RFR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np    \n",
    "import csv\n",
    "import copy\n",
    "import pandas\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel\n",
    "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C, ExpSineSquared\n",
    "from sklearn.gaussian_process.kernels import Matern, RationalQuadratic\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import WhiteKernel, ExpSineSquared\n",
    "from scipy import stats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "    # Read Data\n",
    "ifile  = open('Data.csv', \"rt\")\n",
    "reader = csv.reader(ifile)\n",
    "csvdata=[]\n",
    "for row in reader:\n",
    "        csvdata.append(row)   \n",
    "ifile.close()\n",
    "numrow=len(csvdata)\n",
    "numcol=len(csvdata[0]) \n",
    "csvdata = np.array(csvdata).reshape(numrow,numcol)\n",
    "ID = csvdata[:,0]\n",
    "formula = csvdata[:,4]\n",
    "latt = csvdata[:,5]\n",
    "dir_gap = csvdata[:,6]\n",
    "ind_gap  = csvdata[:,7]\n",
    "form  = csvdata[:,8]\n",
    "X = csvdata[:,9:]\n",
    "\n",
    "prop = copy.deepcopy(form)\n",
    "\n",
    "XX = copy.deepcopy(X)\n",
    "n = ID.size\n",
    "m = int(X.size/n)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data into training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = 75\n",
    "\n",
    "X_train, X_test, Prop_train, Prop_test, ID_train, ID_test, formula_train, formula_test = train_test_split(XX, prop, ID, formula, test_size=t)\n",
    "\n",
    "n_tr = len(Prop_train)\n",
    "n_te = len(Prop_test)\n",
    "    \n",
    "X_train_fl = np.array(X_train, dtype=\"float32\")\n",
    "X_test_fl = np.array(X_test, dtype=\"float32\")\n",
    "Prop_train_fl = np.array(Prop_train, dtype=\"float32\")\n",
    "Prop_test_fl = np.array(Prop_test, dtype=\"float32\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define and train Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    " ##  Define Random Forest Hyperparameter Space  ##\n",
    "param_grid = {\n",
    "\"n_estimators\": [100, 200],\n",
    "#\"max_features\": [10, 30, m],\n",
    "\"min_samples_leaf\": [10, 20],\n",
    "\"max_depth\": [10, 20, 40],\n",
    "\"min_samples_split\": [2, 5, 10]\n",
    "}\n",
    "param_grid = { \"n_estimators\": [100]}\n",
    "\n",
    "rfreg_opt = GridSearchCV(RandomForestRegressor(), param_grid=param_grid, cv=5)\n",
    "rfreg_opt.fit(X_train_fl, Prop_train_fl)\n",
    "Pred_train_fl = rfreg_opt.predict(X_train_fl)\n",
    "Pred_test_fl = rfreg_opt.predict(X_test_fl)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate prediction errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse_test_prop= 0.1797842150936278\n",
      "rmse_train_prop= 0.07499753190802844\n"
     ]
    }
   ],
   "source": [
    "mse_test_prop = sklearn.metrics.mean_squared_error(Prop_test_fl,Pred_test_fl)\n",
    "mse_train_prop = sklearn.metrics.mean_squared_error(Prop_train_fl,Pred_train_fl)\n",
    "\n",
    "print('rmse_test_prop=', np.sqrt(mse_test_prop))\n",
    "print('rmse_train_prop=', np.sqrt(mse_train_prop))\n",
    "\n",
    "np.savetxt('Prop_train.txt', Prop_train_fl)\n",
    "np.savetxt('Pred_train.txt', Pred_train_fl)\n",
    "np.savetxt('Prop_test.txt', Prop_test_fl)\n",
    "np.savetxt('Pred_test.txt', Pred_test_fl)\n",
    "\n",
    "\n",
    "mse_train = sklearn.metrics.mean_squared_error(Prop_train_fl, Pred_train_fl)\n",
    "rmse_train = np.sqrt(mse_train_prop)\n",
    "mae_train = sklearn.metrics.mean_absolute_error(Prop_train_fl, Pred_train_fl)\n",
    "r2_train = sklearn.metrics.r2_score(Prop_train_fl, Pred_train_fl)\n",
    "R_train = stats.pearsonr(Prop_train_fl, Pred_train_fl)[0]\n",
    "\n",
    "mse_test = sklearn.metrics.mean_squared_error(Prop_test_fl, Pred_test_fl)\n",
    "rmse_test = np.sqrt(mse_test_prop)\n",
    "mae_test = sklearn.metrics.mean_absolute_error(Prop_test_fl, Pred_test_fl)\n",
    "r2_test = sklearn.metrics.r2_score(Prop_test_fl,Pred_test_fl)\n",
    "R_test = stats.pearsonr(Prop_test_fl,Pred_test_fl)[0]\n",
    "\n",
    "text_file = open(\"errors.txt\", \"w\")\n",
    "text_file.write(\"MSE_train: %s\" % mse_train + '\\n')\n",
    "text_file.write(\"RMSE_train: %s\" % rmse_train + '\\n')\n",
    "text_file.write(\"MAE_train: %s\" % mae_train + '\\n')\n",
    "text_file.write(\"r2_train: %s\" % r2_train + '\\n')\n",
    "text_file.write(\"R_train: %s\" % R_train + '\\n')\n",
    "text_file.write(\"MSE_test: %s\" % mse_test + '\\n')\n",
    "text_file.write(\"RMSE_test: %s\" % rmse_test + '\\n')\n",
    "text_file.write(\"MAE_test: %s\" % mae_test + '\\n')\n",
    "text_file.write(\"r2_test: %s\" % r2_test + '\\n')\n",
    "text_file.write(\"R_test: %s\" % R_test + '\\n')\n",
    "text_file.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
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j5MiRW3Q646677hrDhw+PAw44IHbfffdo165dlJWVxfLly+Ptt9+OV155JR5//PGYM2dOJEmS9XXHjBlTp/+sAwAAAN+upKQkjjvuuJgyZUpOc7/+9a/jF7/4RUqpoH5RjAQAAAC+1QcffBAPP/xwlV+3d+/e0bZt26zWXnnllZW6LXKnTp3ipz/9aZx88snxne985xvXNmrUKFq3bh077bRT9OnTJyIiSktL44knnojf/OY38Ze//CXn/SMi3nvvvZg9e3YceOCBlZqv6V5++eVYtWrVt64rKCiIU045JS666KLo3r17hWtatWoVrVq1ii5dusThhx8e1157bUyYMCEuueSS+Oijj3LOtnbt2rj//vtr3MmCVWn06NFZvf7bbrtt/Od//meccsop0aZNmwrXbLXVVrHVVlvFnnvuGaeffnp8+eWXMXr06LjllluitLQ052wTJkyIMWPGxFZbbZXz7De59dZb45VXXqnU7J577hmjR4+OIUOGVPh8s2bNokOHDtG3b9/4xS9+Ee+++25cd911cf/992dVDk+SJMrKyiqVDQAAAKj9SktLY9iwYTFp0qSc5q6++uq47LLLUkoF9Y8fXQYAAAC+1c0335xK0WdzxaRNLViwIOfbURcUFMS5554b8+bNi8suu+xbS5GbU1hYGEOHDo0XX3wxxo0bV+mCV64/HV6bZFPK+973vhevvvpq3HvvvZstRVakQYMGcdxxx8Xbb78dI0aMqFS+P/zhD5Waqy2yef3PPffc+Mc//hEXXHDBZkuRFWnfvn389re/jVdeeSW6dOmSc7YNGzbEgw8+mPPcN1myZElcddVVOc8VFBTEqFGjYu7cuVn/3RPx1cmS9913X7z44oux++6757wvAAAAUH+UlpbGCSecEBMnTsxp7sorr4zLL788pVRQPylGAgAAAN/q1Vdf/dY1LVu2jPPPPz+efvrp+OSTT2LDhg3xxRdfxNy5c+O3v/3tP09g/FfZlpNuvvnm2LhxY9Z5mzRpEpMmTYpbbrklWrRokfXctxk+fHi88sorseeee+Y8+8ILL1RZjtrmlFNOiTlz5kSPHj0qfY1mzZrFAw88EP/5n/+Z8+zrr78eH374YaX3rs2aNGkSjz322Bb/WejRo0e8/PLLlfp3P9fTEb7Ntddem/Pt7Zs0aRKPPPJI/OpXv4rCwsJK7bvffvvFyy+/HEOHDq3UPAAAAFC3bdy4MUaMGBHjx4/PaW7UqFFx5ZVXppQK6i/FSAAAAGCLnXPOOfHhhx/GmDFj4pBDDokOHTpEo0aNYuutt45evXrFRRddFDNnzow33ngjhg8fHhERO+ywQ1anr61YsSLuvffenPLcd999OZ0Il4vvfve7MXPmzOjWrVtOc6+//npO5c664rzzzov77rsvGjduXCXXGz16dJx88sk5zz3xxBNVsn9t0rRp03jmmWeqrMi39dZbx9SpU6NDhw45zc2ePTvnIuPmLFq0KO64446cZgoLC+Pxxx+PY445Zov3b968eUyYMCFOPfXULb4WAAAAUHeUlZXFySefHI8++mhOc5deemlcc801kclkUkoG9ZdiJAAAAFBpjRs3jokTJ8att94abdu2/db1e+65Z4wbNy5mzpwZ5513XlZ7jB8/PlavXp11plNPPTWGDRuW9frK2HrrreOxxx7Lqey3fv36mD9/foqpap5jjjkmxowZU+Vv7N5+++3RuXPnnGaKi4urNENNV1BQEA899FD86Ec/qtLrfuc734k777wzp5mNGzfG7Nmzq2T//+//+/9i3bp1Oc2MHTs2BgwYUCX7R0RkMpm4++674/jjj6+yawIAAAC1V1lZWYwcOTIeeuihnOYuueSSGD16tFIkpKRhvgMAAFB3lJaWxsqVK/Mdo0KtWrWq9G0TAajY/57CNnDgwJxn+/TpU+GttSvy4IMPZn3d1q1bx29+85uc81TGbrvtFuedd17ceOONWc/8/e9/j9122y3FVDVHly5d4t57703ljd3mzZvHTTfdlNMJgC+//HKV56jJLrzwwjjiiCNSufaQIUNiyJAhOZ3C+fLLL2/xKa7r1q2Lu+++O6eZn/3sZ3HCCSds0b4VKSgoiPvuuy/mz58ff/vb36r8+gAAAEDtUFZWFqeddlpO72FGRFx00UVx/fXXK0VCihQjAQDYYiUlJfHAAw/EG2+8EaWlpfmOU6HCwsLo0aNHnHTSSdGoUaN8xwGoE66++upKlSJz8cUXX8Tzzz+f9foLL7ww2rVrl2Kif3f++efHTTfdFOXl5Vmtf//991NOVHPcdddd0bJly9Suf9RRR8XOO++c9SmcCxcujBUrVkTr1q1Ty1RT7LjjjvGrX/0q1T1+/vOf51SMfPPNN7d4z0ceeSS+/PLLrNf36NEjbrjhhi3ed3OaNm0aEyZMiO7du8eaNWtS2wcAAAComcrLy+OMM86IP/7xjznNXXDBBXHjjTcqRULK3EobAIAt9sADD8TcuXNrbCky4qvTLOfOnRsPPPBAvqMA1Al77bVX/PznP099n2eeeSbr0mHjxo3j7LPPTjnRv+vUqVP07t076/WffPJJimlqjoMOOij69++f6h6ZTCbOOuusnGbqy63Mr7jiimjSpEmqe/zoRz+KHj16ZL3+73//+xbv+fDDD2e9tqCgIO66667UTwzv0qVL6iVUAAAAoOYpLy+PM888M+67776c5s4777y4+eablSKhGihGAgCwRUpLS+ONN97Id4ys1eRTLQFqk1/+8pdRUJD+2wrTpk3Leu0RRxwRW2+9dYppKpZLAfCzzz5LMUnNcdlll1XLPkOHDs1p/QcffJBSkpqjU6dOMWLEiGrZK5fXf8GCBVu015dffhkzZ87Mev0JJ5wQ++233xbtma3zzz8/unXrVi17AQAAAPlXXl4eZ599dtxzzz05zZ199tkxZswYpUioJoqRAABskZUrV9aqomFpaWmsXLky3zEAarWddtophgwZUi175XIb7WHDhqWYZPP23nvvrNcuWbIkxSQ1Q6dOnaJfv37VsleXLl3iu9/9btbr68OJnSeddFI0aNCgWvY6+OCDs167Zs2aWL58eaX3mjBhQmzcuDGrtQUFBTFq1KhK75WrgoKCuPzyy6ttPwAAACB/kiSJc889N+68886c5s4888y45ZZblCKhGilGAgAAADk5/vjjq2Wfzz//PBYuXJjV2kaNGsUhhxyScqKK7bLLLlmv/fLLL1NMUjMMHz68Wk4T/V977bVX1ms///zzFJPUDNV1WmRERK9evXJ6M39LXv9HHnkk67WDBw+u9hMchw0bFl26dKnWPQEAAIDqlSRJnH/++XHHHXfkNHf66afH7bffXq3vmQGKkQAAAECODjvssGrZZ86cOVmv/dGPfhTNmzdPMc3m5XJi4apVq1JMUjMMHDiwWvfbfffds15b14upnTt3jt12263a9mvVqlV07Ngx6/WVff1XrlwZzz33XNbrR44cWal9tkRBQUGccsop1b4vAAAAUD2SJIkLL7wwbr311pzmfvzjH8edd96pFAl50DDfAQAAqLsmTrwo1qxpm5e9mzdfFkOH3pSXvQHqsqZNm0avXr2qZa8BAwbEK6+8EvPmzYu33nor5s2bF/PmzYsPPvggysvL/23tgQceWC2ZKlJYWBitWrWKlStXfuvaul6MbNq0afTu3bta99xxxx2zXrts2bIUk+Rf//79q33PHXfcMT7++OOs1lb29Z8zZ06UlZVltbZt27YxZMiQSu2zpU455ZS46qqr8rI3AAAAkJ4kSeLiiy+OMWPG5DR38sknx913360UCXmiGAkAQGrWrGkbq1a1z3cMAKrQfvvtF4WFhdWyV5MmTWKvvfb62q2S169fH++8886/FSb79etXLZk2p0WLFlkVI9evX18NafKnV69e0bhx42rds0OHDlmvrevF1P3337/a96yO1/8vf/lL1mv79+8fjRo1qtQ+W2qHHXaI7t27x5tvvpmX/QEAAICqlyRJXHrppXHTTbkdxjFixIi49957o0GDBiklA76NYiQAAACQtQMOOCDfEaJJkybRs2fP6NmzZ76j/FO2ZcANGzaknCS/vv/971f7nttss03Wa1evXp1ikvyrq6//iy++mPXa6r6V+6YGDRqkGAkAAAB1RJIkMWrUqLjhhhtymhs+fHiMHTtWKRLyzFmtAAAAQNY2Pb2Rr2R7O5zS0tKUk+RXjx49qn3PNm3aZL22Lp/YWVBQEN27d6/2fdN+/ZMkiTlz5mS9/qCDDsp5j6rUt2/fvO4PAAAAVI0kSeKKK66Ia6+9Nqe5448/Pu6//36lSKgBFCMBAACArO222275jlCrlZWV5TtCqnbYYYdq37Nly5ZZr63LJ3Zuv/321X4b84j0X/+///3vsXz58qzWtmvXLnbcccec96hKe++9d173BwAAAKrGVVddFb/61a9ymjnmmGPiwQcfjIYN3cAXagLFSAAAACArhYWF0bVr13zHqNXKy8vzHSFVXbp0qfY9mzVrlvXaunxiZz5e+4j0X//58+dnvbYmnGjbvn37vP2zAAAAAKrGNddcE1dddVVOM0cddVSMGzdOKRJqEMVIAAAAICs777yzN/Y28cUXX8QNN9wQn3/+eb6j1AidOnWq9j0LCwuzXluXT+zMx2sfkf7rv2DBgqzXduvWLefrp2HPPffMdwQAAACgkkaPHh1XXHFFTjNHHHFEPPTQQzm9TwKkz3czAAAAgKw4Be0rn3zySTz55JMxfvz4KC4ujo0bN+Y7Uo3QrFmzvNzKuUGDBlmvrcsndrZr1y4v+6b9+udSjMz3bbT/V03JAQAAAOTm+uuvj1GjRuU0M2TIkHj00UejUaNGKaUCKksxEgAAAMjKdtttl+8IebF48eJ48cUXo7i4OIqLi+ONN97Id6QaKV/FvIKC7G+IohhZ9dJ+/RUjAQAAgOpw4403xqWXXprTzKGHHhrjx49XioQaSjESAAAAyEp9KEauWLEiXn/99fjb3/4Wc+fOjTlz5sT8+fPzHatWaNOmTb4j1Gt19fXPpRjZoUOH9ILkoHPnzvmOAAAAAOTg5ptvjksuuSSnmYEDB8aECRPycgcVIDuKkQAAAEBWtt9++3xHqDJLliyJd999N95555149913Y968efHf//3f8dFHH+U7Wq3lTeD8qquv/2effZb12vbt26eYJHs1JQcAAADw7X73u9/FRRddlNNM//79Y+LEidGkSZOUUgFVQTESAAAAyEptPJEuSZJ4++2346WXXorXXnstXnvttXj77bdjyZIl+Y5W5xQWFuY7Qr1WV1//1atXZ722phQSa0oOAAAA4JvddtttccEFF+Q007dv35g0aZJSJNQCipEAAABAVmrLm32ffvppTJ48OZ555pl49tlnY9myZfmOVC/U1WJebVFXX/+1a9dmvbZly5YpJsleu3bt8h0BAAAA+Ba///3v49xzz81ppqioKCZPnhxNmzZNKRVQlRQjAQAAgKzU5Fv1rl27Nh5++OG477774s9//nMkSZLvSPVOQUFBviPUa3Xx9d+4cWNs3Lgxq7UNGzaMTCaTcqLs1OS/KwEAAICIu+66K84666ycZg466KCYMmVKNGvWLKVUQFVTjAQAAACyUhNPjFy9enXcdNNN8bvf/S6+/PLLfMcBqlAup0U2atQoxSS5UYwEAACAmusPf/hDnHnmmTnNHHDAAfHEE09E8+bNU0oFpEExEgAAAMhKTbtV75NPPhlnnHFGfPbZZ/mOAqQgl2Jkw4Y1523OmlTSBAAAAP7P2LFj44wzzshppnfv3jF16tRo0aJFSqmAtNS9e+wAAAAAddr69evjnHPOiSFDhtSIUmSnTp28MQopKCkpyXptWVlZiklyUxdvaw4AAAC13QMPPBCnnnpqJEmS9cz+++8fTz31VLRs2TLFZEBavEsHAAAA1BqrVq2KgQMHxu23357vKHHQQQfFuHHj4v33349tt90233Ggzsnl5MVcSpRpq0lZAAAAgIhx48bFyJEjcypF7rvvvjFt2rRo1apVismANNWce8wAAAAAfINly5bFgAED4q9//WveMvTq1SuOPvroGDZsWOy44455ywH1QZMmTbJeW1pammKS3ChGAgAAQM3x8MMPx0knnRTl5eVZz+y9997x9NNPR+vWrVNMBqRNMRIAAACo8crKyuK4446r9lLkVlttFX369In+/fvHoEGDomPHjtW6P9RnjRs3zmn9+vXrcypTpkUxEgAAAGqGP/3pTzFixIicSpG9evWKZ555Jtq0aZNeMKBaKEYCAAAANd7Pf/7zmDFjRqp7NGjQIHbbbbfYd999Y//9948f/vCHsdtuu0Umk0l1X6BiuZYcly5dGh06dEgpTfZWrFiR7wgAAABQ702YMCGGDx8eZWVlWc/07Nkzpk+fHm3btk0xGVBdFCMBAACAGm3mzJlx0003Vek1mzdvHj169Ijvfe97//zo0aNHNG/evEr3ASovk8lEq1atYuXKlVmtX7ZsWY0oRi5dujTfEQAAAKBemzhxYgwbNiynUmSPHj1ixowZ0a5duxSTAdVJMRIAAACosUpKSuKcc87Z4utsu+220a9fvzjooIOid+/esdtuu0VBQUEVJPxKaWlplV0L+D/bbbdd1sXImlJIrCk5AAAAoD6aPHlyHHfccbFx48asZ7p37x4zZsyI9u3bp5gMqG6KkQAAAECNNWbMmHj33XcrNduyZcs4+eST48QTT4z9998/1Vti5/JGK5C97bbbLv7+979ntXbhwoUpp8nOokWL8h0BAAAA6qUnnngijjnmmJzeq9t9991j5syZsfXWW6eYDMgHxUgAAACgRiopKYmbb74557lMJhPnnHNOXH311dG2bdsUkn3d2rVrq2UfqG+22267rNd++OGHKSbJ3gcffJDvCAAAAFDvPPXUU3H00UfndGeXbt26xaxZs2KbbbZJMRmQL4qRAAAAQI300EMPxWeffZbTTMuWLePRRx+NgQMHppSqYqtXr67W/aC+6Ny5c9ZrFyxYkF6QHChGAgAAQPV6+umnY+jQoVFSUpL1zC677BKzZs2KbbfdNsVkQD4pRgIAAAA10l133ZXT+saNG8dTTz0VP/zhD1NKVLGVK1fmdHueJElSva031CW77bZb1mvffvvtFJNk7x//+Ee+IwAAAEC9MWPGjDjyyCNjw4YNWc/svPPOUVxcHNtvv32KyYB8K8h3AAAAAIBNLV68OF566aWcZm6++eZqL0VGRCxatKja94T6Yvfdd8967euvvx5JkqSY5tuVl5fH66+/ntcMAAAAUF/MmjUrDjvssFi/fn3WM127do3i4uLo0KFDismAmkAxEgAAAKhxpk6dGuXl5Vmv32+//eKnP/1piok2b+HChTmtz+X/F9R3uRQjV65cGe+//36Kab7d3//+91i9enVeMwAAAEB98Oyzz8aQIUNyKkXuuOOOUVxcHB07dkwxGVBTKEYCAAAANc5zzz2X0/pRo0bl7fbUuRaxFCMhe61atYouXbpkvf6FF15IL0wWcj3pFgAAAMjd888/H4MHD45169ZlPdOlS5coLi6OTp06pZgMqEkUIwEAAIAa56233sp67dZbbx2DBw9OMc03yyVrRERZWVlKSaBu+tGPfpT12hkzZqSY5Ns9/fTTed0fAAAA6rrZs2fHoYceGmvXrs16pnPnzlFcXBydO3dOMRlQ0zTMdwAAAOqu5s2X1cu9AdgySZLEvHnzsl4/cODAKCjI389+vvrqqzmtd2Ik5Oaggw6KBx54IKu1M2bMiPLy8rz8nVBeXp73YiYAAADUZS+++GIMGjQo1qxZk/VMp06dori4OKc7UgB1g2IkAACpGTr0pnxHAKAWWrx4caxevTrr9d/73vdSTPPNSkpK4uWXX85pxomRkJuDDjoo67WLFi2K4uLi6Nu3b4qJKjZz5sxYsmRJte8LAAAA9cGcOXNi4MCBOb1v2LFjxyguLo4dd9wxxWRATeVW2gAAAECNsmLFipzW77DDDikl+XZ//vOfY926dTnNbNy4MaU0UDfttNNOOX0D48EHH0wxzeb98Y9/zMu+AAAAUNf99a9/jUMOOSRWrVqV9UyHDh2iuLg4unbtmmIyoCZTjAQAAABqlFze4IyIaNWqVUpJvt2ECRNynnFiJOTumGOOyXrtI488EosXL04xzdctWrQoHnvssWrdEwAAAOqDuXPnxiGHHBIrV67Mema77baLWbNmxc4775xiMqCmU4wEAGCLtGrVKgoLC/MdI2uFhYV5LdAA8O3WrFmT0/r169enlOSbrVu3LsaNG5fznBMjIXfHHnts1mvXrVsX/9//9/+lF6YCv/nNb3I+PRYAAAD4Zq+++mr0798/li9fnvXMtttuG8XFxbHrrrumFwyoFRQjAQDYIoWFhdGjR498x8hajx49alWRE6A+atSoUU7rq/tkuP915513xrJly3Ke27BhQwppoG7be++9c7qd9i233BKffPJJion+z0cffRS///3vq2UvAAAAqC9ef/316NevX07vv22zzTYxa9as6NatW4rJgNpCMRIAgC120kknxV577VWjC4eFhYWx1157xUknnZTvKAB8i6ZNm+a0vrrKT/9q+fLlcc0111RqtqSkpIrTQP3wk5/8JOu1q1ativPPPz/FNP/nzDPPzPmkWwAAAGDz3njjjejbt28sXbo065mtttoqZs6cGbvvvnuKyYDapGG+AwAAUPs1atQoTjvttCgtLY2VK1fmO06FatstvwHqs/bt2+e0/tlnn43/+q//SilNxf7rv/4rpzdm/5UTI6FyfvKTn8TVV18da9euzWr9Y489FmPHjo2RI0emlum2226LadOmpXZ9AAAAqG/efPPN6Nu3b3z55ZdZz7Rv3z5mzpwZ3bt3TzEZUNsoRgIAUGUKCwtzLrMAwKa23377aNiwYWzcuDGr9bNnz461a9dGs2bNUk72lYkTJ8Ztt91W6fn169dXYRqoP9q2bRunnHJK3HHHHVnPnHXWWdG9e/fYe++9qzzPc889FxdeeGGVXxcAAADqq3nz5kWfPn1iyZIlWc+0a9cuZsyYET169EgxGVAbuZU2AAAAUKMUFBTEd7/73azXb9iwIe6+++4UE/2fd955J3784x9v0TXWrVtXRWmg/vmP//iPnE4BX79+fQwaNCjmzZtXpTnmzJkThx9+eJSWllbpdQEAAKC+euedd6JPnz6xePHirGfatGkT06dPj549e6YXDKi1FCMBAACAGqdXr145rb/22muzvr1uZb311ltx8MEHx4oVK7boOmnnhLqsa9eucc455+Q0s2TJkigqKoo5c+ZUSYZp06bFIYccEitXrqyS6wEAAEB99+6770ZRUVEsWrQo65nWrVvHjBkzcn4fEag/FCMBAACAGmfffffNaf2iRYvirLPOSilNxIsvvpjzm7Obo0wFW+byyy+Ptm3b5jTzxRdfxMEHHxy33357JElSqX1LS0vjmmuuiSFDhvhzDAAAAFVk/vz50adPn/j888+znmnVqlVMnz499tprrxSTAbWdYiQAAABQ4wwaNCjnmfvvvz9Gjx5dpTnKy8tj9OjRcdBBB+V0G59volAFW6Zdu3ZxzTXX5Dy3fv36OOecc+LAAw+M5557Luu5JEni8ccfj169esUVV1wRZWVlOe8NAAAAfN17770XRUVF8emnn2Y907Jly3jmmWdin332STEZUBcoRgIAAAA1zh577BE77bRTznOjRo2KM844I9avX7/FGZ577rn4wQ9+EKNGjYqNGzdu8fX+19KlS6vsWlBfnX322dGvX79Kzc6ePTsOPvjg2GuvveK6666Lv/zlL7FmzZp/W7N06dKYNWtWXHbZZbHLLrvE0KFD480336yK6AAAAEBEvP/++1FUVBSffPJJ1jMtWrSIadOmxX777ZdiMqCuaJjvAAAAAAAVOfXUU+Oyyy7Lee6ee+6Jv/zlL/Ff//Vfceyxx0aDBg2ynt24cWM89dRTcccdd8RTTz2V897Z+OKLL1K5LtQnmUwmxo4dG3vuuWcsW7asUtf429/+Fn/729/++ftmzZpFs2bNYtWqVbFhw4ZKZysoKIjy8vJKzwMAAEBdt2DBgigqKoqFCxdmPdO8efN46qmnonfv3ikmA+oSJ0YCAAAANdLpp58eTZo0qdTsW2+9FcOHD4+dd945fvazn8XEiRPj448//reyU5IksXLlynjttdfi7rvvjtNPPz06dOgQhx9+eM6lyB133DHrAmYub/gCm9exY8e49957I5PJVMn11q5dG0uWLNmiUuRpp50W3/nOd7JaW1DgrVkAAADqnw8//DCKiorio48+ynqmWbNmMXXq1DjggANSTAbUNd59AwAAAGqkrbfeOs4777wtusYHH3wQY8aMiaOOOio6deoUTZo0iebNm0f79u2jsLAwWrduHd///vfjJz/5SfzhD3+IxYsX57zHLrvsEs8//3x07Ngxq/Xz5s3LeQ+gYkceeWRcd911+Y4RERE9e/aMW2+9NZIkyWp9LqfZAgAAQF2wcOHCKCoqigULFmQ907Rp03jyySfjwAMPTC8YUCcpRgIAAAA11mWXXRZbb711lV5z7dq1sXTp0igrK9via+2+++7x3HPPRceOHaNLly5ZzbzzzjtRUlKyxXsDX/n5z38eZ511Vl4zdOjQIR577LFo0qSJYiQAAABU4OOPP46ioqL44IMPsp5p0qRJTJkyJQ4++OD0ggF1lmIkAAAAUGO1adMm7r777nzHqNCBBx4Yzz33XGy33XYREbHzzjtnNVdaWhqvv/56mtGg3rntttvi/PPPz8ve7dq1i2eeeSZ22GGHiIisS9eKkQAAANQXn376afTp0yfee++9rGcaN24ckydPjr59+6aYDKjLFCMBAACAGu2II47IW+Fpc0477bSYMWNGbLXVVv98rGfPnlnPz5o1K4VUUH9lMpkYM2ZMXHnllZHJZKpt3+222y6mT58ee+yxxz8fKy0tzWq2UaNGacUCAACAGuOzzz6LPn36xPz587Oeady4cUyaNCn69++fYjKgrlOMBAAAAGq8m2++OY444oh8x4jGjRvH7373u7jnnnuisLDw357r1atX1teZMWNGVUcDIuKXv/xlTJkyJdq1a5f6Xt27d485c+Z87c9+tidGNmnSJI1YAAAAUGMsWrQo+vTpE++++27WM40aNYqJEyfGgAEDUkwG1AeKkQAAAECNV1BQEI888kgcffTRecvQs2fPmDt3bpx33nkVPr/33ntH8+bNs7rWs88+G0uXLq3KeMD/GDx4cLz22msxdOjQVK6fyWTi7LPPjpdeeik6d+78tedLSkqyuk7Tpk2rOhoAAADUGF988UX06dMn3nnnnaxnCgsLY8KECTFo0KAUkwH1hWIkAAAAUCs0btw4Hnnkkbjooouqdd+mTZvGFVdcEXPmzPm32+VuqlGjRnHggQdmdc2NGzfGpEmTqioisIlOnTrFY489Fs8880zsu+++VXbdffbZJ2bNmhW33XbbZovQGzZsyOpaipEAAADUVYsXL46+ffvGvHnzsp4pLCyM8ePHx5AhQ1JMBtQnipEAAABArdGgQYP47W9/G1OmTImOHTumulcmk4kTTzwx3n333bjqqquiUaNG3zqTywl1995775bEA7LQv3//mDNnTrz44otx4oknRps2bXK+RmFhYQwaNCieeOKJePnll+Pggw/e7NqysrLYuHFjVtdt0aJFzlkAAACgpluyZEn069cv3nzzzaxnGjZsGI8++mgcfvjhKSYD6puG+Q4AAAAAkKshQ4bEu+++G9ddd13ccsstsWLFiiq7dtOmTePEE0+MCy64ILp3757T7LHHHhvnnXdeVifGzZ49O958882c9wBy94Mf/CB+8IMfxMaNG2P27Nnx5z//Od58882YN29eLF68OFatWhXr1q2LFi1aROvWreO73/1u9OzZM/bZZ58YPHhwtGvXLqt91qxZk3WmypQ0AQAAoCZbunRp9O/fP954442sZxo0aBAPP/xwHHnkkekFA+qlTJIk+c4AQC2QyWS+9gnD55B/V1JSEu+9916Fz3Xt2jWrE4YAAMjd6tWrY+zYsfHQQw/FX/7yl0r9d2rDhg3jRz/6URxxxBExYsSIaN++faXzLFy4MOtb6W6zzTbRqlWrSu8F1CyfffZZdOjQIau1xx9/fDz88MMpJ6odfD0NAABQ+y1btiz69u0br776atYzDRo0iHHjxsVxxx2XYjJgS2Qyma89liTJ1x+sgZwYCQAAANRqLVq0iHPPPTfOPffc+Pzzz+OFF16IOXPmxDvvvBMLFiyIRYsWxZo1a2LDhg3RvHnzaNWqVbRt2zZ22WWX6NGjR/Ts2TMOOuigKju9rVOnTlVyHaD2WblyZdZrnRgJAABAXbF8+fI45JBDcipFFhQUxIMPPqgUCaRGMRIAAACoM7bbbrs49thj49hjj813FKAeWrZsWdZrt9lmmxSTAAAAQPVYsWJFDBgwIF555ZWsZwoKCuL++++PYcOGpZgMqO8K8h0AAAAAAKAu+OKLL7Jeu91226WYBAAAANK3cuXKGDhwYLz88stZz2QymRg7dmyceOKJKSYDUIwEAAAAAKgSixYtynqtYiQAAAC12apVq2LQoEHx0ksvZT2TyWTi3nvvjZNOOinFZABfUYwEAAAAAKgCCxYsyHptp06d0gsCAAAAKVq9enUceuih8eKLL+Y0d88998TIkSPTCQWwCcVIAAAAAKDGWrFiRTzxxBOxcOHCfEf5Vn//+9+zXtu1a9cUkwAAAEA61qxZE4MHD47Zs2fnNHfXXXfFqaeemlIqgK9rmO8AAAAAAACbs2zZsjjssMMiIqJt27bRo0eP+N73vvfPjz322COaNGmS55Rf+etf/5rVutatW0e7du1STgMAAABVa+3atXHYYYfF888/n9PcHXfcEWeccUZKqQAqphgJAAAAANRYTZs2/eevly1bFs8991w899xz/3ysQYMGseuuu8b3vve9OOuss+JHP/pRPmLGvHnz4sMPP8xq7a677ppyGgAAAKha69ati8MPPzyKi4tzmrv11lvjpz/9aUqpADZPMRIAAAAAqLFatGjxjc+XlZXFvHnzYt68edGiRYu8FSOffPLJrNd+73vfSzEJAAAAVK3169fHEUccETNnzsxpbsyYMXHOOeeklArgmxXkOwAAAAAAwOY0b948WrdundXa119/PeU0mzdp0qSs1ypGAgAAUFusX78+hg4dGtOnT89p7qabborzzz8/pVQA386JkUCNlMlkekTEbhGxTUS0iIglEbEoIv6WJMnH+cwGAAAAVK9OnTrFihUrvnXdf//3f8f69eujSZMm1ZDq/8ydOzf+/Oc/Z73+Bz/4QYppAAAAoGps2LAhjj766Jg2bVpOc7/5zW/iwgsvTCkVQHacGAnUGJlMZrtMJjMmk8l8GhGvR8TDEfG7iBgdEXdFxKSIWJjJZF7JZDLnZDKZBtWcb1Emk0mq4OP06swNAAAAtd1OO+2U1bp169bldEvrqnLDDTdkvbZ169bRs2fP9MIAAABAFSgpKYljjz02pk6dmtPcddddFxdffHFKqQCypxgJ1AiZTOY/IuL9iDg/Irb/luV7RcStEfFKJpPZM+1sERGZTKZNfHV6JQAAAFDNevXqlfXaBx98MMUkX/fGG2/EhAkTsl5fVFQUBQXelgUAAKDmKikpieOOOy6mTJmS09yvf/3r+MUvfpFSKoDceAcOyKtMJtMgk8mMjYgbI6JpjuM9I2JWJpPpXtW5KrBbNewBAAAAVGDvvffOeu0TTzwRb7/9dopp/k9JSUmcfPLJUVZWlvXM0UcfnWIiAAAA2DKlpaUxbNiwmDRpUk5zV199dVx22WUppQLInWIkkG+jI+KULZjfKiKmZzKZraooz+Z0S/n6AAAAwGYceOCB0bhx46zWbty4Mc4///yUE33l8ssvj9dffz3r9S1btozDDz88xUQAAABQeaWlpXHCCSfExIkTc5q78sor4/LLL08pFUDlKEYCeZPJZAZFxM8reGp9RNwcET+MiPbx1UmSO0fEWRExv4L120XEmJRi/i8nRgIAAECeNG/ePIqKirJeP2PGjLjllltSTBRxww03xA033JDTzE9+8pNo1apVSokAAACg8jZu3BgjRoyI8ePH5zQ3atSouPLKK1NKBVB5ipFAPo2u4LG3I2KPJEkuSpLkxSRJliZJsj5Jkn8kSfL7iNgjIm6tYO6ETCbzwxSzbnpi5JQkSTKV/LgnxZwAAABQJx1//PE5rb/gggvigQceqPIc5eXlcfXVV8cvfvGLnOYaNWoUF154YZXnAQAAgC1VVlYWJ598cjz66KM5zV166aVxzTXXRCaTSSkZQOUpRgJ5kclkhkREz00eXhgRByZJ8v7m5pIkKU2S5LyIuKOCp9P87sKmxci3U9wLAAAA2MTxxx8fbdu2zXp9kiQxcuTIuPjii2P9+vVVkmH+/Plx0EEHVeokjBNPPDE6duxYJTkAAACgqpSVlcXIkSPjoYceymnukksuidGjRytFAjWWYiSQL8MqeOz0JEmWZDn/s4jYtEB5ZCaT2WqLUlUgk8k0iogdNnl4XlXvAwAAAGxe06ZN4/TTT89ppry8PH77299Gz549Y+zYsbFu3bpK7f23v/0tfvKTn0SPHj1i9uzZOc+3atUqrrrqqkrtDQAAAGkpKyuL0047LR588MGc5i666KK4/vrrlSKBGi2TJEm+MwD1TCaTKYiIzyNi6395+PUkSXrmeJ1z4uu31T4qSZKJW5bwa/vsERFvbvLwvkmS/LUq96npMpnM1z5h+Bzy70pKSuK9996r8LmuXbtGo0aNqjkRAABA3bJ06dLo2rVrLF++vFLzbdu2jSOOOCL233//2HfffWOHHXaIVq1aRUHBVz8/niRJrFy5MhYtWhRz586NOXPmxHPPPRevvfbaFuW+55574rTTTtu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SqZg0aVIcd9xxCSYDgNZBMRIAAADakcWLF+e8drPNNkswSX5KSkryyrN06dK89zjxxBPj05/+dN5zn+TZZ5+N8847L7bffvvYa6+94gc/+IGSJAAAAJCYVatWxSGHHBJPPvlkzjOpVCpuueWWGD9+fILJAKD1UIwEAACAdmTJkiU5r73uuusilUq1mrd8Sp35fJwbdO3aNa688sq85/IxZ86c+N///d/YfvvtY999942rr746Fi1alOieAAAAQMexevXqGDlyZDz++ON5zd10000xYcKEZEIBQCukGAkAAADtRH19fSxfvrzYMVpEU4qRERFHHHFEfPnLXy5wmo176qmnYuLEiTFo0KA46KCDYvLkybF27doW2RsAAABof9asWROjRo2K2bNn5zV34403xkknnZRQKgBonRQjAQAAoJ145513IpvNFjtGi3j33XebPDtp0qTYaaedCpjm4zU0NMT06dPjhBNOiC233DL+67/+K5555pkW2x8AAABo+9auXRuHHnpoPPbYY3nNXX/99XHKKacklAoAWi/FSAAAAGgnVq9eXewILaa2trbJsz179oz7778/ttpqqwImyk1NTU388pe/jL333jv22Wef+M1vfhP19fUtngMAAABoO9atWxeHHXZYVFVV5TV37bXXxn//938nlAoAWjfFSAAAAGgn1q9fX+wILaa5H+tnPvOZeOyxx+LTn/50gRLl7+mnn47jjz8+tttuu7juuuuirq6uaFkAAACA1qm2tjYOP/zwmDlzZl5z11xzTZx++ukJpQKA1k8xEgAAANqJjlSsK0QJdLvttounn346DjnkkAIkaroFCxbE6aefHp/97Gfz/iYHAAAA0H7V1tbG2LFjY/r06XnNXXXVVXHWWWcllAoA2gbFSAAAAGgnnBiZv8033zweeOCB+OlPfxrl5eUFeWZT/e1vf4uDDjooLrzwwmhoaChqFgAAAKC41q9fH0cccUQ89NBDec1dccUVcc455ySUCgDaDsVIAAAAaCc60omRhfxYU6lUnHnmmfG3v/0tJk6cGN26dSvYs/PV2NgYl19+eRxzzDGRyWSKlgMAAAAonrq6ujjqqKPiwQcfzGvuBz/4QZx77rkJpQKAtkUxEgAAANqJ0tLSYkdoMdlstuDP3HzzzePKK6+MBQsWxHe/+93YaqutCr5Hru6555445phjorGxsWgZAAAAgJZXV1cXRx99dNx33315zX3ve9+L8847L6FUAND2KEYCAABAO9G5c+diR2gX+vbtGxdeeGG8+eabMWXKlBg1alRRSqdTpkyJSy+9tMX3BQAAAIojk8nEuHHjYurUqXnNXXrppXHBBRcklAoA2qZOxQ4AAAAAFEY+xciSkpLYfPPNE0yTrB49eiS+R2lpaYwZMybGjBkTixYtit/85jfxm9/8Jp5//vnE997gsssui8rKyth///1bbE8AAACg5WUymfjKV74SU6ZMyWvu4osvjm9/+9sJpQKAtksxEgAAANqJfIqR5eXl8Y9//CPBNO3LlltuGeeee26ce+658dJLL8Vvf/vb+N3vfhcvvvhiovs2NjbGmWeeGc8991yHuiodAAAAOpL6+vo4/vjj4+67785r7lvf+lZcfPHFCaUCgLbNVdoAAADQTnTr1i3ntStXroyGhoYE07RfO++8c3znO9+Jv/71r/Hiiy/Gd7/73dhjjz0S2+8vf/lL3HzzzYk9HwAAACiehoaGOOGEE+Kuu+7Ka+7888+PSy+9NFKpVELJAKBtU4wEAACAdiKfq7Gz2WwsW7YswTQdw8477xwXXnhhzJkzJ95888245pprorKysuCnO/7kJz8p6PMAAACA4mtoaIgJEybEHXfckdfcN7/5zbj88suVIgHgYyhGAgAAQDvRuXPnKC8vz3n9woULE0zT8Wy99dZx1llnxaxZs2LhwoVx3XXXxZe+9KWCfJPipZdeiqqqqgKkBAAAAFqDhoaGOPnkk+O2227La27ixInxwx/+UCkSAD6BYiQAAAC0IwMGDMh57WuvvZZgko6tf//+8fWvfz1mz54d8+fPj//93/+NzTbbrFnP/L//+78CpQMAAACKqbGxMU455ZT49a9/ndfc2WefHT/+8Y+VIgEgB4qRAAAA0I4oRrY+22+/fVx++eWxYMGC+NnPfpbXv6MPevjhhwucDAAAAGhpjY2Nceqpp8avfvWrvObOPPPMuPrqq5UiASBHipEAAADQjmyzzTY5r33ppZeSC8JHdOnSJc4444x4+eWX47DDDst7fv78+bFo0aIEkgEAAAAtobGxMU477bS46aab8po77bTT4pprrlGKBIA8KEYCAABAOzJ48OCc1z777LMJJmFTevXqFffcc08ceeSRec/OmzcvgUQAAABA0rLZbJxxxhlxww035DV36qmnxs9+9jOlSADIk2IkAAAAtCP5FCP/+te/xvr16xNMw6Z06tQpfv3rX8enP/3pvOZeeeWVhBIBAAAASclms3HWWWfF9ddfn9fc1772tbjuuuuipES1AwDy5U9PAAAAaEfyKUbW1dXFE088kWCatmHlypWxfPnyFt+3W7du8eMf/zivmYULFyaUBgAAAEhCNpuNc845J6699tq85k488cS44YYblCIBoIk6FTsAAADtV31DY4fcG6CYdtppp+jWrVusXbs2p/XTp0+PAw44INlQOVi9enWcfPLJscMOO8ROO+0UO+20U+y4447Ru3fvgu6zePHieO655+LZZ5+N5557Lp577rl47bXX4sc//nFMnDixoHvl4rDDDotevXrFypUrc1q/atWqhBMBAAAAhZLNZuOb3/xmXHPNNXnNnXDCCfHLX/5SKRIAmkExEgCAxCx5J7eSBwCF06lTp9h3332jqqoqp/UPPPBAfO9730s41Sf7/e9/H3fddddH3t+/f//3i5IffNt+++2jtLT0Y5+5YsWKmDVr1vsFyGeffTYWLVq00bXz588vyMeRr7KysjjwwAPjnnvuyWn96tWrE04EAAAAFEI2m43zzz8/rrzyyrzmjj/++Ljllls+8e89AICPpxgJAAAA7cx//ud/5lyMfOGFF+Kll16KnXfeOeFUH+/222/f6PuXLFkSS5YsiT/+8Y/vv6+kpCSWLl0affv2/dhnvvDCC3HEEUfktP/zzz+fc9ZC+/SnP53z2lQq1eR9mjMLAAAA5C6bzcaFF14YP/rRj/KaO/bYY2PSpElKkQBQAM5dBgAAgHbmP//zP/NaP3ny5ISS5Oadd96J6dOn57z+c5/73CeWIiMidtttt5yf+fzzz0ddXV3O6wupf//+Oa/t1q1bk/dRjAQAAIDkZbPZuOiii+L73/9+XnPHHHNM3HrrrUqRAFAgTowEAKBZSkpKIpVKRTabfe8dvYp74tgnSaVSUVLi54OA9m3IkCHRo0ePnK9d/uUvfxnf/va3o2vXrgkn27gbbrgh6uvrc15fWVmZ07q+ffvGwIEDY+HChZ+4tra2Nv74xz/GsGHDcs5RKKtWrcp5bY8ePZq8Tz7fWGlsbGzyPgAAANCRXXLJJfHd7343r5kjjzwybrvttujUSYUDAArFd4QBAGiWkpKSohVpmqJr166KkUC716VLlxg1alTO699555245ZZbEky0aWvXro2f/OQnec3k87Hts88+Oa+977778spRKP/4xz9yXvupT32qyfvkU4ysra1t8j4AAADQUV122WVxySWX5DXz5S9/OW6//XalSAAoMN8RBgCg2fr27RvdunVr1Vd0plKp6NatW05XrwK0B0ceeWRe6y+77LK8Ti4slBtvvDHeeeednNf369cv9t9//5zX77fffjmvvf322yOTyeS8vlBeeeWVnNduu+22Td6nrKws57W5njYKAAAAvOfyyy+Piy66KK+Zww8/PO64445Ip9MJpQKAjsuPHAAA0GwlJSWx2WabRWNjY6u9erOkpMRJkUCHMnLkyLyu0168eHFceumlccUVVySc7F+WL18eP/jBD/KaOfzww/M6+TCfEuXSpUvjt7/9bRx//PF5ZWqOZcuWxeOPP57z+p122qnJe3Xp0iXntcuWLWvyPgAAANDR/PCHP4wLL7wwr5nRo0fHXXfdldcPMgIAuVOMBACgYJQPAVqPbt26xYQJE+Laa6/Neebqq6+O0aNH51UmbI6JEyfG4sWL85r56le/mtf6z3/+8zFw4MBYuHBhTusvv/zyGDduXItdXzV16tRoaGjIae0WW2wRn/70p5u8V7du3XJe+/bbbzd5HwAAAOhIrrzyyjj//PPzmhk5cmTcfffdHylFZjKZqKmpKWS8gikvL3eyJQBtimIkAAAAtFNnn312/PznP49sNpvT+oaGhjjuuOPimWeeiS222CLRbNOnT49JkyblNTN48OC8rsaOiEilUnHYYYfFL37xi5zWv/TSS/Gzn/0szjnnnLz2aYr169fHZZddlvP6L33pS83ar2fPnjmvzed6bwAAAOiorr766jj33HPzmjn44IPjnnvuic6dO7//vrq6upg8eXJUV1dHJpMpdMyCSKfTUVFREePHj3fKJR2KwjK0XYqRAAAA0E595jOfidGjR8d9992X88zbb78dhxxySPzhD3+I8vLyRHLNnz8/jjvuuLznvv71rzdpv6OOOirnYmRExAUXXBBDhw6Nz33uc03aL1dXXHFFvP766zmvP/TQQ5u1X69evXJe+/zzz8fq1aujR48ezdoTAAAA2quf/vSnMXHixLxmhg8fHlOmTIkuXbr82/snT54cc+bMKWS8gstkMu9nPPnkk4ucBpKnsAxtn3sOAQAAoB275JJLoqQkvy//n3/++RgxYkQsW7as4Hn+8Y9/xIgRI2Lp0qV5zfXv3z9OOeWUJu1ZWVkZ22+/fc7ra2tr47DDDou33nqrSfvlYurUqfGd73wn5/XpdDoOP/zwZu3Zt2/fnNfW1dXldQ07AAAAdCQ///nP4+yzz85rZtiwYTF16tSPlCIzmUxUV1cXMl6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EHBQRW4zo2v8iIt4zgsfPN0b5mG0/n4Nc77cFf8eRiIiVIuL17X33KODFlIci52Up4EnAV4ArIuKz7VTKsTfC+9nMiHh5O2X5BOB1LFwocr5lga2AtwBnRMQfImL/iHBIkiRJmtYMRkqSJEmSNCKZ+XvgosIyBiMlLVBErE4TdivxrRq9TLgTgBvn8+f3AMcCbwW2zMx1MvM/M/OwcZuQFBF7RsQxwKnAK4BVOm4JYHXgHcDFEfHpiFip64b6LiK2jIjfAH8Ans8UjksttDjwPODkiPhBRKw/outKVUTEVm2Y+GLgAJrjhru2NLA/cHpEHBYRm3TdkMZPRCwVEe8GLgE+xWjvu8sDrwHObPf+0vDfRIuIRSLixcCFNG+k2WyIl9sJOAg4KyKeExFmAiRJ0rTkF0GSJEmSJI3WDwvXrx8RG1bpRFKfPb5w/b00x99Oa5l5H81Uxfu7iuaI8WcBD8rM3TLzY+N6ZGFEbBsRxwE/A3bruJ0HsiTNkeXnRsRLum6mjyLiwRHxFZoJd4/rshVgX5qgxnsjYrEOe5EWKCIeEhHfAP5MEyYex/tsAE+lCUh+IiKW7Lohde9+IbzzgPcBy3Tc0r7A2RHxyYhYoeNeRq6d7HoS8DWaN4aMyobAd4E/RsSWI7yuJEnSWDAYKUmSJEnSaP2gQo1HV6ghqd9Kg0/HZ+ZNNRrpgZ/TTI58N7AdsEZmvjgzf5SZt3Tb2gNrJ0R9ETiZyXneWBn4akQcEhErd91MX0TEU4AzgJcyPq8JLE4zde/ESTliVdNPRPwXTajshTThw3G3GPBG4E8R8bCum1F3ImJNmuOyRx3CW5DFgTfQTJDco+tmRiUiXgecAjy8wza2o9kb3h0RMzrsQ5IkaaTG5YcgkiRJkiRNC5n5Z5pjzEo8qkYvknpt98L1P6/SRQ9k5rcz85GZ+YHMPDUzs+ueFqQNxJxCc2T2JP4MeC+aF++36LqRSRYRMyPi0zSP53E4On1etgVOioi9u25EmiMiVo6Iw4DP0P2UvUFsRhM6flbXjWj0IuKZwOmM75RogDWAIyLi4xGxeNfNDEtELBER3wf+hyYU2rXFaaaHHhYRy3bdjCRJ0ihM4g/FJEmSJEmadEcVrp+UyV+SOhAR6wAPKSxzeI1eNHptwOwkYJOueym0LvCHiNi160YmUUSsAhxPc0T5uFsa+FFEvKvrRqSI2AQ4leZo6km2JPCDiHhj141oNNqjsz8LHAys2HU/CyGANwG/j4gHd91Mbe1x4b8B/qPjVublyTSf9zW6bkSSJGnYDEZKkiRJkjR6Rxau37h9oUWS5uURheuvycyzq3SikYqIlwI/BJboupdKlgV+YThyaiJiXeD3dHtk51QF8P6I+GjXjWj6iojtgeOAtbvupZIAPhERb+66EQ1XRCwB/Ah4Tde9DODhwAkR8dCuG6klIpan+Z5/5657mY8tgd9ExMpdNyJJkjRMBiMlSZIkSRq9o4CSo1gD2KpSL5L6pzQYeVyVLjRSEfEy4CvAjK57qWwp4JCI2LTrRiZBRGxJE4rcqOteBvSWiPhQ101o+omIbWi+Rn9Q170Mwcci4jldN6HhiIgVgSOAZ3TdS4H1aCYY7th1I6UiYkngl8B2XfeyEDYDfhkRS3XdiCRJ0rAYjJQkSZIkacQy8xrg3MIyW1doRVI/7VC43mDkhImIJwKf77qPIVoBOCwilum6kXEWERvQTKhaveteCr09Il7VdROaPiJiLeBwmim1ffX1iPCNVT0TEcvShCIf3XUvFTwIODIiSr+O7UxEBPBNYKeue5mC7YH/7boJSZKkYTEYKUmSJElSN04pXL91jSYk9dLWhetPqNGERiMiNqM5PnvRrnsZsg2BT3XdxLiKiFWAXwGrdt1LJf8bEeN8BKl6op2U9gtgta57GbKZwHciYmbXjaiOiFgc+CmTMZlwYS0NHN5+bTOJ3gE8q+smBvCyiJjkiaOSJEkPyGCkJEmSJEnd+FPh+i2qdCGpVyJiTcomXs0GzqjUjoasDUV8l35PObu/l07yJKlhaYNdh9OER/tiMeBHEdGXoKfG1yeZPl9XbwH8v66bULmIWAT4DrBH170MwcrAryNi7a4bmYqI2AV4b9d9FPiCk7klSVIfGYyUJEmSJKkbpcHIjap0IalvNi1cf25m3lmlE43CB5heE4QD+ETXTYyhr9Achdk3qwKf7boJ9VdEPBn4z677GLF3RsSKXTehYh9iMicTLqw1gV9GxNJdN7Iw2iPNvwXM6LqXAg8G3th1E5IkSbX1/XgVSZIkSZLG1V8K1y8fEQ/OzKurdCOpLzYpXF+6N2lEImIbhvMCdgJ/pTlS/W/AucBVwNXArcDdwL3Aku1tZWBtYF2a4zy3pwlrDutN+Y+OiB0y86Qh1Z8oEfE84DlDKn8fcDxwLM0k2b8BNwA309wHlqEJr2wC7Aw8Gdigcg/7RsT3MvPQynU1zbWTVr88pPKXAb+nedycDVwOXAPcSLOHzqI52npJYDmaPXRt4GHAI2j20aWG1NtywMuBjw6pvoYsIh4HvGVI5WfT3Hd/C/yZZt+/EbiJ5jXl5YGVgM1p7q+7AI9iOM/5mwGfA144hNq1fQxYp+smKnhTRHw+M6/tuhFJkqRaDEZKkiRJktSBzLwlIq6mmcwwqIfSBFUkaY7SiZF/rdKFRuFT1A0i/JbmWO5DMvO6hfj429vbdcA57e99BaA9/ngv4EXAThV7nOMVwLQPRkbEujShkdrOBv4H+HFmXj+fj7uxvZ0BHAy8NiIeBbwVeGrFfj4TEb/KzLsr1pTeAqxRsd45wEE0j5vzFuLj72xvNwAX3/8P2tDmE4D9gL2pP4XuFRiMnEjt8+u3aSYo13QFzb7/vcy86gE+ZhZwB82bJf4K/LDtaRVgH+B1wMaV+9o/Io7OzG9VrltNRGxP85gq8XfgKOBEmr3kfOAWmjekLAqsCDwI2AbYgWZ/WK/wmvOyDPAymomkkiRJveBR2pIkSZIkdWdhXjSdn2G8GCJpsm1UuP6CKl1oqCLi6cBulcodDmyVmY/JzK8uZChyvjLzmsz8SmY+kiYYeUxxl/9qr4iY1m/6j4igCccsV7HsRTQhrM0y88sLCEXOU2b+PjOfBjyaZtJYDWsDr6xUSyIi1gDeXKncX2mCwJtm5kcWMhQ5X5l5R2b+NDP3pZnC+mWaSX61rBcR25YWycz3ZGYszK3gMi9a2GtUvOZYavf9b1H2xrq53Qi8Clg/Mz85n1DkA8rMazPzizRvznkG5d/jzu3zEVE7cFnTZxksqHoH8A1gp8xcLTOfl5kHZuYRmXlRZl6fmbPa/eCKzPxLZh6Uma+k2RceDxxW8e8xx8siwvyAJEnqDb+wkSRJkiSpO6UvGq1ZpQtJfbJW4XqDkZPhvRVqXA/slZl7ZubpFerNU2aemJm70xz3fHOlsivRTEyazp5LEz6sYTbwEWDzNoyVpQUz8/c0R6t/rbRW6x0RsUylWtLbKD+q+l7gXTTB8p/XeNzMS2ZekpmvoNnzaoWNAZ5UsZZGYz+aSYG1/JQmCP+FzJxVWiwbh9Acsf0B4J7Smq2lgS9WqjUMU/165F7gSzRh1Bdn5olTvWD7uT4iM/cC9qDu3rAude9nkiRJnTIYKUmSJElSdwxGSqqt9FhQg5FjLiKeQBM6KHEesG1mDmPS0Dxl5v8BD6fei/c7V6ozcSJiCeCDlcrdCDwlM9+emXdWqglAZt6VmS8F3l6h3Co0x3tKRSJiJeBFhWVuB56UmR/MzPsqtLVAmfknmgDWjyuVnLZ76CSKiCVpAuw1zAbekpl7Z+bfK9X8h3bvfzdNYO+aSmV3i4hnVqrVpXOAHTLzPzPz6hoFM/NomjcifK9GvdY+FWtJkiR1ymCkJEmSJEnduaJwvcFISf8QEcsDJRPVbq9xjLKG7k2F6y8Dds3MS2s0MxWZeT6wK3BmhXLTeWLka2mOly51KfCIzPxVhVoPKDM/Ary1QqlXtkfJSiVeSTOBblD3AHtm5pGV+llomXkbsC91AlCPqFBDo/MmyqeCA9xFMy364xVqzVdmHkcT2Ks1lfoTbUB0Uv2A5k0pp9Yu3IZRnwu8r1LJp/h8K0mS+sJgpCRJkiRJ3bmqcP3qVbqQ1Bel0yKrTw1SXRGxMfDYghJ30wQiSp9/BtaGbx9L+XPg1uXdTJ6IWJE6ExgvBXZrw6pDl5kfA75QWGYj4PEV2tE0FRGL0AQjS7wuM39boZ2BZOZs4AXAEYWlVoyIdSq0pCGLiNWoEy6fBTwjM39eodZCyczLaSZH1ghHrgO8pUKdLnwW2C8z7xjmRTLzvyl/rgV4CM2Ub0mSpIlnMFKSJEmSpO6UhkJWrtKFpL4oDUZWOdJPQ/W8wvXvy8w/V+mkQHt85PMLy6wbESVT3ybVy4AVCmvcAOyemReVtzMlrwNK738ep60Sj6HsufI3mfn5Ws0Mqj2++7k0j+USm1doR8P3BsqmnEJzfPa+w54QPC/tGyL2AM6qUO71EbFshTqj9OXMfG1m5oiu91/AMRXq7F6hhiRJUucMRkqSJEmS1J0rC9c/qEoXkvqiNCztxMjx95yCtWcDH6vVSKnMPAo4pLDMhhVamRiVpt3dSxOOuaBCS1OSmbNoArH3FpR58jQNxKqOknD5PZQ//qrJzGspPzZ3oxq9aHja/e6lFUq9MzMPrVBnIG048mnAjYWllqfO52NUfgm8apQXbIPTL6c5Nr3EThXakSRJ6pzBSEmSJEmSOpKZN1AWDlgmIhav1Y+kibdC4XonRo6xiNgJWL+gxAGZWfKcMwylQc1pFYwE9gTWLazxjjaU2onM/CvwmYISSwJPqdSOppGImAnsXVDiK5l5Ya1+KvkyZUGz6baHTqL9Kf/67keZ+ZEKvRTJzPOBfYH7Ckv9v4iYUaGlYbsGeEEbVByp9nNd+m++TY1eJEmSumYwUpIkSZKkbt1auH5cj9PePyKyp7ffdv3JlR7ACoXrb6rQg4Znz4K15wA/qtVILZl5AnBmQYm1a/UyIV5TuP4k4JM1Gin0AeCWgvX71GpE08ouwHIDrr2P8pBRdZl5J/DdghLTbQ+dKBERwGsLy1wFvKxCO1Vk5pHApwrLrAM8s0I7w/bydlJmVz4D3F6wfu2IGHTPlCRJGhsGIyVJkiRJ6tbNheuXrdKFpD5YoXB9aVBbw/XEgrUHZmZW66SukqM916zWxZiLiLWAxxaUmAW8JDNnV2ppYJl5I2VTI/doA0PSVJTsoYdk5mXVOqnLPbS/dgc2Lqzx6sy8qUIvNR0AnFtYY6THUw/g6C6PLod/PNceVFjmoRVakSRJ6pTBSEmSJEmSulUyMQlg6SpdSOqDFQrXl+5HGpKIWJXBjzS8G/h2xXZqO6Jg7WrVuhh/TwdKwoBfaY+xHhefAwY92n1l4GEVe9H0UBKM/HK1Lur7HU3weRDTaQ+dRPsWrj80M39apZOKMvMu4OWFZR4dEavX6GdIDui6gdZXC9evV6ULSZKkDhmMlCRJkiSpWwYjJdWyTOF6J0aOrz0YPBT3i8wsnU48TKcAg04xfFDNRsbc0wvWzmLMjgHOzL8DhxSUeEylVjQNRMRqwGYDLr8WOKpiO1Vl5izgLwMuX7lmL6onIhahbN9P4N11uqkvM48FflNQYhHgWZXaqe13mfn7rpsAyMzTgIsLSqxbpRFJkqQOGYyUJEmSJKlbtxWuLw1CSeqPxQrXl+5HGp4dC9b+sFoXQ5CZtwEXDrh8WgQjI2IlYJeCEt/IzMtr9VPRdwrW7lytC00HJXvoTzLzvmqdDMegwcjFI2K5qp2oll2AVQvW/zgzz6jVzJCUTlX8jypd1Pe1rhuYy28L1jpVVpIkTTyDkZIkSZIkdWvQYyTnWKJKF5L6oDQYWbofaXgeMeC6+4Bf12xkSC4YcN0KNZsYY3sCixas/3ytRir7FYNPzh70aHlNT4PuoQC/qNbF8Ay6h8L02UcnzT6F6z9YpYshysyTgCMKSuwYEWvX6qeSW4Efd93EXE4oWGswUpIkTTyDkZIkSZIkdeuewvUlQQlJ/WIwsociYjEGD4GdlJk31uxnSC4ZcN2yVbsYX48rWPvnzDy9WicVZebdNOHIQawXEcvX7Ee9tsOA62YBR9dsZEgG3UNh+uyjk2bPgrV/ao9QngRfKVgbwBNrNVLJLzLz9q6bmMugE2VhmkzmliRJ/WYwUpIkSZKkbpUGI2dU6UJSHxiM7KclgPfSHDt8KnDHFNYeM5SO6rtuwHXTJdDzqIK136zWxXCUTAvbqloX6rvvAl8Cjgeun8K6kzPztuG0VNWgeyhMn310YkTEGsC6BSW+XqmVUTiUsvvvrrUaqeQ3XTcwD2cVrF2pWheSJEkdcaqEJEmSJEndKg0i+b29pDkMRvZQZt4KfHjO/0dEAOsBmwGbt79uBmwCLDPX8j+MqM1SNwy4bmZEzMjM+6p2M0Yi4sE0/96DGrcjPec21WDk9TTTr/4MXF2/HfVRZn4N+Nqc/28fV/ffP+f899zT0fq+hwIsXa0L1bJzwdq7gf+r1ciwZeasiPgu8P8GLLFLzX4qGLtgZGbeEhHXMdj0xxVr9yNJkjRqvngiSZIkSVK3SoNIToyUNEd03YCGLzMTuLC9/XzO77eByXX416DPCV30OIC7CtbOZGpTNCfNIwvW/i0zL6/WyRBk5iURcSWw+jz++GLgNJoQ5Gk0x4JfNrLm1FuZeTVNsPZfjsmOiAfR7J1zgpKHjb67gZTuoRovjy5Ye2xm3lSrkRE5lMGDkWtGxPqZeWHNhgZ0/hg/517GYMHIud9wI0mSNHEMRkqSJEmS1K3S7817OyVL0pTdU7h+8SpdqBNtYPLi9vaLTpuZulkFa5fAYOQD+XW1LobrdzQhtDkByNOA0yYw3KMJl5nXAce2t0lSuodqvDyqYO0vq3UxOscDtwDLDbh+F5o3i3Tt1K4bmI+rgG0GWGcwUpIkTTyDkZIkSZIkdav0e/PSIJSk/jAYqUlVct/t+/324QVrp3pMdScyc7+ue5AmnHtoT0TE0sCWBSV+VauXUcnMeyLiSGDvAUs8AjioXkcD+3PXDczHtQOuWzIiFsnM2VW7kSRJGqFFum5AkiRJkqRpbrHC9aVHcUvqj9JgZOl+JHWh7z/j3qRg7R+rdSGpr/q+h06aTYAZA669IjPPrtnMCJUE+Tet1kWZcQ5G3lSw1vC0JEmaaH7DI0mSJElSt0qDSE6MlDSHEyM1HQ0aIBl7EbEs8JABl1+amdfX7EdSL/V2D51QGxesPblaF6NX0vtm1booc37XDczHTQVrZ9ZqQpIkqQsGIyVJkiRJ6pbBSEm1GIzUdNTnn3E/tGDtqdW6kNRnfd5DJ1HJlOA/Veti9E4H7h5w7aoRsVLNZgYwG7i04x7m546CtX5/IEmSJprf8EiSJEmS1K1lC9cbjJQ0x+2F633hUyMXETOADUpK1OplDJUEI0+r1YSksVeyV/R5D51E0zIYmZn30IQjB9X11Mgr27/DuLqzYO2i1bqQJEnqgMFISZIkSZK6tVzh+lurdCGpD24qXL9UjSakhRERm0XE+4CLgXd13M64Kgk7jfORnpIKRcRDIuLVEXEK8Ouu+1E1JcHIv1TrohslwciSz1sNl3R8/QWZVbDWLIEkSZpovstDkiRJkqRulQYjb67SRX3fzMwXdt2ENM2U7gddH0OoHmsnQ+4APBl4OrB5pw1NhrUK1l5UrQtJYyEiNqbZQ58G7IKBpT5ab8B1d2Tm36t2MnoXFKxdvVoXg7m+4+svyH0Fa2dU60KSJKkDBiMlSZIkSepWX4ORkkbvpsL1K9doQgKIiKAJP+7W3h6D4dupWrVgrcFIacJFxJr8cw/dncFDc5oAEbEksMyAyy+s2UtHSoKRD67WxWBu6Pj6C1ISjIxqXUiSJHXAYKQkSZIkSR2JiEWAZQvLGIyUNMdNhesNrWlgEbEqsC3NVMgd219X7LSpybfKgOvuBiZ9cpg0rUTEMsDWwPY0e+hOlE2N1eQpCfdN92DkQ6p1MZhxD0ZmwVqDkZIkaaIZjJQkSZIkqTurUnY01T2ZeWetZiRNvBsL1zsxUgsUEYsDGwNb0EyE3IomENn1MZZ9NOjEyGsysyQEIWlI2mm66/HPPfRhwDbARng09nRXMiX44lpNdOiSgrVdT4ws/RpckiRJQ2IwUpIkSZKk7pROtvAFGEn3d1XheidG6h8iYiVgk/a28f1uG+LPlUdl0ImR4z65Suq99ljkjfn3PXRjYOkOW9P4KglGXleti+7cAMxmsIBw18FI36woSZI0pvwBliRJkiRJ3SkNRpaGoCT1y5WF650YOQ2108s2A7ajmVq2dfv/JQENFYqIJYBlB1xuMFIaoYhYi+YI7Dl76BbAOngEraZmWgcjM3N2RNzIYF+PDvpGglru6fj6kiRJegAGIyVJkiRJ6k5pMLI0BCWpX66heWF2sQHXrx4R4RG8/dYGIbcBHg/sCuwIrNBlT5qn5QvWXl+tC0n/JiLWo9lDHwM8Cliz247UEyWTu/uy71/PYMHIJWs3MkWzOr6+JEmSHoDBSEmSJEmSurNO4XqDkZL+ITMzIq4C1h6wxExgdeCKel1pHETEosDjgL2Bveh+spIWbGbB2ptqNSGpERHbAc8EnkFzHLZU2xIFa/sSjLwOeOgA6xbv+M09ToyUJEkaUwYjJUmSJEnqzgaF6w1GSprbFQwejARYD4ORvRERGwD/CTwfeHDH7WhqSoKRd1frQprGImIF4MXAS4DNuu1G08DiBWtvr9ZFt+4oWDsTuKtWI1N0X0fXlSRJ0gIYjJQkSZIkqTsGIyXVdjGwU8H69YHj67SirkTElsABNBMiF+m4HQ2mJCDj5CqpQESsArwVeDmwbMftaPooCcT35SjnkmB/l8FISZIkjSmDkZIkSZIkdac0GHlRlS4k9ck5hevXq9KFOtFON/s4zYQzA5GTrSQgYzBSGkBELAK8AXg3sFzH7Wj6MRhZ9vco+fxJkiSppwxGSpIkSZLUgYhYjvJjTc+t0YukXjEYOU1FxG7At4C1uu1ElZRMjOxLQEYamYhYl2YP3bnjVjR9GYg3GClJkqTKfNewJEmSJEndeFjh+lnAJTUakdQrpcHITat0oZGJiEUi4mPAUUxmKPJq4IiumxhDJcHI+6p1IU0DEbEf8BcmMxR5F/CjrptQFQbiy/4ePvdJkiTp3xiMlCRJkiSpG1sXrr8gM2fXaERSr5wDZMH6h0WEp8xMiIhYHPgB8GYm72e9fwReAqwDfK/jXsZRScDDx7C0kCLijTR70KQdnX0JcACwNs1zgCZfyfd2M6p10a2Sv8fd1bqQJElSb/gDEkmSJEmSurFV4XqP0Zb0bzLzjoi4BFh3wBJLAJsBp1drqici4onAtZl5Ste9AETE0sBPgMd33csUXAL8EPh2Zp4x5zcjoruOxldJwGOxal1IPRYRHwDe2XUfU3AzcAjwXeCoOW+Sap8PNPlK9v2SaZPjxKmZkiRJqspgpCRJkiRJ3di6cP1fazQhqZdOZfBgJMC2GIycl/cAO0TEFcBh7e2YzBz5hKJ2qudhwO6jvvYATgd+TtPvHzOzZKLpdFIS8DAYKS1ARLyPyQhFXg4cDvwMOLKL5xyNjMFImFmw1seGJEmS/o3BSEmSJEmSRiwilqI8GDkWE8skjaU/AXsXrN8WOKhOK/0QETOAh7X/uwbwyvZ2W0T8iib0d3hm3jCilj7N+IYirwCOAY4CjsjMKzruZ1I5MVIakoh4FvDurvt4ALcAx9HsoUfef7ques9gZMHfIzOdGClJkqR/YzBSkiRJkqTR25Hy78kNRkp6IH8qXL9dlS76ZTNgyXn8/jLAM9vbfRFxPO00ycw8fxiNRMTLgFcNo/YA7gHOBE4C/gAcn5kXddtSb5QEZEombmly+XrPQoiIrRmv8P/5wB9p9tDfA6fPOSJb0477/uB/D0ORkiRJmie/UZYkSZIkafQeXbj+usy8pEonkvqoNBj58IhYOjNvr9JNP2y7EB8zA9i1vX0yIv7GP4/cPqlG0CUiNgEOLK0zoDtoQpB/aW+nAKdl5l0d9dN3JSGP5at1oUnipNAFiIiZwA+BpTq4/H3AOfxzDz0V+FNm3thBLxpPJcHIFWo10bEVB1x3Z9UuJEmS1BsGIyVJkiRJGr3SYKTTIiU9oMy8MSLOBR46YInFgd2Aw6s1NfkG2bc3a29vAy6KiA0yMwv7+DyjOS7zJpqA7cnAaTQhnvOGOMVsxpDqTrLbCtYOGizRfEREVHgMD5PByAV7O7DRCK4zC/gzzT56CnA68NchBsndQ/uh5A0pK1froluD/j2uq9qFJEmSesNgpCRJkiRJIxQRSwE7F5Y5qUYvknrttwwejAR4PAYj7+8xhesvLg1URcQLKvTxQG4DjgSOAo6lCfCM8ihXf049l8y8JSJmMVgQ1mDkcARgMHJCRcRGNEH1YbiP5hjsI4FjgJMzs2T631S5h/bDtQVrH1Sti24NGows+dxJkiSpx/xmSZIkSZKk0doDWKKwxjE1GpHUa0cBLy9Y//hajUy6iFgb2KCwzLGFPSwFfLywh7nNAg4BvgP8ZsQhnrn5c+p5uxZYY4B1BiOHYxFglIHhqZrZdQNj7lPU/xwdB3wL+Elm3lC59lS4h/bD1QVrJ35iZPu1zpIDLr+mZi+SJEnqD79ZkiRJkiRptJ5cuP4u4IQajUjqtWNoJpvFgOs3iYi1MvOyij1Nqt0r1Phd4foXAqtW6APgXuBzwEcy8++VapYaNAjRd4MGI1ep3YiAJhg5ltpAkRMjH0BEbA48pWLJw4F3ZuZfKtYs4R7aDyXByNWqddGdkr+DEyMlSZI0T2P7jbwkSZIkST1VGoz8Q8dTvSRNgMy8FjizsMxeNXrpgScWrp8FnDjo4ohYBHh9YQ9znAnskJmvG6NQJMCyXTcwpgYNeiwbESvUbETAeAcPV+i6gTH3RgZ/o8D93QDsl5l7jlEoEtxD+6Jk6uH61broTsl0boORkiRJmieDkZIkSZIkjUhEPBJYu7CMx2hLWli/Llz/gipdTLCIWILyKWMnZ+adBev3AjYs7AHgV8AjM/PUCrVqW6brBsZUSdCj9OsN/bvFu25gPpbvuoFxFREPAZ5TodSFNHvo9yvUqs09tB+up5nqPIjpHoz0KG1JkiTNk8FISZIkSZJGp8aLsr+sUEPS9HBI4frtI2KTGo1MsCdQHjg5onD9CwvXAxwFPC0zb61Qaxge1HUDY+rKgrUGI+ctC9Y6MXIy7QfMLKxxJbBrZp5ToZ9hcA/tgcxMYNBpzqu3b+aYZCXByIuqdSFJkqReMRgpSZIkSdIIRMSiwLMKy1yamafU6EfStPAHBn+BfY7pPjVynwo1fjbowjbk8NjC658P7JOZ9xTWGaYHF6ytcTzuuDq/YO26tZoYhYhYLSJ2iIhhTz6cXbB2nENHa3TdwBh7auH6WcCemXl5jWaGxD20P84dcF0AG9VspAMPLVg7rqFlSZIkdcxgpCRJkiRJo/FEYNXCGodU6EPSNNFOHjq0sMzzImJa/gwxIpYFnlFY5vLCo6v3AJYqWJ/AyzLz5oIao/CQgrV9vn8OGpAB2LxaF6PxNOBE4KaIuDIijoqIz0XEayJij4ioFfy7r2BtyWNx2NbquoFxFBErADsXlvlgZv65QjvD5B7aH2cXrN2uWhfdePiA6+4DLqjZiCRJkvpj0a4bkCRJkiRpmnhNhRo/rVBD0vTyE+AVBevXAvZieu4/z6X8GO2Bp0W29ixc/4PM/G1hjVFYt2Btn6edlQQjH1ati9HY8H7/vVp72/3+HxARt9CEhs66369/zcypTNbsazByza4bGFNPoOx1sEuBj1TqZZjWK1jb5z10Ep1VsPbhwEGV+hipNvy+2oDLL8nMWTX7kSRJUn8YjJQkSZIkacgiYkPg8YVlrgWOq9COpOnlaJrjtEumSb2T6RmMLAmUznFY4fodC9d/tHD90EXEykDJ8cm9nXaWmVdExG0MFtB9WEREOzl2Emy44A9hOeAR7W2OE4GdpnCdu6fS1FyWLVg7bGt33cCYKt1D/2dCAlfrF6zt7R46oUomRm5frYvRG3RaJHiMtiRJkubDb3gkSZIkSRq+t1I+jeU7mVky5UjSNJSZ9wLfLiyzXUQ8oUY/kyIiHglsXVjmFuCYgh4WATYpuP6JmXlawfpR2bhwfd9/xn3egOuWATao2ciQDTrh8m9T/Pg7B7wOwAoFa4dt0o5OH5WSz8ss4Ou1GhmWiJhB2WO973vopCkJRm4VEeM82XZ+Hlmw1mCkJEmSHpDf8EiSJEmSNEQRsQ6wf4VSY//CrKSx9bUKNd5ZocYkeXeFGj/IzJLpdBsCSxSsP6Rg7ShtU7h+RpUuxtdfCtbuXK2LIYqIFRj8KOCpHjt714DXgTENRkbE4sBGXfcxprYoWHt0Zt5SrZPh2QRYsmB93/fQiZKZlwPXDLh8JvCYiu2M0pML1v6xWheSJEnqHYORkiRJkiQN13uAxQpr/DEzz6zQi6RpKDPPAX5fWGbniHhcjX7GXUQ8AnhihVIHFa4vnQB3ZOH6Udm2cH3fQz0nFKzdvVoXw7UNg0/Wnmow8rYBrwPwoIK1w7QpsGjXTYybiFgRWK2ghHuoulLyNduTqnUxIhGxJmUh5j/U6kWSJEn9YzBSkiRJkqQhacM1NaZF1pj2Jml6+0qFGgdGxMwKdcbdeyrUOC8zS1+oX7Ng7WymfsRwV3YtXN/3UE/J/WhSJoeVTLY8Y4off3PBtVYtWDtMD++6gTFVsocCTMqbktxD++e4grUTF4ykbFrklZl5SbVOJEmS1DsGIyVJkiRJGoKImAF8jsEnIM1xI/Dd8o4kTXPfB64urPFQ4G0VehlbEfEk6oQKDqpQY9mCtZdl5p0VehiqdkrUBoVl+h7q+Rsw6HG+a0REyRSuUdljwHVXZealU1xTEowsmT44TJMSgB21kj0U4JwqXQzfboXr+76HTqLjC9auHxHbV+tkNJ5bsNZpkZIkSZovg5GSJEmSJA3H26kzweeLmXl7hTqSprHMvBs4sEKpt0fERhXqjJ2IWAL4bIVS9wHfqlBnuYK1N1W4/ig8o0KNxSrUGFuZORs4qaDEs2v1MgwRsTSw44DLBwnE3AjkgNdbZ8B1w1Y6MbCvSvZQmIB9NCK2pDxc3us9dEL9GbitYP1LajUybBGxAWVTgw1GSpIkab4MRkqSJEmSVFk7peOACqVmUSekI0kAXwDuKKwxE/h6RCxaoZ9x8w7KAyYAB2fm5RXqlEw7u7XC9UfhWRVq9PG+OLffFqzdr1YTQ/IUYPEB154w1QWZeS9w/YDXW3/AdUMTEZtRfmR0X5VOjJyEfXSS9tDZA64rnb4/cdp96ncFJfaLiKVq9TNkL6bs3/jIWo1IkiSpnwxGSpIkSZJUUUQ8BPgJdaavfDczr6pQR5LIzOupc8Tzo4GPVagzNiLikTSTfmv4eKU6SxSsHfuwYERsStmUqDmmw7SzwwrWrh8RO1XrpL59C9YOetzs1QOuWzMiSqcQ1jbuwdculeyhMOb7aPsGhRdXKDWqPXTQYOR0Per7xwVrlwP2r9XIsLQTg19WUOL8zDyjVj+SJEnqJ4ORkiRJkiRV0r64cyh1JvfcC3y4Qh1Jur+PAHdXqPP6iKgxqapzEbES8H3qhGCOysxTK9QBuKtgbemktFF4faU6vQ9GZuaZwAUFJWp9rquKiBWBJw+4/Drg5AHXXjHgOoCtCtYOw1gfld6xkj0Uxn8f3RdYo0KdUe2h9w64bmbVLibHIQz+OQN4e0SM++fu1cAqBet/WqsRSZIk9ZfBSEmSJEmSKmiPKzsceESlkgdl5nmVakkSAJl5GfClSuW+HhGbV6rViYiYAXwbWKtSyVrTIgHuLFhb6+8zFO3xvzUmncH0Cc0cWrB2n4jYsFon9bwEWHLAtb/IzEEn0F0y4DqAXQrWVhURjwXG8d91XJTsoTDG+2gbePtgpXKj2kPvGXDdpBwJXVVm3gAcU1BiLeClldqprn1D4ZsKyxiMlCRJ0gIZjJQkSZIkqVA7bexXwK6VSt4NvK9SLUma24eAOyrUWQY4YkwDVwvryww+sW5uJ2fmryvVAri9YO1yEfGQap3U93HqHY86XYKRhxSsXQR4W6U+qmiPAX5VQYmfFawtCUbuVbC2tnd23cCYK9lDATau0sVwvBZYt1KtUe2hg06rXr5qF5PlR4XrD4iIlat0Ut8BlE2LvAo4sVIvkiRJ6jGDkZIkSZIkFYiITYGTgJ0rlv1CO9VNkqrLzKuBz1QqtxpwdESsV6neyETER6k3tRDKJx/N7arC9TtV6aKyiHgS9cKoAEtUrDXOfk9ZoO9FEVFrqnUNLwIG3TfuBkpCyOcWrN1+HCblRsSuwG5d9zHm+rqHrg68o2LJUe2hgwYjxzXYNwoHU/ZGllWBT1fqpZqI2BZ4Q2GZ72Rm1uhHkiRJ/WYwUpIkSZKkAUXEq4A/UfcYw2uA91asJ0nz8iHKQyNzrEUTjly3Ur2hisangLdULHtoZv6uYj0oC8FB3fBhFRGxBvDNymWnRTCyPTb6SwUlFgG+2B4f36mIWIpmWtigfpqZtxasP6tgLXR8PG1ELEEz7Vbzd2nh+nHcQxcFvg+sULHsqPbQQSd4rl61iwmSmTcC3yos89yIeGqNfmqIiMWBrwGLFpSZDXy+TkeSJEnqO4ORkiRJkiRNUUQ8PCKOBT4HLFW5/Jsz86bKNSXpX7ShoprBwHWBP7ZTzMZWRCwGfBd4fcWy91L3czlHaTDyGW2Aaiy0gZ4fUHZ05rwsWbneOPsqMKtg/TbAuyv1UuKDwJoF679eeP1zKPs8vrLjIPhHgYd2eP2JkJl3ANcWlNgwIrav1U8lH6LulHoY3R46aDByg6pdTJ5PA6WTEb8ZERvVaKaCzwFbF9b4WWZeXN6KJEmSpgODkZIkSZIkLaSIeFRE/Aj4I7DLEC5xXGaWTgWRpIWSmd8BjqtYchXgyIh4dcWa1UTEWsAxwH6VS38+M0uO5n0g5wJ3FaxfGdi/Ui9FIiJopjs9agjla79BYWxl5rXAjwrLHNAeZ96JiNgZeG1BiUuAo0p6yMx7gDMKSswEPl7Sw6Ai4j8p+/xNN6cXri897reaiHge8KYhlB7VHnrbgOseOk4h/1HLzLOBXxWWWRE4LCKWq9DSwNr9q8bE3c9UqCFJkqRpwmCkJEmSJEnzERHrRsSbIuLPwPHAPkAM4VJ3Aa8YQl1Jmp9XUTY5bW6LAgdGxPciYtWKdYu0x0ieRv1g3kXAOyvXBCAz76XpucQBEbFMhXYG1oYivwy8bEiXWHpIdcdVaSAkgO9GxOY1mpnShZtw8sGUvS7xpfZY8VJ/LFz/zIh4cYU+FlpE7AUcOMpr9sDJhev3jYjtqnRSICJeCHyT4XwPMqo99KYB1y1G/SmZk+YTFWpsAvwsIjp5zoyIZwCfrVDqjMw8ukIdSZIkTRMGIyVJkiRJup+IWCsi9o6Iz0TE6TShl49TfuTXgrw5M88a8jUk6V9k5pnAAUMovR9wdkS8vA3GdSIiVouI7wGHAStVLp/AizJz0ClYC6M0vLU68LEajQwiImYA36DOhKgH0mnwc9Qy8yTg54VlVqSZ7jqyo1UjYmXgcODBBWVuojmGtYbfVajxhYjYqUKdBYqI/Wimhc4YxfV6pHQPXQT4SkTMrNHMICLi5TTHxw/r9bxR7aE3FKyd1lNS2yBg6dRIaE48OHzU4ciIeArwfZo3z5R6a4UakiRJmkYMRkqSJEmSei0iZkTEEhGxbEQ8KCI2jIjtI+LxEbF/RBwQEV+NiGMj4gbgUuDHwH8BW46ozV9lphOAJHXl4zQTcWtbEfgScEJE7DnKgGS7578NOIf6R2fPcWBmHjuk2nP8ukKNV0bE8yvUmZKIWBs4luEf5738kOuPo7cB9xXWeAhwTERsU6Gf+Wqnxx5N+ddVn8nMWyq0BE0/WVhjceAXEbFrhX7mKRrvAL5DnVDRdHM0cE9hjW2oF8hdaBGxZER8ieZ5dJjPn6PaQ68tWLtnGxCdzt4A3Fuhzq7Ase0E36FrJ+v+lGa/LHVkZv6yQh1JkiRNIwYjJUmSJEld2TUictg3mheQ7gRuoXlB7jya6TG/Bg4C3gu8hGaCxoodfB6uBF7UwXUlCYD2WNr9gVuHdIkdgJ8Bf42Il0bEEkO6DhGxRkS8nybk/mFg2SFd6iyacNqwHQXUmEj59YjYu0KdhdJe6zTqH10+L108d3cqM/8KfKtCqTWA4yPimRVqzVMbvDwZeFhhqVuAT5d31MjMayifJgiwAvDriHhuhVr/IiJWB34BfBBfyxlIZt4M/LZCqZdExCdHFfCPiC1oHjejCAOOag/9e+H6AyPirRExLR8L7ckCX6xUbjvglIjYvVK9fxMRi0XEJ4Gv0RyHXmo28MYKdSRJkjTNTMtvICRJkiRJGhN3AU/PzNIXCiWpSGZeCDyf8glq87Mp8BXg2oj4UUS8oD1et0hErBkRL4mIo2gCke+iCSsNy03AXpl5xxCvAUBm3k35scnQTJo7OCLeOMxgT0RsHhGH0UxeHlXY5kEjus64OQC4vUKdpWjuG9+JiFUq1AP+MbH7zcAfgLUrlHxPZpYcxTsvB1eqMxP4TkT8JCJWKy3WTgp8B83E2ydOcXmtiZp98uNKdd4AfDcihnb0dESsHBH/A5wCbD6s68xl+YgYxTTSKwrXLwZ8BDg9It4REQ+PiNUjYtGImBkRD46Ih0XE0yLiDRHR6RHoQ/LfwHWVaq0CHBkRX6vxtdj9RcTDae7Db6hY9muZeXrFepIkSZomDEZKkiRJktSdF2fmyV03IUkAmXko8IERXGoZYB/gm8DVEXFGRHyvDTo8NSK2jYgNI2LViFiiDVgt3wYgtoyIp0fEmyLiGxFxAXAZ8FVgd4b/887ZwH6Zed6Qr3N/X6pUZxHgE8AREbFppZoARMQGEfE14C/AU2vWXggPHvH1xkJmXk7d6VnPBc5qH4crlBSKiKfShGI+BtSYEHsm8NkKdeb2HWBWxXrPAM6OiE9ExHpTXdw+jj5Ms6d9kGavnIpZ7Tr9q+9RbyLyfsBfIuJJleoBEBErtmHYC4HXUefY4YW+PLDqCK5zUaU6m9Pcz0+mCVveQ/Nms7/TPAcdCnwSeCmwTqVrjoU2HP7CiiUDeDFwbkS8NyKK3mgQERtHxDeBE4EtazTYuhB4U8V6kiRJmkZG8S4wSZIkSZL0796Rmf/XdROSNJf/BrZmdOG2GcAW7W0SvC0zfzXKC2bmbyPiTOp9jvYAzoiIH9Acy3l8Zk55UmgbntuXZtLooyv0dS+D/bx6jQrXnkiZ+aU2hPiUSiVXpgkcvTUiDgZ+AhzVTi6dr4h4KLAXTchmk0r9QDPF9tWZeW/Fmk3hzKvbv2fNY7CXowmsvj4ijgeOBY4DLgauB26mmdK5LLAWsBmwFc1kyI0Lr/1x4NzCGr2TmbdGxLeAV1cquT7wi4g4DjgQOHRhHiNzi4jFgCcBLwD2pJk8WmI2TdBtkKnAawJXFl5/QUb5hoI51qNnj4nMPDwi/pcmQFvLSjRTiN8cEYcChwC/zMwFTqCNiJWAp9F8PfAE6r9B5V7gOQvTiyRJkjQvBiMlSZIkSRq992bmh7tuQpLmlpkZEc8Gfk2dsFuf/G9mfryja3+EZrpdLTOA57S3qyLi1zTTt86kmVZ3HXBn+3ErtreVaI5D3x54BE1Qs9bPl68GXkETxpiqpSJijcwsPaZ1Ur2E5t+t5pHiy7V1XwLMioizgNNpJrLdTDOdbWma+8RGNMG+YQVUP5mZvxtSbYAP0UwBrB3mWQTYpb2Nwt+A99ME7fTvPgW8jLqTGHdub7dFxJE0U/JOAy6heazcQRNWXJ5/7qPr0eyf2wPb0TyOariXJmD5MZqQ41RtBPyxUi/zlJk3RMQVjDbMPuXJrRPirTT3ve0q110SeHZ7mx0RF9Ls/ZfS7P230gS7l6cJdm8DbMhgYdyF9e7MPGmI9SVJktRzBiMlSZIkSRqtD2Xme7puQpIeSGbeERF7AsfQvOgt+Gxmvr7D638PeD31QxAAq9EczfnCIdReGFfQTLG8kCZENEhAbau2zrTTTj18AfAzmiBrbYvTfH63GkLtBTkJeMcwL5CZf4uIbwP7D/M6Q3YvsH9m3h0xzHzS5MrMCyPiczT7aG3LAE9vb12YBeyXmT+JiFcxWDByK+C7dduap5MxGFksM2dFxL7ACQzvGPRFaEKPGw6p/sL4OU3YV5IkSRpY7XdBSpIkSZKkebsPeE1mvrPrRiRpQTLzZpojEc/oupcx8PnMfG2XDbRHXb+B5ljhPrkE2CUzz8nMe4CrBqzzsIo9TZzM/CXwX133UdkNwLPb+8WwvaW93qR6W2b+qesmJsD7aabh9sldwN6Z+ZP2/y8ZsM6o9tAjRnSdOXoZjIQm7EszIfbWrnsZkhOA/8jM2V03IkmSpMlmMFKSJEmSpOG7FXhqZn6u60YkaWFl5rU0x8Ae13UvHfoQ8JqumwBojxP+dNd9VHQGsHMb7pjjggFrDWOS5kTJzC8An+y6j0ruAPbMzItHcbHMvAZ43SiuNQSH0BwTrQXIzBuBl3bdR0U3Ak/OzMPv93uD7qHbVuhnYRwM3D2ia0GPg5EAmXkqsBej/ZyOwt9ongPu6LoRSZIkTT6DkZIkSZIkDdfJwMPbaU6SNFEy8ybg8TThm+nkbuD5mfnOdlrjuHgbcHrXTVRwCPDIzLxsrt8/bcB6u4ZnCAO8mebY9Ul2L/CszDxhlBfNzG8DB43ymhWcBjxvzPaosZaZhwJf7rqPCs4GHpGZx8z1+6cNWG+ViNiirKUFa99w8f1hX+d+eh2MBGjvA/vRHKneBxcCT8zMSZ7iK0mSpDFiMFKSJEmSpOG4D/ggTfDj3K6bkaRBZeZdwD7AAcB0ONLwauAxmfmdrhuZW2beDTydpsdJ9UGao19vm8ef/XnAmqsAWw7eUj+0AbnnA1/pupcBzQKem5m/6Oj6r6I5vnUSXEQzUe32rhuZQK8Fftd1EwV+CeyYmefP488G3UMBdi9YOxWfoAlAj8LKEbHsiK7Vmcz8KfBE4Jaueyl0KvN+04QkSZI0MIORkiRJkiTVdySwdWa+KzNH9cKfJA1NZs7OzPcDj2WyQ3kL8nNgq1FPq5uKzLwIeDIwr2DhOLuRZhLgu+Yz4e7UgvqPK1jbG+1j9eXAx7vuZYpuA56SmT/sqoHMvBN4KnBWVz0spMuBx2bmFV03MonuFzD/W8etTNV9wAdoArE3z+sD2uPnB520N5I9NDPPBD42imu1ej81Ev4xOXJn4MquexnQEcCumdnnrzElSZLUAYORkiRJkiTVczqwV2Y+rn3RT5J6pX3hfUtg7KYpFroVeFlmPnUSXpTPzFNpjjiflKMmfw1skZk/WsDHnQlcP+A19h5wXS9l5ltoJuNNwvGqVwC7ZeaRXTeSmdcDuzG+R9afBzw6My/supFJlpk3Answ+NHTozbn3/3dmbmgyc2DTsN83AinK76PsumWUzEtgpEAmXk6sCPwh657maIDaYLxk/aGD0mSJE0Ag5GSJEmSJJU7BnhSZm6VmYd13YwkDVNmXpuZz6eZLnVB1/1UcAjwsMz8ateNTEU71XJnYJyPnLwdeGVmPjEzFzjFqg38HDXgtXaKiDUHXNtLmflZ4NE0xy6Pq1/STNk+petG5sjMa2jCkYPeF4flOOBRmXlJ1430QWb+HdiV5uv4cZXA52geIycu5JrfDHitmTQTU4eundr5JJrA57BNm2AkQHsM9S404dP7Om5nQa6lmYD6X5l5T9fNSJIkqZ8MRkqSJEmSNJjLgY8Cm2fm7pn5q64bkqRRaqe7bQa8Briq43YG8Udg58x8Rnv86MTJzL8B2wKHd93LXBL4NrBpZn5ximsHDfUE8KwB1/ZWZp4MbAP8X9e9zOVO4C00U8Ku67qZubUTBZ8I/A/N/bnTdoBPA3tk5rUd99IrmXkLzfTdDwMLmsQ4an8AHpmZr8nMO6awbtA9FOA/CtZOSTud+XE0k4KHaVoFIwEy877M/G+a4O+5XffzAH4GbJmZ4/b1iyRJknrGYKQkSZIkSQvvdOBjNFOE1snMt7WhFEmaljJzVmZ+DtgAeCMwCZPMTgKelZk7ZObxXTdTKjOvy8w9gf8Cbu66H+BoYLvMfEE7uWqqDgEGnRz18gHX9Vpm3pyZz6H5+uW0brsBmkDMZpn58czsOnT4gDLz3sx8A01A8vKO2rgUeGJmvs6JasPR/ju/gyakd37X/dD08MzMfNQUpkT+Q2ZewODHVD9llJN32+mnOwDfGuJlpl0wco7M/D2wBfBaYFwC6H+mCXk/rQ3HSpIkSUNlMFKSJEmSpHm7FTgW+ATN9JTV2qOy35qZx7bHfUqSgMy8MzM/BawP7EUzsWqc9sl7aCbm7ZiZO2bmj7puqLbMPBDYCPgycO+ILz8bOIwm7LBHZg4ayiEzrwd+MeDyTSJi90Gv3XeZeSywHfAy4IIOWvgd8Pg2EHNxB9cfSGb+BtgE+CBw14gue3t7vU3b62vIMvNoYHPgzcBNHbTwJ+CFNKHhHxfWGjRoOIMRB8wz847M3B94AnDKEC4xbYORAJl5T2Z+FtgQ+BBwfUetnAk8n+aNE0d31IMkSZKmoUW7bkCSJEmSpBG5F5gF3N3+eivN5Iw5t6toQgJzbpeP8xQjSRpHbWj8MOCwiFgN2Bt4JrAzTeBilGbRTC/8MXDIOB7XW1t7zO4rIuIDNBMkXwqsOMRLXgscBHy+ctDtkww+3Wr1in30TvsY/WpEfB14Es395PE0R5EPw100EyL/NzP/MKRrDF1m3g68KyIOBP4f8AqG89i6Hvga8MnMvGYI9TUfmTkL+EREfBF4Mc3jY8MhXvJO4KfAgZl5QsW636OZFDiIYe0F89UGgH8TEU8FngM8BVi2oOR9NNP+j6jQ3sTLzJuBd0bE+4F9gVcCOw75snfTfA32xcw8bsjXkiRJkuYpfI1HkiRJkiRJ0jBFxAo04chdgV2ArYDFK1/mHuAvwAnA74FftUGAaSsilqAJve0NPBlYpULZM4Gf04TdTnSC8uSLiHVoQkhPAnYHlioseRNwPPAT4MeZeUthvbETETOBPYFnA48FVigodwfwa+Bg4KeZOaqplFqAiAia8Ng+wNNopvKWuoJmKu7PgCMz884KNXunfYzN+XphS2BTYGWasOSywGI0wdLbgb/THHd/EfBXmuep0zLzttF3Pjki4qE0+/4TaL4+K937ofl3+M2cW2beWKGmJEmSNDCDkZIkSZIkSZJGKiIWpZnCtTlN2GFNYLX2tirNi/MzgSVoTr25iyY8dAdNCOJamgDExe2v5wGnGiiav4jYANiBJmiyLrAOzed8GZrP+eI0n9+b29uNwLk0IZMzgNOdYtdvbRhpG5rH5mbtbXVgOf4ZSAqa+8ntNPeRi4Dzae4rJ9LcT6ZNYDYiZgDbAg+neWxtAKzBv+5l99GEuK4HrqT5fJ0BnAT8sZ1UqDEXEavQ7KHbAevT7KNr0jwulgKWpHm+uoV/7qMX0/xbnwGckZkXjrpvaUHavX9rmj1/0/a2Fv/c95eh+RrhtvZ2K83+fz5wFnA2cGZmnjfq3iVJkqT5MRgpSZIkSZIkSZIkSZIkSZJ6Y5GuG5AkSZIkSZIkSZIkSZIkSarFYKQkSZIkSZIkSZIkSZIkSeoNg5GSJEmSJEmSJEmSJEmSJKk3DEZKkiRJkiRJkiRJkiRJkqTeMBgpSZIkSZIkSZIkSZIkSZJ6w2CkJEmSJEmSJEmSJEmSJEnqDYORkiRJkiRJkiRJkiRJkiSpNwxGSpIkSZIkSZIkSZIkSZKk3jAYKUmSJEmSJEmSJEmSJEmSesNgpCRJkiRJkiRJkiRJkiRJ6g2DkZIkSZIkSZIkSZIkSZIkqTcMRkqSJEmSJEmSJEmSJEmSpN4wGClJkiRJkiRJkiRJkiRJknrDYKQkSZIkSZIkSZIkSZIkSeoNg5GSJEmSJEmSJEmSJEmSJKk3DEZKkiRJkiRJkiRJkiRJkqTeMBgpSZIkSZIkSZIkSZIkSZJ6w2CkJEmSJEmSJEmSJEmSJEnqDYORkiRJkiRJkiRJkiRJkiSpNwxGSpIkSZIkSZIkSZIkSZKk3jAYKUmSJEmSJEmSJEmSJEmSesNgpCRJkiRJkiRJkiRJkiRJ6g2DkZIkSZIkSZIkSZIkSZIkqTcMRkqSJEmSJEmSJEmSJEmSpN4wGClJkiRJkiRJkiRJkiRJknrDYKQkSZIkSZIkSZIkSZIkSeoNg5GSJEmSJEmSJEmSJEmSJKk3DEZKkiRJkiRJkiRJkiRJkqTeMBgpSZIkSZIkSZIkSZIkSZJ6w2CkJEmSJEmSJEmSJEmSJEnqDYORkiRJkiRJkiRJkiRJkiSpNwxGSpIkSZIkSZIkSZIkSZKk3jAYKUmSJEmSJEmSJEmSJEmSesNgpCRJkiRJkiRJkiRJkiRJ6g2DkZIkSZIkSZIkSZIkSZIkqTcMRkqSJEmSJEmSJEmSJEmSpN4wGClJkiRJkiRJkiRJkiRJknrDYKQkSZIkSZIkSZIkSZIkSeoNg5GSJEmSJEmSJEmSJEmSJKk3DEZKkiRJkiRJkiRJkiRJkqTeMBgpSZIkSZIkSZIkSZIkSZJ6w2CkJEmSJEmSJEmSJEmSJEnqDYORkiRJkiRJkiRJkiRJkiSpNwxGSpIkSZIkSZIkSZIkSZKk3jAYKUmSJEmSJEmSJEmSJEmSesNgpCRJkiRJkiRJkiRJkiRJ6g2DkZIkSZIkSZIkSZIkSZIkqTcMRkqSJEmSJEmSJEmSJEmSpN4wGClJkiRJkiRJkiRJkiRJknrDYKQkSZIkSZIkSZIkSZIkSeoNg5GSJEmSJEmSJEmSJEmSJKk3DEZKkiRJkiRJkiRJkiRJkqTeMBgpSZIkSZIkSZIkSZIkSZJ6w2CkJEmSJEmSJEmSJEmSJEnqDYORkiRJkiRJkiRJkiRJkiSpNwxGSpIkSZIkSZIkSZIkSZKk3jAYKUmSJEmSJEmSJEmSJEmSesNgpCRJkiRJkiRJkiRJkiRJ6g2DkZIkSZIkSZIkSZIkSZIkqTcMRkqSJEmSJEmSJEmSJEmSpN4wGClJkiRJkiRJkiRJkiRJknrDYKQkSZIkSZIkSZIkSZIkSeoNg5GSJEmSJEmSJEmSJEmSJKk3DEZKkiRJkiRJkiRJkiRJkqTeMBgpSZIkSZIkSZIkSZIkSZJ6w2CkJEmSJEmSJEmSJEmSJEnqDYORkiRJkiRJkiRJkiRJkiSpNwxGSpIkSZIkSZIkSZIkSZKk3jAYKUmSJEmSJEmSJEmSJEmSesNgpCRJkiRJkiRJkiRJkiRJ6g2DkZIkSZIkSZIkSZIkSZIkqTcMRkqSJEmSJEmSJEmSJEmSpN4wGClJkiRJkiRJkiRJkiRJknrDYKQkSZIkSZIkSZIkSZIkSeoNg5GSJEmSJEmSJEmSJEmSJKk3DEZKkiRJkiRJkiRJkiRJkqTeMBgpSZIkSZIkSZIkSZIkSZJ6w2CkJEmSJEmSJEmSJEmSJEnqDYORkiRJkiRJkiRJkiRJkiSpNwxGSpIkSZIkSZIkSZIkSZKk3jAYKUmSJEmSJEmSJEmSJEmSeuP/A1zC3/EAVm1RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2700x2700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(6,6),dpi=450)\n",
    "plt.subplots_adjust(left=0.15, bottom=0.15, right=0.96, top=0.97)\n",
    "\n",
    "a = [-175,0,125]\n",
    "b = [-175,0,125]\n",
    "\n",
    "plt.rc('font', family='Arial narrow')\n",
    "\n",
    "plt.plot(b, a, c='k', ls='-')\n",
    "\n",
    "plt.ylabel('ML Prediction', fontname='Arial Narrow', size=32)\n",
    "plt.xlabel('DFT Calculation', fontname='Arial Narrow', size=32)\n",
    "plt.rc('xtick', labelsize=28)\n",
    "plt.rc('ytick', labelsize=28)\n",
    "\n",
    "## Lattice constant\n",
    "#plt.ylim([3.2,5.3])\n",
    "#plt.xlim([3.2,5.3])\n",
    "#plt.xticks([3.5, 4, 4.5, 5])\n",
    "#plt.yticks([3.5, 4, 4.5, 5])\n",
    "\n",
    "## Direct_gap\n",
    "#plt.ylim([-0.5,8.5])\n",
    "#plt.xlim([-0.5,8.5])\n",
    "#plt.xticks([0.0, 2.0, 4.0, 6.0, 8.0])\n",
    "#plt.yticks([0.0, 2.0, 4.0, 6.0, 8.0])\n",
    "\n",
    "## Ind_gap\n",
    "#plt.ylim([-0.5,8.5])\n",
    "#plt.xlim([-0.5,8.5])\n",
    "#plt.xticks([0.0, 2, 4, 6, 8])\n",
    "#plt.yticks([0.0, 2, 4, 6, 8])\n",
    "\n",
    "## Formation_energy\n",
    "plt.ylim([-1.0,3.0])\n",
    "plt.xlim([-1.0,3.0])\n",
    "plt.xticks([-0.5, 0.5, 1.5, 2.5])\n",
    "plt.yticks([-0.5, 0.5, 1.5, 2.5])\n",
    "\n",
    "plt.scatter(Prop_train_fl[:], Pred_train_fl[:], c='blue', marker='s',edgecolors='dimgrey', alpha=1.0, label='Training')\n",
    "plt.scatter(Prop_test_fl[:], Pred_test_fl[:], c='orange', marker='s', edgecolors='dimgrey', alpha=0.2, label='Test')\n",
    "\n",
    "plt.legend(loc='upper left',ncol=1, frameon=True, prop={'family':'Arial narrow','size':24})\n",
    "plt.savefig('plot.eps', dpi=450)\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}