Verma Net Radiation Sensitivity.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sensitivity import perturbed_run, sensitivity_analysis\n",
    "from verma_net_radiation_sensitivity.verma_net_radiation_sensitivity import process_verma_net_radiation_table\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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       "      <th></th>\n",
       "      <th>Rg</th>\n",
       "      <th>albedo</th>\n",
       "      <th>ST_C</th>\n",
       "      <th>EmisWB</th>\n",
       "      <th>Ta_C</th>\n",
       "      <th>RH</th>\n",
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       "      <td>0.89</td>\n",
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       "      <td>35.39</td>\n",
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       "      <td>13.17</td>\n",
       "      <td>0.966</td>\n",
       "      <td>11.629330</td>\n",
       "      <td>0.814974</td>\n",
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       "      <th>4</th>\n",
       "      <td>19.590548</td>\n",
       "      <td>0.115091</td>\n",
       "      <td>28.03</td>\n",
       "      <td>0.974</td>\n",
       "      <td>26.243547</td>\n",
       "      <td>0.556663</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.813275</td>\n",
       "      <td>0.123871</td>\n",
       "      <td>24.61</td>\n",
       "      <td>0.948</td>\n",
       "      <td>26.553339</td>\n",
       "      <td>0.547092</td>\n",
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       "      <th>244</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109698</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.970</td>\n",
       "      <td>5.191363</td>\n",
       "      <td>0.420223</td>\n",
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       "      <th>245</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059555</td>\n",
       "      <td>8.09</td>\n",
       "      <td>0.956</td>\n",
       "      <td>12.657937</td>\n",
       "      <td>0.599936</td>\n",
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       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>63.251832</td>\n",
       "      <td>0.115424</td>\n",
       "      <td>29.33</td>\n",
       "      <td>0.974</td>\n",
       "      <td>29.333225</td>\n",
       "      <td>0.609216</td>\n",
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       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>32.857876</td>\n",
       "      <td>0.162858</td>\n",
       "      <td>26.43</td>\n",
       "      <td>0.962</td>\n",
       "      <td>29.102583</td>\n",
       "      <td>0.696927</td>\n",
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       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>260.109914</td>\n",
       "      <td>0.107731</td>\n",
       "      <td>30.71</td>\n",
       "      <td>0.958</td>\n",
       "      <td>28.485040</td>\n",
       "      <td>0.585731</td>\n",
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       "<p>174 rows × 6 columns</p>\n",
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      "text/plain": [
       "             Rg    albedo   ST_C  EmisWB       Ta_C        RH\n",
       "0      0.000000  0.027717   0.89   0.972  -2.512519  1.000000\n",
       "1      0.000000  0.036438  35.39   0.940   4.822851  0.630401\n",
       "2      0.000000  0.077051  13.17   0.966  11.629330  0.814974\n",
       "4     19.590548  0.115091  28.03   0.974  26.243547  0.556663\n",
       "5      5.813275  0.123871  24.61   0.948  26.553339  0.547092\n",
       "..          ...       ...    ...     ...        ...       ...\n",
       "244    0.000000  0.109698   1.15   0.970   5.191363  0.420223\n",
       "245    0.000000  0.059555   8.09   0.956  12.657937  0.599936\n",
       "246   63.251832  0.115424  29.33   0.974  29.333225  0.609216\n",
       "247   32.857876  0.162858  26.43   0.962  29.102583  0.696927\n",
       "250  260.109914  0.107731  30.71   0.958  28.485040  0.585731\n",
       "\n",
       "[174 rows x 6 columns]"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "input_df = pd.read_csv(\"calval_final_50_PTJPL_inputs.csv\")\n",
    "input_df = input_df[[\"Rg\", \"albedo\", \"ST_C\", \"EmisWB\", \"Ta_C\", \"RH\"]]\n",
    "input_df = input_df.dropna()\n",
    "input_df"
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>-7.145783</td>\n",
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       "      <th>17395</th>\n",
       "      <td>ST_C</td>\n",
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       "      <td>30.71</td>\n",
       "      <td>6.750828</td>\n",
       "      <td>1.019494</td>\n",
       "      <td>37.460828</td>\n",
       "      <td>232.088111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>17396</th>\n",
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       "      <td>-6.103229</td>\n",
       "      <td>-0.921695</td>\n",
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       "      <td>ST_C</td>\n",
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       "      <td>30.71</td>\n",
       "      <td>16.380250</td>\n",
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       "      <th>17398</th>\n",
       "      <td>ST_C</td>\n",
       "      <td>Rn</td>\n",
       "      <td>30.71</td>\n",
       "      <td>1.997100</td>\n",
       "      <td>0.301597</td>\n",
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       "      <th>17399</th>\n",
       "      <td>ST_C</td>\n",
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       "      <td>-1.156370</td>\n",
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       "      input_variable output_variable  input_unperturbed  input_perturbation  \\\n",
       "0               ST_C              Rn               0.89          -12.224882   \n",
       "1               ST_C              Rn               0.89           -9.695695   \n",
       "2               ST_C              Rn               0.89           -4.786792   \n",
       "3               ST_C              Rn               0.89           -7.145783   \n",
       "4               ST_C              Rn               0.89           -0.262209   \n",
       "...              ...             ...                ...                 ...   \n",
       "17395           ST_C              Rn              30.71            6.750828   \n",
       "17396           ST_C              Rn              30.71           -6.103229   \n",
       "17397           ST_C              Rn              30.71           16.380250   \n",
       "17398           ST_C              Rn              30.71            1.997100   \n",
       "17399           ST_C              Rn              30.71           -1.156370   \n",
       "\n",
       "       input_perturbation_std  input_perturbed  output_unperturbed  \\\n",
       "0                   -1.846173       -11.334882            0.000000   \n",
       "1                   -1.464221        -8.805695            0.000000   \n",
       "2                   -0.722890        -3.896792            0.000000   \n",
       "3                   -1.079140        -6.255783            0.000000   \n",
       "4                   -0.039598         0.627791            0.000000   \n",
       "...                       ...              ...                 ...   \n",
       "17395                1.019494        37.460828          232.088111   \n",
       "17396               -0.921695        24.606771          232.088111   \n",
       "17397                2.473707        47.090250          232.088111   \n",
       "17398                0.301597        32.707100          232.088111   \n",
       "17399               -0.174632        29.553630          232.088111   \n",
       "\n",
       "       output_perturbation  output_perturbation_std  output_perturbed  \n",
       "0                      0.0                      0.0          0.000000  \n",
       "1                      0.0                      0.0          0.000000  \n",
       "2                      0.0                      0.0          0.000000  \n",
       "3                      0.0                      0.0          0.000000  \n",
       "4                      0.0                      0.0          0.000000  \n",
       "...                    ...                      ...               ...  \n",
       "17395                  0.0                      0.0        232.088111  \n",
       "17396                  0.0                      0.0        232.088111  \n",
       "17397                  0.0                      0.0        232.088111  \n",
       "17398                  0.0                      0.0        232.088111  \n",
       "17399                  0.0                      0.0        232.088111  \n",
       "\n",
       "[17400 rows x 10 columns]"
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "run_df = perturbed_run(input_df, \"ST_C\", \"Rn\", process_verma_net_radiation_table)\n",
    "run_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "np.nanmean(run_df.output_perturbation_std)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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"
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       "      <th>0</th>\n",
       "      <td>Ta_C</td>\n",
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       "      <th>0</th>\n",
       "      <td>Ta_C</td>\n",
       "      <td>Rn</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.211720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RH</td>\n",
       "      <td>Rn</td>\n",
       "      <td>correlation</td>\n",
       "      <td>0.170269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RH</td>\n",
       "      <td>Rn</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.028992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  input_variable output_variable       metric     value\n",
       "0           ST_C              Rn  correlation -0.331068\n",
       "0           ST_C              Rn           r2  0.109606\n",
       "0         EmisWB              Rn  correlation -0.182083\n",
       "0         EmisWB              Rn           r2  0.033154\n",
       "0         albedo              Rn  correlation -0.465537\n",
       "0         albedo              Rn           r2  0.216724\n",
       "0             Rg              Rn  correlation  0.877139\n",
       "0             Rg              Rn           r2  0.769373\n",
       "0           Ta_C              Rn  correlation  0.460130\n",
       "0           Ta_C              Rn           r2  0.211720\n",
       "0             RH              Rn  correlation  0.170269\n",
       "0             RH              Rn           r2  0.028992"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_variables = [\"ST_C\", \"EmisWB\", \"albedo\", \"Rg\", \"Ta_C\", \"RH\"]\n",
    "output_variables = [\"Rn\"]\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_verma_net_radiation_table\n",
    ")\n",
    "\n",
    "sensitivity_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/35_x93nx79vg1zmd2fy1vcbr0000gq/T/ipykernel_59317/2022254417.py:5: 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([\"Surface\\nTemperature\", \"Emissivity\", \"Albedo\", \"Solar\\nRadiation\", \"Air\\nTemperature\", \"Humidity\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Verma Net Radiation Sensitivity')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JkiU4cOAAwsLCsHjxYrRv3x537tzReYzt27dHTEwMhg0bhgMHDmDNmjX4+OOP32uQPysrK0ycOBF///232ryRI0eiWrVq+OSTTxAYGIh9+/Zhz549WLlyJbp3747jx4/LdWvXro1Dhw5h+/btOHXqFGJiYvIUz7Rp02BiYiJfuvPy8kLJkiUxZMgQbNmyBTt27MCXX36Js2fPqiynp6eHadOmITIyEl26dMHOnTuxfv16tGjRQqNLHu3bt8eePXvg7++PAwcOYMmSJWjVqhWcnZ1V6llaWsLR0RF//fUX9uzZg1OnTuU64nSJEiUwadIk+XLO33//jZCQEHh7e8PExAT+/v55OkbZxcTEwNHREd988w02btyII0eOYNu2bRg8eDCWLVuGZs2ayf2mlLynmkhKSoKbmxtmz56NHTt24PDhw5g9ezZ27dqFzz777K3LKv3MVK9eHZ6enpgxYwZOnTqlcavQu7YzbNgwpKSkIDIyMs8jsNMHpIA7cBMVuPnz5wsAokaNGjnOf/DggfDz8xPOzs7C0NBQ2NjYCHd3dzFx4kTx7NkzIcT/7ib75Zdf1JbPupvswYMHKuVv3uX1pqZNm4qaNWuqlM2cOVM4OTkJY2Nj4erqKlasWCGv911y205qaqpwdnbO8Xb3Z8+eiR9++EFUq1ZNGBkZCWtra1G7dm0xatQokZCQINeLiooSjRs3FmZmZgKAaNq06VtjyWlbWcaNGycAiMOHDwshhAgPDxeenp7CzMxM2NraigEDBojTp08LACIoKEhl2ZUrV4oqVaoIIyMjUbVqVbF69eoc7zBCtjvCUlNTxdixY0X58uWFiYmJcHNzE1u3bs1x2X379on69esLY2NjAUC+UymnW+uzYqpTp458/Dp16qQ29EBu740m721iYqKYPn26aN68uShfvrwwMjIS5ubmol69emL69OkiJSVFpb6m72lu75Gjo6O8zy9fvhRDhgwRderUEVZWVsLU1FRUq1ZN+Pv7i+fPn6vsX/bjmNtnJqe7ybIsX75cABCmpqYiKSlJbb6S7bypWbNmwsbGRu1YUfEjCfFG2zIREVExcP/+fTg6OuLbb79VG+iUip88daB+8uQJTpw4gfv376sN+d67d2+tBEZERKRtd+7cwY0bN/DLL79AT08PI0aMKOiQqBBQnAxt374dX331FZ4/fw5LS0uVTp2SJDEZIiKiQmvlypWYOnUqnJycsH79epQvX76gQ6JCQPFlsqpVq6Jt27b46aefVO66ISIiIiqKFCdD5ubmOH/+PCpVqqSrmIiIiIjyjeJb61u1aoVTp07pIhYiIiKifKe4z1C7du0wbtw4XLp0CbVr11Z7DlPHjh21FhwRERGRrim+TKanl3tjkiRJyMjIeO+gCpPMzEzcu3dPrbM4ERERFV5CCDx9+hTlypV7a+4C5KFlKPut9B+6e/fuwcHBoaDDICIiojy4ffs2KlSo8NY6fFDrO1haWgJ4fTCtrKwKOBoiIiLSRHJyMhwcHOTf8bfJUzKU9Rya6OhoSJIEV1dXjBs3Dk2aNMnL6gq1rEtjVlZWTIaIiIiKGE26uCi+mywkJAQtWrSAmZkZ/Pz8MHz4cJiamuLTTz/Fb7/9lqdAiYiIiAqK4g7Urq6uGDRoEEaNGqVSHhgYiBUrViA6OlqrARa05ORkWFtbIykpiS1DRERERYSS32/FLUM3btxAhw4d1Mo7duyI2NhYpasjIiIiKlCKkyEHBwfs379frXz//v2864qIiIiKHMUdqMeMGQM/Pz9ERUXBy8sLkiTh33//RXBwMObPn6+LGImIiIh0RnEyNHToUJQtWxZz5szBxo0bAbzuRxQaGopOnTppPUAiIiIiXVLcgbq4YQdqIiKiokenHaiJiIiIPiQaXSazsbHBlStXULp0aZQsWfKtAxg9fvxYa8ERERER6ZpGydDcuXPl4aznzp3LB5YSERHRB4N9ht6BfYaIiIiKHp32GdLX18f9+/fVyh89egR9fX2lqyMiIiIqUIqTodwaklJTU2FkZPTeARERERHlJ43HGVqwYAGA109/XblyJSwsLOR5GRkZ+Oeff1C9enXtR0hERESkQxonQ3PnzgXwumVo6dKlKpfEjIyM4OTkhKVLl2o/QiKiAuQ+bm1Bh1AoRP7Su6BDINIZjZOhrIewent7Y/PmzShZsqTOgiIiIiLKL4ofx3Hw4EFdxEFERERUIBQnQwBw584dbNu2DXFxcXj16pXKvMDAQK0ERkRERJQfFCdD+/fvR8eOHeHs7IyYmBjUqlULN2/ehBACbm5uuoiRiIiISGcU31o/YcIEjBkzBhcuXICJiQk2bdqE27dvo2nTpvjiiy90ESMRERGRzihOhqKjo9GnTx8AgIGBAV68eAELCwtMnToVP//8s9YDJCIiItIlxcmQubk5UlNTAQDlypXD9evX5XkPHz7UXmRERERE+UBxnyEPDw8cPXoUNWrUQLt27TBmzBicP38emzdvhoeHhy5iJCIiItIZxclQYGAgnj17BgCYMmUKnj17htDQULi4uMgDMxIREREVFYqToUqVKsl/m5mZYfHixVoNiIiIiCg/5WmcIQA4deoUoqOjIUkSXF1d4e7urs24iIiIiPKF4mTozp07+PLLL3H06FGUKFECAPDkyRN4eXlhw4YNcHBw0HaMRERERDqj+G6yfv36IS0tDdHR0Xj8+DEeP36M6OhoCCHQv39/XcRIREREpDOKW4aOHDmC8PBwVKtWTS6rVq0aFi5ciMaNG2s1OCIiIiJdU9wyVLFiRaSlpamVp6eno3z58loJ6m0WL14MZ2dnmJiYwN3dHUeOHHlr/fXr16Nu3bowMzODvb09+vbti0ePHuk8TiIiIioaFCdDs2bNwrfffotTp05BCAHgdWfqESNGYPbs2VoP8E2hoaEYOXIkJk6ciDNnzqBJkyZo06YN4uLicqz/77//onfv3ujfvz8uXryIP/74AydPnsSAAQN0GicREREVHZLIymjeomTJkpAkSZ5+/vw50tPTYWDw+ipb1t/m5uZ4/PixzoJt1KgR3NzcsGTJErnM1dUVnTt3xowZM9Tqz549G0uWLFEZJXvhwoWYNWsWbt++rdE2k5OTYW1tjaSkJFhZWb3/ThBRkeI+bm1Bh1AoRP7Su6BDIFJEye+3Rn2G5s2bp4243surV68QGRmJ8ePHq5S3bNkS4eHhOS7j5eWFiRMnIiwsDG3atMH9+/fx559/ol27drluJzU1VX7cCPD6YBIREdGHS6NkKOvBrOnp6Vi/fj1atWqFsmXL6jSw7B4+fIiMjAzY2dmplNvZ2SEhISHHZby8vLB+/Xr06NEDL1++RHp6Ojp27IiFCxfmup0ZM2YgICBAq7ETERFR4aWoz5CBgQGGDh2q0nKS3968XAcAQgi1siyXLl2Cn58fJk+ejMjISOzatQuxsbEYMmRIruufMGECkpKS5Jeml9OIiIioaFJ8a32jRo1w5swZODo66iKeXJUuXRr6+vpqrUD3799Xay3KMmPGDDRu3Bjjxo0DANSpUwfm5uZo0qQJpk+fDnt7e7VljI2NYWxsrP0dICIiokJJcTI0bNgwjBkzBnfu3IG7uzvMzc1V5tepU0drwb3JyMgI7u7u2Lt3L7p06SKX7927F506dcpxmZSUFLmTdxZ9fX0AgAb9xomIiKgYUJwM9ejRAwDg5+cnl0mSJF+uysjI0F502YwePRq9evVCgwYN4OnpieXLlyMuLk6+7DVhwgTcvXsXa9e+vvujQ4cOGDhwIJYsWYJWrVohPj4eI0eOxEcffYRy5crpLE4iIiIqOhQnQ7GxsbqIQyM9evTAo0ePMHXqVMTHx6NWrVoICwuTL9nFx8erjDnk6+uLp0+f4tdff8WYMWNQokQJNG/eHD///HNB7QIREREVMhqNM1SccZwhouKN4wy9xnGGqKjR+jhDb8q6BJWb3r35hSEiIqKiQ3EyNGLECJXptLQ0pKSkwMjICGZmZkyGiIiIqEhR/GyyxMREldezZ88QExODjz/+GBs2bNBFjEREREQ6ozgZykmVKlUwc+ZMtVYjIiIiosJOK8kQ8Hr8nnv37mlrdURERET5QnGfoW3btqlMCyEQHx+PX3/9FY0bN9ZaYERERET5QXEy1LlzZ5VpSZJga2uL5s2bY86cOdqKi4iIiChfKE6GMjMzdREHERERUYFQ3Gdo6tSpSElJUSt/8eIFpk6dqpWgiIiIiPKL4mQoICAAz549UytPSUlBQECAVoIiIiIiyi+Kk6GsB7Jmd/bsWdjY2GglKCIiIqL8onGfoZIlS0KSJEiShKpVq6okRBkZGXj27Jn89HgiIiKiokLjZGjevHkQQqBfv34ICAiAtbW1PM/IyAhOTk7w9PTUSZBEREREuqJxMtSnTx+kp6cDAFq0aIEKFSroLCgiIiKi/KKoz5CBgQGGDRuGjIwMXcVDRERElK8Ud6Bu1KgRzpw5o4tYiIiIiPKd4kEXhw0bhjFjxuDOnTtwd3eHubm5yvw6depoLTgiIiIiXVOcDPXo0QMA4OfnJ5dJkiTfcs9LaERERFSUKE6GYmNjdREHERERUYFQnAw5OjrqIg4iIiKiAqG4AzUArFu3Do0bN0a5cuVw69YtAK/HIfrrr7+0GhwRERGRrilOhpYsWYLRo0ejbdu2ePLkidxHqESJEpg3b5624yMiIiLSKcXJ0MKFC7FixQpMnDgR+vr6cnmDBg1w/vx5rQZHREREpGuKk6HY2FjUr19frdzY2BjPnz/XSlBERERE+UVxMuTs7IyoqCi18r///hs1atTQRkxERERE+Ubx3WTjxo3DN998g5cvX0IIgRMnTmDDhg2YMWMGVq5cqYsYiYiIiHRGcTLUt29fpKen47vvvkNKSgp8fHxQvnx5zJ8/Hz179tRFjEREREQ6ozgZSk1NhY+PDwYOHIiHDx8iMzMTZcqU0UVsRERERDqncZ+hhw8fol27drCwsICVlRW8vLyQnJzMRIiIiIiKNI2ToQkTJiAyMhIBAQH45Zdf8PDhQwwePFiXsRERERHpnMaXyXbv3o3Vq1ejbdu2AIC2bduiVq1aSEtLg6Ghoc4CJCIiItIljVuG7t27pzK+UPXq1WFkZIR79+7pJDAiIiKi/KBxMiSEgIGBakOSgYEBMjMztR4UERERUX7R+DKZEAKffvqpSkKUkpKCDh06wMjISC47ffq0diMkIiIi0iGNkyF/f3+1sk6dOmk1GCIiIqL89l7JEBEREVFRp/jZZEREREQfEiZDREREVKwxGSIiIqJircglQ4sXL4azszNMTEzg7u6OI0eOvLV+amoqJk6cCEdHRxgbG6Ny5cpYvXp1PkVLREREhZ3iZGjt2rVITU1VK3/16hXWrl2rlaByExoaipEjR2LixIk4c+YMmjRpgjZt2iAuLi7XZbp37479+/dj1apViImJwYYNG1C9enWdxklERERFhySEEEoW0NfXR3x8vNoDWh89eoQyZcogIyNDqwG+qVGjRnBzc8OSJUvkMldXV3Tu3BkzZsxQq79r1y707NkTN27cgI2NTZ62mZycDGtrayQlJcHKyirPsRNR0eQ+Trf/5BUVkb/0LugQiBRR8vutuGVICAFJktTK79y5A2tra6Wr09irV68QGRmJli1bqpS3bNkS4eHhOS6zbds2NGjQALNmzUL58uVRtWpVjB07Fi9evMh1O6mpqUhOTlZ5ERER0YdL43GG6tevD0mSIEmS2kjUGRkZiI2NRevWrXUSJAA8fPgQGRkZsLOzUym3s7NDQkJCjsvcuHED//77L0xMTLBlyxY8fPgQw4YNw+PHj3PtNzRjxgwEBARoPX4iIiIqnDROhjp37gwAiIqKQqtWrWBhYSHPMzIygpOTE7p166b1ALPL3iqVW0sVAGRmZkKSJKxfv15utQoMDMTnn3+ORYsWwdTUVG2ZCRMmYPTo0fJ0cnIyHBwctLgHREREVJgoHoHayckJPXr0gImJic6Cyknp0qWhr6+v1gp0//59tdaiLPb29ihfvrzK5TtXV1cIIXDnzh1UqVJFbRljY2MYGxtrN3giIiIqtBT3GerTp0++J0LA69Ynd3d37N27V6V879698PLyynGZxo0b4969e3j27JlcduXKFejp6aFChQo6jZeIiIiKBsXJkJ6eHvT19XN96dLo0aOxcuVKrF69GtHR0Rg1ahTi4uIwZMgQAK8vcfXu/b87Hnx8fFCqVCn07dsXly5dwj///INx48ahX79+OV4iIyIiouJH48tkWTZv3qzSRyctLQ1nzpzBmjVrdN7xuEePHnj06BGmTp2K+Ph41KpVC2FhYXB0dAQAxMfHq4w5ZGFhgb179+Lbb79FgwYNUKpUKXTv3h3Tp0/XaZxERERUdCgeZyg3v/32G0JDQ/HXX39pY3WFBscZIireOM7QaxxniIoanY4zlJtGjRph37592lodERERUb7QSjL04sULLFy4kJ2SiYiIqMhR3GeoZMmSKn2GhBB4+vQpzMzMEBISotXgiIiIiHRNcTI0b948lWk9PT3Y2tqiUaNGKFmypLbiIiIiIsoXipOhPn366CIOIiIiogKhOBkCgMTERKxatQrR0dGQJAmurq7o27dvnp8MT0RERFRQFHegPnz4MJycnLBgwQIkJibi8ePHWLBgAZydnXH48GFdxEhERESkM4pbhr755hv06NEDS5YskUeczsjIwLBhw/DNN9/gwoULWg+SiIiISFcUtwxdv34dY8aMUXn0hr6+PkaPHo3r169rNTgiIiIiXVOcDLm5uSE6OlqtPDo6GvXq1dNGTERERET5RvFlMj8/P4wYMQLXrl2Dh4cHAODYsWNYtGgRZs6ciXPnzsl169Spo71IiYiIiHRA8bPJ9PTe3pgkSRKEEJAkCRkZGe8VXGHAZ5MRFW98NtlrfDYZFTVKfr8VtwzFxsbmOTAiIiKiwkZxMnTr1i14eXnBwEB10fT0dISHh+OTTz7RWnBEREREuqa4A7W3tzceP36sVp6UlARvb2+tBEVERESUXxQnQ1n9gbJ79OgRzM3NtRIUERERUX7R+DJZ165dAbzuIO3r6wtjY2N5XkZGBs6dOwcvLy/tR0hERESkQxonQ9bW1gBetwxZWlrC1NRUnmdkZAQPDw8MHDhQ+xESERER6ZDGyVBQUBAAwMnJCWPHjuUlMSIiIvogKL6bzN/fXxdxEBERERUIxcmQs7Nzjh2os9y4ceO9AiIiIiLKT4qToZEjR6pMp6Wl4cyZM9i1axfGjRunrbiIiIiI8oXiZGjEiBE5li9atAinTp1674CIiIiI8pPicYZy06ZNG2zatElbqyMiIiLKF1pLhv7880/Y2Nhoa3VERERE+ULxZbL69eurdKAWQiAhIQEPHjzA4sWLtRocERERka4pToY6d+6sMq2npwdbW1s0a9YM1atX11ZcRERERPmC4wwRERFRsaY4Gbp79y42bdqEK1euQJIkVK1aFV27dkX58uV1ER8REX0g3MetLegQCoXIX3oXdAiUjaJkaPHixRg9ejRevXoFa2trCCGQnJyMcePGITAwEMOGDdNVnEREREQ6ofHdZDt37oSfnx+GDx+Ou3fvIjExEU+ePMHdu3cxbNgwjBgxAmFhYbqMlYiIiEjrNG4ZmjVrFsaPH4/p06erlNvb2yMwMBBmZmb4+eef0bZtW60HSURERKQrGrcMnTlzBr169cp1fq9evXD69GmtBEVERESUXzROhjIzM2FoaJjrfENDQwghtBIUERERUX7ROBmqWbMm/vrrr1znb926FTVr1tRKUERERET5ReM+Q8OGDcPQoUNhbGyMQYMGwcDg9aLp6elYtmwZfvjhB45ATUREREWOxslQnz59cP78eQwfPhwTJkxA5cqVAQDXr1/Hs2fP4OfnB19fX13FSURERKQTisYZmj17Nj7//HNs2LABV69eBQB88skn6NmzJzw8PHQSIBEREZEuKR6B2sPDg4kPERERfTA07kBdWCxevBjOzs4wMTGBu7s7jhw5otFyR48ehYGBAerVq6fbAImIiKhIKVLJUGhoKEaOHImJEyfizJkzaNKkCdq0aYO4uLi3LpeUlITevXvj008/zadIiYiIqKgoUslQYGAg+vfvjwEDBsDV1RXz5s2Dg4MDlixZ8tblBg8eDB8fH3h6euZTpERERFRUFJlk6NWrV4iMjETLli1Vylu2bInw8PBclwsKCsL169fh7++v0XZSU1ORnJys8iIiIqIPl+IO1FkePHiAmJgYSJKEqlWrwtbWVptxqXn48CEyMjJgZ2enUm5nZ4eEhIQcl7l69SrGjx+PI0eOyOMivcuMGTMQEBDw3vESERFR0aC4Zej58+fo168fypUrh08++QRNmjRBuXLl0L9/f6SkpOgiRhWSJKlMCyHUygAgIyMDPj4+CAgIQNWqVTVe/4QJE5CUlCS/bt++/d4xExERUeGlOBkaPXo0Dh8+jG3btuHJkyd48uQJ/vrrLxw+fBhjxozRRYwAgNKlS0NfX1+tFej+/ftqrUUA8PTpU5w6dQrDhw+HgYEBDAwMMHXqVJw9exYGBgY4cOBAjtsxNjaGlZWVyouIiIg+XIovk23atAl//vknmjVrJpe1bdsWpqam6N69+zs7M+eVkZER3N3dsXfvXnTp0kUu37t3Lzp16qRW38rKCufPn1cpW7x4MQ4cOIA///wTzs7OOomTiIiIihbFyVBKSkqOLTFlypTR+WWy0aNHo1evXmjQoAE8PT2xfPlyxMXFYciQIQBeX+K6e/cu1q5dCz09PdSqVUstRhMTE7VyIiIiKr4UJ0Oenp7w9/fH2rVrYWJiAgB48eIFAgICdH7reo8ePfDo0SNMnToV8fHxqFWrFsLCwuDo6AgAiI+Pf+eYQ0RERERvkoQQQskC58+fR5s2bfDy5UvUrVsXkiQhKioKJiYm2L17N2rWrKmrWAtEcnIyrK2tkZSUxP5DRMWQ+7i1BR1CoRD5S+/3XgeP5WvaOJb0bkp+vxW3DNWuXRtXr15FSEgILl++DCEEevbsia+++gqmpqZ5DpqIiIioIChOhv755x94eXlh4MCBKuXp6en4559/8Mknn2gtOCIiIiJdU3xrvbe3Nx4/fqxWnpSUBG9vb60ERURERJRfFCdDuQ1y+OjRI5ibm2slKCIiIqL8ovFlsq5duwJ4PQK0r68vjI2N5XkZGRk4d+4cvLy8tB8hERERkQ5pnAxZW1sDeN0yZGlpqdJZ2sjICB4eHmr9iIiIiIgKO42ToaCgIACAk5MTxo4dy0tiRERE9EFQfDeZv7+/LuIgIiIiKhCKO1ATERERfUiYDBEREVGxxmSIiIiIijUmQ0RERFSsKe5ADQD79+/H/v37cf/+fWRmZqrMW716tVYCIyIiIsoPipOhgIAATJ06FQ0aNIC9vX2Oo1ETERERFRWKk6GlS5ciODgYvXr10kU8RERERPlKcZ+hV69e8bEbRERE9MFQnAwNGDAAv/32my5iISIiIsp3ii+TvXz5EsuXL8e+fftQp04dGBoaqswPDAzUWnBEREREuqY4GTp37hzq1asHALhw4YLKPHamJiIioqJGcTJ08OBBXcRBREREVCDea9DFO3fu4O7du9qKhYiIiCjfKU6GMjMzMXXqVFhbW8PR0REVK1ZEiRIlMG3aNLUBGImIiIgKO8WXySZOnIhVq1Zh5syZaNy4MYQQOHr0KKZMmYKXL1/ixx9/1EWcRERERDqhOBlas2YNVq5ciY4dO8pldevWRfny5TFs2DAmQ0RERFSkKL5M9vjxY1SvXl2tvHr16nj8+LFWgiIiIiLKL4qTobp16+LXX39VK//1119Rt25drQRFRERElF8UXyabNWsW2rVrh3379sHT0xOSJCE8PBy3b99GWFiYLmIkIiIi0hnFLUNNmzbFlStX0KVLFzx58gSPHz9G165dERMTgyZNmugiRiIiIiKdUdwyBADlypVjR2kiIiL6IGiUDJ07dw61atWCnp4ezp0799a6derU0UpgRERERPlBo2SoXr16SEhIQJkyZVCvXj1IkgQhhFo9SZKQkZGh9SCJiIiIdEWjZCg2Nha2trby30REREQfCo2SIUdHR/nvW7duwcvLCwYGqoump6cjPDxcpS4RERFRYaf4bjJvb+8cB1dMSkqCt7e3VoIiIiIiyi+KkyEhBCRJUit/9OgRzM3NtRIUERERUX7R+Nb6rl27AnjdSdrX1xfGxsbyvIyMDJw7dw5eXl7aj5CIiIhIhzROhqytrQG8bhmytLSEqampPM/IyAgeHh4YOHCg9iMkIiIi0iGNk6GgoCAAgJOTE8aOHctLYkRERPRBUDwCtb+/vy7iICIiIioQijtQA8Cff/6J7t27w8PDA25ubiovXVu8eDGcnZ1hYmICd3d3HDlyJNe6mzdvxmeffQZbW1tYWVnB09MTu3fv1nmMREREVHQoToYWLFiAvn37okyZMjhz5gw++ugjlCpVCjdu3ECbNm10EaMsNDQUI0eOxMSJE3HmzBk0adIEbdq0QVxcXI71//nnH3z22WcICwtDZGQkvL290aFDB5w5c0ancRIREVHRIYmcnqvxFtWrV4e/vz++/PJLWFpa4uzZs6hUqRImT56Mx48f49dff9VVrGjUqBHc3NywZMkSuczV1RWdO3fGjBkzNFpHzZo10aNHD0yePFmj+snJybC2tkZSUhKsrKzyFDcRFV3u49YWdAiFQuQvvd97HTyWr2njWNK7Kfn9VtwyFBcXJ99Cb2pqiqdPnwIAevXqhQ0bNuQhXM28evUKkZGRaNmypUp5y5YtER4ertE6MjMz8fTpU9jY2ORaJzU1FcnJySovIiIi+nApTobKli2LR48eAXj9mI5jx44BeP3MMoWNTIo8fPgQGRkZsLOzUym3s7NDQkKCRuuYM2cOnj9/ju7du+daZ8aMGbC2tpZfDg4O7xU3ERERFW6Kk6HmzZtj+/btAID+/ftj1KhR+Oyzz9CjRw906dJF6wFml33069xGxM5uw4YNmDJlCkJDQ1GmTJlc602YMAFJSUny6/bt2+8dMxERERVeim+tX758OTIzMwEAQ4YMgY2NDf7991906NABQ4YM0XqAWUqXLg19fX21VqD79++rtRZlFxoaiv79++OPP/5AixYt3lrX2NhYZXRtIiIi+rApTob09PSgp/e/BqXu3bu/9bKTthgZGcHd3R179+5VaYHau3cvOnXqlOtyGzZsQL9+/bBhwwa0a9dO53ESERFR0aI4GQKAxMRErFq1CtHR0ZAkCa6urujbt+9bOyZrw+jRo9GrVy80aNAAnp6eWL58OeLi4uQWqQkTJuDu3btYu/b1HQsbNmxA7969MX/+fHh4eMitSqampvLjRYiIiKh4U9xn6PDhw3B2dsaCBQuQmJiIx48fY8GCBXB2dsbhw4d1EaOsR48emDdvHqZOnYp69erhn3/+QVhYGBwdHQEA8fHxKmMOLVu2DOnp6fjmm29gb28vv0aMGKHTOImIiKjoUDzOUK1ateDl5YUlS5ZAX18fwOun1g8bNgxHjx7FhQsXdBJoQeE4Q0TFG8fGeY3jDGkPxxnKHzodZ+j69esYM2aMnAgBgL6+PkaPHo3r168rj5aIiIioACnuM+Tm5obo6GhUq1ZNpTw6Ohr16tXTVlxERESUC7ayvaatVjaNkqFz587Jf/v5+WHEiBG4du0aPDw8AADHjh3DokWLMHPmTK0ERURERJRfNEqG6tWrB0mSVEaY/u6779Tq+fj4oEePHtqLjoiIiEjHNEqGYmNjdR0HERERUYHQKBnKunU9LS0NgwYNwqRJk1CpUiWdBkZERESUHxTdTWZoaIgtW7boKhYiIiKifKf41vouXbpg69atOgiFiIiIKP8pvrXexcUF06ZNQ3h4ONzd3WFubq4y38/PT2vBEREREema4mRo5cqVKFGiBCIjIxEZGakyT5IkJkNERERUpChOhnhnGREREX1IFPcZIiIiIvqQKG4Z6tev31vnr169Os/BEBEREeU3xclQYmKiynRaWhouXLiAJ0+eoHnz5loLjIiIiCg/KE6GchpnKDMzE8OGDeNAjERERFTkaKXPkJ6eHkaNGoW5c+dqY3VERERE+UZrHaivX7+O9PR0ba2OiIiIKF8ovkw2evRolWkhBOLj47Fz50706dNHa4ERERER5QfFydCZM2dUpvX09GBra4s5c+a8804zIiIiosJGcTJ08OBBXcRBREREVCAU9xlq3rw5njx5olaenJzMW+uJiIioyFGcDB06dAivXr1SK3/58iWOHDmilaCIiIiI8ovGl8nOnTsn/33p0iUkJCTI0xkZGdi1axfKly+v3eiIiIiIdEzjZKhevXqQJAmSJOV4OczU1BQLFy7UanBEREREuqZxMhQbGwshBCpVqoQTJ07A1tZWnmdkZIQyZcpAX19fJ0ESERER6YrGyZCjoyPS0tLQu3dv2NjYwNHRUZdxEREREeULRR2oDQ0N8ddff+kqFiIiIqJ8p/huss6dO2Pr1q06CIWIiIgo/ykedNHFxQXTpk1DeHg43N3dYW5urjLfz89Pa8ERERER6ZriZGjlypUoUaIEIiMjERkZqTJPkiQmQ0RERFSkKE6GYmNjdREHERERUYFQ3Gcoy6tXrxATE4P09HRtxkNERESUrxQnQykpKejfvz/MzMxQs2ZNxMXFAXjdV2jmzJlaD5CIiIhIlxQnQxMmTMDZs2dx6NAhmJiYyOUtWrRAaGioVoMjIiIi0jXFfYa2bt2K0NBQeHh4QJIkubxGjRq4fv26VoMjIiIi0jXFLUMPHjxAmTJl1MqfP3+ukhwRERERFQWKk6GGDRti586d8nRWArRixQp4enpqLzIiIiKifKD4MtmMGTPQunVrXLp0Cenp6Zg/fz4uXryIiIgIHD58WBcxEhEREemM4pYhLy8vhIeHIyUlBZUrV8aePXtgZ2eHiIgIuLu76yJGIiIiIp1RlAw9ffoUe/fuRVxcHObMmYMLFy7g0qVLCAkJQe3atXUVo4rFixfD2dkZJiYmcHd3x5EjR95a//Dhw3B3d4eJiQkqVaqEpUuX5kucREREVDRonAydO3cO1atXR+vWrdG+fXu4uLhg3759uoxNTWhoKEaOHImJEyfizJkzaNKkCdq0aSOPdZRdbGws2rZtiyZNmuDMmTP4v//7P/j5+WHTpk35GjcREREVXhonQ+PHj0fFihVx5MgRnDp1Ck2bNsXw4cN1GZuawMBA9O/fHwMGDICrqyvmzZsHBwcHLFmyJMf6S5cuRcWKFTFv3jy4urpiwIAB6NevH2bPnp2vcRMREVHhpXEH6lOnTiEsLAwNGjQAAKxevRplypTBs2fPYGFhobMAs7x69QqRkZEYP368SnnLli0RHh6e4zIRERFo2bKlSlmrVq2watUqpKWlwdDQUG2Z1NRUpKamytPJyclaiJ6IiIgKK42ToYcPH6JixYrydKlSpWBmZoYHDx7kSzL08OFDZGRkwM7OTqXczs4OCQkJOS6TkJCQY/309HQ8fPgQ9vb2asvMmDEDAQEBimJzH7dWUf0PVeQvvd97HTyWr/FYas/7HkttvBf0Go+l9vBYapfGl8kkScLTp0+RnJyM5ORkJCUlqZXlRytK9oEdhRBvHewxp/o5lWeZMGECkpKS5Nft27ffM2IiIiIqzDRuGRJCoGrVqmpl9evXl/+WJAkZGRnajfD/K126NPT19dVage7fv6/W+pOlbNmyOdY3MDBAqVKlclzG2NgYxsbG2gmaiIiICj2Nk6GDBw/qMo53MjIygru7O/bu3YsuXbrI5Xv37kWnTp1yXMbT0xPbt29XKduzZw8aNGiQY38hIiIiKn40ToaaNm2qyzg0Mnr0aPTq1QsNGjSAp6cnli9fjri4OAwZMgTA60tcd+/exdq1r/tKDBkyBL/++itGjx6NgQMHIiIiAqtWrcKGDRsKcjeIiIioEFH8OI6C1KNHDzx69AhTp05FfHw8atWqhbCwMDg6OgIA4uPjVcYccnZ2RlhYGEaNGoVFixahXLlyWLBgAbp161ZQu0BERESFTJFKhgBg2LBhGDZsWI7zgoOD1cqaNm2K06dP6zgqIiIiKqoUP5uMiIiI6EPCZIiIiIiKNcXJUL9+/fD06VO18ufPn6Nfv35aCYqIiIgovyhOhtasWYMXL16olb948UK+i4uIiIioqNC4A3VycjKEEBBC4OnTpzAxMZHnZWRkICwsDGXKlNFJkERERES6onEyVKJECUiSBEmS1EaiBl4/3kLpM72IiIiICpqiEaiFEGjevDk2bdoEGxsbeZ6RkREcHR1Rrlw5nQRJREREpCuKR6COjY1FxYoV3/pwVCIiIqKiQvGgi7du3cKtW7dynf/JJ5+8V0BERERE+UlxMtSsWTO1sjdbiXT11HoiIiIiXVB8a31iYqLK6/79+9i1axcaNmyIPXv26CJGIiIiIp1R3DJkbW2tVvbZZ5/B2NgYo0aNQmRkpFYCIyIiIsoPWnsch62tLWJiYrS1OiIiIqJ8obhl6Ny5cyrTQgjEx8dj5syZqFu3rtYCIyIiIsoPipOhevXqQZIkCCFUyj08PLB69WqtBUZERESUHxQnQ7GxsSrTenp6sLW1VXk8BxEREVFRoTgZcnR01EUcRERERAUiTx2o9+/fj/bt26Ny5cpwcXFB+/btsW/fPm3HRkRERKRzipOhX3/9Fa1bt4alpSVGjBgBPz8/WFlZoW3btvj11191ESMRERGRzii+TDZjxgzMnTsXw4cPl8v8/PzQuHFj/PjjjyrlRERERIWd4pah5ORktG7dWq28ZcuWSE5O1kpQRERERPlFcTLUsWNHbNmyRa38r7/+QocOHbQSFBEREVF+UXyZzNXVFT/++CMOHToET09PAMCxY8dw9OhRjBkzBgsWLJDr+vn5aS9SIiIiIh1QnAytWrUKJUuWxKVLl3Dp0iW5vESJEli1apU8LUkSkyEiIiIq9N570EUiIiKiokxrD2olIiIiKooUtwxlZGQgODgY+/fvx/3795GZmaky/8CBA1oLjoiIiEjXFCdDI0aMQHBwMNq1a4datWpBkiRdxEVERESULxQnQ7///js2btyItm3b6iIeIiIionyluM+QkZERXFxcdBELERERUb5TnAyNGTMG8+fPhxBCF/EQERER5SvFl8n+/fdfHDx4EH///Tdq1qwJQ0NDlfmbN2/WWnBEREREuqY4GSpRogS6dOmii1iIiIiI8p3iZCgoKEgXcRAREREVCA66SERERMWaxi1D9evX12hModOnT79XQERERET5SeNkqHPnzjoMg4iIiKhgaJwM+fv76zIOIiIiogLBPkNERERUrCm+m6ygJCYmws/PD9u2bQMAdOzYEQsXLkSJEiVyrJ+WloYffvgBYWFhuHHjBqytrdGiRQvMnDkT5cqVy8fISYnIX3oXdAhERFTMFJmWIR8fH0RFRWHXrl3YtWsXoqKi0KtXr1zrp6Sk4PTp05g0aRJOnz6NzZs348qVK+jYsWM+Rk1ERESFXZFoGYqOjsauXbtw7NgxNGrUCACwYsUKeHp6IiYmBtWqVVNbxtraGnv37lUpW7hwIT766CPExcWhYsWK+RI7ERERFW4atQzZ2Njg4cOHAIB+/frh6dOnOg0qu4iICFhbW8uJEAB4eHjA2toa4eHhGq8nKSkJkiTlemkNAFJTU5GcnKzyIiIiog+XRsnQq1ev5KRgzZo1ePnypU6Dyi4hIQFlypRRKy9TpgwSEhI0WsfLly8xfvx4+Pj4wMrKKtd6M2bMgLW1tfxycHDIc9xERERU+Gl0mczT0xOdO3eGu7s7hBDw8/ODqalpjnVXr16t8canTJmCgICAt9Y5efIkAOQ44KMQQqOBINPS0tCzZ09kZmZi8eLFb607YcIEjB49Wp5OTk5mQkRERPQB0ygZCgkJwdy5c3H9+nVIkoSkpCSttA4NHz4cPXv2fGsdJycnnDt3Dv/995/avAcPHsDOzu6ty6elpaF79+6IjY3FgQMH3toqBADGxsYwNjZ+d/BERET0QdAoGbKzs8PMmTMBAM7Ozli3bh1KlSr13hsvXbo0Spcu/c56np6eSEpKwokTJ/DRRx8BAI4fP46kpCR4eXnlulxWInT16lUcPHhQKzETERHRh0XxrfWxsbH5nlS4urqidevWGDhwII4dO4Zjx45h4MCBaN++vcqdZNWrV8eWLVsAAOnp6fj8889x6tQprF+/HhkZGUhISEBCQgJevXqVr/ETERFR4ZWncYYOHz6MDh06wMXFBVWqVEHHjh1x5MgRbcemYv369ahduzZatmyJli1bok6dOli3bp1KnZiYGCQlJQEA7ty5g23btuHOnTuoV68e7O3t5ZeSO9CIiIjow6Z4nKGQkBD07dsXXbt2hZ+fH4QQCA8Px6efforg4GD4+PjoIk7Y2NggJCTkrXWEEPLfTk5OKtNEREREOVGcDP3444+YNWsWRo0aJZeNGDECgYGBmDZtms6SISIiIiJdUHyZ7MaNG+jQoYNaeceOHREbG6uVoIiIiIjyi+JkyMHBAfv371cr379/P8fjISIioiJH8WWyMWPGwM/PD1FRUfDy8oIkSfj3338RHByM+fPn6yJGIiIiIp1RnAwNHToUZcuWxZw5c7Bx40YAr299Dw0NRadOnbQeIBEREZEu5emp9V26dEGXLl20HQsRERFRvsvTOENEREREHwomQ0RERFSsMRkiIiKiYo3JEBERERVrTIaIiIioWFN8N1lGRgaCg4Oxf/9+3L9/H5mZmSrzDxw4oLXgiIiIiHRNcTI0YsQIBAcHo127dqhVqxYkSdJFXERERET5QnEy9Pvvv2Pjxo1o27atLuIhIiIiyleK+wwZGRnBxcVFF7EQERER5TvFydCYMWMwf/58CCF0EQ8RERFRvlJ8mezff//FwYMH8ffff6NmzZowNDRUmb9582atBUdERESka4qToRIlSvC5ZERERPTBUJwMBQUF6SIOIiIiogKRp6fWA8CDBw8QExMDSZJQtWpV2NraajMuIiIionyhuAP18+fP0a9fP9jb2+OTTz5BkyZNUK5cOfTv3x8pKSm6iJGIiIhIZxQnQ6NHj8bhw4exfft2PHnyBE+ePMFff/2Fw4cPY8yYMbqIkYiIiEhnFF8m27RpE/788080a9ZMLmvbti1MTU3RvXt3LFmyRJvxEREREemU4pahlJQU2NnZqZWXKVOGl8mIiIioyFGcDHl6esLf3x8vX76Uy168eIGAgAB4enpqNTgiIiIiXVN8mWz+/Plo3bo1KlSogLp160KSJERFRcHExAS7d+/WRYxEREREOqM4GapVqxauXr2KkJAQXL58GUII9OzZE1999RVMTU11EWOhF/lL74IOgYiIiPIoT+MMmZqaYuDAgdqOhYiIiCjfaZQMbdu2DW3atIGhoSG2bdv21rodO3bUSmBERERE+UGjZKhz585ISEhAmTJl0Llz51zrSZKEjIwMbcVGREREpHMaJUOZmZk5/k1ERERU1Cm+tX7t2rVITU1VK3/16hXWrl2rlaCIiIiI8oviZKhv375ISkpSK3/69Cn69u2rlaCIiIiI8oviZEgIAUmS1Mrv3LkDa2trrQRFRERElF80vrW+fv36kCQJkiTh008/hYHB/xbNyMhAbGwsWrdurZMgiYiIiHRF42Qo6y6yqKgotGrVChYWFvI8IyMjODk5oVu3bloPkIiIiEiXNE6G/P39AQBOTk7o0aMHTExMdBYUERERUX5RPAJ1nz59dBEHERERUYFQnAxlZGRg7ty52LhxI+Li4vDq1SuV+Y8fP9ZacERERES6pvhusoCAAAQGBqJ79+5ISkrC6NGj0bVrV+jp6WHKlCk6CPG1xMRE9OrVC9bW1rC2tkavXr3w5MkTjZcfPHgwJEnCvHnzdBYjERERFT2Kk6H169djxYoVGDt2LAwMDPDll19i5cqVmDx5Mo4dO6aLGAEAPj4+iIqKwq5du7Br1y5ERUWhV69eGi27detWHD9+HOXKldNZfERERFQ0KU6GEhISULt2bQCAhYWFPABj+/btsXPnTu1G9/9FR0dj165dWLlyJTw9PeHp6YkVK1Zgx44diImJeeuyd+/exfDhw7F+/XoYGhrqJD4iIiIquhQnQxUqVEB8fDwAwMXFBXv27AEAnDx5EsbGxtqN7v+LiIiAtbU1GjVqJJd5eHjA2toa4eHhuS6XmZmJXr16Ydy4cahZs6ZG20pNTUVycrLKi4iIiD5cipOhLl26YP/+/QCAESNGYNKkSahSpQp69+6Nfv36aT1A4HVrVJkyZdTKy5Qpg4SEhFyX+/nnn2FgYAA/Pz+NtzVjxgy5X5K1tTUcHBzyFDMREREVDYrvJps5c6b89+eff44KFSogPDwcLi4u6Nixo6J1TZkyBQEBAW+tc/LkSQDI8REguT0aBAAiIyMxf/58nD59Otc6OZkwYQJGjx4tTycnJzMhIiIi+oApToay8/DwgIeHR56WHT58OHr27PnWOk5OTjh37hz+++8/tXkPHjyAnZ1djssdOXIE9+/fR8WKFeWyjIwMjBkzBvPmzcPNmzdzXM7Y2Fhnl/uIiIio8NEoGdq2bZvGK1TSOlS6dGmULl36nfU8PT2RlJSEEydO4KOPPgIAHD9+HElJSfDy8spxmV69eqFFixYqZa1atUKvXr3Qt29fjWMkIiKiD5tGyVDWc8neRZIkZGRkvE88OXJ1dUXr1q0xcOBALFu2DAAwaNAgtG/fHtWqVZPrVa9eHTNmzECXLl1QqlQplCpVSmU9hoaGKFu2rMoyREREVLxp1IE6MzNTo5cuEqEs69evR+3atdGyZUu0bNkSderUwbp161TqxMTEyLf6ExEREWnivfoMvXz5Mt8e2GpjY4OQkJC31hFCvHV+bv2EiIiIqPhSfGt9RkYGpk2bhvLly8PCwgI3btwAAEyaNAmrVq3SeoBEREREuqQ4Gfrxxx8RHByMWbNmwcjISC6vXbs2Vq5cqdXgiIiIiHRNcTK0du1aLF++HF999RX09fXl8jp16uDy5ctaDY6IiIhI1xQnQ3fv3oWLi4taeWZmJtLS0rQSFBEREVF+UZwM1axZE0eOHFEr/+OPP1C/fn2tBEVERESUXxTfTebv749evXrh7t27yMzMxObNmxETE4O1a9dix44duoiRiIiISGcUtwx16NABoaGhCAsLgyRJmDx5MqKjo7F9+3Z89tlnuoiRiIiISGcUtQylp6fjxx9/RL9+/XD48GFdxURERESUbxS1DBkYGOCXX37R6UjTRERERPlJ8WWyFi1a4NChQzoIhYiIiCj/Ke5A3aZNG0yYMAEXLlyAu7s7zM3NVeYreWo9EelO5C+9CzoEIqIiQRLveqBXNnp6uTcm6eqp9QUpOTkZ1tbWSEpKgpWVVUGHQ0RERBpQ8vutuGUoMzMzz4ERERERFTaK+gylp6fDwMAAFy5c0FU8RERERPlK8d1kjo6OH9ylMCIiIiq+FN9N9sMPP2DChAl4/PixLuIhIiIiyleK+wwtWLAA165dQ7ly5eDo6Kh2N9np06e1FhwRERGRrilOhjp37qyDMIiIiIgKhuJb64sb3lpPRERU9Oj01voskZGRiI6OhiRJqFGjBurXr5/XVREREREVGMXJ0P3799GzZ08cOnQIJUqUgBACSUlJ8Pb2xu+//w5bW1tdxElERESkE4rvJvv222+RnJyMixcv4vHjx0hMTMSFCxeQnJwMPz8/XcRIREREpDOK+wxZW1tj3759aNiwoUr5iRMn0LJlSzx58kSb8RU49hkiIiIqepT8fituGcrMzIShoaFauaGhIR/VQUREREWO4mSoefPmGDFiBO7duyeX3b17F6NGjcKnn36q1eCIiIiIdE1xMvTrr7/i6dOncHJyQuXKleHi4gJnZ2c8ffoUCxcu1EWMRERERDqj+G4yBwcHnD59Gnv37sXly5chhECNGjXQokULXcRX4LK6VCUnJxdwJERERKSprN9tTbpGc9DFd7hz5w4cHBwKOgwiIiLKg9u3b6NChQpvraNxMnTgwAEMHz4cx44dU+uVnZSUBC8vLyxduhRNmjTJe8SFUGZmJu7duwdLS0tIklTQ4eQoOTkZDg4OuH37Nu94e088ltrDY6kdPI7aw2OpPUXhWAoh8PTpU5QrVw56em/vFaTxZbJ58+Zh4MCBOe60tbU1Bg8ejMDAwA8uGdLT03tnRllYWFlZFdoPZVHDY6k9PJbaweOoPTyW2lPYj6W1tbVG9TTuQH327Fm0bt061/ktW7ZEZGSkpqsjIiIiKhQ0Tob++++/HMcXymJgYIAHDx5oJSgiIiKi/KJxMlS+fHmcP38+1/nnzp2Dvb29VoIiZYyNjeHv7w9jY+OCDqXI47HUHh5L7eBx1B4eS+350I6lxh2ov/32Wxw6dAgnT56EiYmJyrwXL17go48+gre3NxYsWKCTQImIiIh0QeNk6L///oObmxv09fUxfPhwVKtWDZIkITo6GosWLUJGRgZOnz4NOzs7XcdMREREpDWKxhm6desWhg4dit27d8uDGEmShFatWmHx4sVwcnLSVZxEREREOpGnQRcTExNx7do1CCFQpUoVlCxZUhexEREREemc4meTAUDJkiXRsGFDfPTRR0yECoHly5fDwcEBenp6mDdvXkGHU6hMmTIF9erVe+/1BAcHo0SJEhrXP3ToECRJwpMnT9572wUt+74oPRZKSJKErVu36mTdRZm2Psf0YX03C4ObN29CkiRERUXlWic/zyF5ladkiJS7f/8+Bg8ejIoVK8LY2Bhly5ZFq1atEBER8V7rTU5OxvDhw/H999/j7t27GDRokJYizn++vr6QJEnt9bbxrd5l7Nix2L9//3vH1qNHD1y5ckXj+l5eXoiPj5cH/CqMX/7swsPDoa+v/17Hm3Kmq+8/5S63z3P27+aHwNfXF507d1Yrz4/Ez8HBAfHx8ahVq5bGy2Q/nxaGZF/xg1opb7p164a0tDSsWbMGlSpVwn///Yf9+/fj8ePHeVqfEAIZGRmIi4tDWloa2rVr90EMbdC6dWsEBQWplL3PrZsWFhawsLB437BgamoKU1NTjesbGRmhbNmy773d/LR69Wp8++23WLlyJeLi4lCxYsWCDumDoe3vf16kpaW9day4D01un+d3fTczMjIgSdI7H99Ar+nr6ys+1yk9n+YLQTqXmJgoAIhDhw7lOD82NlYAEGfOnFFb5uDBg0IIIQ4ePCgAiF27dgl3d3dhaGgoVq9eLQCovGJjY8W1a9dEx44dRZkyZYS5ublo0KCB2Lt3r8o2X758KcaNGycqVKggjIyMhIuLi1i5cqU8/+LFi6JNmzbC3NxclClTRnz99dfiwYMHWj82b+rTp4/o1KlTrvMBiKVLl4p27doJU1NTUb16dREeHi6uXr0qmjZtKszMzISHh4e4du2avIy/v7+oW7euPH3w4EHRsGFDYWZmJqytrYWXl5e4efOmEEKIqKgo0axZM2FhYSEsLS2Fm5ubOHnypBBCiKCgIGFtbS2EEOLy5csCgIiOjlaJb86cOcLR0VFkZmbK71diYqL895svf39/ERAQIGrVqqW2n25ubmLSpEl5PIp58+zZM2FpaSkuX74sevToIQICAuR5b+6LEP87Flu2bBFVqlQRxsbGokWLFiIuLk5lndu2bRNubm7C2NhYODs7iylTpoi0tDR5/pUrV0STJk2EsbGxcHV1FXv27BEAxJYtW+Q6586dE97e3sLExETY2NiIgQMHiqdPn+r0WGjbu77/Qghx69Yt0bFjR2Fubi4sLS3FF198IRISEuT52T/HJ06cEC1atBClSpUSVlZW4pNPPhGRkZEq6wQglixZIjp27CjMzMzE5MmTtb5vhVVePs/bt28Xrq6uQl9fX9y4caOAIs+b3M6db+5r9s+QEELMnTtXODo6qq3nxx9/FGXKlBHW1tby93bs2LGiZMmSonz58mLVqlXyMjn9fu3cuVNUqVJFmJiYiGbNmomgoKAcj3nW39nPj0FBQaJv376iXbt2KvGmpaUJOzs7le1rC1PffJDVOrF161akpqa+17q+++47zJgxA9HR0WjZsiX27dsHADhx4gTi4+Ph4OCAZ8+eoW3btti3bx/OnDmDVq1aoUOHDoiLi5PX07t3b/z+++9YsGABoqOjsXTpUrkFJT4+Hk2bNkW9evVw6tQp7Nq1C//99x+6d+/+XrFrw7Rp09C7d29ERUWhevXq8PHxweDBgzFhwgScOnUKADB8+PAcl01PT0fnzp3RtGlTnDt3DhERERg0aJD8AN6vvvoKFSpUwMmTJxEZGYnx48fn+J90tWrV4O7ujvXr16uU//bbb/Dx8VF7oK+XlxfmzZsHKysrxMfHIz4+HmPHjkW/fv1w6dIlnDx5Uq577tw5nDlzBr6+vu9zmBQLDQ1FtWrVUK1aNXz99dcICgqS7xjNSUpKCn788UesWbMGR48eRXJyMnr27CnP3717N77++mv4+fnh0qVLWLZsGYKDg/Hjjz8CeP0A5K5du0JfXx/Hjh3D0qVL8f3336tto3Xr1ihZsiROnjyJP/74A/v27cv1/S2s3vX9F0Kgc+fOePz4MQ4fPoy9e/fi+vXr6NGjR67rfPr0Kfr06YMjR47g2LFjqFKlCtq2bYunT5+q1PP390enTp1w/vx59OvXT+v7Vljl5fM8Y8YMrFy5EhcvXkSZMmXyMdrC5cCBA7h37x7++ecfBAYGYsqUKWjfvj1KliyJ48ePY8iQIRgyZAhu376d4/K3b99G165d0bZtW0RFRWHAgAEYP358rtvr0aMHxowZg5o1a8rnxx49emDAgAHYtWsX4uPj5bphYWF49uyZbn6LtJ5eUY7+/PNPUbJkSWFiYiK8vLzEhAkTxNmzZ4UQylqGtm7dqrLeM2fOyC1Cb1OjRg2xcOFCIYQQMTExAoBaa1GWSZMmiZYtW6qU3b59WwAQMTExCvZamT59+gh9fX1hbm6u8po6daoQ4vV/uj/88INcPyIiQgBQ+S9hw4YNwsTERJ5+87+hR48evfU/dEtLSxEcHJzjvDf/kxFCiMDAQFGpUiV5OuuYXrx4UQiR+3+f2bVp00YMHTpUnh45cqRo1qxZjjHokpeXl5g3b54Q4vV/X6VLl5Y/HzntCwBx7Ngxefno6GgBQBw/flwIIUSTJk3ETz/9pLKNdevWCXt7eyGEELt37xb6+vri9u3b8vy///5bpWVo+fLlomTJkuLZs2dynZ07dwo9PT2VVpOi4G3f/z179gh9fX2VlrWLFy8KAOLEiRNCCPWWoezS09OFpaWl2L59u1wGQIwcOVI3O1TI5eXzHBUVVVDhvrfczp0mJiaKW4YcHR1FRkaGXFatWjXRpEkTeTo9PV2Ym5uLDRs2CCHUf78mTJggXF1dRWZmprzM999//9bzYW6f7xo1aoiff/5Znu7cubPw9fVVeHQ0w5ahfNKtWzfcu3cP27ZtQ6tWrXDo0CG4ubkhODhY0XoaNGjwzjrPnz/Hd999hxo1aqBEiRKwsLDA5cuX5ZahqKgo6Ovro2nTpjkuHxkZiYMHD8r/0VpYWKB69eoAgOvXryuKVylvb29ERUWpvL755ht5fp06deS/swb4rF27tkrZy5cvkZycrLZuGxsb+Pr6yi1l8+fPV/mvY/To0RgwYABatGiBmTNnvnVfe/bsiVu3buHYsWMAgPXr16NevXqoUaOGov0dOHAgNmzYgJcvXyItLQ3r16/P9//gY2JicOLECbllx8DAAD169MDq1atzXcbAwEDls1i9enWUKFEC0dHRAF5/hqZOnaryGRo4cCDi4+ORkpKC6OhoVKxYERUqVJDX4enpqbKN6Oho1K1bF+bm5nJZ48aNkZmZiZiYGK3se3552/c/OjoaDg4OcHBwkOtnfXezjmd29+/fx5AhQ1C1alVYW1vD2toaz549U2n9BTQ7X3xo8vJ5NjIyUjm3FEU5nTtXrlypeD01a9ZU6S9lZ2enco7V19dHqVKlcP/+/RyXj46OhoeHh0oLefbvtqYGDBgg9yG9f/8+du7cqbPzIztQ5yMTExN89tln+OyzzzB58mQMGDAA/v7+OHLkCACoNOOmpaXluI43fxhyM27cOOzevRuzZ8+Gi4sLTE1N8fnnn+PVq1cA8M6Oa5mZmejQoQN+/vlntXm67qRtbm4OFxeXXOe/edkq68uWU1lmZmaOywcFBcHPzw+7du1CaGgofvjhB+zduxceHh6YMmUKfHx8sHPnTvz999/w9/fH77//ji5duqitx97eHt7e3vjtt9/g4eGBDRs2YPDgwYr3t0OHDjA2NsaWLVtgbGyM1NRUdOvWTfF63seqVauQnp6O8uXLy2VCCBgaGiIxMTHX5bJfDnyzLDMzEwEBAejatataHRMTkxwvWWRfnxAix23ktu3CLrfv/+jRo3Pcn7ftv6+vLx48eIB58+bB0dERxsbG8PT0lL/jWTQ5X3xo8vJ5NjU1LZKfqTfldO68c+eO/Leenp7a9y6n35nsXQMkScqxLLdzbE7f7bzq3bs3xo8fj4iICERERMDJyQlNmjTR2vrfxJahAlSjRg08f/4ctra2AKDSSvG2MRve5ciRI/D19UWXLl1Qu3ZtlC1bFjdv3pTn165dG5mZmTh8+HCOy7u5ueHixYtwcnKCi4uLyutDOLnWr18fEyZMQHh4OGrVqoXffvtNnle1alWMGjUKe/bsQdeuXdXubHvTV199hdDQUEREROD69esqfWayMzIyQkZGhlq5gYEB+vTpg6CgIAQFBaFnz54wMzN7vx1UID09HWvXrsWcOXNU/qM8e/YsHB0d1fpFvblcVh8t4PV/40+ePJFbEN3c3BATE6P2+XFxcYGenh5q1KiBuLg43Lt3T15H9tvMa9SogaioKDx//lwuO3r0KPT09FC1alVtHoYCkfX9zzoWb/bBuHTpEpKSkuDq6prjskeOHIGfnx/atm2LmjVrwtjYGA8fPsyv0AutvH6eiwNbW1skJCSoJCvv8zuTmxo1asgt5lmyT2eX2/mxVKlS6Ny5s3x+7Nu3r1ZjfROToXzw6NEjNG/eHCEhITh37hxiY2Pxxx9/YNasWejUqRNMTU3h4eGBmTNn4tKlS/jnn3/www8/5Hl7Li4u2Lx5s3wS8PHxUcninZyc0KdPH/Tr1w9bt25FbGwsDh06hI0bNwIAvvnmGzx+/BhffvklTpw4gRs3bmDPnj3o169fjh9YbUpNTUVCQoLKS1sn+djYWEyYMAERERG4desW9uzZgytXrsDV1RUvXrzA8OHDcejQIdy6dQtHjx7FyZMnc/0xAoCuXbsiOTkZQ4cOhbe3t8p/otk5OTnh2bNn2L9/Px4+fIiUlBR53oABA3DgwAH8/fff+X6JbMeOHUhMTET//v1Rq1Ytldfnn3+OVatW5bicoaEhvv32Wxw/fhynT59G37594eHhgY8++ggAMHnyZKxduxZTpkzBxYsXER0dLbfEAUCLFi1QrVo19O7dG2fPnsWRI0cwceJElW189dVXMDExQZ8+fXDhwgUcPHgQ3377LXr16lWknoH4ru9/ixYtUKdOHXz11Vc4ffo0Tpw4gd69e6Np06a5XuZycXHBunXrEB0djePHj+Orr74qfLcqF4C8fp6Lg2bNmuHBgweYNWsWrl+/jkWLFuHvv//W+naGDBmC69evY/To0YiJicFvv/32zu4gTk5OiI2NRVRUFB4+fKhyo8GAAQOwZs0aREdHo0+fPlqPNwuToXxgYWGBRo0aYe7cufjkk09Qq1YtTJo0CQMHDsSvv/4K4PWYGGlpaWjQoAFGjBiB6dOn53l7c+fORcmSJeHl5YUOHTqgVatWcHNzU6mzZMkSfP755xg2bBiqV6+OgQMHyv+BlytXDkePHkVGRgZatWqFWrVqYcSIEbC2ttb52Bu7du2Cvb29yuvjjz/WyrrNzMxw+fJldOvWDVWrVsWgQYMwfPhwDB48GPr6+nj06BF69+6NqlWronv37mjTpg0CAgJyXZ+VlRU6dOiAs2fP4quvvnrrtr28vDBkyBD06NEDtra2mDVrljyvSpUq8PLyQrVq1dCoUSOt7KumVq1ahRYtWuQ4AF23bt0QFRWF06dPq80zMzPD999/Dx8fH3h6esLU1BS///67PL9Vq1bYsWMH9u7di4YNG8LDwwOBgYFwdHQE8LrJfsuWLUhNTcVHH32EAQMGyHeavbmN3bt34/Hjx2jYsCE+//xzfPrpp/J3pqh41/c/a9TtkiVL4pNPPkGLFi1QqVIlhIaG5rrO1atXIzExEfXr10evXr3g5+dXrO+AypLXz3Nx4OrqisWLF2PRokWoW7cuTpw4gbFjx2p9OxUrVsSmTZuwfft21K1bF0uXLsVPP/301mW6deuG1q1bw9vbG7a2ttiwYYM8r0WLFrC3t0erVq1Qrlw5rcebJU/PJiMi7RFCoHr16hg8eDBGjx5d0OEQERUaKSkpKFeuHFavXp1jH0RtYQdqogJ0//59rFu3Dnfv3tXp9XAioqIkMzMTCQkJmDNnDqytrdGxY0edbo/JEFEBsrOzQ+nSpbF8+XI+9JiI6P+Li4uDs7MzKlSogODgYBgY6DZd4WUyIiIiKtbYgZqIiIiKNSZDREQFyNfXF507d5anmzVrhpEjR773erW1HqLigMlQLiRJeusrvx+kmR948iTKna+vr/z9NzAwQMWKFTF06NC3jtKdF5s3b8a0adM0rn/o0CFIkoQnT56813ryiudK+hCwA3Uu3hwNOjQ0FJMnT1Z5HlJRGuAsLS0tx6evfyjbI8ovrVu3RlBQENLT03Hp0iX069cPT548URkX5X3Z2NgUqvW8C8+VRWd7lDu2DOWibNmy8sva2hqSJKmU/fPPP3B3d4eJiQkqVaqEgIAApKeny8tLkoRly5ahffv2MDMzg6urKyIiInDt2jU0a9YM5ubm8PT0VHkY6JQpU1CvXj0sW7YMDg4OMDMzwxdffKH2H19QUBBcXV1hYmKC6tWrY/HixfK8mzdvQpIkbNy4Ec2aNYOJiQlCQkLw6NEjfPnll6hQoQLMzMxQu3ZtlRO4r68vDh8+jPnz58v/0d28eRPBwcEoUaKEyva3bt2q8hyfrLhXr16NSpUqwdjYGEIIJCUlYdCgQShTpgysrKzQvHlznD17VkvvEFH+MzY2RtmyZVGhQgW0bNkSPXr0wJ49ewAAGRkZ6N+/P5ydnWFqaopq1aph/vz5KstnZGRg9OjRKFGiBEqVKoXvvvtO7VlO2VsdQkJC0KBBA1haWqJs2bLw8fGRH5J58+ZNeHt7AwBKliyp0hKTfT2JiYno3bs3SpYsCTMzM7Rp0wZXr16V52d913fv3g1XV1dYWFigdevWKslOTniu5Lnyg/D+D77/8AUFBQlra2t5eteuXcLKykoEBweL69eviz179ggnJycxZcoUuQ4AUb58eREaGipiYmJE586dhZOTk2jevLnYtWuXuHTpkvDw8BCtW7eWl/H39xfm5uaiefPm4syZM+Lw4cPCxcVF+Pj4yHWWL18u7O3txaZNm8SNGzfEpk2bhI2NjQgODhZCCBEbGysACCcnJ7nO3bt3xZ07d8Qvv/wizpw5I65fvy4WLFgg9PX1xbFjx4QQQjx58kR4enqKgQMHivj4eBEfHy/S09PV9l0IIbZs2SLe/Ohkxd2qVStx+vRpcfbsWZGZmSkaN24sOnToIE6ePCmuXLkixowZI0qVKiUePXqkzbeHKF/06dNHdOrUSZ6+fv26qFGjhrCzsxNCCPHq1SsxefJkceLECXHjxg0REhIizMzMRGhoqLzMzz//LKytrcWff/4pLl26JPr37y8sLS1V1tu0aVMxYsQIeXrVqlUiLCxMXL9+XURERAgPDw/Rpk0bIYQQ6enpYtOmTQKAiImJEfHx8eLJkyc5rqdjx47C1dVV/PPPPyIqKkq0atVKuLi4iFevXgkhXp/nDA0NRYsWLcTJkydFZGSkcHV1VTn/vAvPlf/bdyF4rixKmAxpIPuHvEmTJuKnn35SqbNu3Tphb28vTwMQP/zwgzwdEREhAIhVq1bJZRs2bBAmJibytL+/v9DX1xe3b9+Wy/7++2+hp6cn4uPjhRBCODg4iN9++01l29OmTROenp5CiP99wefNm/fO/Wrbtq0YM2aMPJ395JnTvguR8xfc0NBQ3L9/Xy7bv3+/sLKyEi9fvlRZtnLlymLZsmXvjI2osOnTp4/Q19cX5ubmwsTERAAQAERgYGCuywwbNkx069ZNnra3txczZ86Up9PS0kSFChXemgxld+LECQFAPH36VAghxMGDBwUAkZiYqFLvzfVcuXJFABBHjx6V5z98+FCYmpqKjRs3CiFef9cBiGvXrsl1Fi1aJCd7muC50lqljOfKooN9hvIgMjISJ0+eVHmWUkZGBl6+fImUlBT5qeN16tSR52c9WLJ27doqZS9fvkRycjKsrKwAvH6uS4UKFeQ6np6eyMzMRExMDPT19XH79m30798fAwcOlOukp6erPYsn+wMeMzIyMHPmTISGhuLu3btITU1Famqq1p5C7+joCFtbW3k6MjISz549Q6lSpVTqvXjxQqW5m6go8fb2xpIlS5CSkoKVK1fiypUr+Pbbb+X5S5cuxcqVK3Hr1i28ePECr169Qr169QAASUlJiI+Ph6enp1zfwMAADRo0ULtU9qYzZ85gypQpiIqKwuPHj+WHLsfFxaFGjRoaxR0dHQ0DAwOVZ9+VKlUK1apVQ3R0tFxmZmaGypUry9P29vbyJbm84LlSHc+VhROToTzIzMxEQEBAjs9JMTExkf9+s2Nc1nXjnMrefKJ8dll1JEmS661YsULtgZ76+voq09m/uHPmzMHcuXMxb9481K5dG+bm5hg5ciRevXqV+47i9QM1s5+o09LS1Opl315mZibs7e1x6NAhtbrZr6sTFRXm5uZwcXEBACxYsADe3t4ICAjAtGnTsHHjRowaNQpz5syBp6cnLC0t8csvv+D48eN53t7z58/RsmVLtGzZEiEhIbC1tUVcXBxatWr1zu/um3JLtoQQKn1asnfmlSTprYnau/BcyXNlUcFkKA/c3NwQExMjnxS1KS4uDvfu3ZOfzhsREQE9PT1UrVoVdnZ2KF++PG7cuPHOp6Rnd+TIEXTq1Alff/01gNdfwKtXr8LV1VWuY2RkhIyMDJXlbG1t8fTpUzx//lz+EkdFRb1ze25ubkhISICBgQGcnJwUxUpUVPj7+6NNmzYYOnQojhw5Ai8vLwwbNkye/+Z/9tbW1rC3t8exY8fwySefAHjdUhEZGQk3N7cc13/58mU8fPgQM2fOhIODAwDg1KlTKnWMjIwAQO27+6YaNWogPT0dx48fh5eXFwDg0aNHuHLliso5QNt4rox65/Z4riwcmAzlweTJk9G+fXs4ODjgiy++gJ6eHs6dO4fz589j+vTp77VuExMT9OnTB7Nnz0ZycjL8/PzQvXt3lC1bFsDruxH8/PxgZWWFNm3aIDU1FadOnUJiYuJbn3ju4uKCTZs2ITw8HCVLlkRgYCASEhJUvuBOTk44fvw4bt68CQsLC9jY2KBRo0YwMzPD//3f/+Hbb7/FiRMnEBwc/M79aNGiBTw9PdG5c2f8/PPPqFatGu7du4ewsDB07txZrWmaqChq1qwZatasiZ9++glVqlTB2rVrsXv3bjg7O2PdunU4efIknJ2d5fojRozAzJkzUaVKFbi6uiIwMFDtDqg3VaxYEUZGRli4cCGGDBmCCxcuqI0d5OjoCEmSsGPHDrRt2xampqawsLBQqVOlShV06tQJAwcOxLJly2BpaYnx48ejfPny6NSpk1aPyZt4rgx+537wXFk48Nb6PGjVqhV27NiBvXv3omHDhvDw8EBgYCAcHR3fe90uLi7o2rUr2rZti5YtW6JWrVoqt4MOGDAAK1euRHBwMGrXro2mTZsiODhY5YSbk0mTJsHNzQ2tWrVCs2bNULZsWZVRbwFg7Nix0NfXR40aNeTmeBsbG4SEhCAsLEy+xXTKlCnv3A9JkhAWFoZPPvkE/fr1Q9WqVdGzZ0/cvHlT7hNA9CEYPXo0VqxYgc6dO6Nr167o0aMHGjVqhEePHqm0EgHAmDFj0Lt3b/j6+sqX0rp06ZLrum1tbREcHIw//vgDNWrUwMyZMzF79myVOuXLl0dAQADGjx8POzs7DB8+PMd1BQUFwd3dHe3bt4enpyeEEAgLC9PpODc8V055537wXFk48EGthciUKVOwdetWjZpWiYiKK54rSdvYMkRERETFGpMhIiIiKtZ4mYyIiIiKNbYMERERUbHGZIiIiIiKNSZDREREVKwxGSIiIqJijckQEdF7aNasGUaOHKlx/UOHDkGSpLeOPB0cHMznUhHlIyZDRPTefH191UbpzQ+aJA1z5syBtbU1UlJS1Oa9fPkSJUqUQGBgYJ5j2Lx5s9ojMoioaGEyREQftN69e+PFixfYtGmT2rxNmzYhJSUFvXr1UrzerCeS29jYwNLS8r3jJKKCw2SIiLSuWbNm8PPzw3fffQcbGxuULVtW7TlNkiRhyZIlaNOmDUxNTeHs7Iw//vhDnp/T5aSoqChIkoSbN2/i0KFD6Nu3L5KSkiBJEiRJyvFZULa2tujQoQNWr16tNm/16tXo2LEjbG1t8f3336Nq1aowMzNDpUqVMGnSJDnhAV4/AqJevXpYvXo1KlWqBGNjYwgh1C6ThYSEoEGDBrC0tETZsmXh4+OD+/fvq2376NGjqFu3LkxMTNCoUSOcP3/+rcd0+/btcHd3h4mJCSpVqoSAgACkp6e/dRki0gyTISLSiTVr1sDc3BzHjx/HrFmzMHXqVOzdu1elzqRJk9CtWzecPXsWX3/9Nb788ktER0drtH4vLy/MmzcPVlZWiI+PR3x8PMaOHZtj3f79++Pw4cOIjY2Vy27evImDBw+if//+AABLS0sEBwfj0qVLmD9/PlasWIG5c+eqrOfatWvYuHEjNm3alOtzsV69eoVp06bh7Nmz2Lp1K2JjY+Hr66tWb9y4cZg9ezZOnjyJMmXKoGPHjirJ15t2796Nr7/+Gn5+frh06RKWLVuG4OBg/PjjjxocKSJ6J0FE9J769OkjOnXqJE83bdpUfPzxxyp1GjZsKL7//nt5GoAYMmSISp1GjRqJoUOHCiGEOHjwoAAgEhMT5flnzpwRAERsbKwQQoigoCBhbW39zvjS09NF+fLlxeTJk+WyyZMni/Lly4v09PQcl5k1a5Zwd3eXp/39/YWhoaG4f/++Sr2mTZuKESNG5LrtEydOCADi6dOnKvv1+++/y3UePXokTE1NRWhoaI771aRJE/HTTz+prHfdunXC3t7+7TtORBphyxAR6USdOnVUpu3t7dUuF3l6eqpNa9oypIS+vj769OmD4OBgZGZmQgiBNWvWwNfXF/r6+gCAP//8Ex9//DHKli0LCwsLTJo0CXFxcSrrcXR0hK2t7Vu3debMGXTq1AmOjo6wtLREs2bNAEBtXW/uu42NDapVq5brvkdGRmLq1KmwsLCQXwMHDkR8fHyOHcOJSBkmQ0SkE4aGhirTkiQhMzPznctJkgQA0NN7fXoSbzw+MbfLSJro168fbt++jQMHDmD//v2Ii4tD3759AQDHjh1Dz5490aZNG+zYsQNnzpzBxIkT8erVK5V1mJubv3Ubz58/R8uWLWFhYYGQkBCcPHkSW7ZsAQC1deUka9+zy8zMREBAAKKiouTX+fPncfXqVZiYmGiy+0T0FgYFHQARFV/Hjh1D7969Vabr168PAHILTHx8PEqWLAkAav10jIyMkJGRodG2KleujKZNmyIoKEju+Fy5cmUArzszOzo6YuLEiXL9W7duKd6fy5cv4+HDh5g5cyYcHBwAAKdOncqx7rFjx1CxYkUAQGJiIq5cuYLq1avnWNfNzQ0xMTFwcXFRHBMRvRuTISIqMH/88QcaNGiAjz/+GOvXr8eJEyewatUqAICLiwscHBwwZcoUTJ8+HVevXsWcOXNUlndycsKzZ8+wf/9+1K1bF2ZmZjAzM8t1e/3798fAgQMBACtXrpTLXVxcEBcXh99//x0NGzbEzp075RYdJSpWrAgjIyMsXLgQQ4YMwYULF3Idg2jq1KkoVaoU7OzsMHHiRJQuXTrXsZomT56M9u3bw8HBAV988QX09PRw7tw5nD9/HtOnT1ccJxGp4mUyIiowAQEB+P3331GnTh2sWbMG69evR40aNQC8vsy2YcMGXL58GXXr1sXPP/+s9sPv5eWFIUOGoEePHrC1tcWsWbPeur1u3brB2NgYxsbG6Nq1q1zeqVMnjBo1CsOHD0e9evUQHh6OSZMmKd4fW1tbBAcH448//kCNGjUwc+ZMzJ49O8e6M2fOxIgRI+Du7o74+Hhs27YNRkZGOdZt1aoVduzYgb1796Jhw4bw8PBAYGAgHB0dFcdIROok8eYFeSKifCJJErZs2VIgI1cTEb2JLUNERERUrDEZIiIiomKNHaiJqEDwCj0RFRZsGSIiIqJijckQERERFWtMhoiIiKhYYzJERERExRqTISIiIirWmAwRERFRscZkiIiIiIo1JkNERERUrDEZIiIiomLt/wE7NpBGgpPe5QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sensitivity_metrics_df\n",
    "df = df[(df.output_variable == \"Rn\") & (df.metric == \"correlation\")]\n",
    "df = df.dropna()\n",
    "ax = sns.barplot(x=df.input_variable, y=df.value)\n",
    "ax.set_xticklabels([\"Surface\\nTemperature\", \"Emissivity\", \"Albedo\", \"Solar\\nRadiation\", \"Air\\nTemperature\", \"Humidity\"])\n",
    "plt.xlabel(\"Input Variable\")\n",
    "plt.ylabel(\"Correlation of Input Perturbation to Output Perturbation\")\n",
    "plt.title(\"Verma Net Radiation Sensitivity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/35_x93nx79vg1zmd2fy1vcbr0000gq/T/ipykernel_59317/2309426928.py:6: 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([\"Surface\\nTemperature\", \"Emissivity\", \"Albedo\", \"Solar\\nRadiation\", \"Air\\nTemperature\", \"Humidity\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Verma Net Radiation Sensitivity')"
      ]
     },
     "execution_count": 13,
     "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 == \"Rn\") & (df.metric == \"r2\")]\n",
    "df = df.dropna()\n",
    "df = df[df.value>0]\n",
    "ax = sns.barplot(x=df.input_variable, y=df.value)\n",
    "ax.set_xticklabels([\"Surface\\nTemperature\", \"Emissivity\", \"Albedo\", \"Solar\\nRadiation\", \"Air\\nTemperature\", \"Humidity\"])\n",
    "plt.xlabel(\"Input Variable\")\n",
    "plt.ylabel(\"Coefficient of Determination\\nfrom Input Perturbation to Output Perturbation\")\n",
    "plt.title(\"Verma Net Radiation Sensitivity\")"
   ]
  }
 ],
 "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
}