pyoos_ndbc_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pyoos NDBC demo\n",
    "Demo the capabilities of the pyoos [NdbcSos collector](https://github.com/ioos/pyoos/blob/master/pyoos/collectors/ndbc/ndbc_sos.py).   \n",
    "Starting point was a notebook from [Filipe](https://github.com/ocefpaf), for the [OOI Endurance Array](http://oceanobservatories.org/array/coastal-endurance/), which has a spatial extent encompassed within the NANOOS domain.   \n",
    "12/23/2015. [Emilio Mayorga](https://github.com/emiliom/), [NANOOS](http://nanoos.org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from pyoos.collectors.ndbc.ndbc_sos import NdbcSos\n",
    "import owslib.swe.sensor.sml as owslibsml\n",
    "\n",
    "fmt = '{:*^64}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set pyoos filter criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# OOI Endurance Array bounding box\n",
    "bbox = [-127, 43, -123.75, 48]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "dt = 5  # days\n",
    "\n",
    "now = datetime.utcnow()\n",
    "start = now - timedelta(days=dt)\n",
    "stop = now + timedelta(days=dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sos_name = 'sea_water_temperature'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate and configure (filter) NdbcSos collector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************* NDBC Collector offerings *******************\n",
      "National Data Buoy Center SOS: 964 offerings\n"
     ]
    }
   ],
   "source": [
    "collector_ndbc = NdbcSos()\n",
    "\n",
    "collector_ndbc.set_bbox(bbox)\n",
    "collector_ndbc.end_time = stop\n",
    "collector_ndbc.start_time = start\n",
    "collector_ndbc.variables = [sos_name]\n",
    "\n",
    "ofrs = collector_ndbc.server.offerings\n",
    "title = collector_ndbc.server.identification.title\n",
    "\n",
    "print(fmt(' NDBC Collector offerings '))\n",
    "print('{}: {} offerings'.format(title, len(ofrs)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the filters set on the collector don't apply to the server offerings. `server` shows everything available. That's why there are 964 offerings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### FYI: Information available from each offering\n",
    "Sparse but useful metadata. No filtering of any kind applied yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_root': <Element {http://www.opengis.net/sos/1.0}ObservationOffering at 0x7f790f9e97a0>,\n",
       " 'bbox': (-87.313, 44.794, -87.313, 44.794),\n",
       " 'bbox_srs': urn:ogc:def:crs:EPSG::4326,\n",
       " 'begin_position': datetime.datetime(2012, 5, 31, 23, 40, tzinfo=tzutc()),\n",
       " 'description': 'Sturgeon Bay CG Station, WS',\n",
       " 'end_position': datetime.datetime(2015, 8, 18, 11, 40, tzinfo=tzutc()),\n",
       " 'features_of_interest': ['urn:cgi:Feature:CGI:EarthOcean'],\n",
       " 'id': 'station-0y2w3',\n",
       " 'name': 'urn:ioos:station:wmo:0y2w3',\n",
       " 'observed_properties': ['http://mmisw.org/ont/cf/parameter/air_temperature',\n",
       "  'http://mmisw.org/ont/cf/parameter/air_pressure_at_sea_level',\n",
       "  'http://mmisw.org/ont/cf/parameter/sea_water_temperature',\n",
       "  'http://mmisw.org/ont/cf/parameter/winds'],\n",
       " 'procedures': ['urn:ioos:station:wmo:0y2w3'],\n",
       " 'response_formats': ['text/xml;subtype=\"om/1.0.0\"',\n",
       "  'text/csv',\n",
       "  'text/tab-separated-values',\n",
       "  'application/vnd.google-earth.kml+xml',\n",
       "  'text/xml;schema=\"ioos/0.6.1\"',\n",
       "  'application/ioos+xml;version=0.6.1'],\n",
       " 'response_modes': ['inline'],\n",
       " 'result_model': 'om:ObservationCollection',\n",
       " 'srs': urn:ogc:def:crs:EPSG::4326}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'offering' is a list; here's one entry\n",
    "ofr1 = collector_ndbc.server.offerings[1]\n",
    "vars(ofr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('station-0y2w3', 'urn:ioos:station:wmo:0y2w3', ['urn:ioos:station:wmo:0y2w3'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ofr1.id, ofr1.name, ofr1.procedures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_root': <Element {http://www.opengis.net/sos/1.0}ObservationOffering at 0x7f790f9f3a28>,\n",
       " 'bbox': (-124.244, 46.857, -124.244, 46.857),\n",
       " 'bbox_srs': urn:ogc:def:crs:EPSG::4326,\n",
       " 'begin_position': datetime.datetime(2009, 5, 14, 14, 26, tzinfo=tzutc()),\n",
       " 'description': 'Grays Harbor, WA (036)',\n",
       " 'end_position': datetime.datetime(2015, 12, 24, 0, 50, 43, 893170),\n",
       " 'features_of_interest': ['urn:cgi:Feature:CGI:EarthOcean'],\n",
       " 'id': 'station-46211',\n",
       " 'name': 'urn:ioos:station:wmo:46211',\n",
       " 'observed_properties': ['http://mmisw.org/ont/cf/parameter/sea_water_temperature',\n",
       "  'http://mmisw.org/ont/cf/parameter/waves'],\n",
       " 'procedures': ['urn:ioos:station:wmo:46211'],\n",
       " 'response_formats': ['text/xml;subtype=\"om/1.0.0\"',\n",
       "  'text/csv',\n",
       "  'text/tab-separated-values',\n",
       "  'application/vnd.google-earth.kml+xml',\n",
       "  'text/xml;schema=\"ioos/0.6.1\"',\n",
       "  'application/ioos+xml;version=0.6.1'],\n",
       " 'response_modes': ['inline'],\n",
       " 'result_model': 'om:ObservationCollection',\n",
       " 'srs': urn:ogc:def:crs:EPSG::4326}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'content' is a dictionary; here's one entry\n",
    "vars(collector_ndbc.server.contents['station-46211'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply collector `raw` method\n",
    "The result is the filtered time series data. But that information will also be used to extract the set of stations returned, then issue a collector `metadata` request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "collector_ndbc_raw = collector_ndbc.raw(responseFormat=\"text/csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`collector_ndbc_raw` is the entire `csv` string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(str, 220604)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(collector_ndbc_raw), len(collector_ndbc_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2058"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See a piece of the csv\n",
    "ndbc_raw_lst = collector_ndbc_raw.splitlines()\n",
    "len(ndbc_raw_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['station_id,sensor_id,\"latitude (degree)\",\"longitude (degree)\",date_time,\"depth (m)\",\"sea_water_temperature (C)\"',\n",
       " 'urn:ioos:station:wmo:46029,urn:ioos:sensor:wmo:46029::watertemp1,46.14,-124.51,2015-12-19T01:50:00Z,0.60,11.40',\n",
       " 'urn:ioos:station:wmo:46029,urn:ioos:sensor:wmo:46029::watertemp1,46.14,-124.51,2015-12-19T02:50:00Z,0.60,11.40',\n",
       " 'urn:ioos:station:wmo:46029,urn:ioos:sensor:wmo:46029::watertemp1,46.14,-124.51,2015-12-19T03:50:00Z,0.60,11.40',\n",
       " 'urn:ioos:station:wmo:46029,urn:ioos:sensor:wmo:46029::watertemp1,46.14,-124.51,2015-12-19T04:50:00Z,0.60,11.40']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndbc_raw_lst[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the time series data into a DataFrame; but keep only the information/columns we'll use for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date_time</th>\n",
       "      <th>depth_m</th>\n",
       "      <th>sea_water_temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T01:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T02:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T03:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T04:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T05:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T06:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T07:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T08:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T09:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>46029</td>\n",
       "      <td>2015-12-19T10:50:00Z</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  station             date_time  depth_m  sea_water_temperature\n",
       "0   46029  2015-12-19T01:50:00Z      0.6                   11.4\n",
       "1   46029  2015-12-19T02:50:00Z      0.6                   11.4\n",
       "2   46029  2015-12-19T03:50:00Z      0.6                   11.4\n",
       "3   46029  2015-12-19T04:50:00Z      0.6                   11.4\n",
       "4   46029  2015-12-19T05:50:00Z      0.6                   11.4\n",
       "5   46029  2015-12-19T06:50:00Z      0.6                   11.4\n",
       "6   46029  2015-12-19T07:50:00Z      0.6                   11.4\n",
       "7   46029  2015-12-19T08:50:00Z      0.6                   11.4\n",
       "8   46029  2015-12-19T09:50:00Z      0.6                   11.4\n",
       "9   46029  2015-12-19T10:50:00Z      0.6                   11.3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from StringIO import StringIO\n",
    "\n",
    "datacsv_df = pd.read_csv(StringIO(collector_ndbc_raw.encode('utf-8')), \n",
    "                         parse_dates=True)\n",
    "columns = {'station_id': 'station',\n",
    "           'depth (m)': 'depth_m',\n",
    "           'sea_water_temperature (C)': sos_name}\n",
    "           \n",
    "datacsv_df.rename(columns=columns, inplace=True)\n",
    "datacsv_df['station'] = [s.split(':')[-1] for s in datacsv_df['station']]\n",
    "datacsv_df.drop(['sensor_id', 'latitude (degree)', 'longitude (degree)'], \n",
    "                axis=1, inplace=True)\n",
    "\n",
    "datacsv_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract unique station id's as a list\n",
    "To be reused with the collector `metadata` method, next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11,\n",
       " ['46029',\n",
       "  '46041',\n",
       "  '46050',\n",
       "  '46089',\n",
       "  '46211',\n",
       "  '46229',\n",
       "  '46243',\n",
       "  '46248',\n",
       "  'jtaw1',\n",
       "  'sefo3',\n",
       "  'seto3'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations = list(datacsv_df.station.unique())\n",
    "len(stations), stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract station information using collector `metadata` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'outputFormat': {'values': ['text/xml;subtype=\"sensorML/1.0.1\"']}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descsen = collector_ndbc.server.get_operation_by_name('describesensor')\n",
    "descsen.parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(list, 11, <owslib.swe.sensor.sml.SensorML at 0x7f79119ee390>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The metadata method for the NdbcSos collector expects\n",
    "# features to be in the shortened, \"station wmo id\" form (eg, 46098),\n",
    "# rather than the offering name (eg, station-46089) \n",
    "# or the station urn (eg, urn:ioos:station:wmo:46089)\n",
    "output_format = descsen.parameters['outputFormat']['values'][0]\n",
    "collector_ndbc.features = stations\n",
    "ndbc_md_lst = collector_ndbc.metadata(output_format=output_format)\n",
    "type(ndbc_md_lst), len(ndbc_md_lst), ndbc_md_lst[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "urn:ioos:station:wmo:46029\n",
      "urn:ioos:station:wmo:46041\n",
      "urn:ioos:station:wmo:46050\n",
      "urn:ioos:station:wmo:46089\n",
      "urn:ioos:station:wmo:46211\n",
      "urn:ioos:station:wmo:46229\n",
      "urn:ioos:station:wmo:46243\n",
      "urn:ioos:station:wmo:46248\n",
      "urn:ioos:station:wmo:jtaw1\n",
      "urn:ioos:station:wmo:sefo3\n",
      "urn:ioos:station:wmo:seto3\n"
     ]
    }
   ],
   "source": [
    "for sml in ndbc_md_lst:\n",
    "    print sml.members[0].identifiers['stationId'].value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the SensorML responses, for illustration. The corresponding url for the DecribeSensor request for sta0_sml is http://sdf.ndbc.noaa.gov/sos/server.php?request=DescribeSensor&service=SOS&version=1.0.0&outputformat=text/xml;subtype=%22sensorML/1.0.1%22&procedure=urn:ioos:station:wmo:46211"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<owslib.swe.sensor.sml.System at 0x7f790eda75d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta0_sml = ndbc_md_lst[4].members[0]\n",
    "sta0_sml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'capabilities': {'ioosServiceMetadata': <Element {http://www.opengis.net/swe/1.0.1}SimpleDataRecord at 0x7f790ee17cb0>},\n",
       " 'characteristics': {},\n",
       " 'classifiers': {'platformType': <owslib.swe.sensor.sml.Classifier at 0x7f790edab7d0>,\n",
       "  'publisher': <owslib.swe.sensor.sml.Classifier at 0x7f790edab750>},\n",
       " 'components': [<Element {http://www.opengis.net/sensorML/1.0.1}component at 0x7f790ee177e8>,\n",
       "  <Element {http://www.opengis.net/sensorML/1.0.1}component at 0x7f790ee177a0>],\n",
       " 'connections': [],\n",
       " 'contacts': {'http://mmisw.org/ont/ioos/definition/operator': <owslib.swe.sensor.sml.Contact at 0x7f790edab910>,\n",
       "  'http://mmisw.org/ont/ioos/definition/publisher': <owslib.swe.sensor.sml.Contact at 0x7f790edab990>},\n",
       " 'description': 'Station metadata for 46211 - Grays Harbor, WA (036)',\n",
       " 'documentation': [<owslib.swe.sensor.sml.Documentation at 0x7f790edab310>],\n",
       " 'gmlBoundedBy': None,\n",
       " 'history': {},\n",
       " 'identifiers': {'longName': <owslib.swe.sensor.sml.Identifier at 0x7f790edab850>,\n",
       "  'shortName': <owslib.swe.sensor.sml.Identifier at 0x7f790edab810>,\n",
       "  'stationId': <owslib.swe.sensor.sml.Identifier at 0x7f790edaba90>},\n",
       " 'inputs': [],\n",
       " 'interface': None,\n",
       " 'keywords': [],\n",
       " 'location': <Element {http://www.opengis.net/sensorML/1.0.1}location at 0x7f790ee178c0>,\n",
       " 'name': 'urn:ioos:station:wmo:46211',\n",
       " 'outputs': [],\n",
       " 'parameters': [],\n",
       " 'positions': [],\n",
       " 'rights': [],\n",
       " 'security': [],\n",
       " 'smlBoundedBy': None,\n",
       " 'spatialReferenceFrame': None,\n",
       " 'temporalReferenceFrame': None,\n",
       " 'timePosition': None,\n",
       " 'validTime': None}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(sta0_sml)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Available contacts roles, identifiers, and classifiers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['http://mmisw.org/ont/ioos/definition/operator',\n",
       "  'http://mmisw.org/ont/ioos/definition/publisher'],\n",
       " ['stationId', 'shortName', 'longName'],\n",
       " ['publisher', 'platformType'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta0_sml.contacts.keys(), sta0_sml.identifiers.keys(), sta0_sml.classifiers.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract and transform the desired station metadata, then create DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stations_md_rec = []\n",
    "for sta_sml_members in ndbc_md_lst:\n",
    "    sta_sml = sta_sml_members.members[0]\n",
    "    station_urn = sta_sml.identifiers['stationId'].value\n",
    "    # The XPath for the location/point coordinates doesn't follow\n",
    "    # the IOOS SOS convention.\n",
    "    loc_str = sta_sml.location.find(owslibsml.nsp('gml:Point/gml:coordinates')).text\n",
    "    # In some cases the platform type XML element seems to be missing ...\n",
    "    if 'platformType' in sta_sml.classifiers:\n",
    "        platform_type = sta_sml.classifiers['platformType'].value\n",
    "    else:\n",
    "        platform_type = 'Unknown'\n",
    "    \n",
    "    sta_md = dict(\n",
    "        station_id = station_urn.split(':')[-1],\n",
    "        station_urn = station_urn,\n",
    "        # Long name is also available in the offering as the offering description\n",
    "        longname = sta_sml.identifiers['longName'].value,\n",
    "        lat = float(loc_str.split()[0]),\n",
    "        lon = float(loc_str.split()[1]),\n",
    "        operator = sta_sml.contacts['http://mmisw.org/ont/ioos/definition/operator'].organization,\n",
    "        platform_type = platform_type\n",
    "    )\n",
    "    stations_md_rec.append(sta_md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations: 11\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>longname</th>\n",
       "      <th>operator</th>\n",
       "      <th>platform_type</th>\n",
       "      <th>station_urn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46029</th>\n",
       "      <td>46.140</td>\n",
       "      <td>-124.510</td>\n",
       "      <td>COL RIVER BAR - 78NM South Southwest of Aberde...</td>\n",
       "      <td>National Data Buoy Center</td>\n",
       "      <td>MOORED BUOY</td>\n",
       "      <td>urn:ioos:station:wmo:46029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46041</th>\n",
       "      <td>47.340</td>\n",
       "      <td>-124.750</td>\n",
       "      <td>CAPE ELIZABETH- 45NM Northwest of Aberdeen, WA</td>\n",
       "      <td>National Data Buoy Center</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>urn:ioos:station:wmo:46041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46050</th>\n",
       "      <td>44.640</td>\n",
       "      <td>-124.500</td>\n",
       "      <td>STONEWALL BANKS - 20NM West of Newport, OR</td>\n",
       "      <td>National Data Buoy Center</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>urn:ioos:station:wmo:46050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46089</th>\n",
       "      <td>45.910</td>\n",
       "      <td>-125.760</td>\n",
       "      <td>Tillamook, OR</td>\n",
       "      <td>National Data Buoy Center</td>\n",
       "      <td>MOORED BUOY</td>\n",
       "      <td>urn:ioos:station:wmo:46089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46211</th>\n",
       "      <td>46.857</td>\n",
       "      <td>-124.244</td>\n",
       "      <td>Grays Harbor, WA (036)</td>\n",
       "      <td>SCRIPPS</td>\n",
       "      <td>MOORED BUOY</td>\n",
       "      <td>urn:ioos:station:wmo:46211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46229</th>\n",
       "      <td>43.769</td>\n",
       "      <td>-124.551</td>\n",
       "      <td>UMPQUA OFFSHORE, OR (139)</td>\n",
       "      <td>SCRIPPS</td>\n",
       "      <td>MOORED BUOY</td>\n",
       "      <td>urn:ioos:station:wmo:46229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46243</th>\n",
       "      <td>46.200</td>\n",
       "      <td>-124.117</td>\n",
       "      <td>Clatsop Spit, OR - 162</td>\n",
       "      <td>SCRIPPS</td>\n",
       "      <td>MOORED BUOY</td>\n",
       "      <td>urn:ioos:station:wmo:46243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46248</th>\n",
       "      <td>46.133</td>\n",
       "      <td>-124.667</td>\n",
       "      <td>Astoria Canyon, OR  (179)</td>\n",
       "      <td>SCRIPPS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>urn:ioos:station:wmo:46248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jtaw1</th>\n",
       "      <td>46.266</td>\n",
       "      <td>-124.038</td>\n",
       "      <td>Jetty A, USCG Station Disappointment</td>\n",
       "      <td>Center for Coastal Margin Observation and Pred...</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>urn:ioos:station:wmo:jtaw1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sefo3</th>\n",
       "      <td>46.204</td>\n",
       "      <td>-123.759</td>\n",
       "      <td>SATURN Estuary Station #04 at Mott Basin</td>\n",
       "      <td>Center for Coastal Margin Observation and Pred...</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>urn:ioos:station:wmo:sefo3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seto3</th>\n",
       "      <td>46.200</td>\n",
       "      <td>-123.941</td>\n",
       "      <td>SATURN Estuary Station #03</td>\n",
       "      <td>Center for Coastal Margin Observation and Pred...</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>urn:ioos:station:wmo:seto3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               lat      lon  \\\n",
       "station_id                    \n",
       "46029       46.140 -124.510   \n",
       "46041       47.340 -124.750   \n",
       "46050       44.640 -124.500   \n",
       "46089       45.910 -125.760   \n",
       "46211       46.857 -124.244   \n",
       "46229       43.769 -124.551   \n",
       "46243       46.200 -124.117   \n",
       "46248       46.133 -124.667   \n",
       "jtaw1       46.266 -124.038   \n",
       "sefo3       46.204 -123.759   \n",
       "seto3       46.200 -123.941   \n",
       "\n",
       "                                                     longname  \\\n",
       "station_id                                                      \n",
       "46029       COL RIVER BAR - 78NM South Southwest of Aberde...   \n",
       "46041          CAPE ELIZABETH- 45NM Northwest of Aberdeen, WA   \n",
       "46050              STONEWALL BANKS - 20NM West of Newport, OR   \n",
       "46089                                           Tillamook, OR   \n",
       "46211                                  Grays Harbor, WA (036)   \n",
       "46229                               UMPQUA OFFSHORE, OR (139)   \n",
       "46243                                  Clatsop Spit, OR - 162   \n",
       "46248                               Astoria Canyon, OR  (179)   \n",
       "jtaw1                    Jetty A, USCG Station Disappointment   \n",
       "sefo3                SATURN Estuary Station #04 at Mott Basin   \n",
       "seto3                              SATURN Estuary Station #03   \n",
       "\n",
       "                                                     operator platform_type  \\\n",
       "station_id                                                                    \n",
       "46029                               National Data Buoy Center   MOORED BUOY   \n",
       "46041                               National Data Buoy Center       Unknown   \n",
       "46050                               National Data Buoy Center       Unknown   \n",
       "46089                               National Data Buoy Center   MOORED BUOY   \n",
       "46211                                                 SCRIPPS   MOORED BUOY   \n",
       "46229                                                 SCRIPPS   MOORED BUOY   \n",
       "46243                                                 SCRIPPS   MOORED BUOY   \n",
       "46248                                                 SCRIPPS       Unknown   \n",
       "jtaw1       Center for Coastal Margin Observation and Pred...       Unknown   \n",
       "sefo3       Center for Coastal Margin Observation and Pred...       Unknown   \n",
       "seto3       Center for Coastal Margin Observation and Pred...       Unknown   \n",
       "\n",
       "                           station_urn  \n",
       "station_id                              \n",
       "46029       urn:ioos:station:wmo:46029  \n",
       "46041       urn:ioos:station:wmo:46041  \n",
       "46050       urn:ioos:station:wmo:46050  \n",
       "46089       urn:ioos:station:wmo:46089  \n",
       "46211       urn:ioos:station:wmo:46211  \n",
       "46229       urn:ioos:station:wmo:46229  \n",
       "46243       urn:ioos:station:wmo:46243  \n",
       "46248       urn:ioos:station:wmo:46248  \n",
       "jtaw1       urn:ioos:station:wmo:jtaw1  \n",
       "sefo3       urn:ioos:station:wmo:sefo3  \n",
       "seto3       urn:ioos:station:wmo:seto3  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_df = pd.DataFrame.from_records(stations_md_rec, index='station_id')\n",
    "print \"Number of stations: %d\" % len(stations_df)\n",
    "stations_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mayorga/miniconda/envs/ioos_source/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uLldj6/H79b4AKOUpfFb1G+zNY7gQ2YRDtwvW4iOXS3RsVAZL8xd7n/C8tyUK\nzUXsk0a81GQUudGjJN5mJVrFi60S/1bSa7BPGo6ZNiTPJOlmDZFrI1DobhFnuxGt3DXPtP8Gb+Pb\nN+HdJuqU8LqJOiW8bm9jnRItT0+4WR8gKdOZ2PSKeaSQcVfdHOfiLtx9mExYRAJe5YsUeCXyt4Fe\nsiLBej4W6buRsq+nJMlIU7ZGI/dAKyuFRlH53Q2cACQLEqzno475g/1hNXEqZIG9tRKwI+jx1xS1\n+IsH2taUd1E+N6vkVA0PotXcvp+Ap/uLd93Lj0ZRlUTr2Sgzck6O8TLSlU3/HYETgKQgwdoHy7Rt\nSPqUHLt1MntSzT9F0iVinuGPViamSBcEQRAE4c36T7U8EVafWOu1BVqn5tyVKC7fjqdh9RK4Fs87\n+hTerIAz94iMTaFjIzdsrJ4fKOUmPUPLzkM3sbE0o32D0jnW3sjP2/i2RHh3ifokvG5/d51KT0+n\nV6+u9O7dnzZt2qHRaJg5czoREfewsrLCx2ce1tbW7N//Bzt2bEEmk9O+fUfatXu6PlFcXCw9enzC\n7Nnf4eXljV6vZ8WKZfz22x727g14bhn27fuNnTu3oVQq0Wg0dO/e02Qh1g0b1rFt22b27PFHJjO8\nDJ01awbXrl3Bzs4enU5H4cKFmTjxaywsLOjcuT3FihVHJpMZ15/q3bs/3t41jXn+8cdeVq9ejrOz\nYSFSvV5PsWLFmTJlOrNmzaBJk2bUrVvfpJzt2jVn794DnDp1gs2bNyBJEnq9nrt379CtWw+6d+8F\nQEDAPmbOnM6ePf7Y2toB4Ou7iv379+Hk5IRGo6Fw4SJMnWpYQ2v48IGkpaVhYWFhLG+HDh8RFRXF\nqVMnSE5OIjo6Gje3Msa1tD799GM2bNiOubmhW3tQ0Dl27dqOj8/c597vq1cvMGDAAI4fNyzG26hR\nbapVq2489+LFy7lw4Txffjma7dt/Ma4V5eu7Cm/vmnh5edOgQS2mT59Fs2YtjPlOnTqBxMQElixZ\nYXK+4cMH4uFRiaFDR5psW7p05XPLCnDixDHq1KmHQqEwfgZ/h0ePIomLi6ViRc/npk1IeMywYV9Q\nv34jBg7MuWxL9+6dqFv3fYYPHwO82OeTnx9+WEyZMu60afP3zGi8aNF8ypWrQNu2hmEjCxbMxcLC\ngiFDDLNG79ixlbi4WOM1z5s3kytXQtm799e37rtPtDxlKbkMXXLB3l6Xcbbj8u14roc/xvYlH8r/\nDjZWZiiY33TXAAAgAElEQVTe0pYwrVZHovrlJw94lkIhYWOZ971PTs0kMjYFJweLlw6cAFRKOaWc\nrLkbmcS9R8l5ntPCXI658r/1KyMIgpCf9evXGB/wAX799RccHByYNs2HX3/9heDgIGrUqMX69WtY\ns2YDcrmcAQN60ahRU+PilT/+uMS42CgYFsUsXrx4gc5/8eIFfv55J0uWLMfS0or4+HgGD+6Hu3s5\nSpUyjDM9cGA/9vb2nD37J++9V8d47KBBw4wBzrp1q9mxYwu9evVFkmQsWLAkz0VGszRv3pIhQ0bm\nm8aU4cVc3br1jeeNiYlh1KjBfPjhx8ZUBw744+xcisOHD5ps79LlU+MCrLNnf8OJE0eNi9ROmTLd\nuGBqdt279yQo6By7d+/g22/n5CiLSekK8OIwIyODVatWUaSIo3GbjY1tjoAHoGRJZ3x9VzN27IQc\n+0qUKMmBA/7G4CklJYXw8DvY2+c+Jj04OIhHjyIpWrRYgcuaZdu2TdSs+R4KhYLcrvt1OX/+LCkp\nKQUKnm7fvkWpUq65Bk7Xrl0F4PDhg8bgCV7smt+U6tVrcuLEUWPwdOfOLeTyp7PlBgcHGeu0RqPh\n5MkTKJVKbt++jbX1m19KqKD+W0+CFl6QXLDI1sFGRWE7cyJjU9h74s7fW64X4ORgQau6rzbxwN/l\nWNBDIqJe7yLETWs5U9LRKtd9t+4nAuDubJfr/hfh7mzL3cgkjp5/kGcapULGR03KoDQT02YLgvD2\nMD+3B2X4hVz3Zcpl2GpffOmNDBcv0mp0yDdNePgdwsPvUK/e0xaWwMBj9O8/CID27TsChofKihU9\nsbQ0zKpZtaoXly4FU69efc6fP4uVlRVlyrgb8+jcuRsWFhasWWPasjBp0jhmz/7OZNuuXdvp02cA\nlpaG7wkHBwfWrv0JKytrAG7dCkOv19GtWw8CAvaZBE/ZVapUmYMH9z/5Sc8/1Sfn++/nMmDAYGN5\nExMTuXr1MhMnfs2mTX4mwVNWRyGtVktCwmOKFHHKsa/gXu4CN2xYx2effcbs2dlbQHLPq2HDxpw9\n+ycREfeMLXRZnJyKEh0dRXJyMtbW1pw4cRQvL2/u3Lmda159+37B6tXLmTp1hsn2mzfD+P77echk\nMiwtLZkyZQZhYdfZsmUjaWmpVK9eg9DQEMaNG8GiRT8CetauXcmff57Gzs6eb7+dzcCBfVm/fjMx\nMdF06tSOPXv8sbOzp3fv7qxZ8xO+vqu4ePECOp2OTp260KxZS/788zSrVy/H3NycQoUKMXr0BHx9\nV6FQKChWrDjvv9/AWMaDBwPYvn0zCoWCChU8GDFiLEuXfk9U1CNWrvwhRwAVELCP9u0/4vjxIwQF\nnaN69RqAoW5MnjyeyMiHNGzYmN69+3Pnzm2+/34ekiRhaWnFlCnTSEpKYsaMqVhZWdGpUxeSkpLY\ntMkPJ6eiKJVKypRxR6fTMW/eTB4+fIBGo6Ffv4F4e9fMMz8fn2mUKFGSmzfDKF++AhMmTM31c6pe\n3Zsff1xsLK9SqSIzM4P09HRUKhWXL4fw1VeGz/DMmVOUL+9BuXLl2bt3L9269c41z7fRfyt4ekG1\nKxflZkTCmy6GUWRsClHxqTxOSsfe5u2atlmv1/MoLgWVUk7p19DNUavVExaRwI3wx7kGT3q9nlsR\nCchlEi7FrF/5fCUcraheoQgpaZpc9yckZxAZm8LdyCTKlcq5tpYgCMJ/zbJlixgzZgK///6rcVtk\n5ENOnQrkhx8WU7hwEcaMmUBsbIxJi4K9vQOxsTFoNBrWrVvNnDkLWLx4gXG/hUXus7Y+GziBIYAr\nV850nbmsQAQgIMCf5s1b0bBhU1au/JHMzEzMzHKuLXfq1AkqVXp+i8HrdPToYXQ6HY0aPV3w/vDh\nA9Sr15Datesyb95MYmJiKFLE8EZ+x46tHD16iKioR7i7l6Vq1WqvdP5x40YYuyYmJyfh7Jz/i9l7\n98IJC7vBxInjmD37aStWenoG33zzFZGRD2nUqAlduxomapIkiS++GMKKFUvx8ZmXI7/69Rty5MhB\n2rX7kIMH99OlS3fu3FmbI50kSdSpU4+tWzdy82YY7u5Px/0uWbKAYcNG4eFRiS1bNrJ9+2a8vWty\n+/ZNtmzZjUKh4Pff97JgwRIUCgWJiYk0adKcfv0GMnhwX8LDw7G2tkatTubSpWC8vLwJDQ2hUqXK\n2NvbExoawqNHD1m2bBWZmZn07duD+vUbsXv3doYPH03Vql4cO3YEvV5HmzbtsLd3MAmcUlNTWb36\nR/z8tqBSmTNhwmiCgs4xbNgodu/ekSNw0uv1HDoUwIoVvqhUKg4c8DcGT7duhbFz5x5kMjndu3fm\n44+78P338/nyyymULOnMzz/vZNeu7bRs2YawsBvs3v0bNjY2dOrUDl/fjVhZWdOvX0/AEKAVKeLI\nxIlfkZDwmBEjBuPntyXP/K5fv8a3387F3t6ejz76ALU62eT3LIutrR1WVlbExERz7dpVKleuQlpa\nGiEhF3F0dKJ48RLGFt2AgH00b96KsmXLMW3aRBE8/VsUtjOnsF3+zfb/pDsPEzke9JBb9xPx9nB8\n/gH/IHWahkyNDtfiNrzn+eoTUej1emIT0rgflUxahiZHd7no+FSSUjJxK2H7WlqCJEmicj6TRahT\nM9l9+BY3IxJF8CQIwlslrUaHPFuJ/q4xT/v2/UblylUpVsy0e51er8fV1Y0+fQbg57eWjRvXUa5c\nhRxpwNA9r337j4wPYS8zBFuSJHS6vFvWDhzYz6JFP2Bra4unZ2VOnQqkYcPGAKxY8QNbtmxEp9NR\nqZIn7dp1NB6XPbCQJInvvluCUqnMkffVq1eMaZo2bUHHjp0KVO7k5GRWrfqBxYtNu7sFBOyjT58B\nyGQyGjVqyqFDhqACTLvtrV+/Bl/fVfTrNxAwjOHKPuZp8uRpOT6bZ2XvmpjVtS8/S5cuZNSo8Tm2\nDxs2ilatPgBg6NABeHnVMO7z8vJmy5YNhIaazqoqSRJNmjRn4cJ5NGrUlPj4eEqWNG2detbAgUNZ\nvnwJ3323xLjtzp1beHhUAsDbuybr16/G27smZcuWe9JND7K3JFpZWRtbOQsXdiQlRU3Vql6EhoZw\n6VIwn3zSjUuXgtHptHh5eRMSEkxoaCgjRgwy1s+4uFiaNGnO/PmzaNnyA5o3b2kc1/Wse/fuUqqU\ni/E+V69egxs3ruX4ncgSFHSO4sVL4ORUlCZNmuHnt4YxYwzdHj08KhrzcXNz48GD+1y5EsrcuT7o\n9Xo0mkw8PAwvAEqWdMbGxoaEhMdYWVlhZ2d4bqla1QuAS5cucunSBS5evIBeryczMwONRpNnfs7O\nzjg4GF6AODo6kpyce/BkuMaanD9/jrCw69SpU4/U1FQuXDhP0aJFjeMG09LSOHv2DBMmTMXCwgKl\nUpnvfXnbiODpHVLKyRqlQsat+4mULmGDnZXyrZkJMD4xDTB0d3wdJEmijLMt565Ec/XO4xytS9fu\nGtZRcne2fS3nex4rCzOKFbYkMjaFB9FqzFWGgE0vlxuv/WVYW5iJboCCILxzTp06wYMHDwgMPE50\ndBRKpRJHRycKFSqMl1d1AGrXrouv7yrq1WtAYODTxcOjo6OoXLkKf/yxF51Ox65d27h/P4IrV0L5\n9tu5uY7dyYurqxuXL4eYtN6Eh9/B0bEoYWHXiY+P5auvJjxpXVFz8KC/MXgaNGhojkkdDKS/aczT\nUytWLKVz527GViUw3JfLl0NZtux7wDAZh7W1jTF4yq5x42YsWPC09SevMU95k3Ltmpiens64cSOQ\nJIlPP+1J3brvAxATE014+F2++eYrFAoZsbExxkkbsnctrFGjFrduhZkEbgMHDmXRou+MLShZXF1L\n8/hxPL/++jP16zfkeV0JK1b0xMrKivPnz+a6PzMzE0kyPBMpFDlbFwGT8TdgCNirV69BSMhFIiIi\nGDFiLL/9tgedTsf77zfg6tXLtGvXgc8+621yXPHiJahdux7Hjh1m4sQxz4wne8oQ3D+9rsxMjXGS\njtwEBPjz8OED+vbtgV6vJz09nb/+OoNKZfpslRUkW1iY5xhvFhn5EDMzxZN0pmOldDrDsilKpRm9\nevU1jpnLkld+2e+bXk++3VqrV6/BmTOnuH37Jv36fUF6ejq7dm3n0aNIWrduC8CxY0fQanUMHdof\nvV5PYmICBw7sF8GT8PrJ5TJKl7DhengCv524i7OTFU1qOj//wH9AXGI6AIVsX193QrcStpy/Gs2l\nsFguhcXm2G9prqBYYcvXdr7ncXe2JTI2hYN/5VxD62XZWStfeIY/QRCEN23GjNnGf/v6rqJ48RLU\nrPke165d4fTpk3zwQXuuXr1CqVKuVKzoyZw5PqjVyUiSjJCQi4waNc4kcJk1awYffND+mQDg+S1R\nnTt3Y968mVStWh0HB0N3wK++msQ338wmIGAfgwePoFOnLoDhbXfXrh+Smpr6nFz1Jg+8eabKJ0nu\n+wwbg4MvcPNmGOPGTTLZGxDgT6dOXUxmlevW7WPu3494kufTTENDL+Hi8nTdu/xa3/IoYa5bVSpV\nrrPYFSniyNatPwOG1szGjZuwdOlKwsPvsm7daqZN80Gj0XDpUjBNmzY3ucdlypSlWLHiBAYeNwZQ\nWdfSqFETNm3yY/nytTmu0VjSbNsGDBiCj880YytgmTJlCQ0NwdOzMhcunMPDo2KOY2QyGVptVpf8\nnPlXrlyVzZt/ytaSInH9+lUGDBiMTCbjhx8W06PH52RkZLB8+RJGjRrP+vVr6NSpKx06fER8fBx3\n7txBJpOh0Zh2/S9VypX79++RmpqKhYUFFy6cp3fvfjnSQdYECsfYuHGncTIVf//fCQjYR7t2H3L9\n+lXS0w3PWnfv3sHZ2Rl39/KcPn2SOnXqcfDgfhwcClGiRElj/bOzs0OtVqNWJ6NSmXPpUjCVK1el\nYkVPjh07QrNmLYmPj2P79i0MHDj0ufkVhJeXN76+K7GyskalMkelMic1VU1YWDxVqhi6mh44sI+v\nv/7G+H9ARkYiPXr0ZPDg4QU/0Rskgqd3TJWyRTBTyLkR/tgYsLwN4p+UxcH29XVztFApeL9acWIe\n596y41LU+h8NOkoXtyU5JZP0zKdfUhYWZqSmvtwMg1FxKcQlphMdn4pToX8uCBQEQfi7dO7cFR+f\n6ezd+z/jAH6VSsXgwcMYPXoYMpmMvn2/ME7wkJtFi+Zz82YYarWaESMGUb9+Q7p06c6kSWOZPXuB\nSVpPz8oMHDiUsWOHYWFhiVwuZ/To8Tg7lyIw8Dj9+w82pjU3N6devQacOHH0OVchMX78SJNuey1a\ntDZOgJHl0KEArl27AjxtCVi4cBkAK1f+wNatG9Hr9bi5lXnS9crwffXTT74kJiYYu4JJkkT58h4E\nBZ3LMSFCmzZtOXQoAEmS2LnTMOZJr9djbm7O5MnTjOlmz/7GpNuet3dNevfun+81vhrD8S4urhQt\nWowBA3ohk8mpX78RHh6VCAo6Z5K6f/9BdO/eyfidnfV3kybNOXLkIC4upYmMfJjrd3r2bc7OpShf\nvoJxYomRI8excOFcJEmGjY0NkydP49q1KybHeHl5M2RI/yctKlKOfC0sLEhLS6NWrdoAlCnjzpUr\nl1EoFFSuXBVv75oMHNgHgI8+6gxA0aLFGDVqCDY2Ntja2tGt22dYWFgwa9YMHBwcaNGiNWCoc0OG\njGTMmGHI5XKqVKlGlSrVctwfgNOnA6latboxcAJo3LgpK1f+QKtWH1ChQkVmzZpBRMQ9OnbsjJWV\nNSNHjmXevJls2uSHSqVi2rSZT15SPL3Gvn2/YOjQLyhevARlyhjGizVr1pLz588yeHBfdDo9fft+\n8eR+5p+fIU/D32fOnOLhw/t07NjZ5Dqsra2Ry+XG7pQAbm7uRETcw8zMjMTEBG7eDKN27XrG/SVL\nlqRkyZKEhFykcuWqOe7N2+a/tc4TvHXzyL8s/9PhRMWl0r11eeSyN99qsfvwLbRaHZ2buf+nWlFe\nZTzBwxg1B/6MoGwpO+pWKfaaSya8i8Q6T8LrJuqU8LqJOiWAYTKMbds2PSdIL5i3sU6JdZ7+hawt\nzIgilZTUzDzXOEpJ05D6zOxxKqUca8vc+wLnR6PVkZCckWtLv0anQ52aSfEilv+pwOlVFS1siaW5\ngrsPkyhbyg5ZPm8ClWayV1rLShAEQRAE4XWJjY3JMWbqv+KdCZ6uX7/O0KFD6d27Nz169ODhw4dM\nnjwZjUaDmZkZ8+fPp3DhvGdL+7extjAEQMl5BE/pGVr2HLtNpsa0H7QkQfsGpbGzfrGxSYHBDwmP\nzH8Np0Kvscvef4FMkihT0paQm3HsOxn+3PTtG5R+66aoFwRBEAThv+fZtbv+S96J4Ck1NRUfHx/q\n1q1r3LZ48WK6detGq1at2LRpE76+vowfn3MKzX8rqyfBkzo193WJ7jxMJFOjo4SjFfbWhuAqJU3D\nnYdJhEUkUMPDKdfjcpOaruHeo2SsLczyXFNJJpOo4Jr7yuBC3iq5FUKSJLTPWcRSaSbH5iVaDAVB\nEARBEITX550InlQqFWvWrGHVqlXGbdOnTzdO3VioUCGuXLnypor3RmR1vUvOY7KCmxGJSEDdKsWw\nNDd8zFqtjgcxam7fT6R6eUdkBRwrdftBIno9VHRzwKO0CJBeJ5VSjlf5Is9PKAiCIAiCILxx70Tw\nJJPJcixOlzVPvk6nY/PmzQwdOjS3Q/+1rCwMH536SfCk1+tJSdNgZWHG46R0YhPSKOFoZQyc4MlU\n58VtuR7+mBv3Hhe4m93NewnIJChdIu/Bc4IgCIIgCILwb/dOBE950el0jB8/njp16lCnTp03XZx/\nlKW5GRKQnGIInkJuxnHhegyt67pw75FhbJJ7yZwLyLo7G4KnP0OjXuh8pYpaY658p6uLIAiC8Jql\np6fTq1dXevfuT5s27dBoNMycOZ2IiHtYWVnh4zMPa2tr9u//gx07tiCTyWnfviPt2n3IH3/sZfXq\n5caxE7Vq1aZnzz6Ehd1gwYLZSJIMd/dyjB07Ic/zp6SomT37W+Lj49BqtdjbOzBlynQCA4+xd+//\nyMjI4PbtW8b1f6ZOnUGRIo6sXr2cM2dOolSqUKlUjBw5jjJl3AFo0KAWc+d+T716hjVogoLOceHC\nefr0GcCwYYZFP7NPC96+fUcOHPCna9ceeHvXBGD8+JG8/34D4zTOS5cuxNXVjQ4dPgJgzJhhqFTm\nzJ79nfFaPvmkAxs2bDdZRDUo6By7dm3Hx2dunvfgk086ULRoMWQyGampqbRt24GOHTsZ9wcE7GPm\nzOns2eOPra0dYFib69SpE6xe/ZMxXdait8+ec8eOrYSEBDNjxmxSUtSMHDmVyMgo4/2eOnV6tjWS\n8rd9+xYCAvYB8MEH7fnoo86o1cnMmDGV5ORkLC0tmTZtJjY2NgwfPhAPj0om615llXHt2pUcOhTA\npk07jftu3Qrj888/ZenSlXh5eQOG58QPP2zNrl17USqV6HQ62rRpwqpVfri6lgagb98ezJgxm1Kl\nXAp0DQBHjx4yWZg5PxMmjCYtLZ3Fi380bsuq+9u2/YKZmaEn0axZM+jbdyDFiv2zs+8eOXKQxo2b\n5Ztm27ZN+Pv/gUplaMgYNGg41aoZFsPOXv90Oh3m5uZMmvQ1hQv/e3vVvNNPw5MmTcLNze2FWp3y\nm3rwXWNtpSQ1Q0uRItbcPGpY8yA8Ss3dh0molHKqVy6OQi4zOaZIEWsy9RIJSQVfI0qSoHI5RzFZ\nQR7+TXVKePNEfRJet7+zTn3//RoKFy6Era0Fjo42bN68mRIlirJs2WJ27NjBnTtXqVOnDhs2+LJr\n1y4UCgWdO3emU6cO2NiY0759O7788kuTPMeOXcSMGdPx9PRk7NixXL16gQYNGuR6/mXL/HjvvRr0\n7dsXgBUrVnDq1GE++6w7n33Wlfv37zNy5Ei2bNlkPGbVqlWkp6v59dc9ANy8eZMhQ4awY8cObG1t\ncXV1ZcOGtXTo0BpJkrC3t8TSUomjow1KpYJZs3xwd3c3KUdGhpobN0Jp1aoJer2e2Nhorl4NwdHR\nsD5QaOhF+vXrjaOjDXFxcUREhD8JwiSsrQ2Bh1wuo0gRaywsLIz52ttbYm5ulu9nKJfL8PNbh7m5\nOampqTRv3pz+/T83zn577NhBXF1dOXs2kK5duwJgZaVCr9fx11/H+eCDDwBQKhU4OtqYnDMwMJAT\nJw6zfv16lEoly5b5Ua1aNRYvfnq/T548TPfu3fOuJE/cu3ePAwf+4Oefd6PRaGjVqhXdu3/Ctm27\naNDgffr27cv27dv5+ectjB07FjMzOZcvX0SjSaZ48eImZbS2Ngf0xMbex8PDA4CNG4/g4uKCvb2l\nyf3y9q7O/fs3ee+99wgNDcXe3p6bNy9Ts2YVkpOTSU5Owtvb87nlzxIREcGxYwfp3PnDAqUPCbnI\nn3/+abLNxsacQoUc+O23XQwYMAAAc3MzChe2+ke/AzIyMti1ayuffNIxzzS//fYbly4FsWvXDpRK\nJVFRUfTr14+lS5dSunRpk/oH8Msvv7Bhwxpmzpz5QmV5l7773tngac+ePSiVSoYNG/ZCx71t88i/\nCgulnOj4VC5eeUSSOgOAyzdjASjvYkd8nDrX44o7mFPc4cVmxstMyyA6LePVCvwv9DauTSC8u0R9\nEl63v7NOhYff4cqVa9SqVZfExFSio5Pw9w+gf/9BREcn0bixYaHQY8dOU758RVJSdEAGlSpV4fDh\nQJKS0khJyTApn0ajITw8AicnF6Kjk6hZsy4HDx7Fw8Mr10Vyo6JiMTdPMebRqVMP4Ol3fVycGo1G\na3KOTZs289NPW43bbG2daN68NX5+m+jW7TMcHApTqVJl/Pw207ZtBx4/TjGWMzNTS2xsMra2pve0\nfPnKLF36PV27fs6NG9fx8PDkxo3rREcnkZKSQmxsHJaWhYiOTmLXrt3UrVuf5OQkdu/+lTZt2gGG\ncckxMcmYmz+dCOrx4xTS0zX5foZarY6oqEQsLDJ59CgSW1s7YmIMPVASExO5ePEiEyd+zaZNfjRt\nagiU1Op0unfvzfLlK/D2rodcLicz03Cfss4ZFHSZOXPmsmDBMhIS0oF0oqJiKVRIk+f9zo+5uT1L\nlqwylk2pVBEe/ojjxwOZNOlroqOTqFq1Fps2baFXry/IzNTSs2c/5syZb1w8OKuManU6tWrVZdu2\nnQwZYmiZOnLkGB4elXj8OMWkPJ6e1Th6NBA3t4ocORJImzbtOXHiFE2bfsDp0yepUqUa0dFJHD16\niK1bN6FQKIwtXo8eRfLtt18jl8vRarVMnfoNCxfO4cqVy8yf/73JGke3boWxcOE8ZDIZlpaWTJ48\nnZ9+8kWtVtO7d1/mz19sTJuUlEa7dh/xyy+7aNasLTY2NqSlZRIbq0YuT2DevJk8fPgAjUZDv34D\n0el0HDlyiHHjJrJ//z42bVqPn99WYmNjmDFjKl5e3kRHR/HoUSSxsbEMHTqS996rw8GDAWzfvhmF\nQkGFCh6MGDEWX99VPHhwn8jIh5QuXYZr164zadLUJws557R27TqmTJlmrAOSZEGXLj1YvdqXUaPG\no9XqiI5Owtzc0BPK2dmdLVu2vdD/O2/jd987v85TaGgoc+bM4cGDBygUCvz9/YmLi0OpVNKzZ08k\nSaJs2bJ8/fXXb7qo/yhrSzOi4lO5GBYDQLHClkTGpgBQxtnuTRZNEARB+IecuxLF3cjcHzzkMhla\nXf6zeebGtZgNNSrmPyvrsmWLGDNmAr///qtxW2TkQ06dCuSHHxZTuHARxoyZQGxsDPb2Tycbsrd3\nIDY2BoVCwYUL5xg3bgQajYahQ0fh4FAIG5unDy0ODoWIjTV8xz0bOAF8/HEXRo8eyunTJ3nvvTo0\na9aSsmXL5VlmtToZlUqFpaWVyfayZctz8uRxACRJomfPPgwdOoAWLVrnew+yHx8RcQ+NRsPFi0FU\nqVINtTqZ+/cjuH8/gqpVvYxpAwL8GTJkBGp1Mjt2bDMGT69i/HhDAHH37h1GjXo68/DhwweoV68h\ntWvXZd68mcTExFCkiKE7VaFChWjQoDG//LKTTp26otc/XcgxOTmJiRPH0qdPfxwcnn52H3/chXHj\nhnPo0JEC3e9nZbVO/Pnnaezs7HF0dCI2NsZ4juyftyRJ1KlTj61bN3LzZhju7mVN8qpduy4//LCY\nIUNGcvXqFVxdSyOXy3Ocs3r1mvzwwyIALl4MYtiw0UydaggUgoODqF69Jqmpqfj5+bJq1XoUCgVf\nfz2JS5eCuXw5hFq1avP55/24ceMacXExfPppT3bv3pFjcdjFixcwbNgoPDwqsWXLRnbu3MqwYaPY\nt+83k8Api0qlomvX7vj5rWXYsFHG7QEB+yhSxJGJE78iIeExI0YMZtWq9axaZej2FxISjINDIVJS\n1Fy6FIy3d010Oh0xMdEsXLiMW7fC8PGZRpUq1Vi9+kf8/LagUpkzceIYzp8/CxheUixbtorIyIdc\nu3Y5z8AJIDLyAS4upU22lStXgf37/8g1/eHDB6hQwSPP/P4N3ongydPTkw0bNrzpYrx1smbci45P\nw9rSjNqVi/K/o7extVJSxE6suSQIgiD8Pfbt+43KlatSrFhxk+16vR5XVzf69BmAn99aNm5cR7ly\nFXKkAfD0rIK9vQN1675PSMglfHy+ZsGCZWRfjT37A31uSpZ0ZsuW3Zw/f5YzZ04xatQQhgwZwQcf\ntM/zmNyXhtAjkz198La2tqZ167Zs374ZT88qJilnzZphMuZp8uRpFCtWnIoVK3HlSijBwRcYNGgY\nKSlqgoODuH8/wjgW6sGD+0RHR1G1qhcajYY5c74lIeExdnb2+V7n8yxYsASVypyUlBRGjhxMuXLl\ncXFxJSBgH336DEAmk9GoUVMOHdpPly5Pu9h16/YZgwb1pU0b0/t1/fpVBgwYzPr1a6hXr4GxK2HJ\nkrqpdQAAACAASURBVM74+/vj73+4wPf7WSEhl/jxxyV8950hoMjqXgimn3fWvwcOHMry5Uv47rsl\nJvmYm5vj7u5OcPAFTpw4SpMmzTh27EiO85UtW45798LRaDRERkZSrFhxihUrxsOHDwgODqJ9+47c\nvn2TqKhIxoz5P3vnHR5HdfXhd2a2r1Za9V5tuUnuDdN7CQZMCSQQSugtBgwJmF4MBkKLCx8GY9MC\nhBYSSmgmFBuDuy032ZKsLlldq9X2nfn+GGmltaoLYMfzPo8fSTN37tw7M56ZM+ec37kZRVFob29n\n9+5aJk8+grvv/jNOp5PjjjuR/PzRrF+/ttd5lZaWMGLEKAAmTJjEK6+81DmTPo/F6aefybXXXkFt\nbW1oWUHBJgoKNrBp0wYURcHv9yFJEgaDAa/XQ21tLcceewJbthRQULCRY489kbVrVzFx4mQAcnKG\n0tDQQEVFGenpGRiN6vvguHET2LmzEIBRowYfpgg9lZkVRUEUu9JC7rhjJoIgUF1dxdix4/nzn+/e\ni/4PPQ4J40mjd4Zl2BGAoKyQnhhBpNXAcRNSiLDow25GGhoaGhr/u0wcmdCnl+jnCodZuXI51dXV\nrFjxPfX1dRgMBuLjE4iJiWXcODWRfOrUaSxZ8iJHHnkMK1Z8H9q2vr6O/PzRZGRkkpGRCUB+/mha\nWlqw2+20traGte30lPSG1+vFaDQyefJUJk+eytFHH8vSpS/1+TJvtUYQDAZ6GCw7d+4gOzsb6Hpp\nP//8C7nmmstJT88M6+Oeex4kKyu7R98TJkyioGAjVVWVpKSkMnr0WP75z/eprCxn+nQ1P+bLLz/D\n7/dz5ZWXoCgKwaDMf//7VUhYYt8Q6LQ5LBYL48dPZMuWAsxmM1u3bmHBgmdDxyoiwhZmPJnNZmbM\nOJ+33no97L1hwoTJzJhxAW63h6eeeoz77nsk1Ick2fo93h9++B7Lln1JdHQMDz88N2ykO3fu4K9/\nfZQnn3yOuLh4AOLi4mlsbMRisVJfX9/jfI8cmYfVag15TUKzFgROOOFk/vvfL1m/fi3XXntjr8aT\nIAiMHDmKr776PCQKMWbMONauXU1zcxMpKam0tzsZPnwUTz89r8f2r776FqtX/8iLLy7kzDPPJiEh\nsc/z0Inf70cQxD7ahY/tyiuvZfHi/+swRhT0ej2XXXYlJ510aljbMWPGsWbNaqxWK3l5o/nhh+/Z\nsaOQG26Yydq1q5BlZY++xbBlfn8g5PnT6QZfNzIlJYWiop1hHsadOwvJysoJ/d1pvH/wwbtUVpaH\n5e39LzLwmdU4aDEbdYzJjWP88Hji7OqFmpFkG7QEuYaGhoaGxr7w0ENzeemlV1m0aCnTp5/D5Zdf\nxaRJUzjiiCP58ccfANi+fRvp6ZmMHJnH9u3baG934nK52Lx5E2PHjufNN1/jq68+B9R8Ebs9Gp1O\nR2ZmNgUFGwH47rv/MnXqkX2O47bbbmLNmq5k/Lq6OlJSUsPa7Om8Ovfc3zJ//rPIHeGMZWWlLFv2\nZY/wOYPBwO9+dwmvv740bLncRxjk+PET+eabr0lLSwPUUL7S0mJaW1tITk4BYNmyL/jb3/6PJUv+\nztKlb/Loo0/y1Vdf9Dk/dfz9e9/29NRt376V9PRMvvzyc84//0KWLn2TpUvf5M0338fhcFBVVRm2\n9VlnzWD58m9pamrq0fPvf/8HmpqaQqGZt912EytXrgyt7+14z5hxAfPnL+phOMmyzOOPP8KcOU+S\nmNilKDd58hH8979fAfDtt8t6Pd/XXHNjKGytO9OmHcV3331DdvaQkGpdb4wfP5EPPniXsWPV8MnR\no8fy8cf/YvTosQBkZGRSVraLlpYWAF5+eRENDQ0sW/YFJSVFHH30cVx99fVs374NURQJBAI99pGT\nM4QtWzYDsGHD2pDC40BMm3YU9fV1FBcXAQKjRuWFjMDm5iYWLVoIqJ6jd999i5Ej8xgyZChbt27G\nZDKh06l+kIKCDQAUFe0kMTGJ9PQMqqoqcLvdHWNa12NMgiD0Opfu/Pa3F7Nw4XN4vR4AGhoaePvt\nv3PBBReF2nReoueccx7r16+lqGjnoOZ+qKJ5nv4HUAJuFF8rgs6MYDi4cp0afE5i9VbNE6ahoaFx\nGHDBBRcxZ86DfPzxv7BYLNxzz0MYjUZuuOFmbrvtZkRR5Morr8VisXLKKafzyCP38+GH7yPLMrNn\n3wfAzJmz+OtfH0NRFEaNyg+FI82efUeYtDeoXqCnn36cV15ZjCRJ2Gw2br99dlibPR8/F198Ka+/\n/gp//OMlmEwmTCYT9977UCgPqvvz6vTTz+Qf/3gzbNncuQ+Hhe1NmDCJK664mpycodTUVHPaaaoo\ngyocEBHKFykq2onRaCQ7u+uL/dix42lqaqS+vg4QQuFPgiBwyimnk5aWzsaN65k58/rQ/u6996E9\nvB9d2/l8PqZNO4r8/NE8++yTIaGFTs4440y+/vrLsPnodDouu+xKHnig91Cre+55iBtuuJL8/DHc\nc8+DzJ//FE6nq8/j3Rdr166itrY6dG4FQeDGG2dywQUX8fDD93HTTddgs9m4//5HepyHtLR0hg0b\nTllZadg6o9FEfv4YTjihf6nt8eMn8be/Pc1dd90LwLBhIyguLgpJuhuNJmbOvJ077piJwWBg2LDh\nxMXFkZ6ewV//OheLxYIkSdx66x1ERtrZubOQ+fOf5U9/ui20j1tuuYNnnnkCQRCx2WzcffcDHWsG\nfv+5/vqbue46VZnxxBNPYd26Ndxww5XIssKVV14LqAbfpk0buO66m9DpdLjdbqZOnRbqw2Kxctdd\ns6ipqeGWW27HZDJx4423MGvWzUiSxJgx4xg9eiyrV/8U2iY2No5AIMD998/m1lvv4OWXF/UIuTvx\nxJPxeNxcf/2VGI0mBAFuvvmWbiG7XfOTJIkbb5zJM888wfPPLx5w3ocqgjLwJ43/KQ42NY/9RfG2\n4C//FBT1y4GUdBRS5JABtvplKHTu5vXKVZyZkM+0mJ4hDv8LHIwKMRqHLtr1pHGg0a4pjQONdk0d\nfCxZ8iJ2ezTnnffb/ern+efnceONMw/QqAbPwXhN9ae2p4Xt/Qy43a5BuNn3D8XfjuyqIVDzHSgB\nxKjhIOoJ7v4Jua0c2VWL7KpFCf568uKrW8pCPw8zG11DQ0NDQ0ND45AhEAgwefLUX3sYhwRa2N4B\npq2tla1bN5Cenk1KyuCrVe8NiqLgL/8Egmr8qWgfji5hKrIlkUDNdwRqvgm1Fayp6FP7d2f/HDgD\nXnY46wCo87VR7W0l1bR/akIaGhoaGhoaGhrhdIb27Q86nU4zngaJZjwdYFpa1ITLuroakpPTf55c\nn4ALgh4EYzRi1DDEKLX2gWjLQidIKF51DMG2UpT2ahS/C0FvOfDjADxBP1WeFkRBJMMcjdShLrPJ\nUYWMQq41np3t9axvqSA1ad+MJ0WRUTwNoATDlgumOARx8IoxGhoaGhoaGhoaGvuDZjwdYNraVKUW\nr9dDW1srkZEH3tui+B0ACNZ0JHt4/QwxIh0i0tU/JDPBuh+R24qRYkbv2c0B4f2a9Wxz7gbgpLjh\nnBA3DIB1rRWICJyXPI6Fu75jo6OK0xNGoRN7FrAbCNlRQnD3Dz1X6CPRZ56pGVAaGhoaGhoaGhq/\nCJrxdIDweNzo9QaczjZEUUKWgzQ07O7XePL5vLjd7Xu1H6s1EsHXYTwZIvttK9qyCNavIthahGCM\nVT01kmGv9jcQtd42TKIeGZm1LeUcF5vLbq+DWq+DkdZEcPuYaIhnZXsVhe115NmSB+50T4KqzKYY\nmYOgV+cse5tQnOUEd/+IlHT0Xnv4ZFmmra2V/orXAZhMllCBOQ0NDQ0NDQ0NjcMbzXg6ANTX11JS\nUojZbEVRFBITk2lqqqexsZ7MzKFIUk9vi6IobNu2EY/HvVf7MhpNjEgUECFkSPSFIBkQIzKQ20oJ\nVH2FYE1Hn3rCXu2vP2RFodXvJtVsJ94QwbrWCkpdjWx3qpWy86Votm5dTyRwIhEUNJbtm/GkqDU1\nxMihiBa1NoSoyAQqPkNu24VgSUKKyu2vh/DuFIXCwgIcjpYB26rF9cZisx1cEvAaGhoaGhoaGhq/\nPIeV8VS3u5bWpibQWfttZzKZMZkGVx25ydlCya4dACEvktMIYpQNub6BbTUlGKN7vngrLg8ejxub\nLZKoqJhB7cvtdtHYWEdpnUy2aWDPE4AUNwnBGEOwtQilvRIl4EbQHZjKz46ABxmFaL2F8VHprGut\n4IfmEirczVgkA8Y2Dx4gLi6RhobdRDe4cKS4iTT03H+r302drw2DoCPDHB3mSVI6c526VesWBBFd\n8rH4yz4mWLcKRD2CqHrVBHMCgtjz0u70NjU3N+JwtBAZae/XMxgMBqmpqaCoaBvZ2bmYzVbNC6Wh\noaGhoaGhcRhzWBlPq1avGXTbESPGEBUV3W+bYDBIQeFGTApsNnkY5jWiU+Dd5q0YZIGTsFFaW86P\nTlePbce6TGRgIC0te9B5UYqi4Pf7aHW0UC9FkzaIEDxBb0GKyQdBR7B+FbKjBCkmb1D7G4gWvzov\nu95MpjmGaL2F7R35T0faMmmtbMJqjWDIkBHUe9uwt7nYsmsr04ZPDOsnqMgsKluOI6CqB16QPJ5x\nUWndJt5RzV0IV9YX9BHoko4kUP0NwZrvupYb7OgyfhNmQKneps04HM2A6sHLzc0LVebuC1EUqaoq\no7BwM4IgMGrUOCIiBjZaNTQ0NA4HvF4vl112EVdccTVnnDGdQCDAo48+SGVlBVarlTlzniQiIoIv\nvvgP7777FqIocdZZM5g+/RwaGhqYO/dh/H4fsiwzc+Yshg0bQUHBRhYseA69Xs+YMeO49tobe913\nS0sL9913JwA7d+4gPT0ds9nCKaecjsPRyrhxEykvL6WkpJjzz7+Qe++9k8WLX/vZj0ltbc1e7Wv1\n6h9ZsuRFALxeH2efPYMZMy4A4LHHHqKwcBtRUXZkWSYYDHDDDTMZM2bczzZ+DQ2N/jmsjKf0GIGA\nqwkECTEio+NlXEDQW0Mv5oqiUF1dTlHRNnJyhiEIfZfCqtpdhSkAtWaFISk5yF4//mCQ4y2pAAQD\nDcR54FRTJogCCAKyyYAr4COqtYGAJO5VOJggCOTkDGPzxpVUt9sw1u/GYrFgtfZdyKsTMTKLYP0a\nZEcxYvSofVYBdLtdmExmBEGgudN40pkRBYGLksZT0liFhEiiT0cjEBenhtnlDcnjp00/Ymtx0tTU\nQExMXKjPovZ6HAEPmeYYytxNrG0tZ2xkKk6ng2AwSLDdj+I3IbW5EDxNe4woAiXyCBSfWlxN9jug\nvR6h9Ac1xM8QhSDqaW1twuFoxmaLwm6PITY2YUDDCSA1NROj0Yzb3R7yQuXnTxzUtt1RFAWPx4XJ\nZPl5FBg1NDQ0fgVeeWUxkZFdz7GPPvqQ6OhoHnhgDh999CEbN65n4sTJvPLKYhYvfh1Jkrjmmss4\n7rgT+cc//s5xx53A2Wefy+bNm3jxxed56ql5PP30Ezz88GNkZGTxxBNz2Ly5gPz8nqJHdrud+fMX\nATBz5vXMmnUnWVnhBdnLy0vpvOX+krfewe6rtraW5557imefXUhCQiKBQIAHH7wbvd7AmWeeDcD1\n19/MtGlHA1BVVckdd8zkrbc++LmGrqGhMQCHlfGUbHHiV1SxBQLNoeWiZTi6xC5te0nSUV5ezI4d\nWwbss0UKkpY+lInRPWs61ckR7Nq1A+Purn0lJKQg6azU0EiNSd7rF2mD4CPT2kCxM5GSku0AZGUN\nJTExtd/tBMmEEJGG4ixHcZYj2DL3ar8ADQ27KS7eTnR0LLm5ebT41XytaL2FQCBAw86dGL2q96gR\n1diLjU0AwGa00BJnxVLnpnhXIXZ7DKKoGqbrWysBOCNhFJ/VbWWXq5Edu7bTUl/Xbe+J4Nw1iFGK\nHW0VoKbjn4rBYGTYsDx0usGr8wmCQHx8Yuj36upyamoqSE/PHmDLcKqqyqiqKiMpKY3MzCF7ta2G\nhoZGfwRXV6OU9p7DWSeJBIPyXvcpZNmRJqf026a8vJTy8lKOPPLo0LIVK77j6quvB+Css2YAsG7d\nGkaOzMNiUUtmjBkzjk2bNmC322ltbQXA4XAQFaVGYTQ2NpCRkQXA5MlHsHr1j+Tnj2b27NuZO/fp\nXseiKEpYMfbHHnuIE07ovcbhF198xvvv/wNJksjOzuHPf76b//znY9avX0trawulpbu45pob+Oqr\nzyktLeWBBx5h5Mg83nnnLb7++ksEAY4++jguueRy6uvrOrxnfkRRYvbs+8L2tXLlCv75z3d54oln\ne33ef/jhe/z2t78jIUF9zuh0Ov70p1n85S+3hoyn7qSmpuFyuVAURfsQp6HxK3FYGU8BbxuCIQop\nbgKKrwVFDiA3FaB0FJvtJCkpFYPBgNfr6bUfvxyk1e9mc3stZZKHWVG9P2Di45NQFJlgUM3ZaWjY\nTV1dNQBeEbZKTs5WFMReboCyolDrbSXZGBWe/+NrJVLvYVhqJC4hltraCsrKipEkXcgbEpBldvva\nwh4kAGZjKqntVXhrV9DgacGvi8Cvj+ix7yidmSRTV2iay+XE7XaxqyO3q7m5kcrKXbj8rST4dRjc\nfkqqt+P1eoiNjcdiUfu0WCLQ6/UoAQ8oQfLiMtnQsokcn8DW2hL0kTZkRWGbs5Z4QwSpJjvjo9Lx\nOhy0tNYhGY0kxyejOMuRvY1I9jwEnbHXY90dRfajeJqQXVUI+kikjjpYqrdp32XNU1IyqK4u7wj9\nG7zx1NraTFVVGQC1tZXoLGZS4/t/KdHQ0NA42Fmw4DlmzbqTTz/9KLSstraGlStXsHDh34iNjWPW\nrDtpbGzAbu8Kg7fbo2lqauTCCy/mmmsu57PPPsblcvH884sBSElJZePGDYwdO47Vq38KPdv6Mpz2\nFq/XwzPPzMdqjeDmm6+lpKQYUL06Cxe+xEcffcgbb7zK0qV/55NP/s1XX32O3R7NZ599zMsvv4Es\ny1x77RUcf/xJvPbaEqZPn8GJJ57MN98s4+WXF3HVVdeF+nvttSU8/fT8Pg2dsrJSjjnm+LBliYlJ\nIaNyTzZsWEdsbJxmOGlo/IocVsaTEvAiWOPUWkikowS9yE0FXTk1HXT3mPTGW1Vr2OKtAR2Mj0rH\n2Is4QWc/3T1C0dFxbN68FkVRaIkz4/I6aPQ5iTf2DLtb1VLKx7s3c2HKBMZEdvWheNUbapQ9luiI\nDKxWK4WFmyku3j7g/KvFIOsijJwjuUhs3gjA27KVHYTnTokI3JpzIjEGC42NdRQVbQuty8wcSlVV\nGdXVFcQBcVioLSkCwGaLYsiQkeHGXsCDv+Qd0FkYln0eX5kgxwdFNaWscXQpDU6IUgsK5xpicLvM\nBFD4r76RM20ZjBRBbnOgT04ddLFfRc7CX7QNwWJFn9LTK7gvSJKE1WrD6WwjGAz2qqLYYxyKQmnp\nTgRBIDNzKMWlO9i1awfRNjsW089TuFhDQ+PwQpqcAn14ieLjbdTXtx3wfX722Sfk548hKSlcQVVR\nFDIzs/njH6/h1Vdf5o03lpKbO7xHG4A333yNk046hUsv/SMrVy5nwYJnefTRv3LXXffx3HNPIUkS\nubnDaG/fu5IeA2Gz2bjzzlmAarw4HOpzdcSIkQDExsYxZMhQBEEgJiaWgoKN7NhRSF7eGARBQJIk\nRo8eS1HRTgoLt3P99X8CYMKESbzyysuAgNvt5u677+Deex8Kedx6QxAEZDnY6/JOXnhhIW+99QYt\nLc1YLFYefPDRA3UoNDQ09oHDyngCEHTdPC2hfKa9C2ko71CTOy52KGMj0wbeoAOz2cKoUeOR5SB+\nfwPU1VLrdfRqPK1pKQ/97G48yW5VkEEwxQNgt8cyYsQY2ts7cn5Q+KZhJwDZltjQdqLbR5TLQ4SS\nwVZdEGPQS6y3gVMFgeHWVPwdhWYdAQ9F7Q0U1OwkPyKZkpIdiKJESopqqNntsdjtMTQ1NfB9406C\nisLxcbmIokRcXMIeKnkKgdrl6h8BF5Igck7mJKoKt5Hkg9Ni0kGS0IsSE6MykGWZ8pId6BHwxkfh\n9LWxtrWcEVLvghH90jkOpf86TntLZKSd9vY22tpasdsHVkl0Oh14PG7i4hJxRujZbPYw1m1m644C\nJuRPDoUuamhoaBxKrFy5nOrqalas+J76+joMBgPx8QnExMQybtx4AKZOncaSJS9y5JHHsGLF96Ft\n6+vryM8fzbfffh0Sg5g0aSpPPfU4ANnZOfztb88D8K9/fYDTeeCMv0AgwDPPPMmrr75NdHQ0f/nL\nbaF13T+Idf9dURREUUDp9qHV7/chikLHo0bpWOZHFAVAoa6ujtNO+w0ffPAud955b5/jyczMYtu2\nrYwePTa0rLa2hpiYruf39dffxLRpR1NUtJMnnphDRsbeh91raGgcOA67NzdB312mvGP6yuCNJ2fA\nS1vAQ7opmqNihhAxiDCy7litEdhsUSQb1QTbGo+jR5saTyu1XnV5iashJMygKDKKu04VQegmNx4V\nFU1KSgYpKRm02YxsN7iJTkziqKHjQ/+m5k3CbLYitbnx1vtwNAnsao+nzBkLu9vQ1zShr2kitt7F\nVJcFU20LRUXbkOUg2dm5pKZmYLerN3OTyUxSchpb9S7aIo2kpGSQlJTaIyROcZahuNQwxU7DJ8MS\nQ1ZSJgKQ4ZY4JnYoR0RnoxclystLaG9vIy4ukWNzxpFujqa4vZ6A7A/rY3B0GnEH3ngCaGsbuEYU\nqDXAQJVrX99aSbneT6XeT9DtobJyMDlcGhoaGgcfDz00l5deepVFi5Yyffo5XH75VUyaNIUjjjiS\nH3/8AYDt27eRnp7JyJF5bN++jfZ2Jy6Xi82bNzF27HjS0jLYsmUzAFu3biE9XY0SmDv3YYqLiwgG\ng3z++acceeQxB2zcLlc7Op2O6Ohodu+upbBwG36/b8DtcnOHs2VLAbIsEwgE2LZtC8OGjWDEiDzW\nrl0NwPr1axk+fBQAmZmZ3H77nVRXV7F69Y999jtjxvl88MG7VFWpub+BQIAFC57loosu6dF26NBc\nhg8fwQcfvLsvU9fQ0DhAHHaep6pAgPY29YUWRWEI4Ap4qO5c1oEgCGSbYzFK4Yeo06hJNg0sV60o\nCop7N8h+BENUWF2mzpyiGm9XXLOsKJS4Gljb4XUaEZHIduduvmnYyYiIRIy+VtKUAK06Gw17jLeT\nlc3qC/n4qPSw5aIoMXLkGBob60IhE4qiEGzcBIKAFDM65DXa1FpFlbeVkRGJWM0R1BsV6vfYn0f2\nE0TBru+7ZlSwtbhj5waQA6HlCQnJ1NXVUFtbRWSknejoOJqaGti9uwqTyUJWllrwdnxkGhXuZpq8\nbcQDhe31KELX18A0sx2brve6S+pchEEZxm6/i2pnDb4B6n/FGCzE2dTz1hnmAR3n2dMAQQ+CMRqh\nI48sGAzS2FiPwWDEaI1gS20NMQYLNXqR6GaZmppKmmQfoqFnHlaaPZ4Yy+CVGGVZxuFoQVFkIiPt\nSFL//7WDwQBeryeUn7Y/KIqC292O2WzV4vA1NA5zLrjgIubMeZCPP/4XFouFe+55CKPRyA033Mxt\nt92MKIpceeW1WCxWLr30Ch5//JEOEQaBW2+9A4Dp08/h0UcfRBAETj31dLKzcwCYPfsO5s59qtf9\nDvbeExkZxaRJU7jmmssZOnQYl1xyGfPnP8uFF/6+3+2SkpI566xzuemmawCFs846l8TEJK666joe\nf/xhPvroQ/R6PXfddT+BgD+03V/+cg933TWLF198lccff4R77nkQg6ErVD4xMYkHHniERx65HwCf\nz8dpp53Bqaee3us4rr76Bq655nJOPPEU7PbBlTnR0NA4sAjKnqoC/8NUrVjIYtlGdTeb8T6hmQp0\nvKL0DJ0bH5XO+cnhtRSWNxbzWf1WfpcykfzI/pP+5fYqAlXL1D9EA/qcC8JqDz1V9BVeOcBduaci\nCSI/NZfy0e4CAKySgVtyTuCvRV/h7ygSeyQeThbdvC9b2ULfNZ6SjJHclHXsoB4m/upvUZxl6LNm\nhIy7Xa4GXi5fOeC2AMfH5nJy/Igey5WAC3/J+wimWBBEFHcd+txLQ2NyuZxs3rweURQZNiyPHTu2\noCgyeXkTsFhUI8Yd9PNE0Rf8nlayhQCPyHYUuuaUZrJzfVbfXyR9O/+OYLCjzzyz3zlsK/mIIYFm\n3pGtbO/nuOoEkTuGnExZ4Raczjby8iaQnZ1C7Y6fCNZ31BATJHQZZyIa7VRWllJVVUZKSgZ1Nol/\n1m7kpLjh6ASRlbWFHO20ItH7OfLqBI6ZcMygzqGiKOzYsZmWFlXG3Wy2kJc3oc+cLFmW2bp1A+3t\nbYwZMxmzef9yr6qrK6ioKCEuLpEhQ3peCxqD5+fKT9E4fNGuqYOHF198nquvvv6QD9fWrimNA83B\neE3Fx/ddBuiw8zxNjBvBWLHrBVlp+IEYnYkz7KPC2v3QtIvNjmqmJ+SHeZ86PUXJpoG9ArJLlckW\njLEo3kZkZzmiJRkUGUFvZaw1lnWt5exw1jHSlsTa1nJEBE6JH0G2JQ6LZODy9KlUedQQsdzWreB3\nMyRuFBli3y/5w6wJg/4KJ1oSCTrLkN27kTqMpyxzLBemTKAt0LvaYCeSIIblY0GXt01uKwUUxMgc\n5LbyzrV0htNZLBFkZQ1l164dbNumildkZw8LGU4AZknP5elTiaxdjhJwcHpCV3HfTY5qKj0t7Pa2\nkdhLzhjQEebX/7cBV9CH3d8KAlwotlNkS8MtWfDowg2Kak8rGx1VbHRUkp+WxfbtBRQVbUWHA0fV\nNhCjEC1JyM5y2PUDsn00VVVlGAxGkpPT+LxqFQDjotKIkIwYRR1+t5ug29tjTKLTjdHjw+l0YLNF\noSgKLlc7Fku4Z6fT2+RwNNPS0oTNFolOZ6C5uYFduwqJiVFFT4xGE1Zrl4epomJXKEeuoaGW4pQ0\nNQAAIABJREFU9HT1q257exteb8/xhI1NFImMtIce/m1trVRUlHT0tRuTyYzZrJ5DqzUCo7GnZ1BR\nFByOFoLBIBERkWFfYTU0NDT+V8nPH3PIG04aGhqHmfEkiDqmxI4IewH1Nf6ETTJwVEx47R2fHGRZ\nQyGb26qZaO9Sa6vxODCIEtGDUH1TXLUgiEhJRxIo+wi5ZbvqoZAD6FJP5Jj2QqYKPv7bXEyMwUq1\np5Xh1gSOiR0a6iPLEkuWJRbF24K/0QGGKKbEjTwAR0NFMCd1jHU3RKnhcoIg9DCKBovi3k2g8ouO\nzkVEWzays6JjpUJ3R0t8fBIORwuNjXXExiYQH5/Uo79sSxx+nQElKIWdoyidmber17K+tYLTE0b1\n2K5jAAOG7W1yVBGBRGyHaMjQtkIAdKknI1q7PIvtAS+bHdWsb63kqOwhIdnyjVuKgI6Cv20eoEOl\nsbUEQRAYOnQkDtlHqbuJbEts6LqZEp0FXcq9YbS0NFFYWEBDw24iIiIpLt5OY2MdGRk5JCendxxK\nhaKirTQ3NwKg1xvIzc1DknRs2bKexsZ6GhvrQ30OHTqS2NgEmpsbqa2txGQy4/f7aGioIy0tm6am\n+jBVxf6Ijo4jN3cUwWAgtE1OznDKyoqorCwNtRNFifz8CT08W+XlxdTWVqnHWadn9OiJGAx7lzuo\noaGhcajRvR6WhobGocthZTxJRltPj4wg9vqCPS4qjWUNhaxqKcUsqTkpiqLQ4HOSZraHajMpQY8q\n4tDh4BCMdgRDJErQh+JtRjDHIxqjEUzxKJ6ul9lA5ReIgEmAce5SCmu9DMfHMUZjN09NF8HGDaAE\n0cWO67FufxAMUSAZkd21fRbdk911EPCAPgLR1L/CXOccxahhiLYsBMm4h6phVyiZIAjk5AwnJiYe\nuz2mb2+ZEuwhFjEiIhGzqGdDayUZ5t6tkCwU5KCfnW01va4HWN1SxikdJ0+XfCyKr41g40YCtcvR\nJUyhU1TEDBxvMlPpaaCwdg1Go47kKA+iz4XLGIvLqBpQ+kA7Ua5K2o1x+OypVOBie6OqkDhhjzy0\nvoiKikavN9DYWBf6CarHSK3npcfpdNDc3EhERCSxsfHY7bHo9aoHZ8SIMTQ11SHLCqBQWVlKSckO\nVc2wvBhBEMnNHUVtbTX19TVUVZVRU1OJKEqkpWVCH6GEAE1N9TQ3N1BZuYv29nZ8Pi+pqZnExycR\nEREZCh30+73U1FRSVLSV1NSs0PZer5vaWjW3LTLSTl1dNUVFW0lKSiciwqYZURoaGhoaGhoHNYeV\n8aQz23uKkgsCSi+hXdF6CzmWOEpcDbxZtSZsXYqpK0kzsPtHFGc3Y0cQ0aWfAQE3oIQ8O2LUUIKe\negRrGoLOjNy6EyEig3q/h2RvHcneSo4QgZZNBPoQchPtwxFtB1aiVBAEBHOSqozXXokQEf6CL3ub\nCVR81jkC9DnnIej69ropHvXlWYrJDwknhF7Ge0mvE0WRmJi4fseoKDII4fk7OlFibGQqP7aU9jg/\nndwqBPAT7HN9JxE6PcgBhIhM1SgWJYL1awjUfBfW7ihQbSnH1o7BQ6lRx+tKC4pHDefMwc8fTE6+\nlv0sb26BZrWpQZQYZQuvh9IXgiAQH59EdXU5VVVlSJKOzMwhlJQUhgoVg+q1yc0d1cPg0Ov1YfXF\n9HoDxcXbKSlRvWrZ2blYLBHExyeGjCeAIUNGEBeX2O/YYmPjKShYS3W16k2MjLSTmqpek2azJczL\nFAgEqa+vYefOLXvMTzXezGYLgYCPpqYG2tq2EBUVzYgRYwZ1jDQ0NDQ0NDQ0fg0OK+MpKvtomnvk\no/XueQI4N3ks29tqw0wrURAYbesmFBFUc0Sk+EkoQR9y0yYCNd8hGFUPjWhRX0bFyCEg6hCtaWo4\nmzUVwZJCvCJTVrceZB/Remvf6nWSEdGWtfeTHgRS7BgC7ZUEalegz5zezegBpV2VGg/lbTlKkGLy\n++xL9jar6nrdlev2sZ5W1yDkXmXKT4ofTrzRRrCP82dsWo0R+E1MXq/rAQQE4tq2gM8b8nyJ9pEI\n+ggUf3hhRkVRqPa24utQDpQFCV9MKme4ugoc2nwt4NjMCGs8kdYuQzfNZO+zmHJvpKSkYzAYURSZ\nqKgYzGYLJpM5lKsEao2vwXhq4uIS0en0eDwujEZTSHLeZoti6NBR+P1eTCbLoOpWGQxG8vLG09LS\n2GH49p1fl5U1FJstimDQH7bcZrOHcttyckYQFVWHLAdDMvAaGhoaGhoaGgcrh5XxpDNFQtse1lMf\nYXugep+mxeQM0KtqWknRat5NABm5aTOKvw0EKVTMVhBEJFt2124j1DwqPTA0eereT+YAIhqjkeIn\nE6z7kUDN9+jST0PoMFY6i/JKSUcSKP+EoKMYMTqv1xdmRfaD34FgTgxbLyCoR2lfhR17CdsDMEsG\npkZn9bmZr3UDKDJHDnAO/Y7NYRLogiCEzs+e7Ll0T4UY2bWbgGMzaWY7WQNeO30jSToSE8PVHG22\nKGy2wcuXd0c1jHoaR7Gx8Xvdl8lkJilp4OLQoigSH9+/J0uSJBISBueR09DQOPjwer1cdtlFXHHF\n1ZxxxnQCgQCPPvoglZUVWK1W5sx5koiICJYt+4K33/47kiQxYcKkUHHc9evXcv/9s7n77vuZNk3N\nCVIUhRdeWMAnn/ybjz/+csAxfP75p7z33j8wGo14PB5OPfV0Lrzw4p9tzuvXr+X9999hzpwnQsse\ne+whTjjhpNAc+mPJkhex26M577zf7vdYCgu3M2/e0yxc+BIA27Zt4c47Z/Hvf38OgNPp5LLLLuKD\nDz4BYPPmTdxww1UsXfomQ4fm9trnv/71Ae3t7Vx44e957LGHqK2tQZIk7r77AZKTU1i2bBnPP/8C\ner2e6OgY7rvvYfR6PWvWrGLhwueQJB3nnnsBZ5wxnTvuuIU777yHxMSeOc0aGocqmuxLP8bToFBk\nuh9GKXYcUvKxSPGT0aWeHCZNfiARW2rQl21AV1O470ZJ9/6ichFtWSieeoIN64HOory7QW9DNEYj\nWtPB14rcsh25IzxPbacgexpCIXudXrcQnYbPPhtP4cd48Azu3CpKsEdY4D4Tmut+XFMaGr8QNZ5W\nvN1qsGlo7C2vvLKYyMiujzofffQh0dHRvPTSq5x00qls3Lger9fDCy8sZP78RbzwwhLWrFlFWVkp\nVVWVvPPOm4wZMzaszzfeeIXk5MF9VCko2MiHH77PvHkvsGDBi8yb9wLLln3J6tU/HdB57snBUtNu\n2LDhVFZW4PerHv6NG9djMBgoL1fDsTdt2sC4cRNC7b/66nMyM7NYtuyLXvtrbm7mo48+5OKLL+XL\nLz/DZrPx/POLueyyP/LCC/MBeOONN3jmmQXMn78Ik8nEt99+TTAY5KmnHuevf53HwoUvsWrVj4ii\nyA033MwzzzzR6740NA5VDivPU+8IDCRn3S+KAt29LIKI9DOF14XwebB99jeEgBoy2Hbqnwgm7LuX\nA9QHgZQ4DdnTiNy8BdmcCDozyH7ECDX8TIwaiuwsI1i/GgQRfc6FCJIBuXkrwYa1IKohZIIxes/O\nO37Z97A9oRfP08BzEhlUGbM+wgL3idBcD5vyaRqHKLu9Dp4v/Y5jY4dySvyBU/DUOHwoLy+lvLw0\nTEVuxYrvuPrq6wE466wZoeWvvfY2JpNauiAqKorW1haGDx/JY489xdy5D4f1e8EFv8NsNrN48aKw\n5b0VyX3vvX9w1VXXYTarIe8Wi4Xnn18cqnP3u9+dy/DhI5ky5QgSE5NYvFj1mNhskTz88FweeeQ+\nzjnnfCZMmITf7+cPf/gtS5a8wX33zSYQ8OP3+5g1605yc4cP6pgEg0EeffRB6uvr8HjcXHnltUyb\ndjR/+tN15OQMQRAEIiOj2LZtC7NmfUtjYwM33XQrU6YcwbJlX/LOO2+i0+kYPnwEM2fezpIlL1Jd\nXUVtbQ3z5y/qYbQJgsCoUfls3bqZsWPHs3HjBqZPP4eNG9eTkZHJxo3rmTBhIqCWt/jmm6956KG5\nzJnzANddd1OP8f/73x9w2mm/AWDt2tWcccZ0ACZNmsrcuY8AsHTpUurr2wgEAjQ1NRIfn0Bh4TYy\nMjKIi1NzmB966DEAcnOH43Q6qaqqJDV14IgFDY1DgcPeeFJfsPfHS3AAX7wHiaF8A0LAS9AWh9TW\ngNS6e7+NJwBB1KNLPo5AxacEalcgRqg3OsGS1PEzBSn5WGTHLpT2ChR3HYpkJNiwTu1AVo25Hp4n\nDoDnaV88Q4LAoAw2JQgHzEPYtziGhsbBRFF7PQpQ73X+2kPR2E92ut6hzre613WiQ0SW9/4Zl2CY\nTK7lwn7bLFjwHLNm3cmnn34UWlZbW8PKlStYuPBvxMbGcfvtd2Gz2ULGTXFxEbW1NeTlje6zkHdn\n2z3Z03AC1YAbMmRo2LLu/dbUVPPEE8+SmZnFN98s48EHHyUpKZk5cx5g1aofOf30M/nqq8+ZMGES\na9asYtq0o1izZjWJiYnceee91NRUU1HRUwF3/fq1zJypGomKolBeXsYJJ5yEw+FgypQjOP30M6mu\nruK+++4KhfLl5AzlnHPOY8mSF2lpaeaZZxZQUlLMo48+yOjRY3npped59dW3MBpN3HXXLNatU8WO\nAoEACxa82Od5GD9+Ihs2rGPs2PHU19dx/fU389prL3PWWTPYuHE9Z599LgBr1qwiKyubsWPHYbdH\nsWXLZvLywnOY161bw8033wpAU1Mjdrv6MVQQBARBIBBQPdX/+c/HLF78Ascccxxjx45n2bIvkSQd\n998/m4aGes4777ecfPJpAIwZM47169doxpPG/wy/6Fu/LMts2rSJTz/9lE8//ZRNmzbt0039gLLf\nYXtdhV9/KQzFq1AQ8Iw+FQCxvfmA9S2aYpDiJ4PsRXYUq8vMat6KIAhItiwku/oFTnbXEqxbBShI\nSUeDZARBp8qfd2e/PU+95zwNYjaDM2KUIIIWtqdxmLHLpdYIax2gGLaGRm989tkn5OePISkpPLxO\nURQyM7OZP38R2dk5vP76ktC6iopyHn74Xh588LE+Dae9RRBEgkFVtGfz5gL+9KfruO66P4ZCxcxm\nM5mZWQBERdmZO/cRbr75WtavX0trawtTpkyjoGAjwWCQ5cu/5ZRTziA/fzSbN2/iqacep7KynClT\njuix3/HjJzJv3gvMm/cC8+cvYurUaQDYbDa2bdvCDTdcxaOPPojD4QhtM2pUXtj2ADk5Q6ivr6Oi\nooz09IxQYfFx4yawc2dhj+16Y8KESWzYsI7S0l1kZWWTmZlFaWkpXq+XlpbmkNHy5ZefhQyak08+\njS+//KxHXw0N9X3mqnaP5DjjjOm8++6/cTgcHf0o1NXt5t57H+Lxx5/mhRcWhOaekJDI7t27+52D\nhsahxC/ieZJlmZdffplXXnmFlJSUUCxzdXU1tbW1XHHFFVx55ZW/TuVtQWSfX+pBlTnfR8+T4vCi\nNLi6hhJrRogyhbURW2qQmtU6RbILBMWLrn4X/qRcAglq0VjR2URviC01KCYbiimi1/V9IUYN61Cb\ncyLobQh6a9h6wRwPiMiOXRB0I1hTkSJzEE1xKEEvgrjnQ1E9Poqi7LWZqd6s9/EYD9rztI+erV53\n2TlOzfOkcfAiKwqlLvW+4Qi4e6wvdTWSYorC8DPlbGocWHItF/bpJdpT1OZAsXLlcqqrq1mx4nvq\n6+swGAzExycQExPLuHHjAZg6dRpLlqgek7q63dxzz5+5775HeniK9oecnCFs3bqFY489nvz80cyf\nv4j169fywQfvAqDTdV3Dc+c+zFNPzSMjI5Nnn30SUL1UU6YcwZo1qygt3RXyxLzyylusW7eGDz98\njy1bNnPFFVcPajxffvkZbW1t/N//vUxrawtXX315aJ1Op+/Wsnu4v/rsUGvzqfj9gVCYY/h2PRk6\nNJfKygrWrVvDmDFqLciEhAS+/fZrRo9W88l8Ph/Ll3/Hjh3b+eCDd/D7/TidTm699Y4e/XV+74yL\ni6epqYEhQ4aGPE6KovD9998zYsQ4RFHk6KOPY8OGtZxwwsmMHDkKg8GAwWAgJ2cI1dWVREb2VcRe\nQ+PQ5RexVq699lqcTicff/wx7777LvPmzWPevHm89957fPTRR7S1tXHdddf128eOHTs45ZRT+Pvf\n/x5a9tprr5Gfn4/b3fPhP3iE/QuxUmT2xfOkyArBz4uRvysP/Qt+VozS7eZJwEfEFwuwrngdy/K3\n8K0O4l/jQ1HAlzMFxRKFIoiI7b0YTz43tv88i/mnd/Z6bIIgIFpTkezDEa0pPdeLegRTLATV4y5F\nqg9CwRCJaO5FvW1/PE+dHpx9Mp4G73k6cKGXnWF7mudJ49dHVhQ8e0jFA9R6HXhkdbkz4A2T+6/2\ntLK4/AeWlv+oiUlo9MlDD83lpZdeZdGipUyffg6XX34VkyZN4YgjjuTHH38AYPv2baSnqzmzjz8+\nh9tvn01u7rBe++v9Vj3w/fuCC37HkiUv0tysRmDIssy6dWswGAw92rpc7SQmJtLW1sa6dWtCBsGp\np/6GxYtfCHmD1qxZxerVPzF58lRuvfXPFBZuG3AcnbS2tpCcrD43v/nmawKBnv//AAoKNgBQVLST\nxMRk0tMzqKqqCL3PbNiwjhEjBp+LOHz4CD777BPGjlUN19Gjx/LBB+8yYcIkAJYv/46JEyfz6qtv\ns2TJ33n99XfIyMgKhQZ2EheXQF2dWph98uSpfP31MkDNZRs/fiKiKHLffffR2NgAwNatm8nIyCQv\nbzRFRTvx+/34fD4qKytCx6G+vo6EhP6VVzU0DiV+kc+KM2fOZMyY3otfRkdHc9ttt7Fp06Y+t3e7\n3cyZM4dp06aFln344Yc0NjaSkJCwf4MTREBRvSL7pJ4TLhgx6K1qnNDuR0i1IWREoVQ4UCodKFVt\nCOmRAOgrNiP6XPgyxuAR81CKDAQx0Db6UuTscSCIKJaoXo0nqbkaIehHV1+6D3MaGMGShOKpB9GA\nYB0gjnm/1PY6jad98QypYiD9nVs130058Gp7mudJ4yDgX7WbKHBUMWvISUToumqC7XKpLz56QcSv\nyDgCHqL1aoHjRp9a36zC08w7Veu4NH3KLz9wjUOWCy64iDlzHuTjj/+FxWLhnnseoqKinIKCDbz8\n8guh+/FFF12CKAq8+ebrlJeXUVi4nffe+wfPPDOf5577K8XFRbS3tzNz5vUcffSxXHjhxcyefTtz\n5z4dtr8RI0Zy00238Je/3IrBYMDn85KXN5rbbvtLR4uue/+55/6W66+/koyMTC655HKWLHmRo446\nhuHDR9DW5uCUU04HIC0tnYcfvo8333wNQRC56qprBz3/448/iTvvnMWWLQWceebZxMcn8Mori3s8\ng+z2GO66axY1NdXccssdmEwmbrzxFmbNuhlJkhgzZhyjR4/toRrY2zEAGD9+EkuXvhQKURwzZhwv\nvLAgJNywbNnnTJ9+Ttg2v/nN9FC+VycTJkxk48Z15OYO46STTmX16p+48carMRgM3HPPg0iSxCOP\nPMJdd92O0WgkOjqGa665AYPBwKWX/pEbb7wKQRC5+OJLiYpSa/dt3LieM888e9DHUEPjYEdQBiVH\ntn94PB4eeeQR7r//foxG9QG+efNm3n77bR5++OEBw/VkWSYQCPDiiy8SHR3NJZdcgsvlwmKxcOKJ\nJ/LJJ5/0mWC6J3uGL/grv0Rx1aDPvWSf8l58Je8BIoac8/Zqu+C3ZSi7WpB+MxQhwYrS4CL48U6E\nzCikE7IAsC5bhL5mO46zZuNf6UCpVV9qhGExSEemAxDxxQKkuhJaf/8kSF22sGH7d1jW/BOA1vMf\nRjHb9npu/SG7aglUfoFoH44uof86VYH6tcjNW9Cln9G7Z6oflKAHf/E7CBEZ6FOO36ttu87tH/pU\n61NkP/6itxAsqejTTtqr/qFnSIzib8e/631EWza65GP2uj+Nw5sDGWJV3F7P0oofAfh9ykTyItWv\nwEFF5oXS76nxOhgTmcomRxXXZBxFpkUVevmhqYRP67aEdEjvG3bGXhV41ji4+LnC9v6XKC8v49ln\nn+TZZxf+2kMZkOefn8eNN8782fpvamrkzjtv46WXXuuzzd5cUzt37uCll/6PJ5989kANUeN/kIPx\nPhUf3/d78y/yRHzqqacwGo1hRtLIkSMxm80sWLCAmTP7vxGIotjDBW+xWA7Q6Lol+O+F8aQoCkpV\nGwRk6DYvRVFQKtvAF+xvY5TyVog0QnzHPGLNYDehVDiQdzYhBFwEq/34I6YQqJVUwynBAk4/yq4W\n5EQrIOCRs9HJCsr2KhRTZNcuKlsBkBU9bC1HtneE35l1iCl9XxCCpw1dzY6Ql0iOiOlVyU+0JKFL\nPQnBPLDnT/B7O0fVbzslKKNUOiCgIMRbECKNofC3fZEqDwuh62t7peM89cjT2kc0z5PGQYAn6Off\ntQWhvys8LSHj6afmUmq8DsZFppFmtrPJURWW9+ToEJCIM0RQ73PSHvBiNGjGk8b/Jh9++D4fffQh\n99zz4K89lEHRvWbTz0FMTCzTp8/grbfe4Pe//8N+9SXLMosWLeCOO2YfoNFpaBwc/CJPxHXr1vHe\ne++FGU+SJDF79mwuvvjiAY2nnxVh36SllRon8le7YFIAggqKy49g0aOUO5D/WzqoPsSh0SFXviAI\niEOjkdfUIK+oAMDJMdAKrKxU2+fGorR5UTbVIX+vtvGQBqTB6hagJdS3mwz0YjJeJQt/QQDokloV\nOrxdvWFZ+Q/0VVu65ikItJ19N7Itruf4rakDT1KRMexaizs+CgLe/psWNyP/oM4Vqx7pgpHdcp72\nRap8EMp3+2Wc9bZP9Xzun/y9hsa+45eDvFG5ikZ/O1PsmaxuKaPCreaDNPlcfNWwHbOo54yEUZS5\n1ZDfVn+X4l6n8ZRkjFSNp6CPGHq/X2hoHOrMmHE+M2ac/2sPY9B0r6n1c3HOOXsXSdMXoijy1FPz\nDkhfGhoHE7+I8WQwGHoNzTsQ6np7m6e0pxuusdGApx1iYy1I+sGF/gE0/1SNB9Qj6Bew1LmJmBxD\n0/cVeAHbcVmIpn4OryRiHh6HoOvmtTrGiifRhuwPIq/8F7idiMdeiKA3IOglTMPjUAIynjQ7BNWX\nc7m6CGXLCoSR0xDT1ETcYJsH5w+VeMR8/IF4RJ0f20kjCTq8OFdWYKx0EpWX1GNIirOFQPU2iE1B\nGn8ySkMl8oavse/ehD/jeNyBJqJN2YM+RgByZSEObzsQRaSzEnNO38o7LetqcQO6RCuB3e1EuWXE\nBDN1u8BkNhLdjwu1Nxob1HMbF2tF1Jt6bRPwKOwGTGbTXvffSfdrSg4YqCkGo0Eidh/70zi86S9U\nYDC8VbSGUncTE+My+OOIaVSvb6Xa3UpkjJnFm1bgk4NcMWwyWYlxCG0iVIFfHwzt11PjRwCGxsZT\n0FaNZBWJj9Wu5UOZ/b2mNDT2RLumNA40h9I19YsYT36/n8bGRmJjY8OWV1dX4/f3rkQzWPY2ZWvP\nmMqATzVCGhscCLrBKUspviDBHQ1q2J2kGm9tG2pwJZoJljRBrBl3dtQAvUB7c3vPhfFmpIYybO41\n+DLH48qIDq1yNnYUs0zoMvJ0goWIbTvx6LNoT1ZVdgRTKyKt+AKqZ8ioL6I9eRJKogU21uLaVod3\nTHyY4QZg3PodZkXGlTMNX9J4iBtF1OYV+AtWsDxxE03+7RwbPQ9J6Kli1Bfmtd+GIth8xQU4k6f1\n2TZQ7QBRQB6XBJ8X07ymCmFqxwudJ7jX8bCBjtDJhgYHgq7360zxquGNHq+8T/G2PXKeOhTMvF7f\nQRe/q3Hws79x386Al+W1RUTrLZwVO5rGhnaS9VFUtrfw7IZllLqaGBeZxlAxnvr6NuSAev+rbXOE\n9tvoascqGRG96r2tqqmFFHng+5nGwcnBmEugcWijXVMaB5qD8Zr61XOeLr30Uq666ir+/Oc/k5+f\nTzAYZN26dTzzzDPMnj1wLOyWLVt4/PHHqa6uRqfT8fnnn3PUUUexfPlyGhsbueaaaxg3bhx33NGz\nXkF3gk5fL0v3rqipXKmq4hFU1DA7FNDroMWjhpwpajje/mAoUSvF+4ZMHng8EWqSd3fFPV1zNXqx\nCJesyq6a/JtwBs5C0BkQhkSjFNShrC5HVBoRUJCiZQQDGHauRBEl/Fnjkasc4ArQHnUsYkMJTd4C\nZNFPQHEPyngS2+rR1ZVgKN+AP0XNtRBbqhHam1HMkegrNiF0SLgqOj2+1NHQ7EGwgpBoBpsBpawF\nkjpykva5zhP9hmQqeyOFrijoqrYgel0EoxIJxmX20mh/lAV/HXyyA1dwN3Z97q89FI395IfmEgKK\nzNExQ9B1XNPp5mhWt5Sxy9VIkjGSs5JGh9pbJAMSAo6OsD1FUXAEPMQZrCF1vvYBwm01NDQ0NDQO\nJ34R42nGjBnY7XYWLlxIUVERkiQxbNgw7r33Xo488sgBt8/Ly+P111/vsXyg2lB70vLxdjhpj7Cz\nvch5kjvznAAEEIZEQ7UMRrWAnVKhek6E7P0znvQVm5FNEQSSeq+HETYmix1FlJCaqkLLdHUl6IRi\nXMJEJLMbnc+B1FJDMC4TcWg0wYI65MJW5I7TbxDKiZS+BsCXMRa5FeQv1Xl6SKU1WkQW1wEQbHeC\nbYCv0IqC9euXkNrq1W1i0wEnAmDYtQbFYMGy6r3wTfIuIBiMwODciVTcjmdoDvL6WuRNNTCGfa/z\npB6lfsbaaZwNnFOlq9lOxDcv83FKNsW2aC6NuhDo+jLhl4MEleD/s3fecVLU9/9/Ttl6u9cLd8BV\nQOlFqiWgwa6JSTQmmkSMJcYSDWhssQawRX/qFzWR2KIm9lhjAYwdBAQUBA444Hrvu7dtZj6/P2Z3\n75Yr3MF5aLLPx4MHd7Mzn/nM7NzuvOb9fr/epnz6jtQ86SLAF2130WHUcHTyvdjk5EN6OH1HAAAg\nAElEQVQ9pTgHSMjQWdu8lwTFyrSkkdHleQ7zAUuaJYEFI2fHOOfJkoTbYqc1bBgRMDRCQidRdeBS\nwuJJ7+mhU5w4ceLEifO/yZCIp/LycubNm8e8efP6XGfkyJG9vj4YBMvbUNoCpotbhP7cYIcRu8zo\njjx1GNIwF1KCFYRAsqvIx+VDQEdKtiH1Veu0P7Qgsq+V0LAx/XOAkxVCw8dhLd+M0lSJnpSJpWwT\nOK0oxxRhbfgaNph9n/T0PKQkO8pxOdg/fBFhc+ETEwn6RuI94udgk9ByxmJsMI9TmpyF5LLQ0fRa\n5/TKG2Bc30YRSv0elPZ6QsNGEyyajZZohcYNCEXFWrIOYbEjJAnfzDMBCcfal5B37wEmokpNWEoq\nCMyfhey2YpTvMbP+vDoMzOWcfkWBBiCerCVrMYCPsnLpUBSaGveSldNppPFc5RfUB9u5TID0HXHb\nK/Y+Q4dRDUC7VorNGhdP3xX2djTyefNefjhsEnbFQmPQi9/QmJGch6XLZ0eaNYELcueQaXWT0KXX\nU4Qk1UGZrwk93O8JwG2xkRAWT5545ClOHwQCAX71q7NZsOBCTj75NDRNY8mSW6moKCchIYHFi+/G\n5XKxatV7PPfcsyiKwrRp07n44kvRdZ077/wTlZUVGIbBZZddycSJk9m8+UuWLbsfi8XCpElTuPji\nS3vd/9Klt3Hssd9nzpxOI4WzzvoBTz/9Alu3buH3v7+MV155i7Q087PaMAx+9KOTOeOMMzn//Iu4\n/PKLCQQCOBwONE2jsLCIRYuuQ5IkWltbuP/+P1NWVoosSxQUFPG73y3C5XLx9ttvsnt3CZdddmWv\nc9my5St++9sLeOKJfzBqVM+R/eeff5Z3330bm83M6LjkkiuijW7POusHZGUNQ5blaH+sBx/8S8yc\nI8tvu20pmqZx991L8Pv9BAIBCgoKueaaG1BVldNOm8+bb66M7rfr/A3DYPnyR/j888+wWm3YbDau\nvPJqCguLAJg7dxaTJ09FCIGu6wwfns3ChTfgcDi44orf4Pf7Y+bygx/8iPnzT+z1PXvvvXd44YV/\nYLGoaJrGL36xgLlzjwOIGc8wDGRZZtGi66L9q7ry178+xJgxh3HssfN5//2V3HHH7Tz66JMUFJgO\nwa+//i/eeut1FEVh1KjRLFx4LUII7rlnKXv2lGCxWLn66utJT8/guusWcued9+J0xs1x4uyfIRFP\n1113HWeccQY//vGPUZTYm1Rd13n55Zd5/fXXeeaZZ77xuRglzShTu5olmDfYQhj0ZT0hQjqitBXc\nVqRJmUiSFK63EiDJyCMHpyZA9oa7pCf0P3oVLJyBtXwz1t1r0TLykYM+/OPnIGW6MBQzZU5p7oxM\n2fw7cEg78Y0/DUkagVhbRcAoQC7MQGgGYs8ecFqQJ2chyRJNlZ02xlplA2Js3w2FI2mHgfHfR8s+\nDFqKzW3Tc7EXbwQglDOW4Ggz6mip+BoRNgNUpSbUhloUbz1GYRaGpQl8IJoCkN/vU2ISnePBu+0J\nvxetcitVmXl0hK/hytZKxmI2FwwaGru8degI2iSZ5PC4QUPjucovkCWJX4z4djUb9WiVVAc/RZEc\n6MJHu15GOpMP9bTi9JP3G3awu6OBfGcas1LyaQyaNZHp1u5f/gXO7m6ZEbJsbkp9TezpaIwuS1Qd\nOFXzZi4eeYrTF08++TcSEzu//95441VSUlK45ZbFvPHGq3z55UamT5/BX/7yEE8//Tx2u52LL17A\niSeewtdfb8bhcPDww39jz57dLF16G8uXP8W9997F7bcvJTc3n7vuWsyWLZuZMGFiH7PYl04X2+zs\nHFatWsFPf/pzADZsWI/D0dnqRJIkbrjhlugN99Klt7FixbuccMJJLF58C8cffxK33LIYgA8+WMX1\n1y/i//7vr+Ft+57FypXvkpeXz6pV7/UonlaufJcvvljHX//6BBaLhYaGBhYuvIwlS+5h5MhcQOLe\nex/EZos1PJIkiRtvvJX8/NhMmqVLb+PUU3/AvHlmz8I///lOPv98NUcddUz0nMSOY/7/7LN/p6Wl\nhccffxaA0tK9XHfdQh599CncbjdudyIPPviX6HbPP/8UL7zwD8477wKAHufSG1u2fMWLL/6T++9/\nGJfLRUdHB1df/Tvc7sRos96u423c+AX33Xc3DzzwcMw4JSW72LmzmN/85jI2bdrA559/FnOOAwE/\n77+/gkceeQxZlrnyyt+yZctmmpoa8Xq9PPLI41RWVvDAA/dy993/j7PO+hl//etDXZorx4nTO4Pk\nz9w3y5cvZ9u2bcydO5errrqKu+66i7vuuourrrqKefPmUVxczKOPPvqNz0OyKIhdTRg7GqP/RKv5\nVFWUNscs7/bvi2rQDOSilC7CIRxdGKDjX19EapcitUz9QRs+DsPmwrLnC2zbPgQgWGjeqOvJwxCS\njNJcieT3YC35HNuOTxGSRLBgOlJhimnSUBw+zg3VEDKQilKQZAlDaDQ7mqP70gM+xIaK2HNT3IC0\nehPyx+uQP16LUdJGhzqZYFuaeY7rOsx5ZhVFxwkWmfMTukFHwgyCwhR5Rr5ZRxQRYFJKuL6qKYAI\n9dE7q+ezae7jYCJPQiCVfcUTu1Zw2/iZrBlxWPSl8kBb9OcyXzN6+Hqol1RAoAuD5yq/YIe3jmJP\nLZox0Pl/szRr2wDItZ8AgEcrP5TTiTMAPFqAPR0NAGxsNd+3hpBpQJNqGdiT0ymJIwDY0FoejTwl\nqjZUScYhW+KRpzi9Ula2l7KyvTH22Z9++hEnnHAyAKeffgZHHXUMNpudv//9Oex2UwQkJSXR2trC\niSeewhVXLAQgOTmF9nbzM7WxsYHc3HwAZsyYzbp1ZrPn669fNOA5zpgxm/ffXxH9fdWq95g5c3av\n648bN4GKijLKykppa2uLHgvAvHnfR5YVduzYvt/9GobBBx+8zzXX3MjKle/1uM6LLz7H5ZdfhcVi\npv6np6dzzjm/4uWXnw+vIXpNnOjpe83j8eDxdBbdX331dWHhZI7VG6+99jJXXHFV9Pe8vHxOPPEU\n3norknUSu+2kSZOoqOj8vhiIcdeLLz7HBRf8BpfLBZg9O3/zm8t4/vlnexxv3LgJVFZ2/2566aXn\nohbzhx02luuvvxlV7YwH2Gx27r//YWRZxu/34/V6SUtLo6KijHHjxgMwfPgIamurEUJwzDHzWLt2\nDX6/v9u+4sTZlyGJPDmdTm6++WYuv/xyVq9eTXW1mSY0YcIEbr75ZlJT+y8UDgb74en4Ntd29hIC\nRJ4XhoPxVS2SZz9OH5E6p+jGovOFQUL2hMVTwgDOiawQLJyOfdsHyAEvWnoeRlKW+ZpiwUjKQmmu\nxrH+X1j3mrVLoZyxCGcSEiCNTESUtsacl4jpRYu2E50gkiEhZIGuhDA2NwFNMVMw43am0AkyE0LA\najPaJTJaYDToCelmRE0LEhphfniJ7Y2EtgKkIcvt+Gb/GGvVV1j3rMc/+RSkyDnWQZS2Io0awHnp\nV5+nvsWTWrODlbs/YvewPEDhE0xHRtUwKKXTnbHrU/s6oTBaGGxqrWCHtw4ZCQNBU6iDplAH1f5W\nRiVkMNJxcLVxB0tzyIwIZlvnUO5fSbseF0/fFb5ur0YAqiRT4W+hLtBOY9AUT2k9RJ76YqQjhTRL\nAtvaq0lSTSfPRNW8yXWpNrx6XDx920noeAhb6IOeX2yXSDUGnkYcsMzD67ysz3WWLbufhQuv5d//\nfiO6rKammtWrP+Whhx4gLS2dRYuuw+1243CY11ZJyS5qaqoZP34iiqJEs1FeeOEfHH/8SQDk5Azn\nyy83MXnyFNat+zx6U3zHHfcO+DhSUlKw2WxUVlaQlTWMbdu2cvbZ51BTU91tXV3X+fzz1fzgBz+i\nrGwvo0d3rzseNWo0paV797vf9evXkp9fwOTJU0hOTuLrr7cwfvyEmHVqaqqiIjHC6NGH8d57bw/o\nGCOce+55XH/9It5++y1mzJjF8cefxPDh5sMRj8fD7353CWCKk+bmJo488mi8Xg82m61butqoUWP4\n7LOPu+1DCMF7773HYYcdfkBzLCvby5gxh8UsGz16DGVlpT2u/5//rGTMmO77+uKLdVx6qZkyGbm2\neuKZZ57kpZee56c//TnZ2TkUFo7ihRf+yVln/ZyKijKqqqpoaWkhJSWFsWPHs2XLV0yf/u3KEonz\n7WNI28anpqZy6qmnDuUuY0icV0AgxRbzEEUYXoSoR56SiST1fWMuua1I7q41AwNwausn0bS9AUSe\nAPyTTkJPywVDR8saFfOanpKD0lKNpXQjuisN/+STY8wo5DkjEHlJnYE0lwUpybx5qgl8BkCmOoVa\nYyNW59e4LLV0zPgJKOblY9v6H5TmSoIF0xGqFWQZPWVE9HW9MRzda/LhmX8pGAYoFoQQGLuaQJZQ\npiYhp2UiLDaCeVOx7VqNWrODQOR8GxKipBkGJJ72n7a3P7e95r3rWTksj3QhIRQLjUaQYbZEXC3V\nlNiddITMY9vbRTzVo4AQbGgtRwJmpuSzpnkPDUEPr9V8hVcPsqqhmHOHz2Csu3u/raFACIMWrRi7\nnIZdzsCtjKRZK0YTPlSp//3O4hwaNrdVIgHHZxzO23Vb2dhaTlPQiwSkWpz72zwGSZKYmjSClQ3F\nrG8xb2DcYfGUoFhpCHowhEAexAh7nO8+77zzFhMmTGLYsOyY5UII8vIKOP/8i3jqqcd4+unHoze5\n5eVl3H77H7n11qUxKfwvv/wCO3cWc9dd/w+A6667ifvv/zOKojB69Bi83h7aevSJiEkIOfbY77Ni\nxTuMGXM4RxwxvdvaS5feFq2xmTXrSObMOYqPPvoAXe/pu6P3aJCJueMVK96J1v3Mn38iK1a80008\n9fTgVQgR0wPz6qt/F615SklJ5fbb74iZc6TO6IEHHmH8+Am8+OLrrFv3OWvWfMpFF53HbbctZcaM\nWbhc7pjUu7fffpM9e0oAej1OOVw7GRFeQgj27t3DGWf8kB//+Kfdzl9kLjfccEu36yJ6xJKEYcTu\nTwgRcz0sXXobdrudhoZ6cnJGcOONt3Qbx+v14Hbvvy/QL36xgJ/+9Byuvvp3TJo0hdmzj2TLlq+4\n/PKLKSoaTX5+PpGbn/T0DOrqavc7Zpw4QyqeDjWyXUUujH3abzQ4EU0g5SQgOwcYCfi2RJ4ALDZC\n+VN7fElPGQ57vkASgmDRLEIFR8S8LtlVM31vHzThpy64HrucTqpjKrXejeg5KdjLi9EtZYQKjkDy\nteNc8yl6eg7+Yzp7OHWVIsKdgt4Cos6DMbpL7UWTz7Qnz01CmpgX1bTBopnYdq3GunsdgUlhN0a3\nHbHLg/AEkVz97TPVDyfFSOQp/CWhGToBQzML64N+yttqICWRI4dNwKlYea7qC8YkZKK21LFLkiit\n2U2idRgVvmaybYnUBT3UI9NkGJQGmyh0plPkTGdN8x5KvA149SBOxUKHHqLK33rIxJNXryIkPKRZ\nJiJJEi5lJM3adjxaJcmWUfsfIM4hw6+HKPU1MdKRwszkfFbVF7PNU0PA0EiyOFD7YzSzD9OSclnT\nvBdPOMoUiTwlqDYE0KEHo9blPdEW8pPYSyPqON88XudleOk5SpSR4abpG+ifsnr1J1RVVfHppx9T\nX1+H1WolIyOT1NQ0pkwxv4tmzZrD44+bKfl1dbXceOM13HTTnygq6vyMefPNV1m9+hPuvPO+6A10\nQUFhtMbltddeiUlF25fk5GTa2z0xy3Rdj9YJSZLE3LnHsXDh5VRVVXL66WfEpJxBzzU7ubl5PPnk\n8m7727lzByeddBoNDXW0t8fOq6WlmfT0dILBIJ988hE7dmznlVdeIBQK4fF4uOqq2HYqOTk57Nq1\nM6ZWZ+fOYvLzC6O/91Tz1NucA4EANpuNo4/+Hkcf/T3Gj5/IypXvMmPGLHpL20tIcKHrGq2tLSQl\ndRoG7dy5g4ICc/yuwuvhhx8gOzs7RuANpOYpL6+Abdu+5phj5kWX7dhRHLN9ZLzVqz/h9ddfJTU1\nrds4fdVdA7S1tbFnTwmTJ0/FarUye/aRfPXVl0yYMIkLL7wkut7ZZ59BSsrQZD/F+e9hSGqevo2E\nDC9VgY9p18NOBQdkLf1NRJ6aELKCcCQO2ph6illPJJAIFnZ/6tYbdcH16ATIth2FIpkf3oF0cyzb\ntg+x7vgM+8Y3kYRBsLD3nlSSIyx22gMYW+vN+qriRoxN5hMeaZ++WHp6HsGkNBr861FqzNQyKdPM\njza+qO7cfj//RFM44lXW0vs6NWaOvajuwNjRyBtVX3Hf7vfxlW7EtvZt9trN482WXUxIzOHC3COZ\nlz6akTbzS6akvozycL1TUUIGGVYX9UJmY1iTTUsaGS3g39JeBcA4t/lErik00Kep+8fw1SP6MW6z\nZp7XFIuZDuFWcwHw6GUIIWgMbiFkeHrdPs6hozbQjsBMt7PICkUJ6TQEvbRrAdIGWO8UIdFi56rC\nYzk2bQzz0kbjVMy/2U678t5T9/Z2NHJ3yQq+aCk7oH3H+W5y2213sHz5U/z1r09w2mk/5LzzLmD6\n9JnMnn0ka9aYGQvbt29j5EizjvXOOxezaNH1MalwlZUVvPbaKyxZck9Mvcodd9xOSckudF3n3Xf/\nzZFHHkNvHHHETFaufBddNz90V6x4h4kTY41vUlJScbsT2b59a7fXoOeanfz8AtzuRF577ZXosg8+\nWAWYaWZjx05g8+YvaW1tAcyoWnV1FQUFRXzyyUccccQMnnrqOR5//FmefvoFcnPz2bBhfcw+zjrr\nHB566H4CAbPOpqGhgeeee5Yzzzy7y9x6Pu595yyE4LzzfhaTUlhfX0dOTt/uuAA/+tGZ/N///b9o\nRKi0dC+rVq3g5JNPj4weXfe88y7g2WefpampM9ti30hSX5x55s944onltLSY562jw8vy5Y9w9tnn\ndju2OXOOJhgMsnr1J93GSUhw4fH0/h2l66brY6SGaevWr8nNzWPXrp3cccftAKxZ8xmHHTY2uk1D\nQz0ZGZn9PpY4/7sMeeRpx44dlJWVMX/+fNra2khMHDyR0F8MEWJj+59p10sZEcqlgEJ0ERi4kgz/\ngUuDHHkynCkgD54g01NHIGQFLWs0YgAuftXhlL1s65F4dTM/XLPb0DIKUOv3oK41n94JWSWUP633\ngSJPiCSBsbYq9jWHijQisdv6JeNT2Zq8m0kVa0myFyKlJyBUH2JPC2JPS/8OYEQ75ILxdR1SW0eP\nq4jsZigAsb2ZYIvOlhlVBGSDrTv+wxGV4ykb70Q2gmRs8cJR6eQ7zSdgI5NHQNsudrY30m43n1oV\nONNo0/zUBNpYo4NNVhnnHoYsyUh0upaNdQ1jQ0s5TcGe53SgiFA7Wvk7SK5cLDlze11PFwEq/GZf\nrxQ1LJ6UfACqAh/jMxoo879Drv1ERjvP7m2YOIeImrBRSZbN/LsZnZDJNo/5IGKg9U5dsSsWvp8R\nW4sQsTb3aAGyegk8Rer91rbs5Yjk3APef5z/Ds4882wWL76VN998DafTyY033kZ5eRmbN2/iscf+\nEk3tOvvsc9my5Sva2tq45poro8vvu28Zp532Q5YsuRVJkjjhhJOiTnjXX381d9zx55j9zZo1h9LS\nPVx22UVYrVZSU9NYuLC7Y9q8ed+ntHRPt+V9RTD+9Ke7WLz4Fl566TlkWaGwsIglS+4BzIjXVVdd\nww03XIOqqiiKws03L0ZVVVatepfTTvthzFinnHIaK1e+G3WUAzjuuPn4/T4uueTX2Gx2JAkuv/zK\nLilvPc+tpzlLksStty7hnnuWRl/Pzs5h0aJr+xwL4JxzfsXTTz/J+eefi91ux26388c/3obT6ey2\nbUKCi4suuohly+7n5pv/BJhit2va3rRp01mw4EKuv35Rtzq18eMncNFFl7Jo0RVYrRY0TeOss86J\nitp9j+2KK37PjTdew/Tps6LGGgDTpk3nyy83ctRRx/Dmm6/x7rv/ZteunSxdehv5+QXceOOtnH/+\nRVxxxcWoqkpR0RiOPvp7CCEQQnDRRedhs9miTooA27Z9zR/+cGOv5ylOnAiSGIhNykHy5JNP8uab\nbxIMBnn99de58847SUxM5NJLe+/hMNjU17dT7H2WisAqMq3TSfM4SG9TqEoNkZ9+wYDGEqEOQnte\nQnbno2Z/7+AnpwVJfu5aQsNG450/uOdEbq5COJMQtv7dXPn0ej5rvZZk9TCOSLyW5lAxG9rvIt9+\nOqM4DrW2JLqukZgRboTbM4anAq3qfWR1PDKFMa9JaY5ofVVXtnuepDL4ETm+ERR5R2F1HIZImIRo\n6b8TjmHswDCKkeXZyHLPTaIMYyeGsR1ZnsWuEg/P5JniLrujgwu/Oow7Z1aQpAsu+Tob20+mIlnC\nKVGGzkNfvUSD1UaWM5VKfys3jj6JNc17WdlgOjGdlDGco9NMUXlfySqaQqZYun7UCTxa+il+I8TV\nRfPZ4a1jTEJmTG+eA0Fv/BK98UskWxqWvN5rC7d6HqM6+CkjbPM5LOGcbssjpFomMNW98KDmFGdg\nZGS4qd9PitXrNV+xtqWUS/O/R449iZZQB38uMZ+In5w5jqNSi/rcfiCsbd7L67WbOStnGpMTe36C\n/Vzlera0mw9XriiYGxV1cb4d9OeaitM39fV1XH75xTz99AtYrf1NG//vpb/X1MMPP8ill/7uG5nD\nzp07ePTRh7jnngcGZbyPP/6Ades+Z+HCa/e/cpxB59v4OZWR0XtN3ZCm7b355pu88MILJCWZPSH+\n8Ic/8MEHHwzlFKgLrqcisIoEZTjjEi4g02a6qrSEimnVdg9wtEioenBOo+w1IyoDrnfqB0ZKTr+F\nE0B1cDUA2bajAFAk8wvDIIBwJhEqmBb915dwAqKRJynZilyYEvOvJ+EE0GHUmfsVpqBQa3YgpTq6\nbd/XPynVPF45O6HXdUg2j0vOSaJ4pOmelxLQqHY62ZLeQUgWqO5SSg77AFHW2jlBWaFQthOSFSr8\nrWTbk7ArFoY7zGt7nOQnxHI0YfbISreaaYdJqp0E1Uaq1YlXD/JZ827+Wbme56u+QB9g6qghBM1h\nQSaEQG8zBa3Qfb1u49WrqQ5+ilvJY7TzrJjXDkv4BU65s8hXwbLv5nG+BdQE2pCRyAhfU8kWJ5lW\n80M+LbxssIhEnrx92JXXBjq/8Da2VvS6Xpw431UyMjL54Q9/woUX/pJXXnnxUE/nO8OUKX1kpBwk\no0ePYdSoMXz44fsHPVZHRwcvvfQ8F1/ct7tknDgRhlQ8JSQkxBQZyrIc8/s3TfWmf/FRw+vIWJng\nugRFsiGFLaolJKoDn+5nhH2IBO0GyYUq2uPpGxBPA0EIQU3gU2SsZFpNcwk5LJ50cSANM/th3LAP\nHXotdjkNt2KmAXl9OyE0UMvkyH73b1WulG+hWKrHEZI5aa95A/punul86FabqR++k+D2Smp3f0DZ\n3tdp3PU5I43OFKV8nxNjWwNF7Q7Os+icKrViSAFqyz5AhPRo3VOO3ayVivTiWR+uE9nuqeWN2s0D\nOrp36r7mvpJVVPiaEb46CIXzvzVfr303mkNmVGy4bR6yFCuOFMnG9MQbmZl4KwAh0bsIi3NoEEJQ\nG2gnzZoQE6mckjQCi6SQYxucZt0RIsYRraGer4WQodMQ9DDcnoxDtrC6eQ9v1379retpFifOwXLO\nOb/k739/nh//+Kz9rxwHIKb/1zfBb35zGXPnHnfQ4zidTh544JFo76k4cfbHkIqn3Nxcli1bRltb\nG++99x5XXXUVRUWDl2KyP5Z5ytnUNoMs/wm4lEgKinkKLCRQG1yLLkIDGHFwDSM6G+Qe2v4/LdpO\nfEY9mdbpUdtqBfMJtC4OoOeLFGlW27/Iiib8BEQzTjkrKp5qU3xYy748oP32LZ7M17w7P6NNEoxp\nNRjdnMGYZjsdFvM1t6UVXQ2x+bDn2JL8d3YmvsqXycvJ3t55LvKKQxifV2K8t4eRkoYaFtTVgc8Q\nX9dHIwI5dvPmNlKb0hzqIFG1k2Vz80VLGfWB/pk0VPhaWN28BwGsaynD8IbdoxQbIMDo+X1q1kzx\nFDGK2BeL7MSt5qJgRxODW5MV5+Bp0XwEDI1h+6TGHZ1axA2jTxx0x7tIxLQ+2PN1WRc0zStG2JM5\nK2cabsXGp827+bR5oFH8OHHixIkT57vBkIqnm2++GYfDQVZWFq+//jqTJ0/mllu6+/d/UzQI0ybb\nVtElehK+wU5WR6EJLw2hTf0fcJCtymXPgfV4GmyqA6azTSRlDzrT9g4o8hQVl/2LPPl0M2XPoWRh\nl8xITV1aCGnvmgHu13xf2vpKxwxHnvzh9CRXhgvrtATOzp5GoT0Nm6zjUs10vbbUamxGMpmhqQjZ\nQD9aZ5iuIgvB6AwdaUwqGAI96EdCQhIyremVeCt3M96VzdSkkRyRNBIhDJL1Tke8Imc6x6aNQQCf\nNJV0m+K+6MLgtZovEYBdVtnSXkUwYKZ8yk4z7U5o3SMFQgiaQ8VYpSQcct+OQqrs/MbEkxAGhqes\n32I6Tie1/ohZRGwutixJB10z1xMOxYJbtVEX7DkXvdbfHp3PGFcmlxXMRZVkNrSU9xr9jBMnTpw4\ncb7LDKl4ev3117ngggt49NFHWbZsGeeff/4hKb5sb62BYNh4ICKeFDMC1hDc2O9xRLSr7CBHng5h\n2p4uAtQF12GX00hRO523FMkUFwYHEHnqT/pcFzr0GgCcyrDoNppqUCsXI3sa+9o0hsj7U+Z7l6bQ\ntp5XCjvgBV2mi56SOgwmjcaWn8aCvDkcl7kJq6SSrB6OhMyEpN8yPHU+AK2plZw/fg4XlmwhtWUN\n8pRhIJkCUxISOYbZo6rJtZOExhA/yZ5CosWB8JST1NQZRct3pjHOnU2aJYFNreU0Bvu2Gl/TvIfq\nQBtTE0cwK6WAgKGxze8BxY5kDadt9SCeOowaQqKNFMvh++2RYZGc0XqtwcZo3zMNiTUAACAASURB\nVItW9QGGJ25tPVBqAhGxMnSmDBlWNy0hH0FD62E+ppiLRMIcioVx7mwaQ17KfM007XMtd+hBfPqB\npP7GiRMnTpw43w6GVDytWLGiW1O5oeS4HNPprcWi4tjwGtbtH6JWmf1urFIiMire8I17v4iKgcGK\nPDUhJBnhGNy6hYFQF9yAToBh1iORuohCmcGLPBlCpy64HkN0vxkD8yYfwClnRc+xwKB0RBD7hjew\nlqyFfvSVqA2aPTX6qmeTAubNXSDBFE8WqfPpvYQgaNTgVLKY6LqEmYm3kmwZTZJaiIRCc2g7+Rkj\nGevKQm2uRAnUIeW40ZUQEhLpSWbvq6CtA2NTrdnjams9RkU9KXTOP79Kgm0NzA0MQ0fw15KP2LO1\npHP9rfUYJU0IQ9AS8rGqvhiHbOGkzHFMTRoBwFZNR7K4QTHTLIXe3ZUwUu/UVRT3hiqZkadvJDoU\nCn8GhOJ9pPqiXfOzua2Khi4pcy1hg5CDsSQfKBl9pO5FxFNml0hY5Jp8snwN9+1+n81tlYAZ+Xy0\n9BPu3rWC9+q3YcQjU3HixIkT5zvIkPZ58vv9HHfccRQUFMT49T/77LNDsv/jR0zk/ardNFnt2HaZ\nKWD+lCQCowuRMNPEOozaaK+C/fINGEYYCcmD2uNpIGjCxx7fa4BEtu3ImNckSULGeoA1T7GRp4bQ\nJjZ7HmaM81xG2r/fbfUO3exZk6AMA7ELAJeSR13aNkJfbSSh7EuErBAqOKLXXQaMZpq0rWQwFquU\nSFVwPYeJc6M1XFGCXpAg6EoFvS1GPAWMZgyCOJRhWOVErLL5dF2RbCSqhbRquwjqXoKF07FUbMFa\ntpnQmJnoWhBJt+NQTEEWcgUQ2z2IGvPmU2S3oBZARkhghCwkftmIgcQkIJiZwr/zm3kluIPfbcqO\n6SGmpDj4JFRCUOickTWBBNVGAjaskkyrkJGsiUhq+Pj2iTzpIkC5fyUAKZax7A/zPAl0Aqg49rv+\nQIikFPaUWhjH5Ov2ap6rXI/A7B92Qa7599immaI4YuQwFESEUX3ANIaI0BT0sqejgWxbInal8/O8\nyJlBssVBS9hkYkNrORMTh1MX9NAQjkR91LiLbFsSExNzhuw44nwzBAIBfvWrs1mw4EJOPvk0NM1s\nTlpRUU5CQgKLF9+Ny+Vi1ar3eO65Z1EUhWnTpnPxxZ3tOJqaGjn33LO4444/M2XKND755EOeeeYp\nLBYLKSmp3HTT7TH3DPvyzjtv8dJLz2O1WtE0jXPO+SXz5n2fxx9/lOTklF5NHkpKdmGz2RgxYj+O\nsQNk48Yv+Nvf/oIkSfh8Pk488WR++tNzel0/0r+qP/OprKzgwQfvpampCcMwmDhxMpde+jusVitv\nv/0my5c/wogRIxFC4Pf7OfXUH3DGGT8Z0Nxvvvl6brjhZubMMQ0fvF4Pt9xyA21tbWRmZrJs2YMA\nvPDCP1mx4h0ATjnldH70ozP7vZ84cb7LDKl4Gsp+Tj2RZHUgAY2ZhXhHmBbl0u4PzRcDXpwJWXj1\nSoKiDZvUn+jPIFqV6yFkXxuhrFEHP9YBIIRgm/cpfEYdefZTcCpZ3dZRJNsBuu1FjBtMsRk0zKfV\nTaGtMeJJFwFqg2tp1XYhoWCX09HZAUCmfQZ1gW3smn0Yk/+zE2vJ2j7FU3OoGCGZ+0tWi6hgJ3XB\n9eTYYjvVSyE/WCGQkAJtbahdhGskApYgD+s2fop6GK3aThp827FmFACgNFfiHx/EKDeQdAkL5k1n\naLiE7M6PbmtoAYRexTlWCdV1BMqxnd1HZwJ7glv5Wm6gaW4WGbLZpFCyKWhJFr4sqSBBsUaf7gO4\nZBWPrpkpe9HIU6wwKfY+S4dRzQjb/B7f231RJXO/mtGBqjgQwXaE7kN2HHz39WhUrA9L9f91yn3N\n0QrBli5Od22aH6usxIiVb5pI5GnfuqdPmkoQmGYVXZEliYtyjyIkdF6s2kCJtwGvFqDEWw/AtKSR\nbGgtp9LfEhdP/wU8+eTfSEzs/L58441XSUlJ4ZZbFvPGG6/y5ZcbmT59Bn/5y0M8/fTz2O12Lr54\nASeeeAp5efmA2Q9o+PDOz7SXXnqe++5bhtPpZOnS2/jww/eZP//EHvf/1Veb+Ne/XuLBBx/B6Uyg\nubmZ3/72AoqKRu937h9++D6HHz5u0MXT3Xcv4aGHlpOamkYwGOSqqy5l/vwTSU1N63H9SOPf/c1H\nCMEf//gHrrhiYbTZ7nPPPcNddy3mpptuB2D+/BO49NIrAQiFQpx//rnMnn0Uw4Z1/x7bl8rKCl54\n4R9MmjQ5ZvlTTz3OrFlHctZZP+PJJ//G9u3bMQwL77zzJo899gy6rvPzn/+YE088Gadz6KLiceIc\nKoZUPOn6obWvVSQZt2qnBYNQ7iQAZH8dUIXSVI4zcRiEzJobm9wP8TSIkSfZe2jNItq0EuqCa0lS\nR1Ho+FGP6yiSFf0Aap46o3im2NSFefPcou1ACANJkhFCsNXzGHUhM9XOreSZaYPhaFWqdTJKwMZe\n51YKhufjrtyJ5G1GJPTsTNisFUdrnpIUU9zUBzd2E09ofrBaCdrd0BabtheJgPUkNpLVMQA0+HeS\n7RiDYXejNFfiNSqj+1UlOxIyIcWLnNd5PRkNFmiCJDWANb+7GBnTMpyvaxrYldRBmRzEpdgY6x7G\njvZqfEaIo1IKUbqkVLpkiXJdQqhu5EhEQutM2wsYzVQHP8GljOzW26k3IuIpJDqwk4ZWuxrhr8dS\n9DOkgzUmiEae+t/w+H8NT7ivkkO24NH80Wh4m+Yb0qgTdI08dYqnds3PhtZyki0OJvQggJIspoif\n6B5Opb+VrZ4aSrwNAByVWsSG1nKq/a3dtovz3aKsbC9lZXtjLKk//fQjLrzwEgBOP/2M6PK///05\n7Hbz2k1KSqK11TS52bBhPQkJCRQWdorw++9/GABN02hqaiQjw/ycjERouvLyyy9w/vkXRW/aU1JS\neOyxv5OQ0Gk7res6S5bcSn19HX6/j1//+mKysobx2msv8+GH/yE1NZX169fy0UcfoCgyRx75PX75\nywVs2LCe5csfRlUtZGZmct11N7Ny5busWfMZHR1e6uvr+elPf84pp5weM6f29na8Xi+pqWlYrVYe\nfvhvADz++KPU19dRW1tDY2Mjl112JTNnzua00+bz4IN/jZnP4YeP63a+165dQ25uXlQ4AfzsZ7/g\nnHN+QktLS7f1LRYLRUVFVFVV9Es8padnsHTpn7njjttjln/22ccsW7YcgAULLow2NH344ceQJAlV\nVbHZ7Hi93rh4ivM/wZCKp4cffjj6cygUYteuXUybNo05c+YM2RySLA4qfS0YQiBLElrWKKitQmmu\nwFk4BTBvmlMs+68LidTwSINgGCF7Dq1ZRFXQrAkqcPwAWer55liRbNGo0cCIjTxpYfGkCS9t+h68\neiVtWil1ofUkqaPJs5+IW80Pb2KKJ1V2MNr5c7Z3PMnacQ0cV2Xg+OI19HRzvdDwsRhJnSKnJbSd\nhPDlbZFcWEnCo5fHTsvQQQ+BsKCFBZ7a5b30djWu2IeIoPKG6kAFPSUHS3UxvmAFtsh1gYQquQga\n+9T5hR3+0Dqwbv1PNDVPyypET8tljMu8UfikaTftmh8ZiV/nzmF9SykAU5Nin0omIBBIdKgO3D1E\nnjp084l/mmVSt95OvRFJbzTrngQi0GjOW/eBfHC9MCJz66uZ7/86Ht0UT8PsiezpaCRgaCiSTIce\nYtgg93LaHwmKFYdsidY8GULwSvUmNGHwvdRRMUJ+X8YnZvNO/VbWtZTSEPSQbk0gy+YmxeKkJtDW\n/xTpOH2i1a/HaC/t8bWaUhldH3jtouzOQ82Y3uc6y5bdz8KF1/Lvf7/Rub+aalav/pSHHnqAtLR0\nFi26DrfbjcNhfqaUlOyipqaa8eMnomkaTzyxnDvvvJcHHrg3Zuy3336Tv/3tLxxzzFwmT54K0E04\ngSngRo8eE7Osq3ACU8zMnDmbk046laqqSm666Toee+xpZs06kmOPnc/hh4/j6quv5PXX30WWZV59\n9WUA7r33Th544BHS0zO4//57WLHiHSRJYu/ePTz55D9oa2tlwYJzuomnCy/8DRdd9CumTp3OjBmz\nOP74k3C7zYcQDQ313HffMnbv3sXixbcwc+ZsQKKwsCg8n+/3KJwASkv3Mnp093uTwsIiKipMA56u\npYRNTY1s27aV3//+2h7H2xebzdbj8sbGRl599SXWrfucgoJCFi++DSAqhteuXUNycnJU5MaJ89/O\nkIqnp59+Oub3xsZG7r333l7W/mZIUh2U04xH85NocUC4L4oU7MDlM00RIula+2UQDSMOZeRJF0Hq\ngmuxSSmkqj1/aIPZ6+mgap72iTwBfNn+ICFhiguL5GKC6xLscpdoUkRoSAo5tmNo1rZTyxq2jHUw\naeuXEO79ZN29lvZTrwFJImA002HUkqGGu5sLA7dlJI2hLYQMD5bwzb/SWI4hAUho4feyq91zh14N\nhI0r9sEmpwAS3lB9WDwNx1JdTId/D9ZwuiDCwCq7CRjNsRt3aSBq/fod1ICZCmnYXbT9+Fbcqp0c\nWxJVgVZkJASCx8o+i/bTGWaPdVpzhY03vJKFRFkB2RpT8+Q3zCf+drnnlJGeiKbtiQ7T2MEw+58J\nzYdkOXDxJITojIrFa556xaMFsMkqKRYne2jEoweQw58zQx15kiSJTJubMl8TAV1jXUspO731jE7I\nYHpyXp/bplicTHBns6Xd/FsqcmYAkG1LZKunhnYtMOi9qeIMDe+88xYTJkxi2LDsmOVCCPLyCjj/\n/It46qnHePrpx6NpZOXlZdx++x+59dalKIrCk0/+jdNP/1FU7HS1tz/55NM48cRTWLz4FlaufLfX\ntD1JkjD2YyDkcrnYtu1rXnvtFWRZpq2t60NAc5/HHjufK6/8LccffxInnHAybW1tSJJMerp5zU6d\negQbN27gsMMOZ+rUaUiSRFJSMomJibS0tJCc3FkPeMYZZzJ37nF8/vlqPvroA/7+98d5/PFnADji\nCNNIqLBwFA0NDfs7zf06VsMQyOGU81Wr3qO4eBuBQICmpkYWLvxDzNwOhGAwwMyZs1mw4ELuumsJ\nL774IscfbwrGLVs28/DDD/LnPz9wUPuIE+e7xJCKp31JS0tj9+6hbaaYFP6ibo2Ip8hTU0nC3RqC\nxE6r7P0zmGl7kcjT0DfIrQ9uRBM+htuP7TOKJktWDELRVLv+Ext50ukUTyHRTqJSRL7jFBLVwu7p\nkqKzrkySJA5P+CXt2h525tei5UzAIhyo9btR2vcSaHoew5mMV68CwKWMBFoBA5eSS2NoC+16Oamy\naZhg3b2OoE0GSSEUFmlql6hbu16OXU5HDdcdxZ4LFbucaoonhymeAHxaDUlEnr4JrJIbr6hEE35q\nAqvRRYAMQ4r+4QXyJuLPnoJ17xco5Rupr32V9GE/Yowrk6pAK0emFpKgWHmvfhtTEkdwQkb35rYJ\nRgCw4AkLHFRHTFQnIp4ccnov7093LHKneBLBps4XDjZaJDTzH4ARRBj6wacB/hfi0QK4FBvucP8x\njxaI1kAdCrGR50il1NdEqa+RT5pKcMgWzsyeityPz76zcqaRWLeVtS2l0RqnbHsSWz01VAda4+Jp\nEFAzpkMvUaJIitVgs3r1J1RVVfHppx9TX1+H1WolIyOT1NQ0pkwxI0WzZs3h8ccfBaCurpYbb7yG\nm276E0VFZm3v55+vRgjByy8/T2VlBdu2fc2f/nQXtbU1zJo1B1mWOfrouWza9EWv4ikvr4CtW7cw\nd+5x0WVlZXvJyOh86LVixTu0t7fzyCOP0drawoUXntdtnEWLrqWsrJT331/B5ZdfzH33LYtxGw2F\nQiiK+V3WVcAYhtHtFiAQCJCSkspJJ53KSSedytKlt7F27Zrw+gfuMpmXl8+//vVSt+V79pQwcmQe\npaV7ozVPgYCfCy74VY+RqnvuWUp5eRkzZszil788f7/7zcoaxrhxEwCYOXMW27dv5vjjYefOHdxz\nzxLuvvv+qMiME+d/gSEVT9dcc01MikZ1dfWQp2wkhd3IWkM+RjpSouJJSBL2pkbUJGe01mW/iMEz\njDiUaXuNoc0AZFln9blepNeTThCVAdzw7OO2F0nbs0huBAYTXZdgV3qJikTOcfh9UiUHE1yXsr5t\nMXus68zXonXG70GXe3uXmgd8BULgVnIB8OjlpFrGgh7CUroRxhaCYiEUjgZZwk/vAkYrIdFGkjq1\n18Oyy2m0aDsxhIaeaoonr9yMLA2Pzt0im6kalf4P2OV7AQAlOI7MsMAK5I5FHjYB4UyiXnzOV7a3\nGB8cwVGp00iyOJiSOAKLrDAnpQC1B5EhDA2XCAKWaJ2MpNgRwdaoyPXp4chTb+e4B7pGnoxAZ+Ts\noOuU9o02DUIa4H8bhhB49QCpVicupVM86ZEaPnVw3Q/7Q4EzjY+adrG6eQ8ePcCUxBEkqD2n+OyL\nIsmckjWBkzLHR8VWpC9Utb+Nw1z7NzCJ8+3jttvuiP78+OOPkp2dw/TpMyku3saaNZ9xyimns337\nNkaONKOTd965mEWLro9JsXvkkceiPy9dehunnvoDRo7MZeHCy1m+/CnS0tLZunULubm9RzjPPPNn\n3H33EiZNmkpKSgqNjQ3cdNN13H77ndF12tpayc42hfsHH7yPppkPmiRJQtM0vF4PL774HAsWXMiC\nBRfy5ZcbkWUZSZKpq6slMzOLTZs2MGnSVHRdY8uWzQghaG1txefzkZTUGdmpqCjnuusWsXz5Uzgc\nDgzDoKGhgZyc4VRVVbJ58ybgl+zatZOsrEhKuIiZT2/MmDGLRx55kDVrPmP2bNOB8/nnn2XKlGnR\ntMAINpudBQsu4IEH7uVPf7oz5rVrrrmh131E6Jr+N23aDDZsWM+0adMpLt5OQUEBhmFw551/YvHi\nu7scR5w4/xsMqXg68shO+2tJknC5XBx99NF9bDH4JKmRyFPkJq4z8qQ2V+EsGka7XkqZ712ybDPD\n6Vm9MKiGEeEeT86h7/EUEmYKg0Pp+8mREu31FECVBi6eIkYKkdS/6YnXI0tW7HIfglEYRKJOEdzq\nSGYnLcGrV0TXcXz2TxAGviPPBVnGIifiDDrR+AohDFyqWSfUrpl54ZaKLchBH4bFDpLSmbYXjjx5\nwuu5ld5dmOxyOrADv9GE051BwKYQVEMoemdfK6tkfqG1aGY/sZHGDGTRER3DUGVkQE8dQXuKE/DT\nUfshee0ejgao3EUoZyxqSi+uZHqAhPB5jYon1WEu0fxgceI3GsPzHbh4CgkfIjB4kSdPYDddb7mF\n5j+oNMD/Rjr0IAJwq3Zc4c+rdi2AHr5GhzptDyDXmYqMxM6wY16kLm8gdI1SZYdTT2sCcdOI/zbO\nPPNsFi++lTfffA2n08mNN95GeXkZmzdv4rHH/hKtczv77HM56qhYAx8hBIqicO21f+S66xZhs9lI\nSUnloot+C/RsGDF+/AR+85vLWLTochwOJ4qi8Pvf/yHq5Acwd+73ufba3/P115s59dQfkJGRyZNP\n/o3Jk6fywAP3csMNt9DS0sxFF52H05nAxImTSExM5A9/uIFbb70RVVUZPnwE8+efwLvv/pvs7Gz+\n+Mc/UFlZycUXXxYznxEjRvKLX5zHlVf+FofDQTAY5Jhj5jFp0hTWr1+L05nAddctpLq6miuvXBTe\nyvzbiMwnIcHF1q1bmDp1OuPHT4iOLUkS9923jLvvXspjj/0FwxAcfvhYrrrq6h7fi/nzT+SVV15k\n3brPmTGj74ejYEYT//GPpykrK6W4eHvY9fD/uPDCS7j99j/y+OOPkpKSyjXX/J733/+Ympoq7rln\nafQ9vfTS3/VarxUnzn8TQyqeSkpKuPrq2D/yG2+8kSVLlgzZHCIuUK2h8BP0cETDsDpQaqtwK6No\n03ez0/c8lYGPmJF0Ux9CIfJo5iAjT1oQpaXGrHc6BClMIcOLhIpC30+SI5EnY8B1T5G0va41TxIO\nOWv/kUdhdGmy24lDScehdKah2RPLsW/7AG+dGnVSNEJ1kUFwylnIWPHopiiylphRK6GqZtqeEZu2\n1x5ez63m9jq1SBqc32jEacmkNScDaMLi84HFHRN5atHMflXjPymlaXQ2RHwbQmbfGyQJb1oS0ITH\nvxPHxuroftT6vXjnXdDz6dEDuMK1ZO1h8YRiD7/mQwqLJ6uUhCJZez2WfelqVS4Coc79HUSdUrtW\nyh7P8xzOWIRqQ9ICcbvyHoiIYJdiwxVJ29P9BMPX6KEQTzZZJceeRIW/BQkYlXBwKTpJqgOrrET7\nPsX5bvPrX18c/dlms3eLdKSkpLBixcd9jnHDDbdEf541aw6zZnU3kurJMALgyCOPjnH862leTz31\nz+jPxx9/UvTniNlDVwe7CJMmTYk65XVl+PAR0Tqunoik6/XE+PETu/WdevPNFdG5ROYTCAQIhbq3\nBklJSe31PJx88mndlvU0/96YM+foaG+nriQnJ3Pffcuiv9vtdmbMmM1bb63q99hx4vw3MSTiacWK\nFbz33nusXr2aurq66HJN01i7du1QTCFKRDy1hMyn/5Gbd2FPQA54GC2dRIZrGnWh9VQFPqbY+zTj\nXRf1OFY0H/ogI0+Wii1IIT+hMUcd1DgHSkh4sEgJ+xUyshSJPA2w11N03E63PQVbPxsR6z2Kp30J\nFs7Avu0DrLvXRcVT1MgjnL7mUkbQrpcivI2o1dvR0nLD05OjNU8Rw4j2sDOfS+ldPEXS4CI1RU2T\nZ0CwGIsmm+LI0KKRJ014UYQFp8ePj2QMzEicCHmi4/kSLKBDS5qV9mMvREKiUS5FSSqg19iM0Sme\nIg5tyGFlJjSEMPAbjbiV/P2ew65ExJOhd4CmItnSEIHGzh5NA0QTPjZ7HiHFMOfWIjeQgpuOYDku\nBrfHynedyPuYoHbWPLVrAQKGmc5zqGqECpzpVPhbGOlIwan0X4j3hCRJ2GVL9JjixIkTi9VqZfz4\niYd6GnHixOmBIRFPxxxzDKmpqWzZsiXGllySJC6//PKhmEIUl2LDJqs0BCM3rZHIk3mzaGtpIm34\nRFIsY2nXyqkJriZXO7GXCEQkbe/gIk/W3WYUJFg446DGOVBCwtOvlK7OmqeDjzxFxtofopfI074Y\nKTloKcNRK7ci+dsRdneX7cz3ya3m0abvxlv1Di1ON18UjOcYox4pJm3P3MajlaFKjj7Piz0ceYrU\nFHlls8+GYjXz35W63ViyOp3xXD4rEhLCkYShtRKSg1i01mgQKiDM2qKQ5MObnUOHXsPG9pdwBocx\nx9lzdFZ0Sdtr1/x82LiTnFCIPABDIyBaEOg4BlDvZJ4H8+8h0aMDKlJCjpm+d4CRp9rAOnxGHaPl\n4wCDdqWRFNw0+zfi4sj9bv+/RCTy5FZtMTVPPiOEjESC0r+/ncFmjCuTj5t2Mc6Vvf+V+4FFUuLi\nKc53jp6iO/2layRsf0Rc+eLEifPtY0jEk91u54gjjuDVV1/t1kfgrrvu4tpr+9eDYDCQJIkMq4tq\nfyu6MJAjhhE2MyKlNFeiDR+LLKkUOE7jK88yqgOf4VZzEcKgJvg5qZax2OTkfluVKw1lqDXFPb9o\nGKjVxWjpeTF9ioYKQ+howocq7b/upLPmaaCRp33c9oQ/2kdov/RTPAEEi2bgXP8qjnWvoKfkoEsh\nNAsoDXux1TYzwipRmQ5Voc/ZnjmRtQQYLwTpcpe0PVlBFwE6jFqS1dF9Rsc60/ZM8dRhmEYjsnMY\nBBqw7fiEBF8ahLML3S1BtNR8kBUkSUWTOlDDUQYhRIyleVNoCyUdrwCCDqOagNFiXnP7ogdQJXCg\nUO5rZk9HIzmqjQsBDB2/HmtTHjJ0XqjaQLY9iXlpo3t1S1MkB8nBZNI8VlBdKCnjMdpKDrg3U7tu\n9qBJIBOoIct9IviKyZTjT1b3pWvank1WsUgyHi2AVw/iVm39crj7JihwpnF5/txo09yDxSIrndHS\nOHHixIkT5zvCkNY8rV+/nvvuuy/aCTsYDJKcnDyk4gkg0+amwt9CU9BLumKeAmE1b+bV2p0EJswH\nzKaiFslFTXA1o5xnUur/N7t9r5JlncUE12/ol1W5YZDw0ePIHX0XRgeL9l/M+U2gCbPmwCLtvyv4\ngdY8dQqQTre9Po04uiJ0kPt3mYbyj0BsfAtr6SYo3YTmsNMxcSxqQymO0gqGI3DOk6hK9+APmUIk\ngASSjBZJ25MU2rUSoNOhrzdscgoSEn6jkQ69jsbQFlQpAdmehhFoQGmpJrF9F3zPXN/tlQmOmo0Q\ndUiyBUMSSMJACIEmvBhoyFgxCFLsfRoDjQRlOF69kuZQMcNsPVwjeuRG20J92AmvRgsQlEARGr6o\nWYSp4HZ3NLDNU8M2Tw1V/hbOGT6jx5txWVLI7SgEQM3+HpJiRVIciGDLATU29ejlSMioYTMNl3Mc\nIYqR99Of5X+RiKBwqWZqq0u106L58OshhtsPrl/LwbJvj7GDwdLloUWcOHHixInzXWFIxdP999/P\nTTfdxNKlS1myZAn//ve/mT697w7m3wQZVjPKUh/0kO40b+KFoqKl56PW7ETqaEE4k5EllWHW2ZQH\nVrLN+wQ1wTXh7TYQMjqQ+2FVrtbsQO5oJZg7meCo2T2uI1QLekbB4B3gAAhGGtT2wy76gGuezK1B\nCIQwMAii9DfyRP97Sgm7i/ZTr+60fTd80LGZ4PBxeIrM4t1s9RNK1M9oS0oAv04QyTSM0M33UpVk\nmsPOeMmW7v0xYo5IUnGoaXj0SjZ7lqELH+MSLoKQOd+OmWeiKRJgFtqqBScRTJ8De18BWUVIMjIy\nCAN/OOqUahlHQ2gTBhrplsnkO05jfdsSWrSexZPocqMdEU8GUInKKEPr1uOpxGv+nqja2e6ppSbQ\nRo69u8OjMDTcmosO1U+KIxw6U+0Q0M2GuQOoeRHCwKOV41SykfQAQlLAIa3aqwAAIABJREFU4gKk\neKPcHugaeQLzvW32mTWahQkDS7/8NmORFAwEujBQDjL1OU6cOHHixBkqhvQby+VyMWXKFCwWC6NH\nj+bKK6/kiSeeGMopAETTTuoC7XStxwkWzUASAuvu9dF1s23HABI1wdVIyGRYjsAgRF1wLV17M/RG\npJ4pMPb/s3fe8VFU6xv/TtmWbHpCIEjoRUCkCoj8BEHUa1dUFC8qIiACKhbQC6IC4sWCBRsqAiJW\nFCsqiuCliPTeS0JJQkjfJFtmZ35/zO4mm+wmG0oA2efz8SOZnTnnTNmd85znfZ+3F0pKq4D/ues0\nPem8qROFS/UqTyGE7Z1wzhO6OqepHqc9Qrc6V0MzjPDtHl2n3HXVCzFqEdG+bXUSbgCgRNWVQIeH\nPCmaG1nQLdHzXR7yJLcI3Ek5xJgaoGjF2NyHSTH1pJ6pu2+8SmIqQr32eMM6zfHtQBQ9JhiSz1nR\n7bb5QvZi5KaYxQRMYjytI+8nSmqEhIk8V5CwT7c3P8bs9/90ZNAUSt26tbTZ40y4ryQbWRDpGa9f\nm6P2wIqoZs9GRKTAUPa5IHkIbw1NI0rVbNw4iBJT0ZRikCP074xkPmEDin8yijwk2Ou05yVRAtAl\nNni9m3MNRs/zH1afwggjjDDCOJdQq8qToiisXbuW6OhovvnmG5o2bcrhw4drcwiAv/JUlo+j4mrY\nAW3NN5j2rARPOJEJuMzQD4foIFKxIiDyR911ZOX9xAW53XEbQE7bjEHdHaAnDcOhLbijknAnnp2T\nHkXTjTNCCts70Zwn8FxnDcVDvEIxjNA0DTSlzD2uxn163fbKqv1FSHWIlVvg0ByAFSeCz21PFiRU\nTSFf2UukVB+jWH1uR/d6o9mftRUJIzFyU/9+0RAEEYNgxaUVESF6ctpUN4IsoXnCEV3ufBzo5Mkk\nxtI5+mkEZJ8aGGtoTo5rKw61AJPorxJ5ladYo37/+iW1YkHGRg5pMqiKJw9LwCImUaTYyXIU0TQi\nkVSP4poRhDypJXr+Vp4hm4beMD1PcVZNKUUwhh6+5XUujBZSwJ2DYPbYXIv6GMPwR7Hbqec6eciF\n13GvlTWZWEPEmRzaKYW3pppLc2PmBL/jYZxxOBwOBg26g3vvHcI111yHoihMmfIshw8fIjIyksmT\np2G1Wvn991/57LNPkCSJjh07M3ToCF8bubk5DBx4G1Onvkz79h1ZuvR3PvvsEwwGA0lJdXj66YnI\nctXTlTFjRmIymYPaeHuxdOnv9OrVJ6RzW7ToB6xWKz179qp2X7fbzfvvv8Pff6/CYolAlg08/PAY\nmjRpFlJfpxv5+fm89tpLHDqUjigKpKY24uGHHyc6OpoNG9YxYcI4mjRpquffOhx07dqd++8fVmWb\nf/21kr/+WsEjjzzB/v17eeqpx7njjoE+K/YJE8ZRUKCHehcWFtK27UU88cTTLFy4gB9//BaDwcgd\ndwzk8st78+yz/2HAgIHhOlFhnBOoVfL03HPPcfz4cZ588kkmTZpETk4Ow4cPD+nY3bt389BDD3Hv\nvfcycOBAMjMzeeKJJ9A0jaSkJKZNm4bBENoLONYQgUEQdeXJp2qoaEYLrobtMR5Yi2XzIt/+FQPM\nki6RyE48DtnbIaUBpoPrMOUXBu3P2azbKSmkezrg8pKnEML2fMpTjes8AfgrT1IoypPmmVifMHkq\nu7flUc/UA1U7AKBTOUFCURUMokihchAVJ3Fyq5C6MEpWEgxtKmz1dxeMlFJwqoXIYoSHEOrKkyAa\nATcudwEOwUue4irlg8XKLclxbaVA2UsdYyf/rlQHCCKXxjWjviWe1ta6LMveyWFFQ1FdlLgzsYhJ\niILMvuJMQK/Rk2yMQkTgiD0/4HlppVloaBTIBWQ5V1PX1K2c8lSzUDtvweFodxyQg2jWiyILohxW\nngLApjh8ahNAklEn8d3impypIZ0WGMLK0z8Cs2d/QHR02aLO998vJC4ujokTJ/P99wvZtGkDnTt3\n4d133+Ljjz/HbDYzdOi9XHXVv3yFbN9++w3q17/A18brr7/CJ598RUREBNOmTWHZsj/o0+fKoGPI\ny8sjLe0gTqeTkpJiIiKCLwbOmzcnZPJUE2e9Tz6Zg81mY9asTwDYunUzTz/9JPPnf4Uonvmw1EmT\nnuGqq66hX79rAJ1EPv3048yYMROADh06+dXnevjhEWzevJF27doHbM/lcvHOO28yc+ZH2O12Xnvt\nZTp3vqRCn2XtTZ36PNdffzN5eXl89tknzJv3Baqq8vDDw+nevQejRj3KuHGP8f77c071qYcRxilH\nrZKnQ4cOcfnllwMwa9askI8rLS1l8uTJfjbnr7/+Ov/+97/p168f06dPZ8GCBQwYMCCk9kRBINFo\n5bjThuoVJTzqREnX/jibXuKnVlREnLSabP4gv0kzLHYH9ov/hUsObICgSfJZqzpBzcL2vDlP6gkq\nTxpazcL2VL04qyCc6GPqcVKscC/rGDujaroa4kAAUcallSALEvnKTqD6fKeqIFSwSG9nHYmGu9w2\nTXfcE41AKW53EQ6pjDxVRJSsPz9FSnol8qS5HSCasBpMtDHoFtINzTGss9k56rThkoqIlvV8uu1F\neuHdppFJyKJEsimKTEdhpZwTTVXQ7NlgjAFRYmfxHKLlRpjLKU81gbfgsMWlPz+CyXOOgq48nYgB\nxT8VmqZR4nYSZyxTmC6Ja0gLax0SjNWrw+cSyitPYZybSE8/SHr6Qb8CtStW/MmQIfqi6PXX3+Tb\nPnfuZ5jN+u9+TEwMBQX6ws369WuJjIykSZOmvn2jo2MoLCwkIiICm81GbKxulPLUU48HVJaWLPmV\nyy77P2y2IpYt+8OngE2aNIGcnBxcLhf33z+MvXv3sHfvbsaPf5LJk6fx9tuvs2XLZlRV5dZbb6df\nv2sYNWoYTZo0RRAEoqNjiI2N48Ybb2HKlGfJzj6G3V7K4MFDKxWT/fbbr/n44899f7dt244PP5yL\nKIqsXfs3H3zwLgaDgaioaJ5/fipbtmxiwYIvEASB9PQ0evW6giuuuJJp06bw1lvve67ZLCIjI+nQ\noROvvjoNURSJiIjgP/95jr17d1c6/r77AtekTE8/iM1W5CNOAL169eGbbxawa9fOgMe0anUhhw8f\nCkqe/vjjNzp37oLJZEZVVV5++Q3mzZsdpP80iotttGp1ITt2bKNRo0Y+JbFZs5Zs376V9u07kpra\nkHXr1oRt2sM461Gr5Gn27Nn06NGjWvm9IkwmEx988AEzZ870bfv77795/vnnAejduzezZs0KmTwB\nJJmiyHAUUqDYsXpUEQBkE0rd5lUeG6MYoPAPSox2LHYBd3wDtMiUGp3T2QKv8mQMgTx5a/+4PA59\nNYN+jZWaKE/qySpPZUVyy0O3SdeJgEMTQDDgUt2YZQP5yl4A4kLIdwq1X4NYbtLrPSdBRhQtQCmK\n2+anPFWEVdKLyNo84W9+cDsQZP9QrqYR8ayzZbHHWYxkgQgxmeNOGztsmaSYYqhn0kPuUsyxZDgK\nOe60kWwqC8PTHHmgqUgRKbSKvIdtxTPZYnuHTgY9hKMmalGRkkaeaztmMRHRXowKCCZdedLvq1eJ\nq9WforMWDlVBRcNS7pmXBPEfR5wgrDydSqSn7yMnJzvgZ5Ik4nbX3NUyISGJ1NSmVe4zY8ZrjBkz\nlp9++t63LTMzg1WrVvDWW6+TkJDIY4+NIyoqCotF/83dt28vmZkZtGlzEYqi8NFH7/Pii6/w+uuv\n+Np45JHHGTx4IFFRUTRv3tI3mQ4Wkrd48S+MGDGa4mIbX375Oddccx379++loKCAGTNmUlxsY9Wq\nFdx117+ZP38ukydPY9OmDRw8eIB33vkQu93OvffeyWWX6Yu7TZo048Ybb2HWLH3OUVRUxCWXdOPq\nq6/l6NEjTJgwzo88FRfbMJlMlRSvyEj9vWqzFfHss1OoW7cekydP5O+//8JisbBz53bmz1+A2+3m\ntttu4L77HsDlcnH8eDaJiUmsXLmcqVNf4dlnn2bkyEdo1ao1n346jy++mE/Hjp0rHH99UPKUlnaQ\n5s0rv9OaNWtOevpBEhOT/BYZS0pK+PvvVVx55dVB7jysW7eGHj10K1lRFDEag5sIffnlZ9x66x0A\n1K/fgH379lFYWIAsG9i6dRMdOuiLghdf3IH169eGyVMYZz1qdcYSFRXFtddeS+vWrf1C7KZNm1bl\ncYG+mKWlpb42EhISyM4O/OIIBm/eU47ThlUQK02wq4JVSkXCjMN9HEg6a0PyQkFNwvYsUh0ASj31\njGoEQYByypNECDlPHuXp5MP2KquIGvrE3YmAIMoomhuDKGJTDmES4zCKJ2PJXDnXqqxjfaIoCBKi\nJwxOdZfgEPORBEtARc4kxmAUYiqRJ01TQXWC5E+4mkUkIbCdvS4XLYEIqS7Lc/ahAT0TmvlUnhRz\nDOsK4Ii9wJ88ufRnQjBEU9fUijxlJ0cdf3KQ30gFnK5j5Dk3kGTsUOVVULRSttjeQUWhZcTdqHn7\nQTSC7JlgeC3oNYVa/ik6a2FX9GfeFKI9/7kMr/LkDJOncxI///wjbdu2o25d/6LJmqbRsGFj7rvv\nAebM+ZCPP57FiBEPA3DoUDrPPz+eZ599AUmSmD37A66//mYfyfAe/9prL/Phhx9Tr14KzzzzFCtW\n/I8ePXoGHEdGxlGys4/Rrl17FEXhxRcnUVCQT8OGjSgpKWHy5In07NmLvn2v8jtu587ttG/fEdBr\nUTZq1JjDh3WVvHVr/1Bsq9XKjh3b+PbbrxFFkcLCymH6bnfw5zgmJpapUyfhditkZBylU6cuWCwW\nWrRoVWlu06/f1fz++6/06XMVVquVuLg4Dh7c78sF6tixM7Nnv0/Hjp0rHB98HiIIQhACrSF6FjE2\nblzP6NHDcbvdHD6czvDho2jWLPhC8vHjx6lTp07Qz71QFIUtWzbx2GN6SZro6Ggeemg0Y8c+SkJC\nki/PCiApqQ5btmyqts0wwjjTqNU3dO/evendu/cpb7diWFZVSErS8wcakwjHd2E3KgiiiCwLvs9C\nQR3nhSjFeUASsbFWTDGnpnBkbWOn0w4OqJtUF5NU3TlEYS6Mxc6xGl0rgMw0GTSViCgBbBAbHUtS\nbNVtOAoKOQ5EWiOJrmF/AJpb4eheMBpEEiscr+2RAAUHApHWCFzZKmaDhEPLIyWiU43Or+K+NqeF\nguMQHW3CkuD/mWLXyAJMFjNSpBFXzhFkyYlDy8VqSAzab4KjMRnFG4mOx3ef3K5SMgFTRCQJ5Y5T\nFSMXHFQ4pMo0UY3ERDdgY9ouksxWejVp7isM3dpUj++ztrAqfz+NEhNoGasbWhQ5XBQCMYlJWOKj\niEsYyuK0gxy2/49UelLk2MlW2xdc3+RtIg2JQa/LztyllKrHaBV3Ay0TLiHj4EaM0Skk1dGJWm6e\nhVIbxMeakM3n5vfnVCPDUw8uLjKixt+xcw1x9gjIgYhoI0nx/+xzPd1ISgocWnU6sX79ag4fPszf\nf68kMzMTk8lE8+aNSE6uQ58+PUlMjOKqq/owY8YMkpKiyMzMZOLEcbz66iu0atXK08bfrFu3mu++\n+4r09HT27NnJxIkTkWWRdu300OlevXpy6NA+kpL+FXAcCxb8gdutMHToIDRNQ9M01qxZzp133sk3\n3yxg/fr1fPPNN2zYsJopU6Ygivq73mo143K5yn3PVBISojAYJOrUiSUpKYrISBNWq4nVq5fhdJby\n1VdfkJeXR//+/f2+n/q/NSTJRXx8vG/79u3bad26NdOmTeb999+ncePGTJo0iehoC7GxEUREmHzt\neMd1xx23Mnr0aJKS4rj55htJSopCFEXffkeOGDCbjUGPD4T27dswd+6HlT4/eHAfd999J/n5+XTr\n1pXXX38dgAEDBtC588VV/gYZjRJxcZF++3ivV/ltK1eupGPH9n7bbrvtJm67TQ/pfOyxx2jduhlJ\nSVHExFgwmw3/+N++MALjXLrvtUqebr75Znbv3k16ejp9+/alsLCQ6OgTW+GPjIzE6XRiNBrJysoK\naQUEIDtbr2tktOurLQdzc2ipCSguxfdZSP2rzXChW5rnF5QiOkM/9mxCsb0AECjIURGE6s/BLNQh\n37WHrGO5iELoipBb1UB1k1egh6eV2iDbVXV/qk1f3SuxqzhqcG+80DxqotPprHRvXR43RScC+SX6\nar/iyeUxuuuF/CwkJUVV2tddrOeEFRaUYFP9P9Oc+jk5nCouyYAElNhzcZlKMGotg/ZrdNcDNnIw\nawfxhgs9bekTbaci+R2naW6aCwqHNAO5zjrsPaaiaCrtrPXJOV4WchmpGekck8q6gnTe2PoHjzft\ni1U2oeTrdbIKS0Rsbr3dCy3DWON4HjduZLcMaGzPWEwjS+CEak3T2Fu4BAGJOlpfso/oqpkixvjG\nqjj1RY/c4wUIpjOr3mqaxp5DBaQkRWK1nDnnt1KT/ixqTq1Gv0fnIpylegjr8Twb2e5/9rmeSQT6\njToVePrp533/njVrJvXqpdCsWVs6derKjz/+yr/+dT1//bWO5OT6ZGcX8cQT43j44SdJSKjvG88b\nb5SF4r/wwnNce+0NpKa2IC8vn717DxETE8uaNevp0KFT0HP49tvvmD79bRo31g1VNm3awMyZb9Ow\nYXMOHNhPv37XMGLEGEaOHEp2dhGK4iY7u4gGDZoyd+4sbrppACUlJRw8mI7VmoDL5SY3t5js7CKK\nix0YDA7y8oqIj69DdnYR3377HQ5H5XfKjTf2Z+LE5/nPf55FkiQ2b97Iq69OY+bM2RQVFWEwWNm/\n/ygrVqykfv1GRESU4HCUzTtUVfX824DZHMmCBd/w8stvkJ1dRKNGTVi6dBVt2rTljz/+R+PGzcnP\nL8FudwU4vjKs1kRiY+P58MO53HDDzYBuGKGqEBdXj4MHj/qNZdiwUYwf/wzvvRe4lExSUhTR0XHs\n3n2A5OSynO7iYgeybPcbx19/raV+/Ua+bW63m0ceGcErr7xJUVEhW7duJzm5IdnZRezbl05UVNw/\n/rcvjMo4Xb9TJ4OqyFyt5zz98MMPOJ1O+vbty9tvv010dDQjRoyo/uAK6N69O7/88gvXX389v/zy\nCz17Bpb0g8GbQ3DcY1eu1SBsD/RipsdYB4CKu3YLZp1CONUiZCEi5EK0EVJd8pXdlKjHsEr1Q+/I\nY1VeE8OIkw7bCxI+p2kaiud+OxCwad4QDG99ndQT7M/brceogsrPlFYu50mWo9HQrcpBv7bBYJX0\nMdnch8rIkzf3SPQPgRQEiWYoLAGOO+qTLuiksFlkkt9+oiBwU72Lscomlubs4Yg9n5bW5HJhe2Wh\nNJFSPVpZB+HM24xZi0TEQIZjBQ3N1wY0e7C507G5D5Nk6IRBtOJ26k5/grGc1bonNE1TlSoCTmoH\n+w4XsnprFgkxZq6+NBXxDIXilrp14m064Wf+3EHYMOKfif7972Dy5Gf54YdvfeYGhw6ls2XLRj78\n8F2fQcwddwysFIqnaRqiKDJmzFiefPJRjEYj9eql0KdPP6CyYcTevXswmUw+4gR63kxeXi6xsXH8\n8ssivv32ayRJ4q67BgHQvHlLhg69l5kzZ9OiRStGjhyK263w4IOjMJnMAX/PLr+8D2PHPsq2bVu4\n9tobSEqqw+zZH3DvvUN8+9x117+ZO3cWgwcPJDo6Bqs1iv/+91WMRiM333wbw4cPJjW1IQMH3sOs\nWTMZNuyhCr2U9du7dx9WrPifL0/s4Ycf59VX/4sgiERFRfH00xPZtWtHhbHq/w5mr/7cc1OZPn0a\nCxcuQJJEUlIu4JlnJge8h23btqN+/Qv4/vuFfqYf5dGxY2c2btxAz5692LVrJzNmTCczMxNZlli2\nbAlTprxEVFQUOTk51K/fwHecJElcccWVDB9+H4IgMmbMkz43wo0b19fI4TCMMM4UBK0mMW8nif79\n+/PFF19wzz338PHHH6OqKgMGDOCLL76o8rht27bx4osvcvToUWRZJjk5mZdffplx48bhdDpJSUlh\n6tSpSJJU7RjKM9tpexcjIjBazAdBxNj4lhqdz7Ejc4kthkNJGk3j7qnRsRWR49qGgOibGJeHS7Vx\nzLmOuqZuIdVHqgqappHpXEWJOwOLlMTekq+QhUgujZ0a0vFppYvYW/ol7awjSTJ2DLlf18Hv0JQS\nDqeYOWD/jo5RTxJnqNoO3J2/G/exv5DqXoYUfWI2zc7dHyOYEzGklrkMuVQ3z+3+CYD6KPRLkPko\nB1ItpTSN+pnuMVOJkJJDaj+g8lTFuNXSbJRDixDj2iBGNUJJ/5Ej5sPst+7lwsj7SDEFXgQodmfw\nV8F/qGfsQWvr/XpbtnSUo0uREjshxfvH6Dv2fMpbbhP5SETKEbhUhaeaX+XnqufFTlsW8w7/TZ/E\nlvRObIHz4Leg2DE2u6PSvo707xHs+eyuV0KWazWdo54mxlC5jsmu4vkcdvxGO+tokoztceduw318\nHXJKb0Sr/iJVjm9Ezd2MfEE/xIjgxPF0w+1WWbjsACV2ndh2aV2HVo3i2Lovh6PZxfxfhxTMptpZ\nZ0ojl/d3ruC65LZ0i2tcK32eKWwuPMIXR9dzQ/JFXBLX6EwP5x+Ls3FFN4yqMWXKs/zrX9f7jBRq\ngoMHD7Bjx7bTSkKSkqI4ciSHoUPv5b33ZmEyhVj0vgrk5uYwduyYsFX5eYqz8XfqrFGeIiMj/eod\niKIYUv2DNm3a8PHHH1faXhO780BIMEZysCQHl0HEEEAlqA5xcgs0dpPtWks99zVESKGFDlZEgWsv\nm4qmIwtWesZO91tNUjU3m21vka/sIl/ZRevIB07K1vmwYwm7Sz7x22YRQyMKUKaOlLhraBrhLZJ7\nAnWehJNJnvcYVZSHUm6l24nAMWULcBEaNiRMWER/habmfXrrPAUyjCg7J92qHCRNJ/0RVdyHCDEZ\nSbCQp+xE01QEQfQVyEUKQKhFgfZaMUvUWIoUOxda6wYkTgApJl0NOmov0PMHXTYEY2zAfUU5Co08\nUgxdyXKtJsO5shJ5KlLSOepYilGIIcHQVj/tAGP13dczXCh3V3o+JXaFphdEcyjLxsbdx2l6QQy7\n0vTtv685zJVdG2A0VL84c7IoPQ8NI8LKUxhh6HA6nYwaNYzWrdueEHECsNvtdOvW4xSPrDKMRiMP\nPjiKd96ZwSOPPH7S7b355nTGjHnyFIwsjDBOP2r1DZ2amsqMGTMoLCzk119/5aeffqJp06qtUE8n\nEo1WDpTkkItAsqbhLjyAaE5EMIaWtCYgeKr2aGQ6VtIkIrC8HQx5rl3kuLaS6VyFhopLK8Sp5WMS\n4nCpxRxy/I5NOUS+sgsBiUznXwjIGMWY6hsPAA03h+y/YRCstI4cwlbbu7ix49QCF0oNBB95UjNr\n2LunSC765LAmdZ5OPGwPoLKTolLubwcCxR7rdRUbVrlByCGMQeEjtwEIuddZTJB85yVr+tewqrA9\nQRCpY+hEhnM5ecouXaH0EBIhAHlScNJOcPKnIKBoGk0jgxs7RMkmrJKJo/YCcNt1R0BDYGtsQbKg\nAbGkYhLiyHKupnnEACRPDTBFs/sc9i6MvA/Ra0GuBhirN2xPcwUd2+mGqmnsOJCHLAl0alUHi0lm\n675c9h4qoMSuIIkCuYUO1u3MpvtFp18dK3Xr18J8PoTtiWG3vTDCKA+j0Rg0zyhUtGpVOXrldKFr\n1+507dq9+h1DwMSJgUMIwwjjbEStkqdnnnmGuXPnkpyczHfffUenTp0YOHBgbQ7BD4mevKdcRJLd\npbgz/4dmbYiccnmILeiTYwEDGc4VNLbcEPLEu1A5wIail33FU81iInb1OEXKIUzGONLsi0iz66Fl\nFjGJi6wPsaHoZTKcy2t2khUgINI6cgiJxna0i3qIDUWvVCq8WhV0VUag2F1D8iSIoNVMefLlPNXA\nmCJYv+VRvq6MAzCJeu6WiEKMXDkEreaoSnmqTJ4kTUIWLBiEqkl7PdNlZDiXk+FYQbzhwqDKk6op\nOCkhAt0QYk1+Oi0ig6uigiCQYo5hd/ExbPY8TPjnO/nt6ymUi9tOXVN30uw/cdy5kWSTXlk+w/E/\nStUsUs39SDS2K3fanrGWz8/y3tczqDwdOVZMiV2hRWosJqNE/aRItu7LZcu+HAA6tExkz6EC9h0q\noHXjOGKsJxc2Wx1KFT3nySyFlacwwggjjDDCOBtRq29og8FA//796dJFL4DWpEmTKgurnW4kems9\naWWER3Xkht6AZ3Icb2zLIXVFmSLggVtzccj+C4pWWunQLOffaKi0jryfKKkRpeoxNtvexOZOJ0Fr\nS6ZjJbJg4WLrw0TJDZEEE91jXqg5aakAkxiLRdJViHhDGy6LfaXaSXt5iIKMRUyi5ETIEypu1Uue\nQpiEqqcqbC+48uREIMVyLeRvpr65O00tJ1+cT6hCedLKkSdBlNDQkDSJCLFuteGYsXJzLGISx5xr\naakNBHeppymL3345ri0IggtJi+Ca5Lb0TGhOjMESqEkfvOTpaOlxGgOCHKQoq6STXs1tp15kD9Ls\nP3HUubwceVqBgERDcwVbYR/RK/u+nw1he7vTdPfHFql6mGJirAWjLOJw6vepbmIkkRYDy9YfZeOu\n41zeqQYmKSeA81F5ChfJDSOMMMII41xCrbvtvfPOOzRu3BhVVUlPT2f06NHcddddtTkMH7yOe7nl\nBQJXEZrqQghp8qJPjpONXThkX8Geks/oHD0eybOinuFYzr7Sr4Me3dh8A/VMemyy7NYnpUXudHJd\n23Fo+aSYLifWUFYV3CBaiRVPhTJSBpMYV/1OFRAlpXLMtZYMx0rqmS4N8SidGNSkSO6pCtur6KRY\nfqVbQ8Duuf9WKaEszOyk4FWeAoXteYiChzioooCkySEZVAiCQF1TDw6ULuSYcx1JHmt1ZH8VL8Ox\nnGRBRQBEtGqJE+jkCeCovVAnT9UpT0opkVITYuRm5Lq2ku3cgFmcuUdaAAAgAElEQVRMpMidTqKh\nfeUiw24HiEZ/ZdavSG7tw1bq4ujxEurEWYiL1p9HURSolxRJWkYRJoNErNVIrNVIXLSJ9CwbiltF\nlk6ft2ZZkdzzgDwJYfIURhhhhBHGuYdaJU/ffPMNv/32G1FRutJRUFDAoEGDzhh5ivCsgtsrbNcc\neQiWEMwfPMpTtKEpKdrlHHUsY7PtTWKkJtQ39yLDsRwQaB81plKOj4QZq1y2im0S45GFSIqUQ2Sw\nAoAU4+lP+jwRNI3oT07hNnYWzyVabkykVK/6gzyqiqrZkTCFFN548lbl3n4rGEao/qSm2ENovCvh\nJw0hsEW6vs3t2UXvSxNEZE2qMt+pPOoZL+VA6UIyHCtIUlqDaPAj+k61kOOuzdQVPU6IqhtCcKFM\nNulkJ9tVom8IQp7wkCfNo3q1ihjEmsJJbC/+EKuku+h5FwTKQ3M7KhtbCGVW5WcCR7P1XLdG9fyV\n1/oe8pQcb/GpgQnRZvIKHRSXKsRYT59a7lOezoewPTEcthdGGGGEEca5h1otT5SYmOgjTgAxMTFc\ncMEFtTkEP/hWPitUmdFCDd3zKguCSIuIO4mSUsl1beWA/TvWFf6XQvcBEgxtSTC0IUZu6vdfeeIE\nuqpglRpQqmaR5fybCLEe0fKZM9OoChFSHVpG3I2Kk6OOP0M7yEOWHGoBBjHEMEEfeTqZsL3KhhEV\nJ2s2tz55l4VTRZ68X6sAylP5nCdAEI1ImhxyrpVFSiRWbkW+sgvVXewLo/Miy7kaDTcmKcEzhNCI\nSazBggDkeybvwcL2fCGCHtXLKl9Ay8i7UbQS8pVdGIUYEg3t/I7RNA3cjsrGFmc4bC/zuE4U6yX6\nn2uDZCsNkq20alymykZa9LEWl55ec4vzyW3PGA7bO+exevUqFi5cwNKlv59UOxs2rOP66/uxalVo\nOb2PPz6aG2+8qsr99+3by+HDenHuTZs2kJ8fujHS1q2b6dmzC3v37ql23+uu6+v394YN6xg/fiwA\n2dnHeOKJhxk1ahhDh97Liy9OQlH037u8vFwmTnyKBx4YxNCh9zJp0gRsNr3Gntvt5r333uL++//N\nyJFDGT58MGvW/BWw/w8+eJdhw+5j1KhhjBgxxDfm6s65pKTY1+a8ebPZtm1rtecaDIsW/cAtt1zL\n6NHDGTlyKOPHP0lGxlGg7BmpTeTm5vDyy1WXX1m2bMlp6ftkvwthnBuo1Td0gwYNGDFiBD169EDT\nNFavXk1sbCxfffUVoNeBqk3Inkmuq4JAoDnyQjpe8yoagoAkGOkcPZ4idzqH7b+T6VwFQL0aqEdR\nUgPylZ0eU4f7TsqS/HQj0XARQEi5TzmubZjVQgyAotpINIaYV6Qqem7QSbnfCZUUIKUCmSrxFCY1\nnCryFKQ4r77NM1H0TBxlKRrVZSdODt0hKcXUg3zXTnA7EYyxZDhWYXOnA5DtXI+AhEVOBtJCDomT\nBJFo2UKeu1RXzoKpfb6cp7I8vhRTT+Lkljg1GxYxEbGiwYemAGol5aks56n23fY0TSMjp5hIs0xU\npP94jQaJXhVym6wR+j6200SedhRlYpEMlLqdGAUpqK38Pwlhw4hzH16ntSFDBtGrV58TauPIkcN8\n8cV82rW7OORjXn75DV544bkq91m2bAmtWrXmggsa8OOP3zFgwN3ExgYuwVARv/32Cw0bNuL333+l\nWbPm1exd+T3tfXe///47XHvtDb5r8/LLL7J69Sp69OjJpEnPcO21N/gKAM+f/zHTp/+XCRMm8ckn\ncygtLeHDD/USLenpaYwZM5KPPprvtwC9ceN69uzZ5XPo27BhHfPnz+WZZyZVe847d+7g779X06VL\nN+6++96QrktV6Nu3HyNGPAzAmjV/8dhjo5gz57NT5sZXE8THJ/D4408F/Twj4yiLF//C5Zdfccr7\nnjdvzgl/F8I4d1Cr5MnhcBATE8PWrfoKh9VqRVVV1q1bB9Q+eRIEAYMgoXgVAskEqgvNHqry5J0c\n6xMdUZCJkZtgjayPzX0Ip1pEorFDyOOJM7TkkGMxzSz9AxYePZtgEK0YBGu15KlQSWNT0eu0crci\nkUQEBOLkliH1oamuk8x3wmdUUR6KZ6XbgkYpAjaPmYEhhJpjofdJpVwrAM1nVe756okG/dWrKSG7\nCiYZO2HmSwSgmFy2F/sXma5j6IyoWFDRQ+JCpeBxhggOKiUoohFjEOIuiBKIRlD8g10tUh0sBAl1\n9VqqixXD9jznewZynnILHThdKg2So0JapIi0eMhTyaknT7nOEuYfWaMb2Ihgkv75+U5QtngVtio/\nd7Fo0Q+8+OIkRFFk/Pgnee65qUyZ8izZ2cew20u5776hGI1Gli5dwuOPj+PXX3/mk09mM2fOZ+Tk\nHOe558bz0kuvM2XKS7z44iS/tufNm02HDp1p06ZtlWNQVZVp06aQkXEURVG4//5hxMbG8u23C1i2\n7A8OHNjHn38u5cCB/fTo0RNFURgyZDgAjz76EKNGPUqTJs382lu6dAnPPTeVyZMnMmzYQyd8fWw2\nGzZbWeHPxx8fB0B6+kFsNpuPOAHceefdOBz6b+X33y9k7tzPfZ+lpjbk888XIlUIwbbZirDb7bjd\nbiRJokOHTnTo0Ik1a1b7znnKlGksWbKYpUuXoGka3bv34N57hzB9+jRKSkpITW3Ili2b6N27D126\ndGPatCkcPXrEdy27dOnKgAE3c8MNN7Ny5XJcLhfz5s2t8ry7dOlG+/YdWbZsCS6Xi/379zFs2ENM\nmjSBnJwcXC4X998/jEsu6cabb05n587tOJ0ObrzxVq677kZeeOE5LBYLaWkHKSws4KmnJhIVFcWE\nCWNp0KAhhw6l0apVGx57bCzZ2ceYOvV5XC4Xoijx1FMTABg/fiwffDC30tinT3+L6dOnsWPHdmbP\n/gBVVSkoyOfw4cNkZBxhyJAH+fHH78jKyuCll16nXr0UZs58m82bN6KqKrfeejt9+vTjhReeIyEh\nkV27dnLsWBbPPPM8a9euYe/e3Ywf/ySTJ0874ecmjLMftUqepk6tWkY9EzCIIi7PJFeQrYCG5sz3\nFSKtGt6wPf/JlySY6Bz9H1TN5TOPCAWJhg70iH0F8wmYOJwJREjJFCoHUTUloNGCopWy1fYOGgqa\nz9ZdIM4QGnlCdZ1cyB66NbvmreFlikMwxfpWuiO95MljD33qwva8z0NVVuX6syVIBn2vEImippQi\nFKXRwnATkE6+dhBZsHCR9SFkwQIIREopaI6t3gMAcBfuQzQlIJiCr7zGGSM4WCpQIBqJqGoQssVP\neap2zMGK+YpnLucp47ie71Qvscoz9cHqIU+nI2xvRe4+NCDHWYxZNhApnjkH0tqEd/EqrDydPH4+\ntp2thUcDfiYdEHG7a14Evm10ClfXaV3tfrfffheLFv3A5MnTyMvL45JLunH11ddy9OgRJkwYx9tv\nf8DMmW8DsHXrJuLi4ikpKWbLlk107NgZkymweVCoasjixT+TmJjEuHETKCjIZ/ToB5kz51O6dr2U\n3r370r17D/76ayWPPTaO+PgERo0aypAhwykutlFYWOhHnADWrv2bRo0ac/HF7YmNjWHbtq3VErhg\nGDjwHp566jEWLfqRLl26cuWVV1O//gWkpR2kefMWfvsKgoDZbKa42IbJZMJi8Tf6qUicALp2vZQF\nC77gjjtuolu3S7nsssvp1u1SunTpSosWLRkzZix16iQjCALvvPMhgiBw2203cvvtd3HXXYM4cGAf\n119/E1u2bAJ0xc1kMjNjxkyOHz/OqFFD+fTTr3G73TRu3IS77hrExIlPs2rVKtq1u6TKc2/Z8kLS\n0g6SklIfQYD9+/eSn1/AjBkzKS62sWrVCpxOJykpKYwa9SgOh4M77riJ6667EQC3W+W1195mxYr/\n8dFH7zN69Bj27dvL1KmvkJiYxAMP3MPevXv48stPue66m7jiir4sXfo7H374HvffP8z3Gi4/9mef\n/Q/r16/hzjv/zddff8m99w5h1qyZFBUV8corbzBz5tv8/POPvPLKG3zwwbssX/4nLVq0Iisrgxkz\nZuJyuRg8eCA9e/YCQFEUXn31TRYuXMAvv/zEqFFjmD9/bpg4nQeoVfK0cOFC5syZQ1FRkZ4H4cHv\nv5+5GFGDIOFSnbryLkcgiAY958lVDNUVy/WdQ+WVa0kwhWbHXQ6CIGAWzg3iBBAh1qWAfdjV4wEN\nD446llOqHqOe8TI0QVfzjMRgEat3lgNAUxDEEIrpVgVBANWp1/CKaoRc7/98YXuRaBwHin3K06kK\n26vCbU+rYL/uJUwhhq6pBXtx52wgLqYlKuAUXVwYOZh4g/8kxy2U5ROpjnzcmSt85x8McR4ziHzR\nSFUWIIJkRnMWhLjAQPBivmfQbS8zR893qpsQGnmymGUE4dSH7dkUB+sK9JBLFY0SxUmCOYhN/D8Q\nBlEK5zz9gxAVFcWOHdv49tuvEUWRwsJCTCYTRqMRh8NOZmYm//d/vdm2bQtbtmzi//7v5MOmtmzZ\nzJYtG9m8eSOapuFyOX15ReUXsDRNIzo6mgYNUtmzZxdpaQfp3btyeNXixT/Tt+9VAPTtexWLF/9c\nY/LkVbPbtGnLl19+x5o1q/nrrxU88MA9PPfcCwiCgKoGJrSCIKAoZd+JhQu/4vffF2OzFTFgwN1c\ndVVZGQiDwcD06W+xa9dO1qz5ixkzprNkyWKefnoimqb55lkmk4mHHnoASZIoLMynsLAwYN87d+6g\nQwe97mNiYiJGo8m3b7t27QGoUyeZoqKigMeXR0lJCWK5aI6GDRtTWlrC5MkT6dmzl+8aFxQU8OCD\ng5FlAwUFZTlaXbro5Kxt24t49903AWjQIJXExCTftU1PT2PXrp0MHz4KgI4dOzN79oeVxuIde1JS\nHWw2G5GR/oZIF17YBoCEhETfmOPi4ikqKmTr1k1s27aN0aOH+65nTs5xAC6+uOya7NixzdNagEXT\nMP5xqFXy9PbbbzN58mTq1g3NWaw2YBAlShUNBBDkCN9kVnOXIlCdsUGZYcT5CK+9dok7kwipLnmu\nXdjVHJ99ubfmT7OI/uTn6aFlsXKzkMKkNE0LWY2pEuXujVcB8U7WIgU3aBI2Rd8un6r7GJLy5CFq\n3udNrWhbEhiaqo9VLTkCQCPrLRiNF1XesRwx0UozPf+s6CvpjziDTlTzqYZEiuWK20rVqySa29Nv\nxZwnQQTEWjeMcLtVjuWWEms1YjGF9hMoCgKRFsMpV562FB5B0VRiZDMFnvtzPjjteaEvXoXJ08ni\n6jqtg6pESUlRZGdXP9k9FVi8+GeKiop4550PKSjIZ8iQewB98rp27RoiIyNp0+YiVq78H7t37+LB\nB0fXqH2bzYbZbEaWZVRVRZJkDAYDgwYN9guBqwpXX30tS5b8RlZWJkOH+ofkOZ1Oli//k927d/L1\n11/gcrmw2Ww88sjjQdurWKsyPz+PhAS9lqLD4cBkMnHZZf/HZZf9H23aXMRvv/3CwIH38P7771Zq\na9eunbRs2QpNU8nPzyc2NpabburPTTf1Z9asmRQXF/vtr6oqqqrSsmUrWrZsRf/+A7j55n/5LU5n\nZmby2WfzmTNnPiaTmUGD7gh6LoIg+B2rKC4fmZBq+Lu0c+d2rrzyaoqKdPJlMpmYOXM2W7Zs4qef\nfmDlyv9xzTXXsX79Wt566wNEUaRfv8v9zg30NWrvnKG8gqqqKoLgfd3qY9ZD9wIsZpcbuxYgF7m8\nqlf+35qmYTAYue66GwIqoRX3DeP8Qa3O+ps0acIll1xCamqq339nEgZB8rntCXIEgrdmjhJCWFIV\nytP5AItHbSpxZ6FpKttsM9le/AFZjr8pUtL1gr+GdhjFaJ8yFR+qMYJXjTjpukvl7o1nku5Tnjz/\nL/WoPqdeeQrwY6pWdNurmfLkO96luzLJchClspwNuFqSpW/zhs8FQZxnLPla1c9zjcccLGwPdJJX\ny+TpeIEdt6pRN8SQPS8iLQZKHe4TCoEKhgyHPrHoENPAt+18qPHkhVEMh+39E+DN7ywoyKdevRQA\nli5dguJxj2zfviNffvkpF17YhqZNm7F9+1YfCfJvp+p+Xn31v/z551I0TSMt7SCpqQ1p3boNf/65\nFNAd7N577y1An3C73fqzJYqiT43q1q0HGzeux2YrqrSQu3z5n3Tq1IU5cz5j1qxP+PjjL0hNbcT6\n9WuDjqldu/YsXvwzoIdxLVr0I92766ZY99wzgLS0g759s7OPkZJSn9TUhiQnJ/PNN1/5Pvv880/4\n8stPAbjlltt4441XfOMvKSlm+/atlYjahx++x0cfve/7Oy8vl4SEBARB8J1zQUE+8fHxmExmdu3a\nSVZWJi6X0+/6eHHhhW1Yv34NAFlZmQiCiNUapGxFBZS/d6tWreDQoTQuu6ws0mHPnl38+usiLrro\nYh57bCxpaQcpKCggOTkZURRZvnwZqur23adNmzYCeqhno0ZNADh69DC5uTmoqsr27dto3LgprVq1\nYd06fcwbNqyjZcvqQ03LPw/VoXXrNqxY8SeapuFwOHjttZeq3F9VwyTqfECtLnEOGDCAwYMHc/HF\nF/sx9pEjR9bmMPxgECV8XyE5oizZP6ScDg0QzmpXvNOJSNFDntQscpXtODTdpXBH8Wyssrfmj65C\nmaUEVHJ9Ln3VQvWGt5065clLyLwr3RGC5icOnfKcp0CGEUGUJ9whEpGKIW5ykAK45WzAfcqTWjV5\nivUUf82rhjzVNNRQCxa25xmnVsthe16L8lBD9rywWmSygOJShega1npyqgp/56VxSVxDjOXy+DId\nhciCSAtrHZbm6BbD5vPAptyLcM7TPwPNm7dk6NB7ef75Fxk79lG2bdvCtdfeQFJSHWbP/oA77hjI\n5s0bGTbsIWRZprS01OfCtmrVcubP/9gXgvXVV5/z6qtvBjSMGDx4KJMnT+TLLz/l0ksvo27deiQl\n1WH9+nU8+OBgVFVj8OChAFx8cQdee+1lIiIiad++IxMmjOPFF1+hUaPGNGrUmJYtKy/k/f77L76c\nGy+uvfZ6fvvtFxRF4ejRw9x0k7+x1SOPPMErr7zId999g6Io9OlzJZdc0g2AZ5+dwksvveCbI9Sr\nl8Jjj431fPYCr776X77//hsslgiaNWvO2LHjAbjjjoF8/vknPPDAICIiInE4HPTpc2WlsQ0aNJhX\nX/0vw4cPxmQyAxrjx+suhOXP2Wy2MGLEENq1a8+NN97Cq6/+l9GjH+Pdd2eQlFRm9NOnz5Vs2LCW\n0aOHoygKTz75tOeTsndCsOnOkiWL2bVrB8XFxcTHxzNlij/JqFevPu+++xbffvs1kiRx112D6NSp\nM/PmzWbUqGH07NmLSy/tySuvvAiA0+ngyScfJTs7iwkTdDORBg0a8t57b3HgwH7atbuYRo0ac//9\nw3jxxef5/vuFGAwGxo17xkfag429YcPG7NmzizffnF4tOWzbth0dOnRm2LD7AJ3YVgXvd2HmzNlV\n7hfGuQ1Bq0Wt8ZprrqF9+/YkJ/vnvDzyyCO1NYRK4Qsfpa9iX8lx/iPkYW5yC5rLhnL4V8T4dsiJ\n7atsy5X+E5ojF2Pzu0/nkM9auDUnS/OGEye3wijGkOVczQWmvhx2/A5omIQ4Lo39L6Igo2StRi3Y\nhdzwBsQqTAu80JyFuA4uRIxuhlz30hMeo+vwr2glHkdAQxTGxjfzy7Ed/C93L/8SivlJK8svGdPk\nCuKNNcs3CRQSozkLcB38FjGmBXJytwrjWYxWkoGh+d0IgohalI6SsRQhoj5y/SuqJeKuo8vQbGm+\nvw2N+yMYKpMAb7tiTHPUAk+tEkHC2Hxg0LaV4kwmHfqbZNnIiGZXB98vex1q3jbkBtcgWpKqHC+A\ncmw1av4u5NTrEM3xfp85DywE1Ymx6e3VtnOq8MuqdLLzSrn9ymYYDaET5s17jrNpTw59ulxASlLN\nnpP1BYf4OmMjNyRfxCVxjQBQNY3nd/9EktHK4NTuTNnzCwCXxTcNKVH/n4D301aQXprL8y2vO28X\noU43TmfY3nfffUNGxtGTcqSrbTgcDkaOHMrrr79NRETo3+PS0lI+//wT7r13yGkc3bmB0x0K+sIL\nz9G7dx+6d7/Mty0zM8PnoBfGPw+1GV4cKpKSgqfu1OoSZ1JS0lnnuOcL1Wp8K4IhsiwnJRTlSdOV\np/MVkmDELCZQ5E5DVRQixGRaRNxJQ8vVuFQbZjG+zIXPNzEKLeRJ8xXIPdkQpnLKky9sT7/HMagI\n6OJTnCGCKPkkzSl8CK486WMQfEYLgrUBQkQKWskR1LxtSPHVJCZXUp6CjNnzXKvFR8od69aty4Mo\nG6LmJBaVfHfVSsCJhu0FUp4EUUZTSkJr5xTApagczy8lPsZcI+IE5ezKTyDvye5RFo87bb5tOc5i\nFE0l2RSNRTISIRkocbvOiwK5XhhECQ1wa+qpU37DqBVs3bqF+fPn8sQTT1e/81mCbdu28tJLLzBw\n4KAaESfQTQJCzasK4/QgvL4SxtmCWn1L9+zZk6+//poOHTr4xTo3aNCgiqNOL7yFGhUviZL0MCgt\nlJwn1PPWLMKLaLkJx5x6vHF9Uy+PY2A8ZtFfYfBdp1CFTm8ezMlOJMv/2nrD9jzkKQ6V+6LiMca3\nIcUcg3iqfpl9z0QQw4hyk0RBEJDrXobr4De4C3ZXT57K5wdJpuBud97r5iEmgjkJzZ6tE5kg11Rz\n6+QpR1NxqErwSfwJhu0FzXnSFDRNqxXlITuvFFWrecgelBXKLSpx1vhYh+e+HXeWJXxnefKd6pqi\nAUgwWClx52E+j3KeyhfKlaszKgnjrELbthfx2WffnOlh1Aht2rRl9uz5J3TsBRecuXnK+Yann55Y\naVvduvV4//2w6hTG2YFaJU+ffvpppW2CIJxRq3LZQ5p8jk+iQZ/8hmwYcX4vhbSJfIDG5usRBImI\nKi3Iq1BjAsEzMT/pnKcKypOmaSgeFx8ZSDVFIFtCqzofOqo4V83tU4V8e8tmBGMMmiO3ehJRjjwJ\nUpB8J73Rsn+akxDMCWj2bDS3Q1dYA7btxOwhfM4QyJMWsvLkBEEKrHiVL5Rbg5poJ4qaWpSXR6xV\nJ38FRTUnT07PfcspR54yPeQp2ayHBiQYIzlkz8N0Prntlfv9tYS5UxhhhBFGGOcAavUtvWTJktrs\nLiQYy618gscSUwqtCKiGdt4rT6IgY5UvqH7HqtSYACgL2zuFyhMaaKovbE9GCxrCdnJ9Bj9XTVUC\nOwjKEWA/ritDVYUPaopOXkQjgjmhiiGU9SFGNy0LQ63CNEJzO5C9lq9BapDobYeuPGmahuYq0s8v\nEMoZW5x8iGb1yMwpQRAgKa4K4hkEJqOExSSRX1S18UYgOD2LM3muEhRNRRbESspToiffLjIE+/d/\nCrzKkzNsGhFGGGGEEcY5glqd+R85coTRo0fz73//G4Avv/ySgwcP1uYQKqFs5bNssijIFlDs1fv2\nayrnu/IUOmqoPHlze052Ql1xgq8pvnttQDsFVugB4CFsWsCcJxeCWHlyLMjecNHiSp+Vh+YhGYZG\nNyAld69iDOXIU1QjEHXVxFdzKRDcTt9qilLVZLYm5MmZD6oT0VIn4OdCLRbKdbrc5BbYSYy1YJBP\n7Kcvxmqi2K7gdNVssu8N21PRyPOoT1mOIiIkI1ZPOGPXuEb0b9yBZpHVm3D8U2CsqPyHEUYYYYQR\nxlmOWiVPEyZM4MYbb/SRkkaNGjFhwoTaHEIleAujuspP3iQzoIJaXXiOFs5gDBG+3Jyahu2dbChX\nRbKgusopT5y8shUQgc9VL/zrDFhYVpA9oXTVhYtqbhANCKIheL4TeJ5hASGiHoJkLMs3qqrWk1pO\neQqBPIUStud1OhQsQUI6y9WjOt3Iyi1F48RC9ryIjfKE7tlqFrrnLHd+3rwnm+IgRjb7wjQtkpEr\nL7gQ6TxSs+UKyn8YYYQRRhhhnO2o1be0y+WiT58+vslCly5darP7gAiqPEEIE1mVWr6E5y58JDNU\nw4hTE7anKRXJk4JLcyPguXOnJWwvyLlW5SDoCWur1nlOVRBCUMsEUcLQ7E7k+n30v33kKfikX3M7\ndTWOqpWAmoTtqaV6gV4xIgh5Kh+2d5pxNFt3ujs58qQT33xbzUL3yoel5TiLUTUNp+Y+r5z1AsEQ\nVp7OC2zYsI4BA25h6dLA+c0Oh51nnnmKkSOHMmzYfaxcubzK9pYs+Y3hwwczatQwhgwZxG+//eL3\n+bRpU7jvvrt8f69Zs5pRo4YxatQwevXqxujRwxk9ejg7dmxj1KhhHDiw3+/4667rC8CiRT9wyy3X\nMnr0cEaNGsYDD9zDwoULAN06+/LLu7J//17fcYsW/cCiRT/4tZWZmUG/fpf7+hw1ahijRw+nqCiw\nLXNWViY7dmyr8vxPFiUlxUyYMI6RI4fy4IP389RTj1NcrP8+BrtHXuzbt5e0NL1cxrPP/gens+Y5\noOXx/vvvoCgK27ZtZfnyPwGYP38uDzxwD8OG3ceqVSv89t+/fy+9e3cnMzMzpPb37dvL4cOHKm2/\n7bYb+eSTOX7b3nrrdW677YYq2yt/fQJdq/LPzMiRQxkzZhR5eXkhjdWL1atX+Z6zMM5O1Pqbu7Cw\n0Eee9uzZg8NR8/yBU4mKOU9AmeOe2151UJ6mgRhWnkJDzdz2TplVeYXcNU1TUFQVGQFBOAXKVkAE\nUdk8SqYQUHnykqfgYXuapnlynkLLrPfL55K8YXtVKU9OZEEADZSqFMIQyZOmabryJEeCHLgQYW2F\n7RWXuth7uJAIs3xC+U5eeE0j8mtoGuGvPNl8f5/v5MlbEFitvXKDYZwBbNq0gVtvvZ1evfoE/Hz5\n8v/RqlVr7rrr32RmZvLooyO49NLLAu7rcrl4++3XmTfvS8xmMwUF+Tz++Gh69eqDLMsoisLKlcsx\nGo2kp6eRmtqQLl260qVLVwCuu+5K3njj3WpGXPZe79u3H5Y/EHkAACAASURBVCNGPOzr+777BtKt\nWw8AGjVqzLvvzmDatNeqbK1hw4Yh9Klj/fq1lJSUcOGFbULa/0Tw+efzad26LXfeqdeonDt3Fr/+\n+jM339yfefPmBL1PAMuWLaFr1060aRPPs89OOemxOJ1OZFlmz56dXHppTzIyjrJkyW+8995HFBUV\n8dBDQ+jW7VLfvPGtt96okfPhsmVLaNWqdaVjEhISWL58GQMH3uPbtmvXDqpLxfBeH5fLxeeffxLw\nWpV/ZmbP/oAff/yWu+++N+QxewtIh3H2olbf3A899BC333472dnZXH/99eTl5fHSSy9Vf+BpRCW3\nPcrnn1RnGhE2jAgZNazz5FUiTt5tzwPRoE/2VQVFc+skAU6v8lRhQqh5w0AD5jx51JCqlCcvwT+B\nPC0hhLA9ze3QE/i1apSAEMP2fPlO1guCOwgKtaM8bd6Tg6pqtG+RiHQSCx4xVv3eFdRUeVLdGAQR\nRVP9yJPxPCdPF0fXR0OjUURw85Mwzk5kZWUyadIzSJKE2+1mwoTnSUqqw7RpU8jIOIqiKNx//zBi\nY2P58cfvkGWZhIRE4uLimDnzLWTZQJ06dXjqqYn06XOlX7t16tQF9FV8q9VKz569fJ87HA7sdjt2\nux2z2UxMTKyfhfXq1ato0aIVzZu3YPHin7n//mGn7JwNBgNNmzbl6NHDpKTUp2XLC3E47Kxfv5aO\nHTvXuL01a/5i5sx3MJvNxMfH8+ijY5k1ayayLJOcXJfPP/+EMWPG0rhxExYs+ILCwgIGDRrMlCnP\nkp19DLu9lMGDh9K9+2WMGjWMJk2a4narrFnzF3PmfIbZbGbLlk189tk8pkwpm2vZbEUoStlv7qBB\ngwGYP/9j9u7dzfjxT/Lcc1Mr9ZOcXJdvv13AihXLePzxp3nmmaf4+OMvKCoqZOrU53G5XIiixFNP\n6ekYkydPJCWlPvv27aVFi5aMHTve1+exY1l89tk8Nm/exBtvvMKWLZs4dCidJk2a0a3bpUiSRGxs\nLHXrpnDgwH6aNGnKjz9+R+fOl7BqVWVl0u12Bx3vsmV/EB8fT6tWZcXHDQYDkZGRpKUdpGHDRmze\nvJGGDRuTkZEBQHb2Mb9zGjduPH/88bvv+sTHJ7Bv3z5effW/jBkz1m8s5V/9ubk5tGlzEQC//voz\nCxZ8jiRJNG7chCeeeDrg92j9+rXs37+PW2+9vcprGMaZQ62+uVu3bs3ChQvZvXs3RqORxo0bc+zY\nsdocQiUYAihPPgvo6hz3NBUhbBgRImpa5+nUhO2JcW1Q87YhRjVCLdjjCdtTkb237XQYRhAkbM8d\nnDyFFLZ3MrWvQlGe3E5k0QzqqTGMqDbfCcC7UOHIA+vpqaOSW2hn3+ECYqxGGtePPqm2jAaJCLNc\nY+XJoSpYPIpjvqvUZyBxvitPkbKJHvFNz/QwznmY132HMX1jwM9ckki0O8RFq3JwprbH3il4CNPS\npb/TpUtX7rnnfvbs2UVOznE2blxPYmIS48ZNoKAgn9GjH2TOnE+55prriI2N44or+jJwYH9ef/0d\nEhOTmD59GosX/8w111wHwIMPDiY7O5tp06YD+LaXh9Vq5YYbbmbAgJvp1u1SunbtzhVXXInJpP/G\nLV78M337XkWzZs0ZP/7JU0qecnNz2LFjO48+Oha7XZ8fDP1/9s48Por6/v/Pmdkr2WyyCbnDGU4R\nL5RDBQVBbfHW1q+2lVrxAqoVtaD1qlpUPFppq2CLd7Vaf9arVQTxQKsop6BCuM+Q+9j7nPn9Mbub\nLNkkEDa7QT/Px8OHZGfm83nv7Ozs5z3v9/v1vnYm999/NwsXPtPuce397L3++r+44YZZHHvs8Sxf\n/jGapsbO1bhxp/Hqqy+1OcbpdDJ69Fh+9KNzqKzcx1133cbJJ+tRuvLyQVxwwcU8+eR8PvvsEyZP\nPptPP/2Es876cdwYF198KbNmzWTFis8ZPXoskyadxaBBg/nZz67g5Zdf4A9/eJjGxsY28zz99IuM\nGXMKF154XiQypv/WLVq0kHPPvZAzzpjMxx8v4+mnn2LatOvYvLmC+++fh91u56KLpuB2u7Ba9SyE\nwsIirrjiVyxe/C6XX/4LnnxyPjNm/IYXX3wWu72lfYjdbqe+vo78/Hzef/9dHn/8ST7//NM258Xh\ncLRr78SJk+IcpygTJkxiyZL3uOaa6SxbtoQJE85gxYrPE76nZ5/9O7/73T2x81NVtZ+NG79t4zgB\nLFu2hIqKjTQ1NWK1Wpk58yZAT0/94x//gtWaxa9/fS3bt29j5coVbb5H0PIMtqNzKEgfKQubqKrK\nzJkzMZvNjBgxgiFDhiBJEjNmzEiVCQlJqPYUkYo+uMiTcJ4Oio4U6BKQrLQ9JX8kxkE/RzLnRubX\nI0/GyE2/O6TK9SiLdGhpe7JBd6o6uuYiqW0HU/PUdgKDHiVtR6o8KmZhjJyPDqXKJUkfrxPnKVbv\nlFHc7j6ytTdICmHHts7VLbuAqml8+U01GnDSUYVJaYRcXpZNr5wO5OQTEO2blaEY8akh/JH7zQ/d\neRIcuYwaNZbFi//LE0/Mx+8PMHz4CDZsWM+nn37MjTdez513ziEYDMRFOPS0fZn8fF1RcuTIk9i8\nuSK2fcGCZ3jwwce4996OhaSuvXYGzz33T0444UQWL/4v06ZdQSAQwOfzsWrVl4wffzrl5QMxGk1s\n2VLR4ViJaB0p/+CDJdx44/Vcd92vuO66XzFr1m/jFvdlZb0ZOnQYy5YtaXe83bt3xeqdbrjhOh59\n9EEAJk6czCOPPMCLLz7H4MFDyM3Na3eMKFlZWWzc+C3Tp09j7tzf43A4YtuGD9dT/c4++xyWLVsK\n6PVmp5wyPm6MsrLe/POf/+b6639NMBjkpptm8O6770S26vdhm83W7jyt0TSNTZs2csIJJwL6Z7pl\ny2YAevfuTW5uLpIkUVBQgMvlijt2x47t9O8/oJN3rNuzYMFfuPrq65HlxK1ADtbeKJIkMX786Sxf\n/hGqqrJu3RqOP35kbPuB76nlOu38d2ry5LP4858X8sILr3LhhT/h4YfnxmycM+dmfv3ra9m1aycO\nRzOjRo3lvffiv0et6ewcCtJDSn65//Of//CXv/yFXbt2MXx4i/cvSRLjxiXOa04VidSeDj7ypCEE\nIw6SQ+zz1BJlOTznSV/oK3HpYUFVJSNqR6IoUDKQZA58r1q4Y4dQMmR2LFV+GJEnSZJANrcfeVKD\ngIZRNgL+ztXPZGOHaXuapqF5q/WImrH9p2SSYkLO6ovq3IHmq0VqR9K8q2za0Uhdk4/+JTZKC9pp\nDnyInDD00KXEA2oYu1HBICnUqE78kXP3Q0/bEyQH34nntxslKiiwUVubWJzgcCgvH8hzz/0zknr2\nBFOmnIfJZGTq1KuYNOmshMdIkhT3AE1PiZKoqNhEbm4uhYVFDB48hHA4TFNTU5yT0hq/309xcTEX\nXHAxF1xwcUz8obq6mnBYZebMq9E0DYejmQ8+WMLgwUPbfR92ey4uV8v5aWpqolev/Njf0foVv9/H\ntGlTE4515ZVXc/PNv+aSSy5FSdDkur2ap7PPnsKYMaewfPlH3Hbbzdx//0NtzleUcFi//y9duhin\n08mCBU/T3NzE1Ve31OwYDPpvy8CBg2hoqGfTpu8iTmT8b47f78dsNsdqwcaNO41nn/07U6acF9un\no3kOtFG3MyI2FPlMARSlpT5X0+IjcMuWLWXRogXY7XZeeOFpGhubCASCDB06jN27d8X2q62tJT+/\ngNWrV7Jjx3Y0TWPnzu387ne/Zf78BdhstkOytzVWaxYlJaWRGrBj4uxt7z0dKhMmnMGiRQsJhUL8\n8Y8P8/zzr5Cbm8vs2bMA/Xv0/PPx36PWdHQOBekjJSv/c889l/fff5+ZM2eycePG2H/fffcdf/vb\n3wDYtGlTKkxpQ0K1p1jkqbO6BlVEng6aQ+zzpAZBUjqW4z6U2VupuoW0MAYiSomGrosHdDJj+5Gn\n9hw2QyaowXadEk07jLQ9InVP7fV5itgWjTx1mLYHutx6R85ToAnCfuSM4vbrnSLI2Xraltq8tcP9\nDpUdlQ5Wb6rFbFI4aXhynbJDQdU0gloYk2zAIut6ho6ICqSIPAmOVJYtW8L27VsZN+50rr76eioq\nNjF8+AiWL/8YgMbGBp566om4Y2w2G5IkU1OjR6XXrVvD0KHD+frrNbzyyj8APTXO5/O26zitWvUV\ns2ffFIto+f1+XC4nRUUlfPDBYu6++z6eeeYlnn32ZRYseJoPP/zggBHiV58nnjiK999/N/b3O++8\nydixp7SZ12y2cOWV05g//7E223Jz8xg/fgJvvvnvhDa3t+B97rlFKIrC+edfxBlnnMnOnTuRZZlw\nWL//Wq3WWBrX+vVfA+BwNFNSUgrAxx9/SCiU+D48ceIkHntsXpuUPYBZs2ayatVXsb9ramooLS2L\n2Kob29zclHAeSZJaRRP1fY866mhWr14J6JGuoUOHd/i+ASZNOpMJEyaxYMEzXHnlNcye/TtuuulW\nRo48iS+++B+hUIi6ulrq6moZMKCcf/3rLRYufIannnqWIUOG8eCDj8Ycp4O3t4Xo+5w4cTL/+Mez\nTJwYFX7o+D2pqtbhuAe+72+//YY+ffrh8bgxGAzk5uZSXV1FRcVGgsFAwu9Re2MJeg4p/eW+4YYb\n2t32wAMP8MILL7S7vbtIVPMUS3HqqKEoRK5q4TwdFIcYedK0UHLFHGL9hAKENBUDYTBZO13Yd30+\nue1dLyoYkSBtTzcxUz87IS+YEkSnopGnrtZpKWYINKFpahunVIvUYxkUfd7OpKMlyYCmtp8+oHn0\nxZHUnkR567Eyi5FMOS2CGkkgEAzz+foqjAaZyaN6k2FOn5MSPZcmyYAl8lQ65jwleEotEBwJ9OnT\nl0ceeZDMTCuKInPTTbdSVtaH1atXMn36VaiqxlVXXdvmuNmzf8fvf38HBoOBsrLeTJ58FsFgk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jZYwaNYZf/nIaW7ZUUF9fx7p1a8jPL+C22+6iubmJG2+czvPP/5MxY05h4sTJDBs2nJ///CfM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\nRpqLygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f790eed4b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 5))\n",
    "\n",
    "colors = seaborn.color_palette(\"Set2\", len(stations))\n",
    "for k, station in enumerate(stations):\n",
    "    stadatadf = datacsv_df[datacsv_df.station == station]\n",
    "    stadatadf = stadatadf.set_index('date_time')\n",
    "    stamddf = stations_df.ix[station]\n",
    "    label = \"%s: %s\" % (station, stamddf.longname)\n",
    "    stadatadf[sos_name].plot(ax=ax, label=label, color=colors[k])\n",
    "ax.legend(bbox_to_anchor=(1, 1))\n",
    "ax.set_ylabel(sos_name + ' (C)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sefo3` station can predict the future!\n",
    "\n",
    "--------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Postscript: The collector `collect` method\n",
    "It hasn't been customized for NdbcSos, so it is not very useful yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NDBC does not support filtering by > 1 station at a time... returning all stations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(list,\n",
       " 11,\n",
       " <pyoos.parsers.ioos.one.get_observation.OmObservation at 0x7f79012c1090>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The argument offerings=['urn:ioos:network:noaa.nws.ndbc:all'] is not needed.\n",
    "# It is passed by default.\n",
    "collector_ndbc_collect = collector_ndbc.collect(responseFormat='text/xml;subtype=\"om/1.0.0\"')\n",
    "type(collector_ndbc_collect), len(collector_ndbc_collect), collector_ndbc_collect[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed sea_water_temperature at urn:ioos:station:wmo:46029, COL RIVER BAR - 78NM South Southwest of Aberdeen, WA\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:46041, CAPE ELIZABETH- 45NM Northwest of Aberdeen, WA\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:46050, STONEWALL BANKS - 20NM West of Newport, OR\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:46089, Tillamook, OR\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:46211, Grays Harbor, WA (036)\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:46229, UMPQUA OFFSHORE, OR (139)\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:46243, Clatsop Spit, OR - 162\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:46248, Astoria Canyon, OR  (179)\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:jtaw1, Jetty A, USCG Station Disappointment\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:sefo3, SATURN Estuary Station #04 at Mott Basin\n",
      "Observed sea_water_temperature at urn:ioos:station:wmo:seto3, SATURN Estuary Station #03\n"
     ]
    }
   ],
   "source": [
    "for s in collector_ndbc_collect:\n",
    "    print s.description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_root': <Element {http://www.opengis.net/om/1.0}Observation at 0x7f790131e908>,\n",
       " 'bbox': <shapely.geometry.polygon.Polygon at 0x7f79012c1410>,\n",
       " 'bbox_srs': urn:ogc:def:crs:EPSG::4326,\n",
       " 'begin_position': datetime.datetime(2015, 12, 19, 0, 51, tzinfo=tzutc()),\n",
       " 'description': 'Observed sea_water_temperature at urn:ioos:station:wmo:46029, COL RIVER BAR - 78NM South Southwest of Aberdeen, WA',\n",
       " 'end_position': datetime.datetime(2015, 12, 24, 0, 2, tzinfo=tzutc()),\n",
       " 'feature': None,\n",
       " 'feature_type': 'timeSeries',\n",
       " 'location': {'urn:ioos:station:wmo:46029': <shapely.geometry.point.Point at 0x7f79012c1550>},\n",
       " 'location_srs': urn:ogc:def:crs:EPSG::4326,\n",
       " 'observedProperties': ['http://mmisw.org/ont/cf/parameter/sea_water_temperature'],\n",
       " 'procedures': ['urn:ioos:station:wmo:46029'],\n",
       " 'results': <Element {http://www.opengis.net/om/1.0}result at 0x7f790f9c8320>}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The feature object property is not populated, \n",
    "# so the time series data are not parsed and made available\n",
    "ncc=collector_ndbc_collect[0]\n",
    "vars(ncc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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