21-DL-Time-Series-Temperature-Forecasting/Time_Series_Temperature_Forcasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4919064",
   "metadata": {},
   "source": [
    "# Time Series Temperature Forcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a797d980",
   "metadata": {},
   "source": [
    "### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "86b30e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "from tensorflow.keras.metrics import RootMeanSquaredError"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6fd60f7",
   "metadata": {},
   "source": [
    "### Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7d511e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "climate_data = pd.read_csv(\"data/jena_climate_2009_2016.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cf3af7c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Time</th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.01.2009 00:10:00</td>\n",
       "      <td>996.52</td>\n",
       "      <td>-8.02</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-8.90</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1307.75</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>152.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01.01.2009 00:20:00</td>\n",
       "      <td>996.57</td>\n",
       "      <td>-8.41</td>\n",
       "      <td>265.01</td>\n",
       "      <td>-9.28</td>\n",
       "      <td>93.4</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1309.80</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.50</td>\n",
       "      <td>136.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01.01.2009 00:30:00</td>\n",
       "      <td>996.53</td>\n",
       "      <td>-8.51</td>\n",
       "      <td>264.91</td>\n",
       "      <td>-9.31</td>\n",
       "      <td>93.9</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.01</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1310.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.63</td>\n",
       "      <td>171.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01.01.2009 00:40:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.31</td>\n",
       "      <td>265.12</td>\n",
       "      <td>-9.07</td>\n",
       "      <td>94.2</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1309.19</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01.01.2009 00:50:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.27</td>\n",
       "      <td>265.15</td>\n",
       "      <td>-9.04</td>\n",
       "      <td>94.1</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1309.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>214.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  \\\n",
       "0  01.01.2009 00:10:00    996.52     -8.02    265.40        -8.90    93.3   \n",
       "1  01.01.2009 00:20:00    996.57     -8.41    265.01        -9.28    93.4   \n",
       "2  01.01.2009 00:30:00    996.53     -8.51    264.91        -9.31    93.9   \n",
       "3  01.01.2009 00:40:00    996.51     -8.31    265.12        -9.07    94.2   \n",
       "4  01.01.2009 00:50:00    996.51     -8.27    265.15        -9.04    94.1   \n",
       "\n",
       "   VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  \\\n",
       "0          3.33          3.11          0.22       1.94             3.12   \n",
       "1          3.23          3.02          0.21       1.89             3.03   \n",
       "2          3.21          3.01          0.20       1.88             3.02   \n",
       "3          3.26          3.07          0.19       1.92             3.08   \n",
       "4          3.27          3.08          0.19       1.92             3.09   \n",
       "\n",
       "   rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "0       1307.75      1.03           1.75     152.3  \n",
       "1       1309.80      0.72           1.50     136.1  \n",
       "2       1310.24      0.19           0.63     171.6  \n",
       "3       1309.19      0.34           0.50     198.0  \n",
       "4       1309.00      0.32           0.63     214.3  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show dataset\n",
    "climate_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c25a2b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(420551, 15)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset dimension\n",
    "climate_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "71124e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Time</th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01.01.2009 01:00:00</td>\n",
       "      <td>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>265.38</td>\n",
       "      <td>-8.78</td>\n",
       "      <td>94.40</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1307.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.63</td>\n",
       "      <td>192.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>01.01.2009 02:00:00</td>\n",
       "      <td>996.62</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>264.54</td>\n",
       "      <td>-9.77</td>\n",
       "      <td>93.20</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1312.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>190.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>01.01.2009 03:00:00</td>\n",
       "      <td>996.84</td>\n",
       "      <td>-8.81</td>\n",
       "      <td>264.59</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>93.50</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1312.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.63</td>\n",
       "      <td>167.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>01.01.2009 04:00:00</td>\n",
       "      <td>996.99</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>264.34</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>92.60</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1313.61</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.38</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>01.01.2009 05:00:00</td>\n",
       "      <td>997.46</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>263.72</td>\n",
       "      <td>-10.65</td>\n",
       "      <td>92.20</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1317.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.88</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420521</th>\n",
       "      <td>31.12.2016 19:10:00</td>\n",
       "      <td>1002.18</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>272.01</td>\n",
       "      <td>-5.36</td>\n",
       "      <td>72.00</td>\n",
       "      <td>5.69</td>\n",
       "      <td>4.09</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.54</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1280.70</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.36</td>\n",
       "      <td>190.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420527</th>\n",
       "      <td>31.12.2016 20:10:00</td>\n",
       "      <td>1001.40</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>271.66</td>\n",
       "      <td>-6.84</td>\n",
       "      <td>66.29</td>\n",
       "      <td>5.51</td>\n",
       "      <td>3.65</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.27</td>\n",
       "      <td>3.65</td>\n",
       "      <td>1281.87</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.92</td>\n",
       "      <td>225.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420533</th>\n",
       "      <td>31.12.2016 21:10:00</td>\n",
       "      <td>1001.19</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>270.32</td>\n",
       "      <td>-6.90</td>\n",
       "      <td>72.90</td>\n",
       "      <td>4.99</td>\n",
       "      <td>3.64</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.26</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1288.02</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.56</td>\n",
       "      <td>158.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420539</th>\n",
       "      <td>31.12.2016 22:10:00</td>\n",
       "      <td>1000.65</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>270.22</td>\n",
       "      <td>-7.15</td>\n",
       "      <td>72.30</td>\n",
       "      <td>4.93</td>\n",
       "      <td>3.57</td>\n",
       "      <td>1.37</td>\n",
       "      <td>2.22</td>\n",
       "      <td>3.57</td>\n",
       "      <td>1288.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>216.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420545</th>\n",
       "      <td>31.12.2016 23:10:00</td>\n",
       "      <td>1000.11</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>269.23</td>\n",
       "      <td>-8.09</td>\n",
       "      <td>72.60</td>\n",
       "      <td>4.56</td>\n",
       "      <td>3.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.31</td>\n",
       "      <td>1292.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.00</td>\n",
       "      <td>202.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70091 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  \\\n",
       "5       01.01.2009 01:00:00    996.50     -8.05    265.38        -8.78   \n",
       "11      01.01.2009 02:00:00    996.62     -8.88    264.54        -9.77   \n",
       "17      01.01.2009 03:00:00    996.84     -8.81    264.59        -9.66   \n",
       "23      01.01.2009 04:00:00    996.99     -9.05    264.34       -10.02   \n",
       "29      01.01.2009 05:00:00    997.46     -9.63    263.72       -10.65   \n",
       "...                     ...       ...       ...       ...          ...   \n",
       "420521  31.12.2016 19:10:00   1002.18     -0.98    272.01        -5.36   \n",
       "420527  31.12.2016 20:10:00   1001.40     -1.40    271.66        -6.84   \n",
       "420533  31.12.2016 21:10:00   1001.19     -2.75    270.32        -6.90   \n",
       "420539  31.12.2016 22:10:00   1000.65     -2.89    270.22        -7.15   \n",
       "420545  31.12.2016 23:10:00   1000.11     -3.93    269.23        -8.09   \n",
       "\n",
       "        rh (%)  VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  \\\n",
       "5        94.40          3.33          3.14          0.19       1.96   \n",
       "11       93.20          3.12          2.90          0.21       1.81   \n",
       "17       93.50          3.13          2.93          0.20       1.83   \n",
       "23       92.60          3.07          2.85          0.23       1.78   \n",
       "29       92.20          2.94          2.71          0.23       1.69   \n",
       "...        ...           ...           ...           ...        ...   \n",
       "420521   72.00          5.69          4.09          1.59       2.54   \n",
       "420527   66.29          5.51          3.65          1.86       2.27   \n",
       "420533   72.90          4.99          3.64          1.35       2.26   \n",
       "420539   72.30          4.93          3.57          1.37       2.22   \n",
       "420545   72.60          4.56          3.31          1.25       2.06   \n",
       "\n",
       "        H2OC (mmol/mol)  rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "5                  3.15       1307.86      0.21           0.63     192.7  \n",
       "11                 2.91       1312.25      0.25           0.63     190.3  \n",
       "17                 2.94       1312.18      0.18           0.63     167.2  \n",
       "23                 2.85       1313.61      0.10           0.38     240.0  \n",
       "29                 2.71       1317.19      0.40           0.88     157.0  \n",
       "...                 ...           ...       ...            ...       ...  \n",
       "420521             4.08       1280.70      0.87           1.36     190.6  \n",
       "420527             3.65       1281.87      1.02           1.92     225.4  \n",
       "420533             3.63       1288.02      0.71           1.56     158.7  \n",
       "420539             3.57       1288.03      0.35           0.68     216.7  \n",
       "420545             3.31       1292.41      0.56           1.00     202.6  \n",
       "\n",
       "[70091 rows x 15 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Takuing each hour data only \n",
    "climate_data = climate_data[5::6]\n",
    "climate_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9a6dae71",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Time</th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-01-01 01:00:00</th>\n",
       "      <td>01.01.2009 01:00:00</td>\n",
       "      <td>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>265.38</td>\n",
       "      <td>-8.78</td>\n",
       "      <td>94.4</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1307.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.63</td>\n",
       "      <td>192.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 02:00:00</th>\n",
       "      <td>01.01.2009 02:00:00</td>\n",
       "      <td>996.62</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>264.54</td>\n",
       "      <td>-9.77</td>\n",
       "      <td>93.2</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1312.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>190.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 03:00:00</th>\n",
       "      <td>01.01.2009 03:00:00</td>\n",
       "      <td>996.84</td>\n",
       "      <td>-8.81</td>\n",
       "      <td>264.59</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>93.5</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1312.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.63</td>\n",
       "      <td>167.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 04:00:00</th>\n",
       "      <td>01.01.2009 04:00:00</td>\n",
       "      <td>996.99</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>264.34</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>92.6</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1313.61</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.38</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 05:00:00</th>\n",
       "      <td>01.01.2009 05:00:00</td>\n",
       "      <td>997.46</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>263.72</td>\n",
       "      <td>-10.65</td>\n",
       "      <td>92.2</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1317.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.88</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 06:00:00</th>\n",
       "      <td>01.01.2009 06:00:00</td>\n",
       "      <td>997.71</td>\n",
       "      <td>-9.67</td>\n",
       "      <td>263.66</td>\n",
       "      <td>-10.62</td>\n",
       "      <td>92.7</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.72</td>\n",
       "      <td>1317.71</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.50</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 07:00:00</th>\n",
       "      <td>01.01.2009 07:00:00</td>\n",
       "      <td>998.33</td>\n",
       "      <td>-9.17</td>\n",
       "      <td>264.12</td>\n",
       "      <td>-10.10</td>\n",
       "      <td>92.9</td>\n",
       "      <td>3.04</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.83</td>\n",
       "      <td>1315.98</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.88</td>\n",
       "      <td>348.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 08:00:00</th>\n",
       "      <td>01.01.2009 08:00:00</td>\n",
       "      <td>999.17</td>\n",
       "      <td>-8.10</td>\n",
       "      <td>265.12</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>92.8</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1311.65</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.25</td>\n",
       "      <td>213.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 09:00:00</th>\n",
       "      <td>01.01.2009 09:00:00</td>\n",
       "      <td>999.69</td>\n",
       "      <td>-7.66</td>\n",
       "      <td>265.52</td>\n",
       "      <td>-8.84</td>\n",
       "      <td>91.2</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.13</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.13</td>\n",
       "      <td>1310.14</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.63</td>\n",
       "      <td>202.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 10:00:00</th>\n",
       "      <td>01.01.2009 10:00:00</td>\n",
       "      <td>1000.27</td>\n",
       "      <td>-7.04</td>\n",
       "      <td>266.10</td>\n",
       "      <td>-8.17</td>\n",
       "      <td>91.6</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.29</td>\n",
       "      <td>1307.76</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.00</td>\n",
       "      <td>292.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 11:00:00</th>\n",
       "      <td>01.01.2009 11:00:00</td>\n",
       "      <td>1000.87</td>\n",
       "      <td>-7.41</td>\n",
       "      <td>265.68</td>\n",
       "      <td>-8.66</td>\n",
       "      <td>90.7</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.97</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1310.43</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.38</td>\n",
       "      <td>269.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 12:00:00</th>\n",
       "      <td>01.01.2009 12:00:00</td>\n",
       "      <td>1000.30</td>\n",
       "      <td>-6.87</td>\n",
       "      <td>266.27</td>\n",
       "      <td>-8.28</td>\n",
       "      <td>89.6</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1306.98</td>\n",
       "      <td>1.84</td>\n",
       "      <td>2.63</td>\n",
       "      <td>184.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 13:00:00</th>\n",
       "      <td>01.01.2009 13:00:00</td>\n",
       "      <td>1000.03</td>\n",
       "      <td>-5.89</td>\n",
       "      <td>267.27</td>\n",
       "      <td>-7.46</td>\n",
       "      <td>88.6</td>\n",
       "      <td>3.93</td>\n",
       "      <td>3.48</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.48</td>\n",
       "      <td>1301.73</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.25</td>\n",
       "      <td>187.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 14:00:00</th>\n",
       "      <td>01.01.2009 14:00:00</td>\n",
       "      <td>999.81</td>\n",
       "      <td>-5.94</td>\n",
       "      <td>267.24</td>\n",
       "      <td>-7.43</td>\n",
       "      <td>89.1</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.49</td>\n",
       "      <td>1301.67</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 15:00:00</th>\n",
       "      <td>01.01.2009 15:00:00</td>\n",
       "      <td>999.88</td>\n",
       "      <td>-5.69</td>\n",
       "      <td>267.48</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>90.4</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.61</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1300.51</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.88</td>\n",
       "      <td>134.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 16:00:00</th>\n",
       "      <td>01.01.2009 16:00:00</td>\n",
       "      <td>999.94</td>\n",
       "      <td>-5.40</td>\n",
       "      <td>267.76</td>\n",
       "      <td>-6.86</td>\n",
       "      <td>89.4</td>\n",
       "      <td>4.08</td>\n",
       "      <td>3.65</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.27</td>\n",
       "      <td>3.65</td>\n",
       "      <td>1299.17</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.13</td>\n",
       "      <td>145.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 17:00:00</th>\n",
       "      <td>01.01.2009 17:00:00</td>\n",
       "      <td>1000.17</td>\n",
       "      <td>-5.37</td>\n",
       "      <td>267.78</td>\n",
       "      <td>-6.82</td>\n",
       "      <td>89.4</td>\n",
       "      <td>4.09</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.66</td>\n",
       "      <td>1299.27</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.63</td>\n",
       "      <td>142.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 18:00:00</th>\n",
       "      <td>01.01.2009 18:00:00</td>\n",
       "      <td>1000.16</td>\n",
       "      <td>-5.25</td>\n",
       "      <td>267.90</td>\n",
       "      <td>-6.75</td>\n",
       "      <td>89.1</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.29</td>\n",
       "      <td>3.68</td>\n",
       "      <td>1298.68</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.00</td>\n",
       "      <td>183.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 19:00:00</th>\n",
       "      <td>01.01.2009 19:00:00</td>\n",
       "      <td>1000.22</td>\n",
       "      <td>-5.11</td>\n",
       "      <td>268.03</td>\n",
       "      <td>-6.57</td>\n",
       "      <td>89.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.73</td>\n",
       "      <td>1298.07</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.75</td>\n",
       "      <td>176.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 20:00:00</th>\n",
       "      <td>01.01.2009 20:00:00</td>\n",
       "      <td>1000.22</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>268.24</td>\n",
       "      <td>-6.38</td>\n",
       "      <td>89.3</td>\n",
       "      <td>4.24</td>\n",
       "      <td>3.79</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.36</td>\n",
       "      <td>3.78</td>\n",
       "      <td>1297.05</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.13</td>\n",
       "      <td>195.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 21:00:00</th>\n",
       "      <td>01.01.2009 21:00:00</td>\n",
       "      <td>1000.19</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>268.35</td>\n",
       "      <td>-6.14</td>\n",
       "      <td>90.2</td>\n",
       "      <td>4.27</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.85</td>\n",
       "      <td>1296.45</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.75</td>\n",
       "      <td>206.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 22:00:00</th>\n",
       "      <td>01.01.2009 22:00:00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>268.66</td>\n",
       "      <td>-5.82</td>\n",
       "      <td>90.4</td>\n",
       "      <td>4.37</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2.46</td>\n",
       "      <td>3.95</td>\n",
       "      <td>1294.72</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.63</td>\n",
       "      <td>175.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 23:00:00</th>\n",
       "      <td>01.01.2009 23:00:00</td>\n",
       "      <td>999.77</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>268.71</td>\n",
       "      <td>-5.84</td>\n",
       "      <td>90.1</td>\n",
       "      <td>4.38</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.46</td>\n",
       "      <td>3.95</td>\n",
       "      <td>1294.29</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.00</td>\n",
       "      <td>219.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-02 00:00:00</th>\n",
       "      <td>02.01.2009 00:00:00</td>\n",
       "      <td>999.59</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>268.65</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>93.2</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.53</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1294.33</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.88</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Date Time  p (mbar)  T (degC)  Tpot (K)  \\\n",
       "Date Time                                                                \n",
       "2009-01-01 01:00:00  01.01.2009 01:00:00    996.50     -8.05    265.38   \n",
       "2009-01-01 02:00:00  01.01.2009 02:00:00    996.62     -8.88    264.54   \n",
       "2009-01-01 03:00:00  01.01.2009 03:00:00    996.84     -8.81    264.59   \n",
       "2009-01-01 04:00:00  01.01.2009 04:00:00    996.99     -9.05    264.34   \n",
       "2009-01-01 05:00:00  01.01.2009 05:00:00    997.46     -9.63    263.72   \n",
       "2009-01-01 06:00:00  01.01.2009 06:00:00    997.71     -9.67    263.66   \n",
       "2009-01-01 07:00:00  01.01.2009 07:00:00    998.33     -9.17    264.12   \n",
       "2009-01-01 08:00:00  01.01.2009 08:00:00    999.17     -8.10    265.12   \n",
       "2009-01-01 09:00:00  01.01.2009 09:00:00    999.69     -7.66    265.52   \n",
       "2009-01-01 10:00:00  01.01.2009 10:00:00   1000.27     -7.04    266.10   \n",
       "2009-01-01 11:00:00  01.01.2009 11:00:00   1000.87     -7.41    265.68   \n",
       "2009-01-01 12:00:00  01.01.2009 12:00:00   1000.30     -6.87    266.27   \n",
       "2009-01-01 13:00:00  01.01.2009 13:00:00   1000.03     -5.89    267.27   \n",
       "2009-01-01 14:00:00  01.01.2009 14:00:00    999.81     -5.94    267.24   \n",
       "2009-01-01 15:00:00  01.01.2009 15:00:00    999.88     -5.69    267.48   \n",
       "2009-01-01 16:00:00  01.01.2009 16:00:00    999.94     -5.40    267.76   \n",
       "2009-01-01 17:00:00  01.01.2009 17:00:00   1000.17     -5.37    267.78   \n",
       "2009-01-01 18:00:00  01.01.2009 18:00:00   1000.16     -5.25    267.90   \n",
       "2009-01-01 19:00:00  01.01.2009 19:00:00   1000.22     -5.11    268.03   \n",
       "2009-01-01 20:00:00  01.01.2009 20:00:00   1000.22     -4.90    268.24   \n",
       "2009-01-01 21:00:00  01.01.2009 21:00:00   1000.19     -4.80    268.35   \n",
       "2009-01-01 22:00:00  01.01.2009 22:00:00   1000.00     -4.50    268.66   \n",
       "2009-01-01 23:00:00  01.01.2009 23:00:00    999.77     -4.47    268.71   \n",
       "2009-01-02 00:00:00  02.01.2009 00:00:00    999.59     -4.54    268.65   \n",
       "\n",
       "                     Tdew (degC)  rh (%)  VPmax (mbar)  VPact (mbar)  \\\n",
       "Date Time                                                              \n",
       "2009-01-01 01:00:00        -8.78    94.4          3.33          3.14   \n",
       "2009-01-01 02:00:00        -9.77    93.2          3.12          2.90   \n",
       "2009-01-01 03:00:00        -9.66    93.5          3.13          2.93   \n",
       "2009-01-01 04:00:00       -10.02    92.6          3.07          2.85   \n",
       "2009-01-01 05:00:00       -10.65    92.2          2.94          2.71   \n",
       "2009-01-01 06:00:00       -10.62    92.7          2.93          2.71   \n",
       "2009-01-01 07:00:00       -10.10    92.9          3.04          2.83   \n",
       "2009-01-01 08:00:00        -9.05    92.8          3.31          3.07   \n",
       "2009-01-01 09:00:00        -8.84    91.2          3.43          3.13   \n",
       "2009-01-01 10:00:00        -8.17    91.6          3.60          3.30   \n",
       "2009-01-01 11:00:00        -8.66    90.7          3.50          3.17   \n",
       "2009-01-01 12:00:00        -8.28    89.6          3.64          3.27   \n",
       "2009-01-01 13:00:00        -7.46    88.6          3.93          3.48   \n",
       "2009-01-01 14:00:00        -7.43    89.1          3.92          3.49   \n",
       "2009-01-01 15:00:00        -7.00    90.4          3.99          3.61   \n",
       "2009-01-01 16:00:00        -6.86    89.4          4.08          3.65   \n",
       "2009-01-01 17:00:00        -6.82    89.4          4.09          3.66   \n",
       "2009-01-01 18:00:00        -6.75    89.1          4.13          3.68   \n",
       "2009-01-01 19:00:00        -6.57    89.4          4.17          3.73   \n",
       "2009-01-01 20:00:00        -6.38    89.3          4.24          3.79   \n",
       "2009-01-01 21:00:00        -6.14    90.2          4.27          3.85   \n",
       "2009-01-01 22:00:00        -5.82    90.4          4.37          3.95   \n",
       "2009-01-01 23:00:00        -5.84    90.1          4.38          3.95   \n",
       "2009-01-02 00:00:00        -5.46    93.2          4.36          4.06   \n",
       "\n",
       "                     VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  rho (g/m**3)  \\\n",
       "Date Time                                                                     \n",
       "2009-01-01 01:00:00          0.19       1.96             3.15       1307.86   \n",
       "2009-01-01 02:00:00          0.21       1.81             2.91       1312.25   \n",
       "2009-01-01 03:00:00          0.20       1.83             2.94       1312.18   \n",
       "2009-01-01 04:00:00          0.23       1.78             2.85       1313.61   \n",
       "2009-01-01 05:00:00          0.23       1.69             2.71       1317.19   \n",
       "2009-01-01 06:00:00          0.21       1.69             2.72       1317.71   \n",
       "2009-01-01 07:00:00          0.22       1.76             2.83       1315.98   \n",
       "2009-01-01 08:00:00          0.24       1.92             3.08       1311.65   \n",
       "2009-01-01 09:00:00          0.30       1.95             3.13       1310.14   \n",
       "2009-01-01 10:00:00          0.30       2.05             3.29       1307.76   \n",
       "2009-01-01 11:00:00          0.33       1.97             3.17       1310.43   \n",
       "2009-01-01 12:00:00          0.38       2.03             3.26       1306.98   \n",
       "2009-01-01 13:00:00          0.45       2.17             3.48       1301.73   \n",
       "2009-01-01 14:00:00          0.43       2.17             3.49       1301.67   \n",
       "2009-01-01 15:00:00          0.38       2.25             3.61       1300.51   \n",
       "2009-01-01 16:00:00          0.43       2.27             3.65       1299.17   \n",
       "2009-01-01 17:00:00          0.43       2.28             3.66       1299.27   \n",
       "2009-01-01 18:00:00          0.45       2.29             3.68       1298.68   \n",
       "2009-01-01 19:00:00          0.44       2.32             3.73       1298.07   \n",
       "2009-01-01 20:00:00          0.45       2.36             3.78       1297.05   \n",
       "2009-01-01 21:00:00          0.42       2.40             3.85       1296.45   \n",
       "2009-01-01 22:00:00          0.42       2.46             3.95       1294.72   \n",
       "2009-01-01 23:00:00          0.43       2.46             3.95       1294.29   \n",
       "2009-01-02 00:00:00          0.30       2.53             4.06       1294.33   \n",
       "\n",
       "                     wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "Date Time                                               \n",
       "2009-01-01 01:00:00      0.21           0.63     192.7  \n",
       "2009-01-01 02:00:00      0.25           0.63     190.3  \n",
       "2009-01-01 03:00:00      0.18           0.63     167.2  \n",
       "2009-01-01 04:00:00      0.10           0.38     240.0  \n",
       "2009-01-01 05:00:00      0.40           0.88     157.0  \n",
       "2009-01-01 06:00:00      0.05           0.50     146.0  \n",
       "2009-01-01 07:00:00      2.08           2.88     348.8  \n",
       "2009-01-01 08:00:00      0.72           1.25     213.9  \n",
       "2009-01-01 09:00:00      0.34           0.63     202.2  \n",
       "2009-01-01 10:00:00      1.45           3.00     292.6  \n",
       "2009-01-01 11:00:00      1.91           3.38     269.6  \n",
       "2009-01-01 12:00:00      1.84           2.63     184.4  \n",
       "2009-01-01 13:00:00      0.57           1.25     187.3  \n",
       "2009-01-01 14:00:00      1.25           2.00     144.0  \n",
       "2009-01-01 15:00:00      1.17           1.88     134.9  \n",
       "2009-01-01 16:00:00      1.40           2.13     145.5  \n",
       "2009-01-01 17:00:00      0.85           1.63     142.2  \n",
       "2009-01-01 18:00:00      0.55           1.00     183.7  \n",
       "2009-01-01 19:00:00      0.41           0.75     176.7  \n",
       "2009-01-01 20:00:00      0.68           1.13     195.2  \n",
       "2009-01-01 21:00:00      0.44           0.75     206.3  \n",
       "2009-01-01 22:00:00      0.33           0.63     175.7  \n",
       "2009-01-01 23:00:00      0.65           1.00     219.3  \n",
       "2009-01-02 00:00:00      0.41           0.88     155.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the index columns to plot with datetime\n",
    "climate_data.index = pd.to_datetime(climate_data['Date Time'], format='%d.%m.%Y %H:%M:%S')\n",
    "climate_data[:24]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e5e57c",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d8f57096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time Series Temperature Plot')"
      ]
     },
     "execution_count": 79,
     "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": [
    "# Plot temperature\n",
    "temperature = climate_data['T (degC)']\n",
    "plt.plot(temperature)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temprature (C)\")\n",
    "plt.title(\"Time Series Temperature Plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e6c223f",
   "metadata": {},
   "source": [
    "### Features and Target Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "49040ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to make each 5 temp as features(X) and 6th one as target(Y)\n",
    "# group every five hour as features(X) and next(6th) hour as label(Y)\n",
    "# one step ahead(next row) maintaining widow size(5), exclude 1st feature\n",
    "# include the preivous label into feature(X) and take next one as label(Y)\n",
    "# repeat\n",
    "\n",
    "def climate_data_to_X_Y(climate_data, window_size = 5):\n",
    "    climate_data_as_np = climate_data.to_numpy()\n",
    "    X = []\n",
    "    Y = []\n",
    "    for i in range(len(climate_data_as_np) - window_size):\n",
    "        # take 5 columns as feature\n",
    "        row = [[a] for a in climate_data_as_np[i:i+5]]\n",
    "        X.append(row)\n",
    "        # take next one(6th) as target \n",
    "        label = climate_data_as_np[i+5]\n",
    "        Y.append(label)\n",
    "    return np.array(X), np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9675a310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70086, 5, 1), (70086,))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# call above function to split features and targets\n",
    "WINDOW_SIZE = 5\n",
    "X, Y = climate_data_to_X_Y(temperature, WINDOW_SIZE)\n",
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6e874a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[-8.05],\n",
       "         [-8.88],\n",
       "         [-8.81],\n",
       "         [-9.05],\n",
       "         [-9.63]],\n",
       " \n",
       "        [[-8.88],\n",
       "         [-8.81],\n",
       "         [-9.05],\n",
       "         [-9.63],\n",
       "         [-9.67]],\n",
       " \n",
       "        [[-8.81],\n",
       "         [-9.05],\n",
       "         [-9.63],\n",
       "         [-9.67],\n",
       "         [-9.17]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[ 2.44],\n",
       "         [ 1.29],\n",
       "         [-0.42],\n",
       "         [-0.98],\n",
       "         [-1.4 ]],\n",
       " \n",
       "        [[ 1.29],\n",
       "         [-0.42],\n",
       "         [-0.98],\n",
       "         [-1.4 ],\n",
       "         [-2.75]],\n",
       " \n",
       "        [[-0.42],\n",
       "         [-0.98],\n",
       "         [-1.4 ],\n",
       "         [-2.75],\n",
       "         [-2.89]]]),\n",
       " array([-9.67, -9.17, -8.1 , ..., -2.75, -2.89, -3.93]))"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7c0220",
   "metadata": {},
   "source": [
    "### Split Train, Validation & Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2ca5e65d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first 60000 as train data\n",
    "X_train, Y_train = X[:60000], Y[:60000]\n",
    "# next 5000 as validation data\n",
    "X_val, Y_val = X[60000:65000], Y[60000:65000]\n",
    "# remainng as test data\n",
    "X_test, Y_test = X[65000:], Y[65000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8f59f0f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data:  (70086, 5, 1) (70086,)\n",
      "Train data:  (60000, 5, 1) (60000,)\n",
      "Train data:  (5000, 5, 1) (5000,)\n",
      "Test data:  (5086, 5, 1) (5086,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Total data: \", X.shape, Y.shape)\n",
    "print(\"Train data: \", X_train.shape, Y_train.shape)\n",
    "print(\"Train data: \", X_val.shape, Y_val.shape)\n",
    "print(\"Test data: \", X_test.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b515f5",
   "metadata": {},
   "source": [
    "### Build NN Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "54eeb2f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_2 (LSTM)               (None, 64)                16896     \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 8)                 520       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 17425 (68.07 KB)\n",
      "Trainable params: 17425 (68.07 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# model building\n",
    "model = Sequential()\n",
    "model.add(InputLayer((5, 1)))\n",
    "model.add(LSTM(64))\n",
    "model.add(Dense(8, 'relu'))\n",
    "model.add(Dense(1, 'linear'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3b105e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model score function\n",
    "cp = ModelCheckpoint('model/', save_best_only = True)\n",
    "# compile model\n",
    "model.compile(loss = MeanSquaredError(), optimizer = Adam(learning_rate = 0.0001), metrics = [RootMeanSquaredError()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c2d49fa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 36.5345 - root_mean_squared_error: 6.0444INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 9s 4ms/step - loss: 36.4629 - root_mean_squared_error: 6.0385 - val_loss: 1.1142 - val_root_mean_squared_error: 1.0555\n",
      "Epoch 2/10\n",
      "1854/1875 [============================>.] - ETA: 0s - loss: 1.9549 - root_mean_squared_error: 1.3982INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 1.9475 - root_mean_squared_error: 1.3955 - val_loss: 0.6039 - val_root_mean_squared_error: 0.7771\n",
      "Epoch 3/10\n",
      "1863/1875 [============================>.] - ETA: 0s - loss: 0.9221 - root_mean_squared_error: 0.9602INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.9202 - root_mean_squared_error: 0.9592 - val_loss: 0.5418 - val_root_mean_squared_error: 0.7361\n",
      "Epoch 4/10\n",
      "1864/1875 [============================>.] - ETA: 0s - loss: 0.7378 - root_mean_squared_error: 0.8589INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.7379 - root_mean_squared_error: 0.8590 - val_loss: 0.5245 - val_root_mean_squared_error: 0.7242\n",
      "Epoch 5/10\n",
      "1863/1875 [============================>.] - ETA: 0s - loss: 0.6871 - root_mean_squared_error: 0.8289INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6867 - root_mean_squared_error: 0.8287 - val_loss: 0.5064 - val_root_mean_squared_error: 0.7116\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6679 - root_mean_squared_error: 0.8173 - val_loss: 0.5085 - val_root_mean_squared_error: 0.7131\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6593 - root_mean_squared_error: 0.8120 - val_loss: 0.5218 - val_root_mean_squared_error: 0.7224\n",
      "Epoch 8/10\n",
      "1872/1875 [============================>.] - ETA: 0s - loss: 0.6544 - root_mean_squared_error: 0.8089INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6543 - root_mean_squared_error: 0.8089 - val_loss: 0.4999 - val_root_mean_squared_error: 0.7070\n",
      "Epoch 9/10\n",
      "1854/1875 [============================>.] - ETA: 0s - loss: 0.6479 - root_mean_squared_error: 0.8049INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.6493 - root_mean_squared_error: 0.8058 - val_loss: 0.4936 - val_root_mean_squared_error: 0.7026\n",
      "Epoch 10/10\n",
      "1865/1875 [============================>.] - ETA: 0s - loss: 0.6466 - root_mean_squared_error: 0.8041INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6459 - root_mean_squared_error: 0.8037 - val_loss: 0.4916 - val_root_mean_squared_error: 0.7011\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x17961dace10>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model training \n",
    "model.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs = 10, callbacks = [cp])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d33e02b6",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f976460b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load trained model\n",
    "trained_model = load_model(\"model/\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ab54fa2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train Predictions</th>\n",
       "      <th>Actual Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-10.082612</td>\n",
       "      <td>-9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-9.894069</td>\n",
       "      <td>-9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-9.031311</td>\n",
       "      <td>-8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-7.401009</td>\n",
       "      <td>-7.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-7.321931</td>\n",
       "      <td>-7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59995</th>\n",
       "      <td>5.966363</td>\n",
       "      <td>6.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>7.288073</td>\n",
       "      <td>9.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>12.218936</td>\n",
       "      <td>13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>15.956103</td>\n",
       "      <td>15.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>16.409384</td>\n",
       "      <td>15.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Train Predictions  Actual Values\n",
       "0             -10.082612          -9.67\n",
       "1              -9.894069          -9.17\n",
       "2              -9.031311          -8.10\n",
       "3              -7.401009          -7.66\n",
       "4              -7.321931          -7.04\n",
       "...                  ...            ...\n",
       "59995           5.966363           6.07\n",
       "59996           7.288073           9.88\n",
       "59997          12.218936          13.53\n",
       "59998          15.956103          15.43\n",
       "59999          16.409384          15.54\n",
       "\n",
       "[60000 rows x 2 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction on train data\n",
    "train_predictions = trained_model.predict(X_train).flatten()\n",
    "train_results = pd.DataFrame(data = {\"Train Predictions\" :train_predictions, \"Actual Values\" :Y_train})\n",
    "train_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f3af2838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and Prediction \n",
    "plt.plot(train_results['Train Predictions'][:100])\n",
    "plt.plot(train_results['Actual Values'][:100])\n",
    "plt.xlabel(\"Data Count\")\n",
    "plt.ylabel(\"Precitions\")\n",
    "plt.title(\"Actual vs Train Prediction\")\n",
    "plt.legend([\"Training Data Prediction\", \"Actual Data\"], loc = \"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7768dea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Val Predictions</th>\n",
       "      <th>Actual Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.527350</td>\n",
       "      <td>14.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.316591</td>\n",
       "      <td>13.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.093684</td>\n",
       "      <td>12.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.487491</td>\n",
       "      <td>11.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.398828</td>\n",
       "      <td>10.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>17.519211</td>\n",
       "      <td>18.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>17.486084</td>\n",
       "      <td>17.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>17.256996</td>\n",
       "      <td>16.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>15.912534</td>\n",
       "      <td>15.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>15.139518</td>\n",
       "      <td>15.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Val Predictions  Actual Values\n",
       "0           15.527350          14.02\n",
       "1           13.316591          13.67\n",
       "2           13.093684          12.27\n",
       "3           11.487491          11.19\n",
       "4           10.398828          10.85\n",
       "...               ...            ...\n",
       "4995        17.519211          18.27\n",
       "4996        17.486084          17.85\n",
       "4997        17.256996          16.65\n",
       "4998        15.912534          15.85\n",
       "4999        15.139518          15.09\n",
       "\n",
       "[5000 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction on validation data\n",
    "val_predictions = trained_model.predict(X_val).flatten()\n",
    "val_results = pd.DataFrame(data = {\"Val Predictions\" :val_predictions, \"Actual Values\" :Y_val})\n",
    "val_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a8b7b49d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and Prediction \n",
    "plt.plot(val_results[\"Val Predictions\"][:100])\n",
    "plt.plot(val_results[\"Actual Values\"][:100])\n",
    "plt.xlabel(\"Data Count\")\n",
    "plt.ylabel(\"Precitions\")\n",
    "plt.title(\"Actual vs Validation Prediction\")\n",
    "plt.legend([\"Validation Data Prediction\", \"Actual Data\"], loc = \"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "16e9a61a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159/159 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test Predictions</th>\n",
       "      <th>Actual Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.443191</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.262276</td>\n",
       "      <td>13.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.903847</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.496643</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.046687</td>\n",
       "      <td>12.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5081</th>\n",
       "      <td>-1.277396</td>\n",
       "      <td>-0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5082</th>\n",
       "      <td>-1.599110</td>\n",
       "      <td>-1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5083</th>\n",
       "      <td>-1.755187</td>\n",
       "      <td>-2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5084</th>\n",
       "      <td>-3.127067</td>\n",
       "      <td>-2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5085</th>\n",
       "      <td>-3.171355</td>\n",
       "      <td>-3.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5086 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Test Predictions  Actual Values\n",
       "0            14.443191          13.99\n",
       "1            13.262276          13.46\n",
       "2            12.903847          12.93\n",
       "3            12.496643          12.43\n",
       "4            12.046687          12.17\n",
       "...                ...            ...\n",
       "5081         -1.277396          -0.98\n",
       "5082         -1.599110          -1.40\n",
       "5083         -1.755187          -2.75\n",
       "5084         -3.127067          -2.89\n",
       "5085         -3.171355          -3.93\n",
       "\n",
       "[5086 rows x 2 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction on test\n",
    "test_predictions = trained_model.predict(X_test).flatten()\n",
    "test_results = pd.DataFrame(data = {\"Test Predictions\" :test_predictions, \"Actual Values\" :Y_test})\n",
    "test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "25c1dacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nJibqc+fO1e+66y69ubnZPK+4uFg///zz9ejoaD0mJkY///zz9S+//PKI3Uxe/v3vf+vz58/Xw8PD9ejoaH327Nndvq62tla/4IIL9Pj4ePN793Lg35mu6/rmzZv1s846S4+Li9MdDoc+c+bMg7rsvN1ML730Urfj3t9jT115gjDUaLrej4lagiAIgiAIwwTpZhIEQRAEIagRMSMIgiAIQlAjYkYQBEEQhKBGxIwgCIIgCEGNiBlBEARBEIIaETOCIAiCIAQ1IT80z+PxUFpaSkxMTI+jxwVBEARBGH7ouk5TUxMZGRlYLIfPvYS8mCktLR203SWCIAiCIPiXoqKiIy5VDXkxExMTA6gfxoEj4AVBEARBGJ40NjaSlZVlvo4fjpAXM97SUmxsrIgZQRAEQQgyemMREQOwIAiCIAhBjYgZQRAEQRCCGhEzgiAIgiAENSJmBEEQBEEIakTMCIIgCIIQ1IiYEQRBEAQhqBExIwiCIAhCUCNiRhAEQRCEoEbEjCAIgiAIQY2IGUEQBEEQghoRM4IgCIIgBDUiZgRBEARBCGpEzAiDRrvTjdujBzoMQRAEYYQhYkYYFFo6XBz3+4+5/F+rAh2KIAiCMMKwBToAITTYW9VMdXMH9fs60XW9VyvbBUEQBGEwkMyMMCjUNHcC4PLoNHW4AhyNIAiCMJIQMSMMCjUtneb1Op/rgiAIgjDUiJgRBoWa5g7zeq2IGUEQBMGPiJgRBgVfAVPXKmJGEARB8B8iZoRBobq5S8DUtjgDGIkgCIIw0hAxIwwKNS1dZSbxzAiCIAj+RMSMMCj4lplqpcwkCIIg+BERM8KgUONbZmoWMSMIgiD4DxEzwoDRdZ1q324mycwIgiAIfiSgYub+++9n/vz5xMTEkJqayrnnnsvOnTsPef51112Hpmk8+OCD/gtSOCKtnW46XB7zc/HMCIIgCP4koGJmxYoVXH/99axcuZLly5fjcrk49dRTaWlpOejcZcuWsWrVKjIyMgIQqXA4ag4oK0lmRhAEQfAnAd3N9N5773X7/MknnyQ1NZV169ZxwgknmMdLSkq44YYbeP/991m6dKm/wxR6ovBL2PAMnPprag7QnpKZEQRBEPzJsFo02dDQAEBiYqJ5zOPxcNlll3HbbbcxderUI95HR0cHHR1d/o3GxsbBD1SAT/8Iez+C9FnURJ8NwOj4CErq26hvc+L26FgtsmxSEARBGHqGjQFY13VuueUWjjvuOKZNm2Ye//3vf4/NZuOmm27q1f3cf//9xMXFmR9ZWVlDFfLIpqFYXVbtNGfMjEmJAkDXoaFNBucJgiAI/mHYiJkbbriBTZs28dxzz5nH1q1bx1//+leeeuopNK137/LvvPNOGhoazI+ioqKhCnlk01imLqt3mksmR8WGExdhB2Q/kyAIguA/hoWYufHGG3njjTf4+OOPyczMNI9/9tlnVFZWkp2djc1mw2azUVhYyK233kpubm6P9xUWFkZsbGy3D2GQaW+EziZ1vWqXaQBOinKQGOUAZD+TIAiC4D8C6pnRdZ0bb7yR1157jU8++YS8vLxut1922WWccsop3Y6ddtppXHbZZVx11VX+DFXwpams63pzOa2NtQAkRTtIiLSTj2RmBEEQBP8RUDFz/fXX8+yzz/L6668TExNDeXk5AHFxcURERJCUlERSUlK3r7Hb7aSlpTFx4sRAhCwANJZ2+zSsfg+QQlJUWFdmRsSMIAiC4CcCWmZ69NFHaWhoYPHixaSnp5sfL7zwQiDDEo6Eb2YGiG3OByAx2kFCpBIzNSJmBEEQBD8R8DJTXykoKBj8QIS+0VjS7dOU9gJgAcmSmREEQRACwLAwAAtBhreTKSIBgAzXfsDIzBhiRqYAC4IgCP5CxIzQd7xlprxF6gLloUmKcpAYKZkZQRAEwb+ImBH6jrfMNGYxADlaBQlhOuF2q09mRobmCYIgCP5BxIzQd7xlpozZuOwxWDWdGRHVACRGqaF5kpkRBEEQ/IWIGaFvuDqhpUpdjx1Nc8wYAKY5VFt9gpSZBEEQBD8jYkboG83lgA4WO0QmURORC8B4i8rWJEWFAdDU4aLT5QlQkIIgCMJIQsSM0De8JaaYdLBYKLerRZ45ulo8GRNuM7dl10tHkyAIguAHRMwIfaPJmP4bmwHAfosSMxlO1Z5tsWgkRBrLJkXMCIIgCH5AxIzQN7yrDGLTAditjwYgqX0/eFRZyeubkf1MgiAIgj8QMSP0Da+YiVGZmd2diXToNmyedmhQ2RmzPVvEjCAIguAHRMwIfcM7MM8oM1W1uCnQ09Sx6t0AMjhPEARB8CsiZoS+cUCZqbq5kz26EjZU7QR8MzMyOE8QBEEYekTMCH3Dp8zk8ejUtXayx/DNUK3EjDk4TwzAgiAIgh8I6NZsIcjQ9W5lpoY2J26Pzl4MMVO1CxADsCAIguBfJDMj9J7WGnAbAiUmnZqWDgDKHao9m2olZhKNMpNkZgRBEAR/IGJG6D3eElNkMtgc1DQrsdIUlQdo0FYLLdXSzSQIgiD4FREzQu85oJOpxhAr0TExEG9kZ6p2khQl3UyCIAiC/xAxI/Sexu7Tf71iJjHKAckT1W3VO7s8M1JmEgRBEPyAiBmh95idTKotu6ZZeWaSosMgeYK6rXq36Zlpd3po63T7PUxBEARhZCFiRug95l4m1b3k9cwkRTkgxRAzVTuJdFhx2NSflmRnBEEQhKFGxIzQe7wbs42BeV6Db5JvmalqJ5qmmVOAa5tFzAiCIAhDi4gZofccUGaq9i0zjZoKmhUai6F+f1dHk2RmBEEQhCFGxIzQew4oM3XLzITHwui56vZ9K7qmAEtHkyAIgjDEiJgRekdnC7Q3qOtGmcnbzZQUHaaOj1mkLvNXyBRgQRAEwW+ImBF6h9cvY4+CsFjcxl4mgKRoJVzIM8TMvhUkRsp+JkEQBME/iJgRekeTz4wZTaOutRNdB03r2sVE1gKwRUBLJeMoAiQzIwiCIAw9ImaE3nFAJ5O3LTsh0oHVoqnbbGGQczQAk9q+BiQzIwiCIAw9ImaE3tFYoi5jvNN/jU4mo2vJxCg1ZTesASQzIwiCIAw9ImaE3nHgXqZmn1UGvoxZDEByzRqsuKlrcforQkEQBGGEImJG6B0H7mUyZswkezuZvKTNgIgEbM5mZmp7Zc6MIAiCMOSImBF6xwED82pbDpGZsVgg93gAjrFspa6lE13X/RamIAiCMPIQMSP0jgPKTNUtB7Rl+2KUmo6zbsHl0Wlsd/kjQkEQBGGEImJGODJuFzRXqOsHlJkOMgCDKWbmaLsJp0OmAAuCIAhDiogZ4cg0V4DuUbuXolIAKKxpBSAzIfLg8xPHQGwmDs3FfMtO8c0IgiAIQ4qIGeHIeEtMMelgseJye9hX1QLAuNTog8/XNDM7c6xli2RmBEEQhCFFxIxwZCq3q8vEPAD217bS6fYQYbcyOj6i568x9jQda9kis2YEQRCEIUXEjHBkStU0XzJmA7CnshmAsalRWLzTfw8k7wQApmqFtNRXDnmIgiAIwshFxIxwZA4QM7sNMTM+NebQXxOTRnXkGCyaDvmfDXWEgiAIwghGxIxweFydULFVXc+YBXRlZnr0y/jQmrUYgLzyd4cqOkEQBEEIrJi5//77mT9/PjExMaSmpnLuueeyc+dO83an08ntt9/O9OnTiYqKIiMjg8svv5zS0tIARj3CqNoO7g4Ij4ME5ZnprZhJOPZqAI51raaurGBIwxQEQRBGLgEVMytWrOD6669n5cqVLF++HJfLxamnnkpLi+qUaW1tZf369fziF79g/fr1vPrqq+zatYuzzz47kGGHPHe/voWFv/2A8ob2rhJT+izQNDwe3RQz448gZmKyp7PROhWb5qH2s38OcdSCIAjCSMUWyAd/7733un3+5JNPkpqayrp16zjhhBOIi4tj+fLl3c75v//7PxYsWMD+/fvJzs72Z7gjgtL6Nv67shCPDsu3lXNZ9QZ1g+GXKalvo83pxmG1kJ3Yw4yZA9iScQEzi7aSuvt5cN8LVvvQBS8IgiCMSIaVZ6ahoQGAxMTEw56jaRrx8fE93t7R0UFjY2O3D6H3vLCmCI+xSmltYd3BnUxVKiuTlxyFzXrkPx/HtHOo0uOIcVbDjreHJGZBEARhZDNsxIyu69xyyy0cd9xxTJs2rcdz2tvbueOOO7jkkkuIjY3t8Zz777+fuLg48yMrK2soww4pXG4PL6wpMj/fVFB5sPm3ond+GS+zclN53n0iAJ41TwxesIIgCIJgMGzEzA033MCmTZt47rnnerzd6XTy7W9/G4/Hw9/+9rdD3s+dd95JQ0OD+VFUVHTIc4XufLyzivLGduIj7Vg0iGrYBR4nRCRAfA4AuyubgN6LmbEp0bxpOxW3rmEp+BSqdh75iwRBEAShDwwLMXPjjTfyxhtv8PHHH5OZmXnQ7U6nkwsvvJD8/HyWL19+yKwMQFhYGLGxsd0+hN7x7KpCAC6cl8WktFhmWPLVDRmz1YoCujqZxo/qnZixWDTSs8fzkWeOOrD2X4MbtCAIgjDiCaiY0XWdG264gVdffZWPPvqIvLy8g87xCpndu3fzwQcfkJSUFIBIQ5/iulY+2VUFwMULspmbk8A0bZ+6MX0WoH5fu3vZlu3LnOwE/uM+RX2y4TnobBm0uAVBEAQhoGLm+uuv57///S/PPvssMTExlJeXU15eTltbGwAul4sLLriAtWvX8swzz+B2u81zOjtl389g8uKaInQdjhmbRF5yFPNyE7pnZoCqpg6a2l1YNGUA7i1zcuL5zDOdYi0NOhpg88tD8S0IgiAII5SAiplHH32UhoYGFi9eTHp6uvnxwgsvAFBcXMwbb7xBcXExs2bN6nbOl19+GcjQQwqX28MLa5W36JKFqt197ugIJmrqWHvKDKBrjUFOUhRhNmuv739WVjxoFp7uPEkdWPP4IEUuCIIgCAGeM6Pr+mFvz83NPeI5wsD5cEclFY0dJEU5OHVKGgCjO/LRNDc1egy7G6M5KgV2V/TN/OslJtzOxFExvFS+iJ/Zn0Mr3wTNVRCdMujfiyAIgjDyGBYGYCGwPLtqPwAXzMvEYVN/ElqZmi+z2TOGdfvrga4ZM0ea/NsTs7MTqCeGBocSS9TsGWDUgiAIgqAQMTPCKapt5dPdhvF3vs9EZWNY3mY9j3WFdQDs7uOMGV/m5iQAUEC6OlCzu78hC4IgCEI3RMyMcD7fU42uw4K8RHJ9Tb2lGwHY7FFipvtOppg+P86c7HgANrWnqgOSmREEQRAGiYB6ZoTA01qylS/DbqC041jonAmOSHC2QeU2AHZZx9PQ5mRtYR01LaqDbGxq7zuZvOQlR5EQaWdPxygloWv2Dua3IQiCIIxgJDMzwskoeY8MrZZ5tW/CE99QIqN8C+huiEohLVPN/nl+jfLVjI6PINLRdw2saRqzsxPYp2eoA9VSZhIEQRAGBxEzI5y0xi0A6GhQsQX+sRi+fEjdmDGbublq6ec7m8uA3k/+7Ym5OQnkewwDcO0+8Lj7fV+CIAiC4EXEzEjG42Fs53YA9p3yT8g6CjoaYfsb6vaM2czLUWKm3ekBYFxK/8XM7Ox4SkmiA4fa+VRfOLD4BUEQhiGr82u5/93tdLjkDZu/EDEzgnFW7iSWFtp0BzFTT4cr34Kjru86IWMOsw3jrpeBZGZmZsZjt9rY5xmlDohvRhCEEORP7+/k7yv2sWJnVaBDGTGImBnBNO35CoAt+hiS46LBaofTfwuXvAQn3gXjv0F8pKPbXJlx/ehk8hIVZuPMGenk6972bOloEgQh9Khu7jAuZe2OvxAxM4JxFq4CYHfYZCwWreuGCafCop+CRa0smJebYN7UnxkzvlxxTC77DDHTVrZjQPclCIIwHKlvcwJQ1ypixl+ImBnBhJevA6A8dsZhz5uTrcRMakwYcRH2AT3mzKx4XAljAagu3Dag+xIEQRhu6LpOgyFm6kXM+A0RMyOV9kZim1SZpzl51mFPPXVqGkePSeKa48cMykPPmjkXAEf9Xlxuz6DcpyAIXRRUt3D6g5/y1qbSQIcy4mjucOH2qJ2Cda3OAEczchAxM1IpWYeGTpEnhbiUrMOeGhdh57lrj+KaEwZHzBy14CgARlHDx5vzB+U+BUHo4s2Npewob+LRT8Rk72+8WRmQzIw/ETEzUileA8B6fTyjEyL8+tDhscm02uIA+OCLlX59bEEYCRTWtgKwrayRuhZ5QfUn9T7ZGMnM+A8RMyMVQ8x87RnH6Hj/ihkAW8oEAJpLdrCrosnvjy8Iocz+GiVmdB1W7qsJcDQji8Y2XzEjQtJfiJgZieg6ujcz4xlPpp8zMwCOUUrM5Gll/PurAr8/viCEMgU1Leb1L/ZWBzCSkUd9tzKTZGb8hYiZkUjNHrS2Otp1OzvJIS0u3P8xJKmOpjGWMl5dX0Jju/zTC8Jg0NbpprKpw/z8y72SmfEnB3pmPIYZWBhaRMyMRIyszGY9j8TYaOzWAPwZJI0HYLK9ktZON6+uK/Z/DIIQguw3/DKRDisWDfZVtVDe0B7gqEYOvtkYjw5NHa4ARjNyEDEzEilaDagSUyD8MgAkjQNUZgZ01hbWBSYOQQgxCo0S09iUaKaPVkb7L/ZIqclf+GZmQDqa/IWImZGIj1/G351MJoljAI0wVxNJNFJU1xaYOAQhxCg0zL/ZSZEcMy4ZkFKTP2lo6y5epKPJP4iYGWl0NEGlmrz7dSAzM/ZwiFfzbfK0MoqN1LggCAOjsFZlZnKTIjl2rFfMVKPr4t3wBwdmZqSjyT+ImBlplKwH3UONNZVKEgKXmQHTNzPGUkZNSyctUlsWhAHjzczkJEYxNycBh9VCWUM7+dUtR/hKYTA4sINJykz+QcTMSKNY+WU2WyYCBC4zA6ZvZrK9EoBiKTUJwoDxGoCzkyKJcFiZkxMPSKnJX3gzMzFhNgDqWqTM5A9EzIw0itcCsKpTrSYIxIwZE0PMTLJXAFAkpSZBGBBOt4cS401BTlIkAMf4lJqEoccrZnKS1c9fMjP+QcTMSKP0awBWGmImI5CZmWQlZnIpA6CoTsSMIAyE0vo2XB6dMJuFUTFqftSx45IA+Gpvjcw88QMNRpkpNykKEAOwvxAxM5JwtkOzyoLs09NJjHIQ6bAFLh4jM5PqKsGCh6JaKTMJwkAwO5kSI7FYNABmZMYT5bBS1+pke3ljIMMLeVxujzlXpkvMSGbGH4iYGUk0lQLgtkbQQFRg/TIAsZlgC8equ8jUqiQzIwgDxLtg0ltiArBbLSzISwTgyz3imxlKGtu7mhiyE9Xv4MDuJmFoEDEzkmgoAaA5LBXQAi9mLBZINNYaaGXimRGEAbLfGJiXnRjV7fix48Q3M2Q422H5PbB/ZTfzb3KMA5DMjL8QMTOSaFSZmVpbCkBg27K9GDua8rQyiuvaZBaGIAyAAqPMlJsc2e241wS8Or8Wp9vj97hCmt3vwxcPwv9+YZp9YyPsxEcaYka6mfyCiJmRRKPaf1SuK0NgwDMzAMnGrBmtjOYOl2yZFYQBsN/HM+PLpLQYEiLttHS62V4mvplBxXiTSNVOGgwxEx9pJ8EQM9LN5B9EzIQwtS2d/PH9HV3lG+Ofbr8rARgmmRmjzDTBmDUjvhlB6B+6rpszZnKSupeZLBbNPFYmSycHl2b13EVHA+315QDERdhJiLQD0NLpptMl2bChRsRMCPPyuiIe+XgvNzy7XpVvDM/M3o5YYJhkZrwLJzWjPVs6mgShX1Q1ddDmdJOq1ZO1/3V4/y6o3GHenhoTBkBlU0egQgxNWiq7rtfsAVRmJjbcjtFQJtkZPxDAvlxhqPGWbDYWN/DelnKWNCoxs6tdbdIN6MA8L4ZnJslTTRidkpkRhP5QsQ33p0/yjuN9plgK4Q3jeH0hXPRfAFJjlZipapTMzKDSXGVetdftBaYSF2HHYtGIi7BT1+qkrtVJamx44GIcAUhmJoRpc7rN63/83050Q8yU60lEOazERdgDFVoXkUkQHocFnRytQjqaBKGv6Do8fSbpW/+hhAxAfI667JaZUS+mkpkZZIzZXQCRTfsAZQAGTN+MdDQNPSJmQph2HzFTUlWH1qpmTJTqiYxOiEDTtECF1oWmmb6ZPK2cItnPNGS4PTrPrd5PSb38jEOK5kporUFH46bO6/nN1DfhqnfVbXX54FYZ2hSjzFQlYmZwaenKzMS2KDEZH6FETLzhm5Ey09AjYiaEaetUYiYnKZI0rRYApyWcxuEwMM8XwzeTp5VRLJmZIePFtUXc+epmfvv29kCHIgwm9fvVhS2ZNzzHkpw6GmIzwB4FHhfU5gPimRkSdL2bmEluV2Im7oDMjHRpDj0iZkKYVkPMXHF0LjOimwEo8SQC2vDoZPJi+GZytXKK69pkf8wQsXKfyszJSPsQo169gJaSChidTJpmjj2gehfgW2YSz8yg0V4P7q6sS7KrDDsuMyMTZ1zKfqahR8RMCOP1zMRH2rlsqvqnKnYbbdnxkYf8Or/j7WiylNPp9sg7xyFibUEdoLaTu2RwWuhQVwDAPpcajGeuMvCKmZrdQJcBuLq5E7e8YRgcvG3ZYbHgiMGKh2ytoofMjJSZhhoRMyGM1zMTYbcyL0GVb8q8A/OGU2YmUW3wHmNRRjrpaBokdi+Hr1UnS3lDu+mVcbp1Suvl3XnIYJSZ9rrU/7Y5MC95grqsVmImKcqBpinvVG2LvLgOCl4xE51qZpjHaqU+YsabmZGf91ATUDFz//33M3/+fGJiYkhNTeXcc89l586d3c7RdZ17772XjIwMIiIiWLx4MVu3bg1QxMGFNzMT7rBiMZZMlqEWzg0vz4x6EkimjmhapaNpMHC2wwuXwevXQ/kW1u+v63ZzvrHDRwgBjDJTsZ5CcnQYUWHGxI0Dykw2q4WkKK9vRsTsoOCdMRM9qts0c6+YMVcaSJlpyAmomFmxYgXXX389K1euZPny5bhcLk499VRaWrqeaP/whz/wwAMP8PDDD7NmzRrS0tL4xje+QVNTUwAjDw68BuAIuxWMtuzU0WOZmRXP1IzYQIbWnfA4iFL7olR7tnTbDJji1eAyfo77PjZLTF4KqkXMhAx1SswUeVLJ9dmWTZKPmDF2nqWICXhw8c6YiUrBmdC1NNfrmZEyk/8I6NC89957r9vnTz75JKmpqaxbt44TTjgBXdd58MEHueuuuzjvvPMAePrppxk1ahTPPvss1113XSDCDhrancoX4Stmvn3yUXx7wrGBDKtnksZBS5Xani1lpoGT/2nX9X2fsK5hDgDpceGUNbSTL2ImNPC4oUHtXCvSUziqm5gZC2jQ3gAt1RCdQmpMGNvLoKpRxMyg4J0xE51KS0we8cBYSxnRRnYsQQzAfmNYeWYaGhoASExUpZD8/HzKy8s59dRTzXPCwsJYtGgRX375ZY/30dHRQWNjY7ePkUprpwuASIfVXGVA3OgARnQYErs6mqTMNAjkf2Ze1Qu/ZFeJ6mQ6b476/RdImSk0aCoDjxM3VspJJCfRZyeTPQLis9V1s6NJykyDirfMFJVKQ2QuAGMsZeYMr3jJzPiNYSNmdF3nlltu4bjjjmPatGkAlJerpV2jRo3qdu6oUaPM2w7k/vvvJy4uzvzIysoa2sCHMV7PTITWCW1qzgyxGQGM6DAYvpk8i2rPFgZARzOUrFXX7ZFozlam6btJjQnj2HGq40XKTCGCUWKqsqbiwdLVyeTFNAEbYiZWykyDirfMFJ1KlT0TgASaoFU93yZEeYfmOdV+PGHIGDZi5oYbbmDTpk0899xzB9124KRaXdcPOb32zjvvpKGhwfwoKioakniHOx6PbpaZIjuMdw/2KAiPD1xQhyOpawpwWUMbTmkd7j9FK9WwtLhsmLgEgOOsW5ibk0BesnrnXlQnP+OQoN7rl1Ges+yDxIzXN2O0ZxuzZmQK8CDR0tXNVOeyU2J0i3p/3l7PjMuj09ThCkSEI4ZhIWZuvPFG3njjDT7++GMyMzPN42lpaQAHZWEqKysPytZ4CQsLIzY2ttvHSKTDZ+V8RJvx84vNUMO0hiPmSoMyPDqUysj9/uP1y+SdAHmLADjGosTMqJhwwu0W3B5dMmChwAFt2Qctjz1w1owYgAcX0wCcSn1rJ/s86epz4+cdbrcSblcvsw3imxlSAipmdF3nhhtu4NVXX+Wjjz4iLy+v2+15eXmkpaWxfPly81hnZycrVqzgmGOO8Xe4QYXvksmwljJ1Zbj6ZcCcNROvtRBPk3Q0DQSvXybvePQxSszM0vYyP0Nt8s1NUtkZKTWFAHVdmRlNg0QjE2ByyDKTeGYGjK77ZGZSaGhzsk83xIyRmYGuPU39mTWzr6qZO17ZRKF43I5IQMXM9ddfz3//+1+effZZYmJiKC8vp7y8nLY29UKmaRo//vGP+e1vf8trr73Gli1buPLKK4mMjOSSSy4JZOjDHq+Ycdgs5owZYoexmHFEmvGphZNiAu4X7Q1QtkFdzz2efa5kCj2p2DU3Uzq3qMOGmJGOphDAW2bSU4iPsGOzHvCU7m3PrisEZzsp0cZKg8YO8XAMFN9VBlGphpgxPIk1e8zT4gfQ0fTMqv08v6aI59eMTLtEXwhoa/ajjz4KwOLFi7sdf/LJJ7nyyisB+OlPf0pbWxs//OEPqaurY+HChfzvf/8jJibGz9EGFz3NmBnWYgaUb6axhDytTDqa+kvhl6B7VNkubjTr1hbh8kwlx1KJvfAzmLyEXMM3Ix1NIUBd18C8xCjHwbdHp0JYHHQ0QO1eUhMnAaoM3djuMoe7Cf3AW2IKiwN7OA1tTvJ7yMwMZNZMnTGpuaFNSlRHIuBlpp4+vEIGVHbm3nvvpaysjPb2dlasWGF2OwmHxitmIh1WaPRmZoZpJ5MXb3u2pZwi8XP0D9MvczwA6wvr+MIz3bhthbopWZlEJTMT5Lg6wci6FukpJEWHHXxOt4WTuwm3W4kJV+9hq6TUNDB8SkygOpZMz0ztPnArw6+3o6muHyskGtuViGkR8/ARGRYGYGHwafPZy9Q1YybzMF8xDPAunJRZM/3H9MucAMDawjq+8kxRxyq2QHNVl2dGMjPBTWMx6B5cljCqiCepp8wMHLSjyTQBy+C8geEdmBeltpU3tDkpJQmXJRw8TrMEOJCVBo1tSsSImDkyImZCFHMvU7cy0zDPzCR1Dc4rFs9M32mthYrN6nru8dS3drKnsplaYnGlTFXH81eY7dkldW10uqQ9O2gxSkwNYemA1nOZCSBZvUnoGpxn+Gako2lg+MyYAahvc6JjoS0mRx03fDPeKcD9KTN5MzPNImaOiIiZEMVbZoqzO30G5g13z4x60s3Tyqhu7jAnGAu9pMDIyqRMhuhUvt5fD8CY5Chs405Ut+WvICUmjCiHFY8O+yUDFrwYbdnVNjXCoscyE0hH01DhM2MGoNHwtXTGqzdlB86a6V9mxltmch/hTEHETIjSbmRmMrR6dcAepRY6Dmfic9A1C1FaBynUS3t2X/FpyQZYV6iWS87JSYAxi9Vt+z5B0zRypD07+DHKGOWaejE9YpmpZg/oullmksF5A6S5a5UB+GReDpjt01Vm6ntmpqFNPDO9RcRMiOItM6VraicPcaOH78A8LzYHWrxK0Y7RysXT0Vd8h+UBawtVRm5uTgJkHw0Wm3o3X5tvlprkZxzEGGWm/boyoB6yzJSQB5oVOpuhqUw2Zw8WzV0GYI9HN4WHLcWbCeteZuprR5LL7aHFyLBLmenIiJgJUVqNf4JRGGJmuPtlvCR1dTRJ1qAPNFVA9U5Ag5xj8Xh0NhWrxa1zcxIgLBoyF6hz931CrnQ0BT9GZmafS+3bSoo+hJixOSAhV12v3tXlmRED8MAwy0yjaO504THG9oSnT1RX+pOZ2fIK7PkQgKb2LgEjmZkjI2ImRPGWmVI81epA7DDvZPLi45uRrEEf8Ppl0qZDZCJFda20drpx2CyMMbIwGNOAKfxCOppCAcMzs7M9AYCkqEN4ZqBbR1O3zdkeMYD3lbc3lfG7d3eg+6wy8K4qCLNZCBtliJnmCmhvNIfm1bccITPTVAEvfxdevAJ03TT/ArR0uvF4ZMjh4RAxE6J4DcBJppgJksxMYtfCScka9IGCz9VlrvLL7CxvAmB8anTXVNgUNTCN+qKuMlO1GICDEmeb2Rq83RAzhywzQbdZM14D8Oymj+GPY+CDXw5pqKHGL9/cymMr9qB7W7ONVQaAGkIYHgvRxu7Amt2mAbipw3X45a4NRYAOnU3Q0Wi2ZXtpdYoJ+HCImAlRvJ6ZBJeRCh3Oe5l8SepaOCkvtH1g/0p1maN2lnnFzMRRPpOyja4LWirNKcClDW1mFk8IIoysjMcRTZ0ejaZ1eTN6xBQzu0iJdnCz7SX+xIPQVgfblg15uKFCh8tNZVMHsbRg8RiZE2OVAXStLujKhO0hLsJu2hXrD9fR1FTWdb219iCPjZSaDo+ImRDFK2biOg0xEzRlJiVmcrRKKhtbzQyTcBhaa6Fqu7qefRQAOyuUmJmQ5itmjHeLzZUkRTmICbOhS3t2cGKImc6YLEDreS+TL94X18rtxL75PX5ke637fbnlhbI3lDeodvYUTfnRfFcZAF3rIYznMWr3YrVoxIb3YtZMU3nX9bbabmUmEBPwkRAxE6K0GyIgxhQzQVJmissCq4MwzclorUo8HcCO8kbueGUTpfWHaFUvWqUuk8ZDlDKDmpmZtB4yM53NaM7Wrh1NUs4LPuoKAGiNUBnXQ86Y8eIVM83laNvfoBMbtzmvxWNxgMdllDiEI1Far8RMMo0AuCLV/5s34xJnbMj2lssPHJx32FkzvmKmtc6cMeNFMjOHR8RMiNLmdBNOB+Eu4x1EsJSZLFbT2zFVK5QXWuDRT/by/Joi7n59S88n7P9KXeYcDahUuNdv1K3M5IgGW4S63lwpCyeDGaOTqT5M7QI6rF8GIDIRIpPU9agU7kn4HS+5F9MSla2O1e4bqkhDirIG9YYi2cjM1GnxAD1kZoypy4aY6VVHk2RmBoSImRCltdNNumZM/nVEQ1hsYAPqC6PnADDTspd8eaE1hckH2ytZv7/u4BO8fpnso83zXR6dmHAb6XHhXedpWld2prmSvCRve7aUmYIOY8ZMtU2JmUMOzPNl8Z0w+Sy45mPqEtX/WF24UX4WMdMryowyk1fMFDujAahvUyLF9MyYYmYv6HrvVho0+2Zmag8yADe3i5g5HCJmQpQ2p7trYF5sEAzM8yVDPdHO0PaN+MyMruvdurr+9P7O7ic426Bkvbru9cv4mH+1A3/vPZiAR/rPOCgxPDOl3um/h5ox48uCa+Ci/0J8ltnRVGEzys9G2Uo4PN5S78x4NaNnV3MEHo9uloTMzExCLmgWNaiwucLsaDq8Abh7ZuYgA7CsdzksImZClHanm3S8O5mCxC/jxcjMTLfso7CqOcDBBJbalk5zeJbDauHLvTV8sae664SS9WpDb3SamvRKl5jpZv71YpqAK6TMFMwYZaYiXXk2Eg83Y6YHvLNmijD+HiQz0yu8mZnphpgp6oxhe3mjKVLMzIzNAfFGCa9mT+82Zx/QzXRwmUmaIQ6HiJkQpa3TTYZmvOgFi1/GS8pkPNYwYrU23EbNeaRSUKNKQOlx4VyyUD05/vH9nei6MUDL65fJPsrMvnnFzKSexEyUGn1PcxV5xuC8soZ26RoLJtobVUs1sNdpTP/tTZnJB+8U4L0uI1MnYqZXeDMzCXo9ANXE8fnu6oM9M9Ct1HTEMpOrE1pruj5vrTGzPd7kqhiAD4+ImRClzekm12KkLY137EGD1YZn1AwAMlt3jGjjm7cElJsUxQ9PHEu43cKGono+2G50qZli5mjza8y27FGHz8wkRDnMJ9/CWsnOBA1GiYmIRErbbEAvy0w+pBhlpm0dhritzZdpwL3Am5mJdqmsd7Uex2e7q326mXoSM3vMjM0hDcDeAXxe2mppNDKyKUanmoiZw9MvMfPee+/x+eefm58/8sgjzJo1i0suuYS6uh4MioLfaXe6ydMMMeOdeRBE2LLmAjDDMrJ9M94SUG5yFKkx4Vx5jBKmf/7fTjwuFxStVicanUzNHS6K69S7x4k9ipkuAzDA6HjV3eSdnyEEAUaJiYQcappVueOI3UwH4C0zbWuJUQtI3R3dyxzCQbR0uMwMjKNdZb2r9ThWF9SaSzsPlZk5YpnJ1y8DhgFYnZtu/I+O5Dd1vaFfYua2226jsVH12W/evJlbb72VM844g3379nHLLbcMaoBC/2jtdJNriplxgQ2mP3hNwJZ9I3qtgbfMlGt0Hn1/0RhiwmzsKG/i0y9WQEcjOGIgdSoAu4ysTGpMGAk9vcD5GIABEqK86e++bfQVAojRyUR8NrUt6p3+Yfcy9YC5ObvVjR7f1Z79yrpirvjXanPXkNCFty07JsyKpcXYyxSdSqfLQ7UhKr2iBeh6E1mzx8cAfKjMjPFcbTHEUFudKZxGx6uSoGRmDk+/xEx+fj5TpkwB4JVXXuHMM8/kt7/9LX/729949913BzVAoe/ouo7D2UCiZphnE8cENqD+YJiAp2oFFFY1BDiYwGGWmQyzbnykg2tOUL/PjV+8p07Kmg9WVW7Y1dOwPF98ykze+4NebvQVhgfGgDtPbBb1xgteX8tMSVFhWDTQdeiMzQWgsnA7d766mRW7qli+veLwdzAC8Q7MGxfnBrf6f5k0rnvWu1tmxjs4ry6f5CgrAFVNh9hU7s3MpBhLKn0MwOlxKjPTIgbgw9IvMeNwOGhtVe8YP/jgA0499VQAEhMTzYyNEDg6XB5yUf8cnug0cEQFOKJ+kDiWDmsUEVon7aVbAx1NQNB13RQz3sWQAFcfl0dsuI0xbZvUgexjzNt29LSTyRcfA7Dv/IvDdlkIw4vGUgBaI9LQdYy9TH0TM1aLRrLhxWiKzALgyzVr6DQWIZYdatr0CMabmZkQZZRkw2I5emL35orYcFvXJ3GZYA0DdyejLcrcW9fqpLWnFmtviS91srp0tuBxKuHjnRUlZabD0y8xc9xxx3HLLbdw3333sXr1apYuXQrArl27yMwMkh1AIUy707fEFHx+GQAsFpoSpgEQWbUxwMEEhtqWTpo6XJxuWc3YT38E5WoCcHSYjXNnZTDfYsycMebLQFeZqce2bOgqM7naoKPJfBGsa5HMTNBgvPA12FQnU3yEHaul73OkvLNmasPUc7a9scC8raxRPFQH4s3MjI0wMt5RKRw7Ltm8PSbM1n0/lsVqZsVjmguICbN1u59uNBmZsOSJaj4NEI96nLQ4KTP1hn6JmYcffhibzcbLL7/Mo48+yujRSp2+++67nH766YMaoNB32pxu8oxOJkuwihlAz5gNwKjm7QGOJDAUFxfyiP1BHnM8iHXrK/DkGbBf7WH6ziRI0+ro1K3UJkw3v+awbdmgsnQO47aWqi4xI2Wm4KFRiZkaq1pPcMS9TIfA2569y2jvztUqmJyuJoVLZuZgvJmZLIfh4YseRXJ0GFOMn1msb4nJi+mb2UuGYeTtcceaNzMTmw4RiQAkaE3EhNnMJZWSmTk8/RIz2dnZvPXWW2zcuJHvfve75vG//OUvPPTQQ4MWnNA/2nzNv4nBK2aixy4EYIJ790HTMEMaXYfNLzPplW+w1LoaNxa1RLKjAf5zLuz5kAkdqvS2Rc/jtS2qg7C6uYOalk40DcanHkLMAER7S00VYgAONjweaFJlpkpdvej1tZPJi7ej6ZGNqrSUZ6nkJ98YD3S1IAtdeH8maVbDSmH8Hx0/3siQRfYkZrraszMMI2/PYsZ4vo5JU3u0gAStmdgIO1FGRkcmAB+efs+Z8Xg87Nq1i88//5xPP/2024cQWNqcQd7JZBCRMw+ASVoRheU1Rzg7RNB1ePUaeOW7hDnr2ebJ4W/jH4frPoVxp4CzFZ69CL56GIA1nom8sGY/uq6bWZmcxEgiHNZDP4aPCVgMwEFGa7Xaco1GqTsOgOQ+mn+9eMXMXmcyHl0jkjZyI5QX8pAb2kcw3p+Jdy8TUapku3RGOhYNpo+OO/iLfDqaDpuZ8XYzxfhkZmgiNsJOtFfMiAH4sNiOfMrBrFy5kksuuYTCwsKuSaQGmqbhdssPPZC0dbiYEOyeGYC4LBq1WGJppD5/PeSdEeiIhp6S9bD5JbDYeCvhMn5cciI/zZoGjkj49nNK6GxbBuWbAdioTWZXRTMbiuq71hgcyvzrxccEnJDRi50xwvDBMP8SlUJ1m3ru7W9mxtue3YmdxrBU4jsrSHOrckdju4uWDpeZFRjp6LpuZmbi3MYsNcN/NiMzntV3nUJiTyZsn1kzGZlKzJQc6JlxdXRN/43unpmpDbcRFabemEiZ6fD0KzPz/e9/n3nz5rFlyxZqa2upq6szP2prawc7RqGPuJqriNVa8aAF3/RfXzSNkijl7ncXrw9wMH6icpu6zDmWxzgfFzZyjbUD2Bxwwb9g9mXqc81C0uTjAXhhTZFp/j1kW7YX3ynAR5pMKgwvTG9Fhs/AvP55ZnKMv6u02HCi0ycAENW83zSqej0iAjS2uWg1Vn5EOY3XOK+ZHkiODsPSkwnbK2bq95MVo0TJQZkZ7/Rfi10JGSMzE39AZqbT5cHplinNh6Jfsnv37t28/PLLjBsXvCWMUMZSuxeAaksyqfbwAEczMJqTZkDzKqKqNwU6FP9QtQMAPWUSBftUyt+3LRuLFc7+P8iYBWGxnBk9nf9sWskbG0vJSlCD9XotZloqzTJTa6ebdqebcPthylNC4PFmZmIzzIF5/S0zHT8+md+fP50FeUnYvhoLhZ9B7T7S4zNoqmimrKGdcYfzXo0gSg1hlxxpxdrszY6lHuYrDKJSICwWOhrJtVZ2uy8TbydTTLrqs49MAAzPTLi9W3aspcPVfTCfYNKvzMzChQvZs2dkLwAcztjq8wGotAfZgske0Izheekt2wIciZ8wxExz3HiaO1xoGmQlRnY/R9Ng/vdgxoUsyEskLzmK1k63uZPpkDNmvJgG4Epiw21mW6+UmoIAb2YmJp0aQ8z0t8ykaRoXzc9WYtk7WLN2H2nGkLaynlqIRyh1JTu51fYi7+k/NEu8xKYf+Qs1zfzZZriLAfVz9Xh87Bnm79R4kxHhawC2YbdacNjUS7WUmg5NvzIzN954I7feeivl5eVMnz4du727i3vGjBmDEpzQP8KNeRFVjqzABjIIxI5dAF/CaHcxdDRBWIi/U6xSs2NKbNmAi4y4iMNmS9QLUha/e1eJIIfVYk4LPiRmmakSTdNIiLRT3dxJXWunOdNCGKaYmZl0anb2by9Tj3jL0XX5ZCQaXTdSZlI/72U/4Jh9n3CMDdCBiASYeyWkzezdfSSNg7INJLTtx6JNoNPtobqlw2yN79bJBKZnJp4mc6JwdJiNWlenmIAPQ7/EzPnnnw/A1VdfbR7TNA1d18UAPAyIaC4AoCY8+MVMVlYepXoiGVotTflriZl0YqBDGjo6msxR9Tvco4FCcpMjD/81wHlzRvPH93fi9uiMSYnCbj1CwtWbHjeWTcZHOkwxIwxzvGImxrfM1D/PTDd8MjPpeZKZMdn0Iuz7BB2NT93TKR97ARd95/tg68PP3PDNWOv2kRY7g9KGdkrr27vEjG8nE0Ckmh/kLTMBRIVZqW2RzMzh6JeYyc/PH+w4hEEkumU/AA0R2QGOZOBEOKyssownQ19Fw97VoS1mjKwM0aPY06SexLwmzcORGhPOyZNS+d+2iiP7ZaD7skmflQZSZgoCjJKEOzqdulaVORmUzEyikZlpqyMnUmV8JDODuaH8g6RLuabkDG7PndQ3IQPdO5riIwwx08asrHh13JuZie5eZoqn2RzEF+XwtmeLmDkU/RIzOTk5gx2HMFjoOnGtSsw0RwW/mAEoi54MTauw7v8i0KEMLYZfhpRJ5NcYO5l6IWYAfnr6JFwene8d14ulot7WbHcntNfLrJlgwpj+22BPAfb3ay9TjziiVFtwczm5FpWxk8F5mBvK97jU/4x38F2f8J01MzoCCuu6dzT5+KAAs8yUqDWZu566Zs2ImDkU/R6at3fvXm688UZOOeUUvvGNb3DTTTexd+/ewYxN6A9N5Tg8bbh1jfao4C8zAVSkq2xMWsWnUBHCRuBKY21D6uSDtmUfiXGp0fzryvlMz+xhcNeB2MMh3DivubKrPVv2Mw1vOlvUFGigxmKUIiId/drL1CNGdibdo15cy+rbDpojNuKoV28Md7arDiPvBus+4RUzzRXkRisLRkk3MePtZjI8M0ZmJo4WYsPUS3S0IWqaRMwckn6Jmffff58pU6awevVqZsyYwbRp01i1ahVTp05l+fLlgx2j0BeMtuxiPQVHWGiYOSNGT+cd9wI0dFjxu0CHM3QYZSY9eSKFNd627CN7ZvqFjwm4az+TlJmGNUZWBkc0VZ3qdzYoJSYvhm8moV35tlo63SP7xVPXTQ/bpuZ4oGuDdZ8IjzOzoRPtRnt2j5kZr5hRwsmi6SRa1XlRkpk5Iv0SM3fccQc333wzq1at4oEHHuAvf/kLq1at4sc//jG33377YMco9IUaJWby9fTDj7QPIiamxfCg63w1BHDb6+b26JDDKDPVR48127IzE4ZIzJgmYFlpEDQ0lqjLQWjL7hEjM+NoKDS7aEa0Cbi5Alzt6JqF/e54NI3+d/sZvpkcTQkXc3O2qwPajCF8RplJt9pp1lUGKA41biFaPDNHpF9iZvv27d0WTHq5+uqr2bYthMsAwUCtV8ykEREiA9BmZyewmyzecavFkyGZnfHpZMq3qPLgkdqyB0R0V0dToiybDA58NisPdGBej/h2NMVJe7a3xOSMSseFjZTosCN3Ch4Ko9Q0yqkEqZmZ8U7/tTrMjEyHy0OdHg1AtEcttfRmZpqlNfuQ9Os3k5KSwoYNGw46vmHDBlJTezEVURg6jMxMQQiJmbgIOxNSY3jQdR46Gmx/E8pCbCJw1S51GT2KvUYnU14v/TL9wqejSTIzQYI5Y2a0zyqDQRQzvrNm4qU92ytmmsMzAEiP74dfxouRmYlrVYbimpZO2jrd3WfMaMr71NjmpA4lZiKcyiMVbexnkszMoelXN9M111zDtddey759+zjmmGPQNI3PP/+c3//+99x6662DHaPQF2r3AUrMzAuRMhPA3NwEnq3IZFviKUytXQ6f/A4ufjbQYQ0eVYb5N2USBUYnU07SEJWYoFtmxuuZkczMMMd3+m+TEp5J/dzL1CPe9uzmCnJy1Q6gEb2fyWjLrrErL0vGQAZKJqrMjL1+L9FhNpo7XJQ2tDHW+zuNTjNPbWx3UqerEQuWdrXUUjwzR6ZfmZlf/OIX3H333fzf//0fixYt4oQTTuDhhx/m3nvv5a677ur1/Xz66aecddZZZGRkoGkay5Yt63Z7c3MzN9xwA5mZmURERDB58mQeffTR/oQ8MvB4TDETSmUmgHk5KgX7qH4+oMHOt6F0Q0BjGlQqfcRMdQ87mQabbgZgWTYZFPSwlylpMMtMEQlmJ83EMLXFuXQkZ2aMtuxyTQn/fnUyeUmbDoBWtokpsepnWlrfdnAnE9Dgk5mhVflpuspMImYORb/EjKZp3HzzzRQXF9PQ0EBDQwPFxcX86Ec/QtN63ybY0tLCzJkzefjhh3u8/eabb+a9997jv//9L9u3b+fmm2/mxhtv5PXXX+9P2KFPYwm42nFio0RPDhkDMMC8HPUk+35lHO6pagI1n4SQd8Y7MC+1KzOT28sZM/2iBwNwQ5sTt2eEt+IOZ3wzM81DYAAG0zeTaxhVyxtHcmZGlZkK3MlAP2fMeEnMg4zZoLs527YK8IqZA2bMoDZ0ezMzXnOwOWemU8TMoej3nBkvMTExxMT0b1/OkiVL+PWvf815553X4+1fffUVV1xxBYsXLyY3N5drr72WmTNnsnbt2oGEHLoY5t9SbRRurCG1ATkrMYKUmDCcbp2t478PmgV2vQuVOwId2uDguy3bnDHjhzJTSxXxRmZG15WgEYYpvnuZWpRnZlDLTGB6O9JdXUsRRyyGmNnVqd5IDSgzAzD9QgBO6PgYgJL6dh/PzCjztMZ2J/W6NzOjMmRiAD4yvRYzc+bMoa5O1e9mz57NnDlzDvkxWBx33HG88cYblJSUoOs6H3/8Mbt27eK0004btMcIKQzz735UyjKUykyappmlps/rEyD3OHVD8eoARjVI+HQyVUfk0dLpxtLTtuzBxMczY9cgxniylFLTMMXt6up8iR09NGUmgOTx6n7b1At5acMIHZzn8Zj/k1tb1IDJ9IFkZgCmnQ+ahezWbeRo5Sozc+BeJrobgLvKTGIAPhK9NgCfc845hIWFmdf7Uk7qLw899BDXXHMNmZmZ2Gw2LBYLjz/+OMcdd9whv6ajo4OOjg7z88bGxiGPc9jg9ct4DDETQmUmgLk5Cby7pZx1BXWqBp0fIhOBfTqZdjaoLElWYiRhtiH8/XlXGuhuaKslIcpBU4eLehEzw5OWStA9oFlxhSeZAw4HvcyUPAGAyEb1XNLu9FDf6iRhsB9nuNNcDu5OdM3KlmYlLDIGmpmJGQVjFsPejzjH8iVr66eC84CN2UBju4t6s8ykEgiyzuDI9FrM3HPPPeb1e++9dyhiOYiHHnqIlStX8sYbb5CTk8Onn37KD3/4Q9LT0znllFN6/Jr777+fX/7yl36Jb9hhZGb2uFXKMpQyMwDzclW6d93+OjwzJqu0YkUIDNAzO5kmsrlEtWJOG92LtQQDwWpX23lba0wT8P5aqGuRMtOwpLFrSmxdu+o0GrS9TL4YmRlLzW6SIu3UtDopbWgbeWLGKDG5YzLobLNgs2ikxAxCSW/6hUrMWL/gjbpLQT+4m0kMwP2jX56ZMWPGUFNTc9Dx+vp6xozpxaK7XtDW1sbPfvYzHnjgAc466yxmzJjBDTfcwEUXXcSf/vSnQ37dnXfeaZqSGxoaKCoqGpR4goKaPQDs9RhiJsQyM1MzYgm3W6hvdVLiMNpIK7cps0cwYy6YnMwWr5jJGGIxA72fAtxcBTvfA4/U6wNGk+GXiekamDeoe5m8JI5RfrTOJqbEKfPviPTNGGKmNTITgFGx4YPzs558Jh5bOGMtZaQ3bjQzL90yM21Oc2jeQQbgDtfILPv1gn6JmYKCAtzug5/YOjo6KC4uHnBQAE6nE6fTicXSPUSr1YrH4znk14WFhREbG9vtY0TgcUNdAQAFntDzzADYrRZmZsYDsLIpRT3pGpmFoMZrYk6dZGZmpg91Zga6mYAP2579xo3w3EXwwmXQ2Tr0cQkH09g1/XdIBuZ5sYVBQi4AM8ON7dmNI1DMGG3Z9WHKy9KvnUw9ERYDE5YAcKX2ljpmDTOn/0L3OTO01oKum5kZj65Kf8LB9Glo3htvvGFef//994mL63rCdbvdfPjhh+Tl5fX6/pqbm9mzZ4/5eX5+Phs2bCAxMZHs7GwWLVrEbbfdRkREBDk5OaxYsYJ///vfPPDAA30Je2TQ0QgeVSKoJAGrRcNuHXpfk7+Zl5vAqvxaVhW3863EMSobVbm1WzdA0GG0ZTfHjGN/rdrFMm20H0R4tG9mZirQw7JJtxPyV6jrO9+Gp89Cv/h5/rm+kezEKE6flobgB8y9TBnmXqakoSr9JI2H2n1MspYDGZTVj8D2bGNgXpXFmDEzkOm/B2CZeRFse41vWNapAzGjzOm/YLRmY4gZdwc4W4m0dzUDNHe4Qi7rPhj0Scyce+65gOosueKKK7rdZrfbyc3N5c9//nOv72/t2rWceOKJ5ue33HILAFdccQVPPfUUzz//PHfeeSeXXnoptbW15OTk8Jvf/Ibvf//7fQl7ZNDRDIBuceDERrTd6heTtr9R82b2sq6wDrKmKDFTsQ3GnhTo0PpHRzM0qJT2NlcGsJPMhAiz7DOkmIPzKnymAB+QmSndAM5WcMSA1QYla3H+4xSeqbqJYi2d168/duj9PYLPXqYMMzMz6J1MXpLHw+73ydZLgDmUNYzAzIxRZio1BualRA9iC/zYk2nSYogxlkj6djKBysy0EobHYsficUJrLZb4KKIcVlo63bR0uAbHvxNi9EnMeMs7eXl5rFmzhuTk5AE9+OLFiw9b/0tLS+PJJ58c0GOMGDqVmHE7oqCVkJox48ucbJWOza9uoWXGRKJA+WaCFe+wvOhRfF2txKdfSkzgk5mpIjHdKDMdaAAu/FxdjlkEJ98Dz5yPo76AVx338N3O27jt5RjeuOHY/i/gE3qHz/TfmvIhGpjnxehoGuVUfsPSEZmZ6T4wLzlmEH/WNgcbYk/k+Aaj0hHTPbupZj1puMIScLRVqlJ6fBZRYTZaOt1iAj4E/XoGys/PH7CQEQaZTjVozW1TU2MjHKH54hIXaWfCKGWO26Vnq4MVWwMY0QAxzb8T2VKqxgj4LdPRGwNwwRfqMudYSJkA3/2AqphJJGlNPOn4AzVlhTz2yV7/xDuS8ZkUW1ynxEXGIJY+umGImfiWfICRl5nxuKFBeT/3OpMASB7MzAyQn7G065Po7mKm0RhcqXt9ND2YgIWD6XVm5qGHHuLaa68lPDychx566LDn3nTTTQMOTOgjHSpl6bKp2mqkvV87RIOCuTmJ7KpoZmXLKGaDEgQeN1iCMBtltmVPZss2P7Vle+lmAO5h2aTHDftXquu5x6rLmFE8lvt/nLvxGqZbCviT/TG+91E8p01LY8Ko/k0CF46ArvsYgDMorFGzSXISh2jdhdGeHdZSQjgdlDdY8Hh0LIPdOTVcaSpT/kOLjT2t0UDL4JaZAD1zIUVbU8iyVHXLzOi6TmO7EitaZBLUclB7tqw06Jlev+L95S9/4dJLLyU8PJy//OUvhzxP0zQRM4HAKDM5reoJLjyEDWLzchJ4bvV+PiyL4Ae2CHC1qYGBxpNwUGEM/WtLGEe+scbA/2WmCnOlQbfMTPkm6GyCsDgYNc08vLcRfuy8nvcjfs4JbOZiz/+47aV4XvnBMdik3DT4dDSCU/1tEJPOfmM45pBtVY9MUt01bXWMsZSzzZ1DbWvnoGcnhi1GiYm4TCqblXAY7O89IyGSP7ou5LaI18mafJZ5vLXTbe5Hs0SpuVre9m3vFGBZadAzvX7myc/PJykpybx+qI99+/YNWbDCYTAMwJ1W9QQXYQ/dF5V5uSr9uqm0BU/KJHUwGEtNHjcUqz1ju60TARgdHzF0XogD8RqAW2tIiFBPlHWtnV0+Nm+JKfuoblmvotpW9uqjKZxzBwB32p+luWQb//oi3z9xjzS8fpnweJp1B9XGksnsoRIzmmaWmmZFVAEjbNaMIWb0+Byzc2xQPTOopZVveI7lm9qD3d6ENbarzKjNomGNUq+33syMt8zU3C6ZmZ4I3Ve8kYaRmemweMVM6GZmshMjSY4Oo9Pt4esOoxMgGE3AldugowEcMaxpU9+HX1qyvUQmqVk9uocEVInL6dZp6TTe+RUaYsZbYkKlwb2eDdtR18LYkwnHyYP2R3jof9vM7JIwiPiYf/fXqDk/CZF2YsPtQ/eYSeoFdlqY2gdV2jCCTMDGjJmO6EwzSzLYCz1HG36n6uZO2p1dmRbvote4CLsqM4HpmYkSz8xh6ZeYueCCC/jd73530PE//vGPfOtb3xpwUEI/MMWM+icJ5TkEmqbxo1PUk+3bFeofvr1kcyBD6h9eP0rWfDaVKRHgl8m/XixWiFRG/oj2Khw29XRQ19KpFu0VfqnOy+nahVbV3EGHy4NFU6lyznkEPSKB6ZYCvs9L/OvtFbD5ZXj3dnj8FHjr5uCf0BxofMy/+2vV30l20hD5ZbwY2YLxFiWkRtSsGSMz0xyRAShh4f3fGCziIuxEGs/RvgbrxjYlVGIj7BBplJkOyMxkFL8Da5+U/6sD6NdvaMWKFSxduvSg46effjqffvrpgIMS+oFRZmrXlJgJ1dZsL5cdlcMTV8xjvz0XgMo961lTUBvYoPrK/q/UZfbRXTuZMv08s8UoJ2hbXyPR1wRcuQ3a68EeBekzzNOLatWLWnpchGrHjk1HO/NBAG6wvc59+RfDK9+FVY9B8RpY+y/lvRH6j8/030IjM5MzlBvVwfy7yHSrYX0jqqPJGJhXa1fZ0uQhmOejaZrZjebb+u7tZIoNt0GE1zPTJWYytSrO2PVzeOvHsOOtQY8rmOmXmGlubsbhOPgXbLfbR9aW6uGEkZlpNcRMKJeZvJw8eRS/uFplAjP1Cq7+xye8sbE0wFH1El2HQiVm2tIXmOUZv2ZmAI65QV2u/ic54eqFsq61s6vElL1QLaU0KK5T52Qm+LQFTz0XZn8HAKdupSBsEiy4DrIWqts3vzSk30LIY+5lyqCw1hAzQ+WX8WJkZpI79qPhoXQEipkKY/rvUBmfvWLGK1ChyzPTU2YmKszGJdYP0TAyMu/8FNrl9dZLv8TMtGnTeOGFFw46/vzzzzNlypQBByX0A6+YQf2DRIZwmcmXnOwc9MgULJpOnl7MX5bvCnRIvaN+v3qRstjYqo1H1yEtNtz/kz0nnA4Zs8HZwnfcrwOGmCkwhuXlHNvt9KJar5g54MX0rIfY963lTOt4ghMb72b33F/A0YZQ2vKqKlsJ/cMnM+P1zGQPdWYmIRcsNuyedtKpHTllJrcLGlQ2qlhPASB5iP4nZ2XFA/D25q43YF2ZGXtXZqZVLXWOsXu40PqJOmaPUs8fH/16SGILRvolZn7xi19w3333ccUVV/D000/z9NNPc/nll/Ob3/yGX/ziF4Mdo9AbjDJTC+ofbyRkZrxoo5SAnmgpoqCmpZuhbtjiLTGlz2JjheqYCMhaAE2DxXcCcGrrmyTRQF1zR5dfJve4bqd7y0xZiQcMbLNYGTN1ASdOzUbX4aGP9sD4UyEsVu0VKlo55N9KyOLdyxQ7mkLDM5ObPMSeGatdbdAGxljKRs4U4KZS0N1gdbDfqcz4gz1jxsuF8zLRNPhiTw0FRma2oSfPjNGaPan2I5K1RmqtyXDhv9Vtq/8BJeuGJL5go19i5uyzz2bZsmXs2bOHH/7wh9x6660UFxfzwQcfmPubBD9jZGaaPYZnZoRkZgAYpZYkzrIXo+uwp7I5wAH1Aq+YyTmarf7clN0T40+F0XMJ09u5zvYWWs1uaK0GWzhkzOl2apFRZso6MDNjcNPJqjzx1qZS9tQ5wTtDY/NLuNwedpY34fGIcbFPGAZgZ2QaJUYn2ZB7ZsD0zYzVSilrbA+ONwkDxZwxk0V1s8qSDIVnBlR2c9EElf15bo163K4yk49npqMR3E4mFKly7fKIJTD+FJh+IaDDmz9SGaURTr8t2kuXLuWLL76gpaWF6upqPvroIxYtWjSYsQl9wVhn0KSPvMwMqSozM8Oh0rW7K5sCGU3v8HYy+Zh/p2f6sS3bF5/szGXW5WSXva+OZ84HW/cncm9bdtYhXkynZMRy2tRRKjvz4R6Ydj4Azs2vcfZfP+G0Bz/l75/KLKpe4+qEFjXrpVRPwKOr/22/lCOTxgEw2V6GrsP+2tYjfEEIYLRlE59NtbHQcyiHBV6yQK1keXltMZ0uzwFlpnjAmLpc8DlJNetw6lbesp2ijp32WwiPh/LNynA/wum3mKmvr+fxxx/nZz/7GbW1yqC0fv16SkpKBi04oQ8YZaYmzwgUM0aZKc9TAMDuimGemWmtNXcytabNY2+Vitfv5l9fxp1CRex0IrROTig3lrseUGJye3Sz3HBQmckHb3bmzU2lfKlPpcmagL2jjtRqlY16Z3PZEHwDIUpdgbq0R5Lfov63sxMj0TQ/rBYwMjOT7WrWzIiYIeTNzMRnm8MJh1LMnDQplVGxYdS0dPK/beXdDcAWqyFogE//BMD7nnkUOY3niegUOPU+df3j38DG55U/zfsxwspP/RIzmzZtYsKECfz+97/nj3/8I/X19QC89tpr3HnnnYMZn9BbOlU2osErZkZSmSllMqAR7aonmQZ2DXcx483KJE9kW70djw6pMWGkxoYHLiZNY9fkGwGwYph1DzD/ljW04fLoOKwWRsUcOtapGXGcOkVlZy55Yh2vdMwH4IbkrwHYUtpAg+8OKOHQVBjzk1KnsN/Iig3Z5N8DMcRMjq7eoI48MWNkZoYwC2azWrhoXhYAz67a3zVnJtzYNOQtNRnb659xn9J9ncHsy9T/qbMVXrsOXr6q6+OfJ0PphiGLfbjRLzFzyy23cOWVV7J7927Cw7ue1JYsWSJzZgKFkZmpd6t/vFCfM9MNR6RpVpxo2T/8y0zmfJmj2BJov4wPztxFrPWoFzCsDsic1+12r/l3dELEEZcOerMzANuSTgVgXvtXTE2xo+vw1b6aQYw8hCnfoi7TpvlvxoyXZFVmindVE0WbaVINaXxXGZiZmaFdL3Lh/Cw0Db7cW8POCvXcFRdhjEPwmoABZ8I4vvJM6T4BWNPgnEdg/GlquKX3Iz4b0EdU+alfYmbNmjVcd911Bx0fPXo05eXlAw5K6AeGZ6bOqcTMSGnNNjFKTZO0IvbXttLWOYzNit2G5ak5EVOHgZiJjwrjj86LcGGBcd8Ae/dSUlFPM2YOwbTRcTxxxTweuWQOv7vpu+rJtbOZy5NUee2rvdWD/w2EIhWGmBnlI2b8lZmJSIAoZVAdo5Wxb0SIGeWZaYkYTadbZSiHesFmZkIkiw0jcK2xCyrWK2YiusRM5+yrAI02Z9cySgAS8/Bc/AJc9XbXxwVPqdu2vALNlUMa/3ChX2ImPDy8x+F4O3fuJCUlZcBBCX3E7VKbo4F6t3oXMaI8MwCpqqNpur0EXcf0oQw7Olu7Ur85R7OhSLVdzhgGYiYh0sEqfTJLPH+F8/950O3Fh5oxcwhOnjyKpTPSsVgtphF4cecKQL0LFXqBmZmZ7r9VBr4YpaYxWmnoZ2bcTrMNvsqmBubFhNn8kuW+2DACezH3bnkzM/ZIrLMvMW9v6ezKzqwpqGX6ve/zwP92dt1B5lxl4Hd3qincI4B+iZlzzjmHX/3qVzidqu6taRr79+/njjvu4Pzzzx/UAIVe0Nn1wl3jVGJmRJWZwGzPPtGynvFa8fAtNZWuB48TYtKps6ezt0q9QMzJSQhwYGp5IcDuziQ6LQdnX7o6mY6cmTmI6WpSc2rFp8RqLeyubKaycQRNle0PrbXm9F89dYrZTeS3MhOYk4DHWkqpbOoI7SWHjSWge8AaRoVHvbkYSr+ML14jsJfYCMMzE2+InBkXEhadgM0o7/r+HpZ9XUJLp5uHPtrDf1cWdt3pwu+ryzVPqK64EKdfYuZPf/oTVVVVpKam0tbWxqJFixg3bhwxMTH85je/GewYhSPhFTMWO41O9SsdUQZggHGnQOoU4jwNvOj4FY17Vwc6op4p7PLLfF1cD8CYlCgSo4a2Lt8bYsPteK0w9a0HP/kdacbMYRk1FVImo7k7+W6iMrWKb+YIlBvm34RcKjsdtDs9WC0ao3tR5hs0jO3Zk23KPhDSJuD6InUZn0V1y9DOmDkQXyMw+GRmjvoBnPkXOO23aJrW4+bsdYV15vV73tjKil2qlZ8p50BMOrRU4tnyKhuK6mlqD13jfb/ETGxsLJ9//jmvvPIKv/vd77jhhht45513WLFiBVFRfkyBCgrDL0NYNO1OVecdcWUmRyRc+TZVcdNJ0Jq5aOsPIX8YmtFNv8wxrC1QT0JzswOflQGwWDTTeFjXQ7dR1/TffmYGjAF6J0bsBeCLPeKbOSw9+GUy4sPVgk9/4W3PtqryS0FNKIsZn06mpqGfMXMgFy3IJsJuJSsxoiuzHpEA864Gh3pd9W7O9nY0NbQ5TdPwN6aMwu3Ruf6Z9ewob1RTnOd/F4C9b/2Jcx/5nJtf2Oi378ff9Pm/wuVyYbPZ2LJlCyeddBI/+clP+OlPf8opp5wyFPEJvcHoZNIdUaZpbcSJGYDIRPYueYYv3FMJ19vgvxfAzncDHVUXHjcUGRmj7KPMd1TzcoeHmAFIMDJEdQdkZjpcbiqaVFkoq7+ZgdRJAGRram6J+GaOgI9fptAQEbn+9MsAjJ4DVgeZ7iLmaLtC2zfj5xkzBzI6PoJ3fnQ8L1539CHPiQpTz+vezMzX++vQdchNiuSRS+awMC+R5g4XVz+5hvX767i9YC4dup3xrt3M0Xbz0Y4KqgyhFmr0WczYbDZycnJwu4dxt8hIw5gx47FHm4dGXJnJYFxmGlc7b+N/7rng7oDnL4WiNYEOS1G+Wf2uwmJxJk9mo1FmmjsM/DJeEiKVmDmwzFRS14ZuTJ/td0nMaJ+PbS3CZtEormszF1cKPeCdMTNqmumXGfIFkwcSlQwzLgTgGtvbod3R1G2Vgf8zMwB5yVGkxx36zUKUmZlRYsbM7uYk4rBZ+PtlcxmTHEVpQzvn/e1LXtjWxjKPmhf1o+gP8ejw3pbQHFrZr3zlz3/+c+68805z8q8QYIzMjMeu3rVpGoTZ/JiKHkYkR4cRFRXND5w/pjHnVLU0buUjgQ5Lsft/6jLnWLZXtNDu9BAXYWdMcvThv86PeE3AtS3dy0xFPubffk+fNcSMpaWCozOVIJJS0yFwO6HK6E4ZNdX/bdm+HK2GKZ5mWUt7+W7/P76/MDMzOT4D8wLvZfMl+gDPzNpC9Rrsze7GRzp48qr55v/x0WOSmHehGmR7vOtL0qjhzU0iZkweeughPvvsMzIyMpg4cSJz5szp9iH4GcMz47IpMRNht/pn3PkwZXxqNG6srMm9Vh3Y/hY0VwU2KIAdb6nLSUvNEtOc7PgjDqDzJ/GRPZeZvBmUfpl/vYTHQWQyAKemK3EkpaZDUL1btdU6YiA+h0IzMxMAT2LqJJqyT8Ki6Syue8n/j+8vfMpMVQEoM/WGKEdXZsbp9rChqB6AeT7Z3ZykKN750fG88oNjePaahYydfhTkHIdFd/Md2wesKailIgQ7CW39+aJzzz0XTdPQddl+Oywwykwum3qhGZF+GR8mjIphVX4ta9qzOHn0XLWjZMN/4bibAxdUfRGUbQTNAhOXsO519cQ5nEpM0JWZObDMdKQFk70mcQy0VrMwvh5I5Mu9Nei6PqLFd4+Y5t+pYLGYnpmAZGYA23E/gmc/4izPxzRWlxObnBaQOIYMt8ucMaMMwNuBYShmfMpM20obaXd6iI+0Mzale3Y3PS6ie7lqwTVQ+Dnnh63mTy0X8famMq4+Ls+foQ85fRIzra2t3HbbbSxbtgyn08nJJ5/M//3f/5GcnDxU8Qm9wSgzOa3qXduImzFzAONHqX/s3RVNqhOgZB2sewqO+RFYAlR+2/mOusw6CqKSWVeougrm5iQe5ov8T1dm5sAyU++n/x6WxDFQvJoxlgrC7clUN3ewu7KZCaNiBna/oYa3LTttGg1tTuqN34ffPTMGEeMXsV0by2T2Uv/l34k9+56AxDFkNJWqkrTVgR6dSnXzBgBShpmYifYxAK8t7OqGPGJ2d9wpYLGT7i4jRyvnrU3xISdm+vTMfs899/DUU0+xdOlSLr74Yj744AN+8IMfDFVsQm8xykydViMzM0LNv17Gp6oXxl2VTTD1PAiLU9uH930cuKB8Skyl9W2UNbRjtWjMzAr85F9fvObegzIz3jLTYGRmAFt9PvNzlZD7UnwzB+PTlr3f8MskR4eZ78z9jqbxvzg1+DBxy1PgbAtMHEOFj/m3udNDh8tYZTDMPDNdc2bcrC1Qfpm5vemGDIuG7KMAWGTZxPr99ZTUh9bvsE9i5tVXX+WJJ57gH//4B3/96195++23WbZsmXQ2BRpjaF6HMbVVykwqM1Nc10YrDph5kbph3ZOBCai1Fgq+UNcnnWH6ZaakxxLpCNCL0yHwlpkKalq77X/xGoAHnJlJGqsua/M5emwSIL6ZHvFty64NbInJS0XmaRTryYR11sLG5wMay6Bj+mWyzLbsSId12P1/+paZvJmZeb3N7o47GYCzo1UJ7e1NpYMfYADpk5gpKiri+OOPNz9fsGABNpuN0tLQ+qEEHR3KM9OuSWYGICk6jKQoh9rRVNkCc69SN+x4B5oCsAh11/sqhZ06FRLHmGJmuPllAGZnJxDpsLKnspm/LN8FqJS2dwHewDMzRmq7Zi/HjFXl6ZX7arovzhvpNFdCSyWgQepk/2/LPgQ5KXE84VqiPvnqYfB4AhrPoNJtxkxg2rJ7Q0y4EjM7yhupaurAbtWYkdnL7O5YJWZmujZhx8VbIdbV1Ccx43a7cTi6p91sNhsuVwjv6wgGjMxMmyaZGS/jUlV2ZldFk9qonXWUEhRf/8f/wfiUmADW7x++YmZUbDj3nzcdgIc/3sPHOytNv0xchL1rzHp/McpMNJczPcVGhN1KY7srtCfL9hWvXyZpLDiizDJTdoAzM7nJUbzoXkyzFgU1e7pGDYQCPU7/HV4lJujqZtpSohY9Tx8d13uPZNp0iB6F3d3GfMtONhU3mMbyUKBPYkbXda688krOO+8886O9vZ3vf//73Y4JfsbwzLRq4YCIGcA0lO7yLpycZ2Rn1j2tJvH6i85W2POhuj75TFo7XWwtVU9Ew1HMAJwzazTfOUotuLv5hQ2szle1+X4tmDyQiASIUGlxa32BWbYqqw+9VtF+4+OXAYZNmSkvOYoWInjdY2TnQ1LM5AzrzMyBnql5uX1oINA0GHsSABcnqnlBoZSd6ZOYueKKK0hNTSUuLs78+M53vkNGRka3Y4KfMbqZWjEyMyO8zARdvpndFcYSzinnQHg8NBR1iQt/sO8TcLVBXBakzWBjUQNuj056XDgZ8X5cGNhHfnHmFKaPjqO+1cl9b20DBjhjxhdvdqZ2H+nGz6A0xMyIA8L0yygxs98cmBfYvXfZiZFoGnzqnKwOFH4Z0HgGlZ5mzPhpY3ZfiD5AzPT5DdE4tXboOE11U4aSmOmTu+nJJwNkoBQOjzFnpkVXmZmR3poNMN7IzOz2ZmbsETDrUjUNeM3jMOFU/wSy4211OWkpaJpZYpozTLMyXsJsVv526RzOeOgzmtpVGXnAfhkviWOgZC3U7iUjLheA0gYRMyZmZmY67U43ZcaAs0B7ZsLtVjLiIlhTP1EdqNoOLTUQlRTQuAbMgTNmmpUhfXhmZro/t/dZzIw5EdBIaNpJuqWe7WWwt6r5oDk1wcjInHkfahiZmSakzOTFW2Yqqm2jtdPFroomnnGfhAcNdr8Pm14c+iDcrq75MoZfZl3h8NqUfTiyEiP587dmdn0+0E4mL76ZmTgpM3XD1QHVynhN2jQ+2VmFrkN6XHj/d2INInnJUdQSS0O00ZXm3QIfzDSVgccFFjtEp5memZRh6JnxzczkJUf1XXBFJUHGbACuHLUPCJ3RCCJmQgHDM9PkVn/YkVJmIjHKQZLx5L/oj59w6l8+5a7POnjI9U0APG/8qGv3zVBRtBLaalV5K/sYPB7dzMwMp03Zh+PUqWncsWQS41OjOWnyqMG5U5/27PR4JcAlM2NQtUO9sIbHQ+xoln2tMgZnz8wYFlOS85JVqWtfpCFyQ6HUZM6YyQSLJWg8M/P6m901WrQXWzcBmC3ewY6ImVDA6GZq9BiZGREzAExOjwWgqqkDh83CiRNT+DTtKj53T8XiaoUXrzCF4JDgLTFNXAJWG/uqm6lvdRJut5ixBQPfXzSW5bcsYvRgeXy8mZmaveZ9ljVIZgboNl+mod3FRzsqATh39ugABtVFriFm1mte38wXAYxmkGgoUpfxyvRePYw9M93ETH/fEBm+mTFNa7DgMTdvBzvDayKQ0Hc8bnAqg2CDOwxoFc+MwV1LJ7NsQwmzsxI4fnwyUWE21u+v49q/3cA7ljtJrdoOb98K5z6qnP6DSXtDVynLKDEt36ZemObmJGC3juD3EV4x01RKRqSaL1Na3yY7mgDK1btlRk3j3c1ldLo9TEqLGTbiNy9Z+XY+bhvHd0HF294I4cMjvn7hY/4FzMzMcFtlABDlsGLRwKMPYBXK6HkQFoe9o44Zln1sqB9HeUM7aXHhgxusnxnBz6ghgpGVAagzykzimVFMTo/lziWTOX1amvmOZk52Ark5udzYeSMeLLDxOfj6v4P/4J/9GVqrIWk8TDgdgNc3qJLBmTMyBv/xgonIRFVGAdI9aohha6ebxrYRPq/K1QFbXlXXs49imfH3cs6s4ZGVAchLVkbRdXWR6Am5oHugaHVggxoo9YXqMj6H1k4XrZ1qdMNwzMzYrBbuXDKZm04ax9iUfna3WW0wZhEA58epUvu6ECg1iZgJdrxlEs1Ks0v9OiMc8ms9HNecMIZV+mT+j2+rA+/8BJbfDdvfgqaKgT9AbT6sfFRdP/XXYLWzu6KJHeVN2K0aS6aF2Mbh/mBkZ8KbCswVCiPeN7P1NTX5NyadkrSTWLlPzfc5Z9bwEb+ZCRFYLRptTjdt6QvVwWAvNXUbmKdKTOF2C1HDtFx/zQljuOXUiQPLYhq+mUUWlQkMBTEjZaZgx+hkIiyaNqcaLy6ZmcNzyuRR5CZF8mDNGZyXuZ+s6s/hi792nRCfDemzIHUypEyElEnqxbe9ARpKVBtnYynEZ8HEMw4uUX1wD7g7YcximHAaAG9sVCs/Fk1IMTdTj2gSx0DpeqOjaQ51rU7KGtqGTTnF7+h6lwCe/13e2FwFwMK8xGE1j8hutZCVEEFBTSulcXMYxwvB39Hks5epysf8G9IlT2O1QVbLVmJpZl1hbYADGjgBfQv/6aefctZZZ5GRoZz6y5YtO+ic7du3c/bZZxMXF0dMTAxHHXUU+/fv93+wwxVjxgyOGNqcKj0qnpnDY7VofPf4MehYuKLlJtxn/w3mXql2J6GpJ7ftb8CK38PLV8Ojx8Bv0uDPE+Hxk+DFy+C92+H5S+DNH6kWbC8FX8C210GzwGm/BU1D13Ve36DEzNnDqGQQUHzaszPMwXkj2ARctBrKNoA1DOZeZZYkvzlMjL++eDua3m8yfocl64J3i7bHDQ3F6vow38s0qMRnQcokNDz83PYMW0sbaOsM7oXRARUzLS0tzJw5k4cffrjH2/fu3ctxxx3HpEmT+OSTT9i4cSO/+MUvCA8PbqPSoOItMzmizD/G4bbpdThywZxMEiLt7Ktz8Z7tJDjrr/DDL+GO/XD560qIzL4MMhdAmJEt0CwQkwGZ85UPRrPA+qfh+YtVhszjgfd/ps6dcwWMmgrAxuIG9te2EmG3csrk1AB9x8MMb3t2zV4yvO3ZI3kK8KrH1OWMb7G90cGO8iYcVgtLpqcHNq4e8HZW/XFtJ61hqSoLWbIuwFH1k6ZyY8aMDWLSR46YAfjGfeialQttK7jZ8jwbi+sDHdGACOir3pIlS1iyZMkhb7/rrrs444wz+MMf/mAeGzNmjD9CCx58y0xNSsxImenIRDisXHZ0Lg99uJt/fLqXM6anqbRyeKwqD41Z3HWyrkNbnRI1Vp9/mR1vw8vfhd3/o/Zv3+DzsBM4u3KDOu/Eu8zTvO+yvzFllAhNL2ZmJp/03BHent1QorJ5AAu/z7L16u/lpEmpxEUMcLHnEHDOrNHsrWzmoY/28GHrWM6yVqp5M7nHBTq0vtNtxozV9MykxIyAUvCEU9HO+iu8cQPX297gsy/Gw5i7Ax1Vvxm2TlGPx8Pbb7/NhAkTOO2000hNTWXhwoU9lqJ86ejooLGxsdtHSOPtZnJEm2UmMQD3jsuPzsFhs7CxuMGc59EjmqY6cAwh4/HoFNa08N/6adyT8Dtq9BgSG7ZxdqV6d1095waITgHA7dHN/SfDycgZcLxiprGYTDWseeRmZtY8rja65xyHJ3WaWZIcLrNleuLmb0zg4gVZrPJMAqB+xyeBDai/HKIte0RkZgDmXMbasTcCcPzeP8PmlwMcUP8Ztq96lZWVNDc387vf/Y7TTz+d//3vf3zzm9/kvPPOY8WKFYf8uvvvv7/b0susrCw/Rh0AOgzPTFiMWWYSz0zvSI4O46J56u/j+/9dxwtrevZi1bZ08sTn+fzkpY2c/fDnTL3nfRb98RN+vmwLTxelcH7nvZRaVDmg0JPKZVvm0tyhfDSr9tVQ1dRBXISd48en+OcbCwYikyBMLaXNsahx6iMtM9Pp8nD786to+vJxAN6IOJsHlu+ivLGd2HAbJ04avn8vmqZx3znTsOapbIyjdC3bimsCHFXfaGh1surrr9UnI1XMAPZFt/KkSzUq6K9937+LeAeRYStmPB7VmXPOOedw8803M2vWLO644w7OPPNMHnvssUN+3Z133klDQ4P5UVRU5K+QA4PhmdHtkXS4pJupr/zsjMksnZGO061z+yub+dWb23C51c+xpcPFQx/u5oQ/fMx9b23j5XXFbCpuoM3pxmGzMCsrnjuWTOLpn1xMxq2f03LMT/lp2F1sr+rg9pc3dTP+njE9HYdt2P67+R9Ng8Q8ADLcqqxS3tCOx6MHMiq/8tGOSvTNLxHjaaRYT+bHGzJ4+OM9ACydkU6YbXj/H9usFu68/BwatVgitQ4e/M9LQfX7e2D5TvL3bAfgw7Iw6lo6R6SYmTI6jj9oV/C6+xg0jxOW/VDNPAoyhm0BPzk5GZvNxpQpU7odnzx5Mp9//vkhvy4sLIywsJHzh+gtM7lsXQOUZJ1B74lwWHn44tlMHBXDA8t38a8v8tlT1cyJE1N45OM95mjzKemxnDp1FBNHxTAhLYacxEhs3ab4RhF16l38dGId3/7HV7y9uYypK2J5d4sqMZ09U0pMB5E4Bso2kNBejKaNpdPtobqlg9SYkWHw/3BbKVdb3wegcMylXBybS3lDO51uD99fNDbA0fWOcIcd67jjYPc75DZvYHt5I1Mz4gIdVq/4Ym8N39BUC/zb++3c+udPcLuVGEsehksmhwq71cKMzERuy7+Ob0TuIbK5XA1vnHVxoEPrE8NWzDgcDubPn8/Ond2XAe7atYucnJwARTUMMQzATh8xEz7M39ENNzRN46aTxzMuNZpbXtzAp7uq+HSXepLLSYrk1lMncub0dCyWI8+dmJuTwC/OnMLdr2/lD++pv9202HAW5PVz9HgoY/hmrPX5pMZMoaKxg7L69hEhZtyVu7h02/eZbNmP2xrBsRfezLERwbF89EDsecfC7ndYYNnBV3trgkLMVDd3sKeymdEOVeK0JOZQX+00bx+O03+Hknm5CazKr+WTuHM5o/IfsPIRmPntwV/zMoQENO/d3NzMhg0b2LBhAwD5+fls2LDBnCNz22238cILL/DPf/6TPXv28PDDD/Pmm2/ywx/+MIBRDzOMOTNOq9qZEm639OpFVziYM6an8/L3jyEzIYKUmDDuO3caH9yyiLNnZvTpZ3rZUTmc62P2PXNGOlb5nRyMT3t2epy3oynETcAeN3zxV7S/H8csdtJMBJzzCASpkAEg5xgAFlh2smrvYYz0w4hV+2rR8JBpUT6f3129lPvOmUp8pJ2UmLDBW6oaJMw1NnD/veUEsEdC+WYoOHQFZDgS0MzM2rVrOfHEE83Pb7nlFgCuuOIKnnrqKb75zW/y2GOPcf/993PTTTcxceJEXnnlFY47LghbAIcKwzPTYVFiRlp/B8a00XF88pPFWDSt36JQ0zR+e950dlc2s6uiiW/NC3ETen/xac/OSA1nQ1GID86r3QevfA9K1mEBVrhn8MnEn3PPjNMCHdnASJuB2x5NrLOZ+oINuD0Lh714X5VfQyr12FEzZmxxo7nsaBvfmpeF26OPuCaKOdlKzGyssdB+1EWEb3gSvnoE8o4PcGS9J6CvfIsXL0bXD28Yu/rqq7n66qv9FFEQYpSZ2i3qnYSYfweObRA2Wkc6bLzyg2Ooa+00sw7CAXjFTEMRWWPV321IZ2ZevU4NlwuL4w9czt8ajuKRGTMCHdXAsdqw5BwDe/7HDOdmtpY2MCMzPtBRHZaV+2rINPwyxI42xy6MNBHjJT7SwbjUaPZUNrMu/dscu+FJ2PUe1OztyqAOc6S9ItgxDMDtmsrMiPl3+BBut4qQORxRKRAeB+hMsamR8iGbmancAcWrwWKj8ML3+FvD0TisVhZNHL7t131Byz0WgIWW7Xy1d3i3aNc0d7CrorlLzBht2SOdeUap6bPaeDXhHJ99YUGAiJlgx5gz06Ypw5pkZoSgQdMg+2gAJrRtBEJ4c/aG/6rL8afxbrH6Xz1qbBLRYSFSFs5V5YgFlh2s3FsV4GAOz+p8tVTxqBgRM754fTOvri/m/Zjz1MENz0BrcCyhFDET7BiemRYkMyMEIcaLYEb9WgDKQjEz43bBxhfU9dmX8sG2CgC+EUp7utJn4rZHEa+10FCwAacxq2k4snJfDUdZtvGtztfUgawFgQ1omHD8+BRiwmxUNnVw3RdRbNdzwNlK4fLgyM6ImAl2jDJTs67aWSNFzAjBhLHPJ6ZiDVbcVDa1m0MLQ4Y9H0BLJUQmU52+iHX76wA4ZcqoAAc2iFhtWIws20z3FjYVNwQ4oENTumcjf7c/gE13wdTzYPblgQ5pWJAWF86nPz2Re8+awuT0OB53qr2JjvWP89bXhQGO7siImAl2DANwM0rMSJlJCCrSpkN4HJbOJmbaCvHoUNEUfNNHD4u3xDTjIj7aXYeuw7TRsSHnp9KM1QZHWbazct/w9M3UVRZzd8M9xGmtODPmw7mPgkVeBr0kRDm48tg83v3R8Vz9/VtpsCaSrtVS8L+/HbFZJ9DIbzGY8XjAqcpMzR7DMyOZGSGYsFghR70InhKxCwixhZMtNbDzPXV99qUsN0tMaQEMaogwfo8LLDtYuWcY+mY6W9Geu5gsSxUlWhr2S18Ae+gPaOwvU7NTcSz+CQDfbf0X69etDHBEh0fETDBjCBmABrdkZoQgJbfrHT2EhpjJr27hor9/xbJ//wU8TkifRXviJD7brV7kT5kSQn4ZLxmz8NgiSdCaadi/kQ6XO9ARdaHrsOz7xNdtok6P5qWJf4GopEBHNeyJOPYH7IlZQITWSer7PwDn8PW0iZgJZowSE5qFZrfqihDPjBB0GIO5pji3YMUd9Nuzi2pbueSfK1mVX8uEsjcA+HvT0fz+vR20Oz2Mjo9gSnpsgKMcAqx2tBzlm5nj2crGomHkm6naCdtex4mNaztvYdyUWYGOKDiwWAj/1j+p1mPJcuZT//rtgY7okIiYCWYM8y+OGFqdsjFbCFJSp0JEAuGeNqZr+ZQFcWamvKGdSx9fRVlDO6clVjDFUkinbuNv1bN58osCAE6ZnIoWRDtv+oJ33sxRw23eTP4KAFZ6JrNGn8TCPMnK9JbM7FyezfgZAPFbnoId7wQ2oEMgYiaYMcVMFG1OldKNkHUGQrBhsUCOehE82rKN0iDNzFQ3d3Dp4yvZX9tKdmIkD0zYCoBn4lK+9405jIoNw27VOH9uZoAjHULMeTPbWTmc9jTtU2LmC/dUxqVGkzLCFkkOlKNPu4h/uJYC4Fn2Q2gsDXBEByNiJpjxlpnComnrNMSMXX6lQhBivAgeZdkWlJ6Z+tZOLntiNXurWkiPC+eZq2YTtfNVAMLnX8aNJ4/ni9tP4uu7Tx32o/4HRMZsPLYIErVmmoq20O4cBr4Zj9tcmvilZyoLZYN9n5mXk8D7o65hkycPS3sdvPxdcA2vrkN55QtmzMxMtJmZkUWTQlBi+GbmWXZSVd8c4GD6zq0vbmR7WSPJ0WE8d+l4st67GtpqISYdxp4EqJ1fITPx91BY7WjZRwEwV9/KemOmTkAp2wgdDTRrUWzR8zhqjJSY+oqmaVxxwkRuct5AC+Gw/0t47TrVUTtMEDETzHR0lZlaO10AhIsBWAhGUibjiUgkSusgs23H8HhH30ua2p18skt1KT1/hp3cV86AvR+CLQKW/lm1n48gNKM7baFlOyuHg2/G8Mt86ZqEBwsLx0hmpj8smZZGZ2we13bejFuzwdbX4L3bVafYMEDETDDjzcyExdBmGIAjxQAsBCMWi/kieJRlW1B1NK3aV4vb4+FHsZ8w7q3zoaEIEsfCNR/CpKWBDs//+IiZVXurAxwMkP8poEpMqTFhpMbIbJn+YLdauPLYXL7wTOc3jh+jo8Hqf8Bnfwp0aICImeDGt8xkZGakNVsIVjTTN7M9qDqavthbzV22Z7i58x9qpszks+Daj2HU1ECHFhgy5uCxhZOkNdFcvNX08wUEVwcUfgXAF55p5CZHBS6WEOCi+dkkRzv4V8Mcnoi+Th386New7unABoaImeDGxwDcajxhSJlJCFpM38wuSmsbAxxM79m7axtXW99Vn3zjPrjwPxAeF9igAonN0eWbYQtfB9I3U7wWXG202hPZrY8mNykycLGEAHERdp6+egGx4TZ+XX0Cr0Z9W93w1o9h+1sBjU3ETDDj05rdbhqARcwIQUrKJJqt8URqHejF6wIdTa+oaurghLpXsGo6nTmL4NibIERnyPQFLe8EAE60bAjsnibDL7MjYhagkZMkmZmBMjUjjv98dyHRYTZuqTmLFVGnge6B3f8LaFwiZoIZ36F5Zmu2iBkhSNE0SuPnARBXMbz3wHhZs7OQi6yfAOA47saAxjKsmHQmAMdatrBxT1Hg4jD8Ml/p0wDIkzLToDAzK56nrppPpMPG1TXf4Ymk2+hc8kBAYxIxE8wYZSa929A8ETNC8NKasRCAlLr1AY6kd7jX/ZsYrY2q8FwYe3Kgwxk+pEzEmTAOh+YmsfTjwPhmOlugeA0A77VMBCBHykyDxrzcRJ64Yj42m537Smbz01c2BzQeETPBjJGZcdmizO44ycwIwUzGNOWbye3YRVNbZ4CjOQIeN3PLXwCgZtp31SRjwcQ29WwATtFWB2beTOFX4HHhictmc2sCgJSZBpmjxybxz8vnkRBp56L52QGNRf77gplOtTW7w9L1bkPEjBDMpI6dgxMbCVozGzdvCnQ4h6V67Stk6JXU6tFkLb4q0OEMO7TJZwGw2LKRtbtL/B+A4ZepH6XMyCkxYaE/tDAAnDAhhc9uP4mjxwZ2GKGImWCmowmAdmsEAA6rBZtVfqVCEGMLoyJiHABlO74KcDCHR//qbwB8FH0WUdExAY5mGJIxm5bwNKK0Dtp3fuj/xzfETH6s8mFJJ9PQMRxEorzyBTNGmaldU/+k4pcRQgF32iwA9OKvAxvI4SheR0rd13TqVmqmXB7oaIYnmoZ7ohoaOK72E//6ZlproUxl9jZYZwBSYgp1RMwEM4YBuA010VJKTEIokDRBmYBHt+2gqml4LbPzoq98BIA3Pccwa/LEAEczfImZdS4AJ2nrWF9Q5b8HLvgc0CF5IlubVOZaOplCGxEzwYzhmWnV1D+rzJgRQoHovPkATLfs48s9lQGOpgcaimHrMgD+y5nMzk4IbDzDGC37GJqtcSRozZRs/MB/D2y0ZJN3AoU1rYB0MoU6ImaCFV03y0wtupGZETEjhAIpk3FqDmK1NnZt2xjoaA5myytoupuv3FOIyZuDwyZPo4fEaqMyXW0Nj8l/z3+PW7RKXeYcQ2GNetOXK2WmkEb+C4OVzhZA9WO3SJlJCCWsNlqT1F6jloK16MNkK69JwecALPfM5dgAd3AEA9GzvgnA7JYvaOtwDv0DdrZCxVYAmlNmU92sWvwlMxPaiJgJVrzTf9FocjsAycwIoUNkjupAyWzbwf7a1gBH44PbhW4sLlzlmcyx45IDHNDwJ2XmqbQQQZpWy66vPx36ByzbCLobotMocKoSYHK0g5hw+9A/thAwRMwEK4ZfBkc0rU4PIJkZIXSwZ80FYLolny/2BHC3z4GUb0TrbKJBj6QsfAxT0mMDHdGwR7NHsDP2aAA6Ni8b+gcsWasuM+dRUOv1y0iJKdQRMROsGDNmCIuWJZNC6JExG4BpWj5f7qkIcDA+FHwBwGrPJBaMScVikaWSvaF97BkAZJZ/CENdNjRWGDB6rph/RxAiZvpJh8vNroom9tcEKAVuLpmM7loyKWJGCBWSx+O2RRGldVC+dzMezzDxzRh+mZWeyczLlS6m3pK14BzadAcZ7hI63/v50Aoa78b1zPnkV6sMdp5kZkIeETP95IH/7eLUv3zKv77ID0wAHV4x47Nk0h74KYyCMChYrGgZMwHIad/JjvKmAAcEeNzo+5VfZqVnCrOz4wMbTxCRmZbCX8OuA8Cx6mH45HdD80CNZdBYDJoFMmabnUw5MmMm5BEx00/GpKh/jr1VzUc4c4jwZmbCYszJmhEO+XUKoYNl9BzAmDeztzrA0QDlm9A6GmnUI9ljyWVqRlygIwoaNE0jfMHl3Os0piWv+B188dfBfyCvXyZlMoRFU2BkzmWVQegjr379ZExKNAD7qloCE4BPmckrZiIdkpkRQgjDNzPTso8v9gwDMWOUmFZ7JjIpI4FwMdz3iW/OHs1T7tP5o+sidWD53bD6n4P7IMVd5t/mDpc5QVoMwKGPiJl+MtYQMyX1bf7dOeLFp8zUapSZ5MlVCCkMMTNFK2R9fiVOtyew8Zh+mSnMzooPbCxBSE5SFHNzEnjEdQ5f53xPHXznJ7D1tcF7kBKvX2aeWWJKjHIQFyFt2aGOiJl+khjlID5S/YN4TWZ+xduaHeabmRExI4QQiWPQw2IJ05yMdhayqbghcLF43OAzX0b8Mv3jm7NHA/CzhnNgwbXq4If3gWcQhKrHDSXr1fXR86STaYQhYmYAjDFMZfuqA+Cb6TQMkY5o2pwuQMSMEGJoGpqRnZluyWdjUX3gYinfDB0NNOkRbNNzmCP7mPrFmTPScVgtbC9vYse0W8ARA7V7If+Tgd955XZwtqj7TJkonUwjDBEzA8BbatpbGYDMjDlnpssALGUmIeQwxMwMbR8bi+sDF4fpl5lEQnQkmQkRgYsliImPdHDipBQAXt1SD7MuVjeseWLgd+41/46eDRZrVyeTiJkRgYiZAWCagAORmWmrV5fh8eacGcnMCCGHV8xY9ga2zFSohuWt8kxiTnY8mibD8vrLN2dnAvD6hhLcc65SB3e+Aw0lA7tjr/l3tFqFYXYyJUuZaSQQUDHz6aefctZZZ5GRkYGmaSxbtuyQ51533XVomsaDDz7ot/iOhLc9OyAdTe316jIiwWfOjIgZIcQw2rMnakWUVtdR39rp/xg8blPMqPkyUmIaCCdOSiEuwk5FYwdfNqVAznGge2DdUwO7Y7OTaT4ABdWyLXskEVAx09LSwsyZM3n44YcPe96yZctYtWoVGRkZfoqsd4w127Ob/b/Zt61OXUYk+MyZETEjhBhxWRCZhENzM1nbH5jsTMVWaG+ghQi26rli/h0gYTYrZ85IB+C19SUw/7vqhvVPg7ufW7XbG6Fqh7qeOY/WTheVRlu2iJmRQUDFzJIlS/j1r3/Neeedd8hzSkpKuOGGG3jmmWew24dXe112YiR2i05Lp5uKxg7/PrgpZuK7xIxkZoRQQ9MgayEACyzb2RQI34zXL+OeABYbMzJlWN5AOW+O6mp6b2s5rWNPR49KheYKtn70LKvza/t+h6VfAzrEZUN0qtnJFB9pJy5yeL1uCEPDsPbMeDweLrvsMm677TamTp3aq6/p6OigsbGx28eQsOFZHH8/hhuiVwAqO+NX2ox3qD5lJhmaJ4QkuccBsNCygw1FAcjM+MyXmZQWI/9ng8Cc7ARykiJp7XRzwT/W8UTr8QA0fvoYF/79K77eX9e3O/TZlA1SYhqJDGsx8/vf/x6bzcZNN93U66+5//77iYuLMz+ysrKGJrjWWqjazhJNzZ7w61oDtws61JN6pz0Ol7GETzIzQkiScywA8y072FJU49+SrsfjY/6dLC3Zg4SmaZxnGIG3lTXyRNti3LrG0dZtjNOKeXbV/r7dYfEBYkbWGIw4hq2YWbduHX/961956qmn+tQ5cOedd9LQ0GB+FBUVDU2AU78JwISOzaRRw15/moDbu96dtlmjzevimRFCkrTp6GGxxGptpLTsoryx3X+PXbUd2utp08LZIn6ZQeXaE8Zw++mT+O03p/Po9WfDxCUAXGr9kLc2ldHU3kv/jK4f1Mm0pVQ9R0pb9shh2IqZzz77jMrKSrKzs7HZbNhsNgoLC7n11lvJzc095NeFhYURGxvb7WNIiBsN2UcDsNS60r+ZGW8nkyOGNrf6FVotGnartIsKIYjFipZzDAALLdvZ6M9SU+GXAKxzj8eFTTqZBpEIh5UfLB7LJQuzmZUVj3WBWnFwoe1TvuH+jLfXF/TujvZ+CC2VYLFD+gzWFtTy9qYyAE6YkDJE0QvDjWErZi677DI2bdrEhg0bzI+MjAxuu+023n///UCHp5h2PgBnWVf6tz3bt5PJ65exW2X2hRC6GL6Zoyzb/Ds8z9uS7Z5EQqRdyhZDyZgTIXUqUbTxkONhli5fDO/cBmWbDv01q/8Jz1york86gw7NwR2vbgbgonlZzM0R8TlSCKiTrbm5mT179pif5+fns2HDBhITE8nOziYpKanb+Xa7nbS0NCZOnOjvUHtmyjno7/6UWZa9WBsKaOt0+6fUY4qZOFo71SoDKTEJIY0hZhZYdvLk/hr/PKaum5mZ1Z5JzM5OkDcMQ4nFApe/Tuvnf6Phq6dIpxZW/0N9pM+E2ZfB9AsgIkG1cL/7U1j7L/W1My6Csx7isU/2saeymeRoB3eeMSmw34/gVwKamVm7di2zZ89m9mw15fOWW25h9uzZ3H333YEMq/dEp0KucuGfaVnpv4WT3um/EQm0O2XGjDACSJuB2x5DrNaKs2QTHo8fTMC1+6C5AqdmZ6M+ljnilxl6olOIPP0efjP+RS7vvJ0t8Sep8lHZRrVh+08T4eXvwr/PNYSMBqfcC9/8O3vqnDzysXpzfM9ZU4mPdATyOxH8TEAzM4sXL+5TZ0JBQcHQBdNPtGnnQ/4KzrSuZF91M1Myhsij44tPmalVZswIIwGLFS33aNj9P6a7NrOvuoVxqdFH/rqBYJSYtmrj6cAhfhk/cuGCXC7fMpMN9XNZffPfCd/+Cqz/D1RuhS0vq5Mc0XD+4zBxCR6Pzp2vbqbT7eGkSanmUD5h5DBsPTNBw+SzcGNliqWQusIt/nlMr5gJj5fpv8KIwWJkQY+y7PDP8DyjxPRZ5wQApo2WYXn+4rhxyYyOj6Cx3cV7+5xw1A/gB1/ANR/DvKuVv+a7y80OqOfW7GdNQR2RDiv3nTtNyoEjEJn+NFAiEylOPIqc2i9ILngLOHXoH7OHvUyyZFIIeXLVvJkFlu38ZX8N583JHNrHMzIzqz2TSIsNJy5CJsn6C4tF48J5Wfzlg108v2Y/584eDZrGLtt4Hmu5gi+La3D+owSXpxi3Rze9gz85dSKj42Wj+UhExMwg0DDubFj9BdPqP1SmwaF+VyBlJmEkkjYTpy2KOFcLDQUbgZlD91j1RVC/H49mZb1nPHNGDXFJSziIb83L5MEPd7FyXy1vbCzljQ2lfLC94pDnz89N4Ipjcv0XoDCsEDEzCETNOJuOVT8ny12MXr4ZLX3G0D6g716mNm+ZSX6VQohjteEcfRT2wg9JrllDp+s7OGxDVCnfryZ7l0VMoKUtgvGpMUPzOMIhyYiPYNGEFD7ZWcVNz30NqPeJS6alcdlRuSRFO7BaNGwWDZvVQlpsOFaLlJdGKvIKOAhkpaXxsT6L07Q1tKx/keilQy1m6tVlRAJtjd7MjNifhNAnYvwJUPgh89nKjvJGZmTGD80DGSWmjVa1E26CZGYCwuVH5/DJzirsVo1vzh7NdYvGMjZFfhfCwYiYGQQcNgsrIxZzWscabNuXwRn3DW2pyXdoXqcsmRRGDpphAl5g2cGbRXVDKGaU+ffjtnEAjBcxExBOmjSK168/llGx4aTFhQc6HGEYI2/nB4nyUSfQqocR3lwEJeuG9sF8upm8nplw8cwII4H0mXRaIonXWqjcs35oHqO5Cqp3AfC/5jEAjJMyU8CYmRUvQkY4IiJmBonMUcl84JmjPtny6tA9kK5LN5MwcrHaaEydC4Cj6IuheYz9KivTmjCJBqIZFRsmnUyCMMwRMTNIjE2J5k23WjzJ1tfA4xmaB3K2grtTXY9IoM1oSRQxI4wUYiadCMCktg0U1bYO/gMYJaaS2FkATBglWRlBGO6ImBkkxqREs8Izk2YioakUilYOzQN5S0wWGziizMyMlJmEkULYxG8AcIJlI6u37h78BzDMv5sM8++QTxoWBGHAiJgZJMakRNGJnffc89SBLa8MzQP5dDKhaaZnRjIzwoghfQYV0VMI01yw4ZnBve+2eihXk7w/6RgPSGZGEIIBETODRFKUg4RIe1epadvr4HYN/gP5dDIBXYsmJTMjjCA6Zl0OwNyaN3C53IN3x0WrAR0Sx7K2Wi0qHC+ZGUEY9oiYGSQ0TY3f/sIzlUYtFlqqoOCzwX8gn04moGsCsGRmhBHE6OO+QzMR5FLGvjXvDd4dF34OQGfmUZQ1tAPIwDxBCAJEzAwi154wBocjjLecQ1hq8ulkAroWTUpmRhhBWMNjWBdn7EFb96/Bu+MCJWZK41XHVGpMGHGR0skkCMMdETODSFJ0GFcck8ubHlVq0re/Ca7OwX2QA8pMXa3ZMjRPGFm0zbgMgDHVH0Nz5cDvsKMJSjcAsMU2HRC/jCAECyJmBplrjx/DFts0KvV4tPZ62Pfx4D6Az14m8C0zya9SGFnMmHsc6z3jsOGmfc2/B36H+1eB7ob4HDY0Kp+MdDIJQnAgr4CDTEKUgyuOHcvb7oUA6JtfHtwH8O1moiszI4smhZFGRnwEH0QuBcCz9smBz3Yy/DLkHs/uymZAMjOCECyImBkCvnd8Hh9ajwPAtf1tcLYN3p37lJncHp1Ol3oC///27jwuyqpv/Phn2FdBFARkdyNTcSFzzSW3TLOfaWoZ0KKPmqZWt/bUbW5Zdne73LbZ7eOSS7nkEi1WimtaLiAKSrihKEKIGquyzfn9MXHliCLLyIB936/XvJy5rnOd68xhZL6cVcbMiL+jwgcGkakccMi9CGd3VC2zcyXBTGdO/Z4NyJ5MQtQWEszcA64ONrTt3IeLqj7WRbkUn/zJdJnfNJuppFUGZJ0Z8ffUqZkvm4oNm0+qw8srn1F+DqQY9nrK8XyYS9pMJglmhKgNJJi5R17s2ohtOsNA4N/3mXBhr5tmM+X9uZWBTge2VvKjFH8/Dwe5sV4ZVgQmcStkpVYuowt/jpdx8eNUgRsA7s62uDrYmKikQoh7Sb4B7xEXe2vs2gwDoP6lHaiSFpWquqmb6UbBX11MOp3ONPkLUYs42FhRN6Alh/RN0aliSIisXEZaF1OXm8bLSKuMELWFBDP3UP9efTmtfLChkNO7vjBNptoAYFfyCmWTSSG6NnFnR/GfO9af3VW5TG4OZkrGy8hieULUGhLM3EMujjacazgAgOLYL6ueYXER5GcZntvX1RbMk00mxd9Z1yb12atvAYBK2lvxbUQKcuGSYbwMAZ21lhkZ/CtE7SHBzD0W3OdF9EpHcH4cSadOVC2zG5l/Pbdz1YIZaZkRf2fNveqQ7tCEa8oJXUH2X4FJeV04APoicPEFV39O/S7TsoWobSSYucd8Appy0j4EgJNRVVx2vWS8jI0zWFr9tWCetMyIvzELCx39WvmwX98cgKJTURXL4KYuppyCYlL+MCylIDOZhKg9JJipBrbtngGgcep3XMm+UfmMbt2XqVA2mRQC4NXeTYm1bgNAWuyPFbv43D7Dv/6dOf1nF5PMZBKidpFgphoEdB1OPjY00l3ip+0V/EV7s1u2MpBNJoUwcHWwoWPvIQA0yDzGqYtp5buwIBdSog3PA7pwUhv8K60yQtQmsgZ+NdDZuXDZpxc+F7+HY2u5MWBA5Qbt3hrMyCaTQmh6dHiIy1FeuBelsm7Dl/zvxElYWtxlyYILB0FfCHV8oG4Ah88dA2rGeJni4mIKCwvNXQwh7hlra2ssLU3zx7h8C1YTry7hsPZ7eut/5puY8wx9OKjimdyyL1OezGYSQqPT6XAMfhTiV+N15QCrfjlHROfAsi86/2cXU0Bn8gqL+e6YYdG9x1p43uPS3plSirS0NP744w+zlUGI6uLq6oqnp2eV10qTYKaaWDbpRZ61G/ULr3J09yaGtH+t4j+8mxbMg5tbZiSYEQLA4YFeEL+azhbxDP4xkd4PetLQ1f7OF9w0+HdrXBq5BcX413OgfaBb9RT4NkoCGQ8PDxwcHGRBTHFfUkqRl5dHeno6AF5eXlXKT4KZ6mJphWXIUDj8GQ9nb+Pn0+F0beJesTxu2pcJ4HqBLJonhJGAR1DoCLa4gOONDGZEHmdJWOjt0145AxcP/3ldF9ZvuADAkLY+ZgsgiouLtUCmXr16ZimDENXF3t7wh0Z6ejoeHh5V6nKSAcDVyLbtCAB6W0SzaX8l1py5Q8uMdDMJ8SfHeui8WgHQ2eI4O35LJyf/Novo3ciEL0cYxsv4diBZ34ADSVfR6eCpdj7VXOi/lIyRcXBwMFsZhKhOJZ/1qo4Pk2CmOnm1Jr9uU+x0hbQ5/RHpWRWcpn3L1Ow8WTRPiNKCegDQ1z6BYr3iUNJV4/P6Ytj4EmQkgrM3PP05X8VcBKBL4/p4l9UtVU2ka0n8XZjqsy7BTHXS6bAd8D4AYZY/8etP6yp2/S2zmW7IOjNClBbUHYBOujhA8cvZK8bnt8+AUz+BlR0MX4PesQEbY1IAGBrqW61FFcKUAgICWLhwofZap9OxZcuWKuVpijyqgwQz1a1RT04HjgSgU/x0inMyyn/tLd1MsgKwELfh1wEsbXEpyqCR7hK/nLkpmIn9EvYvMjx/8hNo2Jb9Z66Q8sd16thZ0ad5A/OUuZbT6XRlPiIiIiqd961f0GWlK7mfvb09AQEBPP300+zYsaPC94yIiODJJ5+seGFv4+Z6cHZ2JjQ0lE2bNpkk77tJTU3lscceK1faGTNm0Lp16yrlYU4SzJiBz9D3OYMP9bnGlbVjQanyXXjL1Gxt0TxpmRHiL9b2hoAG6GIRz+lL6eQk7oG98+CbVwxpur4OLZ4CYEO0YeDvE629ZfxZJaWmpmqPhQsXUqdOHaNj//nPf6qlHLNmzSI1NZXExERWrlyJq6srvXr1Ys6cOdVy/ztZvnw5qampHDp0iJCQEIYOHcovv/xy27QFBQUmu6+npye2trZmz6M6SDBjBnYOTkQ98A4FyhKPiz/B0XLsqK1U6dlMMjVbiNtrZBg3M8V6PXE2L+L05UCImgXFBRA8AHq8BUDm9UJ+iDesFjy0nXQxVZanp6f2cHFxQafTGR3bs2cP7dq1w87OjqCgIGbOnElR0V8Ds2fMmIGfnx+2trZ4e3vzyiuGoLN79+6cP3+eyZMna60bZXF2dsbT0xM/Pz8eeeQR/vvf/zJt2jTefvttEhMTAcOMsRdffJHAwEDs7e1p1qyZUbA1Y8YMPv/8c77++mvtnrt27QJg6tSpNG3aFAcHB4KCgpg2bVq5Bq6WrKUSHBzM4sWLsbOzIzIyEjC0KL3zzjtERETg4uLCqFGjANi/fz+PPPII9vb2+Pr68sorr5Cbm6vlmZ6ezsCBA7G3tycwMJA1a9aUuu+tXUQXL15k+PDhuLm54ejoSGhoKAcOHGDFihXMnDmTo0ePau95xYoVt80jLi6Onj17Ym9vT7169Rg9ejQ5OTna+ZJWrX//+994eXlRr149Xn755Xu+AKRMzTaTnj16sfDYEKZYr0P//T+w8O8EdQPufEFBrmHmBciieULcTePesH0GjlwHHWRb18e5cUfw7wLtwsHC8Hfct8cukV+kp2kDJ1r5uJi50LenlNL+cKlu9taWVR6g+eOPPzJy5EgWLVpE165dOXPmDKNHjwZg+vTpfPXVVyxYsIC1a9fy4IMPkpaWxtGjRwHYtGkTISEhjB49WvuSr6iJEycye/Zsvv76a6ZMmYJer8fHx4f169dTv3599u/fz+jRo/Hy8uLpp5/m9ddfJyEhgaysLJYvXw6Am5th3SFnZ2dWrFiBt7c3cXFxjBo1CmdnZ6ZMmVLu8lhbW2NlZWX05f7BBx8wbdo0/vnPfwKGgKFv377Mnj2bpUuXcvnyZcaPH8/48eO1MkVERHDhwgV27NiBjY0Nr7zyirZmy+3k5OTQrVs3GjZsSGRkJJ6ensTExKDX6xk2bBjx8fH88MMPbN++HQAXl9L/H/Ly8ujXrx8dOnTg0KFDpKen89JLLzF+/Hgt+AHYuXMnXl5e7Ny5k9OnTzNs2DBat25d6Z9heUgwYyaNPZyJ9g3j4KVY2hckwvf/gGc33PmCkplMFtZg4wj81c0k2xkIcQvPFvDsRmJOJTN+jyV1XAL4YVi3Usk2HDbMYhrazrfGziC6XlhM87ersKdbFZyY1bfKv1/mzJnDG2+8QXh4OABBQUHMnj2bKVOmMH36dJKTk/H09KRXr15YW1vj5+dH+/btAUMQYWlpqbW4VIabmxseHh6cO3cOMAQTM2fO1M4HBgayf/9+1q9fz9NPP42TkxP29vbk5+eXumdJsAGGFpXXXnuNdevWlTuYyc/P54MPPiArK4tHH31UO96zZ09ef/117XVYWBjPPPMMkyZNAqBJkyYsWrSIbt268emnn5KcnMzWrVv59ddfefjhhwFYunQpDzzwwB3v/cUXX3D58mUOHTqkBWeNGzfWzjs5OWFlZVVmPa9Zs4br16+zcuVKHB0N30MfffQRAwcO5P3336dBA8OYs7p16/LRRx9haWlJcHAwjz/+OFFRUfc0mDFrN9OePXsYOHAg3t7epZqyCgsLmTp1Ki1btsTR0RFvb2/CwsK4dOmS+QpsYs90COR/C18CQJ2O+qsb6XZunsn05y9d6WYSogxNeuH/yLNcoj6//Z7DlZx8o9NHL/xB7IU/sLLQ8WSbhmYq5P0vOjqaWbNm4eTkpD1GjRpFamoqeXl5DB06lOvXrxMUFMSoUaPYvHmzUReUKSiljILVxYsXExoairu7O05OTixZsoTk5OS75vPVV1/RpUsXPD09cXJyYtq0aeW6bsSIETg5OeHg4MD8+fP597//bTSoNjTUeGHH6OhoVqxYYVRnffv2Ra/Xk5SUREJCAlZWVkbXBQcH4+rqescyxMbG0qZNGy2QqYyEhARCQkK0QAagc+fO6PV6rRsP4MEHHzRaAM/Ly6vMViNTMOuf9Lm5uYSEhPD888/z1FNPGZ3Ly8sjJiaGadOmERISwrVr15g0aRJPPPEEhw8fNlOJTatfC09mfhPIqcKGNLFIgdNR0HLI7RPfMpMJZNdsIe6mnpMtzRo4k/h7NgeSrtK/5V9Lpn+88zQAg1o3xN255g5wtLe25MSsvma7d1Xp9XpmzpzJ4MGDS52zs7PD19eXxMREtm3bxvbt2xk3bhwffPABu3fvxtrausr3v3LlCpcvXyYw0LBP1/r165k8eTLz5s2jY8eOODs788EHH3DgwIEy8/n1118ZPnw4M2fOpG/fvri4uLB27VrmzZt31zIsWLCAXr16UadOHTw8PEqdvzk4AEOd/c///I82duhmfn5+WuBQkdbEktV2q+LWoPBmNx+/9eem0+nQ6/VVvn9ZzBrMPPbYY3ec8uXi4sK2bduMjn344Ye0b9+e5ORk/Pz8qqOI95StlSVD2vmwY39rmlikkHt8K453DGb+MPz7ZzCj1//Vjy6zmYS4s46N6pH4ezb7z2RowUxiWjY/nfgdnQ7Gdm9k5hKWTafT1equ5LZt25KYmGjUpXEre3t7nnjiCZ544glefvllgoODiYuLo23bttjY2FBcXPkxQ//5z3+wsLDQplrv3buXTp06MW7cOC3NmTNnjK653T337duHv78/b731lnbs/Pnz5SqDp6dnme//Vm3btuX48eN3vOaBBx6gqKiIw4cPa11yiYmJZW5O2qpVK/7v//6Pq1ev3rZ1pjz13Lx5cz7//HNyc3O1AGzfvn1YWFjQtGnTcr67e6NWzWbKzMxEp9OV2ZSWn59PVlaW0aMme66DP79aGpoK8xN+ZEvMHZosb5nJlF/0V5QrLTNC3FnHRoY9jm5eb+aTXYZWmcdaeNLYw8ks5fq7ePvtt1m5ciUzZszg+PHjJCQksG7dOm38yYoVK1i6dCnx8fGcPXuWVatWYW9vj7+/P2AYm7Jnzx5SUlLIyCh7Xa7s7GzS0tK4cOECe/bsYfTo0bzzzjvMmTNHCwwaN27M4cOH+fHHHzl58iTTpk3j0KFDRvkEBARw7NgxEhMTycjIoLCwkMaNG5OcnMzatWs5c+YMixYtYvPmzfegxgyzpn755RdefvllYmNjOXXqFJGRkUyYMAGAZs2a0a9fP0aNGsWBAweIjo7mpZdeKrP1ZcSIEXh6evLkk0+yb98+zp49y8aNG7Up4gEBASQlJREbG0tGRgb5+fml8nj22Wexs7MjPDyc+Ph4du7cyYQJE3juuee08TLmUmuCmRs3bvDGG2/wzDPPUKdOnTume++993BxcdEevr41e7qlr5sDM15+gVydI266bD7fsIlXvjxC5vVbprGVWjDvrz5lCWaEuLMOgfXQ6eDM5VzSs25wLiOXb44axt6N617+v5ZF5fTt25dvv/2Wbdu28dBDD9GhQwfmz5+vBSuurq4sWbKEzp0706pVK6Kiovjmm2+0jTZnzZrFuXPnaNSoEe7uZW/O+/bbb+Pl5UXjxo157rnnyMzMJCoqiqlTp2ppxowZw+DBgxk2bBgPP/wwV65cMWqlARg1ahTNmjXTxtXs27ePQYMGMXnyZMaPH0/r1q3Zv38/06ZNM3FtGbRq1Yrdu3dz6tQpunbtSps2bZg2bZrRztLLly/H19eXbt26MXjwYEaPHn3bLqwSNjY2/PTTT3h4eNC/f39atmzJ3LlztbEtTz31FP369aNHjx64u7vz5ZellwxxcHDgxx9/5OrVqzz00EMMGTKERx99lI8++sj0lVBBOqXKu2LbvaXT6di8efNtV10sLCxk6NChJCcns2vXrjKDmfz8fKOIMisrC19fXzIzM8u8ztz068KxSNjCR8X/j38XDsXbxY5N4zrj6WJnSLB9Bvy8AB4eC4/N5eK1PLq8vxNbKwsS36n5qzMKYU4DPtxLfEoW/xneml/PXuHLgxfo3sydFc+3N3fRjNy4cYOkpCQCAwOxs7Mzd3GEuOfK+sxnZWXh4uJSru/vGt8yU1hYyNNPP01SUhLbtm276xuytbWlTp06Ro/awKKZYYDfCx4n8alrz6XMG2w4fOGvBLfsy3RdNpkUotw6Bhn+yt98JIWvog3Tscf3kFYZIe4XNTqYKQlkTp06xfbt27Vmx/tS496ADocrx5nY3tCHf+Dm3X5v6WbSBv9KF5MQd1UybmZX4mUKixXtA90IDaj8FFUhRM1i1iHyOTk5nD59WntdMvjIzc0Nb29vhgwZQkxMDN9++y3FxcWkpRmWHXdzc8PGxsZcxb43nNyhYVtIiaYLRwAfos9fo7BYj7WlRanZTHmyL5MQ5fZQgBuWFjqK9YZedWmVEeL+YtaWmcOHD9OmTRvatGkDwKuvvkqbNm14++23uXjxIpGRkVy8eJHWrVvj5eWlPfbv32/OYt87TQxdTZ5pu3F1sOZ6YTHxKZmGc7fuyyTBjBDl5mxnTcuGhuXZW/m40LVJfTOXSAhhSmZtmenevTtljT+uIWOTq0/TPrDrXXRnd9HRfzxbE65xIOkqbfzqlt4xu2T1X+vau/6EENXpuQ7+pPxxnTf7P1Bjty4QQlROjR4z87fjGQJODaAwlydczgFwsGTcTMneTNLNJESlPNXOh0Nv9aJD0H089k6IvykJZmoSCwto0huAhwoNizgdOneV4sICyP9z8b+S2UwyAFgIIYQAJJipef4cN1MvdRdOtlZY3rhGyq837aatjZkxLJonU7OFEEL83cmAi5qmUQ+wsEZ39SxR1pNooLsEUX+ec3QHS8OP7HqBYTsDOwlmhBBC/M1Jy0xNY+sMgY8A0KDIsOR6mrUvhDwDQ5ZryfIK/2yZkW4mIYS4K51Ox5YtW8xdDHGPSDBTEw1YAH3mcKr3clrf+Iz++gWoJz+BwK5aksvZhi0bZACwEKIm2b9/P5aWlvTr16/C1wYEBLBw4ULTF6ocIiIi0Ol06HQ6rK2tadCgAb1792bZsmXo9fq7Z3CTFStWlLkhsjA9CWZqorr+0Gk8/g8/yQ1rF67mFnA6PUc7nXTTRnkyM0MIUZMsW7aMCRMm8PPPP5OcnGzu4lRIv379SE1N5dy5c2zdupUePXowceJEBgwYQFFR0d0zEGYjwUwNZmNlQVs/w1Tsm7c2mLs1gcJiRfdm7nRuLIt/CSFqhtzcXNavX8/YsWMZMGAAK1asKJUmMjKS0NBQ7OzsqF+/PoMHDwYM646dP3+eyZMnay0kADNmzKB169ZGeSxcuJCAgADt9aFDh+jduzf169fHxcWFbt26ERMTU+Hy29ra4unpScOGDWnbti1vvvkmX3/9NVu3bjV6L/Pnz6dly5Y4Ojri6+vLuHHjyMkx/MG5a9cunn/+eTIzM7X3MWPGDABWr15NaGgozs7OeHp68swzz5Cenl7hcorSJJip4R4ONLS8lAQzv569wo/Hf8fSQsdb/R8wZ9GEENVBKSjINc+jgguXrlu3jmbNmtGsWTNGjhzJ8uXLjRY//e677xg8eDCPP/44R44cISoqitDQUAA2bdqEj48Ps2bNIjU1ldTU1HLfNzs7m/DwcPbu3cuvv/5KkyZN6N+/P9nZ2RUq/+307NmTkJAQNm3apB2zsLBg0aJFxMfH8/nnn7Njxw6mTJkCQKdOnVi4cCF16tTR3sfrr78OQEFBAbNnz+bo0aNs2bKFpKQkIiIiqlxGIbOZarz2gYbN8A4mXUGvV7zz3QkARrT3pUkDZ3MWTQhRHQrz4F1v89z7zUtg41ju5EuXLmXkyJGAocsmJyeHqKgoevXqBcCcOXMYPnw4M2fO1K4JCQkBDHvuWVpaaq0WFdGzZ0+j15999hl169Zl9+7dDBgwoEJ53U5wcDDHjh3TXk+aNEl7HhgYyOzZsxk7diyffPIJNjY2uLi4oNPpSr2PF154QXseFBTEokWLaN++PTk5OTg5OVW5nH9n0jJTw7Xxc8XG0oLfs/JZGHWK+JQsnG2tmNSrqbmLJoQQmsTERA4ePMjw4cMBsLKyYtiwYSxbtkxLExsby6OPPmrye6enpzNmzBiaNm2Ki4sLLi4u5OTkmGzMjlLKaAuMnTt30rt3bxo2bIizszNhYWFcuXKF3NzcMvM5cuQIgwYNwt/fH2dnZ7p37w5Q68YW1UTSMlPD2VlbEuLrwqFz11gUdQqAl3s2pr6TrZlLJoSoFtYOhhYSc927nJYuXUpRURENGzbUjimlsLa25tq1a9StWxd7e/sKF8HCwqLUPn2FhYVGryMiIrh8+TILFy7E398fW1tbOnbsSEFBQYXvdzsJCQkEBgYCcP78efr378+YMWOYPXs2bm5u/Pzzz7z44oulynWz3Nxc+vTpQ58+fVi9ejXu7u4kJyfTt29fk5Xz70yCmVqgfaAbh84Zds32dbMnolOAeQskhKg+Ol2FunrMoaioiJUrVzJv3jz69OljdO6pp55izZo1jB8/nlatWhEVFcXzzz9/23xsbGwoLi42Oubu7k5aWppR60hsbKxRmr179/LJJ5/Qv39/AC5cuEBGRoZJ3tuOHTuIi4tj8uTJABw+fJiioiLmzZuHhYWhc2P9+vV3fR+//fYbGRkZzJ07F19fXy0vYRrSzVQLlAwCBnij3wPYyUJ5Qoga5Ntvv+XatWu8+OKLtGjRwugxZMgQli5dCsD06dP58ssvmT59OgkJCcTFxfGvf/1LyycgIIA9e/aQkpKiBSPdu3fn8uXL/Otf/+LMmTN8/PHHbN261ej+jRs3ZtWqVSQkJHDgwAGeffbZSrUC5efnk5aWRkpKCjExMbz77rsMGjSIAQMGEBYWBkCjRo0oKiriww8/5OzZs6xatYrFixcb5RMQEKCNF8rIyCAvLw8/Pz9sbGy06yIjI5k9e3aFyyjuQN3nMjMzFaAyMzPNXZRKu15QpJ5evF+98mWM0uv15i6OEOIeuX79ujpx4oS6fv26uYtSIQMGDFD9+/e/7bno6GgFqOjoaKWUUhs3blStW7dWNjY2qn79+mrw4MFa2l9++UW1atVK2draqpu/nj799FPl6+urHB0dVVhYmJozZ47y9/fXzsfExKjQ0FBla2urmjRpojZs2KD8/f3VggULtDSA2rx58x3fQ3h4uAIUoKysrJS7u7vq1auXWrZsmSouLjZKO3/+fOXl5aXs7e1V37591cqVKxWgrl27pqUZM2aMqlevngLU9OnTlVJKffHFFyogIEDZ2tqqjh07qsjISAWoI0eOlF3B97GyPvMV+f7WKVXBuXe1TFZWFi4uLmRmZlKnTh1zF0cIIe7oxo0bJCUlERgYiJ2dnbmLI8Q9V9ZnviLf39LNJIQQQohaTYIZIYQQQtRqEswIIYQQolaTYEYIIYQQtZoEM0IIIYSo1SSYEUKIGuY+n2QqhMZUn3UJZoQQooawtrYGIC8vz8wlEaJ6lHzWSz77lSXbGQghRA1haWmJq6sr6enpADg4OBhtcCjE/UIpRV5eHunp6bi6umJpWbWV7SWYEUKIGsTT0xNAC2iEuJ+5urpqn/mqkGBGCCFqEJ1Oh5eXFx4eHmXuwixEbWdtbV3lFpkSEswIIUQNZGlpabJf9ELc72QAsBBCCCFqNQlmhBBCCFGrSTAjhBBCiFrtvh8zU7IgT1ZWlplLIoQQQojyKvneLs/Cevd9MJOdnQ2Ar6+vmUsihBBCiIrKzs7GxcWlzDQ6dZ+vm63X67l06RLOzs4mX3wqKysLX19fLly4QJ06dUyatzAmdV19pK6rj9R19ZG6rj6mqmulFNnZ2Xh7e2NhUfaomPu+ZcbCwgIfH597eo86derIf45qInVdfaSuq4/UdfWRuq4+pqjru7XIlJABwEIIIYSo1SSYEUIIIUStJsFMFdja2jJ9+nRsbW3NXZT7ntR19ZG6rj5S19VH6rr6mKOu7/sBwEIIIYS4v0nLjBBCCCFqNQlmhBBCCFGrSTAjhBBCiFpNghkhhBBC1GoSzFTSJ598QmBgIHZ2drRr1469e/eau0i13nvvvcdDDz2Es7MzHh4ePPnkkyQmJhqlUUoxY8YMvL29sbe3p3v37hw/ftxMJb5/vPfee+h0OiZNmqQdk7o2nZSUFEaOHEm9evVwcHCgdevWREdHa+elrk2jqKiIf/7znwQGBmJvb09QUBCzZs1Cr9draaSuK2fPnj0MHDgQb29vdDodW7ZsMTpfnnrNz89nwoQJ1K9fH0dHR5544gkuXrxomgIqUWFr165V1tbWasmSJerEiRNq4sSJytHRUZ0/f97cRavV+vbtq5YvX67i4+NVbGysevzxx5Wfn5/KycnR0sydO1c5OzurjRs3qri4ODVs2DDl5eWlsrKyzFjy2u3gwYMqICBAtWrVSk2cOFE7LnVtGlevXlX+/v4qIiJCHThwQCUlJant27er06dPa2mkrk3jnXfeUfXq1VPffvutSkpKUhs2bFBOTk5q4cKFWhqp68r5/vvv1VtvvaU2btyoALV582aj8+Wp1zFjxqiGDRuqbdu2qZiYGNWjRw8VEhKiioqKqlw+CWYqoX379mrMmDFGx4KDg9Ubb7xhphLdn9LT0xWgdu/erZRSSq/XK09PTzV37lwtzY0bN5SLi4tavHixuYpZq2VnZ6smTZqobdu2qW7dumnBjNS16UydOlV16dLljuelrk3n8ccfVy+88ILRscGDB6uRI0cqpaSuTeXWYKY89frHH38oa2trtXbtWi1NSkqKsrCwUD/88EOVyyTdTBVUUFBAdHQ0ffr0MTrep08f9u/fb6ZS3Z8yMzMBcHNzAyApKYm0tDSjure1taVbt25S95X08ssv8/jjj9OrVy+j41LXphMZGUloaChDhw7Fw8ODNm3asGTJEu281LXpdOnShaioKE6ePAnA0aNH+fnnn+nfvz8gdX2vlKdeo6OjKSwsNErj7e1NixYtTFL39/1Gk6aWkZFBcXExDRo0MDreoEED0tLSzFSq+49SildffZUuXbrQokULAK1+b1f358+fr/Yy1nZr164lJiaGQ4cOlTondW06Z8+e5dNPP+XVV1/lzTff5ODBg7zyyivY2toSFhYmdW1CU6dOJTMzk+DgYCwtLSkuLmbOnDmMGDECkM/1vVKeek1LS8PGxoa6deuWSmOK704JZipJp9MZvVZKlTomKm/8+PEcO3aMn3/+udQ5qfuqu3DhAhMnTuSnn37Czs7ujumkrqtOr9cTGhrKu+++C0CbNm04fvw4n376KWFhYVo6qeuqW7duHatXr+aLL77gwQcfJDY2lkmTJuHt7U14eLiWTur63qhMvZqq7qWbqYLq16+PpaVlqUgyPT29VFQqKmfChAlERkayc+dOfHx8tOOenp4AUvcmEB0dTXp6Ou3atcPKygorKyt2797NokWLsLKy0upT6rrqvLy8aN68udGxBx54gOTkZEA+16b0j3/8gzfeeIPhw4fTsmVLnnvuOSZPnsx7770HSF3fK+WpV09PTwoKCrh27dod01SFBDMVZGNjQ7t27di2bZvR8W3bttGpUyczler+oJRi/PjxbNq0iR07dhAYGGh0PjAwEE9PT6O6LygoYPfu3VL3FfToo48SFxdHbGys9ggNDeXZZ58lNjaWoKAgqWsT6dy5c6klBk6ePIm/vz8gn2tTysvLw8LC+GvN0tJSm5otdX1vlKde27Vrh7W1tVGa1NRU4uPjTVP3VR5C/DdUMjV76dKl6sSJE2rSpEnK0dFRnTt3ztxFq9XGjh2rXFxc1K5du1Rqaqr2yMvL09LMnTtXubi4qE2bNqm4uDg1YsQImVZpIjfPZlJK6tpUDh48qKysrNScOXPUqVOn1Jo1a5SDg4NavXq1lkbq2jTCw8NVw4YNtanZmzZtUvXr11dTpkzR0khdV052drY6cuSIOnLkiALU/Pnz1ZEjR7QlScpTr2PGjFE+Pj5q+/btKiYmRvXs2VOmZpvbxx9/rPz9/ZWNjY1q27atNn1YVB5w28fy5cu1NHq9Xk2fPl15enoqW1tb9cgjj6i4uDjzFfo+cmswI3VtOt98841q0aKFsrW1VcHBweq///2v0Xmpa9PIyspSEydOVH5+fsrOzk4FBQWpt956S+Xn52tppK4rZ+fOnbf9/RweHq6UKl+9Xr9+XY0fP165ubkpe3t7NWDAAJWcnGyS8umUUqrq7TtCCCGEEOYhY2aEEEIIUatJMCOEEEKIWk2CGSGEEELUahLMCCGEEKJWk2BGCCGEELWaBDNCCCGEqNUkmBFCCCFErSbBjBBCCCFqNQlmhBB3FRERgU6nQ6fTYW1tTYMGDejduzfLli3T9r0prxUrVuDq6mqysp0+fZrnn38eHx8fbG1tCQwMZMSIERw+fNhk9yiPc+fOodPpiI2Nrdb7CiEkmBFClFO/fv1ITU3l3LlzbN26lR49ejBx4kQGDBhAUVGRWcp0+PBh2rVrx8mTJ/nss884ceIEmzdvJjg4mNdee80sZRJCmIFJNkUQQtzXwsPD1aBBg0odj4qKUoBasmSJdmzevHmqRYsWysHBQfn4+KixY8eq7OxspdTt93eZPn26UkqpVatWqXbt2iknJyfVoEEDNWLECPX777/fsUx6vV49+OCDql27dqq4uLjU+WvXrmnPjx07pnr06KHs7OyUm5ubGjVqlFYmpUrvS6WUUoMGDdL2nVFKKX9/fzVnzhz1/PPPKycnJ+Xr66s+++wz7fyt76tbt253LLsQwrSkZUYIUWk9e/YkJCSETZs2accsLCxYtGgR8fHxfP755+zYsYMpU6YA0KlTJxYuXEidOnVITU0lNTWV119/HYCCggJmz57N0aNH2bJlC0lJSURERNzx3rGxsRw/fpzXXnsNC4vSv8pKurLy8vLo168fdevW5dChQ2zYsIHt27czfvz4Cr/fefPmERoaypEjRxg3bhxjx47lt99+A+DgwYMAbN++ndTUVKM6EULcW1bmLoAQonYLDg7m2LFj2utJkyZpzwMDA5k9ezZjx47lk08+wcbGBhcXF3Q6HZ6enkb5vPDCC9rzoKAgFi1aRPv27cnJycHJyanUfU+dOqXdvyxr1qzh+vXrrFy5EkdHRwA++ugjBg4cyPvvv0+DBg3K/V779+/PuHHjAJg6dSoLFixg165dBAcH4+7uDkC9evVKvTchxL0lLTNCiCpRSqHT6bTXO3fupHfv3jRs2BBnZ2fCwsK4cuUKubm5ZeZz5MgRBg0ahL+/P87OznTv3h2A5OTkO94XMLr37SQkJBASEqIFMgCdO3dGr9eTmJhYnreoadWqlfa8JCBLT0+vUB5CCNOTYEYIUSUJCQkEBgYCcP78efr370+LFi3YuHEj0dHRfPzxxwAUFhbeMY/c3Fz69OmDk5MTq1ev5tChQ2zevBkwdD/dTtOmTbX7l+XWYOtmJcctLCy04KjE7cprbW1d6vqKzuYSQpieBDNCiErbsWMHcXFxPPXUU4BhdlFRURHz5s2jQ4cONG3alEuXLhldY2NjQ3FxsdGx3377jYyMDObOnUvXrl0JDg6+a4tH69atad68OfPmzbttQPHHH38A0Lx5c2JjY41ahvbt24eFhYUWELm7u5OamqqdLy4uJj4+vvwV8ef7KrlWCFG9JJgRQpRLfn4+aWlppKSkEBMTw7vvvsugQYMYMGAAYWFhADRq1IiioiI+/PBDzp49y6pVq1i8eLFRPgEBAeTk5BAVFUVGRgZ5eXn4+flhY2OjXRcZGcns2bPLLI9Op2P58uWcPHmSRx55hO+//56zZ89y7Ngx5syZw6BBgwB49tlnsbOzIzw8nPj4eHbu3MmECRN47rnntPEyPXv25LvvvuO7777jt99+Y9y4cVowVF4eHh7Y29vzww8/8Pvvv5OZmVmh64UQVWDeyVRCiNogPDxcm3JsZWWl3N3dVa9evdSyZctKTYueP3++8vLyUvb29qpv375q5cqVCjCaKj1mzBhVr149o6nZX3zxhQoICFC2traqY8eOKjIyUgHqyJEjZZYtMTFRhYWFKW9vb2VjY6P8/f3ViBEjVExMjJbmblOzCwoK1NixY5Wbm5vy8PBQ77333m2nZi9YsMDo3iEhIVr5lVJqyZIlytfXV1lYWMjUbCGqkU6pWzqKhRBCCCFqEelmEkIIIUStJsGMEEIIIWo1CWaEEEIIUatJMCOEEEKIWk2CGSGEEELUahLMCCGEEKJWk2BGCCGEELWaBDNCCCGEqNUkmBFCCCFErSbBjBBCCCFqNQlmhBBCCFGrSTAjhBBCiFrt/wOcbC/hSXy+kQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and Prediction \n",
    "plt.plot(test_results[\"Test Predictions\"][:100])\n",
    "plt.plot(test_results[\"Actual Values\"][:100])\n",
    "plt.xlabel(\"Data Count\")\n",
    "plt.ylabel(\"Precitions\")\n",
    "plt.title(\"Actual vs Test Prediction\")\n",
    "plt.legend([\"Test Data Prediction\", \"Actual Data\"], loc = \"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "657e025a",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}