FLiES Sensitivity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sensitivity import perturbed_run, sensitivity_analysis\n",
    "from FLiESANN import process_FLiES_table\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>doy</th>\n",
       "      <th>albedo</th>\n",
       "      <th>AOT</th>\n",
       "      <th>COT</th>\n",
       "      <th>vapor_gccm</th>\n",
       "      <th>ozone_cm</th>\n",
       "      <th>elevation_km</th>\n",
       "      <th>SZA</th>\n",
       "      <th>KG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53</td>\n",
       "      <td>0.027717</td>\n",
       "      <td>0.088195</td>\n",
       "      <td>-0.003143</td>\n",
       "      <td>0.418212</td>\n",
       "      <td>0.371734</td>\n",
       "      <td>0.058</td>\n",
       "      <td>110.406482</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111</td>\n",
       "      <td>0.036438</td>\n",
       "      <td>0.237342</td>\n",
       "      <td>3.002028</td>\n",
       "      <td>1.475493</td>\n",
       "      <td>0.371618</td>\n",
       "      <td>0.058</td>\n",
       "      <td>95.128334</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124</td>\n",
       "      <td>0.077051</td>\n",
       "      <td>0.107484</td>\n",
       "      <td>2.628000</td>\n",
       "      <td>1.590333</td>\n",
       "      <td>0.327841</td>\n",
       "      <td>0.058</td>\n",
       "      <td>92.147257</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>145</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>0.094339</td>\n",
       "      <td>0.001261</td>\n",
       "      <td>1.910660</td>\n",
       "      <td>0.317626</td>\n",
       "      <td>0.058</td>\n",
       "      <td>88.451775</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>170</td>\n",
       "      <td>0.115091</td>\n",
       "      <td>0.050474</td>\n",
       "      <td>0.038796</td>\n",
       "      <td>1.494141</td>\n",
       "      <td>0.333815</td>\n",
       "      <td>0.058</td>\n",
       "      <td>86.471424</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>224</td>\n",
       "      <td>0.162858</td>\n",
       "      <td>0.143261</td>\n",
       "      <td>10.702650</td>\n",
       "      <td>4.346220</td>\n",
       "      <td>0.292348</td>\n",
       "      <td>0.529</td>\n",
       "      <td>76.341918</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>165</td>\n",
       "      <td>0.099464</td>\n",
       "      <td>0.121046</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.876024</td>\n",
       "      <td>0.306241</td>\n",
       "      <td>0.529</td>\n",
       "      <td>71.790023</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>174</td>\n",
       "      <td>0.107731</td>\n",
       "      <td>0.109902</td>\n",
       "      <td>0.010211</td>\n",
       "      <td>2.556485</td>\n",
       "      <td>0.321885</td>\n",
       "      <td>0.529</td>\n",
       "      <td>71.627640</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>174</td>\n",
       "      <td>0.107731</td>\n",
       "      <td>0.109902</td>\n",
       "      <td>0.010211</td>\n",
       "      <td>2.556485</td>\n",
       "      <td>0.321885</td>\n",
       "      <td>0.529</td>\n",
       "      <td>71.627640</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>248</td>\n",
       "      <td>0.133559</td>\n",
       "      <td>0.045513</td>\n",
       "      <td>28.339823</td>\n",
       "      <td>1.240811</td>\n",
       "      <td>0.333310</td>\n",
       "      <td>0.548</td>\n",
       "      <td>84.924138</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     doy    albedo       AOT        COT  vapor_gccm  ozone_cm  elevation_km  \\\n",
       "0     53  0.027717  0.088195  -0.003143    0.418212  0.371734         0.058   \n",
       "1    111  0.036438  0.237342   3.002028    1.475493  0.371618         0.058   \n",
       "2    124  0.077051  0.107484   2.628000    1.590333  0.327841         0.058   \n",
       "3    145  0.081791  0.094339   0.001261    1.910660  0.317626         0.058   \n",
       "4    170  0.115091  0.050474   0.038796    1.494141  0.333815         0.058   \n",
       "..   ...       ...       ...        ...         ...       ...           ...   \n",
       "247  224  0.162858  0.143261  10.702650    4.346220  0.292348         0.529   \n",
       "248  165  0.099464  0.121046   0.000000    1.876024  0.306241         0.529   \n",
       "249  174  0.107731  0.109902   0.010211    2.556485  0.321885         0.529   \n",
       "250  174  0.107731  0.109902   0.010211    2.556485  0.321885         0.529   \n",
       "251  248  0.133559  0.045513  28.339823    1.240811  0.333310         0.548   \n",
       "\n",
       "            SZA   KG  \n",
       "0    110.406482  4.0  \n",
       "1     95.128334  4.0  \n",
       "2     92.147257  4.0  \n",
       "3     88.451775  4.0  \n",
       "4     86.471424  4.0  \n",
       "..          ...  ...  \n",
       "247   76.341918  4.0  \n",
       "248   71.790023  4.0  \n",
       "249   71.627640  4.0  \n",
       "250   71.627640  4.0  \n",
       "251   84.924138  4.0  \n",
       "\n",
       "[252 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df = pd.read_csv(\"calval_final_50_FLiES_inputs_clean.csv\")[[\"doy\", \"albedo\", \"AOT\", \"COT\", \"vapor_gccm\", \"ozone_cm\", \"elevation_km\", \"SZA\", \"KG\"]]\n",
    "input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 0s 666us/step\n",
      "788/788 [==============================] - 0s 408us/step\n",
      "8/8 [==============================] - 0s 621us/step\n",
      "  1/788 [..............................] - ETA: 22s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/halverso/Projects/ET-Sensitivity/sensitivity/sensitvity.py:117: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  perturbation_df = pd.concat([perturbation_df, run_results])\n",
      "/Users/halverso/Projects/ET-Sensitivity/sensitivity/sensitvity.py:123: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  sensitivity_metrics_df = pd.concat([sensitivity_metrics_df, pd.DataFrame([[\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "788/788 [==============================] - 0s 391us/step\n",
      "8/8 [==============================] - 0s 558us/step\n",
      "788/788 [==============================] - 0s 380us/step\n",
      "8/8 [==============================] - 0s 604us/step\n",
      "788/788 [==============================] - 0s 462us/step\n",
      "8/8 [==============================] - 0s 567us/step\n",
      "788/788 [==============================] - 0s 437us/step\n",
      "8/8 [==============================] - 0s 556us/step\n",
      "788/788 [==============================] - 0s 493us/step\n",
      "8/8 [==============================] - 0s 822us/step\n",
      "788/788 [==============================] - 0s 454us/step\n",
      "8/8 [==============================] - 0s 620us/step\n",
      "788/788 [==============================] - 0s 429us/step\n",
      "8/8 [==============================] - 0s 574us/step\n",
      "788/788 [==============================] - 0s 405us/step\n",
      "8/8 [==============================] - 0s 585us/step\n",
      "788/788 [==============================] - 0s 437us/step\n",
      "8/8 [==============================] - 0s 617us/step\n",
      "788/788 [==============================] - 0s 406us/step\n",
      "8/8 [==============================] - 0s 577us/step\n",
      "788/788 [==============================] - 0s 392us/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>input_variable</th>\n",
       "      <th>output_variable</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>albedo</td>\n",
       "      <td>Rg</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.443819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>albedo</td>\n",
       "      <td>Rg</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.196975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AOT</td>\n",
       "      <td>Rg</td>\n",
       "      <td>correlation</td>\n",
       "      <td>-0.368927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AOT</td>\n",
       "      <td>Rg</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.136107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COT</td>\n",
       "      <td>Rg</td>\n",
       "      <td>correlation</td>\n",
       "      <td>-0.446447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COT</td>\n",
       "      <td>Rg</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.199315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vapor_gccm</td>\n",
       "      <td>Rg</td>\n",
       "      <td>correlation</td>\n",
       "      <td>-0.527742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vapor_gccm</td>\n",
       "      <td>Rg</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.278511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ozone_cm</td>\n",
       "      <td>Rg</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.016191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ozone_cm</td>\n",
       "      <td>Rg</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.000262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elevation_km</td>\n",
       "      <td>Rg</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.295002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elevation_km</td>\n",
       "      <td>Rg</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.087026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>albedo</td>\n",
       "      <td>UV</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.568893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>albedo</td>\n",
       "      <td>UV</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.323639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AOT</td>\n",
       "      <td>UV</td>\n",
       "      <td>correlation</td>\n",
       "      <td>-0.439479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AOT</td>\n",
       "      <td>UV</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.193142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COT</td>\n",
       "      <td>UV</td>\n",
       "      <td>correlation</td>\n",
       "      <td>-0.453129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COT</td>\n",
       "      <td>UV</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.205326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vapor_gccm</td>\n",
       "      <td>UV</td>\n",
       "      <td>correlation</td>\n",
       "      <td>-0.296498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vapor_gccm</td>\n",
       "      <td>UV</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.087911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ozone_cm</td>\n",
       "      <td>UV</td>\n",
       "      <td>correlation</td>\n",
       "      <td>-0.130323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ozone_cm</td>\n",
       "      <td>UV</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.016984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elevation_km</td>\n",
       "      <td>UV</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.326283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elevation_km</td>\n",
       "      <td>UV</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.106461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  input_variable output_variable       metric     value\n",
       "0         albedo              Rg  correlation  0.443819\n",
       "0         albedo              Rg           r2  0.196975\n",
       "0            AOT              Rg  correlation -0.368927\n",
       "0            AOT              Rg           r2  0.136107\n",
       "0            COT              Rg  correlation -0.446447\n",
       "0            COT              Rg           r2  0.199315\n",
       "0     vapor_gccm              Rg  correlation -0.527742\n",
       "0     vapor_gccm              Rg           r2  0.278511\n",
       "0       ozone_cm              Rg  correlation  0.016191\n",
       "0       ozone_cm              Rg           r2  0.000262\n",
       "0   elevation_km              Rg  correlation  0.295002\n",
       "0   elevation_km              Rg           r2  0.087026\n",
       "0         albedo              UV  correlation  0.568893\n",
       "0         albedo              UV           r2  0.323639\n",
       "0            AOT              UV  correlation -0.439479\n",
       "0            AOT              UV           r2  0.193142\n",
       "0            COT              UV  correlation -0.453129\n",
       "0            COT              UV           r2  0.205326\n",
       "0     vapor_gccm              UV  correlation -0.296498\n",
       "0     vapor_gccm              UV           r2  0.087911\n",
       "0       ozone_cm              UV  correlation -0.130323\n",
       "0       ozone_cm              UV           r2  0.016984\n",
       "0   elevation_km              UV  correlation  0.326283\n",
       "0   elevation_km              UV           r2  0.106461"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_variables = [\"albedo\", \"AOT\", \"COT\", \"vapor_gccm\", \"ozone_cm\", \"elevation_km\"]\n",
    "output_variables = [\"Rg\", \"UV\"]\n",
    "\n",
    "perturbation_df, sensitivity_metrics_df = sensitivity_analysis(\n",
    "    input_df=input_df,\n",
    "    input_variables=input_variables,\n",
    "    output_variables=output_variables,\n",
    "    forward_process=process_FLiES_table\n",
    ")\n",
    "\n",
    "sensitivity_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/35_x93nx79vg1zmd2fy1vcbr0000gq/T/ipykernel_39048/2257911807.py:4: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([\"albedo\", \"aerosol\", \"cloud\\nthickness\", \"water\\nvapor\", \"ozone\", \"elevation\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'FLiES Solar Radiation Sensitivity')"
      ]
     },
     "execution_count": 4,
     "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": [
    "df = sensitivity_metrics_df\n",
    "df = df[(df.output_variable == \"Rg\") & (df.metric == \"correlation\")]\n",
    "ax = sns.barplot(x=df.input_variable, y=df.value)\n",
    "ax.set_xticklabels([\"albedo\", \"aerosol\", \"cloud\\nthickness\", \"water\\nvapor\", \"ozone\", \"elevation\"])\n",
    "plt.xlabel(\"Input Variable\")\n",
    "plt.ylabel(\"Correlation of Input Perturbation to Output Perturbation\")\n",
    "plt.title(\"FLiES Solar Radiation Sensitivity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/35_x93nx79vg1zmd2fy1vcbr0000gq/T/ipykernel_39048/214931233.py:4: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([\"albedo\", \"aerosol\", \"cloud\\nthickness\", \"water\\nvapor\", \"ozone\", \"elevation\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'FLiES Solar Radiation Sensitivity')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAHVCAYAAAA+d8WzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB2BUlEQVR4nO3dd1gU1/s28HspSxEpCiIaBKyAFcEooKKxoEbFlhhJEARNbMEeJVYsscSCGktsYBcTMU1jjdi7YAlYg2KBELBgpe15/+Blf65L2UFcwO/9ua69LvbMKc8MK/s4c+aMTAghQEREREQa0yntAIiIiIjKGyZQRERERBIxgSIiIiKSiAkUERERkURMoIiIiIgkYgJFREREJBETKCIiIiKJmEARERERScQEioiIiEgiJlBERYiIiIBMJsv3NXbsWGU9e3t7dO3atdC+AgICYG9vr1Jmb29fYP9t2rRRqXv69Gn07NkTNWrUgIGBAaytreHu7o4xY8ZotC979+5Fx44dUa1aNRgYGKBatWpo06YN5syZo1H7N7Vp00YtxnclICBA5djI5XLUqlULY8eORXp6eomOFR0dDZlMhujoaJXx3/zdaWrLli0ICwvLd5tMJsO0adOK1e/betvP07uU3+9g9+7dBR4re3t7BAQEvPNxiPLolXYAROVFeHg4HB0dVcqqVasmqY/JkydjxIgRauWenp6YP3++Wrmpqany5127dqF79+5o06YN5s2bBxsbGyQlJeHcuXPYtm0bFixYUOjYK1euxJAhQ9C7d2/88MMPqFSpEu7evYsTJ07g559/xoQJEyTtS2kwMjLCX3/9BQB4/Pgxfv75ZyxYsACXLl3Cvn373unYBf3uNLFlyxZcuXIFI0eOVNt28uRJfPDBB28ZnXRv+3l615o2bYqTJ0/C2dlZWbZ7924sW7Ys3+Rm586dKv9e3tU4RHmYQBFpqEGDBnBzc3urPmrVqpVvubm5OVq0aFFo23nz5sHBwQF79+6Fnt7//dP97LPPMG/evCLHnj17Nlq3bo2ff/5ZpdzPzw8KhUKD6N+9ly9fwsjIqMDtOjo6KsepU6dO+Oeff7B//34kJCTAwcHhncVW0O/ubRX1e39X3vbz9K6ZmppKOjYuLi5aGYcoDy/hEWnR21wGSktLg6WlpcqXXR4dnaL/KaelpcHGxibfbW+2f/XqFUJCQuDg4AC5XI7q1atj2LBhePz4cZHjhIaGonnz5qhUqRJMTU3RtGlTrF27Fm8+tzzvkmdUVBRcXFxgaGiI0NDQIvt/U15S+++//yrLbt68iQEDBqBOnTowNjZG9erV0a1bN1y+fFmt/dWrV9GpUycYGxvD0tISgwcPxtOnT9Xq5fe7W7ZsGVq3bo0qVaqgQoUKaNiwIebNm4esrCxlnTZt2mDXrl24c+eOyiXIPPldwrty5Qp8fHxgYWEBQ0NDNGnSBOvXr1epk3fpaevWrZg4cSKqVasGU1NTtG/fHteuXSvyuEn9PEVGRsLd3R0VKlSAiYkJvL29ERMTo3aMTExMcPPmTXTp0gUmJiawtbXFmDFjkJGRoVJ3xYoVaNy4MUxMTFCxYkU4Ojri22+/Vdu/vEtrAQEBWLZsmfKY5b1u374NQPUS3n///Qe5XI7Jkyer7cfVq1chk8mwZMkSyeO0a9cOjo6Oap9lIQRq166Njz/+OL9DTe8pJlBEGsrJyUF2drbKq6QIIdT6zs7OVvlD7e7ujtOnTyM4OBinT59W+ZLWhLu7O3bs2IFp06bh4sWLyMnJKTCWHj16YP78+fDz88OuXbswevRorF+/Hh999JHaF+Gbbt++ja+++grbt29HVFQUevXqha+//hozZsxQq3vhwgWMGzcOwcHB2LNnD3r37i1pnwAgISEBenp6qFmzprLswYMHqFy5MubMmYM9e/Zg2bJl0NPTQ/PmzVWSi3///RdeXl64cuUKli9fjo0bN+LZs2cYPny4RmPfunULvr6+2LhxI/744w8EBQXh+++/x1dffaWss3z5cnh6eqJq1ao4efKk8lWQa9euwcPDA3///TeWLFmCqKgoODs7IyAgIN8zQ99++y3u3LmDNWvWYNWqVbhx4wa6detW4O83j5TP03fffYd+/frB2dkZ27dvx8aNG/H06VO0atUKcXFxKnWzsrLQvXt3tGvXDr/++isCAwOxaNEizJ07V1ln27ZtGDp0KLy8vLBz50788ssvGDVqFJ4/f15gDJMnT0afPn0AQOU45vefAisrK3Tt2hXr169XO7saHh4OuVyOzz//XPI4I0aMwLVr13Dw4EGVNn/++Sdu3bqFYcOGFRg/vYcEERUqPDxcAMj3lZWVpaxnZ2cnPv7440L78vf3F3Z2dipldnZ2BfY/Y8YMZb3U1FTRsmVL5TZ9fX3h4eEhZs+eLZ4+fVrkfty8eVM0aNBA2d7IyEi0a9dO/PDDDyIzM1NZb8+ePQKAmDdvnkr7yMhIAUCsWrVKWebl5SW8vLwKHDMnJ0dkZWWJ6dOni8qVKwuFQqGy37q6uuLatWtFxi5E7rGrUKGCyMrKEllZWSI1NVWsWLFC6OjoiG+//bbQttnZ2SIzM1PUqVNHjBo1Slk+fvx4IZPJRGxsrEr9Dh06CADi0KFDKuO/+bvLb183bNggdHV1xcOHD5XbPv744wLbAhBTp05Vvv/ss8+EgYGBSExMVKnXuXNnYWxsLB4/fiyEEOLQoUMCgOjSpYtKve3btwsA4uTJkwXGKoTmn6fExEShp6cnvv76a5X2T58+FVWrVhWffvqpsszf318AENu3b1ep26VLF1GvXj3l++HDhwtzc/NC48vbv9d/B8OGDRMFfW3Z2dkJf39/5fvffvtNABD79u1TlmVnZ4tq1aqJ3r17F2ucnJwcUbNmTeHj46NS3rlzZ1GrVi2Vzze9/3gGikhDGzZswNmzZ1Ve+V3+KI6WLVuq9X327FkEBQUp61SuXBlHjx7F2bNnMWfOHPj4+OD69esICQlBw4YNkZqaWugYtWrVwsWLF3H48GGEhoaiffv2OHv2LIYPHw53d3e8evUKAJSTtN+8o+mTTz5BhQoV1P73/aa//voL7du3h5mZGXR1daGvr48pU6YgLS0NKSkpKnUbNWqEunXranqY8Pz5c+jr60NfXx+WlpYYMmQI+vbti1mzZqnUy87OxnfffQdnZ2fI5XLo6elBLpfjxo0biI+PV9Y7dOgQ6tevj8aNG6u09/X11SiemJgYdO/eHZUrV1bua//+/ZGTk4Pr169rvF+v++uvv9CuXTvY2tqqlAcEBODFixdqZ6+6d++u8r5Ro0YAgDt37hQ6jqafp7179yI7Oxv9+/dXOTtqaGgILy8vlbvXgNzLXt26dVOL6fV4PvzwQzx+/Bj9+vXDr7/+WuRntzg6d+6MqlWrIjw8XFm2d+9ePHjwAIGBgcXqU0dHB8OHD8cff/yBxMREALlnIffs2YOhQ4eqXJql9x8nkRNpyMnJ6a0nkRfEzMxM477d3NyUdbOysjB+/HgsWrQI8+bNK3Lyr46ODlq3bo3WrVsDyE1IgoKCEBkZiXXr1mHo0KFIS0uDnp4erKysVNrKZDJUrVoVaWlpBfZ/5swZdOzYEW3atMHq1avxwQcfQC6X45dffsGsWbPw8uVLlfoFzckqiJGREY4cOQIASE5OxoIFC7B161Y0atRI5S7C0aNHY9myZRg/fjy8vLxgYWEBHR0dDBw4UCWGtLS0fCeeV61atchYEhMT0apVK9SrVw+LFy+Gvb09DA0NcebMGQwbNkxtXzVV0Fy1vDs+3zz+lStXVnlvYGAAABqPX9TnKW9uWbNmzfJt/+Z8KWNjYxgaGqrFlJegA7k3LmRnZ2P16tXo3bs3FAoFmjVrhpkzZ6JDhw4axV0UPT09+Pn5YenSpXj8+DHMzc0REREBGxsbeHt7F7vfwMBATJkyBStXrsR3332HZcuWwcjIqNhJGZVfPANFVI7p6+tj6tSpAHInHktVoUIFhISEqLSvXLkysrOz8d9//6nUFUIgOTkZlpaWBfa3bds26Ovr448//sCnn34KDw+PQhNDqf9j19HRUX7hd+3aFXv27EH9+vURGhqKu3fvKutt2rQJ/fv3x3fffQdvb298+OGHcHNzUzvTUblyZSQnJ6uNk1/Zm3755Rc8f/4cUVFR+OKLL9CyZUu4ublBLpdL2qc3Va5cGUlJSWrlDx48AIBCj//byu/zlDfezz//nO9Z0tOnTxdrrAEDBuDEiRN48uQJdu3aBSEEunbtWuSZM6ljvHr1Ctu2bcOjR4/w22+/oX///tDV1S12n2ZmZvD398eaNWvw8OFDhIeHw9fXF+bm5iUWN5UPTKCIyon8vlQBKC9JFbUmlabt27VrByA3CXndjh078Pz5c+X2/MhkMujp6al8Qb18+RIbN24sNLbiMjAwwLJly/Dq1SvMnDlTJY68MzF5du3ahfv376uUtW3bFn///TcuXryoUr5ly5Yix85L/l4fRwiB1atX5xunpmeE2rVrh7/++kuZMOXZsGEDjI2NS+yWe00/D97e3tDT08OtW7eUyeubr7dRoUIFdO7cGRMnTkRmZib+/vvvAutKPbvm5OSE5s2bIzw8HFu2bEFGRgYGDBhQZLuixgkODkZqair69OmDx48fa3zTAb1feAmPqAQlJyerrbME5N5iXdgXzePHj3Hq1Cm1cgMDA+X6Nt7e3vjggw/QrVs3ODo6QqFQIDY2FgsWLICJiUmRizzWr18f7dq1Q+fOnVGrVi28evUKp0+fxoIFC2Btba2cb9WhQwd4e3tj/PjxSE9Ph6enJy5duoSpU6fCxcUFfn5+BY7x8ccfY+HChfD19cWXX36JtLQ0zJ8/Xy2ZKUleXl7o0qULwsPDMWHCBDg4OKBr166IiIiAo6MjGjVqhPPnz+P7779XW7By5MiRWLduHT7++GPMnDkT1tbW2Lx5M65evVrkuB06dIBcLke/fv3wzTff4NWrV1ixYgUePXqkVrdhw4aIiorCihUr4OrqqjyTlp+pU6fijz/+QNu2bTFlyhRUqlQJmzdvxq5duzBv3jyYmZkV70C9QdPPk729PaZPn46JEyfin3/+QadOnWBhYYF///0XZ86cQYUKFSQvPzFo0CAYGRnB09MTNjY2SE5OxuzZs2FmZlbgpUIg9zgCwNy5c9G5c2fo6uqiUaNGhZ71CwwMxFdffYUHDx7Aw8MD9erVKzK+osapW7cuOnXqhD///BMtW7ZUm0NH/yNKeRI7UZmXdxfe2bNnC61X2N10eXcHSb0Lr3r16sp6kZGRwtfXV9SpU0eYmJgIfX19UaNGDeHn5yfi4uKK3I8ff/xR9OrVS9SsWVMYGxsLuVwuatWqJQYPHizu3r2rUvfly5di/Pjxws7OTujr6wsbGxsxZMgQ8ejRI5V6+d2Ft27dOlGvXj1hYGAgatasKWbPni3Wrl0rAIiEhASV/S7qrsXX5d2Fl5/Lly8LHR0dMWDAACGEEI8ePRJBQUGiSpUqwtjYWLRs2VIcPXo033jj4uJEhw4dhKGhoahUqZIICgoSv/76q0Z34f3++++icePGwtDQUFSvXl2MGzdO/Pnnn2ptHz58KPr06SPMzc2FTCZTucMLb9yFl7c/3bp1E2ZmZkIul4vGjRuL8PBwlTp5d4/99NNPKuUJCQkCgFr9N0n9PP3yyy+ibdu2wtTUVBgYGAg7OzvRp08fceDAAZVjlN/vaOrUqSr7vH79etG2bVthbW0t5HK5qFatmvj000/FpUuX1Pbv9eOYkZEhBg4cKKysrJTHMe8z9eZdeHmePHkijIyMBACxevVqte1Sx8kTEREhAIht27ap9Un/G2RCvLEiGBERERWqd+/eOHXqFG7fvg19ff3SDodKAS/hERERaSAjIwMXLlzAmTNnsHPnTixcuJDJ0/8wnoEiIiLSwO3bt+Hg4ABTU1P4+vrihx9+eKs7+qh8YwJFREREJBGXMSAiIiKSiAkUERERkURMoIiIiIgk4l1474BCocCDBw9QsWJFPlySiIionBBC4OnTp6hWrZracx7fxATqHXjw4IHak9SJiIiofLh7967akwvexATqHahYsSKA3F+AqalpKUdDREREmkhPT4etra3ye7wwTKDegbzLdqampkygiIiIyhlNpt9wEjkRERGRREygiIiIiCRiAkVEREQkERMoIiIiIomYQBERERFJxASKiIiISCImUEREREQSMYEiIiIikogJFBEREZFETKCIiIiIJGICRURERCQREygiIiIiiZhAEREREUnEBIqIiIhIIiZQRERERBLplXYARETvI9dxG0o7hDLh/Pf9SzsEoneCZ6CIiIiIJGICRURERCQREygiIiIiiZhAEREREUnEBIqIiIhIolK7Cy8zMxMpKSlQKBQq5TVq1CiliIiIiIg0o/UE6saNGwgMDMSJEydUyoUQkMlkyMnJ0XZIRERERJJoPYEKCAiAnp4e/vjjD9jY2EAmk2k7BCIiIqK3ovUEKjY2FufPn4ejo6O2hyYiIiIqEVqfRO7s7IzU1FRtD0tERERUYrSeQM2dOxfffPMNoqOjkZaWhvT0dJUXERERUVmn9Ut47du3BwC0a9dOpZyTyImIiKi80HoCdejQIW0PSURERFSitJ5AeXl5aXtIIiIiohJVKgtpPn78GGvXrkV8fDxkMhmcnZ0RGBgIMzOz0giHiIiISBKtTyI/d+4catWqhUWLFuHhw4dITU3FwoULUatWLVy4cEHb4RARERFJpvUzUKNGjUL37t2xevVq6OnlDp+dnY2BAwdi5MiROHLkiLZDIiIiIpJE6wnUuXPnVJInANDT08M333wDNzc3bYdDREREJJnWL+GZmpoiMTFRrfzu3buoWLGitsMhIiIikkzrCVTfvn0RFBSEyMhI3L17F/fu3cO2bdswcOBA9OvXT9vhEBEREUmm9Ut48+fPh0wmQ//+/ZGdnQ0A0NfXx5AhQzBnzhxth0NEREQkmdYTKLlcjsWLF2P27Nm4desWhBCoXbs2jI2NtR0KERERUbGUyjpQAGBsbIyGDRuW1vBERERExaaVBKpXr16IiIiAqakpevXqVWjdqKgobYREREREVGxaSaDMzMwgk8kA5N6Fl/czERERUXmklQQqPDxc+XNERIQ2hiQiIiJ6Z7S+jMFHH32Ex48fq5Wnp6fjo48+0nY4RERERJJpPYGKjo5GZmamWvmrV69w9OhRbYdDREREJJnW7sK7dOmS8ue4uDgkJycr3+fk5GDPnj2oXr26tsIhIiIiKjatJVBNmjSBTCaDTCbL91KdkZERli5dqq1wiIiIiIpNa5fwEhISlAtnnjlzBgkJCcrX/fv3kZ6ejsDAwGL1vXz5cjg4OMDQ0BCurq6FXgqMiopChw4dYGVlBVNTU7i7u2Pv3r0qdSIiIpTJ3uuvV69eFSs+IiIier9o7QyUnZ0dAEChUJRov5GRkRg5ciSWL18OT09P/Pjjj+jcuTPi4uJQo0YNtfpHjhxBhw4d8N1338Hc3Bzh4eHo1q0bTp8+DRcXF2U9U1NTXLt2TaWtoaFhicZORERE5VOprUQeFxeHxMREtQnl3bt3l9TPwoULERQUhIEDBwIAwsLCsHfvXqxYsQKzZ89Wqx8WFqby/rvvvsOvv/6K33//XSWBkslkqFq1qqRYiIiI6H+D1hOof/75Bz179sTly5chk8kghAAA5eKaOTk5GveVmZmJ8+fPY8KECSrlHTt2xIkTJzTqQ6FQ4OnTp6hUqZJK+bNnz2BnZ4ecnBw0adIEM2bMUEmwiIiI6H+X1pcxGDFiBBwcHPDvv//C2NgYf//9N44cOQI3NzdER0dL6is1NRU5OTmwtrZWKbe2tla5y68wCxYswPPnz/Hpp58qyxwdHREREYHffvsNW7duhaGhITw9PXHjxo18+8jIyEB6errKi4iIiN5fWj8DdfLkSfz111+wsrKCjo4OdHR00LJlS8yePRvBwcGIiYmR3Oebj4YRQmj0uJitW7di2rRp+PXXX1GlShVleYsWLdCiRQvle09PTzRt2hRLly7FkiVL1PqZPXs2QkNDJcdNRERE5ZPWz0Dl5OTAxMQEAGBpaYkHDx4AyJ1k/uak7aJYWlpCV1dX7WxTSkqK2lmpN0VGRiIoKAjbt29H+/btC62ro6ODZs2aFXgGKiQkBE+ePFG+7t69K2k/iIiIqHzRegLVoEED5aKazZs3x7x583D8+HFMnz4dNWvWlNSXXC6Hq6sr9u/fr1K+f/9+eHh4FNhu69atCAgIwJYtW/Dxxx8XOY4QArGxsbCxscl3u4GBAUxNTVVeRERE9P7S+iW8SZMm4fnz5wCAmTNnomvXrmjVqhUqV66MyMhIyf2NHj0afn5+cHNzg7u7O1atWoXExEQMHjwYQO7Zofv372PDhg0AcpOn/v37Y/HixWjRooXy7JWRkRHMzMwAAKGhoWjRogXq1KmD9PR0LFmyBLGxsVi2bFlJHAIiIiIq57SeQHl7eyt/rlmzJuLi4vDw4UNYWFhoNG/pTX379kVaWhqmT5+OpKQkNGjQALt371auO5WUlITExERl/R9//BHZ2dkYNmwYhg0bpiz39/dHREQEAODx48f48ssvkZycDDMzM7i4uODIkSP48MMPi7nXRERE9D6Ribx1BErB3bt3IZPJ8MEHH5RWCO9Eeno6zMzM8OTJE17OI/of5TpuQ2mHUCac/75/aYdApDEp399anwOVnZ2NyZMnw8zMDPb29rCzs4OZmRkmTZqErKwsbYdDREREJJnWL+ENHz4cO3fuxLx58+Du7g4gd2mDadOmITU1FStXrtR2SERERESSaD2B2rp1K7Zt24bOnTsryxo1aoQaNWrgs88+YwJFREREZZ7WL+EZGhrC3t5erdze3h5yuVzb4RARERFJpvUEatiwYZgxYwYyMjKUZRkZGZg1axaGDx+u7XCIiIiIJNPKJbxevXqpvD9w4AA++OADNG7cGABw8eJFZGZmol27dtoIh4iIiOitaCWBylugMk/v3r1V3tva2mojDCIiIqISoZUEKjw8HEDuI1ESExNhZWUFY2NjbQxNREREVOK0OgdKCIE6derg/v372hyWiIiIqERpNYHS0dFBnTp1kJaWps1hiYiIiEqU1u/CmzdvHsaNG4crV65oe2giIiKiEqH1hTS/+OILvHjxAo0bN4ZcLoeRkZHK9ocPH2o7JCIiIiJJtJ5AhYWFaXtIIiIiohKl9QTK399f20MSERERlSitJ1CJiYmFbq9Ro4aWIiEiIiIqHq0nUPb29pDJZAVuz8nJ0WI0RERERNJpPYGKiYlReZ+VlYWYmBgsXLgQs2bN0nY4RERERJJpPYHKe/7d69zc3FCtWjV8//33as/NIyIiIiprtL4OVEHq1q2Ls2fPlnYYREREREXS+hmo9PR0lfdCCCQlJWHatGmoU6eOtsMhIiIikkzrCZS5ubnaJHIhBGxtbbFt2zZth0NEREQkmdYTqEOHDqm819HRgZWVFWrXrg09Pa2HQ0RERCSZ1jMWmUwGDw8PtWQpOzsbR44cQevWrbUdEhEREZEkWp9E3rZt23yfd/fkyRO0bdtW2+EQERERSab1BEoIke9CmmlpaahQoYK2wyEiIiKSTGuX8PLWd5LJZAgICICBgYFyW05ODi5dugQPDw9thUNERERUbFpLoMzMzADknoGqWLEijIyMlNvkcjlatGiBQYMGaSscIiIiomLTWgIVHh4OIQSEEFi6dCkqVqyoraGJiIiISlSx50BlZmbi3r17SExMVHkVRgiBLVu2IDk5ubjDEhEREZU6yWegbty4gcDAQJw4cUKlPG9yeE5OToFtdXR0UKdOHaSlpXHVcSIiIiq3JCdQAQEB0NPTwx9//AEbG5t876grzLx58zBu3DisWLECDRo0kDo8ERERUamTnEDFxsbi/PnzcHR0LNaAX3zxBV68eIHGjRtDLperTCYHkO8aUURERERlieQEytnZGampqcUeMCwsrNhtiYiIiMoCyQnU3Llz8c033+C7775Dw4YNoa+vr7Ld1NS00Pb+/v5ShyQiIiIqUyQnUO3btwcAtGvXTqVck0nkeW7duoXw8HDcunULixcvRpUqVbBnzx7Y2tqifv36UkMiIiIi0irJCdShQ4feasDDhw+jc+fO8PT0xJEjRzBr1ixUqVIFly5dwpo1a/Dzzz+/Vf9ERERE75rkBMrLy+utBpwwYQJmzpyJ0aNHqyym2bZtWyxevPit+iYiIiLShmKtRP748WOsXbsW8fHxkMlkcHZ2RmBgoPJxLYW5fPkytmzZolZuZWWFtLS04oRDREREpFWSVyI/d+4catWqhUWLFuHhw4dITU3FwoULUatWLVy4cKHI9ubm5khKSlIrj4mJQfXq1aWGQ0RERKR1khOoUaNGoXv37rh9+zaioqKwc+dOJCQkoGvXrhg5cmSR7X19fTF+/HgkJydDJpNBoVDg+PHjGDt2LPr371+cfSAiIiLSqmKdgRo/fjz09P7v6p+enh6++eYbnDt3rsj2s2bNQo0aNVC9enU8e/YMzs7OaN26NTw8PDBp0iSp4RARERFpneQ5UKampkhMTFRbifzu3bsqk8ILoq+vj82bN2PGjBm4cOECFAoFXFxc+Gw8IiIiKjckJ1B9+/ZFUFAQ5s+fDw8PD8hkMhw7dgzjxo1Dv379CmynUCiwYMEC/PLLL8jKykL79u0xZcoUGBoavtUOEBEREWmb5ARq/vz5kMlk6N+/P7KzswHknlUaMmQI5syZU2C7uXPnYtKkSWjXrh2MjIywcOFCpKamYtWqVcWPnoiIiKgUyIQQojgNX7x4gVu3bkEIgdq1a8PY2LjQ+vXq1cOIESMwdOhQAMCePXvQo0cPvHz5EjKZrDghlFnp6ekwMzPDkydPiny0DRG9n1zHbSjtEMqE89/z5iAqP6R8fxdrHSgAMDY2RsOGDTWuf+fOHXTt2lX53tvbG0IIPHjwgMsXEBERUbmiUQLVq1cvREREwNTUFL169Sq0blRUVL7lmZmZMDIyUr6XyWSQy+XIyMiQEC4RERFR6dMogTIzM1NeZjM1NS32JbfJkyerXOrLzMzErFmzVFYwX7hwYbH6JiIiItIWjRKo8PBw5c8RERHFGqh169a4du2aSpmHhwf++ecf5fv3bS4UERERvZ8kz4H66KOPEBUVBXNzc5Xy9PR09OjRA3/99Ve+7aKjo4sTHxEREVGZI3kl8ujoaGRmZqqVv3r1CkePHi2RoIiIiIjKMo3PQF26dEn5c1xcHJKTk5Xvc3JysGfPHt5NR0RERP8TNE6gmjRpAplMBplMho8++khtu5GREZYuXVqiwRERERGVRRpfwktISFAunHnmzBkkJCQoX/fv30d6ejoCAwPfZawFWr58ORwcHGBoaAhXV9dCLyVGRUWhQ4cOsLKygqmpKdzd3bF37161ejt27ICzszMMDAzg7OyMnTt3vstdICIionJE4wTKzs4O9vb2UCgUcHNzg52dnfJlY2MDXV1djfpJTExEfoufCyGQmJioeeT/X2RkJEaOHImJEyciJiYGrVq1QufOnQvs68iRI+jQoQN2796N8+fPo23btujWrRtiYmKUdU6ePIm+ffvCz88PFy9ehJ+fHz799FOcPn1acnxERET0/in2o1zi4uKQmJioNqG8e/fuhbbT1dVFUlISqlSpolKelpaGKlWqICcnR1IczZs3R9OmTbFixQplmZOTE3r06IHZs2dr1Ef9+vXRt29fTJkyBUDuA5PT09Px559/Kut06tQJFhYW2Lp1a5H98VEuRMRHueTio1yoPHmnj3L5559/0LNnT1y+fBkymUx5NilvDaeiEiAhRL7rPT179gyGhoaSYsnMzMT58+cxYcIElfKOHTvixIkTGvWhUCjw9OlTVKpUSVl28uRJjBo1SqWet7c3wsLC8u0jIyNDZUX19PR0DfeAiIiIyiPJCdSIESPg4OCAAwcOoGbNmjhz5gzS0tIwZswYzJ8/v8B2o0ePBpCbaL25InlOTg5Onz6NJk2aSIolNTUVOTk5sLa2Vim3trZWuUuwMAsWLMDz58/x6aefKsuSk5Ml9Tl79myEhoZKip2IiIjKL8kJ1MmTJ/HXX3/BysoKOjo60NHRQcuWLTF79mwEBwerzCV6XV65EAKXL1+GXC5XbpPL5WjcuDHGjh1brJ1484xWQWe53rR161ZMmzYNv/76q9olRSl9hoSEKBNEIPcMlK2trabhE5UZvOyUi5ediKgokhOonJwcmJiYAAAsLS3x4MED1KtXD3Z2dmqPanndoUOHAAADBgzA4sWLS2RukKWlJXR1ddXODKWkpKidQXpTZGQkgoKC8NNPP6F9+/Yq26pWrSqpTwMDAxgYGBRjD4iIiKg8krwSeYMGDZSLajZv3hzz5s3D8ePHMX36dNSsWbPI9uHh4SU2sVoul8PV1RX79+9XKd+/fz88PDwKbLd161YEBARgy5Yt+Pjjj9W2u7u7q/W5b9++QvskIiKi/x2Sz0BNmjQJz58/BwDMnDkTXbt2RatWrVC5cmVERkYW2T6/RThfV9Cz9AoyevRo+Pn5wc3NDe7u7li1ahUSExMxePBgALmX1+7fv48NG3IvTWzduhX9+/fH4sWL0aJFC+WZJiMjI5iZmQHInefVunVrzJ07Fz4+Pvj1119x4MABHDt2TFJsReHlkly8XEJEROWN5ATK29tb+XPNmjURFxeHhw8fwsLCQqN5R40bN1Z5n5WVhdjYWFy5cgX+/v5Sw0Hfvn2RlpaG6dOnIykpCQ0aNMDu3bthZ2cHAEhKSlJZE+rHH39EdnY2hg0bhmHDhinL/f39ERERAQDw8PDAtm3bMGnSJEyePBm1atVCZGQkmjdvLjk+IiIiev9ITqDy8/oSAEVZtGhRvuXTpk3Ds2fPijX+0KFDMXTo0Hy35SVFeaKjozXqs0+fPujTp0+x4iEiIqL3m+QE6vnz55gzZw4OHjyIlJQUKBQKle3//PNPsQL54osv8OGHHxa6FAIRERFRWSA5gRo4cCAOHz4MPz8/2NjYaHTZThMnT56UvJAmERERUWmQnED9+eef2LVrFzw9PYs1YK9evVTeCyGQlJSEc+fOYfLkycXqk4iIiEibJCdQFhYWkuY8vSnvTrc8Ojo6qFevHqZPn46OHTsWu18iIiIibZGcQM2YMQNTpkzB+vXrVR7Hoqnw8HDJbYiIiIjKEskJ1IIFC3Dr1i1YW1vD3t4e+vr6KtsvXLigUT/nzp1DfHw8ZDIZnJyc4OrqKjUUIiIiolIhOYHq0aPHWw1479499OvXD8ePH4e5uTkA4PHjx/Dw8MDWrVv5DDkiIiIq8yQnUFOnTn2rAQMDA5GVlYX4+HjUq1cPAHDt2jUEBgYiKCgI+/bte6v+iYiIiN61EllIU4qjR4/ixIkTyuQJAOrVq4elS5cW+84+IiIiIm3SKIGqVKkSrl+/DktLyyIf2fLw4cNC+6pRowaysrLUyrOzs1G9enVNwiEiIiIqVRolUIsWLULFihUBAGFhYW814Lx58/D1119j2bJlcHV1hUwmw7lz5zBixAiuQk5ERETlgkYJ1OsP+S3OA39fFxAQgBcvXqB58+bQ08sdPjs7G3p6eggMDERgYKCyblFns4iIiIhKQ7HnQKWkpOT7LLxGjRoV2m7RokUl9vgXIiIiotIgOYE6f/48/P39ER8fDyGEyjaZTIacnJxC2wcEBEgdkoiIiKhMkZxADRgwAHXr1sXatWthbW0t+WySrq4ukpKSUKVKFZXytLQ0VKlSpcgEjIiIiKi0SU6gEhISEBUVhdq1axdrwDfPWuXJyMiAXC4vVp9ERERE2iQ5gWrXrh0uXrwoOYFasmQJgNzLfGvWrIGJiYlyW05ODo4cOQJHR0ep4RARERFpneQEas2aNfD398eVK1fQoEEDtWfhde/ePd92ixYtApB7BmrlypXQ1dVVbpPL5bC3t8fKlSulhkNERESkdZITqBMnTuDYsWP4888/1bYVNok8ISEBANC2bVtERUXBwsJC6tBEREREZYKO1AbBwcHw8/NDUlISFAqFykuTCeCHDh1i8kRERETlmuQzUGlpaRg1ahSsra2LNeDrC2XmZ926dcXql4iIiEhbJCdQvXr1wqFDh1CrVq1iDfjo0SOV91lZWbhy5QoeP36Mjz76qFh9EhEREWmT5ASqbt26CAkJwbFjx9CwYUO1SeTBwcGFtt+5c6damUKhwNChQ1GzZk2p4RARERFpXbHuwjMxMcHhw4dx+PBhlW0ymazIBCo/Ojo6GDVqFNq0aYNvvvlGcnsiIiIibZKUQAkhcOjQIVSpUgXGxsYlGsitW7eQnZ1don0SERERvQuSE6i6devi77//Rp06dYo14OjRo9X6TEpKwq5du+Dv71+sPomIiIi0SVICpaOjgzp16iAtLa3YCVRMTIxan1ZWVliwYEGRd+gRERERlQWS50DNmzcP48aNw4oVK9CgQQPJAx46dEhyGyIiIqKyRHIC9cUXX+DFixdo3Lgx5HI5jIyMVLY/fPiwyD4uXbqE69evQyaToW7dumjYsKHUMIiIiIhKjeQEKiwsrNiDnTlzBkFBQYiLi4MQAkDunXv169fH2rVr0axZs2L3TURERKQtkhOo4k70jouLQ7t27eDk5IRNmzbByckJQgjEx8dj0aJFaNeuHU6dOgVnZ+di9U9ERESkLZITKCB3yYHw8HDcunULixcvRpUqVbBnzx7Y2tqifv36+baZOnUqOnTogB07dkAmkynLXVxc0K9fP/Tq1QvTpk3D9u3bi7cnRERERFoi+WHChw8fRsOGDXH69GlERUXh2bNnAHLnNU2dOrXAdtHR0fj2229Vkqc8MpkM3377LSeYExERUbkgOYGaMGECZs6cif3790MulyvL27Zti5MnTxbY7unTp4U+gLhq1ap4+vSp1HCIiIiItE5yAnX58mX07NlTrdzKygppaWkFtrO3t8eZM2cK3H769GnY2dlJDYeIiIhI6yQnUObm5khKSlIrj4mJQfXq1Qts17dvX4wePRpXrlxR23b58mWMHTsWn332mdRwiIiIiLRO8iRyX19fjB8/Hj/99BNkMhkUCgWOHz+OsWPHon///gW2CwkJwYEDB9CkSRN06NABTk5OAHLvzjtw4AA+/PBDhISEFH9PiIiIiLREcgI1a9YsBAQEoHr16hBCwNnZGTk5OfD19cWkSZMKbGdoaIhDhw5h0aJF2Lp1Kw4fPgwAqFu3LmbOnIlRo0bBwMCg+HtCREREpCWSEyh9fX1s3rwZM2bMwIULF6BQKODi4qLRs/HkcjnGjx+P8ePHFytYIiIiorJA8hyo6dOn48WLF6hZsyb69OmDTz/9FHXq1MHLly8xffr0dxEjERERUZkiOYEKDQ1Vrv30uhcvXiA0NLREgiIiIiIqyyQnUEKIfBfDvHjxIipVqlQiQRERERGVZRrPgbKwsIBMJoNMJkPdunVVkqicnBw8e/YMgwcPfidBEhEREZUlGidQYWFhEEIgMDAQoaGhMDMzU26Ty+Wwt7eHu7u7pMGFEACQ7xktIiIiorJK4wTK398fAODg4ABPT0/o6RXrOcQAgA0bNuD777/HjRs3AOQuZTBu3Dj4+fkVu08iIiIibZE8B8rLywt37tzBpEmT0K9fP6SkpAAA9uzZg7///rvI9gsXLsSQIUPQpUsXbN++HZGRkejUqRMGDx6MRYsWSd8DIiIiIi2TnEAdPnwYDRs2xOnTpxEVFaW8I+/SpUuYOnVqke2XLl2KFStWYO7cuejevTt8fHwwb948LF++HEuWLJG+B0RERERaJjmBmjBhAmbOnIn9+/dDLpcry9u2bYuTJ08W2T4pKQkeHh5q5R4eHvk+Y4+IiIiorJGcQF2+fBk9e/ZUK7eyskJaWlqR7WvXro3t27erlUdGRmq0mjkRERFRaZM8E9zc3BxJSUlwcHBQKY+JiUH16tWLbB8aGoq+ffviyJEj8PT0hEwmw7Fjx3Dw4MF8EysiIiKiskbyGShfX1+MHz8eycnJkMlkUCgUOH78OMaOHYv+/fsX2b537944ffo0LC0t8csvvyAqKgqWlpY4c+ZMvme2iIiIiMoayWegZs2ahYCAAFSvXh1CCDg7OyMnJwe+vr6YNGmSRn24urpi06ZNkoMlIiIiKgskn4HS19fH5s2bcf36dWzfvh2bNm3C1atXsXHjRujq6hbZXldXV7n0wevS0tI0ap+f5cuXw8HBAYaGhnB1dcXRo0cLrJuUlARfX1/Uq1cPOjo6GDlypFqdiIgI5arrr79evXpVrPiIiIjo/VLs1TBr1aqFWrVqSW6Xt/r4mzIyMlTu6tNUZGQkRo4cieXLl8PT0xM//vgjOnfujLi4ONSoUSPfcaysrDBx4sRC150yNTXFtWvXVMoMDQ0lx0dERETvH0kJ1PPnzzF37lxERUXh9u3bkMlkcHBwQJ8+fTB27FgYGxsX2DZvjSeZTIY1a9bAxMREuS0nJwdHjhyBo6Oj5B1YuHAhgoKCMHDgQAC5j5zZu3cvVqxYgdmzZ6vVt7e3x+LFiwEA69atK7BfmUyGqlWrSo6HiIiI3n8aJ1CZmZnw8vLClStX0LlzZ3Tr1g1CCMTHx2PWrFn4888/ceTIEejr6+fbPu9sjxACK1euVLlcl/csvZUrV0oKPjMzE+fPn8eECRNUyjt27IgTJ05I6utNz549g52dHXJyctCkSRPMmDEDLi4u+dbNyMhARkaG8n16evpbjU1ERERlm8YJ1IoVK3Dv3j1cvHgR9erVU9l29epVtGnTBitXrsTXX3+db/uEhAQAuQtuRkVFwcLC4i3CzpWamoqcnBxYW1urlFtbWyM5ObnY/To6OiIiIgINGzZEeno6Fi9eDE9PT1y8eDHftapmz56N0NDQYo9HRERE5YvGk8ijoqIwefJkteQJyE04Jk6ciJ9//rnIfg4dOlQiydPrZDKZynshhFqZFC1atMAXX3yBxo0bo1WrVti+fTvq1q2LpUuX5ls/JCQET548Ub7u3r1b7LGJiIio7NM4gYqLi0ObNm0K3N62bVvExcWVREwas7S0hK6urtrZppSUFLWzUm9DR0cHzZo1w40bN/LdbmBgAFNTU5UXERERvb80TqAeP36MypUrF7i9cuXKePLkSYkEpSm5XA5XV1fs379fpXz//v35Pm+vuIQQiI2NhY2NTYn1SUREROWXxnOgFApFoes06ejoICcnp0SCkmL06NHw8/ODm5sb3N3dsWrVKiQmJmLw4MEAci+v3b9/Hxs2bFC2iY2NBZA7Ufy///5DbGws5HI5nJ2dAeQ+bqZFixaoU6cO0tPTsWTJEsTGxmLZsmVa3z8iIiIqezROoIQQaNeuHfT08m+SnZ1dYkFJ0bdvX6SlpWH69OlISkpCgwYNsHv3btjZ2QHIXTgzMTFRpc3rd9OdP38eW7ZsgZ2dHW7fvg0g92zbl19+ieTkZJiZmcHFxQVHjhzBhx9+qLX9IiIiorJL4wRq6tSpRdbp3bu3Rn09fvwYZ86cQUpKChQKhco2TZ6n96ahQ4di6NCh+W6LiIhQKytoMc88ixYtKnSRTSIiIvrfVqIJlCZ+//13fP7553j+/DkqVqyocrecTCYrVgJFREREpE2Sn4X3tsaMGYPAwEA8ffoUjx8/xqNHj5Svhw8fajscIiIiIsm0nkDdv38fwcHBhT72hYiIiKgs03oC5e3tjXPnzml7WCIiIqISI+lhwiXh448/xrhx4xAXF4eGDRuqPTuve/fu2g6JiIiISBKNEqhKlSrh+vXrsLS0RGBgIBYvXoyKFSsWa8BBgwYBAKZPn662TSaTlcpaUkRERERSaHQJLzMzE+np6QCA9evX49WrV8UeUKFQFPhi8kRERETlgUZnoNzd3dGjRw+4urpCCIHg4GAYGRnlW3fdunUlGiARERFRWaPRGahNmzahS5cuePbsGWQyGZ48eaKy/MDrL00cPnwY3bp1Q+3atVGnTh10794dR48efasdISIiItIWjc5AWVtbY86cOQAABwcHbNy4sdAHCxdm06ZNGDBgAHr16oXg4GAIIXDixAm0a9cOERER8PX1LVa/RERERNoi+S68hISEtxpw1qxZmDdvHkaNGqUsGzFiBBYuXIgZM2YwgSIiIqIyr1jLGBw+fBjz589HfHw8ZDIZnJycMG7cOLRq1arItv/88w+6deumVt69e3d8++23xQmH/se5jttQ2iGUGee/56OQiIi0QfJCmps2bUL79u1hbGyM4OBgDB8+HEZGRmjXrh22bNlSZHtbW1scPHhQrfzgwYOwtbWVGg4RERGR1kk+A/W2l+DGjBmD4OBgxMbGwsPDAzKZDMeOHUNERAQWL14sfQ+IiIiItExyAvW2l+CGDBmCqlWrYsGCBdi+fTsAwMnJCZGRkfDx8ZEaDhEREZHWSU6g8i7B1a5dW6VcyiW4nj17omfPnlKHJiIiIioTJCdQvARHRERE/+skJ1DFuQT3+rP0LCwsIJPJCuz/4cOHUkMiIiIi0qpiLWMg9RLcokWLlA8fXrRoUaEJFBEREVFZV6wESip/f3/lzwEBAdoYkoiIiOidkbwO1NvS1dVFSkqKWnlaWhp0dXW1HQ4RERGRZFpPoIQQ+ZZnZGRALpdrORoiIiIi6bRyCQ8AlixZAgCQyWRYs2YNTExMlNtycnJw5MgRODo6aiscIiIiomKTnEBNnz4dY8eOhbGxsUr5y5cv8f3332PKlCn5tlu0aBGA3DNQK1euVLlcJ5fLYW9vj5UrV0oNh4iIiEjrJCdQoaGhGDx4sFoC9eLFC4SGhhaYQCUkJAAA2rZti6ioKFhYWBQjXCIiIqLSJzmBEkLkuwzBxYsXUalSpSLbHzp0SOqQRERERGWKxglU3gKYMpkMdevWVUmicnJy8OzZMwwePFijvu7du4fffvsNiYmJyMzMVNm2cOFCTUMiIiIiKhUaJ1BhYWEQQiAwMBChoaEwMzNTbsubw+Tu7l5kPwcPHkT37t3h4OCAa9euoUGDBrh9+zaEEGjatGnx9oKIiIhIizROoPIWw3RwcICHhwf09fWLNWBISAjGjBmD6dOno2LFitixYweqVKmCzz//HJ06dSpWn0RERETaJHkOlJeXFxQKBa5fv46UlBQoFAqV7a1bty60fXx8PLZu3Zo7uJ4eXr58CRMTE0yfPh0+Pj4YMmSI1JCIiIiItEpyAnXq1Cn4+vrizp07aotiymQy5OTkFNq+QoUKyMjIAABUq1YNt27dQv369QEAqampUsMhIiIi0jrJCdTgwYPh5uaGXbt2wcbGRvKDgVu0aIHjx4/D2dkZH3/8McaMGYPLly8jKioKLVq0kBoOERERkdZJTqBu3LiBn3/+GbVr1y7WgAsXLsSzZ88AANOmTcOzZ88QGRmJ2rVrKxfbJCIiIirLJCdQzZs3x82bN4udQNWsWVP5s7GxMZYvX16sfoiIiIhKi+QE6uuvv8aYMWOQnJyMhg0bqt2N16hRI436OXfuHOLj4yGTyeDk5ARXV1epoRARERGVCskJVO/evQEAgYGByjKZTKZcobyoSeT37t1Dv379cPz4cZibmwMAHj9+DA8PD2zduhW2trZSQyIiIiLSKskJVN4z7YorMDAQWVlZiI+PR7169QAA165dQ2BgIIKCgrBv37636p+IiIjoXZOcQNnZ2b3VgEePHsWJEyeUyRMA1KtXD0uXLoWnp+db9U1ERESkDTrFabRx40Z4enqiWrVquHPnDoDcR738+uuvRbatUaMGsrKy1Mqzs7NRvXr14oRDREREpFWSE6gVK1Zg9OjR6NKlCx4/fqyc82Rubo6wsLAi28+bNw9ff/01zp07p1yI89y5cxgxYgTmz58vNRwiIiIirZN8CW/p0qVYvXo1evTogTlz5ijL3dzcMHbs2HzbWFhYqCy4+fz5czRv3hx6ernDZ2dnQ09PD4GBgejRo4fUkIiIiIi0qliTyF1cXNTKDQwM8Pz583zbaHJmioiIiKi8kJxAOTg4IDY2Vm0y+Z9//glnZ+d82/j7+wPIPdO0efNmeHt7o2rVqsUIl4iIiKj0SU6gxo0bh2HDhuHVq1cQQuDMmTPYunUrZs+ejTVr1hQ+mJ4ehgwZgvj4+GIHTERERFTaJCdQAwYMQHZ2Nr755hu8ePECvr6+qF69OhYvXozPPvusyPbNmzdHTEzMWy+HQERERFRaJCdQADBo0CAMGjQIqampUCgUqFKlisZthw4dijFjxuDevXtwdXVFhQoVVLZr+igYIiIiotJSrAQqj6WlpeQ2ffv2BQAEBwcry6Q8CoaIiIiotGmUQDVt2hQHDx6EhYUFXFxcVJYkeNOFCxcK7ettHwVDREREVNo0SqB8fHxgYGAAAG+9ThPnPhEREVF5p1ECNXXq1Hx/Lo4NGzYUur1///5v1T8RERHRuyZ5DtTZs2ehUCjQvHlzlfLTp09DV1cXbm5uhbYfMWKEyvusrCy8ePECcrkcxsbGTKCIiIiozJP8LLxhw4bh7t27auX379/HsGHDimz/6NEjldezZ89w7do1tGzZElu3bpUaDhEREZHWSU6g4uLi0LRpU7VyFxcXxMXFFSuIOnXqYM6cOWpnpzS1fPlyODg4wNDQEK6urjh69GiBdZOSkuDr64t69epBR0cHI0eOzLfejh074OzsDAMDAzg7O2Pnzp3Fio2IiIjeP5ITKAMDA/z7779q5UlJScqHAxeHrq4uHjx4ILldZGQkRo4ciYkTJyImJgatWrVC586dkZiYmG/9jIwMWFlZYeLEiWjcuHG+dU6ePIm+ffvCz88PFy9ehJ+fHz799FOcPn1acnxERET0/pGc8XTo0AEhISH49ddfYWZmBgB4/Pgxvv32W3To0KHI9r/99pvKeyEEkpKS8MMPP8DT01NqOFi4cCGCgoIwcOBAALkPLt67dy9WrFiB2bNnq9W3t7fH4sWLAQDr1q3Lt8+wsDDlfgJASEgIDh8+jLCwMF5mJCIiIukJ1IIFC9C6dWvY2dnBxcUFABAbGwtra2ts3LixyPZvLoMgk8lgZWWFjz76CAsWLJAUS2ZmJs6fP48JEyaolHfs2BEnTpyQ1NfrTp48iVGjRqmUeXt7IywsLN/6GRkZyMjIUL5PT08v9thERERU9klOoKpXr45Lly5h8+bNuHjxIoyMjDBgwAD069cP+vr6RbZXKBTFCjQ/qampyMnJgbW1tUq5tbU1kpOTi91vcnKypD5nz56N0NDQYo9HRERE5UuxJi1VqFABX375ZbEGnD59OsaOHQtjY2OV8pcvX+L777/HlClTJPf55sroeY+FeRtS+gwJCcHo0aOV79PT02Fra/tW4xMREVHZpVEC9dtvv6Fz587Q19dXm8P0pu7duxe6PTQ0FIMHD1ZLoF68eIHQ0FBJCZSlpSV0dXXVzgylpKSonUGSomrVqpL6NDAwUK7UTkRERO8/jRKoHj16IDk5GVWqVCn0US6aPAy4oDM5Fy9eRKVKlTQJR0kul8PV1RX79+9Hz549leX79++Hj4+PpL5e5+7ujv3796vMg9q3bx88PDyK3ScRERG9PzRKoF6ft1TcOUwWFhaQyWSQyWSoW7euShKVk5ODZ8+eYfDgwZL7HT16NPz8/ODm5gZ3d3esWrUKiYmJyr5CQkJw//59lUfIxMbGAgCePXuG//77D7GxsZDL5XB2dgaQu1p669atMXfuXPj4+ODXX3/FgQMHcOzYsWLtOxEREb1fNEqgKlWqhOvXr8PS0hKBgYFYvHgxKlasKGmgsLAwCCEQGBiI0NBQ5RIIQO6ZJHt7e7i7u0uLHkDfvn2RlpaG6dOnIykpCQ0aNMDu3buVDy1OSkpSWxMq7+5BADh//jy2bNkCOzs73L59GwDg4eGBbdu2YdKkSZg8eTJq1aqFyMhItcfXEBER0f8mjRKozMxMpKenw9LSEuvXr8fcuXMlJ1D+/v7Izs4GALRv3x4ffPCB9GgLMHToUAwdOjTfbREREWplQogi++zTpw/69OnztqERERHRe0ijBMrd3R09evSAq6srhBAIDg6GkZFRvnULWpwSAPT09DB06FDEx8cXL1oiIiKiMkCjBGrTpk1YtGgRbt26BQB48uQJXr16VawBmzdvjpiYGOUlNiIiIqLyRqMEytraGnPmzAEAODg4YOPGjahcuXKxBhw6dCjGjBmDe/fuwdXVFRUqVFDZ3qhRo2L1S0RERKQtkieRt23bFnK5vNgD9u3bFwAQHBysLJPJZMrlDYpaBoGIiIiotGltEnmehISEYrUjIiIiKiu0OokcAOc+ERERUbmno0mlTZs2oUuXLnj27BlkMhmePHmCR48e5fvSxMaNG+Hp6Ylq1arhzp07AHLXifr111+LvydEREREWqL1SeQrVqzAlClTMHLkSMyaNUs558nc3BxhYWFv9QgWIiIiIm3Q6AzU6xISEpTJU3GWMli6dClWr16NiRMnQldXV1nu5uaGy5cvS+6PiIiISNskJ1AKhQIzZsxA9erVYWJign/++QcAMHnyZKxdu7bI9gkJCSqPUsljYGCA58+fSw2HiIiISOs0uoT3upkzZ2L9+vWYN28eBg0apCxv2LAhFi1ahKCgoELbOzg4IDY2Vm0y+Z9//ql8mC8RERGVLNdxG0o7hDLh/Pf9S6QfyQnUhg0bsGrVKrRr1w6DBw9Wljdq1AhXr14tsv24ceMwbNgwvHr1CkIInDlzBlu3bsXs2bOxZs0aqeEQERERaZ3kBOr+/fuoXbu2WrlCoUBWVlaR7QcMGIDs7Gx88803ePHiBXx9fVG9enUsXrwYn332mdRwiIiIiLROcgJVv359HD16VO0S3E8//ZTv3KY3ZWRkwNfXF4MGDUJqaioUCgWqVKkiNQwiIiKiUiM5gZo6dSr8/Pxw//59KBQKREVF4dq1a9iwYQP++OOPAtulpqbC398f+/btg0KhQPPmzbFp0ybUrFnzrXaAiIiISNsk34XXrVs3REZGYvfu3ZDJZJgyZQri4+Px+++/o0OHDgW2CwkJwfnz5xEaGorvv/8eqamp+Oqrr94qeCIiIqLSIPkMFAB4e3vD29tbUpu9e/di3bp16NKlCwCgS5cuaNCgAbKysqCvr1+cMIiIiIhKRbESKAA4f/484uPjIZPJ4OzsXOT8pwcPHqjUcXR0hFwux4MHD/h8PCIiIipXJCdQKSkp+OyzzxAdHQ1zc3MIIfDkyRO0bdsW27Ztg5WVVb7thBDQ01MdTk9PDwqFoniRExEREZUSyQnU119/jfT0dPz9999wcnICAMTFxcHf3x/BwcHYunVrvu2EEGjXrp1KEvXixQt069YNcrlcWXbhwgWpIRERERFpleQEas+ePThw4IAyeQIAZ2dnLFu2DB07diyw3dSpU9XK+OBgIiIiKo8kJ1AKhSLfSd/6+vqFXo7LL4EiIiIiKo8kL2Pw0UcfYcSIEXjw4IGy7P79+xg1ahTatWtXosERERERlUWSE6gffvgBT58+hb29PWrVqoXatWvDwcEBT58+xdKlS99FjERERERliuRLeLa2trhw4QL279+Pq1evQggBZ2dntG/f/l3ER0RERFTmFHsdqA4dOhS68jgRERHR+0rjS3h//fUXnJ2dkZ6errbtyZMnyocMF2XDhg3IyMhQK8/MzMSGDRs0DYeIiIio1GicQIWFhWHQoEEwNTVV22ZmZoavvvoKCxcuLLKfAQMG4MmTJ2rlT58+xYABAzQNh4iIiKjUaJxAXbx4EZ06dSpwe8eOHXH+/Pki+xFCQCaTqZXfu3cPZmZmmoZDREREVGo0ngP177//FvrQXz09Pfz3338FbndxcYFMJoNMJlNbkTwnJwcJCQmFJmhEREREZYXGCVT16tVx+fJl1K5dO9/tly5dgo2NTYHte/ToAQCIjY2Ft7c3TExMlNvkcjns7e3Ru3dvTcMhIiIiKjUaJ1BdunTBlClT0LlzZxgaGqpse/nyJaZOnYquXbsW2D5vJXJ7e3v07dtXrQ8iIiKi8kLjBGrSpEmIiopC3bp1MXz4cNSrVw8ymQzx8fFYtmwZcnJyMHHixCL78ff3f6uAiYiIiEqbxgmUtbU1Tpw4gSFDhiAkJARCCACATCaDt7c3li9fDmtr6yL70dHRyXcSeZ6cnBxNQyIiIiIqFZIW0rSzs8Pu3bvx6NEj3Lx5E0II1KlTBxYWFhr3ERUVpZJAZWVlISYmBuvXr0doaKiUcIiIiIhKRbFWIrewsECzZs2KNWDeZPLX9enTB/Xr10dkZCSCgoKK1S8RERGRtkh+mPC70rx5cxw4cKC0wyAiIiIqUplIoF6+fImlS5figw8+KO1QiIiIiIpU7IcJF5eFhYXKHCghBJ4+fQpjY2Ns2rRJ2+EQERERSab1BCosLEzlvY6ODqysrNC8eXNJk9GJiIiISovWEyiuA0VERETlndYTKAB49OgR1q5di/j4eMhkMjg5OWHAgAGoVKlSaYRDREREJInWJ5EfPnwY9vb2WLJkCR49eoSHDx9iyZIlcHBwwOHDh7UdDhEREZFkWj8DNWzYMPTt2xcrVqyArq4ugNzVx4cOHYphw4bhypUr2g6JiIiISBKtn4G6desWxowZo0yeAEBXVxejR4/GrVu3tB0OERERkWRaT6CaNm2K+Ph4tfL4+Hg0adJE2+EQERERSab1S3jBwcEYMWIEbt68iRYtWgAATp06hWXLlmHOnDm4dOmSsm6jRo20HR4RERFRkbSeQPXr1w8A8M033+S7TSaTQQgBmUyGnJwcbYdHREREVCStJ1AJCQnaHpKIiIioRGk9gbKzs9P2kEREREQlqlQW0rx+/Tqio6ORkpIChUKhsm3KlCmlERIRERGRxrSeQK1evRpDhgyBpaUlqlatqvJgYZlMxgSKiIiIyjytL2Mwc+ZMzJo1C8nJyYiNjUVMTIzydeHChWL1uXz5cjg4OMDQ0BCurq44evRoofUPHz4MV1dXGBoaombNmli5cqXK9oiICMhkMrXXq1evihUfERERvV+0nkA9evQIn3zySYn1FxkZiZEjR2LixImIiYlBq1at0LlzZyQmJuZbPyEhAV26dEGrVq0QExODb7/9FsHBwdixY4dKPVNTUyQlJam8DA0NSyxuIiIiKr+0nkB98skn2LdvX4n1t3DhQgQFBWHgwIFwcnJCWFgYbG1tsWLFinzrr1y5EjVq1EBYWBicnJwwcOBABAYGYv78+Sr1ZDIZqlatqvIiIiIiAkphDlTt2rUxefJknDp1Cg0bNoS+vr7K9uDgYI37yszMxPnz5zFhwgSV8o4dO+LEiRP5tjl58iQ6duyoUubt7Y21a9ciKytLGc+zZ89gZ2eHnJwcNGnSBDNmzICLi0u+fWZkZCAjI0P5Pj09XeN9ICIiovJH6wnUqlWrYGJigsOHD+Pw4cMq22QymaQEKjU1FTk5ObC2tlYpt7a2RnJycr5tkpOT862fnZ2N1NRU2NjYwNHREREREWjYsCHS09OxePFieHp64uLFi6hTp45an7Nnz0ZoaKjGcRMREVH59l4spPn6nXwAlCuZS6n/enmLFi2Uj5kBAE9PTzRt2hRLly7FkiVL1PoLCQnB6NGjle/T09Nha2srfUeIiIioXCiVdaBKiqWlJXR1ddXONqWkpKidZcpTtWrVfOvr6emhcuXK+bbR0dFBs2bNcOPGjXy3GxgYwMDAoBh7QEREROWR1hKo18/QFGbhwoUa9ymXy+Hq6or9+/ejZ8+eyvL9+/fDx8cn3zbu7u74/fffVcr27dsHNzc3tflYeYQQiI2NRcOGDTWOjYiIiN5fWkugYmJiiqxT2GW3gowePRp+fn5wc3ODu7s7Vq1ahcTERAwePBhA7uW1+/fvY8OGDQCAwYMH44cffsDo0aMxaNAgnDx5EmvXrsXWrVuVfYaGhqJFixaoU6cO0tPTsWTJEsTGxmLZsmWS4yMiIqL3j9YSqEOHDr2Tfvv27Yu0tDRMnz4dSUlJaNCgAXbv3q185l5SUpLKmlAODg7YvXs3Ro0ahWXLlqFatWpYsmQJevfurazz+PFjfPnll0hOToaZmRlcXFxw5MgRfPjhh+9kH4iIiKh8KddzoPIMHToUQ4cOzXdbRESEWpmXl1ehq54vWrQIixYtKqnwiIiI6D2j9YU0iYiIiMo7JlBEREREEjGBIiIiIpKICRQRERGRRKUyifzVq1e4dOkSUlJSoFAoVLZ17969NEIiIiIi0pjWE6g9e/agf//+SE1NVdsmk8mQk5Oj7ZCIiIiIJNH6Jbzhw4fjk08+QVJSEhQKhcqLyRMRERGVB1pPoFJSUjB69OgCn1VHREREVNZpPYHq06cPoqOjtT0sERERUYnR+hyoH374AZ988gmOHj2Khg0bqj3ANzg4WNshEREREUmi9QRqy5Yt2Lt3L4yMjBAdHa3yAGGZTMYEioiIiMo8rSdQkyZNwvTp0zFhwgTo6HAZKiIiIip/tJ7BZGZmom/fvkyeiIiIqNzSehbj7++PyMhIbQ9LREREVGK0fgkvJycH8+bNw969e9GoUSO1SeQLFy7UdkhEREREkmg9gbp8+TJcXFwAAFeuXFHZ9vqEciIiIqKySusJ1KFDh7Q9JBEREVGJKtWZ3Pfu3cP9+/dLMwQiIiIiybSeQCkUCkyfPh1mZmaws7NDjRo1YG5ujhkzZkChUGg7HCIiIiLJtH4Jb+LEiVi7di3mzJkDT09PCCFw/PhxTJs2Da9evcKsWbO0HRIRERGRJFpPoNavX481a9age/fuyrLGjRujevXqGDp0KBMoIiIiKvO0fgnv4cOHcHR0VCt3dHTEw4cPtR0OERERkWRaT6AaN26MH374Qa38hx9+QOPGjbUdDhEREZFkWr+EN2/ePHz88cc4cOAA3N3dIZPJcOLECdy9exe7d+/WdjhEREREkmn9DJSXlxeuX7+Onj174vHjx3j48CF69eqFa9euoVWrVtoOh4iIiEgyrZ6BysrKQseOHfHjjz9ysjgRERGVW1o9A6Wvr48rV67wkS1ERERUrmn9El7//v2xdu1abQ9LREREVGK0Pok8MzMTa9aswf79++Hm5oYKFSqobF+4cKG2QyIiIiKSROsJ1JUrV9C0aVMAwPXr11W28dIeERERlQdaSaAuXbqEBg0aQEdHB4cOHdLGkERERETvjFbmQLm4uCA1NRUAULNmTaSlpWljWCIiIqJ3QisJlLm5ORISEgAAt2/fhkKh0MawRERERO+EVi7h9e7dG15eXrCxsYFMJoObmxt0dXXzrfvPP/9oIyQiIiKiYtNKArVq1Sr06tULN2/eRHBwMAYNGoSKFStqY2giIiKiEqe1u/A6deoEADh//jxGjBjBBIqIiIjKLa0vYxAeHq7tIYmIiIhKlNZXIiciIiIq75hAEREREUnEBIqIiIhIIiZQRERERBIxgSIiIiKSiAkUERERkURMoIiIiIgkYgJFREREJBETKCIiIiKJmEARERERScQEioiIiEgiJlBEREREEjGBIiIiIpKICRQRERGRREygiIiIiCR6LxKo5cuXw8HBAYaGhnB1dcXRo0cLrX/48GG4urrC0NAQNWvWxMqVK9Xq7NixA87OzjAwMICzszN27tz5rsInIiKicqbcJ1CRkZEYOXIkJk6ciJiYGLRq1QqdO3dGYmJivvUTEhLQpUsXtGrVCjExMfj2228RHByMHTt2KOucPHkSffv2hZ+fHy5evAg/Pz98+umnOH36tLZ2i4iIiMqwcp9ALVy4EEFBQRg4cCCcnJwQFhYGW1tbrFixIt/6K1euRI0aNRAWFgYnJycMHDgQgYGBmD9/vrJOWFgYOnTogJCQEDg6OiIkJATt2rVDWFiYlvaKiIiIyrJynUBlZmbi/Pnz6Nixo0p5x44dceLEiXzbnDx5Uq2+t7c3zp07h6ysrELrFNQnERER/W/RK+0A3kZqaipycnJgbW2tUm5tbY3k5OR82yQnJ+dbPzs7G6mpqbCxsSmwTkF9ZmRkICMjQ/n+yZMnAID09PRC48/JeFno9v8VRR2novA4/h8ey5LxtscR4LHM87bHsvWkrSUUSfl3ZGa/t2rPz2Suwj6TeduEEEX2U64TqDwymUzlvRBCrayo+m+WS+lz9uzZCA0NVSu3tbUtPHACAJgtHVzaIbw3eCxLBo9jyeGxLDk8liVDk+P49OlTmJmZFVqnXCdQlpaW0NXVVTszlJKSonYGKU/VqlXzra+np4fKlSsXWqegPkNCQjB69Gjle4VCgYcPH6Jy5cqFJnKlLT09Hba2trh79y5MTU1LO5xyi8ex5PBYlhwey5LB41hyysOxFELg6dOnqFatWpF1y3UCJZfL4erqiv3796Nnz57K8v3798PHxyffNu7u7vj9999Vyvbt2wc3Nzfo6+sr6+zfvx+jRo1SqePh4ZFvnwYGBjAwMFApMzc3L84ulQpTU9My+2EuT3gcSw6PZcnhsSwZPI4lp6wfy6LOPOUp1wkUAIwePRp+fn5wc3ODu7s7Vq1ahcTERAwenHuKLiQkBPfv38eGDRsAAIMHD8YPP/yA0aNHY9CgQTh58iTWrl2LrVv/7zr7iBEj0Lp1a8ydOxc+Pj749ddfceDAARw7dqxU9pGIiIjKlnKfQPXt2xdpaWmYPn06kpKS0KBBA+zevRt2dnYAgKSkJJU1oRwcHLB7926MGjUKy5YtQ7Vq1bBkyRL07t1bWcfDwwPbtm3DpEmTMHnyZNSqVQuRkZFo3ry51vePiIiIyp5yn0ABwNChQzF06NB8t0VERKiVeXl54cKFC4X22adPH/Tp06ckwiuzDAwMMHXqVLXLjyQNj2PJ4bEsOTyWJYPHseS8b8dSJjS5V4+IiIiIlMr1QppEREREpYEJFBEREZFETKCIiIiIJGIC9R64ffs2ZDIZYmNjAQDR0dGQyWR4/PhxiY9lb2/PhyprmUwmwy+//FLaYbyVNz+j79L7cLyIStO7/A6Ratq0aWjSpElph5EvJlBERO+xNm3aYOTIkaUdBlGR8vvPz9ixY3Hw4MHSCagITKCIiikzM7O0QyDSGn7eqTSYmJgoH7NW1jCBKif27NmDli1bwtzcHJUrV0bXrl1x69atQtscP34cjRs3hqGhIZo3b47Lly+rbD9x4gRat24NIyMj2NraIjg4GM+fP1duT0lJQbdu3WBkZAQHBwds3rxZbYzExET4+PjAxMQEpqam+PTTT/Hvv/+WzE6XsKKO4f3799G3b19YWFigcuXK8PHxwe3bt5XbAwIC0KNHD8yePRvVqlVD3bp1AQCXL1/GRx99BCMjI1SuXBlffvklnj17pmwXHR2NDz/8EBUqVIC5uTk8PT1x584d5fYVK1agVq1akMvlqFevHjZu3PjuD8Y7olAoMHfuXNSuXRsGBgaoUaMGZs2alW/dw4cP48MPP4SBgQFsbGwwYcIEZGdnK7fnd7m4SZMmmDZtmvL9jRs30Lp1axgaGsLZ2Rn79+9/F7ulVb///jvMzc2hUCgAALGxsZDJZBg3bpyyzldffYV+/fohLS0N/fr1wwcffABjY2M0bNhQ5akKAQEBOHz4MBYvXgyZTAaZTKb8TMfFxaFLly4wMTGBtbU1/Pz8kJqaqmzbpk0bDB8+HKNHj4alpSU6dOignQNQQjIyMhAcHIwqVarA0NAQLVu2xNmzZwHkHpe84/H6Kzo6GgDw6NEj9O/fHxYWFjA2Nkbnzp1x48YNZd8REREwNzfH3r174eTkBBMTE3Tq1AlJSUkqMYSHh8PJyQmGhoZwdHTE8uXLtbb/RRFCYN68eahZsyaMjIzQuHFj/PzzzwXWL+z7IiQkBC1atFBr06hRI0ydOhUAcPbsWXTo0AGWlpYwMzNTW4/R3t4eANCzZ0/IZDLl+zcv4SkUCkyfPh0ffPABDAwM0KRJE+zZs0e5PW+6QFRUFNq2bQtjY2M0btwYJ0+eLO6hKpigcuHnn38WO3bsENevXxcxMTGiW7duomHDhiInJ0ckJCQIACImJkYIIcShQ4cEAOHk5CT27dsnLl26JLp27Srs7e1FZmamEEKIS5cuCRMTE7Fo0SJx/fp1cfz4ceHi4iICAgKUY3bu3Fk0aNBAnDhxQpw7d054eHgIIyMjsWjRIiGEEAqFQri4uIiWLVuKc+fOiVOnTommTZsKLy8vLR8dzRR2DJ8/fy7q1KkjAgMDxaVLl0RcXJzw9fUV9erVExkZGUIIIfz9/YWJiYnw8/MTV65cEZcvXxbPnz8X1apVE7169RKXL18WBw8eFA4ODsLf318IIURWVpYwMzMTY8eOFTdv3hRxcXEiIiJC3LlzRwghRFRUlNDX1xfLli0T165dEwsWLBC6urrir7/+UsYNQOzcuVPbh6tYvvnmG2FhYSEiIiLEzZs3xdGjR8Xq1avVPqP37t0TxsbGYujQoSI+Pl7s3LlTWFpaiqlTpyr7srOzU37W8jRu3FhZJycnRzRo0EC0adNGxMTEiMOHDwsXF5dydbzy8/jxY6GjoyPOnTsnhBAiLCxMWFpaimbNminr1K1bV6xYsULcu3dPfP/99yImJkbcunVLLFmyROjq6opTp04p+3J3dxeDBg0SSUlJIikpSWRnZ4sHDx4IS0tLERISIuLj48WFCxdEhw4dRNu2bZVjeHl5CRMTEzFu3Dhx9epVER8fr90D8ZaCg4NFtWrVxO7du8Xff/8t/P39hYWFhUhLSxOPHz9WHo+kpCQxYsQIUaVKFZGUlCSEEKJ79+7CyclJHDlyRMTGxgpvb29Ru3Zt5d/P8PBwoa+vL9q3by/Onj0rzp8/L5ycnISvr69y/FWrVgkbGxuxY8cO8c8//4gdO3aISpUqiYiIiFI5Hm/69ttvhaOjo9izZ4+4deuWCA8PFwYGBiI6Olr5HfLo0SMhRNHfF5cvXxYAxM2bN5X9X7lyRQAQ165dE0IIcfDgQbFx40YRFxcn4uLiRFBQkLC2thbp6elCCCFSUlIEABEeHi6SkpJESkqKEEKIqVOnisaNGyv7XbhwoTA1NRVbt24VV69eFd98843Q19cX169fF0II5d8aR0dH8ccff4hr166JPn36CDs7O5GVlVWix5AJVDmV92G7fPlygQnUtm3blPXT0tKEkZGRiIyMFEII4efnJ7788kuVPo8ePSp0dHTEy5cvxbVr1wQA5R9iIYSIj48XAJRfavv27RO6uroiMTFRWefvv/8WAMSZM2fe0Z6XnNeP4dq1a0W9evWEQqFQbs/IyBBGRkZi7969QojcBMra2lqZUAmR+0fSwsJCPHv2TFm2a9cuoaOjI5KTk0VaWpoAIKKjo/ONwcPDQwwaNEil7JNPPhFdunRRvi8vCUF6erowMDAQq1evVtv25mf022+/VTvey5YtEyYmJiInJ0cIUXQCtXfvXqGrqyvu3r2r3P7nn3+Wm+NVmKZNm4r58+cLIYTo0aOHmDVrlpDL5SI9PV0kJSUJAAUmNF26dBFjxoxRvvfy8hIjRoxQqTN58mTRsWNHlbK7d++qfOF5eXmJJk2alOBeac+zZ8+Evr6+2Lx5s7IsMzNTVKtWTcybN0+l7o4dO4SBgYE4evSoEEKI69evCwDi+PHjyjqpqanCyMhIbN++XQiRm0C9mTAsW7ZMWFtbK9/b2tqKLVu2qIw1Y8YM4e7uXnI7WkzPnj0ThoaG4sSJEyrlQUFBol+/fmoJVFHfF0II0ahRIzF9+nTl9pCQEJWk/03Z2dmiYsWK4vfff1eW5fdv980Eqlq1amLWrFkqdZo1ayaGDh0qhPi/vzVr1qxRbs/7Xirp/wTwEl45cevWLfj6+qJmzZowNTWFg4MDAKg85+9N7u7uyp8rVaqEevXqIT4+HgBw/vx5REREwMTERPny9vaGQqFAQkIC4uPjoaenBzc3N2Ufjo6OMDc3V76Pj4+Hra0tbG1tlWXOzs4wNzdXjlOWFHYMz58/j5s3b6JixYrK41GpUiW8evVK5TJfw4YNIZfLle/j4+PRuHFjVKhQQVnm6ekJhUKBa9euoVKlSggICIC3tze6deuGxYsXq5zmj4+Ph6enp0qcnp6eZfL4FSU+Ph4ZGRlo166dRnXd3d0hk8mUZZ6ennj27Bnu3bun8Xg1atTABx98oCx7/TNfnrVp0wbR0dEQQuDo0aPw8fFBgwYNcOzYMRw6dAjW1tZwdHRETk4OZs2ahUaNGqFy5cowMTHBvn37Cv27AOT++z906JDKv39HR0cAUPm8v/7vvzy5desWsrKyVP5t6evr48MPP1T5txUTE4P+/ftj2bJlaNmyJQAo//a9/uzTypUrq/z9BABjY2PUqlVL+d7GxgYpKSkAgP/++w93795FUFCQyjGeOXNmkVMvtCEuLg6vXr1Chw4dVOLbsGFDvvEV9X0BAJ9//rlymocQAlu3bsXnn3+u7CMlJQWDBw9G3bp1YWZmBjMzMzx79qzIz+rr0tPT8eDBA43+ZjZq1Ej5s42NjTKGkvRePAvvf0G3bt1ga2uL1atXo1q1alAoFGjQoIHkiZ15X1gKhQJfffUVgoOD1erUqFED165dU6mfHyFEvtsLKi9thR1DhUIBV1fXfOd5WVlZKX9+PVECCt/XvPLw8HAEBwdjz549iIyMxKRJk7B//37lnIE325fV41cUIyMjjevmt4/i/z9VKq9cR0dHWZYnKytLrf7ryuNxy0+bNm2wdu1aXLx4ETo6OnB2doaXlxcOHz6MR48ewcvLCwCwYMECLFq0CGFhYWjYsCEqVKiAkSNHFvl3QaFQoFu3bpg7d67atrwvG0D9815evPlZer08ryw5ORndu3dHUFAQgoKC1Nrm1+fr/enr66tsl8lkyrZ589dWr16t9hB6XV3d4uxSicqLb9euXahevbrKNgMDA7UkqqjvCwDw9fXFhAkTcOHCBbx8+RJ3797FZ599pqwXEBCA//77D2FhYbCzs4OBgQHc3d2LdXOCJn8zX//9vP69V5KYQJUDaWlpiI+Px48//ohWrVoBAI4dO1Zku1OnTik/3I8ePcL169eV/8ts2rQp/v77b9SuXTvftk5OTsjOzsa5c+fw4YcfAgCuXbumsi6Is7MzEhMTcffuXeVZqLi4ODx58gROTk7F3t93oahj2LRpU0RGRqJKlSowNTXVuF9nZ2esX78ez58/V37ZHD9+HDo6OspJ5gDg4uICFxcXhISEwN3dHVu2bEGLFi3g5OSEY8eOoX///sq6J06cKHPHTxN16tSBkZERDh48iIEDBxZa19nZGTt27FD5w3fixAlUrFhR+QfdyspK5Wxdenq68n+7eX0kJibiwYMHqFatGgC8m4mipaB169Z4+vQpwsLC4OXlBZlMBi8vL8yePRuPHj3CiBEjAEB5duqLL74AkPsFcePGDZXPj1wuR05Ojkr/TZs2xY4dO2Bvbw89vffva6B27dqQy+U4duwYfH19AeQm3+fOncPIkSPx6tUr+Pj4wNHREQsXLlRp6+zsjOzsbJw+fRoeHh4Acv9+XL9+XeN/l9bW1qhevTr++ecflbMwZYWzszMMDAyQmJioTMZf92YCVdT3BQB88MEHaN26NTZv3oyXL1+iffv2sLa2Vm4/evQoli9fji5dugAA7t69q3LTApCb9Lz5WX2dqakpqlWrhmPHjqF169bK8hMnTii/p7SqRC8I0juRk5MjKleuLL744gtx48YNcfDgQdGsWTPl9eKC5kDVr19fHDhwQFy+fFl0795d1KhRQzl/5+LFi8LIyEgMHTpUxMTEiOvXr4tff/1VDB8+XDlup06dRKNGjcSpU6fEuXPnRMuWLfOdRN6qVStx/vx5cfr0aeHq6lomJ5EXdQzzJpG3adNGHDlyRPzzzz8iOjpaBAcHK+fY+Pv7Cx8fH5V+nz9/LmxsbETv3r3F5cuXxV9//SVq1qypnET+zz//iAkTJogTJ06I27dvi71794pKlSqJ5cuXCyGE2Llzp9DX1xcrVqwQ169fV04iP3TokHIMlKM5PdOmTRMWFhZi/fr14ubNm+LkyZNizZo1BU4iHzZsmIiPjxe//PKL2iTyCRMmiKpVq4ojR46Iy5cvix49eggTExOVSeTOzs6iXbt2IjY2Vhw5ckS4urqWq+NVmKZNmwpdXV3xww8/CCGEePjwodDX1xcAxN9//y2EEGLkyJHC1tZWHD9+XMTFxYmBAwcKU1NTlc/poEGDRLNmzURCQoL477//RE5Ojrh//76wsrISffr0EadPnxa3bt0Se/fuFQMGDBDZ2dlCiPznTpUnI0aMENWqVRN//vmnyiTyhw8fiv79+wsbGxsRFxenMpk87++jj4+PcHZ2FkePHhWxsbGiU6dOapPIzczMVMbbuXOneP0rdfXq1cLIyEiEhYWJa9euiUuXLol169aJBQsWaO0YFGbixImicuXKyhs+Lly4IH744QcRERGhNgdKk+8LIXLnhFarVk1YWlqKjRs3qmxr0qSJ6NChg4iLixOnTp0SrVq1Uvk+EUKIOnXqiCFDhoikpCTx8OFDIYT6HKhFixYJU1NTsW3bNnH16lUxfvz4fCeR5/2tEUKIR48eCQAqf1dLAhOocmL//v3CyclJGBgYiEaNGono6OgiE6jff/9d1K9fX8jlctGsWTMRGxur0ueZM2dEhw4dhImJiahQoYJo1KiRyuS8pKQk8fHHHwsDAwNRo0YNsWHDBrWJvXfu3BHdu3cXFSpUEBUrVhSffPKJSE5O1sYhkaywYyhE7v72799fWFpaCgMDA1GzZk0xaNAg8eTJEyFE/gmUELl3qLRt21YYGhqKSpUqiUGDBomnT58KIYRITk4WPXr0EDY2NkIulws7OzsxZcoU5URpIYRYvny5qFmzptDX1xd169YVGzZsUOm/PCUEOTk5YubMmcLOzk7o6+uLGjVqiO+++y7fP2rR0dGiWbNmQi6Xi6pVq4rx48er3CXz5MkT8emnnwpTU1Nha2srIiIiVCaRCyHEtWvXRMuWLYVcLhd169YVe/bsKVfHqzBjxowRAMSVK1eUZY0bNxZWVlbKyfdpaWnCx8dHmJiYiCpVqohJkyaJ/v37q3xOr127Jlq0aCGMjIwEAJGQkCCEyJ0s3bNnT2Fubi6MjIyEo6OjGDlypLLv8p5AvXz5Unz99dfKf8+enp7Km1vs7OwEALVX3hfsw4cPhZ+fnzAzMxNGRkbC29tb+QUthGYJlBBCbN68WTRp0kTI5XJhYWEhWrduLaKiot7pfmtKoVCIxYsXi3r16gl9fX1hZWUlvL29xeHDh9USKCGK/r4QIjdRMTAwEMbGxsq/gXkuXLgg3NzchIGBgahTp4746aef1L5PfvvtN1G7dm2hp6cn7OzshBDqCVROTo4IDQ0V1atXF/r6+qJx48bizz//VG7XZgIlE6KAC75ERERElC/ehUdEREQkERMoIiIiIomYQBERERFJxASKiIiISCImUERUpOjoaMhkMpV1wN705kM/iyKTyfDLL7+8dWxERKWBCRQRqWnTpg1Gjhwpqc3YsWNx8ODBdxMQEVEZ8/4tQUtEpSLvGVn0/snKylJ7dAnR/zqegSIiFQEBATh8+DAWL14MmUwGmUyG27dvA8h9qKibmxuMjY3h4eGhfGYikP8lvHXr1qF+/fowMDCAjY0Nhg8fXuC406dPh7W1NWJjYwEA9vb2+O677xAYGIiKFSuiRo0aWLVqlUqb+/fvo2/fvrCwsEDlypXh4+OjjBXIvfT44YcfokKFCjA3N4enpyfu3LkDALh48SLatm2LihUrwtTUFK6urjh37lzxD1wJ+PHHH1G9enW1Z3Z1794d/v7+uHXrFnx8fGBtbQ0TExM0a9YMBw4cUKlrb2+PGTNmwNfXFyYmJqhWrRqWLl2qUicxMRE+Pj4wMTGBqakpPv30U/z777/K7Xm/y3Xr1qFmzZowMDAo8BlxRP+rmEARkYrFixfD3d0dgwYNQlJSEpKSkpTPOpw4cSIWLFiAc+fOQU9PD4GBgQX2s2LFCgwbNgxffvklLl++jN9++y3fZ2kJITBixAisXbsWx44dU0nCFixYADc3N8TExGDo0KEYMmQIrl69CgB48eIF2rZtCxMTExw5cgTHjh2DiYkJOnXqhMzMTGRnZ6NHjx7w8vLCpUuXcPLkSXz55ZfKZ+99/vnn+OCDD3D27FmcP38eEyZMKPWzLJ988glSU1Nx6NAhZdmjR4+wd+9efP7553j27Bm6dOmCAwcOICYmBt7e3ujWrZvaE+2///57NGrUCBcuXEBISAhGjRqF/fv3A8g93j169MDDhw9x+PBh7N+/H7du3ULfvn1V+rh58ya2b9+OHTt2KJNaInpNia5rTkTvhTcf45H3aIcDBw4oy3bt2iUAiJcvXwoh1B+5UK1aNTFx4sQCxwAgfvrpJ/HFF18IR0dH5TMH89jZ2YkvvvhC+V6hUIgqVaqIFStWCCGEWLt2rahXr57y0SNCCJGRkSGMjIzE3r17RVpamgAgoqOj8x2/YsWKIiIiouiDoWXdu3cXgYGByvc//vijqFq1qvIZdW9ydnYWS5cuVb63s7MTnTp1UqnTt29f0blzZyGEEPv27RO6uroiMTFRuf3vv/8WAJSPOpk6darQ19cXKSkpJbZfRO8bnoEiIo01atRI+bONjQ0AICUlRa1eSkoKHjx4gHbt2hXa36hRo3Dy5EkcPXoUH3zwQaHjyWQyVK1aVTne+fPncfPmTVSsWFE5/6pSpUp49eoVbt26hUqVKiEgIEB5lmbx4sVISkpS9jd69GgMHDgQ7du3x5w5c9SeQF9aPv/8c+zYsQMZGRkAgM2bN+Ozzz6Drq4unj9/jm+++QbOzs4wNzeHiYkJrl69qnYGyt3dXe19fHw8ACA+Ph62trbKs4oAlP3l1QEAOzs7WFlZvavdJCr3mEARkcZev8SVdynszfk6AGBkZKRRfx06dMD9+/exd+/eIsfLGzNvPIVCAVdXV8TGxqq8rl+/Dl9fXwBAeHg4Tp48CQ8PD0RGRqJu3bo4deoUgNx5Pn///Tc+/vhj/PXXX3B2dsbOnTs1ivtd6tatGxQKBXbt2oW7d+/i6NGj+OKLLwAA48aNw44dOzBr1iwcPXoUsbGxaNiwITIzM4vsN+/3JYRQ/vy6N8srVKhQQntE9H7iXXhEpEYulyMnJ6fY7StWrAh7e3scPHgQbdu2LbBe9+7d0a1bN/j6+kJXVxefffaZxmM0bdoUkZGRqFKlCkxNTQus5+LiAhcXF4SEhMDd3R1btmxBixYtAAB169ZF3bp1MWrUKPTr1w/h4eHo2bOn5jv6DhgZGaFXr17YvHkzbt68ibp168LV1RUAcPToUQQEBChjfPbsmcqk+Tx5SeLr7x0dHQHknm1KTEzE3bt3lWeh4uLi8OTJEzg5Ob3DPSN6v/AMFBGpsbe3x+nTp3H79m2kpqbme5apKNOmTcOCBQuwZMkS3LhxAxcuXFC7GwwAevbsiY0bN2LAgAH4+eefNe7/888/h6WlJXx8fHD06FEkJCTg8OHDGDFiBO7du4eEhASEhITg5MmTuHPnDvbt24fr16/DyckJL1++xPDhwxEdHY07d+7g+PHjOHv2bJlJID7//HPs2rUL69atU559AoDatWsjKioKsbGxuHjxInx9ffP93Rw/fhzz5s3D9evXsWzZMvz0008YMWIEAKB9+/Zo1KgRPv/8c1y4cAFnzpxB//794eXlBTc3N63tI1F5xwSKiNSMHTsWurq6cHZ2hpWVldocG034+/sjLCwMy5cvR/369dG1a1fcuHEj37p9+vTB+vXr4efnh6ioKI36NzY2xpEjR1CjRg306tULTk5OCAwMxMuXL2FqagpjY2NcvXoVvXv3Rt26dfHll19i+PDh+Oqrr6Crq4u0tDT0798fdevWxaefforOnTsjNDRU8n6+Cx999BEqVaqEa9euKS9HAsCiRYtgYWEBDw8PdOvWDd7e3mjatKla+zFjxuD8+fNwcXHBjBkzsGDBAnh7ewP4vxXgLSws0Lp1a7Rv3x41a9ZEZGSk1vaP6H0gE4KLexARvS/s7e0xcuRIySvJE5E0PANFREREJBETKCIiIiKJeAmPiIiISCKegSIiIiKSiAkUERERkURMoIiIiIgkYgJFREREJBETKCIiLWvTpo2kdZqio6Mhk8nw+PHjAutERETA3Nz8rWMjIs0wgSKiUhEQEIAePXpofVxNEo0FCxbAzMwML168UNv26tUrmJubY+HChcWOISoqCjNmzCh2eyIqfUygiIje0L9/f7x8+RI7duxQ27Zjxw68ePECfn5+kvvNysoCAFSqVAkVK1Z86ziJqPQwgSKiMqFNmzYIDg7GN998g0qVKqFq1aqYNm2aSh2ZTIYVK1agc+fOMDIygoODA3766Sfl9vwudcXGxkImk+H27duIjo7GgAED8OTJE8hkMshkMrUxAMDKygrdunXDunXr1LatW7cO3bt3h5WVFcaPH4+6devC2NgYNWvWxOTJk5VJEpD7QOUmTZpg3bp1qFmzJgwMDCCEULuEt2nTJri5uaFixYqoWrUqfH19kZKSojb28ePH0bhxYxgaGqJ58+a4fPlyocf0999/h6urKwwNDVGzZk2EhoYiOzu70DZEpBkmUERUZqxfvx4VKlTA6dOnMW/ePEyfPh379+9XqTN58mT07t0bFy9exBdffIF+/fohPj5eo/49PDwQFhYGU1NTJCUlISkpCWPHjs23blBQEA4fPoyEhARl2e3bt3Ho0CEEBQUBACpWrIiIiAjExcVh8eLFWL16NRYtWqTSz82bN7F9+3bs2LEDsbGx+Y6VmZmJGTNm4OLFi/jll1+QkJCAgIAAtXrjxo3D/PnzcfbsWVSpUgXdu3dXSdhet3fvXnzxxRcIDg5GXFwcfvzxR0RERGDWrFkaHCkiKpIgIioF/v7+wsfHR/ney8tLtGzZUqVOs2bNxPjx45XvAYjBgwer1GnevLkYMmSIEEKIQ4cOCQDi0aNHyu0xMTECgEhISBBCCBEeHi7MzMyKjC87O1tUr15dTJkyRVk2ZcoUUb16dZGdnZ1vm3nz5glXV1fl+6lTpwp9fX2RkpKiUs/Ly0uMGDGiwLHPnDkjAIinT5+q7Ne2bduUddLS0oSRkZGIjIzMd79atWolvvvuO5V+N27cKGxsbArfcSLSCM9AEVGZ0ahRI5X3NjY2apey3N3d1d5regZKCl1dXfj7+yMiIgIKhQJCCKxfvx4BAQHQ1dUFAPz8889o2bIlqlatChMTE0yePBmJiYkq/djZ2cHKyqrQsWJiYuDj4wM7OztUrFgRbdq0AQC1vl7f90qVKqFevXoF7vv58+cxffp0mJiYKF+DBg1CUlJSvpPjiUgaJlBEVGbo6+urvJfJZFAoFEW2k8lkAAAdndw/aeK1R3wWdIlLE4GBgbh79y7++usvHDx4EImJiRgwYAAA4NSpU/jss8/QuXNn/PHHH4iJicHEiRORmZmp0keFChUKHeP58+fo2LEjTExMsGnTJpw9exY7d+4EALW+8pO3729SKBQIDQ1FbGys8nX58mXcuHEDhoaGmuw+ERVCr7QDICKS4tSpU+jfv7/KexcXFwBQnulJSkqChYUFAKjNO5LL5cjJydForFq1asHLywvh4eHKyd+1atUCkDuh287ODhMnTlTWv3PnjuT9uXr1KlJTUzFnzhzY2toCAM6dO5dv3VOnTqFGjRoAgEePHuH69etwdHTMt27Tpk1x7do11K5dW3JMRFQ0JlBEVK789NNPcHNzQ8uWLbF582acOXMGa9euBQDUrl0btra2mDZtGmbOnIkbN25gwYIFKu3t7e3x7NkzHDx4EI0bN4axsTGMjY0LHC8oKAiDBg0CAKxZs0ZZXrt2bSQmJmLbtm1o1qwZdu3apTxzJEWNGjUgl8uxdOlSDB48GFeuXClwjajp06ejcuXKsLa2xsSJE2FpaVngWlpTpkxB165dYWtri08++QQ6Ojq4dOkSLl++jJkzZ0qOk4hU8RIeEZUroaGh2LZtGxo1aoT169dj8+bNcHZ2BpB7CXDr1q24evUqGjdujLlz56olCx4eHhg8eDD69u0LKysrzJs3r9DxevfuDQMDAxgYGKBXr17Kch8fH4waNQrDhw9HkyZNcOLECUyePFny/lhZWSEiIgI//fQTnJ2dMWfOHMyfPz/funPmzMGIESPg6uqKpKQk/Pbbb5DL5fnW9fb2xh9//IH9+/ejWbNmaNGiBRYuXAg7OzvJMRKROpl4fbIAEVEZJpPJsHPnzlJZwZyI6HU8A0VEREQkERMoIiIiIok4iZyIyg3OOCCisoJnoIiIiIgkYgJFREREJBETKCIiIiKJmEARERERScQEioiIiEgiJlBEREREEjGBIiIiIpKICRQRERGRREygiIiIiCT6f4RXGS9HvfpuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sensitivity_metrics_df\n",
    "df = df[(df.output_variable == \"Rg\") & (df.metric == \"r2\")]\n",
    "ax = sns.barplot(x=df.input_variable, y=df.value)\n",
    "ax.set_xticklabels([\"albedo\", \"aerosol\", \"cloud\\nthickness\", \"water\\nvapor\", \"ozone\", \"elevation\"])\n",
    "plt.xlabel(\"Input Variable\")\n",
    "plt.ylabel(\"Coefficient of Determination\\nfrom Input Perturbation to Output Perturbation\")\n",
    "plt.title(\"FLiES Solar Radiation Sensitivity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/35_x93nx79vg1zmd2fy1vcbr0000gq/T/ipykernel_39048/915107641.py:4: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([\"albedo\", \"aerosol\", \"cloud\\nthickness\", \"water\\nvapor\", \"ozone\", \"elevation\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'FLiES Ultra-Violet Radiation Sensitivity')"
      ]
     },
     "execution_count": 6,
     "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": [
    "df = sensitivity_metrics_df\n",
    "df = df[(df.output_variable == \"UV\") & (df.metric == \"correlation\")]\n",
    "ax = sns.barplot(x=df.input_variable, y=df.value)\n",
    "ax.set_xticklabels([\"albedo\", \"aerosol\", \"cloud\\nthickness\", \"water\\nvapor\", \"ozone\", \"elevation\"])\n",
    "plt.xlabel(\"Input Variable\")\n",
    "plt.ylabel(\"Correlation of Input Perturbation to Output Perturbation\")\n",
    "plt.title(\"FLiES Ultra-Violet Radiation Sensitivity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/35_x93nx79vg1zmd2fy1vcbr0000gq/T/ipykernel_39048/1660787085.py:4: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([\"albedo\", \"aerosol\", \"cloud\\nthickness\", \"water\\nvapor\", \"ozone\", \"elevation\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'FLiES Ultra-Violet Radiation Sensitivity')"
      ]
     },
     "execution_count": 7,
     "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": [
    "df = sensitivity_metrics_df\n",
    "df = df[(df.output_variable == \"UV\") & (df.metric == \"r2\")]\n",
    "ax = sns.barplot(x=df.input_variable, y=df.value)\n",
    "ax.set_xticklabels([\"albedo\", \"aerosol\", \"cloud\\nthickness\", \"water\\nvapor\", \"ozone\", \"elevation\"])\n",
    "plt.xlabel(\"Input Variable\")\n",
    "plt.ylabel(\"Coefficient of Determination\\nfrom Input Perturbation to Output Perturbation\")\n",
    "plt.title(\"FLiES Ultra-Violet Radiation Sensitivity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sensitivity",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}