22-DL-Multivariate-Time-Series-Weather-Forecasting/Multivariate_Time_Series_Weather_Forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "75a25586",
   "metadata": {},
   "source": [
    "# Multivariate Time Series Weather Forecasting  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b148a087",
   "metadata": {},
   "source": [
    "### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0446b16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "from tensorflow.keras.metrics import RootMeanSquaredError\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babb0f44",
   "metadata": {},
   "source": [
    "### Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff95b2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "climate_data = pd.read_csv(\"data/jena_climate_2009_2016.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ebbce5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show dataset\n",
    "climate_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28730d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(420551, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset dimension\n",
    "climate_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed49c1d8",
   "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": 5,
     "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": 6,
   "id": "f8217e09",
   "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": 6,
     "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",
    "# display data for 24hrs\n",
    "climate_data[:24]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1862ec",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bffabe88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature visualization function\n",
    "def plot_feature(feature, feature_name):\n",
    "    plt.plot(feature)\n",
    "    plt.xlabel(\"Years\")\n",
    "    plt.ylabel(feature_name)\n",
    "    plt.title(f\"Time Series {feature_name} Plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28971449",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features\n",
    "temperature = climate_data['T (degC)']\n",
    "pressure = climate_data[\"p (mbar)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e179a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature(temperature, \"Temprature (C)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ae6acd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature(pressure, \"Pressure (mbar)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbbf2602",
   "metadata": {},
   "source": [
    "### Make new dataframe with multiple features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a17f4ed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Seconds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date Time</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>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>1.230772e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 02:00:00</th>\n",
       "      <td>996.62</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>1.230775e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 03:00:00</th>\n",
       "      <td>996.84</td>\n",
       "      <td>-8.81</td>\n",
       "      <td>1.230779e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 04:00:00</th>\n",
       "      <td>996.99</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>1.230782e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 05:00:00</th>\n",
       "      <td>997.46</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>1.230786e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:10:00</th>\n",
       "      <td>1002.18</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>1.483211e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:10:00</th>\n",
       "      <td>1001.40</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>1.483215e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:10:00</th>\n",
       "      <td>1001.19</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>1.483219e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:10:00</th>\n",
       "      <td>1000.65</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>1.483222e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:10:00</th>\n",
       "      <td>1000.11</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>1.483226e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70091 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Pressure  Temperature       Seconds\n",
       "Date Time                                               \n",
       "2009-01-01 01:00:00    996.50        -8.05  1.230772e+09\n",
       "2009-01-01 02:00:00    996.62        -8.88  1.230775e+09\n",
       "2009-01-01 03:00:00    996.84        -8.81  1.230779e+09\n",
       "2009-01-01 04:00:00    996.99        -9.05  1.230782e+09\n",
       "2009-01-01 05:00:00    997.46        -9.63  1.230786e+09\n",
       "...                       ...          ...           ...\n",
       "2016-12-31 19:10:00   1002.18        -0.98  1.483211e+09\n",
       "2016-12-31 20:10:00   1001.40        -1.40  1.483215e+09\n",
       "2016-12-31 21:10:00   1001.19        -2.75  1.483219e+09\n",
       "2016-12-31 22:10:00   1000.65        -2.89  1.483222e+09\n",
       "2016-12-31 23:10:00   1000.11        -3.93  1.483226e+09\n",
       "\n",
       "[70091 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new dataframe with the feature columns\n",
    "temp_pre_time_df = pd.DataFrame({\"Pressure\": pressure,\n",
    "                            \"Temperature\": temperature,\n",
    "                            \"Seconds\": climate_data.index.map(pd.Timestamp.timestamp)\n",
    "                           })\n",
    "temp_pre_time_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a740a73b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Day sin</th>\n",
       "      <th>Day cos</th>\n",
       "      <th>Year sin</th>\n",
       "      <th>Year cos</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-01-01 01:00:00</th>\n",
       "      <td>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>1.230772e+09</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>0.010049</td>\n",
       "      <td>0.999950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 02:00:00</th>\n",
       "      <td>996.62</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>1.230775e+09</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.010766</td>\n",
       "      <td>0.999942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 03:00:00</th>\n",
       "      <td>996.84</td>\n",
       "      <td>-8.81</td>\n",
       "      <td>1.230779e+09</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.011483</td>\n",
       "      <td>0.999934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 04:00:00</th>\n",
       "      <td>996.99</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>1.230782e+09</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.012199</td>\n",
       "      <td>0.999926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 05:00:00</th>\n",
       "      <td>997.46</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>1.230786e+09</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>0.012916</td>\n",
       "      <td>0.999917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Pressure  Temperature       Seconds   Day sin   Day cos  \\\n",
       "Date Time                                                                      \n",
       "2009-01-01 01:00:00    996.50        -8.05  1.230772e+09  0.258819  0.965926   \n",
       "2009-01-01 02:00:00    996.62        -8.88  1.230775e+09  0.500000  0.866025   \n",
       "2009-01-01 03:00:00    996.84        -8.81  1.230779e+09  0.707107  0.707107   \n",
       "2009-01-01 04:00:00    996.99        -9.05  1.230782e+09  0.866025  0.500000   \n",
       "2009-01-01 05:00:00    997.46        -9.63  1.230786e+09  0.965926  0.258819   \n",
       "\n",
       "                     Year sin  Year cos  \n",
       "Date Time                                \n",
       "2009-01-01 01:00:00  0.010049  0.999950  \n",
       "2009-01-01 02:00:00  0.010766  0.999942  \n",
       "2009-01-01 03:00:00  0.011483  0.999934  \n",
       "2009-01-01 04:00:00  0.012199  0.999926  \n",
       "2009-01-01 05:00:00  0.012916  0.999917  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seconds in a year\n",
    "day = 60*60*24\n",
    "year = 365.2425*day\n",
    "\n",
    "# time into sin and cos signals\n",
    "temp_pre_time_df['Day sin']  = np.sin(temp_pre_time_df['Seconds'] * (2 * np.pi / day))\n",
    "temp_pre_time_df['Day cos']  = np.cos(temp_pre_time_df['Seconds'] * (2 * np.pi / day))\n",
    "temp_pre_time_df['Year sin']  = np.sin(temp_pre_time_df['Seconds'] * (2 * np.pi / year))\n",
    "temp_pre_time_df['Year cos']  = np.cos(temp_pre_time_df['Seconds'] * (2 * np.pi / year))\n",
    "temp_pre_time_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d2d396f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Day sin</th>\n",
       "      <th>Day cos</th>\n",
       "      <th>Year sin</th>\n",
       "      <th>Year cos</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-01-01 01:00:00</th>\n",
       "      <td>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>0.010049</td>\n",
       "      <td>0.999950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 02:00:00</th>\n",
       "      <td>996.62</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.010766</td>\n",
       "      <td>0.999942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 03:00:00</th>\n",
       "      <td>996.84</td>\n",
       "      <td>-8.81</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.011483</td>\n",
       "      <td>0.999934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 04:00:00</th>\n",
       "      <td>996.99</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.012199</td>\n",
       "      <td>0.999926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01 05:00:00</th>\n",
       "      <td>997.46</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>0.012916</td>\n",
       "      <td>0.999917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Pressure  Temperature   Day sin   Day cos  Year sin  \\\n",
       "Date Time                                                                  \n",
       "2009-01-01 01:00:00    996.50        -8.05  0.258819  0.965926  0.010049   \n",
       "2009-01-01 02:00:00    996.62        -8.88  0.500000  0.866025  0.010766   \n",
       "2009-01-01 03:00:00    996.84        -8.81  0.707107  0.707107  0.011483   \n",
       "2009-01-01 04:00:00    996.99        -9.05  0.866025  0.500000  0.012199   \n",
       "2009-01-01 05:00:00    997.46        -9.63  0.965926  0.258819  0.012916   \n",
       "\n",
       "                     Year cos  \n",
       "Date Time                      \n",
       "2009-01-01 01:00:00  0.999950  \n",
       "2009-01-01 02:00:00  0.999942  \n",
       "2009-01-01 03:00:00  0.999934  \n",
       "2009-01-01 04:00:00  0.999926  \n",
       "2009-01-01 05:00:00  0.999917  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove seconds column\n",
    "temp_pre_time_df = temp_pre_time_df.drop('Seconds', axis=1)\n",
    "temp_pre_time_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e727051c",
   "metadata": {},
   "source": [
    "### Selecting Features and targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eebbaef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing features(each row in dataframe) and target(pressure and temperature) \n",
    "def df_to_X_Y(df, window_size = 7):\n",
    "    df_as_np = df.to_numpy()\n",
    "    X = []\n",
    "    Y = []\n",
    "    for i in range(len(df_as_np) - window_size):\n",
    "        row = [r for r in df_as_np[i:i+window_size]]\n",
    "        X.append(row)\n",
    "        label = df_as_np[i+window_size][:2]\n",
    "        Y.append(label)\n",
    "    return np.array(X), np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d99131f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70084, 7, 6), (70084, 2))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features(70084 data each window contains 7rows, 6 columns) and target(70084 data each 2 columns) dimensions\n",
    "X, Y = df_to_X_Y(temp_pre_time_df)\n",
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9126b3ab",
   "metadata": {},
   "source": [
    "### Split Train Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cada83b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 7, 6), (60000, 2), (5000, 7, 6), (5000, 2), (5084, 7, 6), (5084, 2))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train test\n",
    "X_train, Y_train = X[:60000], Y[:60000]\n",
    "X_val, Y_val = X[60000:65000], Y[60000:65000]\n",
    "X_test, Y_test = X[65000:], Y[65000:]\n",
    "X_train.shape, Y_train.shape, X_val.shape, Y_val.shape, X_test.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbbf1a2",
   "metadata": {},
   "source": [
    "### Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e2bba66f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mean, std and standardise data\n",
    "p_training_mean = np.mean(X_train[:, :, 0])\n",
    "p_training_std = np.std(X_train[:, :, 0])\n",
    "\n",
    "temp_training_mean = np.mean(X_train[:, :, 0])\n",
    "temp_training_std = np.std(X_train[:, :, 0])\n",
    "\n",
    "def preprocess(X):\n",
    "    X[:, :, 0] = (X[:, :, 0] - p_training_mean) / p_training_std\n",
    "    X[:, :, 1] = (X[:, :, 1] - temp_training_mean) / temp_training_std\n",
    "    return X\n",
    "    \n",
    "def preprocess_output(Y):\n",
    "    Y[:, 0] = (Y[:, 0] - p_training_mean) / p_training_std\n",
    "    Y[:, 1] = (Y[:, 1] - temp_training_mean) / temp_training_std\n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6d6b6aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -0.7934386 , -118.35804908],\n",
       "       [  -0.76312281, -118.41868065],\n",
       "       [  -0.72189335, -118.45020907],\n",
       "       ...,\n",
       "       [   1.48145784, -120.25945506],\n",
       "       [   1.41597574, -120.2764319 ],\n",
       "       [   1.35049365, -120.40254556]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocess inputs\n",
    "preprocess(X_train)\n",
    "preprocess(X_val)\n",
    "preprocess(X_test)\n",
    "\n",
    "# preprocess outputs\n",
    "preprocess_output(Y_train)\n",
    "preprocess_output(Y_val)\n",
    "preprocess_output(Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27cfab64",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7f40f8",
   "metadata": {},
   "source": [
    "### LSTM Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0470c3ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_1 (LSTM)               (None, 64)                18176     \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 8)                 520       \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 2)                 18        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 18714 (73.10 KB)\n",
      "Trainable params: 18714 (73.10 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## LSTM Model\n",
    "model_lstm = Sequential()\n",
    "model_lstm.add(InputLayer((7, 6)))\n",
    "model_lstm.add(LSTM(64))\n",
    "model_lstm.add(Dense(8, 'relu'))\n",
    "model_lstm.add(Dense(2, 'linear'))\n",
    "model_lstm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "37b8a283",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save best model\n",
    "cp_lstm = ModelCheckpoint('model_lstm/', save_best_only = True)\n",
    "\n",
    "#complile model\n",
    "model_lstm.compile(loss = MeanSquaredError(), optimizer = Adam(learning_rate = 0.0001), metrics = [RootMeanSquaredError()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d1c6d82b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1852/1875 [============================>.] - ETA: 0s - loss: 5287.8779 - root_mean_squared_error: 72.7178INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 10s 4ms/step - loss: 5267.0962 - root_mean_squared_error: 72.5748 - val_loss: 3608.6985 - val_root_mean_squared_error: 60.0724\n",
      "Epoch 2/10\n",
      "1859/1875 [============================>.] - ETA: 0s - loss: 2223.9333 - root_mean_squared_error: 47.1586INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2213.9016 - root_mean_squared_error: 47.0521 - val_loss: 1057.6816 - val_root_mean_squared_error: 32.5220\n",
      "Epoch 3/10\n",
      "1850/1875 [============================>.] - ETA: 0s - loss: 428.8394 - root_mean_squared_error: 20.7084INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 424.0567 - root_mean_squared_error: 20.5926 - val_loss: 72.0313 - val_root_mean_squared_error: 8.4871\n",
      "Epoch 4/10\n",
      "1860/1875 [============================>.] - ETA: 0s - loss: 13.3684 - root_mean_squared_error: 3.6563INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 13.2705 - root_mean_squared_error: 3.6429 - val_loss: 1.1271 - val_root_mean_squared_error: 1.0617\n",
      "Epoch 5/10\n",
      "1862/1875 [============================>.] - ETA: 0s - loss: 1.0236 - root_mean_squared_error: 1.0117INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 1.0237 - root_mean_squared_error: 1.0118 - val_loss: 1.0001 - val_root_mean_squared_error: 1.0000\n",
      "Epoch 6/10\n",
      "1861/1875 [============================>.] - ETA: 0s - loss: 0.7767 - root_mean_squared_error: 0.8813INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.7761 - root_mean_squared_error: 0.8809 - val_loss: 0.8261 - val_root_mean_squared_error: 0.9089\n",
      "Epoch 7/10\n",
      "1864/1875 [============================>.] - ETA: 0s - loss: 0.6563 - root_mean_squared_error: 0.8101INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.6563 - root_mean_squared_error: 0.8101 - val_loss: 0.8138 - val_root_mean_squared_error: 0.9021\n",
      "Epoch 8/10\n",
      "1872/1875 [============================>.] - ETA: 0s - loss: 0.5260 - root_mean_squared_error: 0.7253INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.5259 - root_mean_squared_error: 0.7252 - val_loss: 0.4999 - val_root_mean_squared_error: 0.7070\n",
      "Epoch 9/10\n",
      "1867/1875 [============================>.] - ETA: 0s - loss: 0.2422 - root_mean_squared_error: 0.4921INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2419 - root_mean_squared_error: 0.4919 - val_loss: 0.2180 - val_root_mean_squared_error: 0.4669\n",
      "Epoch 10/10\n",
      "1861/1875 [============================>.] - ETA: 0s - loss: 0.1570 - root_mean_squared_error: 0.3962INFO:tensorflow:Assets written to: model_lstm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_lstm\\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 [==============================] - 8s 4ms/step - loss: 0.1569 - root_mean_squared_error: 0.3961 - val_loss: 0.1890 - val_root_mean_squared_error: 0.4347\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x182c072ef50>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model training\n",
    "model_lstm.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs = 10, callbacks = [cp_lstm])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76cf7f0f",
   "metadata": {},
   "source": [
    "### Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "057c5fbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_1 (Conv1D)           (None, 6, 64)             832       \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 384)               0         \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 8)                 3080      \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 2)                 18        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3930 (15.35 KB)\n",
      "Trainable params: 3930 (15.35 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## CNN Model\n",
    "model_CNN = Sequential()\n",
    "model_CNN.add(InputLayer((7, 6)))\n",
    "model_CNN.add(Conv1D(64, kernel_size=2, activation=\"relu\"))\n",
    "model_CNN.add(Flatten())\n",
    "model_CNN.add(Dense(8, 'relu'))\n",
    "model_CNN.add(Dense(2, 'linear'))\n",
    "model_CNN.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1e81145e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CNN Checkpoints\n",
    "cp_CNN = ModelCheckpoint('model_CNN/', save_best_only = True)\n",
    "\n",
    "# CNN compile\n",
    "model_CNN.compile(loss = MeanSquaredError(), optimizer = Adam(learning_rate = 0.0001), metrics = [RootMeanSquaredError()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a3af2fdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1842/1875 [============================>.] - ETA: 0s - loss: 346.5051 - root_mean_squared_error: 18.6146INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 3s 1ms/step - loss: 340.4109 - root_mean_squared_error: 18.4502 - val_loss: 0.2940 - val_root_mean_squared_error: 0.5422\n",
      "Epoch 2/10\n",
      "1865/1875 [============================>.] - ETA: 0s - loss: 0.1150 - root_mean_squared_error: 0.3392INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1147 - root_mean_squared_error: 0.3387 - val_loss: 0.0502 - val_root_mean_squared_error: 0.2240\n",
      "Epoch 3/10\n",
      "1828/1875 [============================>.] - ETA: 0s - loss: 0.0470 - root_mean_squared_error: 0.2167INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0474 - root_mean_squared_error: 0.2178 - val_loss: 0.0437 - val_root_mean_squared_error: 0.2090\n",
      "Epoch 4/10\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 0.0446 - root_mean_squared_error: 0.2112INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0446 - root_mean_squared_error: 0.2112 - val_loss: 0.0400 - val_root_mean_squared_error: 0.1999\n",
      "Epoch 5/10\n",
      "1858/1875 [============================>.] - ETA: 0s - loss: 0.0422 - root_mean_squared_error: 0.2053INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0421 - root_mean_squared_error: 0.2052 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n",
      "Epoch 6/10\n",
      "1822/1875 [============================>.] - ETA: 0s - loss: 0.0398 - root_mean_squared_error: 0.1994INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0398 - root_mean_squared_error: 0.1994 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n",
      "Epoch 7/10\n",
      "1820/1875 [============================>.] - ETA: 0s - loss: 0.0383 - root_mean_squared_error: 0.1956INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0382 - root_mean_squared_error: 0.1955 - val_loss: 0.0287 - val_root_mean_squared_error: 0.1695\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 2s 905us/step - loss: 0.0366 - root_mean_squared_error: 0.1912 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 2s 906us/step - loss: 0.0354 - root_mean_squared_error: 0.1881 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n",
      "Epoch 10/10\n",
      "1826/1875 [============================>.] - ETA: 0s - loss: 0.0344 - root_mean_squared_error: 0.1856INFO:tensorflow:Assets written to: model_CNN\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_CNN\\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 [==============================] - 2s 1ms/step - loss: 0.0343 - root_mean_squared_error: 0.1852 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1603\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x182c857a2d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CNN training\n",
    "model_CNN.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs = 10, callbacks = [cp_CNN])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d1a9140",
   "metadata": {},
   "source": [
    "### GRU Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8ef25de3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " gru_1 (GRU)                 (None, 64)                13824     \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 8)                 520       \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 2)                 18        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14362 (56.10 KB)\n",
      "Trainable params: 14362 (56.10 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# GRU Model\n",
    "model_GRU = Sequential()\n",
    "model_GRU.add(InputLayer((7, 6)))\n",
    "model_GRU.add(GRU(64))\n",
    "model_GRU.add(Dense(8, 'relu'))\n",
    "model_GRU.add(Dense(2, 'linear'))\n",
    "model_GRU.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d68129eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRU Checkpoints\n",
    "cp_GRU = ModelCheckpoint('model_GRU/', save_best_only = True)\n",
    "\n",
    "# GRU compile\n",
    "model_GRU.compile(loss = MeanSquaredError(), optimizer = Adam(learning_rate = 0.0001), metrics = [RootMeanSquaredError()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5bebc7cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1852/1875 [============================>.] - ETA: 0s - loss: 4976.1685 - root_mean_squared_error: 70.5420INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 9s 4ms/step - loss: 4954.3896 - root_mean_squared_error: 70.3874 - val_loss: 3198.0640 - val_root_mean_squared_error: 56.5514\n",
      "Epoch 2/10\n",
      "1868/1875 [============================>.] - ETA: 0s - loss: 1760.0045 - root_mean_squared_error: 41.9524INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 1755.7729 - root_mean_squared_error: 41.9019 - val_loss: 636.9012 - val_root_mean_squared_error: 25.2369\n",
      "Epoch 3/10\n",
      "1851/1875 [============================>.] - ETA: 0s - loss: 201.8489 - root_mean_squared_error: 14.2074INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 199.4380 - root_mean_squared_error: 14.1223 - val_loss: 14.6772 - val_root_mean_squared_error: 3.8311\n",
      "Epoch 4/10\n",
      "1852/1875 [============================>.] - ETA: 0s - loss: 2.7210 - root_mean_squared_error: 1.6496INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.6998 - root_mean_squared_error: 1.6431 - val_loss: 1.0280 - val_root_mean_squared_error: 1.0139\n",
      "Epoch 5/10\n",
      "1845/1875 [============================>.] - ETA: 0s - loss: 1.0338 - root_mean_squared_error: 1.0167INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 1.0326 - root_mean_squared_error: 1.0162 - val_loss: 1.0180 - val_root_mean_squared_error: 1.0090\n",
      "Epoch 6/10\n",
      "1858/1875 [============================>.] - ETA: 0s - loss: 0.7953 - root_mean_squared_error: 0.8918INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.7939 - root_mean_squared_error: 0.8910 - val_loss: 0.8305 - val_root_mean_squared_error: 0.9113\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6408 - root_mean_squared_error: 0.8005 - val_loss: 0.8643 - val_root_mean_squared_error: 0.9297\n",
      "Epoch 8/10\n",
      "1851/1875 [============================>.] - ETA: 0s - loss: 0.6251 - root_mean_squared_error: 0.7906INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6236 - root_mean_squared_error: 0.7897 - val_loss: 0.6652 - val_root_mean_squared_error: 0.8156\n",
      "Epoch 9/10\n",
      "1862/1875 [============================>.] - ETA: 0s - loss: 0.3142 - root_mean_squared_error: 0.5605INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3129 - root_mean_squared_error: 0.5593 - val_loss: 0.1652 - val_root_mean_squared_error: 0.4065\n",
      "Epoch 10/10\n",
      "1873/1875 [============================>.] - ETA: 0s - loss: 0.1260 - root_mean_squared_error: 0.3550INFO:tensorflow:Assets written to: model_GRU\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_GRU\\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: 0.1260 - root_mean_squared_error: 0.3550 - val_loss: 0.1320 - val_root_mean_squared_error: 0.3633\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x182bcdf22d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GRU training\n",
    "model_GRU.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs = 10, callbacks = [cp_GRU])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56f68c0",
   "metadata": {},
   "source": [
    "### General Functions for Model Evaluation and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f24f1223",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reverse standardization to get orignal form\n",
    "def post_process(arr):\n",
    "    arr = (arr * p_training_std) + p_training_mean\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fc589bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction and plot for temperature\n",
    "def plot_temp_predictions(model, X, Y, start=0, end=100):\n",
    "    predictions = model.predict(X)\n",
    "    temp_preds = post_process(predictions[:, 1])\n",
    "    temp_actuals = post_process(Y[:, 1])\n",
    "    df = pd.DataFrame(data={'Temperature Predictions':temp_preds, \n",
    "                            'Temperature Actuals':temp_actuals,\n",
    "                           })\n",
    "    plt.plot(df['Temperature Predictions'][start:end])\n",
    "    plt.plot(df['Temperature Actuals'][start:end])\n",
    "    plt.legend([\"Predictions\", \"Actuals\"])\n",
    "    plt.ylabel(\"Temperature (deg C)\")\n",
    "    plt.title(\"Temperature Predictions\")\n",
    "    return df[start:end]\n",
    "\n",
    "# prediction and plot for pressure\n",
    "def plot_pre_predictions(model, X, Y, start=0, end=100):\n",
    "    predictions = model.predict(X)\n",
    "    p_preds = post_process(predictions[:, 0])\n",
    "    p_actuals = post_process(Y[:, 0])\n",
    "    df = pd.DataFrame(data={'Pressure Predictions':p_preds, \n",
    "                            'Pressure Actuals':p_actuals\n",
    "                           })\n",
    "    plt.plot(df['Pressure Predictions'][start:end])\n",
    "    plt.plot(df['Pressure Actuals'][start:end])\n",
    "    plt.legend([\"Predictions\", \"Actuals\"])\n",
    "    plt.ylabel(\"Pressure (mbar)\")\n",
    "    plt.title(\"Pressure Predictions\")\n",
    "    return df[start:end]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47ee9ba",
   "metadata": {},
   "source": [
    "#### Note: run the corresponding model training again before model evaluation for each one, otherwise mismatch predictions may occur."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0772531",
   "metadata": {},
   "source": [
    "### LSTM Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ab8f5b29",
   "metadata": {
    "scrolled": false
   },
   "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>Temperature Predictions</th>\n",
       "      <th>Temperature Actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.179871</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.981140</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.995667</td>\n",
       "      <td>12.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.245911</td>\n",
       "      <td>11.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.683044</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>13.331116</td>\n",
       "      <td>15.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>12.911926</td>\n",
       "      <td>15.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>12.717041</td>\n",
       "      <td>15.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>12.741943</td>\n",
       "      <td>14.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>12.991577</td>\n",
       "      <td>14.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Temperature Predictions  Temperature Actuals\n",
       "0                 12.179871                12.93\n",
       "1                 11.981140                12.43\n",
       "2                 11.995667                12.17\n",
       "3                 12.245911                11.55\n",
       "4                 12.683044                11.59\n",
       "..                      ...                  ...\n",
       "95                13.331116                15.52\n",
       "96                12.911926                15.27\n",
       "97                12.717041                15.07\n",
       "98                12.741943                14.88\n",
       "99                12.991577                14.98\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "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": [
    "# evaluate and plot temperature\n",
    "plot_temp_predictions(model_lstm, X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b6b2a3bb",
   "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>Pressure Predictions</th>\n",
       "      <th>Pressure Actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>982.200500</td>\n",
       "      <td>982.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>982.395569</td>\n",
       "      <td>982.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>982.618896</td>\n",
       "      <td>983.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>982.882507</td>\n",
       "      <td>983.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>983.264587</td>\n",
       "      <td>983.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>985.588501</td>\n",
       "      <td>985.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>985.697510</td>\n",
       "      <td>985.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>985.834839</td>\n",
       "      <td>985.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>985.883606</td>\n",
       "      <td>985.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>985.881653</td>\n",
       "      <td>985.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pressure Predictions  Pressure Actuals\n",
       "0             982.200500            982.43\n",
       "1             982.395569            982.68\n",
       "2             982.618896            983.02\n",
       "3             982.882507            983.59\n",
       "4             983.264587            983.59\n",
       "..                   ...               ...\n",
       "95            985.588501            985.54\n",
       "96            985.697510            985.68\n",
       "97            985.834839            985.68\n",
       "98            985.883606            985.67\n",
       "99            985.881653            985.93\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "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": [
    "# evaluate and plot pressure\n",
    "plot_pre_predictions(model_lstm, X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8169027",
   "metadata": {},
   "source": [
    "### CNN Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ef2e18a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159/159 [==============================] - 0s 629us/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>Temperature Predictions</th>\n",
       "      <th>Temperature Actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.039551</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.827209</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.735168</td>\n",
       "      <td>12.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.776611</td>\n",
       "      <td>11.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.890503</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>13.893311</td>\n",
       "      <td>15.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>14.034119</td>\n",
       "      <td>15.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>14.186035</td>\n",
       "      <td>15.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>14.494873</td>\n",
       "      <td>14.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>14.912659</td>\n",
       "      <td>14.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Temperature Predictions  Temperature Actuals\n",
       "0                 13.039551                12.93\n",
       "1                 12.827209                12.43\n",
       "2                 12.735168                12.17\n",
       "3                 12.776611                11.55\n",
       "4                 12.890503                11.59\n",
       "..                      ...                  ...\n",
       "95                13.893311                15.52\n",
       "96                14.034119                15.27\n",
       "97                14.186035                15.07\n",
       "98                14.494873                14.88\n",
       "99                14.912659                14.98\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CbHLq56iqSnNzMyUlJWRnZzN06NAuG+N1hnRmJBKJROJ1jEYjqqqSkJBASEiIt82R9AIhISEEBARw5MgRmpubCQ4O7va+pABYIpFIJD6DjMj0L3oSjWm1H7fsRSKRSCQSicRLSGdGIpFIJBJJn8arzsxTTz3FlClTiIiIIDExkYsuuoh9+/Z1uP0tt9yCoii89NJLvWekRCKRSCQ+wCOPPMKECRNsz+fOnctFF13Uo326Yx++gFedmRUrVnDHHXewbt06fv75Z0wmE2eeeSZ1dXXttv3mm29Yv349qampXrBUIpFIJBLHzJ07F0VRUBSFgIAABg0axH333efwXOZOXn75ZRYuXOjUtjk5OSiKwrZt27q9D1/Gq9VMixcvbvX83XffJTExkc2bN3PKKafYlh89epQ777yTH3/8kfPOO6+3zex/HN0Muevh+FvBTeIsiUQi8WfOPvts3n33XYxGI6tWreLGG2+krq6ON954o9V2RqORgIAAtxwzKirKJ/bhC/jUmaqqqgqA2NhY2zKLxcI111zD/fffz+jRo7vcR1NTE9XV1a1uEhf59l74cT7krPS2JRKJpJ+iqir1zSav3LrTtC8oKIjk5GTS09O58sorueqqq/jmm29sqaEFCxYwaNAggoKCUFWVqqoqbr75ZhITE4mMjOTUU09l+/btrfb59NNPk5SUREREBDfccAONjY2t1rdNEVksFp555hmGDBlCUFAQAwcO5IknngAgKysLgIkTJ6IoCjNnznS4j6amJu6++24SExMJDg5m+vTpbNy40bZ++fLlKIrCkiVLmDx5MqGhoUybNq2VRGT79u3MmjWLiIgIIiMjOe6449i0aZPL76kr+EyfGVVVuffee5k+fTpjxoyxLX/mmWcwGAzcfffdTu3nqaee4tFHH/WUmf2DylxxX7wXBs30qikSiaR/0mA0M+qhH71y7N2PnUVoYM9OjyEhIbaxDAcPHuTzzz/nyy+/RK/XA3DeeecRGxvL999/T1RUFG+99RannXYa+/fvJzY2ls8//5yHH36Y1157jZNPPpkPPviAV155hUGDBnV4zPnz5/P222/z4osvMn36dAoKCti7dy8AGzZsYOrUqfzyyy+MHj26w4678+bN48svv+S9994jIyODZ599lrPOOouDBw+2CjQ8+OCDvPDCCyQkJHDrrbdy/fXX8+uvvwJw1VVXMXHiRN544w30ej3btm1zWzSqI3zGmbnzzjvZsWMHq1evti3bvHkzL7/8Mlu2bHG698D8+fO59957bc+rq6tJT093u71+i6kJGsrF47KD3rVFIpFI+iAbNmzg448/5rTTTgOgubmZDz74gISEBACWLl3Kzp07KS4uJigoCIDnn3+eb775hi+++IKbb76Zl156ieuvv54bb7wRgMcff5xffvmlXXRGo6amhpdffpl//etfXHvttQAMHjyY6dOnA9iOHRcXR3JyssN9aGmxhQsXcs455wDw9ttv8/PPP/POO+9w//3327Z94oknmDFjBgAPPPAA5513Ho2NjQQHB5Obm8v999/PiBEjABg6dGg330nn8Qln5q677mLRokWsXLmStLQ02/JVq1ZRXFzMwIEDbcvMZjN//vOfeemll8jJyWm3r6CgINs/h6Qb1Ba1PJbOjEQi8RIhAXp2P3aW147tKt9++y3h4eGYTCaMRiMXXnghr776Kq+//joZGRk2ZwLEhXptbS1xcXGt9tHQ0MChQ4cA2LNnD7feemur9SeeeCLLli1zePw9e/bQ1NRkc6C6w6FDhzAajZx00km2ZQEBAUydOpU9e/a02nbcuHG2xykpKQC28/W9997LjTfeyAcffMDpp5/O7373OwYPHtxtu5zBq86MqqrcddddfP311yxfvtyW09O45pprOP3001stO+uss7jmmmu47rrretPU/kNNYcvjskPes0MikfRrFEXpcaqnN5k1axZvvPEGAQEBpKamtkqrhIWFtdrWYrGQkpLC8uXL2+0nOjq6W8d3xwgITSvUNhOiqmq7ZfZ/n7bOYrEAooT8yiuv5LvvvuOHH37g4Ycf5tNPP+Xiiy/usY0d4VUB8B133MGHH37Ixx9/TEREBIWFhRQWFtLQ0ACIcNiYMWNa3QICAkhOTmb48OHeNN1/sXdmqvLA2OA9WyQSiaSPEBYWxpAhQ8jIyOhSHzJp0iQKCwsxGAwMGTKk1S0+Ph6AkSNHsm7dulava/vcnqFDhxISEsKSJUscrtc0MmazucN9DBkyhMDAwFZyD6PRyKZNmxg5cmSnf1Nbhg0bxj333MNPP/3EnDlzePfdd116vat41Zl54403qKqqYubMmaSkpNhun332mTfN6t/YOzOoUJ7tNVMkEonEHzn99NM58cQTueiii/jxxx/JyclhzZo1/O1vf7NV/fzxj39kwYIFLFiwgP379/Pwww/z22+/dbjP4OBg/vKXvzBv3jzef/99Dh06xLp163jnnXcASExMJCQkhMWLF1NUVGSrHrYnLCyM2267jfvvv5/Fixeze/dubrrpJurr67nhhhuc+tsaGhq48847Wb58OUeOHOHXX39l48aNLjtDruL1NJOrONLJSNxIbWHr52UHIWmUd2yRSCQSP0RRFL7//nsefPBBrr/+ekpKSkhOTuaUU04hKSkJgMsvv5xDhw7xl7/8hcbGRi655BJuu+02fvyx4wqvv//97xgMBh566CGOHTtGSkqKTXdjMBh45ZVXeOyxx3jooYc4+eSTHaa5nn76aVtLlJqaGiZPnsyPP/5ITEyMU3+bXq+nrKyMP/zhDxQVFREfH8+cOXM8XmWsqN3xKPoQ1dXVREVFUVVVRWRkpLfN8X2+uR22fdTy/LSH4eR7O95eIpFI3EBjYyPZ2dlkZWURHBzsbXMkvURnn7sr52+fapon8QG0NFN0hriXImCJRCKR+DjSmZG0RnNmMk8W97I8WyKRSCQ+jnRmJK3RNDOZ1j4D0pmRSCQSiY8jnRlJC6ZmqC8TjzOszkx9KTRUeM8miUQikUi6QDozkha07r+6AIgeCBGiqyNlh71nk0QikUgkXSCdGUkLml4mIhkUBeKGiOcy1SSRSCQSH0Y6M5IWNL1MuOhzQJx1loYDZ2bNoVLmf7WT6kZjLxknkUgkEolj+s7gC4nnsY/MQIeRmc1Hyrnu3Y00mSxkxIVy6wzPDhCTSCQSiaQzZGRG0oITzsyhklpueG8TTSYxUGzFvpLetFAikUgkknZIZ0bSQofOzCFQVUpqmpj77gYq640MThBTYDcdKae2yeQFYyUSiUTSGYqi8M0333jbjF5BOjOSFmyaGaszE50Bih6MddSX5XH9wo3klTeQERfKZ7ecSGZcKEazypqDpd6zWSKRSHyANWvWoNfrOfvss116XWZmJi+99JJnjOpHSGdG0oItMmMtyTYEQowYa/Dqfxaz82gVsWGBvHfdVOLDg5gxLAGAFftlqkkikfRvFixYwF133cXq1avJzc31tjn9DunMSFqwOTNJLcusqaaq/L0EB+h459rJZMaLFNOM4S3OjJ/PK5VIJL2NqkJznXduLv6e1dXV8fnnn3Pbbbcxe/ZsFi5c2Gr9okWLmDx5MsHBwbYp0gAzZ87kyJEj3HPPPSiKgqIoADzyyCNMmDCh1T5eeuklMjMzbc83btzIGWecQXx8PFFRUcyYMYMtW7Z0aGNzczN33nknKSkpBAcHk5mZyVNPPeXS3+nLyGomicBsFN1+oSXNBMKZOfATWUoBp49MYuLAljHwJwyKI1CvI7+igcOldQxOCO9loyUSid9irIcnU71z7L8eg8Awpzf/7LPPGD58OMOHD+fqq6/mrrvu4u9//zuKovDdd98xZ84cHnzwQT744AOam5v57rvvAPjqq68YP348N998MzfddJNLJtbU1HDttdfyyiuvAPDCCy9w7rnncuDAASIiItpt/8orr7Bo0SI+//xzBg4cSF5eHnl5eS4d05eRzoxEYOv+a4DQuJbl1l4zWUoBzSmtR7CHBhqYmhXL6oOlLN9XIp0ZiUTSL3nnnXe4+uqrATj77LOpra1lyZIlnH766TzxxBNcccUVPProo7btx48fD0BsbCx6vZ6IiAiSk5Md7rsjTj311FbP33rrLWJiYlixYgWzZ89ut31ubi5Dhw5l+vTpKIpCRkaGq3+mTyOdGYmgxurMhCeBzi77aE0zZSmF6Ns4MwAzhyew+mApK/aXcMP0rN6wVCKR9AcCQkWExFvHdpJ9+/axYcMGvvrqKwAMBgOXX345CxYs4PTTT2fbtm0uR12cobi4mIceeoilS5dSVFSE2Wymvr6+Q73O3LlzOeOMMxg+fDhnn302s2fP5swzz3S7Xd5COjMSQU2BuI9ofXXQGJVFMDBQKSYsMaTdy2YMS+Dx7/aw/nAZjUYzwQH6XjBWIpH4PYriUqrHW7zzzjuYTCYGDBhgW6aqKgEBAVRUVBAS0v53syt0Ol07HaLR2Lrb+ty5cykpKeGll14iIyODoKAgTjzxRJqbmx3uc9KkSWRnZ/PDDz/wyy+/cNlll3H66afzxRdfuGyfLyIFwBJB27JsK/vrI2hQAwlQzCRZitq9bEhiOKlRwTSZLKw7XNYblkokEolPYDKZeP/993nhhRfYtm2b7bZ9+3YyMjL46KOPGDduHEuWLOlwH4GBgZjN5lbLEhISKCwsbOXQbNu2rdU2q1at4u677+bcc89l9OjRBAUFUVraeZuMyMhILr/8ct5++20+++wzvvzyS8rLy13/w30QGZmRCNo2zLOyp7CWADWZkUouSvkhiB/Sar2iKMwYnsAnG/JYvq+EmcMTe8tiiUQi8SrffvstFRUV3HDDDURFRbVad+mll/LOO+/w4osvctpppzF48GCuuOIKTCYTP/zwA/PmzQNEn5mVK1dyxRVXEBQURHx8PDNnzqSkpIRnn32WSy+9lMWLF/PDDz8QGdmS6h8yZAgffPABkydPprq6mvvvv7/TKNCLL75ISkoKEyZMQKfT8Z///Ifk5GSio6M98t70NjIyIxF05MwU1HBYtS7rYHq21m9mpew3I5FI+hHvvPMOp59+ejtHBuCSSy5h27ZtREZG8p///IdFixYxYcIETj31VNavX2/b7rHHHiMnJ4fBgweTkCB+S0eOHMnrr7/Oa6+9xvjx49mwYQP33Xdfq/0vWLCAiooKJk6cyDXXXMPdd99NYmLHF5Ph4eE888wzTJ48mSlTppCTk8P333+PTucfboCi+nmDkOrqaqKioqiqqmrl1Ura8OGlcPBnuOBVmPQH2+LL3lrLKXlvcKfhvzD5epj9YruXVjcamfTYz5gsKivvn8XAOOfFcxKJRALQ2NhIdnY2WVlZBAcHe9scSS/R2efuyvnbP1wySc9xoJlRVZU9BdVkW6wdgUsPOHxpZHAAkzJE/5kVB2R0RiKRSCS9i3RmJAIHaab8igZqGk0c1qWJBYU7wWJx+HLbaIN9xR41UyKRSCSStkhnRiK6/9ZZVfB2zsyegmoAmuPHQmA4NFZC8W8Od6E5M2sOldFscuzwSCQSiUTiCaQzI4HaYkAVE7JD422L9xTUADA8NRrSjxcLc351uItRKZHEhgVS32xme36lZ+2VSCQSicQO6cxI7PQyrbv/apGZUSmRkHmSWHhktcNd6HQK0waLMQirD3Te60AikUg6ws9rUiRtcNfnLZ0ZSYdl2butzszIlEjI0JyZNR1OlJ0+RER1fj0onRmJROIaer3oHt5RB1uJf1JfXw9AQEBAj/Yjm+ZJHDozNY1GcsvFP9nIlEgImgSGEKgvg5J9kDii3W5Osjoz2/IqqW0yER4k/70kEolzGAwGQkNDKSkpISAgwG/6n0gco6oq9fX1FBcXEx0dbXNmu4s820gcOjP7CoVeJjkymNiwQLEwfQpkrxSpJgfOTHpsKANjQ8ktr2dDdhmnjkjyuOkSicQ/UBSFlJQUsrOzOXLkiLfNkfQS0dHRLk8Md4R0ZiQOe8zssaWYIlq2y5gunJmcX2HKjQ53ddKQeHI35LL6gHRmJBKJawQGBjJ06FCZauonBAQE9DgioyGdGYnDyEwrvYxGxjRxr+lmFKXdrqYPieeTDblSNyORSLqFTqeTHYAlLiOTkpIOnBmRZmrlzKRNBn2giOSUH3a4qxOtFU37imoormn0jL0SiUQikdghnRlJO2fGbFHZV+ggMhMQAgMmi8c5jku0Y8MCGZ0qXrP2UJln7JVIJBKJxA7pzPR3zCaos85TsmpmcsrqaDRaCA7QkRUf1np7W78Zx83zoKVEW/abkUgkEklvIJ2Z/k6d1v1XB2HCCdl9TERlhidHote10cXY62Y64CS7fjOyAZZEIpFIPI10Zvo7Nfbdf4WqvKXzb0T77dOPB50BqvKgwnH55JTMWAL1Oo5VNZJTVu8RsyUSiUQi0ZDOTH/H3pmxssdRJZNGYBikThSPO0g1hQTqmZQRDcBqWdUkkUgkEg8jnZn+TkWOuI9Ksy06UFwLwPAkB5EZaBlt0MHQSbAbbSB1MxKJRCLxMNKZ6e8U7hT3yWMBaDSaOVrZAMCghHDHr8nofOgktOhm1hwqxWyRuhmJRCKReA7pzPR32jgzueX1qCpEBBmIDw90/JqBJwjBcEUOVB11uMnYAVFEBBuobjTx27EqDxgukUgkEolAOjP9GVMzlOwVj63OTHZpHQCZ8WEoDjr8AhAcCcnjxOPDyx1uYtDrOGGQaKC3SqaaJBKJROJBpDPTnynZCxYjBEdBVDrQ4sy06y/TlhHnifvtn3S4iew3I5FIJJLewKvOzFNPPcWUKVOIiIggMTGRiy66iH379tnWG41G/vKXvzB27FjCwsJITU3lD3/4A8eOHfOi1X6ELcU0zjZnKccuMtMp438PKJCzqsPRBtOHCmdm85EKGprNbjFZIpFIJJK2eNWZWbFiBXfccQfr1q3j559/xmQyceaZZ1JXJ06o9fX1bNmyhb///e9s2bKFr776iv3793PBBRd402z/oXCHuLemmAAOW52ZQV05M9HpMHiWeLztY4ebDIoPIzUqmGazhQ055T02VyKRSCQSR3h1avbixYtbPX/33XdJTExk8+bNnHLKKURFRfHzzz+32ubVV19l6tSp5ObmMnDgwN401/9oI/4FF9JMABOvgUNLhTMzc76t6Z6GoihMHxrP55vyWX2ghBnDEtxmukQikUgkGj6lmamqElUvsbGxnW6jKArR0dEO1zc1NVFdXd3qJnGAqrZzZmqbTJTUNAFOpJlA6GZCYqD6KBxa5nCT6UOFAyNFwBKJRCLxFD7jzKiqyr333sv06dMZM2aMw20aGxt54IEHuPLKK4mMdNCdFqHDiYqKst3S09M9aXbfpfIINFWDPhDihwMtepm4sECiQgK63ochCMZdLh5v/cDhJicNFhVNewtrbI6SRCKRSCTuxGecmTvvvJMdO3bwySeOq2OMRiNXXHEFFouF119/vcP9zJ8/n6qqKtstLy/PUyb3bbSoTMIIMIh+MtnOin/tmXi1uN/7HdSVtVsdFx7E6FTheP4qRxtIJBKJxAP4hDNz1113sWjRIpYtW0ZaWlq79Uajkcsuu4zs7Gx+/vnnDqMyAEFBQURGRra6SRxgX8lkxSW9jEbyWEiZIEq8d37ucBOtqkmmmiQSiUTiCbzqzKiqyp133slXX33F0qVLycrKareN5sgcOHCAX375hbi4OC9Y6oc4EP/mdMeZgZbozJYPhBanDScPEbqZ1QdLUB2sl0gkEomkJ3jVmbnjjjv48MMP+fjjj4mIiKCwsJDCwkIaGsRsIJPJxKWXXsqmTZv46KOPMJvNtm2am5u9aXrfx4Ezc7i7zszYS0EfBMW/wbGt7VZPzowhyKCjqLqJg9YhlhKJRCKRuAuvOjNvvPEGVVVVzJw5k5SUFNvts88+AyA/P59FixaRn5/PhAkTWm2zZs0ab5ret6kvhyqrlii5RWzdrTQTiIqmUdbeP1s/bLc6OEDP1CxRoSZTTRKJRCJxN15PMzm6zZ07F4DMzMwOt5k5c6Y3Te/baFGZ6AwxygCoqGumqsEIQGaci84MtKSadn/jcLVttIEUAUskEonEzfiEAFjSy3SSYkqJCiYkUO/oVZ2TNlXc15eJyE8bNBHwusNlNJssru9fIpFIJJIOkM5Mf8RBJVO3xb8agaEQkSIel2e3Wz0yOZK4sEDqm81sza3o3jEkEolEInGAdGb6I5ozk9K+LNulHjNtiR0k7h0MntTpFE6SqSaJRCKReADpzPgzZhPkrIbm+pZlxkYotU4mt5/JVObkgMnOiLWW1ncxRVuKgCUSiUTiTqQz489sWQgLz4N/nwYVOWJZyV6wmEQFUuQA26bZJT1MM0FLZKaifZoJ4GSrM7Mjv5L6ZlP3jyORSCQSiR3SmfFnsleK++Ld8PapkPNra/GvogCiqiynzA1pppjOIzMpUSFEhwZgUeFIWb3DbSQSiUQicRXpzPgzR60N7MKTRZXR+xfC+jfFMjvxb3FNE/XNZvQ6hfSY0O4frxPNjEaGtexbOjMSiUQicRfSmfFX6kqhKlc8vmUFjL5YzE8q2iWW2ZdlW1NMaTEhBBp68C+haWbqSqCx2uEmGbHCWTpijQRJJBKJRNJTpDPjr2hjBeKGQEQyXPouzHqwZX3qRNtDLcXUI70MiAZ8oUIX05FuJjMu1HpMGZmRSCQSiXsweNsAiYc4ukXcp04S94oCM+bBwBOgthgShts27fYYA0fEZkF9qeg1kzK+3eqWNJOMzEgkEonEPUhnxl85ZnVmBkxqvTzrlHabHnZHJZNG7CDI39ihbiYzXkszyciMuymsaiQhIgi9TvG2KRKJRNKryDSTP6Kq7SMzneC2NBN0KQLWIjPHqhpoMpl7fjwJAB+vz+WEp5Zw7sur+Om3QlRV9bZJEolE0mtIZ8YfqT4GdcWg6FsJfR1htqjkWqMk3Row2RabM+NYMxMXFkhYoB5Vhbzyhp4fT0KzycIrSw4AsK+ohps/2MycN9aw5pBsTiiRSPoH0pnxR7QUU+JIMTOps00rG2g2Wwg06EiNDun5sbVeMx0IgBVFkboZN/PdzmMUVjcSHx7EbTMHExKgZ2tuJVe+vZ67PtmKxSKjNBKJxL+Rzow/YksxTex8O1qmZWfGhbpHa6FFZqqPgtFx5EXTzciKpp6jqipvrxSO49xpGfzl7BGsmDeTa0/MwKBT+N/2Y+w6VuVlKyUSicSzSGfGH+lI/OuAvHLhUAyM7UGzPHtCYyEoSjzWRii0QYvM5MrITI9Zc6iM3QXVhATouer4DAASI4J59MIxnDAoDoDdxxz3/JFIJBJ/QToz/oaqtvSYcUL8W1zdCEByVLB7jq8oXQ6c1BrnychMz/m/leI9vmxyGjFhga3WjUqNBOA36cxIJBI/Rzoz/kb5YWisAn0QJI7qcvNCqzOTFOEmZwacrmiSmpmesa+whhX7S9ApcP30rHbrR1udmd0F0pmRSCT+jXRm/A0tKpM8BgyBnW8LFFU3AZDkrsgM2EVmOugCbNXM5Fc0YDJb3Hfcfsbbq4SzePaYZJuDaM+oFOHM7CmoxixFwBKJxI+Rzoy/4UJ/GYAiLTIT2XuRmaSIYIIMOkwWlWOVje47bj+iuLqR/247CsBNJw9yuM2ghHCCA3TUN5tlFEwikfg10pnxN1wQ/0KLM5Pci86MTqfYBMc58iTbLRauycFoVpmSGcPEgTEOt9HrFIYny1STRCLxf6Qz40+YTVCwXTx2IjLTZDJTUW8EICkyyH12aM5MVR6Ymh1uInUz3aeh2cyH644AHUdlNEZLEbBEIukHSGfGnyjdB8Z6CAiD+KFdbl5s1csEGnREhQS4z47wJAgIBdUClbkON5HTs7vPdzsLqG40kR4bwukjkzrdVtPNyPJsiUTiz0hnxp+w6WUmgE7f5eb2KSZFceNwQkXpshNwRryMzHSXzzYKB/GKKQPRddHoUJZnSySS/oB0ZvwJW3+Zrjv/gl1ZtjtTTBpd9JrRIjNyerZrHCyuZWNOBXqdwqXHpXW5/cjkSHQKlNY2UVwjxdYSicQ/kc6MP+Gy+Ndalu1O8a9GV71mYq2RmfJ6OTvIBbSozKzhiU59biGBets0dJlqkkgk/op0ZvwFsxEKd4nHTkZmij1Rlq3RRWQmNToYg06h2WSxRYgkndNssvDlFlGOfcWUdKdfNzpVjJeQqSaJROKvSGfGX6gpBIsRdAEQnenUSzybZuo8MmPQ60iX5dku8fPuIsrrmkmKDGLm8ASnXzdKdgKWSCR+jnRm/IWaQnEfkQw65z5WjzTM09CcmYojYDE73CTDqpvJlboZp/jUmmL63XHpGPTOf3VlRZNEIvF3pDPjL9QcE/cRyU6/xKOamcgBoA8U0aKqfIebyIGTzpNXXs/qg6UAXDbZ+RQTtERmcsrqqG0yud02icSvqcwFixy74utIZ8ZfsEVmUpzaXFVVz3T/1dDpISZTPJYDJ3vMfzbloaowfUg8A60RLWeJDw8iKTIIVYW9MtUkkTjPvh/gpbGw9B/etkTSBdKZ8ReqtciMc85MTZOJ+maR/vFIZAZaes10VJ4dLyMzzmAyW/h8k4huXe6C8NceTQQsdTMSiQtoHdVz13nXDkmXSGfGX9AiM5HOOTNaJVNksIGQwK4b7HWLrsqz7SIzqirLszti5YESCqsbiQkN4MzRnXf87Qipm5FIukFdibjv4DdM4jtIZ8ZfsGlmUp3a3KN6GQ2tPLsix+HqtJgQdArUN5sprXU8w0kCn2zIA+DiiWkEGbrneMpOwBJJN9CcmdpCaJbpcF9GOjP+gn01kxMUVnmwkklD08x04MwEGfSkRIUAUjfTEYVVjSzdWwzAlcd3L8UELQMn9xXVYDRLMWOfpK4Mtn8K/7kOXp8GR9Z42yL/p66s5bGMzvg0Bm8bIHET1QXiPtLJyExNbzgzmmYmG1RVzGxqQ2Z8KEcrG8gpq2dyZqznbOmjfLYxD7NFZWpmLEMSI7q9n/SYUMKDDNQ2mThUUsuI5Eg3WinxKFs/hC3vQ94GQG29PGOa18zqF2iRGRDOTPJY79ki6RQZmfEHmmqguUY8djIyU1TlwYZ5GjEZgALGutY/CnbIiqaOMVtU2/iCK48f2KN96XSK1M30RepK4b93QN56QIWksTDsbLGu7JBXTesX1Je2PJbvt0/jUmRGVVVWrFjBqlWryMnJob6+noSEBCZOnMjpp59Oenr3w+CSHqClmAIjIMi5q3dNM5Mc5cHIjCFI9JupzhfRmfDEdpsMtPaayS2XFU1tWbG/mGNVjUSHBnD2GOf7B3XEqNRINuSU89uxauY4N75L4m20FG1YIty8DKLSxEDZ/YuhXJ5cPYrZBPXlLc9lmsmncSoy09DQwJNPPkl6ejrnnHMO3333HZWVlej1eg4ePMjDDz9MVlYW5557LuvWyRK2Xqfa9YZ52iiDxAgPOjNgJwLOdrh6QLTQzBRUyvlMbfl4vRD+XjIpjeCAnlecaZGZPbI8u+9QJf4HiM0SjgxA7GBxX1cCjfKz9BgN5bRK65U7/g2T+AZORWaGDRvG8ccfz5tvvslZZ51FQEBAu22OHDnCxx9/zOWXX87f/vY3brrpJrcbK+kAF8uywX7IpAfTTCBEwDmrOvwhSLU6M0crGzxrRx+joKqBpXuLAPj9VPdEPEekiKjd3sIaVFVFcaBhkvgYlVZnJsrufyA4EsIShDNTfsjpwbISF6krbf1cRsJ8GqciMz/88ANffPEFs2fPdujIAGRkZDB//nwOHDjAzJkz3WmjpCtcLMu2WFSKa3ohzQROR2YKqxsxW2SvGY3PN+ZjUWFqVs+Ev/YMTYxAUaC8rpmS2ia37FPiYbRRIFpURkOLzkgdh+fQdH5h1vR4TYHD8myj2cL/rTwktWhexilnZsyYMU7vMDAwkKFDh3bbIEk3cLEsu6yuGZNFRVFEq3uPYl/R5ICEiCAMOgWzRaW4RqaaoI3wd2oHwt+S/VB60KX9hgTqybIKrvcV1vTIRkkvoaWZottE5+KszozUcXgOzZmJHwbB0eKxg9+xTzfk8uT3e7nlw02YZNsDr+F0NdOBAwf4/e9/T3V1e++zqqqKK6+8ksOH5RfLK2iaGWfLsq0ppvjwIAJcmL7cLbqIzOh1ii06dEymmoAW4W9MR8Lfxir492ni1uyacHp4sjXVVCCdmT5BlYM0E7R015aRGc9Rb+0xExbfqfOojRrJK2/g+12FvWWdpA1On8mee+450tPTiYxs358iKiqK9PR0nnvuOZcO/tRTTzFlyhQiIiJITEzkoosuYt++fa22UVWVRx55hNTUVEJCQpg5cya//fabS8fxe1yMzBT1ll4GWiIzdSWihNwBLboZGZkB+Hi9iMp0KPzN2wBN1dBYCfkbXNq3zZmRkZm+gSPNDNidXKUz4zFsaaaEDkez7C2sZufRKtvzN5cfkqNZvITTzszKlSv53e9+1+H6yy67jKVLl7p08BUrVnDHHXewbt06fv75Z0wmE2eeeSZ1dS15yWeffZZ//vOf/Otf/2Ljxo0kJydzxhlnUFMjf4xt1Fgb5rk4ysAj07LbEhINITHicQedgDXdjIzMQHFNS8ffKzpKMdl3fnWxC6zWLG9voczv+zxNNcJhBamZ8QY2Zybezplp/X7/xxqVmTY4jpAAPbsLqll1oI1wWNIrOO3MHDlyhMTE9n1CNOLj48nLy3Pp4IsXL2bu3LmMHj2a8ePH8+6775Kbm8vmzZsBEZV56aWXePDBB5kzZw5jxozhvffeo76+no8//tilY/ktFkuLM+NkNZOtLLs3nBloic504cwcrZDOzNpDZVhUGDMgkiGJ4Y43yl3b8jjnV5f2P8IamTlQXCvz+76OJv4NjhIVTPZoJ9eGcmio6F27+gtaNVNYfIvzaKeZMZotfLP1KAA3TM/iCmvV4ZsrpIPpDZx2ZqKiojh0qOMP6eDBgw5TUK5QVSXCdbGxoq19dnY2hYWFnHnmmbZtgoKCmDFjBmvWOL4ibWpqorq6utXNr6kvA4sJUCDcuYnKtrJsT/eY0YjtXAScKiMzNtYeEnn6aYPjHW9gbISjm1ue528Uy5xkYGwoIQF6mk0Wcspko0KfxlbJ5KA0Pygcwq1p5TKpVfQINmcmwaFGaeneYsrqmkmICGLGsARuPHkQBp3CmkNl7Miv7H17+zlOOzOnnHIKr776aofrX3nlFU4++eRuG6KqKvfeey/Tp0+3VU8VFgotSFJS65N0UlKSbV1bnnrqKaKiomw3v+9KrJVlhyWA3nHZfFs0zUxyVC9oZsAuMtORMyOcKtlrBtYdFs7MCYM6mFN1bAuYm0W5aHgSmJtaOzddoNMpDLPpZvzc0e/rVArtlENnBqRuxtNoaaZQOwFwzTGb6F5LMc2ZOACDXseA6BAuGC9S/TI60/s47czMnz+fH374gUsvvZQNGzZQVVVFVVUV69ev55JLLuHHH39k/vz53TbkzjvvZMeOHXzyySft1rVt7tVZw6/58+fbbKuqqnI59dXncFH8C1Bo1cz0XpopU9x3EJmRmhlBQZUYuKlT6HjopqaRyTixZcigi7qZkVZnRpZn+zgd9ZjRkBVNnqXeLjITEiPSfQAVORTXNLJsn9C2/W5yy+dzywzh9Pywq5DsUjlvrjdx2pmZOHEiX3zxBStXruTEE08kNjaW2NhYpk2bxqpVq/j888+ZNKl7A1/uuusuFi1axLJly0hLa/nHSE4WJ+i2UZji4uJ20RqNoKAgIiMjW938GhfLsqElzdQrAmDosjw7xerMVDeaqGk09o5NPsj6w2IOzJgBUUQGdxBl0/QyA6dBxkni8ZHVLh1Hq2jaI8uzfZuOesxoyMiM5zA1ixYIIDQzitJKBPzN1qOYLSoT0qNbNbUcnhzBqSMSUVX4v5Uy/debuDRocvbs2Rw5coTFixdz8OBBVFVl2LBhnHnmmYSGhrp8cFVVueuuu/j6669Zvnw5WVlZrdZnZWWRnJzMzz//zMSJomV3c3MzK1as4JlnnnH5eH6JLTLjnPi3yWSmrK4ZgKTeFgBX5oHZ2C4dFh5kICokgKoGIwVVjUR0dCL3c1pSTHGON7CYRVk2iMiMzvo+5W1w+L52hObM7CuSaSafpsvIjKxo8hhaVEbRtzTMix0Mx7ailh3mP5vERfJlk9s7mrfOGMzSvcV8uSWfe84Y6vn5dxLARWcGICQkhIsvvtgtB7/jjjv4+OOP+e9//0tERIQtAhMVFUVISAiKovCnP/2JJ598kqFDhzJ06FCefPJJQkNDufLKK91iQ5/HNsrAOWemxDrGIFCvIya0l5yGiBTQBwl9R1VeyxWOHanRIVQ1GDla2cCwJPe07+9rdKmXKdol+ssERULSGECBkFhR0XJsK6RPdeo4Wnl2XnkDtU0mwoNc/hmQ9Aa2HjMdlOjbR2ZUVUQPJO7BvpJJZ01gWH+3yvL2cqB4CMEBOmaPb/+7OyUzhvHp0WzPq+SHnYVcOy2zl4zu33i4/WvnvPHGG1RVVTFz5kxSUlJst88++8y2zbx58/jTn/7E7bffzuTJkzl69Cg//fQTERH984TXDheHTBbZyrKDem/QoE7nhG6mf3cBdk4vY00xpU8FnV68rzbdjPMl2rFhgSRGCPG31M34KGZTy4VKR5EZLeLZWAX15b1jV3/BvmGehtV5rDq6F4CzRyc7TAcrisI51s7dmq5G4nm86syoqurwNnfuXNs2iqLwyCOPUFBQQGNjIytWrHBpVpTfU601zHPWmRGRmV5LMWl0oZvp7+XZWlRmbKd6GavQd+CJLcs03Yyr/WZSRHRGOjM+Ss0xUC0ildhRy4XAUIgcIB5L3Yx70SIzoXYpX2tkJrT2CAC/c5Bi0pg1XPRkW3uojIZms2dslLTCq86MxA24mGYq6m3xr0YXAydbnJn+OdJg3SFxZd2hXkZV7SqZprUsz7Q6M7nrxNW8k4yQ5dm+jU0vM6AlzeEIWdHkGewrmTSsGqUUyhgQ1sl3FRiWFE5qVDBNJovtQkXiWaQz05cxNbUMQ3OymqnQLs3Uq8R23gW4ZT5TP43MZHch/i07JELf+kBItasaTBoDQVHQXAOFO5w+3gg5o8m36WgmU1tkRZNncJRmCo2lQSe6cl8+xIRe13GaXlEUZo4Q0RmZauodXHZm2nbX1W41NTU0Nzd7wkZJR2h6GX1Qy/yjLij2Vpqpi8hMfx5pcKyygSM2vUwHn6OWYhpwHATYfXY6vahsApf6zbRMz66Wg/F8kY6mZbdFVjR5BpsAuOXiwmhROWwRDsoZKV13z9ZSTcv2FcvvWC/gsjMTHR1NTExMu1t0dDQhISFkZGTw8MMPY7HIuS8ex75hnpNi3sIqL6WZ7CMzDr7YmjNTWN2I2dK/vvjrs1v0Mh2WpWviX3u9jEY3RMBDEsPR6xSqG022aJ3Eh+iqx4yGjMx4hrr2aaZ1h8s4ZBb6pWGGrqMt0wbHEajXkVfewGHZQM/juOzMLFy4kNTUVP7617/yzTff8PXXX/PXv/6VAQMG8MYbb3DzzTfzyiuv8PTTT3vCXok9LuplAIpqvJRmih4IKGCsawnh2pEQEYRBp2C2qBTX9K+Ta5d6GWiJzNjrZTQypov7I2vE4FEnCDLoGRQfBsBe2TzP9+iqx4yGLTJz2OFFgqSb2I8ysPL9zkJyVOHM6DsoZLAnLMjA8dY2C8v2ylSTp3G5wcR7773HCy+8wGWXXWZbdsEFFzB27FjeeustlixZwsCBA3niiSf461//6lZjJW1wsSwbvJhmMgSJH+aqPJFqCm89gV2vU0iOCia/ooFjlQ2kRIX0rn1eZG1XzfKqC6xaI8VxL5mU8RAYDo2VUPwbJI916rjDkyM4UFzL3sIaZo1I7PoFkt6jsyGT9sRkAorQTNWVtPteSbpJGwGwyWzhx98KmWmxjo1xMhI2c3giqw6UsnxfCTeelCHSwhKP4HJkZu3atbZuvPZMnDiRtWtFKHz69Onk5ub23DpJ51S7FpmpbzZR2yQqXrQ+I72K1mumi/Lso/2oouloZQO55fXodUrXepnkMS3zYezRGyD9ePHYhRLtkbbybFnR5FOoqvMC4IDglm2kbsZ92DfNAzZkl1Ne10x5kDVS1oH2ry0zhwtnaF92DpYXRsLXt7rdVInAZWcmLS2Nd955p93yd955xzahuqysjJgY5wSpkh5Q41qPGS0qExKg907X11jnRMD9qdfMemtUZkxnepncdeJ+oIMUk0baZHFftMvpYw9PkhVNPklDhUjHgijN7gpHuhljA+z4D9TK9IbLGBuguVY8tjoz3+8Sv7WDR4wTy6vywdj1Rdeg+DAGxoYyXM1GV1cEB5d4xGRJN9JMzz//PL/73e/44YcfmDJlCoqisHHjRvbu3csXX3wBwMaNG7n88svdbqykDbY0k3Nl2cU12rTsXuz+a0+XkZn+1wW4yxEG0DKPaeDxHW+jTU2vd76nhVbRdKiklmaThUCD7NTgE2ji37AECHAi3Ro3GA4va4nM1JfDx5dD/gaYcBVc9LrnbPVHtKiMPhCCIjFbVBbvKgLglAkj4XCkGCtSkQOJIzrdlaIozBqeQPX6SrGgoUKOnvAQLv96XXDBBezbt49zzjmH8vJySktLOeecc9i7dy+zZ88G4LbbbuOf//yn242VtMGWZkp2anNNWOuVFBO40Div/zgzW3IrAZja0QiD5vqWaEtaJ7OXtKoLB+LqjkiLCSE8yIDRrHKopNbp10k8jLN6GY1Yu8hMRQ68c4ZwZACKd7vdPL/HvseMorAxp5zS2iaiQgKYNiS+y4uytswckUiCUimeWIzQLCubPEG3cg2ZmZmyWsnbqKrLE7O1IZNem+Lq5EiD/qKZqW402pyI8enRjjcq2A4WE4Qnd17Z0g1nRlEURqdGsj67nO15lTYNjcTL2PQyXVQyaWhppryN8O8zoK5YNFJsqoKKI56x0Z9pM8rg+50ixXTmqCQC9DrRdblwB5Qfdmp3Jw6KI1tvp0trqICgcLeaLOlmB+BVq1Zx9dVXM23aNI4ePQrABx98wOrVq91qnKQTmqpb8urOamaszkyCtyIzWuv1uhIxHK8N/U0zsyu/ClUVEZL48A4+k/yN4j5tcuehaZszU+qSDVOsEaGNORUuvU7iQWw9ZjqYlt0WLTJTc0w4Mklj4SarNqOhHBqlwNsl7CqZLBaVH3aJi8Zzx1p/Z7vQ/rUlOEDPyHC737TGSjcZKrHHZWfmyy+/5KyzziIkJIQtW7bQ1CROkDU1NTz55JNuN1DSAVpUJjhKDJxzAk0A7DVnJjgKIq1Xm4U7263WIjNVDUZb1ZU/sy2/EoDxadEdb6SlC9KmdL4zq1CR5lqRmnISrYJq0xE5ddlnqHIxMhOTIQZSAmTNgOu+h/ihEGJNXVbK6IxL2KWZNudWUFLTRESwgZOGWL9jMZ1HmB2RGdySxlXlhHOP4LIz8/jjj/Pmm2/y9ttvExDQUn0xbdo0tmzZ4lbjJJ1g08s4J/4FH9DMgOiJAlDQfo5QeJCBqBDxP9UfojM78kR0any6g3JrEKnEPGtkxlF/GXuCIoVgEVquLJ1gUkYMigJHyuoplp2AfQNXNTP6ADj/ZZg5H676AoKt6cKYDHEvU02uYXNm4lmyR1SDnT4yqUUg72JkBiCeStvj5dv2u8NKSRtcdmb27dvHKaec0m55ZGQklZWV7rBJ4gz2owycxKaZ6e2GefakWEsbOxiK2J8GTm7vKjJTfRRqC0HRQ8qEznemKN3SzUQGBzAiWZz8Nh2RqSafwFXNDMDEq2DmA2AIbFmmCVVlZMY16qwVgWHx/HpQXBicMqylE7AtMlOZ6/Sk+oCGlu/kkq372CfbIbgdl52ZlJQUDh482G756tWrGTRokFuMkjiBNsrAybJssBcAezEyk2x1ZhxEZgAG9JPy7KLqRgqqGtEposeMQzS9TPIY51KJWqrJZd2MSDVtzJHhb69jbBS6F3BeM9MR0TIy0y2sFwP1ATHsOiaipycNtnNmIgeI4b4WI1Tnd70/U5MQ/VoJt9Twx0+30mg0u9Xs/o7Lzswtt9zCH//4R9avX4+iKBw7doyPPvqI++67j9tvv90TNkocUa01zHMuMmM0WyirE1PNvaaZgZbITMleh02n+kt59va8SgCGJUUQ1lEDw/xN4r4rvYxGNyIzAJOtIuBNUgTsfapFQQUBoRDSw8ajtjRTTs/209+wfn92VwWiqjAsKbx1NFuna3lvnUk1tfk+Jgc2srewhmcX73OXxRK64czMmzePiy66iFmzZlFbW8spp5zCjTfeyC233MKdd97pCRsljtB+oLSrry4orRVRGYNOITY0sIutPUjkAFHyqJrFHKE2tDgz/q3f6DLFBC3N8jzszGiRmd+OVfUL4bVPU2U3xqCnjdVkmql7WBtPbiwRc5Sm2UdlNFwRAdcWtXp6Zpa4mFzwazYr9rv2XZV0TLdKs5944glKS0vZsGED69ato6SkhH/84x/utk3SGZozo/1gdYFWyRQfHoRO58Xuk4rSaaqpv2hmduSL8PW4jsS/pibRYwZccGa6l2ZKiQphQHQIFhW2WZv4SbxEd/QyHWGfZpITtZ1DVW0XAyvyxXs2fYgDZ8YVEXCbkRKpQQ1ce6L4bO77z3bKrRFzSc/odv/y0NBQJk+ezNSpUwkPlw2AehWLueVqS/tSdUGJ3SgDr9OJCLg/aGYsFtWWZuowMlO4C8xNorw21kktWjcjMyB1Mz6DVskU7WQlU2dEpQMKmBrkjCZnaa4Dk4gKb68IQK9TON7RqJEeRGZoqGD+uSMZkhhOSU0TX252Qncj6RKnOgDPmTPH6R1+9dVX3TZG4iTVx8DcLHpLRDoxiA67uUze1MtoOBGZKaxqxGxR0XsziuQhcsrqqG40EWTQ2eYjtcPWLG+K8+mGHjgzx2XG8s22Y7LfjLexlWW7ITJjCBT7qcoTFz8RST3fp79j/e6Y9CE0EMxx6dGOB8DaIjM5Xe9TcyQjBwhNVEMlwQF65kwawLOL99n6TUl6hlORmaioKNstMjKSJUuWsGnTJtv6zZs3s2TJEqKiOgiZS9yLTS8zEHR6p16i9ZjxqvhXQyszLtrVrrQxMSIYg07BZFFtNvsbml5mzIAo0R7dEfbOjLO4ITKzNbcSo9ni8uslbqLG9f5RnSIrmlzDmqKt0olz2UmD4xxvp0VLyw93ncLTnJmE4eLeWtmkRWV3SGfGLTgVmXn33Xdtj//yl79w2WWX8eabb6LXixOp2Wzm9ttvJzJSznbpFbTQppMpJrAfZeDFHjMasYMgMFx0qy07AIkjbav0OoWU6GDyyhvIr2ggJcqJqcF9jO1aszwtxWRshIA2n4vW+TfdFWeme5oZgGGJEUQEG6hpNLGnoJpxnQmTJZ6jxpqScKF/VKfEZMCR1bKiyVmsDScLjUI6cZIjvQxYy+YVMVKmrgTCEzvep5ZmShgBh5baxhloLRnyyhsor2smNsyLhRl+gMuamQULFnDffffZHBkAvV7Pvffey4IFC9xqnKQDNNGZk+JfaBEA+0SaSaeDpDHisYNU08BY0VMlt8z5tvx9CS0yc0JcHSycDU8PhG2ftGxQUyQacqFA6iTnd2wfmXFR8KnTKUzO0HQzskTba9S63gyzU2wVTTnu2Z+/Y41qFpojCAnQM3FgB+XxhqCWVGBXImAtMhM/TNw314KpmaiQALLiwwDYebT9rDqJa7jszJhMJvbs2dNu+Z49e7BYZHi6V7BVMjkfmSmp9SFnBjoVAafHCGcmr8L/nJlmk4XfjlUzW7eW05fPgZxVQuj7zW2w/TOx0VFrCjdxZEtremcItV5FWkzdGmbX0m9G6ma8gqnZVhZMuJucGZlmcg2rM1OmRjI1K7ZlhIEjNEexKxGwFpmJHwpY9W/W7+e4NBGd2WEtCJB0H6fSTPZcd911XH/99Rw8eJATTjgBgHXr1vH0009z3XXXud1AiQO6kWYqsc7d8eooA3tsIuDt7Vala5GZcv9zZg7kHuMp5V9cErgamoEBx4krtu2fwDe3CrFv8W6xcdpk13YeECxmNDVVi1STi03X7Cdoq6qK0tM+JxLX0E56ugAIdVBB0x3kfCbXsI4yKCfScUm2PbFZ4mLE2chMRIoYtttYKXQz4YmMS4vmv9uOsT1fRmZ6isvOzPPPP09ycjIvvvgiBQWiC21KSgrz5s3jz3/+s9sNlDjAxTSTqqo+GJmxDpws3CFSInYnTi3NlOdvzkxjNelfnMNofS4WdOhOuQ9mzBOzlwxBsHkhfH1LS7oorYvhko4Ii7c6MyXWK0HnGZcWRaBeR2ltE0fK6sm0hsAlvUStnV7GXY6k9htRnQ9moxhKKekQS20xOqBUjWROl86MnQi4I5pqha4GIDxJXGBozgx2kRkpAu4xLqeZdDod8+bN4+jRo1RWVlJZWcnRo0eZN29eKx2NxEM0VLSkEJx0ZirqjRjNQkMRH+4jzkzCCHEF2ljVrkPpQH+NzBz8mcj6XErUSD4f8yac+qA4ueh0cN6LMOkPoFpaTmquVDJp9KCiKThAz1jrj6vsN+MFaqwjSsLdWEIdngSGYPF/VSX7mXRFTbn4DJoCYxnRUdsEDWd6zWjf5YAwCApviZY2VAIwOjUSnSIKNIrk1Poe0e2meSAmZcsKpl5G08uEJUKgc1fOWolzTGhA5zng3sQQ2FLF1EYErKWZiqqb/GsYW+kBAJaaJ5EwembrdTodzH5ZODQgfvQ0waAr9MCZAZhsLdGWc5q8QI2bxb8gIjzawEpZ0dQlTVUiJZScmtZ1p3RnugBrKSat2snmzIjvV2iggWFJwmnaLnUzPcKpM9vZZ5/NmjVrutyupqaGZ555htdee63Hhkk6oLwbehlbwzwf0ctodCACjgkNINw6fDG/wn86ARuLxGC5w2qK49JnzaGZ/RJc9r547io9KM8GmJJhFQHL5nm9T62by7I15Iwm51BVDA3iezMkM7Pr7bXITH0pNNU43kb7TLVoW0i0uLeboj12gJZqkrqZnuCUZuZ3v/sdl112GREREVxwwQVMnjyZ1NRUgoODqaioYPfu3axevZrvv/+e2bNn89xzz3na7v5LNyqZbGXZvjDKwJ6UCbD1w3aRGUVRSI8NZU9BNXnl9QxJ9I9xGU1F+wgAKkIyOm5eqNPB5B4I6XsYmZkwMBqAw6V1VDcaiXTU/VTiGWxpJjc7M7KiqXOaamHn51g2/JtYi3DixwxzQm8WHCmG5taXiYtM7eLMni4iMwDj0qP5z+Z8dsjy7B7hlDNzww03cM011/DFF1/w2Wef8fbbb1NZWQmIE8+oUaM466yz2Lx5M8OHD/ekvZKKbvSYsTXM8zFnppOKpoGxIcKZ8ZfybIuFoMpDAIQNGOW54/TQmYkPDyItJoT8igZ25ld13DRM4n7c3TBPw1bRlOPe/fZ16sth+VOix1NzDTqgQQ3kc+VM/jDQyXloMVlWZ+ZwB85M28hMe2dmvJ0IWFYRdh+nq5kCAwO58sorufLKKwGoqqqioaGBuLg4AgLk1Vuv0Y00k0+NMrAnaTSgiEZhtcWtumhqvWb8pnFe9VECLI0YVT1pg0Z47jg9TDMBjE+PJr+igW15ldKZ6U3c3TBPQ6aZHLP2X7Dh/8Tj2MHsTP0dV20axPDMdK511qGIHST6QnUkAnbCmRmeHEGgXkdlvZG88gYGxoV244+RdFsNGhUVRXJysnRkehstVOxKmslXNTNB4RA3RDxuk2rSvtD+UtGkWsW/R9QkJmQmeO5APYzMAEyw6nmkILGX0QTA7qxmAplm6ogyESll+r1w5ya+D7+YasI7Hv7qiK5EwB2lmeyaWgYZ9IxIsYqAZYl2t/GR0haJU5iaRb8IcCnNVOJLE7PbovWbKdjWarFW0ZTnJwLgyrzfADhMKqNTPTiQ1Q3OzPj0aED+sPYqZlNLNC0ixb371tJM9aVCHyIRaKXqAyaBTsf+QiHiHZ7kgjPTVXm2LTLTsWYGWvrNyLEG3Uc6M32JqjzRLyIgtPPBZm3waWdG66Wy68tW84RsIw3K61FdnDPki1TmCmemOiyL4AAP9mPSnJmGCtEkrRuMGRCJXqdQVN1EQZV/OJM+T10xoILOIESl7iQ4quUkKlNNLWjOjHXG0l6rMzPMFWfGFpnJcby+bWQmOFrct3NmxHIZDe0+0pnpS9h3/nVBJFbsa6MM7Bl/BQRGiBb++3+0LU6LEdOya5tMVNR376TsS6il+wEwJHajd4wrhMSAYv1aa3N+XET2vvACWiVTWGL3SvK7QqaaWmNqatEoRaVT02jkaKVw3F1KM2mRmep8sU97LJaWCGknmhmA8VZnZtfRKsyWvn/x5g2kM9OXsFUyOa+XqWsyUdcsGs/5nAAYRN8FrRR59Yu2xcEBepKtzpc/6GYi63IAiM0Y49kD6fQtV/Y90c2ki7D3tjwZ9u4VPFXJpKGlmmRkRlB9TNwbgiE0jv1FIv2WFBlEdGig8/sJTxTdfVWLddK9HY2VYLFeiGkRU/sOwHaDmQcnhBESoKeu2czhEpkK7A7dcmYqKyv597//zfz58ykvF3X5W7Zs4ejRo241TtIGrbTSpUomcbUQGqi3NaLzOU64HfSBkLcOjqy1LfaXGU1NdZXEW0SUJGv4BM8f0B0iYE03IyMzvYOnKpk0bBOeczyz/76GfYpJUdhfZNXLJLvY0V5ROhYBa3qZkBgxew1amuahihlqVgx6HWMGiGPL5nndw2VnZseOHQwbNoxnnnmG559/3tZv5uuvv2b+/Pnutk9ij4sDJsHH9TIakSkwQZT8s/qftsVpsSLV1NcjM9n7RB+dUqJJS3WzuNMRbirPBtH7Qoa9e4GaNiW87kammVrTRi+zzyb+7UaDTpuj2IEzY/+ZGoJEJAc61M3IoZPdw2Vn5t5772Xu3LkcOHCA4OAWDcY555zDypUr3WqcpA3dSDNpPWZ8riy7LdPuFlqPAz9B4S7AfyIzRYd2AlAWPLB3GmK5ITIzNDGC0EAR9j4kw96eR9PMuLuSSUOmmVrTgTPjkvhXo8PITBvxr4aDkQbQUtG0XUZmuoXLzszGjRu55ZZb2i0fMGAAhYWFbjFK4gBVtRtlkOn0y7RRBgm+NsqgLXGDYdSF4vGvLwF2zkwf7wLcULgXAFPMkN45oBucGb1OYcwATTdT6QajJJ1im8vkociMrYQ4p1XVYL+lKk/cR6UD2NJMI1xNM0HLe1t+qPVyR5EZ6FAErM1o2lNQ7RcVnL2Ny85McHAw1dXV7Zbv27ePhAQPNgPr79QWg7FeRC+0KbhOUNwX0kwa0+8R97u+hPJsmzPT19NMQRUHAQhNHdk7B7SlmbrvzABMtKaapDPTC9ga5nlIMxOVBijiN6QH6Ue/wS4yU1LTRFldM4pC9+bApU4U9zm/gtGulYGLzkyatR1Fk8lCeV2z63b0c1x2Zi688EIee+wxjEah0lYUhdzcXB544AEuueQSl/a1cuVKzj//fFJTU1EUhW+++abV+traWu68807S0tIICQlh5MiRvPHGG66a7B9oKabINDA4r7b32VEGjkgZD4NPE5UBa161Nc47VtmI0Wzp4sW+SXFNIykm8cOZPNjB7BZPYIvM9OykNV6KgHuPGg8LgA1BEDlAPJYiYDtnJt0WlcmIDSUksBs9oFInQtRAMNbBwV9alneVZrLrAgwQaNARHy5+2wut7TQkzuOyM/P8889TUlJCYmIiDQ0NzJgxgyFDhhAREcETTzzh0r7q6uoYP348//rXvxyuv+eee1i8eDEffvghe/bs4Z577uGuu+7iv//9r6tm931sKaYMl15W4qujDDpCi85s/ZAEfQNBBh1mi0pBZd/8cm87UkaWIk5UIcm9NITVzc7M3sIaGo3mHhol6RCL2do0D885M2Cn7TjU+Xb+jqq2iszYxL+u9JexR1Fg1AXi8W/ftCx3MTIDkGRtR1EknRmXcblWNzIyktWrV7N06VK2bNmCxWJh0qRJnH766S4f/JxzzuGcc87pcP3atWu59tprmTlzJgA333wzb731Fps2beLCCy90+Xh9mm4MmIQ+Us1kT+Z0UXlReQRd0Q7SY0M5WFxLXkV9nxzAdvjgXs5UjBiVQAJcSA/2CDdoZgBSo4JJiAiipKaJ345VcVxGrBuMk7SjrlREIxVdy2fnCWIHQc6qlplE/ZWGChFFAYgcwL5C0dDSpTEGbRl9sRhcuX+xSDUFhHQSmbHrNdOG5MhgfjtWTWFVU7t1ks5xKTJjMpkwGAzs2rWLU089lfvuu4958+Z1y5FxhunTp7No0SKOHj2KqqosW7aM/fv3c9ZZZ3X4mqamJqqrq1vd/IJuVDKBnWbG1wXAGopinaYNlOwjPaZvl2dX5orKrLrwDNHQrjdwQ2k2iBSy1pl0a25lz2ySdEyr7r9d/49kl9bx+Le7+d/2Y9Q1mZw/Ttxgcd/fIzOa+DcsEQKC2dfdHjP2DDhOiImba+HgErGso8hMByMNoKVLu0wzuY5LzozBYCAjIwOzuXdCzq+88gqjRo0iLS2NwMBAzj77bF5//XWmT5/e4WueeuopoqKibLf09PResdXjdKOSqdlOSNZn0kwACdZ0TMnePi0CNpktKNZp2frEXkoxQcvVvbEOmut6tCutE7AsF/UgLlQyrdxfwoX/Ws2/V2dz1ydbmfSPn7nlg038d9vRrh2bWKsz098jM3YpJotFtWuY1w3xr4aitFRj7v5GzEXTxom4kGbSup4XVUlnxlVc1sz87W9/a9X515O88sorrFu3jkWLFrF582ZeeOEFbr/9dn755ZcOXzN//nyqqqpst7y8PI/b2St0I81UWiuiMgadQnRIgCes8gwJI8R9yb6W6dl90JnZX1RLukX8cIanjui9AweGizbtIEXAfQEnKplUVWXB6mzmvruB6kYTI1MiyYwLpclk4cffivjjp9s488WVnVfB2CIzh/t3ebadM3O0soH6ZjOBeh0ZcWE92++oi8T9vsUt0R9FDyFt0rOdOTNRIoJeVCOdGVdxWTPzyiuvcPDgQVJTU8nIyCAsrPU/wJYtW9xiWENDA3/961/5+uuvOe+88wAYN24c27Zt4/nnn+8wtRUUFERQUB9JqThLU22LQLAb3X8TIoLQ6XqhWZu7sI/MTO27zszWvAqG6EQKQUnoxciMoojoTFWecGY6E43/+CDs/Q4ueQfSjmu3WutKmlteT3ldM7FhLsytkThHF5VMzSYLD/13F59uFCfIS49L44mLxxCo17G7oJrvdxbw+aZ8jlY28OrSAzx8/mjHx4nJAhTRRr+uFML7aSsNux4zmvh3cGI4AfoejipMmyyqTavzYdsnYlm4g8GhnWhmNAFwoYzMuIzLzsxFF13kATPaYzQaMRqN6Nr8I+j1eiyWvlmm2220nHpgRMsXwQn6VI8Ze+Ktk6XrS8kMEX0b+mKaaWtuJWcq1oF28UN79+Bh8VZnphMRsNkEmxaI3iPvXwBXfiYE2HZEhQQwKCGMwyV1bM+vZNbwxA52JnEVs0Xl3V+zOenAfkYCangS9pccRysb+HFXIV9uyee3Y9XoFJh/zkhuPDnL1kl6dGoUo1OjOGFQHNe8s4EP1x1h7rRMx1GGgGDRb6YqT+hm+q0zY1fJVNSDMQZt0VJN616DzQvFMkeC7k4jM7Kaqbu47Mw8/PDDbjt4bW0tBw8etD3Pzs5m27ZtxMbGMnDgQGbMmMH9999PSEgIGRkZrFixgvfff59//vOfnezVD2m2tpMPck1t39Jjpg/pZQACw0RjwMpc0sziKqqi3khNo5GI4L6TLjt4JJcExSpAj+ul7r8azlQ0Fe0UjgyI/7EPL4HLP4ShZ7TabHhSBIdL6sguqWNWLwaY/BmLReUvX+7gi835/F9ANiP18OSqCo4VbCErPoyVB0paDRyMCDLwyu8nMmuEY2fy5KEJnDw0nlUHSnnux33868pJjg8cO0g4M2WHYOAJnvjTfB97ZybHOsagu2XZbRl9kXBmtEi6o1lb9s6MqgonyIqmmamoN9JoNBMc0EtFA35AD+NqPWPTpk1MnDiRiRNFB8V7772XiRMn8tBDDwHw6aefMmXKFK666ipGjRrF008/zRNPPMGtt97qTbN7H03EGehaTlfrzZLUVyqZ7LHqZkIr9xNnTW3klTd09gqforrRiK5MOOrm8BSXHdEe44wzk7dB3GeeDMPOBlMjfPJ7+O3rVpulWSvKjlb2nfffl1FVlUf/9xtfbM5Hr1MYHCwuVrKbIvluZwH/WnaQHflVKApMzYzl77NH8cufZ3ToyGjMP2ckigLf7ijoWOMkK5oc9pgZ4S5nZsDkluaE0IEzEy3uzU2tOwYjIqFBBnFa1kbRSJzD5ciMTqfrdFieK5VOM2fO7HQGRXJyMu+++65L9vklzdarZxedmewy4QRlxfdQ2OYNEoaLoZMl+0iPHUlZXTO55fWMSu1B+WQvsjO/ikFWvYw+YVjvG+BMeXbuOnGfNQOm/wm+uhl++wq+uF5UY4y7DIAB0VZnpkI6M+7guR/38d7aIygKPP+7cQxeVgtGuHfOyYypHEBOaR1Ts+I4Y1SSS527R6VGMmdiGl9uyefJ7/fw6c0ntP+t7u8VTaZmm0apOXwAh0o2A90cMOkInc6aanpdPG/bYwaEQF9nAItJRGcCW/pnKYpCclQwR8rqKaxu7JO9tbyFy87M11+3vmozGo1s3bqV9957j0cffdRthkns0NJMga7ldXNK+7Izo1U07SU9NpRteZV9SgS8La+SwTa9jDecGRciMwOPB30AXPJvCAqHLe/DorshbQrEZjHAOjNGRmZ6zmvLDvL6cuFIPH7RGC4enwr/E6XZo4YOY1TUgM5e3iV/PnMY/9txjPXZ5SzbV8ypI9pEBvp7ZKbmGKCCPoichhBMFpXwIIPNYXcLoy6yc2YcRGYURaSa6krESIM2n3lSZIszI3Eel50ZR513L730UkaPHs1nn33GDTfc4BbDJHZ0I82kqirZVmcms087M/sYOFb80PSl6dnb8ir5nbfEv9C1M1OVL6ouFL1o+AWiYdvsl0UbgJxV8L+74Q+LWiIz0pnpEZ9syOW5H/cB8NdzR3DV8RlQWyKu0FEcX8W7SGp0CNeflMWbKw7x1Pd7OWVoAgb7Kh1bZOZwO71Gv8AuxbS3SFwkDksK7zTb4DJpUyAiVThOHY2n0JwZ2WvGbbhNM3P88cd32v9F0gO64cwU1zRR32xGr1NIj+mDoUotmlFbxJBwMdQ0p6xvODOqqrItr5JhivWH0yvOTBdpprz14j55bOv/K50OLngFDCGQvRK2vM8Aq2amvK6Z+mYXOs5KbJTUNPH4t7sBuPvUIdx8itWpqLWWZYfFi+iYG7ht5mCiQwM4UFzL55vyW6+MyRRjE4x1Lc36+hN2zsyeAiHO7/ZMpo7QvkOT/iC0aI5woqJJRmZcwy3OTENDA6+++ippaWnu2J2kLbY0k/NOyeES4QClx4QQaPCqzrt7BEeKng3ASIPQnhwqrvWmRU5TWN2IqaaETJ31ZJEyofeN6Coyk2t1ZtKPb78udhCc+jfx+Ke/EWUsISJIBHGPyehMt3jpl/3UNZsZlxbFn063SzvWaC3v3TdgMiokgLtOFQ7049/t5qD998YQKNruQ//Uzdj1mNmQLRq/Tkx3vt2F0ww9Ay54VZTDO6ITZ0ZrpSGdGddw+SwXExNDbGys7RYTE0NERAQLFizgueee84SNEltkxnnNTE5ZH04xaVgbzQ20iB+go5UN1Loyi8ZLbMutZILOeqKIGwqhXhjQqDkz9aXgqC+TFpkZ6MCZATjhNpF+aqqGb+9lQLT4Uc6XImCXOVBUwycbcgF48NyRrRtYaj2k3Dwte+60TKYNjqO+2cztH21uHVHrz7oZa2SmOTzVVvF14uC43rejk/lMtl4zMs3kEi5rZl588cVW+UWdTkdCQgLHH388MTEe8HAl3UozZfdl8a9Gwgg4tITQqoMkRGRQUtPEoeJaW4t9X2VbfiUTdWImE2lTvGNEqDXNZDEJkaG9Q9VUC4U7xWNHkRkQ+pkLX4M3T4b9P3Bx/CReYBhN2Wuh5DAc3QKDZsIUqZHriie/34NFhTNHJXH8oDYnTi3N5MRcJlfQ6xRevmIi576yiv1Ftfzt6128cNl48dsdOxgOLe2fkZlKcWF0uDkak0UlLSbENjKlV+koMnPgFwY3igpfOdLANVx2Zk499VTS09MdCqZyc3MZOHCgWwyT2GF03ZnR0kyD+rQz0zLWYGjihZTUNHGgDzgz2/MquUOxNoNM95IzYwhsESEeWgpjL21Zd2wLqGaRxovqJDWcOBJOuR+WP8l15S9wbZCZ4HXGlvX7fhD7DY7y3N/Rx1l9oJRl+0ow6BQeOMfBfC4PpJk0EiKCePX3E7ny7XV8tfUoU7Ji+f3UgTIyA2ytElHuad6IyoDjkQYl++CjSxmhCyBDeZqC6lRUVXWvONmPcTnNlJWVRUlJ+zx8WVkZWVnOD0GUuEAP0kxZ8W5o0+0t7CqahiaKv+NAcY0XDeoas0Xlt/yKljSTtyIzAJOvE/erX2o9WNCml5na9T6m3wNJYwi0NBKsGKnRR8OI2cIRshjFUD2JQ8wWlSe+3wPA1SdkMCjBwXfRFplxvzMDcMKgOO47S1wUPLzoN3YdrWpd0dSfUFWbM7OsSKRypg2O944tjiIzW94HVBRLM38zfESzyUJFvdHhyyXtcdmZ6ajJXW1tLcHBfaxtfl/BxTST2aJyxKaZ6YOVTBpas7nqo4yME1cnB4t8WwR8sLiWFGMuEUoDakAYJIz0njFTbxIOcNFOOGhXaWjTyzjRzt4QCFd/xeapL3Bq0/NcF/cxXPERTLhSrN+zyP12+wlfbclnT0E1EcEG7j6tg4q2LoZMuoNbTxnMaSMSaTZZuP2jLRzBeqzyw471VP5KQ4Utyr2qSHQV94peBto7M2YjbP/UtvoM/WZO0W2XAyddwOk007333guIDoUPPfQQoaEtJ0mz2cz69euZMGGC2w2U4LIzc7SiAaNZJdCgIzXKjc2gepuQGBF+ry1kTID40T/g4xVN2/MqmagTKSZlwCTQu5zJdR8hMXDcXFj7L1j9oqiwsFgg39osz5nIDEBEEroxl3B45RoatB/XURfAymeFk9RUK5rtSWzUN5t4/ifRU+bOWUM6njbuwTSThk6n8MJl45n96mpyy+s55/1adgXo0ZkahAC5h436+gzWqExTUBwNjYEMSgizTanudbSRBpozs3+xEOuHJ4mmexve4iHDB+RXXttnup57G6cjM1u3bmXr1q2oqsrOnTttz7du3crevXsZP348Cxcu9KCp/RgXOwAfLhXbZ8WFta6c6ItYdTOZ1oqmvIp6GpqdH5nR22zLr2Si4mXxrz0n3gG6ADjyq0gvle6DxioICIOksU7vRus1U1TdiNFsgaQxEJMl5jkd+MlT1vdZft5dRFF1EwOiQ7h2WqbjjVTV42kmjejQQP5z64nMGJZAvUnHEbNIr+Qf2unR4/oUVmemVC8q/byml4H2mpmtH4r78VfArL9SrYtmiO4YkTvlOB9ncdqZWbZsGcuWLePaa6/lhx9+sD1ftmwZP/74I2+99RZDh3qhOVh/QIvMBDiXMsop9YMUk4ZVNxNec5DYsEBUFQ6V+G50ZnteJZO8XclkT2Sq+IEEEZ3R5jGlHedS1Cg+LIhAgw6Ligh9K4qYQQMy1eQArYfJ2WOSO5583FAB5mbx2FHbezeTEhXCwuum8Nyl48jXpQLw1lc/8+2OYx4/tk9gdWayjcKR8JpeBlqcmcZKqC5ouSCYeA2ERPNzyi0AjN7/OtQWe8fGPobLmpl3332XyEgZ9upVXEwztZRl+0Ho31bRtI8hVgHlQR9NNTU0mzlaWMgw3VGxwBecGYCT/gQosP8H2PqBWNZRSXYH6HSKbayBrdfMqAvE/f6f2k3/7e9szBHOzJTMTnoMaT1mQmKFNqkXUBSF301OZ9IEMcIijQJe/Hl/rxzb61gb5u1riAaEONpraM5MU7X4TqoWSD/B1i38aOYcdliyCDLXwZLHvGdnH6JbCf2NGzfyn//8h9zcXJqbm1ut++qrr9ximMQOF6uZDpf6QVm2ht3AySEZ4WzIKffZiqbfjlUxBlHFpMZkooQneNkiK/FDhOOx+79wVEwJJt0J8W8bBkSHkF1a1zKjKXWS6CZblSfKv0ec50aj+y7ldc3stwrVp2R20nurRGhqbKXSvUhYynDYBllKIYdL66htMhEe5EV9V29gjcwcU+MYkRzRsY6pN7BvZ7DhbXE/6RrboqToUB4xXstXQY+IFFR9ees5WqkT4eQ/97/ZWp3gcmTm008/5aSTTmL37t18/fXXGI1Gdu/ezdKlS4mKkv0m3I6qdjsy06e7/2pozkxlLiPjRLj+gI9WNG3La9HLKL4SldE46U92TxRIm+zyLmwDJ7XIjKLAyPPF493/7Zl9foQWlRmaGE5ceFDHG5bsFfeJXqh4ixsEwBBDMaoKu49V974NvY3VmTmqxns3xQSiKaXm0NQVCw3bqItsqxMjg9miDmNJwExAhX3fwd5vW25L/wH5G71huc/isjPz5JNP8uKLL/Ltt98SGBjIyy+/zJ49e7jssstkwzxPYGoSDc7AKWemyWS2XTn36e6/GmFxtm62YwJF5Yevppm251f5ll7GngGTRMdeECdPrZrClV3EaNOz7QZ+arqZfYvB1OzgVf2PjVa9zJSsLsZYFIvBkySO8rBFDogbAkA6hShYRP8Zf8cuMuO1kmx7tJEGAGMublURqE3O/rvperjoDZj9Ystt0Cyx0SYpDrbHZWfm0KFDnHeeCCcHBQVRV1eHoijcc889/N///Z/bDez3aFEZcMqZyS2rR1UhIshAfLgXw6juxBqdGYT4Mcopq6PJ5HsVTdtyy21l2d2JfHic0x4WJcCTru3Wy22RGfthk2lTxT6bqiB7hTus7PNssEZmju/SmREN9WzRx94kKh30gQSoRlIp839nxmxEtWqUColnalefTW8QYpeCnPiHVqs0Z+ZYg4GmMZfD5OtbbrMeFBv99pXD2U79FZedmdjYWGpqhGZhwIAB7Nq1C4DKykrq6+s7e6mkO2hl2YYQEZrsAk0vk5UQ5j9tsK0i4MiaQ0QEG7CokFPqW/9rZbVNGCqziVbqUA3BLpU99xoDJsF9++CEW7v38pg2aSYAnQ5GzhaPd3/TQwP7PrVNJptj0Kn419gomtaBdyIzOj3EZAKQqStkp787M9XHUFBpUg2kpqYRFRLgbYtanJm4oe16PkWHBhBoEKfn4uqm1q9LmyxaI5gaWzXa6++47MycfPLJ/PzzzwBcdtll/PGPf+Smm27i97//PaeddprbDez3uKiXsZVlx/lBiknDqilQfvuSE+JE0zZfEwFvt+svo6RM6LXqlN5Ei8wcq2zEYrHrBD7SWtW093sw+/5Uc0+y+UgFFhXSYkJIje6kYWXpflHBEhIL4Ym9Z6A91rEGWUohh0pqW0/W9jfsUkzHD/ERYX6sdfzPcXPbCXkVRbFFZwqr23QBVhTxGhCppg668vc3XHZm/vWvf3HFFaJvxfz587nvvvsoKipizpw5vPPOO243sN9jc2ac6xnjF9Oy2zJ6DkRnQGUuT9X8lSTKfU4EvCG7wrdTTG4gOSoYnQLNZgultXZXixknQWgcNJTDkdXeM9AH0PQyXaYxtBRT4ijvVaRYq6hGBZVg8XMRsGotyy5Q47wv/tWY9Te4/EM44XaHq23OjKORBuMuE33HSvdB7lpPWtlncMmZMZlM/O9//0OnEy/T6XTMmzePRYsW8c9//pOYmE7KECXdw+Xuv9ay7AQ/cmbC4mDutxA9kPjmfD4N/Aclx7K9bVUr1h4u813xr5sI0OtsP7D59roZvQGGnikeH1njBct8B61ZXtd6GU386wW9jEasqGgaEySasvmzbqayMAeAAuKZnOEj56mwOFENqHN8Gk6KEt+1oraRGRCVUGMvFY+lEBhw0ZkxGAzcdtttNDU1db2xxD10tyzbn9JMANEDYe53NISlkaUr4vYjfxKdM32AmkYjh44WMULJFQucnXnUB3GomwGItw4FrTjSyxb5Do1GM9vyKgGYmtVFtYwtMuPFQaSpEwAY1bSdaGrYedR/IzPF+SJqqkalEdZH+ukkR4qy/g6HTR53nbjf/V/Rh6af43Ka6fjjj2fr1q2esEXiCKNV6OqEM1PbZKKkRjiaftFjpi3RA6m67Cvy1XjSLMdQF84Wc4a8zKacCkarh9ErKkQOECME/BSHFU1gE5NSkdOr9vgSO/KraDZbiA8PIjOui7RwiV2ayVukToLkcRgsTVymX+7XkZmmUnGhEZea5WVLnCepI82MxoBJkDIezE2w7eNetMw3cdmZuf322/nzn//Mv/71L9auXcuOHTta3SRuxoU0kyb+jQ8P9A21vgdIGjiMuerDHFNjUcoP+oSaf+3hMqbpfxNP/DgqA51EZmIyxH0/dmY2ZJcBIsXUaSVhUw1UWqN43ijL1lAUmHozANfof+FQcZVPD3HtLs0mC0H1IoqbNciL77eLJHeWZtLQojObF/Z7IbDLzszll19OdnY2d999NyeddBITJkxg4sSJtnuJm3EhzXTYX1NMdiiKQmjiIN4yWTvPatNmvci6w2XM0lmjlUPO8K4xHmZAtIg4tI/MWK94awuh2bfK5nuL9c6Kf7UxBuHJEOrlfidjL0UNiSFdV8JMZSu7C/wv1bQlt4IUSgEYmDXMy9Y4T4fVTPaMvVRc6JYdgJz+Lb532ZnJzs5udzt8+LDtXuJmXHBmskv8sJLJAUMSw/mveRpmxQCFO6Bwp9dsqW40Unj0CON0VkHykNO9Zktv0GFkJiQGgqwDaLWoQz/CZLaw5YhoYNZpfxmwE/96US+jERCCMkk0bPuD/ie/TDWt3Z1NpCIcbF10mpetcR4tzVRU3YTaUdQlKALG/k487uepJpedmYyMjE5vEjejpZkCui7NzilraZjnzwxNjKCSCHaGTRMLtn3iNVs2HC5nhm6beJI6ESKSvGZLb2CvmWn1A6soLammyv4nAt5dUE1ds5nIYAPDkyM637hYm8nkRb2MPZNvQEXhFP1Oig77n1Rg/37xfjcHRLYaGeDrJFoFwM0mCxX1xo43HH2RuD+8rF+nmlx2ZgA++OADTjrpJFJTUzlyRPxwvfTSS/z3v3LYnNtxYWK2rfuvH6eZQAzwA/hanSkW7PgMzJ182T3I2sNlzNKcmaFnecWG3kRzZmqbTFQ3tGmy1o9FwFpJ9pTMWPS6LvrG+EJZtj0xGZSkink/w3M/87Ix7qWkpomGUnGO0kWne9ka1wgy6G2TvTvVzaQfD/ogqCmAsoO9ZJ3v4bIz88Ybb3Dvvfdy7rnnUllZidksBGPR0dG89NJL7rZP4mSaSVVVsktEFMfvIzNJwpn5vGo4algi1JfCgZ+8YsvGQ0WcrLOmubReK35MSKCeOOsPbH5lG22MdGacm/lT7AOVTG3QH38LAKc2LaGxttK7xriR1QdLSFWEMNsQ07ecGXCiogkgIKSl8ODwcs8b5aO47My8+uqrvP322zz44IPo9S2zgiZPnszOnd7TLvgtTjoz1Q0mqhvFlXJGrH87M2kxoQQZdDSYFKqHzRELvZAvrqxvJqxoIxFKA5bQeJFm6gd0qJuJ7p8VTRaLahsu2eWk7PpyIZIG28wxXyB27JnkkEqE0kDJ6ve8bY7bWLm/lBSrM0NU39HLaGi9Zoo66jWjMWiGuM9e6WGLfJduCYAdVS1pE7QlbsbJNJNWXRIXFkhIYNcDKfsyep3C4ATxfvyWICa4s38x1JX2qh3rs8ttKSbd0DM77OTpb3Tca8Za0dTPnJm9hTVU1hsJDdQzdkBU5xtrUZmogUK86SMoOh2rYi4GIHKnf8z7sVhUVh0oIVWx/i70RWfGWp5d0JUzk2V1ZnJWgcXiYat8E5d/fbOysti2bVu75T/88AOjRvlO2NRvcDIyc8x6Yul0uJ0foaWatjWnioiIxQQ7/9OrNqw9ZK+X8e+SbHtszky7XjOZ4r4ixy9Ohs6y9rC48p+SGUuAvouf1BIf6PzbARVDLqFWDSaqLhuO/Optc3rM7oJqSmubSddZu+NG9j1nZlC8+J3bW9hFyXzqJAiMgIYKKOqfGRKXnZn777+fO+64g88++wxVVdmwYQNPPPEEf/3rX7n//vs9YWP/xllnpkpzZoI9bZFPoImADxbVwoSrxMJtH/WqDdkHfmOo7igWRQ+DT+3VY3sTW5qpbWQmOh1QRNfqXo6SeZN1VmfmxMFdjDAA3xhj0AHDMgbws+U48SR7lXeNcQMrD5QAkBkgSub7YmRmXJqI9O3I76JkXm+ADGt15+EVHrbKN3HZmbnuuut4+OGHmTdvHvX19Vx55ZW8+eabvPzyy7Zp2hI34mQH4KP9LDIzJFGE6A8U18KYS0AfKPrNFPROaWl5XTMZ5aJJlXnA8RAS3SvH9QU6TDMZgsQ4B+g3qSazRWW91Zk5YZArzozvRbHHDIhks0U0lTPnrfeyNT1n5f4SFCzEmYVT0xedmdEDolAUkWYqrnFWNyOdGae56aabOHLkCMXFxRQWFpKXl8cNN9zgbtskYBeZ6bzPzLFK8Y8+oJ84M1qa6WBxLZbgGBh+rljRS9GZ9XYl2QEjz+6VY/oKaTHifzGv3EGn335W0bSnoJrqRhPhQQbGpEZ2vrGq+l5Zth0DokM4EGiNGOVtAkvfHW1Q12Ri85EK4qlCr5pA0UFEirfNcpnwIANDrPrAnV1FZ7JOEfdH1oKp2cOW+R7dViwWFxezZ88e9u/fT0lJiTttktgjNTMOyYgNJUCv0GA0iwjBxGvEik0LoOg3jx9/88GjnKiznpj6QUm2PVnxYSgKVNQbKattar2yn81oWmfTy8Rg6EovU1ssNA2KrmXKuA+hKAohaeOoVYPRG2ugZK+3Teo2aw+VYTSrTIqy/n5GpIhUTB9kXFo0ANu7cmYSR0NoHBjr4OhmzxvmY7jszFRXV3PNNdeQmprKjBkzOOWUU0hNTeXqq6+mqsr/WmF7FYsZTNZQfhdpJs2ZSYnqH5oZg15nE8cdLK6FIafBsHPA3Axf3gjGLkKyPaTpwHKCFSP1oaneHRboBUIC9bYI4MHi2tYr+1lkZu0hV/QyVuc3dpDoDeKDTMyIZ5tlsHjSh1NNq6x6mZkpVme7D6aYNDTdzM78ys431OlaojP9sETbZWfmxhtvZP369Xz33XdUVlZSVVXFt99+y6ZNm7jppps8YWP/xWgXxu8kMmMyW2wdIvtLmglgiDXVtL+oRrTTv+BVCEsQJ40lj3nsuAVVDQyrXguAbthZ4tj9jCGaALuk/zozZotqa5bnml7G98S/GpMzY9iiDhVP8jZ615gesCFHiH4nRFj/PzUtVx/EXgTc4YwmDZsz0/90My47M9999x0LFizgrLPOIjIykoiICM466yzefvttvvvuO0/Y2H/RUkyKDgwdR1yKapqwqBCgV4gPD+ol47zPMHsRMEB4Alz4mni87jU4tMwjx1255xin67cAEDzqHI8cw9fR8vgdRmb6wXym345VUdNkIiLIwOjULvrLQEtkJsF3nZnx6dFsVUUKzHRknZet6R41jUb2WUuZM/pwJZPGyJRIDDqFsrpmjjnbbyZvQ8v5o5/gsjMTFxdHVFT7L25UVBQxMTFuMUpixb5hXidX/y0pphB0Xc2F8SM0EfAB+xPqsLNgslWM/s1touOqmzFtXEiKUk5dQFzLlVA/wxaZ6ciZqcr3exGippeZmuXEPCaLRTQ0A0ge42HLuk94kIH6hEkAGCoP98kS+215lVhUSI8NIbShQCyM6nujDDSCA/S24aU78io73zh2kOinYzFCbt90RruLy87M3/72N+69914KCgpsywoLC7n//vv5+9//7lbj+j22smxnxb/9Qy+j0dJrpqZ1+PXMxyFuqBi89r8/unUIZXN9DWeVinbvlVPu8Vntg6fRnJlDbZ2ZsATrhHcVqvJ637BexCW9zOFlIvUWFAVDfLvB4vCsdPZbrGmZvA3eNaYbbLKmmCZnxAqnGiCq76aZwAURsKL02xLtbg2aXLduHRkZGQwZMoQhQ4YwcOBA1qxZw1tvvcWkSZNsN0kPcbKSqb/1mNHIiAvDoFOoaza3bvcdGAqXvA06A+xZBE9nwHsXwLKnROqpsYtump1Q8NNLxCtV5JNIyqyb3fBX9E00Z+ZYVSO1TXbTsxXFTjeT3fuG9RIms4WN1pOmU3qZze+K+/GXd9lmwdsclxHDFotVN5Pf95yZLbnic5mUEQNVR8XCPpxmAjsR8NHKrjfupyJgl2vVLrroIg+YIXGI5swEdNVjRjgz/Un8CxBo0JEZH8bB4loOFNe2duZSJ8L5L8NPfxPlsNkrWl+pRA2EpNGQNErMFKorEVdx2i15LJz7HATb9Q5pqCBx51sArBhwM1cF9B99UluiQwOJDw+itLaJQ8W1jE+PtluZIfQhfiwC3nWsmtomE5HBBkamdNFfpqYQ9n4vHh93neeN6yGTM2N5SR3GFSzHfGQ9fWnSm9misjW3EoApA0Kgrlis6MNpJmgtArZY1M7lBJpu5tg28b8Xkex5A30Al52Zhx9+2G0HX7lyJc899xybN2+moKCAr7/+up2ztGfPHv7yl7+wYsUKLBYLo0eP5vPPP2fgwIFus8NncbL7r9Ywr79FZkCkmg4W13KgqIYZwxJar5x4NYy/Ekr3wZE1kLtW5JGr8qAqV9z2/+B4x8W/Qel+uPpLCLVOQl79EiHmGvZY0oma+nvP/mF9gCGJYZTWNnGwrTPTDyqatBTT8YPiutbLbP0AVDOknyCcZx8nNSqY3NAxYATl2GaRptUHeNssp9hXWENtk2hiODSkRiw0hEBI39ZzDkuKIMigo6bRxJHyerLiO4nWR6aIi7ljW+HzP8AfFkGA/0sQetRFqLa2FkubCZ2RkV1cpdhRV1fH+PHjue6667jkkkvarT906BDTp0/nhhtu4NFHHyUqKoo9e/YQHOz/HwzgcsO8/tJjxp6hieH8gAMhqoZOJ0phE0fCFKswuL5cRA6KdgunpTIXwpNEKDoqDQLC4Id5cGwLLDwPrvkaAMv6N9EB/zRfzvNDk3rl7/NlhiSGs+5weSfl2f5b0bTO2REGFjNsfl88nuz7URkQzfMSMkdTsT+cGHMtFO6AAcd52yyn2HxECP4nDoxGX2OXYurj7RMC9DpGpUayNbeSHfmVnTszABf/H7xzuugV9L+74eK3+vx70BUuOzPZ2dnceeedLF++nMbGFp2CqqooioLZ7HwL7HPOOYdzzum4tPXBBx/k3HPP5dlnn7UtGzRokKsm912arX1mnHRm+luaCWBIUpvybGcIjYXM6eLWEclj4f0LhdOz4GxIGYfO1MgmyzCq0k4jKrRvXKl6ki7Ls/00MmM0W9iYI06aJ3blzBxaKiKAwdEw6kLPG+cmJmXEsXXvEE7VbxP9ZvqMMyP0MsdlxEDVJrGwj+tlNManRbM1t5LteVVcOKELQXPCMLjsffjwEtjxGcQNgRnzesdQL+GyAPiqq66ioqKCBQsWsGTJEpYuXcrSpUtZtmwZS5cudZthFouF7777jmHDhnHWWWeRmJjI8ccfzzfffNPp65qamqiurm5167M4kWaqaTRS3SgEmCn90JnRKpoOtK1o6imJI+D6xRA9UAhZd/8XgGeNlzNzZKL7jtOH0YZ9tqtosndm3PmZ+Ag78quobzYTHRrACGvJbIdssgp/J1zZpyrfJmfG2IZOqn2oE/CmVs6Mf1QyaYwd4IIIGGDQTDjvBfF42ROw60uP2OUruByZ2bFjB5s3b2b48OGesMdGcXExtbW1PP300zz++OM888wzLF68mDlz5rBs2TJmzJjh8HVPPfUUjz76qEdt6zWcSDNpVTxRIQGEB/XN2SM9ISs+DJ0C1Y0mSmqaSIx0Y6otNguuWwwfXASl+1mpjmeDOpJHhklnBlr6/OSU1dFkMhNksEpFo616tqZqIb7WNEd+ghaVmZoZ27kQs+oo7F8sHh831/OGuZGRKZG8oBO/8aYj6+gLccii6kbyKxrQKTAhPRr2aM5M3xb/aoxPF87MrqPVmMyWrmeBgfi/Kz0Aa/8F39wuxPlpkz1rqJdwOTIzZcoU8vI83z9C0+JceOGF3HPPPUyYMIEHHniA2bNn8+abb3b4uvnz51NVVWW79YatHsMJZ6a/lmVrBAfoyYgT749LqSZniRoA1//I/qmPc2fTHSRFBjEypYur8X5CYkQQEUEGLCrklNqP3ggVGiTwy1TT1ly7q/9ON7QKfzNOggTPXvy5mwC9DgZMwqTqCKg91lLi7MNoKabhyZFEBAfYRWb8I82UFR9OWKCeBqO5vU6tM854TMytMzXC//7kl9FS6IYz8+9//5tnnnmG9957j82bN7Njx45WN3cRHx+PwWBg1KjW6v+RI0eSm5vb4euCgoKIjIxsdeuz2NJMHZdmt+hl+p/4V8M+1eQRQmP52HQq1YQza3giip8L6ZxFURQGd9UJ2M/GGqhqS+nvxIGdODNmE2yxCn/7QDm2I8ZmDWCPao2y9YF+M5ozM1lzMjVnpg/PZbJHr1MYM6ClRNtpdHq46HUxEqdoZ59shOgMLjszJSUlHDp0iOuuu44pU6YwYcIEJk6caLt3F4GBgUyZMoV9+/a1Wr5//34yMjLcdhyfxn6cQQcc6+eRGWhJd+z3RGTGyor91im8w2WKyZ4uxxr4WWSmoKqR4pom9DrFpmFoR10pfH4NVB+FkFgYdUHvGukmWjXP6wMnwFZ6GVUV7z/4TZoJsLVA2NHVBO22hMbCmEvF403vuNUmX8FlkcX111/PxIkT+eSTT0hKSurRVWptbS0HDx60Pc/Ozmbbtm3ExsYycOBA7r//fi6//HJOOeUUZs2axeLFi/nf//7H8uXLu33MPoUzmpl+3GNGY6hViHqwyDPOTHZpHdmldRh0CicNcaLbaz+iv03P1qIyI5IjCAl00E7u4C9Cm1BbBPpAOOcZMPTN5oqTBsbwtWUY1/Izxpy1Pq2baTSa+e2oiFYclxEDjZUtkW0/EQCDnQjYlciMxpQbYNuH8NvXcNaTEBbvZuu8i8vOzJEjR1i0aBFDhgzp8cE3bdrErFmzbM/vvfdeAK699loWLlzIxRdfzJtvvslTTz3F3XffzfDhw/nyyy+ZPr2Tklp/wgXNTH/sMaOhnVD3F9fYWgS4k2V7RRfRKZmxIhcvsTG0n0VmNL3MxIHRrVcYG+GXR2D9G+J5wgi45N+ixL+PEhUaQHnsBKgFfdFO0SrCR0cxbM+rxGRRSYwIIi0mBIoOiRWhcX2qiqwrxltnNO0pqGktuneGAZNamult/QCm3+MZI72Ey2mmU089le3bt7vl4DNnzkRV1Xa3hQsX2ra5/vrrOXDgAA0NDWzbto0LL+w7vRp6jNGJNFNV/+0xozE4IRxFgcp6I2V17p/U/O2OYwCcPko2ymuLbeBkSS1mi52w0F+dGevU4onpbfQyX1zf4shMvRluXt6nHRmN9KzhFKox6FSTaCLpo2y2OpmTM2PExYyfzGRqS3psCIkRQTSbLaw7XO76DqbcKO43vSsaOvoRLkdmzj//fO655x527tzJ2LFjCQhofaV6wQV9Mz/sk3QRmTFbVAqrZJopJFBPekwoueX1HCiqJT7cfWH9vPJ6tuRWoigwe1yK2/brL6TFhBJo0NFsspBfUW+rLGsRAOeBqanPplrsaTZZ2GVNZbSKzFTmwr7vAAWu/AyGneUV+zzBcZlxbNo2nNn6dWIcSGeNJr3IZuvQz0maKFub2O5HehkQovvTRibxyYZcftld1H6ES1eMngM//lUI8w8ugWFnesZQL+CyM3PrrbcC8Nhjj7Vb52oHYEkXdOHMlNY2YTSr6HUKiRF9/2TRE4YmhpNbXs/B4hpOHOw+Xcv/rFGZE7LiSHJnDxs/Qa9TGBQfxt7CGg4W17Y4MxEpEBoP9aVQsB3Sp3rXUDewt7CaJpOFqJCA1u3kd34h7rNO9itHBoT+5D3LMGbr12E+ss4nh06qqmoXmbH2NPKzSiZ7zhxldWb2FPHYhaNdS6sHhsKEq2Hda7Dx337lzLicZrJYLB3epCPjZrroAKzpZZIjg51roOTHDLFWNLm718z/thcAcMGEVLfu159wWNGkKDDwBPE4d60XrHI/mvh3Qnp06xOI5syM/V3vG+VhMuNCORA0GrB2Am4zi88XOFRSS2W9kSCDjlEpkULbc+AnsTLa/wYSnzg4jtBAPQVVjew62o0O95OvF/cHfvKr+Wk9OgPaz2aSuBlVbYnMBDgW3bWUZcuIgVbRtK/Qfb1mDhbXsKegmgC9wjljkt22X3+jw/LsgSeK+9x1vWyRZ3Ao/i36TQwr1QfCyPO9Y5gHURSF8IyJ1KlBGJproGSPt01qx4r9pYDQywTqFVh0l5ipFhoPY9oPMO7rBAfoOWWoSC/9vLvQ9R3EDxGjDlBh87tutc2buOzMmM1m/vGPfzBgwADCw8M5fPgwAH//+9955x3/rF/3CuZmsIiZSx2lmWRZdgsTrK2+t+ZWUt1odMs+F20TKaZThiYQHRroln36I5oj2S4qZnNm1vrkFb2rbNPEv/bN8nb+R9wPPRNCuugI3EeZmBnPVou1etUHHdPl+0S14cxhibDmFdj1BegMYtBipH/q3M6wFiP8tLuoezvQhMBb3heaNj/AZWfmiSeeYOHChTz77LMEBrb8wI8dO5Z///vfbjWuX6NFZaBDZ6a/jzKwZ0hiBEMSw2k2W/ilu19wO1RVZdF24cycP16mmDrDVtFUXNt62GfKODCEiPlMpfu9ZJ17KK9rJqdMjGyYYC2PxWKBndbhfWMv9Y5hvcBxGTFsVsU4BjXPt5yZhmYz67NFVc+5Ib+J8niAs5+GzJO8Z5iHOXVEInqdwt7CGvLK67t+QVuGnQMRqVBfBuvecL+BXsBlZ+b999/n//7v/7jqqqvQ61vkYOPGjWPv3r1uNa5fo+ll9EGgd9zbRHb/bc25Y8VV2Pc7C3q8r51Hq8gpqyc4QGe7CpI4JjM+FJ0CNU0mimvsrvL0AS1D7fq4bmZbnkgxDUoIIyrU+n3M3wBVuRAYAcPO9qJ1nmXsgCi2IpwZc45vfY5rD5fSbLIwNbKS1CV3gGqBide0RB78lJiwQNvYhl/2dOPiTW+AUx8Uj5c9AcW+lz50FZedmaNHjzpsmGexWDAa3RPelyBEbNBpwzytx0xqP26YZ895Vmdm5f7SHqea/meNypw2MomwfjiN3BWCDC3DPv1VN2Obx5TuIMU08ny/aszWluAAPU3Jx2FWFQzVeVDd84sFd7F8XwmBGHlF9zxKYxWkTYHzXhACdD9Hu8j6ubuR6AlXwdCzhKTh61vB3LfP3y47M6NHj2bVqlXtlv/nP/9x62ymfo9Tc5mkZsaeYUnhDE4Io9lsYUl3rlasWCwq3+6wVjHJFJNTDE4Q/6ftBNh+UtHUopeJFgvMRtEWHvw6xaQxOnMAe7Whkz6SalJVleX7Spim20Vy42HR7feyD/yip5EzaM7M+uxyquq74YgoClzwCgRHQ8E2WP2iW+3rbZx2Zq6//npqamp4+OGHufPOO3nmmWewWCx89dVX3HTTTTz55JM89NBDnrS1f2Ery3YcmWloNlNu7XYrnRmBoii26Mx3O7qh8reyMaecgqpGIoINzBzuYlOqfop2kl99sLT1irQpoOhEk67qY71vmBuwWFS22ZVlA3BomdAbhCVA1gyv2dZbHJcRw0aLSDX5SpTtcGkdueX1TNNbhxEPP8dvBb+OyIgLY1hSOGaLyjKrCNplIpLh3OfF4xXPQMEO9xnYyzjtzLz33ns0NDRw/vnn89lnn/H999+jKAoPPfQQe/bs4X//+x9nnHGGJ23tX3TRME9LMYUHGYgMlmkQjXOtXXpXHiihppupJk34e/boZNdmn/RjTh0hpomvOVRKo9Gu31RwJCSNEY995CToKodKaqlpMhEcoGNEsqjcsqWYRs8R+gM/57iMGDZbhgFgPuIbUbbl+6yT7EMOiAUZ/iv47QhbqqkHkWjGXipSpRYTfHMbmNw/EqY3cNqZsa9SOOuss1ixYgW1tbXU19ezevVqzjzTfzoJ+gQ2Z8Zxj5mWsuxgtw9W7MsMT4pgUEIYzSYLS/a4frXSbLLwwy4R1ZGN8pxnRHIEKVHBNBotrD1U1nplxjRx30dTTZpeZlxatGhO2VwHe78TK/2wUZ4jEiODORY5HgBd0S5o8syEeldYvq+YYJoYYtScmWneNcgLnDFK9L9asa+EJlM3m9YqCpz3okjTFe2C5U+50cLewyXNjDxp9iJddP+VlUyOaZVq6kZV0w+7CiivayYxIogTB7lvLIK/oyiKLTqzZG+bq8Q+rpvZaq+XMZvg54fEENiYzJZqrX5AeuZQjqpxKKoZjm7yqi1aSfZE3UExBDNyAERneNUmbzBuQBSJEUHUNpm6N3hSIzwBZr8kHq/+J+z6yi329SYuOTPDhg0jNja205vETXSRZpI9ZjpGK9Fesd/1VNOCX3MAuOaEjH4/IsJVNGdm2d6S1v1m0q3OTNFv0FjlBct6htb5d0qSHj6+TMy0ATj5vn5RNaNxXEYMm2y6mfVetUUryT495KBYkDGtX30WGjqdGDwJ3ewGbM+oC+CEO8Tjb26DfO86rK7iUrL30UcfJSoqylO2SOwxdl6abXNmZFl2O0YkRzAoPozDpXUs3VvMhROcGza3NbeC7XmVBBp0XHm8/8108TTTBscTZNBxtLKBfUU1jEiOFCsiU0QUoyIH8jfCkNO9aaZL1DWZ2F9UQ6ZSwMxVD0HFQdEI8OI3YfRF3javV5mUEcOnlmFcqF+DmrsOb7oOrfQytfTLFJPG6SMT+WRDLsv3iYuIHmVQzvwHlB+C/Yvhk9/DTUshum9MHnfJmbniiitITEz0lC0Se7oaMlkhnJkBMTIy0xZFUTh3bAr/WnaQ73YUOO3MLFyTA4hy7Ljw/lHe6U5CAvVMGxzHsn0lLN1b3OLMgOg3U5EjRMB9yJnZebSK45XfeCvwZQwVtSKd8ftPIGW8t03rdYYnRbBbPxIAS94G9BYz6HpfIK+qononABOZDbvFwn4o/tU4cXAcgQYd+RUNHCqpZYh1vEi30Onhkn/DgrOFfubjy+GGHyGoB/vsJZyOo0u9TC/jZJopLcaxQLi/o6WaljuZaiqqbuQ7a2+ZudMyPWmaX9OSamojvrbpZvpWRdPu7KO8FfAikdSKMvOblvVLRwbAoNcRmj6OGjUEvbFWpA29wOHSOvLKG5hoyEFvbhTC1fhhXrHFFwgNNHCCVd+3bG9Jz3cYFAFXfgbhSWKI6hc3gKWb4uJepFvVTJJeoBNnxmxRKbCWZg+QmhmHjEyJICteVDV9vD63y+0/Wp+LyaIyJTOGMQNkKrW7zLI6M5uPVFBRZ1fiqXUCzt/Up0o/Q/Z+SaRST2VIBlz7LUT079EWrYZOHlnjFRu0FNPFsTliwcAT+6Vexp6Zw0Q/rOX7u9lvpi1RaXDFJ2AIhgM/wg/zwMd9AKedGYvFIlNMvYmWZgpoH3kprmnEaFYx6BSSIqVmxhGKonDzKYMAeOGn/fx2rGPhaZPJzMfrjwAwd1pWr9jnr6TFhDI8KQKLKnr92IgfBiGxYGqAgu3eM9AVVJUppaLLb8XoP0CA/K4dlxHDKstY8eTAT16xQZuSfVKAdXhpP04xaWgXERuyy6ltMrlnp2nHwZz/AxQheu+sQ3B1gdedHVmu4at0Ms5A08skRwWj1/XvK5LOuGJKOmeMSqLZbOHuT7bS0Ow4VPrdjgJKa5tJiQrmzNH9+8rbHZw60lqibd/nR1HsUk3euaJ3lYq9KxmiHqFBDSTh5Ou8bY5PMHFgNMtUMbZGzVnV6/1mquqNrD1Uhg4LaTVWp7gfi381suLDyIwLxWhW+bVtF+6eMOpCMYEcYMmjsO2T1uub62HZk/DyeNj7rfuO2w2kM+OrdJJm0vQyMsXUOYqi8Mwl40iMCOJQSR3/+G53u21UVeVdazn21SdkECDLsXuMpptZsb8Ek9nSsiJ9qrg/ttULVrlO49q3AVgeOIPwKNlzCCAyOABDwnCOWBJRzM1weHmvHv/nPUWYLCpnx5eia64RE8uTx/aqDb7KzOHie6el4dzGCbfCtLvF40V3wsElIgqzexG8drwYg2Bugr3fu/e4LiJ/uX2VTpyZ/Aop/nWW2LBAXrx8AooCH6/P5cffWnoxqKrKL3uK2Xm0ikCDjt9PleXY7mBiejRRIQFUNRhtDecASB4n7vvC/JfaEhLzfgBg/8DLvGyMbzE5K5alFutQ4f2Le/XYi3cJkf5lCVYd3MATvFJR5Ytoc+SW7yt2v8b19EdFt2uLCT7/A7x3Pnx+DVTlUhOUzO3Nd/Ogept7j+ki0pnxVTpLM1XKsmxXOGlIPDefLPQzf/lyB8v3FfPU93s45bll3PS+aAx10YRUYsMCvWmm32DQ62w/rK1STVoVUPkhaKpx8EofYusH6FUTWy1DiB92vLet8SlmDEtkiWUSAOqBn8Bi6eIV7qG2ycTKAyKFchx7xEKZYrJxwqA4ggN0FFQ1sq/Izd8vnQ4ufF0MVW2uhZxVoA9CPfk+LtG9xPeWExifHuPeY7pqolePLukYZyIzMs3kNH8+czhjBkRSWW9k7rsbeWvlYfLKGwgO0HHeuBQeOGekt030K7RU01L70QZh8RBhnXflpbJep7CYUTctAOBD0+mMT4v2rj0+xrTBcWxVRlGrBqPUFkHBtl457rK9xTSbLGTFhRJetEEslOJfG8EBetsIFrenmgAMgXD5hzBiNoy5BO5Yz6r0W9lfYSEi2MDs8d6dWC6dGV+lM81MhegOLCMzzhNo0PHKFROJDg0gLFDP+eNTef2qSWz5+xm8duUkGZVxMzOGJaDXKewvquVQiZ1IVNM3+HKq6cDPKFV5VKjh/KKfxvBk328Y1puEBRmYkJXYUtW0/8deOe5i6wDYKwc3otSXibLh1Im9cuy+wqyO+jy5i+BIuOIjuHQBxGbxkbUK9JJJaYQGend6vHRmfBGLRQyyg3ZpJlVVpQC4mwxKCGftA6ex5aEzePX3Ezl3bIrXv4D+SnRoICcPjQfgv1uPtqxIsepmCn24PHvTOwB8bp7B0NQEKQp3wIxhCb2qm2k0mllmLck+OyJbLEybIqIFEhszh7X0eap2cS6dqxRVN/KLNY3sC+Nf5LfUF9HmMgEEthb5ltc102gUOeqUaNn3wlVCAvUEGaRgsDe4yDpG4pttx1oEiZoIuHCnl6zqgvJsOPAzAB+bT2N8erR37fFRZg5PZJnZ6swUbBN9RjzIiv0l1DebGRAdQlq1tRpOppjaMTAulEEJYZgsKr8ecGOJtgM+25iH2dpodFiS96OX0pnxRbQUE4oYameHFpVJigySJ2WJT3Pm6CRCA/Xklte3VDVpaabiPWD27JVjt9jxGaCyNWASR9RkJkhnxiFDE8MJjEpim2WwWODhBnpaiums0cko+RvFwoFSmO2IWdYSbS2S5QlMZgufbBAVZVcdn+Gx47iCdGZ8EduQyTChIrdDE//KFJPE1wkNNHDmKNGE0JZqismEoCgwN0PJXu8Z1xE5qwH4skFU60hnxjGKojBjeCJLtOiMB3UzzSYLv+wRQvLzhoZAhTXNlDLBY8fsy8yy6zfjqTFEy/eVUFDVSExoAGePSfbIMVxFOjO+SKfiX60sW/aYkfg+F04UqaZvdxRgNFtEJ2AtOuNrqSazEY5uBmC9eRixYYGkSZF9h8wcnsBSa4k2h5eBsdEjx1lzqJSaRhPx4UFMMAjBKTGZEBrrkeP1daZkxRAaqKe4poldR6s9coyPrVGZS49LIzjANzIE0pnxRTTNjOz+K+njnDwknriwQMrqmlmttVn31Yqmwh1grKfJEMFBNZXxaVEo/XyAYWecNCSe/bpMCtUY8ZtljWq5m5YUUxL6QqteRkZlOiTIoLf1efp+l/u1TPkV9bYUli81GpXOjC9in2Zqgy3NJK8YJX0Ag17H7HGi/4Qt1WSraPIxZyZ3PQAHg0ajomOCl5uA+TrhQQYmZ8Sx1Oy5qiaT2cJPu0WK6ZwxKXBsm1ghS7I75dyx4jv3/c4Ct6eaPt2Qh6qKfkODEto3dfUW0pnxRZzo/ivD35K+gpZq+vG3IuqaTK0rmrw8abcVeesAWGMcCsD49ChvWtMnmDk8gSVaifbe79ze2Xnd4XLK65qJCgng+EGxLXO9Uie49Tj+xqkjEgkO0HGkrJ7fjrkv1dRssvDZpjzAd4S/GtKZ8UWcaJgnu/9K+goT06PJiAulwWjm591FkDAc9IHQVA0VOd42T6CqkCucmZ9rMgFk518nmDE8gV8tYyhWo6HmmJjbY2p22/4XrhFi39njUghoqoRKq2ZGG40hcUhooMHWhfu7ne5LNX20/gglNU0kRgRxhlXc7ytIZ8YX0ZyZgNYi3+pGI9WNJkCmmSR9B0VRuHC8GGPwzbajoA+AROv4CF9JNVXkQG0RFl0A29XBZMaFEiO7QnfJ8KQIoiOjuKH5PsyGUDi0FBbd5ZaIW3ZpHUusnWyvn57VMjYhdhCEyBRgV7g71VTVYOSVJQcAuOeMYQQafMt98C1rJAKbZqZ1mkmrZIoJDZCdayV9Ci3VtOpAKaW1Tb7XPC9P6GUKw0bQRKBsluckiqIwc3gCO9VBfJr5D1D0sONTWPJYj/f97q/ZqKpImQxOCG9JMUnxr1O4O9X05opDVNQbGZIYzu+OS3ODhe5FOjO+SAdppqNS/CvpowxOCGfsgCjMFpXvdhS0ODO+UtGUuxaADSahlzk+K86b1vQptMqZfxcOQT3/ZbFw9T9hw9vd3mdlfTP/2ZQPwA3Ts8RCKf51idBAg63nTE9TTccqG1iwWqT8Hjh7BAYfHPHhexZJOnZmNPFvtOwxI+l7XDhBpJq+31lgV9HkI5EZayXT99VC1DhrRII3relTnDQknkCDjuzSOn5LugBmPShWfH8/HFzSrX1+siGPBqOZEckRTBtsdSxtzsyEHtvcXzhvnHtSTf/8eT9NJgtTs2I5bWSiu8xzK9KZ8UU6qGay9ZiRkRlJH+Ss0aJT6MaccsrChwKKEI3WeXaGTJc0VEDJHgA2m4cxIjmClCj5HXOWiOAA22f7+aY8OOV+mHA1oMKqf7q8P6PZwntrcgARlVEUBerKoEo0apPiX+dxR6pp97FqvtwiomR/PXekz/Zeks6ML9JBZCbfWskkG+ZJ+iLpsaGMGRCJRYVfDtUJISdAgZcnaOdtAKA4II0yopg1wjevPH0ZTUPx323HaDRZYNZ8QIEjq6HskEv7+n5nAYXVjcSHB3GBNZpHgVUvEzsYgmXJvLP8f3t3HhbVlaYB/L1VBVWshYAKxSIQd0BENJpI3GI0anSME83ilm06pmPUttuO2UwmY6Lpmc6+L51MJ9qaRCXETmfEaMQlcWFRxLigyCaIK4vIVnXmj1sUoECgKOpWle/veeqpy73nXg7nEfnqnO+cY4uhptU/HIUQ8owyR97eg8GMI7IkADcfTmLODDm7O82f4H84XOI4Q035zdeXGcdgpsNG9g6EQa9D2dU6efq9PhS4aZx8MXNNu58jhMCn5tyMebf0atxMl/kyVuvMrKbU4+eQevwc3NQSlk3s1xXVsxkGM46oukx+v+YTCLcyIGfXMByxO+cCagKj5ZNKT882z2TaU9cbvjoN4h3406ejUqsk3GPunfk6TR6SwJC58nvmWsBkbNdz9p++hEOFZXDXqDB7eJOl8rlYntWsHWr65dQFPLE2HQAwZ0Qv9Aq4ft0zR8JgxhFZghm/xlN1RpyvlBej4uq/5Kx69/BGVHcv1BpNSK8z/7FSckZTfa1lc8k0U1+M6tvdIWdqOIN7EsIAADtPnMOZy1eBfpMBD3+gorhdicAmk8A723MAADPiQxDgrW282DAUyZ6ZDvPSNg41bT7UvqGm77OKMe/TfaiorsewiG5YekffrqyiTSj6W5uamoqpU6fCYDBAkiQkJSW1Wvaxxx6DJEl444037FY/xVy9LL836Zlp6JXx1mqg93BToFJEnSdJkmWo6dti8yyVCzlATaUyFSo+CNRXo0zyxUlhsPynTx0XHuCJEVH+EALYmF4IaLTAoHvlixlftHmvEAL/+V02Uo+fg0Yl4dHbIhsvVp4DygoASI1T+qlDppoXrfx8Ty6OlbS95cT/7jmNJ9amo9ZowsTonvjikeHw0Tn+3xxFg5krV64gLi4O77zzTpvlkpKSsHfvXhgMBjvVTGENPTMefpZTlg0m/TwcNpucqD3ujJGDmeST9RA+wQBE4zCCvZn3Y5LXl5Ewuh+nZHfGTHPvzNdphXJ+Rvwc+cKxf7U5a+31lOP435/zIEnA/8yMQ+8ePo0XG1b+DegN6Hy7qOau7c7oIIzq2x3VdSb8fk2avEfaNYQQ+O//O4oXkrMhBDB7eDjem50AnZtagRp3nKLBzKRJk7By5UrMmDGj1TJFRUVYuHAh1qxZAzc3x48OO81YB9SZZzM1GWZi8i+5itgQPQx6HapqjTjrN1g+aQ4q7M6c/HvA1BdxoXoENh3aoA6bFBsEb60GeReqsC/3IhAUIw8NmeqAQ+tbvOeTnafw1jZ5eOmladGYbl4t2oLJv52mUkl4fVYcgnx1OHnuCp5LOtwsGfhqrREL/5GBd7fLM8/+eEdfrJweA7XKeT44O/TgsMlkwty5c7Fs2TJER0e3656amhqUl5c3ezmVhl4ZANA2fgopusxp2eQaJEnCBPNQ0y/mGUQNi9bZVZPNJfeb+mEMh5g6zdNdg7vMC7V9ZV7B19I7k/7FdXs2fXWgACv/Ka/x86cJfTH3lojrH8rkX5sI8Nbi7QfioVZJ2JRRhPX75d2vS8qqMevDn/HPQ8VwU0v4y78PwpO393G6EQCHDmZeffVVaDQaLFq0qN33rFq1Cnq93vIKCwvrwhp2gYZgxt0HUDfuv8SeGXIlDUNN68+ah44L9gEmk30rcfEUUHUetUKDwyKSU7JtZOZQ+f/c77OKUVlTD8TcA2h08sKERemWct9nFWP5Bjn5+z9ui8QTY3u3/MCGYSb2zHTasAh//GmCPMX6heRsfH2gANPe2YWsojJ083TDl48Mx6xhTvY308xhg5m0tDS8+eab+PzzzzsUIT799NMoKyuzvAoKCrqwll2gheRfoMlWBgxmyAUMi/BHgJc79l0NkXdbrimzrMJrN+bF8g6JKPh4eSE2hIux2cKQcD9EdffC1TojPtpxEkKnBwZMky+aE4G3ZJdg0T8yYBLAvUPDWl9ZtrIUKC8Ck39t57FRURjbrztq6k1Y9s0hlFbUoG9PbyQvTMTwKOfdk8xhg5mdO3eitLQU4eHh0Gg00Gg0yMvLwx//+EdERES0ep9Wq4Wvr2+zl1Opviy/N0n+BZonABM5O7VKwh0De8IINU7rBson8+2cN2NeXybN1Aej+3WHyonyAxyZJEl46NYIAMBb23Kw9KuDqImdLV88uA6nv16OlWu3oN4kMH2wAa/MiG05kDEZgR2vyseBfQCt9/VlqMNUKgmvzRqMYL0OgLwOzYbHb0WYv3Pv+af57SLKmDt3LsaPH9/s3MSJEzF37lw89NBDCtXKDhqCmSY9M3VGE86WVwPgMBO5jonRQVi3vwDbqyJxEw7IwcywR+xXAXPPTLqpD+5ivoxNzRnRC7VGgVe+/xWbMopwvNgbGwwjoDvzCyKy38d2jYRsv1sQPWQp1BAArglm6qqBTb8Djnwrfz1ysd1/BlfWzcsdSU+MxMGCy7h9QE+nSvRtjaLBTGVlJXJycixf5+bmIjMzE/7+/ggPD0dAQPMuLzc3NwQFBaFfP8deVrlTWlgwr+BiFUwCcNeoEOjF2RbkGm7tHQAfnQY/VffGo+6w74ym6nKI0iOQABxEX/ylD6dk25IkSXgkMRIDg32xcG06sksqMVK3GCONt+BebMFIdTYGXdkDrN0DdIuUg9jBswFPf6DqIrBuNpC/B1C5AXd/AMTeo/SP5HJ6+uosifiuQNFhpgMHDiA+Ph7x8XJi19KlSxEfH48VK1YoWS1ltZAzszf3IgAgLlTPrnByGVqNGv9xWxQyTL1hhAq4nA+Un7HPNy86AAkC+abuiIq8CXrPG2DZBwXcclMANi9KRFyYHy5UCyTXDcMnUW+gdsHPwPAFgFYPXMoFtjwHvDYA2PQ48Lc75UBG6wvM3chAhtpF0Z6ZMWPGdGjjq9OnT3ddZRxFCwvm7cqRF5sa2TtQgQoRdZ1Hb4vEF7/k4WhNGKJVefJQU0zr607ZjHmIKU30xaQY1/l06oiC9R5Y/7sReOvHE6iqNWL5pP5wd1MDk14Fbl8BZH0D7P9Y3nD04Fr5Jh8DMOcboGf7luQgcticmRvWNTkzJpPAHnMwk8hghlyMp7sGS+/oi/3J/RCtykNN7s/Q2iGYqc79BToAGaY+WOhCXe2OSuemxp/v7H/9BXcvIGE+MGQeUHgAOPA34OpFYMpf5Z23idrJYWcz3bCuyZk5UlyOS1V18NZqEMfdfMkFzUwIRaGPPO320tHUrv+GJhNURfsBAFd7JqCHr67rvye1TZKAsGHA3e8DD6xnIEMdxmDG0VyTM7Pb3CszIsofbtzNl1yQRq3C6NunAgACK4+j5Fzre/jYxLmjcK+vxBWhRf+4EV37vYjILvjX0dFckzPDfBm6ESQmxOGcqgc0kgnfbk7u0u9VkbMbAJBp6o2Jg9gDQOQKGMw4miY5M9V1Ruw/Lc9kYr4MuTJJkqCOkHtJqk7uxtGSrttTrfTITgBAoXcMQrs590JhRCRjMONomuTMpOdfQnWdCT18tOjdg6tfkmvz7z8KAJAgHcNzmw6j3tg1ezV5nk0DAHj1vrVLnk9E9sdgxpEI0SSY0WPXicZZTM62gylRh4XLPTNDVDlIz7uA17cet/m3KD9fguB6eTfnmJtvt/nziUgZDGYcSe0VwFQvH3v4WZJ/mS9DN4QeAwGtL7ylq+gv5eO9n04i9fg5m36LrL0pAIA8VRgiwpxzd2Aiuh6DGUfS0Cuj0qCszg2HiuSvGczQDUGlBkKHAgAejzoHIYClX2Wi1LwvmS1cPi4n/1YExtvsmUSkPAYzjqRJ8u/PuRcgBNC7hzeC9FwHg24Q4bcAACZ7n0D/IB+cr6zFkvWZMJrav1J4aypr6hF4ORMAEDDgtk4/j4gcB4MZR9Ik+XcXV/2lG1HfOwEA6pwteG9GFDzd1dhz8gLe3Z7zGzf+tu1HijAIJwEAQdGjOv08InIcDGYcSZMF83bnXADAYIZuMMGDgJ6xgLEWUWd/wMrpMQCAN7Yex6HCy516dPreVHhItahW+0AK7GuDyhKRo2Aw40jMPTPVGh/knr8CtUrC8Ch/hStFZGeDH5DfM9ZgxpBQTIszwCSAF5OzO7QxbVM5pZUwFchbGJhChwEq/tdH5Er4G+1IzDkzpXVyjszgMD/46NwUrBCRAmJnAioNcCYdKP0Vz0weAE93NdLzLyP54BmrHvnZ7lwMVR0DAHhG3WLL2hKRA2Aw40jMPTMnK+TNzDmLiW5I3t2BPhPl48y1CNLr8MTY3gCA1f86iqra+g497tKVWmxIL0SCyrxuTTj3YyJyNQxmHIk5Z+boZRXUKgnT4gzK1odIKfGz5fdD6wFjPR5JjERoNw8Ul1Xjgx2nOvSotfvy4V9XCoN0EUJSAyEJXVBhIlISg5lOsMV00WbPMwczZcILD4+M4BYGdOPqMwHwDAQqzwInf4TOTY1nJw8AAHy44yQKL1W16zG19Sb8/efTliEmKXgQ4O7VZdUmImUwmLFSTmkFJr+507IRpC3kFRbJBzo9Fo/nbAu6gandgEGz5OPMNQCAO2OCMCLKHzX1Jqz619F2Peb7rGKcLa/BbTp5SnbDOjZE5FoYzFjpwx2ncOxsBR7/Mh0lZZ1fobTgYhXOX5CXbh8/pB+8tZpOP5PIqTXMajr2L6DqIiRJwoq7oqGSgH8eKsaek+fbvF0IgU92yUNSYzzMQ1Nhw7uyxkSkEAYzVvrPf4s2r1Bag8fXpKGm3tip5720+Qh8xBUAQEK/CBvUkMjJBcXKL2MtcHgDAGCgwRf33xwOAJj9yV7M/XQvkg+eQXXd9b9/+3Iv4nBROQI01Qi8ckI+yeRfIpfEYMZKnjn/RJLny5ivS0VG/mW8mHzE6mdtO3oWKUfOQi/JwYzk4WejWhI5ucFz5PeMLy2nlk3sh1F9u0MIYOeJ81j0jwwMe3kr/vT1Qby59QTW7s3HluwSvGNeNfiJPpcgQQDdIgCfIAV+CCLqahzLsNaFk9Cd+QWLe6rw9/xR+Me+fMSG6PHA8PAOPeZ8ZY0lEApQXwVMAHR+tq8vkTOKnQlseQ4ozgTOHQO694Ofpzv+/vDNyLtwBRvSi7AhrRBFl6/im7TCFh8xPSAfyAUQxl4ZIlfFnhlrRd8NAPAv/QXPj+kOAHgh+TDS8i61+xHfZxVjwuupyL9YhRAfN2hN5hkaDGaIZF4BQKR5U8hTPzW71CvAC0vv6Iudfx6LNY8Ox5PjeuO+YWEYP6AH4sL8EObvgUcTI+F/Pl2+IZz5MkSuij0z1vKPBAzxwJkMPNTtEPZFx+OH7BI89sUBPH/XQEwdZIBKJbV468UrtVjx7WFsPlQMAOgf5IO3p0cAn5sL6Hzt8iMQOYWIRODkNuD0TmD4Y9ddVqkkjOwd2PIik8Y6YHWafMyZTEQuiz0znRE9AwAgHUnC/8yKMycE12LxukxMe3cXduc0zrYwmgSyCsvwUepJTHh9BzYfKoZaJWHRuN5IXpiIPj51ckE3L3laKhHJIsw9M6d3AyZTx+4tyQLqqgCdHgjsZ/u6EZFDYM9MZ0RPB1KeB07vgnfteWz8/a34265cfLDjFA4XlWP2J3uR2DsQbmoJB05fQkVN4zLsfXp446+z4jAo1E8+Yd7KAEz+JWrOEA+4eQJXLwLnjgI9B7b/3vxf5Pew4dxcksiF8be7M/zCgdBhAARw5Ft4umuwcFwf7Fg2Bg/eGgGNSsKunPPYfuwcKmrq4aPVYGy/7lhx10B892RiYyADWDaZhE6vwA9C5MDUbo3rw5ze1bF7C5oEM0Tkstgz01nRdwOF+4HsTZbx/ABvLV6cFo2HRkbgqwMF6ObpjhFRARgQ7At1K3k0lp4ZJv8SXS8iETi13Zw387v23SMEkL9XPma+DJFLYzDTWQOnA//3DJD/M1BWBOhDLJd6BXhh2cT+7XuOeV8m9swQtSAiUX7P2y0HKVIrHwqaunQaqCwBVG5AyJAurR4RKYvDTJ2lD2n81HckyfrnMGeGqHWGIYDGA6i6IOfNtEeBuVcmOA5w8+i6uhGR4hjM2IJ5zRlkb7L+GcyZIWqdxr1xnZj25s00JP9yCwMil8dgxhYG/hsASc6duZxv3TOYM0PUtl7moSYGM0R0DQYztuATBPQaKR9nJ1n3DObMELUtokkwI0TbZStLgXO/ysecyUTk8hjM2EpMw1DTRuvuZ84MUdtCGvJmzsv7NLXGZAKSfi8fBw0CvHvYp35EpBgGM7YyYBogqYAzGUBRWsfvtwwzsWeGqEUaLRA2TD7Oa2OoaddfgZwUQKMDpr9nn7oRkaIYzNiKdw8g5t/l4+RFQH1tx+63JAD72bJWRK7FsrVBK8HMqZ+A7a/Ix1P+CgTF2qVaRKQsBjO2NHEV4OEPnD0M7H6zY/eyZ4bot7WVN1N+BvjmEUCYgPi5QPwc+9ePiBTBYMaWvLsDk16Vj1P/ApS2cz0MIZgATNQeIQny8NGVc8D5E43njXXA1w/K+TRBscDk/1asikRkfwxmbC12JtBnImCsBZIXAibjb99TdxUwmXfNZgIwUes0WvN+aAB+TQaOJAM/PAN8OFpeJE+rB2b9nYvkEd1guJ2BrUkScNdrwLsj5HVn9n4I3PL7tu9pyJeR1IC7d5dXkcipRdwm79G07b+an1e7A3d/APhHKVMvIlIMe2a6gj4UmPCSfLztv4CLuW2Xb5ov0549Z4huZP2nyIE/APQYCAx9GJjxCbD4ENB/srJ1IyJFsGemqwx5EDi8Uf4E+eNLwMzPWi/LfBmi9guKAf6QLQ85eforXRsicgDsmekqKhUw/kX5OGcrYKxvvSwXzCPqGN9gBjJEZKFoMJOamoqpU6fCYDBAkiQkJSVZrtXV1eGpp55CbGwsvLy8YDAYMG/ePJw5c0a5CneUIR7w6AbUlLe9kB43mSQiIrKaosHMlStXEBcXh3feeee6a1VVVUhPT8fzzz+P9PR0bNy4EcePH8e0adMUqKmVVGogaox8fHJb6+W4ySQREZHVFM2ZmTRpEiZNmtTiNb1ej5SUlGbn3n77bdx8883Iz89HeHh4i/fV1NSgpqbG8nV5ebntKmyNm8YB2ZvkYGbs0y2XYc4MERGR1ZwqZ6asrAySJMHPz6/VMqtWrYJer7e8wsLC7FfBlkSNld+LDjQGLddizgwREZHVnCaYqa6uxvLly/HAAw/A19e31XJPP/00ysrKLK+CggI71rIFfmFAQB95ifXc1JbLcCsDIiIiqzlFMFNXV4f77rsPJpMJ773X9i64Wq0Wvr6+zV6Ku2mc/N5a3gw3mSQiIrKawwczdXV1mDVrFnJzc5GSkuIYwUlHNQQzp7a3fJ09M0RERFZz6GCmIZA5ceIEtm7dioCAAKWrZJ2IREDlBlw6DVw8df31hlwa5swQERF1mKKzmSorK5GTk2P5Ojc3F5mZmfD394fBYMA999yD9PR0bN68GUajESUlJQAAf39/uLu7K1XtjtN6A2HDgbxd8lDTtXvHcGo2ERGR1RTtmTlw4ADi4+MRHx8PAFi6dCni4+OxYsUKFBYWIjk5GYWFhRg8eDCCg4Mtrz179ihZbevcZJ7VdLKFoSYumkdERGQ1RXtmxowZAyFEq9fbuuZ0bhorbzqZmwoY6wC1m3zeZJRXCAbYM0NERGQFh86ZcSnBg1ve2qBhiAlgzwwREZEVGMzYS7OtDZoMNTUEM26egMaJ8oCIiIgcBIMZe2q63owQ8uymI0nyOfbKEBERWUXRnJkbTtOtDV4bAFQUN17zDVGmTkRERE6OwYw9+YUB3QcA536VAxmVG2AYDITfAgyZp3TtiIiInBKDGXu751M5ZyY4DghJANw9la4RERGRU2MwY289o+UXERER2QQTgImIiMipMZghIiIip8ZghoiIiJwagxkiIiJyagxmiIiIyKkxmCEiIiKnxmCGiIiInBqDGSIiInJqDGaIiIjIqTGYISIiIqfGYIaIiIicGoMZIiIicmoMZoiIiMipufyu2UIIAEB5ebnCNSEiIqL2avi73fB3vC0uH8xUVFQAAMLCwhSuCREREXVURUUF9Hp9m2Uk0Z6Qx4mZTCacOXMGPj4+kCTJps8uLy9HWFgYCgoK4Ovra9NnU3Nsa/thW9sP29p+2Nb2Y6u2FkKgoqICBoMBKlXbWTEu3zOjUqkQGhrapd/D19eXvxx2wra2H7a1/bCt7YdtbT+2aOvf6pFpwARgIiIicmoMZoiIiMipMZjpBK1WixdeeAFarVbpqrg8trX9sK3th21tP2xr+1GirV0+AZiIiIhcG3tmiIiIyKkxmCEiIiKnxmCGiIiInBqDGSIiInJqDGaIiIjIqTGYsdJ7772HyMhI6HQ6JCQkYOfOnUpXyemtWrUKw4YNg4+PD3r06IHp06fj2LFjzcoIIfDiiy/CYDDAw8MDY8aMQXZ2tkI1dh2rVq2CJElYsmSJ5Rzb2naKioowZ84cBAQEwNPTE4MHD0ZaWprlOtvaNurr6/Hcc88hMjISHh4eiIqKwksvvQSTyWQpw7a2TmpqKqZOnQqDwQBJkpCUlNTsenvataamBk8++SQCAwPh5eWFadOmobCw0DYVFNRh69atE25ubuLjjz8WR44cEYsXLxZeXl4iLy9P6ao5tYkTJ4rPPvtMHD58WGRmZoopU6aI8PBwUVlZaSmzevVq4ePjIzZs2CCysrLEvffeK4KDg0V5ebmCNXdu+/btExEREWLQoEFi8eLFlvNsa9u4ePGi6NWrl3jwwQfF3r17RW5urti6davIycmxlGFb28bKlStFQECA2Lx5s8jNzRVff/218Pb2Fm+88YalDNvaOt9//7149tlnxYYNGwQAsWnTpmbX29OuCxYsECEhISIlJUWkp6eLsWPHiri4OFFfX9/p+jGYscLNN98sFixY0Oxc//79xfLlyxWqkWsqLS0VAMSOHTuEEEKYTCYRFBQkVq9ebSlTXV0t9Hq9+OCDD5SqplOrqKgQffr0ESkpKWL06NGWYIZtbTtPPfWUSExMbPU629p2pkyZIh5++OFm52bMmCHmzJkjhGBb28q1wUx72vXy5cvCzc1NrFu3zlKmqKhIqFQq8cMPP3S6Thxm6qDa2lqkpaVhwoQJzc5PmDABe/bsUahWrqmsrAwA4O/vDwDIzc1FSUlJs7bXarUYPXo0295KTzzxBKZMmYLx48c3O8+2tp3k5GQMHToUM2fORI8ePRAfH4+PP/7Ycp1tbTuJiYn48ccfcfz4cQDAwYMHsWvXLkyePBkA27qrtKdd09LSUFdX16yMwWBATEyMTdre5XfNtrXz58/DaDSiZ8+ezc737NkTJSUlCtXK9QghsHTpUiQmJiImJgYALO3bUtvn5eXZvY7Obt26dUhPT8f+/fuvu8a2tp1Tp07h/fffx9KlS/HMM89g3759WLRoEbRaLebNm8e2tqGnnnoKZWVl6N+/P9RqNYxGI15++WXcf//9APjvuqu0p11LSkrg7u6Obt26XVfGFn87GcxYSZKkZl8LIa47R9ZbuHAhDh06hF27dl13jW3feQUFBVi8eDG2bNkCnU7Xajm2deeZTCYMHToUr7zyCgAgPj4e2dnZeP/99zFv3jxLObZ1561fvx5ffvkl1q5di+joaGRmZmLJkiUwGAyYP3++pRzbumtY0662ansOM3VQYGAg1Gr1dZFkaWnpdVEpWefJJ59EcnIytm/fjtDQUMv5oKAgAGDb20BaWhpKS0uRkJAAjUYDjUaDHTt24K233oJGo7G0J9u684KDgzFw4MBm5wYMGID8/HwA/HdtS8uWLcPy5ctx3333ITY2FnPnzsUf/vAHrFq1CgDbuqu0p12DgoJQW1uLS5cutVqmMxjMdJC7uzsSEhKQkpLS7HxKSgpuvfVWhWrlGoQQWLhwITZu3Iht27YhMjKy2fXIyEgEBQU1a/va2lrs2LGDbd9Bt99+O7KyspCZmWl5DR06FLNnz0ZmZiaioqLY1jYycuTI65YYOH78OHr16gWA/65tqaqqCipV8z9rarXaMjWbbd012tOuCQkJcHNza1amuLgYhw8ftk3bdzqF+AbUMDX7008/FUeOHBFLliwRXl5e4vTp00pXzak9/vjjQq/Xi59++kkUFxdbXlVVVZYyq1evFnq9XmzcuFFkZWWJ+++/n9MqbaTpbCYh2Na2sm/fPqHRaMTLL78sTpw4IdasWSM8PT3Fl19+aSnDtraN+fPni5CQEMvU7I0bN4rAwEDx5z//2VKGbW2diooKkZGRITIyMgQA8dprr4mMjAzLkiTtadcFCxaI0NBQsXXrVpGeni7GjRvHqdlKe/fdd0WvXr2Eu7u7GDJkiGX6MFkPQIuvzz77zFLGZDKJF154QQQFBQmtVitGjRolsrKylKu0C7k2mGFb2853330nYmJihFarFf379xcfffRRs+tsa9soLy8XixcvFuHh4UKn04moqCjx7LPPipqaGksZtrV1tm/f3uL/z/PnzxdCtK9dr169KhYuXCj8/f2Fh4eHuOuuu0R+fr5N6icJIUTn+3eIiIiIlMGcGSIiInJqDGaIiIjIqTGYISIiIqfGYIaIiIicGoMZIiIicmoMZoiIiMipMZghIiIip8ZghoiIiJwagxkiIiJyagxmiIiIyKkxmCEiIiKn9v9RxzmBnNNQFgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate and plot temperature\n",
    "plot_temp_predictions(model_CNN, X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "48ae5bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159/159 [==============================] - 0s 563us/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>Pressure Predictions</th>\n",
       "      <th>Pressure Actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>982.404907</td>\n",
       "      <td>982.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>982.581665</td>\n",
       "      <td>982.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>982.721558</td>\n",
       "      <td>983.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>982.876587</td>\n",
       "      <td>983.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>983.251099</td>\n",
       "      <td>983.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>985.508118</td>\n",
       "      <td>985.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>985.549805</td>\n",
       "      <td>985.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>985.560547</td>\n",
       "      <td>985.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>985.549255</td>\n",
       "      <td>985.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>985.544922</td>\n",
       "      <td>985.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pressure Predictions  Pressure Actuals\n",
       "0             982.404907            982.43\n",
       "1             982.581665            982.68\n",
       "2             982.721558            983.02\n",
       "3             982.876587            983.59\n",
       "4             983.251099            983.59\n",
       "..                   ...               ...\n",
       "95            985.508118            985.54\n",
       "96            985.549805            985.68\n",
       "97            985.560547            985.68\n",
       "98            985.549255            985.67\n",
       "99            985.544922            985.93\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "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": [
    "# evaluate and plot pressure\n",
    "plot_pre_predictions(model_CNN, X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7711d99",
   "metadata": {},
   "source": [
    "### GRU Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2ddf598a",
   "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>Temperature Predictions</th>\n",
       "      <th>Temperature Actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.350281</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.181396</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.081238</td>\n",
       "      <td>12.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.174744</td>\n",
       "      <td>11.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.302612</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>14.601807</td>\n",
       "      <td>15.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>14.267578</td>\n",
       "      <td>15.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>14.050415</td>\n",
       "      <td>15.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>14.108154</td>\n",
       "      <td>14.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>14.328308</td>\n",
       "      <td>14.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Temperature Predictions  Temperature Actuals\n",
       "0                 13.350281                12.93\n",
       "1                 13.181396                12.43\n",
       "2                 13.081238                12.17\n",
       "3                 13.174744                11.55\n",
       "4                 13.302612                11.59\n",
       "..                      ...                  ...\n",
       "95                14.601807                15.52\n",
       "96                14.267578                15.27\n",
       "97                14.050415                15.07\n",
       "98                14.108154                14.88\n",
       "99                14.328308                14.98\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 51,
     "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": [
    "# evaluate and plot temperature\n",
    "plot_temp_predictions(model_GRU, X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b576449c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159/159 [==============================] - 0s 964us/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>Pressure Predictions</th>\n",
       "      <th>Pressure Actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>981.422974</td>\n",
       "      <td>982.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>981.669495</td>\n",
       "      <td>982.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>981.916504</td>\n",
       "      <td>983.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>982.176453</td>\n",
       "      <td>983.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>982.558289</td>\n",
       "      <td>983.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>985.014343</td>\n",
       "      <td>985.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>985.140320</td>\n",
       "      <td>985.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>985.279358</td>\n",
       "      <td>985.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>985.343689</td>\n",
       "      <td>985.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>985.355408</td>\n",
       "      <td>985.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pressure Predictions  Pressure Actuals\n",
       "0             981.422974            982.43\n",
       "1             981.669495            982.68\n",
       "2             981.916504            983.02\n",
       "3             982.176453            983.59\n",
       "4             982.558289            983.59\n",
       "..                   ...               ...\n",
       "95            985.014343            985.54\n",
       "96            985.140320            985.68\n",
       "97            985.279358            985.68\n",
       "98            985.343689            985.67\n",
       "99            985.355408            985.93\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "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": [
    "# evaluate and plot pressure\n",
    "plot_pre_predictions(model_GRU, X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f407e9f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39417c10",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f830c98b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4af8043c",
   "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
}