回测连续上涨和下跌(税费)(参数)(指标)(添加因子).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import  backtrader as bt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 1000000.00\n"
     ]
    }
   ],
   "source": [
    "#Create an instance of engine, the engine 创建引擎\n",
    "engine = bt.Cerebro()\n",
    "\n",
    "# Set our desired cash start 设置初始资金,默认10000\n",
    "startcash=1000000\n",
    "engine.broker.setcash(startcash)\n",
    "print('Starting Portfolio Value: %.2f' % engine.broker.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据加载 dataframe 日期一定要asc排列\n",
    "### sma 移动平均线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"query\" : \"SELECT close,high,low,open,time_key, volume,create_time FROM kline_day_b where code='HK.01810' and  1550827319>time_key and time_key>1519291319  order by create_time asc\"}\n",
      "             close   high    low   open    time_key     volume\n",
      "create_time                                                   \n",
      "2018-07-09   16.80  17.00  16.00  16.60  1531065600  462182642\n",
      "2018-07-10   19.00  19.34  17.00  17.00  1531152000  534072218\n",
      "2018-07-11   19.00  19.80  18.30  18.50  1531238400  374721138\n",
      "2018-07-12   19.26  19.70  19.02  19.40  1531324800  132592630\n",
      "2018-07-13   21.45  21.80  19.52  19.60  1531411200  364065075\n",
      "2018-07-16   21.05  21.30  19.40  19.94  1531670400  211621432\n",
      "2018-07-17   20.90  21.70  20.55  21.45  1531756800   97690237\n",
      "2018-07-18   21.55  22.20  21.40  21.40  1531843200  142583771\n",
      "2018-07-19   20.10  21.90  19.82  21.70  1531929600  153563433\n",
      "2018-07-20   19.88  20.45  19.50  20.10  1532016000  111713800\n",
      "2018-07-23   19.02  19.98  19.00  19.88  1532275200   82544900\n",
      "2018-07-24   18.24  19.70  18.20  19.10  1532361600  113828488\n",
      "2018-07-25   18.58  18.88  18.18  18.56  1532448000   72027810\n",
      "2018-07-26   18.30  18.84  18.12  18.80  1532534400   40951672\n",
      "2018-07-27   19.04  19.12  18.30  18.32  1532620800   68204685\n",
      "2018-07-30   18.68  18.92  18.32  18.86  1532880000   43124305\n",
      "2018-07-31   17.60  18.48  17.42  18.42  1532966400  100190678\n",
      "2018-08-01   17.86  18.26  17.56  17.94  1533052800   42088808\n",
      "2018-08-02   17.26  18.14  16.96  18.00  1533139200   80125110\n",
      "2018-08-03   17.00  17.68  16.90  17.32  1533225600   47386901\n",
      "2018-08-06   17.22  17.68  17.10  17.20  1533484800   39896098\n",
      "2018-08-07   17.42  17.62  17.22  17.30  1533571200   29000537\n",
      "2018-08-08   17.14  17.62  17.02  17.50  1533657600   37947677\n",
      "2018-08-09   18.08  18.60  17.26  17.40  1533744000  109541974\n",
      "2018-08-10   18.16  18.60  18.00  18.30  1533830400   54535948\n",
      "2018-08-13   17.44  18.06  17.24  17.98  1534089600   49053111\n",
      "2018-08-14   17.16  17.60  17.00  17.60  1534176000   51105928\n",
      "2018-08-15   16.30  17.34  16.22  17.28  1534262400   79753065\n",
      "2018-08-16   16.24  16.60  15.52  16.16  1534348800   60546391\n",
      "2018-08-17   16.36  16.74  16.26  16.60  1534435200   26655983\n",
      "...            ...    ...    ...    ...         ...        ...\n",
      "2019-01-08   11.10  12.16  11.06  12.16  1546876800   91106400\n",
      "2019-01-09   10.34  11.10  10.34  10.50  1546963200  240922422\n",
      "2019-01-10    9.97  10.04   9.44   9.63  1547049600  376414318\n",
      "2019-01-11   10.34  10.52  10.00  10.18  1547136000  126655283\n",
      "2019-01-14   10.02  10.48  10.00  10.42  1547395200   54264395\n",
      "2019-01-15    9.96  10.16   9.83  10.06  1547481600   82269650\n",
      "2019-01-16    9.70   9.94   9.59   9.70  1547568000  367223812\n",
      "2019-01-17    9.74   9.95   9.67   9.81  1547654400   81046985\n",
      "2019-01-18   10.16  10.24   9.89   9.97  1547740800  139896858\n",
      "2019-01-21   10.30  10.48  10.22  10.22  1548000000   86294625\n",
      "2019-01-22   10.00  10.40   9.92  10.38  1548086400   59735038\n",
      "2019-01-23    9.97  10.02   9.89   9.93  1548172800   37825844\n",
      "2019-01-24   10.00  10.12   9.92  10.00  1548259200   41246163\n",
      "2019-01-25   10.10  10.26  10.02  10.04  1548345600   64613994\n",
      "2019-01-28   10.02  10.22   9.94  10.20  1548604800   44290538\n",
      "2019-01-29    9.91   9.99   9.86   9.92  1548691200   33530991\n",
      "2019-01-30    9.84   9.95   9.84   9.90  1548777600   32802193\n",
      "2019-01-31    9.84   9.98   9.82   9.95  1548864000   50076676\n",
      "2019-02-01   10.44  10.44   9.87   9.90  1548950400  127579302\n",
      "2019-02-04   10.36  10.50  10.28  10.46  1549209600   49681936\n",
      "2019-02-08   10.26  10.32  10.00  10.30  1549555200   57644100\n",
      "2019-02-11   10.70  10.90  10.22  10.34  1549814400  140732181\n",
      "2019-02-12   10.60  11.10  10.54  10.94  1549900800  117906288\n",
      "2019-02-13   11.38  11.42  10.60  10.72  1549987200  156224007\n",
      "2019-02-14   11.52  11.56  11.20  11.36  1550073600  108811783\n",
      "2019-02-15   11.14  11.46  11.08  11.42  1550160000   82217458\n",
      "2019-02-18   11.22  11.50  11.20  11.38  1550419200   63590558\n",
      "2019-02-19   10.80  11.16  10.62  11.10  1550505600  129955818\n",
      "2019-02-20   10.84  11.00  10.66  10.92  1550592000   96070124\n",
      "2019-02-21   11.38  11.52  10.94  10.98  1550678400  152831181\n",
      "\n",
      "[151 rows x 6 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<backtrader.feeds.pandafeed.PandasData at 0x7fa31e357e80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests,json,datetime\n",
    "import arrow\n",
    "start_ = arrow.now().timestamp\n",
    "end_ = arrow.now().shift(years=-1).timestamp\n",
    "\n",
    "url = \"http://es:9200/_xpack/sql\"\n",
    "\n",
    "querystring = {\"format\":\"json\"}\n",
    "\n",
    "payload = \"\"\"{\"query\" : \"SELECT close,high,low,open,time_key, volume,create_time FROM kline_day_b where code='HK.01810' and  %s>time_key and time_key>%s  order by create_time asc\"}\"\"\"%(start_,end_)\n",
    "print(payload)\n",
    "headers = {\n",
    "    'content-type': \"application/json\",\n",
    "    'authorization': \"Basic ZWxhc3RpYzpMT09QMnRoZW1vb24=\",\n",
    "    'cache-control': \"no-cache\",\n",
    "    'postman-token': \"88a2e398-4dc0-439a-deae-3902fa9f4fa0\"\n",
    "    }\n",
    "\n",
    "response = requests.request(\"POST\", url, data=payload, headers=headers, params=querystring)\n",
    "data = json.loads(response.content)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data['rows'],columns=[i['name'] for i in data['columns']])\n",
    "df['create_time'] = df['create_time'].apply(lambda i : datetime.datetime.utcfromtimestamp(arrow.get(i).timestamp))\n",
    "df.set_index(['create_time'],inplace=True,)\n",
    "df.index = pd.DatetimeIndex(df.index)\n",
    "print(df)\n",
    "\n",
    "startDay = datetime.datetime.strptime('2018-7-9','%Y-%m-%d')\n",
    "endDay = datetime.datetime.strptime('2019-2-21','%Y-%m-%d')\n",
    "\n",
    "\n",
    "\n",
    "data1 = bt.feeds.PandasData(dataname= df,fromdate = startDay,\n",
    "                           todate = endDay,)\n",
    "engine.adddata(data1, name='HK.01810')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-27, Close, 19.04\n",
      "2018-07-30, Close, 18.68\n",
      "2018-07-31, Close, 17.60\n",
      "2018-08-01, Close, 17.86\n",
      "2018-08-02, Close, 17.26\n",
      "2018-08-03, Close, 17.00\n",
      "2018-08-06, Close, 17.22\n",
      "2018-08-07, Close, 17.42\n",
      "2018-08-08, Close, 17.14\n",
      "2018-08-09, Close, 18.08\n",
      "2018-08-10, Close, 18.16\n",
      "2018-08-10, BUY CREATE, 18.16\n",
      "2018-08-13, BUY EXECUTED, Price: 17.98, Cost: 179800.00, Comm 179.80\n",
      "2018-08-13, Close, 17.44\n",
      "2018-08-13, SELL CREATE, 17.44\n",
      "2018-08-14, SELL EXECUTED, Price: 17.60, Cost: 179800.00, Comm 176.00\n",
      "2018-08-14, OPERATION PROFIT, GROSS -3800.00, NET -4155.80\n",
      "2018-08-14, Close, 17.16\n",
      "2018-08-15, Close, 16.30\n",
      "2018-08-16, Close, 16.24\n",
      "2018-08-17, Close, 16.36\n",
      "2018-08-20, Close, 16.46\n",
      "2018-08-21, Close, 17.40\n",
      "2018-08-21, BUY CREATE, 17.40\n",
      "2018-08-22, BUY EXECUTED, Price: 17.78, Cost: 177800.00, Comm 177.80\n",
      "2018-08-22, Close, 17.68\n",
      "2018-08-23, Close, 17.44\n",
      "2018-08-24, Close, 17.04\n",
      "2018-08-24, SELL CREATE, 17.04\n",
      "2018-08-27, SELL EXECUTED, Price: 17.34, Cost: 177800.00, Comm 173.40\n",
      "2018-08-27, OPERATION PROFIT, GROSS -4400.00, NET -4751.20\n",
      "2018-08-27, Close, 17.32\n",
      "2018-08-27, BUY CREATE, 17.32\n",
      "2018-08-28, BUY EXECUTED, Price: 17.60, Cost: 176000.00, Comm 176.00\n",
      "2018-08-28, Close, 17.12\n",
      "2018-08-28, SELL CREATE, 17.12\n",
      "2018-08-29, SELL EXECUTED, Price: 17.12, Cost: 176000.00, Comm 171.20\n",
      "2018-08-29, OPERATION PROFIT, GROSS -4800.00, NET -5147.20\n",
      "2018-08-29, Close, 17.14\n",
      "2018-08-30, Close, 17.14\n",
      "2018-08-30, BUY CREATE, 17.14\n",
      "2018-08-31, BUY EXECUTED, Price: 16.88, Cost: 168800.00, Comm 168.80\n",
      "2018-08-31, Close, 16.80\n",
      "2018-08-31, SELL CREATE, 16.80\n",
      "2018-09-03, SELL EXECUTED, Price: 16.80, Cost: 168800.00, Comm 168.00\n",
      "2018-09-03, OPERATION PROFIT, GROSS -800.00, NET -1136.80\n",
      "2018-09-03, Close, 16.50\n",
      "2018-09-04, Close, 16.74\n",
      "2018-09-05, Close, 16.56\n",
      "2018-09-06, Close, 16.46\n",
      "2018-09-07, Close, 16.50\n",
      "2018-09-10, Close, 16.04\n",
      "2018-09-11, Close, 16.12\n",
      "2018-09-12, Close, 15.66\n",
      "2018-09-13, Close, 16.40\n",
      "2018-09-14, Close, 16.68\n",
      "2018-09-14, BUY CREATE, 16.68\n",
      "2018-09-17, BUY EXECUTED, Price: 16.50, Cost: 165000.00, Comm 165.00\n",
      "2018-09-17, Close, 16.42\n",
      "2018-09-17, SELL CREATE, 16.42\n",
      "2018-09-18, SELL EXECUTED, Price: 16.42, Cost: 165000.00, Comm 164.20\n",
      "2018-09-18, OPERATION PROFIT, GROSS -800.00, NET -1129.20\n",
      "2018-09-18, Close, 16.28\n",
      "2018-09-19, Close, 16.56\n",
      "2018-09-19, BUY CREATE, 16.56\n",
      "2018-09-20, BUY EXECUTED, Price: 16.54, Cost: 165400.00, Comm 165.40\n",
      "2018-09-20, Close, 16.48\n",
      "2018-09-21, Close, 16.76\n",
      "2018-09-24, Close, 16.50\n",
      "2018-09-26, Close, 16.36\n",
      "2018-09-26, SELL CREATE, 16.36\n",
      "2018-09-27, SELL EXECUTED, Price: 16.38, Cost: 165400.00, Comm 163.80\n",
      "2018-09-27, OPERATION PROFIT, GROSS -1600.00, NET -1929.20\n",
      "2018-09-27, Close, 15.96\n",
      "2018-09-28, Close, 15.50\n",
      "2018-10-02, Close, 15.00\n",
      "2018-10-03, Close, 14.88\n",
      "2018-10-04, Close, 14.50\n",
      "2018-10-05, Close, 14.28\n",
      "2018-10-08, Close, 13.58\n",
      "2018-10-09, Close, 13.58\n",
      "2018-10-10, Close, 13.76\n",
      "2018-10-11, Close, 12.66\n",
      "2018-10-12, Close, 13.06\n",
      "2018-10-15, Close, 12.48\n",
      "2018-10-16, Close, 12.30\n",
      "2018-10-18, Close, 12.34\n",
      "2018-10-19, Close, 12.28\n",
      "2018-10-22, Close, 12.98\n",
      "2018-10-23, Close, 12.32\n",
      "2018-10-24, Close, 12.26\n",
      "2018-10-25, Close, 12.68\n",
      "2018-10-26, Close, 12.28\n",
      "2018-10-29, Close, 12.02\n",
      "2018-10-30, Close, 11.66\n",
      "2018-10-31, Close, 12.18\n",
      "2018-11-01, Close, 12.34\n",
      "2018-11-02, Close, 13.48\n",
      "2018-11-02, BUY CREATE, 13.48\n",
      "2018-11-05, BUY EXECUTED, Price: 13.06, Cost: 130600.00, Comm 130.60\n",
      "2018-11-05, Close, 13.08\n",
      "2018-11-06, Close, 13.24\n",
      "2018-11-07, Close, 13.30\n",
      "2018-11-08, Close, 13.72\n",
      "2018-11-09, Close, 13.20\n",
      "2018-11-12, Close, 13.14\n",
      "2018-11-13, Close, 13.30\n",
      "2018-11-14, Close, 13.16\n",
      "2018-11-15, Close, 13.06\n",
      "2018-11-22, Close, 14.32\n",
      "2018-11-23, Close, 14.44\n",
      "2018-11-26, Close, 14.30\n",
      "2018-11-27, Close, 13.82\n",
      "2018-11-28, Close, 14.50\n",
      "2018-11-29, Close, 14.20\n",
      "2018-11-30, Close, 14.28\n",
      "2018-12-03, Close, 14.86\n",
      "2018-12-04, Close, 14.64\n",
      "2018-12-05, Close, 14.30\n",
      "2018-12-06, Close, 14.00\n",
      "2018-12-06, SELL CREATE, 14.00\n",
      "2018-12-07, SELL EXECUTED, Price: 13.96, Cost: 130600.00, Comm 139.60\n",
      "2018-12-07, OPERATION PROFIT, GROSS 9000.00, NET 8729.80\n",
      "2018-12-07, Close, 13.64\n",
      "2018-12-10, Close, 13.28\n",
      "2018-12-11, Close, 13.38\n",
      "2018-12-12, Close, 13.52\n",
      "2018-12-13, Close, 13.76\n",
      "2018-12-14, Close, 13.54\n",
      "2018-12-17, Close, 13.52\n",
      "2018-12-18, Close, 13.26\n",
      "2018-12-19, Close, 13.26\n",
      "2018-12-20, Close, 13.08\n",
      "2018-12-21, Close, 13.68\n",
      "2018-12-24, Close, 13.16\n",
      "2018-12-27, Close, 12.92\n",
      "2018-12-28, Close, 12.94\n",
      "2018-12-31, Close, 12.92\n",
      "2019-01-02, Close, 12.20\n",
      "2019-01-03, Close, 11.76\n",
      "2019-01-04, Close, 11.96\n",
      "2019-01-07, Close, 12.00\n",
      "2019-01-08, Close, 11.10\n",
      "2019-01-09, Close, 10.34\n",
      "2019-01-10, Close, 9.97\n",
      "2019-01-11, Close, 10.34\n",
      "2019-01-14, Close, 10.02\n",
      "2019-01-15, Close, 9.96\n",
      "2019-01-16, Close, 9.70\n",
      "2019-01-17, Close, 9.74\n",
      "2019-01-18, Close, 10.16\n",
      "2019-01-21, Close, 10.30\n",
      "2019-01-22, Close, 10.00\n",
      "2019-01-23, Close, 9.97\n",
      "2019-01-24, Close, 10.00\n",
      "2019-01-25, Close, 10.10\n",
      "2019-01-28, Close, 10.02\n",
      "2019-01-29, Close, 9.91\n",
      "2019-01-30, Close, 9.84\n",
      "2019-01-31, Close, 9.84\n",
      "2019-02-01, Close, 10.44\n",
      "2019-02-01, BUY CREATE, 10.44\n",
      "2019-02-04, BUY EXECUTED, Price: 10.46, Cost: 104600.00, Comm 104.60\n",
      "2019-02-04, Close, 10.36\n",
      "2019-02-08, Close, 10.26\n",
      "2019-02-11, Close, 10.70\n",
      "2019-02-12, Close, 10.60\n",
      "2019-02-13, Close, 11.38\n",
      "2019-02-14, Close, 11.52\n",
      "2019-02-15, Close, 11.14\n",
      "2019-02-18, Close, 11.22\n",
      "2019-02-19, Close, 10.80\n",
      "2019-02-20, Close, 10.84\n",
      "2019-02-21, Close, 11.38\n",
      "Final Portfolio Value: 999575.80\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Create a Stratey\n",
    "class TestStrategy(bt.Strategy):\n",
    "    params = (\n",
    "        ('maperiod', 15),\n",
    "    )\n",
    "\n",
    "    def log(self, txt, dt=None):\n",
    "        ''' Logging function fot this strategy'''\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "        print('%s, %s' % (dt.isoformat(), txt))\n",
    "\n",
    "    def __init__(self):\n",
    "        # Keep a reference to the \"close\" line in the data[0] dataseries\n",
    "        self.dataclose = self.datas[0].close\n",
    "\n",
    "        # To keep track of pending orders and buy price/commission\n",
    "        self.order = None\n",
    "        self.buyprice = None\n",
    "        self.buycomm = None\n",
    "\n",
    "        # Add a MovingAverageSimple indicator\n",
    "        self.sma = bt.indicators.SimpleMovingAverage(\n",
    "            self.datas[0], period=self.params.maperiod)\n",
    "    #告诉回测系统 现在的指令是买还是卖或者不操作\n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # Buy/Sell order submitted/accepted to/by broker - Nothing to do\n",
    "            return\n",
    "\n",
    "        # Check if an order has been completed\n",
    "        # Attention: broker could reject order if not enough cash\n",
    "        if order.status in [order.Completed]:\n",
    "            if order.isbuy():\n",
    "                self.log(\n",
    "                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                    (order.executed.price,\n",
    "                     order.executed.value,\n",
    "                     order.executed.comm))\n",
    "\n",
    "                self.buyprice = order.executed.price\n",
    "                self.buycomm = order.executed.comm\n",
    "            else:  # Sell\n",
    "                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n",
    "                         (order.executed.price,\n",
    "                          order.executed.value,\n",
    "                          order.executed.comm))\n",
    "\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "\n",
    "        self.order = None\n",
    "    #告诉回测系统 现在是否可以交易\n",
    "    def notify_trade(self, trade):\n",
    "        if not trade.isclosed:\n",
    "            return\n",
    "\n",
    "        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n",
    "                 (trade.pnl, trade.pnlcomm))\n",
    "\n",
    "    def next(self):\n",
    "        # Simply log the closing price of the series from the reference\n",
    "        self.log('Close, %.2f' % self.dataclose[0])\n",
    "\n",
    "        # Check if an order is pending ... if yes, we cannot send a 2nd one\n",
    "        # 检查是否在买卖\n",
    "        if self.order:\n",
    "            return\n",
    "\n",
    "        # Check if we are in the market\n",
    "        #检查是否可以交易\n",
    "        if not self.position:\n",
    "\n",
    "            # close 价格大于sma \n",
    "            if self.dataclose[0] > self.sma[0]:\n",
    "\n",
    "                # BUY, BUY, BUY!!! (with all possible default parameters)\n",
    "                self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # Keep track of the created order to avoid a 2nd order\n",
    "                self.order = self.buy()\n",
    "\n",
    "        else:\n",
    "\n",
    "            if self.dataclose[0] < self.sma[0]:\n",
    "                # SELL, SELL, SELL!!! (with all possible default parameters)\n",
    "                self.log('SELL CREATE, %.2f' % self.dataclose[0])\n",
    "\n",
    "                # Keep track of the created order to avoid a 2nd order\n",
    "                self.order = self.sell()\n",
    "\n",
    "\n",
    "engine.addsizer(bt.sizers.FixedSize,stake=10000)\n",
    "engine.broker.setcommission(commission=0.001) #交易费 按照小数点算\n",
    "engine.addstrategy(TestStrategy,maperiod=15)#自定义一些参数 \n",
    "\n",
    "\n",
    "engine.run()\n",
    "print('Final Portfolio Value: %.2f' % engine.broker.getvalue())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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KLuwFQTWJ3yMcY+GaafDFwfcH3xDSDLAv9RukIzoakK8vTWO3ylp2qgRoaaCRIVvShXNC//y5QQ1Jbr9s19qN5SQofxLc1HFxIN9cO/Xqwfhc6drXmULOJrrNyHw3S54djMAJfz2wdktpy0szebmYC6sbW5hmWhCcb8eQVivsXD/vA3yTx2UiVYGjBr1KlCGJChmEK0kKGYA0cYISuTsVYiasRIF5g5Hnk9bJFtg4tNnuFOsPrJ6F4HP+c3mBeC/3kh6OUYWjzzwvZY6qVAz+L2eY/FoxxHxOqVkyGE4peIc4Ve2ORLjE1Ai8KCWdIUPnIbYjbtRZPMc5KxXPcXhlDc6dz9thDpzx6qZUobWCarbHU+v3oLvjZ3IkjflGKVh/acgbvNeFPllHnLWqSrmG718Mx6MWYR84/vBo39Hk9dAkqcbXyO89Zd4euZvFDq2HlmLp2xaqAy6a9cuNGOoFZXFmm/RjB0rs9T/7bff8BE9xAE8/ecOghv2Qo7676DoT7NVSiljqnEP0seN2hRbLOY4NPN56ufbGE4uWVHi1h5bXKZNzMkcWNvEQjLxJJ5cvYXgut3TZV1h67AwYJYSHINzjYuLEwkXbbVIEy8zSsbX1xfZs2cXTj+MQFC+fHn88MMPcE9HTEb2tX37dlRiXEczFvY5b948MS+GkGOiHDpBXb16VRuLmplCo6OjMZ8Buy1Qjk+Yi9FTJuHX6Qvg+VXy5D3PQ8IR8FZrZH+7PLz2Ws7p0r9iK7wIe6AhFbq2QEau+e5HX+Lx6b9FK99/diBL/uQJKFMjXQ+mfIvo+etQcPE3yN3+gxRHjvh6HmJWbkXAly1Rf9xQI2dn/eoh7Ych4fAZeB1fg+yvpxzSLCVzEP3ABeaeeWD1jnjmFywIonOONALIq3AcNvfcZH+GEdA93L2IiYN/qaZwec0H3id/NNxY1kgVAXMcms2pkc7MojIH1pkZH1tY28uEJ7jj0xAuXoXhc2GLLUzJbHOgBphRxJRgG/Pnz8PgwTphvcw2knk6kgTFPDhqDqc6pIJvlDp06CAyYo6iXk2vMLIBHZ5TK+YgKCmNwaQ7jJywdu1aMTQJCpP0vP322/iZwfQBWJqgxO06jo9bfYL+rdqj1calySB48vcN8XY0Z9P3UGTtNDNKKGlXHINj+VzbgawF0p/VPvCdjnh2RxO4XNdUS3c0LemqWgFee5ZrLz387mfcH7UAecd+Ac8BnVNc673PRuPRrmO4PaozGg/5wmJ4mKvj8AFTEbtpD4psmYucdaul2G1QnW7iLbjvzd3I4qHJHJXwx0WEfDwAbq0bodBy82uK7vg0hlPWLChx2/AbXnmQMNduMF8/+jLxK90UJP6WMgM138xtuydz7PUnV24huF535GxUE0U2aLJCy5IcAXNgLXG1PAL/vN4M2R7EaiKNGniZZfnZWG4EtRZKt2/fxpdffonTp0+L5IuDBg0SGeSVwohes2bNEokcCxQogAULFojQ5Sz0c+nVq5c4Z+bLlw9jxowRIYmVktZ1SVDMKHtdUkENCmM+00ykX79+WL16tdBUFCtWDNeuXRNZOln/iy++ELGmWUaOHIlOjBepR3YYf5obgNoPhnObO3cuNm3aJMKw5c2bF8uWLUOZMmVSHKM+Q0voFGpkGBpOyehJglKuXDnRx08//YQaNWokISgkOSRYNHlhYQg6bj5uUhKZ69evIz4+HtzAnMuWLVu0cbNTm+fjPy/j24ZtcTB/Fuy4czWZBB4dOIV7nUYgT9cWKDBnuBkllLSr0I7DEX8wqf9Hega7U6IJEuM06ZiL/DwPOd/XmCzplscXr+Nuo8+R68PaKLxmqvaS1t71i3bIP2lAisPf/bAvHp+9ghsze+PD7p+mZ4pWbfNg8gpEL/gxTa2QMHd7lIASQa/M3Z7eDkJQjU5wfa8Kim1bYNY5v4yLh1+JJnAp4wPvPwy/cZcHCbPCb5bO9GWiNSVMRWtplkEdoBNz7PVH+07iXpeRyPPZxygw65VPkAPAZ9QSzYG1UQPKyulC4GLdT5H7qj+8jq5C9gql09WHPTRSQ1B4BqxYsaLIIM8z3507d9CoUSNxhuV59dtvvxVnUp5vK1eujIiICJGRntnlWZgDpVSpUiIM8ZUrV8TZk/cBvzd0XRIUM+4iXROvgIAAlC1bFkePHoWrq6sgDyQqFy5cEISAhYf9Ll26oH///ggKCkLVqlVx5MgRYRrGvrZt2yZIA4XKv8zEuWHDBhw4cEAQFmpgSFpmz56NEydOpDiG7vJIaEgsmDgnD2Np/qdBIWnx9PQUfTIWtq4GRcn2uX//fmTLlk1sSG68qVOninqMW33u3DnBjHv06IFChQph2rRpac7zmd9dnH67JT5+dAWRT5KHy4lZvwsRg6fDc2g35B3Zy4wSStqV1v8jFVKhZuCX8Y/h59NIW7XgsjHI3aZxsqaP9p/Evc7Jf8ATTl9CyEf94fZJAxT6dnyKQwZUbYfnAaG4unwIWrRupWZaGVpHqxUa8wU8BybXCqVm7mYsiTBmkU9vByKoRmfV5EceJIxB1zp19WUS2vV/iN9zAsUOfAfXSppnqizGI2COvf5w5Tbc/3ou8n7TG56Dbf8livEomaeFObA2z0xkL2khcKbDIOQ/dAGFvp8It4/rZVqw1BAUvlAnQeGLaJ4BWZgfhWfVQ4cOiZfuPN8yk7x+4Yvr1157TWhReDZk4XmXvi88Oxq6LgmKGbeergaFZKBPnz6CVCxcuFAIcN26dUKgLLGxsULjkJCQIJLSsHTr1g3MrklVGvuiqoyJbxQywjrt2rUTmTwVvxaakkVFRSEwMDDZGPpLo2lXiRIl8JiB6/8r1KCQoAwcOFCw30mTJgkSpfigtG7dWjimsh4LN+Xw4cMFKSFBofZn+XKNyRL9Wvbs2SNMxdKaJw+iN3wbofyDP8Sm103Uw36i5q9D5JRvDYb5M1V0DyYtR/TC9UiNVKjp/1lACAKrttdWzTexPzz6dkjWNGbdb4j4aiY8R/RA3uH/xfgDhGkYTcRca1VGse0Lk7UTzqc+jeCUwxWXZn6uVZuqmVtG1YnbcQRhPcfCvU9b5J88MNk0ngWHIbByG2Sv9ga8di9Lcl2YYblkMbujrTbvjUrzMXmQyKjdk/q4+jKhaSTJcKEfJsKtReY9RFhaEubY6w8mLkf0ovUouHQ0crdtYukp223/5sDabhdvRxP/ffB4FF1/CJ4jeyLvUM3ZJzMWNQSFL8h5RqRWhD7WLOPGjRMaEb4Yr1ChAhYvXixelD979kxYDfHfuXPnFi/ZmbSRpl9KoUXQ0qVL8ddffxm8bhcEhVoCeyi0q/vf//6nVW3xEE+WSEGSmJw5c0brj0JiQu3J5s2btb4oNJ2i4zoFzL7effddQRbGjh0ryAoLs2zSr0Ux0dLFRX8Mfcy4wT799FP88ssv2ihSHJOkpUWLFjh//jxWrVolNDucH+0GqcYjaWrAZAFggrPLggjNmTMHGzduFJuW9Vh0x09rngyt5N13DqrdO4ktW3+Bs17Uq8KbDiP/wQsI7NsCMW+/ZjHR5ztwHkV+OoLQdnXxoHFysyw1A+e4HYJS0zYg0dkJTi8TEdGkGsLaalSXuqXAzlMotP0kGIkr6v23tJecHz9F+S8X4nHhvLg1uUeyds7xT1B+4CI8LpoPtya+IjZq5pZRdXLevIuSMzYiulpZBPfRRF3SLTnuhKLU1PV4WKUMgvpp7FSVUmbU98geHo2rSwchMZuL2Zbg/ud1FP92J+43rop77eqarV/ZUcYhkG//ORTZfBShbd/HgyYp+zpl3Owca2Sv73bB48w/uDOiPeJfM39iXcdCU642oxHI9U8ASszZguh3Xkfw5+qjOWb0vI0dn2ZbuoXEgy+edQtJB7PI82X1xIkTcevWLaEt4QtvWgjVrl0bDRs2FOdBFvpe80xJIsKX8nQpIMlRCt0AeE5mP4au2wVBsZc8KPqO7cOGDRP2elu3bhWHehItXbJFIkCtCf1QyDDpqK5r4sW6/v7+whmJPiD0M6GdHw//JAP58+cXfih///23aJvSGPobtmDBgoL1Uu3GomhQBg8eLP5PIsI508GJUbxo4sWNt2/fPri4uAhSxXUqJl660b50x09rnhxne7kGGHXrDK4mRIvQlLol7PNxiNt+GEV/W4IcNSoae8+prh+79SDC+0yAx4BOyDe2r+p2uhUf7T6Oe92+gUu5EiLHiVu7D1BoyTfJ+lIicRVeOxW5mtZOcj0t5+2ntwIRVLMzcrxfFWc/rWsXGpRn/iEIrNYerjXfQrEdi5NhoWCWp0crFJiRNIKIEhHN+9xPcPEpmi6ZpNQoevlPeDBmMfJN6A+Pfsk1XPpt5JtOs0Fvto70ZRK38xjCuo+Ge6/WyD9N8/ySxXgEzLHXLXXfGr8a225hDqxte4WZY3a7Vv2IciNWIHulcvA68F3mWFQKq1CjQWEzRnkdMmSIeGHu5eUlXmjzbMhzIS1vDh48qH2JzbNpx44dER4eLjQktM6hC4NSaBG0ZMkSrQYlreuSoJhx6+n6oJA4+Pj4CPOn4sWLp0geSATUOMlT+KxHokNbQGpkKGQSN47Tvn17oXJTQ1DImOvVqyeiMLDoExRqUagx4aYhQVGc5Hfu3Cm0LnSip8ma4iSfGkFh36nNk9eGlq6KxNAIzLx1DlmLaLRDSrnbciAen/wL3mc2wqWklxkllLSr+N/PI7TVYBHel2F+01Mert6O+8PnwK1NI8T9fAA56lVH0c1zknV1r/toPNp5DMX2Lofr2xWSXE8r/K3WNKltY5xuUtEuCIril0PZUYb65eGqbbg/Yi7y/u/zZGGmlZwyxXYthWv1N9MjkhTbPJiwFNGLN6Lg8rHI3fqVz1BqA8iDhNmgN1tH+jLRRvtrUgtFfpxutnEcrSNz7PWAKm3xPDgMJYMPwcmMms/MJgtzYJ3ZMLHF9fy6fTveHLQUcHZCiTt7LZIs2hbWrZag6M+V0WD9/PyEhRBdFfgCXQnIpEtQFB8Talv4cpyFLgwxMTEikqyh65Kg2MIuseIcSGwYsevPP/9MM8yxJaf05MkTVCpcHGudS6Li4TXI/lbZpAf2Wl3w7F/LZyR+et0PQbW7mpTsLXLmSkTNWoW84/oicsIyZKtQGsWPrkoG391m/cDoZd4XtsCleOEk1+8274/HZy7B+/xmuHgnTSAYu+0gwntPgHv/DjhZ2csuCAoX51fyAyS+fImS/vuTYRE5/Xsw+WSB+SORp3NS9bnWr2DVZLg1T24ql959GdZvEuK27EfR7QuRo1Zlg93Ig4RBiKxeQV8mLyIfwr9sc2SrUArFj662+nwyy4Cm7vXEFy9wx6sBsuTzgO8V+zDHzijZmYp1Rs3b0calnKos3IGnl/6Fz+VtyFo4f6aEQC1BuXTpkojERSsavqymfzWJCF+Y0x2BQaEYyIn90f+YL+dp4sVSp04dYbHDF9tKFC9aBylRvNK6LglKptx2qS+KN97Lly+FWo4an4woDE184X8z8c6Jmyi8cRZyNayRZBp+rzVD4uMnFs9v8OJBNPzLfYRsb5ZB8cMr0wVFxPDZiFn9Kwr/OB33PvtGZE73vZo8M31AtQ547n9XhNV1dk2aJPBejzF49NtRFNuzHK5Vk2pXtNnVJ/bHsaI57IagBNbsjGe3AlHCbx+c3XImwTZ8yAzE/rgzRdlHLfgRkZNXIP/0IXDvab6IZSGtByPh+HkUP7Ue2Up7G5S1PEgYhMjqFfRlosle3kR1bhurT9hOBjR1rz8PjUBAxVbIXuV1eO371k5WnTHTNBXrjJm1441KOdXYfRFxWw+i6LYFyPFelUwJglqCMnr0aOHYzpfL9IFmzpNatWoJTOiHTK0IzbnoRE/zL4YdppM8y927d4Wf8vHjx4W2hT7VunlQ0rqeaQhKQOU2eBnzyCKbyDlPLvj8pUliaO/FVh6QD6Z+h+h5a1Fg0Sjk6dBUC6s2BK1PEfic22xRuPmG/07R+shSwE7DYPQAACAASURBVBO+l7ela6x73Ubh0e7f4XXwe4R++j+8CI/UZKbXc/xPKzrV/f/Nx8Pvf0Gh1VPg1qxOknk8mLgM0Ys2CNOkw1nj7YagKGZ6xU9vQLZSSZ1mQzuNQPyBU/A6vBLZ3yyTZL0xm/YgYsBUeAzpinyjXiVzSpdwdBoFvvcpnt3wF+p659y5DHZnK/eJwYk6UIWUZKLI1ffWbmRx1/wgymIcAqbu9cfnruJu0y+Q66O6KLxyknGDO1htU7F2MLgybLmUU+1/IxE1cyXyzxoK98+SOpNn2MTMPLBagmLmYVV3JwmKCqisTVDoGM+wbvTvMFT4FpH2gLt37xbhipnfhCZcDP3GwnBvtBPkNX6YPFLJbK/rM8O6ZMFkuvQ7odO9UhjFgbGw6YxPlR4jiIWFhWmv83ve0GTOjABBByjGxmbx9vbGjh07ki2D4UG3DhuPhe7xQD53MLElw9H1atEKAW+1xqOKJTEyewRu3rwp5j1v3rwU42wbwsfQdf83WuLF/egUSYWhtrwe3PQLPDl3FT5//yIIClXCvnqJ4wzl94iatxaRU79D/plfwb37J0mGDes/BXGb94q3OPsfBNkNQQnrM0Hz9mnHYuSo+SpqGRcX1KCnRnV+9VdkLZg3yXrjD59BaPthyN2pGQouGKlGBKrqiKzjz56jhP9+VfbE8iChClarVkpJJtpkq0dWIfsbmTehmiWBNnWvM6AJA5u4p5Fs1pLzt6e+TcXantZqz3OlnOq/zKUxr04lXL49r0+ZuyQoJkrR1gE0cXkpNjeGoNBxnlG9GJmLB3na+dFhidEVGHGBhITkgvlYSBT69u0rVG4s+lHHUlsLo3gx+SLtEPXLqVOnBDFhn0zio5vkMS1sYrYeRPE2H2Bn7xGovXyqyKfCEMtXtu/Bi46jMNYjFr7tmmHKlCkiggQdsBhUgOswZwmq1wNPr9yE7/XfhA21sUVJoljy7mHc6zoK8YeSZ6Y3lOtEm5hy2GfI+3XPJFMIafsVEo6eRfGT67Dnn7/thqDcH7sYD5f9hELfTYBby/pJ1pQWKXxy9RaC63ZHzoY1UGTjLGPFkWL9lwlP4OfdEFl9i8Hn7CZVfcqDhCqYrFopJZlEjJiDmFXbkVJ0PKtOzo4HM3WvRy/dhAfjliDfpAHw+KKdHSNh+ambirXlZyhHIAKU0wclXkdwg57I2aAGimwyz2+RraFr6+frTKNBsQXBM88Jkxgqmg9qKhgTmt8dO3ZMaAmYYJHOQ3QaYq6R7t27i4M/yQUjY50+fVqEFqYGhWHdSCrYH23+WrVKbpPPpIhMrkiCQps/Zvikw9LKlStFMkUmWbx48aLI4skoX7QNVIiGWoLCGNckIYzspV+ocaHWhv2yqCUoccfPoWjdGlj7UVe0/HUl6JdSt25dXFi2Fgm9J6Hiwz9xK8AfRYponMYbN26M3r17o02bNmYVdUi7oUg48ieK/74W2cqVMKpv3SSKJW7sRPiAqYjdtAdF9DLTG8oW/+jAKdzrNAJ5urZAgTnDk8wh6P1ueHrtDmjCsvPoYbshKNFLNuLB+KXIN3kgPPq01a5JONTSrK5g3hTN6l7cj4L/6y1M8gvSF6KSTNP1nYootnOJKhnLg4QqmKxaKSWZRC1aj8iJy5FvyiB49Dbvs8Gqi8vAwUzd6/e/WYiH325BoZWT4PaRzDGUlihNxToDt4lDDU05fdSwMfx8GyOrb1H4nP0pU65fEhQTxWrrACrLYzZ3JrPZtGmTOGjz8BoZGYl8+fLh/v37ImcJC6/T5IoZ1+lURHOs/fs1kY5Yn05EJChMdEPyweQ4dC5iKGGGatMvdHgnkSHpIfnheCRD/MtC7QpDECvjf/PNNyJkMQsJCskF50qneWpJFNMsZRyGkqO5GPO0cG66JS4uThAIErPy5cuLSyQo3377rRjP09NTmJ99+OGHyeb99GYANlZthiGPbyN34YJ48OABfvzxR9R/5IJ/B05CrYfn8eT5M207kpNy5crhq6++MnFHJW2umFAV2TofOWu/MmtTM8jL2EciWpVLWV94n1iH1DLTG8qs/uTSv5o3NSmES/Ur1xyJcQnCuZ5klflp7KHEbtmH8H6T4TGgM/KN1ew3ludhDxDwRksRuY1+O/pF+AUVq59qsIH0rJ3R0xhFLVeLeij8w0RVXciDhCqYrFopJZnEbTuEsN7j4d63PfJP/NKq88ksg5m61xkc5NGu4yi2bwVcq2h+B2RJGQFTsZa4WgcBRU4BlVrjeUgESgQeSBbcxjozsewotn6+lhoUM8mfZlXMuv77778n63HDhg0iJwgP9PTvIBGhSRMP/wy1xqycTJDIvyQZJCgkO/Hx8cJenm2oYYmNjYWbm1uS/hkumFk5mZ2ThIAmUdTCMBQc+2fCHBIdJtchAWA0Bn7PfqlpYTg45lKhFoZEiSZbuoX1aVrFNegXhiympuaPP/7QXuK6SGRo7sW5NW/eXCSfVHxilIpPIiJRp2gJfFW+Btr/vU+En6OWZN/nw/Fk8U+oFXMeT569IiiM+kBMzE1QtPkxVoxD7lYNjdoNT28HIahGJ+SoXQVFty6ANuKWXjJAOsDTET7vmC/gObBzsjGe37uPgDc/QfbKr8Nr/6soOPSZuFO0HrJ6F4HP+c1C7WwvBCX++DmEth6SLHGllow1fhdF1s9IEW//t1rjxb37Gr+gLFmMkklKleN+O4qwHmPg/nkb5J+qyf9jqNgT1obWklmupyQTrYN2s/dRePXkzLJUq67D1L0e3OhzPLl4HT5XtiNrIc2LMVlSRsBUrCWu1kFAkVNImyFIOHYOXsfXIPvrJa0zuBVHkQTFRLBtHUBleSQoM2bMENoO3RIYGCjMtZgAkVoRmlcx7rNiBsYQbTzAHz58WGhXaJZFUzB9J3niQC2Nh0dSPwk6thctWlTr+E5fEJqPsV86yP/7779Co6EUxrEOCQlBgQJJkyOS/JAcMYwc67CQGJHAMKGOkoRHd201a9YU4eJ69OiRqpSpAWrWrFmyOmfPnkW7mu/jyGuN4HtNE5a3UaNG6ODmhbonbuGtuHO4eeeOxU28TLGfTjj1N0JafAm31o1QaPlYaDPTf9kR+cb102KSWsQypULicxKR+shatAB8Lv6ibfc8JFwEDMhetQK89iy3K4Ly9IYfgt7rihx1q6HoFo35H4tizpb7049QcO6IFPeN9sBjpvjzWoI4ug88B3VR9USSBwlVMFm1UkoyMaSRs+oE7XQwU/e6f/kWeBEdq0nSqBe90E4hsdi0TcXaYhOTHSdBQJFTxNfzELNya6Y1X7T187XUoJjpxiR5oJkTk9WQgCgmXiQD9OGgFoI+Jkxws3nzZkFQaDZFwkGtCMkAtQwkE4x8pZag0PeD2hKajDEGNU3GSCioCaEDPU26aIJFUy7W6dy5szCnYiZPZ2dnbaxqxrhesWKFiNSlFJIuZpxnJC1uZN1Cx3USFJqd6Tquc03U1rBQU8NY2b/99luSqGC8xihgJYsWw88elfBB+BncDQ1FlSpV8HOdNih27DIm1swPrzfLa53k69WrJzDU1yCZKj6tKdLAzsg35pUpkpp+4349grBeY7XRaxJOXEDIJ4OSaQ3CB09H7PpdKPLTbOSs/06KXfuX/xgvomNAZ3sF68cXr+Nuo8+R68PaKLxmql0RFB5Y/Mt8iGzlS6L4sTXaNcf8uBMRQ2bAM4WAAEql0C4jEb/vJLwO/YDsFV9TI4o06zyY8i2i569LFtI6rUbyIGEy7GbvICWZ0CTQz7sRnNxyoMT1nWYf0xE6NGWvJz55KpI0ZmY7fXPuAVOwNuc8ZF9pI6DIidFGmTw476jP4Tmka6aDTRIUE0Vq6wDqLo9EYOjQoeLwz3nTSZ6+Izzk03+AxINaAmo2SFBIGGieRTJDMyuG7+U1ReuiG2Y4NQ0KNR7UotC0jGZV9D0hSaG/BvvlHJQEOjly5BBRvegXcvnyZXTp0kXU4YeaEpIdal+UQqf8qlWrarUzumulqRVN1nS1M7zONtTa0CSNcyYeHIeFGPBD0zCWxRXqYtGNs8hetgReOkH40nzw2994fPIvuO5ahJ5jR+L27dsi4/2cOXOEJsbcJf7oWYS2/Qq5O36Iggv/Z1T32jfzY7+A54DO0GoN6lVH0c1ztH2Fdv4a8fv/gFcaoVCD6nbH06u34Htjp/C/YHm0/yTudR6JPN1bosDMoXZFUEQAgeINkx0cmUGemeTTii0fMXQWYtbuQOENM5GrUU2jZJJS5fCB0xC7cTeKbJ6DnPWqq+pPHiRUwWTVSqnJJLBGJzy7HSRCSDvnymHVOWWGwUzZ68/87iKwege4vlsJxX5dlBngsOgaTMHaohOTnSdBQJFT/JE/EdpuaLKXjvYMF3PNKYUvtXmGtNUiNSi2KhkLzcuWHpChHYZrwvLq2HcGvtsFz24GpJiB3BKQPLl2G8Hvf5ausLaK6VbBxd8gd/sP8CLyIfzLNke2CqVR/Ogq7XS1Jksp5P1QKqUUTSxm3Q5EfDULnl/3QN5h3e2KoHBdAW+3w/PAUI1WKJvGbDBixFzErNqWZljYyJkrETVrFQrM+xp5ujQ3Wewh7Ych4fAZeB1bjezlS6nqz5buE1UTdoBKqclEG4r7xFpkK2tcJD4HgM3gEk3Z66+0xk1QaMlog2M5egVTsHZ07Ky5fkVOz4LDEFi5DbJXeR1e+16ZyltzLuYc63l4JILrvzLJ50twfbeBlMZjMCb6RNPSB05A9uyucM2eXVTly8j4hAQwXx7tbEh6XF1dtd2Ycl0SFHNK3w76sqUHZPiXUxD7014U+WUectapKtDzK/MhyPBLBhywCprPI6IQUL5FqlGl0pqEvulWahGo1Dh9p4RF5JzViJr+AwrMHY48n7awO4IS/GFfPDl7BT4Xf0bWYoUElGoi/jxcvR33h89B3pG94Dm0m8n7QKudMiLXjS3dJyYDkEk6SE0m4UNmIPbHnSi8cRZyNayRSVZrvWWYstf5/Oazy2Pwp8j3TW/rTdpORzIFaztdsl1OW5GTMCH1bQwnFxcR6l/f1N3eFkeTdqYAKLhAYy0SERGRzB9Zf00kGHRhoIUOzflJUh4+fCj+TSJCiyESGLoR8K/uNfZlynVJUOxth5k4X1t6QD6YuAzRizag4PKxyN26EbT2zD5F4HNus4krVddcm5ejcH74/v3KQV1N65RMt/wrtsKL8EiUvHtIRKASpMWrAbJ45oHvVU0wgJSKNkTx0tHI3baJ5uGhJKH7cTpyNalldwQlJTIS3PQLPDl3NQlp0cfj0Z7fRdLLPD1aocCMIWpEkWYdrROvjn+PoU5t6T4xNFdHuZ6aTKLmrkXktO+Qf+ZXcO/+iaPAYbZ1mrLX1Zhsmm2imaAjU7DOBMu3myXoykl5weWThgWEvSwsrN8kkYMs//j+Ysr0kWaQpbQKNSMkMkwpoRA0BlWiaRgjtjJyK9NKkMCw0PT/8ePH4jsSFlOuS4JiLzvLTPO0pQdk9LJNeDCWGYi/hMcX7fH8bhgCKrVB9mpvwGv3MjOt2HA3wkH94X9RaPSCAaTVOiXTraAGPfH00r/wubYDWQt4vjL7eqMMih9ZmWp30Su24MHohcg3vh88+ncU9bQH/APfwbVSObsjKCmZcwVUaYvnQfeSmH3pg/L4wjXcbdIHucwQOja1CGmGdoUt3SeG5uoo11OTSWo5dxwFF1PXacpeN7e/mKlrsfX2pmBt62vLTPPTldO9XuPw6NfDKPrrIuR4t5LdLvNlXDyCandFgfkjkfN9jcWKQlD27dsnAipRO8IosgMGDBD58hhVlgm6SUh4jT7VzKtHrQiTgjMgU8GCBUXkV/pY08eYxGXEiBEiH5/iX02/akZ8pZ/zggULhO8y2/A6rzEIEoM6McVGqVKlhA81r9sFQdm+fbvdbgo58dQRcD99DcW/342ID6ojrE0duPrfQ+nJPyKmcmkE9m9pNehKjV+DHMERuLbgS7zM9cp20tAEyg5fgazRcbi6YgjwX3hNn/m/IPcVP9wc3w1PvAog+937KDNuNWLf8EXA4NQzXef58zq8v92J+42r4l47TTbmklPXI+edUFyf2QfP8+Y2NB2bu15g5ykU2n4Sd7s0RFTdSjRWRfm+8/HSNRuuz9e8wUmpuETGoOyIbxFfqiju/K+TSevKGhWLcsNXIL5kEdwZlTwHjUmdy8Y2gUDOm8EoOWMToquVRXCfj2xiTo4yCf3nnaOsW67TcRAouP0ECu48jbtdGyOqTkW7XXjuv2/DZ9tJFD26Ck7ZNdoO5uSjluONN94QBKFatWoiRUXp0qVF9FYGbmJuverVqwuCwsitzKfHYEpMAk4yw0BM1K4ohd8xOTk1MyQrTH3x9ttv49ChQyJSq/51Rq9lXwywRILCCLbXrl0T7e2CoJBlyWIeBGzpDY5+BK1H+//Avc5fI0+3j1Fg9jDzLFhFL1on2z9+RLYyPipaaBzDRMZzjzzaPC5sqI0Y9fM88ZZCSViYu0NTFFw0KtW+E07+hZCWA+HWphEKLRsr6mmdzEOOwMklq91pULQhhYd3R94RPfAiKgb+rzVLFnpYHxT6IBFbJUGlKoGkUkk/VLPavmzpPlE758xeLzWZaDWv/+ULyuw4mHt9puz1wNpd8ey6H3xv70GWPEmTCJt7npmhP1Owzgzrt5c16Mop9uf9CO87Ce592yP/xC/tZQnJ5hkxcp4woy8472vtNWpQChUqJKK/UvvBFBnXr19H3bp1cePGDZEmgtoQJvxmdFYm0yaRYdLsiRMnCrIxcuTIJOZfJCUkPiQtNA9bvnw5Vq1ahQsXLohxda8fPXoUbdu2FSSGJmIKQfnnn380fSba+OnfnsIM28POtaUH5JOrtxBct7s2gpb+gdZaeNIuM27LfhTdvhA5alVWNaz2sK0XsUvrS7JsDHK3aQzl4eZhIM/K09uBCKrRWZuVPqUwvbYkOzUgKUkZ83RtgQJzhuPpdT+hYtZP3phSX35lmyMxPgElAg+a5Jj4aN9J3OvyKlSzmnmzjr1hrXZd9lwvNZkIPzL6eeXzgO8VqW03Vsam7HW/kh+IlzUl/fYZO6xD1jcFa4cELIMWrSunJ3/fQHDDXuJ3q6CdRqpLfPYcIa0GiUAWbi3qJSEo1FQcOHBAkBA6vjNPHrUkLVu2xF9//SX+UstCrQjTPfTr10+YeFGzsm7dOhEFjP4mJCoffvhhMh+U9957Dx06dBCmYiy6PirdunUTY86fP19LUMqUKSNyArJIgpJBN0BGDWtLD0iGvAuo8LE2glbUvLWInGp9Z9f745bg4dJNKPTdBLi1rK9KNKllSo9evhkPxixCvgn94dGvA7SZ6icPhEeftqn2/TL2Efhj71LWF94n1gmfGP/SHyLb6yVR/Lgm0aEtyU4NSE8u/YvgBj2Rs0ktFPlxOuKPnUNomyEiJDNDM6dVgup0w9N/7ojIKVnc02/exnwqtJP3HNkTeYd+pmbadom16oXZccW09n9A1XZ4HhCKEkEH4eyqCX8pizoE0vtceRETB/9STbXPLHWjOXat9GLt2KhZf/W6cqLvhl8JTeAaey5OuXPB568tSX5PqUGhDwmTidO3hL4gV65cEZqS06dPC+1J8+bNhe8Jk3Lz+sqVK1G2bFmhHaH2hSZh9FVhEnASncKFC4sE4ExMfuvWLbz11luiT+baI9EhAeJ15v4jOTpy5AhKliwJT09PJCQkiATf/D81LZKgZPCOGz9+vEjaSAZprsINw+SN3DTcRGTA06ZNE2+imbTx5MmT2Lt3r7ApfPfdd0ViRyUCw6xZs7BmzRqxkbgJqZpT4mT7+vqKGNdM+MjCMXr16iX6oY2hUjgmbQiZlb5ixYr47LPPxMYtUKCAqFKjRg2h9tOPoMWMrasXLcXaAi/ETcQ3c7xpmFyShUkbybjDw8MF6/7uu+9EIklTS9TiDYicsAz5pw6C++ep+4nojpNa1vjYbQcR3nsCPL7siHzj+uH++CV4uGQTCn07Hm6fNEh1qkJj4tMITq7ZUeLfXXj6rz+Can2aRNtgbz9uz+/dR8CbnyB7pXLwOvAdYjfvRXj/KTCkTSJIWrO7k+uQ7TXfdItYm1Nl7gjk+VS9f4K9YZ1ugOyoYVoyudtyoEjwWvz0BmQrVdyOVpXxU03vXn/yzx0E1+mGHPXfQdGfZmf8QuxgBunF2g6WlqmmqC+nBxOXI+GkxkTJHsvLx0/wIjgMXodWwsX3VdQuEhR+OnXqJEy4lMKE4r179xakg2Zc1JjwXDls2DDhn0KHeuXMqIQWZkJuEh2eCUlAWJiIPDg4GAsXLhSRvXgG5dmN11esWIH169eDZl4MTax/ne0lQcng3WYJgsLNExQUhNWrVwvy0KJFC/To0UPY+vXv3x+07yNBYZQEbkI6PA0fPlyQCKrh/vzzT7GBJk+eLFjzkiVLBEokKAxYQCemtMrGjRsFIbp06ZKoRoLCNoMHD07WTISAfRiHksGHcO3T4Xhnw3xc3XUQPk3rivZ0yuLNwWzyvGnatWsnokFwriRItJc0NTa5Npb/kK7IN+pzVTsidutBhPeZAI8BnZBvbF9tG33iYoz5WEC19njuHyKweHz2CkI+GQS3dq8SoNnbj5uWgBbKB99LWxG1aD0iJy5HvimD4NE7bSIY1n8y4jbvQ5Gt85Gz9ivyq0o4OpW0UYbWz0Cuxu+qbm5vWKtemB1XTEsm2jxC//l+2fEyrT719O51xYQz96cfoeDcEVaftz0OmF6s7XGt9jznzCYnvgC926yfMDt37/EqFDvJCc9WjJxFjQmd5Uko6MxOzUbt2rXx008/ibMXSQSd5Uk26DzPel5eXkLM1HrQX+W3337TvqymhoSRvTZt2iR8W/QLHfIZLaxr167aS0pI4qxZswpiJAmKme4i2uORFFAzwEItAoV35swZEaKNxIAMlCyRTJPMkkWXoJBQkAAoUcvILHmov3jxoqjLUHCTJk0SajAeykkgPvjgg2QroEqOjJgfFoZ2Y190gmrWrJnYSIy8wEKHJ86BZGD27NmCRTMEHAudmugsRYLAopagkEWTFCk2h2kRFF1TnkvtBuG9/Wvw58GjqNCgtpgziRU1J7R/pNqPzlckViwkVhs2bDBZixJ/+AxC2w9D7i7NkziQpbU1opf/hAdjFiPfxP7w6NtBW1Xf9CukzRAkHDuH4qfWI1tp7zR3290P+wpi4v3Xz3j85+VkBMgeH5r+FT4WoZaZTf7B2MV4uGILCn0/EW4fv7KDTQkUbY6c/3x50nubhnYZifh9J+F18HthSqi22CPWatdmr/XSkkl6NWX2ioU5553evf5wza+4P2y22RKqmnNNttpXerG21fVk1nllRjkx8fOTizdQZN00rdiUMMN8qTx16lQ4OzuLl9pDhgxBz549cfDgQXz99dfCHIuEgWdXvgBnYdQtnhdJJngeHTp0qLiuFJ43+eKbzvb6hedNkha+AFcscliH1juM6MU+OWamISg+PsDDh5a5XdzdgYCAtPtmFAKaM5FVEvBffvlFmE5RwCQoVIfRPIrkgmZVJDLvvPOOaoJy584d4Wi0f/9+YXJFMkRTqatXr2pNsJQZMj41SQ2ZK4kSHZdoRsZNMWjQIEGa2A/n+emnn2L37t2ChJAQcFPSBIy2hdxcdIqizSAT8pCgMFso2XjlypWFlqRYsWJJgCGTpoMTcWAbFhIUxsR2c3MT9Wm2Rc0IS0jrwUg4fl4c4Jmcb8vlsxiPENGWuHFuXOf58+e1kSSUAWknyVjdvFFMKU8u30Rw/R7I2fhdFFk/Q1VXVPlGL1qvTTKpNOJh3L9sc2T7z3leS8BURLm599loPNp1DMX2rcDjM5eS5Ihh//b40Ayq3wNPL98UeWEejJqPuO2HUfS3JchRI+1wjfq+PKqEkkIlba6ay9uQtXB+1d3YI9aqF2enFdOSScyGXYgYNB2eX3VD3v/1stMVZsy007vXH0z9DtHz1qLAolHI06FpxkzezkZNL9Z2tky7n25mlBPzi93r9o3Ix5Ylv6fm/KUiUWNGClMSFBXoqyEo7IZEgFoLskhqKmi/x//TZ4Lskj4ZZJokF2SrPFyr1aAsXbpUHOx1CQGJA31K9P0wqKVhRAWq6HjQpyqNMah5yKd2hkSFmhOacTVo0ACLFi0SJISFxIm+HSRU9P0geyZ5Yd2AgADh6ERmS00OSc6pU6eSIDh69GiQTFGzoRSSFYaMoypx165dgrDQR4U+KWFfTETcLwdEEqR/uwxH17t/Yv35k0LVyPlT/UeTNLJwYqnLxql27Nu3r8kEResrUfl1eO3XaI8MFa1JiZ4Jkn7meL9yzZH4SF00Km1iw3XTBEGJXrwRBb8dh9yfNBTTsceHZmjH4Yg/eBpeR1fh/v/m4/Gpv+H95ya4lEhKbPXxjtt2CGG9x8O9Xwfkn5B6zhRDcvKv2Aovwh6gZMhhOGXJYqi69ro9Yq16cXZaMS2ZvDKtfGUSaafLtPq007vXFTPMotsWIMd7Vaw+b3scML1Y2+Na7XnOmVFONLkOrtcDHoM/Re5WmjOFJCgm7lJ7CjNMrQn9NRiiTdGmuLq6Cqchd3d3zJgxQ6iu+Maf6i2ab+kSFLajvR/t+FjoJ0KiQW0I+6VmQ/fgrxba6dOnC00LQ8Lp33jUsrDv33//PVl3tEmk3woJlX6hdoNrYqQFxeSKqkESGGYXrV8/9WhYdKinMz7r3B+zCA+Xb0bBZWOwpscgbHaKwvGH97TDMeQc10ztjaVMvLTZxosVhM9fP6uCNaTdUCQc+RPFf1+LbOVKJGkjDsXhkSgZsF+EP1WbzyNqzhpETv8e+WcPEwRFP/SxPT40wwdPR+z6XSjy02xBUJ7dCUaJgANwzpl2QsyEPy4i5OMBcGvdCIWWa/LCGFsEWWSumrzu8L36q1HN7RFroxZoxqOVdwAAIABJREFUh5XTkskz/xAEVmsP1xpvodhvi+1wdRk35fTudSUwgfeZjXApqbFFlyVtBNKLtcTVughkVjnxvPUiMgaFlmjMtCRBMXFf2RNBUfxOGDWL2gJqPVhat24tTJoYAYEaAGo8qIHQJyg0vaITOB2/aQ7WvXt3oXUhQWG4Npo60Tmc5lUsrE8zMf1CjQeJEMO80eSKfiS84eiovmXLFuHwRDMx+nXQX4Rz+egjTYQj2gRS2xEfHy+if/F7OjLRQYr2iUp0Bq6NURg4P6VQO0ITMmYg1XVc13WmohaH45MwcZyohesROWk5PAZ0xol5K9Az/jou+d0W4ecUO0WugSHoqO2hmZviJM943LSBNNVJnvMXmo64BBGmVE1/QfV64OmVm/C9sVMcgHVLUIOeeHrpX3gd+kGE2c2uMoFczLrfEPHVTHiO6CEIir7vij0+NCOnfY+ouWtQYOH/BEFxcnZGiTt7DT4VlLwwru9VQbFtCwzWT6nC84goBJRvgWxvlkHxwyuN6sMesTZqgXZYOS2ZiOSefBlgxEsGO4TAIlNO715XgnrI0M7qxZJerNWPIGuaA4HMKqf4I3+KsPtKRMuY2Fjk+S/iljlwM3cfmcbEy9zApLc/mnLRKf3s2bNa0ysmu6GvB30+qAmgsxHjSesTFI7JyFTUnFATwYgJe/bs0TrJ00xrzJgxwtmefTDyws6dO5NNlYSGcatJkkhUqKVR/DTohESNiuIMxTnQ1Ewpb775pggxzP45Z47HA/vly5eF6Rr9T/jh/LhOOqorhWOQfCkO+Mr3JFEkPJwPiRd9ZBTn/nk9+uPOpt8wqkV7xB86jQ0lXLEx7q7QynCOzFaqkCcSH4YZJrEi+aIzPzEyRzE2I7Jw/o6KERG3eOjWLYpZEw/lEQOnIdeHtVF4zVSD03y0/yTudR6JPJ99jITTl5JlaLbHh+bDH7bi/sh5Qq0cPX8dXEp7w/vUeoNYKLHnXV7zgffJHw3WT6nCkyu3EFzvVSJQYzqxR6yNWZ891jUkE/+3WmvM+YIPwilrVntcYobM2RCuKU1KaCeLN0QWdzf4XtuRIfO2x0HTg7U9rtPe55xZ5cRwwxGDZwAvXwoR0UVASeNgizKTBMUWpWLBOdnajaeEqsxSpABehEaIw3mBWcMsiEDKXTPLasLvF1D89HpkK5V2tC398Ln6PYYPnIbYjbvh3qetiFqldk1KxloSGhIU/UzqtiY7NUKK23kMYd1HI2eDGoKAutaqjGLbF6ppijs+jeHkkgUlbu1RVV+/UvyhMwjtMAy5OzdDwfkjjerDHrE2aoF2WNmQTBhGk9HvvM9vhot3ETtcYcZM2RCuKc1KP8luxszc/kZND9b2t0r7n7GjyMnWLZQkQbH/e8moFdjajaccypVFeA7vjrwjehi1JnNUDuszAXFbD6LojsXIUfOtNLtUTIcYtpbha/XLg0nLEb1wvTiMM3kcTbbyDu9ucJrPQyMQULGVMEli5KusPkXgc26ztp2tyc7gggARNpnhk7MUyIsXEZFwa9UQhVaMU9MUgdU74plfMEoEHoRzDuOzg5sS2ckesVYFqh1XMiSTsL4TEffzAdAsMIuHJlGYLIYRCAkNQdEir5K3GW4BUMOZcPQscjWrg8Krp6hpIuvYaaATRxScoWdNZsFEEhQTJWnrAJq4PKs3t7Ub73lIOALeaq3FIf+soXD/rKXVcbk/eqEmR8cPE+HWIu0cHU+u3kJw3e7I2agmimyYmWyu0Su24MHohXD2yI2X0bHC6d2928cG15T47DnuFK0HZHMBnj5D9mpvwGv3MrsmKM8CQxH4djvtGtz7tkf+iV8axIIV7jbvL3xxvM/9BBcf4w5QbB81by0ip36H/DO+SpKcSs3gtnafqJlzZq9jSCZKbqLMjoMtrc9zZE/kHfqZLU3JpudiaA/b9OQdaHKOIie152um0WAuPwZTYhsGOGJwJUZiVQpTaNBFgCb4TGuhFPpE042A7gh0c6AbA10H1FyXGhQHuum4VFu78YRza7FXEb8KrZ4Ct2bJs45aWkyKs37+6UPg3jPtvCp0NAttNxS5OzVDwQXJTYditx1EeO8J2ikXXjsVuZrWVrUEOuu/fKBJ6JOr2fsovHqyXRMU2rz6FdeENGTJN74fPPp3VIXFvZ5j8WjHERTbvQyu1d5Q1Ua3UsTIeYj5YSvSs6ds7T4xevGZsIEhmdA37uk/d8BniizqEWCOqvfff199g/9qOrlkRbbXSybzwTO6IwdqYGgPOxAUNr1UR5GTWoLCgEksjDTL5yxTaOTKlQtM8KgU5s1jcm8GQtIlKPQbDgsLE3n5mHKDgZmYZFzJIJ/WdUlQbPo2Mf/kbPHG8yvdFC8fxonFpvcwaipSMRt3C4d2z6HdRGbktErs5r0I7z9FOH7n+6Z3sqpKTgblQrE9y+FatYKqKQa93w1Pr90RdfN0/wQFZn6lbWeLslOzKL8yHwpNEgvDSedu01hNM9wftQAPv/sZhVZNhltz4w9Q97qPxqOdx1Bs73K4vq0Of2Vi9oq1KmDttJKUiWUEJ3G1DK4p9Sqxth7WpozkKHJSS1CYNoMpL5iLjmX9+vUiUfeVK1fE/0lKSDQYOImRaBWCwuBIjMDKFBlKvj6mmKA2hS9GDF2XBMWUXWyHbW3xxgus2RnPbgUKNL3P/gQXX+PNeUwVxaODp3Gv43Dk6doCBeYMT7O7qMUbEDlhGfJPHQT3z9skq/v0hh+C3uuq/d4Yp92Qtl8J224WEiUSJns/NAfW6oJn/waIZRTRS2yZFtBRC35E5OQVUKPVSqkf+r7QB8b7r5/h4lXIqC1ii/eJUQvIhJWlTCwjVImrZXCVBMV6uJp7JEe5J9QSlNWrVwvrG/6lBoURXStUqCBy+zFxN6Opzp8/X0SApbZFISiMYFulShUwBQcjyrIwVQZJTFRUFAxdtwuCwuznsmReBErM3IRc/waLBV5dMhCJ2bNZfbGuAWEoPWkdYiqVRuCXafvAFP7pCPIfOI/APs0RU61csrlmiUvA64OXaL+/unQQEulXoqIU+2E3PE9dEzXvdm2MqDoVVbSy7Sq+szfD7bqGgN6c2B1PiuZTNWGPk1fgtWovwpvVQPgn76lqo1vptZHfIdv9h7i6fAgSs6rPIm/0QLKBREAiIBGQCEgE7AwBxXRLmfa4ceNEWgr9whQPn332GU6dOiUuMSffvn37RF48alKYp++HH37A0aNHkxAU+qw0bdoUcXEaCxkWpuBgXkASG0PX7YKgkLHJYh4EbPHNgOJr4JQrB0r67zfPQo3sRYmgpSapYtgXExH3ywEU/XURcrxbKdlI3K/Cr+bZczjncUOJ2+rD5D6YuAzRizaIPguvn4Fcjd/V9m+LslMDsxJdiXV9b+1GFnd1EZbiD59BaPv0hQmmDPy8G8IpZw6UuJE8V5Chedsr1obWZc/XpUwsIz2Jq2VwTalXibX1sDZlJEeRkxoNCrUiJUuWFFoPhbzw74kTJ7B27VqRQJuakLx58yYjKPz+7bffFnn1FA3KwYMH0bZtW60GJa3rkqCYsovtsK0t3niKM3NW32LwObspQ1BVImjph/ZNaTIhrQcj4fj5NHOm+FdsJfK6qE1MqIyjG4mIIYwZylgptig7NcK6P34JHi7ZBCfXbCJkMB+Kaoo2WlrDGiiycZaaJto6L6Jj4V/mQ+HEW/z4GqPasrK9Ym30Qu2ogZSJZYQlcbUMrpKgWA9Xc4/kKPeEGoLCqFyM1hUUFAQvLy8BNf/t7e0N+pOMHj0abm5u4nuacsXGxgqysmvXLhHViz4of/zxhyAqLLNnz8aOHTtw/PhxrQ9KatclQTH3zrbx/mzxxoucsxpR038QkZroJJ9Rxe+1Zkh88hQlAw6kOQU1WeeDG/YCc7y4vlsJxX5dpHpJsVsPIryPJgKYz+VtyFo4v90TlOhlm/Bg7JJkeV0MgfLifhT8X28h8sIUP7zSUPUk1xU/oBx1q6HolrlGtZUExWi4rNLAFp9dVlm4hQeRuFoYYJ3uJdbWw9qUkRxFTmoICnEsU6YM2rRpA5qAsVCDQkf5GzduIDIyUgs1TcB69eqFq1evomDBgsiWLZuI1kWSw4hfShSvSZMmaaN4pXVdEhRTdrEdtrXFGy9m7Q5EDJ2VLKyuteFVnLlL+O2Ds1vOVIf3K9s8WZZ3/cqhHYcj/uBpuLWsj0LfvQo5bGhN2ghgTk4oefcwGMpTKbYoO0Pr4XWFdBlLQBNfvhSmclnyusP36q9qhtLWiT9+DqGth8Ct3QcotOQbo9pKgmI0XFZpYK/73yrgmDCIxNUE8IxsKrE2ErAMqu4oclJLUK5du4YhQ4bg3LlzwhG+cuXKmDNnjvirW/R9UHiNeVD69OmTJA/K2LFjtc3Sui4JSgbdABk1rC3eePGHziC0wzC4926L/FMGZhQ0uNtyoMj87lr9TWGOlFqheVdW7yLwOf8qy7t+3fCB0xC7cbfRa3p6MwBB73aBc34PlPjntyTd2qLs1Agr4dTfCGnxJXJ9XB+Fv1dP1ti3MJW7dx/ZKpRWM5S2zsuYODwPDIXHgM7IN/YLo9pKgmI0XFZpYK/73yrgmDCIxNUE8IxsKrE2ErAMqu4oclJLUDJIDJAEJaOQz6BxbfHGS3z+HLEb94jM7LomTdaG6MGEpYhe/CrxUFrj5/qoLgqvnJRqlQeTliN64XrkHd0HnoO6qF7Ky7h4UEOT/c0y8Nq7IlMQlMQXLxA1fx1yffAeshtJNML6T0bc5n2q8UtSMUsWFF47Bbka1zK6vS3eJ0YvIpM1kDKxjEAlrpbBNaVeJdbWw9qUkRxFTpKgmLJLAOFQK6N4mQiiTnNHufHSgxj3Gd+648VLg83pTO+UJfXQtYxAxWhfhddNQ453jAsVnHD6ErIWzAuXkhqHNKU4ouwoEyWJp0Gh6FWgeZxzrhzGNhP1HRHrdAFlxUZSJpYBW+JqGVwlQbEeruYeyVHuCbXn68WLF4scKJcvXxZhg3VTf9BE64svvkhiwjVmzBitSEy5LjUo5t7ZNt6fo9x4tiAGHq7VRqxSM18pOzUomaeOxNo8OJqzFykTc6Lp2C8+LIOk4V7lHjaMkS3UcBQ5qSUoW7duhbOzMxgiODg4OAlBYQb5sLAwbNq0SesEP3nyZK0TvCnXJUGxhbvBinNwlBvPipBabSgpO6tBLTUo1oNa9Uhy/6uGyqiKElej4DKpssTaJPis1thR5KSWoCjAM3rXxYsXtQQlPj5ehBE+efIkqlatKqox9PDOnTtx7NgxbRjh9F63C4JitV0pB5IISAQkAhIBiYBEQCIgEZAIOAACxrhQ6BMUJmKsUqWKyH+iJGI8cOCASOoYFRUlEjiact0uCIquvZsD7Be5RImAREAiIBGQCNg8Ar/9VhIrV74BJ6dEvHzprJ2vs/NLJCY6oWfPK2je/I7Nr0NOUCLgiAi0bNkyybKZ50TJFp8SHvoE5ffffxc+KXFxcdrqZ8+eRc2aNfH8+XOYet0uCIoxDM8RN5kxa3YU1aUxmNhLXSk760lKYm09rNWOJGWiFinj6qUX1yNHgAYNgMTE1MdzcgIOHwbq1jVuTpm1dnqxzqx42Oq6HEVOppp4UUPCDPFPnz7ValDop9K2bVutBsWU65Kg2OodYqF5OcqNZyH4MrRbKTvrwS+xth7WakeSMlGLlHH10otr06bAgQPAixepj8dAh02aALt2GTenzFo7vVhnVjxsdV2OIidTCYrig/LHH38IosIye/Zs7NixA8ePH9f6oKT3uiQotnqHWGhejnLjWQi+DO1Wys568EusrYe12pGkTNQiZVy99OAaGwu4u6etPVFmQS3Kw4dA7tzGzSsz1k4P1pkRB1tfk6PISS1BobkWP4zOdenSJWzevFlE9cqWLZuI1nX//n1s3LhRG8Vr0qRJ2iheplyXBMXW7xQzz89Rbjwzw2YT3UnZWU8MEmvrYa12JCkTtUgZVy89uAYFAd7e6scJDASKF1dfP7PWTA/WmRULW16Xo8hJLUGh78mECROSiOz999/H0aNHwTwnffr0SZIHZezYsdq6plyXBMWW7xILzM1RbjwLQJfhXUrZWU8EEmvrYa12JCkTtUgZVy89uEoNinEYK7XTg3X6RpKtTEHAUeSklqCYgqUpbSVBMQU9O2zrKDeeHYrG4JSl7AxCZLYKEmuzQWm2jqRMzAZlko7Si6v0QTFeHunF2viRZAtTEHAUOUmCYsouAUQmbhnFy0QQdZo7yo1nPsRspycpO+vJQmJtPazVjiRlohYp4+qlF1cZxcs4nFk7vVgbP5JsYQoCjiInWz9fSw2KKbvYDts6yo1nh6IxOGUpO4MQma2CxNpsUJqtIykTs0FpFg0KO1m4EBg8GHB2ThrNi9G7Xr4E5s8HBg60zLztsVe5h+1Dao4iJ0lQTNyPtg6gsjzGga5evbr4L/9948YNvPnmm+L/JUqUwLZt29KNxJdffonChQtj9OjR6e5DaWjoxmMM60aNGmHixIkYM2aMaHbr1i3UqFFDRGpIq7x8+VK0GzVqlIjukFLx8vKCm5sbXF1dBU4DBw7EF198YfS6OKf27duDY/bv3x8nT55Ejx49ULt2bWzduhUcR5GH0vmgQYPEdwkJCdi7dy9+/vlno8fNyAaGZJeRc8tsY0usbU+iUiaWkYmpuB49CsyaBezZo4nqxahdNP8aPlzmP9GXmKlYW2YHyF4dVU62fr6WGhQL3Jv+/v6oVKkSoqOjU+z9xYsXyMJXTCqLtQkKw8I9e/YM165dQ4ECBVQTFIahc3FxQWxsrCAhqREUkoM33ngDfn5+eOutt3D69GmUL18+SXVDGE2ZMgWhoaFYvHhxsmG6dOkiCBVx0y2lS5fGuXPnBDGRBEXl5nPQavIgYXuClzKxjEzMhSsd5/mT5+EhQwqnJilzYW2ZnSB7VRBwFDlJgmLinrd1AFNanj5BUTQQ1BZQQ/HRRx+JgzlDtz1+/FhoEkaMGKGNG3337l1069YNISEh8PHxQc6cOVG5cmWhQWFdajaOHDki/l2yZEmsWLFCEAneVKxD8kOCwX937NgxyRQN3Xic38iRI4Umgn2QAOhrUM6cOYP//e9/IrwcScnw4cPRuXNn9OrVCz/88IPQHDFG9u7du1G0aNEk41OzoRAUXqhSpYpYT+7cuTFs2DDUqVNHkAiSi4YNGwrtys2bN4UfUu/evYXG5fvvvxdro/aE/a9btw59+/YV8+Z3n332mcAsf/78oj0/7PObb77Bvn37RPvUCMqsWbOwZs0a4fv0+uuvY9myZciXLx+YaIhzYv/EhdqaoUOHqsLcxFtA29yQ7Mw1juxH2orb4h6Q+98yUpG4WgbXlHqVWFsPa1NGchQ52fr5WmpQTNnFqbRNiaCUKVMGP/74ozjIs0RFRSFPnjyCTNB0igd1HvyLFCmCVq1aoWLFioLAkKzw30OGDBGHcppQsShxppm188qVK1i9ejUqVKiAVatWCTMmHuipwfH09EwXQeFBvly5cjh16pRor5h4RUZGon79+lrywf9Xq1ZNHPx9fX2N0qD8/fffeO+99/DXX3+BmDVu3BjHjh0T5Iilbdu2oNZj2rRp4Dhc17fffivGJxYkd1w/C/shQWnevDlS0qCQnBQvXlyQldQIyq5duwRJ4po9PDwE5g8ePMDatWvRrFkzQRrbtWsnxuN88ubNqwpzc20xR3lomgsvU/qRWJuCnmXaSplIXC2DgPV6lXvYelibMpKjyEkSFFN2iZ1G8UqJoFCrEB8fL97Ms/z777/4+uuvhXaCJIV/t2/fLrQG7u7uuHr1qvCjYOGBm2SBh3ISGfZDHw4Wvs2npoAH+8GDBwtfDB7s6UdCrYt+MXTjKRoUahymT5+OCxcuYOrUqVqCsmPHDqHpIRlRCskWNS1NmzZVRVAUH5QcOXIIEsBDP8elLwl9d5RCkkDypeBAkkHTL87LWIJCk7IDBw4IApgaQaGPCjVRiq8PNTckS/fu3cO8efMEOaJGqkGDBqhVq5aYphrMTbwFtM0Nyc5c48h+pAbFFveA3P+WkYrE1TK4ptSrxNp6WJsykqPISRIUU3aJhQiKpW1lUzPx0nUyr1u3Ltq0aSMO5dwk1JKQCFADQIJC/49ixYolIygkHXPmzBFahJQK2x0+fBgrV64UfSkaF6UuTaSOHz8u/kuTJZpl6RZdgkJn8rJlywoNBg/vnD9vXI6v9KHb1lgflNTGNTdBIenp3r27MNNiSQ9BYTv6zBw6dAgbNmwQpImaFRZDmJt4C0iCYi4AjejHUX6gjIAkw6tKmVhGBBJXy+AqCYr1cDX3SI5yT0iCYuLOMSeAjNs+cyawb9+raCNNmgAjRgD16pk4UZ3maggKfVAmTJiAli1bCn8SvpWndoKkIi0TL7ahU/kvv/wi/CxIIvimnwTnn3/+EX4TLDT54kF6//79SRZm6MbTJShsSJMxkhw6vpOg0OSJDu00V6OWhoVaDkYqy5Url/hQO6SQK31U9X1QlOv64/J7aoJIkCZPnixM4mjitXz5coFVWhoUkj76plDjwkKClTVrVuErkxZBoYkXfYFIZEgSae4VHh4uiMj169eFFovl6NGjwh+G61SDubl2liHZmWsc2Y/UoNjiHpD73zJSkbhaBldJUKyHq7lHcpR7Qu35mn69PE/qRmelRUrNmjUF9LTkoTUM67DQlYFWJzx3mXLdYXxQFiwAhgyxTrx2NQSFTtrUYNCkiL4jly9fFs7iJCiKkzwPx6VKlQJNoUgKeCjnRiBhYNhiOqLT14RmRj179kSLFi1w+/ZtsYloAkazq7ffftskgkKncJIpRsxSNEA0/+JBnmSFWhOSAUbG4qGefjMbN24Uc1bjJJ8WQQkLCxPO74qT/Oeffy40OSxpEZQ///xTaEyIQ58+fYQ2af369aAfkEJQqEmiD4lSPv74YyxZsgQzZ84UhERxkl+6dKkwoeM8aEaXPXt2gTu1XU2aNFGFubkeno7y0DQXXqb0I7E2BT3LtJUykbhaBgHr9Sr3sPWwNmUkR5GTMQSFJvfzmdgohTJu3DhhXbOHscbBMONNxYt2xVc6vdcdgqDIjLevdpSj3HjKioODg/Hhhx/i0qVLpjyvbKKto8kuI0GXWGck+imPLWViGZlIXC2Da0q9Sqyth7UpIzmKnMxFUBiAiBoTui2wbNmyRVigBAQEiP+n97pDEBQmjTpwIGmmW/3Ny7QkNPfatcuUbW37bR3lxrN9SRg/Qyk74zFLbwuJdXqRs1w7KRPLYCtxtQyukqBYD1dzj+Qo94QxBIWYsD4DDdFih5Y7tCah+T2tUWjpwqirLPz3a6+9JiLJ0gonvdftgqAwulV6S0JCVnTq9CESEzXRs9IqTk6J2LBhN3LkeG6oqrwuEZAISAQkAhIBiYBEQCIgEbBLBOgDrVtoikUzff3CaK7UgpBonD17VkRepc8JP0FBQfD29kZERIQwh2fhvwsWLCiu0Q0hvdftgqBwgektQUGAt7f61oGBVEepr29vNR3lzYC9yUXNfKXs1KBknjoSa/PgaM5epEzMiearviSulsE1pV4l1tbD2pSRHEVOajUo+ljSN5e+ugzYpGhQmCqDPtMs/Dd9fnU1KOm5nukJCkMKu7tronYZKkxR8vAhkDu3oZopX2duEDpR00GcWd4ZTYofYwsjVTFqFqNOXbx4UUSQ6tChg7abSpUq4ffffxfZ140tjnLjGYuLPdSXsrOelCTW1sNa7UhSJmqRMq6exNU4vEypLbE2BT3rtXUUOaWXoPCMykixJCgs1K7Qgb5169bi/wya9NVXXyGQb/xNuJ7pCQrBsZYPCgkKzdFIIOgcxNC/J06cAJM0prdwE7BPU8zcdMd2lBsvvXjbcjspO+tJR2JtPazVjiRlohYp4+pJXI3Dy5TaEmtT0LNeW0eRk1qCsnnzZnzwwQfipfj58+e1OfyU1A2M1rVz504RuZWFgYloPqZE8UrvdYcgKNaK4qVLUCgk5u2gAGmHx1C33AzM67Fs2TIRXpjskxoWZkdn+GBmaGd2edoAUjU2atQoVK1aFQ8fPhR5RpikkXlJ2A/VaszbsW7dOjBksVIYEnjRokWoU6eOCK3Lf1ObQ60OGW5ISAgYUlcW+0PAUR6atiAZibUtSCHpHKRMLCMTiatlcE2pV4m19bA2ZSRHkZNagsLzJCOhMq0Ec9zRSZ5Ruugkz8LzK53mdfOg8LypmwclPdcdgqAQwIULgcGDLZsHRZegMK/Ju+++iylTpoj8GiQjnp6egrAwzwkFSaLQvn17dOrUSQg5MjJSOCEpBIUCTkmDohAU5jph4kOagfEvmS1NwRhB4eTJkyJfCm801mMd5kkhYZEExZRHV8a1dZSHZsYh/GpkibUtSEESFGtIQe51a6CsGUNibT2sTRnJUeSklqCYgqUpbR2GoBCko0eBWbMA5pKhTwp9Tmj+xQTjdeuaAqOmra4PCjUWTAbIpIZUiymREfz8/FCtWjWR9HDhwoWgs1HHjh1Rv359vPfee0I7opagMHFOv379RKJEJi6kNobslpoXJlKkBoWaGqUw8SOzqnfr1s30xcoerI6Aozw0rQ5sCgNKrG1BCpKgWEMKcq9bA2VJUKyHsukjOco9IQmKiXvFEgDScT46GvDwSL9DfErL0jfxYp2hQ4emSlB4nVnnDx06JLKvMywbNSvGEBRqTaiFocaGodyoKSFJoabGxcVFZDzXLY5y45m47WyyuZSd9cQisbYe1mpHkjJRi5Rx9SSuxuFlSm2JtSnoWa+to8jJEudrc0rJoTQo5gROLUHZt28fBg0aJEy8qPGgjwl9UkhEGJ2rXLlyois609MH5c6dO0kIyrZt24Sm5Qgdaf4riokX+2OhU36NGjVExATFSYn9UTPDaF8kTgzVzPjVoaGh0sSm5vItAAAgAElEQVTL0hvBQv07ykPTQvAZ1a3E2ii4rFJZysQyMEtcLYNrSr1KrK2HtSkjOYqcJEExZZeAZlhO4nBtDyUlDQrnPXfu3BSd5AcMGCC0JwxNzHVOnjxZRD/Q1aDExMSI7xh2uEqVKkmc5BWCQr8SmpP9n72rAJOq/N5ng+5uJKRBpEFBgUUpQUWRRglB6ZBuUFRCShpBBUUk5C8ioKSkND+6pFs6lmXr/7wH7zI7O7Nz79zYmb3nPM8+xHx13/N9s997Ty1evJizKyiyaNEiGjNmDAc2IVD+1Vdfpbp16wpB8YfN5GKNdvnS9AX1CNa+oIXYaxCdmKMTwdUcXIWgWIer0TPZ5Uz4+v1aLChG72wfH88uB8/H1eDV8kR3XsHmVSfB2ivYTO0kOjEHXsHVHFyFoFiHq9Ez2eVMCEHRuXN8HUCdj2d5d7scPMuBtWBC0Z0FIP83hWBtHdZqZxKdqEVKWzvBVRteeloL1nrQs66vXfTk6/drsaBYt+d9Yia7HDyfANvgRYjuDAY0nuEEa+uwVjuT6EQtUtraCa7a8NLTWrDWg551fe2iJyEoOveUrwOo8/Es726Xg2c5sBZMKLqzAGSxoFgHssaZZP9rBExlc8FVJVAGNBOsDQDRgiHsoidfv1+LBcWCze5LU9jl4PkS5katRXRnFJKexxGsPWNkdQvRiTmIC67m4OpqVMHaOqz1zGQXPaklKKgU37Nnz1iV4idMmBBTKV4P1vH1tSdBMasQillaMnBcuxw8AyHzjaHu36c1ixZR7SZNjC3ek5BP58PnUM5JQm4M13OLTszRieBqDq5CUKzD1eiZ7HIm1BKUYcOGETBZhSrnhALndalRo0Y0dOhQo6GPNZ69CApqiYwZQ7RmzbNS8rVrE/XtS1SjhqlA+8rgdjl4voK37nUkxj3rB88k50T3zjV8ANGJ4ZDygIKrObgKQbEOV6NnssuZUEtQ8uTJQ7CYKGUsUNLik08+oXPnzhkNvf8RlAcPHsRadIoUKSgwMJAePXoUq0YK/g+fRUZG0uPHj2P1ST5zJgV98glFBwZSQGRkzGfRQUEUEBVFNHEiPWzXLlaf4OBgrlEC8xbqiDhK8uTJKSgoiEJDQykK/YmoePHiXL396NGjMZ/VqVOHOnfuTA0aNODPkiZNymNhTEfx5pnAZnPnzk1lypThWid79+6liRMn0vfff0+pUqXi4R8+fBhrnj///JPeeust1c+EztjEKVOm5OfE8zqK0c8EfDBmWFgYP5OjuHsmrXrym2eaMYPC+/enJ+nSEf23x7D25PfvU1BYGIVOnkxRbdrEQOQXevKTZ0LB09dffz0GW9vtPQ3fe1adpz/++IN1It8RT7elUd97Cq7yXf4U1/juEXr33m+//Rbre8XVPcKq86R8uel9JjV3I397JrvoCd8hnuoM3r59mzJmzEgnT56k559/nrcN/l64cGG6c+cOpcP9xCTxCwvKTz/9FOvxcaDwJeJMQnBBA6EAQXEkAFkPH6bqn35KAfEUfIwOCKANgwfTjRIlYubCHJgLF2XnyzK+zEFQcJFWFNylSxe+zIMAwASGgztixAgutFihQgVuj35YG9boKFqfCX1nzJhBBQoUIJAghSQpY+KLD+KMkdZnchwPczh/GRn9TDgw+ME8dn6mlPfu0RsdO9LZV16hnR9/HGuv1Bg5krIeOULrhw6lG8WLx/oMevdVPfnCMwVFRFAKnNukSeO8JJC99+yCpuV7T74j9H+Xy96Tved8UfTl73K5Rzz7tevPenrnnXdi3R/gyoVC4Y5y4cIFyps3L924cYMyZ87MH+HvWbNmJXyGl+RmiV8QFL0WlGRvv01B69fHspzEATQoiCJCQihs2bKYj7S+oYIFZciQIfxz7NgxvmgrFpTq1avT4MGD6X//+x9bIUqXLs0mM1gmrl69Sh999BFdunSJcuXKRWnSpKGiRYvSoEGDaNOmTVxhHn1AhlAxvlWrVrRmzRpq3749W4ywad5//30qVqwY9evXj7Zv3049evSgggULEqrVQ65fv07ly5enqVOnEjYlxl6/fj0Tgeeee44mT57Mm9DZKuRvbz4SzducFi0o2W+/UTjIWtq0sUnInTsUFB5OoRkzUlRICNG8efy5z1tQEuqZtm0jmjmTaNMmCnzyhFLcu0eR9erR4x49iKpUicHW8U2iWFDUW46t2ntiQYltDdf6+8mdnsSCot4TQ6+1wS5v5v39HmEXPWmxoJw6dYrvlBD8vVChQmJBUesj55bBIRAXJqh4rCcxfQMCiO7e9ToIOV++fLR8+XKaNGkSs8pRo0YRiAnIAi48lSpVonb/uZEhIwLcRkA+GjduzO5hsLZcuXKFyUunTp2YycK8ljZtWiYOt27dorJly9KWLVt4/A8++IBefPFFHh+yceNG/vv+/ftpx44dTFqOHz/On40fP55dz+BqdvjwYbasYD7gC7ew3bt304IFC8wiwjKuFgQs3LNalqWrbUI906RJRD17wm+DyNFqGRT01G1u4kSibt3iPJpdfJB16dTizqITcwAXXM3B1dWogrV1WOuZyS56Unu/RgwK7omKxWXJkiXUq1cvOn/+vB6YPfb1CwuKJx+5eJ/ywgWivHk9AhHTAIDnyaO+vUNLhaBkypSJ40IOHjxIzZo1Y9LQoUMHypYtGxMNCKwhICJwX4N/3759+9iSAWnTpg3/HQQFTLVv377s84e++Dc2Bywz8REUjFOiRAmaOXMmVa1alUqWLEmzZs1i09xnn31Gd+/eZesLBO5r8CPcunWrV88tnQxGwMI9a/DK3Q+XEM+EYHxYmOJ7OYGXEuvXE1WvHmvtdvkFZZn+DZhIdGIAiC6GEFzNwVUIinW4Gj2TXc6EWoKCbF2wKuFFOwRhCwhlkCxeAQEeg3ji3ZwWvrlVCAqsGnC1gvXjxIkTTFA+/PBDJgAILHIWEBRYPeBiBQHxwFggKLVq1aI33niDunfvztYOWFCwKbA5PBEUWE1gLenYsSNbU+B2hoMHqw1IimMQsNEHXMbTgYCFe1bHKrV1TYhnqluX6M8/Y1tOnFeNFwbI5LdypRAUbRq1vLVdLg1WAyu4Woe4YG0d1npmsoue1BIUxE3jHvvjjz8yrC1atGCLClzEzJTEb0EBejouKlrAdyQoyG5QpEgRDtpHfMfKlSs5KHf27NkcKH/v3j26fPkyx5ogdVupUqUIAUqIR4Fl5eOPP2aCUq5cORowYAC3+euvv6hGjRq0dOlSJiggLSA36AdxdPHCv2EtwRrAdl944QW2xODgHTp0iGNbfvnlF3Yzg7sXiBTaiPgIAhbtWUuf1spn0kmI7PILylL965xMdKITQDfdBVdzcHU1qmBtHdZ6ZrKLntQSFD1Y6ulrD4Kiw9VDC7iOBAX9vvrqK+rduzcTAVhCBg4cyIHp2BRw1wIBAdFAcDwsHAhkR1YuEJiXX36ZGSvSAiMeBW5jiFOBFQREA/327NnD/cBiYaGBS5cSg6KsGz6Dv/76K2dbyJ49OxOU+vXrc3zMsmXLeC3I+oTge7ihifgIAhbtWUuf1spn0ulSZpdfUJbqX+dkohOdAApBMQdADaPKHtYAVgI2tYuehKDo3GSGATh5MhGCyTUGy+pcvqrusGAoaSZv3rzJwfQ//PAD/2m02OXgGY1bgoznw3vWazyseiaxoHitIl/tKN9d5mhGcDUHV7GgWIer0TPZ5UwYdr82WgH/jWcPC4oC3saNRGPHEq1a9aySPNxO+vSJEyRrEt4uh0WcSMuWLWPqV8CSgSxfZojfHDxcMO/cIUqf3uusambgZ/mYPrpndeFg1TPpcCnzm3OiSxH+1Vl0Yo6+BFdzcBWCYh2uRs9klzMhBEXnzjEFQBtffn3+4MENaMwYojVrnpFIBDH37UtUo4bO3eTH3e/fpzWLFlHtJk0SD2Ez+xzqcCnz+XPix1vZ26WLTrxFLv5+gqs5uApBsQ5Xo2eyy5kw5X5toDLsZUExEDh/HcqnD56XNSv8VRda1+3TutP6MFa199KlTLC2SkHq5xGdqMdKS0vBVQta+toK1vrws6q3XfQkBEXnjvJ1AHU+nuXdffbg6XjbbTmIriY02xpAxAkO3nzzTZ94XL9ahBcuZYK172lYdGKOTgRXc3AVC4p1uBo9k13OhK/fr8WCYvTO9vHxfPbg6YgXSFDItbik6SQxPqu7BFWAhsk14C9Ya8DVoqaiE3OAFlzNwVUIinW4Gj2TXc6EEBSdO8fXAdT5eJZ398mDpzPjkuUgKhOqdUnTQmLieRif1F2CgW/uxIK1ufh6M7roxBvUPPcRXD1jZFQLwdooJM0dxy568vX7tVhQzN3nPje6Tx48nTUrEgRktS5pnToRTZtmSHprn9RdgoBv/qSCtfkYa51BdKIVMXXtBVd1OBnRSrA2AkXzx7CLnoSg6NxLvg6gzsezvLtPHjx/tKCocUlDzZ2oqPh1HBBAtH69qjTXPqk7y3ewNRMK1tbgrGUW0YkWtNS3FVzVY6W3pWCtF0Fr+ttFT0ber8+ePUv58+enVKlSxSipRo0atGLFiph/b926lQuPnzx5kgoXLkzTp0+nKlWquP1cLCjW7HefmcVnD56aC39QEBFSDq9cmbB4aiFUnlaq4Zl8VneentEPPxesfU9pohNzdCK4moOrq1EFa+uw1jOTXfRkBkG5ffs2pUf9Oie5desWFSxYkMaMGUOtW7em77//nvr370+nT5/m9q4+F4KiZxf7YV+fPXhqXaZUWhtMVY1WlzRPi4EV5e5dj/VNfFZ3np7PDz8XrH1PaaITc3QiuJqDqxAU63A1eia7nAkrCco333xDEyZMoEOHDsWoq0SJEvTJJ59QmzZtyNXnQlCM3tk+Pp5PHzwva1ZYDrmRFhRl8efPE+XJE++j+LTuLFeCuRMK1ubi683oohNvUPPcR3D1jJFRLQRro5A0dxy76MkMgpIzZ06KjIykihUrsrWkaNGirKzu3bvTzZs3acGCBTHKa9GiBWXNmpWJi6vP/YKgLF++3NzdKKP7DAKZDh6k55cvp2x791JAdDRFBwTQtbJl6dRbb9HNUqV8Zp2VR46kLPv3U2A8MSbRRBSgYsV4xt9//JEiUqRQ0VqaCAKCgCAgCAgCgoAgoA+Bt956K9YAw4YNo+HDh8cZNDw8nEmHO0mWLBk9fPiQjhw5QmXKlOG/jxo1in7++Wf+vzRp0lC7du0oRYoU9PXXX8cM07lzZwoLC6M5c+a4/NwvCEp0NK56IkYg4DdvBjTUrDACF81jqHFJw6CeAuUlBkUz9FZ08JtzYgUYPjKH6MQcRQiu5uDqalTB2jqs9cxkFz2ptaCAyAATd3LmzBnKly9frI9xb8+RIwd9++23VKdOHbaQIM5k/vz5Me1atmxJWbJkibGgOH8uBEXPLvbDvnY5eJaoxpNLWufORFOnEsVHsCWLlyWq0jqJnBOtiJnfXnRiDsaCqzm4CkGxDlejZ7LLmVBLULzFFwRl3rx5TFAQYzJx4kQ6ePBgzHAlS5akXr16Udu2bV1+LgTFW+T9tJ9dDp5l6tm4kWjsWKJVq54SERAOZCTr0+dp6mBPJGbiRKJu3VQtV3SnCiZDGgnWhsBo6CCiE0PhjBlMcDUHVyEo1uFq9Ex2ORNGEpS///6b0qZNy+mDQ0ND2cUL1pKjR49SunTpYrJ0jRs3jlq1asWf9e3bl06dOkUZMmRw+blfEBSjN5+MJwgIAoKAICAICAKCgCAgCNgZAaNCKBYuXEiDBw+mq1evUsqUKalSpUr0xRdfEKwkimzZsiVOHZSXXnrJ7ed+QVCMAtDOm1B5dru8GUiMuhbdWadVwdo6rNXOJDpRi5S2doKrNrz0tBas9aBnXV+76MlIC4oZ2hGCYgaqPjymXQ6eD6vA66WJ7ryGTnNHwVozZKZ3EJ2YA7Hgag6urkYVrK3DWs9MdtGTEBQ9uwRpWgMCSCwoOkF06G6Xg2ccYr4zkujOOl0I1tZhrXYm0YlapLS1E1y14aWntWCtBz3r+tpFT75+v/Y5CwpyMI8YMSLWTpQ6KNYdTJlJEBAEBAFBQBAQBAQBQSBxI4D0wb5sAPA5guK8HXyd4fnb9rXLmwF/04ua9Yru1KBkTBvB2hgcjRxFdGIkms/GElzNwdXVqIK1dVjrmckuevL1+7UQFD272A/72uXg+aFqPC5ZdOcRIsMaCNaGQWnYQKITw6CMNZDgag6uQlCsw9XomexyJoSg6Nw5vg6gzsezvLtdDp7lwFowoejOApD/m0Kwtg5rtTOJTtQipa2d4KoNLz2tBWs96FnX1y568vX7tVhQrNvzPjGTXQ6eT4Bt8CJEdwYDGs9wgrV1WKudSXSiFilt7QRXbXjpaS1Y60HPur520ZMQFJ17KiEA3L17Ny1atIjGjBnDWcQSk9jl4CUmnSnPIrqzTquCtXVYq51JdKIWKW3tBFdteOlpLVjrQc+6vnbRU0Lcr7VoUSwoTmht2rSJGjRoQPfv36cVK1bQG2+8oRrPfPnyETKOvfjiizF9kCUBPx988AF9++23/LmSleyvv/6ipk2b0pw5c6hevXpx5kEVztmzZ/P/Y01fffUVBQYG0r1796hx48a0Z88eioiIoDt37sTqO27cOPruu+8oODiYf9CvWrVq3AbzHDlyhM6dO0f79u2LtdbTp0/T+++/T9evX6dUqVLx3OXLl3f5/I8fP6aXX36Z1q1bR+nTp+e2kydPpqNHjxLm79Gjh1vccChQXRTPEhoayn/v168fVx71VpCJAutZsGABFShQwOUwwOrzzz+nH3/8keeOjIykKlWqcLXTbNmyxerjCeNGjRrRjh076MqVK3T79m3GwJVgP3Xv3p0/Cg8Pp6pVqzJOyZIl4+wZeO7ff/+d9ZQ2bVqaPn06lShRwuVYdvnS9HYPGNlPsDYSTWPGEp0Yg6PzKIKrObi6GlWwtg5rPTPZRU9CUPTsEovroKxcuZLeffddwuV71KhRNGjQIE0WFC0E5ddff6WPPvqIFi9ezBdrZ9m6dSu1b9+edu7cSSlSpKBatWrRhx9+SC1atOD1bdu2jTJmzEjVq1ePRVD27t1Lb7/9NpMQkAzM06dPHzp+/DhP8dlnnzEJwUXZmUy99tpr9N577/E8f/75J3Xp0oWOHTvmEoPx48fT3bt3aeTIkTzu/v37+dINAlC2bFmPBMXxUr9kyRJ+1j/++IMqVqzo9Y5ZtmwZ/fLLLzR//nyXY+C5b926xeQN2EVFRTE5eO6556hUqVKx+sSHMRoCHxDRrFmzxktQHj58yLiAgGC+d955h1599VXGB+uFlW7Lli38OYgL9uCaNWuEoHi9C4zpaJdfUMagZc0oohNzcBZczcFVCIp1uBo9k13OhBAUnTvHCABx2cSFEG/r8WeZMmXirOqnn36iVq1asUUCF8WuXbtqXrlaggKLytChQ/ky6nwxViYFOciVKxcNGDCA/wtv/fHz22+/xazr7NmzfEl2tKDAPQ3WFhAGWAVgtfn+++9p/fr13E85eM5rvXHjBlsecIFPkiQJty1cuDDP6cqKgs9gYSpSpEgsnGApwpo8WVCcrQ79+/enM2fOsGsdrDJDhgxhfcHq0Lt3b2rTpg1dvnyZSpcuze1Sp07N83788cdMMNAfbXPmzEknT56MY9E4deoUvfDCC3T+/HnKnDmzat26wtixM/ZnfBYUx7bYhyCPr7/+OvXs2ZNAzECEQVDSpEnD9X9g2Zo7d64QFNUaMqehXX5BmYOeOaOKTgRXcxCwblTZw9ZhrWcmu+jJiPu1Hpw99U30Ll5w1WnYsCHB1UYRWAr69u1LISEhbB2YNWsWWzPw93nz5lHr1q1d4gYSg3G+/vprCgoKitMGl368LYfFQxFcpidNmhTj4oXLNgREIn/+/G71A5LRrFkzat68ObeBxQRr/N///hfTx93lGW/lhw0bFnMR37BhAz3//PPczx1BgbsY5jtx4kTM+LhIY064MznKxYsXmSjcvHkzzvq9JSiwJgwePJgtP7jww90JGIMwwSKDS3zu3LmZRMItq1OnTmzBwXPBrUwhHTVr1qRevXrFcc37+eef2Xp04MABt5jDioO9gh9FjCAosF7BMgWSBJdBEEbsE1hUQFTgHpcuXTrKlCkT7y/86Urs8qXp6UvLis8FaytQ1jaH6EQbXmpbC65qkdLfTrDWj6EVI9hFT0JQdO4mPQAilqJu3boEtydcckE8QBZAGiD4P1x2p06dSkmTJiUQELzhdiW4TOKCjJgD59gNpb0aC8rMmTP5Ao4Lq+Ie5Wo+EBSQE5AGCFy+YC3wRFDwbOiDt/NYL2IyQFhwMQeW8REUzKe4gmFOEDnM6UxQtm/fzi5Zhw8fNoWg4CIPAglLCEgK/o3nqVOnDuuyZcuWTGQmTpzIeDhaHPAMr7zyChMrRwFBGT16NFuWtIgRBEWZD4QK64OrGfQP9z1YyODmlyFDBiZQiGtxtJI5rtUuX5pa9GNWW8HaLGS9H1d04j128fUUXM3B1dWogrV1WOuZyS560nO/1oOv2r6J1oICVxlYAGARQJwGNhzeysOFC9YFxGYcOnSIcUqZMiXHY+BC7k7+/vtvqly5Mn+MyzJiCZxFDUHBPDNmzGDrDd7UI2bDlXTu3JkJhlYXLwSo45lhFVIE5OvSpUuUJUsWQ1y8QHZA5P755x/DCIqjixfibWBpQHA5DhCIJFzi4BoHAQGBtQUYgXg4uuyBTL355ptMAhwFJAdWH7h4ubNQuNKDkQQF44Mwwo0N7nFw44NL2sCBA3lq6Aiuc4hbkV9uar/CzGlnl19Q5qBnzqiiE8HVHASsG1X2sHVY65nJLnoSgqJnl3gZJA+XH5ATuCLhsgrLSPLkyXkluHDCtQqZo5DdChdcWAM8ZZDCBfrLL7/kMZD1CdmXvCUoICmI+QBJwTpBKpxl8+bN1KFDh1hB8lgnrAeKuLo8w1UKwf0gVCBkq1atonbt2vHlNz4LCsbEepBVTAmShxsVyI5zqmXEUoDsXLt2jcmdo3jj4rV06dKYIPkKFSpQuXLlmJghYQEyndWoUYPQRiEoeEbEuCD2BDg5SrFixWjhwoWxspMpn8M9DNnZ4MYHiwWyaGFckALEp7gSvQQFViDsNwTBh4WFsQWlaNGibC3B/oO1BDqCyxcyeMH9CxYqISg6vzh0drfLLyidMFnaXXRiDtyCqzm4yne4dbgaPZNdzoQQFJ07RyuAcJtBKl3ER+CyDP9+XA4VQTwG4hQgIC5NmjRRtUJcKhX3JxAFJf2vY2e1FhQlzTDWCGsBsjrBXQnrhtuXEpSOS+w333zDU8CigDZIjwtBKlrEZ4Ak4C08XNXgKoRLN97II5uVEg+Di/BLL73E/WAlAoG7evUqWxIQEA8iB8FlGpaHf//9l4kHrDDusmqBPODCDwIIwTphmYL7GsZEEDvWg3U5i2Oa4UePHnGiABBAZS5kyAI5wvqKFy/OmcTg8qUQFLjb5ciRg6ZMmcKuUorAva127dqsJ1f1axBEDzcvEBi4jmEcJc0wsnE5x6C4wxjzYR64uIH4Af+8efPGEAtHPSJeCcQDexDWO5BAuNyBMIOwwIoCkgUrF54XbbHX5JebqmNpWiO7/IIyDUATBhadmACqQ1yiOaPLqI4IyB72j/1gFz1pvV9brb1E5+KF2A28lUag9NixY2Mu9AqwuJwqgee4CMLNy1XAu6MicKHHRRmB2Li8w2UMRMcfxaiDt2vXLnazcpcS10xs4GIGsgJC5Ug+kU4ZWcVANBKjGKW7xIiN0c8kWBuNqP7xRCf6MZQXH+ZgqHZU2cNqkUrYdnbRkxAUnftMK4DI2oU3923btnX5Fn3ChAlMXpRxUTPD0W3K1XIRJwKrBFyn8He8Mb9w4YLOJ0uY7kYePLhKIRbFXZFCM56wY8eOXLsElgbHIpqwHCnpoRUrkxnzJ+SYRuouIZ/DH+YWrH1PS6ITc3QiuJqDq5BB63A1eia7nAmt92ujcfY0XqKzoHh6YMSOwMUG2alwyVXS1Dq+iXceA/EpcB1T3tzDlQiuSY7phD3N6yuf2+Xg+QreRq5DdGckmvGPJVhbh7XamUQnapHS1k5w1YaXntaCtR70rOtrFz0JQdG5p4wGEDEWSuFCWEMQ74D4CVhcXAliDJBNC0UMkQkKsQfoA9cwxCj4m9jl4PmbXtSsV3SnBiVj2gjWxuBo5CiiEyPRfDaW4GoOrq5GFaytw1rPTHbRk9H3az2Yu+prOwsKCMYff/zBdTRQlwJB0ghuR2A1ApWdZdq0aZzOFm5h48eP5+BtWF4Q6K4EiButFDPHs8vBMxPDhBpbdGcd8oK1dVirnUl0ohYpbe0EV2146WktWOtBz7q+dtGTEBSNe2r48OE0YsSIWL2UrFcah3LZHOlpkToWsSdp0qShUaNGEaqow+UL5MVZkJkKrl3IqAWLCTYuYi+QIUzJKmXEumQMQUAQEAQEAUFAEBAEBAFBwAoEcIdF/G58omQbXbt2LSeJypUrF2dVVbyOhgwZwi/skUwKWUmRbTY+uXz5Micy2rRpE2cuRX+UtnAltrOgZM+endPzAnSwR5ATpPWFGxeyQin1UgAWUuYi/SxqZqCCPLJ9ocAeCiyiUjksKf4mdnkz4G96UbNe0Z0alIxpI1gbg6ORo4hOjETz2ViCqzm4uhpVsLYOaz0z2UVPaiwoKByNGoAIj0CoA2rs1a1blwtOo47fd999x/dklN5AuQVPBAVlNQoWLMhlIhAqAcMA8Mb/O4utCEpkZCS7cYEBoqK4ImCRAAgFDVG3QiEpP/zwA2f4wv/PmTOHmysph1G/BLEo/iZ2OXj+phc16xXdqUHJmDaCtTE4GjmK6MRINIWgmINm/KPKHk4I1LXPaRc9qSEortBr1KgRFztH3T5F4FWEjK7xEZTTp09zYWxYUbJlyy0oogYAACAASURBVMZdEULx4MEDJjq2JSh/v9fJ4y6tvHg6VyBHAUfU00AxQlQaR12V+vXrc39UUUcRQ1QxRzYvRcAowQThQuYor7zyChc0dGfCgkvbnTt3PLJOj4tX2cAuB08lHH7VTHRnnboEa+uwVjuT6EQtUtraCa7a8NLTWrDWg551fe2iJ28ICu7AyH4LIoI7shaCggLiXbt2jSkOjr6wvCDWe9++ffYmKElqVqKZM2dS4cJFqFmzprHAuPDj/1H/G0dp48aNlCpVKkK9lJ49e3Ib+N05un6BnKD6OlINo1o7BEQGZOPgwYMx48JlrGzZsswWEe/iSoSgWPel4+8z2eVL0xf0JFj7ghZir0F0Yo5OBFdzcHU1qmBtHdZ6ZrKLnkBQHAUx17iTuhPEq+CFO7Lbrlu3LlYhdDUWFMR+w10Mrl2KoG7hgAEDOEuus9jGxQsWlLCKJQhuW2XLlqMGDd6IhcW6+9fj3c9QiiIhISG0fv16OnbsGFtaIOHh4RzHghiVihUr8v8B9OvXr9OsWbO40OPKlSv5/5E5bNKkSWyJcSQo3377LQcbKUkBQJZgkdm/fz8H9sO6061bN7bo3L9/n01iS5Ys4bVERESw5QdmNwieEz5+T5484XotYLsVKlRgVzZ/zD6m58smsfQV3VmnScHaOqzVziQ6UYuUtnaCqza89LQWrPWgZ11fu+hJiwUF5ATJpBC3jYD5dOnSxVKIGoICCwrusI6FzhE+MXXqVLGg3Cz+HF/QX6n2CtWoWSMOQYG1A3Lu3DlatmwZoSp948aN2a3LkaCgmjlIh6PrF/r16dOHicOMGTMI8S4IGAKB+N///seEAemNEQPTvHlzDjYaPXq0JoKSP39+goIRMwNLUO/evXkN1atXZ1YKExlIytatW9k3EM8Kyw8IDgL7EXdjl4Nn3VeZdTOJ7gRr6xDwvZlk/5ujE8HVHFxdjSpYW4e1npnsoie1BAXkBLEiO3bsYMsJEkc5ixqCosSgIOkUAushyPyFuzbqEzpLorKgwHzkTgqv2E7n82amtevWUr269ahCxQpuCQo+CA0NpWvXrnGsCS7+jgRl7NixnGYNVonu3bvHjINaKqg6D/ChRFStP3z4ML3zzjvUoEEDTk0M2bBhA5OZ3bt3ayIoxYoV43VBwGIRF3P16lX+tzIfxsTaQIiyZMkSszZYcvAc2GBiQdHz1ZVwfe3ypZlwCD+bWbD2BS3EXoPoxBydCK7m4CoExTpcjZ7JLmdCLUEBOdmyZQt76yA1sKPAewgv5BFnjSB53I+R8TZJkiQu1YK4bATKT548OSaLF7yGEn0WLxAUxQrijMyDEVPpSMbktOPvHfRe4/eoWPFi8RIUxw/37t0bi6DAioEsBmB+cKNylGrVqlGHDh3Y0oG/I45FLUFZsGABp26Dmxhk9erV1L9//1guXgioh8AqAksKXL8gju5gID/YHLDQOItdDp7RX1i+MJ7ozjotCNbWYa12JtGJWqS0tRNcteGlp7VgrQc96/raRU9qCAo8ilDMHPHWwcHBMUpAhlt4C+HFu3MGLqQkRsgCBPUDEeLQokUL/jfiV1AH5a+//qKMGTPS0KFD7VEHxRNB2Zksghlb2zZtKU/ePF4TFATCv/DCC1SnTh1atWpVrHGgKFhWECAP8pA5c2Z2x1q4cCGtWbOGiQMUC4U7u3ghv/R7773HsS3YDG3atOEikY4xKGoICphus2bNaPPmzTwPzHO7du3i2Bi7HDzrvsqsm0l0J1hbh4DvzST73xydCK7m4OpqVMHaOqz1zGQXPakhKHpw1Ns30bl4xWdBWR9+h0lD167dKGPG2D50CJJ319fZgoLCNalTp+ZUayAijgIXrBw5cnDKYVhDIDB/gUEiXgQbonLlymzecg6SR1tYZWA5gWsZCAUIkFaCgnEw95gxYzh4HoHyMJ+B7drl4Ok9GL7YX3RnnVYEa+uwVjuT6EQtUtraCa7a8NLTWrDWg551fe2iJyEoOveUFgA9xaDEt5QTDarEu1LHGBQ0RLFHxHUg1bA7Xzudj25Kd7scPFPAS+BBRXfWKUCwtg5rtTOJTtQipa2d4KoNLz2tBWs96FnX1y560nK/tg79ZzMlKguKJwCReQABPahaqVdgkYAPHXI3FyxYUO9wlvW3y8GzDFALJxLdWQe2YG0d1mpnEp2oRUpbO8FVG156WgvWetCzrq9d9CQEReeeMgpApApGPRCQCVcFYbQus127djR37lx2x4I7l7+IXQ6ev+hDyzpFd1rQ0tdWsNaHnxm9RSdmoEri9msOrC5HlT1sIdg6prKLnoy6X+uAOt6utrGgKJkIXn75ZU6Xplc+//xzjiv5+uuvOT+0v4hdDp6/6EPLOkV3WtDS11aw1oefGb1FJ2agKgTFHFRdjyp72Eq0vZ/LLnoSguL9HuGeRgG4c+dOrlGClL8onqhXFi9ezBm3UAcFWbv8Rexy8PxFH1rWKbrTgpa+toK1PvzM6C06MQNVISjmoCoExUpcjZ7LLt81Rt2vjcZfGc82FpRff/2VCxTC2gGrh15B0UNk/UKxRGTn8hexy8HzF31oWafoTgta+toK1vrwM6O36MQMVIWgmIOqEBQrcTV6Lrt81whB0blzjAJw1qxZ1LFjRxo1ahQNHjxY56qI7t+/T2nTpqUiRYpw3RJ/EbscPH/Rh5Z1iu60oKWvrWCtDz8zeotOzEBVCIo5qApBsRJXo+eyy3eNUfdro/G3nQVl5MiRNGzYMJo9ezZXsTRCsmfPTrdu3SLUPgkKCjJiSNPHsMvBMx3IBJhAdGcd6IK1dVirnUl0ohYpbe0EV2146WktWOtBz7q+dtGTEBSNe2r48OE0YsSIWL2WL1+ucZS4zVGkEBm3ENiOAohGyIABA+jo0aNcKT5btmz04Ycfck2UpEmTcoHEPHnycOHFVKlSeT0dikLC+nPmzJmYeBzE0SDNcXyybt06QmV6PC8q33/zzTduY2WQQAAZyTp16kQI/ocg6xnIV86cOfnfxYoVYwuUVXLixAmaNm0aF5lMnz49x/oAY1fy559/0rJlyygqKorXCTc+pTbNZ599xlnbbt++TQsXLuRMboogE1vy5Mlj2jZo0IBCQkJ4nP79+9Mnn3xCWbNmteqRZR5BQBAQBAQBQUAQEAQsQeCtt96i6OhoS+byZhLbxKA0atSIfvnlF0KwfIUKFbzBKk6fDz74gL777jvCBblWrVqUL18+Apl68cUXWelNmzal0qVLM0nwVrp27UqBgYFMLsB24Vp29epVKlSoULxDfvvtt7wW/GzcuJF69OjBFeldvRnAxbxnz55Us2bNmDEd+7ibKDIy0hTLEQgCnm/OnDlUo0YNmj59OiGGaNWqVXGW8s8//1DVqlVp7969BIsWMC9Tpgz169eP2/7xxx9UqlQpJlrALnXq1DFj5M6dm0lryZIl44z7888/83zz5s3zVnWG97PLWx3DgfNiQMHaC9BM7iI6MQdgwdUcXF2NKlhbh7WemeyiJ7Gg6NklBmbxeumll2j79u10/vx5tmwYIZ9++ikNGTKEL9AfffRRLIKCN/9vv/02B9HDMuFIGDC3IwGAlQXWAYwFuXTpEl+yz549S82aNaNq1arx23xX8tVXX9FPP/3EFpuMGTPyWnC5V0tQLl68SFWqVGFcsFkVcUVQ1q5dy+t45ZVXaPfu3Wwd+v3336ly5cr8dwjie2B9GTduHP977NixhMs+yEymTJkIlixPhS137NhBsG4cPnyYxwgLC+Nnw1pRbNNRvvjiC7pw4QJNnTqV/xvFM7t168ZkTBFgA4uKFoKCOUFqYLlCrJEviF2+NAVrX0DA99Yg+98cnQiu5uAqBMU6XI2eyS5nQgiKzp1jFIC4FONtOy7PyZIl07mqp91BDEAgevfuzRdyWFAwNtyI4DaFAHpc9OFGFB9BOXnyJFsvsD5cpEFUsE5c7jds2EDvvvsu5c+fn4lAvXr1+Afy448/svUGlgbEwMBagnWgzotagjJ//nzut3Tp0liYuCMor7/+Om3atIlJE6Rly5ZuCcr3339PmzdvZlKC9SG9MzKoYez4ZNGiRWyZAvlRpECBArzOF154IVbXjz/+mLHp27cv/z+IFixYcE9TQ1BAmkCeypcvT6NHj45xaUNfEDFYv+rUqWPIftE7iF2+NPXiZER/wdoIFI0dQ3RiLJ7KaIKrObgKQbEOV6NnssuZMOp+bTT+yni2cPGCuxVce0AUbt68aRiWsCLAXQzpi3F5dnTxwlt7xGwg/mTy5MnxEhQsCG5WuOzD6oJxtm7dyhdvCOJQcKnH/+HiDr9BWAxQh2XXrl2ULl06bofnRKwFLulqCQpiTmCZUCwQCjjuCAriO44fPx6DYXwEBW51sGQoFgi4bj148ICJmCeCAnKzcuXKmGbAAl8arggKyEufPn24LYghrE9qCAraPvfccxQeHk6IfYL1BYRKEeALQmZUUgW9G88uX5p6cTKiv2BtBIrGjiE6MRZPISjm4BnfqLKHrcfcmxntoichKN7sDoc+RgCopAQuXrw4uw3toT3UntrTHJpD5aic1ysEGYDrUdGiRenIkSNMKJQYFAwKCwAuzphzwYIFBMvAihUreD7EPiAQW3FFWr9+PQ0dOpRJDawZ7hIDwP0Jl+Z79+5R48aN6bXXXqMOHTrEeQa1BGXSpEl04MABDpJ3FHcEBWsGMVMEcTggBAhihyiuaLDkgGyBeLVt21YTxnDFAylQ4+LlTLC0uHg5Lgq6zJIlCwflI+YH0rBhQ2rSpAm1aNFC0/rNamyXL02z8NMyrmCtBS1r2opOzMFZcDUHV1ejCtbWYa1nJrvoyYj7tR6cPfW1hQUFLlSFCxfmgGsQgTpUh9bQGqpNtWk1rfaEUbyfK65jiEdBCmNHgoLLOqwFyDCFjFp4I4+aKXADa9OmDRMDx1gJBNTjzT/IBbJJQRDkDSuNEnsBSwdculAo8ocffqAxY8YQMnZlzpyZ41AwZrly5VRbUOBCNmjQINq2bZtXBAVZskAk4G529+5dqlSpEr3xxhvsagZrDwgQ3NDgSuW4PrhVlShRgt3FnLNzwdLy/PPPc+YxJUgeXxggdc5y+vRpdjdzDJKHixeIlCKuYlCwVridKUHzU6ZMYcz27NkT0w+xPEis4CqIXtem8bKzXb40vYTH0G6CtaFwGjKY6MQQGOMMIriag6sQFOtwNXomu5wJISg6d44RAMJtB/EEiBfp9WMvqkDPsnjtol1Unsp7vUpYE3CJhusSrCkgCohBwaUY7kOIv1CC8hFIjks2/h+pjpElypGgID4DQe6K5QCLGj9+PF+c4b6FN/s5cuQgBMbjcg/BxRqEBZ9jTrzxR70XtRYUuDeBDOA5YEFQRK0FBS5zIF7Xr19nCxLczUA4lCB5EBTFOoO5mjdvzoH0ly9fZssL3NFcxQTBioLEA6gxg1S/cPmCKxcE5A7WIyUWB8Twyy+/ZAwQ8I9/K+mEkV0NpBCJBxD0jjGwH4D7+++/z33wA7e6CRMmMBYQpCaG9QdppH1F7PKl6Qt4C9a+oIXYaxCdmKMTwdUcXIWgWIer0TPZ5UwYcb82GnvH8WxhQVm8eDFfopFK98hXR2gdraMIiqBgCqYQCtFtRYEVom7duuweBKsGiJA3gpgNjIN6KmaJq4MHEoSgfFhSrBLUJEEMiKOlw6q51cyDvQKLFlzYfEXs8qXpC3gL1r6gBSEoVmhB9roVKD+dQ7C2Dms9M9lFT0JQ9OwSg9IMw8qA1LNdv+tKU1pPibMivVYU5YsHBRShcGxu5e2+msfH23xk6oJLEep9KEUG1fTV2sbVwYNlA8UmlVTBWsdMbO3hYqbsGcfUywn9nHb50kxonOUi4QsaiLsG2f/m6EVwNQdXV6MK1tZhrWcmu+hJCIqeXWIQQYFLEWIlSl4sScdyHWPriSJGWVEwHmIu8MYd7kWoGYLaK74mdjl4voa7EesR3RmBoroxBGt1OFnZSnRiDtqCqzm4CkGxDlejZ7LLmRCConPnGAEgMkJ9s/8bomfJp+KsyggrCtYKIgRXqZdffpnrkfia2OXg+RruRqxHdGcEiurGEKzV4WRlK9GJOWgLrubgKgTFOlyNnskuZ8KI+7XR2DuOZ4sYFGSVWtllJQXVDqLIgMg4eBplRYGykYULGcOQJQo/SrC2mUrUMrZdDp4WTPylrejOOk0J1tZhrXYm0YlapLS1E1y14aWntWCtBz3r+tpFT0JQdO4pIwAs1qoYHZt/zONK9FpRsNYRI0Zwli2QE6QZRjV4iGMRR/y7evXq1KNHD87mhWDsM2fOxKS8RXV0ZPoyI4DcLgfPo7L9sIHozjqlCdbWYa12JtGJWqS0tRNcteGlp7VgrQc96/raRU9G3K/N1IotLCjJNySnsGphRMHuoQyiIKpFtXRl9IKy4d6FdMO9e/dmwoH6JygQ6Y6goCp8q1atOD1up06dmNgg1S3S2yJlsdFil4NnNG6+MJ7ozjotCNbWYa12JtGJWqS0tRNcteGlp7VgrQc96/raRU9CUHTuKb0ARkRFUJKLSYjyel5IXspLZ+gMBdLTSuJaBWuFJQQ1QVCwEHU3YAXp2rVrvAQFRQZbtmzJ1egnTpxI//vf/+JUdte6Fnft7XLwjMLLl8YR3VmnDcHaOqzVziQ6UYuUtnaCqza89LQWrPWgZ11fu+hJ7/3abI0kegvKv//+S1kKZ6FilYvR77//Hi+eaSktZaSMXmMOZZ89e5aJSfr06bkuCmqMIMUxrCKLFi3iKu+QypUrM3mBBQWCQpLINta5c2f6+eefuYihGWKXg2cGdgk9pujOOg0I1tZhrXYm0YlapLS1E1y14aWntWCtBz3r+tpFT2oJCoqIo/j3wYMHuVbf8uXLY5SxZ88e6t69O79Yh9fP8OHDqXXr1m6VBW+ia9euUVBQELcJDg6mO3fuuGyf6AkKqrKXLFmS6tSpw5XbzRTFxevTTz+latWqcRYvkA1UPYdSUZkcblxYU/ny5QnFChWCsmzZMo5JQewJKp2rlejwCLozfRHdm/8rJStZiNJ3aUbJyz2tMu9K7HLw1OLnT+1Ed9ZpS7C2Dmu1M4lO1CKlrZ3gqg0vPa0Faz3oWdfXLnpSS1BwPw0MDOTyGRcvXowhKCAWRYoU4dhrFBjfvXs3vf7667Ry5UqqWrWqS4WBoMBTSLn7xqfVRE9Q1q1bR7Vq1eL6JPPmzTN1h0PZY8aM4UKNiCFBRi8oAuwSblzvv/8+Z/UqVaoUu3P169cvRkkoDpgjRw4uEIiq92okdNt+utF3PIUfPxurefIqpZmopKxVhQICY7ur2eXgqcHP39qI7qzTmGBtHdZqZxKdqEVKWzvBVRteeloL1nrQs66vXfSklqAoyMM6gsLiigUFXkkfffQRnT9/PkY5bdq0oejoaLa4uBK/JigAAGzMURzNSVq36KZNm2jChAnUqFGjeM1OWsf11P7AgQM0bNgwLtbYt29fT805duXzzz+n6dOnx5i+3HUKuv+Isi/eRBm2HeYmD4rmpatNqlOKM1cp85pdlOzabf7/xzky0u1qL9CdSsUoMl0qj2uQBoKAICAICAKCgCAgCAgCiR8BWDFAJtSKM0H57bffCFlnL1y4EDMEXsTDFQwv5d0RlNDQUIqMjKRChQrRkCFDqF69ei7bJnoLCmJAkO4XJAUuVFbJw4cPOQ4lU6ZMdOXKFQJTdScdO3bk+BiQE9RsiU8e7z5MV5r1oag79ykoS0bKNKoLpW5UK2b86KgoerRmK935eiE93nnw6VBBQZQypBKlaVKH1j+5RQ3ffccqGGQeAxGwy1sdAyHzeijB2mvoTOsoOjEHWsHVHFxdjSpYW4e1npnsoifneyleqoOEuBNngnLz5k0mGSNHjiTcY3fu3MnhDFmzZqVTp065HAYhDIjFRgzK0qVLqV27dhzWgLAHZ0n0BKVPnz40btw4jvdo2rSpnj2ruS8yeUFhJ06cYCUaIVEPHtGFV96nlK+9RBkHtqegdGncDht26BTdX7SK7i/5g6L+fRqEFJEmJeUc34dSvxUSL2kyYq0yhrEI2OVL01jUvBtNsPYONzN7iU7MQVdwNQdXISjW4Wr0THY5E3pdvID71q1bCffs48ePc0mNsmXL0o4dO7jEhhpBSEOBAgXoiy++sB9BQY2RBQsW0IYNG7g4opUCyw0sON988w21bdvWsKmjHoZSYKoUqsdDIP2j9X/T/R9X0sPfnwbgp6xTlbKM7U3B2Y2vtaJ6YdJQEwJ2+dLUBIpJjQVrk4DVMazoRAd48XQVXM3BVQiKdbgaPZNdzoQRBMUZ+yZNmnCyJ8RjqxEYDhCXYkuC8tprr3HmAQStFy1aVA1ehrVB7Mzbb79NCBqaO3euYePqGWjtl1Oo0JItFHH2MgWmTU2ZRnahNM3riTVFD6gW9bXLl6ZFcMY7jWDtC1qIvQbRiTk6EVzNwVUIinW4Gj2TXc6EWoISERFB+EGGWqQTRnZaZPVKmjQp7du3jy0nSPQ0f/58jinB/6HchrMgmB6lOOBdhP6//PILJ4+CAQGlN5wl0bt4IWPWoUOH6Pbt2xwTYqVwDZYsWbgGysmTJ62c2u1cOHgNXqtNt76YQ3dnLiaKiqJU9V+hbHNHxcn45RMLlkXEIGCXL01fULlg7QtaEIJihRZkr1uB8tM5BGvrsNYzk130pJaguEpe9eqrr9LGjRv5BTyIBggMkkIh3rtEiWelLvD3gQMHUosWLTh7bfPmzTk+BfVPChcuzIQGJThcSaImKCiUmD17dkr28CFdPnbMs5UgbVqijN4XanQFMJglrDcIlMdaElocD97jPYfp+sefUviZi5RpRGdK38naGJ2ExsLf5rfLl6Yv6EWw9gUtCEGxQguy161AWQiKdSjrn8kuZ0ItQdGPqHcjJGqCgqrs06dNoyvJklG2sDDPCOXJQ3T2LJFT7RDPHd23QGaDWbNmxRRs1DOWEX2dD17Y0X/oYq32PHTutXMoWbECRkwjY5iAgF2+NE2ATvOQgrVmyEzvIDoxB2LB1RxcXY0qWFuHtZ6Z7KInISh6dgkRWz205GlWpvv+++/Ztw0uVueKF6cUW7YQRUa6X01QEFGtWkSrV+tccezu8Mlr3bo1devWjSZNmhTrQ/jivfjii4RqnBA8q9muaK4O3p2pC+nm8GmUtFQhyr16JgUkTWIoBjKYMQjY5UvTGLT0jSJY68PPjN6iEzNQFbcjc1B1ParsYSvR9n4uu+jJ2/u198hq65koLSiodFmlShWCixcC5GukSUNUoYJnZHbtInKRi9lzR/ctQELy589PZcqUiVO4xlcISnRkJF1+qzs93nGAMvR6nzIOeGpREfEtBOzypekLqAvWvqCF2GsQnZijE8HVHFxdjSpYW4e1npnsoichKHp2iRcWlFu3bnHBF1RmHzt2LBdpZKlTh2jdOqKIiLgrCg4mCgnRbT1BdUwEDCHLAQKAUqZMSdu3b+eiNQiYL126NP8/MiHUqVOHsxn4ggUFgISfvUwXqn9A0aFhlGvlNEpe/lmQk04VSneDELDLl6ZBcOkaRrDWBZ8pnUUnpsAqgdvmwOpyVNnDFoKtYyq76EkIio5Ngq5aAESaM1RiX7VqFTVu3JgWLVr0LDB+9+74rSgGWE+QyQDV4P/44w9+apAluG9VqFCB/451IaMB0qkdPnyYP9NKUCIjI7koDrIlvPvuu5rRje/g3Zu/gm70GkNJCuSm3BvmUWDK5JrHlw7mIWCXL03zEFQ/smCtHiurWopOzEFacDUHV1ejCtbWYa1nJrvoScv9Wg+e3vb1Wxevbdu2Ub9+/diNS5GHDx/yxb9YsWJcxTINXLscxZUVxSDrCaaB1Qap12AdCQkJ4T9/+OEHXueDBw/YkpIjRw66efMmp2XLnDmzZoKyadMmLjiZKlUqrlDvKtd0fJshvoOHWJ+rLfrRoz+3U9r336Qs4/6zPnm7u6SfoQjY5UvTUNC8HEyw9hI4E7uJTswBV3A1B1chKNbhavRMdjkTQlB07hx3AHbo0IFmz54dZ3Sk8kVu5iJFisSd2Z0VxQDriSNJQtGZ9evX008//UTIJAY3r5UrV3Jxmh07dsSsyxsXr0GDBtHo0aN5DG8KQHo6eBHXbrKrV9S/dyjLpP6Utnl9nRqU7kYh4El3Rs0j40jgsC/uAdn/5mhFcDUHVyEo1uFq9Ex2ORNCUHTuHHcANmzYkFasWMFB8CVLloyZBcUYkyVL5n5WRyuKgdYTTHjx4kUuBpk6dWqCKxbcuZBiuFGjRuxqBjcv/Bw7dozJijcEBfE1e/bs4TlgMdq9ezeVLVtWNcpqDl7o1n10+Z2eFBAcRDlXfE3JyxRTPb40NA8BNbozb3Z7jSxY+56+RSfm6ERwNQdXISjW4Wr0THY5E0JQdO4cdwDigr9z5066fPkyu02pFmcrioHWE8SYDBgwgNMio6pm7dq1ady4cQSLStOmTTlQPnfu3Bws/9tvv2kmKDdu3KBs2bJR3rx5qX///vTxxx+zSxnGB05qRO3BuzNrCd0cNImCcmTh+ijBWY0tYKlmrdImNgJqdSe46UdAsNaPodEjiE6MRvTpeIKrObgKQbEOV6NnssuZEIKic+e4A/C5556jCxcucAwKMmNpElhR1qwhql1bd+YutfN+++237JLlqh6K2jEWLlxIzZs3pw8//JCmTZvG8SuIuVm2bBm9/fbbqoZRe/BAsq53GU0Pfl5NyauUppxLJ1JAEo04q1qRNFKLgFrdqR1P2rlHQLD2vd0hOjFHJ4KrObgKQbEOV6NnssuZEIKic+e4AhCX5+TJk1O6dOno+vXr2mfYs4eofXuiOXOIypXT3t+LHiBTsHzA7evQoUNejED0wQcf0HfffUdLliyhd955h7OFwUpToEABOnLkSPyubf/NqOXgRYWG72o0/wAAIABJREFU0eUGnSnswHFK1/4dyvx5D6/WLZ2MQUCL7oyZ0b6jCNa+p3vRiTk6EVzNwVUIinW4Gj2TXc6EEBSdO8cVgKi2njFjRipVqhTXHPEXKVy4MJ08eZKuXLlCCObXIiBluXLlomvXrnEWMMS6QOrXr0+///577Jov8Qys9eCFX7xGF19r/zRofkI/StvyDS3LlrYGIqBVdwZObbuhBGvfU7noxBydCK7m4CoExTpcjZ7JLmdCCIrGnTN8+HAaMWJErF7Lly+P9W8Eo3fp0oVjOZzbapzO0uYzZsyg1atXU8+ePTl2RIsgoL5Hjx6cnezLL7+M6QrLTPfu3dmihBossCoZLSmPX6D8Xy3mYc91a0QPSuQzegoZTxAQBAQBQUAQEAQEAUHAIgTeeustjpn2VfHLOihII1yjRg1q1aoVff/9976KbZx1wTULBSTbtm1L33zzjaZ1jx8/nj755BMCgRs2bFisvl27dqWvv/6ahgwZQiNHjox3XG/fDNxftJqud/mMAlKloFwrplKyUoU0rV8a60fAW93pn9l+IwjWvqdz0Yk5OhFczcHV1aiCtXVY65nJLnoSC4qeXeKmkjzqizRr1owv7GPHjtU5g3Xd4ZqVJUsWjkVBUUe1mbewwtdff53+/PNPrqmCSvSOsm/fPk41jDZrEPwfj+g5eLfHf0e3vphDQdkyUa7VMylJ7mzWgSczSbYdC/eAnnNi4TJtNZXoxBx1C67m4CoExTpcjZ7JLmdCCIrOneMKwIkTJ7KbFFL49u7dW+cM1nYHkQChOHXqFBUsWDDW5J9//jnHkyhV5pUPHz16xDE3KVKkIKQads5aFhYWxnVRMmTIwDEq8REfPQcPpsAbvcbQ/QW/UZIi+SjXb9MoKH0aawG08Wx6dGdj2Lx6dMHaK9hM7SQ6MQdewdUcXIWgWIer0TPZ5UwIQdG5c1wBiFojX3zxBS1YsIBatGihcwbjuiPd76effkpRUVEE0pAzZ062egQGBlK9evVowoQJNGfOHCZWM2fOpA4dOsRMfuDAAbaCoO8bb7xBv/76awzRgFWkTp069O6779LixU9jQSDVq1fnuBT4ESLlMMZwVRdGiev5+eefKWnSpPTmm29yf2QUQ/YvLT6I0RERdLXlAHq0bgclf+lF2t26Om3csoWfTa+EhoZyTRtkJlu0aJHe4RKsP7K09erVi7OsQTp37kwrV66kc+fOMTmFrhx1iP9XYodQ1HPo0KH8MVJHw0r48ssv87+dvzRnz55NkydPpqNHj/Kewl5QBFgiHfWOHTt4Hw0ePJjef/99l5ggVXffvn15jdgfSOENogxBIVDEOGG8x48fU5MmTeK4GCYY0CZObJdfUCZCaPjQohPDIXX5vWLOLDKqq+9wQcU3EbDLd40QFJ37zxWAqCeCuiKoIh8SEqJzBmO6X716lS/8uNDlz5+fB927dy+VKVMmlkUDQfJ169al9957L+YSDoJQrVo12rp1K6VKlYorxE+aNIlrpkBw2QUBQFV6XDodL7cKQVFSEOOSCTLkKCAoKAyZOXNmLu4IggJXMVw8d+3apYmgYNyoB4/o0ptd6cn/TlC6j5tQ5pFdDAERKZTxjKjtAgsT1muUREZGUlBQkFHDxTtOgwYN2MJXs2ZNbrdp0ya2llWtWpWQ8MGZoCg6dB4U+wc62rx5s8uLxP79+zm1NCxvILeOBOWzzz5j4gISD9Jarlw5+vvvv9m90FlAgrDnpk6dymQaWeaU4qcvvPACjR49mknzgwcPeI/DxbJKlSqWYJlQk9jlF1RC4evNvKITb1Dz3Edw9YyRUS0Ea6OQNHccu+hJCIrOfeQKQFzwcdHHm2pcmHxB8GYc6zp9+jSTDGfJly8fX04LFSpEadKk4YtlhQoVOBYFl0ccCPRLmzYt13bBZRpvv/EWHZdHVKHHGCBnyht2RwsKMnuhujxSEcMdDBdWFHNMmTIlB9bfuXOH3+iD7LRv355/ypcvz4RFsaDgQoyA+/v37/ObdFipatWqRbjsIlsYspBBwsPDKXfOnLQkZXHafusKbX4xJ63YvJGQvAD9kaFsy5Yt/MYdFiNczCGwGuFNP54RJAnB/o7WG5A0XMjh4oa14ZIPQXpmJENQYm/w+ZQpU2j9+vUEYog+wBHzvfbaaxyXhIs28HnppZdo586d/BnWh4s2sMS/QRRAiIARBOv54YcfGD+Mg78jexoEVqxRo0axJQF7EpYyWLWcBRnmcHk/f/58HFc7ZQ+oJSgYu1ixYrR06VIqXry42xgUkFOM6UhQSpYsSbCwKEQC1jrgCDLiKHAfRMpr6NdVBjiMA+tK69atmbhUqlSJcX/++ed94diZtga7/IIyDUATBhadmACqVJI3B1Q3o8oethRuryezi56EoHi9RZ52dAUgrBJ4e4x4DCPfsutZKlyzkKELb8tx0YZbDqq+w80L4ng5RQ2Tu3fv0p49e7ieCS57uKjDgoI33HjbDrcbXCjx1hpvwJFeGBdtXNK/+uorvhw7EhS8tYeVRHEDw+UehAcXaYWgYC3ADRdXXHgxHi6nmFuZD2/RUVsF7UBOjh07xu5qeJOOyzfiYHBhRkrjXzr3p+mtO9HawAe06sIJ2rx3N/fBc+AiC1KBdrDWgEzi0g8ihznhcgTioxCU48ePM26YA+QGRAeWFAiI0j///MNkAoJnhatRy5YtOTFAv3792JIGKwn+H9iAgAEfpF+GuxwIF+a6desWZcqUif/eqVMn1gv6w/KEP7FWxPPAHeqvv/5igoK5mzZtygQP+sJlHnrA+pR6NMremT9/PhNRYOQs7ggKSFaSJEmYvAITkBJFkPENZBNptd19aboiKCDBwFTZf7CCYB64hDkK6ggBT7hKwh0R6wAOihsg9AUXQuxvEDtkkwNuiV3s8gvKn/QoOjFHW4KrObi6GlWwtg5rPTPZRU9CUPTsEjcEBe4nuCzh4ow35ZBzZd6lqHsPdc7muntg2lT03L4lqsbGhX7Dhg1s4QFZgcsXCIjj5RR/R9wBLnu4AMMagMu7ErOQJ08erg6PCzIsKbh4w90Lbl8gG7BwoK8jQUF2MGCCizguuMAGdWLgjqMQFMTugNwhFTEupvgTgfW4rB88eJAvqorFAA8LS0ifPn3Y6gC3MRAukAIQGFxo8e9J1d+i37ZvpgXte9LRxlXpo48+YlIDgTUJRAXrwsUYl9158+Y91de5c4yJQlAwT0REBLuy4f9A0mDBgAUAb+7xJh/E4N69ezF/x6UZb/3xmSKwCOByDSsK8GnXrh2no4agPaxPICOYCyQRFhZgBMsSxlJSOEN3ICnAA5Yo/D+sU4ogI5ti6XHcGHC3AskC0VNDUGBpASnFMyP1NHQCQqQkQhg4cCCvGyRNK0E5ceJEjKsWiA8SKDgTFJBkWKsU90HEJL3yyivsDgYdgJhB/9A7ngt74scff2S9Jmaxyy8of9Kh6MQcbQmu5uAqBMU6XI2eyS5nQgiKzp3jDCAu63CPwlt4XJgU8RWC4vi4eJOPN/y4/DoSFMUCBCKBt9wQ1EiBOxcEbXGRBwnA5RyixJaAbMCqAlcpZ4ICawEwwSUeb9AVUQgKsp/hYokLMOJ3EAytlqDAIoBLN2IaFGsK5ps7bQb9NGAkTU9akE70aEQDFn/H1hcILvdwPYJ7GcgVgvjnzp0b8xlidXAxh8sYSBnIGN7gQ0AeEDCu1IsBeYKFCnjAnQvuZsABFgwQNlh2nMURH3yGtcOiA3ctWElwWYe7EiwesDgBC8V9Du5gsEzgGfDcsArhYu5JnJ/Tsb0rC4rzeLDuIC4IBBXiSJy0EBS4PsK9zpOLF8hj1qxZ2eUN5BYCstyxY0feX7DAwLqmCGKgYNkDoUzMYpdfUP6kQ9GJOdoSXM3B1dWogrV1WOuZyS56EoKiZ5e4sKAgPiNbtmwctwHrhK/IpUuX+DKrZFy6ffs2uwGNGTOG3+g7Xk7xFhpvqGHlgODNPC7PuBBClLYYA4HWsBLhwo436e5cvHBxhAUGb+QRVA0SAdewokWLxlhQQFBgoQAJgMsQiINCUBQXL1zgEUsDCwvmRqA1rDMcd5I7N7v7gEjAqgBBsoKlc+bRxONRtDNZOH2R/gEdOHSIP3MkKLDQIDsXyAsuxCACiOkAQQH5gYUAF3NF8BywjABXkC2QCFz+4aaEYPqKFSvGXKZBujAWDhv2B2J2QH6cCQosVSAncIUDqYEeYL3A2Pg/WCu2bdvGrnGI9QFJwTMgYB+4wwUK5BIC/bmyIsB6NmjQIB7HWZwJCkgBSJbipojMWSBFeGaFqEEXIAvAXQtBAR4gv0qQPNzEsGaQUmfBHIhFatiwIRNAtMWzw/IH3SOjGkgL9iBwB7GDLhOz2OUXlD/pUHRijrYEV3NwFYJiHa5Gz2SXMyEERefOcQYQF2e4LsHlCFYFXxG4LCEQGW/3EXQNFyK4QOHS60g6YFHAxRkXZCXLF4gHyIMzQUFbXOZhhcGFHZdHuCu5CpLHRRfxH7iEwgUO/WA5wcXW0YLiePAcCQrWqATJYyy4GIE04GKqCGrOIP4FRAJuQQpBwQV/TrHqtGbaNzQ64Bodun6JApImiUVQ0BakBgQJ1i9Yl+CGhTVAl/i3krVMma9GjRqMIcgX8ARBwmUe8SyKgJBgXXAfw16BVQdEBq5bzgQFF+x33nmHiRtIIeJwoDesH4K4GLh74TO4Oa1YsSLGGrRu3ToaMmQIkx+QObg/gdQ4C4gcLvYgz7jcQxAPA5c/kCtYSEA+YOnC2kEOMB5IKMgiCK1CvqAHEEy4zMHi4/ylCesSXM9AZDEm2iANNawmcHXDvNgPwAX7EPEsEMTk4AcWFghIGFzhsB6sA0HxSvpuWNoQkwL8sU64eoGAJXaxyy8of9Kj6MQcbQmu5uAqBMU6XI2eyS5nQgiKzp3jDCCsBHh7i8uW4v6jc4oE6Y74BVys8XYa2bzciRq3IKUvLtR4C47LpnL5dB7XrIMX9egxXazVnsJPnqOkxQtQ1skDKVnpIrGmBylTXM9AVKDLVatWJQj+riZV1gerDlyrYOGARUmrID4IffVe5OFaBlKKRAcQs3Sn9fns0F6w9j0ti07M0Yngag6uQlCsw9XomexyJoSg6Nw5zgAiSxJSnuKNMAJ/E7toISh4K49YDrjoIPjZ6i/I8AtX6Xq3z+nxlr1EQUGUvkszyvDJBxSYPBkvBW/lQUpgNUFsA9yhlFgLX9AjYlzgzgVygRgOxLl4kyUOVhSkVIYbnR7BGLBYKGmr7fKlqQczo/oK1kYhadw4ohPjsHQcSXA1B1erf/9a9xSJfya7nAkhKDr3sjOAcAuCCwr84JGKVuQZAnjzD7ciWALgHqTEMVj5yyg6Koruff8r3Rw+jaIfhtKTgjlpY0hx+m7dKnY3UgSXbqT8VQKzRY+eEbDLl6ZnJMxvIVibj7HWGUQnWhFT115wVYeTEa0EayNQNH8Mu+hJLUH5+uuvOd4YscRwS1fc4qEJZP7EXRwhAkhgBS8ehCwo9eWctYUkTsj2Chd5JDfCi1y4z7uSgGjHSnnm613zDM4AKnEQcI1CNXaR2AggDgXxEojVKVWqVBx4rDp44Rev0Y2eX1Lt5bPpUMQDXkeR5wvRhx915HgMxDZgnUq1dbp1i+jePc/qTJuWKGNGz+0SYQurdJcIodP8SIK1ZshM7yA6MQdiwdUcXF2NKlhbh7WemeyiJ7UEZdmyZRyfinsbPHUcCQpipRHzC1KCOF+UJcC9DiUXXAnKN6DkAeJ9EfeKOyvc2OEZ5Sx+R1DgJoR0r6hTgUBmkdgIIP0rUhAj05UrhVt58JSMa9mDk9PEVIWofOrMlP7jprQo9WPq3KM7VzWHRYyiopC6jOjCBc/qzJMHUd1E/9W/8dwh8bSwUneJBzXvnkSw9g43M3uJTsxBV3A1B1chKNbhavRMdjkTagmKgi8SLiETqyNBSZs2Lb90BkmBIPQCBa9dJRFC8h4kA0LZBiXREu6AaIs7vd8TFMVCgBSqqMcgEhsBFDdE3ALqeiDjlrNYefCw6VC/pHXzFjShZHW6M20hRT9+QlfSJadqp9dSyRIl6KCSkatOHaK1a4kiI92rNCiIqFYtotWrbal2K3VnS4AdHlqw9r0dIDoxRyeCqzm4CkGxDlejZ7LLmTCCoKC4NDKjIvGRYkFBoiaULnAWZFtFjDTidJVi1CjfAG8oZCP1eYIChjZixIhY63Rka0hFi5S7sKK483EzerP603iotg5/QNQQUbI/JdT6QZaQ9hb+hkgjnOTWPcr6yxbKsP0I1b69h05HhtLPHT+hpK+/ROnP/EOvfvKJx6VuGjeO7jz/vMd2ZjdIcv8+BT965HGaiJQpKdyhaKbHDtJAEBAEBAFBQBAQBAQBkxFAGQpHQdkC3MHdiSsLys6dOzmrLsohoJA6xsS9TyEgjmOhRh9iWBAjrQjKVqA0AkoZ+DxBibPAgAAu5qcIsiqhFgVMRWB/IrERgJKRyhf1QBCE7oyRlW8GUEMFPotKvRdlpWEHjlO3Ji1o1vE99Fnq56llyYqUsW9bSjVjDAWsX0/kYqNScDBRSIhvWE8SyCXNSt3Z/VwJ1r63A0Qn5uhEcDUHV1ejCtbWYa1nJrvoSa8FBVYPZJqFFQUWE9zN8YIcRGXhwoVxVAALCoqso6aaQmBwR0QGVb+woMRHUGAWQtYnAIKCiCKuEUCFc7BaFOBzrByO7Gdgttgk6dOnNxU+bFD4GoIwIWuDM5tW6tnUy1mQJj/JwWtJkS6Icp6O64foQLWJ/isQaeri1QyeAC5pdvnSVAO/2W0Ea7MR1j6+6EQ7Zmp6CK5qUDKmjWBtDI5mj2IXPeklKChIDesHCIfyMtyVlUTRlxKDsm3bNiYqEMRMo3D0X3/9FUetfhUkf+nSJa4mXrlyZQ7CEXGNQMeOHWnWrFmEYpCKCQ9FLVFZHIIijvARNFMOHz7MbmZIZOAq+Am1RjJmzMipkM8v/4MeTltEoX/toRy3/0cpwu9QLNuYL1lPFNB27yaKp8CmGaTKLl+aZu5LtWML1mqRsq6d6MQcrAVXc3B1NapgbR3Wemayi57UEhS8aMYPwgaQIfbnn3/mrF4gJnnz5qVRo0YR7p2hoaHUuXNnNiCAqLgSJG/6999/2cKiZPFCf7/P4oXig4j8x6Ubl28R1wig+nmnTp1I8ScEy61atSqFhYVxB7hewYJhpsydO5dJELKKjRkzxuVU9evXp99//z0mI1vE1X8pdNJsSvPFwLjtd+3yHeuJsjpYUdats8wlzS5fmmbuS7VjC9ZqkbKunejEHKwFV3NwFYJiHa5Gz2SXM6GWoLiKDX/11Vdp48aNnJGrX79+dOjQIQoKCqKXX36ZUw4rRbgRc1KtWjUurg6BRw3IjGMdlKFDh7pUoV9ZUFauXMk5lvFwqPIt4hqBHTt2sNntzTffZGsJTGlILICsXvAVRCHHK1eucFFHs6RDhw40e/ZsWrp0KTVq1MjlNCj+A3/F/v37x86ZXacORa9dRwGREYToo9BkGSl82hxK2+Yt34o78mRFQVayggU9Q6yytotdvjQ9A2Z+C8HafIy1ziA60YqYuvaCqzqcjGglWBuBovlj2EVPagmK+Yi7nsGvCIripuQp00BCgekr8yJQCYHyuXLloqJFi3KgevPmzWnBggVM8GC1gJUF2bXMktKlS7MpEEV9sA5XcurUKSpUqBCh0A/iYmLE6eJ/MVNZCgtISakaVKcsE/tRUNrUpix7zZo1tGLFCs7jnS5dOnVzuLKiwCWtZk2io0cNre1ily9NdcCb20qwNhdfb0YXnXiDmuc+gqtnjIxqIVgbhaS549hFT0JQdO4jRwBHjx5NgwYNMv1yrXPJPtG9WLFinPYNgoryiNlJlSoVKRhWr16dNmzYYMpakUIOF/ycOXMS0h7HJyAoICrIo50jx9NgeRZc/NesIapdmx6P/oqufTicIs5foeDnclDWKYMoRZXShq796NGj7D6IIK7atWuz+dFVmrw4k7qzosAlbfBgQ2u72OVL01DFejmYYO0lcCZ2E52YA67gag6urkYVrK3DWs9MdtGTEBQ9u4SIXXqUNMOogTJlypRYwd86h0+03Zs1a0Y//fQTEwXEoDz/X+0QxO7ArQqEAEkHYpECg9CAX2KNGjXo3Xff5axh8QnWAlcvxKy0adPmWdM9e4gQ1D9nDlG5chR59z7d6PElPfxtEzYFpevYmDIO+JACUybXvWoE7CPxwoEDBwhVUeEjiZR5U6dOVedS5mhFcQzo9+QCpqxcZXyNXb40dSvUgAEEawNANHgI0YnBgP43nOBqDq5CUKzD1eiZ7HImhKDo3DmOAKLaJC68sAbgQiniHgGlQOL8+fOpXr16MQ1x8EAgEMQ0efJkJitGyxdffEEDBgygsWPH0iceii/C3QzB8tDtokWL4l0KiOqDJX/QvwMmUtTdB5SkYB7KOmUgJa9QUtcjKMQXpAoxO4jfQXYJYNS9e3fPYzsTEUfCYWAgvV2+ND0Dbn4Lwdp8jLXOIDrRipi69oKrOpyMaCVYG4Gi+WPYRU9CUHTuJUcAkbIWqcv++ecfyp8/v86RE393XOhdFWrMli0bX8KRbWHLli2GA4Esazjg0BWyh8UncKlCuuEUKVLQjRs3VLlVIdvXjZ5f0qO1O4gCAyl91+aUcUB7CggK0vwsyL+NZAKZMmViCwriZf7++2+CCxyynuFzxO14FAeXNFq9+llzT1YUldYTDGiXL02PWFvQQLC2AGSNU4hONAKmsrngqhIoA5oJ1gaAaMEQdtGTEBSdm8kRwCJFitCJEye4WmXKlCl1jmzP7jh4DRs2ZIJ37tw5zu6VJ08ew8AAKULsCcgGXKXU6AkxH0h7jHR1L730kqq1YJ77C3+nm4OnUNT9h5Sq/iuUdfpQCkyRTFV/NIKL2wsvvEC3bt3i4HhHIgILFKw6iNvBuhD0H684uaTFausukD4khGKRGQ8rt8uXpmoFmthQsDYRXC+HFp14CZx8r5gDnBejyh72ArQE6GIXPQlB0bm5HAFUMivdvXtX56j27a4cPFSVhwvW+PHjqVevXpoBAVFEjEuXLl3YAqIISE++fPmobNmyhLo1agSuVD179uTaLYj70CLh/1ykK037UPiZi5SsfAnKMf9zCsqcweMQqHQfEhLCNVjg4jVp0qQ4fZSEAihEhIQDsPJ4JfEF0pcvr3pIu3xpqgbExIaCtYngejm06MRL4ISgmAOcF6PKHvYCtAToYhc9CUHRubkUAOEKhLfZhQsXpuPHj+sc1b7dlYO3d+9ero9SsWJFdmnSIgcPHuTLPawkKMKDjFeoKgpBhdEmTZpwkPm0adNUDYuaLMWLF6c7d+4QUv2+/vrrqvopjSJv3qErrQZQ2K5DlCR/bsrx01hKUiB3vGMoKauR4hh1Y5Ili2t5gZXm7bffZtcq1JABifJa3AXSaxjQLl+aGiAxralgbRq0Xg8sOvEaung7Cq7m4OpqVMHaOqz1zGQXPQlB0bNLHLJ4nTlzhitToiLlX3/9pXNU+3ZXDh4u3yB7SPF7+vTpmKqfnpDZv38/1apVi27evEmpU6cmpBR2vLzDGjNhwgT67rvvqHXr1p6Gi/l8yZIl1LhxY0J8DGJB8KcWiQoNo+sfj6KHKzdRYKZ0lP270ZSi0gtuh2jatCkH5a9evZrTCruTw4cPc5pmFLVE7BNIsivBZ7C0uE1NHF8gvcoHtcuXpko4TG0mWJsKr1eDi068gs1jJ8HVI0SGNRCsDYPS1IHsoichKDq3kQKgUh0dl1i8pRfxDgHHgzd48GAuSvj5559zNXdPApet1157jW7fvs1uYSAglSpVoqioKLZCwK0LMSTIsgYrFwiQFunYsSPNmjWLCQOyeylWGbVjREdG0s1hU+nuzMVEwUGUsX97DqAP+M+64ziOEoODZ/FUlLFFixb0448/ErKT9evXL85yUACzVatW/OyrVq3iVMUuxV0gvavGt24R3bsX6xPE6cSxLqmsQq8WQ2n3FAG7/ILyJ32LTszRluBqDq6uRhWsrcNaz0x20ZMQFD27xMGCsnz5cna3QVpcpMcV8Q4Bx4MHVy0EiZcsWZIrucdXmBBuYCAOiP/BJR2kBpsbNUygExRcBElBgDwC42Fhcc4g5mnFcOOrUKECHTlyRFWKYnfj3Zv/K/07aDJFh4ZRimplKeu0IRScPXNM82vXrlH27NmpRIkSdOjQIU/L4sQMKHyZPn16giXPkYCcPHmSypQpw4kbIHCZg5sa2saR+ALpHRtHRRHly2doFXqPDykNYiFgl19Q/qR20Yk52hJczcFVCIp1uBo9k13OhBAUnTtHAXDGjBkc14A3/gMHDtQ5qn27Ox48uHmhnszOnTuZdMBC4ErwOdy67t+/T7C6jBw5MoZ8OMZp4HKOtnXq1GFLgjcC0oRxIiIiaNu2bUxYvJEnx8/QtQ7D6cmRfygwYzrKOmUApXr9ZR5KSS3ctm1bQiyKGvnggw/YbW3UqFGMAeTJkyecrhnxPL1792bLEdaM2B5YOxyTB6iZI1YbWFvWriWKjHTfFWmVa9XSlAlM8zps2sEuv6D8Sb2iE3O0Jbiag6sQFOtwNXomu5wJISg6d44C4PDhw2nEiBFcSK9du3Y6R7Vvd+eDh/iT8uXLc4D60qVLqVGjRrHAgTUDcT9IxQsdDBs2LA54sJYgDS/S9kLQBm29lenTp3NGr4IFC7JlJ02aNF4NFfU4jG6NmE535yzl/kFZMlBgmlQ05voxmnp2P419uT61rvgKBaVLTYFpn/4EJE1CUQ8fUdS9hxR17wH/GR36mM7eukFVf5lJqYKT0LZarSktBdHIw1to9vlDVCFNFlpY+FUKT5OS2hzZQDuuX6BSOfPQ8n6jKHOG2BnFIpInpZTVylFQpmcWFliu4libPNVPURDBCSDhAAAgAElEQVTRUEfFKxBt2skuv6D8Sb2iE3O0Jbiag6sQFOtwNXomu5wJISgad45CRBy7wb0LFhQENOPtNS7UIsYhsHv3bvr0008pefLkNG7cOMqd+2kGLLhCoSI8yAmKGcKK4M5tC65SQ4cO5XgU/Il4FC1yKt0pmvriVOq8vzMVvFOQXchgjenQoQPVq1dPy1Bx2qbZf5qyL95ISW7eo8CISGp59yDtCL9Lv6UvQ0WDXQe9u5pw0P2TtCjsGnVJkYfKJElD7e4doXQBwbQi/YuUMyg5d3kUHUkd7x2h7eF3qWBQCioalIquRz3hnxtRT+gRRcUZGsH1SPnsnEms8ogRlOXAAQqEy5eTRAUG0o3SpWmHC8KoCyzpLAgIAoKAICAICAKJHgEU1YYXjK9KQLQvr84hBgXxJyAquEzDhUbEOwTcvRlQiGHRokWZGCAeBFXgkeULrlCwXHmKKUFaYaQchiVGa82QOlSH1tAaqk21aTWtZhcpxLwgZTHqrRgl4Q8fUcZsWflQXtuwnQIehFLU3QcUydaSBxQd9oQCU6eiwLT4gVUlFQWmTMGWlfM3rlOJujUpebJklDxZcrpx819avOAHavTOO1zFPvLWXYq8dpPun79MTQb0ovWH9sVadroUKSl90uQUEBpG0RFPXbfuRUfQ7egI+uTNJjRm0fcUkCzpsz6erChiPTFqW8QZxy5v0EwD0ISBRScmgCoJIcwB1c2osocthdvryeyiJ7GgeL1FnnZUAISvP4KwL168SLly5dI5qn27uzt4sHw0aNCAs2fBWnL27FlO9wtiiKxp8QXQ60VzN+2mCvQs1mQX7aIi94twoDmC2aFzT+RI7RqUxAA1atSg9evXq+0W065z584x9V0++ugjgjuaKwkPD6fNmzezVSRHjhz8o5C26KgoCjtwnB6t3konl6+mGjt/oQiKpjUFa1Dpj1tRuvfffFZs0qAq9Jof1OYd7PILyp/ULDoxR1uCqzm4uhpVsLYOaz0z2UVPQlD07BIHgoK0sLg0IzA5SZIkOke1b/f4Dh5S7sJ9DjU9IDVr1qSVK1ey65eZAuvJOlpHERRBwRRMIRTCVhRYyhCAjvVA/0YILEEffvghp1WGG5mj7KE91J7a0xyaQ+XItZUOcTYoKpkvXz4mzFotRc7PAEvOp1160NBpk6la0gw0N01xCkyRjFLVf5VS1alKKTMkocDqr8R9dLGeGLEd3I5hl19QpoJo8OCiE4MB/W84wdUcXIWgWIer0TPZ5UwIQdG5cwAg3u4jdS2K5P377786R7R3d08HD4UYq1evzpdwpMv1NkBdLcrO1hOlH6woC3osoEmTJmku+hjf3O3bt+fMXXAXhKXIUZzdzNyNc/36dd6L7oo2qn12pR1IN9I9o3bMt++0pWq7L1D0w9CnHycJplxRZyjZjQsUEBVF0cHBRCEhFLB6dZxpQHaiHzyiyJt3KfLWHf4zCm5ndx9QNAL/Hz3mcaMehrLrWpJ8ufgnOF9OSpInO7uxmSouaru4nM8Hart4Oiem4iSDu0RAdGLOxhBczcFVCIp1uBo9k13OhBAUnTsHAKL2Borpqa1boXPKRN1dzcFDdXgQQq2FEr0BztF6ovRXrCgfLv2Q3n33XQKpmD17tjfDx+mDmi+oDn/lyhV2H1PElZtZebIuGcOff/7JRRgRMH941x6inYfp4aot9OjPbZTk+iXKffdAzFov5apCUUWKUXDOrEw8OPaFScldoifh3uEUFEQpXi1P6do1opQhlTimxlDxs9ouas6JofjIYB4REJ14hMirBoKrV7B51Umw9go2yzvZRU9CUHRuLQCIN8tFihShkJAQWovaECJeI+BLB8+d9UR5uNXXVlOd7HVY98eOHfP6mZWO9+7d47gWkAC4CzqKOzcz3ZNqGKBx48a0ZMkSGjRoEGdVg0RHRNDjnYco6P0WlPTUEQrNkJOupClO0Y8exx45KIiCMqajwEzp+E/+yZSea8AEpU9DAalSUOB/PwEpklPU7XsUfvYShZ+9zH+ibkzUzbs8ZvBzOSjtB29R2ub1eRzDxI9qu/jSOTEMfz8fSHRijgIFV3NwdTWqYG0d1npmsouehKDo2SX/xaBs2rSJXn31VWrevDn98MMPOke0d3dfOniurCeKdhQryj+F/yFUa4dbVZYsWXQpb926dVxw0jkzWHxuZt5aUdTEszg/zIULFwhZ1FCkEsH8hQsXftZkzx6607gxpV+8mKLLlqXI67co8uq/FJA6JQVlTs/1XQICA73GB0QIFpu7c3+hx1v28jgByZNS+h6tKEO3lhSQJNjrsWM6espKpjQ0Kr5Gh0uZL50T/cAnjhFEJ+boUXA1B1chKNbhavRMdjkTQlB07hwAuGjRIr5U9urVi8aPH69zRHt395WD58l6omipYbuG9OvcX2nZsmWcUUyPjB49mq0TX331FfXs2TNmqPjczBCs742ojWdxHvvLL7/kAH4QKaRadsxeZpXuYE25O3c53f/xN4p+/ISSlixEWScPoGSlCnkDRew+rrKSxbDSp/E15CK+xnGQyMhIdj+MN7ObTpcyq7DWD6h9RhCdmKNrwdUcXIWgWIer0TPZ5UwIQdG5cwDg4sWLaeDAgdS1a1f+EfEeAV85eLjAr6W1FElP64G4kiAKomLfFqNDbQ4xoQCx0CMNGzakFStW0LZt2whpqyGeiBKC9bVaUfTEsyBgvkyZMnTkyBFOYYxUxoo46g7WFsTltGvXjp577jk9sLjt++T0BbrR40t6vOMAUXAQZejekjL0el9fML0nK4oH68mNGzeoWrVqHD+0atWq+LOo6XAp85VzYopi/XRQ0Yk5ihNczcFVCIp1uBo9k13OhBAUnTvH1wHU+XiWd/eFgxdFUZSP8tEFuuDx+XOcykFXCl3h9Me7cHn1UpDhKlu2bHTnzh1CLIqSOlmNm5lWK4reeJY9e/ZQpUqVeI2oRVOwYEF+akV3eIaXXnqJjh49yhd1pIIuW7asl8jE3w01W+7N/YVujppJ0Y9CKUmh5yht6waUumENDtJ3JUhqgfTLSZM6FJ10bOhlbRfHWj0Yrk2bNpyRza0lxRMZUtbkghT5wjkxRaF+PKjoxBzlCa7m4CoExTpcjZ7JLmfC1+/XflNJ3ugNaNfxfOXg3aJbdI/ueVRDmug0VDJnScKbc1zMU6dO7bGPqwZnzpyhAgUKUIUKFWjnzp3cxJP1JOb+qsGKYlQ8y/Dhw2nEiBFsLdiwYQMFBQUxQalXrx7Vr1+fkPULBUtRlwXpjmFlrFu3rlfYqOkUfu4y3ej5JYVufhqfAkle6QVK/VZNShlSmYJzZ+M4ldOnT1PFihWZDG7fvp2z78URd8TBg/Vk4sSJbElDbA4yzV2+fJlmzJhBHTt2dP8IXrqU+co5UaMbu7QRnZijacHVHFyFoFiHq9Ez2eVMCEHRuXN8HUCdj2d5d388eO+99x5fwHEpR2yGN7Jw4UJOsgAXwcmTJ/MQat3MalEtLhypRoyKZ0ElelhR9u3bR+PGjaPevXtz7ZbVq1fTzJkzqVSpUrRlyxaaOnUquz+CwOD/4fJllsAKFXbgOD1Yvo4e/t8Girh47dlUgYEUmTUjvXdhKx24e4P//43qIbR87RpeWxxxJA7/1XaJL/YEBTsrV67M1pK///6bQkNDOXEGZPPmzYyVS/FkRXFDivzxnJild18ZV3RijiYEV3NwFYJiHa5Gz2SXM6H2fv3111/Tt99+y8l78CIUdxFHQQHssWPH0sWLFzmZEerXOdeZU9qjyPW1a9di7gXBwcH88tmViAXF6J3t4+P548GbMmUKdevWjYYOHcpWBW+kR48efGgWLFhALVq0IC1uZnkpL52hMxRI8WfJ8mSR0RrPcujQISpXrhxfynFBB1GZN28eWydgBUK6ZMj8+fOpbdu2nP1ryJAh/JMkiblFF5ms7D5MD/5vPYXtO0YRl67RZyd30uxHF+nF4DR0JyqczkY9pt5Zi1Hfpq0pZc1KlKJ6eQpKl+ap+pyJw65ddLtgQdq6dSvVrFmT6/D8P3tXAmdT+b+fWcwwM5gZ+xqhf3YVShRKKCSylzVZQtYskUopRbsoa/rJmhZJWuzZQkQUIfvYl9nMfv+f5+VM13Xv3HPvWe69c97v5+OjnPMu5/m+77nneb+bIgkJCQIHZnMjuVTi0KZNm4YBAwagdOnSoFtc0aLOXc7ghUtZIO4Tb/ZFILWROjFGWxJXY3CVBMU8XPUeySp7Qi1BYZIiJqZhmQ+SEHuCMmPGDBEfzAy7jJ+lt0tSUpLwWHEmJCj0hnjiiSfcqk0SFLcQ5a4bAnHj0YrAGIvGjRtjzZo1XimEp+88eT906FB2TIdaN7MCKIBYxLodV694FvsUxWsmr8HIkSNRvnx5UbuFcSlMu01XNXvhi6Nt27bgxzxfAGPGjEGPHj1cx4G4fRrPbvjxxx/RvHlzRBcoiK1zvsCFLbvxyPuvICUzA7MLVMWDYTFASAjy1q4qyAqLQYaNGoygH38EmjVD+nffCXc26qhAgQLC2vXss88KvXfv3h2ff/45WrVqJdzclJgTkiQ+I69xbTDrGU9jbhEvXMoCcZ94prHAu1vqxBidSVyNwVUSFPNw1Xskq+wJtQRFwZeu57t3784mKMyoSVdzWlf4+69GJEFRg5JF7wnEjcdNEBsbC7o9MQDbU+tAamqq+OjlH9ZTyTE9rZfrwp31ROlWjRXFPkXx95nf48EHHxSZxygs5Pjkk086neW+ffvwwgsviOxWlDJlymDUqFHixbFnzx5hheEfBt7zY59ZwvQQmmtr1KghsKUrXrt27US3TA/eqVMnxETlx09t+6LwjoOifosi+aKCUOTqASR36YNXzxzGe8sWipfdhQsXQJ1RGG9y8OBBlCxZUsy7cOHCN02Zrl5MGMCXJlORMyaFxThvEQ9dygJxn+ihS3/uQ+rEGO1IXI3BVRIU83DVeySr7AmtBIXZRqtWrQq6gNHDg99ojJHlf+fPf8NbwkE5JCj83eZ3XaVKlYTHB2NrnYm0oOi9sv28v0DdePR7ZPzF1q1bXccbOMGegdQTJkwQ8RncOCtWrDBEQ3rFszhLURxzOAZdu3YVJIAf4O6E2c5YiX758uU53sosYCwMqUWYWYu6ofWiT58+Amd7Ye2i9957T5h+f924ESGHTyF5zTYkr96GlO1/ApmZ+DXtMnrE70NUUAi+L1MfhWpVxXdZlzF/33bs+/ewIJS0nDVq1MjpVJkAgdYXJgyguxfd4G6JVXLiUobatV0+eqDuEy269Pe2UifGaEjiagyukqCYh6veI1llTzge1r788suglcSVOFpQGAfL317+3jLOl8JDSXp8sAyCM2HMKF22GZu6bNkyETfLf2OmVkeRBEXvle3n/QXqxlOKLDIQa8SIEW5RPn78OFj0kMFbrC3CmAb6UTZr1sxtW09v0DOeJacUxZ7qjlaFSZMmgR/wtWrVEi8FukzRHWvcuHEi+5UawpMTHiycSn3wFIVxMfaxI2zHE5VHHnlEuKXRDWvevHnCskPJSk7Byc3bUbdda5y9ehkf13wYzS/aRHFICl24/sxIhC1/BGpFxiIoOBgICgJu/B0U/N9/X83KwMtn9+KbuEOibZ8mLTBx+ChE310VoUVvuOfRinLDpcxdMUhPsfZ0zcj7PUdA6sRzzNS0kLiqQUmfeyTW+uBodC9W0ZNWCwq/MXj4SBfzh1lkGcDq1avRuXNn4VGhRpgEifEq/FaRBEUNYrn4nkDdeBs2bBBZm1hskc/gSrgpGEw/Z84c8XHMtMQDBw4ET/KZXcIo0SOexV2KYr10x4wZtDTQ+kEi5+g2pRYjtmcdlsuXL4tsY9WqVXPalC5gPGFh0D/TDjPQnRYhEhBatWgZ4ykKyaQtPQNp+w8j5ff9SN25Hyk79yPj9DmyFSDLJtqAVeKzbNf/jX/sZGXqBYxPPIQrtgyUC86LWQWrokKpMgivcQciYsMQ9cNiBM37DMH178/xMfXCWi2W8j73CEiduMfImzskrt6g5l0bibV3uJndyip60kpQ6KpF93vWYmNyG28ICi0udPsKCIKi1H+wX5COKc3MXqxyPN8jQCsIA6dZAJABWY6pa5nBauXKlVi0aBGSk5PFSX7Lli1FrIUrX0jfP9XNM3j1vlfxR5E/kBWclX0hOCsYNc/XxMtbX9Z1ujS/8qXCjGbt27f3qm/WPGH6Y1pPJk6cmGMf1N+CBQsEuSTJYFrg2267DUuWLBFkiT6rSvFMjyYjSAoQlJWF0KtJyHPxKhKOnsSU5Uux5eQRNCxQDLPy3oEgEpobkhUWisQq5RB/V0Uk1Lgdmfn/yxjm0djyZomAREAiIBGQCAQoAsykJQ793Ai/r/iHruOMZ+XvNrN6sRgzk9kcO3ZMxJyS8NAiwt92Zy5ePBBlsh/+/rP9119/LZLgsNYbExk5inTxcqeZXHY9kE8G6OtIn0ee+DPugafvdNliilmmIWbAFokLLSb0pYyJiQkY7bkLsmdw/alvT7nMLe7pg5JcMECNKYv5wggPD/e0C7z55puiBgvJCf9WI/Q15QuJbmcUjsvMXTVr1lTTXPU9DLJngD1fiFvWb0StqFhRwyVl824k/bIVtsTk630FByPfg/cgZvDTyFv/ruwECoG8T1SDFGA3Sp0YozCJqzG4OutVYm0e1lpGsoqe1FpQnBkO6NGybt06kVKY31wkG/w9p5cL0w4rB8M8wOT3AQ9D+Y3Gg2ZmU2XGTf5GM0ieB8nORBIULas4ANsG8sZjjQwSD7p70X2LQkLCbBAUxjjQfciVq5E/q0tNiuL+3/bXjaAQizZt2oh0gYwL6datm8fwEG++oHbs2CHiW9QKUyEzboXB7Mz+weB6V2KfcvkeqB+D/dFljKc7JLF0I1PElpqGa5t2IWnlRiT9sDE7s1jeutURM6IH8jWqIxIMuCo0pfY55X36IhDI7y59kdC3N4mrvnjm1JvE2jystYxkFT2pJShasNTSVhIULegFYNvcsPHi4+NFUBZdlOjWxTgTnuYz/a4RKYSNVrM764ky/pT1UzC84XDdpkNrBlMY03rBGBJPsEtMTBS+p4wpYYwJzbWeCkmmu5TR9imXV+E/kqFmLPbPLGVHjhwRlrf69evf0syWmYnE5Wtx+Z15SD9wVFwPv7sy/rm3EhqNG4KgMGMLXqp5DnnPdQRyw7vLH3UpcTVPKxJr87DWMpJV9CQJipZVAibtCVLlI6dxGMs0t8rGCySFqk1RXONsDfxe7HfdHo2+pyz4SBc5pvGlRUStMF0zzbLM1sHYEiPEWcrl2nCdGtjZHGgdYjFHBvAxu4grsWVlIWnFelx+93Ok7bueCSy4UEHkb9cU+Ts/hvCqFY14RNmnBwjId5cHYHlwq8TVA7A03iqx1gigSc2toid//76WFhSTFry/DGOVjecveLubhycpioskF8GZiDMIhufWClfzILmgbygTCnz33XciEI6WKbpH0cLCYLimTZve0nzQoEHCPYtuWiQARkhOKZfVjsfnoQ8siz0yEM9VLRWlP5K25J834+DkmSiw96io00IJq3EHovu2R9STjyAoJETt8PI+HRGQ7y4dwbTrSuJqDK7OepVYm4e1lpGsoidJULSsEmlB0Yjerc2tsvF0B87ADtWmKP515a94+rGndZ0J3aBYVIlFDkk6WKk+Li4uewxm22BxTEdhcNs///wj2rHKu97iLuWyJ+OxgBQD85hkgfVY1LiycZ+0qPcAEr/8CfELVyL97+tB/XnuLI9CLz6LiOYNVPXjyTzlvTkjIN9dxqwQiasxuEqCYh6ueo9klT0hCYrGlePvAGp8PNObW2XjmQ6sCQMapTsWtBw9erR4AsaSMENa7969RXYN1i7ZvHkz6tWrl/2EzMDFwkrVq1cXKQfViKfB7s6SBoQiFA/jYXgai8IkCjVq1BAZRFjxnoUj3Yk91rSqpGzdg0tvzETK1j9E0/DaVVFoXF/kq3+Xu67kdZ0QMGr96zS9gO1G4mqe6iTW5mGtZSSr6Mnfv6+li5eWVRyAba2y8QJQNW6nbJTumFVryJAhInd5z549syu9z549WxCVdu3aYenSpdnz+/TTT9GvXz9RA4X1S9SIJ8Hu7pIGMOWyp7EotAyx3gtzrZNwubOiOMNauH+t3oZLr3+aHafCzF/RAzoholl96fqlZiFouMeo9a9hSrmiqcTVPDVKrM3DWstIVtGTJChaVol08dKI3q3NrbLxdAfODzo0W3cpKSkoW7YsLl68KPKW0xWM0rZtW5HzXK01wtNgdzUplz21orDq/d13340//vgDP/74o9O4GnsV54Q1A+oTv1mDy5PnIv3QcdEsT/nSKNi/A/J3fBTBEXn9YLXkvimYvf5zH4LOn0jiap6mJdbmYa1lJKvoSRIULatEEhSN6EmCojuAPuzQFy/NV199FSzSNHToUFF8iUHnhQoVAivDX7p0Cfny5XOLiCfB7u6sJ8pg3lhRGPDfsWPHW+qiOHsANViTqCT/sgVXpi1GyqZdopvgwtEoPmuCdP1yuyo8v0GNTjzvVbaQuJq3BiTW5mGtZSSr6EkSFC2rRBIUjehJgqI7gD7s0BcvzXPnzgkrSlhYGE6ePIm9e/eiQYMGqj7yCZWnwe5qUy43QROPY1FIripUqCCqyzO2htm9XImnWLNK/ZWpC4RlJSg8DMVmvYrI5g18uFpy39Ce6iT3IWDME0lcjcHV24MP82YjR9Lr/R+oSEqColFz/g6gxsczvbn8MTIdct0G9JXuGIfCeBRaUK5cuYIJEybgnXfewbBhw9w+myfB7p6kXC6LsvgX/3qccpnzZhV7PtPMmTN1IyhKR1dnLcOFMe8DISEo8v4oFOj0qFuM5A3qEPDV+lc3u8C9S+Jqnu4k1uZhrWUkq+jJ37+vZZC8llUcgG2tsvECUDVup+wr3e3btw/VqlUTQfRFihTBjh073Fog+DDu3LWcuWmpTblcAAUQi1i3mDnecPXqVZQuXRpMr0xLStGiRZ32oQXrhGU/49zAiUBGJgpNGIDo/p08nqdscCsCWnQi8XSNgMTVvNUhsTYPay0jWUVPkqBoWSXSxUsjevJHXncAfdihL1+azZo1E0HxlFKlSuHEiRNuM2EZEeyuB/zMWPbBBx+A8TXjx4/XnaCww6RftuJsr3GwXUtF9OCnETu2j1u89Hi23NyHL9e/xDU3I2Des8k1bB7WWkayip4kQdGySiRB0YieJCi6A+jDDn350mTmq+bNm4unZyriOXPm5IiEO+uJ0tibYHetKjhy5AgqVaqEwoUL49ixY8ib99asW3pgnfLbXsR1GYmsq4mIHtgZseP7S5KiQXl66ETD8Lm2qcTVPNVKrM3DWstIVtGTJChaVokkKBrRkwRFdwB92KEvX5qsAUI3LxY7ZGX2Tp1ydlsyMthdDxWwtsuyZcsE0SLhchS9sE798xBOtx2MrMvxiH7+KcSO6ytJipcK1EsnXg6fa5tJXM1TrcTaPKy1jGQVPUmComWVSIKiET1JUHQH0Icd+vqluW3bNlGwceLEiQgPD3eJhBnB7lrVsGnTJpGNjKRrz549t5AGPbFO3XMQp58cgqwrCYge2g2xY3pLkuKFAvXUiRfD59omElfzVCuxNg9rLSNZRU+SoGhZJZKgaERPEhTdAfRhh4H00jQ62F2rGmgRYlX53377zWnBSb2xZhpiQVKuJiLmhZ6IHdlL6yNYrr3eOrEcgC4eWOJq3kqQWJuHtZaRrKInSVA8XCUsCsfgVXv55ptvPOxF3i4RkAhIBHJGYOPGjSJdMivMuwqW1xPDfP/Gody7SxFyLQ3nWtXDucfvB4KC9BxC9iURkAhIBCQCEgFVCDzxxBPgYZ2/ikwz7K+aMWheVjkZMAg+n3Yrdacv/Ew1zMKNzEj2999/4//+7/+yBzAK65Sd+3C63TDYEpOR/6kWKDJ5BILyhOr7YLm0N6N0kkvhUv1YElfVUGm+UWKtGUJTOrCKnqQFReNy8ncANT6e6c2tsvFMB9aEAaXu9Af5jTfewNixY0XRSVpTFDES69S9/4jsXplnLiBfozooPuc1BOeP1P/hclmPRuokl0Hl0eNIXD2CS9PNEmtN8JnW2Cp68vfva2lBMW3J+8dAVtl4/oG2vrOQutMXT/Z25swZlClTBvnz58epU6eQL18+MYjRWGecOitIStr+IwircjtKLJyM0JLOi0bq/9SB2aPROglMVLTPWuKqHUO1PUis1SLl2/usoidJUDSuM38HUOPjmd7cKhvPdGBNGFDqzhiQO3bsiCVLlmDevHno1q2bKQSFg2TGJ+LsM+Nxbd12hBQvLEhKeLWKxjxkLuhVrn9jlChxNQZXZ71KrM3DWstIVtGTv39fSwuKllUcgG2tsvECUDVupyx15xYir25Yu3YtHnroIZHVa8uWLaYRFA5kS8/A+RFTkLDge4QUiUWpn2YgT+liXj1Hbm8k178xGpa4GoOrJCjm4ar3SFbZE5KgaFw5/g6gxsczvblVNp7pwJowoNSdMSAzi0nlypVx4MAB7Nq1C7Vq1TLcxcv+STj+hRFTEP/5coRVrYBSK6YhOCrCmIcN4F7l+jdGeRJXY3CVBMU8XPUeySp7wt+/r6UFRe+V7ef9WWXj+bkavJqe1J1XsKlq9P7772Po0KHo27cvPvnkE1MJimJJies0Atc27ERE0/tR/PM3EBQSomruVrlJrn9jNC1xNQZXSVDMw1XvkayyJyRB0bhy/B1AjY9nenOrbDzTgTVhQKk740C+fPkySpYsidDQUJw+fRpr1qxB69atjRvQSc+ZVxJw6tF+SD90HAX7d0ThCQNNHd/fB5Pr3xgNSVyNwVUSFPNw1Xskq+wJf/++lhYUvVe2n/dnlY3n52rwanpSd17BprpRz5498dlnn2HatGmCrDXCnHAAACAASURBVJhNUDjR9CMncbJ5X2RdjkeRd15AgW6Pq55/br9Rrn9jNCxxNQZXSVDMw1XvkayyJ9QSlKlTp4rfxr179+LRRx+FffH0Ro0aidjNPHnyZKvh4MGD4jfUmcTHx6Nfv35YsWKFyJo5cOBAvPTSS07vlQRF75Xt5/1ZZeP5uRq8mp7UnVewqW7022+/4d5770X16tUxYcIEsMquL+Ta5t043W4oYLOhxKIpiGhY2xfT8Lsx5fo3RiUSV2NwlQTFPFz1Hskqe0ItQfnqq68QHByMX375BSdPnryFoPC3csiQIarU0L17d5w9exaLFi3CuXPn0KRJE7z++uvZGTTtO5EERRWkuecmq2y83KOx/55E6s5YrTJY/Z577hGB8pMmTcKoUaOMHTCH3uMX/YDzg94QBRxLrvgY4VUq+Gwu/jKwXP/GaELiagyukqCYh6veI1llT6glKAq+r7zyCnbv3u01QUlOTkZMTAw2bdqE2rWvH7xNnjxZWFPWr19/ixolQdF7Zft5f1bZeH6uBq+mJ3XnFWweNZo5cyb69OkjXp4bNmzILtzoUSc63Xzp7Tm4PHkuQkoWRelVnyC0RBGdeg7MbuT6N0ZvEldjcJUExTxc9R7JKntCL4Ly559/IisrC7fddptINqPUE3PUCw//7r77bqSnp4t4T8rPP/+MDh06gHGgjiIJit4r28/7s8rG83M1eDU9qTuvYPOoUWJiIipUqCBMz+XLl8e7774rYlH4IjdbaNGhFSVh8SqEVa2IUt9NFRYVq4pc/8ZoXuJqDK6SoJiHq94jWWVPOP6uvfzyy6CVxJU4s6Aw/qRKlSqIiIgQyWVINhiv0qZNm1u62bhxo4hh4e+sItu3b0e9evWQkZEhCYreCznQ+rPKxgs0vaiZr9SdGpS033PkyBF07twZjEmhNG3aFB988AHuvPNO7Z172IMtLR1xXUbi2vodyNeoDkoseBtBea6fPFlN5Po3RuMSV2NwlQTFPFz1Hskqe0IPC4oj9iNHjsTx48dFjImj0IJCN+q0tLRsCwrjWtq3by8tKHov4kDszyobLxB1427OUnfuENLvOrEODw/H4MGDwYwkNEezPsozzzyj3yAqe8qMT8TpVgOQtv8I8ndpgSLvj/KJRUfldA27Ta5//aFlsOqsWbMwduxY/TuXPd6CgFzDgbEorKInIwjK6NGjcfToUacERYlB2bx5syAqlClTpmD58uXCpdpRpItXYOwX3WZplY2nG2B+1JHUnXnKULDmSc+HH36IMWPGiHgUWlcKFy5s3kRujJRx+hxONu+HzLjzKPTaQET362j6HHw9oFz/+muA7harVq3yeVII/Z/MP3uUa9g/9eI4K6voSS1BofsV/zDb1p49e7BkyRKR1YuEg2SDqYZ5oLdu3Tq0a9cOM2bMEFYRZ8L4lAsXLmDhwoXZWbxee+01mcUrMLaGsbO0ysYzFkXf9C51Zx7ujlgPGjQIzAXPAEDGpfhCUv84gFMtnoMtMxOlvv0IeetW98U0fDamXP/6Qh8XF4fSpUuL4FYKPyqeffZZfQeRvd2EgFzDgbEgrKIntQSFsSevvvrqTcpr2LAhli5dipYtW+Kvv/4S18qVKyfSDffq1Sv7Xh6CPPDAA3jxxRfFv7EOSt++fW+qgzJ+/HinC0NaUAJjv+g2S6tsPN0A86OOpO7MU4Yj1nSFYfA8s4/Q5YvZSnwh8f/7DueHvY2QEkVQZs1shBSO8cU0fDKmXP/6ws64Kn5MVK1aVaxpnpAuXrzY5cmnvqNbsze5hgND71bRk1qC4iut+R1BccbU7KtW+gooOa5EQCJgbQRokuYHXOPGjUVsik/EZkOpuasQs3kfEqvchqNDngSCg30yFTloYCPwwgsv4J9//hE+4Mxax7/ptsF4lLvuuiuwH07OXiIgEXCLAAssMlukv4rfERRHoPyd4fmrYl3NyyonA4GmFzXzlbpTg5I+9zjDOiEhQVhR6D/LYlU1atTQZzAPe8lKTsGpR/uKoPmYF3oiduR/5nQPuwqo2+X6109dhw4dQqVKlcSft99+G/xQYbA8XbyYLpSZdZj6U4q+CMg1rC+eRvVmFT35+/e1JChGrXA/7dcqG89P4dc0Lak7TfB51NgV1h999BGef/55tGjRQvjQ+krSDh/HySbPwpZ0DSUWTUbEQ/f6aiqmjSvXv35QMyiVft+se0BrCWv9UEhWRo0aJRJB/P333yhUqJB+g8qeINdwYCwCq+hJEhSN69HfAdT4eKY3t8rGMx1YEwaUujMB5BtDuMKaWb1YD+Xff//F+vXr8eCDD5o3KYeREr9di7O9xyM4tiDKrP8MocXNzy5m5sPL9a8P2nTpqFy5Mg4cOCBICP8oBIUj9O7dG7NnzxaBrEytLUU/BOQa1g9LI3uyip78/ftaWlCMXOV+2LdVNp4fQq95SlJ3miFU3UFOWC9YsABPPfUU7rvvPpFi0RdV5pUHOT/6PcTP/goRTe5DcRZx9EHFe9WgarxRrn+NAN5ozmJpd999t6hDsGPHjltO9S9evIg77rhDFE5jsdLatWvrM7DsRVpQAmQNWOVdIwmKxgXp7wBqfDzTm1tl45kOrAkDSt2ZALIbCwovMy0rP+4Yh/LZZ5+he/fu5k3MYSTGo5x8qBfSD59AkXdHokDXVj6bi9EDy/XvHOHU1FS0atVKEOYJEya4VQOD4xkQz3TZTJvtDNeZM2eiT58+qFOnDrZu3SqC56VoR0CuYe0YmtGDVfTk79/X0oJixmr3ozGssvH8CHLdpiJ1pxuUbjtyh/XatWvRpEkThISE4LvvvkOzZs3c9mnUDSm/78epx55DUN4wlFn3GfKUK2nUUD7t151OfDo5HQZnEdAffvgBXbp0QUyM+vTRbPPYY4+JGaxcuRKsO+BKMjMzRYrs06dP4+TJkyhZsqRTgkISziB5WlBc1ke5dIlFDdw/eYECQGys+/sscEduX8O5RYVW0ZMkKBpXrL8DqPHxTG9ulY1nOrAmDCh1ZwLIN4ZQgzX99Omvz6xHP//8M+6//37zJugw0qVJs3D5nXnIe19NlPzmAwSFhPhsLkYNrEYnRo1tZL9JSUmikvvkyZNBa0ipUqUwZ84cNG3aVNWwzz33HKZPny7uLVGiBP7880/EuiAErPTMNNkPPfQQVq9eLdq4wpXuX3Xr1hV9MV7lpoB5FncsVw44ccL9HMuUAY4elemwc8DaPYjyDjMRyK3vGkcM/f37WlpQzFz1fjCWVTaeH0Ct+xSk7nSH1GWHarHmR+XIkSMRHR0tguZ9lXrYlpaOk837Im3vPyj0ynOIHtDZPLBMGkmtTkyajuZhGKzOSswjRozAiRMnkC9fPkFyFeJA4sGsWpGRkS7HYh+0iNAa8vjjjwuyQQvMF1984bQN3bbovsWUws8880yOBIUX+/Xrh08//dR5wHzz5sAvvwCZma6xIFFu0gRYtUozXrmhg9y2hnODTpw9g1X0JAmKxhXs7wBqfDzTm1tl45kOrAkDSt2ZAPKNITzBesyYMeIEvHjx4vj1119FrRRfSNrf/+Jkk96i8FbpX2YhvPLtvpiGYWN6ohPDJqFTx8nJySJuZM2aNaLH9u3bCwsKycayZcsEIWCwesWKFfH555+7rEnCOCimCaY7Fl29qlevLsgOiU+7du1umi2tM7Sw0GJz5syZbDeynHDlHP7v//4Ply5dujVgfscOoE4d94hs3w7IQHu3ZNA9kPIOsxDITe+anDDz9+9raUExa8X7yThW2Xh+Areu05C60xXOHDvzBGsSAp4001e/fPny2Llzp0cxBHo+1ZVpi3Dx5Y8RVrUiSq36BMF5w/Xs3qd9eaITn05UxeDz589H165dxcf/tGnThMuVvZw9e1YUTWR8U1hYGPbu3SsyazmKUs/kjTfeAIkyCyw+8sgjoo4JXb2KFSsmmpBgvPPOO+B9LMr49ddfZ3flDlelgCMzfzFgPk+ePP9Ng1YUuoplZNz61KGhwMMPS+uJHTLusFaxdOQtJiBgFT1JgqJxMfk7gBofz/TmVtl4pgNrwoBSdyaAfGMIT7Fm8HGbNm3EByVrR/AE3Bdiy8zE6SeHImXTLhTo1RZF3hrqi2kYMqanOjFkEjp1qljd5s6dix49ejjtlcR39OjRws1r0KBB+PDDD2+5jzEi27dvFwSmWrVq4vqAAQME6WFtk8GDBwt3LlplaEHh7+mqVatuim9xhysD5hs2bCisg6+88ooo7pgt7qwo0npyk87cYa3T8pLdaETAKnry9+9raUHRuJADrblVNl6g6UXNfKXu1KCkzz3eYM3YAWb2ouvO8uXL9ZmIF71knLmAE416IOviVRSb8xqiWjXyohf/a+KNTvzvKa7PiFYMPg8tEvfee6/LaV64cAGlS5cWVpRTp04hf/782ffGxcWJLFzlypUDM4ApNXASExNRq1YtHD58OPveAgUKiNo9tMrQJcxe1ODKvhhfxUKlnDPTbGeLMyuKtJ441akarP11zVppXlbRkyQoGle1vwOo8fFMb26VjWc6sCYMKHVnAsg3hvAGa368MdMRT5zpu583b17zJuwwUvLqbYjrNALBBaJQevXsXJF62Bud+EwBbgamu9Y///yDq1evguQhJ+nZs6eot/PRRx9h4MCB2bcqrlf8N16zFxYQbdGihbCqkJQwHoXZ5pyJWlyZKYyB+1WqVBFujNnr25UVRVpPboFbLdb+um6tMi+r6Mnfv6+lBcUqO07Dh5fFIPLbx7XKS9MfFOAt1m3bthX+/XSj8WVtFGJ4ccInuPLRFwi/qzJKrfgYQWF2sQP+ALKHc/BWJx4OY/jtdLUiWWDAOrNvuZPff/9dWCwYr7J///7soomKFebHH39UnZJYC0GhyxnXNFNqs9gjXc+yxd6KotJ6YsvKQsbp80g/dBzpR04iOCqfiJ0Kq3RbwK9VVzrNLWvY3ZoN9OtW0ZPfE5Rvv/3WNn78eHGaU7BgQfC/GfAZHx8v/l6xYoVIf8hTmpdeeil73bm7TrPzsWPHcPDgQVSqVCm7neIf+95772HIkCFu17G/A+j2AfzsBqtsPD+DXZfpSN3pAqOqTrzFWqmN4ipmQNXgOt1kS8/A6daDkLL9TxTs3xGFJ/x3+q7TEKZ2461OTJ2kisEYL0J3KQaz//TTTypaAPXr1wetIgoZSUlJEdY6VninG1h4uPfJEDzBlRnCaJVJSEjAxo0bxbxINDJX/oTQVv8ViEx86W1klq94vfZJRiYyr8Qj61L89b8vxyPj1FlBSmwpabc+f55QhN1RTpCVfPVrIaJJPYQWzR2FHj3BWtXCkDcZgoBV9OTv39dBJUuWtP3vf/8TQXAkHcwecuedd6J79+7ivxctWoRz584J3+rXX38d3bp1EwvC3XUSFJqAn3zySUycOFG04ckR/WlZM4BERRIUQ/ZWjp1aZeOZj6zxI0rdGY+xMoK3WLNCNwvt3X777Th06FB2XIB5M795pPSTZ3GycU9kXUlA8fmTENmsvq+monlcb3WieWCdO1iyZAk6duyI559/Hh988IGq3vk73Llz5+z4JlaMpwsXf1+//PJLVX24uslTXOe8/yGeGToY5QrG4se7WyH08GnYkq+hxNU/EZF+Bcl5ohFX8HrAfk4SXDAKeSqWRZ4KZZHn9lLIik9C2r5DSN13CFkXrtzUlFbAiEfqiT/h1SsFbCFST7F2h6G8bgwCVtGT3xOU6dOn22gpsRfmaI+JicGmTZtQ+0b+cuZopzWFxcjcXWdfJCj9+/fH1KlThSWFJz2LFy/GvHnzRHuapyVBMWZz5dSrVTae+cgaP6LUnfEYayUobM90rLt27cLff/8t3HJ8LUmrfsWZrmMQHFMAZdbOQWip66lnA01yy/pnJqxXX33Vo2xvjG9ijRQeGpL48veY2eIYm8LDQi2SE660jqT/exKpew4iZfNuXNuwE2mHT6B/wl/4Je0SeuYtiZdKVEee/yuHiIKhKPDrd7j2VB9klC4HZNkAm01YUUJi8iM4piBCYgogODo/QosVQnChaKcEnq5kmWcvInXPASSv/g3Jv2xBxvG47Edk+3z310Le+ncjX4O7EHZneQTRUhMAklvWcABArWmKVtGT3xOUyZMn2xhsd+XKFWFF4YkOs4PwRzY9PR2h9CcFhN9phw4dcPnyZfHjm9N1haC8//77wk+VbmPNmzcX/qu9e/fGxx9/LAmKpu3jfWOrbDzvEfLfllJ35ulGC9Z0haW1mXUnhg0bZt6kcxjpwrgPcfXTpchbpxpKfvsRgvJcf68HkmjRiT89J60ntKLwsO/BBx9UPTWSGpIbrim2Z1YvEpYiRYqo7sPZjfa42jIykLRqkyAiqX/+g7R9h4V1xF7y3FkeCXdVQr1Zk3AtPU3UWzGSiJOwpB88iqSfNiN5zW9I3f4nbKn/uYaReNMdLLxqhRt/V7xOWvww5iq3rGFNCy4AGltFT35PUGrUqGFjSkz6s9KSwhceCcWjjz4KpitUhLnWWa02IyND+J7mdN2eoJw/f14Uj+KPNdMbMiiQRMWVBUU5XbJfw998800ALGk5RYmAREAiABw4cACjRo0ScQYTJkzwC0iCMjJRftICRBw9i/OP3YuzbR/wi3lZcRJ07Tp+/LioEO8ug5c9PjwcZEYuCn+H6Yo9adIkXSAMTk5BzMa9KLT6d4RdSsjuMzNvGFLKFEFKmaJIrlASSXeWRUbBSHH9q6++Es9Qp04djB07Vpd5qOkkKD0DEYdPI/Lv44g8cAL5/j2D4IzMm5pm5gvH5fur4nLDGkgtWVhNt/IeiYDlEOB3OA8A/FWCZs2aZXvmmWfE/JjrnAHtJCAPPPCAyHmuWFBIMtq3b59tQWFWEVfX7QkKK+TS3YupEnk/0yE2atRIWlB8tCKscjLgI3gNHVbqzlB4b+pcC9Ys2li8eHGRQpbphu1rV5j3BLeOlH70NE4+1AtZCUkosXgKIh5yXX/Dl/N0NbYWnfjL85BYREZGCmLCwztPhbVMFixYIJop1eM97cP+/vRjp7Fr5CQU3vp3tqUkX6M6yN/lMeSteSdCy5V06T7FQP3KlSvj6NGj4hDyYVaN94HQ6pN+6ISIXRExLHv/wbXNu4G0dDGbvPVqokD31ohq2RBB4WE+mOF/Q+aGNexTAE0a3Cp68nsLyuzZs229evW6iaDQ3YtmY2YNUQoyTZkyRRQf27BhQ3YMiqvr9gSFDK1r16744osvsGPHDuEaJgmKSbvMyTBW2Xi+Q9i4kaXujMPWsWetWPOdN3/+fHHKzArz/iKJ367F2d7jEVw4GmXWzkVo8cA5XdaqE3/QAbNa0h2Krl108fJUWCSRngwU++rxnvaTcfYiLr/7OeL/txxIz0BQ3jDk79AcBZ9tJ9yj1MrSpUuF6zethUyHHBISorapofdlXriM+EU/IH7ecmQcPSXGYuxKVOuHkL9DM4TXqeaTBBa5YQ0bqjg/6dwqevJ7glKzZk3b999/j9jYWOHixSw0jDdhti6mL1y4cGF2Fq/XXnstO4uXu+u0mjAGhQSFfdLtoXHjxmL5SYLiu11olY3nO4SNG1nqzjhs9SYoStYlWqcZ45eT0OLCTIoNGjRAxYoVDX/I8yPfQfzcb5C3/l0ouey9gMmIlBvWP5+Bv4l9+/YVQe6eCt0xmM2LiWbYl1I9Xm0/mVcTcOWjBbg680vYklMQFBWBMw/Vwr1vj0FIoWi13WTfx/mQbP3666+YMWNGtguaxx05NKC3Bb896IFRpkwZr7tjkD/jaeLnfYukHzcJMkYJLVdKEJWIhrVF3EpwZD6vx/CkYW5Yw548b6DeaxU9+T1BGTZsmI2ZtSgkEHTBonsCUw7zJWpfB4WxKYq4u25PUBwXqSQovtu2Vtl4vkPYuJGl7ozDVm+CcunSJWGFLlasmAhmdvUhyQ9NfnDSOt2yZUt89913hj9kVkoqTjXvJ9xhCvR8AoUnDQ2ILEhGrX9+ZM+ZM0fESNLCb6S8+eabePHFF0UyGsaimCVph48jYf4KxM9fIVJO09WpwDNtEfP8U1jx63q0bt3a66kwPrVu3booWrSoqKfmSVyNs0GpDybToU5ITphNVAtJUcbIvHQVid+uQcLSn0SgfbYEByNPpbIIr3EHwmtVFsQlJDq/13jk1NCoNWzIZC3cqVX05PcExebPETKA+GH38ykG1Da2ysYLKKWonKzUnUqgdLhND6wZx8eTZbq+8OPXUVhfqlWrVvjtt9/EJVYWp7XZDGGq2NOtBiDz/GXkf7olirzzgt+TFD104gxbehCQHNJ6RRcsT60SnuiLnge0lrFAIws1GilZ11KR9N1aQUpStvxxfaiQEOTv/ChiR/TITjetB67Kc40ePRokYVrkrbfeAvuhuxiti3SJY1ys1mxl9nPi+k9avhYpu/5C2p6DyDh1Dhez0jHv2mn0LvF/KD+yDwr2fEL3TGB6YK0FW9lWHQJW0ZO/f18HSYKibsHmlrussvFyi77sn0Pqzjyt6oE1MyyNGTMGdI0dN27cTZPnSTMzITIxCatxK/Ut+DdPos2QtINHcbrtEFFzIn/H5ijywWi/dvfSQyeOuPLwizEd27ZtE5fWrl0rXJCNEtYV27lzp8hmyYKeegoJSequv5CybQ9Stu3FtW17YEtMFkOE3lYCBZ5qifydH7sl7kgPXPk8JBIkFKz/Qw8Kb2TZsmVo164dIiIihC6YrptWRcbCrlmzRrN1xtWcGLMyqE8/TP96CXrkK4VxkeWRp3xpxL7cH5GPPaAbadUDa29wlW08Q8AqepIExbN1ccvd/g6gxsczvblVNp7pwJowoNSdCSDfGEIPrPfs2YOaNWuiVq1aIs26Iszs9dxzz4kYP1YC54l6nz59RFC9GSfr9igKSwpJyulziGrbBEU/HougG7WvzENb3Uh66MRxJGafoiUjPDwcqamp6NKli0joYoRkZWWJD2wWLWaGN70sNelHTuL86PdwbdOu7MxVnH9QvnBENquP/F1bIV+Du11ayPTCVanTwqB5FmX2VOgqxlpszA729ddfC7eza9euiRpqTM7Daz/88APy5dM/XoTEqnTp0jhz5gxiC0ZjR5PuyFq/UzxC3vtqotCEAch7V2VPH+mW+/XCWvNEZAc5ImAVPan9vmbBdRaFZWIOHqzZl/7ggQLdMJOSkkS5EsZdOh7I2YPNwwsexCkJNZgpmIm5nIm0oFhso1pl4+VGtUrdmadVPbDm6Tyrf584ccLpxAcPHiyIC1/UzJL4wgsviMK2/NtMYarZ020GI+PEGUS2bIhiM17xy0KOeujEEVdaS5hNa+7cuaA+SFLoZsekMXrLsWPHhGXh3nvvBbNxaRWur4QvvseFsR+KFMHBsQWR997qyHdfTfF3ePU7VLko6YUrP1BoRWHMFV2ymPRBrbAuDHEhQeBeGD58eHZTkjkGy9NVki6RtErSHZJ/SCz1kHXr1mUn8WF/DNB/vNjtuPjKx0jbf0QMEfXkI4gd1xd5Shfzeki9sPZ6ArKhKgSsoie1BIXZKHmwwgMdWkvtCQpJyx133CH2IvcxDxQYZ/f00087xTqn+HTHBpKgqFquuecmq2y83KOx/55E6s48reqFNS0izOjlKExIwlTEiign+axzQUuK2ZJ+8ux1knL0FCIfb4xin473O0uKXjpRsOVHNDNQseAhq6EPHDhQZNYyKoB91apV4vSxR48eghBpkcyLV3B++GQkfb9BxJXEDO+OmKFdvdKZnrjSGsh4FLqy0W2OHzXuhBk+aUnct2+fsCRSB47WJcZrMaaLMUL2EhMTI+K7uGdIWLyV/v37i3Hbtm0rUoOzpgv3pC0zEwkLf8ClN2ci89wlkVygYN/2iBnSFcH5rxes9ET0xNqTceW9niFgFT2pJSgKeiykvnv37psIij2yPIzjO477mRZVZyIJimdr0VJ3W2Xj5UalSt2Zp1WzsWbRPsaeVK1aVXws+0Iy4s7j1OMDkXH0NKI6NEPRj170q8B5e52wOCA/JidOnCh+EL2RZs2aCZc6VkMnWeQJPWMdqlWrBrrn6eWCpczt3XffFZYBLVYyWk2Sf96C88PeFrFDLKRYbNpLyFunmjcQiDZ6rnW6sdESwppnCq6uJsZ76TrCgHi6clEfjDfJkyeP0ya0bLF0AXUfFxcnLF38m21prWGMiqu2OYHD4pklS5YURajZJ9cAP7QOHTqEChUqiKZZicm4MnUBrkxbBNu1VATHFECBbo+jYO8nPaolpCfWXitcNnSLgFX0pBdBocsyXcC4F+k1wPix8uWd11MiQeF9dKtkYfiXXnoJjz32mFOdSAuK26Wau26wysbLXVq7/jRSd+Zp1RdYM2iavrmJiYnImzeveQ9rN1L68TicJkk5dU58gBWeMkL3D3VvH8xeJ++99x6GDRsmfgR5Au/phykzp/FD+vbbbxft6QdNYZrhXbt2CRcsXtdTnn32WVEThx/hzBrmqaT8vh8XX/sUKb/+Lprm79IChSc+j+CoCE+7uul+vdc6M9fR2sH1TGwjI2+1NNAVpGfPntmkgiezI0eOzNaD2gdivArHIiFi2mZavzwV1n1r2rSpILorV64E58LTX7qpkADbS8bpc7g0aTYSlv4IZGQCeUIR1eZhRPftINIUuxO9sXY3nrzuHQJW0ZPjIczLL78s1r8rycmCwgMHHvIQOx7EREc7r6tEyzUPAejazKQYjFnhv9Hq6iiSoHi3fgO2lVU2XsAqKIeJS92Zp1VfYM1TJAYB82OLL3BfiQicbz1InNCzsnihic/7BUmx1wk/bnliR/GmQCADsFl7ZubMmaLmhiLTp08XCQz4b7ympzBb2+bNm0XmNhIjtZJ26DguTZyBpBXXK8/nqXQbCjG7VLP6arvI8T4j1nrHjh2xZMkS8bHDjx5FWICR7m0kI6ylRmsV3cKYSMJbHKw4xgAAIABJREFUIdnhfmHSCSY4YKIDT4S6nj17tlhP3bt3B2OFSHxZD459K+TVvk8SlauzvkL8598i62qiuMTq9PmffARRrRsjpHCM0ykYgbUnzyrvVYeAVfSklwXFHtXJkyeLgwl3BYqVNkyqwfchs146SkASFL7QnIm9T7e6ZWi9u6yy8XKjZqXuzNOqL7DmiS1rSPBjqVevXuY9rJOR0g78i1OtByHr4lVED3oKsS/19TlJsdcJP0h5WkdhET+mbVYbMP3HH3+ID2K2oxtPWFhYNgIMyGYsA2Mn6D6UP78+BfvomsUMN3RtoIVMyWCTk5JZBf3Sm7NE5XdkZiKkRBHEjuyF/J2aexVr4mosI9Y63bAY28PnZNxI4cKFBTHh+uZHPz+MmAxiwoQJqvWWE1arV68WVhCuAVq/atSooWr/kDCRiDDAn3EuBQsWFO1oTWHMELF5/PHHXfZF16+ExatwdcZSMJuakJAQRDSqg6gnmyCyRUMER/xnDTUCa1UPKm/yCAGr6MkIgsI9zr3D5CNqpFOnTiJ5SK4iKI4Vf/ljJQmK++VglY3nHonAu0Pqzjyd+QJrpmbly3rQoEH48MMPzXtYFyOl7v0Hp9s8L06IRcX5N4f4tE6KohPGDERFRQnXIcYe0BLy8ccfC8tHTsIMXUxXy49iuiEx/mHAgAG3NGEQ+7x5826xrjjrm1YCfhAzs1ROhQSZnYrEh8SILmTuhDVNzg14HUnfrRPB2NHDuqHgM08iOJ8+WavsxzdqrRMTfnSwzgxJCbN7UWgppGWlTp067mDw6LpS4JFxI0xZzAB6d6IU6SQJIQ6KKPVY6IpHlzx3QgKaunM/Er78SVSrz7pwPW1qcKGCiH62PQo801ZUpzcKa3fzk9c9Q8AqelJLUPjO5R/WJWJ8Ht97yiEOLf6MH2PtIibGYIA8E47wwM1R+B7g4QXdZ9me6cRptWTMyn333XfL/UGRkZE25V/pi8mXCSU9PR1Dhw7FggULxP8zuwz9fhVzp7vrSvpG9tmkSZPsgWn+oXmXKR0Z8OZOnAFIC4okKO6Qc37dKhvPO3T8u5XUnXn68QXWNIvz1JmZpdSePhmNSOqeg4jr9AIyz19CZIsHUXT6eEM+ktU8h6KTv/76C1WqVBEFFfmbxCxODHKmNcSxRgbz6/Njkx+i/C2i9YJCFx5mjXJWU0OJoahbt252AUfH+fHknb+P06ZNE5eY4IAuYa5O2/kDzFS5nTt3zv5NdfXMGecv40y3MUjdsU8EwZdYOBlhFcuqgcire4xa63ThYhAsLROUJ554QtRHMMp9kSSBNRmYhYvpjsuWvRkzkg2Sf3u/e2Yc4/eEo2sY9UsLG93G+FHlSVFNW0YGrm3YifgFKwXBRFYWgiLzoUCP1thWPhotuj/llZ5kI/MQMGpPmPcE6kZSS1CUuCz7XlmXiAc5TCfMVMOMQeF7mIYCHk4oGfyY+IVkhRxi//79wgWT72pyCaYnZpA804c7E5cuXvQbpZLoE02hyZNZU8aPHy/+3911/njw1IgvI/vCV5wsH4SMSxIUdYtIz7ussvH0xMxf+pK6M08TvsCaWU3oUkSXI2YU0juLlLfopR89jbiOw4ULS9661VF8/iSExBTwtjuv2yk6USxNSlA0P0pJQkhWhgwZkt0/A+F5TalDQ4sLD8tatGghfsvocuVM+KHL3ykSIWfB8rQEtG/fHlu2bBFWE6akVVJJMzaGv2ssyGgvtPDwVPG1117LsYhZ2j/HENf5BWQcixMxDSU+f8NlPIPXQDo0NHKtk5iRMDBBgFq3Ky3PRVJEgk83PmfCeBOSSiZVYIA9iSUPW0miHN35eJDKA1V3Ostpvun/nsKVjxcifuFKUUQzKzQE0V1bIWbw0wgt5X09FS0YybbuETByT7gf3bw71BIU82Z080guCQpPD/jC5wuesnTpUowYMUIEkFHcXSdB4R+6Kvz777/Ct5PmH77AeTLFCH9JUMxXu1U2nvnIGj+i1J3xGCsj+Aprmrn5nuQ7k365/iKZFy4j7qlRSP39L+S54zaUWPyOpoJ13jyXohOewjO7EoMwmQGGlpDq1asLsnDkyBHhakBrBk/LeRJOlwN+IPPET212NCUlMOdJaz1JDf8whoQB4Pyg5e8YiRErkNNCw49fHsrx5J7jMyZCEZITkpQvv/xSzMeZJG/YgbO9XhIudaxFU3TqWFOsVb5a696sATVt6IrCdWAvTB/ME1z+zcNWuqiwzkmbNm2EPqgXR2HsDC0x3IdMbKCmpour+WWcuYCrM77ExZlLEZKSJrJ/FejSAtGDn0aeMsXVPJa8x0QEctuecAWd3xOUmJgYG31j+aLn6RM3IU/vWEmXgYcVK1YUz8b/pjmGJnNaQHK6TjJCckKTLk+xeKLRr18/9O3bV/THHxRJUEzcbXZDWWXj+QZdY0eVujMWX/vefYU135HMSkXfXL4//Umykq7hbO/xSP5lK0KKxKDox+MQ0biuaVNUdEI3KsYFkMiRJFDoNsDq34wvoY/znDlzhAsBD9kYZ+KpNYrxKvQW4Ier48cux+PvGQ/Y7APzL168KOJg+PFLYfpb9kELCy03rNNBF4fKlSvfhBktNqyxcen1GcIdSCQlGNfHtBo0vlrrpi2cGwPRVYvkhDog6SSh/fHHH4W+aBFzJiS1jFtiLM2oUaM0T3nFF4tw//FEXJ35JbISkgRRyd/pUcQM7II8t5fW3L/sQB8ErLIn/J6gZGRk2BhQxlRf9KnlH5rEeQrE4mHMvkFRConxGl+oOV3niZJCUGgq54nXunXrhNWFRchYmMkVQXHm6/bNN9/os+pkLxIBiYBEwI8RoEvtp59+KoLl+cfvJDMLJRauRqF1111ozjerg3NtGsAWGmLaVGkNYWwACYliEaHbFS0mPDyjMECaH5SM6dEi/K1j3wwE3blzp7Cc8LeSpMOVMJUwYzdPnrye1Ymn8PxvuhTRPc0+bW1wShpKzV2FgjsPIissFKe6NsXVelW0TFm2zQEBxiAxyxAPSSlcP/Sjd5UBjuSU/vQkrFxPSoyuVpCDk1JQ+JedKPTL7wi5lgpbEBB/9x04/2hdpJSTFhWt+Mr26hDgIRjfcf4q2S5e9Mtk5Vf63CoWFPtKqvxvBrzZW1BcXbe3oNBPmAGJNK8ykIYnX8ySIi0ovlkSVjkZ8A26xo4qdWcsvva9+wprftyyXgZ/OGhFsRfW6GDsH090tX54a0UycflaUc2c7kjhte5EsU9fNvwEmDrhwRd/O/hbRBcce1Fqo9BiTyLA9LG+EsYT0f2LWW/4u0eh5YSn94qkHT6OM93HIv3AUYTeVgLF505EePVKpk/ZV2vd9Ae9MSDJBr9BGDfEAF9XZQuU+fGAlDFLJDPce84Kyql9FkesM68mIH7uN8L9i4koKPkeuBvRzz+NfA1re2z5UzsPeV/OCFhlT/i9BcV2gz598sknolARCQqF1g6asBV/WZq6WbmXZlI11xULCt3GGKVPn2H2wY0uCYrvXg9W2Xi+Q9i4kaXujMPWsWdfYZ2QkCDi9ej3bu9axIMhHvTwb1q5GSOht/CngPUgmMJXjaSfOINz/SYg5be9IktRkcnDkb99MzVNvbqHOmFgO12nnMUN0ELB7GfMluVpZXmvJqSiES06TINMqxjnzDgV4szaGRdf/EC4+eRrXFcQPF8kHuAj+Gqtq4DPsFuoF7p3Md0p3dXdyTvvvCNicEl66bbO7yNvxBXWWSmpYk1cmboQGUevp2MOr10VsSN6It9DdSVR8QZsDW2ssif8nqBkZWXZaLpmMDx9dVk8iULf2RUrVmDlypXi/5m7nKd6ShYvd9ftCcqlS5dEUS36c/KHQxIUDTtHY1OrbDyNMPllc6k789TiS6xpHaB1mmREKRxHywnjKyiMGaSrrZpif54gxgMpvv+ZnUrtKTFTql6e8hkuv/s5YLMhqkMzFHlrGIKjIjwZWtW91Andrfg79eqrr2b/Fqlq7Cc3MVialqfkn7cAQUGIHtIVsaN6+UV9GT+ByC+nQVLJuCPGhzEbGVNRM+sX/50eJ0yOwG8b7s2cCL6794otMxNJKzbg8nvzkLbv8HWicndlxIzoiYgm90miYtLqcKcnk6Zh+DB+T1BYB4U5vhkkzxMCJVMFU+/R+mFfB4U/YPZ1UHK6bk9QHFGWBMXwdedyAKtsPN8hbNzIUnfGYevYsy+xZsAurc10J6G1gMHXtJ7QQsCYvt27d4OVs2kp0FNYRJDpWZX0vZ70fW3TLpzt/xoy484jT/nSKDrjZeStpS3+w5lOWKGY1n5/TCKQE17ZVpNxHwq3OAZEF/lgDPLdp67iuSe68PReX651T+fqy/v5TcSDWmb/uu222wRZiIuLE/Ep9kKCQqLC7yoe5DZu3Dj7slqsbVlZSPrhV0H+0/78R7Sny1exWRNEwUcpxiKgVk/GzsL43v2eoCguXsZD4d0IslCjd7i5amWVjacvav7Rm9SdeXrwJdaMW6BbLFO0M/BbqcjNDFGsvs1YCx4oMc2uXsIgbsVthR9fTHPsaearzEtXcW7IJCT/8KvITlRoXF8U7NdBt2xU1Mnbb78NxunYxz/qhYFe/TDbWfqh46ALXMaJOGQcPwMWvKQrHK0mBfu2R+yYZxEckVevITX148u1rmniPmhMqyYJBw8JKEwiRDLCPyQwJCz8c/XqVXGdXiNMEKSIp1iT2Cav+hUXJ84QsUoixffCychTtoQPnt46Q3qqp0BFRhIUjZqTBEUjgA7NrbLx9EXNP3qTujNPD77Emq61rKxLEvLGG28I6wmDrlmLgaezxYoVEwG7dCtRW9fDHXKs29GnT5/s23bt2gVaVDwVflAx6Pfi+KmwpaaJ+ArW8wgt6t7P391YtJp0795dZOpiQT7HuhQ7sRO90RuzMAv34B533el6nQX5kn7ajORftuDa5t2iKJ+j0LJU5EP/sJrYz82Xa11XJZjUGdcfSQjTFLOoqjNhLBf3Ld2/aAFVCnd6i3VWYjLOPvvyjRTfsSi+4C3dLZQmwRcQw3irp4B4OLtJSoKiUWMEkNnFHIV5zO2FMS5du3bVOFrub26VjZcbNSl1Z55WfYm1kub9nnvuEVmrGKBLd1rW9KAoldNZoZuF5vQQxhfymZk+l+5jTPfOuBdvJXX/YZzt84o49RU1U6aORcRD93rbnWhH/3/WiWExS8bJOEpzNMeP+BHN0AyrsErTWO4a0wWHRSuTVm4Qrji0mCgSHFMAeetUE6fcoWWLI7RMCVGML6zy7QgKy+Oua9Ov+3Ktm/6wJg7I75H58+fDfp9qwZrxXhfGvI/4z75FUEReFJvxCiKb1TfxiawzlBY9BRJKkqBo1BYBZO0URzIiCYp3wFpl43mHjn+3krozTz++xJpWCLqOsGYDrQR8BzKjl5I2lx88zAhForJ06VLNoNCHntmxKJs2bRKWk7vuukskNtEiWckpwpISP+9b0U3BAZ1Q6MU+Xn+kv/jii6KGBS09zIplLzuwA3VQJ/uftmM7aqO2lumLttQFLUG2xGRkJSQj/XgcklZuFMQk88yF7P7DqtyOiCb1EPHI/chbuwqCQkM1j21WB75c62Y9oy/GYfwuyyvYr1etWGcX9ZzwCRAcjCJTRqBA11a+eLxcPaZWPQUKOJKgaNSUJCgaAXRobpWNpy9q/tGb1J15evA11gyAX7t2rXhgZtZi7IUiDJanmxeJBQsHKu4j3qLz888/o2nTpmjdurUIPmdhwX/++QfHjh0TBXnVCF3QFDLleL9jzRS6OYVXvl1Ntzfdw489fvRNnTpVZPKyF1pPVmM1MpCBUITiYTzslRUlMz4R11ZvQ9LPm3Ftw04wrgbpGbfONSgIee+rgcgWDRH52APCQhKo4uu1Hqi4uZs3i4kWLVoULFzNvcRvGb2wTvxmDc4OeF2szaJTX0T+Ds3dTUde9wABvfTkwZA+uVUSFI2wS4KiEUBJUPQF0Ie9WeWl6UOIs4f2NdasOUWXrsjISBGwTn93e+nVqxfmzp0ralcxLkOL0H3sgw8+EC5UrNJOQjRlypTsIH13fdMljRYXWnSYYcuZ2NdMQWgIogd2Qcyw7gjOF+6u++zrLGDJAHklu5lywdF6ovy7WisK/ftZgyLp+/W4tuUPICPzehehIQiJLShSJgfnj0RQVISoVRLxUF1ENGugS1yN6oc38EZfr3UDH83nXbPOCuum0AuEGfj0xDpp1a8403MckGVDsZmvIOrx/7KF+fzBA3wCeurJn6GQBEWjdiRB0QigJCj6AujD3qzy0vQhxH5DUKhrxoUwDoTxII7CVKePPPKIsHyw4JwWUequMJMXU6PSzatBgwYiHoXjuJOBAwfi448/FifEe/bsQbVq1Zw2oQ/91VnLcOnN2bAlX0NouVKiuGNEo/9cs3IaiyfRrIPCwGNWk1fE3nqi/JsaK0rG6XO4OvNLxM9bLgomUoILRyOSrlrN6ot5GVHPxR2eZl+X7xXjEOfeZc0eEv7hw4frSlA468SvV+NsvwlAcBCKz3sDkU3vN+5hLNSzVfaEJCgaF7UkKBoBlARFXwB92JtVXpo+hNhvCAp9zffv348qVao4TfdLlyqSifPnz+P06dPC5csboSvXHXfcgZo1a2anTmXfTJtKIsD+7cmA4xjMZsRsRUotCDVxMeknz+LC6PeQ/OMm0V1Um4cRM7w7wv6vvMtHSE5OFoXx+MzHj/8XkO7KeqJ05GhFIa6pv+/H1dlfiY87YS0Jy4P8Tz6CAk+3RPg9VXxaNNEbHWptI98rWhF03X7r1q2oV69eNtk3Auv4hStx/vk3ERQeJrJ7RTyoPfbKOEQCo2cj9OSPTy4JikatSIKiEUBJUPQF0Ie9WeWl6UOI/YagqMFAcc1S6qWoaeN4D4vvDh06FAxAnzhxYvZlpjieM2cOvvjiC3Tp0sVl1yzuyyxjrNeybNkyQZbUpCgmUUj6foPISqQEm9NqQdevvPdWv4WU7dixQ9SAadGiBZiGWRFn1hPlmmJF+SFrJVJ37EPid+uQtGI9Mk6eFbcER+dHgR5PoOAzbRFavLA38OWKNvK9YpwaSfYZh8KEF0w3zAx5jPXSW67O+RoXRr0rsnuV/PI9kUVOivcIWGVPSILi/RoRLSVB0QigJCj6AujD3qzy0vQhxAFFULZv3466deu6TLurBke6idGNi25d99//n3vI8uXLxYdUhw4dsHjxYqdd0bpSrlw5UZfk6NGj+PLLL0F3L7b75ptv1AwPxn/E/285rkxfIqrQU8JrVxVEJfLRBtlFHkmWSJpYtJK1YSjurCfKBL7t2BBV19yIKwGQt251RLVtgvydHkVwZD5V88zNN8n3irHa7dy5MxYtWgTuKe4VIwgKn+Dy1AW49Op0BBcqiNKrZiBPuZLGPlgu7t0qeyIgCMq1a9dQvXp1MOsEK6VSWAirX79+4rQqX7584oeH1Y0VcXedP1zMXHHw4EHQx1kRZl+ZNm2aCADlCaA7kQTFHUKeXbfKxvMMlcC4W+rOPD0FAta0QigZt/hupq+7J9XfExISRHphuk4xG1hISEg2wHSpYqpj/ht/F8LDbw1mHzt2rCALzz//vAiyp5sX3/UMmid5ql1bvauJLS1duFzxIyv973/FPPJUKIPoAZ0Q1b4Zho8ZDVp7Fi5ciA6t2yBl2x60KtsL68vtR2ZwlsuFEZIB3L8hBgs/fBpRLRsissWDCC1xc8IB81aVf44UCGvdP5FTNyvWcWMii/79+6NZs2aGERTO5sKLH4i4qjwVy6LUyukiqYMUzxGwyp4ICILCrC3Meb9z585sgsINdfbsWcH8+ePVpEkTvP766+jWrZvQtrvrJCiscsx8/YrrAH/AGOhIn2YSFUlQPN84WltYZeNpxckf20vdmaeVQMGaGa1YdZ4HRj169BCZuPLkUVcMkFYOFnqkCxdduRxFKd74ww8/oHnzm9OYMj7ltttuE6SEFe75XqcoxRQfffRRrFy50mOFkXQl/7IVV6YuQAorsgMIKRKL7umHsOHQfqxt8jTK/nUamWmpaPj7dsSVTnM7RtnM0vg35BiCEez2XiveEChrPVB1w+8o1jDiNxEPZrmvjBJbZibOdB8r4rvy3l8LJZe8I2JTpHiGgFX2hN8TlB07dthINt59911hzqcFhadnMTExwuyvnIJNnjxZWFPWr1/v9jqXAjcjTwyYs56WFObIp6vAvHnzRHtuUklQPNs0etxtlY2nB1b+1ofUnXkaCSSs9+7dCxICZrhiVi+6WtEqYi+0ktMKwvewIkwpPGvWLJdxJkxjzHTGtKRPnz79pv4mTJggMoyxsrt9auH09HRh1WFqZKYEZoCwt5Kycx+uTF2IxBXrUffiViTaMrG30P0Ii86PfA/cg5SHKiGpaCj9gK//CQ5CUFgYwqtVvOnkuAAKIBax3k4j17cLpLUeqMq45557xCEws90999xzhj4G3SZPtR6EtD0HEdWhGYpOHeuRZdXQyQVI51bZE35PUO666y4bTef0jSRpIEFhkCMrtfPHJvRGRVwW8yKB4cmZu+sKQWG/LDA2fvx4cQJH82bv3r3FJpUExTc71SobzzfoGjuq1J2x+Nr3HmhYM0XwY489BpIVVoJ/7bXXRBYwfhTxD7N18RSX9zDQnBbxypUr48yZM8JCrlSSt8eAMSZswxostLA88MADCAsLE9YaHkDx70OHDon/thfWZunZs6cYg78bWmXBh9Pw1OABqFSoKPb8vA7hNe6wXKYtrRjm1D7Q1rqRWBjV97hx44QnCeOoeChgtGScuYBTzfsi49Q5xIzshdgXeho9ZK7q3yp7wu8JSq9evWyzZ8/GunXrsgnKxo0bxYkcM08oQp9inoZlZGTA3XV7gsIfOQZhMtMLi3nxh5RExRVBUfKG2692tQGXuWqHyIeRCEgEJAIeIJCUlIRJkyYJkuIojCe5dOmSOIiiMLaEGYbuvPNO0caV8HCJtU0ojEVkOmK6kPE3gNXuGX/iKOyXWb2Y0YtEifGN3gj7+d///icC7vlDSrdgkh4pEoFAQ4CHBcyUx28gWh7NkPCT53H7pIUISUlDXIdGuNhUfUyYGfOTY/geAX6H063WXyXo4sWLttjY2JsICi0kNEmmpaVlW1BIMtq3b59tQcnpuj1B4Y8YT9h4osb+PvroIzRq1EhaUHy0IqxyMuAjeA0dVurOUHhv6jxQseY7lrGCjAuhFZx/aFGhyy6t3yzq+P3334NxJUx7yvcxE6C4ErZZunSpcO/lbwBdxSh0Ffvrr79EDRVnwmB2xraULVtWxDaSIHkitOp07NhR/C4xZnHBggXi98OoDEiezC233Ruoaz2Q9MCDXe4BurfTSyUiIsKU6Sdv2IEzT4+G7VoqYkY/g5hh3aW7lwrkrbIn/N6CUqhQIUGf6M7FrC4kK8xnT19m+hCTiFBYCZVp8hiUqcSouLpuT1DI0Lp27SpcBJjLnj+YkqCo2CEG3WKVjWcQfD7tVurOPPhzO9a0TrDgIQPd7eNSckKY5ISEYdWqVahatSr69Onj8naeyj399NOCWNDqwTb2WcJyGmfbtm0iuQpjakiu+Ht0++23616F27zV5N8j5fa17i/o84CX8WEk+3SzNEuubfkDcV1GwpaYjOhBXRD7Uj9JUtyAb5U94fcE5cSJE4KgbNmyRcSH7Nu3TxQW4n8zvSRPwpQsXjTXK1m8+HdO12k1YQwKCQpN/QcOHEDjxo3FspAExaxX063jWGXj+Q5h40aWujMOW8eeJdbasabLGd2C6XJmX78kp555+EWrC607/I1hcL5y2ix1ol0nznqQuBqDq2Ov8+fPF4e1jz/+uCDbZkrKrr8Q12E4sq4koEDPNig8aUh2jSEz5xEoY1llT/g9QbHdcECzj0HhImIAJDO02NdBoT+yIu6u2xMUx0UpCYrvtqlVNp7vEDZuZKk747CVBMUYbBlEz0yQV69exddff+02xSpjTkhMaEGha5l9XRe5/o3RkcTVGFwde2VK7pIlS4pYMB4EV6lSxZyBb4ySuu8Q4toPQ+b5y6JIaZEPRkuS4kIDVtkTagkKs/Ey+YmSMVKJC6fxYujQoSK7LzlBhQoVRD0uknBXQoMFDSBsw+QsrOHFjJLOJEghKKbuFA8G86RQo2O3PK2QcjMCVtl4uVHvUnfmaVVirR/WPORirRamPmayFaYhdiUPPvigCMBfvXq1CMK3F6kT/XQicTUGS3e9MhZXyXI3Z84cd7druk6y7ygd6zXE6SeHIvP0ORTo0RqF3x4u3b2coGyVd41agvLVV18JV2DGITLRlUJQjhw5Al7r1KmTIN+Mb+R/8z3vioA3bNhQEBnGP/75558iaRbx5r87Sq4iKIxvUYSpNSVBuXXnWWXjaXqz+2ljqTvzFCOx1hdrWt/pIswfLcaYREVF3TIA3YCZVYzxJkyL7BgbI3Wir06U3iSuxuDqrFe6zLM+HF0Z+XGnFDh1NwMmiGD2PHuLors2JCjOvonSj5zEqVYDkHnuEqIHdkbs+P5u+93WwXntlnuXTHM3jYC8bpU9oZagKEpklt3du3dnExRnyuWaY+IV1tByFCZvYWIVWlGKFSsmLjM7IzMGs0ai5QmKs1MFKxEZq2y8gHwrupm01J15WpVY64s1g/JpRWH2MFfxKC+88IJIxvLGG2+IexxF6kRfnUiCYgyeOfXKNUyC/uabb2L48OFivTsKXcF+++237BpGPGxlxjxmsGPCCLXiiqCwfepfR3C69SBkXY5H7JhnETOsW47dkqCU6dL6pntOLPgWkqCo1YZ/3qc3QaHLFxOv0AquFHm3f3K6+TIFPa0wisycORPTpk0T9RUlQXFxquCfy0f/Wckfef0xNatHqTuzkIbMGGUA1MzKxdMzkpW///77pgKPPCHmaTL980+cOIESJUpIgmKADpx1Kd/EFc1JAAAgAElEQVQrJgGN6++Ve++9V6x9WkSYSY8pwBWJi4sTWe9YN8VReD9juViPSI3kRFDYnoHzp9sMhi3pGgpNHIzoPu1cdisJihrEA+8eR4sca/TQSuJKcrKgkFizfmKZMmWcWkPYJ9fkW2+9JVy7FGGsIQ+kGK8oCYokKLKWQOC9R8SM5YeEeYqTWBuDNQMo+SPXoUMHLF68OHsQngy3a9dOvJtcFeaVOjFGJxJXY3DNiQwyAdGMGTNushbyVJlxV3RvvO+++4TFke4yLO7I+6knnkw3aNBA1YTdERR2cm3TLsR1GgFbSpoImi/QxXn6Y0lQVEEecDfpZUHhARPf3ywEzJiUsLAwp1jQgsLivjyEUmTWrFn4+OOPpQVFYXBWjlWRP0YB9w7JnrDUnXm6k1gbgzVTDzNIntaUX3/9FfXr1xcDNW/eXBSR/O6779CyZUung0udGKMTiasxuOZEUEhCuA9Y0uHo0aM4e/asICeMS2nTpg0WLVp000fe22+/jVGjRonT55EjR6qasBqCwo6SftmKM93GABmZKPrxWORv3+yW/iVBUQV5wN2kB0EhOWGNH1pQ+C4JDw93iYMSg0JLIdc+hfEqzAD2+eef39LOckHyajdtwK00lROWP0YqgfLD26TuzFOKxNo4rJVUwvRRpj8+T9PKly8vssDwYy00NFQSFOPgv6VnudbNA9sea6VwI91bWND02LFj4kOPRa3pzmUvJPMPPPBAjhZGx6fw5Fsn8fsNOPvMeMBmQ9FPXkL+Nk1u6k4SFPPWiJkjqSUoGRkZ4J/XX38de/bswZIlS0QSE7bnmmWQO7M15s2b1+30mamRrr4ffvhhdhYvWs1lFq8bPnDSgnJzsJvbFSVv8AsE5IeEeWqQWBuHNd0A6Ie/Y8cOcWpG3+MJEyZg3LhxItOXK5E6MUYnEldjcHXWqz3WXP916tTJvq1z585iPzgj6NeuXUPBggURHR0trC1qsnl5QlA4icRv1uBs31eBoCAUm/Eyoh6/XlibIgmKeWvEzJHUEhS65dI9115IKPhvrGtIYhISEpJ9+cUXXwT/UKpWrSr++6mnnhL/T+s566Bs2LABsbGxYIZHS9RBUUM8PN20Zi4WM8aSP0ZmoGzMGFJ3xuDq7kPCvFGtM5JyIlyqVCnxscUfLbq3MHhYEhRz14F8r5iHtyPWDIhnzR9mEp07d+5NH3mOs2JcCi2OdA+rWLGi20l7862TsOxnnHvudSA4CMXnvIbIRx+QBMUt0oF7g1qC4qsnlC5eFquXIn+MfLXVtI8rdacdQ7U9SKzVIuX9fQyUZwYXyiOPPIKffvopx86kTrzHOqeWEldjcFVz8MGsdSTrLVq0yJGcsC+mJX733XdFhqRu3XJOC8z7vSEobJeweBXODXoDCA1B8c/fRGST+6QFxbwlYuhItF5z/fBvCi0ZDFL3tdB9kW5fjhJUunRpG1PXscovfckYjMUIfAat9OvXT/iVMa0dA1lYkl4Rd9d5EkafyoMHD6JSpUrZ7ViUhTmP33vvPQwZMsQtLp5UkpcWFLdwykxQ7iHy2zvkh4R5qpFYG4/1v//+KwozMsiSPs38/ZEf0sbj7jiCXOvmYa4FayXTXZ8+ffDpp5+6nbS3BIUdx3/xPc4PmYTgAlEo/css/D5qoqyD4hZx/7+BNXWYhKRy5cpisiTIdLPypVy4cAFly5YVCVJuISiJiYm2yMhInD9/XqR+fPjhh4UvcPfu3YWvI7NJsPgKTZEMkFGYu7vrJCj0S3vyyScxceJEMS6j/Jnrnn6UJCqSoJi/LLS8IM2frRzRHgGpO/PWg8TaHKxnz54tfJFZrMtVakplJlInxuhE4moMrs561YI1q2/TJbJatWrYu3ev20lrISjs/OJrn+DKh18grGpFnC4UjjJd29w0pizU6FYFfncDC4QyVfXKlSvF3LimmJzEl0KDBQ0hypzs55Lt4kWC0qlTJ0Egpk+fLooHbdq0Kbsa5OTJk0Un69evR3Jyco7XOQAJSv/+/TF16lRhSWHEP/Pe07zE9k888YQkKD5YFVpekD6YrhzSDgGpO/OWg8TaPKzVjiR1ohYpz+6TuHqGl5a7tWLNbHf8nuLJNw96cxKtBMWWkYHT7Ycj5dffkVIiBjETBkiCokX5ftCWAe1t27YVtUgUgsJDovnz54vYJtYw4bc5hYdGzLT1119/YcqUKTl+r7Pfy5cvi3hCemN99NFHqFWrluiH6ePHjh0rCvTSWs5K8vTOUiRHgvLmm2/aaOFgmrBChQph1apVwheS7lLp6enZGSV+/vlnYWHhJFiSPqfrCkF5//33hcsYo/SZ575Zs2bC541FWSRB8c1q1fqC9M2s5ahEQOrOvHUgsTYPa7UjSZ2oRcqz+ySunuGl5W6tWDMTElMS8zuN31NGEhT2nXHuEk4+1AuZZy8iT/eWCG1w/aOTIi0oWlbCf23p4sRv5BIlStwURqFP7//1Qpda1p1au3atqMFDoQXl+PHjoiZJr169BAlRCMru3btFTRNaXfi9n5PH05UrV7IJM4sxMrsX2zPWhbyCVhta/s6cOSMSPJAM8XkpqiwoZEnMv01mwwdhyXqSFkW2b9+OevXqiVzIHCyn6/YEhZaZX375Be+8846oiMpqqdxYrgiKs3RmrioL661A2Z9EQCIgEZAISAQkAhIBf0SAbjCsQN+xY0cwLbEZEnHwJMpPWQxbSAiOjOmClLLXC+xJ0Y4Av6t5YM8PfArJgBIfor33m3v44YcfsHPnTuFSq6SppiVOcfGiFcSeoCite/ToIawhakIy2IZeUkzm8McffwirCQnK8uXLwfonf//9t0hLfODAAZE2WzVB4Y3MqMLgK7pz3XPPPcIco+TkJslgEKNiQcnpuj1BYXVUunv17NlT9EfTDycoLSh6Lz91/Wk9wVE3irzLCASk7oxA1XmfEmvzsFY7ktSJWqQ8u0/i6hleWu7WirXivcKYYHq15CRaXbzs+95zX3tEHj6DoCLRCB/XG0EReaUFRcNCYJKpYcOGge5VlPvvvx+bN29GgwYNbiIQGoa4pSm/uWkJobVGEfsYFK0EhaSZYSEM52A4SI0aNcQwXKck04x1v3jxonAnU6w0HhGUhQsXYvTo0cLnjDEoBIxEhEIfNLIgsi8lBsXVdXuCwokwxzetMyxMRIAkQdFz2XnWl9YXpGejybv1REDqTk80c+5LYm0e1mpHkjpRi5Rn90lcPcNLy91asaYHC2NPeALOU3f74niO89KToGxr3x9F4zOQ9fvfCL6zHMKe74STS7/HvUumaYHDb9tq1VNOD8bvYB72Hz16FMWKFROxHo899piwUvz555/iO7tVq1a6YsNCiRyHmbJYT8cIgqL0SQsfx+EfrleS6ZdffhmNGzcWz9e0aVNs3bpVZO5yS1AuXbpk44Lft2+fCJInk+MAzNZF3ziSFiWLF6v8Klm83F2n1YQxKCQoZGk06XCCFElQdF17HnVm5MbzaCLyZo8RkLrzGDKvG0isvYbOsIZSJ8ZAK3E1BldnveqBNTOtrlmzRvj416xZ0+XkdSUoHZ5D6TbNkTr5c9hOnEVInSo4GxmMe5dONw88E0fSQ0+upstQB+quXbt2IiFV4cKFxa20OpCYsPI63aNyIp+eQEE3LhIhWjDi4uJE2RAjCYrNZhOxKxyL4SJdunQR5UYUYc0rpspW0srnGIMSGxtrY/pfBskwJTCDWyIiIkQdlL59+95UB8XeNOTuuj1BcQRTEhRPlpe+9xq58fSdqezNEQGpO/PWhMTaPKzVjiR1ohYpz+6TuHqGl5a79cCa9ehY8oEfdsyU6kr0JihlurSG7UoCUid9BtvFq7hWuhCq7fpGCxx+21YPPTl7OB72kyzcfvvtOHToUHYsCO/lh33Dhg1FjPfcuXPB2A89hDEurGPIeifM0sUx9CQoJEAM3yhevLjolqEiQ4cOxYkTJ4Rxo0KFCsJiwiB5xqDTi2rdunWoUqWKuF9VkLweQBjRhyzUqC+qRm08fWcpe3OGgNSdeetCYm0e1mpHkjpRi5Rn90lcPcNLy916YM1gZ7oEPf3006JavJkEhWNlnbmI1Lc+AxKvodCrAxD9XCctkPhlWz305OzBWFeQ8RjPPvus8FRylC1btggvpjJlygirA2sJapU6deqI8ApaLmrXro033njjJoLCebCaPBNaMUUwxyShYHpgumYx7jxPnjyIiooS5IPJsj755BPhGTVhwgSRkYvPREMH409o7GAcu5JmmF5YHJPXGDRP8vLMM89kz0ESFDsN63mqoHXh+KK9URvPF89itTGl7szTuMTaPKzVjiR1ohYpz+6TuHqGl5a79cCaH4w8Decp/OHDh00nKIKkHDmFlLc+Q1CWDUU/GY/8Tz6iBRa/a6uHnpw9FIkJyQCJCoPKnUmbNm3AzLXMfMtAekpSUhKWLFmCn376CWPGjMkOQHcHHGM+qlevLjKDjRgxQtQlZCYvJYtXwBRqdPegvrouLSj6Im/UxtN3lrI3ZwhI3Zm3LiTW5mGtdiSpE7VIeXafxNUzvLTcrRfWjFPYv3+/8PNXXGsc56XnYey2Ds+BLl72cubD/6Hg/pNAcBCKTR+PqNbXY4xzg+ilJ0cs6O505MgR4fpUpEgRp1AxSRXdoRgbvmzZMkFmWPsmISFB3O9Jpq/hw4eLlL+sR8gaOrSg0AKnxC5JgqJxtUqCohFAh+ZGbTx9Zyl7kwTFt2tA7hPf4i/Xv3n4y7UeeFgrJ/H8gGVlcGdiNEFhocbKbVri/JC3GECBwm8OQcFnnM/FPIT1GcmIPcGsXeXLlxfWDwbB5yQsaK6kIOZ9dLtiUP1vv/0mXL8Yw2EfS+KsLxZaL126tEjty9gPElnWIGSShZEjR4omkqBoXC+SoGgEUBIUfQH0YW9GvDR9+Dh+PbTE2v/UI3VijE4krsbgaiTJZhA1K3/TBYiuQL4iKEwznLhiPc71mwBbahqih3ZD7JjeNwV/m4eufiMZsSdIOEg8GINBq0ZOQkLBWBS68rENrR8s/cFyHYw9IslgbcKchM/ALLotW7YUKX8p7733nvhvZoGTBEWH9SIJig4g2nVhxMbTd4ayN1cISN2ZtzYk1uZhrXYkqRO1SHl2n8TVM7y03K0X1swAValSJZERiTEFviQoHPva5t0403UMsuITkb9LCxR5ZwSCQkO1QOXTtnrpyf4hmG6XAeNMJ9yiRQuvno91RRhPQv2zDiED1l2JEsvy5Zdfigy9FLqPkdwoVdyvXr2a/d9eTUiHRikpKaLeIufpKEE25jbzY5EERV/lGLHx9J2h7E0SFN+vAblPfK8DxxlInRijE4mrMbg661UvrPnZxiD5Y8eOiXiGw8/9Vx1cGfdgq3qCwCjy+++/i6LZ3oizGBS6eNkXakzdfxhxHUcg88wFRDxSD8VmvILgqAhvhvN5G730pDwI9VWiRAnhbsW0vMyW5a3MmTNHZMEiySHZcSZcE6VKlRLkg25cYWFh4jbOY9UPPyArK0v8f8tWrbDihnXF2/no0e7ue+4R+EiC8r//6bZp9VCM2X3ovfHMnr+Vx5O6M0/7EmvzsFY7ktSJWqQ8u0/i6hleWu7WE2v7jFDllq2/JYh9dcI53b511BAU4pJ+8iziOgxH+j/HEFbjDpT44i2EFr9eiDCQRE898bmVbFp029q0aZMmKBhbUrFiRRw/flxYz+xJqNIxXbno/vf888/jgw8+yB4v88JlnGjcS8QMUZgRjq5jvpaodo+g8CsDJEHRM3DM10r1Zny9N543c5BtvENA6s473LxpJbH2BjVj20idGIOvxNUYXJ31qifWTDvLVLU8TX82PswvCAqfOfNyPM70GIuUzbsRWqooii94G+FVKpgHsg4j6aknTockYciQIWCRTdYO0SqsQP/cc8+JBAlMlGAvtJIwS9fevXtBqxkr1yuSsOxnXPlwPoq8cz1I/sKF8yhc2Hk2Ma1zVNs+aeUGpO0/ghKLJkuCIgnKt2jd+uZ0gWoXkrzPtwjo/dL07dP49+gSa//Tj9SJMTqRuBqDq9EEhe5CTFXLon6L6z6Gsk89cdOQvrCgKBNgwPy5YW8jccmPCIqKQPE5ryGicV3zgNY4kt574vHHHxfB6Wqyb6mZOuM2mLKY7lskIkxLrAhJCWM6SFJ27959U3dnB7yOkELRKDxhoPh3f8jidXXO10j+abNzglKuXDnbhQsXhL8aU48xMwQlPj4e/fr1Ez5u+fLlw8CBAwX7U8Td9XLlygn/SKZEYzCXIgMGDBCl7WmCIqN0JzIGxR1Cnl3Xe+N5Nrq8WwsCUnda0POsrcTaM7zMuFvqxBiUJa7G4Go0QWH/rGtBN58lzTujft/ufkNQOBGe5F+e8hkuvz0HCAlBkcnDUaBrK/PA1jCSnnuCge2FChUCXbPoUhUeHq5hZv81ff/990VGMFZx53+TmPDP119/LSrH898GDx6c3SAr6RpOPNANRd4diYhGdbIJCrOLzZ8/X1SE/+qrr0TmLwp5Qbdu3cS/h4aGim/25s2bO507g+3pTsbAfVadZ60WVqinsCL92LFjRRX5tLQ0DBo0SHALRXIkKIcOHbIx2Grbtm149NFHsXjxYjRt2hTdu3fH2bNnRZEYBtw0adIEr7/+upgwxd11EhTmbmb2gIkTJ4o2qampIi8zC9CQqEiCoss69agTPTeeRwPLmzUjIHWnGULVHUisVUNl2o1SJ8ZALXE1BlczCMro0aPx1ltvYcRdD2Do+LF+RVCUySQsXoVzQ98C0jNQaMIARPfvZB7gXo6k557YunWryLbF72p+rOslycnJ4Hf2+fPnb+mS6YkPHDiAwoX/i/9J+mkzLoz9AGU2fo7gvNdJEi0ojGUpWrSoME7wm1whKIxx4r/z+33fvn146KGHRJHJyMjIW8ZjjRZabVjlnsICogx6ZzA+ydnGjRuFlefMmTMifoakRwmKz5Gg2Gfxoj8bO+GiZ+AMg3nI0CmTJ08W1pT169eDwOR0nfcTuP79+2Pq1KnCkhIcHCzIz7x580R7guAvBMXZgvE224Vei8+ofvTceEbNUfbrHAGpO/NWhsTaPKzVjiR1ohYpz+6TuHqGl5a79cZ69erV4vC4YcnyWPDxdL8kKJxU8trfcKb7i7BdS0XMqF6IGd7Dr2ul6KknfuCPGzdOEEmlQKKWNWTf9tNPPxWWizvuuEMEyyt/GHcSFRV10zAXxryPrOSU/2/vWsB0qtb/OzMGgxmahORaqJBCOCp3RVKc5B65dFyOjnAkhSRd6ZRDin86pNwluV9yTVR05Fa55Za7MsxgMJf/81tz9uebPXt/e+3b9+3vm3c9zzzp22uvvfbvXWvt97fey6Ji/x7q+93fxathw4bZCAru9ycSIFi9evUSB0b6F6Q8xr3Q82NiYrJdg9UEBGXRokVUv359+vXXX0VdkCdfquNALl4KQYFPG5gNzELwbcOLwiQF0w7K6tWrqV27dsJEtX379oDXFYKCtsaMGUOvvPKKMA3hFEscOjNx4kRPERR1FgQ76ficGnhutePkxHOrj9wuE5RQjwGeJ6GWQM7ns0zckQnj6g6uWq06jTX0NmwWx6Rl0M+zv6Q8fgpiKGNQtN79ypYddLLTEMpMuUxFnutIia/09SxJsSIneAhNnz6d6tWrR3fddZcPAlge1q1bJ9yuYGVwusCVDqEQgUp6ymX6vXEPunlYbyrUqpEhQUF8U8mSJYXXk1JATvBeyA7mX0A+3nrrLRHzsnXrVkE8kAgAbl4Kd4AbGiwvaBfuZIqVBtcNLSh4QVgMjh8/TmDksJzA3SslJcXXDzwYZir408FcE+i6P0GB+QknXuK0U7A6nJAJoqJnQXn11Vdp1KhR2QBYuHCh0zLl9hgBRoARYAQYAUaAEQhrBKAzIRgaO/R33nmnp98l7tBJKvv+F5Tncir90eg+OtmxCVF0YOXa0y/k1zmEQ+APZAHKedu2bal48eLi5HfEncB7SG1hcPvdYi5eopvX/USJa36k2DyxVGLTdIq+KcH3WJzJAiKC4m9B0SIocPnCIZFqgoIsYnjX5cuXC90eXlawshw8eJAKFCggLHwjR46kRo0aiXTLsMTA7a1MmTLiuQEJSkZGRiZcsRBoBSIB9gMLCZgeAloUCwquoROKBSXQdX+CAvYId6/u3buL9iZMmCCA8JKLF1tQ3J4m3L4TCFjZ1XHiubmxDcbae1JnmbgjE8bVHVy1WnUDa7jfw3VocMcuNLBdZ99jvWZBUTp2dc8BOtl2EKWfPU8FH2tAt4x7kWKKWD+40A3pmZXTpUuXqGzZspSUlCRcmhC3jYJN/S1btmimA3aj30qb1349REmT5lDK/NWEjGoUFUXRCQXptq+nUN5yt/keLevihffDwZCdOnUS5AMxLkqB8QJZyuB5BHIGSwl06lmzZokquAfJspQC/R8WFdyDcn32Ssr45r9UbsH4HJBE9e3bNxNsBpYT5cAWJcYEEfmKSerdd98VfmQbN270xaDoXfcnKCAisM7MmDFDmLjQcSYobg7NwG2bnXih6yk/WY0Ayy54Y4KxDh7Wsk9imcgiZa4e42oOLzu13cAa1hN4p9SpXJUWvPGu5wkKOnjt4FFx6nzakZOUp1RxKj55JOWvfY8daB2916ycsPGOWBAo4x9//LHIYIXwBgSLoyCsAeeWuFUyki8RXOiubN5OqZu209Ude8WjkOIZmdMSej5JZ/qOpvh2zahwtxvpqAMRFJyvU6JECREkDwMGvKZgvEBWX3+CAisM3Nrg3gY3L3hgIZ4dFhS4iCFkBBwD8e3woMJYhbVFiW8PaEGpVq1a5tq1awXr8y/I1oU0Y2BBShav0aNH+7J4GV2H1QQxKCAoAAFBMTDxoDBBcWuYGrdrduIZt8g1goUAyy5YSBMx1sHDWvZJLBNZpMzVY1zN4WWnthtYI1NS0QIFKTntOu35bB4ViisguuhVC4qCX/rFFDo3+F1K+XKNSEOcOKQHFXm+M0WpAq3t4G31XjNyQtgD4rcRJA6yCCUdBfFBSOELywJS9CYk3HCtstov5b6MlMt05budlPrtf+nKtz9lEZKMDF+zecqVpMI9n6SEzi0pOj4r69afY6fS1V376Nbpb/nqQTcHmZoyZYrIBhYfHy+y74JQ5M2bV+j7IBpwTUOYBgLdEZuOQ0JxL9wLkZkLxAMEDcYN1H355ZeFxxUKOMSbb74pEmUhaB5x6PhTgvgDEpR8+fJlKm5caAz+cpMmTRLnoPTu3TvbOSgIdleK0XV/gqIWBhMUu8PT+v1mJp71p/CdbiDAsnMDVe02GevgYS37JJaJLFLm6jGu5vCyU9strJuVqUirjh2gT4eNoqb31wkLgoJOIv45eeYyOvfyOMq8nEr5H6wuskzFls2KiwhVMSOnmTNnUufOnUUiKFgG3CrX9h2m5Lkr6co3P9LVHfuI0tN9j4opUZTiHqpOcQ9Up7iHahAIijpwPvXHPXSq+wgqve4/4rBGFLMHNSYnJwuColhQ8G8QG6QMVp4HqwmsKkoaYTUeMHyA1EH2gnt8sZbS1m/jk+QBlNZJ8hyD4taU4nadRMDMounkc3NjW4y196TOMnFHJoyrO7hqteoW1sNrNaI3tq2nni1b02s9sw7B87oFxR+fa/uP0Om/vUrX9hygqHx5qXCfdnTTgC4UXSjLGhTMAmvA1KlTxXEbOHQwUIGSDZelHTt2EDyRFC8hp/qbee06pSzdQBenfUWpm2+cCh9T/GZBROIevI/yP1CdYm8vZZjJKzM9nY417E43DepK8X9t6iMo/n2FBQV/ekVNULTICGLNQUKU4Ht1W0psOwgN7j/z0Wy6sHQD3fP11ByPjfI/B8UpUJ1sJxgnyTNBcVJi3JZbCLj1cXOrv+HcLmPtPemxTNyRCePqDq7BJCiLWnalVks/o0qly9C68VkneIcTQUF/M1KvUtKEmZQ0YYY4LwVKeOLw3iJuIio6OmhCQmwEYi4Qf9GtWzdCLAZcuLTKihUrRGxGrVq1xGHnRul+ZV/i2oGjwrKUPHuZSCaAAqtIQtcnqGCLehR7e2lLzzo3fDxlJCVTsQ+yDvUMhQVFjcHpD2fS7skzqMmOpUxQ2ILyFbVq1Up2nnA9DyHAikTwhMFYBw9r2SexTGSRMlePcTWHl53abmH9fbu/U+fNi+ng8d9p25TP6dabi4YdQVFwTTt+mv4YPZlSvlgtfspX/W4q+vYAyl+jsh3ope7FQYJIpYs4DOz0I74HBZYRZLtF8Ld/qmDljJP58+dTmzZtpJ6hVyk9KZlSFq6h5Dkr6Oq2PVnVoqOpYPMHKaFba4prcL9tonZ57fd09oV/UUL3rEB5hGqYiY2BxSMjPZ3iCmRZtoSLXnKyyNyl4HLt6jVKS7tOBTROnNd690tbd9Hen3YyQQE4TFCYoNhaRUJ4s1sftxC+kmcfzVh7TzQsE3dkwri6g6tWq25hDYIy+dJxmrpsEb3Ttz89/UgLTYKCGOMOHTrQrl27RLYlJXOrEQJov3Sn7Bubx2Z+RXXmfmh0q7gORR/KrJlzQFK37qZzw8bT1e2/iDS5sB4kDutFMX7neEg93EQlnMGHwG9k48K5MnD1QqA7AuBRcFAhToVv3769yGhVu3ZtqlixIv3yyy9S7wb9M1vJyKQ2t1eh5M8W06Vl32SlBCYSLlvx7R+l+A7NKU/JYibeIHDVjCtX6cxzb4BZiIpLliyhli1bGrYP2WVkZtCB/QcEqalRswZFUZQIfEdiAJC56jWqE8gJAuxxHk+pUqVytIuYFRxVgqRcuBfnrcBaVarzE9RkzPAc9dnF67//FamP/YvsSfKYtOoiO2ENR4RLFdxaIF3qLjfrhwDLLnjDgbEOHtayT2KZyCJlrh7jag4vO7Xdwhq6yKnqFaj1y/+kUrcUow0fTKFvryZl0y2rvcgAACAASURBVG2g1xQtWpRatGghXgEH7yGDk0yxQ1BwPEWfPn1EulkcZ4HsULIlMyNDuDr9MXoSZfx5gaJvLkw3v9KX4js8atuaoO4DlPAqVaoIsvHhhx8KiwkKyNXEVRPppbdeoksbL4nf4PJVpEgRcXTG5MmTCaesyxTfBvn5ixSzYgulfbGG4s5dFLdGJxSiQq0bU3z75pSvVlVLLlxafchBiojE0R8ocEnDexsVrQPUGzRoQOvXrzdMqAUXOKQhRmYvBNSDEAFjFCTTGjBgAPXo0UOzC0xQbBIU/10FMzsKRgPCretuLZBu9ZfbvYEAyy54o4GxDh7Wsk9imcgiZa4e42oOLzu13cJaIRDd33yVVm39jkY88yxVbFI/G0GBMo3z7HCaN1yYkAYXCiYUTaNihaBgpx3KJ6wQSkEmWFgpzJb0Py/QH69PFpYGFJyZcsv7QyhvpXJmm9KtD6tStWrVRNA7Tj73d4VvTs1pJa2kWhtqUcLoBEG0UHBS/OHDhwWeRgVka/HId+iuX09R9KYdFJWWlYUrf51qlND1cSr4eCOKjstn1Izp61peQ2YJiumHOnQDExRJgqLFQist3pLN7MkExaFRyc1oIuDWx43hzokAY+29UcEycUcmjKs7uGq16hbWCoE48Psxavx8byqYP47eGjsmG/nAWRxwV8JuNo6QgLtXpUqVRAYqIwVblqAoehJ2yHFgIbI5Idj8tddeo4EDBwpSBBcg5ZA+s8inbtsjYiiu7d5PlDeWEgc9Q0X6d6ao2Dxmm8pRH65bOJTw7bffFq5cCkHZRtuoFtXy1d9KW+n6luvC+oS4E1gEtPRD3AAikHb6D0qevZwufr6Y0g6fEO1kJhSk9GZ/oV3lE+mpF/rb7nugBpiguAivV7J4aQmZCYqLguemcyDg1seNoWaCEg5jgMe/O1JiXN3BNRQEBc98afIHNH3FEmrWrJk4XA8FB+g98sgjlJSUJLJNIesUXL2QhWrYsGH0+uuvBwTBDEFBTMJ7770n2kOMxrJly0TMAVyh4OqFIHS4mxmRIr0OZaalUdKkuXT+nU8oM/Ua5a1yB90ybijlv+8uy4KEmxPI2oEDB+i3336jnTt3+ggKrCdraA2lURrloTzUhJrQClqR7VnZ9MPMTIo6dIJOLlhFd1+4Lk54p/9ZSy7ccSvFdXyUMupVJ8oXK3BQrBmWO29wIxMUt5D9n48cTJL+BULVihvx/w11QCDUZd/jdXP4ZcrEoDBBcVHI3LQUAqxISMHkSCXG2hEYHW2EZeIonL7GGFd3cA0VQTmbdJ4e7NuDUq9fo8WLF1OZMmXE4dsffPCBCO6ePXu26BoCvxFzgcxM0JfuueceXSBkCcq4cePohRdeEOeHwJUL1hNFAQcJwGGGq1atosGDB9PYsWMtAQ/rDE4vr1OyHJ0f8l7W+SDR0ZTQ5XGR7SpfVe2UwIEehvevWbOmIFQgcMqcUFtPlDZgRbmf7vc1+fnUaVQzLR9Fb/qJYn7YQ1HnLviuRRctQvHtmlPC0y1pzncbc+ifVgmKVgw0HqqOg2aCYmmYyd1kx4KiZeFggsJZvORGnvdqsSIRPJkw1sHDWvZJLBNZpMzVY1zN4WWntltYqwnEuHkzaezM6cKK8tJLL4mzOpBpCZmpihW7kRUKFhSchF6nTh2R1Usvy5YsQYGVZvXq1SJovH///jksBDgEsWrVqiKweuPGjfTQQw9JwwkSMXr0aFq4cKG4ByeVP/PMM9Q+sRwV/uhLykjOCmDPd++dFP90Syr0ZFOKSSgk1f6LL75IY8aMoY4dOwqskHoXfRzbeCz9ctsvwnqiFMWKsuzyQkLa3ktLNlDSkvWU5+p1USUzOooyK5en30slUt1BvShftUoUFRMjrgUiC1Id9askK5OwJih58+bNhEAUoeP9IRiY4pCCLC4ujp577jkaMWKEDxqj64jMBzvft2+fSMGmlH79+onsCPCFRPCUTGGCIoOSfB23Fkj5HnBNqwiw7KwiZ/4+xto8Zm7fwTJxB2HG1R1ctVp1C2u1snrlairV6ttdpHRFvAcC5GG9QPpcf48R/D5x4kTaunWrIDKDBg0Smb7URUYZhlUD1po8efIIKwlSGGu5MEFh7tq1K91+++0i/qVQocAkAhYNEJOlS7MO8sMJ5bD8fP31174MVI3q1acelWtRvd2nKW3vYVEvKi4f5atRWQTS572zHMVWKkt5K5al6MLxFJU/ry9LFiw75cuXFzordNOHmzSh33b9TFdKnaX29f+hOzi+fPg+uuenrL6nx8YQ1a1G6Q1qUEbtykTxBXXfXe3pY8eCIpP6OawJSr9+/TLBav0JCljp6dOnhSnwzJkz1LRpU+GjiEGFYnQdBAX+hQggQtARCsyIyIuM1GwgKrmZoMia5txYNt1aIN3oa6jb9FoaaZZd8EYEYx08rGWfxDKRRcpcPcbVHF52aruFtRaBGLl0Pk2ZMkV0FxYBBH+DDKgV5HvvvVe4N6WlpQmlHdaUxx57TAR/4xp+kyEocO1CljCcvg4XLhQtggJCgAMPoXOCxODMDMR/YDMbBOf48eO0f/9+scGNP6SmRcG1oUOHUvfu3YV+eezYMd85JUePHhV10PfRPfrQvb+eoZQFX1PmpSva4oqOFgQmukAcbU+7SE/uX0818xWhWUWqUXR61uGM3Wfvps31kyg9NmcTMdeJHvyhOM2d1psKPt6QvjpziO6rWydbxZRRE3PcqOXBwwRFf0ZFjRw5MhNBTQpBQTAVBg3MfUqmBfgKwpqyYcMGEWwV6DoeBYKCHNLweQQrxYEsc+bMoU8//VTc37p164glKFpQq30CZSa7nUUw0L1uLZBazwyUf9ut93OyXbWcQp2lLZiycxLHcGyLsfae1Fgm7siEcXUHV61W3cJaS6dYfeGU2CCGsg/CgBS6KFo7+MisNW3aNGGlwIa1Up599lmRreqH9v0CHtSIbF3Q++BGBhcvxQqjFwSOjW/Ew2zZskVsXusVxLJUrlxZePFgg1zrDJX09HQR7A8vHxyeiIIEAG++NpruLlyUru09TNf3HqZr+w7T9YPHhCtYxuVUyrx8Rfz39fP7aFrqCRqZeBe1L1yaYgvE0Y/VLlCHmRsMB4YSi6K2UuBGEBS1hWNN8pkc+Gs9RIa0yOqRYW1BURMUCBgDGCc+wlSHggHXrl07YS40uq4QFARLwacPea9hWoQvJAY7zImRTlCMzkaRHViGs8NCBbcWSD2C4pQ508Kr2r6FCYptCMO2gWDOk7AFKcgdZ5m4Azjj6g6uoSYoUIaxmYxYD7h24ZwPPYKi9BXWDdTDhvSECRNEMDo2mu/fsCcgQVFS9D788MPCTUopRlmqQC5AiGApAZGCVQQuXP4WFUUPNZISDlTERjiykh06dEhYfkBUOnToIDJyxcfH52gC98Ayc+LECWG5gdsYdJYuRbvQzmI7KT0q66wSrRJDMdSUmoqMXnYJihU9SVaPjCiC8s0334ggoZSUFJ9M4J9Yt25dYQI0uu5PUGCag8CR6g6H32AggqgEIihaJ1b6u58ZDVK+zggwAowAI8AIMAKMACNgHQGQBpz+DdICSwzOBtEq0BURFA/rCbKFacWwWO+F+Tuxub5y5UqaN28eXbiQlU0Llhe4sT3wwANUuHBhX6MnT56kjz76SATuK6mWMyiDej3ci84VOGf48Fsu30KTV0+maIo2rOvFCtDFZU6SD1Xfc7h4wUICQWKwKcwVJKNt27Y+C0qg6/4EpXHjxsLsB59BtAdG3rBhQ89bUGRNc1pZwvD+obagBGLWwdwtczJjRSgmCFtQQoG6N54ZzHnijTf2fi9YJu7IiHF1B1etVt3CWuubr+dOZGa3Xjm7pGj+ArT6wylU7KZE32spLs8IYIenDEgKsnKZad9N5EFU1q5dK2KpFyxYIJI/6RWQFCSGUnSW7Ue2U7EKxQhHXCBeRq8kUAIlUqKnLCjqvgaKe4GVKawIihJjsnnzZkFUUBD4tGjRImEqNLruT1DAzuBLN2PGDJFFAgOXCQpJBZzZmbhMUOygd+NeJijO4BiOrbilSIQjFl7pM8vEHUkwru7g6iZB0UrgIhvvYIZAQHnt2bOnCEavU7kqzRn1NsX+z/UfBKXyJ++ITWhYKuCitWnTJs8QFH/8cYI9rCqIVVHHvCQmJoosYchYqxAUHNhYoUIF6YMUveTipd4gD2uCMmzYsEycmjl37lwRzA5TGIKREPQ0a9YsXxYvCFDJ4mV0HQMWMSggKPDt27t3LzVq1EiMl9xIULQWKpn0cFaXTSYoVpHLfh8TFGdwDMdWWGnzntRYJu7IhHF1B1e3CYpaEXWSoPgToNT0NOq99kvam3SOerZsTZ2aNqedB/fTluWraF+hGEKSJWR2RZC9lteEFg4yQeDBk8qNJ3mFoGgdMi6bbCmiCArOlVHE06BBA1q/fr0whfXu3TvbOSgw4SnF6Lo/QVEPstxIULTIiBcIittpdJ108XK7r1qLIROUUHwivPHMUCptoUxD7g30tXsRSpl4GRe7fWNc7SIof79TWGt9m5wmKP7tff/xZ9Rj4yJKSknO8bIlChSiiQ1a0W2FEkhrtx43mLHayKPpfE0vERSroQIRRVAyveyAhsN2oqKEH6B/QWYI/wGPa+rf8P9WT5J3OgZFhqDAX1RdrO4yyFpQ3FbAnSYoRhNWdrnSSn+Me9V4u42PbH+Vek593Mw+NzfWDyXWstlZcptcFJnIzt/cho/V9w3lWLfa53C9zymsg01Q4M51pGpZenHSeLqtaDGqdkcFKnkyiRp06UDlby0pvG9QhwnKjZEpq0dqETgt3TVXWlCYoOQkO7IDSzZIXpagyOwyyJwtwgQl8OdLy19UKx0iE5RwVQPs99spRcJKT5igBLagyM5fK9jnxntCOdZzG95OYR0KgmKkxzBBqZFtOMvqkUxQ9FeBKCYo4UdQjIhMbiEo2K2xYnWSVXCYoOQ29eHG+zqlSFhBkAkKExQr48bqPaEc61b7HK73OYV1biUoMhu0dsaGl128tN7LKmlUvEU8n8WLCQoTFGXgO31SutsuXoGyUwRapJig2FnCI+feQHFNTikSVtBighJcguK20mNlDATznlCO9WC+pxee5RTW4U5QZILAteTlpE4RqH2jLF7qb4daF0HbTltQjMgIngkdjmNQgjjTc1MMipFlBLDLTFAvW1BkFia9IGEzE48JShAnaZg+KpCFzClFwgo0oSAo4RDX4R+DohWDaDVmT2ZNtSLHYN1jl2CFcqwHCyOvPMcprJ0kKFrfZOBllCVMVhlGW2rdRibGwm2CoqVnKETDn6DI4KM+c4YJijMzjl28NALuZZlvqGJQjIiMHYKiNazUwVmyQ0/rw6+HmbpNo90CPX9XGSVFL2e51ns5FZgvi1mgek593JzoSyS04TZBsZp5Tmv+aiXRgAxkxruMrGStijJtuVWHCYo2snYJlhPrSrhnngsWQXcCa4wCpwmK+juXWwiKXuYzNUExwocJijurPhMUDxMULeauNiWCTBkp1rgOhR7lVOdmVGLGSt8tMhNPq30ZxUiWoBiREaX/blpQ1KRUwYsJijsLjxdaDQZBsTJ+9AiKk1YDNf52CIrdHXzZscAERY6g6H0T1BtNitwSEhJ8p2zLrOtavQiF1U923MjUszP+ZdpX6jBBycq4KmNBCWThUPBU2lLLQGZTNdA6ywTFzKh2ry4TFI8TFCOlXMvaoza9+iv4W6NSqVZmfh9hkSEoMoqRmcXErOnYDkGR6RfalyUoWlNRZjF0Ygo79XFzoi+R0IZVgiK7W2w1yYLbBEVvt1hmnmvJ3e4OvtFYUvBWNle0/L21svAZtatct9p/WWKmN16sKlXq+7Q2rWTO2ZINCJaxBDJBkRtt/mu4DK56rUaaBUXLQqwmMcBCbamQJTuypFppnwmK3Hh2uxYTFCYovjGmZTXQMl1iUVAX2TNntOq5aUGRVfbMEBQtUmdF2TA7ud0iKDIfSj2iJ/MOVndlZdq2U0frI6+0529plM0/b1RPLwmFWtGV+TCjn3qpsY3Got5usZqgaO3Ea5Fxqwq+rOwUOSmbK3prksw4kyFnZiwQRu62eEdZZVILD5mzmZwkKDL+9sp3Qt1fGVIkK3O366nXM6dJr7r/apKtXLdiYTUzptTjE/fKxIOgntmNRDNphtVrnB0XKVm3ca0xxS5eUeTloxCZoDBBsURQtJQZI2uP3m6HkwRF6wOrtwj5L1h2CYpTpCUQWTBLUGSIh97HTkbZDqdDufR2sfUsiGpLo5EyJrMDqKfYySiYskq5jNXGDEGRUaDU7ckq+LKkV1FAnCIoRkqbnlVay0XKSYIiQxC1FEeZ8aNFjrUsKE7GVTqdFdJJ0qKeJ7Lzy2of1CQb7egFmWtt/hkp11peE1rWhtxEUMwSLGDDFhSrI9yd+2wRlOvXr9PAgQNp5syZonedO3em999/n/LkyUMNGzakDRs20OrVq6lp06a+3o8dO5aGDBlCzz//PI0bN87wrXJzFi8Zq0SoXLy8TFCMFiaZ3Rq9GBQtBUF2t0nGFczqrr7WRJJRVkNBUGQVU60dcVm3Gq0dTCN3Rn+ZG7lCOkl6ZRRMvTEro7io6+jtFsvMaRkLit6aJNNXLdIrS1AMPyb/q2CVoBjhqOdmJmtBkcHfDkHR679Zd5ZALsT+z9Ai5HrjR903GWuY3pqn/l3rmU4SFJmNIDMExSkLBxOUVr6hIDtmmaDIrqLBqWeLoIwcOZKwq7t8+XLR20cffZSefPJJeuWVVwRBOXXqFNWsWZNmzJjhe5sqVapQRkYGNWvWjAmKn4xlfStllJlgxKDIfEwDKRvKq8suHLIpDWVcyEJBUPR22GWUNkUp8d9BVt9nxzVDrfTIWgOsWlBk3O70gh+tnn2jR/xkLShukl6ZOe3kmNXbLZaZ01rjVab/MvNcz0VElqBoxdAYkRHFXdW/nuymj9aaraVYe4WgOOXOIiNLjBNZCwHqyliiZFQiq5syshYUWUusngVaWcMD4cMEJUvSocymapa0y6zPeCc7pNFJT5NccVBj6dKlhcXkqaeeEgNq3rx5NHjwYDpy5IggKPgbP348HTp0iAoXLkzff/89de/enWrXrk1FihRhgsIEJcdHTGvhtruYO7nbrbWDqfWb0TP13N0CtR/IxcVJFzWZxdasv7FaubCKjx7plVFeItGCYuadFIzsEhQjV05ZBd/spoORi5cXCIrsRoHR+NdS0PSsulYJotXdYrsERZY0+s9pPVxlNniAj9aGjr8M7FooZVx8UYcJinYWL5lvjp6C76RbotU5Yaf/WgRdZqNVZh4aHccQsSfJnz9/nhITE2n//v1UoUIFMf/x70qVKlFSUhK1atWKWrduTT/88APVr1+f+vTpQ7179xZ19+zZwwSl0w3zo5mJZ/VjpAxmI9cVrd10ow+/3seULSg3PouyFrJIJShmLRB6BM6pMWXkwmc0T4KtYMp8AI3eiQlKVnIPIwVZlmDZmdPBHj+BYhTM7hbLKEZmNpWckoksgdNaW5ycX3r9YIKStQJZxd+MniSzaejknJAZP3r9Z4Kiv71o2cXr2LFjVKZMGTp79iwVLVpUPAH/LlasGOHa008/LQgKXLqGDx9O69evJ1hcdu/eTUOHDtUlKK+++iqNGjVKZkOU6zACjAAjwAgwAowAI8AIMAKMgAUEIjKLl2JBwe7LHXfcIWDBvytWrJjNgtK/f38qX768CKDftWsXLV68mLp16yZtQbGAN98SAAGvm/RYeAF2E6K8nRIwkmTH88R70mSZuCMTxtUdXLVaZayDh7WdJ7Gc7KDn3L2WLSjoAiwiyMTVpk0b0aP58+fToEGD6OjRoyL+BBaUAQMG0IgRI+iNN94Q1xFEzwTFOQGabYknnlnEvFOfZRc8WTDWwcNa9kksE1mkzNVjXM3hZac2Y20HveDdy3IKHtaBnmSLoCBb15IlS2jZsmXiGS1atBCkRMnipRCUP//8U/gdNmjQgGJjY5mghFD2PPFCCL7NR7PsbAJo4nbG2gRYQarKMnEHaMbVHVy1WmWsg4e1nSexnOyg59y9tggKzkGBhcT/HBRYVJRzUBSCou4uW1CcE6DZlhDjgz8u4YcAyy54MmOsg4e17JNYJrJImavHuJrDy05txtoOesG7l+UUPKwDPckWQfHGK3AvGAFGgBFgBBgBRoARYAQYAUYgUhBgghIpkuT3YAQYAUaAEWAEGAFGgBFgBCIAASYoESBEfgVGgBFgBBgBRoARYAQYAUYgUhBgghIpkuT3YAQYAUaAEWAEGAFGgBFgBCIAASYoESBEfgVGwCoCOKQJGUu4BAcBxjs4OPNTGAFGgBFgBMIbASYo4S2/kPc+LS2Njhw54jusM+Qd4g5IIZCenk44bLVo0aLESrMUZLYqAW+kW7/lllsYb1tIOnOzMuZ57DuDp38rGRkZdPXqVYqLi+Ox7jy82VrkcewywA41z3PCGpBMUKzhluvvWrVqlTjvZu/evXTnnXfSQw89RO+++26ux8XrACxevJhee+01Onz4MP3lL3+hfv36UfPmzb3e7bDt36ZNm2jgwIH022+/Ua1atei5556jli1bhu37hHPH//jjD5o1axZ9/vnnQnlet25dOL+Op/r++++/0/Tp02nOnDmCnODMs+eff54qV67sqX5GQmd4HIeHFHlO2JcTExT7GEZ8C9j5TUxM9L3npUuX6IknnqCHH36Yhg4dSj/88AM1atSIPv74Y+rUqVPE4xEuL6iW2+nTp6lr167iMNVnn32W3njjDaGsQW6QHxdnEYB1EVjXrl1bzJN33nmHpk6dSlOmTKHGjRs7+zBuzRCBL774QijQefPmFQcHL1y4kCpVqsS7/IbIGVcACT9+/Di1b9+eHnzwQXrmmWeoUKFCNGbMGLauG8MXsMaFCxeocOHCvjo8jm0C6tLtajnxnLAPNBMU+xhGZAuYbDis6LPPPqOSJUtSz549qWPHjlSsWDFasmQJDRs2jD744AOqV6+eeH8QljvuuIPef/99/uCHcERoya1Dhw5UvHhxoSwsX76c5s2b53Ptatu2LV27do0WLVoUwl6H56Nhtt+8eTP95z//oZUrV4odeSi8+D06Opo2bNggrCcffvihsFahQHE7c+aMkAMXZxHQkwfc62JiYoS1NzY2lrDBArlg7Pfu3ZuU6872JrJa08MWJBwHM8+dO5eqV69OFStWFC++du1aQchfeOEFatq0aWSBEYS3OXnypPj+LliwgCpUqED9+/enRx99lIoUKUK//PIL5cuXj8dxEORg9Ah/OWHs/+Mf/xAeCTfddJOw1t5///08J4xADHCdCYoN8CL51kmTJonFcfz48XT06FF66aWXqGbNmvR///d/Yvfx3//+N91666309ttv0/r168VuPOrefffdkQyL599NS241atQQVhIQSlhM4HYEpQLKBSxeW7ZsEcr07bff7vn381IH4bYF0peUlCSICty3hgwZIggfdulBBP/1r38J5a1MmTKi69988w399a9/pW3btlG5cuW89Dph3xc9eShKtPKCFy9eFOsZ5DZjxgwfoQx7AFx8AX9ssV7ANRRjXY3t9evXBQn8+uuvRZ0ff/xRWFK4yCGgxJTgu4q1YsKECWJtxvcW68bIkSN945XHsRymbtQKJCdYzUEulcJzwroEmKBYxy4i7tQKEoUPcZcuXURA78SJE307YtiJx2IJEnLo0CHq27ev+H/sToK8lC1bVsQ3sOLl/tAwI7d27doJhRiKQ926dalHjx5iAd2zZw+NGDFCuLpAjoo1zP3eh98TtPBGkgHMg9tuu00oEN9++63AUfkg4VqVKlVo+/btIk5LKQULFqSvvvqKd5ZtDAMz8lBbSPD/cLPDZgvmBWexyy4II2yxEYVNDox1f2wVyyFa+9vf/ia+C9gw4WIOgVOnTglX3CZNmtCLL74obp45c6bYADl27Bhh/UDhcWwOV6dry8iJ54Q91Jmg2MMvbO6+fPkyzZ49Wyx0+CB37txZuGzBVKwuCMKDLzF216HMKuWee+4RiyTiF2BVGTRoELVp04aefvppUQVxDHfddZcw7SckJIQNNl7uqFNy+/vf/y4IJSwocNuD/OBqhP9HMCuICkhpbs9qZAZv/3GzdOlSoVRg7qAoOILkjx07lrp16+b7DYHDmEeQSW7H22juOSUP9XNg9R0wYAB99NFHgrT7KxJGfYqU6/7YwiUR673eN0E91uGqeO7cuWxQKGMZ1kSsJXAFZot6FkQpKSnCWvfll1+KxAH4ZsKyrTX/4TYEcoLNQf/YwPj4eBFD1aJFCx/uPI6dnY3+csLmEvQkPTmdOHFCbDLJyInnhDU5MUGxhltY3YVFcNy4cWKBhM81dmCGDx8uFKTBgwf73gU7MijY+XrggQeESRnKK9xVUGBBgQ8sdsXee+89sdjCz7JUqVLiOgKBsWB+9913YYWPVzvrpNzgEwtFGe4WICdwD6hatSoh0wjkCstXbg/cNoM3SD6UOqXABQYWKMyH+vXri0xGIP8g+FAEgX3p0qVFqmHEa8GqBb9yJij6s89JeSgERMF737599M9//lMk+oAccluxiy2yNmLDC2Pdn9xh3D/22GNiLsAdiQsJfOCyBbc3uP9gYwhxaWvWrBFxO0rx//7CfRruoyB6Cr4g0g0bNqS33nrL91tuH8dOji8n5YQsdpA5Cs8J61JigmIdO8/d+f3334tgXbiTwAKifIxTU1Pp5ptvFh+Uxx9/XPQbH2cEi8JHXjEZ+78Q/IexQwbXlRIlSohLsIzMnz+ftm7dKoJ84ceNBRRtIcgaMQ4gNdip5yKPQDDkBjcu+I7DZxyKNf4wPiBDZPeCywuIaW4oTuKt4IW59NRTT4kdY5B3kJICBQqIDHdIKIEseLBWwa1IIfawTQ2qwgAABClJREFUrnAhCoY8lLggf7yxbq1YsUKkfz579izBChZpxQq2GL8Yu3pFGeuwBOD74Y8tMkx98sknwm109erV4ruAlM7YIIv0ooc1NoTgWeDv6onvMwp233EWlbq0atVKZO5CAg7ECyrf7F27dhFS/Pu71uWGcezk2NGTE85zw/ptVk7YtMWY15IT+p2b54RduTFBsYugB+4H88duFUzq2MkFSYCVI3/+/L6dFgTpIu5AcdkCUYF5EhYSFOzsIqgXvsWK/zAWPihXCqnBhwvuQZjA+Cih/qeffipiGbCYgqwgiwUHRcoNimDKDcoxkhvgmciw8/LLLwu5IQUuPpK54bwCJ/GGlRCWEMw1ED3EnUBZw5wAzkrBNcwX7KZt3LhRZHcZNWqUsKDk9hIKeQBzEHW44x08eFDsYNepU0dYEWENiJQSCmyxUwxFHIoevgHI/vjII4+IzHWIUYzUEghrvDOC3eFdgHNikOkSBWvA5MmTBQFB1idsEiEuDUkF4NXw888/i80jbCpWq1ZN3IPMczi/ChscWFewBkX6OHZyzIRCTrl1TjglNyYoTiEZhHawcwUXElgv8FFFLIhyPgkyBYH9w4KCXVsQFvhQKllWsKOFhQ1Bdki3id1ypIDEri9cUeBrqcSegKAgoBoHMWLRxPMwuXv16iX+4KesWGfgh4k+gAxx0UbAS3JDD/GRA5FF6ttAO6XhKs9g4A2FFooXLJNKgWURri0IbEUGPMxFKGkocKnDPNOK+QpXnGX77RV5wFqChAa7d+8WyQ3g5x/u498r2C5btky4MGLjA+5fiJ2A5SSSihWs8f5IxIDvMWIZRo8eLSCBqzW+5SAhffr0EQdbAi+sK/BAwIYHNhNh2cNaAgLTvXt3ev31133JNbDBhHU8Esaxk+PEa3KCOz1c5iNxTjgpN622mKC4jbCN9tX+6VB84LeKRQzWEhAD7MQgeP3KlStigcPuOE50x2KGPP+KKRhE5c0336QdO3aIxREWFSyOuA8fbq2SnJwsgqeRreXAgQOCxMDNSzk0iv3ntYXrdbnZGJKevDXUeIN8IGEEdkPhrgGXIfiL47dwV4CtCNzL8sAaGc5Zu7yKLXb3ga1/XJaVseOle5zAWnkfHNAKkgKvBRBkfH+xNmADQ4lVUL87yAcIDSwl2FgEccH3PjeuKYHGBcvJS7PG2b4wQXEWT9utgUDAHQekADvcysm8WKSwQMHNCkx8586dgizA0jFt2jSfpQSp7xA/cu+99woLiFJgdi9fvrxYHBV3HuzwIpMNdmhgfcGujRKfgPvwIQexwS4Ogva46CPAcgvu6PAC3kqgPDK/LF68WByoBnKSGwvLwz2pM7buYatu2S2s8RzEj8AjATGi2DyEpQkbiTgEGUUJkldIHtYXxLLBswHWKS43EGA55Y7R8P/vmVhLvdW1cgAAAABJRU5ErkJggg==\" width=\"897.7778015607676\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[<Figure size 640x480 with 4 Axes>]]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}