api-proto.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48065758-38f4-402c-b343-b5aae6932132",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.shark_api import ApiReader\n",
    "import pandas as pd\n",
    "from utils.calculate import calculate_mining_effort, calculate_time_to_find_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87c0a7a4-d91e-4d09-80eb-7e86f9ee002d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharkapi = ApiReader(config_path=\"../conf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "63f25c27-5fa0-4314-b0a3-724ec33eb60e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime, timedelta\n",
    "import numpy as np\n",
    "\n",
    "def prepare_data(data):\n",
    "    df = pd.DataFrame(data)\n",
    "    df['created'] = pd.to_datetime(df['created'])\n",
    "    df = df.sort_values('created')\n",
    "    df['normalized_shares'] = df['difficulty'].astype(float) * (2**32)\n",
    "    return df\n",
    "\n",
    "def calculate_pplns_participation(df, window_size=1000):\n",
    "    # Ensure index is datetime\n",
    "    df = df.set_index('created')\n",
    "    \n",
    "    # Calculate rolling sum of shares for each miner\n",
    "    miner_shares = df.groupby('miner')['normalized_shares'].rolling(window=window_size).sum().reset_index()\n",
    "    \n",
    "    # Pivot the data to have miners as columns\n",
    "    miner_shares_pivot = miner_shares.pivot(index='created', columns='miner', values='normalized_shares')\n",
    "    \n",
    "    # Fill NaN values with 0\n",
    "    miner_shares_pivot = miner_shares_pivot.fillna(0)\n",
    "    \n",
    "    # Calculate total shares in each window\n",
    "    total_shares = miner_shares_pivot.sum(axis=1)\n",
    "    \n",
    "    # Calculate participation ratios\n",
    "    participation = miner_shares_pivot.div(total_shares, axis=0)\n",
    "    \n",
    "    return participation\n",
    "\n",
    "def plot_participation_over_time(participation):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    for miner in participation.columns:\n",
    "        plt.plot(participation.index, participation[miner], label=miner)\n",
    "    plt.title('Miner Participation Over Time')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Participation Ratio')\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    plt.ylim(0, 1)  # Set y-axis limit from 0 to 1\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Add a stacked area chart\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    participation.plot.area(stacked=True)\n",
    "    plt.title('Stacked Miner Participation Over Time')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Participation Ratio')\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    plt.ylim(0, 1)  # Set y-axis limit from 0 to 1\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Print sum of participations\n",
    "    participation_sum = participation.sum(axis=1)\n",
    "    print(\"Summary of participation sums:\")\n",
    "    print(participation_sum.describe())\n",
    "    \n",
    "    if not np.allclose(participation_sum, 1, rtol=1e-5, atol=1e-8):\n",
    "        print(\"Warning: Not all participation sums are exactly 1. This might be due to floating-point precision.\")\n",
    "\n",
    "\n",
    "\n",
    "def analyze_pplns(data, window_size=1000, block_reward=10):\n",
    "    df = prepare_data(data)\n",
    "    participation = calculate_pplns_participation(df, window_size)\n",
    "\n",
    "    print(\"Analyzing PPLNS participation...\")\n",
    "    print(f\"Total miners: {participation.columns.nunique()}\")\n",
    "    print(f\"Date range: {participation.index.min()} to {participation.index.max()}\")\n",
    "    print(f\"Total shares: {df['normalized_shares'].sum():,.0f}\")\n",
    "    \n",
    "    plot_participation_over_time(participation)\n",
    "    plot_participation_heatmap(participation)\n",
    "    # plot_participation_distribution(participation)\n",
    "    # plot_participation_change_rate(participation)\n",
    "    # plot_cumulative_rewards(participation, block_reward)\n",
    "\n",
    "    # Calculate some statistics\n",
    "    avg_participation = participation.mean()\n",
    "    max_participation = participation.max()\n",
    "    min_participation = participation.min()\n",
    "\n",
    "    print(\"\\nParticipation Statistics:\")\n",
    "    print(pd.DataFrame({\n",
    "        'Average': avg_participation,\n",
    "        'Max': max_participation,\n",
    "        'Min': min_participation\n",
    "    }))\n",
    "\n",
    "    # Identify top miners\n",
    "    top_miners = avg_participation.nlargest(5)\n",
    "    print(\"\\nTop 5 Miners by Average Participation:\")\n",
    "    print(top_miners)\n",
    "\n",
    "    # Calculate rewards\n",
    "    total_blocks = len(participation)\n",
    "    total_rewards = total_blocks * block_reward\n",
    "    miner_rewards = (participation * block_reward).sum()\n",
    "\n",
    "    print(f\"\\nTotal blocks: {total_blocks}\")\n",
    "    print(f\"Total rewards: {total_rewards:,.2f}\")\n",
    "    print(\"\\nMiner Rewards:\")\n",
    "    print(miner_rewards.sort_values(ascending=False))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e6a02807-e591-438c-b2cc-5307657311ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing PPLNS participation...\n",
      "Total miners: 2\n",
      "Date range: 2024-09-17 14:21:45.106828+00:00 to 2024-09-17 21:40:22.150441+00:00\n",
      "Total shares: 203,617,380,412\n"
     ]
    },
    {
     "data": {
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OnNC0adNUs2ZNvfLKK5Kk6tWrK0uWLAoNDVX37t1lMpk0b948m0FVpUqVtHjxYvXu3VvPP/+8vL29EwwNP/74Y7300ksKCAhQx44ddefOHX3yySfKlCmThg4datd5skdwcLD27NmjKVOm6OjRowoODlaWLFm0b98+zZo1S9myZdOSJUusxtKqWLGiChcurIEDB+revXsWXdykh+MHTZ8+Xe3atVPFihXVqlUr5ciRQ+fOndOqVatUo0YNffrppyl2HE/Df/GYAAAAgJRAmAQ8A/r376+iRYtq0qRJRsWOn5+fXnzxRSMkSS1hYWG6ePGiPv/8c61bt04lS5bU119/rW+//VYRERF2bSNDhgyaM2eOBgwYoC5duujBgwcKDw9PcLDu8PBwlS1bVjNnztT777+vTJkyqXLlyqpevXqS+/r00081f/58DR48WPfv31fr1q01depUo8tatmzZ9P3336tPnz4aNGiQsmTJorZt2+qFF15Qw4YNLbb1zjvv6MCBAwoPD9ekSZOUP3/+BMOkwMBArV27VkOGDNHgwYOVIUMG1alTR+PGjXviQckfNXnyZNWrV0+fffaZRo8eraioKPn5+endd99V//79Exz/KigoSKNGjVLhwoVVsWJFq/lt2rRRnjx5NHbsWH388ce6d++e8ubNq1q1aiX7CYFpxX/xmAAAAIAnZTLb0+8DAP7jZs+erfbt22vPnj2qXLmyo5sDAAAAAGkWYyYBAAAAAADAboRJAAAAAAAAsBthEgAAAAAAAOzm0DBpy5Ytatq0qfLkySOTyaTly5cnuU5ERIQqVqwoNzc3FS5cWLNnz071dgL47wsLC5PZbGa8JAAAAABIgkPDpNu3b6tcuXL67LPP7Fr+zJkzaty4serVq6cDBw6oZ8+e6tSpk9atW5fKLQUAAAAAAICUhp7mZjKZ9L///U/NmjVLcJl+/fpp1apV+uWXX4xprVq10vXr17V27dqn0EoAAAAAAIBnm4ujG5AcO3bsUGBgoMW0hg0bqmfPngmuc+/ePd27d8/4PTY2VlevXlW2bNlkMplSq6kAAAApzmw2KzIyUnny5JGTE0NfAgAAx0hXYdLFixeVK1cui2m5cuXSzZs3defOHXl4eFitM2bMGA0bNuxpNREAACDVnT9/Xs8995yjmwEAAJ5R6SpMehwDBgxQ7969jd9v3LihfPny6fz58/Lx8Umdnd6/K+2fJ51YK/2xS/LKKd2+JDm5SrHRia+b93npzz2SXzWpxCvSjfPSzT+lYyv/XSaDt/TCh9I/v0o/z3w4LYu/5J1bOr/TcnuZ80seWSRXT6nU6w+n/fOrtPvzf9fL4v+wzdfPSZF/WbfJt4LknlEyZZDObZce3LFeptq7UqbnpF/XSq5e0ukI6UGU5TI5S0qXjv7/L06SXxWpVHPJyUW6dEz6+SvJ5CKZH/y7jmtGqdhL0uXj0sVDCZ+3AnWkWn2kqCsPt3XsOynyonT32sP55dtId65LJ1Y/PH/1B0lHl0tOzg+P/8Ih6e/DD5ctEyTdvSGd/P+ukwVfkH7bYLm/3GUlz2zS/TuST14pa0Fp68f/v69g6bcIydlNyuAhuWWU8lWT/tgjXTws3bv5cLn8NaTftz382SunlDH3w22d2SLdv/1w/Zh/q+qUp9LDz0IWf+m5ytKuL6RC9R/uwznDw/fPM5tUtNHD93HrRMv145R45eFye8P/3e5fexM+t2nIP/leUvZzaxzdDOA/7ZZfPXmXaSKt7uPopqRLZ2NzyH/g/lTb/s2bN+Xn56eMGTOm2j4AAACSkq7GTKpdu7YqVqyoyZMnG9PCw8PVs2dP3bhxw6793Lx5U5kyZdKNGzdSL0wC0rl1HwWrYdT3jm6GldPPD1WW3ROU1RTp6KYA/1lXKvVQtqbDpaGZHN2UdOkPk6+eG3I81bbPv2MAAEBakK462wcEBGjDBssKkfXr1ysgIMBBLQIAAAAAAHi2ODRMunXrlg4cOKADBw5Iks6cOaMDBw7o3Llzkh52UQsJCTGW79Kli3777Tf17dtXx48f17Rp0/TNN9+oV69ejmg+AAAAAADAM8ehYdLPP/+sChUqqEKFCpKk3r17q0KFCho8eLAk6cKFC0awJEkFChTQqlWrtH79epUrV04TJkzQV199pYYNGzqk/QAAAAAAAM8ahw7AXbduXSU2ZNPs2bNtrrN/f+oNbAkgbTM5ugHAf1yaGEgR6U5MTIzu37/v6GYAAIAnkCFDBjk7O9u17H/+aW4A/kO4ywWANMVsNuvixYu6fv26o5sCAABSQObMmZU7d26ZTIn/GZ8wCQAAxEP9H+wXFyTlzJlTnp6eSf7DEwAApE1ms1lRUVG6dOmSJMnX1zfR5QmTAKQbZm5SACDNiImJMYKkbNmyObo5AADgCXl4eEiSLl26pJw5cyba5c2hA3ADAAAgfYobI8nT09PBLQEAACkl7r/rSY2FSJgEAACAx0bXNgAA/jvs/e86YRIAAAAAAADsRpgEAADiocoE+C8ymUxavny5o5thN39/f02ePDnZ66W340wtQ4cOVfny5R3djHRh9uzZypw58xNtY9u2bSpTpowyZMigZs2apUi7ngTvP54GwiQA6YhZZkc3AQDwnxAZGamePXsqf/788vDwUPXq1bVnz54U3UdYWJhMJpPVq1SpUnZvY9myZWrQoIFy5MghHx8fBQQEaN26dSnazpQQEREhk8mk69evW82LHwz5+/vbPCdxr99///3pNjwJUVFRGjBggAoVKiR3d3flyJFDderU0YoVK4xl6tatq549e1qsN2XKFLm5uWnRokWSpDNnzqhNmzbKkyeP3N3d9dxzz+nVV1/V8ePHjXXinwcvLy8VKVJEYWFh2rt3r1W7vvnmG5UvX16enp7Knz+/Pv7441Q5/vnz56tcuXLy9PSUr6+vOnTooCtXrhjzhw4davN9/PHHHy3md+nSxWK7Bw4ckMlk0tmzZ1O0vQl9ruLeh6TUrVs30c9n3bp1ba7Xu3dvlS9fXmfOnNHs2bON6UuXLlXdunWVKVMmeXt7q2zZsho+fLiuXr0q6eld30FBQapSpYpiYmKMaffv31elSpUUHBwsSapWrZrV+zRjxgyZTCaLY5IefrfVqlVLkjR9+nRlzpxZ58+ft1imW7duKlq0qKKioh673XHfK7ZeFy9etHs78b+LM2TIoFy5cqlBgwaaNWuWYmNjba7TsGFDOTs72/zvQkLf7adOnUp0fqNGjYxtPBp8jxgxQr6+vsZnI87Bgwfl5uam77//3ljvST7jKYkwCYAVE5ENADwWM5Vd6UanTp20fv16zZs3T4cPH9aLL76owMBA/fnnnym2jylTpujChQvG6/z588qaNaveeOMNu7exZcsWNWjQQKtXr9bevXtVr149NW3aVPv370+xdj5Ne/bssTgnFy5c0LFjx5QnTx41bdpU+fLlc3QTLXTp0kXLli3TJ598ouPHj2vt2rVq0aKFRaDyqCFDhuiDDz7QihUr1KpVK92/f18NGjTQjRs3tGzZMp04cUKLFy9WmTJlrMK38PBwXbhwQUeOHNFnn32mW7duqWrVqpo7d66xzJo1axQcHKwuXbrol19+0bRp0zRp0iR9+umnKXrs27ZtU0hIiDp27KgjR47o22+/1e7du9W5c2eL5UqVKmX1ntauXduY7+7urpkzZ+rkyZMp2r74oqOjjZ/jzmH8l73VQsuWLTPW2b17tyTpxx9/NKYtW7bMYvm4AYpPnz6t+vXr67nnnjOqnAYOHKigoCA9//zzWrNmjX755RdNmDBBBw8e1Lx58yQ9vet72rRpOnfunMaOHWtMGzFihC5cuGB8burVq6eIiAiL9TZt2iQ/Pz+r6REREapfv76kh9dIlSpV1LFjR2P+hg0bNH36dM2ePTtFHtJw4sQJq/c0Z86cydpGo0aNdOHCBZ09e1Zr1qxRvXr11KNHDzVp0kQPHjywWPbcuXPavn27unbtqlmzZiW6vfivAgUKJDp/4cKFCbZvwIAB8vPz07vvvmtMu3//vkJDQ9W2bVs1adLEmP4kn/GURJgEIB3hJg0A8OTu3LmjpUuX6qOPPlLt2rVVuHBhDR06VIULF9b06dMlSffu3dN7772nvHnzysvLS1WrVrW6ofryyy/l5+cnT09PNW/eXBMnTrToLpMpUyblzp3beP3888+6du2a2rdvL0n64osvlCdPHqu/jL/66qvq0KGDJGny5Mnq27evnn/+eRUpUkSjR49WkSJF9N133xnL161bV927d1ffvn2VNWtW5c6dW0OHDk30HAwZMkS+vr46ePCgfHx8tGTJEov5y5cvl5eXlyIjI3X27FnjL9/Vq1eXu7u7Spcurc2bNyfntEuScuTIYXFOcubMqZ49eypTpkyaP3++xcCvkZGRat26tby8vJQ3b1599tlnFts6efKkateuLXd3d5UsWVLr16+3mF+/fn117drVYtrly5fl6uqqDRs26IMPPlDVqlWt2liuXDkNHz5ckrRy5Up98MEHevnll+Xv769KlSqpW7duxvsTn9lsVrdu3TR16lStX7/eqEI4cuSITp8+rWnTpqlatWrKnz+/atSooZEjR6patWoW28icObNy584tf39/vfjii1qyZImCg4PVtWtXXbt2TZI0b948NWvWTF26dFHBggXVuHFjDRgwQOPGjZPZbPsPgqdPn1bBggXVtWtXY5mkPr87duyQv7+/unfvrgIFCqhmzZp66623jJAljouLi8V7mjt3brm6uhrzixUrpnr16mngwIE22yZJlStX1vjx443fmzVrpgwZMujWrVuSpD/++MOi8sPf318jRoxQSEiIfHx89Oabb1qdw/gvd3d3Y/7s2bOVL18+47jjB4Nx10/u3LmVI0cOSVK2bNmMadmyZdP06dP1yiuvyMvLS507d5bJZNKVK1fUoUMHo4pn9+7dGj16tCZMmKCPP/5Y1atXl7+/vxo0aKClS5cqNDRUUtLX9+XLl5U7d26NHj3aaOP27duNz3CcsWPHKleuXMqYMaM6duyou3fvWpzfbNmy6YsvvtDw4cN16NAh/fzzzxozZoy++uorZcmSRdLDMOnEiRMWFT+bN29W//79Lb77zpw5o99//1316tWT9LBSZubMmdq1a5dmzJihmzdvqkOHDurdu7eqV69urOfv76+RI0cqJCRE3t7eyp8/v1auXKnLly/r1VdfNSq3fv75Z6vPR86cOa3eUyenh1FGWFiYmjVrpmHDhhkVXl26dLEIGCXJzc1NuXPnVt68eVWxYkUj8F2zZo1V5VV4eLiaNGmit99+WwsXLtSdO3es2hS3vfgvZ2fnROfHnWt/f39JUvPmzWUymeTv7y8XFxfNnTtXy5cvN76PR40apevXr2vSpEkW+07sMx7XbXPdunUqUaKEvL29jWArpREmAUhH+Js/kNqoS8STMJvNiop+4JBXQjfRtjx48EAxMTEWN5iS5OHhoZ9++kmS1LVrV+3YsUOLFi3SoUOH9MYbb6hRo0ZGdcW2bdvUpUsX9ejRQwcOHFCDBg00atSoRPc7c+ZMBQYGKn/+/JKkN954Q1euXNGmTZuMZa5evaq1a9caXU8eFRsbq8jISGXNmtVi+pw5c+Tl5aVdu3bpo48+0vDhw63CFenfwGPu3LnaunWrypUrp1atWik8PNxiufDwcLVo0UIZM2Y0pr3//vvq06eP9u/fr4CAADVt2jTRCh179O/fX7t27dKKFSss9iVJH3/8scqVK6f9+/erf//+6tGjh3FMsbGxeu211+Tq6mrcxPbr189i/U6dOmnBggW6d++eMe3rr79W3rx5Vb9+fQUHB2v37t06ffq0Mf/IkSM6dOiQ2rRpI0nKnTu3Vq9ercjIyESP48GDB2rbtq2WLFmizZs3W9xE58iRQ05OTlqyZIlFNyN79erVS5GRkcax37t3z+Zn948//rDZTfDQoUOqWbOm2rRpo08//VQmk8muz29AQIDOnz+v1atXy2w26++//9aSJUv08ssvJ/sYxo4dq6VLl9oMCiSpTp06RmBhNpu1detWZc6c2bgeN2/erLx586pw4cLGOuPHjzc+Hx9++KFd7di1a5c6duyorl276sCBA6pXr55GjhyZrGMZOnSomjdvrsOHD2vYsGG6cOGCfHx8NHnyZF24cEFBQUGaP3++vL299c4779jcRkJjND16fefIkUOzZs3S0KFD9fPPPysyMlLt2rVT165d9cILL0h62OVx6NChGj16tH7++Wf5+vpq2rRpVtt+5ZVX1KpVK4WEhCg0NFShoaEW72WNGjWUIUMG4/vo6NGjunPnjjp27KgrV67ozJkzkh5WK7m7uysgIMBY18/PT5MnT9b777+vtm3bytvbWyNGjLBqw6RJk1SjRg3t379fjRs3Vrt27RQSEqK2bdtq3759KlSokEJCQpL1fS49rIQ6duyYIiIitHDhQi1btkzDhg1Lcr369eurXLlyFhVnZrNZ4eHhatu2rYoXL67ChQtbhe1PKq7rXFyFUdzvxYsX15gxY/T2229r3bp1GjNmjMLDw+Xj45Os7UdFRWn8+PGaN2+etmzZonPnzum9995L0WOQJJcU3yIApBqiJABIy+7cj1HJwY4Zz+fo8IbydLXvn7YZM2ZUQECARowYoRIlSihXrlxauHChduzYocKFC+vcuXMKDw/XuXPnlCdPHknSe++9p7Vr1yo8PFyjR4/WJ598opdeesn4B3rRokW1fft2Y1yLR/31119as2aNFixYYEzLkiWLXnrpJS1YsMC4MVyyZImyZ89u/NX/UePHj9etW7fUsmVLi+lly5bVkCFDJElFihTRp59+qg0bNqhBgwbGMnGBx/79+/XTTz8pb968kh6GLtWrV9eFCxfk6+urS5cuafXq1ca4N3G6du2q119/XdLDcVLWrl2rmTNnqm/fvnad90ctXLhQEydO1KpVq1SkSBGr+TVq1FD//v0lPTy/27Zt06RJk9SgQQP9+OOPOn78uNatW2e8R6NHj9ZLL71krP/aa6+pa9euWrFihXG+Zs+ebYxnUqpUKZUrV04LFiwwwoj58+eratWqRmjxxRdfKDg4WNmyZVO5cuVUs2ZNtWjRQjVq1LBo65dffinp4fgmxYsXt5iXN29eTZ06VX379tWwYcNUuXJl1atXT8HBwSpYsGCS5ylue3HjCzVs2FC9evVSWFiY6tWrp1OnTmnChAmSpAsXLhhVD9LDKpYmTZpo4MCB6tOnjzHdns9vjRo1NH/+fAUFBenu3bt68OCBmjZtalUhdvjwYXl7exu/lyxZ0qp6qWLFimrZsqX69etnUVETp27dupo5c6ZiYmL0yy+/yNXVVUFBQYqIiFCjRo0UERGhOnXqWKxTv359i2OK07p1a4sKEelhMJIvXz5NmTJFjRo1Mj6zcce9du1aq+0kpE2bNkZ1YRyTyWRUIkoPq+YKFiyoDBky2L1dyfb1/fLLL6tz584KDg5W5cqV5eXlpTFjxhjzJ0+erI4dOxrdzEaOHKkff/zRqjopbtm8efPKx8dHEydOtJjn5eWlKlWqKCIiQq1bt1ZERIRq1qwpNzc3Va9eXRERESpQoIAiIiIUEBAgNzc3i/Xbt2+vL774Qt9995127dplNT/uWN566y1J0uDBgzV9+nQ9//zzRtfffv36KSAgQH///bdxLiXpueees9hO/vz5deTIEeN3V1dXzZo1S56enipVqpSGDx+u999/XyNGjDAqmBJSvHhxHTp0yPj9xx9/VFRUlBo2bChJatu2rWbOnKl27dpZrPf9999bfO5feuklffvttwnOl6QPPvhAH3zwgVH1FldhFF+PHj20YsUKvfzyy+rWrZvN/xYk9hmXHnaPmzFjhgoVKiTp4Xd3XLVlSqIyCQAAxENoi2fDvHnzZDablTdvXrm5uWnq1Klq3bq1nJycdPjwYcXExKho0aLy9vY2Xps3bzaqWE6cOKEqVapYbPPR3+ObM2eOMmfObDWuRXBwsJYuXWpUz8yfP1+tWrWyeQO0YMECDRs2TN98843VeCFly5a1+D0uFIqvV69e2rVrl7Zs2WIESXHtLlWqlObMmSPpYfVO/vz5Lca9kWRRieDi4qLKlSvr2LFjCR5zYvbt26eOHTtq7Nixxk3bo+LvL+73uP0dO3ZMfn5+RpBka3l3d3e1a9fOGPNk3759+uWXXxQWFmYsExwcbAR8ZrNZCxcutKgKq127tn777Tdt2LBBLVq00JEjR1SrVi2rqouaNWvK29tbH374odX4K5L07rvv6uLFi5o/f74CAgL07bffqlSpUjarxx4VV6UR1wWwc+fO6tq1q5o0aSJXV1dVq1ZNrVq1kiSLz825c+fUoEEDDR482Cp0sefze/ToUfXo0UODBw/W3r17tXbtWp09e9ZqkOZixYrpwIEDxmvp0qU2j2PkyJHaunWrfvjhB6t5tWrVUmRkpPbv36/NmzerTp06qlu3rlGttHnzZqvBrytXrmxzP5MmTbJoz4EDB4zPybFjx6y6Nj76uUlKQvuNL7mVNVLi1/f48eP14MEDffvtt5o/f75FUJOcY1q4cKFMJpP++ecfi8Hf48Q/5xEREcY5j185FhERYTPgOHjwoPbt2ydPT09t3brV5v7jf0/lypVLklSmTBmraY9+d23dutXi/Vy9erXF/LhB4uMEBATo1q1bVoOC22I2my26186aNUtBQUFycXn4x4nWrVtr27ZtFhWM0sNugfHbNHXq1ETnHzhwwOrascVkMmngwIGKjY3VoEGDbC6T2Gdckjw9PY0gSbL934OUQGUSAAAAUoRHBmcdHW47GHga+06OQoUKafPmzbp9+7Zu3rwpX19fBQUFqWDBgrp165acnZ21d+9eq7/+PvqXZnuYzWbNmjVL7dq1sxhLRpKaNm0qs9msVatW6fnnn9fWrVutxseQpEWLFqlTp0769ttvFRgYaDX/0QoIk8lkNRZTgwYNtHDhQq1bt86qG12nTp302WefqX///goPD1f79u0tbrCSEtcN48aNG1ZdeK5fv65MmTIZv1++fFnNmzfX66+/nipdL+Lr1KmTypcvrz/++EPh4eGqX7++0c1Qenij2K9fP+3bt0937tzR+fPnFRQUZLGNDBkyqFatWqpVq5b69eunkSNHavjw4erXr5/xfpYpU0YTJkxQYGCggoKCtHjxYuNmNE7GjBnVtGlTNW3aVCNHjlTDhg01cuRIi+oxW+ICtLjBfU0mk8aNG6fRo0fr4sWLypEjh1HtE7/SKUeOHMqTJ48WLlyoDh06JLurzJgxY1SjRg29//77kh4GAV5eXqpVq5ZGjhwpX19fSQ+rQuJ3P0tIoUKF1LlzZ/Xv318zZ860mJc5c2aVK1dOERER2rFjhxo0aKDatWsrKChIv/76q06ePGlVmeTl5WVzP7lz57arPY8rof3GV7RoUf3000+6f/++XdVJSV3fp0+f1l9//aXY2FidPXvWIoCx12+//aa+fftq+vTp2rRpk8LCwrR//36LYKpevXoaNWqU/vzzT0VERBjXZ506dfT555/r9OnTOn/+vDH4dpzo6GiFhIQoODhYderUUZcuXdSkSRMVK1bMYrn45yLu+8XWtEe/uwoUKJBg18AndezYMePaunr1qv73v//p/v37xvh5khQTE6NZs2ZZdAX18vJK9HOW1PzExH13PPodEiepz7it/x48TsCZFCqTAFjhaW4AgMdhMpnk6erikFdygo/4vLy85Ovrq2vXrmndunV69dVXVaFCBcXExOjSpUsqXLiwxSuuS0KxYsWsHhlt6xHS0sOqilOnTlk87SiOu7u7XnvtNc2fP18LFy5UsWLFVLFiRYtlFi5cqPbt22vhwoVq3LjxYx2n9HDMlAULFqhTp05Wj5Fu27atfv/9d02dOlVHjx41BgiOb+fOncbPDx480N69e1WiRAlJD7vWOTk5WT3G/rffftONGzdUtGhRSQ+7X7Ro0UI5c+Y0uoYlJP7+4n6P21+JEiV0/vx5i0FlH11eehjyVK5cWV9++aUWLFhgNXD2c889pzp16mj+/PmaP3++GjRokORTokqWLKkHDx5YdSMqX768NmzYoC1btqhly5bGk75sMZlMKl68uG7fvp3ovqSHXZN8fHysQgZnZ2flzZtXrq6uWrhwoQICAozuM9LDcZS+//57ubu7q2HDhhbjPtnz+Y2KirKqkIsLVx/3xnTw4MH69ddfbT7GvE6dOtq0aZO2bNmiunXrKmvWrCpRooRGjRolX19f4zP0JEqUKKFdu3ZZTLP1uXlSbdq00a1bt2yOXSTJ4il+SV3f0dHRatu2rYKCgjRixAh16tTJosrEnmOKjY1VWFiYXnjhBYWEhGjy5MmKjIzU4MGDLZarXr26XF1dNW3aNN29e1eVKlWSJD3//PO6fPmyZs2aZXSHi2/48OG6evWqJk2apNDQUDVo0EDt27e3CoVSy8GDBy0Gyd65c6e8vb3l5+eX6HobN27U4cOHje678+fP13PPPaeDBw9aVP1MmDBBs2fPfqwxzxKSIUOGFN2eI1CZBCAdIeQCAKSMdevWyWw2q1ixYjp16pTef/99FS9eXO3bt1eGDBkUHByskJAQTZgwQRUqVNDly5e1YcMGlS1bVo0bN1a3bt1Uu3ZtTZw4UU2bNtXGjRu1Zs0am6HWzJkzVbVqVZUuXdpmW4KDg9WkSRMdOXJEbdu2tZi3YMEChYaGasqUKapatarxpCUPDw+Lah97NW/eXPPmzVO7du3k4uKiFi1aSHo4ftNrr72m999/Xy+++KLVGCWS9Nlnn6lIkSIqUaKEJk2apGvXrhnhTMaMGdWpUyf16dNHLi4uKlOmjM6fP69+/fqpWrVqxoDUPXv21MGDB/Xjjz9a3FDHyZo1q1Hts23bNn300Udq1qyZ1q9fr2+//VarVq2SJAUGBqpo0aIKDQ3Vxx9/rJs3byb4tLBOnTqpa9eu8vLyUvPmza3mBwcHa8iQIYqOjraqCqtbt65at26typUrK1u2bDp69Kg++OAD1atXz2alT7ly5bRx40a98MILatmypb755hsdOXJEQ4YMUbt27VSyZEm5urpq8+bNmjVrltWg4devX9fFixd17949/frrr/r888+1fPlyzZ0716jM+Oeff7RkyRLVrVtXd+/eVXh4uL799lubT9fz8vLSqlWr9NJLL+mll17S2rVr5e3tbdfnt2nTpurcubOmT5+uhg0b6sKFC+rZs6eqVKli0aUmOXLlyqXevXvr448/tppXt25dffLJJ8qRI4cxTlTdunX16aefGmPq2CPuHMaXMWNGeXl5qXv37qpRo4bGjx+vV199VevWrUvWeEn2qlq1qvr27as+ffrozz//VPPmzZUnTx6dOnVKM2bMUM2aNdWjRw+7ru+BAwfqxo0bmjp1qry9vbV69Wp16NDBGN+qR48eCgsLU+XKlY1xro4cOWJRpTZlyhQdOXLEGGcoU6ZM+uqrr9SkSRO9/vrrRjjk4eGhatWq6ZNPPlGNGjWM8DCuO2Xc9PiVL3v27NG4ceO0atUqo82ff/65SpcurUmTJtkc1yq5Ll26ZPMJdXHtiI6OVseOHTVo0CCdPXtWQ4YMUdeuXS3C0Hv37unixYuKiYnR33//rbVr12rMmDFq0qSJQkJCJD38rm7RooXVd7Wfn58GDBigtWvX2h3ox+0vPhcXF2XPnl3Swye6bdiwQTVq1JCbm5vxpDd7JPYZf5qoTAIAAAYiWzwrbty4oXfffVfFixdXSEiIatasqXXr1hk3J+Hh4QoJCVGfPn1UrFgxNWvWTHv27DEGOK1Ro4ZmzJihiRMnqly5clq7dq169epl9ZStGzduaOnSpTarkuLUr19fWbNm1YkTJ4yniMX54osv9ODBA7377rvy9fU1Xj169HjsY2/RooXmzJmjdu3aWTzFqGPHjoqOjrb52Hvp4RO5xo4dq3Llyumnn37SypUrjRsj6eENa2hoqPr166dSpUopLCxMZcuW1XfffWeEFNOmTdONGzf0/PPPWxxP3Gv79u3G9vr06aOff/5ZFSpU0MiRIzVx4kRjfCUnJyf973//0507d1SlShV16tQpwafptW7dWi4uLmrdurXV+xN3Pq5cuaKoqCirMa0aNmyoOXPm6MUXX1SJEiXUrVs3NWzYUN98802C57dMmTLauHGjtm/frjfeeEN58uSRv7+/hg0bpqpVq6pixYqaMmWKhg0bZhWAtW/fXr6+vipevLjefvtteXt7a/fu3Vafizlz5hjhwZEjRxQREZHgmF3e3t5as2aNzGazGjdurNu3b9v1+Q0LC9PEiRP16aefqnTp0nrjjTdUrFgxi8/M43jvvfdsdhetVauWYmNjLbqz1a1bVzExMVbjJSUm7hzGf33yySeSpGrVqunLL7/UlClTVK5cOf3www8JjkvzpMaNG6cFCxZo165datiwoUqVKqXevXurbNmyRuVfUtd3RESEJk+erHnz5snHx0dOTk6aN2+etm7danTDCgoK0ocffqi+ffuqUqVK+v333/X2228b7fj11181cOBAffLJJxaDPTds2FDt27dXWFiYxRMP69Wrp8jISKtzXqdOHUVGRlqMl3Tv3j2Fhoaqffv2evHFF43pced80KBBOnHixBOfy2LFilm9p/GrIF944QUVKVLE6Br5yiuvaOjQoRbbWLt2rXx9feXv769GjRpp06ZNmjp1qlasWGF0az548KBRpRRfpkyZ9MILL1h1z0xM3P7iv2rWrGnMnzBhgtavXy8/Pz9VqFAhWecjsc/402Qyp0bnuTTs5s2bypQpk27cuJHsfsPAs2L9R63VIGp10gs+ZaeeH6Zsu8criynxx/MCeHyXKvVWzqZDpKHJr3iAdN6UR35DHm9AYnukpX/H3L17V2fOnFGBAgVs3qA/izp37qzjx48nOPhsWjdv3jz16tVLf/31l8XYTmfPnlWBAgW0f/9+lS9f3nENfExnz55VoUKFtGfPHqsuhPhXev/84tkUFham69eva/ny5Y5uyn+Gvf99p5sbAACIh6e5AfYaP368GjRoIC8vL61Zs0Zz5sxJcIyUtCwqKkoXLlzQ2LFj9dZbb1kNEp5e3b9/X1euXNGgQYNUrVo1gqRH/Fc+vwAcg25uAAAAwGPYvXu3GjRooDJlymjGjBmaOnWqOnXq5OhmJdtHH32k4sWLK3fu3BowYICjm5Nitm3bJl9fX+3Zs0czZsxwdHPSnP/K5xeAY1CZBCBdeab65QIA0rTExs1JT4YOHWo1vkh8/v7+qfJY6dRWt27ddNnup+W/8vnFs2327NmObsIzi8okAAAQDzdeAAAASBxhEoB0wyxGcwEAAAAARyNMAmCFwAZ4hpn4BgAAAEDiCJMApCPc5AIAAACAoxEmAQAAAAAAwG6ESQAAAAAAALAbYRKAdIXnTAGpi6doA/aZPXu2MmfO7Ohm4BFnz56VyWTSgQMHHN0UpJCwsDA1a9bsibfj7++vyZMnP/F2uPaR1iT12a5bt6569uyZ4vslTAKQjpgZNQkAkCIiIyPVs2dP5c+fXx4eHqpevbr27NmTovsYOnSoypcvL+nfkCOhV4ECBezebt26dY313N3dVbJkSU2bNs2YP3v2bGO+s7OzsmTJoqpVq2r48OG6ceNGih5jnJs3b2rgwIEqXry43N3dlTt3bgUGBmrZsmUy/39KHdfuRYsWWaw7efJk+fv7W7W/UaNGFstdv35dJpNJERERdrfr4MGDat26tfz8/OTh4aESJUpoypQpdq0b/zwm9Dp79qwk6erVq8bnydXVVXny5FGHDh107tw5i20mFIxERETIZDLp+vXrdh+bPb788kuVK1dO3t7eypw5sypUqKAxY8ak6D4kadOmTXr55ZeVLVs2eXp6qmTJkurTp4/+/PNPq2WLFy8uNzc3Xbx40WL6kwSBW7ZsUdOmTZUnTx6ZTCYtX7482dtIyfcmoc+Ou7u7xf5MJpPGjh1rse7y5ctlivcwjLt37yosLExlypSRi4uLVRsnTJigLFmy6O7du1btiIqKko+Pj6ZOnWpMW7hwoZydnfXuu+/afTwpLf75cXJykq+vr4KCgqyul/jfdW5ubsqbN6+aNm2qZcuWWW3T39/f6nzHP7dx72PcPjNlyqQKFSqob9++unDhgsW2hg4daizr4uIif39/9erVS7du3dLevXtlMpm0c+dOm8f2wgsv6LXXXjN+/+OPP+Tq6qrSpUsn+zxt3rxZGTJk0E8//WQx/fbt2ypYsKDee++9ZG8zJREmAbCB0gTg2UVki2dDp06dtH79es2bN0+HDx/Wiy++qMDAQJs3vynBz89PFy5csHp99913j3Vj17lzZ124cEFHjx5Vy5Yt9e6772rhwoXGfB8fH124cEF//PGHtm/frjfffFNz585V+fLl9ddff6XosV2/fl3Vq1fX3LlzNWDAAO3bt09btmxRUFCQ+vbtaxFgubu7a9CgQbp//36i23RxcdGPP/6oTZs2PVHb9u7dq5w5c+rrr7/WkSNHNHDgQA0YMECffvppkusGBQVZvFcBAQHGeY97+fn56erVq6pWrZp+/PFHzZgxQ6dOndKiRYt06tQpPf/88/rtt9+e6Bge16xZs9SzZ091795dBw4c0LZt29S3b1/dunUrRffz+eefKzAwULlz59bSpUt19OhRzZgxQzdu3NCECRMslv3pp590584dtWjRQnPmzEmxNty+fVvlypXTZ599lmLbfFJx12D81++//26xjLu7u8aNG6dr164luJ2YmBh5eHioe/fuCgwMtJrfrl073b5922bAsmTJEkVHR6tt27bGtJkzZ6pv375auHChzQDqaYk7P3/++aeWLl2qEydO6I033rBaLu6aO336tJYuXaqSJUuqVatWevPNN62WHT58uMX57tatm9UyJ06c0F9//aU9e/aoX79++vHHH1W6dGkdPnzYYrlSpUrpwoULOnv2rMaNG6cvvvhCffr0UaVKlVSuXDnNmjXLattnz57Vpk2b1LFjR2Pa7Nmz1bJlS928eVO7du1K1jmqU6eOunXrprCwMN2+fduY3rdvX3l4eGjkyJHJ2l5KI0wCYIX6HwDAf9mdO3e0dOlSffTRR6pdu7YKFy6soUOHqnDhwpo+fbok6d69e3rvvfeUN29eeXl5qWrVqjYrYtatW6cSJUrI29tbjRo1svoLdxxnZ2flzp3b4mUymfT222+rdevWxl+Yq1evrn79+lmse/nyZWXIkEFbtmwxpnl6eip37twqWLCghg4dqiJFimjlypXGfJPJpNy5c8vX11clSpRQx44dtX37dt26dUt9+/Y1louMjFRwcLC8vLzk6+urSZMmWXSJ+OCDD1S1alWr4ylXrpyGDx9uLHP27Fnt2rVLoaGhKlmypIoWLarOnTvrwIED8vb2NtZr3bq1rl+/ri+//DKxt0heXl7q0KGD+vfvn+hyu3fvVoUKFeTu7q7KlStr//79FvM7dOigKVOmqE6dOipYsKDatm2r9u3bW9x426poMJlM8vDwsHi/XF1djfMe93J2dtbAgQP1119/6ccff9RLL72kfPnyqXbt2lq3bp0yZMjwWBUgHTp0UNmyZXXv3j1JUnR0tCpUqKCQkBBJ/1bxLFu2TPXq1ZOnp6fKlSunHTt2GNtYuXKlWrZsqY4dO6pw4cIqVaqUWrdurVGjRhnLxFXjDBs2TDly5JCPj4+6dOmi6OhoY5nY2Fh99NFHKly4sNzc3JQvXz5jG3/88Ye6d++u7t27a9asWapbt678/f1Vu3ZtffXVVxo8eLDFcc2cOVNt2rRRu3btrG7G46rzKlSoIJPJpLp161rMT6yNL730kkaOHKnmzZvbfY6/+uorZc6cWRs2bLB7nfiWL1+uIkWKyN3dXQ0bNtT58+ct5sddg/FfuXLlslgmLoRLrFrMy8tL06dPV+fOnZU7d26r+Tlz5lTTpk1thhuzZs1Ss2bNlDVrVknSmTNntH37dvXv319Fixa1GUB9+eWX8vPzk6enp5o3b66JEydadetbsWKFKlasKHd3dxUsWFDDhg3TgwcPjPnnzp3Tq6++Km9vb/n4+Khly5b6+++/bZ4fX19fVa9eXR07dtTu3bt18+ZNi+XirrnnnntO1apV07hx4/T555/ryy+/1I8//mixbMaMGS3Ot5eXl83zlTt3bhUtWlStWrXStm3blCNHDr399tsWy7m4uBj7DQoKUnBwsPEd27FjRy1evFhRUVEW68yePVu+vr5GVaXZbFZ4eLjatWunNm3aaObMmRbLx13HixYtUvXq1eXu7q7SpUtr8+bNxjKjR4+Wq6ur8d+FTZs26auvvtLcuXMtKt3iS+qzvWrVKmXKlEnz58+3Od9ehEkA0hFCLgBI08xmKfq2Y17JGPDrwYMHiomJsfqHuIeHh9GdoGvXrtqxY4cWLVqkQ4cO6Y033lCjRo108uRJY/moqCiNHz9e8+bN05YtW3Tu3Dm7ux3cv39fr7/+unLnzm0RrAQHB2vRokVG1zBJWrx4sfLkyaNatWoluD0PDw+Lm2tbcubMadwQxcTESJJ69+6tbdu2aeXKlVq/fr22bt2qffv2WbRn9+7dOn36tDHtyJEjOnTokNq0aaPY2FgtWrRIwcHBypMnj9U+vb295eLiYvzu4+OjgQMHavjw4RZ/abdl6NChOnz4sJYsWWJz/q1bt9SkSROVLFlSe/fu1dChQ+06/zdu3DBuriVpz549RiXDH3/8oWrVqiV6ruOLf/yP3uh7eHjonXfe0bp163T16lW7thdn6tSpun37thGmDRw4UNevX7eqqBo4cKDee+89HThwQEWLFlXr1q2Nm/rcuXNr586dVtUwj9qwYYOOHTumiIgILVy4UMuWLdOwYcOM+QMGDNDYsWP14Ycf6ujRo1qwYIERinz77beKjo62CCjjix9CREZG6ttvv1Xbtm3VoEED3bhxQ1u3bjXm7969W5L0448/6sKFCxZBR1JtTK6PPvpI/fv31w8//KAXXngh2etHRUVp1KhRmjt3rrZt26br16+rVatWyd6Os7OzRo8erU8++UR//PFHsteP07FjR23cuNHivf7tt9+0ZcsWiyqZ8PBwNW7cWJkyZVLbtm2two1t27apS5cu6tGjhw4cOKAGDRpYhI+StHXrVoWEhKhHjx46evSoPv/8c82ePdtYLjY2Vq+++qquXr2qzZs3a/369frtt98UFBSUYPsvXbqk//3vf3J2dpazs3OSxxsaGqosWbJYhWFjx45VtmzZVKFCBX388ccWAVdCPDw81KVLF23btk2XLl1KdLm479jg4GDdu3fP4rvJbDZrzpw5CgsLM45h06ZNioqKUmBgoNq2batFixbZ/N57//331adPH+3fv18BAQFq2rSprly5Iulh9drcuXP1xRdfaMWKFerQoYM++OADVapUyWY7k/psL1iwQK1bt9b8+fMVHByc5PlJjEvSiwAAAAB2uB8ljbYOFJ6KD/6SXK3/Cm1LxowZFRAQoBEjRqhEiRLKlSuXFi5cqB07dqhw4cI6d+6cwsPDde7cOSMgee+997R27VqFh4dr9OjRkh4GQjNmzFChQoUkPQyg4qp1ktK1a1edPn1ae/bssQi1WrZsqZ49e+qnn34yAo24f/zHH0clTkxMjBYuXKhDhw7Z7PbxqOLFiysyMlJXrlyRh4eH5syZowULFhg3HeHh4RahUKlSpVSuXDktWLBAH374oSRp/vz5qlq1qgoXLqxLly7p2rVrKl68uF3HLUnvvPOOpkyZookTJxrbtCVPnjzq0aOHBg4caHMsmwULFig2NlYzZ86Uu7u7SpUqpT/++MOqwiC+7du3a/HixVq1apUxLUeOHMbPPXr00IULF+weP+vy5cu6fv26SpQoYXN+iRIlZDabderUKVWpUkWS9P3331tUa0kywr043t7e+vrrr1WnTh1lzJhRkydP1qZNm+Tj42Ox3HvvvafGjRtLeli5U6pUKZ06dUrFixfXkCFD9Nprr8nf319FixZVQECAXn75ZbVo0UJOTv/WFLi6umrWrFny9PRUqVKlNHz4cL3//vsaMWKEbt++rSlTpujTTz9VaGioJKlQoUKqWbOmJOnkyZPy8fGRr69vkudq0aJFKlKkiEqVKiVJatWqlWbOnGl8zuPeh2zZslkFc4m1Mf6x2KNfv36aN2+eNm/ebLQljj3vjfTw2v/000+Nqr05c+aoRIkS2r17t/E+37hxw2pbtWrV0po1ayymNW/eXOXLl9eQIUOswh17NWzYUHny5FF4eLiGDh0q6WGVjJ+fn3Ftx8bGavbs2frkk08kPTz/ffr00ZkzZ4yqsE8++UQvvfSSEcoWLVpU27dv1/fff2/sa9iwYerfv7/xeShYsKBGjBihvn37asiQIdqwYYMOHz6sM2fOyM/PT5I0d+5clSpVSnv27NHzzz9vcX7MZrNR4dO9e3eb1USPcnJyUtGiRY0xy+LWrVixorJmzart27drwIABunDhgiZOnJjk9uK+v86ePaucOXNazd+7d68WLFig+vXrS5KyZs2q5s2ba9asWUa14KZNm3T27Fm1b9/eWG/mzJlq1aqVnJ2dVbp0aRUsWFDffvutwsLCLLbftWtXvf7665Kk6dOna+3atUZ3REmqXLmyBgwYoNdee00VKlTQwIEDbR5HYp9tSfrss880cOBAfffdd6pTp06S5yUpVCYBSFcYzQkAkBLmzZsns9msvHnzys3NTVOnTlXr1q3l5OSkw4cPKyYmRkWLFpW3t7fx2rx5s0WFjqenpxEkSZKvr2+if9mOM2PGDM2ePVtLly7Vc889ZzEvR44cevHFF43uB2fOnNGOHTus/oI8bdo0eXt7y8PDQ507d1avXr0SDVHixFU8mUwm/fbbb7p//75x8ytJmTJlUrFixSzWCQ4O1oIFC4z1Fy5caLTH/BiPgHRzc9Pw4cM1fvx4/fPPP4ku269fP12+fNlmF55jx46pbNmyFmFcQEBAgtv65Zdf9Oqrr2rIkCF68cUXreZ/8cUXmjlzplauXGkRMNkjqfPg6upq/FyvXj0dOHDA4vXVV19ZrRMQEKD33ntPI0aMUJ8+fYwAJ76yZcsaP8cFOnGfQV9fX+3YsUOHDx9Wjx499ODBA4WGhqpRo0aKjY011itXrpw8PT0t9nvr1i2dP39ex44d07179xKs3jGbzTZDTltmzZplMXZP27Zt9e233yoyMjLJdRNrY3JMmDBBX375pX766SebN9v2vjcuLi5GKCI9DCMyZ86sY8eOGdMyZsxo17Ykady4cZozZ47F+snh7Oys0NBQzZ49W2azWbGxsZozZ47at29vhG3r16/X7du39fLLL0uSsmfPrgYNGlhcWydOnLD4PpBk9fvBgwc1fPhwi+/GuHGNoqKidOzYMfn5+RlBkiSVLFkywfPz888/a8KECapYsaJVFVRiHv3s9e7dW3Xr1lXZsmXVpUsXTZgwQZ988onRVTSpbUmy2N7hw4eN79gqVaooICDAojKwQ4cO2rJli/HfhFmzZqlOnToqXLiwpIdjyS1btszqM28rMIz/veXi4qLKlStbfRY+/PBDxcbGqn///hbVnnGS+mwvWbJEvXr10vr161MkSJKoTAKQztDRDQDSsAyeDyuEHLXvZChUqJA2b96s27dv6+bNm8bThAoWLKhbt27J2dlZe/futepyEb/SIEOGDBbzTCZTkqHCTz/9pO7du2vatGmqXr26zWWCg4PVvXt3ffLJJ1qwYIHKlCmjMmXKWC0zcOBAeXh4yNfX1+7qjGPHjsnHx0fZsmWzeyDu1q1bq1+/ftq3b5/u3Lmj8+fPG11WcuTIocyZM+v48eN2bStO27ZtNX78eI0cOdLiSW6Pypw5swYMGKBhw4apSZMmydpHfEePHtULL7ygN998U4MGDbKav2nTJnXr1k0LFy60CGiSEnf8CYUAx44dk4uLi8XT+ry8vIwbzji2ujjFxsZq27ZtcnZ21qlTp2xuP/5nMO5GOH5QJEmlS5dW6dKl9c4776hLly6qVauWNm/erHr16iV5fB4eHonOL1q0qG7cuKELFy4kWp109OhR7dy5U7t377YYEywmJkaLFi1S586dk2xLSqhVq5ZWrVqlb775xuZ4XPa+N/ZwcnKy2lZCateurYYNG2rAgAFWVSv26tChg8aMGaONGzcqNjZW58+ft6qSuXr1qsV7Ghsbq0OHDmnYsGF2f4fcunVLw4YNs3hiWZyExvCxJf75KVGihE6fPq23335b8+bNS3LdmJgYnTx50iLQe1TVqlX14MEDnT171iogf1Tc9Rv/u6hYsWJauXKlXFxclCdPHotAWHr41LZ8+fJp9uzZev/997Vs2TJ9/vnnxvwFCxbo7t27FmPOxQV9v/76q4oWLZrkccYXFyDZCpKkpD/bFSpU0L59+zRr1ixVrlzZ7hA4MVQmAbBiov4HeHalwD8u8AwzmR52NXPE6zE/u3EDT1+7dk3r1q3Tq6++qgoVKigmJkaXLl1S4cKFLV62BsC11/nz5/X666/rzTffVKdOnRJc7tVXX9Xdu3e1du1aLViwwOa4FpkyZVLhwoWVN29eu28CL126pAULFqhZs2ZycnJSwYIFlSFDBosuXTdu3NCvv/5qsd5zzz2nOnXqaP78+Zo/f74aNGhgdAVxcnJSq1atNH/+fJvh1K1bt2yOW+Lk5KQxY8Zo+vTpFl1VbOnWrZucnJw0ZcoUi+klSpTQoUOHLJ5IZetx3UeOHFG9evUUGhpqs/Lh1KlTatGihT744AObN8iJcXJyUsuWLbVgwQKrR93fuXNH06ZNU/PmzZUpU6ZkbVeSPv74Yx0/flybN282ulg+qZIlS0qSxbgtBw8e1J07d4zfd+7cKW9vb/n5+alIkSLy8PBIcCDfFi1ayNXVVR999JHN+devX5f0MMioXbu2Dh48aFGp07t3b6NSI+5m3Va3ssTamBxVqlTRmjVrNHr0aI0fPz5Z68b34MED/fzzz8bvJ06cSLS7oz3Gjh2r7777zmIQ9eQoVKiQ6tSpo1mzZik8PFyBgYHKnz+/JOnKlStasWKFFi1aZHH+9+/fr2vXrumHH36Q9DBAebSL56O/V6xYUSdOnLD6bixcuLCcnJxUokQJnT9/3qJq7OjRo7p+/brx+bOlf//+Wrx4scWYbQmZM2eOrl27ZnQNs+XAgQNycnKy2W0tvjt37uiLL75Q7dq1LSoSXV1dVbhwYfn7+1sFSdLDa799+/ZGV2FXV1e1aNHCmD9z5kz16dPH4nwfPHhQtWrVsqq0jP+99eDBA+3duzfZn6WkPtuFChXSpk2btGLFCptPuXscVCYBsIGbSQDAf9u6detkNptVrFgxnTp1Su+//76KFy+u9u3bK0OGDAoODlZISIgmTJigChUq6PLly9qwYYPKli1rjFGTHHfv3lXz5s2VN29e9e/f3yp4kGQEVV5eXmrWrJk+/PBDHTt2TK1bt072/sxmsy5evCiz2azr169rx44dGj16tDJlyqSxY8dKetjNJDQ0VO+//76yZs2qnDlzasiQIXJycrL6q3VwcLCGDBmi6OhoTZo0yWLeqFGjFBERoapVq2rUqFGqXLmyMmTIoK1bt2rMmDHas2eP1dOgJKlx48aqWrWqPv/8c6unXMXn7u6uYcOGWT0VrU2bNho4cKA6d+6sAQMG6OzZs1Y3Ub/88ovq16+vhg0bqnfv3sZ5d3Z2Vo4cOXTnzh01bdpUFSpU0JtvvmnxvtgbHI4aNUobNmxQgwYN9NFHH6l06dI6c+aMBg0aZDMEs8f+/fs1ePBgLVmyRDVq1NDEiRPVo0cP46l09nj77beVJ08e1a9fX88995wuXLigkSNHKkeOHBbdaqKjo9WxY0cNGjRIZ8+e1ZAhQ9S1a1c5OTnJ3d1d/fr1U9++feXq6qoaNWro8uXLOnLkiDp27Cg/Pz9NmjRJXbt21c2bNxUSEiJ/f3/98ccfmjt3rry9vTV27FjNmzdPw4cPV+nSpS3a2KlTJ02cOFFHjhxRsWLF5OHhobVr1+q5556Tu7u7EcIl1kbpYWgZv3rrzJkzOnDggLJmzap8+fJZ7LN69epavXq1XnrpJbm4uBhPLkyODBkyqFu3bpo6dapcXFzUtWtXVatWzaJLWNw1+KicOXPaDIDLlCmj4OBgTZ061Wre0aNHFR0dratXryoyMlIHDhyQJJUvX95iuY4dOxpVXrNnzzamz5s3T9myZVPLli2tru2XX35ZM2fOVKNGjdStWzfVrl1bEydOVNOmTbVx40atWbPGYp3BgwerSZMmypcvnzH+1sGDB/XLL79o5MiRCgwMNI5l8uTJevDggd555x3VqVNHlStXTvCc+vn5qXnz5ho8eLDFGE1RUVG6ePGiHjx4oD/++EP/+9//NGnSJL399ttGdd2OHTu0a9cu1atXTxkzZtSOHTvUq1cvtW3bVlmyZLHYz6VLl3T37l1FRkZq7969+uijj/TPP//YfLJdUtq3b6/hw4frgw8+UOvWrY2qrwMHDmjfvn2aP3++1XhyrVu31vDhwzVy5Ehj2meffaYiRYqoRIkSmjRpkq5du6YOHTokuz1JfbaLFi2qTZs2qW7dunJxcdHkyZOTvY/4qEwCYCWtVialzVYBjmEm9E2TzFR2pRs3btzQu+++q+LFiyskJEQ1a9Y0HuUuPRyIOiQkRH369FGxYsXUrFkz7dmzx+rGNDGxsbFGl4Rdu3Zp79692r9/v/z8/OTr62v1ii84ONj4K3Zy9hknrute3rx5FRAQoM8//1yhoaHav3+/xb4mTpyogIAANWnSRIGBgapRo4ZKlChh1V2lRYsWunLliqKioqwGw86aNat27typtm3bauTIkapQoYJq1aqlhQsX6uOPP060KmfcuHEWlUUJCQ0NtQpRvL299d133+nw4cPGoLTjxo2zWGbJkiW6fPmyvv76a4tzHdc95u+//9bx48e1YcMG5cmTJ8H3IzHZs2fXzp07Va9ePb311lsqUKCA6tSpo5iYGB04cCBZ25IeBo9t27ZVWFiYmjZtKkl68803Va9ePbVr185m5Y4tgYGB2rlzp9544w0VLVpUr7/+utzd3bVhwwZly5bNWO6FF15QkSJFVLt2bQUFBemVV14xBnGWHo7V0qdPHw0ePFglSpRQUFCQxdhg77zzjn744Qf9+eefat68uYoXL65OnTrJx8dH7733nlauXKkrV66oefPmVm0sUaKESpQooZkzZ8rFxUVTp07V559/rjx58ujVV1+1u40///yzKlSooAoVKkh6OH5OhQoVNHjwYJvnpmbNmlq1apUGDRpkDEidHJ6enurXr5/atGmjGjVqyNvbW4sXL7ZYJu4afPSV2Lhqw4cPt+qmKD0MfCpUqKDvvvtOERERFsca3+uvvy43Nzd5enpaXKezZs1S8+bNbXZtev3117Vy5Ur9888/qlGjhmbMmKGJEyeqXLlyWrt2rXr16mXxfdCwYUN9//33+uGHH/T888+rWrVqmjRpklEFZTKZtGLFCmXJkkW1a9dWYGCgChYsaHV+bOnVq5dWrVplPNlPkr788kv5+vqqUKFCeu2113T06FEtXrxY06ZNM5Zxc3PTokWLVKdOHZUqVUqjRo1Sr1699MUXX1jto1ixYsqTJ48qVaqksWPHKjAwUL/88kuiVVMJyZcvnwIDA63Cn5kzZ6pkyZI2H0zQvHlzXbp0SatXrzamjR07VmPHjlW5cuX0008/aeXKlcqePXuy2yMl/dkuVqyYNm7cqIULF6pPnz6PtY84JvPjjJqXjt28eVOZMmXSjRs3rJ6GAOChjR+1Uv2oNUkv+JT9+vxwZd/9sbKakh6oEfivi5WTnGT9D84n9Xfl95WrySBpaPK7ZEA655RX+QYfTbXtp6V/x9y9e9d4ClByxsl4lnTp0kV//PGHxV/Z07rbt28rb968mjBhgsUjxZE8M2fO1DvvvKPFixfbfBJdWhEWFqbr169r+fLljm4K0qjOnTvr+PHj2rp1q6Ob8p9z9uxZFShQQPv377eqMnMke//7Tjc3AOkIf/EHUtuz9ScmIHVERkZq//79WrZsmT744ANHNydR+/fv1/Hjx1WlShXduHFDw4cPlySLqhAkX8eOHZU1a1YdO3ZMDRs2THIgayCtGD9+vBo0aCAvLy+tWbNGc+bMsagCAuIQJgFIV4iTAABp3eDBgzV//nw1b95cXbp0cXRzkjR+/HidOHFCrq6uqlSpkrZu3frYXSzwL1vduoC0bvfu3froo48UGRmpggULaurUqYk+MADPLsIkAOkLaRKQuhjzB3hikyZNshqkOq2qUKGC9u7d6+hmwEHiD9IMSNI333zj6CY8M/z9/ZWeRx1iAG4AANIlQh8AAAA4BmESgPQl/Yb3AJ4BpnT8F0YAAAB7ESYBSDfMJElAquM6ezLPYi9BW4+xBgAA6ZO9/11nzCQA6Qq3uQCQNri6usrJyUl//fWXcuTIIVdXV5mexTQNAID/ALPZrOjoaF2+fFlOTk5ydXVNdHnCJADphkkmRokB/p/ZpFRKV7nKYB8nJycVKFBAFy5c0F9//eXo5gAAgBTg6empfPnyyckp8Y5shEkA0hfucwEgzXB1dVW+fPn04MEDxcTEOLo5AADgCTg7O8vFxcWuSmPCJAAAADw2k8mkDBkyKEOGDI5uCgAAeEoYgBtAusKYSQAAAADgWIRJAACkS/T5BAAAgGMQJgFIN8wyc/sMIE3jWwoAADwLCJMA2EBnMuDZRRgCAACAxBEmAbBix+D9DpJmGwY8dVTAAAAAwFEIkwAAAAAAAGA3wiQAAAAAAADYjTAJgDWGTAKeWVz+AAAASAphEgArjMQCpAdcqQAAAHAMwiQA6QYVE8C/Um8AbkIqAAAAJI4wCQAAAAAAAHYjTAKQrlCdBAAAAACORZgEIB2h+w1gSKXLwUxkCwA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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of participation sums:\n",
      "count    5425.000000\n",
      "mean        0.631705\n",
      "std         0.482386\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         1.000000\n",
      "75%         1.000000\n",
      "max         1.000000\n",
      "dtype: float64\n",
      "Warning: Not all participation sums are exactly 1. This might be due to floating-point precision.\n"
     ]
    },
    {
     "data": {
      "image/png": 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jnTt3ql+/fsqZM6fV4NLMmTPl7e2tJEmSqF69eho7dqzFVLsVK1aocOHCcnNzU7Zs2RQaGqpnz56Z9p8/f14ffvihPD09lSxZMjVq1EhXr161Oj4ZMmRQ6dKl1bZtW+3du1f37t0zqxf9nsucObNKliypL7/8Ul9//bVmzpypDRs2mNVNmjSp2Xh7eHhYHS8vLy/lzJlTTZo00Y4dO5Q2bVp17NjRrJ6Tk5PpvI0bN1bz5s1Nn7Ft27bVd999p4cPH5odExYWpgwZMpiyIY1Go+bMmaOWLVuqWbNmmjVrlln96Pfx4sWLVbp0abm5uSlfvnzaunWrqc7w4cPl4uJi+ndh8+bN+uabbzRv3jyzDLWYXnZvr1q1SsmTJ9eCBQus7gcAAADelDcWKHr27JkiIyMt/ifb3d3dlOIfHBysXbt2afHixTp8+LAaNmyo6tWr69SpU6b6Dx8+1OjRozV//nz9+uuvOn/+fLynAjx9+lT169eXl5eXWdCkefPmWrx4sWm6liR99913ypgxo8qVKxdre+7u7mZfnK1Jly6d6ctOZGSkJKlHjx7asWOHVq5cqfXr12vbtm3av3+/WX/27t2rv//+21R29OhRHT58WM2aNVNUVJQWL16s5s2bK2PGjBbn9PT0lJPT/z/gLlmyZBo4cKCGDh1q9hdya4YMGaIjR45o6dKlVvffv39ftWrVUp48ebRv3z4NGTIkXuN/9+5d0xdnSfrtt99MGQgXL15UyZIl4xzrmGJe/4tf4t3d3dWpUyetW7dOt27dild70SZOnKgHDx6YAmUDBw7UnTt3LDKhBg4cqF69eungwYPKmTOnmjZtavrC7uXlpd27d1tksbxo48aNOn78uLZs2aJFixZp+fLlCg0NNe3v37+/Ro4cqc8++0zHjh3TwoULTQGPJUuWKCIiwiz4GFPMAEN4eLiWLFmiFi1aqGrVqrp79662bdtm2r93715J0oYNG3T58mWzIMbL+phQo0aNUr9+/fTLL7+ocuXKCT7+4cOH+uKLLzRv3jzt2LFDd+7cUZMmTRLcjqOjo4YPH65Jkybp4sWLCT4+Wtu2bbVp0yaz1/r06dP69ddfzbJb5syZo5o1ayp58uRq0aKFReBix44d6tChg7p27aqDBw+qatWqZoFFSdq2bZtatWqlrl276tixY/r6668VFhZmqhcVFaUPP/xQt27d0tatW7V+/XqdPn1ajRs3jrX/165d0w8//CBHR0c5Ojq+9HoDAwOVMmVKi0DXyJEjlTp1ahUqVEhfffWVWfAqNu7u7urQoYN27Niha9euxVkv+jO2efPmevLkidlnk9Fo1Ny5cxUUFGS6hs2bN+vhw4eqUqWKWrRoocWLF1v93Ovdu7d69uypAwcOqFSpUqpdu7Zu3rwp6XnW2bx58zRjxgytWLFCbdq00YABA1SkSBGr/XzZvb1w4UI1bdpUCxYsUPPmzV86PgAAAIgDGUU298YCRUmTJlWpUqU0bNgw/fPPP4qMjNS3336rXbt26fLlyzp//rzmzJmjJUuWqFy5cvL19VWvXr1UtmxZzZkzx9TO06dPNX36dBUtWlSFCxdWcHBwvLMTgoOD9ffff+uHH34wC1g1atRI//zzj9maFNH/Y2+wcqNE9/3w4cOqVKnSS8+bO3duhYeH6+bNmwoPD9fcuXM1evRoVa5cWfny5dOcOXNMQSTp+fQLf39/LVy40FS2YMEClShRQtmzZ9eNGzd0+/Zt5c6dO17XLUmdOnWSm5ubxo4dG2e9jBkzqmvXrho4cKDVL3wLFy5UVFSUZs2apbx586pWrVrq3bt3nG3u3LlT3333ndm0lbRp05oyEEaNGqXLly9r2bJl8bqW69ev686dO/Lz87O638/PT0ajUX/99Zep7Oeff5anp6fZ9sEHH5gd5+npqW+//VZTpkzR4MGDNX78eM2fP1/JkiUzq9erVy/VrFlTOXPmVGhoqM6dO2c6V0hIiFKkSCEfHx/lypVLQUFB+v777xUVFWXWhouLi2bPnq28efOqZs2aGjp0qCZOnKioqCiFh4drwoQJGjVqlAIDA+Xr66uyZcuqXbt2kqRTp04pWbJkypAhw0vHavHixcqRI4fy5s0rR0dHNWnSxCxQkTZtWklS6tSp5eXlZRbMi6uPCdW3b1+NHz9eW7duVfHixc32xee1kZ6/9ydPnqxSpUqpSJEimjt3rnbu3GkKdknPA5LxaatevXoqWLCgQkJCEnwt0apVq6aMGTOafT6FhYXJ29vbFCyIiopSWFiYaRpakyZNtH37dp05c8Z0zKRJk/TBBx+oV69eypkzpzp16mTR59DQUPXr10+BgYHKli2bqlatqmHDhunrr7+W9Dyod+TIES1cuFBFihRRiRIlNG/ePG3dutVsHbjo8fHw8FD69Om1efNmffrpp1azgF7k4OCgnDlzmtYIk6QuXbpo8eLF2rx5sz755BMNHz481gDmi6I/v2K2F9O+ffu0cOFC02dsqlSpVK9ePbMsrs2bN+vs2bNq3bq1qWzWrFlq0qSJHB0dlS9fPmXLlk1LliyxaD84OFj169eXn5+fpk2bpuTJk5u9N4oWLar+/fvro48+UurUqTVw4ECr/Yzr3pakKVOmqFOnTvrpp59Uq1atlw8MAAAA4ubg8Hq3d9Abver58+fLaDQqU6ZMcnV11cSJE9W0aVM5ODjoyJEjioyMVM6cOc2+5G3dutUssyZJkiTy9fU1/Z4hQ4Y4/yIdbfr06QoLC9OyZcuUOXNms31p06bV+++/b5oScObMGe3atcviL79Tp06Vp6en3N3d1b59e3Xv3t1i6oQ10ZlKBoNBp0+f1tOnT82+UCRPnly5cuUyO6Z58+amQJHRaNSiRYtM/YmZ+RRfrq6uGjp0qEaPHq0bN27EWbdv3766fv261Wk1x48fV4ECBcwCbaVKlYq1rT/++EMffvihQkJC9P7771vsnzFjhmbNmqWVK1eaghbx9bJxcHFxMf1csWJFHTx40Gz75ptvLI4pVaqUevXqpWHDhqlnz54qW7asRZ0CBQqYfo4O1kTfgxkyZNCuXbt05MgRde3aVc+ePVNgYKCqV69uFmDx9/dXkiRJzM57//59XbhwQcePH9eTJ09izboxGo1WA5jWzJ4922ytnBYtWmjJkiUKDw9/6bFx9TEhxowZo5kzZ2r79u3Kmzevxf74vjZOTk4qVqyY6ffcuXMrRYoUOn78uKksadKk8WpLkr788kvNnTvX7PiEcHR0VGBgoMLCwmQ0GhUVFaW5c+eqdevWcvjfPzDr16/XgwcPVKNGDUlSmjRpVLVqVbP31smTJy0CDC/+fujQIQ0dOtTsszF6HaGHDx/q+PHj8vb2lre3t+mYPHnyxDo+v//+u8aMGaPChQtbZC/F5cV7r0ePHgoICFCBAgXUoUMHjRkzRpMmTTJN33xZW5LM2jty5IjpM7Z48eIqVaqUWUZfmzZt9Ouvv5r+TZg9e7YqVKig7NmzS3q+dtvy5cst7vkXs7gk888tJycnFS1a1OJe+OyzzxQVFaV+/fqZZWlGe9m9vXTpUnXv3l3r169XhQoV4hyPJ0+e6N69e2ZbfMYRAAAAeFVvNFDk6+urrVu3mr5s7t27V0+fPlW2bNl0//59OTo6at++fWZf8o4fP272xCxnZ2ezNg0Gw0sDBtu3b1eXLl00ZcoUlS5d2mqd5s2ba+nSpXr69KkWLlyo/PnzK3/+/BZ1Dh48qDNnzujBgwcaO3as6QthXI4fP65kyZIpderUL60brWnTpjp58qT279+vnTt36sKFC6ZpJGnTplWKFCl04sSJeLcnPf/ClDVr1pcuxpoiRQr1799foaGhFuuBJMSxY8dUuXJlffzxxxo0aJDF/s2bN6tz586aN2+eWfDlZaKvP7Yv+MePH5eTk5PZU+08PDyUPXt2sy1TpkwWx0ZFRWnHjh1ydHQ0y0iKKeY9GP0l98Usm3z58qlTp0769ttvtX79eq1fv95sDZS4uLu7x7k/Z86cunv3bqxrc0U7duyYdu/erT59+sjJyUlOTk4qWbKkHj58aPEUvNepXLlyioyM1Pfff291f3xfm/hwcHCId1vly5dXtWrV1L9//0SdS5LpKXubNm3Sxo0bdeHCBYvsllu3bsnd3d30GqxevVpz585NUGbW/fv3FRoaavbZeOTIEZ06dSrWNXOsiR4fPz8/9ejRQyVLloxXsFt6nkl56tSpOJ8WWaJECT179izWLKGYot+/MZ++mCtXLtPn/qNHj7Ry5UqzNaYqV66sLFmyKCwsTPfu3dPy5cvNpvktXLhQjx8/VokSJUzj3bdvX23fvl1//vlnvK4zpujgkLUgkfTye7tQoUJKmzatZs+e/dJ/p0aMGKHkyZObba/jaYkAAAD2z/Cat3fPW5FHFb2I8+3bt7Vu3Tp9+OGHKlSokCIjI3Xt2jWLL3rWFpONrwsXLqh+/fr6+OOPTVN3rPnwww/1+PFjrV27VgsXLrS6jkTy5MlNXzzjEyCSnmeaLFy4UHXr1pWDg4OyZcsmZ2dni+kgL36JyZw5sypUqKAFCxZowYIFqlq1qtKlSyfp+Ze9Jk2aaMGCBVafpnb//n2r08YcHBw0YsQITZs27aVf5Dp37iwHBweLx9r7+fnp8OHDZk9usvbI6qNHj6pixYoKDAy0mrHw119/qUGDBhowYIDZY63jw8HBQY0aNdLChQstHvf+6NEjTZ06VfXq1VPy5MkT1K4kffXVVzpx4oS2bt2qtWvXmk0rSqw8efJIktk6KYcOHdKjR49Mv+/evVuenp7y9vZWjhw55O7uHuuUygYNGsjFxUWjRo2yuj/6kfKzZs1S+fLldejQIbMAQ48ePUwZFtFZVzGnPsanjwlRvHhxrVmzRsOHD9fo0aMTdGxMz5490++//276/eTJk3FOQYyPkSNH6qeffjJbkDwhfH19VaFCBc2ePVtz5sxRlSpVlDVrVknSzZs3tWLFCi1evNhs/A8cOKDbt2/rl19+kfQ8OBLz80CSxe+FCxfWyZMnLT4bs2fPLgcHB/n5+enChQtm2V7Hjh3TnTt3TPefNf369dN3331ntkZabObOnavbt2+rfv36sdY5ePCgHBwcTJ9VsXn06JFmzJih8uXLm2USuri4KHv27PLx8THLCIzm4OCg1q1ba+7cuVq4cKFcXFzUoEED0/5Zs2apZ8+eZuN96NAhlStXziJDMubn1rNnz7Rv374E30svu7d9fX21efNmrVixwurT4GLq37+/7t69a7a9ShATAAAAiC/rfxb9l6xbt05Go1G5cuXSX3/9pd69eyt37txq3bq1nJ2d1bx5c7Vq1UpjxoxRoUKFdP36dW3cuFEFChRQzZo1E3y+x48fq169esqUKZP69etnEVSQZApCeXh4qG7duvrss890/PhxNW3aNMHnMxqNunLlioxGo+7cuaNdu3Zp+PDhSp48uUaOHCnp+dSPwMBA9e7dW6lSpVK6dOkUEhIiBwcHi+lEzZs3V0hIiCIiIjRu3DizfV988YW2bNmiEiVK6IsvvlDRokXl7Oysbdu2acSIEfrtt98snpokSTVr1lSJEiX09ddfWzwNKiY3NzeFhoZaPD2sWbNmGjhwoNq3b6/+/fvr7NmzFl+Q/vjjD1WqVEnVqlVTjx49TOPu6OiotGnT6tGjR6pdu7YKFSqkjz/+2Ox1iW9Q8IsvvtDGjRtVtWpVjRo1Svny5dOZM2c0aNAgqwGu+Dhw4IAGDx6spUuXqkyZMho7dqy6du1qenpbfHTs2FEZM2ZUpUqVlDlzZl2+fFmff/650qZNazbVJSIiQm3bttWgQYN09uxZhYSEKDg4WA4ODnJzc1Pfvn3Vp08fubi4qEyZMrp+/bqOHj2qtm3bytvbW+PGjVNwcLDu3bunVq1aycfHRxcvXtS8efPk6empkSNHav78+Ro6dKjy5ctn1sd27dpp7NixOnr0qHLlyiV3d3etXbtWmTNnlpubmynAFlcfpecByZhZV2fOnNHBgweVKlUqZcmSxeycpUuX1urVq/XBBx/IycnJ9IS/hHB2dlbnzp01ceJEOTk5KTg4WCVLljSbphX9HnxRunTprAZ38+fPr+bNm2vixIkW+44dO6aIiAjdunVL4eHhOnjwoCSpYMGCZvXatm2r9u3bS3q+RlG0+fPnK3Xq1GrUqJHFe7tGjRqaNWuWqlevrs6dO6t8+fIaO3asateurU2bNmnNmjVmxwwePFi1atVSlixZ1KBBAzk4OOjQoUP6448/9Pnnn6tKlSqmaxk/fryePXumTp06qUKFCipatGisY+rt7a169epp8ODB+vnnn03lDx8+1JUrV/Ts2TNdvHhRP/zwg8aNG6eOHTuqYsWKkqRdu3Zpz549qlixopImTapdu3ape/fuatGihVKmTGl2nmvXrunx48cKDw/Xvn37NGrUKN24ccPqE+BepnXr1ho6dKgGDBigpk2bmjLwDh48qP3792vBggUW67c1bdpUQ4cONcumnDJlinLkyCE/Pz+NGzdOt2/fVps2bRLcn5fd2zlz5tTmzZsVEBAgJycnjR8/3mo7rq6ucnV1TfD5AQAA3jnv6ILTr9MbzSi6e/euPv30U+XOnVutWrVS2bJlTY8zl54/HahVq1bq2bOncuXKpbp16+q3336z+NIZl6ioKNM0gT179mjfvn06cOCAvL29lSFDBostpubNm5v++pyQc0a7d++eMmTIoEyZMqlUqVL6+uuvFRgYqAMHDpida+zYsSpVqpRq1aqlKlWqqEyZMvLz87OYQtKgQQPdvHlTDx8+VN26dc32pUqVSrt371aLFi30+eefq1ChQipXrpwWLVqkr776Ks5smi+//NIsIyg20QvnxuTp6amffvpJR44cUaFChTRw4EB9+eWXZnWWLl2q69ev69tvvzUb6+j1Za5evaoTJ05o48aNypgxY6yvR1zSpEmj3bt3q2LFivrkk0/03nvvqUKFCoqMjNTBgwcT1Jb0PKjYokULBQUFqXbt2pKkjz/+WBUrVlTLli2tZtxYU6VKFe3evVsNGzZUzpw5Vb9+fbm5uWnjxo1mUw8rV66sHDlyqHz58mrcuLHq1KmjIUOGmPZ/9tln6tmzpwYPHiw/Pz81btzYbC2uTp066ZdfftGlS5dUr1495c6dW+3atVOyZMnUq1cvrVy5Ujdv3rT66Ho/Pz/5+flp1qxZcnJy0sSJE/X1118rY8aM+vDDD+Pdx99//12FChVSoUKFJD1fr6ZQoUIaPHiw1bEpW7asVq1apUGDBmnSpEnxGs+YkiRJor59+6pZs2YqU6aMPD099d1335nViX4PvrjFtY7Z0KFDrU4Dq1GjhgoVKqSffvpJW7ZsMbvWmOrXry9XV1clSZLE7H06e/Zs1atXz+p6UvXr19fKlSt148YNlSlTRtOnT9fYsWPl7++vtWvXqnv37mafB9WqVdPPP/+sX375RcWKFVPJkiU1btw4U/aSwWDQihUrlDJlSpUvX15VqlRRtmzZLMbHmu7du2vVqlVmi4LPnDlTGTJkkK+vrz766CMdO3ZM3333naZOnWqq4+rqqsWLF6tChQrKmzevvvjiC3Xv3l0zZsywOEeuXLmUMWNGFSlSRCNHjlSVKlX0xx9/xJntFJssWbKoSpUqFoGdWbNmKU+ePFYX+a9Xr56uXbum1atXm8pGjhypkSNHyt/fX9u3b9fKlSuVJk2aBPdHevm9nStXLm3atEmLFi1Sz549E3UOAAAA4HV5oxlFjRo1UqNGjWLd7+zsrNDQ0FgfwR0UFKSgoCCzsrp165qt/XDt2jVTVkqFChUStPDzBx98EGv9LVu2xHmstb7FJmnSpKaFs6XnU5JCQ0PNngomPV8rKK6ATvQaFnGtY2Gt36VKlbK4Tmv9d3R01NGjRy2OL1mypCm7IlrM9oYMGWIWUHiRj49PvF+XuMY9TZo0mjhxoikbZNasWerUqZN+/fVXsy/sMbM8YgoICDDrh7VrXbFiRZz9TpEihVlZ/fr145yaE1Nc97qDg4MGDhwY65OWpOdBqSpVqljdV79+/TiDW8eOHTP93K5dO4tpmTHHLLY+vjh+1rw4xbF8+fK6f/++1fPE1XbM+zO2qYrxeQ9aO5+Pj4/VRYPjs86O9HxNqejpfjEdPnw41mNe/Cxs3769KSsp+vfoBZqjVatWTdWqVYu1zSxZspjdry+KbXxKlixpNtYv+6yLVrhwYavTTmOKzz0S7WWfGzGtW7fOoiyu4KOXl5fp/RD9uvr5+WnPnj0vPVds/X/Zvf3iOPr5+enq1asvPR8AAABewvBWrKjzn/JGA0WvU3h4uA4cOKDly5drwIABb7o7cTpw4IBOnDih4sWL6+7duxo6dKgkmWVzIOHatm2rVKlS6fjx46pWrdpLF4UG3hajR49W1apV5eHhoTVr1mju3Llm2TsAAAAA8Lr8ZwNFgwcP1oIFC1SvXj116NDhTXfnpUaPHq2TJ0/KxcVFRYoU0bZt2xI97QH/z9pUK+Btt3fvXo0aNUrh4eHKli2bJk6cGOfi+wAAAMC7izWKbO0/GygaN26cxYLPb6tChQpp3759b7obeENim26Fd1dsj1eH7SVk6isAAADwLvjPBooAAAAAAMB/HE89szlWfQIAAAAAAIAkMooAAAAAAIC94qlnNseIAgAAAAAAQBIZRQAAAAAAwG6xRpGtkVEEAAAAAAAASWQUAQAAAAAAe8VTz2yOQBH+j707j4uq+v8H/p4FGIZRBpFVVgUFQlNREFxAQ0HN7eOSK+aSuZVaSe6WtmiWZrmVJeVulmVuqClaakqi4gZJgpkpuKNCss3r9we/uR+uDHhuX/io9X4+HvPQmTmce/aZe+fccxhjjDHGGGOMsScU3yhV1bhEGWOMMcYYY4wxxhgR8YwixhhjjDHGGGOMPan41rMqxzOKGGOMMcYYY4wxxhgR8YwixhhjjDHGGGOMPal4RlGV4xlFjDHGGGOMMcYYY1Vk8eLF5OPjQzqdjsLCwig5ObnCsMuXL6fWrVuTg4MDOTg4UHR0dKXh/xf4QhFjjDHGGGOMMcaeUKpqfiizYcMGeuWVV2jmzJl07NgxevrppykmJoauXr1qMfy+ffuoX79+lJSURD///DN5enpShw4d6M8//1R87KrCF4oYY4wxxhhjjDHGqsD8+fPphRdeoCFDhlBQUBAtW7aM9Ho9rVixwmL4NWvW0OjRo6lx48YUEBBAn332GZlMJtqzZ8//OOX/xReKGGOMMcYYY4wx9mRSqav3oUBhYSGlpKRQdHS09Jparabo6Gj6+eefheLIz8+noqIiqlWrlqJjVyVezJoxxhhjjDHGGGPMgoKCAiooKJC9ZmNjQzY2NuXCXr9+nUpKSsjFxUX2uouLC6Wnpwsd7/XXXyd3d3fZxab/NZ5RxBhjjDHGGGOMsSeTSlWtj3fffZfs7e1lj3fffbdasjJnzhxav349ffvtt6TT6arlGCJ4RhFjjDHGGGOMMcaYBZMnT6ZXXnlF9pql2URERLVr1yaNRkM5OTmy13NycsjV1bXS47z//vs0Z84c+uGHH6hRo0b/t0T/H/GMIsYYY4wxxhhjjD2hqnfXMxsbG6pZs6bsUdGFImtrawoJCZEtRG1emDo8PLzCHLz33ns0e/ZsSkxMpGbNmv39oqgiPKOIMcYYY4wxxhhjrAq88sorNHjwYGrWrBmFhobShx9+SHl5eTRkyBAiIoqLi6M6depIt6/NnTuXZsyYQWvXriUfHx/Kzs4mIiKDwUAGg+GR5IEvFDHGGGOMMcYYY+zJpHBnsur23HPP0bVr12jGjBmUnZ1NjRs3psTERGmB64sXL5Ja/d80L126lAoLC6lXr16yeGbOnElvvPHG/zLpEr5QxBhjjDHGGGOMMVZFxo4dS2PHjrX43r59+2TPL1y4UP0JUogvFDHGGGOMMcYYY+yJpFKpHnUS/nH4QhFjjDHGGGOMMcaeUHyhqKo9XjfzMcYYY4wxxhhjjLFHhmcUMcYYY4wxxhhj7Mn0mC1m/U/AJcoYY4wxxhhjjDHGiIhnFDHGGGOMMcYYY+yJxWsUVTWeUcQYY4wxxhhjjDHGiIhnFDHGGGOMMcYYY+xJpeIZRVWNZxQxxhhjjDHGGGOMMSLiGUWMMcYYY4wxxhh7UvGuZ1WOS5QxxhhjjDHGGGOMERHPKGKMMcYYY4wxxtgTi9coqmo8o4gxxhhjjDHGGGOMERHPKGKMMcYYY4wxxtiTinc9q3I8o4gxxhhjjDHGGGOMERHPKGKMMcYYY4wxxtiTinc9q3JcoowxxhhjjDHGGGOMiHhGEWOMMcYYY4wxxp5YvEZRVeMZRYwxxhhjjDHGGGOMiHhGEWOMMcYYY4wxxp5UvOtZleMLRYwxxhhjjDHGGHtC8Y1SVY1LlDHGGGOMMcYYY4wREc8oYowxxhhjjDHG2JOKbz2rcjyjiDHGGGOMMcYYY4wREc8oYowxxhhjjDHG2JOKZxRVOZ5RxBhjjDHGGGOMMcaIiGcUMcYYY4wxxhhj7InF81+qGpcoY4wxxhhjjDHGGCMinlHEGGOMMcYYY4yxJxWvUVTleEYRY4wxxhhjjDHGGCMinlHEGGOMMcYYY4yxJxbPKKpqPKOIMcYYY4wxxhhjjBERzyhijDHGGGOMMcbYk0rF81+qGpcoY4wxxhhjjDHGGCMinlHEGGOMMcYYY4yxJxXvelbleEYRY4wxxhhjjDHGGCMinlHEGGOMMcYYY4yxJxbPKKpqfKGIMcYYY4wxxhhjTyZezLrKcYkyxhhjjDHGGGOMMSLiGUWMMcYYY4wxxhh7YvGtZ1WNZxQxxhhjjDHGGGOMMSLiGUWMMcYYY4wxxhh7Uql4RlFV4xlFjDHGGGOMMcYYY4yIeEYRY4wxxhhjjDHGnlg8/6WqcYkyxhhjjDHGGGOMMSLiGUWMMcYYY4wxxhh7UvEaRVWOZxQxxhhjjDHGGGOMMSLiGUWMMcYYY4wxxhh7Uql4/ktV4xJljDHGGGOMMcYYY0TEM4oYY4wxxhhjjDH2xOI1iqoazyhijDHGGGOMMcYYY0TEM4oYY4wxxhhjjDH2pOJdz6oczyhijDHGGGOMMcYYY0TEM4oYY4wxxhhjjDH2xOL5L1WNLxQxxhhjjDHGGGPsycS3nlU5vvTGGGOMMcYYY4wxxoiIZxQxxhhjjDHGGGPsicXzX6oalyhjjDHGGGOMMcYYIyKeUcQYY4wxxhhjjLEnFa9RVOV4RhFjjDHGGGOMMcYYIyKeUcQYY4wxxhhjjLEnFc8oqnI8o4gxxhhjjDHGGGOMERHPKGKMMcYYY4wxxtgTi+e/VDUuUcYYY4wxxhhjjDFGRDyjiDHGGGOMMcYYY08qXqOoyvGMIsYYY4wxxhhjjDFGRDyjiDHGGGOMMcYYY08snlFU1XhGEWOMMcYYY4wxxhgjIp5RxBhjjDHGGGOMsSeViue/VDUuUcYYY4wxxhhjjDFGRDyjiDHGGGOMMcYYY08sXqOoqvGFIsYYY4wxxhhjjD2Z+NazKsclyhhjjDHGGGOMMcaIiGcUMcYYY4wxxhhj7InFt55VNZ5RxBhjjDHGGGOMMcaIiGcUMcYYY4wxxhhj7Eml4hlFVY1nFDHGGGOMMcYYY4wxIuIZRYwxxhhjjDHGGHtS8a5nVY5LlDHGGGOMMcYYY4wREc8oYowxxhhjjDHG2BOL1yiqajyjiDHGGGOMMcYYY4wREc8oYowxxhhjjDHG2JOKdz2rcjyjiDHGGGOMMcYYY4wREc8oYowxxhhjjDHG2BOL579UNS5RxhhjjDHGGGOMMUZEPKOIMcYYY4wxxhhjTypeo6jK8YwixhhjjDHGGGOMsSqyePFi8vHxIZ1OR2FhYZScnFxp+I0bN1JAQADpdDpq2LAhbd++/X+UUsv4QhFjjDHGGGOMMcaeUOpqfiizYcMGeuWVV2jmzJl07NgxevrppykmJoauXr1qMfyhQ4eoX79+NGzYMDp+/Dh1796dunfvTqdPn1Z87KrCF4oYY4wxxhhjjDH2ZFKpqveh0Pz58+mFF16gIUOGUFBQEC1btoz0ej2tWLHCYviFCxdSbGwsTZw4kQIDA2n27NnUtGlTWrRo0f+1ZP42vlDEGGOMMcYYY4wxZkFBQQHduXNH9igoKLAYtrCwkFJSUig6Olp6Ta1WU3R0NP38888W/+bnn3+WhSciiomJqTD8/wQYY4wx9li7f/8+Zs6cifv371dZWI7z8T72vznOf1p+npQ4/2n5eVLi/Kfl50mJ85+Wn0cd5z/dzJkzQUSyx8yZMy2G/fPPP0FEOHTokOz1iRMnIjQ01OLfWFlZYe3atbLXFi9eDGdn5ypJ/9/BF4oYY4yxx1xubi6ICLm5uVUWluN8vI/9b47zn5afJyXOf1p+npQ4/2n5eVLi/Kfl51HH+U93//595Obmyh4VXTz7p1wo0lbRxCTGGGOMMcYYY4yxfxQbGxuysbERClu7dm3SaDSUk5Mjez0nJ4dcXV0t/o2rq6ui8P8LvEYRY4wxxhhjjDHG2P+RtbU1hYSE0J49e6TXTCYT7dmzh8LDwy3+TXh4uCw8EdHu3bsrDP+/wDOKGGOMMcYYY4wxxqrAK6+8QoMHD6ZmzZpRaGgoffjhh5SXl0dDhgwhIqK4uDiqU6cOvfvuu0RENG7cOIqMjKQPPviAOnfuTOvXr6ejR4/Sp59++sjywBeKGGOMscecjY0NzZw5U2jas2hYjvPxPva/Oc5/Wn6elDj/afl5UuL8p+XnSYnzn5afRx0nk3vuuefo2rVrNGPGDMrOzqbGjRtTYmIiubi4EBHRxYsXSa3+781dERERtHbtWpo2bRpNmTKF/P396bvvvqPg4OBHlQVSAcAjOzpjjDHGGGOMMcYYe2zwGkWMMcYYY4wxxhhjjIj4QhFjjDHGGGOMMcYY+//4QhFjjDHGGGOMMcYYIyK+UMQYY4wxxhhjjDHG/j++UMQYY4wxxhhjjDHGiIhI+6gTwBhjjDHLcnNzKTs7m4iIXF1dyd7evtLwBQUFRERVuo1tdcRZ1ZSW0/86vuqiJJ2iYUXDFRcX05kzZ2Rhg4KCyMrK6m/lJTs7m44cOSKLLywsjFxdXf8n+XkcPIq+pqQelda5kvyIhn3U45HI8UXa3KNu79VZl0+Cxz0/VT2+lvW45509RsAYY4yxx8ry5csRGBgItVotewQGBuKzzz6Thd21axc6duwIo9EohTMajejYsSN2794thSsoKMCGDRswfvx49O3bF3379sX48ePx1VdfoaCg4G/FeebMGYwaNQqNGzeGq6srXF1d0bhxY4waNQpnzpyRxSkaVkmcouUkGqeScheN89q1a5g7dy66d++OFi1aoEWLFujevTvee+89XL169W/FqSSdomFFw5WUlGDq1KkwGo1QqVSyh9FoxLRp01BSUiKcn3v37mHAgAHQaDTQarVwdnaGs7MztFotNBoNBg4ciLy8vGrLj9I2d+TIEXz44YeYNGkSJk2ahA8//BBHjhzBg0TjFO1rVd3mlNSjkrBK8iMatjrKSEmdix5fpM096vZeXXVZHeWuZOwU6ZdK8iN67Koe35XUj5LyrI66ZP98PKOIMcYYe4zMmzeP3njjDXr55ZcpJiaGXFxciIgoJyeHdu3aRePGjaNbt27Ra6+9Rl9++SUNHz6cevXqRQsWLCgXtlOnTvT5559TeHg4xcTE0OXLlyksLEwKd/z4cVq2bBl5eHjQjh07yM/PTzjO2rVrU/fu3alp06bUrVs3Wbjdu3dT06ZNafPmzRQTE0M7duwQCmsymYTjFC2np556SijOkydPCpe7aH5q1apFMTExpNfrKTo6murXry+F++ijj2jOnDm0c+dOatasmXCcStIpWkYAhOOcNGkSffHFFzRnzhyLYadPn06FhYUUFRUllJ+NGzdScnIybdu2jaKjo0mj0RARUUlJCe3Zs4deeuklGjduHC1fvrxa8iNa7k2aNKGePXvSwYMHycvLSxZuwoQJ1LJlS/rmm2/I2dlZOM7s7GyhvjZo0KAqb3NfffWVUD3OnTtXuM6DgoKE8yM6zphMpiovIyXjjGg6s7Ozhdpcenr6I23v1VGX1VHuv/zyi1A79vLyEuqXO3bsEM6P6LEBVPn4vmfPHuF++SjHmZiYGGL/Ao/2OhVjjDHGyvLy8sKGDRsqfH/9+vXw9PQEAPj7+2PRokUVhl28eDH8/PwQHR2Nbt26ITc3t1yY3NxcdOvWDR06dFAUZ6NGjTB9+vQKw82cORMNGzYEAOGwSuIULSfROJWUu2icYWFhGDFiBEwmU7kwJpMJI0aMQIsWLRTFqSSdomGVxOni4oLExMQKwyYmJsLZ2Vk4P0ajEQcPHqww3IEDB2A0GqstP6Lp7NmzJ8LDw5Genl4uTHp6OiIiItCrVy9FcYr2NSVxirY50XoExOtcSX5Ew1ZHGSkZZ0SPL9rmHnV7r466rI5yF23Hov1SSX5Ej10d47uSfvkoxxn278AXihhjjLHHiE6nw9mzZyt8/8yZM7C1tQUA2NjYWPyCbJaeng6dTgdbW1ucOnWqwnAnT55UHKdOpxMKZ85TdcQpUk5Kji1a7kriTEtLqzBcWlra3yojJekULSPROPV6PU6ePFlh2NTUVNjZ2Qnnp2bNmvjll18qDJecnIyaNWtWW35E02kwGHDs2LEKwx09ehQGg0FRnKJ9TUmcom1OtB4B8TpXkh/RsNVVRlWdTtE296jbe3XUZXWN7yLtWLRfKs2PyLGrY3xX0i8f5TjD/h141zPGGGPsMdK8eXOaM2cOFRcXl3uvpKSE5s6dS82bNycioqeeeoo+//zzCuNasWIFBQUFkdFopAsXLlQY7sKFC2Q0GhXF6ePjQ9u2basw3LZt28jb25uISDiskjhFy0k0TiXlLhqnq6srJScnVxguOTlZmtpfHekUDaskzqioKHrttdfo+vXr5cJev36dXn/9dYqKihLOz7PPPksjRoyg48ePlwtz/PhxGjVqFHXp0qXa8iOaThsbG7pz506F4e7evSstDisap2hfUxKnaJsTrUci8TpXkh/RsNVRRkrGGdHji7a5R93eq6Muq6PcRduxaL9Ukh/RY1fH+K6kXz7KcYb9O/AaRYwxxthjZNGiRRQTE0Ourq7Upk0b2RoBP/74I1lbW9OuXbuIiOiDDz6gZ599lhITEyk6OloWds+ePZSZmUnbtm2jH374geLi4mj69On0zDPPlAv31ltv0UsvvaQozpycHOrfvz/t27fPYrjExERau3YtERHNmjVLKKzJZBKOU7Sc0tLShOL09/cXLnfR/GRnZ9OIESMoJSXFYrkvX76c3n//fUVxKkmnaBmZTCbhOJctW0adOnUiNzc3atiwoSzsqVOnKCgoiLZu3UqHDx8Wyk+7du2of//+FBISQg4ODuTs7ExERFevXqXbt29TTEwMLVq0qNryI1rue/fupcGDB9OCBQvomWeeoZo1axIR0Z07d2jPnj30yiuvUL9+/RTF6ejoKNTXqqPNdenSRageldT5+fPnhfMjOs6YTKYqLyMl44xoOo1Go1Cbq1OnziNt79VRl9VR7q+99ppQOz5z5oxQv+zZs6dwfkSPDaDKx/fQ0FDhfvkoxxn276ACgEedCMYYY4z91927d2n16tV0+PBh2fa44eHh1L9/f+nLMFHpbKClS5daDDty5Ejy8fEhIqK5c+fSwoULKTs7m1QqFRERASBXV1caP348xcfHK47z0KFD9NFHH9HPP/9cLty4ceMoPDxcilM0rJI4RctJNE4l5S4a54YNG2jBggWUkpJCJSUlRESk0WgoJCSEXnnlFerTp4/iOJWkUzSskjhNJhPt3LnTYtgOHTqQWq1WXJdpaWkW4wsICKCyqiM/IuksKCig8ePH04oVK6i4uJisra2JiKiwsJC0Wi0NGzaMFixYIM0qEs27aF+rjjYnWo9KwirJj2jY6igjJW1T9PhK2tyjbO/VUZfVUe4i7VhJv1SSH9E+VB3ju5J++SjHGfbPxxeKGGOMsX+RzMxMysnJIaLSL3++vr6POEX/DkVFRdLtBLVr1yYrK6tHnCL2d9y5c4eOHj0q60MhISGyE/HHBbc59k8g0o6rq1+K9iHua+yfiC8UMcYYY4+h7OxsOnLkiPSLnpubG4WGhpKrq2u5sMXFxXTmzBlZ2MDAwP/Tl1Ulcebm5sp+ebS3t68wXtGwouGUlJNInEriU5r3goICIiJp1klFqjqdomGVxJmcnFzuF+eIiAhpLRQl+SksLKTvvvvOYnzdunWTZglUZ35E0vl3iMSptP9WZZtTUo+iYZXkRzRsdZWRaDjR44u0ucehvVdHXSopT6V9TXTsfJi/81kpeuyqHN+V9EvROKtznGH/UI9uHW3GGGOMPejevXsYMGAANBoNtFotnJ2d4ezsDK1WC41Gg4EDByIvLw8AUFJSgqlTp8JoNEKlUskeRqMR06ZNQ0lJCYDSnW9GjRqFxo0bw9XVFa6urmjcuDFGjRqFM2fOSMdXEufy5csRGBgItVoNtVoNlUoFtVqNwMBAfPbZZ7J8iYYVDaeknETiVBKfknTu2rULHTt2hNFolMIajUZ07NgRu3fvVlxGStIpGlZJnDk5OWjVqhVUKhW8vb0RGhqK0NBQeHt7Q6VSoVWrVsjJyRHOT0ZGBurWrQudTofIyEj06dMHffr0QWRkJHQ6Hfz8/JCRkVFt+VFSl9euXcPcuXPRvXt3tGjRAi1atED37t3x3nvv4erVq4rrUklfq+o2p6QeRcMqyY9o2OoqI9FwoscXbXOPur1XR11WR7mLtmPRfqk0P6LHrurxXUm/fNTjDPvn4wtFjDHG2GNk2LBh8Pf3R2JiIoqLi6XXi4uLsXPnTtSvXx/Dhw8HAEycOBFOTk5YtmwZsrKykJ+fj/z8fGRlZeGTTz6Bs7Mz4uPjsX37dlhbW6NFixaYOXMmlixZgiVLlmDmzJmIiIiAjY0NEhMTFcX53nvvQa/XY9KkSUhKSsLZs2dx9uxZJCUlYfLkybCzs8O8efMAQDiskjhFy0k0TiXlLhrnF198Aa1Wi759+yIhIQHbt2/H9u3bkZCQgH79+sHKygorV65UFKeSdIqGVRJnz549ER4ebnEL5fT0dERERKBXr17C+YmOjka3bt2Qm5tbLr7c3Fx069YNHTp0qLb8iKYzOTkZDg4OqFOnDgYPHoz4+HjEx8dj8ODB8PDwQK1ataRtz0XjFO1r1dHmROtRSZ0ryY9o2OooIyXjjOjxRdvco27v1VGX1VHuou1YtF8qyY/osatjfFfSLx/lOMP+HfhCEWOMMfYYMRqNOHjwYIXvHzhwAEajEQDg4uIiXeCxJDExEc7OzmjUqBGmT59eYbiZM2eiYcOGiuL08vLChg0bKgy3fv16eHp6AoBwWCVxipaTaJxKyl00Tn9/fyxatKjCcIsXL4afn5+iOJWkUzSskjgNBgOOHTtWYdijR4/CYDAI58fW1hanTp2qMNzJkydha2tbbfkRTWdYWBhGjBgBk8lULozJZMKIESPQokULRXGK9jUlcYq2OdF6BMTrXEl+RMNWRxkpGWdEjy/a5h51e6+OuqyOchdtx6L9Ukl+RI9dHeO7kn75KMcZ9u+gfvjNaYwxxhj7XzGZTOXWqCjL2tqaTCYTEZXudOPu7l5hWDc3N8rLy6Nz587RgAEDKgzXr18/ysjIUBTn1atXqWHDhhWGa9iwobS4p2hYJXGKlpNonErKXTTOixcvUnR0dIXhnnnmGbp06ZKiOJWkUzSskjhtbGzozp07FYa9e/cu2djYCOfHaDTShQsXKgx34cIFMhqN1ZYf0XSmpqbShAkTpB0Dy1KpVDRhwgQ6ceKEojhF+5qSOEXbnGg9EonXuZL8iIatjjJSMs6IHl+0zT3q9l4ddVkd5S7ajkX7pZL8iB67OsZ3Jf3yUY4z7F/iUV+pYowxxth/9e/fH02aNLH4q+KxY8cQEhKCAQMGAAA6deqEDh064Nq1a+XCXrt2DbGxsejcuTMCAgLwwQcfVHjMDz74AA0aNFAUZ+vWrREXF4eioqJy4YqLixEXF4c2bdoAgHBYJXGKlpNonErKXTTOpk2bYuLEieXCmMXHx6Np06aK4lSSTtGwSuIcPXo0vL29sWnTJtntM7m5udi0aRN8fHwwduxY4fxMnz4dDg4OmD9/PlJTU5GdnY3s7GykpqZi/vz5qFWrFmbOnFlt+RFNp4+PD7788styYcy+/PJLeHt7K4pTtK8piVO0zYnWIyBe50ryIxq2OspIyTgjenzRNveo23t11GV1lLtoOxbtl0ryI3rs6hjflfTLRznOsH8H3vWMMcYYe4zcunWL+vfvTzt37iQHBwdydnYmotJf+m7fvk0xMTG0du1aMhqN9Mcff1CnTp0oPT2dGjZsSC4uLkRElJOTQ6dOnaKgoCDaunUrHT58mPr3708dO3ak6OhoWbg9e/ZQYmIirV27lnr27Ckc561btygmJoaKioqoTZs2snA//vgjWVtb065duyg4OJhOnjwpFNZkMgnHKVpOFy9eFIqzTp06wuUump/r16/Ts88+S3Xr1rVY7pmZmbRt2zZq06aNcJxK0ilaRgCE4ywoKKDx48fTihUrqLi4WJrJUFhYSFqtloYNG0YLFiygX3/9Vbgu586dSwsXLqTs7GxpZgAAcnV1pfHjx1N8fLyivqEkP6Llvn//fnr11VfpxRdfpGeeeaZcXS5fvpzef/99Gj16tHCc9vb2Qn3N09OzyttcWFiYUD3a2NgI1/nVq1eF8yM6zhBRlZeRknFGNJ0Gg0G4zT3K9l4ddVkd5b5v3z6hdnzq1CmhftmlSxfh/Ige22QyVfn47u/vL9wvH+U4ExwcTOyfjy8UMcYYY4+htLQ0Onz4sGx72vDwcAoICJCFM5lMtHPnTothO3ToQGp16V3mhw4doo8++qjclrvh4eE0btw4Cg8PVxzn3bt3afXq1RbD9e/fn2rWrCnFKRpWSZyi5aQkTtFyF43zwoULtHTpUovhRo4cST4+PtWaTiVhlcR5584dOnr0KOXk5EhhQ0JC/ladm2VmZsri8/X1LXfc6siPaDo3bNhACxYsoJSUFCopKSEiIo1GQyEhIfTKK69Qnz59FMcp2teUxKmkzYnUo5KwSvIjGrY6ykhJ21RyfCV96FG1d6Kqr8vqKHfRdizaL5XkR/TY1TW+i/bLRznOsH8+vlDEGGOMMcaYoKKiImmdjtq1a5OVldUjThFjjPslY1WLLxQxxhhjj5nCwkL67rvvys3+iYiIoG7dupVbuDQ5Odli2ObNm5eLOzc3VxbO3t7eYhpE48zOzqYjR45I4dzc3Cg0NJRcXV3LxSkaVjScknISiVNpuYums7i4mM6cOSMLFxgYaPFEpqrTKRpWSZzXr1+nFStWWAz7/PPPk5OTk6L8nD17lhYtWmRxttvYsWMpKCioWvOjpC7NCgoKiIikhWUtEY1TSf+tyjanpB6VhFWSH9Gw1VFGSupc5Piibe5Rt/fqqsvqKHclYyfRw/ulkvyIHruqx3cl9SMap9K8Kx0P2T/UI1kZiTHGGGMWZWRkoG7dutDpdIiMjESfPn3Qp08fREZGQqfTwc/PDxkZGQCAnJwctGrVCiqVCt7e3ggNDUVoaCi8vb2hUqnQqlUr5OTkAACWL1+OwMBAqNVqqNVqqFQqqNVqBAYG4rPPPpOOLxrnvXv3MGDAAGg0Gmi1Wjg7O8PZ2RlarRYajQYDBw5EXl4eAAiHVRKnaDmJxqmk3EXjLCkpwdSpU2E0GqFSqWQPo9GIadOmoaSkRFGcStIpGlZJnMnJyXBwcECdOnUwePBgxMfHIz4+HoMHD4aHhwdq1aqFX375RTg/27dvh7W1NVq0aIGZM2diyZIlWLJkCWbOnImIiAjY2NhIWztXR36UtLldu3ahY8eOMBqNUj8yGo3o2LEjdu/eLfUh0TiV9N+qbnOi9aikzpXkRzRsdZSRkjoXPb5om3vU7b066rI6yl3J2CnSL5XkR/TY1TG+K+mXj3KcYf8OfKGIMcYYe4xER0ejW7dush1PzHJzc9GtWzd06NABANCzZ0+Eh4cjPT29XNj09HRERESgV69eeO+996DX6zFp0iQkJSXh7NmzOHv2LJKSkjB58mTY2dlh3rx5iuIcNmwY/P39kZiYiOLiYilMcXExdu7cifr162P48OEAIBxWSZyi5SQap5JyF41z4sSJcHJywrJly5CVlYX8/Hzk5+cjKysLn3zyCZydnREfH68oTiXpFA2rJM6wsDCMGDECJpOpXFiTyYQRI0agRYsWwvlp1KgRpk+fXi4us5kzZ6Jhw4bVlh/RdH7xxRfQarXo27cvEhISsH37dmzfvh0JCQno168frKyssHLlSkVxivY1JXGKtjnRegTE61xJfkTDVkcZKRlnRI8v2uYedXuvjrqsjnIXbcei/VJJfkSPXR3ju5J++SjHGfbvwBeKGGOMsceIra0tTp06VeH7J0+ehK2tLQDAYDBY3BLZ7OjRozAYDPDy8sKGDRsqDLd+/Xp4enoqitNoNOLgwYMVhjtw4ACMRiMACIdVEqdoOYnGqaTcReN0cXGRZgdYkpiYCGdnZ0VxKkmnaFglcep0OqSlpVUYNi0tDTqdTjg/Op3O4gmMWXp6OnQ6XbXlRzSd/v7+WLRoUYXhFi9eDD8/P0VxivY1JXGKtjnRegTE61xJfkTDVkcZKRlnRI8v2uYedXuvjrqsjnIXbcei/VJJfkSPXR3ju5J++SjHGfbvoH74zWmMMcYY+18xGo104cKFCt+/cOECGY1GIipdh+HOnTsVhr179y7Z2NjQ1atXqWHDhhWGa9iwobQIqGicJpOp3DovZVlbW5PJZCIiEg6rJE7RchKNU0m5i8Z59+5dcnd3rzCcm5sb5eXlKYpTSTpFwyqJ09XVlZKTkysMm5ycTC4uLsL58fHxoW3btlUYbtu2beTt7V1t+RFN58WLFyk6OrrCcM888wxdunRJUZyifU1JnKJtTrQeicTrXEl+RMNWRxkpGWdEjy/a5h51e6+OuqyOchdtx6L9Ukl+RI9dHeO7kn75KMcZ9i/xqK9UMcYYY+y/pk+fDgcHB8yfPx+pqanIzs5GdnY2UlNTMX/+fNSqVQszZ84EAIwePRre3t7YtGmT7LaD3NxcbNq0CT4+Phg7dixat26NuLg4FBUVlTtecXEx4uLi0KZNG0Vx9u/fH02aNLH4S+WxY8cQEhKCAQMGAIBwWCVxipaTaJxKyl00zk6dOqFDhw64du1auXDXrl1DbGwsOnfurChOJekUDaskzkWLFsHGxgYvv/wyNm/ejMOHD+Pw4cPYvHkzXn75Zdja2mLx4sXC+fnqq6+g1WrRpUsXLFy4EOvXr8f69euxcOFCdO3aFdbW1vj666+rLT+i6WzatCkmTpxYLoxZfHw8mjZtqihO0b6mJE7RNidaj0rqXEl+RMNWRxkpGWdEjy/a5h51e6+OuqyOchdtx6L9Ukl+RI9dHeO7kn75KMcZ9u/AF4oYY4yxx8ycOXPg5uYmLThtXnzazc0Nc+fOlcLdv38fI0eOhLW1NdRqNXQ6HXQ6HdRqNaytrTFq1Cjcv38fqampcHV1haOjI3r06IGRI0di5MiR6NGjBxwdHeHm5ibduiAa582bNxEbGwuVSoVatWohICAAAQEBqFWrFtRqNTp27Ihbt24BgHBYJXGKlpOSOEXLXTTOixcvIjg4GFqtFk2aNEFsbCxiY2PRpEkTaLVaNGrUCBcvXqy2dCoJqyTO9evXIywsDFqtVlq8VavVIiwsTLrFUUl+Dh48iOeeew5eXl6wtraGtbU1vLy88Nxzz+HQoUPVmh/RdCYlJcHOzg4NGzbEhAkTMGfOHMyZMwcTJkxAo0aNYDAYsH//fkVxiva16mpzIvWopM6V5Ec0bHWUkZK2qeT4om3uUbb36qjL6ih30XYs2i+V5Ef02NU1vov2y0c5zrB/BxUAPOpZTYwxxhgrLysrS7aVra+vr8Vwd+7coaNHj1JOTo4UNiQkhGrWrCmFuXv3Lq1evZoOHz5cbkvm/v37y8Ka40xJSZGFfTBOIqL09HSL2zwHBASUS6do2LS0NIvptBSnaDkpiVO03EXyYzKZaOfOnRaP3aFDB1Kr1YrjVJpOJWGVxFlUVCTdsli7dm2L20EryY8SVZ0fkXReuHCBli5darEuR44cST4+PorjJBLva0Ri7VhpmxOpRyVhleRHNGxVl5GScObjP2yMNVPSh0Q9qv77KMtdtB0r6Zei+RE9ttK+pqTNifZL0Tiroy7ZPxtfKGKMMcYYe8JlZWWRp6cnabXaR52U/7l/Wt4BkEqletTJYFVAtG3m5OQQAHJ1df0fpYw9Sfbt20dhYWFka2v7qJPC/kV4MWvGGGPsMbNo0SKKi4uj9evXExHRqlWrKCgoiAICAmjKlClUXFwshf3rr79oxYoVNHToUOrYsSN17tyZXnrpJdqzZ89DjzNkyBC6fPmy7LVLly5Jv2ISEf300080YMAAat26NQ0cOJB+/vlnIiL64IMP6PfffxfKj5KwRER79+6lWbNm0ahRo2jMmDH0wQcfUEZGRrlwSsrp0qVLdO/evXJxFBUV0Y8//khERFeuXKEZM2ZQu3btKDAwkJ566inq0qULff7551RSUlJhegFQUlISLV++nLZu3UpFRUWy97KysqS0FBYW0oYNG2jlypWyclYSpyUNGjSwWEZlXb58mWbOnEkDBgyg1157jdLT06X3lJRlZc6fP0/t2rWjgoICWZrPnz9PU6dOpUGDBtG0adMoKytLKL60tDSqW7dupWFE8l7WH3/8QUOHDhUKe+vWLVq5cqX0vLi4mFJTU2nnzp20c+dOOnnyZLm6+b/m3cbGhtLS0sq9fuPGDUpKSqKbN28SEdH169dp7ty5NGvWLCn8N998Q/n5+UJ5q4y5HpWGTUtLo4SEBKltpaen06hRo2jo0KG0d+9e2d9t3bqVZsyYQQcPHiSi0n7fqVMnio2NpU8//VQKpyROkTIiKu2HX331FU2YMIH69etH/fr1owkTJtDGjRupsLBQFuf169fpvffeox49elB4eDiFh4dTjx49aN68eXTt2rVKy+bBtnnz5k3q1asXeXl50ahRo6ikpISGDx9Obm5uVKdOHYqIiKArV648vNDpv+24S5cutGrVKvrrr7+E/q4yZevyr7/+ogMHDtDZs2fLhbt//76sX4iWZ2pqKq1YsYIyMzOJiOjMmTM0evRoGjlyJO3cufOh6WvXrp3ss6SyMbSsl156iX766SehsEREV69epb1791Jubi4RlV7Ie++992jOnDl06tSpCv8uKyuLdu/eTadPny73npI296AOHTpUuGC5yGdbZXJycmjWrFlVGif7h3hkN70xxhhjrJzZs2ejRo0a6NmzJ1xdXTFnzhw4OjrirbfewjvvvAMnJyfMmDEDAJCRkQFvb284OzvD09MTKpUKnTt3RlhYGDQaDXr37o2ioiKkpqZafFhZWeHbb7+VngNAaGgotmzZAgD47rvvoFar0bVrV7z++uvo0aMHrKyssGXLFqhUKmg0GkRHR2P9+vUoKCioME+iYXNychAaGgq1Wg2tVgu1Wo2QkBC4urpCo9HIFi0VLafLly+jefPmUKvV0Gg0GDRoEO7evSvFk52dDbVajV9++QX29vYICQlBq1atpLDPPfccjEYjIiIicOfOHQBAx44dcfv2bQDAjRs3EBYWBpVKBScnJ6jVagQEBODq1atIT0+Ht7c31Go1/Pz8kJmZiZCQENjZ2UGv16N27do4d+6cojh79Ohh8aFWqxEdHS09B0q317569SoA4MyZM7C3t4efnx969+6NgIAA6PV6pKamKmpzD3PixAmo1WpERkZi48aNAEq3VLaxsUGjRo3w3HPPoUmTJtDr9eXWY6ksPgCK8i4ap2jYkpISTJ06FUajUVo3xPwwGo2YNm0aSkpKAEA47xMmTLD4UKvViIuLk54DwJEjR2Bvbw+VSgUHBwccPXoUvr6+8Pf3R7169WBra4uUlBSoVCrUrFkTL7zwAg4fPiyUx6oqox07dsDa2hq1atWCTqfDjh074OTkhOjoaLRr1w4ajQZ79uwBACxbtgxarRYhISGoWbMmVq1ahRo1amD48OF48cUXYWtriw8//FBRnKJllJGRgbp160Kn0yEyMhJ9+vRBnz59EBkZCZ1OBz8/P2RkZAAAkpOT4eDggDp16mDw4MGIj49HfHw8Bg8eDA8PD9SqVQu//PKLcNscOnQogoOD8fHHHyMyMhLdunVDo0aNcODAARw6dAjNmzdHXFyconI3r2Fjb2+PkSNH4ujRo3+jxuVx/vrrr/D29pbWPGrTpg3+/PNPKZx53AQgXJ7ffPMNNBoNHB0dYTAYsHv3bhiNRkRHRyMmJgYajQZr1qwBAGzevNniQ6PRYNGiRdJztVqNdu3aYc2aNdI6O5aY8+Hv7485c+bgypUrFYY1r3ukUqng6uqKEydOwMPDA/7+/mjQoAFsbGywc+dOjBo1Svosyc/PR8+ePaUxQa1Wo23bttL7omXUpEkTiw+VSoXAwEDpOQDhzzbROq/KONk/A996xhhjjD1G/Pz86L333qP//Oc/lJqaSiEhIfTll1/SgAEDiIjo22+/pfj4eMrIyKBOnTqRl5cXLV26lFQqFc2dO5f2799P27dvp4yMDOrQoQMNHjyYZs2aRSqViix95JtfV6lUVFJSQgaDgU6dOkW+vr7UokUL6tGjB73++utS+EWLFtGKFSvoxIkTtGLFCvruu+9o+/btVLNmTRo4cCANHz6cgoODZcdQq9VCYfv27UsFBQX05Zdfko2NDb322mt0584d+vLLL2nv3r3Up08fmj59Oo0bN064nCIiIujXX3+lRYsW0e3bt2nSpEmkUqlo165d5ODgQDk5OeTm5kYRERHUvn17mjlzJhERrV69mhYtWkSHDx+mW7duUbt27ahNmza0cOFCUqvVlJ2dTc7OzjR69Gjav38/bd26lXx9fenSpUvUvXt3at68OV25coUA0FtvvUUrVqygnTt3Uv369Wnjxo1kMpmod+/eZG9vT6tWrRKO85NPPqE2bdqUW3tk5cqV1LVrV2kb7ISEBFmc3bt3J5PJRJs2bSKtVksmk4kGDBhA9+7do7S0NOE299FHH1Xafv/88096//33yWAw0NGjR8nf35+ioqKoadOmNH/+fCnc9OnTKSkpiUJDQyuN79q1a7R27VoqKSkhtVotlPcePXpUGmdmZia9+uqrVFJSUum20UREJ0+epMjISHr11Vfpiy++oNmzZ1NMTIy0RXVOTg7t2rWLpk+fTs8//zzNnTuX7O3thfJ+6NAhevrpp6V0m+3fv5+aNWtGdnZ2pFKpaO/evdS+fXvy8fGh+fPn0yeffEILFy6k2NhYWr58ORERDR06lG7dukWbN2+mN998k7799ls6ceIEBQUF0fDhw2nQoEHk6OgoHUO0HktKSoTDhoWFUbt27eitt96i9evX0+jRo2nUqFH09ttvExHR5MmTKSUlhXbt2kVPPfUUjR8/nl544QVKSkqiTp060QcffECjR48mIqIvvviC3nvvPTIajcJxipbRvXv3yM7OjlauXGlxLaS4uDj666+/aOfOndSiRQt6+umnadmyZeVuBwRAI0eOpJMnT9KRI0eE2ubOnTvp66+/poiICGns2blzJ7Vv356IiA4ePEjPPfccXbp0ib7//vtKy93cjgHQ6dOnadeuXbRixQo6c+YMNWzYkIYPH04DBgwgBwcH6W9E67Jr165UVFREX3zxBd2+fZvGjx9PZ8+epX379pGXlxfl5OSQu7s7lZSUUPv27YXK8/r16/Sf//yHpk6dSuvXr6dRo0bRK6+8QtOnTyei0pmnq1evpuPHj5Nara7wM8vM/H5MTAzt3buX7OzsaMCAATRs2DBq3LixLKxarabdu3fTli1baM2aNZSbm0sdO3akF154gTp16iRbS6h169b09NNP05w5c2jZsmX04YcfUvfu3WnRokVERDRx4kQ6dOgQHT58mK5cuULOzs40ZcoUWrVqFa1cuZLCwsLo+PHjNHjwYOrduze9++67wmW0d+9eio6OphYtWkjvA6DZs2fTyJEjydnZmYiIZs6cSYMHDxb6bDtx4kSldZ6enk79+vWjkpIS4ThNJlOlcbJ/iEd1hYoxxhhj5dna2uL333+XnltZWeH06dPS8wsXLkCv1wMA9Hq9NCMFAAoKCmBlZYXr168DKJ0R5OPjg6effhqdO3dGWloaLly4gAsXLiArKwtarRa7d++WXgMAe3t7aXaRs7Oz9H+z3377DXq9HiqVCjk5OQBKZwLNnTsXAQEBUKvVaN68OT799FNpBo5o2Jo1a8ryeu/ePVhZWUlb+q5atQoNGjRQVE7u7u44cuSI9Pr9+/fRpUsXNG7cGDdu3JB+IbW1tcX58+elcCUlJbCyskJ2djYAYNeuXXB3dy+XnwYNGmDz5s2yMvrhhx/g6+sLJycnHD9+XMqLSqXCTz/9JIU7ePAgvLy8FMW5bt06eHh4YMWKFbL3tVotzpw5I3utbJyenp748ccfZe8fO3YMbm5uitqcSqWCu7s7fHx8LD7c3d2hVqthZ2eHtLQ0AICLiwtOnDghO/Zvv/0Gg8EAtVqNpk2bIioqyuKjWbNm0i/Yonk3/6L/4Myfsg9znGV3ibL0ML/v4uKCxMREVCQxMRHOzs4AIJz3d999F76+vtKMmIryAwAODg44e/YsAKCwsBBqtVrWrlNSUlCnTh1ZnR89ehSjRo2C0WiEjY0NevfujV27dimqRyVha9asKc3EKSkpgVarlW2zferUKbi4uACw3H/NOy8CQFZWFvR6vaI4RcvI1tZWdqwHnTx5Era2tgAAnU4n1aUlaWlp0Ol0wm1Tr9dLY62lfGdmZsLOzg6AeDsuW+dA6cyqESNGwN7eHra2tujXr5/UxkTr0tnZGSdPnpTiNJlMGDlyJLy8vHD+/HnZzBLR8rSzs0NWVpYUn5WVlewY58+fh8FgAABpa/my+bJUnua8X7t2De+//z6CgoKkMWXJkiXSZ0fZMiosLMSGDRukWUzu7u6YMmWK1M5q1qyJ3377DQBQVFQErVYrjeMAcO7cOWnmmjnO4OBgrF27VpbWzZs3o379+orK6MCBA6hXrx5mzJghzVC0lG8Awp9tlbWjsu8riZP9O/AaRYwxxthjxNXVVVoTIiMjg0pKSmRrRJw5c0b6VdFoNNLdu3el9/Lz86m4uJisra2JiKhRo0Z05coVSk5OJj8/P+rZsyfdvHmTvL29pZ1g3N3dydvbm7y9vYmIKDIyktatW0dERE2aNKF9+/bJ0peUlER16tSRvebs7Ezx8fGUlpZG+/bto6CgIJowYQK5ubmVy19lYW1sbGS/2qvVaiopKZHWx4mIiJDWaRAtp9zcXNkv6jY2NrRp0yby8fGhtm3b0tWrV6V0lV0bJCcnh4qLi6Vff/39/aV1T4hISuetW7eoXr16sjz6+fnR5cuX6d69e1SrVi0iIrKzsyM7OztZmXh6ekq7KInG2bdvX/rpp5/o888/p549e9KtW7fKlXHZ+MxxqtVqsre3l71vNBrp1q1bitqct7c3LViwgLKysiw+tm3bRkREYWFhtGXLFiIiqlevHqWmpsqOfeLECapVqxb5+fnRhAkTKCkpyeLDPBuEiITz7ubmRps2bSKTyWTxcezYMSlsjRo16N1336W9e/dafJjXyrl79y65u7tXWNZubm6Ul5enKO+TJk2iDRs20KhRo+i1116rdB2qwsJCaSFbKysr0uv1VLt2ben92rVr040bN2R/ExISQkuWLKErV67Q8uXL6dq1axQbG0u+vr7C9UgkXudEJGtvOp1O1uZq1Kghrfni6OgorTVz+fJlKi4uposXL0phf//9d6nviMYpWkZGo7HC9V6ISnfRMs8AcnV1peTk5ArDJicnk4uLi3Db9Pf3p61btxIR0Y4dO0in09GuXbuk93fu3CnNSlLSjssKDQ2lTz75hC5fvkxLliyhP/74Q5qxJFqXf/31l2wBbpVKRUuXLqUuXbpQZGQknTt3TnpPtDxr1KghtdHbt29TcXGxrM3euHGDDAaDVDbPPPMMNWvWTCqvytSuXZteffVVOnPmDB04cIAaN25Mr7/+Orm5uVFcXJwsrJWVFfXp04cSExMpMzOTXnjhBVqzZg01aNCAiIisra3p/v37RFTapkwmk/TcXDbmHcjMbTM7O5saNWokO87TTz9Nf/zxh6IyatmyJaWkpNC5c+coIiKCzp8/X+HfiH621apVi5YvX26xvjMzM2XlKxon+5d41FeqGGOMMfZf06ZNg5OTE4YPHw5fX19MmjQJXl5eWLp0KZYtWwZPT09p3ZLBgwcjMjISaWlpyMzMlNZAMdu3bx88PT2l59u3b4eHhwfeeecd6df5B3+lPHv2LBwdHREXF4fZs2fDYDBg4MCBePvttxEXFwcbGxskJCRArVaX+7W3rNzcXHz66acAIBy2R48e6NmzJ+7du4fCwkKMHz8efn5+UrjDhw/D1dVVUTk1bNgQX3/9dbljFhUVoXv37vDy8oJarca4ceMQHByMHTt2YO/evWjbti2ioqKk8ImJiahXrx6A0l+nO3XqhB49esDBwUFa06lsOl1cXFCvXj3ZDKIlS5ZIs6yA0hkO5vyIxmlWUlKCGTNmwNPTE4mJibCysrI4o8hoNMLBwQFWVlZYtWqV7P1du3bBx8dHUZvr2bMn4uPjK6pKnDhxAiqVCocOHYK9vT1mzpyJjz/+GLVr18a0adOwZs0azJgxA0ajEXPnzkX//v0xfvz4h8ZX1sPy3qVLF0yfPl0ozqioKMydO/ehYTt16oQOHTrg2rVr5cJcu3ZNmgEBQDjvZnfv3kVcXBwaNWqEU6dOWazLgIAA2cyjrVu3Ij8/X3p++PBheHh4PLSvZWRkYMqUKcL1CIjXeaNGjbBjxw7p9VOnTqGoqEh6/uOPP8LX1xcAMGbMGPj7++Ott95CaGgoBg8ejICAAOzYsQOJiYlo2LAhhg4dqihO0TKaPn06HBwcMH/+fKSmpiI7OxvZ2dlITU3F/PnzUatWLcycORMAsGjRItjY2ODll1/G5s2bcfjwYRw+fBibN2/Gyy+/DFtbWyxevFg6xsPa5urVq6HRaODn5wcbGxts3LgR7u7u6NOnD/r27Qtra2ssWrQIgHg7fnBGkSW//vorAPG6bN68OVauXGkxzJgxY2A0GqWZJaLlOXDgQISFhWH16tXo0qULYmJi0KJFC6SlpSE9PR2RkZHo1auX7FjHjx9HUFAQRowYgby8vHKfWZW193v37uGzzz5DRETEQ8vIZDJJs+26deuGZ599FgcOHMCIESPQrFkzdO7cGffu3UNeXh569eqF2NhYqFQqvPjii5gwYQKcnZ2lvzdLSUlB7dq1FZVRWStWrICrqys++eQTi2OC6Gdbhw4dMHv27ArzXravi8bJ/h34QhFjjDH2GCkpKcHbb7+NZ599Fu+88w5MJhPWrVsHT09PODo64vnnn8e9e/cAlN7G1aJFC2nquLe3t+y2jI0bN+Kjjz6SxZ+dnY2OHTuidevWFi8UAaW3xvTt2xc1atSQTkSsrKwQERGBb7/9FgCETk7MRMOeP38e9erVg1arhZWVFYxGI3bv3i29n5CQgEmTJikqp/j4eHTo0MHi8YqKitC1a1eo1WrcvXsXffr0gVarhUqlQkREBDIzM6WwO3fuxFdffQUAeP7552WPDRs2yOKdOHEiYmJi8OKLL2L58uUV5vfdd99Fp06dFMX5oJ9++gm+vr5Qq9Xl6vKLL76QPX7++WfZ+7NmzcKECRMUtbkzZ87gl19+qTBPhYWF0q01hw4dktpn2UedOnXw4YcfAgCuXLkiuxVHiYry/uOPP8ouLjzo3r172LdvHwDg008/xcKFCysMm52djTfeeAMXL15EcHAwtFotmjRpgtjYWMTGxqJJkybQarVo1KgRLl68KP2dSN4ftG7dOri4uFisyzfeeAPr1q2rMJ1TpkzBf/7zH+G+pqQeRcMuXboUW7durTDc5MmTMWzYMACldfDCCy8gODgYI0aMQEFBAebNmwdra2uoVCpERUUhJydHUZyiZQQAc+bMgZubm+zWQ5VKBTc3t3IXDtevX4+wsDBpbDAvHh0WFlaun5pV1i8PHDiA999/HwcPHgRQWr6DBg1Cz5498cUXX0jhRNtxVFQUbt26VWG4skTr8p133kHHjh0rDDdq1CjZBVyR8szOzkb79u1hMBgQExOD27dvY+zYsbKFps23fJWVn5+PF198Ef7+/tBoNBZvPXsYHx8f6Zbshzl37hz8/f2lBaQvXbqErl27QqvVQqvVwsnJCSkpKYiMjJTdJvvgWD979mxERkYqKiNLaWnevDlUKlW5diT62bZp06ZyPxKUdfPmTandicbJ/h14MWvGGGPsCZeRkUEFBQUUEBAgu12gMh999BElJSXRxx9/TB4eHhbDAKCrV6+SyWSi2rVrS9Ptq1N+fj4dOHCACgsLqUWLFrJbR/6O4uJiys/PL7eAaNn3//zzT+nWu/v371NxcbF0C8TfkZeXRxqNhnQ6XaXhsrKySKfTWbxFT0mc9+7do/Pnz1NgYKB02+Hj5Nq1a5SZmUkmk4nc3Nyk2x6rwv8y7yaTiXbu3EmHDx+m7OxsIiq9NSk8PJw6dOggWxDXTGne//jjDzp27Bg988wzitpgfn4+aTQays7OJi8vr3ILLz8p7t+/T0VFRVSjRo0qj9tcRjY2NtJrWVlZsrp8cDHqsoqKiqTt2EXGw8e9X1YHJeVpdv78efrrr78e+vn1/fffU1JSEk2ePFm6FfbLL7+kvn37yuq0qty4cUO2APyePXvor7/+ovDwcNnrFcnMzCRra+tyn69Ky8hkMtHdu3epZs2asn6t9LNNRHXEyZ5cfKGIMcYYY0+8goICIqJqOWF4lLKyssjT01P4AuCjUFBQQJcuXSIPD4+/Xf7FxcV05swZ2QlUUFDQ/+TipCX4/zsBPgrFxcVVXt+i+cnJySEA5Orq+n8+ZnX3yaquo/9lX/u/tHeR9lEVffLvHptVvZycHCooKCAvL69HnRT2L8KLWTPGGGOPmdTUVHrrrbdoyZIl0i/YZnfu3KGhQ4dKz3fv3k0zZ86kvXv3EhHRjz/+SB07dqR27dpRQkICEZVuO2xeOFapy5cv08yZM2nAgAH02muvUXp6OhERdenShVatWkV//fWXUDyLFi2iuLg4Wr9+PRERrVq1ioKCgiggIICmTJkiLVi9detWmjFjBh08eJCIiPbu3UudOnWi2NhYaWHhsnnv1KkTOTg4kF6vJ71eTw4ODtSpUyf64YcfpHBXrlyhGTNmULt27SgwMJCeeuop6tKlC33++edUUlJCRFRukc4TJ07Q4MGDqWXLltSrV69yi3pfuXKFVq9eTdu3b6fCwkLZe3l5eTRr1iwiIkpLS6OEhASp3NLT02nUqFE0dOhQqc4q06BBA8rIyHhoOLO0tDSqW7dulYY9f/48tWvXjohKty3/+eefiah09sewYcPIzs6O6tevTwaDgUaOHCldILh+/Tq999571KNHDwoPD6fw8HDq0aMHvffee3Tt2jUiKv21fNq0aeTk5ERNmjShjh07UseOHalJkybk7OxM06dPl23FvH37dho+fLi0IHpZt27dktKppA9ZYmNjUy5+otLFixcuXEiTJ0+myZMn08KFC+mXX34pF04k74mJiXTq1CmpHGbPnk116tQhGxsb8vDwoDlz5si2Bxdpx6L5uXnzJvXq1Yu8vLxo1KhRVFJSQsOHDyc3NzeqU6cORUREyBZ3r0zZdiTaJ835eVgfKigooNdee43atGlDc+fOJSKit956iwwGA9WoUYP69+9Pd+7cURRnRSrqa6mpqRQXF0d169YlW1tbsrOzo4YNG9L06dNlxxZpm0rau2j7UNInH0yneVx6MJ1/p20+rNwvXbok64s//fQTDRgwgFq3bk0DBw6U8mD2119/0YEDB2QL65vdv3+fVq5cKT3PzMyklStX0ty5c2nevHn0zTffyOrmYcqOcQ9z69Yt2bErkpOTI2tvIp+Bd+/epYEDB5K3tzcNHjyYCgsLacyYMeTm5ka+vr4UGRkp5UvJZ7DI9wQl6WT/Eo/mjjfGGGOMWbJz505YW1vjqaeegpeXFxwdHbF3717p/bLb065atQparRZNmzaFwWBAQkICjEYjhg8fjqFDh8La2hobN26ESqWCRqNBdHQ01q9fj4KCggqPb2tri6tXrwIoXc/C3t4efn5+6N27NwICAqDX65Gamiqt02Fvb4+RI0fi6NGjFcY5e/Zs1KhRAz179oSrqyvmzJkDR0dHvPXWW3jnnXfg5OSEGTNmYNmyZdBqtQgJCUHNmjWxatUq1KhRA8OHD8eLL74IW1tbaX2XL774AlqtFn379kVCQgK2b9+O7du3IyEhAf369YOVlRVWrlyJX375Bfb29ggJCUGrVq2g0WgwaNAgPPfcczAajYiIiMCdO3dki6IePHgQVlZWiIyMxMSJE9G+fXtotVrs378fAJCcnAyj0YiaNWvC1tYWfn5+su3kzXW0Y8cOWFtbo1atWtDpdNixYwecnJwQHR2Ndu3aQaPRSIvv9ujRw+JDrVYjOjpaev4wJ06cEF5DQjRs2XC+vr44fPgwAOC1116Dj48PNm3ahLS0NHz33XeoX78+Jk6ciOTkZDg4OKBOnToYPHgw4uPjER8fj8GDB8PDwwO1atXCL7/8gokTJ8LJyQnLli1DVlYW8vPzkZ+fj6ysLHzyySdwdnaWFt9ds2YNNBoNOnfujFatWkGn02H16tXlyl1JH5owYYLFh1qtRlxcnPQ8JycHrVq1gkqlgre3N0JDQxEaGgpvb2+oVCq0atVKaj+ieW/QoAF+/PFHAMA777wDR0dHzJ8/Hzt27MCHH34IFxcXzJkzBwCE27FofoYOHYrg4GB8/PHHiIyMRLdu3dCoUSMcOHAAhw4dQvPmzREXF6eoHYn2SXMZifShCRMmwN3dHa+++ioCAwMxevRoeHl5YfXq1Vi7di38/Pzw0ksvKYpTSV9LTEyEra0tevbsiYEDB0Kv12Ps2LF4/fXX4efnh3r16uHKlSvCbVNJexdtH6J9UkkfUtI2Rcs9NDRUWqT/u+++g1qtRteuXfH666+jR48esLKykt7/9ddfpb6lVqvRpk0bXL58uVyc9+7dQ69evWTbvbu6ukKj0cBgMEgLg4u24aoMWzac6Gfg2LFjERAQgI8++ghRUVHo1q0bgoODceDAAezfvx9BQUGYMmUKAAh/Bot+T1CSTvbvwBeKGGOMscdIeHi49EXQZDJh7ty5MBgM0qKmZU9yGzduLC3E+8MPP8DW1hbz58+X4nr//ffRsmVLqFQqJCQkoFu3brCysoKjoyPGjRuHU6dOlTt+2cVBu3Xrhi5duki7DJWUlKBv37549tlnpcU1FyxYgIYNG0KtVuPpp5/Gxx9/jJs3b8rirFevHr755hsApV+eNRqN7ORk06ZN8PPzQ1BQkLRT2t69e6HT6WQ7CiUkJCAwMBAA4O/vX+lJwOLFi+Hn54eWLVvijTfekF5ftWoVwsLCAJQu4tm4cWO8/PLLsny3b98eQ4cOlcU3btw4tGvXDgAQHR2NIUOGoKSkBHfu3MGoUaPg6OgoLSRurqPw8HBMnToVQOlCxQ4ODlLdAsCkSZPQvn17qdwjIyPLLWqtVqvRvXt36XlFFwLMj4EDBz70IsiDYRcuXFjpIz4+XorTxsYGv//+OwCgfv365Rbb3b9/P7y8vBAWFoYRI0bAZDKVqxuTyYQRI0agRYsWcHFxQWJiYoX1mJiYCGdnZwDy9g4AGzZsgJ2dHT777LNy5S7ah1QqFRo3bixblDYqKkra+SkqKgpt27ZFz549ER4ejvT09HJpTE9PR0REhLRjk2jey5ZlcHCwtFi62datW6Vd/5S0Y5H8uLm5SYspZ2dnQ6VSyXZtOnDgAOrUqQNAvB2J9klAvA95enpKC9qfP38earUa3333nRTnrl274O3trShO0b4GlLa5pUuXyo4XEBAAoHTh52eeeQbPP/+8cNtU0t5F24donzTnRySdStqmaLnb2dlJGwSEhYVJF5rMPv74Y2nXzu7du6Nz5864du0aMjIy0LlzZ/j6+kppMsc5YsQItGzZEqdOnUJGRgZ69eqF+Ph45OXl4fPPP4der8eaNWsUjXG5ubmVPn766Seo1WqkpqZW+tiwYYMUp+hnoKenp3RR+88//4RKpZLtgLl161Y0aNAAAIQ/g0W/JyhJJ/t34AtFjDHG2GOkZs2a5XZ+WbNmDezs7LBlyxbZSW7ZL94AYGVlhdTUVOl5WloaHB0dZRdBcnJyMHfuXAQEBECtVqN58+b49NNPpW3by4b19PSUflU2O3bsmLRzS9ndZo4cOYIRI0bA3t4etra26NevnzRbxtbWVvqCb05n2V+cL1y4AL1ebzFc2YtZWVlZ0Ov1AEpPoiydtJulp6dDp9PB1tYW58+fl14vKSmBlZUVsrOzAZSe+Lm7u8vy4+bmVm6HsNOnT0tbHTs4OEjbTZu9++67cHBwQHJyslRHNWvWREZGhnRcrVYr25Xu1KlT0pb369atg4eHB1asWCGL19J20E2bNi13IcD8aNasmdQ+RMOqVCq4u7vDx8fH4sPd3V2K09vbWzqRqVOnTrkdlM6ePQs7OzvodDqkpaVVWD9paWnQ6XTQ6/U4efJkheFSU1NhZ2cHoHx7B0ovKBoMBixdulRW7qJ96N1334Wvr69sW3VL5W4wGGR196CjR4/CYDAAgHDey7YzFxeXcvGfO3cOtra2ACDcjkXzo9frZbvNPdjXMjMzpXIXbUeifRIQ70MPGzvKjgmicYr2NaC0LrOysqTnJpMJVlZW0uyWH3/8EU5OTsJtU0l7F20fon0SEO9DStqmaLnb29tLn0/Ozs6yzyqgdLdNc106OzvLyslkMmHkyJHw8vLC+fPnpThr164tm0lz8+ZN6HQ65OXlAQAWLVqExo0bKxrjyu5KZulhfr/svw8+yr4PiH8G2tjYyHZP1Ov1srI1hzOnU+QzWPR7gpJ0sn8HvlDEGGOMPUacnJwsTiFft24d9Ho9li5dKn35NBqNshMzg8EgO5nMzMyEXq+vcAvhH3/8EYMHD4adnZ3spNB865m3t3e5L/OZmZnQ6XQVxpmXl4eEhAS0atVKdruS+Vfuc+fOQa1Wy36h3rZtG3x8fODh4SFdmDL/mrpt2zYp3L59++Dh4QEAaNq0qXQ7hSXx8fFo2rQpvL29ceDAAen1y5cvQ6VSIT8/H0DpiaY5P7/99htyc3Ph6+tb7sSo7EmMg4NDuXIBgHnz5sFoNGLTpk0WL1g8WD8XLlyQTpzNaWnZsiX+85//SL8IP3jyWr9+/Uq3Oj5+/LhU7qJhfXx8Ktzm+8E4p0yZgvDwcNy6dQuTJk1Cly5dcPfuXQCldd+nTx906NABPj4++PLLLyuM88svv4S3tzc6deqEDh064Nq1a+XCXLt2DbGxsejcuTMAyxfwgNJ2YTAYMHXqVKjVakV9CCi9daZ+/fp49dVXUVhYCKB8uTs6OmLfvn0V5icpKUk62RLN++jRo/Hss8+iuLgYI0aMwPDhw2WzkF566SWEh4cDgHA7Fs3P008/Lc3+2b59O2rUqIEPPvhAen/p0qUIDg4GIN6ORPskIN6HGjRogPXr10v5sra2ll3gWb9+Pfz9/RXFaS6vh/U1oHSGRdkZQBkZGdBoNNLtu5mZmbC1tRVum0rau2j7EO2TgHgfUtI2Rcu9a9eumDRpEgAgJiZGNrMJAJYvXy7VZY0aNXD27NlycY4ZM0b6nFCr1TAajTh37pz0fmFhIbRarfQZdu7cOeh0OkVjXM2aNTF37lzs27fP4mP58uVQq9VwdHTE559/jgsXLlh8bNu2TfFnoLu7O1JSUqTX+/XrJ/ucPX36NBwcHACUv1Bk9uBnsOj3BCXpZP8OfKGIMcYYe4y0b98e8+bNs/je2rVrYWVlJX35bNasmew2jNzcXNmX+d27d6N+/fqy9Xcsyc3NlW75UqlUMBqNcHBwgJWVVbkTxF27dsHHx6fCL6llmX8JnTZtGpycnDB8+HD4+vpi0qRJ8PLywtKlS7Fs2TJ4enpiwoQJGDNmDPz9/fHWW28hNDQUgwcPRkBAAHbs2IHExEQ0bNhQuiUsKSkJdnZ2aNiwISZMmIA5c+Zgzpw5mDBhAho1agSDwYD9+/dj3LhxCA4Oxo4dO7B37160bdsWUVFRUhoTExNRr1492a/IKpVKKg+zzZs3S1PuW7duLbsdpay5c+fCxsYGarUajRo1kt0GcurUKek2PqD0Qp2vr6/s70tKSjBjxgx4enoiMTERVlZWspPX/v37Y/z48RWW+YkTJ6BSqRSF7dmzp7QuysPiLCgoQNeuXeHg4ID27dtLs4L8/f1hZ2cHLy8v/Prrr1i0aBFsbGzw8ssvY/PmzTh8+DAOHz6MzZs34+WXX4atrS0WL16MixcvIjg4GFqtFk2aNEFsbCxiY2PRpEkTaLVaNGrUSPqFvVu3bhWuj2FuD2q1WlEfMrt79y7i4uLQqFEjnDp1qly5jx49Gt7e3ti0aRNyc3Ol13Nzc7Fp0yb4+Phg7NixACCc99u3b6NZs2bw8/PDoEGDoNPp4O3tjfbt28PX1xf29vbS2jOi7Vg0P6tXr4ZGo5FuXdq4cSPc3d3Rp08f9O3bF9bW1tKFJNF2JNonAfE+tGDBAuh0OkRHR8PBwQEfffQRXF1dER8fj0mTJsHe3h6zZs1SFKfZw/oaALz55pvw8PDA0qVLsWLFCgQHB8vWCtu0aROCgoKE26aS9i7aPkT7JCDeh5S0TdFyP3v2LBwdHREXF4fZs2fDYDBg4MCBePvttxEXFwcbGxskJCQAAJo3by6tZ/WgMWPGwGg0Sn19zJgx0nvz5s2Dm5ub9PzYsWOoXbu2ojEuKioKc+fOfWjYDh06YPbs2UJxin4GxsbGYtmyZRXGmZCQgIiICAAVXygq69dffxX+nqAknezfgS8UMcYYY4+RTZs2VXpStmbNGukEcdOmTdKJlyXvvvsupk2bJvSF0uyLL76QPR789XnWrFmYMGECoqKicOvWLaE4S0pK8Pbbb+PZZ5/FO++8A5PJhHXr1sHT0xOOjo54/vnnce/ePdy7dw8vvPACgoODMWLECBQUFGDevHmwtraGSqVCVFSULB9ZWVmIj49HmzZtUL9+fdSvXx9t2rTB66+/Lt0ucvfuXfTp0wdarRYqlQoRERGyafg7d+7EV199Ve5X4wdvpfjwww/x3nvvASj95XvgwIEV5nfOnDnw8fHB0qVLsXXr1grDTZ48GcOGDbP43k8//QRfX1+o1WrZyeuVK1dktwxVRjTsmTNnyt2uUlZhYWG5eHbs2IHRo0cjNjYWHTp0wODBg/Hpp5/i3r17Upj169cjLCxMKnvz4qthYWGyX/dLSkqwfft2zJgxAyNGjMCIESMwY8YM7NixAyUlJVK4ffv24Z133qkwnXv37sXzzz+vqA89aN26dXBxcSlX7vfv38fIkSNhbW0NtVoNnU4HnU4HtVoNa2trjBo1Cvfv31ec98LCQixduhSdOnVCQEAA6tevj8jISEyZMgV//PGHFM5SOy47K8DcjkXzA5SuQ/T+++9LaxWdOXMGgwYNQs+ePfHFF19I4ZS0OZE+CYj3IaC0vsaOHYu1a9cCKL2g0bp1a4SEhOCNN96Q2oiSOMuqqK8BQFFREeLj4+Hu7g5HR0f0799fNhvoyJEj2L9/v3DbBMTbOyDePgCxPqkknaLHVlLuv/32G/r27YsaNWpI/cLKygoRERH49ttvpb9555130LFjxwrjHDVqFFQqFVJSUlCrVi24urrCy8sL1tbWWLdunRRu0aJFiIuLUzTGffrpp+VmO5WVnZ2NN954A5s2bap0pt3NmzelfiT6GXjjxo1KP1e3b9+OpKQkABD+DBb9nqAknezfQQWU2duQMcYYY+wxc//+fSoqKqIaNWr8n+IoLi4mg8FQhSmrXvfu3aPz589TYGAgWVtbP+rk/G1FRUXStti1a9cmKyurR5yiyv3xxx907Ngxio6OJjs7O9l7d+7coZSUFMrOziYiIldXVwoJCaGaNWtajKuq8/532vGlS5coJSXFYn5YKXNfCwgIIBsbm0ednH88AHT16lUymUz/535x5coV2rp1KxUUFFC7du0oKCioClPK2L8XXyhijDHGHkMlJSWk0Wik58nJyWQymahJkyaP5EQGAJlMJlmaHgePWzmJ2LdvH4WFhZGtre3fjqO4uJjOnDkju2ARFBQkdML15ptv0pgxY6h27drl3ispKaHr16+TWq0mJycn4fTk5ORQQUEBeXl5iWfCgtu3b9PGjRvp4sWL5O3tTb179yZ7e3uLYYuLiykpKUkK27Zt20rbZ2X5ftIUFBQQEQm3cdE2p6SMROtcNM6CggK6dOkSeXh4VGnfFRm7qurYeXl5lJKSQleuXCG1Wk1169alpk2bkkqlkoXLzMykAwcOSOHq1atH0dHRFV5wrMz169cVt2nRdD4oJyeHAJCrq6vF9//u+MHkHmwfdevWpfbt2z+0fShpx1988QX16NGjwvH1QQAe2j7YP8wjm8vEGGOMsXIuXLiApk2bQqPRIDY2Frm5uYiOjpam6fv6+spui9q2bRuGDRuGiRMnlttp6ebNm2jbti2A0rWFZsyYIe2GtH//fsTGxqJt27ayxWGLioowdepUtGnTRlrL4r333oNer4e1tTXi4uKkhVxFjx0cHIxZs2bJdnOpyOXLlzF9+nS0bdsWAQEBCAoKwrPPPovPPvsMxcXFsnIKCQmpsJzq1q0rldOJEycwe/ZsLF68uNwisrm5uRgyZMhD03XixAlpfZMjR47I0rJlyxa0adMG7u7uCAkJqXQhY6B0JxlLC7VW5uzZs/D19UVJSQmmTp0Ko9FYbqcdo9GIadOmSbevWNra+fbt27CyssKRI0ek14DSbZdbt24trSdi3qVo4MCBsl1w7ty5gwEDBsDLy0tqC6NHj5bWeGrTpo0U54kTJzBo0CD4+vpK66YEBwdj2rRpUpgePXpg48aNAP67s5yTkxPCwsLg4uICV1dXqazGjh0rbRX9xx9/ICAgABqNBi4uLtBoNGjYsCEuXbqkKN+i6XzQvXv3sGLFCkyZMgUff/wxrl+/LntfpM0pbUe7du1Cx44dpfVZzAvVduzYUdpCviIPtjklZSRa50riTEhIwKFDhwAAf/31F4YOHQqNRgO1Wg2tVosXX3xRupVPdJwRHbuUHNtgMGDo0KHS7XkVKS4uxsSJE6HX62VrnalUKnh7e+P7778HUNpuevXqJdsdy9XVFRqNBgaDQVoXyqxs3h8cM8x5V6vVaNeuHdasWSO7/fH/ks4bN26gZ8+e8PT0xMiRI1FcXIxhw4ZJaQ4PD5d2fgPEx4/Lly9j1apV2LZtm/Q5Ynbv3j28+eab0vPly5cjLi5O+nxav349AgIC4OvrK1tn6fr169i7dy9u3LgBoHRR8Dlz5uDNN98UHmfN4ytQOs48uNuniD///BMzZsxA//798eqrr8ra6oO3fh8/fhxxcXGIiIhAz549pdvJlLQPJe3YEkufQ/fv38err76K1q1bY86cOQCA2bNnQ6/Xw87ODv369atwTGT/PHyhiDHGGHuM9OzZE5GRkdiyZQv69OmDli1bIioqCpcuXcLly5cRExOD7t27Ayhdu0Oj0aBz585o1aoVdDodVq9eLcVl3kJ41apV0Gq1aNq0KQwGAxISEmA0GjF8+HAMHToU1tbW0sn6tGnT4OLigldeeQVBQUEYOXIkPD09sXr1anz55ZeoU6cO5s6dK3xsoHTRTUdHR2g0GsTExODrr7+WLeps9ssvv8De3h4hISFo1aoVNBoNBg0ahOeeew5GoxERERG4c+eOonLauXMnrK2t8dRTT8HLywuOjo7SVtIPprMyZRcmLbs4+Pfffw+1Wo24uDgsXrwYw4cPh1arxaZNm9CkSROLD5VKhcDAQOm5CPOFqokTJ8LJyQnLli1DVlYW8vPzkZ+fj6ysLHzyySdwdnaWFm0V3d555cqVqFGjBl599VVMnToVrq6umDRpEpYuXYrIyEjUrl1b2llo7NixCAgIwEcffYSoqCh069YNwcHBOHDgAPbv34+goCBMmTIFiYmJsLW1Rc+ePTFw4EDo9XqMHTsWr7/+Ovz8/FCvXj1cuXIFDg4O0glVx44d0b9/f+kEsrCwEMOGDZN2bHJxcZG2cO/Tpw+io6OlizA3btzAs88+i169egnnG4BwOgMDA6UT0YsXL8Lb2xv29vZo3rw5atWqBWdnZ2ntK9E2J9qOgNK1w7RaLfr27YuEhARs374d27dvR0JCAvr16wcrKyusXLlSuM0pKSPROlcSp6+vr7QY8muvvQYfHx9s2rQJaWlp+O6771C/fn1MnDhR0TgjOnaJHhsoHbueeuopqFQqBAQE4P3335d21Crr9ddfR2BgILZs2YLdu3ejTZs2mDt3LtLS0jB9+nTY2Nhg586dGDFiBFq2bIlTp04hIyMDvXr1Qnx8PPLy8vD5559Dr9djzZo1AMTHd5VKhdjYWFhbW8PBwQFjx47F8ePHLY4joukcOnQogoOD8fHHHyMyMhLdunVDo0aNcODAARw6dAjNmzdHXFwcAAiPH8nJyTAajahZsyZsbW3h5+cn23q9bF0uWLAAdnZ2+M9//gM3Nze89dZbcHR0xFtvvYU333wTNWvWxCeffIIjR47A3t4eKpUKDg4OOHr0KHx9feHv74969erB1tZWtotYRcr+EGBup/7+/pgzZw6uXLli8W9sbW2ltnDmzBnY29vDz88PvXv3RkBAAPR6vbQbXNm+fvDgQVhZWSEyMhITJ05E+/btodVqsX//fkXtQ7QdOzg4WHyoVCrY29tLzwFgwoQJcHd3x6uvvorAwECMHj0aXl5eWL16NdauXQs/Pz+89NJLDy1P9s/AF4oYY4yxx4iTk5P0Jf/27dtQqVT46aefpPdTUlLg4uICAGjcuLFs0c0NGzbAzs4On332GYD/fvEuG+6HH36Ara0t5s+fL/3d+++/j5YtWwIA6tatK83ayMjIgFqtlranNh8jODhY+NhA6RfvP//8E99++y26dOkCrVYLJycnvPrqq7JfNFu2bIk33nhDer5q1SqEhYUBKP31vHHjxnj55ZcVlZN5+2gAMJlMmDt3LgwGg7QbmTmdPXr0qPTRrl07WX7MX/pbtWolbfls9vbbb6NFixbQarWIjY3FG2+8IT1mzpwpbT9tfg0o/YJe2WPgwIFQq9VwcXGRbdf9oMTERDg7OwMA6tSpg86dO2Pv3r3SIt1JSUnQaDRISEiQXgsICJDV8S+//AIPDw9pZ5znnntO2unJ09NTuujx559/QqVSSe0FKJ1Z0KBBAzRu3Fi2E9KuXbsQEBAAoPQC0DPPPIPnn38etra2+O233wCUbt197NgxWX5+/fVX2NvbAwB0Op10McbDwwNHjhyRhT116hRq164tnG8AwuksW+cDBgxAREQEbt++DaB0oeno6Gj069cPAITbnGg7AgB/f/9ys03KWrx4Mfz8/ITbnJIyEq1zJXHa2NhIM03q168v2x0QKJ3x6OXlpWicER27RI8N/LevnzhxAmPHjkWtWrVgbW2N//znP9i+fbvUR9zc3GSzUC5dugSDwSDN6Jg1axbCw8NRu3ZtHD16VAp38+ZN6HQ65OXlAShdfLlx48YAxMd3cxqvXbuG999/H0FBQVCr1WjatCmWLFkimwEimk43NzdpFlV2djZUKhV27dol/d2BAwdQp04dABAeP6KjozFkyBCUlJTgzp07GDVqFBwdHaU+X7YuAwICpAsix44dg1arlfINAJ999hlCQkIQHR2N4cOH486dO5g3bx48PDwwfPhwKdyQIUPQvXt34fHVXOc//PADxo0bh9q1a8PKygpdu3bFli1bZIuNl+2/3bp1Q5cuXaQfQEpKStC3b188++yz5cK2b99e2r3TbNy4cWjXrp2i9iHajg0GAzp37izbpCIhIQEajQZvv/229BpQ2tfNsxPPnz8PtVot2zFt165d8Pb2Bvt34AtFjDHG2GOkRo0a0slwSUkJtFotTpw4Ib2fkZGBGjVqAADs7OxkO3gBpbvWGAwGLF26VPri/WA4Kysr6ZdOAEhLS4OjoyOA0pPxsreI6XQ62RT6zMxM1KhRQ/jYQPltfC9fvox33nkH/v7+0m0Mn3/+OWxtbWU7OZWUlMDKygrZ2dkASr+kuru7KyqnmjVrShcizNasWQM7Ozts2bJFSqdWq0XHjh3x/PPPW3x07drVYn6cnZ1lX+wBID09HUajEQcOHEC9evUwY8YM2QmGVqstt7uS+cQuKirK4qNZs2ZQq9XQ6/U4efIkKpKamgo7OzsApbNsunfvjrZt2+LSpUsVHt/W1la2I5U5zJ9//gmg9FY7o9EIoPTkpGz70Ov1slshL1y4AL1eD51OJ4vTZDLByspKul3lxx9/lG4x+/TTTwEATZo0ke18BJTWuaurKwCgUaNG0glpYGBgudutDh06hFq1agnnG4BwOsvWed26dWUnzUDpLAFPT08AEG5zou0IKC339PR0VCQ9PR06nU64zSkpI9E6VxKnt7e3dPGpTp065XakOnv2LOzs7BSNM6Jjl+ixgfJj1/3797F27Vo888wzUKvV8PDwwPTp01GjRo1yY5dWq5Vmo5w5cwZ6vR5Go1GanQeUXozUarXSzJRz585Bp9MBEB/fLe1qeejQIQwdOhQ1atSAXq/HoEGDAEA4nXq9XrbTnZWVlTSbz1yW5jISHT8cHBzK7Sb57rvvwsHBAcnJybK6tLW1ld2yZmNjI5t9lJGRIcVp/rGhsLAQarVadgE5JSUFderUER5fAXmdFxYWYsOGDYiJiYFGo4G7uzumTJmCjIwMWThPT89yt6sdO3YMbm5u5eJ0c3Mrt5uo+bZbJe1DtB1nZGRIM8Du3r0rq6MH++WD5W5lZSUr96ysLOj1erB/B75QxBhjjD1GWrRoIW1Vu2LFCri4uMhmGsyaNQshISEALH/hBEq3QDYYDJg6daq0jknZk0yDwSA7WcjMzJS+/Lm4uMguRERERMhO+tLS0lCzZk3hYwPyafcPSkpKwsCBA2FnZwdvb28cOHBAeu/y5ctQqVTIz88HUPol1fwlWbScnJycyp2AA6Xbhuv1eixduhRqtRoNGzaU/WL9oOPHj8tOJJKSkpCamgpvb28kJyfLwqanp8NgMAAone3Ut29fhIWFSRcPLH1Br1+/fqVbLZuP36lTJ3To0KHcujdA6docsbGx6Ny5s+z1JUuWwN3dXdpe/MHjBwYGSrceAqUnV9bW1tL6ORkZGdJJobu7u+xWjn79+snq9vTp03BwcEC9evVkM58yMjKg0Wik28oyMzNha2uLrVu3olatWkhISEBCQgJ8fHzw2Wef4eDBg1ixYgU8PT2l24ASEhLg4eGBpKQkrFy5EoGBgfjhhx/w559/Yu/evWjYsKFsNsHD8g1AOJ0qlUo6WXN3d5edNAOlF0vMbVO0zSlpR02bNpXKwZL4+Hg0bdoUgHibEy0j0TpXEueUKVMQHh6OW7duYdKkSejSpYt0EpuXl4c+ffqgQ4cOisYZ0bFL9NhA5WNXVlYWpk2bBk9PT0REROCtt96S3lu3bp10kQ8one3m4OCA9u3bY8yYMdLr8+bNky4mAKUXF2rXrg1AfHyvLI337t3DZ599hoiICKlMRNL59NNPSzPYtm/fjho1auCDDz6Qwi1duhTBwcEAxMcPBwcH2Q8UZcvAaDRi06ZNUl06OjrKZpt6eHjILlxlZGTAYDDAzs5OdpHqwc+233//HTqdTnh8BcpfHCwb18yZM+Ht7S2Vu3lM8Pb2Lpe3zMxMaUxQqVT47bffkJubC19f33IzJ3/77Tfo9XpF7UNJOy4qKkJ8fDzq1asnfcZa6pcNGjSQLsYnJyfD2tpatobh+vXr4e/vX2E5sn8WvlDEGGOMPUYSExOh0+lgbW0NnU6H/fv3o379+ggNDUWLFi2g0WiwYcMGAKXT3csu6llWUlIS7OzsoFar0axZM9n08dzcXOm2AADYvXs36tevDwBo27atNA3dkq+++gohISHCxwYq/uJdVm5uLsaNG4fg4GDs2LEDe/fuRdu2bREVFSUrm3r16ikqp/bt22PevHkWj7l27VpYWVlBrVbj+eefx+jRoytM39mzZ+Hj4yPlp+wisAsWLJCFXbduHYKCgmSvrVixAq6urvjkk09gZWVV7gt6//79MX78+AqPb14j6eLFiwgODoZWq0WTJk0QGxuL2NhYNGnSBFqtFo0aNbK4aPiZM2fw9NNPo1+/fuVOEBYtWgR7e3vEx8djxowZcHd3x7Bhw6T3V69eLa2lFBsbi2XLllWYzoSEBERERODNN9+Eh4cHli5dihUrViA4OFi6fQ0ANm3aJJXR119/DQ8PD1mZqlQq6HQ6jB8/Xrbg8wcffAC9Xg9bW1tYW1vL1sLp3r277Bfzh+UbgHA6VSoVGjZsiCZNmsBgMODrr7+WxbN//37pVhzRNqekHZn7VMOGDTFhwgTMmTMHc+bMwYQJE9CoUSMYDAbs379f9vcPa3OiZSRa50riLCgoQNeuXaWLJ+ZFxP39/WFnZwcvLy/8+uuvisYZ0bFL9NiA2NhlMpnwww8/wMbGBqGhoWjTpg20Wq2sPufNm4d27dohJSUFtWrVgqurK7y8vGBtbY1169ZJ4RYtWiSt/SOad5E0mommc/Xq1dBoNPDz84ONjQ02btwId3d39OnTB3379oW1tbV0IUl0/GjdurXsNs+y5s6dKy2EDZTehlz2drYHbdmyBcHBwQgICJA2aABKb4M0/7AAAIcPH4aHh4fw+Ao8vM5NJhN27dolbSDg4OAAKyurcheidu3aVe4zw1xf5lmUZps3b4afn5+i9qGkHZvt2bMHXl5emDx5ssUxYcGCBdDpdIiOjoaDgwM++ugjuLq6Ij4+HpMmTYK9vT1mzZpVYdmwfxYVADzqndcYY4wx9l8XLlyglJQUCgkJIR8fH8rJyaHFixdTfn4+de7cmdq2bUtERPv376dDhw7R5MmTLcaTlJREK1eupK5du5KjoyO1adPGYrg5c+ZQXl4ezZ49m86dO0dWVlbk6+trMezatWtJq9WSi4uL0LETEhJoyJAh9NFHH1GNGjUqzfe9e/do2LBhtGnTJiopKaHw8HBavXq1lJZdu3ZRbm4u9e7dW7icvv32W/rxxx9pwYIFFeZn+fLllJiYSCUlJaTX6ytNIxHR77//LntuMBjI0dFRer5y5UoiIoqLi5OFy8jIoAEDBtDRo0fp9OnTFBQUJL2XnZ1NBQUF5O3t/dDjm0wm2rlzJx0+fJiys7OJiMjV1ZXCw8OpQ4cOpFarLf5dYWEhTZo0iZKSkmjTpk2yOl66dCmtXr2aCgoKKCYmhqZPn046nU5Kd0lJCQUEBNDNmzdJrVaT0Wi0eIwdO3aQra0ttWrViqZOnSqLc+HChdI23snJyXT//n2pTZaUlNCxY8coMzOTTCYTubm5UUhIiMU2c/v2bdq9e7csbMuWLcnf319xvouLi4XSmZSUJIuzRYsWFBMTIz2fOHEiXbp0idatWyfc5r744gvZ6w9rRxcuXKClS5darPeRI0eSj49PuWNV1uZEy0i0zqOiooTjNEtMTKQtW7aUq8v+/fuTnZ2d8BiXkJAgPHb16dNH6NhERG+++SZNnDhRaFxITU2lr776SmpH7du3txjuypUrtHXrViooKKB27dpVWCeieY+KiqK+ffs+dDt0pek8ePAgHT58mMLDwykiIoLOnj1Lc+bMofz8fOrSpQsNHjxYCisyfhw4cID2799Pq1atsni8uXPn0rJlyygrK4sOHjxIdnZ21LhxY4thlyxZQiaTiW7cuEENGjSgvn37Wgw3depUSk9Pp8WLFwuPr76+vnT06FFZX7Tkyy+/lD1v0KABtWjRQno+e/ZsunXrFs2fP5/2798vC+vm5kb169eXni9cuJAKCwtp4sSJwu3DTKQdl3Xjxg164YUXKCkpiQ4fPkwNGjSQvb927Vr6+eefKSIigvr160f79u2jGTNmSPU+ffr0Cj9j2D8LXyhijDHG2GPl/v37VFxcTAaD4VEnpcqZTCa6e/cu1axZk1Qq1aNODvsX4DbH/q3y8/NJo9EIX0RjjP0XXyhijDHGHmPFxcWUlJREFy9eJB8fH4qKiiKNRlNh+KKiIrpw4QI5OzuTvb393zpmSUkJ/f777+Tj40NqtZoKCgpo8+bNZDKZqG3btuTi4lJpOr29valt27aVppOIKCcnhwoKCsjLy+tvpfNBleW9pKRElp7k5GQymUzUpEkTiycRfyc/Zf/28uXLpNfrpZkpD3P9+nXhsA/Kysqi3377jdzc3Cg4OLjKwlbV8XNzc2UzYB6sm9TUVEpJSaGoqCiqW7cunTlzhhYvXkwmk4l69Oghm7ljSbt27SghIUE2W+Dq1at0+vRpCgkJIXt7e8rJyaEvv/ySTCYTde7cmRo2bFgFOa8aD7bNI0eOUEFBAYWHh5OVlVWlYStqx8XFxXTmzBlZuQcFBZWL70GWyrLssX7++edys5lCQ0OlMADowoUL5OnpSVqtlgoLC+nbb7+lgoIC6tSpU7k2/rB0mmcM/i+8+eabNGbMGKF+OGTIEHr77bfJ3d1d9rrJZLI428JkMtGlS5eksW7v3r104MABunLlCqnVaqpbty517dq1wllxlfnmm2+oY8eOQrOezDIzM8sdv3379lSzZs1K/66yMvq7be5RKioq+p+n7+LFi7Jyf3Dmknk8bNu2Lfn6+ioeDyv6XFUyzjBGRMRrFDHGGGOPkbFjx0pbPP/xxx8ICAiARqOBi4sLNBoNGjZsKC3QOnfuXGk9huLiYrz66qvSui1arRZDhgxBYWEhAGDbtm0YNmwYJk6cKNsJCCjdgrdt27YASnfNcnNzg1qtRnBwsLQmjp2dHQwGAxwcHHDkyBFF6bxz5w4GDBgALy8vxMXFoaCgAKNHj5bWbWjTpo20hfOJEycwe/ZsLF68uNyCzbm5uRgyZIiivF+4cAFNmzaFRqNBbGwscnNzER0dLa0L4+vri19//VVRfh7mxIkT0noU7dq1w5o1a6TtpysiGnbUqFHSOjz5+fno2bOnlBe1Wo22bdtK7ysJu3jxYjzzzDPo3bs3fvjhB9kxr127Bl9f3wrjLLveTtk4ly9fjsDAQNk6Qmq1GoGBgdLC4d988w00Gg0cHR1hMBiwe/duGI1GREdHSzsNmbfJ3rx5s8WHRqPBokWLpOfm9VtUKhVcXV1x4sQJeHh4wN/fHw0aNICNjQ127twJoHRHprJrIG3ZsgVt2rSBu7s7QkJC8OWXX8rqVaRtipbn5cuX0bJlS2g0GrRp0wY3b95E586dpTqqX7++tPvahQsXEBISUmE7rlu3Ln799VeUlJRg6tSpMBqNsvWezOupTJs2DSUlJcJlCQA5OTlo1aoVVCoVvL29ERoaitDQUHh7e0OlUqFVq1bIyclBenq6tNCvn58fMjMzERISAjs7O+j1etSuXVva0Uk0nSqVCvXq1cPbb78t7aJVkcLCQkycOBH16tVD8+bN8fnnn8veN++qlZubW+5x+/ZtWFlZ4ciRI9JrQOl4aOlhZWWFb7/9Vnqem5uL3r17Q6fTwdnZGdOnT5e1K/Oxc3JyEBoaKo1TarUaISEhcHV1hUajkS1YLpoflUqFmjVr4oUXXsDhw4crLaN79+6hV69esnHAfGyDwSCtO6SkjETrEhD/HALEx6Rdu3ZhxowZ0lpF+/fvR2xsLNq2bSstxLxhwwZpgXoA+Pjjj+Hl5QW1Wg1HR0e8+eab0nvBwcGYNWuWxbXelDh79qyURnN+zMcs+2jZsqW0+L2S8VD0c1XJOKNkPGT/fHyhiDHGGHuMuLi4SDsq9enTB9HR0dJJ6Y0bN/Dss8+iV69eAOQ78sybNw8ODg5YsWIFzpw5g9WrV8PZ2Rlz587FmjVroNFo0LlzZ7Rq1Qo6nQ6rV6+Wjll2W+KYmBj06tULp06dwrhx4xAYGIjevXujsLAQRUVFGDhwIKKjoxWlc+zYsQgICMBHH32EqKgodOvWDcHBwThw4AD279+PoKAgTJkyBTt37oS1tTWeeuopeHl5wdHRUdr+98F0iua9Z8+eiIyMxJYtW9CnTx+0bNkSUVFRuHTpEi5fvoyYmBh0795dUX4exnyhSKVSITY2FtbW1nBwcMDYsWNx/Phxi38jGrZsvidPngwPDw/s3bsXeXl50tbo5t3fRMMuXLgQer0eY8aMwcCBA2FtbY133nnnoeVeWZzvvfce9Ho9Jk2ahKSkJJw9exZnz55FUlISJk+eDDs7O8ybNw9NmzaVdmEy78BUdrHU999/H40bN5bK6MEFrx98qNVqtGrVCmPGjMHdu3cxb9481KlTR7aT0GuvvSYtvlw2P99//z3UajXi4uKwePFiDB8+HFqtFps2bVLUNkXLc9CgQYiIiMD333+P5557DhEREWjdujUuXbqE33//HS1btpTSLdqOJ06cCCcnJyxbtgxZWVnIz89Hfn4+srKy8Mknn8DZ2Rnx8fHCZWk+dnh4uGznRLP09HRERESgV69e6NatG7p27YqTJ09i/PjxCAwMRLdu3VBYWIj79++jS5cuGDhwIAAoSucLL7wAZ2dnaLVadO7cGd9++63sZNZs5syZcHFxwbx58zB16lTY29tjxIgRsnIvu6jwg4+yZVJ2If6Kyqls2Jdffhn169fHxo0bsXz5cnh7e6Nz587SxQnzsZ977jl0794dubm5uH//PsaOHSstTrxnzx44Ojriww8/VJQflUqFWbNmoUmTJlCpVHjqqaewYMECXL9+vVwZjRgxAi1btsSpU6eQkZGBXr16IT4+Hnl5efj888+h1+uxZs0aRWUkWpdKPodE+9CqVaug1WrRtGlTGAwGJCQkwGg0Yvjw4Rg6dCisra2xceNGWT9fsWIFdDodZsyYgW3btuGtt96CnZ0dli9fLtW5o6MjNBoNYmJi8PXXX6OoqKhcWT6M+XMAKP2Mcnd3x8cffyxdQJ81axZ27NiBQYMGQa/X45dfflE0Hop+rioZZ0THQ/bvwBeKGGOMsceITqdDZmYmgNItgY8cOSJ7/9SpU9L2uGV3Z2nSpAk++eQTWdjVq1fjqaeeQuPGjbFw4ULp9Q0bNsDOzk6a1VH2C7qDg4O0LXF+fj40Go0sDadPn4ajo6OidHp6ekon1X/++SdUKpU0ewco3ammQYMGCA8Px5QpUwCU7iwzd+5cGAwG7Nixo1w6RfPu5OQkXXC5ffs2VCoVfvrpJylcSkoKXFxcFOWnSZMmlT4CAgJkuxFdu3YN77//PoKCgqBWq9G0aVMsWbJE+kW+bH4eFrZsvoODg6UtyM02b94s7WAnGjYoKEj6lRoADh48CCcnJ0yfPr3Scq8sTi8vL2l3PkvWr18PT09P2fbWJpMJVlZWsi3Oz58/L20RHxsbi86dO5fbkejBXbVq1qwpbQtfVFQErVYru+h27tw52Nvbl8tPq1atpItsZm+//TZatGihqG2KlmfZ7c9v3LgBlUolmzmxZ88e1K1bFwCE27GLiwsSExMrLPfExEQ4OzsLlyVQuuX4g9t5l3X06FEYDAZZGu/du1cujQcPHoSXlxcACKfTXD9FRUX4+uuv0alTJ2mmX3x8vGxXJz8/P9m4kpGRAT8/Pzz//PMwmUxSudepUwedO3fG3r17sW/fPuzbtw9JSUnQaDRISEiQXgOAp59+Gp07d0ZaWhouXLiACxcuICsrC1qtFrt375Ze8/LyQlJSknTsa9euITQ0FB06dMD9+/elY9esWROnT5+Wwt27dw9WVlZS/161ahUaNGigKD9l2/DRo0cxatQoGI1G2NjYoHfv3ti1a5cUR+3ataXZK0DpLB6dToe8vDwApbtqNW7cWFEZidalks8h0T5UNs4ffvgBtra2mD9/vvR377//Plq2bCkro9DQULz33nuyNC5ZskTa2VGlUuHPP//Et99+iy5dukCr1cLJyQmvvvqq9NkIABMmTKj0MXDgQCk/Pj4+2L59u/S3v/76KxwdHaULUC+//DLat2+vaDwU/VxVMs6Ijofs34EvFDHGGGOPkUaNGknbAgcGBmL37t2y9w8dOoRatWoBKP1Sd/XqVQCAo6OjNCPGLDMzE3q9HnZ2dtJFELO9e/fCYDBg6dKlsi/oRqNRuj2ksLAQGo0GKSkp0t+lpaXBwcFBUTptbGxk0/j1er3sBO/ChQvQ6/WyE3yzNWvWwM7ODlu2bCl3wUIk7zVq1JDyXlJSAq1WixMnTkjhMjIyUKNGDcX5GTx4MN544w2LjxdffLHCbasPHTqEoUOHokaNGtDr9Rg0aJCUH5GwZfNdu3Zt2UmnuSxtbW3LlVFlYW1tbaWTE7NTp07BxcUFkyZNqrDcK4tTp9PJTqoedObMGdja2sLV1VU6cb158yZUKpXshDs5ORmurq7S8/nz58PT01N2QvTgxY2y6crLy4NarZZOlIDS24ksXWx1dnaWnUQDpTNmjEajorYpWp46nU7WL+zs7JCRkSE9//3336W6FG3Her1edmL5oNTUVNjZ2QmXJVDav8wXBSxJSkqCo6MjbG1t8fvvv0uvGwwGWZldvHgRNjY2ACCcTkv94tKlS5g1axbq1q0LtVqN1q1bA7Bc7pcuXUL9+vUxYMAA/Pnnn1Cr1bhx4wa6d++Otm3bym4ntZT3goICjBs3DkFBQbKLZQ+GtbW1LTfG3rlzB+Hh4WjXrh0yMzOhVqvh5OQk+7v8/HwpTUDphQBzGYnmx1IZ/fXXX1i5ciWioqKgVqulbdrLju9A6Riv1WqlPn3u3DnodDpFZSRal0o+h0T70INxWllZITU1VXqelpYGR0fHcuNW2b4DAL/99htq1KgBoPxYfPnyZbzzzjvw9/eHWq1GeHg4Pv/8c+lCflRUlMVHs2bNpPzo9XpZfkwmE7RarXTL14kTJ2AwGBSNh6Kfq0rGGdHxkP078IUixhhj7DGSkJAADw8PJCUlYeXKlQgMDMQPP/yAP//8E3v37kXDhg0xfPhwAKVf6t5++20sXLgQbm5u2L9/vyyu1NRUODg4yH5RLGvfvn0wGAyYOnWq9IX2mWeewbBhw3Dp0iW8+eab8PPzk629Mnr0aLRu3VpROt3d3WUXm/r16yf7In769Gk4ODjAycmp3BdToHQKvl6vx9KlS2UXLETy3qJFC0ybNg1A6S0H5hMNs1mzZiEkJERRfkJCQrBkyZIK6/D48ePSrRoPnsCZ3bt3D5999pnFW6AqC6tSqfDiiy9iwoQJcHZ2ls0WAEpnlpS9CCIS1tPTEz/++GO54545cwYuLi6Ii4uTlbtInK1bt0ZcXJzFWzaKi4sRFxeHNm3aYODAgQgLC8Pq1avRpUsXxMTEoEWLFkhLS0N6ejoiIyPL3fJ3/PhxBAUFYcSIEcjLyyt38tqtWzc8++yzOHDgAEaMGIFmzZqhc+fOuHfvHvLy8tCrVy/ExsZK+UlKSkJqaiq8vb2RnJwsO1Z6ero0W0a0bYqWp5eXl2zm2uuvvy5dMABKTx7NdSnajjt16oQOHTqUW0MJKJ3lYp5JJFqWQGmf9/b2xqZNm2Sz4HJzc7Fp0yb4+Phg7NixqFevnmwG0ZIlS3Dnzh3peUpKinSSK5rOyvoFUDqLpH///gAAX1/fcmvZAKWzLerXr4/27dtLdWROn7u7uzQrzlLezbZv3w4PDw+888470oW6smEbNGiAbdu2lfu7u3fvIjw8HE8//TTUajV69OiBnj174t69eygsLMT48ePh5+cnhT98+LBURqL5eVgZZWRkSLPh2rdvL7sNc968eXBzc5OeHzt2TGpzomUkWpdKPodE+5DRaJTdEmkwGHD+/HnpufkHA5VKhZUrV2Lz5s3w8PDAoUOHZPGePn0aNWvWBFD5WJyUlISBAwfCzs4O9evXx6pVqyyGA/77OQAAjRs3xqeffiq9t2fPHuj1ephMJgCl40yNGjUUjYein6tKxhnR8ZD9O/CFIsYYY+wx88EHH0Cv18PW1lZaoNn86N69u7RYsLe3N3x8fKTHggULZPF8+OGHaNGiBbp164YZM2ZYPJZ54V/zF9rk5GQ4OjpKv36fPn0aYWFhcHV1hbu7O2xtbaWTF9F0xsbGYtmyZRXmNyEhAREREWjfvj3mzZtnMczatWthZWUlpVM074mJidDpdLC2toZOp8P+/ftRv359hIaGokWLFtBoNNItUqL5efnllzFu3LgKGPec5AAAnSNJREFU8/Pbb78hKirK4i/9FRENGxkZKfvV2ryuhtns2bMRGRmpKGy/fv0wfvx4i8c7ffo0nJycpHIXjTM1NRWurq5wdHREjx49MHLkSIwcORI9evSAo6Mj3NzccOrUKWRnZ6N9+/YwGAyIiYnB7du3MXbsWGkNFH9//3IzeYDSmRgvvvgi/P39odFoZCev586dg7+/P1QqFQIDA3Hp0iV07doVWq1Wuo3EfIL14Bo0D7ajdevWISgoSFHbFC3Prl27SuvRWLJo0SK0a9cOAITbsXnxea1WiyZNmiA2NhaxsbFo0qQJtFotGjVqVG6R3srKEgDu37+PkSNHSn1Cp9NBp9NBrVbD2toao0aNwv379/Hiiy+Waw9lvfvuu+jUqRMACKdTSR8aNmwYhg4davG9S5cuwc/PT3ahCCi98PD000+jX79+lV4oAkpvd+rYsSNat25dLuxLL71U4Rpmd+7cQVhYGNRqNc6fP4969epBq9XCysoKRqNRNnsxISFBugAomh8lZZSSkoJatWrB1dUVXl5esLa2xrp166T3Fy1aJK2ZZPawMhKtSyWfQ6J9qFmzZvjuu++k93Jzc6WLLwCwe/du1K9fv9z6UuZ1gMw+++wz2a1nDyvP3Nxc9O/fv8I0AqUXYFQqFYDS2+ysrKzQp08fxMXFwWAwyC70Llu2DOHh4YrGQ9HPVSXjjOh4yP4dVADwqHdeY4wxxpjc7du3adeuXZSVlUUmk4nc3NyoZcuWirZPPnz4MNnY2NCdO3fo0KFDNHnyZIvhkpKSaOXKlZSQkEBERHl5eZSenk4NGjQgg8FA9+/fpzVr1tBff/1F7du3pwYNGsjSuXv3bsrMzKwwnTdv3iS1Wk1Go9Hi8Xfs2EG2trZ069Yt+vHHH2nBggUWw61du5aWL19OSUlJwnlv0qQJXbhwQdpm28fHh3Jycmjx4sWUn59PnTt3prZt2yrKj6gvv/yS+vbtK9u2vCrCViYzM5Osra3Jw8NDOOzNmzcpJSWFhgwZYjHc6dOn6ZtvvqGZM2cqOv7du3dp9erVdPjw4XJbqvfv37/SrbjPnz9Pf/31FwUEBJBWq60w3Pfff09JSUk0efJkcnZ2lr1348YN2dbTe/bsob/++ovCw8Ol13///XfZ3xgMBtnfrFy5koiIatSoIdw2T548WSXlmZycTHq9noKDg4mIhNuxyWSinTt3Wiz3Dh06WNy+najysiQiunPnDqWkpMjiDAkJeeiW6mZZWVmk0+nIzc1NOJ379++nli1bVtoGzH7//XdKT0+vcPvwy5cv0+7du2nw4MGy1wsLC2nSpEmUlJREmzZtIl9f30qP89FHH1FSUhJ9/PHHUj+7desWXb58mZ566imLf3P37l06duwYRUZGUn5+Ph08eJAKCgqoRYsWFreaV5KfqKgo8vLyIpVKVWm6za5cuUJbt26lgoICateuHQUFBT30bx5WRqJ1Kfo5JNqHGjVqRI6OjtSmTRuL4ebMmUN5eXk0e/bsSvO3detWsrKyopiYGBoyZAh99NFHVKNGjUr/Jjs7mwoKCsjb27vScGY7duyg1atXU0FBAcXExNALL7wgvXfjxg0iItnYU1ZmZibl5+fLxkPRz9WoqKhK01V2nBEdD+Pi4iqNk/0z8IUixhhjjDHGGGOMMUZERA+/PM8YY4yx/7nk5GT6+eefy/06GxoaKhzHrVu3aMuWLRQXF0clJSX0+++/k4+PD6nVaiooKKDNmzeTyWSitm3bkouLi8U4bt++TRs3bqSLFy+Sj48P9erVi+zt7aX3TSaTxRkKJpOJLl26RF5eXkREBIAuXLhAnp6epNVqqbCwkL799lsqKCigTp06Ue3aten69esV/rpuyY0bN+jkyZP09NNPU61atej69ev0+eefU0FBAfXu3ZsCAwOJiBTlXTQ/SsOaZWVl0W+//UZubm7STBEReXl5lJKSUuEv52Y5OTn0ySef0IwZM+jSpUuk0+mkMv3pp59o2bJldPHiRfL29qYxY8ZQeHi49LeZmZl04MABunLlCqnVaqpbty61b99eNmPkpZdeoj59+lDr1q2F0l1SUkIajUZ6fuTIESooKKDw8HCysrJSnMa0tDQ6fPgwhYeHU0BAAKWnp9PChQupoKCABg4cSO3atSMi8bZhlp2dTUeOHJH1t7CwMHJ1dX1oeRcUFFis6we1a9eOEhISys1A2Lt3b7ly79q169+axWb2sLY5b948RfVYGXObGzp0qKK6vHr1Kp0+fZpCQkLI3t6ecnJy6MsvvySTyUSdO3emhg0bElHpbJbvvvuu3HgYERFB3bp1I2tra1l6Ll26REajkQwGg+z1oqIi+vnnn6U+JNp/U1NTKSUlhaKioqhu3bp05swZWrx4MZlMJurRowfFxMQo7hdmeXl59NVXX0ljQr9+/SqcVWJWt25d2rlzp3D7KDsmPAgA7du3Tzp+TEwMWVlZEZF4/Zj9nfGQiGjIkCH09ttvk7u7u8W//TtxfvHFF9SjRw/ZZ1VZZT/XvL29qXfv3hWGNXvzzTdpzJgxD/2MEh0TioqK6MKFC+Ts7Cw79l9//UXr1q0rNyZ0796dnnnmmUrjrOzzRbRfWFJZHbF/sEd53xtjjDHG5HJyctCqVSuoVCp4e3sjNDQUoaGh8Pb2hkqlQqtWrYTXozhx4gTUarW0XoxarUZwcLC0poSdnR0MBgMcHBykxS579OiBjRs3AihdC6J27dpwcnJCWFgYXFxc4OrqirNnzyI3Nxe9e/eGTqeDs7Mzpk+fjuLiYunYZXewSU9Ph7e3N9RqNfz8/JCZmYmQkBDY2dlBr9ejdu3aOHfuHNRqNdq2bYs1a9bg/v37lebtyJEjsLe3h0qlgoODA44ePQpfX1/4+/ujXr16sLW1RUpKClJTU+Hm5lZp3pOTkxXlRzTsqFGjpHWN8vPz0bNnT2n9B3Neze+L1qWScKGhodKOVt999520Ls7rr7+OHj16wMrKClu2bMG9e/fQq1cvaV0KtVoNV1dXaDQaGAwGLFq0SIq/7FoZc+bMwZUrVyym4/Lly2jZsiU0Gg3atGmDmzdvonPnztIx6tevj8uXLwunEQB27NgBa2tr1KpVCzqdDjt27ICTkxOio6PRrl07aDQa7NmzR7htAKULhQ8YMAAajQZarRbOzs5wdnaGVquFRqPBwIEDkZeXhzt37mDAgAHw8vJCXFwcCgoKMHr0aKk82rRpIy30vHnzZosPjUaDRYsWSc9zcnIQGhoKtVoNrVYLtVqNkJAQqewnTpwolWdhYSEmTpyIevXqoXnz5vj8889l5W1uc6JtU7QeRZjbnJK6NK9Jo1Kp4OrqihMnTsDDwwP+/v5o0KABbGxssHPnTmRkZKBu3brQ6XSIjIxEnz590KdPH0RGRkKn08HPz0/aweny5cto3rw51Go1NBoNBg0aJOtfSssIAL755htoNBo4OjrCYDBg9+7dMBqNiI6ORkxMDDQaDdasWSNcnoGBgdJCwhcvXoS3tzfs7e3RvHlz1KpVC87OztIuXgsXLrT40Gg0mDx5svRctH4AoGPHjrh9+zaA0q3Sw8LCoFKppDV/AgICcPXqVeH6AcTHw9TUVIsPKysrfPvtt9JzJXFWxMrKSrbroujnmvnYDz5u374NKysrHDlyRHpNyZgwd+5c5OfnAyhdzP/VV1+V1vzSarUYMmQICgsLkZGRAW9vbzg7O8PT0xMqlQqdO3dGWFgYNBoNevfuLW0QIPr5ItovACiqI/bPxxeKGGOMscdIz549ER4eLtvJxSw9PR0RERHSoqmWvtCWffz0009Qq9WIiYlBr169cOrUKYwbNw6BgYHo3bs3CgsLUVRUhIEDByI6OhoA4ODggLS0NAClJxX9+/dHQUEBgNKT1WHDhqFDhw54+eWXUb9+fWzcuBHLly+Ht7c3OnfuLIXNzs6WFvLs1q0bunbtipMnT2L8+PEIDAxEt27dUFhYiPv376NLly4YOHAgVCoVYmNjYW1tDQcHB4wdOxbHjx+3WE7R0dEYPnw47ty5g3nz5sHDw0PalQwAhgwZgu7duwvnXUl+RMOW3T1n8uTJ8PDwwN69e5GXl4cDBw6gXr16sgVNK1P2ol9ljw0bNkhf+stuHR0WFoY5c+bI4vz444/RpEkTjBgxAi1btsSpU6eQkZGBXr16IT4+Hnl5efj888+h1+uxZs0aAKUXin744QeMGzcOtWvXhpWVFbp27YotW7agpKREinvQoEGIiIjA999/j+eeew4RERFo3bo1Ll26hN9//x0tW7bEmDFjhNMIAOHh4Zg6dSqA0kVVHRwcpN2cAGDSpElo3769cNsAShcM9vf3R2JiouxEtLi4GDt37kT9+vUxfPhwjB07FgEBAfjoo48QFRWFbt26/b/2zjysqmr94+8+5wAHBGSeZRBUJCdERdDEEVRuDqmVpjjmUNbNa05NlqXp1ZtZZpYmZZZ5K820NCfUMBUFZ0VRcGRwRgYZz/f3B7+zYzO5j8E9nM37eZ796N77Pd93vWu/a23OOnuvhVatWiE+Ph779u1DUFCQWJaKE8JWtalUKjz77LMYNGgQsrOzUVBQgKlTp4oTCe/evRuOjo7iJLRz586Fq6srFi9ejDfeeAONGzfGxIkTxfLqc05ubsq9jkD1Xx4r5pwh17Jr16546aWXkJOTg8WLF8PT01OyGtdrr72G8PBw9O7dGwMHDpSstqYnOzsbAwcORGRkJAAgJiYGoaGhOHLkCHbu3ImQkBB06NABd+/efaw6AoD27duLEx+vX78ednZ2mDdvnliGJUuWoF27drLrs/xEyc8//zzCw8PFgZucnBz07t0bw4cPF229vLwkk/b7+vpCEAR4enrC19cXfn5+BvUJ5f1PmTIFQUFB4jW7du0aQkJCMHnyZNnXB5DfH9bULsoPcBiiaW9vX+UmCAIaN24sOSbnvgZAsohB+a1iOQ3pE8rfCxYvXgx7e3usWbMGZ86cwbp16+Di4oJFixahX79+mDRpkjgh98KFC9GvXz8AZZP0+/r6Yu7cuZU0a7q/yG0X+vyQe40Y5cMDRQzDMAxTj7C2tkZSUlK1548ePSouT6v/o+1Rf9ja29uLv5bm5+dDrVZLlss9ffo0HB0dAQCWlpbiqiru7u6VynL+/Hk0btwY3t7eiIuLE4/funULnTp1QmRkJAoKCiS/Ujo7O4sDPrm5uRAEQbKM9oEDB+Dt7S1+ibl16xaWLFmCoKAgqFQqtG/fHitWrJB8WSwfU1FREVQqlSSmxMREeHp6yo7dkHjk2pb/UtaqVStxiWk9mzdvRvPmzcV4atpsbW0rfVl51B/yjRs3Fn/9dXFxqfRL8MWLF8Unusov/X737l1otVrk5eUBKFsVp127dgCkXzSLioqwYcMG8ckKDw8PvP7660hJSZEshX3nzh3xi7Se3bt3o2nTprLLCAC2trbi0yP6JcrL5+epU6fg6uoqOzcAwM7ODgcOHEB1xMfHw87ODk2aNMGePXsAlC1PLgiC+HQMAGzduhUtWrQAAHE58IpP/lVcMcrW1hanT58W93Nzc2FmZibm+TfffCNqBgQESPylpKQgICAAY8aMgU6nE3PucXKzpusIyP/yaOi11PczxcXF0Gg0kkHhCxcuoHHjxrC0tMSpU6eqvT4nT56EpaUlgLLlwstfZ/0gdLt27XDnzh2D6wgoG2xNS0sDAOh0OpiZmeHkyZPiZy9dugRra2vZ9VnermnTptixY4ckngMHDqBJkyYAgEmTJqFdu3aSJ2OAynlkSJ9Q3n+LFi2wefNmifauXbvg5+cn+/oA8vvDtm3bIjo6GufOncPly5dx+fJlpKWlQaPRYOfOneIxQzStra0RHR2Nr776StxiY2OhVqsxf/588Zjc+xoAeHp6Ijo6Gnv27MHevXuxd+9exMXFQa1WIzY2VjxmSJ9Qvt6Dg4Px+eefS/yvW7cOTzzxBKysrHDhwgXxeGFhIczMzHD79m0AZU/q+fr6VtKs6f4it10AMOgaMcqn6mUPGIZhGIYxCvpVyqojJydHXBnLxsaGPvjgA9qzZ0+V2xdffEFEZfNQ6FdKqfgvEZFarSadTkdERG3atKE9e/YQUdk8IBVXQbly5QpZWlrSrVu3JHOtODk50a5duygnJ4f69+9P+fn54rnc3FxycHAgIqJGjRpRo0aNxJWPiIiaNGlCWVlZEq3p06fTmTNnKD4+ntq1a0ezZs0id3d3cbWVoqIisrS0JCIiMzMzsrKykswd4eTkRHfu3JEduyHxGGKrX4koMzOT2rRpI6nLtm3b0rVr14iIqLCwkMaNG0dLly6tcps+fToRETk4ONCqVasoLS2t0paamkpbt24V9SMiImj9+vVERBQcHEx79+6V+I+LiyNPT08qKSmRzENkbW1NJSUllJeXR0REkZGRlJycTBUxMzOjZ555hrZv306pqan0wgsv0LfffkstWrSge/fukaenp1hmKysrSZ0FBARQRkaG7DJWrE+VSkVarVYyt4eNjQ1lZ2fLzg2isrlOKs5xUx5zc3PS6XR08+ZNCggIICIiDw8PsrS0pObNm4t2rVq1Eq/ltm3bqFevXtShQwfJ9aiIhYWFZKUqlUpFpaWlVFJSQkRE4eHhdPnyZSIiunHjhmTOkYCAANq7dy/9+eefNGrUKCotLSUiw3JTT03XkUh+zhlyLc3NzamgoICIytqyTqcT94nK5mkxMzMjOzs7sQ6q4vLly+KqT9nZ2WRvby+p340bN5Kvry/16NGDbt68aXAd2djYiLly//59KikpEfeJyubCqjjny6PqU3/NCwoKJP0gEZGnpyfdunWLiIhWrlxJb7/9NkVFRdHy5currQND+oTy/u/du0f+/v6ScwEBAZSeni77+hhSnwkJCRQQEEBDhgyhu3fvko+PD/n6+hJRWZvy8fERdeRqHjt2jG7evEl79uyhIUOG0OjRo2nMmDEkCAINGjSIRo8eTaNHj5Z9XyMiOnnyJJmZmdF7771HAQEBFBERQd27dydBEKhTp04UERFBERERBvUJ5ev96tWrFB4eLvEfHh5OaWlpZGdnRzk5OeLx/Px8KikpEfuoNm3aUEZGRiXNmu4vctuFodeIaQAYe6SKYRiGYZi/ePHFF+Hj44ONGzdKnqDJzs7Gxo0b4evri6lTpwIAunfvjkWLFlWrdfz4cQiCgF69emH8+PG4fv063n33XQQEBGDs2LESn08++SSAsl9BHRwcEBsbi9jYWPj6+mL16tU4cOAA1qxZgyZNmmDGjBlo0aIFfv3110o+c3JyEBYWhrZt24q/Uvr7+0ueIFqxYgUePHgg7icmJopzKFU3/1Jubi5Wr14tvu4QGBiI3bt3i+e3bt0qzgEBAIcOHYKXl5fs2A2JR66tIAiYNGkSpk2bBhcXl0pPDyQmJsLJyQkAEB4eLr5mVBX6V88iIyPx3nvv1Winf43g7NmzcHR0RExMDN577z1YW1tj5MiRmD9/PmJiYmBhYYHY2Fj06dNH8lrJ4sWL4e7uLu4nJSWJ5Sz/K3ZV6HQ67NixA97e3pJfsWfNmiXOzaIvp5OTk+wyAkCbNm2wbds2UePUqVPifB0AsH//fvj5+cnODQAYMWIEgoODq3yKLykpCSEhIXj++efh4eEhzmsEAMOHD5fUw+nTp2Fvby/5/LFjxxAUFISJEyciLy+v0pMggwcPxpAhQ5Cbm4uioiK8+uqrCAgIkJTTzc0NAODn5yd5IkvPjRs30Lx5c/Tp0wcqlcqg3JRzHQHIzjlDruXAgQPxj3/8A/Hx8Zg4cSI6dOiA6Oho5ObmIi8vD0OHDkXfvn3x1ltvwd7eHh9++CFOnDiBzMxMZGZm4sSJE/jwww/h4OAgvorTunVr/Pjjj5XKV1xcjEGDBsHb29ugOgKAkSNHIjQ0FOvWrcNTTz2FqKgodO7cGefOnUNycjIiIiLE+b3k1KcgCGjdujWCg4NhbW1dqbz79u0Tn3bTc/36dfTs2RN9+/ZFRkZGpTwypE8QBAH9+/fH4MGDYW9vL3kCBijLOVdXV9nXB5DfH+r57bff4OXlhQULFohPBpaPx1DN4uJizJw5E/7+/oiPjwdQ+akrufe18qxYsQIeHh7ikzoVNQ3pEwRBwPz587Fs2TK4u7tj3759El8nTpyAvb09Ro8ejYiICJw7dw6pqal49tlnxdc1AYhPMuk15dxf5LaL8si5Rozy4YEihmEYhqlHFBQUYPLkyeJEl1qtFlqtFiqVCubm5pgyZYo40fMXX3xR42SmmZmZeOedd5CQkABHR0eoVCo4Ozvj9OnTCA0NhZubGzw8PGBpaSn5Evrjjz/Cy8ur0usMWq0Wr776KkpKSvDyyy+LcyVV5MGDBwgNDRX/+Jw0aRJWrVpVbTk/+OAD9O/f/5FftsrzzjvvYP369dWef/311/H000/Ljt2QeOTaRkREoHv37uJWsQ7ee+89REREAADmz5+Pd955p9p4rl69ijFjxmDjxo345ptvqrW7e/cuvvrqK3H/4sWLeO6552BjYyNeRzMzM4SHh2PTpk0Ayr5QODg4wM3NDd7e3jA3N5fU7fLly8W5c3x9fcXXIGpiwIABNQ58LV++HD179pRdRgD47LPPsHXr1mo158yZg/Hjx8vODaCsvvr27QtBEODg4IDAwEAEBgbCwcEBKpUK/fr1w71799C3b1+sXLmyWs3Y2FhxELM8+fn5mDRpEpo1awa1Wi35snXp0iX4+/tDo9HAzMwMdnZ22Llzp0RTP4fV+PHjMW7cuCp9X79+HQEBAVCpVLJzU+51BGBQzsm9lhcuXECzZs0gCAJatmyJ69evY8CAAdBoNNBoNHB2dha/hC9cuBDu7u6SV20FQYC7u7tkoHzmzJniPDMVKS4uxoABAwyqI6CsD+3Tpw+sra0RFRWF+/fvY+rUqZLJqy9evCi7Pt955x3Jtn37dsn51157Dc8991ylz+l0OixYsECc6Lx8HhlyfcaMGSPZNmzYILGdMWMGoqKiDLo+htSnnszMTPTr1w9PPvlklYMQj6O5e/dueHt7Y86cOTAzM6ukKee+VpEzZ86gbdu2GD58eKVyGtIn+Pj4SOaZWrp0qcT2o48+QufOnZGVlYXOnTuL+eXj4yMZxP7hhx/w8ccfA4Ds+4vcdlGRR10jRvkIAGDsp5oYhmEYhpHy4MEDSkxMlCwHHRISInlFyBDy8vIoOTmZWrRoQdbW1lRQUEDffvstPXz4kPr06SO+FqGntLSUkpKSKDU1lXQ6Hbm7u1NISAjZ2NgQUdlrC+np6fTEE09U6S8nJ4eSkpIoIiLikWVLS0sjrVZLO3bsoOeee058te7vkJ+fT2q1miwsLGTFbkg8tRV7amoqmZubk5eX19+O91EAoJs3b5JOpyMnJyfxtRE9GRkZtHXrViosLKSePXtSUFBQnZYnISGBrKysJK9TPaqMtUX53NBz7tw5OnTokKS9hYWFUWBgIBER3b17l1QqlfiaU0W2bdtGlpaW1L179yrP//LLLxQXF0dz5swhFxcXSVkOHDhAhYWF1Llz52qX3r5y5QolJydTVFRUlefT09Np586dNGDAgFprl38Hudfyzp07kuXgd+/eTQ8fPqSwsLBKy8SnpaVJro+fn5/kfElJCeXn51fbR5aUlNCNGzfI1tb2b9dRamoq5efnU2BgoORV1romMTGR4uPjKSYmRvI6UW2Rl5dHarWatFotEcm7Pn+nP/z4448pLi6OPvnkE0k/+Liad+7coRdeeIHi4uLo0KFDVd7XEhMTKS0trcr7WlUUFRXR7NmzKS4ujjZu3Cjm3d/tE8pz6NAhsrCwoODgYCIiSklJocLCwr+VX/r7i5ubm6x2Ud0rZdVdI0b58EARwzAMwzCMiZOWlkZNmjSR9aXCEFum4QFAMncSwzAM0/DgyawZhmEYxoTIysqiefPmiftbt26lt99+mw4cOEBERHv27KH+/ftT3759xcms9SQkJNCyZctozpw5NGfOHFq2bBkdOXJEYlNYWEjFxcXi/qVLl+iNN96gUaNG0ZtvvklpaWmPVc6/Y9uzZ89Kk48mJSVJyvLNN99Qly5dqEmTJtS1a1f6/vvva/RZUfP69et0+/Ztcf+PP/6g559/np588kkaOXIkHTx4UPL5hw8fUnx8PJ09e7aSdkFBAa1du7ZG/0Q1xw2A4uLiaNWqVbR161bJNamKFi1aUEpKyiN91mR7/fp1ys3NrXS8uLiY9u/fL+6fOHGCYmJiqGnTpmRpaUmNGjWi1q1b01tvvSWZiP3OnTsUFxdHd+/eJSKi27dv06JFi2jevHl07tw50W758uUUExMjXrNvvvmGgoKCKDAwkF5//XVxcmdDfNfEtWvXaNy4ceJ+UVER/fe//6Vp06bR8OHDafjw4TRt2jT64YcfqKioiIiIfvrppyongq6Ohw8f0po1a2jcuHHUr18/io6Oppdffpl2794tW+Nx8+P27dv073//mwYPHkxhYWEUFhZGgwcPpsWLF4sTJRcWFtJrr71G3bp1o0WLFhER0fvvv0/W1tZkY2NDI0aMkNRnRkYGrVu3jn777TexTvTk5eWJ5TTkWlZF06ZNK+Xmzp07ae7cueJkxPv376d+/fpRz549KTY2VrQzpE84d+4cxcbGipO0Jycn05QpU2jcuHGiHzmUv0arV6+m0aNHi2XasGEDtWzZkpo2bUpz584VP2NIDsuNvSYq5rsc2//85z81TiJeE3l5eRQbG0tvvPEGLV++XDL5t5y2pseQe5tc9uzZQ/PmzaMpU6bQSy+9RP/5z39k95vl4yvfH+oXgqgIALp69aoszXv37sm6Z1S8lrXRz1SVHxXr6cMPPzS4nhgFYKRX3hiGYRiGeQz0kxoDwMqVK6HRaBASEgJbW1t88803sLGxwYQJEzBp0iRYWlrio48+QlZWFrp27QpBEODj44NOnTqhU6dO8PHxgSAI6Nq1qzg3UEREBH744QcAZUuDW1hYoE2bNuKkmlZWVvjzzz8NKqdc282bN1e5qdVqLF++XNwHyiY21s/nsmrVKlhaWuKVV17BZ599hldffRXW1tb48ssvZWt26tRJnNj1559/hkqlwoABAzBr1iwMHjwYZmZm4vnz58+LdadSqdCtWzekp6eL8ZRfblhuHfXr1w/3798HULacfGhoKARBgLOzM1QqFQIDA3Hz5k0MHjy4yk2lUqF3797iPgDZtunp6ejYsSNUKhXUajVGjRqFnJycKuPZvn07LC0tMWTIEIwcORJWVlaYOnUqZs2ahYCAAPj7+yMjIwOHDx9G48aNIQgC7O3tcfToUfj5+aFZs2bw9/eHpaUlEhMT8d5778HGxgZDhgyBm5sbFi5cCEdHR7z//vtYsGABnJ2d8fbbbxvk25B6T0lJQdOmTaHVahEREYFnnnkGzzzzDCIiIqDVahEQECAua25ra4sXXngBhw4dqlE/JSUFPj4+cHFxQZMmTSAIAqKjoxEaGgq1Wo1hw4ZJJuGuzfxISEiAvb09PD09MXr0aMycORMzZ87E6NGj4eXlBQcHBxw5cgTTpk2Dh4cHpk+fjpYtW+LFF1+Et7c31q1bh++++w4BAQF4+eWXAQAJCQmws7ODra0tLC0tERAQgNOnT1fKD0Ou5bJly6rc1Go15syZI+5/88030Gg0aN++PaytrREbGws7OztMmDAB48aNg7m5udhfye0Ttm3bBnNzczg4OECr1WLbtm1wdnZG79690bNnT6jVaslk6HKu0dKlS9GoUSM8/fTTcHd3x/vvvy/G/u6778LW1haff/65QTlsSOxy80iurSAIUKvV6N27N77//nsUFhZW+5mWLVuKE9VfvXoVvr6+aNy4MTp27AgHBwe4uLggNTVVdlsD5N/bioqKMGPGDPj7+6Njx4748ssvJWXT52ZWVhY6deoElUoFjUYDlUqFkJAQcc6nihNZy6mj7OxsDBs2DFqtFi4uLnjrrbck8xzJvQ+U1zTEri76mdqsJ8b04YEihmEYhqlHnDhxosZtw4YN4h91QUFB+OKLLwAAe/bsgVarxaeffipqxcbGomXLlhgyZAjCwsKQnJxcyV9ycjLCw8PFiUNtbW1x4cIFAGWDRtOmTZPYv/nmm+jSpYtB5ZRrqx94KT/RaMVNr2lpaYnLly8DAIKDg8V60PPtt98iKChItmajRo2QmpoKAAgNDcXChQslep988om4+sygQYMQHR2NW7duISUlBdHR0fDz88OVK1cA/PUFwZA6Kj+R95QpUxAUFCSW59q1awgJCcHkyZMhCAIiIiIqTUqrUqkwaNAgcV+vKcc2JiYGoaGhOHLkCHbu3ImQkBB06NABd+/eFePRr5rUrl07fPbZZ2K97NixA4GBgQCAoqIi9OrVC2PGjEHv3r0xYcIEPHjwAIsXL4aXlxcmTJggfm7s2LEYNGgQ/P398dNPPwEo+8KiVquxbt060W7jxo3iSmByfVc3OKjfli5dKtZ77969MXDgQMkKg3qys7MxcOBAREZGQhAEzJs3D8HBwRAEAU888QSWLl1a5QTG/fr1w6RJk6DT6QCUTcbcr18/AGWTOPv6+mLu3Ll1kh+hoaGYOHGi6Ls8Op0OEydOROfOndGkSRNxUOXSpUtQqVT4+eefJXXr4+Mj1tHYsWNRWlqKBw8eYMqUKXB0dBQn2dXnuyHXUhAEeHl5SSb49fX1hSAI8PT0hK+vL/z8/NCuXTtxwv5du3bB0tISH374oai5ZMkSdOnSBYD8PiEsLAxvvPEGAGD9+vWwt7fH66+/LtrNnj0bffr0ASC/7woMDMS3334LoGy1PI1Gg9WrV4uaq1evRkhIiOwcBiA7dkPyXa6tIAiIjY3FwIEDYWZmBkdHR/zzn//EqVOnKuVV+dx8/vnnER4eLg5q5uTkoHfv3hg+fLjstgbIv7fNnTsXrq6uWLx4Md544w00btwYEydOFO30fdezzz6LQYMGITs7GwUFBZg6dao4Qf/u3bvh6OhY4+T75dEPrLzyyito3rw5fvjhB6xatQo+Pj6Ijo4WB9XK95vZ2dk1bn/88UeNP5ZUdS3l9jOGaNZmPTGmDw8UMQzDMEw9oqaBjfKDKUDZFyP94AQAmJmZSf6QT0tLg5WVFaytratc/lvP0aNHYW1tDQBo1KgRzp07BwBwdXXF8ePHJbYXL16EtbW1QeWUa9u3b19ER0dXWvmsqhVXHB0dcfToUQCAi4tLleW0tLSUrdm4cWOcOHFC1NP/v7yelZWVeP7kyZPiOZ1Oh8mTJ8Pb2xuXLl0SvzgbWkf6MrZo0UJ8ckrPrl274Ofnh/Xr18PLywtr1qx5ZB3JtfXw8JAsZV9QUICnnnoK7dq1w507dyS/jGu1WqSlpUliNzMzE5+o2r9/P5ydnWFvb4+zZ88CKPsCrFKpJD4SExPh6elZZQ6Xf1rl8uXLYr3L9W3ogGNVX371nDx5EpaWlpLrc/ToUUyZMgV2dnawsLDAsGHDJEtTW1lZiYOtAFBYWAgzMzNxUOnnn38WB0VqOz+0Wq3Yfqvi3Llz0Gq1j6x3fd8BAPb29jh//rxE54MPPoC9vT0SEhLE/DDkWk6aNAnt2rUTc0RPxdwsP4Cr1yzfNs+dOwdHR0cA8vsEW1tb8ckV/dLf5fvHU6dOwdXVFYD8vqti7BYWFpLYU1JSYGdnJzuHDYndkHyXa1s+37KysrBo0SIEBgZCpVKhY8eO+OKLL/DgwQNRU2/btGnTSsu0HzhwAE2aNJHd1gD597aAgADxSU99PQcEBGDMmDHQ6XRibtra2kquR25uLszMzMRBq2+++QYtWrQAUJbvNW22trZQqVTw9vZGXFycqHnr1i106tQJkZGRKCgokPSb5Vfsq2qrmE9yrmVt9DMVNQ2pJ0b58BxFDMMwDFOPcHBwoFWrVlFaWlqlLTU1lbZu3SraOjo6ivPspKenU0lJiWROhCtXrpCDgwNZWFjUOH9LTk6OuAJUaGgobdmyhYiI/P396cSJExLb48ePk4ODg0HllGu7bds26tWrF3Xo0EHy+aro168fffbZZ0REFBERQT/++KPk/H//+18KCAiQrRkREUHr168nIqLg4GDau3ev5HxcXBx5enoSUdm8EOUnghYEgT777DN66qmnKCIigi5cuGBQ3OV1iMrmq/D395ecCwgIoPT0dHruuefojz/+oC+//JKGDBlC9+7dqzYmubbZ2dmSFZQsLCxo48aN5OvrSz169KCbN2+K5zw9Pen8+fPi/qVLl0in04krIHl5eVFubi4VFRWRpaUlERGZmZmRlZWVZEUvJycnunPnDrm5uYnzPKWkpFBpaalk3qczZ86Iq4TJ9e3u7k4bN24knU5X5ZaUlCRq2NnZ1TgXy+XLlyutahQSEkIrVqygjIwMWrVqFd26dYv69u0rroZkZ2dHOTk5on1+fj6VlJSQubk5ERG1adOGMjIy6iQ/3NzcKCEhodp4EhISyNXVlby9vcV5t44cOUKCIEg+d/jwYTHficrm3SrP7Nmz6fXXX6fIyEj6888/iYgMupYrV66kt99+m6Kiomj58uXVltfMzEwyd42FhQVZW1tL9h8+fEhE8vsEor/qUqVSkVarpcaNG4t2NjY2lJ2dTUTy27CVlRXl5eWJGs7OzpJyEpWtLiU3hw2J3ZB8N8RWj4uLC82cOZPOnTtHe/fupaCgIJo2bRq5u7uLNvr6LCgokBwnKmu3t27dMqityb233bhxQ7JyYkBAAO3du5f+/PNPGjVqFJWWlop1JZSboF2lUlFpaak4Z1Z4eLhYtsLCQho3bhwtXbq0ym369OlERHTr1i3JSmFOTk60a9cuysnJof79+0vmNLOxsaEPPviA9uzZU+Wmn3PJ0L5LTj9jiKYh9cQoH17ugmEYhmHqESEhIZSenl7tUrX3798n/P+CpQMHDqTx48fT6NGj6ZdffqGYmBiaPn06qVQqEgSBZsyYQZGRkaTVamn06NG0dOlS6tWrl7hM7oMHD2j37t30r3/9i4YPH05EZRPa9uvXj/Ly8mj48OE0ffp0SklJoZYtW9L58+fp448/pjlz5tDu3btll9OQmKZNm0Y9evSg559/nrZs2UJLly6t8jOLFi2iLl26UEREBHXo0IH+85//0N69e8VyHjp0iDZt2iRbc+HChfTkk09Seno6de3ald544w06cuSIqLdhwwZauXIlEREFBgbS0aNHqWXLlhIN/RfeAQMGGBw3EdGYMWPIwsKCiouLKS0tTbI0dGZmpvglytfXl/bv30/vvvsutW3bllatWlXtKlVybJs2bUonT56kZs2aicc0Gg398MMPNGzYMPrHP/4hHo+JiaEJEybQG2+8QRYWFvThhx/SgAEDxC8nx48fJz8/P9LpdJSamkq+vr5ERPT9999LvkBmZGSQk5MTPf/88xQTE0MDBw6k3bt308yZM+m1116jO3fukCAINH/+fBo6dKhBvv39/SkxMZEGDhxYZZ0IgiDW+4QJEygmJobeeust6tWrF7m6uhJR2STFu3fvpvfff59efvlleu+99yrpaLVaGjVqFI0aNYouXrwoTi7cp08f+te//kUrV64kCwsLmjNnDrVr105cgvvq1avk4uJCLVu2rPX8eO2112jixImUmJhYZTyrVq2iJUuWUFFREY0ZM4ZWr15NiYmJtGTJEnr99dcpOTmZVCoVffbZZ+IX4latWtGff/5Jbdq0kZTvtddeI51OJ/YdhlxLIqLBgwdTp06dKCYmhn799dcqJ2cOCAig5ORkcZnzGzduSJYyv3Tpkrhkt9w+Yc6cOZSSkiIOth08eJC8vb1FzatXr4q5KrcNBwYG0smTJ8U+4dq1axK75ORk8vX1pWHDhsnKYUNib9Gihex8DwkJkWVbXX/y5JNP0pNPPkkff/wxbdiwQTzeq1cv0mg09ODBAzp//rxk8ObKlSvk6OhI48aNk9XWiOTf2+Li4ujSpUtiP0NUNjAVFxdHPXr0oDFjxhARUdeuXentt9+mr7/+mszNzen111+npk2bkoODAxGVDfroB8vbtWtHTZo0odGjR1dZBydOnKB3332XvL296dy5c+L1IiobENqxYwdFRkbS4MGDxePt27cnorIBzKqws7MjALKvD5H8fqZdu3ayNQ2pJ6YBYJwHmRiGYRiGqYqNGzfim2++qfb83bt38dVXXwEoeyz8hRdeQKtWrTBx4kQUFhZi8eLFMDc3hyAI6N69O7KyslBQUIDJkyfD3NwcKpUKWq0WWq0WKpUK5ubmmDJlCgoKCkQff/75Jzp37lzp8XRPT09xfgJDymmIrZ78/HxMmjQJzZo1g1qtrvRaFQDcu3cPs2bNQlBQELRaLczNzeHj44MRI0bgyJEjlewfpXnx4kU899xzsLGxEWM2MzNDeHg4Nm3aJNotWLBAnAuiKqZMmQJBEAyKu+I8Qhs2bJDYzpgxA1FRUZU0/vjjD/j5+UGlUlVZR3JsZ86cKc4NUpHi4mIMGDBAfDWhuLgYM2fOhIeHBxwdHTFixAjcunVLtD98+DD27duHd955B+vXr6+2LK+//jqefvpplJaWYv78+fjHP/6BBQsWQKfTYf369WjSpAkcHR0xZswY5ObmGuR7//792LZtW7W+c3NzsXfvXnF/4cKFcHd3l7weIggC3N3dsWjRIgDS12seRVZWlth+VCoVfHx8JK82/fDDD/j444/rLD++//57hIaGQqPRiHms0WgQGhoq+dy3336LqVOn4rvvvgMAxMXF4cknn0RISAjeeecdlJaWAiibGHrkyJHVlnPhwoXw9fU16FqWR6fTYcGCBeKEueVzc+PGjdi3b1+1vj/44AO8+eab4r6cPuGzzz7D1q1bq9WcM2cOxo8fL/qXc43i4+Nx7Nixau0+/fRTfPLJJ7Jz2JDYDcl3ubaG5Ps777wj2bZv3y45/9prr+G5554DIK+t6csh5942fvx4jBs3rspyXb9+HQEBAVCpVLh06RL8/f2h0WhgZmYGOzs7cY4uoGzOo9mzZwMA5s+fj3feeafaeK9evYoxY8bg5ZdfFuf2q8iDBw8QGhoq9ptffPGFON9UVWRmZuKdd94x6FrK7WcM0TSknhjlIwDlfqpgGIZhGMbkKSgooOLiYsmvz0RlTxAlJiZSZmYmEZW9KhISEiI+YVSRW7duUWpqKul0OnJ3d5f8avu/4pdffqG4uDiaM2eO+NpKXWsCoJs3b5JOpyMnJycyMzOrFb9/h7y8PFKr1aTVaiudy83NpUuXLlHLli3FJxOqoyrbkpISys/PrzYPSkpK6MaNG9U+VfE45Ofnk1qtFl95rA+kpaVJ2kb5JwWuXLlC3t7e1T5pURUpKSlUWFhIgYGBklcV64Kq8qO4uJhu375NRFRv8rgmEhMTKT4+nmJiYvipBYVTU1uriYr3titXrlBycjJFRUVVaZ+enk47d+6k0aNHU35+Ph04cIAKCwupc+fOkldhH4d79+5Renq65Mm+8uTk5FBSUlK1TxHVFrXdz9R2PTGmCw8UMQzDMAzDMAzDMAzDMERExJNZMwzDMIwJsXnzZlq7dm2t2h49epT2798vS1OurbHLuWLFCpo3b16taRoSj1zfdVFHr7/+Oo0bN06WplxbQ8pZ25py69IQ34Zo1kVuytWsi/yQG3td5FFd1Lsh5ZTrvy7KOXr0aOrZs+cj7Yxd73XRdxmzP8zIyJBMgl0dhrTfutCUa1sXuWmIpiExMQrAqC++MQzDMAxjEC1atBDnPagtW/2yx3KQa2vscvbs2RN+fn61pmlIPHJ910UdxcTEoEePHrI05doaUs7a1pRbl4b4NkSzLnJTrmZd5Ifc2Osij+qi3g0pp1z/dVHOOXPmYMyYMY+0M3a910XfZcz+UG67rIt7YF1o1kVuGqJpSEyM6cOvnjEMwzBMAyc9PZ2Ki4tlzUFjiG1tUxe+jRkPoxw4jxim/nHkyBHKz89/5DxBhrTfutA0lf7DVMrJ1A48UMQwDMMwDMMwDMMwDMMQEVHdLsHAMAzDMMzfJi0tjS5evEju7u7UqlWrR9r37NmTYmNjH/mr39ixY2n+/Pnk4eFR6VxpaSmp1Wpx//Dhw1RYWEhhYWGS1ZN0Oh2pVJWnPNTpdHT9+nXy9vaulZgeBQDau3evqBkVFfW3Vnm6f/8+/fDDD3T16lXy8fGhYcOGUePGjSU2e/bsofj4eMrIyCCVSkVNmzalAQMGULNmzSR2qamplez69OlTaZUxAHT58mVq0qQJaTQaKioqok2bNlFhYSH1799fXH1m69atlJCQQFFRUdSlSxfas2cPLVmyhHQ6HT399NM0ceJEUfP27du0Zs0aOnjwoGSVofDwcBozZgw5OzuLtnKvpRz/P/30E/Xr14+srKweq/6bNm1Kv//+u6Qur1+/TlqtVqyHP/74g1auXCleo5deeonCwsKIiOjEiROUmJhI3bt3p6ZNm9KZM2fo008/JZ1OR4MHD652lSQ9ctpQcXExXb58mVxcXCrlBhFRQkJCpXoPCwujTp06iTZyr7keObkkN/Zz587RoUOHKCwsjAIDAyk5OZmWLVtGhYWFNHLkSHFOnbqod0Py4+HDh7R+/fpKcQ8aNIh69epVyf769etkZ2dH1tbWla7XwYMHqVu3bpLjj9N3yO1j9Vy7do3mzp1La9asMaj9GmJbHffu3aMtW7ZQTEyMbNtGjRoZ1H4NaW9y2oWex72/VEdmZiYdPnxY4js0NJTc3NxEm9u3b/+tlb5quq/K8W9o3yW3jgxpF3Lv/4zCMd5bbwzDMAzDVGTKlCnIyckBAOTn52PIkCFQqVQQBAEqlQo9evQQz2/evLnKTa1WY/ny5eL+iRMnqtzMzMywadMmcR8A0tPT0aVLF6jVanTr1g13795FdHQ0BEGAIAho3rw50tPTkZ2djWHDhkGr1cLFxQVvvfUWSkpKxDgyMzPFuQzkxlRUVIQZM2bA398fHTt2xJdffimpm/Ka/fr1w/379wEAd+7cQWhoKARBgLOzM1QqFQIDA3Hz5k3ZmoMHD8YPP/wAADh9+jScnJzg7OyM0NBQuLq6ws3NDWfPngUAZGVloVOnTlCpVNBoNFCpVAgJCYGbmxvUajVmzJgBAMjNzcXQoUPFulOpVKKNtbU1li9fLpYjOTkZPj4+UKlUCAgIQGpqKkJCQtCoUSNYWVnByckJFy5cwMqVK6HRaBASEgJbW1t88803sLGxwYQJEzBp0iRYWlrio48+AgAkJCTA3t4enp6eGD16NGbOnImZM2di9OjR8PLygoODA44cOWLQtZTrXxAE2Nra4oUXXsChQ4eqzfdly5ZVuanVasyZM0fcB4BOnTphy5YtAICff/4ZKpUKAwYMwKxZszB48GCYmZlhy5Yt+Omnn6BWq+Ho6Ahra2vs3LkTdnZ26N27N6KioqBWq/Htt98a1IYWLVqE/Px8AEBJSQmmT58Oc3NzMQfGjh2LoqIiMT+6du0KQRDg4+ODTp06oVOnTvDx8YEgCOjatSuysrJkX3NDcklu7Nu2bYO5uTkcHByg1Wqxbds2ODs7o3fv3ujZsyfUajV2795dZ/UuNz9SUlLg4+MDFxcXNGnSBIIgIDo6GqGhoVCr1Rg2bBiKi4sBlPVdHTt2hEqlglqtxqhRo8R+p3wey+07DMmPR3H8+HGoVCqD2q8htnJ8y0FvK/f6APJzTm67AGBQn/Tpp5+iV69eGDZsGHbt2iUp261bt+Dn54fc3Fw8//zzUKvV0Gg0cHFxgYuLCzQaDdRqNUaOHIm8vDwAgEqlQs+ePfHtt9+ioKCg2rgNua/K9W9IG5JbR3LbBSD//s80DHigiGEYhmHqESqVSvxjec6cOfDy8sKePXuQl5eH+Ph4+Pv7Y/bs2QAgfmHU/xFX1VZ+QOZR5wFg1KhRCA8Pxy+//IJnn30W4eHhePLJJ3H9+nVcuXIFXbp0wUsvvYRXXnkFzZs3xw8//IBVq1bBx8cH0dHRKCwsBFD2x6cgCAbFNHfuXLi6umLx4sV444030LhxY0ycOFGsm/KagiCImlOmTEFQUBBSU1MBANeuXUNISAgmT54sW9Pe3h7nzp0DUDYINWLECDGWoqIijB8/HpGRkQCAZ599FoMGDUJ2djYKCgowdepUxMTEAAB2794NR0dHfPTRR5g4cSK6dOmCU6dOISUlBUOHDsXMmTORl5eHL7/8ElZWVuIf/QMHDsSAAQNw8uRJvPrqq2jZsiUGDhyIoqIiFBQU4KmnnsLIkSMRFBSEL774AgCwZ88eaLVafPrpp2I8sbGxaNmyJQAgNDQUEydOhE6nq5RnOp0OEydOROfOnQ26lnL9C4KAefPmITg4GIIg4IknnsDSpUtx+/ZtSTkEQYCXlxd8fX0lmyAI8PT0hK+vrzjRaqNGjcRrHBoaioULF0q0PvnkEwQHB6N9+/Z4//33AQDr16+HnZ0d5s2bJ9otWbIE7dq1E/3LaUPlc3jx4sWwt7fHmjVrcObMGaxbtw4uLi5YtGgRAGDIkCEICwtDcnJypXpPTk5GeHg4hg4dKvuaA5CdS3JjDwsLwxtvvCHa2dvb4/XXXxftZs+ejT59+tRpvcvJj379+mHSpEliDi9cuBD9+vUDAFy4cAG+vr6YO3cugLLJnUNDQ3HkyBHs3LkTISEh6NChA+7evQvgrzyW23cYkh/VDSjpt6VLl0KlUhnUfuXaZmdn17j98ccfYv8u11bu9QEg+7rLbRcAZPdJy5Ytg5WVFV566SWMHDkS5ubmWLBggairHwQZP348mjVrhu3bt0sGU0pKSvD777+jefPmmDBhgnjN+/btC3Nzc9jb22Pq1Kk4duxYpTIbcl+V69+QNiS3juS2C0D+/Z9pGPBAEcMwDMPUI8p/iWnVqhW+++47yfnNmzejefPmAIC+ffsiOjpatNej0Whw5swZcb9t27aIjo7GuXPncPnyZVy+fBlpaWnQaDTYuXOneAwA3N3dcfDgQQBlv7YLgiD5lXb37t1o2rQpvL29ERcXJx6/desWOnXqhMjISBQUFEh+pZQbU0BAgPjkAlD2NEFAQADGjBkDnU5XrWaLFi0q/aq/a9cu+Pn5yda0tLTExYsXxTpISkqS6J0/fx6NGzcGANja2uL06dPiudzcXJiZmSE7OxsA8M0336BFixZwcnLC0aNHRbu7d+9Cq9WKv1wvX75c/KPf2dlZ/DKSm5sLQRDwxx9/iJ89cOAAvL29YWlpiStXrojHzczMcOrUKXE/LS0NVlZWAACtVisOflXFuXPnoNVqDbqWcv2Xvz5Hjx7FlClTYGdnBwsLCwwbNgw7duwAAEyaNAnt2rUTn9bSUzGHAaBx48biL/QuLi7i//VcvHgRVlZWaNSoEdLS0gCUDYiZmZnh5MmTot2lS5dgbW0NQH4bKh9PcHAwPv/8c4n9unXr8MQTTwAArK2tK+VPeY4ePQpra2vZ1xyA7FySG7utrS1SUlIAAKWlpdBoNJIynzp1Cq6urgDqpt7l5oeVlZX4VBUAFBYWwszMTByw+Pnnn+Hr6wsA8PDwwOHDh0Vb/WBbu3btcOfOHTGP5fYdgGH5IWdAyZD2a0hb0w9mVrWVH7CQayv3+gCQfd3ltgsAsvukoKAgcbAdKGszzs7OeOuttwD8NVBkZ2eHAwcOVOs7Pj4ednZ2Yh1lZWXh1q1bWLJkCYKCgqBSqdC+fXusWLFC7OcNua/K9W9IG5JbR3LbBSD//s80DCq/0MgwDMMwjFERBIGIyuYzaNOmjeRc27Zt6dq1a0REtG3bNurVqxd16NCBtm7dWq1eQkICBQQE0JAhQ+ju3bvk4+NDvr6+RETk4eFBPj4+4lwb9+7dI09PTyIicnBwICsrK8k8HAEBAZSRkUG3bt2SHHdycqJdu3ZRTk4O9e/fn/Lz8w2O6caNG5L5igICAmjv3r30559/0qhRo6i0tLRKzXv37pG/v7/kXEBAAKWnp8vWbNOmDe3Zs4eIyuaNuHLlikTvypUrZGlpSUREFhYWom8iIpVKRaWlpVRSUkJEROHh4XT58mUqKSmRzB1jbW1NJSUllJeXR0REkZGRlJycTEREubm55ODgQEREjRo1okaNGpG7u7v42SZNmlBWVhY5OjqKZUtPT6eSkhK6evWqpJx6HTc3N0pISKDqSEhIIFdXV4OupSH+9YSEhNCKFSsoIyODVq1aRbdu3aK+ffuSn58frVy5kt5++22Kioqi5cuXV1tWIqKIiAhav349EREFBwfT3r17Jefj4uLI09OTbGxs6M6dO0RUNtdUSUmJuE9EdOfOHXGeDrltiOivfLt69SqFh4dLzoWHh1NaWhoRleXHgwcPqtXJyckhCwsL2deciGTnkiGx6+NRqVSk1Wol8yzZ2NhQdnY2EdVNvZenpvyws7OjnJwc0TY/P59KSkrI3NyciMrabUZGBhERZWdnk729vWhrYWFBGzduJF9fX+rRowfdvHlTPCen7yCSnx/u7u60ceNG0ul0VW5JSUlEZFj7kWtrY2NDH3zwAe3Zs6fK7YsvvhA/Y4itnOuj15Rz3eW2CyKS3SelpaVJ2mJ4eLgYx5w5c8TjOp1OzJmqMDc3J51OJznm5ORE06dPpzNnzlB8fDy1a9eOZs2aRe7u7hQTE2PQfVWuf0PakNw6MqRdyL3/Mw0EY49UMQzDMAzzF4IgYNKkSZg2bRpcXFwkv9wCQGJiIpycnCTHjh07hqCgIEycOBF5eXlVPo0BAL/99hu8vLywYMEC8SmCinbe3t6SXx9nzZqFO3fuiPvHjx+Hk5MTWrRogV9//bWSj5ycHISFhaFt27aSX7HlxOTn51dpjgkAuHHjBpo3b44+ffpINPv374/BgwfD3t5e8tQQABw6dAiurq6yNbdu3QoHBwfExsYiNjYWvr6+WL16NQ4cOIA1a9agSZMm4txDgwcPxpAhQ5Cbm4uioiK8+uqrCAgIkPh2c3NDnz59JI/pL168GO7u7uJ+UlKSeC39/f0lT5OsWLECDx48kNSRm5sbXnrpJTRr1gzvv/8+OnXqhNGjRyMwMBDbtm3D9u3b0bp1a4wbNw5A2VMmFhYWeOWVV7B582YcOnQIhw4dwubNm/HKK6/A0tISn376qUHXUq7/8q9qVUVKSorkVafr16+jZ8+e6Nu3LzIyMqrMzbNnz8LR0RExMTF47733YG1tjZEjR2L+/PmIiYmBhYUFYmNjMXLkSISGhmLdunV46qmnEBUVhc6dO+PcuXNITk5GRESE+IqLnke1IUEQMH/+fCxbtgzu7u7Yt2+f5PMnTpyAvb09AODFF1+Ej48PNm7cKD59AJS99rNx40b4+vpi6tSpsq85ANm5JDf2Nm3aYNu2beLnT506Jc71AwD79+8Xn6qpi3qXmx+jR49GREQEzp07h9TUVDz77LMIDg4W7fbu3YsmTZoAAFq3bo0ff/yxklZxcTEGDRoEb29v8WkZOX1HeR6VH0899ZT4FEtVHD9+HIIgGNR+5dp2795dfO2xJt8AZNsa0n7lXne57QKA7D6pSZMm2L9/fyW7M2fOwNXVFTExMVCpVBgxYgSCg4OrfKIpKSkJISEheP755wHUnJu5ublYvXo1wsPDxWNy7qty/RvShuTWkdx2Aci//zMNAx4oYhiGYZh6REREBLp37y5uq1atkpx/7733EBERUelz+fn5mDRpEpo1awa1Wl3lQBFQ9ih+v3798OSTT1b5B+2AAQNqnCB1+fLl6NmzJ15++eVKX7b1PHjwAKGhoeIfn3JjGj9+vPglqSLXr19HQECAqDlmzBjJtmHDBon9jBkzEBUVZZDmjz/+CC8vr0qvkGi1Wrz66qvi3BKXLl2Cv78/NBoNzMzMYGdnh507d4q6sbGxmD17NhITE+Hg4AA3Nzd4e3vD3Nwc69evl9Slfm6jSZMmVaqX8nzwwQfo378/cnNz8cILL6BVq1aYOHEiCgsLsXjxYpibm0MQBHTv3l3yJef7779HaGgoNBqNGI9Go0FoaKhYZ4ZcS7n+y7+6IhedTocFCxaIkzRXlcMXL17Ec889BxsbGzEeMzMzhIeHY9OmTQDKcrxPnz6wtrZGVFQU7t+/j6lTp4pfgJs1aya+ZliemtqQj4+PZA6lpUuXSj770UcfoXPnzgDKXu2YPHmyONm1VquFVquFSqWCubk5pkyZgoKCAtnXHIDsXJIb+2effYatW7dW63vOnDkYP358ndW73PzIyspC586dRQ0fHx/Jl+0ffvgBH3/8MQBg5syZ4jxiFSkuLsaAAQOgUqlk9x0VqSk/9u/fLxl4q0hubi727t1rUPuVa/vFF1+IE75XRWZmJt555x0AkG1rSPuVe93ltgtAfp80fPhwvPrqq1XanT59Wpyg/O7du+jbty8EQYCDgwMCAwMRGBgIBwcHcYLze/fuAZCfmxXroKb7qlz/hrQhuXUkt10A8u//TMNAAABjP9XEMAzDMIw8UlNTydzcnLy8vKo8/8svv1BcXBzNmTOHXFxcqtX5+OOPKS4ujj755JNqtaoiISGBrKysyNPTk9LT0+mJJ56o0i4nJ4eSkpIoIiLikZr6mEpLSyk5ObnapcvT09Np586dNHr06Edq5uXlkVqtpqysLIM0S0tLKSkpiVJTU0mn05G7uzuFhISQjY2N5HP5+fkUHx9PRUVF1Llz52qXU87IyKCtW7dSYWEh9ezZk4KCgh5Z9qpIS0sjrVYreTWpPAUFBVRcXFypnHqKi4vp9u3bRFT2ikL5JY7v3bv3t69lRf9Xrlwhb29vySt6cklMTKT4+HiKiYmRvDJRHgB08+ZN0ul0leKpjtTUVMrPz6fAwEDSaDTV2sltQ+U5dOgQWVhYUHBwsHjswYMHlJiYKFkKOyQkRPIKWU1UvOZ/J5fkxv4oHqfeL126RA8fPpT4NjQ/UlJSqLCwsMbyl5SUUH5+frX1W1JSQjdu3Hjkkvb6vkOr1VZ5/nHy41E8qv0+ru3j8nfar57qck5Ou5DbJ9nb21NiYiKNHTu2SrvTp0/TTz/9RHPnziUiouTkZDp48KDEd1hYGAUGBoqf+frrr+m5554TX4MzhEfdV+X4r4qq6lJuHXXp0qVW2gXRX/f/8q9zM8qFB4oYhmEYxgQA8Lf+aGcYQ+GcY2oDY+dRWloaNWnS5G8NkCkdY18juZhKORlGCfBk1gzDMAxjAlhYWNC5c+cqHb99+zb9+9//psGDB1NYWBiFhYXR4MGDafHixXTr1i3R7s6dOxQXF0d3794VP7do0SKaN29elboVadq0KaWkpMgub1ZWFs2bN8/gcj6u7/T0dJo7dy49//zz9Nprr4mTRFcEAMXFxdGqVato69atVFxc/Fi+z507R7GxsaKf5ORkmjJlCo0bN06cFNtQzYcPH1J8fDydPXu20rmCggJau3YtEZU9WbJu3Tr67bffqKioSGKXl5cnqfeauHTpEvXs2bPa89XlnFz/cuto586dNHfuXPHY/v37qV+/ftSzZ0+KjY2V6BtiW75MsbGx9MYbb9Dy5cslk8MS/e/akJ6KbUNuOcsjN48N0SxPTbkht63p+bt919mzZ+nFF1+k4OBgcnd3J3d3dwoODqYXX3yxyrZSkRYtWhjUd127do3GjRsn7v+dtq6nfH0aEk9d5HtVVHeN/o6mnury/XHsqitnxYmoyx8vPwF4ddy7d0/sX+tSsyJV3QsMyTe594yaqJjv5TH0fskoC36iiGEYhmHqEf/617+qPL5s2TIaOXIkOTo6EhHRhx9+SEeOHKGoqCiysrKi3r17k6urKxGV/cG9e/duys/Pp99//510Oh1FRkbSgwcPyM7Ojnbu3EnDhg0jjUZDOp2O0tPTKT4+ntq3b08ff/xxteWaOXMmubm5ERHRK6+8UmMcJ06coPbt21Npaanscv7555+yfVtZWdGVK1fI2dmZzp49S+Hh4eTs7EzBwcF06tQpunr1Kh08eJBmz55N69evp8aNG9Pdu3epf//+lJCQQE5OTnTnzh1q3rw57d+/X1zVSY7v7du308CBA8na2pry8/Np06ZNFBMTQ23btiWdTkf79u2jHTt20OnTp2VrXrhwgSIjI+nq1askCAJ17dqVvv/+e/G1o6ysLPLw8KBDhw5RZGQk6XQ6Ki4uJk9PT/r555/F1w/0dhVXiKvpGv3zn/+s8nx1OSfH/6+//iqrjtLT02ns2LHUpk0bunDhAn3yySc0bdo0Gjp0KOl0Olq3bh19++23NHToUFq3bp0s27fffpvi4+PJwcGBrl27Rt26daN79+5R8+bN6dKlS6TRaOjQoUPk5+dXJ21Ibr23aNFCdjn79+8vK48jIiJka8opY2lpqey29tVXXxmUR3Lq/datWzRo0CBq3749RUVFSex27txJiYmJtHnzZoqKiqKnn366Sv+bN2+mnj17iq9qbdy4UXbsctt6TQOu5TW3bt0qO566yHe595ft27fXSh5VrE+5dnLL+c4779CECRNoy5YtZGtrS5MmTaK5c+eSWq0W61VOf1je94MHD2pdU+59tXnz5rLzTe49w5Byyu1nnJ2da9RklAEPFDEMwzBMPUKlUlHbtm3Jzs5Ocnzfvn3UoUMHatSoEQmCQHv27KHOnTtT27ZtaeXKlZUexwdAkydPppMnT5K1tTX5+vrShx9+SJ9//jktW7aM+vbtS6tWrSIionHjxtG9e/do06ZNpFKpyNPTs9JrGleuXCEPDw8yMzMjQRDo559/rjGO5ORkGj58OJWWlsou5+HDh2X5Tk1NJZVKRZmZmeTi4kKDBg0inU5HGzduFL+4P//885Sbm0u//vqraPfiiy/Svn37aOvWreTn50fXr1+nQYMGUceOHenzzz+X7Ts8PJx69uxJ77//Pn3//ff04osv0pQpU2j+/PlERDRnzhxKTEykXbt2ydYcPHgwFRcX01dffUX379+nV199lc6ePUt79+4lb29v8Y/+nj17UpMmTWj16tWUl5dHs2bNov/+97+0c+dOCg4Olnw5qO7LiZ4bN27QkiVLCIDsnOvTp48s/6GhobLq6NatWzR27Fh65ZVXaPfu3fTUU0/R/Pnzadq0aURE9J///Ic2bdpE8fHxFBwcLMv2zz//FK/5yJEjKS0tjX777Tdq3Lgx5ebm0uDBg8nZ2Zm+++67OmlDJ0+erLHe9W0DgOxyls/3R+WxHM3OnTvLyo3S0lKD2lpt9135+fk0cODAap80eeedd2jjxo108uRJUqlU1K1bt0qDF2vXrqUBAwaI5Ro8eHCNsaemptL06dOptLRUdlv/xz/+Ias+W7VqJTueush3ufeXvXv3ytaUm+/Hjh2TZafPOTnlbN26NW3fvp3mz59P9+/fp/fff59atWpFGzduJHNzc8rKyiJ3d3e6f/9+jb5PnjxJERERVFpaSv/85z9rXVPufdXNzU1Wvu3YsUP2PWPTpk01lrN8vsvtZz777LMaNRmF8L+bN5thGIZhmEfxwQcfwM/PD7t375Ycr2olFa1Wi3PnzlWrde7cOWi1Wtjb2+Ps2bMAgKKiIqhUKskSuImJifD09ARQtvpWu3btRPvq/OtXYSm/Oph+0x/Xr6Qit5xyfev961emqWqJ5KSkJLi7u0vsWrRogc2bN0vsdu3aBT8/P4N829raIiUlBQDE5ZDLr8R06tQpuLq6GqTp4uKCkydPivs6nQ6TJ0+Gt7c3Ll26hMzMTKhUKtjb2+P8+fOSz37wwQewt7dHQkKCaKevIw8PD8lqXeU3Dw8PqFQqg3JOrn+5ddSoUSOkpqaKx83MzHDixAlx/9y5c3B0dAQA2bblr3nTpk2xY8cOSXkPHDggLqleF21IbtswpJxy81iuptzcqOi7prZWF32XVqtFcnJytXbJycnQarUAgPXr18PLywtr1qyp0X9N16f8dQLkt3W59WlIPHWR73KvkaG5KTff5d4z5JbT29sbcXFx4v6tW7fQqVMnREZGoqCgQOyP9NrVbeV914Wm3HuB3HwD5N8zDMl3uf0M0zDgOYoYhmEYph4xe/Zs2rBhA02ZMoVee+21GucEcHNzo4SEhGrPJyQkkKurKxUVFZGlpSUREZmZmZGVlZVklS79Y+VERCtXrqS3336boqKiaPny5dVqOzg40KpVqygtLa3SlpqaSlu3bjW4nHJ9ExEJgiA+iaBSqahx48aS83Z2dnTv3j3Rlqhszgh/f3+JXUBAAKWnpxvku7ymSqUirVYr8W9jY0PZ2dkGaT58+FDya7MgCPTZZ5/RU089RREREXThwgXxXEFBgeSzs2fPptdff50iIyMlr+/5+PjQ0qVLq7xGaWlp9Ouvv4qfl5tzhviXU0dmZmaSeY4sLCzI2tpasv/w4UMiIoNs9b4LCgoqrRTn6ekpzoFTF23IkLYht5zlbWvKY7macnNDryenrdVF3+Xr6yspS0V+/fVXcbWm5557jv744w/68ssvaciQIWL7r4i7uztt3LiRdDpdlVtSUpLEXk4ey61PQ+Kpi3w35BrJ1ZSb74a0C7nl1OeyHicnJ9q1axfl5ORQ//79KT8/n4jKrtMHH3xAe/bsqXL74osv6lTT0HsbUc35RiT/nvG4+f6ofoZRPjz9P8MwDMPUMzp27EiJiYn00ksvUYcOHejbb7+tcqWX1157jSZOnEiJiYnUq1evSvN8rFq1ipYsWUKffPIJpaamkq+vLxGRZB4DorLJict/6R08eDB16tSJYmJi6Ndff61y0tSQkBBKT0+vdknd+/fvE/7/7Xa55ZTrm6js9ZTmzZuTIAiUm5tLJ0+epDZt2ojnL168SG5ubnTlyhUaM2YMWVhYUHFxMaWlpUmWE87MzJS8kiLHt6+vL6WkpIh/RB88eJC8vb3F81evXhXrV65mYGAgHT16lFq2bCk5rv9SMWDAACIiatWqFf3555+SWPV1rNPpaPjw4eKxkJAQSkxMpGeeeaZKn4IgiNdIbs7J9S+3jhwdHSk5OZlatGhBRGWv6JRf9vvSpUviMtMBAQGybFNSUqhXr16k0WjowYMHdP78eclyzleuXBHnOKmLNmRI25BbTiKSlcc3b96UpWlIbshta0S133c5OzvTiBEjaO/evVXOZbR9+3b67rvvRF1fX1/av38/vfvuu9S2bVtatWpVJf/62AcOHPjI2OXmcfv27WXV57x582THUxf5bsg1kqspN98NaRdyy+nt7U3nzp2TvG5oY2NDO3bsoMjISPE1Q/3cYREREVX6trOzE33XhSaRvHuBIfcWufcMQ/KdSF4/wzQMeKCIYRiGYeoh1tbW9PXXX9P3339PvXv3rnJCypdeeomcnJxo6dKltGLFCtFGrVZTSEgIffXVV/TMM8/QrVu36ObNm+LnoqOjJTq//PILderUSXLM09OTdu3aRQsXLqTg4GDJH5JERJMnT6a8vLxqy+/t7S3+ISy3nHJ9E1GlP7IDAgIk+4cOHaLBgwdLnioYOHCg+Guwnp9++onatWtnkO8pU6ZIrkf5L1BERNu2bZNMbitHc/DgwbR+/XoaNWpUpXPLly8nnU5HK1eupJiYGNq3bx9Nnjy5kt3MmTMJAK1cuZKIiObNm1cp3vIEBQVRWlqauC8n5+T6l1tH0dHRZG9vLx63tbWV2B09elTMjddff12WbcV5QMo/hUFEtGXLFnryySeJqG7akNy2cfnyZdnlHD16tHi8pjwOCwuTpfnWW2/Jzg25ba28z9rqu4jK2s/HH39M//nPfygzM5OIyp5ICgsLo71791aKWaVS0bvvvkt9+vShmJiYSv5nzJhR4/UJCAiguLg4IpLf1v/1r3/Jqk8fHx/Z8dRFvpe3qekazZ07V7am3Hy3tbWVfc+QW87IyEiKjY2l/v37V/rc77//Tn369CEiohEjRohPX1WFm5ubGHNdaOp51L3AkHuL3HuGIfkut59hGgY8mTXDMAzD1HOuX79OiYmJ1Lt3b2rUqFGVNsXFxXT79m0iKntU3szMTLZ+fn4+qdVqsrCwqPJ8YmIixcfHU0xMjOSLy+NgaDlr03dV5OXlkVqtJq1W+z/xXdfx1BZyck5p1GUbqmtqymNjUtd916PIzc2lS5cuUcuWLcnc3LzWdJWEqbT1qsp57949Sk9Plzz1Up6cnBxKSkqq9qmfqqgLzaowlXtBeeprP8PUDTxQxDAMwzAMwzAMwzAMwxAREU9mzTAMwzD1lLVr19LmzZslxzZv3kxr1659bNv9+/fT0aNHJceOHj1K+/fvr6Qp19YQzboo57x582jFihWSYytWrKi0BLWpxCNXU27chtgaknO1rWnseIzZhgwpZ21rmkq9K62cphJPXeRmXfSHV69epYyMDMmxjIwMunr16mPZ1ZVmXfQJxryWjIL53y2wxjAMwzCMIQiCgJYtW0qOtWjRQlzK9nFsq7ILDAyUrVmV7d/V/Lvl9PX1Re/evSXHevbsWWkZX1OJR66m3LgNsTUk52pb09jx1Lc2ZEg5/46mqdS70sppKvH8r3Lzf9XH1sd7YF30Cf+ra8koF371jGEYhmEaEFeuXCEzMzPy8PAQj6Wnp1NxcXGl1Wjk2hqiWRflrG1NU4mHMQ510Ybqyj/D/C8xZh+7b98+srKyoo4dO4rHjhw5Qvn5+ZL5hOTa1ZWmqbRfUyknU3fwQBHDMAzDMAzDMAzDMAxDRESaR5swDMMwDFNfKC4upoyMDPL29haP5eXlUWJiImVkZJBKpaKmTZtS+/btSRAEIiK6ffs2OTk5/W2/clYjevfdd8Wlrw3RzsjIICsrq8cu5/379+mHH36gq1evko+PDw0bNowaN278t8uZlZVFhYWFkvp+FBWvUUlJCZ05c0ayFHZQUFCl+iwtLaUrV66Qr68vqVQqKiwspM2bN5NOp6MePXqQq6vr3447LS2NLl68SO7u7pWWXi4tLSW1Wi3uHz58mAoLCyksLEz2SlT6XOzWrRsREWVmZtLhw4clsYeGhpKbm5vkc6mpqRQfHy/J4T59+lRaEvzmzZt0+vRpCgkJocaNG1NWVhZ9/fXXpNPpKDo6mlq3bl2pTCUlJRQXFyfWUY8ePSRxVqS4uJguX75MLi4uNdYlkWH5YWguVdXW5eSS3Dx6nLrUU1MeVWTs2LE0f/58yZMJtUXFfJMTU2JiIoWEhPwtv9XFpNPpSKWqPAWsTqej69evP/LaV4yH6NFt4+/GU1O+10afUBX6OX308Tg6OlayeZz+sC756quvaPDgwY/sEwCQTqerso+pzfb7uNfd0P6Q6PHu64wCMOqLbwzDMAzDGMTx48fFOQJKS0sxY8YMWFlZQaVSQaVSQRAECIIAHx8f/PLLLwAAlUqFnj174ttvv0VBQUGN+hs2bEBhYaG4/8knn8Db2xsqlQqOjo549913AQDZ2dmVtvv378PMzAyHDx8WjxkSkyHlHDx4MH744QcAwOnTp+Hk5ARnZ2eEhobC1dUVbm5uOHv2rOxyPnjwAM8//zy8vb0RExODwsJCvPjiixAEASqVCt26dTM4ntLSUrzxxhuws7MTr4t+s7Ozw5tvvonS0lIAwIkTJ+Du7g6VSoVWrVrh6tWraNWqFRo1agRra2vY29sjISFBdtwAMGXKFOTk5AAA8vPzMWTIEDFHVCoVevTogZycHKSnp6NLly5Qq9Xo1q0b7t69i+joaLGszZs3R3p6ukGx5+bm4vnnn4darYZGo4GLiwtcXFyg0WigVqsxcuRI5OXlITc3F0OHDhV9qVQquLm5Qa1Ww9raGsuXLxe14+Li0KhRIwiCADc3Nxw/fhxeXl5o1qwZWrRoAQsLC/z++++YOnUqtmzZAgC4du0aAgMDoVar4erqCrVajdatW+P69esAgEWLFiE/Px8AUFJSgunTp8Pc3BwqlQoajQZjx45FUVGRQflRW7lUsa3LySW5eSS3Lg3JoxMnTlS5mZmZYdOmTeI+ABQVFWHGjBnw9/dHx44d8eWXX0piz8zMlDUXSvk6khuTIAjw9/fH/PnzcePGjRr15caUnZ2NYcOGQavVwsXFBW+99RZKSkr+Vjxy24Yh8cjN97roEwDg008/Fe8n5bcuXbrg6NGjknqXk8fW1tYYN24cDhw4UGMZDh8+LLkeW7ZsQbdu3eDh4YGQkBB8/fXXj4zDzMxM7FsBoLi4GG+88Qa6deuGt99+GwDw73//G1ZWVjA3NxfbPlD77ReQf90N6Q9r877OmD48UMQwDMMwJkT5P7pnzZqFli1bYsuWLdi5cye6deuGRYsW4dy5c3jrrbckX4z69u0Lc3Nz2NvbY+rUqTh27FiV+iqVCllZWQCANWvWQKvV4u2338avv/6K999/H40aNcKqVasq/aFffqCq/L+GxGRIOe3t7XHu3DkAQL9+/TBixAjxj/KioiKMHz8ekZGRsss5depUBAYG4uOPP0b37t0xcOBAtGrVCvHx8di3bx+CgoLw+uuvGxTPjBkz4OzsjJUrVyItLQ35+fnIz89HWloaPv/8c7i4uGDmzJkAgKioKAwdOhSnTp3CP//5T7Rs2RLDhg1DUVERiouLMXLkSPTu3Vt23BWv5Zw5c+Dl5YU9e/YgLy8P8fHx8Pf3x+zZszFq1CiEh4fjl19+wbPPPovw8HA8+eSTuH79Oq5cuYIuXbrgpZdeMij28ePHo1mzZti+fbvkC1pJSQl+//13NG/eHBMmTMDEiRPRpUsXnDp1CikpKRg6dChmzpyJvLw8fPnll7CyssK3334LAOjatSteeukl5OTkYPHixfD09JSU67XXXkN4eDhcXV1x6tQpAMAzzzyD3r1749atWwCAO3fu4B//+AeGDh1aqY4WL14Me3t7rFmzBmfOnMG6devg4uKCRYsWGZQftZVL5du63FySm0dy69KQPCrfpipuFfuEuXPnwtXVFYsXL8Ybb7yBxo0bY+LEiaL/zMxMCIJgUB3JjUkQBLzwwgviwGV0dDQ2bdokyVM9cmN65ZVX0Lx5c/zwww9YtWoVfHx8EB0dLbbNx4lHbtswJB65+V7bfYIgCFi8eDE8PDzwySefYNWqVWjZsiXmzZuHbdu2YdSoUbCyssKRI0cAyO8PBUHAE088AUEQEBgYiCVLluDmzZs1xv3LL79ApVIhJiYGn376KSZMmACNRoONGzcCKLu3VLUJgoDGjRuL+2+++SZcXV3xr3/9C0FBQZg8eTKaNGmCdevW4euvv4anpycWLVoEoPbbrz435Vx3Q/vD2rqvM6YPDxQxDMMwTD0iODi4xq38qiPu7u7Yv3+/+Nnr16/D2tpafBpn3rx5CAsLgyAIyMrKwq1bt7BkyRIEBQVBpVKhffv2WLFiheQXQr0tAHTq1An//ve/JeVbsWIFgoOD4enpiejoaOzZswd79+7F3r17ERcXB7VajdjYWPGYITEZUk5LS0tcvHhRrIekpCRJOc+fP4/GjRvLLmeTJk2wZ88eAMCNGzcgCIL4KywAbN26FS1atDAoHldXV2zfvr3aa719+3a4uLgAKPtyov+1Oj8/H2q1GocPHxZtT58+DUdHR9lxV7yWrVq1wnfffSex3bx5M5o3bw53d3ccPHgQQNkXB0EQsGvXLtFu9+7daNq0qVjOmjZbW1uoVCrY2dnV+Ct/fHw87Ozs4OTkJHmS4O7du9BqtcjLywMALF++HO3atQMA2NrairEXFxdDo9FIBhIvXLiAxo0bQ6vVIjU1FQDg5eUlqUcAOHXqFJycnCrVUXBwMD7//HOJ7bp16/DEE08YlB9ybQ1p63JzSW4eya3LinVUUx61bdsW0dHROHfuHC5fvozLly8jLS0NGo0GO3fuFI8BQEBAgKROUlJSEBAQgDFjxkCn04lP4MjNN0B+fujjKS4uxo8//oj+/fuLT1jMnDkT58+fFz8jNyZvb2/ExcWJn7t16xY6deqEyMhIFBQUPFY8ctuGIfHIzXdD+oTBgwfXuPXs2RMqlQq+vr747bffRI3z58/D0dERxcXFAIBXXnkFffr0ASC/P9THc/z4cUydOhUODg4wNzfH008/jd9++w06na5S3F27dsXs2bMlcc+fPx+dO3cGAFhbWyM6OhpfffWVuMXGxkKtVmP+/PnisaZNm4o5nJKSApVKhe+//17U3LBhA1q1agWg9ttv+Zgedd0N6Q8Nua8zyofnKGIYhmGYesTZs2fpueeeIz8/vyrPZ2Rk0IULF4iIKDc3lzw9PcVz7u7uVFBQQPfu3SM3NzcaMmQILVy4UDzv5ORE06dPp+nTp9PBgwdp9erVNGvWLHrttddoyJAhtHbtWiIicW6j1NRUioyMlPiPjIykWbNm0eXLl2n8+PH03nvv0TfffCOWQxAE6tSpEwUFBT1WTHLL2aZNG9qzZw/5+/uTm5sbXblyhYKDg0WNK1eukKWlJZ08eVJWOW/evEkBAQFEROTh4UGWlpbUvHlzUa9Vq1Z07do1g+LJycmpcV4Wd3d3ysvLI6KyeS00mrI/yyr+S0SkVqtJp9PJjluP/lpmZmZSmzZtJP7btm1L165dIwBivTg4OJCVlZVkVZuAgADKyMggIqLCwkKaMmVKtfPXXLlyhd59913S6XRkbm5ebezm5uak0+mopKREMg+RtbU1lZSUUF5eHllZWVFkZCS99tpr4mcKCgqIiKioqIh0Op24T0T08OFDMjMzIx8fH0pISCA/Pz+ysbGhBw8eSHzn5OSQTqerVEdXr16l8PBwiW14eDilpaVRaWmp7PyQm0ulpaWy24XcXDIzM5OVR3LrsmId1ZRH9+/fp5kzZ9KQIUNo3bp1krz08PCQ5NSNGzckcxsFBATQ3r17qWfPnjRq1Cj697//TUTy841Ifn7o0Wg0NGTIEBoyZAjduHGD1qxZQ1999RUtWbKEunTpQvv376eEhARZMd26dUsSn5OTE+3atYuioqKof//+tHr1aoPjMaRtyI2HSF6+63Q62X3Cli1bqE+fPtXOGVRaWkpEZe2iZcuW4vFmzZpRdnY23bp1i9zd3WncuHHUtWtXIpLfH+pp27YtffLJJ7RkyRLauHEjffnll/SPf/yDPDw8aOzYsZLyXLhwgT766CPJsSFDhtDixYuJiOjYsWM0YsQI2rNnD3366adkbW1NREQvvPACDRo0SLxnTJ48mdq2bSvWh7m5ubhPRNSxY0e6cuUKEdV++y3Po6578+bNZfeHcu+XTAPByANVDMMwDMOUIyQkBCtWrKj2/LFjx8RfnMPDw/H++++L59avXw87Oztx/9SpU7C3t5c8dl+R3NxcrF69WnzNRBAErF27Fps3b4aXlxf+/PNPif3p06dha2sr7q9YsQIeHh7iUwYajQZnzpx5rJgMKefWrVvh4OCA2NhYxMbGwtfXF6tXr8aBAwewZs0aNGnSBDNmzJBdTg8PDyQmJor7w4cPl5Tl9OnTsLe3Nyie/v37IzIyUnzMvzy3bt1C3759ER0dDQDo1asXxo8fj+vXr+Pdd99FQEAAxo4dK9q/+OKLePLJJw2KWxAETJo0CdOmTYOLiwt27NghKUNiYiKcnJzg7e0t+ZV51qxZuHPnjrh//Phx8Rfn8PBwfPTRR9XGrn91ZsSIEQgODq70xBMAJCUlISQkBM8//zz69OkjeYVl8eLFcHd3l9jqfQ8cOBD/+Mc/EB8fj4kTJ6JDhw6Ijo5Gbm4u8vLyMHToUPTt2xexsbHw8vJCXFwc1q5di5YtW2LXrl24ceMG9uzZg9atW2PChAliHc2fPx/Lli2Du7s79u3bJynriRMnYG9vb1B+yLU1pK3LzSW5eSS3LvV1JCeP9Pz222/w8vLCggULUFpaWmWf4OfnJ3lCRc+NGzfQvHlz9OnTByqVSna+AfLzo6Z+BgB27dqFESNGSI49KqYWLVrg119/raSVk5ODsLAwtG3b1uB45LYNQ+KRm++G9AmtW7fG6tWrq/Wvz+N27drhiy++EI/v3r0bVlZW4lM/ycnJsLGxASC/P6wp9rS0NLz55pto0qQJBEFAXFwcTpw4AR8fH3GeHz3JycmwtrYW94uLizFz5kz4+/sjPj4eQOV7hqurK06ePCnuh4eHi3P9AMC5c+fEe2Vtt18Asq+7If2hHjn3dUb58EARwzAMw9QjXnnlFfzzn/+s9vzFixfRvXt3AGV/CFpYWKBTp07o1q0bNBoNli5dKtouXrwYPXv2lDx2/ygqzsNRfiAKAFavXo3g4GDJsTNnzqBt27YYPnx4lX9Qyo3JkHICwI8//ggvL69Kc4hotVq8+uqrleZqqKmcffv2xcqVK6v1FRsbKw5SyY1HPxGpRqNBcHAw+vbti759+yI4OBgajQZt2rTB1atXAQAJCQlwdHSESqWCs7MzTp8+jdDQULi5ucHDwwOWlpbiF2u5cUdERKB79+7itmrVKkk533vvPURERGDAgAE1fnldvnw5evbsCaDsFY133nmnWturV69izJgxuHv3Lvr27QtBEODg4IDAwEAEBgbCwcEBKpUK/fr1w71795CYmAgHBwe4ubnB29sb5ubmWL9+vcR3TEwMgLJXh5o1awZBENCyZUtcv34dAwYMgEajgUajgbOzszhA85///AdWVlawtLQUJ+vVb4MGDRInZ/bx8YGvr6+4lW8/APDRRx+hc+fOBuWHXFtD2rrcXJKbR4bUpdw8Kk9mZib69euHJ598sso+Yfz48Rg3blyVcV+/fh0BAQFQqVSy8w2Qnx+G9jNyYnr55ZfFeV4q8uDBA4SGhhocj9y2YUg8cvPdkD5hzJgxePHFF6u1PXv2LHx9fbFhwwaYmZnhmWeeQUxMDKytrSWvgK1cuRJhYWEA5PeHcmLX6XSV5pqqGPf69esRFBRU6bO7d++Gt7c35syZAzMzM8k179GjB7766qtq/f73v/9FSEgIgNpvvwAMuu5y+8PyPOq+zigfAQCM/VQTwzAMwzCPx4kTJ+i///0vFRYWUlRUFPXp06eSzddff03PPfccWVhY/G1/W7duJTMzM4qKipIcLyoqotmzZ1NcXBxt3Lix2tdpauJxyllaWkpJSUmUmppKOp2O3N3dKSQkhGxsbKq0r66cd+/eJZVKRXZ2dlV+btu2bWRpaUndu3c3KCadTke///47HTp0SLIkclhYGEVGRkqW0s7Ly6Pk5GRq0aIFWVtbU0FBAX377bf08OFD6tOnD7Vo0UISd2JioviayKPirorU1FQyNzcnLy+vGu0SEhLIysrqkcugV0VycjIdPHiwUuyBgYGiTUZGBm3dupUKCwupZ8+ej3y94c6dO5LltHfv3k0PHz6ksLAwyfH79+/Tjh07JHXUpUsXatasmezyHzp0iCwsLMjHx0d2fhg7lwzJI7l1WRM15dHHH39McXFx9Mknn0jOX7lyhZKTkyv1I3rS09Np586dNHr0aFllKM+jYtq3bx916dJF8kqPIVQV07179yg9PZ2eeOKJKj+Tk5NDSUlJFBERYZAvOW3j78ZTHn2+l3/FrirK9wmFhYVUWlpKVlZWj9Tftm0brVu3TrxfvfDCC+K5O3fuEBGJ105OHr/77rs0Y8aMR/rWvwKmx9raWpIj+teuY2JiKn32zp079MILL1BcXBwdOnRIbD8XLlwgMzOzau913333HWk0GnrmmWeIqPbbr6HX/f79+7Rz507JvfJR/WFt3NcZ04UHihiGYRiGYRiGYRiGYRgiIuLJrBmGYRimHqLT6SRPm5Q/fv36dfL29jbIFgBdvnyZmjRpQhqNhoqKimjTpk1UWFhI/fv3JycnpxrL07NnT4qNjZVMaFqRtLQ0unjxIrm7u1f59ElCQkKVT5d06tTpsexq4t69e7RlyxbxF2JTqSM5tkVFRfTzzz9XqqPw8HAaOHBgjZNIlycrK4s+//xzevvtt4mI6Pr162RnZydO3qqnuLiYDh48SN26dSOisl/YT548SW3btiUHBwe6ffs2ffnll1RYWEjDhg2TTFhbkaZNm9Lvv//+yKd65NgBoL1799LFixfJw8ODIiMjJZMV/y/zCABdu3ZNbJd/J5fk5BGR/FxqyHkkx7Z8Hrm7u1NUVJQkj2qjnh63juTa/p26rI06Iiqb6Pzw4cOSOgoNDSU3N7fHsquJvLw8SkxMlNSTHEpLS0mtVov7hw8fpsLCQgoLC6sUT0XGjh1L8+fPr3FS6uLiYrp8+TK5uLhQ48aNK52vqk8KDw+njh07PpZdTTzOPZDo7/VdjMIwzhtvDMMwDMNURXZ2NoYNGwatVgsXFxe89dZbkrl29MssG2KbnJwMHx8fqFQqBAQEIDU1FSEhIWjUqBGsrKzg5OSECxcuAChb6rqqTa1WY/ny5eL+lClTxHkN8vPzMWTIEHH+B5VKhR49eojns7Ky0LVrVwiCAB8fH3Tq1AmdOnWCj48PBEFA165dkZWVJdtODvpJYU2ljqqy1c+pUd42JSUFTZs2hVarRUREBJ555hk888wziIiIgFarRUBAAFJSUgyqo/T0dHTs2BEqlQpqtRqjRo2SzFlRPucOHz4sLjFub2+Po0ePws/PD82aNYO/vz8sLS2RmJiIZcuWVbmp1WrMmTOn2vPV2QFAv379cP/+fQBlS3aHhoZCEAQ4OztDpVIhMDAQN2/eNGoeAZCdS3LzqLr8qCqXGmoeAZBtKzePANRaPT1OHcm1lVuXdVVHubm5eP7556FWq6HRaODi4gIXFxdoNBqo1WqMHDkSeXl5op1KparRzpCcKyoqwowZM+Dv74+OHTviyy+/lNjp6yg9PR1dunSBWq1Gt27dcPfuXURHR4ttqHnz5khPTwdQNqF3VZuZmRk2bdok7i9atAj5+fkAgJKSEkyfPl2c/0ej0WDs2LEoKioCYBr3QEB+38U0DHigiGEYhmHqEa+88gqaN2+OH374AatWrYKPjw+io6NRWFgIoOyPOkEQDLIdOHAgBgwYgJMnT+LVV19Fy5YtMXDgQBQVFaGgoABPPfUURo4cCQCVJv2saqu4QtmcOXPg5eWFPXv2IC8vD/Hx8fD39xcnKh0yZAjCwsKQnJxcKd7k5GSEh4dj6NChsu2AskGymrY//vgDKpXKZOpIrm3v3r0xcOBAZGdnV6qj7OxsDBw4EJGRkQCq/8Kj3zZs2ACVSoWYmBiEhobiyJEj2LlzJ0JCQtChQwfcvXu3Us717t0bEyZMwIMHD7B48WJ4eXlJVswZO3YsBg0aBEEQ4OXlJZk419fXF4IgwNPTE76+vvDz85Ntp693fR1NmTIFQUFBSE1NBQBcu3YNISEhmDx5slHzCIDsXJKbR4bkR0PNI31+yM05OXmkL6eceqqLOpJrK7cu66qOxo8fj2bNmmH79u2SAYiSkhL8/vvvaN68OSZMmCDbTg76QZC5c+fC1dUVixcvxhtvvIHGjRtj4sSJop2+jkaNGoXw8HD88ssvePbZZxEeHo4nn3wS169fx5UrV9ClSxdxhbma2mX5QdfybW3x4sWwt7fHmjVrcObMGaxbtw4uLi5YtGgRANO4BwLy+y6mYcADRQzDMAxTj/D29kZcXJy4f+vWLXTq1AmRkZEoKCiQ/Pon19bZ2RnHjh0DUPbrryAI+OOPP8TPHThwAN7e3gAgLtNb8VfLiquelP8i0apVK3EZXT2bN29G8+bNAQDW1tZVLpOu5+jRo7C2tpZtp/dffuWWipv+vKnUkVxbS0tLnDp1qto6OnnyJCwtLSV19KgvPB4eHpKlsPVfCNq1a4c7d+5Ics7e3h5nz54FABQVFYlPNOhJTEyEp6cnJk2ahHbt2om21dWRXLuKddSiRQvxaRs9u3btgp+fn1HzCIDsXJKbRxVjryk/GmoeAfJzSW4eAZBdT3VRR3Jt5dZlXdWRnZ0dDhw4UG0dxcfHw87OTrYdUJYfNW22trbiUy9btmwRNVJSUhAQEIAxY8ZAp9OJdeTu7o6DBw8CKHtCShAEcfUwoGx1s6ZNmwIA2rZti+joaJw7dw6XL1/G5cuXkZaWBo1Gg507d4rHytdRcHAwPv/8c0k869atwxNPPAHANO6BgPy+i2kYVH5RkWEYhmEYo3Hr1i3J3CROTk60a9cuysnJof79+1N+fr7Btrm5ueTg4EBERI0aNaJGjRqRu7u7+LkmTZpQVlYWEZWtStOrVy/q0KEDbd26tcayCoJARGVzTrRp00Zyrm3btnTt2jUiIrKwsKAHDx5Uq5OTk0MWFhay7YiIbGxs6IMPPqA9e/ZUuX3xxRcmVUdybe3s7Ojy5cvV+rt8+bK42paDgwOtWrWK0tLSKm2pqali2bOzs8ne3l7UsLCwoI0bN5Kvry/16NGDbt68KZ4rKioiS0tLIiIyMzMjKysryZwVTk5OdOfOHVq5ciW9/fbbFBUVRcuXL6+2vHLtKtbRvXv3yN/fX3IuICCA0tPTjZpHRPJzyZA8Kh/7o3KpIeYRkWG5JCePiEh2PdVFHcm1lVuXdVVHOp2uxnmazM3NSafTybYjIiosLKRx48bR0qVLq9ymT59OREQ3btyQzLsVEBBAe/fupT///JNGjRpFpaWlYgyenp5EVJbPVlZWkvYcEBBAGRkZRFQ2P1BAQAANGTKE7t69Sz4+PuTr60tERB4eHuTj4yN+Vl9HV69epfDwcEk84eHhlJaWRkSmcQ8kMuw+yDQAjD1SxTAMwzDMX7Ro0QK//vprpeM5OTkICwtD27ZtxV//5Nr6+/tLfhVcsWIFHjx4IO4nJibCzc1NonHs2DEEBQVh4sSJyMvLq/IX50mTJmHatGlwcXHBjh07JJ9PTEyEk5MTAODFF1+Ej48PNm7cKHmFIzs7Gxs3boSvry+mTp0q2w4AunfvLj7WXxXHjx+HIAgmU0dybd966y3Y29vjww8/xIkTJ5CZmYnMzEycOHECH374IRwcHDB37lwAQGRkJN57771H1lHr1q3x448/VjpfXFyMQYMGwdvbW8y5wMBA7N69W7TZunWrOE8HABw6dAheXl7i/vXr19GzZ0/07dsXGRkZVT4JItdOEAT0798fgwcPhr29veRJAr1vV1dXo+YRAINz6VF5pI9dTn409DySYys3jwDIrqe6qCO5tobWZW3X0YgRIxAcHFzlkzBJSUkICQnB888/L9sOAMLDw/HRRx9Vrsj/R//qmZ+fn+TJID03btxA8+bN0adPH/GpmvJPWc2aNQt37tyR6On7Yj2//fYbvLy8sGDBApSWllZZR/Pnz8eyZcvg7u6Offv2ST5/4sQJ2NvbAzCNeyBgeN/FKBte9YxhGIZh6hGRkZEUGxtL/fv3lxy3tram33//nfr06WOwbe/evSk5OZm6du1KRERTpkyR2O/YsYPat28vOdauXTs6evQoTZs2jdq1a0cAJOe7detG58+fJyKioKAgunLliuT8b7/9Rk888QQREX344Yek0+noueeeo5KSEvFX5aKiItJoNDR+/HhasmQJEZFsuxEjRtDDhw+rrUc3NzeaO3cu3blzxyTqSK7tvHnzqFGjRrR48WKaPn26+Is2AHJzc6NZs2bRzJkziYho8uTJlJeXV20deXt7U2xsLJ09e5a++OILGjJkiOS8RqOhH374gYYMGULXr18nIqLnnntO8tRDdHS05DO//PKLZFUxT09P2rVrFy1cuJCCg4Mr1ZEhdqNHjxb/P3DgQMkv4UREP/30E7Vr186gfKvtPCIyPJcelUdEhuVSQ84jObZy84iIZNfTpk2bar2O+vXrJ8vW0Lqs7Tpavnw5jRgxgkJCQsje3p5cXFyIiOjmzZt0//598eklALLs9DHcv3+/2vp0cHCgmJgYUqvV9N1331GvXr0k5z08PGjPnj3UvXt3IiprYwcPHhTrYeHChRL7+Pj4Sk/f9evXj44ePUpjx46lbdu2VSqDt7c3rVq1iojKnhhKSkqSrMIWFxdHLVq0ICLTuAcSPd59kFEuAmrqaRmGYRiG+Z9y7949Sk9PF7/0VSQnJ4eSkpIoIiLCINuaSEtLI61WK3nEvDy//PILxcXF0Zw5c8Q/7h9FamoqmZubk5eXl3jswYMHlJiYKFnyNyQkhGxtbSWflWsnB1OrI0Ns09LSJHXk5+cny29FSkpKKD8/v9r6LSkpoRs3bjxyuXYiovz8fFKr1eLrEeVJTEyk+Ph4iomJkbxS87h2FcnLyyO1Wk1arZaI6mceEdWcS4+TR0Tyc6kh5ZGhtnoq5pGe2qgnQ+qotuqzprokqr06Sk5OrrSke1hYGAUGBko+K9dODleuXKHk5GSKioqq8nx6ejrt3LlTMvBVFQkJCWRlZSV5ja08H3/8McXFxdEnn3wiq78mIjp06BBZWFhQcHCweMyU74FEj74PMsqCB4oYhmEYhmEYhmEYhmEYIiLiyawZhmEYpp5y9epVcYJNPRkZGXT16tXHtmXN+u3bENv9+/fT0aNHJceOHj1K+/fvr6Qp19aYmnXh21SupTE1lZZHpqKptHgYeZhCn2CoLaNQ/vfTIjEMwzAMIwdBENCyZUvJscDAQHHiycexZc367bshayotHlPRVFo8pqKptHj0tkFBQbI05djVhaYxfettTeVayrVllAlPZs0wDMMw9ZS4uDiysrKSHFu7dm2liUUNsWXN+u3bENu0tDQyMzOTHNu9ezcVFxdX0pRra0zNuvBtKtfSmJpKyyNT0VRaPEREa9asITs7O8mxDz74gLKzsx/Lri40jembyDT6BENtGWXCcxQxDMMwDMMwDMMwDMMwRMRzFDEMwzCMSfDVV19V+evk37E1tiZTO6SkpNDu3bvp4sWLtWZrTM268M1UTWlpqWQ/ISGBDh06RIWFhY9tq0RNorI5Ww4fPkxHjhyhO3fuVGlTF3ampElEVFhYWG0dPo5dXWga0zfDmAzGfveNYRiGYZhHY2ZmhrNnz9aq7f9K8/DhwygpKRH3t2zZgm7dusHDwwMhISH4+uuvDbKrC01j+jbEdsGCBdi1axcA4O7du+jVqxcEQYAgCFCpVOjbty/u3btnkK0xNevCt7W1NcaNG4cDBw7gUci1VZrm5cuXERISArVajb59+yI7Oxu9e/cW67Np06Y4f/68QbZK1ASATz/9FN7e3lCpVJKtS5cuOHr0aJ3ZmZLmjh070K9fP9jZ2Yl2dnZ26NevH3bu3GmwXV1oGtP3ozh79iz8/Pxqzc7Ymowy4IEihmEYhqlH2NvbV7kJgoDGjRuL+4bYGltTpVIhKysLAPDLL79ApVIhJiYGn376KSZMmACNRoONGzfKtqsLTWP6NkTTy8sLSUlJAIAJEyYgODgYSUlJePjwIY4fP47OnTtj/PjxACDb1piadeFbEAQ88cQTEAQBgYGBWLJkCW7evFlle5NrqzTNIUOGICIiAlu2bMEzzzyDLl26oHv37rh+/TrS09MRFRWFQYMGGWSrRM3FixfDw8MDn3zyCVatWoWWLVti3rx52LZtG0aNGgUrKyscOXKk1u3qwnddaX711VfQaDR47rnnEBsbi99++w2//fYbYmNjMXz4cJiZmWHt2rWy7epC05i+5XD8+HFZk0TLtTO2JqMMeKCIYRiGYeoR1tbWiI6OxldffSVusbGxUKvVmD9/vnjMEFtjawqCIA6CdO3aFbNnz5bEPH/+fHTu3Fm2XV1oGtO3IZoWFha4fPkyAMDX1xf79u2T2B09ehTu7u4AINvWmJp14Vtfl8ePH8fUqVPh4OAAc3NzPP300/jtt9+g0+nEz8i1VZqms7Mzjh07BgC4f/8+BEHAH3/8IfpLTEyEq6urQbZK1PT19cVvv/0mnjt//jwcHR1RXFwMAHjllVfQp0+fWrerC991pdmsWTMsX74c1fHpp58iICBAtl1daBrTNwBMmzatxm3kyJFQqVSy7YytyTQMeKCIYRiGYeoRKSkp6NixI2JiYpCTkyMe12g0OHPmzGPZGluz/CCIi4tLpdcWkpOTYWdnJ9uuLjSN6dsQzebNm2Pr1q0AAD8/v0qvGB07dgy2trYAINvWmJp14bt8XQJAQUEBvvvuO/Tq1QsqlQpeXl546623AEC2rdI0bWxskJqaCgAoLS2FRqPB8ePHxc+lpKTAxsYGAGTbKlHTysoKaWlp4jmdTgeNRoP09HQAZU9YWFtb17pdXfiuK00LCwskJyejOpKTk6HVamXb1YWmMX0DZU+Mtm/fHt27d69y69Chg/jqmhw7Y2syDQMeKGIYhmGYekZxcTFmzpwJf39/xMfHA6h6AMYQW2NqCoKAuLg4nDhxAj4+PkhISJCcT05OhrW1tWy7utA0pm9DNBcvXoyWLVsiJSUF//nPfxAWFoaLFy8CAFJTU9G9e3cMHToUAGTbGlOzLnyXf42vImlpaXjzzTfRpEkTAJBtqzTNzp0748033wQArFmzBq6urpKn2ObNm4eQkBAAkG2rRM127drhiy++EM/t3r0bVlZW4pNZycnJsLGxqXW7uvBdV5rt27fHjBkzUB0zZ85E+/btZdvVhaYxfQNlg9zffPNNtbbHjh2DSqWSbWdsTaZhwANFDMMwDFNP2b17N7y9vTFnzhyYmZlVOahjqK0xNPWTDesni126dKnk/Pr16xEUFCTbri40jenbEE0AePnll2FmZobAwEBotVqoVCqYm5tDpVKhQ4cOyMjIED8n19aYmrXtu+JTNVVR8VWtR9kqTXP79u3QarUwNzeHVqvFvn370Lx5c3Tq1AmdO3eGWq3Ghg0bAEC2rRI1N2zYADMzMzzzzDOIiYmBtbW1ZFBp5cqVCAsLq3W7uvBdV5pxcXFo1KgRWrdujWnTpmHhwoVYuHAhpk2bhjZt2sDa2hr79u2TbVcXmsb0DQAjRozAq6++Wm2bPH78OARBkG1nbE2mYSAAgLFXXmMYhmEYpmru3LlDL7zwAsXFxdGhQ4eoRYsWf9v2f6155coVia21tTU5OjqK+2vXriUiooiICFl2MTExta5pTN+GaMbExBAR0blz52jr1q2UmppKOp2O3N3dqUuXLtS7d28SBEGiJdfWmJq16fvdd9+lGTNmkJWVFT0KubZK1Lx8+TIlJiZSSEgI+fr6UlZWFn366aeUn59P0dHR1KNHD4Ntlai5bds2WrduHRUWFlJUVBS98MIL4jn9cvGOjo61blcXvutK8/Lly/TZZ5/RoUOHKDMzk4iI3NzcKCwsjCZPnky+vr4G2dWFpjF9Z2ZmUmFhIfn4+FBNyLUztibTMOCBIoZhGIZhGIZhGIZhGIaIiFTGLgDDMAzDMPIpKSmhq1ev1qota9Zv34bYFhcXy9aUa2tMzbrwbSrXkvO94WkqLR6GYUwXHihiGIZhGBPizJkz5OfnV6u2rFm/fRtie/bsWdmacm2NqVkXvk3lWnK+NzxNU41nxYoV1Lt3b3rmmWdo9+7dEtvbt29T06ZNDbKrC01j+laiJqN8eKCIYRiGYRiGYRiGMZiPP/6YZsyYQYGBgWRhYUH9+/enDz74QDxfWlpKV65ckW1XF5rG9K1ETaZhoDF2ARiGYRiG+Yv27dvXeP7hw4cG27Jm/fbdkDWVFo+paCotHlPRVFo8RESff/45rVq1ikaMGEFERFOmTKFBgwbRw4cPad68eQbb1YWmMX0rUZNpGPBAEcMwDMPUI86ePUvPPfdctY//Z2Rk0IULFwyyZc367bshayotHlPRVFo8pqKptHiIiNLS0ig8PFw8Fx4eTnv27KHevXtTcXExvfrqqwbZ1YWmMX0rUZNpIIBhGIZhmHpDSEgIVqxYUe35Y8eOQaVSGWTLmvXbd0PWVFo8pqKptHhMRVNp8QBAkyZNsH///ko2Z86cgaurK2JiYqBSqWTb1YWmMX0rUZNpGPAcRQzDMAxTj+jSpQudP3++2vM2NjbUrVs3g2xZs377bsiaSovHVDSVFo+paCotHiKirl270saNGyvZBAUF0e7du2nbtm0G2dWFpjF9K1GTaRgIAGDsQjAMwzAMwzAMwzCmxcmTJykxMZHGjh1b5fnTp0/TTz/9RIMHD5ZlN3fu3FrXNKZvJWrOnTu3yvOMwjD2I00MwzAMw/zFu+++i7y8vFq1Zc367bshayotHlPRVFo8pqKptHhMRVNp8Rhbk2kY8BNFDMMwDFOPUKvVlJGRQS4uLrVmy5r123dD1lRaPKaiqbR4TEVTafGYiqbS4jG2JtMw4DmKGIZhGKYeYcjvN3JtWbN++27ImkqLx1Q0lRaPqWgqLR5T0VRaPMbWZBoGPFDEMAzDMPUMQRBq3ZY167fvhqyptHhMRVNp8ZiKptLiMRVNpcVjbE1G+fCrZwzDMAxTj1CpVNS4ceNH/rF29+5d2bb3799nzXrsuyFrKi0eU9FUWjymoqm0eExFU2nxGFvz7t27NZ5nlIHG2AVgGIZhGEbKu+++S40bN64127Fjx7JmPfbdkDWVFo+paCotHlPRVFo8pqKptHiMrck0DPiJIoZhGIapR6hUKsrMzJQ1maRcW9as374bsqbS4jEVTaXFYyqaSovHVDSVFo+xNZmGAc9RxDAMwzD1CFOZn0BpmkqLx1Q0lRaPqWgqLR5T0VRaPKaiqbR4jK3JNAx4oIhhGIZh6hGmsuqJ0jSVFo+paCotHlPRVFo8pqKptHhMRVNp8Rhbk2kY8KtnDMMwDMMwDMMwDMMwDBHxE0UMwzAMU69o3bo1vffee3Tt2rVas2XN+u27IWsqLR5T0VRaPKaiqbR4TEVTafEYW5NpIIBhGIZhmHqDIAhwdHSEWq1GVFQUfvzxRxQXF/8tW9as374bsqbS4jEVTaXFYyqaSovHVDSVFo+xNZmGAQ8UMQzDMEw9QhAE3LhxA5s2bcJTTz0FjUYDZ2dnTJ8+HWfPnn0sW9as374bsqbS4jEVTaXFYyqaSovHVDSVFo+xNZmGAQ8UMQzDMEw9QhAEZGVlifvp6elYsGABmjVrBpVKhbCwMHz55ZcG2bJm/fbdkDWVFo+paCotHlPRVFo8pqKptHiMrck0DHigiGEYhmHqESqVSvKHWnni4uIwcuRINGrUyCBb1qzfvhuyptLiMRVNpcVjKppKi8dUNJUWj7E1mYYBDxQxDMMwTD2i4i96VZGdnW2QLWvWb98NWVNp8ZiKptLiMRVNpcVjKppKi8fYmkzDgFc9YxiGYZh6xOjRo8nS0rJGG1tbW4NsWbN++27ImkqLx1Q0lRaPqWgqLR5T0VRaPMbWZBoGAgAYuxAMwzAMwzAMwzAMwzCM8eEnihiGYRiGYRiGYRiGYRgi4oEihmEYhjEpTpw4QWq1ulZtWbN++27ImkqLx1Q0lRaPqWgqLR5T0VRaPMbWZJQBDxQxDMMwjIlhyFvjcm1Zs377bsiaSovHVDSVFo+paCotHlPRVFo8xtZkTB+NsQvAMAzDMMxfPP300zWez87OJkEQDLJlzfrtuyFrKi0eU9FUWjymoqm0eExFU2nxGFuTaRjwQBHDMAzD1CO2bNlCffr0IVdX1yrPl5aWGmzLmvXbd0PWVFo8pqKptHhMRVNp8ZiKptLiMbYm00AAwzAMwzD1htatW2P16tXVnj927BhUKpVBtqxZv303ZE2lxWMqmkqLx1Q0lRaPqWgqLR5jazINA56jiGEYhmHqESEhIZSUlFTteQsLC/L29jbIljXrt++GrKm0eExFU2nxmIqm0uIxFU2lxWNsTaZhIAA8IxXDMAzD1BcKCwuptLSUrKysas2WNeu374asqbR4TEVTafGYiqbS4jEVTaXFY2xNpmHAA0UMwzAMwzAMwzAMwzAMERHxq2cMwzAMU8+Jjo6mjIyMWrVlzfrtuyFrKi0eU9FUWjymoqm0eExFU2nxGFuTUR48UMQwDMMw9Zz9+/fTw4cPa9WWNeu374asqbR4TEVTafGYiqbS4jEVTaXFY2xNRnnwQBHDMAzDMAzDMAzDMAxDRDxQxDAMwzD1Hh8fHzIzM6tVW9as374bsqbS4jEVTaXFYyqaSovHVDSVFo+xNRnlwZNZMwzDMAzDMAzDMAzDMEREpDF2ARiGYRiGqUxqairFx8dTRkYGqVQqatq0KfXp04dsbW0f25Y167fvhqyptHhMRVNp8ZiKptLiMRVNpcVjbE1G4YBhGIZhmHpDbm4uhg4dCkEQIAgCVCoV3NzcoFarYW1tjeXLlxtsy5r123dD1lRaPKaiqbR4TEVTafGYiqbS4jG2JtMw4IEihmEYhqlHTJw4EV26dMGpU6eQkpKCoUOHYubMmcjLy8OXX34JKysrfPvttwbZsmb99t2QNZUWj6loKi0eU9FUWjymoqm0eIytyTQMeKCIYRiGYeoRTk5OOHr0qLh/9+5daLVa5OXlAQCWL1+Odu3aGWTLmvXbd0PWVFo8pqKptHhMRVNp8ZiKptLiMbYm0zDggSKGYRiGqUfY2dnhwoUL4n5RURE0Gg1u3rwJALhw4QK0Wq1BtqxZv303ZE2lxWMqmkqLx1Q0lRaPqWgqLR5jazINA5Wx50hiGIZhGOYvOnbsSMuWLRP3ly1bRs7OzuTs7ExERLm5uWRtbW2QLWvWb98NWVNp8ZiKptLiMRVNpcVjKppKi8fYmkwDwdgjVQzDMAzD/EViYiIcHBzg5uYGb29vmJubY/369eL55cuXIyYmxiBb1qzfvhuyptLiMRVNpcVjKppKi8dUNJUWj7E1mYaBAADGHqxiGIZhGOYvMjIyaOvWrVRYWEg9e/akoKCgv23LmvXbd0PWVFo8pqKptHhMRVNp8ZiKptLiMbYmo3x4oIhhGIZhGIZhGIZhGIYhIiKNsQvAMAzDMIyUoqIi+vnnn+ngwYOUmZlJRERubm4UHh5OAwcOJHNzc4NtWbN++27ImkqLx1Q0lRaPqWgqLR5T0VRaPMbWZJQPP1HEMAzDMPWIixcvUlRUFKWnp1NoaCi5uroSEVFWVhYdPnyYvLy8aNu2bRQQECDblohYsx77bsiaSovHVDSVFo+paCotHlPRVFo8xtYMCAggRvnwQBHDMAzD1CP69OlDjRo1orVr15Ktra3k3IMHDygmJoYePnxIv//+u2xbnU7HmvXYd0PWVFo8pqKptHhMRVNp8ZiKptLiMbbm77//TkwDwHjzaDMMwzAMUxFLS0ucOnWq2vMnT56EpaWlQbasWb99N2RNpcVjKppKi8dUNJUWj6loKi0eY2syDQOVsQeqGIZhGIb5Czs7O7p8+XK15y9fvkx2dnYG2bJm/fbdkDWVFo+paCotHlPRVFo8pqKptHiMrck0EIw9UsUwDMMwzF+89dZbsLe3x4cffogTJ04gMzMTmZmZOHHiBD788EM4ODhg7ty5BtmyZv323ZA1lRaPqWgqLR5T0VRaPKaiqbR4jK3JNAx4oIhhGIZh6hkLFy6Eu7s7BEGASqWCSqWCIAhwd3fHokWLHsuWNeu374asqbR4TEVTafGYiqbS4jEVTaXFY2xNRvnwZNYMwzAMU09JS0uTLE/r5+f3t21Zs377bsiaSovHVDSVFo+paCotHlPRVFo8xtZkFIyxR6oYhmEYhqmZ+Ph4FBQU1Kota9Zv3w1ZU2nxmIqm0uIxFU2lxWMqmkqLx9iajPLggSKGYRiGqefY2Njg0qVLtWrLmvXbd0PWVFo8pqKptHhMRVNp8ZiKptLiMbYmozx41TOGYRiGqefAgLfE5dqyZv323ZA1lRaPqWgqLR5T0VRaPKaiqbR4jK3JKA8eKGIYhmEYhmEYhmEYhmGIiAeKGIZhGKbe8/nnn5Orq2ut2rJm/fbdkDWVFo+paCotHlPRVFo8pqKptHiMrckokFp9kY1hGIZhmDohLi4O+fn5tWrLmvXbd0PWVFo8pqKptHhMRVNp8ZiKptLiMbYmoyx4oIhhGIZhTAAzMzOcPXu2Vm1Zs377bsiaSovHVDSVFo+paCotHlPRVFo8xtZklIXG2E80MQzDMAzzF+3bt6/yeElJCQ0ZMoS0Wi0RESUlJcm2rQ7WrB++G7Km0uIxFU2lxWMqmkqLx1Q0lRaPsTWTkpJq1GaUAQ8UMQzDMEw94tSpU9S7d2/q3LmzeAwAnThxgnr06EEuLi4G277//vusWY99N2RNpcVjKppKi8dUNJUWj6loKi0eY2syDYRafT6JYRiGYZi/RXx8PPz9/fH222+jtLRUPK7RaHDmzJnHsmXN+u27IWsqLR5T0VRaPKaiqbR4TEVTafEYW5NpGPCqZwzDMAxTj+jSpQslJibShQsXKDw8nC5duvS3bVmzfvtuyJpKi8dUNJUWj6loKi0eU9FUWjzG1mQaCMYeqWIYhmEYpmrWrFkDNzc3fP755zAzM6vxFz25tqxZv303ZE2lxWMqmkqLx1Q0lRaPqWgqLR5jazLKhQeKGIZhGKYec+HCBXTs2BGCIDzyDzW5tqxZv303ZE2lxWMqmkqLx1Q0lRaPqWgqLR5jazLKRAAAYz/VxDAMwzBM9eh0OsrJySFbW1sSBKFWbFmzfvtuyJpKi8dUNJUWj6loKi0eU9FUWjzG1mSUBw8UMQzDMAzDMAzDMAzDMERExJNZMwzDMEw9Y8WKFdS7d2965plnaPfu3ZJzt2/fpqZNmxpsy5r123dD1lRaPKaiqbR4TEVTafGYiqbS4jG2JtMAMO6bbwzDMAzDlGfZsmWwsrLCSy+9hJEjR8Lc3BwLFiwQz2dmZkKlUhlky5r123dD1lRaPKaiqbR4TEVTafGYiqbS4jG2JtMw4IEihmEYhqlHBAUF4dtvvxX3Dxw4AGdnZ7z11lsApH+oybVlzfrtuyFrKi0eU9FUWjymoqm0eExFU2nxGFuTaRjwQBHDMAzD1CMsLS2RlpYmOXbq1Cm4urpi9uzZkj/U5NqyZv323ZA1lRaPqWgqLR5T0VRaPKaiqbR4jK3JNAx4oIhhGIZh6hFNmjTB/v37Kx0/c+YMXF1dERMTI/6hJteWNeu374asqbR4TEVTafGYiqbS4jEVTaXFY2xNpmHAA0UMwzAMU48YPnw4Xn311SrPnT59Gs7OzuIfanJtWbN++27ImkqLx1Q0lRaPqWgqLR5T0VRaPMbWZBoGGmNPps0wDMMwzF/Mnj2bEhMTqzz3xBNP0J49e+inn34yyHbw4MGsWY99N2RNpcVjKppKi8dUNJUWj6loKi0eY2syDQMBAIxdCIZhGIZhGIZhGIZhGMb48BNFDMMwDFMPSUhIoIMHD1JmZiYREbm5uVFYWBh16tTpsW1Zs377bsiaSovHVDSVFo+paCotHlPRVFo8xtZkFI6x331jGIZhGOYvsrKy0KVLFwiCAB8fH3Tq1AmdOnWCj48PBEFA165dkZWVZZAta9Zv3w1ZU2nxmIqm0uIxFU2lxWMqmkqLx9iaTMOAB4oYhmEYph4xZMgQhIWFITk5udK55ORkhIeHY+jQoQbZsmb99t2QNZUWj6loKi0eU9FUWjymoqm0eIytyTQMeKCIYRiGYeoR1tbWSEpKqvb80aNHYW1tbZAta9Zv3w1ZU2nxmIqm0uIxFU2lxWMqmkqLx9iaTMOA5yhiGIZhmHqEhYUFPXjwoNrzOTk5ZGFhYbAta9Zv3w1ZU2nxmIqm0uIxFU2lxWMqmkqLx9iaTAPA2CNVDMMwDMP8xYsvvggfHx9s3LgR2dnZ4vHs7Gxs3LgRvr6+mDp1qkG2rFm/fTdkTaXFYyqaSovHVDSVFo+paCotHmNrMg0DHihiGIZhmHpEQUEBJk+eDHNzc6hUKmi1Wmi1WqhUKpibm2PKlCkoKCgwyJY167fvhqyptHhMRVNp8ZiKptLiMRVNpcVjbE2mYSAAgLGfamIYhmEYRsqDBw8oMTFRsjxtSEgI2draPrYta9Zv3w1ZU2nxmIqm0uIxFU2lxWMqmkqLx9iajLLhgSKGYRiGYRiGYRiGYRiGiIhUxi4AwzAMwzBSHj58SPHx8XT27NlK5woKCmjt2rUG27Jm/fbdkDWVFo+paCotHlPRVFo8pqKptHiMrck0AIz75hvDMAzDMOU5f/48fHx8IAgCVCoVunXrhhs3bojnMzMzoVKpDLJlzfrtuyFrKi0eU9FUWjymoqm0eExFU2nxGFuTaRjwE0UMwzAMU4+YNWsWtWrVim7evEnnz58nGxsb6tq1K129evWxbVmzfvtuyJpKi8dUNJUWj6loKi0eU9FUWjzG1mQaCMYeqWIYhmEY5i9cXFxw8uRJcV+n02Hy5Mnw9vbGpUuXJL/oybVlzfrtuyFrKi0eU9FUWjymoqm0eExFU2nxGFuTaRjwQBHDMAzD1CNsbGxw9uzZSsdfeukleHl5Yf/+/eIfanJtWbN++27ImkqLx1Q0lRaPqWgqLR5T0VRaPMbWZBoGPFDEMAzDMPWIjh07Yu3atVWee+mll2BnZyf+oSbXljXrt++GrKm0eExFU2nxmIqm0uIxFU2lxWNsTaZhwANFDMMwDFOPWLBgAfr161ft+SlTpkAQBINsWbN++27ImkqLx1Q0lRaPqWgqLR5T0VRaPMbWZBoGAgAYe54khmEYhmEYhmEYhmEYxvjwqmcMwzAMwzAMwzAMwzAMEfFAEcMwDMMwDMMwDMMwDPP/8EARwzAMwzAMwzAMwzAMQ0Q8UMQwDMMwDMMwDMMwDMP8PzxQxDAMwzAMwzAKYcyYMTRo0CBjF4NhGIYxYTTGLgDDMAzDMAzDMI9GEIQaz8+dO5eWLVtGvKgxwzAM83fggSKGYRiGYRiGMQEyMjLE/2/YsIHefvttOn/+vHjM2tqarK2tjVE0hmEYRkHwq2cMwzAMwzAMYwK4ubmJW+PGjUkQBMkxa2vrSq+ede/enV5++WV69dVXyd7enlxdXWnVqlWUl5dHY8eOJRsbGwoICKBt27ZJfJ0+fZr69etH1tbW5OrqSqNGjaLbt2//jyNmGIZhjAEPFDEMwzAMwzCMgvn666/JycmJEhIS6OWXX6YpU6bQsGHDKDw8nJKSkigyMpJGjRpF+fn5RER0//596tmzJwUHB9PRo0dp+/btlJWVRc8884yRI2EYhmH+F/BAEcMwDMMwDMMomLZt29Kbb75JzZo1ozlz5pBWqyUnJyd64YUXqFmzZvT222/TnTt36OTJk0REtHz5cgoODqYFCxZQYGAgBQcH05o1ayguLo4uXLhg5GgYhmGYuobnKGIYhmEYhmEYBdOmTRvx/2q1mhwdHal169biMVdXVyIiunnzJhERnThxguLi4qqc7+jSpUvUvHnzOi4xwzAMY0x4oIhhGIZhGIZhFIyZmZlkXxAEyTH9amo6nY6IiHJzc+mpp56iRYsWVdJyd3evw5IyDMMw9QEeKGIYhmEYhmEYRqR9+/b0008/ka+vL2k0/HWBYRimocFzFDEMwzAMwzAMI/LSSy/R3bt3afjw4XTkyBG6dOkS/f777zR27FgqLS01dvEYhmGYOoYHihiGYRiGYRiGEfHw8KADBw5QaWkpRUZGUuvWrenVV18lOzs7Uqn46wPDMIzSEQDA2IVgGIZhGIZhGIZhGIZhjA//JMAwDMMwDMMwDMMwDMMQEQ8UMQzDMAzDMAzDMAzDMP8PDxQxDMMwDMMwDMMwDMMwRMQDRQzDMAzDMAzDMAzDMMz/wwNFDMMwDMMwDMMwDMMwDBHxQBHDMAzDMAzDMAzDMAzz//BAEcMwDMMwDMMwDMMwDENEPFDEMAzDMAzDMAzDMAzD/D88UMQwDMMwDMMwDMMwDMMQEQ8UMQzDMAzDMAzDMAzDMP8PDxQxDMMwDMMwDMMwDMMwRMQDRQzDMAzDMAzDMAzDMMz/839TKGQNaC0gGQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index([('9eg7v2nkypUZbdyvSKSD9kg8FNwrEdTrfC2xdXWXmEpDAFEtYEn', '9ehJZvPDgvCNNd2zTQHxnSpcCAtb1kHbEN1VAgeoRD5DPVApYkk')], dtype='object', name='miner')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[39], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m data \u001b[38;5;241m=\u001b[39m sharkapi\u001b[38;5;241m.\u001b[39mget_shares()\n\u001b[0;32m----> 2\u001b[0m \u001b[43manalyze_pplns\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwindow_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_reward\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m27\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[38], line 79\u001b[0m, in \u001b[0;36manalyze_pplns\u001b[0;34m(data, window_size, block_reward)\u001b[0m\n\u001b[1;32m     77\u001b[0m plot_participation_over_time(participation)\n\u001b[1;32m     78\u001b[0m plot_participation_heatmap(participation)\n\u001b[0;32m---> 79\u001b[0m \u001b[43mplot_participation_distribution\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparticipation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;66;03m# plot_participation_change_rate(participation)\u001b[39;00m\n\u001b[1;32m     81\u001b[0m \u001b[38;5;66;03m# plot_cumulative_rewards(participation, block_reward)\u001b[39;00m\n\u001b[1;32m     82\u001b[0m \n\u001b[1;32m     83\u001b[0m \u001b[38;5;66;03m# Calculate some statistics\u001b[39;00m\n\u001b[1;32m     84\u001b[0m avg_participation \u001b[38;5;241m=\u001b[39m participation\u001b[38;5;241m.\u001b[39mmean()\n",
      "Cell \u001b[0;32mIn[20], line 62\u001b[0m, in \u001b[0;36mplot_participation_distribution\u001b[0;34m(participation)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_participation_distribution\u001b[39m(participation):\n\u001b[1;32m     61\u001b[0m     plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m---> 62\u001b[0m     \u001b[43mparticipation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mboxplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparticipation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     63\u001b[0m     plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistribution of Miner Participation\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     64\u001b[0m     plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMiner\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/plotting/_core.py:533\u001b[0m, in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, backend, **kwargs)\u001b[0m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;129m@Substitution\u001b[39m(data\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m, backend\u001b[38;5;241m=\u001b[39m_backend_doc)\n\u001b[1;32m    517\u001b[0m \u001b[38;5;129m@Appender\u001b[39m(_boxplot_doc)\n\u001b[1;32m    518\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mboxplot_frame\u001b[39m(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    531\u001b[0m ):\n\u001b[1;32m    532\u001b[0m     plot_backend \u001b[38;5;241m=\u001b[39m _get_plot_backend(backend)\n\u001b[0;32m--> 533\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mboxplot_frame\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    534\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    535\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    536\u001b[0m \u001b[43m        \u001b[49m\u001b[43mby\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    537\u001b[0m \u001b[43m        \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    538\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    539\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    540\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrid\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    541\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfigsize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    542\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlayout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    543\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    544\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    545\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/plotting/_matplotlib/boxplot.py:492\u001b[0m, in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mboxplot_frame\u001b[39m(\n\u001b[1;32m    478\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    479\u001b[0m     column\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    488\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds,\n\u001b[1;32m    489\u001b[0m ):\n\u001b[1;32m    490\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m--> 492\u001b[0m     ax \u001b[38;5;241m=\u001b[39m \u001b[43mboxplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mby\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrid\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfigsize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlayout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    505\u001b[0m     plt\u001b[38;5;241m.\u001b[39mdraw_if_interactive()\n\u001b[1;32m    506\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/plotting/_matplotlib/boxplot.py:469\u001b[0m, in \u001b[0;36mboxplot\u001b[0;34m(data, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m    467\u001b[0m     columns \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 469\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    471\u001b[0m result \u001b[38;5;241m=\u001b[39m plot_group(columns, data\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mT, ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    472\u001b[0m ax\u001b[38;5;241m.\u001b[39mgrid(grid)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4106\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   4107\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 4108\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   6198\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m   6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m   6204\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[1;32m   6248\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[0;32m-> 6249\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   6251\u001b[0m     not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m   6252\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index([('9eg7v2nkypUZbdyvSKSD9kg8FNwrEdTrfC2xdXWXmEpDAFEtYEn', '9ehJZvPDgvCNNd2zTQHxnSpcCAtb1kHbEN1VAgeoRD5DPVApYkk')], dtype='object', name='miner')] are in the [columns]\""
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = sharkapi.get_shares()\n",
    "analyze_pplns(data, window_size=1000, block_reward=27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ed7cd652-0f5e-46a9-8f0b-639c4947dc74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = prepare_data(data)\n",
    "pplns_data = calculate_pplns_participation(df, 1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4b41759b-11df-4e62-aeb9-8e0493b7c500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.last of miner                             9eg7v2nkypUZbdyvSKSD9kg8FNwrEdTrfC2xdXWXmEpDAFEtYEn  \\\n",
       "created                                                                                 \n",
       "2024-09-17 14:21:45.106828+00:00                                                NaN     \n",
       "2024-09-17 14:22:20.896486+00:00                                                NaN     \n",
       "2024-09-17 14:22:35.447983+00:00                                                NaN     \n",
       "2024-09-17 14:22:44.193679+00:00                                                NaN     \n",
       "2024-09-17 14:23:04.594473+00:00                                                NaN     \n",
       "...                                                                             ...     \n",
       "2024-09-17 21:40:09.254304+00:00                                                1.0     \n",
       "2024-09-17 21:40:10.275768+00:00                                                1.0     \n",
       "2024-09-17 21:40:15.867156+00:00                                                1.0     \n",
       "2024-09-17 21:40:18.415044+00:00                                                1.0     \n",
       "2024-09-17 21:40:22.150441+00:00                                                1.0     \n",
       "\n",
       "miner                             9ehJZvPDgvCNNd2zTQHxnSpcCAtb1kHbEN1VAgeoRD5DPVApYkk  \n",
       "created                                                                                \n",
       "2024-09-17 14:21:45.106828+00:00                                                NaN    \n",
       "2024-09-17 14:22:20.896486+00:00                                                NaN    \n",
       "2024-09-17 14:22:35.447983+00:00                                                NaN    \n",
       "2024-09-17 14:22:44.193679+00:00                                                NaN    \n",
       "2024-09-17 14:23:04.594473+00:00                                                NaN    \n",
       "...                                                                             ...    \n",
       "2024-09-17 21:40:09.254304+00:00                                                0.0    \n",
       "2024-09-17 21:40:10.275768+00:00                                                0.0    \n",
       "2024-09-17 21:40:15.867156+00:00                                                0.0    \n",
       "2024-09-17 21:40:18.415044+00:00                                                0.0    \n",
       "2024-09-17 21:40:22.150441+00:00                                                0.0    \n",
       "\n",
       "[5425 rows x 2 columns]>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pplns_data.last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f69d3ff6-0a97-49e7-a2d3-a89b1d2cec6e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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