various_ML_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import Lasso, Ridge\n",
    "from sklearn.svm import SVR\n",
    "import sklearn.gaussian_process as gp\n",
    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor\n",
    "from sklearn.model_selection import ShuffleSplit, KFold, GridSearchCV, train_test_split\n",
    "\n",
    "import sys, os\n",
    "sys.dont_write_bytecode = True\n",
    "\n",
    "import conditions, analysis\n",
    "\n",
    "import random\n",
    "random.seed(1107)\n",
    "np.random.seed(1107)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "PATH = 'Output_Figures'\n",
    "os.makedirs(PATH, exist_ok=True)\n",
    "\n",
    "PATH2 = 'Output_CSV'\n",
    "os.makedirs(PATH2, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20220318 rwgs_250 all data\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Group', 'EN_Allred', 'm.p.', 'd_fus_H', 'Density', 'BG_Oxide',\n",
       "       'Ox Num_Oxide', 'Eads_CO2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition = conditions.calc_condition()\n",
    "Reaction = condition['Reaction']\n",
    "data_sheet_name = condition['data_sheet_name']\n",
    "date, pgm_model, add_model, supp_model = condition['date'], condition['pgm_model'], condition['add_model'], condition['supp_model']\n",
    "converted = analysis.analysis_data_convert(condition, data_sheet_name, use_models=[pgm_model, add_model, supp_model], idx=None)\n",
    "data, add_desc, feat, target = converted['data'], converted['add_desc'], converted['feat'], converted['target']\n",
    "\n",
    "feat, target = converted['feat'], converted['target']\n",
    "feat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso prop2\n",
      "Train ... RMSE: 0.762, MSE: 0.582, MAE: 0.618, R2: 0.336, RMSE detail (sd: 0.031, min:0.718, max:0.866)\n",
      "Test ... RMSE: 0.832, MSE: 0.704, MAE: 0.651, R2: 0.185, RMSE detail (sd: 0.104, min:0.627, max:1.124)\n",
      "Ridge prop2\n",
      "Train ... RMSE: 0.739, MSE: 0.547, MAE: 0.595, R2: 0.376, RMSE detail (sd: 0.034, min:0.663, max:0.851)\n",
      "Test ... RMSE: 0.829, MSE: 0.702, MAE: 0.633, R2: 0.185, RMSE detail (sd: 0.120, min:0.614, max:1.432)\n",
      "GPR prop2\n",
      "Train ... RMSE: 0.656, MSE: 0.431, MAE: 0.505, R2: 0.508, RMSE detail (sd: 0.019, min:0.606, max:0.691)\n",
      "Test ... RMSE: 0.731, MSE: 0.541, MAE: 0.563, R2: 0.378, RMSE detail (sd: 0.078, min:0.547, max:0.952)\n",
      "SVR prop2\n",
      "Train ... RMSE: 0.563, MSE: 0.324, MAE: 0.444, R2: 0.631, RMSE detail (sd: 0.083, min:0.372, max:0.661)\n",
      "Test ... RMSE: 0.734, MSE: 0.590, MAE: 0.556, R2: 0.315, RMSE detail (sd: 0.225, min:0.557, max:2.290)\n",
      "RFR prop2\n",
      "Train ... RMSE: 0.164, MSE: 0.027, MAE: 0.133, R2: 0.969, RMSE detail (sd: 0.004, min:0.149, max:0.174)\n",
      "Test ... RMSE: 0.444, MSE: 0.199, MAE: 0.362, R2: 0.769, RMSE detail (sd: 0.037, min:0.365, max:0.551)\n",
      "ETR prop2\n",
      "Train ... RMSE: 0.007, MSE: 0.000, MAE: 0.001, R2: 1.000, RMSE detail (sd: 0.005, min:0.000, max:0.011)\n",
      "Test ... RMSE: 0.396, MSE: 0.158, MAE: 0.324, R2: 0.815, RMSE detail (sd: 0.033, min:0.313, max:0.466)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\nprint('XGB', f'prop{pgm_model}{add_model}{supp_model}')\\ncvmodel = GridSearchCV(XGBRegressor(n_jobs = -1, random_state = 1107),\\n                        param_grid = {'n_estimators':[100, 250, 500]},\\n                        n_jobs = -1)\\nerrors['XGB_{}'.format(f'prop{pgm_model}{add_model}{supp_model}')] = analysis.crossvalid(feat, target, cvmodel, cvf)\\n\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MODEL_TYPES = [f'prop{add_model}']\n",
    "errors = {}\n",
    "cvf = ShuffleSplit(n_splits = 100, random_state = 1107, test_size = 0.2)\n",
    "log_param1 = [1e-2, 1e-1, 1.0, 10, 1e2]\n",
    "log_param2 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5]\n",
    "log_param3 = [1.0, 10, 1e2, 1e3, 1e4, 1e5]\n",
    "\n",
    "print('Lasso', f'prop{add_model}')\n",
    "cvmodel = GridSearchCV(Lasso(),\n",
    "                        param_grid = {'alpha': log_param1},\n",
    "                        n_jobs = -1)\n",
    "errors['Lasso'] = analysis.crossvalid(feat, target, cvmodel, cvf)\n",
    "\n",
    "print('Ridge', f'prop{add_model}')\n",
    "cvmodel = GridSearchCV(Ridge(),\n",
    "                        param_grid = {'alpha': log_param1},\n",
    "                        n_jobs = -1)\n",
    "errors['Ridge'] = analysis.crossvalid(feat, target, cvmodel, cvf)\n",
    "\n",
    "print('GPR', f'prop{add_model}')\n",
    "kernel = gp.kernels.Matern(nu = 2.5)\n",
    "cvmodel = GridSearchCV(gp.GaussianProcessRegressor(kernel = kernel,\n",
    "                                                    normalize_y = True,\n",
    "                                                    n_restarts_optimizer = 10,\n",
    "                                                    random_state = 1107),\n",
    "                        param_grid = {'alpha':log_param2},\n",
    "                        n_jobs = -1)\n",
    "errors['GPR'] = analysis.crossvalid(feat, target, cvmodel, cvf)\n",
    "\n",
    "print('SVR', f'prop{add_model}')\n",
    "cvmodel = GridSearchCV(SVR(kernel = 'rbf'),\n",
    "                        param_grid = {'C': log_param3,\n",
    "                                        'gamma':log_param2,\n",
    "                                        'epsilon':log_param1},\n",
    "                        n_jobs = -1)\n",
    "errors['SVR'] = analysis.crossvalid(feat, target, cvmodel, cvf)\n",
    "\n",
    "print('RFR', f'prop{add_model}')\n",
    "#model = opt_RFR(n_estimators = 100, random_state = 1107, n_jobs = 4)\n",
    "\n",
    "cvmodel = GridSearchCV(RandomForestRegressor(n_jobs = -1, random_state = 1107),\n",
    "                        param_grid = {'n_estimators':[100, 250, 500]},\n",
    "                        n_jobs = -1)\n",
    "\n",
    "errors['RFR'] = analysis.crossvalid(feat, target, cvmodel, cvf)\n",
    "\n",
    "print('ETR', f'prop{add_model}')\n",
    "#model = opt_ETR(n_estimators = 100, random_state = 1107, n_jobs = 4)\n",
    "\n",
    "cvmodel = GridSearchCV(ExtraTreesRegressor(n_jobs = -1, random_state = 1107),\n",
    "                        param_grid = {'n_estimators':[100, 250, 500]},\n",
    "                        n_jobs = -1)\n",
    "\n",
    "errors['ETR'] = analysis.crossvalid(feat, target, cvmodel, cvf)\n",
    "\n",
    "\"\"\"\n",
    "print('XGB', f'prop{pgm_model}{add_model}{supp_model}')\n",
    "cvmodel = GridSearchCV(XGBRegressor(n_jobs = -1, random_state = 1107),\n",
    "                        param_grid = {'n_estimators':[100, 250, 500]},\n",
    "                        n_jobs = -1)\n",
    "errors['XGB_{}'.format(f'prop{pgm_model}{add_model}{supp_model}')] = analysis.crossvalid(feat, target, cvmodel, cvf)\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = ['Lasso', 'Ridge', 'GPR', 'SVR', 'RFR', 'ETR']\n",
    "use_method = ['{}'.format(m) for m in methods ]\n",
    "\n",
    "mean_trn = [errors[m]['trn_rmse'] for m in use_method]\n",
    "mean_tes = [errors[m]['tes_rmse'] for m in use_method]\n",
    "\n",
    "width =0.3\n",
    "plt.figure(facecolor='white', figsize=(12,8))\n",
    "ind = np.arange(len(methods))\n",
    "plt.bar(ind, mean_trn, width, color='blue', alpha=0.4, edgecolor='blue', linewidth=2, label = 'Training')\n",
    "plt.bar(ind+width, mean_tes, width, color='red', alpha=0.4, edgecolor='red', linewidth=2, label = 'Test')\n",
    "\n",
    "for x,y in zip(ind, mean_trn):\n",
    "    plt.text(x, y+0.01, '%.2f' % y, ha='center', va= 'bottom', rotation=90, fontsize=14)\n",
    "\n",
    "for x,y in zip(ind, mean_tes):\n",
    "    plt.text(x+ width, y+0.01, '%.2f' % y, ha='center', va= 'bottom', rotation=90, fontsize=14)\n",
    "\n",
    "plt.xticks(ind+(width*0.5), methods, fontsize=14)\n",
    "#plt.tick_params(length=0)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel('RMSE CO form rate (mmol $min^{{-}1}$ $gcat^{{-}1}$)', fontsize=14)\n",
    "#plt.legend(bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=0, fontsize=10)\n",
    "plt.legend(bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=0, fontsize=18, frameon=False)\n",
    "plt.ylim(0, 1)\n",
    "plt.savefig(f'{PATH}/{date}_{Reaction}_ML_methods_prop{add_model}_RMSE_v2.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t2ik0NFRms9noiAAcBKUfAAAAqCQOHTqkkSNHauXKlYXWWa1WmUwm3XTTTYqNjdXIkSMNSIiq4ssvv9SuXbsUHR1tdBSUEY/sAwAAACqBn3/+WZ06ddKKFSvUunVr9e7dW61bt5bValWrVq309ttv6/HHH5fVatVzzz2nZ555xujIcGJLlixRXFyc0TFQDij9AAAAQCUQHR2t9PR0ffbZZ9qxY4e++OIL7dixQ59//rl++OEH3XTTTZo3b57S0tL02GOP6cMPP9SSJUuMjg2gkuPyfgAAAKASWLlypR555BE99thjBZaHh4frkUce0eTJkzVs2DDVqlVLCxYs0Hfffafp06erd+/exgSGQ5k/f36JxqemplZQEtgbpd9eEhOlnByjU5Qfd3cpPNzoFAAAAE4jIyNDfn5+Ra5r2rSp/vOf/9h+NpvN6tWrlxISEuwVDw5u6NChJXoE39U5JOD4KP32kpMjHTsmZWUZnaTsLBbJ27vcNleWP0zmzp2roUOHlluW67n62JKhQ4fKx8fHLvsEAABVR4MGDbRr164i1+3evVsWi6XAsjp16ujChQv2iAYnYDabdeutt2rEiBHFGv/ZZ59p586dFZwK9kDpt6esLOnIEcmRH7GSmys1blyupf+WW24pcvmvv/6q8+fPX3eMu7t7ueW4kasTmQQHB1P6AQBAuevRo4dmzZql6dOnF5iZ//3339eqVavUr1+/AuOPHj16zX8jAX90991368iRI/r73/9erPH79u2j9DsJhyr9CxYs0Lfffqvt27frhx9+UG5ubqnO9Obn5+v999/Xhx9+qJSUFNWqVUtdu3bV66+/fs1LqsqN2Sx16FCx+6hIW7aU+yZ/+eWXIpfHxsbaiva1xgAAADiL6OhoLVmyRM8995xef/113X777Tp8+LBOnjypWrVqFZhJPS8vT19//bW6du1qYGI4krZt22rHjh06evSoGjVqZHQc2JFDlf5XX31Vhw8flpeXlxo2bKjDhw+XajtPP/20Zs6cKX9/f40aNUonT57Up59+qlWrVmnjxo3y9/cv5+QAAADA9Xl7e2vTpk167rnntGrVKv3yyy9ycXFR165dNXnyZDVr1sw29vLly0pMTFTjxo0NTAxHEhgYqJUrVyolJaVYpb9Lly52SAV7cKhH9s2aNUtpaWk6deqUnn766VJtY+3atZo5c6YCAwO1Y8cOvfXWW/roo4+0bNkyZWdn87xTB5OVlaXXX39dHTp0UN26deXm5qZGjRpp4MCB2rx58zXfd+bMGUVHR+vee+9VnTp1ZDab1aBBA7Vq1UpPP/20Vq9ebRv7x0lPunbtKpPJZHtxqT8AACgvvr6++uqrr3Tu3Dn9/PPPys7O1jfffKNWrVoVGFejRg117tyZM7YotoiICB06dEjdunUr1vjhw4dr7ty5FZwK9uBQZ/r/9Kc/lXkbM2fOlCS99tprcnNzsy0PCQlRWFiYVqxYoZ9++qnAN6monLZs2aJHHnlEJ0+elCS5uLioZs2a+vnnn5WQkKBPP/1Ur7/+uv7xj38UeN/PP/+szp0768iRI5KkatWqyWKxKCMjQydPntQPP/ygffv2KSQkRJJksVh0yy232PZTt25dmX83L0P9+vXt8XEBAEAV4ubmpltvvdXoGACcgEOd6S8PSUlJ8vDwUOfOnQutCwsLkyStW7fO3rFQQmlpaerRo4dOnjypPn36aPv27bp48aKys7N18uRJRUVFycXFRWPGjNGSJUsKvDc2NlZHjhyRj4+PvvnmG+Xm5iozM1OXLl1SWlqaZsyYoYCAANv4d999t8CcAomJifrll19sr61bt9rrYwMAAABAiTjUmf6yOn/+vE6cOKG77rpLLi4uhdZfncQvJSXlmts4ceKETpw4Yft579695R8UN/Tyyy/r7NmzevzxxzV//vwC626++WaNGzdOdevW1YsvvqjY2Fj17t3btn7jxo2SpAkTJtjO5ku/XSlw++23l/rWEQAAAHs5ffq0pk+fLpPJpKioKKPjwA4SE397CrgjcneXwsONTlF1VanSn5WVJUmFnnF6VZ06dQqMK8oHH3xQYOZU2F9mZqYSExMlSaNHj77muCeeeEIvvviidu3apZMnT9oeaePp6SlJBb68AQAAcCQZGRmKjY2l9FchOTnSsWO/PQXckVgs5fq0b5RClSr95WHEiBF6+OGHbT/v3btXgwcPNjBR1bNp0ybl5+dLUrEnIjl8+LCt9D/44IPatGmTRo8erX379ik8PFydOnWyfekDAABQ2Xl5eSk6OrrAZMNwfllZ0pEjvz0F3BHk5kqNG1P6jValSv/VM/zXOpOfnZ1dYFxRGjZsqIYNG5Z/OBTb8ePHbb++OrnejVy4cMH265dfflm7du3SokWLNHPmTM2cOVMmk0ktW7ZUjx499OSTTzKRIwAAqNTq1aun2NhYo2PAAGaz1KGD0SmKZ8sWoxNAqmIT+Xl4eKhhw4Y6dOiQ8vLyCq2/ei//1Xv7UTld/f/O3d1dVqu1WK/g4GDb+6tXr65PP/1U33//vaKjo9WtWzfVrFlT//d//6d33nlH/v7+mjRpkkGfDgAAAADKT5Uq/ZIUFBSk8+fPKzk5udC6lStX2sag8mrQoIEkKScnR6mpqaXeTuvWrRUXF6fVq1fr7Nmz+uabb3T//fcrLy/PdjUAAACAvR04cECvvvqqAgMD1aBBA7m7u8vd3V0NGjRQYGCgoqKiyvRvIABVi9OW/oyMDO3bt08ZGRkFlj/11FOSpFdffVW5ubm25atXr9bKlSt1//33c2l3JdepUyfb/WsJCQnlsk1XV1eFhIRo2bJlcnNzk9Vq1TfffFNgzNV9Wq3WctknAADAH02cOFH+/v6aMGGCkpOTlZ+fL29vb3l7eys/P1/Jycl6/fXX1bJlS02cONHouAAcgEOV/lmzZmno0KEaOnSoPvvss0LLfv889mnTpqlFixaaNm1agW107dpVkZGR+vbbb3XPPffolVde0ZAhQ9SrVy/VqVNHM2bMsOdHQincfPPNeuSRRyRJb7/9tn766afrjs/MzCzw86VLl6451s3NzfY4xz8+1vHqRH9nz54taWQAAIAbio+P15gxY9SsWTMlJCQoMzNT6enpSk1NVWpqqtLT05WZman4+Hj5+flp7Nix5XYCBIDzcqjSv2HDBn300Uf66KOPtGPHDklScnKybdn3339frO188MEHeu+992QymfTee+9p2bJleuihh/Tdd9/J39+/Aj8BysukSZNUr149ZWdnq0uXLpozZ06BCRozMjKUmJio8PBwDRw4sMB7b7/9dv3jH//Q5s2bC3wBkJqaqoiICF24cEHVqlVTWFhYgffdddddkqRPPvmkwMSAAAAA5WHq1Kny9fXV5s2b1a9fP9tjhn/P09NT/fv316ZNm+Tj46MpU6bYPygAh+JQpX/evHnXnazt9zOYxsbGFlp2VbVq1TRq1Cj93//9ny5evKiMjAx99tlnXNbvQHx9ffX111/Lx8dHp06d0vDhw1W3bl3ddNNNql27turXr6/HHntMixcvtj3e76qTJ09q4sSJ6tixo2rWrKmbbrpJ7u7u8vPz02effSaTyaRJkyapRYsWBd739NNPS5K++OILeXp66rbbbpOPj4+6dOlit88NAACc1549exQeHi4PD48bjq1du7bCw8O1Z88eOyQD4Miq1CP7KoXcXMd+dsXv5kEw2j333KMff/xRc+bM0ZIlS7Rr1y6dOXNGZrNZfn5+at++vR5++GH17NmzwPtWrVqltWvXasOGDTpy5IjtsX9NmzZVYGCgRo4cqbZt2xba3+DBgyX9dqXIDz/8oBMnThT6QgEAAKC0zGbzNR8tXZTs7GyZHeWB7QAMQ+m3J4tFatzY6BRlZ7HYZTexsbE3fP6su7u7Ro4cqZEjRxZ7u6GhoQoNDS1VpsGDB9vKPwAAQHkKCAhQQkKCRo4cqVatWl137K5duxQfH6/AwEA7pQPgqCj99uLuLnl7//ZyBu7uRicAAABwKnFxcQoMDFRAQIAiIiIUGhoqPz8/Wf57wiUrK0spKSlatWqVFi5cqPz8fMXFxRmcGkBlR+m3l/BwoxMAAACgEmvfvr1WrFihyMhIzZ49W3PmzClynNVqla+vr2bNmqV27drZOSUAR0PpBwAAACqJ4OBg7d+/X2vWrFFSUpJSUlJs9/lbLBb5+fkpKChIISEhhR4vDABFofQDAAAAlYiLi0uZ5iACgN9zqEf2AQAAAACA4qP0AwAAAADgpLi8HwAAACgniYlSTo7RKUrH3Z25pwFnROkHAAAAyklOjnTsmPTfufcchsXiPE+WBlAQpR8AAAAoR1lZ0pEjktlsdJLiyc2VGjem9APOitIPAAAAlDOzWerQwegUxbNli9EJAFQkJvIDAAAAAMBJUfoBAAAAAHBSlH4AAAAAAJwUpR8AAAAAACdF6QcAAAAAwElR+gEAAAAAcFKUfgAAAAAAnBSlHwAAAAAAJ0XpBwAAAADASVH6AQAAAABwUpR+AAAAAACcFKUfAAAAAAAnRekHAAAAAMBJUfoBAAAAAHBSlH4AAAAAAJwUpR8AAAAAACdF6QcAAAAAwElR+gEAAAAAcFKUfgAAAAAAnBSlHwAAAAAAJ0XpBwAAAADASVH6AQAAAABwUpR+AAAAAACcFKUfAAAAAAAnRekHAAAAAMBJUfoBAAAAAHBSlH4AAAAAAJwUpR8AAAAAACdF6QcAAAAAwElR+gEAAAAAcFKUfgAAAAAAnBSlHwAAAAAAJ0XpBwAAAADASVH6AQAAAABwUpR+AAAAAACcFKUfAAAAAAAnRekHAAAAAMBJUfoBAAAAAHBSlH4AAAAAAJwUpR8AAAAA4NC+/PJLjRs3zugYlRKlHwAAAADg0JYsWaK4uDijY1RKlH4AAAAAAJyUq9EBAAAAAAD4vfnz55dofGpqagUlcXyUfgAAAABApTJ06FCZTKZij7darSUaX5VQ+gEAAAAAlYrZbNatt96qESNGFGv8Z599pp07d1ZwKsdE6QcAAAAAVCp33323jhw5or///e/FGr9v3z5K/zUwkR8AAAAAoFJp27atMjIydPToUaOjODzO9AMAAAAAKpXAwECtXLlSKSkpatSo0Q3Hd+nSxQ6pHBOlHwAAAABQqURERCgiIqLY44cPH67hw4dXYCLHxeX9AAAAAAA4KUo/AAAAAABOisv7AQAAAACV0oYNG5Senq6OHTuqYcOGkqSsrCxNnTpV27dvl5ubm7p166bhw4fLbDYbnLZyovQDAAAAACqVS5cu6cEHH9SaNWskSe7u7oqPj1enTp0UEBCgAwcO2MYmJiZq3rx5WrdunWrUqGFU5EqLy/sBAAAAAJXKrFmztHr1arVr107/+Mc/1LJlS0VGRuq1115Tenq6ZsyYof3792vDhg0KDQ3Vtm3bNGnSJKNjV0qUfgAAAABApfLRRx+padOmSk5O1muvvabk5GR5enpq2rRpmjx5skaMGCE/Pz916tRJS5cuVZMmTfTZZ58ZHbtSovQDAAAAACqVQ4cO6YEHHpCr6293pLu6uqpHjx7Kz8/Xww8/XGCs2WxWWFiYUlNTjYha6VH6AQAAAACVyqVLl+Tm5lZg2dWfi7pvv0aNGrpy5YpdsjkaSj8AAAAAoFJp0qSJtmzZUmDZ1q1bJUkbN24sND45Odk2uz8KovQDAAAAACqVRx55RBs2bNDLL7+sNWvW6JVXXtH69esVERGh5557Tnv27JEkXblyRTExMdq6datCQkIMTl058cg+AAAAAECl8re//U2ffPKJJk2apMmTJ8tqteq+++7TBx98oD/96U9q1aqVbrrpJmVnZ+vKlSuqXbu2/vGPfxgdu1LiTD8AAAAAoFKxWCzasWOHJk6cqKeeekrvvfeekpKS5O7urqVLlyoiIkJWq1Vubm7q3r271q1bpzvuuMPo2JUSZ/oBAAAAAJWOxWLRK6+8Umh5vXr1NH/+fAMSOSbO9AMAAAAA4KQo/QAAAAAAOCku7wcAAAAAOKzTp09r+vTpMplMioqKMjpOpcOZfgAAAAClEhoaqkmTJunUqVNGR0EVlpGRodjYWMXGxhodpVKi9AMAAAAoldWrV+uVV17Rbbfdpj59+mjFihWyWq1Gx0IV4+XlpejoaEVHRxsdpVLi8n4AAAAApebv769ffvlFiYmJWrx4sby9vfXnP/9Zw4YN0+233250PFQB9erV4yz/dXCmHwAAAECp9e3bV8ePH1d8fLxCQkJ0/PhxjRs3TnfccYfCwsL02Wef6fLly0bHBKosSj8AAACAMqlevbr69++vVatW6eDBg4qOjpa3t7e+/vprDRgwQLfeeqtefPFF7dmzx+iocDAHDhzQq6++qsDAQDVo0EDu7u5yd3dXgwYNFBgYqKioKKWmphods1Kj9AMAAAAoN40bN1ZsbKzS0tK0fPlyPfbYYzp37pymTp2qVq1aqVOnTkZHhIOYOHGi/P39NWHCBCUnJys/P1/e3t7y9vZWfn6+kpOT9frrr6tly5aaOHGi0XErLUo/AAAAgHJnMpkUFhamRYsW6fjx45o8ebL8/f21ZcsWo6PBAcTHx2vMmDFq1qyZEhISlJmZqfT0dKWmpio1NVXp6enKzMxUfHy8/Pz8NHbsWCUkJBgdu1Ki9AMAAACoUDfddJP++te/6ocfftDmzZuNjgMHMHXqVPn6+mrz5s3q16+fPD09C43x9PRU//79tWnTJvn4+GjKlCn2D+oAKP0AAAAA7KZ9+/ZGR4AD2LNnj8LDw+Xh4XHDsbVr11Z4eDhzRlwDj+wDAAAAUCpz585VmzZtjI4BJ2Q2m5WVlVXs8dnZ2TKbzRWYyHFxph8AAABAqQwZMkStW7c2OgacUEBAgBISErR79+4bjt21a5fi4+PVsWNHOyRzPJzpBwAAAABUKnFxcQoMDFRAQIAiIiIUGhoqPz8/WSwWSVJWVpZSUlK0atUqLVy4UPn5+YqLizM4deVE6QcAAABQKp988olCQkLUoEEDo6PAybRv314rVqxQZGSkZs+erTlz5hQ5zmq1ytfXV7NmzVK7du3snNIxUPoBAAAAlMrjjz8uV1dX9erVS8OHD1fPnj1VrRp3EKN8BAcHa//+/VqzZo2SkpKUkpJiu8/fYrHIz89PQUFBCgkJkYuLi8FpKy9KPwAAAIBSy8vL05dffqmlS5eqQYMGGjZsmP785z/L19fX6GhwAi4uLgoNDVVoaKjRURwWX8MBAAAAKLUxY8bo888/V/fu3XXy5ElNmDBBfn5+CgkJUXx8vC5dumR0RKBKo/QDAAAAKLXq1asrPDxcy5cv1+HDhxUXF6fbb79da9eu1eDBg3Xrrbfq+eef165du4yOClRJXN4PAAAAoFx4e3srKipKUVFRWr16tWbNmqUlS5bon//8p6ZNm6a2bdsqMjJSTz31lNFRYQf3piWqfkaOvM8YnaQU3N2l8HCjU5QLSj8AAACAchcSEqKQkBCdPXtWH3/8sWbPnq1t27Zp+/btlP4qonpejmqfOybLsSzpR6PTlIDFInl7G52i3FD6AQAAAFQYT09PjRo1SqNGjdL27duv+eg1OKcal7JUM/eI9IvZ6CjFk5srNW5M6TfS1q1bFRMTo02bNik3N1ctW7bUX//6Vw0aNKjY2zh79qwmT56sJUuW6NChQ3Jzc1OTJk00ZMgQRUZGqkaNGhX4CQAAAICqqW3btmrbtq3RMWBn+a5mqUMHo2MUz5YtRicodw5V+pOSkhQWFiaz2awBAwbIYrEoMTFRERERSktL05gxY264jbNnz6pt27Y6ePCgunTpohEjRujSpUtavny5Ro0apcWLF+vrr7/m+aIAAADADQQFBcnHx8foGACuw2FK/5UrVxQZGSmTyaT169frnnvukSTFxMSoY8eOiomJUd++feXn53fd7Xz44Yc6ePCgXnjhBU2ePNm2PDc3V126dNGaNWu0YcMG3X///RX6eQAAAABHt3btWqMjALgBhzmdvWbNGh04cECDBg2yFX5Jql27tqKionTlyhXNnTv3hts5ePCgJKlnz54FlpvNZoWGhkqS0tPTyzE5AAAAAADGcJjSn5SUJEnq3r17oXVXl61bt+6G22nZsqUkacWKFQWWX758Wd98843c3d3VsWPHMqYFAAAAAMB4DnN5f0pKiiQVefl+3bp15eXlZRtzPZGRkfr44481adIkbdu2Te3bt9elS5e0YsUKnTlzRgsXLpT3dWZqPHHihE6cOGH7ee/evaX4NAAAAEDVcvr0aU2fPl0mk0lRUVFGxwGqDIcp/VlZWZIki8VS5Po6dero559/vuF23N3dlZSUpBEjRmjBggW2qwOqVaumv/zlL+rSpct13//BBx8oLi6uhOkBAACAqi0jI0OxsbGUfsDOHKb0l5eMjAw98sgjSk9P17Jly9S5c2ddvHhRS5cu1d/+9jd99dVX2rZtm+rWrVvk+0eMGKGHH37Y9vPevXs1ePBge8UHAAAAHJKXl5eio6NlMpmMjgJUKQ5T+q+e4b96xv+PsrOzr3kVwO+9+OKL2rhxo3bt2qVWrVrZtv3kk08qLy9PzzzzjKZOnXrNs/kNGzZUw4YNS/kpAAAAgKqpXr16io2NNToGUOU4zER+V+/lL+q+/TNnzigjI+OGj+uTpGXLlummm26yFf7f69atmyRp+/btZUwLAAAAAIDxHKb0BwUFSZJWrVpVaN3VZVfHXE9ubq6ys7OVm5tbaN2pU6ckSW5ubmWJCgAAAFQpBw4c0KuvvqrAwEA1aNBA7u7ucnd3V4MGDRQYGKioqCilpqYaHROokhym9IeEhMjX11cLFy7U999/b1t+7tw5jR8/Xq6urho6dKhteUZGhvbt26eMjIwC2+ncubOuXLmi8ePHF1h+6dIl27KuXbtW2OcAAAAAnMnEiRPl7++vCRMmKDk5Wfn5+fL29pa3t7fy8/OVnJys119/XS1bttTEiRONjgtUOQ5T+l1dXTVr1izl5+crMDBQTz31lF566SW1bt1ae/bsUWxsrJo1a2YbP23aNLVo0ULTpk0rsJ2JEyeqdu3aeu2119ShQwe9+OKLevbZZ+Xv76+VK1eqbdu2ioyMtPfHAwAAABxOfHy8xowZo2bNmikhIUGZmZlKT09XamqqUlNTlZ6erszMTMXHx8vPz09jx45VQkKC0bGBKsVhSr/02xn4DRs2qEuXLlq0aJHef/991atXTwsWLNDYsWOLtY02bdpo+/btGjZsmH755RdNmzZN8+bNk4eHh+Li4rR+/XrVqFGjgj8JAAAA4PimTp0qX19fbd68Wf369ZOnp2ehMZ6enurfv782bdokHx8fTZkyxf5BgSrMYWbvv+q+++7T8uXLbzguNjb2mrOD+vn5ac6cOeWcDAAAAKha9uzZo2effVYeHh43HFu7dm2Fh4drxowZdkgG4CqHOtMPAAAAoPIwm83XfKR2UbKzs2U2myswEYA/ovQDAAAAKJWAgAAlJCRo9+7dNxy7a9cuxcfHq2PHjnZIBuAqh7u8HwAAAEDlEBcXp8DAQAUEBCgiIkKhoaHy8/OTxWKRJGVlZSklJUWrVq3SwoULlZ+fr7i4OINTA1ULpR8AAABAqbRv314rVqxQZGSkZs+efc15s6xWq3x9fTVr1iy1a9fOzimBqo3SDwAAAKDUgoODtX//fq1Zs0ZJSUlKSUmx3edvsVjk5+enoKAghYSEyMXFxeC0QNVD6QcAAABQJi4uLgoNDVVoaKjRUQD8ARP5AQAAAADgpCj9AAAAAAA4KS7vBwAAAKqwe9MSVT8jR95njE5SCu7uUni40SmASo3SDwAAAFRh1fNyVPvcMVmOZUk/Gp2mBCwWydvb6BRApUfpBwAAAKq4GpeyVDP3iPSL2egoxZObKzVuTOkHioHSDwAAAED5rmapQwejYxTPli1GJwAcBhP5AQAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpV6MDAAAAOKK9e/cqNTVV/v7+uuOOOyRJ+fn5mjlzppKSkuTq6qqePXtq4MCBBicFAFRllH4AAIASevrppzVz5kxJkslkUkxMjKKiotS3b18tXrzYNm7hwoVasmSJPv30U6OiAgCqOC7vBwAAKIHExER9+OGHatGihf7617/qrrvuUmxsrCZPnqxly5bprbfe0vfff69ly5apVatW+vzzz7Vo0SKjYwMAqijO9AMAAJTA9OnTdcstt2jLli3y8PDQxYsX1bx5c40ZM0bjxo3TSy+9JElq1aqVOnToIB8fH3300Ufq16+fwckBAFURZ/oBAABKYP/+/XrooYfk4eEhSapRo4Z69uypy5cva8CAAQXG3nTTTerVq5d27NhhRFQAACj9AAAAJZGRkaGbb765wLKrPzdq1KjQ+Ntvv12ZmZl2yQYAwB9R+gEAAEqgbt26hUq8yWSS1WqVyWQqNP78+fOqWbOmveIBAFAApR8AAKAEmjRpotTU1ALLXnjhBR06dKjI8YcPH9att95qj2gAABRC6QcAACiBdu3aafPmzcrPz7cts1gsuv322wuNzc7O1tq1axUQEGDPiAAA2FD6AQAASmD8+PHauXNnkZfy/9GhQ4cUGRmp4cOH2yEZAACF8cg+AACAErBYLLJYLMUa27p1a02ZMqWCEwEAcG2c6QcAAAAAwElxph8AAKAcnDx5UrNnz9a2bduUk5MjHx8f9evXT127djU6GgCgCqP0AwAAlMC4ceOUmJioHTt2qFq13y6aXLdunR599FFlZWXJarXaxn744YcaMWKE3n//faPiAgCqOC7vBwAAKIEvv/xSjRs3thX+8+fPq1+/frpw4YJeeeUVbdiwQf/3f/+nTz/9VK1atdIHH3ygOXPmGJwaAFBVcaYfAACgBA4ePKigoCDbz8uXL9epU6f0z3/+UyNHjrQt9/f3V48ePdS6dWv961//0p///Gcj4gIAqjjO9AMAAJRAbm6u3NzcbD8fOHBAJpNJAwcOLDS2du3a6tWrl/bs2WPPiAAA2FD6AQAASsDX11c7d+60/Xz18X0XL14scvylS5fk6srFlQAAY1D6AQAASiA8PFzffPON1q9fL0l68MEHZTabNX369EJjjx8/rsWLF+uee+6xd0wAACRxTz8AAECJvPzyy/rkk0/Us2dPvfzyy+rTp4+mTJmi5557Tvv27dNDDz0kT09P/fjjj/rnP/+pzMxMvfzyy0bHBgBUUZR+AACAEqhVq5bWrl2rxx57THFxcRo3bpxt3ZIlS7RkyRJJktVqlZubm95991316tXLoLQAgKqO0g8AAFBCjRo10ubNm/XZZ5/p008/1fbt25Wenq78/HxZLBY1b95c3bp107Bhw+Tj42N0XABAFUbpBwAAKIVq1aqpf//+6t+/v9FRAAC4JibyAwAAAADASVH6AQAAAABwUlzeDwAAUEFOnz6t6dOny2QyKSoqyug4AIAqiNIPAABQQTIyMhQbG0vpBwAYhtIPAABQQby8vBQdHS2TyWR0FABAFUXpBwAAqCD16tVTbGys0TEAAFUYE/kBAAAAAOCkONMPAABQCgcOHNDcuXO1bt06paSkKCsrS5JksVjk5+en4OBgDRkyRE2bNjU4KQCgKuNMPwAAQAlNnDhR/v7+mjBhgpKTk5Wfny9vb295e3srPz9fycnJev3119WyZUtNnDjR6LgAgCqM0g8AAFAC8fHxGjNmjJo1a6aEhARlZmYqPT1dqampSk1NVXp6ujIzMxUfHy8/Pz+NHTtWCQkJRscGAFRRlH4AAIASmDp1qnx9fbV582b169dPnp6ehcZ4enqqf//+2rRpk3x8fDRlyhT7BwUAQJR+AACAEtmzZ4/Cw8Pl4eFxw7G1a9dWeHi49uzZY4dkAAAURukHAAAoAbPZbJu0rziys7NlNpsrMBEAANdG6QcAACiBgIAAJSQkaPfu3Tccu2vXLsXHx6tjx452SAYAQGE8sg8AAKAE4uLiFBgYqICAAEVERCg0NFR+fn6yWCySpKysLKWkpGjVqlVauHCh8vPzFRcXZ3BqAEBVRekHAAAogfbt22vFihWKjIzU7NmzNWfOnCLHWa1W+fr6atasWWrXrp2dUwIA8BtKPwAAQAkFBwdr//79WrNmjZKSkpSSkmK7z99iscjPz09BQUEKCQmRi4uLwWkBAFUZpR8AAKAUXFxcFBoaqtDQUKOjAABwTUzkBwAAAACAk6L0AwAAAADgpLi8HwAAVDmJiVJOjtEpSsfdXQoPNzoFAMBRlFvpP3r0qL799lsdO3ZMOTk5io6Otq27fPmyrFarzGZzee0OAACg1HJypGPHpP/OvecwLBbJ29voFAAAR1Lm0p+RkaGRI0fqiy++kNVqtS3/fekfNmyY4uPj9d1336lt27Zl3SUAAECZZWVJR45IjnJOIjdXatyY0g8AKJkylf5z584pKChIe/fuVaNGjfSnP/1JX3/9tY4dO1ZgXGRkpBYuXKjExERKPwAAqDTMZqlDB6NTFM+WLUYnAAA4ojJN5PfWW29p7969euyxx7Rv3z7Nnj1bt99+e6Fx999/v9zd3bV27dqy7A4AAAAAAJRAmUr/559/Ljc3N82aNUvu7u7X3km1amratKmOHDlSlt0BAAAAAIASKFPpT0tLU7NmzWSxWG44tmbNmsrIyCjL7gAAAAAAQAmUqfTXqFFD586dK9bYEydOFOvLAQAAAAAAUD7KVPpbtmypo0eP6vDhw9cd9/333+vIkSNM4gcAAAAAgB2VqfQPHjxYeXl5euqpp3ThwoUix5w5c0bDhw+XyWTSE088UZbdAQAAAACAEijTI/uefPJJxcfH6+uvv9bdd9+tvn376uTJk5KkOXPm6P/+7/+0YMECZWRkqHv37howYEC5hAYAAAAAADdWptLv4uKir776Sk899ZQ+/fRTvf3227JarZJ++0Lg6q/79eun2bNnlz0tAAAAAAAotjJd3i9JtWvXVnx8vHbt2qWYmBg99thj+tOf/qRHHnlEY8aM0datW5WQkCAPD4/yyKutW7eqZ8+eqlu3rjw8PHTfffdp4cKFJd7OuXPnFBMTo7vuuks1a9aUp6en7r33XsXFxZVLTgAAAAAAjFamM/2/d/fdd+vuu+8ur80VKSkpSWFhYTKbzRowYIAsFosSExMVERGhtLQ0jRkzpljbOXLkiLp166aDBw/qT3/6k3r16qVLly4pNTVVX3zxhWJiYir0cwAAAAAAYA/lVvor2pUrVxQZGSmTyaT169frnnvukSTFxMSoY8eOiomJUd++feXn53fd7eTl5alPnz46fvy4Vq9era5duxbaDwAAAAAAzqDMl/fby5o1a3TgwAENGjTIVvil324viIqK0pUrVzR37twbbufzzz/X1q1b9dJLLxUq/JLk6uow34MAAAAAAHBdZZ7IryRMJlOpz6QnJSVJkrp3715o3dVl69atu+F2Pv30U0lS3759dfToUS1btkxnz57VHXfcoQceeEC1atUqVT4AAAAAACqbMpX+q7PzV9T430tJSZGkIi/fr1u3rry8vGxjrmfbtm2SpA0bNuiFF17QpUuXbOvq16+vRYsWKTg4+JrvP3HihE6cOGH7ee/evcX9CAAAAAAA2FWZLu/Pz8+/5uvXX3/V999/r5EjR6pmzZr617/+pfz8/FLvKysrS5JksViKXF+nTh3bmOtJT0+XJI0aNUp//etfdfToUZ06dUrvvfeesrKy1Lt37wKl/o8++OADtW3b1vYaPHhwKT4NAAAAAAAVr8Lu6a9Zs6ZatWqlf/7zn5o+fbqeeeYZLV++vKJ2V2xXv3h48MEHNXHiRN12223y8vLSqFGj9MILLygrK0uzZ8++5vtHjBih7du3214LFiywV3QAAAAAAErELhP5DRkyRA0aNNAbb7xR6m1cPcN/rbP52dnZ17wKoKjtPPzww4XWPfTQQ5L+dwtAURo2bKh7773X9mrRosUN9wkAAAAAgBHsNnt/w4YN9f3335f6/Vfv5S/qvv0zZ84oIyPjho/rk6TmzZtLkjw9PQutu7osJyen1DkBAAAAAKgs7FL6z58/r/3796tatdLvLigoSJK0atWqQuuuLrs65nq6desmSfrxxx8Lrbu6zMfHp7QxAQAAAACoNCq89O/du1d9+vTRhQsX1Llz51JvJyQkRL6+vlq4cGGBKwbOnTun8ePHy9XVVUOHDrUtz8jI0L59+5SRkVFgO8OGDZObm5v++c9/6tixYwW2M2HCBElSv379Sp0TAAAAAIDKokyP7PP19b3mOqvVqlOnTiknJ0dWq1W1atWylerScHV11axZsxQWFqbAwEANHDhQderUUWJiog4dOqTXXntNzZo1s42fNm2a4uLiFBMTo9jYWNvyJk2a6O2339Zzzz2n1q1b69FHH5Wbm5uWLVumtLQ0PfXUUwoJCSl1TgAAAAAAKosylf60tLQbjrFYLAoLC1NcXJztfvrS6tq1qzZs2KCYmBgtWrRIubm5atmypcaPH6+IiIhib2fUqFHy8fHR22+/rYSEBF25ckUtW7bUmDFj9OSTT5YpIwAAAAAAlUWZSv+hQ4euuc5kMsnDw0P16tUryy4Kue+++4r16L/Y2NgCZ/j/6KGHHrLN1g8AwJdffqldu3YpOjra6CgAAADlpkyl//bbby+vHAAAGGrJkiWaP38+pR8AADgVuz2yDwAAAAAA2Fexz/QfOXKkXHbYuHHjctkOAADXM3/+/BKNT01NraAkAAAAxil26ffx8ZHJZCrTzkwmk65cuVKmbQAAUBxDhw4t0d9bVqu1zH/PAQAAVDbFLv2NGzfmH0MAAIdhNpt16623asSIEcUa/9lnn2nnzp0VnAoAAMC+il36i/N4PgAAKou7775bR44c0d///vdijd+3bx+lHwAAOB0m8gMAOKW2bdsqIyNDR48eNToKAACAYcr0yD4AACqrwMBArVy5UikpKWrUqNENx3fp0sUOqQAAAOyL0g8AcEoRERGKiIgo9vjhw4dr+PDhFZgIAADA/sql9J8/f17//ve/tWvXLmVmZury5ctFjjOZTJo9e3Z57BIAAAAAANxAmUt/QkKCnnnmGWVnZ9uWWa1WSSow2//VRyFR+gEAAAAAsI8ylf5Nmzbp8ccfl7u7u8aOHatPP/1Uqampmjlzpo4ePapdu3bp3//+t9zc3PTqq6/q1ltvLa/cAACUSWpqqi5cuKDGjRvL09PT6DgAAAAVokyl/5133lF+fr4++eQTPfTQQ1q7dq1SU1ML3BO5b98+9e3bV9OnT9f27dvLHBgAgOL47rvvdNtttxX6wnnmzJmKiYnRyZMnJf12VVqPHj00Y8aMYk34BwAA4EjK9Mi+TZs2ycvLSw899NA1x9x555364osvdOLECcXExJRldwAAFFvHjh01a9asAsveeecdPf3008rMzFSXLl3Uu3dvNWrUSP/5z38UFBSkrKwsg9ICAABUjDKV/tOnT6tx48a2n81ms6TfJvb7vWbNmqlly5Zavnx5WXYHAECxXZ1f5qrTp08rJiZG3t7e2r59u9atW6cvvvhCqampev7555WWlqbJkycblBYAAKBilKn016tXTzk5Obafvby8JEkHDhwoNDYvL892KSUAAPb29ddfKycnR2+++aZatmxpW+7i4qJ33nlHzZs319KlSw1MCAAAUP7KVPp9fHx04sQJ28/33nuvrFarPvnkkwLjdu3apZ9++kn169cvy+4AACi1w4cPy2QyqWvXroXWubi46P7771dqaqoByQAAACpOmUp/aGiozp49qz179kiSBg0apBo1auidd97R4MGDNX36dEVHRyskJET5+fl67LHHyiU0AAAlVbNmTUm65kz9np6eunLlih0TAQAAVLwyzd7fr18/bdy4Ufv371fLli3VqFEjzZgxQ0899ZQWLlyo+Ph4Sb/dVxkQEKDXXnutXEIDAFAcSUlJtl+npKRIko4cOaJmzZoVGnvs2DHbbWoAAADOokylv2XLlvr6668LLBsyZIgCAwO1aNEipaWlyd3d3TZDsouLS5nCAgBQEklJSQWKvyR99dVXevHFFwuN3bhxY5FfBgAAADiyMpX+a/H19dXo0aMrYtMAABTL2rVri1xe1PwymzdvltVqVY8ePSo6FgAAgF2VqfR/9dVX6tGjh1xdK+S7AwAASi0oKKjYYwMCAnTo0KEKTAMAAGCMMk3k9/DDD6thw4Z6+umnC10+CQAAAAAAjFWm0n/PPffo9OnT+vDDDxUSEqLbbrtNL730krZv315e+QAAAAAAQCmVqfRv375d+/fvV0xMjJo3b67jx49r8uTJuu+++9S8eXPFxcVp//795ZUVAIAKcfr0aY0bN07jx483OgoAAEC5KlPplyQ/Pz/FxMToxx9/1I4dO/TSSy+pUaNGSklJ0bhx4+Tv76+2bdtq0qRJ+vnnn8sjMwAA5SojI0OxsbGKjY01OgoAAEC5KtcZ+Nq0aaM2bdrorbfeUnJysj755BN98cUX2rlzp77//nuNHj1aly9fLs9dAgBQZl5eXoqOjpbJZDI6CgAAQLmqsGn3O3furM6dO2vMmDF66qmntGLFCuXn51fU7gAAKLV69epxlh8AADilCin9WVlZSkxM1MKFC5WUlGQr+3Xr1q2I3QEAAAAAgCKUW+m/ePGili5dqvj4eK1YsUK5ubmyWq1yd3fXQw89pEGDBumBBx4or90BAFAsBw4c0Ny5c7Vu3TqlpKQoKytLkmSxWOTn56fg4GANGTJETZs2NTgpAABA+StT6b9y5YpWrlyp+Ph4LV26VOfPn5fVapWrq6vCwsI0aNAgPfroo/Lw8CivvAAAFNvEiRMVExNjm0/Gy8tL3t7ekqTs7GwlJycrOTlZb731luLi4jR69Ggj4wIAAJS7MpX+Bg0a6MyZM7JarTKZTOrUqZMGDRqkfv36qV69euWVEQCAEouPj9eYMWPUsmVLRUVFqXv37vL09Cww5uzZs1q5cqXGjx+vsWPHysfHRwMGDDAmMAAAQAUoU+nPzMzU3XffrUGDBmngwIFq3LhxeeUCAKBMpk6dKl9fX23evPmaV5x5enqqf//+6tmzp9q0aaMpU6ZQ+gEAgFMpU+n/v//7P/n7+5dXFgAAys2ePXv07LPPFusWs9q1ays8PFwzZsywQzIAAAD7qVaWN1P4AQCVldlstk3aVxzZ2dkym80VmAgAAMD+ylT6AQCorAICApSQkKDdu3ffcOyuXbsUHx+vjh072iEZAACA/ZTbI/sAAKhM4uLiFBgYqICAAEVERCg0NFR+fn6yWCySpKysLKWkpGjVqlVauHCh8vPzFRcXZ3BqAACA8kXpBwA4pfbt22vFihWKjIzU7NmzNWfOnCLHWa1W+fr6atasWWrXrp2dUwIAAFQsSj8AwGkFBwdr//79WrNmjZKSkpSSkmK7z99iscjPz09BQUEKCQmRi4uLwWkBAADKH6UfAODUXFxcFBoaqtDQUKOjAAAA2B0T+QEAAAAA4KQo/QAAAAAAOCku7wcAVFqJiVJOjtEpSsfdXQoPNzoFAACo6opV+k+dOqXjx4/rjjvuUK1atQqtz8jI0H/+8x898cQT5R4QAFB15eRIx45J/517z2FYLJK3t9EpAAAAblD6r1y5oieffFLz58+XJJnNZo0YMUJvvPGG3N3dbeMOHDigYcOGUfoBAOUuK0s6ckQym41OUjy5uVLjxpR+AABQOVy39L/33nv69NNPNW7cOLVt21br16/Xe++9p3Xr1mnFihW65ZZb7JUTAFCFmc1Shw5GpyieLVuMTgAAAPA/153Ib86cOYqKitLYsWPVo0cPTZgwQVu3blVOTo46deqk1NRUe+UEAAAAAAAldN3Sf+jQIXXq1KnAshYtWmjjxo3y8vJS586dtWPHjgoNCAAAAAAASue6pd/Ly0snTpwotPymm27SmjVr1KpVK3Xt2lWrV6+usIAAAAAAAKB0rlv627Ztq8WLFxe5zsPDQ8uWLVNISIheffXVCgkHAAAAAABK77qlf9CgQTpy5IhOnz5d5Hqz2azPP/9cTz31lBo3blwhAQEAAAAAQOlcd/b+Pn36qE+fPtfdQLVq1fSvf/2rXEMBAAAAAICyu+6Z/vKUm5vLlwMAAAAAANhRhZf+CxcuaNKkSWrSpIlGjhxZ0bsDAAAAAAD/dd3L+68lKytLq1atUlpammrWrKk2bdqoc+fOBcb8+uuvmjx5st577z2dOXNGVqtVt956a7mEBgAAAAAAN1bi0v/JJ59o5MiROnfuXIHlXbp00dKlS2WxWLRw4UI9//zzyszMlNVqVYsWLfTSSy9p8ODB5RYcAAAAAABcX4lK/7Zt2zR06FDl5eXJw8NDzZo104ULF3TgwAFt2LBBzz77rNq1a6eXXnpJVqtVAQEBGj16tB5++OGKyg8AAAAAAK6hRKX/3XffVV5envr27atZs2apdu3akqSUlBQ9+uijWrRokRYvXqx69epp5syZeuSRRyokNAAAAAAAuLESTeSXnJwsd3d3ffjhh7bCL0l+fn6aOnWq8vLydOnSJX355ZcUfgAAAAAADFai0n/ixAk1bdpUFoul0LoOHTpIknx9fdWxY8fySQcAAAAAAEqtRKX/0qVLRRZ+SbYz/w0aNCh7KgAAAAAAUGYlKv3FYTKZynuTAAAAAACgFEr8yL709HTNnz+/1OufeOKJku4SAAAAAACUQolLf0pKioYNG1bkOpPJdMP1lH4AAAAAAOyjRKW/cePGXL4PAAAAAICDKFHpT0tLq6AYAAAAAACgvJX7RH4AAAAAAKByoPQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMA4EAyMzN1/vx5o2MAAAAHQekHUOlRclCV/Pjjj4qMjNTDDz+sd999V3l5eZKkJUuWqEmTJqpfv77q1KmjDh06aNOmTQanBQAAlZ2r0QEA4Mcff9TkyZOVnp6ukJAQ/eUvf5GLi4uWLFmiF154QUeOHJEktWvXTlOnTlXHjh0NTgxUjEOHDqlTp07Kzs6WJC1btkz79+9XRESE+vbtK3d3dwUEBOjUqVPaunWrQkJCtG3bNvn7+xucHAAAVFaUfgCGouQA//Pmm2/q3LlzmjJlinr16qVly5bplVde0Q8//KC2bdvqq6++kpeXlyTpo48+0rBhwzRx4kTNnz/f4OQAAKCy4vJ+AIb6fcn56aefNHnyZM2ePVujR49W27ZtdfDgQSUnJ+unn37S3LlzdfHiRU2cONHo2ECFSEpKUlhYmJ5//nk1bdpUzz//vEJCQrRx40ZNmTLFVvglaciQIQoNDVVSUpJxgQEAQKVH6QdgKEoO8D9Hjx5V69atCyxr1aqVJKlNmzaFxrdu3VonT560RzQAAOCgKP0ADEXJAf7HYrHYbnW56ty5c5KkM2fOFBp/5swZmc1mu2QDAACOidIPwFCUHOB/mjdvri+//NL2tIpff/1VS5culYeHhz7++OMCY7Ozs7V06VLdeeedRkQFAAAOgtIPwFCUHOB/Ro0apePHj6t169Z64okn1Lp1ax0/flwffPCBoqOj9fLLL2vZsmWaN2+eunTpooyMDPXv39/o2AAAoBJj9n4Ahho1apT69Omj1q1bq1OnTkpOTtbx48e1YMECDR06VBkZGQoODtapU6c0efJkZWRk6OWXXzY6NlAhwsPD9cILL+jdd9/VwYMHZTab9fbbb2vgwIE6cOCAoqOjNXnyZEmS1WpVcHCwnn/+eYNTAwCAyozSD8BQlBygoEmTJumVV15RWlqa/Pz8dNNNN0mSXn31VXXq1En/+c9/lJubq86dO6tv376qVo2L9gAAwLVR+gEYjpIDFHTLLbfolltuKbS8W7du6tatmwGJAACAo6L0A6gUKDkAAABA+aP0AwDgIE6fPq1t27bJ1dVVnTp1kru7u9GRAABAJcc1sgAqtdOnT2vlypVavXq1cnJyjI4DVLh169apQ4cOqlevnoKDg7Vz505JUkJCgpo0aaKePXuqe/fuuu2227R48WKD0wIAgMqO0g/AcJQc4Dc//fSTevTooa1bt0qS1q9frwceeEA7d+7UsGHDdMcdd+j5559Xnz599Ouvv2rgwIHau3evwakBAEBlRukHYChKDvA/b775pvLy8rR69WqdPn1aycnJysrK0oABAxQaGqpt27Zp8uTJ+vTTT/Xvf/9bubm5evfdd42ODQAAKjFKPwBDUXKA/0lOTtYDDzygrl27SpI6duyoXr16KTU1VTExMXJxcbGN7d69u+6//34lJSUZlBYAADgCSj8AQ1FygP85evSoWrRoUWBZs2bNJEn+/v6Fxrdq1UpHjx61SzYAAOCYKP0ADEXJAf7HbDYrNze3wDI3NzdJKnKmfnd3d1mtVrtkAwAAjolH9gEwFCUH+J+GDRvq2LFjBZYFBwdfc/zRo0dVv379Ck4FAAAcGaUfgKEoOcD/tGnTRhs3biywLCgoSEFBQUWO37p1q+666y57RAMAAA6K0g/AUJQc4H9GjBihO+64Q7m5uTKbzdcd++233yojI0PPPfecndIBAABHROlHuVm6dKl8fHzUqlUro6PAgVBygP+53hdefxQYGKgzZ85UcCIAAODoKP0oN71799aTTz6pDz74wOgocCCUHAAAAKDiUPpRLN99912xxqWnpxcYe99991VUJAAAAADADThc6d+6datiYmK0adMm5ebmqmXLlvrrX/+qQYMGlWp7ly9fVvv27bVr1y41b95c+/btK+fEziEgIEAmk+m6Y0wmk5YuXaqlS5faluXl5VV0NACokk6fPq3p06fLZDIpKirK6DgAAKCScqjSn5SUpLCwMJnNZg0YMEAWi0WJiYmKiIhQWlqaxowZU+Jtjh8/XqmpqRWQ1vl4eHiod+/ecnFxKbTOarVq/vz58vPzU6dOnQxIh6qAkgP8T0ZGhmJjY/n9AAAArsthSv+VK1cUGRkpk8mk9evX65577pEkxcTEqGPHjoqJiVHfvn3l5+dX7G3u2LFDb7zxhiZPnszEYDcwceJExcTEKCUlRXPmzFGLFi0KjZk/f76CgoL04YcfGpAQVQElB/gfLy8vRUdH3/AqLAAAULVVMzpAca1Zs0YHDhzQoEGDbIVfkmrXrq2oqChduXJFc+fOLfb2cnNzNXToUAUEBOgvf/lLRUR2Kq+88op27NihvLw83XvvvZowYQKX7sPurpac6Ohoo6MAhqtXr55iY2MVExNjdBQAAFCJOcyZ/qSkJElS9+7dC627umzdunXF3l5sbKxSUlK0a9cuzpIUU4sWLbR582ZNnDhR48aN0xdffKE5c+aodevWRkdDFXG15AAAAAAoHoc505+SkiJJRV6+X7duXXl5ednG3MjWrVv11ltvKS4uTs2aNStRjhMnTmjHjh221969e0v0fkdXrVo1jRkzRtu2bZPJZNJ9992nV199VZcvXzY6GgA4jQMHDujVV19VYGCgGjRoIHd3d7m7u6tBgwYKDAxUVFQU89EAAIBicZgz/VlZWZIki8VS5Po6dero559/vuF2Ll26pKFDh+qee+7R3/72txLn+OCDDxQXF1fi9zmbu+66S1u2bNHrr7+u119/XYsXL+aKCZTJgQMHNHfuXK1bt04pKSkFfs/7+fkpODhYQ4YMUdOmTQ1OClSsq3OoXP0y1cvLS97e3pKk7OxsJScnKzk52fbl9ejRo42MCwAAKjmHOdNfXqKiomyT0RU1C/2NjBgxQtu3b7e9FixYUAEpHYOLi4uio6P13XffqXr16rJarUZHgoOaOHGi/P39NWHCBCUnJys/P1/e3t7y9vZWfn6+kpOT9frrr6tly5aaOHGi0XGBChMfH68xY8aoWbNmSkhIUGZmptLT05WamqrU1FSlp6crMzNT8fHx8vPz09ixY5WQkGB0bAAAUIk5zJn+q2f4r579+6Ps7OxrXgVw1Y4dOzR58mRFRUXp7rvvLlWOhg0bqmHDhqV6r7Nq3bq1vv/+e6NjwEFdLTktW7ZUVFSUunfvLk9PzwJjzp49q5UrV2r8+PEaO3asfHx8NGDAAGMCAxVo6tSp8vX11ebNm+Xh4VHkGE9PT/Xv3189e/ZUmzZtNGXKFH4/AACAa3KYM/1X7+Uv6r79M2fOKCMj44aP69u9e7fy8vJsj/z6/UuS9u/fL5PJVKhwAKg4vy85/fr1K/L339WSs2nTJvn4+GjKlCn2DwrYwZ49exQeHn7Nwv97tWvXVnh4uPbs2WOHZAAAwFE5zJn+oKAgvfHGG1q1alWhMxqrVq2yjbmeZs2aafjw4UWumz17tiwWi/r06aOaNWuWT+gq5PTp09q2bZtcXV3VqVMnubu7Gx0JDmLPnj169tlnS1RyZsyYYYdkgP2ZzeZrXtFWlOzsbJnN5gpMBGdz+fJl/fDDD3J1ddXdd999zfl4du/ere+//15PPPGEnRMCAMqbw5zpDwkJka+vrxYuXFjgUvJz585p/PjxcnV11dChQ23LMzIytG/fPmVkZNiWderUSbNmzSryJUkNGjTQrFmz9N5779nrYzmUdevWqUOHDqpXr56Cg4O1c+dOSVJCQoKaNGminj17qnv37rrtttu0ePFig9PCUVBygP8JCAhQQkKCdu/efcOxu3btUnx8vDp27GiHZHAGn3/+uW699Va1b99e99xzjxo1aqSFCxcWOXbx4sUaNmyYnRMCACqCw5R+V1dXzZo1S/n5+QoMDNRTTz2ll156Sa1bt9aePXsUGxtb4PF706ZNU4sWLTRt2jQDUzuPn376ST169NDWrVslSevXr9cDDzygnTt3atiwYbrjjjv0/PPPq0+fPvr11181cODAKvc4Q5QOJQf4n7i4OF26dEkBAQF68skntWjRIu3cuVMHDx7UwYMHtXPnTi1atEiRkZHq2LGjcnNzeaIMiuW7777TgAEDlJ2drdDQUPXs2VOnT5/W448/rmeeecboeACACuQwl/dLUteuXbVhwwbFxMRo0aJFys3NVcuWLTV+/HhFREQYHc+pvfnmm8rLy9Pq1avVtWtXbdq0Sd26ddOAAQMUGhqqxYsX256GsGrVKvXo0UPvvvuu/vWvfxmcHJVdXFycAgMDFRAQoIiICIWGhsrPz6/A5J0pKSlatWqVFi5cqPz8fEoOnFb79u21YsUKRUZGavbs2ZozZ06R46xWq3x9fTVr1iy1a9fOzinhiN566y1Vq1ZNa9asUefOnSVJR44c0eOPP64PP/xQOTk5mjt3Lo/fBQAn5FClX5Luu+8+LV++/IbjYmNjFRsbW+zt8ri560tOTtYDDzygrl27SpI6duyoXr16afHixVq4cGGBxx92795d999/v5KSkgxKC0dCyQEKCg4O1v79+7VmzRolJSUpJSXFdguMxWKRn5+fgoKCFBISUqpHz6JqSk5OVu/evW2FX5IaN26s1atXa9iwYZo/f77y8vI0f/58ij8AOBmHK/0wxtGjR9W7d+8Cy67eTuHv719ofKtWrWy3AgA3QskBCnJxcVFoaKhCQ0ONjgInkZmZWeRTjlxdXTV//nyZzWbNnTtX+fn5+vjjjw1ICACoKJR+FIvZbFZubm6BZW5ubpJU5Ez97u7uXD2BEqHkAEDFadCggdLT04tcZzKZNHv2bFmtVs2bN0/5+flq2rSpnRMCACoKpR/F0rBhQx07dqzAsuDg4GuOP3r0qOrXr1/BqQAAQHHceeedWrdu3XXHzJ49W5I0b9481a5d2x6xAAB2QOlHsbRp00YbN24ssCwoKEhBQUFFjt+6davuuusue0QDgErl3rRE1c/IkfcZo5OUgru7FB5udApUgAceeEAvvviikpOTC9zX/3u/P+P/0UcfcW8/ADgJSj+KZcSIEbrjjjuUm5t7w2ekf/vtt8rIyNBzzz1np3QwWmKilJNjdIrSoeOgvFXPy1Htc8dkOZYl/Wh0mhKwWCRvb6NToIL069dPJ0+e1KlTp647zmQyac6cObr99tt1+PBhO6UDAFQkSj+K5Xpn9f8oMDBQZ8444ikulFZOjnTsmPTfufccBh0HFaXGpSzVzD0i/XL9L0krjdxcqXFjfkM4sVtvvVVvvPFGscaaTKYSPQEJAFC5UfoBlIusLOnIEekGF4JUGnQcVLR8V7PUoYPRMYpnyxajEwAAgApC6QdQbsx0HABwSKdPn9a2bdvk6uqqTp06FflkHgCAY6pmdAA4n9OnT2vcuHEaP3680VEAAMB/rVu3Th06dFC9evUUHBysnTt3SpISEhLUpEkT9ezZU927d9dtt92mxYsXG5wWAFBeKP0odxkZGYqNjeV+QAAAKomffvpJPXr00NatWyVJ69ev1wMPPKCdO3dq2LBhuuOOO/T888+rT58++vXXXzVw4EDt3bvX4NQAgPLA5f0od15eXoqOjuZRPwAAVBJvvvmm8vLytHr1anXt2lWbNm1St27dNGDAAIWGhmrx4sVycXGRJK1atUo9evTQu+++q3/9618GJwcAlBWlH+WuXr16nOUHAKASSU5O1gMPPKCuXbtKkjp27KhevXpp8eLFWrhwoa3wS1L37t11//33KykpyaC0AIDyxOX9AAAATu7o0aNq0aJFgWXNmjWTJPn7+xca36pVKx09etQu2QAAFYsz/SiRAwcOaO7cuVq3bp1SUlKU9d8Hs1ssFvn5+Sk4OFhDhgxR06ZNDU4KAACuMpvNys3NLbDMzc1Nkoqcqd/d3V1Wq9Uu2QAAFYvSj2KbOHGiYmJidPnyZUm/3bvv/d+HnGdnZys5OVnJycl66623FBcXp9GjRxsZFwAA/FfDhg117NixAsuCg4OvOf7o0aOqX79+BacCANgDl/ejWOLj4zVmzBg1a9ZMCQkJyszMVHp6ulJTU5Wamqr09HRlZmYqPj5efn5+Gjt2rBISEoyODQAAJLVp00ZbtmwpsCwoKEgxMTFFjt+6davuuusue0QDAFQwSj+KZerUqfL19dXmzZvVr18/eXp6Fhrj6emp/v37a9OmTfLx8dGUKVPsHxQAABQyYsQIPf7444Uu8S/Kt99+q4yMDPXo0cMOyQAAFY3L+1Ese/bs0bPPPisPD48bjq1du7bCw8M1Y8YMOyQDAAA3EhQUpKCgoGKNDQwM1JkzZyo4EQDAXjjTj2Ixm822SfuKIzs7W2azuQITAQAAAABuhNKPYgkICFBCQoJ27959w7G7du1SfHy8OnbsaIdkAAAAAIBr4fJ+FEtcXJwCAwMVEBCgiIgIhYaGys/PTxaLRZKUlZWllJQUrVq1SgsXLlR+fr7i4uIMTg0AAErq9OnTmj59ukwmk6KiooyOAwAoI0o/iqV9+/ZasWKFIiMjNXv2bM2ZM6fIcVarVb6+vpo1a5batWtn55QAAKCsMjIyFBsbS+kHACdB6UexBQcHa//+/VqzZo2SkpKUkpJiu8/fYrHIz89PQUFBCgkJkYuLi8FpAQBAaXh5eSk6Olomk8noKACAckDpR4m4uLgoNDRUoaGhRkcBAAAVoF69eoqNjTU6BgCgnDCRHwAAAAAATooz/QAAAFXEgQMHNHfuXK1bt67I2/SCg4M1ZMgQNW3a1OCkAIDyQumvQhITpZwco1OUjru7FB5udAoAABzXxIkTFRMTo8uXL0v67d59b29vSVJ2draSk5OVnJyst956S3FxcRo9erSRcQEA5YTSX4Xk5EjHjkn//VLfYVgs0n//TQIAAEohPj5eY8aMUcuWLRUVFaXu3bvL09OzwJizZ89q5cqVGj9+vMaOHSsfHx8NGDDAmMAAgHJD6a9isrKkI0cks9noJMWTmys1bkzpBwCgLKZOnSpfX19t3rxZHh4eRY7x9PRU//791bNnT7Vp00ZTpkyh9AOAE6D0V0Fms9Shg9EpimfLFqMTAADg+Pbs2aNnn332moX/92rXrq3w8HDNmDHDDskAABWN2fsBAACcnNlstk3aVxzZ2dkyO8plgQCA66L0AwAAOLmAgAAlJCRo9+7dNxy7a9cuxcfHq2PHjnZIBgCoaFzeDwAA4OTi4uIUGBiogIAARUREKDQ0VH5+frJYLJKkrKwspaSkaNWqVVq4cKHy8/MVFxdncGoAQHmg9AMAADi59u3ba8WKFYqMjNTs2bM1Z86cIsdZrVb5+vpq1qxZateunZ1TAgAqAqUfAACgCggODtb+/fu1Zs0aJSUlKSUlxXafv8VikZ+fn4KCghQSEiIXFxeD0wIAygulHwAAoIpwcXFRaGioQkNDjY4CALATJvIDAAAAAMBJUfoBAAAAAHBSXN4PAADgAO5NS1T9jBx5nzE6SSm4u0vh4UanAIAqidIPAADgAKrn5aj2uWOyHMuSfjQ6TQlYLJK3t9EpAKDKovQDAAA4iBqXslQz94j0i9noKMWTmys1bkzpBwADUfoBAAAcSL6rWerQwegYxbNli9EJAKDKYyI/AAAAAACcFKUfAAAAAAAnRekHAAAAAMBJUfoBAAAAAHBSlH4AAAAAAJwUpR8AAAAAACdF6QcAAAAAwElR+gEAAAAAcFKUfgAAAAAAnBSlHwAAAAAAJ0XpBwAAAADASVH6AQAAAABwUpR+AAAAAACcFKUfAAAAAAAnRekHAAAAAMBJUfoBAAAAAHBSlH4AAAAAAJwUpR8AAAAAACdF6QcAAAAAwElR+gEAAAAAcFKUfgAAAAAAnBSlHwAAAAAAJ0XpBwAAAADASVH6AQAAAABwUpR+AAAAAACcFKUfVVpmZqbOnz9vdAwAAAAAqBCUfji1H3/8UZGRkXr44Yf17rvvKi8vT5K0ZMkSNWnSRPXr11edOnXUoUMHbdq0yeC0cDRHjhxRXFycgoKC1LBhQ7m7u6tWrVpq0qSJ+vXrp8TERFmtVqNjAgAAoApzNToAUFEOHTqkTp06KTs7W5K0bNky7d+/XxEREerbt6/c3d0VEBCgU6dOaevWrQoJCdG2bdvk7+9vcHI4gvfff18vvfSSLl68WGjd4cOHdfjwYX3xxRe67777tGjRIjVq1MiAlAAAAKjqONMPp/Xmm2/q3LlzmjJlin766SdNnjxZs2fP1ujRo9W2bVsdPHhQycnJ+umnnzR37lxdvHhREydONDo2HMC///1v/eUvf9Ett9yiqVOn6ssvv9TUqVPl4+Oj1q1bKyUlRWvXrtWf//xn2xdKv/76q9GxAQAAUAVxph9OKykpSWFhYXr++eclSc8//7xWrlyplStXasOGDfLy8rKNHTJkiBYuXKikpCSD0sKRTJo0Sbfccou2b9+um266ybY8IiJCd911lyZNmqT3339fQUFBCgsLU79+/fTOO+8oNjbWuNAAAACokjjTD6d19OhRtW7dusCyVq1aSZLatGlTaHzr1q118uRJe0SDg9u5c6ceeeSRAoVfkurVq6fevXsrMTHRtqxPnz4KDg7W559/bu+YAAAAAKUfzstisdju57/q3LlzkqQzZ84UGn/mzBmZzWa7ZINju3Llitzc3IpcV716dWVlZRVYdt999+nQoUP2iAYAAAAUQOmH02revLm+/PJL2yP5fv31Vy1dulQeHh76+OOPC4zNzs7W0qVLdeeddxoRFQ7mzjvv1PLly3Xp0qUCyy9duqQVK1bIx8enwPKLFy+qevXqdkwIAAAA/IbSD6c1atQoHT9+XK1bt9YTTzyh1q1b6/jx4/rggw8UHR2tl19+WcuWLdO8efPUpUsXZWRkqH///kbHhgMYNmyYUlNT1b17d61cuVL79+/XihUrFBYWpgMHDmjQoEEFxu/du7fQFwEAAACAPTCRH5xWeHi4XnjhBb377rs6ePCgzGaz3n77bQ0cOFAHDhxQdHS0Jk+eLEmyWq0KDg62TfoHXM/IkSOVlJSkxMRE9ezZ07bcarWqa9euGj16tG3ZuXPndPToUQ0YMMCIqAAAAKjiKP1wapMmTdIrr7yitLQ0+fn52SZee/XVV9WpUyf95z//UW5urjp37qy+ffuqWjUufsGNmUwmff755/r888/15Zdf6uTJk6pfv74eeOABDRw4UC4uLraxtWvX1o8//mhgWgAAAFRllH44vVtuuUW33HJLoeXdunVTt27dDEgEZ9GnTx/16dPH6BgAAADANXFaEwAAAAAAJ0XpB/4rMzNTR44cMToGnBDHFgAAAIxC6Qf+629/+5t8fX2NjgEnxLEFAAAAo1D6gd+xWq1GR4CT4tgCAACAESj9AAAAAAA4KWbvh9P6/WPTgPLEsQUAAABHQemH07JarapZs6YaNWpUrPEnTpzQuXPnKjgVnAHHFgAAABwFpR9Oy8fHR25ubtq7d2+xxg8bNkzz58+v4FRwBhxbAAAAcBTc0w+n1bZtW6WkpOj8+fNGR4GT4dgCAACAo6D0w2nde++9ys/P186dO4s13mq1MsM6ioVjCwAAAI6C0g+nFRkZqbVr16pZs2bFGj9v3jzl5+dXcCo4A44tAAAAOAru6YfTql+/voKCgoyOASfEsQUAAABHwZl+AAAAAACcFKUfAAAAAAAnxeX9gKTTp09r+vTpMplMioqKMjoOnAjHFgAAAIxE6QckZWRkKDY2lmKGcsexBQAAACNR+gFJXl5eio6OlslkMjoKnAzHFgAAAIxE6Qck1atXT7GxsUbHgBPi2AIAAICRHG4iv61bt6pnz56qW7euPDw8dN9992nhwoXFfv+GDRv0t7/9TW3btlW9evVUo0YN3Xnnnfr73/+us2fPVlxwAAAAAADszKHO9CclJSksLExms1kDBgyQxWJRYmKiIiIilJaWpjFjxtxwG3369FFGRoa6dOmiJ554QiaTSUlJSXrrrbf0xRdfaOPGjbr55pvt8GlgLwcOHNDcuXO1bt06paSkKCsrS5JksVjk5+en4OBgDRkyRE2bNjU4KRwNxxYAAAAqO4c503/lyhVFRkbKZDJp/fr1mjlzpt555x3t2rVLLVu2VExMjFJSUm64nRdeeEFHjx5VUlKSpkyZosmTJ2v79u165plndODAAcXFxdnh08BeJk6cKH9/f02YMEHJycnKz8+Xt7e3vL29lZ+fr+TkZL3++utq2bKlJk6caHRcOBCOLQAAADgChyn9a9as0YEDBzRo0CDdc889tuW1a9dWVFSUrly5orlz595wO3//+9/VsGHDAst+P6v2unXryjc4DBMfH68xY8aoWbNmSkhIUGZmptLT05WamqrU1FSlp6crMzNT8fHx8vPz09ixY5WQkGB0bDgAji0AAAA4Cocp/UlJSZKk7t27F1p3dVlZCnv16tUlSa6uDnXHA65j6tSp8vX11ebNm9WvXz95enoWGuPp6an+/ftr06ZN8vHx0ZQpU+wfFA6HYwsAAACOwmFK/9VL9/38/Aqtq1u3rry8vIp1ef+1zJkzR1LRXyr83okTJ7Rjxw7ba+/evaXeJyrWnj17FB4eLg8PjxuOrV27tsLDw7Vnzx47JIOj49gCAACAo3CY09q/nyCrKHXq1NHPP/9cqm1///33iouL080336xXXnnlumM/+OAD7vt3EGaz2XbcFEd2drbMZnMFJoKz4NgCAACAo3CYM/0V5dChQ3rwwQeVl5enhIQEeXl5XXf8iBEjtH37dttrwYIFdkqKkgoICFBCQoJ27959w7G7du1SfHy8OnbsaIdkcHQcWwAAAHAUDnOm/+oZ/mudXcvOzr7mVQDXcvjwYXXt2lWnTp3SF198oa5du97wPQ0bNiw0ESAqp7i4OAUGBiogIEAREREKDQ2Vn59fgWMpJSVFq1at0sKFC5Wfn89VHCgWji0AAAA4Cocp/Vfv5U9JSVHbtm0LrDtz5owyMjLUqVOnYm8vLS1NXbt21fHjx/XZZ5/pwQcfLNe8MF779u21YsUKRUZGavbs2bZ5G/7IarXK19dXs2bNUrt27eycEo6IYwsAAACOwmFKf1BQkN544w2tWrVKAwYMKLBu1apVtjHFkZaWpuDgYB0/flyffvqpHnnkkXLPi8ohODhY+/fv15o1a5SUlKSUlJQC80P4+fkpKChIISEhcnFxMTgtHAnHFgAAAByBw5T+kJAQ+fr6auHChXruuefUpk0bSdK5c+c0fvx4ubq6aujQobbxGRkZysjIkJeXV4H79K8W/mPHjunTTz/Vo48+audPAntzcXFRaGioQkNDjY4CJ8OxBQAAgMrOYUq/q6urZs2apbCwMAUGBmrgwIGqU6eOEhMTdejQIb322mtq1qyZbfy0adMUFxenmJgYxcbG2pYHBwfr8OHDCggI0O7du4uciOv34wEAAAAAcFQOU/olqWvXrtqwYYNiYmK0aNEi5ebmqmXLlho/frwiIiKKtY3Dhw9LkjZv3qzNmzcXOYbSDzi/e9MSVT8jR95njE5SCu7uUni40SkAAADgAByq9EvSfffdp+XLl99wXGxsbJHl3Wq1VkAqVBSKGSpK9bwc1T53TJZjWdKPRqcpAYtF8vY2OgUAAAAchMOVflQtFDNUpBqXslQz94j0i9noKMWTmys1bsyxBQAAgGKj9KPSo5ihIuW7mqUOHYyOUTxbthidAAAAAA6G0g+HQDEDAAAAgJKrZnQAAAAAAABQMSj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATorSDwAAAACAk6L0AwAAAADgpCj9AAAAAAA4KUo/AAAAAABOitIPAAAAAICTovQDAAAAAOCkKP0AAAAAADgpSj8AAAAAAE6K0g8AAAAAgJOi9AMAAAAA4KQo/QAAAAAAOClKPwAAAAAATsrhSv/WrVvVs2dP1a1bVx4eHrrvvvu0cOHCEm0jPz9f06ZNU6tWreTu7q769eurX79+SklJqaDUAAAAAADYn0OV/qSkJHXp0kXffvut+vTpo2eeeUYZGRmKiIjQhAkTir2dp59+WqNGjVJeXp5GjRqlnj17aunSpWrfvr1+/PHHCvwEAAAAAADYj6vRAYrrypUrioyMlMlk0vr163XPPfdIkmJiYtSxY0fFxMSob9++8vPzu+521q5dq5kzZyowMFBff/213NzcJElPPPGEQkND9cwzz2jdunUV/nkAAAAAAKhoDnOmf82aNTpw4IAGDRpkK/ySVLt2bUVFRenKlSuaO3fuDbczc+ZMSdJrr71mK/ySFBISorCwMK1fv14//fRT+X8AAAAAAADszGHO9CclJUmSunfvXmjd1WXFOUOflJQkDw8Pde7cudC6sLAwrVixQuvWrVOzZs2KfP+JEyd04sQJ28/ff/+9JGnv3r033LfRDh2SfvlFOnFCWrbM6DTF43/2kKzWX3TOdELZjhL68uXf/tfdXdqxw9gsdsKxZSccWw6BY8sxcGzZCceWQ+DYcgwcW3biIMfW1f6Zk5Nz48FWB9GnTx+rJOu2bduKXO/l5WWtX7/+dbfx66+/WiVZ77rrriLXf/XVV1ZJ1pdffvma24iJibFK4sWLFy9evHjx4sWLFy9evAx9LViw4IZd2mHO9GdlZUmSLBZLkevr1Kmjn3/+uczb+P24oowYMUIPP/yw7eczZ85o7969uueee+Tu7n7d/aPk9u7dq8GDB2vBggVq0aKF0XHgRDi2UFE4tlBROLZQUTi2UFE4tipOTk6O0tLSFBYWdsOxDlP6K4uGDRuqYcOGBZaFhIQYlKbqaNGihe69916jY8AJcWyhonBsoaJwbKGicGyhonBsVYyiblkvisNM5Hf17Py1zsJnZ2df8wx+Sbbx+3EAAAAAADgyhyn9Vx/Fl5KSUmjdmTNnlJGRccPH9Xl4eKhhw4Y6dOiQ8vLyCq2/uu0bbQcAAAAAAEfgMKU/KChIkrRq1apC664uuzrmRts5f/68kpOTC61buXJlsbcD+2jYsKFiYmIK3VIBlBXHFioKxxYqCscWKgrHFioKx1blYLJarVajQxTHlStX1Lx5cx07dkybN29WmzZtJEnnzp1Tx44dtX//fu3Zs8f2qL2MjAxlZGTIy8tLXl5etu2sXbtW3bp1U2BgoL755huZzWZJ0urVqxUaGqrAwMBiPfoPAAAAAIDKzmHO9Lu6umrWrFnKz89XYGCgnnrqKb300ktq3bq19uzZo9jYWFvhl6Rp06apRYsWmjZtWoHtdO3aVZGRkfr22291zz336JVXXtGQIUPUq1cv1alTRzNmzLD3RwMAAAAAoEI4TOmXfivsGzZsUJcuXbRo0SK9//77qlevnhYsWKCxY8cWezsffPCB3nvvPZlMJr333ntatmyZHnroIX333Xfy9/evwE8AAAAAAID9OMzl/QAAAAAAoGQc6kw/AAAAAAAoPko/AKcwdOhQmUwmpaWlFWt8WlqaTCaThg4dWqG5AAAAACNR+lFqV0tTjx49jI4CJ3P12Pr9q3r16vL29la/fv20bds2oyPCyX3//fd6+umn5e/vrzp16shsNqthw4bq3r27pk6dqtOnTxcY/8fj1dXVVQ0bNlTv3r21fv36AmPnzZtXaLy7u7uaNWumUaNG6ZdffrHnR4UdXbhwQRMmTNC9996rWrVqqUaNGrrtttsUGBiof/zjHzpw4IBWrlxZ7L9bH3vsMZlMJiUmJkqSYmNjCx1bNWvW1F133aWxY8cqOzu7oj8iDFKavzevfll+rVdsbKxtLMdW1VXUsfXHV5s2bRQcHHzDcb9/JSUlSZJ8fHwKLHdxcVG9evUUEhKizz77zNgP70RcjQ4AANdyxx13aPDgwZKk8+fPa/v27frss8+0ZMkSffPNN7r//vttY9944w2NHj1a3t7eRsWFE8jPz9crr7yiSZMmydXVVffff7+6d++umjVrKj09XRs3btQLL7yg6OhoHTx4sMAjYevVq6e//OUvkqScnBzt2rVLX375pZYuXapFixapT58+BfYVEhKiLl26SPrtMbNr1qzRtGnTtGTJEu3YsUP169e33wdHhTt37py6dOmi3bt3q2nTpho8eLA8PT119OhR7dmzRxMnTtQdd9yhP//5z2rcuLG+/vpr/fzzz7rtttuK3F5GRob+/e9/q379+nrooYcKrHvsscd01113SZJ++eUXLV++XBMmTNBXX32l7777Tm5ubhX+eWGMkvy9edXw4cOLPM6Cg4MLLePYqrp+f2z9UYMGDVSjRo1Cx8y8efN0+PBhPf/88/L09CywzsfHx/ZrFxcXvfrqq5Kky5cvKyUlRUuWLNGaNWts/75DGVmBUjp06JBVkjUsLMzoKHAy1zu23njjDask6/33318u+xgyZEiZtgPnMnr0aKska7t27aypqalFjvnuu++swcHB1qNHj9qWSbI2b9680NiZM2daJVl9fHxsy+bOnWuVZH3jjTcKjM3Ly7P27NnTKskaHR1dTp8IlcW4ceOskqzDhw+35ufnF1p/8OBB6969e61Wq9UaHR1tlWR97bXXrrm9yZMnWyVZ//a3v9mWxcTEWCVZ4+PjC4zNycmxtm7d2irJOmfOnHL6RKhMSvP35pAhQ6ySrJs2bbrh9jm2qq6y/Hs/KCjIKsl66NCha465/fbbrW5uboWWb9iwwVqtWjWru7u79fz58yXeNwri8n5UuOPHjysmJkYBAQG6+eab5ebmJh8fHz377LNKT08vND4rK0vR0dHy9/dXrVq1ZLFYdOedd2rYsGE6evSobdzFixc1adIktW7dWhaLRbVq1dIdd9yhgQMH6ocffiiwzStXrmjKlClq3bq13N3dZbFY1LVrVy1btqzCPz/K1/DhwyVJ27dvL7D8Wvf05+Xl6c0331TTpk1Vo0YNNW3aVG+88Yby8/OvuY9169bp/vvvl4eHh+rVq6f+/fvr6NGjtkvX/shqtWrOnDnq3Lmz6tSpo5o1a6pdu3aaM2dO2T8w7CYlJUVvv/22br75Zi1fvlx33HFHkePat2+vNWvWqGHDhjfc5p///Gd5eHgoLS1NGRkZ1x1brVo12xwTfzy+4fg2bdokSfrLX/5S5J8jTZo00Z133ilJGjZsmEwmk+bNm3fN7c2dO1fSb8fYjdSoUUMRERGSOLaqomv9vVkeOLZQUTp37qw777xTOTk5+vHHH42O4/C4vB8Vbv369Zo0aZJCQkLUoUMHVa9eXTt37tSMGTO0cuVK7dixQxaLRdJv5SksLExbtmxR586d1aNHD1WrVk1paWlavHixhgwZokaNGkmShgwZokWLFqlVq1YaNmyY3NzcdOTIEa1du1ZhYWG6++67bdvs37+/EhMT1axZM40cOVLnz5/XokWL9OCDD+rdd9/Vc889Z9h/H5SOq2vx/vh66qmnNGfOHDVp0kQjR47UxYsXNXnyZG3cuLHI8atWrVKvXr3k6uqq/v37q2HDhkpKSlKXLl1Ut27dQuOtVqsGDx6shQsXqlmzZho0aJDMZrO+/vprDR8+XD/++KPeeeedMn1W2Me8efOUl5enESNGFLhsvyhX7zssDmsJnox7dWxxj284jptuukmSlJqaqjZt2lx3rI+Pj0JCQvTNN99o/fr1hS7J3rZtm3744Qd17NhR/v7+xdo/xxYq6v97ji1UFI6t/2/v3mOiOOI4gH8PEA5BwEqBngYEH5Tw0sMKrQi2pUKTCodAWtvQg1Kt1VjqswWiPCwqKDbE00gjopRHUESwJmKVqxQtUglCtYoNSKrSyCugKA+Pc/oHuY3XWwSRE4q/T3KJ7s4us8eE2d/O7G9GDn2DROveeecd3L17F8bGxmrbMzMzIZVKIZPJEBMTAwC4evUqKioqEBgYyCUmUunt7YVCoQDQPxvg6NGjmDdvHi5evKh2861UKtHZ2cn9PysrCwUFBfD29sbPP/8MfX19AEBMTAzc3NywYcMGLFmyBLa2tlq5fjKy0tLSAIB7F/ppzp07h4MHD8LV1RUXLlyAkZERACA6Opr3plupVGLFihV4/Pgxzp07B3d3d25feHg476jbgQMHkJOTg4iICOzfv5/rmB49eoTg4GCkpKRg2bJlcHNzG8bVkhdJNRL79ttvj9g5Dx48iK6uLkyfPn3QBwlKpZKbHTKU9k3+X0JCQpCdnY2IiAhUVlZi8eLFmDt3Lu/DRKB/dPbs2bPIyMjQCPpV7UQ1gjuY7u5uZGVlAaC29TIarN88cOAAiouL1bYJhcIhvUdNbevlUVdXp5bc8UkeHh4jntj7119/xY0bNzBlyhRuFhQZPgr6idZZWFjwbg8NDcWaNWtw9uxZLuhXMTQ01ChvYGDAJYgRCARgjMHAwEBjtE1XV1ctWYgqUEtOTuYCfgCYNm0a1q5di6ioKGRnZ3MJRMjY8WQH8/DhQ1y6dAmlpaWwsLDAzp07Bz0+MzMTALBlyxYu4AeAqVOnIjIyEps3b1Yrf/78efz9998IDAxUC/gBYOvWrfjxxx+hVCrVtstkMhgZGUEmk6k9idbX10diYiJ++ukn5ObmUtD/P6DKmi8SiTT2yeVyjSz8Pj4+aje5ra2tXHvt6elBdXU1Tp8+DR0dHd7ZHmfPnkVPTw8AoK2tDWfOnMGNGzfg4eGBL7/8cqQui4wRAQEBSE5ORkJCApKSkpCUlASgPzmWn58fIiMjMWvWLK58YGAgJk+ejKNHj2LPnj3cg/Oenh7k5ubCyMgIH374Ie/Pys/PR21tLQCgqakJJ0+exJ07dxAQEIClS5dq+UrJaBpOv5menq6xzdTUlDfop7b18qqvr0d8fDzvvsjIyOcK+vv6+rh2+2QiP4FAgL1790IoFA773KQfBf3khSgoKEBaWhqqqqrQ3t6uFjj9888/3L8dHBzg7OyMnJwc3L59GxKJBAsXLoRYLFYL7k1MTODn54fi4mKIxWIEBwdj4cKFcHd3VwvsAeDy5cswNDTE/PnzNeqlyjJaXV09shdMRgRfB2NhYYGysjLMnj170ONramoAAAsXLtTYx7dNVf6tt97S2Ddt2jRYW1ujoaGB29bV1YUrV65AJBJhx44dGseoZqaobpDI2Pa0afhyuRyJiYlq24RCoVrQ39bWxrVXXV1dmJubQyKRYN26dbztraSkBCUlJWrb3nzzTcjlcrrBGac2btyIlStXori4GL/99hsqKytRUVGBvXv3Ij09HXl5efD39wfQ/6D7k08+gUwmw5EjR7h39wsKCtDR0YHw8HCNGXQqx44dw7Fjx9S2LV26FPn5+bz5BMj4MZx+s7y8HB4eHkM6P7Wtl5evr6/GjJCRolQqNdqtrq4u8vLyEBQUpJWf+bKhRH5E61JSUhAUFITLly9j8eLFWL9+PWJjYxEbGwtTU1P09vZyZfX09CCXy7F69WrU1dVh/fr1mD9/PqysrJCQkKD2sCA/Px/R0dHo6OhATEwMvLy8YG5ujq+//hpdXV1cufv378PS0pK3blZWVgD6XxcgY4+vry8YY2CMobm5GTt37kRrayskEgkePHgw6PH37t2Djo4O77RqvjahWmd4oKXS/ntMe3s7GGNobGxEfHy8xmfbtm0A+kdbyNin+v02NjZq7Pvuu++4tqhKoPZf9vb2XJm+vj7cvXsXx48f5w34gf5lJhljUCqVqK+vR2hoKMrLy7F8+fKRuygy5kyaNAkhISH4/vvvUVZWhpaWFqxatQo9PT2IiIjAo0ePuLKq6ftPtrmhTO3Pzc0FYwwKhQJXr16Fn58fCgoKsGXLFi1dFRkrnrffHAy1LaINBgYGXLvt7OxEUVERzMzMEBYWxg3IkOdDQT/Rqr6+PmzduhUikQh//vknsrOzkZSUhLi4OMTGxqrd3KiYm5tDJpOhsbER165dg0wmw5QpUxAbG4vk5GSunJGRERITE3Hz5k3cvHkT6enpeP3115Gamoq1a9dy5UxMTNDU1MRbP9V2ExOTEb5yMtJeffVVbNiwAdHR0bh+/fqQXscwNTXF48ePebOm87UJVTtoaWnhPd9/j1GVd3Nz4zorvs8vv/wyaF3J6FPN8HjRvy8dHR3Y2dnh8OHD8PLyQlZWFgoLC19oHcjoMTU1hUwmg42NDVpbW9VWn5kzZw7EYjHOnz+Pv/76C7du3YJcLoe9vT0WLFgw6Ln19PTg6OiI48ePY+bMmUhMTERVVZU2L4eMIcPpN4eK2hbRFmNjY/j7+yMvLw8PHjxAWFjYMyXEJfwo6Cda1drainv37sHDw0Nj9LSyshLd3d0DHisQCODg4IDVq1fjzJkzAIATJ07wlrW1tcVnn32G0tJSGBsbq5WbO3cuuru78fvvv2scV1paCgCDZlImY0d0dDREIhH27dunsTzff7m6ugIAysrKNPbxbVOV58vsf+fOHbUlI4H+ETsHBwdcv34dHR0dQ7wCMlZJpVLo6Ojghx9+GHR5PW0QCARITU2FQCBAVFSURv4IMn4JBAJMnDiRd59qRP/QoUPIyMgAY2zICfxUhEIhdu3aBcbYkJKzkfHlWfrNZ0Vti2jLu+++C4lEgurqauTm5o52df73KOgnWmVhYQFDQ0NUVVWpTblvb2/HmjVrNMo3NDTwrsWpGmFVJfhraWnhDeLb29vR29urlghQKpUCAKKiorh3rIH+Kby7d++Gnp4et8YsGfsMDQ3xzTffQKFQYOvWrU8t++mnnwIAEhIS1KbYNzY2IjU1VaO8p6cnrK2tUVRUpNG+Nm/ejL6+Po1jvvrqK3R1dWH58uW80/gbGhpG/CaLaIe9vT3WrVuH5uZmvP/++6ivr+ctp80HPHPmzIFEIkFtbS1ycnK09nPIi5eWloZLly7x7isoKEBtbS3MzMzg5OSktu/jjz+GUChEZmYmDh06BD09Pe5v27MICAiAWCzGmTNneB96kvHrWfrN4aC2RbQlLi4OAoEA8fHx9CD8OVEiP/Lcrly5grCwMN59YrEYq1atQkpKClxdXbFkyRLcv38fp06dgo2NjUaW7JqaGgQGBuKNN96Ak5MTrKys0NjYiMLCQujq6mL9+vUA+oM2d3d3ODo6QiwWY+rUqWhra0NRUREUCgU2bdrEnTM0NBQFBQUoKiqCi4sLPvjgAzx8+BBHjhxBW1sbUlJSYGdnp7Xvh4y8FStWICkpCZmZmYiOjsaMGTN4yy1atAjh4eHIyMiAs7MzAgMD0dvbi7y8PHh4eODkyZNq5XV1dbF//374+/vD29sbH330EaysrFBaWorGxka4urrijz/+UDvmiy++wMWLF3H48GFcuHABPj4+EIlEaGpqQm1tLSoqKpCTk4Pp06dr6+sgI2jHjh1QKBRITU2Fvb09vL294eLigokTJ6K5uRnV1dWorKyEiYkJXFxctFKHuLg4FBYWIiEhAcuWLaP1iceJU6dOYeXKlZg5cyYWLFgAkUiEBw8eoLq6GmVlZdDR0cG+ffu4VWpUzMzMEBQUhOzsbACARCIZME/NYOLi4uDv748tW7bQa0cvmaH2m8NFbWv8e9qSfQCeum+4XF1duWW8s7KyuIE8MgyMkGFqaGhgAJ76CQgIYI8ePWKJiYls1qxZzMDAgFlbW7N169axzs5OZmNjw2xsbLhz3r59m3377bfMw8ODWVhYMH19fWZtbc2Cg4NZRUUFV669vZ3FxcUxLy8v9tprrzF9fX0mEomYn58fO336tEZdFQoF27VrF3N2dmYGBgZs0qRJzNvbmxUVFb2Ir4o8I1Xb8vX1HbDMnj17GAAWGhrKGGNMKpUyAKyhoUGtXF9fH9u+fTuzs7Nj+vr6zM7Ojm3bto3V1dUxAEwqlWqcWy6XM09PT2ZoaMheeeUVFhISwm7dusWcnJyYqakpb33y8vKYj48Pmzx5MpswYQKbOnUqW7RoEUtJSWEtLS3D/SrIKKmsrGSff/45mz17NjMyMmITJkxglpaWzMfHh+3evVvjdwqA2dvbD+ncGRkZDADbvn37gGWCgoIYAJaenv5c10HGjtraWpacnMzee+89Zmtry4RCIRMKhWzGjBlMKpWyysrKAY+Vy+Vcv3rixIkBy8XGxjIALDc3d8Ay8+bNYwBYSUnJc10PGVuep98sLy8f9PzUtl5eQ7nfHyik9Pb25r03e5KNjQ0zMDAYcH9NTQ0TCATMzs6OKRSK572cl5aAMcqMQAghg+ns7ISlpSWcnZ1RUVEx2tUhhBBCCCFkSOidfkIIecLDhw/R2dmptk2pVGLjxo3o7u6GRCIZnYoRQgghhBAyDDTSTwghT6iuroanpyd8fX1hZ2eHzs5OlJWV4dq1a3B0dERFRQWMjIxGu5qEEEIIIYQMCQX9hBDyhJaWFmzatAmlpaVoampCX18frK2tIZFIEBMTAzMzs9GuIiGEEEIIIUNGQT8hhBBCCCGEEDJO0Tv9hBBCCCGEEELIOEVBPyGEEEIIIYQQMk5R0E8IIYQQQgghhIxTFPQTQgghhBBCCCHjFAX9hBBCCCGEEELIOEVBPyGEEEIIIYQQMk5R0E8IIYQQQgghhIxTFPQTQgghhBBCCCHj1L8sYr0vcTJDagAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = ['Lasso', 'Ridge', 'GPR', 'SVR', 'RFR', 'ETR']\n",
    "use_method = ['{}'.format(m) for m in methods ]\n",
    "\n",
    "r2_trn = [errors[m]['trn_r2'] for m in use_method]\n",
    "r2_tes = [errors[m]['tes_r2'] for m in use_method]\n",
    "\n",
    "width =0.3\n",
    "plt.figure(facecolor='white', figsize=(12,8))\n",
    "ind = np.arange(len(methods))\n",
    "plt.bar(ind, r2_trn, width, color='blue', alpha=0.4, edgecolor='blue', linewidth=2, label = 'Training')\n",
    "plt.bar(ind+width, r2_tes, width, color='red', alpha=0.4, edgecolor='red', linewidth=2, label = 'Test')\n",
    "\n",
    "for x,y in zip(ind, r2_trn):\n",
    "    plt.text(x, y+0.01, '%.2f' % y, ha='center', va= 'bottom', rotation=90, fontsize=14)\n",
    "\n",
    "for x,y in zip(ind, r2_tes):\n",
    "    plt.text(x+ width, y+0.01, '%.2f' % y, ha='center', va= 'bottom', rotation=90, fontsize=14)\n",
    "\n",
    "plt.xticks(ind+(width*0.5), methods, fontsize=14)\n",
    "#plt.tick_params(length=0)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel('$R^{2}$ value', fontsize=16)\n",
    "#plt.legend(bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=0, fontsize=10)\n",
    "plt.legend(bbox_to_anchor=(0, 1), loc='upper left', borderaxespad=0, fontsize=18, frameon=False)\n",
    "plt.ylim(0, 1.1)\n",
    "plt.savefig(f'{PATH}/{date}_{Reaction}_ML_methods_prop{add_model}_R2_v2.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = ['Lasso', 'Ridge', 'GPR', 'SVR', 'RFR', 'ETR']\n",
    "use_method = ['{}'.format(m) for m in methods ]\n",
    "\n",
    "r2_trn = [errors[m]['trn_mse'] for m in use_method]\n",
    "r2_tes = [errors[m]['tes_mse'] for m in use_method]\n",
    "\n",
    "width = 0.3\n",
    "plt.figure(facecolor='white', figsize=(12,8))\n",
    "ind = np.arange(len(methods))\n",
    "plt.bar(ind, r2_trn, width, color='blue', alpha=0.4, edgecolor='blue', linewidth=2, label = 'Training')\n",
    "plt.bar(ind+width, r2_tes, width, color='red', alpha=0.4, edgecolor='red', linewidth=2, label = 'Test')\n",
    "\n",
    "for x,y in zip(ind, r2_trn):\n",
    "    plt.text(x, y+0.01, '%.2f' % y, ha='center', va= 'bottom', rotation=90, fontsize=14)\n",
    "\n",
    "for x,y in zip(ind, r2_tes):\n",
    "    plt.text(x+ width, y+0.01, '%.2f' % y, ha='center', va= 'bottom', rotation=90, fontsize=14)\n",
    "\n",
    "plt.xticks(ind+(width*0.5), methods, fontsize=14)\n",
    "#plt.tick_params(length=0)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel('MSE CO form rate (mmol $min^{{-}1}$ $gcat^{{-}1}$)$^{2}$', fontsize=16)\n",
    "#plt.legend(bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=0, fontsize=10)\n",
    "plt.legend(bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=0, fontsize=18, frameon=False)\n",
    "plt.ylim(0, 1)\n",
    "plt.savefig(f'{PATH}/{date}_{Reaction}_ML_methods_prop{add_model}_MSE_v2.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = ['Lasso', 'Ridge', 'GPR', 'SVR', 'RFR', 'ETR']\n",
    "use_method = ['{}'.format(m) for m in methods ]\n",
    "\n",
    "r2_trn = [errors[m]['trn_mae'] for m in use_method]\n",
    "r2_tes = [errors[m]['tes_mae'] for m in use_method]\n",
    "\n",
    "width = 0.3\n",
    "plt.figure(facecolor='white', figsize=(12,8))\n",
    "ind = np.arange(len(methods))\n",
    "plt.bar(ind, r2_trn, width, color='blue', alpha=0.4, edgecolor='blue', linewidth=2, label = 'Training')\n",
    "plt.bar(ind+width, r2_tes, width, color='red', alpha=0.4, edgecolor='red', linewidth=2, label = 'Test')\n",
    "\n",
    "for x,y in zip(ind, r2_trn):\n",
    "    plt.text(x, y+0.01, '%.2f' % y, ha='center', va= 'bottom', rotation=90, fontsize=14)\n",
    "\n",
    "for x,y in zip(ind, r2_tes):\n",
    "    plt.text(x+ width, y+0.01, '%.2f' % y, ha='center', va= 'bottom', rotation=90, fontsize=14)\n",
    "\n",
    "plt.xticks(ind+(width*0.5), methods, fontsize=14)\n",
    "#plt.tick_params(length=0)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel('MAE CO form rate (mmol $min^{{-}1}$ $gcat^{{-}1}$)', fontsize=16)\n",
    "#plt.legend(bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=0, fontsize=10)\n",
    "plt.legend(bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=0, fontsize=18, frameon=False)\n",
    "plt.ylim(0, 0.8)\n",
    "plt.savefig(f'{PATH}/{date}_{Reaction}_ML_methods_prop{add_model}_MAE_v2.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mine",
   "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": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}