{ "cells": [ { "cell_type": "markdown", "id": "ab64a426-f870-4c7a-8814-14b1f9fc68c6", "metadata": {}, "source": [ "# Анализ ордербука" ] }, { "cell_type": "code", "execution_count": null, "id": "6e6d0daa-c0ad-4e19-8d0d-01e15efe1fa1", "metadata": {}, "outputs": [], "source": [ "# Декомпозиция класса" ] }, { "cell_type": "code", "execution_count": 54, "id": "74db1419-af56-4ef8-9dc1-464cd80321b2", "metadata": {}, "outputs": [], "source": [ "from typing import Dict, Any, List\n", "\n", "class OrderBookOutOfSync(Exception):\n", " pass\n", "\n", "class OrderBook:\n", " def __init__(self):\n", " # books: symbol -> {'timestamp': float, 'bids': {price_str: volume}, 'asks': {price_str: volume}}\n", " self.books: Dict[str, Dict[str, Any]] = {}\n", " self.last_update: Dict[str, int] = {}\n", "\n", " def process_message(self, message: Dict[str, Any]) -> None:\n", " data = message['data']\n", " symbol = data['s']\n", " ts = message.get('ts', 0.0)\n", "\n", " if message['type'] == 'snapshot':\n", " self.books[symbol] = {\n", " 'timestamp': ts,\n", " 'bids': {str(float(p[0])): float(p[1]) for p in data['b']},\n", " 'asks': {str(float(p[0])): float(p[1]) for p in data['a']},\n", " }\n", " self.last_update[symbol] = int(data.get('u', 0))\n", " elif message['type'] == 'delta':\n", " update_id = int(data['u'])\n", " prev_update = self.last_update.get(symbol)\n", " if prev_update is None:\n", " return\n", " if update_id != prev_update + 1:\n", " raise OrderBookOutOfSync(\n", " f\"Update out of sequence for {symbol}: expected {prev_update + 1}, got {update_id}\"\n", " )\n", "\n", " if symbol not in self.books:\n", " return\n", "\n", " def update_side(side_name: str, updates: List[List[Any]]):\n", " for entry in updates:\n", " try:\n", " price_f = float(entry[0])\n", " volume_f = float(entry[1])\n", " except (ValueError, TypeError, IndexError):\n", " continue\n", " price_key = str(price_f)\n", " if volume_f > 0:\n", " self.books[symbol][side_name][price_key] = volume_f\n", " elif price_key in self.books[symbol][side_name]:\n", " del self.books[symbol][side_name][price_key]\n", "\n", " update_side(\"bids\", data.get(\"b\", []))\n", " update_side(\"asks\", data.get(\"a\", []))\n", "\n", " self.last_update[symbol] = update_id\n", " self.books[symbol][\"timestamp\"] = ts\n", "\n", " def _get_basic_info(self, symbol: str) -> Dict[str, Any]:\n", " \"\"\"Получить поля symbol, timestamp, lowest_ask, highest_bid для symbol.\"\"\"\n", " if symbol not in self.books:\n", " return {\"symbol\": symbol, \"timestamp\": None, \"lowest_ask\": None, \"highest_bid\": None}\n", " bids = self.books[symbol][\"bids\"]\n", " asks = self.books[symbol][\"asks\"]\n", " lowest_ask = min(map(float, asks.keys())) if asks else None\n", " highest_bid = max(map(float, bids.keys())) if bids else None\n", " timestamp = self.books[symbol].get(\"timestamp\")\n", " return {\n", " \"symbol\": symbol,\n", " \"timestamp\": timestamp,\n", " \"lowest_ask\": lowest_ask,\n", " \"highest_bid\": highest_bid,\n", " }\n", "\n", " def get_order_book(self, symbol: str) -> Dict[str, Any]:\n", " base_info = self._get_basic_info(symbol)\n", " if symbol not in self.books:\n", " return {**base_info, \"bids\": {}, \"asks\": {}}\n", " bids = self.books[symbol][\"bids\"]\n", " asks = self.books[symbol][\"asks\"]\n", " sorted_bids = dict(sorted(bids.items(), key=lambda x: float(x[0]), reverse=True))\n", " sorted_asks = dict(sorted(asks.items(), key=lambda x: float(x[0])))\n", " return {**base_info, \"bids\": sorted_bids, \"asks\": sorted_asks}\n", "\n", " def get_aggregated_levels(self, symbol: str, levels: List[float]) -> Dict[str, Any]:\n", " base_info = self._get_basic_info(symbol)\n", " if symbol not in self.books:\n", " return {**base_info, \"bids\": [], \"asks\": []}\n", " bids = self.books[symbol][\"bids\"]\n", " asks = self.books[symbol][\"asks\"]\n", " if not bids or not asks:\n", " return {**base_info, \"bids\": [], \"asks\": []}\n", "\n", " highest_bid = base_info[\"highest_bid\"]\n", " lowest_ask = base_info[\"lowest_ask\"]\n", "\n", " sorted_bids = sorted(((float(price), volume) for price, volume in bids.items()), reverse=True)\n", " sorted_asks = sorted(((float(price), volume) for price, volume in asks.items()))\n", "\n", " def aggregate_levels(\n", " sorted_prices_volumes: List, base_price: float, percents: List[float], is_bid: bool\n", " ) -> List[Dict[str, Any]]:\n", " agg_results = []\n", " for pct in percents:\n", " pct = float(pct)\n", " if is_bid:\n", " lower_bound = base_price * (1 - pct / 100)\n", " volume_sum = sum(\n", " volume\n", " for price, volume in sorted_prices_volumes\n", " if base_price > price > lower_bound\n", " )\n", " else:\n", " upper_bound = base_price * (1 + pct / 100)\n", " volume_sum = sum(\n", " volume\n", " for price, volume in sorted_prices_volumes\n", " if base_price < price < upper_bound\n", " )\n", " agg_results.append({\"level\": pct, \"volume\": volume_sum})\n", " return agg_results\n", "\n", " aggregated_bids = aggregate_levels(sorted_bids, highest_bid, levels, True)\n", " aggregated_asks = aggregate_levels(sorted_asks, lowest_ask, levels, False)\n", "\n", " return {**base_info, \"bids\": aggregated_bids, \"asks\": aggregated_asks}\n" ] }, { "cell_type": "code", "execution_count": 139, "id": "50d63e2e-1ed8-499e-b710-85976713dd4e", "metadata": {}, "outputs": [], "source": [ "from typing import Dict, Any, List, Optional\n", "import time\n", "\n", "class OrderBookOutOfSync(Exception):\n", " pass\n", "\n", "class VolumeDeltaOrderBook:\n", " def __init__(self, max_depth_sec: int = 900):\n", " self.max_depth_sec = max_depth_sec\n", " # Сохраняем историю объёмов: symbol -> side -> list записей {timestamp, in, out}\n", " self.volume_history: Dict[str, Dict[str, List[Dict[str, float]]]] = {}\n", " # Запоминаем последнее время обновления для каждого symbol\n", " self.last_update_time: Dict[str, float] = {}\n", " # Для хранения текущего объёма по каждому symbol и side (по цене)\n", " self.current_volumes: Dict[str, Dict[str, Dict[str, float]]] = {}\n", " # Последние id обновления message для контроля последовательности\n", " self._last_update_id: Dict[str, int] = {}\n", "\n", " def process_message(self, message: Dict[str, Any]) -> None:\n", " data = message['data']\n", " symbol = data['s']\n", " # Используем время получения сообщения как timestamp для истории\n", " now_ts = time.time()\n", "\n", " if message['type'] == 'snapshot':\n", " self.volume_history[symbol] = {'bids': [], 'asks': []}\n", " self.last_update_time[symbol] = now_ts\n", " self.current_volumes[symbol] = {'bids': {}, 'asks': {}}\n", " self._last_update_id[symbol] = int(data.get('u', 0))\n", "\n", " # Инициализируем текущие объемы из snapshot\n", " for side_name in ['bids', 'asks']:\n", " side_updates = data.get(side_name[0], data.get(side_name, [])) # data['b'] or data['a'] обычно\n", " for price_str, volume in side_updates:\n", " price = str(float(price_str))\n", " self.current_volumes[symbol][side_name][price] = float(volume)\n", "\n", " elif message['type'] == 'delta':\n", " update_id = int(data['u'])\n", " prev_update = self._last_update_id.get(symbol)\n", " if prev_update is None:\n", " # Без snapshot не обрабатывать delta\n", " return\n", " if update_id != prev_update + 1:\n", " raise OrderBookOutOfSync(f\"Update out of sequence for {symbol}: expected {prev_update+1}, got {update_id}\")\n", "\n", " if symbol not in self.volume_history:\n", " self.volume_history[symbol] = {'bids': [], 'asks': []}\n", " if symbol not in self.current_volumes:\n", " self.current_volumes[symbol] = {'bids': {}, 'asks': {}}\n", " if symbol not in self.last_update_time or now_ts > self.last_update_time[symbol]:\n", " self.last_update_time[symbol] = now_ts\n", "\n", " def update_side(side_name: str, updates: List[List[float]]):\n", " volume_in = 0.0\n", " volume_out = 0.0\n", " side_current = self.current_volumes[symbol][side_name]\n", "\n", " for entry in updates:\n", " try:\n", " price_f = float(entry[0])\n", " volume_f = float(entry[1])\n", " except (ValueError, TypeError, IndexError):\n", " continue\n", "\n", " price_key = str(price_f)\n", " prev_volume = side_current.get(price_key, 0.0)\n", " delta_volume = volume_f - prev_volume\n", " delta_value = price_f * delta_volume\n", "\n", " if delta_volume > 0:\n", " volume_in += delta_value\n", " elif delta_volume < 0:\n", " volume_out += abs(delta_value)\n", "\n", " if volume_f > 0:\n", " side_current[price_key] = volume_f\n", " elif price_key in side_current:\n", " del side_current[price_key]\n", "\n", " # Запись с теперь актуальным timestamp\n", " self.volume_history[symbol][side_name].append({\n", " 'timestamp': now_ts,\n", " 'in': volume_in,\n", " 'out': volume_out\n", " })\n", "\n", " cutoff = now_ts - self.max_depth_sec\n", " self.volume_history[symbol][side_name] = [\n", " rec for rec in self.volume_history[symbol][side_name]\n", " if rec['timestamp'] >= cutoff\n", " ]\n", "\n", " update_side('bids', data.get('b', []))\n", " update_side('asks', data.get('a', []))\n", "\n", " self._last_update_id[symbol] = update_id\n", "\n", " def get_volume_delta(self, symbol: str, depth_sec: int) -> Optional[Dict[str, Any]]:\n", " if symbol not in self.volume_history:\n", " return None\n", " depth_sec = min(depth_sec, self.max_depth_sec)\n", " now_ts = self.last_update_time.get(symbol, time.time())\n", " cutoff = now_ts - depth_sec\n", "\n", " def sum_volume(records: List[Dict[str, float]]):\n", " volume_in = sum(r['in'] for r in records if r['timestamp'] >= cutoff)\n", " volume_out = sum(r['out'] for r in records if r['timestamp'] >= cutoff)\n", " return {'in': volume_in, 'out': volume_out}\n", "\n", " bids = sum_volume(self.volume_history[symbol]['bids'])\n", " asks = sum_volume(self.volume_history[symbol]['asks'])\n", "\n", " return {\n", " 'symbol': symbol,\n", " 'timestamp': now_ts,\n", " 'depth': depth_sec,\n", " 'bids': bids,\n", " 'asks': asks,\n", " }\n" ] }, { "cell_type": "code", "execution_count": 140, "id": "66a16f4a-9c2a-499b-880b-5662ebe8b07f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'symbol': 'BTCUSDT',\n", " 'timestamp': 1756684801829,\n", " 'lowest_ask': 108241.8,\n", " 'highest_bid': 108241.7,\n", " 'bids': [{'level': 0.005, 'volume': 0.024432999999999996},\n", " {'level': 0.01, 'volume': 0.720067},\n", " {'level': 0.015, 'volume': 2.188207},\n", " {'level': 0.02, 'volume': 3.6439869999999983}],\n", " 'asks': [{'level': 0.005, 'volume': 0.12923800000000002},\n", " {'level': 0.01, 'volume': 1.229285},\n", " {'level': 0.015, 'volume': 2.2872159999999995},\n", " {'level': 0.02, 'volume': 3.177030999999999}]}" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "levels[3]" ] }, { "cell_type": "code", "execution_count": 171, "id": "08e0b593-47aa-4d59-b14a-8733d37d9027", "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "def process_json_lines(file_path, handler):\n", " with open(file_path, 'r', encoding='utf-8') as f:\n", " for line in f:\n", " line = line.strip()\n", " if not line:\n", " continue # пропуск пустых строк\n", " try:\n", " obj = json.loads(line)\n", " except json.JSONDecodeError as e:\n", " print(f\"Ошибка декодирования JSON: {e}, строка: {line}\")\n", " continue\n", " handler(obj)\n", "\n", "\n", "ob = OrderBook()\n", "volob = VolumeDeltaOrderBook()\n", "\n", "orderbooks = []\n", "levels = []\n", "volumes = []\n", "# пример обработчика\n", "def my_handler(json_obj):\n", " #print(json_obj) # здесь можно делать что угодно с объектом\n", " ob.process_message(json_obj)\n", " orderbooks.append(ob.get_order_book(\"BTCUSDT\"))\n", " levels.append(ob.get_aggregated_levels(\"BTCUSDT\", [0.005,0.01, 0.015, 0.02]))\n", " volob.process_message(json_obj)\n", " volumes.append(volob.get_volume_delta(\"BTCUSDT\", 20))\n", "\n", "\n", "# пример вызова\n", "#process_json_lines('./data/2025-09-01_BTCUSDT_ob200.data', my_handler)\n", "process_json_lines('./data/testob2.csv', my_handler)" ] }, { "cell_type": "code", "execution_count": 142, "id": "d7c702ff-03e1-4f40-b53b-ec04a6a822f7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.823819,\n", " 'depth': 10,\n", " 'bids': {'in': 0, 'out': 0},\n", " 'asks': {'in': 0, 'out': 0}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.824225,\n", " 'depth': 10,\n", " 'bids': {'in': 267825.1921611, 'out': 144300.90123040005},\n", " 'asks': {'in': 162848.7712041, 'out': 193219.8091764}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.824451,\n", " 'depth': 10,\n", " 'bids': {'in': 434006.80804489995, 'out': 353783.707776},\n", " 'asks': {'in': 403914.7605707, 'out': 277329.900226}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.824672,\n", " 'depth': 10,\n", " 'bids': {'in': 707685.6404085999, 'out': 1136577.1175236},\n", " 'asks': {'in': 657794.1375483, 'out': 613306.2864753001}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.824901,\n", " 'depth': 10,\n", " 'bids': {'in': 1087650.2383775997, 'out': 1401142.0233726},\n", " 'asks': {'in': 934629.9921611999, 'out': 899587.3122018}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8253162,\n", " 'depth': 10,\n", " 'bids': {'in': 1394798.2319957996, 'out': 1478476.0530396001},\n", " 'asks': {'in': 1079812.5444328, 'out': 923695.8137408}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8254871,\n", " 'depth': 10,\n", " 'bids': {'in': 1515477.1922628996, 'out': 1662637.8632248002},\n", " 'asks': {'in': 1127373.5341794, 'out': 959929.4989198}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.825662,\n", " 'depth': 10,\n", " 'bids': {'in': 1622656.9443586995, 'out': 1729471.2006568003},\n", " 'asks': {'in': 1174989.832958, 'out': 1018609.5373818}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.825838,\n", " 'depth': 10,\n", " 'bids': {'in': 1748500.1911307995, 'out': 1859853.8151610002},\n", " 'asks': {'in': 1222245.2463612, 'out': 1063574.5614261}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.826016,\n", " 'depth': 10,\n", " 'bids': {'in': 1978227.4428747995, 'out': 2032903.8450020002},\n", " 'asks': {'in': 1277955.9460451999, 'out': 1113442.3371838}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8261912,\n", " 'depth': 10,\n", " 'bids': {'in': 2101342.9670808995, 'out': 2115828.4432673003},\n", " 'asks': {'in': 1326196.5799411, 'out': 1200036.0015884}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.826367,\n", " 'depth': 10,\n", " 'bids': {'in': 2186606.7783189993, 'out': 2291406.2453213003},\n", " 'asks': {'in': 1471898.9791091, 'out': 1384340.8210784}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.826602,\n", " 'depth': 10,\n", " 'bids': {'in': 2629419.6023500995, 'out': 2552981.2285976},\n", " 'asks': {'in': 1822566.1382271, 'out': 2003293.7614737}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8268192,\n", " 'depth': 10,\n", " 'bids': {'in': 2744426.6155696996, 'out': 2760997.0181854},\n", " 'asks': {'in': 2186791.201626, 'out': 2198742.8590527}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8270159,\n", " 'depth': 10,\n", " 'bids': {'in': 2919763.0509190997, 'out': 2928973.3080213},\n", " 'asks': {'in': 2449333.9407672, 'out': 2255491.8147432}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8272052,\n", " 'depth': 10,\n", " 'bids': {'in': 3143378.0325658997, 'out': 3029319.8554058},\n", " 'asks': {'in': 2576341.4302286003, 'out': 2481795.84332}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8273852,\n", " 'depth': 10,\n", " 'bids': {'in': 3281538.8971273997, 'out': 3154419.9291593},\n", " 'asks': {'in': 2726425.2965156003, 'out': 2619954.2698376}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.827552,\n", " 'depth': 10,\n", " 'bids': {'in': 3366779.1366016995, 'out': 3239660.9152002},\n", " 'asks': {'in': 2761249.1254374003, 'out': 2682805.0895006}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.827708,\n", " 'depth': 10,\n", " 'bids': {'in': 3429509.4754397995, 'out': 3302631.45708},\n", " 'asks': {'in': 2987346.3925456004, 'out': 2816379.9278307}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8278651,\n", " 'depth': 10,\n", " 'bids': {'in': 3626070.2711404995, 'out': 3498261.9480399},\n", " 'asks': {'in': 3132404.8720800006, 'out': 2959013.0721734}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.8280342,\n", " 'depth': 10,\n", " 'bids': {'in': 3748259.2630156996, 'out': 3824299.6576489},\n", " 'asks': {'in': 3181710.3500442007, 'out': 3120891.1145141}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385335.828222,\n", " 'depth': 10,\n", " 'bids': {'in': 3840162.4290713994, 'out': 4037993.2536334},\n", " 'asks': {'in': 3320882.9663327006, 'out': 3316213.9624412}},\n", " {'symbol': 'BTCUSDT',\n", " 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'depth': 10,\n", " 'bids': {'in': 265169611.0796411, 'out': 265268612.50565726},\n", " 'asks': {'in': 225264855.798403, 'out': 225101315.92643964}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385336.122875,\n", " 'depth': 10,\n", " 'bids': {'in': 265364547.0949076, 'out': 265453480.98741326},\n", " 'asks': {'in': 225475475.8565394, 'out': 225305436.35844234}},\n", " {'symbol': 'BTCUSDT',\n", " 'timestamp': 1760385336.1232572,\n", " 'depth': 10,\n", " 'bids': {'in': 265770404.4342719, 'out': 265761053.46088704},\n", " 'asks': {'in': 225485367.3975672, 'out': 225314835.66742694}},\n", " ...]" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "volumes" ] }, { "cell_type": "code", "execution_count": 121, "id": "a5c1507c-f012-4600-b3cf-e1da9bc4be14", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 162, "id": "f0d9f779-c974-44c4-9f0b-72f42fa4ddd8", "metadata": {}, "outputs": [], "source": [ "prices = np.array([volume[\"lowest_ask\"] for volume in orderbooks])\n", "\n", "bids_in = np.array([volume[\"bids\"][\"in\"] for volume in volumes])\n", "bids_out = np.array([volume[\"bids\"][\"out\"] for volume in volumes])\n", "asks_in = np.array([volume[\"asks\"][\"in\"] for volume in volumes])\n", "asks_out = np.array([volume[\"asks\"][\"out\"] for volume in volumes])\n", "\n", "\n", "bids_balance = bids_in - bids_out\n", "asks_balance = asks_in - asks_out\n", "balance = bids_balance + asks_balance\n", "\n", "x = np.array([vol[\"timestamp\"] for vol in volumes])\n", "\n", "data=np.concatenate([prices.reshape(-1,1), bids_in.reshape(-1,1), bids_out.reshape(-1,1), asks_in.reshape(-1,1), asks_out.reshape(-1,1)], axis=1)\n", "df = pd.DataFrame(data=data,index=x, columns= [\"prices\", \"bids_in\", \"bids_out\", \"asks_in\", \"asks_out\"] )\n" ] }, { "cell_type": "code", "execution_count": 163, "id": "ce0f8c96-1d15-48d9-9b13-f0f88a3a48a9", 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oBIyAETACRqD8CVgNjYARMAJGwAgYASNgBPomYBMvfXMxrREwAkbACExMAlZrI2AEjIARMAJGwAgYASNQVgRs4qWsusMqYwSMwOQhYC0xAkbACBgBI2AEjIARMAJGwAgANvFio8AITHYC1j4jYASMgBEwAkbACBgBI2AEjIARGDcCNvEybuinXsHWYiNgBIyAETACRsAIGAEjYASMgBEwAlONwFSceJlqfWztNQJGwAgYASNgBIyAETACRsAIGAEjMBUJlEWbbeKlLLrBKlEKAq6pCdXn/hItyzdixSpg9Wo5l/OBVWucl5evAFatcgV582ZXiuLNhhEwAkbACBgBI2AEjIARMAJGoB8Cph5rAh2dabS2tY91sX2WZxMvfWIx5UQk0LAFiC1ahHvudbj40hguvCSGCy4NcQH9Cxm+6OIAv6d86R9juOjSgHExSD77tyHThqhvsAmYidjvVmcjYASMgBEwAkbACBiBIRCwpEZgEhFYuWYDdn3fv2PF6vW9WnXR9CTxdgAAEABJREFUn2/CGV/4cS/9eCiC8SjUyjQCo0HgxZei4eynT/wmKsXl5CwcgiI5J0LL6jUODz1SrJHWnBEwAkbACBgBI2AEjMBoETC7RqBcCKxauxH7nvB5nH3R1YUq3XzXo/6G/txLri3oJPz1uru9/pK/3qKguUESWLw0i7vuy+CxpzJoah5kphEmO/3UI3DW/35hhFZKkz26Uy2NLbNiBMaVQG1tUfFZIMt5FK4U/Ao/ASM9k3mZfvG6erVPXawy2QgYASNgBIyAEZj8BKyFRsAITGECWxqb8YX/OhvNLa19UtAES/2WJh+nn65cduVtXrbN4An86e9p/OLcTlx9QxqXXZHG//txB9asG3z+4aZ8asFC/PX6e332xUtX4UOf+QEuv/oOHHfGd7y7+qb7fJw2La3t+MV5f8Ohp30Fp37yv6EJNukUVwpnEy+loGg2yoJAItFVjW5TKMWBYrkruZdSKe/ZxggYASNgBIzAOBGwYo2AETACRmAsCXSm0/jOTy7AXu95O4573369in7XLm/0cdfcfL+Pu/uBeZg7awb23G0XH56Km42bgBtvTw/aabLloccy3VC1tACX/qVz0DZU3qNPdrfRzWA/gU219Xh9+Rof29LahpdfXYanF7yC//n6x/HJ04/Hj87+E/KTamdy0uWZ51/Fr/73C/ge4/963T2458F5Pm8pNjbxUgqKZmNcCTw5L8Bf/hYWXlnLZrMAJ1i4Rq+6REFIjWIZ3Zc9dx/6ztzdgoWMgBEwApOEgDXDCBgBI2AEjMAUIPDL8/+O9vZOfO9rH+u3tZ/+15Nw0V9uRnNLGy756834zMdO7jftVIjYsCmLm+/IDNrd9c++77FeXz40O/qJUin4/vb/vopD938v/vVfjsKsGTWY//wi6M2Wa265H+8//hBMr6nCtOpKHLzvbrj7IZt4KQXzktjYflYK5saPwb33JHDzrQEWverQ2uqnWtivTvMrmD4dmDPHYdZMOWAu5ZkzHWbOBObMpj/DobKSczOcjNlnzwDHHpa0vrTxbGOgzMZAOR9fd5hdgcnsZk9LTOr2Tea+K0Xbynnfs7ql7Fxl5yobAyMYA9Or4rxfAK684V488NizOOdHX0Y8HvO6vja6Sd9+m1n41o9+7ydfjjhoz76STRndXN5HnXJ8gMG6Y4/s+12PN73BDdqGyjpw377tjAR8DSdYWlrasXb9Jm/mutsexE9/c4V3L7+6HLEw9PpSbEpf+1LUqshGJpNFOt33LJni1m6oRWc6XZRj+OKmugZsrm/sZeCeh57Ghk2be+mlWFPbCnPjw2DVxlb/cSb/dkt+zoWdou+3KHjg/hl89Usd+PpX6b7S6eVvUP4G5a99uQOSDzs4rex49vk0vvuTDpx9YQfueKDN+tTGdc8xYGEbE73GwOpNLZjMblND+6Ru32Tuu1K0za5txufaxrgbdxsDoz8G6ps6eMcAXH7VHXjjTtvior/chF+efyVefGUpHp33IvRNF58gtwkCh8/828l48PEF+NzHT0EYlv0tdK7mo+PNmQ28/4Rw0O7000IcemB3ZhUVwKc/Hhu0DZV30H7dbZSydbNmTvPmfvyd/w9XnPe9gjv7h1/y+lJsRq/2JaidfjLyo7Mvx4/P+VMvaw88tgD7n/QFHPXhb2L3oz6Fq3O/u1PCQ0/7iv/StP6tVLHTB3UU39PpK9Yf+dyPcNi/fBUHv//L+Pev/wKahMmn++7PLsGiJSvzQfPLhID+/XN7dNzsVaMEJ7J3aF4CV9/3hJkyrN8A3HlPNIvZ2emwcZPDywsd/nF9iAcfLtddQzU3ZwSMgBEwAkbACBgBI2AEjMBICPzHR0/APru/AzOmV3unCZVUMuF/ZtLT7vFH7OcnXU46+sCeURYeBIFPfjTE//t6DKdzEuZTHwvxi/+NY/ttB5FxCEmWr1qPpcvXFFxfL1T0Z04/L9p/z3fh57/7G9asr4U+ovwCJ+L+dM2d/WUZsr5s7y7vvP9JPxFy7S0P9GqUfoP17R9fgC//x79gwb2X4Tc/+Qp+9OvLof/hrcR/+/33cdsVZxbcWf/7BamhV4m80GNzyRW3YOaMavzzmnPwyI3noaWlDb++8KoeqSzYjUAZBGbOyGLWzCxctndlZnMm9t1P/BXh6lW9I3OaF14Kou++5MLF3jMLXHHQZCNgBIyAETACRsAIGAEjYAQmEYGPvP9IfPZjpxTcO9+mD+nuAul7NlM/Rfrqpz6IpJ7u9oy08KAIvO3NDsceEUA/GaqqHFSWISX67HfOwsmf+G7B6ZstzhXd0xXLRZbz6p//92dRXVWBo0//JvY4+lPQixn1Db1/DVOUdUhi2U68HLr/7rjmkh/h5GMO7NWgJ595Gc0trTjj/UdCv7s6+tC9/WtiDzz2rE+78w7b+LBeHZO78c6H8YETD8O2c2f6+OJNQ2Mz1ClnnHa0j9eMp14hu/HOR3hT3vuOXm/CqFP1b6iK7Zg8PgROOTmDRDL6TktxDXZ5Wxbxps19Tsrk023e7Py3YPxvjfLKnF9b65DJ5ALmGQEjYASMgBEwAkbACBgBI2AEjMC4E+hZgZ22n4sX77+8l9PPwz7+oWPxh3P+y2d5zzvf7NM41zUZc9sVZ+KEI/f38ZoruOAX38DTd17sX8jQCx6abPORJdiU7cRLZUUS282dharKil7NXLexzk+sJIpmHN/6xh2wdn1dr7RPPbsQDz3xPL7wiVN7xUkR5MAHQReKHbaboyjUbt7i/fxG/2rq09/6pZ8J+9gHj8mrzR9HAm99cxZvflPGz53suUc0UXbcCRkcdWQ6qtXmOiRvvyWaYIk0he2b3xjl8//tqKCNhLe+JYuiIREpbWsEjIARMAJGwAgYASNgBIzApCRw9g+/iG9+7vRC20459iBce8mPCuFi4Yrzvue/+1KsM3lyENDPzTQJoxc8StmirtmGUlodZVsNW5pQWZHqVkoymcCWxuZuOn0j5qwLrsInPnwc8pMp3RIwoNeJDjtgd/zgrD/g+tsfwi13P4bf/+kGxnRfm5pb8aXvnoudd9wGZ/7P55HviNnTkjA3vgw2buQw5sTlO94Sfa+lev1KVD77FMBJtVi6Haln52FGVbxXPx19WALv2dUpGYqXmmr43x9av45vvxp/429jYHzHgP7rg/XB+PaB8Tf+NgZsDGx9DBijoTKqTsWKL/1NNgJjQoB3rGNSTkkLmVZT5X9qVGy0ra291zdc7n14PvRRnE+dcWJx0l7yL//nczjlmIOgnxfp2zLtuS+2zppRU0j7vV9cimdeeBXf/vxHEI9FN/iKbGzpgLnxY7CpvgObaoFEAognOtUlcHV1wOvLgGzW/1wokwWa2zr77KePfTSN7/+/DL7y+QwOOiDr34x5z65ZTJ/Rd3rr6/Hra2Nv7G0MjO0YaGlP93nctH4Y234w3sZ7wowBuya2Y+YEGQOtHbk34/2dg22MwNgQCMammNKWsu2cmVi2ch06OqIbbVnXfx3abpuub7h0ptM4+6Kr8flPnIo5s6YrSb9OH939xmc/jMvP/X/43U+/hvb2Thx58J5wzhXynHLsQdCbMZ//r7NR/IXkto4MzI0fgxdfZhdxYmW77bLoSFNQkB7nXChp7kUBoL0z028/xRMZzN0mg7fvkoF6fNUa9JvW+rp/jsbG2NgYmFxjoN3Ob3YumKBjwI5Fk+tYZP1p/VnqMdCZu2fwNwu2MQJjRKBsJ17S6YyfWElzAqWzM+3ljF5dIJh993gnt8CVN9yLTsbf89DT/j8aHX7gHl6vzU13PoINm+r9z4wULnb6MO7Hv/KzgqqxqQX6KZG+4fLX6+7GE8+8jM9+/NRCvISjDtkLv/7BFzF9WjW+8N1z0NzSJrW5cSJQ3+Bw4SUhrrqWQ5izJatWOSxfMbLKbL991r/xsnK1Y/+OzJblNgJGwAgYgSlPwAAYASNgBIyAETACRsAT4F2r98tu849bH8Aex3wa197yAG6442Ev33DHQ76e+vCu3kw58/wrsftRn8LXvv87/M/XPw590VgJ2to7cM7F1+CzHzsZ02uqpOrmNmzcjIWLlxd0C156Dfud+HkcdMqXcNWN9+GK874HffW4kICCPsKrci/4+Tegfyv1zR+ehzQnhxhl6zgQePBBh9VrOOOSK5vzb5g3P8yFhuc98GDg33hJdwK/+FUM1143MnvDq4XlMgJGwAiUmoDZMwJGwAgYASNgBIyAERhPAsF4Fj5Q2aefeoT/d0/F/xpK/xI6n0c/BXru3j/grr+fhWfvvhRnnHZUPgrJRBwP3fC7fr80/Z0vfhRP3X5hIf3+e70Ld/ztl153059+hj1326UQJ0FpD9xnV4nQv5vWv5268MxvIQzLFp+v62TeLF/Vm317+/BbvGKlw6OPd7f53AsOLy/srht+CZbTCBgBGAIjYASMgBEwAkbACBgBIzAFCUzou0pNfOy43RzE4yP7MrX+Q9HOO2zT6z8lTcHxMGGaXFOdLWldNfFSbFDW5RYtBlZyUmbNGmD1asqrnPeLZcXpp07SrVgF6KdKq1Y7rJLM9AqvpOx11K+Ujk5hyavWOJ8nn877TKf8SiO3YiUgXy4vNzUV19jkoRCwtEbACBgBI2AEjIARMAJGwAiMDoHWFavRcNd92HLb3YNzj85DJv+RztGp0rhbndATL+NOzyow5gTq6hzmPe3gP8bSo/TtttVUSQ/lIIOFiRyZkGO+bAZ4en6Aiy4LccHFMVx4SSyS6Xv50hAXXBrDBQpTvpDyxXQXXRLiIoYvuiyGi5n3YsoXU69v0hTiqLvokjBKl0+f9xl3MeULmd+np3zxH2KQLBuXSGaaM38dw1//brswu8pWI2AEjIARMAJGwAgYASNgBMqEQGbjJlR96pOoOenYQbnwn/foH9KWSe1Hpxp21zY6XCeY1YlR3ZdfCXDO70LcdGuIxUucn3vRxGjAUfy+wzI4+CDOlAyzKbvskkVlJTNzTkcfepEnu5qD0T+3ykrBaL9KpuNamACSrLoorfIrn9JKJ18673Pj09DXKtmnYQbJ8BvGeIM580VyPpop/PrKooCTQ0rgg7YxAkbACBgBI2AEjIARMAJGwAhMGQIr12zAru/7927uP75xpv8vyOUEgbesZVQdq8qUJLBlC/DsAodbbw9wMydVbr4t4ORKgFsYvoXyzbfGvHzTLYGfbMlD0nSDJiIyGSAepxvBL85SSeBzn04jVcEZkFwBmhBx+SB9Tb6oTEUrDgyo/G4yI6lWVGHWllmpRaRDtChNsd5HMsqXVxyhhNT7+LyscJFbvbqfiKI0JhoBI2AEjIARMAJGwAgYASNgBMqKQAkr85ff/TfuuerXuPqiH6KpuRVnXfD3Elofualg5CbMghEYPgF9z+Sc82K47oYQTzwZ4KmnHZ58iv68wIefpP/U0/C6pkaWk5+UoFi8btg48smHmTOy2G6b3FsmMk6T+eIoSuOdl1yELYgAABAASURBVP3GB/1Gky8+LTdepq9JmR7JfNrCJhepCZ28jtmgfPlwLokP+kkZL3XfVNV0D492SHUc7TLMvhEwAkbACBgBI2AEjIARGCsCVs7EJzB39gxsv+1s7PqON+G9734LXNB1J/VfP70Ih572Ff9WzKmf/G/cef9ThQb/4ry/4W/X34sL/nwjPv6Vn+G2e58oxJVSsImXUtI0W0MmoP8k1NHObNwvCjf0lLn6CQg/icHowqqIQiASwhDYZ+5yuIb6SDGC7W67sRZaVTDtOJXHMOhzLczKSPZJGKc0CudfcZEsV3g9h2mUlh4t0gQFrhS4Ssg5pYkScCsdPW8jL+fD8nNOZe/27kwuNLpelpVRVaovPA+xhS+NbmFm3QgYASNgBIyAETACU49A6Vvc3Az32qu5q7jSmzeLRqBcCNx05yP463X34Fe//ztuuOMRfPGTpxWq9t53vQVn/eCLuPGPP8Wpxx2Mb/7wfNQ3NPn4ZSvX4ae/+QteXbIKRx+2Nx/Ez/L6Um+CUhs0e0ZgKAQ2bHCa04iyOE5EUKIHKbtNRIBxjOBKqfs6d04Wb1/6TwT690Ldo4Yc2m/vDD7y4Qx22xWQ3WnTgRkzspg+DXRZyvRrgGk1WcycCUxn/PTpWcygPGOGg9LO4r5aIx3zzZyVxbRpWcycAcyirLSSZ87MYrr0M6mXzPSzKOutm5qc3su0H5WTRQ3rIHka08Zi5MFZkJNPSGObuUNu5rAyxJ9/DomnnkRsxetR4cOyYpmMgBEwAkbACBiB8idgNZwsBIL6OlRffhlcR+dkaZK1wwj0SeD5hUvw9HOL8PrKtT7+/D9ej+aWNi9/9P1HoaaqAs+9/Bo6O9Net2LNeu9r85l/Oxln//CL+OSHj8Ne79lFqpI7m3gpOVIzOBQCc+fqPYpcDk4kaGKFHm/sAb3NAS1S0vceIzUhQw/v2CWL0z+Yxqc/lUbY0Qanf0PEdCNd9QbJRz6Uxle/mMa3v9aJb34tjW99vZMuTZn+Nzrx7W9Q/ipl6Rn/TclM+y3KX/9KJ77zdcZT/sZX0viO0jJO8re9vhPf/Gq6YOMbkqn/Bm2oLKX3MvXfoX3J36at77DcvLzvvhnPp6XVUxlpkweVP9jSgHDl8ihtJoPAy9kobNuyIBDUb0bs8YfLoi5WCSNgBIxASQiYESNgBEZMINy0AbpikxuxMTNgBMqUwPe+9nE/eXL+z76Ou/9+Fh6f/zIeeuI5/72Xf//6L/DJr/0CTzzzMlrb9HMLIJPOFFpSVZkqyKMl2MTLaJE1u4MicNABGf9hXJ0NClMIPCtw9b/c0eRLQS+LDEi37TbAv52Rxm67ZpGIKWJquZ134IQVIS1eDKxe47BylcOq1S4nA/p2jnerIrm5pbR8Ap7AK/9xDdRvpbVs1kZEoKUZlVdfCdfWNiIzltkIGIHeBExjBIyAEZioBLLJJBLz5yH+1JMTtQlWbyMwJAIzpldj7uzpWL5qHR5/+iXMf34R7rn61zjze5/D1z/zoSHZKlVim3gpFUmzMywCO3EC4QOnpQFOqNTUAPvuncV++2aw7z4Z7L9fBvvR33dvINJlcchBWfzbRzP48uc7MZWXefMDIcPrywJccHGIiy8LceGloZcvou/dJdLHcDHDv/hVDNdeF5YOWYYzxG2tpbM3SEtZve40yLRTNVnQOvb9MlVZj1O7rVgjYASMgBEwAkMk4BCuWY3YujX20Ay2TFYCGzZtxuq1G/HKayvw+8tvgL7dcsBe70b+bZa162v9d130Id3xYBCMR6FWphEoJtCSexvj7W/P4pST0jjlxAxOPSmDk0+go3zKSZ1ePuXENI49Oo13vJ03/cUGppj8zLMOS5Y65P8bkt4A8vMRWUCy3+RldC3PveDwwksTcZfPgs3xLnnXHYi9bB/27erV8ZSsbCNgBIyAETACRsAIGAEjUB4E9B+Jjvnot/GBT30fDzy2AOf97Gt4z7vegv32fBeOOWwfrz/o1C/hsXkv+Ao7xyf/XgJvn7pkjNIyEe/CRgmFmR0vArW10UCfNVO31+NVi4lT7sqVnHQRKjquvuKeoN8A/t9O52TkFqXT5Mwri6KfHq1aBf/TJP08acXKSG6KPuydy9HlZZtbUPGPq4Fx+Cib6h1/5hmEq1dBTUq89Bxc05auykkyZwSMgBEwAkbACBgBI2AEjMCUJLDT9nPx4v2Xd3NXXfQDHHHQnp5HEDic++Mv44HrfoOHb/wdfvfTr/m0mpRRggt+8Q18+l9PkjiqziZeRhWvGR8MgdraKJX+G1AkTcztWNW6qgbQBK0ccovefvETLgxrsqIgc7ZFYf+6CDfPPhfg4ktCXHhZDBfJp7vkD5QvDXHmr2P46997HxKCbAbJe+/ihI63xBLGbtVkS+rWm+BWrUT8xefHruAJUpL99GqCdJRV0wgYASNgBCICvJRIPP0UwtcWR2HbjguBcOnS3HUVO2RcamCFTnYCwdzZaLz0cmy59a5BufSRR/v7m9HkMmfWdMyczhup0SxkANu977IGSGxRE4LAhKvkprpoGM6aObV/QjTYjtN/XdKkC+dUfBbv87zJ1X+QWMqC7BMiOpBJBhfNZjAB10hPVX59ZVGAJ5+O+iOvKwc/1tTIyZ87o6oEIdup2kfBSbGtq0W4ciU4Nzb45rDjK66/BvF5Tw4+j6U0AkbACBgBIzCeBHgNEnv6CYSbNo5nLQZV9mR+uBGuXhFdP0yyy6lBdawlGhMCqZ12wPTjjkTNiccMzh20D4L8vcqY1HDsCxnFO6yxb4yVOL4E2juAF150uPOeELfcHuCW2wLcfGuIW28LvX/jLSF1IeTfeHNAXeDlDRtYb95EVlfRt3WrBLaZC3z2U2nsv28G226bxeyZwMxZWUyfnoXeGpo5I4tp0yhTH/ACJ2/QvwWTP8FSzzUf1c1fubK/mG7JugK02TOH/yoL+7QrUXcpWLUCwcZhXnQ1N6H6wt92NzjBQwk++Uvd8I8hzbuoyeHmzXDj8JFjlW3OCBgBI2AEjMBwCDj9dHmAa4Th2Cx1HlWv8s9/QHzBM6U2bfaMwCQmYE0biIBNvAxEx+IGTUDfB/nteSGuujbEI486PPlUgCfmBXjqaYfHn2J4nsPT8yN/HnVPPxPgScZLp5ObXr248JIQsjPoQqdwwh13yOLkEzP48ufT+PpXO/HNr6Tx7a+n8Y2vdOKbX0vjO99I4xvUb7MNkJ8U4fwIBjORXF2llBj0Er70AirPP7frLRR2aOUfL+v3YkXWK+69B+Frrw66jOKELpNBsPT1IU9SFNsoO5nM0NE+omrJxIgMWGYjYASMgBEwAlOBwCBOmLpeiq1bD6Qn19vYmVgcwaJXAF5LTequtsYZgTIkYBMvZdgpE7FK858N0LCFt/hcs7kG6A0Lndt08pIr6HNppMsl9d6WRgfZ8QHblITAe3bN+AkK9YM3qE6Q8wFuimUG1Sd77D60iwxNhIQbN4AzL7TAlUbia9cCmR7GGZVf9ZaGS6fzQfO3QqB/kkA2CJk7izgnwJxe3Q4CuHVr4dauod5WI2AEjIARMAJGQAT006HkP++GHhjpzdzEww8g9sJzioqulbxU2k3ZWauqQtVVV8B1pgtVG+gao5DIBCNgBEZMIBixBTNgBEhgw8bcbIqO3nLU+ZVq70snp4B8Ock9nLfTQ2fB4RM47JAMPvSBNN75jizmzsmiZloWM2cC+jlSzXT6lKdNzyIe5w08++T4Y9LQT5mGWmKmZhqSd9+B2IJnEKxc7i9ghmrD0vdNgN2CirtuR2zhi/0kyKLqgt8h8cxTCOrqkE1VIDl/HuILXy7MhfWd0bRGwAgYASMwRQhM6GbqcZAeIMmNpCHOOcRfWYiwvgHx559H7PUlCDZv9iYTTz2BOJ0PTPJN2O3BTBaxZ+ej4i9/tGuGSd7v1rzxJ2ATL+PfB5OiBrqphyZZ8i7fKt01Si7WF8uKK3LeTlHYxJET2P09WfzbR9P46pe6foL0za9S/nqn/znSt7+Wxn776LIGeOXVACtWqIOGVm42kUDy2Wf8BUzynrs4FPIdPzQ7g04dBAiWL0Owbu2gs0zUhOqN2IvPweV+h6enc8HrSwvNcZk0YkuXFMISXFsbn2Z1SjRnBIyAESgjAlYVI9CdQPy5BQhffqm7skco3LgR0372QwRNjT1ihhns6EDlFZd3yxyuXoWYrimy3dSTOpCZOQupG69HuKUBwYb1k7qt1jgjUA4EbOKlHHphEtRhrz0ymFbDsxVX3SiqSf5fHDOgJxRyFKX2M+qSpfOK3KamOgvZyQXN648AGQevLgIaS3MBoreM9E0ePgjCkqUOl/wxxE23DO/QoJ8P6QTeX9VLpc9WVSH12MN8WtU1AVEq22Nlx61bA7dqOYJlbEPnICZJWluRvPtOpB5+EPE1q8eqmlaOEZicBKxVRsAIlAWB+KJXEPJ8OFBldH0SrlwxUJKSxuknSbzUKqnNcjSWjcdR8cC9AB/g5OvX89o8rzffCBiBkRMY3t3VyMs1C5OMAO+DcfqH0/5Nh2QK2G/fDPbfJ4N9987igH2z2G+fLPbeK/L3oW7vPTPUZbzuoAMyOP2DaXz1y2nIziRDU/rmOKD62ivhv6tSAuuPPeGgfzAgU/6Ey6uNefMDrF0nzei5TBAiXLoU+p11vpSAT5zCVSvzwQH9QG+AdHYMmKYcI/PtjbHtidcWo/qyC4H2tq1WVb/Hjj/5WCEdu6kgDyT4dH4zUCqLGy8CVq4RMAJGYEoTSPPacYBvwo01G50u4/OfQuqmG8a0aF1/6Roo0DfzxrTkqDD9ZDx1x22Iv7owUtjWCBiBkhMISm7RDE5ZArWbOCPAxxJve3MGJ59Ad2IGp5yUxkknpr3//pPTOJmy/PefkqEuA8nHH5vBbrtmkUxMWXRbbbhu1sPXXgNyr9mG6zkrks1sNd9gEmzU93mYUBcb7D5w9gxaLro0hpWr2acKjIJz6Q5U/uEiJJ5/DrFFC30JeuU49uIL4GyMD0+2TWz+PKQeesC3z3W0w7W2IayvH3Izs7EQieeexdYW9WnywfuQvPdOIlWo3xwWYQSMgBEwAkZgwhDQREWpK5ueNg0V112NoLkFQeOWfs1nWDjXfuOHE+F4lk7eexdieqN5OAZGmkc/GZ//JK8zW3iNYtcLI8Vp+Y1AXwRs4qUvKqaDW78eVeeexQNw06BprFkX3aRvt+2gs1jCQRJwnA2p+vOlJXnLxZ9Oi64Y5szxGpYQVUYhRfMhFC65LMQ/bgh9RFoRXoo22coqhM89A5ebDIq0g986FhK0tvj/xhPkPvTmOjgZMYi3PwZfSnml1CRLsGolsq4HzOJqDhBVSNbSisprriwE+xKy8ZhXu6YmuBL9LM0btI0RMAJGwAgYgXEikE1nUP3LnyJcvaLkNXC0mLrvbk48ZCj1vyZeeB5VF58PPRRztZuQeOJxykXpeX1TFBpA2tbgAAAQAElEQVS0GHR0oPhnP4POWMKErrkRqdtuLqFFM2UEjECegE285ElMIX/NWuCKK0P86P9i+N+fhPjfH9Pv4W69zSH+ysv4+9WM/wnjc+4H8pW2OF9OfuyxwJ94Vq8B+DB/YhMtw9qHPLl7wCOsW+KJx5C64Vr4j+3Q1oH7Z5G7R/cqXXjIMcqHdR0gmffv8gouU1WFyhtvgOPkSUE5HCGTgSZhhpO1nPO45mYEry1mlw1mJqWrJcFrr6CCFz3DvG4rGMoGMUz71f8hKPrtdiHSBCNgBIZGYGi78dBsW2ojMIkJlHTX8a/lAvqprsuMLbT4U48j/ujDPKcDLt0J/+YxAD3cqLzyT7yOiSoULnkNqVtvwkRdArYt8ciDvp0TtQ1WbyNQrgSCcq2Y1SsiMBrbq68NsWiRAx8aQL9WKdzg8ezI1ReZ/7mtD+tcIoG+0iuB96VjwMv09cqEbthffiXAI4/b0BKScnSuvQ1BQ0OhavpPUt/6emchXBgPBU0k2GRaxGHQ200bUf2nS3kxlttRBpkxbGxC7OmnoLdi9IG/QWbrlUyvLcdWLO+lN4URMAJDJxCsWYGac38J8KZk6LkthxGYugQqr7umNP+mOR5H4vFHkOAEyHBp9nd9szV7OouHPKeH+q9HUKh7DtfeXlBoIiah77ENt7DArp8LME0wApOMwGD37knW7KnbnLrNDptqOT3CVecE5xy4RqcRRy6584lEhvyq/07k1VLSSXaOAte8DZ+waKP/jlMUnDBiljNO4etLkW1t7VbnDCeduinGMBCsX4/An+x7FKo+6KEaKKhXYoPFr/LGoXdjKiuBww6lnn0qs9vXNOOUXRYhmVJvD2R1lOJYj1GyPGZm1YThfgA5M3sOqi+/DInnF5Smvv5CzkH7a2kMmhUjMMUI8FAYLnplijXammsERk4gaKiHa9v6B+S3VpKuRf0btkWTHFvLUxzvVi5H6q7bilVezsRCxJ6Z7+W+NlkXRNfIfUWWUMdDjLeWYXnV53CSt6XFh21jBMaRgBVdYgI28VJioBPJ3JzZGVRUTKQaj25dg7WrOcGxBpUX/hbhlq43QmLPzkf1Hy/EqNy1rlsHlTtQy8KlryH2Qh834Fu2IHXjdaxWFvrQm1vyqpf7tZXOourPf4Tr6OgzydFHZHDqSZx8YezbZ63HUS9ehB22jcJUIZHQdvRdetp0VP3mLOhbKKNfWvmWEHJsoLUNsZdeGHElM2Ra88ufIrZ+7YhtmQEjYASMgBEwAqNNwHHCxv9TgfyMRI8Cs2E4pAmRoKUV+ql1DzMI+MCt6k+XgRdSPaN8OGhvQ8355/L6ygeHvSm8wdrHE5DgtVdRfelF3nbAJ30xTvI6HxrfTZYTZsHixeNbCV+6bYzA5CBgEy+Tox8H3YqZM7KYPSsL+V/CpfjA9k/6k4k/wGdpxgvodjLTd0C9WvF0kv0JRDIDXmbW4vUtb2ZksWICyMnHH0eMT0TChq4v2asVLp1GsGnTkFsQ0JbTfx8aIGfy+WeRePwx8lZJfSd0nZ0IeryBo5SuswOpe++i6BA0NaL68j8gqKvDQEu4Yf1A0dh37wz0n6iqKoGwdiNcUerKqqLAaIq8mIovXIhgcy2m+hPmQB+56+MJ3XDw6zfxfmcfTmbLYwSMgBEwAkZgDAkEq1ej4rqrkC2+ECkqP/b6UlRey/gi3aiI2QziixZCbwOjuABeq4QrVkAfoy1W95SzVKSu+isSTz+F2LNPI/nQAwjXrkFs3pOMiVbHyZ9w5YooUEZbPYSs+tMlsA/0l1GnWFUmNAGbeJnQ3Te8yp/+oTTe+hbmXbMelYk0TnjbYiQSPDXw5MaVEUDgvAfvaZRIoO/oFON96RjwsnyGq6uzOOiADA6mo2pire3tcPrwTVGtUzddz5NqU5eGTyqyIKsuTb9S8uEHESx7HcGKZbzf7TuP04dPObHSr5EeEdlGTgqtXd1DGwWH+7MWNgnhKwsBPl2SpX33yuCgAzMArzLev+cKvGVGNOlUvxnYXA+0tTvU1Tpwrgd8gOR/utbSwnAT9ZsdWlsdmhlevtzhtSUcFOha9LOp+AvP9SbIsrpSRVKwqZaTUo9GAdsaASNgBIyAETACnsBU2DhOeAR1tf021bW18gHRpn7jRzsiU12N5D13IhygjqqDroJitbXQfywKN9cj1EO5zZtRqTeWlaDMXbhxY5nX0KpnBCYOgdxt9MSpsNV05AS23w449eS0f+vlrds249iVV+B7/y+NH/9vZ8GdfCJvvFnUGR+h/vvU59yP5Cvd96mXL5eTFfef30zj+GP5dGCMfpbCKo7eyhmJ1BOPAEUfU4y/sABVf7i0+ytB/dRAb6nIVf/ubLjeUw395BpYHeNkRPKF57sSccIiXLUCTm+6UM5y8sStXwu3chnQ2taVbgCJ2VB5w7XIT9xUXHUFYqtXIptMIcmnM+9IrvC5O9NAOs1LCM4hZTg8iAfyNXekBHxgA81beR3j6zYDf7oixKuLmYcJ9GaUq9/MJ1jXMtR9TcfiSN58PbmykO5RFjICRsAIlAWBbBAitnAhwpdf8vUJX37R33jxkOjDxRsdH4M1q4BlyxB/el5x1GSSrS1GYEoTCJqbAV38BAHiLz6PGB8sYbBL0bXlYLNYOiNgBCY2AZt4mdj9N+La+ycKvBkesaEpYiDLmYVw3WroP8701+SeF+H+xNxf4iHq9fMitLQUcmlyJP78c4it4gV+GCL5ykLEeFNQfe6vEW7u/0kReix67VWvukrtn26kowmQTEs7sh2R7BTZl+s3Ikr8zIIAekuo+o+XQG+8RNruW6VI3Xs3mLB7hIWMgBEwAlslMEYJ4nHEFr+K+JLF/lil/w4Xe+klL/esgePBLHX3XYivXoGqP15Uko+L9izDwkZgohDIhgGCJUvg6uu7qszJiq5AJOn6SZOWUWhk22DlCj6UGvx10EhKy/LYEHCSVT9/GomdQt7AwW3agGDt2oLKBCNgBCY+AZt4mfh9aC0YBQLZPi4IBlOMLhrCNauRvKP3l/MHk79nGv3UCe3tPdXdwoEmYtIdXuf0+gknh7zOawbehOvXwa1bM2AiN2Ds1iM3bHTQn7i4Ul1Rbb3YskqRaW1F+PRTQG5Cq6wqZ5UpHQGzNPkJ6BgmN4iW+onybAbQa4CDSG9JjMCkJVBRiYrrrka4elWhiVnuG8mbbuQUZUHlv3+S0tuvXarBS9wvY8/MK3yYv/IfV8F/22zwFkaU0mk/lxuRlShzNp5AfMEziD/zNLoBiqJtawSMwAQlYBMvE7TjrNqjSyD+ystI3HNn90J4Ui9W6C2ObmHGJ++9G+Hy5Ug+/EBx1LDlijtugePFybANDJTROf8EKr7gWQy0aDKpZ3xfup5pHJ/YzJiexS5v5Y1Hz8ipEg5DOE5uhbWbUHP5pRirG7CpgtfaaQTKkQBPBeVYLauTERhXAmF9HVB0PePa2pF66J/d6qR/JhDqW3bD2Yl4TVN5w3WFb6649g5eP/FKjRcs4dKlcC3N3coqiwDbGXviUTi1uUeFHB/UZCuSnHyZV/gGX48kFjQCRmCCEbCJlwnWYVbdEhHgiZin4/6NdXQi3LKlEJ+eNgOpO29DUPRvpuPPP4/qSy8opHHOIaEv37e2FnR9Cfr3wH39DthtWA/9a+mKm66D21S6j5nFnn0GqN/crSrpuXNR9duz/X9Dcm1tPi7LSQJi8XJ+U5HKS7xe6hIHJW0zrR1fnnUNDjskM6j0kzGRnlolH3sUseWvwyZdJmMPW5uMAPhAWmeTrqOn/jte4q47BkbjBo62WCNgBAZPwNXVIuBDDqe3fntk46UZkvffjaChvkdMeQQrb7u5358iZmNxVF52EUJ9SwZjvPCQxnmhMS7UijMCk5tAUA7N6+Ssbn/12Fhbj5bWgX9q0V/evvRr1tciow9h9RXZj+6eh57Ghk3db1z7SWrqsiTQu1IBb4Qrbrqhd0R/mlQSiScf7/FTkSz0G+LsIM9MShcsWYzw9aXwvwPmSa24uMRzCxBb+DKSt9+G+OJFiD838JsoxXn7lTmBVH3R+b1O2o6TLImFLwFFF/96Bbjymr+heNF/qZo9O6rorJnA9OlZxONZ6E2WykqgsgKYOR1IJrOUs1B8KpWFLnSCTDvmzL8PVUtenNL/nUhPrTDEY05xH5hsBIxA+RJIz56D6osvQGzRK6xklo4rJ99Tjz9Koe81XLYMVWediUGeOvo2YlojMJkIDPPn3XkE4eLFSP7znnyw4Gfa2xF7/jkE+rZMiX4GVDBeIsGxjiUyVTIzmYoKxF9cgLCudA8BS1Y5M2QEJjCBcZ94Wb5qPXY/6lNYvbb7zr181Tqc+LH/wuEf+Br2Of6z+P4v/4AO/VsVwr7+9oew6/v+vZf75g9/z9i+1z9fcyeOO+M7+Lcv/YQ2v4qzL7q6kPDeh+b3siX7be0dPs13f3YJFi1Z6eURbSxz2RAImps4GfAIn1SOXZX0c5Oqv/0Z/nf/fVwAOD2pyU1CusZGhOtG9lE1fadGz2EH3UKWrSdGuVuHQrYgNzkTCwE5XR/xIQwChjXBEuNEjHScy0GcOi8Huew0FjRs9pNNOc3oeSwrpo/36U0lyqNXkFk2AkZgshEIVixD4oZ/cDJkkAeP3HHR8YAXW7kMwZYGpG69Ra+/9InG1W70x0E/2ZLNRB/o7TOlKY1AeRMIVywHeA1VylqmeaNf/cufAZ3RdfdQbTtdzXX0zhu2tSF1zx1DNTex0/MQpgd8rrbOt4NB7w9pk0gg9dCDCOo2DymbJTYCRqAPAkWqoEgec/GML/4EJ/zbf/ZZ7v+d+xe8821vwLw7LsYtf/457rjvSdzxzyd82mMO2we3XXFmN/fed78Vs2fW+PiemxdfeR1nnn8lfvKfn8I/rzkHf/rtf+OyK2/Dgpde80l1c1pZkepmT/YT8ZiPn2qbYPVKuKbGqdbsQbc3M206Urffhm5f5x9k7nD9+iilZiwiaXS2tK8Tr570jE4BA1t1uSPLsE74A5vuP5Y3QhVXXYHY0sWIPTufN1D9J7UYI2AEjEAxAdfaioqHH4R/XQ8DL+mZs1B9zi+hPPmUOmfG5z+F/v7jXWzFSiTuvTuf3HwjMGEJVF3wW8RWrkSg/6Y4xFZonwlfegE9rw0cZyRjr0fX5N1MxmII1qwC+DCqmz4XyPI6PXyN+To6c5o+PD3U6kM9aVW6FrrpesRfeQlV5/5y0jaz1A2r39KE9Rsn90RTqZmZvaETyN0eDT1jKXL85sdfwZW//34vUxr8jzz1Aj7+oWNRkUrgzW/YHqcdfzDueuApn7a6qgJv3GnbgqtvaMRznET5+IeO8/E9N43NLV613TYzvf8W2ps1owbLV67zYW1SyXjBXt62czx6KbLIbaprwGe/cxYuv3pyzqDrZJi6504E+QmCorYXi27tWujr8UpfrC9n2TXUI1i6hFUcYa0TSST0NrHaNgAAEABJREFU5fxhPplhBZCpqkb1eecAA10sKOEIXLB5M2Ibusb4CEwNOWv+LZkRkh56uXV1cHSph+9DbMF8hKtWDtmGZTACRsAIbI1AbNEiJhnCES7dCb0Vw0zdVt5vdguXLMCqZXvd3pbMuhmawgSCtlYEq1eh4uYbhvyAQx+RrbjztkHTy6YqUHHP3QjXrukzT3bGLFRe/TdOgkbX+T5REA65Xj7fJNjk93l/rOnsRLh6dTm2qqzqpE9a6BcWB53yJRzxoa/j1E/+N26+q+unopL1K4hzL7m2W73/et3d/tcSl/z1lm56CxiBgQiM68TLNnNmYNu5s3rVb+OmzV630/Zzva/NG3bcFmvW10rs5c6++Bp89P1H4g07btMrToo9d30b9nrP2/HBT/8Af7n2Llzw5xsxZ9Z0HH7QHor2rnbzFvz3zy/Bj87+E26993F0ptNeX7zRhNCnv/VLaOLnYx88pjhqUslBW7v/EvxAjQrrNqHy2qsxYc5uvAgNeKGgE3QWuQm1/MRadqCWjlKcXk9/9RXkq4JJtuTfeBmvZmVTlai69aatTiCOVv1G7WZqtCpsdsuKwLDGDzOFK5YBTU1l1RarTN8EMqkUUnfdjuSD3f+rS9+pB6flECickvX2TfLBB5hxPE5wLNbWsiNQypHgshlgjL5N4jhpWRjYfVAN62q7XUq5xgbuV/f1kbKnaujhbGUl4vqHBW1tW82s/dEtXz6m05+xF19A4onHt1o3S9BFQN/9PO34Q3DvNWfj8Vt+j+OP2A8/PufPvb4vqgkW3Qcqpz59oV9OSDZnBIZCYFwnXvqraENjs49KJOLe1yaZTKB2c4PEbu6hJ57HU88uxGc/dko3fXFAdg474L3YfptZuOXux3DeH67HYQfsjsqKpE+myZ//76Mn+DdrpPjPn1yIM8/7m8SCa2puxZe+ey525uTOmf/zecT0QQvGTquMYSK6qlSIgBMPvP8HPSisdlRLH4BxbBzXikTYZ/sSsSCXb/zbX1MRQxg4hGoIWPdAzlFyCKjHtGqkXnwWiaZGhA2bfVtV/+zMmaj66x9R8dw8yIa+X5Iz4fMpqwNtUCmZoufCoEQ4KgPnUJ2KoXLzBqSefxYqTw5c5MtRRFUyRCrBijGgeiofRdpzHEvw5Yeh62qDA23BL0orp0BAE2qr5DBggEJIr3s8lVwVnden4oFvN9Xebl4vX/kdI/iQCGwO6wSfhirPNQicRK+T7OCYRg5wubKlc45bn9YhoB5aeKUXUscVjPZ67ztQdkrh/XzykJFyilA+yVTB0UDgXFRuIB9QGTHKGrdyGsNMQr3zZTkA8Zjrc/wqfWlcnPa7u2qOx8p770Ji03rWADmGXvR1C4OA/R2H6h6w/opRO+WUNx4LpGIbXOTgutoNwDGPHLgEzjEOfolkV5BdZIbxDupbcEn6/Tneq87TKk1XTgyqmutQffYvUdnWNOi+qqpgH553LirrNgw6z9barPPB1tJMhvgaskvyBMDdicfJWJ/8UvGQexC4P9MpIUPaZ13guIc6HrsDSK3zQZJpJWv/DlwALTq+K6x93MfDId7ciFh7mz8eSF/Fc8lweaoNFa8vRuXSRb7+8aYtSK5egZpU3IeHa9fyjQG/UT7+1uictH4Nqm6+nuM7HMJ4iDFtd1fJa0THAe2cQzSeZa97mmlF18Uq25/Lc/uJ8gTKKxfIEmiHzsEvild65dN1GpMx3nkHLlE8Ba6SFe9dAJ7rqHRA0NKM+MIX4ZxjPsAB3g+YxlF2LDego8g8jg5+CRkfMo8CAf3IAUHopOK+7+CqKlDzp0sRS/MBpYPPGzCfEvj8gUOC1x2OpVa0bEHNb3+FytZGBNTrGOAdZZ/GARW8NgTTKi+DTAdvE1xUbMh6UESS14/FXPuTk9zv46uWQ9lUZsDMujZK8RqQom8Di5foy1K5CgRBwHIdHAMBEyi/c87rwEX17K/MiarPX5PrJQDdQ243dxZqqitx6nEHo7mlFS+/+jpbHq3v2uWN0AP8a26+3yvufmAe5s6agT1328WHbWMEBktA++Rg045Zumkc+Cqso+hnGG1t7Zg1Y5rUBZdOZ3D2RVfhU2eciG3nRj8jKkQWCQ898Rz0itgV5/8PrrroB7j4V9/G366/F/+4RU+DgPe888349uc/gs/828n4wTc/iZ/853/4+M6it16+94tL8cwLr/p0cV6g5c2zCpiITt92zeYakXUhMq8tRevCV9Ex72noQUYuCvpnLH21L5+3v/i+8oyajpXhCj1dUL29LEGOgUwiheTtd0RPghXmgxp60GOIUB8f4zjzPKjM28hllde/U3rGpuXXbUb8lptlkppopToSuFUasaLYLY3C6VQFOl5dgizHW3GevJz3lVaynOS86xnO6+Xn41S22iidXF6f96XrVTEqFZ9n4mXquq1SUpHzKEUrz9te8HkVWeQkFpelNF7HHHmfol99WBs6rlE2Clx9/8nPj6vi9vnM3Mh2Pn50/Az3/+4uw4rEF74AtLayBr6ahbGpUJatUBrVXc4n4kZypJdERdEaaZRTSoa4RlJOYKBLUildCp8rF6n/rJVm/cxlevVbOTHJ8IARf+VljpvskOqJ5qYh5xmo3TpuDBQ/GeL05LLzuuuQqd3EPRPQPthXuzI6mHTtVpTg00vQ7iUnOc10SuvDlAupqMjGYuh8bQnSfAigtFQxOurjznv/iew11yDNA1Vf5W9VxxN3uPAlBIsWoWXhwuh8wgK2mq/oeGBpy/u4MJz+6eB1RceNtyC9fDkSd92BTh5bBm8HPP50dxwuGroFpzCHbK90BR3HIFcelwpZCvtNQcMEXKMgBe02yk+R+4dfozhtpZSjLE/OG6TAldq+V9nsK8bn8RuWk/OL03mV3+Tic7Ivszgh5XwZOm4yCPlBS4tvu49jXvl5Jxvip7QFxzR5uUj0NsRka87blgFlpsty8qSjk8eY3Ev8VCm24ArhgsAoyXIU86vsbq3siRavNuXbV+w/xYf5Cr9p5+3lFdyn//UkXPSXmzkp04ZL/nozPvOxkwtxJhiBwRIoy4mXObNn+PqvWB09MVbg9RVroTdWJOfdHfc/Cf23Ib2tktf15T/93CK89Y07ID+hc/C+u2G/Pd8J6ftKP3d2NInTmfsvSkpzyrEH+bdkPv9fZ2NzfaNU3ukp1UR0lXyypptjpxEwfRrifEIfrlyB1LVXI6DOKZIt1BsvfbUv/yRCs+B9xY+lTk8JA9Y3YL1ZZT9DHzDM8w0C6jSDL9nldHrSEAsDSOfjM53AY48hYLyeRkQ2QDuUHLzP1FF6ZuJK2TE94ABU8olFQvZohyZ8eqq9r3A2Fkfw0osI1q6TmnrmCrzobaCiEsnfn4eAZ2CnDIxyziHw1gGp8vqAeq7QEjptwXgH55wPRPGR7JzzceCiJ6zqK4o+rdoqOVBSpvNyADj+BQwH9MElcEBXWhfJDrQLpnBgUuoQhR0iH+DFHTe5dQ2b3dnpmNZBT44cgCCAD4OLZDmKUX4VykBA44EDHNMqniLjHR0g2TmHkAnyYy3fPqrAKDqHeCxAPn50/Djtd3eVfMKsOsiBi/xAFaLsnEMQOOjtBNWdTYMWqqnnWGLeeBggah+itjrQd945wPuyQdHLyhvJipNEP5BzPhAwAVcvaxxUV3Svb1/hKtajL73pts6uFIxS8dD3VyoR6zW++rNfkYz5PAmOeY0v9/pSJBsbBp2/L7upRDii/H3ZHIRuTMsUt+SD9yPU8ZcEFe6rjtp3GB3tc0HXvhUEgPYvqaStTMaQ4NNlyc457teSEPk105C46QaEnKgHl8DHB769Md4gxzhxVslzc1/lD0YXBA6ZqiqkLrkEjvYU1lgYTF5LE/f9MNk4VPJYrjd+g/Z2jjigv/Hdd7tjZNLdVSRDb4dDjWMaqPTjtXua6oqucBXjNQ7l4BDtP7QQ5XeUQDsOIfcFcHGMUNoUy9H5O8rDNI6RXBWnNBRpC95FaRxlJxE+DQCZdAF9wMcFcJRAOXIKsDgEgSTqFFAmBgPKEuUoUhPFS69AEATemuIiqSteb9oqn44DSqtjB5ND15chMwR0MR6nlUZvssgPXKCkcM75+oFLQNnl9LJRzLU/WWXIvnMOgUxW1yD597/CLY/e3vA2fQTgnPMOXAIHpnc+7BhgFJRMjtGIzgWxXuOhv3pMBL2Yqm3F7tWlK/Gz3/4VX/jE+/mwv6Y4CofuH/1y4ls/+r2ffDnioD27xVvACAyGQDCYRKOVRk+a2js6vHnJcgpMr6nCgfvsir9ce7f/jd3S5Wtw012P4tjD91W0d+3tHfj1hVfhS//fv2Dm9O47x1OcrfzQZ36AZbmP5779LTvjtWWroTdf9MR3+ar1uP/RZ/Hut7/J29LbL5qEaWltx9oNtbj4ipux/57vQiqZ8PHaHHXIXvj1D76I6dOq8YXvnuN3Ouk31rdhIrq6Le3ws9OcBdesfLZhC5+0pTUBn9NH090NzR19tq+5Le1n8zc3tvcZP5ZMahvaWPcs6x3VOc0GZTozfEKQRSdlzWrzmtrHd86ajc6LL0X7ytW+/kqbbk8jvO5adPqnAl02xEfjxaehET5Q9PZkS080pZe/mSyb2zpZHnL10MiQTMfyeRUOzF+Azg0bfEQnDeQ/sp/2T5+yCBq3RHlVKFPJthxF1lt2MhIpZ6Gn4Qp05NJ2sgw9pZUub69LjvJtac9AfSV9hum7bNMew2q15hnVHsXJptJ6e7lypM9z9bKYMCOv78lSdlhPtkd2ci97yAT0U+iGxqyvu562MRtlpmW5SqD8uSKoZzrakF5pfTlsgveZMUN2qhtFZJi/g/2cH2ubORY1gJWPyaC2tDW1ovbpF7Fhc9uYjdNo38pC9VA7xFB19TLr3MnGbuKYVd3Vrrxe6ZS3lX1FrFAbFK92ZBhQvPTy0wwX8pFPl6wUZEuG+XGSZplirDSNrZ1b5bDhpSWou/9RbNzcijzbqemP3ZjpyXdLS6e6C/0df3umV7i+KTqX6liksPvrldjyypIR9WE99ynZmsxOxw0dTzq4z8jvr81N3HfUKdH+lNvP/L6V5bE/649d2hVlr4XnR9nK5OKVT/u9wmhsRloHSSq1L0svvh08LnSEITY9/wo2rtww5H7bxGuRDrZBNsPaTTx+ZHkMykDHGtk3N37787ix5zG8ltcnfsxqQHLM6Tgxkvoov0a/bHZyvNVuKeLK8nqea/PXZ508Z/nrKY5zndOUP02ZVfL7Tif3FS9T115Vjaa/XY0WXo+r2mmWI5ePL8jMQ7Pc/3jOo6y3zZRe9ZKv/U3nPu2XklWGr7vsSUmDLI7XdRS4qk5KR5H7TtbvQ7KvfNKpXKWR3MmMMkGP+WUVPk8nryNb2zJQG5tbo2NyY0uHb2M7r1c6mEF5W3ndySrzcNDh6y+drKh8JlMRPk9aFWBoa+fuDdz/N5B/R0cGqqfKly+bqG9ApiWZer4AABAASURBVIMX+7QjXZp1oOjtq7y8rHZG+VR/eDvSKX4o56KRjK+xzKt+UdvybtXajfjcf/4aRx6yJ77wyffn1QU/4ISUfhnx4OML8LmPn8KJtHG9hS7Uy4SJRWBcR81Bp3wJx//rf3pi+qL0kR/6upe1+d5XP4YXX1mKfY7/LE7+xHc56bKP/+CR4uSuu/0hbGlswcc/2Psjt43NLXj51WVobYtm+PWhpC//x7/g/879C/Y78Qv496//HNp5/i2Xd+36TfjEV3/myzrqw9/kwTONH//nf6iYggs4/VtZkcQFP/8Gj2GN+OYPz+NBiQc4HjnTE9Dp4ArdpfqjspdyGzY5p6PkTx759mVfXQQ8t4B8sjxRMBFXHbTz8ePp64TO6qjKvh3FsppZrAw2cgJEZ+MoNdsCuLbWKBmiRSftSPI5u8Xl9VKmZ89G6s9/ANat61IXMotTXs0acc2HIqv5baTtFh2pclvGcPUB+lzzovdVj2JdofgoFnyEgYBPv4MFz0KLj/cbhViH4sxU5YMUZVpe5BjB1cvyveOGrfQ6WvJ+flLJB3IbFef1TC+V96RUoKgU6fP25CtcnCQflp93+XHH3bDIks+FbEsznzBfhwwvXvLpxsJX3aIaRFQKYQl0qgO9fBJfb4W9vicXplJc3jEYGfVC943S5DXFckFHSCpDLsOJwOyS1/z+rHDeaf+ouIETkUVp83HmZ/3F6mhz0PFMfSZ/sGVl2F/KI0953JZ6ZEc67jmIZGsyO/ESN7GWz3uyPvs4Omdy1yMTHZuU1vsMF8tile8LRfk0TKDdWmFa8Ps7Vd6XTnkUyNRMR/UfLgVWre67DmvWIHj88W5xnezjTjYisiFrsqxSIl91UZy5sdl3y4mz4zVb/L5/+sGg8acRMdLxoPyyI5eZsw0qf/874OWX/HkkduvNwKJF3canePh9Kzc0c56GO/J1yvuy6V0sjtQjD8LlJguk65ZPCrm8UrJccTgv5/ycp1Te+TpFkt9qo3rk91dBK+TJCYpTGp82t8lFKeSdT+MlWtDMDGV/7FDCvKMub4e7rkKeB4WCn5dlz8tMKJb9ufDJJ5G68q8+v4rxebiR7F2uwLw9RrGCXBXJgNfn0jDYtebi1e/9lT1R9URaaOfipavw0c//yL/V8tP/9+l+J1V0P6lJl5OOPrCQ1wQjMBQC4zrx8tTtF+LF+y8vuIdu4AE8V3v9C+k7r/wV/nnNOXjytgvxU+4I8XgsFwv/X4yUXx9CKihzgl7/kt13vHVnr9EspV4bk70b//h/uOeqs/H1z3wI8Vj0yuQ3P3c6nr7zYtzxt1/ikRvPg/7FdfF/VFI5egNHxmZMr8ZtV5yJC8/8Vr87ptJNRhdbvQrJBc/A+UP7ZGzh8NoUrl8Hx4vf4eUeg1wOCDbXIcZ6jqS0CZmXT3b09HdC1n2UKx1bthTJu+8ccinJe+5C7MXnhpzPMhgBI9AHAdddFzRuoSJ3t0OpeA0a6lFx3TXdzsCp225F8qEHCjexxelNnuIEtmxB/OXnRw8CH0jGlr0Oxwccoa4Pn3wMQWvz6JU31S1zYiT2LK/BVy7vRcJ1tCOo39xLXzJFczPcyhU89vR9bCpZOeNg6JXXVuD9/9/3cODeu0LfcVm3oQ56+6WuXsfi7hXSfehXP/VBJBPx7hEWMgKDJDCuEy+DqaM+mltVmRpM0kGl2WG7OdBETM/E+lnRzjtsA02s9IyzcBEBHviLQuMhWpllTiAIe5+YeX2GIJrnHPPaZysqEXt+AcLly8a87HIsUE+2gqVL4dp4odbYOOQqxl55GQEv6IecsUcG/8SRxxONFrke0RYsBYFgENPk7INSFGU2hkcgU1mNiuuvBdLRz8v6s+L3F0a6zg5uu9aA+7BrbelSmGQERkggNn8eEprM4232YEwF9Q1IPPHoYJJampEQ4IVUxW03IbZp00isDCtvuGoFavR29yQ8WS9ZttozufXex/2vMI796Lchd+b5V3q9bYxAKQmU/cRLKRtbWltTw5q+iJ7hTHfFP64BOge+MJwaRKyVWyMQcoJFn0eSr7S890NVFcBrBozHkqmsQsWdt0NvJo1H+eVUpmtuQrhhPar+chnQFv3XpeHUTzeB8SefGORled8lJBY8gxQvIqsvvRDxhS/R1iS8ouu76aOqzWqHy5XgGpuQuvlGsgVcXR3i85/qerccQPzZ+ai84nJ7W4Isxm11QOreO7v1S8+6aH/Tm2b6GWDPOAsbgVITCOvrEVuxDC53SJZX/eufIVj2ep9FOV4bOk4AFkfG5j+NYEXvNzOK05g8fAKaL4+/8Bz0n8yGb2XwOZ0KbG8bfIYJlPKEI/cv/PJCv5bIu1/892d9K0459iBce8mPvNxzc8V53/Ofruipt/BkJjCytgUjy265JzOBbDKJ+NNPI/HqIiTvu4snYZ1+J3OLrW2lIhCPZzF7Ztd4icdLZdnsjIRAsHIl9+l50IV1/qJ6OPZcRyeqrvwzbxYHnzu2+FVgS0NXBl6so7YWsZUr4PgEL3XHbV1xJg2bQLBuHeKP5p4+80I58cICJO+9C7HXXkX1JReg2wxoRweC9ethS2kIZGbMRMUVf0K4bm1pDOasOOeQWPiCf0stpzLPCIwJgXDpEsTo4q+/PrhrwEQC+nZGxb13I2bHllHrIx4SEL68ECHPn5oTGbWCzHD5ErCaTUgCwYSstVV6bAgEAWJrV0O/K4ctRmCIBFzu6KKLsHL+BM4QmzWhkzt9VJo32yVphCZOhmCo4pq/9ntD6jrakJj/FPQzqCGYtKR9EAiaWxBbwkmuXFw2DBFf+DJc8b8ay8WZV2ICvBuKbeRElg56pTLduAXB2jUDWwvdwPEWGxHgHWrX44BIZduBCejbLfGXnh84UVGs27Ae1X+/AvBf0y+KMLHkBFw2M7jJsJKX3N2ghYyAERg8gdyt0eAzWEojYASMwGAJ8D7EJ9X9vhdsU1YE0nPmovqS3yP+0gujXq9wwwbYb1pGHXN5F2A3voPun/wEQWzlSlRe9bcB86WTlaj+za/s58ADUBLP5N13Ir5o4QCpLGqkBPzPjjT5OFJDQ89vOYyAETACZU/AJl7KvousgkZgAhPIPYidKA+/dHE+2rSzVVWIP/ss9HOd0S5ra/ad3mpbsQzo65UkweCN8tZsDCc+/szTcCP4xsxwypxMedQ1wZrVcBs3TqhmpW65AcnHHoXqLzehKj+GlXVtbUgsmO85+WLb27zX38ZxP429vrS/aNOTgE5FCf1HmMYmhkZznRi2OWSgD6WHixZNjApbLY2AETACk4CATbxMgk60JhiBciWgi13VrTOjbXk7t2kDKq67uutmp5/q6oI1o00/8VtTZyqqUH3R+UAZvQaUdUG3dqdnzkbFXy/nBNH8rTVnWPGVV14BtLcPK69lgn+9PHXnbeX59L7HvlH887GgoQHZMEDFP65CuKUetvRNINhch9RNNzByBNNTzG3rVCfQ//jR26jxl15EbOliexFxAgyTbHMzgnXrJ0BNrYpGwAgMRMAmXgaiY3FGwAgMm4Duv/KXfXpg27gF0HVDeyf0TVVsrI2mZTZuctiUe3C/ebNDW2ukH0zBepPmxltC/PyXIS65LEQb7+U30e6w7uk7OpC6504EfNo8UNk1Z/0UsUn23xqCxgZU3HFrV7N5ZoivXw//TZicNly+HIm778iF6IUhwpXL4datY4ArO5wrBVvHgoBe6Ud2eDOa6qfsKFTSrV2H5E3XQf+FR+arzjsHsT5+2pG6925kO9JKYs4IGIESE9C+XXnh+dBEZ4lNm7lxIhBfvRLJB+4dp9KtWCNgBEpFICiVIbMzNQhkYzG45cvgtnJzOjVoWCsHItDUBBTuC3UlOFDiXJxeAtnCCZpccKteHSdq5s13aG5xyH/PspP3c20dQCddXwZizz2D8NXur1dX3HgdYkuW9JW8ly7OSRe92t8rYiIrWloQf2Yeit9O6Nkc19qMxLwnCmr9ZCr58AOIvR5xSzz2CJ/S/6MQP2yBswJc4f8VaV1tl5mGBoSvvcpB1aUagmRJiwi4jnbU/PwnKPUHd126HRV33ganGVGWp++TuHSG0vivGlPDqYXyhTznobFxONmHnScbTyDGyU7X2jJsG9F/sNJE9iAPwMMvyXKWEQGdn+IrRvbTs2x1NWLPzO//RDqI9mbCGM8pg0hoSbZOQD8HHuIH7bdu1FIYASMw1gRs4mWsiU/w8rJV1ai+/h9wQ7k7nuBttuoPj0B7By/489f7FHtZycf1ihi8Qv+oxdGOXpvumUtvvejn/B2sR2urw6q1Ae65D2i9fz62rKhH+zW3YvmdC3HX3QEaVjageUt0g/jk0w433xbipltCvH7XIqy76wWsfGAJHryrBQ8/GkATPC8vdNCbNjfeHODmWwPcwLR5ed78AP1fH/Ws5cQOB5ywcfkZrnQnQh0XdKfaX7N4E6lvk/QXLX348ouovOZvqLjhH4gve10q74IN61B59d+R7Wss+RS2GTQB9lFMkwmDzjDxE4b+p4TXgIeLITVGx5bEffcgrKsbUr6RJs5Om4aKm28Y0VsLmW22QfVZP0WwcuVIq2P5pxiBTEUlqi+5ACOZONX5oOqPF08xctZcI2AEjED/BGzipX82FtMfgaZG6IlKf9GmnwAEJlEVU8mixhTdVekBUVsrH9h1Au16+4UzJrs2L0BbSxqNzQES857ClrUteHpBgKZmoDX3/cpFiwI8Nc9h+UqH5Ma1qN68Gu+68wKk12zEwkUOunNbuSrACy86bNe8BGsXN+FpTtbMe4b5ng7w0MMO9fXO2yyqmYkkENbWIvFU11szVPVa9fOmYP06aFLHz3IxReUVf0RszRoE+d+kUTdZVz+E/WYoLcwio4G5lSzZZBKJxx9B+NriraQsbbRjv4Wrx/nmv6MdyXvugOvQwWBw7Qs21yJYtQKB3nbJvcUzuJylSeWaeWDiJFlf1tzmOuhjsf6A1FeCnC6+dMn4nq85lsNFryD/87NctcwrFwIhz2mjVRdNxvO4PVrmze74EdBhKf74o4g/+Rj37fGrh5VsBCYagWCiVdjqO7kIWGsmL4FEnFfc+Ws6ir1amo/rFREp9JZKJHXf6udIcptqAVeRwH+863Gc/s7ncNS2L1GBfpdURyPe+OR1CIqPeqxXX9V4647N+EziSlTF2gHO4IS8ydHPpgppKWy7TRZHPn8R9tx5I3baAaAKxUtzc09NcezUlDWpMpyftwTkj3G48R2PXqq89u8Il3afGOEw5cWttkDQsBnhylW+atkgRPD6EiQffhDJJx7zugE3HPzhqtUI6jcPmKzUkfElS5G4/75Smx11e8Hy5ZysuXPUyxlOAUF9Ayr/8gdmdXRlvDZtQeXfr0BYu6mMKznyqrmNG+HWrBrE9OfIyxqJhWyPGqYrqlHzq58hM8bHhJG0wfKOPwG9CRiuW4tQ4378q2M1MAI81NJEAAAQAElEQVQThkAwYWo6uhU160bACJSYQFUV4HiE4b0eBQx50Tdi+srkHz7rV0G8D43FHd55xwV4Q7gB229e1Gvyo6/8xTrXzz1LKshg23l30h4LKc5QLDMqqK/HOxNL8Yn0XzCthoqieE3UFAUnhOgvyrs3Y3zr3dmJ8MXnx7cOY1k6HyMGmzcD3b6LkvWTKon58zj5Arhly1Fx07XQovFb/cdL4LizBC3NUpWn46SZa2kpz7oNVCtOugZNjQOlmBBx2eZGhCtW+PEzHhUO164Zj2LHtMz44oVI3XnHmJY51MIyqRQqH3oAsQXPFLLq7eVw5TJUzHsCyTtv47RMOZ0ACtU0wQgYASMwGgTG3CZvi8a8TCvQCBiBKUBAN4UhjzA77pjFzJlAdQ2w7TZAIgbMmgXMmRVd4M2ZHfk9kaQ7e2rgbxw6OvqYLdEsh16D6Z1lQI1Krkhq25VM9Qbr3aXpR8pXo7UN2Y11PlFFBdtZHdnTpJNXTpBNpmYaKu64Ha6BN/4jrDPnD9hZWv1UzrCtaTKh4vZbZWirNjZtdLj97gDnXxTinN/FcPZvQvzqHMq/jeSzzo15/a+9njL1Z/Uj//q3Ic6ljd9dwPy/k4t5e+cxfNsdAfTfubZaoZIlcAhXr4KeLmrIOd4aFT64StBBbS1sMQIDEYit2wB9p4YzyQMlG904HhCjIyMQnzcPlX/5oz+ej26ho2Q9k0W4/PXu9aeu8L2rUSp2xGZjcQTLlyLQt7h6GPMTox0d0M/C9JZnj2gLGgEjMKoEzPhUIRBMlYZaOyc+gWxrK+KPPQynVx4mfnOsBUUEglhRICeGfeh045mL7tvbaoLu2WbXtOFz29+CWJjTx+M4ZO4i7L/Dypyi+z1//saB977dL7qZWnGnvXkB3j/tIegtn8pKaRgxUdZkCnqrIujsY8ZriG2IP/8sb66e8B/IDcfg2x5NTcCFl4Z49NEAa9c6aC6ibrNDQwPlOqCunnI9fc5RbKa8pYEy9fX1DsVyA/W1dVlsrnPQT9nWr2f+WjlA+dZvcHj8yQAXXRLD5s1DHGxDZGjJjUBfBLKpCsRfeA6u+D9+9ZWwWMdZ7GCc3zgKX3oBlddfzQNqFo71AW/yE08/ifD55xCf/xQPqdniGpe17Bo2o+q35yDczINIWdd0aJXL8ulI1dVXAuqfoWW11JOFwGDakfG78WBSWhojYAR6ELCJlx5ALFi+BFxnByqu+Tugf1dTvtW0mg2DgN4U6ZlNP1XqqdMT27i+HdMrIlLEOVmjn/wk4gDnUMC5BMSoU5j3K9AbOBUpoKYGSCaA6dVZzH3yblRWAvpIb5azPTtufAHbZ6JX45VW+WR9px0yeOfbs1AddtqR8juzCHjvnaIdlbPXHmlsG6tH9dolCEKgijYxBZasf0WoqKEMB41NCNauQcUD/4Rra+01SVWUuiTiolcd2tppiv3BFawCA0CxHAUQLT6CIruTW/g4yl6dy+w9KrjCy+haOtPAcy8opktnkhEYCwJZHqhSDz2AQDOGY1FgicoIeDPvNtdzgiUyqJv8ihv/gXDjOiQfuA/673RRzMTYTuSfoGV4IvQ/N+IxryftcvlZWM96Wbh8CAQbVqPm1z+32Zfy6RKryQQiEEygulpVJyqBPk7ug21KsHY1qs/5FfL/ciZobR1sVks3gQjwXsLXNpnsGiypVJfsI3Ob6uqcUORpcqWakyi6QdZkiSZVNBlSXQVOwGQhW5KVThM606bBT8pMp688yltZFZWndDW5MpS2mhMousV+FydaDjko4ydbJB8qOeQEC9NW7zQdJzXdiO1mtPib9AxvzDPZogoOQswmkghWrsBEm1h0ziF5/bW8COOD0jlz+ST4bE62tBRaHGzZMur/Raeu3kWTO2TO1cvqM8mqiG7qsrlAN5mJcmpQLNwUKo9Pz0iuCnZz8QSwXdMSuNwT777SdMtggbIhoL6y/7BTHt3hOjr9cQOaySyPKvWsxaQMB9wJqv50GewnRZOye0e5UTxTcvyEq7reCh7lAs28EZhUBIJJ1RprTFkS0GvECT6h63ZXM4Saxl5dCN7bDSGHJZ2oBFKprppv2eL6vC4MOdmRHw/Tp/MKgFlmzsgimaQwTmu2shIVd96KTHsG4HUJuNRuigR9fobBra4ZzgZV/f0vmGhPUrOZDCruvp3ty8KxcxILX6LctQYbNyL5wrNdilGQ3vxGlh3h9tYd5SydD+Q3PcN5fd5XvBzD8mSDYrfVT8ZQM2smsNv8q+By/12o8rprEN/Kv8lmNlvHmYD+nXbyqScRf+bprdYk9sw8TiC2bTXd2CSYHKXoGJe69UagpWlyNMhaYQSmEIFsVTVS992FYPXqKdRqa6oRKC0Bm3gpLc8pZ01PDoN1a3khO6/feZWgoQHhqhXI6lHzlCNkDR4KgYYG3fJGOZqbgcYmh74mLvI3wPppUZS6PLZbiurL+QhfqY21Do1siw9sZeNa7I2urSDqM/rNb8pij/dm4CdLOIT8+NAmJ2sSxh9+suBxCKCaAryTrKRgXCGNohRWJJ3iGYS3zzitmdoGLH1NEhA01E+Nm/SouRN364LoQ8UbNyDfp/01puYPF0PfIekv3vRDJ5DlzHrFfffCDfV1wKEXZTmMwOQloN8yj0PrsnywEn/lFTh783wc6FuRk4WATbxMlp4ch3bov6Ck7r4LsdcWo+Kqv5WuBroiLrKWjcURvvYqZ9nXFGlNnIwEenS9vzFubgE2bnJoaXbQxMzGjbmWO94XdebkMvH0E6O+qtKSm3jZuIlt4USM0tTWAZso658x1dJvb2ODFDGKrqMDZBhxU13EU9/cFNMtfAit71ZvzLPeAjz/YoD//XHMu4cfDfx9aN1m1Rv+1wGbaoHauqje69Y5tgeav6DvUMc26Tu9alvdZufzynY987ezHptYzpYGB/Wv5PoG4He/DzFvPnXs6y2NDhvY161twGam20h2ynfJZSHydZL/g5/EILfgeZ7OODuin4p94l/T+Nync+4z9D/Vic/K/2wan/2PTnxOMt1nqJf8+SJZ4c9+uhNf+EwnvvqFTnyedr7AfDtsR+M9+qa2Dn2+ldUjmQXLjEC2qgqp665CuKW+zGpm1Sl3AjpHyZV7Pa1+k5hA0xZUXHbRJG6gNc0ITF4CvFKdvI2zlo0ygXgciZefh+toL1lBFZdfgtiLz3e3F4sh/vJLiK2ziZfuYKZYqHDfG93o6w6/oT4njw+K4ZVaaAez5+Q0J5A0wTDaD4K3NHLChGWp2EHdPBQnlMwq91rzevp5mxR7JfOKooh82p569avX9dwU5VWafH69ESVZDhwO+g9HmoTZaUdgpx2ykcvJO26fxc47ATtSL9evzPQ77ADMmRul3YH5tnAiqGeVFK7jpJL8yeyEPlyzGm7d+knRTI2VirvvRLYjPSnaU66NyGpHLdfKDbFeGjPKkrrzNsReeVni1HPOTaIenbjd5zrTCJcvn7gNsJobgSlMwCZepnDnl2PT42vWcSKnsxyrVsZ1mrpV0w1hObW+vzeAOR/QVc1ugS61JE3AyB8tl795kP0BqqHoIpe72B58Bs1/FOUvEotsFInIZxiwP3MZfBrJcuh7WfL6AJF9Z9mq9m1v9SV3S1dZE8PsWb313RJN4IAfL2yeaMYfewTxhS9O4NZY1ceSgCZdkg8+iHDpkrEsdtTK0n9fqvrNWQi3NPAaJfewiRMRo1ZgGRp2bHtsyatlWDOrkhEwAkZgYhCwiZeJ0U8To5ZWSyMwQgIVFbzLG6GN8cxeU5VFPD6eNbCyR4vAEYdlsM3c7tbfPnsTKi+9qLtyCKFg0yYEy5dhsI+R/UTIEOyPKCkLq7r2SsSfetybCfQfaMIQweJFrO7E3k99g/raxGL+bYZgBfukr/gpqtO33NjpQ2q942xqbNUKBHoFbUg5yy9xvv2xZa93VY77gt58iT/y0KD3367ME1MK169H4rnnJmblrdZGwAgYgTIgEJRBHUpeBTM4jgR4Pc51HCtgRU9kApWVQEXV4FvgBp90TFKGMWD6NGDatCwk5wvttk90C+RTRL7ubevqgKZmuiaHujqHtlYH/cylbjOgb6bU1QN1mx30PZm6OmDz5ihvXZ1DPfUK1dGvy+n1HZLanKy4vBugGvkkOT/LWyiKWbpBrv0mLYooEgs3LgP2Zy6DTyNZrp/6vOVNA0T2k2dr6pkzs/jyF6LvwyRyk2stW9LA2rVRVna4/ltOtrUlCg9iGy55Danbb+E9rd4PGDiD/nNWYsE8uCHYH9ji1mPdli1dT/eVPAhQ/Zc/8qn/FoUmndP3xBIvvYiY/avUrr7lBFzNhb9FuHhhl26KSRU3XItw2dLurXYOQe0mxFavxLj+4wD2D2wxAkbACBgBTAQENvEyEXppAtUxWL4UNb/99dZrzPuikV4v0MTWy7EUE45AVe6tlyBk1f1dNn2tedn/6xmAD6cxbXp5jgI+DMWMGVHdeH0eTVyoDXL5dkju4bRPaEIFzKrvvejDu/L1HRPpqUY2w0mXTDRXobj8f0+Sn6ZeJpVPLi8rr2Tllz9op7qqAcogWX5Pl9fT31rSYhD5tN4c83pfm2JZ4bwr1lPO53cBIJlzAkilgD3em8Vxx3BCBKOz3HpbgPaOyHZbO/xHgxXWf06qvvA8hPqCcRS91a1jp2U421h52UVIPN/7SXKWDcv3mf4jTLX+044K26rlkSUIlyxBFdvSywoHaLBxA2KvL+n9La5eiU0xGQhoDMZeWwIdkyZDe4bThrC+nhOQ5fcTaLd+A5I3XQf/Rs5wGmZ5jIARGA0CZtMI9EuAl6z9xlmEERg6Ad74xRYv5oVAV9b8jUNeo3Dqxn8gtnZ1XjUsP/7MfKC2dlh5LVMZE+BNtWo3d04W02qyqKoC5szOoqKS4WlZyvA/55k+PfKVdixc1v+GSKN3cKWFQZROdZ8zJy+zLblvgsyaCcSTkb7fbY5Fv/EDRPSVtVjHe3rozZyKCtaJ9ash5+pqynnWNcB7ds3gx//b6d0hB2U895kzgNmzgBgnxuTPmhkx2XbbrNerDH33ZCbbqcmxWfRnchJK+MRiOvPrjZHZLKeG/VlZAUjWm0Jf+WIa++yVRSX7uqY6i7msV4qMZjDdnNmA8n3mU2lfn3y9fvT9Tsj98H868d//2YkPnJZGDes+AJphR23Y6LBylVrY3UTxf6TSjerWboRELP7kY5y1aQc4cxRbzWNhfnYMXUvspRdRcd01XYrRkNhJISdSwlcXIvbUE7zBZu2yGehjun0WxxnRYNVKxF7t/pOjrbW5T1umnDQE3MpliD2/ALEFzyJcXvSTnFFuYcUVf0Rs/lOjXEp5mnfpdlTceRtcH8eO8qyx1WpsCVhpRsAIlBuBoNwqZPWZPARccyOqzvkVnxR1FBoVe+pxJO6/BxX33Q20tRX03QQ+0Q31HYFsN22vQM3lFyO+ehV0c9Ir0hRGEt7pvQAAEABJREFUoMQEYkte49i9t6RW82+llNRozljx7tN7qoCJmCCToW/roAls5MRLcWIi9EEesuA/ZeEcOp97BYmHH/D6fjfpDCr/dgXcVgaA4zEyGOXJ5QxnwipuvQnhqtWovP3mfqvcLSLLlsvllZST/7wHwdo1eY35U4xAuHETko8/hIq7b0cwxP9+pZ/RhQueQbBqxaCpcchBP9ALN6znxMPoHMg4ygddn0JCVawQGHtBTMa+1K2UaNFGwAgYASPgCdjEi8dgm9IRKLpUyTrEOYECXh7l7Qf6N3iNTflgn77raEPFLTcyrsgWQ32tjhddicce6SvKdEagpARcexuCrYzdoRaoNzgGzLP1XWDA7PnIvswEIaCf5+TTTETf8cl6sG7tmFX9jW/I3eD1AKoJrNZWB3BtXF6Huvm8gdzKDZjraEe3pbUV8WfmdVONVSCs38zDNBuljwgNp1BOOCVefQWurXU4uccxj+PZie0exxpM1KI1vONPP4nEE492NUG/fewKDVrKVlah8pabEG7c6Psj/vyzCJb2+KZKD2sxjrfk/f/soR04ONTY2LKlSN51x6CzZaZNhyYxg5XLB52n1AkTL76A+LwnSm3W7BkBI2AEjEAJCNjESwkgmglAT00r//ZnxFav8jh0KTvcJy96bTbYsMHb6XPDu0XZ7zPOlEZgAhFIVWR1rz7mNeb8APSzojEvuIQFZmmr4pGHEBb/pxHqRnOtrAROPSkNTVr5clQJL3Tf6CPJixYP7fQacCIm9c97uhuy0JAJBJxEClYs8zfwA2bmZFF80UIk5s9nsn46kjGjsE4OkzyI6G2TYM2arbMeQosdj4iJBx9AbP3Ab0/pI9OJ556JLLMvI6G0W9fWxomlwT/Y0XeYUvfdjaC9vbQVGYI1t6UBseWc+B1CHktqBIyAETACY0NgaFeGY1MnK2UiEggDhLUboY+7ZBNJpB59CPElrxVakpkxExXX/wNo7+fnRYWUWxE46RIuegWJ5xdsJaFFG4HyJxA6+Jv42bOzqK7KQt870bdr9JHYSk7KTJ+RRSqZRVV1FtNnwH9QWN9FmT49C+4KmF4DTJsWtXP6NKahU0jxM6ZLAuTPmBHJ06mTi8fgv5cSaSfuNmhs7PZTxrFoyT57Z5F/82Wg+72Nm4ZRG06+DCPXMLOUJlu5fdslWLEcVVdewXPRVtrnALelHuH6sXtjClNkybqAlwKln8zSWzZ9Icy2d0DfjRvuw56+bJrOCBgBI2AEjECpCQSlNmj2jIC+uhmuWsWL2qJ/OZpI+N9+67949Eco219EsZ53OrHaTQjWryvWmmwEJjQBvUEhx+GNWBzQf0WS0wQJ72HAeU3tVnwWDMRDRLKjz7QxTqKo8T5fTla+vF4ftdWHcH0a5pVdyWXjJmBF9IFhVbu/G0HF6WPA8ie6S4dxBCv7f4Ief+pxpPxPQ8ukpewU1zzwz1nLpKbjU41BnWhHVjX9gCt5680jM5LLHXvpBf8h56qzfoFw4/qctrsXdHYg+cD9VPKgyK2tRsAIGAEjYATKkUBQjpWyOk0NAtmmJgS5nwmEL7+M1CMPT42GWyvLkoBVyggMlsCu785Ck2To5z5PE19vf1umT3PxF55D+HrX24B9Jhonpf5zV7BqJdDcUqhBkO5A1YW/A7KZgq5YCNIZBPX1xSqTy5hAbOFLSNxzF8fgEriGofdbxZV/3vq3iFpbeT5/sCQUwsWL/YOW+JLFHIPZktg0I0bACBgBI2AExoOATbyMB3Ur0xOIbdqIxKMPQZdSYWMDgtde9bKPnLoba7kRMAJlTqCmJouKCv2MKwu9WSQ5ldKRLKp4VWUWa9cCq1c7OmDtOsprHFbRdS5+HVhFRZS0rLbZqiokb78Z4ebuv5MKeCNdVhXtUZlwxXLEnn3Gbsx7cOkrGDRuQbhuLSqvvQqhJtn6SjSALty4Aa69o5AiWzMNFTddD9cyOm8ZOU34Zbv2rULBJhgBI2AEjIARmGAEbOJlgnXY2FV39EtynZ0ImkbnYm30a28lTGkCTY1w9m2IKT0E9MZLRQr+Z1+aaNF3efJANtc7XP6XGC68JMSFl8bw+4tiuOYv7Vh132vYsDaL2k3RjWTR/Ws+67j7/sOgUfXGvS6DrYD+s1Xyn3dx4r77K0gZu2HvF2FQvxlumP+FqNhoNhFHxX33AOm+34gqTmuyETACRsAIGIGpTCAo98ZneGGQ7ueEnmHc2g216EynS9KM5pZW1Df0ngi456GnsWHT5pKUYUYGRyBbU4PU/ffCrVkzuAyWygiMIYFw0wYk5j89hiVaUeVOoLm5qIaOMl2WjrMBDADv2HYTjnz9T0glum5QGxt9lG1GiUD8tcWoPuesfBeMUilmdqwI6KO9Y1WWlWMEjIARMALjSGCSFl3WEy/6bwk/Ovty/PicP/XC/8BjC7D/SV/AUR/+JnY/6lO4+mZ9WC1KduhpX8Gu7/v3Xm7x0lVRgh7bdRvq8JXv/QaHf+DrOPoj38LHv/IzvPzqskKq7/7sEixasrIQNmH0CWSDAPFXFiJs7frWAGwxAmVCwHWmEbS1lkltrBrlQKCjU7MsXTXRyxYzZnDC5V0Z/Ns75uOdszYg3LChKwGlzq5fbDBka6kIZMMQsdeXwunNtLpaJB+8D8Ga1aUyPyntaLzK5RsnOVy9Gq6+fB46hctfR/IhXuupcvmK9vTbWhB/dj5gH1juScbCRsAIDIOAZTECpSRQthMvd97/JA77l6/i2lse6NXeltZ2fPvHF+DL//EvWHDvZfjNT76CH/36cqxcE13U/u3338dtV5xZcGf97xe8jZrqSu/33Pz6wqvQ1t6BR286D4/dcj7etPN2+M2l1/ZMZuFRIjDB3mofJQpm1ggYgYlMIHC9a3/U7AX4WN0leNNrD2FGtqF3AmpaWgCeftCwBejgRExTM9Da1ocxpp2qq+MEQPyJx4ABbrizVdWIL5jPyZYmZGbMQOruO+AaGuDCAPElr8EJNGzpj0DshedQdcl5yPLPp+EQTN51G8LlXQ+hvH4UN9n+vladKzPcXIeQfZkL9um5tg5UXXQ+J8bb+4w3pRGYoASs2kbACEwCAmU78XLo/rvjmkt+hJOPObAX5iefeRn6WdAZ7z8SMT7ZOvrQvfHGnbbFA48969PuvMM2Piyd3I13PowPnHgYtp0708f33KxetwlzZ89APB7z9vZ6zy79vuGyqa4Bn/3OWbj86jt6mrHwMAjoJ0WJ+fOAtrZh5LYsRsAIGIGtEFi3Dm7VyoHu2bdiYHDRyWS2W0LHG9dsewZu/YZu+uKA5hE06aIJl+Zmh0wa0HdsGzkJo3Rtdu8oDAjr61Bx+83ICqrX9N5kKitRecO1cLk30YKGejgB7p3UNH0QcOlOhBs2dosJODBdpvu47pagxIGgsQFxfS+mxHaxqRbBSntruTRYzYoRMAJGwAgMl0DZTrxUViSx3dxZqKqs6NW2dRvr/MRKIhEvxL31jTtg7fq6QjgvPPXsQjz0xPP4widOzat6+f/x0RNwwx0P46vf/y3ue/QZXPLXW/DFT57WK139liZ8+lu/RHVVBT72wWN6xZti6AQylVWo+csfgM7OoWe2HEbACBiBAQhUXP8PJF5+AamH7uez9NG9gazInaqSSYBz+Jg2LYtkCggCujALPiMYoKZ9R9m3x4u4tNssVBGNcRUzLvRvF5V8XosTPckFzwyubUNIFVu5HImH7mOO0T0GsABbjYARMAJGwAj0S4CXhP3GlW1EAydAKit4RVtUw2QygS2NzUUa8AlnFmddcBU+8eHjsMN2c7rFFQfe8bY3QG/GBC7Af/7kIm9nj13fVpwETc2t+NJ3z8XOO26DM//n8/7NGCWoTIaYiC4VD+Gcg24KHAC9Jh8w7CjTYxwFrtLlB4lzjukdHAAXODhHP+8AOP05+MXbVQKGAvouZ8Q5hy4ZUDomicpn/sCBsoNzyDkKQKSjr9XHKSED8uQcN7kionw+4Hw+OMA5RxmgCIqUHRxA57wMLvm6UIRkpXNMEVAInLRgWoBBOADOOe/gJAN53+USy5cdcHHOwfk6wdsInKMWlB0d/BI4UHZeVnQ+vXMOXCM9beRthrkE8orjQ6ZRYumccxKZ38EFzsvynItkebKnkPcpKF5OiQMmcI5KBuQ52nZwvp6BA7xOGzg4FzlqvRwoDCCkz2xwlANt6HzYBwDnHByiRaqAYYXkyynOOQfn8g6UI6d4cFG9AgdwhaPgKHBlPcHFUU/nQJ+OfkjHNRcvnaMsDbyv/OAi36myXnbokgHnHLTIY5ESvU5hB+ftgEtABVdKgPK7wEGLvC7Z+fSOUXLg4pyjFScJzjk41sMpxI1z3ORkqikBjn9BQc+Qg1+kUt8qUCxL5xwTcQ3ociKcc97BAUEqiUTMIfXCAoRbGqRiPal3sgYwKcNRIGBAdQQX5xxkH1woMl0uDRz/4F3gEOV18L7SpRIhj63OxzvnqAfo0Tkvg4M7tWIpUnUbUJEIqAdVrs9jcBIZJO+6HUE6jbCjHRUlPFZXsJ5wYJ2cPDg2hiu0zJgObLcN8M2vAHvtDqQ4ETNrBjBntmKH5vTdGNmNnPOZicCXWaxzAeBcFF+sD6hzzrGeoHPQon6hSiJ1YD46wMuAg+ICJ4mOfqAAAEffMQwuAQXtQ+KgeAZz+aIEOr+k4gEUirHCmnQKmM85B+ccJSARd332W1/n0zhtKJds9hUfsjKqkwwHTOicNmBZACRSyRVa5CtasnOO7XZMQt9HgGE5By05VXcdGxIwXxTvIC6ghcAFkNqBfwFADwrn04aBQwDG0YkZuAQBoDTg4pxDQAeHyAcXhrlKRecQBA4U4HWSAergw+ASMCJwFLjKd3CUQHuRq+A+o3o456CoWNjVB5Kl1j4oxkqrcL7MONN6HTPG0u2o/sPFft9TOeASKDHNgm0K6KiCV0nHgOScGgF1XCEXMMA1kpWIksKqZwX3M/nOKSXgnGN7nd+XVR8Gvc77ADQ+Ehx3FDm+gsL4irkMQn3vhwljNK72DcZ5JjTmmCfIVd45xzKp5CpdwLhKX88AAdM46pVvMPZHmiYeY4EsLxXvug4NcvVz4J9jpByTSc8QnHNgEFoCxjEoEZK76xnJGMXLUWReRA6IfGZSKuecD4NLIJ0Dw67Aw1HHFY5/gU/LOAeW6aAloEe1RDjnvN6BPivkAET5JcHHhYgW5xyYTGvkGJQge/LB+CBvg3HS05MasgkuzjkwCbRQRKANA/LlFAwC5YKPy4mU4e0oLAcu8kNmUP8DDvH8WGQ/OecgOwELCygrjQOQYByYNmSAKxgFJoGWgIpACgbkS08VJEudt6VxlN9PAiaQoxelY94orTTwdVCY6qgsGWUgoFLO5WT5VDGNJPh93TnHMOAKeeDLqCjhuVVtGSsXsYctRmBMCeR2nzEtc8SFTaup8j81KjbU1taOnt9wuffh+XjhlaWUZngAABAASURBVKX41BknFiftJX/zB+fj5GMOwrk//jL+ec3Z2HePd+GML/4Exf8t6Xu/uBTPvPAqvv35jyAeCws2EjzhTUSnE4Jz8AdRyI828IvCimRAnguoyMsSqeQKn4VhR8EBkE4OXBQOigIOjlpAqkjKy1FIaRUHBSXkfIngIt/lRmvAOIWphnw5RTnHCCqdc9AfRSiPc4BctCmSc3ofB4BBBDIOwDkHh2ihSMFBvvNStCmEqQwYoAcloggtPozc1nt+A+foc82ncS4KyHMuLzs4JaCTyuVCTuGcHCjAsNoe5HUMIyc758AVWuTLSZYyLzMFnFcC0jlEf1AA8F5QkHNxgNdz69fAeQ/0kEuK4kU6OR9JQenAjdOGCamCHEX61DpJ4AkdDANwXOUAidQ77ztHnw5cKPmtIlwkwcuM5wo59a1855iCzjlHvYMWv/UbUAfkeTrn4BAt8uUUcgUtKDnkFyZH3iGnVjjQhokclTkRoOAQLQE9BgEquMqDwgUH+Do5+oqQPhKp4So5kC/HgI/Py3DwYa+nLN87yo4CVwf9eQFK6yRqw8mW+GWXIvHPuxA0N0EqxXvBp6HGKwB5DEFLJEch5yJfelB0qqh86eUY4RghF+dFqL+A9GFFOFCET8ZNdto0xB56CMHS13nBGnq97PU8BuPVV4Flr0MLs/l0cR63e6YbbjjOeuZtg1VU/0EFUSkvM70G7vob4bbUe7VzzvuMHtKaiMPnc075HbQ456A/0PeOSgdH0VECAuq5+rDzGlCmQ7RIF0Bb5NI6gBmcc/LALcPwi5epUcDLThLApMhzL96vctGIkY9nREUsDKJyAtCP8oJLPAgwWP4hbcCB596gzzxh4ADnuNJBzgdZnoOWQBvG5z0lz8tQJJMFjKfH3IBzkgBWEU5/jjqlA+Cco4NfnKNMyfV0eT19QLHcSqYoL8jrABbvuAWklwMX+dLKBQG3WuUUR6fEDk6S3wbKwJA85yI96LuASq4UAaoT3Adkz4cBuHQHgteXIh4PIRvSq998vzCtwnkbYRBE6QJAtsDFx2tDOaB9xwhH2TltAXnO5WQ4OEcH0KeLMlB28As9rvCOm4DxqodzztcNXChCLs66Kd5Bf8htgXgs4MRtAC2xMCiMlUCZqJSnfLLb04Wd7cg++aQ/zuXjwiCyFW0dLQDOOQTIyfSdcxA/egpBS3HZeVuj4ceCqB7a3/L2A+kita+ny9VK9VPdXGEDOKblCi1O6QJJdI4hR58rRcVQAiQjtzjn4CRzEzCfcw7O0VGnrZPvN2A94OPg5NMBFB2YHFrkOwl0kqMMYDy1XGmeAfgliqeSIcle4kZtcY6C9HCQ6CRrk/elpKzIgujD2gABlQ7OByh2+ZEK0jkXBaK08LpoQzlwUD0C+Q5+sgJcdPxyDrQPJnVwbJDSwMEfK+U7xwAA5+gH8ItD9KeAc5RpF/IdwNU72VHfUw05hRWngMKSneOWK7jICxSWTF9hikxOiWEKiBy4ODhutfqonOBcpHXOQeWp/InoYprtgi1GYGwJ5HbvsS10pKVtO2cmlq1ch46OzoIp/deh7baZWQhr0uTsi67G5z9xKubM4iPIQkx3QW+yaHLmnW/d2Udo8kYTNfqGzNLla7xOm1OOPQiHHbA7Pv9fZ2Nzfdf/AN3c2I6J6LY0d0D/jpsPg6HXhdPcKJxlY7MZ+DiKSGey3klWfDqdZfosMvJ9OqZVXibIyKeOIvOALgoon5z08jORmmUojUoEOlVOLn+GCVSHLHUZOuVTPaiWSLssk+UroP80HjnWk/mlUx45gDqmky2F5VSa7GS4UXLVWbaVT3Yy+fKYT2JWNigondJTDQbJAFBar2d7ZEPxvs5KRIM+XolzsuIosv5Zn9fLzKS8XvZ2uGEgykuBq5eZjiLzweeX3ME2yFe91X8FuahM5ZVevvqsIOfT0K7yi4vGgtojW0qvtKpbOleOdHLZYiasbpq2pFNexYPx+o9kCstu1LfUsizF0wOzIFNsVwlZoGzJUYzGBBP69PRlT07xDDI/+7ebDdaCdjKM5Or7SGnzddHYlV7x0udtqSzJ0ktWnBhIli5XBKI00sLLsquQ17NcL7MApfc6ytJ5eznZ2+tjfHSykUrnxyptcYVPS73KkT3FEzc0jhQn2/JzpqE6K5zXy5aXaUN962UmLvQ3ZZ+ehfm89Jm0q1wWlm3vhFu/ljq2WelpRPnVRorUZ+mYkAFfvwwFrrKrMEXGZ6E2eJkFKI4eFJ+RIZabZmXVRh2X2joyyHAMKZxhmYzyNrzMtK6+HmlOtNf7YxjQ2Znxx+DGpxdgy/znvJxZtBjZ115TkT6v+r6haejH6rotfedpYNmsom9DV1tYOZaoemaCuP9pQ5r81HavY1sYzQvtKJ3krbmKiizLAJ38iHMnx0+GhUc2I12x7Pc3lqVy06yc95lMsspT/+VlpVXeKE3UTxF3sAQgkpmZGTPsK/UFRV8f1aOeTMWfUciwzLzdxpYObGnpZL8DrWTQ0ZllnqgdSicbTW1Rvw3m/CkbbIq32Vf6do0ZVsK3gwlVb9VVY0wNkZ8v17c/apKvs46LytfJCGYF8SJNW6pjZ5oMaIBR0BiSTn6abfUy08kuySHNAqXOMH1kk3mpyFCvtLKfYQFyncwnnbdLnWTZ8XY5POTTI7+sL1dJlM/rGREdA6JGeBssRzYULydZ9uQkK42K1D7Twf3F62mnc2MdstffgPotbWj3ekD7oBgrrfKk2XdsElo70mho6oDs+/w0XMyik3UQRzVXekaTb8TAy2xEmo7F0kakZ5Dty4BZ2VaODyo8SzZN9fTji5VPqyI0onJlu4Hjrk19zvTiIie7GnPNrZ1MCchXO+TUNiaF8kuWrqdrbGhF8qIL0Fzb4I8him9jm2U3zQoqrwxnWBe1Q7L0adavq56qvcZ8umBDdkbLtbRzgPLmt+XZ59H42DxfpsaZ+kA1UT0LfcI2qM5ilRPJPuv7QnqlZVMkek6ZXCI2F8qjCMniKCaKl1M4zZ0mkmUv69Mrj847Pp62xEp1ke/jGCFZdlUuk0iE7ET7D23RLpOxjrSZS6A8ckqstIqXkyynsaq2eAaM8HVgYtVd8aq7339om2qoLjnTvpy0MjJCZfg4jkXVh6ooPpdY412ir7sEJhAH8dfxSG1tbWP/UN/S1gmV3ckylUYuShONUw1+2VPdlE51ZTakVf9CfTJQnWRXvopU2Z3caHx10rbyyqdIk+THRKy+L7uvNig9s6so2s4wXcbny7ACLDqSJTCF7IpHMTvVo5MG6ifofVBzrn/YPFuNwJgRKNuJlzR35g5OrKR51Ozk1Y/kDA8iIrPvHu+UhytvuBedjL/noaf9fzQ6/MA9vF6bm+58BBs21fufGSlc7C6/+g7/L6Olq6pMYaft5+Lqm+/jxUeTn8y57d7H/U+P3vyG7ZXEu6MO2Qu//sEXMX1aNb7w3XPQ3NLm9bYxAkagDAlYlSYcgcz0Gai4/VY4TqwMp/Lxl15EsHoVkjffyDu46OZrOHaiPFkkb7oBseXRWzORrjTbGTOA6dMjWxWV4FN5IB4HKiuzCELwKWkUp23dZofGJkmTzPFmMfbqK5yceoD3SbrdmGTtmyDNcZm0/y9Q+epm4zHEXl+KoK42r+ryXdAll6GUTaaQeOkFuA3rfO10iXbPPwNceGmIha846HtJTU0OCxc6/Po3Ic75bYhzfxfirHMp/yaGm26L2nfPfdLHcP6FIV5d7JC77/U2y3IThoivWo7Y4ldsXyrLDrJKGQEjYAS6CERnmq5w2Uj/uPUB7HHMp6F/J60P30q+4Y6HfP0qK5L43U+/hjPPvxK7H/UpfO37v8P/fP3jfgJFCfSvoc+5+Bp89mMnY3pNlVTd3IaNm7Fw8fKCTj8x0od6DzrlSzjo1C9jyfI1OPN7n0OMJ7R8Ir2ap3Iv+Pk3UN/QiG/+8Dw+Lcjko803An0SMKURMAKDJBBPIP70PASdHYPM0DuZo42Ke24Hck/p8iky06Yh/sw8oK4urxrQz8Kh4sH7gNx/yBkw8TAiE4mszzWtBqiogJ98kRzwjMxnDj4uv1EV2ifhPH+wpR6xFcvgiELdleVtY77N5peeQDaVQuKJxwD926x+zGdnzELFLTchWLGiVwpXtwmp++/lvtUralQUWU7ODclwIoHEk48jqI0mjV57ajPSS1dg7VqH9nYHPsT3LsZ97/AdlqKlxWFTrUN9vUPtZqCp0fnimhuBWh4m1q13kOtIA/rPYz6yjDbxZ56Ga9gS1UgPJXseOKIY2xoBI2AEjEAZEeBlXhnVpqgqp596BF68//JuTv8SOp/kyIP3xHP3/gF3/f0sPHv3pTjjtKPyUUgm4njoht/hM/92ckFXLHznix/FU7dfWFC9a5c34rc/+SqevO1C3HPVr3HBL76B97zrLYV4pT1wn119eMb0atx2xZm48MxvIQzLFp+va9HGRCNgBIzA1CVQWYnqyy5G2Mi7qhyFYMkSxF54bsxuJHPFDuhpAqKvBB3Dn4vqy1zZ6eIvPo+Ku+8csF56oyHO/gqWLhkwnUX2TSAzfToqb7gG2Mp/EHQN9ZwM6/1QKeCETfg895dofqLvQkqoVTGpm64ftsWdO5fjwNaHkExyZq/IyvSwGQc/dylmz85yejWK8HM8KlDBvC8559rb+lDm4sbF44Gi8tor4Zon4+tw40LUCjUCnJjNYu2GWuiXFIbDCIwWgTGeOShtMzTxseN2cxCPx0piWD87mj6tqiS2zIgRMAJGwAiUL4HYmlX+DYByetPC3wD2gUxvwvShnjSqoKkJ4YsvFF5UyvbRsmwqidRDDyC2ZnW32PiTj8O1tXTTWaBvAq5t4rw6leUEUfK+e3pNjGZjMYScNMVWZiOzHZ3INLdxEqkHC05auMboTRE/zjinQlWvcopzDfQySXr2HFSfdSZce3txljGXs9XViM+fV56v54w5DSvQCAyNwAOPLcD+J30BR334m/6XFFfffP/QDFjqrRCw6DyBCT3xkm+E+UbACBgBI2AERkqg6rxzeFO3uGAmGzjezDwFjNGTZb1E2dfkSwvnFTo7eYdYqNnkEXTzK1fcoprfnIVg2dJiVb9yxc03YGs34f1mtogJRyAzYyaq/v4XBM3Ng657z/FVnFE/detrnytOo28wFYd7yvHXeMwYqJCeGUYhnK2oQM2lFwLp9ChYN5NGYJIQ6KMZLa3t+PaPL8CX/+NfsODey/Cbn3wFP/r15Vi5ZkMfqU1lBEZGwCZeRsbPchsBI2AEjMAYE8gGAfTfHUpdbHzpEjh9LyFvOJ1F1SUXImhrz2tG1+fcyoxpAB/qdytHxTc0dFNNikCaN9EVN14P18qZpaIWxRcvgm6Ii1T9iq5jjPqm3xpYxFgTCPy3mgae6aiqyhZ+SrS1+umNl/zki2R9bqhKH7vm/hgwcyqV5XbsV9XFLV/OY93Yl20bx3dgAAAQAElEQVQlji4Bs14+BJ585mU0t7TijPcf6b/tefShe/t/sPLAY8+WTyWtJpOGgM4pk6Yx49GQRDzARHTxmLreIf+PCnzIMSyIjhs5edQpjiIo0jk4BfgkWIJ03qfO6/0GiPRRwDkXhcGFMhx9rfQVlChzASOoUhAUI4doUbp8nJe1YZS8yDmfhSpIkA5cnHNQ2G8ogotU8BtAqpyIvA8ukhUHODgnB784bbnhCnDDtZvvdWoMAOcccqKX86yp9mFwUX45itQBjn/gojQ5Ec45gCu4eDEnBwr00EkV5OIlOxcFHA0455ga8J7fUAaiMABfPyYJgC6do0IOXAqiY7wc6Ms5RAt9n5Y+6LhKH1AnUU5quUj2Wzjn4JSQjiKCXMg5h3xeqZSGKomRo0Lx4OL1DHOFC7iNVjj6gDYOkh3g/VwSBuAXB/4pAUPecxS4dpMZZjJtvfNxlJS0IFPwYekleN+BakrwvnMOWrznN/D6gDJXdC1KFznpvVMkVZJzIhiU6H3nciH6XL3ebwK/hXRyCsl3Lkrvt9xwBbjhGvkUuCKXDF4GUBxWvcHF+0pAWQmK0+TVgOOftnROjhvA67TxIb8BFwpcvT7nS5ZzvCOpuP0WpgGCwCHB45kvz3kVJDsXBZyjL8eoMGTa3DE7CODTxRWePw/Je++CloBpYnRezuWLxRyKj/NxlgeaRS7eOYcA0UKRah9Z8KFgFA3FB9ooTH1Alw9KFfbzy9n8LytUb6VjNtrSFvTpEMmyF+QMeg1l+fSgxftSMCBP4bwP2sjL3gdQsOUYkMt5Lhb675w555grUgb0tIpPnMwcA2EYwAXwaZxzcM5BizyXTKLirts4yZL15eTzKT4MUWAuG9KBWV3gCvp8GY4Rzrl8EoYoMsgVCjjnECBaKEJOIfnOKZVDQLugyBWB1wE5D910NBR4jeIZE4CLo8aBG62AdI5BuUgD2QpABZ1z8nPxkQipnIsCDg7R4pBTwVHhAm3yThpAOheJ8PXOyZGX29LLp5HvHBXgQk+i9oFAtiMVAo5/sWUQQcBEFPyWG65QHqq8n5d9Mh8Jr4cWhvPxuaA8H8+oSGZGyd5pQ628IJfRsV7OSQPmc3CBgxYHJw9OnhxDzjkygF+qKukxr6ZMdnlLBlVV8G72zCz0BsuHP5DBl7+QxZe+kMFX6J9wjFICR74vi29+NYOv7XQj3lhdC0cbIct0uTJUYF5WnHNRhDw5lgqlLz5eDFeOt2xBzW9+hWTj5sKYly3tJ4BDoW9YMKsILRTBKICbgswqOscNIC0d/OIckFMjivVqLzsXabwXiVCEDyNaHNlIcs6Bq8SCD1DXK546pxhAUcV5ussO6JE/yCWQp1hoyQnyvPMbMCeQ33TVEYh0gHwXKLFEpyC0yHaQCzlHvZOWLqDj6pwDV0qIfEcfiGTA+wETBD69Q8j9CFzkU+3jI98h9GnASXYagYP04CLf0dcqP4C2DDFCknfccIULAOecHxv0KCtMB8DHc8MVigsCSdQzEEmS6QL4xTnHdHQ+lNs4UAe/KDuDADdMCi0UETCg44fG5URzsVz/rNtYhzfutC30T1bULrm3vnEHrF1fJ9GcESgpgdwuV1KbU8rYnGlJTEQ3qyYB7L0Hwmk1yO6zD7K7vQdhTTXSBx4MvOMdlKvQcfTRCHbaEcHMaWg/9gT6MxHuuD3S++6D2LRqZHffE5n37s6005Dea29kd30P9TXoOOxw4A07U6aN445HOGcWgjlz0H78iYjNmI7Y9tuh45jjEFKO77AD2o88BuHM6XC7vAWZffZGOL0G2T1Zp3e9G6pfx1HHIHjDGxAwfccxxwNzZtPebG8jNl353g687W2IVVei4/gTEGN9Q9Y7ffBhCBiPd78L2b3UVtZ5z71Y5/fS7jRk9madd3svYtOr0fm+IxDVuTpnYwYc25A+5FCmnYFgl12Q3v+AyN673sV67ouQtjPv2Z0c9qKNGmT23R9ZxsWmVSF9yGHIsk7hNNp735HIvmFnBGTdcexxcNttH7Xl6OMQzJ2DcPZMtuVYLwdzKJNTMHMGwm228cwCsgm2JTOyFIM8s4Tsve99CHbeyfdBR97edtvQXsQ9FOtjKc+dhXC7ueg44US4mTMQbrstbZ8AlRMwTedRRyM2bRrcru8FOBZi1TVoP5rcaVvt6eK6E/JM3Nvejsz++5MDub7nPcjuvSflGqTfuxvUj3H2Y5pc46r/m9/IcbMvpMvsfyDwjrcjXlODjiOOhmOc2HQeTQbbb8/xIgZRP7q3ckzsV8x6D8SmT/d9gbe/HbHKCnR4XjMRcCypH32fsx/S+zIfGWV23w2+/6dPYx32A96+C2I17CPGx2sq2feHw731zdRVIz/W4uo3jqVg1kxE/XCCH6N+7JJnjG1yO+7kx24wazoc691BfTBjBvDmN3N/4rhi+/IMQ8od3IdCjt1wmzko9INk7gtqk3vLm3M8pzH/u5Hnmdn9vcjsrvE7DZn99ucYezfH0nR0HHwo8I5d2Pc15Hgk3FveErXhiKOAN3J/If/2Y06A225bxGbMQMRpBnx/H3kUtP+5N78J6YMOQZ5ZZh8ymz4dflzvwTLJLLPn3si8970IOT40xrHrrpSr0XHo4bnyud8dd6Ifx3lW2s/z41RjDNwfO7kfi1vwpjegg+MinM66vPWt6DjgAF8XsM8yub7OsjzfZ+yHzB57+WNLfBrrldvHglQCYRiy/Lcz7zQkVyxFyLGqfmvnfgAeb8JtON7Vh2x7frwnM+2oWrcaYRAgNmsW8M53onLF60hU0B55dZJLUJlCYksDx8URqKhOeW7xlkYkNq1DxbKlqFy/BlWUO3k80P6d2W8/aF8PuN+3H38Sx+9shNtugzSPgyFtZvbcw/dZyLHYeRTH+I47kpX64wT2y3So37QfxZR2H45P7hut8UqsPfBkNMdqsGXHXdA4c0c0UW7j8S/G46D6xL3zHXDVPMbymBKyrcHsWejgGNSxNOBY6jzgQL+/Zdl/6rMYWbazDwJySigt2cRmTGPdeWzjvqD6ZVhX7Lqb79/Ogw7yfNWujiOORPaNb+RYm8YyjkOg8nZ+AxL3/ROxqgq24c2Ajtc8lnZwLMyYXomZ1Ql0kkFqRg2Ss9jOd7yN6d6EUMdp7QusQzh3rj8OxXnMBvu8klxTTQ3o4LEuwcm1yjUrUbFyGRLxkPvDvuT4LqjOqWVLULVxLSobalnG+3hMiMaS2hyyTX787r4X9TU8VhwI9U/I/ungcS54w87UV7OME9kPXX0VY/+kDz7YH091nFUdYhz/jucI58c8WfNcFey4A/s4OkaprOBNb0aG+5Dqhd12hfbVuGxxfOhY4/f9405CnO11u7wVGtfxmmp0HnWkLyusmYYOz2M2Yhw3OobJVnY3HgP22gMh65DJn6ty+6DOM/HpVTz+HAXszPawbzt5vHY8lsc53lX3cO5shJ7viZ6ZY739Psjx4978Jmi/V/2x9z5IkXG8Isk+fCeP3fsi4MxE+NprcLwWSJCbrxOPedndeCwlj1hNDc8Px/tjSXzmLNbjGO6H05mf8Z4728e+D3hsD9lWjYlw5kwEO2wP7WMh6xDwOKDxoeOAe8cuPLbt69ua3WMPnod0jKlCR543+7SD41XHkth22/l90zPi9Ud29z0QJ5c0x7v6OcYZFx3rMtNmoHGHt+DEty3GrG0rELz7nXD77IHU7BpkePx5U2Y13h5biV06l+AdqbXYKVnHff0EbJtqxBtj61GRyqIzjKNhtwPQtvPb0JGoRgvPVx3b8hyYqkHLkcehc9pcdM6YixYeZzsqp6NzzvZoPfRotLdm0b5oJdpefB0tL69G28LlaH95GVpfofzScrS8tAKtC1eh/aVljFuJllfXofXFpWhbtIr6lWh/YQnaFq9B+9oGtB38PrRvaETz4rVe37p4NTqXrYfOwzGevzp4vNPxJuC5pZ3nQnHRPq54cca7cn3Cc17n4YfD8dgQUO7Q9d3OO0b7wtHHw3HsxWbxuMR9z48d7uPReWo68tcf3p7G0ZFHwx9n3vQm7n+HI2D/uLe+DX7/43j1x29dD0wj69zxMV5dhczBhyDGvs/ssSd0nInxeKGxhTdwDKtOPHaEs2dD7fHHz6L8gdp6lOq5LbQvqW6qg2MbZCPQMeQtb+U17CHR+axw3VfD68m9oHNYXOcQfw7lccSPa/LguT9Q2dx/Al5DBBznut6N0V7A/cTvr9XV0XjnuTQ+a7Yf77HpHO88Zvlja3UNOnT9wvEd7rQ9KsgjfdCBqNH547gTUFOZBN79bsRmz0Sc5/yQdlM8LmYOPQzTKxM8VxyGxLRpSPNcqGOG6tN5zDF+f1Ff6PgQp83gLW/y+6fk9IEHAjvv7O3NnZ5EYvYM6HwWnzUDsTdyjOqYzn1P1626VvLHP+1PO/H4RVsd2p9mzUCM15bt1Pv2vO1tvEbal/shme0dHW8DHls6eF7LVk1D2257ov1tvP7l+G89/Fi0b7MjOmdvB5U/Z1oSE83VVMShpWFLEyorUhILLplMYEtjcyFsghEoFYGgVIamqp32jgwmokuns4ideiqShxyMxKmnIH7yiUgdehASH/wXxE88HhX774vEh09H4qgjkDrwACTO+CgqDmXaQw9B4gP/guTBTPv+k5E45WSfL3na+2mH8oH7I3n6h5A6+khUHLA/4mecgeRhhyB10AGRrPIOPwzxj34EFdTHDz+Utj+CJC/yE8ceg8S/vB8ppTntFCRPOoE29kPi9A8j8b7DUHHQ/oif8RFUUk4dfCBkI3nYQYifcCwSxx2D1D57QuWlePGcOOJ9iH/4gyzjICRPPAFx1i+lurOtiVNOQeqwg5GUfCrrrLI/cnpUZ7ZbNpIsS2XGP/whb8PX7UMfoMx8xx2L+AdOQ4o8kuSWUF3Fg7rkySeR6UFIfOhfkFSdWM/Ehz+M1DFHoYJs4h/5CFJHHI6Kgw+A2pI88n1IMW/8ox9F0usPQvxfyfqwQ8lEaciPPFLkpDQV8ukS5JB473vI+sM5NvtF9sSGE2MJ2hD3pMr81zOQIOskLwTi7EfxSx7CctQ3bEMq1x9imiDzmNp04H5IsN4J1SnXjynP9XByjZgkjz8GcY6F1KGs60knRoxZ79RJJ/l+THLcqC3JvfdC4uijfFrVJ/GB93OMnYCEGLOPEkcdhUrVk32QPII89tmLDM6A8qtfozIO5lg7CXH2XZLskh84DYnjj0Nqv7192tTB+3OcHYo4x17lIQeS/bG+PHFLnXwy4u8/FX7Msr6J449Hco/3RvEH7Iek2nnsseyHAwtjTfWMk4/GfDI/dtnORI5V8pBDkMj1Q4qTc0lyj5NtUvsQx36C4ypJbnmGleTpWXDcRfvCR/04Vp00jlPsD40X31baTZ54YsSTYzbJNmvMVhzK+nH/8PuFGIid2kI58aEPcR/oaoP2v0r2t8ZJ6n2H+7ap/BTZ+P4mOvbbJAAAEABJREFU6xT7Pnns0ezPD6Li8INzzIrGNfcP1S9x2qlkfwoquK9J9scK1Zf7g1imWE6cYyy1P1nmWXHMFvZtlcM6xFlmku2JH3kEOWsMHYwE94vkhz7IfekQJNmfvv1KX7TPJnWcEU+1k/2ufaxSaT54mh9HjpOXOP98gBeiSY6jBI8tGuMp7cuq1/s4Pllf7T/pNevQefc9CPmoLsYbg/gxR6O9rgHZN70JyVwdq972VlRsOwfxj/L49463Q20LkhVIZwJkzv89b5AWInPzrYh94ANQ2xM8tqjvKnLlJbXPqzyOxQqySWrscb+q4FjzDLSPcIJZ9VFa3wccl0mm1fEvccIJeK16H5xXfzr+8sq+uHz9sbhi5SH4Z91eSGq/5uSUxlf8hONRxfbKToI8UpI1BnU8Y7+Kq/bzBI9P6rMk+yZxOtvEfTq51+6+XUnug8njOAZYvvYVjTWlrWD/Jdgv2scqyVX5UuyrioP2JRceqzjeNdZTJxwH2YgzTseOFG8cxCubyUL/vlf7Y5w843vw5phjNcGbicr99mHZtME6+7zczxI8tsTZx9kddoCbNcuXka2vR8d9DyDzt78h25lGguNQfe+23wHtry5Bx8OPo+OpeYjxGK26JFgHtdmPax3D2G4/fnlOS+pYLL659uucpnomuT8mcn2VYr0SajPbltIxiMdknXMSPB/qmJjS/swxoXHi4zW2eOxOHPU+7kMfIIeDkNCxj+fEpMpiudo/ZCOufttrD+6jPGZqPJC7mPrj60H7RTxUF44djTsd+5I6Bigty8ifq5Lqlw+cBnHwZbA9KR5vNM7iPLfo+JnkPh4nU3/8Zzle5vFK9Y5rH6SNJMdHnMcPHbsdj93uyivhXIgkx1Sc9it4zK7c9Z2Ise0JTgBFdTrQl6ux549tYkFbiQP2hT/OkE+S40HjTeMgSfs6tlfsv49vn47RKe3/rEOK56NuxwGe++Mag6xngufQXry177BNumbQuSyqzwFQWn9O0Nj+MPtA/Zw7J6za/iDcmzkcBy64DFnuv6qbrgOSZBzjmMk+9BDalixH5pJL0fbYE0g/+zziHz0D2SXLkL7/IfwD78efFu2Pc1eeiv+bfyR++s+98fPXPogfP3YYfnr/fvjp8g/jxw8fhP+7Zy/8bPnp+L/7D8BPHzkEP132QSydvxntDz6C+LVXoumlV4C77kb2uhvQ9NwrCO+4GbjpBrS8sAix265HB48lTc8vRNV1f0Xr3Q+g+ZWlqL7mj2i+7zFsevQV/GL16Vj7+BK0PPMSav58AVoWvILGq2/B7zedhiUJnse4zyc4hlM8LuT7WuPKX7to7KgPuS9UsN3aPxK8ftGYTOj6jsdi7Qu+/yQzTZzjPho7ByBO2zoH6tggvfjnx1GKfRU/8n3RsfzwQ5A49ijIvvo4yTEV13FR40M++yW5x3sQ5znDj9tTToKOMykdqzQeuO/mj9fF+6SOmYlc/goeXxNnnA6dz5Icc6qbzpsJHbd1rOWYSrIOcY47fz7TOUT7j+rAc5nOoUmdQ1QHjrEkmSVz537Pg/VIHvU+pDSW2O6Ujns8P/h2qOwe4z3J8a79Xe1I6PzOfTHJY3uc1wkxTooneE4OZ0xHnONWk6dxMknutSfiPM7EeD0U40MH9VGgN0Y+/CEk9t2b11wf5HXN8VB94qd/xJ+TdKzx13M8psSZL/6B05DgA68k26F9P8E+1j1IYq+9EGObdb0VP+IIxLk/Veh6ROm5X3c7frGNqpe/5uNxOMHrSR0r/XGB+VJiljveYrd98KfO0/H9f7wbP33xBPzfc8fjx3dzzL/+Afwfx/uPHjgA//XDDry6JD3h7oU6cl/NnsaJt+aWVhQvbW3tqKmuLFaZbARKQsAmXkaIcWNDGyaqq2tsR21jG+roy9Vuae8mb25qZzzdljZI3kS/tpC2KF+Rrjhe6ZVPdmuZ18ssT7LKU3xers3plb+gYx7JUb4e9WBcHcuVbe/nZKXtZpf9I5s+TS6Pl5ne+9QpfbEsG3m70iu+2EaxrHiFa1n/OtpUffN5vZ5MI3ttkN7LSkfnZcYrXU85X6b03i7Ty/d61llybb5MxhVs92Ovrkift9GnbdqqyznFq25KL1l+ba5sL+fS5XXKV9utTm1+PPm0uXzFaeuYX3aLy5CsNF7PPJKVLu97mfm87+Pb/dhU+5VX+k399nlUH9lS+nxa+ZEuGueyo3jVwded5UmuZduUro7hWsp19OVqi+S6vE51oz7K1w7Z8bL6gXGSpVNZyuNl6iXny/AybciX28S88hVfm9NLrmOZsidZLi/LZl7u0nM/YnrlUbz0kmv7YKa61ebKqWOefPmSxSfy2wrj2ttjOl8m8+VtK13e1VJfxzRySi9fTmlVXl6WLyd93i+OL87b3JZGWLsJW1o6+q4LucqO6tXZ3oF0c4tPu5n12NzUgZZ3vwdNFdP8WFVZsq30eXkzj4Nbps9Gc81MX47+W0q2uRmbm1Vem8+nuomJ0spXOJ9fvuzJ+XiWq3gv5+qWT5P3d9+zA+94OyfH41ls2QI0NTnsvXc6Kot58ulU18hOV7/WkrHKqlM5DVH9vMzx49NKn5fpK04un0ey7MqXTrLPx3LVtkhmeQzn5XzafHz+nCi9ZNmQ3N0ebbAu3kauzlvItIF9orTtbR3IsK9cfQPaKEun/I3si5bDj0C6tY2uI2LC/IqX01j2Pm3X5vTina9bN5ntV7iOaQt1pKxyvI788vGF/Gy34n29KSudXG2urDrlz8lKJ6e0si9Z8ZLly3aX3YhHpG/z7SqWlbe2m938sa+t6/iislknlSe7su/lXL46xVOWLS8zbT2ZQ4xb+ymfjHxa5vM+89RS9nYl09XJLn2VF7mo/l5m/ihtl/18+5VPstJJrqXdOtqSU/2jfG3cr6Njs0/Lcnx8cdp8HvrK98DjHbjm5gwatgSof9ueaAnCLp7MX899umn/Q9AxcxbCulpkeJOVbm2FyktzXG3e1IaXFznoXqyh3qGp2aG5xaG+3lHHMPfH+s2UqWtm3GavB/SAXHKaE4XZ5hY4ckV7J7gDw21p9BOIrrERrrEJ2XQarkE7N5+qp7MI6jYjywI1yag6QXXiuFc5WdrI8til41xLrAINs96ANescrrgmDdVZbRZDLxdx8bzEhE6y58b2K32Uln1S6J8uzoov2GO8ZOXN25Bc262cdtSxDLn82PJyLo3yy6bXsXz5cnl7yqN4X6c+ysvnj+K711N25Lqdn4r321wdlEZ2vM86qLxiuadt1U31yqcpjve6IrsK+3gyUBnKK52OfdLLly3pVW7PNEpbm7OndEqjfEqnsGTlraN9udqitNLLvvw6xstXHi+Tg3yFCzZ926P9STrF+TQ5m15mmrx/670deO5FQJ/S2tLgwOGLVo77esr66LvcpjrgqhvSE+5eqLGF+ybvAbedMxPLVq5DR0cUpgqLlqzEdtvMlGjOCJSUgE28lBSnGTMCRsAITBoCU6IhbrCtDBxajj4OCEb/tJlMACefkMEZp2cQiwGORV51TYCzfxNi1WqHqb44uKmOwNrfD4EHHw7wj+tDrF0HrF8P/L7+w7jluTf1Sp3ZYQdkq2t66aXQ95SyFDTKstxkFaDjSi3gqIMcuNDnSiFa9VHoIBmHmz4tUmhbnEBhuViI7Ny5krpcvoCcpq9sL27eCX+rP5ZzOY4TS85PzOaSm2cESkpg00aHBc+76OPOHIyFfaGPUtauZYI+9BNBte8e7/TVvPKGe9HJCdF7Hnra/0ejww/cw+ttYwRKSYCXc6U0Z7aMgBEwAqUmYPaMwPgTcM6h9QMfRrq6GulZY/Mk7PY7A/A6sNB4PU2/8247bQtIx267If2Wt4LdoqC5KU7gyXkB/nxFiPse6L5/bKoN8Mxzgf/p22AQZTmgQn08m5Mg+QkXqrqNM0ZBEyy97DEiy/vPWzbti9tqPohsJpeC+pxU8DZ0zsDVO3y+2/4N5i0koKBsBZUC1HW0ZVFfH2lVL300mGpbjUBJCTQ1ARdeGqKuNhprxftCz3GqgsfrP3+p7JG6yookfvfTr+HM86/E7kd9Cl/7/u/wP1//OHbavsfE6EgLsvxGgAS6n6GosNUIGIF+CJjaCBiBKU1ANzqde++L5g9+tK9rz5Ky0TdS+nq7ZSI/WSwloM53vAudu7yjlCanpC0+z0b7PvshW1E1Ydt/8y0Bbr41wKLFDunO3s1oawf004jeMb01nTvthMy73kE7zu/jTkk46eFvPCl7X+G8/P+zdx5wUhRpG396Z4AlLhmJ5nSeAbOeGUVEzDkrGZRozgGz5Cz4mc98Z0IxZzFHjGcWBSTuAhInfO9TMz3MLruzu+zCTnjmN9X1Vuyqf1VXd73dXW22+5sfEzD+/PleuU+mhZatwi8/W0qmM8v9k2XnASS8mHHcj54sR9u2Ub6R5PvKFoFqI/D1d3mJvsXzHrsfjd/3Su7onzskemrJoIxwH/Kvjvjy1bvx0iPD8fnLd+G0YztlRLlVyMwjIMVL5rWZSiwCIiACIrAeBMJt2mL1Hnvb/MhdQq5HDrEknle19LFcUm/5RlNpd7Mz+c5i6hortCoE1j+thxXdjkFoyy3WP4saTEkF5RczY5eyPGb4xEnJ4rS0G9dNGldsYhg2hV7+3jvj3LPC2GO3CFq0jKJJkyiaNY2ioCCKpo2BJr7c1GTza1RgtsmNKTcy2eI3bAjwFUGWybf5IbY8GzrorlULaNTIQ14AYBwblJzNOLBfwKrE1wy5z6DFdXHMP2jxGdfzgD/+8DB6XBCFheawMP1FoLoILFywNicq+fyHt/LrRtG8mR0LjWgD22wdxbFHhdH5UD/G2nSZJgXsoGu7SXPUqhXMtKKrvBlEwIb2DCqtiioCIiACIiACSQQ4nYo0qdirP+HNt0TonzuCExdkwG+XnWIXs7zwZT1pCos8XHtDENcOM3NjENfQNnON+dE4/7jswuKy87d4tBmPhjLzonzNsACcHY/vy4xTWj70o2G4b667MYjrrExjxgcwflIQw24O4ubbA7jxtoDzZxjT+Mbft0t/QzBWJysj9/3tj0EsCRZg+Qrgo4/jdU6KwzSM50w8jcvX4qy1rU4W5sfl/pxsfoxD+aZbg3j0PwGQawZ0iQ1SxNi0PbbdIDuopkx/+dXD3ffF+hLbzrWh9bfVa5J2YAcLjxPfhwqOI7qEfWeF7U07RHFUtwgG9AtjyMAwBg8I46LBYQwZFMJQXx5g8iD6hzDE5At92eKfdXoI9eoCTU0hU78uUL9+1BQ4QJ38KBo2iGJrm7D26h4G31psaooam/NZ3CiovGEh6de2TRSDLwijndlN44qjUImqcHHTjz5J/7ZjnWQygwCPp1Vr1vYpKvmoMOR584xTIxhkffJCOxYGnh/CmaeFsWvH5CMuM+qoUopATRGQ4qWmyGu/IiACIiACVSfQpDmW9ewHBJRpDwQAABAASURBVJLvUlU923TIgXcRj+gcBidlfMTbs0LxEpfqGJtfunUruI4E7/rTbcFuXYlkOcIEFuDimcwwGvOKxaVghuEJf7otrllwfrbDuNO56cf4LBNtf/+0wxZ3wUIP8+bDfdBl5UoPq1d5rqwM99O6dNyBGZbRGUvLOOaFaQv2wLRGJwPmRzfDWQaXzgTGY14MYxwnm3+UQjxNNOIhajK9XFktMmUa57b4fA3l668996qKBeufpgTYTg89EgCVL2z7iHUItq1/TNBm0T3P4/yQzQtuTj8ljC03t4ZmYE0ZrhmzAfc9f8EGzFxZ5wQBPjU1Zw4wezbw8KMBfPqp546f5MrzCTAqJJP9JIuACFSOgBQvleOl2CIgAiJQcQKKucEJRO22cmTzLTf4fmpqB40be7A5pptMciKZMFYgLvBpl8ewuaa54tfJziMmJ/z9eadvW2ynfGDcuOx5XiwfxqHxLA/aZuxvsWL/dWSLB5pYsFt01LKKuSwy9+NMzCcWbrKLY+F+fZiF87Mw+i1a5NkkgL70MGMi8zEploe5KTML7p9Oyp5nkv2ZB8MZ5qdjOP3IjXay+fEn42xKmmQ/yelDYPZsz6054Tet53mwf6yAbFgv3l9jPjCne1Voyy0YGPfcyNaq3fdCpKAAq/be3/bMEplVlX9e6ZfsLZpXJVOlzWUCS5cCk6cGMGJsAJOmBE0OgmOhY8Iua4cPx88O7aI46kgNkI6LNiJQBQKlj+JVyFBJRUAE0o+ASiQCIpD5BOwauPKVsIvnxAS1AqlL7qOkuwJZVCpKafmXtk4HM13felQmHfcjk1kE/Pa1rg5OErseHsYVl4Tcq0I1VROWZcWRRyO0y+4I7bhjlYuRt3wZlnXvifwW9YrlVacOTMFU2lFULJocIlAqgXdm5CUU3OxFHHv948klsI5M96JCE5yHNiIgAlUhIMVLVegpbWUJKL4IiIAIiEAlCHRoH0Gd2nY339LwAtjdyo9fA/Mi2V0sc8NwM+5JD3MzCiehzk1/GnrSNuPysngmgjJf0WF8ymA8C6PsDCPFDYPioovm8re4vp8rk++2yC497XgEF24y98UMGE6bSZyfhTFPJ9OTbpok2eURd1vWDg6dlFkPpme+dFNmchrnNoHpzSr232rLqFvctJinHGlDoE2b6NrjwErFdnZ9xOTE3xo43xQRO+0YRX5+wrfGBPZB9u2qFiDKVXbzAliz2x7Yr1sTnHd2GJt1iLquvdtuETSOr/9S1f0ofe4RmDPXA8dDO3RcV6XNsbQkCY6PJf3kFgERqDyBHFW8VB6UUoiACIiACIjAxibALxudclIYHWyiZXMvcJFDnrg5qeObB545nM0rZisc3QwzEZQZP1n2LB6N72dOimBcL+6glZDpsH3QYkT6O2N+vFJnOn//tLkeDb96wS/J1A7CJsBR1K4TU2ow3E/r0jFDMyyjM5anH8e3Ldj9Gc4yuHQmFAu3dMzXlYeCuWOJomB8z+K7MPOkTEO3Od2/eXPoMXpHIn03VD6efmoY/KoPFS551ohsW5bY82L9q127KHis1Cv+UAijZLRZvf+BWHHkMdZl2ZGBzTeLonXrqFM4zpiR5xalvubGILjYMA0Xkeai09cNC+L6m4KY8n8BfPZFnvvE9Z9/An/86eGP2R7+nOM5+U+T6efclGkYz2z6zfoDYByaYrLFmT0b4NeV/PVBmA/9aJiWNhdrzegGSIPCJ6/B4nOdbe03f0GsTyxa7GHuX4Bvk7sfbxbbyeL6bU5/yjSuE/n1i2XlXuX0vWhzAeiDD4hQlBGBDCdQ88X3L09qviQqgQiIgAiIgAiIwDoEeLex13lhXHdlCNdfbeYaM7SvCuEG2mZuMD8aP5yyC0v2t3gMd2HmT/l6s5376jCc7bvjNuOUlg/9aBjum+usPDT86sUF/UK4+ooQrrgkjKsuDYP+NEzjG3/fLr3tz9lWRpaDcr8+IdSrD+yxexSM64yFM4yG8Zwxv9Jtq5OF+XH99P7+Tz8lNpng54L9r8msA18eaUOgWTOAizfXqg3XH1w7un4T61+9u4fBYyVtClyNBTE9UyK3WbM8vPdBHuhHJZQzYZtCUxdjXdr+Li5Zhcx/1h8e/vtUHu68K4A7/88UMWZPmRrA5CkBOD+T17EZj/4WZ8rdQdxpMtcCmWr+lGMmiMl3BXGnKXYmxtcHoTxpajDmzzQWfsvtQbz/YZ4rkzaVI1C0xAO5J9ZgMZ53WptMJmPjO25C7Ctfo8YFMHFyEL7NdmH70mabTbI0fptPnhLvC5b+l9/y4PQt1ndAw+I5DwoAv67FrxdxjIz5aFstBJRJzhLIy9maq+IiIAIiIAIiIAI5S6BVy9hM4y+7U5yzEKpY8Z9+9vD6W3mY9nye+zLUs89R9vDaG3lgWGWznzMXeHsG82B+QUybbvL0AJ55NoAnn85zc8NNWsTarbJ5Z0v8H4y5X5fEHNkEz7BQGVMszPezcAfPAvlqiVnw41oUJ1OBwzj0Z170RzwdZfo7N/3M8KmzYv7M1IyLZ7b/D5vy5/kX8rBsme8ju6IE3nrLK7YGC9O59jP+lNke/MoX2825bcM2MYtBpo2jtNa4tuHGItFiNia6Bdxp5+dH0awp0K5tFAcdGEEvU2ayndfmIEkERKAqBKR4qQo9pRUBERABERABEchIAryLW6sWsMTuKq9YmZFVqLFCL1zoYdykAO57MIDXXs/Dhx/HzSe0A3jjrTwXxvCKFvLNt/Mw8c4gXn6ZeeTho0+ADz7Kwwcfevj4M88pcjhR5CsS9K9ovukSL1onH6t32nWjFMcpUQyW/d2c3Nm2cf42w7a/K4d5ITZDN8s5sPbnu822v/N36Z1kc3rLhAqauBNUCLh45u/7Jdtz5rrQZC9fll0Ggd//NGWjYbM/qChx/JP4JkSL4PN38Zgf/cxmGhPhx2Wb0W1B7k+ZyhWm2/EfUQy6IITePcI4xBQvfNXVRdJGBESgWghI8VItGJWJCIiACIiACIhAJhH49TfPTWY4I5lwZwBvmrIgk8pfU2X96isPoycEMH++R3SAh8QvSXR+fOpl1p8lfV3QOpu33s2LZWXROVmkMRExTxT7UUlTzKNaHBs4k7p1sergQxGt3wDggk3rubutt/Cn0Kb88PMwL068ycx5eW4L+oFhZmhzck0TCy2+Zdp4MjCdLxePZS7mZRbbJUmkT8yUkbBBAz92LJq25RNoWIIZ264Y3rKQmj/bk3tgGtpsL9oWRKtUY7rBUv3lKQIiUD0E8qonG+UiAiIgAiIgAiIgAplBYNVq4JHHA1htNickS4o8vPpGHr76uti0pmYqk+Z7fWtGYO2MP2kWl5jglSj/gvklPEpxcpHQNWwLC/MnjCa6P+/QOyFpw9dWMu4ppUgEZLSiy5FYs+lmSbWpnNi+fRTHdAujRXMgYE3BPD3ftqt6+8Pzs4zDpJsG1l5xL9A2p2tLykxCty8zrjMMoGGgZUKLX5WiF/ftx7Egl5fvZrhvWjSPolVL3yW7ogQ2aRWNtWXUUtCstUyyv4Nutv2dyDhm2C5007A9zMu1TaJtLT79afmGaXbZOeI7ZYuACGwAAhyfN0C2ylIEREAEREAERCCTCWRz2WfP9rB8+bo1/OUXN1VZN0A+joDpDrBooYk+Jt82r2KTOnP7/+YtfKlsmxNzLprLGJwA0vYNn77wZd9u0ACom++7MsuOdDClSzPTmlSh2LvtGsWA/iG3aLVbPPqqkFto+wYuJn2NyWauvCyEww+LYtMOEfDLXY0KomjcOIqmTaMoMLlpY6AJ5UZRNGkSW0jV+ZvcxMIbFTAc4MLTDRvBxWF4I5ObNvHAvBiHdoH5FTSKmh/gyw3q21zfZvwtWkTR/Zww+DpLFaqcFUkXLPQwbx6wdCkwezbw9/KYzXWm+NWo5K9OjZ8cxDsz8px+JGIcPZuxNTLGzZsBTawd+eW47beNYo/dIui4UxRt20SxySawtmY7RK1trS2sjRs3iaK5tTPbsam1LduffaGJyWw7ts+eu0fc60XMMytAqxIikKYE7DBO05KpWCIgAiIgAiIAiEEOE4jWqWO1T5rdm0v/miPAyXNTm/ixRahooe1mhvEi2fwwLsWsLbeIon3bkr6xsJLbA/4ViWVl0al8oTHRZu8lYwIH7q878+tSKe5Tpzaw374R9DwvAn6Z5qLBYQwdFMaQAWFQHjIohKGUh5j/wBCGDIz7m8x4Fw02vwEhXGhpLh5isvlflCRfyPjMw/wutLgXuvwtvsWlfNopYbANWY5cXytk3ny41/PGTgiACpU7RgUx6a4gbh1u9tSg83NfluJXo+4KgF8uooKGLcpjjMcdj7cD94+twcK24pfjTjfGRx0ZwfHHhtGnZxj9+4SsrcMYam0zxLVdyMmDrJ3ZJkOsDYeYfJG1FWXGG9AvjG5dI05xw/3JiIAIbDgCUrxsOLbKWQREIKsIqDIiIAIbl0AUy844D2vad6j23baxu8P16sWy5YSGM37aS/8GlizlVCcWpu26BA7YN+w8OammQHuLzSLgXfM97e77nruHEaxl+hLTjSxYCIwYE8To8UGMHBvA8NFBjDJ7xJgA7jB5pNkjzB4xOoBPP4/d3WfuO+0YsTv5wF57RLDXnlHs3jFq7iiOPCKCC/qGnD/3nW0mmhfAsrO6I0qoGV45PmHBKixcpOPpldfysGih517vIhMan4prajqi9I0buuNisrVgQbJLsgiIQKYRkOIl01pM5RUBERABERCBHCDgwSYqHdoDBQXVXlvehT/1pDDatI7PdjzY3oDvvsvDmHEBfPSJeUC/0gj8859R92oDyW2/XRSD+odx7tkR8M47TTTiwa3XYggLizwUFcImncDiQpOLgEWLgULzX7oEKCw0P7OLlnhYbP55liZgZpFN1o86MoRupmjpdkQYRx8VtvzDTuHSMovXCvFCaxDauaNhNwi2zeR/w4ax0q9YAaxeFZNzdfvHn7HpVqJVefAYDLopOts2VP6aZSGl//nKWOkh8hUBEcgEArGRIBNKqjKmHQEVSAREQAREQAQylcBmm0axxeZR8I4zJzzgjMfMmhDw7HMBvPSKLpHKatvFpkAxVNh7zwiaNY8Wizbz6xg3ck0EMDKjme15trEAOh1zk0v+//jTAxfcLemfC+44nqyoqr9mCPtLVlRoPSuReNUq1vUR9W0eBGbc+GO23/a0zVlsb02bRLHjPyPF/OQQARHILAKxs2NmlblkaeUWAREQAREQAREQgUoT4KswTMSJDu1koy8cAavXxBb/nD3bc4uBzpkDzJ3rgYoRTgz5ylYyMy4aupJPN8QnlvwiEeMxTtyLojO+2006nU/xzYIFfozi/tnminp5iHDhnGyrmNWnoHGs9QsLc3u6seMOcYVJDAdgduzLUCYYJ/Z0Gudvbv7p5rFBs922UQweEEZ+HYbIiIAIAMhICLk9EmZkk6nQIiACIiC945KRAAAQAElEQVQCIiAC1UFgq63cHKjUrPj6C7/iU2pgDnjOeC8Pt9wexOSpNHluMdBJJk+cEkAoBPewyptvFb+M5OslrVpZWGw+iZI/Tibp52yLY3+4jLDub7PN4pPVdYOyyycYwPLjT0akbj0gWNvm3lGEG9ZHND8fpSkEM6ny/PoOy1tYyG3umgP2i2C3jtafrePXrxfFZh2i4MLTm20GtGsbQcsWURQ0iYJfGXJfHYp/vWjbbaI47ugwuIhu7tJL95qrfCJQcQJ5FY+qmCIgAiIgAiIgAiKQPQS4GOx220RssrtunTa1yRG/JrJuSPb7FC318MLLeQhxpVubLEZhm1Kqzc/dzp5dPODILmE0bBh1C4nylQqm5F17P1bUBKfQYoDJDj49KZupXQs4+shwxn4u2qpQqT+VK5EtNsfqPfdGaIstjbSHVZ27YvX+B1Uqn3SMvNT6ET+F/Jop6EaNC2D0+ABGcjHlMZSDGD46gFFjghhl/iNMHjsxiPsfDODDj7NveuI+0GaNtM/eUfQ4L4xzz4qgxzlh9OkZwQX9w+BXovg1Kf+rQ/wS1ZmnhbFrx6SDw9KX+VeACIhA2hPIvpEt7ZGrgCIgAiIgAiIgAulC4IxTIzjbJjgNG8SVBVYwTnUKi4AZ7+fmZdLsP+NaEQNhf1A5EPcxOsX/f80rHsK1cy4eGsaAfiH07WkTy15h9O8TRq8eIZtkhtHP/Hqb3LenubtbWO8Q+vYKoXePMOh31eUh7L4b91p8P9noWmXKlVDrNq5qkfabIrLJJk4uTtR5ZczGL+iz0/LwzbcePNOsrVgOLF7sYeFCD4uLoigs9NyCy0VFZhfCZA98wmzBAuDHnz1Mez4PTO/nlQ32Aqs769EqixeHZv1kREAEyiaQm1cUZfNQiAiIgAiIgAiIQI4R2HrrqFMO8GtHntWdptAmhy+8lIefbCJoXjn1z8+PKT6ocPErHvPxXWvtBg3WyskSvz7Utk0UNK03iaJ9OziZ68LwY1VtTN/Qrl0UrVsDTm4bdXZyHlWQMyJpaKedEemwWUaUtTKF5BNNX8y0KYYdSJ5nG0vM/uNE29jffGL/ZDnmE9v6izTHXJm/nTcvVofmzUgiJmsrAiKQWwRsVMytCqu2IiACIiACIiACIlCSwO+z8txisiX9q6Z4KZlbZrj5mlWL+NeKOG3mq0JcKBcl5oz16gHt25fwzIwqqpQbkACfXuHCzP4u2H982e9PoGCGYWb5wQmbizQvXZpwZrSwejVQWOQhGASaSfGS0W2pwotAVQhI8VIVekorAiIgAiIgAplAQGUUgUoQ4No23c8Jo169KLhGR+OCKBoXAI2bRNGsKZ9MiWK/fSPu1SF9aaV8sJEGDQFCLT9qVsRo0tj6iSkYPL82Jtg/tu6P6en4lItTuPiyHy/J5iLNXKw5yStjRb5CxcK3aM6tjAiIQK4SkOIlV1te9RYBERCBGiCgXYpAuhLo0D4CvmrE8nFSSIUDn/D4/EsPY8YHMGlKAC+9kofFhZxCMlZ2m/r1geXLPeRZdS8cHMbQwSEMHRjGoAu4FksYnQ+NgBNs6FcugRVdj8Sa9h3KjZdNEbp1jcCtm2SV4vHEY4nrvfBJF7qpfEmWTQdjMWN/PhnCRZpjrszfzltgB5FVw3+KzET9RUAEcpBAWiheQuFwmegXLCrCipWrywzfGAGvvP0J5i8s3Bi70j5EQAQ2DgHtRQREQASKEaCi4ZSTwuDkiJNC/wJp2VIPCxd5mDPXA7/ic+ddAfA1iGKJs9AxZ26sUuQRk7RdXwKRzTbnOybrmzwj0225eRRukeW+IZzfNwwuoNy3VwT9eobQnwsq9wy7J6a4oHIfk/uaOfXEiHsqplYtw9U0WRWTkQgShV7gK15aZE+dEpWTIAIiUGEC/nVFhRNUd8Tf/5yHnTv1wOy5C4pl/fuff6HrmZfiwOMHYfcuvXH17XdjjfuuIfDk9Lexw0HnrmOGXjexWB6+o7z4r7796Tp5Mf9V8RdUL795Kv738x9+drJFoBoJKCsREAEREIF0IbDVllHwFQfenecUiYZ35ZPLt3w58MsvNX75lFykDSL7XytyPDbIHpRpLhDgIsutW/H1NIALK3Mh5diCylG32HLbtojZbaIoLDIiHrB8BXDHyCCG3RLA6LFBjB4fwPDRAYw0edRYyvQLmtvkMWaPMdvCGWf0OIs3OogRYwIYZfGHjzLZwph2OP1NHjUuiDvMHsk4Fp/pRjEfJwcxZkIQoy3OaHOPnRhw+2Feo8zNNKMnBFzedI+xso2wvMZaGipl77o3gHvuC2DqPWbfH8DdZj7+1CplVVu5MmabqL8IiEAOEqjRK4fT+g/DEWdcUir2G0c/gO226oCPX5iCafffghde/xAvvPaBi3vYAbvj+QdvK2Z2+seWaNakoQsvuSkvftSusOrVzS+WH/OvXStYMqvsdatmIiACIiACIiACmG93p6M2P+JTLzTJSPiKBM2PP8M9AfP7LGD2bBrP2XPmxOQ/Z3uYZfdrZs/x8IfJq2r2wd3kKlRYnjfPIFjsli2d+skk/UVgwxFYtgx44eU8cBHnWM8DVq/2sHAx3Geoi4o8LDZ5UaGHIlPQLFwYda/9FRXCbGDJEs8ZPp1WWASL42GRxS9aQoWOh0LK/JR1kYfFi4ClZhcWeli0yIvFLQQWL/YsD2Ch3QteZHEWWdj8+R4KzX+xpV9sdqGlW2RjxGK6LZyfieZiwgsWAjzuf/vNw8+/2vE/yzMFrYdfTV6+PFajd9/z8OEneRsOonIWARFIawI1evSPuWEAHp54dTFAdBQt/RvvfvQVzjqxM+rm18bmHVrj2C7/wktvfsRgNKhfF5u2a5UwRUuW4ctvfrL4h7vwkpuKxM+vUyuRn5+358UGyuT8Fi5egt4XD8e9j72Q7C1ZBERABERABEQgCwhsu03EPeRCBQuNqxJ1DzTm4JXBRx/nYeKUAKbcHcTkqUFMmhrApLuC5hd0Mu98T3VhFueuAG66NYgXXqrRSy4recX+CxZ6ePjRPMx4P8+99jHrDw+ZqDiqWG0VK10IzJnLI8tKY1biuDOZl+JmuWOSst0rjcv0jcX3PJPt+GQ6ioxgTjgZFmbR6EbMw1xgNoAF+V7Otkj2d/6+bVHgfr5gAfZfm94FFt8Uy6t4EL76ys+oRICcIpDDBHKl6jV6FdCyeWO0atF0HdYLFpra2XzbtW5h29i/Q9tWmDPP1M8xZ7HtyCmP49RjDkGHti2L+ZflKC3+osKluOKWqbh+5H147tX3ESpl3RkqhHpeeLtT/Jx5wmEu+1oBDzJioD6Qo30gaPWWQS0xyE4GPL/lYNse8K8o+DllTo/cBMqd7WMTtWR3ItwE+7tYDHcyZ2bOZ+2Gioy//kr/MeOpZ/Pw7fd5iEQA1ueHHzy8825e1vXxYPwKmO3leR6COdjXU47dPP43omlQD+5H5Qlco8BZdPNwooH9rKliSg+T+ecTMrQZzmRONofv7zIxT6Zz4dyYoduiWYj9zW1b0M9PRzmx73g44xd7Gs487M+kTknphPjGjxd3JiwqNnXN6NX43CnAVcMTrZIQJIjABiUQP+1s0H1UOvMly5a7NLVr13I2N3Xq1MaiwiUUi5m3P5iJjz7/Dr3PPKqYf1mO0uJT+XPeqUe4J2uY7pJhk3Hb+IcoJszfy1fi/MtHo70pd267qi+CgYALK2hQGzJioD6Qo32gntVbBgVikJUM6ucHs7Je5fXXNs1r49KBQdx0dQCXDc7D5UPyULu2nfI9M/Z3Ey23MYf9OTmDhdk/NiE0ISnYYqz9L1xQK62Z5gdqY9Ysq8DaIjvpxx8DaV3u8tq0tPD82kFXtzybgAXNlBYnp/028vXtDtvWxiatXJPwcEooMqgAifmu3fp+rqe6jYWZ7Y4727hwuikzyGQenDxW46LTpFKmv69ssajub8nc/l0+9KEHbTNMw3xoXEHNz/0ZYELcckFJySwk9v/n9l4F5g21FWcD97+6dQKxBtFWBDYigbyNuK8K76pRg3ou7po1IWdzs2rVajRt3IhiwoTDEYy881H0OK0rWrVokvAvSygr/o7bbY6L+p6CXmd0w7VDz8GwS7rjoSdfLfbUy5W33oXPvvrBxasVXHuwLihaBRkxUB/I0T6wxOotgwVikJUMCv9ek5X1qmh/DeetQr1Gq1G34WrUrgO39gTiP97NpuglNjaP4yzLjJuQ0b8U4wVCac10vh3LpRQboUg0rctd0TZNjrdsZchVNWx1W2PXkwut7snhOS/z+nYjmkVLV+GcM0M4+KAI6tWzA8m1jm3iIo81d2zRHTd8KstiUHdiByASyg5rUvDnWYiLahv7g4qURB7MkJHiJuEfdycHU3bhDLOM6GZedDpjfgnbAv24JRU6LVsA++xtY8BG5KrrU7tOK4X3shWx49+1mzYisJEIpKXipXmzxq76s2bPczY3v86ai9Yti7+W9MIbH7qvDfFpFcYpz1Q0fotmMSVOKP4VJeZ7VOd9ccDeO6PvpSNRWLSMXjIiIAIiIAIiIAI5QGCXnSI2hbO5HSdYNrGiIxqfXTkn/elpLOimiTvNJ/avZ/eUNt88EnOk6TbfFEzt2rnKFCvhNlvVbLmLFUaOrCVQvz5wyIERXHZRGJcMDaFvzxD69DK7V9hsMz3N9A6jN91muzDG6R5Gr+6MF0I/C+tvafqZ6dMzgn4WzrB+5t/H0vfuEVqbl8m96WdhfS0/yn1M7m9y/74ht//z+8X2x3CXB8P7hF1Y/z4h9LO4LE9f8x9wfhgXDg6jn+37ikti+6HNeIMHhHFBvxCaNFn3+MraBlXFREAEihGoUcXLGlNsrF6zxhWIMg0dBQ3rY5/dd8ADT7yMFStX45ff5+CZl2ag84F7MNiZ1avXYMTkR3H+ecehSUFD5+dvPvr8O5zY61r89sdfvhdSxefTLZ98+T+3r7nzF2HKg89ir47bI79O7UT6TvvtihHX9kdBowbod/koLF+xKhEmQQREQAREQAREIHsJdD40guOODmPbbaJo0TyKxo2BZs2AgsZRNGoURbOmUbsW8cw/isZ2j6hRQSysKSdZTgsDnH1G2K4r0p/RcUdH0MTqRb0SFTF77hHBAftL8ZL+LZddJWxol/Zt2gBtnYmaHUW7tma3NrtNzG7fFmCcdqYs7NA+JrexMPq1bg20tfiUY2GWxsLatwPamd2WxtIzT+dmvhaf/q03ibrXnph2k5Zr99fG4jB8Ewt3Ya0A+vnpOTYUNIq6MuXnA4xLexOL58aC7Goi1UYERKCSBGpU8bLvUeejy+mXuCJ3PfNSHHLiYCdzc+XAM/H1979g9y690e3sy03psju6HLwng5z57/S3sXTZCpwVX+TWecY3y5avwLc//IaVq1bHfYBU8efOW4izB97s9tXppKHuFaMbLumeSEshz/NQr24dTLplCPgVpaHXjQdfXWKYjAiIgAiI9oPD6wAAEABJREFUgAhsPALaU00Q2LVjFGeeFsbA/nZXe1AIgy8I48KBYVxkd7h5N3vo4BAuHBTG0AEh84vJ9N9qi9gd7sLCuAamJgpfiX02b2bKI1Mc2WUPTj4xjG5HRJB0H6oSOSmqCIiACIiACIiAT6BGFS8fTZ+Mr9+4N2HefmqcXy630O2LD9+B1x4fhQ+fn4ybLuuJWrWCifBTjzkETN+wQb2Eny8cvG9Hl+e2W5r6O+6ZKv7QPifjkxen4IWHbse7T4/HwxOvRvIXlbgfPoHDrBoXNMDzD96GybddiECgRvGxODIiIAIiUHMEtGcREIFyCfAVIz49wq8FjRwTwJjxQdAePdbscQEMHx3A2IlB3P9gAB9+nB7XFXP/iimJWlbsY5HlMlAEERABERABEch1Aulxhk/RClw0t369/BQxqieIrxW1b9MSVKxUT47KRQREYGMR0H5EQAREIB0JzPzKw5czY0qMFSuiWFzoYcFCOHvhYrMXeViyxMOCBcCPP3uY9nwenp1Ws5dmS5cCK1YCtWoDjRrGntZJR7YqkwiIgAiIgAhkEoG8TCqsyioCaU5AxRMBERABERCBBIHPvsgDn3bhazv8pAptGkbwbcqMQxUHv8by4ad5GDk6iBFjAhg1Nojho4IYMTqAkZTj/qPHBZzfqDHxp2ZGxcJHjQ24J2iYbqTJd1j80eMs/VjzZ9wxZltefMqGcUdY+HBLO8LCRsT3c+ddQVfmunWj+Ptvlk5GBERABERABESgqgSkeKkqwbRMr0KJgAiIgAiIgAjUNAE+yRJ73gXgp2WpXAF/5klli1lOyQFEQTmPmyhQWAQUFXlYtNjsJVEUmlxo8hL6F3pYuMhzfosKgcUmFy3xsNjCFy32sMTiUl5s8ZZY/IWLLL3JRYxrhk/Y0Cy2uEVLgCVLPTCMctESS78UoFJoieVz930B+J/shX4iIAIiIAIiIALrTWDDKl7Wu1hKKAIiIAIiIAIiIAKZTWDrLU2LElemmASKrBGVMFRuRM3D/vA8bk39YpGcaE4Tzd/F5sZUMxZOycJcHJNpmxOwDWXwZzLdTOBEP8Acnmcby5hKHxfH4psTvowSv/kLPPw1r4SnnCIgAiIgAiJQFgH5l0lAipcy0ShABERABERABERABNafwAEHRMHPzfrKDafwcA5TotA2D1rcA21Ti1CMPwXjRPhKEepMaBgP8Z8lp37FuSgzPW16lIxLZQ/9mZ7xSpPpV9IUFupSsSQTuUVABNKfgEooAulGQGfTdGsRlUcEREAEREAERCArCBQ0iqJvr7D7zHT/XiH07x1y7j7m1693GH16RnDGqZG1daVGhJoR83GiLztHTFlD0YKdPobKFSdbPMpmwQUg9kuOm+xfTLZE9o8lKGXbvl1S+UoJz1SvqEEIbbZ5phZf5c4cAiqpCIiACDgCUrw4DNqIgAiIgAiIgAiIwIYh0LhxFK1bw5k2baJoa4ZPwrRrG8V220bQ5bAIggHbd9SUK2aZToBbs8zDJD7FQonKFXPGI5lFT+cRk4uF+2Fm++u0mBiLbQLzTDwuE/Mttg1Yebp2iaBBg2LeWeOItGqFvwdfDHi5cikM/URABKpAIBKJYsGiIhQt1arjVcCY00l1tsnp5lflRUAEREAEREAEaprAvvtEcPklfBomhH58GqYn7ZB7Iqafyb26080nZMKg3Ld3GL0tHu0+PcPgEzTJNsP6WDrnb/H69AqhL93d4+nN7fZjdl8zfXqEbb/xOBafcVmevffcAE+71DTs+P79p4EiplmK1K8f95UlAiIgAusSeO/jr7HXkf1w4PGDsO9R5+Pcwbfiq+9/SUS8+va7scNB5+LtD2Ym/CgMuHKM8/905g90yuQ4ASlecrwDqPoiIAIiIAIikIsE0q3OtWsBbdrQRJ3NJ2Tato3JHdrH/PmkTPt2JreOol2bKNrStjj059Mzzm3+Lqwt3JM1DGtvMvNu1y6KWF6wfUTN0AaYluE0zIM2y5NujDZEeVYf1AmrDjoU8KCfCIiACJRKwMvzcM2QszHj2Ql47fFRqF8vHxPueWqduFMefDbh9/Pvc/Dau58l3BJEQIoX9QEREAEREAERqDkC2rMIiEBNEvA82L8mS6B9i4AIpDmBvXf9B47qvC8KGtZHqxZN0OWgPfHW+18gFA7D/x13xP74dOb/8NlXPzivB554CWccf5iTtREBEpDihRRkREAERCDnCQiACIiACIiACIiACIhAeQTe/fgrbL/1pghyMax45CYFDdHjtK6466HnMH9hIR575nWcecKh8VBZIgBI8VLFXtC4QS3IiIH6QDX2AR1TGlMypA80sXJms2lUL4hsrp/qVitl++q8VktjsY1x6gfqB9nYB+rVCbgZYDgcwdR/TyvT8HUhFzFp8+xLM0BzYZ+Tk3xj4unHH4o3ZnyOa+64Byd1OwitWjSNBWgrAkZAiheDUJX/qtURyGQnA7Wr2lV9QH0gVR9YYeN/NptVayLI5vqpbqnbN1XfV5jGRvUB9YFM7gNrTOHC+V8UUSwqXFqmWbMmxGgJ8+5HX+Gym6fg2qHnYJ/dd0j4+8Impmg5vusB7jWkc04+3PeWLQKOgBQvDsP6b1asDtuF6QY1yl+M1QfUB9QH0rAPrLQyZbOh4iWb66e6hZGKga5vdG2nPqA+kK19YE0o6iZ/fFXo0vNPQ1lm2y3bu3jcvPjGh+h98XDceGkPnHz0wfQq1fQ640hcOegsbN6hdanh8sxdApVQvOQuJNVcBERABERABERABERABERABEQg9wg8/eK7GHrdRFx2wenYs+P2+HPuAmeWr1i5DowObVvh9OM6reOfmR4qdXUSkOKlOmkqLxEQAREQAREQAREQAREQAREQgeojUMM5ffHNT64Et45/CJ1PvShhXnzjI+fPjb6ORgoyqQhI8ZKKjsJEQAREQAREQAREQAREQAREAIAg5CaBa4acja/fuHcdc9wR+zsgwy7pjqGlLLZbp3Ytl2bXHbd28bTJbQJSvFSx/ds0qwsZMVAfUB9QH1AfUB9QH1AfUB/YSH1A1566/lYfqEIfaFFQp4ozQCUXgcoTkOKl8syUQgREQAREQAREQAREAEIgAiIgAiIgAiJQEQJSvFSEkuKIgAiIgAiIgAikLwGVTAREQAREQAREQATSmIAUL2ncOJUtWjQaRSgcrmyylPEXLl4CmpKRVq9eg7/mLwb3WTJslYX9MWc+FhUuLRmUcM+ZtwiRSOxTbgnPCggVyTtVNlx9vGjJ36miuPqWVueUibI4kG2sfpW6gcvrV+vb31PvNbtCOR6Ew5FqrRSPY5qSmS5dthyLi0ofn8obY0I2xnLsK5mn705lL/t7BWbNnoe/l6/7FYRU6fww1qWwaJnvlF0KgQ0xXrG9SjuGU50H2U/mzl+EOX8tRLiMfr1gURFWrlpdSi1Se4WsD5aXd6ocWJ95CwpLPX+vWLkas+cuWK/zc6p9ZkMYuVdnPdgOpfUrjoVs39L2Rz+GZVq/qk5u2ZYX27Q667RmTch9bYfjU3K+qfoVx01es//+5zzwHJiczpfTcbzyy1ZWnf1w2SKQLgSkeEmXlqiGckx7+T23ynZ5We1/7ADscNC565gff/nTJeXgPPXf08B4Bxw3EF1Ov8T5c8PBeeJ9T6Nj51445KQhYLi/0jfDr7rt/7CrhR1+2sUu/VkDbkbyROH+x18Ew844fxgOPH4gRt75GJM58+rbn65TJpbTPwmUl7fLxDZvvveFy4e2Od2fE6UBV46xfQ7GoadcCJbr2x9+c2HcpKrzsy/NcPmxLDR7HNEXV956FzjZZtpsN+pXsRZmf2L70475wCkfU/WrVP19A/crv4gZYXNcuX7kvbhh1H0py/vbH38VOxbZHjQ8Jv2EqY5lHrNsr7279cd+xwzAaf2HgReTftpUYwwvjoeNuh8HnzAYJ/a6Bl3PvBTPv/qBnxSpxi/u97juV2GvI/u58XTPrn1x05gHSp2Qs3+xTrT9zP+0ifApfa534+2/jrkA5w6+1SmIGU4lN+Mnm+5DbgNZMTzXTHWNV+TGNmA7s70OPXkofvw1do5kf011Hnz06dewc6ce6HTSUHe+OezUC/HV978wS2fe/egr1/fYJ/Y/diAGXj0WVMoxkJOf5Lb05fc//YbBKC9vF8k27DM8LpLPsZyIXXrTnWB9Dj5xMDpZnWZ+t7ZcPDZ279Ibh516kZ0rB2LE5LXn51zvZ5yQsk2plDK8Zf6vuGVqqWPUY8+8nkhTVr9iBIZxnGDf4f4ee/YNejtTXtuna79yhbcN+x+vvU7sda25Yn/1q3lurKiOfvXL73Pcte0uh/V0c4H/Tn87Btm2qfrVl9/85M4tvOY/4oxL3LnxyaS0qfqVZY2jz7linT4/8d6nGFTh8aq0vkG/VOMVd1BWnXO9X5GNTHoSkOIlPdulUqX6/c+/nDLjspunVCjdQxOvxvMP3pYww6/p59I1bFDP2aOmPA5OGPuefQzefmocnr3/ZufPzedf/4gJ9zyJB8Zdgc9fvgvHdtkfQ64dn7g71r5NSzx253X4/JX/w/R/34ZfZ83BY8/GLji+/v5X3DbhYQy7pAdee3wU7ht7Bf7v4efhK26iiKJe3fxEufwy1q4V5K6RKm8XwTbf/zQLF90wyaTi/xGTHwUVODOeGY/3pk3AZu03wZi7nkhESlVnRmrauCFeeWwkXnpkOMYOG4CX3vwY/3nuLQZlramefgXkar8qr7+z4+Riv2K9k82Lb3zoLvqemPZmsnepcptNmq8zPpx90uFo0awgET/VsfzQk6/ifz//gdefGI33p01EIC/PxoH/JNKmGmOemv4OnjEl7NP33uzGxV5ndMO1w+9JKGBTjV9UBnXabze88NDt+PLVuzH5tqFgWb745sfEvimUNX5NfXAamjRu4MbNd58ejxUrVtmk+FEmSRiOya88OsKNv7yTPnzSI4mwXBCqe7x6Y8bn6H/5KHQ+cA9Mu/8WkDv7B1mWdx7keYxt/NH0O+18MxFbbdYWI+NKDCrwel88HAfsvRPeenIs3vzvaPCmh3+epFKH+5h824XF+vrO/9iK3u4cOdn6T2l5uwi24RNd/S4dmeib5uX+L7/9CWaY0ueJqde7c/SB++yCi66f6M6NjLDtlh3w5N034pMXp7jz9N2PPI+Z3/7MoITJxX5GBS0npAkIKQR+1cS/dqH9n7tucLELGjVw9hsp+hWfNuL1ywXdj8MXr/4fxti1xvUj7gUnkUycqf2KZWe/vn7kffh05v/oXMeoX62DpJhHef3qr/mL0e3sy9GqRRN3ff7xC1Nw+EF7uDzK61d89nxQzxPdeY3X7mef1Bm8CcFr5vLGK7cD2wzscUKx8er04w41X1RovCqrb5Q3XqWqs9u5bXKxX1m19U9jAlK8pHHjVLRonIzcN/ZyXDnorAol4cXjpu1awTdPv/gOju96gBuw5y8sBC+2OHoC7sQAABAASURBVMifcfyh4MRwkxZNE/m+9s5n2Gf3HbDrjtuglilEzjqxs7vr//1Pv7s4fc46CjtsuxlqBQNo3bKZ82scv+BYtnyFc2/Ssomzt+jQ2uX/u93Fdh62ya9TK1Euv3ye51kIkCpvRmDZ+102EtcOPccN9vTzzey/FtrkrDFqWZmDgYCVf2s3AWM406WqM+PQtG7ZFG1t4sf6c6IXsHzon61G/SrWsuwf69OvKtLfuYdc61esc7LZf6+d8bhNBLsdtk+yd6kyxxV/XKDdvGkBqLDpZ0piJmBbpTqWX3j9Q5zY7UC0bN7YKQTPOvEw/Pf5txKvXKQaY+YtWOzGq/r18rkr7LbTNm5iuzjp1Z+yxq8G9euCkymOvYFAHtq0io2NjRrUd3lxw7KX1s+WLFuOx6e9gdOOPRS8qG5c0MCNhU+/+G6i3EzfolljtLZ8Of7u9I8t4OXFxk2G5YKpzvGKEwEq5o/qvC8G9zoRm9u5itzr5td2KMs7DzLd/nvtZOehOmhkNzQaNayPxgUNwd/q1SFa4HnV8zyLk49ddtgKv/w+1/n7m3atmxc7F9aN7ztV3kzLidLFwyaB5+jDD9qTXgnzsCke+enT7bfeFDyWBvY43k3qqfhhJPbRbbZoh/w6tXHQvru4/vbeJ18zKGFysZ+NuWEAHrYbVgkIKQSOSRybfPPex1+jXesW6LT/ru54TdWvPvzsWzemnHbMIeB1yqH77+b6wJvvfe72mKrt07lfsfB3PfQcvvnfr6V+bpfh6lekULZJ1a+Y6r7HXnDnp1uv7O2OfY4XTeJjTnn9aud/bOnOi03tBiPHhVZ2zU85z25MVLRf8ZrY7/O0G9t5iuVK1WcZTlNW3yhvvEpVZ+ZLk4v9ivWWSV8CeelbtOws2YaoFU/QvIhrEh/oKrOPjz7/Dm9/MBP9zj7aJfvym5+d/dV3v7hHFntedIfd5X3X+XEzZ95CbN5+E4rOcAJDge+L06bh44GT738GZw+6BR133BpdO+0N/jraxSUvBk/oeS0eeOIlTLr/afBkcqBd4DGcZlHhUvBRXd4Zee7V99dZs6asvKnRv+CKMTj+iANQ2gSu+6lH4KkX3nGPdL8+4zPwVar+5xzLXaK8OjPSylVr3B1qPgk09LqJqFO7Fo48NFYvhmejUb8CqtKvKtLfc7FflTxW6tWtA45f9evVLRlUrvv+J140hWoBjui0l4tb3rHM1286tG3l4nJDRQhtKjdo05Q1xnBc4StDp/a9HtNf+wDDJz8KXlRSGct0NOWNX7xzzVc/Blw11sbcY7DV5m2ZLGU/y7PJOSPxIpg2DZUMtLk/2jTPmCLm3/99BXdMfMTGunfhj28MywVTneMVlWl8Murvv1eg76Uj3Llw4r1PYWV8PZaKnAfJ/JmX3sXga8a7CWfvM7vRyxQtddDjtK7ujjL7EM9zb3/wJU4/rpML9zfsJ7zrzHNO0dK/fe+EXVreDLx9wiPgZOnKQWfSWcxwHYTkftQkPjFjfYpFNAePFd5R5lMw5kz8c7Gf8TqHk9EEhAoKbDe+kja0z0lOkVJev/rLlLuctNa26wt/F1tu2gZz5y32nc4ure05jqZrv+ITwuzHk24biob1Sx/n1a9c01ZoU7JfMdE7H840hX5zXHT9JPC11OuG3wuuBcSwivarT778n3uKc7Jdm1824AynnK1ov3p82ptuTOM4yacPud9kU1qfZXiqvlHeeJWqzsybJhf7Festk34E/BJJ8eKTyEGbd/WGT3oUZ590OPwLeX+gbt6sAOed0gW8q3v5zVPBi0MiWmIXgPl16lBMGD7+6t/dp2c4EnVPkxQtWYYlS5djqV280p8XE3y8mnf4+R7++LufxAF77+wuRBnOC5vzTEHCu4t0XzJsMm4b/xDFhCktbz7GzzVX2tpdpf7nxpQpiQRxYdutOrg7R3leHi4Zdif4KPYupghicHl1ZhxOuD7+4nt8/vVPWLLsb1e/B015xDCZ4gTUr7ZyQMrr74ykfkUK62e4NgvHkAv7nOImNcwl1bHMfkne+XY3n3FpqEClvTxpsdvSxhjGadGsiVMkN2tagDsmPeLWdDls/90Z5ExFxq+ly5aDa0XQZll5YVne+MWnZThOXjv8bvC9e46dE+97yu0zeTPzu5/BC+df/4g9OcFXQpevWJUcRXIpBNgvSp4H/5q/yMVs1qQAx3c9EMcc/i/c8+gLuHVc7HxUkfMgM/j5tznggsjhcMSdC+lHs+8e/3R3p7k+wSV2nuMTKP45mH2SSpid7C407zrzJsG5dhODCkGm9U1peT/81Kvg0xGjrr8AfLrTj+vbVFAyv3utLi++8SGopPPDkm2+qjb4mnHuzvl+e+6YHAT1s2I4UjrueWQ62rVujsMO2MPFq0i/4vWUixzf1LHxiuNF3Oms0tqeAenYr2baTTxen028dahTsLOcpZmZGr9Kw1KqX8l+xUg//TYb9evlo9N+u6L7aUeAa0pxrS+eYzheVaRfccFm3kRlmsKkxedT9Svu+/CD9rBr+Z3cPOK1dz/DCXZztaTypbQ+O7OcvlHeeJWqziwXzUz1K2JINpJrmIAULzXcADW5+1ff+RQcnHvY3bfkcvAOS7+zj8EhNoDT5l3dl9/82EXhI9OrVq92sr9ZvmIlGiTdra6bXxsjr+uP5x64FcFgwK0Jw7i8qzd66hN4cMJVePTOazHljovcUyT/MU05w3fcbnNc1PcUuLUThp6DYZd0d+GhcJjBzpSWNydgvIhs2KAuhtuE6PYJD4Nl4jvzL77xkUs39NoJ6HbYvhh9wwV47fGR2GOX7d3ihn7eqerMDHgBzDrR3DX8Ytw7+jJMvO9p8CTFcJm1BNSvhrkntcrr7ySmfkUK62f4ePI/t93cxqmOSP6VdSx7Xuy1jlWr1ySi+3K9erHXhxhQt4zxi3cBqUiecvtFePmREW6s4qKoP/zyB5OhIuMXJ9gcg6bZ2MixafrrH7jFfV+0SXCq8ev2q/rgKBu/+HoR4/qTcPYft3PbXDnoLDfuTrh5sJVvON7/9FuwD1qQ/ikIlDVeMcnAnieg84G7u8fwLx9wursBQUVNRc6DsB9fU+IaA8d3PQAXXj/BfADere4x9HYMv7Y/2FbT/30b5s5bhOtH3OfCqWhjW/I8yFd+7x97hVP0f/dj7HVeF8k2peVNhQqfmLjzgWfA8+DX3/+CGR9/7Z7wtCQ4x26yXD3kbPP7Cv957i0sX7mK3mgdfy2YjhUrV4PrtoVNWTTuxoHgq3H09w3LxvMgy/7yI+pnPpeSNhWrVHIN7nUS8vKKv/aXql/x2iU5r1WrVrvXIpP9Bvc60a3jkQn96qnpb7unEqe/+r7rk8+/9oFb+Jv9M1mhpH6V3MJly6n61RnHH+aewjz8oD1xx9V9Heeff58DjlcV6VfdDtsHk24dgtE3DMDNY//tvsBX3njFkp5/3nHuCc7+5xzjXsnjuYyLzTPMN+v2WaC8vlGR8aqsOvv7Vb/ySchOFwJSvKRLS2zkcoRMmcFHmfuefbR73cfffbvWLUAt8ppQ2PdCyOQ1oZBz8wLt11lzncyNr3jgo7h0JxvP88B1XHiioD/vxnJSxHfe6f6X3fXbs+N27i4t3SUN7zDTj/unnWw8b23eDernY5BdIPORf75XSsO4DerXdU/T8O4dFUzbbdme3u4ihsomnoh4x7G8OrtEJTb+Uzl8daBEUE471a+6OqUf+1Vl+zs7jvoVKZRv+EnmB554CbyY87y1k5ryjmVOSpPvxDEf7s0fkyj7xvPWjjH0e/+Tb0DFCSdRnIyec3IXeuPTL//n7JKbVONXQcP6bjLy1/zFaFDO+MV8uUD1kN4ngQrfcTcNcq+SHPKvjvA8j8HrGI6BfOc+ua7rRJIHyhqv/KdP/pg9L0GJ5yGeM6JRoDLnQWbA45qvhXF//utw224ROx/x1bcTux2IspRkLZs3YRZYYRNwJ5TYJOfNV2p333lbsP1p2E/5hJffvz3Pw6nHHALe9KBp3bKpnSPzscWmbVyufOWO6wwVLfkbVPgwDxdQxobh6melw5l83zPgU0sH7rNzIkJ5/aqVtTVf8eITB34ivvK2SctYH/D9fDu57dO1X3GtoGO77Jfok3wqI79OLedm//Trkmxv1H6VvOMMkEvrVyw2z03J430kEqE3Vq8JobL9iudJJuZNzcr2q1q1gmjRtHGFxqvy+obnpR6vUtWZ5S9p1K9KEpG7JghI8VIT1Kt5n7wDxxM1LwyZtZNNsUKZZuh1E916BJR9w/ce5y8scq8Z+X60O+64tbsQu/P+Z9ynTr/45ie3nsG/9tiRwe7uMj8t9+nMH7DGFDL3P/EiWrVoAr4Hzs9hUpnDSSfDmPbJ6e9gj523c2m3sQtNKnV4gcky85F7rvD/j202c+H8ygcnq7zjRmXNlAefxV4dt3cL/aXKm49Q9j7zKCQb+nU9ZG9wgUOe6Dkh4xMw1N6Tz/N294UnF164lFdnVzjbzLG7klS08NN7N4990D0qvu2WsYtnC866P9uIrNSv1vatyvSr8vq732FyrV/59fbtsN1ZZz8L25jFvkY5ErHZbTxCaePXhHufwh67bOcW+o5Hc1bHHbdOOX7xkejHn33DPanGMeWBJ14G7xp7nuc+55tq/PrHtpth2ssz3KtCPDZeefsTt8/99trJ2anGr8+++sE9vUdFNZ9WedLuAnOCxTWv2KeSxy7K9PPHL2bOslKBzPHr3/99GR989i16n3U0gxJm/sJCzJ67AN//NAt8z575773rPxLh2S6wTdh32IdYVydbn6JMc+9jL4CfsqXsm7LOg1SM7b/Xjhh/z5OgsoXnKj4hctgBu7unFw7ZryPKOg8yb/Ln+Y9rwvCzzvc8Ot2dy7gOzRabtmYUsB1ZRio7uM4BFSYMePO9L8CnmtjW3PeYu/7j+vR2W3VgsGvbsvI+xZQq7D++2W6rTbGrHRP0Z2IeV+wnLBfPw1P//Ry4wC6f8lq+YhXOPP9GOzYW44ZLuuPvFSvBsnN8YlrfMH2u9TNez6xeE3tSjjKNz6O0fsWnDB6f9gaG9DrJj+bsVP2KCl2OaYz48FOvOqUgxxhec/DrU/TPxH7FazC/P9I+cO+d7ZqxKShznGO9aNSvwu6amixouP7iib2udU+t0E1TVr9iWNdOe7kPY/CY5djBcxufiNxqs7buXMk4ZfUrno9ee+dT9zQen0Li09xsG6Ytb7yisofHAK/ZeVxMe/k99yQ9r925z1R9try+kWq8Yt6p6sxwmlzsV6y3TPoSyEvfoqlkFSXw06+zscthPXHZzVPcF4Yoc1E+P/0vv892F+S+m4/X85Orvc/sBl4I+P60eWds7LABuO/xF7FTp+44vf8wt+jfyUcfxGD39QU+JXPWgJuwy6E98OjTr2PEtf3dxajneeBjzfykHcOYlo9pn3tKF5e2y8F7ui973Dj6AezZtR/OHXyZ8VrZAAAOY0lEQVQL+Dj1GSccBv7mzluIswfejN279Eank4a6Cw9eADLM81LnzTipzOgbLgDX3Nj3qPOx79EXgCew267sA14Il1dn5su7lYeePBSHn3Yxelx4B8jw7lGXundqGZ6N5if1q3KbNVW/Kq+/M/Nc7Fesd7L5z3NvgmMWv07EBbApP/XC24koJccvKhaefWmGe8otESkulHcs8xOXvLt/8ImDsdeR/cCJ74Dux7vUnpd6jBnU4wR02m83nNDzGjd+UTHMMYRP2jGDVOMXx5lJ9z0F7rdj515uEcJLzz/NraHFtOUZTrT37NoXHL845j44/kr3alNyOioVDjv1Ihzf42pw8j7+5kHYcfstkqNktVzeeDV/QSGSX9fhGF7WeZCgrhp8Nnh87nFEX/BTwlTgXzX4LAalPA8yAic/PP/tdnhvdLY2CeTlOWUGw9hf+KrOe598485FPNfxJsC1F57DYLtDvcb6x92urbnv6a+9D77y45+rU+XtMkixWblqFQ46YTBYLi5if3H/U3HWiZ1dCk64fvpttpvosQ+x3DQn977WhfubXOxnPO66nH6JQ9D1zEtxiI0fzmGbkv3KvDD+7v86pTCf6KU72VyVol9xIVM+0XbbhIexc6ceGHT1OLDP8cYR80jV9unar1juihj1q+L9iusmfvvDb6CS1OeXql+defxh2MsU7Z1tvGF/feuDL9yrjFSqltevqDDhgu9Mt3e3/nhjxmduzOGTluX1K5aNCydzHNvF5gSX3nQnks9tqfos06YyqcYrpktVZ4bT5GK/Yr1l0peAFC/p2zYVLhm/jPH1G/ci2dx6Re9E+ifvvhEjrzs/4ebifW8/Nc4pPRKeSQI/l/zetAl46ZHh+Gj6ZPAdSU4cGMXzPHCi8smLU9w6Ah88Nwkd/7k1g5wS4omp1+PD5yeD761/NP1O3HRZT/cFIEbIy/Pce6AvPnwHnr7nRrzy6Ej3qgA/X8dwvs/OfF946Ha8+/R4966of8HBi95UeTN9svlo+mQkP+LLRxLHDhvoyvbKoyPA91iTJyWp6nxU532LsWXefL99683bJe8y62T1q3WblG1f0X5VXn/P1X5VkurJRx9c7PjiOManUPx4JccvPmXGOP6448fz7VTHMscRHvsznp2AN/87xq011bJ5Y5eUYanGGD6mfN1F54Jj47P33wzG5TvxLrFtUo1fHGveenIs3nl6HDi+ff7K/63ztKFlkfiX7Gd77bq9S0f/Z+67OTHmMgHHSPJINlxD6+B9OzI4Q03li13eeHWxKRnIz8+5vPMgubKNX39iNHi+pKKdX+Fjes8r+zzI8JvsvPfZS1PBcx3PZVSUMT+G0XANBvo9/+Ctrk8wPr/sxTA+VcM+9trjo0DDfrP3bmufXGLcVHkzD9+MvK4/2C99N+9iv/r4SDBP1unkow7yg8AnV5P7kC8zHiOx/L6fb+dKP2O/8etM22dCLiX7Ff1G2vUW14KjXNKQY1n9inH5CuGXr97trr8+f/kunHZsJ3o7U17bp2O/cgVP2nC8Z/19L/Ig02SjfgUcbOM3mfB857NK1a94Y3H4Nf3s/DTRrq1H4NXHRrpX3fy0qfoVx4EvXv0/N95wfOC1efKYk6pf8VVJ7otjCs9tzIcf7PD3W16f9ePRLtk3Uo1XjJ+qzrncr8hGJn0JSPGSvm1ToyWjooWabg58pRWE743zfWVOLkuGcwLDwZha9pJhvrustMyXn3jlJMePm2xXJO/k+CVlpi9oVL+kt3OXV2cXSZsqESiPMdu/rL7BtsvEfkVgZdWJYTLVT6C8fsanB/xJdMm9F+tnJQPNzbz9SbI5i/3Zf8savzzPQ5OChmC4r2wuljiFg/tkurLG4xRJFVRFAlTMNW3csNRc2N5lHducFPDiv6xzGTNs0axx4sYE3b5he1MRQuN5nu+dsCuSdyJyCYF9t1mTRiV85dzYBFL1K659wusvrpdRslwVaXv1q5LUcsfNpz5bt2pW6vpfqfqVP+ZwfCjtup4Ey+pXnueBYwrPUcyHcZNNRfpscvxkmeVh3sl+JeVUdS4ZV24RqGkCUrzUdAto/yIgAiJQDgEFi4AIiIAIiIAIiIAIiIAIZC4BKV4yt+1UchHY2AS0PxEQAREQAREQAREQAREQAREQgUoSkOKlksAUPR0IqAwiIAIiIAIiIAIiIAIiIAIiIAIikBkEpHipSjsprQiIgAiIgAiIgAiIgAiIgAiIgAiIQPYTqEINpXipAjwlFQEREAEREAEREAEREAEREAEREIGNSUD7yjwCUrxkXpupxCIgAiIgAiIgAiIgAiIgAiJQ0wS0fxEQgQoSkOKlgqAUTQREQAREQAREQAREQAREIB0JqEwiIAIikN4EpHhJ7/ZR6URABERABERABERABDKFgMopAiIgAiIgAqUQkOKlFCjyEgEREAEREAEREIFMJqCyi4AIiIAIiIAIpA8BKV7Spy1UEhEQAREQARHINgKqjwiIgAiIgAiIgAjkPAEpXnK+CwiACIiACOQCAdVRBERABERABERABERABGqGgBQvNcNdexUBEchVAqq3CIiACIiACIiACIiACIhAThGQ4iWnmluVFYG1BCSJgAiIgAiIgAiIgAiIgAiIgAhseAJSvGx4xtpDagIKFQEREAEREAEREAEREAEREAEREIGsJSDFS6JpJYiACIiACIiACIiACIiACIiACIiACGQ/gY1bQyleNi5v7U0EREAEREAEREAEREAEREAEREAEYgS0zQkCUrzkRDOrkiIgAiIgAiIgAiIgAiI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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "\n", "\n", "start_idx = 10000\n", "end_idx = start_idx+500\n", "\n", "# Создаем субграфики с двойной вертикальной осью\n", "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n", "fig.add_trace(go.Scatter(x=x[start_idx:end_idx], y=prices[start_idx:end_idx], mode='lines+markers', name=\"Line\"),secondary_y=False,)\n", "fig.add_trace(go.Bar(x=x[start_idx:end_idx], y=balance[start_idx:end_idx], name=\"Bar\", marker_color='red',), secondary_y=True,)\n", "fig.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "ecf34523-9e9a-4f3f-a0c8-308cedefefff", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "15459f27-bff5-403a-b0c9-22018d7acd51", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "7ed61f2e-47ca-4a22-b95b-077d3d678cea", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0bfb48d8-8472-4325-b459-3796615da3da", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "9bf0e249-c38a-44e6-b19e-b00762aa5a0e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "06fbe4da-11c3-4103-8e0c-0ddf35f7176e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "1463e24a-8329-4b57-8502-05fa85c84da0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "9174b812-232c-4490-ad7e-c6b5a289fe53", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ab2da23e-6cf5-4eff-9627-52bf717404cf", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "9872b5fa-e30e-430c-bf38-c841c3d7caac", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "4848d00e-565d-49a4-b87a-03dca4ba2598", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "b0177e6b-13d6-4952-9fbc-f2db57f8b185", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 57, "id": "c0343502-c2ab-48d1-ab46-a035420a2cee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'symbol': 'BTCUSDT',\n", " 'timestamp': 1756684801229,\n", " 'lowest_ask': 108241.8,\n", " 'highest_bid': 108241.7,\n", " 'bids': {'108241.7': 1.041892,\n", " '108240.1': 0.003,\n", " '108239.7': 0.000433,\n", " '108239.3': 0.039114,\n", " '108239.2': 0.098379,\n", " '108239.1': 0.003,\n", " '108237.7': 0.009,\n", " '108237.4': 0.13,\n", " '108237.3': 0.006,\n", " '108237.2': 0.03,\n", " '108236.9': 0.191506,\n", " '108236.8': 0.092382,\n", " '108236.7': 0.003,\n", " 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