      "2023-12-26   5.430008  8.073858  8.692729  \n",
      "2023-12-27   6.730011  7.881880  8.552535  \n",
      "2023-12-28  12.419998  8.530183  8.828782  \n",
      "2023-12-29   7.760009  8.420158  8.752441  \n",
      "\n",
      "[2516 rows x 18 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "193bd4a2-7898-4a93-b365-34948a892b6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2014-01-02      9.970667\n",
      "2014-01-03      9.800000\n",
      "2014-01-06      9.957333\n",
      "2014-01-07     10.085333\n",
      "2014-01-08      9.835333\n",
      "                 ...    \n",
      "2023-12-22    256.609985\n",
      "2023-12-26    261.440002\n",
      "2023-12-27    253.179993\n",
      "2023-12-28    248.479996\n",
      "2023-12-29    248.419998\n",
      "Name: next_day_close, Length: 2516, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1f4301-710a-4b3a-bfb4-85927aa57f19",
   "metadata": {},
   "source": [
    "## Spliting Dataset for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "74820f29-97bd-456b-a8c2-3f80730176bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5b8fa24-04ad-41b9-827a-cfdeac4ab972",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2516, 18) (2012, 18) (504, 18)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X_train.shape,X_test.shape,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7336161c-8af4-41df-8f0f-93be2b7e0273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  open        high         low       close     volume  \\\n",
      "date                                                                    \n",
      "2016-07-22   14.799333   14.966667   14.592000   14.818000   38695500   \n",
      "2020-02-20   60.796665   60.799999   57.329334   59.960667  264523500   \n",
      "2016-03-21   15.689333   15.992000   15.666667   15.888000   79617000   \n",
      "2022-02-03  294.000000  312.333344  293.506653  297.046661   78855600   \n",
      "2015-01-20   12.924667   12.941333   12.469333   12.795333   67548000   \n",
      "...                ...         ...         ...         ...        ...   \n",
      "2016-09-06   13.268000   13.550000   13.266667   13.522000   65859000   \n",
      "2023-07-28  259.859985  267.250000  258.230011  266.440002  111446000   \n",
      "2018-05-15   19.000668   19.130667   18.700001   18.945333  142788000   \n",
      "2023-05-01  163.169998  163.279999  158.830002  161.830002  109015000   \n",
      "2020-05-22   54.811333   55.452000   54.133331   54.458668  149812500   \n",
      "\n",
      "                rsi_7     rsi_14       cci_7      cci_14      sma_50  \\\n",
      "date                                                                   \n",
      "2016-07-22  52.708013  53.575524  -60.607939   13.971244   14.453640   \n",
      "2020-02-20  75.528429  74.352347   81.929886  116.049156   37.138760   \n",
      "2016-03-21  90.383736  78.298705  128.127301  157.042120   12.736227   \n",
      "2022-02-03  39.319470  40.869472    9.713902  -50.735089  339.571332   \n",
      "2015-01-20  20.339633  28.844051  -65.854370 -109.880208   14.970760   \n",
      "...               ...        ...         ...         ...         ...   \n",
      "2016-09-06  27.540532  31.545188  -77.102722 -127.897865   14.716880   \n",
      "2023-07-28  47.814213  51.956498  -11.216892  -65.166045  244.834800   \n",
      "2018-05-15  34.700905  42.986866 -189.350725 -112.033157   19.993213   \n",
      "2023-05-01  37.626891  38.578465   20.567958  -57.106100  185.576600   \n",
      "2020-05-22  59.481649  60.100135   67.282408   80.799395   43.772853   \n",
      "\n",
      "                ema_50     sma_100     ema_100       macd   bollinger  \\\n",
      "date                                                                    \n",
      "2016-07-22   14.608542   14.952647   14.625031   0.156244   14.433767   \n",
      "2020-02-20   40.157168   28.599727   32.414407   6.293906   48.751033   \n",
      "2016-03-21   13.375313   13.837993   13.804161   0.839234   13.598433   \n",
      "2022-02-03  329.532887  324.060899  317.635133 -13.643584  324.898830   \n",
      "2015-01-20   14.628975   15.870853   15.132539  -0.555486   14.094067   \n",
      "...                ...         ...         ...        ...         ...   \n",
      "2016-09-06   14.639446   14.829260   14.681827  -0.364489   14.591567   \n",
      "2023-07-28  247.568855  211.510600  226.311770   4.536680  273.222500   \n",
      "2018-05-15   20.098494   21.150540   20.717572  -0.041611   19.593600   \n",
      "2023-05-01  178.917071  169.800200  184.293783  -7.568984  176.198999   \n",
      "2020-05-22   47.702756   43.744040   42.599274   2.485081   52.862866   \n",
      "\n",
      "            TrueRange      atr_7     atr_14  \n",
      "date                                         \n",
      "2016-07-22   0.374667   0.421142   0.452327  \n",
      "2020-02-20   3.831997   4.249445   3.755424  \n",
      "2016-03-21   0.476000   0.546326   0.608269  \n",
      "2022-02-03  18.826691  21.197967  21.319359  \n",
      "2015-01-20   0.472000   0.506321   0.506741  \n",
      "...               ...        ...        ...  \n",
      "2016-09-06   0.364667   0.373380   0.359301  \n",
      "2023-07-28  11.539993  11.699829  11.316795  \n",
      "2018-05-15   0.764665   0.752170   0.797396  \n",
      "2023-05-01   5.479996   7.375881   7.933951  \n",
      "2020-05-22   1.318669   2.223670   2.731407  \n",
      "\n",
      "[2012 rows x 18 columns]                   open        high         low       close     volume  \\\n",
      "date                                                                    \n",
      "2020-01-08   31.580000   33.232666   31.215334   32.809334  467164500   \n",
      "2022-08-19  299.000000  300.359985  292.500000  296.666656   61395300   \n",
      "2018-02-06   21.680668   22.414667   21.566668   22.264668   76326000   \n",
      "2020-05-18   55.185333   55.647999   53.591999   54.242001  174987000   \n",
      "2021-10-18  283.929993  291.753326  283.823334  290.036682   72621600   \n",
      "...                ...         ...         ...         ...        ...   \n",
      "2020-04-01   33.599998   34.263332   31.673332   32.104000  200298000   \n",
      "2017-08-04   23.133333   23.818001   22.886667   23.794001  139033500   \n",
      "2018-05-25   18.508667   18.642668   18.374001   18.590000   58126500   \n",
      "2016-08-15   15.068000   15.300000   14.995333   15.039333   30514500   \n",
      "2019-08-12   15.532667   15.718000   15.250000   15.267333   69958500   \n",
      "\n",
      "                rsi_7     rsi_14       cci_7      cci_14      sma_50  \\\n",
      "date                                                                   \n",
      "2020-01-08  90.984023  86.182158  142.592608  202.850274   24.343280   \n",
      "2022-08-19  50.857035  57.053684  -48.916011  -24.865227  259.200333   \n",
      "2018-02-06  37.671688  46.620408 -148.987747 -199.419112   21.991293   \n",
      "2020-05-18  62.424317  60.923640   83.862931   95.521728   42.679600   \n",
      "2021-10-18  91.006054  81.193722  164.468442  226.271055  249.624535   \n",
      "...               ...        ...         ...         ...         ...   \n",
      "2020-04-01  41.505370  41.088338 -106.308975   17.457432   43.516200   \n",
      "2017-08-04  69.987073  59.848609  126.651259  154.810942   23.055093   \n",
      "2018-05-25  40.411921  43.015340  -49.986758  -74.236521   19.442227   \n",
      "2016-08-15  45.615944  50.657585  -25.843357  -52.350186   14.716227   \n",
      "2019-08-12  38.342897  44.505068  -29.851921  -48.608525   15.292987   \n",
      "\n",
      "                ema_50     sma_100     ema_100       macd   bollinger  \\\n",
      "date                                                                    \n",
      "2020-01-08   24.644717   20.182660   21.871033   2.135017   27.359033   \n",
      "2022-08-19  274.459637  275.180168  275.101777  11.900382  292.820833   \n",
      "2018-02-06   22.312313   22.230113   22.172021   0.213543   22.883200   \n",
      "2020-05-18   46.526638   42.699520   41.610238   2.790665   51.468066   \n",
      "2021-10-18  252.110795  232.926235  240.615013   8.568383  263.074171   \n",
      "...                ...         ...         ...        ...         ...   \n",
      "2020-04-01   38.434961   34.972087   35.230339  -3.138830   35.143867   \n",
      "2017-08-04   22.336412   21.448853   21.265306  -0.032845   22.079333   \n",
      "2018-05-25   19.709457   20.937940   20.417065  -0.329764   19.368133   \n",
      "2016-08-15   14.890166   15.105953   14.786546   0.098260   15.168133   \n",
      "2019-08-12   15.695918   15.868867   16.279069  -0.046592   16.130633   \n",
      "\n",
      "            TrueRange      atr_7     atr_14  \n",
      "date                                         \n",
      "2020-01-08   2.017332   1.169920   0.990828  \n",
      "2022-08-19  10.369995  11.700566  12.662630  \n",
      "2018-02-06   0.847999   0.719017   0.683726  \n",
      "2020-05-18   2.369999   2.807747   3.148753  \n",
      "2021-10-18  10.743316   6.737289   6.724255  \n",
      "...               ...        ...        ...  \n",
      "2020-04-01   3.260002   3.551319   3.909967  \n",
      "2017-08-04   0.931334   0.948247   0.879757  \n",
      "2018-05-25   0.268667   0.602630   0.690736  \n",
      "2016-08-15   0.304667   0.340519   0.385669  \n",
      "2019-08-12   0.468000   0.554743   0.582786  \n",
      "\n",
      "[504 rows x 18 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_train,X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a890e861-d448-4f15-b787-4ca394968339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2516,) (2012,) (504,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape,Y_train.shape,Y_test.shape,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0d8b4ea8-dea0-4935-9714-2f6132517404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2016-07-22     15.334000\n",
      "2020-02-20     60.066666\n",
      "2016-03-21     15.616000\n",
      "2022-02-03    307.773346\n",
      "2015-01-20     13.104667\n",
      "                 ...    \n",
      "2016-09-06     13.447333\n",
      "2023-07-28    267.429993\n",
      "2018-05-15     19.098667\n",
      "2023-05-01    160.309998\n",
      "2020-05-22     54.591331\n",
      "Name: next_day_close, Length: 2012, dtype: float64 date\n",
      "2020-01-08     32.089333\n",
      "2022-08-19    289.913330\n",
      "2018-02-06     23.000000\n",
      "2020-05-18     53.867332\n",
      "2021-10-18    288.089996\n",
      "                 ...    \n",
      "2020-04-01     30.298000\n",
      "2017-08-04     23.677999\n",
      "2018-05-25     18.917334\n",
      "2016-08-15     14.907333\n",
      "2019-08-12     15.666667\n",
      "Name: next_day_close, Length: 504, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Y_train,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9d69870-5ad6-4ff1-bfe5-b6f49635c80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardization makes it easier to compare and interpret the coefficients or feature importance scores of different features within a model.\n",
    "#Since all features are on the same scale, their coefficients or importance scores are directly comparable in terms of their relative \n",
    "#influence on the model predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ecd235e7-c2e2-47ca-8a98-1ab45af439b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "Scaler=StandardScaler()\n",
    "X_train_scaled=Scaler.fit_transform(X_train)\n",
    "X_test_scaled=Scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "572a93c4-d0aa-42d8-8e0a-84158a9d683a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2012, 18) (504, 18)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_scaled.shape, X_test_scaled.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "605ef361-ba82-4630-982e-cdb73115f6ca",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "22850cb2-ca16-4788-a1d9-1ccba20e06f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bd7c9a72-7c6a-444d-be40-10a7e8dae6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.fit(X_train,Y_train)\n",
    "lr_predict=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e2bf8e49-ec9a-40f4-8938-2bc069919ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For regression tasks, mean absolute error (MAE) and mean squared error (MSE) are common evaluation metrics.\n",
    "#You're already using mean_absolute_error, but you might also consider mean_squared_error for your regression evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e11af0f-576d-4f79-ab04-83dafb6d69a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0322865822549976"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "lr_mean_abs_error=mean_absolute_error(Y_test,lr_predict)\n",
    "lr_mean_abs_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4db5a28-51f0-4567-9ed7-c6e7f8ee7bf5",
   "metadata": {},
   "source": [
    "## Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "82cb2c31-0bab-4a16-a5da-3873972676b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dr=DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "abd10c73-3477-4405-9097-a29207bf54e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dr.fit(X_train,Y_train)\n",
    "dr_predict=dr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c64b95f9-9b0d-4df7-b1c8-6fe9a76c4619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.252852111111112"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "dr_mean_abs_error=mean_absolute_error(Y_test,dr_predict)\n",
    "dr_mean_abs_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3381682-902a-4a7d-868c-d1d644873ccb",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors (KNN) regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "41b4ef92-d030-45ef-b4b2-9cc89afbdb7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "kr=KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e91e2ad3-f774-4fe2-a352-68aa8e80cf71",
   "metadata": {},
   "outputs": [],
   "source": [
    "kr.fit(X_train,Y_train)\n",
    "kr_predict=kr.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "be308bf6-2c16-4847-ac53-7b7ee84e1c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.252852111111112"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "kr_mean_abs_error=mean_absolute_error(Y_test,dr_predict)\n",
    "kr_mean_abs_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a7fd83-ab20-4c35-a94b-c0ada09b8460",
   "metadata": {},
   "source": [
    "## Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "09fd6d9a-b78e-4c50-83e6-cec1d5366afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cb4d7550-f789-4df9-ab90-526d3e1b4ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr.fit(X_train, Y_train)\n",
    "rfr_prediction = rfr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "41816b1b-d898-4e0c-8bd8-2e3538ad5938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0907509561507935"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr_mean_abs_error = mean_absolute_error(Y_test, rfr_prediction)\n",
    "rfr_mean_abs_error "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3abc91f-3166-4160-b83a-59e3e8ea8ab2",
   "metadata": {},
   "source": [
    "## Comparing The Mean Absolute Error Of Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d06a7825-c984-44f3-aa62-3c65e7ec4c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Classifiers=['LinearRegression','Decision Tree Regressor','K-Nearest Neighbors regressor','Random Forest Regressor']\n",
    "mean_abs_errors=[lr_mean_abs_error,dr_mean_abs_error,kr_mean_abs_error,rfr_mean_abs_error]\n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(Classifiers,mean_abs_errors,color=['cyan','red','green','magenta'])\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Classifiers')\n",
    "plt.ylabel('Mean_Absolute_Error')\n",
    "plt.title('Comparing Mean Absolute Error Of Different Models ')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6eff6b26-17d5-4dfa-918c-cc40eb8fc57e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e28ed71-d538-4743-a842-86bf66a780b3",
   "metadata": {},
   "source": [
    "## Linear Regrerssion Model Relationship Between Label and Predicted Value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1bfe1efe-d2a1-4e58-9b06-41ebca2ffce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Actual Values')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=lr_predict,y=Y_test)\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.ylabel('Actual Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f52b9e70-d7d6-4edf-85c0-edf2295462f3",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}