examples/extreme_response_full_sea_state_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extreme Conditions Modeling - Full Sea State Approach\n",
    "Extreme conditions modeling consists of identifying the expected extreme (e.g. 100-year) response of some quantity of interest, such as WEC motions or mooring loads. \n",
    "Three different methods of estimating extreme conditions were adapted from [WDRT](https://github.com/WEC-Sim/WDRT): full sea state approach, contour approach, and MLER design wave. \n",
    "This noteboook presents the full sea state approach. \n",
    "\n",
    "The full sea state approach consists of the following steps: \n",
    "1. Take $N$ samples to represent the sea state. Each sample represents a small area of the sea state and consists of a representative $(H_{s}, T_{e})_i$ pair and a weight $W_i$ associated with the probability of that sea state area. \n",
    "2. For each sample $(H_{s}, T_{e})_i$ calculate the short-term (e.g. 3-hours) extreme for the quantity of interest (e.g. WEC motions or mooring tension).\n",
    "3. Integrate over the entire sea state to obtain the long-term extreme. This is a sum of the products of the weight of each sea state times the short-term extreme. \n",
    "\n",
    "See more details and equations in\n",
    "\n",
    "> [1] Coe, Ryan G., Carlos A. Michelén Ströfer, Aubrey Eckert-Gallup, and Cédric Sallaberry. 2018. “Full Long-Term Design Response Analysis of a Wave Energy Converter.” Renewable Energy 116: 356–66.\n",
    "\n",
    "**NOTE:** Prior to running this example it is recommended to become familiar with `environmental_contours_example.ipynb` and `short_term_extremes_example.ipynb` since some code blocks are adapted from those examples and used here without the additional description. \n",
    "\n",
    "We start by importing the relevant modules, including `waves.contours` submodule which includes the sampling function, and `loads.extreme` which inlcudes the short-term extreme and full sea state integration functions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mhkit.wave import resource, contours, graphics\n",
    "from mhkit.loads import extreme\n",
    "import matplotlib.pyplot as plt\n",
    "from mhkit.wave.io import ndbc\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain and Process NDBC Buouy Data\n",
    "The first step will be obtaining the environmental data and creating the contours.\n",
    "See `environmental_contours_example.ipynb` for more details and explanations of how this is being done in the following code block. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter = \"swden\"\n",
    "buoy_number = \"46022\"\n",
    "ndbc_available_data = ndbc.available_data(parameter, buoy_number)\n",
    "\n",
    "years_of_interest = ndbc_available_data[ndbc_available_data.year < 2013]\n",
    "\n",
    "filenames = years_of_interest[\"filename\"]\n",
    "ndbc_requested_data = ndbc.request_data(parameter, filenames)\n",
    "\n",
    "ndbc_data = {}\n",
    "for year in ndbc_requested_data:\n",
    "    year_data = ndbc_requested_data[year]\n",
    "    ndbc_data[year] = ndbc.to_datetime_index(parameter, year_data)\n",
    "\n",
    "Hm0_list = []\n",
    "Te_list = []\n",
    "\n",
    "# Iterate over each year and save the result in the initalized dictionary\n",
    "for year in ndbc_data:\n",
    "    year_data = ndbc_data[year]\n",
    "    Hm0_list.append(resource.significant_wave_height(year_data.T))\n",
    "    Te_list.append(resource.energy_period(year_data.T))\n",
    "\n",
    "# Concatenate list of Series into a single DataFrame\n",
    "Te = pd.concat(Te_list, axis=0)\n",
    "Hm0 = pd.concat(Hm0_list, axis=0)\n",
    "Hm0_Te = pd.concat([Hm0, Te], axis=1)\n",
    "\n",
    "# Drop any NaNs created from the calculation of Hm0 or Te\n",
    "Hm0_Te.dropna(inplace=True)\n",
    "# Sort the DateTime index\n",
    "Hm0_Te.sort_index(inplace=True)\n",
    "\n",
    "Hm0_Te_clean = Hm0_Te[Hm0_Te.Hm0 < 20]\n",
    "\n",
    "Hm0 = Hm0_Te_clean.Hm0.values\n",
    "Te = Hm0_Te_clean.Te.values\n",
    "\n",
    "dt = (Hm0_Te_clean.index[2] - Hm0_Te_clean.index[1]).seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Sampling\n",
    "The first step is sampling the sea state to get samples $(H_s, T_e)_i$ and associtated weights. \n",
    "For this we will use the `waves.contours.samples_full_seastate` function. \n",
    "We will sample 20 points between each return level, for 10 levels ranging from 0.001—100 years return periods. \n",
    "For more details on the sampling approach see\n",
    "\n",
    "> [1] Coe, Ryan G., Carlos A. Michelén Ströfer, Aubrey Eckert-Gallup, and Cédric Sallaberry. 2018. “Full Long-Term Design Response Analysis of a Wave Energy Converter.” Renewable Energy 116: 356–66.\n",
    "\n",
    "> [2] Eckert-Gallup, Aubrey C., Cédric J. Sallaberry, Ann R. Dallman, and Vincent S. Neary. 2016. “Application of Principal Component Analysis (PCA) and Improved Joint Probability Distributions to the Inverse First-Order Reliability Method (I-FORM) for Predicting Extreme Sea States.” Ocean Engineering 112 (January): 307–19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return levels\n",
    "levels = np.array([0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100])\n",
    "\n",
    "# points per return level interval\n",
    "npoints = 20\n",
    "\n",
    "# Create samples\n",
    "sample_hs, sample_te, sample_weights = contours.samples_full_seastate(\n",
    "    Hm0, Te, npoints, levels, dt\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now plot the samples alongside the contours. \n",
    "First we will create the different contours using `contours.environmental_contours`. \n",
    "See `environmental_contours_example.ipynb` for more details on using this function. \n",
    "There are 20 samples, randomly distributed, between each set of return levels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAEYCAYAAABRMYxdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydd5wU9f3/n7O93O713o87jt6LSEcFC4K9G2wxid0kXzXGWJJoNMnPGDWJMbFFjWhs2MUCohTp9Sh3XO97bXud+fz+WDhA2lVAnOfjsWyb+exndo/5vOZdJSEEKioqKioqKionE5rjPQEVFRUVFRUVlb5GFTgqKioqKioqJx2qwFFRUVFRUVE56VAFjoqKioqKispJhypwVFRUVFRUVE46dMd7Aj0hKSlJ5OXlHe9pqKioqKioqBxn1q1b1yKESP7u699LgZOXl8fatWuP9zRUVFRUVFRUjjOSJFUd6nXVRaWioqKioqJy0qEKHBUVFRUVFZWTDlXgqKioqKioqJx0qAJHRUVFRUVF5aRDFTgqKioqKioqJx2qwFFRUVFRUVE56VAFjoqKioqKispJhypwVFRUVFRUVE46vpeF/lRUVFRUDo9fVmgJR2gJRWgJhTsfuyIyXlnBI++5jygEFYWIEEQEyEIQFgIB6CTQShJ6SUK3596q0xCj1WLVRu9tOg0Jeh1JBh1J+93bdVokSTreX4PKDxxV4KioqKh8jxBC0BKOUOMPUR0IUROI3lf7o4+bQmG8snLIfXUS+wSKLnpv0miwajRoJdDtETOSFBU7EQERRRDZI3zqAmHckQAeWcErywQUccjPsWg1ZJsMZBkNZJsNZBn15FuMFFlM5JuN6DWq+FHpf1SBo6KionICIoSgLhhmpzfALm+AnXtuu3yBgwRMgl5LjsnI0BgzpxvtB1pU9jxONOiwarV9OseQotAWlg+wEjlCEeqDIWoDYWoDIda5vHRE5M59dBLkm40UWkwMspoYabMw3GYmw6hXrT4qfYoqcFRUVFSOM4oQlPmCbHL72OjyscntY7v3QCGTbNBRbDFxaVoCBRYjOSYD2XtuMbqeCRe/HxwOCATAZILkZDCbu76/QaMhzaghzag/4nbuiMxuX5AyX4BSX5BSb4BSX4BPW5zsPcJEvY4RNjOjbBYmxlkZZ7f2+LhUVEAVOCoqKirHHEcozKoOL+tdXja6fWxx+/HsETMWrYbhMWYuTUug2GpioNVEsdVEgr5vT9d+P1RVgdEIViuEQtHnubndEzldwabTMspuYZTdcsDrPllhu8fPJrePLR4/m90+/lrVhFIVzYAZFmNmYpyVSXExTIm3YVcFj0o3kIQ4tA/1RGbcuHFC7SauoqLyfaEmEGJVh2fPzctufxAAo0ZiaIyZkTYLI21mRtktFFlMaI+Bq6a6GmQ5KnD2EgyCVgs5Of3+8YfFE5FZ5/LxrdPDt3tEoF8RaCUYa7cyPd7GzAQbI+2WY/I9qZz4SJK0Tggx7ruvqxYcFRUVlT7GEQqzrM3N0nY3K9o91AXDAMTqtEyMtXJlRiKnxFkZFmPGoDk+1ToCgajlZn8MBvB6j8t0OonRaZmeYGN6gg2Ixvmsd/lY2uZmSZuLP1c28qfKRhL0WuYkxXJWUizT4m2YtGrVE5UDUS04KioqKr0kpCisdfpY2uZiaZubzR4/EA3+PTUuhkl7boOsJjQniNXhRLXgHI3WUIRl7W4Wtzj5vNWFW1awajXMSrBzbkocZyTaMati5wfF4Sw4qsBRUVFR6QFt4Qiftbj4tMXJV+1uvLKCVoJxdiszEmzMSLAzwmY+Yd0o+8fgGAzRGJxgsH9icPqLoKKwvN3Dxy1OPmlx4ghFsGk1zE2J48LUeCbFxZyw379K36EKHBUVFZVeUuUP8mmLk49bnHzb4UUB0o16zki0MyPB9r0LhO1KFlVvM62OFbIQrGj38GZTOx86OvDICulGPRenxnNlRiK5ZuPRB1H5XqIKHBUVFZUeUOYLsKipgw8dHZR4AwAMspo4KymWOUmxjLSZj1n9Fln2EQg0EAw2Egq3EQ53EA6373frIBJxoygBZDmAogRQlOCex0FAIEkaQLNnzhokSYMkadFqrWi1VnRaK1qdFZ02hnAkluamHGJiYrFY4pCkBGQ5kYEDE7HbE0/YujV+WWFxq5P/NbbzZasLAcxIsHFVRiKzE2PVQoMnGarAUVFRUeki1f4gi5o7WNTcwVaPHwmYEGvlrKRYzkyOJa+frAGKEiEQqMHrK8fnKyfgryMQbCAQqCcQqCcS6TjkfjqdDb0+Hr0+Hp3WhkZrQqsxodGa0GhMaDVGNBojSBoQCgKx514BoaCICLLsQ454iMgeZNmLHPFSW2clFPKg0ez73HBYj1Yrk5bmxGzOwmTKxGTKwmLJw2otwmoZgNGYdlzEz6GsTW1SiNca2vhvQyv1wTCpBh0/ykhiQWYSSQY1z+ZkQBU4KioqKkegORjm3eZ2FjV3sM7lA2Cs3cL8lDjOTYkj3Wjos89SlAg+XzluTwlezy58vnK8vnL8/mqECHdup9PZMBkzMJoyMJnS9zxOx2RMQ69PQG9IQK+LRaM5cqG9nrJrVzTTSlH8hEJthMNthEJttLe3kpGxA3+gloC/Fn+ghkjE1bmfVhuD1VqILWYwNtswbLahxMQUo9H03Xf4XY4WUxRRBF+2uXixroUv29yYNBIXpSZwY3YyA62mfpuXSv+jChwVFRWV7xCQFRa3unijsY0lbS4iAYmicAwzYmI5O9PG6Gxjr+NNFCWI27MDt3sbbvc2PO4SPN6de1xGIEl6zOZcrJZ8LNYBWCz5WC0DsFgK0Otj++Aoe05XM62EEITCrfi8ZXi9ZXh9ZXg8u/B4SohE3ED0OGNiiomNHUtc3HjiYsdhNCYf87kC7PIG+Fetg/81thFQBKcl2LkjL5Xxsd/Jm1f5XqAKHBUVFRWii/EGt483Gtt5t6mdjohMulHPebHxTAwmUBxv6lVWUTDUgtO5DqdzPU7nelyurQgRAkCns2OLGUKMbQi2mCHYbEOwWAr6zQLTW3qbaSWEwO+vxu3eitu9DadrIy7XJhQlGstkNucSFzeBxIQpJCRMRq+P7/Fc91qb9veMCRGt6zNw4KH3aQlF+E99C/+uddAWlpkaH8OduWmcGh/T43moHHtUgaOiovKDpi0c4Y2GNl5taKXUF8SkkTg7OY5L0xKYEh9DXY3Uo7owgUA9be0raG9fibNjPf5ANQCSZMBuH0Zs7Bhi7aOx2YZhMmWesIG5h6Ovs6gUJYzbvY0O51o6OtbQ0bF6j3tLwm4bTkLiVJISZ2C3j9oTEN01elPXxyvLvFzXyt9qmnGEIpwSa+WX+WlMibf17CBVjimqwFFRUfnBIYTgW6eXl+tb+cDRQVARjLVbuDw9kXkpcQekdHfVAhAKtdHesYr2thW0ta/A768CQK9PIC5uPLGxY4iLHYPNNjQa2KtyRBQlgtu9hda2b2hrW4bLtQkhZAyGZJKTTic5eTbx8accNX6nL+r6+GWFVxtaebqqmcZQmBnxNn49IJ3hNsvRd1Y5bqgCR0VF5QdDRzjCm03t/KeulV2+ADathovSEvhRRiKDYw692h3OAqDRKMTFbaaldQmtrUtxu7cC0UDa+LgJxCecSkL8qVitRd2yOKgcmnDYSWvrVzgci2lt+wpZ9qHVxpCSPIe0tPnEx5+CJB261lBfWZsCssKLdS38taqJ9ojMBanx3J2fptbSOUE57gJHkqTngblAsxBi2J7X/gScC4SA3cC1QoiOo42lChwVFZVDscPr57naFt5sbMOvCEbbLFydmcj8lDis2iMX4NvfAiBJbhyO1TQ1rcFo+gCNpgHQEBs7msSEaSQkTMZmG45Go6YZ9yeyHKS9fTnNzR/T7FiMLHswGFJITZ1Letp52GxD+/XzXRGZv1U382xNMxEB12cl8Yu8NGzfo2KOPwROBIEzDfAA/9lP4MwGvhRCRCRJegxACHH30cZSBY6KispeFCH4vNXFv2sdLGv3YNJIXJgaz7WZSQzrhmshGGqhuvoLSnetwtGyDYPBS2KiICNjIkmJM0lMnNqrIFiV3iHLAVpav6SxcRGtrV8hRBibbRgZGZeSlnouOl3/xcs0BsP8qaKB/za0kWTQ8euCdC5JSzhh+or90DnuAmfPJPKAD/YKnO+8dz5wkRDiyqONowocFRUVT0RmYWMbz9U6qPCHSDfquTYziSvTE0nsYgG3QKABh+NTmh2f0tGxFlAwm3NJSZ5DUtJp2O2jVCvNCUg43EFj03vU1y3E492JVmshNWUuWVlXY7MN6bfP3eT28etdtax1+Rhts/DwwEzG2NXU8uPN90HgvA+8LoR45TD73gjcCJCTkzO2qqqqP6eqoqJyglLlD/LvWgevNbThkRXG2S3ckJXMOclxXSrBHwq10NT8EU2N7+F0bQDAai0iJflMklPOJMZa/L3LdPqhIoTA5dpEff3rNDa9j6L4iYubSE72dSQlzTxsrE5vUITgraZ2fre7HkcowrWZSdxbkE6M6rY6bpzQAkeSpF8D44ALRBcmpFpwVFR+eGzz+PlbdTOLmtuRgPkp8dyQlcxo+9HdUJGIF0fLZzQ1vUdb2zcIIRNjLSYl9RxSks/Cai3o/wNQ6VfCYRf1Da9TW/MfAsF6zOYcsrOvIyP9YrTavq9U7I7IPFrewPN1LaQb9Tw6MIvZSce3MOMPlRNW4EiSdA3wE+A0IYSvK+OoAkdF5YeBEIKVHV6erm7iyzY3Vq2GqzMS+Ul28lFbJwgh09a2nIbGt3E4PkdR/JiMGaSmzSMtdR4xMcX9MufvS/ftkxVFieBoWUxN9fM4XRswGJLIybmBzIwr0On63p20zunlFztr2OENcG5yHI8MzCTZcGIWbjxZOSEFjiRJZwKPA9OFEI6ujqMKHBWVkxtFCD5tcfJ0dTPrXD4S9TpuzEpmQWYicfojx8T4/TXUN7xJQ8NbBIMN6HRxpKaeTVrqfGJjx/RrKndf1GJR6Tva21dTWfk32tq/QaeLIyf7WrKzF/R5QHJIUfhHtYPHqxqxajX8uTibs5Pj+vQzVA7PcRc4kiS9BswAkoAm4AHgV4ARaN2z2SohxE+PNpYqcFRUTk4iSjS+4enqJkp9QXJMBm7KSeHStATM2sMLE1kO4nB8Sn3D/2hvXwFIJCRMISPjEpKTTjtmBfd6U01Xpf9wOjdSWfV3Wlq+QK+PJy/3Z2RmXoVW27d/Fzu9AW4tqWKzx8/FafE8XJR1QDFJlf7huAucvkQVOCoqJxdhRfC/pjb+WtlEVSDE0BgTt+akMjc5Dt0RAoc9np3U1b9GY+N7RCJOTKYsMtIvIj39QkymjGN4BFF60g9J5djhcm9l9+4/09b2NUZjGgX5d5CWdn6fZsqFFcFfqhr5a1UTaQY9fx2co7Z86GdUgaOionLCEVYE/2ts44mqJqoDIUbEmPllfhpnJNoPm8mkKGEcjsXU1r5Mh3MNGo2B5OQ5ZKRfTHz8pONaTVi14Hw/aGtfye7df8bl2ojVWkRR0X0kJkzp089Y7/Jya0k15f4gd+Sm8ou8tCOKdZWeowocFRWVE4aQovBGYzt/rWqiJhBipM3ML/KOLGyCwSbq6hZSV7+QUKgZkymbrKwryUi/6IQpwKfG4Hx/EELgcCymbPej+P3VJCWdTlHhr7BY8vrsM7yyzK931bGwsY1TYq38Y2juUYPjVbqPKnBUVFSOO2FFsLCxlb9WNVEbCDPKZuGX+WmclmA7pLARQtDRsYbaupdxOBYjRITExOlkZV5NYuK0fqlz0lvULKrvF4oSpLrmRSor/4aihMjJvpa8vFv6NOPqf41t3L2rFpNG4snBuZyeaO+zsVVUgaOionIcUYTg3eYO/ljRQKU/xBi7hV/kpTHrMMJGUcI0NX9IdfVzeDwl6HR2MtIvJjPzij69wlZR2Usw2Mzu3X+iofFtTMYMiot/S1LSzD4bv8wX4MatlZR4A/wyL42f56WqrR76CFXgqKioHHOEEHzW6uIP5Q1s9wYYGmPinvx0Tj+MKyocdlFf/xo1tf8hGGzEYikkJ/ta0tLmo9WqZhCV/qe9Yw07dtyHz1dGSsrZDCz6DUZjSp+M7ZcV7t5VwxuN7cxOtPP0kFw1y6oPUAWOiorKMeWbdjd/KG9gnctHvtnA3fnpzEuJO+RVq99fS03ti9TXv4Ese4mPn0RO9vUkJk4/rkHDfYEQAr/fj9frxefzdd77fD78fj+hUIhQKEQ4HO58HIlE2HtuFkJ03iRJQq/Xo9Pp0Ol0nY+NRiMWiwWz2YzFYul8bLPZsNvtaI/SSV3lQBQlRFXVs1RW/Q2NxkhR4X2kp1/YJy08hBA8V9fCA2V15JuNvDAsnyJr31da/iGhChwVFZVjwgaXj0fLG/iq3U2GUc8v8tK4JC3hkH2iXO6tVFU9i8PxCSCRmnIOOTnXY7MNPfYT7yFCCLxeLy0tLbS1teF0Og+4uVwuIpHIIffV6/UYDAYMBkPnY71ej16vR5KkzgV172MhBOFwmEgkQiQS6XwcDAbx+XwoinLIz7HZbMTGxnbekpKSSEpKIjk5GbMaIHRYfL4Ktu+4l46O1SQlncag4ocxGpP7ZOwV7R5+vK2SoKLwz6F5nKbG5fQYVeCoqKj0K1X+II+UN7CouYMEvZbbc1NZkJGE6RAF+to71lBV+Xda25ah1caQmXk52VkLMJnSj8PMj8zeoGG/XxAKOYlEGnG5mmhpaaG1tZXW1laCweAB+3xXUNjtdqxWa6d1Ze9Nr++7kv5CCILBIH6/v9NC5Ha7DxJcTqcTWZY794uJiSE5OZnk5GQyMjLIzMwkMTERjeb7bTnrK4RQqKl9id27/4RGY2ZQ8W9JTT2nT8auC4S4ZksF2zx+HhmYxTWZSX0y7g8NVeCoqKj0C+3hCE9UNfFCbQtaCX6ancJNOSnYvhNbIISgre1rKiv/TodzDXp9AjnZ15OVdWWfl87vLbIs43A42L27ni1bXDidTbS3N+P3y8iylthYJ0lJVhITEztvSUlJJCQkYLfb0en6rnBcX6MoCh0dHTgcDhwOBy0tLTgcDpqbmwmHwwAYDAYyMjLIyMggOzubvLy8H7ylx+stp2T7/+FybSQt9TyKix9Cp4vp/bgRmZ+WVPFZq4ufZCVzf2EGWjX4uFuoAkdFRaVPCSoKL9a18JfKJpwRmcvSE7grP+2gOh9CKDhaPqOy8h+43VswGtPIzfkxGRmXnjCBwy6Xi9raWmpra6mrq6O+vp5wOIzTaUerNZKenkBSUhIpKSnY7UmkpiZRWHhs2j/0NYdLY1cUhZaWls7jr6uro6mpqdPak56eTl5eHvn5+eTk5GAy/fDiRhQlQmXVP6ioeBKzOZthQ/+K3T681+PKQvBAWR3/rm3hzCQ7fx+Sh+UIrUlUDkQVOCoqKn2CEIL3HB08sruBqkCImQk2fjMggyEx5u9sp9Dc/BEVlU/j9ZZiNueQm/tT0tPOR6M5vsXOOjo6qKqqorKyksrKStrb2wHQarWkpaWRmZlJVlYWfn8WGRlxaPdbbL7PrRe6W4gwEolQV1dHRUUFlZWV1NTUIMsykiSRm5tLcXExxcXFJCQkHPuDOY60d6xh27Y7CIVaKRxwF9nZ1/RJMPxztQ5+U1rHWLuVl0fkH7WxrEoUVeCoqKj0mtUdHh7aXc86l4/BVhP3D8hg5neCI4VQcDgWU17xV7zeXVitReTl3kRKytl92vOnO7hcLsrLyzsFTUdHBwAmk4m8vDxyc3PJzs4mLS3tAPfSydZ6obfHEw6Hqampoby8nF27dtHc3AxAcnIyxcXFDB48mIyMjD7JNjrRCYc7KNl+Ny0tn5OcdAZDhvypT1ytHzR3cFNJFQUWIwtHDiDN2HdxWicrqsBRUVHpMTWBEA+V1fGBw0maQc9dBWlcmpZwQKyAEIKWli8or/grHk8JFksB+fm3kZpyzjFP9ZZlmZqaGkpLSyktLe1ciM1mM7m5ueTl5ZGXl0dKSsoRg2lPttYLfd0MtK2tjV27drFz506qqqpQFIWEhASGDx/OiBEjSExM7LvJn4AIIaipfZGysj9gNucyfPjfibEW9Xrcb9rdLNhSQYJex+sjB1Bg+X66Q48VPRY4kiR1xfaoCCE6eji3bqMKHBWVY4NXlnm6qpl/1DQjAbfkpPLTnGSs+9VVEULQ2vYV5eVP4HZvwWzOJT//NtJSzz2mrRRcLhelpaWUlZVRXl5OMBhEo9GQk5NDYWEhhYWFRxU0h+Jkar3QnxYpv9/P9u3b2bx5M5WVlQBkZGQwYsQIRo4ceVIHKbe3f8uWrbeiKAGGDP4jKSln9nrMjS4fV2zejQaJN0YNOMgFrLKP3gicAFAPHMnmqBVCHDODrSpwVFT6F59P8Op2J38pdeCQQpxXGMODQ9PJNO2LnRFC0N6+gvLyv+B0bcBkyiI/71bS0s47Zq6olpYWduzYwfbt26mrqwPAbrdTWFhIUVER+fn5xzQYVoQVFH8ExR9G8UWit0AEEVEQYWXffViJmk5gz5l1T70bDUg6DZJBi2TQIOmj9xqTDo1Vj9aqR2PVI+l6ZhE7VhYpp9PJ1q1b2bJlC42Njeh0OoYNG8a4cePIzMw8KV1YgWAjW7bcgsu1gby8WyjIv73XlssyX4CLN+4mqCi8MXIAw2yWPprtyUVvBM4GIcTo3m7Tl6gCR0Wl/1jT7OOer5vZFPQyONbIz7PSGWy0HrAIOp0bKNv9Jzo6vsVoTCc/72bS0y/s9+BhIQSNjY2domav6ykjI4PBgwczcOBAUlJS+mUBVYIykVY/kdYAsjOI7AoiO0PIrhCKK4jsDiFChy60dxA6CUkjwX6nXyH2/CMfPWxAMmrRxOjRxRrRxpvQxhnRxZvQxhvRJZnR2g2H/Q6OtUWqoaGBtWvXsnnzZsLhMGlpaYwbN44RI0ZgMHw/Omt39TtTlCA7dj5AQ8P/SEk5myGD/4RW2zuBXekPcuGGMryywuujBjBSFTkH0RuBYxJCBHq7TV+iChwVlb6nJRThsYoGXtrowq7RcvuAFM5LjUcrSZ1ujMTEcnaX/xmH41MMhiTy8m4mM+NSNJr+jRFoampiy5YtbNu2jfb2diRJIicnh8GDBzNo0CDi4uK6PeahFi2TUSB3BAk3egk3+Yg0+6Kipi2A4gkfOIBOQms3orUb0MYa0doMaKx6NBYdGrNuz70ejUkbtcToJCS9NipujiDAhCwQERkRUhAheZ9VyBtG9oZR9txkTxi5I4jcHkB2hw4QS5JRiz7Fgi7Fgj51zy3LhtZ6/AJWA4EAW7ZsYc2aNTQ3N2OxWJgwYQITJkzAYjlxF+3uWr2EEFRXP0vZ7j9ht49gxPB/9rr6cZU/yIUby3BFZBaOHMAYe991Oj8ZUIOMVVRUDklEEbxQ18KfKhvwyQrzQmncOjCJWMM+N1Mg4GDnrv9gNP4TjcZEbs6PSUq6jvZ2a79ZAtrb2zvdHM3NzUiSREFBAUOGDKG4uJiYmJ4XWfP7oaJURtPuRWrz4qv34WsMkRpxYhL7hIw21ogu0YQu0Yw20YQuIfpYF29EMutOGFeLiCjIziCR9gCRFn+nOAs3+Q4QZtoEE4asGAxZNgzZNgxZNiT9sQ0AF0JQVVXF8uXLKS0tRa/XM2bMGCZNmtQjodrf9DRuqdnxKdu2/QKDPp6Ro57vdfBxTSDERRvKaAtHeHN0oWrJ2Y9eCxxJksYBvwZyAR1Rp7EQQozoy4l2BVXgqKj0Das7PNyzq5YSb4CZCTYeKszE1GrqPKFHIh5qa1+msuptNJowo0fPIj/vJmQ5qV9iObxeL9u2bWPLli3U1NQAkJ2dzfDhwxk6dChWa8+uXIUsCDd5CdW4CdW4qdgSINgcwKCJnv80Fi0i3oox1UzecCP6VCv6VAsa0/e/DonsDUctUrVuQrUeQjVu5I49rSV0EsYcO8YBcRgHxEYFTw/je3pCU1MTK1asYMuWLQCMHDmS6dOnn1BCpzeZZy7XFjZtvgFFiTBq5L+IjR3Tq7nUBULM31CKT1Z4e3Qhg6xq4DH0jcDZCfwfsAXodDQLIar6apJdRRU4Kiq9ozUU4ffl9bzW0EamUc/vijI5KykWSZKi1o2KIO3t79LQ+DyBgJ/Y2DOZfOrVJCRkA32bjSPLMrt372bDhg3s3LkTRVFISUlh+PDhDBs2jPj4+G4fnwjLBKvcBCucBMudhGvd0cBeQGPRUa1NJLbAgiHTiiHNijbG8L0u4NddZE+IULWbYLmT4O4Owo1eECDpNRgHxGEakoB5UCJa+7GJkeno6GDlypXsPa+PGzeOqVOn9spK11f09m/d769mw8YFBIPNDB/2NElJM3s1nwpfkPkbSpGARWOKyDOrKeR9IXC+EUJM6fOZ9QBV4Kio9AxFCF5taOWR3Q24ZZmfZqdwZ15qZ9q3EILm5g/ZVvJXmpp8mM1TGFh0Hfn5xQdYZvqinkpraysbNmxg06ZNuN1uLBYLI0eOZOTIkaSlpXXvuEIyoSpXdMGucBKqcUeDdSXQZ8RgzLNjyIm6ZLQJJmpqpJOqgF9vUXxhguVOArs7COxoQ26PWnj0WTGYBydiHpaIPrX/4z46OjpYtmwZGzZsQKfTccopp3Dqqaf2KsW8t0HVfZF5Fgy1sGnTdXg8Oxg86FHS0y/o2cHsYYfXzwUbyrBoNSwaXXRAduMPkb4QOKcBlwNfAJ2tc4UQb/fVJLuKKnBUVLrPZrePe3bVst7lY1KclUcHZlNs3Zfh4XRtorT09zid64mJGUxR4a9ISJh8yLF6elUbCoXYtm0bGzZsoLq6GkmSKCoqYvTo0RQVFXW5SaUQgkizj8CudgK72glWOCEiQAP6TBvG/FiMBbEY8+yHdDOdbAX8+hIhBJEmH/7trQS2t0XFogB9mhXzqGQsI5PRxfdv6n1raytLlixh69atWCwWTjvtNEaPHt2jGkZ98Tv3ReZZJOJh85af0d6+guLi35GVeUX3BvgOm90+LtxQRppRz3tjioj/Abd16AuB8wowCNjGPheVEEJc18X9nwfmAs1CiGF7XksAXgfygErgEiFE+9HGUgWOikrXcYYjPFbRyIt1LSTodTxYmMGFqfGdAbKBQD27d/+ZxqZFGAxJDCj4BenpFx6xSF93F47W1lbWrl3Lhg0bCAQCJCYmMnr0aEaMGIHdbj94h0OgBCIEyzqiomZnO7Izep2lS7FgGhiPqSgOQ54djbFrJ/qTqYBffyK7Q/i3tODb2Eyo2g2AIc+OZUwKlpEpaIz9V8yxtraW8vJy9Ho9Wq0Ws9ncrU7tkUj0/ruWRoDj0fBdCEE43IaiBNDpYnvdjTyoKDhCEQwaiWT9iRP03l+YTCaysrLQ6w/MBuyTGBwhRHFPJyZJ0jTAA/xnP4HzR6BNCPGoJEn3APFCiLuPNpYqcFRUjo4Qgreb2nlwdz2toQjXZCZxd34asXuu9CIRL1XVz1Jd/W9AISf7enJzf9rlk+7RBIIsy5SWlrJmzRp2796NRqNh8ODBjBs3jry8vC6djGVXCH9JK/6SVoK7O0AWSEYtpsI4jMXxmAbGo4v74XW1Pl5EWv34NjnwbWwm0uxHMmqxjE7BOiENQ0bfx8tUVFRgs9kwm8243W4URcFsNmO329Fqjy6sAgE4lNFHUaJ/s8cDIRT8/hoiERdGY1qvU8g7whGq/CFi9VpyTYevf/R9RwhBa2srbreb/Pz8A947nMDpjoZdIUnSECFESQ8nt0ySpLzvvDwfmLHn8UvAUuCoAkdFReXIVPiC/N/OGr7p8DDaZuGVEQWdaaVCKDQ0vM3u8v9HKNRMauq5DCj4P8zmzG59htl8aHeUx+Nh/fr1rF27FpfLhc1mY+bMmYwZMwab7ejNCMMOH/5trQRKWjstBtpEEzGTMzAPSsSQa0PSHtvUZpUoukQz9lk52GZmE6p2413VgHdtI95VDRiybVhPzcAyIqnPfp9AINAphs1mMx6PB4/HQyAQwG63Y7FYjrigS1LUYvNdC87x1ACSpMFszsYfqCUYbATolciJ0+sIC0F9IEydFCbTqD8pRY4kSSQmJuJwOLq8T3cEzinARkmSKojG4PRFmniqEKJhz+NGILUXY6mo/OAJK4J/1DTzeGUjeknisYFZXJWR2NkUs8O5jl27HsLt3obdPpoRw/9ObGzfFCGvr69n5cqVbNu2DUVRKCgo4KyzzmLgwIFHvdqOtPjxbXbg2+Qg0uQDQJ8Zg/2MXMzDEtGlHHkhUzm2SJKEMdeOMddO3LkFeNc34/22gfbXd+L6pJKYqZlYx6f1iftq7++u0Wg6RU1HRwdOpxO/309cXNxh3VZ6fdR1qtHsEzuKcmDs2PFAkjSYTdkEgGCwEUmSMBiSejxeskFPSBG0hCIYNRLJhpOzA3l3zwHdETi97x52BIQQQpKkw/rLJEm6EbgRIOeHmOagonIU1ju9/GJnDdu9AeYmx/L7oizSjNETXTDooGz3YzQ2voPRmMbQIX8hNfXcXosGRVHYtWsXK1eupKqqCoPBwPjx4xk/fjxJSUc+YUc6gvj3iJpwnQeIxnbEnluAeWii6nr6nqCx6LFNySTm1AwCu9pxf1WD84NyXJ9XEzMpnZjJGWhj+i7LR6fTkZiYiM/nw+Vy4XA4sNlsWK3Wg/6eNZqomAmHo8JGkqLPuxmr3C9IkoTJlIUQgkCgAdBgMHSlt/WhyTDqOy05Ro0Gu+7YNbo9UemywOmnejdNkiSlCyEaJElKB5qP8PnPAs9CNAanH+aiovK9xBOR+UN5A8/XtZBm1PPS8HzmJMUCoChhamv/Q3nFkyhKiNzcn5GX+zN0ut6l/IZCITZu3MiqVatoa2sjNjaW2bNnM2bMmCM2t1R8YXyb9wSsVrqAaCpy7Nn5mEcko4tTa3p8X5E0EuZBCZgHJRCsduFZVot7aQ2e5XXETM7ENjUTjaVvLAuSJGG1WjEajTidTlwuF4FAgPj4+IOshXtFTk/55JNPuP3225FlmRtuuIF77rnnoG2CwSA/+tGPWLduHYmJibz++uvk5eUB8Ic//IHnnnsOrVbLk08+yZw5cwC47rrr+OCDD0hJSWH16g8IBOpAkjDou1/3CaLfSbbJQFAJUOUPUmQxYeqiqzAcDvOb3/yGt956C5vNhtFo5P777+ess87q9jzeffddBg4cyJAhQ7q9b19zVIEjSdJ6IcQRyy92ZZvD8B6wAHh0z/2iHoyhovKDZXGLk3t21dIQDHNtZhK/KkjHtufKra1tOTt3/Rafr4zExOkMLPoNFkv+UUY8Mi6Xi9WrV7N27VoCgQCZmZlcdNFFDB48+LBuKKEIgqXteNc14S9phYhAl2rBPjsXy4hkdElq6tLJhjHHjvGqIYQdPlyfV+NeUoNnZT22qVnETM7oswrROp2OhISETmtOc3MzcXFxvaqbsz+yLHPzzTfz2WefkZWVxfjx45k3b95Bi/dzzz1HfHw8ZWVlLFy4kLvvvpvXX3+dkpISFi5cyLZt26ivr+f0009n165daLVarrnmGm655RZ+9KMfYTbn4PNXEvDXIqFFr+9aZuF30UoS+WYjpb4gFXtEjk5zoFVLCIEQ4oCU+9/85jc0NDSwdetWjEYjTU1NfPXVVz2aw7vvvsvcuXP7ROBEIpFuZc19l67sOViSpM1HeF8CYo82iCRJrxENKE6SJKkWeICosHlDkqTrgSrgki7MR0XlB09TMMyvS2v5wOFkkNXEv4bmMTY2apXx++soLXsEh+MTzKYcRox4lqTEWb1yR7W0tLB8+XI2bdqEEIJBgwYxadIksrOzCQQk6uoOzqYKN/vwrWvCu6EZxRVCY9ERMyEdy9hU9BkHuxN+yJysKev6ZAuJlw8iNCMb12dVuD6rwrO8DvsZuVgnpCNpu/438ND72yipdx3wmhD7Bw0LIpFI5+LdlYVxSIadB84detj3V69eTWFhIQUFBQBcdtllLFq06KDFe9GiRTz44IMAXHTRRdxyyy0IIVi0aBGXXXYZRqOR/Px8CgsLWb16NZMmTWLatGlUVlYC0ZgcizkXn68cf6AGScpHp7Pwox/9iAsuuIDzzjsPgCuvvJJLLrmEuXPncs8997B06VKCwSA333wzP/nJT/B4PMyfP5/Wtna8oSC/eOBBfnLxRVRVVTFnzhwmTpzIunXr+Oijj8jNzQXA5/Pxr3/9i4qKCox7TF2pqalcckl0OX7ttdd45JFHEEJwzjnn8NhjjwEQExPD7bffzgcffIDZbGbRokXs3r2b9957j6+++orf//73vPXWW7jdbn7605/i8/kYMGAAzz//PPHx8cyYMYM///nPjBs3jpaWFsaNG0dlZSUvvvgib7/9Nh6PB1mWeyy0oGsCZ1AXtpGPtoEQ4vLDvHVaF8ZXUVFhXyXi3+2uJ6gI7i1I52fZKeg1ErIcpLr6WSqrngGgIP9OcnJ+jFbbc/t8Q0MDX3/9NSUlJeh0OsaOHcukSZNISIjGCuxfD8dqhYBbpuS9VpIaGtA2ukADpoEJWOelInITaGnX0OwFU83Js4j3lu9+h6FQ9PnJVHTQkG4l6UdDCNW6cX5UQcei3XhWNRA3twBTUc9cMntjavbqZCEk9Ho9siwjyzLhcBidrne1Yerq6sjOzu58npWVxbfffnvE7XQ6HbGxsbS2tlJXV8cpp5xywP51dXWH/CxJ0mI250VFjr8Si2UA119/PX/5y18477zzcDqdrFixgpdeeonnnnuO2NhY1qxZQzAYZPLkycyePZvs7Gzeeecd7HY7u+obOX3KZOadOw+A0tJSXnrppQPmA1BWVkZOTs4h61HV19dz9913s27dOuLj45k9ezbvvvsu5513Hl6vl1NOOYWHH36Yu+66i3/961/cd999zJs3j7lz53LRRRcBMGLECJ566immT5/O/fffz0MPPcQTTzxxxO99/fr1bN68ufM801OOKnCOR68pFRWVg6nyB/n5jhq+afIyRonlF5npDJSMRILg9C1l186H8AeqSUk+i6KiezGZMnr+WVVVfP3115SVlWE0GpkyZQqnnHLKQb2BHI49QZtOH871Tfi3thD0CRwpJorPzscyOgWtzYDfD9Un+SLeU/Z+h3vjRPbeOxwnX9sIQ5aNpB8PJ1DSSseHFbQ8txXT4ATizik4qqtyf0tLcE8t/UMV8DMao+nlHR0dCCH61GXV32g0+j0iZzd+fwVTp57KTTfdhMPh4K233uLCCy9Ep9OxePFiNm/ezJtvvgmA0+mktLSUrKws7r33XpYtW4ZGo6G5oZ7ttXUko5Cbm3uQuDkaa9asYcaMGSQnR9PYr7zySpYtW8Z5552HwWBg7ty5AIwdO5bPPvvsoP2dTicdHR1Mnz4dgAULFnDxxRcf9XPPOOOMXosb6F4WlYqKynFAEYLn61p4eHcDmpDEL805XJgdh9Eo4XI3s/GzZ9DpFxIfn8noUf85bHuFoyGEoKysjK+//prq6mosFguzZs1i/Pjxh1wgRFihbX07UkljNAtKJ2EenED8mFTCsTHYivetPj+kRby7BAJR0bc/BkO0r9fJiCRJmIcmYSpOwP1NHe4va2h8Yh3203OxTc3sUg0dIQ7OhJKkqFUHohVvk5KSaG9vp729nXA4jM1m67Y1JzMzs7OrPUQrK2dmHlwvau92WVlZRCIRnE4niYmJXd5/LzU1NZx77rmAwoJrzuPGH/+Iq6++mldeeYWFCxfywgsv7Dl+wVNPPdUZsLyXF198EYfDwbp169Dr9dEaQuEw9YqC5bt/ZHsoLCykuroal8vV5ariAHr9vno7Wq2WyN6y0V1Ep9Oh7PnBAoHAAe9ZDzPX7qIKHBWVE5jdvgB37qhhtdPLrAQbd1iySdYZ0Osj1Ne/SVX1vwiFJHJz72LihMvRaLrvjhJCsGvXLpYuXUpDQwN2u52zzjqL0aNHYzAcnN4bafHj+bYB37omgo0apFgzcafnYBmRhMasJxgE83fijX9oi3h3MJmiFq39M31CoeNXafdYIek02GdkYx2TSsf7u3F9Uol/k4P4C4swZB25IGRXCvjtTSd3uVx4PB7C4TDx8fHd6mc1fvx4SktLqaioIDMzk4ULF/Lf//73oO3mzZvHSy+9xKRJk3jzzTeZNSsa8zZv3jyuuOIKfv7zn1NfX09paSkTJkw47OdlZ2ezceNGAMJhJ35/NZdddgbTp19AWlpaZ+zPnDlz+Mc//sGsWbPQ6/Xs2rWLzMxMnE4nKSkp6PV6lixZQlVVFVkmPRW+IGFFoAiB5jsiz2KxcP3113P77bfzz3/+E4PBgMPhYOnSpUyZMoXbbruNlpYW4uPjee2117j11luP+J3ZbDbc7miBztjYWOLj4/n666+ZOnUqL7/8cqc1Jy8vj3Xr1jFhwoROS1RfowocFZUTkIgi+Getgz9VNGDUaHhycA4Xp8ZTWioRCG6jpORPeLw7iY87hYKCX6AoWd2u7SGEoLS0lKVLl1JfX09cXBzz5s1jxIgRBwVoCiEIlnXgWV5PYEcbaCTMQxMZeEE6jdpY9CYJyRB1HeztSbU/J/IifrwDfJOTo+46OLiv1w8Brd2A5YLBtKa20vRRDYZHSsg5LYW0eYc37XW1gJ9GoyE2Nha9Xo/T6aSlpYWEhIQuZ+bodDqefvpp5syZgyzLXHfddQwdGnWV3X///YwbN4558+Zx/fXXc/XVV1NYWEhCQgILFy4EYOjQoVxyySUMGTIEnU7H3/72t85sw8svv5ylS5fS0tJCVlYWDz30ENdff/1+xxiLoqSSkNBEcfEALrhgXw7ODTfcQGVlJWPGjEFRBElJyfzvf+9y+eVXMn/+uQwfPpxx48YxaNAgDBoNGSYDCoL6YJisQ3Qe//3vf899993HkCFDMJlMWK1Wfvvb35Kens6jjz7KzJkzO4OM58+ff8Tv7LLLLuPHP/4xTz75JG+++SYvvfRSZ5BxQUFBpxXql7/8JZdccgnPPvss55xzTpd+j+7SpV5UkiQNItpWYa9trQ54TwixvV9mdRTUXlQqJzM7vH7u2F7DRrePs5JieXRgFqlGPeGwi2+++Rd19e8RY40lv+AOkhJnEgpJR+3ivT97XVFLly6lrq6OuLg4pk2bxsiRIw9K9RZhGd8GB+7ldUSafGhi9MScko51Qjpae/RE2RWBcCJ271ZkmbZmF6U7vCghN0rES8AXxueJkBzXgRY/4WAQORI+5P4arRa90RS9mYzoTWb0RhNGiwWLPQ5LXBwma0yX3CLHW2QdT/b/29ApEVo/r6FjbSu5hRL+c0wMGXboLCdFiRbw22u50euPXMAvGAzS1taGJEkkJCQc0jp5oiGEoK2tlHHjTmPNmm9IStqnehXl8CLvUN9DfSCEIxQhz2zo7Ef3fWT79u0MHjz4gNd63ItKkqS7gcuBhcDqPS9nAa9JkrRQCPFo76esoqISVgRPVzfxeGUTNp2GZ4bkMj8lDoDGxvcoLXsYf8BLYsLPKCxcgMUS062rfSEEu3fvZunSpdTW1hIbG8u5557LyJEjD7qilV1BPCsb8H7bgOKLoE+3En/xQCwjk5F0B549D9eT6rvb5OZGF3GvN7qI96e4CQX8uJqbcLU6cLc4cLXsu/c52/G5nAS9Xtq9SShCg067L34gIuvQSArx1hYkSYNWpztk8yJFjqDIR04g1Wi1WOyxWGLjiUlIIDYlbc8tldjUNOLTMtAZDF36Dk9WDozP0pFybj7GongcH5djdoeQ3SE0MQf3V+puAT+j0UhSUhJtbW20trYSHx9/xKKUJwJffPEF119/PTffvACj0YMsB9Bqo3MOh/eJG4jeazTR1w/1vaQZ9XhkhZpACLNWg+FEKOfczxzVgiNJ0i5gqBAi/J3XDcA2IURRP87vkKgWHJWTja1uH3fsqGGrx8/8lDgeLsoiyaDD56ti587f0Na+HLttBMWDfodeN6xbV/tCCMrLy1m6dCk1NTXY7XamTZvGqFGjDhI2oXoPnmW1+Da3gBCYBidim5KBIT/2hKtbI4TA5Wimrb6W9vpa2upraauvo72+Fk972wHbarRaYhKSsCclY42Lx2yPxWyz43ClE5dkwWKzYbBY0BmM6PRGghEDQ4YZ0R4lzViOhAkHgoSDAUIBP5FgkIDXg8/lxNfRgc/Vgc8ZvblbW3A2NxLy+zv3lyQNcWnpJGXnkpidQ1J2HumFA7ElJZ9w33d/sWtXND7ru/E0LkcYObydgen5SEYtunjTQeK6J8iyTFtbG+FwmNjY2D4LaO1PFCWM11uGJGmwWguRJG2POqUHZIVSXwCzVsMAs/F7+TfWpxYcQAEyiBbi25/0Pe+pqKj0kLAieKKqkb9WNRGv1/H8sDzOTo5DUSJUVf2T8oq/Ikl6Bg58kKzMK5CkqAupq1f7tbW1fP7551RWVmK32znnnHMYPXr0AcJGCEGw3In7q1qCu9qRDNpoD6FTM9Alnhh+kkg4TGttNY7KcpqrynFUVuCoqiDo2xelbLRaSUjPImf4KBIysohNTcOelIxtj6jRaA6utFxdDbJ84BXv3iBpXRe6Cmh1erQxekzfSZ8/HEIIAh43zqZGOpoaaK2rpbWmipbaasrWrEKI6CnVEhtH2oAi0goHklk8lIyBg9B9D1wqPeFw8VnWBD1erx5tvAm5I0i42YcuwdTrKsharZbExETa29txOp0IIQ4qf3CiEU0fz9lTI6cWszkHSZK63SndpNWQaTRQs8ddlWI8OZty7qUrfyl3AF9IklQK7M13ywEKgSOHU6uoqByW7R4/t22vZovHz0Wp8fyuKJN4vQ6Xeys7tt+L27ON5KQzGFj8ICZjWrfGdjgcfPHFF+zYsQOLxcKZZ57JuHHjDhQ2iiBQ0opraQ3hWg+aGD32OXnEnJKOxnz8fPRCCJzNTTTs2k596U4aSnfiqCrvdAfpjEaSc/MZNHk6ybn5JGZlk5CRhdnefSvTsQ7wlSQJs82O2WYnrXDgAe9FQiFaaqpo3F1KY9lOGsp2Ub5+DQBavZ6MokFkDx1B9rARZBQNQnOUDu3fF470G3i9oLXqkQwa5NYAkRY/2ljjIV1W3UGj0ZCQkEB7ezsul6tT5JzIFg2dzorRlE4w0EAo1IJen9yjTunxei3OiJbGUBi7TtvlflXfR7oaZKwBJgDZRK02tcBaIcRRKxj3B6qLSuX7jCwE/6hu5o8Vjdh0Wv5UnMXZyXHIsp/yir9SU/M8en0CxQMfIiVlztEH3I+Ojg6WLl3Kpk2b0Ov1TJ48mVNOOaWzBDuAiCj41jfjXlYbXTASTdimZWEdk4qkP/YnO0WRaa4op2bbZmp3bKOhdCd+lxMAvdEUtWQUFZOaP4Dk3ALi0tIOaY3pKSdygG/A66FuRwk12zZTs20LzVXlUddhjI38UWMpGDOevFFjMVlPbAvE0Tjcb7C/O0IoArk9gOKPIJl06BJMSJreCRIhBB0dHfj9fqxWK3a7/YQWOUII/IEaImEnFksBGo21W4HWewkrCju9AQwaDUWW75erqq9dVAghFEmSbgDOIdqWoQ7YLEnSZiHEU30wZxWVHwTlviC3ba9ircvHOcmxPDYwmySDjra25ezYcR/+QDUZGZdSOOCebjXc83q9fPPNN6xeHc0DmDhxIlOnTj0gvkAJyXhXN+L+qhbFHUKfGUPCFYMwD0vq9ULRHYSi0FxVQen6rZSsLadmZyUi5CTG6CIlO5mC0eNILxpEelExSdm5/W6pOJEDfE3WGAaMncCAsdHaKX6Pm+otmyhfv5qKDWvZ/s1SNFotOcNHMejUaRSOn4TRYjnOs+4+XfkNJI2ENsGE5AkjO4NEmn3oksy9isuRJIm4uDgkScK7pyjTiSxyJEnCbMrEK/sIBGqwWIowGrv//0Ov0ZBlMlDlD9EcipB6krqqumOHngpkCSFkSZIygZHAiP6ZlorKyYUiBC/UtfD73fUYNBr+NjiHC1LjiUSclJQ8QkPjW5jNeYwZ/V/i4yd2edxgMMiqVatYvnw54XCYkSNHMmPGDOLi4vZ9dkjGu6oB97JaFE8YY0EstksGYiyMO2Ynck9bKxUb11GxYS012zbjdoZo9yaTkBbH4IkjSR4whIScQQwZHnfCWE9ORMwxNoonTaF40hQURaahdBe7165i58pv+OTvf0Grf5qC0eMZPHUGBWMmRDPATiIkSUJrMyDpNUTaAtG4nEQzmh4s8vuPGRsb2ylyJEk6oOrxJ598wu23344sy9xwww3cc889B40RDAb50Y9+xLp160hMTOT1118nLy+P1tZWLrroItasWcM111zD008/3eN57puvFpMpGo8TCNZhNmV3+f9xY2Mjd9xxB2vWrCEuLg57UjJ3/uExzhg5HHM3XVVPPPEEN954I5YTWFB356//WyARaBZC1BG14nzUL7NSUTmJqA2EuHNHNV+3e5iZYOPh3Gy0Tj0rVy6htu5xbLZqBhb9jLy8W7vcGFNRFDZu3MiXX36Jx+Nh0KBBzJo1i5SUlH3bBCN4Vjbg+boWxRvBWBSH/bQcjHmx/XWo+z5blqkv3UHFhrVUbFyHo7IcgJjEJAaMPwV94nhSCweTkLav30wwqLZu6A4ajZbM4sFkFg9m6hXX0FC6kx0rvmLXym8oXb0CS2wcQ2eczvBZs4lP63lfshMRjUmHLtlMZG9cTrwRraXnVghJkrDb7Qgh8Hg8nSJHlmVuvvlmPvvsM7Kyshg/fjzz5s07qJv4c889R3x8PGVlZSxcuJC7776b119/HZPJxO9+9zu2bt3K1q1be3vYneh0FozGVILBRsLaGAyGg/s2RSKRg5IJzj//fBYsWNBZiHDdho1sbXZQGwhR2E1X1RNPPMFVV13VJwLnu3PtK7oz4j+BryRJeo6o2NkshHD2+YxUVE4ShBAsbGzj/tI6FODPxdlcEJdAaVkLtTV/xu1Zhtk0nOTkv5KRMYCuemJ2797N4sWLaWpqIisri0svvfSAjsdKIIJneT2e5XUovgim4nhsp+VgzOm6y6snBH0+KjasoWzNKio3ryfo9SJpNGQOGsLUK64hf/Q4krJzkSSpMzV4f/qidUM4JONzhvA5g/g9YUKBCCF/hKAveh8KyMhhBVlWUCICRVaQZYFQBJJGQqOV0Oy912rQGTQYzDqMZt0B99ZYI9Y4Ixa7Hs0JEKQpSRIZAweRMXAQM66+gYqN69jy5aesff9t1ix6k5xhIxk79zzyR45F+r7WP/n4Hmjc0vlUA+gRiLACikDoNF3qY3UAacPhrGgpt72WHCEEbrcbSZLYsmULhYWFFBQUANEqvYsWLTpI4CxatIgHH3wQgIsuuohbbrkFIQRWq5UpU6ZQVlZ22CkoikJxcTErVqwgOTkZRVEYOHAgK1euBOCnP/0p1dXVQFRUTJ48mdWrV3P77bfj87kwmfS8+OLLDB48nBdffJG3334bj8eDLMt89dVXnZ+zZMkS9Ho9P/3pTztfGzt6FAXhCFW+ILf+4pcsXfwpkiRx3333cemll7J06VIefPBBkpKS2Lp1K2PHjuWVV17hqaeeor6+npkzZ5KUlMSSJUt47bXXeOSRRzorHj/22GMAxMTE4PF4AHjzzTf54IMPePHFF7nmmmswmUxs2LCByZMn8/jjj3fvt+sC3RE4rwDP7tnnJmCEJEkmIcSAPp+Visr3nKZgmF/urOGzVheT4qw8MSiHHJOBNWs+pLzi72i1fgYU3EZGxiWEQtouWS6am5tZvHgxZWVlxMXFcfHFFzNkyJDOqy7FH8GzvA73N/WIQATT4ATsp+Ucta9Pb/C5nJSvW03p6hVUbd6AHIlgiY2jcPwkCkaPI2f4qEMGwPakdYNQBD5XCGeLH3eLH2dLAHeLH3dbAO8eURMKHD7vQaePihWdQYNGq0GjldDqNJ2iRlEEirz3pqDIgnBIJuSPEAkduiKGJIHZbiAmzogt0URcioW41H03k/XYxzZotNrOuB1PWytbl37Ops8+4p1HHyI+I4uxZ89nyLSZ6I0ndpG7riAhgV4DEQUR2fMbaaXo6z0Zb09MjhACl8vF7t27D7h4yMrK4ttvvz1ov7q6us7tdDodsbGxtLa2kpSUdNTP1Gg0XHXVVbz66qvccccdfP7554wcOZLk5GSuuOIK7rzzTqZMmUJ1dTVz5sxh+/btDBo0iK+//hqNRuGDD/7DPff8H++++zEA69evZ/PmzQd1494rUL5LnE7Lfz98j3UbN7BmwwZcbW2MHz+eadOmAbBhwwa2bdtGRkYGkydPZvny5dx22208/vjjLFmyhKSkJOrr67n77rtZt24d8fHxzJ49m3fffZfzzjvviMdeW1vLihUrDqqg3ld0R+DUCiH+sP8LkiR1v7OfispJzqLmdu7ZWYtfUfhtYQY3ZCUTCjawcdO9lGyvITW1mIFF92I2ZwFHt1x4PB6WLl3KunXrMBgMnHHGGUycOLHTpKuEZDzL63Evq0X4I5iGJmKflYMhs38yazztbZSuXkHZ6hXUlGxFKAr25BRGzZlL0YRTSR9YfNQspyOlBiuKwNXip63eS3ujl7Z6L20NXtobfcjh/YSGRFRYJJhIzIwhe0gC1lgDFrsRa6wBU4w+anWx6DCYdGh7EYwqywphv0zQHyboi+B1hvB2BA+4tdZ5qdjYgqLsy0w12/QkZdtIzrGRnG0jJdeGLdF0zGKfYhISOeWCSxk/70J2rfqGtR+8w+f//hvL33iF8edewKjZ56A/wav5dnLWoYvmS0StpUpHEMUbRhOjRxvb88wgSZKIj4+nra0Nn8+HfJRq1X3Bddddx/z587njjjt4/vnnufbaawH4/PPPKSkp6dxub+NQp9PJggULKC0tBRRCIT+hUAsAZ5xxxkHi5khIksTO1d9y5kUX0xRRyEtNZfr06axZswa73c6ECRPIyoqeq0aNGkVlZSVTpkw5YIw1a9YwY8YMkpOTAbjyyitZtmzZUQXOxRdf3G/iBroncDZKknS7EOKve18QQgT7YU4qKt9LXBGZe3fV8mZTO6NtFp4cnEOhxUh9/UJKyx4FFAYPepDk5PMxmfYttvtbLvZPl9XpwpSXr2b16q+IRCKMHz+e6dOnd2ZGibCC59sG3EtrUDxhTIMSsM/OxZDR98Im4PVQunoFO775ipptWxBCISEzmwnzL6ZowiRS8gd0a0HZ27qhsVGhptSHx+Em4nax+W0X7Q1eIvsJmZh4IwkZVjKL44lLNmNLMhObZMaWYEJ7jNLatVoN2hgNppgjW2RkWcHdEqCjyUdHs4+2ei+OGjcbF1d3Ch+jVUf6gDgyB8aROTCexKwYNP2cxabV6Rg8ZQaDJk+ndvtWvn3nDZa9+gJr3n/7+yd0DoEkSWjjjKCRUNwhUEAb33uRk5mZSUVFBaFQCIPBQG1tLZmZmQdtn5mZSU1NDVlZWUQiEZxOJ4mJiYcd/5133uGhhx4C4N///jfjxo0jNTWVL7/8ktWrV/Pqq68CUffVqlWrDmopccsttzBz5kzeeecdKioqmDFjKsFQE4oSPmxl5qFDhx62a7dWkrBrdTjDMi79gYJu/xITWq2WSCTy3d2PyP6/QSAQOOC9/q4i3R2Bkwqcvqc31XpgE7BRCPG/fpmZisr3iBXtHm7dXkVjKMwv89K4IzeVcLCODRt/RXv7CuLjJzF40B+AbKqqoq6N71ou9jYdNBgEVVXb+eqrFXR0+Bk/vpBzzpmF1ZqEwwG11QpKqQPjxioMviDGAbHYr87DmNu3MTaRUIjyDWvY8c1XlG9YgxwOE5eazsQLLmHQqdNIzOpeNLAQAldLgIbdHTRXuGiudtNS40He41owWnQk59gYOj2ThHRr581wHIsOdhetVtPpntqfSFimrd5Lc5Wb5ioX9aUdVG6OXnEbzDoyiuLIGZJA3ogkbAn9JzQkSSJ7yHCyhwynbud2Vr75X5a9+gJrP3iHUy++guGz5nwvCwhGG29KCKMBIUso3hAIEU0r76HI0Wg0nH766dx0001s3LiRYcOGsXDhQv773/8etO28efN46aWXmDRpEm+++SazZs064ueef/75nH/++Qe8dsMNN3DVVVdx9dVXd1o1Zs+ezVNPPcX//d//AbBx40ZGjRqF0+nsFFovvfQSkqRDQkM43M7hatvNmjWLe++9l2effZYbb7wRgM2bN+N0Opk6dSrP/POfzL7scrbW1LFs2TL+9Kc/sWPHjsMeg81mw+12k5SUxIQJE7jttttoaWkhPj6e1157jVtvjdYBTk1NZfv27RQXF/POO+9gs/Wfy/y7dPnMIYS4BDrdUkOB4USL/6kCR+UHS1BR+GNFI3+vbibPbOD90UWMtpupq3uVst2PARoGFf+ejIzLOk94h2s6WV0NHR2NLF++hPr6elJSUpg9ezZ5eTlYrVBZIRDlrQRW1hJoDSFSbAy5aiDxw+L67HiEENTtLGHrks8o/XYFIb8PS2wcI08/i0FTppM2YGCXFwyhCNoavNSXdtBQ1kF9aQdeZwgAvVFLco6N4TMyScmzk5Jrw55kPmHrj/QWnV5LSq6dlFw7EF2YPO1B6kvbqdvVQd3Odio3t7Bs4S4Ss2LIH5FE3ogkUnJs/VajKLN4MBf9+nfU7dzO1/99gc///XfWf/w+06+6jvzR4743v8X+XbU1GglhNRBRAF8IpGCvLDlGo5GnnnqKyy+/vDNNfOjQaHfz+++/n3HjxjFv3jyuv/56rr76agoLC0lISOjMUgLIy8vD5XIRCoV49913Wbx48UFByhAVSddee22newrgySef5Oabb2bEiBFEIhGmTZvGM888w1133cWCBQv4/e9/zznnnLNnrmkoSghFObRjRZIk3nnnHe644w4ee+wxTCYTeXl5PPHEE0yZMoWVK1dy8eRTCAvBrx9+hLS0tCMKnBtvvJEzzzyTjIwMlixZwqOPPsrMmTM7g4znz58PwKOPPsrcuXNJTk5m3LhxnQHHx4KuNNssBnaJrpQ8PkaolYxVTgR2eP3cUlLNVo+fqzMSeXBABppwHSXb76Gj41sSEqYyeNAjmExHT9H1eDz85z+rKCvbhMViZtq0qQwZMhRJ0uDxCKTadjqW1KFp96FPtRAzPRMpOx6dTuqTtGpPWyvbln3JtqWf0d5Qj95kZuDEyQyeMoPsocO7dFUfbbHgp2Z7GzXb26gv7SDoi5qzrXFGMgpjySiKI70wjvh0KxqNdEJXET7WtDd6qdzcSsVmB427nQgBtkQTAyekUjwxjfi0/jPnCyEoW7OSZa++QEdjA7kjRnPa9T877unlh6pa+12Ce9bz7/ZkUjwhtP4gGqsebVzvqvUGAgHa2towmUzEx8f3i/hbu3Ytd955J19//XWP9hdC4PNVoCh+rNaBaDQ9C3Av9wXxyjKDrCb0J2DGXXcqGXdF4GwBcoFdwGZgy957IURzX026O6gCR+V4ogjB83Ut/G53PTFaLX8ZlM0ZiTZqa1+mbPefkCQtA4t+TXr6xUc9EcqyzOrVq1m6dCkOh4mRI8cwderETp+7q8KN76tqPBU+bKkG7DOyMA1KQNJEG+15vTBw4BE/4vCfHQlTvm4NW5d+RsWGdQihkDV4GMNmnkHRxFMxmI6uNALeMHU726ne3kZNSRvu1qiP3Z5kInNgfKegsScd7CrY65IzGg921/1QRc5eAp4wlVtaKF3TRM32NoSA5BwbxRPTGDgxFXPMoRtv9lYwypEwmxZ/xPI3XkGORJh43iWMn38ROv3xqXTbFYFz+K7aAl0whOIO9TrwGKIXIS6XC5vN1udulkcffZR//OMfvPrqqwcF8HYHWQ7i9ZWi09qwWHrWUC0oK+z0BYjTackxn3h5RH0qcPbsbCRatfgjYCEwhKibCiFE97oA9gGqwFE5XlR0hLl1ZR0rHT6mp1p54pQMkvT1bN9+Nx3ONSQmTmdQ8cOYTOlHHWv37t18/PHHtLS0MGDAAKZPPxOfLxmjETQeP62f1+Eq6SA7QyY0OhfjsBRMln1n8mAQtNruF8ZrrathyxefULJsCX63i5j4BIbOOJ2h008jPv3gAMr9EULQ3uCjYrODys0tNFW4EAL0Ji1ZxfHkDEkge0gCsclHL/51uE7ePTmmkxmvM0jZ2mZ2ftuIo9qNVqehaHwKw2dk7XF5RelLwehpa2Xpf/7NzpVfE5+eyeyf3ErW4GF9fGRHpzcWHIjGs8nOUNSaE2tEa+t5R3YhBO3t7QQCARISEg4K/D1RCAabCQabsFjy0Ol6JsTq93QbH2g1dbvCcX/T5wJnv0EqhBD5+z2PF0K092ayPUEVOCrHgzerOrjr6yZCOoVfFqZybnwcNTUfEgw9hMkUoajoPtLTLjzqVWJbWxuffvopO3fuJD4+njPPPJOBA6OxLR5HiIpF9bSvacFkEuSckUrKGRkEFV2vFi85EmH3um/ZtPhDqrduRqPVUThuIsNmnkHuyNFHTOtWZIWGMicVm1uo2NyCy+EHohaF3OGJ5AxOICXfjrabJ8K9xf6+uzD1xip1stNa52HrV3Xs+LaRSFAmNd/O8BlZFI5Loa5O0+eCsXLjOj5//h84m5sYe855TLn0anSGnouE7tIVgbN/DM53u2prNFFhIrcHUHwRtPEmtL2oTaQoCi0tLciyTHJycr9U3+0tQih4vaWAhNVaSLRXdveIKIIdXj8WrYYCy4kl5PpT4JQLIQp6P8WDxr0TuAEQRF1g1wohAofbXhU4KscSd0Tm16W1vLbZw2CziUeHZpImOSkr+wPNjvUkxI9l1qz/O2qsTSgU4uuvv2bFihVoNBqmTZvGpEmT0Ol00Vo2X9fh/qoWEZGxjk/DfnruAVecPXE/uNta2PLFp2z+4lO87W3YkpIZefpZDJt5Bta4+MPuJ4cVara3UbaumcotLQR9EbQ6DVmD4skbkUTe8CRi4ntnvlYtOD0n6I+wc1UDW5bW0dHkw5ZoImFIASMmp6Az7FvQ+kIwhgJ+lr3yAps++4iEzGzOuulO0gqPjQLtisCBvVlUh++qLYQg0uJHBGV0SWYw6HrUhRuibQUcDgdarZbk5OQTMhg7HHbh91dhNKZhNCb3aIzmYJiGYJgCixGb7sTJrOvrGJy/EU0LXw+8vb8Fpy/Y07jzG2CIEMIvSdIbwEdCiBcPt48qcFSOFd92eLhlezV1gRBXKhncVJRER9tiysv/H4oSIS/3Fuz2CyguPvxJTgjBjh07+OSTT3A6nYwYMYLTTz892vtGFnjXNeL6rBrFHcI0NJHYM/PQd8HFc6TPq9m2mY2LP6RszSqEEOSPHMPI2eeQP3rsYa01ciQqanava6Z8UwshfwSjRUfeiCTyRyaRPTgBg6nvrljVGJzeIxRB1dZW1nxUyY4tQUw2I6NmpjNwYhp6g7ZPBWPlpvV8+sxf8Tk7mHr5AsbOPb/fF/euCpyuIBRBxOFDDgtErBmNQXtIi09X8Pv9tLe3Y7VaiY3t/95uPcHnq0SWvT0OOFaEYIc3gE6SKOpmn6r+pDsCpytnq03AKOBHgE2SpBJgG1AClAghXu/1jKPzMEuSFAYsQH0fjKmi0mMiiuD/VTby16omsk0G3htTRHxLgO3b7sft/gK7fQQDi+5Do8k+Yg+ptrY2Pv74Y0pLS0lJSeHaa68lNzca/Bcoa8f5QTnhRh+GXDuxVw3uVS2bcCjI9q+XsP6j92itrcZkszP2nPMYefpZxKUdOiZIlhVqd7RTtq6Zio0Ogr4IBrOOgpFJFI5LJWtQfK8qAB+JvcX+DpUyr9I1JI1E3ogkcocnUrahnU9eb2DFokq2LK1j2Gk5ZA1JJT+/bxamvJFjWPDnv7H4mSf56pXnqdm+lTNvuhNzzLGra9IbJI2ELtFMqMGPcAbRJplA0iBJUWETDh9oTTwSZrOZYDCI1+vFaDSekPE4RmM6Xm8pwWBTZ9X07qCRJNIMemoCIZwRmTj9ieeOOxrdclEBSJKURbQGzghgmBDi6l5PQpJuBx4G/MBiIcSVh9jmRuBGgJycnLFVe+u8q6j0MVX+IDeXVLHW5ePStAQeLsrE1/45mzb9jro6O4UDriIv71LCYe1hLQ7hcJgVK1bs6RejYcaMGUycOBGtVku4xY/zw3IC29vQJpiIPSsP87CkHl8hedrb2LT4QzZ99jF+t4vkvALGnj2f4klTDxkvIYTAUe1mx6pGStc0EfCE0Zu0FIxMpnBsCtmDE45ZhWCVvsXvhy3fdrDqgxqcDS5yCvRMv6SA3GGJfXYFLoRgwyfv89XLz2ONj2fenb/qN5dVX1pw9uJ3yyjtASSDBl2CCfb0rVKUQ/dC++STT7j99ts76+Dcc889e7aPxuMoioLdbufaa69l3bp1JCYm8vrrr5OXl0dlZSWDBw+muLgYgFNOOYVnnnmmT4/nSAQCDYRCLVitRWi1JhobG7njjjtYs2YNcXFxpKam8sQTTzDwMD5MIQQ7vQGQoNhyYCbkE088wY033tgn3cS7Q7/F4PQHkiTFA28BlwIdRAsHvimEeOVw+6guKpX+4u2mdu7eWYMkwR8HZnNOgpZdpQ/R2Pgutpih5Of/Gb9/4BHjYMrKyvjoo49oa2tj6NChzJkzB7vdjuKP4PqiGs/KeiStBtusbGyTM5F6KCaaK8tZ9+G77Fi+DEWRGTB2AmPPnk/WkOGHXMw87UF2rW5kx6pG2hu8aHQS+SOSGTghlZyhCej0J46fXaV3CCHYvd7Bqnd343T4yR2eyNRLBhKb3HfmsYqtpbzxp2dwtvs5/UdXM+mcSX1ufesPgRMMguILo7iC0fRxm7Ez6+q7FhxZlhk4cCCfffYZWVlZjB8/ntdee62zUF84HMbhcPDqq69SVlbGP//5TxYuXMg777zD66+/TmVlJXPnzmXr1q19egxdIRKJoNGA17sLrdaC2ZzLqaeeyoIFCzo7im/atAmXy8XUqVMPO057OEK1P0Su2XCAFScvL4+1a9d2qaFoV+ba1YDtvnZR9TenAxVCCAeAJElvA6cS7V6uonJMcEdkfrWnj9SEWCtPD87B6lvNt6vvIRRykJd3C/l5N6PRHD6DxOl08umnn1JSUkJCQgJXXXUVhYWFCFngWVWP67MqFF8E67g07LNze5SyKoSgYsNa1r7/NjUlW9AbTYw4/UzGnHXuIVO8wyGZ8g0Odq5qoGZHOwhIK4hl+hXFFI5NOS7drlX6H0mSKBybQv6oJLYsqWX1+xW89ttvGXtmLqNn5/RazPr9ENQVce4vH2DJ80/y+Qv/oqG6kfN/Mh+LtX+sf4+tfowdbYevrNsdFAWQFYrMhfxi3D1g1B/SPbV69WoKCwspKIjm1lx22WUsWrSoU+Do9XrsdjsffvghDzzwAAAXXXQRt9xyy2FbJhw8F4Xi4mJWrFhBcnIyiqIwcOBAVq5cCcBPf/pTqqurgajVZPLkyaxevZrbb7+dQCCA2WzmhRdeoLi4mBdffJG3334bj8eDLMt89dVXGAzJBIONfP75R+j1+k5xAzBy5Eggel656667+Pjjj5Ekifvuu49LL72UpUuX8uCDD2KMi6e0pIRJ48fxyiuv8NRTT1FfX8/MmTNJSkpiyZIlvPbaazzyyCOdlYwfe+wxAGJiYjqrF7/55pt88MEHvPjii1xzzTWYTCY2bNjA5MmTefzxx7v5Kx6dE0HgVAOnSJJkIeqiOg1QzTMqx4z1Li8/21ZFTSDEL/PSuCUrhsryhymtexWLZQDjxv4Du33EYfeXZZlVq1axdOlShBDMnDmTyZMno9PpCJS20/FBOZEmH4b8WOLOLehRM0xFltm5YhmrF71JS00VtsRkpl15LcNPm4PJevB4LbVuSr6uZ+fqJkL+CLZEE+POzqN4YhpxKcfWpNzXqNWPu45Wq2HU6TkUjk1l+ZulrH6/gtI1TZy2YAip+T2P93I4otYOo93OvDvv5pvX/sOWL97H31rDggd+gt5w4hWI2x+NBhQ0CK0GxRXClKZFc4gI47q6OrKzszufZ2Vl8e233x6wjdVqpampCbvdjizL6HQ6YmNjaW1tBaCiooLRo0djt9v5/e9/f5C1RKPRcNVVV/Hqq69yxx138PnnnzNy5EiSk5O54ooruPPOO5kyZQrV1dXMmTOH7du3M2jQIL7++mt0Oh2ff/459957L2+99RYA69evZ/PmzZ0dxQ2GRELhVjZuXMWYMWMO+X28/fbbbNy4kU2bNtHS0sL48eOZNm0aABs2bGD5xk2E45P4yZmns3z5cm677TYef/xxlixZQlJSEvX19dx9992sW7eO+Ph4Zs+ezbvvvnvUbuK1tbWsWLGi3zqK91jgSJKUDrT1tqO4EOJbSZLeJJqlFQE2AM/2ZkwVla4gC8Hfqpv5Y0UDaUY9744upFgqZf3aX+D3V5OTfT0FBT9Hqz18AGF1dTUffPABzc3NFBUVcfbZZxMfH0/Y4aPlw50EdkTjbBKvGoxpaPfjIMLBAFuXfMbaD97B5WgmMSuHs27+OTmjp9HWrqO6bt8ir5UilK1rZtvX9TRXutDqNAwYm8yQyRlkFMb1W0+jY8n+mVdWazTzqqpKDU4+GjHxRub8eBiDT21lySs7eOuPaxk9O5cJc/N7FG8VCES/fwCtTs+0q64jLi2TpQvf5u1HHmD+/913SOHdG+6ecHefjgcgIgrhZh9KRwBNcs96oUmShEajibYqcTo7hQVAeno61dXVJCYmsm7dOs477zy2bduG3X6guLzuuuuYP38+d9xxB88//3xnP6rPP/+ckpKSzu1cLhcejwen08mCBQsoLS1FkiTC4XDnNmecccYBc5AkDUZDCkKEEWLfdvvzzTffcPnll6PVaklNTWX69OmsWbMGu93OhAkTGJqXy05vgAHDhlNRUXFQteU1a9YwY8YMkpOjKelXXnkly5YtO6rAufjii/tN3EDvLDgvAwMkSXpLCPHL3kxCCPEA8EBvxlBR6Q71gRC3bK9mRYeH+SlxPFqURnvtP1lX9TeMhlTGjH6V+PiJB+yzv+UA/Gze/CVbt0ZPApdddhnFxcWIkEzHRxV4vqlD0muIPSuPmMmZSN3MRAp4PGz89APWf/wefreLjIGDmXXtTygYPZ5AUHPAIt9Y6WHp6024quqRImESMqxMuaSI4olpJ50LqtNysMdAsPfe4VBr5xyKg6xdBYlcdv9Elr9ZyvpPq6jc0sKcG4aRkNG9PlcmU1Rc7v3+JUli0LQzMcbEsumtx3jjwXu44N7fEhOfcOSBjjOSToMu3kikNYDsCqGLPdDylJmZSU1NTefz2trazi7e+5OVlUVHRweBQAC3243T6SQxMXpBY9zzJY0dO5YBAwawa9cuampqeOihhwD497//zbhx40hNTeXLL79k9erVvPrqq0DUfbVq1aqDsrRuueUWZs6cyTvvvENlZSUzZszofM9qPfi31OvjGDJkEIsW/Q0hRLeEnNEYTRFPMegRGi2e0KFF0uHY/7MCgQPL2x1qrn1Jj52lQojTgQLghb6bjopK//Oho4NZa3ay0e3jr4NyeDxPoWzzFVRUPklq6rlMnPjRIcVNVRVEIoKamh288MJ/WLq0nNGjT+Xmm2+muLgY/yYHjf9vHZ5ltVhGp5D2y3HYpmd3S9x42lpZ+vJzPHvztSx/4xXSi4q59KHHuPx3f2LA2IlIGg0OB+i0CjXbmvngb5v45JmN1GxpJjE/lQv+byyX/WYCI2dln3TiBqIL9XcTwwyGvaJTZX/2/s3KclQIy3L0uYKOWVcP5pybR+B3h/jfH9ZQsry+yzEjELUYBoPRmxD7Hk86exLn330/HU2NLHzgLlwtx6VdYbfQmPVorHoUdwglEDngvfHjx1NaWkpFRQWhUIiFCxcyb968g8aYN28e//vf/9Dr9bz66qvMnDkTSZJwOBzIsgxAeXk5paWlFBQUcP7557Nx40Y2btzIuHHR2NgbbriBq6666gCrxuzZs3nqqac6P2fjxo1ANN5vr9B68cUXj3qMkqThjDPOJRgM8MwzT3a+vnnzZr7++mumTp3K66+/jizLOBwOli1bxoQJEw4YI06vRSNF4xUBbDYbbrcbgAkTJvDVV191Vnl+7bXXmD59OgCpqals374dRVF45513jjrXvqTLFhxJkh4TQnzXRvjoIV5TUTkh8coyD5bV83J9KyNtZv4+OBdTxzusXvMwGo2BYUOfJDX1nEPu63BAMOjk88+/oKKigpSUFObPn09GRhqadi+ORTsJVTjRZ8aQeNVgjDndi29wt7awetH/2PLlYhRZZtCp0xg/70KScw+sq+ntCLLqwyaqNzcS9ISITTYzcV4+BaNTiCh60gf0+Os5pgghUNxuIi0tyK2tyG4PiteD4vEgezwoHi8i4EdEZIQcAVlGRGQ62q20SXqMZi2SXo+k1xHWmNCZjbTn69DGxaGNi91zH4cuMRHpODWKPN4czdqVNzyJS++bwGfPl7Dk5R3U7WxnxpWD0BuP7jI4Ug2jvJFjuPj+h3nr4ft546FfcckDf8CelNKPR9p7tLFGRFAm0h5An2rtdOfqdDqefvpp5syZgyzLXHfddQwdOhSA+++/n3HjxjFv3jyuv/56rr76ak455RTsdnun6Fi2bBn3338/er0ejUbDM888c4D7aH/mzZvHtdde2+meAnjyySe5+eabGTFiBJFIhGnTpvHMM89w1113sWDBAn7/+99zzjmHPmd9F4Mhntde+xt33/0of/7zk5hMJvLy8njiiSeYMmUKK1euZOTIkUiSxB//+EfS0tLYsWNfULdGkjBrNAQUBZ8sc+ONN3LmmWeSkZHBkiVLePTRR5k5c2ZnkPH8+fOBaCPRuXPnkpyczLhx4zoDjo8FXU4TlyRpvRBizHde2yyEOHz0ZT+hpomrdJetbh8/K6mizBfklpwUbs/Us3vXr2lp+YKE+MkMHvJHTMZD942VZZn//W8j69d/gyTB5MmTGT16NAQFjZ/VkV5Tjcakwz4nD+v4tG7Furhamln97ptsXbIYIQSFk2aTf+rFGGwpnbE1JpOgsdzFliU17F7voMVlIL0onpHT0sgoikfSSCdciwPF5yNcV0eoppZwbS3hulpCdXVEmh1EWhzILa2IUOjwA+h0aEwmJK0WdLo991qCwkytNxG97EUX8hEOQTAkkaGvx6Q5RDigRoMuJQV9RkbnzZCTjbGwEENhIdqYvo0TOZHoaq8vRRGs/6SSb9+vICkrhrN+Ohx7Yu8DmhrLdvHmw7/BFBPTY5HTH2nih0MJyUSafWgs+j31cXpGR0cHPp+P5ORk9N0Q12vXruXOO+/k66+/7vFnH41QqI1AoA6zORe9vvtB5rIQbHVHe9GNtB+fZIU+TROXJOlnwE1AgSRJm/d7ywas6OVcVVT6FSEE/6p18PvdDSTodbwxcgCDlTWsX3MPsuymqOg+srMWHLYhXX19Pe+//z47dnjJySngzDNnYrfZ8W9toWVxDVIgjPWMNOyz87rVxM/laObbd99g65LPARg283SGz76YDn9qZ+sCv1dhyaIW2nZW42xwYTDrGD4zi8KJmXT4LNErcmmfe2BPgeRjSqStjWBZGaHduwnuLie4u4xQ2W4iDscB20lmM4asTHQpqRjz89EmJaJLSkaXlIQuMQGNzY4mxorWZkMTE4NkPHxp+AHfiStJShKYNEFkpxO5owO5Y899ezvhpkYi9Q2E6+vxb9iA65NPILLPDaFLS8NYVIR5+DBMw4djHjECXWJiv35nx4rvxslA9Pl3i9lpNBLjzs4nOdfO4n9v439/WMuZNw4jc+Dhe5V1hbTCgVz069/x5sO/4Y3f3svlv/3TEfufHW80Bi0amyHqqrLo0PSwLYnNZsPv9x8Qh3M0Hn30Uf7xj390xt70F3p9HMFQM6FQS48Ejnb/eBpZwXSCdRr/Ll3pRRULxAN/AO7Z7y23EKKtH+d2WFQLjkpXaA1FuH1HNZ+3upiTZOfPRcm0Vj5GXf1rxMQMYuiQx4mJKT7kvsFgkCVLlvDtt99itVqZOfNsLJbBaDp8BL6soLUiQLs1lty5GaQUW7ucquxsbuTbd95g21dfIEkSw2bOZsJ5F2FPSulsPokSZueqRkqWN+DuCBOfambW+akMnJDa2QvqWKdKCyGINDUR2LZtz60Ef8k2ZEdL5zYaiwVDYSHGAQMw5Oaiz8rCkJWJPjsbbULCCdHLRsgy4bo6gmW7CZaVESwrJbhzF8HS0j3FUUCfkYFl/Dgsp0zCespE9OmHbnNxotOTXl8dTT4+/PtmXA4/s340iOJTen/sDaU7eeN39xKflsGlDz6K0dL1wNJjacGBPf2qmn0IAfpUS48zD71eL06nk/j4eMwnWHpfMOggGGzEah2AVtt9K4xPlin1Bkkx6kg3HrvO8nvpl0rGkiSZgAuAPPaz/AghftubyfYEVeCoHI0V7R5uKqmiLRzhwcIMLrDVUVLyC/z+KnJybmBAwZ1oNIeu1bFz504++ugjnE4n48aN47TTTsModDS+X03NVy24JCOhYVnkT0skNk7q0sLhanGw6u2FbFv6OZJGw/BZc5gw/yJsifuqgK771k/1+gZK1zYRDslkFMUydGomsRnxR2zm2R8oPh/+zVvwrV+Hf+NGAttKkPfU9UCjwTigANOQoRgHD8JYWIRxQAG6tLQTQsT0BMXnI1BSgn/zFvybNuFbvRq5vR0AQ24u1qlTsZ02C8u4cUh6/femFk9P5hn0R/j4mS3U7Wxn0vkDGD07p9e/a+XGdbzzx9+SUTyYC3/120O2EDkUx1rgAChBmYjDhyZGjy6uZ64qIQQOhwMhBCkpKSfU/wshZDyeHWi1MVgsPTP7lvuC+BWFwVYTmmN8bP0lcD4BnMA6QN77uhDi//Vqtj1AFTgqh0MWgscrG/lLZRP5ZiP/GJJFTOuLVFY+hcGQwtAhfyY+/pRD7uv1evn444/ZunUrycnJnHvuuWRnZ+Nb34zzowoUf5iYSRl0FOci9LoDTP+Hi4HxdrSz+t3/semzjwAYftqZTDjvImwJ+4RNc5WLjZ9Vs/orF0JA0Zgkhk3LICEj5pjF1kRaWvCtX49/3Xp8GzYQKCmJunIkCWNhIaZhwzANHYpp6BBMgwahORFX8z5EKArB0lJ8q1bhXbES76pViGAQjd2OdtIsOkbOJWHyeIwxhm51Qf++CCM5rPDFSyWUrm1mxKwsplxU1Os6Stu/WcpHT/2ZgadMYe7tdyF1oXX38RA4AJH2AIo3jC7VgqaHVZ8DgQBtbW3Exsb2ezp0d/F4SlGUABZLPjpd9+PQXBGZCl+QHLOB+GPchLO/WjVkCSHO7O3kVFT6i/pAiJtKqljl9HJxWjz3Zwkqd16Lw7WBtNT5DBz44CH9zkIItm7dyscff0wgEGDGjBlMmTIF0Rai5V9bCJY7MeTYiDtvGIaMGJp3gfUQqcpe777nAY+HNe+/xfqP30MOhxk6/XQmXXRZZ6ClEILqkjY2LK6ibmcHepOWU8/Kwj4gk8QUEwZD/8bWyB4vvrVr8K1ciXfFyqiLBpCMRswjRpB4/fVYxo7BPGoUWnvPK94eiRN5sZc0GkzFxZiKi0lYsADF58O7YgXuL75k1yfbCH+wGr/dTMysmdjnzMZQOAyHQzqiEP0+FSnU6jWccd1QLHYjm76sIRJWmHF5ca9EzuApM/C0t7HsledZmZXDqRdf0Ycz7lu0dgOKP4LcEURK6lkBQKPRiMFgwO12YzabD1kp+XhhNmfi9e4mFGrtkcCxaTUYNBItocgxFzjdoTszWyFJ0nAhxJZ+m42KSg9Z3OLkjh3VBBTBXwdlM5UlbFv/EJKkY+jQJ0hLPfeQ+zmdTj788EN27dpFZmYm8+bNIyUhCdeXNbi/qkXSa4k7v/CA7KgjBW+G/D7Wf/Qeaz94h6DPy6DJ05l00RUkZERrVghFUL7JwbqPq3BUu7HGGTn1gkKGTM3AaNZ1LvrfTb3tLUKW8W/ejHfFCrwrVuLftAkiESSjEcvYsdjnnYt1/HhMQ4YgddF90Bu+T4s9ROOLbKefju3003FfHUEqWYN78WLcH3+Ca9EidLl5GM6+mMyfzEIbc+ir9e9bkUJJIzH54kJ0Bg3rPqlCyIKZVw3qlcgZN/d8WmuqWfnmf0nKzmHgKVOOvtNxQNJq0NoNyB1BFH8EraX7pQYkScJms9Ha2orX68Vms/XDTHuGVmtBr48lEnEjhHLYJIvDIUkSiXodDcHwCR1s3JUsqi2A2LPttZIklQNBoj3mxfFIE1dR2UtQUXh4dwPP1joYFmPm/+Uk0rT5ryyuX05y0nROPfVe4uMzDtpPCMG6dev47LPPkGWZ2bNnc8oppxAqc9L40nrktgCW0SnEnpOPNubABT85OboYw77gTa8nhLP0Ez74+HX8LicDxk1k8iVXddaxUWSF0rXNrPukivYGL7HJZmZePYjiiWlo9ysEaDb33WIne7x4ly/Hs2QJnq++isaUSBKmoUNJvPZarKdOwjxmDJpDdRnsJ4QQBOQA5fURIkJBFgregEAWMrKipbxeT2GeAYPGcELFLeyPOUaHPHYS6adOQvZ68SxZguOtD3D/7XHK/vsQseefT8I1CzBkZR2w3/7tDfbyXcvfiYYkSUycX4CklVj7YSUAM68e1OPfRpIkTv/xzbQ11PLx3/5CXFoGKXkFfTjjvkNj1aN4w3z07gf88qF7kGWZG264gXvuueegbZctW8Ydd9zB5s2bWbhwIRdddBEQteIYjUY8Hg9Wq/WYWXFWr17NL3/5S5qamrBYLIwdO5Ynn3wSi2VfULFen0g47CQc7sBgOHRtno6ODv773/9y0003HfRevF5LQyhMWzhChvbYBxt3ha5kUR3RQC6EqOrTGXUBNQZHBaDCF+QnJZVsdvu5PjOJH1ubWPHV/0PQyICCBaSkXEUopD3IKtDW1sZ7771HZWUleXl5zJs3j1idlY4PyvFvbkGXZCbuvEJMhXGH/ey9lhafT6Z60zfs/PxlQq5GcoaPYsqlV5NeFM3OksMKO1Y1sP7TKlwtARIyrIw9K5fCMSlo+uGqJ1RbFxU0S5bgXbMGwmE0sbHETJ1KzIwZWCefii6+71J1w3KYBm8D9d56WvwttPpbaQu00RZo63zsDXvxhX14I9F7gSDQkIPG6D+oRosSNGNKj3ZO1mv0WPVWYo2xxBpjiTPGEWuIJcmSRLo1fd8tJh27oX/caIfiUNlJgYAg1bkZ/9uv4vr4E1AUYs89l8Qbb8RYEBW5e7PkuhK7dSLy7XvlrP2oklGnZ3PqhYW9EqDejnZeued29CYTV/3hCQzmQ2fzHK8YnL2EfUGKBw/i0/c/JndQAePHj+e1117r7Ca+l8rKSlwuF3/+85+ZN29ep8ABCIVCtLS0YLPZ+sWKE4lE0On22SqampqYMGECCxcuZNKkSUC0i/fUqVNJTU3t3E4IgddbiiRpsFoLDzl2ZWUlc+fOZevWrYd8v8IXxKcoDLGauvT3IMtyr3tP9WkMzvEQMCoqR+Ptpnb+b2cNekni+aE5DPK8zMqv38RgzGTYsKew26LVRiVpnwtgb1+XL7/8Eq1Wy7nnnsvoUaPxfdtI46c7ELKC/fQcbDOO3l7BZBJEWtay6tUXaK2tJr2wmKl33Eb20KhBMxySKfmmng2Lq/F2BEnJtTH5oiLyRyT1edPLYEUF7k8/xfXJpwT3VB415OeTcPXV2GbOwDx6NJKu537yQCRApauS8o5yKlwV1LnrqPNEb82+ZgQHXiTpNDoSTYkkmBJIMCWQbcvGqrdi1pmx6C1YdBZczbGgRAO1JSQ0koZgEGQpiC2lg5AcIiAH8IV9OINOOoIdOHwOytrLcPgdhJUD++EkmBIoiC1gQNwACuMKKYwrZEjiECz6vi9Gdqgqvnl5EmbzSDhlJCm/+AVtz79A++uv41y0CPtZZ5J8xx0kJ+ccZPk7XvWLesKEc/MJesNs/LwGs83AmDk9n7g1Lp5zbruLN357L5/962+cfesvj7pANj7yCMHtO464TXcxDh5E2r33Hvb9tZvWU1hQSG5SFnqdnssuu4xFixYdJHDy8vIADmmhMRgMGI1Gtm3bxk033cT69esBKC0t5dJLL2X9+vWsW7eOn//853g8HpKSknjxxRdJT0/nX//6F88++yyhUIjCwkJefvllLBYL11xzDSaTiQ0bNjB58mQef/xxFAXCYfjrX//GlVcuYOLESZ1z2Cu42trauO666ygvL8disfD03x5jYFEC99//a2prGygvL6e6upo77riD2267jXvuuYfdu3czatQozjjjDP74xz9y11138fHHHyNJEj+/51eMOfc8PvziS5554i988MEHQLRX1rhx47jmmmvIy8vj0ksv5bPPPuOuu+7isssu69Vv1h2606rh54d42QmsE0Js7LMZqagcAa8sc19pHa81tDEh1srjBQY6dv+ECud67PYfM2zorej1+/wAe10Azc3NLFq0iLq6OgYOHMg555yD2a3B8fdNhOs8GAvjiDuvEH3S0QNAGst2sezVF6gp2UJcWjrn/vxXFE04FUmSiIRkti6rY/2nVfjdYTKK4pj1o0FkD+7bOjDB8grcn34SFTU7dwJgHj2alLvvxjZzBoY9J1zYY22qP3owryIUql3VlLSWsLN9J+Ud5ex27qbWXdspYjSShlRLKhkxGUxMn0hmTCYZMRlkxmSSZE4i0ZyITW876rH2pEbL/vNsC7TR4GmIWo889VS4KtjdsZsPyz/EE/Z0zrUwrpDhScMZmTyS0SmjybXn9snvcCRXoj41ldRf3UPiT26k7cWXaHv5ZVyffU785ZeRec3PaI/E93mM1bFAkiSmXjqQgC/Cynd2Y40zUjzx0NW/u0LWkGGcevEVLH/jFbKHjmDEaXP6cLZ9Q11dHdn5OaAIZHeIrKwsvv32226PExMTQ05ODjExMWzcuJFRo0bxwgsvcO211xIOh7n11ltZtGgRycnJvP766/z617/m+eef54ILLuDHP/4xAPfddx/PPfcct956KxBt/LlixQq0Wi2KEv3/o9FASclWrrpqAcFg9P/X/prrgQceYPTo0bz77rt8+eWX3HD9bSz7+lVkxc+OHTtYsmQJbreb4uJifvazn/Hoo4+ydevWzh5Yb731Fhs3bmTTpk20tLQwfvx4XppwCh5Z/u4hH0BiYmKnsDuWdOeybtye2/t7ns8FNgM/lSTpf0KIP/b15FRU9me7x8+N2yop8wW5MzeVKy1rKdt0HwBDh/yFUGge3/1/5vNF2LhxDTt2fIbJZOLCCy9kSNFg3J9V0byiHo1VT8JlxZhHJh914etobODrhf9h18qvMdtjmXXdTxlx2plodToi4ajFZt0nVficIbIGxTP+nHwyiuL67PhD1dU4P/gA9yefEty1CwDzmDGk3vsrbLNno087eLE5XDBvTo6gJVLD5pbNlLSWUNJawo62HXjD0YAQnUZHnj2PIYlDOLfgXPLj8hkQO4Bcey6GPvC3H6mX0dHQSBqSzEkkmZMYnjz8gPeEEDT5mtjVvoutLVvZ7NjM4qrFvFX6FgAZ1gwmZUzi1IxTmZg+kVhjbK+P5XDoEhJI+fmdxF91JS1PPU37K6/ifOddkm+9lewrr4i2n/ieIWkkTlswGJ8zyJcvbyc22UxaQc+/wwnnX0zN9q0seelZsocOJz7t4Hi5vRzJ0tKfSBoJjVmH4gkj9hSD7C4GgwG9PmoBev755/nLX/7C66+/zurVq9m5cydbt27ljDPOAKJunPQ9xSW3bt3KfffdR0dHBx6Phzlz9onA/ZtyhsNRIbP/KUyjib6+v0v0m2++4a23ov8XZs2aRWtrG36fBkUOcvbZZ3fGDKWkpNDU1HTQcXzzzTdcfvnlaLVaUlNTmT59OpWbNqBYrBwp2OXSSy/t0ffWW7qVJg6MEUJ4ACRJegD4EJhGtDaOKnBU+gUhBP+pb+WBsjrsOi2vDcsg2fFHdla+Q2zsGIYO+Qtmc1bnYg5Rq0BNTSMfffQFslzB6NFDOOuss9DUBml+Yj1yRxDrxDRiz8xHYz7yfwOfy8mqtxeyafHHaHRaTrnwcsafez4GswU5rLD1q1rWflyFtyNIRlEcc24YSkZR38S5yB0duD75BOei9/Bv2ACAeexYUu+9F9uc2ej386kfir2ZOxp9mJ3tu9ni2MLmxh1s+2IL3pjtABi1Rorji5lbMJehiUMZkjiEgrgC9Jr+bVK51wqyN56ppqb36eKSJJFmTSPNmsa0rGlA1OJT6apkbeNaVtSv4NPKT3mr9C00kobxaeOZnTub03NPJ8F06EDL3qJPSSH9d78lYcGPaHr0MZoeeQTnokWkPfQQ5mFD++Uz+xOtTsOZNw7nf4+t5aN/bOaie8b1uHeVRqPlzJ/dwUu/vJlP//EElz7waJfq4xwrMjMzqampQWM3ovi91JRXd3bx7gq//vWv+fDDDwFYuXIlZ555Jo8//jinnXYaY8eOJTExkfr6eoYOHcrKlSsP2v+aa67h3XffZeTIkbz44ossXbq08739a+sIsc9SM2TIUDZsWMe8efPpih7T62MBhf3bZmm1WiKRyGH32R+rVotPqyO039VlIBA4cJvjVAeoOwInhWj21F7CQKoQwi9J0iG63Kmo9B5nOMIvdtbwgcPJzAQbD2e5adx1KY3+WvLzbiMv72Y0muif8V6rQGNjhM8/X8PGjStJTdVy/vkXU5iZT8f75fg3OtClmEn+6QiMeYe+8ty74LpdAcpWfEbZsoVIspvhs2Yz6aIriIlPQJYVtn1dx9qPK/G0BUkfEMvp1wwmszi+1y4QJRTCs3Qpzvfew/PVMgiHMRYVkvyLnxM7d26XWgeElTDbWrbx7oYSdnjWsaN9O4FI9KSTakljZPxETht7ESOTRzIgbgA6zfGpZXEs0sU1koaC2AIKYgu4pPgSIkqErS1bWVa7jMVVi/ndqt/x8LcPMz51POcOOJfZebMx6/reb2QsLCT7X8/i/vhjGv/wByovuYSEBQtIvuP2Y5rJ1heYYvTMvXkEbz62jo+f2cKFd41F18OCeLbEJGZecyOf/P0vrP/4fcaeM7+PZ9tzxo8fT2lpKVW1VaSaEnj9f2/w34X/7fL+Dz/8MA8//DAQvVAzmUxMmzaNn/3sZzz33HMAFBcX43A4WLlyJZMmTSIcDrNr1y6GDh2K2+0mPT2dcDjMq6++elhxJUlRkSNJ8LOf3cKUKRM488xzmDBhIgBvv/02kydPZurUqbz66qv85je/YenSpSQlJe3JMtUgy/6DxrXZbLjd7s7nU6dO5Z///CcLFiygra2NZcuW8cc//pESp4ftJSUEg0H8fj9ffPEFU6Yc/xIA3TmrvQp8K0nSoj3PzwX+K0mSFSjp85mp/OBZ7/Lyk21VNARD/KYgjbOUd6jc/ARGQwpjx7xGXNxBQfN0dDTwySfv0tTUxLRpI5kzZw5iu5umx9ehBGVsp+Vgn3n4IGK/HyorFGq3rmLdB2/ganWROXQy82+cR1ZhFoqssH1FPWs/qsTVEiA1387Mq/omxiZQUkLHm2/i/PAjFKcTbXISCVdeSey8czEOHnzE8YUQVLgqWFW/ipUNK1nTuAZv2Eu4JY0C+0DOyjuLYcnDGJY4DLs2+YTJ3DketWF0Gh2jUkYxKmUUt46+lV3tu/i08lM+rfyU+5bfx2OrH2PugLlcNPAiBsYPPPqA3UCSJOxnn411yhSa/9/jtL3wAt7ly8n4058wFfftZ/U38WlWTr92CB/9fTPL/1fG9CsO3detKwyZNotdq77hm9deYsDYCcSlnRj9v3Q6HU8//TRz5sxBjsgsuOhKBudFf6f777+fcePGMW/ePNasWcP5559Pe3s777//Pg888ADbtm07YCxJkrDb7Zx//vksXryY2bNnA1H31Ztvvsltt92G0+kkEolwxx13MHToUH73u98xceJEkpOTmThx4gFiY3/0+n0xOKmpqfznPwu5555f0tLSjEajYdq0aZx55pk8+OCDXHfddYwYMQKLxcJLL72EJElotSYUJYiiRDovGCEaOzN58mSGDRvGWWedxaOP/pFvvlnJiBEjkSSJRx/94/9n77zDo6jaNv6bLclm03svQAiBhAQIvRfpTUSkKU1AUBRsIIKKHdEXEcWC0lQEpCO9914ChNAhvfe2fef7Y0kgECBZEoh+3NeVa7O7M2eenZ2dc5+n3Xh6eiI6aunc7zlCQ0OpUaMGDRs2rKJvpGIot1QDgCAITYCWt54eEkXxidRqPy0T/2+jWAH80+vJuFvK+D7QFov498jJOYabWw+C63x+T0divV7PgQMHOHDgAEqlkt69e1PLxY/sddfQXM3Bws8Wx/61kbs/2FV6fO9VDq9cQVb8FVz9AmgxYAjOfsFIBBFtRhrHNtwgN02Fq58tTXvXwD+0fGrB94MhP5+8jRvJWbkKdXQ0gqUltl26YN+nD9Ytmj+w+qlIV8TR5KPsS9jHocRDpBaZYuY+Nj4092pOC88W1LdvSm6qg1nJvI8DV66YPDd3l4sXFkLQY57vRVHkZOpJVl1ZxY7YHeiMOiLcI3g59GVae7eukr48Bfv2kfT+NIx5ebi9+w6OL71Ubfv/3A+HVl8jckccXUaHULvxg0OmD0J+VgaL3hyPb71Q+k35CHjyZeJ3Q5ehQtQYkHsoEcxo82A0GpkxYwZFRUV88803lWpbcRVVsSdHLi+dYPwgGAwqCguvoVB4YWHhfN/xi0lUscfIaDQtStSiSYDTR2GBs0XVeoOrRIuqOuEpwfnvIkenZ9KlOLZm5NHNxY7pLtdIvPoeoqgjKOgjPD363zMBpKSksG7dOlJSUggLC6Nbl24YTmeRtyMWJAL23QKwbub5wPLsvIw0Dvy1hP1bruLgrKB5vwEENWuFIJGQcDmbw//EIy/KwMnLmmZ9alIj3MXsiUgURVSnT5Pz90rytm1DVKuxDA7GYcDz2PfqhdS+dOjsTkmDPGMaF9X7OZyxi+PJx9EatdjIbWjh1YIWXi1o7tkcX1vf++5f3SQRqmtvmBx1Duuvr+eP6D9ILUqljmMdRoWOoktAl0oP5+kzM0me/gEFe/Zg16MHnp99ikRZ+eXtVQWDwci6/50hM6mAQR80NTsfB+DEP2vY/+dCnp38AbUimlU7gmMo0mHIUiPIJQ9dLJWFfv36ceXKFVasWEHdunUfuSdMZcHUE+cKEokFSmWNMrfR3EpEuXsxAmBhIXKxUI2VREINZdWGWyuV4AiCcFAUxdaCIORDqUTp4k7Gj6/D1i08JTj/TZzOK2TshRhSNXqm13SlTeE8kpOXY2tbn9CQb+/54RkMBg4ePMi+ffuwsrKiV69e1LLzJXvNVXSJBSjqOuHQNxCZw/1/cFq1ihPrV3Hyn7UA+LQYSmjHHtjaW5Een8/prTHEXc7D1tGCni/5ULupBxIz+9gYcnLIWbOWnJUr0d68icTaGrvevXB4fgCKkHplEiaVCvafj+VY2j6OpR/kamYsok5OzQAZHWu1oL1vexq5N6ryhOCqwqOUiz8O6Aw6Nt3cxMKohdzMvUlN+5pMbDSRDr4dKtXTIooimfN/JX3OHCyDgvD5fi4W1SGGWE7kZahY9ulx3APs6Duxgdm9ngx6PX9MeQOdRsOI2T9y7fqNakVwRFFEl2hqQyD3tjHrGihu/FfdRDjV6hS02gxsbIJLhaluv1+2R8hoNC2cEtRasnR6Qm2sqlRh/KkH5yn+Vbg7JPVdDQmSmEkUFV3H3/8VataYiERSujQ5NTWVdevWkZycbIoPd+6K/nAG+QcSkCjlOPSphVX9+3tZRKORC/t3c3D57xRmZxHcqh1thgxHbu3G+VNFRO+PIyE6A7mVBXVa+vJMf3ds7G7/uiviFVFduED2X3+Rt3ETokaDVcOGOAwYgF23rmWu1EVR5GrOVbbHbGf9ydPE5iYilekJdQmlhVcLIlxaEGDvh7//vyuUcT9UZw9TMYyikV1xu5h7ei4xeTE0dGvIu43fvadM/VFRcOAgie+8gyCR4PvzT1iFh1fq+FWJCwcS2bv0Mm0HBVG/vc/Dd7gP4qLOsvLTabQdOhKb2vWqFcEBMBTqMGSrkblaIbGsuDdPFEXS09MRBAFXV9cqsNA8GAxFFBZeR6HwLlO64UEeHEtLyNcbuFGkoYaVBXZVKMBZJQRHMM0UQ4Eaoih+KgiCL+ApiuLxSrC5QnhKcP47yNHpefNSPFsycunqbMcU+2Ok3vwEmcyekHr/w8mpVantDQYDhw4dYu/evSgUCpPXRuFF9uqr6DPVKBu749CjBpIHiOMlXIxi7++/kXrjGp6BdWg/fDReQXUpyNZwYvNNzu5LRa2zoFZTbxo944WXj6zUhPswr4NKBalJWjK2H0C7eQ1Wlw9hZS3BvndvHIcOQVHn3mRMURS5kn2F7bHb2R6znZi8GFOjOl0POgY2o51PW5ysnG5t+2RyVJ4C9EY9a6+t5cfIH8lUZfJ80PNMbDSxUvvpaGNjiRszFn16Ot7fzsa2fftKG7sqIYoiG78/S9K1HIbMaI6tk8LssdZ8+RHJVy/T4e0PqBdSvUrpRaOILqUQiaUUWTnCcWXlxhQVFZCXl4ebm1spmYUnCVOY6jISiQKlMuCe9x+UgyORgFEUuVCgwlEuw0dRddpUVUVwfgKMQEdRFOsKguAIbBdFsUkl2FwhPCU4/w3cWSU1LcCJlnlfkZGxFWenttSr9zUWFi6ltk9LS2PdunUlfSO6PdMV/b40Co8mI3VS4Phc7QfqR+VnZrDvjwVcPnIAGydn2g4ZQXCrdmjVBk5vi+Xs7gREo0hIW28adw9AaVf2j/RBeSOWhSkc/P4A+fv2osxPxd7HHofevQgZ1Qkb93ujuSmFKWy8sZGN1zdyPfc6EkFCY/fGdPHvQif/ThSlu1TLHJX/7yjQFjAvch5/XfoLB0sHpjadSrca3SptfH1GBvGvjEN96RKen3+Gw7PPVtrYVYm8TBXLZhzDL9SZ7q+Y791Ki7nBH1PeoOO7H9Gw8WOfYh4KfY4aY6EOuYf1A5ON70cKZDID6emp2NjYYGf32LM87gu1OgmtLgtbm3plKow/LJE5RqWhyGCkbjm1qcxBpWpR3YFmoig2EgThDIAoitmCIFQKTRMEwQH4DQjFlOczShTFe7sePcV/AneHpP4KMiKNeZFMTSqBge/h5/tyqR+X0Wjk8OHD7NmzB0tLSwYMGEAtCy+yf7mEIVeDTSsv7LoGILEoO2FPr9NxauNajq5dgWg00rz/YJr26Y9EbsG5vYmc2HQTTZGeoKbuNOtdE7uHyDWUpQptuHGZq4s3kLQ/Cqmox7VJCFadxyAJDsPOXUKWBmxubVugLWBH7A423tjIiZQTiIg0cG3A9GbT6eTfCRer28ROVYZy+b9Jv+i/ChsLG6Y0nULfwL58cuQT3t3/LrvjdjOt+bRK8ebIXFzw/30JCa+/TvLU9xEEAfu+1ac/zP1g52xFRI8Ajq2/QeyFTPxDyq7IeRjcAmqaFh8qFQa9Hmk18XIUQ6KUYyzQYVTpkdrcfxq8u8OwINzydhilWFpaolKpsLV9uLTJ44JUZgvaTAyGQmSye4VBJZLSi627YSuVkqszoDGKKKRP/jNV5KrRCYIg5VaisSAIrpg8OpWB74Ctoig+f4s0/XtKCJ6iQrg7JPWW9S4yL3+N1NKLiIi/sbcLL5WToVZnc+jQelJTY6hbty49numGfm8aGSeikLlY4frK/Rv2Adw4c4I9i+eTk5JMYJPmtB82GjtXd26ezeDwmmvkpqnwCXakZf9AXH3Lp/SrUJiIhoXcSOHhw+QsX0Hu2WiypT7YdO6Cz3OdsLglm6DVQn4+iIKe/QmH+ef6P+yJ34PGoMHP1o/xDcbTq0YvfO18yzzWo0gaPEXVI9gpmN+7/86C8wv4+ezPnEo9xcy2M2ni8eheB4m1NT7z5hH/6qskTX0fJFLse/eqBKurFg2f8ePy0RQOrLiCz0fNkJpRTg3QcsAQrly9RlFuDrbOLg/foYqwdetWJk6ciMFgYPTo0bz33nsIcgmCXIKxyERwFi9ezLvvvlvSiG/ChAmMHj26VIfhYgiCyRNiZWVFTk4OOp0OC4vKDekcP36cd955h9TUVJRKJREREcydOxflQ6rzZFJTzwa9Ph+ZzJacnBz++usvXn311XId1+ZWf7F8gwGFmd97ZaIiIaqhwEAgAlgMPA9MF0Vx5SMZIAj2QCRQUyynMU9DVP9O3BmSmupvT4vsj8jOOYSbW0/qBn+OTGZbkt9iYSFy6dI5tm07TEGBLV27Nibc1Q35gWtYqDTYtPXB/hk/hPt0T81JSWbPkvncOH0CR09vOo4YS0CDCNJi8zi06hpJV3Nw9FDSsn9ghXvZFGapiFq4E9X6lZAUg9HVG2Wv57Hs2BWdzAaj0eRtAUgtTGXThYOcNv5NvnU0DpYOdA3oSu9avQlzCas2K7eneHREZ0YzZf8U4vPjeTPiTYbVG1Yp369RpSJ+3HiKTpzAZ94P2HboUAnWVi1izmWw6cdztBscRGg78xOOTx07io+bK65+AUieQEm1wWAgKCiIHTt24OPjQ5MmTVi2bBn16tXDkK/FkKtB5q7k96V/cPLkSX744YdS+z8oMVcmM5Ca+uhhKr1eXyqPJzU1laZNm7J8+XJatDApiq9atYo2bdrg/hBpF4CiopsYjTpsbIKIiYmhV69eREVFldueiwUqFPcpFzcYDI9cGl8lISpRFJcKgnAK6HTrpWdFUbz4SJaaUANIBxYJghCOSddqoiiKhXduJAjCWGAsgN/T5IN/FURR5LeEDD65noS7pYw/ahUijRlLrqGQusFf4uk5oGQiSE8Hvb6AnTu3c+VKHNbWdejcoTmWF3LJ3H0Ng4OSsJfrYl+7bG+LTq3m2LqVnPxnNRKZnLZDR9KoRx+K8gzsWHSBK8dSsbKV025wEPVaeyGpwCpDn55O1tKl5CxbjkW2Gn3tFihe+gSXzq1x85CRng4FBZCSquNo8kn2Ju3kXMJVjAYpndu5MzB0DO182iGX/jtLup/iwajnXI9lPZfxwaEP+ObkN5zPOM9nrT5DITM/2RZAYmWF74/ziB02nMS33sb/jz+qvYaVf31nPAPtOb4phqBmHlgozAsxWSqViEYj2cmJRB9WkxFfUKl2uvja0OaF+2frHz9+nMDAQGrWrAnAoEGDWL9+PfXq1UNiJcOQq8Gour9m050dhm/cuM6QIQM4fPg0lpZw/foN+vfvz86dO7l69SpvvfUWBQUFuLi4sHjxYjw9Pfn111+ZP38+Wq2WwMBA/vjjD5RKJSNGjEChUHDmzBlatWrF7NmzS445b948hg8fXkJuAJ5//nkAsrKyGDVqFDdu3ECpVDJ//nzCwsKYMWMGcXFx3Lhxg9jYGMaPH8Q773zCe++9x/Xr12nQoAGdO3dm1qxZTJ48mS1btiAIAtOnT2fgwIHs3buXb775ho0bN2IjlTJ54ht0adGMkSNHEhAQwMCBA9mxYweTJ09m0KBBj/q1lRvlvuoEQbAEGgH2t/YbIAgCoih+Ugk2NAJeF0XxmCAI3wHvAR/cuZEoivOB+WDy4DziMZ/iMeHOkFQXZ1smWa4n++o8rKyDCA1dio117VLbnz9/lUOHtqPX62jYsBM1LHxQb4snL9+Ib09PLBp7k2sp4e6glCiKXDl6iH1/LCA/M526rdvTduhILJT2nNgYS+SueBChUVd/GnXzx/IhApt3QhsfT+aCBeSuWYuo02H7TCd8RozAqlGjUiv0AvlNFl/Yza643WTn6HGU+NK7Zj/GdWtBXd97lb6f4r8HGwsbZrefzaILi5hzag6phanM7TgXR8Wjia9KrK3x/fknYgYOIn7cOGqsWI68AqKPjxuCINDyuUBWzzrFuT0JNO4eYNY4UplpMaDTaG67Ph4jEhMT8fW9HT728fHh2LFjAAgyCYKFFPEWwVm9ejX79+8nKCiIb7/9Fl9f35KcFZ0OatSohZ2dPRcvRtKoUQMWLVrEsGHDUKlUTJgwgQ0bNuDq6sqKFSuYNm0aCxcu5LnnnmPMmDEATJ8+nQULFvD6668DkJCQwOHDh+/xiERFRTF8+PAyP89HH31Ew4YNWbduHbt372bYsGFERkYCcOnSJfbs2UNOTjp164by2mtvMXPmTKKiokq2Wb16NZGRkZw9e5aMjAyaNGlC27ZtSx3DRiZBBLR3fF/Ozs6cPn3avC/hEVARWr0eyMXkYalMcc0EIEEUxWO3nq/CRHCe4l+OO0NS0/2VNM2cTHZGJN7eQ6gdOA2p9PbKVqVSsXnzZg4ejMXNzZMez3Qi/p8iimJvIjoq8ezli11D65IS6TuRmRDH7kU/Exd1Dlf/GvR44x28atfl4uFkjm2IRpWvo3YTd5o/W7NCXVbVV66Q+etv5G3ejCCRYP/sszi/PAqLgICSbfRGPXvi97Ds0jJOpJxAorOmiW93nmnSi2Z+DfBwlz7Nl/l/BkEQGBU6Cl9bX97b/x4vbXmJXzr/grfNoxESmasrvvN/IWbwEBLemIj/X0urtUinR017/EOdObsrnvBOvsjvUwTwMNi5upGXnkaTnt4obGwevsNjRLEXp1f3ngwePBhLS8sSMcrdu3ebtrkjMXfs2NEsWbKI8PDZrFixgsOHD3PhwgUuXLhA586dAVMYx/OWoG5UVBTTp08nJyeHgoICunbtWnLsAQMGVDjcc/DgQVavXg1Ax44dyczMJC8vD4CePXtiaWmJm5s3rq7OJCXFIpe73rP/4MGDkUqluLu7065dO06cOFEqxGZzyyuuNd4mOAMHDqyQnZWFihAcH1EUK68O8hZEUUwRBCFeEIQ6oihexhQCeyre+S/GnSEpNwsZi/zTkMe9i0oQCA39AXe37qUSiVNTYzh0aD1abS7du3fAuTAY1V/xiNkCQrgnyobuuPvd+tFoTYm2ADqNmqOrl3Ny41osFFZ0GjWesM7dSL6ax99fnCAzsRDPWvb0fLU27jXKH+NWRUaSMf9XCnbvRlAqcXrpJZxGjkB+R/w6Q5XB6iur+fvK36QVpeFt482kRpN4NvBZnK3Mqxx57NCpQJ0LmnzQ5IGmwPS/Xg2i0fRnNJgeBQFkCpArQa4AmRVY2oDSBZTOIKu6vhf/VnT274xLVxcm7JrAyK0jWdh1IT625uejgEmR3OurmSS8+hqpM2fi+dFHlWRt1aBRN3/WfnOai4eSCOtQdiL9w2Bla0dhdhaq/NzHTnC8vb2Jj48veZ6QkFBK0VuiMBEcR6U9UkvTb2D06NFMnjwZgGnTprFp0yYAIiMj6d+/Px9//DEdO3YkIiICd3d3Ll68SJ06dThx4sQ9xx8xYgTr1q0jPDycxYsXs3fv3pL37tcFOSQkhFOnTtG3glV3lrdYmEl8U4ZWm3cPwbkfZDIZRqOp5kgukaDXqNEYb9cgPamOzRUhOIcFQagviuL5KrDjdWDprQqqG8DIKjjGUzwGpOTpef1oErtTCmjvbs8bdn+iv7kIa7uGhIbMwcrKpySRWBC0HD26n9Ono7C1dWH4oH44nS0k6/QV8hzs8epVkzyJEncPE6nRaG6XSF8/dYzdi34hLz2NkHbP0PbFkeh1Fmz/LZrrp9OxdVLQdUwotRq5livRUxRFCg8fJnP+rxQdO4bE3h6XCRNwHDoEmaNjyTZn08+y/PJytsVsQ2/U09KrJR80/4A23m2QSqqHrgwA6jzIjoGc2FuPcVCQCgVpt/+0ZSsTmwVLe7B2AQdfcPAHR39wDADXYHAJggfkHf0bOhmbi4ZuDfm1y6+M2T6GUdtGsaDrgnu0wioK244dcXp5FFkLFqKMaIx9r56VZG3lw+tWX6oDK65Sr5UXMjO8OIIgYGVrR0F2FgadDqn88eWwNWnShKtXr3Lz5k28vb1Zvnw5f/31123b5BIEmYTE2Hj8QmsBsGHDhpIk2M8//5zPP/+8ZHuFQkHXrl0ZP348CxYsAKBevXpkZGRw+PBhWrZsiU6n48qVK4SEhJCfn4+npyc6nY6lS5eWIlf3w4QJE2jatCk9e/akWbNmAKxZs4ZWrVrRpk0bli5dygcffMDevXtxcXEpM8FZECQYjTqsrRWlFMzbtGlT4qHKyspi//79fP311+h0OqKjo9FoNKhUKo7t20vDFi3vGfdx46EERxCE85hKw2XASEEQbmAKURVrUYU9qhGiKEYC92RAP8W/C8fTinh5dyLpopY3akoJTfuQG8nJNGkygXp1JyC5pZeUng5ZWUns2rWFnJwcmjWLoL5DPbIXx2NlqcK9lz+BbX0QpELJ5FdcIu1onca2H+Zz/eRRnH38GDhjJu616nJmWyynt8chAE1716BhZ79y3UxFUaRw/37Sf5iH+vx5ZK6uuE2ZguMLA9BIrElKh/wUHUcz9rA5dQlXCs9hI7dhUJ1BDKwzkAD7gKo9qQ82HnITIO0ipF+89XjZRGhUWaW3tbQHWw+wcQOvBmDtBjauYOUIFrZgaWvyyFjamjw1gvRW0w6p6X/RaPLs6FSmP73K5PEpyoDCW38FqSYidWkjFGXePrZEbiI67iHg0xj8moNbPZBIS3WFtrY2eehiY/9bpfD1nOvxW5ffGLNjDGO3j+WPHn+U6nVkDtzefBPV6TOkfPIJyiZNkLu7VZK1lY+Qtt5c2J/IuT0JNOpqXgMnxS2Co8rPw8bp8XlIZTIZP/zwA127dsVgMDBq1ChCbnVW/vDDD2ncuDE92nXlh59/ZNOerchkMpycnFi8ePF9xxw6dChr166lS5cuANja2vLLL78wZcoU8vLy0Ov1TJo0iZCQED799FOaNWuGq6srzZo1K0U27gd3d3eWL1/OO++8Q1paGhKJhLZt29KtWzdmzJjBqFGjCAsLQ6lUsmTJkjLHKF4UOjhY0apVK5McTvfuzJo1iyNHjhAeHo4gCMyaNQuPWy0xXnjhBUJDQ6lRowZhDRpgEEW0xsrqJGMeyiO2+cArUhTF2Eq1qBx4WiZevSCKIn8mZ/LewVQcJHKmet7AMvlLpFJrAgI+xsW5SUnXXb1ez9KlJzh79jB2drZ07dgZuwsGis5nonOypsmEGsg97nVnGvR6Tm1ax5HVywBo0X8wjXr0JeZcNodWX6UgS0NgYzdaPhdYrhbxdxMbubc3zmPHYt/vWSQWFqhUEHU1n12Jm9gQs5qMwjx8FbUY2aYTz9XrjlL+mFs1GQ2QcQUST0PSaUg+ZyI0d3phbD3BtQ441TR5TxxueVEc/U1E5nFCk28iWmmXIDUKUi9AynkoSDG9b2kHvk2Js+6Dwb8dlh4Bt3f9j3ZqPpd+jpe3vUwth1os7Lrwka8hbUwMN57th3WzZvj8/FO1bTlgNIr8+cERbJ0U9Hu7UYX2vbMkOCspAaPBgItv9epyaVTr0WeokLlYISlHtdg333xDbm4un376KWDKuUlNTcXW1hZb2/L14qpqiKKR/PxoLCxcUCgqXiBRZDBwtVCDv5UFDpWsS1WpZeJPgsA8xb8HhQYDUy4nsCo1m4aWtoxV/oU2cTP2Dk2pXftDLCycS5KC09LSWLt2LZcuFRIcHErb2g1Rb05AVajDsoU3bq29kHvcW7adcOkCu377kYz4WGo1bkbHEa+g1Sj5Z+55kq7m4OxjwzMj6uEd9PBJvCxi4/nZp9j37Ytwy/UdnxfP3D3r2HZjB1qhgAj3CKY0fYH6jo2RyQQeIHNVeShIh7jDEH/cRGqSz4Lu1om0sAXPMGgw2OQZcasHbsGPn8Q8CJa24FHf9McA02uiaPLwxB2FuCMQcxD10e+wtvgCnGpArY4Q3AsL17oUFlXPyfpREOYaxtftvmbinolMOTCF7zp8h6SMdvjlhUVAAG5vTiL1y5nk/fMP9n36VKK1lQeJRKBeay+Orb9BbroKe1fzXHMKaxvyMtLRaTXILapPcrVgIQUBjBrDQwlOv379uH79ekkCMoBUKkUmk6HRaKoNwREECVKpAoOxyKz9FRIJCKAyGnGoXNMqhKdq4k9hNq4Wqhl9IYYrhWre8JJQ+8LnaNRpBNYagY/PSwiC5FYPCJHk5GPs2LEDS0tLnunQC/0RJfroNKzcFCi71UR0srknLFGUl8v+pYu4sHcnti6udBzxCj51Izi24QYXDiRiqZTTrG9NUz8byYMnRFEUKTxwwERszp1D7uWF8/hxOPTti2BhgSiKRKZHsuTCEnbH7UaXWoPOtVvyfFB/Ah0Db41RhSKXuQkQexhiD5keM66YXpdamsiMV0PwagTejcC59r3tUf+liDsXh+H6ASzjd0HccRB1aOzqIg3piV/X3qaw2n8MSy8uZebxmUxsNJHR9Uc/0lii0UjM4MHokpII3LoVyRNK5nwY8rPU/D7tME16BNC0d81y73fnat2g15MeexMbR6fHGqYqD3RpJiIgdzPPK5eTk4NKpcLDw6PaeOJUqkT0+hxsbOqZZdPlQhVyQULNMhr+PQqqSovqKZ6iBOtSs3nrcjwKicAP3jE4JU3F6OCBrc1cXF1NInsaDWRmFnD27Abi468QFBRE17D2aDclUJCejaaFL/LGPljZSkollopGI+f37ODAX4vRqopo0qc/zfoN4uqJTP788AhatYHQ9j407VUDhfWD3SllERuPTz8pITZG0cieuN0sOL+AcxnnsLOwY3T90bRpPgQHC5dSuit3VnA9MlTZcGMfXN8FN/aaPBtgCt34tYAGQ8C/FXg2+E9XKLnW9iPWYig0GIqFIRvthW1ozm/H/8x0iJ4Koc9B8/EmgvcfwZDgIZxNP8v3Z74n1CWU5p7NzR5LkEjwmDqVmEGDyfj1V9wmTao8QysRtk4KfIMduXQ0hSa9aiAIQoWTy6UyGRZWVqgLC6sdwREspRjztYhGEeEhi62yIJfLKSoqQq/XI3+MSdQPglRqhU6XhdGoRSqtOElRSCQUGp5sDk5FGv0JwFBMkgqfCILgB3iIoni8yqx7imoHjdHIjGtJLErMoLGdgrdkixATVuHo1JZ69f6HweBUkhScmHiNw4c3IIpF9OjWg9ppThT+eR2ZswK/CaFlakhlJsSxff4PJF2Oxjs4hGdeHo/B4MT6OVGkx+XjXceBNi8E4ez98HLRwuPHSf92DqozZ+4hNnqjnq03NrLg/AKu5VzD28ab95u9T99afVHKlSXJr1BJIpcGPSSeguu7TaQm8ZQpcdfSDmq0heavmgiNe4gpsff/CUppbekcUTQchH+XQVgVXYfjv8KZP+DcCtO56TANAlo9aZMfGYIgMKPFDC5nXWbawWms7bsWOwvzW/VbNWiAXe/eZC1chOMLLyD38qpEaysPQU092LXkIulx+di62ZmVXG6pVJKfmVntBDglllKM+SBqDQhmdG0u1qLS6XTVhuBIbvUpMxrVZhEcK4mEHJ0BvVFEZgbpqwxURIvqJ0zimh1FUawrCIIjsF0UxceuZf80RPVkEKfSMPZCLJH5RbzsLqFrzpvoNfHUrPk2/n5jShTAtVotW7du5fTp03h6etK7eVckOzLQZ6iwaemFXbd7lb/1Oh3H1/3NsbUrsbCyot2Lo6jVuB1HN9wg+mASSjsLWj9fm8DGbg91l6ouXCD92zkUHjyIzN0dl/HjcXiuH4KFBRqDhvXX1rMwaiGJBYkEOgQyuv5ougZ0RSYpfWN65PJlTT5c2wWXNsHV7aDOAQTwjjDlmwR2Au/GIK0+N+pqB3UunPkTDs01JSjX6ggdPzCF6qoYVV2+HpURxYubX6RXzV581vqzRxpLl5TEta7dcBzwPB4fflhJFt5GZZwLdYGOhZMP0qirH14Na2EwlFamLiu5/O5whE6jJjMhHns3d6xszSeFlQ3RYESXXIjU3hKpbcU9rqIokpycjLW1Nfb2j65GXxkQRQP5+dFYWrpjaVnxKr18vYEbRRpqKS2xkVXeoq2qQlTNRFFsJAjCGQBRFLNv9a15iv8H2JGRy+sX4zCIIrM84/BJnoLEwolGDf/CweH2dZWYmMjq1avJysqiVctWROhqULQ8HsHeEpfR9VHc6otxJxIuXWDHL9+TlZRAcKt2tH9pNLHRKv6acQyNSk94R1+a9qqBxUPkFTQ3bpI+dy75W7citbfH7d13cRw6BIlCQaGukJVRf7EkegkZqgzqu9RnSpMptPNtd99ETysrMyp58lPg8ma4tBlu7gODFqycoE4PqN0ZarYHpVMFB/1/DIU9tHgNIkaiOriI9O1/oj42BkWj3rg+OxErp6pJrH4c5euhLqGMCh3Fr+d/pXuN7rTyNt87JffywuHZvuSsWo3zuHHI3SqvbLyyzoXCRo5XbXtuns3AqW4t7k4XsrC4t0v53ZBZWCKRStGqiqoVwRGkEpAKiDqDefsLAnK5HJ1OV8mWmQ9BkCJI5BiN9xcuMBpNMhSiaOoqIZffTg+0vOW10RiN2PBkvNIVITg6QRCkmHriIAiCKyaPzlP8h6E3isy6mczcuDRCrC141/JP5EkrcHJuR72632BhYZqsjUYjBw8eZO/evdjY2DC010BsDxRRlJqMdRMP7HvWuKfCQF1YwIG/FnNu51bsXN147r0Z2LjUYeuvl0m5kYeTvwMRHeugdLQmJf3+q0ZdcjLp8+aRu3YdgqUlLq+Ox2nkSKS2tuRqclkauZClF5eSp82juWdzZraZSVOPppWXzJebABfWwoV1kHjLs+gYAE3GQHBP8G321EvziFAZlMR6vYbl8GFYn/oB7YllxF48hP8L47Fq8lylHy893TShF3sYih/T0yu3fH1c+Di2xWzjqxNfsdpzNXKJ+eEJ59GjyVm5imtt21H3UmXoIJtQmeeiRpgrB1deRadSoZVbVTjHTRAELKys0KpUFTvwI2DUqFFs3LgRNze3+6pqi6LIWx9OZuvObShtrVm8eDGNGlXMyyiXy1Gr1ZVhMsePH+edd94hNTUVpVJJREQEc+fORamsWBJ0fp6GFSuW8+abM+55z2i8LSQqkZhIjkZjuj4kEpALAoIAGuOTK2SqyF13LrAWcBME4XPgeWB6lVj1FNUCaRod46JjOZxTwAAXgefyJyEWxlCz1uRSIans7GzWrFlDfHw8oaGhtLVvgHZtCkalHOcRIVgF3+uxuHrsMLsW/UxRTg4RPZ8lovdAzmxPJmr+CRQ2cloNqouluwcKhVCSA3P3qlGflUXmL/PJXrYMRBHHoUNweeUVZM7O5Gnz+DPyR/6M/pN8XT4dfDswpv4Y6t9KgH5k5KdC9DqIWgPxR02veYZDx+lQpye41TUtaSoR/+WOvw/D7UnWFjpOxTKkL2z9jPS/puMXtwV6/g8UlbeiV6sxy8NQUVhILXin8Tu8secN/r78N0PrDjV/rDsSxIxFRUgqOJndD5V5LnyCTR43XXYOGkurkrEqkuMmt1SgLih4bHk4I0aMYMKECQwbNuy+22zZsoVrMde5sP8Mp+MvMH78+BJRzvKiWO7AaDQiqUCVpF6vR3bHeUhNTWXAgAEsX768RFF81apV5OfnV5zg5Kv59delTJr00T0LQp3ORGSKXxYE03OdzvRbFQQBC0EoRXAMBkOF9bMeBeW+OkRRXCoIwilMWlEC8KwoipW3THiKaoXD2QWMi44hT29ghnsCQanvIrdwIrThX1haNiY+HlQqkZs3ozl2bBMWFgb6dumN9zkZ2pPJWNV3weHZQKR3VTnlZ2Wwe+HPXDtxFNeAmvR9Zzp5mXas/PIsqnwtoW29adanJmmZ8lIx+jtXjT4uRWQuXkzWbwswqtXYP/ssrq+9itzbm3xtPn9G/sQf0X+Qr8vnGb9nGBc+jjpOdR79pBRlQfR6iFptKucWjeAWYiI1Ic+Bc61HP8Z98P+h4++DcM8k614Pixf/oHDfQoiaAvHHYNBf4BFaKcdTKEznuMqq6O5Ae9/2NPdszs9nf6ZfYL9HagDov/RPYoe+SN627Tj0e7ZS7KvMc+HkaY3CWk5mbDYtm3mW6lL+sGt5z+L5pMXeMOW7aDXILCyQPOJk6eZfkw4jxj5wm7Zt2xITE/PAbdavX89LQ19EEASaNWlKTk4OycnJJaKZANevX2fAgAElqtpXr15l4MCBnD59mlOnTjFp0iRyc3Nxd3fn999/x9PTk19//ZX58+ej1WoJDAzkjz/+QKlUMmLECBQKBWfOnKFVq1bMnj275Djz5s1j+PDhJeQG4PnnnwcgKyuLUaNGcePGDZRKJfPnzycsLIwZM2YQFxfHjRs3iIuLY9KkSbzxxht88MFMbt6Mp2HDBnTu3IVZs2YxefJktmzZAghMnTqdAQMGsm/fXr799hvWrduI0WiSi2jcuDHtXhhM67pBvDhoEDt27GDy5MkMGjSogt+S+ahIFdVbwApRFOdVoT1P8YRQ7B0oUomsyEzn57xkajjI+Mx6NdYpS3Fybk+9ul9jMDgRGwtGo4pdu3Zz5kwStrb16d+kLrYbU9DJNTi+EISyYelkYNFo5OyOLRxYthij3kCbISOo1bgzB/6+TuLlBNwC7Oj1Whhu/qZVuDrx3lWjXGogef1ONCu/QJ+Whm3nZ3CdNAnLWrXI1+bz29lbxEabT0ffjoxvMJ5gp+BHOzEGPVzbCWf/gstbTDk1zoHQ9l0TqXF7xPHLiccVMqmuKHOS1UtRtB0DbcNg5XBY2BX6/wZ1uj/y8VxdK7mK7gEQBIHXGrzG0PWj+XnfJnr5DTDbQ2fVqBFyX1/y/tlQaQSnMs+FIBHwCnIg6WqOeTlumErjAUSjyBNK7bgHiYmJ+PoXt2s34uPjQ2JiYimCU6tWLezt7YmMjKRBgwYsWrSIkSNHotPpeP3111m1ahUAO3fuZNq0aSxcuJDnnnuOMWPGADB9+nQWLFjA66+/DpiEPw8fPnyPRyQqKorhw4eXaedHH31Ew4YNWbduHbt372bYsGFERkYCcOnSJfbs2UN+fj516tRh/PjxfP75J0RFnefUqSNIpUpWr15NZGQkZ8+eJTExg1atmtC6dduS8YtzcYphcSsPx8nJqYTYPU5UxL9nC2wXBCELWAGsFEUxtWrMeorHiWLvgEai5+O4RPanF9DGyoLRllOwUl2iVq3J+N0KScXFQVpaHNu27SApSUKTRg3xT3MgY0s20iAHwsYEYO1VemmXmRDH9l++J+nKRfzqN6DjiPHcOKvl7y9PI7eQ0m5IHUJae5XqH3H3hFZ4/ARJP8zHcOMaLk088J7zLcpGjSjQFrDo7M/8Hv07+VpTKGp8+HjqOpfOsq8wUqLg7DI49zcUppkUsxu/DOGDTKGox9yM63GFTKorHjjJWjWDMbth+RBYNhh6z4GIEY90vFLl6+X0MDwK6tg2QJvhwc+5v9GrVg8MBmuzPHSCIGDfuzcZP/2ELjUVubv7I9tW2efCPcCOG2fSURfqHtrH6k7c6WnJiI9FKpfj6FF9SuKL71+i/v45J6NHj2bRokXMnj2bFStWcPz4cS5fvkxUVBQ9evQoSTIuFtWMiopi+vTp5OTkUFBQQNeuXUvGGjBgQIXDPQcPHmT16tUAdOzYkczMTPLy8gDo2bMnlpaWWFpa4ubmRmpqaol+oNGoQyo17T948GCkUik+Pu60adOOkydPlAh2Go2lFyEWEgEReP6FgRWys7JQkRDVx8DHgiCEAQOBfYIgJIii+EyVWfcUjwXp6XBNp2LazXjSCvQMkWTje+MX4qzdeLbvR3h6RgCmWO+uXUc5f/4YguBFrxbNsYnMx6DKx7qFF57d3cjWCxQ3Gy/I17Pzr80c37gFKysJPUa/jUdAA7b9dpnslCJqN3aj1YDaWNvf22OheEJTX79G/m8/kXsiEqO7Pw2+nYpbn86oDWoWnF/AwqiF5GnzaO/TnvENxlPPuZ75J0KVDWdXQORSSDlnEokM6mpquhfY+Yk23HucIZPqiIdOsnZeMGIz/D0M/pkIBh00HfPIx3xc3rH0dBgY8iyrbvzFPzc3MDh4cMnrFbXBvk9vMn78kfxt23Ea9lKl2FeZ58LF19TDKiM+H58y8vPKA5ncAr1OW/L8QdU8jwPe3t4kJCXSrHYjRL2RhIQEvL29mTZtGps2bQIgMjKS/v378/HHH9OxY0ciIiJwdnYmKSmJkJAQjhw5QkpKCgqFAgcHB8CU/7Nu3TrCw8NZvHgxe/fuLTmm9X26VoeEhHDq1Cn69u1boc9gecfNRSqVotfrEYRigqO9Z/vi5GLT/6b8oeIE4+JkafmthaBFJeWDVRTmXAJpQAqQCVRfCdunKBdEUeSvmCzGXrqBXiPySuFRamf8Dy/PQGoEfENubgQqFWRmZrJw4ULOnTtOcJ36tHNsgdWhbAQrGbb9g3Fs6o6lQqC4CCD20k1+eHMmxzesIygilF5vf8L5M778Pessep2RXhPC6TI6tExyAyDLS0W+4COSXxlDVnQcbq+Oo/WWn3Hq3YGVV1bSc01P5pyeQ5hrGMt7Luf7Tt+bR25E0aT3tHYc/C8Ytk4BQQLdZ8Hbl2HQUlMl1BPuJuzqavJYaDS3qxU0GtPr/19QPMkGBZke7/EgWChN31edHrD5HYj864nYaQ7Uanil0Sg8rD04mWqqxLOwAHOKaiwCArAICKDg0MFKtrJy4OprWgKlxxeYPYZULseg0yGKYkk1D9yecDUaE+l5XOjTpw+///47SASOHjuGvb09np6efP7550RGRpaEgRQKBV27dmX8+PGMHDkSgDp16pCens6RI0eQSCSo1WouXLgAQH5+Pp6enuh0OpYuXVouWyZMmMCSJUtKJTmvWbOG1NRU2rRpUzLO3r17cXFxKfG+lAU7O3sKCooQRT0Abdq0YcWKFRgMBtLT0zlwYD+tWjUlKMifS5ei0ek05OTksGvXLuA2wdE/IUmoiuTgvAq8ALgCK4ExoihGV5VhT1H1KDIYmXIlnuXJBTSxEuiW9iMSbSI1aryIk1M/pFIJlpawf380p06tQyKRMLhnVzTbZWQkZaNo4IZtcy+0BgnOziaPglym49DfK9i2dDdyKzu6jx8Pgj+7F1+jMN9AaOsAeo/0RW5ZtmvVWFhI5oIFZC5cBAYDIWNfxGXcKwh2tmy9uZUfdv1AfH48Dd0a8nW7r4lwjzDvw6tzTeGnk4sg7QJY2Jg8NREjTdpP1QyPO2Tyr4XMEgYsgb8GwIbXTVpWtTo+aaseCoUC9DoJ3QO6s+jCIpILk3GSeZrtobNu04aclSsxajRILMteRDwpWNlaYGUrJyfF/Piq7JZ+nEGnwyBaPLCa51ExePBg9u7dS0ZGBj4+Pnz88ce8/PLL/PzzzwCMGzeOHj16sHnzZuq2qI+VworFfyy573hDhw5l7dq1dOnSBTB1MV61ahVvvPEGWVlZ6PV63n77bUJCQvj0009p1qwZrq6uNGvWjPz8/Ifa6+7uzvLly3nnnXdIS0tDIpHQtm1bunXrxowZMxg1ahRhYWEolUqWLLm/nQAuLi40a96IiIj29OjRh1mzZnHkyBHCw8MRBIFZs2bh4WHSi3vhhRcIDQ2lRo0aNGxoklYp7mCse0Kl4hXpZPwlpiTjyCq1qBx42sn40RGr0jAq6ibRBWpGWGcRdPEb8nJqERY+HitFPTQacHPTcOjQbiIjr9KooQ1d3JvB4Sy0CgWFLYNI1Ntjawvu7qYOpAlXbnJx0xzyU69jF9SPJj37ErkjhcQrObj42NCiXyAKB5syxSpFo5HcDRtI/99s9Onp2PXogetbbyL39uZg4kG+O/0dl7MvE+QYxMRGE2nj3ca8PjZJZ+DkQji/CnRF4BEGjUdB/edNCtj/T/GfK0FX58KiHpAbD68cAMcqyA6uRBTnwWXqEhm18yVeCZlAD7/nzCaxBfv2Ef/KOPwWL8K6uflaV1WFVV+dRG4ppe+kB2uMldW1FkCjKiI7KREnL2+MgrLMcJTR+PhDuPosNUaNAQvP+4uefvPNN+Tm5vLpp5/e8152djZarRb3SsidqiwUFl5FEOQolQEV3tcoipzPV+FhKcfdsnIkKKqkk7EoilMFQXAUBKEpoLjj9f2PYuxTPH7szMzjtehYQORzhz34Z/+AMqgDev0naLWOpiZN8hRWrdpERkY+rSIa0aHQBv3BLJQNXPHqG4jESkbgrUkxP0/DhV3ruXngbxxcren77gdcvujFph+vIpdDsz41CG7hhU4nUFZOnOrsWVK++AL12XMowsLw+X4uVg0acCbtDHO2TuN02ml8bHyY2WYm3Wt0v2/n4fvCoIeLG+DYz6ZyYrkSQvtD45Emhe5qot77pPCfLEFX2MPAP+GXtrBqFIzaCtLqofFTFoo9dMp0b3wtgzmSeojxbZ4z+/wrwkxeSHVUVLUkOHbOClJj8szevzi51mAwIJHfW71z9/PHBokAD/BW9OvXj+vXr7N79+6yd5dIMBgMiKJYbVTFBUFWEqKqKCS3mv0Z/gUhqtHARMAHiASaA0eA6u//fQrAdJH9LyaF2TGp1FNKmGj8CpvsY9QIeIMaNSagVku5edNIdPRpjhzZj0JhS9fQzgRdzsOg0OA0OBhl+O2kDysrkKoucmzBd2QnJdCwcxfqtn2BQ6viSI69jksND1r3C8DBVVFmaakuNY302bPJXb8emasrXl/NxK53b27mx/Dt7tfZG78XFysXpjebznO1n0Ne0QmqKAtOL4Hjv0Fegqm7cNcvoeFQ0wT4iPiveD3+syXoTjWgz1xYOQIOzoF27z5pix6I4hyj3s3rsChqEYW6QtLTrc26vmSOjsh9fFBFXahao82ErbMV10+nmz2RS251BjcaDFgqb3fUFQQTubm7mudxQZCYDLifqvjatWsfuH9FGvw9LgiCrMwk4/JCKgjVn+BgIjdNgKOiKHYQBCEY+KJqzHqKyka2Ts+r0bHsycqnj0MRz+e+gUIKIeELcHZuB4DBUMCRIxuIikrB17s2rYUaOF7Kwb6uHY4DgpDdkRCsU6s5uPx3Tm/9BzsXV56d/DEpMfb8MzcapZ0FfV8LwTPYtVTOiJvbrV47eRrU/6yCFfNQGApwHjsW57FjyZVp+OL4l6y8shKFTMHERhMZEjyk4o3P0i6ZvDVnl4NeBQFtoMfXpoqoSlLq/i95Pf7TJegh/UwyGge+MYUhnWo8aYseisbujZl/6g92Rl6hmW9Ds68vRUgI6ujqmSapsJFjNIro1IaHasyVBUEiAQGMBj0Siel3qNOZiI0g3JYLeOwoPqYoYuqHWzEUk73q5MEBAVE0P2NbCpin0PXoqMiVpRZFUS0IAoIgWIqieEkQhEpoD/sUVY1z+UW8HBVDikbH2w7naZg9A3u7BtQP/R6FwtRH4tq1a6xduxaNRsMLXdrif8YCY0EO9n0CsGnjXWo1khAdxdaf55CbmkKDrj2p1eRZDv4dQ256HPXaeNHyuUAsb920ij0AKhXExIjoTx4k95d5qJLTkTfvTsMZI7Gs4cKii3/y2/nfUOvVPB/0PK82eBUnRQVKSEURbuyFw9/D9V0gtYSwAdBsfKV1t70T/yWvx3++BL3bTJOq+44PYeAfT9qahyLMNQxDniPX8i/Q1tKUo2LO9WUREED+zp2Iej3CY5A0qAgU1iZ71IU68wiOIJikYm6Fg4pJzr8ddxKc6gKTJM8jEJx/iQcnQRAEB2AdsEMQhGwgtiqMeorKw7LkTN67koCTTGCm1UI8sjfi4zOM2oFTkUgs0Ov17N69m8OHD+Pq6spzgc9gub8AibMMl1dDsPC5nXir02o4tPx3Tm3egL2bO8+99xlxF63YNO8idi4K+k5qcN++FoknYsic+wOGyONY1KhJjW+/QghtxJqk3Sw7O4vkwmTa+7TnzcZvUtO+Zvk/oNFgkk849B0kR4KNu0k6IWIkWLs84tm7P/5LXo/H2bX3icDOC5q/CvtnmUhwzfZP2qIHwtbCFm+rQG4UXC71ekWvL7mXFxgM6NPSTP9XI1gqTeFmTZF5uR1gIgPViQgAtxN/HtGs6vW5JIii0fxw4oPTkqoUFUky7nfr3xmCIOwB7IGtVWLVUzwy1AYj068m8mdyJs1sDIxRvYONPpO6Id/h7t4LMPW2Wb16NUlJSUTUb0hEig/GYwUoI9xx6FMLyR2l3CnXrrBl3myykhII79KTGo36sv/vmxTkZBHe0ZdmfWuWWfptLCwk/ccfufrLXpQ2Ai6TJuHQpw9ns6L4ef9rXEyJo2GIA5+1+oymnk3L/wF1alNDvsPfQ/ZNk3xC77mmTsOyql/K/Ze8Hv8vStCbjzcRnN/7wozcKjlEZeZk+Tq5kpCbUuq1il5fxaRGl5RU7QiOziAhK9+SK1dEVJh3rqonwTE9iKI5ASqqUVjqNoSSog4zw24IiI/K+MyEWX5LURT3VbYhT1F5SFBreTnqJmfzVbxkH0/nnLexs65J/dC1WFubBCHPnj3Lpk2bkEgkPNu0G67HjIjCvYnEBr2Oo6uXc2zdSqwdnejz9kfERluz9ZdLOHoo6f9uBB41703YFUWR/G3bSZ05E31KCs7dR+H48hhyrAqZffRjDiUdwlnmydRWkxjRulO5KqNUKkiPz0V9chWKqEW4ClexCgiBzp+YmvFVUn5NefBf83o8zq69TwRKJ1NiuToXCtLBpnI7JFZ2TlYdbyeOx0ZRpDJgpZCadX1JnUzK3YbcqiF05kKlgqRkCUbRdG4MBjPPlSDwyK6SciAgIABbW1ukUikymYyyWpSIosjEiRPZvGkzVhYKFi1ZROOmTcw+5qMSt+PHj/POO++QmpqKUqkkIiKCuXPnVlhNPCcnh99/X8SIET3MtsX0LVVzgiMIwp/APuCAKIqXKtsQQRCkwEkgURTFXpU9/n8dxavHAykFfBQXj2iv40OHjdTJWYSHe1+Cgz9DKlWi1WrZsmULZ86cwc/Hl06Khkj3FyL3t8NpUB1kjreXiOlxMWyZN5v0mBuEtOuEf4N+HFiVgLqggIju/jTpUQOp/F5ioo2JIeajr0g4cAljzbr4zv4er4ia/HhwFetjVyCTiwwLGk0Pv37UqWVFGcUG936+zCxiNyzD8sIfWBuz0fq0JzbsW/xbNsZK+fhXPf8vvB7/NYzcAj+1NLUMaPJypQ5d2TlZvs4uCE5xqIz5GAsdzLq+JFamycxYpKq4AVWI9HSwsASZVARE88/VY6wF37NnDy4u9w95b9myhatXr3LpbDRHdh/gtQkTOHb82H23fxgq4snR6/XI7sixSk1NZcCAASxfvrxEUXzVqlXk5+ebRXB++eURCY4AxTnKBoOhwvpZj4KKeHAWAG2A7wVBqAWcAfaLovhdJdkyEbgI3L9v9FOUCZUKbsaILM/I4OfkVPykAi+mzaeG4SB16n+Kt9dgBEEgPT2dv//+m/T0dFo1aE69y46IOYXYPeOHbQc/BKnpR2U0GjixYQ2H/16KwsaGbq9NIf6SA7t/v4GLrw29Xw8vabd+p1veAhXC+gVkL15CEv54TngDl+d6sT12P7/+9TW5Ftfo4Nubl4LG4O3oXD63dGEmHPme9G2bsNSqsazbEZq9gqV7PdBAesb9b4pVXcb9n/d6/NfgVg+casLlzZVOcCo7J8vO0g6JhQZ79zz87BzMGkNiZVqsGFVF5hlRRVCri8nN7Ym8POcq55/raJNub6RTq9BLpWjk5ms+W3hZ49C7ltn7F2P9+vUMGzYMAWjWqCk5uTkkJyeXUhO/fv06AwYMKFHVvnr1KgMHDuT06dOcOnWKt956i7y8POzt7fnjjz/w9fXl119/Zf78+Wi1WgIDA/njjz9QKpWMGDEChULBmTNnaNWqFbNnzy45zrx58xg+fHgJuQF4/vnnAcjKymLUqFHcuHEDpVLJ/PnzCQsLY8aMGcTFxXHjxg3i4uKYNGkSb7zxBu+99x43bsTSuvUAunTpxddff83kyZPZsmULgiAwffp0Bg4cyN69e/nmm2/YuHEjYJKLaNy4MSNGjKB1cBDdnnue0/v2MHnyZAYNGvTI57u8qEgOzh5BEPZjKhXvAIwDQoBHJjiCIPgAPYHPgbcedbz/b7iZYuCDmAT2F+TTziafvoUfYGXpiJ/vX/h4m/SZzp49y8aNG5HL5TxXvytOxw1I7AScXgnDMuB2iCkrKZGtP84m+eplajdrSe1mAzmyLgmNKoNmfWvSsIsfUqnJa3OnW148c4ib3/1IUXI2tp2ep/b40SRZZvH5wbeJyoiilm09Pmo0n+6NQsr3oQoz4PBcUw8bXRFqn3FYt38ZXGuXbPKgm+J/qYz7KSoJggBB3eHEb6DXVqq+WGXnZNlZmNZ5eVrzm+GVdNU0PKki3bKhUEBRgWlJL1eYbKzO+WuCINClSxcEQeCVV15h7Nix92yTmJiIr69vScTMx8eHxMTEUgSnVq1a2NvbExkZSYMGDVi0aBEjR45Ep9Px+uuvs379epRKJb///jsffvghixYt4rnnnmPMGJNo7PTp01mwYAGvv/46AAkJCRw+fPgej0hUVBTDhw8v87N89NFHNGzYkHXr1rF7926GDRtWopN16dIl9uzZQ35+PnXq1GH8+PHMnDmT8+cjOXhwJba2IaxZs4bIyEjOnj1LRkYGTZo0oW3btg89hw5OTiXE7nGiIiGqXYA1puZ+B4AmoiimVZIdc4DJwH175QuCMBYYC+D3dNlcgmtFal46kUACakbYnicify5Oji0Jqv0hWq19qZCUv48fHXQhyE5osQpzwbFfbSS3SjRFo5Ez2zZx4K/FSOUyOo99k5RYd3b/EYuLrw19JzXE2dum1LHT00GSm0rGj99RdOgg+NbGcuosomS+/H5mOQey1+FgreDtiLfpGtANtaocrsmCNBOxObEA9GpTx+E276BQB6M1wJ3pww+6Kf6XyrifohLh2wSOzjPpj3k9WCagIqjsnCypYPqtGB+h/4h4S6lTUFQvRu/qCucKDOgNAnILaYlw7MPO1Z2eFtFoJPXmdWycnLBxdK5Sew8ePIi3tzdpaWl07tyZ4ODg+0/qD8mdGT16NIsWLWL27NmsWLGC48ePc/nyZaKioujcuTNGoxGdToePjw9gIivTp08nJyeHgoICunbtWjLWgAEDKhzuOXjwIKtXrwagY8eOZGZmkpdnItE9e/bE0tISS0tL3NzcSE292zMmcPDgQQYPHoxUKsXd3Z127dpx4sSJBwp2AnS/5UF63KhIiOocEAGEArlAjiAIR0RRfKQAryAIvYA0URRPCYLQ/n7biaI4H5gPJi2qRznmfwU7MnJ5NToWUZDylmQVAfnb8PN9GT+/UWi1EgoLM/j11xWkp6fTMrQp9S46IGj1ODxfG2WEO2q1QHocZKZkcXTlAnJvHCGocX3qdXiJo2tTKMpPp0nPACJ6BJR4bYohGgykLPsH1R8/ISBi/fJr5DXpw4H0fSw9+ieidQrP+L3Iq+374Gpni0bzkBWaKsdU6n3sZxOxqT8A2r4LLiaPjauqYhPIkyjj1hmMJGSriM0sJCVXTUaBhvR8DekFGjIKtBRp9RRpDBRpDRRp9SUpBFKJgEQQUFpKcbCywN5Kjr1SjreDFb5OSvyclNR2s8HTXlEtqyz+VfBsYHpMPlupBKeyc7L0RlP5tPQREueNtwhOcaiqusDKCpxttUgE0BllWEkrfq6Mt7xSxR2NqxLe3t4AuLm50a9fP44fP34PwfH29iY+Pp7moU1AEEhISMDb25tp06axadMmACIjI+nfvz8ff/wxHTt2JCIiAmdnZ5KSkggJCeHIkSPk5eVRUFBQ4vkZMWIE69atIzw8nMWLF7N3796SY1rffYO7hZCQEE6dOkXfvn0r9Dkt73A/SqVS9Po7SvhN/e/uu69MJsN4h3y7+ta1V2Kr8v7aXFWJioSo3gQQBMEWGAEsAjwovag2B62APoIg9MCkcWUnCMKfoii++Ijj/mchiiLfxaby1c0Ugq0MjHF4H32yhMDQr/Fwb41WC2fPXiQycgNKpcBzdTvjdMqIzM0S5yHByN2tS0I4SZdOcnj5QrRqI2HdX8dK7smuxXE4elrT49Uw3PzvZebqy5dJ/uBDCk6mYdmkGd7vvsHhrAJ+PfMJcXkxBPuF8WLwBLxtfdDkgcbyAWREW2QiNYfmmCpcQvtD+/fBJbDUZhWdQKqyjFutM3A5JZ+LyXlcTM7jRkYhsZlFJOaoMNzV8MHeSo6rrSVO1ha42SpQOktRWkhRWsiQCAJGUcQoiuiNIkUaPbkqHTkqHYk5KnZEp6LV375puNhYEOptT5MAJ9oFuVLP0w5JeTK0n+I27H1BkEBuQqUPXZk5WTqjDgCZYP4EbiwoAEBSwcTSxwF9kQp3Vz0h9aVm5QnfJjhVm7BaWFiI0WjE1taWwsJCtm/fzocffnjPdn369OGHH37g+c59OR55Ant7ezw9Pfn888/5/PPPS7ZTKBR07dqV8ePHs2DBAgDq1KlDeno6R44coW7duhgMBqKjowkJCSE/Px9PT090Oh1Lly4tIVsPwoQJE2jatCk9e/akWbNmAKxZs4ZWrVrRpk0bli5dygcffMDevXtxcXF5oPfF1taW/PwChFstmtu0acMvv/zC8OHDycrKYv/+/Xz99dfodDqio6PRaDSoVCp27dpF69atS8Z5UnepioSoJmBKMo4AYoCFmEJVjwRRFKcCU28doz3wzlNyc38U6g28cSmOTem5dLPJ4IX8iTjZ+VK70c8UFgaQna3l6NE93Lx5glo1vOigqYf8jA5l41u9bSxMN4SkBBXHVy/n2rE9uPj6E97lRU5uz6coN5NOz/nRtHcNZPLSNw+jWk3GvB/JXLQIqZ0d9b6eTlxgK767tph1J0/ipLRlfOgkno1oiiAIZGZCdjZ4eJRBRvRak07U/q+hIBVqd4GOH4Bn2H0/e0UmkMoKGegNRi6l5HM6LptTsdlEJeZyM6OwpHGVtYWUWm42NPB1oG8DL/ydrfF3VuLlYIWLjQWWsordgEslbVuIiAoN6aoiLqXkcS4hl/MJuXy97TJfb7uMi40lvcI86d/Ih1Bvu6fenfJAKjM1g8xPftKWPBDZ6mwAHBWOZo+hSzZ9RpmHR6XYVJnIz1Jj62S+R9JgMHkXqtqDk5qaSr9+phZwer2eIUOG0K1bNwB+/vlnAMaNG0ePHj3YvHkzwY1DsbKyYvEfS+475tChQ1m7di1dunQBwMLCglWrVvHGG2+QlZWFXq/n7bffJiQkhE8//ZRmzZrh6upKs2bNyM/Pf6jN7u7uLF++nHfeeYe0tDQkEglt27alW7duzJgxg1GjRhEWFoZSqWTJkvvbCeDs7EyLFo1p1uxZevbsy6xZszhy5Ajh4eEIgsCsWbPwuHV9vfDCC4SGhlKjRg0aNiztHX1SdyahvPX2giC8g4nQnBLNlRZ9+DHaYyI4DywTb9y4sVhWL4L/OmJUGkacv8mVQjVjrI/RpmAW7m49qBs8E5nMulSVVIuQJoREOyAYwLFfbZQN3UrGSbl2hQWfLkGdnUCDZ7ojyCK4eCQVWycFjXrWpnWXe/vaFB45QvJHM9DFxWH/3HO4vfMOW7IP8tXhuWSmC7RWDGdg3Wep4WddQmQ0GlOeYylSYjTA+VWw9wvIjgG/ltDpQ/Bvcc8xHxXmVFFp9AZOxWZz5Homp2KziYzPoUhrWi262VoS5uNAPU9b6nnZUdfTDl9HZaV5Ue5MjL6blN1pd3q+hoPX0tkRncrO6DS0BiPhPvZM6FibTsFu1carU23FSL9vDB71YcCiJ23JffHDmR+Yf24+p186jUxi3iSe+dtvpH3zP4JOnkBqY/PwHR4jVnx+HKWdBb1fb/DA7S5evEjdunXveb0wJ5v8zAzcAmpWuRenItClFCLIJcic73+hf/PNN+Tm5vLpp5/e8156ejqCIDywJP1xo6goFqNRi41N7YdvXAauFKqRCQI1lZXTgLWsa0IQhFOiKDa+e9uKhKi+qQTbHnaMvcDeqj7OvxF7s/IYdyEWMPKR5W8EFmwjsNYU/PzGIAgC58+fZ8OGDcjlcvrVeQbnUyJyTyuchgQjd73VD8No4Pi6VRxZ9RdSaS1aDprE9ZNGctNTqdvCk9COASiUpW8W+uxs0mZ9Te7atcj9/fBbvIjUum68cvQdjqccJ8wljF97TCdAWZfYWJMmjCjex2NyYy9smw6p500TzNBVEPhMlfWyKI/Hx2gUuZiSx6FrGRy8lsnxm5modUakEoG6nrYMiPChkb8jEf6OeDtYVamXpLyJ0a62lvRr6EO/hj7kFunYcDaR+QduMOb3k4T72PN5v/qEej+6Wvqj4GkV26MhpTAFZytns8kNgDY+AYmdXbUjN0ajSE5KEd61zfdO6XVaJFJptSI3oigiGkQExf2blvbr14/r16+ze/fuMt83GAylcmGqA0RRj+QRrkO9KKJ4Qouu6qXA9hT3QBRFfopP57PrSdRS6JmgeQ9PcghtsBgnp1bo9Xp27NjBsWPH8PXyoaMmBPlZPdbNPHDoVRPhVpgpNy2VLfP+R+KlaOq0aIOtd192r83Bxk5G55dDcPV3RKMxrbKLkbdjBykzPsaQm4vzK69gM2YEv135nUUbFmEls+KD5h/wfNDzJV2I75sjk34FdnwAV7aCvR/0XwAhzz0huV9TDs3h6xkmD8jFNNLzNQDUdrNhUBM/Wge60KymE7YK+eO1y4zEaHulnJdaBDC4qR9rzyTy1dbL9PnhIG90qs0bHWs/MW9Ota5i02tAWnkl4lWBaznXqOXwaP1Z1BcvoqhT/fSQc9OK0OuMOPuYT7wMWh0yeTX7Dg0iiCKCrOzfnNEIy5evLSkuMBpL3wJFUcRoND7WRnjlgYngmJ/HZRBFpE8ofP6U4FRjFBmMvH0pjrVpOXRUpjG0cBKutoHUD12ClZU3eXl5rFy5kvj4eBoHNSD8iisSUcTxLrmFiwf3svO3HwGRDiNeJ/aiC1G7kgiJcMe9URDZGjmGFKhTx0RI9NnZpH72OXmbNmFZry5+C34jyqGAGTteJDYvlt41e/NW47dwsXqIG7UwE3Z/CScXgoU1PPMxNBsH8sdf1ZFbpGPXpVS2X0hl/9V0irQGrC2ktK/jRodgN1oHuuBh/2SrTR4lMVomlTCgsS9dQjz4eMMF5uy8SnRSHj8MaYSF7PETyWorRiqKUJACtu5P2JD7w2A0cC3nGgPrDDR7DFGnQ3PpEo5Dh1aiZeXDw0KTGQmm5GcXMwmOKIrotRoUNvftKvJEIN4qCBDK6O5uNJo82hLJbS+3RmP6rReTnOKqJVkFld+NRtDpbjd2lssrb+0oiiJGUWd2srupiIJ/J8ERBMFDFMWUh2/5FBVFvFrLyPM3uVCgYpTyCB0Lv8HLoz916nyCVKogJiaGlStXotVq6R7YFu9zcuTeSpwHByNzMd1NdGo1uxb9zIW9O/EKqku99iM4sSkTg6GAlgODUXh4olAIJfkeaWmgO7yL3C8/wpCXh8sbr6MYPoRvzv/AimMr8LbxZn7n+bTwujdfplRIwlKD9vhfxB5agr/1Fayaj4D2Uytd/+dhUGkN7LyYyoazSey9nIbOIOJuZ0m/ht50rudOi1rOFU4CrkpURmK0vZWc/70QToi3PZ9ujOb1Zaf5cWgE0sfsyam2YqSFGWDQgq3nw7d9QriacxWNQUOwU7DZY6ivXEHUalGElLOxZiWhPKHJ1Jt5SOUSnDzNKx3W67QYjUbkT/xiKg1Rd4vglLGg0OlMpKN4nhcE03Od7vZvxByCUx7i9GgwmrxSZhIcw60c3/s4taocj+rBWYCpA/FTVCJO5BYy8vxNNEYD0ywXUa9oC7Vrf4iPzzAADh8+zI4dO3BycKSPsgXWUaIpJNW7VsmPKyMuhn/mfEVWUgJN+gxAq4vg4MpU3ALs6DyqHnlqJQbD7R+XVJVD9v9+IGnPLgIauOG3cAHHrVP5ZPPzpKvSGVZvGK81eA2lvGxXZUlIIn437P0Cy9x48OtEervf8GsUWOY+VQG9wciBaxlsiExi24UUirQG3O0sGd4igF7hXoR521ebJNy7UVm9VARB4OXWNQD4dGM0P+29xoSO5iUImotqK0aacs706FbvydrxABxLNmkYNfVoavYYhYcPA6BsYr7gozkoT2gy8Uo2HjXty9SxKw90t3qsyC2rB8Ep9qDoi0QQJcgEyT1VQ6J4L+EoDlMVwxyCUx7i9CgwGrUASCTmhQN1twiO/N/owRFF8Sm5qWSsTMni7UvxuBmNjM2Yg4MuB5f6S3FxaYJWq2H9+vVER0dTx782LZL8kakEHAfUxjrC5HIXRZHzu7ezZ9EvWCiVdBz1HlEHJORnpNG4RwCNe5qa9qVduR1CyN+3j7TZszHkFWA9bAz2bz/DR2f+x+abmwl0CGR2+9mEud6/fBtAnRKH9bHPIWYvOAVC/wVY+Ld+bCGJuMwi/j4Zz8pT8aTmabC3ktO3gRd9wr1pWsPpsXswzEVl9lIZ1SqAM3HZfLP9ChH+TrSoVbUdX+9EtRUjTTpjevQMf7J2PABHk49Sw74G7tbmh9EK9x/AMjgYubvbwzeuRDwsNKku1JGRUEDTXjXMPoZOrUYilSKVP94cubJwpwdFMBgQZZIyPSiCcK826N3P9Xo9EokESQVcL+UhTo+CRyU42lv9NOTVPclYEISyNKJyMZWNR1aaRf9PYRRFZt5IZm5cGg2FInrHf42jhRNePj+Snu7NtWtZXL26CrU6mXZBzQi8YINOqUDTpy6JttYo4sDOuohDS+dx6dA+/EIb4F3vBY6sy8TGwZJn326EV6BDyfEUClBlF5D74xwKtm/HsnYQLl/O5oRVPO9uep58XT6vhr/K6PqjkUsfcCPRqeDgtyg2/o1WsMSy7XvQ6EWQytE+rHvxI0KjN7D9QiorTsRz8FoGEgHa13Hj4z6+dAx2q9Tck2pb8vwACILAW52D2HgumcG/HiVm5uNdj1RLMdLre8A9FKwcnrQlZSJPm8ex5GMMCjZfkNCQl0fRmTM4jxxZiZaVDw8LTSZcygYRvOuYV0EliiJaVREWiqqtaLwTo0aNYuPGjbi5uREVFVXyelZWFgMGDCQ2NgZ//wCWzFmIi7c7wl0elCVLlvDZZ58hivDee9MZNmw4omgiIXeeJ51Oh7yCpK08xOlOpKSkMGnSJE6cOIGDgwPu7u7MmTOHoKCgMrd/EMGZM2cOY8eOfaBCebEHx0J4MgUlFfHgNL7198+t570wyTeMEwRhpSiKsyrbuP8vKDQYeD06js0ZufRS3qTdzW+xtmuNg/00LC2tSU+/wdatB5BKbBnVsC7e50Cs6Yy6SW2sHOQoLCDp2k1+/20BctUlWvYfQlZaMGd2ZFC7sRvthtTBUln6h2Mde5Lod+YizUzBfcRIjM/25evTv3JMs5JwryA+bvkxtR0fENYQRbi8BbZOgZw4XBsNJjboE3Bxw0IC2nLqy5iDtHw1fx6NY+nRWDILtXg7WPFW5yCej/DBy6HyWce/ueS5pqsN1hZSinQGRFH8/90MUJUNcUeg9ZtP2pL7YlfsLnRGHT1q9DB7jLytW0Gvx7ZL50q0rHx4WGjyRmQ6VrZyPGqa18ZAr9Vi0OuxcXx8rf9HjBjBhAkTGDZsWKnXZ86cSfv2nZgy5T1mffkFs+fN5suvvy7lQcnKyuLjjz/m5MmTiKJA48YR9OzZBycnx1JeHqPRiF6vx6qCNxS5/A4P0i2yU0yc9Hp9qXCXKIr069eP4cOHs3z5csAkwpyamvoAgqNBEKQIwr25inPmzOHFF198IMHRGkWTHM1Dbjt321pZqMiIPkAjURQLAARB+AjYBLQFTgFPCY4ZSFJrGXb+JtEFKsYodtGu8EeMTlOwt38Zg0Hg3LkTnDp1CncXF+pkB8F5FXb9Pcmu5QtFAunpIlEHDhG1fS12jjIi+k3lWqQKTWEe7YfWoV5rr1KTmqjVkv79D2T+9hs+3rWw+PhbTiqL+H7fOIoUcUxq9gojQ0c+uP9GTjxsfsdU9u0aDMM3YlWjDf6qqg1JRCXmsvDgTf45l4TeKNKxjhvDWgbQJtClSvNqqnXJczkwvVc9pq45T1xWEf7OT0YTplogag2IBgiuvpH1f278g6+tLyHO5icH565dh0WtWihCQyvRsvLhQaFJg85IzPkMakeY14xyy5YtJCYkYNDrsLBUVEr/LA8PD7p37/7Abdq2bUtMTMw9r69fv55t2/YiijDk+aF06/MMMy1ml/KgbNu2jc6dO+Pk5ARAly6d2bt3K4MHDy4Zx2g0EhQUxNq1a3Fycip5fuTIEcDUKTkuLg4wkYpWrVpx/PhxJk6ciFqtxsrKil9+WUTt2nX444/FbNiwhsLCAgwGA/v27Ss5zp49e5DL5YwbN67ktfBwU6hWFEUmT57Mli1bEASB6dOnM3DgQPbu3ccXX8zF3d2XqKgoIiIi+PPPP/n+++9JSkqiQ4cOuLi4sGfPHpYtW8YXX3yBKIr07NmTr776Co1RpIWnG4W3ZENWrVrFxo0bWbx4MSNGjEChUHDmzBlatWrF7NmzK/jtPRwVIThugOaO5zrAXRRFlSAImvvs8xQPwOm8Qkacv0mhXs97sl+or9lLcL3/odM+S3S0htOn9xIbe5NangEExbsjl4gon6uDXScHYs5BeoqayG0riT93Avfa9bHz6MiBdRmE1JPR+/VwXHxKl1Fqrl8n8d130URfxGHAAGzeeZ3/XZjH6qurCfQJ5Ms2ix9cuWE0wonfYNfHIBqhy2emsu9bIayqCEmIosjey+n8tPc6x2OyUFpIGdrMn+EtA6jh8ngm62pb8lxOFHu1Mgo0/38JjijCyUXgXr9SRTYrE1eyr3Ai5QQTG00029OmuXkT1ZkzuL791hPz1t3vPhAXnYlObaBGA/OrKUWDwdR3qxp4IlNTU/Hz80SjATcHV9Iy0hERSoWeEhMT8fX1LdnHx8eHxMTEUuNIJBJeeOEF1qxZwwcffMDOnTsJDw/H1dWVIUOG8Oabb9K6dWvi4uLo2rUrFy9eJDg4mAMHDiCTydi5cyczZrzP6tWrkcvhzJnTnDt3roRUFaOYoJSFNWvWEBkZydmzZ8nIyKBJkya0adMGUdRy7txFLlxYjZeXF61ateLQoUO88cYbzJ49mz179uDi4kJSUhJTpkzh1KlTODo60qVLF9atW0edZ7qWebxiJCQkcPjw4Srr/VMRgrMUOCYIwvpbz3sDfwmCYA1EV7pl/3GsT8tm4sU4nKV6ZojTCRByCGu4DHv7BiQlZbNly15yc7NoXjMUz2tKRAclfkP8sfcyBbNT4tLY8+efqLNjqd+hOzmZ/lw5kU2NUBcGTPXHQlHaNZn911+kzfoaiVKJz7wfuBLqwLSdL5FUkMTI0JFMaDABiwc1P0u/DBteh/hjUKsj9PoWHAOq7PwYjSLbo1P4fvc1LiTl4e1gxfSedRnQ2Bd7q8ebXFiRkufqmKtjLBEAffKTwhND7CFTB+2e/6sWk2NZWHpxKQqpgudrP2/2GFm//44gl2NfQSXpx4HoQ8ko7Szwref08I3LQOdOnciIj8XW2QVrB/O7IFcmJBKwkBlR6Y0lhNKcEu2BAwfy0ksv8dFHH7Fw4UJG3sqf2rlzJ9HRt6fXYrXx3Nxchg8fztWrVxEEAZ1OV7LNnR6j8uLgwYMMHjwYqVSKu7s77dq14/jxI8jlIo0bN8LHxweABg0aEBMTU0pIE+DEiRO0b98e11udYocOHcreffuo0bHLA487YMCAKm1sWBGphk8FQdgKtLz10jhRFIsFoR5/N6l/IVQqSEsTWXQzi3mpKYS7q5igew0va2/CwteisPQgJiaGv//+G3t7C+rK2+AQLcMqxB7vPr5ILaW4usL1U8fY/etKVFobGnQZxs2zEtRFRTTq4k+Dti5YKG7fwA25uSRNm0bBzl1Yt2mD22ef8GvSSn7d+iveNt4s7raYRu6N7m+0XgsHv4UD35ia9T37M4QPqrJJwmAU2XguiXl7rnEltYAAZyWzng+jX0Nv5NLy3zUehWjcva+NjalHEDy45Lm65urEZRUB4OVQPcpqHztEEfZ8ATYe0KB63qpSClPYeH0jfQP74qBwMGsMfWYmuWvWYv9sX+Ruj7d66mEoyNYQez6Dhl38kVbgd3wnVAUmocnq0uDP3d2d5ORk3OxcyMxIxs3N7Z7SbG9vb/bu3VvyPCEhgfbt27N27Vo+/vhjAObPn4+Pjw/u7u7s3r2b48ePs3TpUsAUvjp69CiKu1ZTEyZMoEOHDqxdu5aYmBjat29f8p713e7mWwgJCWHVqlXl/nxGoykwo1DcvnlJpdKScvaHobgHjuSOuUJ9q8T/YbZWFip0pYmieEIUxe9u/f3/U7t8BKhUEH1J5IPDqcyJzCQ8J49nr3yBh7IJjRotQ2HpwYkTJ/j999+xslQw1CmCTqip388F/xf8sXWU4utr4PSGP1g361Pcva1p3uMFoo8YEaUyOo+oS9NnXHF0vH0xqc6d4+Zz/SnYuw+3KVOQzZ7B2NOTmX9uPn0D+7K6z+oHk5uUKJjf3iSMWbc3vHYCGgyuMLlRqSAuDq5cMT2qVPduI4oiey6l0XPuASYujwTgu0EN2PlWO15o7FthchMbCwaDiWgYDKbndx73fjaVtW9aGri5mYRDCwtNj2WRljtzdQTh9v/p6RU6XZWOw9cz8HawwsPu/ynBubbL5MFp8zbIq2dW+E9nf0JEZEz9MWaPkfXHH4haLU4jR1WiZZWDi4eTEEWo28q8BouiKKIuyMdCqURaBcmo5qBPnz4sWbIEUa3nz9V/0advn3u26dq1K9u3byc7O5vs7Gy2b99O165d6devH5GRkURGRhIWZmrBMWrUKF588cVSXo0uXbrw/fffl4wXGRkJQG5uLt7e3gAsXry4XPZ27NgRjUbD/PnzS147d+4cBw4coE2bNqxYsQKDwUB6ejr79++nUaMQEASE+1RA2dralqibN23alH379pGRkYHBYGDZsmU0a9MWADd3dy5evIjRaGTt2rXlsrWy8NBZQxCEg7ce8wVByLvjL18QhLyqN/G/gasxRt4/kcTGrFw621+hj+5rlLJncbD/AYnEis2bN7Np0yZqePnTO68h0jQ5Yve6yBt6Y2UlYKPIY8ucGRxds4J6bZ+hZr3niD6YS91wG0ZNq0tgiDWCYPJUiKJI1pIlxAx9EUSRgKV/EtnRlwEbB3Al+woz28zk01af3rdpH0YDHPoOfu0AhekweDk8v9CsTsTlIRtn4rIZNP8oIxefQKUz8P3ghmyd2Ja+DbyRmbHaexjReJBN99u3oMCUVxAUZHosyyOjVps8PHfCwsL0+oPOz8PI36MgPV/D7ktpdAlx//9ZQaVTmRLinQMhYviTtqZM3Mi9wbpr6xhYZyCeNuYRAF1aGllLfse2a1csa5rfY6YqoNMaOLcnAf/6zji4madppCkqxKDTobS1q2TrHo7BgwfTokULLl++jI+PDwsWLADgvffeY8eOHQQ3rs/uw/uYOnUqACdPnmT06NEAODk58cEHH9CkSROaNGnChx9+eE/4SK1WIwgC/fv3p6CgoCQ8BTB37lxOnjxJWFgY9erV4+effwZg8uTJTJ06lYYNG5bboyIIAmvXrmXnzp3UqlWLkJAQpk6dioeHB/369SMsLIzw8HA6duzIrFmzcHW1RiJYcL/Q9tixY+nWrRsdOnTA09OTmTNn0qFDB8LDw4mIiKB9z15IBJj55Zf06tWLli1b4un5eDuIC6IoPnyraobGjRuLJ0/+exxImVo93RclcVWn4nnrwzTSrMXPbySODs9SUKAlI2M1V65coXHNcMIuu6C3tqKobV0kTtYUFEDizRQOLf8dR8lZOg8bxI3z7mQmFhDepRa+Df3QaIWSMIyFNpek96dRsGsXNp064fzpR3x7ZT7LLy8nxDmEr9t+ja+dKemtzDCOOhbWjoO4wxDcC3rPBWvzG8TFxVGqYzKYwjtSKcjtVXy26SKbziXjYmPBxE61GdjE76H9ax4Wfrpyq4nh3b0hCgtNBOVBNhUnFN9vX3M/a1lJl3eGtO4OfVVWSOuLzRf59cANdrzZjkC36qUq/Viw+zPY/zUMWw812z9pa+6BKIqM2T6G6Mxo/un3D85W5v3WkqZPJ3f9Bmpt2ohFNSvtO783gf3Lr9Dv7UZ41Xao0L4XL16kbt26ZCUlYNDpcPELqFZE3VCkw5ClRuZihURRcc+SKIqkpqZiYWHBjRs3ePPNNzlw4EAVWFpRuwzk51/EwsIFhcLDrDGuFKqRCBCorFzPcfE1cScEQTglimLju7d96DciCEITIL5Yc0oQhGFAfyAG+FgUxazKMPq/ihiVhiFnbxBTJOVFq38I1R2mVuAUnJxakZVVyNatO7C3v0rHWs2pecEay1r2FLati8QgJyMDMm5e5MjKRRiwxqXt+5zclY2FTM0zL4dj5eZcapIXYi5yc8Lr6NLScJ/6HgX9OjBs73guZ19meL3hTGw0saRpX5n5Ils34n9uElZyNTz7E4RXPBx1N8qqQBKkRhbvj2f5lYsYjCITO9VmbNuaWFs+/AZRnjyXhyUFP6gq6lE0lCoqT1DV5efX0vJZdOgmAyJ8KkxuqmOydIURexgO/A/Ch1RLcgOw8cZGjqUcY0qDGRSmO5NpxvlWX7xI7uo1OA0bVu3IjUFn5Mz2ODxq2uEZaF7vG51Gg1alwtbZpVqRGwBjoQ6kEgRL8xJltVqTrta8efP47bffSnJvnjQMBhUgIpOZlyNjFEXURiOu8icbTizP0X8BngEQBKEtMBN4HWgAzAfMT/n/j+N8fhGDz95Ab9TzhutilOlqatX/AkfHYFJSstiwYRcKRRq9fNvifkGGMsIdx36BZN+QkJ8vcuPEfs5tX4uDhwd+Yf04vbeA2rWV9JwYTK7KpCUlkUB8PBxfdBSbZV/g7SIh8M8/OOWSz5RNpl4L8zrNo61P21K2lZpctSos93wKZzeQ7tsSv5c/BcfK6dB3N2E4E5fNl/9cJia7kG7NnPmodwi+TuV3W5eHFDyMaDyIxNxvXzc3k4fmQRN+ReUJqrL8XK0zMOGvM9gp5LzbtWKijdU1WboiUGVmkf7bdNRiWxT1v8ZVVf1sz1Bl8PWJr6lnF0Eji34lIdOKnG9Rryd52nSkTk64jB/34I2fAKL2J5Kfpab9i3XMJieF2VkIEglWTyA89SCIeiOixoDE1sLsz6a6FZOeNm0aH3zwQWWa90jQ6019a6RS80KKKoMRUQSlmQnllYXyEBzpHV6agcB8URRXA6sFQYisMsv+5TieU8CL529gLRh43/geHt4GJD6Lycz04ezZFE6dOoCzk5qXAsJxvyJi94wftp38EAQBuVzPvuUbSIrai2+9+li7tOP8gVxq1HWgw0s10AkyLC1NbWkS4w1kr1hB8oYDiP6dkLz1Ijty/2Fx5GyCHIP4tsO3+Nr63mNfyeSaeR3+mQSZV7FoOZ7C8AmoFFLSHzKZlxfFhEGtM7Dw8HWWHYvHTankl9Gh9GxUca2dnByTXcV6L87OJhvvJAUPIxoPIkBl7evmZko0Ls+EX5FeQFWluG0wirz1dySXUvJZMqoprrYVU937tzc2VOVrif35PSwLsrEe+h1aqU21I2hG0cj7B95HpVfxeujHWCkkZp3vzIWLUEdH4z1nDlIHhyq1uaLQqPSc3ByDb11H/OqZF3oz6PWoCwuwcXRCUoXlxObAWGQqzZZam9e2wmg0olKpsLKyqpD+1OOA3pCPVGpdZgfj8qDAYGrlrHzC31m5CI4gCDJRFPVAJ2BsBff/f4c9mXmMirqJu0zH29pJ+CptqFPnT2JjPIiNvUZU1H7c3W1pL9TDLlGD46BaJWKZmqJCDi/6ihuntdRt3RW9piZXT+VSs7EHHfr5YGMrlJCT+Mv5pH7zI8ln47Bv2wp53z78HLuA08fO0r9zHz5rPw0rWdl3dIUCtOc2Y7l3Gsgt4blf0Xq1AV3lrt6trCBfmsuk36OJSVHzXFMvPhpQGxeHil86WVlw8SLo9eDgYLJPpTIRkLs9IQ8iGg8jQHfvGxd374SvUkFk5G2CZQ4JrArFbaNRZPq682w+n8L0nnVpF1TxxPB/dWNDUSR9+cdYph3GsvcX4FWfYnpXnQjakgtLOJJ8hI9afIQb/mUmpz/sfKsvXyHjhx+w7dIFu24Pbqj2JHBqcwzqQh0t+gWaPYamsACJrRKlvUPlGVYJEEURQ6EOwVKKYKbmnUajQRTFCsszVDWMRi1GgxpLS/NybwDyDQYUUskTE9ksRnlmmWXAPkEQMgAVcABAEIRATGKbT3EH/knL4dXoWGpaqJmofhVfuwAahP9GcrIjsbFnOXduJ6HBnrTO9MeoFuHZelhHOABQkJXJmi8/IjMxgRdefZ09WzzIyyqiRU8/wtu6l1RJpadDQUwiMVNnk5NUiNuQweiahvL9uW/JlF1kcPAoxtXujJXsPheX0YDr2c+I3fYP+NbHou8stJYeJZomlbV6F0WRP4/F8ck/F3C2tmTZu2G0NWPCBROhOHsWHB0hP99EBFQqsLeHlBRo3rxiY1Ukv+TuCb94f73edE7MJYGVrbit0Rt4+++zbDyXzIQOgYxuU9OscarKs1TlEEXYNg312a1YtxkD9W6X7VYngnY48TBzTs+hs39n+tfuT3x8xc+3oaCQxEmTkNjb4fFh9QltFCMjIZ/IXfHUbeWJq595fWvio8+j12pR2jtWQ++NHgwiUkfzVLYBCgsLkUgkWN7dPOcJQ683lX7LZOZ9bwZRpMhgxOUJ599AOQiOKIqfC4KwC/AEtou3y64kmHJx/t+jeMJbGZfFV4nJhLkX8ob6FXydIqgfOg+pVMnBgyc4eXI/Nbz9aJ7kj1Qmw2lkMFprU4wzKymB1V98iCo/n44vv8vpbSIettn0HBaCk49jqYnYJjWKCxM+R1Rboxz3Hhn+Un4+8i2CXSpTIqYR5lYfzf3EMzT5sGYsVpc3499xHOkNP6NQJ0dxq7dLfHzZpc4VnRzUOgPT10Wx6lQC7eu4MmdgAxyU5t8M0tNNITlnZ1PjvZwcU+m2SgUBARVr4ldRD9XdE35Wlin32tb2dil5sY0VJYGVJW+Rlq9mwl9nOH4zi6ndgxnb1jxyA1XjWapyiCLsnAFH56Fo/C7aJm9w57RRXQjazdybvLPvHQIdAvms1WcIglDh8y2KIikffog2Nha/RYuQubg8vg9QDhiNInuXXkZhLaPlc+Z5bwx6PbsX/kzdfoOrpffGWKBFkJufXKzT6dBqtdja2la7xGm9Pg+JxAKJxDziVXgr/8ZW9uRJabl8a6IoHhVFca0oioV3vHZFFMXTVWfavwPFE+aqpCy+TEoiXJ7P8zGf4Wn7DOFh89FqlSxdup/t28/jYFWfBjEByKwscHmpLqKDEoUCkq5cYtmHk9FrtXQc9T7HNxqQSAUGT21I846OpXqv5O/ZQ9rYYfg65FH/h4+4oFQx+9iPKJ3ymdHqQ4Ls6mNjc5+beU48LOwGV7ZBj2+weu4r/GrIS41fPJnfiYpODrkqHS8tOMaqUwm80ak2C4c3eSRyAyYviq0t6HQmWzw8oFYtE0mpSOqBOc34XF1NE45GY5pH8/Juk61iPKzfTVXi8LUMenx3kHMJOXw3qAGvtKv1SDfNYs/SwxobVhsYjbDjAzg0ByJG4vrCNDRaoeT7Kv7uXM2XQKoUZKgyeG3Xa8ilcr7v+H1JH6qKnu+sRYvJ27wZ14kTsW7W9DF+gvLh3O54Um/m0er52ijMzE85u30TGfGxKKxtnnh+SkBAAPXr16dBgwY0btwYUW1A1BnJ0RbQpUsXateuTefOncnOzi5z/yVLllC7dm1q167NkiVLAJP3BnigEndVICUlhUGDBlGrVi0iIiLo0aMHV65cKXnfaNSh1xcgk9k/9B4yZ84cioqK7nk9T29AEJ58gjFUsJPxU9yL9HTYmpPFl/FJNLHKZYT+Xfw9muDq+jUqlYwlS/Zw4sQZmtf3ovZNT5K1tlj3r4vB2soUZkk5y8pPp6Gwtqbti+9zaFUuSjsLnns3Aiev0okQOatWkfDaBCxr1SJ45RKu+J3gH4vxBHjY8H6jGfjZ++LiYgoz3XMzT78CC7uaSM7QldC07I6pd0/mFZ0cMgo0DJ5/lMj4HH4Y0pC3OgdVitJ3sWSCVmv6K+5NU+ZnfQDMacZ39wSkVIK7e+kJ6El4CAo1ej75J5qhC45hbyVj/Wut6dvAu1LGLvYsPaixYbWAXgNrRsPh76HJaOg5GyulUO0IWq4ml7E7xpKhymBux7l42XiVer+85ztv23bSvv4a265dcR4z+jFYXjbu16AyIyGfI+uuExDmQlDTihcRAORlpHPo76UEhDdCXk3CN3v27CEyMpITJ05gyNOATMKsud/QqVMnrl69SqdOnZg5c+Y9+2VlZfHxxx9z7Ngxjh8/zscff0xGRkZJcnFV6jDd3QBQFEX69etH+/btuX79OqdOneLLL78kNTX1jn1MvXvlcgeMRtO9v7iow2gsPX5ZBEcURfL0BuykUqQVWGSVt1lhRfHEg2SCIPgCvwPugIipSuu7J2tV+bEqzuS5aWSVx1DVVDxd2lO79gcUFUn5++9dXLoURdM6dQm66IAxUEDTthZJuXJqOgM5p9n6w2c4eHrRqNcb7F+eiLOPDb1fD8fKtvQsnPXnUlI/+wzrNm3w+W4Oy2LXMvP4TNrWacUXzSZTkG11/5ySpDPwZ38QpDByM3iE3vfzPEpeSIFGz/CFx7mZUchvw5vQLsi10vqpuLpCURG4uJhCUzk5JnITHl6x8czNL7kzlFTstdNonlwIZ8/lNKavjSIxR8XQZn6836NuufoI/adQmAkrh0PMAXhmBrSaVNK3qSqU7c1FvjafV3e9SkxuDPM6zSPcNdyscYrOnCFp8mSswsPx+momwhPybNwvzOvtaWD7gmgUSjkdXwo2y4soGo1s+/k7RKORTi+/SnLWba/IlSufkl9wsTI/CrY2dQkKKn8Ok1GlR9QZkTop2LBhQ4nO1PDhw2nfvj1fffVVqe23bdtWSvyyc+fObNiwgW7dumFjY+pNZTQaqVOnDocPH8bV1RWj0UhQUBBHjhwBYNy4ccTFxQEmUtGqVSuOHz/OxIkTUavVWFlZsWjRIurUqcPixYtZs2YNBQUFGAwG9u3bV2LLnj17kMvljBt3u51AeLjpWhRFkcmTJ7Np03oEQWD69E949tmBHDiwl88/n4GzswsXLpjUyJcu/ZPvv/+epKQkOnTogIuLC3v27GHZsmV89vkXaIwGevToydxvvgbAxsaGggJT2fmqVavYuHEjixcvZsSIESgUCs6cOUOrVq2YPXt2ub+H8qI63BH1wNuiKJ4WBMEWOCUIwg5RFKu9QvnSm1l8fjadIIOe7rL/4VyzM0FBU9FqpRw5sodTp6JpVq8eQZfssfS0xmlQMIKljMJC0GeeYOsPX+Dk7UvDHq9zYHk8noEO9Hg1DEur0l9L5sJFpM2ahU2nTnh/O5uFl3/nu9Pf0cmvE7PazsJCaoHT/fLBEk7B733ByhGGrQPnWvdsUhYJqejkYDSKTPjrNJdS8vltWOMScvMoFVl32+XmZiI3MpkpRPWkKpcqOzm4IohOyuPLLRc5cDWDWq7WrBzXgiYB5qkz/6uReAr+Hg4FqdBvPoQPfNIWlYlsdTbjdo7jStYVvmn/DS28Wpg1jirqAvGvjEPm4Y7PTz8ieYIJRWW1ERBFkY2/XSc7uZDeb9y7QCsvIndsJu58JM+Mfg0Hd49SBOdJQRAEunTpgiAIvDxoBGOGjUJiJSM1NbVEesDDw6OUJ6QYiYmJ+PrebtPh7e1NTEwMCoUCudwUvpNIJLz44ossXbqUSZMmsXPnTsLDw3F1dWXIkCG8+eabtG7dmri4OLp27crFixcJDg7mwIEDyGQydu7cyfvvv8/q1asBOH36NOfOnbtHEiIqykRQysKaNWs4c+Y0hw79TX6+jFatetCiRVsEASIjz3DmzAU8Pb3o0KEVhw4d4o033mD27Nns2bMHFxcXkpKSmDJlCpsOHUFnY8vb/Z9l3bp1PPvssw88twkJCRw+fLjKPFlPnOCIopgMJN/6P18QhIuAN1CtCc6G+Fwm7U+lvnMR3ZNmY2/XGoXl6+TlSTl48ACxsYdpHNyUmhccsPCyQvlsMMmZMvLyIDv+Aje2fI1nDT8a95nInj9j8Qx0oNfr4cgtSn/RGb/+Svr/ZmPbrRveX89i0eU/+O70d/Ss2ZPPWn2GTPKArzDtIiztD0onGLkF7O8NX1RWU7clR2LYezmdT/uG0CHYpGT8KP1UyrIrLe3RiURlkZOKeAgqw4t1I72AeXuus+ZMAnYKOdN71uWlFv5YVoNEvscKUYSTC2HreyZ18FHbwPsBgrFPEOlF6YzdMZb4/Hi+6/jdPc02ywt1dDRxL7+M1NYW/4ULkTk6VrKlFbSnjDYC108mcfFEJj2HBpjd8yYzIY79SxcR0CCCsGe63fN+RTwtlYmDBw/i7e1N8o0EuvbqTr0GobTv3KHUNoIglMtjpdVqkUgk2NqWXpGOGjWKvn37MmnSJBYuXFiiR7Vz506io29PhXl5eRQUFJCbm8vw4cO5evUqgiCg0+lKtrnTY1SRzzhgQE+kUhk+PnVo06Ydp06dwM7OjsaNm+Lj4wNAWFgDYmJiaN26dan9T5w4Qfv27ZE6OmEtEXhx6FD279//UIJzp7BoVaBa5eAIghAANASOPWFTHojjOQW8eiSBIFstoyTvUq+2F/XqvobRKGPPnpPExu6nRVgorRJsEO2skXUPJjFdhkoF2Yk3ObX6Z7RWjXBr9C4rfkhDYu9Mx1Fh95Cb7L//Jv1/s7Hr0QPvb75m9c31fHvqW7rX6M7nrT5/MLnJiYM/+oHU0qTDUwa5gcpRwM4s0DBr62U61HHlxea3XSHm5LtUpl33w+PMLymP2OiDEJWYy2tLT9Np9j42nktiTJua7H+3A6Pb1Pz/R27yU2HZINj0FgS0gVf2VVtycynrEkM2DyGpIImfnvnJbHKjirpA3MhRSKyV+C1Zgty7cnKsHgV3FyIkX8th/5p47HyccahdwyzRWK2qiA3/+wILhRVdX3mjWlUWeXt7I+qNOFs60LdnH05EmnQQ3d3dSU5OBiA5ORk3N7cy942PjwfAYDAQGxuLr68vGzdupEGDBjRo0ICTJ0/i6+uLu7s7u3fv5vjx43Tv3h0wha+OHj1aojyemJiIjY0NH3zwAR06dCAqKop//vkH9R03Veu72ecthISEcOrUqTLfE0URg6EImdwOiaR0YnhxGbsoglQqvW++jM4oojOKON5VHn7nd6m+6+Z/P1srC9WG4AiCYAOsBiaJoniPSrkgCGMFQTgpCMLJ9MqY5czEpUIVL52/iZNR5GX9VFxsa9KgwSf4+lqg1Z7j4sUjRIQGU+ecO7lya6z71OZKrJyiItDkJhK1YTb2Lnb4txjJjhUpePgpaP9SPZJTZaVuCnk7dpAy42Os27TB66uZ7EzcwydHPqG1d2s+b/05UskDJjdtESwfYnp8aS043V9duCwSYjDAjRvlV7j+82gcar2BaT3rlrqYH6Ui61HIUXWCOURNqzey8VwSg+Yfodf3B9l/JZ3x7WpxcEpH3u9RF3uleZUp/2pcWAc/Nofre6DrFzB0lckzWQ2xO243w7YMQxRFFndbTBOPJmaNU3DwELHDhiGxtsZ/yRIsfJ48uYHShQiZSYVsXnAFjdSOLkNrYmMrVJjEi6LItp/nkp2cRK+Jk7FxMl/ct7JRWFhIfn4+hlwNhUWF7Dq4h/r16wPQp0+fkqqoJUuW0Ldv33v279q1K9u3byc7O5v4+Hj27dtH37596devXwlpadzYpBE5evRoXnzxxVJejS5duvD999+XjBcZGQlAbm4u3rfI7uLFi8v1WTp27IhGo2H+/Pklr507d44DBw7QsmUjVq/eglRiT3p6OocO7adRo6YUN4URRVOS8Z1pX7a2tuTnm3rmNG3alAP795GXlYGNAMuWLaNdu3aAiQhevHgRo9HI2rVry2VrZaFaEBxBEOSYyM1SURTXlLWNKIrzRVFsLIpiY9cnVO8Zm6Oj/7Y4jAkyXsj8CSujJyH1vkEqteLatWts3bqbWjU8qX/Vl6QCBfbP18HNzwI7OxB12Rz7azZWVgKhPV7l4t4kFLaWdHk5BDsHWalJT3XuHElvv4Oifig+383hSv4N3j/wPmGuYcxuPxu55CET3Ka3ICUK+v8G7vUeuOndJESlgpgYkMvL73HYfTmNRn6OBLqVdrs+SkVWZZSrVwdUhKjFZhbyzbbLtJy5mwl/nSEhW8WUbsEcmtqRyd2C7yu5cL+Klv8CVElxxM19jSs/fUKc2ATV8IPQ4rXSd9pqAqNo5NdzvzJpzyQCHQJZ1nMZdZ3rPnzHMpD7zz/EjxuHhZ8f/suWYXFHHseTRnGYV5WvZuMvl1Ab5Ax4rQYu7jKzvK0nNqzmytGDtB48DN+QsKo1voJITU2ldatWNGrTlNZ9OtCzV0+6dTOFz9577z127NhB7dq12blzJ++99x4AJ0+eZPRoU4Wbk5MTH3zwAU2aNKFDhw5MmTKlTE8PmAhTQUFBSXgKYO7cuZw8eZKwsDDq1avHzz//DMDkyZOZOnUqDRs2LHcFkiAIrF27lp07d1KrVi1CQkKYOnUq7u7udO/RnNDQYCIiWtGxY0dmzZqFv3/pTsbFi7RijB07lm7dutGhQwdc3T14fcYnjOnVg4YNGhAREVFC+GbOnEmvXr1o2bJlSc7S44Jwu2/fk4FgWvIvAbJEUZxUnn0aN24snjx5skrtuhMqFVyPNTJqRxKxQhGTXX/BTRuDjc3PBAe7otGk88cfK7CzdWGITSDZ8SL2g+ph629yv8XGaNg47yc0OckMnjKOdQsL0Kv1dB4ZTHB9U3ykuOy5pnMmN/s/jyCVErBqJQVWAgPWDKMwx4pvW/+Ep4PTg3M4ojfA3y9Bu/egw9RyfbbiXBcLCxO5UamgRo3bx9BoTKW298s5ifh0B11DPfiiX/0yx69o/olKZWo4eOOGqfeNu7vp+HfqRf1bEBdnIol3Vm3deT7T8tVsOpfM+sgkIuNzEAToWMeNF5v70zbIFelDSuzv/v7u1tX610KvQbXnJ2I3rsRSpsWi9Vi09Ueg0cur5WfLUGUw7eA0DicdpnuN7nzS8hMUsoqzcdFoJH3uXDJ//gVl06b4zPsBqe39KgieHIrytKybfZrCXC1h/SLwqWVdavIrvp8FBT14nMtHDrBxzlcEtWhDr4mT7wlNXbx4kbp1zSOJlQHRYESXpkIQQOauNK8yTBTJyMhAr9fj5uZ235yTkydP8uabb3LgwIFHNbtC0OnyUKliUSh8sLAwL78rXasjSa2jtrVlletPlXVNCIJwShTFxndv+8STjIFWwEvA+TvEO98XRXHzkzPpNoonkC9Op3JVKGSY1WEkySmEtvsIS0tXUlNV7NixCYVCxhC/ulhdzUPVPRQbPxO5EUWRi9v/JCshiRYDh3PhoIqiXA1Nnq2Df+Dtu7RWC5YWRhLffgdDdjYBy/5C6uDAh9vfIyFeyv+emYGPs9ODk4DVubD5HfAIg7bvluvz3Z10q9Pd2xn4YZ2M7a3kpOeX3Tq5ouW6d07YgYGQmgpXr5qa+lXHie1hKKtqKzb9/9o77/A4qrNv32f7ruqqd8mWe+/GxgZjisG4UG0IhJ6EEkoISUihhDjE5EveBAgBQicQCMFgTA1gbOOCe5G7JduSrN7LavvO+f6YlSzJtiyti9bO3Lrm2tXslDMzO3N++5ynOMlvqWbFlxWs2V+DImFIajS/vGwQs0emkRbb/YM80wtjHoGiwI5F8M3vqC7yY86dgfmSn0F0mpqZ2BN+x7aufB0Pr3yYJk8Tj5zzCNcOuDakjjDQ1ETZz36OY8UKYq6+ipTHHkPX2fwXBjibvCz+yxaaa93Mvm8kfnNESKkXSvbs5PO//Zn0QUO47O6fhJXfDQT9Uho8EFDQJ4UmbgCcTic+n4/Y2NhjipuFCxfy/PPP8/bbb59Ik0PC661GpzNiNMaEtL6UkhqvH5te1+vFNTvT6wJHSrkKCK9vdjtaE/l9Xt3M5fFlDHJ8QHruvfh8I0hIUPjssy8xm4uZP2E21uVNRF+UhTPLTmGh+qwu3raG/A1rOO+KK/EF4ijIK+S8udlkj4xCp1N/6bT+6o5a9S5Va7ZgfPD3FBuGsHbNcj7fsp3bR97EqFRVsXbZga37hxo2e/07oO/+pW0vQiwW1eLQnuM9rKb2T+Cd9Ycoqm0hO/7EnMY6d9gpQStpWdmJVzY/2XTHOmW1QlqGwsq8RpbvrmN9cTUHHfXojAp9EyK454J+zBmZRv/k0H6ln9GFMdsjJRQshaWPQ8V2SB6O+7I/EjF0coenQzgdm9Pn5Nktz/L27rfJicnhhYteYGDcwJC25d69m9KfPIi3pITkRx/Bfv31YdfhQ3tx42LWj0eS1t/e9qMEup96obroIB/9vwVEJyYz96HfYAhDIac4/SguP/poMzpTaB13IBCgqakJk8nUZVHNhx9+uG2I63Ti97cQCDixWFIRIrRh3yZ/AK8iSbGGn29grwuccGdvvZs/lVQwPEYwoeE5ElMuJD3tEurqYPPmHWzfXsvcS2agW+bCPCAWw6QsnPlq5xdwV7H+44+ISp1M6pDz2fbBZiaeZ2fmzam43R1DlVP1JRx89lkaRs4i+5KZKDoXz216nlT3FK7sd02HNh31Ie/3wtq/w4BLIf3ouQ66Qyh5Yn50fi4fbCnljjc28vYdE0mKDt1Rpn2H7XJBSYnaDp3usD9QOFhyugqvN5klu8qaWLO/hu8O1LL+YB1ObwC9TjAu286Ngwdy4eAk+iZGnnA7ztjCmK1ICfuXwsr/g6LVEJsNV70Mw67GUqIL22NbX76ex9Y8RomjhPkD5/Pg2AfbSi/0BCkl9W++SdWf/ow+Npbs117FNj40p+RTTWO1i4+f2UpLo4dZ94wkfaA6nNHT1As1h4r4z+9+jcFk4qpf/hZrVPRpPIruofgUAg0ehFmPLiq0jltKSUNDA1JKYmKOX/rgdCOlxOOpQAgDRmPoDvvVPj9GnSA2DKM6NYHTBT5F8rtDpZgVwfes/w/FmU5Kyo9oaoKCgnrWrdvGyBHxZOyLoMztI3XOQGpqBTExEBWp8O7CdxA6M1OvuZLtXxcSESnaMnx2HropffD/qPfbSX/oXiwWwVu7FtGoVPPwuGuoqTIQ1S4Q6qgP+QPLwVUH4247oWMOJU9MWqyVF78/ljve2MjMZ1by+JyhXD48NaQbun2HXVt7uHOzWsNr+KW9pamuxcPOsia2FTWyZ0kT+c5qmt2q41+/pEiuHpPB5Nx4JuXGn3BNrs6ckYUxAZQA7F6iCpuKPIhKg8v+CGNvBYN6jsLx2Ord9Tyz5Rne3/c+mVGZvDrj1ZCjpPzV1ZT9+te0fLuSyAsuIPX3CzD0MH/J6aK6uJmP/7YNJaAw5/7RpOZ2HM7o7lB0bekh/vO7X6MzGJj36JPEJqccf6XTjFQkgVoXCDDYLSc0NOXxeIiOjm5L6hdO+P3NQetNWsjWm5ZAgBa/QqrZGHYCDjSB0yXPH6pir8HB3cadRDoPMGzy87S0WCkqE8J9PwAAPHNJREFUUsjPX0VSUgsXpE0mcKCGxMv7UecytVkgCtZ/h6dqE3NuuwO94qP6kIPr7u0HBhPFxR2HNUTxXpo++xwx65c0iGRK8328s24F47KmMGXQAAoKDpcFaGqCiorDhR7bhkUKV4LeBH2nndAxh5qUbnJuAh/cPZkH/72NH/9rC39P3c8tk3OYOSKVyB6UEGjfqbndajSXzwet6T9MJlX4tH5+OoetFEVyqN7JnopmVm5wcajZwe7KRsrq1ZAonRDkxEQze1waE/vEMalv/AlZs7pDb2ZVDgl3I2x9B9b/A+r2Q1wuzHkWRswHQ8cosXA6Nr/i57297/Hc1udo8bVw85CbuWf0PVgNPW+MlJLGDxdTuXAh0u0m+ZHfYP/e98KygwAo2lHLf1/agTnCwBU/GUtcamjD0FWFB1j05KMAXPvI77GnhkfYe3uklPjr3Ei/giHBijCE1vH7/f62oalTneslFFqtNzqdCaMx9MSRlR4fegHxpvCUEuHZqjCgxO3lL4UVzEiWjK/4Pba4H2KzDSYuDsrK8oF9XDj1IpRv6rD0jyV6RBytdccK8j188sZyIu0jyRwxmY+f3UZypoWc0alHHdYwvfEeHnMs/vOvxO2G3c2baXI7mGC9Ep8P+vZVo25qa9UpORliYjplHa4/CPacIzqJnnCiWY0HpUSz5MfnsmRbGc8tK+Dni/J4bMlOzu2XwPkDE5mcG0+f+Igui2+279QURZ3S0w/vv6lJPQdRUSeWebkrmt0+imqdFNU6Kaxtoai2hb2VDvIrm3F6VQclf5OF1GgrQzKiuWZMBkPTYuhjjyLSajjt1qVwqrt0TCp3wvqXIO898LWow6jXvg6D50AXOZ3C4djWlq/lqfVPUdBQwMTUiTw8/mH62fuFtC1vSQkVjz5Gy5o1WMeNJfWJ32Hue+w8Vb2JlJItXxbz3eL9xKdHMuuekUTaQ3u+lOzawYd/fAKTzcY1v/od8enhE/benkCTF+n2o481o7OE1j0qikJdXR0AsbGxYSlcfb46FMWD1ZoVuvXGH6DZr5BiNvaosObpRBM4x+DxglIkMN/3DNFRMZxzzm34fFBe7mPZsi1ERPQhsSwGxV9J1IVZ+HzqBXY6Yc/6bbiaa5l89Y9Y9VUjVRV+bnqoLzU14sgaLl4vRZ+sJWLKHNJyo6ipgc3l27FZTAxLGE5FBZxzzuHOOyrqGBEzUqrFNE+AkxGRY9DruGpMBleOTmdzcT0fbill+d5qvt6t1mmJMhsYlh7D4NRo+iTYyEmIIMUWgc5rIeDTdaiF1WrNae+MXVGhCrxQ2iilpNnjp9bhpaLRTVWzm4pGN5VNHiqb3JQ1uiiudVLb0jEBT0KkiX5Jkcwbl8nAlCgGpUSRGR1FTYXhiPDsXkrRFJ64G9UEfdvegeLvwGCBYVerVb/DNANxe7ZVb+PZzc+yrmId6ZHp/HXaX5meNT2kDktxOql9+RVqX3kFodeT8tijxM6f32sFM4+Hzxtg2T/3kL+hkn5jk5h+02CM5tCeLwUb1vLp038kOjGJq3/9O6ITwvMmCbT4UJq96GxGdBGHh5Ruu+02PvnkE5KSktixY0fb/Lq6OubPn09hYSE5OTm899572O12Ghoa+OUvf8ny5cuJiIjg9ddfZ8yYI7/vmzZt4pZbbsHlcjFz5kyefvrp0yKGFMWPx1OJXh+BwaD6P/l8Ph555BEWLVpEVFQUZrOZRx99tC2j8tGo8KrWm4RO1pvFixczYMAAhgzpOgfb6UATOEdhp8PFJ9WN3J3kwVK5gj4DF+DzRVFUBPv27Uavr2DYwKvY+00D/SclokRa8XjUjjg6WtK47xMS0lKJzx7Avg/2YU/QM3hCAvkFR0a8KIX7cLf4iR57LjExaodduHE/2ZbBRNoMWCyHxU2XETP2HCj4WnU2NoTm53EyI3KEEIzNjmNsdhxSSg7UtLCpsJ5tJQ3klTTyr/VFuH0Kik+Hv8mK0AeIthmwW0xEmy1kZEiiI3UYpAHpNmOQeiIidLiadUTUKOh0h4NrfH6FJockOtmDx6/g9gVw+xWa3T4anD4aXT4anF4aXT6Uo6R9ijDpSY6xkBJt4ZKhyWTHR5AdZyM7PoKseNsxh9gizOExfBJWKAE4sEwdhtrzCfjdkDAALn4CRn8/bLMPt2dv3V7+tuVvLC9ZTpwljp+P/znzBs7DrO+59UJKSdOnn1H1pz/hr6gg+vLLSXropxiPk/DsZNQvC5WaEgdfvryD+konE+f2Zeyl2SHnf9mwZBEr33mDlNz+XPmLx7BFdx2K3Pm4T1eaNsXlJ1DvRpj16O3mDsd7yy238OMf/5ibbrqpwzoLFy7kwgsv5OGHH2bhwoUsXLiQRx99lE8//ZTi4mIKCgpYt24dd911F+vWHVl96K677uKll15i4sSJzJw5ky+++KJLQXGycLvLCQT82Gxpbcf5yCOPUF5ezo4dOzCbzVRWVnaoRN6ZZn8AR9D3prP1ZvHixcyaNeukCBy/34/BELpM0QTOUfhrYSVReh3nuZ7DYE4lNfVqCgqgpkbyzTf7iIzMZJDeQJkuQEN6Ckl6tXM7dAhaastwVBUy5bqbyEwPsLKuhn7nZCB04qgRL449BzDjxT5iAF6v+hDzRRYyJGkkiYnq0FQrXUbM9L0Avvsb7PlY/ZUcAqFG5BzvYSyEIDcxktzESOaNV03TiiKpavbwXZ6T4loXjW4PdU4vdS1eqhu97Cn0QaQLlzeA0xvA5Ts8NCQVEPrDTz4ZEOh0EJXgw2zQYTHqMRt0RFuNxFiNZNitxNqMxFpNxNqM2G0mUmIsJEdbSI42E2UJPwfAM4qAT/UB27VEFTUt1WCJhdE3wsjvqdaaMDVhtyKlZHPVZl7Z/gorS1cSZYrivtH3ccPgG0KKjgJwbtpE1Z//D9fmzZiHDCb9z3/Cdoxqzu05WQVwe4qUkh0rSln9fgFmm4E5944ic0hogtTv9fLlP55l98plDJw0lRl33Y/R3PWD5GjH7fcfLhHwSH4JOxwnN033sEgrv81KwV/nRpj0GOKtR4i58847j8LCwiPW/eijj1i+fDkAN998M+effz73338/X3/9NbfeeitCCM455xwaGhooLy/vkMW3vLycpqYmzjnnHABuuukmFi9efITAuemmm7jqqqvailbecMMNzJs3j1mzZvHwww+zfPlyPB4P99xzDz/60Y9wOBzMnTuX+vp6fD4fCxYsYO7cuRQWFjJjxgzGjx/Lpk3rWbz4HWJj1evhdDp56aWXOHjwYFvdqeTkZObNmweoZReefPJJpJRcfvnlLFy4kFKPl0lpSTxw3318+umnWK1WPvroI/bv38+SJUtYsWIFCxYsYNGiRTQ3N3PnnXfidDrJzc3l1VdfxW63M23aNP70pz8xbtw4ampqGDduHIWFhbz++ut88MEHOBwOAoFAl0LreGgCpxNVHh+f1TRwe4qRQNlq+vT7JR6PiQMHwOOpwumsYdSoaZRvbCatnwn7MFuHHDK7V2wHIGfEGGpKmvH7IXuQagaMjIRt29QbNipKnVx1DuyGelIGxlGs1m3DoDPiditHRIx0GVVivgASBsLXj0P/S8Dc87wqoUSthPow1ukEKTEWhiZbmNCX42ZBVRSJN6C07c9kApNJ4vGA4teR21dgs52+TrS3OqGecEqtAD43HPwWdn8Eez4FVz0YI2DAJTD0SjVdwQn4g50uFKmw/NByXt3xKtuqt2E327ln1D1cP+h6YsyhJT5z5eVR/fQztKxejT4xgdQFvyPmyisR3UyC1hvJG51NXpa/vYeD22rIGhrPhTcPxhYdmiW4saqST55+ioqCfZw770YmXjW/Wxagox23w6EGGZhP0VdJBiT+GjdCLzDEWxDHyRzensrKyjbREh8fT2VlJQaDgZqaGrLaXaiMjAxKS0s7CJzS0tK2Ct3tl+nM7bffzl/+8heuuOIKGhsbWbNmDW+88QavvPIKMTExbNiwAY/Hw7nnnssll1xCZmYmH374IdHR0dTU1HDOOecwZ84cAPLz83n++Sd44YVHiYjo37aPgoICsrKyiI4+Mly/rKyMX/ziF2zatAm73c4ll1zCP99fxMgZM3G1tDBp0iSefPJJfv7zn/PSSy/xm9/8hjlz5jBr1iyuuUZNbzJixAieffZZzj//fB599FF++9vf8te//rXLc7t582by8vJ6XBW9M5rAaYfLBS9tbcBdbGKI3IDXayMleS6VlaoY2bevCCEgNyONuqX7aRiYQqrl8LpuN2xY56TaM5qi8jhKd1fg8hjoPzQClwuqqlT/EYcDmpvVTjwjTsGv82DWe8nONlFdDfZALnuK6/EOUW/61o6p66gSPcx+Gl6/HN67Ca57B4w9i+AJJWrlRB/G3bUa6XQCi06PxQiWfoc77ihb7yT/C/cMwqdEgNUdVIdB879SLTY+J5ijVTEzZC70uxCMYaLujkO9u57FBYt5b+97lDhKSI9M51cTf8UV/a4IKTIKwLVzJzV/ew7HsmXo7XaSfvYz7N+7Hl0PT/jpTN4opWTf+kpWvrcPnzvAudf0Y+T0zB519O3J3/Ad/33+r0hFMuenv6L/hMndXvdox622UX39Xf+MIz88ARRPAH+NC3RCjZjSh+YPFQgEqK+vRwhBXFzcSfWjOf/887n77ruprq5m0aJFXH311RgMBr788kvy8vJ4//33AbX4Zn5+PhkZGfzqV7/i22+/RafTUVpaSmWl6v+YlZXBuHFDsFgyEN3019ywYQPTpk2jtf7jddd/j69XrGDy5bMwmUzMmjULgLFjx/LVV18dsX5jYyMNDQ1thTdvvvlmrr322uPu9+KLLz5hcQOawGmjtUP4ttbBoHgTlqZVtPgvRlEScbtVYVJWVoPdnoKuPgASvNFRJCYeXldRIOCsxhIdR0UFOB0BEGC2Gjp0iLGx6j49HnCWZqIDPPvysY0ZTWIijE0fwWvNL9CoP0BUoG+HjqnLqJLsSTD7r7DkXnhjFsz7J0T3rLhZT6NWTvRhHIrVyGpV12sVOe1F4Oki3DMInxQB5qqHou9UMZP/FdTmq/PtfdThp/6XQJ/zzghLDaideV5NHm/nfcBneRvweASj0gazYMJPuHzgdAy6nj8OpZS0rFpN7auv4PxuLbroaBIfeAD7jTeijwwtPPh0JW9srnOz/O29FO+sJaVvNBfcOJi4tNDa7Pd6Wfmv19n8+RKS+/Zn1gO/6HGOm6MdN5ya0U3F7cdfG7TchBgOnpycTElJCSaTifLycpKSkjAYDKSnp3Po0CEURbU+HTpUQkJCeodq3Onp6ZSUlLRtq6SkpG292bNnA3DnnXdy5513ctNNN/HWW2/x7rvv8tprrwHq9+7ZZ59lxowZHdr0+uuvU11dzaZNmzAajeTk5OB2u/H7W7DZTJhM8RgMHROM9uvXj+LiYpqamo5qxWlPo9+PBNLNJozGw7lv9Hp9t4t+tmIwGFAUBQB3p+rDJyu0XhM4QaqrAaPCTo+Ta5Ki8ZTvIjn5VqqrD5cvUJRikpIG4aj2EZAwYIQJq1UtqCgl7N8PDpeJyEjIzITSJh1Wk5+yEj8YTEftEF0DR4LBQPN/v8A2ZjTV1TAzdzr/LnyJV3e8woIpCwBx1I7pqEMQY24CSwx88CP4+0S4+HdqZ9RFKO6JcKIP41CsRuEwPBTuGYRDEmDOOjXaqXCVKmoqdgAS9GbImQLjb1dFTXzuqWz6SafaWc1nBz9jyf4l7KkswtDYlxl9Z3LFwJlk2Pri8YDPAz0x3Eivl8bPPqPu1dfw7NuHISmJpJ89ROy8eSdcHPNUJzgM+BXylpWw4ZODSCmZMq8/w6dldJm+oSsq9ufzxd//Qm1JMWMum8PUG27FEEJiu6Mdt5RqLqyTSQdxkxi65Wb27Nm8+OKL3HXXXXzyySdtfjJz5szh2Wf/xty517Fx4zpiYmJITU3F41GfFzodpKamEh0dzdq1a5k4cSJvvvkm9957L5mZmWzdurXDfm655RYmTJhASkpKm+PujBkzeP7555k+fTpGo5F9+/aRnp5OY2MjSUlJGI1Gli1bRlFREYrix+2pAHSYzUeKTpvNxu23387999/Piy++iMlkorq6muXLlzNlyhTuu+8+ampqMERFs+jf/+ZH99yDtYtzFhUVRXNzMwAxMTHY7XZWrlzJ1KlT+ec//9lmzcnJyWHTpk1MmDChzRJ1stEEThC3G6qEF48i6Wd2AgqxMTm43apYKSyUeDxukpNNiPoALV4DOpMBlwsaGqCuTu14Y2L0NDc6qKkBTBb0Okl5cQt9hpiO2iFGJseiv/RS6v/zPnG33ILbnUpCdAy3DL2FF7a9wKL8RVzd/5ojOqYuO/khcyF5mGrJ+fg+1fn4/F+owwj6k/u0OBkP455ajcJheCgcs+y257gCTAlA9R4o2QilG6FkE1TtAqQazp0xHqb9UhU26WN7PNzZ2zh9TpYWL+WTA5+wtnwtilQYnjCcu/s9xrT0C7FHdnQc7u53x1tSQsN7/6Fh0SICtbWY+/cn9Q9/IObymYiTVE/pVCU4lFJStL2WVe/n01jlIntYPOddN4DohNA2HPD7WLvoXdYt/g8RsXau+uVv6TMq9DIxRztug+Gw1eNkEGjxEqj3IIy6bg9LXX/99SxfvpyamhoyMjL47W9/y6233soPfvADbr/9dt555522MHGAmTNn8vHHnzFsWD9sNhsvvfQaQqjHMXr0KLZt2wrA3//+97Yw8csuu+yYEVTJyckMHjy4TUAB3HHHHRQWFjJmzBiklCQmJrJ48WJuuOEGZs+ezfDhwxk3bhyDBg3C4ylHSj86nfGYOW8WLFjAb37zG4YMGYLFYiEiIoInnniC1NRUFi5cyAUXXIA7oHD+jEu5+Zqug1iuu+46fvCDH/DMM8/w/vvv88Ybb7Q5Gfft27fNCvXQQw8xb948/vGPf3D55Zcf9zqEgpCnKw7vJDJu3Di5cePGk7rN4mJYU+vg3oJCnslxozt4L9lZzyPlKOLj1XHWZ555gezsyYyJSsO6rZjU+0fh1xupqFC/vA4H5C39koMbVzP9zocxGoxs+3ATSQOTmXplDrW1avHI6OjDHWJSEtTnl5F/y11ED+lL/BNPYbKZMJoUHlv9GKvLVnP3sJ9wxYDZHR7AxcWqVam1A3O51BwxrYkBExPBapFqOvxlT6qdWWQKjL0FRsw7qb/CT3dI6759qqg7nmPyqaY3Q3mPR3sBbDIE8FYX4ykrIJvvsFavg7ItasI9UC1+6WMha9JhQXOGDDu1p8XXwsrSlSwtWsqKkhW4/C7SI9O5vO/lzOo7iz4xfUL67ki/H8e331L/7ru0rFwFQhA5bRr2668nYsq5YZnIrTN1ZS2sfj+f4l11xCbbmHJtf7KHxYe8vdI9u/j65eeoOVTEkPOmc8EtP8QSceK11Tqze/duBg8efMLbkVISaPKiNHsRFgOGuJ45FLdHURRqa2vx+XzY7fajFtF0u48uzBSl51Zep9PJ8OHD2bx5MzExPXN893iq8HgqsVjSMJlCv94lbi+1Xj+5NjORvVxz6mjfCSHEJinluM7LahacIImJ0FIqkT6BUQiaWiIoLjYxfHirhUSP2x2P1+tAxJppdOtJqHZhyTbi8ahfWpsN7FmD2P3dOg7t3k2/MWMwJiVTXVCFRZ9KcrK5TYTExqripqoKzElpZP/sbkp+/0eanvwrSQ88gD3BxK/P+Q2PfbuAZze8SLVhO79MfaAtZLV7RSmFas0ZNEt1Dl3/Eqx4ClYshJThQcfQiyFlxAn9TDrd2WbDZXgoHLLsHoGrAap2Ya3YQXbxXqoPHKKlogQLDWTbarGaA+q1H30DpI9TxUx8btiHcR+LBncDy0uWs7RoKWvK1uBVvMRZ4tpEzeik0eja/Wrt7ndHSoln924al3xM06ef4q+uxpCURMLddxN7zdXHzWMTLjRWO9nwSSH71ldgshqYcm1/hk1LRx/isIyzqZFv336Nncu/JiohkSt+/gi5Yyee5FafXKQiCdS7UVx+dBFG9LHmkEVpIBCgtrYWv99PXFwclmM8dIRQhXNnId3T3X799dfcfvvt/OQnP+mxuPH7m/F4KjEaY0+omGajz0+t10+CydDr4qanaAIniNUKfbKBAsGBA/E0HRjAiOG1mEzql1JRQK9Pp6SkiZjpEVQgOLjBwYCMaMzmoEBqgb5DMtmTmsihLcvJGT6US65N5puXy1i7uIALbx6C1SrQ69WOsbj48FCL+dIZBOrrKP/7yzQuqCb20Z/jj0rkiSmP817pc7xV8BLra1bw8ISHmZY5DYtFdL8opU4PA2aoU2MJ7PpIzTD7zQJ1ssapzqJ9pqqdXvLQkz6UdTIJ9+GhU44SgIZiqC2AmnzV+bcmX/2/ubxtMavVTlbyMBh5vnpNk4dC4uAzbripPYpU2F27m1Wlq1hVuoq8mjwUqZAakcq8gfO4KPsiRiWOQn8Mn7PjfXd8paU0fvoZjUs+wluwH4xGIs8/j9grriBy2jTECSQdO50017nZ+OlBdn9XgV4vGHVRFqNnZGGNDG0YLeD3k7f0C9a89zZel5Pxc69h0lXXYQwXp7NjoPgCBOrcSJ+CPsaMLjL0opA+n4+6ujoURelS3IDqN9Sa/LVV7ChKz8PdL7roIopav7A9IBBw43IVo9NbgsU0QzxmReGQ24tFryPVHL59wrE4M+7W00QfmwUUQSDdSnxZPVBASclUMjJUEZGbG8emTaU0uZuJ7BNFS0Et5YeSSUvXoyiQkABpaQLrHVP5+OlnqNv5EZMmz2PczGzWLTnIxs8LGXtZDk6n+mXrbIVxTr0evzud8ldeJ/7Oa8h+9CGiZ83iFzn3cXG/KTy25jHuW3YfIxNHctuAe0j1nQOIYxalPKpDaUwGTLpHnZor1Fwm+5ep1ch3LVaX0ZshdQSkjQl2ioMgcSBYY0/p+e8u4VSE8ZQgpRrB1FAMDUXB1+BUXwR1ByDgOby8JRYS+qvJHhP6qxaa5KEQlRpWlplQh/QqWypZX7GeNWVrWFO2hjq3WudnaPxQ7hh+B9OzpjMkbki3HuJH++6kyEJa/vkVFV99hXu7msfKOmYMKY8/TvSlM9C3hj2eAdRXtLD160PsWasK3eHnpzPm0mwiYkIbcpRSsn/Ter59+zXqy0rIHDKc6bfdSUJm+P+aCDh9BOo9alXwBGvItaUAPB4PdXV1CCGIj4/HdBx/K51OFTM+nypshDjsYHyqURQ/LlcRoMNmze52SHhnpJQUub0oQLbFhC6MniXdRRM47dA1GkiO0LO+wcN5jGfvvoMYjQpC6BACRo/uy5YtO9iwYRvTxo2j5u39lHxWS8z3k7BY1C+zzwdZQ4dywdUTWfXRZ2zKjGXspRfTWOVix7eluJ0KE+f0AXRt5nJFUYeYzGaInX4elgG5VL38RwI/fYTYt94i6ac/ZfT48Xww9wMWFyzmhW0vcP/qH5JpHsClSTfSN3ARdn1Uh6KU3RqyiUpR/XFGzFM71YZiKN2kTmVbYMtbh/00QPXhSRwIcX0gNhtis9RXezZEJJ7WzvRkDg+dVl+agB9aqlRLS3MFNFfgqqmhusyBu74Bi/sQiYEdWGV1x/VMUep5jusD/S9Syx/E91cFjS0+rITM0ehJ5Fupo5SNFRvZWLmRjRUbKXGo4bR2s53J6ZOZkj6FSamTiLeG5lNgMSskNu7EsWw5zV99RXm+Gv5uGTGCxAcfJPqySzFlhmcxyGNRcaCRzf8t4mBeDXq9jsGT0xh7aTZRcaFbWEr37GL1v//JoV3bsadlMPdnj5A7dkLY+xxJKQk0elAcPjU7cZwl5KrgAC6Xi/r6evR6PfHx8d0uHdAqck4nUiq4XEUo0ofN2hedLnTH9wqPjxa/QobFhCXEIc3eRhM47fB4BNNjo/j3Dgez0idQv+MtGhq24XCMJj0dDAYrl1ySybp120lPH4A7IQF7STmRDjMiKqaDqXvQxTezd0+Az99cSnODm/PmzUKiY9u3lSjORhLuHEJiooWiItU61PqDwOeDrDHpiOf+D+fX/8X/9kKKb7oZy8gRxN96G9dcdCWXpM1l0ZYVLNr5Gc+VPofJ9ldGWC5kmm8S52dPxkhEtzIQd+zUBVZ7UKwMu0pdSFGgsRiq96pOyq2vuz8GZ23HDRqsqmCKTIbIpOBr6/sktRO2xKjWBktM2AyThBxyLiX4XOB1gKdZDbF21amvztrD79vm1aklDFqqgcOO/S6fhaLGTMxRUUREx+CNzKTIOpnsgdFYk9ODIjJLPW9h3rF0xbEi38orfLii9rG9Zjt51XlsqtxEWUsZADHmGMYkjeH6QdczLmUcg+IGdfCn6Qn+2lpaVq/GsXIVLatWEaivB50O29ixJP/qV0RdfNEZ41fTSsCvcGBrNduXlVC+vxGzzcC4y3IYPi0j5CzEoAqbNe//i+LtW7FGxzD9tjsZceGl6M+A4TnFGyBQrw5J6SKN6GNC97eRUtLU1ERLSwsmkwm73Y6+m9moewNV3BQTCDixWrMwGEIrMQJQ7/NT5fUTZzIQbwr/634stCiqdhQXw/p8Dz/aXcictGjOqfkddXV2sjIfIT3dQmwsWK1e/vOftykrM3DRtNmkbS3G6PEQ/70hyLiINpOk2Qx6nZ8vXnmLzSv2MHBUFpf94Ht4at2s/WAvvoCO3Ml9SBmcRn6+DrtddTyOi1M71tbIjn6ZLho+/JC619/AV1yMz55G0+TriL9sOtGDc9lZmc/SAytZ2/wxFXXN6PwRDEvN5YLBw5meO4kB9gFHdAouF+zdq24/EFDrXUVEwMCBPbBceBzthk6CwyjNFeCoBEeV+upuOPb6BsthsWONBVMEGG1qJlyjVRVMRmvHeTpDp0l/5P8ASPUESiU4tX8fnAJe8LspLjES8Pkw65zg90HAg8cdQC/dZNkrgwImKGJa33ub1VcZOPbxCb1aWNIad/g1IkEdNopKVl8jkyluSCdgjsdsO/wQ8Xho89M6W9i3D6w2hfKWMnbX7WZv3V521e5m76FqFEsdis9MXJSNif36MjFrBOOSx9Hf3j9kQaO0tODcshXnhg20rFqFe+dOAPRxcUROnULElKlEnDsZw0nIlnq6aa5zs3NlKbtWl+Nq8hIVb2HkhZkMnpyK6QSGYToLm/FzrmbUxTN71c+mu1FUUkoUh49AkweEwGC3oLOGfi5asxN7vV5sNhsxMTFhbbmSUuJ2H8LnazzhiClXIEC+04NVpyPXZg67oameRFFpAqcdLhesWAFvNVTwWUMDv0314jvwe4YOGU9Ozk/JylIT7pWV1fHKK5+SlOTj+itm4/ugBMXjJ/aK/hxSYsnOPvwLVUrJ5i+/ZuOSf5MU72TK/O+T2H8qS14+ROWBOuKSLcQPyiIhJ57cfro2gdG5k5OBAI7ly9n55jKa12zAJF0Ys7KJPHcyhglTsQ4bSEPEDlYcWsHqstXsqdsDQLQpmpGJIxmdNJpRSaMYljCMQwesFBertbFafXccDnVfJzXM2u9RLRbNlapPibtBnVwN4G4MTsH/fU7VItL5NeA9iQ06kn01uUSYWg4bR/RmpM5MSyCWAWnlYIoEc+ThV3N0u/dRwffRYLWrViqbXRUzlphuWVzCJeT9ZNPiayG/Pp999fvYW7eXLXtr2F9fiEeoCcAsBgs5puGkKKOZNDSV4cmDiDUk4fWKkPypAg4Hrs2bVUGzfj3unbvUSo16PdaRI4k8byoRU6ZiGTIYcTocIU4yAb9C0Y5adq8pp2h7DRLIGRbP0PPSyRoaH3KSPkUJULBhLZs+WUzZvt3YYmIZP/sqRvaysGnleAJHUcDnCuBr9IIvQECnp8FvxuPThTzc7PV625yJY2Nj+ctf/sK//vUv9Ho9Op2OF198kYkTT03kWPsClN1FFTdl+Hx1mM0pmM2JIe/fpyjkO1X/vv42M8YwvFc0gXMC7NsHhRUB7ttRjN8Y4MmclXirXqNvnx8yefKNbcutX1/O++9/iE5nYMzQy2ha6sFd58E4MJ7pP0giIvLwF0NKKCko5eA3z1C4I58W8yRyz72UmNg0ijYcoqHSiUsXTd8R8Qw5JwGv34hOByNHqhadzu0z+xpwLPsG56rVOLdsQfr9eKJSGTotDduE8URMnEhlbDRf7drCltI97GveRinb0Jk86IWeiOKZ9IlPIzc+g5yYHLKjsrEKOx4PTJt2Sk5rl3TpAxPwg9+lFndU/KD4gq+B4Kv/8P8Bn7qO0LWbxNH/15vAYKG43EIAM2abSY0cE+K0WlA65zOCM8uCU++up7CpkIONBylsLORg00EK6gva/GYAooxR9LENIdEzjgEJ2QxN7k+aNYeSYgPJyYdLl0D3jl0qCt4DB3Bty8O1PQ9XXh6ePXvV3s5oxDpsGLbx47GNH4919OiQSyb0JmrtOklpgYOSXVXU5legeD1Yo00MmZzKkKlpRMeH7ijmcTrZsewrNn++hKbqSmKSkhlz2RyGT58RFsKmla4ETsAvcdf7kE4fQgceg4niSgNRUaLNv7F1qL47IkdKicPhoLm5Gb1ej91uZ9OmTTz44IMsX74cs9lMTU0NXq+XtLS0k3ykKj0VOKq4KcXnq8dkSsRi6Vl5jPb4Fcl+lxuvIsm1mbGF6XCclgfnBMjMBEXR87fzU7h9z0Geckzlvog6GhqfZP+Bavr2eQAhBMOHp9LcfC3vvruKfy/+jhHDB5ESbadlRz3r/9zMmO8lE9Nfrevh9UJqn3SG/2Ihy5dsZum737Dlw5eITEij34TpDDsvh7yVtaz6vJZdayroPyKaIRPiqCiPwWrVdbg5LRYIGGOxX3UV9quuIuBw0LB6A6713+Ha+hXNX3yBWzFTETmEwcP6MWHIAHT97qIlLZ3GxP3scWxhabWPPbW7WVt1uAx9hIgjxZTFcpOP7OhscqJzyI7JJjMyE+MpDBk/rg+M3gD6qJCqo3eHRFMwbDgAJj14Pac35DzcQ96llNS56yhzlFHqKKXEUUJRUxGFjYUUNhXS4GloW9aoM5Idnc3g+MFc0e8KBsYNZIB9AKkRqQghOghZsxHi46Fzao/O0X9SUfAVF+Peuw/3zp248vJwb9+OElxIFxmJdcRwou78kSpoRo3qcXHLcKN0fwtrvqqjZFc1LXVO0OlJGZDIlBlxDBgTh+4EHD6riwvZvvS/7FyxFK/LSdrAIUz7/u3kjp+I7hSVczkVKC4/7lovMiDR2/Too0xUlKpWm9bQ7J5kOPf7/dTX1+Pz+bBarcTExKDT6SgvLychIQFzcGMJCQkAPPHEE3z88ce4XC4mT57Miy++iBCCadOmMXr0aFauXElLSwtvvvkmf/jDH9i+fTvz589nwYIFFBYWcumllzJ27Fg2b97M0KFDefPNN7HZOvrMfPnllzz22GN4PB5yc3N57bXXiIyM5OGHH2bJkiUYDAamT5/EE0/8GLM5CZMpKfTzKSWFbg8eRdLHGr7ipqdoFpyj0PogXlPZwi8OFhGfCL+O+AzvgQ+JiLiQYUN/RmpqFIcOwaZNLtat20RV1UHS0yMYkTaCpjUBUk1O+k+OwjwxHSXS2haaGghAVWWAg3lb2bvqK2rKqjCbjaQPG0dyn5GY/DoObK3G5wlgsJjoMyyOSRfHkdY/Fr1R1zFDbacO0WKR+A4dYt+n22nemoeyJw9/aSkAXmnEkpFG33HJHIoaQ5VtAOacWMqjWthff4gDVVW0ROymPnp9WxgugE7oSLIlkRaRRmpkKmkRaaREpJAWmdb2vjX5YCicLgtGV1ai3s5I3Jv79wV8VLuqqXZVU9lS2SZkSh2llDnKKGspw+V3dVgn3hJPTkwOfWL6kBOtvvaJ7kNaZNox888cjc7XPtDSQvOeg/gP5pNYsxXP3r248/ORTqe6gMGAZeBArCNHYBk+AuvIEZhycs7IIaf2SCmpLXWwf3M1+zdXsT8/gAJk5EaTOzaR7GEJoDOEfE943S72rP6W7d/8l4qCfegNBvpPPJcxM+eQ2m/gST+ek0nnX+uKL0Cg0Yt0+/GixxhrQgSdYFuHe6U8HEF63CzVUuJ0OmlqagIgNja2Q2Zih8PBlClTcDqdXHTRRcyfP5/zzz+furq6tmrX3//+95k3bx6zZ89m2rRpTJw4kaeeeoqnn36ap556ik2bNhEXF0dubi7btm2jubmZPn36sGrVKs4991xuu+02hgwZwkMPPdRmwcnJyeGqq67i888/JyIigqeeegqPx8M999zD5MmT2b17F253KTU1h0hO7o/ZHLq4aQ0Hb/QFyLKasBvD2+5xxllwhBCXAk8DeuBlKeXC3mxPawhyVlYEgwflcOOGg9y753J+mDScQU0L2Lx5NxkZP8Nmm0B6upXvf38KBw+msnr1albu/pq4vv2wR2ZRv7MR885a0sfHoI9Mx+2OIiICEhL1eAaNJXv4GOpK9rPt243sWb+LqrwvSEizkztyHBFxg6gtM7NnUx3Vu4sxmHSkD7STPTSehD5xeLHS0iI65YARmLKyMF2YRdacyxECAk1NuPfuxb1rD3XbD+DauhLzoa/xe7JpVqwYTFZGZqQwtU8yg0f3JzptGt5+cZTbJUWylsKmQsod5ZS1lLGlcgtfOL8g0M65VvGaMbnSiNGnkBAZQVqKidTYWBKsCW1TnCWOaHM0seZYIo2RHZz1TkdV7uNZiXo7I/HJ3r9P8dHoaaTB3UC9p54GTwO1rlpVyDirqXJVUe1U39d76o9YP8oURXpkOjkxOUxOn0x6ZDppEWmkR6mvkabQU/JLKfFXVOA9eBD93mKKttShKy9EKT2Iu7IOr2ImzVhGk92CZeBAYq++GsuggZgHDsLcvx+60x13e4rwewOU5jdQtKOWou01NNW4EQLSBtgZdWkKg8bFdchd09pRdxdFCVCyayd7Vi9nz5qV+Nwu4jOymHbTDxg8dRq26J5lxe1tZEBRSy20+EAI9DFmjCYjcPhZYrXSVtCyla7SZfh8PhobG/F6vceMkoqMjGTTpk2sXLmSZcuWMX/+fBYuXEhUVBR//OMfcTqd1NXVMXTo0LYq4HPmzAFg+PDhDB06lNRgdF7fvn05dOgQsbGxZGZmcu655wJw44038swzz/DQQw+17Xft2rXs2rWrbRmv18ukSZOIiYnBYjFzyy3Xccklk5l7xfyTJm7SLMawFzc9pdePRqhZiJ4DLgZKgA1CiCVSyl292zKVkVE23swewC+dJTzXnM6E6L9ztec5CvY/BMwmNeVGfL5U+vbNJTMzk82bd7B1ax4bvHko5yQyytIPdtZS9bdqmq12fCMTiRmZQHq6nro6gSWuHxd8rx+RlhYO7dxAyfb15H3zBVL5DEt0EtmDBpHbPw2vJ43SfCdF29Xw7IgYE6n9YkntF4OVWMwZkW2Ohu3T0eujo4kYPx7DiPHE3KB2pAFHC9k791O25RBN+SWI4nwi8z/FsbYER/C4jcDAmBiGZWVhysrCmDYCQ+ol6NKTcNgtVEUGOOhzsHu/k2alhkZ/DTWORvL2NbIuah1OUXvU86kXemLMMUSbVMGja8wm0hCDPSKCSGMkNqMNoxJJhMVCVYSBCGMENoONCGNE22TS9ywENhyKc/YERSq4/C4cXgcOnzq1eFto9jXT4muh2au+OnwOGj2NNHoaVSETFDTN3uajblcv9MRb40m0JpIWmcaoxFEk2hJJsiWRaFVfUyNTiTZFn1j7PR58pWX4SkvwlZbiKynBW1qKt6gIb2HRYYsMEG2x40gbgRw8nsRZqaSNycI+cgCGlJSwjloJhaYalypodtRSurcev0/BYFR/uIyZkU3fUYlYo0xtlq32dCevlZSSygMF7Fm9nL1rVuKor8NotjDgnCkMv3AGaQMGnXHnVK0h5SHQ7AMp1dDvKBNCr0MoHbMFx8dDYaEaOCHlsYd7FUXB4XDgcDgQQhATE4PNZjvmudHr9UybNo1p06YxfPhwXnzxRfLy8ti4cSOZmZk8/vjjuN3utuVbh7N0Ol3b+9b//X4/wBH76vy/lJKLL76Yd955p1PbvXzzzb9YtuxbPvlkDa+++jHffPNNj85p+320iptUs5FE05mXqfh49LrAASYABVLKAwBCiHeBuUBYCByAiICRf4zK4T8VdTxTXMk2eS/T7RUEyrbBgXfAMxRLpAUhwCt0WM/LwBevZ6mrjv8GvsM01oTNYMHfCE2FNvSlCjorSGkg4NMTnaTKiiZHJPoBWYgh6QQ8Cl4nFMTXs0ruAdMe9CP0CL0eqQiklOpUBVSpv2NU/1mB36OnuToKvTGAzqCg+HUEfHqiEpsxmNs9OROC06QcIEedFxyylDIYao0Mpm2RQCVUV0IwB11zTRQyINCbDEAqFpFKKjpEy3iiEh10QLa+BLfvBbzg9+upLI+ixhBAZ3Cj+L0E/M1EJTaz8lAXYdh070EtgIayGAxm/xGRSn6Pgdi0xm5tpyecyKCv7MHaAiOQCCRiA2xAmg5E+45QiI5nSgJO8DrVXxMleIBDwemEGk7rd+WIY8jQIzKz4ZzsYLiYCH5Z1aM4TCM4tsPq7cfb0RmFeivJ1lsLkSQQKeq9KgRsQUAN8LX6+eH719/u/jWo9++m1ntCHrGTgD+AlAqYQX/RWHQGIzq9nn1C8En+Sshf2fO2h3bEIa11BApcnz6aEn8jRARFgHBDBx8tgRLQtdV5siYrVNWBp0GH2aIQnyCp9gG+dptVFBRFoo8wYjAYaBQ+Gl1Hfw4U7MtHp9PRt59anHjlhrVk5Oawdds2vDYD+VUlvPvev7n8yrmUORvwKn6q3c2UORuocTtwB3yUORsA2j4LuEwUFxezZNmXjJs4gZfffJ3hE8Z2WD9nxGC+vXslq7dvpk9uX5wtLZSXlpKYHIvLFWDK5Tcz8rx5TBw8kEqP76htPx6OQACHXyHVbCTpDCzD0B3CQeCk0/HpWgIcEYMnhPgh8EOArNP8s9tigYBPcH1aPNPionjuUBWfloESlYIuxk+gykCg1oSUEkOiH328D2FOgM7WdCtIu0BpNKB4BDqzRBfjR5iDnb6t3WcxEl3W4c96iow/9n5OFj6vCWFRjhAO0q3DmNj98G6ZeGrbGsCIDIAwHt6m9AmEHvRJoT0cNDROJTK23T0RdWru3zOBuTpBg6GLIVFdcGrFCLYIVewDuILTMdfp6jcUUNroZeHPf4qjsRG9QU9mn1weeeZZDFFxTBt3LvHJyQwcM44WRU+134RX6qj3G6n2m2gIGPBKHdV+1eLc+pn0G8npP4AXX3iVB+68j74DB3HfrXd2WD/Nnsbjf/8HP7z5B/i8atj2Pb95jKG2eB64/na8HjdSSn7y+z9QEaLA0QtIs5ydlptWet3JWAhxDXCplPKO4P/fByZKKX98rHVOtZNxZ47m2NvikqRnyWBSPgVFdv0lCwT8Hf6XioTA8c99Z9OlogSQfuWYy0tFElCO/Xl399MdSkoEiiJoX5bF4/QjhCQj4+htEMfaV5f7P8ZnXa0jDu9Hrbauw2QO5v3xCzxuSXqGgskSQJEKAqEmlRMgWv9a359hZn2N8EKnP/XfIZ0x9CKSYYuAogNFDBo0qLdbclIpLCxk7pw5bMvL69mKwefRySLcEvh1lzPNybgUaF/4JSM4L2w4WoG+3D4Cq7X1C6LjuKfyLBTJEZFHCj+DMTwLX8YnHo5UiumFSCkNDY2eoxMC/RkeJdeZ1uM5244rHAkHgbMB6C+E6IMqbK4Dvte7TTqS3o60CUfOpKre2vXT0NAIB3JyctixY0dvN+N/gl4XOFJKvxDix8B/UcPEX5VS7uzlZml0E004aGhonEqklGff8JtGSPTUpabXBQ6AlPIz4LPeboeGhoaGRvhgsViora0lPj5eEzn/40gpqa2txdKDUiJhIXA0NDQ0NDQ6k5GRQUlJCdXV1b3dFI0wwGKxkJGR0e3lNYGjoaGhoRGWGI1G+vTp09vN0DhD0dy4NTQ0NDQ0NM46NIGjoaGhoaGhcdahCRwNDQ0NDQ2Ns45ez2QcCkKIaqCot9txiklArVCjcWagXa8zB+1anTlo1+rMoTevVbaUMrHzzDNS4PwvIITYeLTU0xrhiXa9zhy0a3XmoF2rM4dwvFbaEJWGhoaGhobGWYcmcDQ0NDQ0NDTOOjSBE778o7cboNEjtOt15qBdqzMH7VqdOYTdtdJ8cDQ0NDQ0NDTOOjQLjoaGhoaGhsZZhyZwNDQ0NDQ0NM46NIEThgghCoUQ24UQW4UQG3u7PRqHEUK8KoSoEkLsaDcvTgjxlRAiP/hq7802ahzmGNfrcSFEafD+2iqEmNmbbdRQEUJkCiGWCSF2CSF2CiHuD87X7q8wo4trFVb3luaDE4YIIQqBcVJKLcFVmCGEOA9wAG9KKYcF5/0RqJNSLhRCPAzYpZS/6M12aqgc43o9DjiklH/qzbZpdEQIkQqkSik3CyGigE3AFcAtaPdXWNHFtZpHGN1bmgVHQ6MHSCm/Beo6zZ4LvBF8/wbqja4RBhzjemmEIVLKcinl5uD7ZmA3kI52f4UdXVyrsEITOOGJBL4UQmwSQvywtxujcVySpZTlwfcVQHJvNkajW/xYCJEXHMLShjzCDCFEDjAaWId2f4U1na4VhNG9pQmc8GSKlHIMcBlwT9DMrnEGINUxX23cN7x5HsgFRgHlwJ97tTUaHRBCRAKLgAeklE3tP9Pur/DiKNcqrO4tTeCEIVLK0uBrFfAhMKF3W6RxHCqDY9KtY9NVvdwejS6QUlZKKQNSSgV4Ce3+ChuEEEbUDvNtKeUHwdna/RWGHO1ahdu9pQmcMEMIERF02kIIEQFcAuzoei2NXmYJcHPw/c3AR73YFo3j0NpZBrkS7f4KC4QQAngF2C2l/L92H2n3V5hxrGsVbveWFkUVZggh+qJabQAMwL+klL/vxSZptEMI8Q4wDUgAKoHHgMXAe0AWUATMk1Jqjq1hwDGu1zRUE7oECoEftfPx0OglhBBTgJXAdkAJzv4Vqm+Hdn+FEV1cq+sJo3tLEzgaGhoaGhoaZx3aEJWGhoaGhobGWYcmcDQ0NDQ0NDTOOjSBo6GhoaGhoXHWoQkcDQ0NDQ0NjbMOTeBoaGhoaGhonHVoAkdDQ0NDQ0PjrEMTOBoaGhoaGhpnHZrA0dD4H0IIERBCbG03PdzbbYIO7dohhPiPEMLWw/XX9HD5x4UQDx1nmTvanSel3fu/dGP7OUIIlxBiaxfLWIPb8wohEnrSfg0NjeOjJfrT0PgfQgjhkFJGnsTtCdTniHLchbveTlu7hBBvA5s6pes/qfsXQjwOOKSUf+rGsunAGilldg+2nwN8IqUc1o1lC4FxUsqa7m5fQ0Pj+GgWHA2N/3GC1obdQoiXhBA7hRBfCiGswc9uFEKsD1oaXhRC6IPL7xVCvIlaayZTCPFIcN4qIcQ7rdYRIcQTQogH2u3r90KI+4/TpJVAvx7u39FuHw8GLUE7Ou3710KIfUKIVcDAHpyiYagp6dufsz5CiI+EEBuD7etye8Eac58KIbYF2zW/B/vX0NAIAU3gaGj8b9E6LNI6tXa0/YHnpJRDgQbgaiHEYGA+cK6UchQQAG5ot/zfg8snAVcDI4HLgHHt9vcqcBOAEEIHXAe8dazGCSEMwW1s7+7+pZRF7dYfC9wKTATOAX4ghBgdnH8dap2cmcD4bp8xGE67ooHBKsovAw9KKccBjwPHG+q7FCiTUo4MWnW+6MH+NTQ0QsDQ2w3Q0NA4rbiCYqGN4HDKQSnl1uCsTUAOEAuMBTaoI0FYgSrgW6BISrk2uPy5wEdSSjfgFkJ83LptKWWhEKJWCDEaSAa2SClrj9Iuazt/lZWolYp/2M39t2cK8KGUsiV4bB8AU1F/zH0opXQG5y/p6iR1YhjwVbv/rwCGAouC7TIE29wV24E/CyGeQh26Ot7yGhoaJ4gmcDQ0NAA87d4HUMWEAN6QUv6y/YJBQdTSg22/DNwCpKBadI7G0YTXydr/iTIcaO9YPBL4tZTyle5uQEq5TwgxBtV6tEAIsVRK+cRJbqeGhkY7tCEqDQ2NY7EUuEYIkQQghIgTQhzN0XY1MFsIYRFCRAKzOn3+IeoQzXjgv6dg/+1ZCVwhhLAJISKAK4Pzvg3OtwohooDZrSsIIZYGHYmPIDis1h/Y3W52OTAj+BlCiOFBMXZMhBBpgFNK+Rbw/4AxxzkODQ2NE0Sz4Gho/G/RfigIVF+QF462oJRylxDiN8CXwc7cB9wDVHRabkNwyCcPqEQdjmls97lXCLEMaJBSBrrb0O7uv9M6m4UQrwPrg7NellJuARBC/BvYhjrMtSE4T4fq0Fx3jE32A0qklN52814FLgB2CyFcwA4p5Y3HOZzhwP8TQijB47jrOMtraGicIFqYuIaGxgkjhIiUUjqEmr/mW+CHUsrNwc90wGbgWillfm+2szNCiGHAbVLKB0/ydnPQwsQ1NHoVbYhKQ0PjZPCPoGVoM7ConbgZAhQAS8NN3ABIKXecbHETJADEiG4k+gOMwAnlEdLQ0DgSzYKjoaGhoaGhcdahWXA0NDQ0NDQ0zjo0gaOhoaGhoaFx1qEJHA0NDQ0NDY2zDk3gaGhoaGhoaJx1aAJHQ0NDQ0ND46xDEzgaGhoaGhoaZx2awNHQ0NDQ0NA46/j/E131b1t2a58AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the contours\n",
    "Te_contours = []\n",
    "Hm0_contours = []\n",
    "\n",
    "for period in levels:\n",
    "    copula = contours.environmental_contours(\n",
    "        Hm0, Te, dt, period, \"PCA\", return_PCA=True\n",
    "    )\n",
    "    Hm0_contours.append(copula[\"PCA_x1\"])\n",
    "    Te_contours.append(copula[\"PCA_x2\"])\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "labels = [f\"{period}-year Contour\" for period in levels]\n",
    "\n",
    "ax = graphics.plot_environmental_contour(\n",
    "    sample_te,\n",
    "    sample_hs,\n",
    "    Te_contours,\n",
    "    Hm0_contours,\n",
    "    data_label=\"Samples\",\n",
    "    contour_label=labels,\n",
    "    x_label=\"Energy Period, $Te$ [s]\",\n",
    "    y_label=\"Sig. wave height, $Hm0$ [m]\",\n",
    "    ax=ax,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Short-Term Extreme Distributions\n",
    "Many different methods for short-term extremes were adapted from WDRT, and a summary and examples can be found in `short_term_extremes_example.ipynb`. \n",
    "The response quantity of interest is typically related to the WEC itself, e.g. maximum heave displacement, PTO extension, or load on the mooring lines. \n",
    "This requires running a simulation (e.g. WEC-Sim) for each of the 200 sampled sea states $(H_s, T_e)_i$. \n",
    "For the sake of example we will consider the wave elevation as the quantity of interest (can be thought as a proxy for heave motion in this example). \n",
    "Wave elevation time-series for a specific sea state can be created quickly without running any external software. \n",
    "\n",
    "**NOTE:** The majority of the for loop below is simply creating the synthetic data (wave elevation time series). In a realistic case the variables `time` and `data` describing each time series would be obtained externally, e.g. through simulation software such as WEC-Sim or CFD. For this reason the details of creating the synthetic data are not presented here, instead assume for each sea state there is time-series data available. \n",
    "\n",
    "The last lines of the for-loop create the short-term extreme distribution from the time-series using the `loads.extreme.short_term_extreme` function. The short-term period will be 3-hours and we will use 1-hour \"simulations\" and the Weibul-tail-fitting method to estimate the 3-hour short-term extreme distributions for each of the 200 samples.\n",
    "\n",
    "For more details on short-term extreme distributions see `short_term_extremes_example.ipynb` and \n",
    "\n",
    "> [3] Michelén Ströfer, Carlos A., and Ryan Coe. 2015. “Comparison of Methods for Estimating Short-Term Extreme Response of Wave Energy Converters.” In OCEANS 2015 - MTS/IEEE Washington, 1–6. IEEE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sea state 1/200. (Hs, Te) = (2.851166377141435 m, 10.195736685047796 s). Tp = 11.282805679200564 s\n",
      "Sea state 2/200. (Hs, Te) = (3.5862460438066304 m, 10.936985799409186 s). Tp = 12.103086741331571 s\n",
      "Sea state 3/200. (Hs, Te) = (2.7751615384993915 m, 9.506986218875468 s). Tp = 10.520620668805236 s\n",
      "Sea state 4/200. (Hs, Te) = (3.0820450802745007 m, 9.728717644182273 s). Tp = 10.765993088865644 s\n",
      "Sea state 5/200. (Hs, Te) = (2.7162732683836195 m, 9.177186568411022 s). Tp = 10.155657794203384 s\n",
      "Sea state 6/200. (Hs, Te) = (2.7916623630702304 m, 8.810734371427328 s). Tp = 9.75013448019444 s\n",
      "Sea state 7/200. (Hs, Te) = (2.592821066032464 m, 8.374599835754573 s). Tp = 9.267499299629163 s\n",
      "Sea state 8/200. (Hs, Te) = (2.4021986215575373 m, 8.348365892255037 s). Tp = 9.238468294234675 s\n",
      "Sea state 9/200. (Hs, Te) = (2.327723379324664 m, 8.299620704291481 s). Tp = 9.184525896487639 s\n",
      "Sea state 10/200. (Hs, Te) = (2.00764351315584 m, 7.416472871792525 s). Tp = 8.207216881170892 s\n",
      "Sea state 11/200. (Hs, Te) = (1.951087921529932 m, 8.122015735790558 s). Tp = 8.987984694104963 s\n",
      "Sea state 12/200. (Hs, Te) = (1.642063924746251 m, 7.997394534817154 s). Tp = 8.85007638619882 s\n",
      "Sea state 13/200. (Hs, Te) = (1.5614901176199742 m, 8.156813011698352 s). Tp = 9.02649205403011 s\n",
      "Sea state 14/200. (Hs, Te) = (1.3223442301926103 m, 8.373988391532727 s). Tp = 9.266822663251443 s\n",
      "Sea state 15/200. (Hs, Te) = (2.310517824251443 m, 9.135556192896424 s). Tp = 10.109588789893749 s\n",
      "Sea state 16/200. (Hs, Te) = (1.546312740170234 m, 9.978412702019654 s). Tp = 11.042310622719521 s\n",
      "Sea state 17/200. (Hs, Te) = (1.9019087805024188 m, 10.44123868584584 s). Tp = 11.554483092459133 s\n",
      "Sea state 18/200. (Hs, Te) = (2.3338262351351116 m, 9.930633820011005 s). Tp = 10.989437558425541 s\n",
      "Sea state 19/200. (Hs, Te) = (2.4479576516446064 m, 9.760511705407653 s). Tp = 10.801177031491845 s\n",
      "Sea state 20/200. (Hs, Te) = (2.651578075055819 m, 10.749317530563182 s). Tp = 11.895409289966368 s\n",
      "Sea state 21/200. (Hs, Te) = (5.030844895631051 m, 14.282752593036674 s). Tp = 15.805579042430452 s\n",
      "Sea state 22/200. (Hs, Te) = (4.463644749835009 m, 12.34746631098706 s). Tp = 13.663952622633797 s\n",
      "Sea state 23/200. (Hs, Te) = (4.903918051881774 m, 11.272595420034712 s). Tp = 12.474479044855961 s\n",
      "Sea state 24/200. (Hs, Te) = (4.37807740903193 m, 9.956362967463496 s). Tp = 11.017909946440787 s\n",
      "Sea state 25/200. (Hs, Te) = (3.9498715792778443 m, 8.856270002839082 s). Tp = 9.800525118612159 s\n",
      "Sea state 26/200. (Hs, Te) = (4.070115900693828 m, 8.307318836624022 s). Tp = 9.193044803348798 s\n",
      "Sea state 27/200. (Hs, Te) = (2.736299341121807 m, 7.4381867053480155 s). Tp = 8.231245842699135 s\n",
      "Sea state 28/200. (Hs, Te) = (2.4118215576759034 m, 6.537612647601825 s). Tp = 7.234652618770482 s\n",
      "Sea state 29/200. (Hs, Te) = (1.9552243695320324 m, 6.321440801925461 s). Tp = 6.995432540474677 s\n",
      "Sea state 30/200. (Hs, Te) = (1.801478730354882 m, 6.510603277357231 s). Tp = 7.204763510665695 s\n",
      "Sea state 31/200. (Hs, Te) = (1.3679970074652714 m, 6.468135086563519 s). Tp = 7.157767363248965 s\n",
      "Sea state 32/200. (Hs, Te) = (1.0743335950058275 m, 7.090922017059409 s). Tp = 7.846955808713215 s\n",
      "Sea state 33/200. (Hs, Te) = (1.2357391850105406 m, 7.849764893906227 s). Tp = 8.686706479507244 s\n",
      "Sea state 34/200. (Hs, Te) = (0.9213431748668424 m, 8.443600703410047 s). Tp = 9.343857036740465 s\n",
      "Sea state 35/200. (Hs, Te) = (0.4814455431778253 m, 9.340126880987539 s). Tp = 10.33597079569597 s\n",
      "Sea state 36/200. (Hs, Te) = (1.0933659724162896 m, 9.979135574932235 s). Tp = 11.043110568310052 s\n",
      "Sea state 37/200. (Hs, Te) = (1.0123053762369256 m, 11.850902621257452 s). Tp = 13.114445334280386 s\n",
      "Sea state 38/200. (Hs, Te) = (1.7075134960575675 m, 11.578239068080656 s). Tp = 12.812710405130463 s\n",
      "Sea state 39/200. (Hs, Te) = (2.518274653405948 m, 12.53479278415886 s). Tp = 13.871251836085138 s\n",
      "Sea state 40/200. (Hs, Te) = (3.529852197639782 m, 13.30774814454997 s). Tp = 14.72661966279441 s\n",
      "Sea state 41/200. (Hs, Te) = (5.148223472776434 m, 15.586005564766051 s). Tp = 17.247784788330833 s\n",
      "Sea state 42/200. (Hs, Te) = (5.556129509905438 m, 13.785131436466855 s). Tp = 15.254901540169124 s\n",
      "Sea state 43/200. (Hs, Te) = (6.576702748036527 m, 13.803319512494747 s). Tp = 15.275028828058206 s\n",
      "Sea state 44/200. (Hs, Te) = (6.326628174866466 m, 11.194121656530978 s). Tp = 12.387638412160275 s\n",
      "Sea state 45/200. (Hs, Te) = (5.4962832147142935 m, 9.623068260891554 s). Tp = 10.649079373003927 s\n",
      "Sea state 46/200. (Hs, Te) = (3.8010066164603415 m, 7.1969594306047 s). Tp = 7.964298926598074 s\n",
      "Sea state 47/200. (Hs, Te) = (3.376615764959727 m, 7.194776215525951 s). Tp = 7.961882937224246 s\n",
      "Sea state 48/200. (Hs, Te) = (2.6216876932404376 m, 6.227995589253622 s). Tp = 6.892024203363135 s\n",
      "Sea state 49/200. (Hs, Te) = (1.6315497369764984 m, 5.243725558813215 s). Tp = 5.80281134583553 s\n",
      "Sea state 50/200. (Hs, Te) = (1.3669420800943772 m, 5.356634348657379 s). Tp = 5.92775846585616 s\n",
      "Sea state 51/200. (Hs, Te) = (0.8083067025753863 m, 5.521508218837552 s). Tp = 6.110211180778536 s\n",
      "Sea state 52/200. (Hs, Te) = (0.6856869087958939 m, 5.632227381764594 s). Tp = 6.232735215957039 s\n",
      "Sea state 53/200. (Hs, Te) = (0.40402171887623717 m, 6.179589350227688 s). Tp = 6.838456893274449 s\n",
      "Sea state 54/200. (Hs, Te) = (0.18050092680749552 m, 7.8131070731262104 s). Tp = 8.646140203497934 s\n",
      "Sea state 55/200. (Hs, Te) = (0.09436285952322665 m, 9.01492519221614 s). Tp = 9.976096117259395 s\n",
      "Sea state 56/200. (Hs, Te) = (0.47347906604424894 m, 10.821566270980709 s). Tp = 11.975361187889881 s\n",
      "Sea state 57/200. (Hs, Te) = (0.527437042017636 m, 13.504929376019787 s). Tp = 14.944824348437386 s\n",
      "Sea state 58/200. (Hs, Te) = (1.7419771172383736 m, 14.110799728601373 s). Tp = 15.615292571198744 s\n",
      "Sea state 59/200. (Hs, Te) = (2.4488493325170952 m, 14.752553677478932 s). Tp = 16.32547029770514 s\n",
      "Sea state 60/200. (Hs, Te) = (4.087706649607579 m, 16.122641924784478 s). Tp = 17.841637293305862 s\n",
      "Sea state 61/200. (Hs, Te) = (0.33176145271261115 m, 13.75930528117036 s). Tp = 15.22632180133784 s\n",
      "Sea state 62/200. (Hs, Te) = (0.8855174001154404 m, 15.076157859092664 s). Tp = 16.683577142841592 s\n",
      "Sea state 63/200. (Hs, Te) = (2.5427157275613133 m, 16.843426458663085 s). Tp = 18.63927184228866 s\n",
      "Sea state 64/200. (Hs, Te) = (4.337377283702386 m, 17.077378007829093 s). Tp = 18.898167295273343 s\n",
      "Sea state 65/200. (Hs, Te) = (4.836290692740213 m, 16.880304961749243 s). Tp = 18.680082329741825 s\n",
      "Sea state 66/200. (Hs, Te) = (7.138210033996883 m, 15.883932531728718 s). Tp = 17.577476715326455 s\n",
      "Sea state 67/200. (Hs, Te) = (7.04607811580354 m, 13.695319484323035 s). Tp = 15.15551384166231 s\n",
      "Sea state 68/200. (Hs, Te) = (6.683515089328116 m, 11.949932510664025 s). Tp = 13.22403378611297 s\n",
      "Sea state 69/200. (Hs, Te) = (6.127794684752878 m, 10.242888484924617 s). Tp = 11.334984801893572 s\n",
      "Sea state 70/200. (Hs, Te) = (6.0852360496095255 m, 10.21048065086315 s). Tp = 11.299121645998666 s\n",
      "Sea state 71/200. (Hs, Te) = (4.284337177258922 m, 7.5594798415804405 s). Tp = 8.365471247748896 s\n",
      "Sea state 72/200. (Hs, Te) = (3.9700754639265234 m, 7.320993637781578 s). Tp = 8.101557655455082 s\n",
      "Sea state 73/200. (Hs, Te) = (2.4674726025092806 m, 5.567536470063029 s). Tp = 6.161146962112716 s\n",
      "Sea state 74/200. (Hs, Te) = (2.4411565850268797 m, 5.576064022637772 s). Tp = 6.170583721965596 s\n",
      "Sea state 75/200. (Hs, Te) = (1.5027020575734593 m, 4.974528663747209 s). Tp = 5.504912689730755 s\n",
      "Sea state 76/200. (Hs, Te) = (1.371060685104678 m, 4.955398811863336 s). Tp = 5.483743213885607 s\n",
      "Sea state 77/200. (Hs, Te) = (0.9867265408067029 m, 5.015636588492398 s). Tp = 5.550403539593066 s\n",
      "Sea state 78/200. (Hs, Te) = (0.4019223940784351 m, 5.424495697652933 s). Tp = 6.002855188878451 s\n",
      "Sea state 79/200. (Hs, Te) = (0.3041167158650624 m, 6.175974380713595 s). Tp = 6.834456495870742 s\n",
      "Sea state 80/200. (Hs, Te) = (0.04419855678833473 m, 7.035319486906778 s). Tp = 7.785424939256372 s\n",
      "Sea state 81/200. (Hs, Te) = (1.1406640868177353 m, 16.99689554825793 s). Tp = 18.80910379942441 s\n",
      "Sea state 82/200. (Hs, Te) = (2.088875590556972 m, 18.853554386717196 s). Tp = 20.86371952107473 s\n",
      "Sea state 83/200. (Hs, Te) = (3.3670302274138626 m, 19.204362993751015 s). Tp = 21.251931326265645 s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sea state 84/200. (Hs, Te) = (5.125297529462439 m, 18.672283211728097 s). Tp = 20.66312122143036 s\n",
      "Sea state 85/200. (Hs, Te) = (7.5395641905378215 m, 17.827501744997704 s). Tp = 19.728269192102534 s\n",
      "Sea state 86/200. (Hs, Te) = (7.077565789406247 m, 16.565463530565825 s). Tp = 18.331672519098774 s\n",
      "Sea state 87/200. (Hs, Te) = (7.660797463748704 m, 14.340144866170789 s). Tp = 15.86909047718626 s\n",
      "Sea state 88/200. (Hs, Te) = (8.267146386057341 m, 13.752524476289345 s). Tp = 15.218818027340491 s\n",
      "Sea state 89/200. (Hs, Te) = (7.5585377867557515 m, 11.41873618959678 s). Tp = 12.636201336801353 s\n",
      "Sea state 90/200. (Hs, Te) = (6.189724705513505 m, 9.943266929556025 s). Tp = 11.003417609551178 s\n",
      "Sea state 91/200. (Hs, Te) = (4.985820233723228 m, 8.101640035422118 s). Tp = 8.965436537464955 s\n",
      "Sea state 92/200. (Hs, Te) = (3.950449104550366 m, 7.032331054963992 s). Tp = 7.7821178808320965 s\n",
      "Sea state 93/200. (Hs, Te) = (3.349526273174492 m, 6.2239567755115734 s). Tp = 6.887554771478645 s\n",
      "Sea state 94/200. (Hs, Te) = (2.5305938620600323 m, 5.471413955273054 s). Tp = 6.054775869049686 s\n",
      "Sea state 95/200. (Hs, Te) = (1.864284800127662 m, 4.692418833126264 s). Tp = 5.19272432145363 s\n",
      "Sea state 96/200. (Hs, Te) = (1.3594583232962816 m, 4.600545483005879 s). Tp = 5.091055438809216 s\n",
      "Sea state 97/200. (Hs, Te) = (1.0278889995696616 m, 4.580250801273515 s). Tp = 5.068596939008678 s\n",
      "Sea state 98/200. (Hs, Te) = (0.7073568060165395 m, 4.792230763150695 s). Tp = 5.30317819504014 s\n",
      "Sea state 99/200. (Hs, Te) = (0.508628205353712 m, 4.912581414426901 s). Tp = 5.43636062742936 s\n",
      "Sea state 100/200. (Hs, Te) = (0.1887138929931016 m, 5.815845769563984 s). Tp = 6.435930988137664 s\n",
      "Sea state 101/200. (Hs, Te) = (0.1871884572595377 m, 17.031553776651883 s). Tp = 18.847457286595947 s\n",
      "Sea state 102/200. (Hs, Te) = (1.8255533228167624 m, 19.044007965006898 s). Tp = 21.07447925145326 s\n",
      "Sea state 103/200. (Hs, Te) = (4.085461042668986 m, 19.625018339020407 s). Tp = 21.717436925831777 s\n",
      "Sea state 104/200. (Hs, Te) = (5.26393200451075 m, 19.6875070497272 s). Tp = 21.78658818010872 s\n",
      "Sea state 105/200. (Hs, Te) = (7.7652549552321455 m, 18.932000395608686 s). Tp = 20.950529439962647 s\n",
      "Sea state 106/200. (Hs, Te) = (8.242537266166547 m, 17.662905419000868 s). Tp = 19.54612361311204 s\n",
      "Sea state 107/200. (Hs, Te) = (9.27591707540523 m, 15.26409450321619 s). Tp = 16.89155158364453 s\n",
      "Sea state 108/200. (Hs, Te) = (8.866267940605884 m, 13.700480521607233 s). Tp = 15.161225148513351 s\n",
      "Sea state 109/200. (Hs, Te) = (7.32630515058675 m, 10.931360540476366 s). Tp = 12.096861717540277 s\n",
      "Sea state 110/200. (Hs, Te) = (6.499223554458602 m, 9.489259188129742 s). Tp = 10.501003582825902 s\n",
      "Sea state 111/200. (Hs, Te) = (4.677064608710481 m, 7.3713129233161805 s). Tp = 8.15724198098653 s\n",
      "Sea state 112/200. (Hs, Te) = (3.760226861005709 m, 6.226071145112327 s). Tp = 6.889894575072661 s\n",
      "Sea state 113/200. (Hs, Te) = (2.9941197640583335 m, 5.514672741901748 s). Tp = 6.102646905594336 s\n",
      "Sea state 114/200. (Hs, Te) = (2.5717123084913123 m, 5.108974693146778 s). Tp = 5.653693348835068 s\n",
      "Sea state 115/200. (Hs, Te) = (1.8783735128639163 m, 4.539471617179031 s). Tp = 5.023469880110703 s\n",
      "Sea state 116/200. (Hs, Te) = (1.2752130858691124 m, 4.242007181700333 s). Tp = 4.694289799685399 s\n",
      "Sea state 117/200. (Hs, Te) = (0.9793803265738974 m, 4.2470174592346845 s). Tp = 4.6998342727887366 s\n",
      "Sea state 118/200. (Hs, Te) = (0.7124811539135159 m, 4.424785222214989 s). Tp = 4.89655562679098 s\n",
      "Sea state 119/200. (Hs, Te) = (0.3588409389431 m, 4.705576661939675 s). Tp = 5.207285037392834 s\n",
      "Sea state 120/200. (Hs, Te) = (0.18838257732596975 m, 4.92177034404479 s). Tp = 5.446529280316457 s\n",
      "Sea state 121/200. (Hs, Te) = (1.1238727812408893 m, 19.89593130663322 s). Tp = 22.017234622062354 s\n",
      "Sea state 122/200. (Hs, Te) = (3.256336956058525 m, 22.014360984983714 s). Tp = 24.361531178968573 s\n",
      "Sea state 123/200. (Hs, Te) = (4.659638373417929 m, 20.55339693581869 s). Tp = 22.74479920753578 s\n",
      "Sea state 124/200. (Hs, Te) = (8.409675325429806 m, 20.5323227793156 s). Tp = 22.721478125398956 s\n",
      "Sea state 125/200. (Hs, Te) = (9.29875873352201 m, 19.828872472868778 s). Tp = 21.9430259784096 s\n",
      "Sea state 126/200. (Hs, Te) = (9.628455421584839 m, 16.805889531372728 s). Tp = 18.597732729471783 s\n",
      "Sea state 127/200. (Hs, Te) = (10.40162875212416 m, 15.193034921745538 s). Tp = 16.812915632742182 s\n",
      "Sea state 128/200. (Hs, Te) = (9.514119089597632 m, 13.496075929567501 s). Tp = 14.935026947916162 s\n",
      "Sea state 129/200. (Hs, Te) = (8.425241115023425 m, 11.273431425724304 s). Tp = 12.475404185435181 s\n",
      "Sea state 130/200. (Hs, Te) = (7.181441955239233 m, 9.927469647992638 s). Tp = 10.985936022526603 s\n",
      "Sea state 131/200. (Hs, Te) = (5.875841398493123 m, 8.165254125537952 s). Tp = 9.035833159053732 s\n",
      "Sea state 132/200. (Hs, Te) = (4.792485900332849 m, 6.9990505762219986 s). Tp = 7.745289038919442 s\n",
      "Sea state 133/200. (Hs, Te) = (3.1248928086261785 m, 5.482392746553494 s). Tp = 6.066925218570586 s\n",
      "Sea state 134/200. (Hs, Te) = (2.6573548488075835 m, 5.066441779515841 s). Tp = 5.606625577835072 s\n",
      "Sea state 135/200. (Hs, Te) = (1.7636422458226333 m, 4.351913064671034 s). Tp = 4.8159138430346085 s\n",
      "Sea state 136/200. (Hs, Te) = (1.5014369679854003 m, 4.266948107278621 s). Tp = 4.721889925645097 s\n",
      "Sea state 137/200. (Hs, Te) = (0.9816361473032175 m, 4.15497551695166 s). Tp = 4.597978822692763 s\n",
      "Sea state 138/200. (Hs, Te) = (0.8127603228313202 m, 4.141264736044045 s). Tp = 4.582806198931542 s\n",
      "Sea state 139/200. (Hs, Te) = (0.4006290776350021 m, 4.287345227405981 s). Tp = 4.744461785817772 s\n",
      "Sea state 140/200. (Hs, Te) = (0.18188378189464394 m, 4.611357261437197 s). Tp = 5.103019968578412 s\n",
      "Sea state 141/200. (Hs, Te) = (0.2805216324632074 m, 20.448028838143248 s). Tp = 22.62819676794919 s\n",
      "Sea state 142/200. (Hs, Te) = (3.4488490911962097 m, 22.44173356304588 s). Tp = 24.834470202390577 s\n",
      "Sea state 143/200. (Hs, Te) = (6.128032867857982 m, 22.368550725950417 s). Tp = 24.753484614442662 s\n",
      "Sea state 144/200. (Hs, Te) = (8.0257433252404 m, 21.09485355189949 s). Tp = 23.343985904061235 s\n",
      "Sea state 145/200. (Hs, Te) = (9.624239654696968 m, 20.397290291584074 s). Tp = 22.57204847980129 s\n",
      "Sea state 146/200. (Hs, Te) = (10.880424233644302 m, 18.09569728837873 s). Tp = 20.025059732449993 s\n",
      "Sea state 147/200. (Hs, Te) = (10.980286858713281 m, 15.648662372262576 s). Tp = 17.317122061869792 s\n",
      "Sea state 148/200. (Hs, Te) = (9.626205932332264 m, 13.035460355521655 s). Tp = 14.425300561749852 s\n",
      "Sea state 149/200. (Hs, Te) = (8.48145364001674 m, 10.969828551388684 s). Tp = 12.139431186073685 s\n",
      "Sea state 150/200. (Hs, Te) = (7.496113729159182 m, 9.77133103334069 s). Tp = 10.813149915690298 s\n",
      "Sea state 151/200. (Hs, Te) = (5.616718937684953 m, 7.69244938169326 s). Tp = 8.512617994344046 s\n",
      "Sea state 152/200. (Hs, Te) = (4.8696012734631715 m, 6.889172832566847 s). Tp = 7.623696135098361 s\n",
      "Sea state 153/200. (Hs, Te) = (3.1127279534654217 m, 5.146140131826839 s). Tp = 5.6948213649424595 s\n",
      "Sea state 154/200. (Hs, Te) = (2.3851224952288668 m, 4.573133253276174 s). Tp = 5.060720518358844 s\n",
      "Sea state 155/200. (Hs, Te) = (2.1405588081566735 m, 4.408339089583258 s). Tp = 4.87835600822587 s\n",
      "Sea state 156/200. (Hs, Te) = (1.4376356173488731 m, 3.9651074597519202 s). Tp = 4.3878670416369 s\n",
      "Sea state 157/200. (Hs, Te) = (1.0717947445845732 m, 3.797209276299676 s). Tp = 4.202067561295225 s\n",
      "Sea state 158/200. (Hs, Te) = (0.7714659563414245 m, 3.8906591392370555 s). Tp = 4.305481044483412 s\n",
      "Sea state 159/200. (Hs, Te) = (0.4675473016122803 m, 4.01891894221551 s). Tp = 4.447415901979359 s\n",
      "Sea state 160/200. (Hs, Te) = (0.22912012469457133 m, 4.117183864360642 s). Tp = 4.556157825774942 s\n",
      "Sea state 161/200. (Hs, Te) = (0.8868405022143149 m, 22.262946721786285 s). Tp = 24.636621116028003 s\n",
      "Sea state 162/200. (Hs, Te) = (3.3435032701311083 m, 23.466210376684526 s). Tp = 25.968176688561687 s\n",
      "Sea state 163/200. (Hs, Te) = (6.767304773171118 m, 23.562164689248302 s). Tp = 26.074361645684604 s\n",
      "Sea state 164/200. (Hs, Te) = (10.473453427011934 m, 21.667662928997025 s). Tp = 23.977868191595526 s\n",
      "Sea state 165/200. (Hs, Te) = (10.16869748664882 m, 20.88428094047033 s). Tp = 23.11096205012028 s\n",
      "Sea state 166/200. (Hs, Te) = (12.592753558411193 m, 18.630634795246163 s). Tp = 20.61703225262625 s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sea state 167/200. (Hs, Te) = (11.673289407430126 m, 16.196134650574198 s). Tp = 17.9229658102669 s\n",
      "Sea state 168/200. (Hs, Te) = (10.377818028562164 m, 12.9398233046888 s). Tp = 14.319466692789565 s\n",
      "Sea state 169/200. (Hs, Te) = (8.744961663025443 m, 10.648293741704611 s). Tp = 11.783614349210453 s\n",
      "Sea state 170/200. (Hs, Te) = (6.859939603914331 m, 8.737652031185462 s). Tp = 9.669260104070206 s\n",
      "Sea state 171/200. (Hs, Te) = (5.960600437241585 m, 8.015473102288261 s). Tp = 8.87008248973364 s\n",
      "Sea state 172/200. (Hs, Te) = (4.693969291807187 m, 6.476150181500641 s). Tp = 7.166637027253579 s\n",
      "Sea state 173/200. (Hs, Te) = (3.1828656644648836 m, 5.082363302518114 s). Tp = 5.624244652915248 s\n",
      "Sea state 174/200. (Hs, Te) = (2.7310116880872317 m, 4.561878698618447 s). Tp = 5.048266003581575 s\n",
      "Sea state 175/200. (Hs, Te) = (1.960761965106161 m, 3.9966122086956775 s). Tp = 4.422730825518815 s\n",
      "Sea state 176/200. (Hs, Te) = (1.745663077932535 m, 3.8392198892428673 s). Tp = 4.248557343931261 s\n",
      "Sea state 177/200. (Hs, Te) = (1.2054393707718805 m, 3.807490373589555 s). Tp = 4.213444828723166 s\n",
      "Sea state 178/200. (Hs, Te) = (0.7359796530337633 m, 3.6471354786545067 s). Tp = 4.035992901986565 s\n",
      "Sea state 179/200. (Hs, Te) = (0.6244103771848238 m, 3.805058507193115 s). Tp = 4.2107536768391585 s\n",
      "Sea state 180/200. (Hs, Te) = (0.1947445590996868 m, 4.049025575814537 s). Tp = 4.48073250352036 s\n",
      "Sea state 181/200. (Hs, Te) = (2.1148904507720747 m, 24.527579096070692 s). Tp = 27.142708493838484 s\n",
      "Sea state 182/200. (Hs, Te) = (5.047569685078679 m, 24.918912509361533 s). Tp = 27.575765858336137 s\n",
      "Sea state 183/200. (Hs, Te) = (6.420147613959171 m, 24.768168426047513 s). Tp = 27.40894944753028 s\n",
      "Sea state 184/200. (Hs, Te) = (9.266139293031646 m, 23.421568304895782 s). Tp = 25.91877488105444 s\n",
      "Sea state 185/200. (Hs, Te) = (12.41274872958178 m, 20.666780023774187 s). Tp = 22.870271195311382 s\n",
      "Sea state 186/200. (Hs, Te) = (12.957910005068928 m, 18.230023846708807 s). Tp = 20.17370818248467 s\n",
      "Sea state 187/200. (Hs, Te) = (12.2843220215601 m, 15.409966126505479 s). Tp = 17.052976032953453 s\n",
      "Sea state 188/200. (Hs, Te) = (10.519991169667653 m, 12.528633985612458 s). Tp = 13.864436386710274 s\n",
      "Sea state 189/200. (Hs, Te) = (9.450056877314221 m, 11.348957852297204 s). Tp = 12.5589832362669 s\n",
      "Sea state 190/200. (Hs, Te) = (8.160919020315601 m, 9.81433166497037 s). Tp = 10.860735272761364 s\n",
      "Sea state 191/200. (Hs, Te) = (5.587707137405241 m, 7.232570607296176 s). Tp = 8.00370696537244 s\n",
      "Sea state 192/200. (Hs, Te) = (4.8308252936215474 m, 6.3910826892692425 s). Tp = 7.072499642765086 s\n",
      "Sea state 193/200. (Hs, Te) = (3.9608488792601535 m, 5.641529187160236 s). Tp = 6.2430287794323185 s\n",
      "Sea state 194/200. (Hs, Te) = (2.8150456982143615 m, 4.58212563012944 s). Tp = 5.070671662034252 s\n",
      "Sea state 195/200. (Hs, Te) = (2.214607067395854 m, 4.085048800640139 s). Tp = 4.520596522982679 s\n",
      "Sea state 196/200. (Hs, Te) = (1.649511190726026 m, 3.713958023766156 s). Tp = 4.1099400639008214 s\n",
      "Sea state 197/200. (Hs, Te) = (0.9652307901744451 m, 3.456495828266831 s). Tp = 3.8250272605112143 s\n",
      "Sea state 198/200. (Hs, Te) = (0.6126122310648106 m, 3.484511462758769 s). Tp = 3.8560299207128654 s\n",
      "Sea state 199/200. (Hs, Te) = (0.468987733159132 m, 3.55586551038935 s). Tp = 3.9349917337440825 s\n",
      "Sea state 200/200. (Hs, Te) = (0.10978529871712417 m, 3.772910684185013 s). Tp = 4.175178254364618 s\n"
     ]
    }
   ],
   "source": [
    "# create the short-term extreme distribution for each sample sea state\n",
    "t_st = 3.0 * 60.0 * 60.0\n",
    "gamma = 3.3\n",
    "t_sim = 1.0 * 60.0 * 60.0\n",
    "\n",
    "ste_all = []\n",
    "i = 0\n",
    "n = len(sample_hs)\n",
    "for hs, te in zip(sample_hs, sample_te):\n",
    "    tp = te / (0.8255 + 0.03852 * gamma - 0.005537 * gamma**2 + 0.0003154 * gamma**3)\n",
    "    i += 1\n",
    "    print(f\"Sea state {i}/{n}. (Hs, Te) = ({hs} m, {te} s). Tp = {tp} s\")\n",
    "    # time & frequency arrays\n",
    "    df = 1.0 / t_sim\n",
    "    T_min = tp / 10.0  # s\n",
    "    f_max = 1.0 / T_min\n",
    "    Nf = int(f_max / df) + 1\n",
    "    time = np.linspace(0, t_sim, 2 * Nf + 1)\n",
    "    f = np.linspace(0.0, f_max, Nf)\n",
    "    # spectrum\n",
    "    S = resource.jonswap_spectrum(f, tp, hs, gamma)\n",
    "    # 1-hour elevation time-series\n",
    "    data = resource.surface_elevation(S, time).values.squeeze()\n",
    "    # 3-hour extreme distribution\n",
    "    ste = extreme.short_term_extreme(time, data, t_st, \"peaks_weibull_tail_fit\")\n",
    "    ste_all.append(ste)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Long-Term Extreme Distribution\n",
    "Finally we integrate the weighted short-term extreme distributions over the entire sea state space to obtain the extreme distribution, assuming a 3-hour sea state coherence. \n",
    "For this we use the `loads.extreme.full_seastate_long_term_extreme` function.\n",
    "The integral reduces to a sum over the 200 bins of the weighted short-term extreme distributions.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lte = extreme.full_seastate_long_term_extreme(ste_all, sample_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the short-term extreme functions, the output of long-term extreme function is a probability distribution (`scipy.stats.rv_continuous`). \n",
    "This object provides common statistical functions (PDF, CDF, PPF, etc.) and metrics (expected value, median, etc). \n",
    "Here, we will look at the survival function and the 100-year return level. \n",
    "\n",
    "The value of the survival function at a given return level (e.g. 100-years) \n",
    "may be calculated using the `return_year_value` function. This gives a 100-year wave of about 11.1 meters.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100-year elevation: 10.949955788933982 m\n"
     ]
    }
   ],
   "source": [
    "t_st_hr = t_st / (60.0 * 60.0)\n",
    "t_return_yr = 100.0\n",
    "x_t = extreme.return_year_value(lte.ppf, t_return_yr, t_st_hr)\n",
    "\n",
    "print(f\"100-year elevation: {x_t} m\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the survival function and show the 100-year return level (dashed grey lines). The 100-year value is about 11.1 m (where the grey line intersects the x-axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27ff4207fd0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEJCAYAAAC+I6F6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABHa0lEQVR4nO2deXxU5fX/3ycbYUdWgYAgm7IlLAIqm1ULKEpVVNBatSw/q6iobb92RdpabS1Qt8qiuFUBtW4gSlc2RQUCMRICIRBDQiSMBEIgJAxzfn/MBEMMZDL3Tu7MzfN+ve5rcp/cee7nnnmSM892jqgqBoPBYDCESozTAgwGg8EQ3RhHYjAYDAZLGEdiMBgMBksYR2IwGAwGSxhHYjAYDAZLGEdiMBgMBksYR2IwGAwGSxhHYjAYDAZLxDktIBREpDHwN6AcWK2qrzksyWAwGOotEdMjEZHFIlIoIl9WKR8rIjtEZJeIPBwovh54S1WnAdfWuViDwWAwnCJiHAnwEjC2coGIxALPAuOA3sBkEekNJAF7A5edrEONBoPBYKhCxDgSVV0LHKxSPATYpaq7VbUcWApMAPLwOxOIoGcwGAyG+kikz5F05NueB/gdyFDgKeAZEbkaWF7dG+MaNtG4Rs1PnTdo0ID4hARiRDjp8xETE8vJkyeJjY3l5EkvMbH+c3+5Dw1cpwonfUpFaMv4GKFRQizxMdC0YQKcPEGTxo04duwYjRp9+1paWkpiYiLl5eXExcUF6vb7PJ/PR2xsLF6vl4SEBI4fP07Dhg3PWEdZWRkJCQl4vV5iYmKoCLQZExOD1+slPj6e8vJyEhMTv1NHxevx48dJSEjgxIkTxMXF4fP5ABARfD4fcXFxlJeX06BBg2r1HD58mBYtWrjqmZz6nIqKimjevLmrnsnJz+ngwYO0atXKVc9U15/T0aNHKSwsREQoLS0tU9XEIP4/f/v/tjYXRwqqehS482zXJIiPY9/k23K/kz5le0Exn+7+hs/3HOTL/MPsO3yc44Hfd2jdmMvOO4eLurRkUJdzOL91Y0TElntHCgMGDGDLli1Oy3AFVm25b98+OnToYKOi6Ma0TXsRkVpPF0S6I8kHOlU6TwqU1SmxMULfjs3p27E5U0ecD8DBo+Vs23eY9PzDpH5VxL+27+fNzXkAtGycwJAuLblxcBKje7UlNib6nUrFNy6DdazactGiRcyaNcsmNdGPaZvOE+mOZCPQQ0S64ncgk4BbnJXkp2XjBEb0aMOIHm0A8PmU3Z4SNuUUsTGniDU7D/DRtq/p2roxPx/Ti7F9z3VdL8VgMBggghyJiCwBRgOtRSQPmKWqL4jIDGAVEAssVtVtwdTXqlWrsGmtjpgYoXvbpnRv25RJQzpz4qSPVdu+5sl/Z/GT11IZdn5LHr2uH93aNKlTXXZx++23Oy3BNRhb2ouxp+0cqO0bxK0ZEvv3769ffPGF0zLwnvSxdONe/vxRJsdP+PjJ6G78ZHQ3EuNjnZZWK3JycujSpYvTMlyBVVuuXr2a0aNH26Yn2jFt015EZLOqDq7Ve9zqSAYOHKipqalOyzjFgSNl/OGDDN7buo+urRvz6A/6ckn31k7LCpqSkhKaNInO3lSkYWxpL8ae9hKKI3HtHozy8nKnJZxGm6YNeHLSAF758RB8qtzy/Gc8uGwr35SUOS0tKPLy8pyW4Bqs2nLOnDk2KXEHpm06j2sdSYMGDZyWUC0je7Zh1cyRzLisO8u/2Mflc9ewbGMuPl9k9wy7d+/utATXYNWWJSUlNilxB6ZtOo9rHcnx48drvsghEuNj+emYXqy8bwQ92zbl//6Rzr1LtlDujdxljNu2BbXGwRAExpb2YuzpPK51JA0bNnRaQo30aNeUpdOH8fC4C/ggvYCpr2zi+InIDB2WnJzstATXYNWW7du3t0mJOzBt03lc60iOHTvmtISgiIkR7hrVjT/d0I91WQe4b8kWvCcjr2eyefNmpyW4Bqu2nD59uk1K3IFpm87jWkfSqFEjpyXUipsv6sxvx/fmnxn7+c1724i01XSDBg1yWoJrsGrL5curDS9XbzFt03lc60iipUdSmTsv7cpPRndjyee5vPhxjtNyTsN867MPq7aMpGXtkYBpm87jWkcSbT2SCn72/V5c2bsdj67czme7v3FazinMtz77MLa0F2NP53GtIyktLXVaQkjExAhzbkrmvJaNuOf1LXx9ODJWn6WnpzstwTUYW9qLsafzuNaRJCbWKpx+RNEsMZ4Ftw3iWLmXmcu2RMQek549ezotwTVYteWDDz5okxJ3YNqm87jWkUTazvba0qNdUx65pg+f7j7I8+t3Oy2H3NxcpyW4Bqu23Ldvn01K3IFpm84TlY5ERM4XkRdE5K0zXRMXFzGBjUPmxsFJjOnTjidW7SBjX7GjWtq1a+fo/d2EVVsuXbrUJiXuwLRN56lzRyIii0WkUES+rFI+VkR2iMguEXn4bHUEcrhPOds1J09G5sa+2iAiPHZ9f1o0SmDmsi2OblY8dOiQY/d2G8aW9mLs6TxO9EheAsZWLhCRWOBZYBzQG5gsIr1FpJ+IrKhytA3mJhW5j6Odlo0T+MuNyezcX8LjH2Y6piOa55wiDWNLezH2dJ46H/9R1bUi0qVK8RBgl6ruBhCRpcAEVX0MGF/HEiOOUT3bcMclXXjpkxxG9WrDZb2C8qUGlzJ+fL3/kzBEGJHytb0jsLfSeV6grFpEpJWIzAcGiMgvqrtm//79pKSkkJycTHJyMvPmzSM7O5vS0lIyMjLw+XynNnZVbGhKTU3F5/ORkZFBaWkp2dnZFBUVkZ+fT0FBAR6Ph5ycHEpKSsjMzMTr9ZKWlnZaHRWv6enplJWVkZWVRXFxMbm5uRQWFlJYWEhubi7FxcVkZWVRVlZ2avli1TrS0tLwer1kZmZy78hOnN+yAQ8t28K2Xbnk5+dTVFRUZ890/Phx25+ppKSEnJwcPB4PBQUFdf5M4ficgnmm0tJSS8/UuHHjiHsmJz+n/Px81z1TXX9O8+fPp2/fvgwcOBCg1omSHElsFeiRrFDVvoHzicBYVZ0aOL8NGKqqM0K9x4ABA3TLli12yI0Ydnx9hGufWc/F3Vrx4h0X1WkO+OLiYpo1a1Zn93MzVm05e/ZsZs2aZaOi6Ma0TXuJ5sRW+UCnSudJgbKQ8Xq9lgRFIr3Obcqvrr6Q1TsO8ML6PXV67/3799fp/dyMsaW9GHs6T6Q4ko1ADxHpKiIJwCTgfSsVJiQk2CIs0rht2HmM6dOOP67czuodhXV2386dO9fZvdyOsaW9GHs6jxPLf5cAG4BeIpInIlNU1QvMAFYB24E3VNVStppITmxlBRFh7k0p9Dq3Gfe+vqXO9pfs3LmzTu5TH7BqS7OT+3RM23QeR+ZI6oLBgwfrpk2bnJYRNvIPlTLxuU8o8/pYMm0Yvc5t6rQkg8HgAqJ5jsR2ojGMfG3o2KIhS6YNIz5WmLzoUzZ/VRTW+5lQ3fZh1ZZLliyxSYk7MG3TeVzrSKI1jHxt6NK6McumX0yzxDgmL/qU5Wnhi8FkQnXbh1VbmqGc0zFt03lc60jc3iOpoEvrxrx996X079ice5ds4ZH3t1HmtT+UivnWZx/GlvZi7Ok8rnUk9aFHUkHLxgm8Nm0od17q3/0+8bkN5HiO2noP863PPowt7cXY03lc60iiNbFVqDSIi2XWNX1YcNsgvvrmKOOeXMfi9Xtsy2VSsTvXYB2rtjSbEU/HtE3nca0jqa+B3Mb0OZdVD4xk2Pkt+d2KDG5asIHsAyWW6+3Tp48N6gxg3ZZmKOd0TNt0Htc6krKyMqclOEb75g1ZfMdFzLkxmZ37jzDur+t48t9ZluZOdu3aZaPC+o1VW65YscImJe7AtE3nca0jcevO9mAREW4YlMS/HxrF9/u0Y96/d3L1U+vZmHMwpPqSkpJsVlh/Mba0F2NP53GtI3FjrK1QaNs0kWduGciLd1xEaflJbpy/gV+8nc7h0hO1qsfj8YRJYf3D2NJejD2dx7WOxC2Jrezisgva8s8HRjJ1eFeWbczlirlr+OCLAoKNbNCkSZMwK6w/WLXlpEmTbFLiDkzbdB7X/rd1a+gXKzRuEMevx/fmvXuG065ZA+55PZUpL2+i8EjNcclOnKhdD8ZwZqzaskOHDjYpcQembTpPVDoSEfmBiCwSkWUi8n2n9UQb/ZKa8+7dl/Lrqy/kk2wP1zy9nq17D531PT6fr27E1QOs2nLu3Lk2KXEHpm06jxPRfxeLSKGIfFmlfKyI7BCRXSLy8NnqUNV3VXUacBdwc3XXmKGtsxMXG8PUEefz9k8uJT42hpsWbODdLWdOAVOfNniGG2NLezH2dB4n/tu+BIytXCAiscCzwDigNzBZRHqLSD8RWVHlqJyw/NeB930HM9keHL07NOP9GcMZ2LkFM5dtZfEZEmYdPBjaai/DdzG2tBdjT+eJq+sbquraQKrdygwBdqnqbgARWQpMUNXHgPFV6xB/jtnHgQ9VNbW6+8THx9uq2820bJzAyz8ewn1LtvC7FRkcLj3BzCt6nJbK14zL24dVWwbyahsCmLbpPJEy/tMR2FvpPC9QdibuBa4AJorIXdVdsG/fPlJSUkhOTiY5OZl58+aRnZ1NaWkpGRkZ+Hw+UlP9Pqhip3Bqaio+n4+MjAxKS0vJzs6mqKiI/Px8CgoK8Hg85OTkUFJSQmZmJl6v91R4hoo6Kl7T09MpKysjKyuL4uJicnNzKSwspLCwkNzcXIqLi8nKyqKsrIz09PRq60hLS8Pr9ZKZmUlJSQk5OTl4PB4KCgrIz8+nqKjItmc6cbyU+wY15oYBHXjyP1n86aMdVORz2bx5M3v27Im6Z4rUz2n37t2WnqlHjx4R90xOfk5paWmue6a6/pzmz59P3759K76ktKaWOJLYKtAjWaGqfQPnE4Gxqjo1cH4bMFRVZ4R6D7cntgoXqsqv3/2S1z7L5YErenL/FT0A/4SmmXeyB6u2XLhwIdOnT7dRUXRj2qa9RHNiq3ygU6XzpEBZyNSXMPJ2IyL8fkJfJg5KYt6/d7JgTTYAW7dudVaYi7Bqy4KCAnuEuATTNp2nzudIzsBGoIeIdMXvQCYBt1ip0KzkCJ2YGOFPN/Tn+ImTPPZhJonxsdx+iRmXtwszx2Evxp7O48Ty3yXABqCXiOSJyBRV9QIzgFXAduANVd1m5T6mR2KN2Bhh3s0pXNm7HbPe38YT/1jntCTXYDV6r9nJfTomGrLzODJHUheYORJ7KPOeZOrLm/h4l4dnbhnIVf3aOy3JYDCEkWieI7Ed0yOxhwZxsSy4bRA9WsZz/9It/G9HodOSop6KVTOhsnr1anuEuASr9jRYp1aOREQaBzYPRjxmjsQ+GiXEsezuUfRs15S7Xt3Mp7u/cVpSVJOSkmLp/WvWrLFHiEuwak+Ddc7qSEQkRkRuEZEPRKQQyAQKRCRDRJ4Qke51I7P2HD9ecyBCQ/Ds+yqbV348hKRzGjL15U2k1RCby3BmMjMznZbgKow9naemHsn/gG7AL4BzVbWTqrYFhgOfAn8SkR+GWWNI1PfEVnbTtWtXWjVpwGtTh3FO43huf/Fzdnx9xGlZUUnXrl2dluAqjD2dpyZHMk1Vf6+qX6jqqRCbqnpQVf+hqjcAy8IrMTRMaGl72bdvHwDnNk/ktSnDSIiN4YcvfEaO56jDyqKPCluGyrRp02xS4g6s2tNgnZocyRIAEfnPmS5Q1Yj8jx0XFylbZNxBy5YtT/3cuVUjXps6FO9JH7c+/xn7DpU6qCz6qGxLg3WMPZ2nJkcSIyK/BHqKyINVj7oQGComR4G9VF0F16NdU1758VCKS0/ww+c/w1NS5pCy6MPqisJFixbZpMQdmBWazlOTI5kEnMS/A75pNYehnlBdLKN+Sc1ZfOdF7Dtcym0vfM7Bo+UOKIs+TFwoezH2dJ6zjv+o6g78E+pfqOqHdaTJFiqHQDdY50xh+S/q0pKFtw1m2iubmLRwA3+fOpS2TRPrWF10YVIc2Iuxp/PUtPy3YgjrQjO0Vb8pKSk54+9G9mzDi3dcRF5RKTcv+NTMmdTA2WwZDKNGjbJJiTuwak+DdWrqE1YMYQ0GfoI/R0hH/CluIzpSmplst5fWrc+eouCS7q15dcoQPEfKuHH+Br76xqzmOhM12bImRo8ebY8Ql2DVngbrnNWRqOpsVZ2NP6z7QFV9SFUfAgYBnetCYKiUl5vxejvJy8ur8ZpB57Xk9WnDOFru5aYFG8j8urgOlEUfwdjybMyZM8cmJe7Aqj0N1gl2lqodUPk/c3mgzBFE5EIRmS8ib4nIT6q7pkGDBnUty9V07x5cEIN+Sc1ZNv1iAG58bgMbsk04laoEa8szYYZyTseqPQ3WCdaRvAJ8LiKPiMgjwGfAy6HcUEQWi0ihiHxZpXysiOwQkV0i8vDZ6lDV7ap6F3ATcGl115gQKfaybVvwUf17nduUt+++lHObJ3L74s9ZnmY2jFWmNrY01Iyxp/ME5UhU9VHgTqAocNypqn8M8Z4vAWMrFwQCQT4LjAN6A5NFpLeI9BORFVWOtoH3XAt8AKys7iYNGzYMUZ6hOpKTk2t1fccWDXnrrktI6dSCe5ds4fl1u8OkLPqorS2r0r69CeVfGav2NFgnKEciIsOALFV9UlWfBLJFZGgoN1TVtcDBKsVDgF2qultVy4GlwARVTVfV8VWOwkA976vqOODW6u5jNinZSyjJg5o3iueVKUO4qt+5/OGD7fx+RQY+nzvz39QGq4mYTL720zGJrZwn2KGt54DKA7MlgTK76AjsrXSeFyirFhEZLSJPicgCztAjOXz4MCkpKSQnJ5OcnMy8efPIzs6mtLSUjIwMfD7fqTwGFQ0xNTUVn89HRkYGpaWlZGdnU1RURH5+PgUFBXg8HnJycigpKSEzMxOv10taWtppdVS8pqenU1ZWRlZWFsXFxeTm5lJYWEhhYSG5ubkUFxeTlZVFWVkZ6enp1daRlpaG1+slMzOTkpIScnJy8Hg8FBQUkJ+fT1FRUZ0906BBg0J6pm1fbOXpyQO5qnsjXli/h8nP/pfDR49HxDM59TkNGDDA0jO9+uqrEfdMTn5Obdq0cd0z1fXnNH/+fPr27VuRtrjWy+CCypAoIltVNaVK2Req2r+2Nwy8twuwQlX7Bs4nAmNVdWrg/DZgqKrOCKV+gN69e2tGRkaobzdUocKZhIqq8sqGr5i9fBu9zm3G87cPpmOL+jn8aNWWs2fPZtasWTYqim6s2tNwOuHMkLhbRO4TkfjAcT9g56B3PtCp0nlSoCxkTGIre7H6hyoi3H5JF168cwh5B48x4ZmPSc0tsklddGH+6dmLsafzBOtI7gIuwf/PPQ8YCtg5ULsR6CEiXUUkAX+Mr/etVFhaanZX20lF19oqo3q24Z17LqFRQiyTFn7Ke1stfV+ISuyypcGPsafzBDW0ZesNRZYAo/GPw+0HZqnqCyJyFfBXIBZYHFgpFjKDBg1SMwlnH2VlZbbuzSk6Ws5df9/MZ3sOcs9l3Xjwyl7ExtSP+GhWbXnkyBGaNjUxUyuwu23Wd8I5tFX5Jqm1fU9lVHWyqrZX1XhVTVLVFwLlK1W1p6p2s+pEwOxst5vc3Fxb6zuncQKvThnKpIs68ez/srnjxc8pqifRg63a0iRyOh2726ah9oQSfzkqvjaaWFv20q6d/YEMEuJiePyG/jx+fT8+232Q8U+v54u8Q7bfJ9KwasulS5fapMQdhKNtGmpHKI7kA9tVhIGTJ086LcFVHDp0KGx1TxrSmbd+4g+rMvG5DSz93N3fMMNpy/qIsafz1NqRqOqvwyHEbkyyG3tJTAxvjpH+SS1Yfu9whp7fkoffTufBZVspKfOG9Z5OEW5b1jeMPZ0n5P+2ImKWShhspWXjBF66cwj3X96Dd7fmc/VT69i695DTsiKO8ePHOy3BYDiNs04kiMj1Z/oVcK79cuzDJLayl7oKghkbIzxwZU+G92jNzKVbmfjcJzxwZU/uGtXNNau6rNrS7Js4HROg1Xlq6pEsA64FrqlyjAciuj8ZGxvrtARX0aJFizq930VdWrLy/hGM6XsuT6zawS2LPiX3G3fET7Nqy9mzZ9sjxCXUdds0fJealjZ9AfxFVb+s+gsRuSI8kuzB63Xn+LpT7N+/n2bNmtXpPZs3jOeZyQMY1bMNv1+ewZi/ruXnY3tx+8VdiIni3okTtnQzxp7OU1OPZCZwpjR319krxV4SEhKcluAqOnd2JiGmiHDT4E6semAkQ89vyezlGdy8cAN7PNGbytcpW7oVY0/nqSnV7jpVrXYtpqpuCo8kezDjpvayc+dOR+/foUVDXrzjIv5yYzI7vj7C2L+u5bnV2ZR7o28uzKote/bsaZMSd+B02zTUECJFRH4N/E1Vq+YPqfj994BGqroiTPpCZvDgwbppU0T7OkOI7C8+zm/e/ZJ/ZuynR9sm/P4HfRl2fiunZRkMriAcIVLSgeUi8h8ReUJEfi4ivxWRVwPLf6/Bn3Y34jCJrewlkuKWtWuWyMIfDeb5Hw2m9MRJJi38lAeXbeXAkTKnpQWFVVsuWbLEJiXuIJLaZn0l2HwkPfDnRm8PlALbgbWqGrEhdk2PpH5QWn6SZ/+3iwVrs0mMj+VnY3px69DzXLNUuDpMPhJDOAlb0EZVzVLVl1T1MVX9q6quctqJiEhjEdkkItXuzjI9EnuJ1G99DRNi+emYXnw0cyT9k5rz2/e2ce0z69n8VbWjsRFBpNoyWjH2dJ46jyMiIotFpFBEvqxSPlZEdojILhF5OIiq/g9440y/NImt7CXSN8F1a9OEv08ZyjO3DOCbknJueG4DD72RRuGRyFt0Eem2jDaMPZ3HiXwkI/HnfH+lUqrdWGAncCX+xFkbgcn4c5M8VqWKHwPJQCv8myI91U329+nTR7dt2xaux6h3pKWlkZyc7LSMoDha5uXZ/+1i0brdJMbFMvPKnvzo4vOIj42M+GvRZMtowNjTXkIZ2qpzRwLV5my/GHhEVccEzn8BoKpVnUjF+x8FGgO98c/ZXKeqp60DNYmt7MXr9UZdaP7dB0p4ZHkGa3ceoFe7pjxybR8u7ub86i6rtjQ5yk8nGttmJBO2ORIRaSMivxSRhYGhqcUisjg0mdXSEdhb6TwvUFYtqvorVZ0JvA4squpEwJ/8JyUlheTkZJKTk5k3bx7Z2dmUlpaSkZGBz+cjNdWfo6vC4aSmpuLz+cjIyKC0tJTs7GyKiorIz8+noKAAj8dDTk4OJSUlZGZm4vV6SUtLO62Oitf09HTKysrIysqiuLiY3NxcCgsLKSwsJDc3l+LiYrKysigrKzuVKrRqHWlpaXi9XjIzMykpKSEnJwePx0NBQQH5+fkUFRXV2TPt2rUr6p6pbUP4xcVNee7WARSVHGPyok+59dn/UHC41NHPKSsry9LntGLFinrV9mp6ps2bN7vumer6c5o/fz59+/Zl4MCB4M9eWyuCXbX1CbAO2AycSvShqv+o7Q0D9XXh9B7JRGCsqk4NnN8GDFXVGaHUDzBw4ECtMJjBOiUlJTRp0sRpGSFz/MRJ5q/J5rnV2cTGCPd+rwdThnclIa7uh7us2tKs2jqdaG+bkUYoPZJg+4ONVPX/QtAULPlAp0rnSYGykDGxtuzF4/FE9R9rYnwsM6/oyQ0Dk/jdigz+9FEmb27ay6xr+zCqZ5s61RLttow0jD2dJ9ivYytE5Kow6tgI9BCRriKSAEwC3rdSoUlsZS9u+UPt1LIRi340mBfvvAgFbl/8OdNf2cTeg3W3XNyqLSdNmmSTEnfglrYZzQT73/Z+/M7kuIgcCRxnCuZ4VkRkCbAB6CUieSIyRVW9wAxgFf7Njm+oqqUlV04sInAzJ06ccFqCrVzWqy0fzRzBz8f2Yl2WhyvmruHJf2dx/ET4UzRbtWWHDh1sUuIO3NY2o5FgNyQ2VdUYVU0M/NxUVUOK26yqk1W1varGq2qSqr4QKF+pqj1VtZuqPhpK3Ybw4cZEYQ3iYrl7dHf+89Aorujdjnn/3smV89bwr4z9Yf0iYtWWc+fOtUmJO3Bj24w2gh7/EZFrReQvgSPic32aoS17cfMGzw4tGvLsLQN5fepQEuNimfbKJu58aWPYQtW72ZZOYOzpPMEu/30c//BWRuC4X0Sq3eMRKZjJdns5eDByQ47YxSXdW7Py/hH8+uoL2ZRTxJh5a3liVSbHyu1tS/XBlnWJsafzBPu1/SrgSlVdrKqLgbHA1eGTZZ34+HinJbiK+jIuHx8bw9QR5/Pfh0Yxvn97nv1fNpfPWcMHXxTYNtxl1ZaBtf6GAPWlbUYytRn/aVHp5+Y267Cd8vJypyW4ij179jgtoU5p2yyRuTen8OZdF9OiUQL3vJ7KD1/4jKz9RyzXbdWW11xzjWUNbqK+tc1IJFhH8hiwRUReEpGX8W9MjOgJ8cTERKcluIoLLrjAaQmOcFGXlqy4dzi/n9CH9LzDjHtyHY9+kMGR46GvFLJqy4ULF1p6v9uor20zkgh21dYSYBjwNvAP4GJVXRZOYVYxYeTtZevWrU5LcIzYGOG2i7vwv5+OZuKgJJ5fv4fvzVnDO1vyQhrusmrLgoICS+93G/W5bUYKZ3UkInJB4HUg/qRWeYGjQ6AsYjErOezFjMtDqyYNePyG/rxz96V0aJ7IA8vSuGnBBjL21W5LlbGlvRh7Ok9NPZIHA69zqjn+EkZdljE9EnsxkZS/JaVTC965+1L+dEM/sg8c5Zpn1vOnjzKD3sxo1ZZmJ/fpmLbpPMEGbUxU1eM1lUUSJtWuoS44dKycRz/Yzpub8+jSqhF/vL4fl3SrdfBUgyFiCFsYeeCTIMsiBtMjsRcTSbl6WjRK4Ikbk3lt6lAUuGXRZ/z8rTQOHzvzZLxVW65evdrS+92GaZvOU9McybkiMghoKCIDRGRg4BgNRPQkhJkjsZeUlBSnJUQ0l3ZvzUf3j+SuUd34R2o+l89dw4ov9lU7GW/VlmvWrLH0frdh2qbz1NQjGYN/LiSJ0+dHHgB+GV5p1jh+PGJH3aKSzMxMpyVEPA0TYnl43AW8d8+ltG+eyIzXtzDtlU3sO1R62nXGlvZi7Ok8Z3Ukqvqyql4G3KGq31PVywLHBFV9u440fgcRGS0i60RkfqB39B0SEhLqVpTL6dq1q9MSooa+HZvzzt2X8KurLmT9Lg9Xzl3DKxty8Pn8vRNjS3sx9nSeYOdIBolIi4oTETlHRP4Qyg0DaXoLReTLKuVjRWSHiOwSkYdrqEaBEiAR/3Lk72BCS9vLvn37nJYQVcTFxjBt5Pn8c+YoBp53Dr99bxs3LtjArsIjlm05bdo0m1S6A9M2nSdYRzJOVQ9VnKhqEf74W6HwEv5YXacQkVjgWWAc0BuYLCK9RaSfiKyocrQF1qnqOOD/gNnV3SQuLtjkj4ZgaNmypdMSopLOrRrxyo+HMPemZLIPlHDVk+t5Z8cxvCdN6HO7MG3TeYJ1JLEi0qDiREQaAg3Ocv0ZUdW1QNVwnUOAXaq6W1XLgaXABFVNV9XxVY5CVa34Kyw6kw6To8BezCq40BERrh+YxL8eGMUVvdvyzNpcfvC3j2u9kbGCRYsW2awwujFt03mCdSSvAf8RkSkiMgX4F/CyjTo6AnsrnecFyqpFRK4XkQXAq8Az1V1z4MABUlJSSE5OJjk5mXnz5pGdnU1paSkZGRn4fL5TywYrNjSlpqbi8/nIyMigtLSU7OxsioqKyM/Pp6CgAI/HQ05ODiUlJWRmZuL1eklLSzutjorX9PR0ysrKyMrKori4mNzcXAoLCyksLCQ3N5fi4mKysrIoKysjPT292jrS0tLwer1kZmZSUlJCTk4OHo+HgoIC8vPzKSoqqrNniomJcd0z1fXn1CTOx4wBDfnDuC7s9ZRw7TPr+dkrqyn3+mr1TEDEPFMkfE7ffPON656prj+n+fPn07dv34ooAbXeCBXUhkQAERkHXB44/ZeqrqrtzSrV1QVYoap9A+cTgbGqOjVwfhswVFVnhHqPlJQUNTF47MPj8dC6tdloZwcej4fYhs343YoM3tmST692TXnixv70T2oR1Ptnz57NrFmzwisyijBt017CuSERVf1QVX8aOEJ2ImcgH+hU6TwpUBYyZmjLXiq+CRusU1JSwjmNE5h3cwov3D6YQ6Xl/ODZj3n8w+DCrIwaNaoOVEYPpm06T7AZEq8XkSwROSwixSJyRERCG+Ctno1ADxHpKiIJwCTgfSsVmsl2ezHf+Oyjsi0vv7Ad/3xgFDcO6sT8Ndlc9dQ6Nn919ox/o0ePDrPC6MK0TecJtkfyZ+BaVW2uqs1UtamqNgvlhiKyBNgA9BKRPBGZoqpeYAawCtgOvKGq20KpvwKT2Mpe8vKqXWVtCIGqtmzeMJ4/TezPq1OGUHbCx8T5G5i9fNsZU/zOmTOnLmRGDaZtOk+wX9v3q+p2O26oqpPPUL4SWGnHPQAaNAhpUZnhDHTv3t1pCa7hTLYc0aMNqx4YyZ8/yuTFj3P4z/ZCHr/hu0EgzVDO6Zi26TzB9kg2icgyEZkcGOa6XkSuD6syi5gQKfaybZulDqKhEmezZZMGcfxuQl+WTR+GiD8I5K/eSbeUkdHtmLbpPME6kmbAMeD7wDWBY3y4RNlBw4YNnZbgKpKTk52W4BqCseXQ81vx0f0jmTq8K69/nsuYeWtZvaMQgPbt24dbYlRh2qbzBL38N9ro3bu3ZmRkOC3DNWzevJlBgwY5LcMV1NaWqblF/PytL9hVWMLEQUn85ureNG8UH0aF0YVpm/YSyvLfYBNbvYg/vtVpqOqPa3OzusQktjK4ieMnTvL0f7OYv2Y3oxNzmTzxOq7o3c5pWQYXEoojCXayfUWlnxOB64CIjpR28OBBZs/+NgxXRaC7yuElRo0axejRo5kzZ86pCcz27dszffp0li9fflrCnAcffJB9+/axdOnSU2Xjx49n0KBBp92nZ8+eTJ48mSVLlrBz585T5bNmzWLz5s2sWPGtKSdNmkSHDh2YO3fuqbKBAwdyzTXXsHDhQgoKCgB/atWHHnqI1atXn5aLoi6fqWfPnuzcudNVzxRtn9OxT5fxowaAwp9f+5CV/VNonvleVD+TGz+naH+mUAhpaEtEYoD1qnpJSHetA0yPxOBWZs+eTZOLJ/G31dm0apzAH6/rZ3onBtsI6872KvQA2ob43jqhtLS05osMQVMR68dgHTts+dD3e/HePZfSsnECU1/ZxIPLtp41va+bMW3TeYKdIznC6XMkXwO/UNV/hEuYVQYNGqQVQckM1ikrKzN7c2zCqi2PHDlC06ZNASj3+nj6v1mneiePXd+Pyy+sX70T0zbtxfYeiYhcGvixTWBHe8XRM5KdCJid7XaTm5vrtATXYNWWlRM5JcTFnNY7mfLyJh58o371TkzbdJ6ahraeCrx+Em4hdmNibdlLu3b161tuOLFqy8qTuRX07dic92cM597vdee9rfu4ct4a/rN9v6X7RAumbTpPTY7khIgsBJJE5KmqR10IDJWTJ2uOomoInkOHDjktwTWEy5b1tXdi2qbz1PS1fTxwBTAGiJgJh8Cqsd/j33G/SVW/k2QrJibUdQSG6khMTHRagmsIty0reicVcyfrszyunjsxbdN5zvrfVlU9qroUf+Tfl6seodxQRBaLSKGIfFmlfKyI7BCRXSLycA3VTMCfs+QE/myKBkO9Yfz4mqMT1dfeicEZgvrarqppNt7zJWBs5QIRiQWeBcYBvYHJItJbRPqJyIoqR1ugF/CJqj4I/KS6m5jEVvZigmDah1Vb1iYcSH2YOzFt03nqfPxHVdcCVTP3DAF2qepuVS0HlgITVDVdVcdXOQrx90KKAu+tdjIkNjY2XI9QL2nRooXTElyDVVtW3iUdDG7vnZi26TyRMpHQEdhb6TwvUHYm3gbGiMjTwNrqLvj6669JSUkhOTmZ5ORk5s2bR3Z2NqWlpWRkZODz+U6FN6jYb5KamorP5yMjI4PS0lKys7MpKioiPz+fgoICPB4POTk5lJSUkJmZidfrJS0t7bQ6Kl7T09MpKysjKyuL4uJicnNzKSwspLCwkNzcXIqLi8nKyqKsrOzUhqqqdaSlpeH1esnMzKSkpIScnBw8Hg8FBQXk5+dTVFRUZ8+0f/9+1z2TU5/T119/bemZgJCeqW18Gc9O6Mz0Szvx7pZ8rpy3hsUffR71n9OOHTtM27P4TPPnz6dv374MHDgQoNYpJ8+6IVFEHjzbm1V17tl+f5Z6uwArVLVv4HwiMFZVpwbObwOGquqMUOoHsyHRbsymL/uwasvZs2cza9YsSxq+zD/MT99MI/PrI1w/sCOzrulD84bRGVHYtE17CUeIlKY1HHaRD3SqdJ4UKAsZM25qL5WDyxmsYdWWPXv2tKyh6tzJ2L+uZV3WAcv1OoFpm87jSD6SanokccBO4HL8DmQjcIuVvO0maKPBEBxpew/x4BtbyT5wlB9dfB4Pj7uARglmQ299JWxBG0UkUUTuEZG/BZbvLhaRxSGKXAJsAHqJSJ6ITFFVLzADWAVsB96w4kQAjh07ZuXthiqYYUL7sGrLJUuW2KTET3KnFnxw3wimDO/Kq59+xVVPrmPzV1XXw0Qupm06T7CT7a8C5+LfmLgG/9DTkVBuqKqTVbW9qsarapKqvhAoXxmI4dVNVR8Npe7KNGrUyGoVhkqYDHT2YdWW4RjKSYyP5Tfje/P61GGcOKncOH8Df/ookzJv5EeIMG3TeYJ1JN1V9TfA0cBGxKuBoeGTZR3TI7EX863PPiLZlhd3a8VHM0dw46BOPLc6mwnPfEzGvmKnZZ2VSLZnfSFYR1Kx4PyQiPQFmhPh+UhMj8RezLc++4h0WzZNjOdPE/vzwu2D8ZSUM+HZ9Tz7v114T0bmJt9It2d9IFhHslBEzgF+A7wPZAB/CpsqGzCJreylYi28wTpWbWl16W+wXH5hO/75wEi+3/tcnli1gxsXbGD3gZI6uXdtMG3TeYJNbBWrqpE/WFoJs4/EXrxerwnNbxNWbbl58+Y6/Rauqryfto/fvreNMu9JfjHuQm4bdh4xMVJnGs6GaZv2Es5Uu3tEZKGIXC4ikdF6aqCsrMxpCa5i165dTktwDVZtuWLFCpuUBIeIMCGlI/98YCRDu7Zi1vvbuG3xZ+w7FBm9ftM2nSdYR3IB8G/gHiBHRJ4RkeHhk2WdhIQEpyW4iqSkJKcluIZotWW7Zom8dOdF/PG6fmzJPcSYeWt5a3MeTuxFq0y02tNNBBv995iqvqGq1wMp+POArAmnMKt4vV6nJbgKj8fjtATXEM22FBFuGdqZj+4fyYXtm/HTN9OY/upmPCXOjQBEsz3dQtBBG0VklIj8DX+Cq0TgprCpsgGT2MpemjRp4rQE12DVlpMmTbJJSeh0btWIJdOH8aurLmTNzgOMmbeWf2c4E57etE3nCXZnew4wE1gH9FPVm1T1H2HUZRmnu9tu48QJd4QcjwSs2rJDhw42KbFGbIwwbeT5LJ8xnLbNEpn6yiZ+8fYXHC2r29EA0zadJ9iv7f1V9TpVXaKqR8OqyBCRmERh9mHVlnPnhhR0O2z0Orcp795zCf9v1Pks3biXq55ax+avimp+o02Ytuk8Z10zJyI/V9U/A4+KyHe+4qvqfWFTZhEztGUvZoOnfbjRlg3iYvnFuAv5Xq+2PPhGGjfO/4QZl3Xn3st7EB8b3r9FN9oz2qjpE94eeN2Ef26k6hGxmMl2ezl4MHqC+EU6brbl0PNb8eHMEVw3IImn/ruLG577hOwwb2J0sz2jhbP2SFR1eeDHdFVNrQM9QSEiI4Bb8evvraqXVL0mPj46k/REKpEyLu8GrNoykMUuYmmWGM+cm5K54sK2/OKddK5+ah2/uupCfjjsPMKxDc20TecJts85R0S2i8jvA7G2QiYQgr5QRL6sUj5WRHaIyC4RefhsdajqOlW9C1gBvFzdNeXl5VZkGqqwZ88epyW4Bqu2vOaaa2xSEl7G9WvPqpkjGdK1Fb95bxt3vLiRwmL7E86Ztuk8we4juQy4DDgALBCRdBH5dYj3fAkYW7lARGKBZ4FxQG9gsoj0FpF+IrKiylE5WOQtwOvV3SQxMTFEeYbquOCCC5yW4Bqs2nLhwoU2KQk/7Zol8vKdF/G7CX34dPc3jPnrWj76ssDWe5i26TxBz4Kp6teq+hRwF7AV+G0oN1TVtUDVQc0hwC5V3a2q5cBSYIKqpqvq+CpHIYCIdAYOq2q1eVFMGHl72bp1q9MSXINVWxYU2PuPONyICD+6uAsf3DeCpHMacdffU/npm2kcOW7Psl3TNp0n2H0kF4rIIyKSDjwNfII/uZVddAT2VjrPC5SdjSnAi2f65eHDh0lJSSE5OZnk5GTmzZtHdnY2paWlZGRk4PP5SE31T/tUBHdMTU3F5/ORkZFBaWkp2dnZFBUVkZ+fT0FBAR6Ph5ycHEpKSsjMzMTr9Z6KPFpRR8Vreno6ZWVlZGVlUVxcTG5uLoWFhRQWFpKbm0txcTFZWVmUlZWRnp5ebR1paWl4vV4yMzMpKSkhJycHj8dDQUEB+fn5FBUV1dkzDRw40HXP5NTnlJKSYumZgIh7pmA+JzmynxcmX8jtF7Xj7dQ8xsxdw/sbtln+nFq3bm3ansVnmj9/Pn379q2Yf2tNLQk2+u8G/L2EN1V1X21vUk19XTg9Z/tEYKyqTg2c3wYMVdUZod6jd+/empGRYVWqIUBdR5x1M1ZtOWfOHB566CEbFdU9m78q4sE3tpJ78Bh3jerGA1f0JCEutGXCpm3aSyjRf2t0JIH5i1dV9RYr4qrU2YXTHcnFwCOqOiZw/gsAVX0s1HsMHjxYN23aZINag8EQDo6Wefn9igyWbtxL7/bN+OukFHq2a+q0rHpPWMLIB/KQdBKRcIbT3Qj0EJGugftMwp9AK2TMHIm9VHSHDdaxasvVq1fbI8RhGjeI4/Eb+rPoR4PZX3yc8U+vZ/H6Pfh8tQtvZNqm8wSdjwT4WER+IyIPVhyh3FBElgAbgF4ikiciU1TVC8wAVuHfBPmGqm4Lpf4KzG5Xe0lJSXFagmuwass1ayI68HatubJ3Oz6aOZIR3VvzuxUZ/Gjx5xQcDj7XiWmbzhOsI8nGv2cjBmha6ag1qjpZVduraryqJqnqC4HylaraU1W7qeqjodRdmePH7V+vXp/JzMx0WoJrMLb8Lm2aNuD52wfzx+v6sfmrIsbMW8vytOCmY409nSeo/JSqOjvcQuzGJLayl65duzotwTUYW1ZPRa6TS7q14oE3tnLvki38N7OQ2RP60CzxzJEqjD2dJ9jlv/8Tkf9WPcItzgomtLS97NtnebGeIYBVW06bNs0mJZFJl9aNefP/XczMK3rwfto+xv11HRtzzhxPy7RN5wmqRwL8tNLPicANQERHRYyLC/bRDMHQsmVLpyW4BmPLmomLjWHmFT0Z0aMNDyzbys0LNnDPZd25r5powsaezhNsiJTNlY6PVfVBYHR4pVnD5CiwF7MKzj6s2nLRokU2KYl8Bp13DivvH8H1A5N4+r+7mDh/A3s8p6dEMm3TeYId2mpZ6WgtImOA5mHWZoggTH4X+zC2rB1NGsTxlxuT+dutA8nxHOXqp9ax9PPcU1lQjT2dJ9jxn82AAoJ/SGsP/hAlEUs4wlXXZ0xYfvswtgyNq/q1Z0DnFjz0RhoPv53OfzMLefyG/saeEUCwQ1tdVfX8wGsPVf2+qq4PtzgrmKEte6mI8WSwjlVbjho1yiYl0Uf75g35+5Sh/OqqC1m94wBj/7qW/23/2mlZ9Z5gh7ZuFJGmgZ9/LSJvi0hEZ9cxk+320rp1reO4Gc6AVVuOHj3aHiFRSkyMMG3k+bx7z6U0bxjPzz74itnLt3H8xEmnpdVbgh1c/I2qHhGR4cAVwAvAc+GTZR2T2Mpe8vLynJbgGqzacs6cOTYpiW56d2jG8nuHc+2FzXnx4xwmPPMxmV8XOy2rXhKsI6lw9VcDC1X1AyCid/w1aNDAaQmuonv37k5LcA1WbWmGGb8lMT6WubcO48U7L+Kbo+Vc+/THPL9ud63jdRmsEawjyReRBcDNwEoRaVCL9zqCCZFiL9u2WQp9ZqiEsaW9bNu2jct6tWXVzBGM7NmGP3ywndtf/Jz9YUjra6ieYJ3BTfgDKo5R1UNAS+Bn4RJlBw0bNnRagqtITk52WoJrsGrL9u3b26TEHVTYs1WTBiz60SAeva4vG3MOhiWtr6F6gl21dUxV31bVrMB5gar+M7zSzoyIdBaRd0VksYg8XN01ZpOSvVRkUzNYx6otp0+fbpMSd1DZniLCrUPP44P7RtApkNb352+lcbQsogNxRD11PjwV+OdfKCJfVikfKyI7RGTXmZxDJfoBb6nqj4EB1V1gwsjbi8lAZx9Wbbl8+XKblLiD6uzZrU0T/vGTS7h7dDfe3JzHVU+tY0tukQPq6gdOzHO8BIytXBDIwvgsMA7oDUwWkd4i0k9EVlQ52gKfAlMCgSM/qu4mpkdiL6ZHYh9WbWkSOZ3OmeyZEBfDz8dewNJpw/CeVCbO38BT/8nCe9LsMbObOnckqroWqBrKcwiwS1V3q2o5/vzwE1Q1XVXHVzkKgTuBWar6Pfwryb6D6ZHYi+mR2Iexpb3UZM+h57di5f0jGN+/PXP/tZObF37K3oPmi6adRMrKq47A3krneYGyM/ERcJ+IzAdyqrsgLy+PlJQUkpOTSU5OZt68eWRnZ1NaWkpGRgY+n+/UN7uKbzSpqan4fD4yMjIoLS0lOzuboqIi8vPzKSgowOPxkJOTQ0lJCZmZmXi9XtLS0k6ro+I1PT2dsrIysrKyKC4uJjc3l8LCQgoLC8nNzaW4uJisrCzKyspIT0+vto60tDS8Xi+ZmZmUlJSQk5ODx+OhoKCA/Px8ioqK6uyZ0tPTXfdMTn1OX3zxhaVnAiLumZz8nD755JMan6l5w3h+fGEMcyb2Y/u+w4z961oWrNrKgQMHIvKZ6vpzmj9/Pn379mXgwIEAtd4xKxWBz+oSEekCrFDVvoHzicBYVZ0aOL8NGKqqM0K9x6BBg9QMx9hHWVmZ2ZtjE1ZteeTIEZo2DSlBqSuprT33HjzGQ2+k8XnOQa7u354//qAfzRuZeF0ViMhmVR1cm/dESo8kH+hU6TwpUBYyZme7veTm5jotwTVYtaVJ5HQ6tbVnp5aNWDJ9GD8b04tVX37N2CfX8km2J0zq6geR4kg2Aj1EpKuIJACTgPetVGhibdlLu3btnJbgGqzacunSpTYpcQeh2DM2Rrjnsu68ffclNIyP5dbnP+OxD7dT7jUT8aHgxPLfJcAGoJeI5InIFFX1AjPwb3rcDryhqpa2/548aQK42cmhQ4ecluAajC3txYo9+ye1YMV9w5l0UWcWrNnNdX/7mF2FR+wTV09wYtXWZFVtr6rxqpqkqi8Eyleqak9V7aaqj1q9j0l2Yy+JiYlOS3ANxpb2YtWejRLieOz6fiy8bRAFh49z9VPr+funX+HE/HG0Yv7bGgxRxvjx452W4Eq+3+dcPrp/BEO6tuTX737J9Fc3c/ComWsNBtc6EpPYyl5MEEz7sGpLsw/ldOxsm22bJfLynUP49dUXsiaQOGt9lpmIrwnXOpLY2FinJbiKFi1aOC3BNVi15ezZs+0R4hLsbpsxMcLUEefzzj2X0DQxjh++8BmPrTQT8WfDtY7E6zVB2uxk//79TktwDcaW9hIue/bp0JwV947glqGdWbB2N9c/9zHZB0wumOpwrSNJSIjovFtRR+fOnZ2W4BqMLe0lnPZsmBDLH6/rx4LbBpFXVMr4p9az9PNcMxFfBdc6EjOmby87d+50WoJrsGrLnj172qTEHdRF2xzT51w+un8kAzq34OG307n7tVQOHTMT8RU4EiKlLhg8eLBu2rTJaRkGg8FF+HzKwnW7+cuqHbRp2oC5N6VwcbdWTsuylWgOkWI7Joy8vZi4ZfZh1ZZLliyxSYk7qMu2GRMj3DWqG+/cfSmJ8bHc8vynPLEqkxP1PDS9ax2JCSNvL2bJqX1YtaUZZjwdJ9pmv6TmrLh3ODcN6sSz/8tm4vwN5HiO1rmOSMG1jsT0SOzF9Ejsw9jSXpyyZ+MGcfxpYn/+dutA9hwo4eqn1vHW5rx6ORHvWkdieiT2Ynok9mFsaS9O2/Oqfu35aOZI+nRszk/fTOPeJVs4XHrCUU11jWsdSWlpqdMSXEVFch6DdazactasWTYpcQeR0DY7tGjIkmn+0PQffvk1Vz25jo05VRPBupeodCSBfO5viMhzgaRY38EExrOXPn36OC3BNVi1pRkaO51IaZsVoenfuutiYmOEmxdsYO6/dtaLHPFOhJFfLCKFIvJllfKxIrJDRHaJyMM1VDMOeFpVfwL8qLoLysrKbFJsANi1a5fTElyDVVuuWLHCJiXuINLa5oDO5/DBfcP5wYCOPPWfLG5asMH1OeKd6JG8BIytXCAiscCz+B1Eb2ByoNfRT0RWVDnaAq8Ck0TkCaDaRdxmZ7u9JCUlOS3BNRhb2ksk2rNpYjxzb0rhyUkpZO0v4aon1/HeVktJXyOaOk8jqKprAznbKzME2KWquwFEZCkwQVUfA84UM/uegAN6u7pfmlhb9uLxeGjSpInTMlyBsaW9RLI9J6R0ZGDnc5i5bCv3L93K6h0H+N2EPjRNdFeO+EiZI+kI7K10nhcoqxYR6SIiC4FXgCequ8bj8ZCSkkJycjLJycnMmzeP7OxsSktLycjIwOfzkZqaCnw75pyamorP5yMjI4PS0lKys7MpKioiPz+fgoICPB4POTk5lJSUkJmZidfrPTXRV1FHxWt6ejplZWVkZWVRXFxMbm4uhYWFFBYWkpubS3FxMVlZWZSVlZGenl5tHWlpaXi9XjIzMykpKSEnJwePx0NBQQH5+fkUFRXV2TM1adLEdc/k1OfUuHFjS880fPjwiHsmJz+nY8eORfQzHdybxbLpw7i5dxPe25rP5U/8h9Tcooj6nObPn0/fvn0ZOHAgQGtqiSMhUgI9khWq2jdwPhEYq6pTA+e3AUNVdUao90hOTtZIWM3hFgoKCmjfvr3TMlyBVVseOXKEpk2b2qgouommtrkp5yD3L93K18XHmXl5D+6+rDuxMeK0rNOI5hAp+UCnSudJgTJDhGAShdmHVVvOnTvXJiXuIJra5uAuLflw5giu7teeOf/aya3Pf8rXh6M/wGykOJKNQA8R6SoiCcAk4H0rFZqc7fZiNnjah7GlvUSbPZslxvPkpBSemNiftL2HGffkWv6dEd05apxY/rsE2AD0EpE8EZmiql5gBrAK2A68oarbrNzHTLbby8GD9WdzVbgxtrSXaLSniHDj4E6suG847Zs3ZOorm3jk/W2UeU86LS0knFi1NfkM5SuBlXbdJz7eXasinKZDhw5OS3ANVm0ZmBA1BIjmttmtTRPeuecSHluZyUuf5PD5noM8fcsAurWJzFVoZ8K14z/l5SbpjJ3s2bPHaQmuwaotr7nmGpuUuINob5sN4mJ55No+PP+jwRQcLuWap9fz5qa9URX80bWOxIRIsZcLLrjAaQmuwaotFy5caJMSd+CWtnlF73Z8eP9I+ic152dvfcHMZVs5cjw6gj+61pGYMPL2snXrVqcluAartiwoKLBHiEtwU9s8t3kir00dxkNX9mR52j6ufmo9aXsPOS2rRlzrSKJtJUekY8bl7cPY0l7cZs/YGOHey3vwxv+7mJM+5YbnPmHh2mx8vsgd6nKtIzE9EnsxEWftw6otIzUciFO4tW0O7tKSlfeN4IoL2/HHlZnc8dJGDhyJzGC0juxsrwsGDx6smzZtclqGwWAwWEJVef3zXH63PIOmifHMuzmZET3ahO1+0byz3XZMj8ReKuLzGKxj1ZarV6+2R4hLcHvbFBFuHXoe788YzjmN4rnthc957MPtnIigPCeudSRmjsReUlJSnJbgGqzacs2aNfYIcQn1pW32Orcp788Yzi1DO7NgzW4mzt9A7jeR8YXZtY7k+PHoj18TSWRmZjotwTUYW9pLfbJnw4RY/nhdP/5260D2HCjh6qfW8X7aPqdludeRmMRW9tK1a1enJbgGY0t7qY/2vKpfe1beP4Ke5zblviVb+PlbaRwrdy4slGsdyYkT0bGRJ1rYt8/5bz1uwaotp02bZpMSd1Bf22bSOY1YNn0YMy7rzpub87jm6fVsLyh2REvEOxIROV9EXhCRtyqVNRaRl0VkkYjcWt374uLqPIyYq2nZsqXTElyDsaW91Gd7xsXG8NMxvXht6lCOHPcy4dmPef2z3DoPrxJWRyIii0WkUES+rFI+VkR2iMguEXn4bHWo6m5VnVKl+HrgLVWdBlxb3fsOHDhgSbvhdExYDvuwastFixbZpMQdmLYJl3Rrzcr7RzC0a0t++U469y21FF6l1hkSw90jeQkYW7kgkGf9WWAc0BuYLCK9RaSfiKyocrQ9Q71JfJuat9q4y9EYWjqSef31152W4BqMLe3F2NNP6yYNePnOIfxsTC9WphdwzdPr+TL/cChV1XqTSlgdiaquBar+Rx8C7Ar0NMqBpcAEVU1X1fFVjsIzVJ2H35lAFAzPuQGRyEoHGs0YW9qLsee3xMQI91zWnaXTh3H8hI/r//YJr2zICftQV9h3tlvNzy4irYBHgSuB51X1MRFpDDwDHAfWq+pr1bzvOKf3Vg4AHtserP7RGmM/uzC2tBdjT+u05tueSKyq1ip8esTPSKvqN8BdVcqOAnfW8D4TR95gMBjqACeGhfKBTpXOkwJlBoPBYIhCnHAkG4EeItJVRBKAScD7DugwGAwGgw2Ee/nvEmAD0EtE8kRkiqp6gRnAKmA78IaqbrPxnkEvLTbUjIjkiEi6iGwVERNOuZZUtwReRFqKyL9EJCvweo6TGqOJM9jzERHJD7TRrSJylZMaowUR6SQi/xORDBHZJiL3B8pr3T5dFUY+sLR4J/6J+Tz8vZ/JqprhqLAoRkRygMGqaiYzQ0BERgIlwCuVFpz8GTioqo8Hvuyco6r/56TOaOEM9nwEKFHVvzipLdoQkfZAe1VNFZGmwGbgB8Ad1LJ9um3pbLVLix3WZKjHnGEJ/ATg5cDPL+P/4zUEwRnsaQgBVS1Q1dTAz0fwjxB1JIT26TZH0pFvNyqCv1fS0SEtbkGBf4rIZhGZ7rQYl9BOVSsSr38NtHNSjEuYISJfBIa+zFBhLQls0xgAfEYI7dNtjsRgP8NVdSD+SAT3BIYWDDah/rFl94wvO8NzQDcgBSgA5jiqJsoQkSbAP4CZqnpa1Mdg26fbHIlZWmwzqpofeC0E3sE/fGiwxv7A+HTFOPWZIjgYgkBV96vqSVX1AYswbTRoRCQevxN5TVXfDhTXun26zZGYpcU2Eoiy3LTiZ+D7wJdnf5chCN4Hbg/8fDvwnoNaop6Kf3oBrsO00aAQf2yZF4Dtqjq30q9q3T5dtWoLILD0769ALLBYVR91VlH0IiLn4++FgD8KwuvGnrUjsAR+NP4QFPuBWcC7wBtAZ+Ar4CZVNRPIQXAGe47GP6ylQA7w/yqN8RvOgIgMB9YB6UBFAvhf4p8nqVX7dJ0jMRgMBkPd4rahLYPBYDDUMcaRGAwGg8ESxpEYDAaDwRLGkRgMBoPBEsaRGAwGg8ESxpEYDAaDwRLGkRgMAQIh81vbXOcvq5x/YlO9qwPpEq6txXsaBsKsl9v9nIb6jXEkBkN4Oc2RqOolNtZ9q6oGHblBVUtVNQXYZ6MGg8E4EkP9Q0R+KCKfB76dLwjksanxGhG5S0SeqHTNHSLyTODndwMRkrdVREkWkceBil7Aa4GyksCriMgTIvJlIHHYzYHy0YHexlsikikirwVCWdT0TKtFZJ6IbBKR7SJykYi8HUhO9AdbDGcwnAHjSAz1ChG5ELgZuDTw7fwkcGuQ1/wDfyynCm7Gn/MG4MeqOggYDNwnIq1U9WGgVFVTVPW0ewDX4w/rkQxcATxRKWbUAGAm0Bs4H7g0yMcrV9XBwHz88ZHuAfoCd4hIqyDrMBhqTZzTAgyGOuZyYBCwMfBFvyHfjW5a7TWqekBEdovIMCALuAD4OPCe+0Skwsl0AnoA35xFx3BgiaqexB9tdQ1wEVAMfK6qeQAishXoAqwP4tkqhrnSgW0V8aZEZHdA09n0GAwhYxyJob4hwMuq+osQr1kK3ARkAu+oqorIaPy9iotV9ZiIrAYSLWgsq/TzSYL/O614n69KHb5a1GEw1BoztGWob/wHmCgibQFEpKWInFeLa97Bn4p0Mt8OazUHigJO5AJgWKW6TgRyPlRlHXBzYO6lDTAS+NyG5zMY6hzjSAz1ClXNAH6NP33wF8C/gPbBXqOqRfhzW5+nqhX/+D8C4kRkO/A48Gml6hYCX1RMtlfiHeALIA34L/BzVf3atgc1GOoQE0beYIhCAsNnP1XVTSG8NwcYrKoeu3UZ6iemR2IwRCcHgZdC2ZAIxPNtIiODwTKmR2IwGAwGS5geicFgMBgsYRyJwWAwGCxhHInBYDAYLGEcicFgMBgsYRyJwWAwGCzx/wF9i0pjiX6KSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 20, 1000)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# plot survival function\n",
    "ax.semilogy(x, lte.sf(x))\n",
    "\n",
    "# format plot\n",
    "plt.grid(True, which=\"major\", linestyle=\":\")\n",
    "ax.tick_params(axis=\"both\", which=\"major\", direction=\"in\")\n",
    "ax.xaxis.set_ticks_position(\"both\")\n",
    "ax.yaxis.set_ticks_position(\"both\")\n",
    "plt.minorticks_off()\n",
    "ax.set_xticks([0, 5, 10, 15, 20])\n",
    "ax.set_yticks(1.0 * 10.0 ** (-1 * np.arange(11)))\n",
    "ax.set_xlabel(\"elevation [m]\")\n",
    "ax.set_ylabel(\"survival function (1-cdf)\")\n",
    "ax.set_xlim([0, x[-1]])\n",
    "ylim = [1e-10, 1]\n",
    "ax.set_ylim(ylim)\n",
    "\n",
    "# 100-year return level\n",
    "s_t = lte.sf(x_t)\n",
    "ax.plot([0, x[-1]], [s_t, s_t], \"--\", color=\"0.5\", linewidth=1)\n",
    "ax.plot([x_t, x_t], ylim, \"--\", color=\"0.5\", linewidth=1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}