trajectory_generator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trajectory generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory saved to trajectory.txt.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def generate_trajectory(start, end, T=0.2):\n",
    "\n",
    "    x0, y0, z0, h0 = start\n",
    "    x1, y1, z1, h1 = end\n",
    "    \n",
    "    # Calculate the number of samples\n",
    "    distance = np.sqrt((x1 - x0)**2 + (y1 - y0)**2 + (z1 - z0)**2)\n",
    "    num_samples = int(np.ceil(distance / T)) + 1\n",
    "    \n",
    "    # Generate linearly spaced points between start and end\n",
    "    x = np.linspace(x0, x1, num_samples)\n",
    "    y = np.linspace(y0, y1, num_samples)\n",
    "    z = np.linspace(z0, z1, num_samples)\n",
    "    heading = np.linspace(h0, h1, num_samples)\n",
    "    \n",
    "    # Format the trajectory as a list of tuples\n",
    "    trajectory = [(xi, yi, zi, hi) for xi, yi, zi, hi in zip(x, y, z, heading)]\n",
    "    \n",
    "    return trajectory\n",
    "\n",
    "def save_trajectory(trajectory, filename):\n",
    "    with open(filename, 'w') as file:\n",
    "        for point in trajectory:\n",
    "            line = ','.join(map(str, point)) + '\\n'\n",
    "            file.write(line)\n",
    "\n",
    "start_point = (-40, -40, 3, 0.0)\n",
    "end_point = (-32, -32, 3, 0.0)\n",
    "trajectory = generate_trajectory(start_point, end_point, T=0.2)\n",
    "filename = 'trajectory.txt'\n",
    "save_trajectory(trajectory, filename)\n",
    "\n",
    "print(f\"Trajectory saved to {filename}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def generate_trajectory(waypoints, hover_times, T=0.2):\n",
    "    trajectory = []\n",
    "    for i in range(len(waypoints) - 1):\n",
    "        x0, y0, z0, h0 = waypoints[i]\n",
    "        x1, y1, z1, h1 = waypoints[i+1]\n",
    "        hover_time = hover_times[i]\n",
    "        \n",
    "        # Calculate number of samples for the hover period\n",
    "        num_hover_samples = int(hover_time / T)\n",
    "        trajectory.extend([(x0, y0, z0, h0)] * num_hover_samples)\n",
    "        \n",
    "        # Interpolate  waypoints\n",
    "        distance = np.sqrt((x1 - x0)**2 + (y1 - y0)**2 + (z1 - z0)**2)\n",
    "        num_travel_samples = int(np.ceil(distance / T))\n",
    "        if num_travel_samples > 0:\n",
    "            x = np.linspace(x0, x1, num_travel_samples + 1)\n",
    "            y = np.linspace(y0, y1, num_travel_samples + 1)\n",
    "            z = np.linspace(z0, z1, num_travel_samples + 1)\n",
    "            heading = np.linspace(h0, h1, num_travel_samples + 1)\n",
    "            trajectory.extend([(xi, yi, zi, hi) for xi, yi, zi, hi in zip(x[1:], y[1:], z[1:], heading[1:])])\n",
    "    \n",
    "    # Add hover time at the last waypoint\n",
    "    x_last, y_last, z_last, h_last = waypoints[-1]\n",
    "    num_final_hover_samples = int(hover_times[-1] / T)\n",
    "    trajectory.extend([(x_last, y_last, z_last, h_last)] * num_final_hover_samples)\n",
    "    \n",
    "    return trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def generate_trajectory(waypoints, hover_times, T=0.2):\n",
    "    trajectory = []\n",
    "\n",
    "    # Add the first waypoint\n",
    "    x0, y0, z0, h0 = waypoints[0]\n",
    "    trajectory.append((x0, y0, z0, h0))\n",
    "    \n",
    "    for i in range(len(waypoints) - 1):\n",
    "        x0, y0, z0, h0 = waypoints[i]\n",
    "        x1, y1, z1, h1 = waypoints[i+1]\n",
    "        hover_time = hover_times[i]\n",
    "        \n",
    "        # Calculate number of samples for the hover period\n",
    "        num_hover_samples = int(hover_time / T)\n",
    "        trajectory.extend([(x0, y0, z0, h0)] * num_hover_samples)\n",
    "        \n",
    "        # Interpolate waypoints\n",
    "        distance = np.sqrt((x1 - x0)**2 + (y1 - y0)**2 + (z1 - z0)**2)\n",
    "        num_travel_samples = int(np.ceil(distance / T))\n",
    "        if num_travel_samples > 0:\n",
    "            x = np.linspace(x0, x1, num_travel_samples + 1)\n",
    "            y = np.linspace(y0, y1, num_travel_samples + 1)\n",
    "            z = np.linspace(z0, z1, num_travel_samples + 1)\n",
    "            heading = np.linspace(h0, h1, num_travel_samples + 1)\n",
    "            trajectory.extend([(xi, yi, zi, hi) for xi, yi, zi, hi in zip(x[1:], y[1:], z[1:], heading[1:])])\n",
    "    \n",
    "    # Add hover time at the last waypoint\n",
    "    x_last, y_last, z_last, h_last = waypoints[-1]\n",
    "    num_final_hover_samples = int(hover_times[-1] / T)\n",
    "    trajectory.extend([(x_last, y_last, z_last, h_last)] * num_final_hover_samples)\n",
    "    \n",
    "    return trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" \n",
    "waypoints = [\n",
    "    (0.28, -13.91, 344.88, -2.16),\n",
    "    (12.62, -27.13, 345.56, -2.16),\n",
    "    (24.96, -40.34, 346.24, -2.16),\n",
    "    (37.30, -53.56, 346.92, -2.16),\n",
    "    (49.64, -66.78, 347.60, -2.16),\n",
    "    (61.98, -79.99, 348.28, -2.16),\n",
    "    (74.32, -93.21, 348.96, -2.16),\n",
    "    (86.66, -106.43, 349.64, -2.16),\n",
    "    (98.99, -119.64, 350.32, -2.16),\n",
    "    (111.33, -132.86, 351.00, -2.16),\n",
    "    (123.67, -146.08, 351.68, -2.16),\n",
    "    (136.01, -159.29, 352.36, -2.16),\n",
    "    (148.35, -172.51, 353.04, -2.16),\n",
    "    (160.69, -185.73, 353.72, -2.16),\n",
    "    (173.03, -198.94, 354.40, -2.16),\n",
    "    (185.37, -212.16, 355.08, -2.16),\n",
    "    (197.71, -225.37, 355.76, -2.16),\n",
    "    (210.05, -238.59, 356.44, -2.16),\n",
    "    (222.39, -251.81, 357.12, -2.16),\n",
    "    (234.73, -265.02, 357.80, -2.16),\n",
    "    (247.07, -278.24, 358.48, -2.16),\n",
    "    (0.28, -13.91, 344.88, -2.16)\n",
    "] \"\"\"\n",
    "\n",
    "#0_1\n",
    "\"\"\" waypoints = [\n",
    "    (0.00, 0.00, 8.87, -2.38),\n",
    "    (12.32, -13.29, 9.55, -2.38),\n",
    "    (24.58, -26.58, 10.22, -2.38),\n",
    "    (36.90, -39.75, 10.90, -2.38),\n",
    "    (49.22, -53.04, 11.58, -2.38),\n",
    "    (61.55, -66.33, 12.25, -2.38),\n",
    "    (73.87, -79.62, 12.94, -2.38),\n",
    "    (86.19, -92.91, 13.62, -2.38),\n",
    "    (98.52, -106.20, 14.29, -2.38),\n",
    "    (110.84, -119.49, 14.97, -2.38),\n",
    "    (123.17, -132.78, 15.65, -2.38),\n",
    "    (135.49, -145.95, 16.32, -2.38),\n",
    "    (147.82, -159.24, 17.00, -2.38),\n",
    "    (160.14, -172.53, 17.68, -2.38),\n",
    "    (172.46, -185.82, 18.35, -2.38),\n",
    "    (184.79, -199.11, 19.03, -2.38),\n",
    "    (197.11, -212.40, 19.69, -2.38),\n",
    "    (209.44, -225.69, 20.35, -2.38),\n",
    "    (221.69, -238.86, 21.03, -2.38),\n",
    "    (234.01, -252.15, 21.72, -2.38),\n",
    "    (246.34, -265.44, 22.39, -2.38),\n",
    "    (0.00, 0.00, 8.87, -2.38)\n",
    "] \"\"\"\n",
    "#0_2\n",
    "\"\"\" waypoints = [\n",
    "    (0.00, 0.00, 16.36, -2.38),\n",
    "    (12.32, -13.29, 17.04, -2.38),\n",
    "    (24.58, -26.58, 17.71, -2.38),\n",
    "    (36.90, -39.75, 18.40, -2.38),\n",
    "    (49.22, -53.04, 19.08, -2.38),\n",
    "    (61.55, -66.33, 19.71, -2.38),\n",
    "    (73.87, -79.62, 20.39, -2.38),\n",
    "    (86.19, -92.91, 21.06, -2.38),\n",
    "    (98.52, -106.20, 21.75, -2.38),\n",
    "    (110.84, -119.49, 22.43, -2.38),\n",
    "    (123.17, -132.78, 23.10, -2.38),\n",
    "    (135.49, -145.95, 23.79, -2.38),\n",
    "    (147.82, -159.24, 24.47, -2.38),\n",
    "    (160.14, -172.53, 25.14, -2.38),\n",
    "    (172.46, -185.82, 25.82, -2.38),\n",
    "    (184.79, -199.11, 26.50, -2.38),\n",
    "    (197.11, -212.40, 27.19, -2.38),\n",
    "    (209.44, -225.69, 27.86, -2.38),\n",
    "    (221.69, -238.86, 28.54, -2.38),\n",
    "    (234.01, -252.15, 29.22, -2.38),\n",
    "    (246.34, -265.44, 29.90, -2.38),\n",
    "    (0.00, 0.00, 16.36, -2.38)\n",
    "] \"\"\"\n",
    "\n",
    "\"\"\" waypoints = [\n",
    "    (0.00, 0.00, 23.92, -2.38),\n",
    "    (12.32, -13.29, 24.59, -2.38),\n",
    "    (24.58, -26.58, 25.26, -2.38),\n",
    "    (36.90, -39.75, 25.94, -2.38),\n",
    "    (49.22, -53.04, 26.62, -2.38),\n",
    "    (61.55, -66.33, 27.30, -2.38),\n",
    "    (73.87, -79.62, 27.98, -2.38),\n",
    "    (86.19, -92.91, 28.66, -2.38),\n",
    "    (98.52, -106.20, 29.34, -2.38),\n",
    "    (110.84, -119.49, 30.02, -2.38),\n",
    "    (123.17, -132.78, 30.69, -2.38),\n",
    "    (135.49, -145.95, 31.37, -2.38),\n",
    "    (147.82, -159.24, 32.06, -2.38),\n",
    "    (160.14, -172.53, 32.73, -2.38),\n",
    "    (172.46, -185.82, 33.41, -2.38),\n",
    "    (184.79, -199.11, 34.09, -2.38),\n",
    "    (197.11, -212.40, 34.73, -2.38),\n",
    "    (209.44, -225.69, 35.41, -2.38),\n",
    "    (221.69, -238.86, 36.09, -2.38),\n",
    "    (234.01, -252.15, 36.77, -2.38),\n",
    "    (246.34, -265.44, 37.44, -2.38),\n",
    "    (0.00, 0.00, 23.92, -2.38)\n",
    "] \"\"\"\n",
    "\n",
    "waypoints = [\n",
    "    (0.00, 0.00, 31.43, -2.38),\n",
    "    (12.32, -13.29, 32.10, -2.38),\n",
    "    (24.58, -26.58, 32.77, -2.38),\n",
    "    (36.90, -39.75, 33.46, -2.38),\n",
    "    (49.22, -53.04, 34.13, -2.38),\n",
    "    (61.55, -66.33, 34.81, -2.38),\n",
    "    (73.87, -79.62, 35.49, -2.38),\n",
    "    (86.19, -92.91, 36.16, -2.38),\n",
    "    (98.52, -106.20, 36.85, -2.38),\n",
    "    (110.84, -119.49, 37.53, -2.38),\n",
    "    (123.17, -132.78, 38.20, -2.38),\n",
    "    (135.49, -145.95, 38.88, -2.38),\n",
    "    (147.82, -159.24, 39.56, -2.38),\n",
    "    (160.14, -172.53, 40.21, -2.38),\n",
    "    (172.46, -185.82, 40.89, -2.38),\n",
    "    (184.79, -199.11, 41.57, -2.38),\n",
    "    (197.11, -212.40, 42.24, -2.38),\n",
    "    (209.44, -225.69, 42.92, -2.38),\n",
    "    (221.69, -238.86, 43.60, -2.38),\n",
    "    (234.01, -252.15, 44.28, -2.38),\n",
    "    (246.34, -265.44, 44.95, -2.38),\n",
    "    (0.00, 0.00, 31.43, -2.38)\n",
    "]\n",
    "\n",
    "#0 hover time for each waypoint\n",
    "hover_times = [0] * len(waypoints)\n",
    "trajectory = generate_trajectory(waypoints, hover_times, T=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0hPPPPx8UReHIkSM455xzcPPNNwMAmpub8eabb+KXv/wliouLUVFRgT179uBHP/oRDh8+jDPOOAM33XQTkpKS8Nhjj8HpdOJ//ud/eH3ea665BpdddhkA8L6vWq0WBw8eZOosXNJgb7/9Nm6++WZcfvnl2LJlCzweD/70pz9BpVL5pApvuOEG/OxnP8MNN9yAnTt34r333guYMr3wwgvxpz/9CVlZWdi2bRuOHTuGN998U1Db7XXXXQcATJqLjdbWVlRWVuLCCy/ED3/4Q1x33XVobW1FV1cXnnzySZ9GCuBUVJqdnY3f/OY3yMjIQHp6Ovbs2ROw3nXRRRfhnHPOwd13343x8XHs2LEDb7zxBl566SXcdtttPoV6GQmK+DWknb7485//TO/fv58uKiqik5KS6JycHHr//v30Sy+9tOZnzzrrLKYlGQCt1WrpzZs301dffTX9xhtvCDp/sHbjb3zjGwF//ne/+x29efNmOiUlha6pqaF///vf0/fccw/t/3j4txvTNE2vrq7Sd911F71p0yY6OTmZzs/Pp1tbW+n77ruPdrlczM95PB765z//OV1TU0MnJyfTBQUF9IEDB+iTJ08yP9Pf309/4hOfoFNTU2kAPudqa2ujzzvvPFqr1dJpaWn0OeecQx89etTnWki78fHjx4PeG6fTSefk5NBZWVmClAf+9re/0QDo9evXr2nDpem1bb6jo6P0V77yFbqqqorWaDR0bm4ufc4559Bvvvmmz+/ZbDb6+uuvp7OysuiMjAz6iiuuoBcXF9e0G5vNZvq6666j8/Pzaa1WS5933nl0f3//mr8Nl3bjjRs3+jx77H+kbdjhcNDf+ta36PXr19Opqan0vn376GPHjtFnnXXWmmfspZdeordt20YnJSX5HCNQm/Pq6ip9++2308XFxbRaraY3b95M//znP/dpP6fp4M9toGdRhnSgoGm5AiaDG0pLS3Heeefht7/9bbwvRTA8Hg+Ki4tx0UUXrakbyJAhQxzINRYZnEBmJvjUJKSIF198EXq9Htdcc028L0WGjNMWco1FRlgcOnQITz/9NOx2Oz71qU/F+3IE4cMPP0RnZyfuvfdeNDY2BuyAkiFDhjiQiUVGWPzsZz/D8PAwfvzjH+PTn/50vC9HEB599FE88cQTaGhoCCj2KEOGDPEg11hkyJAhQ4aokGssMmTIkCFDVMjEIkOGDBkyRIVMLDJkyJAhQ1TIxCJDhgwZMkSFTCwyZMiQIUNUyMQiQ4YMGTJEhUwsMmTIkCFDVMjEIkOGDBkyRIVMLDJkyJAhQ1TIxCJDhgwZMkSFTCwyZMiQIUNUyMQiQ4YMGTJEhUwsMmTIkCFDVMjEIkOGDBkyRIVMLDJkyJAhQ1TIxCJDhgwZMkSFTCwyZMiQIUNUyMQiQ4YMGTJEhUwsMmTIkCFDVMjEIkOGDBkyRIVMLDJkyJAhQ1TIxCJDhgwZMkSFTCwyZMiQIUNUyMQiQ4YMGTJEhUwsMmTIkCFDVMjEIkOGDBkyRIVMLDJkyJAhQ1TIxCJDhgwZMkSFTCwyZMiQIUNUyMQiQ4YMGTJEhUwsMmTIkCFDVCTF+wJkfLxA0zQoioLD4YBKpUJSUhJUKhUUCkW8L02GDBkiQSYWGTEDTdNwu93wer1wOp0AAIVCAaVSCZVKBbVaDZVKJRONDBkJDgVN03S8L0LG6Q+KouByuUBRFBQKBVwuF5RKJRPB0DQNmqYZoklKSmKiGZloZMhILMjEIiOqoGkaXq8XbrcbNE0zZOJ2u9eQBXkUAxENSZvJqTMZMqQPmVhkRA3s1BdwKu2lUChAURTcbjfztVC/D5wimg8++AA1NTXIysqSIxoZMiQOucYiIyogUQpFUVAqlQGjk3BkQL5PiINELzRNw+l0wuVyAQCUSiVTn0lKSgp4PhkyZMQOMrHIEBU0TcPj8aC3txcFBQXIzc0VZZFnEwtwimxIuoymaTgcDubnCNGQiEYmGhkyYguZWGSIBpLioigKS0tLyMjIEG1BVygU8M/aErIBZKKRIUNKkIlFRsQgnV2hUl+xAFei8W8EkIlGhgxxIROLjIjgX6Ani3SgCCMSCDleMKIhA5rkev2bAWSikSEjMsjEIkMwSJTi9XrXLMahiCBei3YwovF6vfB6vXA4HDLRyJAhAmRikcEbZDH2eDxBU1/hIgy+C7XYERA5JrshwJ9onE6nT+qM/C+boGTIkLEWMrHI4IVgqS9/RIMIoo1gROPxeJiBTqIakJaWhpSUFJ9WaBkyZJyCTCwyOCPcbAobUqixiHHOQERz4sQJ1NbWIjMzk4lo2Dpn5OdlyPi4QiYWGWFBdu0ej4eRZeEy3JhoEUs4EKJRKBRMaixQRCMTjYyPO2RikRESFEXB4/GETX3543SIWIKBXEe41BmAgPIzMtHION0hE4uMgGDPphD5FT51BCkRQawQjGjcbjdcLpdPtCMTjYzTGTKxyFiDYOKRfHA6RyxcEYhoCFmzRTjZREO6zmTISGTIxCLDB6FmU/ggGkQgJWIRcl9I/YWATTSBIhrZIkBGokImFhkAuM2m8EEoYnE6nRgbG0N6ejpycnKQkpLC6XinG7gQjeyuKSMRIROLDNA0jaWlJbhcLmRmZooyaR6MWIxGIzo6OpCeng6j0Yje3l6GYHJycpCdnQ21Wh30Ok9ncCUa2YtGhtQhE8vHHMQyeGZmBna7HfX19aIc159YKIrCyMgIxsfHUVNTg4KCAigUCng8HpjNZpjNZoyMjMBmsyEjI8OHaD6uiyebaNimZy6Xi1EFIF9PT0+XiUaGZCATy8cUwSyDxQJ70XM4HOjo6IDL5cLevXuh1WqZ86rVahQWFqKwsBDAqTQZIZqBgQE4nU5kZmbC6XTCarUyabqPG9gaZ8BHRLOwsIDJyUk0NjbKEY0MyUAmlo8hAs2mKJVKUBQl2jkIsej1enR2dqKwsBDNzc1ISkoKSWApKSlYt24d1q1bBwCw2+0wm80YHR3FzMwMpqenkZWVxUQ0GRkZH2uiYYtmkvZmf3dNmWhkxBoysXyMEGo2JRpdXGazGdPT09i2bRs2bNgg6BipqalITU2FXq9HXl4ecnJymIhmcnISNE0zJJOTk4P09PSoL5xSqvWw/47BvGicTieTOpNNz2TEAjKxfEzAlmUB1s6miJkKs9vtmJubA0VRaGlpgVarFeW4CoUC6enpSE9PR0lJCWiahsVigdlshslkwujoKJRKpQ/RpKamntYLZyhrAtldU0a8IBPLxwDs2RT2wB4bCoVClFTY4uIiurq6kJqaioyMDFFJJZA1cUZGBjIyMlBWVgaKorCysgKz2YyFhQUMDg4iOTnZh2g0Go0o1yMVkIglHGR3TRmxhEwspzH4zKZEmgqjKAoDAwOYnp7G9u3bYbVaYbfbBR9PCJRKJbKzs5GdnY2Kigp4vV4sLy/DbDZjZmYG/f390Gg0PkSTnJwc02sUG1yJxR+yu6aMaEImltMUXH1TCCJJhdlsNuh0OgBAa2sr0tPTMTIyEndJF5VKhdzcXOTm5gIAPB4PlpaWYDabMTExgZ6eHs4zNIGuRyoQ41pCEY3T6ZTdNWXwgkwspyGEyLIITYXNz8+ju7sbxcXFqKmpYdJsUtT2SkpKQn5+PvLz8wEALpeLIZpQMzRShtCIJRz8a3DB3DVJjUZ215TBhkwspxECzaZwfcn5RixerxcDAwOYnZ3F9u3bmfZggkQQoUxOTl4zQ2MymWA2m9Hf3w+Xy+XT2kxUCaSEWJF3KBtnj8fj40Xjr3MmE83HDzKxnCbgm/ryB5+IxWq1QqfTQalUorW1FWlpaQGPF27Ri9ZuWyhSUlKwfv16rF+/HjRNMzM0pG3a6/UiOzsbFEXBYrHEpLWZC+JxDcGIxmaz4fjx42hpaWHmZmTTs48fZGI5DcDHMjgYuEYEs7Oz6OnpQWlpKbZs2RJ0oUiEiCXc+dLS0pCWloYNGzaApmlYrVYfVYDBwUFkZ2fHdIbGH1IhZzbReL1eJCWdWlpkd82PJ2RiSWD4z6ZEUkgNlwrzer3o6+vDwsICduzYwaSPguF0k81XKBTQarXQarUYGxtDY2MjaJqG2WyG0WjEyMgIVCpVzGdopFbHIkRHCEN21/x4QiaWBAUp0JP0VaS57FCpMIvFAp1Oh6SkJLS2tiI1NZXT8cSOWKQCsnhmZGQgMzMTGzdujNsMjVQiFgKKogJeT7DUGVFuBmSiOZ0gE0uCgbSAjo2NITMzE5mZmaIsLMEilunpafT19WHjxo3YtGkT5xf9dItYwiHcDE1fXx/S0tJ8Os7EmKGRGrGQppFwCEQ0srvm6QOZWBII7AL9zMwMVCoVsrKyRDm2PxF4PB709vbCYDCgoaEBBQUFER1PjOtLJISaoRkbG4PVaoVWq/UhGlKX4Asp3ZtgEUs4cPGiYRON7K4pbcjEkiDwn02Jhsw9SYWtrq5Cp9MhJSUFra2tglI4p3vEwndBCzRDQxoBhoaG4HA4fGZosrKyOM3QSOmeAOJFUFxNz2R3TWlCJhaJI5gsi1jaXgRENn9ychIDAwMoLy/Hpk2bBL+o4YiF74J4ui0YycnJKCoqQlFREYBTnjWEaPr6+jjP0EgtFRYtvxyuRCNbBEgDMrFIGKFmU8T2T/F6vYzLY1NTE/Ly8iI6XihiMRgM6OzsRFJSEnJycpCbm4ucnJyQcipSnOQXExqNJuQMDUVRa3xoyD2R0uIZq+thE00wd02ZaOIHmVgkinCzKWISy/LyMtrb2wGc0vpKSUmJ+JiBiICmaQwPD2N8fBxbtmxBUlISlpaWMDY2hu7u7oSTU4kWws3QjI+PQ6FQICcnBx6PB2q1WjIEw7V4LybYGmfkGoCPiGZ5eRnz8/OoqqqSiSZGkIlFYmDPpoSSZRGjxkLTNCYnJzE4OIiysjKMjY2JpvbrTyxOpxMdHR1wOp3Yu3cvNBoNvF4v0xTgcrkYORViSUx26KQAfjpHLKHAnqEpLS1lJv9NJhNmZ2extLSEf/7zn2tmaOIBocV7MeFPNF6vF4uLi6isrJTdNWMEmVgkhECWwcEe9EgjFrfbje7ubiwtLWHnzp1IS0vD2NgYKIoSJVJgE4vRaERHRwfy8vLQ1NSEpKQkpqWUIDk5mbEkDpQK8ng80Gg0SElJiduUu1SgVCqZVnMS1RYWFsJsNmNubg4DAwPMfSL/xIhCuSAeEUs4kKifkEc4d03S2iwrNwuHTCwSQCjL4GCIpHi/tLQEnU6HjIwM7Nu3D8nJycxCL1ZUQK5vZGQEo6OjqKmpQUlJCWelZf9UUG9vLxwOBzPlTuozJKKJpYGXlCInspCTewGc2qGT1uapqSn09vb6zNCEq2dFAilELP7wbygIZhEgu2uKB5lY4gz/Aj3XCXohEQtN0xgfH8fw8DA2bdqE8vJy5lzkf8Wq23g8HjgcDszMzGDPnj3IzMz0+T5fgczk5GQkJydj8+bNoCiKGT4kO3SNRsM0AURz4ZQi/O+lSqVCXl4e04Dhdrt9Zmi6u7tFm6Hxh1RqPWyE61TjSjSyuyZ3yMQSRwjxTSHgW2NxuVzo6urC6uoqdu3ahezs7DXHA8TZjZvNZvT09AAAWlpaAi7yQtqNCen579D9hw8/To0AXBZytVqNgoICn3qWGDM0gRCtduNIwPeaghEN211TJprQkIklDuBjGRwMSqVyTZ0iGMxmMzo6OpCVlYV9+/YFXOjJ+SMhFnZEVFJSgvn5eVEjh2DXFmj4MFQjQEZGhuQWP6EQ8vfyn6Gx2+0MMff29sLj8axpbeZ6vxIxYgmHUEQju2sGhkwsMUakvikEXFJhNE1jdHQUo6Oj2LJlC8rKyoKeK9JUmNvtRldXF1ZWVrBr1y7QNI35+XlBxwp1fVwQrhGAoiifeoOQRgCpLBhiLOSpqalITU1lZmhsNhtzv6ampkBRlI89gFarDXpOqRbvxYxY/dPVsrvmWsjEEkOQvvpIfFMIwhXvnU4nOjs7YbfbsXv3bk6aYkKHEJeXl6HT6aDVatHa2ork5GQsLy9LQtIlUCOAxWLxkbuPZyNApBA7QlAoFEhPT0d6ejpKSkp87hdJNZIZGvIvLS3NZ2MitcUz2um5YMrNH2d3TZlYYoBILIODIVTEYjQa0dnZiZycHDQ2NnIuzPKt29A0jampKQwMDKCqqgoVFRVrdnJiQawXUKE4JXefkZGBsrKy06IRIJqLU6D7tbq6CrPZDL1ej+HhYR9idrlckoxYYnlNwYjG3/SMpmmmKeV0swiQiSXKoGkaJpMJSqUSGo1GtLxrIBJgT7bzae8l4NPC7PF40N3dDbPZjObmZmaIMdT1RYpotPkKaQSQEmLd+qxUKpGVlYWsrCyUl5fD6/UyPjRzc3NYXl6GSqVCX19fzGdogoE0x8QLwYimt7cX6enpTIr6dHLXlIklSmDPpgwNDSErKwuVlZWiHd8/YnE4HOjs7GQm2zMyMgQdk8tCtbq6ivb2dqSmpoaUgAl1LKnWNEI1AvT398PlcoGmaUxPT6OoqCjujQDxLpazXTMBYGhoCDabDWq1mpmhSU9P9yHmWEeAUutU849YSEPA6eSuKRNLFMCWZQE+6iQRE+zogog65ufnM5PtkR4zGIjxV0VFBaqqqkI2AyRCxBIOgRoBPvzwQ9hsNnR2dorSCBAJ4k0sgZCamopNmzYB8J2hGR0dhdVqXdPaLNYMTTCIXbwXC+wxg0ARDdtd86tf/Sq++MUv4rLLLovnJXOGTCwiw98ymLQhiqlETI7r9XoxODiIiYkJbN26FSUlJREdMxQZsI2/GhsbmR29kGMJvbZ4gzQCAMCWLVuQmpoqiUYAKdwbAn+i85+hcTqdTCMAaQXPzMz0sQcQmwQoipJknczr9Qb8rIGIZnR0VPQ1JJqQiUUksFNf/l1fhATEhMfjgcVigdfrRUtLC7RabcTHDJYKI573arWas/FXOGIRshhKSUoFkEYjgNTuCU3TIYkhJSWFiQAB+LSCz87ORjRDEwxSS4URBCMWfygUClitVqSnp8fgqsSBTCwiINxsikqlEpVYFhcXMTAwAKVSiZaWFtF2eIFSYbOzs+jp6UFZWRk2b97My/MeENdRUOoI1ghgMpmipgggtVQY33ZjMkNTXFy8ZoZmcnISNE1znqEJdU1STYVxdQm12WyibB5jBZlYIgQXWRY+U/LhzjU4OIipqSmUlZVhcXFR9MEvsgP2er3o7+/H/Pw8duzYgcLCQt7HAsQlFqntzsPBvxGAnQYijQBiKAJIiVgi+XtzmaFRKpXIzs5mosDU1NSw55NqxMKH8OSI5WMCPrMpYtRYbDYbOjo6QFEUWltb4XA4RJ1sBz5KhdlsNuh0OigUCrS2tgry9uAiEZNoRMGGkMWTnQZiKwKYTCZMTU2BpmnejQBSjFjEWsRDzdAsLi5iaGgIarXa554FStNKlVj4tEGTpodEgUwsAsBXliXSGsv8/Dy6u7tRXFyM6upqqFQqZoJfTCgUCphMJnR1dWHDhg2orq4W/EKGIhaKojA6Ogq73Y68vDzk5OSE7QxKxIglFIIpAphMJl6NAFK7J9GUdAk0Q0NqWjMzM+jv74dGo/EhmuTkZEkSC9mYcolYXC4X3G63TCynM8JZBgeC0IjF6/ViYGAAs7Oz2L59O1PwjOSYwUCUW6emplBfX+9zLiEIRixsJ8msrCyMjIzAbrcjIyMDubm5yM3NRWZmpuQWgmiDvTvfuHGjTyPA7Oxs0EYAKUYssboelUrFPDOA73DrxMQEenp6kJ6eDpfLBYvFgtzcXMl0h5F3lwuxWK1WAJBTYacj/GdT+EzQq1Qq3iRgtVrR0dHBpKNImyuBmJPtdrsdOp0OHo8HVVVVEZMKEJhYzGYzdDodcnJyUF9fz3QQORwOJiXU1dXFiB6SRZSkhKS2O48m2I0AlZWVQRsBnE4nrFYr591vtBFPovOvabndbqaeNT8/j4mJCcnYKZAMBpfzWywWADKxnHbwn03hKx7HN7qYm5tDT09PyHSUWBHL4uIiurq6sG7dOqhUKlE97wlomsbExASGhoYYlWUS+QGARqPB+vXrGXVdq9W6JiWUkpLCWMnGWyIkHgjWCDA0NIS5uTlMTU0hKyuLIeN4KQJIKe2kVqtRWFiI4eFhbN26FWlpaSFnaLKysmJ27ew0ejiQwr1U7isXyMQSAqFmU/iAKwl4vV709fVhYWEB9fX1ITuxSGuw0B0iRVEYGhrC5OQkamtrUVxcjJMnT4pqTQycyg/39vbCbDZj586dTCtuqN/TarXQarU+syETExNYXl7GP//5T6SnpzMLqJjuh4kE0ggwPT2N0tJSZGRkMFEfadNl12fYCsTRhNRSc8BH3Vf+zRMkUvafoWGTc7Q+C7kmLscnxCK1+xoKH783kiPE8k0hvxuueE+GEJOSkjh1YrGncvlel8PhQEdHB9xut89wJR8RynAg13Ty5EmkpaVh3759gqIhkhKy2WwAgNraWmYBHRwcjIuJl5RScuTvz7URgCya0VIEkKofi/81KRSKkDM0ExMTAOAzQyPm4s63I8w/FS51yMQSAJFYBgdCuBrLzMwMent7eQ0hCrUSZuuKNTc3++z2xazbzM3NAQAKCgqwdevWiO8h+X2S3iDRnN1uh8lkYlp2ATD1mdzcXE5zDomMQBuLUI0A7O6paCgCJKofS7AZGkLOo6OjPnUvrjM0wcCnJmaxWOSIJZEhhmVwIARLhXk8HvT19WFxcRENDQ2MnhLXYwLch6xomsbIyAjGxsawdetWbNiwIeCCFCmxUBSF/v5+hljKy8sD3kOxJF1SU1OxYcMGZqe+uroKk8kEvV6PoaEhJCcnMyRD2k9PN4S7l1wbAUhEE4nnvdRSYSSdzTeKCkTOxB5gYWEBg4ODSE5ODjtDEwx8iMVqtSbU1D0gEwsDsihNTExg06ZNovpVByKW1dVV6HQ6JCcnY9++fbxTE2xiCQe2m2QoSf1IU2Gku4ymabS0tOD9998XPbUW7mcyMzORmZnJzDn4t59qtVqGaCJZQKWyeApZyEMpAvT19TGKAEIaAaSWCiObkUiviUz8Z2dno6KiIugMDbv2F2oTw3fqXk6FJSBIh5LD4cDExAS2bNki6vHZNRbi5dHf34/y8nJUVVVFJOMRbuE2mUzo6Ojg5CYZSSrMYDCgo6MDRUVF2Lp1K+fCJB/wvTaVSoW8vDzk5eUB8PVW6evrg9vtZhbQ3NxcQTpU8YYYqctQigB8GwGklgpjq4yLiVAzNOPj47BYLNBqtT6tzex3T45YTmOwZ1NomoZarYbX6xU9nCc1Fo/Hg56eHhiNRjQ1NTELnhCQludgxELTNMbGxjAyMoLq6mqUlpaG/UxCIhYi6T06OrpGuj9Uai0eRl/+3iqkWGsymTA+Pu6TMiL1GakjGp73kTQCSC1iiRax+COQQRwhmuHhYWYImDxfbrdbJpbTEWShZ3d9kT+02C8riQSOHj2K1NRU7Nu3T5RZjGARhsvlQldXFywWC3bv3o2srCxOx+NbYyHnsVqt2LNnDzIzM3kdj+89FtvfhV2sJTpUJpMJ8/PzGBwcjLrkvRiIdk2DSyNAamoqc4+8Xq+kIhbyfsf6mpKTk32aTNitzX19fXA6nVCr1RgbG2N8aIKRX6IpGwMfQ2Jhz6aQl5I8dIRYxPTIpmkas7OzAID169dj06ZNUa3dLC0tQafTITMzE62trbwWQz6psOXlZeh0OmRkZKClpSXgecSclo/2wsDWoaqoqAhY4CbDdAC32lasEMtFM1wjADGEy8/Pj7iOJQbEbMKJBP5DwIODg7BYLLBarZiengZFUWt8aMg1k66waOBnP/sZ7rrrLvz7v/87HnjgAZ/v0TSNz3zmM3j99dfxwgsv4ODBg5yP+7EiFv/ZFP8JekImXq9XlN2p2+1Gd3c3lpaWAAAbN24UPRIiCxx7un3Tpk1Bu7FCQaFQhJ23YdeIqqqqUFFREfQ8YhNLLOdHAhW4SVszAPzrX//ySZvFqx003l1Y/vfpnXfewfr162Gz2SJuBBADUlICIFAoTrlDZmRkYMuWLYzahP8MjdFoxMDAAJaXl3l1jHLF8ePH8dhjj6G+vj7g9x944AHBz9bHhli4+qaINSS4tLSEjo4OpKeno6WlBe+++25U1IjJ5yIExmW6PRjCRSxerxc9PT0wGAycakSnk75XSkoK1q9fj3Xr1mFhYQF1dXXMYjA6OupTd8jNzY2Z7IwU729BQQHS0tIibgQQA1I1+aIoiinms9UmSktLmQ7Vt956C2+++SbTPTo6OopPfvKT+NSnPoXKysqIzm+xWHDVVVfh//2//4cf/ehHa76v0+nwi1/8AidOnMD69et5H/+0Jxa+symRuj0GixzEnGonUCqVsFgs6O7ujmi6nSAUEVitVuh0OqhUKtHsicW6tnhAq9UiLy9vjSXxzMwM+vr6kJaWxpBMNGVn4h2xsEHTtE/xPlAjAPFTiZUigBQjFuDUJi3Yu0ra5j/72c8y/7Zv346cnBw88cQT+OUvf4m+vr6Izv+Nb3wDF1xwAfbv37+GWGw2G6688ko8/PDDggVpT2tiESLLEgmxkGL26urqmshBbJl7QpgkJVVZWSnKdHuga1xYWBDk0SI1MogW/OsObrebqTsMDQ3B4XAgMzOTIRqx00FSIhYg+PWw54y4NAKI0TAhZWLhGknZ7Xbs2LED1113He65556Ibc6ffvpptLW14fjx4wG/f/vtt6O1tRWXXHKJ4HOctsRCURRjhsWneCfUlMtsNqOjo4MpmvvvRsQkFlIgdblcqKqqQlVVlSjH9U+FsYUq6+rqeO9eTueIJRTUajUKCgqYvDg7HUQKtexdeiTpICndE77DiLFQBDgdiMW/3TiS1N7U1BT+/d//HYcPHw4YHb788st4++230d7eLvgcwGlILHwsgwOBr3cKe15k8+bNQQv0kbpIElgsFrS3tyMlJQVarVbUbhH24k0MuVwul49QpdDjiQEpLaJ84C92SOZC9Ho9hoeHoVarGZLJzc3llc6UUiqMbSshBOEUAdxuNxP5cW0EELPDU0xwJRYybyVWu/HJkyexuLiIpqYmn2t577338NBDD+HrX/86RkZGkJ2d7fN7l156Kc4880y8++67nM5zWhGLGIrEfFJhTqeTmeMINy8ixOzLH0SskkzsHz9+XNT0GkmFEUOu3NxcNDU1Ca4PhCIWs9kMvV7P2TFSKotnpPCfCyHSIEREs7e3l5nYJvWZUAuQlIhFLPkUgkCKAEQ5gWsjwOkQsZApfjHwqU99Cl1dXT5fu+6661BTU4P/+I//QH5+Pm688Uaf79fV1eH+++/HRRddxPk8pwWxhJpN4QuuKSu2VAqXeZFIUmFsnxa2WKXYdRuFQgGbzYYTJ05wntYPdzx/YqFpGuPj4xgaGkJubi5mZmZ8UkOhFIkTNWIJBX9pEJfLxaTNiBkVW3YmkEeIVIgl0oglFNiNAER9mDQCGAyGoI0AUiUWvlphYmUmMjIysH37dp+vpaenIy8vj/l6oJR3WVkZKioqOJ8n4YnF3zI4ElIBwkcsbJVgPouvUBLw78Ziy4yIKXPv8XgwPT0Nu92O3bt3rwmFhcCfWDweD7q7uxnTL7LD9E8N+SsSq9VqySye0Sa35ORkFBUVoaioKOAuHYAPCUuJbCPd1PFBsEYAk8nk0wigVquZTIaUlBP4pMKsVmtQ4VipIqGJxd8yWIydSShiIXUHh8MRUMIkFITUWObn59Hd3Y2SkhJs2bJlzecTK2JZXV1Fe3s7M7QlBqkAvsRitVrR3t6O5ORkJsJzuVwBU0OBJt5TU1N9WsY/DvDfpbNlZ4h0O9FqKywsjLvsTDwFKNmNAMCpTQwZNrRarThy5Iho1gBigGvtx+Vywev1RlXSJVzdRMjmJSGJRSzL4EAIRgDEICsvL09Q3YEPCRBPk9nZWdTV1aGoqCjgz4kxGzM7O4uenh6Ul5cjPT2dmfoVA4RYFhcX0dnZ6UOQwa7bX5GYTLzPz8/D5XLhyJEjnNJmpyP8ZWfcbjeOHDkClUrl00XFlm6PJQlLqd6TlJSEgoICrK6uIj09HZWVlUyKka1szZZQieW94hqxWCwWAJC1wqINmqaxvLyMlZUVFBQUiK4D5F9kpygKw8PDmJiYCGqQJeS4wWCz2dDR0QGaptHa2hrShyGSiIVtyEXqNvPz86KnVubm5mAymbB9+3ZBE7xk4l2j0cBms6G+vj5s2uzjArK5qaysREpKCtNFZTKZ0NvbC4/Hw7hp5uTkRN0WQGrKxkBwv/tAKUZiQxxtRQCKokDTNGdiIZFrIiGhiIVEKcTzINhOPhKwU2Fsb/hQBllcwIUEFhcX0dXVhfXr16OmpibsSyq0xuJvyEUeWjFrNi6Xi3kpIr13wEe1My5pM0I0XLrNIrmeeMN/INF/8bTZbIy+2djYGJRKpU9bczSm3KVwX9gI5nfPpRGArWwt5r0i6wAXYiEzLFK7r+GQEMTiP5uSlJQUNXVZkgrT6/Xo7OxEYWEhtm3bFnE+NhSxsAcR+ezshUQs5HOtW7cONTU1Pp9LrLmTlZUVtLe3Q6FQoLKyUpTCY6BrC5Y2M5lM6Orq4txtlqgIZ0lAbAFKS0sZa12TyYTZ2VkMDAwgNTXVZ/GMVHZGihGL1+sN+7n8GwG8Xi9zr6KhCEA2rlyJJdp6atGA5Ikl0GxKUlIS0wUmNpRKJfR6PWZmZlBbW4vi4mLRjhuoduNwOKDT6eDxeHgPIvIhFnY327Zt27Bhw4aIjhcMZNaG5LRjudCQtBmRJmd3mw0NDSElJeW0SpuFk1Bhg22tS6bcSdpsZGQEdrudsQUQGu0lSsQSDiqVKmAjgNlsDljL4tsIEEoI1x9ithrHEpImFhKl+BfoIxWKDAa73Y6ZmRlBi3w4BKqxBLLz5QMuMvfAqbRUZ2cnbDZbWM97oRFLoJpNW1tb3CRdpJQ2izaELOakuE1mohwOB1NzINEeqc9wrTlIMWIRo4vQ/16xa1n+jQDBZo3Y4KMGQLxYpEbY4SBJYvGfTfFn92gQy8LCArq7u5Geno6kpCTRuzD8vVOGh4cxPj4eNHrge8xgWF5eRnt7OzIzM4MachEI7TIjURdFUT41Gynpe52OaTM+EUs4aDQaFBcX+8jOsFWI1Wq1z/BhIFuA0yViCQeujQDBtOD4TN0nonskIEFi8Z9NCTRwRWosYrQ3UhSFgYEBJvXl9XoxNzcX0TEDQalUwu12M7MwTqdTlIaAYAs3TdOYmprCwMAAZ+MvIcV7k8kEnU6HgoKCNbUoKYtQng5ps2iRNjvaKysrY2RnzGYzIzuTnp7uYwugUqkk1W5MEO25p1CNAKRz0b8RgK+cS6J1hAESIhY+sylsC+FICo42mw06nQ4AmNbeubm5qKTZlEolbDYbjh49GrEGF/uYgSIMtiFXc3MzIxcSDnwWb7bvTCgFAqlELKEQLm3W09PD5NTFGh4VA2JGLKHAlp2pqqpiOjP9ZWfUarVoGz6xEOuBWi6NAMnJyaAoCnq9HtnZ2SE3Lf7KxokCSRALX/FIMYhlbm4OPT09azxGxNbfAk59PvIibt26NWINLoJA10om3NVqNWdDLgKuqTCPx4Oenh6YTCbs2rUr6GIbLgLicw9imVYLlTabmZkBAB9f93inzWJ9brVajcLCQhQWFgIAkwqanZ2FzWZjhlhJ6iye9yfeDpL+jQButxvj4+NYWFjA6OgoI9cSrBEgmsTi73dvMplwzz334I033sDk5CQKCgpw8OBB3HvvvSEFdgMh7sTCxTLYH+TnhEQWxBxrbm4u4FS72PUbUjhfWVlBdnY2ysrKRDu2PxGEk4AJBy6pMH/iCmfBG+54fHa38Yp+2Gkzj8eD9957D5mZmXFPm8UqYgmH1NRUZnB4YWEBVVVVMJlMWFxcZO4Puz4TicspX0hNAkitViMtLQ1arRYNDQ1BGwFUKhVMJhOjHCA2Avndz87OYnZ2Fvfddx+2bduGiYkJ/Nu//RtmZ2fx7LPP8jp+3IiFr2WwP4QQgMViQUdHB5RKJfbt2+cj6BjJcYOBmH9lZWWhqqoKBoNBlOMSkIiFoigMDg5ienoa27dvF2wnGi4qCCTNEu76Qh2PD6nEe/EkINdRUlKCysrKkGmzaHebSSnlBHy0iJNUUHl5uc/9mZiYYO5PrDS7pOjHwq6xBGsEePvtt3HnnXeCoiisX78eDz30ED71qU+hpqYm4r95ML/77du347nnnmP+u6qqCj/+8Y9x9dVXw+Px8MoOxYVYxPJN4TPLQjSxysrKsHnz5qAPmxiGXOz6AzH/mp2djYrnvdfrxfHjx+F2u9HS0hLR7oYQgf+Cxe5i4yvNcroafbFb3/l0m4lZiJXS/QACtxv73x+Xy8XcH/YOndwfsafMpRaxAMG7wtiNANdccw2uvPJKXH/99VhZWcHLL7+M73znOygpKcHAwEBEnymU370/lpeXkZmZybvkEBdiIQ9OJBLbXCMLj8eDvr4+LC4u+niZhDpuJATgdrvR1dWFlZUVn/qDWA6SbNhsNqysrGD9+vVobm6OuBmA/C3YxOJ2u9HZ2Qmr1cq7i01MiRgp7cxDIVC3mdFo9EkLiZU2k2LEEu56kpOTA8rOEJkmolJM7k+grALfa0oUYvFHUlISvF4vzj33XNxxxx1wOBzo7++P6POE87tnw2Aw4N5778XXvvY13ueJWyos0kWH3PRQWF1dRUdHB9RqNfbt28epkE0IS8hLu7y8DJ1OB61Wu8b3XgwHSQJiljU6Oork5GTU1dWJssCQY5CXkUizaLXasDMwoa5VLEhthx4O7G4z/7SQf9osLy9PkMKulIiF7zsTTHbGbDZjbm4OAwMD0Gg0Pm3NfJ9BKRKLUJMvjUaDhoYGwecN53fPxsrKCi644AJs27YN3//+93mfK+7Fe6EIFbHQNI2ZmRn09fUxNr5cHy7yc3yLymRmpKqqChUVFWt+V6xuM7fbje7ubiwvL2PLli2YmpoS1TIAOPV5SOqwsrISlZWVgs4RqmazurqKiYkJZGZmIi8vL2wTgJQWUKHwTwuRaXfSbcY3bSY1oo10EWfLzlRUVMDj8TBETGRn/DuoQp2PjDBIjVj4dLOK2RUWzu/e6XRCpVJhdXUV559/PjIyMvDCCy8I2lCedsRCWmGNRiMaGxuRn5/P+7gA96If2xUx1MyIGMRCDLnS0tLQ2tqK1dVV0a2JAWBgYGCNDbLQ4wW6vrm5OXR3dyM/Px+zs7Po7+9nBu7y8vKCFnSltpBGikDT7v5ps7y8vKDCh1JLhYl9PUlJScjPz2feYYfDwXRQzc7Owuv1+ky4+0ufkOclnu3GgcBnQFJMYgnnd69SqbCysoLzzjsPKSkpePnllwWrOseNWCJ9AAMRy8rKCnQ6HTQaDfbt2xd2FxzsuMCpP344piYLfWpqatjW20iJhYg7VlRUoKqqCgqFQtQaBnCqsAoAS0tLPtIsQuEfsdA0jcHBQUxNTaG+vp6pP3k8njUF3ezsbOTl5UXdG0MqCJQ2I4vo6Oioz26dpM2kSCzRjA40Go1P/cpqtcJkMjGyM+wJ99zcXOZdlmLEwtWWWExJl3B+9ysrKzj33HNhs9nwxBNPYGVlBSsrKwCAgoICXgR9WkQs7FQUe+EVAtJQEI4EpqenmVTbpk2bwp5PaBszRVHo6+vD/Pz8mghCzGFOs9mM9vZ2AEBDQ4Mo3UtsYiHzPHa7HXv37kV6ejpDZGq12sfn3X/BUKvVDAm5XK6YzkH4I5ZDmv67df+0WUZGBiiKgs1mk4TsRyy1whQKBbRaLbRaLcrKytZ43vf19TH3xGw2i2ILIBb4SrrEavK+ra0NH374IQBg06ZNPt8bGxtDeXk552NJ404LAGk3drvd6OnpCZuK4nvsYCTg9XrR29sLvV7PK9UmhASI5IxCoUBra+uaDhkxiIWmaUxOTmJwcBBbtmxBX1+faIsDIZbV1VW0tbUhIyMDe/fuhVqtDrpA+y8YpOCt1+sBAP/85z/XqBJLadceLfinzVZXVzE7O4vl5WV8+OGHTNpMaJFbDHB1RYwG/D3v3W43FhcXMTAwgOHhYTgcDp/nJtZWxGzwLd7Hyu/+7LPPFm3jlNCpMKK9lZ6ejn379om2kw1GLBaLBTqdTpBcCiEBrukLtiHX1q1bA74EQtWICYimmNFoxM6dO5GTk4PBwUFRW4RtNhs++OADwZEkKXhnZGRgdnYWe/bswdLSEoxGI6ampgCAWSy4NAGcDiB6VBRFwWg0MvfE31sl1ouolNSNiRqzUqlES0uLjwLx1NQUaJqOm5o1n1SYrBUWQ5Adm8lkwubNmwN2YUWCQDMnXAcsQx2TXHuoa2UPI4YzGoukxmKz2dDe3o6kpCSf+pBYmlw0TcNoNGJpaQmNjY2MrlSkYO/cKYrC6uoqjEYjryYAMSCFBZQ8S4GK3CRtNj09HXARjeb1SAXsBhwiO7Nhwwaf9YMoEKvVap/5omimW7k2BtntdtA0LRNLLOByuZh225ycHFRWVop+DvbMCdEWm5+fx44dOwQvkORBCtX+6HK50NHRwdQhwg0jBpuUDwcSDRUXF/sIcAKRR0HAR/UUi8WCvLw8UUiFPbxJoFQqkZWVhaysLFRWVjKqu0aj8WPTBBDo8wRKm5lMJiwsLGBwcNBnNkTM2oPUWnuDXQ9bgZg9X2Q2mxnZGa1Wy9wfYgsgFrhGLFarFQBkYuEDIS/40tISdDodMjMzGaG7aICkwtg1jki7pMIpMpPPlpWVhdbWVk4vO5usuIbWxJ44WDQUaacZu56yceNGrK6uCj5WIIS6NrbqLumoMRqNPk0AJJqRqscKH3DZUPgvooFmQ8RKm0ktYuFKdIFkZ0hHXn9/P2+HyHDgSiwWiwUqlUpwy288kRARC5k0Hx4e9tHeioZvCnBqcTWbzeju7l4jqy8U7Kl2NoQYcrGvkxwz3IPKVZolklTY/Pw8urq6mHrKxMSEoOMEuy6+P0+mutlNAOyp90RvAhDydwqVNiM1K6FpM6lZEwuNoJKTk326FEl9hghpKhQKn7ZmvveI60bQZrMhPT1dUveUKyRPLCStYrVasXv3bsYXIFq+9xRFwW63Y3l5GfX19YKVgv1B5k7YxML2NRHS0cYmllAg8zbp6elhpVmEdJqx51PY6UIx0mqBziUEoabeE7UJQIwIQcy0mZSK94A4Xiz+DpGkrmcymTA/P8/cI7a+Waj3i7wPXCOWaEjmxwKSToWZTCZ0dHQgOzsbra2tPn+waBCL3W6HTqeDx+NBeXm5aKRCwF60/TvMhCxkgeoO/iBNB1y7svhGLP7zKex8sJiLjNgLlv+CSlz+ZmdnMTAwgLS0tDXWu1KE2Pc4krTZ6RKxhAK7rseWnTGbzRgbG0N3d7ePbYK/7Axb0T0c2DphiQZJRiw0TWN0dBSjo6NBbW/5yuaHAyloFxUVITU1NSoLCSFDkjIS2mFGEGqYk6IoDAwMYGZmhlfTAZ8og9RTtFotM58i9FhcEY0BRYVC4bNYsJsASI6dNAFkZmaKfn6hiHZNg2/aTIoRS7SJzv8eEdsEs9mMnp4eeDweRnYmNzfXx6k2HAixSOmeckVciSXQ7tjpdDI74D179gR9kcWKWCiKwvDwMCYmJpiCdnd3t+gLInDq846Pj8NgMKC+vn6Ne6UQBEpdOZ1OJvJqbW3l1XTAtXhPyDGU8oCY0VEsX65wTQAAMDg4iPz8/Lg2AcRaOy1c2kyhUGB+fp6x4433pHs8utT8bROsVivTCDA2NsZ4T83PzyMnJydkYV6OWESC0WhER0cH8vLy0NjYGPLBFINYHA4HOjo6GJMsksaJRprN4XDA5XJheXk5YkMuNvyJxWw2Q6fTITc3F9u3b+cdeYVb7P31vkKRY6JELKHg3wTgcDhw9OhRqFQqH+l70tIcyyaAeHZhBUqb/etf/4JCoYjrkCYb8W5/ZqtIEFuA2dlZDA8PY2ZmBv39/UhNTfVJubLXvFj63QOn1qhvfetbePrpp+F0OnHeeefhkUceEbQBlgSxUBSFkZERjI+PY+vWrYx/digkJSXxmmT3ByGx/Pz8NSZZYptykXMplUpUV1eLugshize7u4x0zgmVug9GBqHqKcGOJRakkg4gRF1VVYWkpKSQTQC5ublRbRWVUntvUlISVCoVNmzYgPz8fFG7zYRCarbESqUSaWlpSElJwc6dO+F2u5ka1tDQECM7k5aWhunpaSwvL8fM7x4Abr/9dvztb3/DM888g6ysLNx888343Oc+h3/+85+8zxH3VJjdbkdHRwdcLhcvh8JwcyHBwJ7lqKmpQUlJScD6DRFIjAQ0TWNsbAwjIyOoqalhXjAxQUiwq6vLR5olkuMFigrC1VMC4XSIWIKBPDPxbgKQCrEAvsX7eA1pshHviCUQ2J1qarUaBQUFjLCs3W6H2WzG8ePH8Y1vfANutxsFBQV48MEHsX///qj63S8vL+N3v/sdnnrqKXzyk58EAPz+97/H1q1b8cEHH2Dv3r28zhNXYtHr9dDpdCgsLORtrSuEWNiT7dGu37AtikmbdLRmb3p6epCcnIyWlpaId8iBUmFc6inBjiUWpLSABgPXJgDS0hypEoBUSJYgWPE+lkOa/tcjNWIJFUWlpqYiNTUVl1xyCS688ELcfvvt6O/vxyuvvII77rgDhYWFjKumUATzuz958iTcbjf279/PfK2mpgZlZWU4duxYYhHL7OwsampqsGHDBt6/S4pgXBdqUnvIyckJW7+JNBVGfGGIIRfRHRLTnhg4RcwOhwMFBQVobGwU5SVi12xomsbQ0BAmJycFNRuIpTsWreNFG8GaAIjHSqRKAFJKhQHc2439O6nITl3stJlUiYVL1KpSqaBQKHDGGWfg5z//ORwOB+M1JRSh/O7n5+eRnJzM2FMQFBUVYX5+nve54kosDQ0NES3gXCIL9tT+li1bUFZWxsk7RSgBEJ+WQHMjYvmnsNuxNRoNSkpKRHuByOLtdrvR0dEBm83GqZ4S6liBYDQa0dPTg9TU1NNay4uAixIA3yYAqRGL0HZjslMXO21GUVRcvXsCga975MaNGwGcSi3yjRrY4ON3LwYkUbwXinCzLC6XC11dXVhdXfWZ2udyXL6E5/V60dfXh4WFhaA+LWIQC0mxra6uYs+ePejp6RHdnthut+PYsWOcJvXDHcufWNj+L5WVlYwK8sjICJKTk5mFNdACkmgRSyjwUQII1QQgJWIRY0CSb9osFAEncsQCQFQDt3B+94cOHYLL5cLS0pJP1LKwsCBoUDzhiSUYARBRx4yMDJ90FNfj8lmswxlyEUSaYmN73re0tCA5OVl0e2Kn04nFxUVUVlbyqqcEgj8RUBSF3t5eLC4uorm5GVqtFjRNMzt4kg4hxkzsekSi9vNzhZAmAKmRbDQiqEBpM7avChA8bSZFYomXyVc4v/vS0lKo1Wq89dZbuPTSSwEAAwMDmJycREtLC+/zJTSxJCUlrVmo2TtivqKOBHwIgEzsr1+/HjU1NSEf5EhSbHNzc+ju7l5TQBczvTY0NITl5WWsW7cOmzdvjviYbGJxOp1ob28HRVFMk4Hb7WYWI38rXpvNxtgTj42NMa6TBoMB69evP62HErk2AZDnVCopsVhM3gfyVTEajT66XYRkPB6P5IiFTwu0mMQSzu8eAK6//np885vfZCLBW265BS0tLYJScHFvN44E/hGLx+NBd3c3zGZzRG23XGs3XA25CISQAEVRGBwcxPT0dEBpFrHSa6SeUlBQIFr4TYhleXkZ7e3tyMnJYYY2wy3Q/sJ/S0tL6OjowMzMDEZGRpCZmcmkkrRarSQW1mghUBMA8XYnLqrxtgMgvkCxXMjZaTOi22U2m2E2mzEyMgKbzQa73Q632y0ZBWuv18v572Oz2WLqxXL//fdDqVTi0ksv9RmQFIKEjljYBEA6sVJTUyO2KQ4XsZC2ZYfDwWv2hm8qjEizEGWAQOmgSInFX/lYbGtiMpEtNHoETn3G3NxcqFQq1NXVISkpiemumpiYgFKpZGozubm5kivYigl2EwAh7Q0bNjCRHWkCIEQTq8WUPDPxXLiTkpJ85kKOHz8OrVYLi8UStyFNf3i9Xk7F81jYErP97oFT6diHH34YDz/8cMTHTnhi8Xg8mJqaQn9/v2Bf9UDHDbZYsw25WlpaeM3eKJVKuN1uTj+7tLTE7PJDzfiI4Z/CTq+JaU08OTkJt9uN5uZm5mUX47gajYZJh1AUheXlZZhMJkxOTqK3t5cp7kZzYY33zhf4qKZBFkpgrS0xEBslAPLMSC31RBxMw6XNYqVtxrcrLBHdI4EET4UpFArMzs7C6XSiqamJ6bCJFCQSYueu2bUboZIpXCIWvtIsQv1Tgs2niDEtT1JrFouFmS4WA4Hug1KpRE5ODnJyclBVVcWoyxqNRp+FlUQ0ieCzwhWBaivBmgDm5uaiqgRAnhkpEC4Bu3gfLG3GbhZhb0gidYkMBplYJA6LxQKDwYCkpCS0traKuhMjDyN5cdmGXJHWbkIt2l6vF729vdDr9ZyNv/gSS7j5lEg71ywWC9ra2pCeno76+nq0t7cLPlYghIum/NVlV1ZWYDQaMTMzg76+Pmi1WoZk/L0yEg3hivbBmgDYlrtiKQFIMWIJ1RXmnzZju0RGM23GtVONoqiY11jEREISCzGv0mq1yMzMFD28Z8vFkFbi5ORkwYZcBKFIwL9lmetn4kMsXJwkI4lY9Ho9Ojo6UFpaii1btsBisYg+ec/358nCWllZCZfLxSwexBqB7RqZaN7ifLvBgjUBGI3GiJUApFBj8Qef1l7/bjMS6ZG0WSgVYj7gGrHYbDbQNC0TixDwfQjZQ4gNDQ1YWlqCw+EQ/brIH56kDyI15CIIRgIGgwEdHR2cWpb9wZUIuOp9CZmLYYttsjvkojHQGMnxkpOTsW7dOqxbtw40TcNisfjk3EmaKC8vj2nnPV3BbgIoLS2F1+vF8vKy4CYA0mosNWIR8jf0j/TYaTO2CrGQtBlXYrFarQAgE0u0YbVaodPpoFQqmSFEi8USFVFHsngNDAzwcl8MB/80E1uaZdu2bYI100LdA1JPmZiY4KT3xZcMvF4v0+Ltr24Q6lhCFiGxRS0zMjKQkZGB8vJyn1mRvr4+uN1u5OTk+MjNSA1izq+oVCqfJgCn08l03pFaFft++Ed3UpmlYUOsAUkuaTN2E0CotBkfYlGr1QlbE0wIYpmfn0d3dzc2bNiA6upq5mGJliGXTqcDAFFJBfCtsXg8HnR2djLSLEItb0N1mrHrKWwjs3DH45oKs9vtaG9vh1KpREtLy5qXQGoRSyj4p4msVitMJhP0ej2Ghoag0WiY3alUEM3FPCUlhVcTgNRsiYHoTd6z02YURTHaZuTehEqb8SGWRNbOk3QqjKIo9Pf3Y3Z2FnV1dWt222ITCzHkKigogM1mE30egizapMCdmprKSLNEekx/cKmnBAJXMjCbzWhvb0dhYSG2bdsW8AWOhrpxLMB2/isrK2P0qkgtAgDjdCqG/H2k1xqLc4RrAiDyPBaLRRI+7cT8LtrpTKVSGTZtlpWVxRANV2Ih9zFRIdmIxWazoaOjAzRNB/VtF4tY2DWCrVu3oqSkBP/4xz9Ej4aUSiVcLheOHTuGjRs3YvPmzRG/gIGIRah/CsCtZkPmhqqrq1FaWhr0+OxWbbEWmnjoY7H1qjZu3IijR48iLy+Pkb9PTk72KXrHyus9XumnQE0AMzMzWF1dxYkTJ5gmAPIvHkoA5BmOdZ0sVNpsYmICADA0NIT8/PyQc0WJ3GoMSIBYAu1qFxcX0dXVhXXr1qGmpiYow4tlyNXZ2QmLxeJTIxDbO4WiKExPT8PlcqGxsVGQj3QgsImAbz0l2PGCLd4kgpybm+M0NyQ2scR7F8y+hpKSkjXy90R9Nysri6lFRFNuRgp1DdIEUFhYCL1ej7179zJNAOPj4+jp6VlT6I7FYh8vYvEHO23mcrnw/vvvIz09PWzazGaziRb5Pfroo3j00UcxPj4OAKitrcX3vvc9HDhwAMCpjeh3vvMdHD58GKurq6iursbdd9/NiFEKQdyJhQ2KopjBPS76W+Fk88NhZWUF7e3t0Gq1a1JSYqbZnE4n41yZlJQkGqkAH3VxCamnhDqeP1wuF9rb2+HxeNDS0sKpmM0mllDf5wopyeaTa2fL32/evJnZoZKFlXw/Grv3WGtzhQKpZ0TaBCDm9QDxJxY2yDVVVlYy83H+abP09HS8+uqr0Gq1oqXCSkpK8LOf/QybN28GTdP44x//iEsuuQTt7e2ora3FNddcg6WlJbz88svIz8/HU089hSuuuAInTpxAY2OjoHNKhlgcDgc6OjoYXSwuC2Mkiz8x5KqsrGT+0GxEOihIQCRgsrOzUV1djX/9618RH5MNdnotUv8UIHAqbGVlBW1tbcjKyuJlIR2OWKRCEmLCv7DL3r339vYyZl5iTXfHO2IhCEZy/k0ARFaFvWMnJCO2EgBxmZUKiLIxuaZAabPh4WG0t7fj+PHj8Hg8+PznP49zzz0X5557LkpLSwWd96KLLvL57x//+Md49NFH8cEHH6C2thZHjx7Fo48+it27dwMA/vM//xP3338/Tp48mbjEolAomME6Ugjm+nAFks0PBy6GXIA4qTBSiyACjHa7XdT0GnCqSL+8vIyqqqqI/VOAtRELqdcEI+BQCEcsJpMJFEUhJyeH085SShELF7DlZjZt2sTs3o1GI6amphidL7Kw8m3ikEIqjIDLtfjLqoRSAsjNzY0oFZSIXiypqamoq6vDK6+8gp///Oc4cuQIamtr8fvf/x5f//rXMTs7G3S94gqv14tnnnkGVquV8VlpbW3FX/7yF1xwwQXIzs7GX//6VzgcDpx99tmCzxN3YhkaGsLo6ChTNOcDsvhzfcHY0+379u0LGYZHEg0R8iKObaQWQRZtMR56Uk+ZmppCWlqaKP4pwEeLN9sWIJJ6DblW/2sfHBzE5OQkc5+5TsAnErH4g717pyiKaeGdmpryiWaIxHu4Z0RK90JIu3EwJQDSFBFJE4AUiYWPF4vT6URZWRm+973v4Xvf+x4sFktExfyuri60tLTA4XBAq9XihRdewLZt2wAAf/3rX/H5z38eeXl5SEpKQlpaGl544QVs2rRJ8PniTixqtZqX9DwbbOmVcOmZxcVFdHZ2ori4mNN0u9BUGJntCCTNQs4Z6UPPrqds2bIFMzMzgo/lD/K529vbYbFYBP9tgMDE4vF40NHRAavVil27diElJQU2m23NBDxJF7H1vKSyOxcDSqUS2dnZyM7O9pGbMRqN6OrqAk3TPmQbaFBOahFLJM90MCUAk8kkqAmAzyIeK/AVoGTXWCLtEKuuroZOp8Py8jKeffZZXHvttfjHP/6Bbdu24b/+67+wtLSEN998E/n5+XjxxRdxxRVX4MiRI6irqxN0vrgTS0VFheDIgAuxsDultm/fjvXr13M+Nt+0ldFohE6nw7p167B169Y1Dza5XjH9U5aWlkTdubpcLthsNqSkpGDv3r0Rzdj4E4vNZkNbWxtSUlKYMNzr9a6ZgCcLLFvPKy8vj4lOT0f4y82QWgSxJk5PT2fuAyFbKRGL2AOS7CYAkkYkz8XMzAxomg7ZBCDViIUPsYilCg6cer5IBNLc3Izjx4/jV7/6Fe644w489NBD6O7uRm1tLYBTg+FHjhzBww8/jN/85jeCzhd3YokEpBAWjJicTic6OzvhcDh4d0rxSYUFmoMJdr2AcGIJNJ8iljUxcEqzrK+vD0qlEs3NzaK9mDRNw2Qyob293UcPLZBigFqtRlFREYqKitYssDabDUNDQ1hZWWH0q2K9eMTKmti/FkEW1Z6eHni9XuTk5MDpdEpG8iPaJOevWh2uCeB0IJbKysqoXQtFUXA6nbDZbADWds9FWmNOaGIBghOA2WyGTqdDTk4OGhsbeQ+tKZVKTq3MHo8HXV1dWF5eXqOV5Q+ijyXUPyXQfIoYxELTNCYmJjA0NISysjLMz8+L8lKSzzs7O4vR0VFUV1ejrKzM5/vhfp+9wP7rX/9CdnY2nE6nT7ooHl4rsYwU/MmWiGdOTU1hcnISBoMhKp1VfBDLhTxUE8DAwABcLhdSU1Ph8XgkowQA8CMWm80mmj7dXXfdhQMHDqCsrAyrq6t46qmn8O677+LQoUOoqanBpk2bcOONN+K+++5DXl4eXnzxRRw+fBivvvqq4HPGnVjEcHtkEwB7kRRqyEWO63K5Qv6MxWJBe3s7NBoNWltbOaWN+NZuyAAn6eII5J8SCbF4vV709PTAaDRi165doGkac3Nzgo/HBkldjY2NiWLERuoS/o6AxGuF3corBX/zaIAtnrm6uoqMjAykp6fDaDQynVXERyQvLw+pqakxuQ/xTMsFagIYHx+H0WiUjBIAwE/GP9JiPRuLi4u45pprMDc3h6ysLNTX1+PQoUP49Kc/DQB47bXXcOedd+Kiiy6CxWLBpk2b8Mc//hGf+cxnBJ8z7sQSKdgtxx6Ph1HajcSQCwifCiNpKb7SLHxCTK7+KULTMw6HgzHiamlpgUajEa1m43a7GTHPhoYG0dw92b4f7F0ru/jd2dnpE83k5eWJrvsmBdA0DZVKxcxCkEXVaDTCYDBgeHgYKSkpzD2IxEeEy7VIIfVEmgCys7Ph8XhQV1fHKCPEUwkA4NdQYLVaRRM8/d3vfhfy+5s3b8Zzzz0nyrkIEp5YCAGQ6CElJSViQy4geGRB1AGmpqYEteFyjTD4+KcIiViWlpbQ3t6OvLw81NbWMjspMVJrVqsVJ0+eRHp6OpRKZdC/xZrPRNNQmEyAxQJotaBzcwHWz4Qib//iN3GOJIOwp2s0o/C7P6SzisjN+E92Z2dnM2kzMVNEUlM3Jqk5pVLJqQkgFmZv8UqFxQNxJxYxUmFklyqWsCM5rv8C63K50NHRAYfDEdDWlwvCLdzsekpdXR3WrVsX0fECYWZmBr29vQFThZEOIRoMBuh0OsZF8u233w5/vKUlqJ96CsmPPQbl2BjzZaqiAq4bb4T7yiuB7GwA3IrngZwjyWDi9PR0xIOJUkG4e6FSqRjxTAAhHSNzc3MjimakErEQBKv5BGsCYDtFRqtexZVYiG2DlCwa+CLuxBIJKIqC3W7H8vIyGhoaRPdOYUcsy8vLaG9vR1ZWFlpaWgS/hKGIIFw9JdjxyEAjFxuCgYEBzM7OBlUdEGpNTNM0JicnMTg4uMa0LNQCmPTWW0i75hrg/7pTfK5lfBwpd92FlHvvhf1Pf4Li//Sn+CI5OZlZTIh/hsFgwNTU1JpoRgyZlViBb10jLS0NaWlpKCkpCegYmZmZydwHvuKZUo1YQsE/nerxeJgBTdIEwBYUjTTC83q9nDcxsrpxnEDqAx6PB+Xl5aKSCuBLLCSdQqRZInm4gqXYhPqnsFuYQ+2GSLTldDqxd+/eoAJ3QqyJKYpCb28vFhcX19S2Qh1PefgwUq644lQKLMDPkK95rTakXHoZfnfZPXivshnAIP78pR3YXha8Ay8Y2P4ZVVVVPqkRIrNCFpK8vLw1fwcpzdFEUjD3F4v0l3dnfz/QfQh0LVKKWIQMSCYlJUVNCQDgV7yXiSUOIB7xRUVFSEtLi0p7JSGA7u5uLCwsiNLVBAROsUXqnwKEXvAIaWm1WuzduzdktMWWdOFyHf6qx4FsWQNe29ISUq666hSphImQVKDhpYFHX/wpWm76A1Y0WnzxTx1hr43g3vNKcXBnRcDvsVMjRGbFaDRicnLSJ5rJz8+X3IsuZidWIPFMk8mEyclJ9Pb2+hS8A9WoEjFiCQV/JQCKopgmgImJCUFNAFxTYV6vF3a7XTb6igR8Hka2RzwZROzp6YlIOj8YPB4P7HY7VldX0draGtLHmg/YqTC+9ZRgxwOCD10uLCygs7OTM2mR43FZtFZXV9HW1obMzMygqsfBajaqJ54AbLaAkUogqEAj1e3E57rfxh92Xszpdwj+69AU/uvQFOeff+xzNWjdXeUjGkl0zcicktvtjlvbarTBFs/0j+qI9L3//JDUIhaKokT9+7CbAAAIagLgGkVZrVYAkGsssQDbkIvtES9E4TgciJwIAOzevVvUiIgQi5B6SrDjAWuJhU3CfEiLSwQEfKS9tnHjxpCEFZBYaBrKRx7hdD3++PLJl/GH5ot8usXExo3P9wPo9/uqAgAFwAxACRz7JwAl0tXAn764FZtK8mO+Y4/V7Ih/wZtEdWR+iNgSZ2RkSGbiPdrXwaUJgJAMaQLg43cPRK4PFk8kBLEsLy9Dp9NBq9WitbXVZyfCZZCRK2iaxvj4OIaHh1FRUYGxsTHR02xKpRI2m01U/xT/gjuZ51laWvIhYa7HA4Lng9n3iIv2WkBiMRqh/D/veD5Qgkb50jyyHatYSuX+mcTHRwuW1Q187vFenCKewEgGcPjf9yJXK273WTyGEgN13JEaxOLiIvR6PWel6mginkoAxMDLaDT6NAE4HA64XK6wfzei1ZeoETFN0/EnllA3mKZpTE9Po7+/H1VVVaioqFjz82I5PbIXY6K6Ozw8LPrL63K5oNfrUVFRIYp/CuCbXrPZbGhvb4dareasBuB/LCBwxMKe0g8nX0PgTyw0TQOrq7yuyR9alz3OxOKP0H9DF4CzfvUBpyNVpQNP/FsrtJrwr6YURCjJ/JDRaGSEMk0mk49SNXvnHqvFnk+hXGywDbxomobdbofRaMTy8jJjRRHKVdRisSAtLS3uf1uhoCgq/sQSDF6vF729vdDr9SEL52IQi/9wZXJyMhMFifXyEn+TpaUlFBYWiuafAny0eAcSehRyLHK9bDidTrS3t4OmaWZKn8+1kWNSFAUqwl2sJVmcepcUMWIFWn5xlPPPP3bxiugdkUJAIgSycydK1WTn3tfXx8jNkJZmseqWoa4n3lAoFEybN/E28nq9AZsAcnNzkZGRweibiYFwfvcAcOzYMdx999348MMPoVKp0NDQgEOHDkX095EEsfjvasmuW6VSrfE08UekxLKwsICuri6UlpZi8+bNzMNI/lcMXwd2PaWoqEj0F4oIPU5OTqKmpkawhSk5FuBbsyHWxDk5Odi+fTuvnSCbqLxe76m/c14eqMpKKMbGOBfvAYCCApPZRVjSJG5RU2zc+PIs8PIsAODBSzbj7O3cbCHERqDivb+Gl9VqhdFohF6vx9DQEDQajY/cjJgRhlSIhQ1i75GVlbWmCYBsCm+44QZUVVUhNTUVk5OT2LhxY0TnDOd3f+zYMZx//vm466678OCDDyIpKQkdHR0R3ztJEAsbZKHfsGEDqqurw35AocQSriOL7fUSSa7TYrGgra2NqaeMjIyIak9MURQoisLU1FTE+mjARzUbQvSkFTpYKpLL8SiKgtfr/ciHXKmE59/+Der/+A/e1/eH5oujWrhPZNzy0hDw0hC67v5EzM8drt1YoVBAq9VCq9Vi48aNTB2CPYxIbInz8vIiTgVJjVhItO5PnuwmgJqaGvzhD3/A448/zszNVVVV4fzzz8edd94pqGs0nN/97bffjltvvRV33nkn8zPV1dXCPuT/QaFQQDJ3nkyFd3Z2ora2NqBRViAIIRaXy4UTJ05gYWEBLS0tAf9gZIGNNBo6duwY1q1bh6amJqjVasHOlIHgdDrxr3/9CxRFYdu2bRGTCgH53MPDw+jq6kJ9fT1vv3v2sTweD7OjJcfwXHUVkJYGmuPL71UoYFen4Pntn+R9DR831P34vZifk2/KmNQhqqur0dLSgl27diEvLw8mkwnHjx/HsWPH0N/fD71eL2icQGoOkuSdDxWVqVQqnHnmmfjEJz6BHTt2wGg04mc/+xkcDocoTRBerxdPP/0004m6uLiIDz/8EIWFhWhtbUVRURHOOussvP/++xGfSxIRCxmyc7lcvFtvk5KSeD14fKRZhJrdsP3i/aOhSA10CMjnyMnJgcvlErWDRKFQoL+/H1arVbA1MRmyTElJQV9fHxYWFlBQUID8/PxTopTZ2XA++SRSLr0UtFIZckjSCwUABf7ts9/FiiZxWzBjiXe752KaFoskQghkS7y0tASj0YiRkRHY7XZGWiUvL4+TtIrUIhYuxEJAbIkzMzNx8OBBHDx4MKJzB/O7/+CDUw0l3//+93HfffehoaEBjz/+OD71qU+hu7s7ojqwJIilra0NGo0GTU1NvDW4+EQsRJqFa1pHSDTErqcEWpTFUA+enZ1FT08P8zmOHj0qmtSIw+GA1+uFy+US1FUGsIr0FIXt27czxlTsuYeCggLk796NnOeeQ8pVV4H+P60wds2FJn8fTSq+fNGdOFLRJMpn/DjglpeG0BVDYhGzQ02lUjEkAoDpqiLS9+zv5+TkBNxUSZFYiONrONhsNlFnWIL53ZN16MYbb8R1110HAGhsbMRbb72F//3f/8VPf/pTQedTKBTSIBZCKEINucIt/hRFoa+vD/Pz80HFF4Uemw3/ekqgBz4SYqFpGoODg5iamkJDQwPjiS2WPTGR0lcqldi6datgUmGK9AAzrc6eezAYDDAYDJicnIQyORmFL72Ejf/4B3KeeAIqlrqxp7QU9C23wHPVVXiMQ2szwfFBI659upP3tcsQjmhO3qempqKkpAQlJSU+0ips8Uy2tAqp60mJWPiafIkp5xLM757UVbZt2+bz81u3bsXk5KTg80mGWDQajeC6A0ktBdsxEbFKmqZ5S7PwWbCJdEo46X6hNRb/SIi9oxEzCtq8eTPGxsYEkTw7UmHXU9hITk5GcXExiouLGU0qg8GAjrPOgrW5GWkOBxQWCzY1NiJ/yxYoBCwOu7bkofd756z5OruJgKTqeieX8aW/9PE+hwxfxEorzN9fxeFwMNIqk5OTzPc9Ho/oihyRgK/JVzR1wojffXl5OYqLizEwMODz/cHBQZ92ZCGQBLFEAnb3ln8azWg0oqOjAwUFBdi2bRvvdkYuEUuoekqwY/IlATJnk5qaGjASEjsKmpiY4H08EqmEIhV/sDWpNm7ciPb2djhTUpBeXIzOmRkk6/VMXSYnJyfidlRi/AR81E1XV5GLtjtafFKJ5Of8F4LO8SVc9WQnTkm7SGc37AsagALfPePU8y92G2/Qs8ZJK0yj0fhsVIjcDE3TaG9vX2OJEK8oho/Jl9VqDatowRWh/O4VCgW+853v4J577sGOHTvQ0NCAP/7xj+jv78ezzz4b0XlPS2Jhy45UV1ejtLRU0G4qXHQRrp4S7Jh8Fm29Xo+Ojg7GOCvQ5xDqoeLxeBj9NXYUxNfsiyzSfEiFDavVyiwCO3fuZAjdZDLBYDCgr68PLpcLubm5yM/PR0FBQcRdMv4kA8AnmiENISQvrlAoUF+ejQ9v34l//etfOPvs4C29+hUnPvnghxFdn3CcuvdN64rQ398fs6FEKagbK5VKZGdnIzMzExMTE9i1axcsFgtMJpOPXTVJm0XqMssHfN0jxYpYwvnd33bbbXA4HLj99tthMpmwY8cOHD58GFVVVRGdVxLEEqm/CbstmO17v2vXLmT/n/OgEISKLkg9JS0tjbd/ChcSYJNjbW0tiouLIz4mGzabDW1tbUhJScHevXt96ilcPVlIOonceyGkYjKZ0NHRgZKSEh+JG38vd6vVCoPBgIWFBQwMDCA9PZ1xR8zKyopoJ+o/FMsmSpI+I+CSXinITOE8SzIwu4rLft8u4KqDg5x7y5YtsNlsPkOJxCFRbIkVKakbk2dXo9EgIyNjjVDk7Ows+vv7odVqGZKJ9BkKB74Ri1jF+3B+9wBw5513+syxiAFJEEukIC3HZOebnJwsiu99sFQY13pKIHAhAeIDYzabOWly8TXnCif9wiVi8S/SC3kpZ2Zm0N/fj5qaGh/HSX+wh+uIVIjRaGR8eWiaRl5eHgoKCpCXlxex1bB/NMP+rEtLS1AqlXC5XD7RjNBFqbo4g9dA47vdc6cGIQPAf/Ke3cZbVlbmI45IJFbY8veRRDNSiFgIyPvF/pv4C0W63W6mNtPT0wOv18tEdpHei2DXxKd4n8jKxsBpQiwqlQoGgwGjo6MoKSnBli1bRNl9+BML33oKl2P6w+FwoK2tDUqlEi0tLZzIkU/EQlquq6urUVZWJuh4XIr0oUDu4/T0NBobGxl5C65Qq9VYt24d1q1bx8i46/V6H+0lkjLja7HrD3Y0Mz4+jrGxMaZe5x/NBKvNiImzt69n2ojfe+89NDU1cV6E/MURicTKwsJCxIKRUhDEJAhELP5Qq9UoKipCUVERaJpmWuLJvSCRXW5urih1Kr4RSyJ7sQASIZZIHkiymxweHkZdXZ1oRS/Ad4EVUk8Jd0x/mM1mtLe3o7CwENu2beP8YnMhFrbffTg3zFARi5AiPRskGltdXcXu3bsjziWzZdxJlxCJZsbHx5GUlMSkzHJzc3nPSQEf3TtivUxsCNjRDDt1BnyUFowkmgmHSGaXAkmskB18b2+vzw6ei/y9lFJhZGaE67OpUCiQkZGBjIwMlJeX+0R27DoVId3U1FRBzz3X+2Oz2ZCWlsbr+FKDJIhFKFwuFzo7O+H1erFlyxZRSQX4KLoQWk8JhGAkMDU1hf7+fmzZsgVlZWW87YlDEYvb7UZHRwccDgdaWlrCPrTBjhdpkd7hcECn0yEpKQm7d++OOGUVCBqNxsdi12w2w2AwYGhoCHa7nWkAyM/P5/Tyer1edHZ2wm63Y/fu3T4pErJQkJ2ofzszuYdipMwCQcwowd/vnezg2fL3hGQC1SOklgqL5D77R3akTmUwGDA8PIyUlBSfAU2udsNcfo5EknLEEiesrKwwnUSZmZlRWaSUSiVWVlbwwQcfoKysjHc9Jdgx2Ys2RVHo7+/H3Nxc2EiC6zHZsFqtDCmG87tnH8/fQyXSIv3Kygp0Oh3y8vI468BFCqVSySwA1dXVTAOAXq9n0h2EZHJyctZck9PphE6ng0qlwq5du8JuKILVZoKlzMj/F4popZ/8d/Bs+XtSjyC7d1LTklLEIuZwpH+dyuv1MvdicHAQTqcT2dnZTNosmNwM31RYIvvdAxIhFr4vx8zMDHp7e1FZWYnKykqcPHlS9GEoUqhdWlrCjh07BNVTAoGQAE3TcLvd0Ol0jEaa0PA3WFu0wWCATqdDSUkJqqureaUGCFH5F+n5pBgIFhcX0d3djYqKCpSXl8dtZ0sWCHbqx2AwoKenBx6PB3l5eQzReDwetLW1ITs7G7W1tbwXqnDRTKB25kj8c6IJf/l7Es3Mzc1hYGCAeW5XVlaQnJwcd4KJ5tS9SqVinhEATDRDXDTVarWPiRfZyHm9Xs7NRDabTY5YYgn27p4taSKWiyQBqadYLBbk5uaKRirARwvN8vIyOjo6kJmZKUgjjQ2lUgm32838N03TmJycxODgILZt2xay4yrY8dhRSiRF+snJSYyMjKC2thZFRUW8fj+a8E/9rK6uwmAwMJsW0kUkdAbKH4GGMwO1M/NpABBLH44PAkUzZNZoYGAA/f39PrWZWM6KEMTSPZKYeBHxzOXlZRiNRoyOjqKnp4fxXnE6nZxmrzweDxwOhxyxxAokP09R1JrdvUqlEiStHQjsekplZSVMJpMoxyUgC8bx48dRUVGBqqoqUdNrRBdtYWFBsD9LQA8VntdINgF6vR7Nzc2cbIzjBXYranp6Orq7u1FUVASKohjtNNLOHMhKli8CpcwIyXCNZtgRZDyhVqtRUFCAvr4+7N27lxGMnJ2dZeaNCMlkZmbGJJqJl06YSqViIpXNmzfDbrczDRFmsxnLy8uw2+1MRBPoObJYLAAgRyyxgMlkgk6nQ35+Pmpra9fsRpKSkkSJWIjJGKmnzMzMiGrKRdM0RkdHAZwSeispKRHluIQIXC4XdDod3G43WlpaBPfiKxQKOJ1OJi/Md/EiEZ/L5cKePXtE8ZKINmiaxsTEBEZHR1FfX89Ew2w9s9HRUXR1dSE7O5tJh3CRcA+FYCkzQjbBohmpEAvg297LnhVxuVzMwtrV1cVMvrNrM9G6nnin44BT4pmkkaStrY0RyJyYmEBvby8jN5Obm4vMzEwoFApYrVYAEGWOhYstMXDq2f/MZz6D119/HS+88ELEMv2ARIgl2MtBXvahoaGQ0iyRpsKCzaeImWIj8imrq6sAwHt2IxRIKuzYsWMRpdZI+iszMxMjIyOYm5tjtLpyc3M5pRdsNht0Oh1SU1Oxa9euiFJ8sQJN0xgYGMDCwsKa6IqtZ0Z2oUSdeWRkBMnJyVHVM2PXuNjRDCGWeKTD/BFsSDY5Odln3oidbuzr6/PR8SILqxiQmskXcOpvmZmZyaSDnU4nU5uZmpqCQqHAyy+/zBCvGKm8cLbEBA888IDoGxTJvPX+sxMejwc9PT0wmUxhUzoqlQoul0vQeUPNp4hFLGz5lJaWFh8vBDFgtVphMplQWVnpI4vCB+x0TGlpKTZs2IClpSUYDAb09/fD5XL5FLcDRSFmsxkdHR1Yv359UF0zqcHr9aKrqwtWq3VNO3EgpKamorS0lMmpR1vPDAgczSwsLDDvjMvlislwZjCw26qDgZ1uJPYJJJrp6OgAAIZkcnNzI4pmpBKxsOHfFZaSkuIjnrm6uorXXnsNzz77LIxGI3bu3IkDBw7g/PPPx969ewWlX8PZEgOATqfDL37xC5w4cULUcQ3JEAsbRJpFrVZzkmYRSgDh5lMiUQ0mMBqN0Ol0KC4uRnV1NfPyi0EsRE9sZmYG6enpgh3f2EV6ks9XKpUMiZBWXb1ej7m5OUZniezUMzMzMT8/j97eXmzZsgWlpaURf7ZYgKQOFQoFdu/ezfvlDaZnNj8/L7qeGfBRNDM1NYWhoSHU1dUhOTk5LsOZbAhpNfaPZogq8dTUFHp7exmPlfz8fCaFxBWJQCxsKJVKZGVl4ac//SkOHDiAG2+8Ed/85jdx6NAhXHrppXjuuedw5plnRnz+Z555hrElBk5teK+88ko8/PDDojYoARIklsXFRXR2dmLDhg3MQhwOQojFv54idoqNncbzr6eIQSwURaG7uxtGoxGVlZUwGo2CrzNckZ49pU1y5yQdNDExwVxPRUVFSLFMKYFsXjIzMwPW7fiCi54ZIRmh9QVSo5ucnERTU5OPwGqshzPZiHQ4kq2eQKIZo9HIbMoUCoVPbSbcBkCKxMK1U81msyErKwtXX301rr766ojXiWC2xABw++23o7W1FZdccklE5wgESRHL0NAQxsfHw6r5+oMPAfDR+wo2HxIOFEWhp6cHBoMhoMJypMTidDoZ87KWlhaYzWbo9XrexxHaTkzMuoqKitDd3Y2lpSUUFhZibm4O4+PjyMnJYaKZaMm0R4KlpSUmihRj6DUQ/PXMSAOAUD0zmqaZLrtdu3atKe7yHc4UWwFAzOMlJydj/fr1WL9+vY/HyuTkJBPNsD1W/O+dFImFa93HarX6dLxG+jmC2RIPDw/j7bffRnu7uMraBJIgFpqm0dbWhtXVVUE6XFyJha/elxBTLuJYCQAtLS0B8+xCCQs4NYTW1taGnJwcbN++HSqVire6MSkERyLP4nQ6mdx4S0sLM31ts9mg1+t95O0JyWRlZcW97rKwsMA4ZcYqZadQKJCdnY3s7GxBemZEX81isWDXrl1hyTpWw5kE0RSgVCqVzL2rqqpiit4kbaZQKHxqM2q1OqZzLFxASJ7LNYmtbBzMljg1NRUjIyNrNr2XXnopzjzzTLz77rsRnVcSxKJQKFBaWoqsrCxBRSoucyzhXBiDHZcUS7m8OMQzPi8vL2R6RQhhAcD8/Dy6uroYxQFyTXwiIDahkN/luyiQe5mdne3jzMmWv2Cng/R6Pdrb26FQKJhdel5eXsw7xiYnJzE8PIzt27ejsLAwpudmg4+emVqtRkdHByiKwq5duwSl0KIxnMlGLHXC/IveJJohLbxEIFSj0UhGcZm8a1yIJVa2xD/4wQ9www03+Hyvrq4O999//5qivxBIglgAoLCwUHB6KNwcC5d6SiCwd3zhHgoysb1582Zs3Lgx5Dn4psJomsbIyAjGxsZQX1+/ZoKd6/HE8FAxGAzMvWSTWyCw00FkHkSv12NkZARdXV0+KbNoqrkS++W5uTnJDWuG0zMDTi2mNTU1ohCxGMOZ/oiXTliwaGZ8fBwrKyswm80MSefk5EQ82CoUfIhFTPfIULbE5L30R1lZGSoqKiI+t2SIJZKdRbBUWKT+KewXMNhDQVEUBgcHGW8RoiEU7rhciYW0wy4tLQVN33GxJmaTihC9L+DUjn9oaAjbtm3j3ZrIngchzobsBTQtLc0nZSbWQsVOI+3evVvycuQk4isoKMDJkyeRmpqK1NRURsqe3fIdqVyK0OFMf0hF2ZhEM0tLS9BoNMjJyYHRaMTY2BhT1yIkHqlPDx+wxVvDQUz3yHC2xNGEZIglEgQiFjH8U8gL5/V6A+52iIgkkaPnutPgWmMhpl8qlSqk6Ve4GkukpELIc35+Hs3NzRHZPROkpaWhrKyMcTYkKTN2BxVJmQndaZJ2YgCC00jxAKmjsZsLAg0Y+rd8iyENFGw4M1Q7s5SUjYGPNoJkI8Oua5G0mUql8qnNRDMtSwr3XP4+YtZYuNgSsyHmsO1pQyzkRVAoFILqKYFAXp5AJEBmYNLT09HS0sLrweRSYyH1GiJjE+rFDRYBiVGkJ4oBDocDe/bsiUqXV1JSko+bH+mgGhsbQ3d3NyOhUlBQwJm8bTYb2tvbodVqmSaHRAAZGKysrER5eTnz9UADhqTle3JyMip6ZgA3rxmPxyOJiIUgUFeYf11raWnJJ5rJyspiiCZSmR5/8JHMt9lsoqpyxAuSIZZIU2EAmEnozs5O0fxTApEAmbUReo5wqbDZ2VmmcylcvSbY8cQo0tvtduh0OqSkpHDyIxED/h1UREKF1GY0Gg1DMsHsc5eXl9He3p5QCgDAqeaMnp4ebN26NWy7PWn5JkVsQsakfiWmnhkQPJrxer1YWlqCUqlkFABiOZwZCOHajZVKJSMWCcBHLHJsbIyRvidGXpFGMx83LxZAQsQSCcgfbXh4GFNTU4L96IMdm0QsZEBtdHQU27dvFyyBECrCGBoawuTkpI8tAN/jiVGkX15ehk6nQ2FhIedB1WjAX0KFtOl2dXWBoiifmkNycjLj/bJp0yaUlZXF5ZqFgHSs7dixg1Odjo1wemYpKSk+hmZi6JmR/x0bG4PBYEB9fT2TFovlcGYg8J1jYYtFsqOZkZER2O12xsgrLy8PaWlpgmyJP05+98BpQixk4Z+fn4/Ijz4QCLF4PB5mGHDPnj1MW6MQBCIWknKyWCzYu3cvrzwrycETIolE7h4AI8+yadMm0fxIxIBKpfLxUFlZWWFSQb29vdBoNHA4HDGdUYkUpONvenp6zTS9UMRCz4ymafT19cFoNGLXrl1MU0SshzMDIZI5FnY0Q0ia1GaIkReZN+IazfC5HjliERlCFy9ST1EoFKivrxed7ZVKJex2O/r7+5GUlITW1taIi8D+xXtSD0hOTsbevXt5Hz9Q+6gQUqFpGmNjY0wXHdeIKR7wlwHp6+vD/Pw8srKyMDw8jMnJSR9lZikVlwmIZw0RHRRzMI4gGnpmRE6IDGyyyYldmyGbp2gOZwa7PrGOmZqaipKSEpSUlDBpP6PRiOHhYTgcDk7RDB+1ZZvNFpXnINaQDLEIAal1lJaWYnZ2Nio7a7IzKy4uFs2rXalUMi+Y2WxGW1sb1q9fj5qaGkHHJ7/jdruZHaGQzq/e3l6YzWbs2rUrYcJxr9eLnp4erKysYM+ePUhPT/fZpff29q6xHY6Hq2Gg6+7q6oLNZluzOEcLYuiZeb1edHZ2wul0YufOnSE3QeyUGRCd4cxAiJakC7uTDPjIlphEM8nJyT61GXbtl2vEYrFY5IglXgg0n7K4uCi67/3k5CSsViuKi4t9/AsiBZH5n56eRl9fH6qrqwXXA9h1lJMnTzK7Uz6KsC6Xi5nu3r17tyQWXi4g7d40TWP37t3MIsfepdfU1MBisUCv1/v4gJDvx3KeIdB1x6opIhC46JmRqE+r1cLr9TLX3dzczPu6Q7UzixnNxMqPxd+W2Gw2w2QyYXBwEC6Xi4lmHA4HJ2IhEWWibOpCQTLEwvXldrvd6OrqWqMrJqYpF0V9ZO+blZUlSt6bDYVCgaWlJSwuLqKpqYnZAfEFeTEB4IwzzoDJZIJer8fExASSkpKYxTNUKoio/GZkZCRUW67dbmfavevq6oJeN9ujvbKykpnO1uv1Pjpd5D5F+/MTLTmNRoP6+nrJ3O9AemakAWBsbAwqlQo0TUOj0aCxsTFq7cx8hzMDIR4ilCqVion2/GszJpMJKpUKg4ODyMvLQ3Z2dtC/u1xjiQP851PYYbhYxOJ0OqHT6eD1etHS0oL+/n5RTbncbjdmZ2fhcDiwb98+wZPg/vMpKSkpPoqwRPG4r68PbrebmXEg3VPAqZkJkkqsqqqSTJE+HEjHWlFREaqrq3ldt7/WFLlPxMwsNzeXuU9ip6esViva2tqQm5srWlo1WtBoNExtwWaz4eTJk8zMypEjR3z0zMRQMwilzszXaybe6sYKhcInmhkYGIDD4WCcSl0uF3JycpjhTPb9E6vGEsqW2GQy4Z577sEbb7zB1CIPHjyIe++9VzS5o4QhFnY9JdBsghjEQiaes7OzmV2wmJEQWVhI4TkSUgnV+eWvP0VSQcREKSsrC8nJydDr9di2bVvCeKgAgF6vZ4Q4ucz4hEIgnS5/MzMSzUQ62U5mazZs2CDY5TMeIO6nbDL01zNLTU0NO1vEB3yGMwOlzOJNLIGg1WpRVVXFKICTqHloaAipqanQ6XTIy8sDRVGipMJC2RLTNI3Z2Vncd9992LZtGyYmJvBv//ZvmJ2dxbPPPivCpwUUtBRMs/8PTqdzzdfYAoyh5lOI2q5QAbW5uTl0d3ejqqoKFRUVzIvf3d2NlJQUwe6MBMS0aMOGDdBqtZidncXu3bt5H4e9gxNSpHc4HOjp6YHZbAZwquuFpMzE1OiKBqanpzEwMIDt27evEeIUG8RsSq/Xw2g0QqlU+qTM+AzNkWn6qqoqbNy4MYpXLS6IukRRUVHQQVOPx8OkYA0Gg+h6Zv7wj2bYyxd5H/7xj3+gtbU1Jg0RXNDb24vU1NSAa5PH44HZbMb//M//4Pnnn4fJZML+/ftx8cUX48CBA6IIQhLk5ubi5z//Oa6//vo133vmmWdw9dVXw2q1iiJvI+mIhcx2cPFpERpZsIcSd+zYsUZOXYyIZXJyEgMDA4yT5NzcHO/0GilwsgXthMiz9Pf3w+FwMJbPbI0uAHGVtQ8G0qwxMzODpqYm5OTkRP2c/mZTS0tLzA6TSNtzMTObm5tDb2+vIOHOeIJE76WlpSFVrJOSknxmi/z1zDIyMhiSEUvPDAgezdjtdp+vx1MBgCBUVxiphf785z/HzTffjIaGBnzyk5/Es88+i9tuuw1/+tOf8PnPfz7i8/vbEvtjeXkZmZmZor3z0lg5/g9k0A8IXU8JhHDS+YHgL1QZKLfJbg3mC3YTwM6dO5kFUYhsPnuSXoiQpMPhgE6nQ1JSko+/O3tR8Je1Zw/SxcsJkrhxLi8vY9euXXEpbLKH5tjS9mwzM3Kf2GZmZJq+oaFBcINGPGA2m6HT6Zh0I1fEWs8M8K3N2O129PT0oLCwcM16EKvhzEDg2m5st9uRkpKCb3/727jjjjuwsrISUXNHKFtiNgwGA+6991587WtfE3wuf0iKWAjC1VMCgW9kQeod4YQqSWswXxBlXbfbjZaWFp+FmQ+xRCoiCZzafZIcbrCiMbsraPPmzYwTJMmjEyl3MeoNXOF2u9HR0QGv14tdu3ZJpg2aSNtv3LjRZxaEKCnn5+czKQ6p+b+Eg8FgQGdnJ6qrq7Fhw4aIjhVLPTOHw4GTJ08iJycH27ZtWyPAGqvhzEDg4x7Jvg+RqHsAwW2J2eSysrKCCy64ANu2bcP3v//9iM7HhqSIhaQ8wtVTAoEPAZDUDxfiEpIKI7npjIwMNDU1rQkvuR5TqCc9G0Q7i2+xOy0tDRs3bmQWT1KsbWtrg1Kp9GlljkbLrN1uZyLWxsZGybTl+sN/FsRsNmNgYABWqxXAKf06Es1I3QtmYWEB3d3dqK2tFU1rjyCaemZ2ux0nT55kGgzYET07ZRaL4cxA4CrpInarcTBb4sceewwAsLq6ivPPPx8ZGRl44YUXRJ2nkgyx0DSN9vZ2rKysRM33nqZpjI+PY3h4GLW1tZy6ofgSCyGtjRs3Bu3+4RKxRFqkp2kaExMTjGBmJFa8arU6YL1hYGAATqfTp5VZjKhiZWUF7e3tcRfA5AuKojAxMQHg1FwRRVHM4km6f8h9EqN7SkzMzMxgYGAA9fX1MZHyEUvPjLRCFxQUhGw9j9VwZiBwHdi0Wq2CRC65gtgSA6fesfPOOw8pKSl4+eWXRW90kAyxKBQKrF+/HrW1tYK0uMIRAJH+MBqN2L17N+f0BNe0FZu0wikfhzqmGEV6UtshGlSRhtRssOsNW7ZsYVp0SbGWTGsT7xS+105SMWK0E8cSZJoeAHbu3Mns/vzNzNjKzGSHTrzt4wWyAWloaIiLF0ggPTO9Xh9Wz8xqteLkyZMhu9YCIZrDmYHANRUmpntkKFvilZUVnHvuubDZbHjiiSewsrKClZUVAEBBQYEo2QHJEAsAJhcrBKGIhTgxKpVKphtKjOMSkAKzwWDgRFqhZPMjLdKTuoTH48Hu3buj2nLJ1p6qqKiA0+lkUmajo6NISUlhFgwuO3TSTpxoHVTk+UpLSwuqAuBvZrayssJM/xOjKRLNiG00FQzEBmJqagpNTU2SqAX5P1PB9My0Wi0mJiZEmQsSczgzEPjWWMRAKFvid999Fx9++CEAMKkygrGxMR+DOaGQFLFEavYVqHuLdLhwcWIMdtxQxOJ0Opmho5aWFk4LOVE3Jo6XgG89RWgYTlSS09PT0dDQEPN24ZSUFMbXgqQ3yEAj2aEHshsms0pTU1NobGxMKAc90r2Yl5fHWUSUrcxM5FPIHAipNxCSycnJiUrKjKZpxm46WsrKYiCQntns7CyGh4cBnHq/x8fHGbKJdjuzEK8ZrsQiprJxKFvis88+W1Qb4kCQFLFEgkAEQEQet2zZgrKyMkEPXai0Fen1z8nJ4aWzRX6OEIsYRXqz2YyOjg4fn/R4wj+9QXboxG44JyeHIZmxsTFGVVmqC1wgkGn6kpKSiCRxNBrNmnqDXq9HT08Po8zsL8cTCWia9lGylnpTAQFZxBcXF1FVVYXi4mIfPTOi+0bUmcVI6YSKZrikzMjvxKN4H0+cNsTC7lunKAoDAwOYnZ2NSOQRCB6xLCwsMLWAUANkgcB+UBUKRcSkMjs7y6gkl5SU8P79aMN/h26326HX67G4uIiBgQEolUps2LABHo/HJ4qTMkgtSGynSn9CXl1d9ZHj8VccFlJ/I14qO3fulMx0OhcsLy+jra0NFRUVTLqG6JkR3TeDwYDBwUE4HI6o6JkBwaOZQA0AZFMa6xpLvHHaEAshADI/4nK50NLSEvED5U8sbHvi+vp6QdIi5AElCyn5mpDOL5JCSqQhvNTUVBQWFmJmZgY5OTkoLi6GyWRCe3u7j3SKWLtOsUGIPBptuWywBw6rqqrW1LCSk5OZe8WlRdfr9aKjowMulyusl4rUsLS0hPb29qBDm4F036KpZ0bOGc5rJpBMVTBYLBaZWKIBMWosx44dQ2ZmZsD5ESFgp8KIOVOk9sTkc9psNsY3RYiPdnd3N1ZXV7F79+6ECqFXV1fR3t6O/Px8pi5BGjdIK/Pg4CCcTicjnVJQUCCJAcnx8XGmgyrWRO5fw2IrWLtcrpBt3x6PB+3t7QBOda1JRa6HC8xmM9rb23lZTrOHWNl6Zl1dXVHRMwuWMpudnYVGowFFUXC5XCFrMzabTdKurXyQOE9XGJhMJtA0jeLiYlHVY0nEQgb2lEolWlpaBD+MpACYl5eH48ePM6ZThYWFnLuBiLS/Uqn0MbhKBBCp/vLycpSXl/t8Xv9WZjL9T9SG42nQRTTlZmdnRW/hFgK2/wdN07BYLAE1ugghE+vrHTt2SDIKDAaTyQSdToctW7YITvPGQ8+MZBMMBgOzyQ3XziynwiQEtvoxAB9lYjFAXsJjx46hoKBAUGcZ+1pJTraxsZGZaF9cXMTY2BjTDVRYWBhUaXh1dRU6nY6RrpDSkF04kBQSl3ZihULB7DrLy8sZ3SnSoqtWqxmSiVbnFAGxbV5aWoqbXlkosM3MKioqfO7VxMQEKIqCRqMRVSk3FiCq0DU1NaJZO8RCz4woiMzPz6O5udnnefEfzmS3M8/PzyfUJjEUJCWbT1EU3G4355/3eDzo6urCysoKGhoacOzYMZx99tmiFiSnp6fR3d2NzZs38y7SsxGu88vr9TJKw3q9HsApzanCwkKm1kBC+fLyctEJNJogdSmiIB1pOzE7DaTX6+HxeJjdudjDhsTj3eFwoKmpSRLpOK4gU+np6elIS0uDwWBg0otcptrjCfKsb926NWYzTSQVS4jGZrPx1jMjG92ZmRns3Lkz7CaERDAnT57Eeeedh5tvvhn333+/mB8rLkhYYiEGRMnJyWhoaEBycjIOHTqEM844Q5QdJVtO3+PxCCYsfzE8LkV6mqaZWsPi4iKcTidSU1Nhs9lQU1Mjyc6vYGCrADQ1NYke6rM7p/R6PSwWC7MYkOl/oSCNIEqlEjt27IjrdDxfWCwWnDx5EuvWrWOm0slUO4lmlpeXRTUzEwuLi4vo6uqKie9OKLD1zEwmEyc9s5GREUxPT6O5uZnzs67T6XDBBRfgrrvuwne+8x1J/A0iRUISCzHNKi4u9tGSeuutt7Br166I89/EB8ZisaCpqQlHjx7Fvn37eC9S/qGu0CI9merXaDSw2WzMlHakC2e04fF4mC6kxsbGmOyOybChXq+HyWRCWlqajz4X1/tPamrp6em8ZpSkADJfE85LhaRiyT/SkUfmQOJR4CdCmHV1dRHp24kNtp6ZwWAIaGNNonI+A6fd3d34zGc+g9tuuw133333aUEqgMSIhabpkArFNE1jcnISg4ODjGkWG++++y527NgRkRGU3W5HW1sb1Go1Ewm9+eab2LNnDy9hTH95FiE1ALfbja6uLjidTjQ0NCA1NRUOh4Opy7AXTn8vkHjD4XCgvb0dKSkpqK+vj8siRfS5yFQ7wM3IjKhT5+fnM2q5iQKTyYSOjg7eXiqB0kCx9uMhpmixEsIUCraemcFgwPLyMtRqNTweD2pqarB+/XpO73tfXx8OHDiAG2+8ET/84Q8T6jkLh4QhFqLHpdfr0djYGJA8jhw5gq1btyI/P1/Q+Ulb47p163zkOd555x00NjYiOzub8+eIdOiR7Jg1Gk3QhZksnIuLi8yOM9py9lxA2olD+b/EGmwjM71ezyyc5H6RaIrMS5SVlUVUU4sHSF1CDC8V0pFnMBhgNpsFR35cMTs7i/7+ftTX1wt+f+OFkZERTExMICcnB8vLy4yeGYn8AhXkBwcHceDAAVxzzTX46U9/Kol3REwkBLEQPS6KotDU1BQ0pXL06FFUVVUJyssS+Zfq6uo1k9TvvfceamtrOc0t+E/gCnkBl5aWoNPpmPw4l4eO7DgXFxeh1+vhdruRl5eHwsLCmKrnkh2z1Bdm9o5zaWkJWq0WaWlp0Ov12Lx5s6jT9LHA/Pw8enp6olKXcLvdPr72AHwWzkifLSLZHy915UhAlKGbm5uRmZnJbGBI5GexWBi1BK/Xi8rKSoyPj+P888/HZZddhl/+8penHakAEiMWYO2kKpFxyMvLQ21tbchd+IcffojS0lJerYk0TWNgYAAzMzNBh97ef/99VFdXhwzPhRTpA4GkAyJZ3MhcAyEZi8XCaHNFM61B2om3bt0qWntoLOByuTA8PIzZ2VkoFIo1rcxSr69MT09jcHAwJrt9duRnMBhgtVqRnZ3to8zMB1NTUxgaGgqahZAyJicnMTIyElIZmqSuDQYDvvCFL8BmsyEtLQ21tbX4y1/+Evd5qGhB0sQyOzuLnp4ebNq0ac0wXSCcOHEChYWFnBdkIjFvt9vR1NQU9KU4duwYKioqgsp3iFGkZ7fk1tXVibpAEG0uvV4Ps9mM9PR0FBYWoqCggJn8jwQ0TWNsbAwTExOor69PGGkZ4CMfnfHxcezYsQPZ2dk+rcwk8hNTBFJMsL1U4rEwk84p0izBRzqFLMx80sxSwdTUFIaHh3nZDYyMjOC6666Dx+OB1WrF5OQkzj77bPzqV79CTU1NlK84tpAcsbhcLkZEcnp6Gjt27OBcyGtvb0d2djanQTC25324VtJ//etfjJSGP8Qo0nu9XmYAr7GxMarTt2ybYYPBEPGgIUVR6O/vh8FgQGNjI2/nz3iCLR3f2Ni4ZvdIIj9CMqurqz4dedF0++Ny7WwvFSnsfNnSKQaDgVGYIM0SbFIeHx/H2NiYZHxg+GB6epqJsrgS4vz8PM4//3y0tLTgf//3f6FSqTA0NIS//e1vuPrqqxOurhQOkiMWq9UKnU4XNooIhM7OTqSlpa0xr/EHaVfesGFDSDtTgmCREFuiQWjqi8xKAMCOHTtiOoBHURRMJhOTMiOeKWQoM1wnF2nLdjqdMWsnFgukGWR5eRlNTU2cxEpJWoPszjUajejChlxACHFhYSEqs0FigG2VQGoNhJSdTidmZ2eZukQigdSD+KTuFhcXceDAATQ2NuLxxx9PKJ02oZAUsVAUhXfffRcajQY7duzg/Qfo7e2FSqVCdXV10J+ZnJzEwMBAwHblYGhvb0dOTo6Ps5oYnV/EJCorKyts/SjaIAsBIZlgXVMEpKFCrVajvr4+oYYHCSGS+RohZM5WSiC7c5Iuy8/Pj9riQQZOzWYzmpubY9IGLAbIfNHk5CRsNhtSUlKYdGy0JXnEAulc40MqRqMRF1xwAbZs2YI///nPCfWeRAJJEQtwyudCqMDgwMAAvF4vtm3btuZ7JGVD0h588tGdnZ1IT09HVVUVc6xIi/REjLG0tDQik6hogXRNkQntjIwMZiGgaTph9cpcLhfa29uRlJQkaPMSCIFamaPRLEFRFLq6umC1WkN2R0oRRD9rdnYWDQ0NcLlcDClHw8xMbBBS4dO5ZjabcdFFF6G0tBTPPPOMJD9XtCA5YnG73YJ974eHh2G321FXV+fzdZJucrvdaGpq4v2id3d3IyUlBZs2bWIiFUB4O/HU1BQGBwcTxtudLAJkIaBpGpmZmdi8eTNycnIkR4rBQIZfMzIysH379qgRos1mY1JmpFmCkIxQ2RTipeJ2u9HY2JhQixSRRwokyshWGyZ1rMzMTCbFGGsV60CYm5tDX18fduzYwbkxZXl5GRdffDHy8/Px4osvJpTGnBg4rYhlbGwMy8vLaGhoYL5Gpqi1Wq3gCfC+vj4AwObNmyMq0pPc+NzcXMQKAfHA3Nwcenp6sGHDBlAUxYhlkkVTqsZcwEdDm4WFhZzqamIhkGwK3yFWt9vN1OEaGxsTKkdP2vn1ej2am5vD1rKImZnBYIDRaIRarWbSi/EY+p2fn0dvby8vUlldXcVnP/tZpKWl4ZVXXkmYdKWYkByxeDyegFbAXDA5Ock8wMCpSWQyrBeJD/zAwADcbjczrCjkOESJ2W63o6GhIWF8xoGPWnLHxsZ8ZiWIWCapyzidTialUVBQIJldtdlshk6nw8aNG+OqCs02MvO/X8EMp1wuF9ra2hhpHKkSdyDQNM10DO7cuZP3AktUrEk0Q8zMCNFEOxVIdMt27NjBuWvLarXi0ksvhUKhwGuvvSZpLb9o4rQilpmZGUxPT2P37t0YHx/H8PAwamtrIxrWo2ka09PT6O3tRXZ2NmMYxOehJrpZycnJCVfoJq3fi4uLAVtyCdj6SYuLiz6tuYWFhXEj0sXFRXR3d0dkFBUNsO+XXq/HysoKM6FNxEWdTicTbUczdRcN0DSN3t5e0ZoMAulzabVahpTFVmYmCst8dMvsdjsuv/xyuFwu/P3vf0+o1nuxcVoRy/z8PEZGRpCZmck4t0XSI88u0pM6w+LiIpaWlnyK2aHaPZeXl6HT6VBQUOCjP5YIIF4kdrsdjY2NvBaHQCrD5H7FSp6dTKRv375dUkq5gcD2sycpII/Hg5ycnISLVIgx2vLyMpqbm6MSWbANuoxGI5NiJDIzkdwvQip8FJYdDge++MUvYmlpCW+88UbCzeaIDckRi9frZbS2+GJ2dhbd3d3QarURdc0QeZZgRXryUC8uLsJoNEKj0TCRDHvRXFhYQE9PD6qqqlBWVhb3IiQfEPtjlUoVsReJx+PxGcoki0BhYSFyc3NFJ1u2EkC8JtIjAZEx0mg0zMAwO2Um5YiXoih0d3fDYrGgubk5JkXrQClG0pWXn5/Pa0Ok1+vR2dnJi1RcLheuvvpqzM3N4fDhwwmndxYNnDbEsrq6iuPHj8Pj8eBTn/qU4B2L/yR9uM4vr9fLLJp6vR4qlQoFBQWgKArz8/OS85XgAqJKkJ2dHZEVcyBQFLVGMkVM90dSLCbDg4mWjiBeKmVlZYyCBBk01Ov1sFqtyMnJYe6ZlGp1pB3aZrOhubk5LjU2mqZ9lJmXlpaYrrz8/PyQ1hIGgwEdHR28hDzdbjeuvfZajI2N4a233jrtJuiF4rQgloWFBXR2dmLDhg2YnZ3F/v37BZ070qFHiqJgNBoxODgIm80GlUrFpH/y8/MTIp1hNpvR0dGBDRs2YNOmTVGNstjuj4uLi8yiKXT+g+yWV1dXBbWVxxsmkwk6nQ6bNm0KqndHdN+Iq6FU/HgoivKxcJZK44bb7fYZZFUoFAE9eYxGIzo6OrBt27agmoD+8Hg8uOGGG9DT04N33nkn4TaQ0URCEwvRSxodHUV9fT20Wi3++c9/4txzz+V9XjEm6V0uFzo6OuD1erFjxw64XC4sLi5icXERDofDR8ZeKi8eG0R6vbq6Oi6FbrJokjoWKc4WFhaGnWcgbpUejyfh5jwAYV4qbCMzvV7POEDGuvWbPWPT1NQk2VQdRVE+ysxkkDUtLQ2zs7O85sq8Xi++/vWv48SJE3jnnXcSYh4tlpAcsXC1J/Z6veju7obZbGZE+BwOB959912cd955vIhBjEl6q9WK9vZ2ZviO/VKTjhZCMsSXXUiHWTRA0zSjkltXVycJ9z4y/0HqWEQss7CwcI0uF2nJVavVok3TxxJieKmwF83FxUU4nc411rnRgNfrhU6ng9frRWNjo2RJJRBsNhsmJiYwPT0NhUKBtLQ0hpizsrKCpoApisKtt96K9957D++88w5KS0tjfOXSR0ISC2nfVSgUPlpPbrcbb731Fvbv389pcQlXpOcKYm5VUlLCKX3kcDgYkvHvMEtPT49pOoNdk2hoaJBkNwvxGyc7c6LLRdJlnZ2djN5aInXdAdHxUmHXGdiSPOSeiTXN7vF4oNPpQNN0wg1uAh85xlZXV6OoqMgnZUZcIEn0RwiToih861vfwhtvvIF33nnHRz9QxkdIOGIhHTP5+flrFhKKovDGG2/g7LPPDrtDE8NDBTg1O9Pf34+amhpBdrBcO8yiAa/XyxRb+bYTxwtsXa75+Xk4HA5oNBqUl5ejsLAwoaQzyNBptDvXyDPGbmWOxCoBOEUq7M1dItQP2VhaWkJbW1vA+Sa2CyRpmPjDH/6AyspKmEwmHD58GO+++y6jHShjLSRHLKF87+fm5tDd3R3S+OvQoUM444wzQk68suspCoVCsDzL8PAw4xkjRothsA6zwsJC0RVgiRijUqlEQ0NDQqUwgI+ixOLiYiQnJzNDc/5DhlJs8aZpGiMjI5ieno65lwqZZifPmMfj8dmZc6lNud1uHyHPRCSV9vZ2bN68mVMt0W6346GHHsJzzz2Hnp4elJaW4nOf+xwuvPBCnHnmmQn37sQCCRG7kkWczCWEqgEkJSWFHLAUo0hP6jurq6vYvXu3aLINKpUKRUVFKCoqYtpyyeQ4O/0TaYcZqQdlZmbGXa5fCIjUBjtKrKioYIYMFxcXMTo6Co1GwxBzPDum2CCpx8XFRezcuTPmXioqlYqRRKmpqWG68iYmJtDT0+NjZBbouXa73Whra2NUJBLt2SHt3Js2beLcoKLRaOBwOLCwsIAPP/wQs7OzeOWVV3DVVVfh6aefxllnnRXlq/4IDz/8MH7+859jfn4eO3bswIMPPojdu3fH7PxcIfmIhWhskRbScC/iu+++G1TgUQxScTgcPoODseg+YnulRNphtrS0xJicRbudOBogHunhpumJX8ri4iIMBgOA+ItlSt1LxV8tITU1lbln2dnZDKloNBrU19cnXD1rZWUFJ0+eZAaWuYCmafzP//wPHn74Ybz99tuor69nvkfS6LG6D3/5y19wzTXX4De/+Q327NmDBx54AM888wwGBgYk1+osOWIBPvK9JzLnarUaDQ0NnBbQI0eOYOvWrT6FUFKkpygKNE0LrqesrKxAp9MhLy8PW7dujcuLFUiTi2uHGVEC2Lx5c8J1spDW8snJSd41iWDij7Fs/WZ7qcRqIj0SkFZmkpoFTv0N0tPTOb+LUgIhlcrKSmzcuJHT79A0jV/96le47777cPjwYUbcNl7Ys2cPdu3ahYceegjAqWeqtLQUt9xyC+688864Xps/JEsspGOjqKiI1yJ+9OhRVFVVMW2bYhXpSUqqoqIiaH0nHvDvMNNqtQzJsGsMExMTGBkZSQjdLH8QlVy9Xh+xFS+79ZvtY0+68qIxyZ7IXirAqWfs+PHjUKlUoGkadrs9pLuo1LC6uoqTJ0+ivLyccxcXTdN45JFH8JOf/ASvv/469uzZE92LDAOXy4W0tDQ8++yzOHjwIPP1a6+9FktLS3jppZfid3EBIMkay8zMDHp7e1FdXc05ZCVQqVRMjcVfnkVokZ7MeNTW1gqeM4gWNBoNysrKUFZW5tNhNjY2xtQYHA4HjEYjmpubJdlOHAqknmWxWLBr166I00cKhQJarRZarRaVlZU+6Z+hoSFRTLnYIF4qCoUCzc3NCdeS63A4cPLkSeTk5KC2thYKhQJWqxUGgwELCwsYGBhgBlkLCgqQkZEhmU0X8BGpbNy4kRep/Pa3v8WPfvQjvPbaa3EnFeCU3IzX612z/hQVFaG/vz9OVxUcknvKvV4vJicn0dTUxNlYhw2VSgWPx+NDKkKjFGJnTDxepL4oJycno7i4GMXFxfB6vVhcXMTQ0BCcTieSk5MxOzvLKOYmQn7c7XYzSga7du2Kyk5fo9GgtLQUpaWlPvIfbW1tTFceMeXie88S2UsFOJWKPnnyJHJzc7F161bmHUpPT0d6ejo2btzIDLKSBoCkpCSfVuZ4fmaLxYKTJ0/66K6FA03T+OMf/4j//M//xCuvvIJ9+/ZF+SpPT0iOWFQqFVpaWiL6fa/XGzGpuN1udHZ2wuVyYc+ePZIP9/3h9XoxNTUFjUaD3bt3M+mfnp4eeL1e0TrMogWn08l42MRqTkKtVmPdunVYt26dj1hmb2+vT1suF7FMstOPtg1ytGCz2XDy5EmmeyzYO6RWq7F+/XqsX7/e55719fXB7XbHzcvearXi5MmTKC0tRWVlJaffoWkaTz31FO644w689NJLOPvss6N7kTxA3tOFhQWfry8sLHDWNoslJFljEWpPTNM0urq6AJyyEVar1YJIxWazQafTITU1FXV1dQmXvrDZbGhvb2cMovzlZcTqMIsWbDYbo668bdu2uC/KRCyT1GWIWCapy/hvOsiiTJo8pJQa4gKyKBcVFWHLli2Crp+maVgsFibNyDZ+y8/Pj+qMkdVqxYkTJ7BhwwZUVVVxOg9N03j22WfxjW98A8888wwOHDgQlWuLBHv27MHu3bvx4IMPAjiVUSkrK8PNN98sF++5QAixkCK9Xq/H4OAgHA4H8vPzmQWT6xATUfddv3694JcqniB9+lyvn7z8/h1mQtSFxcDKygpz/ZHYSUcTdrudIRl/sUyapiV//aFA0kfFxcWitqM7nU6fVuaUlBSfVmaxNg82mw0nTpzA+vXreV3/iy++iK9+9av485//jIsvvliUaxEbf/nLX3Dttdfisccew+7du/HAAw/gr3/9K/r7+yVX+z0tiCVQkd5isfiIPubm5jLdUsF25XNzc+jt7cWWLVsSrh0X+KhzLZTseiiQDjO9Xg+z2Ry0wyxaMBqN6OzsZDrvEgFsuRSDwQCKopCVlYWqqqqEqWUREFLhs9MXAjJjxL5ngXS5+IKQyrp163iR+quvvorrrrsOjz/+OC699FJB544VHnroIWZAsqGhAb/+9a8l0VzgD0kSCx97Yi5DjzabjSGZlZWVNXMfRGJjamoK9fX1gpoG4o3JyUkMDw+L1rkWTMMsWr4fROF369atKC4uFvXYsYDJZGIiFeCUDL6/kKGUU6qke6qsrIxzTUIMBNLlEuLJY7fbceLECRQWFvLKNBw6dAhf+tKX8Nvf/hZf+MIXIvkoMlhIaGIRMklP2ksXFxdhNpuRkZHBCF9GOiMRD9A0jaGhIczOzqKhoQHZ2dmin4M9xR4NDTMyTS8VyX6+IF4qNTU1DCmyxTJJLYs9+yGlAUki7FpeXs65eypasNlsDMmYzWZO7d+EVAoKClBdXc2ZVN555x18/vOfxyOPPIIvfelLCZe2lDISlljE8FCxWCzQ6XRwu93wer1IT09HYWEhioqKJCtgyIbX60VPTw9WVlbQ2NgommZZKLA1zPR6Pbxer08ti0/3FluMMVqkGG2Q9Gk4LxW2WsLKygojlknSjPECEWTkM5EeK/i7PyqVSp/2b5VKBYfDgRMnTiAvLy9k95o/jhw5gssuuwwPPPAAvvKVr0j+XU80SJJYQrlIijVJb7FY0N7eznQeURTFpH4MBkNM5euFgAze0TQdN4mNSDrMyIyQ0WhEY2NjwkWKwEdeKjt27OCVPvUvZEc7zRgMZrMZOp2Ol3ZWvBBIlic7Oxurq6vIy8vD9u3bOd+3Y8eO4bOf/Sx+9rOf4etf/7rk3u3TAQlFLGJM0gOnpli7urqYfLL/g0VSPwsLCzAYDEhKSmJIJjs7O+4PItFQS09PR11dnWTmULh2mLF9YJqamhJuRgj4yEulsbExokiLbS/M9mQvLCxkduXRgMlkgk6nC+hHInXQNA2z2YzOzk4Ap+4hV7uEEydO4OKLL8YPfvAD3HrrrXF/l09XJAyxkEjF6/UKTn0Bp4rcQ0NDnP2tKYqCyWRiduUKhQIFBQUoKiqKS9fP8vIydDodioqKeOWTY41gHWa5ubkYHBwEgISzsgWi66USaFfOHsoUKyo1Go3o6OhAdXW1IHO6eMPpdOLkyZOM7YPb7WbumdFoREpKCnPf2O+oTqfDBRdcgO9+97v49re/Ldl353SAJInF30VSDLl7iqIwODjItOkJ2WWSF5+QDJlgLywsjIkUOykSk9RForwYpMNsfn4eRqMRSqUSJSUlKCoqkoxPChewvVSam5ujWhthDxiSlvns7GxmVy5ULNNgMKCzsxNbt27ltLGSGlwuF06cOMGQSqBsA9vG+sSJEzhy5Aj27t2LBx98EN/+9rfx3e9+N2GeuUSF5IlFjCK9x+NBZ2cnHA6HaBa8pL6wsLCAxcVFuFwunyK22K2lpHNKikKYXGC1Wplp+oKCAqbzR6lUMmlGKc99UBSF3t5eLC0txcVLxb+bUYhY5uLiIrq6ulBbWytJGZBwcLlcOHnyJKMoEe4z0zSNjo4OPProo3jjjTdgMBhw1lln4eKLL8ZFF10kWwtHEZIlFpfLxUQqgPAivd1uh06nQ0pKCurq6qKSeiG7SxLJ2Gw25ObmoqioKOIUBnHPnJmZCWpgJnUQNQB/czExO8yiCeKlQmpC8W4VJt1SpNGECD+GImfiuhmue02qIKSSnp7OS3ttcHAQBw4cwLXXXosbb7wRf/vb3/DKK6/Abrfjvffei/JV+6K8vBwTExM+X/vpT3/qI8fS2dmJb3zjGzh+/DgKCgpwyy234I477ojpdYoBSRKL1+uFw+FgivRCSYXUIwoLC1FdXR2z3TARfCRFbKIrVVhYyGtRoigKPT09WF5ejlk7sdgg+fyqqqqQ7ayBOsyIWkJBQUHcNMy8Xi90Oh08Hg+ampokVxMKRM6kM49MsZPh0/r6+oScE3K73Th58iSj3cf1PR4dHcX555+Pyy+/HL/4xS98fo9kQGKJ8vJyXH/99fjqV7/KfC0jI4N5r1dWVrBlyxbs378fd911F7q6uvCVr3wFDzzwAL72ta/F9FojhSSJ5etf/zomJydxySWX4IILLhDUiTU/P4/e3l5s2rQJpaWlccupEl2pxcVFLC8vM6ZShYWFIdMpbMn4hoaGuO+ShYDMeHBtlGBDChpmbrcb7e3tUCqVaGhokPTkPPAROZP6gtVqRVpaGmw2G7Zt25aQigaEVPjaIU9MTOD888/HhRdeiAcffFASKdby8nLcdtttuO222wJ+/9FHH8Xdd9+N+fl5ZiN155134sUXX5Sk50ooSJJY+vv78Ze//AXPP/88+vr6cM455+CSSy7BhRdeiLy8vJAkQdM0xsbGMD4+LrlJbjK/sLCwwHRKFRUVrRmSs9vtaG9vR2pqakL6eAAfSczU19f72EQLQTw0zEjqhSxoifg3GBsbw8jICNLT02G1Wpn7VlBQAK1WK/kCttvtRltbG5KTk7Fjxw7O5DAzM4PzzjsP+/fvx29+8xtJkApwilgcDgfcbjfKyspw5ZVX4vbbb2c2LNdccw1WVlbw4osvMr/zzjvv4JOf/CRMJlNCpcElSSwERK7k2WefxfPPP4+Ojg6ceeaZuOSSS3DRRRehqKjI5+UgBVaz2YyGhgZkZGTE8epDg7RIEi2u1NRUZqEcGBhAUVERr0liqYBdE2psbBTdHM3/vkVjuJB4qZDOI6ksTHxAhjcbGhqQm5u7RiwzWurCYsHj8aCtrQ1qtZoXqczPz+O8887Dvn378Lvf/U5SG4Jf/vKXaGpqQm5uLo4ePYq77roL1113HX75y18CAM4991xUVFTgscceY36nt7cXtbW16O3txdatW+N16bwhaWJhg0Qizz33HF544QUcP34ce/fuxSWXXIJLLrkECoUCN998M77+9a/jE5/4REKljjweDwwGA6amprC0tISkpCQUFxcnXDsuRVHo6+uDyWRCU1NT1GtC/hpmYnSYke61RPVSAU5FiyMjI0GHN0lLLin+s8UypdA0QUglKSkJO3bs4Hw9i4uLOHDgABobG/H444/HJHV555134r//+79D/kxfXx9qamrWfP1///d/ceONN8JisSAlJUUmlniDpmlMT0/j+eefx/PPP4/3338fWVlZ2Lx5Mx5++OGEXBCmp6cxMDCAmpoaJCcnM3UZlUrlM/UvtZ0lgdfr9WnpjvU0Pdu9kMwYkQ4zrsrCq6uraGtrE92LJJaYmJjA6OgompqaOEWLRCyTkHO8xTI9Ho9PXYsrqRgMBlxwwQWorq7Gn//855g1WZChzFCorKwM2HzS09OD7du3o7+/H9XV1XIqTEo4fPgwLr/8cuzduxculwtHjhxBfX09E8lIfYFgS/bv2LEDubm5zPfYHT+Li4ugadpnIFMqJEN0ywCgoaEh7p1TQjrMiBhjeXk5ysvLJf3MBMPY2BgmJiYiUgQgHY16vZ4RyyT3LRYRaHt7OwDwsqM2m8248MILUVZWhmeeeUYSLqhc8OSTT+Kaa66BwWBATk4OU7xfWFhg3qHvfve7eP755+XifSzR0dGB1tZWPPLII7j22mtB0zQMBgNefPFFPPfcc3jnnXdQXV2NSy65BAcPHpRczYJdEwonxEjTtM/Uv9vtZkgmnukLh8OBtrY2pKWlSUq3jA3/9m//DjOimyXUIC3eoGkao6OjmJqaQnNzs2i1RX+xzNTUVCaSETtFS9q6KYpCU1MT5+doeXkZF110EQoLC/HCCy9INgV+7NgxfPjhhzjnnHOQkZGBY8eO4fbbb8eBAwfwxz/+EcCpz1JdXY1zzz0X//Ef/4Hu7m585Stfwf333y+3G8cSRGIjUP6SCNW9/PLLeO6553D48GFUVFTgkksuwWc/+9m4F2Xdbjc6OzvhdrvR2NjI64Vge7AvLCwItmGOFKQekZubi61bt0omggoF/w4zjUYDh8OByspKVFRUSGrjwQUk4p2ZmUFzc3PUVKLZYpmknuUvYS8UbFJpbGzkXBtZXV3FwYMHodVq8fLLL8fFSpsr2tracNNNN6G/vx9OpxMVFRX40pe+hG9+85s+7z57QDI/Px+33HIL/uM//iOOVy4MCU0sfLC8vIxXX30Vzz//PF5//XWsX7+eIZmGhoaYLooOhwPt7e3QaDSoq6uLqMhI07TPjpyrDXOkINP0JSUlUbWxjSampqYwMDCAjIwMWCwWaDQaJgpMhKYJ0jU5NzeHnTt3xmyAli2WSaLnvLw8hmj4bGy8Xi86OjqYAVSu74LVasWll14KpVKJv/3tbwk5PHw642NDLGxYLBa89tpreP755/Haa68hNzcXF198MQ4ePIhdu3ZFNZ2zurqK9vZ25Ofno6amRnRCC2fDLAaIkGGipo6Aj7TXiJdKNDrMogmapjE4OIiFhYWoC2KGuw4iZ6TX6xmxTC7DrBRFoaOjg3Fv5Uoqdrsdl19+OVwuF/7+979Leqzg44qPJbGwYbPZ8MYbb+C5557Dq6++ivT0dFx88cW45JJL0NLSImrLotFoRGdnZ8wKxCTts7i4iKWlJaYQW1hYKFgdl0zTJ6qQIQBmgDZYO64YHWbRBE3T6O/vh8FgwM6dOyWVArLb7Uy6jC2WWVhYiIyMDB+duM7OTjidTl5SOQ6HA1/84hexvLyMQ4cOiT4nJUMcfOyJhQ2Hw4G33noLzz33HF5++WUkJSXhwgsvxGc/+1mcccYZEdUuZmZm0N/fL0jeRAy4XC6fwUIhNswTExMYGRnh7ZgoFbCHN7kWudkdZnq9Hna7Pa4aZjRNM7NC8VBZ5gO32+0zlKlWq1FQUIC8vDzMzMzA4XCgubmZ83vlcrlw9dVXY25uDm+++WZCtd9+3CATSxC43W68++67ePbZZ/Hiiy/C6/XiwgsvxMGDB3H22WdzXlBIx87k5OSaduJ4gbzwXG2YSS5/dnY2KtP0sQDZ5ev1+ohSR+E6zKIJmqYZUdLm5uaEct5kG+bNzc2BoihmY8PFZsLtduPaa6/F2NgY3nrrrYhlgmREFzKxcIDH48H777+PZ555Bi+++CKsVisuuOACXHLJJdi/f3/QF5w9iS5VX3ev1+tDMv42zDRNMy3RsZimjwai5aXi75ESTQ0zonS9urqK5uZmybbVhgJFUeju7obFYsGWLVuY9nliM0GK//7vk8fjwfXXX4/e3l688847KCwsjNMnkMEVMrHwhNfrxbFjxxhpGZPJhPPPPx+XXHIJzj33XGbhNZlM6OnpgVqtRkNDQ0LsLgPZMJOitdRy+Vzh9XrR1dUFu90eVS8Vtj0u0eIiJBNphxnbD6a5uTlhBgDZoGka3d3dWF1dxc6dO30+A2k40ev1WF5eRkZGBmiahkqlQkNDw/9v77zDorq2Nv6OVFGKSpeiIAgKCDbEXtGIAqJGLNgLiuaqsRIbMfYSY4uaEMtVLICKig0UURELIEpHEUG60qswzPr+4JtzHYEICswMnt/zzJPMOfvM7D2M856991rrxaJFixAaGoqAgACxdL38HmGF5Rvg8Xh49uwZIzJpaWmwtrbGoEGDcODAAQwePBi///670Dd7vwa+rziXywURgcfjNakNc0PA5XIZ6wELC4smy++pLcKMn/NRnwgz/iZ3WVkZunfvLraiEhUVhYKCgi/OtvjFMj08PLBlyxYoKiqivLwcf//9N+zt7cXy39L3CCssDQQ/dPLw4cM4efIkOnToAENDQ8ZTRhzyIvjws+k/devj15NqChvmhoDvpSIhIYFu3boJrY/fEmHGr79WXl4ukiZjdYG/lJqXl4eePXvWecbI4/Hwn//8B8HBwTA1NcXdu3dBRJg6dSr++OOPRu3zli1b4Ovri/DwcEhLSyMvL69am+TkZCxcuBABAQFo3bo1ZsyYgW3btgn8Pe/du4fly5cjKioK2traWLduHWbOnNmofRcVRO8XQUxp0aIFcnNz4enpiXXr1mHcuHHw9vbGgQMHsHjxYgwePBj29vYYM2YM2rZtK7IiU1RUhLCwMCgrKwsU81RSUoKSkhIMDAyYvIU3b94gKiqqwWyYG4qPHz8iLCyMcRwU5uyqRYsWaNeuHdq1a4fOnTszEWYJCQmIjIysNcLs88RBcRWVmJgYZm+rPqKyZs0a+Pn5ISAgAPr6+swS9OfWvo1BeXk5Jk6cCCsrK7i7u1c7X1lZCRsbG6irq+PRo0dIT0/H9OnTISUlha1btwKoCmm3sbGBs7Mzzpw5gzt37mDu3LnQ0NDAyJEjG30MwoadsTQgc+fOxYABAzBjxgzmGD+RzdvbW8BTxt7enqlxJCoiwy/EqKOjAz09vTr1q6FsmBuK0tJShIWFiYWXSm0RZm3btkVsbCyIqF4lTkSJT8Oie/bsWec9Rh6Ph/Xr1+P8+fO4d+8eDA0NG7mntXPixAksXbq02ozlxo0bGDNmDNLS0qCmpgYAOHLkCFavXo33799DWloaq1evhq+vLyIjI5nrHB0dkZeXh5s3bzblMIQCKyxNCD/0+FNPmb59+8LOzg62trbQ1NQUmsi8f/8eERERMDAwgLa29le9xtfaMDcU/Npl/KoGoiLYdYEfYcZ3F5WQkICOjg7U1dUbzSWzseCHdmdnZ9dLVIgImzdvxvHjxxEQEIAuXbo0ck//ndqEZcOGDbhy5QpT0RuomqHo6ekhLCwMFhYWGDhwILp37459+/YxbY4fP46lS5ciPz+/aQYgRET3dq4ZwuFwoK+vj1WrVuHRo0d48+YNHBwc4OPjA2NjYwwfPhx//PEHkpKS0JR6n5aWhpcvX6Jr165fLSoA0LJlS+jq6qJXr14YMGAANDQ08OHDBwQFBeHx48dITExEcXFxA/b8fxQWFiIkJATq6upiJyoAICsrC3V1dfB4PCgpKcHQ0BDFxcV48uQJgoKCEB8fj7y8vCb9XnwN/MKwHz58qFeuDRFhx44dcHd3h7+/v9BF5d/IyMhgZip8+M8zMjL+tU1BQQFKS0ubpqNCRPzm2M0EDocDHR0dLF26FP/5z3+Qnp6OS5cuwdvbGxs2bICZmRns7e1hZ2fXaEUeiQhv375lyps0ZPKmjIwMtLS0oKWlJWAn/ObNG8aGWU1NrUG81z/1UunYsWMDjaBp+dTf3czMDBISEtDS0hKIMOMbYPGj8+obYdbY8Jd9379/X6/wdCLCvn37cPDgQfj7+8PU1LTB+/YtTo8s9YcVFhGAw+FAU1MTLi4uWLRoET58+MCIzObNm2FkZMSITEPdjfN/BDIyMtCjR4+vNoaqC1JSUtDU1ISmpiZjw5yVlYVnz55BWlr6m/I9srOz8eLFi29awhM25eXlCAsLg6ysLMzMzATE4lMH0U8jzKKjo0Wqhhm/OkNmZma9ReXQoUPYvXs3bt26he7duzdK/37++ecvRmTp6enV6bXU1dXx9OlTgWOZmZnMOf5/+cc+baOgoPCvnw0Rid1suybYPRYRhu8p4+PjA29vb/j7+0NPT48p99+lS5evumPlZ3Hn5+eje/fuX12Q8lvhe69nZmbi/fv39bZhzsrKQmRkJIyNjcU2ca68vByhoaGMUVpd/56iVMOMX4MtLS2tXuX7iQh//fUXNm7ciBs3bqBv376N3NP68aXN+/T0dKYKwLFjx7By5UpkZWVBRkYGq1evxvXr1xEREcFcN2XKFOTk5NS6ef+pqERFRYGIoKOj06g3fY0FKyxiRH5+Pq5evcp4yrRv355xx6yrpwyXy2VyI+prMNaY1NeGOT09HTExMTAxMRHbEh/8JFR5eflvjmATZg0zfmHP+orKyZMnsXr1aly7dg2DBg1q1D7Wh+TkZOTk5ODKlSvYtWsXHjx4AADo1KkTWrdujcrKSpibm0NTUxM7d+5ERkYGnJycMHfuXIFwYxMTE7i4uGD27Nm4e/cufvrpJ/j6+n4x3HjXrl04cuQI0tPTMWLECDg6OmLy5MmNPu6GhBUWMaWwsFDAU0ZZWVnAU6amH6ny8nI8f/4ckpKSQk0a/BKf2zBzuVyBhMy0tDQBLxVxpKysDKGhoVBUVETXrl0bdPmjKWuYJSQkICUlpV7ulUQEDw8PLFu2DD4+Phg2bFiD9achmDlzJmMX/CkBAQEYPHgwgKpK3wsXLsS9e/fQqlUrzJgxA9u3b6+WILls2TJER0dDS0sL69evr3E57tOZyqNHj+Dk5IRdu3ZBWloaR48eRUFBASZPngxnZ+dGGW9jwApLM6CkpAS3bt1iPGXk5eUxduxY2Nvbw8rKChISEoiPj8fTp09hamparyUXYfPpkk9WVhYTUdOhQwfo6uqKZeJgaWkpQkND0aZNG3Tp0qVR19Qbs4YZv2p3z5496yUqnp6eWLx4Mby8vDBq1Kivfv/mxrNnz+Dl5QVZWVm4ubkBqBKwdevW4e3bt3B0dISLi4uQe1k3WGFpZpSVlcHf35/xlJGSksKQIUNw+/Zt2NvbY//+/WK5OcjfHE5NTYW6ujry8vJQXFzcJDbMDUlpaSlCQkKEkmtTUw2zr40wS0xMRFJSUp19bfhcvnwZ8+bNw7lz5zB27NivGUazJDs7GzY2NoiMjIStrS08PDyYcykpKVi3bh0SExNhbW2NX375RYg9rRsiLSyHDh3Crl27kJGRgW7duuHAgQPo3bu3sLslNlRUVGD//v1wdXWFrq4usrOzmZlMfTxlhM2njomflu5vChvmhqSkpAQhISFQVVVF586dhSrwfN96/udXnwgzfoh6fUXl2rVrmDVrFv773//CwcGhIYYh9ny6DBYSEgJXV1e8e/cOv/32G8aPH8+0y8jIwKJFi6CgoAB3d3eRLwIrssJy/vx5TJ8+HUeOHIGlpSX27dsHT09PxMXFie1mbVNz5coVTJkyBXv37sXs2bPx4MEDeHp6wsfHByUlJbCxsYGtre2/esoIG34EW0FBAbp3717rRnRj2DA3JMXFxQgJCYGGhgYMDAxEatbIX27k78v8W4RZUlIS3rx5U+8Q9Vu3bmHatGn4559/MGnSpMYYhljBF5SPHz9CRkYGFRUVkJKSwvPnz7Fy5UpISkpi3rx5AuKSm5sLOTk5kQm4+TdEVlgsLS3Rq1cvHDx4EEDVD4y2tjaWLFmCNWvWCLl34sF///tftGrVqtrdYWVlJR49esSUlsnLy8PIkSNhb28Pa2trkfghBr7eS4Vvw5yZmYmcnJyvsmFuSIqKihAaGor27ds3WrJrQ/J5hBm/NA+Xy0VycjK6d+9eLxfRu3fvwtHREX/++SemTZsm8uNvbPii4ufnB3d3dxQWFqJt27bYuHEjOnXqhMjISCxfvhwAMH/+fEyYMEHIPa4/Iiks5eXlkJOTg5eXF+zt7ZnjM2bMQF5eHnx8fITXuWYGj8fD06dPGZHJyMjAiBEjYG9vj1GjRtVrqaMhaSgvlfraMDc0hYWFCA0Nhba2dp0Le4oS/Aiz5ORklJSUQE5ODhoaGnWOMLt//z4mTpyIffv2Yfbs2WI3/sbi1q1bsLW1xaJFi1BUVITXr18z3k4jR45EZGQk1qxZg7S0NLi5uYndfpRICktaWhrat2+PR48ewcrKijm+atUqBAYG4smTJ0LsXfOFx+MhPDwcXl5euHjxIpKSkjB8+HDY2dlh9OjRTeYp01heKl+yYW7osRUUFCAsLAy6urpiW2oGqNo8jo+Ph5mZGTMbrEuEWXBwMMaNG4cdO3bA2dmZFZX/p6ioCDY2Nujfvz+2bNnCHFu5ciVOnTqFkJAQGBsbIyYmBjt37sSePXsatNxSU8AKC0uN8K1kvby8cOnSJcTFxWHIkCGwt7eHjY1No3nKNJWXCt+GmZ/1z+FwoKKiAjU1NbRp0+abw7Hz8/MRFhaGjh07okOHDg3TaSGQmpqKuLg4WFhYoE2bNszxTyPMPnz4wHx+qampGDBgACIiImBnZ4dff/0VS5Ys+W5FZd++fZCQkMCSJUuYYzk5ObCyssLatWsxc+ZMprBoUVERU7Zp3759kJaWBo/HE5vUgE8RyQw5ZWVlSEhI1Fhrh1+Lh6Vx4XA4TM7Lpk2bEBcXB29vb/z1119YsmQJBg4cCDs7uwb1lOHndygpKX11uZq60qJFCygrK0NZWVkgQioqKgqVlZXfZMPML4qpr68PHR2dRhpB45OWllajqADVa5jl5eUhJSUFCxYsQGFhIeTk5DB27FjMmjXruxUVoOoG7fPZatu2bdGhQwdcu3YN06ZNg6SkJIgI8vLy0NTURGZmJhMwIY6iAoho2XxpaWn06NEDd+7cYY7xeDzcuXNHYAbD0jRwOBwYGRnhl19+QUhICGJjY2FtbQ0PDw8YGhrihx9+wJ9//onU1NSvLuvOj5pq165dkxt0tWjRAm3btoWRkREGDBgACwsLSEtLIz4+HoGBgXj58iUyMjLA5XK/+Fq5ubkICwtDp06dxFpU0tPTERsbC3Nz82qi8jn8z8/MzAyXLl2CtrY2TExMEBoaChUVFYwZMwYvXrxokn5v2bIFffv2hZycHJSUlGpsw+Fwqj3OnTsn0ObevXtMwEinTp1w4sSJr+rPsmXLmPHv2bOH+Q7Z29sjOTkZu3fvRkVFBSO+0tLSUFBQQEVFxVe9n6ggkkthQFW48YwZM3D06FH07t0b+/btw4ULFxAbG1vN54BFOBARkpOTcfHiRVy8eBHBwcHo1asX7OzsYGdnBx0dnTrdrfI3uNu3b49OnTqJzB0uEaGoqAiZmZkCYbi12TDzKy137twZ7du3F1Kvvx1+Hbb6lsyJjo7G6NGjsXDhQmzatAkcDgdxcXG4fPkyJk6cWOfqwd/Cxo0boaSkhJSUFLi7u9foV8/hcHD8+HGBrH8lJSUm5J5f58vZ2Rlz587FnTt3sHTp0jrV+fo0L4UfQgwAJ0+exKxZs/Drr79i3bp1KCsrw9q1axEUFARlZWUMGzYMMTExOHfuHIKDgxvFOqApEVlhAYCDBw8yCZLm5ubYv38/LC0thd0tlhogIqSlpTHl/h8+fIhu3box5f5ri4gSJy8VfhhuZmYmioqKBGyYCwsL8fLlSxgZGUFTU1PYXf1qMjIyEB0dXW9RiY+Pxw8//IAZM2Zg27ZtQr85qK0yMVAlLJcuXRKIOP2Ub7UV5vF4SE9PR/v27REdHY2YmBiMHz8eJ06cwJw5c7BmzRps2bIFFRUVOHXqFG7evIlXr16hY8eOWLduHXr06PG1wxYZRFpYWMQTIkJWVhYuX74Mb29v3Lt3D8bGxozI8LPOr1+/Di6XCwsLC7HzUvnUhpn/46WhoQF9ff0msWFuDDIzMxEZGQkzMzOoqKjU+bqEhAT88MMP+PHHH7F7926R2Bf4krBoamri48eP0NPTg7Ozs8Be0NfYCvNnKpWVlViyZAkyMjLg4uKCESNGYNOmTdiwYQOAqtyyWbNmYdmyZdi1axdzfVFREaSlpcWmGsaXEMnNexbxhsPhQE1NDQsWLMD8+fORk5PDeMrs2LED+vr6sLCwgJeXFw4dOiR2ogL8z4a5ZcuWyM/Ph6amJkpLSxEUFITWrVtDTU2NyfUQB/jeNvUVlbdv32LMmDGwt7cXGVH5Er/++iuGDh0KOTk53L59m8kl+emnnwB82Va4phsHfiSrhIQEevbsiaNHj8LGxgazZ8/Ghg0bmL1HJycnSElJwcnJCQCwefNmyMrK1rmIp7gg+t+CJoa/Nvzp41O70rKyMri4uKBdu3Zo3bo1xo8fXy16jeV/cDgctGvXDrNnz4avry8yMjLQv39/nD9/Hjo6OtixYwc2btyI58+fg8fjCbu79SIjIwMREREwMzNDly5d0KNHDwwaNAg6OjrIy8vD48eP8ejRI7x+/RqFhYUi61eflZWFiIgImJqa1ktUUlNTYWNjg1GjRmH//v2NJipr1qypccP900dsbGydX2/9+vXo168fLCwssHr1aqxatUpg9lBfYmNjMXz4cGbpbOrUqSgoKICCggKKiooQGxsLDocDIgIRwdHRERcuXMCePXuwatUqkf1efAvsjKUGunbtCn9/f+b5pwl6y5Ytg6+vLzw9PaGoqIjFixfDwcEBQUFBwuiq2HHmzBmcPXsWN2/eRO/eveHr64uLFy9i1KhRjKfMuHHj0LNnT5G+++VvcH9+h9+YNsyNwfv37xEREVFvw7T09HSMHj0agwcPxuHDhxv1b9WQtsI1YWlpic2bNzN1u77GVvjgwYMwMTFhZjTHjh1DYmIijh8/DldXV7i5ucHU1BSVlZWQkJDAuHHj4OPjI5QSQ00BKyw1ICkpWWO+TH5+Ptzd3eHh4YGhQ4cCqFp7NTY2xuPHj9GnT5+m7qpYQURISkrC7du3mc/K0dERjo6OKCkpwc2bN+Ht7Q17e3vIy8szxmV9+vQRqWquaWlpiI2N/eIGN/97pK6uLpBQyK8qUB8b5sbgw4cPePnyJUxMTOoVaZmZmYkxY8agd+/e+Ouvvxr9b6OiolKvmVR9CQ8PR5s2bZhadFZWVrh+/bpAGz8/v1pTHYyMjGBkZITS0lKmFNKyZcswYMAA8Hg8nDp1Chs2bICbmxvMzMzg7e2N9+/fi5VxV31hhaUGXr16BU1NTcjKysLKygrbtm2Djo4OQkNDUVFRgeHDhzNtjYyMoKOjg+DgYFZYvgCHw8HOnTtrPCcnJwcHBwc4ODigrKwMfn5+8Pb2xqRJkyAjI8OU++/Xr59Qzb345U3Mzc3rVWbj84TC3NxcZGZmIiIi4os2zI1BdnY2Xr58ia5du9ZLVD58+ICxY8fCxMQEJ06cEDkXUr6tcHJyMiorKxEeHg7gf7bCV69eRWZmJvr06QNZWVn4+flh69atWLFiBfMazs7OOHjwIFatWsXYCl+4cAG+vr7V3u/TzPj3799DW1sb7u7u4PF4zExLSkoKp06dwqxZszBo0CDs27cP//3vf5vk8xAWbFTYZ9y4cQNFRUXo3Lkz0tPT4ebmhtTUVERGRuLq1auYNWsWPn78KHBN7969MWTIEOzYsUNIvW6+lJeXIyAgAF5eXvDx8QERwcbGBuPGjcOgQYOaNIrm3bt3eP36dZ2SBuvKl2yYG2M2kJOTg/DwcBgbG0NDQ6PO1+Xm5mLMmDHQ1dXFhQsXRDKC6Uu2wjdv3sTatWvx+vVrEBE6deqEhQsXYt68eQKCXhdbYS6XC0lJScalU1NTE0lJSTh06BCuXLmCmTNnMpXYr127hlu3biEmJgbOzs5iWbG4PrDC8gXy8vKgq6uLvXv3omXLlqywCBEul4v79+/Dy8sLly9fRmlpKWxsbGBvb4+hQ4c2qqcM34fEwsKi1ozub+VzG+aysjIBkWmImRpfVOqbb5Ofn4+xY8dCTU0NFy9eFAtPkMaEv1dSUVGBESNGoGvXrnB1dUX79u3x9u1bHD16FJcuXYKTkxPj+EhEKCsrE9tw9PogurujIoKSkhIMDQ3x+vVrqKuro7y8vFpsPFvDrGmQlJTE0KFDcfjwYbx79w4+Pj5o164dli9fjo4dO2LWrFmMiVlD8vbtW7x58wbdu3dvNFEBqpYKFRUVYWBggL59+8LS0hKtW7fG27dvERgYiLCwMKSkpKC8vPyrXj83Nxfh4eHo3LlzvUSlsLAQ48aNQ5s2beDt7f3diwpQtbRJROjduzfk5eUxa9YsKCsrAwA6dOiAxYsXY/z48fDw8MC6desAVP19vwdRAdgZyxcpKiqCjo4ONm3ahBkzZkBFRQVnz55lnN3i4uJgZGTE7rEIER6PhydPnjCeMpmZmbC2toadnd03e8q8efMGycnJ9bbhbWi+1YY5Ly8PYWFhMDQ0hJaWVp3ft7i4GA4ODpCQkICvr6/Y5OU0BVu3bsXly5fx9OlTAFV/o4cPHwIAunXrBjk5OWzfvh0nT57ElStX0L17d2F2t0lhheUzVqxYgbFjx0JXVxdpaWnYuHEjwsPDER0dDRUVFSxcuBDXr1/HiRMnoKCgwJTDfvTokZB7zgJUiczz588ZT5nk5OSv8pQhIiQkJCA1NRU9evQQqQS2+tow88vmdOrUqV7JqKWlpZgwYQIqKipw48YNoQqrKLJy5UrExcXhypUr+OeffxAQEIDLly9DVVUVffv2hbu7O1NnrjmUaakPrLB8hqOjI+7fv4/s7GyoqKgwZjz6+voAqv5R//zzzzh79iw+fvyIkSNH4vDhw+xSmAjC95Tx9PTEpUuXEB8fj6FDh8LOzu5fPWWICK9fv0ZaWprIicrnlJeXMyJTkw0z32ysviX8y8rK4OjoiIKCAty6dateVsTNkZp8US5evIgJEybAzMwMBQUFmDZtGmbMmIGAgAD89ttvCA4OrldwRHOCFRaW7wIiQmxsLLy9vXHx4kVERkZiwIABsLe3x9ixY6GiogIOhwMej4egoCAQEXr06CFWSz+f2zBLS0vj48eP0NbWhqGhYZ0T8T5+/Ihp06YhMzMTfn5+DRYBJ67wo78A4PXr1wCqcmsUFRVx7949hIWFwdbWFpqampCTk4O/vz9cXV1x9uxZ5ob0e4MVFpbvDv4yF98dMywsDFZWVrC1tcWjR48QGRmJoKAgsRKVz+H7wrRq1QqlpaV1tmGuqKjA9OnT8fbtW9y9e7deFY6bI5/OVBwdHREeHo6SkhJISUlh//79sLGxYdpmZ2fj1atXGDNmDFxcXODm5iasbgsdNipMxLh//z7Gjh0LTU1NcDgcXL58WeA8EWHDhg3Q0NBAy5YtMXz4cLx69UqgTU5ODqZOnQoFBQUoKSlhzpw5KCoqasJRiDYcDgedOnXCmjVr8PjxY7x69Qq2trbYs2cP/Pz8oKGhgX/++QfJycliWcepsLAQL168gL6+Pvr06YNBgwbByMgIXC4XL168wP379xEdHY3s7GyB+mxcLhdz5sxBQkIC/Pz8vntRAf7n4Dh58mTExcXhyJEj+PvvvzF06FA4ODgwBmBZWVnYsmULpk2bhjlz5nzXogKwmfciR3FxMbp164bZs2fDwcGh2vmdO3di//79OHnyJDp27Ij169dj5MiRiI6OZqKDpk6divT0dPj5+aGiogKzZs3C/Pnz4eHh0dTDEXk4HA60tbURFRUFeXl5XLlyBY8fP4a3tzfWrVsHc3NzxrisNk8ZUaKoqAihoaHQ1dVFhw4dAFT9OPLLonxuw1xUVIQTJ05g7NixCAgIQFRUFAICAupVN6y5k5ycjMjISOzevRuDBw8GAFhbW0NdXR3z5s1Dr1690LVrV4wbNw5WVlaYOHGicDssChCLyAKALl26xDzn8Xikrq5Ou3btYo7l5eWRjIwMnT17loiIoqOjCQA9e/aMaXPjxg3icDiUmpraZH0XJ+7fv09du3altLQ05hiPx6P09HT6888/afjw4SQlJUXdunWjDRs2UFhYGBUVFVFxcbFIPTIzM8nX15ciIiLq1L6oqIgiIiLIycmJVFVVqUWLFjRmzBg6d+4cFRQUNMlnn5iYSLNnz6YOHTqQrKws6enp0YYNG+jjx48C7V68eEH9+/cnGRkZ0tLSoh07dlR7rQsXLlDnzp1JRkaGTExMyNfX96v69OrVK3ry5AmlpKRQaWkppaSkkIyMDD148ICISKBvlpaWtHbt2q96n+YMuxQmRiQmJiIjI0OgVpmioiIsLS0RHBwMAAgODoaSkhJ69uzJtBk+fDhatGiBJ0+eNHmfxYEBAwYgLCxMIIKHw+FAXV0dzs7OuH37NtLT07FkyRKEhISgT58+6N27N3777TdERUWJRLn/4uJihIaGQltbu86VfjkcDjp06AAZGRnIycnh6tWrsLCwwObNm6GiooKwsLBG7nVVyXkej4ejR48iKioKv//+O44cOQJXV1emTUFBAaytraGrq4vQ0FDs2rULmzZtwrFjx5g2jx49wuTJkzFnzhw8f/4c9vb2sLe3F3CBrAsHDhzA2LFjMWTIEPTp0we///47VFVVMWjQIGzfvh2FhYWQlpZGZWUleDweFBQUxHK5tNERtrKx1A4+m7EEBQURAIE7ayKiiRMn0o8//khERFu2bCFDQ8Nqr6WiokKHDx9u1P5+D/B4PMrNzaWTJ0+SnZ0dycrKkoGBAa1YsYKCgoKosLCwyWcqWVlZdP36dXrx4kW9ZlKFhYW0aNEi0tbWpoSEBIFxxsbGVps1NBU7d+6kjh07Ms8PHz5Mbdq0EejP6tWrqXPnzszzH3/8kWxsbARex9LSkhYsWFDn9929eze1bt2a/vrrLwoICCB7e3tSUFAgb29vOn36NFlZWdGcOXOorKyMiIjS0tKoU6dOdODAga8darOFnbGwsNQDDocDJSUlTJ8+HZcvX0ZmZibc3Nzw9u1bWFtbw9TUFK6urnj69GmTzGRKSkoQGhoKTU1N6Ovr13kPiMfjYf369bh8+TL8/f2rzXI6d+4stCKT+fn5ApWjg4ODMXDgQIH+jBw5EnFxccjNzWXafDqT57fhz+S/xD///IOVK1fiwoULmDt3LgYPHoxLly5BXl4egYGBmDp1KqZMmYKXL19CW1sbw4cPh5WVFUxNTbF48eIGGHXzgt28FyP4SZiZmZkCyzaZmZkwNzdn2mRlZQlcx+VykZOTwyZxNgIKCgqYPHkyJk+ejOLiYsZTxs7ODoqKirC1tYWdnV2jeMqUlJQgJCQE6urq6NSpU51FhYjw22+/4ezZs7h79y4MDQ0btF/fwuvXr3HgwAHs3r2bOZaRkYGOHTsKtOOX+s/IyECbNm1qtRPOyMj44ntWVlbi7Nmz0NTUhISEBEpKSpgKBgYGBigrKwNQVU5/2LBh8PX1xcePH+Ho6Ii5c+d+03ibK+yMRYzo2LEj1NXVcefOHeZYQUEBnjx5wpgQWVlZIS8vD6GhoUybu3fvgsfjwdLSssn7/D3RqlUrpvBgRkYGDh48iMLCQkyaNAmGhoZYunQpAgMDweVyv/m9SktLERoaCjU1NRgYGNRLVHbs2AF3d3f4+fmhS5cu39yXmvgaO+HU1FSMGjUKEydOxLx58xqlXzUhISEBHx8fmJqaYs2aNbh27RoA4Pz583j48CEWLVoEoKoIqrGxMVasWIFffvmFFZV/gZ2xiBhFRUVMdi9QtWEfHh6Otm3bQkdHB0uXLsVvv/0GAwMDJtxYU1MT9vb2AABjY2OMGjUK8+bNw5EjR1BRUYHFixfD0dGxXhVtWb6Nli1bwtbWFra2tigvL8fdu3fh5eWF6dOng8PhMJ4yny/x1IXS0lKEhIRARUWlXhn1RITff/8dBw8exJ07d2Bqavo1Q6sT9bUTTktLw5AhQ9C3b1+BTXkAtVoF88/9W5u6zNK5XC7k5OTg4+MDe3t77N69Gw8ePMDJkydx4cIFdOvWjSmTz1JHhLzHw/IZAQEBBKDaY8aMGURUtXm8fv16UlNTIxkZGRo2bBjFxcUJvEZ2djZNnjyZWrduTQoKCjRr1iwqLCwUwmhYPqeiooL8/f3J2dmZ1NXVqU2bNjRt2jTy8vKi7OzsL264Z2dn061btygkJKReG/VFRUW0fft2UlJSoidPngj7YxAgJSWFDAwMyNHRkbhcbrXz/M378vJy5tjatWurbd6PGTNG4DorK6tqm/c8Ho+IiD58+CBwnP++XC6XHBwciMPhkLOzM3Ocfx1L3WCFhYVFSHC5XLp37x4tWbKEtLS0SEFBgSZNmkQeHh70/v37WkXl2bNn9RaVvXv3koKCAgUFBQl72AKkpKRQp06daNiwYZSSkkLp6enMg09eXh6pqamRk5MTRUZG0rlz50hOTo6OHj3KtAkKCiJJSUnavXs3xcTE0MaNG0lKSooiIiKqvefLly9JW1tb4D2ISEDUJk2aRCYmJnTq1CkqLi5uhJE3b1hhYWERASorKykoKIiWL19OHTt2pFatWtG4cePo5MmTlJmZSdHR0WRtbU2BgYH1FpWDBw+SvLw8BQYGCnuY1Th+/HiNM/TPF1M+TZBs3749bd++vdprXbhwgQwNDUlaWpq6du1aa4JkUFAQaWlpUU5OTrVzNYnLwYMHqbS09BtH+n3BCgvLFwkMDKQxY8aQhoZGtdwaIqIZM2ZU+1EYOXKkQJvs7GyaMmUKycvLk6KiIs2ePZtdnquFyspKevbsGa1evZoMDAxIVlaWVFVVaeDAgfTu3bs6C0tRUREdPXqUWrduTXfu3BH2sESGjx8/ko6ODnl6etZ4/lNx+eGHH8jJyamputZsYKPCGhmqEm9hd+ObKP7/+mWHDh2qtc2oUaOQnp7OPM6ePStwfurUqYiKioKfnx+uXbuG+/fvY/78+Y3ddbGkRYsW6NmzJ7Zv34779+9DU1MT6urqyM7Ohr6+PiZMmIBTp04hJyen1u8WEcHT0xPLly+Hp6cnhg4d2sSjEA0+zyXi8XggImhoaCAxMbHGayQkJFBZWQkAuH79Ok6ePNno/Wx2CFXWmjlFRUXC7kKDg1pmLHZ2drVew9Yv+zpycnLIxMSEJk+eTFwul3g8HkVFRZGbmxuZm5uTlJQUDRs2jA4cOECJiYkCM5nTp0+TnJwcXb16VdjDEDqvXr2iCxcu0Lt37ygvL4+IiHbs2EGOjo5ERDUGDBBVzRxZvg5WWBqRSZMmCZSAIPrfl1Vco0xqExZFRUVSUVEhQ0NDcnZ2Foi6cXd3JyUlJYFrKioqSEJCgi5evNgU3RZLuFwu/fnnn1RRUVHtHI/Ho/j4eNq2bRv16tWLJCUlaeDAgbRnzx46cuQIycnJffefLY/Ho6KiIho6dCgpKSmRgYEBqaqqkp2dHenq6pKRkRGzMV+buLB8HazRVyPy4MED2NjY4N27d1BUVAQRgcPhICMjQ2yz4DkcDi5dusTkzQDAuXPnICcnh44dOyIhIQGurq5o3bo1goODISEhga1bt+LkyZOIi4sTeC1VVVW4ublh4cKFTTyK5gURISkpCd7e3jh//jyePXuGo0ePskuN/092djYUFRURHR2NkJAQ5OTk4M6dO0hKSoKpqSn+/vtvyMvLs7kqDYlwda15k5CQQF26dCEPDw8iIiooKKB9+/ZRy5Yt/7UgJI/HE9kZDWqYsXxOQkICASB/f38iYgtjNiU8Ho8iIiJE9vsjDGpa0iorK6MzZ85Qr169yN7envLz82tty1J/2M37RoLH40FPTw9KSkqIjo5Gfn4+pk6diiNHjmD37t3MXTrVMGHkl7wAquoY1dRGlNHT04OysjJTQYCtX9Z0cDgcmJiYiLwhWVPCd4Hk/zvi8XiQkZHBhAkTsGTJEnz48AGjR49Gfn4+05bl22A/xUaC/wV1cXGBt7c3TExMkJ2djbNnzzK1h3g8noCAAMC9e/fg5uaGp0+fAqiKUBG3H4mUlBRkZ2czhTLZ+mUsogD/31GLFi1ARJCWlsaUKVMwc+ZMqKmpiYSvTrNBuBOm5gl/Op2Wlkaurq7E4XBo2rRpNSZk8eEvXcyfP59atmxJ0tLS1KZNG1q8eDElJSU1Sb9ro7CwkJ4/f07Pnz8nALR37156/vw5JSUlUWFhIa1YsYKCg4MpMTGR/P39qXv37mRgYCAQtDBq1CiysLCgJ0+e0MOHD8nAwIAmT54sxFGxfO/w/81VVlZSSUmJkHvTvGCFpYHhR5ckJiZSz549adSoUcThcOiPP/4gIhKod/Q5BQUFNHDgQPrll1+orKyMbty4QVZWVuTk5CTUzN9/q19WUlJC1tbWpKKiQlJSUqSrq0vz5s2jjIwMgddg65exiCLsXlTjwApLIxAYGEja2to0fPhwSk9Ppzlz5tDYsWO/GC9/9epVsrCwoH/++Yc5d/r0aeJwOPTw4UOBayoqKth/FCxCoy5e9YmJiTXekAQHBwu8VkN51bOIDuweSwPC5XKxYMECTJs2DaNHj8b169ehrq6O8ePH4/79+yguLv7X62/fvg0tLS1YW1szx+Tk5KCiooKUlBQA/9uLkZSUFLu9F5bmQ1286vn4+/sLVGXo0aMHc66hvOpZRAxhK1tzgsvl0oEDB8jLy0sgETIhIYE6d+5MZ8+erfXakpIS6t+/PxkaGtKVK1eIqCrzul+/fmRiYkKpqakUHx9Prq6uNGDAAFq2bBnjU/75zOV7mMls3bqVevbsSa1btyYVFRWys7Oj2NhYgTalpaW0aNEiatu2LbVq1YocHByqLdElJSXR6NGjqWXLlqSiokIrVqyoMSGR5ct87lXPn7E8f/681msawqueRfRgZywNiISEBBYvXozx48cLhC3q6emhbdu2CAwMBCAYYsyPRAkMDERubi40NDTw008/QVZWFmZmZnj79i2WLVuGnJwc9O3bF/7+/hg5ciSio6NhZ2eHV69eVZu58J9zuVzm9SsqKvDo0SPk5OQ06mfQVAQGBsLFxQWPHz+Gn58fKioqYG1tLTArXLZsGa5evQpPT08EBgYiLS0NDg4OzPnKykrY2NigvLwcjx49wsmTJ3HixAls2LBBGEMSez73qudja2sLVVVV9O/fH1euXBE4961e9SwiirCV7XvBz8+PXFxcqh3nz2yWL19OI0aMoJiYGCIiioiIoDNnzlB8fDwVFBTQ9OnTqUePHsx12dnZ1Lt3b3J2dmaO5ebmkqenJ4WGhlZ7n4KCApo5cybNnTtX4Hhzmd1kZWURAKY0fF5eHklJSQlUsI2JiRFY479+/Tq1aNFCYBbz559/koKCgsBeAcuXefXqFSkoKNCxY8eYY+/fv6c9e/bQ48eP6enTp7R69WricDjk4+PDtJGSkmISiPkcOnSIVFVVm6zvLA0PKywiQGlpKfXv359++umnGn/QHj58SCYmJnT8+HEiIiaMd926ddStWzciqvKr6Nu3L1lYWJCysjJpaWmRm5tbtUKY/GuTk5OZSDO+uIizyLx69YoAMMZOd+7cIQCUm5sr0E5HR4f27t1LRETr169nPj8+b968IQAUFhbWFN0WOVavXl2rPwr/wb/54ZOSkkL6+vo0Z86cL76+k5MT9e/fn3nOCkvzhF0KEwHi4+NRWloKfX19SEtLM6X26f+XzAoKCpCUlIQxY8YIXBcWFgZDQ0MAwOnTp1FcXAxvb2+8f/8ebm5uyMzMRF5eHrhcLm7cuIGQkBDIyMgAADw8PGBubo7MzExwOBxwuVxwOBz8/fffSE9Pb9oP4Bvh8XhYunQp+vXrBxMTEwBARkYGpKWloaSkJNBWTU0NGRkZTBs1NbVq5/nnvkd+/vlnxMTE/Oujrl71NWFpaclUZAC+zaueRXSRFHYHWAAzMzM8efJEYH+Aw+EwwiIpKYnKykpwuVwAgIyMDMrKyvD06VOsX78eAGBkZAQPDw88efIEHTp0wMyZMzFo0CAoKyvjw4cPWLVqFYyNjXHhwgX8/vvvuH79OoyMjJgfUknJqq/C6dOnweFwMHv2bLGJOnNxcUFkZCQePnwo7K6IPSoqKlBRUalT29TUVAwZMgQ9evTA8ePH61QOJTw8nKnIAFRVZbhz5w6WLl3KHPPz84OVlVW9+84iQgh3wsRSF1JTU6lfv360fPlyIiKKjY2lBQsWkJaWFr169YqIqpa4XF1dafDgwbR161aB6+/du0fq6up069YtIiKaN28ecTgcatWqFVlbW4u1u6CLiwtpaWnRmzdvBI6zS2GNS1286k+cOEEeHh4UExNDMTExtGXLFmrRooVAnlZ9vOpZxAdWWMQEDw8P0tDQoPbt25OxsTGZmZmRt7c3ERHz41laWkqnTp0ifX19cnJyooqKCiovL6cdO3aQmpoa81oHDx4kY2Njunz5Mi1YsIDWrFlDRER79+6lp0+fVntvLpcrcn4VPB6PXFxcSFNTk+Lj46ud52/ee3l5McdiY2Nr3LzPzMxk2hw9epQUFBQEytGwVKcuXvUnTpwgY2NjkpOTIwUFBerdu3eNdsB19apnER9YYREzbt++TadOnWLKfBMRLVy4UGDW8ccff1Dv3r0pMTGRsrKyaPjw4Yxb3rt372jKlCnVcgeIiDgcDu3Zs0csSocvXLiQFBUV6d69ewJ3y5/WfHJ2diYdHR26e/cuhYSEkJWVFVlZWTHnuVwumZiYkLW1NYWHh9PNmzdJRUWF1q5dK4whsbA0G1hhEXPy8/Np8uTJpKenR5MnTyZ3d3cyNzenIUOGUG5uLj19+pTatWvHWNT6+/tTjx49mNpl/B/ia9eukaKiIoWHhxNR1eznr7/+opEjR9KwYcPo9OnTNc5ahCVCtd0t8yPniP6XINmmTRuSk5OjcePGCSzVEBG9ffuWfvjhB2rZsiUpKyvTzz//zCZIsrB8I6yDZDMhKCgIx44dQ0JCAvr374/Zs2fD0NAQe/bswd69e5GamgoA2L9/P44ePYqrV69CT08PHz9+hIyMDBwcHFBQUABfX1+UlZXh559/xvnz5+Hs7AwZGRmcP38eU6dOxaZNm2p8fx6PByJiHfhYWFjYqLDmQr9+/dCvXz8AVRn3kpKSyMjIwKVLl2BkZASgKmw5NjYWioqK0NPTQ2VlJRN+fOvWLWzbtg0yMjI4c+YM7t69C09PT4waNQoAoKSkhGPHjmH8+PEwNTVFREQEzp8/DwcHB5iamkJKSko4A2dhYRE5WGFphvBDh9XV1eHu7s6ELSsoKEBaWhrS0tIAwMwubt++jcrKSgwZMgQfP35EcHAwTE1NMWLECOY1p0+fjl9//ZVxggwNDcXx48cRFRWF1q1b48WLF/jll18wYcIEdtbCwvKdwyZINnM6d+7MzFgAYPz48SguLkaLFi2wZ88eAMDx48fRv39/6OrqorCwEK9fv4aenh4kJCRQUVEBAPjw4QOKiorQoUMHAMCzZ89QWFgIQ0NDuLi4YPz48XB1dcWLFy+afIyixLZt29CrVy/Iy8tDVVUV9vb2iIuLE2gzePBgxn6a/3B2dhZok5ycDBsbG8jJyUFVVRUrV65k8phYWEQdVli+MwYMGIBnz54hOjoaAwcOBACcP38egwYNgqysLJSVlZGTk8PMOvizH3d3dxgbG6N9+/Z4/fo1oqOj4eDggB07dqBPnz5YsmQJSkpKvvvigXUpjgkA8+bNEyglv3PnTuYcWxyTRdxhl8K+Uz6dxSQnJ0NKSopZIps2bRrOnj2LFy9ewNjYGCdOnIC7uzs2b94MWVlZBAcHo6SkBLa2tsxrZGVloUuXLsjPz2/ysYgSN2/eFHh+4sQJqKqqIjQ0lBFyoMpnp7ayJbdv30Z0dDT8/f2hpqYGc3NzbN68GatXr8amTZuYvxMLi6jCzlhYoKWlJVAza+bMmejQoQMsLS3Rs2dP7NixA9OnT8eSJUsAAE+ePIG8vDz69+/PXBMSEoK8vDyYmpo2ef9FGb7Qfl5O/syZM1BWVoaJiQnWrl2LkpIS5hx/j+vTv8nIkSNRUFCAqKiopuk4C8s3wM5YWKqhoqKCixcvIj09HQ8ePIC+vj7j+hcTE4P4+HiYmppCVVUVQFWo8fPnzyErKytwV/69U1NxTACYMmUKdHV1oampiZcvX2L16tWIi4vDxYsXAbDFMVnEH1ZYWGpFQ0MDP/74o8CxtLQ05ObmwtzcnDn2+vVrxMfHw8zMDIqKik3cS9GltuKY8+fPZ/7f1NQUGhoaGDZsGBISEqCvr9/U3WRhaXDYpTCWejFs2DDcv38fkyZNYo49fPgQ0dHRGDZsmBB7JlosXrwY165dQ0BAALS0tP61raWlJQAw5eRrKyXPP8fCIuqwwsJSb1q2bAlZWVnmec+ePTF+/HgMHTpUiL0SDYgIixcvxqVLl3D37l107Njxi9eEh4cDAFNO3srKChEREUzOEFBVSl5BQQFdunRplH6zsDQkbEkXFpYGZNGiRfDw8ICPjw86d+7MHFdUVETLli2RkJAADw8PjB49Gu3atcPLly+xbNkyaGlpITAwEEBVuLG5uTk0NTWxc+dOZGRkwMnJCXPnzsXWrVuFNTQWljrDCgsLSwNSmzna8ePHMXPmTLx79w7Tpk1DZGQkiouLoa2tjXHjxmHdunVQUFBg2iclJWHhwoW4d+8eWrVqhRkzZmD79u1MXhELiyjDCgsLCwsLS4PC7rGwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQorLCwsLCwsDQo/wfhKCcZ3+/bYgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_trajectory(trajectory):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    x, y, z, _ = zip(*trajectory) \n",
    "\n",
    "    ax.scatter(x[0], y[0], z[0], color='red', s=100)  \n",
    "\n",
    "    ax.plot(x, y, z, marker='o', linestyle='-')\n",
    "    ax.set_xlabel('X Position')\n",
    "    ax.set_ylabel('Y Position')\n",
    "    ax.set_zlabel('Z Position')\n",
    "    plt.title('3D Trajectory Visualization')\n",
    "    plt.show()\n",
    "\n",
    "plot_trajectory(trajectory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory saved to /home/rivermar/inspel/inspel/data/staov_cable_trajectory_0_4.txt.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "file_location = os.path.expanduser('~/inspel/inspel/data/staov_cable_trajectory_0_4.txt')\n",
    "\n",
    "def save_trajectory(trajectory, filename):\n",
    "    with open(filename, 'w') as file:\n",
    "        for point in trajectory:\n",
    "            line = ','.join(map(str, point)) + '\\n'\n",
    "            file.write(line)\n",
    "\n",
    "save_trajectory(trajectory, file_location)\n",
    "\n",
    "print(f\"Trajectory saved to {file_location}.\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}