glove_max_avg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_filename = \"data/glove_embedding_salinfo_avg_max_ENG.pkl\"\n",
    "salary_yvals = pickle.load( open( pickle_filename, \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 100000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_yvals[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy_filename = \"data/glove_embeddings_avg_max_ENG.mat.npy\"\n",
    "\n",
    "embedding_matrix = np.load(numpy_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10930, 300)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Now feed this to linear model and to SVM\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, LassoCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, median_absolute_error, r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_examples, GLOVE_SIZE = embedding_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_options = [\"avg_salary\", \"min_salary\", \"max_salary\"]\n",
    "salary_model_data = {}\n",
    "dataset_dictionary = {}\n",
    "\n",
    "for option in salary_options:\n",
    "    dataset_dictionary[option] = []\n",
    "\n",
    "##convert min and max salaries to floats\n",
    "for ex in range(num_examples):\n",
    "    dataset_dictionary[\"min_salary\"].append(float( salary_yvals[ex][0] ) )\n",
    "    dataset_dictionary[\"max_salary\"].append( float( salary_yvals[ex][1] ) )\n",
    "    dataset_dictionary[\"avg_salary\"].append( float( (salary_yvals[ex][1] + salary_yvals[ex][0]) / 2.0 ) )\n",
    "    \n",
    "\n",
    "seed_val = 1991\n",
    "\n",
    "for el in salary_options:\n",
    "    X_train, X_test, y_train, y_test = train_test_split( embedding_matrix, dataset_dictionary[el] , random_state=seed_val, test_size=500)\n",
    "    salary_model_data[el] = {}\n",
    "    salary_model_data[el][\"X_train\"] = X_train\n",
    "    salary_model_data[el][\"X_test\"] = X_test\n",
    "    salary_model_data[el][\"y_train\"] = y_train\n",
    "    salary_model_data[el][\"y_test\"] = y_test\n",
    "\n",
    "    salary_model_data[el][\"regressor\"] = LinearRegression(fit_intercept=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary data for avg_salary model \n",
      "MSE:\n",
      "_test: 680986235.7182943\n",
      "_train: 707327136.6199983\n",
      "MAE:\n",
      "_test: 19846.27985521488\n",
      "_train: 18833.213483305655\n",
      "MEDAE:\n",
      "_test: 15104.054636826484\n",
      "_train: 14501.766998059473\n",
      "r2:\n",
      "_test: 0.23541222627744007\n",
      "_train: 0.32901622488184346\n",
      "Summary data for min_salary model \n",
      "MSE:\n",
      "_test: 607710257.6976526\n",
      "_train: 629137754.5753332\n",
      "MAE:\n",
      "_test: 18834.689547828042\n",
      "_train: 17710.333816362723\n",
      "MEDAE:\n",
      "_test: 14503.66428704308\n",
      "_train: 13620.204023096514\n",
      "r2:\n",
      "_test: 0.17820521574939996\n",
      "_train: 0.2903694829337846\n",
      "Summary data for max_salary model \n",
      "MSE:\n",
      "_test: 954996285.9551293\n",
      "_train: 963343337.5650089\n",
      "MAE:\n",
      "_test: 23369.310065393172\n",
      "_train: 22538.858607903116\n",
      "MEDAE:\n",
      "_test: 17502.152857722627\n",
      "_train: 17553.906220778576\n",
      "r2:\n",
      "_test: 0.23963689614866102\n",
      "_train: 0.3249509965204661\n"
     ]
    }
   ],
   "source": [
    "for el in salary_options:\n",
    "    salary_model_data[el][\"regressor\"].fit(  X=salary_model_data[el][\"X_train\"], \\\n",
    "            y=salary_model_data[el][\"y_train\"] )\n",
    "\n",
    "##Calculate how good the model is\n",
    "for el in salary_options:\n",
    "    #mean_absolute_error, mean_squared_error, median_absolute_error, r2_score\n",
    "    y_pred = salary_model_data[el][\"regressor\"].predict( salary_model_data[el][\"X_test\"] )\n",
    "    y_true = salary_model_data[el][\"y_test\"]\n",
    "\n",
    "    y_pred_train = salary_model_data[el][\"regressor\"].predict( salary_model_data[el][\"X_train\"] )\n",
    "    y_true_train = salary_model_data[el][\"y_train\"]\n",
    "\n",
    "    salary_model_data[el][\"y_pred\"] = y_pred\n",
    "    salary_model_data[el][\"y_pred_train\"] = y_pred_train\n",
    "                  \n",
    "    datasets = [ (\"test\", y_pred, y_true), (\"train\", y_pred_train, y_true_train) ]\n",
    "\n",
    "    for d in datasets:\n",
    "        salary_model_data[el][\"MSE_\"+d[0]] = mean_squared_error(d[2], d[1])\n",
    "        salary_model_data[el][\"MAE_\"+d[0]] = mean_absolute_error(d[2], d[1])\n",
    "        salary_model_data[el][\"MEDAE_\"+d[0]] = median_absolute_error(d[2], d[1])\n",
    "        salary_model_data[el][\"r2_\"+d[0]] = r2_score(d[2], d[1])\n",
    "\n",
    "metric_names = [\"MSE\", \"MAE\", \"MEDAE\", \"r2\"]\n",
    "dataset_names = [\"_test\", \"_train\"]\n",
    "for el in salary_options:\n",
    "    print(\"Summary data for \" + str(el) + \" model \")\n",
    "    for m in metric_names:\n",
    "        print(m + \":\")\n",
    "        for d in dataset_names:\n",
    "            print(d + \": \" + str(salary_model_data[el][m+d]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "i=1\n",
    "fig=plt.figure(figsize=(18, 16))\n",
    "\n",
    "for el in salary_options:\n",
    "\n",
    "    y_train_predictions = salary_model_data[el][\"y_pred_train\"]\n",
    "    y_train_true = salary_model_data[el][\"y_train\"]\n",
    "    \n",
    "    #true_v_pred = list(zip(y_train_true, y_train_predictions))\n",
    "    #true_v_pred.sort() \n",
    "    #true_sorted = [x[0] for x in true_v_pred]\n",
    "    #pred_sorted = [x[1] for x in true_v_pred]\n",
    "    \n",
    "    true_sorted_indices = np.argsort(y_train_true)\n",
    "    true_sorted = [y_train_true[true_sorted_indices[i]] for i in range(len(true_sorted_indices))]\n",
    "    pred_sorted = [y_train_predictions[true_sorted_indices[i]] for i in range(len(true_sorted_indices))]\n",
    "    \n",
    "    true_sorted = [x for x in true_sorted if x < 200000]\n",
    "    pred_sorted = pred_sorted[0:len(true_sorted)]\n",
    "    \n",
    "    xvals = range(len(true_sorted))\n",
    "    plt.subplot(2,2,i)\n",
    "    plt.scatter(x=xvals, y=pred_sorted)\n",
    "    plt.scatter(x=xvals, y=true_sorted)\n",
    "    \n",
    "    plt.legend([\"predicted\", \"actual\"])\n",
    "    i+=1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIioc5qwgIiLKy1AfsOPzQLVi/T12wPobYA4LIiIiIhf2rCAiIsrLzk0nAhWOasUqJyIiIqJpDFYQERHlZWw4WjkRERFRi2KwgoiIKC/lOf7lnfPzXQ8iIiKigmOwgoiIKA9DfcDxX8wsb58FrLor//UhIiIiKjAGK4iIiPKwcxMwVZ1ZPutUJtckIiIi8mCwgoiIKA+6vBSVI/muBxEREVEDYLCCiIgoD7q8FMxXQURERDRDR71XgIiIqOkM9QHP3A5UDgc/r1RmvgoiIiIiHwxWEBERpWmoD+i/yT8/hdf8DzJfBREREZEPDgMhIiJKky6Rpp8fv5DtuhARERE1KPasICIiSpMukaYfNZndehAREVFrMxmWWp4LXHJfIXt6smcFERFRmqIkzJT27NaDiIiIWpczLDUsf1blMLDtZuv5BcNgBRERUZrO/qj5c5dfl9lqEBERUQuLMix1ctx6fsFwGAgREVFahvqA7z8e/jxpA5ZfD1z6xezXiYiIiFpPlGGpcZ6fAwYriIiI0hLUitG5ALj1B/muDxEREbWmzvnA2IFozy8YDgMhIiJKS1CrRAFbLIiIiKhJrboLaCuZPbd9lvX8gmHPCiIioiSevA0YeASACn5eAVssiIiIqEk5s3s08GwgDFYQERHF9eRtwMDD4c8raIsFERERNbElawsZhDDFYSBERERx7X40/DnSBqz5UkNXFoiIiIjyxmAFERFRXGrS4DmKgQoiIiKiiBisICIiikvaw5/DXBVEREREkTFYQUREFNfy64IfZ64KIiIiolgYrCAiIorr0i8CK24AIDMfK89lrgoiIiKimDgbCBERURKXftH6ISIiIqLUsGcFERERERERERVKaLBCRB4RkbdF5Aeuso0iMiIie+yfj7keu0NE9ovIayKy2lV+sV22X0TWu8oXisjLIvK6iGwRkVl2+Wz77/3242el9aGJiIiIiIiIqLhMhoE8CuCvADzuKb9fKfU/3AUi8usAPgngXABnAPhHEfk1++EvAfgIgGEA3xOR7UqpfwVwn72sr4nI3wC4AcCD9v9HlFL/UUQ+aT/vqhifkYiIWlT/4Ag2P/sa3hqt4IyuMtatXoSeZd3JFvrkbcDAIwBUbXl5LnDJfcxRQURERJSC0J4VSqnvADhsuLw1AL6mlDqulPoRgP0APmj/7FdK/VApNQ7gawDWiIgAuAjAN+zXPwagx7Wsx+zfvwFglf18IiKiUP2DI7jjm69gZLQCBWBktII7vvkK+gdH4i/0yduAgYcxI1ABAJXDwLabgaG++MsnIiIiIgDJclZ8TkSG7GEic+yybgAHXM8Ztst05e8FMKqUmvCU1yzLfnzMfj4REeWkf3AEK3t3YeH6p7Cyd1eyG/2cbX72NVSqkzVlleokNj/7WvyF7n40+PHJcWDnpvjLJyIiIiIA8YMVDwL4VQBLARwE8Od2uV/PBxWjPGhZM4jIjSIyICIDhw4dClpvIiIylEnPhBy9NVqJVG5ETYY/Z2w4/vKJiIiICEDMYIVS6qdKqUml1BSAv4U1zAOwekYscD11PoC3Asp/BqBLRDo85TXLsh/vhGY4ilLqIaXUCqXUinnz5sX5SERE5JFJz4QcndFVjlQeynR4R+f8eMsnIiIiomkmCTZnEJHTlVIH7T9/F4AzU8h2AF8VkS/CSrB5NoB/gdVL4mwRWQhgBFYSzv+ilFIi8jyA34OVx+JaANtcy7oWwHftx3cppXx7VhARUfoy6ZmQo3WrF+GOb75SE3Apl9qxbvWi6Asb6gP6bwp/XvssYNVd0ZdPREREFMdQH/DM7VbuLB1pA5ZfD1z6xfzWKwWhwQoReQLAhwGcJiLDADYA+LCILIU1LOPHAD4NAEqpfSLSB+BfAUwAuFkpq8+siHwOwLMA2gE8opTaZ7/F7QC+JiL3ABgE8LBd/jCA/y0i+2H1qPhk4k9LRETGzugqY8QnMBG7Z0LOnFk/UpkNZOcmYKoa/BzOBkJERER5chpTwuooaspOEI6GClhIs3VWWLFihRoYGKj3ahARNTwnZ4W3Z8K9VyxOPv1no9nYBU3aJAACbBzNc21iE5HdSqkV9V6PVsD6CBERZe7+84CxA+HPc0g7sMF0os9sRKmLxBoGQkREzS/VngmNrnO+vjLAHBVERERUD1GTepskCi8QBiuIiEirZ1n3dHCif3AEm599Dbdu2dM6gYsnbwMGHoG2VwVzVBAREVG9BDWm+JH27NYlA3GnLiUiohbS6NOYxvLkbfb4Tk2gYtYpwJovMUcFERER1cequ4C2kvnzl1+X2apkgcEKIiIK1ejTmMay+9Hgx8tzGaggIiKi+lmyFuj5a6tOEkTagBU3NFRyTYDDQIiIyECjT2Nqyhnq8tZoBT88aRIS9OSo40SJiIiI0rZkbdM2njBYQUREoRp9GlMT3tlPJlUbOmRK/wIm1iQiohbgDuS3TM4qKgQOAyEiolDrVi9CuVSblKlcase61YvqtEbp8w51+f+m/hO0s3szsSYREbWAlsxZRYXBYAUREYXqWdaNe69YjO6uMgRAd1cZ916xuKlaVtxDWi5vewEr2l6H+I0DKc9lYs06EJFHRORtEfmBq2yziLwqIkMi8g8i0mWXnyUiFRHZY//8jes1y0XkFRHZLyIPiFh7WUTmishzIvK6/f8cu1zs5+233+c38/7sRET10pI5qxrBUB9w30JgY+eJn/sWWuVNhMNAiKjpsLtiNtzTmDYj91CXjaXHcbKMz3xS5wLg1h/MLKc8PArgrwA87ip7DsAdSqkJEbkPwB0Abrcfe0MptdRnOQ8CuBHASwCeBnAxgGcArAewUynVKyLr7b9vB3AJgLPtn/Pt15+f7kcjIiqmVslZ1VCG+oD+m4Cpam155TCw7Wbr9yZpUGHPCiJqKuyumK/+wRGs7N2FheufwsreXblt5yze1xnqcnnbC5iDd/2fxKSadaOU+g6Aw56ybymlJuw/XwIQmEhERE4H8B6l1HeVUgpW4KPHfngNgMfs3x/zlD+uLC8B6LKXQ0TU9HS5qdLMWVWvukTD2rlpZqDCMTluPd4k2LOCiJpKUHfFZu4VUA/ehJROYAhApts60vsO9QHP3G61NoToAbCmHUA79LOAMKlmkX0KwBbX3wtFZBDAzwHcqZT6ZwDdANwRp2G7DADer5Q6CABKqYMi8j67vBvAAZ/XHPSugIjcCKvXBs4888zEH4iIqN7WrV5Uc80F0s1ZVa+6REMLazhpooYV9qwgoqbC7or5qdc4VuP3dbpJGgQqHIKAQAXApJoFJSL/HcAEgK/YRQcBnKmUWgbgNgBfFZH3wH/36tKoTi/e9DVKqYeUUiuUUivmzZtntvJERAWWdc4q5sSIoTwn+PEmalhhzwoiaiqtMMVmUdQrMGT8vkHdJOMoz22aMaDNRESuBXApgFX20A4opY4DOG7/vltE3gDwa7B6RbhrcfMBvGX//lMROd3uVXE6gLft8mEACzSvISJqelnmrNJd00dGK1i4/inmHouqyWYrY88KImoqSafY5LhJc3mMY030vml3g7zkPt9iHjP1IyIXw0qCeblS6pirfJ6ItNu/fwBWcswf2sM8fiEiF9izgFwDYJv9su0ArrV/v9ZTfo09K8gFAMac4SJERJRMUJ2Bucc0Kkf0jzXZbGUMVhBRU0nSXZHJOaO58Jx5M/rHpzmOVcc4IJVmN0hNr4q0jhkGPMKJyBMAvgtgkYgMi8gNsGYH+Q8AnvNMUfo7AIZEZC+AbwD4jFLKGQ/0WQB/B2A/gDdgzQQCAL0APiIirwP4iP03YM0Y8kP7+X8L4KYMPyYRUUvxu6Z7cViIh65+07mgqQIVAIeBEFETittdkck5zfUPjmDr7pGagfsC4Mrl2U9v6iw/dHraVXf5T+0VVVu7tldFGscMk4uZUUpd7VP8sOa5WwFs1Tw2AOA8n/J3AKzyKVcAbo60skREZMR7TdclEWLuMZdVdwE7Pg9UXdukVG6q4R8OBiuIiGxMzmnO7yZdAXj+1UO+z+8fHAkPLkRgFJByWhcMZwPxNesU4NK/0LZUmB4zQZ+fQTIiImpl7mv6yt5dzD0WxqmT7NxkDXntnG8FKpqsVwXAYAUR0TQm5zQXJbBT154DS9amevH2Bh26Ti7hyLGZPTfcx0zY52eQjIiIyJL1VKlNI+X6TVExWEFEZOMF0lyUwE6z9BzwCzqU2gSldkF18kTHVe8xE/b5GSQjIqK8pN3TMW3GQz2LbqgP2HELUD2a7fuU51pDVVMIXBTx2GCwgojI1jQXyBxECew0S88Bv6BDdUqhq1zCKbM7tMdM0LRsK3t34cJz5mHr7hEGyYiIKFONkiMpy6lSczHUB3zz0wCmsn+vymFgm51WKUHAoqjHBoMVREQuDX+BzIk7sDMyWkG7SE22bvc2zKznwFCfeT6KkNwTJnRBh7FKFXs2fFT7Ot3nB6zKwNbdI7hyeTeef/UQg2RERJSZZunpWHg7NyGXQIVjctx6zwR1nKIeG5y6lIiIYulZ1j095diksoZB+E3daTzVqIH+wREsvftb+Pwf3YHjWz9jnjhz/CjQ/xkrwBGTLrgSFnQJm5atUp3EEy8fYKCCiIgy1Sw9HQtvbLjh3rOoxwaDFUREBdU/OIKVvbuwcP1TWNm7qyYAUBRBkXhHz7Ju3HvFYnR3lSEAurvKuPeKxZFvyPsHR7Du63sxWqniDzv6MFsmw1/kNjVpt3bEEzfo4v78OpNKQcE/2ENERJSGuEF3iqg8J//37Jyf6OVFPTYYrCAiKiBn7OCIPed4UW9iTSPxPcu68eL6i/Cj3o/jxfUXxeo5sPnZ11CdsnpwnCE/i76yQKKWhyRBF+fzBwUsHN5gDxERURrS7OlIGkN9wPFf5Pue7bOsqUsTKOqxwZwVREQFVNSxg15p5qMIy0LtDoCM4lTMxbvRVzhhy0PSnCZ+iUn91LvbJRERNR8mEs/Bzk3A1MwpzTOT0mwgRT02GKwgIiqgoo4d9EpruleTLNROYOTythdwCmJsh7b2xC0PUQQFX5zyNpHpfB9u9e52SUREzYmJxMMlmsJT24NTgI2jqa1jFop4bDBYQUSFVcT5nvOS2QwaKUsrEm/Sk2Td6kVY9/W98fJVpDAbSBRhwRfnM3mfBxSj2yUREVErSjyFZ+d8YOyAfzlFxmAFUQNppZv3os73nJe0eixkJe1j0aQnyXQPi23vaJaSTatFnM9qOoynqN0uiYiI3FqlDpp4GO6qu4AdnweqrnpNqZxrz85mwmAFUYNotZv3RsnZkCZvReDK5d14/tVDhasYZHEsmvYk6VnWDXw7v1aLsM+qq7xFGcZTxG6XREREjlaqg+qu3351FF9OD86dm6whIZ3zrUBFTj07mw2DFUQNotVu3hslZ4Mpv5ta4ESLeme5hKPjE6hOWvkLRkYr2Lp7JNYUn6bvH3e5sY/FoT7gmduByuEZD70AALN9XvNLABsNViqjVouwqVl1lbdGGcZDREQUppXqoLrrt8CqS0Wq55TnJg5UtEqPFh1OXUrUIIp+894/OIKVvbuwcP1TWNm7K/EUm0Wd7zkOv2lI1319L9Z9Y+902WilOh2ocKQ1hWXa06DGOhaH+oD+m3wDFYBVCRDx+TFdqfkfzKTVIuizBlXe/KYAEwAXnjMv9XUkIiLKUtHroGlat3qRb91DAcF1Mr96TuUwJv/hJuuxGBplGvssMVhB1CCKfPOexcm0qPM9x+F3U1udUjOCE37SqAiE9Q6IKtaxmPVUXj9+YUZRGgG0oM8a1FX01i17YFVtTlAAtu4eaalKBhERNb6kddC0G7Sy1LOsG7raWWCdTFPPaVdVHNlxZ6x1Sbv+1ogYrEhZI30ZqbEU+eY9i5Npz7Ju3HvFYnR3lSEAurvKqQ2JyFuSgEMawai0W0RiHYvaqbxSomqPv7QCaEGfNWjfKACV6tSM8larZBARNaqi1+nzXL8kddBG7B3QHSc4E1DP6Rx/O9bnbaUeLTrMWZGiVko+Q/kr8qwBWZ1MmyXxoG78Y5i0glFp50+IdSzOOhkYPxrr/YxIbSUqrfG1YZ/VO2OLiVaqZBARNaKi1+nzXr8kddBGzHcRa0Y23ZSlAN5S742YL+dgAAAgAElEQVT1eZn/isGKVDXil5EaS1Fv3uOcTFspYZDfRa/UZiVkcA8FKbUJTj2pA6PHqqlukyymQY10LD55W7aBCgBYfl3Nn2kG0HSf1Vt5Cx/UY2mlSgYRUSMqep2+HusXtw7aiL0DYgVnVt2F8W9+BrNQu1/GVQf+bGJtrM9b9Gns88BgRYoa8ctIlIaoJ9M8WgSKFAzRXfT8yurRIpL5ttr9qG+xcv9iZ7MSTbkuEjAFQftvfQq49Is15Xm1Rrgrbyt7d4X2oGm1SgYRUSMqep2+6OvnVvTeAbo6UOTgzJK12PvjI/jAwCbMlXcBAEdwKjZWr8H2qQ9ph5YEiRI0Ma3LFal+bILBihQV/ctIlJWoEeisWwSK2H0zrHW+Xu+fy7ZSmmESClh4/KvTf5ZL7bhyeTe27h4xHlrR3VXGi5deNKPcL4AmsD7fyt5dmVyc161ehFu27NE+PufkEjZcdm6hKwVEVEyNdoPR6Ipepy/6+rkVuXdA2nWg37r807hz6v/EV156s6aNJcnnNQmamH6OItaPwzDBZoqKnACRKGs9y7rx4vqL8KPej+PF9RcFnvSybhFg9mRzuWwrTz4Jx6TnElSpTuKJlw8YByoE0J5f3Qlanec6FYcoyb2iJDDrWdaNOSeXtI+fPKujsJUBIiquRkxQ2OiKXqcv+vq5FTlhehZ1oHt6FuP+q5bm+nlNP4fueV/o21vYRLKhPStE5BEAlwJ4Wyl1nl22GcBlAMYBvAHgeqXUqIicBeDfADhb5iWl1Gfs1ywH8CiAMoCnAfyBUkqJyFwAWwCcBeDHANYqpY6IiAD4SwAfA3AMwHVKqe8n/8jZKXICRGptRWuRybpFoJG6R9abd5tc3vYCNnQ8jrmVd4GN2b2vUsBXJmf2iJhUZpkfBMDvX3Bm4HHstEb4Dc8w6ckTpwViw2XnantX8PgjojiKnj+hGRW9Tl/09fMqas61ZkkQb/o5dM9z6l5F7GlhMgzkUQB/BeBxV9lzAO5QSk2IyH0A7gBwu/3YG0qppT7LeRDAjQBeghWsuBjAMwDWA9iplOoVkfX237cDuATA2fbP+fbrz4/06eqgqF9Gal1F7PKVdZfARuoemZa4ASn3trq87QVsLn0ZsyXa7BZx/IsswYaJT80obxfxDViUS22Ye8rsWJWyuJWRODcIPcu6cfeOfThybOZc6818/BFRdhiAr4+i1+lN169oDVb15t4ebZo6R831eqgPeOZ2oHI42huV5wKX3AcsWZtwjcOZ1ntNZqcrWiA0dBiIUuo7AA57yr6llJqw/3wJwPygZYjI6QDeo5T6rlJKwQp89NgPrwHwmP37Y57yx5XlJQBd9nIoB0WfW5rMFXFIRNZdAhupe2QaknQRdm+rjaXHcwlUAMDi8iHffXT1+QusmVI8JqYU1q1eZDTMyEsXJAgLHsS9Qdhw2bktdfwRUbbinsOo+LKubzf7EKKo28+7PfwbR1zX66E+oP+m6IEKwHrNtputZWRMV++98Jx5NdvnwnPmzXien7CARp7SyFnxKVg9JBwLRWRQRP5JRH7bLusGMOx6zrBdBgDvV0odBAD7//e5XnNA85oaInKjiAyIyMChQ4eSfRpq+hNbqylqi0yUHBdxll3U8ZFZSBKQcrbVdaf+C+bg3axWcYaTK//uu4/u6VmMU0+a2emvOqliB9jiBq/i3iC02vFHRNlqtQB8q8ijvl3EBqu0xNl+ftsDsHp1+l6vd24Cpmb2lDQ2OW4tI0TSoJVfvcNJWO7ePlt3j+DK5d3Tz9Npl6BH85VoNhAR+e8AJgB8xS46COBMpdQ7do6KfhE5F/DdHmEDk41fo5R6CMBDALBixQrTqe5Jg2Mjm0srDokAitN9M4/ul0kDUj3LutHz7a3AWJprFaJzvnYfjfoMoQDiB9jiju1NMlypKMcfETW+RstPEEcrDlXIo75d1AarNMTZfrrPPaUUftT78ZkPjA3PLIsqZBlpDdf21jtW9u7y3T7Pv3oIL663coadtf4p32WZ5g/LQ+xghYhcCyvx5ip7aAeUUscBHLd/3y0ibwD4NVi9ItxDReYDeMv+/acicrpS6qA9zONtu3wYwALNa5pGEU/OzXxi8yrS9s9qXYo8ZVSjiLtv8soXkkpAauxA+HPS0j4LWHWX9uEsAmxxggetcINARI2hmQOgUaZdbKbzcR717WZusIqz/SJvj875yetHnYHZEjILWplsn27N9ugu0PERaxiIiFwMKwnm5UqpY67yeSLWHHUi8gFYyTF/aA/v+IWIXGDP8nENgG32y7YDuNb+/VpP+TViuQDAmDNcpFkUdbhFq4yNLNL2z3Jd2CU9mST7Jq/ul4m7CD95W+DDCtbsHTN+4qxseS6w5kuBCaeK1OU5y+FKRERxNFteMZNrZZHqbGnJo75dpOtp2uJsv8jbY9VdQJt+SvJQIY0zQHZBK5Pt0wjHh8nUpU8A+DCA00RkGMAGWLN/zAbwnBV7mJ6i9HcAbBKRCQCTAD6jlHIyknwWJ6YufQYn8lz0AugTkRsAvAngE3b507CmLd0Pa+rS65N80CKq93ALXYS6VVri89j+pq0AcdYlSgtDM7fIZC1oTupbt+wJ3PZZXICC9nusFqehPmDgEf3j5bn4kHpYG3l3uhKmyf15RkYraBepqbgW7VjuHxzBxu37MFqxhq/MObmEDZedW7j1JKLGV8QZvpIyuVbWu86cJuc6PjJagaA28J92fbuZewjGuV+JvD2chpUMZwPJqveLyfZphOMjNFihlLrap/hhzXO3AtiqeWwAwHk+5e8AWOVTrgDcHLZ+jayewy1MLnZFPnDTkPX2j1KhiLouzVhZiSLPrqAmc1LfumUPbtmyB92edUn7AhS232Ntg52bENRHYqpyBCO/zP9c5XyWoh/n/YMjWPf1vahOndiGR45Vse4bewEUZz2JqDk00027w+Ra2SxDlL3XcQVMByy8dYi0BNUPGnloTdz7lcj1pSVrM51+NKtGYtPtU/QGzUQJNimZeo4jC7vYFf3ATUPW2z9KhSLqutSjslKUC1regRqTOamd21TvuqR9ATLa70N9wI5bgOrRWO/h9dbUe7WPBX1X/I4XIFqlohEq5Zuffa0mUOFwZi9p5vHWRJS/ZrlpdzO5VjZL7gW/65oTqAjqqZjF9SPN+lS9rm/NcL+SZSNxM2wfBivqqJ7DLYpwsat3xT3r7R9lG0ddlzx6hbj3zYXnzMPW3SM1F7R1X9+Lu3fsw+ixaq77L+8bWL99E6RSncTG7fumt19nuYSTSm2pbCfd/h0ZrWBl7y6s+Plz+PNZD6IjXjaJGcZVB/5swr81Iej49KsArfv6XkCsm3inLKxSlPd5yn3cd5ZLEEHofgtaF3fFutV7QxFROprlpt3N5GatWYYox7muZXX9SKs+xetbcnkEFep93xUXgxV1VM/hFvW+2BXhxJb19tdt485yCSt7dyXKOZDl/vPbN1956c0Zt7/VKYUj9hSTee4/XS+HrG5gvfumTSR0SqfRSnU6f8FopYpyqR33X7U08bbpOrk0vc3dBNZ22TKrL5VAhVLAUZyEP6p+CtunPuT7HG+SVvdF0G8b+fU+CKsU5Xme8h73zv4Dgo/voJ43Yi+3Z1l3Q/QSIaLia5abdq+wm7VmGaIc57pWz9kiTOjWz91w06j7q0iSBBuKcN8VF4MVdVav7jn1vtgVpeKe5fb328alNsHR8YnpG6G4OQey3H+6Loph8th//YMjM5JRObIMtLn3jfeEbyKNbdM/OIJ3fznh+5izPc6Qn8Vevnd55x3XJ93s7irPCFS4t0mU+bmDKkV5nqf8jns33T5ct3rRjJwVDmUvt2dZdyF6sxFR42uWm/Y4mqFLe5zrWprXj7CGBSB6fUq3Hu6Gm3reHDdqjwK3uMEGdzJXr0ZpMGGwokWlcbFL8uVvhYq73zY+Nj4xo2U8zskiy8pKkn0QlBA0jXXd/OxrvoEKgVUBCHofv6Etz796KHCdwmbe8MvkreNsm7jbQpcboeY91GmYn0LA4iBO0z7mV6kKu9EP0lnWTwmWZ6Xc5Lj3e46zLrds2RP4mnr3ZiOi5tEMN+2tKs51La3rh0nDQpwGAZPcXkB9bo4buUeBW9RGXu8sZTqNcN/FYEULS3KxS/rlb5WKu7ONnRtUvy78QLyTRVaVFd2+Mbkp99t/umNl4CeHQ4MFXrrt5KyX7pj0e+zvX3pz+jG/49d05g1v8MEvIOVsm6BlAsGVl6Bj5PK2F7Ch43HMlXehFGDNKB3PhGrDi79yE7rfLhvnbjA5fkttVguON95ydHxieqiEn7SPc12wyKSy5RzffsvoDjmn1bs3GxERFUPU61pa1w9dw0K7CKaUit0gECW3V943x/XsyZ1mj44ojbxRegA3wn0XgxUUS9Ivf54V93p3/zI5adTjZKHbLrp9c+Xy7ungQme5hKPjE9PJEp3n+O0/3bHizoNhGuzS3VB2d5UDj0nn9yDe49f0GPdWOvz2t7NtgsZ1Hp+YCgz+6T775W0vYHPpy5gt+s9nOirDyVOx++1lgVnJvUwCXKee1IHxiSkcHa9dT79ZM6Iy/Y4HBYvCKlvOPtQt48rl3TVJaN2vAfStaQC0OWyIiChcvet5WUurl6HuhndKKfyo9+OJ10/Xw9At7/puvXpyp92jI0ojr2lv10ZpMGGwgmJJ+uXPq3t3Ebp/hZ006nGyMNkuYfvGtHIQ1hvCYRLsCgpy3RrSDd+E+7lxj/Gg7adbR79uet7tofvs69v7MBsBF6XOBfjQ8QeMumg6JOJFXJefxT37h65XEZCs0hDlOx4UgHKCM2Gzgazs3eW7jOdfPYR7r1gc+J0IC2w1avdUKr5mv5mj1tUq59E0ehlm2avZSSQdVNeoR323Xj250+7R4VfPEgAXnjNvxnNN61TeROlFxWBFA6tn5SONL38eYy6LkMgz6KTRHWG/pbm/w7aLyb4x3X+mYxmBZIEA3UXSOSZN1sF9/CY5xnXbJsq2AGq3h+6zn77tneCFjA1j3ZpFRi0e7vWMwjQ/S1rv53b3jn2+x/ItW/bgli17ar5jQVO/Llz/lNH3KiiIFfWcVoTzEzW/vG/mGBgphlbZDzyPmsu6V7Puhlqhtr6b57FZryGYaffo6FnWjYGfHK7plawAbN09ghW/Mrdm+5nUNbvsfGGN0LOTwYoGVe9IcpHGXwed9IqQyDNo6IJpV/u093ee2yXKWMYkgYCwY9JkHY658idkcYzrlnlSqU2b58LN97N/ez4wdkD/pp3z0bOsG3fv2GccPBgZreDX/99nMLvUHpirImjdFq5/yui9kmzT/sGR0M/k/q7opn4FrIu+yfcqaEriqIpwfqLml+fNXL3rJmRppf3QaOfRegaRsu7VbLL8vI/NKJ85zX2TRY+O51895Nsr+Qt9e3Hrlj3T62xS7/75L6tY9429071fi3yOYLCiQdU7kpzXMI4wYSe9IiTyTOOmN+39HXTDlXaU1Xus6KbKcmb0SOt9/NbfOxvIk3sP1gzBOHKsGnk4TJJ1vPbUf8Ed8r8wqzoGzPZ5wS8BbLR+nd5iCtbGOvGfXvssYNVdAIANl53re/Ga1S7oaBMcq07VlB+rTk2XxbmI6Y6xrnIJp8zuSK2HkAknL8jR4/5Tv3qfG/S90k1VGpYo1E8Rzk/U/PK8mat33aSZJLlxaqX90Ejn0SIEkbLu1Ry2/HocmyafOenUoKb535LUc3XnbKdO7azzvVcsrhmWCpmZu2xKAVOTtYVFPUcwWNGg0ujOnFQewzjChJ30opwssop2+930XnjOPGx+9rWaSGjcrudx6HIMHB2fyGRObPexomtxVym/j8ljz796aEa+iKjDYWKv45O3AQMPn3ggJPIgM34JMesU4NK/AJasnX5fwD/4srJ3F46FHEtRL2K6797Gy89NbZtGOf7Dpu8yXa6ul0qcRKFF6qFGzSvPm7lGa+UuqqQ3ta20HxrpPNpKQSSdoh6bun2z56mH0PPtrcDYMNA532oAsutVaeR/i8JkeIc7D1dYvdtPvfeDHwYrGlTQAWvanTlMI4x3DDvpmZ4sso52u29647xXUGUzzn4yzTGQxUU0aFhM3oKCfit7d2V3zA/1AQOPpL9cl+FflnHV06dh3eSJ1n5d8MX04hRlu+TR+ypqDpAoyw0yqhlKEvUiX5QeatTc8ryZa6RW7iJLelNruh+89YcLz5kXeUrxemuk82hRb9TzlNY5Iu17FL99cHnbC/jD6t8BY+NWwdgBYMfnrd+XrE0l/1sUpsOqvZ8lSl2piOdqBisalK4rsluSG80idFUzYXLSMzlZ5BntDpq+MqjruV9l88Jz5gXup6CTuWmOAd0cznEvEkVqBQk6gWd6zO/chJnzoaTrDHkHI6MV3LJlD+7esQ8bLtP3aIhyIYuyXdK4UAcda6YX7qC8IH7PDTsW07whK0IPtUYhIo8AuBTA20qp8+yyuQC2ADgLwI8BrFVKHRERAfCXAD4G4BiA65RS37dfcy2AO+3F3qOUeswuXw7gUQBlAE8D+AOllNK9R8YfNzV53sylfX5vhEaTLCS9qTXZD371vL9/6c3px4ta7/OT9bUmLQ0VzBvqA3bcAlSPprrYF4DQYa9hFIA1CljjLKsCoB9Q22qnTI8ybPaHJ8G3WibeF1YrVh1uydrcg0+mw6q9x5PJjG1AcXsktdV7BSienmXdOPWk8FhT3C9M0M17kaxbvQjlUntNWZwvW54nHN0yRytV9A+O+D7Ws6wb916xGN1dZQisXgj3XrEYz796SLufnIrIyGilpreN7j10F0u/lpgoyzX9LPWoDPkdP26xj/mhPuC+hcDGTv+foISYKXlLvXf6dycXh24frVu9yLpwGUr7XNA/OIKVvbuwcP1TWNm7a3o9w44177HUVS5hzslWost2u4bhHF8bLjtXu6+dT+53LPqtW1rnHYrsUQAXe8rWA9iplDobwE77bwC4BMDZ9s+NAB4EpoMbGwCcD+CDADaIyBz7NQ/az3Ved3HIezSMnmXdeHH9RfhR78drugdn8T5pnd+TXmsamen1WMdkP4RNqw4Us96XhbyOtYa5dgz1Ad/8dOqBCsC63or4/KS4DHE/B2bL1i3T19gwgOTf0zjc5/I/X/sbRseT3/lg8yd+A5t/7zcKURcPI8onItPIVqxYoQYGBuq9GrlYuP6p0LbZKDNOmCxbAPyo9+NGy8irRcT9Pp3lEkRgPIOBY2XvrsQzdpjSvVec9wvaT1FnIfG2sgDWSe/K5d013UJ1U1Jmsa3y4Bw/un0S5ZgHYF3k+28CpsxzJKRtXHXg/6neiO1TH6opd+8jv+6/7oSjNa0TPiJvFw3dceckh0rze9k/OIIv9O31bYnwW2bQugHxWqrr3VIsIruVUitye8OUichZAJ509ax4DcCHlVIHReR0AN9WSi0SkS/bvz/hfp7zo5T6tF3+ZQDftn+eV0qdY5df7TxP9x5h61qU+ki9j7m48rwuF03QuSetfWdShwTSO9cXWdRjLcl3qiG+j/efl0ujSsPqXADc+oNcvqdhGuJ48hGlLsJhIA0srOt2kmht0q5qft0Lb92yBwM/OYx7ehbHWicdp/tfkqEreQ5NWLd6EW7Zssf3sag9OYL2U9TeIrpEoFt3j9RsU51GHXPpHD+6ykrkCPnOTbEDFWr6Hw/xPEnTrVEBOKJOxcbqNTMCFcCJfeT3Xdm6e6TmAhuWkCnKdgm6mOp6cd3at2dG9mrv54jznrdG+O4F9TCL0jrtDoi5g0CN1M26wN6vlDoIAHYw4X12eTcAd2172C4LKh/2KQ96jxlE5EZYvTNw5plnxv1MqWmUIZ1+Wnl8f5Fy/hRyiELKohxrSb9TuiErd/a/gq+89Ob09cG5VnS79n1uN6Zjw+HPaWX2LGtJvqdp7cuoQ6AaMbjBYEVK6rHz/W6w/U5uaS07ys27XyVfAfjKS29ixa/M1a5X2HbUPa5rMfXLOxF245T1PtTNJgBErxQE7Sddq3TQe3hPeit7d4V2EzVZbiNILWCV4CKvlOADx7/i+9hfXLU09Hjc5lSipvz3mbOPTHK0BFVko2yXsIqdrpIY1Okv7FgLes8ogdg0bpa86+I3R3orZYLPkV8HXhWjPBKl1EMAHgKsnhVRX5+2Rp59oKHG92cg63w2Jjl/6jFEoR716SjHWhbfqTv7X6nJFwLMDGoP/OTwjMYjpzz1pKid89mzQmMU/wHLvnoKznj6RLLxqNu7XkHkRg1eM1iRgnrt/CxvsHXLBqwb2LD3096A2Mv0e03YdtQ97pzA/bp2e9fFbxnrvr4Xd+/YNz105H6Dm8KkNlx2bio3xmHHQNL3ML0py7pCk0flJWxbGq9Dgov823Ka9jHTc8rsjjbfyqd7H5nchOsqsnNOLgUm6/QKq9iFte55h6P4HWvefXNsfEL7nmFBKfeyTJNXBTEZF94KLcUZ+qmInO4aovG2XT4MYIHrefMBvGWXf9hT/m27fL7P84Peo/AauXdCkZIx11PU65/p83W9Kes5G0i96tNRjrUsvlNPvBxcZ6hUJ/HEywd8G+TcvTHS2l7f+9X/G8t2344OqXu8tVAmVBvuqv63xLMu3r1jn28d5Qt9e2Mtz1SjBq8ZrEhBPXd+lpF377KjXESCbkB0J/Sw7ah73O8E7l2XoPeoTqnpXg6NGGjSHQNpvIduP3aVSzhldkcuFRq/485khotIhvqAZ25HT+UwegDgJFiZqbdZP2GZp1PRPgsHfmMdyt9r9725DTun+I2d1PW00u3XznJp+ve0jtGwil3QsCi41j+ot5X3+AhaF/fnGhmtoF1kett6W678ziumN0thuVDcWqWlOCPbAVwLoNf+f5ur/HMi8jVYyTTH7GDDswD+1JVU86MA7lBKHRaRX4jIBQBeBnANgP8Z8h6F18i9E/Ls8VhUUW/eoz4/694bUdWrPh3lWMviOxVUhw17Tha99W7517OxvPpZ/EnHwzhVjkdfQMSEmXGEDpsN26QR1/EoTsId1U/VDLGNs637B0e0M5NNKpVdbxk0bvCawYoUFH3np9UqHeUism71Ity6ZY/vuUJ3Qg/bjrrHg07y3hsLk33SDIGmtN5D19qw8fKUggQGdC3TzgwXQMLAkkFCTJn+JyPlucAl9+G3lqzFvQtGYuU00Q298ksQppv6+Oj4BPoHR6a3p9/xE/V8ElaxCxoWBc36u5n0XPB7TwAzKvXebriOdhFMKWV8/vQLHOm0YktxXCLyBKxeEaeJyDCsWT16AfSJyA0A3gTwCfvpT8OatnQ/rKlLrwcAOyjxxwC+Zz9vk1LqsP37Z3Fi6tJn7B8EvEfhNXrvhCyuk400ZjvqzXujtpw68qpP644Bk22UxXeqXdOLz00keHik28hoBUvv/lashPPO60fwIWwfn5n7CghOwJ1XgklB8Hc56DocZx3P0ySkjXps3r1jX+DjafSW0W2XRg1eM1iRgiLv/DS71EW5iPQs68bATw7XfOGA4BN62HbUPa47ybeLzDgZmSaUKkqgqd6K0LIVtC9SqYQlSIgZiZ09OozTiyjqOSXq99MvQFCdVJF6b5icT0wqdhsuO9c3eFJql9AKYNyhSlGCHFNKRcqGH7bstHILtRql1NWah1b5PFcBuFmznEcAPOJTPgDgPJ/yd/zeoxEU4RxeJI02ZjvqzXvUHq1Fk0d9Oo0EmUC636mrz1+gDZYD1vVLoHCsOmW8TGdmLyDaZ+wfHAkNRrhniYsyC1+cQGHQa4ICTH69KCeVin3dTevY1DXMuCXpLRN0fEcZBlukawWDFSkocstFmlH2qF/Ue3oWY8WvzDU+8MO2o+7xK5d313TdBqybgUmlpucHd97TJKFU0GfyU7Qvd9rrE9bakNV4WkdYgCnJzBAA8st6HfA+ftOIeo/psHNK1O+n7oIZtK3jnE9MKnbO7xu375uuYJnmxog7VClK5T1qZSRo2QxQUN6K1tW/npLWifK+3kc5rwfdZLaJYOH6pwpRRwmSR3067jGQ5b53ZskLmg1EN5OVKdPjfPOzr2kDFSeX2jC71IavvPRm5BxvQTfRzvv6Jc/PYuaVKHSzeQH53uib1lnCZjFznpP2ts4SgxUpKHLLRZpd6uJcRKKcKMK2Y9DjTlAkbGpA7zI6yyUcHZ9AdfLE6SfpTAdZTdEad31Sz+8Q8n66k1v/4EjNzaj3+UDtfnEi9Z3lEkrtUrOP3ExmhnC32jtJVafXMa+s153zfYt104i6Wy5MzilRv59B3U51ldq45xOT80DQc4IqAaZDlfoHR2qSA588qx1Hx7MZpqG7wQgb0kJE6dCdM5LUiepxvY+aEFh3k+mc64t0A+Inj/p0nGMgjxu5e3oWBx5Huh6XTmA+Sa9hv/qZHwWJneNNdxO9cfs+HJ+Y8t229R7W5Debl25KWdNAzMmltsAeMrqAo2mjSdjxratr1XtbB2GwIiXOzncuHLdu2TOdeT5JF6ek0uxSl8dFJOymRve4U76yd9eMz+v9snmXkWT76/IEhE3RGibuOmWe38Hg/fxObkFjB/0uVu4L5mililKb+J7gTW4kN27fh+qUwuVtL2BDx+OYK+9aD9jJM3PRPmt6Xm4v3TZ8/tVDkW5so34/g8bH6jJd12PIm8kMPmGBnShJON3i9oIocm87omaX1tTFXlld74MEnde9n9MkSSNQnBsQnax7AsU5BtK6kUtS3wwLzPvVf710PXLWfX0v7mx7GP919j+izfP4EZyKjdVr8JT67UTbQHcT7RcgcZabdg6TqNvfNBdYUCDm6PGJmsayNgBtAkz5fF27Y/asdTM9vr3boshDyBisSFGcyGvW0dq0K81F704a58SW5DPFmaI1jMkUrrqTbVh+h43b96UabDLd3mFj+MOi+dUphfe95yT86epFkdd/tFLF5YRYUzUAACAASURBVG0vYHPpy5gt4S3p2gzThsSbiNNOnokla32fn+bFOMqx3G2Qv8VbEUnrfBKlwmAyg8/W3SOBybKi5KcAkicIK3JvO6Kksu7unHT5QTeWSc5hWVzvTfg1sJjcmAbxfpaiDWfNUpxjIOl1OqxnqemQBkB/XQkb5qz7jJuffQ13tj2Ma9r/cWb9BcBcvIv/UXoIqALb1cyEm2+NVoyOH9Occe7l6l6jAKzs3RXpOI1zv2W636MEYqYAlDvaMPeU2drtFWUIvZfJ8e23LZL26MgSgxUpihN5zbrbTatVmvNu+c0iGhl0TAAzZzBwn2zDLgajler0yTONwFjY9o4yfWMYZ9rJqOOKAeAPO/qMAhWA1QVvBKdh5fEHtM/RDaGI0s3fWUddXCTJMXtn/yvTU/q2i+Dq8xfM6F5qmr/FfRyncT6J2pU6jRl8onwX08opUfTALlEcWTewpLH8oBuMJOewIrQ+RplpCDDrUl7ksepZiHMMJKlbhvUsDUtq7V1PXR3Db5izSeLLt0Yr+P3Zu3wDFY5ZMoE/7OjznR2ks1wyOn50N9Enldp8c2g566zbdmkNQwna/qb7PWogplKdCqwruusP7h77JseqyfGt6zESlJOjnhisSFGci1ge0zQ1aqU5TqT/wnPm+WZUvvCceZmso5P0KM1oZNAxEXayNb0Bdb924/Z9sY8P3cXnwnPmYend3wrtMeE8X3excjPdnn4VgzPkZ0avPfH8d9BVLvmuf6ldcNVvLUjUTS+swpnkAnFn/ys134FJpfD3L72Jf/j+CP7kd0/0FvBe0No0AZi0A31Ru1KnMYOP6TKYU4IoWNYNLGksP+wGI26dKIvrfVQmvcTcUy2bdCkv8lh1IJteH1GHbifpkRO2z4LySEQNIsU5ts/oKqO9Ej7LyBnyzoyycqkdIgg8ftz7r7NcwkmltpoACgDttnXXU/y+02kMQwnqZaTLmXbMnubdvW5+N/pRenT6idtjP+z7EtRLrLurXLjGbQYrUhKUhTnoIlaPMeCNIG6k//lXD0UqT6pnWfQpWoP0D44E3jSaJM4BYJQoyTFaqaJ/cCTWCckvgutXOdJxZnwA4Dt1pWN6ew71ATtuAapHtctcA2BNG4DZkT/ONOmcj1OOd/huw1NmdUSe6cZ78Tg2PqHdPgLgyuXxA4xPvOyfLPTo+OSM75A3eh+n62BarZ66rtRpzOBjsoxSm+DY+ERDZMwnqpc0G1j8KtVpLD/JjWXYNIlB1/s8hlKEbQe/IWxh16o8Gs3iyrLXR5RlJ+mRE7YdddeuvIJI61YvwmR/GzoQHLB4S713Rtm9VyzWzlLiDA9xb+PRShXlUrvvLCJB37ueZd04a/1T2vcxYXK/5be+fjnTjhyrWonaBdOBDL/km35TxANW3ddE1GPA9JhutCTgDFakRNedW4DACyQTsfmLe5Kux0U36o2rjnOS8QtUOMeELrp8Rld5xphIa4qp9ukI9uixce3sB1/oc82OoVm3sAuJY2XvrtAbS29X+/7BEUxokoO1i+DeKxaj+8CTmPj+7egISSgh0//EZCfDfOur/sfMmL19owxJiZLcUSFZcC0oyVrQdyhu18G0Wj0B/++pX9fWoBl8go5Vb1DNPU/80fGJmjwYt27Zg1u27OFUo0QuaTWw6CrVXSeXtF3CTcW9sTSp6Ouu90DwEM20BJ0/deeqsGtVkRvNsrxhv3vHvkjLjtsjJ2ifBdX386rP9izrxht71uIDP/matuo0rjrwZxO1ebe6u8roWdYdWC813X9h2zaNaXlN7rd0ObL82tH8Gtf8km+u+8bemvpKqV2mG+nCRD0GTLd3o917MliRkqDWwqRji5pdktYV72vTqOjEkcZQG11XQedm3Vm+btiFt2fCseoUqlMK91+1FACmp+v0M6lU4JSjUSphYRdS74ncWb7uHnvKfuCM3X+GDkmQ+dJjxvsJIK5kmGc87Z/ALOqxFDW5I5CsMhI0JWnYssOO4ygXTl3QIE5Xau966ZYddqzqPtvK3l0zetHopj8mamVpVXJ1lerZHW0zuk/HWX6ca3JQRv+wGyu/IH0WSa112z9JQuAi37hkdcPePziiHXqadjBA17PP6VmaNF9CGn71+i8DT54CDDwC5+rnJBt3ZgPZPnUiX4X7+Ag6fsJ6XURJtJ10Wl6T+y3dvjedace7jKT3eFGPAdPvS6PdezJYEUHQFyuoS02YRs0pkYQ78aI7WhqldcXvxqTUJjPGlxXlohtGd5JxbtZX9u7Sjvnb/OxrvlHe6qSaTsypG2Lh0LUoRG3ZiNqKEHYz70Tn/xnR8k6YWHj8q9Pr5a3smWZUjjs2MEiSysjV5y/wzdviaBOJPewnypRYQUGDpEOndOfMtHtkRVkGUZFkNSQhrUqu7js3Vqni/quW1qUSHZTRP+ycGfRad1LrW7bswd079gXepAbJ4ibDr/eaCELzOCSVZAaJpDfsG7fv0z4WZ6aJIHH3We5BpEu/aP3YPqSZccbbgBb0+XS9LvyScnqnJHdvI9N6VNi1Om4vo7BGIO8yorynw+/7EPUYiPJ9aaR7TwYrDIVVvoscmU5b0kqQd1t6v/6mrSu67lpd5RJOmd3RENFCN91JxntS9xvzp4teAyemJDLhd0GI2rIRtRUh6CLkjs6/Nes0zI+YKDPIpLRBAO0xElbBSDo2sKtsjVn0tugnPW84M2p4gwGOoF40YUwDOF/o2zvjwu6uRKQ1dMor6rEaNiOL24jdEtQI5xJqbf2DIzU97ZwbASCd3kFpVHKDKtX1qkQHBdrDgpVRZgM4cqyaqLdWnO0TVm9zlpnXzCCm75NV3Tosp1fan9tbn3AakYrc8zqoAc10qJFu//kl5fROSR5lpjuT9TahW98rl3cb5WJLkq/O7/tw7xWLce8Vi42Ogf7BERw+etx3+VlNMpAXBisMhY1ti3pSMbnhTxIUyKpVJY0LmUm3eJPWlaCWmT0bPmq0LkWg62UCmGVaBoIrSgJoe6p4+bW6R23ZiPpdcC//8rYXsKHjccyVd6dXXrYBa04CoKyhG0HTa0XRseJ6/OjSjwc+J6hSGNSK7zyuyyZdLrVj4+XnTlcO0/6u3tOzGPf0LDYKHERhGsDRtUCMjFZqxpamncgpyrEadQpAABwOQg1h4/Z9M3rSVadUopmf0tQ/OIKjxydmlJfaJdMGHu+51p23xvlb1yst7AYozkxcefXWilJvyzJHhHv7+yUS173P7I626XUKGzqRpjT3kd8+COpJ4Khn63cavVp0dYagBjaHe/v7fb/iTGoQd317lnVjxa/M9a1POdpFauqAUfZb0PfuxfUXhS7rxPHlnyQ1q0kG8sJghQHTsW1RuvqEXTiSBAWyjIyncSEziXqatK4UOSmU6Q2oXy8TbzbhoDF/jnWrF2ln01CwbvJNplGaVAq3btmDgZ8cnm751gVRgiqVUS6wzkXoI5P/hM2lL2O2zFxHv6SZyt5YkWMX0gYsv76mu2McuuPY+b65e8IAwCmz2nFsfDI0QWmaepZ1Gx0/UZcZJYDjpXBiGw385HDNzUKcXlphM9HojtWgddVVgrznujyy/xNFpWs1Np0hKkuBQcL0UhKFvu/IaKUmMDEyWsHW3SM4ZVa7byLqsHqF303OMVfSXj95zbYRpd6WZY4I9/bX3fA57+NNGO74peZmLKo5hg04ae0jv31wCf4ZGyYex9zZ7wIVAP2A2pYsP3gq7Nxd61avTKVXi1+dQTc8xMvZ/n7fryjX+6Tr65QHBVlMc2f4Sfq9C6t7FWFmnyQYrDDgRMn8hF3A/CqzJheOJEGBLCPjaVzIwrpzmZ5sknYPLELvE7995c0mHJRp2eEs9xbNidSvp8qF58zDEy8fmFFpUAD+/qU3seV7BwKnZErrpsxZzgXbPofZMG/lFgHQuQC49QeR3m96v7+QbIrKoLGNfheNY+OTvtN1Ga9vzOM0j6Ceu3eQqUp1smaoStQLvN/3bOvuEVy5vNsoAKI7ZwmA+69aqv0uuSvTeXSXJmomQZXq6pTKrLeBSSC1Up1EV7mEcgmx6hXem5yw3lt5NaxEqbdldb0wTTTtzGym225p1WU3XHbujFkadOuTBu+2vrztBW3jTN1VDgPbbkbPmi8BV6zMpJ5s2hPJmz/E+95ZDCcNYjocJepxmvR7F3YPVoRG3CTa6r0CjSDoIAi6gDkn3JHRSk1rosm0fUmCAiav7R8cwcreXVi4/ims7N2F/sGR0OUC+gM+yhdh3epFKJfaa8qcSHJ3V9k4q3XPsm5rSsuuMiTia3X7xnQ7BAkbHuBmsq/8tpdf5alnWbc2oavTU+XF9RfhR70fx4vrL8I9PYunk3f68V7E3UGUtC8GPcu68X/ESaA5Nhzp6Wnud91+0bUYKQQHPrNaX9PjJy73Okbll6/GdBvpvmfPv3qo5jgPGn6kKw/7LgW9f9R9TJS2OSeXIpXnKawOk1ULoOlyxyrV2PUKL6eO4uQncot6Do5bZwOi1dvSul5419fk+uCeoj3tVmLv+gDA5t/7jen93GUP1/RbnzR4t/UfdvQVM1DhmBwHdm6aUW/0G64e57j01t/9tr8jqN7jrJ8z692tW/ZE/n5E4ff90IlynMb53rm3fVvA+OhmyJ9o1LNCRB4BcCmAt5VS59llcwFsAXAWgB8DWKuUOiIiAuAvAXwMwDEA1ymlvm+/5loAd9qLvUcp9ZhdvhzAowDKAJ4G8AdKKaV7j0SfOIagBHlBFzBdZTbofcLe0yQoEPbaJC2CaSQ70o0Jc8qiZKCO24W+KL1PTPZzlBwQaWUO1gmbOjZRVLtzPjB2IPprIkhzv+v2S1APA9OLV1BPhajrm3WSLtMWM1Mjo5WamW9EECk7uOk2DvuuhD2eVXdpoqT8Wo1L7YINl50b+Lo8hjWFXXOyagE0vdalneDTWVaSPGVJe3FFqRekcb3wW98w7h6bYfkMoh4jQckLvdOoZ3X8e/fBGSkmC89MSGNQ0uPSryeS39AfILjek1YvR5P97/wdlLvCYXKcut/Tb6Y/3fqbDqvKM8dLlkyHgTwK4K8APO4qWw9gp1KqV0TW23/fDuASAGfbP+cDeBDA+XbgYQOAFbAa1HaLyHY7+PAggBsBvAQrWHExgGcC3iNXuhP9xsuDL/xRKq3eC0eSoEDYa5PcsKV14xPWXTLrLtVZ3miYTLvqMN3PppWnqPvHyYmhO+X6JbzERutXBWCNAtYAwGzkP+ayfRaw6q5ILwnKM3Fn/yuR8yfo9otum5pevMK6R7pv6E3WNcu8GHG/M7q8EIITlVt3pcU0O7hpRTbsuxL2eNL3Z74Lykqc63Re1+Cg7t9ZtgCadDvP8v3DzsFB219XZ/tCn9kML1GPh6TXi6gBbAFqggZBgaWgfaQ7p5rWebPOHwWc2Af/LqfhjAymY09VSGNQ2o1+zr7S5dbR1TXiroc3UHB0fGI6wBt0/jMJqJmcS7zfeb+Z/nTr65eg1i3t4dr1ZhSsUEp9R0TO8hSvAfBh+/fHAHwbViBhDYDHlVIKwEsi0iUip9vPfU4pdRgAROQ5ABeLyLcBvEcp9V27/HEAPbCCFbr3yFXcG3TTSL7fQZUkKBD22qQ36lmc0LPs6eBHt2/izK/tPeH9/JczT7S6LOdR97Np5DdKq/vATw7PmOay1Ca4tO0F9Lbrx1T6Jb3MjZ0Aqn9yJTZHuGkP+k56k62ZZOr2o9umphVhk4qe+4a+3rkSTM9z3imF/ZJj6QIYjrDs4HF6ecUN8sR5f93MP/Xeh9R8ol6n87oGu695I6MVtNuV7qwr19739Z5rBMCVy+s380JQQEJ3UxJlGuo0621h9ZCoAWxvgFcXWApqJQ4K9hSlF1zNPhg6isl/+Cza1cxZcQrBoDEoi+0a9Fonn4n32IuzHn6BAq+g819QvafL7hEa1ks8ShDStCcFUJvzrlkkSbD5fqXUQQBQSh0UkffZ5d0A3H25h+2yoPJhn/Kg98hdnBO9SSTfG1FO+p4mr817Fg2TG+ysLyYmswY4ksy8oosInzKrIzRSGrXbV1o3OPf0LPZNUvTRb92G2ZU6j6mUdmDDYd+H4myPKNPL6eb8BsKDS7ptarKfTI55Xa6HelS0TVssnala3bzbKGl28DxbEuIEGr0z/7jVcx8S5XlDl2ULtsn7+uVQUKjv9H667TypVGAQN+/zhsl1N8rwUl1PUiDauT0o2FbImeOWrEU7ADxzu5XQMoCa/sdDMxua6fOV+xfXA0fUqXhA/i8snVyJnoD1MhlynlYjrwC48Jx5vsdelN7Mjrt37DOqB+qOY11jxZXLu2vuLYLqpUHfee9rovRWasahqFnMBqL77kQtN39DkRthDSPBmWeeGeWlmfJG8v3U42SZRoukKdMbyiwvJmGzBiTJC2B6whvzCWJEvdlO0vIVdtHwrTxu+/fQz5U5pd+2uu2xcfu+0K79upkegjjLPj4xVbPPdD0w4lbIwyL2UbtIZk3XUmrSYurdRiaJ2Lz5XOp5cx/l/U0qG81YyaDGUMgbuowUpaXdLei8756Ny0/c9Y5zM2lSD7nwnHkzehb6iXJtCBO0T++/amludd5Ilqy1fkJ8SHNd1LWgC8L3behw0+NAOUbjj7Nd4zau+S1TAPz+BWfi+VcP+R57szvaUC61G+/f/sERo2lrAWt2Nz+6gFqUenrQd977mqgzLiZVtGGqSYIVPxWR0+0eD6cDeNsuHwawwPW8+QDesss/7Cn/tl0+3+f5Qe9RQyn1EICHAGDFihUZztQdnXPC9TsxxMkGbXLwmNyYAvm0SJp+cbMMoITNGrBw/VO+F9Wwk0OUE57fySNq8CEo38LC9fppOEMvGkN9RtH9epiSNu2URbrtMVqpTt/Q+10gnXGRcWav8AsU6HpgxPk+9Q+O4Ojxmd1Cy6V23HvF4sDxnG0i6B8cqWuLZVJhvTQKUcGMyaSy0Yw3hpS+LCqSeTZi1FsRAzPrVi/Cuq/vRXVKP5tUu2acepz1jnszGRbo6R8cwdbdI4GBCud6lua1Kmif1rsXXlJxgmth12TTqXyDGsOCtuvK3l2xGteClrlw/VO+rxmrVHH/VUuN92+UGbuChlv4bWNdLgu/fRVW33HnJgvLUeFI45xdxGnZkwQrtgO4FkCv/f82V/nnRORrsBJsjtnBhmcB/KmIzLGf91EAdyilDovIL0TkAgAvA7gGwP8MeY+Gk/RkaXrwmD4vrxZJ05NslheTsHWIW3ExPeG5I80mXd516xvW8qLb14FBkfYXgf6bgCmzoEuelAL+QT6KKzWPm3Y1NQ2OpSVut1xdS4d7nG5QUqco45eLynseCJoNpNGEHa/NemNI6cqqIln0G7o0AzRFDMz0LOvG3Tv2aRtAnF4Iaa133J6aYfWlsJtgJzcIgEhJosOEbZt698KLwnus64Y5dJZLRjNn+TFtqQ97nm67JpkRTbfMsICUX0Od3zkjSi8F3RTmOlHuJ5z11eWlcecm83vcGXbi9BBvF5n+DruXH1XeOQRNmE5d+gSsXhGnicgwrFk9egH0icgNAN4E8An76U/DmrZ0P6ypS68HADso8ccAvmc/b5OTbBPAZ3Fi6tJn7B8EvEdhRLmAJjlZ+g038Dt46nmQ+W2LqF/cLNYxbB3iVgCCTnhzTi7VXDQAzKhg6rp16oIkJjfYfvs68KKxc1PiQIV2fKSG06su6HVTEPz95CpsPH6NNlgRJeBgEhy78Jx507OBeDNDA9YxcXxiEpqGr8D3M6Gr5J3syncSdsPrt/+zaIXNsotgkSuVST63rnurQvNl7qbsZHmNd3/3nGP91i176h64SDtAU9TAzOj/z97dh9lR1fmi//56ZwcaPCdNNONIJyiew8GRIZChfXluOPcZyBleFEgPOvEd3+5Br3gc0ZshzHhJE1GCGYVxxlFx4Kh3VGhFm/DiiRiYey+cQe2YF4jCHQSRdHg0GhKVNKRf1v1jV3Wqa6+1aq2qVXvX3vv7eZ5Oumu/VdWuqrXqt9b6LUtPzeT6hVjvvENhsupLWa9XAO7c+bTzuH5XpkB3VpLD0IqWjbpjvd4nqNdk/lTEfYJnD0/P9bS0zZylYwqApOXtteNbv3XhU1e3XTNcG7ryBAJ97yfi78dUN0iriWBWqXnHVujrYxWHybnOBvIWw0OrNM9VAC4zvM/NAG7WLB8H8Mea5b/RfUZVtKqrjG24QfrgaddBZtoX6WQzQOtbMFwi7kC42V4G+uvYftU585bpusTpxqG6XNSy5ndOfteZhUbGPNp6AowcmPvLNJ5SpyaCT685DcMrBp1eZ4tq6763Q4ennRMtZUXidS0Vrrku8hTILueuS4Am/f2HvkZVsYtgK4SYUx6o3g0SdZYQZbzveHaXY932nkVv4MoI0FQxKGqrUySHMeYdYujSWp9VdmVdx1xuBF1nXvA9buJ9064yKv7cdepLePtR30efz3Tu0Qxnm7a8qOlYn5pVTbNpmeo6Mdv5MbZ9Ar9/LnsWkiK9dkzTkut6GbtcD+LXTE7NZObDGts+oa0nx/tEl1MlrieHmJ3oqAV9c9+hbQabmO6cMp1Ds0rhiY2vn7cs9PWxisPkykiw2TOyDpAQEdassfXpg6ddB5ktL0Q81r5dFXSXm4Q8FQBTEGTkolOanmuqSMatqj6Fcdb8zsnvOqvQwL8sBQ4+pXmGRWrubZ9KcnKoQtbrXArK9PeWNzfM2PYJjGzePa8SpZvz2iXXRZy12pfLueuyHunvP3Qlv4pdBFshxHZX8QaJOkvRMt7lRs73WLe9J9Dcq9D3xrGKLX02LnU/3XN86hS+6+PSWu96c2q7jvkE9dN8Au22fdyuMmrTlkexTn0Jl9S+D0NeRrPJ/cDtl2Fo8n/DBM5sevjg5BR2rD/SCGbK35BkOj82bXlUmxulv96HxcceVVqvnfgT8wRC09N2xseq7rxa+y1zg16cYD8dqHjba0/ANcOnGrfJ9ZxOn7/PTc0a3zMpfU6Zko3rrvOhr49VHCbHYEUBtgNEd6H98K07cPUduzOjbIBDpt5I+uBp9UGWFVDZe2CyqyvorhFUUwUzz3zI73zBD/HfDv8TFsvv9U94DsBI49f7FYCj9E+TPBlgNHNv21qDfvfctDG6bYse541q52m9tp1r6QqO7vzqA5AsjhSA27ZNYOili73W3/XcNbUe6Z5fRiW/024cQunV7aZqKVrGu9zI+R7rtveMf7d9XpYqtvSZuASDTM+59uJTS2nc0X0/utb6EJ81vMKee6O/XsPR9b7MXh1Zx5RtH+e5Vhdp7U+2hr/tqHv9AxWxmcO4cuE3cftzzcEK14ZJ22tipv3w3NSsd33U9Lmm+m6eQJLPa66+Y/e8AJyOrpezbdpi1546IYNkWdf55LFnSr6Z9/pYxV6gDFYUYCtATePPnzk0hctv3YHxJ/dbo3gumXqTXQNjrTzIXAIqPidLWePgQ3dp1b0nYI+g5q1gptfjhlf+G/7PmX9ArS+7Cx8A70JTF4yee4+om2J6qi1ba5AtM7JpOrGiWcJ9g2NZ51qyYHcdemIqoGzHle+56/L8Mir5nXTjEFKvbjdVS7p3lW9SNZcbOd9jPc/NoU+QL0/52a6p91xuWGzPeWDd2cHX07Sv0631Nqb9qVu+/sJTrMmigeYx+j6B9qx97Hv85q0jpl8DADW4taSbvBi/dpqGs8jMWab9E2pGMdv5aqoTxjNf6IaP+STrdJ2hz+W9Yq5BiJANGrb6na6nSVrRRuqqNTIzWFFAnhMSaETxvvbgL6wtry5d5E1dA1t1kGXd5PncjKcTToYcYxiyS6tLZSN+3NQ9MW+L/8SBSRy/7VOoiVugIg8RYM/si3Dm4c8CcOv5Yds2U0ETZ282vS4Ulwpr1rmWruCkzy9Td8z0+7ocV77nbtbzy+hpVcUugq3Qq9tN1ROf83mGV7jcyPke61nvWTTI51tW5OnZGiq44XLD0upeWmUNHRp/cn9ToszLb92Bt732BKceInkD7Vk3r77Hb6jWfgCYQR8WFAhYyKKluPZPs/dd+pxI5tjKSi5qCnSEnFHM1OvYFnywDR/TCdlQYHsv1/M1dIOGqX6Xde/lkidDp10BXhcMVhQwvGIQ40/uxzd+8BRmlEJNBG84YzDzhAQaN+W2m2XbnLohMseHOChtBavLOqYLwPTWhhpjGLJLa1Y32fhia6pAFm3xPx6/dn59XsfLbwD43YiZts0lualvV8vkrB22Y9e1tcRW+XHZB64FVDvG0ZYREKpiF8FWKGO7q1w5oGrLez1xuZHzPdaz3jNEkM+n/LT1bC0y5bsLl/Kg1b20yho69M8P/qLpucnGOFtDR95Auy45Yizef6YbedPNe8ieQV+bOTtfzgpgbpjt8HK3Y123D7PyxyT3iW52s6J1kqxex1k9QmzDx5JMx+9Af12bwNUm61xwPV9b1aCRFdRMziDnquqJ0xmsKGBs+wRu2zYxF1SYUWpurLpv1v7ke1757YeM3XqKdpFPfkbRg7JoHgaXoS4hWhpCdWlNdlOz3eCGuiFNr8dFfffPzSBSpr3qhcGmUix6g6c7VpMVJNux61qZN52rrtFp1wKqXTkPyuhpVbUugq0ScrurXjmgast7PXG9Jvsc6y7v2cqgnG0f6MqAVo41d31OSEXLYd8yStcYlyfp6BvOGGxqmNi0JSNpeGKbdV3mdddZWx3RtN6m16yffg8A4O2176PPtpMkVZczDLP1ZTqWRzbvxvPTs3OP2W7oi9RJss6l9DC2PJ9vq5+OXHQK1n5zpzaBaJJuClATn3xigP0882mg8D32Ynm+v3YlpXXFYEUBWeMOATTNMJCku1k23cDXRIIFKmxT+vi8f9EC1+WECtHSEKpLK2CfktXG5+IRX6CS39BFfffj+vrn0Vd2pKKvhqUXX4sHlhdPshQrcoPnEtAyLQkOgAAAIABJREFUHbuulfmiFTnX1zPnASVVvXJA1VbketLqAGarg5u+lflWjTUH/KZgDCF9w5Oc3cqVSzLHtOS+y5t09LZtE031XtMQawV9kNflOqvrrRH34jCtt61Bcv30e+aCFiZ5kqtnGds+YfyefHobFKmTuJxL8fUga8aLPI2hul41zx6ebpr1xud+yqeOaLvW+TRQ2J6b1Rie5/uregJxBisKyPpy44P2Y2MPaS+Euptl03vOKjUXJS7aSm2b0ufEdXdlJk+KP6/oTV5WARiqpSFPl1ZbZHZyqnlKVtuwHSD/2FCgEajYVP8iaqJ/fzX3T4Z0JD9t4bHABTdkRvZb2XXd9UKpe55PZb5oZdrl9cx50FpVH2JR9coBVVs7rydlnlsh3tu3Ml/GWHPgSJ0j7tYOzB8SY5uCMYRQvbdcegqnJfdd0aSjLsM2B3Mmf417SKensox7dRRpkLQpcp3XnSMA5g33yKvoNcTnXCpr+Fi6PhbimhIi4OrTQGHrIRMnxdUde3m/v6o3pjFYUYDpy13UX5/39zXDp2LopYudTpas7mhFCp6r79idWdgo2JMnFU0ImKS7UMVJNnUtDaYLtGu0M/ncuPJgeq5t6i2geUpW25zXRceG/tWCURwl5u9NpIax1Q9lBk5CRPJb3XXdtUUnT0HYakWDe+ROd5xefusOfPjWHblbMUPfoFW9ckDVFmKIXZ7XllkGhHrv+LmulfnQZYVpO/pEP0z0w4ZcCkWF6r2VPtayGmfyTKHtGrwNnfxVt4/iqSyzhgTH584Fp70E9z2yD+lE8TZ5r/OmY+voep+xft9fr6FPgGcPNz9eZPpa3TXE5/spY/iY6bpWhXqW6zGe1UMmnrGlaAN2UtXqy2miLBecTjQ0NKTGx8db8llj2ye0LfD1mmDTG0/zOmBMs2IAR7osmcZ4udyEjm2fwIctM5To1AwF0kB/3Xm6qyyuJ5qux0G9r9FVwKV7l+71tq5gJ667y1rgpPe5qTtbTQSfXuN+LOg+9/Gj3po9/GPkoPU9gEYg6ImNr3daDxPTdpbRpRFwmx7X9j1mJed0TdZJncV0nMZ8u4H6Xj9cfGzsIW2Cure/9gTrtNZFicg2pdRQaR9Ac1pZH/FR5Hguswwo47196hihgpFZ1x+TUHnJYmXVBXTHT1NDU+0B4LtXAJP7zb0/43qNrbKl6RGqkr/I/LdKs322QD9d+7z1c71F0jz3GbwA16p3YWxmZWY91fX4y3NsHVPvw6Gp5hlK8tyrJNfXdA0BwjXK+OZ4CF1Oh+RyfdP1xLc9P6RW90b1qYswWFHQig3f07bA+xxMThf+FYPWG2gBgl/gbG7IMe6xSKUha3aVJN2+960E2fZXf72Gr77qSbzqpxuByf0A7AXiHEvBqpp+Sb2NLVixaBlw+cOZ6x7iAldmIMQkVMChaOCDOkdWsBHwOx/KOKdaHfiLMVjROmXVR4pWKosce2WWAbbzdnCgv3AlulWVcZfrj0nI89/0Pbu2ptv2l3Vf7hoFxj4AzPoPj+g2h7EAO//kk/jwT06yJl10vcn2PbZs8ZYiDY++15A8s7r5Bh/aVaa6ytqese0TuPzWHZnfb5n17VbyqYtwGEhBBwxDBXzGo5m6oaVPMFuX+OTwDaC5y6RtfY47pm4d8qAzsnm3V6Gf1b3T1LMkPRWoC922+o4Pt80QcePpT+BVO/5mXkEsc/9YWB4Xh+doRVNdJbl053KttH1s7KF5U/Mes7Cm7UpYZtf1rIRFyd5NEwcmsfabO+del1QkWaePqudK6AUuw4d8rtFl5JdgzgrKI8RQiSLHXpnDl0zvLTiSbC/v0JBWDmHMk5AyFvL819UF6n2CZw9Pzw2PMe2HrP1l7Vq/dQMDFZGFmMarfvb3eGDdw8bn+AzXMR1bA/31ebN9ANkdQw7myLUB2Icp6I7fvLO6+Q5jamWZmqeelzXsxTTTTVovDhW1zqxD2UwHzaL+OlZuvBcnrrsLKzfei7HtE8b3sM08kbT23JPRX69Z1yc5R7HLeg70N6ZnzHrftAOTU5g4MDkvSJLcxrHtE/O2X5cvI17X+EIWb2/6ZJ2cmvGas1q3rabtNy0fXjGIay8+FYMD/RA0Akc3vOl0bL/qHLzqZ3/f9oJYAY2prlZ/rikhpm7dk5Ho5P42fX/AkW7qyal5nz08g1pqTEq7xrXFUej0MKypWYWRzbubnl8kWafPOpn2bfqcsF0TqBiXa6VPge97/WjXe1L3s1XgXRU59nTnlqkM8L3m6d5bd8Plu71AmP3myuX6YxLy/NfVBV5w9IJ5QxIA/X4otL8O7im66l1l9sAe7fE/tn0Cp1/9Pa8bf9P5N3LRKU3fddaNb55jLa7j+LynT0NRkm/woVVlqmsdWmd4xSAeWHc2ntj4ejyw7ux5AQ6XumeV8ki0EntWFFQkch0z5Yaope7Q01E504Vo4sDkXAIW23rGF7jhFYMYf3K/dvy0q2S0UxdFNYmTXWYm/nTs92Y6kfMkjzG2HFSiIBbgiieMj9paPVyj1d/4wVPa18/OqiBdcouIjzHTYaHLzl0kWacr1znOy05M2uuS10pTHiCfAr+M5FNVT2hF1RSi9dDn2NO1ICZnwnLtwu1yzdO1PPrcyNm0stU1ff1xVcb5n64LmJKBu07nqqtfNlm0FDiorz/0or3qhdrexLZZ5wDzzGWAuXU++b3YhjMLkOtYs9XXfWc5zHqeby+uVpWpeRLXuvTEyKqjljnFcdUxWFGQ7sJx6PB007AK24FsyqqsW54seGwXIt2sHen1TE5Petu24q288YXGJfgQOz666Q3BdiIHnYmhAgWxLFqa+7WulTbTcamAto//8znGYi7TrxUt2Ez7Vhc8mZyawUdHd+LyW3dwuEgJktfKokNzypjJpYz3pO4XYhiG67FnCjhce/GpxjIgOaQzzWWYXfrm2lTP8Q0qt3r2nXg7bMm33/KaZcESPLte41z3g+3GKTPQvuoq5qyIHFYL8KnpRu/X5PG/acuj1kCFrS7iOrvF2nNPNibWV8jXUGKrr5tySeRtKPINPrSqTPUNfLoGbk0zJL6t5KTbnYDBigDyRq5jg4YT2TRvdMx286WrFJgucFk3fnEypqyLTXyhcQ0+xBcd39YHHZcEOq4X+Hl2jc5ltK6MvlpTngofrpUV1x4/ReW5kcw6xo47pt60TFeQhZwNZGz7ROaUbmnxc9nToly5zv0S3qMV70ndLVTrYXzsxdffyzXTZ/q2ILokMfZtnAi1ve3qyWT63JCJnH16sbjuB9/65Tzx0NSMupMxMTmgnQVE+/r4F8usILYiWVedsa7XvBdbZiFRjdlARqYuwebZM+eWx8d/1nkQ4vgYXjGIq+/YbZwEIA9T/XFwoN+4vnkbivIEH1pRpvoGPk3X0fSUxWzAMGOwogSt6roUH8CmyGmIMfoCYOSiUwAAHx3dabwRS66vLQGQKQO1aTYUF6VVODwzWjtNgeU4VVfiv/kWHgtccENTngofrsfbW16zTDs06C2vWZb7s9PyJj2zRerrNcH6C0/RPlZWQRZvh+786K/XcHS9LzOJrakCyISdRBQLWaHNuv76tiC69Hjz7ckQanvbdSOQ1as1xPrYcoKl3891PxSuXy5fk1lPEeSfXcZnpogzLbOi6GbDEGRPIZn+rPR3qethDRw5/m11GNuNv6/1F54SNEiXd0g1kK+hqIoBfd99YDtXvBLX9jAGK0rQyq5LcXcy3UWvTyR7bCGyZxkBYLwRAxqt2OsvPGXuc7LyY+i2AWjeftsUPrqpXYPf1PlktF60DGc+/1mnaZPaPRd01vGW3I/99T48Pz2LWXWk22rI7mh5xv4B9tlaksdiq5gq6TWRuXnHXWa1SRdqrcxgT0SdIW+FVndDZbv++ja8ZN3A5r1J0m1v3mz8ZQWr43VZ1F+HSGOmuOR6uQ6xidczOZwm7uWoG+o6tn3CGAg3fR+u+8FWvww1fCbv8ByfusPac0/W5od49vC0sY583yP7rNPoputM6e+y3ieo12ReMtPk8W9ap3pNnM4R1+M/dJAu7/t100247z7IGgYTYha6IjqhQYzBihK0uuuS6cZtRimnGxtbYGBwoN96I/bpNafljtynX5N+3FRI6j63lJs6n0SaB/dg7Wq3IFUVunqZjrf0fpycmm2aB3rlxntzr3f6opg3eVoV9mGSaX1nlZq3TvH6moaLpCtotoSdVStMiKgadJVPAE1lpEmcRNG34SWrtdjnGm2rQKdbvdsZxE2Xmcn8RLb1Ml3bPzq6E+NP7sdt2ybmHrcNF7TNzhEioFD28JmzXrGkqQdD8v1Nx4FPrx/TcIipGeU9DaagOWeX7rucmlXW3sTx/yObd88dM66NLb713dCBgm4KPOTlsw9chsG0a8ryTmkQY7CiJK08mePP0Q3TcE1oNf7kfmOBcbmhG2D6Riz9nkW332esZ94WeiufRJqLlnrdQIc8PkJGRbOmKytyUdNdFE3DfVwqWcl9OLZ9AiObd891WW11DwtTJX1R/5HcGen1dakA2hJ2uvSaIqLul27Zf/bw9FyLbnydPrre55WUOE6k6TLzR8ynzLaVW7YKNABt9/x2tU5mDX1Jz5SWNZvbjFLW4Qfp7bTd5Jz1iiWOW2EWqmHAFEC7bdvEvG0VAG8440g+FdNx4Nsj44Bn7xOf9ze9x8HJKe0wk1jeemAp9V0KxjaDkimY264pyzvlWGKwoo1C3mQOrxg0BhVckldeM3wqhl66WLs+phMseSNWBl3k+eh6n/a5psJi6Lf3ANe9t9wEmYmEl62OOIeOitpaK4pe1HSvj9N4FJlWUjcF2DOHprD2WzsBtCY67NvN1LUCaGupLKsw6YQuge3GfVQtInIygFsTi14O4CoAAwD+K4B90fK/VkrdHb3mSgDvBTAD4ENKqS3R8vMA/B2AGoB/UkptjJafCOAWAIsB/BjAO5RSh0vetEy2lv3Y5NSM9+xJ8bX9gXVnOx/brte1rHIrK2huupEP0Trpe267fObeqKeKy1BAIDtfV/IzbWXEbdsmMPTSxU29UJu2r/aANRnmcPSDowE8B+D26MfR8/UB3P/82zFx+H8BYA+gKTSGYAD2GymXHh/JbXXtzRjz6VHS6plmWjkNL/nJmkHJtaGqVTrlWGKwIocQFdU8N5lZn2u6YEr02rxjyvKM9wvp+enZud+fOTSl3U+6bb+o735sWvhFYNKvkuYlQMLLIkJHRW2FbtGLmul5ce6RvMnHTFOA2bp4hpanm6lLYMs29VgZhUmndAlsJ+6j6lFKPQrgdAAQkRqACQDfAfBuANcrpf42+XwReSWANwM4BcDxAL4vIv8pevhzAP4MwB4APxKRzUqpnwC4LnqvW0TkC2gEOj5f+sZlyDONc9JAf10b4ACODAfxOa5drmtZ5VbesqbozWGec9tlWsbjLcNp80hup8+sHbrtu++b/4Dz61/EUVJePemoqQP4pHwBh/tm52bGsAXQsmbM2HtgMjMwlt5WU/Jr3U1iXPeYnJqx5guJlTVUxlQHanVwhNxlXduqNoS5U44lBis8haqo2sYrXn7rDq9ukfFzTLknFIq1wua5EQvF9WZcV1hcUR/FQgQugKUGrK/ONKaho6K2Qtcn0ZaukLVNeZUeA+pzntm2tZXRYd9upi5M5x5QTmHSKV0CdVrV26GT91GPWAXgZ0qpJ8U8zfJqALcopZ4H8ISIPAbg1dFjjymlHgcAEbkFwGoR+SmAswG8NXrOVwCMoALBCtfry0B/Hc9Pz2oTX9u6J5cRiMsqt2wVaNMsCwIUvjm01csA/T5wGY9uWuc80jfB8Tq5BLV127d2wWipgYrYQpnGXy0YxebDZ2Y+N2vGjPhxW2DMlmttVinnXj8zSs3tc9uw5/gzTeWPb/lkqwPpjjmJnrNy471By75WlKvd1FPRpU5ehZwfyQS+RXs3t4K+Tz0ZZXVPdGU6oGeUgsKRC9PY9gnnzx1eMVha90jTjdjEgUmcfvX38LJ1d+Fl6+7Cig3fm1vnEFxvxodXDOLai0/F4EA/BI0b4OPlN8HWY44qv1CPxcksT1x3F1ZuvFe7X003rHlvZHX7MR5vvPbck9Ffr817vu6iFheyE9G43PhYPusVS5xeD/idZ7ZtbWV0OPR3EVt/4SnO+62oTukSmGY65kJei2Kduo96yJsBfCPx9wdFZJeI3Cwix0XLBgEkExLtiZaZlr8QwAGl1HRqeRMRuVRExkVkfN++fbqnBOV6fRm56BSva3ssT/1GJ1me9RmCSPG26NZHALzshf34/XPTmlcCb3vtCYVvAGz1MtP1JF1mDvTXcUxquOozh6b0U5FHz89Si/ZX8jtLr8OgQ/mj277j5deZnx+KS50sPWNG3rLPlvT6iY2vNw5vylvHH14xiAfWna197zzlU1Zy7fiYA+YPpQ1Z9oUsV0312VaW3a1QVj0wpOQ+B44MxwbM15h2Y88KT6Eqqi5dB126Raa7aQ6W1KXHtr7JLqShcwX4dFFqilZe75Eg05XoK3RZQkbVs3qUFL2RNUV9XbuvmQrZ+x7Z55yszec8KzoFWChldQV16e4aqlWiU7oEprWyt0On7qNeICILAVwE4Mpo0ecBfByN+tjHAXwawHsA7b2jgr4BJ1mXSy9vXqjUjQBuBIChoaGs9AOFZU3zDTRuiOPzwHZtL2vImW+X/OEVzUm/FYD/+bP92u0c6K8HmVLbVs+xXU/SZebKjffiUOp9TPmZRi46xdh7Ttfr0MSl/NFt3171IixtUcBir3qh9fH0UItk2RdP4ZoMHNiu7Xmv02UEo/OUTy7JtYdXDGLlxnubtjNU2ee63ll1EFt9ttt6KpY9e44v3Xdjyh/nc71pNfas8BQqamZryUhKdos0SUYhi0SiY7oIqKmlQyceHhJCoe1ZdRXQFzgJ6BnvAuDW6yEWMqqe3q+2nhBlsLUexLLGmWa9HvA7z4ZXDGLTX5w2r4XquGPq2PTG5ml1y1Tmd2Hab6FbJUJcP9qhlb0dOnUf9YjzAfxYKfVLAFBK/VIpNaOUmgXwJRwZ6rEHwLLE65YC2GtZ/msAAyKyILW87Ww9KoEjN8SAvdxybZ3Pw9Yl33StvO+RfdohrToHDTk3fGXVy0LlZ0pvc4jecy7lj277PjW9Bs+rfI0wPmakjn9a+Hbj4/GNkq7XSLzeySlcL791Bz429pDurQDkv06X0TKep3yyfV6yHmhryHSpn9q4rLdLHcRWn+22noqtrpPbmL4bU0C2yvucPSs8hYqapVtLszIVuyZR8k3eko66nfWKJfPm905msk23itt6hoQ66Aslo4mTXlqyXDuTPuCMd+Nj0+/G19bdNa/SlJW3JE902lQp0+3zKox/SwrR8ux7nlVlH7R6PUK3SlQt+ZOrVvZ26NR91CPegsQQEBF5iVLq6ejPPwfwcPT7ZgBfF5HPoJFg8yQAP0QjBn9SNPPHBBpDSt6qlFIich+AN6IxI8g74TUfQrlMPSprInMVZde8V3nqN1nTkJrqCnGXfB2fOkSo8zxeZ9008D6f45OfKfm5Ra8pWeVPuqeCAI2El1PA+gVfxWL5PSDmhiggChjpKiiS6jmS6I8k/YtRO/86jCxfg9NzzIZgagn+2oO/aJrtRLetPvu0jJbxPOWTa3JtW108eYMK+Pd2dllvlzqILSDRjT0Vq1IfNX03Nc+ZcapAlGaFO9nQ0JAaHx8v9TPKSAZjms4mGZEb2z5hvHgJYCz0fT4z3U0xpitkdd3PYllJjNotz3f4sbGH8M8P/sL4uKkicmIquBFLfmeu05oJgOvfdHrl9meSblvqfYIXHL0ABw5NOQfQ4m6fWZm4e5nLsdULXK6f1CAi25RSQ+1ej9BE5Bg08k28XCl1MFr2f6ExS4gC8HMA74uDFyLyN2gMCZkG8GGl1Hej5a8DcAMaU5ferJT6RLT85Tgydel2AG+PEnQataI+Argd/6byOl1uFR2yGH/2G84YxJ07nzbONKL77CTT+qbrKOmy5axXLMF9j+ybt/6A302rz/VEt78AtP165PI95q3Lml7nut98P9dUzgHldF0PXcfPWz6t2PC9zOFBrnXHPPvJZb1d6iC2a48pOMSyuzjbedNfr7V9n/vURdizIocyomYuUeDhFYNeMzJkMUWrdXxyBQCY112vatP65Z3R5Rs/sOe/MEWP80andYrO7NIK6WN5UX8dzyayodv2d55M3L2sG1sl8mBvB1JKHUIjEWZy2Tssz/8EgE9olt8N4G7N8sdxZBhJpbgc/7bWzSI3tqbWu2S+CZ2s1mrTTcwbzhicC0boypZkg8LEgUms/eZOQBrDU+NlWeW96/XEVJfQ9URt5fXItY6Tpy5rO1Zce/r5fq6t90AZXddD1/Hzlk/rLzwls5dH+r1DJtl3WW+XOoitt0refdNNM4j48NluWw+vOHdFp+w/9qzoMCFbEG1RtzRTVHZs+wRGNu+eaz3pE0ATu3CL6t75EWD8ZphDJgX0LwbOvw5Yvsa5hSntZevusn6EbR/ljU7rdFqruc/+zvvd9Cr2KCBf3dqzooqqVB8xXVtNU5qme3WarjNZyT1NbnDoIZhVMbf17swSokyx7dMd688p9N5FlFWOZpU3WT0g8t4QjW2fMB5n3V438L0pb3UdqqzeNCE+s9v4bnfV9xN7VnSxkC2Ipqibz5y76Qj0iYYb+nRUN33h+uqLb8V/ePIW721wNrkfuP2yaF2OdVrHNNM4L8A+80SR6LROp7Wa+yRQ6rZkS2VjjwIiArJvBkytmyLIbA23tZj7lF2xwYF+p2tUVgt3kXLB57WmfWubseFjYw81ZijZNRomb5aH+xWAozQPTAIYsbxw4bHABTccyfeVktVzwnYsFOllO7yieXYYIF8uiaI3za1u0fft5dHq2Shc6yAhe6vkzdXV6b0xfLe7m+qHDFZ0oKInfTIngC4wkexm6Xtwu3QJ03VRfOnkqD2rUwgzh4GtG3D8wGe9u86PbZ/AwgWCySl9sOLYhQsyE1vZHtcVMPU+mdd9FejMWQd8hipwWIO/qiRzIqL2cOn2b6q4Xu6QxM/WBf/6N53unPsKsJdh6Z6axx1Tx/oLT5nXwyO5/gPH1LVj+l24lim2fWu7Of/ag7/A6r7/iVft+BtgNt865iV561KHnwXG3t/4XROwyGpMsCWCB4olf75m+FQMvXRx4UBDniHAoV7fClk3qGXcsLe6DpJnSFAnfHdZ8jTmdUv9kMGKHpM+YZNzf4dIYugS1dVFB2uYzf2ZPtTBPVi72i/yfGSfmdex6NRppgJGt6zTLjw+kf4yWgU6PZpORGRTJFdAVh6sse0TxuDD8YkeErZZxWLp4EPS2PaJphxYzxyawtpv7Zz7O32zUe9zuyuv1yR30N+2b20zNigAy368CUBrAxWFzc4AWzdogxVZjQnJY6GMHBNFb7yKzp4Vevatspj2k8sNe9XrS1nXI5NQ3107908vN+YxWNFjTEk1Q41ny5voaxaCWhm5KlL2qkb+NZ/kVy7JL0NcLEwFTJUKijx8uqKF7rbWDdF0IiKbIsPnbAHise0T+OjoTmO2//iGX1d2+baCb9ryqDZZ99SMwqYtjwJoHq4yNasgAthSrw301zFy0Sm5yxTbvh1eMYir79ht7N3xB2pf+T1Gy3Bwj3axS2NCfCyYcieUfWNlu5ksOsy004epZt2wd0J9adOWRzOvRzohvrt2759WD/GpEgYrekDy4h0yU7CJLfo9tn0CfSK4qnYT3l77PvqCfWq2w2oBrptag21bHsUD684uXFmJ9crFogifFpEqjG0kIuoURVrcbL36rvz2Q8Y8TQr2CrrvddxWztoeU6p5Gr5Yf72GkYtOKVSmZO3b9ReeYkz++CtZgj/Evlyf21aLlmoX246VlRvvnbesHTdWWTeTrsOUTcGOTm/ZzrphN9WXRjbvrkx9ybQNWdejEN9du+uT3ZSDwheDFRUVqquR6xzMrbjYxuvy3xdcg//ctztzXGVTHUn8GimSr38GL8DI1CXYPHsmcGASJ667y3m/2salhhg60+mq3G2w01tCiIiy6G4MBcBZr1ji9HrdzfzKjfda6w2DOesMpvLCVs7G9ZOsafgmDkzOJcIOVTZn3XTbkj8+9Sdr8YdtyFlRSF8NWHWV8eH0sRJq+tYQ9QjTzeTloztw+a07sKi/3jQkKHmeZAU7Or1lO+uG3ZYwdmz7RCXqdbbpOG1CfHdVqE92Sw4KXwxWVFDIrkYuQxhadbHdtOVR/NnM/43/XM8OVACAguDlz38NQL7pds60TGum4L5fTRe5qkz/E0qeykK7u8Vl6fSWkF5Q5WAXUSfQ3TArALdtm8DQSxfnOp9sFXBB41q/cuO9XuerqbwYf3I/nn1+Wvua5ExbppuNMivwLq2ZpuSPr1pxHvCy41o+G0huGbOB6Nham117sIaqRxhb3aOT4sDkVFNv3uR5ktVy3ukt21k37LaAYVV6o+YNOoT47lifbB8GKyooZFejrApHKy+2ew9M4taFo86Zqn8lLyq0jme9YklTa0fa5NQMPjraSOCVfv/kTdSi/jqOrvfhwKGpjiugXLhWFtI3locOT1d6mEWnt4R0u6oHu4g6xX2P7Gsq64pci203LvHn+J6vprqNqZzWJeT0meXA9nwfLsEQ43OWr/G6+S9TGYHhEK3Noeq8LtPo6tKkx5/lsi2d3LKddcNuSxhbld6oRYIORb871ifbJ3ewQkROBnBrYtHLAVwFYADAfwXmBur9tVLq7ug1VwJ4L4AZAB9SSm2Jlp8H4O8A1AD8k1JqY7T8RAC3AFgM4McA3qGUOpx3nTtFyK5Gti5TuRJq3vkRYPxmpHPxquQvUTAiHZN4/Oiml1kI/vDiT+KJ5a/3X0c0CuXbtk04fdyMUtqMyMmL0oHJKfTXa7j+TaeNVw1GAAAgAElEQVR3bEFl41JZ0N1YmnRDwRYCew3YtXsMKFG3CN1F2TS0pEhAxDbePE1XR/GZ5SB909XrgdCyAsMhWptDHbu2m22XdeiFlnPbDbstYWyV9kG7Akbtrk/2stzBCqXUowBOBwARqQGYAPAdAO8GcL1S6m+TzxeRVwJ4M4BTABwP4Psi8p+ihz8H4M8A7AHwIxHZrJT6CYDrove6RUS+gEag4/N517lThLxgBo0E3vkRYPwm7UPS9IvhOa5JJ4beU6g1wmX4S1I6I/JHR3c2JRaLe2FcfuuOrrlIxTfTLtOM+exTFmzsNeCiCmNAibpB6BstXcW86HSULi3f6fd0Cfi6lk26AHzeG4+x7RMY2bwbB6Jpy23TslZBWYHhEHXMosdu8nvMmh3Gtg5sOW8kjO31fWDTyT1rOlmoYSCrAPxMKfWkmPv4rwZwi1LqeQBPiMhjAF4dPfaYUupxABCRWwCsFpGfAjgbwFuj53wFwAh6IFgR8oIZNBK47cv+r/HVvxg4/7rC3Sbz3OzsPTA5d4NpyoAeL++GG0+X5KvJyoLrPmXB1sBeA9l6oSWLqBXKuNFKV8x9pqPUBQJce2vE7+ka8PUp75NBkLzB5LHtE1j7zZ3zplp95tAU1n5LP6S0CsoKDIeoYxY5dpvqMRmBinpfI1N7MslmMvdJ1rZ0e29J9h5oKON77vZjp0yhghVvBvCNxN8fFJFLAIwD+KhS6hkAgwAeTDxnT7QMAJ5KLX8NgBcCOKCUmtY8fx4RuRTApQBwwgknFNsSR2UedKEvFlmRQOdtUe49FXwoADJyMOh7mm6CBvrr+N1z09pgxPED/V69Bzr9xjNrW9OVBds+PfaoBbwAp7DXQDa2ZBGF0YqbDNfz1WeGiLNesQS3bZvQvqdrwNenx0YcWCkSTN605VFMzSpc1Hc/1i/4KhbL7488eHv0k5YjeWVIeQPDLvXDoq3NRY5dUz2mJoJZpbCovw4RzMs3Zvss27b0Sm/JXu89UMb33CvHTlkKBytEZCGAiwBcGS36PICPo3EP+nEAnwbwHugHACigKTlvvNz0/OaFSt0I4EYAGBoaytEBzE8rDjqfi4VLYWJ6jte2SK2UgMVe9SL8KPC0SKZK1chFpwAwZxW/3HO8YyffeNrWXTftm22f8mLbjL0GsrEVhyicsm8yXM9X3xkidDNpDK8YNJbH6bJLVzbpJAMrRYLJew9M4qK++7Gp/kUcJY51osPPAmPvb/zegoBFus5nCwrZ3mPtt3bO9UKYODBZWu+RvMeu6fuaVQpPbDTnPMvzWewt2RvK+J557BQTomfF+QB+rJT6JQDE/wOAiHwJwJ3Rn3sALEu8bimAvdHvuuW/BjAgIgui3hXJ57eV6aAb2bw7aIDB9X2ygg2251hPoNoDpU+5dVgtwHVTa7At8AnrUqnSPWbL36DTyTeevslXeWPph70G3PR6Kw5Rp3Ctt/gGAkzXANeAb7JsmjgwqR1aks4pUSSYfPxAP/7q0Kh7oCI2OwNs3VB6sEJX57tt2wTecMYg7ntkn1P5PbZ9ApeP7mjK/zA1o3D1He513bK1slGAvSV7QxnfM4+dYkIEK96CxBAQEXmJUurp6M8/B/Bw9PtmAF8Xkc+gkWDzJAA/RKMHxUnRzB8TaAwpeatSSonIfQDeiMaMIO+EvnNdy5kOrgOTUxhz6CEQsmeGS7TO9hzTtgz99h5g7EvAbHNWYB2X2UCA+YmPnsELMDJ1CTbPngkp4YTNynqse8x0g/mGMwa9WyWqLs/NNG8s3TG4Q0TdwqfeEuoG0qeMSpZNLkGVIsHkteeejOPHfu21LXMO7rE+HKIhy1Tnu++RfU6zwMXftSlRpW62iHZpZaMAe0tWW6hG4DK+Zx47xRQKVojIMWjM4vG+xOJPicjpaNy2/jx+TCm1W0RGAfwEwDSAy5RqjCkQkQ8C2ILG1KU3K6V2R+91BYBbROQaANsB6KeiaDHb+EjX8Y6hugNlRevGtk9YM3ibtuXKhd90C1QsWgZc/rDzJB9neiToagfbDaapq6pJ1ZPp8Ga6fAzuEFE38Km3hLqBzFtGuVx3i5R/wysGceh7L8Exk09nPrfJoqXz/kzWExb11/Hs4el5wy7yNGQVbcX1nU2tnVpZj2FvyeoK2QhcxvfMY6eYQsEKpdQhNBJhJpe9w/L8TwD4hGb53QDu1ix/HEdmDKkM21zOruMdfZbbbnpt0bqPjT2Erz34C+N62KZqejEcWw0yWgnS26DrolmVEza9n69/0+nzLnK+eUQ6IZkOb6aJiCiLqX4ycWASKzfe25SkEAhzA1lmGVXkvY85fwMw9gHn3qcAgL4asOqquT/T9YR4GtSkPA1ZRVtxs+qxA/11ANVvkAmNDTzVFbIRuIzvmcdOMaFmA+kpwysGcfUdu7Vd4VzHO/pM/2W76TUFG856xRJ87cFfzAsKNGWufg6Q24HVNTT6tGQM4dBKtRLopLch/hgFfSLHdggdXGAyHSIi6ha2HqW68rLrA+Fx3gnXvF6a2UBcezD4jmsv2opr+67rfYKRi06pTINMq9ej64/rDhU6J0QZ3zOPnfwYrPAUR5KfOTSVu4eAT0GSddNritZt2vLovHW7esHNeEft++jTRCGk6RdHtYXzWglMdNsQByps4yeLRO19Xxs6uMBkOkRE1C2yZtzohWB8c71iJYaveCL3+7nWB9INWVn1m6KtuKbvOpmkdOXGe7V1po+O7sTlt+5oWcsxG4YIYE6IbsdghYdQPQR8ChKXm15dtC455ddFffcbAxW59S8Gzr/OKat1nhv3rGi5rbDOE2kPHVzghZOIiKrMJ6ifnnFDpxuD8ckhrEkhWvBtPRhi6YYs1/pNnlbcdP6Mo+t9OHBoSntsmL7rGVUs34YvNgwRwJwQ3Y7BCg95ewgkZeVFSD8+cEw913CTZCH4VwtGAwYqBBg54PWKPDfutulhRzbvnje2M10o5om0hw4u8MJJ1NBr45qJOkGeoH58A7wyZ6Js3bUACDOOu4zrTHofpRVtwdfVE+p9ghccvcAYJCirJ4Euf0Z/vdZUR425BFpa0cOhFxqGeqkMzbutzAnR3Ris8FA0gqurHFx+6w6MP7kf1wyfqn283ieo12QuOzTgdtObLAQHJecUWzoOOSps6xLL2gbb9LA6yUIxz/ekW0cBcNYrlhhfY8MLJ1HnJJol6lamyn+Rm948ZbruWrD2mzsBQb7ZL3aNzuWLUABWK2A1ABwFYBLAGKBu9x/dmrQawOq+6D1NJgF8sjkfhYs89YSyehL4Hg9Zw4JCrVeWbm8Y6qUytOi2MidE92KwwkPRCK6pZ8bXHvzF3LSY6cenZhUG+us49qgFflN31R7A+Uf/H1hYOzg/sUYRjjkqmtYlR4HsErVPiwvFPN/T8IpBjD+5f15SUgXgtm0TGHrp4splMSfqBBxPTNQ+tsp/kZvePGW6qX6T5nJ9+NHmL+K0H1+JhWi8n8z9E5bz+x5+Fhh7f+P3HAGLVs70YeJyPKQDX284YxD3PbIPew9Mok9kbghIyPXK0u0NQ71UhnbitvZSr5d2YrDCg2sE13TwmgoDhSMXWp2Dk1PYsf4c9xXdNQqMfQBHxVNqhSjEPXJUmLbf5wR2jdonxYVi3kj7fY/sa4rrVP1CSVRlHE9M1D62yr/vrGRFy3Sfcz4rn9Wrtn0KC8W9btASszPA1g3ewQpfZfUkyDoedIGv27ZN4NqLT53LIxZivfLc/HVzw1AvlaGdtq291Oul3Ris8OASwbUdvLbeAvH7BYmYb93gNvf3yEG/93UQ6uTV7etDh6e1+TuA+YVi3kh7p10oiaquF8YTE1WVrUy7/k2nOze+hCjTfXpLZuWz+n8RcGhrSAf3lP4RZfUkyAqC5J2Zzme9ePPXrJfK0E7b1k7sCdKpGKzwlBXBtR28a889GZffukM7KiO+sAeJmLegwDQJefKm97Up2VVyOi3Ta1102oWSelsndD/s9vHERFVmK9Ncby5DlemmZJLJnBWAWz6rvQtfhKUhc3GFkiOnVx6hexLEZcnk1Axq0XCO9Ax3eWem88Gbv2a9VIZ22raygbN1GKwIzHbw6vIiAEdOxmAR80VLgYNP2Z/Tv9jvPR2VefKWPTax0y6U1Ls6pQWq28cTE1VZVpnmcnMZqkw3XQt0y7LyWX3qt2uwqf5FHFWhoSDTqg8LcuT0ard0WTKjVFOdFGhNYw5v/pr1UhnaadvKBs7WYbDCg0tLZtbBe83wqXPJNHXv4xWZ3jUK3PFhYOpZvw2RWiP/RAlM298nghPX3VX44lPm2MROu1BS7+qkFqhuHk9MVGUhyrSQFXLTtcC/h8ZhrJ0C1i/4KhbL7xsPyLz/nKi5f1IaHT7Mj6c8i6Pxt/X3Y6TkfBVlMJUlHx3dCeDId9OKxhze/On1UhnaSdvKBs7WYbDCkWtLpsvBG+Rk3DUKfPt9AGb9XueRKDMPU2LMOEt0VVuAY510oaTexRYoInJRtEyz1WnaMRQtfv+RzX04Y/LMeY/112tzCR9dCOyNULdrhp6ahq5c+/pTC25Ze5jKjBml5tXVWtGY41J/7oThj9Qb2MDZOgxWOHJtyWzZwbt1A7wCFVID1u8Puw4a6e3XTWdV1RbgVmBBSyGwBYqIWsE2fMN3KFqo8m94xSA2bXkUBybnJ9zOU7ewBXNCDV2pMlvi0/T+LLsxJ6v+3CnDH6l3sIGzNRiscOTTktmSg9c3iaZq3djO5PafuO4u7XN6sQW4Fwra0MGYbg3uFN0udj8kolbR1WlWbrzXayha6PKvVb3LQgxdKUuRciR+7cSBySNDXjRaXVez1Z87afgjEYXDYIWjIi2ZwW647vwIMH4znAZRpknN+yV51jv9moFj6trpRnuxBbjbC9rQldFuDe6E2C52PySiMriW+77BgtDln2+drNsC30XKkfRrbTXKKtXVOPyRqDcxWOEob0tmsBuuOz8CjN/kv+KxM97l9fQ86617Tb1PUK+J19Rk3arbC9rQldFuDe6E2i52PySikHzKfVOwYFF/XfveQcq/XaPAd68AJvfjfgA4CpiF4J9nVmH99HuMdYtuDHwXKUd0rwXQ1MOianU1Dn8k6k197V6BTjG8YhDXXnwqBgf6IQAGB/qdEjnZChQv277s9/yY9AFD7wUu+IzXy/Kst+41U7MKxy5c4L3fupGpQO2WgjZ0MKZbgzvdul1E1Nl8yv21557cSDaZ8uzhaYxtn2haXrj82zUKjH0AmGzk3hIAIkBNFC6pfR+f7v+qsW4RrB5WIUXKEdNzFFDputrac09Gf31+L+GqBVR63dj2CazceC9OXHcXVm68V3stIPLFnhUe8rRkBrsxcco5IcDIAb/3Nciz3qbHDk5OYcf6c4KsVyfr9jwDoVs9qtiKEqIrcRW3i4i6j+/1yjc319V37G4a5jk1o7St+4XLv+9eAcw2DykFGkGLN+AewLBt3RggLlKOmF47ONCPB9adHWT9ysDhj9XWjT2YqBrYs6JkwVrTXXJOLFrq954Weda723sOFJW3d06nCN3qUbVWlLggnjgwCYUjBbFvy0HVtouIuk+e65VvGX5Ak48KMAc3cpd/u0bnelQYWRp0urFuUqQc6eQyaHjFIB5Ydzae2Ph6PLDu7K6pP3WDbuzBRNXAnhUly9WakBiX6WpG6qituqrIqs6TZ727vedACN2cZyB0q0fVWlFC5pqI368K20VE3SfP9cq3DPdt3c9d/m3dkP0cS4NOu+omZSb1LFKOsAxqnW5L7GrTjT2YqBoYrCiZd6EQj8s0dHeMqUQWpGfwAnxy9l04c2Ylhtu13jlfQ90ldDCmSsGdkAVxlbaLiLpPnuuVbxnesiCAy1TtliTi7aibtKJLfJFyhGVQ+XptWASHuFJZGKxoAa9CYeuGzEAF0BijuWf2RTjz8Gfnlv1r4FkS8hRmLACpW7EgJqJOkfd65VOGtywIsGgpcPAp/WPSB5zx7swk4q2um7RyNqsqtN5XYR2qpltnNDNh72oqC4MVVePSghA5Xn4z7292tSIqDwtiIuoUrbpetSQIsOoq4I4PAVOJOk69H7jws8DyNeV+dk6t6hJfhdb7KqxDFfXasAj2rqayMFhRNbYWhJS96oXz/u61Fl5G8qmVWBATUafoqutVHJDYuqHRoLNoaSOAUXKgokgdo1U98arQel+FdaiiXuyNyd7VVAYGK6pk1yhwyC2p5mG1AJ+aPlJQ91oLLyP51A4siImoU+iuVx0b5F++pqW9KIrWMVrVs6UKrfdVWIcqYm9MojAYrKgKx8SaAID+xdj5R+uw7ScnQTqtwhEII/lERBQTkZ8D+B2AGQDTSqkhEVkM4FYALwPwcwBrlFLPiIgA+DsArwNwCMC7lFI/jt7nnQA+Fr3tNUqpr0TLzwDwZQD9AO4G8JdKJVNdt1aeoAOD/Pb9lnysTwQzqa/Xp44RqmdL1vdchdb7KqxDFXVV76Ye1LGB3S7EYEVV2BJrLloGXP7wvEWvAvDAReWvVlUxkk9ERClnKaV+nfh7HYCtSqmNIrIu+vsKAOcDOCn6eQ2AzwN4TRTcWA9gCIACsE1ENiulnomecymAB9EIVpwH4Lut2az58gYdCgf5c0yrHtzCY4ELbsjVy8K23wDMeywdqIj51DGK9sRz+Z6r0HpfhXWoKvbG7EwM7FZLX7tXgCK2xJoeSTd7hSli3+uRfCIimrMawFei378CzM3uvRrAV1XDgwAGROQlAM4FcI9San8UoLgHwHnRY/9eKfWvUW+Krybeq+VsQQebQkH+uPdnOwMVAHD4WWDs/Y318WTbb7rHdELUMca2T2Dlxntx4rq7sHLjvRjbPuG9vrHhFYO49uJTMTjQDwEwONCPay8+taU3VFVYB6KQ8l5jqRzsWdEud34EGL8ZjcabDIuWlr46nYaRfCIiSlAAviciCsAXlVI3AnixUuppAFBKPS0ifxA9dxBAMpP1nmiZbfkezfK2yBt0KNRd33Fa9ZaYnWmsj2fviqI9MkPUMXxabF3Xtwqt91VYB6JQ2Hu7Wtizoh3u/AgwfhOcAhW1hY2s1zQPI/lERJSwUin1J2gM8bhMRP5Xy3NFs0zlWN78xiKXisi4iIzv27cva51zyduzcO25J6O/Xpu3TNC4Yba18AOoXg/PHOtj22+mx2oiQesYPi227EFK1B4896qFwYp22PZl56f+6LRrsPLuF2V2F+xFwysG8cC6s/HExtfjgXVnM1BBRNSjlFJ7o/9/BeA7AF4N4JfREA5E//8qevoeAMsSL18KYG/G8qWa5br1uFEpNaSUGlqyZEnRzdLSBR0A4NDhaWsdIRnkBxqBijjiErfwG19ftR6eOdZHt9/66zWc9YolePb56abn99dr+PSa04LWMXxabE3ryx6k1eI6rIc6B8+9amGwoh1U9rhIoFGJuORHL8XEgUkoOFQmiIiIeoyIHCsi/y7+HcA5AB4GsBnAO6OnvRPA7dHvmwFcIg2vBXAwGi6yBcA5InKciBwXvc+W6LHfichro5lELkm8V8vFQYeB/vq85c8cmsqsI8RB/sGB/qauIXELv/bma9VVQF9d+54t11fL1eNU1yPzDWcM4rZtEzgwOX+Iy3HH1EvprenTYssepNUXD+thPb278NyrFmnjzFulGBoaUuPj4+1eDburFzsFLKbRh//43D83LR8c6McD684uY82IiKiLicg2pdRQu9cjJBF5ORq9KYBGLq6vK6U+ISIvBDAK4AQAvwDwF0qp/VHA4R/QmNHjEIB3K6XGo/d6D4C/jt7rE0qp/x4tH8KRqUu/C+C/ZU1dWnZ9ZOXGe7U5KFzqCCeuu8s4ELW/XmvKB3XtxadiuPaAdjaQrGqk6AbR5FVgNhCdIvswj3TOCiCxf3kj1HFWbPgenjnUnMuF9fTOwOlJ28enLsIEm622axSQmlOw4uvT+gsdE7y0Di9kRETVppR6HMBpmuW/AbBKs1wBuMzwXjcDuFmzfBzAHxde2YCKJIEzJdusiZinN123RhskONNwww9U/6at1Yn04voD6xWdb2z7hDZQAbCe3gk4PWnnYLCileKpv7IyaksfcMa78cWHLwDyZu6mwnghIyKiqioyu4dpRi3T9J22m6+1556Mtd/cianZ+V0s6jWp/BjvQjOk5MSZM8rXioYm2zSWrKdXny3ZLc/PamHOilayTf21aBkwcrDxs/4Z4ILPMMFLm3GeZSIiqqoidQTTmOzBHFnwh1cMYtNfnDYvh8Zxx9Sx6Y2nVb7Sz3pW92lVHomsAB5VG6cn7RzsWdFKtqm2NI+xu2B78UJGRERVVbSOYGrhT/a4uKjvfnyyfhOOfe55YCR6giZvRKf2FmA9q/u0qsXc1CtnoL/O46cDtKNXFeVTOFghIj8H8DsAMwCmlVJDIrIYwK0AXgbg5wDWKKWeiZJa/R2A16GR1OpdSqkfR+/zTgAfi972GqXUV6LlZ+BIUqu7AfxlVlKryuo/rikx1RzDNFydWgHoBryQERFRlYWuIyRv3od+ew8+vfDzWJBOxXn4WWDs/Y3fAyW6bCfWszqXbrhHqxqaTEOpRi46JejnUDlM3x97xVRPqGEgZymlTk9k9VwHYKtS6iQAW6O/AeB8ACdFP5cC+DwARMGN9QBeg8bc6OujacMQPefSxOvOC7TO5ds1Clx3IjCyqPFjClTUFuaahovKxe6hRETUa+LpTf9uyR3NgYrY7ExjaCtRm5iGewwco59iN3RDE6e37Gz8/jpHWcNAVgP40+j3rwD4FwBXRMu/GvWMeFBEBkTkJdFz71FK7QcAEbkHwHki8i8A/r1S6l+j5V8FMIzGtGHV5pNMc/XnuqJ1otuweygREfUs29BVl8eJSmQa7nHUgj7t9LtlNDSxV05n4/fXGUIEKxSA74mIAvBFpdSNAF6slHoaAJRST4vIH0TPHQTwVOK1e6JltuV7NMurz5ZMM0kpBioqjBcyIiLqSYuWAgefsj9O1CamYR0HJ6dw/ZtOZ0MTUZcIEaxYqZTaGwUk7hGRRyzPFc0ylWP5/DcVuRSNoSI44YQTste4FVxbHFjYExERUdWsugr49vsAzDY/1lfj8FVqK1teMTY0EXWPwjkrlFJ7o/9/BeA7aOSc+GU0vAPR/7+Knr4HwLLEy5cC2JuxfKlmeXodblRKDSmlhpYsWVJ0k8JwCkIIC3siIiKqnuVrgIu/CNSPnb984bHA8BfYK5TainnFiHpDoZ4VInIsgD6l1O+i388BsAHAZgDvBLAx+v/26CWbAXxQRG5BI5nmwWiYyBYAn0wk1TwHwJVKqf0i8jsReS2AHwC4BMDfF1nnlll1VXbOiqH3sLAnIiKialq+hvUUqiTmFSPqDUWHgbwYwHcaM5JiAYCvK6X+h4j8CMCoiLwXwC8A/EX0/LvRmLb0MTSmLn03AERBiY8D+FH0vA1xsk0A/zuOTF36XXRCck3gSOH+3SuaZwHRzFFORERERERuONyDqPsVClYopR4HcJpm+W8ArNIsVwAuM7zXzQBu1iwfB/DHRdazbdgiQUREREREROStcM4KIiIiIiIiIqKQQswGQkm7RpuHfvQvBs6/jr0siIiIqD109RNXrMcQEVEbMFgR0q5RfVLNyf3A7dHoFxb0RERE1Eqm+okr1mOIiKgNOAwkpK0bzBWBmcONx4mIiIhayVY/ccV6DBERtRh7VoR0cE+xx4mIiIhCC1X/KKkeM7Z9glNQEhFRE/asCGnR0mKPExEREYUWqv5RQj1mbPsErvz2Q5g4MAkFYOLAJK789kMY2z4R/LOIiKizMFgR0qqrgL66/rHawsbjRERERK1kq5+4Kqkes2nLo5icmpm3bHJqBpu2PBr8s4iIqLMwWBHS8jXA8D82smYn9S8GVn+OSamIiIio9Uz1E1cl1mP2Hpj0Wk5ERL2DOStCW76GQQkiIiKqlorWT44f6MeEJjBx/EB/G9aGiIiqhD0riIiIiKgt1p57MvrrtXnL+us1rD335DatERERVQV7VoS2a7QxtdfBPY1EVKuuqmRLBhEREVG7xbN+cDYQIiJKY7AipF2jwB0fAqai7owHn2r8DTBgQURERKQxvGKQwQkiImrCYSAhbd1wJFARm5psLCciIiIiIiIiJwxWhHRwj99yIiIiIiIiImrCYEVIi5b6LSciIiIiIiKiJgxWhHTSOQBk/rJ6fyPJJhERERERERE5YbAilF2jwM6vA1CJhQKc9lYm1yQiIiIiIiLywGBFKLrkmlDAv32vLatDRERERERE1KkYrAjl4FOG5UyuSUREREREROSDwYoQdo2iKVdFjMk1iYiIiIiIiLwsaPcKdLw7PwKM32R4UJhck4iIiIiIiMgTe1YUYQ1UAIBick0iIiIiIiIiT+xZUcS2L9sfX7SsJatBRERE1GTXKPDdK4DJ/fOX9y8Gzr+ODSpERFRpDFYUoWbsj3MICBEREbXDrlFg7APA7FTzY5P7gdsva/zOgAUREVUUh4HktWvU/nj/YlYAiIiIqD22btAHKmIzhxvPISIiqigGK/KIWytM+mqN7pVERERE7eAydTqnVyciogpjsCIPa2uFAMNfYK8KIiIiah+XqdM5vToREVUYgxV5ZLVEMFBBRERE7bTqKqCvbn68tpC5tYiIqNIYrMjD1hLBVgoiIiJqt+VrgOF/bOTQSutfDKz+HBtXiIio0jgbSB4nnQOM39S8nK0UREREVBXL1zAgQUREHYs9K3ztGgV2fr15+cJj2UpBREREREREFACDFb62bgCmJpuXc6pSIiIiIiIioiAYrPBlSq7J6b+IiIhaTkSWich9IvJTEdktIn8ZLR8RkQkR2RH9vC7xmitF5DEReVREzk0sPy9a9piIrEssP1FEfiAi/yYit4rIwtZupaddo8B1JwIjixo/153YWEZERNRBGKzwZUqgyXoviroAACAASURBVMSaRERE7TAN4KNKqT8C8FoAl4nIK6PHrldKnR793A0A0WNvBnAKgPMA/KOI1ESkBuBzAM4H8EoAb0m8z3XRe50E4BkA723VxnnbNQqMfQCY3H9k2eR+4PbLGLAgIqKOwmCFr1VXAfX++cvq/UysSURE1AZKqaeVUj+Ofv8dgJ8CGLS8ZDWAW5RSzyulngDwGIBXRz+PKaUeV0odBnALgNUiIgDOBvCt6PVfATBcztYEsHUDMDvVvHzmcOMxIiKiDsFgha/la4ALPwssWgZAGv9f+FnmqyAiImozEXkZgBUAfhAt+qCI7BKRm0XkuGjZIICnEi/bEy0zLX8hgANKqenUct3nXyoi4yIyvm/fvgBblMPBpyyPccgqERF1Dk5dmgenAiMiIqoUEXkBgNsAfFgp9VsR+TyAjwNQ0f+fBvAeAKJ5uYK+AUdZnt+8UKkbAdwIAENDQ9rnlE5qgJrRP8Yhq0RE1EEYrCAiIqKOJiJ1NAIVX1NKfRsAlFK/TDz+JQB3Rn/uAbAs8fKlAPZGv+uW/xrAgIgsiHpXJJ9fPaZABcAhq0RE1FFyDwNh9m0iIiJqtyinxE0AfqqU+kxi+UsST/tzAA9Hv28G8GYROUpETgRwEoAfAvgRgJOiusdCNJJwblZKKQD3AXhj9Pp3Ari9zG0qZNEy/XJOsU5ERB2mSM4KZt8mIiKidlsJ4B0Azk41lHxKRB4SkV0AzgJwOQAopXYDGAXwEwD/A8BlSqmZqNfEBwFsQSNJ52j0XAC4AsBHROQxNHJY3NTC7fNjSgR+/nXtWR8iIqKccg8DUUo9DeDp6PffiYhz9m0AT0QF/qujxx5TSj0OACISZ9/+KRrZt98aPecrAEYAfD7vOhMREVF3UUrdD31eibstr/kEgE9olt+te11UR3l1enklxb0ntm5oJNRctLQRwGCvCiIi6jBBclaksm+vRCP79iUAxtHoffEMGoGMBxMvS2bTTmfffg08s28DuBQATjjhhOIbRERERNSpmAiciIi6QOGpS9PZt9Ho+fAfAJyORs+LT8dP1bzclmXbK/u2UmpIKTW0ZMkSzy0gIiIiIiIioiop1LOC2beJiIiIKmDXKPDdK4DJ/UeW9S9u5KpgLwsiIupARWYDYfZtIiIionbbNQqMfWB+oAJo/H37ZY3HiYiIOkyRnhVx9u2HRGRHtOyv0ZjN43Q0hmz8HMD7gEb2bRGJs29PI8q+DQAiEmffrgG4OZV9+xYRuQbAdlQ5+zYRERFRO2zdAMxO6R+bOdx4nL0riIiowxSZDYTZt4mIiIja7eCeYo8TERFVUOEEm0RERETURouWFnuciIioghisICIiIupkq64C+ur6x2oLG48TERF1GAYriIiIiDrZ8jXA8D82Zv9I6l8MrP4c81UQEVFHKjR1KRERERFVwPI1DEoQEVFXYc8KIiIiIiIiIqoUBiuIiIiIiIiIqFIYrCAiIiIiIiKiSmGwgoiIiIiIiIgqhcEKIiIiIiIiIqoUBiuIiIiIiIiIqFIYrCAiIiIiIiKiSmGwgoiIiIiIiIgqhcEKIiIiIiIiIqoUBiuIiIiIiIiIqFIYrCAiIiIiIiKiSmGwgoiIiIiIiIgqhcEKIiIiIiIiIqqUBe1eASIiIiLKadcocMeHgalnG39LH3DGu4ELPtPe9SIiIiqIwQoiIiKiTrRrFPj2+wDMHlmmZoHxmxq/M2BBREQdjMNAiIiIiDrR1g2YF6hI2vblVq4JERFRcAxWEBEREXWig3vMj6mZ1q0HERFRCRis8LVrFLj+j4GRgcb/u0bbvUZERETUixYtNT8mtdatBxERUQkYrPCxaxS440PAwacAqMb/d3yIAQsiIiJqvVVXwViVO+NdrVwTIiKi4Bis8LF1AzA1OX/Z1GQ0ZpSIiIiohZavAS7+IlA/9sgy6QOG3svkmkRE1PE4G4iPg08ZllvGjBIRERGVZfmaxg8REVGXYc8KV7tGAYj+MduYUSIiIqIyMI8WERF1MfascLV1AwCleUCiMaNERERELRLn0YqHp8Z5tAD2tCAioq7AnhWujEM9FCsFRERE1FrfvYJ5tIiIqKsxWOHKNNRj0bLWrgcRERH1tl2jwOR+/WPMo0VERF2CwQpXq64C6v3zl9X7OQSEiIiIWsvWe4J5tIiIqEswWOFq+Rrgws9GPSmk8f+Fn+UQECIiImotW+8JNqIQEVGXYIJNH5wejIiIiNpt0VL9dOr9i1lPISKirsGeFURERESdxDQ09fzr2rM+REREJWCwgoiIiKiTcGgqERH1AA4DISIiIuo0HJpKRERdjj0rfOwaBa7/Y2BkoPH/rtF2rxERERERERFR12HPCld3fgQYvxmAavx98Cngjg81fmfLBhEREbVCuj6y8FjgghtYFyEioq5T+Z4VInKeiDwqIo+JyLq2rMSu0fkVg9jUpH2ucyIiIqJQ7vwIMH4T5tVHDj8LjL2fvT2JiKjrVDpYISI1AJ8DcD6AVwJ4i4i8suUrsnUDmgIVMdtc50RERNQVKtF4su3L+uWzM2w8ISKirlPpYAWAVwN4TCn1uFLqMIBbAKxu+VrYAhKLlrZuPYiIiKjlKtN4ombMj7HxhIiIukzVgxWDAJ5K/L0nWtZaxoCENOY6JyIiom5WjcYTqZkfY+MJERF1maoHK0SzrGk8hohcKiLjIjK+b9++8Gux6iqg3t+8akPvYUIrIiKi7ufUeFJ6feSMd+mX99XYeEJERF2n6sGKPQCWJf5eCmBv+klKqRuVUkNKqaElS5aEX4vla4ALPwssWgZAGv9ffCNwwWfCfxYRERFVjVPjSen1kQs+Awy9d/7qLDwWGP4CG0+IiKjrVH3q0h8BOElETgQwAeDNAN7aljVZvoYVASIiot7k1HjSEhd8ho0lRETUEyrds0IpNQ3ggwC2APgpgFGl1O72rhURERH1mLnGExFZiEbjyeY2rxMREVFXq3rPCiil7gZwd7vXg4iIiHqTUmpaROLGkxqAm9l4QkREVK7KByuIiIiI2o2NJ0RERK1V6WEgRERERERERNR7GKwgIiIiIiIiokphsIKIiIiIiIiIKoXBCiIiIiIiIiKqFAYriIiIiIiIiKhSGKwgIiIiIiIiokphsIKIiIiIiIiIKoXBCiIiIiIiIiKqFAYriIiIiIiIiKhSGKwgIiIiIiIiokphsIKIiIiIiIiIKkWUUu1eh6BEZB+AJ0v8iBcB+HWJ798ruB/D4H4Mg/sxDO7HcMraly9VSi0p4X0phfWRjsH9GAb3Yxjcj2FwP4bR9rpI1wUryiYi40qpoXavR6fjfgyD+zEM7scwuB/D4b6kLDxGwuB+DIP7MQzuxzC4H8Oown7kMBAiIiIiIiIiqhQGK4iIiIiIiIioUhis8Hdju1egS3A/hsH9GAb3Yxjcj+FwX1IWHiNhcD+Gwf0YBvdjGNyPYbR9PzJnBRERERERERFVCntWEBEREREREVGlMFhBRERERERERJXCYIUjETlPRB4VkcdEZF2716fqRORmEfmViDycWLZYRO4RkX+L/j8uWi4i8tlo3+4SkT9p35pXh4gsE5H7ROSnIrJbRP4yWs796ElEjhaRH4rIzmhfXh0tP1FEfhDty1tFZGG0/Kjo78eix1/WzvWvEhGpich2Ebkz+pv7MAcR+bmIPCQiO0RkPFrGc5sysT7ijnWRMFgfCYN1kbBYHymuE+oiDFY4EJEagM8BOB/AKwG8RURe2d61qrwvAzgvtWwdgK1KqZMAbI3+Bhr79aTo51IAn2/ROlbdNICPKqX+CMBrAVwWHXfcj/6eB3C2Uuo0AKcDOE9EXgvgOgDXR/vyGQDvjZ7/XgDPKKX+I4Dro+dRw18C+Gnib+7D/M5SSp2emMOc5zZZsT7i7ctgXSQE1kfCYF0kLNZHwqh0XYTBCjevBvCYUupxpdRhALcAWN3mdao0pdT/A2B/avFqAF+Jfv8KgOHE8q+qhgcBDIjIS1qzptWllHpaKfXj6PffoXFBHgT3o7don/w++rMe/SgAZwP4VrQ8vS/jffwtAKtERFq0upUlIksBvB7AP0V/C7gPQ+K5TVlYH/HAukgYrI+EwbpIOKyPlKpS5zWDFW4GATyV+HtPtIz8vFgp9TTQKPgA/EG0nPs3Q9RlbQWAH4D7MZeou+AOAL8CcA+AnwE4oJSajp6S3F9z+zJ6/CCAF7Z2jSvpBgB/BWA2+vuF4D7MSwH4nohsE5FLo2U8tykLj4XieJ4VwPpIMayLBMP6SBiVr4ssKPsDuoQu+sY5X8Ph/rUQkRcAuA3Ah5VSv7UEg7kfLZRSMwBOF5EBAN8B8Ee6p0X/c1+miMgFAH6llNomIn8aL9Y8lfvQzUql1F4R+QMA94jII5bncl9SjMdCebhvM7A+UhzrIsWxPhJU5esi7FnhZg+AZYm/lwLY26Z16WS/jLsLRf//KlrO/WsgInU0KgZfU0p9O1rM/ViAUuoAgH9BY9ztgIjEQdvk/prbl9Hji9DclbjXrARwkYj8HI2u52ej0bLBfZiDUmpv9P+v0Kiwvho8tykbj4XieJ7lwPpIWKyLFML6SCCdUBdhsMLNjwCcFGWZXQjgzQA2t3mdOtFmAO+Mfn8ngNsTyy+Jssy+FsDBuPtRL4vG090E4KdKqc8kHuJ+9CQiS6JWDIhIP4D/gsaY2/sAvDF6Wnpfxvv4jQDuVUr1dBReKXWlUmqpUuplaFwD71VKvQ3ch95E5FgR+Xfx7wDOAfAweG5TNtZHiuN55on1kTBYFwmD9ZEwOqYuopTij8MPgNcB+P/QGFv2N+1en6r/APgGgKcBTKERiXsvGuPDtgL4t+j/xdFzBY3s5j8D8BCAoXavfxV+AJyJRveqXQB2RD+v437MtS+XA9ge7cuHAVwVLX85gB8CeAzANwEcFS0/Ovr7sejxl7d7G6r0A+BPAdzJfZh7/70cwM7oZ3dcpvDc5o/LD+sjXvuKdZEw+5H1kTD7kXWR8PuU9ZH8+64j6iISfTgRERERERERUSVwGAgRERERERERVQqDFURERERERERUKQxWEBEREREREVGlMFhBRERERERERJXCYAURERERERERVQqDFURERERERERUKQxWEBEREREREVGlMFhBRERERERERJXCYAURERERERERVQqDFURERERERERUKQxWEBEREREREVGlMFhBRERERERERJXCYAURERERERERVQqDFURERERERERUKQxWEBEREREREVGlMFhBRERERERERJXCYAURERERERERVQqDFURERERERERUKQxWEBEREREREVGlMFhBRERERERERJXCYAURERERERERVQqDFURERERERERUKQxWEBEREREREVGlMFhBRERERPT/s3f/cXbV9b3v39+Z2Qk7cM0QSVuZgNA+LNxiImmmyuPGnhY4JVIEUtDQHlv8dY5a7LUgj0hsvSSm9jCaKtbe1uqtXuWWakbhDkHkEW1CTy88itekE4Icya2KQjYcjSaTChnJnpnv/WPvNVmzZn3X77X32jOv5+ORZLL22mt914+9H/P9rM/3+wEAVArBCgAAAAAAUCkEKwAAAAAAQKUQrAAAAAAAAJVCsAIAAAAAAFQKwQoAAAAAAFApBCsAAAAAAEClEKwAAAAAAACVQrACAAAAAABUCsEKAAAAAABQKQQrAAAAAABApRCsAAAAAAAAlUKwAgAAAAAAVArBCgAAAAAAUCkEKwAAAAAAQKUQrAAAAAAAAJVCsAIAAAAAAFQKwQoAAAAAAFApBCsAAAAAAEClEKwAAAAAAACVQrACAAAAAABUCsEKAAAAAABQKQQrAAAAAABApRCsAAAAAAAAlUKwAgAAAAAAVArBCgAAAAAAUCkEKwAAAAAAQKUQrAAAAAAAAJVCsAIAAAAAAFQKwQoAAAAAAFApBCsAAAAAAEClEKwAAAAAAACVQrACAAAAAABUCsEKAAAAAABQKQQrAAAAAABApRCsAAAAAAAAlUKwAgAAAAAAVArBCgAAAAAAUCkEKwAAAAAAQKUMdLsBRTvrrLPseeed1+1mAABQOfv37/+xtXZlt9sBAAAQZ8EFK8477zzt27ev280AAKByjDE/6HYbAAAAkmAYCAAAAAAAqBSCFQAAAAAAoFIIVgAAAAAAgEohWAEAAAAAACqFYAUAAAAAAKgUghUAAAAAAKBSYoMVxphzjDEPGWO+bYx5whjzx+3lK4wxXzfG/Fv73zPby40x5hPGmO8YYw4aY37Vt603t9f/N2PMm33L1xljHm+/5xPGGBO1DwAAAAAAsHAlyayYknSrtfZ/lnSJpHcbY35F0hZJe6y1r5C0p/1/SbpS0ivaf94h6ZNSK/Agaauk10h6taStvuDDJ9vreu97XXu5ax8AAAAAAGCBig1WWGufs9b+a/vnn0r6tqQhSddK+nx7tc9L2tj++VpJd9mWRyUNGmNeJmmDpK9ba49aa49J+rqk17Vfe4m19l+stVbSXYFthe0DAAAAAAAsUKnmrDDGnCdpraRvSPp5a+1zUiugIenn2qsNSXrG97bD7WVRyw+HLFfEPoLteocxZp8xZt+RI0fSHBIAAAAAAKiYxMEKY8wZku6RdLO19t+jVg1ZZjMsT8xa+2lr7bC1dnjlypVp3goAAAAAAComUbDCGFNTK1Bxt7X23vbiH7aHcKj974/ayw9LOsf39lWSno1ZvipkedQ+AAAAAADAApWkGoiR9BlJ37bWfsz30i5JXkWPN0u6z7f8xnZVkEskHW8P4dgt6QpjzJntiTWvkLS7/dpPjTGXtPd1Y2BbYfsAAAAAAAAL1ECCddZL+gNJjxtjDrSX/YmkEUmjxpi3S3pa0hvbr31V0m9L+o6kE5LeKknW2qPGmD+T9M32etuttUfbP/+hpM9Jqkt6sP1HEfsAAAAAAAALlGkV4Fg4hoeH7b59+7rdDAAAKscYs99aO9ztdgAAAMRJklkBAADKcnBUevA2abKdbFhfIV35YWnNpu62CwAAoIsIVgAA0C0HR6Wxm6SZ5qllk0el+97d+pmABQAAWKQSly4FAAAF27N9bqDCM32y9RoAAMAiRbACAIBuOX4422sAAAALHMEKAAC6ZfmqbK8BAAAscAQrAADolstvl/pq85f3L2m9BgAAsEgRrAAAoFvWbJI2/k2rAoinvkK69q+ZXBMAACxqVAMBAKCb1mwiMAEAABBAZgUAAAAAAKgUghUAAHTLwVHpzldK2wZb/x4c7XaLAAAAKoFhIAAAdMPBUen+90jNydb/jz/T+r/EsBAAALDokVkBAEA37Nl+KlDhaU62lgMAACxyBCsAAOiG44fTLQcAAFhECFYAANANy1elWw4AALCIEKwAAKAbLr9dqtXnLqvVW8sBAAAWOYIVAAB0w5pN0tWfkJafI8m0/r36E0yuCQAAIKqBAADQPWs2EZwAAAAIQWYFAAAAAACoFIIVAAAAAACgUghWAAAAAACASiFYAQAAAAAAKoVgBQAAAAAAqBSCFQAAAAAAoFIIVgAAAAAAgEohWAEAAAAAACqFYAUAAAAAAKgUghUAAHTDwVHpzldK2wZb/x4c7XaLAAAAKmOg2w0AAGDROTgq3f8eqTnZ+v/xZ1r/l6Q1m7rXLgAAgIogWAEAQBYHR6X7b5aaLxSzveaktGc7wQoAAAARrAAAIL2Do9K975Q0U+x2jz9T7PYAAAB6FHNWAACQ1p7tKjxQIUmmv/htAgAA9CCCFQAApHX8cDnbtdPlbBcAAKDHEKwAACCt5atK2u455WwXAACgxxCsAAAgrVdcUfw2+5dIl99e/HYBAAB6EMEKAADSODgqPfYPxW6zvkK69q+pBAIAANBGNRAAANLYs71VZjRo+TnSLd/qfHsAAAAWIIIVAACk4ZpcM+Wkm2PjDe3YfUjPTkzq7MG6Nm+4QBvXDhXQQAAAgN4XOwzEGPNZY8yPjDHf8i3baYw50P7zfWPMgfby84wxk77X/tb3nnXGmMeNMd8xxnzCGGPay1cYY75ujPm39r9ntpeb9nrfMcYcNMb8avGHDwBAAgdHpQ+fL21bLsmGr5Ni0s2x8Ybef+/jakxMykpqTEzq/fc+rrHxRiHNBQAA6HVJ5qz4nKTX+RdYa2+w1l5srb1Y0j2S7vW9/F3vNWvtu3zLPynpHZJe0f7jbXOLpD3W2ldI2tP+vyRd6Vv3He33AwDQWQdHpbGbpMmj7nVSTo65Y/chTTbnlimdbE5rx+5DWVsJAACwoMQGK6y1/ywp9De0dnbEJklfiNqGMeZlkl5irf0Xa62VdJekje2Xr5X0+fbPnw8sv8u2PCppsL0dAAA6Z892aaYZvc6SM1JNjvnsRMicFxHLAQAAFpu81UB+XdIPrbX/5lt2vjFm3Bjz34wxv95eNiTJP5j3cHuZJP28tfY5SWr/+3O+9zzjeM8cxph3GGP2GWP2HTlyJN8RAQDgl2QuisljqTZ59mA91XIAAIDFJm+w4vc0N6viOUnnWmvXSnqvpH8wxrxEkgl5r2PQ76zE77HWftpaO2ytHV65cmWCZgMAkFCSuShSzFchSZs3XKB6rX/OsnqtX5s3XJBqOwAAAAtV5mCFMWZA0nWSdnrLrLUvWmt/0v55v6TvSvpltbIi/L/JrZL0bPvnH3rDO9r//qi9/LCkcxzvAQCgMy6/XeqruV9POV+FJG1cO6Q7rlutocG6jKShwbruuG411UAAAADa8pQu/Y+SnrTWzubHGmNWSjpqrZ02xvyiWpNjfs9ae9QY81NjzCWSviHpRkl/1X7bLklvljTS/vc+3/I/MsZ8UdJrJB33hosAANAx3lwUD942f5LN+grpyg+nmq/Cs3HtEMEJAAAAh9hghTHmC5J+U9JZxpjDkrZaaz8j6Xc1f2LN/yBpuzFmStK0pHdZa73f7P5QrcoidUkPtv9IrSDFqDHm7ZKelvTG9vKvSvptSd+RdELSWzMcHwAgxth4Qzt2H9KzE5M6e7CuzRsuoBMdtGZTpoAEAAAAsjGt4hwLx/DwsN23b1+3mwEAPWFsvKH33/v4nDKa9Vo/QxIWKGPMfmvtcLfbAQAAECfPMBAAQI/bsfvQnECFJE02p7Vj96HFG6w4OBo+5MMTMvSD7BQAAIBiEawAgEXs2YnJVMsXvIOj0thN0kzTvc7kUem+d7d+XrNpXnZKY2JS77/3cUkiYAEAAJBR3tKlAIAedvZgPdXyBW/P9uhAhWf6ZGtdRWenAAAAIBuCFQBQsLHxhtaP7NX5Wx7Q+pG9GhtvdLtJTps3XKB6rX/OsnqtX5s3XNClFnXZ8cPx6wTWJTsFAACgeAQrAKBA3pCAxsSkrE4NCahqwGLj2iHdcd1qDQ3WZSQNDdYX9+Say1elXpfsFAAAgOIxZwUAFKgXJ6zcuHaosm3ruMtvj5+zQpL6l7TWVSs7JayiyqLNTgEAACgAwQoAKFDVhwT0StWKrrXTq/CRohqI165eOK8AAAC9gmAFABTo7MG6GiGBiSoMCeiVqhVdb+eaTXPKkiZBdgoAAECxmLMCAApU5Qkre6Fqxdh4Q7eOPlb5dgIAAKBcZFYAQIGqPCSgF4aovP/exzVtbejrHWvnwdFWWdLjh1uTaF5+e+pMi14ZbgMAAFBVBCsAoGBVHRJQ5SEqUnjmh19H2nlwVLr/PVKzfZ6OP9P6v5Q4YNH1YSwAAAALAMNAAGCRqPIQFSk6c6Jj7dyz/VSgwtOcbC1PqBeG2wAAAFQdwQoAWCQ2rh3SHdet1tBgXUbS0GBdd1y3ujJP+12ZE/3GdK6dxw+nWx6i6sNtAAAAegHDQABgEanqEBWplfnhHz4htTIqOhpQWb6qNfQjbHlCVR9uAwAA0AvIrAAAVEIlMj8uv12qBYIKtXpreUJVH24DAADQC8isAABURpLMj1IrbXiTaOaoBpKkIgzVQgAAAKIZ6ygR16uGh4ftvn37ut0MAEAJgpU2pC4MFcmpm8dgjNlvrR0udScAAAAFYBgIAKBnLIRKGwvhGAAAAMrGMBAAgFPVhiv4K2pc0/ewtg7cpRXmeWlS0raCd1ZfIV354VRDQJKgWggAAEA8MisAAKG84QqNiUlZSY2JSb3/3sc1Nt7oWpu8ihrX9D2sHbVP6aV9z8sYyZgSdjZ5VLrv3dLB0UI366oKQrUQAACAUwhWAABCVXG4gldp430Do1pqpuPfkNf0ydZkmw5j4w2tH9mr87c8oPUjexMFcqgWAgAAEI9hIACAUFUcruANQTn7vp90bqfHD8/+6A2LaUxMykjyT1HtZZ742xkmSbUQAACAxY5gBQAg1NmDdTVCAhPdHq6wce2Q9E+rpOPPdGaHy1dJml/FI6yWlpd5Ehd4SFKiFQAAYDFjGAgAIFSlhytcfrvUVyt/P/1LWvtS+LCYMEyUCQAAkB+ZFQCQUTcrZXRi35UeruBV6HjwttZEmCnYsJSItjkTdQaqgSQNQnQ78wQAAGAhIFgBoKuqVhozqeCQgKTzFfTavis9XGHNpkxlRV87sjd0eMvQYF2PbLnM+T7XsBi/ymSeAAAA9DiGgQDomiqWxkyqm5Uyqlilo2qiqnRkHd4S9j6/M5fVdMd1q6sb3AEAAOghZFYA6JqoTnfVO3zdrJTRiX33asaLFJ95knV4i/99jYlJ9RujaWs11GPnBwAAoBcQrADQNVUsjZlUNytllL3vbg5xSeTg6Ny5KgJzSyQJgmUd3lLpYTEAAAALCMNAChCVbgzAzdW57oUJCouolJH1uyPrvpPur6xhJoV8Vx4clcZumjup5uRR6b53t15TbwfBAAAA0EJmRU6VfwKJntTLKfhpbN5wwZzPj9Q7ExTmrZSR57sjy77T7K+Mzn5h35V7tkszzfnLp0+2XluzKVfmSZbPXtx7FsvnGQAAoEgEK3Lq5TH3qKbFFACrdGnMBPIMCcj73ZF232n2I9y1bQAAIABJREFUV8Ywk8K+K48fjn0taxAsy2cv7j2L6fMMAABQJIaB5ES6MYq22Co9bFw7pEe2XKanRq7SI1suWzQduE5/d6TZX9wwkyzDOQo73uWrIl/zshgmm9PqN0ZSqyRpkiodWT57ce9ZbJ9nAACAohCsyKmXx9yjmgiALQ6d/u4YXFZLvL+Na4d0x3WrNTRYl9Hczn6WcrNj4w31tQMHSfYf6fLbpb6QY+lfom/+0v862zZJmrZ2NsjiBSqiAi1ZPntx7+HzDAAAkA3BipyKmGQP8CMAtjh06rtjbLyhiz/4NR07MX+eh1q/ce7PlfGSNlPAC25MWzvvtUzHu2aTtPFvWhVAZje0Qrr2r3Xzf39FZNvCAi237DygD4y1hmVk+ezFvYfPMwAAQDYEK3KKegIJZFGFABgVbsrXie8Or3M+MRkyIaWk05cMpN5f2kyBsOCGJPUbk/1412ySbntK2na89ee2p6Q1m2LbFtYWK+nuR5/W2Hgj02cv7j1V+DwDAAD0otgJNo0xn5X0ekk/sta+sr1sm6T/IulIe7U/sdZ+tf3a+yW9XdK0pPdYa3e3l79O0l9K6pf0d9bakfby8yV9UdIKSf8q6Q+stSeNMUsl3SVpnaSfSLrBWvv9Ao65cHkm2QOCuj3pJBMCdk6Z3x1j4w3dOvpYaEaD57gjiBEl7eSbrgDCjLWFH3tc21xtsZK27XpCpy8dmJ3rYtpaDTk+e8HqHtevG9JDTx4J/bwm+TxTLQQAAGA+YyN+kZUkY8x/kPS8pLsCwYrnrbV/EVj3VyR9QdKrJZ0t6R8l/XL75f9P0m9JOizpm5J+z1r7340xo5LutdZ+0Rjzt5Ies9Z+0hhzk6Q11tp3GWN+V9LvWGtviDug4eFhu2/fvoSHDyBo/cje0A7f0GBdj2y5rAstQlrBgFOYDw58Vr8/8I/qd64Rzs7+FWCksFkp0q6fiumT1r1Vev3HJIUfd73WP5vB4bq3w/jf5xe3j7SK3l4cY8x+a+1w4RsGAAAoWOwwEGvtP0s6mnB710r6orX2RWvtU5K+o1bg4tWSvmOt/Z619qRamRTXGmOMpMskfbn9/s9L2ujb1ufbP39Z0uXt9QGUiAkBe59r6IXngwOf1Y396QMVUivAYEzIn4LWT8XOSPs+I33lvZLih9Zs3nBB4v265uEouroH1UIAAADCxQ4DifBHxpgbJe2TdKu19pikIUmP+tY53F4mSc8Elr9G0kslTVhrp0LWH/LeY62dMsYcb6//4xxtBhAjbZo/qicusPSmgb3FBAuqYv/nZrMroobWbFw7pH0/OKq7H306NNkjKOw8Fh3MIzgIAAAQLusEm5+U9EuSLpb0nKSPtpe7soDTLo/a1jzGmHcYY/YZY/YdOXIkbBUACTEhYO9zBZb6jdHHb7hYA5rpcItKZt1ZJEEf2rhad95w8ZzsizNTlHUturoH1UIAAADCZQpWWGt/aK2dttbOSPo/1BrmIbUyI87xrbpK0rMRy38sadAYMxBYPmdb7deXyzEcxVr7aWvtsLV2eOXKlVkOCViw0lb2oMJN73MFnD666VWt62iyDACpsJTHEyzLuvXqixIH6MLOrSSdODmVqWoOwUEAAIBwmYaBGGNeZq19rv3f35H0rfbPuyT9gzHmY2pNsPkKSf+vWlkSr2hX/mhI+l1J/8laa40xD0l6g1rzWLxZ0n2+bb1Z0r+0X99r42YDBTBH1soeVLjpbbEVKNa9pTXXw0Kx7i2JVnNV3UhTgcdbtm3XE3NKwh470cxUNafb1X8AAACqKkk1kC9I+k1JZ0n6oaSt7f9frNawjO9LeqcXvDDG/Kmkt0maknSztfbB9vLflvRxtUqXftZa++ft5b+oU6VLxyX9vrX2RWPMaZL+L0lr1cqo+F1r7ffiDohqIPBb7CUBXdUPBus1Hdh6RRda1Juqch8V2o6vvFfa91k5Rtd1XKaqIYFqIFGKrrrRq1VzqAYCAAB6RWywotcQrICn0yUBq+j8LQ84u6Ifv+HiRXMe8qjKfVSVdpSl7M5/0duP+mwNDda7HthyIVgBAAB6RdYJNoFCpJ1PIQ1KAkZP0reYzoOU/V6ryn1UlXaUpeyqGEVv3/XZMmoNt7I6NeyqyO81AACAxSJP6VIgl6zzKSTVKyUByxxisHnDBbp554HQ16p2HsqU515znafGxKTGxhuprlWea535fj44Kj14mzQZOj+xVF8hXflhac2mRO0oS9klc4ve/uYNF8zLdDGaP5LFCyi5rnNVhhgBAABUDZkVJSszc6DXlf2kuBdKAnqd6LKexG5cO5SqLONCledeizpPaa5V3mud6X4+OCqN3eQOVEit1+57d2vdLiq7KkbR2w+rmuMaFuIKKJX9+QcAAOhlBCtKxC+i0crOfMjaOelkgKkTqf2usoyXXrhy0QTS8txrrlKVUrprlfdaZ7qf92yXZpru1z3TJ1vrdpGrZK6kQu7TMkryBkugDqUMKH3w/icW9NAeAACAPBgGUqKozglpvuWnfWcpCVj20JSgPJ3opOnjYefh0gtX6p79jUKPs8rp7HnuNe8Y4obTxB1/3uBcphKXx59JtO3WuoeTr1uSYMncD4w9rrsffXo2YyHLfdrJ+zJsaIgroDQ23tCxE+GBpMU0RAsAAMCFYEWJemXOhG5J84t9VsHOT5xOB5iydqLTBlWC52H9yN5Cj7PTQZ608t5rG9cOacfuQ85rleT4iwjOpb2fZfolOx2/nqQT9V/Qb43szdypLzooMDbemBOo8Ew2p3Xr6GOS4u+tTt+XaQJKUdkTi2mIFgAAgAvDQErUC3MmdFMZadl5dTrAlHWoSpIhBWHDWbxlYZ1mKftxVrVShXe8t+w8oKUDfTpzWS3zvRZ1rZIcf9lzMoRKGKiYNjXd/sL1mYeshQ15u2XnAX1g7PHMTd+x+5BzDohpaxO1rxv3ZXBoiOsei/qslXpPAAAA9AgyK0rUicyBXpf6SXHJyh6aEpQptV/xQZWwJ8qbv/SYZKTmtKsLmP04o9rTreEhwXMwMdlUvdavO2+4ONP+o67VLQkqrmS91rksPyd+KEh9hf6seaO+PPnqOYvTZNqEBQWspLsffVrDL1+R6RjjAmdJ2lfl7DbXd81gvVap70QAAIBuIVhRoq50TpCLK8DkTUZZxnXMErCJC6qEdR6bM+4ghZQukBYMQAwuq4WOv19er3VteEiWIT1xgRXvWnnr3bLzgHbsPuQ8/mDwp+PBuctvl+5/j9T03Su1unT1J6Q1m2aPo/F8+k69/1y57iwrJQp4hJ131z2etH1S54OPabi+a7Zdc1EXWwUAAFAdBCtKVrXMAUTr1GSUecVl7aR9cjzU7iBKig3KhGVt1PqMav1mTtZGvdYvY9S1SWbTPlVPOr9BmuPvehbVmk2tf/dsb02guXxVK4DRDlQE76EgV6c+yXs9cfei67xfv25ozucuTfs8ebLb/AGU5fWaTk5N60RzRpJ05rKatl59Ue5KIhLBbAAAABeCFUBA2ZNRFiGuo5PkqbRnaLCuR7Zclriz7sraGKzXdPrSgTntSTI8Io00Q0qinqqHbSdpJkaa4891fxwclR68TZo8mn0bfvUVs4EKKfw45qwe0amPe69fXEDBdd4fevKI7rhu9ezEpkaak8GRJOiQNSAQNoTI79iJpjZ/OX6Sz6SZOgAAAJiPYAUyqXKZyqKVOe49z3mM6uiEPVGu9Zl5c1b4O3xJO+uu4z4+2dSBrVfMWRZVQSOtJMGU4NPwsGyHSy9cGbodV+c7eLyu458IOf7MDo5KYzdJM+GlLTOZPCrd9+7Wz2s2Rd6/QzH3YtJ7P0lAIerz5b/Hs35WsgQEkgRjmtM2dkhRlSvkAAAAVB3VQJBa2Mz/aSoH9JqyqrqUeR7DKq3seOOrtOMNr3JWX0kalElzPoqsgBFX2SF4Picmm5LVvAogDz15JHQ7/caE7tdKc6pauI7ftNtQiD3biw1UeKZPtrYt93F4mTZRHWrXewfrtdTVfZLeT16VjTtvuFiSdMvOA7NVboqWNBgTtV5VK+QAAAD0CjIrkFqWiQt7WVlVXco+j64nylmGTfilOR9FjsuPC6a4hmcsWzKg8dtPZTy4hqZMW6t6rT/0ifrfP/q0JOlDG1fPDm8JTiqZdDLJRI4fzr+NmG1nua9nJ+QMGZZh1AoQnb50IFXFlTTtKDpbwZWtkXQYVVTAsooVcgAAAHoJmRVIrcrlAMsQlqWQ5IlxnKqdx6RZEGnPh/dE/KmRq2Kf2EeJewKfNzPEOw6XL3yjVQJ049ohZ/WLooYG/Q+dlXs7Lv9DZ2lsvJH6OvozV6RWoMLLRfEHLrwyuWu3f03nb3kgNvshTTuKzFYIy2y6eecBrd3+NV164cp5n4WgWr+JDOy47jOrVsBssWSmAQAAZEVmxSKS5WlempKCVSgHmFTac1HGRHhpzmMnnsSmyYLoxsSAcU/g82SG1PqMTpyccmZdSK3MC89QSZ8BrwP9W9Nv1I7ap7TUJJvEMqmTdkD/tflGfd2XjZD0OoYFCqykfmPmnBupldHilXJNkv2QtB1pAnxj4w1t2/XE7OSYwQoernkpjp1o6p79DV2/bkgPPXkkczWQsPvMEwx2LeTMNAAAgKwIVlRAJzqiWdKn05QUrESZxoTSlKgs87okTX/v5ER9Va5OEBdMSXo+g9tZXq/phZNTs51rF/+cFmUPDdql10pNaevAXVphnne/wZzKbrCzf4U7pjO0rXmjds28VpoJ7xxH3fOuQEEwUBGmqM540oDU2HhDm7/0mJozp9oWrOARlQXjVSN5ZMtlmdvqv8+SDClZqJlpAAAAWRGs6LJOdUSzzI+QpKRgL465TnIuOnFdkmYyLLY5QqJEBVOyZoasH9k7rzRlmN97zTmZ9pWGv8O6a+a12nXytbP/D86nUa/1zxkucd94Q+/deUAzGfYlxd/zacrhJtlfFkmDRDt2H5oTqPD4K3jEHU8R7fXus/O3PBAVR5LUW5lpAAAAnUCwoss61RHNMj9C0pKCvSbJuejUdUlyHtNeu6pP3ldm+9Lel2PjjdgOeL8x+r3XnKMPbZw7n4W3L+94btl5QDt2H8p1PK4OtFdKNOq8eT8Hhz5ICs0aCXaO4+75sEBBcJLNuGPLKywrxhjp5p0HdOvoY5q21jlEx+N9bqKGaRTVXv+2otrUS5lpAAAAnUKwoss6NclilnkmFsLcFGGSHFeVJr9MO7dFXEZIN4MZnRzSkrQtLl4JzyTbyHM8/uvxu6c9qj9d+imdrhdnX39Bp+nbv7Jdv7Y2fnLSsGBNsI1Sq3N86YUrtX5k7+x94OpMe/d8WDZJ0kyLIjvj/iCR/7i84ShhlUr8vM9NWHCnjPZKclaQkVrBsCIm7AUAAFhoqAbSZXEVDoqStNJD3vf0giTHVcZ1GRtvaP3I3kQVEtK21xNXLSGsAkInKxEUWc2hjLZ4kt7neY/Hfz2u7ntY2+1f6QzzoozR7J8zzM+0bvz92vahranvHSm82oY374z/PjCO9/vv+WBllyHH52GwXiu8ek5Q1PVzBSqCFTw2rh3Sga1X6OM3XDx7LP3GzF7Doj4XG9cO6U2XnDvvHNdr/froplcRqAAAAAhBZkWXlTVRX1CWMfZljcvvtiTHVdR18Z6aB5/2pnkCn+Y6xGWEdHv+iyplrETtM2nnOu/x+K/H+wZGVTPh3ew+O63/fPLv9Tm9OlP2RjDjYv3I3tDKHsGMBNeEr/5hGLV+o+a0nfOebddEV8ooQpJzPFivOauB+HnLysz6+dDG1Rp++YoF930KAABQFoIVXdbJgIC/w+IfZ1+1EpWdkOS4lg70zXZckpQqDAqmqecpV5j0OsQNGXF18BoTk3MmAcxyvElUaWhR1PwQSY877/H4r8fZ5sfR+zI/mf3Zf+9kGdbjug+sWsfv2lbwnp6YbKrWZ3TmspomTjQzf39lOYa4YShJhvH49x22raIDeQv1+xQAAKAMBCsqoNO/wIaNs7955wF98P4nSumgJm1TcGLApG0peg6GsDH+P2smrbFwSlSauqfojIK4jJCoDp4/mBIs81h2+4LzJ3TiiXMR2TN5t+G/Hs/as7QqImDxrH3p3P9PTGaeMyPpRJ7ecBZ/UDV4TzdnrJYtGdD47VfEHW6orMcQNUFm0msQ9lkPKvozWvUJcAEAAKqCOSsWIVcn+tiJZkfnL/CMjTe0+UuPzZnkzussx7WljDkYippXIUknp+iMgrD5CfxDGsLmv3DxyjyW3b6w+RM6cR/GnatObMN/PT4ytUlNGz5zxJTt00emNs1ZdvZgPfO96poH5dILV0Z+nooYxhOcu+WD9z+R6Rj8596v3xhdvy5ZADhJQLHIz2jU91XWOW0AAAAWKjIrFqGo1OlOzl/g2bH7kJoz88fqe53lsOoG3pPJPmNmqwB48h5DUfMqxKWpByf7K0pUpo7/CXmSSg5lzCWRZP6ETt2HabKaXE/E82RG+a/HronXSk3pzwc+ozPMqWogJ8xp2mr/s3bN/C+zy7zMgVt2Hgjdbtx1cw0/i5vTJO+wl7AsCpck917YXBPT1uqe/Q0Nv3xF5uEwnqLnD3Kd3227ntCLUzOVqJIDAABQFWRWLDJj4w3nrP+eMjqoUU8No/YXfC34ZDIYqPAkLakYpqhKIHFZDKcvGehKR8Sr6OCq5ODXibkkqjTppkuZVVS862Ek7Zp5rVaf/D91/ov/MPvnlT/7rF77OzeFZm/kuVeDlT02rh2KvRZ5KwQlyWTwJL338mRCRe2j6ComY+MN5/fSxGSzMlVyAAAAqoLMipyKGn/cqXHMO3Yfcpb18xTdQY0bkx6VgRBsS9LOjmnvN8s5LKoSiLfvmx1Pv4/7hr10Q9SYf6m8zI+gKk266ZK1ikrc5zouS0hqnQdX9kbR1YTirkXeCYGTBqDSHEOeYNfmDRdo85cem5PZVesz2vHGU+VEi/hu9r4D06pSwA4AAKDTCFbkkHViuLK2k0Sn056l+I5eWIdBCu8sJ/3l3bb3m+X8FVmhZePaIeeQi253xoNDQvxlK8uqBhKmU+V7Ezs4Kt1/s9R8YXbRw1bS0pB1JyVtC9+MlXStla5V+72TksYkOyZ56U1zXvc5pjO0rXmjvt7/G5GlQ89uz/nx0JNHEgVE4u7lJNciz7AXVzBksF7T6UsHMn3ecge7gqlmvv9/YOxx3f3o05lKDvtFBVnrtX6dVuvTsRPzg5fd/o4AAADoJoIVOWR92lrWdpKIymIYKimjI+7Jp7e/JNVA4uaBSLLfJIqs0JK1M96JbJtOVaKJOpa0waFSz8vBUened0qaW/3FxI2dCmFm/4pbab4Vel5/seTTeuxXz9OvrX3d7PKwwOY9+xvO4QppA6Fll1IO+yzU+oyMUeb95Ql27dh9SM3puUFS/8Sy/kCFJ8t3c9R30R3XrZakagXsAAAAKoBgRQ5FjbXv5Jh91y/2RY7NDkry5DNppzms/f6MANf2u1kuMKoD6GpXJ7NtypbkWJJe/9LPy57tCgYqumWJpvRr3/0rSe+cXZY2sJklEFpmACv4WVher+mFk1OzWQVZrmeeAEvUd2/UkLmiJtsdag/x8VDSFAAA4BSCFTksr9fmlNv0L0+jk2P2y35yGqbINP+w9l964Urds7/h3H4VOv5hHcCodnUy26ZsrmO5dfQxSfnT6Seb07p55wHt2H0o/718/HD295Yh0J6kgU0vCObKQurmXAj+z8L6kb3zvkOz3OdZAyxR371R5yjLZLtlDq8BAABYiAhW5OBKDU+bMt7pMftpfynOm5VQdIAkrP3DL1/h3H5VO/5R7eqFChlJudo8bW3qoFHU8RcShFq+Sjr+TLb3luBZvVTrtzwwe08nCWwGg2BhigqE5v1u6PZ9HvXd6wr2mPb70uhGkBgAAKDXEazIYSJkQrSo5S5V/kW2qKyEIp4axs174Np+tztELlHtStopreI9ExQ1z0jaoFHcnCW5g1CX3x46Z0U3nLQDGmlumlMq9fp1Q5FZRFJ8xZzg+kkrljQmJtXfrlgyFJLRlOW7odOVYMKO9Y7rVjuPP2zI2ZsuOTfzZLtV/HwCAABUFcGKHIr8Rbuqv8hWJSshT9CkqqUxo9rlKit64uSUxsYbktT1oS1JxZVI9YI2wY7kpReunFflIm5bUdtLFMxZs6n1b6AaSBJ29i8H418xmlcNZNfMa2eXTTan9YVvPKPfe805kdU/ooJwwUl04z5Xwde90qqNiclCJp+MyyorMiDnOtY7rlutR7ZcNm/9KgeRAQAAFgNjbYLfnHvI8PCw3bdvX0f2FZZuXfZklZ12/pYHQvtWRtJTI1d1rB3rR/Y6J6gL62j4FXmdyuw8Bds1Nt6YUyHFv46r1OFgvaYDW6/I1J6kbc5y/GPjDd06+thsZ9dvKCI441frN9rxhldp3w+O6gvfeCZ0W1Hb68Rnc+32r4Vel+B96vpcJRF2HP7r0tfOfohrgxT/uXK9HiXtd0PSSWalfNcwz3fIQmKM2W+tHe52OwAAAOKQWZHDYnjyljcroajOfZqhHGlTvZMqY6LOpQN9s9s7c1lNV615mXbsPqRbdh7Q2YP10PlPJpvTzk79xGRTY+ONUu7BPMe/ce2Q9v3g6Lyn8UbSpReujB26ILVKSv7JvQdlFd4Zl+bON9CNjKCtV1+UaP6ZNCV4g4LH4cp+8HPNgRP3ucoyVCptxpIrq6zoa9jJ4WC9MkQLAACgymKDFcaYz0p6vaQfWWtf2V62Q9LVkk5K+q6kt1prJ4wx50n6tqRD7bc/aq19V/s96yR9TlJd0lcl/bG11hpjVkjaKek8Sd+XtMlae8wYYyT9paTflnRC0lustf+a/5Czcf3y2cnhG934BTjP5J9Fdu6TBk3SpnqnUWTn6QNjj8/ruD//synt/OYzak6fSrXP2s4yJlBNc/xhQzru2d+Yl01gpXlzMEQ50XTPJTFYr8kY6ZadBworOZlW0gBm2Oeq1md0xmkDmjjRnC3p6d0LQf7jcAV6+o3RjLWR1zTucxUXVAmWDS5yYuCigwudGg5WhepDAAAAC0GSzIrPSfrfJd3lW/Z1Se+31k4ZYz4s6f2Sbmu/9l1r7cUh2/mkpHdIelStYMXrJD0oaYukPdbaEWPMlvb/b5N0paRXtP+8pv3+16Q6uoJU4ZfPbrUhT/ZIns69q7MbFzQp84l6UZ2nsfFG6Hj/5kzygQGDjrK5aduT5r5KevzBQIxrfgPPZHN6duLGPF6cmgnttF/T97C2DtylFeZ5yUh2m1o97HbWirN4T32FdOWHNTa9PtX9nySAmeRzFTV0xt/Bdl2XGWtjh2PEBSOjhufUa/26ft2Qc/6MqCBYkgBZ0cGFTlVdqso8PwAAAL0uNlhhrf3ndsaEf9nXfP99VNIborZhjHmZpJdYa/+l/f+7JG1UK1hxraTfbK/6eUn/pFaw4lpJd9nWpBqPGmMGjTEvs9Y+F3tUBSvql8+kT7DD1uvmL8BZs0eydu7DOtD37G9Edozy7jOJojpPO3YfyjxfgdTqYG275iJ98P4nQudHSNOeNPdV0golYYGJuOOdtlb1Wn9shoUxUlhMo9+Y0Pd+cOCz+oP+f1RfMCKRpLzw5FGdvPcP9d+m3qnG1HpJxQYJ4z5XrooUwQ52nvsyLmjifz1YDSQqaBP2Gb5l5wHt+8FRDb98RaIAWdHBhU4N26tq9SEAAIBeU8ScFW9TaxiH53xjzLikf5f0AWvt/yNpSNJh3zqH28sk6ee9AIS19jljzM+1lw9JeibkPR0PVhTxy2fSJ9iu9eIqKVRR1k6UqwP90JNHYodylJnqXVTnKe01G6zXdPrSgUSlFdO2J829neT4swZihnxBuWcnJrW8XtO//6wpf7JJrc/ohlefE5ph48qoCA1UpLBEU7q1b6f+b62fXVZmoDIoSQc76rok2W+SoEnaDn3YZ9hKuvvRp/XAwecSBcjijj3LOe3EsL2qVh8CAADoNbmCFcaYP5U0Jenu9qLnJJ1rrf1Je46KMWPMRQp/jhnXp0n8HmPMO9QaYqJzzz03SdNTcf3yubxeS7yNpE+wXeu50uSr/AvwpReunPeUPUlnOk9wqMxU77DOkzc5pDchZpIOU9Q8ALV+M2eeAi+LImybRTwpTtOxSrK/qGsUnN/A412fYEfS1RkdfvmK0Myj4HG8b2A0V6DCc7b5ybxlZQ21CZM0AyN4TqTulbd1nR8rhWYDud7jOvYqDM1z6dRwEwAAgIUuc7DCGPNmtSbevLw9VEPW2hclvdj+eb8x5ruSflmtrIhVvrevkvRs++cfesM72sNFftRefljSOY73zGGt/bSkT0ut0qVZj8ll84YLtPlLj82bU+CFk1OJKy8k7YC71gtLk6/yL8Bj4415EyoaSdevi3+yWWZae17+zlPWDlNYZ8ZIetMl54Z2xIt+6h3Xlqj7Km5/rmvnHd9DTx5JPJzAtS/X8uBxnG1+7GxnGs/al85bVtZQm6zCzsn6kb1dGzqWpdpJ1c5pVl7lG6+8br8xib73AAAAMFemYIUx5nVqzSvxG9baE77lKyUdtdZOG2N+Ua3JMb9nrT1qjPmpMeYSSd+QdKOkv2q/bZekN0saaf97n2/5HxljvqjWxJrHuzFfhdT65TNsfoDmtE38y3HSDrhrvWCafNXL4bnSwB968kjse/M+mexUhZasHaak8wR0QtHBnahAzIc2ri6iyZLc5Wn9E1I+a8/SqpwBi5N2QB+Z2jRnWZlDbYqUZ795Kw9t3nCBsyrLYL02bzLUuHMabI8rEOI6trHxhrbtemJ2Utozl9W09erwjKW8vECtdx9OW6t79jc0/PIVlf2+BgAAqKIkpUu/oNYEmGcZYw5L2qpW9Y+lkr7eqjA6W6L0P0jaboyZkjQt6V3W2qPtTf2hTpUufbD9R2oFKUYClmrXAAAgAElEQVSNMW+X9LSkN7aXf1WtsqXfUat06VvzHGheEylSl8Mk7YBHrdfJMqkuSTsxeTpKZWdH5K1SEHcsjYlJnb/lgcj3V+FaeopsS9HXLux6SOHDG+64brU+uulVs699ZGqTRmp/p2Xm5Oz2rP+HkGog1rfSMZ2hbc0b9aB+XWcua5UULXuoTVJlVtMoYoiFl10QNgxs2zUXSUp+j4S1x2V5vab1I3vn3S/BzLhjJ5ra/OXHUh1TUlXO+gAAAOglxuYsF1g1w8PDdt++fYVvd/3IXmfGQ9ykj5481UCq8EtusNMgtTofd1y3OjQFPe/5KkPUMUjhE1aGHZ/kPka/qPcXqcr3TNZ2ua7VabW+0HkPvHvL22djYlLX9j2szQOjOtv8RM/al+ojU5v09f7fiLwmRZ/LNJ+bJPtPur20+/W47ut+YzRjbapzkuZcutZN8jmTpD5JM4FlUfeLVM730flbHgjNKDFSbCnZTjDG7LfWDne7HQAAAHEIViSU9Rf/XhbsPJw4ORXZSQy+t4rnKyqIIoU/te03Rh/d9Kp57Q47xjBlB2iqeq6ztssfbEjD3xmMuzadDpqlCVTGnbM0gcAsgRdXZ9uv6Psr6rhdw0n8Buu12SEeaaQJICQ9l1UN1HoIVgAAgF5BsCKFJE88q/h0O4ukHXHJ/Qt/Fc9H1FNPyV2ixtU58x+j671lP1EttXN0cFS6/2ap+ULqt/qHVMxhwkv9RL4nCRO4jr7tzMjo76cv19apt3mrVuIpd1CSa1n2k/ukmQxZMi3S7jMqiOgxyjahp7f9JJ+RNIG3qgYPPQQrAABAr8hVunSxiRrbX+VSelmEjbt2cY2BL3NehqyBkLhx/K4Oj2vMuf8YXR2uosvL5p1sMLGDo9K979T85PpkzOxf5b4nyXb6ZXVj/z9KkrZOva2yJX+j5kHx5mLoK7mMcdi8OWG8NhTxXRc1x82dN1wc2Z6zB+uR9/pgvaYXXpyaV82p1m+ck3qGZZUlnYei7Dl3AAAAFguCFQVZaJOqJe3odqN8ap7AUNxEp1GdorhzkreKSRJhx24UnoyQu/P64G3KGqioImOkN/Xv1Yj5L3OuSVETrgZleW9U6VdveVigosj7zN/ZTpqtMNmc1rZdT2Q+V67jtu12XL9uSF957Ll5Qz2843a11UizE3omrQaSZkJP13dClSbQBQAA6FUEKwrSrfKEZXF1HgbrNZ2+dKCrTwzzBIZcTz1d2/WL6/x34omqqyRsMGCRu/N6cFSaPBq/Xo/pNzNz0vGjAl9SeNURaX5QLBiYuPTClbpnfyN1QM1V+jUsGFXkMIwgb1tJh4JJ0sRkczYYkDbbIiqbozExqXv2N2YnwXV9vlwlc9OWBC4iqwwAAAD5MWdFQaLGeQ/WazJGmUsfdpJ/csOwDnAVxl0XPWY/yfwc3rkY6vL1i5r8cKidDl/IPXbnK6Xjz2R/f1WZfmnrqSBMlrkSgvMchN0/rgBDkjkSxsYbc7IAnIei7syFkkaWakmufcZtq6g5cpJMMCpV5/swLeasAAAAvYLMioJEPRn0dzryju8uc9LKYKfL/8S+2510v7h5J1xc5871JLW/PTeAv+PZ7blIooYJFHp9FmKgQpLWvWXOf6Myolwd1uB7XNkuSd7r8uJU/PCbsp/qR7W1XutPlH2QJrPMGzrhChbEbauooRdVzioDAABYTPq63YCFZOlAstPpDVlIywsmNNodKa/jPDbeSL2tMK5Ol/dEsxu/mI+NN7R+ZK/O3/KA1o/s1dh4Q5s3XKB6rX/OenHDHqLOnasTNGOthgbr8zpOWa9fETZvuCB0/klvbH8hDo7KNculteEdcev9sad+7oTZfYb98a9o+qTht0uv/9ic97s6/MvrNec8n8H3pOmQJwkwJBmG0Im5YlxtHRqs647rVmtosC7T/v+Zy2qptpFlv50acuH6ftl2zUV6ZMtlemrkqq59HwIAACwmZFYUIE2ZT09jYlLnb3kg1RO6tHM1pM3CqNq8G675BO64brXuuG51qmOLOndRmRpFnpOk1yNqvY1rh3TzzgOFtSnUnu0KCzfMWOnm5k3aNfPaOUNOgvMzSJ1LkTeS1m7/mo6dmD9kIskQBNekqMY4q67OCxJEZbtkmUck6jp6ZTo78VQ/asLYYBaDq1xnloBKJyaqjZJ27pkqlmgGAABYCAhWFCDNhGx+/if8UvywgjQd5ywVM7IOryhLVIAh7ZPNqHP3pkvO1d2PPh3asXSNoU97TpJejyTrDZV9nY4fdr60a+a1cypTNCYm5507qbOVcCZCAhVSsuCNq2N6iyMgZDX/8+PqXF+/bkgPPXlEjYlJ9Rsze072/eDonMoWwcoUrs9hmvkfipCm017k5LJVKP2ZdEjJQitZDQAAUCUEKwqQ94l20o5dmmCCq6N/6+hjksJ/ke72E82gIrMaXOdueb2me/Y35nS2jaTr153qrBRxTpJmxSRZr9DrdHC0VaI0QeWPZ+1ZoRNH5p2fIa+8QbawjqkrSDUUss2oznVYZ/bvH316zvuPnWhq85dPfS6r9DlMMw9EljkjXFkJeeef6FS2w0IrWQ0AAFAlBCsK4OoseaUFl/uqgeTp2KXpxLi2N22t88lfFZ5o+sV1QtN0SFwlIZvTM6HzdDz05BFJxZ2TpIGXJOtFlV9dP7I3eTsPjkpjN0kz0VUnJOmkHdDfLfl92ZOxq85aXq+la09KcZVr8nTu0wYMXJ3rpFlXzWk728HNes/12nCEsrISysx2CJ5jV+WSXi1ZDQAAUCWULi2Aa7y2lwbu7zxEPbFNkuLt/2V5eURJ1Liyg51OKc/CdV7vuG61pPCMh6h5Ej4w9njokAWX7xdYFjKqRKb/OiRdLyjqXDk7aGnKk9ZXSLc95WxfMFhQ6zOSaXXCE7dH6eb1cJULLapyTRGd/6RlMKV8pUgzXf8uy3qvd2u7RZeo7RZKlwIAgF5BNZACbFw7NG92/OvXDeme/Y151ScuvXBl6koWwX09suUy3XnDxXpxakbH2tkawcogYTPa+/XCkz//eZU0Z9z/B+9/wpl+7fLQk0cSdxz7jasWRDZJK5hkqXQiRaejO0XMTTHP5LHI9r3pknPn3P9nnDYwJ1CRpD1pqt1krVwzNt7QxR/8ms7b8oDO2/KA1m7/mrOajvdZy1L9watikyYUnGfOkUzXv8vKmtC3rO267rngN0U3h84BAAAsJAwDKUgwDXz9yN7QzsNDTx5JXckiTNxYaW97t44+pumQ7JmyJ80sKiU9bN6IqIyRqA5Jms5K2DnLI2lqf9YhAK5jizpXWr4qcWbFifovaFmK9p2/5YFU7fS2mXT8f5YO6dh4Q5u/9JiaM6eubXC+iCJkqQ5U6ze5OrhVq+STRFkT+hax3bDvL9e59IJkvTL8BgAAoFcQrChJVOch7+Rxcdv3pJkgssjx7kWPGU9TbSWqQxI1xjwobCLFvJJe9yz3R1T5zLHxRvj2Lr890ZwVU7ZPH2neoG0h7fPum1t2Hphz32TpMKbpcGfZ/o7dh+YEKjz++SKiJP2MRN2vQ+1Sr1HVQLKoWiWfJMqaSDTvdl3fX4PLaplL5AIAACA9hoGUxNVJKKrzELV9LwX9/C0PaMfuQ7p+3dCcFP3gOPY06fdJFJ2SnvTpcFyHJG5ojMe01+0lmzdcMC8dXWo99XWd97Hp9drW9279ZOYM2fa61s7987w9Te9tvkuff/7V898fcd9kGc6S5jOTZft5sm7SfEZc2zKSHtlymT60cbUObL1C3x+5St8fuUrjt1+RO3iZdfhQN4UNnytijo2823V9f1mrnjvHAAAAvYzMipLEPd3Lm8ng2v6lF66c91Twnv2NyF/Wiy6/V3RKuuup8WC9ptOXDiQ+h8EhDMvrNb1wcmrO3ApG0psuObeyadxRpR5v3nkg9D1h5/3U0+NX63NqBSLqtX6dVutzPj0Oct0323Y9oQNbr5hdJ+n1SfNEPMtwmajMmrggYprPSDeyHKpWySepIrLMit6u63vq+GRTd95wcc+dYwAAgF5FsKIkUZ2HIoZJuLafJfDQqeBCnzHuIQkRXGVHJyabOn3pgO684eJU5y2YVZK389GpkpFx981Qik6y6z5ZOtCneq0/0bAhV8d/YrI5e53TnIe0He6029+84YJ5c1ZIyeaLSPMZKWt4Q5gqliutYpvSiAo2lRVcAQAAwHyULu0CV2m9fmP00U2vcpZqTNIBcJVKDCuL6G3T1enMOhY7aoLBrOUU/W0NlgvsZonGXCUjD45K998sNV9ItC87+1eA8ZVQjHh9zrYC683I6O+nL9e2qbfFPj1OMoFkVcfxj403tG3XE6nni0hbDrMTHfYqlivtREnZslXxvBaJ0qUAAKBXkFnRBa6ntNPWhmZYpMnESJqCHtfhzPMkOKoSSdbhJd4TzbBOY54hK3llHkJzcFS6952SZhLvy8z+le31OesG1uuX1Y39/6gzagPauPaqyLYnmfC001UokgYHsj4ZT5st0Ykn8EUP3yqrTd43QN6JdjulV4fUAAAALDQEK7ogaux8WGcjTackaacqrmJB3l/ON64d0i2OORQaE5NaP7I3U0egaiUaM7dnz3alCVR0gjHS7+hrseslOdedrEJRdPWZMFXswFbts5Bk32UEU8rIYskabOr1ITAAAABVQrCiC8ICCn7BX/jTdEqSdqriKhYUIaqcprc8bceyaiUaM7fn+OGSWpRPn40PoMSVgO10hYROZRhUbb6Cqn0WvH3HlQcuMpjSiUBVL7YFAABgIaB0aRd4pfX6g7n4bVatMfJeWcQkJR2D5Uo3b7hAT41cpUe2XBb6i3LS0qf+driWu4SVUwzONyGlK2taZInGtMeTpj2XXrgyetvLV6XeV0eY+NKurusqteZd8a5n1tK3YaKuVVEZBkXcD51UxXKlScoDFxlMKbpM8kJpCwAAwEJAZkWXeAEEV4aF/6lckjKoaZ/opSl9evPOA3rflx/TSV+JzyT7CMvycD11TdqxLCodv8inoEsH+ma3c+aymq5a8zLds78Rve3Lb089Z0XZrCSz7i2x64Vdg0svXBl/zBGi0ufjrlURGQa9+FS8ikNT/G1yTYZbZDClSkNhqtQWAACAhYBqIB0U1iGTTv1iH2awXtOBrVdEdubSVipwtefSC1fqC994Zt6kmFHSVn3I2taiFdEOV9WA02p9OnaiGb9tXzUQZyUP6VTagqvMi3yvm1OL03y0Z2T0/fNu0C+99VOzy9KMv89zPsPOY63P6IzTBjRxoqk+Y0LvSW/bRVRvqMp9udCUPYdDla5bldoShWogAACgV5BZUQJXUCLsye0d163WI1suc5YcnZhsamy8ETlePssTvbBAxT37G6kCFXH7CJO2qkJZingK6kr7ds1FMq8js2ZT64+k+2LKvUZV3wi+7nXUXUEwbwLVNOVJ4zIN8pzPsPPYnLGzAR/XPeltu4gMA56Kl6PseT6q8n1StbYAAAAsBAQrCubq5J1W63OOZ45KZZcUO1Hg4LJa6JN8Vxp8WBvvfvRp54P9KGnHn8d1LDs1m37eoQNj443YiQSDTPt9kvv4XeVe+x3ZBd4cEcH1vXlLXJ2nuE6kKxBz884Ds9v2vz/P+cwaEPBvO2+nuIqTVcah8kS1hsJUqS0AAAALAcGKgqV92u511DZvuEA3O0p9xmVIPP+zqXnLa/3G+UQvrI1ZAhVGmrOPpJ0nV8eyk/MG5HkK6rXTZbBe0/HJ5rxzaiV9476/1Wb7WV2r56WlkialY2P/k775zP+mjde801nuddra0PH/UfdVns5T1D0Xdl3ynM8kFSSCgvdeXlV9Ku76TPXiHBtlqVKVliq1BQAAoNcRrChY2qfE3pPbjWuH9MH7n0iVISG1OqLNmfmhhtOXDKQeNpKWleZkROTtPHWiBKW/87e8XtNptT5NnGim6siHtdNTr/Vr2zUXhQaerul7WNvsp7TUzH3vCv1UZ/zrn0jnnamzB89ylnu1gf9fv25IDz15JDIjIGvnKS6AELwueQIjcaV8w/jvvSJU8al41GeqU+Vai0QmCAAAANIgWFEwVydvsF7Ti1MzkU9ut159Ueqnu67Aw/HJ+UGPuDYGeR1kY8InaxzyBVGK6DyVPW9AsPM3MdlUvdavO2+4uJD5DSTNTuoYNl/E+wZG5wUqPEs0Je3Zrs0bds+7B8LKvVpJDz15pLSMgCQBhOB5yBoYCQYKltdreuHklJrT7nyfoRKGZ1TtqXjUZ6qsz0pZAQUyQQAAAJBWX7cbsNBs3nCB6rX+Ocu8p+13XLdaQ4N1GbU6W8FqBRvXDsWuE+TKuojKxghrY1C/Mbrzhov1/ZGrdOemi0OPyd8hLqLzlOVY0ojq/KXhas/QYH3OsIjgOTvb/CR6w8cPh94Dri67N9Qj7T2ThH+7LkXO57Bx7ZAe2XKZnhq5Sge2XqEdb3jV7L5NYN0qDM/ohKjPVBmfFS+g0JiYlNWpgII3z0oeRX32AAAAsHiQWVGwuHTyuE5k2qe7WZ6s+9voyrCYsTZVin8RExSWPW9AUU+jk7Qz7Jz9zPyClk0+597w8lWz7/WfW1dJxLxDPeJ423WVBi0zYOA/psU6fCDqM1XGZ6XMoSWuz1hjYnK22hEAAADgR7CiBJ1MJ8861t5ro6sjbNXqJPsrRwS3GZz/odZv5qTup+08lT1vQFEVH5K209/Z37H7kN5//He0Y8mntEQhQyv6l0iX3x66v25P/tjt+RyqNjyjU+KquUjFXpMyh2FFDT1LOxxksQavAAAAFhtjwyYj6GHDw8N237593W5Gocr85TzsqblfvdYfOqwg7H21PqMzThtIPWFlp7gyBIoYNpF0n9f0Paxttbt0pnn+1PCG+grpyg9LazZFbifvPZBkG3QEq6WT18MVuBwarOuRLZfl2nbc90zSfXTjM7zQGGP2W2uHu90OAACAOAQrMupUJ6ITQQHvWFxPPsM6EmV2bMrU6c543vNUVHuTdPK6FcwhOFINZV//sfGGszyzkfTUyFWx2+jV750qIVgBAAB6BcNAMujkzPZh48ibM3a2xGkR+/bS7M/f8kDoZI5haeBlV+4oS6eHFOQ5T3nus2AQ4MTJqdj5CFxzFmzb9UThAYWx8Ya27XpCE76qNZ2uEEGgZK6yh/u4quRIyYdi9er3DgAAANJLFKwwxnxW0usl/cha+8r2shWSdko6T9L3JW2y1h4zxhhJfynptyWdkPQWa+2/tt/zZkkfaG/2Q9baz7eXr5P0OUl1SV+V9MfWWuvaR64jLkCZE9EFJfklvKh9p5nTwbVunzE6f8sD5XT+Do5KD94mTR4tbpsl+95pml93VNKMMdJX3ia9/mPO92a9z8KCHC7++8t1r01MNmeDCv6AgtfGtB3bqCEBZX2O4trQ7VKaVQmclB3Myzv/SlHzzgAAAKD6kpYu/Zyk1wWWbZG0x1r7Ckl72v+XpCslvaL95x2SPinNBje2SnqNpFdL2mqMObP9nk+21/Xe97qYfXRV3NO9sfGG1o/s1flbHtD6kb25Sv/lfeLoSdImV9nVsI6Eq/zptLWRZQ8zn5uDo9LYTT0VqJBa6e3GzP/TLyvt+4z0lfc635v1KXJYkMPFf38lvde8bIusZS7j2teJp+RVKqVZZsnQqslbajfNdxQAAAB6W6JghbX2nyUFe4nXSvp8++fPS9roW36XbXlU0qAx5mWSNkj6urX2aDs74uuSXtd+7SXW2n+xrQk07gpsK2wfXeXq1J09WC+84+EKCiRtk5S8M5SmIxFct9+YeesEO39h7bhl5wF9YOzxee+dZ892aaYZv16v2f85SeFBnKj7LErSzn6wk5f0XpNa2RZJOvthxxXXvk48Ja/ScIIqBU46YePaIT2y5TI9NXKVHtlyWapMjrzBDgAAAPSOPHNW/Ly19jlJstY+Z4z5ufbyIUnP+NY73F4WtfxwyPKofcxhjHmHWpkZOvfcc3McUjJRqcxFDxEJjiNfXq/phZNTqUqEpmlTmjRw/7rnb3kgdB1/5y+sHVbS3Y8+reGXr4je7/HD7td6mZ12Dkm4ft2Q7tnfSJ0yH1Um0tNvzLxOXticBSdOTs3Oj5KE/3q7jmtwWc25zU49Ja/ScIIqBU56wWItZQsAALDYJB0Gksb8R+ytPmna5YlZaz9trR221g6vXLkyzVsziXq6V2THw3sqfUt7Bv07b7hYB7ZeoR1veFWqJ4ud6AwlyQJw7c9KunX0sejsk+Wr8jSvuky/M5j00JNHMj1FTpIhMWOtM2PG/9R769UXhabdn7msFrpd//V2HZe1Cm3fmctqHXtKXqXhBFkzaNAbihwWCAAAsJjkyaz4oTHmZe2Mh5dJ+lF7+WFJ5/jWWyXp2fby3wws/6f28lUh60fto+tcT/eKemIbNwFgmg5dJ54iJ5k4L+qJ/7S10RMcXn57a86KhTYUZN1b9OzD7mBSlqfI/gwJ1/lOeu1dFSIkxV5vV3Dq+GRTd95wcVcnlCy78kUaeSedRHVVbSJXAACAXpInWLFL0psljbT/vc+3/I+MMV9UazLN4+1gw25J/9U3qeYVkt5vrT1qjPmpMeYSSd+QdKOkv4rZR2UV1fEocjhJJzpDXpv85ShPq81N3Nm84QLdsvOAM20meHxzKyScpY9f/Of6tW+PzJtk087+lUzI9BqntuP90F7HsWr4e6PaYALbMn3SurdKr/+Yzv7W3sKDSV6QI6zyRtprHxUwiersRwXJqpDKX4U2eO2QqhE4QbE6WTkKAABgoUlauvQLamVFnGWMOaxWVY8RSaPGmLdLelrSG9urf1WtsqXfUat06VslqR2U+DNJ32yvt91a6/U6/1CnSpc+2P6jiH1UVlEdjyKHbnSyM/Ti1Mzsz8dONOdlg+z7wVHd/ejTzn69v6JK8Inkjd98ue647uF57Tbt9f2BkijfH7lq3jJXpz7psITXjoQHHDxDg3U9suWy0NeKDCaFlcC847rVpVz7uM4+GQPJVSVwgmIxHwkAAEB2plWAY+EYHh62+/bt63Yzclu7/WuhkxB6nd6wTmm3OzvrHR32YEd9bLyhW0cf07Tj3hsKmdjxmr6H9b6BUZ3d9xP1LV/VGhayZlOi/fsN1ms6sPWKxG0frNd0+tKB2clNjZEmTjTnnfPztzwQl1ihp0aucl63Iq5n3oBLGap4nwKdkvQ7sZOMMfuttcNd2TkAAEAKeYaBoCRj4w09/7Opectr/UabN1xQqXHQ/s5oXLaEx2tjsGPtCf5yf03fwxqp/Z2WmZOtBcefke5/T+tnX8Ai7mllrc9o2zUXJWqjZ2KyOZut4c/aCJ7zuAoc/rK2RcxDEqaKKedkDGAxI7sIAAAguzKqgSCnHbsPqTkzv+t/+pIBbVw7FNkp7SSv892ICFRI4XMv+Cuq+F3T97D2L3mHnlr6n2b//GXtb04FKjzNSWnP9tj9eIYG69rxxlc5O85Z5ofwn/OoChxJytoWgZRzoFqiKkcBAAAgGpkVFRRVRSHq9U53SsM630FRTxG9p+7eEIpr+h7WjtqntNREb3PW8cNz/ut6ipm03Kcr0yOKd86DFTj6jdG0tRryDX3wStC6tpFX2VVfGNIBpEd2EQAAQDYEKyoortPZiVKkSUR1so2UuEPrHc/7BkaTByokafmqOf/NM5Fo2HuD82a42u7fRtS+yr5uZaacV2noURYEWgAAAIDeQrCiguI6nVUZB+3qfKedPM47nrPNj9M14PLb5y3K8xQz+N6wCSv9an0m1Tkv+7qVWfWlivNhJNXrgZZeR6AIAAAAWVANpKLifsEPe13qTHlSfxvCOt/XrxvSQ08eSdWOsfGGLrnvN/QLOpJs5/UV0m1P5Wl+It55DgvK9Blpeb0WWh0kbnu91nFzVTvxqpxUWRUrMiwWVaxQs9hRDQQAAPQKghUpVbWz2a1OQfB8XHrhSt2zv5GtHQdHpbGbpJnooRfqXyJd+9fzSpeWKUlZ1IXcCevlDn8vB1p6XS/fNwsVwQoAANArGAaSQpXTyV1p+jfvPKAduw+VFlQJDp1YP7I3+3ABL/jw4G3S5NHwdeorpCs/3NFAhZRsEsxeGRaR1th4Qy+8OL+Ubq+UYKzKHC9V04nAa1UmAwYAAEDvIViRQpXH7Uf98t/JoEruzsmaTR0PRCTh6vAGLbROmGvejjOX1bT16ou6ft8nUZU5XqqkU4FXAkUAAADIqq/bDeglVX5KGPfLvxdU6VY7OtE5GRtvaP3IXp2/5QGtH9mrsfFGYdvevOEC1Wv9sesttE6YqzztsiUDPRGokFqd7zuuW62hwbqMWkMQFupwnaSiAq9FCvvcLPZAEQAAAJIhsyIF11NCq9bwh27OXxH29Dio0KDKV94r7fusFJgN4GFJWhqy/s8kbUux/SWnS6//+LwsC1fqetlPioOVNpbXa3rh5JSa06eO398Jq+rcJmlVOUCXRp4qMVF69Tp36rqWWaEGAAAACxvBihSiAgLdnr/C3ylwDVcoLKjylfdK+z4T+pKZ/Sunky9IY+9q/dwOWEQFJOKeFBfRWQorbdqNwEknpU3j79XOexa9fJ07OTyjrEARAAAAFjaqgaQUVcpSqsYs9655Bjy5q1Z8cIVk3RkchVp+jnTLtyRFVxZ4dmIytOKD1DreTlZJWUgVENJUmVlsZSp7+TovtmuFU6gGAgAAegVzVqS0ce2QHtlymTN5oArp8f4x+mHyjk23nQpUSNLxw7NzUbgCRN5T/DD9xnRkbH6wPWmWV1ma+R46NQ9CVfTydWYeDwAAAFQdw0Ay6vQs92nT673U6/O2PBD6ep4O1bT6NKCZzO9P40T9F2Ln4vDOR3A9I2nakTmU5/jjrsVCq4CQNI2/lzvvWfT6dWZ4BgAAAKqMzIqMOjnLvZey3WgPdfDGxsdVuxgbbzgzQPJ0qO6euiPsx/oAABzoSURBVEwdGT3U16+PNG+IDFTU+oxOnJzSLTsPaOlAn85cVpPUClRENTHr8Se5Fou1AkI3K8F0w2K9zgAAAEAnEKzIwHuyPtmcVr9phQPKTKPOml6/Y/eh0A67kTJ3qMbGG9o29TbdNf0fNW0l6/+TaYsOS06XNv6tPv/8q52rDNZrkpGOnWjKSpqYbOpnzRmduawW2ZY8HUrXtdi264nZsqk7dh/S9euGFl2K/WLrvDOUAgAAACgPw0BSCk5MN23tbIfMqwRRdDWErOn1rtetslcr8AIgW6fepq1Tb5tdbiTdecPFhXfUzv6qexJDqRWg8JtsTkdmYgzlvCauczox2ZxtS2NiUvfsbyy6jutiLFPJUAoAAACgHAQrUorLciijlGHWsfGu97km3kyijABIlLC5KLzg0C07D6TaVhFVGlznNMi7JxZbR5bOOwAAAIAiEKxIKSrLISqQkacDF9VhD/Jndiyv11TrN2pO29j3zTo4Kj14mzR5NPTl752mOeM9jukMbWveqP0v+a3Ux5VE1NN6VwnZwXpNL07NJDpfaYVdC5eFOrEkAAAAAJSNYEVKUVkORVVDCBtKcsd1q0M77MHgxL//rKmZdjBhYrKpPklnLqtp4kQzPi3/4Kg0dpM00wx/Xa3hHv5ZO1foef1F7dN67FfOk5Qva8HF9bTeFcTZds1FksoZjhAWPDlxckrHTsw/Zwt1YkkAAAAAKJuxHSnr0DnDw8N23759pW0/OGeF1Ooge8EE17CLpMMPorYf7GyHrRtmsF7Tga1XxO/8zldKx59J1M55lp8j3fKtbO/NoYw5QrK0Iek1A4BuMsbst9YOd7sdAAAAccisSCluEsGkwzVc0gwlCVs3THASSqfjhxO3s8j35gk4VGGOhMU4sSSQVxUCjQAAAKgughUZuDrIRXRa0wwlKXxOhOWrcmRWrMr0tmBWQlGTknZaFYImQK9YKJ97AAAAlIdgRcHSdlqDTxcHl9USz3+QtDLFmctqyRpz+e2xc1aE6l/Sem8K3nGHtX+xVtIAFouyJiMGAADAwkGwoovCni7W+kziCh5JKlPU+o22Xn1Rsgat2dT6N6IayDz1FdKVHz713hDBgMylF67UPfsbke2mkgaqiuEL+RU1GTEAAAAWLoIVXRT2dLE5YzVYr+n0pQOxnaGwYSeXXrhSDz15JHtHas2myMBDWmEBmbsffVpx07pSSQNVxPCFYkRVVQIAAAAkghVd5RrCcXyymax6h6o/V0JYQCYuUJF2UlKgUxbK8IVuZ4e4yg7zuQcAAICHYEWXjI03ZBTecc/9dPHgaLqhHC4JhnjESZvWPURaPSrs/2/v/oM8r+/6gD9fWfZ0NfYuROIkd9gkljLScEK9iczQPzTXBogimIk3ib/QZorTkElSLQK2c1KqJUzaoEyTdKIyoqPCVgmQNBTTSzrtdGqSS4hLIlIxRjmOhrMXzjjuhON494/vZ8Pe3e7t7ne/u9/P7j4eMzvf7+f1+Xx23/ueW4bP8/v+sRmmL/RhdIgddAAAWIqwYkze89BjCwYVlazu08WZ6eEWyVzI7NHk/usG71ewJsX8h47FhnufGtRMTU7k1jde6GGFdbeSUQarmb4w7tEMc/oyOqTvo8IAABivF427ARvJfQ8/mUvf/fG86sb/kkvf/fHc9/CTQ3+vxT6JbVnlp5sP3jCaoGLOiWeTA7csenruU9onn5lNywuf0s71zfWXnZ+pyYmT7pmanMiPXvLt2bljKpXBaIqFgopR9jcsZKl/v6da7N/zUgHjSn/OWtoMo0MAANj8jKxYplEPnV7sE9qdq5kCMjO9+qkfCzl2aNFTS31KO+xw7z4MVWdt9WGkwUpHGQz777kvoxkSi1sCALAxCCuWadQPG2uywNwZRkCsyvZdi55a7NPY+Q9Dwwz37tPDHaPXlzBqmFEGw/x77tNoBotbAgCwEZgGskyrfdg4dUpDktz6xguXnAqxIseeGP7exUxsS/buX/T0Yp/GVrIm02QMVd8czhRGrafF/v2OepTBev2c5bj64p2j/28PAACMmJEVy7TahfUW+hT51jdemP914+tG18iaSNqJpa9brmXsBnL9ZefnX9zzudMWC23JqkZBGKq+ufUljFqvUQZ9G81gcUsAAPpOWLFMq3nYWLcpDWcKKm4+NrqfM8/VF+/Mu+753ILnVvPg2beHO0arL2HUem2haatOAABYGWHFMq3mYWNdPkWemc7pG4J2tp87up+zgJ1r8ODp4W5zW+swaiWLd67XKAOjGQAAYPmGDiuq6vwk98wrvTrJ/iQ7kvyzJEe6+s+31j7a3XNTkrcmOZHkHa21h7r65Ul+JclEkl9rrb27q78qyd1Jzk7y2SQ/3lp7dtg2r9awDxuLfYr8oqrc9/CTy/ueH/mZ5OCdWTCMOKM645oTo7BWD54e7javtQyj+rJ4JwAAMLxqbaUPvwt8k6qJJE8m+Z4kP5Xkb1pr//6Uay5I8rtJXpvkFUn+W5K/353+P0n+SZJDST6d5C2ttT+uqukk97bW7q6q/5Tkj1prHzhTW/bs2dMOHjy46t9plE59eJpvanJi6cXtPvIzycFfH74BazQFZL4+bEMJSXLpuz++6LbAI10jBjagqvpMa23PuNsBALCUUU0D2Zvkz1prf1FVi11zVZK7W2tfS/LnVfV4BsFFkjzeWvtiklTV3UmuqqpHk7wuyY9019yV5OYkZwwr+mjuof1np/8oJ04Jh5a1dsVnfmP4H77GU0DmGAVBX/Rl8U4AAGB4o9q69M0ZjJqY8/aqmqmqO6vqJV1tZ5L5e2se6mqL1V+a5JnW2nOn1E9TVddW1cGqOnjkyJGFLhm7qy/emecXGcWy5EPUanb4WOMpINA3fdomFAAAGM6qw4qq2pbkB5P85670gSTfkeSiJE8l+Q9zly5wexuifnqxtQ+21va01vacc845K2j9+hr6IaomhvuBU2efcdtR2Iyuv+z8TE2e/DdjJxkAANhYRjGy4ookn22tfTlJWmtfbq2daK09n+RX88JUj0NJ5s9J2JXk8Bnqf5VkR1WddUp9wxr6Ieq7f3LlP2xiW3LFbSu/Dza4qy/emVvfeGF27phKZbBWxZLrwgAAAL0yijUr3pJ5U0Cq6uWttae6wx9K8vnu/QNJfqeq3pvBApvnJflUBiMozut2/ngygyklP9Jaa1X1iSRvymBHkGuS3D+C9o7N0Dsg/MB7B6+n7AbS5r/pxqFUMhhRccVtC46qsBAmW4E1VAAAYGNb1W4gVfVNGaw38erW2rGu9lsZTAFpSb6U5Kfnwouq+ldJ/mmS55K8q7X2YFd/Q5JfzmDr0jtba7/U1V+dF7YufTjJj3ULdC6qj7uBrIWFdhhZameRYe4BYPOwGwgAsFGMZOvSPtkqYcUw2zPa0hFgaxNWAAAbxai2LmVYM9PJgzcks0eXd303xePwM9+84Okz7SxiS0cAAAA2glFtXcowZqaT+962/KAiGVx7/3W55sWfWvD0mXYWsaUjAAAAG4GwYpwO3JI8f3zl9514Nj83ec+KdxaxpSMAAAAbgbBinI4dGvrWb5r9vyventGWjgAAAGwE1qwYp+27kmNPDH3vMNsz2tIRAACAvjOyYpz27k9eNLni255tZw3uBQAAgE1IWDFOu/clV79/sMPHAlqS1k7+OtpenH83+fbBvQAAALAJmQYybrv3LRo83P/wk7np3kcye/zE12tTkxO59fsvXK/WjdR9Dz+Z9zz0WA4/M5tX7JjK9Zedb0oKAAAApxFW9Njcg/xmeMC/75Tg5clnZnPTvY8kyYb8fQAAAFg7woqe2ywLYr7nocdOGiGSJLPHT+Q9Dz22KX4/AAAARkdYMU4z08mDNySzRwfHU2cnV9y2KdejOPzM7IrqAAAAbF0W2ByXmenkvre9EFQkg/f3Xzc4t8m8YsfUiuoAAABsXcKKcTlwS/L88dPrJ54dnNtkrr/s/ExNTpxUm5qcyPWXnT+mFgEAANBXpoGMy7FDw53boDbTYqEAAACsLWHFuGzflRx7YvFzm9BmWSwUAACAtWUayLjs3Z+8aPL0+sS2wTkAAADYooQV47J7X3L1+wc7gMyZOju56n2bcjcQAAAAWC7TQMZp9z7BBAAAAJxCWEHv3PfwkxbiBAAA2MKEFWtlZjp58IZk9ujS106dnVxxm1EWGQQVN937SGaPn0iSPPnMbG6695EkEVgAAABsEdasWAsz08l9b1teUJEMrrv/usF9W9x7Hnrs60HFnNnjJ/Kehx4bU4sAAABYb8KKtXDgluT54yu758Szg/u2uMPPzK6oDgAAwOYjrFgLxw6t732byCt2TK2oDgAAwOYjrFgL23et732byPWXnZ+pyYmTalOTE7n+svPH1CIAAADWm7BiLezdn7xocmX3TGwb3LfFXX3xztz6xguzc8dUKsnOHVO59Y0XWlwTAABgC7EbyFqY29XDbiBDufrincIJAACALUxYsVZ27xM+AAAAwBBMAwEAAAB6RVixFmamk9tfk9y8Y/A6Mz3uFgEAAMCGYRrIqM1MJx9+R3J8dnB87InBcWJaCAAAACyDkRWjduCWF4KKOcdnB3UAAABgScKKUTt2aGV1AAAA4CTCilHbvmtldQAAAOAkwopR27s/mZw6uTY5NagDAAAASxJWjNrufcmVdyTbz01Sg9cr77C4JgAAACyT3UDWwu59wgkAAAAYkpEVAAAAQK+semRFVX0pyVeTnEjyXGttT1WdneSeJK9M8qUk+1prX6mqSvIrSd6Q5G+T/GRr7bPd97kmyb/uvu0vttbu6urfneQ3kkwl+WiSd7bW2mrbPTIz08mDNySzRxc+P3V2csVtRloAAADAMo1qZMX3tdYuaq3t6Y5vTHKgtXZekgPdcZJckeS87uvaJB9Iki7c+IUk35PktUl+oape0t3zge7aufsuH1GbV29mOrnvbYsHFcng3P3XDa4FAAAAlrRW00CuSnJX9/6uJFfPq/9mG/jDJDuq6uVJLkvysdba0dbaV5J8LMnl3bm/01r7391oit+c973G78AtyfPHl77uxLODawEAAIAljSKsaEn+oKo+U1XXdrVva609lSTd68u6+s4kT8y791BXO1P90AL1k1TVtVV1sKoOHjlyZAS/0jIdO7T0NcNcCwAAAFvYKHYDubS1driqXpbkY1X1J2e4thaotSHqJxda+2CSDybJnj171m89i+27kmNPLH3d3LUAAADAklY9sqK1drh7fTrJhzJYc+LL3RSOdK9Pd5cfSnLuvNt3JTm8RH3XAvV+OO/1y7tuYluyd//atgUAAAA2iVWFFVX1zVX1LXPvk7w+yeeTPJDkmu6ya5Lc371/IMlP1MAlSY5100QeSvL6qnpJt7Dm65M81J37alVd0u0k8hPzvtf4/ekfLH3N1NnJVe+zGwgAAAAs02qngXxbkg8NcoScleR3Wmv/tao+nWS6qt6a5C+T/HB3/Ucz2Lb08Qy2Lv2pJGmtHa2qf5vk0911t7TW5rbY+Od5YevSB7uvflh0HYpKbn5mXZsCAAAAm8WqworW2heTfNcC9f+XZO8C9ZbkukW+151J7lygfjDJa1bTzjWz2JoV1qcAAACAoa3V1qVbw979yeTUybXJKetTAAAAwCoIK1Zj977kyjuS7ecmqcHrlXdYnwIAAABWYRRbl25tu/cJJwAAAGCEjKwAAAAAekVYAQAAAPSKsAIAAADoFWEFAAAA0CvCCgAAAKBXhBUAAABArwgrAAAAgF45a9wN2LBmppMHb0hmjw6Op85Orrgt2b1vvO0CAACADU5YMYyZ6eS+tyXPH3+hNns0uf+6wXuBBQAAAAzNNJBhHLjl5KBizolnB+cAAACAoQkrhnHs0HDnAAAAgCUJK4axfddw5wAAAIAlCSuGsXd/8qLJ0+sT2wbnAAAAgKEJK4axe19y9fsHO4DMmTo7uep9FtcEAACAVbIbyLB27xNMAAAAwBowsgIAAADoFWHFMGamk9tfk9y8Y/A6Mz3uFgEAAMCmYRrISs1MJx9+R3J8dnB87InBcWJaCAAAAIyAkRUrdeCWF4KKOcdnB3UAAABg1YQVK3Xs0MrqAAAAwIoIK1Zq+66V1QEAAIAVEVas1N79yeTUybXJqUEdAAAAWDVhxUrt3pdceUey/dwkNXi98g6LawIAAMCI2A1kGLv3CScAAABgjRhZAQAAAPSKsAIAAADoFWEFAAAA0CvCCgAAAKBXhBUAAABArwgrAAAAgF4RVgAAAAC9IqwAAAAAekVYAQAAAPSKsAIAAADolaHDiqo6t6o+UVWPVtUXquqdXf3mqnqyqj7Xfb1h3j03VdXjVfVYVV02r355V3u8qm6cV39VVX2yqv60qu6pqm3DthcAAADYGFYzsuK5JD/bWvvOJJckua6qLujO3d5au6j7+miSdOfenOQfJLk8yfuraqKqJpK8L8kVSS5I8pZ53+e27nudl+QrSd66ivYCAAAAG8DQYUVr7anW2me7919N8miSnWe45aokd7fWvtZa+/Mkjyd5bff1eGvti621Z5PcneSqqqokr0vye939dyW5etj2AgAAABvDSNasqKpXJrk4ySe70turaqaq7qyql3S1nUmemHfboa62WP2lSZ5prT13Sn2hn39tVR2sqoNHjhwZwW8EAAAAjMuqw4qqenGS30/yrtbaXyf5QJLvSHJRkqeS/Ie5Sxe4vQ1RP73Y2gdba3taa3vOOeecFf4GAAAAQJ+ctZqbq2oyg6Dit1tr9yZJa+3L887/apKPdIeHkpw77/ZdSQ537xeq/1WSHVV1Vje6Yv71AAAAwCa1mt1AKsmvJ3m0tfbeefWXz7vsh5J8vnv/QJI3V9U3VNWrkpyX5FNJPp3kvG7nj20ZLML5QGutJflEkjd191+T5P5h2wsAAABsDKsZWXFpkh9P8khVfa6r/XwGu3lclMGUjS8l+ekkaa19oaqmk/xxBjuJXNdaO5EkVfX2JA8lmUhyZ2vtC933uyHJ3VX1i0keziAcAQAAADaxGgxg2Dz27NnTDh48OO5mAEDvVNVnWmt7xt0OAICljGQ3EAAAAIBREVYAAAAAvSKsAAAAAHpFWAEAAAD0irACAAAA6BVhBQAAANArwgoAAACgV4QVAAAAQK8IKwAAAIBeEVYAAAAAvSKsAAAAAHpFWAEAAAD0irACAAAA6BVhBQAAANArwgoAAACgV4QVKzUzndz+muTmHYPXmelxtwgAAAA2lbPG3YANZWY6+fA7kuOzg+NjTwyOk2T3vvG1CwAAADYRIytW4sAtLwQVc47PDuoAAADASAgrVuLYoZXVAQAAgBUTVqzE1EsWrm/ftb7tAAAAgE1MWLFcM9PJ1756en1iW7J3//q3BwAAADYpYcVyHbglef746fVtL7a4JgAAAIyQsGK5FluXYvYr69sOAAAA2OSEFcu12LoU1qsAAACAkRJWLNfe/cnk1Mm1ySnrVQAAAMCICSuWa/e+5Mo7ku3nJqnB65V3WK8CAAAARuyscTdgQ9m9TzgBAAAAa8zICgAAAKBXhBUAAABArwgrAAAAgF4RVgAAAAC9IqwAAAAAekVYAQAAAPSKsGIlZqaT21+T3Lxj8DozPe4WAQAAwKZz1rgbsGHMTCcffkdyfHZwfOyJwXGS7N43vnYBAADAJmNkxXI9eMMLQcWc47PJgVvG0x4AAADYpIQVyzEzncweXfjcsUPr2xYAAADY5HofVlTV5VX1WFU9XlU3jqURZxo9sX3X+rUDAAAAtoBehxVVNZHkfUmuSHJBkrdU1QXr3pAzjZ7Yu3/92gEAAABbQK/DiiSvTfJ4a+2LrbVnk9yd5Kp1b8Vioyemzra4JgAAAIxY38OKnUmemHd8qKudpKquraqDVXXwyJEjo2/F3v3J5NTJtcmp5IrbRv+zAAAAYIvre1hRC9TaaYXWPtha29Na23POOeeMvhW79yVX3pFsP3fQpO3nDo6NqgAAAICRO2vcDVjCoSTnzjveleTwWFqye59wAgAAANZB30dWfDrJeVX1qqraluTNSR4Yc5sAAACANdTrkRWtteeq6u1JHkoykeTO1toXxtwsAAAAYA31OqxIktbaR5N8dNztAAAAANZH36eBAAAAAFuMsAIAAADoFWEFAAAA0CvCCgAAAKBXhBUAAABArwgrAAAAgF4RVgAAAAC9IqwAAAAAekVYAQAAAPSKsAIAAADoFWEFAAAA0CvCCgAAAKBXqrU27jaMVFUdSfIXa/gjvjXJX63h999K9OVo6c/R0p+joy9HazX9+Xdba+eMsjEAAGth04UVa62qDrbW9oy7HZuBvhwt/Tla+nN09OVo6U8AYCswDQQAAADoFWEFAAAA0CvCipX74LgbsInoy9HSn6OlP0dHX46W/gQANj1rVgAAAAC9YmQFAAAA0CvCimWqqsur6rGqeryqbhx3ezaCqrqzqp6uqs/Pq51dVR+rqj/tXl/S1auq7uj6d6aq/uH4Wt4/VXVuVX2iqh6tqi9U1Tu7uv4cQlV9Y1V9qqr+qOvPf9PVX1VVn+z6856q2tbVv6E7frw7/8pxtr+vqmqiqh6uqo90x/pzCFX1pap6pKo+V1UHu5q/dQBgSxFWLENVTSR5X5IrklyQ5C1VdcF4W7Uh/EaSy0+p3ZjkQGvtvCQHuuNk0LfndV/XJvnAOrVxo3guyc+21r4zySVJruv+DerP4Xwtyetaa9+V5KIkl1fVJUluS3J7159fSfLW7vq3JvlKa+3vJbm9u47TvTPJo/OO9efwvq+1dtG8LUr9rQMAW4qwYnlem+Tx1toXW2vPJrk7yVVjblPvtdb+R5Kjp5SvSnJX9/6uJFfPq/9mG/jDJDuq6uXr09L+a6091Vr7bPf+qxk8EO6M/hxK1y9/0x1Odl8tyeuS/F5XP7U/5/r595Lsrapap+ZuCFW1K8n3J/m17riiP0fJ3zoAsKUIK5ZnZ5In5h0f6mqs3Le11p5KBg/gSV7W1fXxMnVD5i9O8snoz6F1UxY+l+TpJB9L8mdJnmmtPdddMr/Pvt6f3fljSV66vi3uvV9O8nNJnu+OXxr9OayW5A+q6jNVdW1X87cOAGwpZ427ARvEQp/42UZltPTxMlTVi5P8fpJ3tdb++gwfRuvPJbTWTiS5qKp2JPlQku9c6LLuVX+eQVX9QJKnW2ufqarvnSsvcKn+XJ5LW2uHq+plST5WVX9yhmv1JQCwKRlZsTyHkpw773hXksNjastG9+W5Icrd69NdXR8voaomMwgqfru1dm9X1p+r1Fp7Jsl/z2AtkB1VNRfizu+zr/dnd357Tp/itJVdmuQHq+pLGUyTe10GIy305xBaa4e716czCNJeG3/rAMAWI6xYnk8nOa9b2X5bkjcneWDMbdqoHkhyTff+miT3z6v/RLey/SVJjs0Neebr8/9/PcmjrbX3zjulP4dQVed0IypSVVNJ/nEG64B8IsmbustO7c+5fn5Tko+31nx63Wmt3dRa29Vae2UG/338eGvtR6M/V6yqvrmqvmXufZLXJ/l8/K0DAFtM+f/D5amqN2TwSeFEkjtba7805ib1XlX9bpLvTfKtSb6c5BeS3JdkOsm3J/nLJD/cWjvaPYz/xwx2D/nbJD/VWjs4jnb3UVX9oyT/M8kjeWFNgJ/PYN0K/blCVbU7g0UKJzIIbadba7dU1aszGBlwdpKHk/xYa+1rVfWNSX4rg7VCjiZ5c2vti+Npfb9100D+ZWvtB/TnynV99qHu8Kwkv9Na+6Wqemn8rQMAW4iwAgAAAOgV00AAAACAXhFWAAAAAL0irAAAAAB6RVgBAAAA9IqwAgAAAOgVYQUAAADQK8IKAAAAoFeEFQAAAECv/H8xr4q5Fgb7dQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "i=1\n",
    "fig=plt.figure(figsize=(18, 16))\n",
    "\n",
    "for el in salary_options:\n",
    "    y_test_predictions = salary_model_data[el][\"y_pred\"]\n",
    "    y_train_predictions = salary_model_data[el][\"y_pred_train\"]\n",
    "    \n",
    "    y_test_true= salary_model_data[el][\"y_test\"]\n",
    "    y_train_true = salary_model_data[el][\"y_train\"]\n",
    "    \n",
    "    #true_v_pred = list(zip(y_test_true, y_test_predictions))\n",
    "    #true_v_pred.sort()\n",
    "        \n",
    "    #true_sorted = [x[0] for x in true_v_pred]\n",
    "    #pred_sorted = [x[1] for x in true_v_pred]\n",
    "    \n",
    "    true_sorted_indices = np.argsort(y_test_true)\n",
    "    true_sorted = [y_test_true[true_sorted_indices[i]] for i in range(len(true_sorted_indices))]\n",
    "    pred_sorted = [y_test_predictions[true_sorted_indices[i]] for i in range(len(true_sorted_indices))]\n",
    "    \n",
    "    true_sorted = [x for x in true_sorted if x < 200000]\n",
    "    pred_sorted = pred_sorted[0:len(true_sorted)]\n",
    "    \n",
    "    xvals = range(len(true_sorted))\n",
    "    plt.subplot(2,2,i)\n",
    "    \n",
    "    plt.scatter(x=xvals, y=pred_sorted)\n",
    "    plt.scatter(x=xvals, y=true_sorted)\n",
    "    i+=1\n",
    "\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_sal_buckets = range(50000,210000,10000)\n",
    "num_buckets = len(min_sal_buckets)+1\n",
    "\n",
    "def min_class_bucket(salary):\n",
    "    proper_bucket = -1\n",
    "\n",
    "    if salary < min_sal_buckets[0]:\n",
    "        return 0\n",
    "\n",
    "    elif salary >= min_sal_buckets[-1]:\n",
    "        return len(min_sal_buckets)\n",
    "\n",
    "    for i in range(1,len(min_sal_buckets)):\n",
    "\n",
    "        if salary >= min_sal_buckets[i-1] and salary < min_sal_buckets[i]:\n",
    "            return i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for el in salary_options:\n",
    "    y_test_true= salary_model_data[el][\"y_test\"]\n",
    "    y_train_true = salary_model_data[el][\"y_train\"]\n",
    "    \n",
    "    y_test_bucket_true = []\n",
    "    y_train_bucket_true = []\n",
    "    \n",
    "    for t in y_test_true:\n",
    "        y_test_bucket_true.append(min_class_bucket(t))\n",
    "    \n",
    "    for t in y_train_true:\n",
    "        y_train_bucket_true.append(min_class_bucket(t))\n",
    "\n",
    "    salary_model_data[el][\"y_test_bucket\"] = y_test_bucket_true\n",
    "    salary_model_data[el][\"y_train_bucket\"] = y_train_bucket_true\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC, SVC\n",
    "\n",
    "for el in salary_options:\n",
    "    salary_model_data[el][\"svm_model\"] = SVC(gamma = 'scale')\n",
    "    \n",
    "    X_t = salary_model_data[el][\"X_train\"]\n",
    "    y_t = salary_model_data[el][\"y_train_bucket\"]\n",
    "    salary_model_data[el][\"svm_model\"].fit(X_t, y_t)\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg_salary train accuracy: 0.21860019175455417\n",
      "avg_salary test accuracy: 0.164\n",
      "min_salary train accuracy: 0.25330776605944394\n",
      "min_salary test accuracy: 0.238\n",
      "max_salary train accuracy: 0.2031639501438159\n",
      "max_salary test accuracy: 0.164\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for el in salary_options:\n",
    "    X_train = salary_model_data[el][\"X_train\"]\n",
    "    X_test = salary_model_data[el][\"X_test\"]\n",
    "    y_train_pred = salary_model_data[el][\"svm_model\"].predict(X_train)\n",
    "    y_test_pred = salary_model_data[el][\"svm_model\"].predict(X_test)\n",
    "    \n",
    "    salary_model_data[el][\"svm_train_pred\"] = y_train_pred\n",
    "    salary_model_data[el][\"svm_test_pred\"] = y_test_pred\n",
    "    \n",
    "    y_train_true = salary_model_data[el][\"y_train_bucket\"]\n",
    "    y_test_true = salary_model_data[el][\"y_test_bucket\"]\n",
    "    \n",
    "    \n",
    "    \n",
    "    print(el + \" train accuracy: \" + str(accuracy_score(y_train_pred, y_train_true)) )\n",
    "    print(el + \" test accuracy: \" + str(accuracy_score(y_test_pred, y_test_true)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DFvUYZmbWd5VmlJP0TUlT8/J4Sbv08bgjgKUljQCWAR4HtgcuzuvPBjzek5lZjQZ8RrmI+BvwQ2A2KTE8S7oy+5mImF84xuqLegwzM+u7AZ9RTtKKwG7AWqT5rZcFPlayaTTZf7KkGZJmdHV1LWoYZmbWgzpmlNsBeCQiuiLiNeBSUu1kZG5yAhgDzC3bOSKmRsSEiJgwatSoPoRhZmatVBrum4VnlJsGfK0Px5wNbJn7NgRMBO4DbgT2zNtMAi7vwzHMzKyPWp7mmr/AHwA+Tj/NKBcRt0m6mHQq63zgDmAqcDVwvqRjc9npi3oMMzPru5YJIiJC0mUR8T7SF3i/iIijeeukQw8DW/TXMczMrG+qNDHdKmnztkdiZmYdpcqV1NsBh0p6FHiR1MwUEbFxWyMzM7NaVUkQZaegmpnZEFclQZRej2BmZkNblQRxNSlJCFiKdIHbg8AGbYzLzMxqVmXK0Y2Ky5I2Aw5tW0RmZtYRqpzFtJCImAn4rCYzsyGuypzUXyosLgZsBngQJDOzIa5KH8TyhfvzSX0Sl7QnHDMz6xRV+iC+MxCBmJlZZ6kyYdD1kkYWlleU9D/tDcvMzOpWpZN6VEQ801iIiH8C72hfSGZm1gmqJIjXJa3RWJC0Jr54zsxsyKvSSf0N4BZJ0/PyvwCT2xeSmZl1giqd1Nfmi+Ma80F8sS/zQZiZ2eBQpZN6D+C1iLgqIq4E5kvavf2hmZlZnSpNORoRzzYWcod198l+zMxsiKnSB1GWRKrsZ7ZIxk3p++SFs47fuR8iMRveqtQgZkg6UdI6ktaWdBJwe7sDMzOzelVJEIcB84ALgIuAV4DPtTMoMzOrX5WzmF6UdCxwTES8OAAxmZlZB2iZICR9FpgCLJuXXwBOiIifDkBsZsNKX/te3O9i/a1pE5OkbwK7ANtGxMoRsTKwHfCxvM7MzIawVn0Q+wEfj4iHGwX5/ieA/dsdmJmZ1atlJ3VEvFJS9jLwRtsiMjOzjtAqQcyRNLF7oaTtgcfbF5KZmXWCVp3UhwOXS7qFdN1DkOai3hrYrS8HzfNLnAZsmB/3QOBB0qm044BZwCfy0OJmZlaDpjWIiLiX9AV+M+lLe+18f8O8ri9OBq6NiPWB9wL3k86WmhYR44FpednMzGrS8jTX3AdxRn8eUNIKpCHDP5WPMQ+YJ2k3YNu82dnATcCR/XlsMzOrrsqV1P1tbaALOFPSHZJOk7QssGpEPA6Q/5bOWidpsqQZkmZ0dXUNXNRmZsNMHQliBLAZcEpEbAq8SC+akyJiakRMiIgJo0aNaleMZmbDXqsL5ablvyf08zHnAHMi4ra8fDEpYTwhaXQ+5mjgyX4+rpmZ9UKrGsRoSR8CdpW0qaTNirdFPWBE/B14TNJ6uWgicB9wBTApl00CLl/UY5iZWd+16qT+NqnpZwxwYrd1AWzfh+MeBpwr6W3Aw8ABpGR1oaSDgNnAXn14fBtAHkPIbGhqmiAi4mLgYknfiohj+vOgEXEnMKFk1VsuzDMzs3pUGe77GEm7kk5NBbgpIq5qb1hmZla3Hs9iknQccASpn+A+4IhcZmZmQ1iVuaV3BjaJiDcAJJ0N3AEc1c7AzMysXlWvgxhZuP/2dgRiZmadpUoN4jjgDkk3AiL1Rbj2YGY2xFXppD5P0k2kkVwFHJmvZTAzsyGsSg2iMTbSFW2OxczMOkgdYzGZmdkg4ARhZmalWiYISYtJumeggjEzs87RMkHkax/ukrTGAMVjZmYdokon9WjgXkl/IM3dAEBE7Nq2qMzMrHZVEsR32h6F2SDT1xFswaPYWuerch3EdElrAuMj4gZJywCLtz80MzOrU5XB+g4hzfr2/3LR6sBl7QzKzMzqV+U0188BWwPPAUTEX4B3tDMoMzOrX5UE8WpEzGssSBpBmlHOzMyGsCoJYrqkrwNLS/owcBFwZXvDMjOzulVJEFOALuBu4FDgGuCb7QzKzMzqV+UspjfyJEG3kZqWHowINzGZmQ1xPSYISTsDpwJ/JQ33vZakQyPi1+0OzszM6lPlQrkfAdtFxEMAktYBrgacIMzMhrAqfRBPNpJD9jDwZJviMTOzDtG0BiHp4/nuvZKuAS4k9UHsBfxxAGIzM7MatWpi+tfC/SeAD+X7XcCKbYvIzMw6QtMEEREHDGQgZmbWWaqcxbQWcBgwrrh9X4f7lrQ4MAP4W0Tsko9zPrASMBPYr3gFt5mZDawqZzFdBpxOunr6jX489hHA/cAKefkE4KSIOF/SqcBBwCn9eDwzM+uFKmcxvRIRP4mIGyNieuPWl4NKGgPsDJyWlwVsTxo1FuBsYPe+HMPMzPqmSg3iZElHA9cBrzYKI2JmH477Y+BrwPJ5eWXgmYiYn5fnkIYVfwtJk4HJAGus4ZlQzczapUqC2AjYj/QLv9HEFHm51yTtQrq24nZJ2zaKSzYtHc4jIqYCUwEmTJjgIT/MzNqkSoLYA1i7HzuMtwZ2lbQTsBSpD+LHwEhJI3ItYgwwt5+OZ2Zmi6BKH8RdwMj+OmBEHBURYyJiHLA38JuI2Be4EdgzbzYJuLy/jmlmZr1XpQaxKvCApD+ycB9En05zLXEkcL6kY4E7SGdOmZlZTaokiKPbdfCIuAm4Kd9/GNiiXccyM7PeqTIfRJ9OaTUzs8GpypXUz7PgjKK3AUsAL0bECs33MjOzwa5KDWL54rKk3XFTkJnZkFelD2IhEXGZpCntCMbab9yUq/v8GLOO37kfIjGzTlelienjhcXFgAk0uYjNzMyGjio1iOK8EPOBWcBubYnGzMw6RpU+CM8LYWY2DLWacvTbLfaLiDimDfGYmVmHaFWDeLGkbFnSPA0rA04QZmZDWKspR3/UuC9pedIEPweQZn37UbP9zMxsaGjZByFpJeBLwL6kSXw2i4h/DkRgZmZWr1Z9ED8APk6ae2GjiHhhwKIyM7PatRru+8vAasA3gbmSnsu35yU9NzDhmZlZXVr1QVSZK8LMzIYoJwEzMyvV67GYzGzw6OvYW93H3fJYXsOLaxBmZlbKCcLMzEo5QZiZWSknCDMzK+VO6g7X352MZmZVuQZhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVmrAE4SksZJulHS/pHslHZHLV5J0vaS/5L8rDnRsZma2QB01iPnAlyPi3cCWwOckvQeYAkyLiPHAtLxsZmY1GfAEERGPR8TMfP954H5gdWA30qx15L+7D3RsZma2QK19EJLGAZsCtwGrRsTjkJII8I4m+0yWNEPSjK6uroEK1cxs2KktQUhaDrgE+EJEVJ6hLiKmRsSEiJgwatSo9gVoZjbM1ZIgJC1BSg7nRsSlufgJSaPz+tHAk3XEZmZmSR1nMQk4Hbg/Ik4srLoCmJTvTwIuH+jYzMxsgToG69sa2A+4W9KduezrwPHAhZIOAmYDe9UQm5mZZQOeICLiFkBNVk8cyFjMzKw5X0ltZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMr5QRhZmal6phy1MxsUBk35eo+7T/r+J37KZKB5QTRj4brh8jMhiY3MZmZWSnXIMysVq55dy7XIMzMrJRrEGY2pPS1RgKulTS4BmFmZqWcIMzMrJSbmMzMBthgaQbruBqEpB0lPSjpIUlT6o7HzGy46qgahKTFgf8GPgzMAf4o6YqIuK+/jzVYMriZWV06rQaxBfBQRDwcEfOA84Hdao7JzGxYUkTUHcObJO0J7BgRB+fl/YD3R8TnC9tMBibnxfWAB9sY0irAU218/P7gGPuHY+wfjrH/tDPONSNiVE8bdVQTE6CSsoUyWERMBaYOSDDSjIiYMBDHWlSOsX84xv7hGPtPJ8TZaU1Mc4CxheUxwNyaYjEzG9Y6LUH8ERgvaS1JbwP2Bq6oOSYzs2Gpo5qYImK+pM8D/wMsDpwREffWGNKANGX1kWPsH46xfzjG/lN7nB3VSW1mZp2j05qYzMysQzhBmJlZKSeIHkg6LA/9ca+k79cdTyuSviIpJK1SdyzdSfqBpAck/UnSrySNrDumhk4f3kXSWEk3Sro/fw6PqDumZiQtLukOSVfVHUsZSSMlXZw/i/dL+kDdMXUn6Yv5fb5H0nmSlqorFieIFiRtR7qSe+OI2AD4Yc0hNSVpLGmIktl1x9LE9cCGEbEx8GfgqJrjARYa3uVjwHuAfSS9p96o3mI+8OWIeDewJfC5Doyx4Qjg/rqDaOFk4NqIWB94Lx0Wq6TVgcOBCRGxIelknb3riscJorXPAMdHxKsAEfFkzfG0chLwNbpdWNgpIuK6iJifF28lXePSCTp+eJeIeDwiZub7z5O+1FavN6q3kjQG2Bk4re5YykhaAfgX4HSAiJgXEc/UG1WpEcDSkkYAy1DjtWBOEK2tC3xQ0m2SpkvavO6AykjaFfhbRNxVdywVHQj8uu4gstWBxwrLc+jAL98GSeOATYHb6o2k1I9JP1LeqDuQJtYGuoAzczPYaZKWrTuoooj4G6mlYjbwOPBsRFxXVzwddR1EHSTdALyzZNU3SK/PiqRq/ebAhZLWjhrODe4hzq8DHxnYiN6qVYwRcXne5hukJpNzBzK2Fnoc3qVTSFoOuAT4QkQ8V3c8RZJ2AZ6MiNslbVt3PE2MADYDDouI2ySdDEwBvlVvWAtIWpFUg10LeAa4SNInI+IXdcQz7BNEROzQbJ2kzwCX5oTwB0lvkAbQ6hqo+BqaxSlpI9KH6S5JkJpuZkraIiL+PoAhtnwtASRNAnYBJtaRZJsYFMO7SFqClBzOjYhL646nxNbArpJ2ApYCVpD0i4j4ZM1xFc0B5kREo/Z1MSlBdJIdgEciogtA0qXAVkAtCcJNTK1dBmwPIGld4G102CiQEXF3RLwjIsZFxDjSP8FmA50ceiJpR+BIYNeIeKnueAo6fngXpcx/OnB/RJxYdzxlIuKoiBiTP4N7A7/psORA/p94TNJ6uWgi0O9zzfTRbGBLScvk930iNXakD/saRA/OAM6QdA8wD5jUQb98B5v/CywJXJ9rOrdGxKfrDakjh3cpszWwH3C3pDtz2dcj4poaYxqsDgPOzT8GHgYOqDmeheSmr4uBmaSm2DuoccgND7VhZmal3MRkZmalnCDMzKyUE4SZmZVygjAzs1JOEGZmVsoJwmon6XVJdxZubbt4SdK2ecTbgwplm+ayr+Tl70pqedFfk8d+oR/j/LSk/fP99fPrcoekdST9bhEfc/fiIH+L+jxt+PBprlY7SS9ExHI9bLN4RLxeWB5RGPyv1X4LbZeHgfgJ8PeI+EguOwH4KPCLiFjkEXurPI9FfNwpwNIRcXQfH+cs4KqIuLhfArMhzzUI61iSZkn6tqRbgL0k3STpe5KmA0dIWlPStDzHxDRJa+T9zpJ0oqQbgRNKHno2sJSkVfPVqjtSGDww779nvn+8pPvyMX6Yy1ZVmtPirnzbqlvcy+V4Zkq6W9JuuXxZSVfnfe6R9O8tjvGfSvN77AR8ATg4P5+FaiqSvpaPcZek43PZIZL+mMsuyVflbgXsCvwg10bW6fY8J+Yayt2SzpC0ZOE9+E7huazfpzfVBhVfSW2dYOnCFcIAx0XEBfn+KxGxDaRmF2BkRHwoL18JnBMRZ0s6kFQz2D3vty6wQ7HW0c3FwF6kK1VnAq9230DSSsAewPoREVowydFPgOkRsYfSfBLdaw2vAHtExHNKkzfdKukKUiKaGxE758d/e4tjABAR10g6FXihe+1G0sfy831/RLyUHwvS+GE/y9scCxwUEf8nx/BmDSLlRlCakOYs0hhZf5Z0Dmmo+x/nx3sqIjaT9FngK8DBTV5TG2Jcg7BO8HJEbFK4XVBYd0G3bYvLHwB+me//HNimsO6iFskB4EJSgtgHOK/JNs+RvuxPk/RxoDGG1PbAKQAR8XpEPNttPwHfk/Qn4AbS8OGrAncDO0g6QdIH837NjlHFDsCZjbGtIuLpXL6hpN9KuhvYF9igh8dZjzRA3J/z8tmkeRMaGoMD3g6M60V8Nsg5QVine7GH5aJih1qr7RoDt71GmoVvWpNt5pMmFLqdwEO5AAABZUlEQVSE9Ev92p6CzfYFRgHvi4hNgCeApfIX8PtIieI4Sd/uwzEgJaKyTsSzgM9HxEbAd0ijq/b0OK00alev41aHYcUJwgaz37FgOsZ9gVt6uf+3gSOb1TSU5l94ex4U7wvAJnnVNFITTGMO5hW67fp20twIrylNW7tm3nY14KU8tv8Pgc1aHKOK64ADJS2TH7/RxLQ88LjSEOH7FrZ/Pq/r7gFgnKR35eX9gOm9iMOGKP8asE7QvQ/i2oiocqrr4aTRdr9KmqOjVyNzRkRPp4suD1ye2+gFfDGXHwFMVTpV9nVSsvh9Yb9zgSslzQDuJH0BA2xE6iR+g1R7+UyLY1SJ/1pJmwAzJM0DriFNHvUt0oxzj5JqK42kcD7wM0mHA3sWHucVSQeQJqcZQRoC/dSqcdjQ5dNczcyslJuYzMyslBOEmZmVcoIwM7NSThBmZlbKCcLMzEo5QZiZWSknCDMzK/X/AWmolQFjNJAjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "#Lets look at how far we are off\n",
    "Y_train =  salary_model_data[\"min_salary\"][\"y_test_bucket\"] \n",
    "Y_train_pred =  salary_model_data[\"min_salary\"][\"svm_test_pred\"] \n",
    "\n",
    "dif_matrix = Y_train - Y_train_pred\n",
    "unique, counts = np.unique(dif_matrix, return_counts=True)\n",
    "my_class_counts = dict(zip(unique, counts))\n",
    "\n",
    "dif_matrix_abs = np.abs(dif_matrix)\n",
    "unique, counts = np.unique(dif_matrix_abs, return_counts=True)\n",
    "my_class_counts_abs = dict(zip(unique, counts))\n",
    "\n",
    "x_count = [int(v) for v in my_class_counts.keys()]\n",
    "y_count = [my_class_counts[v] for v in my_class_counts.keys()]\n",
    "\n",
    "x_count_abs = [int(v) for v in my_class_counts_abs.keys()]\n",
    "y_count_abs = [my_class_counts_abs[v] for v in my_class_counts_abs.keys()]\n",
    "\n",
    "plt.clf()\n",
    "plt.bar(x_count,y_count)\n",
    "plt.xlabel('Error Misclassification')\n",
    "plt.ylabel('Number of Occurences')\n",
    "plt.title('Number of Buckets SVM Was Off By')\n",
    "plt.show()\n",
    "\n",
    "plt.clf()\n",
    "plt.bar(x_count_abs,y_count_abs)\n",
    "plt.xlabel('Magnitude of Error Misclassification')\n",
    "plt.ylabel('Number of Occurences')\n",
    "plt.title('Number of Buckets SVM Was Off By')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage off by 1 bucket or less on test set: 0.472\n"
     ]
    }
   ],
   "source": [
    "\n",
    "total_rec = sum(counts)\n",
    "zero_to_one_bucket = sum(counts[0:2])\n",
    "print(\"Percentage off by 1 bucket or less on test set: \" + str( zero_to_one_bucket / total_rec) )\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cs229",
   "language": "python",
   "name": "cs229"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}