{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f75a8ad3",
   "metadata": {},
   "source": [
    "# Quickstart\n",
    "\n",
    "This page walks through a typical analysis: load a recording, look at\n",
    "it, deal with blinks, filter the signal and compute the average pupil\n",
    "response to an event. Every step links to a page with more details.\n",
    "\n",
    "We start by importing the package. `u` holds the units (e.g., `u.ms`,\n",
    "`u.sec`) that can be attached to times and distances. By default,\n",
    "`pypillometry` logs every step; here we only show warnings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b649eaf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:05.011561Z",
     "iopub.status.busy": "2026-11-10T13:34:05.011119Z",
     "iopub.status.idle": "2026-11-10T13:34:06.176992Z",
     "shell.execute_reply": "2026-11-10T13:34:06.174780Z"
    }
   },
   "outputs": [],
   "source": [
    "import pypillometry as pp\n",
    "from pypillometry import u\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "pp.logging_set_level(\"WARNING\")  # show only warnings, not every step"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0918172",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "`pypillometry` comes with a few example datasets. `rlmw_002_short` is the\n",
    "first 40 seconds of a binocular recording from a reinforcement-learning\n",
    "task, recorded with an Eyelink eye tracker at 500 Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db88ea43",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:06.187586Z",
     "iopub.status.busy": "2026-11-10T13:34:06.186914Z",
     "iopub.status.idle": "2026-11-10T13:34:06.607464Z",
     "shell.execute_reply": "2026-11-10T13:34:06.606094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"font-family:system-ui, sans-serif; border:1px solid #ddd; border-radius:6px; padding:12px; max-width: min(600px, 50vw); min-width: 300px;\">\n",
       "  <h3 style=\"margin-top:0;\">EyeData — <span style=\"color:#555;\">rlmw_002_short</span></h3>\n",
       "\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-bottom:10px;\">\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Samples</div>\n",
       "      <div style=\"font-weight:600;\">20,465</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Sampling rate</div>\n",
       "      <div style=\"font-weight:600;\">500.0 Hz</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Duration</div>\n",
       "      <div style=\"font-weight:600;\">0.68 min (start 0.00, end 0.68)</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Eyes</div>\n",
       "      <div style=\"font-weight:600;\">left, right</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Variables</div>\n",
       "      <div style=\"font-weight:600;\">left_x, left_y, left_pupil, right_x, right_y, right_pupil</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Events</div>\n",
       "      <div style=\"font-weight:600;\">40</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Size</div>\n",
       "      <div style=\"font-weight:600;\">1.4MiB</div>\n",
       "    </div>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    <details open style=\"margin-bottom:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Signal overview</summary>\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-top:8px;\">\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_x</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_x sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_y</div>\n",
       "        <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAALQAAAA8CAYAAADPLpCHAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAADIJJREFUeJztnQlUVXUex7/Ag8e+y76LuMQiSIlomg5OkjlWtk5mNe2d0rLFtlFnrKkcrczOqabFabE0bUqTFMqF0EhAUWSRRWTf9+WxCnN+v/cuE4hwaUDee/0/53Auwn33/u////3/tv//okFvb28vBAI9wXC8GyAQjCZC0AK9QghaoFcIQQv0CiFogV4hBC3QK4SgBXqFVgu6qFaF/OqW8W6GQIcw0OaFFf8Xvkd3Ty8KXls83k0R6AhabaFJzER5Y9t4N0WgI2i1oCdYKfmYkFsz3k0R6AhaLegLGgtd39o53k353fBlUhF8nouBFkeiuinonp5eNKjUQlZ1Xhjv5ugsDapO7Ewukn3+9uOFfKxq7oAuorWCbm7vhsZAo61LCPq38vd9mVjz9RnUyfRyrjZmfDxb0QxdRGsFXaexzkRrRzf0mbKGNhzOrhqTa7dpvFtdqzyLa2NmzMfsiiboIlor6HqNoK1MFX2Doq+s+DgJ925LHpNrKxWGIwohWtrVxiOrfGQWuutCD0/M8UZrBS3Fz+62ZqMaQ2+Oy8byD49Dm6hsbOdjU3vXqF/b1NiIj9UyBS21IbmgbkT32RSXjcjXDqF9nMNDrRM0ZdcUYuRVtcDQQCPoUeykrYfycDSvBqUyrAkNDrVjrDHRWNHyBrWwx6KWX9PSKTt3cbRUoqS+DcV1Ktn3OV3c0O84XmiVoFWd3bh7WzKuWBeLjQeycWOoB2zMjaEaxRg6wNmSjyu/TEVH99ATZcO+TES9Ec/tGgndF3p+U1hQNgYLSM0aizsSCx011Ym/P1lUL/s+FiYKPqYUyv+MFKqM5vYGrRH0+/HnMG1tLJLO1+Kv10/DA3P9sCZ6MsxNjEY15Ojs7kGQuw1OFNYj+fzQnZ9epk6MkgvkD9KZkkb4v7gfKSNw2QqjsbPQzZqY+GBWpaxcpKmtC5725rBSKlChCYXkUKY5l/p1JDzwaQoWbI6HTgh6JMmctBr47vIZuG+OL9YsmgInK1OYmyguWbYj6/PwZyewfm+G7PtQ+So6yAV25sbDxomSpT2aWy37+htiMvkYn1M94nxhLJb4mzWCzq1q6asxDxXu0fnWZsZwslbKTiTpcyV1KigMDZBe2jiisTiSLb+fxl3QZ0dQ+imuV+HBuX6YP1nt7iTMjI0u6fIPZlXhQEYF/v1zATLLmoZd3SKBNrV3w8HCBDO87ZBSWDfkKqUUP3+TWiYr2SG3Lk0Sao9cj0FtInYkF6OySZ5V/CyxAHM3Hh42zm1q78LCac78fX5N65AhERkOirmtTRVwtjaV3Zamtm40d3RjUaALT4IqmZ8rrR/9CTymgs6tbJE9qDQwvo4WF/2OQ46OwcWUWf4/0Vz3dgJiMyqHvE9jmzqetDU3wbyACUg8V4sPE/LxRlz2ReeSpeno7sE/bgxCbWsHth8ffrXtiKaW/OeZXkjMr8UrMZn4Krl4yIkmlSc3LgtGR9cFnpxy2BCThaI6FX7Jrx3yvOb2bkz3tMWiK1y4j7PKmxC0Pg5rdqchp7L5ImES1qbGLOiqpg7us+e+TsN78ef6tiIMZoyI6EBXPqaV9LfSNS0dg/aBNGFszdW179FAHcmPEQM77FLQwFBfDSpopYKrHNQhBgYG/X5HVjDMyxYni9SZ9ZnSBrYSe0+XobalA/fO9u13fr1KLWh7CxPM9nfEur0ZeDkmi39W09rJ1/v0vqt4QHckF8HVxhS3hnuwCDbFZnMSuSzMA6lFDTBRGKC8sR0zfe3h72TF1/glvw5XuFnj4bkT8VNONT4+VsAiyKpowsKpzjyRHCxNWCwSdG1iqqs1bgrzwK6UYjwZFdBX+bhUqEVGYOCklvaQE14O5txnFBOTxaV/x2VU4J3DeWyJd6YU43xNKz9vT28vh3aSMKl9TlZKTgoPn61iz0HEpJVjxSxvRPg5cJwtIXmJCD97+E2wwH9SSxCl8Qr0fNdvPYq7Iryxbsk0Dn1obYFWJCub22FkaMCTTTcELbPkRR1L+A0maGMjFkXnhR7Ut3axSyfRUghwprQRd0f69AmahJZb2cwVDOLHrEosDXHHvxLysfWOULRoqiUUP1sqFdj9SCTOV7fiqV2n8YXGAq/eeRovXDcFe06VcQhECdtz0VM4MSVRU/VlIJETHbDplhCkFtdjjr8jiyf+mfnc7ke3n8C2YwX8RThammD/qrm8k5BE+UpMFoc/NBGUxl5soV+OyWSRLQl2w+YfcljgsyY69N2PSmqEm40pMsqaUFKvQmvHBRb6ze8lwsLECHsemw0na1MOIaxMjeFpb4CCWhUnb/Q8JKoXv0nHwjfjOdfZfn8EeyWaSJOcLfmzZEEpLCORvr4sGBsPnMUzu9NgbGSAD1aE4xpNeEjtIU9KhuIvs32xdk86CmpasT+9Ahtjz3IFhJ7rh8xKLpdSpSnuyXmobOrABEslXlsWDJ0QNM3OwSzrQM7XtPAgSNtFfw11FLEzuRhfpRQjvbQJ4d523PG0gWlZmDuiA11wIL0CWw7mYuGbP8HfyRL3zvbBP2OzcSyvlpMVShynuKgtKVlKIszLDqGetixogoTzzuFcngjEbVd68tFCqcDmW0OwPMILL32bjpcWTwM9ElmxOz88jp/P1XI8S+J5fIE/f4YsD31tvSOMY+u86mb8lFODfWlluO+TZOx4MAKfJhZyXLvv8TkwNDRAgLMVrvKx559TDf7zX4r4Pnd/nIQtt09HdJBrP0EvDXXHu0fOYc7rh/v6y0RhyP14/ycpuD7YjZ893McO+dVqo0GTiPrPy96cn6W4Tn2tLQdzeBFmqosVjI0M4etojvauHm4Deakrfeyx6+FI9nwrd6Tikc9PwtPeDE9EBbBl97Az43EmD0aLLNdsOsLPcKW3Pe6a5c0GiMaPJgcVAKhUR7G2s/XFY661gqaBPFfd0ueSh7LQvhMsBhW+JL61ezLYqjxz7WS89WMOi4WshLeD2qqTGCgzp6XbOyO82QIHu9sio6wRZiZGWP3VaaQWN+CG6W6w11yToHuShaWJsypqEse/HyTkc7ghbdSRCPWyQ8zKq/v9LPH5P3DyuPjtBBbE7ImO/X5P9yaLTV8Lpjjj5hkeuO39RERvSUBhrYorOhRuSLy7PIyvR8/17alSdu8fHT2PR784iQ1LA7E8wptdPNWun1oYwIsgpsaG7A3W7snA4iBXPBE1ia9P4cWdM73gYWfO50kEuttwW+Ofns97N+g+f/suAwpDQ9wS7sHnUHJ+/xxfHMquwtLp7n2fdSCLelMwXt2fhe/PVHA4QpPG086873mfj57C935o7kRur8Qt4Z7sDUI3xGHRWwnsdSlk1JlXsAJe3M8CpJryUNAAk4ujsGAgZIUpliN35mil5PiW9keTRZM20siBkhvqeLK2YwG5UmoPTaThOFXcgE9+LmDvteGGQA4JhoL6gHbNkdum8/eeKkWDqgs/rJ7X77xvUkswy88RLjamfI9DZ6vw2Hz/vnic9jqnlTTg1Zv6u3gKxQLXxfL3Xz8yCzO87WU9M000SnyJR6/xx9PXTpb1ueP5tYg5U86eiEOUJdOgE4JetSOVE4nvV13NFnQg5IYoJvvudBlWLvDH6j/K65DfIzRMz+5Ow64TJZzkvbd8BiL9+3uD/wcSO1VMttx+sVG5FBQy0PhRWZAsu1KhDg/lQlscyNNIC0taL2iqgQavj8Pbd4TiTyFu/X5HoQjFtbSvIsTDlt1pkIfNWDVFLyBLTXHoTD+HQStCgjGOoWn5lGJdqf4rQXNoxUdJ7KYp3npo3kQxFjKgMOv2q7xEX43XwgolXOQeqRY6MAkkMT+7aDKXxgQCndmcRInSQEHTPgdKVO6J9Bm2pCcQaJWgaaPLwJAjpaAe0z1seXVKINA9Cz3gTYzUonqEjnL9USAgFJfDQkvbIylLTyqo4+VXWqQQCHRP0KbGKKxt5ZrjPduSeLM8LUHPmTR6NVSB4LIJWp0UduNYXg2L+Y1bQ3BdkGvfy5sCgc4JmpJCKTFcEuLGm18EAh2tcig4KaS3MmgTjRCzQOerHLS4Xt7QNuwmHIFAJwRN0MogrRoKBLodcmisMm0CFxZaoDcWmt6yoA36AoFeCJo2pNMii0Cg04L+tVUWMbRA5wVNbyNIryWJGFow1lyWFQ4p7KAN/wKBzgvaQqle5hZJoUAvBJ2j+ZNg4T7y3iYWCH4rlyUGoDdTsiua+e9BCAS/2/8aWSAYKWLbm0CvEIIW6BVC0AK9QghaoFcIQQv0CiFogV4hBC2APvFf+muuUutp54QAAAAASUVORK5CYII=\" alt=\"left_y sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_x</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_x sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_y</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_y sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "    </div>\n",
       "  </details>\n",
       "\n",
       "\n",
       "\n",
       "  <details style=\"margin-top:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Data glimpse</summary>\n",
       "    <pre style=\"margin-top:8px; background:#f9f9f9; padding:8px; border-radius:4px;\">EyeDataDict(vars=6,n=20465,shape=(20465,)): \n",
       "  left_x (float64): 655.6, 655.7, 655.0, 654.5, 655.0...\n",
       "  left_y (float64): 599.9, 598.9, 597.6, 597.8, 597.8...\n",
       "  left_pupil (float64): 1121.0, 1122.0, 1124.0, 1126.0, 1126.0...\n",
       "  right_x (float64): 773.1, 773.8, 774.8, 776.3, 776.7...\n",
       "  right_y (float64): 594.2, 593.9, 596.5, 597.4, 597.3...\n",
       "  right_pupil (float64): 949.0, 951.0, 956.0, 959.0, 960.0...\n",
       "</pre>\n",
       "  </details>\n",
       "\n",
       "  <details>\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Recent history</summary>\n",
       "    <ol style=\"margin-top:8px;\">\n",
       "      <li>reset_time()</li>\n",
       "    </ol>\n",
       "  </details>\n",
       "</div>\n"
      ],
      "text/plain": [
       "EyeData(rlmw_002_short, 1.4MiB):\n",
       " n                 : 20465\n",
       " sampling_rate     : 500.0\n",
       " eyes              : ['left', 'right']\n",
       " data              : ['left_x', 'left_y', 'left_pupil', 'right_x', 'right_y', 'right_pupil']\n",
       " nevents           : 40\n",
       " experimental_setup: {'screen_resolution': (1280, 1024), 'physical_screen_size_mm': (300.0, 200.0), 'eye_to_screen_distance_mm': 600.0, 'camera_position': 'not set'}\n",
       " calibration       : not available\n",
       " duration_minutes  : 0.6822\n",
       " start_min         : 0.0\n",
       " end_min           : 0.6821\n",
       " params            : {}\n",
       " nblinks           : {}\n",
       " blinks            : {}\n",
       " glimpse           : EyeDataDict(vars=6,n=20465,shape=(20465,)): \n",
       "  left_x (float64): 655.6, 655.7, 655.0, 654.5, 655.0...\n",
       "  left_y (float64): 599.9, 598.9, 597.6, 597.8, 597.8...\n",
       "  left_pupil (float64): 1121.0, 1122.0, 1124.0, 1126.0, 1126.0...\n",
       "  right_x (float64): 773.1, 773.8, 774.8, 776.3, 776.7...\n",
       "  right_y (float64): 594.2, 593.9, 596.5, 597.4, 597.3...\n",
       "  right_pupil (float64): 949.0, 951.0, 956.0, 959.0, 960.0...\n",
       "\n",
       " History:\n",
       " *\n",
       " └ reset_time()"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pp.get_example_data(\"rlmw_002_short\")\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6180ebc2",
   "metadata": {},
   "source": [
    "The result is an `EyeData` object holding pupil size and gaze position\n",
    "(x, y) of both eyes, the time vector, the events (triggers sent by the\n",
    "experiment) and a description of the experimental setup. At the bottom,\n",
    "you can see the history of all operations applied to the data.\n",
    "\n",
    "Your own Eyelink files are read with `EyeData.from_eyelink()`:\n",
    "\n",
    "```python\n",
    "d = pp.EyeData.from_eyelink(\"recording.edf\")\n",
    "```\n",
    "\n",
    "See [Loading data](../user_guide/loading_data.rst) for other formats.\n",
    "\n",
    "The data is stored under keys of the form `<eye>_<variable>`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67d06e5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:06.612006Z",
     "iopub.status.busy": "2026-11-10T13:34:06.611422Z",
     "iopub.status.idle": "2026-11-10T13:34:06.619351Z",
     "shell.execute_reply": "2026-11-10T13:34:06.618164Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['left', 'right'] ['x', 'y', 'pupil']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "masked_array(data=[1121.0, 1122.0, 1124.0, 1126.0, 1126.0, 1124.0, 1123.0,\n",
       "                   1122.0, 1122.0, 1122.0],\n",
       "             mask=[False, False, False, False, False, False, False, False,\n",
       "                   False, False],\n",
       "       fill_value=nan)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(d.eyes, d.variables)\n",
    "d[\"left_pupil\"][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e90eb1d9",
   "metadata": {},
   "source": [
    "## Look at the data\n",
    "\n",
    "All plotting functions are available under `d.plot`. Here we plot the\n",
    "pupil size of both eyes for the first 20 seconds; the vertical lines\n",
    "mark events and the shaded areas are blinks (none have been detected\n",
    "yet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd05c94c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:06.623517Z",
     "iopub.status.busy": "2026-11-10T13:34:06.622971Z",
     "iopub.status.idle": "2026-11-10T13:34:07.328931Z",
     "shell.execute_reply": "2026-11-10T13:34:07.327481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "d.plot.plot_pupil(plot_range=(0, 20) * u.sec);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce7ee63d",
   "metadata": {},
   "source": [
    "For long recordings, the interactive viewer is faster: `pp.view(d)` opens\n",
    "the data in a separate window (see [Interactive tools](../user_guide/viewer.rst))."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a931dfb",
   "metadata": {},
   "source": [
    "## Data quality\n",
    "\n",
    "Before processing, it is worth checking how good the recording is.\n",
    "`quality_report()` summarizes missing data, dropouts and noise of the\n",
    "pupil, missing and off-screen samples and precision of the gaze, the\n",
    "agreement between the eyes and the calibration errors stored in the\n",
    "Eyelink file. Each measure is labeled \"good\", \"ok\", \"poor\" or \"bad\"\n",
    "(the thresholds are rules of thumb); after preprocessing, the report also\n",
    "covers blinks and interpolated data. See\n",
    "[Data quality](../user_guide/quality.rst) for the measures and how to\n",
    "change the thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "96e68886",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:07.333205Z",
     "iopub.status.busy": "2026-11-10T13:34:07.332560Z",
     "iopub.status.idle": "2026-11-10T13:34:07.387730Z",
     "shell.execute_reply": "2026-11-10T13:34:07.386080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"font-family:system-ui, sans-serif; border:1px solid rgba(128,128,128,0.4); border-radius:6px; padding:12px; max-width:720px;\">\n",
       "  <div style=\"display:flex; align-items:center; gap:12px; margin-bottom:8px;\">\n",
       "    <h3 style=\"margin:0;\">Quality report — rlmw_002_short\n",
       "      <span style=\"opacity:0.6; font-weight:normal;\">(EyeData)</span></h3>\n",
       "    <span style=\"background:#9a7d0a; color:#fff; border-radius:3px; padding:3px 10px; font-size:1em; font-weight:600; text-transform:uppercase;\">ok</span>\n",
       "  </div>\n",
       "\n",
       "  <div style=\"font-weight:600; margin-top:10px;\">Recording</div>\n",
       "  <table style=\"border-collapse:collapse; margin-top:4px; min-width:60%;\">\n",
       "    <tbody>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"length of the recording\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">duration</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"length of the recording\">0.682 min </td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"number of samples per second\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">sampling rate</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"number of samples per second\">500 Hz </td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"number of samples in the recording\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">samples</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"number of samples in the recording\">20465 </td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"number of events (e.g., triggers) in the recording\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">events</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"number of events (e.g., triggers) in the recording\">40 </td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"number of gaps in the time vector, where the eye tracker stored no samples (filled with missing data when the data were read)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">time gaps</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"number of gaps in the time vector, where the eye tracker stored no samples (filled with missing data when the data were read)\">0 </td>\n",
       "      </tr>\n",
       "    </tbody>\n",
       "  </table>\n",
       "  <div style=\"font-weight:600; margin-top:10px;\">Pupil</div>\n",
       "  <table style=\"border-collapse:collapse; margin-top:4px; min-width:60%;\">\n",
       "    <thead><tr>\n",
       "      <th style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\"></th>\n",
       "      <th style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right;\">left</th>\n",
       "      <th style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right;\">right</th>\n",
       "    </tr></thead>\n",
       "    <tbody>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"percentage of samples without valid pupil data (e.g., blinks, lost tracking) (labels: good &lt;= 10%, ok &lt;= 20%, poor &lt;= 40%, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">missing data</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 10%, ok &lt;= 20%, poor &lt;= 40%, else bad\">2.7% <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 10%, ok &lt;= 20%, poor &lt;= 40%, else bad\">1.9% <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"number of periods with missing data per minute (including blinks; many short dropouts indicate unstable tracking) (labels: good &lt;= 30 /min, ok &lt;= 60 /min, poor &lt;= 150 /min, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">dropouts</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 30 /min, ok &lt;= 60 /min, poor &lt;= 150 /min, else bad\">16.1 /min <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 30 /min, ok &lt;= 60 /min, poor &lt;= 150 /min, else bad\">14.7 /min <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"duration of the longest period with missing data (long periods cannot be interpolated) (labels: good &lt;= 2 s, ok &lt;= 5 s, poor &lt;= 30 s, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">longest dropout</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 2 s, ok &lt;= 5 s, poor &lt;= 30 s, else bad\">0.14 s <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 2 s, ok &lt;= 5 s, poor &lt;= 30 s, else bad\">0.12 s <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"high-frequency noise in percent of the median pupil size, estimated robustly from sample-to-sample differences (insensitive to blinks and slow pupil changes) (labels: good &lt;= 0.5%, ok &lt;= 1%, poor &lt;= 2%, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">noise</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 0.5%, ok &lt;= 1%, poor &lt;= 2%, else bad\">0.094% <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 0.5%, ok &lt;= 1%, poor &lt;= 2%, else bad\">0.11% <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"high-frequency noise (SD) in the units of the pupil data\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">noise (SD)</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"high-frequency noise (SD) in the units of the pupil data\">1.05 </td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"high-frequency noise (SD) in the units of the pupil data\">1.05 </td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"number of changes larger than 5% of the median pupil size from one sample to the next, per minute (ignoring 50 ms around missing data); indicates artifacts, e.g., during saccades (labels: good &lt;= 1 /min, ok &lt;= 10 /min, poor &lt;= 100 /min, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">jumps</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 1 /min, ok &lt;= 10 /min, poor &lt;= 100 /min, else bad\">0 /min <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 1 /min, ok &lt;= 10 /min, poor &lt;= 100 /min, else bad\">0 /min <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "      </tr>\n",
       "    </tbody>\n",
       "  </table>\n",
       "  <div style=\"font-weight:600; margin-top:10px;\">Gaze</div>\n",
       "  <table style=\"border-collapse:collapse; margin-top:4px; min-width:60%;\">\n",
       "    <thead><tr>\n",
       "      <th style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\"></th>\n",
       "      <th style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right;\">left</th>\n",
       "      <th style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right;\">right</th>\n",
       "    </tr></thead>\n",
       "    <tbody>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"percentage of samples without valid gaze data (x or y missing) (labels: good &lt;= 10%, ok &lt;= 20%, poor &lt;= 40%, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">missing data</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 10%, ok &lt;= 20%, poor &lt;= 40%, else bad\">2.7% <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 10%, ok &lt;= 20%, poor &lt;= 40%, else bad\">1.9% <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"percentage of valid gaze samples outside the screen (looking away or poor calibration) (labels: good &lt;= 5%, ok &lt;= 15%, poor &lt;= 30%, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">off-screen</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 5%, ok &lt;= 15%, poor &lt;= 30%, else bad\">0.21% <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 5%, ok &lt;= 15%, poor &lt;= 30%, else bad\">0.24% <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"precision of the gaze: median distance between consecutive samples in degrees of visual angle (lower is better) (labels: good &lt;= 0.05 deg, ok &lt;= 0.1 deg, poor &lt;= 0.2 deg, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">precision</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 0.05 deg, ok &lt;= 0.1 deg, poor &lt;= 0.2 deg, else bad\">0.0202 deg <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 0.05 deg, ok &lt;= 0.1 deg, poor &lt;= 0.2 deg, else bad\">0.02 deg <span style=\"background:#2e7d32; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">good</span></td>\n",
       "      </tr>\n",
       "    </tbody>\n",
       "  </table>\n",
       "  <div style=\"font-weight:600; margin-top:10px;\">Binocular</div>\n",
       "  <table style=\"border-collapse:collapse; margin-top:4px; min-width:60%;\">\n",
       "    <thead><tr>\n",
       "      <th style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\"></th>\n",
       "      <th style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right;\">left/right</th>\n",
       "    </tr></thead>\n",
       "    <tbody>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"correlation of left and right pupil size (samples valid in both eyes); the pupils of both eyes should move together (labels: good &gt;= 0.9, ok &gt;= 0.8, poor &gt;= 0.6, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">pupil correlation</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &gt;= 0.9, ok &gt;= 0.8, poor &gt;= 0.6, else bad\">0.866 <span style=\"background:#9a7d0a; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">ok</span></td>\n",
       "      </tr>\n",
       "      <tr>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:left;\"><span title=\"median distance between the gaze positions of the left and right eye; large values indicate calibration problems for one eye (labels: good &lt;= 1 deg, ok &lt;= 2 deg, poor &lt;= 3 deg, else bad)\" style=\"text-decoration:underline dotted rgba(128,128,128,0.7); text-underline-offset:3px; cursor:help;\">gaze disparity</span></td>\n",
       "        <td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25); text-align:right; white-space:nowrap;\" title=\"good &lt;= 1 deg, ok &lt;= 2 deg, poor &lt;= 3 deg, else bad\">1.61 deg <span style=\"background:#9a7d0a; color:#fff; border-radius:3px; padding:1px 6px; font-size:0.8em; font-weight:600; text-transform:uppercase;\">ok</span></td>\n",
       "      </tr>\n",
       "    </tbody>\n",
       "  </table>\n",
       "\n",
       "\n",
       "  <details style=\"margin-top:8px;\">\n",
       "    <summary style=\"cursor:pointer; opacity:0.7;\">Thresholds</summary>\n",
       "    <table style=\"border-collapse:collapse; font-size:0.85em; margin-top:4px;\">\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">pupil.missing_pct</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 10, ok &lt;= 20, poor &lt;= 40, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">pupil.dropout_rate</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 30, ok &lt;= 60, poor &lt;= 150, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">pupil.longest_dropout</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 2, ok &lt;= 5, poor &lt;= 30, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">pupil.noise</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 0.5, ok &lt;= 1, poor &lt;= 2, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">pupil.jump_rate</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 1, ok &lt;= 10, poor &lt;= 100, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">gaze.missing_pct</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 10, ok &lt;= 20, poor &lt;= 40, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">gaze.dropout_rate</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 30, ok &lt;= 60, poor &lt;= 150, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">gaze.longest_dropout</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 2, ok &lt;= 5, poor &lt;= 30, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">gaze.offscreen_pct</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 5, ok &lt;= 15, poor &lt;= 30, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">gaze.precision_deg</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 0.05, ok &lt;= 0.1, poor &lt;= 0.2, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">binocular.pupil_correlation</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &gt;= 0.9, ok &gt;= 0.8, poor &gt;= 0.6, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">binocular.gaze_disparity_deg</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 1, ok &lt;= 2, poor &lt;= 3, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">blinks.rate</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 20, ok &lt;= 35, poor &lt;= 70, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">blinks.time_pct</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 10, ok &lt;= 20, poor &lt;= 35, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">artifacts.pupil_masked_pct</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 10, ok &lt;= 20, poor &lt;= 35, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">artifacts.gaze_masked_pct</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 10, ok &lt;= 20, poor &lt;= 35, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">artifacts.interpolated_pct</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 10, ok &lt;= 20, poor &lt;= 30, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">calibration.mean_error</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 0.5, ok &lt;= 1, poor &lt;= 1.5, else bad</td></tr>\n",
       "      <tr><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">calibration.max_error</td><td style=\"padding:3px 8px; border-bottom:1px solid rgba(128,128,128,0.25);\">good &lt;= 1, ok &lt;= 1.5, poor &lt;= 2, else bad</td></tr>\n",
       "    </table>\n",
       "    <div style=\"font-size:0.85em; opacity:0.7; margin-top:4px;\">The overall label is the worst label of all measures. Hover over a measure for a description.</div>\n",
       "  </details>\n",
       "</div>"
      ],
      "text/plain": [
       "Quality report: rlmw_002_short (EyeData)\n",
       "Overall: \u001b[33mOK\u001b[0m\n",
       "\n",
       "Recording\n",
       "  duration       0.682 min\n",
       "  sampling rate     500 Hz\n",
       "  samples            20465\n",
       "  events                40\n",
       "  time gaps              0\n",
       "\n",
       "Pupil                   left           right\n",
       "  missing data          2.7% \u001b[32mgood\u001b[0m       1.9% \u001b[32mgood\u001b[0m\n",
       "  dropouts         16.1 /min \u001b[32mgood\u001b[0m  14.7 /min \u001b[32mgood\u001b[0m\n",
       "  longest dropout     0.14 s \u001b[32mgood\u001b[0m     0.12 s \u001b[32mgood\u001b[0m\n",
       "  noise               0.094% \u001b[32mgood\u001b[0m      0.11% \u001b[32mgood\u001b[0m\n",
       "  noise (SD)            1.05            1.05\n",
       "  jumps               0 /min \u001b[32mgood\u001b[0m     0 /min \u001b[32mgood\u001b[0m\n",
       "\n",
       "Gaze                  left          right\n",
       "  missing data        2.7% \u001b[32mgood\u001b[0m      1.9% \u001b[32mgood\u001b[0m\n",
       "  off-screen         0.21% \u001b[32mgood\u001b[0m     0.24% \u001b[32mgood\u001b[0m\n",
       "  precision     0.0202 deg \u001b[32mgood\u001b[0m  0.02 deg \u001b[32mgood\u001b[0m\n",
       "\n",
       "Binocular            left/right\n",
       "  pupil correlation       0.866 \u001b[33mok  \u001b[0m\n",
       "  gaze disparity       1.61 deg \u001b[33mok  \u001b[0m"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.quality_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa9e6ff5",
   "metadata": {},
   "source": [
    "## Events\n",
    "\n",
    "The events are the triggers that the experiment software sent to the eye\n",
    "tracker. `d.events` holds them as an `Events` object, which\n",
    "displays as a table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "115c5142",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:07.391763Z",
     "iopub.status.busy": "2026-11-10T13:34:07.391355Z",
     "iopub.status.idle": "2026-11-10T13:34:07.403619Z",
     "shell.execute_reply": "2026-11-10T13:34:07.402300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Onset (ms)</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5177.0</td>\n",
       "      <td>F_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5677.0</td>\n",
       "      <td>S_1_1_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6773.0</td>\n",
       "      <td>C_1_5_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7010.0</td>\n",
       "      <td>R_1_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8776.0</td>\n",
       "      <td>F_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9575.0</td>\n",
       "      <td>S_2_6_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10581.0</td>\n",
       "      <td>C_2_6_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10808.0</td>\n",
       "      <td>R_2_20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p style=\"font-size: 0.9em; color: #666; margin-top: 8px;\">Total: 8 events</p>"
      ],
      "text/plain": [
       "Events: 8 events | units=ms"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = d.events\n",
    "events[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4ed0fa",
   "metadata": {},
   "source": [
    "In this task, each trial has four events whose labels start with `F`,\n",
    "`S`, `C` and `R`, followed by the trial number and some trial-specific\n",
    "information. `filter()` selects events, e.g., all labels containing\n",
    "`\"S_\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c9b4d8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:07.407140Z",
     "iopub.status.busy": "2026-11-10T13:34:07.406650Z",
     "iopub.status.idle": "2026-11-10T13:34:07.416799Z",
     "shell.execute_reply": "2026-11-10T13:34:07.415599Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Onset (ms)</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5677.0</td>\n",
       "      <td>S_1_1_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9575.0</td>\n",
       "      <td>S_2_6_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12574.0</td>\n",
       "      <td>S_3_2_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16473.0</td>\n",
       "      <td>S_4_4_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20388.0</td>\n",
       "      <td>S_5_3_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23987.0</td>\n",
       "      <td>S_6_4_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>27002.0</td>\n",
       "      <td>S_7_3_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30601.0</td>\n",
       "      <td>S_8_6_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>34799.0</td>\n",
       "      <td>S_9_4_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38415.0</td>\n",
       "      <td>S_10_2_5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p style=\"font-size: 0.9em; color: #666; margin-top: 8px;\">Total: 10 events</p>"
      ],
      "text/plain": [
       "Events: 10 events | units=ms"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.filter(\"S_\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb5c08f9",
   "metadata": {},
   "source": [
    "More in the [Events tutorial](../notebooks/events.ipynb).\n",
    "\n",
    "## Blinks\n",
    "\n",
    "During blinks, the eye tracker loses the pupil. `pupil.blinks_detect()`\n",
    "finds blinks and marks (masks) the affected samples, so that they are\n",
    "ignored in later steps. Short gaps between blinks are\n",
    "merged with `pupil.blinks_merge_close()`.\n",
    "\n",
    "Processing steps can be combined into a pipeline with `|`\n",
    "(`pp.pupil.<method>` is a step for the method `d.pupil.<method>`);\n",
    "`d | step` returns a processed copy of `d`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3e309a15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:07.423032Z",
     "iopub.status.busy": "2026-11-10T13:34:07.422538Z",
     "iopub.status.idle": "2026-11-10T13:34:08.680190Z",
     "shell.execute_reply": "2026-11-10T13:34:08.678604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>total_duration</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left_pupil</th>\n",
       "      <td>13</td>\n",
       "      <td>235.230769</td>\n",
       "      <td>260.023121</td>\n",
       "      <td>48.0</td>\n",
       "      <td>984.0</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right_pupil</th>\n",
       "      <td>13</td>\n",
       "      <td>138.0</td>\n",
       "      <td>84.85644</td>\n",
       "      <td>44.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>ms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              n        mean          sd   min    max total_duration units\n",
       "left_pupil   13  235.230769  260.023121  48.0  984.0         3058.0    ms\n",
       "right_pupil  13       138.0    84.85644  44.0  404.0         1794.0    ms"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = d | pp.pupil.blinks_detect() | pp.pupil.blinks_merge_close(distance=100 * u.ms)\n",
    "pd.DataFrame(d.blink_stats()).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c9dd77b",
   "metadata": {},
   "source": [
    "`pupil.blinks_interpolate()` then replaces the pupil size during blinks\n",
    "with a smooth interpolation (Mathôt, 2013). The interpolated samples are\n",
    "used in the analyses like the recorded ones, but they keep their mask\n",
    "layers (`\"blink\"`, `\"interpolated\"`), so you can still tell them apart\n",
    "(see [Masks](../user_guide/data_model.rst#masks-layers-and-policies))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f06d241",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:08.684046Z",
     "iopub.status.busy": "2026-11-10T13:34:08.683619Z",
     "iopub.status.idle": "2026-11-10T13:34:09.247279Z",
     "shell.execute_reply": "2026-11-10T13:34:09.245708Z"
    }
   },
   "outputs": [
    {
     "data": {
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qX716VehDZ2szhmEYhrEQpFYt8UXPaln2kX279w5SUlNRx/U1nkYmYvSXe9GyYiAuPYlEeFyKcr1eP5/AwwVdLbD1jK0z57/MFRf1S+fP9nUeLiqRpT6/nMCZT9uZfNsYhmFyg/Lly2t9nFTQ6SYRGxuLkydPokOHDsJ9Cqj//PNPzJgxI8v3ouCbbnI8PT3RqFEjmBMuU2cYhmEYDaZvuapcJtEsTb7uU0Pt/k+H7gtiW5suPBV6e/fdDFELxCVS0y2v1MrYBxUDs1cRpp5yiVDuG2cYJg/i6uqKhQsXolKlSmjcuDGqVauGN954A+PGjYM1wmXqDMMwDCNDXsqmi371S+CTTVfUHruSjeAbEZWQioLeKiVshtFk/03tgkEujpw/YRiGyQ6yLNuxYwcePHiAZ8+eoVy5cggMDIS1wmd2hmEYhlHwMDweDeftN9v+uBsay/ua0Ulaegbe/vOc8n4BL1cMqF8CQxqWgo+7s157rnM1VZnlqD/P6TW5xDAMY28EBQUJ4m3WHIgbFYzfuHEDEyZMEP65PXv2ZHqeTvobNmzA4MGDBeU5WvfmzZuZ1qNm+JEjRwrN8mPHjhXq+Y1Zh2EYhmFMxTd7bquV9/48qI5Jd+6h22EmfT/Gvhi7+oLa/WGNS2FBnxr4rHtVvd+joLeLcpnaJW69jFUqrH+29Rqu6lHBkRNoHPg0Ul0AkWEYhjFBMP7rr7+ib9++KFOmDI4fP47Q0NBM60yaNEkoDSA5+A8++EBQn6tXr54QWMsD+iZNmggS8x9++CFevXolNMdTKYEh6zAMwzCMKYnRsJRqXbGQznXn9apu8PuzxRmTFRQ8S7SrXBijW5QxeIfl91Rvg5Aml3r8dBwrTz7CpA2XzPoljF55Ds2+OojSU7fjzxMPzfpZDMMYD1fNWMc+NCgYJ+l4CpIpONbFF198geXLlwvrdu7cWVCv8/Pzw9atW9XWqVGjhmDITkE7ych7eXnh22+/NWgdhmEYhjEl3m7qUiruMnVqTd5qWFJQR9/zUQu1x/vVLSH8ndq5UqbX/H7sAQ+AGK2QFZkcqsogSz1jkKuqJ6em49bLGLyIFr3H74TEmfwb2HXtBap/vhs/7LuLfTdViZrP/71u8s9iGCZnODuLLS/koc3kDGkfSvvU7AJuFFRnB0nAy7l16xYiIiJQvboqg7Bv3z4hgy7h6OgoqNzJzdT1WYdhGIZhTMmDcNXg5Ks++mW+nWQK1m83C0KrlpXRu15J1C3lj+FNSgse48uPP8S/l58L66w69QhDG6t8yRmGmLhePWPt4mS8rM9/7zdHq2+PCMuJqenYd0NdFC4lLQN02DqZQBTuZXSSsrz++313Mj3/44G7eK+NdksihmEsD8VUFNNJFc4eHh6ZLDvNkUFOSREdRpKSksz+eeaG/h8KxGkf0r6kfWpVauqkXjdkyBDEx8fj/v37WLx4MXr06CE8R4+Fh4ejWLFiaq+h+48fP9Z7HW0kJycLN4mYmBgT/2cMwzCMPfMqXryGrHq7AZqXL6jXa0rm90DpAE8UynCFl6sTPFyc0CBI9IR2dHBE7ZL+CA6LVwbjPx64x8E4k4mzDyOVy+0q626P0IcS+T2Uyx+su4SRTVUevESFGTuFv9dnd4Sna86GgndCshYl/N+eOxjfupzND74Zxp6QRM20tRybK3iVYjSyHrOX84Gfn1+OBeLMEowXLlwYCxYsQFRUFNavX4/p06cL/d6UHU9NFfvx3Nzc1F7j7u6unDHRZx1tzJ8/H7NnzzbDf8QwDMPYOwkpaQiJEQcL1Yr66v06yi7u+KA5Thw/pnOA0at2MUzacFlY9tIohWcYonXFgjioEPj7SsPHPqcsO/5A6+O/HgnGxPYVcvTe8clpmR6jrPuQRqXw58lHyokGaYKKYZjch65VRYoUQaFChZRxlzlJT0/H+fPnheUKFSrkKJNsLVBpuin+D7OMCKjcgdTWCSotp+W5c+di3bp18Pb2FjaeStfl0P38+cUTtT7raGPatGmYOHGiWma8RAmxd49hGIZhsuLCoyjhr7+HM/w9VYrU+uDs6ACHLGb6HRzyYdHA2piw9qKQJafAnzLoDCPhq7Au+7RLZQR45dyLngTg5IJw2li0/y4+bFseH/x9CWUKeOIjIwLzOC3BeKdqgZjdo5oyGB/151lcmdXR4PdmGMa8UDBpicCYgvGMjAxlstUegnGb8hmn8nKpDIJ2PmXIpdkRiTNnzqB27dp6r6MNKnvw8fFRuzEMwzCMPjyMiBf+msuVuWpR1TWJAnKGkavsx6ekC8s5LRuXaFGhgF7rlZm+A9suP8cP++8aJS6oLTM+uGEp4W/1YmKFSUxS5nUYhmEYMwTjpHZOjfnyAHrnzp2C57gEeYeTFzmJuxEnT57E/v37hccNWYdhGIZhTMWJ++HC345Vctb/pYuyBb2Uy28sPmaWz2Bsj/OPXqHG7D3YqxBZ83Q1TcborQYlDX5NgmJCwBCkSYR+9YoLwXd+TxdBJ4GY00P0Ry/onfNMP8MwjD1i0PTruXPn1GzNyH5syZIlgvXYlClThMeoBCEoKEjoQUhMTMSLFy8wfvx4tfLxd999F1euXEGtWrVQtmxZQeTt448/Ru/evQ1ah2EYhmFMRUScqElSwNuwEnVjofJeEnxj8jZ9fjmpdt9UxwRpGSwfXh8jVpxVPkZCbrr6x4noxFSDM/NSZpzaLraMa4K0jNdKSzZ/D/G3FBabjFfxKUKgzjAMw6gw6Ixbvnx5QZhNE7mK3OTJk4WA/fbt20Lfd6lSpTIJsTk4OODXX38VxNaePHkiBO8FChQweB2GYRiGMQWp6Rk4/eCVsFyrhJjVMzchMUnwkmXLGYbwUwSwpqB1pUIY3TwIS4+KAXj7KoUxoW05fLzhCu6HxeFBuHq7RHKa2NNpTDadJhFoAsDJMXMfPFHni70oHeCBgx+3shslZYZhGIsG476+vkphtqygILxatWrZrkdBfHZy8PqswzAMwzA54cR9lWBouULmC5BndK2ML7ffVAbj8tJ1hiFql/Az6Y4Y27KsMhh3d3EUgv3fh9UT7ofGJmHBzlvYfOGZcD85zYgydSkzrqW8XlMI8WFEAub8dwOfdxPL1xmGYfI6FhFwYxiGYRhrhspoJSh7Zy6GNSmtXKZgnGFK5HdX7oS/xzQSlPdNiVQqThTS6N0u5O2G//WtqbyfkoPMuIeiNF0TJ43/Z5VCYZ1hGIbhYJxhGIZh8DwqUdgL/euVMGsJLVmgkec48TyKg3EGiE4QPX73TWyBhmUCTL5LKLj/6a06+LpPDRT1c9f6fFABT6OD8cRURTCuw6pv78SWavepp5wy8C+iE7Hh3BPBXo3EEz/feg2JRgjIMQzD2DKsHMMwDMPkeaRgvIifusaJOZCyk6SePb51uTy/7/O6VoFk+2XKXnFNutYokuXzLo4ORvWM0/YfuCVa17o6ay+2pED/4YKuCA6LQ5tvDwuPVZyxS+u6+T2cUFPVZs4wDGP3cJk6wzAMk6dJy8jAurNPhOUivuYPxiW1bDcdwQuTd7j0JEq5nN+MwXh2SIG0oZnxJYfuK5fddZSpS0jZ96x4FJFg0OczDMPYOpwZZxiGYfI0TRaqrKV0ldqakmrFfJXWZkzeZvKGy8plU/eKG4IqM25Ymfi3e+8ol0kcLiv0af/459IzhHqGoUctsZWDYRjG3uFpeYZhGCZPE6rwFydK5DefeJuE5OMcn8z9sXkd6XirWtQnV7fDxcm4MvV6pVQ2gBmvs1/fM5uAXZqk+us0i7wxDJM34GCcYRiGYRTUMrGtlDY8FRZQnBnP2xy+E4ajd8OF5eEylf3cwNXIYLywj6qto6QeE1k/DKit93s/ecUl6wzD2D8cjDMMwzB5ljRZNm9A/RIW+UypZ1zyZ2byJsOWnVEuF/HNrHKeG5lxQ3vGpbL2HrWK6tUTXqGwt9p9EnaTbuNbl1V77of9dw3aFoZhGFuEg3GGYRgmz/IsWdUzO69XdYt8plSmTv7MGfrU9jJ2j5db7kr4uDo5GpUZJ5syonn5gnp7qhdViCR+2qWy2nOTO1bC1vFNlfefvkrA69f8+2AYxr7hYJxhGIbJs1yOE0WlvN2cLCagJWXGiS6LjsLSJKWmIyZJ9LZmcgdrq4owNjOeli4Gy056/nZIxG3PxJY4+2k7jG5RJtPzVYr6onXFQsLy+ceRqDl7D0JjkgzaJoZhGFuCg3GGYRgmzxKfbvnMpNSfS9x6GQtL03HhEdSYtQfRiRyQ5xarTqkLlFmPgFu6wbaAhJNjPoMmowp6u+p8voifqg+dPNgnyRTnGYZh7A0OxhmGYZg8CSX1pj0Qg/B6pfJb7HP1sXgyF1T2K3k5X3gUmWvbkde5E6KahKF+aWeFtVhu4WqhzLg+5Pa+YBiGsSR8xmMYhmHyJBfSVKJZlu5NPTaltXI53YJ946mK4IngzHjusfnCM1gTUmY8MdXQzLh4PDk6OJjc81xCUpxnGIaxRzgYZxiGYfIkL9JVpenPoxMt+tnyMt0jd8Ms9rlJsjLk0FjuxWXUBdyWH3+I/648N2uZenZoe68Q7htnGMZO4WCcYRiGyZN8E69SgG5ZQRSNsnTwQ9x6Ybm+8aQUVTD+LNKyExCMSGR8inJXlNHDDszSOgbvrblodWXqS48Em+z9GYZhrAkOxhmGYZg8yZMMF+XyiKalc207vtp1y2KfJS9DDg6Pt9jnMio+/eeqcnnju02sYtfIFf6NKVN3MmGZuoNMU8FZkSUPi1N4EDIMw9gZHIwzDMMweY6XaarL36YRdeDmrMpUW4oCXqrJgGdRlslSJ6VmqPXizvznmkU+l1Gx53qIcjm/p+oYyE38jdwOSe/AlGXqROMyAehdpzi+6lNDuM9l6gzD2CscjDMMwzB5jlVRHsLfBt4ZqFvCN1e2Yd2YRsrlAb+dtJjHuKbFVmq6YQrajPGQdZiUTf5b9v3nNv4ezka9Tjp2TFmmTjQsEyAE4oE+os3ZqeBXmLrpikk/g2EYxhrgYJxhGIbJUyRkAGuixWB8ZJHcC0TLFPBSLj95ZanMeHqWPcyMeVl27KFyuWSAeAxaA/4eOcyMm7BMXU6gr8pzfN3ZJ2b5DIZhmNyEg3GGYRgmT/FnhCsiMxxQ0iEF7f0ta2kmx8HE2URdkPjV5A2XkZHxGklafKSXHVcFiIx5SEhJw8n7EWr6AD5uxmWjLVGmrm3SJiurPFOXqUuUDvA0arsY80HVENM2X8X6czw5wjCmgINxhmEYJs8Qkw4sCRezbR94hsNMMYTezHyjilm9ztPSMzB3x01sOP8U5x9Hag1mlhy+b/LPZVTQPq/y2W4MXHpKbbd4uFhep0AX+TUy4w/0FPdLl6zNzDSxRBNWD+Z3Ud4/fo89x3OD68+jMW/HTVSftRsdvz+CtWce45ON3DbAMKaAg3GGYRgmz/BTmDui0x1QziUNPV1jcntzMKhhSeVybHKayd8/MiFVuTx3+01lMN6kbECmoJ0xD2//eVbr4/lkquG5jbvGxMCdkFjD1NS12JGZCvl+evvPc3gZnWS2z2IyQ5OEXRcdw29HghGblKbmwpCipdKGYRjD4GCcYRiGyRPcSnLAH+GuwvK0gNhcz4oTpOIu2UpFxJm+d/uVrB/80pMofLf3jrDs4eKES5+1Vz736FWCyT+bEQOZ4/cispyEsRZ+GVRH63FjaZ9xbZSW9dcv3n/XrJ/FqNP5h6M6d8my4w94dzFMDuFgnGEYhrF7UjKAKc88kYZ86OiTgrZe1uNbHKfIiJ998Mpk70n94V9su4GOC4+oPf4oQgy6yxT0hJ+HC8oVEkXkHrLnuFn4bOt1rY/P6VEN1kbn6kXQv14JYXn2thtCn3tuWZtpsu39Zsrl9eefmKWlg8lMWGwybr3UXSWxYOct5THAMIxxcDDOMAzD2D0LQtxxOdEJPg4ZmFXEOrPA3+8Ts9amgMqMV57SLcxWyFusECivCMY3X3hmss9mRCLikgXrOE3m9qoGRwuJ9xnKlkvP9MqISqQqesbN/f94uzmjX73iyvs/7L+Lp5HW+Tu2J64+i8p2nfA465nYZBhbhINxhmEYxq5ZFeWOZRGiaNu3xRNQxNm6MjmNy4j926b0+w7PpuQ9wEsU7CpbUAzGt199gXpf7hUy6oxpuPxUPZCZ37s6Hi7oikENS1ntLp7Xq7paFcXVp9E616VjRUpQO5vJ2kzO1M6V1e5vPP8Uvx1h8UFzEhym6g+nqonLn3XI1GJx4VGkWbeBYewdDsYZhmEYu2VVnDdmhvkKyx8UTER7H5WgmbXQvVZR4a+rk6OQTc1JQEwB/bmHr3DukXrJu4uTg1Zf6VYVC6oF8LuvvzT6sxl1nkaqe8cPqC+WgFszfesWx9DGqsmCbj8e07luUlq6zuPLHOT3dBFucr7ZfRu/Hw3msnUzQK0AX26/qbz/Vd8a8PVwxtxe1fH3mEbKx/84xn3jDJMTOBhnGIZh7I641AxMv5KAmdFisDk8IAkfFrJOFeYKhb2Fv8+iElH3y30YtvyMUe9z62UMqny2C8e02D993q2KWplvcX9REKte6fxq613OIhPKGMYTDVE8a1JPzwpNpX1dxCWJPeVUoW4pm7Z1siBQggLGqZuuIjGFPchNyaf/XFMu965TTO25hmUC0KZSIWH53KNIrqjJAprgnLzhslZbSYYhOBhnGIZh7Iak9NdYfT8B7XeHY80jsVR7ckAsPg9MhLXGQlL/tsTRu/p7Kd8LjcWA305i2bEH6LRQd49v79rFUTK/SpFaEm4jfh1S12BLKyb7CoWlR8WMYcOg/Ng/qaXN7LJifqrjJCvbO8mKj9wALDXRQBNXF2aqXAAk/j73BB/9fcki25AXSE5Lx5rTj5X3p3SqlGkdN2dVCNHm20NCVQ+TmXdWnceG80/x5wndGh5M3oaDcYZhGMamg+9Lr1Kw6n4C3jsVhbr/hmLGhRi8SMxAcXcHrAp4gfH54602ECeo9FMTyqJ0W3wM83bcFMpFRyw/g04Lj2D9uSdqwdEH6y7hVPArzPnvhtb3rlbMBzsmNBd8pLvWEMvhKwWKmXiJRoqedcLHTbRZY3IGTY5I9K9fQtmbbwtUKeqjdj8qUXtrR7TicRJXsyT0efkVbRZydl1/ib9OP+IMpAnQtFnUnDAkhjcJUi4/jEjAz4e4f1/ftpXLT6Lw7+Xnwrm8+qzdKD11Ow7eChWeY6OAvAdfdW0EGoxFxKeggFfmEyLDMExeOQ/eiUnDydAUXItKw/XIVNyNSUOaRot1cQ9HjKrggf5FHOB+hQZA6pk+a8Nb4TMup9rnu5GW8RpXn0Xj9INXwuCN+GTjFeF2alpbHL4TiuvPY7S+Z586xbGgURNUKuqnfCyogCdOTG2Tqe/W190ZH7Yrj4X77iIumUspTcEVWbl/gyD1VgBrR1MZ/ZWWsQeVhE/ddEVNDNCS9G9QAr9oCf4+3XJNCHq0ZXIZ/YlJUk3ALBlcR2vlg+ZxTb3jM9+owrtZB3Q+l9pXevx0XFiO6lEVsYp2jxErzqJiIU88CHNCO+eieMslDU1dqL2Fd6m9w8G4jbD4wD18t/cOpnephDEtyub25jAMw1iM0MR0/BWcgI0PE/EsIXPJbH6XfKie3xk1/J3Rtqgravo7i4PHJOvsEdeEtpVEs1aefJRp4EZIgbicRvP363y/ioW9USK/B8oretHlFPVz1/qaMorM7b6bIcJgkV7PGA+p0xMl8rsr+/PNTnoqEB8OeOjX850Vq99uiMF/nBaWSVFd0jUgZvxzFatPqUqYc6MXlsQOyYXgyh2xhPqrXbeUz1GQXq6gF/rUVWkkMIbxMFyld9CpWhHefUYi92A/HRwhZMDlbL8inickboeSen0+7EjxwY5nQIPIVEwPTEQtD54ktWeMLlNPSkpChsJfMifo8x6m+BxbZ9H+u8LfeTtuqc1YMgzD2CvJ6a/xw404tNgZhh9uxAuBuKsD0KKwCyZU8cSSJn441qUgzncvhD+b58ekat6old/FZoSy5Hi4mGZuPNDHDe2rFjb4dfJ+8uZfH8QNHRl3Jnvmble1DDx5pa6oblY2vQ2c/R0I1d6yYAjNyhdQLk/acBk7r74QKlOiElLUAvHcLKslEbE7X3bGu63KokxBT7XnaJsZ47jyJApjV5/Xa12qqJFz7RkLQMqJlY3Xg8NVNnESVPWkjaFukXDJ9xpnEpzRK9gbs164I57jcbvFoGA8OjoaixcvRtWqVeHu7o41a9ZkWufEiRPo0aMH/P394eXlhVatWuH0aXF2Vc7SpUtRsmRJODs7o1y5cti4caNR6+QV5FkSPtkxDGPvPE1zQt/jcfj+ehyS0oFa+Z2xuJEvLvcsjJUt8mNiVW90KuaG4p6ONhl8axIZn7UvuL6Mah4EJyM8n2sWF+3fJMguijGME/fD0XTBAaVwG7FseD3L7cbbO8S/TzKPuXKqqv7uXxcEvYK/ZKJeEt+8WRO5hfTb3/NhCxSTVX04O9r+OSE3oAmXPktO6L3+B23LC04NEn+ffWKmLbNNYhLFEnRDqOGZgTneIThSOgy9/ZLxGvmwIsINXe/74FqiZVwLGMti0BWbgu/bt2/j77//1rnOt99+i1GjRuHBgwd4/vy5ELh36NABT56ofqDbtm3D+PHj8b///Q+xsbGYOHEiBgwYoBa067NOXqKqTFAlKoEz4wzD2C/3UhzRK6workanw88lHxY19MWWNvnRrYQ73Ox0kD20SSmhVzen/cX965c06nWaExqvEkwzOZCXGLbsjGBPJ6dlBdH+yRbpXlMU/JOYsumq4OsthyYbapVQ6RLkFk6ODtjzUQvl/dT0XErX2zgxiv5lia/71sj2vEFODRKrTj3i6k21/Wn4eL2QQoIh0CkD3xVPwMpSsSjmnI6HKY7oHeyN5VEeuJzqhrrnnPDHKfNNfuy9EYIVxx+wbZ21BePvvvsufvzxR1SrVk3nOps2bUK3bt3g5+cHHx8ffP/990hISMC+ffuU69Bj3bt3R79+/eDh4YFx48ahXr16WLRokUHr5CXiFRYiBAfjDMPYK49THDDgaX6EZTihkrcD/mtXAN1LuttF9jsrqhb1xYUZ7bF2dCNUL+artQSUlM7HtCij8z261igCN2fjMyfNyqlKk19G20a/vTWhLQDUFEOzCM6m6VEnBf7saFPJ8JYIc+EpE0IskAuicrbO86hEbL/yXHmfKg361SthsBuE3BItr/PbEf0qjG7O6aRc9tH42bXwTsP2srHo4J2ClNf5MDvMBz2iSiMiLR++2HMPa8+Yfn+TQOPolecwa9sNvLH4mMnfn7GwtVlYWBjS09OFsnWpBIay2y1bqntutm7dGidPntR7nbwmAEG2ERJRiZyxYBjG/iBttjGPPBGe7ohKTslY09hLKEPPK9CgloK3f99rigfzu+DDdhVwZVYHzO1VDedntMOVWR0xvUtl9KilylgGz+uiXHZ1ytklPdDXTbl862Ws2iQwYzitKha03G5LlvnDV+5ukreMz0ZZf2AD46owzMmQRqWEvw2Dci5il9egNoTQWJVX+MZ3G+v92kUDayuXF+xUienldci+TGJB7+pq54TlI+qjVIAHVo5soDbx5eaQeVLPz+k1fi0Zj9lFEoRecjnTNl81abvUmJXnUPmzXcrHbrxg/RCbVlOnoPr9999H6dKl0bFjR+ExKjmnTHnBguoXqUKFCiEkJETvdbSRnJws3CRiYuzjAFp+XNV/RkRzmTrDMHbI5889cCvZCQUc07GiwEvkd7WerJslkVcB+Lg5Y1BDMcCQmNG1CsJikzGgQUk4OOQT+r0vP43GACNL1CU+6VgR98PicPGxqN7+35XnRpe9M8CyYfVNvxt0qaVRNrzWECCyIOBimsw4Wd7pgpTM5/euDmtDqipJSOGJJEMgv2s5RX3dUMRXu/OCNtpVVm/HSE3PMH+2z8ag8zXd5LSenLmNxY12nJZ5MLosDAtIRi2nBPR4oqpikva3s2PO9/hvR4Ox50aI1njO3qvTchOz/lY+/vhjHDhwAJs3bxYE37JSSKf7ml+0PuvImT9/Pnx9fZW3EiWyL6+xBb7cfjOT5yfDMIy1sO9GCLbJMgDGcDjWCRuiXJEPr/FTkSgEOrJ0rC4KertizehGyp5eWt79YYsc95sX8nHDlnFNUaek2AN8OviVMAhjsoasvUhlXBOaKDEpd/YAZ5YCSVoSDQ6OQLfvgaDmJvu4jlULY1DDkvhhQC1lxlliWJPSsEY8XMUMY3hcCpLT0vNcQE1BmTHIqy+JkgEeBrtBUKZXYte1l0Zth70hnUt71y6m92sqeWR9zq3ploYtfg/VHrsbEgdT8CAss+I7kZzGrlY2GYxPnToVv//+O/bs2YNatWopH/f29oanpydCQ0PV1qf7gYGBeq+jjWnTpgmK79JNLhpny9QvLZb4S2w4/zTXtoVhGEYODXhHrTyH99deFDxU5++8aXAAl5iRD9Ofi4O/4QHJaOjOIpWG9spWDMzsKW4sPRUDx80XnyFomkKhm9EK9WtWmrkLPx28Z/499PwCkPgKuLwu83P3DwAbhgFnlwFPz5lMFG1ur+roUasYvuhZDTsmqAJ9TSsxa+sbv/osGhVn7MozYmJ0zqXe3lbfHDIqIP/18H21+52q6h5r66J1RVWWN1ZDCC6v4u0mVpc0kelx6GLN6Ib4oEVp9CmY/fWztnMSppVUTTaFxGav8RGdmIov/ruBK0/Fyic5KWkZWeqE0DmOsbFgfPr06fjll1+we/duNGjQQO05ymw3bdoUBw8eVHt8//79wuP6rqMNV1dXQTROfrN1aHbx7MNIZTaEYRjGmngepX4B//VwsFAybQgr4rzxLNVRUIydXNiCvsyMVjR7zynQjOP+ca1I/ZpyO7Pi/u44Pb2t+Y6uZ+eBWb7AX29SyaD42N29wK3tQHwo8PSsWT7W203V2VhEpi9gTXi6qHdf9vzpOPICSakZgs4Dqfk/fqWe5daHfy49Uy5PaFMeQxobV/nQpboYxGsL+PIi0sSIPlZ7TcoWwEetgqCvYcg7RTPQsqxYDbXt0nOdAfjlJ+J3sXj/Xfxx7AG6/5j5NzHo91NoNH8/dl3XXdHAtspWEoxTmXhSUpJwI1JTU4XltDTVDNhnn30meJH/+++/QkZcWl++zuTJk7F9+3b89ttvePHihVBefuXKFcG+zJB18gJjV59XLr/dLEhN1I1hGCa3od5lTQzp14x+7YhfosWJ00mFkuDBjYZW541LdlbvrDJNtjUvcGxKGxT2MXGwunOqavn6JvHv3T3AuoFi6TrdJJKiVUG6CaFJBiq3Hda4lDLjZ214aCjAB4fFY//NEDwxIkC1JSizKUH/KyVyMvQcJ8pfS7zftrzRLgDnFMmjdWef4F6oTFQwjwfjLibo59ZGIW8XZRWTtoq0mrP3oMdPx4XKh4cRqhJ0mizZefWFUM324bqLyqSfJn4ypXx2cjIfBh0dhw8fFizL6EZZaLI6o2USaZMg6zEK0kmwTVqXbgsWLFCu065dO/z1119C0F6pUiXBDo18xWvWrGnQOnmN9lVUYkYz/rmKJvP34/yjV7m6TYw6u6+/xKHb6u0VDGOvULa036+ZHS70qVKPTUpF++VXUDO+BmJeO6KSaxp6+LEehrV4nmty/F5ErmyLtfIiOlHIMmlSyYTtAmpE6bAvurML2PExkKFRjp1g+u+Lqha/618Ls3votre1tmCcePvPc2j+9cFMImX2hLw/fvjys0Ii5+9zT/SysFq4745Ob3lDkauxd/7hKGwBmjRouuAA/jr9yGx2h9TyYQ4mtlIl6SIUelJbLz3DmQfqeh/zd95CAS9VdS1lx9/964Kw/I+OrDq5eFyc2V55/04IT65YhZo6WYtJWXFdREXpV5rSv39/4ZbTdeyZkBj1fV0yv0pQY+0Z8SQ7YvlZwe6GMR3BYXHCrO6bdYsjqICn3idRKgd6Z9V5pWekPh6tDGPLvL3irE5Bq+zYdP4p7kaoStI/LJSkd3keY15cnRzxcEFXIWsi51FEPEoFWGevsKUZ+scZ3A3NLJq0dGg983zgvT1k2KX/+jHPAV/D+35tHRIS00XTrw5g38SWVpvVzwnaBLZ2XH2Rrf3c70eD8fOh+8rMXJmCXjnajoENSijHpwRl500uZGgiqML0h/13sWj/XeH+p1uuZXKusGSZujEU8XGDi5ODUN1APvEUMH+w7pLwnI+srYSgca2+7JvYAuUKqU8szvnvBkbKKnQZ08EFgVbMg3B1VUNttgUxLJJhcrosOorfjgSj/fdHMHrlOYP8GSXk5UASNFM5ddMV7r1k7AKafDr94JWa8nKD0vn1Vl6Vr1PSKRXtffKG0JItcW5GO7X7d0yk2GsPaAvEG5XJL5RymxxDSs69CgPF6wOxeVPNOqtJ8JCYZCw7pq5CbQ9QyfEILROj+lhdHZBV8vl7iCXPOWF6l8pq9x+ZqT2Agk8aT8l9vAnKBk9YexFdfjgqVF9lV8koBeK2WqYuh7Ldby09naP4YGrnSrj8eYdMgThjXjgYt2LkFmb964k2bec1BkeMeYRQJA7eDtO5XmhMkhBgSwSHx2mdSKHSuG6LjwnlvDQz+c2uW1b1tZE408FbXFrPGEafX05kKsdzdXbQOzMuZ4R3LGfFrRAqa6QMeeuKBYX797QEoIxYmk77ad2Yxubx4k3Jpjy063dAv5XAxFtAkRqigNv6QUB03nNe0VamLuf7fXeMtv+yVj7ZeEXrbzMrn3gpcL34WFXN6qY4f+cEqjqQq+5TybQ5+OjvS8J4igJv4sT9cEGsj9wfKEC/8SIG1WdRNYlheiemJs3MZeraev6NpUJhL4xtWTbTcSNvvTH35EVehYNxK+bTLaJKK/FZtyrC3wAvV9QqIfoWMpZBV5/Zm7+eFALsI3fChFKsnVdfaj3JrznzWLBZkQi3Ip/4vTdCBHEmmlVnP2FG399D228PZRr8jW9dTihv1pzQ0oW8rfxNTw7yrBkvRVnv9qs585K3V+6Hmfn4TZBpw5RsDEx7LgbeLSYD5TsCVXsBVXoAngWAwrJ+7v/yluCtvtng32XK9/YAqahrIzshzb9Oq+sQGOhIqZMqRdWdjExtL3f+USS2X32hFoxSRviSQjVczr4bIXq5A2gr6zYFKWYuUydWjlR3rTKWz96oqvXxhQNU9tTf7VXpCzCmg4NxKyYyITWTdyaxaEBttfU4iDKPCIqmKAZBM+rUk0MByaMIsfxq6LIzglCJ3P+dMuO03nd7buOzrdfV3q+gTEQjt5FboGizu2AYTX4/9gD3w1SVH83KFcCRya1Rt5S/MrOi7XekSWiMOGE1wCkcXg7sDmHNVCkiDq7zwTp7Py2NZmb11yF1zfuB8eHiX1cfwNkdmF8UCL8DtJkBDFoPeIjtIQKOslLjON2BiD2zeGBtjGtVVufzG87r3ztry5CYmq5SbQpgZ/xzTe0xfdqL9OVTWbn6/pumrbw7+1BduJgEjXUxauW5TAkVGjMvOXxfEDWT4ytTDjd1ZlyfSSJjaVFBrFzSxdlP1StqPV0cUb6QF05Oa4Mve6om75qV1+6FXlpDJyRUD09zxjBMPw3EmJ2SAR74a1RDDPpd7A25/jwG1Yr58p43AZHxmS9cFFRLNjXlP92p9XW3NVQmV5x4KNy0IQUqpOC55HAw3m9TLtdEkeQlfZS9pz7g7ErbmLwNVVPIWfV2A2Vprpuz/pnxZcfF7FRpB/OXCpqM9DTgVTBQoDxJSyOvQL3Qmq1TeZlYjV7MMgVyJnqVJYmRwKa3xeXkGOD+AXF53VvAdC0lwHRcFqoChJ4GnKxn4teSdKtZVLjdfBGjtdWM7M7siRL53fHklUoMU4JK0KlU+/eh9ZCW8RrH7oUJ2U8S/Jq9TT1JYMpyZ6J7raKYv+O6UvjRlGieedefy7odY/WpRxjeVCU8djI4Ags0AnFT//+ZBdxyJ/dJWi4FvV2F6/Ttl7EY3kT0j6drNtnXDW5UCp2qBcIvi3GfdF2XaDB3Py7MbI/8njnXGGBEODNuozQtV0A4oRLf7rmd25tjN0QlZh5sDvnjjEG+ydkRlywG471+OoGN559i2mbds7rmhmxNND0pmbxFRkYG9u/fj6NHjwrLEi9evMD27dszqc/KB1akHC3vkXV10i8zLhc7tJmQloLwLWOAn+oDZ35DXiLA0zVXgvHgp0+xbvduWBua2UazTmB+VRqI0mK5lJJFaXwJRdlqxD3YG+np6fjqq6/0WndOj2pCv+vkjhXRqWog2lYqpHzu+nNV65itIy8vp0mINrL/U8oOk9XZ6lOPBYV1QlP4jKhd0nQtkHJ/auJMsOWsEfvWLQ4vWTXpuUfqHtrXZG2DBB0fksCfLi5cuIBdu3YhISFB7Vj8888/c71MXZMDk1oKf2sU98XPg8SqneblC2JU8zJC7zrd5D7ypA2SXU/7jK7qwnxs4WtaOBi3Ul5Gq8pAJrWvoHUdaRYvK5ExJueZcWLOthtCIJITpAtdfLIY2Mcq/p64H4FlWvxqLYG2srSoBM5+5ZQnT57g22+/xU8//YToaOse9A0ePBhDhgzB6NGj0bJlS0RGigOX27dvC/+DHPJhDY8Tj4+Zb1RB+yqF1Z7XNzOeJAvWE17bwGUoJQFYVBu4tkm8v/MT2AIPHjzAv//+i5AQ9WqGrVu3IjxcUfqsB/6e4sA6MTU90wSeObnz+DFWakwIWVtmfETT0vA3V4bo/Ar1+9XfVC0HtdD9Oo8AoFwHoO4IID1VLXhYsGABbJnU1FTMmTNHr3VL5PfArg9bCHoWS4bUxazuqp5YeauNtXD0bhjaf3cY5x+pl2Fnp1fwNFKVFW8YlB/964uCv9qIU4w7WpRXlTYPa1wKy4bVR52S/jAVkn6IxKA/VCrfcmjSd9myZSYrjb86qwP+92ZNNC4boHz8vysvsOe6StPn5gv1SsaOVVUWgNrERzdu3Ii2bdti+vTpqFatGq5du6Y8FseNG5frZeqawXJRP3dBUPLf95qpBd05QcqoS6w8aXpP9rwMl6lbKZJCJPFOS929T3Jl70KKUmrGeKJlmXESypMEQUi181ONmUFD6V6zqFA2FqfFbiK3/Bu1ZTCp3M3PBBYneYmff/4Zt27dwtixY4XAp3bt2mjVqpUQ2P7yyy+4dOkSnJys73T78uVLISt+//59+Pr6CtvaoUMH7NmjvULif7tvZ6m8q29mPFkWrAcJZepWnh+/viXzY4e+AhqPA1y8rLJk/erVq3jjjTdQuXJlPHz4UPhu+/btKzz3xRdfYMmSJShQQHuPoCaUZSJrHsryRMQno7iLh8m28/aDB5i/eLFWFfLwqCgE+PpabTBetqAnPu+mXfTIJOnObR+oPxZQFmg+EQhMBip31f1aR2dg4BogLRFIiAC8A5XBw6xZszB16lRYM2lpaULg4+/vn+m4kFfvGAoF56R/QErb8uoca4Gq8IgxK8/j/Mz2er1msKJdUT4hWsxPt70e9YlTD7BcVI3CxeYVCuJoiGmdXqj8+fktldCaNp49eyZcP0eOHKnXe5I47rd7MouI1Szhh9Etyijvf92nBmrf2Ku8P2bVeeG7p4kZsoKTQ79juZYJtYLKWbt2Lfbu3YuGDRvi0KFD6N27NzZv3oxy5cpZTZl6PYWtqLayclNAmfPP3qgijFUJGhtTgspUwX5ex/pGh4zAGZlAhVSOrsncXtXw6RZxhu7y02i0r8LBeE6JUojmUTnbH8PrY9Sf57DvpphVStCSEZrTo6owk9qvXgks3HcXgxuVRLvvjmh979IFxBP+s6hEhMRkFsAgURGz2OLokRl/u1mQIIpy5Wm0sG3VYX0DYGuGBrkpKeLgbvXq1UJAu2bNGuF+3bp1ceDAAeExayM2NhYlS5YUAnHi3Xffhbu7u7Ctn376aab15RdeN43MhyGZcXlFRjcnysTLBKiskbJtMj92aJ54a/I+0OFLWBubNm0SMogTJ07Eo0eP0KNHDyEz2r9/f4Pfi85L1B/4MiZJKFUv7m+6YDw6JgYvw8MxeejQTM9duXsXl+9an5WOVK5v1vJ06g+XU7q5SqCtUlvAwSHrQNY1A/leXAIcXYGg5jkOZC0JbefFixfxxx9/wEHj/6Rz7ZEj2q+x+hAWJ5Yiz91xE8M0sn3WAonGkpvOmBZlsOXiM+F8OaVTJbV1wuOSUe/LfZleS6XJFQp7C+X5ulTWB/9xWhDxMjdf9qiOkbcuIu5VCF7uWoKOF3/KNG8ZE6NxnGfD+DUXlKXf8n752bKqB4KqVf57vxneWHxM+RhNwtDr5dA6dH7z93AWRJNT0tMzjcvi4+NRvXp14T5NstO1vVevXli3bl2W20qvpV59S5Sp1yzuK2THyxc2nz84JYx+PHhPef4jETya3GByDgfjVoi+NhDUFyMF4yS8Zc/suvYSmy88xdd9a5g1aysp2EufMbJZaWUwrmkDQlAQLgUgUglc7zrFsPmCurDOwv614K3oYaJgvOG8/Zneq/nXB1HU1x3rxzY2+f9FugI0afBpV9EiTzNDSRnNQt40mRONEFbKzBHPnz9HgwYqq5F69eoJ5cLWSFBQkJC9p1LBIkWKCI8NHz4ciYmJQtl6lSrqx4uHi5PyN6Jt9l3Klq8981j4HdSXzdZLPAyPR8eFqsG0BVvpjOfIN7qfO7EYqNILKKKyf7EGaJArDSBLlSqFffv2CaWWxk74eZHtTwy12Zi+TD2wQAEM7tIl0+O7TpywymD8bqgY5JQpaEbhtuBDquUu/wMqdAEu3dU/kP1mJhyOkIBWBlAqDag5CKmvkaNA1pLQcTpo0CA4OqqfZ5KSkoRJQ2ORbEepzS83JsD1hcYb5NAitSNWK+qLrjWKKMcQTRcohPxkLBlcVwjECSrPvxsSi/bfa/++42XJhXGtzBNQSerkqUmJSH31HOdd2ghjITl0baS2GX0580CVqKLx4Jt1i4PiXW0Z2qoaFmuaUCm3ell9aqaJZDo+6tSpI5w/u3fvrrymL1++XMiQZ0WqokTd3D7j0nZST7i52T+xJWp/IVYckC0uB+OmwQaa9fIe8lJQue2AJnTyaKmwNPh6l2nLi6yFC48j0eH7w4L4yJ4bIZgu8143p4CbJD7i6SKzlNsvDoSkc767s6PWgOS7fqqLDZVF0Qm/Z+1iavZ02qC+L6qI+OeiFoXcHEA9nosP3MPSow+UWgTTNl9B6anb8SI6UXksFfYRRZpCZHoFjP5QFqdnz57YvXu3Wkm6s7OzkM2xRmg7Fy9ejIMHD6o9ToPdL7/8Ek2bNtXpy6ptDCvvE/zo70taP7PvkpOwKZa2Ac79IS5X7g6M1FLC/3sb4NlF0xn1moD69esLA0gJKkknAaLPP/8c9+4ZLuylbwuCoVQICsKsd97R+lyLOnXwx2efwdq4GyKKp1UobKZg/NkFYL2iUsCjANBgtLLUXO9AdshQDK7hIt58z2JwwesYOHAgbAE6L1ErhTZcXV2FFgxjGdhA1U99UYsvtbkxxIpWru5NGd0N557gwK0QrYG4VBYuh1S05Zo1lEXW5OLM9gj0NV9VZWFhkh9wcPeGV9XW6Nanv6BTIt06d+5s9Hu3qlBQqQquDUMmWqQKVG06OtQXThNccpo1a4YVK1agW7duelkgUpuPPWA2fYw8DmfGrRC5MIKmQJImh++EKf0k7ZFBS08LokESO66qRDj0gdTKA7xc0LqiurqoLqIVWT8qWdIl5kHl5iR2Ig9MdOEuKwXz1zOjT3YsFLybo9KCVIBf4zXWnhF9VndeE/dnMX93pXfkogP3BN9K6kEif05zz+jaA9SbS/2NUkllzZo1lc9RmXqxYqb7Pk0NDYY0s0/EO++8I5Q1k8o6leCHhYUpJ59eZ6Rjx+Z1uLI9WXi99P/K+8jlokK6BAILeNjAJejZedVyz58BV2+g9mDg4mr19W7vEDOX1EPerBlymz59+gjibZRJdHMTB8RU/UB6AJMmTRKOV2OCcVPb//j5+KBB+fJan/Nwc4OriwvuRUYiJTUVF2/fxtZDh3AjOBjR8fEIDAhA67p1MahzZ7i5msfGKyY5DWsO38cbNYoI5fl0zd2uUKQ2W0no0taq5ZoDjAtkvcUsqpKzS+Ha5RvcuCH2fFozdB6l/lxdAVaZMmWEXmOC1KwfP34s6HVQVdL8+fOFfUATii1aZBa5ox5/6fonXe8tBWV1+/16Es3LF8CqtxtmqfCtjckbrxj0eXJF8YENSuLOy1j8LhOLrVvK3+zBVZcaRfA0LBIBbUcJ9/ffDEHvOsWVz1P1zj///GPUe+uz7Xs/aqG1OmBa50paz2/aKlOLFy+udSKrdevWwjFG10iCjklq+9IMxtMTorFi2VK8O3Ys7IEvelTFzK2ZrfEY4+FRtpUj+VvrgpQz7VkFWx6IG8rjiAR8vOEyRiw/i2N3w/WakY5U7ENfReBMoiCBGt8BXeAoIA/w0j34m9i+Apwc8mGWTNxH39nnmzr6vIzlUUSCWulb4/mZZ9UrFvZWExih7CX1pLX85hBG/XnWpNtjj5QtW1boyaXMON2o/FtixIgRyn7xqKgoof/M1tixY4cgBCYNLsK3foVDm5bjzp07aN68Oc6cOaNVQVcb8mTFkFpZTzZaHRSIE12/Bwb+rf7cq3tAfBgQ+QBIigKin4oq7LHqSuaWwsXFBT/88IMyEJcoUaIE1q9fLxyzRGhoqF6VG1lljizBtv370Xj4cIRFRuKN5s3xTu/eaFStGlbt2IHmo0cjNc109pNyvjv+VPAk7vXzCeH+sGXisU5UDsy6DNYkNH7PuEDW0QmYqt5alS89RQhkJaSA1lY5f/483n77bWGikNprBgwYgMaNG6NSpUro2rWrEKRrQhOKksUZlXtbiu/23BYCceLo3XBcVmTlacKfxOSGyo4rQ32kidIaomOEfCK9ejFfFJBlyknvZtO7TWBuSFfB2c0dbsXFlqd5O27ixL1wodIwI+O1UOlAwS5B5yE6H+mLPqJoNGG26d3GwkQxlbRLSL3cEtJ5jcaNxkAT1hUqqDsfSb3tGQmR+OnHH2EvyNtilx+3zhY8W8MG0hJ5C8l2gtj2XvbZlV8G10UdRf/GsXvheKNGUdg6N57HYPGBuzqDV8rM6BK1kyOf4STBEuq3+nFgbUFNlHr9SLRME6kfUurvpgv3sSmtUe7TnQZdACa0LY93W5XNtC4JbHy5/abaBfHng/cFYSSJI3fCsP7sE/TLwp7EEGJkJ84VJx5qXadcIS9BIEb+fKeFRwQbK0sOWOydGTNmCAPF994zbICd21AfKt0oGE+LDYfDi2s4+ewpvL29hf+HlLmpT15TYZ0GWw6yEsKLjyPV+ujya3jRWh1pKUDH+YBvcaUIloCTC1CxE9DiE+DI1+JjN7fR9Ki4/D/K9GZktpsacwjwU2VOrIEuXboI3x/1QWaFNNGSW8H46cuXUbpoUSyZPl3t8XFvvonagwbh6MWLaFO/vsk/9/yzOGWvsabwplnKe0OuaxFy08hy64ubL9DjJ2DrePF+zHMgv3jdo4oJmpChv7YKeT9TVQ9ZSFLJM7lYkGAhcfPmTUH5eqgWYcAifm4WrSg8FRwhVJzJ2XD+iTDZ/+7q8zmypxVsvMo8Refq2o+Rv8c0Ev7PykV8UL6QlzCxVCrAA0MalUJuQGOKtxQK8KT63kcWIF++fFmocDh37pzO13etXkRZmaIvdUvlx9VZHYXxGPmr0zmsZnF1T3WPHArakYsKBeSdOnVSPkYTLSHU456aBH9f69QmMIaqxVQCv7O33cCIppZ3ArI3ODNuZXyy8bJyuUo24hMEKdzaSy8KkZCShi6Ljgrl08uPaw8cn0aqz1z+sO8u3lh8FFsvPVN6gd8LjcP+m+ozrNuvvMAnG68I2eEv/rshfJYmUlm6vBecZpeXj1AN8s4/En2Ys0Nb0C6fAAjwdBEuiJ93UxfJIj7ZZFg5WlYk6FFdQOX0dOtfTzUBIPlJM4x8Iiw97hVKly0vBOJSb7IkUOeqoaEg92MmpOyiRMvSVq7av6oXsHsaEHoDcNdS1h1rwKCQLKYS9Tt3WCPm6hm3dor5qEph5cKb75lLRfj0r6rlmeFAwYo5e7+qvYGitQHPQsDqPjRDBnuEytf17RGWVPDlE9XmgsqyB/x2KtPjq089Fqr1chKI1ynpB283ZwxvGqSzirJhmQB0q1lUOZYhDZvDk1tbVLhOV0+3vmLF2t5L7qttyHhs/6SW+GNYPTQtp/IhlyoHNBNihkCip/R9yvvhu/fpD8+qreFbUXvLha1CvfpyaJKdyRmcGbcy5D3R+vr3Uf/R/luhWv2rbQ3KEuuCgley/NA8WX6/T/Sc/GDdJaH8i2aK2313WOt7kDqpBFleHJjUSmtZvLzXm2gUpH7iNha6AF6d1QEPwxNQrZiPcJ90AbTZkFApkCmsc5K0WLIR52e0E7L0bRQle8TEDhXw9zmxn07OkD9OY+XIBoJXZ3xyGj5qXwET1l7E9C6VlRd6xr5JSEjAleUzkJiUhMAAZ60CdVLAJh9sSYq6mr3Gez5qgZKOVqx1QW0tjxS2OIfmA620eDPTYwmvgNvb9XvP2zuBIio9AVtCmmiRe8Rbkvo1auDXNWvw7vz5aFitGjzd3RESEYGN+/cL59FmtUyrZp/8GuibUBFX72ofaE7qoF6SahIOfAlc+FN1n44t7xy2cpAn/PDtwLyiQHwo8CoYKGAfdkSkck02U+PHj0d4eLjQLlOwYEHExcVh+/bt+PprRdWKBj5uzkYHg4ZA7//2n7qzvGRJqwlN0FOLGFXIyRXPNfltSF00KVcAtsCoZmVQqmpddFl8XO1xfSocNUlTTCYZ81qCdB+0WTNKArs0vjEWOg9REC5x62UMFj0+Cs/4Z8BL49oQrBHNiRyaZJcr0zOGw8G4HSD1pVx5Fg3DpF6srzydPAy1cW5GO/T95YQQjFNZDInpUKb7nEaWmgTb3mmhn71DcFi80KslFwGRgnE3jRO9PDinyY+cQDPZ1YurMoI0W002JMLnp6Sj8me7hOWzD16hXTYCfvqgrQKAoJ737zVsRmh2/eyn7VB/rrp/KU1y3A6Jxd4bYv/rifti79v7ay/i6rNoDG1cyqTew4x1UbhwYaEfLrlmB2GAOXGoqoWGBr/t2rUTlj1l7gPShFIJDT0GyRNVsOAJt+JgPF1WGdJulvZ1qHx94BpRzO3+USC6MPAki0EXBfUVOgFFrcsGzbDMePbBOFUvkbaGKW0oSxUtikbVq8PH0xObDhxAbHw8CuXPj37t22PYG2/Axdm0LQ/nk11xNUP3Oc0smUW5hZ6LN+Cln/Botrh4im0SVJ1BN9hHME7iXyTWRuegmTNnKgXcqG9+27ZtamJacnwUk9zmrv56lc379/xJPTglvlA46FCWdcgfZxCfkoYVwxtg0YG7qFfKH+/+Jfpkt61cWO+ETW5D46eKgZnFDjVtxPRBanNy0vCfzymeruIYLyGLCZCsKFSokJqLinwC2rtQSWzYqWp1tAcGNyopVHfoakljDIODcStCLjBGPbz6QoESseb0Y8zrJfrK2hrkPUzl6do4PLkVCni5KsVIqEw8q1JxXb6a2th1/aWgMiohZX00M+PEhZntBWuRXnXMp4wt/9xRK8+ZZLbR0L44skNZM6qhsq9LotNC7d/Pb0eChSD94MfqVQaM/UCiX9R7+VdiLaQlpaFxE1UwXrFiRcyaNSuTlQ4hKvSLE0+U7ZHQXM8qSZEJ7TVS9NzqgtTVawwESFXXPwi4Ivvt1BsJnFumun/yJ6DPUthrmfrA307hZHCE8pxJrVSmoGBAAHq3aYOJgwbBEmRA98CSqorMzpDN2v0DjcWnqBiIL+sAvLkCKGu8pZS1QP3iJOAmnX8CAwOxYcOGbF8nZT8p+2yusdyRu+FqYn8EuZSMbFoaw5drF0WlYFs+2bN6lKq8eeYbYjvbjTli77OtBOJZoc2tJjvI4YVwcjTt/y9lxo0tU6e2rZEjR2oNxt3cXIVrqD3xRY9qasE4jd9pwmVa58pmtcqzV+yn2dgOkGd5DQmqeylssCQ7LlvkJx0ZcaKIr7uyD9zUaAqsK8vUtfiH06DynZZlUUjhm2krhMQkK1VXV4yoL1QVbBmXtYpq47IBmNxR/17FB+G2pxCeGzRp0gRVq6oU9q0NKjcn79QFCxaoeflShunjjz9WVuHIy9GfPHmClStXav3djFxxTqg+uRsSi7Grz6vZC1k90Yp2DSc3UbBNX0gga7Ks3ablFKDOMNX9GOtSsCb1f8osZoc+Am4XHkcqA3Fiz/XsrSijY2Nx/qZK1PLirVvoPXkyWo0Zg0Xr1iknqYOKF0f/9u3x7+HDePRCvVd//5kzePj8OUxJZLr24dGZ6W2zdNIwGcXqmvb9yHJPIiVByOKR04O1C7Tt2rVLaJGRILtFsjMjJPE2bezdu1fp8qCJKSs2dLUbagbiBKl5t9Jis1qrhJ8g2LtRD3VzDxcnvURkrZHxrUUHB4lkjWCczkN0PsoKSQXd2cTBuJcRZep0LH711Vc6j0Vla1ZSDH777TfYEzRZJB8jUpvl1kvPMeOfq8J5X96WRrZ9159nb92Xl7HNX7SdQvZbEpq9l1nRUiGmEJmQilfxtim6Je/lllPE183o3qCsKOwjDqZWnnyI07LBI5WJawq4WRpN/0tTlak3K1dAGAj8+FYd1C7pn+2JdryBAkW/Hw3G8yhRxIRRsXv3bqU67FtvvSV4k1oLV65cESyAJN58803BDuv27dto2rSpYB2krURQ/pt89OiRcqBBLR9jW6oPuGjGnKpVnrwSVfkblwlAifw20NLg4CRmxAtXNU7Fetwp4IPLgHcg0GkBUKKR+NyjzKWp5uD69euIiYlRTrLMnj1bsKEbNGgQgoOD1RT+S5XKXlnZVaGUr61nnH73TRccQG8Ngb6pm68KAXpW3A4OxsxffhGW4xIS0GnCBNQoVw5j+/TBj+vXY/WOHcJzT1++RPUBAzB76VLUHTwYi9etU77HLxs34pyJ/bOjMhwy9ejSZGahbOxGjSZNVsE05RHg4GjaQPbxSUHgdMWlFMGij4LxXxT73RrZuHEj2rZti+nTp6NatWq4du2a8lgeN25ctq+nnvETJ9SPRwnJ2kx4v/QMpfCrKSD3kfFrxFJyOaS5QhPhhOaYhs6Z8tY1e+XDdiqdhZSwh1g681215+k8NHnqNHT+4ShKT92OUA33AuF1iiDP1GXqNMlBZNWnrwkdi3PmzNH5fLJkbRYfhUWLFsHeoDEi6R3J2XczVNAWqjBjp9DSRuNP0mfquuiYUAHLaIfL1K2Isw9fKZfLGlCmLg/cp2++iiVDTDyjbmZ2XVPPctQo7ov6pfPjj2MPsGRwXb0nJA5nUXKmKZBGqubzdtwSHuv/2yk8mN9FeDwpLfeDcRJ0m7/zlsneT7q4SBcbQyCRrbGrziNYj5MoicHRjSxCqO+KhPT6ymxL8irUu0j2X9lZR+UGZFdGKrBSUH3kyBEh0+3p6Yny5ctj6dKlQvBGaunz5s3H83vihOFb539QZmfIO13eKze1cyWhimXfTVFf4EV0Uqbj2yagILztTCDDSPXwQpXVRbSaTgDWKVSVw+4ABc0gACaDBn90zJHn9DfffIOtW7di2rRpQoBCEzAU3FA2x9DrTEp65v3x8YbLOi0QKUDPqt2GtiFBYa91+c4dlC5SBLPeeUe4n/H6NbYePox3K1TAfwcOYEiXLvjh44/xKjpayJ6npKVhkkwwyZTMjFQX7exQNRBmRejlpplQB5WffQ4CWapWKVeunPD7/Pfff1HNtyRSIx5h7H9JGF5rCDDLujNVa9euFbLbdPySRVnv3r2xefNm4X+SoP+LesS19e/funULH374YZY948Q/F5/hqx030C6lBBYg58ECTUppQhamVKIusfOD5mj7rUpk1hRCrdYMXVM6dOggfE9BGa8Fq7eMlERERD0T9Eao1UnKKlecIWrmEKRCT17ocl0f82XGHbVmxtPS0oQJIX9//0zHGV0/JV6+fCnYRMrXodaskOcxiM2XiiJigafdsWx4fTTRcswTpOskr9jt/uMx/NTWBibicwEOxq2Exfvv4sT9iEwlM/ogZSykHmhbgrKoY1dfUOuLokCZHp/YvoKyj0cK0q9oUR8lVXKyqiBhE0lgTA5dBGlWmmZapWChqJ/6mTEmMU3Yj1JSV1vPuKUggTf5cfF+W/IsNp4ExcVFEigxBBLZOvBxK0GIq+bsPcrMprwUNdPnKYJ/GqDnlWD8n3/+0TkoJL9bCsatHRI/qlKlihCIS5ZlBw6IF1lSJ757PxieVXsI94e9WVMp1nLnzh3lehJLh9ZF0DQxo6mJ3FfWqnl+CTi/AmgzE3DVf3JUJyT2ZkGEIFeRFaWghsop27dvL1Q/UBBDEyzywEbvMnWNzDgF4fJrl6GUKlYMV+7eRfDTpyhSoACi4+KE8z/9lsKjouDmIg7EY+Li0K52bWE5v68vdi5ahM4TJmTR2W0cNNb/KkT9+mCRFjBXH6DvciD6aY6z4loD2d8WoNy+4aqV6GJnQXsrQ6DvPz4+XhBoI1q1aiWopvfq1QvrZBURjx8/Fkqb5d7OEqtXr9b5/vJ+68kbRRvRdSiAua/jkZM9f1XL+MTPwxlDG5dWe6xsQS8hASCdI+uUUve8tjdIUO/SpUuYMGGCEHgH3XiJLYcv4vXtAxg1ahTy58+vLGeWQ0mA2l/sxZLBddCpWhH1nnGTC7hpL1OngPvixYv4448/hP9DMzNOE9gETWrfuHFDGAcIz6VnYNXJR/D0j0ZxlwSk3dZfy8iWoLE0TbBrG3tP26xqdZO3gVIFL+kFeBowGWzvcDBuBVCJ1Ld7RXsuY7Bl29DnGlmzYoogmQZi8kCc+HtMY6XSOF1MP2xbXghUpYFbnzrF1U4I9F5lCnrip7fEAdwnnSpiz/UQDG5UCpefRKm9d3h8smCdJqGppm5JCniptoOOi5wG4znJjMtn7qXqgbDYZOy+EYKZ/4hlg1lBJ9zcrDKwFJRVJsVxmv3XROqntlaonJn69Chwk2e45ZZlhK9/fryuKpbYDx2qynQeO3YsUzBOv8dN7zZGn19E1X2JW190sp3j4Z93RX9xsiKrZ4LeWnqffiuB/GXNnhUnypQpg02bNgltEcWKFUNEhBgwJycnIzY2Fm5ubjlSUyf1XArCR63MLEZFmXBp8pOgst0f+tfSOuAo4O+PDwYORP1hw4TMd/HChdH6nXdQvmRJ/HvkCP797jtQzr1e9erYd+YMRil6St3d3PDfwoVoP3487j5+jAFafnuGkpgBTHzqiZ0x6j3FVOVjdmjCp1pv8wWyAwdiXWvZYCEpCnDPul0pt6DzB9mW7du3D927dxceoyqP5cuXCxMLcmg9uZ2UhNQaZAg7EzzwRg6qxbv9qLBClP1m/hzRQOf/mFfsoOj8QxU577//vjDZ26RdL+x8BKQ/OYugBu0FF51yYXFYoKMikCrupGBcqaZu4sy4qkw9Tet3RRVimpVESUlJePddVam9r6+v8ljs9fNxBPsFwMsPCA17iPywz2Cc+PGt2qj35T7E6mGvvPvaS9x8GYNNz8/hr9HZayTkFTgYtwI01RsntDGsV1c+WdeojDjDaCv0W6I+WHdx0n2CpWz17g9bgKpjyxVSlfFJ2cgOsvJXN2cHHJ/aRu3141qVE25EIQ0154i4FKG8Wip/kpTbcwP6fyoX8cHNF2K/54voRKWInTFIM73S/5eT7SKoZ5K8UFedfIg7IVmL6n25/Qa+7GmbCv+GQoNFbYPCU6cUpclWSFBQkNDvLc34FyhQQM2qRepvL126NMZMmomvLwMVyZJM4//++++/M7133VKZz0U2E4inp4mBOJHDkmE1qoiVBZaAjkUS3aPKjAYNGmDSpElClpSyPDRpVLy4YZl6qQLr38vP8TI6CX3rFccniqyinM0KccjbX3ZSlpxSuSKdn3tU0B78fT5mDLo2a4Y1u3bBw9UVnn5+KBkYiIt//YXCAQE4evcu2jRujLjISIRFRqKgv/g+Xh4e2LV4Md7/+msUK5QzG7D7yQ4Y/8QTt5Kc4JLvNbp5xCM8KQNzR7ZA8bJmbq2gGfW1ZEz6Ghi4LkeZ8SwD2YH96Yqg8jC30mCcoL5wOlal/4Fo1qyZIDD5888/C/ffeOMN4TFtkBYCTSgawk8xvugaGG9UwYBm3/mdLzubRe/GVqEqHNIwWLhwIX6YNBSpgY2FMhRqEyTG/XVB6f+uiacskSD5jJtaxE4l4KbehkMT1F988YXW17i6ugpCp8+ePRMm4xcvXqx87uJjVcKnaqVKWDbLvqzNNKumSEV9+hb1TLg2KBAnTuagmsoe4TOFFdo7yEUu9KFlhUIo7i8Ga7Ym4KbZZ5hd0EnWCfJAXA6VzR6Z3Br96hXHf+83z/J9yCpNzr+Xnyk9L90U5Zi5iVxgZsWJhzl6r9ik1Ex9cqbgFz36+eXWF/YMDRjlQmhyyHZnyJAhsEZoJr9Hjx5CZpxu8oEtlayTd6806ChQrJRyoksOZVgp80GkpKQgLEyl3XB9dkfYJBdEtWaBVJWSs0lITQSSTe8MoQnZPFHVAwUs1LNZt674e6XvdNWqVQa/n1SmTpx5+EprIH5sSmvUUYhDytcn5Jod2mhYrZrQD/7v999j7bx5mDlqFIrKVN5TUlMxZdgwZSAu4evlhZVz5qBxjRrCfeonT1T0oOsDVWtviXJBt/s+QiBewDEDq0rH4duACPzpfh/FfS2gnE4K+3d3A3f3AI9PmSyQlSMEsitXo3s1X/UedSLkBrCwOrDiDVgLNFlEAbUmNEEolarTJKGLoo1BE5pYpPMbER0dLbTayNHmFnIz1QUH44zLUcnHXm0qFeJAXAs06Ttx4kRMnvMN4i7uRDqJZMrOD3Re0WbtW62YqlwhTekzbmprM0etyTHaZmr30DXxRRVIhIeHB8qWVRcvlZjWrZrS2ox60ENCMpd02zrtqhQSKkGallPX2sgKqp6iiVqGg3GrQAqWiO0Tmil7MfWFSra/7iMORChT+cshma2OFXM/TP3iOLt7VSEjnBNKBnjg6741s/Vppz6u2iX91ILGmETxe3DLxX5xiQENVJ6UOZkcoJJFUtkndM06Gwv1vS0dWg9f9KyGK7M6IHheF8GehXr+5WhTRLU3KMNMA8PsBoUkpkQlpLYG9cJ9MXGMsOyaRXabrISor1NCs9XEZkiUKYBXydpqxyD2zATmBgKHRTscc+Pl5YWPPvpI6J+lPvElS5YIwblc2yA0NFStHUEX2WX5mpQNQHF/j0xWThJ0XSo95yCWPc1AQprh6tWXbtzA0M8+y3a9MXPnYvsx9XJhXbxIdcDox5746KknEjLyoZFnKnaUi0FDT+O8ho0mWTZR4VfCrIHs+nFi2xaenRcyeuLnxwBRj4GHR9W3xcqhyb8KFbJPXnz++ef4/fff1R7TpWeyONQ9k+WpPtfZ+nP3qSnvM7qpV7MaAgd/jbIjv1Pfj+lpSI+PyuQmdPSuaoI3VZEZN3X1oqci+05q7dTvbcpjsWKgj5qwILkE2Btk+Xt2RjusHNkQNTWcAWjia8PYxlpfRy1Ma888xsgVZ/HJxst4Gpn15Dc9b4w/vbXDmXErYMLaS8rlqkWNa1iSZz2/2mU6JW5zQVY4cjVRYlgT7cGMOaDB6OZ3mwgz2Jpe2do8xi0NDWrHtRJnWY/ILkSGcvNFrFkVW0m4g0rWKdCnSSSyZyERvlPTVBebESsy95TmVWiATOWitohUhultYIC9f1JLYZKNsqY2g39pwK+U6A/ulrMJQjWk95IH+7kMKQBfvnw52/Wys9vU1i84440qmR6bc+81mu+PwZLbcUgxoaWUISSlv8bvsb5o/7gA9sW6wDnfa3xUKBF/lY5DIedc2CapJYLwK2nez3otDmSTXt5RZfLk1R9xocgLFJZZ1JX0dcUxj2twy5eBi4lO2B3jnKMKv9xsc7MFJGvZ8Dj1Ss6U0AcI3TgLX/etISSmJEJjVbZ/qYqJPBdTB+Oy61qCRqm6oZx/pDq/Lx5YW6mFZO/QOJCSgyvfbpjJAo0cknQxbfNVHLgVivXnnqKvhs6MBPmUb7v8HM2+OohOC48I7lP2ZKPLZwwr4IaiNzgnBBUQFZAN4eDtUNSas0cI3i19UGtaIawZpb0MyNwBubz8Sbqgapbh5hamyCrKBe0s+X8F+qoGOtef5/z4ZqwnGPdyczK4goKsfDSzplZN9b7Ah1eA7ib2hpV6dK0oGNeX7ILxIY0ze5XTBCC5V2gSkfIa316Lwx93E3D4ZTLux1omE03B//oHCWh7MAZfxgQgLsMBtd3TsL1sDD4olAQTa0Lpz66plvus+qOByt2BUk3VWyck0uy/kkni8mcdsGx4PRx4uyaKO6RijLd4rZof4o4UjeQoxYDbbkUIziIS0rjpnVXnlY95G3h+zIv4e2hvLZAnpej2gRbxWqln3NQCbvLKn//tuZ2j9+rzi8rfvlvNoshr0Hl/49jGgv6SvvbEEi9jkoTy9VsvY5S/LxIN7v7jcby/Vmy9eRiRgDeXnBRiGHvBOqKOPIxc9CMnLiOagRv5G+qCSjyoDGfE8rOISkgVygc3nn8KS/FQi2e13EfSkoxqHpRpu9xzoDpuSpqXF8W0yLP5xP1w/H3W8P5rebm+NtstS0HKy4xtQ57P1qKpYLMog3F1NwdbQLMHXKJ37WL4972m6FtHe9nvOy3EnkqJGt7At7U8EOTpiOT017j4KhWd9oTjrcOv8PeDBEQmm94eJCQxHT/ejEOz7WH45FwMniW+RqBDGr4qFI2NZWJRwS2XLUksKaRWZwjQaqp6z3hKgvbA3M7x9XBGm0qFlf3H7/jEoKBTBh6lOGLFK3WtgE1p+fH+f/fQ6puDOH4vXMjOkad4SEyS2oQzVYYxWaNvK6Z8fCYJ0Upq6qYWcJOz6tQjs713XqFe6fy4O7cLOlULVD42StHC2KV6EbzVIOsKoE4LjwrWf2TvSy0gmgKJxN9nn8Be4GA8l6EZH4m1oxuZ7H3n79Beqr7h3BNUmrkL5T9VV3Ykr81gjR5uc9Fx4RGt2bPcKqshH3Jig2JCoqwRVQbmQCptovLPt5aexpRNV3HxsWEZtZT0dLXAPrdo8+2hXP18JudI10JJVZsxAg+FuE2c7Qn46Prem5YrgBrF/XQOsGkSkFwwJOaUd0CfEi7Y1bEAepR0RxkvJ8Er/ERoCqaci0H9baEYcuQVfr0djyuvUpFuRNUWveJeTBpW3ovHgEOv0Oi/MPzvWhxCkzJQyM0B0yq74VDgE/T3Tcy9bLicof8CTSYAH2QWxTMLv7UGdk4GMtIyl6nnoWBcE0+H15hcWPz/vwtxx4Nk1TE/JVms/CANlkG/nxYEx8iadYBCDVyiX72c9/znNaj1qVJgZmFebzdneCo0fN5bc0HwcZf6uSnrak6S04wrVX8UoUo28cSMOp90qoTRzcugQmFvzO5RTa/9+W0Wts8FNVyRbBkeVeUyVM5L3rukAt6ojP4qhNog8SyJTRe0Z7op6NbF0GVnYAkkn1oJEgHLTQuQi7L+Hmv6gftpKeW6+izaoPdIVijEZ1diam6orEjbzCZjG+TPnx/5SojCT2lZfI+k4N2tWzfYNBT8zS0C/NQIuLLBtO/tU9x2g3Et5xAS5ulVW1TS16dHlCiqWHTIlw9BXo7oXtIN+zsVwORqXqji5ySUAx8NScH8K7Hovj8CdbaGYt2DRNxP94Z32TrY+DARh14m42RoMk6HpeBEaDK2P0nCqvsJ+PJyDJ7mr4bP77uj3e5wfHYxFqfCUoTgvG6AM75v4ItjXQvinXJucMtnJeejiPvAn28AFbsA/plL/U1OWjKQrkgCZKRnDsBtKBgn3+fhw4dnu17jxo1RrZp+g/++filo6pmKpNf58PEzT6S+BnYl6O75lbRmCBZu05+PO6jEzv6b0CyTw41mb//B22GCj3uaBTLjxIGboUYdiy2/USUe+tQppvVa2qdPH+RFaGJWXsUrj1k0XYSyY2jjUvhCz4DeFrCOetw8Dnnvkgp4TiHbLzm7rr1UKxHJjqeR5r8IawvIqhY1oUCSkRkfWYGCIEJmDZAQhiafbb2OoY1LGzzxoavE1Jzsm9gS7b5TifRRD5CxAoX2QpMmTVCkSBHYAo8ePRJ8qUuVKoWSJUsio7pYSrvm9GPM66XyjifbrIMHD2Lo0KGCl+yUKVNg05xZKmYKw24CBTPbH+UIj/wq9WoqDXbJ/T56srQrKLMQ04W2cwiVoOtTckoTiwPql0C+9DQUdMk8EVHULR8qRF9DrYJuCGxYFQdfpuBUaAqO3X+O8JcP8LJCEcCtEC74lcSRNXvgUigITr6qgVviw0tw9i8CJ9/CQNkO4vY6AHULuKBFYRe8UcIdxT2ttL1iaRsgKQpY3gn49CXgbGaxJ4WtHs2tjKjtAqSlAC6yarA06wjGyQKKLMzIpaJp06b4888/cfbsWWG5X79+ygDonXfeEbzH33zzTcEy6qeffoKnpyfGjh2LokXFft3+/clfXT/ocP6qWAI63fPB+QQnfP7cA1f0LBrsUFX/8VZe57025YWbxNxe1fAq5BkK+6oHqmEaLZcp6ebpGdfkl8P30aGK9uAwOTkZNWrUwO3bqt5yOhYXLf4RFWbuVj4W8uQB+k+YgN27VY/RMTl79myzbrutQGPtczPaofviY/jmzZpClRVV6LbREHfWZE6PqkKZe262XpoaDsbtCM2ynbGrz+PB/C4GHbBp6RlmVQLVtCTY81ELFM1lpUkSm5Crelp7Bnf39ZfoqOdFXyq1yo3MOPWrH5jUUnli7broGB4u0O7Fbc/QhTggIAD16tXDW2+9BVvhwYMHWLZsWaaBg6YQIAXtv/32mxCM2wVPZGWnRUTLSJPh5gf4lgDijXdIMPQ7JFs68sktXLiw8vGtW7cKQQ3Z7mmzwNKG/BxCtpA/vlXHoGvFArLfTEgAjmTOOPWbOhXnbt6Eh5sbShQujC3ffINRFfyxPd8NLDi5E13ajMOtJ8+xa/k8eOYvjNBXoQhq/RaKNe4hlPfdurELleq1RrNyQajg64QKPk6o5u8MN6uoQc+C9FQxEJcwUSCeVSA7cOBAYPxZOP1UH790dQNin4t95Nc3A/cPWE1mfOHChbh//74g4kSK//v27ROqbuh4ff78OerWrSuce9577z1hknP79u2CK0CHDh3w8uVLIRt+584duLoaXulW3CUDC4vHC7Z3ayLVX1+rhJ9QyXfmgeiJrc3Gj8meq1ev4tNPPxUsF4lSAZ7Y8WlvUqHI1qWBcHYw/Zjmv/eb4Y3Foi1iFYXNLv2WaILZz89POZbOyMjAvXv30K5dOzg7OwvP0zFXvk5TpfJ7gKcrhh92EP5PWo/sT5cuXWrybbZ1qCLihMx9p0xBL+z6sDmO3Q3Hl9tvZopxvulbEz31qMbKE8E4ZULopFe7dm0UK1bM6HXoREkn0/Lly+v06NVnHUZEW9BN2e4S+T2yDDJHNC2N5ccfCstRiak6y4XMUaJOvSO5zVd9aqDvEpWdQqsK+pfK5Aak3PrP+KbCoEDvMvVc6vOlE6sm3++9gx/23xVUlsmT3NzlZoawdu1aNCxfHuqSUzlj27ZtqFSpkhCMWxs02J0/f77Wc8erV6/g7u6uDOpC1v8IOpoqB3qj09kflOuRd7qTk53M68a+BK5tEpf7LjP6bWjfnTp1Svje6dolcfL0afi2+gNVKlU0e1acBoHkKV65cmU8fPgQv/zyC/r27Ss898UXXwie4xSMG1LBJdf4MJVdz+Pnz3H62jXc3bIFXh4eWLhmDTpNmIBdixbBMV8+eDjlE4LrA5eP4Z3uXfHDxx/jVXQ0ek+ejK7hbpg0eDD6HnDBgIqe6Fs7d6usDOaFrG2s43y9X0YZOTq+evXqhYSEBCyifSUr2c4qkKWggbzn4V1UDMT3fg5UfxNoMRmoNxIoUgvWAFXl3Lx5U/j/6HcUHBwsBDT0f7z99ttCMH7s2DHhf168eDF69+4t+Kj/+OOPwutbtWqFPXv2GN02084nFbOKJOLzF6rfaf/qBfHVoAZ4FZ+CBnP3qbXszOhqv8JtdA2jfUn72RgoHqBJEvl1JjY2Vvh+KVCtWLGiUNFgCM5mSDCQu84nnSri6123se7sE3zZQ/xO6fxJ1RgtW7YU7qempgrH3qhRo5T/E/19eu8mvGt3gXOBkviibw1EhL7E06dPhfUomGe08+LFC2HykMYRlLCoFBiASoE+6Fy9iCCSSFA71Pf9rePcZA4MGkFRcP3ZZ5/h0qVLePz4MVatWoXBgwcbvA7NNA0aNAg7d+5E9erVhXXphEonUenA1mcdJjNkEfbrkWAcviNmXpp/fVCY7fNwccTUTVe17rL3WpdTBuMn70eY1YrBWFEMc6s+Ut8+ZX6oBCo3Srp1UTrAQ+i3nta5EubvVIny9fzpuF5ZZqmky1r+p8cRCUIgTizYeQurTj7C8altYC1QBsl/xAiDg/F//vlHZ1BLAw4aTFojNCCi8zRlKLRtNw04iLi4OLyODYVno/7o2aac2iQLTZgeOKBuVWizrBHLXwUKqHoaDSEyMhK1atUSqiFokPHuu+9izpw5wnN//fUXKr2+iyqFzwMd5wENx8BcbNq0SfjciRMnChPaPXr0QHp6ukElu3L8PVTey4kaFU45ITY+HqWLFhUCceLDt96Cu6urEJB/SFlcBTFxcWhXW9QtyO/ri52LFqHzhAmC+JvNIvcXr6NfZUl4eLjwXTZq1Ah///03IiIihKQHBd6U+V6+fHm2gawQjJdqAtz4R7zd3QNMuCQ+ZgXQ/0I36q+lCSMXFxdh+4kKFSoI5egEHc/0OyNKlCihlrCRr2cswwKS4ev4Gh8+Fcv4qxUS/+b3dMH1OR0Fr2tKMMgnqmwdGmvTZJ38Wkb7MSwsTDimaMJD2/UiKxwcHIT3nTBhghB4E3RM0rFMgSp9z7r4YUAtfLDuUqbHPcy0z2MS09SWKUCkCjEKFk+ePClc5+n/GT16NAYMGCAcg0ePHkWioxeKDv8O4buXIJ+zK4YOGijsL2qhoPUYFXSOOnfuHD7++GNhMr9OnTpC9VZSUhJ++OEHXL9+XahoKerrhlYVCyIyPgXf9DVxlZotB+NUGjRixAjhhK4rE6LPOrSz9+/fjytXrggnT/qR0oWFLiRSGac+6zCZaVKuABqWCUDZ6TuUj0llN3LIiubTLdcwsX0FBMgy4bP+vW7WYDxCVg4+o2tlWAvSxdRaglaJ9WMb43TwK6H3/05InE5hvux7xnMv+0x2Fr8feyAst/jmoNpz5O1OFRva+uPNxR9//KEzcD5//jwwYoTB70nBDpUB0+y/JitXroQ1U6hQoUwTpgQN6KVgnHDy9IVX1dbo0bcpasqqMmgduwnG23wG/KXoWQxU9cUbwt69e9GsWTMhUIqPjxf27eTJk/HNN9+IK8SFAgVTRTVrMwbjMTExwkQ2QX3/lB1t27at0ZPZ5mpfKleqFF6EhyM8KgoFFNmjd/r0QWJyMsYtWIC6lcXrRL3q1bHvzBmM6tlTuO/u5ob/Fi5E+/HjcffxYwzQ8tuzeu4qekm7fgu46ucoQhlxynZTFc+YMWPg5eWlLH+liaAjR47oFcii7x/AF9so9SFqJHxbAei5BKilmgDJLegYpSzkf//9J/SC01/5OZrGgUSLFi3w66+/CgkgmvSSKnkIGjN+8MEHOd6Wnn4paJQRiVMRaehUrb7ycWmsYE+BOEHHDo27p0+frtQ3oSCU2hwocKZziaFQdeyJEyfw/vvvC/38NEF44cIFbN68OdtAtVuNonBycBD83advuWqwPZqhkA7GksP3heV6c/diRt18KFPQF6tXr8aWLVuETL62nm8qT3f2LYxCb85CxYjjwnqkW8BkhioLUlLEWICuk/Xr1xcSGtJvmlpOqNKFzgMrRjTIE7vQoGC8c+fOJlmHBqdU8iHNYlIWoWPHjsLjUqCtzzqMdiiwKVvQE/fDMvt5S1Qv5ott76uUDJuUDcCJ+xGIiE8RTsbmqj746eA95fKo5qYsBrZPCnm7KSdHvu1XUy0YpyqD7CYPkhUZrNxUq/+4Y0VlMK4NUojXp+TeVFCJJ53w6ZyiCWUyjYXK0LUFtTR4tlZokD537lytz9FMNVU2UQaDzsPFOrwN6ibVbCug/5suqHZB+XbALMMcC7IKgmnguWHDBuFa9sknn4gryHtySbndTOdaGuBQAN6+fXvhPgVmu3btQps2bXJ0nAuYUFbD1cUFCydNwqFz59C3XTvl45Qhd3RwQESM6OHcpnFjxEVGIiwyEgX9RTFByqbvWrwY73/9NYoVsu72Iq0kRau0BPSEMnFSewFlg+Xie9QSQS0SWQWyNFGkejN1cSz8MxYoWgsolPsT5RTIUGafspDSMUzQZMN3330nlD7TeWnatGl49uyZWvURZd2osrJq1aom2ZZAp3T0dI6kRmXYO1RlQWXplLWk70C6ptEkTk4yvCTuSecfaqGg73PkyJF6vY6C7q41igh+7tgCs+Pv6SL0i994IZ53qMpUqgSjNh/6bY0bNw5ubqLKu5RUOHE/nERGhLHztiVf4umjh0IVijGaBXkJSuA2aNBA7bpFbXF5DYufWaj8nEoQatasqfY4DYypxF3fdXQpHNJASH7Lq/w2NOv+VM1gu3cdlfhIsMyqwxw9OYxp2Hcj1CYy49llDhJS0vDniYf4/WiwcD82KVWwi9EU+zMlVGpHgwzNm7FK5927d0fXrtrbBmbNmoUhQ4bAGvH19RWEjrRBWSZJ74MGFE5+4r5xcVI/d9CgRFqPZrupnNFmOfs7cFMVvBgDTU5QECxBFWJU4njt2jXhLyq9oVo5VF2gxpSQfQ5l5qn0T4KOb+r9pAoOf0VAaw10a9FCLRCXeH/AAMwcNUpYTklNxZRhw5SBuISvlxdWzpmDxjXEMkbqJ0+U/c9WDdmaEW76Xxfp90plsfRbo4BTbu1F2Uay8KIgiibVtQWyFAwJJOkYH91WVdXlJj4+PsJkgiZUQi0vR6dxIU1KaP4G5Vnx6OhoodWG0Q8KiKTKVMpQks6AKaDjkbLiP//8s6BhYUigShZnw5uI33tfM4vlybXhKCMvH4vQ5BdNsFJ5tcS2y8+Fv2mxEZjSqZKQJClbtqxQXSZU28mg+Can7RP2ALVskZMHiYk6yaqoSRCPMud5DYuP0OmESDO7mj0i1PcjHdz6rKMNKj2lwaV00zxB5yVIYEcXy4ZnDtTbVVZlFdpmYytgisDsjRq2Ye9kbZD6vMT4NReEAZe1WpvJWTO6odr9rePFMkPi77NP8Pm/1wXlzL03QlB91h60/t8hVJq5CxlmULanNhoS/9HVy9RCR3CaXYZZl8AkZbHofETQ+YsCJFuDFLmD188TlrMS3Dtz5owgKmWTPLsAbJ8E/D0ISI41+m0oOKDsCantSlCpMJVkUoBcpm5r1cqrxJJrc0CfSe1e8gwOQdfF9evXC4NFIjQ0VO/BD/XIEs3K6y/8Ziou3biBoZ99lu16Y+bOxXZZe4XV8vA4EPNMXJZbi+nRVvL1118LrTFlypRRCgRSxQ9lvclySa9ANvopnsUoBFXbyvZrunb1amuEJiSo9D47Pv/8c/z+++8W2SZ7gQJlOs6oxYbaIOh8Yiqob5zam44fP672OJ2H6Hyki1ndq+L67I5m7x/WrNTbcTXrSiIK1tPTUvH01zHZViHeunVLaBfKy9BkMJ2LaCKRkhV9ZL7rlIStUsV+xRCtJhiXftBUfiSH7kvP6bOONqhciWZApRuVMeVlpnfJLBo1uWNFtKmksriR+8DKyS7IM5bEFPFC7+VqJ+rLFkZTff67vXf0szbL5fK6JmUL4OacTvi6bw3BV5J6jiU15q2XxFllYv5O9UxhZIJKY8BUkLq0Ll9lyhx6KISkwuPikGziGVpSM6aA3xaRzgm52fJgVpbKguQEddsiQ6EeVirLlENBMfW3dqEKijrDEJX0GvEFREGy3IT6j7OqOJOzf1JLfN+/JvrVy7sT3SbjJvVrK8gvTozoCw3m5Sr9UoAjF9YiccasqgOTfIJQ9scE4VhE80lAA4V+QUbey0oxWVdiUKWqpvZJYmKi0BJhSug8ROejrPB0dTK7iLOLo3ry4vEr9VhEEz93lbhlwyDdYnSMCE0IkgglZcbpVl52LiPrRao0JOj8ReexvIDFR1U00KWZXZL7l0OBsyQyos86umbyaEZYfsvLjGlRFiemtlEGvhQIjW+tPkCUE+DpotOCzFSQXQRB1iBMzll8QJV900aKFZSpS7i7OAqDeMk6j/qsNAnW0DnYdd005XHGMHjFChy8ab4yYltCVDcWl63Jis5seAea9/1LN8eMA0lYvnkPbAmyyexVu7hFBRftltqDgZJNgEbjAO/ME+Q5hezOKLOpEwpoHJyB7ovE+46K6386X5uZ7Nm67HuMG6LuCW4vlFWo5ssJU/iHS6w/+wQdvz+CL7bdECyBCZok4FZM0zFv3jyDLe9slVwZVXXq1ElQzpMyLVRqRIIR9Lgh6zDZU9TPHRc/ay+UN7+ZTZ9Nh6qqAWj1WbsF6ylTQ77nxP0w7t/Kid2ZvlhLmbo2vuhZLdt1SPHfXFUajP7IuwXyRDDuZGbRncRI8W+ajfQ2M6YnsBowcifQSX9/cbPi4GRzZeqMBcnIAI4vAp6cEYUnD34JPDoORNlfBWqfOsUxrHEpQTRX4skr9UTBJ5uu4HZILFaeEm2BibxwaWTMg0GHDnkCkjKnpM5J1hG0TCIPhqxDJXzUT0flCKTWS+UKGRkZmDRpkkHrMPpBg2cqb86utOe9NqqseWr6a8HeIVXhU20qvBVZ+vfbqJfYMfqzZIh6vzPZfdDkhragNTnVejLjmuhbzkX95DklLT1DEIXjwN44MmTHltQ3bLcY6S9uEOVkPYN395r/8xgmOxwVpbacGWe0cWMLsHcm8Ed74LHMIcQOJxRJ22h2j2roXrOYcsyakJKO4LA4Qcdm6RFRbDYzXDHEGIdBoyqyj1iyZIlwI+VgEiKg5cOHDxu0DgnHkMIg9WdS9psa9snDUN7Hqc86jGmhHl4/D1XvC9Hh+yMmVbUO9BXFhAr5sN2DsVQK9EHbSirBvTWnHwuie6tOPcq0bpKiZ9wa+3zdssjWF/RWHR8rT2b+vwxlzn830OH7w8JMNmM4/v754V62XrbHUmBgILp162a7FlPESIX3szkJkPUI/9XX/J9nBxQMCEBHPcQV2zVogCCFur/VQpNb/00Enp4H0q2kR5tK1m2sZ9zR0VFNTT6rvmdSmWeM5NpmYKPMimy5rEK1gH0nVqhHnRix4izafHsYX+++jbk7Mrev5XNwQMee/bJ9PxKmlguWMQxhkIoWBcRyz0pj1yFIBfT777/P8TqMaYlKUL8QUzZxxYmHGNvSMIEZXaSkW2+m1tas68pOV7eg+WzrdQxtXFqrAJqvTGDEWnBz0X0MrB3dCO2+O6wm7Dats3Het5HxKUJAT5+272aIMJmRWzRp0sRo+zRzQROlkZGRagMEcrRYsGCBoEJMFClWHH4NewsT/5r9wuQJSurcU6ZMEUTL6K/NQQFR+Q5A6C3Aw8ICPN5FkZuQgE5uT3LvO3NGsCbt3rKl8rHouDh8u3o1Phs9WrgfVLw4hraWiewpoGqXtbt3o0ThwmheuzbG9rWByY0np4Fzf4i3KY8Ad/19xk0F2QmRs4SSSl0B/9JAgG5dGUtCdlrkJ05q3nJIhXmUwu6OgnFdPaU7duwQbKRIDKp///4W2Wa7ZaPsONEkPgLwDDDJx9B5iM5H1lZZmhAdgejrp+DdoI9QMSoRdXwtfGt1BHzJDs0RH8/5Rut7UJJy2bJlmDlzJooWLYrZs2db8D9gbAGOiJhsiYhTF67ICamKHuY80XdqRigg2jA2c5bouYYoWlyS2P/n7WZ9wXghb3XLJX9FVcaVWR1QrpC6Nd+vh4Nx8n6EUZ+z5sxj5XJa+ms8f/5cL4XOTlWqoISGvWJWkH+z5OBAF1/KENeuXRt//PGHcp233npL8Di3FshCcvTo0WjYUN16zsvLS/ALX7FihbhexmukhD1ExI6Fgtot2QSRDQ2pN5MnraRAb7N4FgAGbQA+uprjt6L9Ru1a2dHkrSmoWtABcNVtQ5lT1qxZo/QZP3XqFJo2bSpMCJFPtVzhv1SpUsgtUtPS8O68eWhQtWom//BHL14IgbYUdP+8YQO6f/SRYHF259EjXL13D03ffhvf/fUXihSwvN2a0VzbJP71K2lwIE4K1tn5PtNkGlkokt82QQ4OZLlH5yRJGJeCcfJ6Vuthr9kfKK7d9tHS0LZJ9ntyihcvLgQ1cr9i8sKmgJuqKR8+fIjOnTsLftYU+DCGQccO2eZlR1l/B7QJcgIW1QaOfJOtAwV9LwcOHFBrRa1evbpQ2SB5wNN5iM5H1gS1al7btxFOfpkn0Z28C+DVkdXK++nRL4SJIrpGkoAiXV/nzp0rJCnlXtp5nYiIiCwt7CQaNGgg7Lu8AEdETLYsPfrAZL22Kel2bo9kQeqXzo+HC7riv/ebKR/rtPCI2jrU50R4uFifgBshBd396hXH8altcG12R/goJg7KFFRXNL36LMqoz9CUSvh83tfYf0gMRigQJb9TbXzYpg2qFs9a9FCCgnCyVqQybYK0Lcgrc9y4cZgwYYIwELFGaGDu7u4uDHA1ad++PfbuFfuZk1KS8XL9LCETRR6h8+fPFyYVyNv42rVreP/993Nh660TCoD//vtvYfnLL78UggVtvNW/L1rTYDYia0cEY6GB765duwRLNTp/Dx06VBj4tmrVCoMGDRIGitbAw6dPkd/HB4Fagun2DRti7ymxP3XvsWOYv3w5WtSpA38fH3T76CP0nDQJY3r1wtmVK1GuhI3YraUlA2d+E5dLNzf45du3b1dmg6kFkLzkNaEJQApsKMtI5x7KJlOQQwGBrejuXLhwAW3atMnyvESaRHR+pUkHCtxJW4jaI+kcReclaTKC0Z8zZ84I1zKpumDSB+9pXa9+MUeMqesCJEcDB75Ut4bUwsiRI5XWxHReotucOXME2zRrroClYDz84S24l8rsbT6wV1e4hFwTHIsKe7lg9OB+guVbx44dhd9my5YthfHFxYsXlfuUATZt2qS0eKXql6VLl2rdLTTJRhNreQGeqmH04tDtMLSW9SkbS4qih5kz46ZDbqURo8iES9lMSU3dWoPxVW83wO5rL/FmvRLwcHHKVKo+6PfTuBcqzprP23ELo5qVgYOBtkrpigkgiZjEVGy58BR9eopBtDQrn9NSb8o6OTs7C4EPZWjWrl0riCbSoIOykuStaW3QwEFXUEaZb8quEU+fPoODizuCek3Chx+2Fwb6H374IY4cOQIHB55Y0wVlMP38dGQ+XRXtEq8zgJjngI9ps3j79+9XDgBpwFupUiVh8okgz2A69q3hmExMSkI6KTXryJrHKzL7l2/dwqcjR2J8P7Evs+WYMejeogWG25pGwU5ZG4dvziYQqAJDqnyQc/DgQWVFDp1/xo4di759+6Ju3bpo3lzHBEDMC+D5RcDNFyjd1PCN2fu5WOXRQr2s3BjoHKrr3CQ/L1Ew/u6772L69OnCfcroki3uRx99lONtYESf52ePggF/LXvD2QNIlflvRz7M8jilSaFmzcTEAbWyzpo1S8gg0wSRNdtX0bGYlpKE168z1OTZaEw1sV05rJ2WjBFNgxARHoZ/Y2OxevVq4bpfo0YN4TdH1wAaFzDaoQx5SgrbKfIoilFjdHPRx/3tZup+7iReYQrSFB5JzjyANxulp27HV7tuIVEmvKcZ6FoLRXzdMbxpkFIkRU5hHzfsm6jqISXKTN+BRxHqFiPZoc0R4KkWj3Ni2+XnKP2/0ziTntlnNCuo35XcHoi7d++iQoUKSvcCyjxLz1kbQUFBePHihZCF0oQmExo1aiQsx8XGwsHdRzmJRr19JUqUsJ9A/NxvwNyiwH8WHMTLy5NfPTD521OQJh139P1SO4EEZcut5ZgsW7Ik7j97hmv3MlcIrNuzB42qVxeW4+LjUdBfFRUU9PNDKUUlis0QckPl500EtTDLx8gDdPl3n+W56PFJYN1A4OA8wz+QJpOOLxQzpCmyAM1I6NxJk0ekRZHleSkuTk3vgJZzs+XCLknXaFN8ex8wKxp4/7z647WHZHl9lFdXUqZYqlqw5usjUaOEP/yLBiFf8Em1x7/rV0t5LFLbIE0QBQQEKK/7dCySPgwH4ow+WOcInck1pnSqhM7Vi6B6MV94uzlh4b67yudexacgv6dsIJGDYNzJkS0gTAn1j7+5RHWx+OXQfQQVEANKuja4Odtu0NS7TjFsvvBMeb/lN4eE8nx9SZWbZCu4s3sV2nQ+hIvnLgg9XfMX/oQ7L2OFrLlvkwHoV7gCHkJ/Ffc6deoIgmdUKrl161ahXJIgASEq+6OybmvE1dVV6N2jsk7KdNNsflRUlDDIoEkFKiW7evUqqLgg9dVT3Fs7G30v/CL0fJFTBs38E5UrVxZKYW2WlDggNR7IME3pNu03qhqgzN3x48dx6NAh5XPUU9ipUyfA3R9o8QmQEg+UagJTQ8cklYFSdut///ufkA0nHj9+LAx+SSDVGvD08MDU4cPR5t138dFbb6FqmTKIpAzTjh14GhqK0T174tLz58K61Bu+XlGifOraNTwLCxMCduLjIUOUgbvV8uIScOZXoEgtoOt3QLE6Rr3Nli1bcOfOHeE3SNlj+ktQwFO1alWhP5wyxvS7pgqJxYsXC8/Tcait9FvAyc14q6rESPVlFw/RrzzsFhB6A6iRvcq0JlTF8cknnwj/G2Xz6f+k/4VaQKgFQ9L9oOP6xIkTwvLly5fh6empbD0igToqW2cMg/YnndupjenZw3voez8ByOco6Ak08zuODz+sn7mSh77jyEeAf+bJEGrfogz4xx9/rOzjpwqGbI9JK6BWCT9MGDcW/5s3G/meP4Rr8arIhwys+XYztm7eqHYsUrWRdE2k8678fsmSJYWSbEaExhg0UUjtJM7Ozjh37pxy1wwePNjqhPzMDQfjjPoB4eiAOiX9tZY2kzhYToNxKp0mNBWZmZz3j2vyycYrwl+akM7OY96a+aZvTbVg3BiPcaJt5cIIv/cCQXVb4V5kGi6l5UO+2r2RXMhLLIUvCJChmoO72Mcen5EP+ubHSaiNyu7oAkPL1B8nlYvSgJCyyNbKBx98IARmv/76qzCwpcEsDY5WrVqlLLEuWCgQ/k0HIr+XG1q1Eq1s5OrrNi+UlKqolHAxrCJCG9STTb83qhqgZU2KyW232nwKc9GvXz8hU0MifOPHjxcCNCmQs7YezSnDhqFCiRJY+s8/WL5tG7zc3QWLsr/nz4e3p/iddG3dGm4ZGXBQnMta1VUXGpNnza2WhAiVl72RQmmSqJG244smWSjooSCWJgU3bNgglKvTpBtBwSxN0GjF2chg/PFpYFkH9YqPtBTgx3pAlGJC09UbqGhY7yf9j7S9dKySYBv9pijDf/LkSVSsWFEQIaSe3BYtWigrdDT3hzYtDCZrqKqLFOyVVU8PjgA3twGBNYA6A4XnlUx/LpanH1oA/NkNaDcbaPZhpvckrZHNmzcLx15wcLCgLE5QKwJNKklCodZKy8b10FlxLF68+I/gKpKvQQO1Y5HOtTQhLa8WIyV/CXqeESEhUaoo0HWNLFmyZJ7bVRyMMzrpV6+E0KcrMXPrNWwZZ0QvmQKatedgnDEUmrhxd3ZUK7s3hFRFz3iFQl5o7VsC618DoWlFkKHo0iFNTx8tsfKmeE8MNUDoeMyYMUI2SlNsiG7WDs1Ca5uJlno2vf384VP3DZQJ9MV775mntDZXeXVPvY87B1AQQZk8GoBmS1IM8PSsOAlQUiy9NRWUbaCqD83toMkXa6Rvu3bCTROpn7wB7dfy5eFoy60Rklifh/EDcxr86zq+6PcqKeVTwKO5ji6hpBxlxq9tVC0HlBeP5bA7qkCc2PmJwcE4QT3uNMGp67xEatx6/9YYvaAJjIEDB4r79ORPQIWKwOcLgYw0Uf1fDn3XhauqtA/kFRIa0HdFAbkcKlHX1opgjWR3LPr4+AiTnnwsZg9V7/DvVh0bvqox5sbPwwVrRqksjy4+Nk7NWkJeLezEmXGTQ/3Vfwyrh9YVc9cz2Bx831/M6kkY0jcu9YxTa0RRP3e9X/dnrI9QVcDQb9eOXRCSw4C7u8TlghUt+9lkCbS6N7Cso5hNZOyb84oMYFzW1mS5ghSM04TBZdENQC/SU1XLJOD27Dzwm0a2K0plL8nYCNRmsHs6cGIRcGEl4JxF1RC13BCJWdubMQyjHTscWTGmpHFZ9Rn8JCOzk0SaTKTDUEVsRj+bMCrF/mNYfdQuqUrpWquSuiF0rEr/Vz21vvHmXx/ApSdRegfjkvjYOy3Koqhv9kH5/TRnXE2y/X1HRCem4vejwXgYbpj4nYSkgWeXLghx11XLQeqCgWZH3j6SRVaJsXHo2jfLV3sAay2QOrbEljFAUrR+r/OSuayQGvvSNqL+gib0/8eGiH1TjPWzRGWZikPzgegn2YtR8jmMYYzCDkdWjCmhPq2v+6j8FdecNn6GWy6YyZlx80ETHRvHNsnkNW7rxyFNNMh58ioRPX86nu1r0xRl6s4K0UB3F0cc/qQ1xrRQCVjRc+vfaYyrszrg7Lt18IaTGBhtjcqZRoK1UHP2Hny5/SZGGumKcDdEFKg5/8gOA0af2mJ5evV+gKeF+/rI1kzCM7PPNmMnHP2f+v1umb3Bcx0SXZPz8pp+r6PyZX35toJYCaIJBegbhgOLaoutG0zuE3ZT92SNJqQJQFBvOU+2MIzBcDDO6KVmLTHnvxumyYzbsKCYrfRZl8gvZn+/6FEV9sKVWTKhIAWJ2Uw2SGrqThq9ptO7VMbFme1xb25n3J3bBQ2C8sPbzRkFPZ3R00kst/s32kVQErdlwuNU1jTBRmbGbyuCcbvk8VIgsBbQfKLlP7uYqtoDLy5b/vMZy/AqWLU87al1Trxo9rHHvtDvdWka1ldy3r+Q+X3vHxBLoKXydbJ7u70DuL5F3E+/tgTu7eegLjfRFlA7u+t3DMi9xxnbgmwJd38qivFFP83trclTsIAbk/1BolGampCSZpRvtSTeJrwnl6mbnaOftBFE82xZSV0THzfnTFZn3+y+jTshsUJwXaVoZgGu1DRFmTr1O2u05frrcAdo4RgLn3zpCEtzxMUER9Sz4TPlzms560+V1OiJpuXsTBH2dTIQdxWIlSmqW5LK3cVghVS2qW98Zpjlt4ExPyRyJfytrsoiWhskxlX9TeDqBpXdnz5U6QnkDwK2T9J4vAeQvwwwaINYui4nPQVIjgEWarGii3kCxJwVRcFCCwH5MoBAK7esszdoMsTNV1T9J4HJ7DLjVXsBF/4EKnQyiSMFY0Eo6H54XGyZ2jxa9fj3VYEyrYEOXwKB1fgrMTOcGWcMRp8+3eyCcbY2swz2FIhLfNevFr59s6by/rLjD3DsXjgmbbicZUWGswETQC75XqOVu6gqvC/WtkvVd2sE4xlafNezYs+NEOXykEalYVdkhKuWc6BwbTRUrdFvpSpAeXre8tvAWCbjRBhpZ2bx7SSS9ayGKVEfqD9K9KGWQ8c1XX+K1QVmRQMVZX7f64eKGbisuL4Z+LWZ2Lss3y7G/KwbIGoGSIG4tjYGOW4+wOgDQMtP+NuxdvZ+Luo3/NIUiLgvBt2kESEPxCWCD4q/VcbscDDO6IVcEOyXQ/dzFIxTTGSPQSJjOfrUzewfG5+svXdx300yLwNSZBlefWjrLg4A98c6w1aJTkgVJirkPHqVoFPojn6jMUmpWLT/rlLsjaoOJAJ9FYrL9kKGrAfev1TubEMJmaXZ722AFONaCRgrxrcYUKqpmGm0Zp6cMjwYl2j4TtbPy6377u0FLq/R/70T1M9hjBmRVwg5ugDNPwY6zsu6TF0qbQ+/B4TIBDEZ64K+o+MLxeWQa8DiOtm/5tV9dcEnxixwMM7oRedqgcrlNpVk6qkGkKajd5dhTEGVIplL1OXq/+4GtlZQZtwRr3E32RGPU21TVX38mguZHnsVn7nH815oLMp/uhMfrLuIWf9ex3d772DAb6fUqlmIGsVkitD2QIaiaqDWsNzbBkcnwEPWQ2xqAaTIh8C2D8SBMpM71B4MjNgBNB5v3d8AtUsYGoy/vAoEHwbqvQ0UrCw+RqWtmlD23FgSc2aryugJnXtu/Ku6T9U6bWfqd9z++x7wY13g6Le8u62VsNvGvY7sChmzwlERoxfDmwQpl92cjQtMpIE9l6gzpqB0gIdWL2w5ciX57jWLGvT+vg4ZqO8pZtv3xbnC1iC9AHlWvExBsZcvJjFzBcHWS8+Fv/9deaHsx38Zk4TtV8XHiZUjG9iXJWHqSyDlhLpPbm4x8SZQujnQ5H3Rq9mU/NRQ9Lf+q69p35exP9p/If5tM1Psc9fH3uzwV8DK7mJJ6/hTwMwI8TjWJKsyZ4L6yykDqw22zLIMpIYe+UB1/13F+VEfAsqJf5P11BpgzA9NxK4ZADw4CvzWGvi5Yfavof7/KY8yZ8cZs8LBOKMXLk4O6FqjSI68xjkYZ0zJhrFN0KhMfrXe5k3nn2YSG5SOX2M8stt6i37AhxNsKxinkv2gaTvUHgv0EUvMqQxdE09X7VUDk9ar+vDrl1bta7vg5deq5Xpa+uUsiZMLMPw/7RnFnPDgGJAmah+gSnfTvjdjfzSdIPZ3k1/41vHi3+yQPNOdXFWVHrpoNT3zY6MPAoM2Am/vBRq8A/RfAzR+TxSTK6rosY9RTQoyZiRU5pZDYmyS8KA+FKoi/r272/TbxRjHP+OBOzuBP98AnmeuktMKtSWQb/x75wB/hUbMnpmiyBtjNjgYZ/RGUkC/9sw4H9B0ReaSM+OMKSjo7Yo/RzZAUVkfs6aIm2R75uFiXDVHCy9xoHk60QVJr3MvK0yTCr8evo8Hij7u849eYeXJh0L2mybHBv9+Gp9svIyfD91DaGwSqn6uPiBaNLC2oERPxCSmGmwPp81VwabJ0NgH7lYw0RD1BDj7O3BFoWZtCm5tVy07e4oWRNz/Z1koq0uCSXS7swc2QcIr/TPSkq0V9RdnR4uPgaFbVfenPgGK1QHKtxft3iiQr9BBrA4JKKtSnqf+VsZ8nPoF+ELWKuMZCLz1t2HvQZUNRD4HIFUxAcjkHjTefnRM+3M+xYFPQ8SJsHeOqrt7SMrpBcoDQS3F5fhQYEUXIF7WxsKYFDsaXTHmRipl3XThaY4y42xrxpgKVydHzO1VXaeQW6KiisPDyNaKCq4ZKOSUIQTi51OzEbAxI//bfQfzd95C31/EssE+v5zEZ1uvY++NEPxz8ZlQjr7+3FN8ves2Gszdn+n1Jfzd4eMuZqyiZcE4Lc/dfhNPI7O29apQ2ErtmHJSoi5Rcxmsgnv7RHuozaNM1zdOQY7EoXnAl4XEkmLGMtD3+JXMgcBW9FIoMyYPyvXJjOsTjDs4AmVaAVMeAjPCRBXurJBEw07+yL7j5mTXVPX7/RUOD4ZAZeoOzsDrDC5rtjb9B03B0O6LAGc3cSKsSA2xRYq+vz5/qK9Lv1U5e2aYb3vzODZyZWDsgbR0hZq6PfWdMrmOpjB/3yUnM/WMuxmZGaf3bqbIjh9NyT3/1L03xeAxIj5FCMAlxqw6j7uhWffoTe1cCbVL+is91ek9iJS0DCw//gArTjzIdoJNLuBoFyQryl4dS2W2ZMotpJJAebYxJ1C/b8EKYg+vnIeyTAhjXl5cUr/vaiMCiFJJ6vaJ2a+bnqxepq4PpNFArRnZUURlYYmDOvrJmZyhea6p9IYYpBlzsSxaS1wOu8XfSm6jzZWj4Vjg7d1Aubbqjw/9Fxh/JvNv0kOjYuyhjkw7k2M4GGf0pmbxnA0kJIEtzowzpqRMAXXBq5svYrD/Zoh6ZtzIYJxo4SVm2o+m5l4wnpiishYZvfKc2nMktKaLeqX8MbZlWWG5oJc4WA6LFQdfWy+JQm1yVr/dEA8XdMWaUaLQi7NjPgxrXNr+rAhTXoh/HYxzhjALUklgVlkNfUlPE0tPF9UCTiwSBbnkmFqxndHOP+PU77vZSDCerIdwm1xxW9/MuKFQ37jEEZnGA2M6Di1Qv++YAyvP/OK1BpEaAmCM5bkpU8WXaDBG+7pUsUNVK5oUVpSsS1TsbKKNYzThYJzRmymdKwl/S+Q3rlyXfIwJ7hlnTElJDVV1Yu6Om+o9486G2ZrJaarIjF9Pc0N45nZri6DLQ53YfkUMLNtVzhxYzu6hEuCResbpvajX/FlU5tL0/IrseZNyBYSg/MaczsqMul3hVRvwfQNwEs9pVoG8hPmcRrmgoSTLdD32zwZqD1F//tfmwP8qAlc35uxzGP0FsWwpGO/8jfi31bTs100zYzDuXwooIVOAZosl03PsO/X7hlQ4aFLjTaDTV0DZNjneLLviyVng0hrLfR79TuQWc29tEDPfpMNgCKTjMP4sUG+k6rrCE7lmgYNxRm+8XcXBfLqi3NxQktPEYNzViQ87xrRollEHh8ULAWdOy9SJgk6vUcVVjMKPR+dOhriITKROFx93rKh2//jUNqhaVDX4l/bBwdthqDBjp9b38PXIQVbElnArBfi0AZxUlo1WRciNnIkgaZYoehcGqvRU94aOewlsetv4z2B0c2EVcP7PzI/bSjDecAzw/gXRoiy7wXfTD0RLNEMH+vrSf7Vq2cvO2mWsAU0HB79Sxr9XuXZAo7GqcnVG5I92wD/vWkaRPCkGWNpGJb7Yc4koilhQfXygN9TqRBUq5HBweS3wrxbbQibHcFTE6I27i3i4JBhpbUY9qoSLk5X0aDJ2w8+D6uD2l53UHpu97UaOBdwkmnuIpd0f3HNCvMIuzZJo2gkObFASkzWC79IBnngwvwsuf94BwfO6oJifegWLPvvAzz2PBOPWyrjT4l+yo5lbWMw6kgL3lrFAcqz+7+NbXCxJrPEW8OFV8bF+fwLD1e3uGDOQmgj8+x6wbULm50g0yVa4sh6YVxSY7Zd1QF5roGiJRsecOfAqBEy4CNQaBBz9n3k+Iy8SHw5sfgco1QSY/gJo/6VYQZPTliTqKybnAGqTYdTRp5eevpcUWZtSRgqQkQS8zmLcHf0UuL0TWNIMWFBC9Xi72eLvM6eUbAyEXBeXL67K+fsxmeBgnNEbDxex1DchOafBOB92jGmhnmZSVt/2XjPlYytOPESiInB2z0FmnGjgpqpP7/77eViaJMVv562GJVGnpB8+6VgR41uXU1vHzdlR2A++7s5aRRL12Qc56a23GVIjgLgLQGoYrI78Gpn6G/8AW8aIGYkFJfV7jzNLgcNfAS+uAD0WAX6y15VuKtrXyGGrM9NnpjQp1VTMLNkSh2W9xFG53ANMiu2X/gKubc7d7bAn6DxxZZ2YRQ29KWa0fYvl/H1XdNWu0M6ISvNZQZNeq7sCNyYCGQph1vCNQPAk4P4E4N4EIPhj4PksIH4pkPhE1Gz4viqwdoBY8SSnkYZmRU4IPpi1OByTIzgqYvTGUxGMp6Rn4MbzGOPL1O3Jr5ixKqpriAzGKSaOchyMuyv6IgHcD09AbmXGx7Yoi83jmir7uKsXE//fcoXURey0oWsfLBtWX/jr4+Zkf0Jt2ki4Abz8A4jaAqsO5Eh5u1ofICVBNZBLjMr+PXYr+nyfnNJe6q7ZB5yWta0dkwMFdUdF/+3w7UDHuba7K/d/ofu5J2eAp+fN6y3tofDATooCHiuqRxjtRD8Dzv4hVmhkRaosoPq9jfZJJEPRPAYkPYG8TIYseVW1t/YAnAQ3CSotj36seFyxL1/LK/HSgYxEID0GyHgB3J4B3Nqq/XPrj9LPsUAf5OMCn2KAkw1V+NgIxqsaMXkOD1fVYL7LoqOCwJMhpKSLJyVXZw7GGcvwMjrRJGXq3o6v0c4lFvtSRL9t6ke3VOBKnyUF424av53fhtbFjwfuYURTmS2WDty17APqRW9eoSDOfNpWOdlm96QobM2ci8Dq8Coo9nA6ewD13waS41Rq1VIwInlA64O2sujCVYAPLgNPzwGeBc0jvJVXoaB0TT/V/cl3xQG2rU9y0XGni5U9gNQE8ZiS2/OZErnF0rIOwEw9/M/zGq+CgYurgTO/i2r41GZANlbkFU9WctIxSJUw9PwNDbXt8Hs53wY637SfA+z9LOfK7PaCfFLE2T2z/aRU8fTBFfVWJAcf8W+hwUDBAcBrKldPFf+GbwMSLorPb9fSDkMTuZ1N7D5A9mcnfwT8g8QJx2J1Tfv+eZw8MvpiTIFzDjPayjJ1zowzZuSrPtUxZZNYrrX+nOif/VwRlOeEn32eoUK4qL7dcN5+nPm0HSxBavprZChaNl01Auoivu6Y26u6Xu+jrQS9hL+oRF/IOw/NdCfLgvFcUsfPEhLNevVADHKCD+n3GlqffJgp0JbwzMK2jYImcwVOeRnKMMohxfoZL2HzuGv4DVvK2kxCPpnhVdh8n2OrUIC9qLb6Y1QZ8+gksLwTULo50H62GJRrridRvA7w4GjOt4UE/UivwoECcRufhDIJr4EqPcQKJ82Msrzt4twysZ2FcC8F5FP8nvI5KJZdAOkS7t9fFYxro/0s7VZlOaFMS+D5RWDf58CZX4EZoTlT3mfU4BQlYzRpCqsyfeGeccYS9K+fubf2zAOFsmgOcMkHlHETo+LQ2GSlbZq5kUTotGXGDUEzM96+SmHUD8pikG2PUEmgNWfG5T2y8kCc/F4/jxKD7rhQVVbl7j6xDHL3dODqemDfLNVr6ivsaLIqKb27V1UGz+QMbb331AIQn0PP+NzivXNAw3eBusOBSl10/88Zaeol+eZiyD/as4uM7soFCsSJh0fF3nBdgXjf5abdi/QdnVwMbB7LVliu3kClbmJF0/XNQMR9YEEpMfimyRGJ4wuBNW+Kyw7Z/JYcdEx8TbgE9PoNqDMcZuHY96plqrhgTAYH44xB/DBAZVkRm2SYqvQ2hR/y/TCFMAXDmInBjdQD8iWD65jkfTdWVR3z2ny6zUFyWroyOZSTqhJ5z7iPu7Nge5bTahebIz0ayKDg0wFwtuIMm2Ypesg14PI6YFVP4E+FABspIf/VRywJjdQQ2HLJXkMAvzQB/uoLbBxhwg3Pw8h7cOWYqm/T0hQoD3ReAHT7Qd0WT468hcLcJcmUNfz4HuDmB2x5l4M8XWX8hvLJA6Call7mnAZtNDF4fSMQ+dC0722LvLwMXN0gZpYX1xEnT/77CNgwTPv6qUYkD6hihARAa/YHHMx0XZcm3iQFd8Zk5LGRGJNTetQqBk/FoD4mybAazzMPxJm0OyEcjDPmZXb3amr365aSzUDngPzOotAZcf15NCxBcqqYcXNzEtXSTZEZj0m0xvpsC5D8TPzrXAjIZ8VdWh4B6vcDygH/jBWXw26KvaFkf0ZQH1+ownZGIqh59p/x6r74984uk2xynoOqFA5/LfaES72e054Cb/6pnmnUZ2LEmnl0AviqlOidrkm6aPkoYO6SVZrUOPK12K96bYPKR5kBnp0H6hoxqTbmUM4CeV1Qv7pEmuwYyYvIK49S9Bz7FumT/TouLcW/b/wMTH8OjD8Ds/PuCXXvdMZkcDDOGEy8ojx3zRmF6qMevIw2o9Iqw2jg6JAPvesUUwbiphRbi1FUhHyw7hIypGZuMyKJt+VU+NApr2XBtSGVqLsWhVVD/X6V3lDdr64oX5TYOj770kjGvKzuDRycK/b2zy8u3igoqtoTKNdOVB0mNXxbF2/7q5/YEkHe6SQoKCdG8XsihB5hM3PmN9VyvBVaE+YG64eJJejnsyk1DygvOilMvAXMihZvRXWUreeUtp/rb+dl79Bk54nF4rLm70ei1XTVcu0RgH/j7N/XpQVQ7WegSi/AxdMwYU9j8S8F+JUy/+fkQXh0xhjNr4eD9V53yqYryuV/31OIVDCMGfmuXy0Ez+uCTe82Mdtn9PzpOMyNZAlImfGcYutxQY7xrg8EjgZ8W8DqafEx0G0RUKoZUKaVYa910SMYJ7stCclah9Gfsm3Fvy8uqx7bpbCNc/MBPrwG9F1m+3s0Rabw/FND9Yznz41U981VGquLvJ5xlbih6KXXZOxxYOA6oM4wlRBYq6mAjwW0Mip2AkYfBIbvEkun8zJy7+9rG4EgxbWHnCxIkZyy2q2mAFMeii0hLWfofzF38oTFGbjWtAr8jIDBdXpJSUlYv349Lly4gCFDhqBu3czy9tHR0fjrr7/w6NEjlC9fHoMGDYK7u7tZ1snTkBhRLo6unRz0/+wrT1UCIzWKW2AGj2GE8aF5fx83X8bg6N0kNC9f0GyfocvWzBi61yyKrZdk2ay8hpMf4KXQvUi28sE8Za3oVlcxmB72HxAfCmyUCbPlLyNaGhFFaorXhOiXgKsepdElG4tBPvUapiUBjjZeTm1pKBulSegNsV+22UeWD07NRdE6wPML4nLMU1H4jyysHhyx/LaQ/RP12T6/DNzeAQTq5ySRZ/AuCsQ+F3/XgdXEm3cRUTSsnSxbbQmK1QHIzpbE4/IyURp6HvS76fwN0HCM+uMk5kZiiQlWLqhJx5PE3d1AgXK5uTV2g0FXi3///Rdly5bFnj178MMPP+DmzZuZ1gkNDUWdOnWwevVquLm5YfHixWjSpAni4+NNvk6ehMrCaHb6y0Dg6yD1WTcLsbC/OJh1csyntwBVZEIe7VFl7I4Nw9VL+84/irRIZtzVBJnxz96oIpTvrx0ty2gxtgH1gQfWUN1/cwUw4aLK73Xg38DYo8DHt/RTnKZSeFKojn0BrOwuKrgz+qOrF7x8R/vaiyMU2gQSh+aJfykYlvhIQ7PAnGWy/Vdr71nPS5CK/cmfxcBOrsj93hmxr5cy4hJFawEjtgMlGlh+O8ntgezS5F7beQ25yKEETaraKqQxULyBWK5eQaHWz1g2GKfs9JUrV4QAWRdffvklnJyccPDgQcyePRuHDh3C06dPsWjRIpOvkydPwBuGA2G3RMsUQTjG8mJoxf3FgV5SagZeUyYmG04HswUCYz/ULOZj0c8zZWY8wMtVKN+vVzqPWZpJCrWvdgMJmSeRbQYfWa97kELAZ8QuscTRmPJTuobQgJ56nVf1Mt122ju03w5+qf05EtuzJygLPuaw6v7xH4DLf4vWTESbGYBvccttj/yz7DnIIwtDXRNk1B++exqwpj+QqKg6fO+8qBVRuKr12L/tmgo8Oq6yY8yLaPsOaw6ETUOl6qQIL7fSZHKEQaO7ypUrIyBAQ+VVg61bt+LNN9+Eq6uorOnv749u3brhn3/+Mfk6eY61A4Anp9Uf88vsqWxuyBJJ4sT97D1UD9+xkNDK0e+A76vlTvkck2fQZi8m2Y9Ze2Y8T5MUDLz6F4j4DzZdGv3WevEmqSCTyrQ8O2YIKbJKMyonvbPbNNtp77xQaaCoUXuI7VqZZQVlV0fuUd3fIiuxrfmW5bfHp7h9B+MU5PyvvHqwE3xItDNMilFVRKZSSbMiIeJfGlZb0iw/z+T1zHjj94CAsrBpyKaNRB1v/pvbW2I3mLSpKTk5GY8fPxZK2eXQ/bt375p0HV2fHxMTo3azq/J06s/QhJRcLYw8Qzfzn2vZrv/HsQfK5RldK5t+gyg7/884YP9sIPqJOFvMMGbk0mftBe9ySTchKSXD6tXU8zy2oqSeHRU6ijdT4FcC6PSV6v7+L2BTJLwAjr4L3F4BvDgGvDbfpJga64dmfqz2YKDHj7BbSjYEmk3MLALoK7pWWBRSqifsMeNK4xnSHZBsC6UJMhrrXVkHLCghBkKaOFqhVaOHYpIw6nHe9YWXMuMkqunoChQywxjY0sirf07LHA4YozHp6C5BITzg7a2u5Orj46N8zlTraGP+/Pnw9fVV3kqUKAG74d8J2h+PuAssMpM9hQ7kNlHB4VnPeGpmDEc0NYOyJvWuXfpLdZ9mi9O09OkwjInw83BBp2pF4OMm2vmExiZh9amHuBcqUx42EdQOQrjJfMIZI0hReIy72HgwbmoajRUFx4iG78CmCN4ERN0G7qwEzn0G3NHdQmdSSPBOE81A1R4hkUCJ3kuB0s1yZztIuJCIVE3021UmlSZ25DaGxzXaM69vVr9frS+sEmeFrgJ5w/9moCOEvVCyEVCxCzB4E/DpC6B6P9g85durlndOzs0tsRtMGox7enoKgVpUlEo5m4iMjFQG1qZaRxvTpk0TFNil25MnT2AXUInPvb3qj8k9aElNlxROLUjPWuKAtnbJrJXRzz6IzOT/bHKk3jU5lCXXNnvMMCYkJV0MlLdffYHZ226g3XdHzNgznkeC8bRoIPkBkGHiEtRkRWbcJRcyedZOm8+ADy6rBwG2QLKGeOK9tcCTPcCt5ebNxGkLxm299FQfqvTIHBDn5qRAfIR9ZVxDbgAHvgDazVH3U987M+vXUd++NVJMligKvQ4km36y2uqh74Z6rKmyhEQz7aWNRbLPpBYp0hxhrCcYd3FxQbly5XDr1i21x+l+lSpVTLqONqi/nLLn8pvNQ/6vmjOKpJ7bW6M0JNmyJfkDGoi96hcfRyE9Q/fFMI1E58zNvX2ZH6PyrgUlgciH5v98Js/SuGxAtpUh6848RnQO3AReRItBqZuTDZSppyQAsSHGvTY9Dni5DHj4KRC6GIj+EcgwUdlxejyQptC3cLWg2JStQDZc5NtMYkskTmYzaFx7MlKASwuAu6uAfWZqV6IAMK9CVXFv7xX904vXy73tqDdSLJVNCAc2j4ZdcHUj8Etj4MRi4Jsyoop9v5WGle1bG9X6qN+PYF9qu2HMQfEvXS+WthHH2s8umO6anccw+eiuX79++Pvvv4UsNkHZ6f/++0943NTr5AnIMzL8jur+2GPA6AOikA/ZC0jQQMqC+HmI5bnElE3axWyodHf48rPm3ZCXsp71JhOAtzaoP/+DwlOYYcxAQkrmC09MkirwbvbVQUzdfBU158jEjwxk6VGxFPNemOWdEwxmx2RxcszQ8xFlwZ98A8SdVwVYGdHAs12m2a5URVbcKQBw9DDNe9obG0YAK7oCzy/CZnB00/1cUjjw2gyTwZ4Boh9wgYpAd0WPON3PK5BFlmaQlRuTR1Jgd8NORH0pkJFDyuglm6juexbK/JrhO4BR+60320oik62mqu5fVLSRkO/5oxPIE9hT5YacAuWBtjLv+h9qAktbA0f+l5tblTeC8Tt37uDDDz8UbgRZnNEyBc0SU6dORdGiRVG/fn0MGzYMjRs3FvzBx4wZY/J17P4HTDNMmmrpgdVVyz1/US1f32K5baN+fkWvLLHr2kut67T+5pDa/SpFzFCpcEfmgdpmJlBA01bmNbC8q3WcEKOeABf/EvvZzy0XL0iMTXP8Xnimx8JjVYFomGxZk5CYJLT53yH8fOge0hTl7llBVShWz6XVwNZx+glLkirw2q5A+i4gnxvgVVsMlotPUa1z6ycgQfv5xSBSnop/Xe1IR8TUxCh66i+tgc1Q6xOgwTzAp5z2wHyPGXppQ28CQS1ET+c6Q8Re8fY2JnzH5A40DqGAlMrR5QJfZ/8ATv2kvu6pJcDfg1T3J98FpspaL1tNA0o3zd0KBUM5+7s49llcB1jeWVSIt3cW1QLmFACenoPdoW3S/dC83NgSm8cg+UV3d3eULi3aJ3z//feqVimZ3ZmXlxeOHz+O3bt3C4roQ4YMQdu2bdVEv0y1jl1DpVcPjwNv7xF7MqgUxF9D/EweeFJPUVMdIm9moLC3aDlHjGyq3VIjXiNr+NvQuqbvFT+g8HoNrCHODvtp2ZZHx4CMNMBRNYGQK6zoIqqKUrBCODgDn2UO5hjbYUSTIJw/80Ltsd+PPcC8XrJJMy1QkN5w3n5h+etdt4XbmtEN0aRsAdgFT/WoiKGBWITUivQmENAD8O8gZq7zuQKvFRf6az8CDXR4OuuLdwvAJ+vvJM9DvrGSbU2f3827O1JiAGcj7di0ZcBj7gGexYH4pxqfE2X6trGfG4nLcWGi+F07WXaIsRwkHvfwmLj8/JJov2bt3NwmCrIRsxSaNtQjTt7tchycgBYfi4GcHDcfoNevQMg1oIWNCmf9JybzBK5tBspYgbAbTY5QiwAx6Q7gXdh0702TLRmpYr+4vVHrLeDwAvXHPAqIE+10rDLmCcZJnVzKimf5pk5O6Nq1q0XWsVtoQEQsrKZ6rGDFrF/z4CgQ1ByWwMnRAW/WLY4N55/iVPCrbNf/a1RDFPc3YXnokzPAfwoFYEISBqHyNW3kdiBOUCAuh07Q5Bcqr3ZgbIoxLcvgnTPq5XZrTj9WBuNFfd3wPDqz2NMX/8kyIwreWnoaDxeon+9eyyo6fhhgA4NNCZr8yo4HMrG7jDggn6yEPGAIEK4ICFNNUJ6fzxFwtdK+SmuhxgDROsncxD8ETk8DAlsC3iZoO0tTOKz4VQTKDwacPQFHd+DUx6pjkYIbU/D0jGqZBJmY3KNwVVUw/ltLYMojwD1rQdlc5/Ep9fs3tmYOxEcfFMd61IrY5w/RSYcEwCRqDoDNUbIx8Ph05setJUCVC8tR77Opg3HC0UpbCXKCfylg+nMg9qVY7UCQjgPZ731wRXye0QsbUARilNBskybjZYMDso+wIGcfikH4GcXfrGgQlD9nH0ZBCWXS4hWZ5D9k1gpE1Z6q5d6/iwMyiaYfGPY5+2YBXxYGjn4Ls+PM/au2TICnCyoFeqNcQYWFi4KnkZktGD/6+5Igdkjq6P9eVvQwaxCdqC70JhdHbFVBS8+gLRMty2JSmbquS9OrK8DhUaYJyhndtJ+tPrFrLtITgFJ9gCQTCKGd/Ry4sURcfnUNKNEBCGwKFKipOoZCTsJkHJSVYBa1rKUoowFVDMp79be8o7qG3z8oVi5YGykaauInf1a/P+0pUKyOGIgT1fsC058BZVrCpqHtd9GSKc1nJSFImsy5IyXO9FZ19hqME3Ssys+LEj/UYDE3A7CSXwKjdSZNTqlmwACZl7YEzaBSsFmoiqgwakE+bFdB53PyjN72Cc3g7JjDQ41EWqgPlfqMNAeK+cuK/eISNd4UL2ASNPOcHKd/T9Ox70Xrmv0ye5GcoqtnPeyW/tvGWB3UNkN+42/ULIruNVX+1StPPhJ+A/JvfcvFZ2jx9UGdgofE2QfqE1up6ap3cHaysRad7YrMpK7fg6T3kK864OCe9SAtJhi4uVRcjrgCHBwBhOlpp5L2HIhYC8Roycww6uWFVXuJy3++YT6BJcpaP9wAJKi3dxhF6CntA2qqhJB+fQ//hcmQJoM9C5ruPRnj8Q5ULd/ZBYTfBU7+BKzqCfyVyyJz2WVgN78DPNHIlGsTHLSX1swpwaLgoXcR1WOpicaNj6mffv1Q4IqGYK+xyK2ByUrYlCgz41ZQnWkOyDHp2kbtz9FvkVpIqAKEyRIOxq2NnVOA76uKZT1yqDSJlCm10X4O0PYz4MIqiwqVta5UCJJtuGS/JBGbrCpTLeanMdA2hp0KRU5SlqeBogQJ6Uy4kLncSfMCFnJdv8/R9E411f7UtAui7XZyB9a9JZaqJ0YBh75iUTcbpndtlWXWb0eCETRtB15olKg/i0rE1kuqrPjqtxtiyziVYu6olefwOCIhk485keMJLUtzdinwKlj7c/T7LNcOcM8POLTI/LxbBcDvA6D0m6rH/CqJfy8uAOIeAaf07JlMfQQknAXiNNSKGXUcnYA3V6ju3zXeASBLHFx1e3UbQnqy2OojUai++vOV3hb/xphQKDPspuKzdFusMhamkaIHm/ixnhiUEy8uW99XUXMgULw+0HKK9pYQVzvvsyXBw0m3gM5fGx/4XvgT2DVFDPB2fmKa7UozZzBu55lxXa2hkpYVtZDQxAlNoOyeKbaWGDMJY+fY2OjOziGV7dNLgLgQ4PFJoMEYsWeo9QygRhaeqcGHgbUDxBOUBW1pfN2d4ekq9uJN33xVraR23GrVwNfPwyXnM29xOhSV+8oGj5p8HqXy39R34Oen0eOSpBBZySnO7kAvmTd8tb6qTM7yTsBXpUQVSqnvhrE5mpTL2nNckyGNSqFZ+QKoXdIfgxqqXBMW7ldZGabKgnEnaebL2iB16W0fANGyahSJZZ11v27gOmDYMSCfjlYNx4JAQVlfrruiTN/NQJG7dMXkByup6wdNkhBUIWQOoi+rvN9zQqosy1hhKFBNQ8C0WBugSAug2nswGXQOr9hFVLJmrCfAtYWAlsZ31OdO/uEUjGtC7jj2kgXXtz0vNXM7V7bEyFq8vAqZIRiPM7Erkh33jEt8cBloOFas4NUFxSdnfhGDcapWpd58RgkH49bE6t7q9wtWEnuGWk7O2kfyweHMirgWIjZJzIAfvB2GZl8dUPosH9Ni+WQUdOI9qfBy1YTK88nzVRd0YZNO1vr6Hu+X9U0ScaHIsfpuApUe5xMFfySv0GJ1TScqxFhNyfra0QqlZT34pJNKkLFGcV/lclSCKtuXkiYG4y6ODtbhJEF2i3QxlZdb0kTg+RXA91UAF2/19XVNot3ZI3qSPzme9ef5VQHqzAQazAecPICkV6qMp2cJ/cvUCTcNm0hGO2G3zbdnqCXn6SbV/Zz4gEvBuIsvUHF45mPPIxCoNwsIqAlEKjLaOaVmf6D/X0Apjco1JvegVr1xsnLv29tVy9ZgaSpBiRKqevyiAPCPwlFFThU97CDtBRdFMJ6SkPXkBSWa5CXkhFdh9Ta/++K40yqDcbpelmsPBLUEnLRYL9oL/qWBzv9v7z7Ao6i6PoD/IZBQE3rvvYv03gVEVEBRQFEEBAVfFAEVRAHBAoL6CgKvoiIKiCJ8IkoHEaX33hTpvZNQAtnvOXN3srOb7T2b/+95QnYns5tlJ7s7595zzxkLlGujsj7d+VuWLJY1H6m/ga/bAdPbWw+0pDGMBsKFVJr812Yt9JavgdrmE09njB84s7sCA/cAWYPfIklScquOXJIcoIv8sZYWaB6R/5MMMjjrV9zAjVZu+huguzPjtvt9Vhuo0xdoZ06r8pRkOBjT6uWkIf48UKCKOk4TytvPBHCW+kNhKzqD+8ctmzmrRHSqUQSv/7RLu7xy/7kUM+MZo8IgENffk34dpDJ1OpkzPYwj3FKgqGxrdYKkV1SXwovG9jVyfZY5/VzqbMHFe1Xh5io1/YQ5bTrGPAB3142Z1Xu3gHvm5zOGwbhbJFV9Wkt1+eLfQO7S8JuVI4C7hoEc010gXbT37dFExuwufmd39XfQ9AsgtrRvBVRlECqOVfnDigxS5quYcnurUcGfaT5/UA3oy4CWFLaS1k/bZwIPfQR81dqynzFF/QVzRXi9aFtaoBfYTXTyHv7HOOCPD4GazwEPf2LZbpvi7I8ATpYMBiJNXZb+PO1gPXUkkvbKeovlo+tU1qcz8loxToB9VNHS8i+N4Rl/uLBXCb31u+7dttZzqme1HkwGowq4Wb9m1ic3xkBc/DrAi1ZrMqAgI8jOAvEus90bcMgQ49nMuL21LBv/53ofR+vDjYG42LdQjRzqxWdsU+xkWQID8VTr9t17Dn9mnP0e+1hVq5luWQ9ep0TKmhDJwbgHQX5A6cUG9QJr8rqS16KRpOP1NQwsbp9ted1cP2v9us5jXgfuih6Ia4/housTOd11WbNuAtJnBzKEedujcCFZO1oBNKjZCn/aNcvzFniOJJizLuxVadad+l0F4nq1dV9Iux7J/lgx2rf7ocAwFn18dZ86Lwo2GbzfvxC4eEid08maZlm7rg9u2Wr+pmptmtbam0oPaimCmMnJe7IE4voAsLMJE6m344+Ml+dXAo9/BVQJw8J/qZFkD0lg/dYFoNkwy/bobEDt3o5v96+LbLkIFSZneKTNltrSgzZXchQDnl/hfj9yPxrSxvHv+vDxasiTzYuZ8QO/AdfsrD81Km4peuXWzPivr6qUp/iLwIktKVOfbN/opbKwvSq6Z/cC7xYARsapFHSj0zvV9rElgC/bqN9ny1hwSDz8qeWypDLJsgRKtSSpwRGptn7k/XbY/vYDeLJ2ylna8Z21aWKNXn/hzl1TeBVv09f4yZo/+c+OyQd8bzOgdGgZkLuMJW24SC3VX1deNxNsOjBkt1Sg91jSbdfB3LXD6nu0myntpGYT9ZaZ10/5v6CRrvgjQJS3mVP3gO1jLZcd2WLoiJE5f2B6RVN4kP7cOlkOJinC8nksX9f8ULnfFW+CwqZ+KkCW2sj525DDwDP/53gfKe6pO7zC8WSIbXFcXwYhJRC3l2VB3pMq8k0GqwGn/FWA+v2dT6RtnwVsthmASQPC5AyPkkcB9VHdwYc9S7GSlBCdFJgJEpnd2/aWTc9vs861/HwC3MOwFiyTZZbRKX1t6+1rwIxHgA9LAdNaAO/mdx6MN37VevvKMer7nx9Zttm2c/ifIQtAWpbI77NV+3nr61IL4GVzq6snv1MnELZBPqUaJfJYFyP7X/eaGN2hCkY/WhnPNiihvV4cFTQsnNOSKnf43A2rmXFZMx4W9MBM1vxJVVsjvUBhW1nfHQ0MOwEUawD8Nhj4qo39+8uU073fm9VOarCsHXfVp/aWDHKmA6Itle7JDZKarhfCOmBuQecPhWpZLuetDZxZCxxbBNz2MJDZbyjcWdvJTHVhczE6kd/9eg4pGAOAjlO8vx8KHDnR131aHRhX0nJ9pqErQ6Bs/tL1Pm8cAx6ZaLnuaFIgrZMCX2UM2QTrJzueGQ9ynSSPXDkOvFsQGO+4DXCaIN2O+vwB9F7hOK555meg3Xhg+3fAwleAc/uD/ShDimvGw42cXMZ6MVsklQp1s54A+vyOYMmZNRql8mTFPxcsMyjZDethPWKvIrOo/xJQopHn60kuHXG+Lt32jUFPZ5eqp+UetPRCllQp23SpI3+olnPukAEWR8c1Z3H1//rucbXWVoLyCn4cUJGCWzJ4kdZS4UKgSM4sGNy6HMYvPYgiOTOjTWVDH1wXogzV0n/ZcQrlC5QPvzXjeobIWsMJpWj0qkr1ky+jU07aicm689ySWWMng8RWzZHAukFAonmdsMgY6zoYL9cLuF4JyBTBlWwDQd4X9QKTP/VSFdZnPq6CUlmC4O1SGmlHpru8F7hufn/e9h7QYLz793N4puWys+r61QYCBRqlbHvmqQ2GpUqxHNgJS7I+V3f1eMrswUAzzuRmzqnWRUs72taj1dIcWTcun8P3dzd/JucAMkZwUS9PyPOxbSZQuAZQojGw7G37HR5EubYqxf3kVlWszx8z49I5Yt1kNSsuEzHGGie+tjWTbDIW61WfGSZDC+LKjwF7fgQqdwJaDFcDwMYlbOsmAo9+hrSCwXg4kP57upo9vLsPeaOXVGwRxPZmukndamD80gO4djMRN27fxVc9vDz5+dHO/1/SXdu4uX7eVp3ewApDqqLR9TNq3bYxINdHXWVdS7fv1YeCjNLaI4VZtCIx6dWMuzPuDLBIuqV8+audmpBK7tMfUpdl1LGOzcw8+d1LLcqieYV8KJrLQcsuN0xadRj3F8uBzNFR4ZWm7iglWDob2OOocGKf1UCh6sAFN7suxJUGmn0JLDPMcOWr495t08UAUd4fizQrrghw85I5cD4CnNikLktLRm+LTek9d8XfUkvA3Lru4nbg9BqgoBs1RowV2Ku/4bowUyE3l3s5I/9/HWt6hK8G/0k5UKgXmZRlNTIYJG1GA0Fm8/TK0tLqycg4iSDnG3rxy7RKiiHOelLV/ZDlBX+vAnbMUl/2LH5DDd7K4Ebp5upL0pklGN/9k/oSw8+r9wdPBznk7yP+HHDkHHAv0Y/BuN7WzFzTiSykKF+9vubOQulTPjMPpK3aHGFyhpfGGWe1JbjzRq2e1teNrYeCoFKhWC0An/tiAyx+pQkK5fDyA++Eea2iruLDQO/l3j+wOAep8rV6AR9VAEblUD2SrxxT26UNRYX26k1f2LbLsTW+jOtAvNcy9x6rnhbqz2Nn7Lm6xFBEQ88MCMZaujSocqE4xGby7QO41zebtcGtsArG7RUvHPIPkNlBIZ58lexvlxNWT8UYfkeemsC//2cp4kX+Jye8OmOquqsCls60+xRI7yBLYfMI4LYbs1xn11tX2nflzF/A4g7AusHwyvav1MydzHS+tMW7+6DgaD3G/H5kUwzz3B7gnZwqZTiQrfuEq2wdUsHpsbWquN2p7cAaN7JiZNmBdHew7VVuNCavWoKoB8HukEEaac1pL3vHV/rgYyT3GPeWDIoVrW0diLd5T32v/jQwoQKwIe0MWvFdIxyrTHpDRlulGqSxxUZqJ2lckrKdz82Ky/ZUewIYcQXIZQiYh5+zXmckb8SXj6rL7T8Cusy07B/jIhi3VaeP5fIru9WIeFE3Z/D033X7amBS9+SDwVhUbtlbakBiz3z//T7yyaKXrWcGX/hua3hVU9cLuOlajgCymluNOarzIIF3ofvVa1nnKHh3Rip8P7QUePA34OY54O85QMJZx/uf3wpsGQYk+DCYl5Y1Ncw6G7ODbP8GPFGgGlDbptZAjCFw2jIa2DfNeX9ovWe4tCnTu4i4Co5kecMFJ0smnDm7E7h4GGg6BMhTxrv7oOCR9yOpim2PdC+xV1jVn9xpuZrWGXtuSw0fd0n9ETm3lWK5zmavZfDMXca+9Hp/c3/hzLhn6vcH3r6k1o3LoMiiIcBqQz2tCBYmZ3hpXDbzutKuc3y7Hy3dw3xyos/spjbGtV1S7MEf9IEKaRs27LRqd2abVptgbpdkr/CErTzlHVe6l1Squi+oInqxHvaj1YvSBTKr4cpRYPEwVWFWT+dbODBwv488UrFgrNaFwJY+Qx5yxuJDQrJInJH3IUnZlBoWknUirxtjmxNPydo7OZHTq3BLRfWrf6vAO9Hcdk138wxwaTtwl9kfXpEiffaWG/gyM64Hx3UMfYPzVLdclnT1w7NUSzJHbpmXNpRw0vrSKK6s5XK8iy4d9lw2z8bl9WFQmIJLArUqjwPVn7LeLmnQRwIQjBtnYivaKdxK1twpTvzEjJTp/pLVIAG5FMuVrh2OfNtBtZF1x9UT1tf9OTOuD1y6M2hI9s+5V5mLJ0c4rhkPB/qJjnH21lsyEyVFkxyt1QxnUiRHTxeX4NmfsxBSUMXYNsz2uT6zE6j0qKpmbpxNNr4x9Fyi3lRlLaUEGTJTt2QosGEqcF83tR5bCpBIKydv6AMoMgtVpA5Q0Q89fnd8r1L29LWfC/6Tch9v6xRQQEgXgiFzzRX2zY4YiiOGdJ1fNpsuBHk9qBIr6/ie/cU/j0UPxuXEactIFWQ1/BTIZaiofO1vy5px8o60npP2Zka+zIxvmw6cOQSUeBAo/xxw1gRkLQKU6QYc/w24bX6f2jrafgr6ljHArTNArVFAnhru/U5jgbe/XgZa23TBcMZ0F7iwV13Ok8YrIqe2YO9xc3XzC4esl7/JYLykJvtz7f+Nc5bBQjnXIN90/J86HxMvrgOm1FeXpeWt3hbL1brwVe+pSRFnx1la1cp6dJG/KnB2l3/PnU9u8S3jlSxr+r1Z2paKcGY81CT400cJ/fEmLhW+5c1l/RTHv8+bfpiBJsXUFr1mnc4YSJISPuggULa1ur5mglo/Pjo3MCqX5TmStLa6L6q2C8XqAUVqAtnzqzVP8ibf9gM1etthsgrEfWFs1zbHZkTflzcxPRB3Vkl0dD5glwcnqRRQa9/wIHUvWH7uB4zOY5nd7O6kR2yg6cH4lf2W2U4pBiQpzHunAncTgGv/mB8r1+t5rdXIlNt8mRlfNQr49yvg7g2gTFdLlfPyPYBowwmrBOdSKE4GWxJOq8wHaX925g91zGX9tnx5SoJ9YwE4V0yGwm0RfjIYsbrPA+r0tQw6S7EvWT/ur/7UYt8C9T3pLgv8uc3O7LgUzn39qHWXmvyVgIcmWLcL04szPjJJXZZlCdJGU25vrBMwua4aeHHkh+6Wy7JE0d9p6tKVRzLCmFXjW92FmMgfzGAwHmoy8/r6v8CbZy0jfv5ILTy4WM2CymyW0Q/PAGOLWxfCCAfXbGZfglF9UoLq3IYURmPFaH1NkxSZePADx+uTZCBFTtI86QnviG16mz/6jeut2lyRk15pYXRml++/k3wmBRA/6BRmrejkPUU3YLt1ga9g0wviHJaK3GbXDgN/9gf+/gFY1N7Suzqjod8weaZq55RLcpyt53ZGbqe/H9lL26wj/emzAve/qQLwRQ8Bvz0IHPk/dWyPmgMeEePhZ2WctNAzu+jBe5zJPKAj7/H2lixR+JOB93bjgHr9rLfP7ua/3yEtSckzL29PuW3IYfv1RKT9lU6v9yMZDjW6A8NOAVUeA3osBAYdANq8b9n3wkFgzzzHj0FqQdhOhvgzTb3sA8DTc4GOho5J5NqL64CWb6suUfJdlpZ887CaXIpQDMbDhaTc+COg09t23DgLbJ2hZnyN9pvX0Wx2UOAkFKT91uqxofndTQ2z8UbyRh9s2fJZ1w2w7bXpSzXPGs+4V2l+aiPffyf5RZc6xRCXWQUt0rM8rBhbVIVClJ0Uxf3mtFTdDXNRxgxutBUk+yT7R1oi6qSaeImG3j1bMmsIk+NgPEt+4MFfgD2TgFOrLNtjy6Q83pk8rIlS11ylV4J9qSXgtpvu1Uag8JfXMCAjzu7xz/3eiQdqP6/qacggJXlOum5IgU9HbedkCd/T5vZlemaLvq/eZlEGy2KyqUkWI5lksMc4qCi/X9rcyky7BID+IAOPsq5d/j7IM/kqAI0HAQWrq9bE0vJYsn7/e1/EPpMMxiONbeqePooo67HHlTbsF0Yn95Kebpx1e+Gv4P1uGYWViudS3M3IHwMj3ijfFqjcUV1e/xmwx8dUYH0mKms+1SZOtB2r/s8DdwMFI/fNLRJ8+WwtNCmXF9Ofc7MifyBlyBI+7XvSezBYlj7yU9wCPkiof67kNnyG+JKl46igkQzy3LHpJnHnMnDHZmmVBNWeiMkJFG0D3I23FIBzJvEScElSYG8D1XupmTdK/XobZrBjC/p+f5LS/F4hYFZnlYmYi1k4HtWjkBnQHr8B/dZZzk8cOb7RcUV2o0odUrb6tReI/9Tbcr39x2pmXGbaqxhm4X0x6wlg5uP26/SQe46tTbnt4NKIfPYYjEdyBVx9beXueSrgTTCchNj24QylXT9aLtfrDxQwFGEKFgmAszhp0RRMsk5d9+OzwDc+VGfVU67kg+vxr4F+G4C6fZ2nnHqbhkp+V6tELszoWQdl8hnWwoWKscWgvayKYMrhZjGtsj1ZwM1XUstEUkplOZUvg5TGbApHwbi9+98/zXK5YFOgzf959zgy5VXfb553ve+d08Ddk4DpGHDjtO/1QCg8SM0XmWGVAmFSB8ZXi1/3x6NKmzJEq/Xg7mba1LKZ4XY0oSQz5BJcv2JejhITl/Kc5uhaYLe5Ro4sVZR6QDp/1VT653dLjQLyTvl2KbfJwFdiKixQ7QKD8UiT0SYY3/g/YO5zQFHDm43+RiHryWXGXNpcSQpIKNi+SYYq/VXewF/7B3jtCPCWGzMngWRbvM6XVix6MRL54JN1+BJQGU9k9fWgeiE7wbQqskd/bT45U/09hVJJc/aIM8XaASUMHRTIt9nxTV8CX7YGdv7g48x4etWJwh5XLYBkhttY6M0TelV1d2bGHT0+Sv3KtFKzp5KW7IvbN6yX+zXgDGjA34OMXLXvjS2iisG1fV9VVjeea043BHkXD1mKG8usq9RUmlABuGQo3ugp4+9qMsT7+0nrcpdWhfpsndqGSMPWZpEejOuOr7e+fmgJ8IFhdkuKI0gROVftIvxh6Vtq/aB8eNmmGkmP7lAKh/7s/kifMw4yyHPsKKUrUw71Ziejw40Hq+yAcFrCQOFDH9gxVv0PFWP7mZKPqeJemfMDlfoCGbMBuaqqVPpbkTeCHjJSPOf4BuuBO2+OmauAO1894Nx6lYouaeW66Dh1rL3lSTCe0VAg7qq59gBFDn+c58hrQR+4kVashar7fp/kmG02jKvuQ1LvQqpwSxcQIeec0nc+Z3HHt5FZV3H9NPBpdaBOfyCLF+93xh7oDQZ4fnuyaD40Zf2kr9uqSbNgFHoOEgbjkUbWLLUapQpZ/DrI85Srh/+LgJPCcrKWveZzwEcVLdsfGO3f3uKRxNu+qNJyTb4caTIYaMqRW3JDicZA7hJAVnO6byidN6wfLNcdKP0EkDkMHldaGOj1trWZ9KjvPBvYtdP5fjWGAZd2q8GV1ebU1FKd1aChFHnzRzDuaCmOFIc6PQXIKp0M5EQvEWg6yvvfSeFp549qQqLZUO/rIJw2F2uT4I6BeHD0/QNYOBCo08e9/Y+YU8XFnx+pL1sD96rv9rolbJwCNG4BRHkQKsl7y4FfLdfZY9w39foDeSuqAoz/NWSN/jY4OPFKkDBNPdJI6lWjV4DavdXJjye2TFepOoGmF5UzjnZlLwg05AhiMjlJMFoxKjDp48bRZlmqsOh1YPss//8eSv3ajQW6zbFeOx4qxmBK0pYZiAeenjHjbTAu9UxKNgNyuJhBlMyG/PWA2JKq33ilfvZTFT2l/41Igbh1/YDr36fcJ34HkLAXuLgAyDMEiOoFFLjf999N4eWvT1StmkM+FIPa8Ln6LpXUKTik4KxkIRj7kDsjVdKdGX4eiCvsfB9pdeuJA4vUubQYdtqz21JKMhBSrrUa9JLCwzr9OY4QDMYj2VOGwmh6Wk+5ts5v8++a4KS6ioOLLJd7RWaFRK81ewN484z1yYNtmzp/k56bG6b6XsGdKNAKNALKPQvU91MbGvJgZjzB+X7Xz/ivwE7F3kDJDv65r+gcQLMvgVojgfijwJ3d1ifapkTgormoV1wzIH12IF0YLFsi/5PlWeLKMe8HA/XBcc6Khy+9XZk9spbbtvaJsXiuyFfZ9bIaW4eXWy6nZ/IxuYfBeKSPIhrfXAYftk7rkOqUuUoBXWZbts14NLCPKdHO7G7uMkCOYoH9vamR7dpt29Ye7lg8FJj5BHB8k+t9Ywtb1ksRhXOVfUkpLP8skIfrNMNqZvzSP8CE8sC7+dUacz0lWNoISUGkHd8Bl9x4LwoEyQLKXhL4x1DdOMnwf7m8DEg8D0TFAjlbheQhUpDo1fHXO1nC5cxNabd3XV0uxMyJsCbtyux1D6pop0tNhynA8HNAJ3MHh3N7PJ+gylnCcjnUhU4j0YDtQFwx1aI3gnDYJtJJ8C1vRNU6q3QP40hdewezSrevAzHZA/N47iSkHDDwR4uRtMD4Ju+uY+tU5UlXfTeN1UpvnPP891Dk+6C4mj38zxYgR4hbm1EIg3EnM+MLDEuN/nsf8PJOYJ65n++Ns2opTJZiQPEWCIkr+4GL5rW+wmSewY/fA1z6TV3O0xFILwW+QtTZgwKvaF3vBxllMGrdZ+qyLAVkwdPwJ51yDi4BitV1XvhNBuwyxKjzZRlY/P0D15lAtvRsi4av+PaYyXFdrIHmtnURhDPjkU7eWNq+Zxm9zZoH6L8JGHTQej/jzPQOO2vp/MV23fPpHa6rYqZlNZ61tGPJVxFIuOR9azNXpBWIftJsO2hCaZucsN69qfrWR3G0P02SAdro7MC+BcCURinfy8/amUUyFtzR22dGOej4EQwXjC1xpN1jHBC/Ezj9P/kjB7LXA7LXCd3jo+Ao09Jy+c4NzwvQXjkKPPldyt7XFJ4kyC7f1rNzTTlX1lvYeULvXMTCbeQBBuNpUd5yQHab4m7G2WmpUuhLj0Vn9sxLue2Cuc8jpVS9G9B6jFqHtGQYMK6k6gt/bp97z5YETyIqxr0PH2ltJielFw5410eYs+qRSapMy9+FYOpd2iRFk4adUJfP7rKuLbFvoSqI5Y5MPlRE91V6w/tg1geBdBmBdDILfg/IVhPIZ7NmlCKTtPqUQUVpS+ZpsCWDSqd3AucPAM1eD9QjpFDTCyBf+tuzvtaFa6n6GtLLnshNDMZJkbXjXQ0z4qveDcwzc8CcCmgk/YDJvmL1VD92W5PreTgzHuPe6LG0kBC7fwJuGSpXumNGB9Wq7uLfnt2Owp+xywJnxtO2oub3HuNSpl8GAH9+bLnurPVQpoIImaKGnsHpzbNkmUoCBfsC+Z8D0nHlXpogn3UPjlUz5Hq/cHcc/QPYv1ANVjOjL7LJEkqdpLi7ky0omYt1XwBe2eV9yzxKkxgFkf11VDLL8fdK/z879+5aX5eWNXzTcq1aFy+f7zueBVCSCi/WTgQ+KGZd/d4e+fn+34CbV4Bja4GkuyqNlSKLseq0O1kWFLkyxanvJzer7/EXgISL1vsYi+vIQK+uSB2gwIMIGWmb9vBK4IHfgOgyaptUS85azbrNI0U+OReR1mY/mpeCuWP9+JQ1VigyxZmX7elWvuteW7PJdYH5fQP2sCgyMRgniyw2FSe/7ej/ZycpUX1/+FOg/SfA6+aKu+RctSfsn+ye2Q1s/CLlIIdtmro7M+PCttf7By4Kda39FPi+KzC7K3D/02rb8pGq+AlF3sy4ZLFIIUhKey4cBr7tBBxaYlmWIrUEvm5nvV+vZUD69MDAPcALfwHtPwZKNQNavg10+xmIkrRwohCT4qa62e2AJDfanJV8wHJZW9JFEUsG5zp9Ybm+6X/AIUPbMqNj64G/PgX2/p+lpRqRB3hWRdZeO6LWJQcqfVxmTkXeCqqyJblHTmYlXT1HcSCuKFCuDXBiMzCtpWWd/6OTgfufUoGwtH4wVgF1d2Y8Opv19bu3gCNrgJKN7e+/7Tv1XWbF5Uu3+kNVFHD1B+r6G8csM2qU+uivW86Kp13yXvD3Csv1DVPVl9HIq9YzS/rskrx/iQQWhqQwUf5BSw2bi1Ij5QBgMhQctGW6A5RuC2Q0qWyKYvWD9lApRCp3BOaZ25yJXweqFHRb//xu/m5e8pC9UJAeIEUKv0dbSUlJ+OKLL9CuXTvUrl0bvXv3xtGjR1Pst27dOnTq1Ak1a9ZEly5dsHv3bq/2oQDMjutroZoP8//Tq8/gGluskXt9laWQW53n1Wz0pFqWQFz3cz9V3O3T+1W/+E+qqhTRN8+438fd3jq4b9o73t9Rj9UdsyyBuJCU90VvBK4wIAX+769UC6BUUz7TaVW0iyroslaSKDVlm2W3qV/gbALCdBCY/SBweAXQ8i0ua0grijdM2bbMSDKEbNkWSCYKdjA+cOBADB8+HN27d8fEiRMRHR2NBg0a4OJFy5qybdu2oXnz5ihTpgw++eQTZMuWDY0aNcK///7r0T4UIJI6PuIK0GRI4NLUmerqPamkfvGw832O/gncugKc3AL8s1oFU+6mZsmxly9XpC2dfLlrwxTg0+rAvl/U9fMHPbs9hY4U63rqB6DbHB6FtEoqBDuT27wGmyi1GLTf/X2TFqUsXEhppJOIE7YtHgWXMFAog/HExERMnToV77zzDrp27Yp69eph8uTJiI2N1QJz3ZgxY1C/fn2MGzcOjRs31mbS8+fPj/Hjx3u0DwWQpB/LLOsc8zpgf6e7SpoXeSfGJpVcTwF1ZPaTnt2/BOSuihkl3gT+10S1/dArj8rMWMkmru9f/qZG5gA+q63uw9Pe6UQUfBkzO/95nnLBeiRE/qN3EEFuICkBOP0FcNNmsDvxuOUyC86mLTLIqC+xK1DVuhWv1MzYPtP+bYhCFYzHx8fjzp07yJs3r9X2PHnyYPlyS+GDVatWaWnsunTp0mnXZbsn+1AALXhJfZdZzKQk4PZ19cbjqyavAS3esvRwJM8ZU6WaD1c94o0Vi2119XI2s/t8y2XbAnG26XytRqlWMc/+Arx9CXjSvJbcIT/8LZFn5Bi6056FyJOZ8fu6Ac+vUm0YiVKbztOBip2BqE5A/HIgfjtwagqQYJg1T1htudz0jZA8TAqRuMJAvReBuv2AM7vUEkGZQFg+ChhfFlgxynrpZdZ8DMYptMF4jhw5UKNGDW02XAJzsWbNGmzYsAHHjqkAQrZfvnwZBQtar9WR68ePH3d7H3tu376Na9euWX2RF2yD7ndyAu8XAZYO9/3prNsHaDIYyMpKpH5Rq6f6XraNZZsUctO1Gw+Ub+v7Wqmxxa3/LmyrsxvbvEhKfMWHVdG2ty8DDWwqtLuq4k+B8V0n4MMy3mUiyFr/D0oA053UD6DIFmUnm6leP6DjFKBwDfc7NhCFk3wVgJYfAOlyANnaApnLAqZbwKmJwKnPgDNfA7f3qH1LtgJyuOgwQpFpg+G86scewJ8fAfHnLduk/sDQU8CAba6ziIgCvWZ81qxZuH79OgoUKICSJUuiT58+eOyxx3DvnmqNc/eummGTteRGMTExWpq7u/vY8/777yMuLi75q2hRvml6xVGK8rpJ3t0fBUZ0dsugRtlWlm3Gtho1nvH+/o0V2O/cAPb+bP3zmj0sl/OUT3l7Se2SFketRgIdpqivEjZV2RsP9v7xkWeOrAYS44H9Cz1/5uT4J95Q/eSJqj+tWlNKlhNRpEgXDRTsB8SaB6IT9gI3Npt/VhRoNyWkD49CqEQT689SWzmLAxmiUy4jJHKD30taly9fXpsJv3TpEq5cuaIF5FLMrUgR1eIke/bsWpBtLOgm5Lqks7u7jz1Dhw7Fq6++mnxdZsYZkHtJWmNddaPvpqdObFEzp/kr259pIde6zAI2fq6CW13pliqtXNa/ySx1pQ5qfZMvs1UyKCPrQC8cVNd/fBaofNXSS3zLdKDZUKCZi7Q9Od7Vu6nL1boAUxsCsYWBdh+qWdprp4HYguryoWVAxfZAdFbvHzelZMxquOVhxpAU2TtgLl501k5bF0o76r8EHN+oWv7oA4BEkSR9NJCvG5CjhQrGk+4ASQWA63GBafdKqcO/fzj/eZHawXokFIEC9s6SK1culCpVSksdX7p0Kdq0UWm06dOnR/Xq1bWA3Wjt2rVaCzN397FHZs6lWJzxi7z04p/WacrensgbydrzaS2Az5sCtwz9aMkzFR5S68RjC1kHzqVbqKBWgt8nvlHLAXzVz/o1qBX1k6D5z4/V9d/f9+z+ZKa83zrg6bnAyjHq72GPeW269Lef3wd4rxBww5D+Rf7pEe1udVj9taq3bNEDcaI27wK9lzEQp8gXXUAF5LnaAtGl2MosrevyPVD9Kfs/K9uay3QovILxn3/+Gbt2qdmTW7duoX///siYMSNefvnl5H369u2LuXPnYsuWLdp1CdZ///13LaXdk30ogCTFuOtsoHAt6+3epLjaVlIX7DOeOkjw/MwC622/GNaAR8X43rdYUqdt6xSML6MKpJB/3LtjuZxPrx5sQ/rAy2DL0XWqSM2YfMDlo9b7PP0TjwgREaUtkgnUYTJQ5THLtgyZgKfmqZoZROGUpl6hQgU89dRTOH/+vFaE7b777sPKlSuRO7elYFfPnj1x4MABNGzYUNsu+8l6b2P1dHf2oSAE5L2XA6NyWLatGG1JOfa2x7hginrqUaqp9XW9T7infVptxZgzV7Z+C9Trn/LnUiCl1Qjv75+sX8sjDdko5hoeKfrAi68NBf8mSSZSXcv1HMX5rBIRUdrUaRpQvAFQvJG5ano64OSaUD8qSuUCsmZ88+bNOHHihJY2btvmTDd27FgMHz4cZ86cQeHChZElSxav9qEAk/RnOYmXGTNx/ZT393XPEIyzz3jq0nulSik3kmrpev9Nb2QyD/JcOQosc1AISl9PToF1R3W/cClPWR4JIiJKu9mCtXs7H9gmCpc141KwzVEgrpNCbWXLlnUaZLuzDwVBFkMrMn0tqaeYpp56FakJdJhqf2bbWxUMWS6bptnfRy8eR/6RdA9IvGm9TXqPf97c9W37/smjQERERORHLA1J7nnOUMRpyTDfgnGpSCqji5S62FZmd9QCz11SUd9eSzRHa53Je8fWq+yWd3IB8/qkbFl44UDK2xSoZrmcNZ/qx0tEREREfsOIiNyTt7zrWUx309SZop76/wZ6/Oqf+3x+RcptcUUtl7d+45/fk9bdvGy5bDsz/sc4y+UnvgViVRtKPDXXst1YuZ+IiIiIwnPNOJFDMdmApm+wRUhqJTPZEqDFFXFckdtTtm22yjyg2p59/5RaGlHxEd9/x/kDwNqJwAPvIs0yrgk3tjm7ecVyueXbQKVH1Jcurhggdd/yVwrSAyUiIiJKOxiMk+eav+nds5Y5J9B8KJ/x1KzsA/69PykA99IW1ebsxlkgVym1vcMU4OxuoFh933/HZ3XU920zgWbmnuZpORhPTLBcPrnVcll/7o16LQN+X86ZcSIiIqIAYDBO7usyGzi+Hmg8mM8a+U+eMilToTPFqvYh/ib9zH1d654a3bxkuZxonhm/cgw4sdGyvcLDKW+XNTcDcSIiIqIAYTBOnlW/NlbA9pSsVb38L5AhE5CrJJ95cuzcPmByPXW56/dA+Qf982xJEcG01uM+4RKwfKTl+u1rwNm9wO6fLNuGnQai+HFAREREFEws4Ebui78IbPgfsPEL7561M7tVgDXDD+uAKbLJoI1udhfL5ZNbgIt/e3Zf7cb73pYvNTu9w/r61ePA502sn4vrp4P+sIiIiIjSOgbj5L7488Ci14BV73n3rCWa161GZ+OzTq7rCxj9+YlqzfVFC2BiDc+evVq91Nr0tBqMX3IxeFGquf314kREREQUUAzGyX1SZEtffyprb70tIhWdlc86OVe0rvX15SOsry95Ezi7x71nUXraJwfjhkrikU5eo+smA78OsmwbYaieLhoMALr9kDbX0RMRERGFGINxcp9xRnu/F32mb99Q3zOag3oiR1wFh+smAVMaACtGOx8YSrqnglEpVibO7U07z/m/a4Alhu4F9z+tnteybdT1+7qoATYG4kREREQhwWCc3Gec0b70j/frgGML81kn156e53pJw5rxzteQy0z4pmnWa84j2antQPwFdXnNBOufPfqZ+t5lFvDmOSBnieA/PiIiIiJKxmCc3JchxnK5dHPPn7nz+9T3fBX4rJNrZVoCw0663m9STeDYBlWt35a9NeLeLLEItZtXgB1zLNkl9kgth8+bAh+WBuZ0B/753fKzev3V909rAKNzqx7uRERERBRSDMbJMyOvAm+e8W5W7dx+9T1vRT7r5D5Z06ynWRdz0Hv8q9bAmV0pt99LdDwopPtnNbB1RngfkXl9gPl9gIUDnbdt0+1bYLl8X1fggXfUZVOS+n4nIVCPlIiIiIjcxGCcPCP9id8tALxfBDiyxr3byEzk8U1Aje5A3ReAAlX5rJP7yrUBhvwNPDzRstShROOU+9nrH37PPDMeZcjq2LsA2Py15bq02lvwH/f/nkPh0BL1fZd5YMLW5q+AQ8vs/6zCQ5Ye4tLWTMxoH4hHSUREREQeYDBOntn6jeXynnnu3WblaODLVsCSYSqwii3IZ508kzWPqor+6EQgRzGg8atA63et95HK4Y5mi6OigRyGbI5FQ4CES9b7Ht+Qeo/K+YPAmZ32f5a7rOWyPjNORERERCHHYJw80+A/1sWi3GEsJJWnHJ9x8l6ZVsAru4DSLYAGLwFDTwDpzTPiR9cCu+cBiYb2ZffMwXj6KJWVYWRbhFBfX77qTeDeovBaW/7Yl+p7sfrOB8lqPGPZ9vhXQIep1jUa9MA8tkjAHioRERERuYfBOHkmrgjQapS67G6fZ9vbE/lLTHag+TB1+doJYO5zwNI3U86Mp88A1OwBFKxu+dm/f9oE3CZVIG3P94BpN2AKo57kWfOq77az+dK6TdaTJ5rXgFd8VA1WvLoPqPIYUL2r9f5dZwMlmwBt3g/SAyciIiIiRxiMk+ckqNErqtsrkGVkLKrV3BAkEflL7tLW142tzHKXAQZsB3ovU+umy7e1BLbLRwDbvgVqPy+NzVUqu7RC06UzrDMPtSy5gLiiQLZ8arZf1s5fPwOMLQnsnGPZT2bBJY0/tpDj5+rZX4AKDwbtoRMRERGRfeaqPkQeyJxDVVV3x2zDzFwdCXqI/KziIym3XTulAtIM0UCukmrbvXvqe+UOwMaF6rIUbuvxG/DgOLUm/cox8x1kANKF0VjlX/8FitQCijdUrcnsafoGM0+IiIiIUpEwOtukiKRXbxaZcoTykVCkSpcOqPqE9XrqKQ3V9wuHU9Y2yGITzE5vByTGq8und5g33gWuLwBuG/5+Q2n/b8Ce+cBvgx3v03xoMB8REREREfmIwTh57048cOO8832ymyunl22jgiaiQJBiblU7A9W6qOs3LwG3rgKTagKfNwWWvmW9vxQ2M5JWfWLO05ZtN9cBlxaH/nites8yWEBEREREEYPBOHln11zgvULA+DL2+zMnJQFLhwPXT6s15l1m8ZmmwCl4H1C7FxBX2LLtg2KWy2s/td6/6uMp72Phqym3xW8Hbp/y7bHZFl3z1OqxKbe9dQHovRIYfg7o8Ssw7LRvv4OIiIiIgo7BOHknb3nL5W/aAye3WlepficnsHaiur5luiqeRRRo9+64v68EsUabze3DRNQzQExldfnyEtWf+94ZwGRed37xb+D6WdXf25nts4BxJS2vBX9oNgyIyggUqQlkiAFKNAKis/jv/omIiIgoKBiMk3fyV7G+/kVzy+XpD/FZpdBoJLPbbi6HkCC2du+U2wvWAtLlB7K0UNdvbAGuzAcSPgd29AQmVQUm1gAmlAOmNgBO73T8O/T16pJJYtvT/NAytdTDE29fBpq97tltiIiIiCgsMRgn78j676L13Nv39aN8lil4LcB6mCulG0mBN3uk3V6seb24LnNO9T1jISBrVdV//MZflp8nXLDe/8Bv1teld/n8F4CfngcuH1HbjK3GVowGxuQDZj6uCs1JYC7LOhzJaJ71bviyqvhORERERBGBZ3bkvZ42xa2OrQfeM6zZNbZCIwoWmfFuOUJdfmou0H8T8Ohn7gfvhQ1V2XO1dz89ft9ClZZ+5SiwYzaw6wfg0FL188tHVV/wr9sBa8ZbbifBugTmsqzDXu0FIano4r5u7j0WIiIiIkoVuJCXfJsdb/k2cP4A0OY94MPSKffhrDiFQuNX1ZeR3mfc1WBRXlkrvlldjikCZKsNIBq4eQK45yDL4/IxYM0QqVwIdJhiP6D+ogVw7aTjx7xuElCyccrtiTfVd64LJyIiIoooDMbJN40Hqe8HFtn/eaY4PsMU3iQtXWbQz+9X6eQFalmCcZH/WeDOHSDdFaB4AWB3P3Wbm5ct++w2rAlPuJjyd5wyFDh05OBiYOYTwFM/WLZJ+vrdW+pyhsze/f+IiIiIKCwxGCffSVAy29zf2ajp6+wtTqlD2QfUl7hwIWUGiC5DVuC100AW8zruXwYBW/ZY7y8t/Vy5vztw6QjQdRawdwGw8XPgzE7g0BIg8RaQMZPld0t2icyOZ8nt2/+RiIiIiMIK14yT78aWSLmt6RtA82F8dimySW9zRxwVjWsxHEi6C9TprTJHanQHOky2P4suwbik0ccWZPE2IiIiogjDYJz8r/5LqvIzUaTLWx5IZ34bbfWO9c86TgVe3Qf0Wm7ZJqnmTYaon1XuaNleoKrlfnb9GIxHTkREREQhxmCcfPfkd9bX27zLYlOUdjR9DWjyGlCnD1DjWbWtSB0gfZRag15UCsDZ1Fiwp2QT9V1anemungQW/AdYYRPoExEREVGqF9A14zdu3EC2bNkc/txkMmn7ZM+e3ad9KMQqPgyMvAoc+QPIViDUj4Yo+KT/d1QG4JFP1ZetduOBvT8Ddfs4vo8K7YGja60LwN04C2ydoXqhS+cCIiIiIooYfp8Zv3btGrp3746sWbOiQIECiI2NxYsvvojbtw2zPQA+/vhj5MmTB7lz50bBggUxY8aMFPflzj4URmRmL2+5UD8KovCy6UvVY/yxac67CxSrp/qVS1X1eX2s25plZCV1IiIiokjj92B88ODBWLt2Lfbu3avNaMvlH3/8Ee+++27yPnPnzsUbb7yBb7/9Fjdv3sTYsWPRs2dPrFmzxqN9iIjC3q+vAmvGAweXON9P1o3rs9875wAHlzIYJyIiIopgfg/G9+3bh+bNm6N48eLa9SpVqqBu3brYv39/8j4TJ05Ex44d0a5dO0RFReGZZ55BvXr1MGnSJI/2ISJKNX4Z4Hqf4o0sl2d1BhIT1OWM5lZqRERERBQx/B6M9+nTBwsXLsSyZctw8uRJzJs3D+vWrUPv3r21nyclJWHjxo1o1KiRdQ2kpk2xYcMGt/chIoo4xepaX799XX2PZjBOREREFGn8XsDtqaeewpYtW9C6dWtkyZIFt27dwqhRo7TrQlLXZVvevHmtbifXz58/7/Y+9si6dOPadFm/TkQUUrlKAZf+AQre597+A/cAH1cGyrYBzu5W22JiA/oQiYiIiCgCZsalWNvixYtx+PBhxMfHY9u2bZg8eTJGjx6t/TxdunTa93v37lnd7u7du0gvFYnd3Mee999/H3FxcclfRYsW9fd/j4jIM0/NBWr3TtkC0JG4IsBbF4BTW4H1k1O2OyMiIiKiiODXYFzSy7/55hsMGDAApUuX1rZVq1ZNS1H//PPPtevSoky+zp49a3VbuS4V093dx56hQ4fi6tWryV/Hjx/353+PiMhzuUsDD00AchRz/za/DQbiDVlA1bvymSciIiKKMH4NxmXWOlOmTEhIMBcdMpMZcml1pmvcuDGWL19utc/SpUu17Z7sYysmJkZrpWb8IiJKdeq/ZH290qOheiRERERElFrWjHft2hXjx49HxYoVtVnx9evXa7PiQ4YMSd5HWpa1aNECH330ER5++GFMnz4dBw8exOzZsz3ah4goIuUpC/TbACwdDjwwKtSPhoiIiIhSQzD+8ccfo0iRIhgxYoSWVl64cGGMGTMGL71kmemR2e358+drvccnTJiAsmXLYsmSJahUqZJH+xARRax8FYCn54b6URARERFRagnGJU39zTff1L6cad++vfbl6z5ERERERERESOvV1ImIiIiIiIjIOQbjREREREREREHGYJyIiIiIiIgoyBiMExEREREREQUZg3EiIiIiIiKiIGMwTkRERERERBRkDMaJiIiIiIiIgozBOBEREREREVGQMRgnIiIiIiIiCrIMwf6FREShEBUVhWbNmoX9fQaDu487tf7/QiFcnqtweRyuRKVPj2bly/ttv0jnznFNLcfembTy/ww3/Hz07jnh32Jo/8YiBWfGiYiIiIiIiIKMwTgRERERERFRkDEYJyIiIiIiIgoyBuNEREREREREQcZgnIiIiIiIiCjIGIwTERERERERBRmDcSIiIiIiIqIgYzBOREREREREFGQMxomIiIiIiIiCjME4ERERERERUZAxGCciIiIiIiIKMgbjREREREREREGWARHMZDJp369duxbqh0JEqcX168CtW8Dly8DNm2rb3bvqckKCvKGo6xSY51p/vuXr4kUgUybv71/uW46XHLcMNh93PKbBJ8chPh5ITARiYhAyzv4ujPvIl/yNRkcH+xFSOLwP6fi3QMEi70nyJX+T8hll7/2Jn12phh5/6vGoI+lMrvZIxU6cOIGiRYuG+mEQERERERFRGnP8+HEUKVIkbQbjSUlJOHXqFLJnz4506dIhnEdOZNBADlZsbGyoHw45wOMU/niMUgcep9SBxyn88RilDjxOqQOPU/i7lopiJgmxr1+/jkKFCiF9+vRpM01d/uPORiLCjfxRhfsfFvE4pQZ8LaUOPE6pA49T+OMxSh14nFIHHqfwF5tKYqa4uDiX+7CAGxEREREREVGQMRgnIiIiIiIiCjIG42EgJiYGI0aM0L5T+OJxCn88RqkDj1PqwOMU/niMUgcep9SBxyn8xURgzBTRBdyIiIiIiIiIwhFnxomIiIiIiIiCjME4ERERERERUZAxGCciIiIiIiIKsojuMx5Ozpw5ozWoL1WqFHLnzh2w25D3bt26hQMHDiBXrlxaf/p06dI53X/79u24ceOG1bYCBQqgTJkyPAwBcPv2bWzatCnF9sqVKyNnzpwuj+3evXuRPXt2lC1blscngHbs2IHr16/b7bVZtWpVu7c5deoU/vnnH6tt8vpr2LBhwB5nWn0NbdmyRXufks8Ve65du4aDBw8iX758KFasmFv3681tyPln/+HDh7XXi70etffu3cOhQ4eQPn167ThmyOD8VE5eW/IaM8qSJQtq1KjBw+CD3bt3a+cA9erVS/GzjRs34s6dO1bb5LXh6vWRlJSkfVbJMa5SpQqioqJ4jHyQmJiIzZs3I0+ePCk+++29Llx99shnm3zG2apevTqyZcvGY+WFpKQk7f1OSpiVLFkS0dHRdveTY3Xy5EntHNvVOZ8vtwkJKeBGgXPv3j1Tnz59TDExMaZKlSpp34cNG+b325D3Ll26ZHrhhRdMOXLkMFWrVs2UJ08eU40aNUx79uxxerv77rvPVKJECVPDhg2Tvz744AMeigA5cuSIFJs01axZ0+o5/+uvv5ze7ueffzblzJnTVKZMGe0Y16tXz3T27FkepwDp1auX1fGRr/Tp05s6duzo8DYff/yxKUuWLFa3adq0KY+Rn1y8eNE0ZMgQU6FChbTnuX///nb3++yzz0yZM2c2VahQQdvv0UcfNSUkJDi9b29uQ/Zt2bLF9Pjjj5vy5cunvdctW7YsxT7vvvuuqUCBAtrzXbJkSe2Y/t///Z/Tp1SOd65cuaxeX926deNh8NI333yjnSPI50pcXJzdffLnz28qV66c1XP++eefO71fOeeQzyk5voULFzYVK1bMtGnTJh4nL1y9elU7by5SpIgpW7ZspmeffTbFPh999FGKzyo5ngULFnR4v+vWrdNem/Xr17e63f79+3mcvCDHoHDhwtprpXTp0tp73/fff2+1T2Jioql79+6mTJkyJcdE8j7ojDe3CSUG4wE2adIk7cW9b98+7fratWtNGTNmNP30009+vQ15Tz4Ap0yZYrp9+7Z2/datW6ZHHnnEVLFiRZfB+IcffsinPsjB+KFDh9y+zalTp7QAYdy4cdr1+Ph47STKWWBI/rV161btuMmgiLNgvHLlynzqA2Tnzp3aQOG5c+dMdevWtRuMy0l/unTpTPPmzdOunz59WjuRlSDeEW9uQ47NmDHDNGfOHNOxY8ccBuMSYJw/fz75+ujRo7UTzhMnTji8XzneMkhC/iHHYPPmzaaJEyc6Dca//fZbt+9TJmHkPVAGY5KSkrRtEkAWL148+dyE3Hfw4EHTmDFjtPekli1b2g3GbckgogzYDx061GUwfv36dR4OP3jrrbdMZ86csToXkHjHeJ4nn10ySfbPP/9o11esWKEN8C9ZssTh/Xpzm1BiMB5gcuLfu3dvq21t27Y1PfTQQ369DfmXzDTIG67MKDkLxt98803Txo0bTSdPnuQhCFIwLieoEuBdu3bNrVHX2NhYq5OZ7777zhQVFWW6cOFCgB8x6YGAzN7dvXvX4RMiH8Ay07djxw7T3r17TXfu3OGTFyCOgvF+/fqZqlSpYrVt5MiR2gmNHhz44zbkmgQQjoJxW/I+5mqwS463nEPIzPvhw4e1wI985yoYl88fGbByJxNLJl3kOG7fvj15mxwr2bZo0SIeLh+4G4zL4IkMLsrz7ioYl3OQbdu2mW7cuMFj40c3b97Unt+ZM2cmb5NZ81deecVqv0aNGpmefPJJh/fjzW1CiQXcAkjW/OzatQs1a9a02l6nTh1s27bNb7ch/5O1ybJ23NUak08++QTPP/88KlSooB0jWetFgfXMM8/g6aef1uoo9O7dGwkJCQ73ldeMrLs0rkGS4ySvs507d/JQBZis1Z81axaee+45l2sfpV5Dly5d0KZNG+TNmxdffPEFj08QyWvF3ufOhQsXcOLECb/dhvxLr6NRunRpp/stX74cPXr0QIMGDbR1ywsXLuShCLCRI0dqn1GyDrZFixY4evSow33ltSRr/6tVq5a8TY6pnIfw3C84vvzyS+04uXotiUcffVT7vJLj8+qrr2rnFOS7zZs3a9/12kvx8fFaPRJPYiJvbhNqDMYDSAo9SPEI2+Jrcv3SpUt+uw35/81gwoQJGD58uNMibgMHDtROOqWQm5x4yjHq2LGjViSJ/C9z5syYP3++VpBjz5492pvqggULMHToUIe3kdeMvdeS/jMKrHnz5uHKlSvo1auX0/3kBFQ+PGUw69ixY/jwww/Rt29frFy5kocoSLx5rfD1FVoXL15E//790alTJ62QpSMPPPCA9hklA5CnT5/WBjQ7d+6sveYoMN577z3t+Mj5wb///qsNGkvwJhmpjl5LEtjZnnPw3C84/v77b6xevVqbXHFGjpF8Lsnn1P79+7XbfP755xg3blyQHmnkunbtGvr06aMNyEvgLC5fvqx99yQm8uY2ocZgPIAyZsyYPDtkdPPmTYfVAr25DfnPvn378NBDD2kzr6+88orTfZ999llkypRJuxwbG6sFEHJys3XrVh6SAMifPz86dOiQfF1OPgcMGIDvv//e4W3k9WTvtST4egrOTEOrVq20mSFnZDbC2IVAToik0vOcOXOC8CjJ29cKX1+hPXFt166ddoL59ddfu5zFk/dPIRXYx4wZo1VT//nnn4P0aNOenj17Jle5l0wfec7Xr1+vBebuvpYEz/2C46uvvtICbZlQcaZcuXJo3rx58nWppC8ZJ87OQ8i1hIQEPPzww9rrQLLp0locxWA8gLJmzap9UEpZfSO57qi9hTe3If+QUU4JCiQYl5FOV63NbOknO7bHjgJHnvNz587h7t27dn9evHhxu68lwddTYB05cgSrVq1yOdPg7NjytRQ8jl4r8j5YtGhRv92GfCcZdG3bttVSY5cuXaoNBntCAnJp9cTXV/C4Oj+Q15IMsBjbQkprtPPnz/OzKsDkdfTNN99oEyzeBGv8rPLNzZs30b59e23WWpbTyKCITgayZODQk5jIm9uEGoPxAJP0sF9++cXqRf/rr79q23WS7iIjpp7chvxL1qvKaKec4EybNs1uIC5p0dILUR/Fs003k5Mi4SxdkLwn64BsyXNevnz55BkIGQn9888/k9OU5DUjKe2SgqaT2SDps+yo5zX5b1ZcTvhlVs6WfCj+9ddfDo/t1atXtT690meXgkNeK5J+aTwW8lqRmR+9f64sOZDXlz5j7s5tKDCBuARqy5YtQ44cOVLsI+93xgwt27oa8nPpsczXV3A/q+RzSurL6DZs2KCd/wk5/5CfG8/9lixZoh1nyS6iwFm0aJH2mSTr+23JAIm850k/eUfHVl6HfC35FoifP39e+yyRQNp24FAmyWRJok5eE3LMjDGRZJzo9TPcvU1YCXUFubTQNitr1qxa3/AFCxaYOnfubMqbN69VG5IRI0ZYVeR05zbkP9JGRqo916pVy7R69WrTmjVrkr+kFZZO2o5ID2UhFdSlz6T0DZVWCe+//74pe/bspueff56HJkDefvttU48ePbQelL/88ot2WVpgGHvsSjsMeVuTnwup6Cz9qqXy/dy5c00TJkzQbvPll1/yOAWQVE6XFleDBw+2+3NpCSgV7XVNmjQxjRo1yvTrr79qVVSlo4T02JWq0uQ7qZ6tv6dJz9VOnTppl6UisE6qApctW1arPCyvKWnvkyFDBtPKlSuT95GqzvL60ttuunMbcp+0npPjIpXR5XmWLgNy/ejRo8m9cxs3bqxVq5f3OONnlfG10rdvX1P58uWTX4vSpnP8+PGmxYsXm6ZNm2YqVaqU9hqTysXkOen4IM/5wIEDtR7W+jHQK2svXLjQ1KpVK9NXX32lPefSvknaz8l3o9y5c2sdWXTSElC2ye2ksrf0u5bzQPKOflzk3O7BBx/ULtvr296hQwet0raj+5DXon476SAh3Qmk1fD8+fO199LMmTOb/vzzTx4mL7Ru3VprJyftMY3vZ8Z4R7pAyOtnwIABWkwkrYflnN3Y4nHQoEFav3JPbhNO0sk/oR4QiHRSHf2jjz7SRkDLli2L1157DaVKlbJaq/Ljjz9qozbu3ob8Z+3atdrza8+3336bvN5Vit7IrPfrr7+uXd+xY4eWzn7o0CEULlxYW2v0yCOP8NAEiLxV/fDDD9rMm8zQydqtF198UZsZ18no9pNPPqkVU5GqwfpItqznl5nY7Nmza8fRuPac/E9eG1JYSt7b5DjZkvV1U6dOxe+//548+/DZZ59pr8WYmBitCupLL72kHS/yz+yDvRkB+UyZMWNG8nVZ8vHBBx9oxy9fvnzo168fGjdubDWTN2jQIG1Nn57u5+o25D6ZPX3nnXdSbJf0WVnuIbNzMituz5AhQ5KzUMaPH69lcs2cOVO7fubMGUycOFGbLZeZdDk+cn/62kryzLBhw/DHH3+k2D59+vTk2hfyXiZr+eUcTl4r3bp1s1prLGRGUI6ZvpQnKSlJy8yTzzi5LDUB5DNOz/wizzRq1CjFtoIFC2rn28b3Rlma+J///MfuenE5F5djIJlecq6hp7QvXrxYu23FihW123JZjufkuWzatKndn8nnvxQ81G3ZsgX//e9/tXM8yS6R83BjyrmcP6xYsUIrGuvubcIJg3EiIiIiIiKiIOOacSIiIiIiIqIgYzBOREREREREFGQMxomIiIiIiIiCjME4ERERERERUZAxGCciIiIiIiIKMgbjREREREREREHGYJyIiIiIiIgoyBiMExERUcDNnz8fJ06c4DNNRERkxmCciIiIAu7555/H+vXr+UwTERGZMRgnIiIiIiIiCrIMwf6FRERE5J2kpCRs2rQJ586dQ6VKlVC6dGmrn9+8eRPr1q3TvlerVg1FixZNcR+3bt3SZqgTEhJQr1495MqVy+rnly5d0u5D1K9f3+rnJpMJc+bMQYsWLXDnzh3s2rULuXPnRp06dVL8nuPHj2Pr1q0oVqyY9lg8/b8QERFFOgbjREREqcDVq1fRvHlzXL9+HZUrV8b+/fvRrFkzTJ06Vfv56tWr8eSTT6JkyZJaAL127Vq89NJLGD16dPJ9/P777+jSpQty5syp7Sc/nzRpEtq1a6f9/IcffkDPnj1x3333add37NiBr7/+Gp07d9au37t3D127dsVDDz2EgwcPonz58lrg3qRJE8ybNy/598yYMQN9+vRB3bp1tYGBTJky4fbt227/X4iIiNICBuNERESpwNy5c3Ht2jXs27cPGTNmTC6Kpge3nTp1wmeffaYF2+LIkSNaUN2yZUst0JUZ744dO2prt8eOHYt06dJp97dlyxZt/4sXL6Jv374YNWoUBg0apG2T/SSolplwmQE3zmrv2bNHexwSlFesWFELymUmXe5HgvxPPvkEL7zwgrb/4MGDsWbNGrf+L0RERGkF14wTERGlApkzZ9ZmkiXI1klwLX7++Wdt5jlDhgz48ccftS9JAS9evDhWrVpltc8777yjBeIiNjZWm6EWixcvxt27dzFgwIDk+x84cCASExOxZMkSq8fSu3fv5CC6XLlyKFSoEA4cOKBdX7RoEaKiorSgX/f666+7/X8hIiJKKzgzTkRElAo88cQT2uxyjRo1tBTzVq1a4cUXX9SC4X///VcLxGXG2UhSwGXNtjh27Ji2hlxSxu05evQoihQpkhxki+joaG2b/MzIdp15TEyMthZd/z1yGwnIdXnz5kXWrFnd+r8QERGlFQzGiYiIUgEJtqdMmaKlf2/YsAHTpk1DzZo1tRlpmeGW4mqzZ89OnvW2lSNHDi2F3JE8efJoqey2ZJv8zF2Szn758mWrbVLsTQrGufN/kVl2IiKitIBp6kRERKnAqVOnkmehpWDaF198gfj4eOzevRutW7fW0r4lGDeStHQ9AH/ggQe0y7/99pvVPufPn9e+N2zYULv8119/Jf9MisJduHABDRo0cPtxyv3IY928eXPyNlkPLoMF7vxfiIiI0grOjBMREaUCsuZ7+vTp6NChAwoWLIgFCxZoaefSVkxmvUeOHInnnnsOGzduRNWqVbX12D/99JN2G5mtlvZhw4YN0yqjv/zyyyhRogSWL1+O2rVrY8iQIVpKuxRck7Xbr732WnIBN0kfl5+5q0qVKnjmmWfwyCOPaPcr1dQnT56spby7838hIiJKK9KZjEPVREREFLZktlkCbOnNLW3FJPiW9dg6mdWWFmOSWl62bFk8/fTTyWvGdcuWLdOCXynMJrPljz32WIo+4hKkS7q7rOWW9d166rtUUe/WrRvefvttLbjXvfLKK2jfvr22v5BCcDLbLSno8vslOB83bhx69eqltTtz5/9CREQU6RiMExEREREREQUZ14wTERERERERBRmDcSIiIiIiIqIgYzBOREREREREFGQMxomIiIiIiIiCjME4ERERERERUZAxGCciIiIiIiIKMgbjREREREREREHGYJyIiIiIiIgoyBiMExEREREREQUZg3EiIiIiIiKiIGMwTkRERERERBRkDMaJiIiIiIiIEFz/D2S1GEZnUSOaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.pupil.blinks_interpolate();\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "d.plot.plot_pupil(plot_range=(0, 20) * u.sec);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "730cf357",
   "metadata": {},
   "source": [
    "Blink detection often needs some tuning per participant. The\n",
    "[blinks tutorial](../notebooks/blinks.ipynb) shows how to check and\n",
    "adjust it.\n",
    "\n",
    "## Filtering\n",
    "\n",
    "Pupil size changes slowly, so high-frequency noise is usually removed\n",
    "with a low-pass filter (here, 4 Hz):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e541f5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:09.251464Z",
     "iopub.status.busy": "2026-11-10T13:34:09.251067Z",
     "iopub.status.idle": "2026-11-10T13:34:09.261945Z",
     "shell.execute_reply": "2026-11-10T13:34:09.260389Z"
    }
   },
   "outputs": [],
   "source": [
    "d.pupil.lowpass_filter(cutoff=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc28235",
   "metadata": {},
   "source": [
    "Every step was recorded in the object's history:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac09cc04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:09.265601Z",
     "iopub.status.busy": "2026-11-10T13:34:09.265174Z",
     "iopub.status.idle": "2026-11-10T13:34:09.270366Z",
     "shell.execute_reply": "2026-11-10T13:34:09.269152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* rlmw_002_short\n",
      "└ reset_time()\n",
      " └ pupil.blinks_detect()\n",
      "  └ pupil.blinks_merge_close(distance=100 ms)\n",
      "   └ pupil.blinks_interpolate()\n",
      "    └ pupil.lowpass_filter(cutoff=4)\n"
     ]
    }
   ],
   "source": [
    "d.print_history()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b5cf56",
   "metadata": {},
   "source": [
    "Note that the method calls (`d.pupil.blinks_interpolate()`,\n",
    "`d.pupil.lowpass_filter()`) changed `d` itself (they work *in place*),\n",
    "whereas `d | step` returned a changed copy. Use `inplace=False` or\n",
    "`d.copy()` to keep the original with a method call; see\n",
    "[Pipelines and history](../user_guide/data_model.rst#pipelines-and-history).\n",
    "\n",
    "## Event-related pupil dilation\n",
    "\n",
    "How does the pupil respond to the stimulus? The short example contains\n",
    "only ten trials, so we switch to the full 20-minute recording of the same\n",
    "participant (about 300 trials). It is downloaded once (37 MB) and then\n",
    "cached. We apply the same steps as above, this time as a single pipeline\n",
    "(a pipeline is a value: it can be printed, stored and applied to other\n",
    "recordings, too):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "52245544",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:09.273632Z",
     "iopub.status.busy": "2026-11-10T13:34:09.273231Z",
     "iopub.status.idle": "2026-11-10T13:34:11.748823Z",
     "shell.execute_reply": "2026-11-10T13:34:11.747360Z"
    }
   },
   "outputs": [],
   "source": [
    "preproc = (pp.pupil.blinks_detect()\n",
    "           | pp.pupil.blinks_merge_close(distance=100 * u.ms)\n",
    "           | pp.pupil.blinks_interpolate()\n",
    "           | pp.pupil.lowpass_filter(cutoff=4))\n",
    "d = pp.get_example_data(\"rlmw_002\") | preproc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e35ac0",
   "metadata": {},
   "source": [
    "First, `get_epochs()` selects a time window around each stimulus\n",
    "event, from 500 ms before until 2500 ms after its onset. The result is an\n",
    "`Epochs` object (see the [Intervals tutorial](../notebooks/intervals.ipynb)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18e4b6c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:11.752838Z",
     "iopub.status.busy": "2026-11-10T13:34:11.752538Z",
     "iopub.status.idle": "2026-11-10T13:34:11.761017Z",
     "shell.execute_reply": "2026-11-10T13:34:11.759773Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Epochs 'S_' | 300 epochs, window (-500, 2500) ms"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs = d.get_epochs(\"S_\", (-500, 2500) * u.ms)\n",
    "epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edbdb9fb",
   "metadata": {},
   "source": [
    "`get_segments()` cuts these windows out of one variable, here the pupil\n",
    "of the right eye. After subtracting the mean of the 500 ms before the\n",
    "stimulus from each segment (baseline correction), `plot()` shows the\n",
    "average response with its standard error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd405fbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:11.764925Z",
     "iopub.status.busy": "2026-11-10T13:34:11.764605Z",
     "iopub.status.idle": "2026-11-10T13:34:11.819059Z",
     "shell.execute_reply": "2026-11-10T13:34:11.817253Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"font-family:system-ui, sans-serif; border:1px solid #ddd; border-radius:6px; padding:12px; max-width: min(600px, 50vw); min-width: 300px;\">\n",
       "  <h3 style=\"margin-top:0;\">SegmentedEyeData — <span style=\"color:#555;\">stimulus</span></h3>\n",
       "\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(140px,1fr)); gap:8px; margin-bottom:10px;\">\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Epochs</div>\n",
       "      <div style=\"font-weight:600;\">S_</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Segments</div>\n",
       "      <div style=\"font-weight:600;\">300</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Time points</div>\n",
       "      <div style=\"font-weight:600;\">1,500</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Window</div>\n",
       "      <div style=\"font-weight:600;\">-500 to 2498 ms</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Missing</div>\n",
       "      <div style=\"font-weight:600;\">0.0%</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Sampling rate</div>\n",
       "      <div style=\"font-weight:600;\">500 Hz</div>\n",
       "    </div>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    <details open style=\"margin-bottom:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Channels (mean over segments; red line = event onset)</summary>\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(180px,1fr)); gap:8px; margin-top:8px;\">\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_pupil (0.0% missing)</div>\n",
       "        <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAALQAAAA8CAYAAADPLpCHAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAACcxJREFUeJztnXlUVNcdx7/Dvg0CIpuA7IKVRUAxxI2oadWYuItNokk1dU1t2tPYak5to0c9bdUY4xqrnlrt0aiYBGNSV3BHUFQCyCI67MgyDjLDNjM9v3vKqVoXljfLm7mfc+bcf2beu+e979z3u7/tSbRarRYcjolgYegJcDhCwgXNMSm4oDkmBRc0x6TgguaYFFzQHJNClIJWy+V4+G0qGzkc8QtaoYDi+HE2csTPdVmDYMeSiDGwomlqQnN+PuzCw2Hh6Gjo6RgthdWNOJlXjWyZHFWKZtCddnGwRqiHFEOD3DAs1B0ONlYGm19zmxqbzxRie9pdFK8Zb76C5ryY3AoF1p7Iw/nCWjjYWCLW3xW+rvawsJCgtrEFeVUKlNarILWzwrQ4X8x5JQAB7vpdGLLuN+Djwzchq1diSVIolo4JNV9Bk6mhzMiAw5AhsHR2NvR0jAaNRosvzhZh0+lCBLo74sPXQvCzgV6wtbL8v++W1DbhUGYpDl4rhVzZiglRPlicFIxwL91ez9J6JTacLMCx7HJE+brgL1Oj0N9LKtjxRSnoVpkM1WvWwnP5H2Dj72/o6RgF7WoNlh7MxvFblfjV6FAsSQqBjZVFpx77X2WVYfu5YpTLVRg7wJP9NtrPRdD50bF3phXjQIYMLg42WDo6FLOG+MPSQiLoeQxnQHEEg9akld/8iO9zqrDt7ViMi/Tu9G/trC3x7tB+SB7sh2M3yrH1XDHe2nIRw0PdmbATgnr3aG55lQrsTL+Lb25WwMnWCr8eE4b3Xw3Qme3OBW0C7Dpfgv1XZVg3JbJLYn4ca0sLTI/3w5RYX3x3uxJbzhZh5s4rGBzgijmJARgT4cnE3xkeqtrYMVKulyPjXj36utjjkwkRmBHvB0db3UpOp0dXa7SCP1I4T/J9TiXWnMjDwlHBSB7Sc/OL7tfEaB+8EeWN03k12JZWjCUHbrDVdUSYO4YEuCHc2xkeUls42Vmxe6xQtTPbOL9KgUvFdci814B2jQavhrhjU3IMxkd6sz+MPtCpDT1mQxo+GhOGcQO92A5bKNpqaiA/fBgu06bB2sMD5kp2qRzJOy9jdIQnNicPEvQaP72B/Dq7HBcKa3GzTI429bMlQ6JPCHRjQqY/hIezHfSNTgU9e3cG0gseIMLbGb8dG4bRER6QSPiKLQS0Ik7eehH+bg448MHQTpsDPYU2kWUNKjxobEFTSzusLCXMjKB59HGy1dmfymi8HNfu1eNvP9zB1ZJ6DPB2xoJRwRg/0AtWPXgEadvboVGpYGFvD4mV+W0DyEaduu0SWts1SFmUiN5OtoaektGgF7cdneJycR2zx8jZ7+dmj2mxfngzxof5S7uKObvtSMTv7cnAjxUKHF2UiOA+ToaeklGhl+WNzIzEEHf2ySl/iN0XS7AzvRgbTxUgyrcX2zSQnd2vNw9jv2xhWJFymz319s1N4GJ+Bnp/Xg/s2wsbZsRANUmN0/nVSL1Zic9OFWDdiXxmkoyP9GLuI08DbCiMnS/OFLEgyMaZ0RjaQ/+wqWIwA9TexhJvRJF7yAfK1nacu/MAJ3KqmGP/s1OFbJe85LUQhHgIFxYVM/uv3sf6kwX4zdgwTB7ka+jpGC1GsaOiqBGZHfRRNLfhq8wy7L5Qgtc3pjNnPEWXvHqZ74qdeqsCnxzLwXuJASw/gyPCXI6WdjX2X5Gx9EJlqxrzhgdiwchgSO2sodVooG1pgcTWFhILUaZ0d5p/ZciY3fxWTF+snx5tcLeYsWO0gu6AVuwdacUsvMtyAcaGYdZgvx65/cRAY3MbVqfm4WBmKWa/0g8rJ/6ER11NQdAdVMhVWP/vAhy9UYZB9m1YqspB/Idz4ejTvdwFQ0Bh4otFtczlRvsGZztreLvYsVyHvq72kEACWX0TzuTX4J9XZCyD7o8TBzCziwekTEzQHZDb7+8H0xH99R4cGjoNCcOime0d5+9q1I/jM/nV+PTbXNyrU7InjbOdFeSqNmZOPQ0l3k+N9cX8kUHw7mVvkPmKFdEJuiOwcvdPq5A6PBmHKrSoaWxhyTLky6ZsMfJtG9OKRplrf/3hDkaE9WFeiuj/zo8uPSX2lMmVKG9Qse+SgCO8pSZvUukK0Qq6I1Jo5evHiiy/u13FvAEk7hAPJ5bfSwnkuk5XfBm7zt/F6uN5LKGdPsb8FDEFRC/ox0PfHTbqketlLB+X3IFzhwXig+FBzO+tb07lVmPePzKZd+b348L1fn5zRJSCVjc2QpmVBYe4OFhKpc/dRFKlxIGrMvSR2rIE8+4mv3eHqofNGLcpHXH93PDl7DijMoFMGVEKuivcq23C6uO5OJVXg58n+GPlxAHPLBoVErqklDpbUN2IE0tHwM3RRqfn4/wPUe481I+a0HTlKhtfBpXnfzk7HmunROJwZhmrvmhTa3Q6v5Qb5SyrcN3UKC5mPSNOQdfXoX7vXjZ2Bnrc0wZxx7txOHenBsuP3tbZ3OoetWBVai7ejPZBUn/zraYxFKIUdHdJCvfA2ilRLGONPCK6gDwaZMNRQISjf8xK0MTU2L6YEOmNFSk5qH3UIuixqdyMzI0V4yPgzqtIDILZCZrMj1WTBkKj1eLz04WCHZdC2ctTbiMxuDdrr8UxDKIUNGXZ2QQGsrE7kNdhcVIIc+lRRbMQbDxZwApH10yO5C46AyJKQVt7esJz2cds7C6UW0zhcuqzJkh+yYUS1nBQ300POSYgaCGgsv+FSSFsc1hU86jbx6GMuGVHbiHMU8oikhzDIkpBU+i7dMFCNvaEGfG+8JTaYevZom4fgwp+cysVzOesr+5AnOdj1neAIoYLRgax1q4UUewqxQ8eMZOFzJcYgbt1crqHWQuaoH5w1KiFUjy7AkUbPzqYzdI9f/fT/jqbH6drmL2gyZaePyKI+Y+pvVZn2Xy6kFWebJwZY9DXOnCexOwFTbyd0I+9e4RaKHSGS0W1rFM+5TdzU8O4EKWgrb284PXpn9koBJQrPW94EA5nlbJO8y+irEGJxQeuIzHYHYtGBQtyfo6ZC1piY8Pa6NIoFNTFnmr9XmRLq1rVmL8vi/VF3jxrEC+TMkJEKej22lrU7d7DRqGgUi2KHlIfDHpD07P6hPxyXyaLLO54Jx6uPMfZKBGloDVKJXsLFo1CQu63QX4uWLz/+hMbREoJ/cXea8goqceuOfEY4MPfvGWs8O354xfD0gLb3onD9O2XMWnLRSZwYt+V+6xecc/7g5ntzDFeuKCfgrqeHlmYyF5cuT2tGBYSCXvzwLJx4bxHhgjggn4GVFRLLX8xQ/83hNMzTL5IlmNeiHJTyOE8Dy5ojknBBc0xKbigOSYFFzTHpOCC5sCU+A8h5NjVDYhwCQAAAABJRU5ErkJggg==\" alt=\"right_pupil (0.0% missing) sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "    </div>\n",
       "  </details>\n",
       "\n",
       "\n",
       "  <details style=\"margin-top:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Epochs</summary>\n",
       "    <pre style=\"margin-top:8px; background:#f9f9f9; padding:8px; border-radius:4px;\">Epochs &#39;S_&#39; | 300 epochs, window (-500, 2500) ms</pre>\n",
       "  </details>\n",
       "</div>"
      ],
      "text/plain": [
       "SegmentedEyeData(stimulus): 300 segments x 1500 time points (-500 to 2498 ms, 500 Hz)\n",
       "  channels: right_pupil (0.0% missing)\n",
       "  epochs: Epochs 'S_' | 300 epochs, window (-500, 2500) ms"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erpd = d.get_segments(epochs, \"right_pupil\", name=\"stimulus\")\\\n",
    "        .pupil.baseline_correct((-500, 0))\n",
    "erpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3c1da52c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:11.822929Z",
     "iopub.status.busy": "2026-11-10T13:34:11.822562Z",
     "iopub.status.idle": "2026-11-10T13:34:12.313469Z",
     "shell.execute_reply": "2026-11-10T13:34:12.312399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6ivmZ6o2IMXZk4KHAI26Hn5+fTJ8+fah3gxDSBUaLTXYUYr6oj4QGdv8VUt1klhc35squIoeFCfovpqZFy/SMaBmTFC5x4YFqcwJMFpumaiEatxfU6V+n2BudEC7fmZUmYxIjevR8QHCimze3qkm+OlIli8YkSHAXaUNyYqBpAq+Dg+UN3VrjWFta5KWNefA7lgWj4mR8MhsrBhMKPEIIIT1mX0m9lBxnvuhXOVXy8sZ8jcxBCJ48NkHOnpws0d2k8FC3NSIuTJfTJyZr6nfVvgpZfaBcDlU0ysMf79fb+M7MtGOKSie4T4xJO1zRpOJu/sg48e/nqRrDHURkIcYxiizQv+tju2JvuY4sCwv0k4tmpQ/6Pg53KPAIIYT0eFrF3pIGrW3ryhIFtVX/3VYkH+0q1fMwsv3+wuwubTOO1zBxyZwMOWNSkrzzTbF8eahS03w7i+rkR0tGysiE8OPeBlKBadEharoLkTczM2bApmsMN+B5uCmnSsKC/LpNgUOkv7u9WE9fNCuDqfIhgD9piNuB8WTr1q3ThaPKCHGflBzEEorquyqmt7XYdQSVU9whYvezM8f3Wty1BRG/axZkyV2nj1NTY6R9H/5kv6zaV96j60PYwcJle0Gt1gKSE6eq0SRfHakWq80u8d00wkDov7A+V2v0xiVFyILRcTz0QwAFHnFLLBaLLoQQ9wDjpWAknBQZ3KW4e+7LHNmUW62RvR8szJYLZ6b3m/HxuOQI+eW3JsiszBi9r1c25csbWwp61I2JCBNsXLbk1WjBPzkxa5yNh6uk2mDS6Gh3fLG/Qg6UNWrqFgJ9MKebkKNQ4BFCCDlu9A4RMLPN1mkElU4o2JjnEndIoZ40qv8jNqi9+9HJI+XCGWl6/rM9ZRoxRCH/8UDEEQa7aLqAZxvpW3MNjl9hbbNkxoR129mMcWRvbS3U09+ZkaaRVzI0UOARQgg5JmiqyKlskqSIztG7T/eUydpDlWp7csOSkTIjM2bAjiZExdlTUjRCiHmnEJUQeYjqHQ9EHpEy3HCoSkUI6d28YdTcoWkFzSvdRWYh9v+1IVdM1hYZkxguy8Yn8jAPIRR4hBBCugVf2hhBhXRoxw5WTC+AZx24ZHaGNjIMBogQ3rRslAoNpF6RHu6JyENasc5oViPkOgNLQHoCjuvWvBptrsHxQ/NKd6ARZl9pgwT6HTs1i9tk0nbgocAjhBDSLfCng6ccmhU6Fts/vy5HPc6Wjk2QUwc5WjMtPVpuPNkh8hDJU7+149TkIQKYER2qxrzrDldKk+nER6QNB6+7HUV1GgE9lp8gJpW8ucUh9r89I63LWk0nZQ1GSYsJkahejrgjvYMCjxBCSLdf8AfLGjRF1zZ6h1Flz36Zo9MnMMHg0rkZQzI8HqbJSAvjrmGlgokZxwNWKUgzFlQbZOORKq0tI910TRfXy9a8WokLDexUe9kWvD7+sfaImG0tOmru1Andi33MIMYEkynpUf3WhEO6hgKPuCURERG6EEKGjspGs86NTQhvH435cFeJGt0GB/jKDxdnuyZSDAVIC18xb4Sefn9HiWss1rHA/kLkoSsYtWUQKKQ9eH4xOzgyxF+7kI/Ff7YVSUFNs4rA7y/sPjWLHwZVTWaZlBKlXodkYKHRMXHLUWWzZs0a6t0gZNhzpKJRp1GkRB21xCipa1YhBS6fO8Itvqgx5aLGYJYPdpTIy1/laeoP0b2eGCGjtizI309mZ8UyotQK7GRghxIS6Nel52FbdhXVaUczuHZhVrfTSpzpfqRux6dwZNlgQIFHCCGkE3XNFjlS0SSxbb6wkbZDrRuK5KemRcn8kbG9OnKIlDWarNJstmk6z1Ez56MWJqjvwmQEpO/6ku49f1qq1Bosmqr9+5ojctcZ4zR9fCxwnylRwbK9sFYFH0ThcJ92kVfVJBsOV6rY7c7I2AlE9XPrcvT0KeMStS6yO5AKx/M/NT1KhSMZeCjwCCGEdCK/qknqjBYZ1UYkrTtc5TKwvWxeZo+EGEQcxGK1wayiAcbDKLBH2g/dlujOheirajTr/ZXUGyXY30/iwgKPWdTfEezLlfNHaI3XruJ6eWzlQfnFWROO68OG2kI0kGzLr5EAPx+ZnBY1JPWE7gDqEtcdqhTo7uNNILHaWuTpLw5Lg9EqGTEh8t3jzJotrmuWsUkRx5xhTPoXCjzidmA82ebNm/X0nDlzNGVLCBk8EG2BkIsKDnCJHYPZ6rJEQbTseNEd5+QDTMCIDA6Q6enRkh4bKnHhgZoS7ao+q751+5yKRimuNUqL2NV7r6dCT42WTx4lD3+8T2vC/rrioPz8rPHHbBAAEJ2ISm7OrZEAf18Znzz8UogwgIa4s9jsx5xS4eTNrwvVFw8R1xuXjlLR3x0YMYfnYEpa1LCPkA4mbLIgbonRaNSFEDL4FNY0S1WTSWLDjqZnP95dqpG25MjgY3ZJAoilghqD1ButMiMjWs6emiJzR8ZJanRIl+IOIDWK+i1EeZZPTJbTJydLdly4Cr6imuYe+dwBiMHbTh0jMaEBWvP15OpDPWqiwH2HBfrpnFU0Xww3cbf2YIUK+56IO3Qfr2ydB3zd4uxj1mFi0ghSuRB3cT34UUD6Dwo8Qggh7SJph8obJMjPz9V0gAiMs5D+OzPTjtk1a7LY1DcPKdbTJiapsOut3xnEHoTG0nEJKiZjwgL0NhuNPfOtg1iDyEN0CZHIf67L7dHcWggQpGk3HKmU3MomGS7i7suDlVoXmR4Tetzt86sM8uLGPD19zpSUY9bdgdI6o2TEhMqYJLoiDDYUeIQQQlwgYlZSa9RUqpN3vynS1B3GTx2rOxVpXMwqHZccIadOSOpRNOiYX1C+PjIiLkwjeogEYsg9TIqPZ2gMIFbUCLl1pNk724p6dJ+IRvnYfXTaBWrSvBk8vrUHKqTZbJX0HjxXqG98fNVBHfk2KSVSzpuWesztVZD7iEzNiOpVPSXpHyjwCCGEuECkzNpid30hI825/kiVnr5oVnq3DQiYCoFt0SW5cHS8hB2n7q03oOtyTnasnDwuUfz8fCS/xqCRxuMxMTVSrjrJ4ZH34a7SHnnkATQYWG12rUlDhMtbu2URucPcWIjh4zWWmKw2eWLVIakxWPT43HDyyGPW0yFiiokV45MiTljok75BgUcIIcTVFIHUJOrXnHy4s0S7KqelR8nIhPAujxTSe4isIV03Jyv2mPNK+woEyKiEcDllfKJ2veZWQ4gev7YOYvPcqSl6+pWv8mRnUV2P7g/1giZLi9ameZvIQ40hHheOX0/EF8Qa0ty5VQZtlrj1lNGd5hJ3BK8HdDBPTo8etl3JQw0FHiGEEAVCBpYmzskFFQ0mLagH50ztOh2HBgbMIUW0bOaIGPEfAHHXMYV68thEjTrlVRvUruN4IJW4YFScIOgHaw/UkfWE1OhgMZptKoYKazw/XYvU9u7iOo1MYtpEWwPrY4ERcFvyarQm86alo45rbq0+h1a7TMuIPm4HMxk4KPCIWxIaGqoLIWRwQJfqobJGCQ30c42a+mhXiYqiSamRXZoGI7KTX22QUYlhOgliICJ3XREVGiCLx8TLiNhQyasxHDeShwjSVfNHyPjkCE1J/nXlQalqNB33fnA9CEmTuUXTmT0Vhu4IhPC2/FrHhIoAvx5PINlwuEonhAD4DKLL+VjgNVFc3yxjksIkm553QwoFHnE74Hs3d+5cXeiBR8jggJQaaqac1igQQDA2Bue0pjg7gsgdhMKc7LhBL6KHdx3Sr+jQhMg8Xk0eIouIPiEqhyjlYysPaVNIT4AxMxoL1h6q0PFtngbsTxCJ3ZJXrc9vW/ub440he2F9rp4+c1KyLBod36MmnbjQIJmWEUPPuyHG4wReTk6OvPbaa/Lvf/9b8vPzu9zGZDLJ+++/L88++6x89dVXg76PhBDiaUAk2VvE5VP3+b5yjeoh6jUmsXPUBiIJkb7ZWTG9tkHpb5GHVGNeTdNxrVBQN3bbKWN0f5GOfmr14R6leJ01eWIXWXuwUvaW1Peok9cdqGkyy+r95TrdIzUqRI9ZT0At5lNfHBab3S7zsmPlwplpx70OUrOwyZmeGT1krwnigQIPb6aLLrpITj/9dBVvr7zyiowbN04eeeSRdtuVl5fLjBkz5Gc/+5msWLFCzjrrLLnuuuuGbL8JIcTdQQcsvtCjW5srEPFBShKcPjGpy7q7ykaTTEmPkozYoS2lgJBAfV1saFCPmiHgdQeRF+TvK3tLG9TTradiLSkyWIL9fWX9oSpNd/ZUHA4VSCnDkLigulmyYkN7HGUtbzBqGhvp7AkpEXLtgixX2v6Yqdk6h0XOyOPMACaDg8dUP+INeMkll8ibb77p6sh59dVX5YorrpBvf/vbMmrUKF3385//XAICAmTjxo0SEhIi33zzjcyaNUvOP/98Offcc4f4UZCejir7+uuv9TSeO6ZpCRlYkGpFRM5ZZ/floUpptth0agVms3bavq5ZRsSFyqTUzpcNBRBtC0bHyep9FZpqhhA7FplxoTrS7PGVB2X94Sodu3Y8T7e29xXob9F0Z6PJorWHx+soHWwgwJFe3VFYBxs6yYo7vg2Kk/pmizz6+UHXjNmbTh7do8aZkjqjpuvRWHEs+xQyeHhMBM/X11e++93vtnuRnnbaaSr89u/fr+dbWlrkrbfekmuuuUbFHZg+fbosWLBA3njjjSHbd9J7DAaDLoSQgQW1a6grC/Z3NFfg/Iq9jjFUp01I7BS5gRAM9POV6Rkxx5w/OtggTTtvZKxYWlrUkPd4YHTW5fNGuLpE0VnaU5DmRLpzT0mDikpEvNwpJQu/v8051drBitRyT8UdIrePrTyo3dPx4YFy+2lj1YOwJ/Y6iODNHBHd4xQwGXjc593ZB9577z2N7kybNk3PFxQUSENDg0yYMKHddji/Z8+ebm8HNXv19fWuBbdBCCHDgcomk5TWH507u72wVioaTdpNe9LIuHbbQvwhNYvIHcxu3Q349M3KjNHZp0g7H4+TxybIWZOT9fSLG/JkT3F9j+8L6U5Exkrqm2XFnnLZX9rQ43m5AwHu+2BZg3y6p0wFO7p/e1MHh6gfau6cXncQdz25PtLUaKyAYM4c4nQ98RKBt3v3bvnpT38qd911l6SlOYo/ncIsOrr9KJ2YmBgVbt3x4IMPSlRUlGuZOHHiAO89IYS4B0U1zRq5cUZqPttb5hI/QR1qtsobTWpeOz7FfeeKQnwirYypGuh8PR7fnpEmc7NitZngb6sPSU4vZtBiJu+IWEdae82BcvnyUIVGOAcbzAqGVx+aKWwtLWpP0pvoKsThP9Yekd3F9Xo9GBkjPX88kEErqDVouh7HnIbG7oVHCrxDhw5ps8V5550nv/vd71zrnWnZjhE4iLtjeardfffdUldX51qOFe0jhBBvAQIIgiayNa0GM98DZY2CEqpl4xI7RXgMZpuOInO3mrO2oP4LhsujE8NVfBzPPgUp6GsXZskEp0feioNak9gbIHqRIt5X0iCf7CqVfaX1erwGGnSt7iis1fs8UNag+6CzdHsxOQKpVVihbM2vFX9fH7l56ehuJ5Z0BJE7pGRRh8hZs+6Hxwm8w4cPy9KlS3V54YUXtDbPSWZmpgQGBqqVSluOHDkiY8aM6fY2g4KCJDIy0rVERLjvr1NCCOkv0JCA6I9zNJlzVuuMjJhOXmmldUbJjA2RER5gXgurl7nZsZISGawi73hdsjBo/vGy0ZpybTRZ5dHPD/TICLktEDjoHkU0bM2BCvlsT5l2Jg9Epy2EHaxaYEQNI2JMmBgZH95rkYXj8upX+bLhSJWK+huWjNSJJD2h0WhVQTx7RKw2qRD3w6MEHoQahN3JJ58sL774YqfuSnTPwhYF3bXON3RhYaGsXr1ao32EEELa22gg2IMuSfiXbTxSreuXjG1vaIsULj5RJ6ZEDdq0ihMFkaX5o+IkPChA07XHA+LotlPHSEpUsNQYLPLnzw5oR2lvQOQM0TyYL5fXG2XF3jL5dHeZRtfQiHAiQDiimWNbXo38b0eJikgIPaRje2pc3BZ8R769tUhWH6jQTtsfLMyWGZkxPbouopOlDc2alh2V4P6C/1h89tlnqhvgxDF16lS5+eab1W6tLdARl19+uWRnZ2vj5qOPPiqegPvG2TvQ3Nwsy5YtE7PZrBMOnnrqKddlp5xyiqtu7uGHH9au2XPOOUfmzZsnL7/8sp7Hk0M8h+Bg9yvgJsSbQKSqoMYgUa3p2c25NWqNkhAeJBNS2kdxICwy48IkPaZns0vdBaQrYdKLyCQaL2JCA48rCn9y2lh56ON9UtZgkr+sOCh3nT6uR52kbYEIRpMDhBA6UnGcI4L91b4FXa1oXggP9teRYV0JZogvRMfQKAK7EkRZkT6vNph1fXRwgAq7vtqR4Pbf+rpQPtnjqLe8Yv4ImdehoeZYKV08nlEJ4TItw7Pr7rZt2yZnn322/OIXv5AnnnhCKisr5cc//rFceOGF8uWXX7qaMOHYMXLkSHnnnXc0Q3jVVVepFoHfrjvjMQIPFihOH7uDBw+2uwxeaU5gfrxr1y556aWXpKysTJ84iDt/f495qMMeRGbnz58/7I8DIQNJaV2zRqiyWr3vvjhY4YretbVGQfQOYKKFJ/qb4fHBrw6pTNi7hAUd+7sA0bA7lo+Vhz/ep9M9Hl91UG4/dWyfLGEg3iDoIIqQ0iyoNsjh8kZNqQYF+GoqGYbLaGbBOtQLQhQaLS36F2LObLWJj/ioyIwPCzrhWjeIu9c3F8iKfY4o1ffmZGhDTU9BfSJSsnOyYl1TTzyVdevWaYnWAw88oOcRxbvtttvk6quvFovFollBTM1CaRi2jYuLU9cO+O0imHTHHXfoNu6Kx6iesLAwVdg9ISUlRf7v//5vwPeJEEI8EXzJo7kCogViDkIG5yEyFo6K7zJ6B983TwWp5SaTTb4pqNX06fHEGjpIbz91jPzx0/3adAKRd/Oy0X0WNDjGkSEBujjTrRDOZhuidDapN1pdZUXYFs0OEKMRQf66r/0VJYPQfOWrfFet5ZXzR/RK3KEu0c/PR+sbo48TDfUEli1bJlarVd59910dhgDv1f/+97+yfPlyl3Bbs2aNTseCuHNyxhlnyD333KPBJFzmrnhGMQUhhJB+AzVmaJpwpiyPNldEu0QIQF2eiI+MS/LM6J0T7Dvqy8YmRWh6sSd+dWgmudU50qykQZ5YeUhMVkc080SBkEYkEccfNXsQlOiAxYI0LqZl4HlAZK+/xB2aPf65Llefa9ziNQuyeiXukNLHgqYKpJ/dnYaGhnb+tki1dmTSpEnqp4uIHRosYZNWU1OjAxOcFBUVSVJS+3F9zvPFxcXizlDgEbcdVYYFpwkh/UtJXbPW20FkQMR9lVOl6zt+4cP3Li0mRNOMng4iYXOyYzWCl1/T1KP5sxCEiOQ559Y+DpHXmrL2JNCMgdmyzm7Z7y/KlkWj20dqj2eng45rmBlD7HsCEydObOdvC7/bjuzYsUMuvvhiTcvi+wbRuqampnY1+3iddCzxcp539+8nj0nRkuEFp4kQMjAgepVT0SShAY6P/20FtVrzheYK1Nk5QQ0YtoXIQcTJG8CEBowzW73fJkW1zT2KRI1JitDGi7+sOCD7ShtUKCFd685egG3B2DZ4+xXUNKtQxQxeCLWegtcAop5jEiNkemaMx0Ry9+zZ4xqCAFBr15HHH39c6+6cNXiwU0MpGBo0t27dKjNnzpSEhAR18GhLRYUj4p2Y2N4r0t1gBI8QQoYRGDWGUWRO77uNRxzRu/kjY9ulA7FdYkSQx3XOHg+kP2GfEuDvqx2uPQGmyRB56HpFTd7DH+/XzlZ3B3WVv/twr4q7yGB/ueuMcb0SdzqpoqZJ0qJDZO7IWLeaPXw8IiIi2vnbdiXw0EiB+v62hIc7TJ7RJQvg2gGxBycPJ4j0welhypQp4s54zrNFCCHkhEEXJFJuqO/CWK3dJY4xjm1tMtDNiakV45IjPcb3rjdAsMA+BY0OPR0tBlsQCCRYnCD69+BHe/Wvu/LlwUrtBEa9JWr87j5rgmT10qS6uNYo0SEOQYzop7dx5plnyvr167WxAjQ2Nsr9998vqamp6okHvve976mYw8QriD7YpPzxj3+Ua6+9tpM4dDe8751LCCGkS5B2bTuabHNutaAULTs+rN3sUfWMCwuUjFjvit51FGyzsmLUW85gtvboOpmxoXL3WeP1WEE4PfTRPu3Mdbfn+OWNefLChlyxtthleka03HP2BG3m6A2IbmKWAFLa3jqp4tJLL5WHHnpIbrzxRo3cxcfHa1PF+++/7xpvGhsbK//73//UEBmRwPHjx8uSJUvkT3/6k7g73ifJPaC4GT5Lnu4fRAjxPFAoX9NkkdTo4Pbp2ezYdtvVNlvUCsNT6sz6AtLRk1KjxGCyyvbCOk1F9+RzGWLn52eOl7+tPiQHyxvliVWH5JwpKXLetNQhr09DRPEfa49IYU2zdsqeNz1VvjUlpZ2vYU9AVNNotcnC0fGSEev+HbMnwp133qkLoneI1HXlmQtf1t27d+usemzTVbrXHWEEbxBB2mNTTrWs2lfeozZ9QgjpT4pqmqXF3qJpV9ik5FYZtKsSprVOYMgbGuTnETNnTxQ0j8wcESvjkiNUFFlbejY3FlMofnr6WDl1vKPI/oOdJdrIgIaGoQD+dhiL9psP9ujjwNSMW04ZLedOTe21uEM0s6rJrJY5YxId9WjDgfDw8OMOREA3rqeIO0CBN4jYW8PnedUG2VNcN5h37XHAZNKdHcIJ8TRQb5ZXZZCoEIf33cZWaxREsdp631U1mdRKpC/zTT0RNA4gWjkiLlQNnyGWeoK/r698b26m/GBRtpoSo5bxV+/tVsuZnliw9BcYYYZau9c2F2hKdnJapNx/7iSZmh7d69uCBUxJHexQImVKerRHjyEjTNEOCb7iI1vza/VDdTj8Su7LqLKFCxcO9W4Q4lUgYldntEhmTKgKkLbds07wA9RZnzacQCr6pJHxYrSUqyUIjlFPxc1JI+NkRGyoPLcuRwX0P9bmyFc51XLxrAxJjhq4mdoNRot8uLNUVuwrEySEYIHynZnpsmxcQp+EGZ77wtpmjWbOGhHrNdY4wxnvLbBwQz7bUyb/3lIoF85ME5OlRdO1GG49XH4pE0KGDggXfGfji/tQeaNUNppVFKAI3wlSczpZYQCFibsSFRogC0bFyer9FRrF6o25M7ZF88VHO0vlgx0lsqOwTnYX1etc3zMmJfdrkwLq41Dm89neMp1VC2ZlxsglczL6/F2CkiFELzG3d152nEfZoZDuocAbxOLmW1/f5rAn8PfVGYD4tbcpp0qWjks84QHShBDSHRgxhfq7aGd6tjV6NzMzxtVYgKhek9Gq67zRGqUnJEYGy0mj4uSLA+XaRdqbzlOkbM+dlqr1jG9+XaAib9X+Cll9oEJmZsRoN+rk1Kg+iSd8b+wpqdegwNf5Na4abnT1XjgjTSb3wtuuI0hJQ9zBOgYCNySQ30XeAgXeIIH5gr+7YLLc9dYOWXOwUs+jQBcib1t+jf5qGuoOLHcB41927typp2EkiZQtIeTE0rP1Rov6oGEmKexROqZnMfA+IsRf0r3YGqUnoGt0/sg4WXuw0mEX0zqvt6cg+okZtntL6uXjXaVamwdRhgXibmR8mIxOCNftICBhWRMc4KuRVYvNrmIOzRqIpqJhArY2OVVNut4JbgORwZmZJ1Yn5zAyNuh+oGMWGSXiPVDgDSIXzkyX1fvL5X87S+XfXxdKdEiA/vLaU1yv7uoYCUQc1Na6l7cUIZ5MfpVBAnx9taNyR3GdNJltatg7PjnStU2NwSQTkqNcHnnDGdQgoinlqyPV4u/r0yfhMyElUhc0Qaw/XCVbcmvUcw/jzrD0FqRf0dmKKFt/1G47xJ0jqgtxB99D4l1Q4A0yS8clSH2zVdYeqpTn1+fqIGvUZ3ydWyPRoQGSGDH8al8IIQNHncEiJfXN+vnSNj07N+toIT0K7H3EVzLjvNvzrKcgKjYxJUrMVrtsyatWYRzWx0kOmHd78exQ+e6sdCmpNcqhikaNyiEFXN5gVLHdNjoX4OejoguCLikySE2oR8aHq3dhf3W1Qtw57VQg7nprgkw8Awq8QQZv0HOmpWhx7Kbcannqi8Pyi7MmiMlmky05NbJsfCJrIAgh/UZpvVG97RLDg9TjbHthbaf0LOaqxkcEDsvmiu5Aycy0jGgVv9sLaiUlKuSEPpshEtNiQnQ5eWxCJ49Um92ugru3vnV9Ene1zRIa5BB3fM69l+FZSTtE+LQuFqtdrl2YJaMSwnTe42MrD2q6JL/GINsKavTNTggh/fFlnlvZqE1c+HEJeybUeaVEBWuBftsmDKQlh2tzxbGNkGNkUlqUFNc1a9p2oMQkjv1giDtMu4AtzOIx8b3qFCaeB9/Ng3mwfX205sVsa5EGo1V+vHS0xIcHSnmDSf6xJkcSwwNlb3G9hvAJIeREQWQOny8xHdKzaCJwpvuckyvQRUk6A+GFztiJqZEqjgZK5A002i1bY5DwoAC1b6G4834o8AYZfEigXb7RZFHX8VuWjdEOqv1lDfLfbcUSEuCn9XiozSCEkBNNzyJLgIgNxN7+1uL+eW1mz6LwPy0qlEX2PZh2MSElwiNFnvrcVRkkNjRIxR3SzcT7ocAbkuLdSJk/Ml5FHn45X794pODHNBovvimsFYPFqh1XnvYh0p/4ouPPly9PQvoKSj1yKpokLNBRag0PNRR/YL6o03gXX/z4oZkVz+aK4wG/QHxuT2qN5KGe0RNADWFudZPW2p08LkG9/sjwgN+gQyTy8EsQ3ndI1aIWBmNtACZdVDWapaDa4Y83HOvx4Hu3ZMkSXeiBR0jfqGw0SWWT6Wj3bM7R9GzbqQi4nOm6nkfy5o2Mk2npURodxbgwdwYiFJG7UfFhKu44NWl4QYE3lJG81EiZkxWjH7Jzs2JkyZh4/YX93Jc5Kuzgj3eY9XiEkD5QUtesIxHRYAEzW9hioGlg1ogY1za1zWa14eAknV7W5GXHyewRsZr2rmo0ueXrEybNZQ0mmZweJYvGJNDEeBhCgTfEIm9SapR2aVU3W+S8aakyLilCLVQwuBrBu6/zatQviRBCegqmVeRUGtTnDMCwF0xNi5LwVj83k9WmJr7pMazH6i0QyjMyo2XB6HhtmkPKFh2q7tJMgbF0zWabzM+O1YgtBfzwhALPHbyW0qN1QbHztQuy1HQSg8Df3FIgdc1mHSuEN+twoaWlRXbs2KELThNCekdFo0mqkJ4NCdQv/K+6SM/WGCxai5fQWo9H+lJqEyknj03UOsfcKoPWuw0lqNuGiTJGzsFTdUp6tMvMmgw/KPDcRORhpiCieXVGi/xoyUjtpj1Y3ihfHKiU/KqmYeWPh1/C1dXVurjLr2JCPInimmZtoEDN2IGyBhVz+EyZmn50KH2TySojE8LFn953JwSmf5wyIVH/5lcbhqQuD5+TyPSU1BtlXHKEnDYxSWfqkuENBZ6bgA/Z2Vkx+ua0tNjlukXZ2lmLGYZ7Sxu0Hu9Aee/nFxJChhdIvSKa5Jwpu7E1PTt7RIzLyBjiLjTQTw2PyYmD5gVMp0B9Y73RqjWP1kHKPqCRIqeqSQMFS8YkyOIxCZwnTBQKPDdrw0dnLSZchAf7y0Wz0nX9e98Ua1ct6vFQOE0IId1RXm/SAvvokABNGeJzo3N61qy2Geyq7D9Q5waBd8r4RE1751UbtAEDKfKBEvKIGMJ1YXxyhJwxKUkDBEzJEicUeG4GZh2eNCpew+sQes7O2tc3F6jIQ7F0vZu35hNCho7CGoPYxa5ZAcydbbbYJDY0UMYkhevlEBxo5MqKC+u34fXEAY4nPruRIl0AQe3jI0cqm9SyBinz/gBj5fKrm6S0zqRzbXFfiNpFhwbyaSDtcLRTEbcCXW4LRsXJamuLLBoTL2X1Jp10AY+8y+dlamcc3tCoryGEECdoxsqvbtbmirbpWUzPcc45hfcm0rccMj+w0Tw0OGTFh2nTw8GyRh0Thn6HiKAA/Yzv6ec3hCHSsHjeINaRWs+KC1fBDv9CRuxId1DguSn4NbZwdLx8sb9Czp+eIi+sd8yU/N/OEgny91VPI9TUoO6CEEIAzHfhqzkiNlRnzO4squuUnoX33dikCPqiDQL4nJ6aHq3Hu6TOqM0vxXXNUtZgFIvNLj5iV+Ht7+urNdfA3irqbK01fLgcmZ2kyCDJiA3Tv0itM/pKjgcFnhsDu5SFY+JU5F0yO0Oe/TJHDlc0ycp9Fa0iz1/b9AkhBCB1h8AQojpb8qpVKGTEhEhatMPrziEcsI4dloMd0YOhNBaztUVFNgQ45gQbTFYxWlu0XhLizs/HUY8dFuSv0Tr8jQxxRP0I6Q18xbg5GAq9YFS8rDlYLpfOSZd/bciTr/NrdIZtUICf+i+hPd+bwHiypUuXDvVuEOJRwJ6juMYoUR3Ss51HkwUyPTuEIDWbGBEsiRFDuRdkOECB5wFAwM23xQs8NL87M13e/LpQ1h6sVF8r/NILDnR8YBBChi+ldUZtwEKUCJ5ohyoaBVm/udmxrm3qmy3qhcfJBoR4PxR4HsLoxHBNrcDs+FtTUrQW79M9ZRIc4Ku/CJeOYxcVIcMVGN3mVjVp6QZqtpyTK2CfEdPaXYkUIGp202iAS8iwgALPgxibFK4zJlGn0Wiy6JSLD3aU6DxJiLwlYxO8ok4D48n27t2rpydMmCC+vuwWJuRYYFIFIngQcxB7G3M6p2drDRa9PDGCo8kIGQ54vhoYRqBramJqpPpYQQQ1mWyyJa9G3vmmWEUfhB5sVUIDPftp1bE7FRV6evz48UO9O4S4PRB3KNhHzS4ieTgf4IcRiDGubRpMVhmfEuGaZkEI8W48WwkMU5E3OS1KBR0WiL2t+bXy7jfFejlSMAtHxWtbPSHE+0HpxpGKRm24AhuPONKz0zOiXZ8DJotNBR980wghwwMKPA8VeVPSnEPDHeZJTpHnNEuHUbKnR/IIIccHUxIqGk2SGB6kYm9TF+lZpHAxPis+nOlZQoYLVAAeLvLgc+zjY3eJPMythb8S0pwYeeYNNXmEkO6BeS4aKGCbtKuoTofd430/KfWoR2aT2SozRkRz6gEhwwh++3u4yJuUGiUBfn7qhB7o56vF1Sv2lWu9DeZNLhodzxmFhHgpGDiPWacYPQY2tnbPzsmK0c8EgDFXMMxNjqSVEiHDCQo8LxB545IjtIsWAi882F8+31uuaRqYmjYZrbJsfKIk8sOdEK+jvN4ktQazTqowWmwaxe8qPZvcOt6KEDJ8oMDzEmBuCk+84EA/Laz+cGep7C9tkEc+O6D1Od+amqrbEEK8h/wqg5bh+vv5ypa8Kh2DhVq7ka3vdZRqQPhlxYdzdikhwwwKPC8CFgmnjE/UdEx0SIA2XcDR/olVh3TA9aVzMmViSqR+Gbgz8L1bvHix6zQhpDONJqsU1Bgk2jWazJGenT8y1iXmsA2i+kzPEjL8oMDzMjBncum4RB1OjZTMf7cVS361QV79qkCOlDfJ9UuyZd7IeB1g7a7gywnzaAkh3VNS26xlGIjM4+/uknpdP6+DufGIuDCJCnXU6BFChg/u+y1P+gzmTM7PjlOBFxcWKO9tL1FDZDRg5FYb5PrFI+XsKSkcOE6Ih4LUK5orgv39XKPJ7HbR1KwzWgePTEtLi86yJoQMP7xS4G3btk2efvppKSsrkylTpsgdd9whMTFHHd2HAzA8Hp8cKbGhgRIfESwjYkPlo92l6nD/0Ef7ZG9pvVyzIEu38YPXihuBKR0HDhzQ02PHjmWalpAOVDaapQyjycIckbkNhx3p2ZPaRO8ajFbtrk2OYvcsIcMRrytw2rhxo5x00kkSEBAgF110kaxevVoWLlwoBoNBhiPonkVd3qXzMuWGk0eq0DPbWuTfWwrl52/vlE/3lEqD0SLuFp0oLS3VBacJIe0prjVo8wTMzItqmqWgpll/qM3JinVto921MSH0wiRkmOJ1Au8Xv/iFnHXWWfLEE0/I5ZdfLh988IHk5+fLc889J8M5ZTt7RIxcOCNdbjt1jHrjoQZ7Z1Gd/OI/O+X5L3O1G49iihD3B52yhyuaJKLV+25Da3MFjM/RUAEw0cJmt0tGDNOzhAxXvErgNTc3y5o1a+SCCy5wrYuKipLTTjtNPv74YxnOoHEhIzZUzpicLD9eNkquOSlLokIC1CPrrysOyK8/2C2bc6s1KkAIcV9QZlHTZJaY0ABpabG7umfbpmfRdIGGK6ZnCRm+eFUNXkFBgdhsNsnIyGi3Pj09XVatWtXt9Uwmky5OGhoaxFtB9yxMUGGpMioxTF7emC/7ShvUHPlQeaPcePIoOWVCkiREcGYlIe5IblWj/oXd0Z7ieqlttqg10tR053xqkfpmi55H9J4QMjzxqgie2WzWvyEhIe3Wh4aGui7rigcffFAjfc5l4sSJ4u3RvKz4MDl/eprcc/Z4OWdKigT4+UhulUEe+GCPPL7ioJokIzpACHEf6gwWKaxpdk2lcKZnUXsX0Opvibm0eI+nxrT/HCSEDC+8SuBFR0fr3+rq6nbrq6qqjtlFe/fdd0tdXZ1r2bNnjwwHUMOzcHSC3L58jNx6yhhJigwSg9kmL27Mk3vf2SlrDlToHEtCiHtQVNus3bHhQf5i0tFkNZ3Ss/C+Q3dtEscTEjKs8SqBh1RsfHy82qS0BeenTZvW7fWCgoIkMjLStURERMhwslMZnRghV8wfIfefO1GHlIPNuTVy77u75N2tRVLeYBzq3SRk2GO1tcih8gYts0CEbmtBrZgwmiwiSEYlHB1DWG90mB87I3qEkOGJ130CXHXVVdoxW1lZqec/+eQTFXhXX331UO+aWxMTFiinTkiWu8+aIN+bkyHB/r6aCnrw433y4vo8rc8brC5bjCdbsGCBLhxVRoiDkjqjVDSY1by8rffd/Oyjo8kQ1Qv095XUaKZnCRnueFWTBfj1r38tO3fulDFjxuiC07///e9l0aJFQ71rbg++GGZkRqvYS4sJlX+uy5GqJrM8/cVh7dy7fH6mWjEM9CxbfFkFBjq+xAghDnKrmkR87BqZq24yq1k5QNOUE3TFJ4QHSXw4m6QIGe54ncALCwuTTz/9VOvoMMkCDRNJSUlDvVseA8QV0juXzMmQpIggeXrNYfXcentroTSYLHLl/BEyOyuW3XmEDCIwLS6oNuhkGrD+cKWOJhuTGN6u1q7JbJUZI6LdbjoNIWTw8TqB5wTCztu7YQcS1PWcNTVFokMD5MnVh2VbQa18srtMmoxWuWahTU4aFa+1QAM1quzQoUN6evTo0UzTkmEPxB2aKxCdw4zZdYcc6dlFY+Jdx6bJZFW7FFggEUKI1wo8cuKgU2/x2AQJDvCVv6/JkbWHKuXLw1VitLaoFcOC0QlqltzfoNavuLhYT48aNarfb58QT8JktcmhckyucDRX7C+tl4pGk74vZ2cedQeoaR1NBgNkQgihwCPHBEap80fFS6C/n4QG+WkUb0tejaaArDa7CkA45hNCBgbMmq1sMklGq6/d2oOOBrK5WbES1GpkjKgeOmqz4sJcDReEkOENBR45LijqRt0dLFV8fXzko12l8lVOtaDMB321SyjyCBkQYDZ+uKJR/H19xN/XV30pnd53bdOzSN9GBgdwNBkhxAUFHukRiNjNzIwR+0LHIPNP95TJhiPVKv4g+haPSZAopoYI6VfKGoxqV4TaO/DVkWqx2OySFh0i2XFh7ZowxiZHqHk5IYQACjzSS5EXLdcuzNKUEObXrjlYqbVBiO4tHhPPLxhC+pEj5U1is9ldXetfHnakZxeNjnelYq0tLfp+zIwN5bEnhLigwCO9wr81XWu2tehYs/WHqzRlGxbsrwJwyZgECQnkgHNCThR43eVUNUpceKCrkzavyqDvs/kjY9vNp40NC+JoMkJIOyjwSK9BWnZedpy65mMs0q6ienl3W7GEBvhLkL+vLNCmDK8bkkLIoHKkolGaTDZJjnQ0V6w+UKF/Z2REt4uU1xktMnsEvSkJIe2hwCMn1F3baLTK46sOS05lk7yzrUjTtRB3EIB9NVvFeLL58+e7ThMy3MAPJ4wHjG0dS9ZstsnGIw7vu6XjElzb4UcWfnBxNBkhpCMUeKTPoGtv0ZgEqTda5W+rD0tFg0ne314sIQG+EhLgJ9Mzovtk2YDrBAcfdecnZLhxuLxR6potMjLe0Uix4UiV2qCkRAXLuKQI13bVBrMkRgSpMTkhhLSF4RFyQiRGBqtNyiWz0lXU5VYZZPWBStmaVyMHyxt5dAnpJQ1Gixwoa5SY0ED9sQPj79X7y/WyZeMSXT+asB51sCMTwjmajBDSCUbwyAmD2bWnTkySykaTvPF1oWzKqdY5tsGBfjo6KT0mtNejynJychy3nZ3NNC0ZViA1C9sTvK8AxF5xnVHrW9s2VzSarBIe7C+pHE1GCOkCCjxywiCiMDktSuqmpEh5o0lW7quQ/+0s0e6/YEzAGO/vqiXqCYhMFBQU6OmsrCw+Q2TYgLTs/tIGjd7BXxKsao3ezcuOldBA/3ajyRC9o/8kIaQrmKIl/QIKvWeNiJFzp6bKlLQoabGL/HdbkVo7oDgcDvyEkGOzv6ReRZ5zniysUrbl1+rppeMSXdvB+w6G484oHyGEdIQCj/QbsG6Ykx0rF0xPlaTIIKlrtsqHu0okt7JJ07YWWwuPNiHdUN5glP3lDTq1wllnt3JfudjsdhmbFN7OyLimyeF9lxzFZiRCToSWlhb505/+JBMmTJCoqCg588wzZf/+/e22OXDggJxxxhkSEREh6enpcs8994jNZnP7A0+BR/oV1Ns5RR5qhlA/9E1+rRwoa5CdhXWafiWEdJ45u6e4XkzmFokMcUTvjBabfNHqfXf6xORONiojE8IkyJ+m4oScCD/+8Y/lz3/+szz22GNSXFwsv/jFL+S5555zXd7U1CSnnXaaxMbGyr59++T111+Xv//973Lfffe5/YGnwCP9zsSUSJk/Ml7OmuL4Uvp0b5mU1Rvlm4JaHZxOCGlPXrVBDpc3tYvIrT1YKc0WGB0Hy9T0KNd6lDugY723zUuEkPZs27ZNnn76afnHP/4hy5cvl7CwMFmyZIn84Q9/cG3zxhtvSFlZmTzzzDOSlpYmixYtkp///OcqCE0mk3hFk0Vvit1zc3P7uj/ES8aZzcyM0fqh0jqjbDxSLa9tKpAfLx2tqdrwoACmlghpI9i2F9RKgJ+Pa+Ys6us+31ump5dPTHI1XICqJrOkx4RIfOsIM0JI33jnnXc0Moe0bHesW7dOZs6cKZGRka51p556qtx5552yc+dOmT17tucLvHvvvXdg94R4FejsmzkCIs8kJbVGjVC8saVArpiXqU0Xp0xIVKNkQoYzKFlA6UJpvVFGxh1tmPg6r0aFHCbDnDQyzrUews9qa9Hu2b6YiBMyXGhoaJD6+nrX+aCgIF3acvjwYa29++1vfytPPvmk1uPNmzdPI3hYD0pKSiQx8WiDE0hIcEyTKS0tFXemxwLvuuuuG9g9IV5HVlyoTEuPlpqpFvnXhlzJrzbI2kOVsmh0vGw6UiWLxyZ0WUOE8WRz5sxxnSbEW8F7ArV3yRHB4ts62q/FbpcPdhTr6VPGJ7ab6wx/vJjQIEmLdsynJYR0zcSJE9udR83c/fff324dBN2GDRtk1KhR8s0334jVapXbbrtNTj/9dNm9e3e7qF1bnN9L7l5TTh88MmAgwjA1I1oqG81y/rRUTdOirmhUfJjYxe7ous2KdX2xtb0eaiEI8WZgh4JIHWY2w7DYCdbB2Bgm4aeObx85qG22yNysWFcqlxDSNXv27NGaOScdo3cgJSVFRdoTTzyhHbIAtXXolF2/fr2mbpOSkjp11ZaXO7wpcZlX1eChvu549XiswSNO8EWkqVqDSU6dkCif7y2XVzcXyE9OHSM7i+r0i21S6tECckKGAyarTTbnVuv0l6w2qVlE7zDPGSyfkNTO2LjJZFXRlxHH5gpCjkdERES3ETgnCxculEcffbRdpshZ+uD8u2DBAnnttdc05esUgStXrpTw8HCZOnVq/zwR1iaRnJe6udBHxC9YJHKcSNw87NjA1uCxHo/0BnQGTs+I1tFKRTXNsre0Qf61IU9uWjpKZ9aiFi+jjccXwub5+fl6OjMzk2la4lWgjm5bXo0cLm9Ub7u2DRRbcttE7ya0j96hJg9iMK4XU2EIId1z7rnnyvjx4+UnP/mJ1t0hRfvTn/5UMjIyVNiBSy+9VFO7t9xyi/rlYYzmww8/LDfeeKMEB/eTD6WlUWTPH0SackR8/ERCUkRsJhFThYiPr0hIqkhzsUjMDJFTV4oEHFu4nlANHuvxSG8ZlxwpZfUmOX1SkhaUY3lve7GcMzVFvjpSrV9oceGOEDpC5s4oMN5ohHiT3x06ZncU1UtKVLBOgHGC5on/flPUZfQOlyG6B+87NlcQ0j8EBATIxx9/rF54SNcijYuo3qeffuqK1uEvzkPQJScna1Tw6quvlt///vf99zSEJImknC5iM4rM/JNIUGtjVf0BkQ1Xioy5SST5NJG13xXZcb/IrD8PfA0ePGT27t3rKmicPn36idwc8WICWq1TUI938ewMeXZtjmzJq5HRieEyKiFMvsqplqXjEtp9qRHibZE7iLuv86p1WkXH1/rK/eVS0WCSqJAAtUbpGL1LiAiSVDZXENKvZGZmyvvvv3/MbSZNmiRr1qwZuCNvbxHJf1Pk26Uifm0i9JFjRWY9JvLNz0RGXi0y/UGRbXf1+Gb79G0Kt+fLLrtMvvjiC81D4xcl8tPLli2TV155RZUwIR2JCQuUWSOipdFokfOnp8p/thXJv78ulJ8uH6sza+GRt3B0PN23yaCCiRGohWswWnWcno/4SFCAr4QH+Ut0aEC//OhoNtu0HGF3SZ2KOzQYtaXRaJUPdpToaUyBadtEgcgdyhtmZMa066glhHgJ5hpHHZ50Mc7TbhMxOCL7EpQgYu/5XPc+fXIhRYs0GqJ3yF8DjPC4/vrr5Yc//KF88MEHfblZMgwYGR+u5sdmW4vMyoyRr/NrNJr3szPHyf7SBgkN8JMZGWy6IINjMHygtEEOljdqR6vD8QD/oSbOrrVxYUH+khQZLBmxIZIQEazRtd6Az0nU1G3Pr5WCGoOkRoVISGDnDliUKxjMNjUwXjgqvt1l2DfcL/aBEOKFBMWJRIwW2XityMxHRUJaRxPW7hLZcrNI4hLH+dLPRZJOHViBt2rVKhV0I0aMcK2D0HvxxRc7ec8Q0hZYokxvTdWePjFJCmsNWpv34sY8+cHCbNlRVCeB/jRwJQMLIsawI8EIveiQABVW/h08F60tLdJkskluZZPOUobpcHJkSKvYC9LmoI4WP23TseUNRjlU3qjj+SAeR8SFdroPgMtX7XfYLlwyO6PTbWIizKwRMZ2ifoQQL2Lh6yLrLhX5b4pIULxIi1nEUi+ScobIjD/i16LjB+iU+wZW4CEF6+/v32XBItOz5Hgg9YUvrBX7yuTK+SPksRWHZHdxvQ5WXzwmXrbm10pwY4ukhjMdRfoXRNT2lzXI5pxqaWkRyY4L61akQYxFhWAJ0OshTZpX1SQHyxu0KQjrMSc2IiRAa0zRDIsUb32zVYprm3WKC+4DYhCRwK5A88SLG/I0boiJFRNSIjulbnFfWfH0hSTEq4meInL2TpGKL0UaDon4BjrWxUw7us3423t1k30SeOggueGGG3RAr1PQYZwH1uEyQo4HbFEmp0bJlrxquWxuhrywIU/TVOgSjA8LlH3VNmG5EelvIO42HKpydG1HdTY+7Q7UGSOChgVir9li05q98oa6Vjd7R1oXf6EXUbeH1G5Xk1ra8r+dJVJU26w/ei6end7p8oomo4xPjpT41g5zQogX4+PrSMc6U7InSJ8E3htvvKH1d7CwgFO01pkUF4vNZpMjR47Im2++6dp2165d/bKjxPuYnBalxe2IfiwZEy9rDlbKP9bmyL3fmiDpGRnS5OcrpfUmSYuhsSs5cRB9Q+SurSVPX4DYg4A70eYL1Jx+sNPRWHHZ3MxOKVg0ZuC9MSbRYddACPFyKtaLVG9xpGbbAt+7cbf2+ub69AmFZgpC+m3KRZOjHi+3yqCzOf+26pD87MzxUtFoknWHqmTJWF81Syakr2CG6+acGi1jORFx11/UN1vk2S+P6P4sHBUnc7NjO21T3miU7LhwSYoc+v0lhAww234msu8RkcjxIv4dftQFJw6ewLv99t7lgQnpjsSIYLV/+PJQpVy3KFse+XS/FNQ0yz/WHpGbTh4lxfVGWXuwQhaNiZeUKHYRkt6DOjc0VGBcHmruhhqztUX+tvqQ1BgsWsOH6F1X1i1I+o5LiaCxMSHeTotFZP9fRE5bK5LgmKDRH7CKnQw5Y5MiZFxSuH6p3bR0tAT4+cj2wjp5af1BSYsK0hmcaw5USmGNYah3lXgghyoa5UhFo6RFhwy5WMIki+fX5cjhiiZNFf942SgJauN556SswSiZcWGSEsnINSFej80o4h/er+KuzxE8jOs4FqWlpX3dHzIM8fP10ShedZNFagxmuXZBlvx9bY58mdMgmQmVsmxcohTXGmXNgQqZPzJORiaED/UuEw8BHnLbC+okPCjguA0PAw1sV57/MlcnuOA1j1nMXUWlUXvnKz7aUdtdhy8hxIsIiBAJTnKMJsP0iqEUeI888ki78xgOf/DgQXn88ceZviV9AgXms7NiZNW+chmdECbzU/xkY4lNXttUoBYT87Lj1LNs7cFK/QLklx/pCftK6qXOYJbsLmxGEE37prBWtuXXSm5Vk9QaLGp1Ar+7jJhQHaM3PSO6XzpYYar87Jc5sqOwTsXd9YtHandsV5TWN8uYpAhJZd0pIcMDa7PD727V6SLjbhMJ7TCD3S9UJO3swRF4V1xxRZfrFyxYII899lhfbpIQSY8J1S/UdYcqZXqCrxgsdtlR6Yh6oCFjWnq0Rvg2HKlST7JpGdHtRjoR0hYYDcOgGD50HVOzOwpr5bXNBTr7tSOYKAHzbUTaXt9cIJmxoTI3K1bmZMX0qUHjSGWjPPdljt6mv6+P3Lh0lL6Wu6LBaNHX9MSUyCFPJxNCBnFU2ZF/Ok7vfKDz5aFpgyfwumPJkiU6o5aQvoLIXGWDUT46IrIk3U9CIqLlq5xqeWr1Ybnt1DF6eaCfr3xTUKvptzlZsTrjlpC2wLoJ0TuTpUVSogLaTZj499cF8vlex+QI+M8tGBWngio2LFAjeOjqzqsyyK7iOh1jhs5uLG9tLZTRCeHa8Tp7RIxEHmdsGcTjhztLtIEIDnmxoYEq7rqKJjr3ubzBpD9yEll7R8jwITRV5KLafr/ZfhV4n376qYSHsz6K9B1/P1+dcrHpG1+pam6Ra04aISZriwq6x1YelBtPHiVT06M1qoK0GkQetseXJiMexAmEUk6lQaN3bbtpn/zisKZJwfKJSXL+tNROUWDUxU1KjZKzp6RoRA2TVb7KqZKDZY3asIHltc35Mj45QsYmRkhaTIhEBPlr6hWR5cKaZoc4LGtUYSetUypgZHyscWMY3xcTGigTUrtO3RJCyIALvKVLl3ZaV1NTIzt37pQ//elPfblJQlyg5m5crK9sL7erEfINS0bKM18c0Xqpv606LNctztbIHSwvkPZavb9CoyUQfl0NcifDj4NlDWKxtrhGhKHezlkDhwjwDxZl6w+D4wFBdvLYBF1QHrA5t1o25VSrZ+PekgZdjsXk1Ej51pQUrak7Fhhx1mCyyOIxCTrjlhDi5VgNIvlviviHiaR+y3G6O7BN5kWDI/AWLVrUaV1MTIwsXrxY5s6d25ebJKQd0UG+Mi5WpLnFrjYpP1o6Uv65LlfTtX9fe0SNYk+dkKQGyIiaIMKHJgwYJ6OWjwxfqhpNklPVJHHhR1P3724vbte9iikqvQXRtdMnJuuC+j505yJ1W1pv1Nco0r+wPkEEEBHlmZnRPa7ZK65rlhFxYdrYQQgZBljqRbbfKxKSIpKw2HG6O7BNHwSej90xSJG0obCwUMewFRQUSHp65/mQZGDBS7KuzpFGy2+wy8acakmKCJaQAD95ZVO+fHGgQi9bNi5BLp2TqV/a+HLFgHfYSkxKjdQUG6N5w5MtudXydX6NjIp3iKWdRXXy1xUH9TTMtGG1406g5s/S0qIpYxh/E0L6Dr+/+6kGb9u2bTqTFkycOFGmT59+IjdHiIJauuhoR5dhZCQGuztq8GBUe8W8TEkID5K3txbKqv0VmqJFChepuIzYUK2ZwtQCiD1466XHDL25LRk8EEmDiXBMiCN6h+gujIWdPwjcTdyZLDapbTZrowfFHSHEhbFcpPRzkeipItGTZdAEXnFxsXbLfvHFF9pUgS/QhoYGWbZsmbzyyiuSkpLSp50hpCOIyCHtarLatN4J/mRnTk7W+Zz/+DJH9pTUy2/+t0duWOLoTkTNFIbAI+W1Yl+ZTEyJkilpjOYNF9DgAMHkHEn21teF0mC0SkpUsFw8u4O31BCDusDC2mYZlxzRrSceIWSYsO9REUORyMxHRKxNIh/PcdintBhFln4kknzq4Iwqu+666zSNhugdhF19fb2etlqt8sMf/rAvN0lIO+PsoqIiXXA6wM9X5mbH6UizghqDFqQjOnf3meO1SxLdhw99vE9W7C3T1yVSthCCUcEBsi2/Rj7bU8oxZ8MApOnRXBEa4Ce+Pj56GhYl4KqTRujryF3A67Sg1qDCE80e6B4nhAxjdj8oMvkex+nCd0WCE0S+WyUy668ie/vWvNqnT5VVq1bJiy++KOPHj3etw2msW7lyZZ92hJC2X36YjILFWSIKKwuk10YlhmthOwa2IyX7y29N0GJ2fLnDuPbpNUd0agBANC8rLkxrnFbuLZdteTUaCSTeCZodMMMVfnZ43fz760Jdv2RMvIxJPHYX62BTUmfU8Wl4TR/LOoUQMgwwVSO0IRLY2tlftlIk8xIR3wCR5OUijYcHT+AhBevv3zm7GxAQMKDp2RUrVshZZ52l9VlxcXFy/vnny/79+ztt98c//lHS0tJ0f2bMmKGpZOL5oGli4ah47TREJA/1S0jHwhvv0jkZ4ufjo/V3v/5gj+RUNul1EM1DVy1q9DblVssX+ytU8BHvo6DaIPYW0Zmz2wpq5UhlkwT6+8r509OOeT2MvoPNDuo2i2qbtRsb/oqYHTsQ4H78/XwcdXc0NCaEBEY7xpXV7XWkZ4veP5qSNRQ4JlkMlsC7+uqr5YYbbpCSkhLXOpzGOlw2ENhsNnnwwQd11m1eXp7s3r1bReby5cs1Tezk73//uzzwwAPy3HPPSWVlpZx99tm65ObmDsh+kSEQeaPjtW4J9UuI1qEG9LQJSfKzM8dJfHigK2X76Z5SVwQwKiRAzZHzqwzy+Z4yOVLR6LqMeEdzBYyvo0MDNJr7n21Fun75hCR97rsCIu5wZaNUG8z6ukqPDZGsuFC1Q0FXa1FNs/5QwA8C1MudKC1Iy9YYVHQuGp2gEWhCCBEfX5GxN4t8PFvk3WyRiLEisTMdBybvdZER3+vTQeqTTQo6ZlFz5+fnp5Ey3AQaLyDCJkyYIL6+R3Xjrl27BuzZg41JZmamTtCA0APjxo2TM888U/7617/qeewbtrn88svloYce6tHtss16aMHraO3atXoa3op4nXUEKdotedWyq6hO4sOCXGOjIPj+tT5PbTLAtPQouXZBtoQH+7teD2UNJjXBxSzbKelRblWbRfrGofJGWbmvTFPyW3Jr1CsxLNBPHrxwikZ52wIBWFRr0Od9fEqkXicuLFAbetpG9aqaTJpKzalo0sYNRAbjwwIlqA/zj40Wm0YH0Sk7f1SseuURQvofj/7+LlstYqoSST1bxD9ENCVx6BmRUdeL+PoNThft9ddfL+5AWVmZ/nVaalRXV8uBAwc00ucE0R1M3li/fv2Q7SfpfxAFwQD4kNZ0HMaZoeECX+Y/OnmkTrd4Y0uBbC+skwc+2C3XLxmpdVh4PSRHBqudCvzS6o2OebbOiQfE84BoR/QOgg0S7ePdpbr+tIlJncQdGnRQwwnLndlZsWqU3RUa0QsM1fT+5NQobdLBXNrSumZB5haRQkycaCsKuwL3h/Qv/iLqjOag7iKKhJBhTtLSzjYpMEHug7gDffpWQ5q0P0CHJJZj0VWtH7BYLPKTn/xEZs+eLbNmzdJ1paWOD/aEhIR22yYmJsqmTZu6vQ+TyaSLk7YpX+K+oPNwema0hAX76wgp1GBhLig6KJeNT9SGjGfWHFavvD9+sl8umJ6mFiu4HIXtGFm1v7RBDGabzgqNCTs6+YB4DrUGi5TUNWtqFVY6EHD4AbBsbGK77VBTh9cI7HQWjI6X8B6Keog9jBobmRCu9Xl5VU2SV22Q3GqDQN9hekWwv58rEoz7MVpapMlsER/xkaTIYJmYGqmTKlATSgghbmuT0l/AUiU4OPiYy+HDnbtHIAq///3v62VvvPFGu5RwV2D7Y5ndIuIXFRXlWpCCJp4BnlfYpywblyjRoYEayUG0BKDm7pffmijzsmMFJVSoy/rr5wc1egeQahsRF6q1Voj4ldcbh/jRkL52zxpMNk3JOqN3iyDgWtPyzihfYXWzpMeG9krctQXiLDU6RE4aFS/nTUvVyRNT06L0tsy2Fqk1mjWVC3EHUTg1LVpOn5QsZ0xOVnFIcUcIGUyblCHNS6ERAktvwAc1fPg+//xztWsZOXKk6zJnB295eXm761RUVEhycnK3t3n33XfLHXfc4ToP/zWKvKEDgn3KlCmu0z0BX7ynjE+UzblVOskAo83wBQ97FYynmpASKa9uypfdJfXyuw/3yq2njNHr+Pv6qshDCg4j0PDlj/Qd8QzQ/ABRjzF2EHowvsZvOTRXtAWX4QcAbEn6Iu46gtRvdjyWMP1MgqiDyBO7SIC/j0b0jpe+JYSQHtmk7PuL9AWPqi53irsPP/xQ/fba+vCBmJgYbfKA8Gt7HZxfsGBBt7cbFBQkkZGRriUiwr08s4ZjVA42OFh6M2YsKjRAFo9N0MkX6IzEQHjn7SGic+/ZE1zGyA9+tE8bNABStjBGRifm2gMVmsYjnkGNwSwV9SatiVtzwGFqjKganmcnGFeGqO6srBj1yOtv8PpCxA61dXgNQvxR3BFCPNImZSiAUPvRj34kH3zwgXz22WcyZswYnZyBpW0j8F133SXPP/+8vPvuuxrJu/POO3XSxo033jik+08GB3Q6zh4RI0vGxqtwy6tu0q5JgIjdPWdNkLFJ4dJssclfVx6U1fvLXV/SKKhHs8aXBysp8jwE1FcarTatf1t/2CHwTh6b0M6aBHVziODCAoUQQoaLTYrHtA6iQ9aZzoV5cVvgfYeaPHDttddqkwQaMNBli1QfbFTQNk08A9RMOtPsaJDpaZrWCcTa6MQItU7ZdKRaU3hIuyJdi7TtHaeNlRc35sn6w1Xy8lf50mS2ydmTk/V62A52FusOVWo0kOlaz0jPbsmr0ecRETp0vTpBB2t8eJBMSY/uVTSYEEIGlRkPi6SeddQmBcAmJWa6yEiHvhkUgZefny+33XabrFu3ToVXRxBV62+Qruvp7d566626EM8EEdl9+/Z12RHdG+A5hm5adNgeKGtw+eWh+/baBVkqBj7YUSL/3Vak/nnfnZnuEnmoyVt3sFJOHpegXZDEPdOzVY0mTY2uOVCh6xaPiXelR5GWbTJbZU52bL/U3RFCyKDZpDgje2P6nn3s06ceomVms1nNhFH3Roi7An871N9FBQfItoIal18ehBxsU2Bx8eaWQvlkd5mYLC1y+bxMl8grqHFE8pDyiws/WtNF3IOKRpNa3MCKBB510HWLR8e3a6xA2h2NEIQQ4lZYDSL5b4r4h4mkfstxujuwTeZFgyPwNm7cKIcOHTpmZyoh7uaXFxHiL5tyqnVcVHp0iAq50ycmS2iAv/xrQ66sPlChM0IvmZ3RWpMXIvnVTSrylo5PVGNb4j5RXvjdIe2+MadK101KjdJOWWCy2jSFOyk1kpNKCCHuh6VeZPu9IiEpDjNjnO4ObDNYAg92JL2tiyJkKHHW5YUE+svGw1XafJEZE6bpvEVj4gUjEF5Ynyuf7y3XRo1vz0jTJg1sk1vdJBsOV8mSMQnaLUmGnnqjVevrIoP85asjjjKR+dmxrsvL603qeYcIHiGEuB0hySLfLjx6vu3pfsK3rynae+65R9O0hHgSSL0uHZeg9Xk51U06dQAgjXvZ3Ew9/b+dJfLRrhI9DQEICxUMnYfHntNEmQwtEHdNJpuUNRg1VRvk7yvTMxwjC/EcoXt2fHIEzYUJIcOWPkXwXnrpJdm7d69OkUB3asfutF27dvXX/hHS76Ce7uRxiWqrkVdl0IkXsNmAUbLZ2iJvbS2Ut7cWaboPI8xwGVK6+0obJDwoQGZksiNzqCmqNYi/j4+szXFE7/CcYDKJU/ylRIWwA5oQ4llYGkUaDjjSt23xDRJJOGlwBN7111/fl6sR4jag83LxmARZ71Op0TmnyMOsWowy+2RPmaZsY0IDZHxypNZ6JYQHyTcFtRIZ4q/pXjI0NJttUlJnlLAgP9mcW6Pr5mfH6V94HsIXb2xyuNZeEkKIR5DzisjmG0WsDZ0vC0nrUwq3TwLv9ttv78vVCOkRqO90joobyFpPWGeg/g4BaIw3G9Eq8r4zK12qmszqrfa3VYfl52eOl7SYEIkIDlCD5M05NdpwkUj7lCGhstEkjUar1DdbdEpFRLC/GhmD6iazxIUFsfaOEOI5wO9u849Epj0okn2FiH94hw365uHJn7jE7UDKHwbHWAbanBZjpRaOjlcrDXRloiYPzRU/WJQtYxJbJ16sOCh1zRbdHrV7zRarduPCO48MPphMgZmviKaCmZkxrlq7eqNFxiSFa8SVEEI8AnONiI+/yLibHWPLfP07LH37POuz++eePXvknXfeUdPjjgbEzz77bF9vlpBBxynykN6DyMuKDdNI3o+XjZYHP9qr47Ce/uKw/PT0seLv6ytp0aE6QWFbfq3W6HHu6OBhtbVIQXWzdjNvcwk8R3MFonqhQX7aFEMIIR5DYKxIYJRIU55I2Ih+u9k+RfDee+89mTVrlqxZs0aeeeYZqays1HFgGCVWUuLoPiTkRDzOMKoMS9s5wwMJ0rULR8VLcmSwFNQa9H6x7pZlY3QUFox039hcoNsiWpQSFSz7SuvlSGXjoOwfcVBtMEtts1kbKRog6AL9ZFyyox6y2mBScRcT5vDCI4QQjwCZqjlPi6w+R+TgUyJFH7RfSlcMXgTvvvvuUzF32WWXaQoNkTyLxSI33XSTzhEl5ETAawgRYrB48WLx8xucdFtUaIAsGB0vq/dV6DxaeKglRwXLdYuz5YmVh2TV/gptxkBzBqJ+wf5W2ZpXq7NOnQa7ZGCpbDBrp/POojo9D2sURFWRWkcEllMrCCEeSeUGkbpdIpt/LOLb4fskNF3kvEODI/AwJ/T888933IC/vzQ3N0tISIj89re/lcmTJ/flJglxCyDWThodJ6v3l2uUCGPNpqVHy/nTU+Wdb4rlla/yteFiZHy4JEYESU5Vk9aCwQSZqdqBp7DWoJ53W/NrXPV3oKbJIrFhQSrICSHEo7AZRfY8JDL/nyJZl4v4BgxditZoNEpYWJhrqsXBgwcd+2izqdgjxNPNkOdmxWozBTo1wdlTUtRrzdpil3+syVGrDkSvU6NC5HB5oxypbBrq3fZ6YF9T2WDShpcag0WFHkaROZsrRiaE6RQSQgjxKFrMIn6hIiOv6Tdx1y9dtOeee6784Ac/kL/85S9y6aWXakqNEE9ndGK4zMiMkcomkxgtNu2svXZBlsSFBerkhJe/ytM6PXRrQlTsKKxlV+0AU9VolkaTTfaWOHyipqZHaTMMnp+gADS/hAz0LhBCSP8TECkSGCPS0Ps0bL8LvPfff991+qGHHpLZs2fLCy+8IKmpqeygJV4BonNT0qJ03FVhbbPWeKHu7oeLRwocOb7KqZYNRxxD7pGqRTp3X6vwIANDeb1RfH3ssqPI0T07I6M1PWswS2J4sKbXCSHE47A2iySfKrJyuci+R0Xy32q/FH3Yp5vtUw3eOeec4zodEREhTz31VJ/unBB3BpMQZmfFatSosMYgI2LDNLJ33rSj9XhjkyJUWCCyt7e0XkbEheooNNL/9igQ2marXYprIfTElZ41mG0ya0QYayAJIZ7rg5f/b8fpnQ90vjw0TSTt7MHzwQOot8vNzZUJEyacyM0Q4rYgajc3O1Y+32uR8gaTJEUGy9mTU2RPSb0cKGuUFzfkyU9OG6NdtLBM2VtSr556A23QPBztUVBnl9ta6wihHRbkL00mh1UKmysIIR5LaKrIRY7MxJCnaBsbG+WKK66Q8PBw10gpcPHFF8vXX3/dn/tHhiEQR+PHj9fFHYQSInRzsmLFZLVpoT+6Za8+KUsC/HxU6K0/7EjVJkUE68gzGCOT/gUjyGCPguMNpqY5zI1rmy2aIsfMYEIIISco8O6++24pKiqSzZs3t1t/zTXXyAMPdBFeJKQXYP5scnKyLgM5i7Y3jIwPk6np0RrFs9haNJJ3/rQ0veyNLQXa2YmIksVmUwPkwTJoHi4U1TTreLJ9pUcbLHCM0WCRFR/uFj8ECCHEnejTt+d///tfNTqeOXNmu/Xz58+XVatW9de+EeJeTRfpUZIVH6ZiA+Ji+cQkNT5GDdg724pcs2rzqgyM4vUjmPmLJpaSeqPa1MSHB+okERx3jCZDBI8QQkg/CDyMJsMgeND2lzNq8hi5ICcKXkNVVVW6uNPrCXYos0fESHiIv1Q2mnVk2WVzM/WyLw9VSn6VQWv2LNYWOVTOjtr+tUexysGyRld6Fp87mp7VKSJMzxJCSL8IvOnTp8uHH37YSeD99a9/lXnz5vXlJglpN6ps586durjb6Dt0yGJ6QpPZqmbHKPaHKTJk6Otb8lWQxoUH6oSLqkbW4vUHlY0msbW0yK7W8WRIzwKkZzPjwpieJYR4F81lIiWfipR9IWLpe7CgT120GEl24YUXyvr16/X8ww8/LB9//LGsW7dOVq5c2eedIcQTGJ0QLqV1zbK3tEGy48Lku7PSdVwZumrxFwbJqNVDqpaWKSdGS4tdU+L1zVaN2AX6+8q45AhN26J7lulZQohXceBvIt/8TCRirIitWcRUIbL4PyKJSwYngnfaaafJJ598Ijk5OVoIjykWoaGh8sUXX8iiRYv6cpOEeAzoop2eGSNxoQ6D49iwQDltoqNk4d3txdJit0tMaKAcKm/kdIsTBNYoMDLOrXLYo0xMidTpFRB8cWHonu0wlJsQQjyFFquIvU2WymYW+ebnImduFTlrq8g5e0VmPS6y7Wd9uvk+tyiedNJJ8u6772o3bUlJifzvf//TdYQMByKDA2R6ZpQ0W2yaKjx9YrKEBPhJYU2zbMuvleiQAKk1mB3dn6TPVDWZNRW+v7V7FtNFgMFilcy4UJobE0I8F2O5yKcnidTubF3RKvb8w45uExAhYrf16ebdw4OCEA8kOz5ca/BK6owSHuQvp05wRvGKRHxEggL85FBFo6YZSd/Hk1laWuRIq8ExplfAjxBRPKZnCSEeb3A8+Zciay4Q2X4vuhpEJv5M5H8TRVacIvLpQpF1l4lM+dXg1eChW/bJJ5+UL7/8Umpqajpdvnr16j7tDCGeBLpoEVEqrm3WNOLyCUmyYm+5jtLaWVQn45IipKzeKBWNjgkYpHfAbxDHsqzOKNDISZFBajqNtDhSs0iNE0KIR5N2jkjiySLbfyHy0QyRuU+LjLhUpHqriG+ASMJCkWBH8GBQBN7111+vfncXXHCBTJo0qU93TIg3gCaKCSmRsulItWTHh8mSMfHyyZ4y+WxPmUxLj9bpC0jTUuD1npomx3iynCqDnp+U4kjPwjIFjRaYFUwIIR5PQITI7MdFKjaIbLpeJP4kkRl/FAl0fOb1lT4JPNTebdmyRcaOHXtCd05IV8B6Z8yYMa7T7s745EjJqWyS8kaTnDI+UT7bW6YTF/KrDRIVHKCXTUqLVB890rv6O0TxnNMrJqZGirWlRfCSSKC5MSHEWzDXitTtEfELEjl9vci+R0U+mi4y808iGRf2+Wb79BMYHbPx8fF9vlNCjgXGk6WlpeniLqPKjkVIoJ+mamHdERkSILNHxOp6RPGiQh3NFuWcT9trSuqapdHomGLhh/nEyRF6PiI4gAKPEOIdHH5O5J0MkY3XOGrxPhgnknKGyNIPHUJvzbdFDMV9uuk+fXteffXVOnPWZutbZwch3gZGmKVHh6qQc1qmbM6tFqO5RetmC2scaUbSM9A5Cy/BolpHF/KoxDAJDvBTe5TU6GA9TQgh/UlTU5PU1tZ2e7nJZOpf8/0Wi8jXt4ss/1Lk3AMiFxSITP2NyLY7RaImiJy2RiTldJE15w2ewLvlllvkpZdekqSkJJk1a5bMnj273ULIiYBpEHiTYXGnUWXHAl2dE1IjteMzIyZUZ9RiburGnCq1VMmvblY7FdIzqg1maTJa5XBFo8v/Dq8Fq90uKVEhPIyEkH5l9+7dkpCQIDExMWK1WttdtnXrVtU64eHhutxwww0q9vrFBw+EpB5dF5opYm0NCKAeZcyNIqd8Nng1eNdee60ehIsuukiio6P7dMeEdAd+IX3zzTd6evHixeLn5xnRmoyYEEmNDtEo3qLR8fLqpnxZe7BSlo5NkIIag3bUjohr429EugVj3lB/t7/UIfAmpSIFbpOQQF+JD2f3LCGk/2hubpZLL71UzjvvPHnjjTfaXVZXVydnnnmmfOc735G1a9dKfn6+LF++XIKDg3U86wnhHyIy9maR/00QiV8gYjOJVG4Qmf98++0CY/p28325EuxRoHZHjhzZpzslxBtBVyfqxIpqDDIzM1r+/XWBphgLaprVkBd2KhR4xweROhhGVzdZ1Eg6LNBPRsSGasoWXctRIQGD8GwSQoYLt99+uwYTFixY0Engvfrqq9LY2CiPPPKI9h+MHz9e/u///k9+/vOfy4MPPqjrTojpD7baomwR8Q0UmfcPkbBM6Q/6lKLFeLLIyMh+2QFCvImM2FC1RIEwmZnp+NW19mCFRAQFqNBjmvb4NJis6iuY1zqeDDY0EMhNmF4RG+oRndWEEM/grbfekjVr1qiA64qvvvpKZs6cKWFhR7MvJ598shgMBtm50zmB4gSJmSYy6gci2Vf2m7jrs8BDavaee+4Rs9ncbztCiDeAWrzxKZHaJLBgZJyu+zqvRkIDfKWh2SKVjf1Qt+HlVDeapclklYOt9XeYXoF0rb+vj8QxPUsI6QENDQ1SX1/vWrqqmcvNzZWbbrpJXnnllW4jceXl5Z1cQxITE12XuTN9StG+//77sm/fPnnttdckIyOj0y/qXbt29df+EeJxoMkiJixQIoJbJDLYX+qNVjlQ3ijhwf5SUW+S9JgTDOl7OZj8Ybba1D/QWX/XYLRqs0pcWNBQ7x4hxAOYOHFiu/P33Xef3H///e3WXX755Tq4AeVmaOpDVM5Zd4dmiqAgx+dNx85ZZxOGu2cT+iTw0EFCCOneFw8zajfnVKsn3sr95bI5t0bOnZYiedUGmZoRrWPOSGdsLXad/FFSaxI0UKdEBetIsrzqJjWUDvR3f19EQsjQs2fPHvVSdeIUa205fPiw9hM88cQTet5isejfUaNGqRhEbR5uY8eOHe2u54zcpaa26X71FoGHB00I6R7Uiu0qqpPJaZEq8L4pqJWLZ6dLXbNFqpvMNOrtBphC4xjlVjujdw57FAi/5CjO8yWE9IyIiIjj9gqUlpa2O//yyy/LlVdeKZWVleLv75BHixYtkn/9619SU1Oj7iHgs88+k6ioKJk8ebJbPx38OUzcDoS9ETLH4u4h8O5A1CktOlQ7PmNCA7Tp4lB5o5gsNqlqYh1ed0D8Nlusst85niwlstUexU/imZ4lhAwyl1xyiWRmZqo93IEDB+Tjjz+Whx56SO68804JDHRvyyYKPOJ2YDwZ3lBYPGFUWVdAmGYnhAkqN5zdtFvzazXFiBQk6Rp4BTY226Sy0axp7HFJEVp/FxcaJJEhfUo4EEJIj4BgQ2SubWABfneff/65fhehe/a2226Tu+66SxtN3R1+YhIyQKB+LCYkUEYnhMmKfSI7i+rkwplpOlsVc2tDA/n2a4vZ2iIldUYprHUUOo9JDJegAD8xWJplSkykx0ZzCSGewcUXX6xLR7KysuQ///mPeBqeGR4hXg1qrpyt7Z4yqqwrMC81K97RURsa6CeNJqtOucDfqkZaDHUE3ncNRourexbpWWtLi/j5+Ep8BOvvCCGkN1DgEbcDLemY/YelXwc7DwFpMSGalp2U4ij2RRSvxW7XUVykPRC9JkuL7C9rcDVYNBqtai8TF+betS6EEOJuUOARMoAkhAfpeK2RCQ4X9O2FtZqaxQgzT45ODgQY5Yb0tdHSIuFB/joVBPV3yVFBGg0lhBAyDAQefGjeeeedbkeFbNu2TbtdCgoKBn3fCGk7nzYrLlRSY0LEz8dHa8wwrgzpSBggEweoScSUD4xzc6ZnfX18xGxrkZSoEB4mQggZDgLPZrPpuDQUQz733HOdxpOg0+WMM86Q3/3udzJu3LhO7tWEDCbJUSE6hWFUaxQPdimw/qhpYh1eW3sU1CYeajOeDEIYkTumZwkhZJgIvF//+teSlJTUaRQJuPfee6WoqEj2798va9eulQ8++EAeeOABWb169ZDsKyHx4UESExooWfEOgbevpEF8WkUNcYCaRIPJKrlVrQ0WqZGanoWPYHQo6+8IIcTrBd6aNWvUVfqZZ57pdBlqmuBE/YMf/MDlOH3KKafIrFmz5KWXXhqCvSVE1M8NaVo0XIB9ZfUS5O8nhTUGaWlhHR7et0U1RimuM+p4stToYBXEjSaLpCO1zbFuhBDSazzKiKuqqkquuOIKeeGFF1wCri2FhYVSXV0t06ZNa7d++vTpsn379m5v12Qy6dI2zUtIf5IUFazCJSzQT5rMNo3e+fiKI0oVGjCsDzZqEasNJsmrcvjfTUqJ0k5jSN+EiM7zIwkhhLi5wIMNRn5+/jG3Wb58uYSFOVJbGBVy6aWXalSuK+rq6vRvR/EXFxcntbW13d7Hgw8+qGlc4h7A0BbGks7T3kBcWJDEhqKbNlytUo5UNkpoUJQ2Wwx3gQexi/TsgTb2KE0mq3bSYuQbIYQQDxN4X375paxcufKY28yfP18F3ptvvqnpWUTw0D3rFHSHDx/W8+eff75rLpzB4IgEOGlsbJSgoO4jAXfffbfccccdrvOo4euqvo8MDhgJ4xR43gK88JBuzIgNUYG3t6RBpqZFq7hx1uYNVyrqjVJjsEhVk1n8fX1kTFK4euIlRQZJRPDwFr+EEOKRAu/WW2/VpSeEh4fL0qVL5dVXX3WtQzp2z549mrI977zzdHapn59fJ2sUnM/Ozu72tiH+2gpATFAgpL9JigyWrDiHmDtS0SQITpbUwQ8v2msilb3F1mLX2rvCVnsUHU/m76cdtGkxoUO9e4QQ4rF4TA3e2WefrUvH2jqIvr/85S+uocDLli2Tt99+W6655hpdV1NTIytWrJA//vGPQ7LfpG9F984obGhoqNeIn/iIIMmICVXbD0SrSuuMEtI6wmy4RqqQoq5rtkhOm+5Zi61F/Px8tPuYEELIMOmiPR6op/v888/lhz/8oTz//PNy5plnyqhRo+T73//+UO8a6SEYT7Z582ZdPH1UWVtQU4amAWdKFpYg8MOrNVhkuIIUNertDpY5/e+itPEEgjcmbHiKXkII6Q88WuAhWjdlypR262bPni1btmyRkJAQ+eSTT+Tcc8/VWj9E9wgZalKjQyQtyvFaPFjeqN2iiGANVxDFLKs3isnaIhHB/lqniPmzKVHBmqolhBDi5Snarnj00Ue7XD9p0iR57LHHBn1/CDkeceGB2kkL8qsNaniM8WWT06KG3cEzWW1SWn+0/s45nsxix3gy/iAjhJBhG8EjxNOA7UdGbKjEhAYIPI7L602apkRTwXCjpskiDUZLu/FkzWabhPj7qdExIYSQvkOBR8ggEuDnK8lRwdpsAQprm6XJZBmWadqqJpPW2+W3GhwjgtdgsqgvIAUeIYScGBR4hAyBXUp6rGNs2eGKRrG2DM86vKLaZimqadaJFWnRITpz1mCySXp0iPhyPBkhhJwQFHiEDDJIz45urcODH569xS5VjUdH5Q0HYA1T1WDWOkSnPQrm8raIXRIiWH9HCCHDusmCeCfwvcvIyHCd9jYQqcqOD1PbFAid6iaLlNWbVOAMl8hVdaNZGozmo+PJUiKlyWyViKAAiQ1n/R0hhJwojOARtxxVBu9CLDjtbfj5+qhdSkZMiCtVidoz1KMNFxCxrGqy6Igy53gyPH6IOwhfQgghJ4b3fXsS4iFTLZyjuJCmRPfocKnDQ6SyoMZwdDxZUut4MqtNffAIIYScOPypTNxyVJnJ5KhJw4xgb0zToks0O94h8I5UNuFBDxuBh8eJBZM8wKSUKMd4Ml8fiQvjeDJCCOkPGMEjbgfGk23cuFEXbxpV1paokAAZmxihogZ1eI0mm5Q3GGU4gDm8eLyHyo/63yE9G8nxZIQQ0m9Q4BEyBEDYwSrFObGhosEkVY1mne7g7VQ0GKWkzqDjySKD/SWtdTwZ/AE5nowQQvoHCjxChoiEiCBJbRV4aLQwWKxS3+zdjRZIxRbXGqWg2lF/NyElUse1cTwZIYT0LxR4hAwRUSGBkhnnqMPLqzaI2doi9UbvrsOraTJLfbNFDle01t+lRorR0iKhAX4Sy/o7QgjpNyjwCBkiokMDZGxShJ4urG4WW0uL1DaZvfr5qGw0S12zWQqqO4wnCwmQ6JCAod49QgjxGijwCBnCubRjk8LV981mxzQLh+GxN1Os48mMnceTxYQOG5NnQggZDCjwCBlCEiNCVOiA0nqjpmgNZu+sw8Pjqmw0aTramZ6FJx7+xYfTHoUQQvoTCjzidsD3LjU1VRdv9MDrmKZ1mvsW1TTruC5vbbRAl3CD0SIHy1vHk6U6xpOFczwZIYT0OxR4xO3AeLKxY8fq4o2jyjoKvFEJ4S7DY6vNexstKhtMWoPnGk+WGCH1RqvERXA8GSGE9Dfe/e1JiJsTGugvE1MjBeVnmO4AA2B0mnrveDJHehbNJYH+vur7lx7t6CQmhBDSf1DgEbccVWY2m3XBaW9nRGyoJEY4atBQo1ZWb/S6x+0cT5ZTebR7Fp54Ab6+EhceONS7RwghXgcFHnE7MJ5s/fr1unjrqLK2RIcFSkqUow6vvN6ko8uazN410aKqyaT1dx3Hk0WE+OtcXkIIIf0LBR4hQww84DJjHWnKwtpmaTbb1AzYmyipM0ppnUnMtqPjyeB/B2GLVC0hhJD+hZ+shAwxEDwjE8P0dH6VQawt3tVogTo7CDzU4AHUHKI32mazS1KkY1QbIYSQ/oUCj5Ahxt/PVyanRWpnabMF0TurWop4C9VNDnsU13iylCh9nCGBfhLP+jtCCBkQKPAIcQOSI0MkKdLZaGGWigaTdp56AxCraLDIbzU4npASofV3qL2LDOZ4MkIIGQgo8Ahxkzo8Z6MFJlrAABiLp4NuYAg7mDiDjBjHeDI8Nhg8czwZIYQMDBR4hLiJwMuKD3XNa9VGC6PnCzw8hhqDWXJb7VGmpEVpjaGvj4/Et1rDEEII6X/8B+A2CTkhMJ4sOTnZdXo4EBbor5MdACJe1ha7dtI659R6KlWNJmk0WmR/Wb2en5wWJU0mm4QH+0tcGAUeIYQMFBR4xO3AeLLx48fLcAKpSkS3Avx8xGRtkVqDWZsTPB2YNpfVwdvPJiEBfjIyIUw7arPiwrTJghBCyMDAFC0hbgJSlilRDtuQygbPb7QwW1ukuNYo+U57lBR0CvuqF16qh0cmCSHE3aHAI25ZmG+z2XTxtpFdxwIdpU7hg8gXGhEaPbjRAhHIumaza3oFrGCMFpsE+/txPBkhhAwwFHjE7cB4srVr1+oyHEaVOYkM8ZcRsWFeM9ECc3Wx/3lVBlf9HexR0FDC8WSEEDKwUOAR4iaEBvrL+BRHo0VBtUEsthY1PfZEEHnF5IrCmmZBDBbNIhB1jWaL2qP4+Q6P5hlCCBkqKPAIcSMmpUZKkL+vdtHWNFmkuskkngiEKVK0ua3ROzSQoJ4QGfcE2qMQQsiAQ4FHiBsRGxZ4tNGi0SQVDWaPbLSobGq1RyltcNXfoaYwIihA4sJpj0IIIQMNBR4hbtpoUVqHRguLNJg8L01bWmuU8gaT7ntwgK+MTghX0+P4iEAJD6I7EyGEDDQUeIS4EZEhAZId72i0QA1bs6VFGoye1WiBTtmiumYpqHaMJ5uQHCn+fr5istgkLdoxrYMQQsjAQoFHiBsRHOAn45MdjRbFdUaxWNFJ61kRvKomszQ0W9vYo0SJyWqTAH9fjeARQggZeJgrIW4HxpMlJCS4Tg83JiRHSGignxjMNqlutGgtnidRAQ8/k0VyqppcDRawR0F0MjaUAo8QQgYDRvCIW44qmzRpki44PdyIDgtq32jRaBKbhzRaoCEEs3SLao3aMZsaHayNI40mi2REh2iqlhBCyMDDT1tC3NDwGL5xoKTeKAZMtDB6Rpq2xmCWWoNFjlS2pmdTo6TFbpcWu48kRjpEKyGEkIGHAo8QN+ykzWrTaGHERAsPabRAtBF2KHtLHPYo09KjpclklfAgjicjhJDBhAKPuB2YQbt69WpdcHo4N1qU1BnFbLV7jMDD5IrKBrM0mqxaRzg60WmPEiQRwQFDvXuEEDJsoMAjxA0ZnxwpEUH+WscG02BPaLSAnUt5vVFyqhpdzRUYSYYO2nTaoxBCyKBCgUeIG4KO0+TWRouKBpNUNpjEamsRdwb7icidMz07NT1Kve8CaY9CCCGDDgUeIW7aaJEec3SiBSxTYDXizpTUGqW+2aJpZV8fR4MF0rNRwYG0RyGEkEHG43zw7Ha7vP/++7Jy5UoJDQ2Vq666SsaPH99um9zcXPnnP/8pZWVlMmXKFPnBD34gwcHs4CMe2Gixv0IKWydaoA4vJizQfadX1B6dXjEmMULCgvw1qjcmKZz2KIQQMsh4VATPYrHI+eefLzfffLMkJSXpcuWVV8rOnTtd2+zatUumTZsme/bskTFjxsjTTz8tS5cu1esS4kmNFhjxBUrrTWK0WN16ogWEHATo/rKj6Vl49+FfYgR/XBFCyGDjURG8P//5z/LFF1/I7t27JT09Xdf96Ec/kqYmh2M++NnPfiZz586Vf//733r+iiuukBEjRsiLL76okTxCPAV0oEaFBEhds0WqmyxS3mAUkShxR8rqjWIy2+Rg63iyaRnRWo+HzlmOJyOEuDNWq1UqKio0aNSduT6yh8XFxRIRESGRkY4f3+6OR0XwnnnmGY3YOcUdCAoKktjYWD1tNpvls88+k0suucR1OZ6wU045RdO6xDPAeDI8p1iG46iyto0WzokWEHeY8Wq2ul+jhcXWInnVBimuN2rULikySJIjg7UeD40ioYEe9TuSEDJM2L9/v3zve9+TmJgYmTlzpgq3n//859LS0v5zFoGl7Oxsna4UHx8v3/nOd6Sx0fFj1p3xGIFXV1cnOTk5Mm/ePHn22WflxhtvlN/85jdy6NAh1zb5+fmais3Kymp3XZw/fPhwt7dtMpmkvr7etTQ0ONJMZGjAL6ipU6fqMhxHlXU50UIbLawazXM3YOFSazDLodbo3dT0aP1rtrVIauv+E0KIu7Fq1Sq54IILpLKyUkpKSlTIPfXUU/LII4+4tkFkD6Vhl112mVRXV6vO2LFjh9x2223i7gzpT2sItS+//PKY2/zhD3+QxMREl+i6//77VeQtWrRIvvrqK1XUH3/8sSxbtkyamx0F3mFhjikAThBSdV7WFQ8++KA88MAD/fKYCOnPRovshDCRfSIF1QaX4XFCRJBbHeSyOqNG8XYX1+v5aelRKkZhdBwf7l77SgghTlDi1ZZZs2bJOeecI59++qn83//9n6577bXX1HD/V7/6lQYckpOT5c4771SB95e//EX1hbsypAIP3a/+/sfehZAQRwQgKspRezRu3Dh59dVX9fRNN92kwu++++5TgefMi9fW1ra7Dahu5/W74u6775Y77rjDdb6oqEgmTpx4Ao+MkP6daFHZaJZmi0Vqm8wiCe5zdJGSRXq2qsP0CuxvbFigRIdwegUhxLPStpMmTXKd37x5s8yYMaOdEwcCTMj8ocFzwYIF4q4MqcDDQcLSE6CS0SwxefLkduvxRLzyyit6OiMjQ4UcmjDOOuusdp21Ha/XFtTxYXGCNC0ZOvBrad26dXp64cKF4ufnN2yfjpHx4RIbFqBNFtWNFimrd6+JFlWNJqluMsuhitbmivRo8ff11Xm06KT1hSEeIYQMMg0NDe2+yzt+z3fF448/runXv//97651SN/GxcW12w51eM7L3BmPKnBCg8Unn3yiyhmg3g7p2Tlz5uh5hE/RYPH888+7CiCRxsWC/DnxHFDk2rHQddhOtIgMcVmRoAavyWR1q+5Zs8Um2wvr9PzMzGhN1wb4+tAehRAyZEycOFEDPs4FpVjH4q233pKf/vSn2syJhgsn0BUdbdbQ0AncPfjgUe1tSKVu3LhRJkyYILNnz5atW7eq2THsU5zgSTzttNPU4BgLBtbfcsstcsYZZwzpvhNyIhMt9pTUq5Gws9ECJsJDTQvSs1UGqTNaNYqHkWQTUyO1ezYqJFDiwt3TlJkQ4v3s2bNH0tLSXOePFb3773//K5dffrk88cQTcu2117a7DJnBLVu2tFtXWlqqf9s6ergjQ/8t0Qsg5lD8CJGXl5cnt99+uzZctFXRsNbYtGmTrFmzRidZ/P73vz9mepYQj2i0QJd4tUFr3iDw3KE7FbYtWA63pmenpEZJkL+flJiaZWpatAT4eVSCgBDiRUT00K/unXfekUsvvVQee+wxuf766ztdjkEJaAiFnoDtGvjwww81Tdu2Vs8d8SiBB+CLdtJJJ+nSHWjcgPcdId4x0SJCUMlWY7BIs8WmqdoJKe6RnsWIsh1t0rM6vcIukhw19AKUEEKOxUcffSQXX3yx/OQnP5FTTz3VZbsWGBgomZmZehqedwgUQQT+7ne/kyNHjqi7B9Ydr0l0qHHvvSOESGZsmMSHB0pFo1lToRB4VlvLkM53RXo2t7JJDCabevT5+frIlPQoaTBaJCKE0ysIIe7P6tWrVci9/fbbujgZNWqU1vuDgIAA+fzzz+Xee++VG264QaOCjz76aJfRPneDAo8QNycqNEAnQkDglTeYtMmi3mhVG5KhorLJpOnZI5WO9CyijJhYUdlgkFFJYZxeQQhxex5++GFdjge8eNt21noKLJIhbkl0dLQuxFGHlx4TqocChsdGq00nRwy1ubHJapOdRc70bIzOarS2tEhatGNfCSGEDB2M4BG3A00z06dPH+rdcBsigv3VPBgcqWzC2GupGULDY9TZ5VZhskaL/kV94LSMaGky2SQs2N/tJm0QQshwhBE8Qjyh0SIlUgL8fMRgtqmQQt0bImZDZW5c1WTSGjwA8RkVEqDdvRB3kcH83UgIIUMNBR4hHkBqVLCktHamYppFndEiDUNkeFxS1ywma4vL3HhGpiOVjtRxZmyodroTQggZWijwiNuOKsOC0wSNFoGSHu2YhVhYYxCDySq1Te3d1QcDdO/mVBq0i/ZQuaPBYvaIWDVgxhzaxIij8xoJIYQMHRR4xC3BaJiO42GGM0h7joh3GB4frmgSJGdrDIM/lxZdvEjPHi5v1H0Yk4hZuYGano0LC5LokIBB3ydCCCGdocAjxENm0o5NitDTqL+DuirG30GmuLZZmyy25tfq+TlZsfoXtYEj4kPF15fpWUIIcQco8AjxADD2a0RcqMv7rrLRpJ20jYNYh4epFbmVBrHZ7NrNi1K7WSNidD0aQdg9Swgh7gMFHiEeAurb0GwBimqNGjVTu5RBorzeJLXNZjlQ3qDnxyVFuLpnITzjw2iPQggh7gIFHiEeAsRUeoyjkxYTJFrsdqluGrw6vLzqJvW825Jbo+fntqZnMVkD0UWmZwkhxH2gwCPEgyZajHQaHlc0SbC/nxTVDI4fHmbMFlY3i9nWIgU1zeLn46PTK0wWmwT4+0pyJLtnCSHEnaAjKXFLIiIcDQWkw0SL+HAJ8vdVH7p6o0X8/Hx0Li2iewMJGjvqTRbZW1Kv5yekRkh4sL+U1RslLixQ4sKZniWEEHeCAo+45aiyWbNmDfVuuB1IgaZEB0tGTIgcqmiSvCqDir3qJvOACjx43h2uaJQgP1/Z3JqedXbPNpqtMjktUvzYPUsIIW4FU7SEeBCIlGFaBDhQ1qCdrBX1A2uXgo5dNFg0mx0j0vx9fWRGRrSmZwP9fCW5dcIGIYQQ94ECjxAPApG6kQkOw+MDZY0SGugvBbXNOmFioCisaVYrlK/zHdG76RnRer+1am4cKPFMzxJCiNtBgUfcDown27hxoy4cVdZZ4GUnhEuwv680W2xqUVLfbNE07UAAYYf0bESQv3yVU63rFoyKc3XPZsWHMT1LCCFuCAUecUuMRqMupD0wFEbELDPOkabNqWwSs7VFKhoHxi6lqLZZag1m/dtgtGqjx8TUSBV+gQFIz7J7lhBC3BEKPEI8jJQoR6MF2F/aICEBflJYbeh3uxQ0VxypaBR/P19X9G5edqz4+/pKrcGixsY0NyaEEPeEAo8QDyM6JFAy447W4YUH+Ut5o0nTtf0JooLFtUYJDfCTbwocs2cXjIzXvwazIz1Lc2NCCHFPKPAI8TCiwxyNFsEBjjo8dLlibFl5Q/+maXMrm8Rktcmu4nqxttglLTpEMmJDVNwhVcz0LCGEuC8UeIR4GGh4iMJUi3jHVIvdxfXqUZdfbei3+4CJMur7YkMDZd2hSl130sg48fHx0fRsQmSQXkYIIcQ9ocAjxMOAyEqNDpHseEejxc6iOk3bltY5GiL6g/yqJk35YkTZkcomHU120qg4rfND1BDikulZQghxXyjwiFsSGhqqC+ma2LBAGRnvqMNDpK3Fbpcmk01K+8H0GB2y+0sbdfbtmoOO6N30zGi1aGk0WXVEGWfPEkKIe0OBR9xyVNncuXN1wWnSmZjQQEmMDJbU6GBB7yxmxKKb9khFk3a/nggYgVbVaJKwID/ZeMTRPbtkjKO5osZgkdSoEIkKHdjZt4QQQk4MCjxCPBBE0yKCA2RMQoSe31FUp1G9snrjCTVbIHoHsRga5C9b82s1HZsQHiQTUiJVOFpbWmREqwcfIYQQ94UCjxAPBPVvaTEhkhkX4mq0CPDzFYutRfKqmk4oelfeYJT48EBZc6BC1y0ZGy++Pj5SZ7RIVEggu2cJIcQDoMAjbgfGk23atEkXjirrHky0QLNFWKCf1sYdKGvQzlbU5KELtrc0m22yu7hOwgL9df6ss7liwShHerbOYJGsuFCdQ0sIIcS9ocAjbonBYNCFdE9MaIA2PExJi9LzW/JqNHWL7ld42PWW/aX1UtFg0pTsZ3vKdN2c7Bi9TUQGfXxF0mOZniWEEE+AAo8QDwVdrjEhgTI+xVGHtzW/RtBfER0SIPtKGjSq11PQVLGnpF7r+GqbLbIlt0bXL5+QpH+rm8waMUyKCBqgR0MIIaQ/ocAjxMPr8JIigzVN22B0pGljwgKl2mCWA6UNPbodq61FthfUqc0KunNX7CsTm90u45IiZETrSDSIxVEJ4TqXlhBCiPvDT2tCPBhE1dBcMSMzRs9vOFKlDRFIs+4pqZPyHvji7Sutl0MVDWq5gi7aNQcc3nenT3JE72B2DMsUjCojhBDiGVDgEeLhAi88yF+mZ0TreaRWMSsWdXMma4vW5UG0dQc6brfm1WrkLsjfT1bsK1drFBgZO2v7agxmSY8J1cggIYQQz4ACjxAPJiTQT21LYkMDJDUqWMy2Fvkqx2FOnBYVIgXVBtl4pKpLkQdxt/5Qlfj4OIyTIQw/2V2ql50zNUUjgUjfoq4vu3VqBiGEEM+AAo+4JcHBwbqQ4wOrFEuLXRa3TpuAfx1mxqJeLj06RPaXNMgX+8tV7KGWrrLRJF/nVsvq/RVqXJwS5Ui9onPWYLZJSlSwzM2K1XWo5YsLC3JtQwghxDOgoRVxOzCebP78+UO9Gx4D6u1CA/00TfufbUVSUNMs+0obdPpEUICfZMaFSlFtsxTWGCUkEGbIdo3oIb2LVC6Atcrne8v19HnTUrWBAyKx3miVqelREujP34LEu2lpaRGz2TzUu0GOQ0BAAEdY9hAKPEI8nOjQAIkLD1Krk8WjE2Tl/nL5cGeJCjyAJozM2DBNtxqtLeLv6yPBAe1n/L69tVBr7zCGbNYIR8MGxF1ksL9kxDA9S7wbCLucnBwVecT9iY6OluTkZPFBfQnpFgo8QjwcfMhBmCEFe8akJPniQIXsLW2Q/aUNMi7Z4ZEHkLIN78Lm5GBZg6w/XKWnL5+bqbV3oKbJLBPTIiUq1BHlI8QbQaS6pKREo0IZGRni68totTs/VzDALy93ZBtSUlKGepfcGgo84nZgPNk333yjp6dPn85wfA+AFx6ichgjhlq81Qcq5NVN+fKrcyaKn2/3v3KRqn1+Xa6eXjQ6XkYmhLvW+/n5yMh4x3lCvBWr1aqiITU1VUJDOanF3QkJcdQDQ+QlJiby++EY8KcKcUsaGhp0IT0DM2gTIoLU0uSC6Wlak4e6u49bu2K7+zX84oY8qWg06QSLi2enuy4rbzBJekyIJHJyBfFynPOuAwNpA+QpOIW4xdL7mdvDCQo8QrwANEXAygR1dDAlvnh2hq5/55si2VtS36W4e2troWzKrRY/Hx+5blG2Rv8A5s7i8rFJEXq7hAwHWM/lOfC56hkUeIR4CbA3CQ/2VyuUhaPiZMGoOLHbRR5fdUi25FaraAPwu0Na9pPdZXr+ypNGqJhzUl5vkpToEE6uIMSL2Lp1q3z55Zded1+ke1iDR4iXEB0aqKLscEWjRAQHyBXzRkh9s0V2FdfL02uO6CiymJBAOVTRqFMuEJy7bG6m1t45QfTObLPJ+OQIzp0lxEPZsmWLmEwmWbhwoWvd888/L4WFhbJo0aIBv//BvC/SPRR4hHgRaIo4XN6oQg3edTcvGy3v7yjRCRXFtUZdnNG+y+dlyvhkh5WKk9J6o6TFhEpGLIvNCfFUnn32WamsrGwn8GbNmiXZ2dlDul9kcPEogdfY2Ci//e1v5cMPP5SamhrJzMyUH/3oR3LllVe22+7FF1+Uv/71r1JWViZTpkyRhx9+WKZOnTpk+03IYJESHawdtZhWgekTsEb59ow0WT4hSfaXNWiNHsQd6vWcdihO0DkLG7BJqZHqnUcIcW/y8vJk165dEhcXJzNnztRGkZ07d8rhw4e1Se3111/X7ZYtWybTpk3TbmEnX331lZZtTJ48WV0L6urqZMmSJRIREaHXXbdund7eggUL2k0VWrt2rYSHh8uMGTNc65zXP/nkk7vcz55ep6vHQ4aJwLvxxhtl/fr18sILL0hWVpZ8/PHHcu2110pYWJhceOGFus2bb74p1113nfz973+Xk046SR555BF9ce/Zs0eSkpKG+iGQXriVk94DYQbvu1X7y3UMmX+rpxdq85wGxt1RUt8so+IjJCOG0TtC3B0EO/7whz9oGhTBj/r6enn77bdl7969atqMFO0777yj20LEdUybPvXUU1orh+tNnDhRRSEEF27zvvvukwkTJsiBAwe0YxVi0GlPgoDJ6NGj24m1l19+WYVZdwKvJ9fp7vGMGjVqQI+jN+NRAg+/Aq655hpZvHixnv/hD38oTzzxhK53Crzf/e53KvqwHXj66aflvffekyeffFIeeOCBId1/0jNgONo2tUB6B0aTIYpX0eCI4vWEWoNZQgL8ZHJ6JDtnybAGUS1EuocCvAd70iEKe5Df/OY38sEHH8jy5ct13cGDB6W5uVkuvvhiWblypaZonRG87jh06JBG0saOHavTPMaMGSM333yzbN++XdO5iPjh72uvvSbf//73++1x9ubxkGEi8L71rW9pevbHP/6xJCQkaDQPvzoQpQP49bFjxw65995724mFU045RUUgIcOBIH8/mZQaJSv3lYnJatPzxwL1elVNZpmbHSuJEUdTMYQMRyDuJv7qkyG57z2/PsNlV3QsMG0jKChI07Gnnnqqnoc46y3IbkHcAaRDkRbFbTlr9RC9Q3kTInkDSX89HtIejyq0eeyxxzRci1Qr8vl4cf7tb39zKf7i4mL92zEVi/MYRdMdCGUjHOxcaLBLPJ2suFAZGR8mJbVGlz1KV+CywppmyYoPk4mp7RsuCCHuCQIXL730kn7/4fvtu9/9rrz11lu9vp2YmPZlGxBZXa0zGh3NWe7+eIgbRfBuueUWDf0ei82bN7t+Tdx6663a/r1ixQoVep9++qncdNNN+oI488wzXV9keLG0xd/f/5hDpB988EGmb93MWR6/5ACaZDo+n+T4oLliRmaMVDVZpKzeJMlRnSNzeL9g2gWmWMzJij1upI+Q4QDSpIikDdV995Tzzz9fF6QyP/roI609z8/PlzvuuGNA9xHRtY7fp8cTgD25zlA9Hm9mSAUehBWKOY9FbGys/kXXLIpCX331VY3cAbwAPv/8cy3ghMBD2hZUVFS0uw2cd17WFXfffXe7F1FRUZEWnZKho7a2lof/BIkLD9K065cHK9T+JCkiyFXfgwYMiLuIoABZMDpORR4hxDEloSdp0qEEtWkQSIi2IZWJ5euvv5aNGzfq5chwoaFiIEhLS9PaPScQbmvWrNFZvn29zvEeD+kbQ/oqxosQS0+jOog4dBwGjc4eDIsGEHGI9sFB+4ILLnBtgxeSswmjKxCCxuIEaVpCvAHYoYBNOdVypLJJIwQ2u13M1hY1Rda6u0jW3RHiSaCMCPYl5513nmY5IObQcYpOWTB79mz1wkNzIYIkzqBIf3DFFVdo5+udd94p48aN01RqaWnpMQXe8a5zvMdD+oZ7/0xpQ3x8vMydO1ceeughmTNnjqSkpKiQwwvlF7/4hWs7pHHvv/9+ueiii9TY8c9//rPW5t1www1Duv+EDKXIiw0NlNyqRqloNEuAn49212bGhkpwL1JChBD3IDExUcuVYBmG70FEvlCyBJEE0EmLqNiGDRs0YAGblI5Gx/PmzeuUNu3oeQfgjZecnOw6D4eDVatWqSUZbE5uu+02zbAVFBS4tul4X8e7zvEeD+kbPvZjVWC7GTBBhID77LPP9DyiebBKQXs16uwAHs4999wjjz/+uDZPIDQMKxV04PYU/HrIyMjQF196evqAPR7SfbTW2fUMSxzW4BFCBgqkBuEbB0HSUdwQz3vO+P3tgRE8MGLECHn33XcdPkXNzZ3Stc76id///vdqmtjU1KSu3IQQQgghwwmPsklpVwTbhbjr2LVDcUcIIYSQ4YhHRfDI8AECnRBCCCF9gwKPuB2ouUNhLyGEEEL6BsMkhBBCCCFeBgUeIYSQYY8HGUoMe/hc9QwKPOJ2wJtpx44duhxrxBwhhJwoThsms9nMg+khGAwG/RsQEDDUu+LWsAaPuOWvs+rqatdpQggZKOChClcGjLSEYGCDl/uC7wOIu/LycomOjqZH6nGgwCOEEDJsge0WJiPBOBdm+sT9gbhrO12DdA0FHiGEkGFNYGCgDrhnmtb9QZS1v6cbNTc3y549eyQqKkpGjx4t3gIFHiGEkGEPUrMcVTb8+O9//yvXXnutJCQkaJp+0qRJOjErPj5ePB02WRBCCCFk2FFYWCiXX3653HfffXLw4EE9jxGnN910k3gDFHiEEEIIGXa89tprGrW95ZZb9Hx4eLj85Cc/0ahebW2teDpM0XaB05qjpKSkXw+2b1OOiN3Sr7fpjdhsLWIo3aGni/dZxc+Pv0MIIcTbaAkdKeLbvzLE+b1dV1cnkZGRrvVBQUG6tGXbtm0ydepU7aR2MnfuXLFarbJz505ZvHixeDIUeF1QVlbmeqIJIYQQ4llMnjy53XmkYe+///5262DHFRcX126d87zTqsuTocDrghkzZsimTZskKSmpXz2RGhoaZOLEidqtExER0W+3643wWPF48bU19PB9yGPlaa8rZODy8/P19ttG5jpG75wduUajsVNHrbOz2tOhwOvqoPj7y5w5c/r9YNfX1+vftLS0dqFjwmPF19bgwfchjxVfV979HszMzOzRdiNGjJAvv/yy3bqioqJe3YY7w+ImQgghhAw7li9friMx2xpcwyIFwnPChAni6TCCRwghhJBhx7nnnisLFiyQb3/723LvvffKkSNH5M9//rM899xzXjGyjgJvEEENAAo9u6oFIDxWfG3xfehu8DOLx8qbX1e+vr7y0UcfyR//+Ed56qmnNF389ttvy3nnnSfegI+d09wJIYQQQrwKz49BEkIIIYSQdlDgEUIIIYR4GRR4hBBCCCFeBpssBgiMQMHQ4rakpKTIqFGjOm27d+9eNVuE8zaMF7uiJ9t4E2hbr6iokHHjxg1LU2iM2zl8+HCn9YsWLeq0DscpNzdXPZ0SExO7vL2ebONp5OTkqGcVPCu7K9Y+dOiQjiyC6WlISMiAbuPOVFZWyr59+9T6oaNzP0xhsbQFJq9dTfLB6xID2fE5Fhsb2+V99WQbdwb7jikG2P+wsLAut8GcUrwmkpOTJT09fUC3cWdwnPBZjc+Vjs+12WzWgQEdwXuo47b4boPpMWbBjh07tsv76sk2pANosiD9z6RJk+zZ2dn2hQsXupY//vGP7bbJzc21T5061R4fH2/PysqyJyUl2VetWtXrbbyJpqYm+znnnGMPDQ21jx8/Xv8+88wz9uHG448/bg8JCWn3+sHSkTvvvNMeFBRknzhxov695ZZb7C0tLb3expP4/PPP7aeffro9NjbWjo+wnJycTttUVVXZFy1aZI+MjLSPGTPGHhUVZf/3v/89INu4M7t27bJffvnl9uTkZD1WXe37Pffco4+v7evsvPPOa7eN1Wq1X3PNNfbg4GDX6+iBBx7o9TbuzHvvvWefPHmyPS0tzT5lyhR7WFiY/Ve/+lWn7fA5jsc4YcIEfY9ecskldpPJNCDbuCtbt261n3baafa4uDj79OnT9XP6sssuszc3N7u2KSgo0NfczJkz27221qxZ0+623n//fX0vjxo1yh4dHW2fO3euvbS0tNfbkM5Q4A2gwHv00UePuQ2+OE499VS72WzW8z/96U/1DVNXV9erbbyJ22+/XYVsWVmZnn/ttdfsPj4+9m3bttmHm8AbN27cMbd5+eWX9Yvh66+/1vPbt2/XD9rnnnuuV9t4GnhfffTRR/pF0Z3Au/TSS+3Tpk2zNzQ0uK4DwZGXl9fv27gzb775pv3FF1+019bWHlPgnXzyyce8nUceeUS/YA8dOqTnV69ebffz87P/73//69U27sxf//pXFcROvvjiC3tAQID9jTfecK3DY8Ln0SeffKLn8TpITExsJ2T7axt3BscEP7ScYP9TUlLsd911VyeBt3fv3m5vByItPDzc/vvf/17PGwwG+5w5c+znnntur7YhXUOBN4ACD7/+Nm3aZC8uLu50+YEDB/TF3/ZNUllZaff397e/9NJLPd7Gm7DZbPaYmBj7Qw891G49Iie33XabfbgJvNGjR9t37Nhh37Nnj0vgt+WUU06xf/e73223DoKkbaSvJ9t4KmvXru1S4OHHD76YX3jhBdc6i8Wi4uPBBx/s1208BURWjiXwFixYoD+i9u/fr4+xI4jI3Xzzze3WLV261P6d73ynV9t4GohOIeLt5KqrrrLPnz+/U4R8xIgR/b6Np3HdddfZTzrppE4C7+OPP9YfmF0FJR577DEVb20jf6+//rrd19fX9SO/J9uQrmGTxQDypz/9SX74wx9qvcC8efO0BqZtjR6YNWuWax1qY0aOHOm6rCfbeBOoEaupqWn3eAFqrLzx8R4P1OBdfPHFcuaZZ0p8fLw8/fTT7S7HMel4rFA31fZY9WQbb2PXrl1isVjaPW7Ml54+fbrrcffXNt7Cxo0b5YorrpClS5dKamqqvPHGG+1qn1ADfKzXUU+28TTwWYT34OjRo4/7fkIdGrbvz208CQSLvv7663bHysk111wjV111lX6GXXvtte1q03EcJk2aJMHBwe2OQ0tLi2zfvr3H25CuYZNFD8GLt7m5udvLOxYl33XXXXLppZdq8TeKsy+66CK58MIL5ZtvvtFtUZzq5+cnUVFR7W4HAg6XgZ5s4004H1PHInCcx7zA4QSaafCDwFlM/M9//lO+//3vq7g//fTT9QMVBdpdHSuDwSAmk0lfZ8fbZqid5Af7ddT2vdUf23gDGNUEYYEif7yuHn74YRV7eO3NmDFDX0NYf6zj0JNtPAk8luuvv15iYmLk6quvdq3HY+nqMTovw/b9tY0ngdcMBP6//vUv1zoIsrfeeku+853v6HlcvmzZMvm///s/+dvf/qbrjncceroN6RoKvB6CUSboruoOvOAwpNhJ2w8FCLQ//OEP+mGJXxyISKET1mazaYQAX8ROICKd53uyjTfh7A5GNKAt3vp4jwUiKW3BL99nnnlGIysQeD4+PhpN6upYOY9lT7bxRnryOuqvbbyBs88+23Uar5mf//zn+lrDlzM+s4bjsbr11ltl5cqVurT9gY3H2d37qe2x6I9tPAX8+PzVr34lr776qkyZMsW1HhE7p7gD6OC+/fbb9bvUKfBwHBoaGo57rI63DekaCrwe8vrrr8uJkJSUpH+dtg5oKwfFxcWSlZXl2g7nMzMz9XRPtvEmnI8Xx6gtOO+Nj7cvr6G2xwbHpKtjhUiMc1B2T7bx5tdRdna2az3Oo1SiP7fxVmCl43zdwNICVkXHel/2ZBtP4Sc/+Ym88sor8vnnn8u0adPaXYbXRFePESIEVif9uY0n8OKLL8oNN9ygf7/73e/26DMMUTeIW0T4cBx27tzZbhvncWn7PXi8bUjXeOcn/BCD9FfHEb+ffvqp/kUtATjppJPUY+m9995zbbNhwwYpLy+X5cuX93gbbyI6Olpmz57d7vEivb169WqvfLzHoqOHYn19vXz11VeaunWCY/LBBx+4Xmv4i2PX9lj1ZBtvA96JGRkZ7V5HSEGiPML5uPtrG298rcHHDvWHztcaonqnnnpqu+OArMKHH37oOg492cYTuOOOOzTN+Nlnn8nMmTM7XY7Hgs9ylDc4QeYGEXdnFLO/tnF3XnrpJa0xx/FCOdLxXlcAjxn+gs56OhyH/fv3y4EDB9odh4SEBJe47sk2pBu6ab4gJ8D69eu1S/Ef//iHtsH/7ne/0y6gG2+8sd12Dz/8sHotPfXUU9oVBI+fb3/7273expv49NNPtUv4l7/8pf2dd95R+wbYhaA1fjixbNky+3333Wf/4IMP7K+++qp99uzZ9vT0dHtRUZFrG3SPousYPmfw8Lr66qvVz+zgwYO92sbTgCUDOmiffPJJV2cozrftqIM9DF5Hf/jDH+xvv/22enGhaxGd2v29jTtTXV2tx2bFihV6rH7zm9/o+cOHD7frFEVX8Icffqgdw/BlgwclrFWcoMMWdjs//vGP9XV0wQUXqLde22Pek23cGXzmoDMTdik4Rs6lrc0HjmdmZqb97LPP1sd4xx132AMDA+0bNmzo923cmf/85z9qgYOu6bbHavPmza5tfv3rX2u3MKyu4GP3gx/8QN9LeB85gR8nOv3hO4j3MWyIcBzaep/2ZBvSNT74rzvxR/oOOn/+8Y9/qEs50mFosDjnnHM6bYdUAOqq8EvulFNO0RqFjoXvPdnGm/jiiy/kqaee0ukL6FhETRB+rQ0nUHPy5JNPyrp167TOBNGEm2++WSIjI9tth1+1qGlBtx/SiHfeeafWuvR2G0/i+eef16Uj99xzj5x11lmu84hcoj4IUWBEw9H41PH49dc27sr69eu1qL0jqI1CKhJUVVXJ448/Lps3b9YpAfPnz5cbb7yxXdei8zPtL3/5i9YiI7r5s5/9zJXG7s027sp1113XzunACRoDfvOb37RLD6KpYPfu3Tqd6JZbbumUsu+vbdyVRx55RN55551O6/E4/v3vf+tpSAvUcWI7dAaPGTNGX1fjx4/vFOmD48TatWv19XfllVfq92VvtyGdocAjhBBCCPEyWINHCCGEEOJlUOARQgghhHgZFHiEEEIIIV4GBR4hhBBCiJdBgUcIIYQQ4mVQ4BFCCCGEeBkUeIQQQgghXgYFHiFkyNm6dat8+eWXQ7oPq1at0lFkAwVMqw8ePDhgt08IIW2hwCOEDCpbtmxRsdMWTKaAO/5QgQkGmKc5kBMqSktL5eKLL5aWlpYBuw9CCHFCgUcIGVSeffZZefTRR9utmzVrlixevHjInol7771XR1XFxMQM2H1gPJjBYNCxg4QQMtD4D/g9EEJIKzt37tSZuJi1+/rrr7tmfU6bNk3Fj5OvvvpKZ1lOnjxZvvnmG50Du2TJEomIiNDrOmf0LliwoNPM1Lb3k5GRofOM/fz8un0OMBMU8zL37t3bL/ePNO+uXbskLi5OZwhjOydXXHGF/O1vf5Pvfe97fE0QQgYUCjxCyKABEZWTkyMmk8k1rBwiCilaDKhftGiRrnvqqae0Lq++vl4mTpyoYg0i6w9/+IPcd999MmHCBDlw4ICEhoaqGAsJCdHrQXwhDQqBNWPGDN0Gouz999+X5OTkLvfpo48+0iHpGIbupK/3/9vf/la3weNobGzU67/99tsyatQovfyUU06R+++/X6qrqyU2NnbAjzchZPhCgUcIGTQgvlauXCmVlZWuCF53HDp0SKNnY8eOFbPZrALs5ptvlu3bt0t2drZG/PD3tddek+9///t6nTvvvFN8fHxUkCFyhnq3iy66SNe//PLLXd4PhBxE3Inev8Vikd/85jfywQcfyPLly/U20FTR3Nzsus0pU6boPn399deubQghZCCgwCOEuCVI3UJcAYg1pDt9fX1VVAFEz6ZOnaqRNGC1WlXE3XTTTfLee+9pihVLenq6vPXWW93eD8RmV7V3vb1/XBYUFKTp4VNPPVXPt40KAjRxIF2M+ySEkIGEAo8Q4pZ0FF0QT+Hh4Z3WGY1GPV1eXq5RNUTdCgoK2m23dOnSbu8Ht1lRUXHC9w/h9tJLL8kdd9whDz74oJx88snamfvd737XtT22tdlsmjYmhJCBhAKPEOIVQDQhPfvDH/5QU8E9BVG6TZs29cs+nH/++bogNYvaPnTm5ufnq+gDqD8E48aN65f7I4SQ7qBNCiFkUEEUzBn16m+Bh67WZ555RlOzHTtluwPp1D179khNTc0J3T9q7Zy3gdTsrbfeqmJv48aNrm3QfZuZmdkpdUsIIf0NI3iEkEFl9uzZ6oX35JNPaicpat36C9wmBBu6VdFcASG5YsUKGTlypDz++ONdXmfOnDlq0wJ/uh/96Ed9vm908EJgnnfeedpMga5gdNCiQ9gJ7gNRPUIIGWgo8AghgwrSp4h2bdiwQW1EYJMCo2Nn8wKYN29ep4kPXXnOwZuurf0Jmh52794tL7zwgqZd4+PjNZJ2xhlnHHOffvnLX8qvfvUruf7667U5oi/3n5iYqFM6cN8Yu4Yavk8//VSvBxAlRH3g8bqHCSGkP/Cxd8xlEELIMOSuu+6SK6+8UkXiQPDcc89pevqSSy4ZkNsnhJC2UOARQgghhHgZbLIghBBCCPEyKPAIIYQQQrwMCjxCCCGEEC+DAo8QQgghxMugwCOEEEII8TIo8AghhBBCvAwKPEIIIYQQL4MCjxBCCCHEy6DAI4QQQgjxMijwCCGEEEK8DAo8QgghhBDxLv4fHxnfghiMvEwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "erpd.pupil.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6f9fdf",
   "metadata": {},
   "source": [
    "The pupil first constricts (a light response to the stimulus on the\n",
    "screen) and then dilates. The orange line (right axis) shows the\n",
    "percentage of masked (unusable) samples at each time point; it is zero\n",
    "here because all blinks were interpolated.\n",
    "\n",
    "With several participants, `pp.group_segments()` combines their\n",
    "responses into a group-level curve (see the\n",
    "[ERPD tutorial](../notebooks/erpds.ipynb)).\n",
    "\n",
    "Instead of whole curves, `stat_per_event()` computes one number per\n",
    "trial, e.g., the mean pupil size in the second after each stimulus. This\n",
    "is useful for statistical models of trial-by-trial data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4b17a49b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:34:12.317452Z",
     "iopub.status.busy": "2026-11-10T13:34:12.317055Z",
     "iopub.status.idle": "2026-11-10T13:34:14.801916Z",
     "shell.execute_reply": "2026-11-10T13:34:14.800793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>left_pupil</th>\n",
       "      <th>right_pupil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002.5</td>\n",
       "      <td>831.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1174.6</td>\n",
       "      <td>965.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1270.0</td>\n",
       "      <td>1077.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1152.6</td>\n",
       "      <td>955.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1098.4</td>\n",
       "      <td>912.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   left_pupil  right_pupil\n",
       "0      1002.5        831.2\n",
       "1      1174.6        965.8\n",
       "2      1270.0       1077.6\n",
       "3      1152.6        955.6\n",
       "4      1098.4        912.2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = d.stat_per_event(window=(0, 1000) * u.ms, event_select=\"S_\",\n",
    "                         variables=\"pupil\")\n",
    "pd.DataFrame(means).round(1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161d2dd5",
   "metadata": {},
   "source": [
    "## Save the result\n",
    "\n",
    "`write_file()` saves the processed object including its history; read it\n",
    "back with `pp.EyeData.from_file()`:\n",
    "\n",
    "```python\n",
    "d.write_file(\"subject01.pypil\")\n",
    "d = pp.EyeData.from_file(\"subject01.pypil\")\n",
    "```\n",
    "\n",
    "## Next steps\n",
    "\n",
    "- The [User guide](../user_guide/index.rst) explains the concepts used here\n",
    "  in more detail.\n",
    "- The [Tutorials](../tutorials/index.rst) contain worked examples for\n",
    "  specific tasks.\n",
    "- The [API reference](../api/index.rst) lists all classes and functions."
   ]
  }
 ],
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