gaussian_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import GPy as gpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>well</th>\n",
       "      <th>X_UTM</th>\n",
       "      <th>Y_UTM</th>\n",
       "      <th>Q_lpm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BalajuPark Well</td>\n",
       "      <td>332622.4245</td>\n",
       "      <td>3068903.385</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bansbari Bhaktapur Well</td>\n",
       "      <td>347876.3542</td>\n",
       "      <td>3063728.237</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bode solar tube well</td>\n",
       "      <td>341850.6854</td>\n",
       "      <td>3064433.453</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bode Treatment Plant</td>\n",
       "      <td>341610.7512</td>\n",
       "      <td>3064641.583</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bode Tubewell Dugwell Compound</td>\n",
       "      <td>341852.2304</td>\n",
       "      <td>3065309.970</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Sitapaila well</td>\n",
       "      <td>330476.6749</td>\n",
       "      <td>3066710.112</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Suichataar well</td>\n",
       "      <td>330309.6899</td>\n",
       "      <td>3064993.617</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Sukedhara well</td>\n",
       "      <td>337060.4114</td>\n",
       "      <td>3068666.772</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Swayombhu Geeta Mandir</td>\n",
       "      <td>330923.4266</td>\n",
       "      <td>3066921.091</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Tripureshwor well</td>\n",
       "      <td>333694.0924</td>\n",
       "      <td>3064786.137</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             well         X_UTM        Y_UTM  Q_lpm\n",
       "0                  BalajuPark Well  332622.4245  3068903.385  600.0\n",
       "1          Bansbari Bhaktapur Well  347876.3542  3063728.237  400.0\n",
       "2             Bode solar tube well  341850.6854  3064433.453  600.0\n",
       "3             Bode Treatment Plant  341610.7512  3064641.583  600.0\n",
       "4   Bode Tubewell Dugwell Compound  341852.2304  3065309.970  500.0\n",
       "..                             ...          ...          ...    ...\n",
       "79                  Sitapaila well  330476.6749  3066710.112  200.0\n",
       "80                 Suichataar well  330309.6899  3064993.617  350.0\n",
       "81                  Sukedhara well  337060.4114  3068666.772  400.0\n",
       "82          Swayombhu Geeta Mandir  330923.4266  3066921.091  250.0\n",
       "83               Tripureshwor well  333694.0924  3064786.137  300.0\n",
       "\n",
       "[84 rows x 4 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qdata = pd.read_csv('data\\cleaned-qdata.csv')\n",
    "qdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of the original data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = qdata.iloc[:,1]\n",
    "y = qdata.iloc[:,2]\n",
    "q = qdata.iloc[:,3]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot between x and Q(lpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24b5483c590>"
      ]
     },
     "execution_count": 62,
     "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": [
    "plt.scatter(x,q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot between y and Q(lpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24b54e5dd50>"
      ]
     },
     "execution_count": 63,
     "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": [
    "plt.scatter(y,q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter Plot of Discharge Data (Colorbar indicates discharge in lpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x24b54e57fd0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_aspect('equal')\n",
    "plt.grid()\n",
    "plt.scatter(x, y, c=q, cmap='gist_heat_r')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Triangulated Contour Filled Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = q\n",
    "# create plot\n",
    "fig, ax = plt.subplots(nrows=1)\n",
    "\n",
    "# contoure\n",
    "ax.tricontour(x, y, z, levels=14, linewidths=0.5, colors='k') \n",
    "# filling\n",
    "cntr2 = ax.tricontourf(x, y, z, levels=14, cmap=\"gist_heat_r\") \n",
    "# colorbar\n",
    "fig.colorbar(cntr2, ax=ax)\n",
    "# dots\n",
    "ax.scatter(x, y,s = 20, facecolors='none', edgecolor='k') \n",
    " \n",
    "# Equal aspect ratio \n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "# plt.clim(0.13,0.4) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Regression Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the discharge values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize discharge data:\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "q_scaled = scaler.fit_transform(qdata.iloc[:,3].to_numpy().reshape(-1, 1))\n",
    "df_normalized = pd.Series(q_scaled.flatten(), index=q.index)\n",
    "qdata['Q_lpm_normalized'] = pd.Series(q_scaled.flatten(), index=q.index)\n",
    "# df_normalized\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shuffle the dataframe and divide train and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>well</th>\n",
       "      <th>X_UTM</th>\n",
       "      <th>Y_UTM</th>\n",
       "      <th>Q_lpm</th>\n",
       "      <th>Q_lpm_normalized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Ratnapark well</td>\n",
       "      <td>333820.4642</td>\n",
       "      <td>3065731.906</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-0.898627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>BB-2</td>\n",
       "      <td>336978.2194</td>\n",
       "      <td>3070694.725</td>\n",
       "      <td>650.0</td>\n",
       "      <td>0.432337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hepali Height Well</td>\n",
       "      <td>336348.4159</td>\n",
       "      <td>3071073.341</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>4.805502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Sallagari Well</td>\n",
       "      <td>343112.2146</td>\n",
       "      <td>3062290.601</td>\n",
       "      <td>240.0</td>\n",
       "      <td>-1.126792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Damaichaghat well</td>\n",
       "      <td>332649.6046</td>\n",
       "      <td>3065867.665</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.002750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kausaltar Tube Well</td>\n",
       "      <td>338605.9825</td>\n",
       "      <td>3061868.624</td>\n",
       "      <td>540.0</td>\n",
       "      <td>0.014034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Sukedhara well</td>\n",
       "      <td>337060.4114</td>\n",
       "      <td>3068666.772</td>\n",
       "      <td>400.0</td>\n",
       "      <td>-0.518351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Danfe Colony well</td>\n",
       "      <td>335603.5148</td>\n",
       "      <td>3070813.024</td>\n",
       "      <td>350.0</td>\n",
       "      <td>-0.708489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Tripureshwor well</td>\n",
       "      <td>333694.0924</td>\n",
       "      <td>3064786.137</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-0.898627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>BH-4</td>\n",
       "      <td>342001.1615</td>\n",
       "      <td>3065006.619</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.002750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  well         X_UTM        Y_UTM   Q_lpm  Q_lpm_normalized\n",
       "72       Ratnapark well  333820.4642  3065731.906   300.0         -0.898627\n",
       "30                 BB-2  336978.2194  3070694.725   650.0          0.432337\n",
       "10   Hepali Height Well  336348.4159  3071073.341  1800.0          4.805502\n",
       "73       Sallagari Well  343112.2146  3062290.601   240.0         -1.126792\n",
       "8     Damaichaghat well  332649.6046  3065867.665   800.0          1.002750\n",
       "..                  ...          ...          ...     ...               ...\n",
       "12  Kausaltar Tube Well  338605.9825  3061868.624   540.0          0.014034\n",
       "81       Sukedhara well  337060.4114  3068666.772   400.0         -0.518351\n",
       "45    Danfe Colony well  335603.5148  3070813.024   350.0         -0.708489\n",
       "83    Tripureshwor well  333694.0924  3064786.137   300.0         -0.898627\n",
       "37                 BH-4  342001.1615  3065006.619   800.0          1.002750\n",
       "\n",
       "[84 rows x 5 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Shuffling the dataframe\n",
    "shuffled_df = qdata.sample(frac=1, random_state=30) # frac=1 shuffles all rows, random_state for reproducibility\n",
    "shuffled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 70%\n",
    "mid = int(len(qdata)* 0.70)   # m = integer which divis the overall dataframe for distribution of training and testing data in the ration of 0.70.\n",
    "mid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.89862667,  0.43233676,  4.80550231, -1.12679183,  1.00274966,\n",
       "        0.24219913, -1.20284688,  2.75201588, -1.0887643 ,  0.43233676,\n",
       "       -0.70848904, -0.13807614,  0.05206149,  1.38302492,  0.43233676,\n",
       "       -0.5183514 , -0.89862667, -0.21413119, -0.70848904, -0.32821377,\n",
       "        0.24219913, -1.27890193,  0.24219913, -1.0887643 ,  1.19288729,\n",
       "       -0.89862667,  1.38302492, -1.27890193,  0.62247439, -0.32821377,\n",
       "       -0.5183514 ,  0.24219913, -0.97468172, -0.5183514 , -0.70848904,\n",
       "        0.62247439, -0.13807614, -0.13807614, -0.89862667,  0.43233676,\n",
       "        2.52385072, -1.0887643 , -0.32821377,  0.24219913, -0.32821377,\n",
       "       -0.70848904, -0.5183514 ,  1.00274966,  0.24219913, -0.32821377,\n",
       "        1.00274966, -0.13807614,  0.62247439, -0.44229635,  0.24219913,\n",
       "       -0.5183514 , -0.5183514 , -0.13807614])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled_df.iloc[:mid,1:3].to_numpy()\n",
    "shuffled_df.iloc[:mid,4].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       ".pd{\n",
       "    font-family: \"Courier New\", Courier, monospace !important;\n",
       "    width: 100%;\n",
       "    padding: 3px;\n",
       "}\n",
       "</style>\n",
       "\n",
       "<p class=pd>\n",
       "<b>Model</b>: GP regression<br>\n",
       "<b>Objective</b>: 109.67223174033467<br>\n",
       "<b>Number of Parameters</b>: 3<br>\n",
       "<b>Number of Optimization Parameters</b>: 3<br>\n",
       "<b>Updates</b>: True<br>\n",
       "</p>\n",
       "<style type=\"text/css\">\n",
       ".tg  {font-family:\"Courier New\", Courier, monospace !important;padding:2px 3px;word-break:normal;border-collapse:collapse;border-spacing:0;border-color:#DCDCDC;margin:0px auto;width:100%;}\n",
       ".tg td{font-family:\"Courier New\", Courier, monospace !important;font-weight:bold;color:#444;background-color:#F7FDFA;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}\n",
       ".tg th{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;color:#fff;background-color:#26ADE4;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}\n",
       ".tg .tg-left{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;text-align:left;}\n",
       ".tg .tg-center{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;text-align:center;}\n",
       ".tg .tg-right{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;text-align:right;}\n",
       "</style>\n",
       "<table class=\"tg\"><tr><th><b>  GP_regression.         </b></th><th><b>value</b></th><th><b>constraints</b></th><th><b>priors</b></th></tr>\n",
       "<tr><td class=tg-left>  rbf.variance           </td><td class=tg-right>  5.0</td><td class=tg-center>    +ve    </td><td class=tg-center>      </td></tr>\n",
       "<tr><td class=tg-left>  rbf.lengthscale        </td><td class=tg-right>200.0</td><td class=tg-center>    +ve    </td><td class=tg-center>      </td></tr>\n",
       "<tr><td class=tg-left>  Gaussian_noise.variance</td><td class=tg-right>  1.0</td><td class=tg-center>    +ve    </td><td class=tg-center>      </td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<GPy.models.gp_regression.GPRegression at 0x24b58edbe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       ".pd{\n",
       "    font-family: \"Courier New\", Courier, monospace !important;\n",
       "    width: 100%;\n",
       "    padding: 3px;\n",
       "}\n",
       "</style>\n",
       "\n",
       "<p class=pd>\n",
       "<b>Model</b>: GP regression<br>\n",
       "<b>Objective</b>: 82.48002481734494<br>\n",
       "<b>Number of Parameters</b>: 3<br>\n",
       "<b>Number of Optimization Parameters</b>: 3<br>\n",
       "<b>Updates</b>: True<br>\n",
       "</p>\n",
       "<style type=\"text/css\">\n",
       ".tg  {font-family:\"Courier New\", Courier, monospace !important;padding:2px 3px;word-break:normal;border-collapse:collapse;border-spacing:0;border-color:#DCDCDC;margin:0px auto;width:100%;}\n",
       ".tg td{font-family:\"Courier New\", Courier, monospace !important;font-weight:bold;color:#444;background-color:#F7FDFA;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}\n",
       ".tg th{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;color:#fff;background-color:#26ADE4;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}\n",
       ".tg .tg-left{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;text-align:left;}\n",
       ".tg .tg-center{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;text-align:center;}\n",
       ".tg .tg-right{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;text-align:right;}\n",
       "</style>\n",
       "<table class=\"tg\"><tr><th><b>  GP_regression.         </b></th><th><b>              value</b></th><th><b>constraints</b></th><th><b>priors</b></th></tr>\n",
       "<tr><td class=tg-left>  rbf.variance           </td><td class=tg-right> 1.2060059955249935</td><td class=tg-center>    +ve    </td><td class=tg-center>      </td></tr>\n",
       "<tr><td class=tg-left>  rbf.lengthscale        </td><td class=tg-right> 461.23515760064316</td><td class=tg-center>    +ve    </td><td class=tg-center>      </td></tr>\n",
       "<tr><td class=tg-left>  Gaussian_noise.variance</td><td class=tg-right>0.04489972622599393</td><td class=tg-center>    +ve    </td><td class=tg-center>      </td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<GPy.models.gp_regression.GPRegression at 0x24b58edbe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a model with RBF kernel\n",
    "# Variance=5 and lengthscale = 200 applied on hit and trail basis.\n",
    "\n",
    "kernel = gpy.kern.RBF(input_dim=2, variance = 5. , lengthscale=200.)\n",
    "\n",
    "in_data = shuffled_df.iloc[:mid,1:3].to_numpy()\n",
    "out_data = shuffled_df.iloc[:mid,4:5].to_numpy()\n",
    "\n",
    "m = gpy.models.GPRegression(in_data,out_data,kernel)\n",
    "\n",
    "display(m)\n",
    "m.optimize()\n",
    "display(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_predicted_mean, q_predicted_var = m.predict(shuffled_df.iloc[mid:,1:3].to_numpy())\n",
    "q_predicted_sd = q_predicted_var**0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting and Visualizing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_trans_mean = scaler.inverse_transform(q_predicted_mean)\n",
    "q_trans_var = scaler.inverse_transform(q_predicted_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalized Plot with error bar\n",
    "The axes are distorted for better clarity in data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "q_test= shuffled_df.iloc[mid:,4:5].to_numpy()\n",
    "\n",
    "# Plot act = pred line.\n",
    "x = np.linspace(q_test[:, 0].min(), q_test[:, 0].max(), 100)\n",
    "plt.plot(x, x, '--', color='gray')\n",
    "\n",
    "# Plot scatter plot for actual vs predicted q_lpm for test data set. \n",
    "plt.scatter(q_test, q_predicted_mean, color='k')\n",
    "plt.errorbar(q_test.flatten(), q_predicted_mean.flatten(), q_predicted_var.flatten(), fmt='o', color='k', alpha = 0.2)\n",
    "\n",
    "# Labels and graphics\n",
    "plt.xlabel(\"Q_lpm Actual\")\n",
    "plt.ylabel(\"Q_lpm Predicted\")\n",
    "plt.title(\"Actual vs Predicted Q_lpm\")\n",
    "plt.ylim((-2.5, 3))\n",
    "\n",
    "\n",
    "# Visualize\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Denormalized Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tLcWcOXPEpk2bhEKhEACEp6enOHjwoBBCiLS0NDFy5EhRr1490adPHzFp0iSRlpYm7ffTTz8VVlZWok+fPtKQY5VKJRYsWCA8PDyEl5eXGDFihDhy5Ij0GpVKJb799lvh7u4ufH19xYcffij+9a9/ST/3rVu3lvkzf/z4sZg3b55o27at6Nq1q2jYsKFo27atuHTpUpmveZHp06eLWrVqCQDCx8dHGv5cv3590a5dO/Haa6+Jbt26iVatWok///xTet2mTZuk3+vatWuL0NBQ8ddffwkLCwsBQNStW1dER0eLWbNmCVNTUwFAuLi4iCtXrlS6ViIhhJAJwTs1ERHpkqioKHTt2hW9evWq8OR05eHh4YFOnTph48aNGt830cvg5RsiIh3zyiuvoE2bNjhw4ECV3biQSBexpYSISAelpKRg5syZiIqKgr29PerXr49vvvlG6jz9Ih07dsR//vOfEstVKhWcnJzQsGFDREVFSZ16iXQBQwkRUQ1x6dIlvPnmm7h27RoAwM3NDWFhYejUqZOWKyN6gqGEiIiIdAL7lBAREZFOYCghIiIincB735SDSqXCnTt3YGVlxU5hREREFSCEwMOHD+Hq6gojo+e3hTCUlMOdO3eq9J4TREREhi4xMREKheK52zCUlEPxvUcSExOr9NbhREREhiY7Oxtubm7SZ+nzMJSUQ/ElG2tra4YSIiKiSihP9wd2dCUiIiKdwFBCREREOoGhhIiIiHQC+5RoiBACRUVFUCqV2i6FdICJiQnkcrm2yyAi0isMJRpQUFCAlJQUPH78WNulkI6QyWRQKBSwtLTUdilERHqDoeQlqVQqxMfHQy6Xw9XVFaamppxgrYYTQuDu3btISkpC48aN2WJCRFRODCUvqaCgACqVCm5ubqhdu7a2yyEd4eDggFu3bqGwsJChhIionNjRVUNeNHUu1SxsLSMiqjh+khIREZFOYCghIiIincBQQnpHJpNhy5YtAIBbt25BJpMhNja22usYN24c3nzzzWo/LhGRoWIoISQmJmL8+PHS6KH69etjypQpuH//vrZLeyE3NzekpKSgVatW5dqeQYKISHcxlOgQpVKJ8PBwrFmzBuHh4dUyEdvNmzfRqVMnXLt2DWvWrMH169exbNkyHDhwAN7e3sjIyKiS4xYWFmpkP3K5HM7OzjA25kAyIiJ9x1CiI8LCwuDh4QFfX1+MHDkSvr6+8PDwQFhYWJUed9KkSTA1NcXevXvx6quvwt3dHf369cP+/fuRnJyML7/88oX78PDwwPz58zFixAhYWFigXr16+PXXX9W2kclk+O233zBo0CBYWFjgm2++AQBs3boVHTp0QK1atdCgQQPMmzcPRUVF0uuuXbuGHj16oFatWmjRogX27duntt/SLt9cvHgRAwYMgLW1NaysrODj44MbN25g7ty5CA4OxtatWyGTySCTyRAeHg7gSWvR0KFDYWtrCzs7OwwePBi3bt2S9qlUKvHJJ5/A1tYW9vb2+OyzzyCEqOBPm4iInktoUUREhBgwYIBwcXERAMTmzZvV1qtUKjFr1izh7OwsatWqJXr16iWuXr2qts39+/fFyJEjhZWVlbCxsRHjx48XDx8+VNvm7Nmzonv37sLMzEwoFArx/fffV6jOrKwsAUBkZWWVWJebmysuXbokcnNzK7TPp23atEnIZDIBQO0hk8mETCYTmzZtqvS+n+f+/ftCJpOJb7/9ttT17777rqhTp45QqVTP3U/9+vWFlZWVWLBggYiLixNLliwRcrlc7N27V9oGgHB0dBR///23uHHjhrh9+7Y4fPiwsLa2FitWrBA3btwQe/fuFR4eHmLu3LlCCCGUSqVo1aqV6NWrl4iNjRURERGiffv2ar8r8fHxAoA4c+aMEEKIpKQkYWdnJ/z9/UVMTIyIi4sTf//9t7hy5Yp4+PChGDp0qOjbt69ISUkRKSkpIj8/XxQUFIjmzZuL8ePHi3PnzolLly6JkSNHiqZNm4r8/HwhhBDff/+9qFOnjti0aZO4dOmSmDBhgrCyshKDBw8u9Weiid8LIiJD8LzP0GdpNZT8888/4ssvvxRhYWGlhpLvvvtO2NjYiC1btoizZ8+KQYMGCU9PT7U/9H379hVt27YVx48fF5GRkaJRo0ZixIgR0vqsrCzh5OQkRo0aJS5cuCDWrFkjzM3Nxe+//17uOqsylBQVFQmFQlEikDwdTNzc3ERRUVGl9v88x48fL/XnXuynn34SAERaWtpz91O/fn3Rt29ftWXDhg0T/fr1k54DEFOnTlXbplevXiUC0cqVK4WLi4sQQog9e/YIY2NjkZycLK3ftWvXc0PJjBkzhKenpygoKCi11qCgoBJBYuXKlaJp06Zq4Ss/P1+Ym5uLPXv2CCGEcHFxEQsXLpTWFxYWCoVCwVBCRPQCFQklWr0Q369fP/Tr16/UdUIILFq0CDNnzsTgwYMBACEhIXBycsKWLVswfPhwXL58Gbt370ZMTAw6deoEAPjvf/+LN954Az/++CNcXV2xevVqFBQU4O+//4apqSlatmyJ2NhY/PTTT5g4cWK1nWtZIiMjkZSUVOZ6IQQSExMRGRmJnj17VkkN4gWXIUxNTV+4D29v7xLPFy1apLas+D0qdvbsWRw9elS6lAM8uUySl5eHx48f4/Lly3Bzc4Orq2uZx3lWbGwsfHx8YGJi8sKan67j+vXrsLKyUluel5eHGzduICsrCykpKfDy8pLWGRsbo1OnTryEQ0SkQTrbOzA+Ph6pqanw8/OTltnY2MDLywtRUVEYPnw4oqKiYGtrq/Zh5+fnByMjI0RHR2PIkCGIiopCjx491D5Y+/Tpg++//x4PHjxAnTp1Shw7Pz8f+fn50vPs7OwqOksgJSVFo9tVRKNGjSCTyXD58mUMGTKkxPrLly/DwcEBtra2GjmehYWF2vOcnBzMmzcP/v7+JbatVatWpY5hbm5e4dfk5OSgY8eOWL16dYl1Dg4OlaqDiIgqTmc7uqampgIAnJyc1JY7OTlJ61JTU+Ho6Ki23tjYGHZ2dmrblLaPp4/xrAULFsDGxkZ6uLm5vfwJlcHFxUWj21WEvb09evfujaVLlyI3N1dtXWpqKlavXo1x48aVa1/Hjx8v8bx58+bPfU2HDh0QFxeHRo0alXgYGRmhefPmSExMVAtkzx7nWW3atEFkZGSZo3tMTU1LjGrq0KEDrl27BkdHxxJ1FP8OuLi4IDo6WnpNUVERTp069dxaiIioYnQ2lGjTjBkzkJWVJT0SExOr7Fg+Pj5QKBRl3itFJpPBzc0NPj4+VXL8X375Bfn5+ejTpw8OHz6MxMRE7N69G71790aTJk0we/bscu3n6NGjWLhwIa5evYpff/0VGzZswJQpU577mtmzZyMkJATz5s3DxYsXcfnyZaxduxYzZ84E8KTVq0mTJggKCsLZs2cRGRn5wtFAkydPRnZ2NoYPH46TJ0/i2rVrWLlyJeLi4gA8GSl07tw5xMXF4d69eygsLMSoUaNQt25dDB48GJGRkYiPj0d4eDg++ugj6dLalClT8N1332HLli24cuUK/vWvfyEzM7NcPxsiIl324MEDbNy4Ue0KgbbobChxdnYGAKSlpaktT0tLk9Y5OzsjPT1dbX1RUREyMjLUtiltH08f41lmZmawtrZWe1QVuVyOxYsXAyh5E7fi54sWLaqyO802btwYMTExaNCgAYYOHYr69eujX79+aNKkCY4ePQpLS8ty7WfatGk4efIk2rdvj6+//ho//fQT+vTp89zX9OnTBzt27MDevXvRuXNndOnSBT///DPq168P4MlNDjdv3ozc3Fy88soreOedd9T6n5TG3t4eBw8eRE5ODl599VV07NgRf/75p9TH5N1330XTpk3RqVMnODg44OjRo6hduzYOHz4Md3d3+Pv7o3nz5pgwYQLy8vKk937atGkYM2YMgoKC4O3tDSsrq1IveRER6ZMHDx5gxYoVuHjxInbt2qXtcrQ7JPhpeGYUiEqlEs7OzuLHH3+UlmVlZQkzMzOxZs0aIYQQly5dEgDEyZMnpW327NkjZDKZNGJj6dKlok6dOmqjMWbMmCGaNm1a7tqqekiwEE+GBT87CsfNza3KhgM/z+zZs4WlpaWIiooq1/b169cXP//8c9UWpWc4+oaIdN39+/fFTz/9JObOnSv++9//iuzs7Co5jt6MvsnJycH169el5/Hx8YiNjYWdnR3c3d0xdepUfP3112jcuDE8PT0xa9YsuLq6StOEN2/eHH379sW7776LZcuWobCwEJMnT8bw4cOlERsjR47EvHnzMGHCBHz++ee4cOECFi9ejJ9//lkbp1wmf39/6fJBSkoKXFxc4OPjU2UtJM8zb948eHh44Pjx43jllVdgZKSzDWpERFQJGRkZWLFiBR4+fIi6deti7NixJUYgaoNWQ8nJkyfh6+srPf/kk08AAEFBQVixYgU+++wzPHr0CBMnTkRmZia6d++O3bt3q43MWL16NSZPnoxevXrByMgIAQEBWLJkibTexsYGe/fuxaRJk9CxY0fUrVsXs2fP1onhwM+Sy+VVNuy3ot5++20AT4YslzVsG3gSLImISH/cv38fwcHBUiAJCgoq96X6qiYTghMtvEh2djZsbGyQlZVVon9JXl4e4uPj4enpWelhrLosNzcXycnJZa5v1KhRNVajPwz994KI9JMQAn/88QdSU1Ph4OCAsWPHVnkged5n6LN0dp4S0g3m5uYMHkREBkImk2HIkCHYvXs3AgICSswfpW0MJRrCBid6Gn8fiEiXKJVKqY+io6Mjxo4dq+WKSscejC+peKjp48ePtVwJ6ZKCggIA0EpHZSKip927dw+//vqr2p3PdRVbSl6SXC6Hra2tNF9K7dq1y5wIjWoGlUqFu3fvonbt2jA25n8xItKeu3fvIiQkBDk5OThw4ADGjx+v059R/IupAcWTsD07kRvVXEZGRnB3d9fp//xEZNju3r2L4OBgPHr0CE5OThgxYoTO/01iKNEAmUwGFxcXODo6lnnPFapZTE1NOb8LEWlNeno6QkJC8OjRIzg7O2PMmDGoXbu2tst6IYYSDZLL5exDQEREWpWeno7g4GA8fvxYrwIJwFBCRERkUKKjo/H48WO4uLhgzJgxMDc313ZJ5cZQQkREZEDeeOMN1K5dG127dtWrQAJwSDAREZHey8rKkuZHksvl6NWrl94FEoChhIiISK+lpqbi999/x86dO/V+4kaGEiIiIj2VkpKCkJAQ5ObmIjU1Ve9HgLJPCRERkR4qDiR5eXmoV68eRo8eDVNTU22X9VIYSoiIiPTMnTt3sHLlSuTl5UGhUGD06NEwMzPTdlkvjZdviIiI9IihBhKALSVERER6JSsrC/n5+XBzc8OoUaMMJpAADCVERER6pXnz5hg1ahQUCoVBBRKAl2+IiIh0XnJyMrKysqTnDRs2NLhAAjCUEBER6bSkpCSEhIQgODgY2dnZ2i6nSjGUEBER6ajExESsXLkSBQUFsLGxQa1atbRdUpVinxIiIiIdlJCQgNWrV6OgoAAeHh4YMWKE3s9D8iIMJURERDrm6UDi6emJESNGwMTERNtlVTmGEiIiIh2SlJSEVatWobCwsEYFEoChhIiISKfY2trCxsYG1tbWGD58eI0JJABDCRERkU6xtLTEuHHjYGpqWqMCCcDRN0RERFoXHx+Ps2fPSs8tLCxqXCAB2FJCRESkVfHx8QgNDUVRUREsLCzQqFEjbZekNQwlREREWnLz5k2sWbMGRUVFaNSoETw8PLRdklYxlBAREWnB04GkcePGGDp0KIyNa/bHcs0+eyIiIi24ceMG1q5dy0DyDP4EiIiIqtH9+/elQNKkSRO89dZbDCT/hz8FIiKiamRnZ4fOnTsjIyMDgYGBDCRP4U+CiIioGslkMvTu3RtCCBgZcWaOp/GnQUREVMWuXbsmXbIBngQTBpKS+BMhIiKqQlevXsW6desQFxeH48ePa7scncbLN0RERFUkLi4O69evh0qlQvPmzeHt7a3tknQaQwkREVEVeDqQtGjRAv7+/pDL5douS6cxlBAREWnYlStXsGHDBqhUKrRs2RL+/v7sQ1IODCVEREQalJ+fj+3bt0OlUqFVq1YYMmQIA0k5MZQQERFpkJmZGUaMGIEzZ86gf//+DCQVwFBCRESkAfn5+TAzMwMAKBQKKBQKLVekf3Q+vj18+BBTp05F/fr1YW5ujq5duyImJkZaL4TA7Nmz4eLiAnNzc/j5+eHatWtq+8jIyMCoUaNgbW0NW1tbTJgwATk5OdV9KkREZKAuXryIJUuW4M6dO9ouRa/pfCh55513sG/fPqxcuRLnz5/H66+/Dj8/PyQnJwMAFi5ciCVLlmDZsmWIjo6GhYUF+vTpg7y8PGkfo0aNwsWLF7Fv3z7s2LEDhw8fxsSJE7V1SkREZEAuXryITZs24fHjx4iNjdV2OXpNJoQQ2i6iLLm5ubCyssLWrVvRv39/aXnHjh3Rr18/zJ8/H66urpg2bRo+/fRTAEBWVhacnJywYsUKDB8+HJcvX0aLFi0QExODTp06AQB2796NN954A0lJSXB1dX1hHdnZ2bCxsUFWVhasra2r5mSJiEjvXLhwAWFhYRBCoG3bthg0aBD7kDyjIp+hOv2TKyoqglKpRK1atdSWm5ub48iRI4iPj0dqair8/PykdTY2NvDy8kJUVBQAICoqCra2tlIgAQA/Pz8YGRkhOjq61OPm5+cjOztb7UFERPS08+fPM5BomE7/9KysrODt7Y358+fjzp07UCqVWLVqFaKiopCSkoLU1FQAgJOTk9rrnJycpHWpqalwdHRUW29sbAw7Oztpm2ctWLAANjY20sPNza0Kzo6IiPTV+fPnsXnzZggh0K5dOwwePJiBRAN0/ie4cuVKCCFQr149mJmZYcmSJRgxYkSVvvkzZsxAVlaW9EhMTKyyYxERkX4RQuDs2bMQQqB9+/YYNGgQZDKZtssyCDo/JLhhw4aIiIjAo0ePkJ2dDRcXFwwbNgwNGjSAs7MzACAtLQ0uLi7Sa9LS0tCuXTsAgLOzM9LT09X2WVRUhIyMDOn1zzIzM5OGdRERET1NJpNh2LBhOHXqFLy8vBhINEjnW0qKWVhYwMXFBQ8ePMCePXswePBgeHp6wtnZGQcOHJC2y87ORnR0tHTTI29vb2RmZuLUqVPSNgcPHoRKpYKXl1e1nwcREemnlJQUFI8NMTExQZcuXRhINEznW0r27NkDIQSaNm2K69evY/r06WjWrBnefvttyGQyTJ06FV9//TUaN24MT09PzJo1C66urnjzzTcBAM2bN0ffvn3x7rvvYtmyZSgsLMTkyZMxfPjwco28ISIiio2NxdatW9G9e3e89tprDCNVROdDSVZWFmbMmIGkpCTY2dkhICAA33zzDUxMTAAAn332GR49eoSJEyciMzMT3bt3x+7du9VG7KxevRqTJ09Gr169YGRkhICAACxZskRbp0RERHrkzJkz2LZtG4AnU1VQ1dHpeUp0BecpISKqmZ4OJJ06dcIbb7zBVpIKqshnqM63lBAREWnD6dOnsX37dgBA586d0a9fPwaSKsZQQkRE9IxTp05hx44dAIBXXnkFffv2ZSCpBgwlREREzygOIF5eXujTpw8DSTVhKCEiInpGhw4d4ODgAIVCwUBSjfRmnhIiIqKqdP78eTx69Eh67ubmxkBSzRhKiIioxjtx4gTCwsIQEhKC/Px8bZdTY/HyDRER1WjR0dHYvXs3AKBRo0YwNTXVckU1F0MJERHVWE8Hkm7duqFXr168ZKNFDCVERFQjHT9+HHv27AEATh+vI9inhIiIapzTp08zkOggtpQQEVGN06BBA9jY2KBNmzbw9fVlINERDCVERFTj2Nra4r333kOtWrUYSHQIL98QEVGNcOzYMVy5ckV6bm5uzkCiYxhKiIjI4B05cgT79u3Dhg0bcO/ePW2XQ2Xg5RsiIjJokZGROHjwIACgR48eqFu3rpYrorIwlBARkcE6fPgwDh06BADw9fVFjx49tFwRPQ9DCRERGaSnA8lrr70GHx8fLVdEL8JQQkREBicuLk4KJL169UL37t21XBGVB0MJEREZnCZNmqBdu3aoW7cuunXrpu1yqJwYSoiIyCAIISCEgJGREWQyGQYNGsQhv3qGQ4KJiEjvCSFw6NAhhIWFQaVSAQADiR5iKCEiIr1WHEgiIyNx8eJFXL9+XdslUSXx8g0REektIQQOHjyII0eOAAD69OmDJk2aaLkqqiyGEiIi0ktCCBw4cABHjx4FAPTt2xdeXl5aropeBkMJERHpHSEE9u/fj2PHjgFgIDEUDCVERKR3MjIycOLECQBAv3798Morr2i5ItIEhhIiItI79vb2GD58OB48eIBOnTppuxzSEIYSIiLSC0IIPHr0CJaWlgCAhg0barki0jQOCSYiIp0nhMCePXuwbNky3Lt3T9vlUBVhKCEiIp0mhMDu3bsRHR2NR48eISkpSdslURXh5RvSO0qlEpGRkUhJSYGLiwt8fHwgl8u1XRYRVQEhBHbt2oWYmBgAwMCBA9GuXTvtFkVVhqGE9EpYWBimTJmi9k1JoVBg8eLF8Pf312JlRKRpQgj8888/OHnyJABg0KBBaN++vZaroqrEyzekN8LCwhAYGFii6TY5ORmBgYEICwvTUmVEpGkMJDUTQwnpBaVSiSlTpkAIUWJd8bKpU6dCqVRWd2lEVAUKCwuRkpICABg8eDADSQ3ByzekFyIjI5/buU0IgcTERERGRqJnz57VVxgRVQlTU1OMHj0at2/fRtOmTbVdDlUTtpSQXij+xqSp7YhI9wghcOPGDel5rVq1GEhqGIYS0gsuLi4a3Y6IdIsQAtu3b8eqVasQFRWl7XJISxhKSC/4+PhAoVBAJpOVul4mk8HNzQ0+Pj7VXBkRvSwhBLZt24YzZ85AJpNJM7ZSzcNQQnpBLpdj8eLFAFAimBQ/X7RoEecrIdIzKpUK27ZtQ2xsLGQyGYYMGYLWrVtruyzSEoYS0hv+/v7YuHEj6tWrp7ZcoVBg48aNnKeESM88G0j8/f0ZSGo4mShtjCWpyc7Oho2NDbKysmBtba3tcmo8zuhKpP+EENi6dSvOnj0LmUyGgIAAtGzZUttlURWoyGcohwST3pHL5Rz2S6TnZDIZHBwcGEhIjU5fvlEqlZg1axY8PT1hbm6Ohg0bYv78+WoTaAkhMHv2bLi4uMDc3Bx+fn64du2a2n4yMjIwatQoWFtbw9bWFhMmTEBOTk51nw4RET2lW7du+Ne//sVAQhKdDiXff/89fvvtN/zyyy+4fPkyvv/+eyxcuBD//e9/pW0WLlyIJUuWYNmyZYiOjoaFhQX69OmDvLw8aZtRo0bh4sWL2LdvH3bs2IHDhw9j4sSJ2jglIqIaS6VSISIiAvn5+dKyunXrarEi0jU63adkwIABcHJywl9//SUtCwgIgLm5OVatWgUhBFxdXTFt2jR8+umnAICsrCw4OTlhxYoVGD58OC5fvowWLVogJiYGnTp1AgDs3r0bb7zxBpKSkuDq6vrCOtinhIjo5ahUKoSFheHixYvw8PDA2LFjyxziTy+mT33rKvIZqtMtJV27dsWBAwdw9epVAMDZs2dx5MgR9OvXDwAQHx+P1NRU+Pn5Sa+xsbGBl5eXNPlOVFQUbG1tpUACAH5+fjAyMkJ0dHSpx83Pz0d2drbag4iIKufpQGJkZAQvLy8GkpcQFhYGDw8P+Pr6YuTIkfD19YWHh4dB3JRUpzu6fvHFF8jOzkazZs0gl8uhVCrxzTffYNSoUQCA1NRUAICTk5Pa65ycnKR1qampcHR0VFtvbGwMOzs7aZtnLViwAPPmzdP06RAR1ThKpRJhYWG4dOkSjIyMMHToUE4d/xKK75b+7EWO4rul6/v0CDrdUrJ+/XqsXr0aoaGhOH36NIKDg/Hjjz8iODi4So87Y8YMZGVlSY/ExMQqPR4RkSFSKpXYtGkTA4mG1IS7pet0S8n06dPxxRdfYPjw4QCA1q1b4/bt21iwYAGCgoLg7OwMAEhLS1O750laWhratWsHAHB2dkZ6errafouKipCRkSG9/llmZmYwMzOrgjMiIqo5du3ahcuXL0Mul2Po0KFo0qSJtkvSazXhbuk63VLy+PFjGBmplyiXy6FSqQAAnp6ecHZ2xoEDB6T12dnZiI6Ohre3NwDA29sbmZmZOHXqlLTNwYMHoVKp4OXlVQ1nQURUM73yyiuwtrbGsGHDGEg0oCbcLV2nW0oGDhyIb775Bu7u7mjZsiXOnDmDn376CePHjwfwZPKdqVOn4uuvv0bjxo3h6emJWbNmwdXVFW+++SYAoHnz5ujbty/effddLFu2DIWFhZg8eTKGDx9erpE3RERUOY6Ojvjwww9hbKzTHzV6oybcLV2nhwQ/fPgQs2bNwubNm5Geng5XV1eMGDECs2fPhqmpKYAnzVVz5szBH3/8gczMTHTv3h1Lly5VS+UZGRmYPHkytm/fDiMjIwQEBGDJkiXlvhMlhwQTEb2YUqnEli1b0KFDB3h6emq7HIOjVCrh4eGB5OTkUvuVyGQyKBQKxMfH69Tw4Ip8hup0KNEVDCVERM9XVFSEDRs24OrVqzA3N8eUKVPYN68KFI++AaAWTIqHWOvi6BuDmaeEiIh0X1FREdavX4+rV6/C2NgYAQEBDCRVxNDvls6WknJgSwkRUemKA8m1a9dgbGyMESNGoEGDBtouy+AZ6oyu7H1ERESVUlRUhHXr1uH69esMJNXMUO+WzlBCRESVcvz4cSmQjBw5kp1b6aUxlBARUaV4e3vj7t27aN++PTw8PLRdDhkAhhIiIiq3oqIiyOVyyGQyyOVyDBkyRNslkQHh6BsiIiqXwsJCrFmzBjt37ix1ngyil8VQQkREL1QcSG7evIlz584hIyND2yWRAeLlGyIieq6CggKsWbMGt27dgqmpKUaNGgV7e3ttl0UGiC0lRERUptICibu7u7bLIgPFlhIiIipVQUEBQkNDcfv2bZiammL06NFwc3PTdllkwNhSQkREpUpOTkZCQgIDCVUbtpQQEVGpPD09ERgYCGtraygUCm2XQzUAQwkREUny8/ORn58v3aOkRYsWWq6IahJeviEiIgBPAsnq1auxfPlyZGVlabscqoEYSoiISAokiYmJyMvLw6NHj7RdEtVAvHxDRFTD5efnY9WqVUhKSkKtWrUwZswYuLq6arssqoEYSoiIarC8vDysXr1aCiRjx46Fi4uLtsuiGoqhhIiohsrLy8OqVauQnJwMc3NzjBkzhoGEtIqhhIiohlIqlSgoKIC5uTnGjh0LZ2dnbZdENRxDCRFRDWVhYYGxY8fi0aNHcHJy0nY5RAwlRKTflEolIiMjkZKSAhcXF/j4+EAul2u7LJ2Vm5uL27dvo1mzZgAAS0tLWFpaarkqoic4JJiI9FZYWBg8PDzg6+uLkSNHwtfXFx4eHggLC9N2aTopNzcXK1euxLp163Du3Dltl0NUAkMJURmUSiXCw8OxZs0ahIeHQ6lUarskekpYWBgCAwORlJSktjw5ORmBgYEMJs/Izc1FSEgIUlJSULt2bfYfIZ3EUEJUCn4D121KpRJTpkyBEKLEuuJlU6dOZZD8P48fP0ZISAhSU1NhYWGBoKAgODo6arssohIYSoiewW/gui8yMrLE+/M0IQQSExMRGRlZjVXpJgYS0icMJURP4TfwitPGZa6UlBSNbmeoCgoKEBISgrS0NCmQODg4aLssojIxlBA9hd/AK0Zbl7nKO8FXTZ8IzMTEBA0bNoSlpSXGjRvHQEI6j6GE6Cn8Bl5+2rzM5ePjA4VCAZlMVup6mUwGNzc3+Pj4VFkN+kAmk8HPzw/vvfce6tatq+1yiF6IoYToKfwGXj7avswll8uxePFiACgRTIqfL1q0qEbOV5KTk4N//vkHRUVFAJ78PDgPCekLhhKip/AbePnowmUuf39/bNy4EfXq1VNbrlAosHHjRvj7+1fZsXVVTk4OQkJCEBMTg507d2q7HKIK44yuRE8p/gYeGBgImUym1hJQ07+BP01XLnP5+/tj8ODBnNEVTwJJcHAw7t27BysrK3Tv3l3bJRFVGEMJ0TOKv4FPmTJFrTVAoVBg0aJFNfIb+LN06TKXXC5Hz549q/w4uuzhw4cICQnBvXv3YG1tjaCgINjZ2Wm7LKIKk4nSLgqTmuzsbNjY2CArKwvW1tbaLoeqCe+pUjalUgkPDw8kJyeX2q9EJpNBoVAgPj6eP7Mq9vDhQwQHB+P+/fsMJKSTKvIZypYSojLwG3jZeJlLNwghsG7dOty/fx82NjYICgpCnTp1tF0WUaWxoysRVQo7mmqfTCZD37594eTkxEBCBoGXb8qBl2+IysbLXNVPCKE2QuzZ50S6hJdviKja8DJX9crKysL69evRv39/uLq6Aig5VwuRvuLlGyIiPZGVlYXg4GDcuXMHO3bsKLWTMZE+YyghItIDxYHkwYMHqFOnDoYNG8YWEjI4vHxDRKTjMjMzERwcjMzMTNSpUwdBQUGwsbHRdllEGqfzLSUeHh6QyWQlHpMmTQIA5OXlYdKkSbC3t4elpSUCAgKQlpamto+EhAT0798ftWvXhqOjI6ZPny7dF4KISJc9G0jGjRvHQEIGS+dDSUxMDFJSUqTHvn37AABvvfUWAODjjz/G9u3bsWHDBkRERODOnTtqQxGVSiX69++PgoICHDt2DMHBwVixYgVmz56tlfMhIqqIiIgIZGZmws7ODuPGjeMIQDJoejckeOrUqdixYweuXbuG7OxsODg4IDQ0FIGBgQCAK1euoHnz5oiKikKXLl2wa9cuDBgwAHfu3IGTkxMAYNmyZfj8889x9+5dmJqavvCYHBJMRNpSWFiIXbt2oWfPniX+/nA4NumDinyG6nxLydMKCgqwatUqjB8/HjKZDKdOnUJhYSH8/PykbZo1awZ3d3dERUUBAKKiotC6dWspkABAnz59kJ2djYsXL5Z6nPz8fGRnZ6s9iIiqy+PHj6WRNSYmJhg0aFCJP+ZhYWHw8PCAr68vRo4cCV9fX3h4eCAsLEwbJRNphF6Fki1btiAzMxPjxo0DAKSmpsLU1BS2trZq2zk5OSE1NVXa5ulAUry+eF1pFixYABsbG+nh5uam2RMhIipDRkYGfv/9dxw8eLDMIb9hYWEIDAxUu2EkACQnJyMwMJDBhPSWRkPJlStXNLm7Ev766y/069dPmjCoqsyYMQNZWVnSIzExsUqPR0QEPAkkwcHByM7OxpUrV1BQUFBiG6VSiSlTppQaWIqXTZ06FUqlssrrJdK0cg0JTkxMfOEkPUVFRfjoo4+wd+9ejRT2rNu3b2P//v1q3wCcnZ1RUFCAzMxMtdaStLQ0ODs7S9ucOHFCbV/Fo3OKt3mWmZkZzMzMNHwGRERly8jIwIoVK/Dw4UPUrVsXQUFBpf4dioyMLNFC8jQhBBITExEZGcmZdknvlCuU1K9f/4WT9FT1vReWL18OR0dH9O/fX1rWsWNHmJiY4MCBAwgICAAAxMXFISEhAd7e3gAAb29vfPPNN0hPT4ejoyMAYN++fbC2tkaLFi2qrF4iovK6f/8+goOD8fDhQzg4OGDs2LGwtLQsdduUlJRy7bO82xHpknKFEg8PD2zcuBH29vYQQmDKlCmYNm0a6tevL23z8OFDbNmypUqKVKlUWL58OYKCgmBs/P9LtrGxwYQJE/DJJ5/Azs4O1tbW+PDDD+Ht7Y0uXboAAF5//XW0aNECY8aMwcKFC5GamoqZM2di0qRJbA0hIq27f/8+VqxYgZycnBcGEgBwcXEp137Lux2RLilXKPnkk0/QoUMH6XnLli3Ro0ePEttVVSjZv38/EhISMH78+BLrfv75ZxgZGSEgIAD5+fno06cPli5dKq2Xy+XYsWMHPvjgA3h7e8PCwgJBQUH46quvqqRWIqKKSEpKQk5ODhwdHTF27FhYWFg8d3sfHx8oFAokJyeXelldJpNBoVDAx8enqkomqjKVmqekf//+2LRpE2rVqiUtKw4E4eHhmqxPJ3CeEiKqShcvXoSHh8cLA0mx4tE3ANSCSfEl9I0bN6pNIkmkTRX5DK3UvW8GDBiA5s2b46233oKbmxsePHiA9evX8z8BEVE53Lt3D+bm5lIIadmyZYVe7+/vj40bN2LKlClqnV4VCgUWLVrEv8Wktyo9o+uKFSvw3XffISEhAQ0aNMC7776LDz/8EEZGejX1SbmwpYSINOXu3bsIDg6GhYVFuS7XPA9ndCV9UJHPUI1NM19QUFCuKdv1EUMJEWlCeno6QkJC8OjRIzg7O2PMmDGoXbt2ie0YNsiQVPk08+fPn0fnzp3x6quvAgCysrLw3nvv4ebNm5XZHRGRwUtPT0dwcPALAwmnj6earFKh5Msvv8TYsWPh4eEB4MnQ3N9//x1z5szRZG1ERAYhLS0NwcHBePz4MVxcXDB27NgyAwmnj6earFKhpFWrVvjwww9hZ2cnLTM1NWVLCRHRM9LS0hASEiIFkjFjxsDc3LzEdpw+nqiSoeTu3bsAoDaD6++//4579+5ppioiIgNhZmYGExMTuLq6lhlIgIpNH09kqCo1JHjIkCFo164dcnNzceXKFVy4cAHJycnYsGGDpusjItJrtra2GDduHGrVqqU2t9OzOH08USVbSt544w2EhITAz88PMpkM/fr1Q1RUFMfGExHhSXB4+q7ptra2zw0kAKePJwIqOSR4//798PPzK7F827ZtGDRokEYK0yUcEkxE5ZWSkoKQkBAUFBRg9OjR8PT0LNfrlEolPDw8Xjh9fHx8PIcHk16p8iHBf//9d6nLf/rpp8rsjojIINy5cwchISHIy8uDq6trhVo15HI5Fi9eDAAl7rhe/HzRokUMJGTQKtSnpPgmdhcuXFC7oZ0QArdv30ZsbKxGiyMi0hd37tzBypUrkZeXB4VCgdGjR1f4TuScPp5qugqFkkOHDgF4MsSt+N/FzMzM8Msvv2iuMiIiPZGcnIyVK1ciPz8fbm5uGDVqVIUDSTF/f38MHjyYM7pSjVSpPiVvv/02li9fXhX16CT2KSGismRkZOCPP/7QSCAhMkRV3qfk66+/xqefforw8HBp2WeffYb09PTK7I6ISG/VqVMHLVq0gLu7OwMJ0Uuq1DwlH374IW7duoU2bdpIy+bMmYOPP/4Yf/zxh8aKIyLSdTKZDAMHDkRRURFMTEy0XQ6RXqtUKGnWrFmJezBYWFiojcsnIjJUiYmJOHPmDAYMGAAjIyPIZDIGEiINqFQouXTpEu7evQsHBwdp2Y4dO/DgwQONFUZEpIsSEhKwevVqFBQUoE6dOvDx8dF2SUQGo1KhZMqUKWjcuDHatm2LunXr4ubNmzh37hxCQkI0XR8Rkc5ISEjAqlWrUFhYCA8PD3Tp0kXbJREZlEp1dPX19cXx48fRtm1b5OXloXPnzoiIiMCoUaM0XR8RkU64ffu2FEg8PT0xcuRIXrIh0rBKDQkuS2RkpEE2ZXJIMFHNduvWLYSGhqKwsBANGjTA8OHDGUiIyqkin6HlunyzaNEi1KtXD2+99RYAYP78+SXuzSCEQEhICG7cuFHJsomIdE9BQQE2bNjAQEJUDcrVUlK/fn00atQIBw4cAPDkHg1eXl5q4/GFEIiNjUVmZmaVFastbCkh0l1KpbLKZz+9efMmYmJi4O/vz0BCVEEabym5cuWK2n/ysWPHljqj6//+978KlkpEVHlhYWGl3idm8eLFL32fGKVSKf3da9CgARo0aPBS+yOiFytXR1dzc3OYmppKz8uaYt7Z2VkzVRERvUBYWBgCAwPVAgnw5D40gYGBJeZSqoibN2/i119/xb179162TCKqgHJdvlm5cmWJPiTPKioqwg8//IDLly9rrDhdwcs3RLpFqVTCw8OjRCApJpPJoFAoEB8fX+FLOTdv3sSaNWtQVFSEdu3aYfDgwZoomajG0vjlm6CgINja2sLGxgbAkxtQyWQy1KlTR9omPz//JUomIiq/yMjIMgMJ8KSPW2JiIiIjI9GzZ89y7/fGjRtYu3YtioqK0LhxY/Tv318D1RJReZUrlHh5eeHIkSPSN45x48bhl19+gaWlpbRNUVERFi9eXDVVEhE9JSUlRaPbAcD169exdu1aKJVKNGnSBG+99RaMjSs1vyQRVVK5/sd9/fXXak2gjo6OaoEEAIyNjXn9lYiqhYuLi0a3ezqQNG3aFG+99ZbGR/AQ0YuVq6Nrr1691J6fPXsWly5dUlt28+ZNhIeHa6wwIqKy+Pj4QKFQQCaTlbpeJpPBzc2tXJM5CiFw5MgRBhIiHVCptskvv/wSHTt2ROvWreHm5oYHDx7g+PHjWLlypabrIyIqQS6XY/HixQgMDIRMJlPriF8cVBYtWlSucCGTyTB8+HAcPXoUPXv2ZCAh0qJK3fumR48eOHv2LDp06IDc3Fy0aNECR48eRUBAgKbrIyIqlb+/PzZu3Ih69eqpLVcoFNi4ceML5yl5+q7mtWrVQq9evRhIiLSs0ve+ycjIwJ07d9CqVSvk5+fj5s2baN68uabr0wkcEkykuyozo+vVq1exfv169OrVC97e3tVUKVHNpPEhwc/atGkTRo8ejQ4dOuDo0aMAgNDQUHTv3h19+vSpzC6JiCpFLpdXaNhvXFwc1q9fD5VKhaSkJAghyuybQlSTVMctG16kUpdvVq9ejRMnTqBDhw4AADMzM8yfPx9//fWXRosjItKkpwNJy5YtERAQwEBChCczJHt4eMDX1xcjR46Er68vPDw8Xmpm5MqoVChp1qwZWrduXWIM/7MjcoiIdMXly5fVAom/vz+MjCr1J5DIoFTlLRsqqlL/I1NSUpCeni59w7h+/TpGjRolzfhKRKRLLl++jI0bN0KlUqFVq1YMJET/R6lUYsqUKaXeSqZ42dSpU6FUKqulnkr1KZkxYwa8vLyQlpaGZcuWIT8/Hw4ODjhw4ICm6yMiemkPHjyASqVC69at8eabbzKQEP2fqrplQ2VVKpQ0adIEV65cwbZt23Dr1i0oFAoMGjQIFhYWmq6PiOilde3aFXXr1kWjRo0YSIieUhW3bHgZlQolI0aMgEqlwrp16zRdDxGRRly/fh0KhQK1atUC8OTLFGmPLozsoJI0fcuGl1WprwyJiYklpp4HgFu3br1sPUREL+3ChQsIDQ3F6tWrUVBQoO1yajxdGdlBJWnylg2aUKlQ8uGHH0rDgZ/21VdfvXRBNYVSqUR4eDjWrFmD8PDwautERGToLly4gLCwMAghULduXd7pV8t0aWQHlVR8ywYAJYJJRW/ZoAmVCiUjRoyAl5cX5HK52iM4OFjT9SE5ORmjR4+Gvb09zM3N0bp1a5w8eVJaL4TA7Nmz4eLiAnNzc/j5+eHatWtq+8jIyMCoUaNgbW0NW1tbTJgwATk5ORqvtbz4rYGoapw/f14KJO3atcPAgQPZh0SLdG1kB5XuZW/ZoEmVmmbe09MTS5cuRe3ataVlKpUKP/zwA/755x+NFffgwQO0b98evr6++OCDD+Dg4IBr166hYcOGaNiwIQDg+++/x4IFCxAcHAxPT0/MmjUL58+fx6VLl6Rryf369UNKSgp+//13FBYW4u2330bnzp0RGhparjo0Oc188beGZ3/sxYm0un8BiAzFuXPnsGXLFggh0L59ewwcOJATo2lZeHg4fH19X7jdoUOHqmVkBz1fVfX7qdBnqKiEU6dOlbo8KSmpMrsr0+effy66d+9e5nqVSiWcnZ3FDz/8IC3LzMwUZmZmYs2aNUIIIS5duiQAiJiYGGmbXbt2CZlMJpKTk8tVR1ZWlgAgsrKyKnkmTxQVFQmFQiEAlPqQyWTCzc1NFBUVvdRxiGqa8+fPi3nz5om5c+eKrVu3CpVKpe2SSAgRGhpa5t+7px+hoaHaLpWqUEU+Q8vdrimEwA8//ICBAwfizz//LHGJBECJpp+XtW3bNnTq1AlvvfUWHB0d0b59e/z555/S+vj4eKSmpsLPz09aZmNjAy8vL0RFRQEAoqKiYGtri06dOknb+Pn5wcjICNHR0aUeNz8/H9nZ2WoPTajIeHAiKj8nJyeYm5ujQ4cObCHRIbo2soN0X7lDycyZM/H5558jOjoaq1evhpeXF+7cuVOVteHmzZv47bff0LhxY+zZswcffPABPvroI6nvSmpqKoAnf5Ce5uTkJK1LTU2Fo6Oj2npjY2PY2dlJ2zxrwYIFsLGxkR5ubm4aOR9dGw9OZCgcHBwwceJEDBgwgIFEh+jayA7SfeUOJZs3b0ZkZCTS09Px4MEDTJs2DT/++GNV1gaVSoUOHTrg22+/Rfv27TFx4kS8++67WLZsWZUed8aMGcjKypIeiYmJGtkvvzUQaU5sbCzi4+Ol5zY2NgwkOkbXRnaQ7it3KOnYsSO6desG4Mkv2pdffol79+6pbbNv3z6NFufi4oIWLVqoLWvevDkSEhIAAM7OzgCAtLQ0tW3S0tKkdc7OzkhPT1dbX1RUhIyMDGmbZ5mZmcHa2lrtoQn81kCkGadPn8bWrVsRGhpa4u8Q6RZdGtlBuq/cA/iNjY1x7do1tVEjRUVF0jKVSoWff/4ZvXv31lhx3bp1Q1xcnNqyq1evon79+gCejAJydnbGgQMH0K5dOwBPevlGR0fjgw8+AAB4e3sjMzMTp06dQseOHQEABw8ehEqlgpeXl8ZqLY/ibw2BgYGQyWRqP0t+a3g5nC2y5jh9+jS2b98OAOjQoQPs7e21XBG9iL+/PwYPHsz/o/Ri5e09K5PJhJGRkdrj6WXF/9akEydOCGNjY/HNN9+Ia9euidWrV4vatWuLVatWSdt89913wtbWVmzdulWcO3dODB48WHh6eorc3Fxpm759+4r27duL6OhoceTIEdG4cWMxYsSIctehqdE3xTZt2lRiFI6bm5vYtGmTRvZf05T281QoFPx5GqCTJ0+KuXPnirlz54p//vmHo2yI9EBFPkPLPU9Jp06dsHDhwjKTbVFREWbNmoVjx45pJCwV27FjB2bMmIFr167B09MTn3zyCd59911pvRACc+bMwR9//IHMzEx0794dS5cuVbvPRUZGBiZPnozt27fDyMgIAQEBWLJkCSwtLctVgybnKSnGb/aawXlfao5Tp05hx44dAAAvLy/06dOHfUiI9EBFPkPLHUq2b9+OgQMHPneb/fv3qw3PNRRVEUro5SmVSnh4eJQ5zFomk0GhUCA+Pp6BT89dv34dq1evBgB06dIFr7/+OgMJkZ6oyGdouTu6viiQADDIQEK6i/O+1Byenp5o3rw5AwmRgeOdqkhvcd4XwyeEgEwmg1wulzqIM5AQGS7eqYr0Fud9MWzR0dHYuXOn1F/IyMiIgYTIwGk0lGRlZWlyd0TPxXlfDFd0dDR2796NU6dOlZgWgIgMV6VDSWFhIZKSkpCQkICEhATcvn0bEyZM0GRtRM/F2SIN0/Hjx7F7924AQPfu3dG0aVMtV0RE1aVSoeSHH36ApaUl6tevD09PT+mxefNmTddH9FycLdKwREVFYc+ePQCetIS99tprvGRDVIOUe0jw0xwdHbF06VJ06tRJ+oMhhMCXX34pDdszJBwSrPs474v+O3bsmHSrih49eqBnz54MJEQGoCKfoZUafdOxY0cEBgaWWP77779XZndEL00ul6Nnz57aLoMqKSMjAwcOHADAQEJUk1Xq8s2kSZOwc+fOEss/++yzly6IiGoeOzs7BAQEoGfPnvD19WUgIaqhKtVSkpSUhClTpqCoqKjEuqVLl750UUS6gJeEql5+fj7MzMwAoMQdwYmo5qlUKJk5cya++eabEn1KFi5cqNHiiLQlLCwMU6ZMUZsxVqFQYPHixew8qyGRkZE4ffo0xo0bBxsbG22XQ0Q6oFKhpF27dvj0009LLG/evPlLF0SkbWXd5C85ORmBgYEc1aMBhw8fxqFDhwAAcXFxeOWVV7RcERHpgkr1KXnnnXdw4sSJEsuL54wg0ldKpRJTpkwpEUgASMumTp0KpVJZ3aUZjIiICCmQvPbaawwkRCSp1JDgBg0a4P79+7C3t5eWFd/8rLR+JvqOQ4JrjvDwcPj6+r5wu0OHDnG0TyWEh4cjIiICANCrVy90795dyxURUVWr8iHBKpUKP/74I0xNTdWW/fnnn5XZHZHO4E3+qs7TgcTPzw/dunXTckVEpGsqFUqCg4Px6quvlljOZljSd7zJX9UoKCjApUuXAAC9e/dG165dtVwREemiSl2+AZ58UwwNDcXNmzfh7u6OiRMnok6dOpquTyfw8k3NoVQq4eHhgeTk5FL7lchkMigUCsTHx3N48HOUNpw6NzcX165dQ/v27bVdHhFVo4p8hlaqo2tERAQaN26MhQsXIioqCkuWLEGLFi14N0/Se7zJ38sLCwuDh4cHfH19MW3aNPj6+sLDwwN79+5lICGi56pUKJk8eTKWLVuG1NRUnD59GsnJyVi3bh1mzpyp6fqIqh1v8ld5xcOpk5KS8Nprr2HixIlo166dNJw6LCxM2yUSkQ6rVChxd3fH6NGj1b5J9ujRA02aNJGeP3r06OWrI9ISf39/3Lp1C4cOHUJoaCgOHTqE+Ph4BpLneHo4da9evdCjRw/IZDKYmZlxODURlUulQkmPHj2Qm5urtkwIgfT0dOk5W01I3xXf5G/EiBHo2bMnL9m8QGRkJJKSkuDn5wcfHx8AwD///IPo6GgA/3/agMjISG2WSUQ6rFKjb3bv3o0VK1aojUBIS0tDZmYmXnvtNahUKkRHR+Pnn3/WWKFUObx/i+HTlff4zp076N27tzTUd+fOnYiJiSmxnaaHU+vK+RPRy6tUKImKisKkSZPK7EUrhEBaWtpLFUYvj/dvMXy68B4rlUocPnwYcXFxLwwkgGaHU+vC+VP5MDxSuYhK+P7771+4zbZt2yqza52UlZUlAIisrCxtl1JumzZtEjKZTABQe8hkMiGTycSmTZu0XSK9JF14jzdt2iQUCoUAIPz8/MTcuXNFp06dStRU/LC3txdFRUUaO7a2z5/K5+nfk+KHQqHge1RDVOQztNLzlJRm2bJleP/99zW1O52hb/OUFM+18fS3x6dxrg39pwvvcWk3LlQoFGXWBAD29vZIS0t76Zp04fypfMq6wSXw5H3iiDbDV5HP0HKFkl69epX6C/U0IQRiYmKQk5NTsWr1gL6FEkO/fwubgbX/HheHAmdnZ8TGxlbonleaqEnb50/l86LwCABubm4MjwZO4/e+OXnyJN577z1YWFiUuY14ZvQNaY8h37+FfQie0PZ7fPjwYbRu3RpeXl5o2rQpQkNDX/jFRZM1afv8qXyKR2Q9T/GILIZHAsoZSqZMmYKvvvrqhdu99tprAID4+Hh4enq+XGVUaYZ6/5aymoGLJ+aqSc3A2nyPhRA4e/YsvLy8IITApUuXyh1INFWTof6OG5rk5GSNbkeGr1zzlJQnkABP5i8BgGnTplW+InppPj4+UCgUJaZJLyaTyeDm5ibNJaEPnp6Y61miBk7Mpa33WAiBf/75B1lZWRBCYNu2bThz5ky5XqvJmgzxd9wQ3b17V6PbkeGr1ORpL6LBvrNUCYZ4/5YXNQOLGjYxlzbeYyEEdu7ciZMnTwJ4cgknNja2XK/VdE2G+DtuiBwcHDS6HRm+KgklZX17oepjaPdvYR+Ckqr7Pd63bx9OnToFABg8eDAmT54MoHz/36uiJkP7HTdEz743L7sdGT6NDgku5u/vb1A33tK30TdPM5SRKhxtUbbqeo+Tk5OxatUq9O3bF23btgVQesdjNzc3/Oc//4GDg0O1/N4Zyu+4IeLoGwKqYEhwRTGUkKYV/3FLTk4uc74DzktR9fLy8lCrVi21ZQwF9DzFHdQB9Uv7xS1sbNUyfBX5DK2SyzdU8yiVSoSHh2PNmjUIDw/XeIdT9iGofkII7Nq1C3fu3JGWPRtIAN64kJ6Pl9moIhhK6KWFhYXBw8MDvr6+GDlyJHx9feHh4aHx1jL+cas+KpUK27Ztw4kTJ7B69Wrk5+druyTSY/7+/rh16xYOHTqE0NBQHDp0CPHx8fw/SyVUyeWbIUOGYPPmzZrerdbw8k3Zypo7pCqbZnm5oGoVB5KzZ89CJpPB398frVq10nZZRKSnND6ja0U1bdq0KnZLOuZFc4fIZDJMnToVgwcP1mhoKL5cQJqnUqmwdetWnDt3DjKZDAEBAWjZsqW2yyKiGqLCl28uXbqEL774AoMGDUJgYCBmzZqF27dvq23z3XffYf369RorknQT5w4xLAwkRKRtFQol33//Pdq0aYOFCxfiyJEjiIiIwDfffIMmTZpg2bJlattu2LBBo4WS7uHcIYbl+PHjOHfuHIyMjBAYGMhAQkTVrtyhZPPmzVixYgU2bNiA7OxsZGRk4O7du3j8+DGWL1+O//73vzh48CCAJ3NKXL16tcqKJt3A+48Yls6dO6NRo0YIDAxEixYttF0OEdVA5e7oOmjQIPz1119lTgd8//59DBw4EDY2Nti/fz8++OADLFmyRKPFags7upaOc4foP5VKBZlMJnVMLu4LRESkKVUyT0ndunWfe38Ce3t7NG3aFMePH8d7772HBQsWlL/iMsydO1f6g1n8aNasmbQ+Ly8PkyZNgr29PSwtLREQEIC0tDS1fSQkJKB///6oXbs2HB0dMX36dBQVFb10bWT4c4dU9dwr2qZSqRAWFoaDBw9KoZKBhIi0qdyjb8rzB9nIyAgPHjx4qYKe1bJlS+zfv196bmz8/0v++OOPsXPnTmzYsAE2NjaYPHky/P39cfToUanm/v37w9nZGceOHUNKSgrGjh0LExMTfPvttxqts6Yqnjvk2anGFQoFFi1apLfzEJQ2fbpCocDixYv19pyeplQqERYWhkuXLsHIyAht2rThTdGISOvKHUqKiooQExODzp07l7o+NjYW9+/f11hhxYyNjeHs7FxieVZWFv766y+EhobitddeAwAsX74czZs3x/Hjx9GlSxfs3bsXly5dwv79++Hk5IR27dph/vz5+PzzzzF37lyYmppqvN6ayN/fH4MHDzaYuUPKmnslOTkZgYGBej9Rm1KpxKZNm3D58mUYGRlh6NChDCREpBPKHUpmzJiBnj17YujQoXjzzTehUCggk8mQlJSELVu2YM2aNdi9e7fGC7x27RpcXV1Rq1YteHt7Y8GCBXB3d8epU6dQWFgIPz8/adtmzZrB3d0dUVFR6NKlC6KiotC6dWs4OTlJ2/Tp0wcffPABLl68iPbt25d6zPz8fLUZLLOzszV+XobGUOYO0dbcK9Xl6UAil8sxdOhQNGnSRNtlEREBqEAoadWqFTZu3IigoCD8/vvv0nIhBJydnREaGopXXnlFo8V5eXlhxYoVaNq0KVJSUjBv3jz4+PjgwoULSE1NhampKWxtbdVe4+TkhNTUVABAamqqWiApXl+8riwLFizAvHnzNHoupB8qMveKvoUwpVKJjRs34sqVK5DL5Rg2bBgaN26s7bKIiCQVmtG1Z8+euHbtGvbt24fLly9DCIHmzZujd+/eMDMz03hx/fr1k/7dpk0beHl5oX79+li/fj3Mzc01frxiM2bMwCeffCI9z87OhpubW5Udj3SHIc+9cuvWLQYSItJpFZ5m3tTUFP3790f//v2rop7nsrW1RZMmTXD9+nX07t0bBQUFyMzMVGstSUtLk/qgODs748SJE2r7KB6dU1o/lWJmZmZVErIMmaHcj8aQ515p2LAhBg4cCGtrazRq1Ejb5RARlaBXdwnOycnBjRs34OLigo4dO8LExAQHDhyQ1sfFxSEhIQHe3t4AAG9vb5w/fx7p6enSNvv27YO1tTUnh9Kg6rpLcHXw8fGR+kuVRiaTwc3NDT4+PtVcWeUUFRXh8ePH0vMOHTowkBCRztLpUPLpp58iIiICt27dwrFjxzBkyBDI5XKMGDECNjY2mDBhAj755BMcOnQIp06dwttvvw1vb2906dIFAPD666+jRYsWGDNmDM6ePYs9e/Zg5syZmDRpEltCNKR4pMqz/TCKR6roWzAxpLlXioqKsGHDBgQHB+PRo0faLoeI6IV0OpQkJSVhxIgRaNq0KYYOHQp7e3scP35cGr74888/Y8CAAQgICECPHj3g7Oys9iEol8uxY8cOyOVyeHt7Y/To0Rg7diy++uorbZ2SQXnRSBUAmDp1qt5NOlY890q9evXUlisUCr0ZDlxUVIT169fj6tWryMjIwL1797RdEhHRC5V7mvmajNPMly48PBy+vr4v3O7QoUN6N1IF0N9+MsWB5Nq1azA2NsaIESPQoEEDbZdFRDVURT5DK9zRlaiYIY9UAfRz7pWioiKsW7cO169fh7GxMUaOHAlPT09tl0VEVC4MJVRphjxSRR8VFhZi3bp1uHHjBkxMTDBy5Eh4eHhouywionLT6T4lpNsMbaSKvsvNzcX9+/cZSIhIbzGUUKUZ0kgVQ2BtbY2goCCMHj2agYSI9BJDCb0UQxipos8KCwtx69Yt6bmtrS3c3d21VxAR0Uvg6Jty4OibF9PXkSr6rLCwEGvWrMHt27fx1ltvoVmzZtouiYioBI6+oWqnjyNV9FlBQQHWrFmDW7duwdTUFLVr19Z2SUREL42hhEjPFBQUIDQ0FLdv34apqSlGjx7NG0YSkUFgnxIiPcJAQkSGjC0lRHqisLAQq1evRkJCAszMzDB69GgoFAptl0VEpDFsKSHSE8bGxrC3t4eZmRnGjBnDQEJEBoejb8qBo29IVwgh8ODBA9jZ2Wm7FCKicqnIZyhbSoh0WH5+PsLDw6FSqQA8mZSOgYSIDBX7lBDpqPz8fKxatQpJSUl4+PAhBg4cqO2SiIiqFEMJkQ7Ky8vD6tWrkZSUhFq1aqFTp07aLomIqMoxlBDpmLy8PKxatQrJyckwNzfHmDFjeKdlIqoRGEqIdEheXh5WrlyJO3fuwNzcHGPHjoWzs7O2yyIiqhbs6EqkI4QQWLt2LQMJEdVYDCVEOkImk6FHjx6wtrZGUFAQAwkR1Ticp6QcOE8JVaeioiIYG/PKKhEZBs5TQqQnHj9+jNWrV+Pu3bvSMgYSIqqpGEqItOTx48dYuXIlrl+/jk2bNoGNlkRU0/ErGZEWPH78GCEhIUhLS4OFhQUCAgIgk8m0XRYRkVaxpYSomj0bSIKCguDg4KDtsoiItI4tJUTV6NGjRwgJCUF6ejosLS0RFBSEunXrarssIiKdwJYSomq0f/9+BhIiojKwpYSoGvXp0we5ubnw8/NjICEiegZDCVEVKywshImJCQCgVq1aGD58uJYrIiLSTbx8Q1SFcnJy8McffyAqKkrbpRAR6TyGEqIq8vDhQwQHB+PevXs4fvw48vPztV0SEZFO4+UboipQHEju378v3cvGzMxM22UREek0hhIiDXs2kIwbNw516tTRdllERDqPoYRIg54OJDY2NggKCmIgISIqJ4YSIg26evWqFEjGjRsHW1tbbZdERKQ3GEqINKhjx45QqVRo3LgxAwkRUQUxlBC9pOzsbJiamqJWrVoAgM6dO2u5IiIi/cQhwUQvISsrCytWrMDq1as55JeI6CWxpYSokjIzMxEcHIzMzEwAQH5+Pof9EhG9BLaUEFXC04GkTp06GDduHKytrbVdFhGRXmNLCVEFZWZmYsWKFcjKyoKdnR2CgoIYSIiINIChhKgCHjx4gODgYAYSIqIqwFBCVAFFRUUoKiqCvb09goKCYGVlpe2SiIgMhl71Kfnuu+8gk8kwdepUaVleXh4mTZoEe3t7WFpaIiAgAGlpaWqvS0hIQP/+/VG7dm04Ojpi+vTpKCoqqubqyRA4ODggKCiIgYSIqAroTSiJiYnB77//jjZt2qgt//jjj7F9+3Zs2LABERERuHPnDvz9/aX1SqUS/fv3R0FBAY4dO4bg4GCsWLECs2fPru5TID2VkZGBW7duSc8dHBwYSIiIqoBehJKcnByMGjUKf/75p9p9RLKysvDXX3/hp59+wmuvvYaOHTti+fLlOHbsGI4fPw4A2Lt3Ly5duoRVq1ahXbt26NevH+bPn49ff/0VBQUF2jol0hP379+X5iFJSEjQdjlERAZNL0LJpEmT0L9/f/j5+aktP3XqFAoLC9WWN2vWDO7u7oiKigIAREVFoXXr1nBycpK26dOnD7Kzs3Hx4sVSj5efn4/s7Gy1B9U89+/fR3BwMB4+fAhbW1vY2dlpuyQiIoOm8x1d165di9OnTyMmJqbEutTUVJiampa4x4iTkxNSU1OlbZ4OJMXri9eVZsGCBZg3b54Gqid9de/ePQQHByMnJwcODg4YO3YsLC0ttV0WEZFB0+mWksTEREyZMgWrV6+W7itSHWbMmIGsrCzpkZiYWG3HJu17NpAEBQUxkBARVQOdDiWnTp1Ceno6OnToAGNjYxgbGyMiIgJLliyBsbExnJycUFBQIE3zXSwtLQ3Ozs4AAGdn5xKjcYqfF2/zLDMzM1hbW6s9qGYonqk1JycHjo6OCAoKgoWFhbbLIiKqEXQ6lPTq1Qvnz59HbGys9OjUqRNGjRol/dvExAQHDhyQXhMXF4eEhAR4e3sDALy9vXH+/Hmkp6dL2+zbtw/W1tZo0aJFtZ9TMaVSifDwcKxZswbh4eFQKpVaq4X+PysrK7i5ucHR0RFjx45lICEiqkY63afEysoKrVq1UltmYWEBe3t7afmECRPwySefwM7ODtbW1vjwww/h7e2NLl26AABef/11tGjRAmPGjMHChQuRmpqKmTNnYtKkSVq7eVpYWBimTJmCpKQkaZlCocDixYvVhjNT9ZPL5QgICEBBQQHMzc21XQ4RUY2i0y0l5fHzzz9jwIABCAgIQI8ePeDs7IywsDBpvVwux44dOyCXy+Ht7Y3Ro0dj7Nix+Oqrr7RSb1hYGAIDA9UCCQAkJycjMDBQrXaqHunp6di/fz+EEBBCQC6XM5AQEWmBTAghtF2ErsvOzoaNjQ2ysrJeqn+JUqmEh4dHiUBSTCaTQaFQID4+HnK5vNLHofJLT09HcHAwHj9+jF69eqFbt26QyWTaLouIyGBU5DNU71tK9ElkZGSZgQQAhBBITExEZGRkNVZVc6WlpUmBxNnZGR07dmQgISLSIp3uU2JoUlJSNLodVV5aWhpCQkLw+PFjuLi4YMyYMbxkQ0SkZQwl1cjFxUWj21HlpKamIiQkBLm5uQwkREQ6hJdvqpGPjw8UCkWZlwhkMhnc3Nzg4+NTzZXVHAUFBVi9ejVyc3Ph6uqKsWPHMpAQEekIhpJqJJfLsXjxYgAoEUyKny9atIidXKuQqakp3njjDbi5uWHMmDHVOlMwERE9H0NJNfP398fGjRtRr149teUKhQIbN27kPCVV5OlBZs2bN8fbb7/NQEJEpGM4JLgcNDUk+GlKpRKRkZFISUmBi4sLfHx82EJSRe7cuYMdO3Zg2LBhsLGx0XY5REQ1SkU+Q9nRVUvkcjl69uyp7TIM3p07d7By5Urk5eVh//79CAgI0HZJRERUBoYSMljJyclYuXIl8vPz4ebmhgEDBmi7JCIieg6GEjJISUlJWLVqFfLz8+Hu7o6RI0dq7V5HRERUPgwlZHCeDSSjRo2CqamptssiIqIXYCghgyKEwJ49e5Cfn4/69etj5MiRDCRERHqCoYQMikwmw7Bhw3Do0CH06dOHgYSISI9wnhIyCI8fP5b+bWlpiYEDBzKQEBHpGYYS0nsJCQlYsmQJzpw5o+1SiIjoJTCUkF67ffu21Kn1woUL4FyARET6i6GE9Nbt27exevVqFBYWwtPTE8OHDy/zZodERKT72NGV9NKtW7cQGhqKwsJCNGjQAMOHD4eJiYm2yyIiopfAUEJ65+lA0rBhQwwbNoyBhIjIADCUkN6Jj49HYWEhGjVqhGHDhsHYmL/GRESGgH/NSe/07NkTtra2aN26NQMJEZEBYUdX0gvJyckoLCwE8GSCtPbt2zOQEBEZGIYS0nk3btzAihUrsHbtWimYEBGR4eFXTdJp169fx9q1a6FUKmFsbMwhv0REBoyhhHTW04GkadOmCAwM5CUbIiIDxr/wpJOuXbuGdevWSYHkrbfeglwu13ZZRERUhRhKSOc8HUiaNWuGwMBABhIiohqAoYR0Tu3atWFsbIwmTZogICCAgYSIqIZgKCGdU69ePUyYMAF2dnYMJERENQiHBJNOuHr1KpKTk6XnDg4ODCRERDUMQwlp3ZUrV7Bu3TqsXLkS9+7d03Y5RESkJbx8Q1p1+fJlbNy4ESqVCo0bN4adnZ22SyIiIi1hSwlpzdOBpFWrVhgyZAiMjPgrSURUU7GlhLTi0qVL2LRpE1QqFVq3bo0333yTgYSIqIZjKKFqd+vWLWzcuBFCCLRp0waDBw9mICEiIoYSqn716tWDp6cnLC0tGUiIiEjCUELVzsTEBMOHD4dcLmcgISIiCT8RqFpcuHABBw8ehBACwJNgwkBCRERPY0sJVbnz589j8+bNEELAxcUFzZs313ZJRESkgxhKqEqdO3cOW7ZsgRAC7du3R7NmzbRdEhER6SiGEqoyZ8+exdatWyGEQIcOHTBgwADIZDJtl0VERDpKpy/q//bbb2jTpg2sra1hbW0Nb29v7Nq1S1qfl5eHSZMmwd7eHpaWlggICEBaWpraPhISEtC/f3/Url0bjo6OmD59OoqKiqr7VGqcs2fPSi0kHTt2ZCAhIqIX0ulQolAo8N133+HUqVM4efIkXnvtNQwePBgXL14EAHz88cfYvn07NmzYgIiICNy5cwf+/v7S65VKJfr374+CggIcO3YMwcHBWLFiBWbPnq2tU6oRHjx4gK1btwIAOnXqhP79+zOQEBHRC8lE8XAIPWFnZ4cffvgBgYGBcHBwQGhoKAIDAwE8ubFb8+bNERUVhS5dumDXrl0YMGAA7ty5AycnJwDAsmXL8Pnnn+Pu3bswNTUt1zGzs7NhY2ODrKwsWFtbV9m5GZLTp08jNTUV/fr1YyAhIqrBKvIZqtMtJU9TKpVYu3YtHj16BG9vb5w6dQqFhYXw8/OTtmnWrBnc3d0RFRUFAIiKikLr1q2lQAIAffr0QXZ2ttTaUpr8/HxkZ2erPejFlEql9O8OHTrgjTfeYCAhIqJy0/lQcv78eVhaWsLMzAzvv/8+Nm/ejBYtWiA1NRWmpqawtbVV297JyQmpqakAgNTUVLVAUry+eF1ZFixYABsbG+nh5uam2ZMyQKdPn8Yff/yBR48eabsUIiLSUzofSpo2bYrY2FhER0fjgw8+QFBQEC5dulSlx5wxYwaysrKkR2JiYpUeT9+dOnUK27dvR3p6OmJjY7VdDhER6SmdHxJsamqKRo0aAQA6duyImJgYLF68GMOGDUNBQQEyMzPVWkvS0tLg7OwMAHB2dsaJEyfU9lc8Oqd4m9KYmZnBzMxMw2dimE6ePImdO3cCALy8vNC1a1ctV0RERPpK51tKnqVSqZCfn4+OHTvCxMQEBw4ckNbFxcUhISEB3t7eAABvb2+cP38e6enp0jb79u2DtbU1WrRoUe21G5qYmBgpkHTp0gV9+vRhHxIiIqo0nW4pmTFjBvr16wd3d3c8fPgQoaGhCA8Px549e2BjY4MJEybgk08+gZ2dHaytrfHhhx/C29sbXbp0AQC8/vrraNGiBcaMGYOFCxciNTUVM2fOxKRJk9gS8pJiYmLwzz//AHgS/nr37s1AQkREL0WnQ0l6ejrGjh2LlJQU2NjYoE2bNtizZw969+4NAPj5559hZGSEgIAA5Ofno0+fPli6dKn0erlcjh07duCDDz6At7c3LCwsEBQUhK+++kpbp2QQiud9ARhIiIhIc/RunhJt4DwlJWVmZuLChQvo1q0bAwkREZXJIOcpIe178OCB9G9bW1t0796dgYSIiDSGoYTK5fjx4/jll19w+fJlbZdCREQGiqGEXigqKgp79uyBSqV67qRzREREL0OnO7qS9h07dgz79u0DAPj4+KBnz57aLYiIiAwWQwmV6elA0qNHD/Ts2ZN9SIiIqMowlFCpjh49iv379wMAXn31VbaQEBFRlWOfEipBCIGMjAwADCRERFR92FJCJchkMgwYMABNmzZFkyZNtF0OERHVEGwpIcnly5ehVCoBPAkmDCRERFSdGEoIABAREYH169cjLCwMnOSXiIi0gZdvCOHh4YiIiAAAuLi4cIQNERFpBUNJDfd0IPHz80O3bt20XBEREdVUDCU1lBAC4eHhOHz4MAAGEiIi0j6Gkhrq8OHDUiDp3bs3unbtquWKiIiopmMoqaHc3NxgbGyM1157Dd7e3touh4iICDLBoRYvlJ2dDRsbG2RlZcHa2lrb5WhMVlYWbGxstF0GEREZsIp8hnJIcA0hhEBkZCTu3r0rLWMgISIiXcJQUgMIIbB//34cPHgQISEhyMvL03ZJREREJbBPiYETQmDfvn2IiooCAPj4+KBWrVparoqIiKgkhhID9mwgeeONN9C5c2ctV0VERFQ6hhIDJYTA3r17cfz4cQAMJEREpPsYSgzUiRMnpEDSv39/dOrUScsVERERPR9DiYFq27YtLly4gHbt2qFjx47aLoeIiOiFGEoMiBBCuplerVq18Pbbb8PIiAOsiIhIP/ATy0AIIfDPP//g2LFj0jIGEiIi0idsKTEAxYHk5MmTAIBGjRrB0dFRy1URERFVDEOJnhNCYOfOnTh16hQAYPDgwQwkRESklxhK9JgQAjt27MDp06cBAG+++Sbatm2r5aqIiIgqh6FETwkhsH37dpw5cwYymQxvvvkm2rRpo+2yiIiIKo09IfXUjRs3GEiIiMigsKVETzVq1Ah+fn6wtrZG69attV0OERHRS2Mo0SMqlQpFRUUwNTUFAHTr1k3LFREREWkOL9/oCZVKhW3btmHlypXIz8/XdjlEREQax1CiB1QqFbZu3YqzZ88iOTkZiYmJ2i6JiIhI4xhKdFxxIDl37hxkMhkCAwPRqFEjbZdFRESkcexTosNUKhW2bNmC8+fPw8jICAEBAWjRooW2yyIiIqoSDCU6SqVSYfPmzbhw4QKMjIwQGBiI5s2ba7ssIiKiKsNQoqOys7MRHx8PIyMjvPXWW2jWrJm2SyIiIqpSDCU6ytbWFkFBQcjIyEDTpk21XQ4REVGVY0dXHaJUKpGamio9d3BwYCAhIqIag6FERyiVSoSFheGvv/5CfHy8tsshIiKqdgwlOkCpVGLTpk24dOkShBAoLCzUdklERETVTqdDyYIFC9C5c2dYWVnB0dERb775JuLi4tS2ycvLw6RJk2Bvbw9LS0sEBAQgLS1NbZuEhAT0798ftWvXhqOjI6ZPn46ioqLqPJUyKZVKbNy4EZcvX4ZcLsewYcPQpEkTbZdFRERU7XQ6lERERGDSpEk4fvw49u3bh8LCQrz++ut49OiRtM3HH3+M7du3Y8OGDYiIiMCdO3fg7+8vrVcqlejfvz8KCgpw7NgxBAcHY8WKFZg9e7Y2TklNcSC5cuWKFEgaN26s7bKIiIi0QiaEENouorzu3r0LR0dHREREoEePHsjKyoKDgwNCQ0MRGBgIALhy5QqaN2+OqKgodOnSBbt27cKAAQNw584dODk5AQCWLVuGzz//HHfv3pVubvc82dnZsLGxQVZWFqytrTVyLkqlEhs2bEBcXBzkcjmGDx/OmVqJiMjgVOQzVKdbSp6VlZUFALCzswMAnDp1CoWFhfDz85O2adasGdzd3REVFQUAiIqKQuvWraVAAgB9+vRBdnY2Ll68WOpx8vPzkZ2drfaoCkZGRjA2NsaIESMYSIiIqMbTm3lKVCoVpk6dim7duqFVq1YAgNTUVJiamsLW1lZtWycnJ2lobWpqqlogKV5fvK40CxYswLx58zR8BurkcjkCAgKQnp4OFxeXKj0WERGRPtCblpJJkybhwoULWLt2bZUfa8aMGcjKypIeVXVXXrlczkBCRET0f/SipWTy5MnYsWMHDh8+DIVCIS13dnZGQUEBMjMz1VpL0tLS4OzsLG1z4sQJtf0Vj84p3uZZZmZmMDMz0/BZEBER0fPodEuJEAKTJ0/G5s2bcfDgQXh6eqqt79ixI0xMTHDgwAFpWVxcHBISEuDt7Q0A8Pb2xvnz55Geni5ts2/fPlhbW/OOu0RERDpEp1tKJk2ahNDQUGzduhVWVlZSHxAbGxuYm5vDxsYGEyZMwCeffAI7OztYW1vjww8/hLe3N7p06QIAeP3119GiRQuMGTMGCxcuRGpqKmbOnIlJkyaxNYSIiEiH6PSQYJlMVury5cuXY9y4cQCeTJ42bdo0rFmzBvn5+ejTpw+WLl2qdmnm9u3b+OCDDxAeHg4LCwsEBQXhu+++g7Fx+TJZVQwJJiIiqgkq8hmq06FEVzCUEBERVY7BzlNCREREhouhhIiIiHQCQwkRERHpBIYSIiIi0gkMJURERKQTGEqIiIhIJzCUEBERkU5gKCEiIiKdwFBCREREOoGhhIiIiHQCQwkRERHpBIYSIiIi0gkMJURERKQTjLVdgD4ovpFydna2lishIiLSL8WfncWfpc/DUFIODx8+BAC4ublpuRIiIiL99PDhQ9jY2Dx3G5koT3Sp4VQqFe7cuQMrKyvIZDKN7Tc7Oxtubm5ITEyEtbW1xvarLYZ2PgDPSV/wnPSDoZ2ToZ0PUDXnJITAw4cP4erqCiOj5/caYUtJORgZGUGhUFTZ/q2trQ3mFxowvPMBeE76guekHwztnAztfADNn9OLWkiKsaMrERER6QSGEiIiItIJDCVaZGZmhjlz5sDMzEzbpWiEoZ0PwHPSFzwn/WBo52Ro5wNo/5zY0ZWIiIh0AltKiIiISCcwlBAREZFOYCghIiIincBQQkRERDqBoUSDFixYgM6dO8PKygqOjo548803ERcXp7ZNXl4eJk2aBHt7e1haWiIgIABpaWlq2yQkJKB///6oXbs2HB0dMX36dBQVFVXnqUh+++03tGnTRppIx9vbG7t27ZLW69v5lOa7776DTCbD1KlTpWX6dl5z586FTCZTezRr1kxar2/nUyw5ORmjR4+Gvb09zM3N0bp1a5w8eVJaL4TA7Nmz4eLiAnNzc/j5+eHatWtq+8jIyMCoUaNgbW0NW1tbTJgwATk5OdV9KgAADw+PEu+TTCbDpEmTAOjf+6RUKjFr1ix4enrC3NwcDRs2xPz589XucaJv7xHwZDr0qVOnon79+jA3N0fXrl0RExMjrdf1czp8+DAGDhwIV1dXyGQybNmyRW29puo/d+4cfHx8UKtWLbi5uWHhwoUvX7wgjenTp49Yvny5uHDhgoiNjRVvvPGGcHd3Fzk5OdI277//vnBzcxMHDhwQJ0+eFF26dBFdu3aV1hcVFYlWrVoJPz8/cebMGfHPP/+IunXrihkzZmjjlMS2bdvEzp07xdWrV0VcXJz497//LUxMTMSFCxf08nyedeLECeHh4SHatGkjpkyZIi3Xt/OaM2eOaNmypUhJSZEed+/eldbr2/kIIURGRoaoX7++GDdunIiOjhY3b94Ue/bsEdevX5e2+e6774SNjY3YsmWLOHv2rBg0aJDw9PQUubm50jZ9+/YVbdu2FcePHxeRkZGiUaNGYsSIEdo4JZGenq72Hu3bt08AEIcOHRJC6N/79M033wh7e3uxY8cOER8fLzZs2CAsLS3F4sWLpW307T0SQoihQ4eKFi1aiIiICHHt2jUxZ84cYW1tLZKSkoQQun9O//zzj/jyyy9FWFiYACA2b96stl4T9WdlZQknJycxatQoceHCBbFmzRphbm4ufv/995eqnaGkCqWnpwsAIiIiQgghRGZmpjAxMREbNmyQtrl8+bIAIKKiooQQT36ZjIyMRGpqqrTNb7/9JqytrUV+fn71nkAZ6tSpI/73v//p/fk8fPhQNG7cWOzbt0+8+uqrUijRx/OaM2eOaNu2banr9PF8hBDi888/F927dy9zvUqlEs7OzuKHH36QlmVmZgozMzOxZs0aIYQQly5dEgBETEyMtM2uXbuETCYTycnJVVd8OU2ZMkU0bNhQqFQqvXyf+vfvL8aPH6+2zN/fX4waNUoIoZ/v0ePHj4VcLhc7duxQW96hQwfx5Zdf6t05PRtKNFX/0qVLRZ06ddR+7z7//HPRtGnTl6qXl2+qUFZWFgDAzs4OAHDq1CkUFhbCz89P2qZZs2Zwd3dHVFQUACAqKgqtW7eGk5OTtE2fPn2QnZ2NixcvVmP1JSmVSqxduxaPHj2Ct7e33p/PpEmT0L9/f7X6Af19n65duwZXV1c0aNAAo0aNQkJCAgD9PZ9t27ahU6dOeOutt+Do6Ij27dvjzz//lNbHx8cjNTVV7bxsbGzg5eWldl62trbo1KmTtI2fnx+MjIwQHR1dfSdTioKCAqxatQrjx4+HTCbTy/epa9euOHDgAK5evQoAOHv2LI4cOYJ+/foB0M/3qKioCEqlErVq1VJbbm5ujiNHjujlOT1NU/VHRUWhR48eMDU1lbbp06cP4uLi8ODBg0rXxxvyVRGVSoWpU6eiW7duaNWqFQAgNTUVpqamsLW1VdvWyckJqamp0jZP/8EpXl+8ThvOnz8Pb29v5OXlwdLSEps3b0aLFi0QGxurl+cDAGvXrsXp06fVrhMX08f3ycvLCytWrEDTpk2RkpKCefPmwcfHBxcuXNDL8wGAmzdv4rfffsMnn3yCf//734iJicFHH30EU1NTBAUFSXWVVvfT5+Xo6Ki23tjYGHZ2dlr9/QOALVu2IDMzE+PGjQOgn793X3zxBbKzs9GsWTPI5XIolUp88803GDVqlFpN+vQeWVlZwdvbG/Pnz0fz5s3h5OSENWvWICoqCo0aNdLLc3qapupPTU2Fp6dniX0Ur6tTp06l6mMoqSKTJk3ChQsXcOTIEW2X8tKaNm2K2NhYZGVlYePGjQgKCkJERIS2y6q0xMRETJkyBfv27SvxbUhfFX8zBYA2bdrAy8sL9evXx/r162Fubq7FyipPpVKhU6dO+PbbbwEA7du3x4ULF7Bs2TIEBQVpubqX99dff6Ffv35wdXXVdimVtn79eqxevRqhoaFo2bIlYmNjMXXqVLi6uur1e7Ry5UqMHz8e9erVg1wuR4cOHTBixAicOnVK26UZPF6+qQKTJ0/Gjh07cOjQISgUCmm5s7MzCgoKkJmZqbZ9WloanJ2dpW2e7W1f/Lx4m+pmamqKRo0aoWPHjliwYAHatm2LxYsX6+35nDp1Cunp6ejQoQOMjY1hbGyMiIgILFmyBMbGxnByctLL83qara0tmjRpguvXr+vt++Ti4oIWLVqoLWvevLl0Waq4rtLqfvq80tPT1dYXFRUhIyNDq+/T7du3sX//frzzzjvSMn18n6ZPn44vvvgCw4cPR+vWrTFmzBh8/PHHWLBggVpN+vYeNWzYEBEREcjJyUFiYiJOnDiBwsJCNGjQQG/PqZim6q+q30WGEg0SQmDy5MnYvHkzDh48WKJpq2PHjjAxMcGBAwekZXFxcUhISIC3tzcAwNvbG+fPn1f7hdi3bx+sra1L/IHWFpVKhfz8fL09n169euH8+fOIjY2VHp06dcKoUaOkf+vjeT0tJycHN27cgIuLi96+T926dSsxpP7q1auoX78+AMDT0xPOzs5q55WdnY3o6Gi188rMzFT7hnvw4EGoVCp4eXlVw1mUbvny5XB0dET//v2lZfr4Pj1+/BhGRuofI3K5HCqVCoB+v0cAYGFhARcXFzx48AB79uzB4MGD9f6cNFW/t7c3Dh8+jMLCQmmbffv2oWnTppW+dAOAQ4I16YMPPhA2NjYiPDxcbdjf48ePpW3ef/994e7uLg4ePChOnjwpvL29hbe3t7S+eMjf66+/LmJjY8Xu3buFg4OD1ob8ffHFFyIiIkLEx8eLc+fOiS+++ELIZDKxd+9evTyfsjw9+kYI/TuvadOmifDwcBEfHy+OHj0q/Pz8RN26dUV6erpeno8QT4ZrGxsbi2+++UZcu3ZNrF69WtSuXVusWrVK2ua7774Ttra2YuvWreLcuXNi8ODBpQ5tbN++vYiOjhZHjhwRjRs31upwU6VSKdzd3cXnn39eYp2+vU9BQUGiXr160pDgsLAwUbduXfHZZ59J2+jje7R7926xa9cucfPmTbF3717Rtm1b4eXlJQoKCoQQun9ODx8+FGfOnBFnzpwRAMRPP/0kzpw5I27fvq2x+jMzM4WTk5MYM2aMuHDhgli7dq2oXbs2hwTrEgClPpYvXy5tk5ubK/71r3+JOnXqiNq1a4shQ4aIlJQUtf3cunVL9OvXT5ibm4u6deuKadOmicLCwmo+myfGjx8v6tevL0xNTYWDg4Po1auXFEiE0L/zKcuzoUTfzmvYsGHCxcVFmJqainr16olhw4apzeehb+dTbPv27aJVq1bCzMxMNGvWTPzxxx9q61UqlZg1a5ZwcnISZmZmolevXiIuLk5tm/v374sRI0YIS0tLYW1tLd5++23x8OHD6jwNNXv27BEAStQphP69T9nZ2WLKlCnC3d1d1KpVSzRo0EB8+eWXasNE9fE9WrdunWjQoIEwNTUVzs7OYtKkSSIzM1Nar+vndOjQoVI/i4KCgjRa/9mzZ0X37t2FmZmZqFevnvjuu+9eunaZEE9NvUdERESkJexTQkRERDqBoYSIiIh0AkMJERER6QSGEiIiItIJDCVERESkExhKiIiISCcwlBAREZFOYCghIiIincBQQkTVYtasWbC2toavr6+2S6lWOTk5GDZsGGQyGebNm6ftcoh0mrG2CyAi3XfhwgV8++23kMvlMDY2xt27dzF+/Hj4+/uXex/z589HXl4eYmJiqrDS0uXl5aFNmza4ePEiTExMqvXYlpaWWLduHW7evAlOoE30fAwlRPRc4eHhCAgIwM6dO9GlSxcAQHp6Onr06IG4uDjMmDGj3PuysLCoqjKfKywsDNeuXcPOnTvx5ptvlus1c+fORXh4OMLDwzVSg7bOnUif8PINEZUpPz8fY8eORWBgoBRIAMDR0RHffvstZs6ciQsXLmixwvLZtm0bGjZsiOXLl7/UfgoLC5GXl6ehqojoWQwlRFSm/fv3IzExEUOGDCmxrnfv3lCpVAgJCanUvr///nuYmZmhZ8+eWLBgAd544w20bNlSapkIDw9H/fr14enpiTVr1qBXr16wt7fHb7/9hu3bt6Nv375wcHDAwoULn3ucxMRE1K1bF2PHjsU///yDtLQ0tfW3b9+Gv78/pk+fjsDAQBw8eBALFy7Exo0bcenSJQwfPhyLFi3Czz//jNq1a+P7778HAMyZMwd16tTB22+/Le3r999/x7/+9S9MnDgRPXr0QHp6eqV+NkQ11kvfZ5iIDNbChQsFAHH+/PlS19va2or+/fuXe39z5swRr776qvT8448/Fk2aNBE5OTlCCCFCQkJE7dq1RVJSkhBCiN9//11YWlqKqKgoqR4TExOxbds2IYQQ69evFyYmJiIvL6/MY86fP18cO3ZM3Lx5U8hkMvHjjz9K6/Lz80XDhg3F1q1bhRBCTJ06VbRv377UWoUQolu3bmLOnDnS89GjR0u3g09LSxMAxL59+4QQQvTr10988cUX0ravvvqq2muJqCS2lBBRmcT/dcyUyWSlrpfL5TA1Na30/q2treHi4iL1txg1ahQsLCwQGhoKADA1NYW9vb106ahTp04oLCzEwIED1Z4/2/rxdP2RkZHw9vaGp6cnunfvrnYJZ/v27YiPj0e/fv0AAIGBgXjvvffKrNfYWL0bnlwul/7t6OiI8PBw9OjRAw8ePEB+fj5SU1Mr+iMhqtHY0ZWIytS8eXMAQFJSElq2bKm2Ljc3F/fv30ezZs00djwjIyPUq1cPN2/eLHX9s+Go+LlKpSp1+4iICDx48ADvv/8+gCch4uLFi4iJiUHnzp1x48YNWFtbSyNyunXrhm7dulW6/ry8PHzwwQdo0aIFzMzMONqGqIIYSoioTL1794azszO2bNmCPn36qK3bvHkzjI2N8e6772r0mGlpaXBzc9PIvpYvX46wsDAoFAoAT4KUi4sLli9fjs6dO6NevXrIzMzEgwcPUKdOnRKvfzZUGBmV3bh84MAB9OvXD5cvX0bTpk1x/vx5jZwDUU3CyzdEVKZatWrh77//xpo1a3D69Glp+f379/H1119j0aJF8PT0LPf+hBAlPujT0tKgVCoBAIcPH4ZSqZQuoZS2ffHy5z0HgNTUVNy6dUsKJABgbm6O4cOHY82aNXj06BH69+8POzs7zJ8/X9rPl19+iYyMDJiZmSEpKUmqDQAUCgVSUlKkbW/fvi2tj42Nhbm5ORo0aAClUolbt26hoKDguedORM/QRkcWItIvUVFRYtCgQWLcuHEiKChIdOnSRezdu7dC+wgPDxfNmzcX5ubm4tdffxVCPOlM6uzsLMaPHy9mzJgh/P39xcWLF4UQQpw/f1506dJFyGQy8fnnn4uUlBTRt29fAUC89957Ijs7W4wYMUIAEEOGDBEZGRnSsXbt2iUaN24srKysxAcffCAePnwohHjSMfaVV14RAETnzp3FqVOnRExMjOjSpYto166dGDp0qNSp9vz588LV1VW0aNFCfP3110IIIWJjY0Xz5s3FRx99JH7++WcxaNAgYW1tLYKDg0ViYqJo06aN6NChg/jiiy9ESEiIqFOnjvjPf/4j/ve//wkrKyvh4eEhDh48+NLvB5GhkgnB6E5E5WdrawsvLy/s2bPnpfel6QnKiEi/sU8JEVXIqFGjsGzZMhw6dEi6j83nn3+OuLi4577uP//5Dxo2bKi2TAhRZidVIqp52FJCRBWSk5ODL774AhEREWjSpAneeustDB8+vML72bZtGz777DPEx8fj/fffx9dffw0rK6sqqJiI9AVDCREREekEjr4hIiIincBQQkRERDqBoYSIiIh0AkMJERER6QSGEiIiItIJDCVERESkExhKiIiISCcwlBAREZFO+H/uN4998BoHsAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "q_test= shuffled_df.iloc[mid:,3:4].to_numpy()\n",
    "\n",
    "# Plot act = pred line.\n",
    "x = np.linspace(q_test[:, 0].min(), q_test[:, 0].max(), 100)\n",
    "plt.plot(x, x, '--', color='gray')\n",
    "\n",
    "# Plot scatter plot for actual vs predicted q_lpm for test data set. \n",
    "plt.scatter(q_test, q_trans_mean, color='k', label='Q_predicted')\n",
    "\n",
    "# plt.errorbar(q_test.flatten(), q_trans_mean.flatten(), q_trans_var.flatten(), fmt='o', color='k', alpha = 0.2)\n",
    "\n",
    "# Labels and graphics\n",
    "hfont = {'fontname':'Georgia'}\n",
    "plt.xlabel(\"Q_lpm Actual\", **hfont)\n",
    "plt.ylabel(\"Q_lpm Predicted\", **hfont)\n",
    "plt.title(\"Actual vs Predicted Q_lpm\", **hfont)\n",
    "plt.legend()\n",
    "# plt.ylim((-800, 2000))\n",
    "# plt.xlim((-800,2000))\n",
    "\n",
    "\n",
    "# Visualize\n",
    "plt.savefig(\"images\\Qact_Qpre.png\", dpi = 150, bbox_inches='tight' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Denormalized discharge plot with error bar ( scale distorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "q_test= shuffled_df.iloc[mid:,3:4].to_numpy()\n",
    "\n",
    "# Plot act = pred line.\n",
    "x = np.linspace(q_test[:, 0].min(), q_test[:, 0].max(), 100)\n",
    "plt.plot(x, x, '--', color='gray')\n",
    "\n",
    "# Plot scatter plot for actual vs predicted q_lpm for test data set. \n",
    "plt.scatter(q_test, q_trans_mean, color='k', label='Q_predicted')\n",
    "plt.errorbar(q_test.flatten(), q_trans_mean.flatten(), q_trans_var.flatten(), fmt='o', color='k', alpha = 0.2)\n",
    "\n",
    "# Labels and graphics\n",
    "hfont = {'fontname':'Georgia'}\n",
    "plt.xlabel(\"Q_lpm Actual\", **hfont)\n",
    "plt.ylabel(\"Q_lpm Predicted\", **hfont)\n",
    "plt.title(\"Actual vs Predicted Q_lpm with varience\", **hfont)\n",
    "plt.legend()\n",
    "plt.ylim((-800, 2000))\n",
    "\n",
    "# Visualize\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize 2D input and Discharge output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from random import randint\n",
    "# ran = randint(0,len(xy)) # Take a random y point from dataframe xy; 0< ran < len(xy)\n",
    "\n",
    "ran = 8 # Here, we take ran = 8 for reproducibility\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//GWv834SERERUfJiwElERERERERERESqxVXUiYiIiIiIiIiISLUYcBIREREREREREZFq6WJdQH9SFAV1dXWw2WyQJCnW5RAREREREREREamKEAIulwuFhYXQaOKjdzKpAs66ujoUFxfHugwiIiIiIiIiIiJVq66uRlFRUazLAJBkAafNZgMQ+QWkpqbGuBoiIiIiIiIiIiJ1aW9vR3FxcTRniwdJFXB2DEtPTU1lwElERERERERERNRD8TT9Y3wMlCciIiIiIiIiIiLqAQacREREREREREREpFoMOImIiIiIiIiIiEi1kmoOTiIiIiIiIiJKDEIIhMNhyLIc61KIEopWq4VOp4urOTb3hwEnEREREREREalKMBhEfX09vF5vrEshSkgWiwUFBQUwGAyxLqVbGHASERERERERkWooioKKigpotVoUFhbCYDCoqtOMKJ4JIRAMBtHc3IyKigoMGTIEGk38z3DJgJOIiIiIiIiIVCMYDEJRFBQXF8NiscS6HKKEYzabodfrsXPnTgSDQZhMpliXtF/xH8ESEREREREREf2CGrrKiNRKba8vdVVLREREREREREREtBsGnERERERERERERKRaDDiJiIiIiIiIiGivli5dCkmS4HA4AAAvvvgi0tPT+/Q+L7nkEpxxxhl9eh+x8MvHNXnyZFx//fX9Xscvf6dqx4CTiIiIiIiIiKgfXHLJJZAkCffdd1+n7QsXLlTVSvDnnnsutmzZEusyIMsyHnnkEYwePRomkwkZGRk4+eSTsXz58liX1m3vvPMO5s6d2619Ey2U7E0MOImIiIiIiIgoKckysHQp8Nprka+y3Pf3aTKZcP/998Nut/fq7QaDwV69vX0xm83Izc3tt/vrihAC5513Hu655x7MmjULGzduxNKlS1FcXIzJkydj4cKFfXbfvflcZ2Zmwmaz9drtJSsGnERERERERESUdN55BygrA6ZMAS64IPK1rCyyvS8df/zxyM/Px7x58/a539tvv41Ro0bBaDSirKwMDz30UKfry8rKMHfuXFx00UVITU3FFVdcER06/uGHH2LYsGGwWCw466yz4PV68dJLL6GsrAwZGRm47rrrIO+W5r788ssYP348bDYb8vPzccEFF6CpqWmvtf1yiHpZWRkkSdrj0qG6uhrnnHMO0tPTkZmZidNPPx2VlZXR62VZxg033ID09HRkZWXhpptughBin8/PG2+8gbfeegv//ve/cfnll6O8vBxjxozBc889h9NOOw2XX345PB7PPm8DAO666y4ccsghePbZZ1FcXAyLxYJzzjkHTqczuk/HsPJ7770XhYWFGDZsWK89rl8OUQ8EArj55ptRXFwMo9GIwYMH4/nnn0dlZSWmTJkCAMjIyIAkSbjkkksAAIqiYN68eSgvL4fZbMaYMWPw1ltvdbqfRYsWYejQoTCbzZgyZUqnOhMBA04iIiIiIiIiSirvvAOcdRZQU9N5e21tZHtfhpxarRZ///vf8cQTT6DmlwXssnr1apxzzjk477zz8OOPP+Kuu+7C7bffjhdffLHTfvPnz8eYMWOwZs0a3H777QAAr9eLxx9/HK+//jo++eQTLF26FDNmzMCiRYuwaNEivPzyy3j22Wc7BWChUAhz587FunXrsHDhQlRWVkbDs+5YuXIl6uvrUV9fj5qaGhx55JE45phjorc9bdo02Gw2fPXVV1i+fDmsVitOOumkaCfkQw89hBdffBEvvPACli1bhra2Nrz77rv7vM9XX30VQ4cOxfTp0/e47sYbb0Rrays+++yzbtW/bds2vPHGG/jggw/wySefYM2aNbj66qs77bN48WJs3rwZn332GT788MM+e1wXXXQRXnvtNTz++OPYuHEjnn32WVitVhQXF+Ptt98GAGzevBn19fV47LHHAADz5s3Dv//9b/zjH//A+vXrMXv2bPz2t7/FF198ASASxM6cORPTp0/H2rVrcfnll+OWW27p1nOjGiKJOJ1OAUA4nc5Yl0JEREREREREPeDz+cSGDRuEz+fr0c+Hw0IUFQkBdH2RJCGKiyP79baLL75YnH766UIIIY488khx6aWXCiGEePfdd8XuEc0FF1wgTjjhhE4/++c//1mMHDky+n1paak444wzOu3zr3/9SwAQ27Zti2678sorhcViES6XK7pt2rRp4sorr9xrnStXrhQAoj+zZMkSAUDY7fbo/aSlpXX5s9ddd50oLS0VTU1NQgghXn75ZTFs2DChKEp0n0AgIMxms/j000+FEEIUFBSIBx54IHp9KBQSRUVF0eeqK8OHD9/r9W1tbQKAuP/++/f68x3uvPNOodVqRU1NTXTbxx9/LDQajaivrxdCRH5veXl5IhAIRPfprcc1adIkMWvWLCGEEJs3bxYAxGeffdZlrb/8PQghhN/vFxaLRXz99ded9r3sssvE+eefL4QQ4tZbb+30f0cIIW6++eY9bmt3+3qdxWO+xg5OIiIiIiIiIkoaX321Z+fm7oQAqqsj+/Wl+++/Hy+99BI2bty4x3UbN27EUUcd1WnbUUcdha1bt3YaWj5+/Pg9ftZisWDQoEHR7/Py8lBWVgar1dpp2+5D0FevXo3p06ejpKQENpsNkyZNAgBUVVUd0GN67rnn8Pzzz+P9999HTk4OAGDdunXYtm0bbDYbrFYrrFYrMjMz4ff7sX37djidTtTX1+OII46I3o5Op+vysf2S2M8wdoPB0K26S0pKMGDAgOj3EyZMgKIo2Lx5c3Tb6NGjO91eXzyutWvXQqvVRp//7ti2bRu8Xi9OOOGEaB1WqxX//ve/sX37dgCR/0+719HxGBOJLtYFEBERERERERH1l/r63t2vp4499lhMmzYNt9566wENB99dSkrKHtv0en2n7yVJ6nKboigAAI/Hg2nTpmHatGlYsGABcnJyUFVVhWnTph3QYjpLlizBn/70J7z22ms4+OCDo9vdbjcOPfRQLFiwYI+f6QhBe2LIkCFdhsMAotuHDh3a49v/pV8+133xuMxm8wH/jNvtBgB89NFHnUJaADAajT2qQ43YwUlERERERERESaOgoHf3+zXuu+8+fPDBB/jmm286bR8xYgSWL1/eadvy5csxdOhQaLXaXq1h06ZNaG1txX333YdjjjkGw4cP3+cCQ13Ztm0bzjrrLNx2222YOXNmp+vGjRuHrVu3Ijc3F4MHD+50SUtLQ1paGgoKCvDtt99GfyYcDmP16tX7vM/zzz8fW7duxQcffLDHdQ899BAKCwtxwgkndKv+qqoq1NXVRb9fsWIFNBpNdDGhrvTF4xo9ejQURYnOnflLHR2ku3fxjhw5EkajEVVVVXvUUVxcDCDy/+m7777rdFsrVqzYxzOiPgw4iYiIiIiIiChpHHMMUFQE7LbIdyeSBBQXR/bra6NHj8aFF16Ixx9/vNP2G2+8EYsXL8bcuXOxZcsWvPTSS3jyyScxZ86cXq+hpKQEBoMBTzzxBHbs2IH3338fc+fO7fbP+3w+TJ8+HWPHjsUVV1yBhoaG6AUALrzwQmRnZ+P000/HV199hYqKCixduhTXXXdddJGlWbNm4b777sPChQuxadMmXH311XA4HPu83/POOw9nnHEGLr744ugq4z/88AOuvPJKfPjhh3jllVf26FzdG5PJhIsvvhjr1q3DV199heuuuw7nnHMO8vPz9/ozffG4ysrKcPHFF+PSSy/FwoULo7f5xhtvAABKS0shSRI+/PBDNDc3w+12w2azYc6cOZg9ezZeeuklbN++Hd9//z2eeOIJvPTSSwCAq666Clu3bsWf//xnbN68Ga+++uoeC1apHQNOIiIiIiIiIkoaWi2wa/HpPULOju8ffTSyX3+45557osPFO4wbNw5vvPEGXn/9dRx00EG44447cM899/R4KPu+5OTk4MUXX8Sbb76JkSNH4r777sP8+fO7/fONjY3YtGkTFi9ejMLCQhQUFEQvQGRO0C+//BIlJSWYOXMmRowYgcsuuwx+vx+pqakAIoHu7373O1x88cWYMGECbDYbZsyYsc/7lSQJb775Jm677TY88sgjGDZsGMaMGYO33noLa9aswZQpU7r9GAYPHoyZM2filFNOwYknnoiDDz4YTz/99D5/pq8e1zPPPIOzzjoLV199NYYPH44//OEP8Hg8AIABAwbg7rvvxi233IK8vDxce+21AIC5c+fi9ttvx7x58zBixAicdNJJ+Oijj1BeXg4gEmK//fbbWLhwIcaMGYN//OMf+Pvf/97t50cNJLG/GVkTSHt7O9LS0uB0OqP/2YiIiIiIiIhIPfx+PyoqKlBeXg6TydTj23nnHWDWrM4LDhUXR8LNX4yyJpX4/vvvcfzxx+Oyyy7Dgw8+2K2fueuuu7Bw4UKsXbu2b4tTmX29zuIxX2MHJxERERERERElnZkzgcpKYMkS4NVXI18rKhhuqtm4ceOwePFipKSkRFcQp+TAVdSJiIiIiIiIKClptcDkybGugnrT2LFjMXbs2Oj3o0aNws6dO7vc99lnn+2vsqiPMeAkIiIiIiIiIqKEtGjRIoRCoS6vy8vLg81mw1133dW/RVGvY8BJREREREREREQJqbS0NNYlUD/gHJxERERERERERESkWgw4iYiIiIiIiIiISLUYcBIREREREREREZFqMeAkIiIiIiIiIiIi1WLASURERERERERERKrFgJOIiIiIiIiIiIhUiwEnEREREREREVE/qq6uxqWXXorCwkIYDAaUlpZi1qxZaG1tjXVpRKrEgJOIiIiIiIiIqJ/s2LED48ePx9atW/Haa69h27Zt+Mc//oHFixdjwoQJaGtri3WJRKrDgJOIiIiIiIiIVE0IAW8wHJOLEOKAar3mmmtgMBjw3//+F5MmTUJJSQlOPvlk/O9//0NtbS3+8pe/7Pc2ysrK8Le//Q0XXXQRrFYrSktL8f7776O5uRmnn346rFYrDj74YKxatarTzy1btgzHHHMMzGYziouLcd1118Hj8USvf/nllzF+/HjYbDbk5+fjggsuQFNTU/T6pUuXQpIkLF68GOPHj4fFYsHEiROxefPmA3oOiHqbJA70lahi7e3tSEtLg9PpRGpqaqzLISIiIiIiIqID5Pf7UVFRgfLycphMJgCANxjGyDs+jUk9G+6ZBotB161929rakJ2djXvvvRe33nrrHtdfccUVeOutt9Da2gpJkvZ6O2VlZXC5XPj73/+OqVOn4pFHHsGCBQswceJEXHrppRgzZgxuvvlmbN68GevXr4ckSdi+fTvGjBmDv/3tbzj11FPR3NyMa6+9FmPGjMG//vUvAMALL7yAgoICDBs2DE1NTbjhhhuQnp6ORYsWAYgEnFOmTMERRxyB+++/Hzk5ObjqqqsgyzKWL1/eg2eP4lVXr7MO8Zivde8VSEREREREREREv8rWrVshhMCIESO6vH7EiBGw2+1obm5Gbm7uPm/rlFNOwZVXXgkAuOOOO/DMM8/gsMMOw9lnnw0AuPnmmzFhwgQ0NjYiPz8f8+bNw4UXXojrr78eADBkyBA8/vjjmDRpEp555hmYTCZceuml0dsfOHAgHn/8cRx22GFwu92wWq3R6+69915MmjQJAHDLLbfg1FNPhd/v3yMII+ovDDiJiIiIiIiISNXMei023DMtZvd9oPY3mNZgMOz3Ng4++ODov/Py8gAAo0eP3mNbU1MT8vPzsW7dOvzwww9YsGBBpzoURUFFRQVGjBiB1atX46677sK6detgt9uhKAoAoKqqCiNHjuzyvgsKCqL3U1JSst+6ifoCA04iIiIiIiIiUjVJkro9TDyWBg8eDEmSsHHjRsyYMWOP6zdu3IicnBykp6fv97b0en303x3D2bva1hFSut1uXHnllbjuuuv2uK2SkhJ4PB5MmzYN06ZNw4IFC5CTk4OqqipMmzYNwWBwv/fdcT9EsRD/r34iIiIiIiIiogSQlZWFE044AU8//TRmz54Ns9kcva6hoQELFizANddc0yf3PW7cOGzYsAGDBw/u8voff/wRra2tuO+++1BcXAwAeyxSRBSvuIo6EREREREREVE/efLJJxEIBDBt2jR8+eWXqK6uxieffIITTjgBQ4cOxR133NEn93vzzTfj66+/xrXXXou1a9di69ateO+993DttdcCiHRxGgwGPPHEE9ixYwfef/99zJ07t09qIeptDDiJiIiIiIiIiPrJkCFDsHLlSgwcOBDnnHMOSktLcfLJJ2Po0KFYvnx5p8V8etPBBx+ML774Alu2bMExxxyDsWPH4o477kBhYSEAICcnBy+++CLefPNNjBw5Evfddx/mz5/fJ7UQ9TZJ7G9m2wQSj8vYExEREREREVH3+f1+VFRUoLy8PGFW7b7zzjvx8MMP47PPPsORRx4Z63KI9vk6i8d8jXNwEhERERERERHF0N13342ysjKsWLEChx9+ODQaDrglOhAMOImIiIiIiIiIYuz3v/89AOCrr77CySefvNf93G53f5VEpBoMOImIiIiIiIiI4sT48eOxdu3aWJdBpCoMOImIiIiIiIiI4oTZbMbgwYNjXQaRqnBSByIiIiIiIiIiIlItBpxERERERERERESkWgw4iYiIiIiIiIiISLUYcBIREREREREREZFqMeAkIiIiIiIiIiIi1WLASUREREREREREe7V06VJIkgSHwwEAePHFF5Gent6n93nJJZfgjDPO6NP76GtlZWV49NFHY11GUtDFugAiIiIiIiIiot7wY42zX+9vdFHaAe1/ySWX4KWXXsK8efNwyy23RLcvXLgQM2bMgBCit0vsE+eeey5OOeWUWJcBWZbx+OOP44UXXsDWrVthNptx5JFH4q9//SuOOuqoWJeHlStXIiUlJdZlJAXVdHDOmzcPhx12GGw2G3Jzc3HGGWdg8+bNsS6LiIiIiIiIiKjbTCYT7r//ftjt9l693WAw2Ku3ty9msxm5ubn9dn9dEULgvPPOwz333INZs2Zh48aNWLp0KYqLizF58mQsXLgwZrV1/C5ycnJgsVhiVkcyUU3A+cUXX+Caa67BihUr8NlnnyEUCuHEE0+Ex+OJdWlERERERERERN1y/PHHIz8/H/Pmzdvnfm+//TZGjRoFo9GIsrIyPPTQQ52uLysrw9y5c3HRRRchNTUVV1xxRXTo+Icffohhw4bBYrHgrLPOgtfrxUsvvYSysjJkZGTguuuugyzL0dt6+eWXMX78eNhsNuTn5+OCCy5AU1PTXmv75RD1srIySJK0x6VDdXU1zjnnHKSnpyMzMxOnn346Kisro9fLsowbbrgB6enpyMrKwk033bTfbtY33ngDb731Fv7973/j8ssvR3l5OcaMGYPnnnsOp512Gi6//PL9ZkZbtmyBJEnYtGlTp+2PPPIIBg0aFK3tsssuQ3l5OcxmM4YNG4bHHnus0/4dw+nvvfdeFBYWYtiwYdHnZfch6g6HA5dffjlycnKQmpqKqVOnYt26ddHr77rrLhxyyCF4+eWXUVZWhrS0NJx33nlwuVzRfRRFwQMPPIDBgwfDaDSipKQE9957b7ef60SlmoDzk08+wSWXXIJRo0ZhzJgxePHFF1FVVYXVq1fHujQiIiIiIiIiom7RarX4+9//jieeeAI1NTVd7rN69Wqcc845OO+88/Djjz/irrvuwu23344XX3yx037z58/HmDFjsGbNGtx+++0AAK/Xi8cffxyvv/46PvnkEyxduhQzZszAokWLsGjRIrz88st49tln8dZbb0VvJxQKYe7cuVi3bh0WLlyIyspKXHLJJd1+TCtXrkR9fT3q6+tRU1ODI488Esccc0z0tqdNmwabzYavvvoKy5cvh9VqxUknnRTtdHzooYfw4osv4oUXXsCyZcvQ1taGd999d5/3+eqrr2Lo0KGYPn36HtfdeOONaG1txWeffbbP2xg6dCjGjx+PBQsWdNq+YMECXHDBBQAigWJRURHefPNNbNiwAXfccQduu+02vPHGG51+ZvHixdi8eTM+++wzfPjhh13e39lnn42mpiZ8/PHHWL16NcaNG4fjjjsObW1t0X22b9+OhQsX4sMPP8SHH36IL774Avfdd1/0+ltvvRX33Xcfbr/9dmzYsAGvvvoq8vLyAHTvuU5Uqp2D0+mMzKuRmZm5130CgQACgUD0+/b29j6vi4iIiIiIiIhoX2bMmIFDDjkEd955J55//vk9rn/44Ydx3HHHRUPLoUOHYsOGDXjwwQc7BY9Tp07FjTfeGP3+q6++QigUwjPPPBPtQDzrrLPw8ssvo7GxEVarFSNHjsSUKVOwZMkSnHvuuQCASy+9NHobAwcOxOOPP47DDjsMbrcbVqt1v48nJycn+u9Zs2ahvr4eK1euBAD85z//gaIo+Oc//xnt6vzXv/6F9PR0LF26FCeeeCIeffRR3HrrrZg5cyYA4B//+Ac+/fTTfd7nli1bMGLEiC6v69i+ZcuW/dZ+4YUX4sknn8TcuXOjP7N69Wq88sorAAC9Xo+77747un95eTm++eYbvPHGGzjnnHOi21NSUvDPf/4TBoOhy/tZtmwZvvvuOzQ1NcFoNAKIBNQLFy7EW2+9hSuuuAJAJFB98cUXYbPZAAC/+93vsHjxYtx7771wuVx47LHH8OSTT+Liiy8GAAwaNAhHH300gO4914lKNR2cu1MUBddffz2OOuooHHTQQXvdb968eUhLS4teiouL+7FKIiIiIiIiIqKu3X///XjppZewcePGPa7buHHjHovkHHXUUdi6dWunoeXjx4/f42ctFks03ASAvLw8lJWVdQoq8/LyOg1BX716NaZPn46SkhLYbDZMmjQJAFBVVXVAj+m5557D888/j/fffz8aeq5btw7btm2DzWaD1WqF1WpFZmYm/H4/tm/fDqfTifr6ehxxxBHR29HpdF0+tl/a3zD2vYWNuzvvvPNQWVmJFStWAIh0b44bNw7Dhw+P7vPUU0/h0EMPRU5ODqxWK5577rk9npvRo0fv8/7WrVsHt9uNrKys6PNgtVpRUVGB7du3R/crKyuLhpsAUFBQEP1dbdy4EYFAAMcdd9xe72Nfz3UiU2UH5zXXXIOffvoJy5Yt2+d+t956K2644Ybo9+3t7Qw5iYiIiIiIiCjmjj32WEybNg233nrrAQ0H311XK3Tr9fpO30uS1OU2RVEAAB6PB9OmTcO0adOwYMEC5OTkoKqqCtOmTTugYc1LlizBn/70J7z22ms4+OCDo9vdbjcOPfTQPYaBA507Pw/UkCFDugyHAUS3Dx06dL+3k5+fj6lTp+LVV1/FkUceiVdffRV//OMfo9e//vrrmDNnDh566CFMmDABNpsNDz74IL799ttOt7O/1dLdbjcKCgqwdOnSPa7bfT7Tff2uzGbzfu+jL55rNVBdwHnttdfiww8/xJdffomioqJ97ms0GqNtv0RERERERERE8eS+++7DIYccEl2UpsOIESOwfPnyTtuWL1+OoUOHQqvV9moNmzZtQmtrK+67775oU9iqVasO6Da2bduGs846C7fddlt0mHmHcePG4T//+Q9yc3ORmpra5c8XFBTg22+/xbHHHgsACIfD0Tkq9+b888/HBRdcgA8++GCPeTgfeughFBYW4oQTTuhW/RdeeCFuuukmnH/++dixYwfOO++86HXLly/HxIkTcfXVV0e39aQbcty4cWhoaIBOp0NZWdkB/zwQCXXNZjMWL16Myy+/vMv72N9znahUM0RdCIFrr70W7777Lj7//HOUl5fHuiQiIiIiIiIioh4bPXo0LrzwQjz++OOdtt94441YvHgx5s6diy1btuCll17Ck08+iTlz5vR6DSUlJTAYDHjiiSewY8cOvP/++9H5KLvD5/Nh+vTpGDt2LK644go0NDREL0AkPMzOzsbpp5+Or776ChUVFVi6dCmuu+666CJLs2bNwn333YeFCxdi06ZNuPrqq+FwOPZ5v+eddx7OOOMMXHzxxXj++edRWVmJH374AVdeeSU+/PBDvPLKK3t0Q+7NzJkz4XK58Mc//hFTpkxBYWFh9LohQ4Zg1apV+PTTT7Flyxbcfvvt0flFD8Txxx+PCRMm4IwzzsB///tfVFZW4uuvv8Zf/vKXbgfKJpMJN998M2666Sb8+9//xvbt27FixYroPK7dea4TlWo6OK+55hq8+uqreO+992Cz2aIvlLS0tP226BIRERERERFR4htdlBbrEg7YPffcg//85z+dto0bNw5vvPEG7rjjDsydOxcFBQW45557ejyUfV9ycnLw4osv4rbbbsPjjz+OcePGYf78+TjttNO69fONjY3YtGkTNm3a1CkYBCLNahaLBV9++SVuvvnmaJA4YMAAHHfccdEuwxtvvBH19fW4+OKLodFocOmll2LGjBnRBaa7IkkS3nzzTTz66KN45JFHcPXVVyMYDCIzMxNr1qzByJEju/0c2Gw2TJ8+HW+88QZeeOGFTtddeeWVWLNmDc4991xIkoTzzz8fV199NT7++ONu335HvYsWLcJf/vIX/P73v0dzczPy8/Nx7LHHRldB747bb78dOp0Od9xxB+rq6lBQUICrrroKALr1XCcqSexvRtY40bH60y/961//6vYLvL29HWlpaXA6nQn/iyUiIiIiIiJKRH6/HxUVFSgvL4fJZIp1ORRHvv/+exx//PG47LLL8OCDD8a6HFXb1+ssHvM1VQ1R7+rSF2cviIiIiIiIiIhIXcaNG4fFixcjJSUl4VcNp85UM0SdiIiIiIiIiIhoX8aOHYuxY8dGvx81ahR27tzZ5b7PPvssLrzwwv4qjfoQA04iIiIiIiIiIkpIixYtQigU6vK6A5n7kuIbA04iIiIiIiIiIkpIpaWlsS6B+oFq5uAkIiIiIiIiIuqgkjWTiVRJba8vBpxEREREREREpBp6vR4A4PV6Y1wJUeLqeH11vN7iHYeoExEREREREZFqaLVapKeno6mpCQBgsVggSVKMqyJKDEIIeL1eNDU1IT09HVqtNtYldQsDTiIiIiIiIiJSlfz8fACIhpxE1LvS09OjrzM1YMBJRERERERERKoiSRIKCgqQm5u71xWyiahn9Hq9ajo3OzDgJCIiIiIiIiJV0mq1qgtiiKj3cZEhIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFSLAScRERERERERERGpFgNOIiIiIiIiIiIiUi0GnERERERERERERKRaDDiJiIiIiIiIiIhItRhwEhERERERERERkWox4CQiIiIiIiIiIiLVYsBJREREREREREREqsWAk4iIiIiIiIiIiFRLF+sCiIhI3RRFICgr0EgSdBoJGo0U65KIiIiIiIgoiSRlwLm10YWMgAZ6rQYGXeSrXivBoNPAqNPGujwiorimKALekAxPIAx3IAxfUIYQP18vSYBWEwk7tbtdUgw6pJn1DECJiIiIiIioVyVlwBkMK/AEZADyHtdJEnYFnZpo4NnxvV7LEf1ElHyEEPAE9x5o7rk/EJYFwnLnneyeEGodPqSZ9chIMcBqTMo/QURERERERKqlKAK+4J55Wqzx6PIXhAACIQWBkLLHdRoNYNJrYdJrYdZrYdJrYNJp2Y1ERAnL7gmiod2/R1jZU0IADm8IDm8Iep2EdLMB6RY9THp2zxMREREREcWTsKzAF5LhDynwh2T4QzICYQWudnesS9sDA84DoCiANyDDG+icVBt3BZ0mvQZmgxYWgw5ahp5EpGLeYBh1Dn+fnpkLhQWaXQE0uwIwGzTITDEiw6KHJPH9k4iIiIiIqD8JIeANyvAEw/AGZPhCcq81uvQHBpy9oKPj0+n7eVtH2Jli0MFs0LI7iYhUISQraHD64fCG+vV+fUEFtUEf2jwBFKabYTHwzxMREREREVFfkRURDTM9wf1PRRbveATZRyLtuwrsnkhIoNVIsBi0sBi1sBn1MBsYeBJR/BBCoNkdQFN7IKZ/1HxBBdubPMi0GpCfamI3PBERERERUS/o6NBs94fg9ofh72JqRjVjwNlPZEXA5Q/D5Q+jEQFoNRJsJh2sRh2sJh0XMCKimHH6Qmhw+hEMx88fuDZ3EO2+EArSTEi3GGJdDhERERERkeqEZQXuQCSLaveHoMTPIV+vY8AZI7IiogttAJEh7daOwNOo4xx0RNQv6hw+tLqDsS6jS2FZoLrNhzZPEIXpZk71QUREREREtB/+UKRL0+VX/7DzA8GAM05EhrQH0eIKQqMBUk16pJr1sBl1XKWdiHqdEJHw0Onr37k2e8ITkLGtyY0cmxE5ViPfE4mIiIiIiHYTlhU4fCE4vEH4ggncprkPDDjjkKIg2t25e9iZamJnJxH9erIisLPVA0+g71ZI721CAE3tAbj8IZRkpsCg47QeRERERESUvIQQaPeH4fAG4fKHk6ZTc28YcMY5hp1E1JtCsoLKFo9qJ5T2BRVsa3KjJMsCq5F/woiIiIiIKLl4g2HYvSE4vSHISpKnmrvh0aGK7B52ajUSMlL0yEwxwKjjvHREtH/+kIzKVg9CYXX/EZQVgcoWD/LTTMi2GmNdDhERERERUZ8SQsDpC6HFHUjaIej7w4BTpWRFoMUVmbMzxahFVooRqWZ2dRJR1zyBMHa2ehPmDJ8QQL3DD19QRlGGme99RERERESUcBRFoM0bRIs7oPpGlb7GgDMBeAIyPAEvtBoJmSkGZKTo2dVJRFFOXwjVbd6EnJPF4Q0hEFZQkmnhvJxERERERJQQQrKCVncQrZ4AFDZsdgsDzgQiKwLNrgCaXQFYTTpkWw2wmfSxLouIYqjNE0Sdw5eQ4WYHX1DG9mY3SjItSOG8nEREREREpFL+kIwWdwAObyihj+H6Ao8EE5TbH4bbH4bZoEGO1YQ0C4NOomTT7g+h1u6LdRn9IiwLVLR4UJBmQhbn5SQiIiIiIhUJyQoanH44vKFYl6JaDDgTnC+ooKrNC6NLgxyrEekWPeeqI0oC/pCMqlZvrMvoV0IAdQ4/grKCgjRzrMshIiIiIiLaJ0URaHZHRuKyY/PXYcCZJAIhBTV2HxpdfmRbjci0GKDRMOgkSkQhWUFlqydp/0C2uIIIy4KLDxERERERUVwSQsDuDaGx3Y+wnKQHbr2MAWeSCYUF6h1+NLUHkG0zIMdqZABAlEAURWBnqzfpV9hzeENQhEBxhoUnc4iIiIiIKG60+0NodPrhD3H1oN7EgDNJyYpAozOANk8Q+akmpFsMsS6JiHpBjd0HX1COdRlxod0XRoXiQVlWCrQMOYmIiIiIKIb8IRn1Tj/c/nCsS0lImlgXQLEVCgtUt/mwrckNT4AvMiI1a3D64fRxUurdeQMydjS7EZJ5dpSIiIiIiPqfEAJN7X5sa3Iz3OxDDDgJAOALytjR7MHOVg/8IXZ/EamN3RNEsysQ6zLikj+kYEezB4Ew39uIiIiIiKj/+EMytje70djORYT6GgNO6qTdF8a2JjdqHT6E2fFEpAqeQBi1Dl+sy4hrwXAk5OTwfSIiIiIi6mtCCDS5Il2bviCzlf7AgJP2IATQ5g5ic6MLrW52hBHFs0BYxs5WL88GdkNYFtjR4oab03EQEREREVEfiXRtetDoZNdmf1JVwPnll19i+vTpKCwshCRJWLhwYaxLSmiKAtQ5/Nje7OawdaI4JO9aMV1W+FezuxQFqGzxoN3PuUqJiIiIiKh3NbsCu7o2maH0N1UFnB6PB2PGjMFTTz0V61KSijcgY1uTG03tfgiefiCKG7V2HwIhDnc4UEIAVa1eOL0MOYmIiIiI6NcLhhVsb3ajweln12aM6GJdwIE4+eSTcfLJJ8e6jKQkBNDYHoDTF8KADDMsBlX91yFKOHZPkCum/wpCAFVtXgwQZmSmGGJdDhERERERqZQ7EEYVR9bFXEKnVIFAAIHAz3NItre3x7CaxNCxGnGW1YA8mwkajRTrkoiSTiAso87JRYV6Q63dB0UIZFuNsS6FiIiIiIhUpsUdYNdmnFDVEPUDNW/ePKSlpUUvxcXFsS4pIQgBtLiC2NrExTqI+psQAjV2HxSOTO819Q4/mtr9sS6DiIiIiIhUInJc5kW9g+FmvEjogPPWW2+F0+mMXqqrq2NdUkIJhhVUNHt2na3gK5qoPzS7AvAGOGF1b2tsD6CeXbFERERERLQfIVnB9mYP7B5OGRZPEnqIutFohNHIYYd9rdkVgDsQRkmmBQZdQmfmRDHlDYbR5Arsf0fqkRZXELIiUJRhiXUpREREREQUhzyBMKravAjLbPKKN0yjqFf4gjK2Nrm46AlRH1EUgeo2H4c/9DG7J4TqNi+70omIiIiIqJM2TxAVLR6Gm3FKVR2cbrcb27Zti35fUVGBtWvXIjMzEyUlJTGsjABAUYCqVi8yrQYUppkgSVyAiKi31Dl9CIY58WZ/cHhDCCsCpZkWLqRGRERERESodfjQ5g7GugzaB1V1cK5atQpjx47F2LFjAQA33HADxo4dizvuuCPGldHu2txBbGtywx/iPIFEvcHpC3F+l37m9oexo8WDsMxQmYiIiIgoWQkhUN3mZbipAqrq4Jw8eTKHDaqEP6RgW5MbA9LNyEgxxLocItUKyQpq7Vz8JhZ8QRnbmz0oy7bAqNPGuhwiIiIiIupHQghUtXnR7gvHuhTqBlV1cJK6CAHU2H2odfgYTBP1UI3dB1nh6ydWgmEFO5o98AXZkU5ERERElCwURaCyleGmmjDgpD7X5u6YiJdDPYkORIs7ALeff1BjLSwL7Ghxw+XnNAFERERERIlOVgQqWj08FlMZBpzULzyByFBPzstJ1D2BsIwGpz/WZdAuigLsbPXC4eXcO0REREREiSosK6hoccMbYHahNgw4qd8Ewwq2N7MLiqg76hx+cGaH+CIEUN3mQ7MrEOtSiIiIiIiol4VkBRUtHviCHH2qRgw4qV91dEG1uBkQEO2NwxvkcIg41uD0o97JuYWJiIiIiBJFx9z7/hDDTbViwEn9Tgig3uHn4kNEXZAVgXoOTY97LS7OLUxERERElAgCYRk7WtwIhvnZXs0YcFLMtLmDqGz1coVoot3UO30Iy3xNqIEnIGNbsxveILttiYiIiIjUKCwrqGzxIhTmMZjaMeCkmHL7w9jezDMlRADgCYRh93COWjUJhQV2NHvQymk3iIiIiIhURVEEKls9zCMSBANOirlASMGOFjdXWKekJoRAncMX6zKoB4SILApV3eaFwo50IiIiIqK4J4TAzjYvFxRKIAw4KS50dEH5ggw5KTk1uwKc0FrlHN4QdrS4EQjzfYyIiIiIKJ7V2H1c2DXBMOCkuCErAjta3PAE+CZDySUQltHk4hDnROALKtje5IHLz6kGiIiIiIjiUb3TB4eXn9cTDQNOiiuKAlS0eNDOcICSSJ3DD8GRzQlDVgQqW7xocPo5ZJ2IiIiIKI60uANocQVjXQb1AQacFHeEAKpavXB4+aZDic/hDXJoRIJqdgWwpckFp48nbIiIiIiIYs3pDaHe4Y91GdRHGHBSXBICqG7zcWViSmhhWUEd/8AmtFBYoKrVi51cnZGIiIiIKGbcgTCq7d5Yl0F9iAEnxbU6hx9NLgZAlJga2v2QOYQ5KbT7wtjS6EKTyw/B+QiIiIiIiPqNPyRjZ6uH04IlOAacFPcanQE0OBlyUmLxBMKwezh0OZkIEXk/29rkhpuLqRERERER9bmQrKCixQOFg6kSHgNOUoVmVwCN7Qw5KTEIIVDn8MW6DIqRQEhBRbMH1W1eBMJyrMshIiIiIkpIQghUtXkRltm6mQx0sS6AqLua2gOQAOSmmmJdCtGv0uoJwh/iKcRk5/CG4PCGYDXpkJliQKpJB0mSYl1WrwnJyq6LQEhWEJYFwrtOnUuShI5HKkmABGnXV0Cn1cCk18Co00KrSZzng4iIiIj6V0O7H94AGwqSBQNOUpXG9gAkSUKOzRjrUoh6JCwr7EamTtz+MNz+MPQ6CZkWAzJSDNBr1THAIhCW4Q8pCIQiX4OygrASCTN7Y44jvU6CSaeFSa+FUaeJftUw+CQiIiKifXB6Q2hxBWNdBvUjBpykOg1OPyQJyLYy5CT1aWj3c/4X6lIoLNDYHkCTK4BUkx6ZVgOsxvj4Mx2WFfjDCvwhedcl8u++nqg9FBYIhcNw+X+es1SSAKtRh3SLHjaTnl2eRERERNSJPyRzxfQkFB9HTkQHqN7hhwQgiyEnqYgvKHNhIdovIQCnLwSnLwSjXgOLQQuzXguLQQeTXtOnw9gVRSDQEWSGfw4y42neIiEAlz8SekqSDzaTDmlmPVJNenZ2EhERESU5RYnMu8kV05MPA05SrTqHH5IkITPFEOtSiLqlzsmFhejABEIKAiEFdkSCcUkCTHotLIbIxaSPXLpLUQRCSmRezPBu82OGZAWBcOS+1EQIoN0XRruPYScRERERATV2n+o+01LvYMBJqlZr90ECkMGQk+KcwxvkBNf0qwkR6QT2BWW07rZdkn5erEej2fW1Y5skQVEEgrKS0NMj7B52ajV+ZFsNyLIaOYSdiIiIKEm0uANw+jhiLlkx4CTVq3X4oJEkpFn0sS6FqEuKIlDv5MJC1HeEwK5hOAKyEvmazGQlMp9pszuArBQjsq0G6FSycBMRERERHThPIIwGHnMlNQacpHpCANV2LySNBakmhpwUf5pcgbiaw5AoWSgK0OwKoMUdQGaKAdlWIww6Bp1EREREiSQkK5x3k8BP+ZQQhACqWr3wBsP735moHwXCMlrcgViXQZTUhABa3UFsaXShxu5FIMzpIoiIiIgSgRAC1W1eNpQQA05KHEIAlS1e+EM8cKX4Ue/w80wiUZwQArB7Qtja6Eatw4ewnMCTkhIRERElgcb2ADxc64DAgJMSjKwIVLZ6EOJBK8WBdn8ILj+7ionijRBAmzuILY1utLgDEDwLQURERKQ6nkAYzS6OlqMIBpyUcEJhgcoWD2SFB6wUO0II1Ds4yTVRPJOVyOt0W5Mb7gBPRhARERGphaII1Nh9sS6D4ggDTkpI/pCCna0eduVQzDS7AwiG2UlMpAb+kIKKZg92tno4PycRERGRCtS3+3m8RZ0w4KSE5QnIqG7jGR3qfyFZQVM7h0oQqU27L4ytjW40OP1QOAqAiIiIKC65A2G0uYOxLoPiDANOSmhOXwi1Doac1L8anFxYiEithACaXQFsaXLB6QvFuhwiIiIi2o2sCNTYvbEug+IQA05KeG3uIJraORci9Q9vMAyHl6EIkdqFwgJVrV7sbPVw+BMRERFRnKhz+BAKs5uE9sSAk5JCY3sAbR62sFPfq+PCQkQJpd0XxpZGF5pdXG2diIiIKJba/SE2k9BeMeCkpFHn8HGVXOpTDm8QviAXKCFKNEJEpp7Y3uyGN8i/I0RERET9LSwrqOWq6bQPDDgpaQgB7Gz1wB9iAEW9T1EEGjgVAlFC8wUVbG/yoNbhg8xFiIiIiIj6TZ3Dj7DMz1+0d7pYF0DUnxQF2NnqxaCcFOi0zPep9zS7A5wLhlRFCAFfSIbLH4Y7EEYorCAkKwjJAiEl8jW4a1tYViAA6DQa6LQSdBoJem3Hv3/eZjXqkGrWw2bUJfR7bJs7iHZfCAVpJqRbDLEuh4goZhRFIBBW4A/J8Idl+EMKAmEZQgBajRS5SJGvOu3u/9bAatRBq5Fi/RCISAWc3hAXf6T9YsBJSScYVrCzzYuB2SmQJH6ool8vGFbQ7ArEugxSCVkB1q4BWlqA7GzgkLFAb2WBihBwekNocQfQ4gmi1R1AizsIpy8Ilz+86xKCKxCG2x9GuA+7EFMMWqSa9Ug16WEzRYLPdLMeeakm5KeakJ9mQo7NCL1Kg9CwLFDd5kObJ4jCdDNMem2sSyIi6nP+kAynLxQJNEPKPhdh21+nlSQBKUZd5G+ESQ+DTp1/D4iob4VkBbUODk2n/WPASUnJG5BRY/ehONMS61IoATQ4/eDaI9Qdny8B5s8Hmhp/3pabB8yZA0ydsv+fV4RAqzuIBqcPdU4/Gpx+NLn8aHEH0eKOLKZ2oKGlXhvpvDToNDBoNdDvuui00m7fS4AkQVYUhGWBsCJ2dXZGuj3DcuR7967gVADwBGV4gjLqnXufukEjAdlWI/JTTchLiwSfA9LNGJiTgrxUEzQqOAnlCcjY1uRGltWAXJuJ3UhElHCEEHD6Qmj1BOEN9N5UT0IAbn/k70Y9/DDpNZFRACYdLAYephJRRB2nBqJu4l8OSloObwhGnR+5qaZYl0Iq5gmEOVyCuuXzJcBNNwH4xeezpqbI9gceiIScQgjYvSFUt3lRbfei3ulHvdOHBqcfDe1+hPbXEQMgI8WAbKsB2VYjslIMSLcYoh0yVpMOqSYdrMbIQWRvdx7KioA7EEa7P4R2Xwjt/vCuryHYPUE0tgfQ0B55LMGwgiZXAE2uAFDr7HQ7Zr0WZdkpKM9OwcBdX0syLd2uty87ZX9JCKDFFYTDy2HrRJQ4gmEFbZ4g2jzBfgkX/CEF/lAATe0B6LQSMlMif8d44ogoeTm9IbT7uMAjdY8kRPL0HbW3tyMtLQ1fb6iC1ZYa63IoTpRkWpBm0ce6DFKpbU0u+IJ7H55FBETCtunTO3duAgLalAB06V7o0r2w5fswfLwXNXYvPMG9d8hoNRJybUYUpJmQn2ZGfqoR2dafLxkWvSrmvxRCwOENRcPOhl0dqVVtXuxs83QZ5GokYEC6GcPybRhRkIqRBakYkG7eY7qRX9sp+2ulGLUctk5EqtXuD6HNHZnaJNa0Ggk5tsjJOg2DTqKkIisCWxpdXFgoTrld7Zg4sgROpxOpqfGRrzHgpKQnScDAnBQOhaED1uYJotbO+WBo32RFYPHXftz9UCTI1KV5oUv3QZfmhUbfdTiukYD8VBOKMy0oSDOjIM2062JGji3xu1lkRaDG7kVFiwcVLR7saPGgssUDRxfd0qkmHUYWpmJEfipGFqaier0Vt96i2aNTFrueso5O2b4mSUCW1YAcq1EVgTMRkScQRq3Dh0Ao/k7c6nUScm0mZFj0nEOfKEnUO31ocQVjXQbtRcIHnDfddBMeeOCB3rq5XseAk/ZGq5EwONfKyc2p23hGkX4pJCuoc/hQbfdFh5dXt3lRY/ftdV5MIUsIt5sRdlgQdppx1kkW/GayBYXpZr4fdcHuCWJbsxsb69uxob4dWxpde3R6ClmDYJMNgbp0BOoyEG5NQTTdlIC8XOD9D/puuPovaTSIdtcmejBNROokKwL1Th/snvifcseg0yA/1cTRV0QJzh+KzHGePO146qPagPO4447D/nYLh8P47rvv4PfvfTGBWGPASfti0mswMMfKA1DqlgannyunJyl/SEatoyPEjHytavOi3unD3qYo02k08DVHgszQrjAz7LBAdpkA8XPS9uyzwKGH9tMDSQAhWcH2Jjc27Ao8f6huhy/ceUil4tdFw85AXToUrzEmz7NWIyHbZkB2ipHDLIkobji8QdQ5/KpbwMNs0CAv1QSbiUEnUSLa0eyGpxcXNaPeF48BZ7fG5C5fvhxXXHEFsrKyAABvvvkmDj/8cJSWlkb3cbvdSE9P75MiifqDP6Sgus2LsuyUWJdCcS4QltHiZriZyDpWjK22+1Bjj3Rhdnxt2kewbTFoUZxhQXGmGcUZFpRkWlCUaUFWihGnnybB0YQ9h04D0c7CQ8b22UNKSHqtBsMLUjG8IBUzAXzyicCdD/pgzHfCOMAOQ74TGlMY5oEtMA9sAQCE7Ba8uzEdmpwMHDQgDfp+auWUFYFGZwCt7mB0PjkOsySiWAmEZdQ5/HDHwTybPeELKqhs8SLTakBBqoknjogSiMMbZLhJPdKtgPOMM87A448/Hv3ebrfj0Ucf3WO/hx56qNcKI4oFlz+MBqcf+WlcWZ32rsHp53CJBCErAo3t/k4BZo090pnpDuz9oC/VpENxpmVXmBkJMoszzMjcR2g1Z86uVdQldA45d+1+45z+GzadqHJyJMhOC7xOC7ybCwBJgT7HBWOhA8YBDuizXNBneLHO4cW69+tgMWhxaGkGDi/LxPjSTFhNfT8Xc1gWqHf40eIOcD45Iup3Qgg0uyMrlSfCZ5k2dxDeQBjFmRYu7EaUACJTZsTvqGCKb936JP/CCy90+t7j8XS5348//vjrKyKKsWZXACa9BukWQ6xLoTjk8ofQ7lNnt0My231YeY3DFwky27yodex9fkwJQF6qCUUZZhRlWFCUYUZxpgUD0s1IMx/4kLipUyIL3Pxyde+83Ei42R8L3yS6Q8ZGVktv6uiUFRqEmtIQakqDe20pJGMIucOdOPECO1btbIPDG8JXW1vw1dYWaDUSRhWm4ojyTBxenoX81L490RUKC9TafWhy+ZFhMSDDYuC8q0TUp/whGdVtXvjjcBGhX8MfUrCtyY0B6WZkpPDzO5GaNbb7ucYB9ViPFhn63e9+h4KCAlxyySUoLi6G3W7HSy+9hGXLluHTTz/tizp7BefgpO6SJGBQjhVmA88E08+EENja5I7L1UWp58PKDVoNBmSYUbxbkFmUYUFhuglGXe+/B8gKsHYN0NICZGdHQjl2bvaez5fs6pQFuuyU7VhFXRECWxvd+LaiFd9WtKGqzdvpdkozLThqcDaOGpyNkkxLv9RuNemQaTEg1axjVycR9SqnL4TqNm9CdG3uS7pFjwHpZg5ZJ1IhLiykLvE4B2ePAs62tjaceeaZ+OKLL6IfwMeNG4d33nkHxcXFvV5kb2HASQdCp42srN5f87NR/Gt2BdDAIRMxJ4RAizvYaaXyjsV+XN0YVl6UbkZR5q6OzAwLcmxGaBgmJZTPl3TRKZu3707ZeqcP31W04duKNqyvc3ZaMKok04KjB2fj6MHZKO6HsFOrkZCRokeGxcAhl0T0qzW2+9HUnjxzhxt0GpRkWtioQKQy25vd8HLuTdVImICzw4oVK1BVVYWBAwfi0EMPjftuAwacdKDMBi0G5aTE/f9t6nshWcGWRhcUNm/2m475MavtkVXKa9p8qLJ7UWv3wRfq+sNPbw8rJ/X6NZ2yLn8IKyvbsGxbC9ZUOTpNY9DfYafFqEWqSQ+rUceDdSI6ILIiUGP3JuXUOpIE5KeZkG01xroUIuoGuyeIGrsv1mXQAUiYgDMUCuH//u//4HK5cPPNN8PpdOLxxx/HX//617gOghhwUk+kW/T9chBL8a26zQuHNxTrMhKSIgQanH7saPF06sqsdfgQ2sscPFqNhML0yLDyjsV+SjLNKEw398mwckpe7kAY31W04qutLVhb3Tns7BjG3l9hp0YDpBh0SDHqkGLUwqzXxuXnLiEEFBF5bStCQAhA7Pa9RpIiFw2glSRoNVJcPg4iNQuEZexs9Sb9tDrpFj2KMsx8jyGKY7IisLnBBXkv8+JTfEqYgPOaa67BihUrkJ+fj48++ggAsHbtWrzzzju45557er3I3sKAk3oqP82EHBvPACcrbzCM7U1dL65GByYsK6i2+7Cj2Y3tzW7saPFgR7Nnrx2ZBp0mOpS8eNdK5cWZFhSkmqDj9BHUz9yBML7d0Ypl2/YRdg7JRnFG/5wU6wg8LUYtDFoN9FoNdFoJeo2m1+afE0IgrAjIuy5hWSCsKJAVgZAiIO/+vfxzoHmgJAnQ7Ao7tRoJJr0GJr02ctFp+HonOgAufwhVbV6OOtnFZtKhJNPCeTmJ4lStw4c2dzDWZdABiseAs1urqP+SoihYvXo1Zs+eHd12yCGH4C9/+UuvFUYUTxrb/TDpNbCZOMQ1GdU5OFyiJ4QQaHIFsKG+HRvr27G1yY2drZ4uuzINWg1KsywozbL8HGZmWpDL+TEpjliNOhw3Ig/HjciD2x/GtxU/h50727zY+V0VXv2uCqWZFhw9JLJAUV+GnYoCuPxhuPx7Dj/VaiTotRJ0Wg302kho2GH3AHL3V6OidISZSjTU7K+ARAhAFiLaveELygB+7prXaaVdgacGJp0WKUYdV50n6kKTy49GZ/LMt9kdLn8Yla0elGWlMOQkijO+oMxwk3pNjwJOq9UKAJ1a/auqqrB+/freqYoozggBVLV5MTjXyuGvSabNE4QvyBaI7pAVgcpWDzbUtWNjQzs21LWj1bPnBxaLQYuB2SkYmGPFoBwrBuWkoCjD0imAIYp3VlPnsHNFRSuW7x52fluFBd9WoSzLgmOG5OCYIdkoSDP3W30dHZdIkOGpYVnALYfh3m2dN5Neg1SzHqkmPecnpaQnhECN3cfpdPbCE5Cxo8WDsiwLO8KJ4kgtG0moF/Uo4NTpdJg/fz4qKirwxhtv4IcffsDzzz+Ps846q7frI4obigLsbPViUI6VQUySkBXBVdP3QQiBqjYvVu20Y121A5saXHsMNddqJAzKScHIglQMzbNhcK4VeakmdmX2E0mKhEB6rQYaSYoOA45cIicqO97PwrKCoKwgJAuEZAXBsNKjocbJyGrS4fgReTj+F2HnmmoHKlu9qGzdiZdX7MTgXCuO2TWMPddminXZqucPKfCHAmhqD0Cvk5Bq0sNm0sFq1HG+PUoqihL5e9xVNzf9zBeUUdHiQVl2CvQMOYlizukN7RqxQdQ7ejQHZzgcxm233YZnnnkGHo8HJpMJv//97/HQQw/BZIrfD+ycg5N6g82kQ1l2SqzLoH6QTPPBdHfFaX9Ixg81DqzaaceqnXY0uzoPg7MYtBien4qRhakYmW/DkDwbTHp2VvWXjkAzsgiNDikG3a86IROSlcglLBAIy/AEZXiDYc7r1k0ufwjf7IgsUPRDjQO7z50/It+Go4fk4OjB2chMMcSuyASk0QCpJj2yrAZYDD06l0+kGh2jJ7wBhgTdZdBpUJ6dwmkuiGJICIGtTe6kXwhNzeJxDs4er6Ku1+shhEBLSwuys7MhSRIaGxuRl5fXF3X2Cgac1FtyU43IS43fMJ9+PX9IxrYmd1J0sH2+BJg/H2hq/Hlbbh4wZw4wdQpQ7/RhZaUdqyrb8GOts9PCKgatBgcXpWFcSQYOGpCKkswUdjj3M7Oh9wLN7vKHZHh3hZ3eoMwPp93g8Abx9fZWfLW1Gevr2qNzX0oADhqQhmOGZGPioGykmTnXc28yG7TIthqQZtazq5MSTlhWUNnq4VQ6PaDXSSjLSuFJWKIYaXUHUOfgSDk1S5iA84ILLsCrr766x/bzzz8fr732Wq8U1hcYcFJvKsm0IM3CA9FEtb3ZnRTdEJ8vAW66CZ1XGgGgMQdhKmvB0ElNaPS7O12XazPisLJMjC/NwEED0nhwECOpZh1ybaa4mHtQVgS8wTA8ARnuQIgH2/vR6g5g+fYWfLW1BZsaXNHtGgkYU5SOY4ZkY8LAbFhN7D7sLTqthKwUAzJTDJx/jxJCMBwJN3mCqee0Ggnl2Slx8XeUKJkoisDmRhfCXSw8SuoRjwHnAX1yrqqqAgB4PB5UV1ejIxsVQmDnzp34/PPPe79CojhVbffCqLcy3ElADm8wKcJNWYl0bkbDTa0MU3EbzIOaYBxgh6QBGv2R0OWgAWkYX5qB8WWZKEo3sxMqhtLMeuSmGuPqvUerkWAz6WEz6QGYICsCbn8Y7mAYbn8YwTAPwHeXZTXitDEDcNqYAWhq92PZtkjYua3ZjTXVDqypduDppdtxSHE6jh2agyPKMznU+lcKywKN7QE0uQJIM+uRbTUy1CDVCoQjc0mGwgwHfg1ZEdjR4sagHH6eJ+pPLe4Aw03qEwfUwanRaKIHtUKITv8GgOnTp+O9997rgzJ7Bzs4qbfpdRIG51jZDZJAkumM4urVwJVXChjynTAPaoKptBUaw8/BbrDZCt/2XMy9NhuTjuQcgbEkSZFgM8cWX8FmdwXDCjyBMNy7Lsnw+uqJOocPX21rwbKtzahs9Ua36zQSxpakY+LAbBxenolUDmPvFVaTDnmpRobHpCr+UCTc5Pto79FpJQzKsXJOTqJ+EJIVbG5wJcU0YIlO9R2cFRUVEELguuuuwxNPPNHpOqPRiPz8/F4tjijehcKRVSvLs1PY0ZYgGl3+pDho8AVlLN7WiJyZddCl/jz/TdhthG97Lnw7ciA7LQAAvzNWVZIkAemWSLBp1Kkv2Oxg0Glg0BmQsWsxnUBYhicgR0PPZHjNdUdhuhnnji/GueOLUd3mxVdbm/Hl1hbUOiLz4K6stEMjAaMHpGHCoGxMGJjFBYp+Bbc/0mGcatYhL9WkypMHlFy8wTAqWjxc6K2XhWWBna0eDMyxch5xoj7W5Aow3KQ+06M5OL/77jtYLBaUl5cjJSWymvSCBQtw4YUX9nqBvYkdnNRXsqwGFKabY10G/Uq+oIztzYm9sFCrO4APf6jHx+vr4dk1DF8JauGvzIZvey6CjamILHvys2efBQ49NAbFJjmzQYOiDEtShC67B57eoMwh7bsRInIi7Zsdrfhmeyt2tHii10kAhufbMGFQFiYMykY+F7/rsY6TCbk2E7u4KC65A2FUtngS+jNKrKUYtWxaIOpDgbCMrY2JfayVTOKxg7NHAeett96KBx54ADfffDP+/ve/AwC+/vprrFixAjfccEOvF9lbGHBSXyrKMEe7k0idtjW5EnZxlIoWDxauqcWXW5ujq6AXpJlQ+/UANK3NhQh1EaJJQF4u8P4HAGdh6F/ZNgPyU01Je5AVlhX4QjJ8IRn+YOTfDD0j6p0+fLO9FV9vb8XmRlen6wbmpGDioGxMHJiF4kxLjCpUN0mKnLTMsRo5/QzFDYab/Sfdouf7J1EfqWr1wukLxboM6iXxGHD2aNIhp9OJNWvWYODAgdFtEydOxH333RfXASdRX6p1+GDUaziXl0q1uAMJF24KIbCmyoF319ZibbUjun1kQSrOGDsAh5dl4osiCTetQqQVbPcDp1252o1zGG72J51WQlGGeddiPclLp9XAptV0eh5kRURCz6CMQFhGSBYIhhWEZCWpDvoL0syYOa4IM8cVodUdiHZ2/lTnxI5mD3Y0e/DKip0oyjBj4q5h7INy2JHUXUIALa4g2jxB5FiNyLYaoeGQVYohhpv9y+ENwaDzI48d8US9yhsMM9ykPtejJCYQCODggw/utK2trQ1btmzplaKI1EgIYGerF4NzrdAzEVKVYFhBg9O//x1VZH2dE/9aXhnt8NJIwMRB2ZgxdgCG5tmi+02dAjzwQGQ19abGn38+LzcSbk6d0t+VJy+rSYeiDDPfP/ZCq5FgNepgNXb+6CKEiISdshINPINhBbIiIAsR+brrkmgBQZbViN8cXIjfHFwIpy+EbysiYefaagdq7D68saoab6yqRq7NiAkDs3B4eSZGFqSyM7EbFAVobA+gzRtEQZoZaVzYiWKA4WZsNLUHoNdqOMcxUS+qT7BjLYpPPQo4CwsLcdZZZ+HUU09FdnY2duzYgaeffhqTJk3q7fqIVCUySbkXA7NT2PGhInUOX8IcPFTbvXjp60p8W9EGADDpNThxZD5OG1O4126EqVOASZOAtWuAlhYgOxs4ZCw7N/uLJAF5qSbk2IyxLkWVJEmCQSdF5k3cz1OoKAJhRUARHRdAEZHgU+z62rFd4OfvRaf9sNvPR24vLMc+PE0z63HiyHycODIfnkAYKyvb8PX2VqyusqPJFcB76+rw3ro6WAxajC3JwGGlGTi0NAPpFh7A70soLFDV6oXVpENBGhciov7j8oews9Ub8/eWZFXn8EGvlZJ+RAVRb2j3h+DdNfc/UV/q0RyciqLgzjvvxOOPPw6XywWj0YgLL7wQjz76KKxWa1/U2Ss4Byf1F87fox5ObwhVbd5Yl/Gr2T1BvLayCp+ub4AiIh2bJ47MxwWHl3Bu2Dhm0GlQkmmB2cDQRO06wlNZEQgrkQ7Sjs5Sf0hGIBTZ1t/8IRnfV9nxbUUbVu+0dxoeJgEYmmfDYWUZGF+WiYFcXGOfJAnIthqRa+OwdepbDDfjgyQBg3Ks/BtN9CsIIbC1yY1AKLGmAqP4nIOzRwHn7pqbm5GdnQ1JkuDxeKKrqscjBpzUn/LSjMi1cf6eeCYrAlsaXQjL6j2C8AVlLFxbi3fW1MC/64PDEeWZuHhCGUP2OGcz6VCSaWFQkkRCsoJAOBJ4+kNy9N9KP33mV4TA1kY3Vla2YeXONuxo9nS6PivFgPFlmTisLANjitK71a0oK8nX/a3XSShINSPNws4u6n0MN+OLTithUI41MkqAiA5YmyeIWrsv1mVQH1BtwFlVVQWr1YrMzEwAQHV1NXb/MUmSoCgKrr/+erz77rt9V+2vxICT+ltJloXzdsWxGrsXdo86J7tWhMB/1zdiwXc74fBGHsOwPBt+f1QZRhWmxbg62p90ix5FGWZ2yxGEEPAGZbj8Ybj8oeiJiv7Q6g5g1U47Vla2YW21A4HdVqrXayWMHpCOw3d1d3Y1xcXnS/acvzc3D5iTJPP3phi1KEw3c9g69RqGm/HJpNdgUI6VJySJDpCiCGxWeTMJ7Z1qA87s7GwMGzYMy5cvBwBoNBpIkgQhRPTgrOPfshy/cysw4KT+JknA4FwrD37ikCcQ3qN7SS2q7V48sXgrNjZEFhAqSDPhogllOGpQFgMzFchI0aMog9211LWQrMDlD8PtD8MVCPVbd2cwrODHWmeku7OyDU2uQKfrSzItOKwsA4eWZmJEvg1ffqnBTTcB+OWnyF1vQQ88kBwhJ4etU29huBnfMq0GDEg3x7oMIlVpcvnR6Azsf0dSJdUGnO+++y7S09MxZUrkk+phhx2GBQsWwGj8eTZ/RVHwl7/8Ba+++mrfVfsrMeCkWNDrJAzOsXLV2jii1rlgwrKCd9fU4rWVVQjJAma9Fr89sgQnH1TAlbdVIttmQEEaD5Coezq6Ox2+EOyeYL8FH0IIVLV5o92dG+vbsfv0oWa9Ft7qdDh3ZCBQmw7F84vuTgnIywXe/yDxh6t3MOg0KEw3cUES6hGGm+pQkmnh1BRE3aQoApsaXDGZf5z6h2oDzl/6/vvvMW7cuD22NzY2Ii8vr1cK6wsMOClWLEYtF2+II43tfjS1q+ts4o5mNx77fGu063RcSQaumTKI87yqSF6qEbl7WcmeaH9kRcDhDaLNE+zXYexAJHz5vsqBVZVtWFPt6LRQEQCE7BYEajMQqM1AsDEVUCKp5rPPAoce2q+lxlyaWY+CdBNPOlG3MdxUD40mMjLLqOPILKL9aXYF0OD0x7oM6kMJE3B2RVEUnHvuuXjzzTd74+b6BANOiiUOS40P/pCMbU1u1RxIBMMK/rOqGm9/XwNZEbAadfjDMeWYMiyXgbmKFKSbkG017n9Hom7wBMJo8wTh9IX6/b1MEQKvvO/GC+/ZYRxghz7HBWm3LE8JaRBsSEOgNgPXnpeJc36TfKG+RgPkp5qQxdc87QfDTfUxGyLzcfIzGNHesXszOcRjwKnrzk5aLc9SEf1adk8IJn2AIUeM1Tp8qjmQ2FTfjsc+34qaXSsPHjUoC1dOGoQMiyHGlVF3SRIwIN2MjBT+zqj3pBh1SDHqUCAraPMGYfeEEAz3T1enRpIwqsgG9w82uH8ogWQIwVjogHFAJPDUWkIwFdthKrbj5codWPyyCYeWZmBcaQZGD0hLis4nRQHqHH7YvSEUZXARIupauz+EKoabquMLKqh3+lHI+TiJ9qrVE2S4STHRrYAzJycHTz/9NLKysiCEwNy5c3HxxRejtLQ0uo/L5cKSJUv6rFCiRNDg9MOo03COrhhpdgXgDcTvQmgdQrKCl1fsxMI1tRCIrLj9x0mDMHFQdqxLowMgSUBxBufror6j02qQazMhx2qE3RtCk8uPULjvDygOGRtZLb2pCRBBPfyVOfBX5gAQ0GV6YBxgh63cDl22C3VOP+p+qMcHP9TDoNXgoAGpkcCzJAMD0s0J3QXlC0ZGDGRZDcizmbgIEUUx3FS3VncQKUYd0sz8+070S4oi0OxS11RglDi6FXBeccUVmDlzZvT7Tz/9FBdddNEe+/3000+9VxlRAhICqGrzYlAOV1bvb76gjMb2+J8HpsnlxwOfbMbmxsgK6VOH5+Lyo8sZiquMJAElWRak8vdG/UCSJGSmGJBh0aPFHUSzK9CnnRNaDTBnDiKrqEvYbSV1CWG7FWG7FXdeVIwjJoaxrsaB73fasbrKjhZ3EN9XOfB9lQNABfJSjRhXkoHxpRkYPSAdZkPi/V0UAmhxRaYTKEw38z2BGG4miBq7F2a9DQYd59sl2h27NymWejQH55lnnom333670zYhBE455RR8/PHHvVZcb+McnBQv9DoJg3KsXISgnyiKwLbm+F81fVVlGx7+bAtcgTBSDFrMOn4oJgzMinVZ1APFmWakcyoBihFFEWhxB9DsDkDpw7e9z5cA8+cDTY0/b8vLA26cA0yd0nnfjpXZv6+yY/VOO9bXtSO82wGQTiNhVGEqxpVk4NDSDJRkWhKyuzPVrENBmpmhSJJy+kKobmO4mSjMBi0G5XARUaIOiiKwudGFsMw3uWSg2jk4f+mQQw7BxIkT8bvf/Q5FRUWw2+145ZVXMHjw4N6ujyghhcICO1s9GJht5ZC1flDn9MV1uCkrAq+s2Im3vq8BEFmh8+aThiOfK26rUn6aieEmxZRGIyE31YTMFAOa3QG0uoN9EqhMnQJMmgSsXQO0tADZ2ZHh612du5MkCaVZKSjNSsGMsUXwBWX8WOvA6ioHVu9sQ2N7AOtqnFhX48S/vq5Ers2II8ozcXh5FkYVpibMCcF2Xxguvwt5qSZkWw0MRpIIw83E4wvKaGj3oyCN83ESAZHuTYabFEs96uBUFAX33HMPHn74YbjdbqSkpODyyy/HvHnzYDLF7wE5Ozgp3thMOpRmJWaXSrxw+iJDweJVqzuAB/+7Gevr2gEAvxldgEuPLk+Yg/lkk2U1cOEBijshWUGD0w+HNxTrUrokhECdw4/VVW1YvdOBH2sdCO12gGQxaDGuJAOHl2difGlGwkzZYTZoUJhuhsXQo34DUhGnN4RqO8PNRFWazSlpiNi9mXzisYOzRwFnh3A4jLa2NuTm5vZmTX2GASfFo4wUPYoyLLEuIyGFZAVbG91xOw/Mmio7HvpsC5y+EMx6Lf40dTCOGZIT67Koh9LMepRk8bVM8csTCKPO4YM/jjvaAcAfkrG22oHvKtqwsrINDt/PwaxGAkYUpOLI8ixMHJSF3ATodM+0GpCfaoKWIzoSUpsniDqHj+FmAtNqJAzJ49RTlNyaXQE0OON/vQPqPQkTcNbU1OCGG26ARqPB66+/DqfTiRtuuAEPPPAAsrLid744BpwUr/JSjQlxkBZvdjS74YnDVdNlReA/K6vw+spqCABlWRbcctIIDMhg559apRi1KM/mPFwU/4QQaHEH0djuV0XgogiBrY1ufFvRipWVbaj8RUf+4FwrjhqUjYmDslTdPa3VSMhPi0wrQImjyeVHo5OrCScDm0mHsuyUWJdBFBPs3kxOCRNwnnnmmSgrK0NdXR1ee+01AIDD4cCf//xn/N///V+vF9lbGHBSPCvKMCODBza9Jl4PKnxBGfP/uxnfVbYBAKaNzMMfjh0Ioy7xVg9OFia9BgNzrOy+IlUJyQrqHX44ffE5bH1vGtv9+LaiDd9sb8GG+nbs3qBfnp2CiYOycNSgbBRnqrObmsPWE0e904cWVzDWZVA/4gKDlKxa3AHUO9i9mWziMeDs0aenwYMH4/7778fs2bOj29LT07F9+/ZeK4wo2dQ6fNBppYSZWyyWfEEZTe3xF262ugO456MN2NHsgV4r4dopgzF1eF6sy6JfQa+TUJadwnCTVEev1aAkywKXP4Q6hx/BcHwPW++Ql2rCaWMKcdqYQji8QazY0Ybl21vwQ40DFS0eVLR4sODbKhRnmHHMkBxMHpajqgVAfEEF25s8SLfokZ9m4pBXFRJCoMbui9s5b+OFrHRvgTI1qXP4YTXqoFP7AyE6AIoi0OyKv+MuSk49CjjdbjcAdBqK9/HHH6OysrJXiiJKRkIAVW1eDMqxwqRnN19PKYqIy4n8dzS7cc+HG9DqCSLNrMdfTxmB4QXxcaaLekarkVCWlcIAglTNZtJjaJ4Oza4AmlyBuHvv3Jd0iwEnHZSPkw7KR7svhO8qImHn2moHqu0+vPpdFV79rgrD8myYPCwHxwzJQZpZHScRHd4Q2v0h5Nq42rqadHwGafeFY11KXPt8CTB/PtDU+PO23Dxgzhxg6pTY1fVryYpAvdOv2g5yop5o83LldIofPRqi/sILL+Dtt99GU1MTDjvsMPzwww/45ptv8OSTT+KPf/xjX9TZKzhEndRAp5UwKMcKg46hSU/U2L2we+Kra2JlZRse+HQT/CEFRRlm3PmbUchP45yraiZJwMCcFA4jpYTiD8mosfvgC8bf3MUHwhMI49uKVizd3Ix1NY7oMHaNBIwrycCkoTk4cmCWak4mGvUaFKSZOMIjzsmKQGWrB944nPs7nny+BLjpJgC/PALdleE/8IC6Q06Aq6pT8uDcm8ktHoeo93gV9UWLFuGpp55CZWUlioqKcPnll+Pss8/u7fp6FQNOUguTXoPy7BQOcTlATm8IVW3e/e/Yjz5YV4d/LtsBRQBjitJwy8kjYDUyFFO7kiyLajrBiA5UizuyEqqaujn3xu4J4sutzVi6pRnbmtzR7Sa9BhMGZuGEkfk4qDBVFR2SVpMO+akmmA3qCGaTSUhWsLPVA19QHVM9xIqsANOnd+7c7EQC8nKB9z9Q93B1vU7C0FwbNJy+hhIc595MbgkTcH7wwQcIh8OYMWNGX9TUZxhwkpow5Dww/pCM7c1uKHFybCErAv9ctgMf/lAPADhhZB6unjSIv88EkJtqRF4qO3ApsQXCMmrtPngSqButxu7F0i3N+GJzMxrafz4gK0wz4cRR+Zg6PBcZKlggJN2iR16qiSM94kQgLKOyxauaeWxjRQiBz78J4I75Hugz3dCl+qJdm5EdAEACBHD4EUBWFpBq0mNong3D8m3IsxlVcSKiQ5bVgMJ09cz/S3SghBDY1MDuzWSWMAFnZmYmxo4di8WLF/dFTX2GASepjdmgQXk2V2fen5CsYHuzG6FwfPyB9QbDePDTzVi10w4AuHhCGc4cN0BVH8ypa6lmHUqzUmJdBlG/afMEUe/0xc3Jo94ghMDmBhf+t7ERX25tgS8UCXG1GgmHl2Vi2qh8HFKcHtd/eyUpEqDk2kxxXWeia/eHUN3mTajXR2+QFYEauxc7WjzY0eze9dUDd6Dnc5NmWPQYlm/D8PxUDM+3YXCuFUZdfHczD8xJQQpH7VCCavMEUWv3xboMiqGECTj/9re/4eabb4Ze33l43gMPPICbbrqp14rrbQw4SY0Ycu6bogjsaHHHzbAwuzeIu95fjx0tHhi0GtxwwlAcNTg71mVRLzDpNRiUY+WQM0o6IVlBrd0Hlz/xFk7xBsNYtq0F/13fiM2Nruj2HJsRJ4zIwwkj85BtNcawwn3TaMCFiGKksd2PpnauHNxBEQLra51YuqUZy7e3dNn9rZEkBFotCLWmIOywQMi7upAlABCAFPnnWWcDAwYItLgD2NTgwo4WD2Sl8yGrViOhPDsFEwdl4eRRBbCa4i9INOo1GJJr5WuTEtKWRhcCofg4/qLYSJiA8+yzz0ZFRQUOPvjg6DYhBD7++GM0NDT0aoG9iQEnqZXZoEV5dgpDzl8QQmBnqzduDrqbXH7cvvAn1Dn9SDfr8ddTR2JYvi3WZVEv0GokDM7l4l+U3BzeIOoc/j2ChkRR2eLBfzc0YMnm5minmUYCJg7KxvQxhRiRb4vboEKvk5BrMyHDoo/bGhOFrAhUt8XPZ49YEiKysNLSzc34cmszWtzB6HVmvRZl2SkYlJ2CgTkpKM+2oijDgplnaNDUhD0XGQL2OgdnICxje7MHm+rbsanBhc0NLrR5f74vk16DE0fm4/QxhciNsylkOK0NJSKnL4Sq1vha94D6X8IEnAaDATNmzIDFYoluE0Jg8eLFqK6u7tUCexMDTlIzhpx7qnX40Lbbh+lYqrX78Nf3fkKLO4BcmxFzTz+Icy8lCEkCyrJTuDgUEYCwrKDe6YfDG4p1KX0mGFbw9fYWfLK+Aevr2qPbB+dYMX1MAY4ZkgN9nM6nrNdJyLEakZnCjs6+4A/J2NnK+Tab2v34Yktk8a7dF3dMMWhx1OBsTB6ag5GFaV1+Zo2uog50DjkPYBV1IQSaXQGsq3Hg/XV1qNwVtGgk4OjBOZgxdgAG51p/xSPsPZIEDM61wqSP7+H0RAdiW5MbvmDizNFNPZMwAef999+Pm2++eY/tH3zwAaZPn94rhfUFBpykdhajFuVZKRwiC6DZFVnlNx5UtHhwx3s/weELYUC6GXNPPwg5tvgd0kgHpjDdhKw4HqJKFAvt/hDqHL64mfu4r1S0uPHBD/VYurkJoV0LKaSb9TjpoHycclABMlLic1EivU5CttWILAadvcbpDaHa7sWBHzkljg317fjPymp8X2WPbtNrJRxWlonJQ3MwviyzW+H/50uA+fM7r6aelwfcOGf/4eYvCSGwptqBd9fUYm21I7r94KI0zBg7AIeWZMT8NWA2aDEoJyXmdRD1BncgjIpmT6zLoDiQMAFnh9WrV2PHjh0oKSnBEUcc0Zt19QkGnJQIGHLG17CITQ3tuOuD9fAEZAzMTsHdp41CugpW4aXuyUjRoyjDsv8diZKQrAg0tPvjppO+Lzl9Ifx3fQM++rEerZ7I49VpJBw9OBszxg7AwJz46Bb7JZ1WQo7NiEyLIak/N/waQkT+n7e4Ev//eVeEEPih1on/rKzGj7VOAJFmy9ED0jBpWA4mDsru0QgHWQHWrgFaWoDsbOCQsZ2HpffEjmY33l1bi6+2tkSn0ijNtOCPkwdhVGHar7vxXyk/zcST35QQKlo8cHOKDkICBZw1NTU4/fTTsWbNmui2448/Hm+++SbS0mL7x2NfGHBSokgxalGWpCGnNxjGjmZPXHRQrKtx4G8fbYA/pGBEvg13TB/FYcwJxGLUYmA2Oy6I9scTCKPW4UuKxQbCsoJvdrTig3V12Njw86JEh5Zm4KxxRRhVmBqX7xk6baSjMzPFwKluDkAgLKPW7utywZxEJ4TA6p12/GdVNTbt+r+u00g4bnguzjq0GPlp8TuvZLMrgPfX1eLT9Y3whWRIAGaOK8KFR5TEbHoJSQKG5tk4lzepmj8kY2ujO9ZlUJxImIDz+OOPR1lZGWbPno3i4mI0NDTgP//5DyorK/H888/3RZ29ggEnJRKLUYvSTAt0cToPWF8IhhVsb3YjLMc+3fyuohX3fbIJIVngkOJ0/OWUEZxfKYHodRIG51iT6vVF9GsIIdDYHkCLOxAXJ6D6w9ZGFxaurcWybS3oWHdpeL4NZx1ahMPKMqGJw6BTkoDMFAOyrAYYdfybtTdCCDS7A2hqT57/zx0UIfDtjlb8Z1U1tu8ahmrQanDiqDzMHFukqi5EdyCMF5ZV4LONkbHwZVkW3HjCMJRlp8SknnSLHsWZHBVC6lXd5k3oObjpwCRMwDlx4kR8/fXXe2yfM2cO5s+f3yuF9QUGnJRoDDoNSrMsSRGsyYrAjmY3/HHQIbR0cxMe+d8WKAKYMDALf542LG4XnKADJ0nAoBwrzIbEf10R9TZ/SEadI7k63hqcfryzpgb/29gYnaezONOCs8YNwLFDcuL2RInNpEOW1QCbSR/rUuKKJxBGncMXF583+tuqnW146evK6KI9Rp0Gp4wuwIxDBsTtfLPdsWJHK55csg1OXwg6jYTfHVmK0w8ZEJNu5kG5KbAYONqH1CcYVrCl0ZV0J31o7+Ix4OzRu+vEiRO73L57Vvrwww/jhhtu6FlVRNQtHR2NJZmWhD5ACckKKls8cXGw8en6Bjy1ZBsEgCnDcjDruKEc7pdgijLMDDeJesik12JgjhUObxD1Tn9cdNz3tfw0E66ePBjnH1aC99fVYdFP9ahu8+KR/23FyyuqMHPsAJw4Ki/uOiZd/jBc/jCMeg2yUgzISPJ5OmVFoN7pg92TfN1JNXYvnl9WgVU7I4sHWQxa/ObgQpw2phBpZvV/vjxyYBaG5dvw5Ofb8F1lG/71dSW+q2zD7OOHIi+1f4fa1zn8cbPCO9GBSKYRGqRePergvPbaa9He3o7BgwdHt9XV1WH58uU4++yzoSgKXnjhBVRVVfVqsb8WOzgpUUkSUJCWmCs9+0MyKls9cbFS76frG/Dkkm0AgFNGF+DKYwfG5RBE6rlsmwEFaeZYl0GUEGRFoMnlR6s7mFQHRZ5AGB//1ID31tVGh/JlWgw489AinDQqP27n4NNoIsPX082GpDvJ4/AGUefwRxemSRaeQBivr6zCBz/UQ1YEdBoJ08cU4pxDi2E1JV6XoRAC/93QiH8u2wF/SIFZr8UVxw7EccNz+3Xu3OJMMxekJFUJywo2NbB7kzqLxw7OHgWcGo0GY8eO3euDEEJgzZo1cDqdv7rA3sSAkxJdltWAgjRTXC5w0BPuQBg7Wz1QYt+4if9taMTjn2+FAHDamEJcfnR5wjzPFJFi1KKciwoR9bpkHLYOREZZLN7UiDdX16DZFQAQCRDPPrQIJ46M36ATAEx6DdItBqRb9Ak9BUuy/t+UFYH/bWzEyyt2wumLhPCHlWXgsqMGYkBG4p/kq3f68MhnW6ILhR01OBuzjx/Sb13Wep2Eobm2pO6YJnVpavejsT0Q6zIoziRMwDljxgy8++67+9znkUcewezZs3tcWF9gwEnJwGrSoSTTovph03ZPELUOX1ycKVy8sRGPLY6Em9MPLsAfjhnIECzBcFEhor6XTMPWdxeSFfxvYyPeWFWDFnfkADFrV9B5QpwHnZIEWI06pFv0SDXpEyaQcflDaHUH4fKHY11Kv1tf58RzX+3Ajl0LCBVlmHH50QNxaGlGjCvrX7Ii8M6aGrz6bRXCisCowlTcfupIpBj7p3M1N9XY78PjiXpCUQQ2NbiSrsOd9i9hAk61YsBJycKk16A0KyWuD5r2JZ7OEn6+qQmP/m8LBIBTdw1LZ7iZWLioEFH/URSBFk8Aza5AXHTn96eQrOCzDY14c3U1WtxBALuCzvHFOHFkXtx3Smo0QJpZj1SzHlaDTnVhp6IIOHwhtLoDcTGnd3+ze4L457Id+HJrCwAgxaDFBUeU4JSDCpL65N5PtU7M/WgDvEEZA3NScPf0Uf0yfFySgKF5NtV+Vqfk0eIOoN7hj3UZFIcYcMYYA05KJlqNhKJMM1JVtPiQEAK1jviZ4H/31dJPPigff5w0iOFmAirKMKt6dVgiNZIVgRZ3JOhMnk+iESFZwX83NOLNVdVo9USCzlybERceUYJJQ3NVMQKjo7PTZtLBZtLHdUgTkhW0uoNo8wSTsgNJiMhw9OeXV8ATkKGRgGmj8nHhEaUJsYBQb9je7Mad76+H0xfCgHQz7jltFHL7obsy3aJHcaalz++HqKeEENjc6IqLtRAo/jDgjDEGnJSMUs06FKab474zRFEEqtq8cTNc7MstzXjos81QBDBtZB6unjKYCwoloEyrAQPSE3++MaJ4FZYVNLkCaPMk10JEQGSOzs82NOCN1TVo2xV0lmZacNGEUhxWlqmqE2pGvSYadqYYtDGvXVYE3IEw2n0hOH2hpPu/1aHO4cNTS7bhh9rIugiDc6y4dupgDMrhKt6/VGv34fb3f0KzK4BsqwH3nHZQv4SPg3JTYDEk3oJOlBgc3iCq23yxLoPiVMIHnJs2bcLw4cN76+Z6HQNOSlYaDZCXakJ2nK6y7g/JqLF74QvGx5CxZdta8OCnm6AI4ISRebiW4WZCshi1GMhFhYjiQjCsoMnlh8ObfGGUPyTjox/r8ebq6uhiNyMKUnHxhFKMKkyL7icrwNo1QEsLkJ0NHDIWiMdzl5IEmA1amPW7LgYtjDpNn77XhmUFnqAMTyAMTyCclEPQdxeWFby7thavf1eNoKzAoNPgt0eU4LQxA1TRIRwrLe4Abn/vJ9TYfbCZdLh7+igMybP16X2aDVoMzmXgTPFpa6Mr6d9Pae9UG3BWV1djf7uFw2FcddVV+O9//9trxfU2BpyU7MwGLQakm+NmrsGQrKCx3R83Q9IBYPm2FjywK9w8bngurjtuCMPNBKTTShica437zmaiZBMIy2hqDyRl153bH8Zb39fgg3V1CMqRA8rDyjJw0ZFl2PFjCubPB5oaf94/Nw+YMweYOiVGBR8ASYrMD27aFXoa9VroNBI0khT52o3QTQgBWREIK7u+ygKeIAPNX9rS6MKTS7ahoiWyiNAhxem4ZvJg5KdxQZvucPpCuOuD9djW5IZZr8VfTx2Bg4vS+/Q+OVUOxSOXP4TKFm+sy6A4ptqAU6PZ/1lXIQQkSYIsy71WXG9jwEkUOcjIshqQZzPFbIEAIQSa3fG3yMR3Fa34+8ebICsCU4dFwk12OiQeSQLKs1P6baVUIjpwYVlBmyeIVk8w6VZdb3UH8NrKany2oQEdU0Z6t+fAvaYUsnu3kGrXn6cHHlBHyLkvkhQ58aSVJGg1EnQaDRSxW5ipKHH1eSEe+UMyXlmxEx/8UAdFADajDpcfMxBThuVwpMIB8gbDuHfRRvxQ44ReK+HP04ZjwsCsPrs/nVbCsDyb6hbuosS2o9kdHVVA1BXVBpwDBw7EW2+9haysLAghMGvWLNx4440oLS2N7uNyubBw4UL89a9/7dOCfw0GnEQ/0+skFKSZ+32Ceac3hPp2X9xNVr2u2oG7P1yPkCwweWgOrj9+KMPNBFWYbkJWnE7XQESdCSHQ7gujxROAN8kOtGrtPvx7xU58vT2y6rWQJXg358P9QzEU/65uLwnIywXe/yA+h6tT//h+px1PLd2GJlcAADB5aA4uP2YgFxH6FYJhBQ/+dxNW7GiDRgJuOGEYJg3N6bP7y001Iq8fFjYi6g5/SMbWRnesy6A4F48BZ7faV2644QaMGzcu+v2oUaNw7LHH7rHfwoULe60wIupbobBAVasXOq2EdIseGRYDTPq+G7ruC8qoc/ri8gB1U0M7/rZoA0KywISBWQw3E1im1cBwk0hFJElCmkWPNIsevqCMFnfyDF8fkGHGibnD8f6jLtgO3QljoQMpI+thHtIEz/pCeH4aABHWobExMjfnoYfGumLqb05fCP9ctgNLNzcDAHJtRvxx8iCML82McWXqZ9BpcMtJI/Dkkq3438YmPPq/LUi36DGmj4arN7sCyLAYYNDxTAXFXvOukyVEatOtd9Brr7220/fr1q2D3+/vtC0QCOB///tf71VGRP0iLAu0uILY2ujG1kYXml0BhOTeGQcWCMto8wRR1erFtiZ3XIabFS0e3PXBevhDCg4pTsefpw1juJmgUoxaFHIOMiLVMhu0KM60YHi+DQXpJliM8TGfdF9qaQFCrTa0/fcgtH56EIItVmj0MmyHVCPnrFWwjKgFNApaWmJdKfUnIQSWbG7CHxesxtLNzdBIwGljCvHk+eMYbvYirUbCn6YOwdGDsxFWBOYt2oidrZ4+uS8hgMZ2//53JOpjIVmB0xc/6yMQHYgeTUD2m9/8BiNGjMDZZ5+N4uJi2O12vPHGG5g5c2Zv10dE/cgfUtDg9KPB6YfVpEO6WQ+bSQddN8e9+UORFUy9QRnuQDju502rc/jw/+zdd5icdbn/8c8zvW7v2ZLeQ0ICgYCU0AIiRVEUjopHRUTA8zvKQTwqHPUgiuDRg6IcUWIFAUGQHnpvIQnppGeTbO+zO32e3x+TLAmpuzu7U/b9uq69kp19ZvbO5slk5vN8v/d9wyOr1BuOa1qFX9/56DSGzuQou81QbZGHPmRADrBZLSrxOVXicyoSS74R6wpGFIzkXpPGkpIPfh9pKFDbo7PlqmuTf+5W2fJDyj9ui7wzdmmHWat4oowLdKNAY3dIv35ho97d3ilJGlvs0TWnTdLkYZ72PVpZDEP/fsZktfVGtLahW99/dI1u/eRsFQ3DUKDOvqhK/fFh3VEFHE57b2RU7JJAbjqiHpwHsnjxYv34xz/W9u3bNX78eF1++eW65pprZLFkbjhAD05g8CwW7W78v2fqqUUWi2SzWBSJJdQbyfxAc2/NPSFd/+BKtfSENb7Eq5s+Pks+hs7kJMOQJpb5eMMA5LhwLK6uvqi6gtGcmaodT0jnnSc1N0va+79YIyH3pGb552yX1RORJNUVefT5BWN17NhCLubkoHjC1D9X7NKf39ymcCwhu9XQZ46t1SeOHnPEF6IxeN3BqK77+3va2RnUhFKvbv74UXI7Uv+6Is9tU12xN+WPCxyJRMLUusYexRPZ854O6ZOJPTgHHXB+WCQSkcOR+itZqUTACUCSOvoi+vaDK7WzM6gxBW79+BOzVODJ7OcvDF5tsYdBC8AoE4rG1ROKqScUVV8kntWrUZ57Xrruut2f7BNySrLG9alvNGhZV33/tNvplXn6wgljNa2S17q5YlNLQL98fqM2NieHfsysytPVCydpTKE7zZWNLg1dQf3HA++pKxjVMXWF+u6504dl1fTEMt+whKfA4bT3RrSzI5juMpAlMjHgHNTlvpUrV+rYY4/VKaecIknq6urSFVdcoc2bN6e0uAP51a9+pbFjx8rlcum4447TW2+9NezfE0DuCIRiuvGR1drZGVSp36kfXjCTcDOHleU5CTeBUchlt6rU79T4Up+mV+aprsSjIl92DvA4baF0yy1SWdm+t5eXSbfcbNW3Lq7WXZ87VhfNHSOH1aI1Dd267u/v6b8fW6Pt7X3pKRopEYzE9btXNusb9y3XxuaAvA6rrl44UTd9fBbhZhpU5rv1vXOny2Gz6J1tHbrzpU1K0VqhfTTSixNp0hpguBCy26BWcJ5//vk688wz9c477+gPf/iDpOQKzi996Uv605/+lPIi9/jb3/6mz3/+8/rNb36j4447Tj//+c91//33a/369Sr78Ku+A2AFJzC6BSNx3fDIKq1r7FGBx66ffOIoVRXwBiFX5bvtqi32pLsMABkmHEuu7gyEYuqNxJTIkt3s8URyWnpra7I355yjpQ/vTG4NhHXPW9v1zNomJUzJYkinTS3TpfPrVOp3pqdwDMpbW9r06xc39wcOJ00q0Zc/Mn5Yej9iYF7f3KabH18rU9JlC8bqk/OqU/49xpd65aV1EkZQTyiqra1cFMORy5kVnDNnztQ111yjoqIPpvQ5HI5hX8H5s5/9TJdffrn+9V//VdOnT9dvfvMbeTwe/f73vx/W7wsg+0ViCf3oibVa19gjr9OqH5w/k3Azh7nsFlWzugXAAThtVpX4nBpb4tX0yjxNKPOqPN8pn8umTG5dabVI8+ZJixYlfz1Q28USn1PXnDZJv7xkrhaML1bClJ5Z26wr/vyOfvfKFnUzGTfjtQbC+tHja/XDx9aqNRBWmd+pG8+brusWTSXczBALxhfryyeNlyT94fWteun9lpR/D1ZxYqS1BiLpLgEYskFdFmppST6J793A/M4771Rra2tqqjqASCSipUuX6tvf/nb/bRaLRWeccYZef/31A94nHA4rHP5gmXV3d/ew1Qcgc8UTpn769Dotr++Uy27Rf503Q+NKaOCeq6wWQ3XFXlmYJgzgMAzDkMdhk8dhk/ySaZrqi8TVG44pEI5lbf/OmiKP/vOj07SusVt/eG2rVu3q1j+W79RTqxt14ZwqXTBnDKvDMkw8YeqxlQ368xvbFIzGZbUYunDOGH3m2BqG5GWg82dXqak7pEdW7NL/PPO+in0OzajKT9nj94Xj6glF5XfRZgfDLxSNKxCKpbsMYMgG9crm4x//uObMmaNgMKh169Zp1apV2rlzp+6///5U19evtbVV8Xhc5eXl+9xeXl6udevWHfA+N998s77//e8PW00AMl/CNPWLZ9/XG5vbZbca+u650zW1IjOW0CP1DEOqK/ZkZZ89AOlnGIa8Tpu8TpvKlJwo2xtJhp294ZiCkSzZz77b1Io8/ejjs7R0e4f++Po2bWnt1T1v1+uf7zXoE3PH6LyjqgjPMsDG5oB+9cIHQ4SmVvh11akTNZaLsRntiyeOU0tPWK9vbtN/P7ZWP7t4tirzU7d7pKk7TMCJEUHvTeSKQU9Rf++993TnnXdq69atqq6u1pe+9CXNnz8/1fX127Vrl8aMGaPXXntNCxYs6L/9uuuu04svvqg333xzv/scaAVnTU0NPTiBUcI0Tf3mpc16fGWDLIb0nx+dpuPGFae7LAyj6kK3CtnCB2CYxOIJ9Ybj6glHFQjHFI1lz/LOhGnq9U1t+sub21S/e0pugduuT86r1jkzK7kwlAbtvRH96Y2tenZts0xJXodVl50wVotmVMiSyf0S0C8ci+s7D63S+qYeTSz16ZZPHiX7gfpHDFJtsYdhiRhWsXhC6xp7snK3AobX4Xp/Z2IPzkEFnG+//baOPfbY/s9Xr16tpUuXSpI+//nPp666vUQiEXk8Hj3wwAO68MIL+2+/7LLL1NnZqYcffviwj7FnyNCxNzysitIilficKvU5VeJzqsTvUHmeSxV5rpT+pwQgff74+lbdv3SHDEnfOHOyTp1y+GFkyF7l+U6V+V3pLgPAKBKKxtUdiqo7GFMwEk93OUcknjD10oYW3fPWdjV0Jfv8FXsduviYGp05vZzXwSMgHIvr4eW7dP/SeoWiyVXBp04u1RdPHMdFuizUGgjr6/cuU08opvOOqtRXTp6Qssd22S2aVO5P2eMBH9bcHVJTNys4sa/nnpduvVVqbvrgtrJy6dprpdMWJj/PmYDTarXq8ssv1y233KK8vDxFo1G99NJL+vrXv67Vq1cPR52SpOOOO07z58/X7bffLklKJBKqra3V1Vdfreuvv/6w998TcNb8v/tkcR54sq4hqdTvVGW+S5X57t2/Jn9fke9iGw+QJR5YukN/eH2rJOlrp07QOTMr01sQhlWxz8HQKABpFY0n1B2MqjuU3M6e6athYvGEnlvfrHvfrldLT/LNbZnfqU/MrdYZ08rktPGaN9VM09QrG1u1+LWtat79M59S7teXTxpH+5ws987Wdn3/0TWSpP88Z6oWTChJ2WPXFLlV4CH4RuqZpql1jT2KxTP8PyyMqOeel667TtKHT4vdGwt+9OOEJs3u07rtzfrWBXOzP+A8++yz1dnZqW3btukXv/iFLr74YknSNddc0x8+Doe//e1vuuyyy3TnnXdq/vz5+vnPf6777rtP69at268354HsCTh/+8xK9ZpOtQbCag2E1dKT/LWpO6xg9NBX38vznKor8qqu2KO6Yq/qijwaU+jmajeQQR5f2aBfv7hJkvSFE8bqornVaa4IwynfbVdt8YEvWgFAOsQTpnp2r+zsDkUzOuyMxhN6enWj/vZOvTr6klPWCzx2XTB7jD46qyI5gAlD9n5Tj+56ZYvWNiSHnpb4HLpswVidPLmU7eg54u5Xt+jBZTvldVr1808frYq81Owqcdgsmlzu22fAL5AKHb0R7djdsgSQktvSzztv98pNw5TVH5S9sE+2gj7ZCntlL+iTNS8owyIlwn2q//nFGRVwDuoVy+TJk/WLX/xCv/rVr/SVr3xFf/jDH3THHXcM+5Pupz/9abW0tOiGG25QY2Oj5syZoyeffPKIws29zajKP2APTtM01RWMqqErpIauoHZ1hdTQuef3QfWG42rqTgahb21t77+f1WKoqsCtscUejS32alKZT5PK/PK5eEEIjLTn1zfrN7vDzU/NqybczHFep1U1RazcBJBZrBZDBR6HCjwOxRPJ15ftvZGM3MZut1p07lFVOmN6uZ5Z06S/L9uplp6w/vD6Vj2wtF4fnVWp82dXsYJskJq7Q/rLm9v13PpmSZLTZtEn51Xrwjlj2BmWYz53fJ3WNHRrXWOPbnlynX5yUWr6cUZiCXX0RVVE+wKkGMOFsIdpmmrpCeuJ13sUrOlR8dyA7MUBGbYDD1d0WW2qrfSrfoTrPJxBreA8//zz9cgjj0hKDv/5+te/rieffFI1NTVau3ZtyotMlT0rOAc7ZKgrGNX2tl5ta+/Ttra+3b/2qu8gL1ar8l2aXO7XpHKfJpf5Na7Uy3YfYBi9uaVNP3p8rRKm9LFZlfrKyeO52p3DXHaLxpf6ZLXwdwwgO4SicXX0RdTRG1U8kZnLOmPxhF7a0KIHlu7oH0bksFl01vRyffzoMfQ6PkK7OoO6f2m9nl/f0v93fdqUMn1+QZ2Kfc40V4fh0twT0r/du1yBcEwXzK7Sl08an5LHtdsMTSn387oWKRMIx7SlpTfdZSBNgpG43m/q0fqmnv5fO3fv4thbImpRrNOT/OjwKtrpUazDox98x6GPnNiTGz04Fy5cqGnTpuknP/mJ/P5k0+PHHntMV199tbZs2ZLyIlNlqAHngZimqdZARNvae7WtrU+bWwLa0Bzob9q+N6vFUF2xR9Mr8jRzTL5mjslnKh6QIivqO/X9R1crGjd12pQy/dsZk9jylcPsNkMTSn20BwGQlUzTVHcwpo6+iHpCsXSXc0AJ09Sbm9t0/9Id2tAckJR8LXvSxBJ9dFalplYQthzI9vY+3f9OvV7a0KI9Gfbs6nx9fsFYTWZYzKjw5pY2/fdjyUU/3z13mo4bV5ySx60scKmEcBwpsrW1N2P//0HqxeIJbWgOaHl9p1bs6NS6xp79LrRaLYbK3B5tXOpXtMWvSKtf8S63+ptv7uXOO6Upk3NkyNDBrFq1SjNnzkzVw6XccAScB/1ewag2NAe0oTmZiG9oCqgzuH8iXlvk0awx+Zo1Jl8zqvLY/gMMwrrGbn3v4VUKRRNaML5Y3zp7Kqv6cpjVYmh8qZetfQByQiSWUFtvWO29ESUOvBMsrUzT1IodXbp/ab3e29HVf/vYYo8+OqtSp04uk9vB8/HmloDue6der21q65/LcExdoT59TI2mVmbGGz+MnLte3qyHV+ySz2nTLz49R2Up6MdpsyZXcVp4jYshCkXj2tAUSHcZGEamaaq+I5gMNOs7tXJn137zZkr9Tk0p92tKuV+TK/yaUOqVzWJN9uBs1v5DhiTJkMrLpEf+KQV7szTgfPDBB1VWVqaPfOQjkqQ//elP+vDdTNPUz3/+cy1btmx4Kk2BkQw4P8w0TbUEwnq/KaDVO7u0aleXtrb17XdcTZFHR43J19zaQh1Vnc8beOAwtrT26tsPvafecFxzagp0w8ems6ovhxmGNL7Uy9ALADknnjDV1htWWyCSsRNtNzT16PFVDXrp/VZF4sk01m23auHUMn10ZoXqir1prnBkmaap9U09uv+dHfv0518wvlgXH1OjiWW+NFaHdIrGE7r+wff0flNAU8r9+vEnZsmWgtenFfkulfpZxYmh2dkZVHsgku4ykGKxeEKrdnXrtU2tenNLu9p79/079jttOqqmQHOqCzSnpkAV+Qe+8NI/RV3aN+TcfW3llluk0xZKgZ4sDThLS0s1adIkvfbaa5Iki8WiiooKOZ0fPLmapqldu3YpEsncfyjpDDgPpCsY1epdXVq1s0srd+4feDqsFs2qztcxdYU6ZmxRyibxAbliV2dQ33rwPXX2RTWtwq8fXDCTiwI5zDCk2mKP8ly09gCQu0zTVEdfVC09YUViGbikU1JPKKpn1zXriZUN2rVXW6YZVXk6Z2aljh9flLV95+MJafkyqbVVKimR5hwtfTiX6gpG9eL7zVqypqn/9bsh6aRJJbr4mJpRF/TiwBq7Q/p/9y5TbySuTxw9Rv964rghP6bNatAeAkMST5ha29Ct1O3jRTpF4wktr+9Mhpqb29UT/qDtgMNq0fSqPM2pKdDs6gKNL/UecQu3556Xbr119zT13crLpW9emww3pSwOOOvr6+V0OlVWViZJ+vjHP66HHnpov+Nuuukmfec730l9lSmSaQHnh3UHo1rd0K3l9Z16Z2u7mnv2nWpWU+jWvLoiHTu2UNMr8w54FfBIXpQBuaC5J6TrH1yplp6wxpd4ddPHZ8nnZFVfriLcBDAadfVF1RIIZ+T0dSnZp/O9HV16fGWD3tzS1t9z0mW36NixRTpxQonm1RVmzcXHA72hKyuXrr1WOuUUU8vqO/TMmia9uaVdsd1/WLvV0MmTSvXJedWqLvSkqXJkqtc3tepHT6yTJN3wsek6dmzRkB+zqsDFoCoMWktPWI0HmBeC7BGKxvXu9g69tqlNb29t32fodb7bruPHFWnBhBLNGpMvh23wYdDhsqWsDTg/rLu7+4B/gPr6etXU1KSksOGQ6QHn3kzT1Pb2Pr2zrUPvbG3XmoZu7d0D1ue0acH4Yn1kYomOqs6XzWo55IuyPSk7kAs6+iL69oMrtbMzqDEFbv34E7PoX5vDDEMaW+IlwAYwavWEomrqDikYycwVnZLUFgjrqdWNemZds1r2ukjvtFl0zNginTihWMeOLcrYsLN/S96H3hlZ84JyT2xW5dwmBWIf7FSbWOrTGdPLdcqkUvlc/P+Eg7vzpU169L0G5blsuuNf5g15yCwT1TEU6xt7MnZ3AA7ONE1taA7o6dWNemlD6z79NIu8Dp0wvlgnTCjW9Kr8EZtFkbUB58svv7xfz80PM01TN954o1544YVU1ZZy2RRwflggHNOy7R16Z2uHlm7vUNdeA4v8Lpvq3MV68W+lijTkS+ZeJ/SH+iQA2ebDV44mTovpe4+s1JbWXpX6nfrJJ46iF1EOs1ikcSX03AQAKbmis6knpHA0c9+c7nkT9urGVr2ysXWfHUkOm0Xzagt1/PhizajKU5nfmREhTTyh5FCFJknWuBzl3XJWdspZ2Sl7SW//cX6nTQunlumMaWUaV0J/TRyZaDyhb9y3XFvb+vSRiSX61tlTh/yYYwrdKvJycR8D0xWMavsB5oAgc/WEonp+fYuWrGncp6Vhmd+pEyYU68QJJZpc4T/ireeplLUBp8VyZMtaDcNQPJ6ZW2ik7A449xZPmFqzq0svb2zVa5va9gk740G7QtuKFdpaokjT7rBzr0lXbFdHNvnwqmTDFlf5uatkFPaowGPXTz5xlKoK3OktEsPGajE0rsTLdF4A2MueHp1N3aGMHUa0h2ma2tTSq1c2turVja1q7N53W2SR16FplXmaXunXtIo8jSvxpmQQy0DEE6YefSmg//ljpxyVnXKUdcuwfvBzNRNSeFehghvKddv1RTruWF5MY+A2Ngf0zfuXK2FK1589VSdOLBnS4zlsFk0u92XEBQJkj80tAfWGMzevQVLCNLVyZ5eeXt2k1ze3Krr7/3qH1aITJhbrrOkVmlmVl/Z//5kYcB7RkpgTTzxRTz31lDyeg/eVMU1TV199dcoKw8FZLYZmVRdoVnWBrjh5gh58oUu/fqhV7rpWWd1Reac2yju1UfFeh/o2liu4sUxNTW4tXybNm5fu6oEjs99WMWtChaetlVHYo0TYpvMmzCTczGE2azLczNStjACQLoZhqMjrUIHbrtbesFp6wkpk6IJOwzA0scyniWU+XbagTptbe/Xqxlat2NGpTS29au+N6NXd4aeU3M4+pdyvaZV5qinyqMTnUKnfqWKvc8hb7uIJUy2BZO+5hq6gGrtCqu/o0+pd3eqLxOWfu9exvQ6FdxUo0lCgcEOBEsHkSrnO9oM8OHAYE8t8+tS8Gv3tnXr9+sVNmjkmf0hb1SOxhLqCUVo04YiFonHCzQzXF4npqdWNenxl4z4XBMeVeLVoerlOmVxGS5TDOKKfzu23337IcFNKvoD5xCc+kZKicOSsFkN50QJ1v16g7jfGy1HRJfe4Vrnq2mT1RuSfXS//7HqFG/L1yuYyzTiqhMAAGS+eSK7c7A83jYQKT14nZ1WnElGL2p+ZoT8859VFi1iVnIvstmS4ma0TeAFgJFgshsr8LhV7nWrpCas1EM7oqbiGYWhCqU8TSpNbu0PRuDY0B7S2oTv50dit3nBc7+3s0ns7u/a5r8WQirxOlfqdKvUlfy30JMMh00yudjG1+1czufAiYSa3YzZ0hdTYFVRTT1jxxIF/QC6rVR2bPgg0490u9fd52kvJ0BbdYZT79LE1enNLm7a29ek3L24a8lb15p4wASeOWGsgfPiDkBYdfRH9c8UuPb6qoT+EdtutOnVKqc6aXqEJpd60r9bMFoMaMiRJS5YsUWNjY39vzkgkoh/96EfavHlzSgtMpVzZov5hS5dKV1zxoRutCblq2uSe1CRnVaf2/Htw2606eVKJzpheTnNqZKx9zmnDVMHJ6+Ue1yozbqh9yQxFGgskSXfeyarkXOO0WzS22DukiX8AMBpFYgk1doX2aV2UTRKmqfr2Pq1t6NH6pm41doXUGoioNRDun1g+VDaLoYp8lyryXKrMd6ki362pFX6NLfbpwgsMNTdrvyFDkmj3hJRJ9Vb12iKP8j1DG1qE3BeLJ7SusSejL4KNRg1dQT20bKeeWdvUvw19TIFbHz96jE6ZXJrxC9Oydov6h11++eW6//775XA4ZLfb5XA41N3drcsvvzzV9eEIzDk6OS19nxdlcYtCW0sV2loqizes0tnNqjymSU3dIT21pklPrWlSTaFbH51VqdOmljHAAxmltXX3bwxTBSe93x9udjw/rT/c3Oc45ASX3ZKW/msAkAscNotqiz0KhGNq6AwqlMGDiA7EYhiqK/aqrtirs2dW9N+eME119kXV0hNWSyCs1t2/dvZFZRiSYUgWGclfDWP3bYYMJQdxVuS7VJmXDDOLfY6DDmK49trdrXEM7Rty7j78m9cSbmYSq8WQzWooGk9kbIuGA0n1VvWWQIiAE4fV3hch3MwgG5sD+vu7O/TaplbtuX43pdyvi+aO0XHji9MyMChXDCrVam5uVmtrq7Zv366XXnpJX/jCF9Tc3Kz7778/1fXhCFgth35Rluhz6psfr9Gpp1Zr9a5uPbO2Sa9ubFV9R1B3vrRZf3x9m06fVqZzZ1WquvDQrQiAkVBSIskwlf+R9+Ue3yIzYajjhakK7yja/zjkhAKPXWMK3LIMsccaAIx2PqdNE8t8au+NqKn74Nuys4Vld8/RIq9DU+Qftu9z2kLpllv2HW4oJVdufvPa5NcxMgxD8jpt8jisslkM2SwWWa3G7t8b+10IjcQSCsXiCkXjCkcTCsfiCkUTGRvopHKrejCSUHcoqjwXIScOzDRNtfdG0l0GJK1v7NGf39ym5fWd/bfNqyvURXOrM2JoUC4Y1Bb166+/XjfddJOsVqu+8pWv6Je//KUcDofOPfdcPfbYY8NRZ0rk6hb1PT48cVqSyssP/KKsLxLT8+ua9ejKBu3oCPbffnRNgT52VJXm1RUOuZk7MFjRuKkLv7NBlupmmQklw83te6WZbBXLGYYhVRW4VeSlhxQApFo8YaqpO6T2XlbvHKl4Qlq+LLlLpKQkuVOK1xrDz2Y15HfZ5HfZ5XfaUnLBMxSNKxiJq603rGAks5Z5pnKrutth1cQyXwqrQy7p6otqe3tfussY1XZ1BvXHN7b1D9SzGNLJk0r1ibnVGlfiTXN1g5czW9TD4bBcLpdefvllzZ07VzNmzNCECRO0atWqVNeHAThtoXTKKUf2oszjsOnco6r00VmVWl7fqUffa9DbW9u1rL5Ty+o7VZHn0kdnVejMaRVM6sKISpimfv3ixv5ws/Ol/cNNia1iucBpt6i2yJPx/WUAIFtZLUb/RaSGrpACoVi6S8p4Vgv9vUeK22FRnssuv8sutyP1rwVcdqtcdqsKvQ4FwjG19oTVkyH/BlK5VT0YiSsQjsnn5D0b9tfay3ChdOnsi+hvb9fridWNiidMGZJOn1amzxxbq/I8V7rLy0mDeha85ZZbdMIJJ2j27Nk67rjjtGvXLi1fvlyLFy9OcXkYqIG+KDMMQ0fXFuro2kI1dof0+MoGLVnTpMbukH7/6lbd81a9Fs0o1/mzx6jU7xy+wgElw807XtikJWuaZDGks2un6J/BEoX2OoatYrmBLekAMHJcdqvGlXjV1RdVQ3dQ0RjLOZEeFotU7HWqyOsY0YGCPqdNPqdN4VhcrYGIOjJgVXMqt6o3d4fkK2UVJ/YVjMTVt3sqN0ZOKBrXwyt26e9LdygYTf78j6kr1GULxmpsFq/YzAaDnqJ+IC+88IJOPfXUVD1cyuX6FvVUCUXjevH9Fv1zxS5t272c3WoxdMrkUl00t1q1RfTpROqZpqlfv7hJT6xqlMWQ/v2MyTp1ShlbxXIMW9IBIL0SCVMtgbBaesJpD3gwethtRn+wmQltsGLxhNr7ImoLRBSLp+8fQiq3qo8v9crLKk7spb69T5190XSXMWrEE6aeWdukv765Xe19yb6nE0t9+sKJYzW7uiC9xQ2DTNyiPqiAs6+vT/fdd5927NihxO6xdaZp6i9/+Yvef//9lBeZKgScA2OappZu79CD7+7Uyp1d/bfPH1ukT8wdoxlV+WmsDrnENE3938ub9eh7DTIk/b8zJum0qeXpLgspxpZ0AMgc4VhcDZ2hjNmyi9zkdlhU4nMq323PyAEapmmquSe9gf+f39imv71Tr3y3Xb+6dO6gt6r7XLas7ueH1IrFE1rX2MOFrBGyrqFbv3x+Y/8CsfI8pz53/FidNKkkZ6eiZ2LAOahLPOeee67Wrl2rqVOn7vMfVXNzc8oKQ/oZhqFj6op0TF2R1jf26O/v7tAbm9v01tZ2vbW1XdMq/LpoXrWOHVuUs/9oMfxM09Rdr2zRo+81SJK+fhrhZq4xDKnI61B5nisjVm0AACSnzaqxJV51h6Jq6AwpEsusISzIbn6XTSV+Z8b3hTQMQ+V5LvmcNtV39KWlfcPeW9XvenmzvnnWlEE9TiAUUzASH5Z+psg+DJcbGb3hmP74xjY9sbJBpiS/06ZPH1ujj86qlJ1thyNuUCs4x48fr5UrV8rr3fcK0eLFi/WFL3whVbWlHCs4h25HR5/+sWynnl3XrFgieeqMLfbo08fW6oQJxQSdGJB4wtSvXtioJWuaJElXL5yoRTMq0lwVUqnAY1dZnlNOGy+2ASBTJRKmWgNhNbNtHUPkc9lUkefKypAtnjC1qzOYli297zf16Nr7V8iU9N8Xzhz0dtY8t011xaziHO1M09S6xp60tl8YDV7f1KrfvLRZ7b3J7ehnTCvTv54wTnmDXIWdbXJmBedJJ50kh2P//mnz588fckHIbNWFHl192iRdelydHlmxS4+vbNDWtj795Ml1qi3y6DPH1uiECSWs0sJhReMJ3bbkfb26sVUWQ7rmtEk6YxorN3NFNr/BAYDRxmIxVJbnUoHHocaukLqC9GzDwLgdFlXkuzN+xeahWC2Gaoo88rsi2tkZVGIEFzVPLvfr3FmVenRlg379wibdfsnRg1r91R2MKRSN0w5olOsKRgk3h1FrIKzfvLhJb25plyRV5rt01cKJOdlnM9sMagXnli1b9MMf/nCfgUKmaeqOO+7Qm2++mcr6UooVnKkXCMX08Iqd+ueKXeqNJCeE1RS6dfExNTppUilBJw4oFI3rx0+u09JtHbJZDF171pQhNVVH5siFNzgAMNoFwjE1dAYVirJtHYfmsFlUkedSvie3VixFYgnVd/SN6ATq3nBMV/5lqTr6orp0fq0umV87qMcp8NhVw1DYUW1jc0DBCNPTUy2eMPXEqgb98fVtCkbjsloMXTS3WhcfUz0qd6tl4grOQQWc5513np5++mlVVlbu04Nz586dikQiKS0wlQg4h08gHNM/V+zSwyt2qnf3C4ExBW59+tganUzQib30hmP64WNrtHpXtxw2i75zzjTNrStMd1kYolx9gwMAo1lbIKym7rDiCVYCYV82q6Eyf3IqeiYOD0oF0zTV0jOyrRte3tCiW55aL7vV0C8vmauqAveAH8MwkitCHTb6/41GfZGYNjX3pruMnLO9vU//++wGrW/qkSRNrfDr6oUTR3VLiJwJOMeMGaO3335bVVVV+9z+s5/9TN/4xjdSVlyqEXAOv95wTI+ubNDDy3aqJ5ycylmV79Il82tZ0Ql1BaO68ZFV2tTSK4/Dqhs+Nl0zqvLTXRYGyWY1lOe2K99tZ8UmAOSoeMJUU3eIgRWQlAzPyvxOlficsoyS1/W94Zi2tvWOyJZ10zR14yOrtay+U3NqCvSD82cMKkAu8TtUmT/wcBTZr769Ly19ZHOVaZp6YlWjfvfKFkXiCXkcVl22YKzOnlkx6ueP5EzAeckll+iee+7Z7/YtW7Zo3LhxKSlsOBBwjpy+SEyPvdegh5bvVE8oGXTWFnn02eNqdfz44py90ouDawuE9b2HV6m+I6g8l00/uGCmJpT60l1WyhmG5LRZ+l/0G/t8bd/zPp4wFU+YisYTWfOm0WY1lL871PQSagLAqBGKxtXQFVJg9+s6jD6FXrvK81yjcjJwKBrX5pbeEVnNvKszqKvveVfRuKn/OGuKTp5cOuDHMIzkCjPbKPy7Gs2i8YTWN/ZkzfuKTNcdjOr25zfojc3JXptzawv19dMmqtjnTHNlmSFnAs7Fixdr/fr1Ouecc/pvM01TN9xwg1588cWUFphKBJwjry8S0z/fa9BD7+7o79E5sdSnzx5fp7m1BQSdo0RDV1Df/ccqNfeEVex16IcXzlRNYfb2BjIMyW61yGmzyGHb+1froLcDJRKmYrsDz1gioVg8+Xk0nuj/iMTMEd8maBjJ7ec+p41QEwCgnlBUTd0hBSP05xwtvE6rqgrco35wTSga19a2XkVjw/9a7N63t+svb25XoceuO/5l3qB2ypTnOVWW5xqG6pCpGrtCaukJp7uMnPDejk7dtuR9tfdGZLMY+sIJY3Xe7KpRv2pzbzkTcFosB34DbxiG4vHMbWZLwJk+gVBMDy3fqUdW7OxvWD+9Mk+fPb5Os8awRTmXbWnt1X89slrtfRFV5rv0wwtmqjwLX2xZLYb8LpvyPXb5nba0hfOJhKloIqFo3FQ0llA0kUiGovE9AWnya/GEOaCrt3abIYfVIqfduvtXS/JXm4ULEQCA/XT1RdXUE1KYQUQ5y2m3qCLfpTwX/bX3iMQS2tLaq0hseM/7aDyha+5Zpp2dQZ07q1JfPWXCgB/DajE0tcI/aloJjHaJhKl1jT30TB6iWDyhv761XQ8s3SFTybki/7FoSk7uPByqnAk4zznnHD3yyCOy2z/4z840TX3961/X7bffntICU4mAM/26glE9sHSHHl/ZoEg8+cJgTk2BPntcnaZU+NNcHVLtrS1t+unT6xWKJlRX5NEPL5ipQq8j3WUdMavFUL7HrjyXTb40hpqDtWc16B6G9q1/zx/Hahi8+AUADJhpmuroS67ojMV5U50rrBZDZXlOFefwAKGhiMaTIedwh/srdnTqu/9YJUPSrZ+arcnlA3+vVFngUgnbaUeF9t6IdnYE011GVmvoCurWp9fr/aaAJGnR9HJ9+aTxo371+sHkTMD5yiuvyOl06thjjx2OmoYNAWfmaAuEdd/SHXp6daNiu68yzR9bpH85rlbjuTqS9UzT1EPLdmrxa1tlSjqqOl/Xnz1V/ixYAbCnx2Qeg3MAADgiiYSp1t6wWnrCIzKIBcPDMKRin0NlfheDQQ8jFk9oa1vvsLdquG3Jer2wvkXjS7362afmDPjvxW4zNKXcT1A9Cmxo6unfKYmBe359s379wiYFo3F5nVZds3CSTpxYku6yMlrOBJz5+fmqq6vTe++9Nxw1DRsCzszT1B3SvW9v13PrmrVnNf1HJpbo0uNqs7pH42gWjSf06xc2acnaJknSohkV+urJ4zO+ybnFIpX6nSrxjp6poAAApFI8Yao1EFZrgKAzmxiGVOCxq8zvGnQv8dEonjC1ta1XfeHha9HW0RfRlX9Zqt5wXJefNE7nzx4z4MeoKXKrwJM9O6gwcIFwTFtaetNdRlaKxRP67Stb9PjKBknSjKo8ffPMKSr1s/L5cDIx4BzU/2Bf+9rX9Pbbb+93++9+97shF4TRpTzPpX87fbJ+delcnTwpeYXklY2tuvqv7+p/nnlfjV2hNFeIgegORnXDw6u0ZG2TLIZ0+UnjdNWpEzI63DSMZLA5tSJPZX4X4SYAAINktRgqz3NpakWeKvJdsln5PzXT5bvtmlTuU3Whh3BzgKwWQ+OKvfK5hm/HT6HHocsWjJUk/fmN7WoLDHyADENncl8rf8eD0hWM6rsPr9LjKxtkSLp0fq1uunAW4WYWG9QKziuvvFI7d+7UMccc03+baZr685//rA0bNqS0wFRiBWfm29Laq7+8uU1vbmmXlHzhcNb0cl18TA39YzJcfUeffvjoGjV0heS2W3Xd2VN0TF1Russ6KMOQCr0OlfmdsmdwAAsAQLba06OzNRBmGFGG8blsqshzye2gt9xQmaapbW196gnFhuXxE6ap6x54T+ubenTihGJdf860AT9GXYmHYVE5KhyL6/3GQLrLyDqbWwL678fXqqUnLLfdqmvPmqL54zL3vWsmysQVnIOeon7sscfK4/lgC7Fpmlq+fLk6OztTWV9KEXBmj/ebevTnN7ZpWX2nJMluNfTRmZX65LxqtlhkoHe3d+iWJ9epNxJXmd+pGz42XXXF3nSXdVAFHrvK8pxy2nhRDwDASOgKRtXSE1YwMnzbeXF4bodVFfku+oynWCJhanNrYNh6cm5pDej//W25Eqb0/fNmaG5d4YDu73FamQKdo3Z1BtUWiKS7jKzy8oYW/fzZDYrEEqrKd+m7505XTRHt8QYqEwPOQf3PdtVVVx1wWvrixYuHWg8gSZpc7tcPLpipVTu79Oc3t2n1rm49vGKXnlrTqPOOqtInjq4e1u0gODKmaeqxlQ367cublTClaZV5+s5HpynfnZlXiN0Oi8YUeFitAADACMt325XvtisQjqktEFZPKKaBL7PAYPlcNpX4HFkx8DEbWSyGxhZ7tamlV5FY6kPOcSU+feyoKj2yYpf+7+XNur366AHtQOoLx9UbjslLsJ1T4glTHX2Em0cqnjD15ze26YF3d0iS5tYW6j8WTeGCTw4Z1ArOPerr61VfX69JkyaptLQ0lXUNC1ZwZifTNLWsvlN/fmObNjQnl997HVZdePQYnT+7Sh4HT0jp0BOK6pfPb9Rrm9okSadNKdPVp03M2O3exT6HKvNdTJEEACADROMJdfZF1dEXYfv6MDGMZLBc6nfKZefi7kgIx+La1NyreCL16X1vOKav/nmpOoNR/esJY/WJudUDur/fZdPYkszdYYWBaw2E1dDJzIoj0RuO6dan1+udbR2SpIvmjtHnjh8rK/MXBi0TV3AOKuDs6enRpZdeqsceeyz5IIahT3/607rzzjvl9/tTXmSqEHBmN9M09eaWdv3lzW3a2tYnKfkf9SfnVuvcoyrZbjyC3tvRqZ8teV9tvRFZLYYuW1CnC+eMycjw0GKRqgs8yvewYgEAgEzUG46pvTeirmCUVZ0pYLFIxV6nin2OjL3wnMv6IjFtbukdlnP5mbVN+sWzG+S2W/Xrf5mr4gHOKJhU7iPsziHrG3uGZcVwrtnR0af/fmytdnYG5bBadM1pE3XqlLJ0l5X1cibgvPzyy+X1evXJT35S5eXlamxs1H333adAIKC77757OOpMCQLO3JAwTb2yoVV/fWu7dnYGJUlFHoc+dUy1zppewQTIYRSNJ/TXN7fr7+/ukClpTIFb1541RRPLMrOnj9thVU2Rm/AbAIAsEE+Y6gpG1d4boVfnINhthoq9ThV5HaxKSrOuYFTbdy/ISKW9Bw6dMrlU1541ZUD3L/DY6TWYI4brHMs1q3d16YePrVFvOK4Sn0Pf+ej0jH3vmm0yMeAc1N7evLw83Xbbbf2fT5o0SSeddJKuvPLKlBUGHIzFMHTy5FKdOLFEz69r1j1vb1dzT1h3vrRZ971Tr08cXa2zZ1ZwdTLFdnUG9dOn12vj7jYBZ00v1+Unjc/YnzNb0gEAyC5Wi6Eir0NFXocisYR6QlF1h2LqDdOv82AsFinPZVeBxy6f08brngyR77arqsClXSnePmwxDH31lAn6xn3L9eL7LTp7RoVmjsk/4vt3BaMqjyVYEJID2gLhdJeQ8V7f3KZbn1qvSDyhKeV+fefcaSpkYHFOG1TAabEc+AnR7XYPqRhgIKwWQ2dML9cpU0q1ZE2T7l+6Q62BsH736hbdv7ReF84Zo3OPqqRH5xCZpqln1jbp/17erFA0IZ/TpqsXTtSJE0vSXdoBWSxSdaEnYwcdAQCAw3PYLCr2OVXscyqeMBUIxdQdiqo7FFVilO/INIxkm6YCt0N+l00WVmtmpGKfU9G4qZae1AZRE8t8OmtGhZ5a3ag7X9qkn3/66CNesWuayb6NVQW8b89moWhcvWFWuR/KE6sa9JsXNylhSvPHFuk/Fk3J2IU5SJ1BJT+hUEhXXnmlLr30UhUXF6upqUl/+ctf5HQOrAcIkAp2q0UfnVWpM6eX6/n1zXpg6Q41dIX0xze26e/Lduj8o6p03uwqpkYOQk8oql89v1Gv7h4kdNSYfP37mZNVMsB+PyPF7bCqtsjDVWkAAHKI1WIo32NXvscu0zTVG4mrJxRVbziuUDQ+alZ3epxWFXocynPZZKO3ZlaoyHf1D9RKpc8dX6dXN7Zqa1ufnlzVoHOPqjri+7b3RlTmd3IOZbFWVm8elGma+utb23Xv2/WSkrsOv3bqRNp2jBKD6sHZ19enL37xi7rvvvv6t0F86lOf0l133SWfL3P7GdCDc3SIJ0y9vKFF971Tr/qOZI9Ot92qj86q1AWzq1ToZVn64cQTpp5a3ag/v7lNPaGYrBZDnz2uTh8/ekzG/ufgc9lUV+RhFQMAAKNIImGqLxpXXzimvkhcvZFYzqzwdNot8jpt8jls8jitDAzKUqZpaktrb8pX3D22MrlCzeu06s7PHjOg3UtleU6V57lSWg9GRiye0LrGnlFzYWcg4glTd7ywUU+vaZIkfebYGl06v5bWHcMkE3twDirg3KOlpUWbN29WZWWlamtrU1nXsCDgHF0SpqnXN7Xpb+/Ua0trryTJZjF08qRSnTe7iubCB7G8vlN3vbxZ29qTTatrijz699MnaVK5P82VHVye26baIg//eQEAgN3bN2MKRuMKxxIKRxOKJzI/DXDZLfLsDjS9Tisr7HJIPGFqU0tA4Wjq0vd4wtS/37dcW1p7tWh6ua4+bdIR39dqMTS1ws/CgCzU3B1SUzcrOD8sFI3rp0+t11tb22UxpK+eMkHnzKxMd1k5LecCzg+75pprdPvtt6fq4VKOgHN0Mk1Tb2/t0ANL67W2saf/9hlVebpgdpXmjyvO2FWJI2lXZ1C/f3WL3tzSLknyO236l+NqdfbMyoz++RR47KoudBNuAgCAg4rFEwrFEgrvCT1jCYVjccXi5oiuhDKMZHslp80ih+2DX912As1cF4rGtaklkNIVxqt3den6B1fKkHTbp2YPaEFCRb5Lpf7MbDuFAzNNU+saexSLZ/4Fm5HUHYzqh4+t0brGHtmthv7jrClaMCEz50XkkpwJOO+55x59+9vfVn19/X5fi8czt9ktASfeb+rRIyt26ZWNrf1X8sv8Tp13VJXOnF4ur3P0DSTqDcf0t3fq9c8VuxRLmLIY0rmzKnXJ/NqM71ta6LWrutCT7jIAAEAWi8UTiiVMReMJxeKmoonkis9Y3FQsYco0TZlKhgumqd2/l0yZSiSSAw6thiHDMGS1GLIaRvK2/t8bH4SZVgsXZUexrmBU29v6UvqYty1ZrxfWt2hKuV+3fPIoWY7w/LJZk6s4OR+zR3tvRDt3t2AbreIJafkyqbVVKimRxkwM6/uPrtKOjqC8Tqu+d+50zajKT3eZo0LOBJxlZWX6xje+oeOPP77/CdE0Td1yyy16/PHHU15kqhBwYo+2QFiPrWzQk6sb1ROKSUr26TxtapnOmFauCaXenP/PPhpP6Nm1zfrzm9vUFUw2Pp9bW6gvf2ScaooyPzQs9jmYAAkAAICs0tgVSulk9fbeiL7656UKRuP6t9Mm6Yzp5Ud83zGFbhUxnyBrvN/Uk9I2B9nmueelW2+VmpMtNmX1hVT60ZUyPGEVex36/vkzVFfsTW+Ro0jOBJynnnqqXnjhhf1u37Ztm+rq6lJR17Ag4MSHhaJxvfh+ix5esUv17R9cTa0t8ui0qWU6dXKpig8yMfzDV4/mHC1lw86itkBYT65u1JOrG/snOlYXuvWlj4zTMXVFaa7uyJT6narIpzE6AAAAss/W1t7+RRap8OC7O3T3a1tV4Lbr15+dJ98R7kpz2i2anMF99vGB7lBU21pTu/o3mzz3vHTddUouoZdk9QdVvGiVrL6wYt0uXTFvpi5cxPvDkZQzAeef/vQnTZ06Vccee+w+t3/nO9/RTTfdlLLiUo2AEwdjmqaW13fq6TVNenNLm6K7+5oYkmbXFGjhlDItGF8st8Mqaf+rR5JUVi5de6102sI0/AEOwzRNrWno1qPvNej1zW392/OLPA5dNG+MPjqzMmv6PpXnOVXG1EcAAABkqXjC1MbmgCKx1KzGi8YTuuaeZdrZGdT5s6t0+Unjj/i+tUUe5Xsyuy0VpM0tAfWGM7cd4HCKJ6Tzzttr5WZeXzLc9EYU63Kr7amZKvU79cg/s2PBUa7ImYBz/Pjxam1tVUnJB41bTdNUfX29YrHUXYlKNQJOHIlAOKZXN7bq+fXNWr2ru/92l92iBeOLlRcs1Z0358uMWve94+4d7bfckjkh554Vqo++t0tb9+r3M6MqT+fOqtSC8cVZE2xKNEMHAABAbghF49rYHEjZkKt3t3foxkdWy2JI//uZo494q67bYdXEMl9qisCwCEaS58potXSpdMUVyd/b8vtUtGilrJ6ooh0etT81U4lQss3CnXdK8+alsdBRJhMDzkFPVLn11lvldH4QNCQSCf32t79NSVFAOvmcNi2aUaFFMyrU2BXS8+ub9fz6ZjV0hfT8+hZJLSr/jKFIc57CuwoU3lmoWLtXMg3JkG67VTrllPRdPQqEY1q5s0vLtnfopQ0t/Vf6HDaLFk4u1blHVWpcSfa9iCnPdxJuAgAAICe47FbVFHq0vT01247n1hZqwfhivb65TXe+tFk3XTjziGYKBCNxBcKxI97WjpHXGkhdz9Zs1Nqa/NVW0KuiRatkdUcVbfeo/alZSoTt+x2H0WtQz2J/+MMfdNJJJ+13+/z58yVJW7Zs0bhx44ZWGZABKvJdumR+rT5zbI3WN/bob680641NHbL5wnJWdslZ2SXN26Z4yKbIrkKFdxWoZVeBli9zjtjVo2g8oXWNPVpe36kV9Z3a0NyjxF5XgivyXDp3VqXOmFYunys7X7gUeu0q87MtHQAAALkj32NXadSZsqFDX/rIOC3d1qGVO7v0ysZWnTSp9Iju19ITJuDMUJFYon8g7GhVUiLZigIqPmuVLK6Yom1etT09U2bYvt9xGN0G9Sx2oHBTkmbMmCFJ+uY3v6kHH3xw8FUBGcYwDE2tzNPxeXn65wOmrP6QnGM65KzqlKOyS1ZXTO7xLXKPb5Ek/eQtuyZsc6umyKPqQo9qCt2qLfKoyOsY9HR20zTVG4mrtSes1kBY9R19Wl7fpdW7uhT+UP+eMQVuzakp0LFji3R0bYEsWTwR3ueyaQzT0gEAAJCDKvJdCkbjCqRg6FB5nkufnFetv761Xb9/dYuOqSvqnyFwKIFQTMFI/IiOxchqDYRT1sYgW/nG9Kjk7NUyHDFFWnxqXzJTZmSvKMuQysuSQ38xug3LZZpBtPUEskLyqpCheI9bfevc6ltXJRkJ2Ut75BzTKWdVh+wlAQXjUa3aFdWqvXp4SpLHYVV1oVtlfpccNoscVoscNovsVkN2a/Jzu80iu8VQdyim1kBYLbsDzdZARMHogRtLF7jtml1ToDnVBZpdU5AzW7lddotqizyDDoUBAACATFdb5EnZ0KFPzB2jZ9Y2qbknrPuX1uvzC8Ye0f1aesKqLfYM+fsjdeIJU+29kXSXkVbrG3t04yOrZDjiijT71f7MjP3CTUn65rUMGMIwBZyEEchVc45OTktvbpa0J8c3LYo25yvanK/A8jqVV8Z12/8FtaurT/Xtfarv6FN9e1ANXUH1ReJ6vymg95sG3yTa77SpxO9Umd+pmVX5ml1ToLpiT1av0jwQu83Q2BKvrJbc+nMBAAAAe7NaDNUVe1IydMhps+rLJ43Xjx5fq4eW7dTCKeVq3uJWa2tyscacow8cBHUFowpF43LZWcWZKdp6R/fqzfebevS9h1cpGI1remWeFk6drv9dauufpi4lV25+89rMGfKL9KLRBjAAVot07bXSddcpebVo7/9w9lw9+nerplT4NKVi30E+0XhCuzqD2tERVFtvRLF4QpF4QpFYQtF4QpG4qWjsg9v8LptKfE6V+pwq8TtV4nOoxOccFS86LBZpbLFXdi7DAQAAYBRw2a2qKnBrZ0dwyI91/Lgiza0t0LvbO3Xlzzer8fEZ/V8rK0++nzlQINQaCKu6kFWcmcA0TbUFRu/qzS2tvbrxkdUKRuOaWZWnG8+bIZfdqjNPk5Yv02EDe4xOBJzAAJ22ULrlFunWWzWgq0d2q0V1xV7VFXtHptAsZRjJbTqjIcgFAAAA9ijyOtQbjqmzb2hDZQzD0CzHeC2NL5NR1iFndbvCO4okJXeiXXdd8v3Mh9+3dPZFVZ6XYJFBBujsiyoWH53LN+s7+vS9h1cpEI5paoVfN3xsRv97Q6tFIzbMF9mHgBMYhNMWSqecwtWj4VBV4JbfZT/8gQAAAECOGVPgVigaVyg6+H6c8YT0+9s9ClZXyTdrp/Lmb1ZLQ4EUtyR3oBnSbbcm38/s/f7FNJOrOCvzGfCZbq2BcLpLSIvG7pC+949V6gpGNb7EqxvPm8HwKxwx4hhgkPZcPVq0KPkr4ebQlfqdKvI60l0GAAAAkBYWi6GaIo+G0l5/+bLkTrPAihrFex2y5YXkm7HzgwNMqakpedyHtQWSrbSQPj2h6JAC7mzVGgjru/9YqbbeiGqKPPrBBTPlc7ImD0duWCIZpqgDGKgCj10V+a50lwEAAACklctuVXXh4FdRtrYmfzVjNnW/M1aS5DuqXhZv6IDH7c00pbZRPrk73VpHYe/Nzr6IvvuPVWrqDqsy36Ufnj9D+W529WFghiXgnDJlynA8LIAc5XZYhvQiDgAAAMglBR6HinyD29lUUvLB70NbShVuzJNhSyjvmK0HPW5vrYGw4gkWLaVDKBpXIBRLdxkjqicU1fceXqWdnUGV+Jz67wtmqtjnTHdZyEIDDjjXrFmj66+/Xueff74++clP6nvf+562bdu2zzE//vGPdd9996WsSAC5y2LR7m04Q9iHAwAAAOSYqnyX3I6Br0mac3RyWroMSTLU/eZ4mQnJPa5VjspOyZDKy5PHHUgiMXp7QKZbS8/o+rn3RWL6r3+u1ta2PhV47Lrpwpkqy2NXHwZnQM+WP/nJT3TUUUfplltu0SuvvKIXX3xRN910kyZPnqzf/OY3+xx7//33p7RQALmputAjp43G0QAAAMDeDMNQbZFXlgFmnFaLdO21ex5EinX41Le+UpKUd9wmyUjom9ceeoYAqzhHXiSWUFcwmu4yRkwoGtcPHl2j95sC8rts+u8LZqqqgF19GLwjfqp86KGHtHjxYt1///3q7u5We3u7Wlpa1NfXp7vvvlu33367nnvuOUnSCy+8oPfff3/YigaQG0r8DnqrAAAAAAfhsFlUU+QZ8P1OWyjdcotUVpb8vGdZneIhm+wFQX3mPxp02sJD3z+RkNpYxTmiWgJhjZZxJrF4Qjc/sU6rd3XL47DqB+fPVF2xN91lIcsd8Uiqu+++Wy+99JJKS0v3ud3lcunSSy/VokWLdN555yk/P1/PPPOMrrzyypQXCyB3eJxWVbD9AAAAADikPJddpX7ngLcvn7ZQOuWU5LT01labtsXH6p+bN+qt9u3q6C1VoffQPT5bAmEV+5yyWmglNdyi8YQ6Rslwp4Rp6hfPbtC72zvktFl043kzNLHMl+6ykAOOeAVnSUnJfuHm3oqLizVlyhS98cYbuuKKK3TzzTenpEAAucdqMVRL300AAADgiJTnOeVxDrytk9UizZsnLVokffmcck0s8ykYjWvx61sPe19WcY6clp7RsXrTNE39/pUteuH9Flkthq4/Z6qmV+aluyzkiCMOOOPx+OEfzGJRR0eHfvnLX8rrZXkxgAOrKXLLfqimPwAAAAD6Jftxeoa0mtJiGPrqyRMkSc+ta9bahu7D3qc1EFGCXpzDKhpPqH2UrN78+7s79fCKXZKkfzt9ko6pK0pzRcglR5wwxGIxvf322wf9+vLly9XW1paSogDkrvI8p/wu+m4CAAAAA2G3WlRbPPB+nHubUuHXmdPKJUl3vrTpsIOE4glTrb2s4hxOraOk9+Yza5r0h90rh7904jgtnFKW3oKQc464B+e3v/1tnXrqqbr44ot14YUXqrq6WoZhaMeOHfrHP/6he+65R08++eRw1gogy/lcNpXRdxMAAAAYFJ/TpvI8p5q6Bx86fn5BnV7b1KpNLb16ek2jzplZecjjW3siKvE6ZaEXZ8rF4gm1BXJ/9eZbW9p0+/MbJEmfOHqMLjx6TJorQi464oBz5syZeuCBB3TZZZfpzjvv7L/dNE1VVFTor3/9q+bPnz8sRQLIfnaboZpCd7rLAAAAALJaWZ5LvZG4AqHYoO5f4HHo0uPq9NuXN+tPr2/TiRNKlOc++A6reMJUW29EpX7nYEvGQYyGyelrGrr1kyfXK2FKp00t0xdOGJvukpCjjjjglKRTTz1VGzZs0JIlS7R27VqZpqlp06bpzDPPlNPJkx2AAzMMqabQIxt9NwEAAIAhqyl0a2NLQNHY4NKxc2dV6unVjdrW3qc/v7lNXzt14iGPb+kJq9jrYBVnCo2G1Zvb2nr1g0dXKxJP6Ji6Ql2zcCKDZjFsDNPM9esFH+ju7lZ+fr5eW7NdPj+TuoCRUpHv4oovAAAAkEK94Zi2tPYOegXgyh2d+s9/rJLFkH528RxNKPUd8nhe06dWY1dILT2529+0uTuk//j7e2rvjWhahV8/uGCmXHZrustCigR6unXC9Fp1dXUpLy8z8jWWUwEYVl6nlRdCAAAAQIp5nTaVD6G//azqAp00qUQJU7rzpc063Nqn1kCYieopEosn1BrI3XCzKxjVDY+sVntvRLVFHn3vY9MJNzHsCDgBDBuLRaouHNqkRwAAAAAHVup3Ks89oM5z+/jiiePktFm0tqFbL7zfcshjY3FT7X25vaV6pLT1RnK292YoGtcPH12jnZ1Blfic+v75M+R3HbzHK5AqBJwAhs2YArccNp5mAAAAgOFSXegZ9GvuEp9Tnz6mRpJ096tb1Bc59OCilh5WcQ5VPGHm7OrNeMLUbUvWa31Tj7xOq35w/gyV+NjNh5FB8gBgWBR47CrwONJdBgAAAJDTrBZDtUUeDXZ2y4VHj1FlvksdfVHd81b9IY9lFefQJbf6p7uK1DNNU3e9vFlvbG6XzWLoe+dOV00Ru/kwcgg4AaSc3WaoqsCd7jIAAACAUcHtsKoyf3D9OO1Wi75y0nhJ0j/f26X69r5DHs8qzsHL5dWbDy3bqUdXNkiSvnnWFM2oyk9zRRhtCDgBpFx1oUdWyyAvIQMAAAAYsGKfUwWewfU6PGZskeaPLVI8Yer/Xj70wKFY3FRLjoZ0w60tR1dvvvR+i+5+bask6UsfGaePTCxJb0EYlQg4AaRUid8hn3Pwjc4BAAAADM6YArfcjsG9zf/ySeNktxpaXt+pFw8zcKilJ6xoPAeTumEUT+RmMLxyR6f+55n3JUnnz67ShXPGpLkijFYEnABSxmW3qCJvcFtjAAAAAAyNxWKopmhwu6kq8939A4fuemWLekLRgx5rmlJjV2jQdY5Gubh6c1tbr256fK1iCVMnTijWlz4yLt0lYRQj4ASQEoYh1RR5ZAy2uzkAAACAIXParKopGlw//E/MrVZNkUddwajufnXrIY/t7IsqFI0P6vuMNrF4IudWb7YFwvqvf65RbySu6ZV5+saZU2ThvSDSiIATQEqU57nkslvTXQYAAAAw6vlddpXnOQd8P7vVoqsXTpQkLVnbpJU7Og95fAOrOI9IU09urd7si8T0X/9crdZAWNWFbn333Gly2IiXkF6cgQCGzOu0qtQ/8BdQAAAAAIZHWZ5LftfAe+NPr8zT2TMqJEm/emGTIrGDJ3OBUOyQW9khhaJxdfRG0l1GykTjCd38xDptbetTgceu/zpvhvyuwQ23AlKJgBPAkFgsyanpAAAAADJLTZFnUCvrLjthrAo9du3sDOq+pfWHPLaxK3TIqeujXUNXSLny4zFNU7c/t0HL6zvlslt048dmqJwZDMgQBJwAhqQq3812BAAAACADWS2G6oo9GmhrRJ/TpitOniBJ+vvSHdre3nfQY0PRhDr6WMV5IN2hqAKhWLrLSJk/v7ldz69vkcWQvnX2VE0s86W7JKAfqQSAQfO7bCr0OtJdBgAAAICDcNmtqi4c+NChEyYU69ixhYolTP3y+Y1KHGIZYlN3SIlEjixTTBHTNHNq0vyTqxp13zvJ1bxXLZyoY+qK0lwRsC8CTgCDYrFIYwbxQgkAAADAyCrwOFTsG9jCBMMw9NVTJshlt2htQ7eeXt100GNjcVOtOTYlfKjaeiMKR3NjstBbW9r16xc3SpIuObZGZ02vSHNFwP4IOAEMSlW+W3YrTyEAAABANqjMd8njtA7oPmV+lz57XJ0kafFrW9R+iGE5zT1hReO5EegNVSyeUFN3bqzefL+pR7c8tU4JUzpjWpkumV+b7pKAAyKdADBgbE0HAAAAsothGKor8shuG1hDzo8dVaWJZT71RuL6v5c3H/Q401TOhHpD1dQTViIHst6GrqB+8OgahWMJza0t0FWnTpQx0IauwAgh4AQwIGxNBwAAALKTzWpRXZF3QEOHrBZDVy+cKIshvbqxVW9taT/osZ19UYWi8RRUmr1C0bg6DrHSNVt0BaP6r0dWqysY1fhSr7519lTZ2MGHDMbZCWBA2JoOAAAAZC+3w6qaQs+A7jOh1KcL5oyRJP3mpU0KRg4cYpqm1JBDg3UGo6ErpEPMY8oKoWhcP3x0jXZ1hVTmd+rGj82Qx2FLd1nAIZFSADhibE0HAAAAsl++x67yPOeA7nPp/FqV+Z1q6Qnrj29sPehxgVBM3aHoECvMTt2hqAKhWLrLGJJ4wtRtS9ZrfVOPvE6r/uu8GSriPSCyAAEngCPC1nQAAAAgd5TluZTvth/x8S67VVedOlGS9Oh7DVpR33nQY3d1BhVPZPkyxgEyTVONWb561TRN/fblzXpjc7tsFkPfO3e6aooGttoXSBcCTgBHhK3pAAAAQG6pLnTL7Tjy1/hz6wp19owKSdLPn31fgfCBVytGY6Z2dQZTUmO2aOuNKBzN7slC9y/docdWNsiQ9I0zJ2tGVX66SwKOGGkFgMNiazoAAACQeywWQ7VFXtmsRz516IsnjlNlvkutgYjufGnTQY/r7IuOmq3qsXgi6yfIP7OmSX96Y5sk6csnjddJk0rTXBEwMAScAA6JrekAAABA7nLYLKot8hzxZHW3w6pvnDFZFkN6YX2LXt3YetBjd3YEFYtn96rGI9HQFVIii/+Yb21p1+3Pb5AkfXJutc6fXZXmioCBI+AEcEhsTQcAAABym9dp05iCI1/UMLUyTxfNrZYk/er5jWrvjRzwuFjczPmp6p19EXX2Ze9K1XWN3frJU+uUMKXTppbp8wvq0l0SMCikFgAOiq3pAAAAwOhQ6HWoxH/kr/0vmV+r8aVe9YRj+t/nNsg0DzxUqLMvqq5g9gaAhxKOxbUzi3uN1nf06Qf/XKNILKF5dYW6ZuFEGUe6lBfIMAScAA6IrekAAADA6FKR55LfZTuiY+1Wi75xxmTZrYaWbuvQU6ubDnpsLm5VN01T9e3BrN2a3hYI68ZHVqsnHNOkMp+uP3uqbOzcQxbj7AVwQGxNBwAAAEYXwzBUW+Q54snqdcVeff74sZKk3726WQ1dB17NGE+Y2tWZW1vVm7rDCkbi6S5jUALhmP7rn6vV0hNWVb5LN543Qy67Nd1lAUNCegFgP2xNBwAAAEYni8VQXbFXdtuRbVU+f06VZo3JVyia0P8seV/xxIG3qncFo+rK4l6VewuEY2rpCae7jEGJxBK66bE12trWp0KPXd+/YKby3fZ0lwUMGQEngH1YLFLVABqMAwAAAMgtdqtFY4u9shxBYmAxDP2/0yfJ47BqbWOPHnx3x0GP3dkZVDTLt6rH4gnt6OhLdxmDEk+Y+tmS9Vq1q1tuu1X/dd4MVeS50l0WkBIEnAD2UZnvlsPGUwMAAAAwmrnsVtUVe3UkM2fK8lz6yknjJUl/fWu7NrcEDnhccqt69g7lkXaHtLEDr1LNZKZp6o4XNurVTW2yWQx959xpGl/qS3dZQMqQYgDo53PZVMTWdAAAAACSfE6bqo9w8OhpU8u0YHyxYglTty15X5HYgVdqdgdj6uiNpLLMEdMWCKs7GEt3GQNmmqbuemWLnl7TJIshffOsKZpdXZDusoCUIuAEIGn31HS2pgMAAADYS4HHofJ852GPMwxDVy2cqAKPXdvb+/SbFzfJNA+80nFXV1ChaHYN6AlF42roys5BSX95c7seWbFLkvT10ybpIxNL0lwRkHoEnAAkSRV5LramAwAAANhPmd+lQu/hB9Hku+365plTZDGkJWub9OTqxgMel0hIW1p7FY5lR8hpmqbq2/t0kLw2o/393R362zv1kqSvnjxep08rT3NFwPAgzQAgr9OqYt/hr8oCAAAAGJ3GFLjld9kOe9ycmgJ97vixkqT/e2mz1jV2H/C4WNzU1ta+rBg61NAVUiia+XV+2GMrG7T4ta2SpMsWjNW5R1WltyBgGBFwAqOcYUjVhZ50lwEAAAAggxmGodoij9yOw8cIF80doxMmJPtx3vzEOnX0HbjnZiSW0NbWXsUyOORsDYTVFsi+nqHPrm3Sb17cJEm6+JgafXJedZorAoYXAScwylXmszUdAAAAwOFZLIbqir2Hff9gGIb+7fRJqil0q703op88ue6gIWYomtDWtj7FE5m3/7u5O6SGzuzru/nqxlb973MbJEnnz67SZ4+rTXNFwPAj1QBGMbamAwAAABgIu9WisSUe2azGIY/zOGz6z49Ok9tu1epd3bp791bpAwlG4trW1qtEBoWcjV0hNXWH013GgL2ztV0/fXq9EqZ05vRyffkj42QYh/67AnIBAScwSlksbE0HAAAAMHBOm1XjSryyHCZRqC706N/PnCxJemTFLr2wvvmgx/aG46rv6Dvo5PWRtLMzqJae7As3V+7o1M1PrFM8YerkSSW66tSJhJsYNQg4gVGqMt/N1nQAAAAAg+KyWzW22KvD5WcLxhfr4mNqJEm3P79RW1oDBz22OxjTjo5gKssckD3T0tuzsOfm8vpOff/RNYrEEzpuXJH+/YzJsloINzF6kG4Ao5DfZVOR15HuMgAAAABkMa/Tppoiz2FDzkvn12pubaEisYRuenytekLRgx7b2RfVrs6RDzlN09T29j519h28tkz11pZ2/eDR1QrHEppbW6jrFk2VzUrcg9GFMx4YZawWQ2MK3ekuAwAAAEAOyHfbNabg0O8vrBZD1541WeV5TjV1h3Xr0+8fcqhQWyCiXZ3BEevJmUiY2trWp+5gbES+Xyq9srFVP3piraJxUwvGF+u7505jpx5Gpaw562+66SadcMIJ8ng8KigoSHc5QNaqKnDJztU8AAAAAClS6HWoIt91yGP8Lru+89Fk+Pbu9g795c1thzy+LRDR+8096hrmFZXxhKktbb0KhLIv3Hx2bZN++lSy5+Ypk0v1rbOn8l4Po1bWnPmRSESf+tSndOWVV6a7FCBr5bvtKvCwNR0AAABAapX6nSr1Ow95zLgSn65eOFGSdP/SHXp4+c5DHh+NJbeNb24JKBSNp6xWKbklvasvqk0tAfWFU/vYI+HxlQ36+bMblDCls6aX03MTo54t3QUcqe9///uSpMWLF6e3ECBL2ayGqgoOfVUVAAAAAAarIt+lWCKhjt6Dr7pcOKVMjV0h/fWt7brrlS1y2a1aNKPikI/bG45rY3NARV6HyvNcQwryEglT7X0RtQbCisbSP7F9MB5atkO/f3WrJOm8oyr15ZPGy8K0dIxyWRNwDkY4HFY4HO7/vLu7O43VAOlVVeCm0TQAAACAYTWmwK14wjxkP8vPHFujYDSuh5bt1K+e3yiX3apTJpce8nFNM7ltvbMvqop814CHpkbjCbUFImrrDSuRGNBdM4Zpmrr37Xr99a3tkqRPzavW546vk0G4CWTPFvXBuPnmm5Wfn9//UVNTk+6SgLQo8NiV77anuwwAAAAAOc4wDNUWeeRzHXw9lWEY+tcTxuqcmRUyJf1syXq9sbntiB4/njC1syOojc09aguE1dkXUU8oqmAkrnAsvt/wolA0rvr2Pq1v7FFLT3aHm394fWt/uPnZ4+v0+QVjCTeB3dIacF5//fUyDOOQH+vWrRv043/7299WV1dX/0d9fX0Kqweyg91mqOowUw0BAAAAIFUMw1BdkUdep/WQx3z1lAlaOKVUCVP6yZPrtGx7xxF/j2AkoV2dIdW3B7W1tU8bmwN6vzGgNbu6tWpnl9bs6tb6xh5taAqosy8qMzt3o0tKrj793+c26O/vJnuWfukj4/TpY1jABewtrVvUv/nNb+oLX/jCIY8ZP378oB/f6XTK6Tx0k2Mg140pcNNsGgAAAMCIslgMjS32anNrr4KRAw/xsRiG/u30yQrHEnptU5v++/G1+sH5MzSjKn9I39s0pbhp7reaMxt19EV08+NrtbaxRxZD+uopE3TOzMp0lwVknLQGnKWlpSotPXSfDQCDV+RzyO9iazoAAACAkWexGBpX4t09Bf3Ae8OtFkPXnjVFNz2+Vku3dej7/1yjmy6cqUnl/hGuNvNsbgnoh4+tVWsgLK/DqusWTdXcusJ0lwVkpKzpwbl9+3YtX75c27dvVzwe1/Lly7V8+XIFAoF0lwZkJKfdoso8pqYDAAAASB/r7pDTaT94/GC3WvTtc6Zq1ph8BaNx3fjIam1t7R3BKjPPa5tadd3f31NrIKyqfJdu/dRswk3gELIm4Lzhhht09NFH68Ybb1QgENDRRx+to48+Wu+88066SwMyjmFINYUeWdiaDgAAACDNbFaLxpV45bAdPIJw2qz67rnTNKXcr55wTN97ZJW2t/eNYJWZITkpfbtufmKdwrGE5tQU6LZPzVF1oSfdpQEZzTDNbG61OzDd3d3Kz8/Xa2u2y+fPS3c5wLCpyHep1E//WQAAAACZIxyLa3NLr2Lxg8cQgVBM//mPldrS2iu33aprz5qs+eOKR7DK9AlF4/rFsxv0ysZWSdJ5R1XqSx8Zz0wFZJxAT7dOmF6rrq4u5eVlRr6WNSs4ARwZv8tGuAkAAAAg4zhtVo0r8R4ysPO5bPrhBTM1oypPwWhcP3xsre59e7sSOb42qzUQ1vUPvqdXNrbKajF09cKJ+srJEwg3gSNEwAnkEJvVUHWhO91lAAAAAMABuexWjS/1ynKINCLfbdd/XzBTH5uVnBb+lze368dPrFNfJDZCVY6s1ze36d//tlybWnqV57Lpvy+YqUUzKtJdFpBVCDiBHFJd6JbNyj9rAAAAAJnLZbdqfInvkCGnzWrRFadM0NdPmyibxdDrm9v0Hw+8p12dwZErdJh1BaO65al1+tHja9UZjGpssUe3XTxHM8fkp7s0IOuQhAA5otTvlN9lT3cZAAAAAHBYbkdyu7pxmB3YZ06v0M2fmKUij0Pb2/v0jfuX693tHSNT5DAxTVMvb2jRVX99Vy9vaJXFkD45t1q3fWqOKvJc6S4PyEoEnEAOcDusKs+j7yYAAACA7OFx2I4o5JxakaefXTxbU8r96g3H9f1/rtaD7+5QNs5M7uiN6OYn1umWp9arKxhVXZFHt35yti47Yewhp8wDODT+9QBZzmKRaorcMg73qgAAAAAAMozXaVNdseewIWexz6mbPzFLZ04vV8KU7n5tq255ar3aeyMjU+gQmaap59c366q/vqvXN7fJajH0mWNr9D+fnqNJ5f50lwdkPVu6CwAwNGMK3HLarOkuAwAAAAAGxe+yq7bYo+1tfTrUoky71aJrFk7UhFKffvvyZr2ysVVvb23Xx48eo48fPUYeR3oijnhCWr5Mam2VSkqkOUdLe49GaO4J6TcvbtLbW5Nb68eXevVvp03S+FJfWuoFchEBJ5DFCjx2FXgc6S4DAAAAAIYkz2VXTZFH9e2HDjkNw9C5syo1sdSnu17ZrHWNPbr37Xo9uapRl8yv1VnTy0d08Opzz0u33io1N31wW1m5dO210vhZvXpw2Q69tKFV8YQpm8XQZ+bX6qKjxzAcFkgxw8zGphWD1N3drfz8fL22Zrt8/rx0lwMMicNm0aQynywWtqYDAAAAyA1dfVHVdxw65NzDNE29vrlNf3htq3Z1hSQld7hdtqBOx48vHvY2Xs89L113naR9ajXlqOySd8ZOuao/GIZ01Jh8feXk8aor9g5rTcBICPR064Tpterq6lJeXmbka6zgBLKQYUi1RR7CTQAAAAA5Jd9jlym36tuDhz3WMAydMKFE88cW6ak1Tbrnre3a2RnUj55Yp2kVfv3rieM0rXJ4wpd4Irlysz/cNEy56lrlnblTjpKAJMk0pRMnluiiuWM0mT6bwLAi4ASyUHWhW24HfTcBAAAA5J4Cj0MJU9rZcfiQU5JsVovOnVWphVNK9eCynfrHsp1a29ij6/7+nqaU+zWvrlBzaws1scwna4oWiSxfJjU3mbL6Q3KO6ZB3+i7Z8pKrSM2YRX0bytW7pkpn3erW5PKUfEsAh0DACWSZEr+DvpsAAAAAclqR1yHTNLWrM3TE9/E4bPrscXU6Z0aF7nlru5asbdL6ph6tb+rRX9/aLr/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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xy = shuffled_df.iloc[:, 1:3].to_numpy()\n",
    "\n",
    "divisions = 200\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "\n",
    "# Plotting Prediction points\n",
    "x_lin = np.linspace(xy[:,0].min(), 340000, 20)\n",
    "y_lin = np.linspace(xy[ran,1], xy[ran,1], 20)\n",
    "q_pr, q_var =m.predict(pd.DataFrame({'x': x_lin, 'y': y_lin }).to_numpy())\n",
    "plt.scatter(x_lin, q_pr, color='blue', label=\"Normalized Q_predicted\") \n",
    "\n",
    "\n",
    "#Plotting prediction line and interval\n",
    "\n",
    "# x_lin = np.linspace(xy[:,0].min(), xy[:,0].max(), divisions)\n",
    "x_lin = np.linspace(xy[:,0].min(), 340000, divisions)\n",
    "y_lin = np.linspace(xy[ran,1], xy[ran,1], divisions)\n",
    "q_pr, q_var =m.predict(pd.DataFrame({'x': x_lin, 'y': y_lin }).to_numpy())\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(x_lin,q_pr, label=\"Q_mean\")\n",
    "plt.fill_between(x_lin, (q_pr - q_var).flatten(), (q_pr + q_var).flatten(), alpha = 0.2, label='Normalized Q_varience')\n",
    "\n",
    "# Labels and graphics\n",
    "hfont = {'fontname':'Georgia'}\n",
    "plt.xlabel(f\"x_utm and y_utm = {round(xy[ran,1])}\", **hfont)\n",
    "plt.ylabel(\"Q_normalized predicted\", **hfont)\n",
    "plt.title(\"x_utm vs Q_normalized\", **hfont)\n",
    "plt.ylim((-2.5, 2.5))\n",
    "plt.xlim((331000, 338000))\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('images\\interval.png',dpi = 300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Metrics:\n",
      "MSE 0.6821085085675241\n",
      "MAE 0.6682820465503551\n",
      "R2 Score 0.022063898229291\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "q_test_norm = shuffled_df.iloc[mid:,4:5].to_numpy()\n",
    "mse = mean_squared_error(q_test_norm, q_predicted_mean)\n",
    "mae = mean_absolute_error(q_test_norm, q_predicted_mean)\n",
    "r2 = r2_score(q_test_norm, q_predicted_mean)\n",
    "print(\"Model Metrics:\")\n",
    "print(\"MSE\", mse)\n",
    "print(\"MAE\", mae)\n",
    "print(\"R2 Score\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
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