{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python-AWIPS Tutorial Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "\n",
    "# Objectives\n",
    "\n",
    "* Retrieve an Upper Air vertical profile from EDEX\n",
    "* Plot a Skew-T/Log-P chart with [Matplotlib](https://matplotlib.org/) and [MetPy](https://unidata.github.io/MetPy/latest/index.html)\n",
    "* Understand the **bufrua** plugin returns separate objects for parameters at *mandatory levels* and at *significant temperature levels*\n",
    "    * *Significant temperature levels* are used to plot the pressure, temperature and dewpoint lines\n",
    "    * *Mandatory levels* are used to plot the wind profile\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "[1 Imports](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#imports)<br>  \n",
    "[2 EDEX Connection](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#edex-connection)<br>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[2.1 Initial EDEX Connection](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#initial-edex-connection)<br>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[2.2 Setting Additional Request Parameters](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#setting-additional-request-parameters)<br>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[2.3 Available Location Names](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#available-location-names)<br>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[2.4 Setting the Location Name](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#setting-the-location-name)<br>  \n",
    "[3 Filtering by Time](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Sounding.html#filtering-by-time)<br>  \n",
    "[4 Get the Data!](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#get-the-data)<br>  \n",
    "[5 Use the Data!](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#use-the-data)<br>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[5.1 Prepare Data Objects](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#prepare-data-objects)<br>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[5.2 Convert Units](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#convert-units)<br>  \n",
    "[6 Plot the Data!](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#plot-the-data)<br>  \n",
    "[7 See Also](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#see-also)<br>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[7.1 Related Notebooks](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#related-notebooks)<br>  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[7.2 Additional Documentation](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#additional-documentation)<br>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Imports\n",
    "\n",
    "The imports below are used throughout the notebook.  Note the first import is coming directly from python-awips and allows us to connect to an EDEX server.  The subsequent imports are for data manipulation and visualization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from awips.dataaccess import DataAccessLayer\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import numpy as np\n",
    "from metpy.calc import wind_components, lcl, parcel_profile\n",
    "from metpy.plots import SkewT, Hodograph\n",
    "from metpy.units import units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 EDEX Connection\n",
    "\n",
    "### 2.1 Initial EDEX Connection\n",
    "\n",
    "First we establish a connection to Unidata's public EDEX server.  With that connection made, we can create a [new data request object](http://unidata.github.io/python-awips/api/IDataRequest.html) and set the data type to ***bufrua***, and define additional parameters and an identifier on the request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the edex server\n",
    "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
    "request = DataAccessLayer.newDataRequest()\n",
    "\n",
    "# Set data type\n",
    "request.setDatatype(\"bufrua\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Setting Additional Request Parameters\n",
    "\n",
    "Here we populate arrays of all the parameters that will be necessary for plotting the Skew-T. The `MAN_PARAMS` are the *mandatory levels* and the `SIGT_PARAMS` are the *significant temperature* parameters that were both mentioned in the [objectives section](#Objectives) above. \n",
    "\n",
    "Also request the station name and ID to use in the figure title later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAN_PARAMS = set(['prMan', 'tpMan', 'tdMan', 'wdMan', 'wsMan'])\n",
    "SIGT_PARAMS = set(['prSigT', 'tpSigT', 'tdSigT'])\n",
    "request.setParameters(\"staElev\", \"staName\")\n",
    "request.getParameters().extend(MAN_PARAMS)\n",
    "request.getParameters().extend(SIGT_PARAMS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Available Location Names\n",
    "When working with a new data type, it is often useful to investigate all available options for a particular setting.  Shown below is how to see all available location names for a data request with type **bufrua**.  This step is not necessary if you already know exactly what the location ID you're interested in is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note:</b> It is important to note the location names are listed by their WMO Station ID. Their corresponding location and site identifier can be looked up in <a href=\"https://data.un.org/Data.aspx?d=CLINO&f=ElementCode%3a15%3bCountryCode%3aUS&c=2,5,6,7,10,15,18,19,20,22,24,26,28,30,32,34,36,38,40,42,44,46&s=CountryName:asc,WmoStationNumber:asc,StatisticCode:asc&v=1\"> this table from UNdata</a>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['21824', '21946', '24266', '24343', '24641', '24688', '24959', '25123', '25703', '25913', '31004', '31088', '31300', '31369', '31510', '31538', '31770', '31873', '32061', '32098', '32150', '32389', '32477', '32540', '32618', '47122', '47138', '47158', '47401', '47412', '47582', '47646', '47678', '47807', '47827', '47909', '47918', '47945', '47971', '47991', '70026', '70133', '70200', '70219', '70231', '70261', '70273', '70308', '70316', '70326', '70350', '70361', '70398', '70414', '71043', '71081', '71082', '71109', '71119', '71603', '71722', '71802', '71811', '71815', '71816', '71823', '71845', '71867', '71906', '71907', '71909', '71913', '71917', '71924', '71925', '71926', '71934', '71945', '71957', '71964', '72201', '72202', '72206', '72208', '72210', '72214', '72215', '72230', '72233', '72235', '72240', '72248', '72249', '72250', '72251', '72261', '72265', '72274', '72293', '72305', '72317', '72318', '72327', '72340', '72357', '72363', '72364', '72365', '72376', '72381', '72388', '72393', '72402', '72403', '72426', '72440', '72451', '72456', '72469', '72476', '72489', '72493', '72501', '72518', '72520', '72528', '72558', '72562', '72572', '72582', '72597', '72632', '72634', '72645', '72649', '72659', '72662', '72672', '72681', '72694', '72712', '72747', '72764', '72768', '72776', '72786', '72797', '74004', '74005', '74006', '74389', '74455', '74560', '74794', '78016', '78384', '78397', '78486', '78526', '78583', '78866', '78954', '78970', '78988', '80001', '91165', '91212', '91285', '91334', '91348', '91366', '91376', '91408', '91413', '91610', '91643', '91680', '91765', '94120', '94203', '94299', '94332', '94461', '94510', '94578', '94637', '94638', '94653', '94659', '94672', '94711', '94776', '94995', '94996']\n"
     ]
    }
   ],
   "source": [
    "locations = DataAccessLayer.getAvailableLocationNames(request)\n",
    "locations.sort()\n",
    "print(locations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Setting the Location Name\n",
    "\n",
    "In this case we're setting the location name to the ID for `KLBF` which is the North Platte Regional Airport/Lee Bird, Field in Nebraska."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set station ID (not name)\n",
    "request.setLocationNames(\"72562\") #KLBF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Filtering by Time\n",
    "\n",
    "Models produce many different time variants during their runs, so let's limit the data to the most recent time and forecast run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all times\n",
    "datatimes = DataAccessLayer.getAvailableTimes(request)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Get the Data!\n",
    "\n",
    "Here we can now request our data response from the EDEX server with our defined time filter.\n",
    "\n",
    "Printing out some data from the first object in the response array can help verify we received the data we were interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parms    = ['staElev', 'staName']\n",
      "site     = 72562\n",
      "geom     = POINT (-100.7005615234375 41.14971923828125)\n",
      "datetime = 2023-05-19 12:00:00\n",
      "reftime  = May 19 23 12:00:00 GMT\n",
      "fcstHour = 0\n",
      "period   = (May 19 23 12:00:00 , May 19 23 12:00:00 )\n"
     ]
    }
   ],
   "source": [
    "# Get most recent record\n",
    "response = DataAccessLayer.getGeometryData(request,times=datatimes[-1].validPeriod)\n",
    "obj = response[0]\n",
    "\n",
    "print(\"parms    = \" + str(obj.getParameters()))\n",
    "print(\"site     = \" + str(obj.getLocationName()))\n",
    "print(\"geom     = \" + str(obj.getGeometry()))\n",
    "print(\"datetime = \" + str(obj.getDataTime()))\n",
    "print(\"reftime  = \" + str(obj.getDataTime().getRefTime()))\n",
    "print(\"fcstHour = \" + str(obj.getDataTime().getFcstTime()))\n",
    "print(\"period   = \" + str(obj.getDataTime().getValidPeriod()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 Use the Data!\n",
    "\n",
    "Since we filtered on time, and requested the data in the previous cell, we now have a `response` object we can work with.\n",
    "\n",
    "### 5.1 Prepare data objects\n",
    "\n",
    "Here we construct arrays for each parameter to plot (temperature, pressure, and wind components).\n",
    "After populating each of the arrays, we sort and mask missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize data arrays\n",
    "tdMan,tpMan,prMan,wdMan,wsMan = np.array([]),np.array([]),np.array([]),np.array([]),np.array([])\n",
    "prSig,tpSig,tdSig = np.array([]),np.array([]),np.array([])\n",
    "manGeos = []\n",
    "sigtGeos = []\n",
    "\n",
    "# Build  arrays\n",
    "for ob in response:\n",
    "    parm_array = ob.getParameters()\n",
    "    if set(parm_array) & MAN_PARAMS:\n",
    "        manGeos.append(ob)\n",
    "        prMan = np.append(prMan,ob.getNumber(\"prMan\"))\n",
    "        tpMan, tpUnit = np.append(tpMan,ob.getNumber(\"tpMan\")), ob.getUnit(\"tpMan\")\n",
    "        tdMan, tdUnit = np.append(tdMan,ob.getNumber(\"tdMan\")), ob.getUnit(\"tdMan\")\n",
    "        wdMan = np.append(wdMan,ob.getNumber(\"wdMan\"))\n",
    "        wsMan, wsUnit = np.append(wsMan,ob.getNumber(\"wsMan\")), ob.getUnit(\"wsMan\")\n",
    "        continue\n",
    "    if set(parm_array) & SIGT_PARAMS:\n",
    "        sigtGeos.append(ob)\n",
    "        prSig = np.append(prSig,ob.getNumber(\"prSigT\"))\n",
    "        tpSig = np.append(tpSig,ob.getNumber(\"tpSigT\"))\n",
    "        tdSig = np.append(tdSig,ob.getNumber(\"tdSigT\"))\n",
    "        continue\n",
    "\n",
    "# Sort mandatory levels (but not sigT levels) because of the 1000.MB interpolation inclusion\n",
    "ps = prMan.argsort()[::-1]\n",
    "wpres = prMan[ps]\n",
    "direc = wdMan[ps]\n",
    "spd   = wsMan[ps]\n",
    "tman = tpMan[ps]\n",
    "dman = tdMan[ps]\n",
    "\n",
    "# Flag missing data\n",
    "prSig[prSig <= -9999] = np.nan\n",
    "tpSig[tpSig <= -9999] = np.nan\n",
    "tdSig[tdSig <= -9999] = np.nan\n",
    "wpres[wpres <= -9999] = np.nan\n",
    "tman[tman <= -9999] = np.nan\n",
    "dman[dman <= -9999] = np.nan\n",
    "direc[direc <= -9999] = np.nan\n",
    "spd[spd <= -9999] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Convert Units\n",
    "\n",
    "We need to modify the units several of the data parameters are returned in.  Here we convert Temperature from Fahrenheit to Celcius, convert pressure to milibars, and extract wind for both the u and v directional components in Knots and Radians. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign units\n",
    "p = (prSig/100) * units.mbar\n",
    "wpres = (wpres/100) * units.mbar\n",
    "u,v = wind_components(spd * units.knots, np.deg2rad(direc))\n",
    "\n",
    "if tpUnit == 'K':\n",
    "    T = (tpSig-273.15) * units.degC\n",
    "    Td = (tdSig-273.15) * units.degC\n",
    "    tman = tman * units.degC\n",
    "    dman = dman * units.degC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 Plot the Data!\n",
    "\n",
    "Create and display SkewT and Hodograph plots using MetPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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psQxDXVe6nM/y15VCoVAYiSUKUSFESyFEthBirsNrYxymwzkrhMi0/8HrVMIxwoQQPwltScJ4IcQtxhkYixCitxDCVuTzWVr2nu6BlHKwlPJrACHEbUKIPytznBKuqyuEEKuEEKeEEMeFEN8LIRqUcox19mMUfs7OXiDBZVDXlUKhUCiKYolCFPgQbRLrc0gp50kpgwsfaPMpxgH/lHKMXLS1pccAHxdOJG0W9gnT9crhUcfPR0o5xJkH95BWvAuuK7R11Wejrc7TFG0u0bLmgb3f4XNuXca2uqOuK4VCoVAYhccXokKIUWgTbpe1DGOxE1TbjxEEjACelVKetU/OvQRtEvTizukthJgphDghhIhDW8Pc8f0aQojPhRBJQohEIcTLQghvh33ftO97UAhxv+OtSXsL2itCiI1AJtqSjm0cWuFihBA3OZzL3x7LYSFEshDik8KJvSuKEOJaIcS/Qoh0IUSCEOL5YjYbYz/XCSHE0w77Pi+E+EEIMVdoE2rfVsbncJsQ4k977Kftn0XRybqbCiE2CiHOCCFWCoelRu0tk5uEtrb2TiFEb4f31gkh7hRCtEWbqLubvXUutQKfRbHXlZRyhZTyeyllun3J0g+A7uU9bhnnVNeVh19XCoVCYTU8uhAVQoSgLWP4aBnbNUVb8WVOCZu0AgrsSygWshMoqUX0LuA64FK0VW5uLPL+12hLV7awb3M12lKChfsORltS8DK01WuKMha4G21lnONo82jOB+qirRf9kfivtXaGPf6O9vOFA8+VEHdZZKCtpBOKVgTdK4QoGt9VaCu29AOes/9RLuR64Af7/vMo/XMA6Iq26lBt4HW0lYwc1za/BbgdzdsPeAy05QvRVrB5GQizv75ICFHHMVApZTTaSkOb7a1zoeX5EMp7XdnpSdlri79mL7A2OhY2xaCuKw++rhQKhcKSSCk99gG8Czxhf/48MLeE7Z4F1pVynB7AsSKv3VXSPmjrbU90+P5qQAI+aLf2c4BAh/dHA2sd9r3H4b3+hfvav18HvOjw/s3AhiLnn4W2PKNA+yMf6fBeN+BgGZ9bb7RlM1MdHjcVs907wNv25xH2OBs5vP8XMMrh8//D4b2yPofbgP0O71WzH7++w+fwjMP7k4Bf7c+fAL4pEutvwHiHfe90OM+fOl1XF6MtEdmjlGN1RSv8/NFa5c845ktdV9a5rtRDPdRDPaz48Nj+VEKIjmh/bC8tx+bjgFdLef8sEFLktRC0oqE4GnL+mujxDs+bAr5AkkMjjJfD9kX3PW9t9WJeawp0LXL7zwf4Bm099GrAdodzCcC7hLgdOSqlbOT4ghCiK9r65R3QWor8ge+L7HfM4XkmEFxK3KV9DucdS0qZad8uuLj3i5yrKTBSCOHY/9AXWEsVKe91JYRoAawAJkspN5S0nZRyq8O3XwshRgPXUPza9Oq60vC460qhUCisiscWomitLxHAYYc/NN5CiHZSyssKNxJCdEf7I/1DKceKBXyEEC2llPvsr11Cybdck4DGDt83cXiegNZiU1tKmV/Cvo5/qBsXs41jP9YEYL2UckDRjYQ24CQLaC+lTCwh1oowH63P42ApZbYQ4h2025vlpWjcpX0OVSEBreXqrgrGVB56U8Z1Ze/qsRp4SUr5TQWPL9GKuuJQ11XxeMJ1pVAoFJbEk/uIzgYi0fqwdUQbPPALMLDIduOBRbKUJSKllBnAj8CLQogge/F6PVrrUHEsBB4UQjQSQtQEnnQ4VhKwEnhTCBEihPASQkQKIXo57DtZCBEuhAhFux1YGsuAVkKIsUIIX/ujixCirZTSBnwKvC2EqAtaPzchRNHPoLxUB07Zi4XL0frSVYpyfA5VYS4wRAgxUGiDdAKENnVQo2K2TQYaCSH8ynnsUq8rez/C34EPpZSflHYgIUSoPcYAIYSPEGIMWp/S30rYRV1XZeDG15VCoVBYEo8tRKWUmVLKY4UPtNvr2VLK44XbCCECgJvQBjechxDiKSHECoeXJgGBQArwLXCvlLKkFtFP0YqJnWjTQf1Y5P1xaLcg9wCn0VpjGzjsuxLYBfwLLEcbeFFQgucZtL6Co4CjaLcVZ6Dd3gSt4NgPbBHaqOLVaIM+KsMktGL8DNrAlIWVPE4hpX0OlUZKmYD2j8JTaINuEoDHKf56/x2tZfuYEOJEOY5d1nV1J9AcmCYc5sss3L/IdeWLNvDlOHACeAAYJqUsaS5RdV2VD7e7rhQKhcKqqLXmXRyhTS3ziZSyqdmxKDwHdV0pFApF8Qgh3gNWSymXmB2LFfDYFlF3RQgRKIS4xn6rNhxtlPJPZselcG/UdaVQKBTl5kpgsdlBWAVTClEhxBdCiBQhxG6H18KENnn2PvvXmg7vTRVC7BfapNqV7YfmLgjgBbRbiv8C0VR+fsaST6LdIj5bzGNF2Xsr3BB1XSkUCkX56AUgHBbPUOiHKbfmhRA90frWzZFSdrC/9jragIXpQogngZpSyieEEO3Q+mRejja6fTXQSkpZbN82hUKhUCgUiqoghFgD9AW8pOrDqCumtIhKKf9Am+jbkev5b9DQ1/y38sv1wHdSyhwp5UG0ARKXGxGnQqFQKBQKS1K49O8bpkZhAVypj2g9+9QrhVOw1LW/Hs75k1Efsb+mUCgUCoVC4XSklLloM248ap872anYp4IcLYRYKoSQ9kdLZ5/HHXCHCe2Lm9y72GZyIcTdaGtlExgY2Klx4+Lm7DYfm82Gl5cr/Q9gDMr7QnJtuRzKOARARFAEfl6eM+2kJ+U7OC4OkZ9PRrNm2Hx9S93Wk7wrgvK2FlbwbtWqFbGxsYSEhBS0bq3NTlcZ76ysLNLT0zlz5gw2m63YbWrUqEFaWtomtJXrLIUrFaLJQogGUsokIUQDtPk6QWsBdawoG6HNa3gBUsrZaBOO07lzZ7lt2zY9460UO3bsoG7dujRs2NDsUAxFeZfsfffSu/n0n0/p3K4z348surKle+Jx+R44EFauhHfegaFDS9zM47zLifJW3p7K3XffzaeffsrJkyfZvXt3qd7x8fF8++23zJs3j927dxe7TZ8+fRgzZgw33HADNWvWPO89IUR8sTt5OK7078wStFWOsH9d7PD6KCGEvxCiGdAS+MuE+KpMamoqGRkZNGhQ5bm13QrlXbr3tF7TCPQJ5Ic9P7D1yNZSt3UHPDLf7dppX6NKWsPCQ73LgfJW3p7MrFmzALj++uvPeZ89e5Z58+Zx7bXXIoQ494iIiGDq1Kns3r2byMhInnvuOaKjo5FSnnv8/vvv3HHHHRcUoVbGlBZRIcS3aGt21xZCHEGb03A6sFAIcQdwGBgJIKWMEkIsRFslJR+4zx1HzEsp2b17NxdddBFClLSUuOehvMv2Dg8JZ3LXyUzfOJ0n1zzJ7+N+d9vPymPz3b699nXPnmLf9ljvMlDeytvTEUIwZcoUXn/9dZYvX37B+35+ftxyyy3ccsst9OnTBx8fV7rR7B6Y8olJKUeX8Fa/ErZ/BXhFv4j05+jRo1SvXp0aNWqYHYqhKO/yeT9x1RPM2j6LdYfW8duB3xjUYpDOEeqDx+a7jBZRj/UuA+WtvK3AAw88wIoVK6hTpw633HJLsbfVFZXHlW7Neyz5+fnExsbSpk0bs0MxFOVdfu/QgFCe6vEUAE+sfgKbLL5Duyvj0fkuLESjo6Hg/BsyHu1dCspbeVuB/Px89u3bx99//82aNWvUbXUdUIWoARw4cIAmTZrg7+9vdiiGorwr5n3/5ffTOKQxu5J3Mf9/83WKTj88Ot+hodCwIWRnw6FD573l0d6loLyVtxWwqreRqEJUZ7KyskhKSqJZs2Zmh2Ioyrvi3gE+AbzY50UAnl37LDn5Oc4OTzcske9i+olawrsYlLfytgJW9TYaVYjqTFRUFO3atfP4+daKorwr5z324rG0r9OeQ6mH+GTbJ06OTj8ske9i+olawrsYlLfytgJW9TYa9enqyMmTJ8nPz6du3bplb+xBKO/Ke3t7efNav9cAeHnDy6TnpDsrPN2wTL6LtIhaxrsIylt5WwGrepuBKkR1QkpJVFQUHTp0MDsUQ1HeVfe+rtV1XNXkKk5knmDmpplOiE4/LJVvhxZRS3k7oLyVtxWwqrdZqEJUJw4fPkxYWBjBwcFmh2Ioyrvq3kIIZvSfAcCbm9/k2NljVT6mXlgq3w4j5w8fOmQdbwcslW8HlLfyVuiHKkR1IC8vj7i4OArXprUKytt53lc2vpLrW19PZl4mL65/0WnHdSaWy3fNmtCgAWRlcXTTJut427Fcvu0ob+Wt0BdViOpAbGwszZs3x9fX1+xQDEV5O9f71X6v4iW8+PSfT9l3cp9Tj+0MLJlvez/RyJwca3lj0XyjvJW3Qm9UIepkzp49y8mTJ2nSpInZoRiK8na+d7s67bjtktvIt+XzzNpnnH78qmDVfOe2bAlAnZQUkyMxFqvmW3krb4X+qELUyURFRdG+fXvLrMNbiPLWx/v53s8T4BPAwqiF/J34ty7nqAxWzfcR+9KGIjra5EiMxar5Vt7KW6E/qhB1IikpKXh5eVGrVi2zQzEU5a2fd+Majbm94+0A/Lz3Z93OUxGsmu/jUVHUWbFC+yY52dxgDMSq+VbeylthDKoQdRI2m43o6GjaF841aBGUt/7euQW5ADSo3kD3c5WFZfO9YwfV+/Wj+s6d0KgRzJhhdkiGYNl8K2+zQzEUq3q7CqoQdRKHDh2iXr16VKtWzexQDEV56+8dfUK7Ddy2dlvdz1UWlsz34sXQvTsBycnQtSv89RdcconZURmCJfON8lbeCiNRhagTyM3NJT4+nhYtWpgdiqEob/29pZREH7cXonXMLUQtl28pYfp05PDheGVmYhs9Gtat06ZwsgCWy7cd5a28FcaiClEnsHfvXlq2bImPj4/ZoRiK8tbfOyUjhdPZpwnxD6FBsLkFkKXynZ0N48fD1KkIKUl74gm85s2DgACzIzMMS+XbAeWtvBXGogrRKpKenk56ejrh4eFmh2IoytsYb8fb8maO5LRUvs+ehT594JtvkEFB7Hn1VUJeew0sNJLWUvl2QHkrb4XxqH8BqoCUkt27d1tuugflbZx3SoY2X2XtarUNOV9xWC7f330HW7YgGzdm50sv0XToUGt427Fcvu0ob+WtMAfVIloFjh07RmBgIDVr1jQ7FENR3sZ5d6zfEYBtR7chpTTsvI5YLt8xMQCcveUW5MUXW8fbjuXybUd5K2+FOahCtJIUFBQQExND27bmj2Q2EuVtrHfLsJbUqVaH5Ixk4k7HGXpusGi+DxwAIMHPz1reWDTfKG/lrTATVYhWkri4OMLDwwmw0OAFUN5GewshuLLxlQBsTNho6LnBovm2F6LBl1xiLW8smm+Ut/JWmIkqRCtBdnY2iYmJNG/e3OxQDEV5m+NdWIhuSthk6HnN9jYFKZH2QjS8Z0+TgzEWS+Yb5a28FWajCtFKEB0dTevWrfH29jY7FENR3uZ4d2/cHTC+RdRsb1NISUFkZGCrUQPvOnXMjsZQLJlvlLfyVpiNKkQryOnTp8nKyqJ+/fpmh2Ioyts8704NO+Hn7UdUShSp2amGnNMVvM0g/d9/ARAWm9zaqvlW3spbYT6qEK0AUkqioqLo0KGDpaZ7UN7megf4BNC5YWckks0Jm3U/n6t4G42UkqQ//wRAREaaHI1xWDnfylt5K8xHFaIVIDExkZCQEEJCQswOxVCUt/neRt6edyVvI0lMTCT05EntGwsVolbOt/K2Dlb1dgdUIVpO8vPz2bdvH23atDE7FENR3q7hXViI6j1gydW8jaLQu3Z6uvaCRQpRq+dbeVsDq3q7C6oQLSf79++nadOm+Pn5mR2KoShv1/Du1rgbAFsTt5JXkKfbeVzN2ygKvb0PHtResEghavV8K29rYFVvd0EVouUgMzOT5ORkIiIizA7FUJR3hNmhnKNuUF1ahrUkMy+Tnck7dTmHK3obwXne9qmbsMBgJZXvCLNDMRTlHWF2KIoSUIVoOYiKiqJt27Z4eVnr41LeruXdvYm9n+hhffqJuqq33pzzzsiAlBTw94eGDc0OS3csn2/lbQms6u1OqMyUwYkTJ7DZbNStW9fsUAxFebuet54DllzZW0/O8y5sDW3eHDz8j5bKt/K2Alb1djc8+7dtFZFSsmfPHtq3b292KIaivF3T27EQlVI67biu7q0XF3gXFqIe3j9U5Vt5WwGrersjqhAthcOHD1OrVi2Cg4PNDsVQlLdrereu3ZqwwDCOnjnK4bTDTjuuq3vrxQXeFilEVb6VtxWwqrc7ogrREsjLyyMuLo5WrVqZHYqhKG/X9fYSXnRrpI2e33B4g1OO6Q7eelCstwUKUZVv5W0FrOrtrqhCtARiYmJo3rw5vr6+ZodiKMrbtb37N+8PwNQ1Uzl29liVj+cu3s6mWO/Tp7WvYWHmBGUAKt/K2wpY1dtdUYVoMZw9e5ZTp07RpEkTs0MxFOXt+t73dr6XKxtfyZH0I9yw4AZy8nMqfSx38nYmJXo3bqx9PXLE+KAMQOVbeVsBq3q7M6oQLYbdu3fTvn17y61Hq7xd39vfx58fb/qRxiGN2XxkM/csu6fSA5fcyduZlOjdtKn2NT7e+KAMQOVbeVsBq3q7M6oQLUJycjI+Pj7UqlXL7FAMRXm7j3e94HosGb2Ear7V+Hrn17y1+a0KH8MdvZ1Bqd6FE14fOmRkSIag8q28rYBVvd0dVYg6YLPZiI6Ottx0D8rb/bw71u/InGFzAJiyegor9q0o977u7F0VyvQubBH1sEJU5Vt5WwGrensCqhB14ODBgzRo0IDAwECzQzEU5e2e3iPajeD5Xs9jkzZGLRrF3hN7y7Wfu3tXljK9HW/NO3GeVrNR+VbeVsCq3p6AKkTt5OTkcPjwYSI9eOqW4lDe7u39bK9nGdF2BOk56Qz9diins06Xur2neFeUcnmHhmqPzEw4ccKo0HRF5Vt5WwGrensKqhC1s3fvXlq1aoWPj4/ZoRiK8nZvby/hxdfDvqZj/Y7sO7WPm364iXxbfonbe4p3RSm3t4fdnlf5Vt5WwKrenoIqRIG0tDTOnj1Lw4YNzQ7FUJS3Z3gH+QWxeNRi6gbVZXXcal5c/2Kx23mad3mpkHfhNh4wcl7lW3lbAat6exKWL0SllJac7kF5e5Z3Nd9q1AyoCcD+U/sveN9TvcuiQt6LFsFvv2nP3fyPmsq38rYCVvX2NCxfiCYlJREUFERoaKjZoRiK8g41OxSnkZadxqC5g4g5GUOHuh344JoPLtjGE73LQ7m9f/sNRo8Gmw1eeAGuvNKQ+PRC5TvU7FAMRXmHmh2KogpYuhAtKCggNjaWNm3amB2KoRQUFBATE6O8PYCsvCyGfjeU7UnbaV6zOStvXUlY4PlLVHqid3kot/fGjTB8OOTlwcMPw7PPGhOgTqh8K28rYFVvT8TSheiBAwdo1KgRAQEBZodiKAcOHKBx48bK2wOY/7/5/BH/B+HVw1k9djUNqje4YBtP9C4P5fI+cgSGDYOsLJgwAd58E9z8Fp/Kt/K2Alb19kQsW4hmZWVx9OhRmjdvbnYohqK8Pcs79mQsoK1B36xmswve91TvsiiXd14e3HyzNlXT1VfD7NluX4SqfCtvK2BVb0/FsoVodHQ0bdq0wcvLWh+B8vYs78QziQCEh4QX+76nepdFubyfego2bYLwcJg7F7y9jQtQJ1S+lbcVsKq3p2LJLJ46dYqcnBzq1atndiiGorw9z/tcIVr9wkLUk71Lo1zeixfDzJng4wMLF0KdOsYFqBMq38rbCljV25OxXCEqpSQqKooOHTpYarqHQm+rTXPh6d6J6cW3iHq6d0mUyzsuDsaP157PmOH2I+RB5Vt5WwOrens6litEjxw5QmhoKNWrVzc7FEMp9A4JCTE7FEPxZG8pZYktop7sXRplemdnw8iRkJamDVJ6+GFD49MLlW/lbQWs6u3pWKoQzc/PZ//+/bRu3drsUAxFeXumd1pOGpl5mQT7BRPi/98vZk/3LolyeT/yCPzzDzRvDl9+6faDk0DlW3lbA6t6WwFLFaL79u0jIiICPz8/s0MxFOXtmd7nbstXDz/vNpWne5dEmd7z58PHH4OfH3z/PXjIJNgq38rbCljV2wpYphDNyMggJSWFpk2bmh2KoShvz/UubsS8FbyLo0zv6Gi4+27t+bvvwmWXGRecjqh8K28rYFVvq2CZQjQqKop27dpZbroH5e253kfSjwDn9w+1gndxlOqdkaH1C83IgFtugXvuMT5AnVD5Vt5WwKreVsESWT1x4gQAdTxgipaKoLw929vx1jxYx7sopXpLCZMmQVQUtG0Ls2Z5RL9QUPlW3tbAqt5WwuMLUcfpHqyE8vZ8b8db81bydqRM788/hzlzoFo1+OEHCA42NkCdUPlW3lbAqt5Ww+ML0fj4eOrUqUNQUJDZoRiK8vZ8b8epm6zk7Uip3jt2wP33a88/+QTatTM0Nj1R+VbeVsCq3lbDowvR3NxcDh48SMuWLc0OxVCUtzW8C2/N1w2saynvQkrNd1qa1i80JwfuugvGjjU+QJ2w2nVeiPJW3grPxKML0ZiYGCIjI/H19TU7FENR3tbwLmwRzUrJspR3ISXmW0q44w7Yvx86doT33jMlPr2w2nVeiPJW3grPxGML0WPHjpGamkrjxo3NDsVQzpw5o7wtQG5BLikZKXgLb3yyfSzjXUip+X7vPVi0CEJCtPlCAwKMD1AnrHadF6K8lbfCc/HYQjQxMZGEhATLrUdr1XV4readdCYJgDC/MC7ucLFlvAspMd9btsBjj2nPv/wSWrQwPjgdsdp1XojyVt4Kz8VjC1GAu+++mx07dpgdhmEkJyfj6+tLWFiY2aEYihW9C2/L1w2saylvKCXfJ0/CTTdBfj489BDccIMp8emFFa9zUN7K2xokJyebHYJpeGwhGhYWRkZGBkOGDOHo0aNmh6M7NpuN6Oho2nnQyODyYFXvhLQEACLrRJocibGUmG+bTRuQlJAAV1wBM2aYE6BOWPU6V97K2woUelsVjy1EIyIiuOqqqzhy5AhDhw4lIyPD7JB0JS4ujgYNGhAYGGh2KIZiVe9dB3cB0CS0icmRGEuJ+Z4+HVasgLAwWLBAW0/eg7Dqda68lberMHXqVPLz83U5dqG3VfHYQlQIwU8//UTz5s3Zvn07t956KzabzeywdCEnJ4eEhARaeFh/uLKwsve+lH3A+evMezol5nvtWnj2We353LnQxLOKcytf58rbOri69/Tp01m3bp3Tj+vq3kbgsYUoQO3atfnll18IDQ3l559/5sknnzQ7JF2Ijo6mdevWeHt7mx2KoVjZO9c/Fzh/nXlPp9h8HzsGo0drt+affhoGDzYvQJ2w8nWuvK2DO3gXLjfqTNzBW288uhAFaNOmDT/88AM+Pj688cYbfPbZZ2aH5FTS0tLIyMiwXLO+1b1P5Z0CrNMiWmy+8/O1IjQ5GXr3huefNys83bD6da68rYG7eB8/ftypx3MXb73x+EIUoF+/fnz88ccA3HvvvaxZs8bkiJyDlJLdu3fToUMHS01zobw7nLe8p6dTYr6nTYN166B+ffj2W/DxMS1GPVDXufK2Au7k7cxC1J289cYShSjAnXfeyZQpU8jPz2fEiBEeMULt6NGjBAcHU6NGDbNDMRSre4eEhJxb3tMKLaJHjx4lKCjo/HwvXw6vvgpeXloRWr++eQHqRLHeFkB5K29XxZmFqFX/jhWHZQpRgNdee40bbriBtLQ0rrvuOl36exhFQUEBsbGxtGnTxuxQDEV5t+FU1ilyCnKo4V+DYL9gs0PTlULvtm3b/vfi4cP/rR3/8svabXkPo1hvC6C8lbcrU1iIPv7448TGxlb6OFb9O1YSlipEvby8+Oabb+jUqRNxcXEMGzaMnJwcs8OqFPv376dJkyb4+/ubHYqhKG///27LW6A19IJ85+Zqk9afOgXXXANPPGFugDqhrnPlbQXczbuwEJ05cyYzZ86s9HHczVtvLFWIAlSrVo0lS5bQqFEjNm7cyB133IGU0uywKkRWVhZJSUk0a9bM7FAMRXlr3uduy3t4/9Bi8z1lCmzdqk3RNGeOdmvew1DXufK2Au7o7XhrvrK36d3RW28877d4OWjYsCHLli0jODiYefPm8dJLL5kdUoXYs2cPbdu2xcsD/wiXhvLWvK3SInpBvn/4Ad59F3x9YeFCqFXL3AB1Ql3nytsKuKO3MwpRd/TWG8t+EpdccgnfffcdXl5eTJs2jfnz55sdUrk4deoUeXl51KtXz+xQDEV5/+d9JP0I4Nktohd4798PEyZoz2fOhK5dzQtOR9R1rrytgLt6O44rqUwh6q7eemPZQhTg2muv5a233gLg9ttvZ9OmTSZHVDpSSqKiomjfvr3ZoRiK8j7f29NvzV/gnZUFN94IZ87AiBHwwAPmBqgT6jpX3lbAXb0nTZoEcG6qpdjYWE6ePFnu/d3V2wgsXYgCPPjgg0yaNInc3FyGDRtGXFyc2SGVSEJCAjVr1qR69epmh2Ioyvt8b0+/NX+B94MPws6d0KIFfP45eOice+o6V95WwF29P/zwQzIzM3njjTfOvVa7dm2EEISGhjJr1izy8vJK3N9dvY3A8oWoEIJ3332XQYMGcfz4ca677jpSU1PNDusC8vLyOHDgAK1btzY7FENR3hd6e/Jk9hd4z5kDn30G/v5aH1EPnXNPXefK2wq4u3dgYCCPPfYYUkqklBw5coS7776btLQ0Jk6ciJ+fH0IIunTpwu+//35uP3f31hvLF6IAPj4+LFiwgA4dOhAdHc3IkSNL/c/GDPbt20ezZs3w9fU1OxRDUd4XenvyZPbnee/eDRMnam988AFccom5wemIus6VtxXwNO/w8HBmzZp1rjDdunUrffr0Ydu2bfTr1w8hBEIIrr/+ery8vDzG29moQtROSEgIy5Yto27duqxevZr777/fZaZ1ysjI4MSJEzRt2tTsUAxFeV/onZ2fzcmsk/h4+VA3qK4J0elHRkYGx48f17zPnoWRI7X+oePGwR13mB2ebpznbSGUt/L2NC6//HJ+//13pJQUFBQwf/58GjVqxIoVK+jdu/e5wvS5554jPT3d7HBdBlWIOtC0aVOWLFlCQEAAs2fP5u233zY7JACioqJo166d5dajVd4Xeh89cxSAhtUb4iU868e3sCO/ALj7bti7F9q3h48+8th+oeDg7cGOxaG8lbcn4+XlxejRo1m0aBHHjx/nzJkzvPjiiwC89NJL1KhRAyEEDRs25JtvvqGgoMDkiM3Ds/6SOYGuXbvy9ddfA/DYY4+xePFiU+M5fvw4Qghq165tahxGo7yL9/bUEfPnec+apa0fHxSk9QsNCjI7PN1Q17nytgLKuzbBwcE8++yz527jx8XFMXbsWJKSkhg3bhw+Pj4A3mbHbAaqEC2Gm266iVdeeQUpJbfccgv//POPKXHYbDb27NljuekelHfJ3p44Yv487+3bYfJk7Y1PPwUPXotZXefK2woo7+K9mzVrxpw5c84VpocPHwawZLOoKkRLYOrUqYwfP57MzEyGDBlCYmKi4THEx8dTt25dqlWrZvi5zUR5l+ztiS2i57xzcrR+obm5cO+9MHq02aHpirrOlbcVUN7l827cuLHOEbkuqhAtASEEs2fPplevXhw9epQhQ4Zw9uxZw86fm5vLoUOHaNmypWHndAWUd+nenraq0jnvFi3g9tvh4EHo1AlcpH+2XqjrXHlbAeVtLe/KogrRUvDz82PRokW0bNmSf//9lzFjxhjWoTgmJoYWLVoU9huxDMq7dG9PuzV/zvu992DxYm2e0IULtXlDPRh1nStvK6C8reVdWVQhWga1atVi2bJl1KxZkyVLljBlyhTdz5menk5qaiqNGjXS/VyuhPIu29uTJrM/5x0fD088ob349dfQvLm5gemMus6VtxVQ3tbyrgqqEC0HrVq14qeffsLX15e33nqLWbNm6XauwvVoO3ToYJlpLkB5l9fbUyazL/S+qH59xKhRUFAAjz0G119vdmi6oq5z5W0FlLe1vKuKKkTLSa9evZg9ezYA9913H6tWrdLlPMnJyfj7+1OzZk1dju+qKO+yvW3Sdm4eUXdvEU1OTsbfx4fQ++6DxETo3h1efdXssHRHXefK2woob2t5VxVViFaA2267jalTp1JQUMCNN97Inj17nHr8goIC9u7dS7t27Zx6XFdHeZfP+0TmCfJseYQFhhHoG6hzdPpR6H3RkiWwahXUrg3ffQcevvxdoXfbtm3NDsVQlLfytgJW/TvmDFQhWkFefvllbrzxRtLT07n22mtJSUlx2rEPHjxIw4YNCQgIcNox3QHlXT5vT5m66eDBg7Q4dAjfV17RVkyaPx8s0J+qMN+Bge77T0RlUN7K2wpY9e+YM1CFaAXx8vJizpw5XH755Rw6dIjrr7+erKysKh83OzubI0eOEBkZ6YQo3QflXX5vTxgxn52dTcq//xI+ZQpICc89BwMGmB2W7qjrXHlbAeVtLW9noQrRShAYGMjixYtp0qQJW7ZsYfz48VWe1ik6OppWrVrh7W2tFb6Ud/m9k84kARDiH6JXWLoTHR3NpXPmII4fh/794dlnzQ7JENR1rrytgPK2lrezUIVoJalfvz7Lli0jJCSE77//nsmTJyOlrNSxUlNTycrKokGDBk6O0rVR3hXz7li/IwA/Rv/ItqPbdIhMXwq9A44f116YOhUs8ItbXefK2woob2t5OxNViFaBiy66iMWLF+Pn58eHH37Iq5UY9SulZPfu3Zab7kF5V9y7S3gXHur6EPm2fG798VYy8zJ1itL5nOfdpIn24rFj5gZlAOo6V95WQHlby9vZqEK0ivTu3Zv58+cjhOCZZ57hs88+q9D+R48epXr16oSEuO/t1sqgvCvn/Wq/V2lXpx0xJ2N4YtUTTo5OPxITE//zbtpUezE+3tygDEBd58rbCihva3k7G1WIOoERI0bw0UcfAXDPPfewePHicu2Xn59PbGwsbdq00TM8l0N5V9470DeQucPn4uvlywd/f8Bv+39zYoT6kJ+fz759+/7zLmwRPXzYvKAMQF3nytsKKG9reeuBKkSdxMSJE5k2bRo2m41Ro0axYcOGMvfZv38/TZo0wd/D19UuivKumvelDS7lxT4vAnD74ts5mXnSGeHpxgXeFilE1XWuvK2A8raWtx6oQtSJTJs2jXvuuYfs7GyGDh3K7t27S9w2MzOTY8eO0axZMwMjNB/l7Rzvx698nO6Nu5N0NomJv0ys9EA5vSnW2wKFqLrOlbcVUN7W8tYLVYg6ESEEH374ITfccAOpqakMHDiQ+BL6we3Zs4d27drh5WWtFChv53h7e3kzZ/gcgv2C+WHPD8zdNdcpx3U2xXpboBBV17nytgLK21reeqE+RSfj7e3NvHnz6NmzJ0ePHmXgwIGcOHHivG1OnjxJQUEBdevWNSlKc1DezvVuXrM57w56F4D7V9xPfKprDf45efIk+fn5F3qHhUG1apCeDmlp5gSnIyV6ezjKW3lbAav+HdMTVYjqQEBAAIsXL+biiy8mJiaG6667joyMDECb7iEqKor27dubHKWxKG99vG/veDvD2gwjPSed8T+PxyZtupynohR6d+jQ4cI3hfDYVtFSvT0Y5a28rYBV/47pjSpEdSI0NJQVK1YQERHB1q1bGTlyJHl5eRw+fJhatWoRHBxsdoiGorz18RZCMPu62dQNqsv6+PW8tfktXc5TUcr09tBCVF3nytsKKG9reeuNKkR1pGHDhvz222/Url2bFStWMHbsWPbt20erVq3MDs1Q8vLyiIuLU946UbtabYa2GgrAa3++ZvrApVK9MzLg1Vdh82bt+5QUY4PTEXWdK28roLyt5W0EqhDVmVatWrFixQqqV6/OggUL+Oqrr/Dx8TE7LEOJjY2lefPm+Pr6mh2KoRjhnZWXxfifx/PZv9pCCo9c8YjpK3wU652fD7NnQ8uW8PTTcOYMDBwIQ4aYF6iTUde58rYCytta3kagClED6Ny5MwsXLsTPz4958+bx1FNPmR2SYZw9e5aTJ0/SpPBWrEUwwvtw2mF6fNmDb3Z9QzXfaiy8cSFP93xat/OVhwu8pYQff4QOHeCeeyApCTp1gtWr4ddfoXZtU+N1Fuo6V95WQHlby9soVCFqEDVr1jzXGjp9+nSmT59udkiGUNix2+xWOqPR2/uP+D/oPLsz25O20yy0GZvv2MzI9iN1OVdFOM/7jz+gWzcYMQJiYiAyEhYsgL/+gn79zA7VqajrXHlbAeVtLW+jsNY9YpNISUnB29ub0aNHAzBmzBimTp1KjRo1uPfee02OTj8KvWvVqmV2KIaip7eUkg///pCHf3uYfFs+A5oP4LsbvyMsMMzp56oo57yPHoXx4+GXX7Q36taFadPgrrvAA29rqetceVsB5W0tbyNRLaI6Y7PZzk1+CzB69Gg+/vhjAO677z7mzZtnZni6UdTbKujpnZ2fzZ1L7uSBFQ+Qb8vnsW6PsXzMcpcoQm02GwfWrqXjO+/AJZdoRWhwMLzwAhw4AJMmeWQRqq5z5W0FlLe1vI1GtYjqzKFDh6hfvz7VqlU799o999xDWloaTzzxBOPHjyckJIQhHjRoA4r3tgJ6eSemJ3LDwhv4K/EvAn0C+Xzo54y+aLRTz1FpTp4k/Ykn6PrNN3jl5moF58SJ8MwzWmuoB6Ouc+VtBZS3tbyNRrWI6khubi7x8fG0aNHigvemTJnC1KlTKSgoYOTIkaxdu9aECPWhNG9PRi/vjYc30ml2J/5K/IumNZqyccJG1yhCMzNh+nRkZCShn3+uFaG33AJ798J773l8Eaquc+VtBZS3tbzNQBWiOrJ3715atmxZ4nRNr7zyCpMmTSInJ4ehQ4eydetWgyPUh7K8PRU9vGdtm0Wfr/uQnJFMn4g+bLt7G5c2uNRpx68U+fnw2WfaVExTpyLS0sju2RP++QfmzYPmzc2NzyDUda68rYDytpa3GahCVCfS09NJT08nPDy8xG2EELz//vvceuutnD17lsGDB7N7924Do3Q+5fH2RJztnVuQyz1L72HiLxPJs+XxUNeHWDl2JbWrmTjdkZTw889w0UXawKOjRyno2JH/vf02/uvWwaUmF8gGoq5z5W0FlLe1vM1Clfo6IKVk9+7ddOjQoczpHry8vPjiiy9IT09nyZIlDBgwgD///JPIyEiDonUeFfH2JJztnXQmiRu/v5FNCZvw9/Zn9pDZjLtknBMirQIbNsATT/y3IlJkJPLll9naqBHtVL4tgfJW3lbAqt5molpEdeDYsWMEBgYSGhparu19fX1ZsGABffr04dixY/Tv35/ExER9g9SBinp7Cs703npkK50/7cymhE00CmnEnxP+NLcI3b0bhg6Fnj21IrRuXfjgA9izh2O9ehEYFKTybRGUd6jZoRiK8g41OxTLoApRJ1NQUEBMTAxt27at0H4BAQEsXryYrl27cujQIQYMGMDx48d1itL5VNbb3XGm9xf/fkHPr3py9MxRejTpwba7ttG5YWcnRFkJEhJgwgRtKqalS7WpmJ5/Hvbvh/vuo8DbW+XbQihv5W0FrOptNqoQdTJxcXGEh4cTEBBQ4X2rV6/O8uXL6dChA9HR0QwaNIi0tDQdonQ+VfF2Z5zhnVeQx/3L7+eOJXeQW5DLfV3uY/W41dQLrufESMvJqVMwZYo2EOnLL8HLC+6/XytAp02D6tUBlW/lbQ2Ut/JW6I8qRJ1IdnY2iYmJNK/CqOGwsDBWrlxJZGQk//zzD0OGDCEzM9OJUTofZ3i7I87wTslIof83/fnw7w/x8/bjsyGf8cE1H+Dn7efESMtBVhbMmKEtw/nGG5CTA6NGaVMxvf8+1PuvKFb5Vt5WQHkrb4UxqELUiezZs4fWrVvj7e1dpeM0aNCA1atXEx4ezoYNG7jxxhvJzc11UpTOx1ne7kZVvbcf3U6n2Z34I/4PGgQ3YP1t67njsjucHGUZ5OfD559rLaBPPgmpqdC/P2zbBt9+qxWmRVD5Vt5WQHkrb4UxqELUSZw+fZqcnBzq16/vlONFRESwatUqatWqxYoVKxg3bhwFBQVOObYzcba3u1BV7zk759D9i+4cST9Ct0bd2H73dq5odIWToywFKWHxYrj4YrjzTkhM1KZfWrkSVq2CTp2K3U3lW3lbAeWtvBXGoQpRJyClJCoqivbt2zt1uoe2bdvy22+/ERISwoIFC5g4cSJSSqcdv6ro5e3qVMU735bPw78+zPifx5NTkMNdl93F2vFraVC9gU7RFsPGjXDVVTBsGERHaxPQf/ut1go6YECJu6l8K28roLyVt8JYVCHqBBITEwkJCSEkJMTpx+7UqRPLli0jICCAzz77jMcff9xlilE9vV2ZynqfyDzBwLkDeWfrO/h6+fLJtZ8we8hs/H38dYq0CHv2wPXXa0Xopk1Qp47W/zM6WusP6lX6rwOVb+VtBZS38lYYiypEq0h+fj779u2jTZs2up2jR48e/Pjjj/j4+PDmm2/y6quv6nau8mKEtytSWe8dx3bQeXZnfj/4O/WC6vH7+N+5p/M9OkVZhCNH4I47tBWRliyBoCBtBPyBA9qIeL+yB0apfCtvK6C8lbfCeFQhWkX27dtH06ZN8SvHH/OqMHjwYObNm4cQgmeeeYYPPvhA1/OVhVHerkZlvL/937dc+fmVxKfFc3n45Wy7extXNblKxyjtnD6trYbUsiV88YXW4nnffVoB+vzz56ZiKg8q38rbCihv5a0wHlWIVoHMzExSUlKIiIgw5Hw33XQTs2fPBuCBBx5gzpw5hpy3KEZ7uwoV9S6wFTBl1RRu+fEWsvKzuK3jbay/bT2NQhrpG2hWljYFU/Pm8PrrkJ0NN9+s3YL/4IPzpmIqDyrfEWaHYijKO8LsUAxFeUeYHYrlUWvNV4GoqCjatWuHVxl965zJnXfeSVpaGo899hi33347wcHB3HDDDYadHzTvtm3bGurtClTU+9m1z/LGpjfwFt68M+gd7utynzGd4a++Gv78U3vet682P2jnyq/QpPKtvK2A8lbeCnNQGagkJ06cwGazUadOHcPP/eijj/Lss89is9kYNWoUv/zyi2HnLvSuW7euYed0BSrjHRYYBkCBLCAt28AVsrKytK8PPwyrV1epCFX5Vt5WQHkrb4V5qEK0Ekgp2bNnD+3btzcthhdeeIFHHnmEvLw8RowYwerVq3U/pyt4m0FlvR/t9ihvDHgDgeCZtc8w7udxZOdn6xSlA489pn39+WeowtyzKt/K2woob+WtMBdViFaC+Ph4atWqRXBwsGkxCCGYOXMmkyZNIicnh6FDh/LHH3/oek5X8DaDynoLIXjsysf4edTPBPkGMXfXXPrN6Ud6TrpOkdoZOVIboHTwIMyfX+nDqHwrbyugvJW3wlxUIVpB8vLyOHjwIK1atTI7FIQQvP/++0yYMIGsrCyuueYaNm7cqMu5XMnbSJzhPbT1UDbdsYkmNZqwKWETX+34ynkBFoe3Nzz1lPb8qae0ZTsriMq38rYCylt5K8xHFaIVJCYmhsjISHx9fc0OBQAvLy9mz57NrbfeSkZGBoMHD2bLli1OP09MTAzNmzd3GW+jcJb3xfUu5rFu2i3zPcf3OCO00hk7Frp105bufPjhCu+u8q28rYDyVt4K81GFaAU4c+YMp0+fpnHjxmaHch7e3t589dVXjB49mjNnzjBw4ED++usvpx3/zJkznDp1iiZNmjjtmO6As71b1moJQOzJWKccr1S8veHLLyEgAL76CpYtK/euKt/K2woob+WtcA1UIVoBXHk9Wm9vb+bMmcNNN91Eeno6V199Ndu2bXPKsaOioujQoYNLeuuJs71b1dJuB+07tc8pxyuT1q2hcBWuu++GU6fKtZvKt/K2AspbeStcA1WIlpPk5GR8fX0JCwszO5QS8fHxYe7cuYwYMYK0tDQGDBjAP//8U6VjuoO3Hujh3aRGE3y9fDmSfoTMvEynHbdUHnxQW1s+KQkmTy5zc5Vv5W0FlLfyVrgOqhAtBzabjejoaNq1a2d2KGXi6+vLt99+y7Bhw0hNTWXAgAHs3LmzUsdyJ29nope3j5cPzWs2B2D/qf1OPXaJFN6iDwyEuXO1KZ1KQOVbeVsB5a28Fa6FKkTLwcGDB2nQoAGBgYFmh1IufH19WbBgAUOGDOHUqVP069ePXbt2Vfg47ubtLPT0Pnd7/qRBt+cBWrTQVlcCuOceOHGi2M1UvpW3FVDeylvhWqhCtAxycnI4fPgwLVq0MDuUCuHn58f333/Ptddey8mTJ+nXrx+7d+8u9/6F3pGRkTpG6Xro7d0yTBuwZFg/0ULuuw969YKUFHjggQveVvlW3lZAeStvheuhCtEy2Lt3L61atcLb29vsUCqMv78/P/zwA4MGDeLEiRP069ePPXvKN3VQobePj4/OUboWensbOnLeES8v+OILCAqC776DH344722Vb+VtBZS38la4HqoQLYW0tDTOnj1Lw4YNzQ6l0gQEBPDTTz8xYMAAUlJS6Nu3L3v37i11H0/wrgxGeBs+ct6R5s3h9de15/feq7WOovKtvK2B8lbeCtdEFaIlIKVk9+7dLjtdU0UICAhg8eLF9O3bl+TkZPr27UtsbPEtcp7kXRGM8j53a97IPqKOTJwIfftq/UQnTULabCrfytvjUd7KW+G6GF6ICiEaCyHWCiGihRBRQojJ9tfDhBCrhBD77F9rOuwzVQixXwgRI4QYaEScSUlJBAUFERoaasTpdCcwMJClS5fSu3dvkpKS6NOnD/v3Xzhy29O8y4tR3uEh4QT4BJCckaz/mvPF4eUFn38OwcGwaBGnZ81S+bYQyjvU7FAMRXmHmh2KohyY0SKaDzwqpWwLXAHcJ4RoBzwJrJFStgTW2L/H/t4ooD0wCPhICKFrh82CggJiYmJo27atnqcxnGrVqrFs2TJ69OjB0aNH6dOnD3Fxcefe91Tvsij0btOmje7n8hJe5reKRkTAm28CUP3JJ2ljsV/WRubblVDeytsKWNXbnTG8EJVSJkkp/7E/PwNEA+HA9cDX9s2+BobZn18PfCelzJFSHgT2A5frGeOBAwdo3Lgx/v7+ep7GFIKCgli+fDndu3fnyJEj9OnTh0OHDgGe7V0ahd4BAQGGnK9wwJIp/UQLuesuMrp3xzc9nYCHHgIpzYvFYIzOt6ugvJW3FbCqtztjah9RIUQEcCmwFagnpUwCrVgF6to3CwcSHHY7Yn9NF7Kysjh69CjNmzfX6xSmExwczIoVK+jWrRuHDx+mT58+xMTEeLx3cZiR78IWUcNHzjuQlZ3NrgcfRIaEwOLFMH++abEYiRV+votDeStvK+Cu3nl5eUybNg3Akh1ahTSpJUQIEQysB16RUv4ohEiVUoY6vH9aSllTCPEhsFlKOdf++ufAcinlomKOeTdwN0C9evU6fffddxWOKzMzEz8/P0tM93D27Fkef/xx9u7dS/369XnzzTctN8LQjHwvT1rOG7FvMKDuAJ5q+5Rh53Wk0LvRypW0eeMN8qpX5+8vviC3dm1T4jEKK/18O6K8lbcVcEfvXbt2Mfm/5Zf/lVJeZmY8piClNPwB+AK/AY84vBYDNLA/bwDE2J9PBaY6bPcb0K2sc3Tq1ElWlJMnT8pNmzZJm81W4X3dldOnT8uOHTtKQEZGRsojR46YHZJhmJXvPw79IXke2fXTroaet5DzvG02KQcPlhKkvO467XsPxYo/31Iqb+VtDdzN22azyUGDBklAhoaGypycHAlskybUZGY/zBg1L4DPgWgp5VsOby0BxtufjwcWO7w+SgjhL4RoBrQE/nJ2XFJKoqKi6NChg6Wme6hRowavvPIKHTt25MCBA/Tp04ejR4+aHZbumJlv0ya1pxhvIeDTT6FGDVi2DObMMTwmI7Dqz7fyVt5WwGjv8ePHI4QgOjq6UvsfOHAALy8vfv31Vz7//HNOnz6Nn5+fk6N0H8zoI9odGAv0FULssD+uAaYDA4QQ+4AB9u+RUkYBC4E9wK/AfVLKAmcHdeTIEUJDQ6levbqzD+3SHDlyhMaNG7NmzRo6duzIvn376Nu3L0lJSWaHpitm5rteUD2q+1XndPZpks8mG3ruYr3Dw+G997TnkyeDB+beyj/fyts6KG99vXfs2AHA7NmzAWjXrh3vvPPOedt07NiRTZs2lXiMqVOnnlsy/NSpU0yYMEGXWN0JM0bN/ymlFFLKi6WUHe2P5VLKk1LKflLKlvavpxz2eUVKGSmlbC2lXOHsmPLz89m/fz+tW7d29qFdGkfvsLAwVq1axUUXXURMTAy9e/f22JZRs/MthKBzw84ADP1uKCczTxpy3lK9x46FK6+EtDT44w9D4jEKs/NtFspbeVsBI70vvfRShBBkZ2cjpeSuu+7i4YcfpnXr1oVdB8nOzqZ79+48/vjj5+2bmpqKEILp06fz6KOPIqWkZs2axZ3GcqiVlYDY2FgiIiIs1zRe1Lt27dqsWbOGiy++mNjYWHr37k1iYqLJUTofV8j3p0M+JSI0gr8S/6LnVz1JTNf/cy7VWwjw9dWe16qleyxG4gr5NgPlrbytgN7eK1asYNasWQAkJGgT+ISGhrJ+/Xpmz57NqlWriI2NxcvLi2PHjrF3716ef/55Zs6cia+vL/n5+cyZM+dc0RkbG8vMmTN1idVdsXwhmpGRwfHjx4mIiDA7FEMpybtOnTrn3abv3bs3R44cMSdIHXCVfEeGRfLn7X/Svk579hzfQ/cvurP/1IUrXTmLQu+mTZuWvFHhPx3hus2OZjjl8vZAlLfytgJG/D6Pj49n4sSJNGnShPDwcAoKCmjUqBG9e/fmwQcfpH///pw6pd3AbdCgAT/++CPTpk1j27Zt5Ofn4+vry/jx4+nfvz82m42WLVvqFqu7YvlCNCoqinbt2lmqYzeU7l3YMnrppZeyf/9+evXqxeHDh02I0vm4Ur7DQ8JZf9t6Lg+/nPi0eK764ip2Htupy7kKvb28SviRl9IjC9EyvT0U5a28rYCev8+FEKxZs4aJEyeydu1aEhIS8PLyIikpiYSEBF599VXef/99hBAEBwcjpaR79+6MGDGC4cOHk5WVde5Yy5cvZ9WqVS7xd8cVsdZVW4Tjx48DWiuglSiPd1hYGGvWrKFTp07ExcXRu3fvcyswuSuumO9a1WqxZtwa+jXrR3JGMr2+6sWmhJI7uleGcnmnpUFWlrYGfUiIU89vFq6YbyNQ3srbCujt3b59e/r378/EiRPp3bs3qampADRq1IjvvvuOqVOnnhu85OfnR3R0NH/++SdffPEFP//8Mz169CA0NJScnBwGDx6sS4yegmULUZvNxp49e2jfvr3ZoRhKRbxr1qzJ6tWr6dKlCwcPHqRnz57ExMQYEKXzceV8B/sF88stvzCszTDSctJ4bOVjTjt2ub0zM7WvubmwebPTzm8WrpxvPVHeytsKGOG9e/duZs6cyaxZsxBCEBgYiJSSfv36MXr0aK655houueQSsrOzAW0E/cMPP3xuFPxLL71k+WmZyotlC9H4+Hjq1KlDUFCQ2aEYSkW9Q0NDWbVqFVdeeSUJCQn06NHj3H+B7oSr59vfx59eTXsB/80z6gzK7d2wIYwfrxWigwbBtm1Oi8EMXD3feqG8lbcVMMr70UcfZffu3QD4+/uze/duVq9ezdy5c1mxYgVCCHJzc8+NmC+cyunUqVM888wzusbmSViyEM3NzeXQoUO0atXK7FAMpbLeNWrUYOXKlQwYMIDjx4/Tu3fvUudJczXcJd/rDq0DoE9EH6ccr8Len30GI0dCejpcfTW44T8c4D75djbKW3lbAaO927dvT05ODgAXXXQRM2fOZMyYMefGTYSEhJzr+6mmZaoclixEY2JiiIyMdKv1aJ1BVbyDgoJYunQpw4cPJy0tjQEDBrB69WodonQ+7pDvAlsB6+PXA9A7ordTjllhbx8fmDcPrr8eTp+GAQMgKsopsRiJO+RbD5S38rYCZnj7+fkhpWTixIk8/vjjREZG0qhRI77++utz26hpmSqP5QrRM2fOkJqaSuPGjc0OxVCc4e3v78/ChQsZN24cmZmZXHvttfz888/OC1IH3CXfu5J3kZqdStMaTYkIjajy8Srt7esLCxbA4MFw4gT06wdu1C/YXfLtbJS38rYCZnt//PHHrFmzhri4OLy8vNS0TE7CUoWolJLdu3fTvn17S02j4ExvHx8fvvzyS+6//35yc3O58cYbmTt3rpMidS7ulO+1h9YC0KdZ1W/LV9nb3x8WLYL+/SE5Gfr2hQMHqhyX3rhTvp2J8lbeVsBVvPv27cvp06dp0aIFa9euVdMyOQFLFaLJycn4+fkRFhZmdiiG4mxvLy8v3nvvPZ5++mkKCgoYO3YsH330kVOO7UzcKd+F/UN7N+1d5WM5xTswEBYvhp494ehRrRiNj69ybHriTvl2JspbeVsBV/IODQ09t+CLoupYphC12Wzs3buXdu3amR2KoejlLYTg5ZdfZsaMGQDcd999TJ8+3annqArulO8CWwF/xGvru1e1f6hTvatVg2XLoFs3OHxYK0ZddMlXd8q3M1HeytsKWNXbKlimEI2Li6NBgwYEBgaaHYqh6O09ZcoUPv74Y4QQTJ06lSeffPLcVBZm4k753nFsB2k5aTQLbUbT0Kot0ed07+rVYcUK6NwZ4uK0YvTYMecc24m4U76difJW3lbAqt5WwRKFaE5ODgkJCbRo0cLsUAzFKO+JEycyd+5cvL29mTFjBvfddx82m03Xc5aGu+X73G35KraG6uZdowb89htccgnExmoDmOyrmrgC7pZvZ6G8lbcVsKq3lbBEIRodHU3r1q3x9vY2OxRDMdL7lltu4aeffsLf35+PP/6YcePGkZeXp/t5i8Pd8r0ufh1Q9flDdfUOC4NVq6B9e9izR5va6dQp55+nErhbvp2F8lbeVsCq3lbC4wvR1NRUMjIyaNCggdmhGIoZ3kOGDGH58uUEBQUxb948Ro4ceW75M6Nwt3zn2/LP9Q/tFdGr0scxxLtOHVi9Glq1gp07YeBAbY16E3G3fDsL5a28rYBVva2GRxeiUkqioqLo0KGDpaZXMNO7b9++rFmzhpo1a7J48WKuu+46zp49a8i53THfO47tID0nneY1m9OkRpNKHcNQ7/r14fffoXlzbRnQwYPhzBl9z1kC7phvZ6C8lbcVsKq3FfHoQvTo0aMEBwdTo0YNs0MxFLO9u3btyrp166hXrx5r1qxhwIABnD59Wvfzmu1dGZyxrKfh3uHhWjHapAls3gzXXQeZmcac2wF3zLczUN7K2wpY1duKeHQhGhsbS5s2bcwOw1AKCgpcwvviiy9mw4YNNGnShC1bttC7d2+Sk5N1O5+reFeUwonsKztQyTTvpk21YjQ8HP74Q1sW1MBuGO6a76qivJW3FbCqt1Xx2EI0OzubJk2a4O/vb3YohrJ//36X8W7ZsiV//vknrVq1YteuXfTs2ZPDhw/rci5X8i4v+bZ8NsRvACpfiJrqHRkJa9ZAvXpa39ERIyAnx5BTu2O+nYHyVt5WwIre/5rUxckV8NhCNC8vj2bNmpkdhqFkZWWRlJTkUt6NGzdmw4YNXHLJJcTGxnLVVVcRGxvr1HO4ond5+DfpX87knqFFWAsahTSq8P4u4d26tVaM1q4Ny5fDzTeDzrMluIS3CShv5W0FrOgtpeQBJ/9ddCc8thANCAjAy8tj9Yplz549tG3b1uW869aty9q1a+nWrRsJCQn06NGDXbt2Oe34rupdFuduy1dyWU+X8W7fXmsRrVlTWxZ0zBjIz9ftdC7jbTDKW3lbASt6f3/8OBtVi6jn4evra3YIhnLq1Cny8vKoV6+e2aEUS82aNVm5ciX9+/cnJSWFXr16sWXLliof19W9S+PcQKVmFR+o5HLel1wCK1dCSAh8/z3cfjsUFDj9NC7nbRDKW3lbAat6N8jL41ILzwzgsYWolSic5qJ9+/Zmh1IqwcHBLF26lGHDhpGamkr//v1Zs2ZNpY/nLt7FkW/LZ8PhyvUPdVnvzp3h118hKAjmzoV77gEnrrDlst46o7yVtxWwsjdxcay79FKzQzENVYh6AAkJCdSsWZPq1aubHUqZBAQE8P333zN27FgyMjK45pprWLx4caWO5U7eRdl+dDtnc8/SqlYrGlZvWKF9Xdq7Wzetr2hgIHz+Odx/P0jplEO7tLeOKG/lbQWs6J1js53zDgkJMTsc01CFqJuTl5fHgQMHaN26tdmhlBsfHx+++uorJk2aRG5uLiNGjGDevHkVOoY7ejtybn35CvYPdQvvnj1hyRLw94ePP4ZHHqlyMeoW3jqgvJW3FbCq92179jDywAH8mjY1OxRTUYWomxMbG0uzZs3crk+sl5cXH3zwAVOnTqWgoICxY8fyySeflHt/d/UupHB9+Yrelncb7/794ccfwdcX3nkHnnqqSsWo23g7GeWtvK2AFb03paXx3YkT7PTyws/Hx+xwTEUVom5MRkYGJ0+epKmb/jclhODVV19l+vTpSCm59957mTFjRpn7ubt3XkFepeYPdTvva67RBi75+MD06fDii5U6jNt5OwnlrbytgBW9bVLyQEwMAI81bkxEYKDJEZmLKkTdmN27d9OuXTu3X4f3iSee4KOPPkIIwZNPPslTTz2ldeAuAXf33p60nYy8DFrXak2D6g3KvZ9bel9/PcyfD15e8PzzWkFaQdzS2wkob+VtBazoPefYMf7JzKSBjw9TLVSAl4QqRN2UlJQUvLy8qF27ttmhOIV7772Xb775Bm9vb1577TUeeOABbMWMuPYE77UHK76sp1t7jxwJX38NQsDUqfD22+Xe1a29q4DyVt5WwIre6fn5TNm/H4A3WrYkyNvb5IjMRxWibojNZiM6OtrjprkYM2YMixYtws/Pjw8//JDbbruNfIeJ0T3Fu7B/aJ+I8s0f6hHet94Kn32mPX/kEfjoozJ38QjvSqC8lbcVsKr3K/HxHC8ooGtwMLfUrWt2OC6BKkTdkEOHDlG3bl2qVatmdihO5/rrr2f58uUEBQXxzTffMHLkSHLs65d7gndeQR5/Hv4TgF4Rvcq1jyd4AzBhwn8F6H33/VeYloDHeFcQ5a28rYBVvbPT0vAH3m/VylLdEUpDFaJuRm5uLvHx8bRs2dLsUHSjX79+rFq1itDQUH7++Weuu+46Tp8+7RHefx/9m8y8TNrWbkv94Pplbu9x+b733v9uzd99N3zzTbGbeZx3OVHeytsKWNl7WFoah7p2pYuF5w0tiipE3Yy9e/fSokULfDx8uodu3bqxbt066taty+rVq+nTpw916tRxe+9z84eWs3+oR+b7oYe0QUtSwm23wYIFF2zikd7lQHkrbytgde/6Fh8lXxRViLoR6enppKen06hRI7NDMYRLLrmEDRs20KhRI3bu3Mmtt95KSkqK2WFViYoUoh6d7yeegBde0JYAHTMGfvrp3Fse7V0Kylt5WwEreufbbPTbvp3vU1NpGB5udjguhypE3QQpJbt376Z9+/aW6lfSsmVL3n33XSIjI9m5cyc9evQgISHB7LAqRW5BLhsTNgJlF6KWyPezz2qj6AsK4Oab4ZdfrOFdDMpbeVsBq3p/cvQov585wzc+PuQ5acljT0IVom7CsWPHCAgIoGbNmmaHYijHjh0jIiKCjRs3cvHFFxMbG8tVV13Fvn37zA6twvydqPUPbVenHXWDSh8taYl8CwGvvKKNos/LgxEjOLVgged7F4Ml8l0Mylt5ezon8/J4Ni4OgLdbtiRATdd0AaoQdQMKCgqIiYmhXbt2ZodiKI7e9erVY926dVxxxRUcPnyYHj168L///c/sECtEedeXt1S+hYCZM7VR9Dk5hN52G+1PnjQ7KkOxVL4dUN7K2wpMi4sj1Wajd0gIwyw0X2pFUIWoGxAXF0fDhg0JCAgwOxRDKepds2ZNVq1aRb9+/UhOTqZXr15s3brV5CjLz9pD2kT2fZqVPn+o5fItBLz3HmkjR+Kdk4P/8OGwaZPZURmG5fJtR3krb08nKiODT5KS8ALeU9M1lYgqRF2c7OxsEhMTiYyMNDsUQynJOzg4mGXLlnH99ddz+vRp+vXrx9q1a02Ksvzk5OewKUErrno1LXn+UMvmOzeXfydOxHbrrZCRAYMHw99/mx2W7lg238rb7FAMxdO8pZSlLkNduM3k2FgKgHsaNOCi4GBjgnNDVCHq4kRHR9O6dWu8LdavpDTvgIAAvv/+e8aMGUNGRgaDBw9m6dKlJkRZfv4++jdZ+Vl0qNuBOkF1StzO0vlu2xavL7/UBi6lp8PVV8OOHWaHpiuWzrfytgye5v3www/j5eVFXl5eiducyMsj9swZQr28eKl5cwOjcz9UIerCpKamkpWVRf36ZU987kmUx9vX15c5c+YwceJEcnJyGD58OPPnzzcwyopxbn35UvqHqnzXBx8fbZL74cMhNRX694fdu80OURdUvpW3FfAU70WLFiGEIC0tjRkzZgDg5+dX4va+mZnM8fZmVceO1PL1NSpMt0QVoi5K4TQXHTp0sFS/kop4e3l58dFHH/HEE09QUFDArbfeyqxZswyKtGIUri9f0rRNKt8O3r6+8N13cO21cPIk9OsHe/eaG6iTUflW3lbA3b0PHTqEEILDhw9zww03ABAaGoq/vz877HdrHnnkkQv2K/S+7KKL6FzOFZTOnj3rtLjdDVWIuiiJiYlUr16dEIstA1ZRbyEE06dP57XXXkNKycSJE3n99dd1jrJinNc/tIT15VW+i3j7+cEPP8CAAZCSohWj+/ebE6QOqHwrbyvgjt5SSoQQ/Pvvv0RERADQtGlTpJTn5rC+5ppruOSSS3jxxRd5++232bx587n9j+XkcNeOHeQHBZXpfeLECYYMGYIQgurVqwN4Rt+FCqIKURckPz+fffv20bZtW7NDMZSqeD/55JN8+OGHADzxxBM888wzZXYmN4qtiVvJzs/moroXUbvahdN3qHyX4B0QAD//DL16wdGjWjEaH29ojHqg8q28rYC7ehe23F522WXk5OSQlpYGQPPmzWnUqBHffPMNK1as4IcffuDZZ5+lZs2aXHnllWRkZADw5IEDfJ6Wxicl3I7Pz8/n6aefRghBnTp1WLZsGddcc03hqoEFRji6GqoQdUH2799P06ZNS+1/4olU1XvSpEl88803eHt788orrzB58mRsNpuTo6w4ZS3rqfJdine1arBsGVx5JRw+DH36wJEjxgWpAyrfytsKuLN3bm4uoA2MDQkJ4ffffyc+Pp4333yTW2+9lauuuoqRI0dy7Ngxjh8/DmgzumxLT2dOSgq+cMEApe+//x4hBL6+vrz66qs0atSIf/75Byklv/zyC3XqlDyI1dNRhaiLkZmZSXJy8rlbAlbBWd633norP/zwA35+frz//vtMmDCB/Px85wRZSQoL0T4RF84fmpmZeW71KCtRIe/gYFi+HLp0gYMHoW9fSErSPUY9UPmOMDsUQ7Gytzv/HfP19SUqKgqAe++9lz59+nDXXXfx2GOPER0dzYYNGwBo0KABXl5e7Ld3Gxr0yy9I4MFGjWhZrRq7du2iadOmCCG46aabAJg/f/652/yXXnqpKX6uhipEXYw9e/bQtm1bvLyslRpneg8bNoxffvmFatWq8fXXX3PzzTeTk5PjhCgrTnZ+NpsSNiEQ9Gza84L39+zZQ7t27SyZ7wp516gBv/0GHTvCvn3aaHp7S4Q7ofKtvK2AJ/wda9euHa+//jqffPIJ69evZ/bs2edez8vL48SJEwB07NiRyMhIJsydy8kGDaien8+eqVMRQnDJJZdw+PBhpk6dSm5uLlJKRo8ebaaWS+K+V4kHcvLkSQoKCqhbt/R1yD0NPbz79+/P6tWrCQ0N5ccff2To0KHn+vAYydYjW8kpyOHiehdTq1qt895T+a6gd82asGoVtG8Pe/ZoxeipU/oEqQMq38rbCniS9+OPP06jRo3o3bs3Z86cISsrC4Bq1apRq1Ytli1bxq5du3jro49YGBYGwJm332bFDz8wePBgkpOTkVLy6quv4qumcCoRVYi6CFJKoqKiaN++vdmhGIqe3t26dWPt2rXUqVOHlStXcvXVV3PK4MKl8LZ84xqNyS3IPfe6ynclvWvXhjVroHVr2LVLm/TehH8wKorKt/K2Ap7oHW8fIBkSEkJAQADbt28nPz+fJ598kuzsbAAe/f57zgYG4nvwIJueew4pJcuXL/eIYtwIVCHqIhw+fJhatWoRbLFlwPT27tixIxs2bKBRo0Zs2rSJ7t27c+jQIV3OVRw2qQ2WWha7jGbvNmPGnzNIzU5V+a6Kd716WjEaGQnbt8MDDzgvQJ1Q+VbeVsATvb28vM4Vo8OHDz/XsjljxgxuvPFGAF4bOJCva9Rg44gRdOva1bRY3RVViLoAeXl5xMXF0apVK7NDMRSjvFu3bs3mzZvp0KEDe/fupVu3bvz777+6nrOQZ3s9y1fXf0WHuh04euYoT655ksZvN+aRVY/gV9f9RpNWBafmOzwcfvpJm+Lpyy+11ZhcFPXzrbytgCd7N2nShC+//JKff/6Ziy++GIAJEyaQk5NDbm4uXbt2ZXSHDnRxo/lSXQnhKnMtOpvOnTvLbdu2mR1GuYiKiiI4OJimTZuaHYqhGO2dmprK8OHDWbduHcHBwSxatIirr77akHNLKVl5YCUzN89kddxqALyEFyPbjeSxKx+jc8POhsRhJrrk+7PP4K67IChIax1t3dp5x3YS6udbeVsBK3hv2LCBVq1aUa9ePQD+Tk9nb1wcPWvWdIq3EGK7lNLz/xgUQbWImszZs2c5efIkTZo0MTsUQzHDOzQ0lF9//ZXRo0dz9uxZrr32Wr766itDzi2EYGCLgfw0/Cc+6/wZYy8ei5fwYkHUArp82oXeX/VmaczSc7fyPQ3d8n3HHTB6tNZP9KabwD6YwFVQP9/K2wpYxbtHjx7nitACKblr717GpaayNTDQ5MjcG1WImkxhx253XIe3Kpjl7e/vz9y5c5kyZQr5+fncfvvtvPzyy4atwhQVFcWwK4YxZ/gcDk4+yONXPk6Ifwjr49cz9LuhtPuwHZ9u/5Ts/GxD4jEK3fItBHzyCbRooQ1eKmbdZzNRP9/K2wpY0fvLpCR2ZmbS0NeX62rVKnsHRYmoQtREUlJS8Pb2ppbFLmKzvb28vJgxYwbvv/8+QgieffZZ7rnnHt0nvi/q3SikEa8PeJ2EhxN46+q3aFKjCTEnY7h72d00ebsJL65/kROZJ3SNyQh0z3dICCxcqK1P/8kn2nMXwOzr3CyUt/L2dNLy83nywAEA3mzRgmrellwi3mmoQtQkbDbbucmOrYQred9///0sWrSIgIAAPv30U4YNG6bbXKOleYf4h/Bwt4fZ/8B+5t8wn8saXMbxzONMWzeNJm83YdIvk9h3cp8ucemNYfm+9FJ46y3t+Z13gv2PhFm40nVuJMpbeVuBlw4d4mRBAd2Cg7lZTdFUZVQhahKHDh2ifv36VKtWzexQDOXgwYMu5T18+HDWrFlDWFgYv/zyC7179yYlJcXp5ymPt6+3L6MvGs22u7axdvxarm15LVn5WXy87WNaf9Ca4QuGs/HwRsO6ETgDQ6/zSZPghhvgzBm4+WYwaTUtsO7Pt/JW3p5ObGYm7x05ggDeb93aUt0R9EIVoiaQk5NDfHw8LVq0MDsUQ8nJyeHw4cMu533llVeyadMmmjVrxrZt2+jWrRv79jmvBbKi3kIIekf0Ztkty4iaFMUdl96Br7cvP+/9mau+vIorv7iSRXsWUWArcFqMemD4dS4EfP45RERoI+inTDHmvEWw8s+38rYOVvV+5sAB8oDx9erRqXp1s8PxCFQhagJ79+6lZcuW+Pj4mB2Kobiyd+vWrdm0aROdOnUiLi6OK6+8ki1btjjl2FXxblenHZ8N/Yz4h+J5psczhAWGseXIFm78/kZafdCKD/76gIxc11xZyJR8h4bCggXg6wvvvQc//2zcue248nWuJ8pbeVuBSfn5TKhRg9eaNzc7FI9BFaIGk56ezpkzZwgPDzc7FENxB+/69euzbt06Bg8ezIkTJ+jbty9Lliyp0jGd5V0/uD4v9X2Jww8d5sNrPiSyZiRxp+N4YMUDNH67MU+veZqkM0lVOoczMTXfl18OM2Zoz2+/HeyrohiBO1zneqC8lbcVSE9Pxycjg886dqS+v7/Z4XgMqhA1ECklu3fvpkOHDpbqV+JO3sHBwSxevJg77riDrKwshg8fzscff1ypY+nhHeQXxKQuk4i5P4ZFNy2iW6NunM4+zat/vkrEuxHcsfgOolKinHKuyuIS+X7oIbjuOkhNhVGjIC9P91O6hLcJKG/lbQW2p6fzz//+ZzlvI1CFqIEcO3aMwMBAQkNDzQ7FUJKSktzK29fXl08//ZTnn38em83GpEmTmDp1aoUHCenp7e3lzQ1tb2DTHZvYOGEjN7S9gbyCPL7Y8QUdPu7ANfOu4feDv5sysMklrnMh4KuvoHFj2LIFnn5a91O6hLcJKO9Qs0MxFCt6n8jNpd+OHYwrKCDPQgOzjEIVogZRUFBATEwMbdu2NTsUQykoKCA2NtbtvIUQTJs2jc8//xxvb2+mT5/OuHHjyM3NLdf+Rnpf2fhKFt20iNgHYpnUeRKBPoGs2L+CfnP60Wl2J+b/bz55Bfq3CIKLXee1asG334K3N7zxBixfrtupXMrbQJS38rYCzx48SJrNRqvq1ant62t2OB6HKkQN4sCBA4SHhxMQEGB2KIbi7t4TJkxg2bJlBAUFMXfuXK655hrS0tLK3M8M7xZhLfjw2g85/PBhXurzEnWD6vLvsX8Z8+MYIt+L5M1Nb5Kek65rDC6X7+7d4eWXtefjxsGRI7qcxuW8DUJ5K29P539nzzI7KQlv4N1WrdRteR1QhagBZGVlcfToUZpbbJSdp3gPGjSI9evXU69ePdasWUPPnj1JTEwscXuzvWtXq80zPZ8h/qF4Ph3yKW1qtyEhPYHHVj1G47cb8/jKx0lIS3D6ec32LpEpU2DgQDh5Em65BZy8gpbLeuuM8lbeno6UkgdiY7EB9zZsSPugILND8khUIWoA0dHRtGnTBm+LLQPmSd6dOnVi8+bNtG7dml27dnHFFVcQFVX8oKDo6Ghat25tuneATwB3XnYnUZOiWDZ6Gb0jepOek87MzTNp/l5zxv40lh3HdjjtfK7ifQFeXjBnDjRoABs2wPPPO/XwLuutM8rbet6e8vu8vPx84gTr09Op6eXFC82amR2Ox1JqISqECCvtYVSQ7szp06fJycmhXr16ZodiKJ7o3axZMzZu3MiVV17JkSNH6N69O+vWrTtvm9OnT5OdnU39+vXNCbIYvIQX17a6lrXj1/L3XX8zqsMopJTM3TWXS2ddSv85/fl1/69VGthUmG9X8j6PunVh/nytKH31VVi1yimHdXlvnVDe1vT2pN/nZZFdUMDD9oVNXmzenDDVN1Q3ymoR3Q5ss38t+timb2juj5SSqKgo2rdvb6l+JZ7sXatWLVavXs0NN9xAWloaAwcO5LvvvgP+83bl6T06N+zMtyO+5cCDB3j4iocJ9gtmzcE1DJ43mIs+vogv//2SnPyKLY3pNvnu3RumTQMp4dZb4dixKh3ObbydjPJW3lbAVwhuzc+nb/XqTGzY0OxwPJpSC1EpZTMpZXP716IP63QUqSRHjhwhJCSEkJAQs0MxFE/3DgwMZOHChTz44IPk5uYyevRo3nzzTRISEtzGu2loU94a+BYJDycwo/8MwquHE3U8iglLJhDxbgSvbXiNU1mnynUst8r3009Dnz6QkgJjxkBB5ZdJdStvJ6K8lbcVOJqYyE2hoazp1AkfL9WLUU/K/ekKIWoKIS4XQvQsfOgZmLuTn5/P/v37adOmjdmhGIpVvL29vXnnnXeYOXMmAI899hgPP/wwLVu2NDmyihEaEMqU7lOImxzHnGFzuLjexRw7e4ynfn+Kxm835sEVDxJ3Oq7E/d0u397eMG+edqv+99+12/SVwO28nYTyVt5W4FhWliW9zaJchagQ4k7gD+A34AX71+f1C8v92bdvHxEREfj5+ZkdiqFYyVsIwaOPPsq3336Lr68vP/74I2PHjiUrK8vs0CqMn7cfYy8Zy457drBq7CoGRg4kMy+T9/96n5bvt+Sm729i65GtF+y3b98+mjZt6l75btAA5s7VJr1//nlYv77Ch3BLbyegvK3nbZXf54X8lZ5OxNatLA4NtZS3mZS3RXQy0AWIl1L2AS4FjusWlZuTmZlJSkoKTZs2NTsUQ7Gq99ChQ3n99depUaMGixYtYsCAAZw8edLssCqFEIL+zfvz662/snPiTsZfMh5v4c33e77nis+voMeXPVi8dzE2aTuX74iICLPDrjgDBsDUqWCzwejRcLz8v87c2rsKKO8Is0MxFCv+PpdS8kBMDDlAgMW6IphJeQvRbCllNoAQwl9KuRdorV9Y7k1UVBTt2rXDy2L9SqzsPWbMGDZu3EijRo3YuHEj3bt35+DBg2aHViUurncxXw37ikMPHeLJ7k9Sw78Gfx7+k2ELhtH2w7a8uPxFmrVq5r75fuEFuOoqSErSJru32cq1m5Wvc+VtHazoPT8lhb8yMqjr48PTFirAzaa8V9gRIUQo8DOwSgixGDiqV1DuzIkTJ7DZbNSpU8fsUAxFedehffv2bNmyhYsvvpiYmBi6devGP//8Y3aIVaZh9Ya81v81Eh5O4J2B79C0RlNiT8YyI2oGneZ24sX1L5Jvc+4k8Ybg46MtAVqrFvz6q7YMaBmo61x5WwErep/Nz+cx+3RNMyIjqe7jY3JE1qFchaiUcriUMlVK+TzwLPA5MEzHuNwSKSV79uyhQ4cOZodiKMr7P+/w8HD++OMP+vXrR3JyMj179mTp0qUmRuk8qvtXZ/IVk9n3wD6ebfcswb7BnMg8wbR101gSs8Ts8CpHo0bw9dfa82eege3bS9xUXefK2wpY1Xv64cMcy8/nsmrVGGexeWLNpryDla4QQlQHkFKuB9ai9RNVOBAfH0/t2rUJstgyYFb2rlWr1gXeNWrUYPny5YwbN46MjAyGDRvGe++9Z1KUzuf3Xb+z5NgSzuadBWB4m+EMajHI5KiqwLXXwoMPakt/jhsH2dnFblZSvj0d5W09b6v9Pj+UlcUbhw8D8EHr1niVMF/q66+/zsqVK40MzRKU99b8x8BZh+8z7K8p7OTl5XHw4EG3m76nqljdu1WrVsW+7+fnx1dffcWLL76IzWZj8uTJPPjggxRUYd5Ks8nOz+ap1U9x3dLr2HlqJw2CG/DjTT/y480/Us23mtnhVY3XXoNWrWDPHq1ltAhl5dtTUd7W9Lba7/Pc/Hw6Ssmo2rXpVqMGH330Ef/++y8A/v7+dOnSBYAffviBgQMHmhmqR1LeQlRIh/X/pJQ2QHWgcCAmJobIyEh8LbYMmPIu2VsIwbPPPsvcuXPx8/Pj/fffZ9iwYZw9e7bEfVyVP+L/oOMnHXlt42vky3wmdppI9H3RDG873OzQnEO1atp69F5e8NZb8Mcf572trnPlbQWs6p0bH8/C5s35sm1bAF588UUuu+wyAGbPns22bdtISUlh7dq1ALzyyiumxeqJlLcQjRNCPCiE8LU/JgMlz3JdCkKIACHEX0KInUKIKCHEC/bXw4QQq4QQ++xfazrsM1UIsV8IESOEcLl/R86cOcPp06dp3Lix2aEYivIun/eYMWNYvXo1YWFhLFu2jB49epCYmKhzlM4hNTuVe5beQ6+vehFzMoYm1Zqwfvx6Pr7uY2oE1DA7POfStSs89ZS2BOhtt8GZM4C6zpW3NbCit01K0tPTOX36NE2aNCHA2xuAnTt3AjB37lzGjx8PQJs2bQgKCqJ3794888wzOLTNKapIeQvRicCVQCJwBOgK3F3Jc+YAfaWUlwAdgUFCiCuAJ4E1UsqWwBr79wgh2gGjgPbAIOAjIYR3Jc+tC1ZchxeUd0W8e/TowZYtW2jRogU7duyga9eu7NixQ78gncCP0T/S7sN2zP5nNr5evkyInMCW27bQM8KDF1V79lm49FI4eBAefRRQ17nytgZW9P4sKYne//4LzZuf512vXj2aNm3K2LFjAfjxxx85ffo0CQkJ/PrrrwBMmTLFlJg9kfKOmk+RUo6SUtaVUtaTUt4ipUypzAmlRuG9SV/7QwLXA/bhq3zNf6Pyrwe+k1LmSCkPAvuByytzbj1ITk7G19eXsLAws0MxFOVdce+WLVuyZcsWrrrqKhITE7nqqqv45ZdfdIiyaiSmJzJ8wXBGLBxB0tkkujXqxqobVzGp7SQa1Glgdnj64uen3aL384NPP+X0vHnqOrcQyts63ql5eUw9cIB/pSSpmK4IhQ0FH374IcOHa12QmjRpgr+/PzfccAMzZ87EVs65hxWlU95R868LIULst+XXCCFOCCFurexJhRDeQogdQAqwSkq5FagnpUwCsH+ta988HEhw2P2I/TXTsdlsREdH065dO7NDMRTlXXnvWrVqsXr1asaMGUNGRgZDhw7lww8/dGKUlccmbXz898e0+6gdP+/9mep+1fnwmg/547Y/kMnSOvnu0AFefhmAapMn085iU7mon2/lbQVeOHSIUwUFdK9enZHFzJcaGhrKpZdeyv333w/AqlWrAIiNjWXhwoUA3H13ZW8MayQlJfH222/TpUsXGjVqBOW/S+1RiPL0cxBC7JBSdhRCDEdrqXwYWGu/vV75k2uT5P8EPAD8KaUMdXjvtJSyphDiQ2CzlHKu/fXPgeVSykXFHO9u7F0G6tWr1+m7776rSnhlkpOTA2ij6qyE8q66t5SSr7/+mq/tc1iOGDGCe++9F29vc3qdHMo4xJuxb7I7fTcAV9a6kodaPkQd/zrWzHdBAZdMnkzNqCiS+/Yl+tlnzY7IMCyZb5S3lbwPAxOkxAbMEoKS5gjIysrimmuuYcKECYwdO5Y+ffoAsHbtWmbOnMkvv/zC6tWry/y9nZuby+bNm1mzZg0bNmwodpu2bdsSHR39j5SyU+XN3BQpZZkPIMr+9VNgkP35zvLsW45jTwMeA2KABvbXGgAx9udTgakO2/8GdCvruJ06dZJ6kp2dLX///XeZn5+v63lcDeXtXO85c+ZIX19fCcghQ4bIM2fOOPX4ZZGdly2nrZ0mfV/0lTyPrD+zvvw+6ntps9m09y2c703ffCNtQUFSgpTffWd2SIZg5Xwrb+sw6N9/JWvXyjujo8vctl+/fhKQNptNbt68WQLy33//lTabTQJy+PDh57a12Wzyr7/+kg899JCsW7euROt2eMFj1KhRcunSpTInJ+e8cwHbpBPqKnd7lLdYnA7sBf5F69NZB9haqRNq+4banwcCG4DrgDeAJ+2vPwm8bn/eHtgJ+APN0Ebre5d1Hr0L0R07dsgjR47oeg5XRHk7n3Xr1smaNWtKQF522WUyMTFRl/MU5c/4P2XbD9pKnkfyPPKuJXfJU5mnztvG8vn+5BPt12RYmJRHj5odlu5YPt8Ww4rev5w4IVm7VlZfv14mFykEiyMnJ0cC8pFHHpFSynPFZGJiogwKCiqx2ARk9+7d5UcffSRTUlLKFZtVC9HyDlZ6EugGdJZS5qFNaH99efYthgbAWiHELuBvtD6iy9CK3QFCiH3AAPv3SCmjgIXAHuBX4D4ppamzgqelpXH27FkaNmxoZhiGo7z18e7VqxebN28mMjKSf/75h65du7Jr1y5dzgWQlp3GpF8mcdWXVxF9IppWtVqxbvw6Zg+ZTc3Amv9tp/INd98NgwbBqVNw553a1E4eisq38rYC+1NT8QemRURQ18+vzO39/PwYOXIkb7311nkj68PDw8nIyACgQYMGPPnkk+zateu8AuvPP//k3nvvpU4xfVAV/1GuPqIAQogOQDsgoPA1KeUcneKqMp07d5bbtm1z+nGllGzatIkOHTpQo4aHzaNYClb3bt++PaGhobqe68SJEwwbNoyNGzcSHBzM999/z6BBzl06c/HexUxaPomjZ47i4+XDE92f4JmezxDgE3DedkZ6uxLFeicmagOYUlPh00+1gtTDUPlW3lag0Ltmq1a0qFULP6/yjQ0qKCjAx0dbw+eWW25hxIgRXHfddfiVo5CtCEKI7VLKzk49qBtQ3lHz04D37Y8+wOvAUB3jclmSkpIICgqyVDEGytuIX9a1a9dm9erVjBo1irNnz3Lttdfy0UcfOeXYNmnj1h9vZdiCYRw9c5Su4V355+5/eLnvyxcUoWCstytRrHd4OBTObPDww9ocox6Gyneo2aEYitW929WpU+4iFMDb2/tcK+e8efO44YYbnF6EWpnyZuJGoB9wTEp5O3AJWp9NS1FQUEBMTAxt7cuAWQXlbZx3QEAA8+bN45lnnsFms3Hffffx0EMPVXmN+hOZJ/hu93+zSBxKPcR7W99jWewysvKyzttW5bsY79GjYeRIOHsWxo6FKubDlVD5Vt5W4MkDB3gzJoZWbdqYHYqiCOUtRLOktr58vhAiBG3+z+b6heWa7N+/n8aNG1tqmgtQ3kZ7e3l58dJLL/H111/j6+vLu+++y/XXX88Z+5KTlaFuUF0237GZBy9/kKY1mpKckcxn/37GkG+HUPuN2gxfMJyvdnzF8YzjKt/FeQsBH38MDRrAxo3w+uvGB6gTKt/K29PZceYMryck8J4QJKpJ6F2O8hai2+xzfn4KbAf+Af7SKyhXJCsri6SkJJo3t1b9rbzN8x43bty5Nep/+eUXrrrqKg4fPlzp43UJ78K7g9/l4OSD7Jy4k5f6vETnhp3JzMvk570/c/vi26n/Zn1GLh/JT8k/EXsy1ok2rk258l2rFnz5pfb8uefgn3+MCU5HXOE6NwPlbR1vKSUPxsYigUnh4bSsVs3skBRFKPdgpXM7CBEBhEgp9RvW6wScPVhp+/btNGrUiHr16jntmO6Alb3Dw8Op7wKr6uzfv59rr72W2NhY6tWrx5IlS7j8cuetcpuYnsjS2KUsjlnMmrg15Nnyzr3XpnYbrm99PUNbD6VreFe8vcyZcF9vKpTvBx+E99+HNm1g+3Zw4z9srnSdG4nyto73DykpjNyzhzBvb/ZfcQU1i1nO01VQg5XKQAhxgxDiLbRVkCL1C8n1OHXqFLm5uZYrxpS3a3i3aNGCLVu20LdvX5KTk+nVq9e5JeacQXhIOBM7T2Te4Hks772chTcu5NaLb6VmQE32ntjLjI0z6P5Fd8LfCmf9ofVOO6+rUOF8z5gBbdvC3r0webLbTunkate5UShv63hnFRTwyL59ALzSvLlLF6FWpryj5j8CJgL/A3YD99iX3vR4pJRERUXRoUMHs0MxFOXd4bw548ymZs2a/Prrr9x5551kZ2dz880388ILL1DROxolUejdtWNXRrYfyTfDvyH5sWTWjl/LA5c/AEByRjJbE7c65XyuQqXyHRgIc+eCnx989hk89pjbFaOuep3rjfK2lvfMhAQS8vLoEBjIXRabL9WdKG+LaC9goJTySynll8A1QG/donIhEhISCA0NpXr16maHYijK2/W8fX19mT17Nm+++SZeXl48//zzjBo1iszMzCofuzhvX29fekf0JiI0AoBGIY2Y2Hlilc/lSlQ635ddBj/8AL6+8NZbMHWqWxWjrnyd64nyto63TUp+SkoC4L1WrfC2UAHubpS3EI0Bmjh83xhw6T6iziAvL48DBw7QunVrs0MxFOXtut5CCB555BGWLl1K9erVWbhwIb169SIxMbHSxyzN+8CpAzzz+zMAfHLtJ4T4h1T6PK5GlfM9ZAgsWAA+Ptrt+ueec26AOuEO17keWNU7Pz/fkt62ggJez87mpzZt6FOzZtk7KEyjvIVoLSBaCLFOCLEObbnNOkKIJUKIJbpFZzL79u2jWbNmlpu41sreERERbuF9zTXXsHnzZpo1a8a2bdvo0qULlR2cV5K3lJK7lt5FVn4Wt1x0C9e2utYZobsMTsn38OHw7bfg7Q0vvwwvvui8AHXCna5zZ2JV79jYWMt6t2jWjGEWGpjlrpS3EH0OGAxMsz+uAV4C3rQ/PI6MjAyOHz9O06ZNzQ7FUKzuHRERYXYo5aZ9+/b89ddf9OzZk6SkJHr06MGCBQsqdIzSvD/75zPWHlpL7Wq1eXfQu06K2jVwar5vvFHrM+rlBdOmwauvVv2YOuGO17kzUN4RZodiGDYpeTo2lr0pKZb7O+aulKsQlVKuB/YC1e2PaCnl+sKHngGaRVRUFO3atbNUx25Q3u7mXbt2bVatWsUdd9xBdnY2o0aN4vnnn8dWzkmbS/JOTE/ksVWPAfD+4PepXa2202M3E6fne9Qo+PprbeL7p5922Qnv3fU6ryrK2zre3yQn8+rRozzh54f79Nq2NuUdNX8T2gT2I4GbgK1CiBv1DMxMjh8/DkCdOnVMjsRYlLd7evv5+fHpp5/y1ltv4eXlxQsvvFCuQUwleUspufeXe0nPSWdIqyHc3P5m3WI3A93yfeut8MUXWjH6xBPw9tvOPX4VcffrvLIob+t4n8nPZ8r+/QA817w5XhYqwN2Z8t6afxroIqUcL6UcB1wOPKtfWOZhs9nYs2cP7du3NzsUQ1He7u0thODhhx9m2bJlVK9ene+//56ePXuWOIipNO8FUQtYGruUEP8QPr72Y49qTdE937fdBrNna88feUSb+N4F8JTrvKIob2t5vxIfT0p+Pl2CghhjoflS3Z3yFqJeUsoUh+9PVmBftyI+Pp66desSFBRkdiiGYmXvOnXqeIz34MGD2bJlC82bN2f79u106dKFv//++4LtSvI+kXmCB1Zo84bOHDCT8JBwQ+I2CkPyfeed2rr0oK3CVPjcRDztOi8vyts63nFZWbydkADA+61bq9ZQN6K8xeSvQojfhBC3CSFuA34BVugXljnk5uZy6NAhWrZsaXYohmJ171atWpkdilNp164dW7dupVevXiQlJdGzZ0++++67c++X5j3518mcyDxBn4g+3HnZnUaGrTuG5nvixP9aQydNgk8/1f+cJeCp13lZKG9reT+ybx+5wJg6dega4jnTzFmB8g5WehyYBVwMXALMllJO0TMwM4iJiaFFixb4+PiYHYqhKG/P865duzYrV648txLT6NGjee6557DZbCV6L4tdxvz/zSfQJ5BPh3zqUbfkwYR833+/Ntk9wD33wFdfGXPeInjydV4ayts63tEZGSw5dYpqQjCjRQuzw1FUkPIOVpohpfxRSvmIlPJhKeVPQogZegdnJOnp6aSmptKoUSOzQzEU5e253n5+fsyePZu3334bLy8vXnrpJYYPH05SUtIF3mnZaUxcpq2a9HLfl4kMizQjZN0wLd8PP6yNoJcSJkzQpnkyECtc58WhvK3lHV5QwCwvL2a1bk24v7/Z4SgqSHlvzQ8o5rXBzgzETArX4W3fvr3HtQKVhvL2fG8hBA899BDLli0jJCSEJUuWMGXKlAsGMT2x+gkSzyRyefjlTO462aRo9cH0fD/+OLzyilaMjh+vTYBvAKZ7m4Tytqb3iA4duFVNXu+WlFqICiHuFUL8D2gjhNjl8DgI/M+YEPUnOTkZf39/wsLCzA7FUKzs7efnZynvwYMHs3TpUsLDw9m5cyddunThr7/+AmDdoXXM2j4LXy9fPh/6Od5e3iZH61xcIt9PPQUvvAA2G4wdC99/r/spXcLbBJS3dbxP5+XxfVycJf+OeRJltYjOB4YAi+1fCx+dpJRjdI7NEGw2G3v37qVt27Zmh2IoVvdu166d2aEYis1mw2azsWXLFnr37s2xY8fo1asXX33zFXcu0QYlPd3jaTrU7WBypM7FpfL93HPwzDNQUAC33AI//aTbqVzK20CUt7W8px08yM0JCSyqXt3sUBRVoNRCVEqZJqU8BLwLnJJSxksp44E8IURXIwLUm7i4OBo2bEhgYKDZoRiK8ramd6NGjfjtt9+46667yM7O5vZxt3Ng0QHa127P1B5TzQ7T6bhcvl98EZ58EvLz4eabYelSXU7jct4Gobyt470nI4OPjh7FC7i5QQOzw1FUgfL2Ef0YOOvwfYb9NbcmOzubhIQEIiM9a2BGWShva3v7+fkxa9YsHnn+ERDAH1B3eV3ysvPMDdTJuGS+hdDWon/0UcjL09apX77cqadwSW8DUN7W8ZZSMjk2lgLgrvr1uTg42OyQFFWgvIWokFKeW7ZVSmkD3H5uiL1799K6dWu8vT2rT1xZKG/lnWfLY2XdlXAL+FXzY+3ytfTo0YME+4TQnoDL5lsIeOMNmDwZcnPhhhtg5UqnHd5lvXVGeVvH+5eTJ1mdlkYNLy9eat7c7HAUVaS8hWicEOJBIYSv/TEZiNMzML1JTU0lIyODBhZr0lfeyhtg+p/T2Z2ym8jLI9m8eTORkZH8+++/XH755WzdutWkaJ2Hy+dbCG0t+kmTICcHrr8e1qyp8mFd3lsnlLd1vHNtNibHxgIwrVkz6vj5mRyRoqqUtxCdCFwJJAJHgK7A3XoFpTeF0z106NDBktNcKG9rUJL37pTdvPzHywB8NvQzLrv4MrZu3XreIKb58+ebFXaVcZt8C6GtvnT33ZCdDUOGwPr1lT6c23g7GeVtLe8PExOJy82llb8/94d71hLEVqW8KyulSClHSSnrSinrSSlvKbL2vFtx9OhRgoODqVGjhtmhGIryVt4FtgLuWHIHebY87ul0D70jegNQq1YtVq5cyd13301OTg5jxozhmWeewWazmRR95XGrfHt5aWvRT5gAWVlw7bXw55+VOpRbeTsR5W0t70FSMs7fn/dat8bXq7xtaQpXprwrK7USQqwRQuy2f3+xEOIZfUPTh/z8fGJjY2nTpo3ZoRiK8lbeAO9tfY+/Ev8ivHo4M/qfvziar68vn3zyCe+++y5eXl688sorjBw5koyMDCNDrxJumW8vL5g9G8aNg4wMGDwYNm+u0CHc0tsJKG/reR87cIDZl13GQDVvqMdQ3n8nPgWmAnkAUspdwCi9gtKTAwcO0KRJE/wttgyY8lbeB04d4Onfnwbgk+s+oUbAhS0pQggefPBBli9fTo0aNfjxxx+56qqr3GYQk9vm29sbvvhCm1/07FkYNAjsCw6UB7f1riLK2zreR7Kzidq/33LeVqC8hWg1KWXR34r5zg5Gb7KyskhKSqJZs2Zmh2Ioylt5Sym5a+ldZOVncctFt3Bdq+tKPcbAgQPZsmULLVq0YMeOHXTp0sXlBzG5fb69veHrr+GmmyA9Ha6+GrZvL3M3t/euJMrbOt5SSkZFRTHw2DFS69QxOxyFkylvIXpCCBEJSAAhxI1Akm5R6cSePXto27YtXhbrV6K8lfdn/3zG2kNrqV2tNu8MfKdcx2nTpg1bt26lT58+JCcnu/wgJo/It48PzJ0LI0ZAWhoMGAA7dpS6i0d4VwLlbR3v748fZ+OZM+R7exNpoUn7rUJ5r+T7gFloa84nAg+hjaR3G06ePEleXh716tUzOxRDUd7KOzE9kcdWPQbAe4Peo05Q+VsUwsLC+O2335g4ceK5QUxPP/20yw1i8qh8+/rC/PkwdCicPg39+8P//lfsph7lXQGUt3W8MwsKeHTfPgBei4wk1NfX5IgUzqa8hWgi8CXwCvAdsAoYr1dQzsZxmgsrobyVt5SSe3+5l/ScdIa0GsKoDhXv2u3r68tHH33E+++/j5eXF6+++io33ngjZ8+eLXtnA/DIfPv5wcKF2ij6kyehXz/Ys+e8TTzSuxwob2t5v3H4MEfy8rg4MJAJFpov1UqUtxBdDAxBG6x0FG25T7cZSnv48GHCwsIIttgyYMpbeS+IWsDS2KWE+Ifw0bUfVXq+QSEE999/PytWrKBGjRr89NNPXHXVVRw+fNhZ4Vcaj823vz/88AMMHAjHj0PfvrB377m3Pda7DJS3dbwTsrOZbv8d837r1nhbaL5UK1HeQrSRfR7R16WUbxY+dI3MSeTl5REXF0fr1q3NDsVQlLfyPpF5ggdWPADAGwPeoFFIoyqf5+qrrz43iGnnzp1cfvnlbK7gVEPOxOPzHRAAP/2k3Z5PTtaK0X37PN+7BJS3tbwf37+fbCm5sVYteoaGmh2OQifKW4huEkJcpGskOhEbG0uzZs3wtVi/EuWtvCf/OpkTmSfoE9GHuy67y2nnKhzE1LdvX5KTk+nTpw9z58512vErgiXyHRgIixdDnz6QlAR9+nBw9WrP9y4GS+S7GKzqfUVuLi18fZnZsqXZoSh0pNRCVAjxPyHELuAq4B8hRIwQYpfD6y7N2bNnOXnyJE2bNjU7FENR3sp7Wewy5v9vPoE+gXw65FOnLwEYFhbGr7/+em4Q09ixY3nqqacMHcRkqXxXqwZLl0KPHpCYSJPbbqOpiw0Y0xtL5dsBK3tfdvYsMd260TQgwOxwFDpSVovodWh9QwcDLYCr7d8Xvu7SREVF0a5dO0utwwvK2+re6TnpTFymTWrxct+XiQyL1OW8vr6+fPzxx3zwwQd4e3vz2muvMWLECMMGMVku30FB8MsvnLnoIgJSUhD9+oEL9NE1Csvl244VvbMKCs55W2maKqtSaoallPGlPYwKsjLk5+fj5eVF7dq1zQ7FUFJSUpS3hSjOe8qqKSSeSeTy8MuZ3HWy7jHcd9995wYx/fzzz4YMYrJsvrOyiH33XejaFQ4d0m7XHzlidli6Y9l8W9A7PT+fFps3805eHsE1a5odjsIAPPZfjaysLNq3b292GIZis9mIjo5W3hahOO8/4v9g1vZZADzW7TG8hDE/4gMGDGDr1q20bNmSnTt30qVLFzZt2qTLuaye77Zdu8Kvv0LnzhAXpxWjR4+aHZ5uWD3fVvN++dAhjubnczAgAH8LtYa62tzMRuKxWfb19aVatWpmh2Eohw4dom7dusrbIhTnvfLAynPPb/rhJurNrMeNC2/kva3vsePYDgpsBbrF07p1a7Zs2UK/fv1ISUmhT58+fPXVV04/j8p3NQgNhd9+g0svhf37oXdvSEw0O0RdUPm2jvf+zEzesbfwv9+qlaW6Ixw6dMjsEEzDYwtRf39/s0MwlNzcXOLj42lpsdGFyvt87yevepKPrvmIm9vfTP3g+hzPPM6i6EVM/nUyl866lNpv1GbIt0N4Y+MbbD2ylbyCPKfGFRYWxooVK7jvvvvIzc3l9ttv59FHH6WgwDkFsMq3g3dYGKxapRWj+/ZpxaiH3aZX+baW9yP795MHjKtbly4hIWaHYxiF+bYqQkppdgy60LlzZ7lt2zazwzCMXbt2ERYWRqNGVZ8r0p1Q3iV7SynZf2o/f8T/wR+H/2D9ofXEp53/yy7IN4grG19Jz6Y96dW0F13CuxDg45wRqrNmzeL+++8nPz+fQYMG8e233xJaxbkAVb6L8T51SluT/p9/oHlzWLsWmjQxPkgdUPm2jveqU6e4etcugoRg3xVX0MBCjUmF+W7cuPF2KWVns+MxGh+zA1BUnfT0dNLT07noIrec6rXSKO/SvYUQtKzVkpa1WnLHZXcAEJ8az4bDG7TiNP4PYk7GsCpuFaviVgHg7+1P10Zd6dmkJ70ietGtUTeC/IIqFec999xDmzZtGDFiBL/++itXXHEFS5YsoVWrVpU6nsp3Cd5hYbB6tVaMbt+utYyuXQtuPt2Pyrd1vPNtNh6MjQXgmYgISxWhVsx3UVSLqJsjpWTz5s20bduWmhYaYai8neN97OwxNsRvONdquiv5/OmBfbx86NSg07kW0+5NuhMaEFqhcxw8eJDrr7+e//3vf9SoUYOFCxdy9dVXV+gYKt/l8E5Nhauvhr//hogIrRiNiDAgSuej8m0t75O5uYzYvJnDvr5EX3GFywxS2r17N/v372fYsGG6HL9ovoUQlmwRVYWom5OUlERSUhKXXXaZ2aEYivLWx/tU1in+PPznuRbTf5L+oUD+179TILik/iXnWkx7NOlBnaA6ZR73zJkzjB07lsWLF+Pl5cWbb77J5MmTyz0YQeW7nN6pqdra9H/9pbWIrl0LzZrpGqMeqHxb07vZRRdR04VWjxo3bhzffPMNetVJRfOtClEPwwqFaEFBARs2bOCKK64gwEIrTyhv47zP5JxhU8Im/oj/g/Xx6/kr8S/ybOcPcGpbu+25FtOeTXsSHhJe7LFsNhvTpk3j5ZdfBmDChAl89NFHZQ4sVPmuoHdaGgwaBFu2aH1F167V+o66CSrfyttV2LJlC926dcNmszl9BH9x3qoQ9TCsUIju27cPwHIjK5W3ed5ZeVlsTdx6rjDdnLCZrPys87ZpXrP5eYVps9Bm5/0SX7BgAbfffjtZWVl0796dRYsWUa9evRLP6QreZlAl7/R0rRjdvBkaN9aK0Uh9VthyNirf1vHeffYst+3axZM1anCji86XKoRg1apV9O/f36nHLS7fVi1EXaMjhqLCZGdnk5iYSHM3aulwBsrbXO9A30B6R/TmuV7PsWbcGlKfTGXThE1M7zeda1peQ4h/CHGn4/jq/+ydd3hU1daH352ekJ4Q0giBQBJCL0F6B0GagBQFVBTbtWC5Fq5Xr/rpxYq9gFdURKUoSBOQ3qT3hJBOeu89k5n9/TFJJNJSpmbmfR6eaeecvX6snTNrdlnr3Hcs3LSQoE+CaP9he+ZtmMfyU8uJyoli9uzZHDp0CH9/f44cOUJ4eDjnzp27bnuGolvXtFi3s7M6z+iQIZCSot7AFBenURu1gdnfpqNbSslTMTGcrq5mj5Vh75v+6KOPNHo9U/T3zTCPiBopZ8+exdvbGx8fH32bolPMug1bt1Kl5HzW+foR00NJh8iryGtwTFuHtgzrMIw+jn1Y9+o6Lp6+iIODA6tWrWLmzJkNjjUW3ZpGY7pLSuCOO+DwYfDzU4+MGvCIm9nfpqN7U24ud0ZE4GphQdygQXgY0NrQq/H29iYrKwspJVJKSkpKcG5hjtMb+ds8ImrGaCgoKKCiogJvb299m6JTzLoNX7elhSV9ffry9MCn2ThnI9nPZxPxWER9kn0fRx9yynPYELWBV06+wsUJF7HuY015eTl33XUXi55dRJWiCjAu3ZpEo7qdnGD7dhg+XF15acQIiI5u+XW1gNnfpqO7SqXi6dp0Ta936mSwQSjA008/Xf/8p59+wsXFpUXXM0V/3wrziKiRIaXkyJEj9OzZs8W/yowJs+7WoVtKSXxBfP2I6cGkg1wpuAJHgV2ABMtulox4egSdbDsxo98MRgWP0liSfUNHa/4uK4PJk2H/fvDxgb17ITRUc9dvIa2tnzcWU9X9dlISSxITCbWz48KAAVgbSLqm65GXl4enpyf5+fnU1NTg5eVFUVFRs/x1K3+bR0TNGAVpaWk4Ozub1E0LzLpbi24hBJ3dO/NAnwf4/s7vSVycSNIzSax+bzUTXp2AhZ0Fykgle/+9l/+d+R93rL8D17ddGfHdCF7Z+wq74ndRVl2mbxlaQ2v+btMGtm6FUaMgI0P9GBWl2TZaQGvr543FFHVnVlXxZm1d9Y+Dgw06CAXw8PAA4JtvvqFt27b1z5uDKfq7MRh2DzDTgJqaGmJjYwk1oJEMXWDW3bp1B7gEMK/nPLa/tp3Is5F0DOoIWWD3rR1BJUFUK6s5mHSQNw+9yfjV43F9x5WB/xvIC7teYGvMVgorC/UtQSNo3d91wejo0ZCZqQ5GL13STltNwFT6+d8xVd1niotBpWKSmxvj3d31bU6j+fDDD+ufN2fzkqn6uzHoLRAVQlgKIc4KIbbWvnYXQuwSQsTWPrpddewSIUScECJaCHG7vmzWN3FxcXTo0AEbGxt9m6JTzLpNR3doaCg///gzQ4cOpbKokuRPkvnI6yM2z93Mc4OeI9w3HCklx9OO896f7zHl5ym4v+NOn+V9WLx9Mb9e+pXssmx9y2gWOvG3gwNs2QJjx0JWljoYjYjQXnuNwBT7OZiu7k55eez19+fLkBB9m9JoJk6cSHp6Os8//zwAycnJVFRU3OKshpiqvxuD3taICiGeBfoDzlLKyUKId4F8KeXbQoiXADcp5YtCiDDgZ2AA4AvsBoKlvKrcy3VobWtEy8vLOXnyJMOGDcPCwKcyNIlZt2nqHjRoEC+++GL9yMPixYt5//33sbKyoqSqhKOpRzlw5QAHkw9yIu0E1crqBtepS7Jfl8/0Rkn2DQWd+7uiAu68E/74A9q2hT17QA+1rk29n5t1Gwfx8fF07ty5Ucfa2NjQo0eP+n/du3cnKCiIlJQUhg8fflPdprpGVC+BqBDCH/geeAt4tjYQjQZGSikzhBA+wH4pZYgQYgmAlHJp7bk7gdeklEdv1kZrC0RPnTpFQEAAXl5e+jZFp5h1m7bulStX8uijj6JQKBg3bhxr1669pgZ3haKCE2kn6jc//Zny5w2T7NeVJv17kn19oxd/V1bC9OmwYwd4eqqD0Z49ddc+5n5uSrp/zcnhaGwszwQF4XeTAhbGgkKhIC4ujosXL9b/i4iIID4+vknXCQwMpEePHowaNYpnn33WHIjqrFEhfgGWAk7AP2sD0UIppetVxxRIKd2EEJ8Bx6SUq2vf/wbYLqX85TrXfRh4GKBdu3b91qxZowM12kepVFJZWUmbNm30bYpOMes26wa4ePEir776KoWFhfj7+/PWW28REBBww+soVApiSmK4UHSB80XniSiKoEzZcIOTp40nt3ncxj86/QMHKwet6Gks+vS3RXU13V59FY/jx1E4O3P+vfcoDQ7WSdvmfm46uiuAe6UkVwheAUbr2yAdUufv6upqEhISSExMJDExsf55VVXV1Yefk1L20ZeteqMuSauu/gGTgS9qn48EttY+L/zbcQW1j58D8696/xtg5q3a6devn2wNqFQqeeDAAVlSUqJvU3SKWbdZ99UkJSXJXr16SUC6uLjI7du3N/raNcoaeSb9jHzzwJvS/k17yWvU/zueelxTEpqFQfi7slLKSZOkBCnbtJFy2zatN2kQuvWAqep+JSFBsm+f7H38uFSqVPo2R2c01d/AKanjmMwQ/uljkcYQYKoQ4gqwBhgthFgNZNVOyVP7WLfjIBVof9X5/kC67szVL8nJyXh4eODo6KhvU3SKWbdZ99UEBARw5MgRZs6cSVFREZMmTeKjjz6q+3F6UwoqC/jt8m98cPSD+in7bm278dOMnxjgN0CjOpqKQfjb1hZ+/RXmzVPnG506FZYv12qTBqFbD5ii7qTKSt5NTgbg0+BgLAxoSYy2MUV/NwedB6JSyiVSSn8pZSAwF9grpZwPbAbuqz3sPmBT7fPNwFwhhK0QoiPQBTihY7P1gkKhICEhgWAdTZUZCmbdZt3Xo02bNqxbt45XX30VlUrFM888w6JFi/4+tVVPUmEST21/ioAPA3jj4BsUVBYwuP1gNs/dzIXHLnB3j7u1IafRGJS/bW3hhx/g3/8GpRIefRSWLAGVSuNNGZRuHWKquv8ZG0uVlMz29GSoq6u+zdEZpurv5mClbwOu4m1gnRDiQSAZmAUgpYwUQqwDLgE1wOPyFjvmWwsxMTF06tQJawMuf6YNzLrNum+EhYUFr7/+Ot27d+e+++5j5cqVxMTE8Ouvv9Zv/IjIjuDdI+/y08WfUNbeKiZ1mcRLQ19iaMBQrWppCgbnbyHg//4PAgPhkUfg7bfhyhX47jt1oKohDE63jjBF3QcLC/klLw87IXivkbvOWwum6O/motdAVEq5H9hf+zwPGHOD495CvcPeZCgtLSUvL4+wsDB9m6JTzLrNuhvDrFmzCAoKYurUqRw+fJjw8HDeWPEGv+b/ypaYLQBYCkvm95zPC4NfoEc73acmuhkG7e8HH4T27eGuu2DNGnWN+t9+Aw0kHzdo3VrEVHUvT0kB4MWAAALsTKNML5iuv5uLIY2ImrmKiIgIunXrZlApZnSBWbdZd2Pp27cvJ06cYPQdo4k+H839U+6HGWDXw45FfRbx3ODnCHQN1LjNmsDg/T1+PBw6BJMmqR8HD4bff4dOnVp0WYPXrSVMVffjpaUM9ffnvptkuWiNmKq/m4vxZJQ1IbKzs7GysqqvcWsqZGVlmaRuU/V3S3QrlApWX1jN7ZtuJ3pyNPQEFMBaeLr6aT6Z+InBBqFG4+9eveDYMXVu0ehoGDgQTjR/eb7R6NYwpqzbxtqaxzp3xsHSUt/m6AxT9XdLMAeiBoZKpeLSpUt069ZN36boFJVKRVRUlMlNZdT526y7cZQryvnsxGd0+bQLCzYuICI7Aj93P97/8n3eeOsNhBC8/cbb3HPPPU0uwacLjM7f/v7qEdFx4yAnB0aOhE2bbnna3zE63RrCVHWvzsxkn4l+j5ni93dLMU/NGxiJiYl4e3tjb2+vb1N0Sp1uBwf9JhfXNWbdjdOdX5HP5yc+55MTn5BbngtAiEcILw55kXk952FjaQODoXeP3txzzz2sWbOGuLg4fvvtN/z8DKe8p1H629kZtm1T76RfuVJdjemjj+Cppxp9CaPUrQFMUXdMeTkPXL6MtRBcsbTElL7JTPX7u6WYR0QNiKqqKpKTkxtd07a1YNZt1n0jUotTeW7ncwR8GMCr+18ltzyXAX4D2DB7A5cev8TCPgvVQWgtU6ZM4ejRo3Ts2JFTp04RHh7OiRZMJ2sSo/a3tTX873/qXfVSwuLF8Mwz6lRPt8CodbcAU9X9TGwsCmCOlxdtbWxueXxrwVT9rQnMgagBcfnyZYKDg7GyMq2BarNus+5rjsm9zAObHqDTx51YdmwZZYoybg+6nb337uXYg8eY3nU6FuL6t6/u3btz4sQJRowYQUZGBsOHD+enn37SlpxGY/T+FkKdZ/SHH9SB6UcfwezZcIslEEavu5mYou4deXn8XlCAo4UFS4OC9G2OTjFFf2sKcyBqIBQVFVFaWoqvr6++TdEpRUVFlJSUmHWbCLfSfTz1ODPWziDs8zC+PfctSqlkTrc5nHn4DDvm72BUx1GN2onq6enJH3/8wcMPP0xVVRXz5s3jX//6FyotJGhvDK3K3/Pnw86d4OICGzbA6NHq9aPXoVXpbgKmqFuhUrE4JgaAVwMDaWdCo6Gm+v2tKcyBqAEgpTTJdA91urt3725yuiMjI826r3p/Z9xORn0/ioHfDGTj5Y3YWNrwaL9HiXkihjV3raGPT58mt2djY8NXX33Fp59+iqWlJUuXLmXGjBmUlJRoUtYtaZX+HjUKjhyBgAD1zvpBg6A2CKmjVepuBKaq+/O0NGKqquhkY8Nif399m6Mz6vxtat/fmsQciBoAGRkZODg44GpC5c/AtHXb29ubvO4aVQ1rItbQd0VfJvw4gf1X9uNs68xLQ17iytNX+HLylwS5t2x6TwjBE088wY4dO3B1dWXTpk0MGTKEK1eutFxQI2m1/u7WTR2E9u0L8fHqYPTIkfqPW63uW2CKuiuUSt5ITATgo+BgbCxMJ7QwRX9rGtPpLQaKUqkkJiaGrl276tsUnWLWbdq6t8ZsJeSzEO7+9W7OZZ7DycaJpWOWkvx0MkvHLsXb0Vuj7Y8dO5YTJ04QEhLCxYsXCQ8P59ChQxpt43q0en/7+MCBA+rE9/n5MGYMbNnS+nXfAFPVbQO8r1Lxgq8vk00of6ap+lvTCCmlvm3QCv3795enTp3Stxm3JCYmBgsLC5PbaWfWbdq6Qz8LJTovusExtpa2dHTrSJBbEEFuQXRy60SQu/qxo2tH7K1bnhKlsLCQuXPnsnPnTqytrfnqq6944IEHWnzdG2Ey/q6pgccfhxUroH9/Yn780TR0/w2T8fffMOvWjG4hxGkpZX+NXMyIMG/v0iMVFRWkp6czfPhwfZuiU8y6zbrX3rWWX6N+JaEggfiCeOLz48kpz+Fy7mUu516+7nX8nPz+Ck5d1Y91Aaung2ej1me5urqydetWnn/+eT766CMefPBBIiMjeffdd7HUcPUXk/K3lRXcey+sWIHKwsJ0dF+FSfm7Fikle7KzsUxPZ4QJ6QbT9Le2MAeieiQqKorQ0FAsTGg9DZh1m3VDL+9e9PLu1eC4kqoSEgoSGgSnCYUJxOfHk1SURFpJGmklaRxKvnZK3cnGqT5IrR9NrX0McAnA2tK6/lgrKys+/PBDunXrxmOPPcayZcuIiori559/xsXFRau6WzW1O+dLbG1NS3ctJudv4LfcXGZERTHd2ZlRJqQbTNPf2sIciOqJgoICqqqqaNeunb5N0Slm3WbdN8LJ1um6ASqoNzalFKVcN0iNL4inuKqY81nnOZ91/ppzLYUlHVw7NAhOg9yC6D+5P5s6bOLeu+9l+/btDBo0iC1bthCkgfyHJunvXHXFqypnZ9PSjWn6u1Kp5JnYWADGeHnp2RrdYor+1ibmQFQP1KUt6tWrl0mlezDrNutuLlYWVnR060hHt46MYcw17eRX5DcMUmufJxQkkFqcWj/Supvd11zbbZEb9qvsiYqKonf/3mzeuJlRI0c121aT9XdODgJwCQoyLd0m6u9lqakkVVcTZmfHIyaUP9NU/a1NzIGoHkhNTcXV1RVnZ2d9m6JTzLrNurWBEAIPBw88HDwI9wu/5vPKmkquFF5RB6d/C1ITChIosC+A+4BfoDSulDFjx/DKu6/w+rOvN8seU/V3UWkproDt/v1QXKyuUW8CmKK/06uqeKs2BdonISFYmdD0tCn6W9uYA1EdU1NTQ1xcHEOGDNG3KTrFrNusW1/YWdkR6hlKqGfoNZ9JKckozSChIIEzU8+w5KUllB8s543n3mDzkc3s+n4Xno6ejW7LkHTrkpqaGi4OGMDQ4GDEhQvq0p9btqhLgbZiTNXfL8bHUy4l09zdGePmpm9zdIap+lvbmM7PGAMhNjaWwMBAbEyo/BmYdZt1GyZCCHydfBkaMJSnBj1Fxh8ZjF08Fizg3IZz+A3w4/PDn6OSjSsNaiy6NU1sbCz+PXsitm+Htm3VJUAffxxaaXrAOkzR3yeKi1mdnY0NsKxLF32bo1NM0d+6wByI6pDy8nKys7Pp0KGDvk3RKWbdZt3GgrOtM7s+2sU367/Bqo0V1VHVPDHzCfq/158zGWdueq4x624JDXR36gSbN4OdHXz9Nbz7rr7N0xqm6u9AIVgALOnQgU72Lc/tayyYqr91gTkQ1SGRkZGEhYWZXLoHs26zbmPjgRkPEHkmEp8OPpANZ984S79X+vGPbf8gvyL/uue0Bt3N4RrdAwfC6tUgBLz0Eqxdq18DtYSp+jvp8mU+6NaN1zp21LcpOsVU/a0LzP+jOiI3NxcpJW3bttW3KTrFrNus21gJDg4m8mwkI0ePhDLge/hy5ZeEfBbCN2e+aTBd35p0N4Ub6p45E95/X/383nvh8GHdG6dFTNHf5UolV7KyTE43mKa/dYk5ENUBUkoiIyPp1q2bvk3RKWbdZt3GjpubG3/s+IPHHnsMaoANkLs1l0WbFjH4m8GcTj/dKnU3hlvqfuYZ9TrR6mqYNg1iYnRroJYwVX+/lZREv6goUvz89G2KTrmev3/55Rfi4uL0aFXrwhyI6oCkpCTatm1LmzZt9G2KTjHrNutuDVhbW/PFF1/w6aefqqflDoLdb3YcTzxO+NfhTPp+EpdqLmFrb6tvU3XKLf0tBHz0EUyeDPn5cMcdkJ2tUxu1QWvt5zcjsaKC95OTyQd8HR31bY5OuZ6/Z82axTvvvKNHq1oX5kBUyygUChITE+liYrsLzbrNulsbTzzxBL///jvOzs5UXqjE+xdvLMos2J60nX8c/gf+y/x5dueznM+8trpTa6PR/raygp9/hr59IT4epkyB8nLdGKkFTKGfX4/n4uKoBu5u25ZBGiyDa+jczN+jR4/Wg0WtE3MgqmWio6MJCgrCupXn0/s7Zt1m3a2R22+/naNHjxIYGEhmTCYeP3iw0GMhIR4hZJVl8eGxD+m9vDe9v+rNsqPLyCrN0rfJWqFJ/nZ0hG3bIDAQTpyAuXOhpkbrNmoDU+nnV7O/oICNeXnYC8G7Gih/a0xcz99KpRKAMWPG3Og0M03EHIhqkZKSEgoKCmjfvr2+TdEpZt1m3a2ZsLAwjh8/zoABA8jOzGb9kvV80PEDji86zuPhj+Nu7875rPM898dz+C3zY9JPk1gXuY7Kmkp9m64RmuVvb2/Yvh3c3NSJ7p96yuhyjJpaPwdQSsmTtWt7l3TogL+dnZ4t0h038vfZs2cB8PLy0odZrRJzIKpF6hY4m1o9WrNus+7WjpeXF++++y4zZ86ktLSUqVOncnzDcT674zPSn01nw+wNTAuZhhCC32N/Z84vc/B+35tHtjzCkeQjSCMLwq6m2f4ODVXnGLW1hS+/BCNbY2eK/fx/GRlEVFTQ3tqaf5pQAA439vfu3bv1ZFHrxRyIaomsrCysra1xd3fXtyk6xazbrNsUyMrKwtHRkfXr1/Of//wHlUrFU089xZNPPokllkzvOp3f5v5G+rPpfDLhE/r59KOoqogVZ1Yw9NuhdPm0C28ceIMrhVf0LaVJtNjfQ4fCjz+qNzItWaJ+bgSYaj93q6igg4UFy7p0wd7SUt/m6Iyb+XvXrl16sKh1Yw5EtYBKpSIqKoqwsDB9m6JTzLrNuk2Bq3ULIXjttddYvXo1NjY2fPbZZ0ydOpXi4mIA2rZpy5O3Pcmph08R8VgELwx+AV8nX+IL4vnP/v/Q8eOOjPhuBCvPrqS4qljPym6Oxvw9cyZ8+KH6+cKFsHdvy43TIqbcz71SUrjQrx8zTSh/5q38vdfA+6sxIox5iuhm9O/fX546dUovbcfHx1NTU0NISIhe2tcXZt1m3abAjXQfPnyY6dOnk5ubS48ePdi6dSsBAQHXnK9UKdmTuIdV51exIWoDFTUVANhb2TO963Tu63UfYzqOwdLCsEagNO7v556DZcvA2Vmd8L5HD81cV8OYYj9XSsmVhAST0w239rcQgtGjR7Nnzx6Nty2EOC2l7K/xCxs45hFRDVNVVUVycjKdO3fWtyk6xazbrNsUuJnuoUOHcvz4cUJDQ7l48SIDBgzgxIkT1xxnaWHJ+KDxrJ6xmsx/ZrJy6kpGdBhBRU0FP138idtX307ARwG8uOtFIrMjdSHrlmjF3++9B7NnQ3ExTJwIqamau7aGMNV+PvXCBZ5OTsbNxOqqN9bf48aN05FFpoE5ENUwUVFRhISEYGlC62lArTs4ONis20Qw676+7k6dOnH06FHGjBlDVlYWI0aMYP369Te8nrOtMwv7LGT//ftJXJzIGyPfoLN7Z9JL0nn3z3fp/mV3+q/ozyfHPyGnLEdbsm6JVvxtYQHffw/DhkFamjoYLSrS3PU1gCnez3/Py+P3ggIOWFqqfWRCNNbf5tRNmsW0epmWKSoqoqysDB8fH32bolPqdPv6+urbFJ1i1m3WfT1cXV3Zvn07Dz30EJWVlcyePZv//ve/t9wpH+gayCsjXiHmiRiOPHCER/o9goutC6czTrN4x2J8l/kye/1sSqpKNCnrlmjV33Z28Ntv0LUrRETA9OlQVqb5dpqBKd7Pq1UqFtema/pPYCBeNjZ6tkh3NMXfAwYMYPr06VRVVenAstaPORDVEFJKIiIi6N69u0ml9zDrNus2BZqq29ramuXLl/P+++8jhODll19m0aJFKBSK6x6vVCmJzo3m10u/8vqB11l2dBn7ruyjpPqvoLNGVcP6S+s5l3lOU7JuiU787e6uzjHq4wP79sHIkZCZqZ22Gomp9vNPU1OJq6qis60tT/r769scndEUfyuVSh577DF+++037Ozs6NmzJwUFBTqytHVipW8DWgsZGRm0adMGFxMqfwZm3WbdpkFzdAsheO655wgKCuKee+5h5cqVJCUl8fHKj0muSiYiO4KL2ReJyI7gUs4lqpTXjq5YCktCPUPp4dWD7l7dGdJ+CMM6DNOktJuiM3936KAOQu+4A06dgoED1cFp167abfcGmGI/z66u5vUrVwD4ODgYGxOalm+Kvy0sLPjiiy/44osveP/993n++edxd3fHwcGBiIgIOnbsqAOLWxfmXfMaQKlUcvDgQQYPHoytra1O2jQEzLrNuk2B5uouqiziQtYFLmZfZO/hvWx+bTOKYgV4AvMAt4bHt3duT492Pejetrv60as7oZ6h2Fnpp5qNXvydnQ1Tp8Lx4+DqChs3qkdIdYip9vOHL1/m68xMxru6srN3b32bozM04e/169cze/bs+td1ldeaiqnumjcHohogOjoaKysrgkywDq+lpaXJ7Sg16zbrvhUxeTH0Xd6XMsVV6x0LgR+BHKANeC7yZNLISYwPGs/YTmPxamNYJQP15u/ycpg/Xx2EWlurR0Z1uDnEFO/nKimZceIEv1dUcD48nK5t2ujbJJ2hSX8fOXKEoUOH1r/+7bffmDZtWqPPN9VA1HTG3rVERUUFGRkZJjccX6e7U6dO+jZFp5h1m3U3BkcbRwJcAhBctd7MFXgQ6ASUQe7nuXy/5nvmbZhHu/fb4f6OO92/6M64H8Zx78Z7eXHXi3x07CPWRa7jUNIh4vLjKKvWzUYevfrbwQHWr4d//AMUCpg1C+LidNK0qd7PqyorebqigtgBA0wqCNW0v4cMGYKUkpiYGKysrLjzzjsRQvDpp59q5PqtFfOI6N/w9PQkMDBQ8waZMVmuXLlCbm6uvs1oEadPn8bf35927drp2xSd0lLdNaoassuyySjJIKM0g/SSdFILUln37jqid0WDAJepLpT1K6NGVdOoazrZOOHj5IOvky8+jj74OPrg7+zPgl4L8HTwbJadf8cg/K1UqnfRb9miXiv655/q6XotYhC69YBZt3Z05+bmMnToUKKjowFYvHgxy5Ytw+IG629NdURUL5uVhBBXgBJACdRIKfsLIdyBtUAgcAWYLaUsqD1+CeqxBCXwlJRy53WuaQmcAtKklJNramoYN24cV65cITAwkHXr1uHm5vb3066hQ4cO6Ksik7Fw4MABAEaMGKFnSwyfkpISRo0apW8zWkR+fj4KhcLkvqQ0odvKwgpfJ198nRqmPnp9zOssXbqUl19+maJNRSwOXMxL//cSWWVZZJRmkFGiDlozSjPqX9c9llSXUJJXQkxeTINrJhQk8OkdLR95MRh/W1qqa9EPGgSRkTB4MGzeDFpaKmAwunVIhVLJjHPnmFJVRT8T0g268benpyeXL1+moqKCmTNn8vHHH/Pxxx8zZcoU1q1bh52dftZ/GxxSSp3/Qx1oev7tvXeBl2qfvwS8U/s8DDgP2AIdgXjA8jrXfBb4CdgqpaRdu3Zy6dKlUkoply5dKl944QXZGPr169eo40yZb7/9Vn777bf6NsNoMOY+pVKp5MGDB2VxcbG+TdEputL9448/SmtrawnIu+66S1ZUVNzSrrzyPLn81HLp9F8nyWtIXkP2+rKXjMqJarE9BunvK1ekDAuTEqR0c5Pyjz803oRB6tYBrycmSvbtkz2OHZNKlUrf5ugMfflbqVTKJ554QgISkGFhYTI3N7f+c+CU1ENMpu9/hrRGdBrwfe3z74E7r3p/jZSySkqZCMQBDbajCSH8gUnA/+reKyws5L777gPgvvvu47ffftOm7WbMtEpSUlJwdXXFyclJ36boFF3pvueee9i5cyfOzs788ssvjB8/nvz8/Bsen1iYyCNbH+GRrY9QUl2Cj6MPK6eu5PTDpwn1DG2xPQbp7w4d4OhR9W76ggKYMAE+/BA0uKwsJSUFNzc3w9KtZVIrK3k7KQmAT4KDsTChfKn68reFhQWffvopUko++OADLl26hKenJ/b29iQkJOjUFkNCX4GoBP4QQpwWQjxc+147KWUGQO1j3RZSPyDlqnNTa9+7mo+AFwBV3Rs1NTX1FRJ8fHzIzs7WtIZWi0KhoKREnUg7LS2N48ePs3v3bn7//XfKy8tRKBTk5uayefNmtm7dypXa3HNnzpzh8uXLpKamUlpaqkcFZjSBQqEgPj6ekJAQfZuiU3Ste9SoURw+fBg/Pz8OHTrE0KFDSaoNEOoorCzk+T+ep+vnXfnl0i/YW9nz6vBXiXkyhoV9FmJp0fISlAbtb2dn9S76f/8bVCp49llYuBAqK1t8aYPWrUVeiI+nQkqme3gwshHL1loLhuLvZ599Fiklv/zyC5WVlXW79k0nX9hV6Cuh/RApZboQwgvYJYS4fJNjr/czrf6nsBBiMpAtpTwthHgfGCqEOCWEYP/+/fUn1NTUNHh9NVu2bGHr1q0AVGrgxmYsKJVKcnLU9au9vb1Zu3YtV65cQaFQ4O/vz/33309eXh55eXk4OTnh7OyMhYUFFhYW2NnZ4e/vj1KpxN7eHikl6enplJSUUFpaipeXF9OmTeO3336jsLAQX19ffHx8TK5SCajXid6o7xkylZWVWFhY8Oeff+rbFJ2iL90ffvghL774IlFRUfTr14+3336bwE6BbMnYwndXvqO4phiA8e3Gs6jjItqKtpz6U3Pr2Y3C32PG0NbSktB33sHy++8pPnGCiP/7P6o9PJp9yTrdR44c0aChhk0E8DNgLSWz8vKM8v7UXAzN3x4eHuzbt4+ioiLuvPNOk6wZqvdd80KI14BS4CFgpJQyQwjhA+yXUobUblRCSrm09vidwGtSyqO1r5cCC4AawA5wBjbY2trOS0xMxMfHh4yMDEaOHFm/c+1m9O/fv9VuVqqurkapVGJlZcV3331HdnY2bm5u9O3bl4EDB5KTk0ObNm2wt7e/abC4bt06gAYJfG9EWVkZGRkZZGRkUFhYyJQpUzh69CjR0dH4+vrSpUsXAgICsLRs+YiOoWKMfaqsrIxTp04xfPhwk/rhoG/dhYWFTJ8+nf3792PvYI/HQg9S26YCMKLDCD4Y/wH9fPtpvF19624y587BtGmQnAy+vurR0mYkEDc63RpAJSXhJ09yprycfwUE8JYJpWQzdH+b6q55nQeiQog2gIWUsqT2+S7gDWAMkCelfFsI8RLgLqV8QQjRDfUmpAGAL7AH6CKlVF7n2iOBf0opJ3t7e8unn36al156ibfffpv8/HzefffdW9pnjEHDzaiqquLixYtER0eTnJzMhAkT6NOnD6mpqXh5eWFjY6NzmyoqKkhPTyclJYW4uDjuvvtuSktLycnJoXPnzq1uJ6Ex9qkTJ07QqVMnPD01kw7IWDAE3SeTTjJlzhSyjmeBBXjd48Xyl5czLWSa1r48DUF3k8nOhrvugkOHwNYWvv4aFixo0iWMUncLiS4vZ8CJEzhYWRE7cCCOVqZT6dvQ/W2qgag+emA7YGPtDdUK+ElKuUMIcRJYJ4R4EEgGZgFIKSOFEOuAS6hHPR+/XhD6d7y9vdm1axfffPMNAQEBrF+/Xlt6DI7S0lLOnj1LYGAgnp6eJCcn07t3b2bOnFkf5Pn7++vNPnt7e4KCgggKCmJkbfm+/Px8Lly4wJYtW/Dz82Py5Mm4u7vrzUZTpm65hqHerLWFvnVnlGTwyr5XWHl2JfJ2ia2tLVUHq8henc2lrpeYtqTxFVqagr51NxsvL9i9G556CpYvh3vvhfPn4Z131KmfbkFOTg5CCOPT3ULcy8r41cEB59BQkwpCTdXfxoDep+a1RXMT2hvj6FUdZWVlbN++nfj4eMLCwhg0aJBW/uh2794NwNixYzV+7erqahISEujcuTMJCQnExcURHh5O27ZtNd6WrjCmPqVSqTh06BD9+/enjQlVWNGn7nJFOR/8+QHvHHmHMkUZVhZWPBH+BK+MeIUf//cjixcvRkrJo48+yqeffoqVBoOHVuPvr76CJ5+Emhq4/Xb4+We4yQacOt3h4eE4ODjo0FD9YtZt2LrNI6JmjJKqqirOnz+Pu7s7HTt2pEOHDkyePFmr09upqalau7aNjQ2hoeo0NN7e3qSlpbFq1So8PT2ZNWuWQd9EWgNJSUl4eXkZd1DSDPShWyVV/HjhR5bsWUJaSRoAd4beyTtj3yHYIxiAJ598Ej8/P+bNm8dXX31FWloaP//8s8bsbDX+fvRRdfWlu+6CnTvhttvUye9Dr5/Sqk63Kd1PEioq+Co6mnvatjUp3WCa/jYmDCmPqJkmoFQq2bdvHx999BFJSUk4ODhgaWlJeHh4q1lj6ezszKhRo3j66ae57bbbsLe3JyIigtOnT6NSqW59ATNNorq6mitXrtClSxd9m6JT9KH7YNJBBnw9gHt/u5e0kjT6ePdh33372DhnY30QWseMGTPYvXs37u7ubNmyhdGjR9dPp7eEVufvESPg5Eno1QtiY9XB6LZt1xzW6nQ3kmdjY3mvsJDvTWg6HkzX38aEORA1MmpqasjOzq6vVfvwww8za9YsfH19b3Gm8WJpaUloaChCCNzd3YmIiOCLL77g0qVLtNalJfogOjqazp07a3Tq1xjQpe64/DhmrJ3BiO9GcDrjNL5Ovnw37TtOPXyKkYEjb3jekCFDOHLkCIGBgZw4cYLBgwcTHx/fIltapb8DA+HIEfXIaHExTJkCb7/dIPl9q9R9C/YUFLApPx8HIXi+Qwd9m6NTTNHfxoY5EDUSVCoV586d47PPPuP48eMIIRg1ahRuJpSIGMDX15d7772XiRMnkpSUhBCC4uJifZtl9BQXF1NYWKjXTWz6QFe6CyoKeHbns4R9HsbGyxtxsHbgtRGvEfNEDPf1vg8LcetbcWhoKEePHqVPnz7ExcUxaNAgTpw40Sx7WrW/27SBdevgjTfUAeiSJTBvHpSXt27dN6BGpeKp2tSF/+rQAV9b08mZbor+NkbMPxGMhG3btpGTk8OMGTMICAjQqy3Ozs56bV8IUb/rXkrJunXrsLOzY9KkSSYXmGsCKSWRkZF069bNIHPraQtd6FYoFXx56kteP/A6+RX5CAQLey/kzdFv4uvU9FkMb29vDhw4wKxZs9i5cycjR45k3bp1TJ48udHXMAl/CwGvvAI9e8L8+fDzz8joaGJfeYXuI0a0Xt3XYXl6OpcqKwmwseG59u31bY7OqOvnplhExdgwj4gaMAqFgn379lFRUcG4ceNYuHCh3oNQUK9ZmzFjhr7NANRB6cKFC+nYsSNff/11o4oWmGlIVlYWtra2JpcuS5u6pZRsjt5M9y+7s3jHYvIr8hkVOIrTD59m5bSVzQpC63BycmLLli0sXLiQiooKpk2bxooVKxp9vkn5e9o0dZ36Tp0QZ87Qc9Ei3C5d0rdVOiNfoeCV2hrmH3bpgl0rLhzyd+r6uXlwwvAxB6IGSkpKCsuXLyc/Px8AOzs786+6G2BpacmQIUN44IEH8Pb2pqysjIKCAn2bZRSoVCouX75M165d9W2KTtGm7rMZZxmzagzT1kwjJi+GYI9gNs3dxJ5799DHp49G2rC2tuabb77h1VdfRaVS8cgjj/DKK6/ccs20Sfq7e3dUx45R0Lcv1nl5MGoU/O9/+rZKJ6zMyKBApWKEszPTTSh/pkn2cyPGHIgaIGVlZfz666+MGTOGmTNnYm9vr2+TGrBjxw527NihbzOuwdPTExcXF1JTU/n66685ceKEeTPTLUhISMDX19fg+pi20ZbulWdX0m9FP/Zd2QfAkPZDWHvXWu7ocofGf0gKIXj99ddZsWIFlpaWvPnmmzzwwAPU1NTc8ByT9XdRETk//ACLF4NCAQ89BO+9p2+ztM60qired3Xl0+BgkxrIMNV+bqyYA1EDIi8vj0OHDtGmTRuefPJJg/01l5mZSWZmpr7NuCEhISE88MADXLx4kbVr15qD0RtQWVlJSkoKQUFB+jZFp2hTd155HpK/+tuRlCP0Wd4Hx/860m9FPx7c9CCfHP+Eg0kHKaws1EibDz30EJs2bcLBwYHvvvuOGTNmUFFRcc1xpuzv1NRUgkJC4KOP1KVAAf77Xygv16tt2qSyspK0tDSe7tGDHo6O+jZHZ9T728T6uTFj3qxkIMTFxbFx40bGjBkDqKebzTQfT09PFi5cSEZGBkIIysrKjD9pt4a5fPkyISEhJtfXtKn7+SHPM6vbLM5lnuN85nnOZakfEwsTOZNxhjMZZxoc38GlA729e9OrXS96efeit3dvAl0DG7WL/momTZrEnj17mDRpElu2bOH2229n8+bNuLq61h9jyv4ODg7+S/eiReqp+ePH4aef1K9bGceKisiOi6P/1bpNhKioqIb+NmPwmANRAyAxMZFNmzYxZ84cg9iM1FqwsLDAz8+P/Px8vvnmG+68805zUuNaCgsLKSsrw8fHR9+m6BRd6A50DSTQNZA7Q++sf6+osogLWRc4n3VeHaRmnSciO4KkoiSSipLYFL2p/lgnGyd6efeiV7te9UFqd6/u2FvffJpx4MCBHDp0iPHjx3Po0CFGjBjBjh078PHxMfv777off1wdiH72GTz4oHqXfSuhWqViXmQkydXV7A0KovVmmL6WwsJCysvLTa6fGzvmQPQqdD2FW1NTQ2FhIR06dODhhx/GyclJp+2bCu7u7sydO5f169dz2223MXjwYJNaL/V3pJRERETQo0cPk/p/0KduFzsXhnUYxrAOw+rfq1HVEJMXw/nM8w0C1MzSTA4nH+Zw8uH6Yy2EBSEeIepR03a96wNVb0fvBlrCwsI4cuQI48eP58KFCwwdOpSdO3eSmZlp9vfVzJoFzz0H58/Dn3/CkCH6MVILfJyaSkJ1NcG2tgzUc6o9XWKq97XWgDkQvYr09HSdtVVWVsaaNWvw9vZm0qRJRhWEenh46NuEJtO+fXsWLVrE4cOHkVKa9I0qPT0dJycnXFxc9G2KTjE03VYWVoS1DSOsbRh397i7/v2s0izOZ51vMLV/OfcyUblRROVGsSZiTf2xXm281NP6daOn3r0I8Q/h8OHD3HHHHZw6dYrBgwfz6aefMnToUH3I1Bs39bednXpKfulS+PzzVhOIZlVX88aVKwB8EhKCtYXpbAMxtL9vM41HtNaNHP3795enTp1q9PE1NTUcOnSI559/nqac1xxKSkpYtWoVoaGhjB492qSDIn1QUlLCgQMHmDBhgk7KvvXv31/rfaqx1PXzwYMHY2tCFVaMXXdlTSWR2ZENRk7PZ56nqKrommNtLW3p5tWNMOcwjn9wnNhTsTg5O7F1y1aGDx+uB+t1T6P8nZwMHTuCpaX6ube3bo3UAg9ERfFtVhZ3uLmxrVcvfZujM4z977sOIcRpKWV/fduha8wjorXExcXpbH1meXk5ffr0YfDgwTppz0xDHBwcqKysZM2aNcyZMwdra2t9m6Qz4uPjCQgIMOqbdXMwdt12Vnb08+1HP99+9e9JKUkqSlKPnNYFp1nnSShI+Gtj1ASgHEoulTBy7EjCnwpnwqQJLdoYZQw0yt8BATB1Kvz2m3on/Suv6Mw+bXCmpITvsrKwRp283pSo+/421r9vU8cciAIVFRVkZmZqfbSguLiYU6dOMWrUKNq1a6fVtrTJli1bAJgyZYqeLWkelpaWzJgxg02bNrF+/XruvvtukxiVrqioICMjw2RGxeporbqFEPUbo6aFTqt/v7iqmAtZFziRcoK9EXvJeiqLsyvPojyh5MSyE5yIOwG1efWdbJzo2a5n/aao3t69G7UxypBpkr8ff1wdiC5frq5Jr4MZEm3xXGwsEnjS359gBwd9m6MzdPX9bUZ7GO9fnQaJjIwkLCwMCy2upyktLWXVqlX06dPH6IOevLw8fZvQYiwsLJg2bRpZWVkIIVCpVFr1vyGgi35uiJiabmdbZ4YGDMUu24750+fj5eWFYpGCxS8s5stlX8ImCLEPoah/EZmlmRxJOcKRlCP151sIC4I9ghsEp9fbGGWoNMnfY8ZASAhER8OmTTBzpvYN1BJP1NTg4eLCq4GB+jZFp5ja33drxOQ9l5eXR01NDV5eXlpro7KyklWrVtGjRw+GtJJF8a0BCwsLfHx8iIiIYP369ahUKn2bpDV00c8NEbNutW5rS2u++OALPv74YwCi10Rzb/a9ZD6Xyaa5m3is/2MEugYCoJIqLudeZk3EGpbsWcLEHyfiu8yXwSsHo5KG/TfSZH8LoR4VBXUqJyMlLy8PP5WKX/r0wcWIR3Wbiqn+fbc2TDoQlVISGRlJ9+7dtdqOjY0No0ePNk8dGChdu3alsrKSvXv36tsUraCrfm5omLruDsEdiMmL4cCVA6yJWMOyo8tICU1h8NODEZaCd999l4CxAUz7aRpfnvqSK4VXbnrdsuoygw5Em+3v++4DBwfYvx9iYrRimzaJLC0lIiLCZPu5qelujZjOT6frkJycjIeHB45aLH924MABgoODCQ0N1VobZlqGpaUls2bN4uuvv6ZDhw6tLul9cnIy7u7uWu3nhkhr1C2lpKiqiPSSdDJKMsgozah/TC9JJ6M0g5SCFLLKsyjfd4Pyla7AbGAdVB+vRlQI/O/1x9fFF18nX3wcffBx8sHH0Uf9uvZ52zZtDXpjU7P97ewMc+fCypXqikvvvqsdA7VAcmUl/U+fJszSksMmVle9Nf59myomG4gqFAoSEhK0mlvv7NmzXLhwgQEDBmitDX3g3QrSnPwdBwcH7rvvPpycnFpVnlFd9HNDxNh0SynJq8hrGFTWBZp/e11ZU9moa9pb2dcHkT5OPvg6+jZ4nTIzhcX3L6bsQhl9DvZh7dq12NnZaVmpdmixvx96SB2IfvcdvPkm2Nho1D5t8XxcHJVS0snFBXsTKmlpbH/fZm6OyQaiMTExdOrUSWupe1JSUti9ezf3338/9q3sl+qECRP0bYJWcHV1pbq6mu+//55Zs2bVFxmQUiJLP0TY34XSwhWBDZYWxvGFre1+bqgYim6lSklOeU6D0cu6kcurX2eWZqJQKRp1TUcbx2tGK+teV+VV0cG9A/1C+uFi63LzH1RB0KN9DyZMmMDmzZuZNGkSmzZtMsoRphb7+7bboEcPuHhRvWlp1izNGqgFDhcWsi43F1sheL+VzeLcCkP5+zajGUwyEC0tLSUvL4+wsDCttWFra8vMmTNp27at1towo3lsbGzo3Lkza9eu5f7771cnvK9YB2VfUVP6FUk1Stw8vsTDYby+Tb0luujnhogudNeoasgqzforqPzb9Hjd66zSLJRS2ahrutq5/jV6WTdF/vfXTj442lw/UCwtLeVM4RmG9RzW6BH9AQMGcODAAcaNG8fevXsZN24cv//+O25ubo3+v9A3GvG3EOpR0aeeUucUNfBAVCUlT9auZ30hIIAORjqS3RxM9b7WmjHJQDQyMpJu3bppZfpVSsnRo0cJDw9vtTv5NmzYAMCMGTP0bIl2GDZsGJmZmeov5tGhyJK3AbAUYC8EFYo4wPADUW32c0OmJbqraqrILM28dnr8b4FmTlkOksZVpfN08Gyw7vKa0czax5bm7myu7h49enD48GHGjh3LsWPHGDlyJH/88YfR5DrWWD+fPx9eeAF27YLERHXVJQPlu8xMzpWX42ttzYs6KsRiKJjqfa01Y3KBaHZ2NpaWllqrl37y5EmioqIYOHCgVq5vCBQXF+vbBK0ihGDy5MkUFeUji14AWQZAhUqSrFThqUjQs4W3Jjs7GwsLC631c0PlVrqllOyI20Fsfux1RzPzK/Ib1Y5A0K5Nu1uOXno7emNjqf31hi31d+fOnTl06BDjxo3jwoULDBs2jN27d+us2lxz0Wg/d3NTj4T+8IN609Jbb7X8mlqguKaGF+PiAHivc2famNDaUFO9r7V2TCoQValUREVFER4erpXrFxQUsH//fh544AFzcl0jx8HBATvVT1CqrhEvsSCuphoJtSOihou2+7mh0hjdP138ifkb59/wc0thibejd8Nd49fZRe7VxgsrC8O4fWrK3+3bt+fgwYPcfvvtnDt3jmHDhrF3716CgoI0ZKlm0Uo/f+ghdSD6+efqEdKuXTV3bQ1hBdypVJLo7MzdrXTW7XqY6n3NFDCMO2kLEUK0B1YB3oAKWNGvXz/y8/OZM2cOV65cITAwkHfffZd27drhoKXyZ0lJSQwbNgxPT0+tXN+M7pCKGCj98K/XDvdTVrUCgApFgkHvrL9y5YpW+7mh0hjdozqO4s7QO9mbuJfiqoYj+652rkwNmcrowNGMCBxRn+Dd0NGkv728vNi3bx8TJ07k2LFj9cGoIaaf00o/HzoU7rxTXfZzwgQ4ehR8fTV3fQ2QmZzMc97ehISEGOw9SBuY6n3NFGgtw3Y1wHNSyq7AQODxiooK3n77bcaMGUNsbCwjR47k3XffpXPnzloxoKysjN69ezNo0CCtXN+M7pCyWj0lj3oXc1aeJ2kFs7C0cAZAKUupVmbp0cIbU11dTVJSktb6uaHSWN2+Tr5snLOR/BfyOfXQKd4f9z5TgqfgYutCYWUhq86v4v5N99Px444EfhTI/b/dz7dnvyWxIBEpG7cmVJdow9+urq788ccfjBgxor5m+4ULFzR2fU2gtX4uBPz4IwwcCMnJcMcdYEBLkfIrKup1m1IQaqr3NVOhVQSiUsoMKeWZ2uclQJRCoWDTpk3cd999AAwcOJATJ06od0FrmIKCAr744gvKy2+QQLqV4e/vj7+/v77N0Bqy9EuouVT7yoZyq/+Qnp6Ng/VfN0FDnZ6/fPkyXbp00Uo/N2SaqtvSwpJ+vv14bvBzbL57M3kv5HHm4TMsG7+MqSFTcbVzJakoie/Pf88Dmx+g0yedCPw4kHs33svKsytJKEgwiMBUW/52cnLi999/Z/z48eTk5DBq1ChOnz6t0TZaglb7uYMDbNkCXbrA+fPq+vPV1Zpvp4n8kZ9P0IkTnGnXzvz3baZV0eq8KoQIBPq0adOGxMREfHx8KC4uxtbWlvz8xm1EaCp79+5lwIABrXbKoLi4mMTEREpKSqipqaFv377Y29uze/durK2tsbKyon379ga/saExyOrzUPZV/Wvh9BxB3rcTBERnBwJnAPX0vKu9YSVTLi4upri4mB49eujbFJ2iCd2WFpb08elDH58+PDPoGZQqJRezL3LgygH2J+3nwJUDJBcl88OFH/jhwg8A+Dv7MzJwJCM6jGBk4EiC3IJ0OkqlbX87ODiwefNmZs2axZYtWxg9ejTbt29n8ODBWmmvseikn3t6wo4dMGgQ7N4NixbB99+rR0z1gEKlYnFMDIVSomjTRi826AtTva+ZEq0qEBVCOAK/Ak9bWlr+Cupdsreqw7tixQpWrFCv/8vJyWlSm5mZmVy5coUpU6Y0225DIj8/n+TkZDIyMsjMzGTBggVkZGQQFxeHs7Nz/S9SIQS2trYoFAqqqqrqd9KvWLECOzs7fH19ja5cppQVtVPytXkfrQeAg3pEvaSkhDPHM+jQU/2RoY2I1vVzU0troi3dlhaW9PbuTW/v3iweuBiVVBGRHcH+K/s5kHSAA1cOkFqcyuoLq1l9YTWgnvYfGTiSSV0mcXf3u7XqB13529bWll9++YX58+ezfv16xo8fz9atWxk5cqTW2rwZOu3nnTrB77/DiBHqDUz+/vDf/2q3zRvwZXo6lysr6WhjwzPt2+vFBn1gqvc1U0PoY3pJCOEK/A/oDkjgASAaWAsEAleA2VLKgtrjlwAPoo4QnpJS7rzONa2BrcBOKeWy/v37y5KSEtauXYuUEm9vb0aOHEl0dPRNbevfvz+nTp1qtJbq6mpyc3PxNbAF7U2hoqKC+Ph4unfvzqFDh8jOzsbHxwdfX1/at2+P5d/Sg6xbtw6A2bNnX3Ot0tJSMjIySE9PRwjB8OHDOXjwIK6uroSFhRn01Iqq+E0oX6V+IdogPLYgrP5agrB931u4dPoGAGfbQXT3/rFR121qn2oOdT8c+vTpo9V2DA196VZJFZHZkey7so+Pjn1EYmFig8/33LuH0R1Ha619XeuuqanhgQce4IcffsDe3p7NmzczduxYnbR9NXrx9/btMGUKKJXwzDPw/vugw6woeQoFnY8epVCl4rfu3ZlmQpthTe2+JoQ4LaXsr287dI2+1oh+DOyQUoYCvYAo4CVgj5SyC7Cn9jVCiDBgLtANmAB8IYRoEBkJ9U+lb4AoKeWyuvcnT57MF198QdeuXfn++++ZNm2aRkWkpaWRlZVltEFoVlYWmzZt4pNPPiEmJgalUsmwYcOYOXMmgwcPJjAw8JogFKC8vPyG62EdHR3p0qULI0aMYPjw4YC6Nv358+f56KOPOHz4sFY1NRdZdfSvIBQQTv9qEIQC9Ov516h3RU28zmy7FUqlkujoaLoaYKoZbaIv3dXKanYn7Obzk5+z9PDSBkGop4Mnj4c/ziB/7W1a1IduKysrvvvuOxYtWkRFRQVTpkxh585rxgO0it76+cSJsHo1WFvDhx/C7NlQUaGz5l9NSKBQpWKUszNTTSh/pqne10wRnQ9PCSGcgeHA/QBSymqgWggxDRhZe9j3wH7gRWAasEZKWQUkCiHigAHA0asuOwRYAFwUQpwDdYLmOXPmsHjxYnr06EFAQADr16/XmA4pJdu3b+e2226jvRFNlUgpiY+PJygoiMzMTNzd3XniiSdoo8V1R8HBwQQHB5OXl0deXh4A+/bto0uXLgax6UmqSpBFL/31hu0osL/rmuPauncjrsQahAKFMpsaVTFWtTvp9UlCQgJ+fn7YmVCZP9Ct7rLqMnbE7WDj5Y1sjdlKUVVR/WcBLgFMD53O9NDpDAkYovX8ovryt4WFBcuXL8fS0pLly5czbdo0NmzYwB133KGT9vXaz+fOhXbtYPp0+PVXSE+HzZvVa0m1yMXSUr7KyMAC+CQ42KSmp031vmaK6GOetBOQA3wrhOgFnAYWA+2klBmg3gUvhKjL1OsHHLvq/NTa9+qRUh5GPTD6MPAwqNf0ZWdns3Tp0vrjNJmC5PLly9TU1Nx07amhUVZWxrZt28jJyeHBBx+kV69eOm3fw8MDDw8PpJS4uLiwZs0aevXqxahRo/Q6ZS+L3wRVhvqFcEU4v3ndG74QljjYdKRcoa7xXKGIx8n21lNGJSUl7N+/X5Mm1yOlpKysDEdHR9LS0rTShiGiC93FimL+zPuTQ7mHOFVwimrVXzunAx0CGeY5jKGeQ+ni2AUhBPKK5PAV7Y74G4K/58yZQ1ZWFr/99ht33nknr732mtY3MBmCboTA4cMP6fnSS9gdPUp5795cfOcdKvz8bn1uM7ksJQEqFb0tLck9dYr9WmvJsDAIf5vRGfr49rcC+gJPSimPCyE+pnYa/gZc7yfgdRe2SilXACsAwsLCZL9+/fDx8Wmpvdfl4sWLjBgxwmh+oRYUFPDNN9/Qu3dvZsyYodfATwhB3759CQkJ4ffffyciIoLevXvrxRZZuQsqN/5lm8sbCMu2Nzze3rpzkwNRJycnrW3uOHv2LKGhoVrr54aKtnSnFqfy2+Xf2Hh5IweuHEAplfWfDfQfWD/y2cVDP5vwDMXfI0eO5JlnnuHjjz/mtddeY+3atUyfPl1r7RmKbkaOhNtvh8mTcTh7ltueflqd6klLJZ1dzp7lYLt2uLdrZ1KlPA3G32Z0gj6ikVQgVUp5vPb1L6gD0SwhhE/taKgPkH3V8VfPffsD6bdqRKVS4e3trUGzG3LXXddO3RoiZWVl5ObmEhAQwL333ouXBkrCdezYUQOWQZs2bZg1axZSSqKiokhNTdXp6KhU5iGLX/nrDbupCLsJNz3H3vqvcoellTF4OWrLultTUFBARUWFVvu5IaIN3bvid/Hy3pc5mX6ywfvDOwxnbre5TAudhq+TfteCG5K/hRB8+OGHWFlZ8cEHHzB79mx+/vlnrdwXDUk3oK60dOCAeq3ojh0wahRs3QpjxmisCSklhYWFVFRU4O/jYzQDHprA4PytIwoKCvRtgt7Q+WYlKWUmkCKECKl9awxwCdgM3Ff73n3Aptrnm4G5QghbIURHoAtw4lbt2Nvba+2P98iRI2RnZxt8Pfn4+Hi+/PJLkpOTEUJoJAgFGDFiBCNGjNDItUD9pRYQEEBhYSHLly+vX0eqTaSUyOJXQVWbW9aiHcL5lZufRMNANLvgnJasuzVSSiIjI+nevbtJfUlpS/c7R965JggFOJh0kP/s/w9Tfp7C9LXTeWr7U7x35D3WRKzhSPIRkouSqVHVaMyOG2GI/hZC8N577/Hiiy9SU1PD3Llz+fXXXzXahiHqBsDJSb1GdMECqKyEjz/W6OXfSU7m7gsXcDexdaEG628tU6fbVNHX/OyTwI9CCBsgAViIOiheJ4R4EEgGZgFIKSOFEOtQB6s1wONSXjVfdgOut9tbE1RWVnL48GGdr69sKrGxsfz222/MmjWLwMBAfZtzS+pGR0+fPs3u3buZM2eOdhus/A2qdtW/FC5LERYutzzt6kDU2j5XG5Y1irS0NJydnXF21v9mKV2iLd2rZ6zml0u/kFKUQkpx7b+iFNJK0sgpzyGnPIczGWeue66FsMDXyZf2zu1p79Ke9s7tCXAJaPDaq41Xi75YDdXfQgiWLl2KhYUFS5cuZe7cuaxbt05j0/SGqhtQ76IfP16dY1SDmz0zqqr4vytXKJeSVAsLQjV2ZcPHoP2tRep0myp6CUSllOeA6+XKuu7chpTyLeAtbdrUWM6fP09QUBCOjnqck70FUkratWvHggULtDK98eOP6vyZ8+bN0/i1+/XrR58+fSgrK6OgoEAru+qlMgNZ/H9/vWF/D8K2cVWS7K06oV62LKmqSSYzMwVvb91mTaipqSE2NpYhQ4botF19o03d3o7ePDHgiWveV6qUZJVlkVyU/FeQWvuYXJRMSnEKmaWZpBanklqcytHUo9e5OthY2jQITOueXx2wuti6XDdYNXR/CyF46623UKlUvPPOO8yePZtffvmlxenyDF03AJmZ6kcN3mdfio+nXEomu7kx1t1dY9c1dIzC31rAVHVfjeFmFzdQUlNTCQ8P17cZN+TChQvEx8czffp0rf3CUigUWrluHRYWFuTm5rJu3TrmzZun0TytUqrUqZpkqfoNywCE0wuNPt/Swh4rSx9qlOkgVJw4u5WpEx/TmH2NIS4ujg4dOmBjY6PTdvWNPnRbWlji6+SLr5MvA/2vvyGlWllNWnFafZBaF6BeHbTmV+QTXxBPfMGN88862jhedzRVFkk6uHWgRtRgg2H6vG5kVKlU8v777zNr1ix+/fXXFlWcM4p+npWlftRQIHqyuJhV2dlYAx8aUVU6TWAU/tYCpqr7asyBaBOZOXMm+qhG1Riio6PZtWsX9957r75NaTEdOnRgypQp/PTTT9x33320bXvjnexNonw1VNeNWlkgXN5FWDg0+vSC6mJOFTvRzrYXPZxGcPRyOlWjq7C1tdWMfbegvLycrKwshg0bppP2DAVD1m1jaUNHt450dLvxJr6y6rIGgenfR1VTilIorS4lKjeKqNyo619kE3jYe9xw+r+9S3v8nPywtrTWktKbI4Tg3XffRaVSsWzZMmbOnMmGDRuYPHlyk69lyP5ugAZHRKWUPBmjzsjxtL8/nR0af18ydozG3xrGVHX/HXMg2gROnDiBu7s7nTt31rcp11BQUMDmzZuZN2+e5oI2PRMaGkp1dTU5OTka0SRrEpAl7//1RptFCJu+jT5fJVV8GPM95wvV60SDS2vo2TaYxMREQkN1s5IrMjKSrl27GvxGOU1j7Lrb2LQh1DOUUM/r9xMpJYWVhdeMqp5LOEcRRWRWZJJSlEJeRR55FXmcyzx33esIBD5OPjddr9rOsR0WQjv/j0II3n//fVQqFR999BEzZ85k48aNTU56bzT+1mAg+nN2NsdLS2lracm/jWBdvyYxGn9rGFPV/XfMgWgTOHXqFFOnTtW3GdfFzc2Nf/zjH1qtkKQPevbsiZSSK1eutGjTlZQ1yKIXgEr1G1YhCMenmnSNDam7OV8YDai/8Od1mExIcAedVf7Izc1FpVJpLPuBsWAKuoUQuNm74WbvRs92PQG17njHeG677TZA/UMouyz7pqOqGaUZpJekk16SzvG049dty9rCGn9n/2vWq9YHrS7tcbNza/bmKiEEy5YtQ6VS8cknnzB9+nQ2bdrEhAk3T41Wh1H5uy4Q1UC+y1O5uQjg7c6dcdZjnmddY1T+vgFCCI4ePcrAJuSTbQ26NYXp9PYWkp+fT3l5OX5arKLRXPbu3Yu/vz/BwcE6aU9X7dShVCrZtm0bI0eOpFu3bs27SNnXoKirrGWNcHkPddKGxhFVnMCPSVvrX8/0H0dv11CklJw8eZL+/ftr9VetlJJLly7Rt2/jR3BbA2bdf+m2EBZ4O3rj7ehNuN/116krlAoySjMarlX9WyaAnPIcEgsTSSxMvGH7DtYO+Dv71//zc/K75nnbNm1vOLIqhOCjjz5CpVLx2Wefceedd7JlyxbGjRvXZN0Gy5kzcOWK+nkLR0SllNyZl8ecsDDCW8mMVmMwKn/fgOTkZEC9nKyxtAbdmsQciDaSzMxMQkNDDS63WUpKCmfPnq0fNdEF2i7n93esrKyYNm0aa9asITAwsMmjvlJxCVn6af1r4fgUwrrxU+mlNeV8EP0tKlQAhDp14u6ASeprCcGZM2fw9vYmICCgSXY1heTkZDw8PAw6W4M2MOtumm5rS2sCXAIIcAlgCNffhVuhqCC1OPWGo6opxSkUVxUTkxdDTF7MjduysMbXyff6waqz+vkHH36ASqXiiy++YOrUqWzfvv2mVcaMwt/5+fDvf8NXX4GU0Ldvi2vO1+nuZmKjY0bh71vw+OOPAzSpClRr0K1JzIFoIwkLC6Nr1676NqMBCoWCTZs2MXHixFY3Jf93/P396dWrFydPnmxSuUwpq2qn5GuTjlv3gTaLmnC+5LPYH8mpUle9aGNpz3Mh92Nl8Vee2pCQEKKjo7UWiCoUChISEhg6tHEpploLZt3a0W1vbU8Xjy43LVNaVFlEWkkaacVp9ampUotTSSv563VeRR5JRUkkFSXd8DoCgVegF57DPMk9lMu4ieN48IMHGTp0aH3g6ufsh4O1g+H7W6WCb7+Fl16C3FywtITFi+E//4EWzIY8HxuLZ2oqzxqqbi1h8P5uJFu3bm1S6qXWoluTmAPRRlBZWcn+/fsbvcZJVyiVSvr3709YWJhO2/3uu+8AuP/++3Xa7ujRo7GwsEBK2eiRaVn6MdTUjuoIe4TLOwjR+GIHOzIPczTvfP3rJ7vMw8uuYW6/4OBgNm7ceMtpx+YSHR1Np06dsLbWz25ofWHWrT/dLnYuuNi5ENb2xveWCkUF6SXp1wSoV7/OKMkgqzwLRgHFUHO+huXPLGf5vcvVxZprcbNzw9PGEz8nPzqXdGZk4Eju6XGP4cxAJSbC3XfD8dp1tyNGwGefQffuLbrswcJC3k9Lw87CggekxHQm5Q2jn7eUurKcX3/9daPPaQ26NY05EG0EaWlpZNYtSjcQiouLUSgUTVocbexYWlpSVVXFt99+y6JFi25Zk15Wn4Syb+pfC6cXEFaBjW7vSlka3yT8VbLwDp/hDPLsfc1xvr6+WknuD1BSUkJ+fn7z18YaKWbdhq/b3tqeIPcggtyDbnhMjaqmPuF/0l1JvPPcO5zddRbrn63p9nw3ilzVI68FlQUUVBYQWxzL/rT9/O/s/wCY11M7f1dN5pNP1EGok5N6Sv7uu6GFQbJSSp6MVm9+fCkggLYmlEfSmPr5zXjhBXUO6sbOlrYW3ZrGtHMGNJL09HSNJlXXBEePHuXs2bP6NkPn2Nra4uDgQFTUDXIt1iJVpciiF4HanK82Q8D+nka3U6ms4r3LK1FI9ZR+oIMfCztev2yhEIKqqioKCwsbff3GEhkZSbdu3QxnZEhHmHW3Dt1WFlb4O/sz0H8gc3rM4fi240yfPh1FmYLUz1LZNn4bFS9XsG3kNvbO2cvmuZt5btBzACzesZjdCbuprKnUswpg/nz19HtpKXTq1OIgFGBlRgYXKirws7bmeS2uLzdEWks//9///tekdI6RkZF0797d6HVrGnMg2giysrKatBBZ2ygUCs6fP0+/fv30bYpeCA8P5+TJkzc9Rpa8A8pU9QvhrK4l34Q//q8TfiG1Ql01xdbChudDF2JjceOplPPnz3Px4sVGX78xZGVlYWVlhYeHh0ava+iYdbde3dbW1vz8889MmDCB3NxcxowZw8kTJ/Fq48Wo0FFMCZnCe+PeY2ynseRV5DHuh3G4v+POxB8nsuzoMiKyI/RTUKRfP5g+Xb056fvvW3y5QoWCJfHqKlsfdOmCg2XjlwsZO62ln1dWqn8gfd/I/pCVlYW1tTXuJlS2tbGYA9FGMGPGDJ2vw7wZERERtG/fHjc3N32bohdCQkJo27YtNTU11/1cVh2AirX1r4XzqwjLxqdXOZB9kt1Zf9UMfyRoNv4ONz/f19eXjIyMRrdxK1QqFVFRUSY3hWPW3fp129rasmHDBkaOHElGRgbTpk3DxcWl/nMhBGvvWssLg1+gV7teVNRUsCNuB8/98Rw9vuyB3zI/7vvtPn688CMFFQW6MTo5GbZtUz+/p/EzKzfijStXyFMqGezkxGwTStfUmvr5W2+9BTQui0ydbkOKIwwJcyB6CyorK4mIiMDSgH6xhoSEcPvtt+ut/W7duun1RmJhYcGUKVOuO8IpVYXIopf/esN2Atg1vt51RkUOX8b/FcSOaBvOaK9bp8by8fHRaCCamJiIj48P9vb2GrumMWDWbRq67e3t2bx5M7169SIrK4vJkyeTnZ1d/7m7vTvvjHuHc4+eI+O5DH6Y/gPjg8YDkFGawarzq5i/cT6DV+ooldyyZVBZCT16wKBBLbpUhVLJz5mZCODT4GCTmqZtTf38zTffbHT6pdakWxuYA9FbkJaWZlBrMXNzcykoKNDr8H54eDjh4ddPqK0rqqur+fjjj+unR+qQxa+DqvYLzcIT4fJao2/0ClUN70WvpEKpvqaPXVseDZrTqPM9PDwYO3asRqYNq6qqSE5OJijoxptAWiNm3aal28bGhjfeeIOePXsSExPDxIkTKS4urv88tTiVHy/8yKv7XuX/Dv4ff8T/0eB8eyt7xnQcoxtjBw9WrxG9eBFuvx2uCpqbikVNDV/X1PBzaCh9nZw0aKRh0xr7eWlp6TXfQX+nNerWNOZd87cgIyMDbw3UEdYUf/75Jx4eHnqt8KRQKAD0mn7CxsaGgIAALl68WB8Uy4qtULmt/hjh/CbConEB+4XEDD45vpkc/xQArIQl/wxdiINV48p3CiEICgpCoVBg08Ldr5cvXyY4OPiWWQFaG2bdpqe7X79+7Ny5k6FDh3LmzBkGjB5A+AvhHMk4ck3lpzbWbRgSMIQRHUYwosMIwv3CsbHU0U7z2bPBywvmzoW9e6FPH1i3DpqQP7KOy5cv0yc4GD8D+l7RBa2tn584cYIBAwZgb2/P77//zsSJE697XGvTrQ3M/zO3oKioCM8WVs3QJElJSQxq4dRQS/nxxx8B3ecR/TtdunQhLi6O8PBwpDJLPRpah/1dCLvRNz1fSsnRy8ms/OMEp2LVG5sGj+pFnk8U8zpMobNj03aybt++nY4dO9K7d++mSqmnqKiI0tJSevbs2exrGCNm3aalu7CwkEtZlyitKeVg8kHK55bDJxB9OproV6NhNjg7ODM0YGh94NnXpy/WlnrMvThypLqs55w5cPiw+vV776mT2jdi1kShUvFefDwDSkpMzt+tsZ+Hh4dTU1NDaGgod9xxB+Hh4Rw/frzBDFpr1K0NzIHoLRg4cGCLR7g0RWVlJaWlpUa/21BTtG/fnuLiYqSU6nWhskj9gYUfwulfNzxPqVKx51wc3+46SVRKwym2EwfK+fk/z9HZvenpupycnCgpKWnyeXVIKYmIiGgVaU2agll369ctpSQqN4oDVw5wIOkAu2N3k1ed99cB1uD8oDMVX1egiFYw6fIkNv60EWsrA0v67eurHhFdsgQ++ACeeQb+/BO++UadY/QmfJ6WxstpaQx3dGRsK/f31bTmfm5paUlsbCy//PILs2bNwsLCol5ra9atacyBaCMwlAXGVlZW3HvvvVi0oJxca8Ld3Z1hw4Yhy9dA9cHadwXC9R2ExbWLyBU1SraeiOK73adIym6429bSQjCxfyj3j+tPZ4/mjYA7OjqSn5/frHNBvQykTZs2uLq6NvsaxohZt6u+TdE4KqniYtZFDiYd5EDSAQ4mHSSnPKfBMW0d2jIicATDA4YzInAE3b26c2LOCcaOHcu29dt4we8Fli1bZnhf4tbW8P776k1LCxfC+vVw4QL8+ivcYBNnTnU1/0lULzV4PjBQh8bqn9bcz+u46667KCkpwcnJie7du/PII4/w6quvtnrdmsIciN6CH374gfvuu88gUiUVFxcbhB2GxK4dKxnV6yMs6r6rHO5D2AxocEx5VTW/Hr7ID/vOkF1Y2uAzW2tLpg/qwYIxffHzcKEl+Pv7Y2fXuDWlf0epVBIdHa33ZRe6xqy7dehWqpScyzzHgST1iOehpEMUVDb8sefj6MPwgOH4VPtw34j76OXX65ogc+DAgWzcuJFJkybx0Ucf4eHhwb///W9dSmk8M2eqd9HPnAkRETBgAKxYAdepsvZKQgLFKhXjXFyYZEIzWq2tn98MR0dHpJS89dZb/Pvf/2b58uUkJSXp2yyjwByI3gQpJaWlpTgZyM7GvXv30qVLF3r16qVvUwwCKZX07bwOC1G7a9EyCOH0bP3nhaUV/HzgHGsOnKOovOHORkd7W+YM68W8UX1wd3LQiD3+/v74+/vf+sDrEB8fT/v27ZsdyBorZt3GqVuhVHA643T9iOfh5MMUVxU3OKa9c3tGBI6oX+PZ2b0zsbGxWFhY0Nn/xtVoxo0bx48//sicOXN45ZVX8PDw4LHHHtO2pOYRHAzHjsFjj8EPP6grMP35pzrdk60tABdKS/k6MxNL4GMTS9dk7P28Obz88ssMHTqUkSNH0qFDBz799FOeeOIJfZtl0JgD0ZtQUVGBtbW1wex2y8jIYPjw4fo2o0WbcTRK+Xe4OSbUvrBEuL6LEHZkFZTww94z/HrkIhXViganeDg5MH90X+4a2hMne1uNmlNaWsqGDRu49957m3ReRUUF6enpBuFbXWLWbTy6q2qqOJl+sn6N558pf1KmKGtwTCe3TvVB54jAEQS6Bjb4vCm6Z82aRWFhIQ8//DCPP/44bm5uzJ07V5OSNEebNupqS0OGwFNPwRdfwKlTsH49sn17noqJQQU86etL1zZt9G2tzjDGfq4JKioqkFKiVCq55557ePLJJ3n66acpLS01qYC8KRhGhKVFduzYweLFi1EqlSxatIiXXnqpSecPGzZMS5Y1DYVCQXFxsUHs4G9uICqlRKWUVNbU8NXWozw6eRBt7Jq3EUwqYpAly+pfC8d/kJzvz7e7/2DriShqlKoGx/t5OHP/2P5MHdgNW2vtdHsbGxtSUlKafF5UVBShoaEmt/bXrNtwdVcoKjiWeqx+fefR1KPX1HwP9ghuEHj6O998NqCpuh966CHy8vJYsmQJCxYswNXVlQkTJjRbk1YRAh55RF0K9K674MQJ6NuXAz/9xAEbG9wsLXm9Y0d9W6lTjKGfa4Orda9Zs4bnnnuuPs3T9u3bDbcP65FWHYgqlUoef/xxdu3ahb+/P+Hh4UydOrXRZbYcHBwaVb5LV4wfP94g/qjLy8sB9f9PU4iNymDJUz9i6WVHkqjk+OVkPnpsKr5NXJspZTWy6HlAPdpZoQrh9XUB7Dr7HX/PJ9/F15OF48IZ3zcYK0vt/t9ZWVmhVCqbdE5+fj5VVVW0a9dOS1YZJmbdhqW7rLqMP1P+rF/jeSLtBNXK6gbHdGvbrT7oHBYwDB8nn0Zfv7m6X3zxRfLy8nj//feZMWMGu3fvNqh78jX07w+nT6un6HfsYPiECfzv1VdxeOQR3PSYd1nXGGo/1zbX0311mqeJEydy2223cfToUZNaonErWnUgeuLECTp37kynTp0AmDt3Lps2bWp0IJqZmcnevXu5RwO1hVuKtbW13qsZ1bFu3Tqg6XlED++PoqS4AoorsHW3Ii4tl/lv/8z7D0+mb5fGr62UpV9ATRQACqUV81f0ITEvocExvTv58sD4cIZ166izP3ghBC4uLkgpG9WmlJLIyEh69+5tUjcls2796y6uKuZw8mEOXDnAweSDnEo/RY2qpv5zgaBXu14NAs+2bZpXE70luoUQvPvuu+Tn57Ny5UomTZrEgQMHDDsvo4cHbNuGfPNNxGuv8eDrr8Px47B6tfqzVo4h9XNdcjPdN0vzZEYPgagQIgRYe9VbnYBXgVW17wcCV4DZUsqC2nOWAA8CSuApKeXOxrSVlpZG+/bt61/7+/tz/Pjxm56Tl5fHd999B6hLc+Xn53Py5EnCw8NRKBT1ydyvpnfv3vTu3Zvy8vL6IO1q+vfvT/fu3SkqKmLjxo3XfD5o0CBCQkLIzc1l69at13w+fPhwXFxc+Omnn667cWrMmDG0b9+elJQU9uzZc83nEyZMwNvbm4SEBA4ePHjN55MnT8bT05Po6GiOHj16zefTp0/HxcWFiIgITp06RWZmJkD9/9Ps2bNxcHDg3LlznDt37prz582bh5WVFefOnaX7QPWQpdJeicqqDCjj0Y9/5V93j8FLFBMTE9PgXGtra+bV7kI9cOAAifEXkTUxCMYCkFLkSGKeOpNAWJtKOrpY4+PmhKN9BQmnDpIbc44ZM2YA6mUadbbX4eHhwZQp6lr0W7ZsIS8vr8Hn3t7e9VMpGzZsaFCCENR9auxYtS3r16/HxcWF77//vv7zjjeZjktNTcXV1dVgNsPpCrNu/ep+5/A7/Gvvv1BJ1TWfOds6MzVkKjNCZxDsEUzbNm3xsPfA0sKy2e21VLcQguXLl1NQUMDGjRu5/fbbOXLkSP0AgyGSoVAQOW8eQe3a0fHll2HHDvWu+vPnoZH1yY0VQ+nnuqYxuv+e5umxxx7jiy++0KGVhonOA1EpZTTQG0AIYQmkARuBl4A9Usq3hRAv1b5+UQgRBswFugG+wG4hRLCU8pZzoNer+329X2grVqxgxYoVwF/lKw0NKSU1NTW3PtBASUrIobCgDE8fsLAQdOzQloTMfGqUKmqUKt5YvYtZvTzxtr1+kRKFUklMWgaqyhhsa3ttcaUNqQUuCCS39wulr2sN5UXNz+OpCQoKChqVYqumpoa4uDiGNKNEoDFj1q1/3dll2dcNQkE9Urr6wmpWX1hd/55A4G7vTts2bWnr0Pavx9rn7dq0w8/ZD39nf3ydfBuU3dSUbisrK3766ScmTZrE3r1764NRLy+vFl1XWzwfF8fP2dl8eccdPHzHHTBmDMTGwr59UPvDtzViSP1clzRF99/TPH355ZdkZWUZbF/WBeJ6wZrOGhdiPPAfKeUQIUQ0MFJKmSGE8AH2SylDakdDkVIurT1nJ/CalPLaobur6N+/v/z000957bXX2LlTPYC6dOlSAJYsWXKz8zh16hQA2dnZHD58uH5ETZ8UFRWxcuVKnnnmGX2bUj8Sev/997P/1+O4tHWmz/CuNz1n9TcHWbV8PwDDx4Tx76V3kZ5XzLNfbSYm9a9E14PDAlm66I76He35+aVs//08J1LSGdBtDXcPuAhAaZU181feTZ+gfsjUCN741/OaF9pEpJS88cYbvPrqqw1+8Fzdp+qIiorCzs7upiOmrRGzbv3rllKSWZpJTnkO2WXZ5JTlkFOe89fj1c/LcsivyEfS+O8JrzZe+DmpA1MHpQP+Tv5079C9/j0/Zz+cbZ2bZXtxcTEjRozg3LlzhIeHs2/fPtoY2E70Y0VFDDp7Fhsg6rbb6HTqlDq3aEoKLF0KTdwwa0wYUj/XJc3VnZKSQkCAupT0Z599xhNPPHFaStlfGzYaMvpeIzoX+Ln2eTspZQZAbTBa9/PADzh21Tmpte/dkvDwcGJjY0lMTMTPz481a9bw008/Ndo4Ly8vgwhCQb0j28+vUbI1Tm56PokXkuh/e8O1L6ve2cwP//4JK2tLPtj3GmG3dbnhNQ7vjap/PmRUKAC+Hs6sfG42r3y/k33n4gD489IV7nvnZx4a0Z8TB2I5ciQGpVJFv16Z3L3wYv01jqbO43/PPIWnswMVFYaRHqRubeit1kWVlZWRnZ1tMBkZdIVZt2HoFkLg4+TT6M1GNaoa8ivyySmrDVz/FqhmlmWSVpxGanEqGaUZZJdlk12WzdnMs39d5FzDazrZONWPotYHqFcFqn5OfrRt0xYL0XCDobOzM9u3b2fQoEGcPHmS2bNns2nTJoNJsaeSkqdqlxc96+1Np5degk8/BSmhe3cw1BRUGsDQ+rmuaInu9u3bI6Vkzpw5dblGNZtT0EjQ24ioEMIGSAe6SSmzhBCFUkrXqz4vkFK6CSE+B45KKVfXvv8N8LuU8tfrXPNh4GGAdu3a9VuzZg3Hjh3j888/R6VSMXHiRObPn39Tu/75z3/Wj16VlpZy6tQpRo4cqRHNxsqXz3zHho+30aVfJx55/16qbCvYsvIAx384i6yqAsDGwZYNOd9g+7fcnDmZRWz75SQ/rzyEtLLE2tqSdTv/SRvHv45TqSRfbT3K/7b/tX5XKCVt0hVYl6to41DFind+w8tDvVu/xmoE1h4rEEJQWlpKfn5+/a9KfVJdXc17773Hyy+/3OD9kJAQli9fXv+6vLwcGxsbg/ny1BVm3a1ft1Iqya/OJ7cql7SSNAqVheTX5JNTlUNuVS451erHKlXVLa9lJazwsPGgrW1bPG091Y826kdlrpKPl3xMaXEpd9xxB//85z8NYmPMH8BSwLO6msjHHsMrIQGVpSXJ99xD0oIFyFa8c96U+vnVaEq3lJLRo0ebR0R1zETgjJQyq/Z1lhDC56qp+eza91OB9led5486gL0GKeUKYAWop+ZHjhzJyJEjm5w7tA4LCwuOHz9uMIHozp076d+/Px463HlZkF3EthW7AIg9nUDs+WQ2fX+Y7Lj0+iAUYNHSedja26JUqkiKzeLM8XgO74rk8sVUQF3LPbiHHy5ubRoEoVJKLl1KoygiF+esGorbWoKFQFoKSv2tsc+u4b+PRdQHoQhXrN3+W/+lEx8fT2xsrEEEolZWVjzyyCPXvO/k5FTfh3Jzc0lISGDAgAHXHNeaMes2665DSklBZQFpxWmklahHUutGVOtfl6SRX5FPVlUWWVVZ12kBmAV8D7///jtnLM4Qfk94/chqB9cOjA8aj1cb3a27K62pYe7Ro6BU8sEHH+CVkAC9emHx7bcE9ulDoM4s0T3mfm5aujWNPgPRu/lrWh5gM3Af8Hbt46ar3v9JCLEM9WalLsAJXRhob2+PQqFAoVBgbQC/ZEtKSkhNTdVpILrhw61UVajzCbbt7Md37/2OhUUNVqKKuiyDXW4L5vKlLB4b9zZpiTl4eLuSUdIwB6FKJZk0rS+jJ6nLk5aWVrJ7dyRbt5wlMVG9RtQScKpUUupng7QSIAQV7azZk+xCnzALrCxVCJf/Q1j+lUYmPT0dH5/G5zPUJpWVlahU198EAn+l9+jf37R+8Jp1m3VfjRDqzU/u9u70aNfjhtcpV5STXpJ+3SA1tTiVNKc00melI9dIMrdmssVyC/T563xLYcmEzhOY12Me00Kn4WCtmVK+N2JpcjJZSiUDoqKYv28fvPYaLFkCNs0r2mEsmPu5aenWBnoJRIUQDsA44Orho7eBdUKIB4Fk1L93kVJGCiHWAZeAGuDxxuyY15CdODo6Ulpa2qid0NrGx8eHjIwMndWaL84vYdPnO0AILF1dKChRZxQInuQKSicufp8I1tYkpJSQmPrXerDCnGKsnNpQo1BiYWlB7wGdGDauGz36BXJ4bxSH918mLjWP5JS8a9rsHujN8LFhbLocy+UU9aD4sfj2lFdb4ew2AWF3e4PjMzIyCA0N1d5/QhNITEwkMjKS2bNnX/fzpKQk2rZta3CbK7SNWbdZd3NwsHags3tnOrvfuC59jaqG9/q/x7+e+xeW2yx5dMyjtAltw8Xsi+xK2MW22G1si92Go40jM7rOYEHPBYwKHNWidFQ3YqJSybnycv792WdYuLjAiy+2+iAUzP3c1HRrA70EolLKcsDjb+/lAWNucPxbwFs6MO0aFi1a1OQKQtrCx8eHpKQknbW36dMdVJSqy/pZ2tlQN9gnLC2QQoClJZbubteszbJ3tGPK/EH4B7al75DOREZnEHUuhc/mfIGiWv0bIqx/YP3xdnbWjBnbjcmT+xAc7A3A1Oo+vL7qD/6MvMJHD/fE2fE0wvnVa2wcPnw4vr6+WlDfdEpLS3G8QY7A6upqEhMTGTp0qI6t0i9m3Wbd2sTKwoolzy6hILOA9957jx/+/QNHjhyh+7ju5JTlsC5yHasvruZY6jFWnV/FqvOr8HH04Z4e9zC/53x6teulkbWl1dXVKFJT+W3sWKxtbCA3F9avhwULNKDScDH3c9PSrS30mr5Jm/Tv31/+PWVOI89rkGonIyMDe3t7XF1dNWhd86jzlS4W5ZcVl7Og4z8oKSgDYPxjd7B3wykQgrApriCgIMKG9l28Cejcjva1//yD2uHoYk9MXBbbd0ewZ/8liksqCfRxI+NiRv31Xd3b4BXkyfjxPRgzthtt2ly7WVBKSUpOIQFebkipQvxtB211dTVKpRJ7e3ut/l80lt27d2Nra3vN7sn+/fvz7bff4uLiYhBrWXXJxYsXzbpNCH3pVqlUzJkzh19++YX27dtz7NixBj9QY/Ni+fHij6y+sJr4gvj697u17cb8nvO5p8c9BLg0z+aMqiqyo6Nxc3VV6165Eh58EMLD1TXnWzHmfq5Z3UII82YlM9dy5swZPD09ue222/RtCkIIzp07R8eOHXFxaVp99qay5cs/6oNQ36B2LHhxCq4+boT27kBk8kls7axZ+NXCBufk5Zfy+94Itu+KIDEpt8FnV9Lz8fVywsXFgaGjQhkyqiuBndreNKgWQhDg5Vb7/No68VFRUcTGxnLXXXe1VK5GCAoKws7O7pr3VSoVhYWFdO/eXQ9W6Y+SkhKzbhNCn7otLCxYtWoVaWlpHD16lClTpnDgwIH6GYouHl14beRr/GfEfziedpzVF1azJmINkTmRLNmzhCV7ljCiwwjm95zPXWF34Wrn2qh2a1Qqxp87R01FBb93qU1fd/fd8PzzcPKkOhBtpRtZzP3ctHRrE3MgegtcXFwoLCzUtxn1JCUlUV5ezuDBg7XWRmV5Fb8u21L/eu5L0/EO8OShf98JQPx35+s/UyiUHD0Rz/bdFzl+MgGl6toRdm8vZ24f251xI7rSvr3mNlrFxsbSoUMHjV2vpXTo0AELi2sD5oqKCrp162YQ6WV0SWRkpFm3CaFv3fb29mzatIlBgwZx5swZ5s6dy2+//dYgrY4QgoH+AxnoP5APb/+QnfE7WX1hNZuiN3Eg6QAHkg7w+O+PMyV4CvN7zmdi54nYWt04teP/MjKIqKigvbU13nXrQe3t1SOi770Hn30Gq1ZpW7pe0Le/9YWp6tYm135rmmmAt7f3NfXJ9Um/fv04derUdcuXaorfv95NYY66pnrb9h6MXdAwYfzAgQPpEBjKx1/tZuaCL3jlrd/483h8gyDUztaa28d048Olc/h55SM8MH+oRoPQ0tJS4uPj6dHjxrtudYmUkvfff5/KysprPhNC4O7urger9EdWVhbW1tZm3SaCoehu27Ytv//+O+7u7mzbto3Fixff8F5pbWnN5ODJrLlrDVn/zOLbad8ypuMYFEoFv0b9yvS10/H5wIdHtz7K4eTD15RFLVAoeDkhAYBlXbpgb3nVBqh//ENdq3jtWsjOprVhKP7WNaaqW9uYR0Rvgb+/P4MGDdK3GfX4+flha2tLamoq7du3v/UJTaS6SsH69zfXv57zwp1Y26hTV+UXlLFr3yV27I4g4UrOdc/v2c2fCeO6M2poCA4O2i0SMWXKlOtOheuD/Px8bGxsrmuPodioK1QqFVFRUQaxnEWXmHUbhu7g4GA2bdrE2LFj+eKLLwgKCuLZZ5+96TnOts7c3/t+7u99P2nFafwc8TOrL6zmfNZ5lp9ezvLTywl0DWRej3nM7zmfUM9QXrtyhXylkmFOTsxs27bhBQMD1TXlN2+Gr7+GvxW5MGYMzd+6wlR16wLzZqVrz7umLnhVVRUWFhYGkUsU1Jt0bLSUFmTbil189OgKANy9Xfnm8seciUhl+66/pt4d7NQjA+WV6gH1dm2duX1sN24f0x1/X+2nuVKpVBQUFOg0n+qtuHjxIlFRUddN3XS9PtWaiYuLQ6lUEhISom9TdIpZt2HpXrt2LXPnzkUIwS+//NKscs0Xsy6y+sJqfrz4I2klafXvh3WYSHTg86gQnOnXj95OTteevGsXjB8Pfn5w5Qq0kopDhupvbaML3aa6Wck8Nd8IfvvtN6Kjo/VtRj02NjYcPnxY42tXaxQ1rHl7IxJQOTngNXkA9zz8Da+82XDqPaSjgq5BNYwf3Y0P/zuHNd8+woMLhukkCAWIiYlh06ZNtz5Qh1hbWxtMPlN9UlVVRUpKCp073zj3Y2vErNvwdM+ZM4e3334bKSX33nsvERERTb5Gj3Y9eGfcOyQ/k8zee/fyQO8HsLd24JLzSJQIFnl7Xz8IBRg7FoKCIC0NWskPUUP2tzYxVd26whyINgJfX1/S069bVVRvlJaWcvr0aY1ec+9Ph0mTFtQM7kbNkG5cRNfwcQAAMuRJREFUSC2gqLiiwTE9u/nT3t+dbl19efmfk+jbuwMWFrpdtH3y5EmDq2YRGhpKz5499W2G3omKiiIkJARLS80nDDdkzLoNU/cLL7zAvHnzKCsrY/r06c3+8W4hLBjVcRQrpqzA1c4N8k/QVqh4q1OnG58kBAyvXV/fSgJRQ/e3tjBV3brCHIg2grqKRoZEeHg4Z86coby8XGPXTDh/BVU7N6RzwwT+Xm2duHfuIH7830N8+t49eLi1wfI6u8N1QXp6OllZWYSFheml/eshpeTbb7+lpqZG36bolaKiIsrKygym5KquMOs2XN1CCFasWEHv3r2Ji4tjwYIFNy3Deyv+iP+DjJI0/AuOkzp0BG1vtUSq7gfzyZPNbtNQMAZ/awNT1a1LzIFoI/D39ycwMFDfZjTAw8Oj/uaqKR5ddj8LHx4NgK2NFeNGhbHsv3NY++2jPHiv7qbeb4anpyd33313g5Qs+iYjI4OysjKDsknXSCmJiIige/fuJpXWxKzb8HU7ODiwYcMG3N3d2bp1K2+88Uazr/W/s98B8EDvB7BpzOhYeLj60chHRI3J35rEVHXrGtP95mwCdnZ2jBgxApVKdd08kfpi7NixCCE0unlp1tzBePi6M2p4KG20vOu9qZw8eZKOHTvi5+enb1MaEB0dTXBwsL7N0Cvp6ek4OjpqvdCCoWHWbRy6O3bsyM8//8zEiRN5/fXX6du3L1OnTm3SNS4VZbLBeSbCHxYNeKhxJ/XsCdbWEBUFJSVwo/WkBo6x+VtTmKpuXWM4UZWBc/78ebZu3apvMxoghKC4uJjPP/9cY1P0Dg62TJ7Q66ZB6PDhwxk+fPgNP9cGaWlpHDhwwCBTIZWVlZn0RiWlUklMTIzJ/R+YdRuX7vHjx/PWW28BsGDBgiZvQH3w/BGw88LTdzztXfwbd5KtrToYlRLOnm2qyQaBsfq7pZiqbn1gDkQbSfv27YmJidFqIvnm4OzsTFhYGNu3b9dZm506daLTzRbpa5iamho2bdrE7bffXl+yz5CYPHmyydVavpq4uDgCAgKwtTWsEXRtY9ZtfLpffPFFZs6cSXFxMdOnT6ekpKRR550pLuaY0g1UNbzs18Rk5ka+TtSY/d0STFW3PjAHoo3E3d0dBwcH0tLSbn2wjhk9ejQZGRk6SzGVmZmp02pTBQUFdOjQwSBr+168eJFz587p2wy9UVFRQUZGBh07dtS3KTrFrNs4dQsh+PbbbwkLCyMqKoqFCxfecnBBSskDkWdBWGCXvZ1Hw+5oWqNGvE7U2P3dXExVt74wB6JN4LbbbkOpVOrbjGuwtrZm7ty5BAYG6mTEdseOHezYsUPr7QCkpKTg4eHBpEmTDHKx+Pnz57VWXMAYuHTpEl27djWotdO6wKzbeHU7OTmxceNGnJ2d+fXXX3nnnXduevz6nBzOV0moLuR+V4ub1p6/LkY8Itoa/N0cjEG3UNMqvnwM93/ZAOnXrx8BAQEGNz0P6t3kVlZWrFq1itzcXH2boxFiYmJYu3YtRUVF+jblulRUVJCSkkJQUJC+TdEL+fn5KBQK2rVrp29TdIpZt/HrDg4OZvXq1QC8/PLL/PHHH9c9rkKp5NnYGPWLKyt5tPf8pjcWFgZ2dhAfDwUFzTVZ57QmfzcFI9KtAqqEELL236dCCKP8MjIHok3kp59+Ijk5Wd9mXBdLS0t69erFDz/8QH5+vr7NaREJCQls2rSJu+++Gzc3/aeNuh7p6el069bNJNcQSSmJjIykW7du+jZFp5h1tx7dU6ZM4T//+Q8qlYq5c+eSmJh4zTGZ1dXI8nwojacP6fTy7tX0hqytoU8f9XMNFyHRFq3R343BWHQLIV6vfToA+Kz2+RNA3FWB6XEhxGwhhMFnRzIHok0kKCjIoOuG9+7dm+HDh/PDDz+gUCj0bU6zuXLlCrNnzza4VE1XExQUxJQpU/Rthl5ISUnBzc0NJyNNR9NczLpbl+5XX32VyZMnU1BQwPTp06/JPmKZk0OHuHfhwvM82Hth8xsysun51urvW2EMuoUQtsCrwFop5Ukp5ZNSSiGlFIAtcB9wAXWQuhZQCCE+0J/Ft8YciDaRuiTypaWl+jblhvTr14+FCxdibW1tsNPaN+LcuXOkpqYyevRoOnTooG9zbkhCQgJ79+41yHWr2kahUBAfH09ISIi+TdEpZt2tT7eFhQU//PADXbp04fz58zz88MP1S68UCgV/Rv7Jsew/sVWVc3ePu5vfkBFtWGrN/r4ZRqT7eO3jNR1SSlktpVwlpex1VXAaBvxHpxY2EXMg2kTs7OwYOXIklZWV+jblpjg7O1NcXMzXX3/Nnj17NFp+csyYMYwZM0Zj1wMoLy/nl19+4fDhwwaZK/TvnDx50mSTHMfGxtKxY0esra31bYpOMetunbpdXV3ZuHEjbdq04ccff+STTz7hz6IiJp06xW5FBBLJ9K7TcbdvYtqmqzGiEdHW7u8bYQy6hRAdgV7A/bKRm1WklFFSSsMdOUNPgagQ4hkhRKQQIkII8bMQwk4I4S6E2CWEiK19dLvq+CVCiDghRLQQ4nZ92Hw1t912Gy4uLi2qWawLnJ2defTRR8nJyWHFihUaGx1t37497du318i1QL0uZ82aNTg7O/PII4/g6empsWtrg6KiIpKSkujRo4e+TdE5ZWVl5ObmGvRotTYw627durt168a3334LwLP//Cf3nz7Nrqoqfq1QT9UvbMm0PEBICDg6QkoKZGW11FytYSr+/jtGpDsBQEr5vb4N0SQ6D0SFEH7AU0B/KWV3wBKYC7wE7JFSdgH21L5GCBFW+3k3YALwhRCiEUV+tcvatWt1lrezJTg6OjJnzhzGjBmDo6Mjubm5LR4dTUlJISUlpcW2lZeXs3v3blQqFQsWLGD8+PEG/Wu0jtzcXAYMGGCSaZsiIiIICwszuSUJZt2tX/esWbN44YUXUI0ZQ6yFBa6yhsKYL2nv3J4xHVs4A2RhAf36qZ8b8PS8Kfn7aoxBtxBieu1Tw95J1Qz0NTVvBdjX7uZyANKBaUBdlP89cGft82nAGilllZQyEYhDvQhXr/Tt25eDBw8aZCqnvyOEICQkBEtLS06cOMGKFSuIjY1ttu179uxhz549zbZHoVBw9uxZvvzyS5RKJVJKowhAAVQqFUFBQYwcOVLfpuicnJwcLCwsDH7EWtOYdZuO7hdffx3rxx8HoOabz6Gqkvt734+lhQbGPgx8nagp+huMQ7dQR8gbgBQp5SV926NpdB6ISinTgPeBZCADKJJS/gG0k1Jm1B6TAXjVnuIHXD38llr7HgBCiPeEEJeFEBeEEBuFEK51ny1dupTOnTsTEhLCzp07NaqjLtltZGSkRq+rbSZOnMiIESPYt28fGzZsANBZkv78/HyqqqpIS0vj0qVLzJ49m9tvvx0rK4PPLlHP7t27OX78+K0PbGWoVCouXbpk8GlNNI1Zt2npfictDYWTEzZxsZT+tAl+h/t63aeZixvwOlFT9bcR6f689rGrXq3QEjqPAGrXfk4DOgKFwHohxM2yBF9vrPzqobxdwBIpZY0QYgcQJYRI8/Dw4H//+x9ffvkleXl5PPDAA6xatQpLS83M6gshmDx5ssaupyuEEHTr1o1u3bpRVVWFlJIvv/wSPz8/wsPD8fPz0+j0hEqlIjY2llOnTpGens6cOXMIDAwkMDBQY23oiqKiIs6dO8djjz3WpPNKSkrYv3+/dozSEdXV1ahUKk6cOKFvU3SKWbfp6E4DPpQShGB0xSV2WEo4C5+/+TlTp05t8fXtamoYCFT/+Sd/7tsHBjQNbIr+BuPQLYRoAzwGfCalLNO3PdpAH0NRY4FEKWUOgBBiAzAYyBJC+EgpM4QQPkB27fGpwNU7Y/xRT+UDUDuaWsdy4C4p5Tw/Pz+5aNEixo8fD8B3332Hg4MDgwYN0pgQHx8fysvLSU5OJiAgQGPX1RV1idgfeOABzp07x4YNG+jatSvjxo0jIiICJycnvL29m5SwvaysjIyMDNLT0wkLC0NKyaFDhwgPD2f27NlGMwV/Pfbv30+/fv2anGPOycnJqKfyq6urOXLkCMOGDTOq0euWYtZtWrpXpKSgjItjgZcXsQ7HYAqwET777DNmzZrV8u8OKeHJJ7EpKGBkly7g768Ru1uKqfrbiHRHA0gpn9S3IdpCH//7ycBAIYQDUAGMAU4BZagTsb5d+7ip9vjNwE9CiGWAL9AFuNHPlwdQJ3BFoVA02Nnt7+9PWlqaxsUUFRWxfv16nnzySaPdvOLg4MDgwYMZNGhQfVqqjIwMjh07RlZWFkFBQcydO5fIyEhyc3MpKipCCMHhw4epqalBoVAwatQoYmJi2Lx5Mz4+Pvj4+CCEwNPTk0WLFulZoWZo3749YWFh+jZD51y+fJnOnTsb+s1a45h1m5bugQUF/BEQgLV9BSNSj+EU7sR83/l8+fmX3HXXXZw+fRpvb+/mNyCEenp+1y719LyBBKKm6m9j0C2E6I56KeKdejZFq+jcA1LK40KIX4AzQA1wFlgBOALrhBAPog5WZ9UeH1k7NF1ce4kM4Hzt9PHLUspNAEKIl2uv9+ON2r7RlPOKFStYsWIFoF643BR8fHzo0KEDx44dY/jw4U0619AQQmBvbw/AuHHjAPX60bIy9WyAhYUFSqWSgIAAVCoVFRUVWFlZYW9vj0KhICQkhBdffNGgdx42l5SUFPr06dMqtd2M4uJiioqKTC5VlVm3aeoe3aMHL+5+EYC53efy8T8/JuJCBIcOHWLWrFns2bOnZQMO4eHqQPTUKZg+/dbHaxlT9ndxcbEx6L4IUBfntFb08lNASvkfrs30X4V6dPR6x3e+2fWEEPcBk4ExdUlera2tG6QYSk1NxdfX97rnP/zwwzz88MMA9K9bUN4ExowZw7fffsttt93W6uqOW1pa4uzsDKg3aHXt2irXSt+UqKgodu/ezWOPPWbQv541TV3d5e7du5tUAG7WbVq6f8rKIjk2loe7daNGVcOq86sAeKDPA1hbW7N+/Xr69evH4cOHee655/j000+b35gBbVgyVX9fXU/ekHULIR6ofdpRr4boAKOvrCSEmAC8CEyVUtYXCnZ1dWXNmjVUVVWRmJhIbGwsAwZoJ+uTm5sbjz/+OLa2tkaRzqmlJCQkkJCQoG8zdEJ5eTm///4706ZNM6kgFCArKwtbW1vc3NxufXArwqzbdHTnKxQ8ER3NkpoaLgjB9rjtZJVl0dWzK7f53QZAu3bt+PXXX7GxseGzzz5j1apVzW/w6hROev6uMEV/g3Hork3X9A1wRkp5Rc/maB2jD0SBzwAnYJcQ4pwQ4isAe3t7Zs+eTVhYGBMmTODzzz/X6g53W1tbjh07xrFjx7TWhqFw8OBBDh48qG8zdMLJkyfp1q2bUW5GawlKpZLLly+b3JpYs27T0v2fxEQKVCpGOjszwtWVlWdXAurR0KtHy2677TY+++wzAB555BHOnDnTvAb9/KBdOygoAD3+mDdVfxuR7rW1j5rbXW3AGH0gKqXsLKVsL6XsXfvv0brPXn75ZeLj44mOjmbixIlatyU4OJhDhw6Rl5en9bbMaB8pJcOGDWPs2LH6NkXnJCQk4Ovri52dnb5N0Slm3aajO7KsjC/T07EAPgkOJrssm22x27AUlszveW1GwYceeoiHHnqIyspKpk+fTm5ubtMbrduwBHpNbG+K/gbj0C2EcEe9R+Y1KWW1vu3RBUYfiBoS7u7ujBgxgk2bNhl8HXozN6e8vJyvv/4ahUJhclPylZWVpKWlERQUpG9TdIpZt+nollKyOCYGJfCIjw89HB1ZfWE1NaoaJgVPwtvx+rvjP/30U2677TaSk5OZO3du88ol103P62mdqCn6G4xKdyaAlPJ1fRuiK8yBqIYZMGAAvXr1Mom1oq0VKSXbtm0jICCg1W0+awxRUVEEBwcbXbGGlmLWbTq6t+blsaeoCBcLC97o2BEpJSvP1U7L937ghufZ2tryyy+/4OXlxZ49e/jXv/7V9Mb1PCJqiv4Go9HdBrAGRunbEF1iDkQ1jBCCfv36kZaWRnx8vL7NMdMM/vzzTwoLCxkz5rpJHFo1hYWFVFRU4OPjo29TdIpZt2npfv/KFQBe79gRTxsbTqSd4FLOJbzaeHFHlztueq6/vz/r16/HysqK9957j7Vr1970+GuoC0RPnwYdz5yZqr+NSHcogJRyv57t0CnmQFSLbNiwoXnriAycyZMnM3nyZH2boTW6dOnCnDlzjLoKVHOQUhIREWGS6VzMuk1L9ysVFSz19+cffn4AfHvuWwAW9FyAteWt/+6HDx/OBx98AKgr0128eLHxBrRrB+3bQ2kpREc3XUAzMWV/G4Puuv4EtKBqgnFiDkS1REBAAGPGjGHNmjX11YpaC56ennh6eurbDI2TnZ3N9u3b8fLyqs+dakqkpaXh5ORkctrNuk1Pd1tnZ17q3BlrCwvKFeX8HPEzAAt7L2z0dZ588knmz59PeXk506dPp6CgoPFGXJ3GSUeYsr+NQfc///lPgCIpZVZzryHUGPTag+thDkS1SN++fenVq1d9ZaLWQnR0NNE6/CWvC8rLy1mzZs0Nix60dmpqaoiNjSU0NFTfpugUs27T0v1zRgZn/6Z7Q9QGiquKuc3vNrp5dWv0tYQQLF++nN69exMfH8/8+fMbv0lVx4ntTdXfxqS7dvY0rrnnCyE8ABXwmoZM0hnmQFTLDBs2DBcXl6ZN3Rg4R48e5ejRo/o2Q2OoVCp++eUXQkND6dWrl77N0QtxcXEmuTnLrNt0dJ8qLmZedDQPW1mhuioTxtW5Q5uKg4MDGzduxN3dnd9//53XXnutcSfqeETUFP0NxqXbw8Oj2efWBqF16wD/TyMG6RBzIKoDFAoFhw4d4tChQ/o2xcx1EELQs2dPk8wXCurR4MzMTDp2bPWV5Bpg1m06uqWUPBUTgwTm+fhgX7tzOrEgkX1X9mFvZc+cbnOade3AwEDWrFmDhYUF//d//8emTY0oC96vn/rx7FlQKJrVbmMxRX+D6ej+WxBqa4y5R82BqA6wt7dnwYIFnDt3rlWNJBo7KpWKrVu3kpmZSe/evbGwMM0/h0uXLhEWFmZy+s26TUf32uxsjpaW4mlpySuBgfXvf3fuOwBmhs3Exc6l2dcfN24c//3vfwFYsGABMTExNz8hIwOcnaGyEiIjm91uYzBFf4Np6P5bEBpqjEEomANRneHk5MS9995rzi9qIKhUKjZv3kxeXl6r3HjVWPLy8lAqlXh5eenbFJ1i1m06usuVSp6LUy+9e7tzZ1yumpbfFK0evVzQc0GL23nhhRe46667KCkpYe7cuVRVVTU8oLoa1qyBkSOhWzcoLgZLS9BiwQxT9DeYhu6/BaEAl4UQpUKIdvqyqbmYA1Ed4uLiwuDBg0lKSuLPP//Utzkmzc6dOykpKeHuu+82uTRNdUgpiYyMpFu3xm/QaA2YdZuW7neSk0lXKOjt4MD93n9lxskpy+F81nkAzmSc4UzGGZQqZbPbEULwzTff0KlTJ86ePcsLL7zw14eHD6tTNt19Nxw4AG3awCOPwJkz0L17s9u8Gabqb1PQfZ3peAFMQZ0QP1MIsU1vxjUD06pdaCC4ubmxZcsWqqqqGDlypEHnNrse06dP17cJzUahUCCEYODAgTg5OZlc+c6rSU5OxsPDA0dHR32bolPMuk1Hd051Ne8kJwPqevKWV91rlVKJg7UD5YpyluxZwpI9S3CycWJIwBCGBwxneIfh9Pftj61V4ze6ODs7s3btWgYPHswnn3zCzz//TG5uLgFOTrxVXMy8zp3h2Wdh3jz11LwWMUV/Q+vXfaM1oVLKreqPxX3ANH3Z1xzMI6J6wNnZmfvvv5+4uDh2796tb3OajIuLCy4uzV9PpS+Ki4v57rvvOH/+PG5ubiYdhCoUChISEggODta3KTrFrNu0dLsKwRsqFc/7+THM1bXBZ96O3kQ/Ec3KqSu5v/f9BLkFUVJdwo64Hfxr778Y+u1QXN9x5Z9//LNJbfbv35+77roLgJycHKSUJBUX8zDwY1gYPPaY1oNQU/V3a9fdmI1JUsrvpZQzdGtZyxD6WLMohFgMPAQI4Gsp5UdCCHdgLRAIXAFmSykLao9fAjwIKIGnpJQ7b9VG//795almpMbo378/zTmvOdTU1FBaWkqbNm2oqKgw+IS7dURERADQXUtTStogNTWVdevWMWDAAIYMGaLTUWhd9qnGEhkZiaOjIx06dNC3KTrFrNus+2akFadxKPkQB5MOcij5EBHZ6nvd3nv3Mqpj48t/d+jQgeTakdgG71tYcCU1FbRcatLsb+PULYQ4LaXsf4PPjH53/I3Q+YioEKI76iB0ANALmCyE6AK8BOyRUnYB9tS+RggRBswFugETgC+0VTmgtLRUG5e9IVZWVri6upKYmMjXX39NSkqKTttvLqdOnTK4wOpWJCYmMmnSJIYOHWp0SyE0TWlpKXl5eQQEBOjbFJ1i1m06upVScjw7u8m6/Zz9mNt9Ll9M+oKLj13kzVFvAvDy3pebtNH0RvfyJJUKFi7Uao15U/Q3tG7drTkIBf1MzXcFjkkpy6WUNcABYDrqNQ3f1x7zPXBn7fNpwBopZZWUMhF15YEB2jAsUstpNG5EcHAwU6dOZc2aNZw9e1YvNrRGVCoVu3btIikpiWHDhhESEqJvkwyCuoX8phaQm3Wbju7vMjMZdOkSmzw8WqR78cDFtHVoy9HUo2yLbfz+j5sFQ4/u3Enx++8326ZbYYr+htaru7UHoaCfQDQCGC6E8BBCOAB3AO2BdlLKDIDax7q8C//f3r1HR1Weexz/PoQwCZBwCxGINNxCuEgFKqK40IKUetCj9hxh2WOVVi2Cxxun1qrpWVq7PN7q8fTU1kIVL22VWlsFWyqKhQPtQhGRoOFOJNRwCQl3Akkmec4fe08c4uR+2TN7P5+1ZmXmnZmX9zd7k7yz937fNwuI/nr5mVvWpkpKSkhK8m6J1pycHL7zne9QXe2M2gyHw561xQ/Kysp44YUX2L9/v6+n8GiuyH7emlU8EpHlDk7uY+Ew9+7ciQLntXJqtu5dupM3OQ9wjorWaNOOZD788MN07dr1jLLk5GSSOnViAXDOD37A8gULWtW2WIK4vcG/uYPQCQUPRs2r6hYReQx4BzgB5AMN9bpifb2JeY5EROYAcwDOOussVq1a1eR2nThx4gu/ODpaRkYGGRkZHD58mOeff54ZM2YkxBq58UZVWbp0KaNHj+b888/3/Bvy8ePHm7UvtqfIfh4v7ekolnuV103pML/E+cs9WpV+W7awasuWVtU3smYkmaFMNh3YxAO/f4BLMy9t9D1ZWVnMnz+fZ599lpKSEjIzM7n55psZOnQoP50/n/yjR7ls7lxmvP468267rc1GeAdxe4M/cwelEwoeDVY6owEi/4VzlPNO4Kuquk9E+gOrVDXXHaiEqj7ivn458KCqNrhEUXMGKxUWFlJZWcmIESPiZmBJUVERS5Ys4eyzz2bGjBmkpKR43aRaL7zwAgDf/va3PW1HXWVlZaxZs4YrrriCTp06xc2KGvGyT0Xv50FiuYOTe0d5OaPXraMK+GD8eM5rowGgz254lu+++V2G9R7G5ls3k5zU8rmHw0eO8OTQoTxw6BAVOJ3WBQsWcPnll7eqjUHc3uCv3JHBSkHqhIJH0zeJSKb780vAvwCvAEuB2e5LZgORBXuXAteKSEhEBgM5wLq2aktFRQVFRUUMGzasrapsE9nZ2cybN48+ffqQlJTEqVOn4mZVplmzZjFr1iyvm1GrpqaGtWvX8txzz9G/f3+SkpLiphMaLyorK+NyP29vljtYuefv2EEVMDszs806oQCzz51NTu8cdh7aybde/xYlJ0taXFfnnj35wbJlfNSpExcAxcXFXHHFFcycOZOioqIW1RnU7e3H3EHrhIJ384j+QUQ2A28C/+5O0/Qo8DUR2QF8zX2MqhYArwKbgbfc17d8+Ys6tm7dSk5OTlzOKZmcnMwll1xCcnIyb775Ji+++GJcjKzv2rWr55cxgHMKvrq6muLiYnbu3MnNN9/MxIkTPT8VH4/ieT9vT5Y7OLmXHzrEnw8fppsIjw4d2qZ1Jycl87N/+hkpnVN4teBVRjw9gtuX3c7P1/2cdwvfpfhYcfMOFEycyMinnuJvnTrxE6Ar8NprrzFi+HAe+OEPKS8vb1b7gri9wZe5kwhYJxTi4NR8e2nKqfljx46xadOmM+aVjJfTqHXV1NSQn5/PqlWrGD16NNOnT/esLRs3bgRg7NixnrWhsLCQFStWMG7cOCZMmOBZO5rC630q1n4eBJY7WLm3lZZyV0EBUwYP5p52msJn16Fd3LrsVt7e9fYXnuvepTu5fXIZkTHi858ZuYzJHFP/digshEcf5bPnn+eecJhX3OKB3brxxB13MOtHP0IaWYI4qNvbb7nLysrI+HxwXWA6oRDgjqiqsnbtWkaNGkXPqBU3vO40NCYcDnPs2DF69OjBihUrmDhx4hnt7wheXiNaXV3NK6+8wuHDh5k6dSqjRo2K+19CXu5T9e3nfme5g5u7R48e7fo7QVVZtXsVH+77kK2lW9lWto2tpVspLS/9wmszu2Vy4O4DjVf6j3/ASy+xZtEi7iwsJDKJ34NTp/LAu+822Jagb2+/5I7aZwPVCYUArzW/f/9+UlNTE24n7ty5M71796aqqork5GQWLFjA0KFDmTx5MmeddZbXzWsX1dXVbNu2jZMnTzJhwgQmTZpEdna2p9NtJYpE3c9by3L39LopHeZkdTWHDhzosNwiwpTBU76w0lJZeVltpzTSQU0PNfE61YEDIS+PyXl5fPDxxyy6/34efustbszLa/BtQdze4M/ce/fuZcCAARuC1gmFgHZEIx2bCy64wOumtFhycjJTp05l0qRJ5OfnU1lZyenTp9m0aRPnnnsuoVDI6ya2ib///e+8//779O7du3Z7DRkyxONWJQY/7OctYbmDlfv7O3eyfN8+XvJ4yeE+XfswqeskJg2c1Kp6ksaM4btvvsl3qqro3MBp+aBub7/m7u8s++rPU9SNCGRHdNeuXWRlZcXVlEgtlZKSwsSJEwE4evQoRUVFrFy5ktGjR3PRRRfRq1cvj1vYPFVVVRQWFnLo0CEuvPBC0tPTue6663x7tLc9FRYW+mY/bw7LHZzcH584wYJ9+xCgh89yN9QJhWBubwhubj/zzRw3InK3iKiI1F7t+8gjjzBs2DByc3NZvnw5AKdPn2bv3r2+PKrWo0cPZs6cybx580hLS6OqqorDhw/zxhtvsHnzZioqKrxuYkyR1aSWLVvGk08+yXvvvUeXLl0AGDNmjHVCW+D06dMUFxf7cj9viOUOTm5V5fbt26kB5vbvzzltNCl8Igji9obg5vY7XxwRFZGBOFM+7YmUnTp1isWLF1NQUMDevXuZNm0a27dvZ/PmzeTm5vr6+sL09HQuueQSwPkcsrKy2LBhA0uWLOHqq68mNzeX4uJi+vXrR3Ij37pjue6661rdxt27d7Nnzx62b99OKBTi+uuvZ+zYsUyZMoXU1NRW1x90QdjPY7Hcwcn9Rmkp/3fsGL06deKhgHVMgri9Ibi5/c4XHVHgKeAePp8EnyNHjnDbbbcRCoUYPHgww4YNY8WKFaSnp9OvXz/vWtrBUlNTmTBhAhMmTKg9IlpeXs5f/vIXDh48WHvt5bhx4ygpKSE9PZ1QKNTgqNPmdF6rq6spKipi79697Nu3j379+jF58mTy8/NJSUlhypQpDBo0CIABAwa0KqtxHD58mIqKikDt52C5g5T7dHU183fsAOChIUPo3YIv1IkqiNsbgps7CBK+IyoiVwLFqpof3Xmqqqpi4MCBtY+zsrJYt24dd955Z9xP99NeIgOYQqEQc+bMIRwOU1JSUtuxXLZsGfv27aOmpoacnBxmzZrF+vXrOXjwIGlpaYRCIcaPH8/f/vY3Dh06xKBBg6ipqaG6urq2I7tmzRpOnDjB8ePHmTZtGiNGjGD16tX069eP3Nzc2m1y1VVXefY5+JmqUlBQwJe//OVA7eeWO1i5n/rsM4oqKxmZksLcAH2BDer2DmruoEiIjqiIrABifQ3KA+4HGp3dvby8nG7dupEeY9m3hQsXsnDhQgAOHjzYusYmkM6dO59xFDIyL2hlZSWVlc4MEn379qWqqorjx49z5MgRVJWtW7dy/Pjx2vXcRYRwOExaWhrnnnsu3bt3Jy0tjbS0NJKSkuJuTXo/Ky4uJj09PeZ+7meWO1i5QydP0kuE/x0+nM4BWs43qNs7qLmDIiE6oqo6LVa5iIwBBgORo6FnAxtE5PwBAwbULocZDofZtWsXt956a8z658yZw5w5cwBn8vGg69KlS+1goezsbLKzs894PhQKEQqFvnBUMzU11X5ReCgcDrNjxw4uuugir5vSoSx38HKPO3CAXRdcQK8AXU8e5O0dxNxBktBfJVX1Y1XNVNVBqjoI+AwYr6r7e/bsyeLFi6moqGDlypUcOHDAdmTjazt27CA7O7v2S0RQWO7g5FbV2txB6oRCMLc3BDd3kCR0R7QhqampzJo1i5EjR3LTTTfxzDPPNGmknV+XPDXeOHr0aIf8O+Xl5ZSUlNQO/AoKyz3I66Z0GFVl2kcf8eSBA/Rrp7Xk41UQtzcEN3fQJMSp+aZyj4rWysvLY/r06WRnZ5OZmdmkOsrKyjh16pRNIWRarbS0lF//+tcd8m8VFBQwcuRIOgXoejmw3EHK/XJJCX89doy+SUk85XVjOlgQtzc4uUeNGhW43EHjq45oXaWlpdTU1DS5Ewpw4sQJJk+e3I6tqt/Bgwfp27dvq+sJh8NUVFTQrVu3NmhVbAcPHuTpp59ut/pboqHcbfXZtsTu3bvbtf6W7Od+YLmDk/tEOMzd7nRNjw0bRlpnX//pOkMQtzd8ntur39um47Tb/2YRWQRcAZSo6jluWW/gd8AgYDcwS1UPu8/dB9wEVAN3qOpyt/wrwAtAKrAMuFObeP588+bNjB8/vlntLi0tbdbr29J5553H+vXrW1WHqrJmzRrGjx9P93ZcaaQt2tqWGssdb+1tK6raov080VnuYOV+dM8e9ofDjOvaldkBmkcyqNs7qLmDqj2Pd78AXFan7F7gXVXNAd51HyMio4BrgdHue34hIpELOp8B5gA57q1unTFVVlbSp0+fdu2MxaOioiLLHSCW23L73e5Tp3hij7No3tO5uXQK0DySQdzeENzcQdVuHVFVXQ0cqlN8FfCie/9F4Oqo8sWqWqGqnwI7gfNFpD+Qrqpr3aOgL0W9p0EVFRUMHz68dSESTFVVFZ9++qnlDgjLbbmD4Hs7d1IJXJuRwaQePbxuTocJ6vYOcu6g6ugrgM9S1X0A7s/IRS9ZwD+iXveZW5bl3q9b3qhQKNSiddS9FJnLtKW2bdvGkCFDOiR3a9valpqSO57a21Y6cnvHE8sdnNynqqspOnqUFBEeHzbM6+Z0qCBubwh27qCKlyu+Y51r0QbKY1ciMgfnND5AhYh80gZt61C33HJLa6vIADrkQtc2aGtbajR3nLW3rXTY9o4zljtYMr4U0NxY7iDJ9boBXujojugBEemvqvvc0+4lbvlnwMCo150N7HXLz45RHpOqLgQWAojIelUN3DJJljtYLHewWO5gsdzBIiL+G1HbBB19an4pMNu9PxtYElV+rYiERGQwzqCkde7p++MicoE4a3jeEPUeXxCRJ0Rkq4hsEpHXRaSnWz5IRE6JyEb39kuPm1pLRC4TkW0islNE7vW6PXWJyEARWSkiW0SkQETudMsfFJHiqM90htdtNcYYY4KsPadvegX4KpAhIp8BDwCPAq+KyE3AHmAmgKoWiMirwGYgDPy7qla7Vc3j8+mb/uLe/OQd4D5VDYvIY8B9wA/c53ap6ljPWhaDO5vBz4Gv4Ryx/kBElqrqZm9bdoYw8D1V3SAiacCHIvKO+9xTqvoTD9tmjDHGGFe7dURV9Zv1PHVpPa9/GHg4Rvl64JwWNGFhC97T4VT17aiH7wHXtLLK9s59PrBTVQsBRGQxzqwHXndEa3O7R9Ijg+KOi8gWmjjILQElxH7eDix3sFjuYLHcASK2tnr8EJE3gd+p6m9EZBBQAGwHjgE/VNU1XrYPQESuAS5T1Zvdx9cDE1X1Nm9bFpv7Oa7G+TLzH8C3cT7P9ThHTQ971jhjjDEm4GwB1w4gIitE5JMYt6uiXpOHc0r5t27RPuBLqjoOpwP1soikd3zrv6BZMxl4SUS6A38A7lLVYziLIwwFxuJ8vk961zpjjDHG+K4jKiK3uwNpCkTk8ajy+9zBNdtE5Osd2SZVnaaq58S4LXHbNhtnOdTrIsuXupP7l7n3PwR2ATFn+G1oEE475K5vhgPPiMjdIqIikhFVloczK8NA4CSAqh5Q1WpVrQF+hXOZQcIRkR+7g9s2isjbIjIg6jnP9vP2Vt/APvc5P+ee6f4+qxGR8+o859vcEP8DI9uKiCwSkZLoKQdFpLeIvCMiO9yfvbxsY3toYGCpr7OLSIqIrBORfDf3j9xyX+eul6r65gZMAVYAIfdxpvtzFJAPhIDBOJ26JK/b67btMpzrK/vWKe8baSMwBCgGetdTx4PA3THK2zw3znXFhW59Xdz6R3v4+Q0ElgNFQEZU7kPAz6JzA/2j3jcfZzUvz/eBFmROj7p/B/DL9tre8XQDpgOd3fuPAY8FJPdInPkFVwHnRZX7PXeSm2lI1O+aUV63q52yXgyMBz6JKnscuNe9f29kf/fTDegPjHfvp+FcijbK79lxzix2d+8nA+8DF/g9d303vx0RnQc8qqoVAKoamac05hKiHrWxrqdx/gO+U2eapouBTSKSD7wGzFXVukumNqbNc6tqGLgNp/O3BXhVVQtaU2crPQXcw5mXB9wB9AIuAV7H6dTfCTwuIh+LyCacLy3zO7itbUKdywwiuvF59njez1tNVd929z9wBvZF5hj2e+4tqhpr2RVf5yZqYKSqVgKRgZG+o81bEts3VHWfqm5w7x/H+ZuShc+zq+OE+zDZvSk+z12feFlZqa0MByaLyMPAaZyjhB/g7NjvRb2uyUuFtjdVjblunar+Aef6xqa6TURu4MxBOO2SW1WXActaW09riciVQLGq5ouccelqGLheVX/jvu45YI+q/rcHzWwX7j5+A3AUp1MNcbyft4Mbgd+594OUO5rfc8da+nmiR23xwhlLYotIZmNvSGTuwNJxOEcHfZ/dnQrxQ2AY8HNVfV9EfJ87loTriIrICqBfjKfycPL0wjnEPQFnztIhJNAAm/o0kvsZ4Mc4mX6MMwjnRvyf+36c07VfeFuMMt/kVtUlqpoH5InIfThHqB8gALnd19Qd2BeI3LHeFqMsoXI3wu/5jKvuwNI6BxZ8SZ250se617q/LiItmabSFxKuI6qq0+p7TkTmAX9U5wKLdSJSg7NmbdwNsGmuhnJHE5FfAX9yH/o2t4iMwbkuLnI09Gxgg4icj49zx/Ay8Gecjqjvc0cN7LvU/X8OAchdj4TP3Qi/52tMfUti+4qIJON0Qn+rqn90iwORHUBVj4jIKpzxIoHJHc1v14i+AUwFEJHhOBe4l1LPEqJeNbKtuTtsxDeAyMhL3+ZW1Y9VNVNVB6nqIJw/WuNVdT8+zg0gIjlRD68Etrr3/Z77MpxVx65U1fKop3yduwF+z/0BkCMig0WkC3AtTuagqG9JbN8Q5yjCc8CWOpdO+Tq7iPSVz5fzTgWm4fwe93Xu+iTcEdFGLAIWuVNgVAKz3aMmDS0h6gePi8hYnNNWu4FboNGlU30rALkfFZFcoAZntoC5EIjcT+OMEH/HPQr+nqrO9XtuEfkGzgwQfYE/i8hGVf2633Ors+xxZGBkErDI44GR7UaasSS2z1wEXA98LCIb3bL78X/2/sCL7nWinXAG/f5JRNbi79wx2cpKxhhjjDHGE347NW+MMcYYYxKEdUSNMcYYY4wnrCNqjDHGGGM8YR1RY4wxxhjjCeuIGmOMMcYYT1hH1BhjjDHGeMI6osaYhCAiD4rI3V63I0JE+onIYhHZJSKbRWSZu5BGQ+850cjzyyITXRtjTBD4bUJ7Y4ypl4gktcWk7+6KMK8DL6rqtW7ZWOAsYHtL61XVGa1tmzHGJBI7ImqMiVsikici20RkBZDrlg0VkbdE5EMRWSMiI6LK3xORD0TkocjRRxH5qoisFJGXcVZwSRKRJ9zXbRKRW6L+ve9Hlf+ogaZNAapU9ZeRAlXdqKprmlKPiPQXkdUislFEPhGRyW75bhHJEJFB7gpxkdffLSIPuvfvcI/AbhKRxS3+cI0xJg7YEVFjTFwSka/grC8+Dud31QbgQ2AhMFdVd4jIROAXwFTgp8BPVfUVEZlbp7rzgXNU9VMRmQMcVdUJIhIC/i4ib+Os1Z7jvlaApSJysaqujtG8c9y2xGr39CbU82/AclV92F3mr2szPpp7gcGqWmGn8Y0xic46osaYeDUZeF1VywFEZCmQAkwCfu+uNw/O+vMAFwJXu/dfBn4SVdc6Vf3UvT8d+LKIXOM+7oHTcZzu3j5yy7u75bE6og1pSj0fAItEJBl4Q1U3NqP+TcBvReQN4I1mts0YY+KKdUSNMfFM6zzuBBxR1bHNrOdk1H0BblfV5dEvEJGvA4+o6oIm1FcAXFPPc9JYPaq6WkQuBi4Hfi0iT6jqS1EvCXPmpVMpUfcvBy4GrgT+U0RGq2q4CW02xpi4Y9eIGmPi1WrgGyKSKiJpwD8D5cCnIjITnEFDInKu+/r3gH9171/bQL3LgXnu0UhEZLiIdHPLbxSR7m55lohk1lPHX4GQiHw3UiAiE0TkkqbUIyLZQImq/gp4Dhhfp/4DQKaI9HEvH7jCfV8nYKCqrgTuAXriHHE1xpiEZEdEjTFxSVU3iMjvgI1AEbDGfeo64BkR+SGQDCwG8oG7gN+IyPeAPwNH66n6WWAQsMEd/X4QuFpV3xaRkcBa97T/CeBbQEmMtqmIfAP4HxG5FzgN7Abucq9dbayerwLfF5Eq9/kb6tRfJSIPAe8DnwJb3aeS3Iw9cI68PqWqR+rJaYwxcU9U6575MsaYxCMiXYFTbifxWuCbqnqV1+0yxhhTPzsiaozxi68AT7tHOY8AN3rbHGOMMY2xI6LGGFMPEekDvBvjqUtVtayj22OMMX5jHVFjjDHGGOMJGzVvjDHGGGM8YR1RY4wxxhjjCeuIGmOMMcYYT1hH1BhjjDHGeMI6osYYY4wxxhPWETXGGGOMMZ6wjqgxxhhjjPGEdUSNMcYYY4wnrCNqjDHGGGM8YR1RY4wxxhjjCeuIGmOMMcYYT1hH1BhjjDHGeMI6osYYY4wxxhP/D63uTDX/Os78AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create SkewT/LogP\n",
    "plt.rcParams['figure.figsize'] = (10, 12)\n",
    "skew = SkewT()\n",
    "skew.plot(p, T, 'r', linewidth=2)\n",
    "skew.plot(p, Td, 'g', linewidth=2)\n",
    "skew.plot_barbs(wpres, u, v)\n",
    "skew.ax.set_ylim(1000, 100)\n",
    "skew.ax.set_xlim(-60, 30)\n",
    "\n",
    "title_string = \" T(F)   Td   \" \n",
    "title_string +=  \" \" + str(ob.getString(\"staName\"))\n",
    "title_string += \" \" + str(ob.getDataTime().getRefTime())\n",
    "title_string += \" (\" + str(ob.getNumber(\"staElev\")) + \"m elev)\"\n",
    "title_string += \"\\n\" + str(round(T[0].to('degF').item(),1))\n",
    "title_string += \"  \" + str(round(Td[0].to('degF').item(),1))\n",
    "plt.title(title_string, loc='left')\n",
    "\n",
    "# Calculate LCL height and plot as black dot\n",
    "lcl_pressure, lcl_temperature = lcl(p[0], T[0], Td[0])\n",
    "skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')\n",
    "\n",
    "# Calculate full parcel profile and add to plot as black line\n",
    "prof = parcel_profile(p, T[0], Td[0]).to('degC')\n",
    "skew.plot(p, prof, 'k', linewidth=2)\n",
    "\n",
    "# An example of a slanted line at constant T -- in this case the 0 isotherm\n",
    "l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\n",
    "\n",
    "# Draw hodograph\n",
    "ax_hod = inset_axes(skew.ax, '30%', '30%', loc=3)\n",
    "h = Hodograph(ax_hod, component_range=max(wsMan))\n",
    "h.add_grid(increment=20)\n",
    "h.plot_colormapped(u, v, spd)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
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    "[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
    "\n",
    "---"
   ]
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    "## 7 See Also\n",
    "\n",
    "### 7.1 Related Notebooks\n",
    "\n",
    "* [Grid Levels and Parameters](https://unidata.github.io/python-awips/examples/generated/Grid_Levels_and_Parameters.html)\n",
    "* [Model Sounding Data](http://unidata.github.io/python-awips/examples/generated/Model_Sounding_Data.html)\n",
    "* [Forecast Model Vertical Sounding](http://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html)\n",
    "\n",
    "### 7.2 Additional Documentation\n",
    "\n",
    "**python-awips:**\n",
    "\n",
    "* [awips.DataAccessLayer](http://unidata.github.io/python-awips/api/DataAccessLayer.html)\n",
    "* [awips.PyGeometryData](http://unidata.github.io/python-awips/api/PyGeometryData.html)\n",
    "\n",
    "**matplotlib:**\n",
    "\n",
    "* [matplotlib.pyplot](https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.html)\n",
    "\n",
    "**MetPy**\n",
    "\n",
    "* [metpy.wind_components](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.wind_components.html)\n",
    "* [metpy.lcl](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.lcl.html) (Lifted Condensation Level)\n",
    "* [metpy.parcel_profile](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.parcel_profile.html)\n",
    "* [metpy.skewt](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html)\n",
    "* [metpy.hodograph](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.Hodograph.html)"
   ]
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  {
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   "metadata": {},
   "source": [
    "[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
    "\n",
    "---"
   ]
  },
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