heasarc_queries_v7_arb.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# v8 Draft notebook:  HEASARC access through python\n",
    "In this notebook, we go through a specific science case with a number of examples for accessing HEASARC data, including catalogs, images and spectra, using various VO protocols. <i> Our hypothetical science case: we would like to prepare an observing proposal to target a sample of potentially interacting galaxies. There is some evidence that these types of galaxies have a higher frequency of ultraluminous X-ray binaries, and we would like to test this hypothesis in our study. Therefore, we will upload our sample list, make images, cross-correlate with HEASARC catalogs to get additional information, and produce a spectrum to justify our exposure request. </i> The various services are described on the __[HEASARC VO](https://heasarc.gsfc.nasa.gov\n",
    "/docs/archive/vo/)__ page. In summary:\n",
    "\n",
    "* [1. VOTable](#votable): [Accessing](#votable1) and [creating](#votable2) VOTables\n",
    "* [2. SCS](#scs):  Using the Simple Cone Search to query a different catalog in a loop\n",
    "* [3. SIA](#sia): Using the Simple Image Access protocol.  \n",
    "* [4a. TAP1](#tap1): Using the Table Access Protocol to find available VO resources\n",
    "* [4b. TAP2](#tap2): Using the TAP to cross-correlate our objects with a catalog\n",
    "* [4c. Combining data](#combo) from different catalogs\n",
    "* [5. SSA](#ssa):  Using the Simple Spectral Access protocol to retrieve spectra.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes:\n",
    "\n",
    "There are a number of ways to use VO services through Python.  From the AstroQuery web page (https://astroquery.readthedocs.io/en/latest/):\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\"There are two other packages with complimentary functionality as Astroquery: astropy.vo is in the Astropy core and pyvo is an Astropy affiliated package. They are more oriented to general virtual observatory discovery and queries, whereas Astroquery has web service specific interfaces.\"</div>\n",
    "    \n",
    "For this notebook, we will stick with the generic python requests method. In general, the queries are simple enough that this is almost as readable, and it has the benefit of depending on no specialized packages. There is as yet no single standardized set of Python tools for accessing VO data.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "import numpy\n",
    "## There are a number of relatively unimportant warnings that \n",
    "## show up, so for now, suppress them:\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "## The main HTTP request tool we will use:\n",
    "import requests\n",
    "## For simple astropy tables\n",
    "import astropy\n",
    "import io\n",
    "from IPython.display import Image, display\n",
    "\n",
    "## For handling ordinary astropy Tables\n",
    "from astropy.table import Table\n",
    "\n",
    "## For handling VO table type objects\n",
    "from astropy.io import votable as apvot\n",
    "\n",
    "## For reading FITS files\n",
    "import astropy.io.fits as apfits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"votable\"></a>\n",
    "# 1. VOTable:  working with VO Table objects\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __[IVOA TAP document](http://www.ivoa.net/documents/TAP/20100327/REC-TAP-1.0.html)__ describes the Table Access Protocol (TAP) that consist of an HTTP request to a given TAP service URL formed in a particular language (ADQL in this case) to specify what you wish to search for. \n",
    "\n",
    "In this example, we show how to list, fetch and look at VO tables from the HEASARC archive and also how to create your own VO Table, which can be used to cross-correlate with other VO Tables (see examples [4a](#tap1) and [4b](#tap2) below). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<a id=\"votable1\"></a>\n",
    "\n",
    "## 1.1 Retrieving a catalog as a VO Table\n",
    "\n",
    "We can use W3Browse to get a HEASARC catalog as a VOTable object.  W3Browse can be used to retrieve tables in a number of formats, one of which is a VO Table.  \n",
    "\n",
    "The requests package of python is a generic interface to web services.  The requests.get() method formats the given parameters for you and returns the results of the HTTP request in a useful object.  In this case, the result is a string containing the VOTable result.  The standard astropy.table package can read these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "params = {'name': 'rosmaster'}\n",
    "r = requests.get('http://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/getvotable.pl', params=params)\n",
    "#r.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "table=Table.read(io.BytesIO(r.content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.table.table.Table'>\n"
     ]
    },
    {
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       "&lt;Table masked=True length=11431&gt;\n",
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       "<thead><tr><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th></th><th>s</th><th>s</th><th></th><th>MJD</th><th>MJD</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>d</th><th>d</th><th></th></tr></thead>\n",
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      "text/plain": [
       "<Table masked=True length=11431>\n",
       "    col1      col2     col3     col4     col5   ... col25 col26 col27 col28\n",
       "              deg      deg      deg      deg    ...  deg    d     d        \n",
       "   str16    float64  float64  float64  float64  ... int16 int32 int32 int16\n",
       "----------- -------- -------- -------- -------- ... ----- ----- ----- -----\n",
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       "        ...      ...      ...      ...      ... ...   ...   ...   ...   ...\n",
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       "RH800780N00 261.1700  85.8900 118.5825  28.5800 ...   190  2161  2165  5000\n",
       "RH702064N00  35.7700  86.3200 124.4960  23.7300 ...    54  2160  2162  7000\n",
       "RH202532A01  37.9600  89.2600 123.2826  26.4577 ...    47  2529  2530  2900\n",
       "RH202532N00  37.9600  89.2600 123.2826  26.4577 ...   254  2370  2370  2900"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(table))\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id=\"votable2\"></a>\n",
    "## 1.2 Creating a VO Table from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can create a table from scratch using an array of values named whatever we want.  \n",
    "\n",
    "There are several ways of doing this, and there are a few object layers here, which can be confusing.  There are standard astropy Table objects, there are votable Table objects, and there are votable VOTableFile objects that might contain multiple votable Tables. Though some things can be done with generic astropy Tables, other VO operations can only be done with VO Tables or VOTableFile objects.  \n",
    "\n",
    "This is easiest to see with an example.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.table.table.Table'>\n",
      "   RA     DEC  \n",
      "------- -------\n",
      "19.0186 46.7304\n",
      "20.2887 40.4703\n",
      "125.886 21.3377\n",
      "136.002 21.9679\n",
      "141.057 40.6372\n",
      "  146.7 22.0116\n",
      "148.785 14.2922\n",
      "149.751 17.8168\n",
      "175.039  15.327\n",
      "191.542 30.7317\n",
      "194.913 28.8959\n",
      "199.026 41.5011\n",
      "206.577 43.8511\n",
      "209.963 38.1821\n",
      "213.556 15.6214\n",
      "219.967 42.7421\n",
      "226.693 12.8502\n",
      "237.489 20.8057\n",
      "241.519 20.8014\n",
      "317.088 18.2002\n",
      "329.235 6.64845\n",
      " 333.83 37.3012\n"
     ]
    }
   ],
   "source": [
    "##  Create a table with only two columns starting from an astropy Table:  \n",
    "from astropy.table import Table as apTable\n",
    "aptable=apTable(\n",
    "    numpy.array([\n",
    "            [19.0186,       46.7304],\n",
    "            [20.2887,       40.4703],\n",
    "            [125.886,       21.3377],\n",
    "            [136.002,       21.9679],\n",
    "            [141.057,       40.6372],\n",
    "            [146.700,       22.0116],\n",
    "            [148.785,       14.2922],\n",
    "            [149.751,       17.8168],\n",
    "            [175.039,       15.3270],\n",
    "            [191.542,       30.7317],\n",
    "            [194.913,       28.8959],\n",
    "            [199.026,       41.5011],\n",
    "            [206.577,       43.8511],\n",
    "            [209.963,       38.1821],\n",
    "            [213.556,       15.6214],\n",
    "            [219.967,       42.7421],\n",
    "            [226.693,       12.8502],\n",
    "            [237.489,       20.8057],\n",
    "            [241.519,       20.8014],\n",
    "            [317.088,       18.2002],\n",
    "            [329.235,       6.64845],\n",
    "            [333.830,       37.3012] ]), \n",
    "    names=[\"RA\",\"DEC\"])\n",
    "print(type(aptable))\n",
    "print(aptable)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.io.votable.tree.VOTableFile'>\n",
      "        RA                DEC        \n",
      "------------------ ------------------\n",
      "19.018599999999999 46.730400000000003\n",
      "20.288699999999999 40.470300000000002\n",
      "           125.886 21.337700000000002\n",
      "136.00200000000001            21.9679\n",
      "141.05699999999999            40.6372\n",
      "146.69999999999999 22.011600000000001\n",
      "           148.785 14.292199999999999\n",
      "           149.751 17.816800000000001\n",
      "175.03899999999999             15.327\n",
      "           191.542            30.7317\n",
      "194.91300000000001 28.895900000000001\n",
      "199.02600000000001 41.501100000000001\n",
      "           206.577 43.851100000000002\n",
      "209.96299999999999 38.182099999999998\n",
      "213.55600000000001            15.6214\n",
      "219.96700000000001 42.742100000000001\n",
      "226.69300000000001 12.850199999999999\n",
      "           237.489 20.805700000000002\n",
      "241.51900000000001 20.801400000000001\n",
      "317.08800000000002 18.200199999999999\n",
      "329.23500000000001 6.6484500000000004\n",
      "333.82999999999998 37.301200000000001\n"
     ]
    }
   ],
   "source": [
    "## Then convert this to a VOTableFile object.\n",
    "votablefile = apvot.from_table(aptable)\n",
    "print(type(votablefile))\n",
    "## Which contains a nested set of RESOURCES\n",
    "for r in votablefile.resources:\n",
    "    ## And tables (in this case, only one of each)\n",
    "    mytable=r\n",
    "    for t in r.tables:\n",
    "        print(t)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"scs\"></a>\n",
    "# 2. SCS:  Using the Simple Cone Search to query a different catalog in a loop\n",
    "The __[Simple Cone Search (SCS) protocol](http://www.ivoa.net/documents/latest/ConeSearch.html)__ is used to search a given table with a given position and radius, getting again a VOTable XML result.  \n",
    "\n",
    "In our science example, <i> we might want to search what available information exists for our sources within different HEASARC catalogs. For example, suppose we were considering proposing for observing time with Chandra. We might first want to check whether any of our sources were already observed by Chandra, XMM or ROSAT? </i> Below, we show the search for the table 'chanmaster', the master catalog of observed Chandra targets, with a search radius of 0.1 degrees. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"scs1\"></a>\n",
    "The SCS is quite straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid  status      name        ra    ... type    pi    public_date Search_Offset\n",
      "                              deg    ...                   mjd                  \n",
      "----- -------- ----------- --------- ... ---- -------- ----------- -------------\n",
      " 7937 archived NGC2563-P14 125.81833 ...   GO Mulchaey       54461        4.7670\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid  status    name      ra    ... type  pi  public_date Search_Offset\n",
      "                          deg    ...               mjd                  \n",
      "----- -------- ------- --------- ... ---- ---- ----------- -------------\n",
      " 2043 archived ARP 242 191.55083 ...   GO Read       52429        0.5376\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid  status    name       ra    ... type    pi   public_date Search_Offset\n",
      "                           deg    ...                  mjd                  \n",
      "----- -------- -------- --------- ... ---- ------- ----------- -------------\n",
      "19370 observed NGC 5297 206.59875 ...   GO McHardy       58417        1.5692\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n",
      "obsid status name  ra dec time ... exposure type  pi public_date Search_Offset\n",
      "                  deg deg mjd  ...    s                  mjd                  \n",
      "----- ------ ---- --- --- ---- ... -------- ---- --- ----------- -------------\n"
     ]
    }
   ],
   "source": [
    "# len(mytable['ra'])\n",
    "for i in range(len(aptable)):\n",
    "    params = {'table': 'chanmaster', 'RA': aptable[i]['RA'], 'DEC':aptable[i]['DEC'], 'SR':0.1}\n",
    "    r = requests.get('https://heasarc.gsfc.nasa.gov/cgi-bin/vo/cone/coneGet.pl', params=params)\n",
    "    table=Table.read(io.BytesIO(r.content))\n",
    "    print(table)\n",
    "               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"sia\"></a>\n",
    "# 3. SIA:  Using the Simple Image Access protocol.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __[HEASARC VO](https://heasarc.gsfc.nasa.gov/docs/archive/vo/instructions.html#_accessing)__ page describes how to access SkyView images with the SIA protocol. But first, how do you know what sorts of images are available?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1:  Finding SkyView SIA resources from the NAVO directory with TAP\n",
    "\n",
    "First, we can find out what SIA resources are available through SkyView. These are listed in a registry at STScI which we can access through the__[IVOA Table Access Protocol](http://www.ivoa.net/documents/TAP/20100327/REC-TAP-1.0.html)__ (TAP). We submit an HTTP request to a given TAP service URL formed in a particular language (ADQL in this case) to specify what we wish to search for, in this case the HEASARC SIA resources: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "For now, let's forget about using Xamin to find the SIA resources, of which there are far fewer listed than there are in SkyView.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=88&gt;\n",
       "<table id=\"table4712033744\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>short_name</th><th>ivoid</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>object</th></tr></thead>\n",
       "<tr><td>1420MHz</td><td>ivo://nasa.heasarc/skyview/1420mhz</td></tr>\n",
       "<tr><td>2MASS</td><td>ivo://nasa.heasarc/skyview/2mass</td></tr>\n",
       "<tr><td>408MHz</td><td>ivo://nasa.heasarc/skyview/408mhz</td></tr>\n",
       "<tr><td>AKARI</td><td>ivo://nasa.heasarc/skyview/akari</td></tr>\n",
       "<tr><td>ASCA</td><td>ivo://nasa.heasarc/ascamaster</td></tr>\n",
       "<tr><td>ASCA/AGN</td><td>ivo://nasa.heasarc/tartarus</td></tr>\n",
       "<tr><td>ASCASIS</td><td>ivo://nasa.heasarc/ascasis</td></tr>\n",
       "<tr><td>BATSIG</td><td>ivo://nasa.heasarc/skyview/batsig</td></tr>\n",
       "<tr><td>CDFS-LESS</td><td>ivo://nasa.heasarc/skyview/cdfs-less</td></tr>\n",
       "<tr><td>Chandra</td><td>ivo://nasa.heasarc/chanmaster</td></tr>\n",
       "<tr><td>...</td><td>...</td></tr>\n",
       "<tr><td>SwiftUVOT</td><td>ivo://nasa.heasarc/swiftuvlog</td></tr>\n",
       "<tr><td>SwiftXRT</td><td>ivo://nasa.heasarc/swiftxrlog</td></tr>\n",
       "<tr><td>TGSS</td><td>ivo://nasa.heasarc/skyview/tgss</td></tr>\n",
       "<tr><td>UKIDSS</td><td>ivo://nasa.heasarc/skyview/ukidss</td></tr>\n",
       "<tr><td>VLSS</td><td>ivo://nasa.heasarc/skyview/vlss</td></tr>\n",
       "<tr><td>WENSS</td><td>ivo://nasa.heasarc/skyview/wenss</td></tr>\n",
       "<tr><td>WFCF1</td><td>ivo://nasa.heasarc/skyview/wfcf1</td></tr>\n",
       "<tr><td>Wise</td><td>ivo://nasa.heasarc/skyview/wise</td></tr>\n",
       "<tr><td>WMAP</td><td>ivo://nasa.heasarc/skyview/wmap</td></tr>\n",
       "<tr><td>XMM</td><td>ivo://nasa.heasarc/xmmmaster</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=88>\n",
       "short_name                ivoid                \n",
       "  object                  object               \n",
       "---------- ------------------------------------\n",
       "   1420MHz   ivo://nasa.heasarc/skyview/1420mhz\n",
       "     2MASS     ivo://nasa.heasarc/skyview/2mass\n",
       "    408MHz    ivo://nasa.heasarc/skyview/408mhz\n",
       "     AKARI     ivo://nasa.heasarc/skyview/akari\n",
       "      ASCA        ivo://nasa.heasarc/ascamaster\n",
       "  ASCA/AGN          ivo://nasa.heasarc/tartarus\n",
       "   ASCASIS           ivo://nasa.heasarc/ascasis\n",
       "    BATSIG    ivo://nasa.heasarc/skyview/batsig\n",
       " CDFS-LESS ivo://nasa.heasarc/skyview/cdfs-less\n",
       "   Chandra        ivo://nasa.heasarc/chanmaster\n",
       "       ...                                  ...\n",
       " SwiftUVOT        ivo://nasa.heasarc/swiftuvlog\n",
       "  SwiftXRT        ivo://nasa.heasarc/swiftxrlog\n",
       "      TGSS      ivo://nasa.heasarc/skyview/tgss\n",
       "    UKIDSS    ivo://nasa.heasarc/skyview/ukidss\n",
       "      VLSS      ivo://nasa.heasarc/skyview/vlss\n",
       "     WENSS     ivo://nasa.heasarc/skyview/wenss\n",
       "     WFCF1     ivo://nasa.heasarc/skyview/wfcf1\n",
       "      Wise      ivo://nasa.heasarc/skyview/wise\n",
       "      WMAP      ivo://nasa.heasarc/skyview/wmap\n",
       "       XMM         ivo://nasa.heasarc/xmmmaster"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warning:  'query' is bizarrely case-sensitive:\n",
    "tap_params = {\n",
    "    \"request\":\"doQuery\",\n",
    "    \"lang\":\"ADQL\",\n",
    "    \"query\":\"\"\"\n",
    "        select b.short_name,a.ivoid from rr.capability a \n",
    "        natural join rr.resource b \n",
    "        where a.cap_type='simpleimageaccess' and a.ivoid like 'ivo://nasa.heasarc%' \n",
    "        order by short_name;\n",
    "    \"\"\"\n",
    "    }\n",
    "r = requests.post('http://vao.stsci.edu/RegTAP/TapService.aspx/sync', data=tap_params)\n",
    "##  If you want to see what requests gives you, you can look at the URL it actually constructed:\n",
    "#print(r.url)\n",
    "##  And the raw text of what it got back:\n",
    "# print(r.text)\n",
    "## The astropy.table  module will read this VO Table formatted result into an astropy Table:\n",
    "table=Table.read(io.BytesIO(r.content))\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "This would be useful as a function, e.g.,<br>\n",
    "    astroquery.heasarc.list_sia_services()<br>\n",
    "But then we have to resolve the SkyView SIA versus Xamin SIA ambiguity.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Using SIA to retrieve an image from SkyView:\n",
    "\n",
    "Now we pick one of these catalogs, SwiftUVOT, and look for our sources.  See __[the SIA definition](http://www.ivoa.net/documents/WD/SIA/sia-20040524.html)__ for usage.  In short, you specify the central position, POS, the angular SIZE, and the NAXIS, i.e., the number of bins.  (SkyView will bin/interpolate as needed from the archive data.)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Survey', 'Ra', 'Dec', 'Dim', 'Size', 'Scale', 'Format', 'PixFlags', 'URL', 'LogicalName']\n"
     ]
    }
   ],
   "source": [
    "pos='{},{}'.format(aptable[0]['RA'],aptable[0]['DEC'])\n",
    "#  Note that there's currently a bug in the default interpolation \n",
    "#  method with this particular query, so we have to set it to 'LI' here.\n",
    "params = {'survey': 'swiftuvot', 'POS': pos, 'SIZE':'0.03', \"NAXIS\":\"300,300\", \"interpolation\":\"LI\"}\n",
    "r = requests.get('https://skyview.gsfc.nasa.gov/cgi-bin/vo/sia.pl', params=params)\n",
    "table=Table.read(io.BytesIO(r.content))\n",
    "print(table.colnames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the fields you're interested in, e.g., the URLs of the images made by skyview.  Note that specifying as we did SwiftUVOT, we get a number of different images, e.g., UVOT U, V, B, W1, W2, etc.  For each survey, there are two URLs, first the FITS IMAGE and second the JPEG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://skyview.gsfc.nasa.gov/cgi-bin/images?position=19.0186%2C46.7304&survey=swiftuvotuint&pixels=300%2C300&sampler=LI&size=0.03%2C0.03&projection=Tan&coordinates=J2000.0&requestID=skv1512519860425&return=FITS\n",
      "https://skyview.gsfc.nasa.gov/cgi-bin/images?position=19.0186%2C46.7304&survey=swiftuvotuint&pixels=300%2C300&sampler=LI&size=0.03%2C0.03&projection=Tan&coordinates=J2000.0&requestID=skv1512519860425&nofits=1&quicklook=jpeg&return=jpeg\n"
     ]
    }
   ],
   "source": [
    "###  Inside the table is a numpy record array that you can access like this, for example:\n",
    "#print(table.array[4][8])\n",
    "#  Or like this\n",
    "print(table[4]['URL'])\n",
    "### To download the FITS image itself:\n",
    "print(table[5]['URL'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resulting image\n",
    "You can display it in python by\n",
    "(give it ~10 seconds to appear)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://skyview.gsfc.nasa.gov/cgi-bin/images?position=19.0186%2C46.7304&survey=swiftuvotuint&pixels=300%2C300&sampler=LI&size=0.03%2C0.03&projection=Tan&coordinates=J2000.0&requestID=skv1512519860425&nofits=1&quicklook=jpeg&return=jpeg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  Note that there are bugs with some of these datasets, \n",
    "##  since they are new in SkyView.  Selecting the U-band image.\n",
    "##  (The decode is needed in Python 3.5 because it comes back a byte type.)\n",
    "display(Image(url=table['URL'][5].decode(\"utf-8\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or download the FITS image and display it with:\n",
    "\n",
    "(This often errors off with a time out message.  Just try it again, possibly a couple of times.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x119dbafd0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J/6vtW1tb2tra0o0bN9rncjBdu3ZNOzs783KSc3q7ybg8MVAxJR0xUJHteN1d+1Nw8GvJ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119bf3290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hdu_list=apfits.open(table['URL'][4].decode(\"utf-8\"))\n",
    "plt.imshow(hdu_list[0].data, cmap='gray', origin='lower',vmax=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "Why does it so frequently time out?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now with aplpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zaZRbCe/De1WwM0ubsU6YjgpYXnoGCzJCeKYVY7iYS1HJiXXrreDIpRel4WUe\n8FvzM6uvCmwL9nxFdpYec/Fk+XtE/Z9Z14q5YjKroxiYXGg9M1cLvjfQX2NB88w9VmUoEP6+/f39\nTV8ZgeyXL18+da6W2QnTNVipszBbHoNNe+ihh+w73/nOqbweeOCBjSuRhQksZSARink2255zqkwq\nsj8VWVggfzQeFUOZIWNmxydz6ak0GfPK0lf1yOZrnHPHESLMhczWBMZEqeNU2MYVBGvj6lEr1RCD\nU+mndzQajUaj0Wg07gnNcO0YwwqKtv+Pe8xOn7iLv7M4mwFlfTANPguQV8yZT9dfU/FAzKJVaVbj\njTJ4NgutKRWvhawXyxctWhXUqg4mzCxXFVuCfcbnW7XQo8B3ZvlW4k2QMWOsoEJ27AGTvXpf5QRq\nFjAdyaSCadn4YQHYAxmrVYmVRCZ3fF6+fHnz3sQRc/WOd7xjw0BVrPUqrly5sslrpHvlypUN61Vl\n7gaiOacyR+D1LJ4wkqvKtLE81BwdyTyejU5c9xuK8Bm2sWdp7FElNthfY/d5JogF8uN9jOEawPg4\nnI/8syyGVsV34Tysys1izaJ6QbTCtWMcHR2F7iS2ALPJeanbyHcKHIwq8JwtttG9ZwVTPFgeasck\nDkaWFlP48JwkPzHgwMRPr/BEQbIYIM1kGL+xa9GCygY8bqDw6bA0/D1sIWKLjU+PTZxs8mU7e/Al\n7azvMfdPRaZskcgWByU7psH6mX9rgWpHVu8RvHzRhgzWFj4PtgAvPdetitVqtdXGrK9GO3aVkoqo\nzEPVuYrlH/0/oAy+qtudKbrMsML7hptYyVR1Q2eKtg+PULtE8Rr7PeqzTEHy6x5z1/pnzGKSwqer\nNgv4Z8Z9LL2sntul2Gg0Go1Go3HOaIbrAiDS0JXli9p9dFxD9Cxzow0wBoWxblW6dQn97tNDmZbQ\ntyhn9RRgRV/7381OrHYvU8RSeYaLsSX+mXFfxS02EKXp3QsR66cYHpUuc3WgzOjS9NYt9t9qsKzP\nn1mbR0dHW+Ninmd6jcnNrPBqW7Bxw/KIUHGpR3mxscLyx5eAj0B+vHY/wdofv1fdpsotp57PfsO0\nlSzV85ayo1RY2mxOHiENivWK5hyVl8+TXcuYcmwL38+y+jnLuFDrDmPkFUM6wOaeKnAOX7LGNcPV\naDQajUajcc5ohmvHGNa5YomUXz2KZ4isbMYanCV2ohqgX9X+lSXG4nZYfowZUixIZk2pOA607Fhd\nYD0pxoP4e/lcAAAgAElEQVRZway+sz7g5VWBoVifKng9s5SV1c76L8qiWFNkPli6Kmgf4esuiw9U\nMkWWddZvI7BxVI2zYXkomRjju16vN8zWG2+8YWYn7zQcRzXcT6zX6624tuPjt96vWGW4kCnzfZ4d\nbIxg40f1M3ymytxUwOI82QYcFtMUzdOeSa7GqSEqMZgog2KaIrC5jJWXHaPj02DXovph5fDX8H7F\nbuM1vxkpiqFFtMJ1AcAWNrZQmukzsdhC7cGCZaMByqhipXCNCTw6m4WdkVJxGbGFkgUMs9O7lbzV\nAVpND68PWXBThFLCMjesmhBRXqZMj4UtS5e5+zzYqe5VWfb396kbiSlamA5eY8oDK1d2yjfrl1hn\nqi3URBwpdR5KNrbYRkpBZYcjUyDv3LmzOQNp9NXXXntto3y9+uqrZnZ/zuN64403NoqczxOBcx6e\nYebrqqqksvklcrmyuSlCNI7Uwo+fbLONz48pJdgv1RmFVcVQ7eBjiPp21ahmdarmEuW2HNd9Wtma\nFqXBzgFj7m+WjjK8Pdql2Gg0Go1Go3HOaIbrAoBZLozhilwsjOHxyII2FVDj9wwOpreEoRnX/LOM\nlmWMwv7+/ilGDX87Ojra/MbSqNaFcgHicQbR7+M3xcRgGsqtUZGTWe+Zu5hZZ2rbNpMzStfngda9\nsqhH3UXuTsXEZe4KVm7ltlVuZcUkR1B9O2I1vBxZ+ooJG/kdHh7anTt3zOzkXYdmZt/61rfsoYce\nOpXvlStX5Et8FcY5W6+++qq99tprZmanmK5K3THGgc0HEYPNGBTFdlfqORsXKtwCZVJ9NWKS2NhT\nMis3a8TK+ftZGTM34tLjKJhnZ8iGm3LwNyZzdqyR/9/nhd4Htcbhd7V2eTTD1Wg0Go1Go3HOaIZr\nh7h27Zo9++yzG634xo0bdnBwYGY8VsZMB3Qzq8NbBmgxqoBptH5YLBOyECwmAtMZn+r4ChZvEsk2\n8mfWiZmdYrdQdsYaKIuSyaDiR6J4k4zZ8mlV2QwVd8KsaJTT962MScgORFT1hzFckex4jTEFUWxH\nlmf0LItLxPsV86agLGCUBa/5A0ej2EbFCjJmEce5t9rX67Xdvn37VP7r9dre+c53nnp2f3/fHnnk\nETPTB2wiRrp//ud/bmZm3/72tzfXxufh4eEWIx/VXfa7RxaHVIHqzxGLWMmP9S2M4VKxQpms1WBv\nNjYUE8ag5s1qbC67Fo1bn14U36zmqepGL1+PLHYNr+3t7dljjz1mjz/+uM3zbJ/73OdCGVrh2iGu\nX79uH/nIR8zsrQ6EO3fYji7lzmFKC1tgPNhgxEmauRQHosBqn/YSirqicEVKmAcu9mzQVgMevZyM\n0s7ccipdhFpYB1CpVKeNVwM/mTIdyevlQqUS65gtLKxto8UzcnksdeOp/oa/o0z+Pv9GCCavl42N\nWxYcrUIHmMEQyT3uY25Tv0vw6Oho40rEV09985vfPJXenTt3Ns+OAPorV65sXkw98hhp3blzx779\n7W+bmW3S+s53vrO5hvfh+V9DdmbksTIOMEVBuZ2i9hlAWXybYb2qeRjlqOycxH6ebe7xYPOlL9NI\nN3IrsufwTLZsDmNQu0KxHqubPli6bM5hckTzRWZEqk1r+H1vb89eeukle/HFF2V6Zu1SbDQajUaj\n0Th3NMO1YwyWxQf7Re6uCvvBrB60LtSzGNSsXJUKzLWGRwKgBaoYHsUgoOXi6yRyrbEAzUp9Rr9V\njx2ousCWWpIsXe+aYPUUsYN4lIVPj+WrmDX/ffzv81asJEPE0jFGT535xcDqU7F+jAmLGEUvCx4N\noCz0Klh7Y54jX2RG0W04fhtuwIHXXnttk85wB16+fHnzwmvPnN2+fXtz32C1Xnnllc0xE+O3N954\nY3NExJAJmdLMPebHbeZaZGMe86rMOZhWZW7O2jNjnFmf9u5nxv5UXN+RfFjvZ2G2hozKJY7hKL4P\nRusTc7lWxgtjNLFeVeA9y4cF5WN6mUzNcDUajUaj0WicM5rh2jEG88LYGRXvEt3j4S03FguDWjmz\nPtDqqcTPsHuOjo62jgSI5K8wQmidMOaKXRtQLAxja5iVxiwxxgqyuLcl7biUeWMxEd6yZGAxSpF8\nlbqN5FbHQqgYu4EoJkMxE1mfZWPEMzcRg6IYLsaasEMvlzJcUbySzx+vsVPIffzV8fHxhuEavz34\n4IOb+9/97ndv8hwB9CP/ccQEHnL6rW99a5P+yGMcD7FerzfPDIaLnUiPsURZe/q6UEwJ3hex+Uva\nBVmypUwT5s8YHBZXy8qE5fbsWDYnVpCyNxDT5NcznK+xDTxjd7+hmLqoPIyZ9+WJ+la2HrfCdQHA\ngjDZIMwUFf88fqqBzCZhFpiKgbvVwGXlskMFLguK9uVVAaD36qZRbs7o/3GNLQ4VhUspgRlY3bHX\noETuGS+7UoYwPxVUGrlI1cI/4F/5goj6htqBpPoCG2dRHupsrEgpx08GNvYjWZny6b9nYwdPcPfK\nktlb7TPaAF/3M06hN7NN0Lx3Q9+5c+fUTkSzEzeiP2n+8PBwy82JrxvCdJXyq8aqcrVH9ym3JcLP\nk0xpQ9daZlgqo4idg8YUFRaEz3AWxX7Iq5TY8YnlZmkx2bGuWb5LN8pUjHb2OzPy2HqigvEV2qXY\naDQajUajcc5ohmvH8FYDYxyqWjpjMNgxDp6ZiCxGZnV5a2bJMROMcVAnLCtrU1Hk2XZfJqcKflXM\nkL/GGK4Kw1Q9SmOA1WcmU1R2/79is6KTsjMWySwPKh11oNwMEcuqtpdn6VVcrlG5B/A3nw5rx4wl\nUwwBO0cuO07A1w+6kLG/je+DpcLjIwZLtb+/vynjqNvBXI17zU6/FHvcz16Ojaysbwt0h2I/8vUX\nldXPg2x+w+9s3Kj2ZvOseo7lhSw4ln/ULfNOMEYo8z4oxo6hch/O4UNedq4YtoWaa5mbHvPIXtCt\n2tlvXMnaR43HiBVN3a7y10aj0Wg0Go3GPaMZrh1jnJjOWJXKYXhLt8mexR9ehYpVQatHxYwxSzFi\nonwActVyU5YWk4k9y9JhbcZiO9Daq7JxA4xpYiwH2+iQxYllcSFRvqwcLAYQv6uYKyVjFPPk8z0+\nPt7qH/g9avsILN8sjiNipby8zKKvpBHVgwrsZX0fGRlfF/jOUDy+wW/nH4wYpjdYL3y3KSsjsjos\n3tEDGQ8VTxhhSYwdorr5go2jjPllzJ5iibD+VcxRdgTPUrC8fH/Ddx9maSiZWLwUxnf6d+ky7w3+\nXvGEZEwcm7er/cesFa6dY57n0JXBJh/lQhjADs9cikuDIRUVHblQ/OSDEwObJDNFxpfn8uXL0i3m\nwRQf9izKnk1QFbccXsP6ZMpApRxM8fHpm+lNEv7eKA/8zs6NYvcjmCuInfVVNRSUvH5xWq1WUkGJ\n3N6YBl5jrhP2DFPCWFspJcfL6tNQ9YWGjVIqWfmxTVAx9sH1OEZ8G2Mw/EiDvWqLlSFTwrGd/Phh\ni3zUZ7x8OP/huF2qxClXe9Xdh8+x8rAyeeWX4axB3kw2D193qKRnoQ0o3/istOM8zxvlC5UgNe/6\n/1kds/ywPEqBzYxBs3YpNhqNRqPRaJw7muHaMdSZMRWwreEs4DM7H0il61kOvBaxY8P6GJ/7+/vU\n1VGxMLz80f0ok08DaXtfhoocS1kidv4NpsUs6qpry8vBrDrWPvhZod4RipHzeZtxN8l6vZYsBnO9\nsnt9f8S6y46s8PlHDIpyP+BveG7dgGcaqm5Qdj8rB6vDamgBY40H2FEiCHV0C7obWXoqXfU7qzvG\nlmdsBRtn2RsV2LiKME2TDHHI2B3ffzMmjJVh6VyC6Xiw+szWKCZzdhaZmnPUGhOxkr7N1FhmDDCW\nYfRp7IOYLmPQs3pvhqvRaDQajUbjnNEM147hrRFmZTPtmzFbaPFH22eniR+K52OukAVh29D9cz4P\nv8UfLQL2LGMa8FrlRGJmvUexbEMmZTnh1vdKwGUkU8VSjKzHCvOpDoTM4oPwUMVKXFeVFcTAavau\nPMViVeO18H5fXiYnYznx3oz1w77s81CMY3RfJAeCxXqpwzsxplKxB/jsAGO4MIZLHaabsWMsLocx\nm2ys+HapzkN4DRlnP16ieB/fforhqyIqI2PxsvGFst0vqLmJ/Zb1SzXesrGv1ilkkxTrr/JjcwR6\nh3ANZX1QpROhFa4dI1rIsZFVJ2TuKVwcKmcw+XzNThZi5U7BtBjdyhQunz/rqOj2yQIUo3KhwsnK\nnClNnt5nLh5UwrKJ4azAZ5WyyiY37Atswqko01F+A+y8IQy2ZkHzbLGJJmfmNvDP+vKwCRmDtn15\nonHm82OuiWgiruwuZvmosmJ6rD5Rdj/2o4Wlksc0TalbyKdbMSiY4qHcW5X8lZGFxijbScwQbbqY\n53nLAKy6T1FO1Y9wzmNn9Kl2jObQpYgULC/ngH8jgE+jovBEslZ2r2Z5+PbEvoCGA7vm334R5d8u\nxUaj0Wg0Go0doxmuHeLatWv2yU9+csMu3Lx5027dumVmpy1qtFQrgdoq4D1zcWGQe8WyjCz0iust\nopvZVnuWH7NYKkDWzT8bneUy6oW5aRSbiFAuv6yuM/Zjyf0RK+pZL3QLYhl8mpGLq3okyVL4voVu\nDUwvO5Xa/8Y2hWRMoQcLgGbPVF031U0vWFbmKlTvr8M+WD2zLWKnsJ6qLm7G1iLu5dkB5vJUDDXL\nP/q/AjW/sHk9CqFgTJpvM3buXSSTqgvP/LJ3Q6ILG0MIFEPMNnep8kT3DWQuxWjDT5WlqsxRV69e\ntatXr9o8z/bLv/zL4X2tcO0Q169ftw9/+MOn4puWnP3SaDQajUZjt7h165YdHByk7vdWuHaMoWz5\nwHe0VFEzZ4cqsq3p0fvcWKB4xRoYz1YsDXb4YhQk65+d5+0DB7ODKH0a0dEDLK4Mmb/xLGPY1JZv\nPEWblY0xCSomirFOWcza+PQBzlFevhxsUwNCbaFnrAaTZakxsYT1YyyIZ0jRUmaxeNHRDwqV+KZq\numwjzAAGUSO7gOzDAJs3KnJiviy+ZlzL3jnJ+rtidFnAsuqz7NoSptSng0fWqPtY27G2UP0yYmEY\no+qBc5N6NoKvo4w1ZeyXZ97Y8S/R3MbWBF+GiAWv9AdWfrbhSd2P19E7lG1AUXIiWuHaMUZwule4\nWIBx1JHZQIpcVThxq11ZIx1/zSsjqEix9HBwsWBW5bpRAd1MqcPJzStS0zRtBT6uVqvNNbYjEcvP\nBq0/NZ2VMVLCfPnxvsy15esQ4U+dxuB1ZFHV5MPcgkOpjBZ+/yy2mToJGydYNqlVg34rbh6mpDNX\nR5YeLjA+ID9SrqJ2ZC5a/7uXJ3OheDmz9BCVDSDR2U8qjYqSHu2S9DiLSw8x+iHbhXZW11W2sUe1\nFZvzUBac57I5OwKbh7LdrgOVHeJRXgOs/JFyV5mbIkPSy8yMVjUGsrkMZVLzeoT2XzUajUaj0Wic\nM5rh2jG89o1bltk2cP8cWlaVrb+orWcv91WWZNVNkjFcFevpLNY9Y8S8FcncAPhdMWwRfaysHVUO\nZOVYvqx/YNlGGp7FY+yprwMvG7M8meXHys1cyNhXGfPoy12NZ1RuEITqM8wCzvLLTs5X7ill3eO9\nqhzMfTgQMVSMBfbpMrBxi3MOczt52Vh5sN2V+72KqD5ZvirwPEozwzTxDQcqrWzeYt4ExRwpROz7\nQGWtmWf+3t/I84Ly4XzA1gS2FjG2L+u3Ph3G5leh1jVEdSyZNcPVaDQajUajce5ohmvHGNZf5QA2\n9OGjVV4NiDXjLEdkVangRuXfRmAaFSuS+caXWHEeykpRlnqWRxbIifWoGA/G/kQxImanA/R92TC4\nPzuqYumu2Ox4D386M4v7w+BTVneMiaseuqniHVn/ZTEzWR6sDdQWds82oiyKyWGbU6I4Et8/kGnB\nPNWz6j4W+1ONs1GMT3SSugIby4ylYTGVTPYoNmh8snimCBFbdNb5EtPx5UJELL1Pj9VFFYy1RhnZ\n2FPzOmPPfJnwk8XHjTTxM7qmysXi2tRcUm27CK1wXQAwupe5K5gbT+1MzJ7F/9UEyq6pzs4UyHs9\n6bjqtowQuUZUMC+7li2UDMrtxBQfthD4lwOj7GzH3VjsmaIbuWP9BF+dmDEwH6/5s8uinZA+qD5z\nu3klg7keqvKzRcT/Pj4rigzKU03Xg+1Gi8ri54H1ei3TzpSWyuLpn8/kZJtkIgOsCuZKZXNOVeHy\n6aKrWRmATKFRimamILEztPzmHLM8FIMp9iocYSBy6Y00VP9YqtCxuZbNEWiALFGoPJhLtXItUpKr\nruNTZVksdaPRaDQajUZjEZrh2jGG69BbH8hwMfcMWrbohhxg1K+/H9NSVpdimhhtHrkjGRRTlt03\nwCwsli97qS9jBRVrwe5j1Dd+si3nqh4HWD1i/7h06VIop7JUWRmjvDxrULXqok0KzF3ALOkoXYbo\nPCFlefsx4MtY6VORS4+NUZ8H/n8Wa109q1wiUTrRfVGfjWTGuvPXM0R1rNJgDDq7xlidKlui+k/G\nIDEwNpAx6CzfaKxkz+LxJYyBq8xREdT5WyyNyqaSCBG7OKCO0vDtw9Zdli7r08zr4b8zNMPVaDQa\njUajcc5ohmvH8LE5+L+31CK//dC02XZx/z45FhQYMWMVhoEFQ1atvegU4Go8WcRgMEuZxT8oKx2f\nmaZpq+4xQJ09y4LD1cGwrBxHR0db7Tj6RxQvlsX84D0eLOjYW68RY+fB2CzW3hifwRi2SjySf8aX\nh/0WpePTQ/jnV6vVFvOHGwMqwdZR31ZjAPNUcXesDKo+s+MS2CYALyeybhk7xWRj8jE22JeHIWLb\n1HzKPAGMNfHMphrHKHs01yqGS7FOjD3F+Rffc6hik1hM6RKvgv/ONnexY2/UWGH1N4DehDEn4j0q\nTk0ds4FQrFzUdhm72QxXo9FoNBqNxjmjGa4dg1kiZqe1ZbUjhcVkoebOLINoezGiGnvFLKdM00eG\naSmbhfJF1j3LGy0ixnaxOsHX/fj6ZHFveJ/aqcQQWbS+jKzuFNOzJD6FxTj4emFHFvjfvXzqIFdm\nIat+FLEWjB2LmDPMn70jMtpVyg5VHGmyV4moHZlejugaYzCQcVFlRFTitDCeDuelpa92YXGMrGxe\ntiqqzATWD4sZVPJlsUKMVfdzOGOm9vf36ZhiYLvUWd2yvu9lYWz5YCxZue/lwFBktzE99j5azxRG\nu+5ZGQfYs6x+WFq+7rG/R+3nZV+CVrh2jDF5sE5RoVtZp/Df8X4WYIiLhJrI2LPMRRktWGqyUGB5\nKIUropb979FEp/JSbpXM7cXS95Ogv88v6JguC5BXilZ1gshcCL7+IpeHOnJDuU4yhYvl5dNgCwyO\nKSan6iv4nY1L5nLFPJjrKcqLjTPl/vLPVMYUtmPUx1CmKpiyqNyDFajFVsmAZUSZmHLhjdZsLlUK\nvjeizU6fi+fzYO4vVsZoXKq+z64xZdLnwRSKbH5j/Ye5KJWrPQpeZ+EOzEBjczwjJKIyRuWsHPXC\n5rytdOSvjUaj0Wg0Go17RjNcO8awfpklr9xtqN2z+yoW5FmofP89OwkcrZ6Kpcjui2SpuFMya25p\n0KRis6K01DMMivJGC5RR9EwW1j9UXTHrEANTlRUXMaj+WsUVw8aF2Vt9Ci3VCsOFUEeiRC4rVs+e\nkagGG0f/j2sVdgGvKwYb5WT3VYL7M1kUo1odZ8plZ8ZdRwPZ4ZiMgWSu7gqQcce+y8JDKoeHsrwr\nR7n49FhdZfPqknJH7cjeiXlWFyUrT+QBqoQMMDCGGvuCn3PUpgVEM1yNRqPRaDQaFwDNcO0Q165d\ns2eeeWajwT///PP2/PPPb923NPYminnCzywN9qxiizyq8TuVd+VlgdqYB/s+8vLXsrwzhoJZQire\nhDFXGF9QCXRXcRBVNiBiKFjaLB3WFqy/+bpYr9c0boXd5/PCelKB/Hi/P9IiY3WqYwRRiUth/VLV\nIWPdEJVjOTzY0QZepqjfVVi5e4kPZGmreC0WB2VWi39iMXar1WqrXdh8sRRLmUNEFKtYZT6ZLD4e\nMAoy9zjLmuHTy1hExmaxWE4WM6dkxWfZcUCenTs6OjoTO3f16lW7evWqzfNsv/IrvxLe1wrXDnH9\n+nX74Ac/SN9/Fg04BUXJqwms6l7ATr50gmV0NBtcODDV4sTyYGVg8rKFjS1AeJ9asHBhZzQ32200\nMILwI7ey7w9L3SDR4sTAlGTWj9Skr9yIOHFVXLIsEBmV1YrykpWRIVM8lCszCqQ/6+KN6TPXDWuf\niiKG7cjmmmi3mM9/abmU8RYttiwvtqBXjTJUtMy4G1j182nafkMFU/zY71V3PpsHWd+KZPDPHB+/\n9W5TVDz8+05Z+mpuzIw31sfY/Mz6AEtbbajJ8vO/4bmAaj7COTSS7eDgwG7dupUTEfLXRqPRaDQa\njcY9oxmuHSPSmJkFWgnKG89GW5TRKli6DZxZWJG7T1nP7JwarAcWFO3vY3WRBUJXrKnMYlcuB8ZS\nRa4Jb22ixYxpsPc/RoisTc86RTIxq5hZ/or1YszfAJ7Pg/0nsjIxL+y//mgORvlnLpnMTVN1f/gy\nMkYxGsssPSa/kqPKYPuxhxsiWF7srRUoWzRvRa5nfz9rd6xPxRTgaf4Zc4Qyj89RNnZ22hI3LSI7\nv4m1twIL6MYzvBizx9h3dKexzQLVIxq87BEDVi2TAmO62ZzD+pRHdZ1gfRWvsTkU82DfGZrhajQa\njUaj0ThnNMN1AYBxKai1s9N6q0yH19Lxs2JpMNbg6Oho86wKOM/kZNeGvOhXx/S8hcEO0mMMnGdI\n/G/KemdWKWMIlFWMFvW4//Lly3bp0qVNeb3sLFbHs5HsOBBEFitTbRdft5k1i3XiGUVsWywjYwFG\nnopBYXJjn2Btplhj7GPsmrduWbtjX2VBydH/0W8qXiyLd1HA/sNYYyy/il85KxO3hJ1DhmfIphjD\nDIp9Z8fYMOaVyemfY8Byq3mQjdGsjKwMmBd7U0IUk5XJycqTgcWyVp+tMGtVBk2xjVieaqwZ1nvG\nkLbCdQGAL8FVwePRRKMmAEXRVyd9lq4aKHt7/MXOA7gg+ddM4HPZgsUUrSivaHJjrsfK5B0ttv5Z\n9sLmy5cvbwWropI6cHR0dOr1G+PaEjmZO4m5Gf13lG2UY6DidmGnXc/zfGqTAMvT5zvSYK8hyV4e\n7hUfNn6YcYBjjxkOmTvQK2usf6pdn1FfZe7qs4IpUplrEdsseobNR2xRzlxSTAlU/V3tzM6uefmj\na2ohznbxqjwipcWXe71ep8bd+KzcN/JmnypMJAI+wxR3ZsBXjFbMmxkCTD78Hrlco/Isnf+RhMj6\nV7sUG41Go9FoNM4ZzXDtGIPdYlq4srzR6qpYlAOZ9ZwxWBmzhZ8VKLeg2gadBXyyOqmW0cvG0o0s\nYV+O1Wq15RLZ39+n9L53JSJFrVyEAyxYlyFiFxSLyOqsehyDYjMwTxZszo6AYH2FWc+MpVIMV5SW\nl73qVh2oWu/ZmK4yIyx9dp86ekG56pgbmI1FZC9Uu7NrbM5jyJgc5jb18mVMR5VVUswr1gVjnNV8\nVWWDszqNZMLvKKcaF1h3fkMWY9gyRj5zFfo6QBcp679MFs+mResHG2dqUwWml431ZrgajUaj0Wg0\nzhnNcO0Yg8VgwYvMeoyCHPH7Uv/7PM/y+IHMilbxWj4fRBYkO3AWy40di+HrJzoiQ8WlZIyQD4Lf\n39/fitfCuC4VmIkWk6+r6FBQDxZvg9ZrFIsWlZEFlWbb4DH+qsJOIfyxGdkREAgf/8bSZQwXA47H\nLB6yEgMS5TE+K2xa5S0NXqalcU0Y2Fw5OiFikn0arL5Yv4yYswqzF/UxxVzjPewonZFuNm5GGmpO\nVu2NjJmKt2TsS9b/VB9gcWrZ/OvzZ3GjPk2PjHXy+UZt4Ovg+JgfGDx+Y3Pn0tjIahyaWStcO8dY\nfNnkWZ242WBBt1R2v5ltne/CkAX/ZrKxjnlWmldNmjiQ2Mtl2cTA3AvRBIefkSyoWPhzf6KdUL7s\nZ6Gvq5MFc1coRQLLwxYgpaSibCw91edYsGwFuBHFp4Xf2cIR5aPqh7mWqi5Inz4zGKJFUrn+GJYa\nL/g7uswqZVKyR7tNmXt1oFqP0cabkQarMz//ROfi+TSUMhSNAX8tUpzVZgG1Wzfa/cfGQzRGWD1F\nfcfPnayMzM3IECmylfGP9ehDIfB7tg4p9/u9ol2KjUaj0Wg0GueMZrh2DGZRmeXBogzMyhrnPaGG\nzoIIFR3NKNlsqzZzK3jWqepqOAvTxxgC9f6zjNmruGmnaXsLO1pnaHWpU6nRfcYCSFl5R1ojPXbO\nVWbtMTcnKysrN3M9DjDX7dK8qn0A679i0S9BleFSzzEmVbXjuIYv9EZr3zPY98J0MdcW9h/2LGML\nvCsucit5d3F09AhjgNh84fsSY0+j8eAZK6wLf7TOarWiR3lU2MOo3f3YV+wTPqtcexEYi+XTzTwN\nzPWpPAIsCB9/Z+mpNmbnKkYbEqI8EdhXq+7NgSXrdDNcZjZN0wemafqfp2n66jRNfzhN03959/p7\np2n6yjRNN6dp+r1pmh4Onv/YNE03pmk6mKbp78P1fzJN07Vpmv6XaZo++BdVnkaj0Wg0GhcLzXCd\nYG1mf3ee5z+YpuldZvavpmn6ipn9pJn983mef+GuIvVzZvYP8MFpmvbM7NNm9tfN7N+Y2e9P0/SF\neZ5vwG3SrF4S46HiIdCq8AwKszaH1Yx+a7Tm1LZbZtWoeC1knVQcVGQx+bIy/7vyw2OMFLPw2NEB\nLMAbLbGKRY1g8VcREzT+j7Y0YxlZ22bxQyrQUzE50TVfZ4wNjfpvhCh9FbCsGDOsM7xWiZ3L6kRZ\n0qrc7LmMoUi3npM6YFY7Y3qygHj/vbqZw4/p6PgXL1t0mKRncnHssfhAxhxl8WH+PlZ32VxVCWSP\n+k0RHasAACAASURBVIC/zmIMWf/F+mbxomyuZVD1nsVreUT5MIZrQG3gYvXDkDFcbH1kfbsyR1TQ\nCpeZzfP8J2b2J3e/vzpN0wtm9gEze8bMrt297dfM7H81p3CZ2Q+Y2a15nr9mZjZN0+fvPnfDzF4x\nszfN7JtmRiMjhyLCdnoMKIqTLfzT9NZuuaFU4SBTi15Gp6rAeRUkix2ZLcbsRbL+VHL/fSBTLny6\nTOHKXFd+AosmX6b8jTbAemKLHSuPdymxTQARvR7JiVDBpYilGw2iSdLLxdwa6GpiLhnmtvWuNUyX\n1RPKodx86hqO26zOPNRzWBZ0t7GFTSkyLL+sr/qyVhcWXLCGzNjvfTuyOY/ly5Rk/3387+cSTA//\nr5QJ0/djnikFKHN1px3OecxA9eMMjcYsfd+/onViwNdx5F5W40KVtfr7NE2beZ/tMmayoEI8vrN5\nmqXF6t2XMTOkUY6svK1wOUzT9GEz+3fN7H83s/fN8/yy2YlSNk3TXyGPfJ+Z/RH8/3U7UcJsnue/\nc/faj5yXvI1Go9FoNC4+WuEC3HUn/rqZ/cxdpsur7mePuA2ArjsiT/p8ZLlEli8L0s7yqrj4PCq0\nPaO0I+tMsVjKRcnSrdSrv48xCMwq9NYzukSYZZxZ2xHDdXR0RLer+/Qx38gy988yK1dtGsjqM2sz\nxc4wJpC1LXuOBW8zF9iwpNnRKKz8eJ9qPxU0rp7LGBR2H7tHbUTB78olzeYS/K5YEJYn3sfaourq\nrrixGBgzEqWj2G/Gllfbls3Xnh3DemfzR5S2KkNljh/Ixr5itTIWTs0luMmIpYn15DfgmNmWN0G1\nZ+TqZ/NLpX81w7UA0zSt7ETZ+qfzPH/h7uWXp2l63zzPL0/T9FfN7E/Jo39sZhgQ/4G711Jcu3bN\nnnrqqVPXRge4efOmHRwcLCtEo9FoNBqNc8Vjjz1mjz/+uJltG4+f+9znwuda4XoL/4OZPT/P838H\n154zs58ws583sx83sy+Q537fzL5/mqYPmdk3zOxHzezHKhlev37dPvrRj566ho3ng9YjjZzFxXgo\nixXjQ5hFlDEklZgIZRFF5cFYHhVnoqxdFStlpq0xZq2wPDGQtPLMEkbIl5sxXIz9wXgWxiBFrJCS\nRcnOLOSl6ft+vLe3t7UlH7+ztlXbxud5piyWP3Yg2r6+5GR3n69Htf4VaxL1ac9cYR9QsTdHR0db\nc0h2bIfKH1kGxahm7BiLnavMJ4wZxzzUcRMqbieqk0osE84RioWO2rvC7o578fco/iqKv80YrvsF\nLz/b/IBgmwCY10b1PbxX1SfbkDHq5MUXX7SbN2+eulbpk61wmdk0TT9oZv+pmf3hNE3/2k5ch5+y\nE0Xrf5qm6T83s6+Z2d+6e//7zexX5nl+ap7no2maftrMvmInx2x8dp7nF6p5DwrVd3w8dwcnCN+o\nOAiXDgyloKFMVbrey3UWmTCgHBdb9poNtfj7QaYmV5XW+I1N3L5sWGfqvqryyZQR5v5hwFPtVbCs\ncgtiAHT0vELUr/xvbEH1vykXNasnlj/2I6VoR3XLFiWmaCo3qF/s2WSulCJ/jSkoftGJFmo2RpXb\nUimJ+KlcQii3lz1yvVaCvVm9o6uOKRKsfdT8UjUYGFj9qI0taqdpBqwLZmCgLN6gwT7BjB2fBgNr\nswzoHqzML2yXK9YtljHq02xeZ0RDpHwrEiBCK1xmNs/z/2Zm0QsB/wa5/xtm9hT8/2Uze+x8pGs0\nGo1Go/GXHa1w7RhDox6Bu4PZwnfBMZYBNfOqBYif/j7lNlQsyFnA0mEWP2MKmFXo3R/Milzi0lTu\nF2wLbz0imzXak1nUzHUTWeg+fyXv8fHxViBp9FJuVsdVN5eXKZKzwgpmDIpvvyjgdolb3afjzxaK\n2AUvAzvagLna2QnqeE4eC8BW8jMX6UDkPmRMnGLL1TUFZAjY8QxsTLN+NIBnWWEeTHZ2jY0zj4gx\njNygS1h7XzaUnTGQrM18IHhUjqWyYluxvl1xlUVtxsBkUWsMu18dy8Pkqfbf7MgPnz/OZTgHZO2y\njPdrNBqNRqPRaCxGM1wXAMrP7K+xmA2lVSsrBa0KdjRFxTJgbBKLI4kOMGTwjAOzCpkPXwXyZzER\nS+PV8IgBlI0d1urjQ9jxBAxo3bNDAH05WPxDZuGpeKQqk4DpsGvqEEtkaJicGMvo08AAef8s1nH1\nWAbWZoydUzFPS45l8M8oS5n1N3bf0dGRPDCSsWgZ++3TiH6Pfov6jL+f1VMWj4R1x/qvH3uMMYtk\nY32/guw+xmQzdlXN9VnMHJOhsukD+1i2Zniw/q7qIjtSCP/3fQDbEdcunybGBFf6LPM0sGfZupfl\nYdYK187BFouBygKYTYyVSWK1WpWod5Yvo2CxkytZ2QQS5aGoZ/WsOnUaoQJY8VnmzsEyVFxB7Dfm\nWkIXodo95hXUKM/qgoGKDAuc9flF7hyvcGFfx3J4dy32I58X61uYF8qbnffjy+PTxM9ISVa7+NjY\nqATrsyBcrDtcFP1ChC5FlQdzR2J+2cIR9aVISVfjC9ukoswzwyKCr2/MI5ujmNIbPVd1S0YLtc+f\npREt7JW6wHHD6pgpoeyl4vh7JEc0r1d23OI8iNf8uM2UMJ8npldd4/A+FqrA5uRMqW2XYqPRaDQa\njcY5oxmuHWOeZ1uv1xs3gGINEGhNMIu6QuUqqjWyVBkdroCWSZVxYPJVtpoPRDSychEwVkmBWbRV\nBok9w86fYeVm7hSEZ4kwQB9RCWBdr9fU8lVuMfY/OxNLuSTYNVZPFTYiSp+1vWIIo/QUw6WuKWsf\nMfLHs/IUS4ZsteorjPHIGNpsvvDfFZPC2Cycy6ruSyanyoMhOx6m4laP+od3eym2KLqu2KyM0WTP\nKdZbMePVdojurTBrrM1w/kDGv7JOsI0tKE+lTKx/7O/v0yNmsvHcDFej0Wg0Go3GOaMZrh1jaOAj\nONifLl9BxaJeGvCKVhyzOllMBkuH+fAxDRZj4Z9lcQ9o2TELzAfYorXJAkPxOAVvKU8TP0DRlzVi\nX9h9LB7EW2zRgXtDNhZf5eskOpTP/86YPdYHswDdiNka93kWDw9mZQwFQ4WRip6rBhir9JjslbRY\nGbLAYQyA9301Stf3mczqrrLUqm+jjIrhQpkUU6cY0CgGyOfHDo9WLHsGxtSy/FHO0X7YZxQ7pubp\n7I0gDGrTUuaxWLqRCOVW7ByTCT8VO4afLGieMcks5hTlyGSfpokeR8FkVJubzFrh2jnG6fFZQ+Ln\nWZC51vxkmnV8dMENRBNHBFVmL7NahL2czM2K17zyhN9xILNASSw/UxZVcHQ20VUWNqYsZlQ9a2+m\nfLJdYAOXLl3aknNp/lhnSuFCqDrB9qzu+mSouhq87NHOOF8vahGLFHO1YLF6r5Q/gtrswtosC/y/\nl3kKX+eFMuAnk92MK+yszpR82Zzk01AB4Kzu8BqbQzNjWLlhMX9fZ1FIRwWsD2ZKt//O5nKsR5x7\nfJ2xfh6tZ0uJhso1nKPULtKKC7tdio1Go9FoNBrnjGa4dozI6mDWTGSdLQ1qrLArkUVUsewY+4Ks\nCgNaM+xZDAIfcuJp7ig7WklZ3ZyVJUKZsc7YNQbPsmVt5+VEJoGxf+pMKUbls63XZrblEolcBCz4\n1VujmC5S9J7VYBsoWOAqaxO1LTtyKeKp7yMNZuWOaxgAzeq24lLzcmXliphF1gcYg+vzYuwcc1mx\nzRzKys+QMdTM7cT6D2M/KgxgNIcq9xljlfxvrO5Wq5WsO+xPFZcnY64imX25quyWSre6TkR9gr23\ncVxjY7/CpkUyq/JW10k2R6l02Jrt0QxXo9FoNBqNxjmjGa4dY7ANQyPH4yFYkOPAUutZWbvRcyqG\niVmR6r4saJNZJCw+jAVKDqBFomK4EMyyVSwiPufzWK1WW+8yxCBKzGNYdKyMjPVi9a7qk1nMioFk\nZWVlZAfJRocVqhg3ZIlU3AyLgRnIjhnxzF+Wly8XyunTwf+9vF4uHMuVWMSMvRjXjo6O6HEPrG19\nOozhYgwK1hnbWIL5Yjo+f8WQMgaLxb2xflllsDOwumdjfmCUd8Q2svuiTTRsc4rPizG1UQwX+86e\nZYjqB+ORFCuMv2cxhX5+w40/eE+FsYvYamSpI6g+M8/z1hzOmNURe+3zzvpbK1w7xhhsvtOyawhU\n0KoBjB5MGWEv58Q8/WQa0aiKZlaLvdn2YNnb29uintmEjLS0ymNpECh7ltVZ5HZSExcunsolMoCT\nFnObKgWbtQErP1NQcKJjbievSOB9kSvRy8z6ezV/thAxBcnvIpqmie729EBlRCkeiEghw7yYm4bd\nzxQa3CyQuaS87JGLlilB7G0HUT9jaWSusMwd6+sAFRk8DZ3NDUy+pe41pXBkblu/eKt8PNQYZmXE\nOs6UCvXs+PTlZ32VpYX9Eu/z81rlVUMe2byh2pb1hQG2I129MgjzYqRChFa4dohr167Zs88+a2Yn\nDX3jxg176aWXdixVo9FoNBqNKq5evWpXr161eZ7tV3/1V8P7WuHaIa5fv24f+tCHQmtcWSfsPsU6\nZZaY1+Azxic9UddZpRkdnuXrLTGkdH2dHB4ebj0XuVeWBMvifZGlztw0yi2GLFDF4lMsGesLZttu\nLEab4+/4WamfqC8wl2LF0mduA8amoRyKJUO20ZcbWRVl3eM5ZYxZy1hbX8aqK4gxW8j2suM9PNg5\nRVhGJgdzi+F9UTmjcc7GhfquWBiE8gIwVqU6b7E+xcDmlewaCx5n8rE5vOqxUFDzlbo/W5Oy4y18\nXbCxzNKN5suBzJ3tf0N52PhlayGbc9B9eePGDXvhhRfS8d9B841Go9FoNBrnjGa4doxhxXvrObIQ\nvIbP4leYZefT8XmoWB522JwC20oeBUNWrFeG6MBV/xyLeVJ5VA8hZM9m9c7SZYyDspQxFsfLx7br\nR/E2jNXxvzHmk1nj1f4WIYojYf0IrWLVZ5n1zpgWJh8yayxuSI2bKIZJ5anqR7EgaGWz8YAbcDxT\nWGVBojF7VqYlY3UYu+Blz5gZlsfSeCHMl8nkZWf9EudmZFS9nCz2hzF8WAZMxx9rEh1hwOIBK8iY\nvspcn601A+y4G5Z31AeqTOa4lzGgLJ7ay45xamot8miFa8dQHdMjci8ol+JZFsCKvNXBq1xBGapn\nfQ1Z8PVIKjg52g3l/88maaaI+ms4gM9yFo5yw6qdX6yeWF3gJ+sr3h3JJsQoD1UuBvWsv8d/V8YB\nC7jP0mAuSqb0+nSyxaYKJqdyH7IFGPNXyilLgz3L5KsYdJlrD68xZdqP80gmlp5yLalysTmCKXxs\nQwjm75UhNraXKIPKLRbdM+5T/TJzhfn7EdVwFNUWzM3I+g9ujGB9kLkIq31lwI8zrDvlumc7sz3a\npdhoNBqNRqNxzmiG64KBaeZ4ZpO3zqKg1giR68jnz2SKoLR6tFIU08NoW/yNna/i72NHWmTntqht\n95m8vn3wGZbHvYBZtpV2z9yXUfCpB1p93gJkdTdNU/oi13F/xDgw5tA/uwQ4fliwdeSaVHkpty67\nR8m8dFzgNbVdPnpW5cugGIVq4LLK//DwcIu1rZzwHYExR5FMFWYkY4BVXbD/Vb2zUBE8+iLrZ8zr\nsdQ7wdJlv/nfs/ARvK8iC87n6tgitokF5VOsMWsXdswQIlp/FJrhajQajUaj0ThnNMO1Y6jA2hGT\nhFq4t9ouXbpENe2ILchiMgYyizULpIzSjX5H5oHFezA2hQVSjnvYOwAxiNinwQJSs5gDZkX6NKZp\n2rQjY/uyOATFFlTj89jxDJX8EVj/Pp6CyRmdsO/TY/IpObws6j6Wl4+piWKz/CGaWAbc4KLGEo6R\njFnxYON33IuWtDqkEcvD4v2yGMnxqeICWRuwNKoY9T76z9K4z7OAsSVmb9WVZ5XYvZGclWMPsH4Y\nK8nyVQxXNr+z2Et2H2Ns2GYSH3PFYhvxd9aPozrwMqsYVpaXYoXxWBUcs/5atO6pTQoRWuG6IFCD\nmJ27g4pCtJvGbLvjRYsEO2fEf68u8AxRUDZLx0/mqHCxZ9iCqZQMfL4qv1pgMD2/2OKmhiyImC1U\nUd1n9akQKQCV/HFxwknQTz5LJiHfp1EeX59odCxdjFl5Irebf6E23svcFZgHS89fq7qWlAIdtb9S\nuBiUi5C5ypgrJuszCswVxn5nho1SDKP0BjLFiY1bny9DNqcopRbv8X10mqbSyfVRHqzN2Lyi0qqM\nOZwPMgXf58vqIoKfN1h5UH7//5L7lUzo5sz6ersUG41Go9FoNM4ZzXBdAERatme4WDAiat+V4w4y\n6+Fegl8VGBXMzjyJ8s2CRCtgTEqlbJF1yCws3xYRw8We9XkwZi8L9mYsgH+WbYFm+fvvUXrz/NbJ\n9dnWfSUfK8/SNlZgTBzmUXHHnidY3bCjKpC5YuPcM97R2GHvkPRs1nq9TvuN//R9hm2XZ2Pw0qVL\nlNXxZWRHzGRsReZm8/lGR4NU0sdripn2dY3fIwa9Oo8rLwFj1Rl7W0G2Xizd7BLNG6zcrP5YXtHv\nUR0zFpGtu+oomgjNcDUajUaj0WicM5rhumCoMAr+2tCsWdyF0rrZYZb3I06hGrMRHXJXyUvFX0Ux\naiz4V0HFpTCr1Gw7dmme503QPGOEkL1kFuhAJf6MxbFkMRQZw6VwloDyCrPFGNuBNEaiyEhVgmqj\n39UYyfqk6kcIxQgNHB8fb/U3jFWsHviK17L+7f+vshoVZpHFy2VtkcXzZe0X/caYKJVmlL4fh9Fc\nUmEFkdFUx7mwdmQbj1hskor1zfpMFg9VmVei9q6wR1Umi8mRHR90P7w8Zs1wNRqNRqPRaJw7muG6\nIPCWEGr0uEWasQDMOhrwr5vIGA+2WyRjyyoWBANaE8h0VdmHpfmp+1UcS2T5q7THJ+4iZfdhHVde\n8VGN4WB54f/MylZlyw4o9IwEs05ZbBK7xn7DtNjW9HvpK16+jBFayopFsTwqjWjnJoL9Fh0B4Vnw\nDNh//bWzHNXA+geLzfLyRm3hj3OJYnVUHBtr02rMooopwv8rngA29nGMKsaXMV1YFyqGi6Wtxi2b\nN5bMjQxsbVGxlGwXqYqZY/1ClZHFRTJPw9LYtIFWuHaM4VZjncyfRcMUrozKZ4GflcW6Su1G9LVH\ntCji7+NZtWhjvuw7+99DUf5nSS/Lg7lk1AKAMjFl4F7lYG2WuXKrk6lqO+wDVSXMAw2MLPCdjRXv\nws5cgN5gwe/KJZOVA8uzdKFiMmEdq3OMlJucgSlcSvmMFkumTFfmA5Qd5fAKCnPLsTGdHYnC2o+5\nbdnzFaWt8vu9IpqTfb7YLpcuXdpKx5cT02Bn8C2Rz4z3ATSoWP+I3l+Kz+M1Jh/OH6pfsn6O5WZr\nUjbm26XYaDQajUajcc5ohuuCgAXJMsrda9oIPEZCadpVq5RZjMqaOQulrORTboVMFgYVmJq5GRWU\neyxydTCXIoNi9vyzkeU9fh/B+1VWlOUfQTFMLJBeHWGCUK6EjNVS/Zy5ISJGY9zv2bGIma7UGbZB\n9QTzqtvW589O1I4YIc8aRG9gqMjCApGx3Vkanr1kdbJerynbptywqr/h5gM2hr0s6HZSrD5CsSZY\nP1n4Bsrs78M6Ye3Nxs3wooxPJp9ixvHYEFZetpEL29jPXVi3rA6i+htQYRn+LQbR2FUeDqz37ABi\nhla4dohr167Z008/vaEib968aTdv3ty1WI1Go9FoNIp49NFH7dFHH7VpmuyXfumXwvta4dohrl+/\nbh/5yEfMTAeVqoM6p2n73U/M4kDNvxp3otiVyvVKHiyNqpUXpcdYC7OcQVDyKWYm2+qu6lHFA+E1\nb7kxBjSKI/GWZXQMiCq3ihsa8vjye0sS+zR7tto+LH9mvXtWJepjvoxL6tYDn/XxLlEZKuOHyaTK\nb8bjd1RMDzIjjNVg5WDsLjsE17NFEVPr644Fex8dHW0dB8FYt729vU1sEms7ZF/8s+x1SIypxbwq\nR9xkR2osPWiXsVlRfJ5fH/b29rbaKhtvLM7Up49rEjuaxOfpr1U2GTEwjwWrWzXPRXMEKzdeu3nz\nph0cHKRrVitcFxjZxG6mdyTid7ZwZztTfCc8i9tNncu0JLjUl0O5MLI8VNAmmzgjZXHkx17UrJTK\naFCyBbCidCqFAr+zIFO8h5315SdHVp943wBbsLL6ZjIpt1emGCuFC19IXAnoxrZg7hy28KqT3BnY\nmFbuGv99pO/bOVqUWTC6WrwZfH/f39/fcs9N07RRfCIXYQSm8KGLlLl4ULm7fPnyqfJEu9z8HHIW\nY8+3H2t3pvxiOuzF5Mzgxr7I3MBszEeuci+LLw8z5Nlakxmjapxh/Xs50R2qFD10W7J1TCGa1/x3\nlu8Sl2IHzTcajUaj0WicM5rhuiAYFgayJQOZK0PRqAPDOkSNn7kZmRsgY7jYdUbv4zP4G0tT/e9l\nrlgfzHXDLDJl6XlZVCAw/s+YBAZ/nVl2I30830udTYO/K+uQBatiH/DpR9ewTiosH3Ox4P/+/oj1\nY8xZNbCbWdnMteXvQ3ZBuYKU9Rwxi1G5ovswL+U+VNfwu2LClCysPjE4mrklFZsWyc5c7N5tuVqt\ntupof39/izlCV5Q6UoMxRGwsMag6xnTY2MvOfFLuQ4ZsnlT3+/kc25Y9ozYU4XXGSOEnrl8+HTb/\n4f9VtlKl4bGE1UI0w9VoNBqNRqNxzmiG6wJgnuetuAcVKI9gMUc+bTPNgmQBucwSyyxgZqUwtoKB\n/c5ieZTlwixldo/arIBxCiwgtsJMLLGwWFtFdcYYu+rWdC+f/y2LY1FxcVhPFYYrs7Yr1iZeU4HN\n+DuietyCLyM7OmUpGCPEwNi+aIyqGK5qXNfS4w68rPhbdHiqYrPwWvVIi0o7MoaU9QlWjoyZ95im\naav+2FyK7CkybSqWFNPz40Z5HNjz+Il5VOd1Nl8zxtn/Fh3f4dsx2nRSaYdoHoiej9jlbM0av2Xz\nQStcO8be3h517UWdoUrvR4MvmugqHYot8lkHyxaHs0INboQ6KZsFWUbB1szFpMDcBkreKPi08ixz\nF6tncJFXAaxqEY/SR5fnUoVLlbGilODvURnZ/VVjgz1bcedkMntXGAMGimcLMLuWLRT+PuX2UjJg\nGiwYHvNiIQsVJZCBjdsoX1YeZUTcC1jf8q6wyP2tFBOUsTInZQZQpW+xclV3Z2Zpq3mItdOStcj3\nMzZHKCWdlcGMvwEhQ7sUG41Go9FoNM4ZzXBdAEQav9p+zizFCgV8dHSUnoUzUKXQ2X3McmLU99KA\nTy9vJpOylDHgU1k2zCpl9zH3KnN/sHJUqXmfNn4qy9Hnpc7IYgG+jCnEdH197+/vbxgOlMEHLDP5\n8H9V/iz4lh1PwPJg5VGBysji3SvDhXKOoxMil9Dh4eGp/HGOqIQVePh5IBuDzNXL3G7+jCwEO+oA\n24elpxgUdn4TexbL4PsK6+/RePCy+7rJrmG+arNN9Fylrarzh6rbKsMVsUAqnSxtLxPOoVg21raV\nuVZt7ImOYVHuWnXOnkczXI1Go9FoNBrnjGa4dowoPgG1esU+4bPIwkTxOOyQzmgLv/Jv4z3MYqtY\nKcwCRdaAsU5qm7Gy7jFdxrqp+JSojNXyKKbSp4XI2C0Vu6HigaLYBRY/w070Vowmyu6txSUbF6I0\n2H0RWJ9R9c76KspeidlABkUxzwhfRjyiA9k01qbMuvbXWD1dunRpq/9m7Biyl75Mqu7wWcYuINPm\nj8XB+sS8WNv6WB38rmRi4xZRYbQYgx7Fxlb6L2P/ozJkbzLwMmd1sRSMWayMPYQ6jgffLDDA0mPH\nhQx5UD6MmWQMl/IUsblRsW4erXBdAGAHxUZkHZTt2PG/Vc8EYkoE/qaUHOUKw8lCuaKY0sCCrdFd\nMKAUCkwP81YLJipF/tydzBWFZfCTLgYO4wLMzltTCrZ3b7CdjKi84HMVBRLbDPPwgc/RZM0CgQdQ\nVnY+W9QPI0NAKWHsWeW2ZAsb65fZQpQZLGpR9EHzuBDgmVLMKKsA+yq+rNiXjRkHzFDCRYkpOayO\nmQsbyzvy8unheXOYh+9HbPFEhQevMUPAz1er1Yqe9eXlYH1GzeGR0u/HPFNcWXps7GVgfdnPefv7\n+1TJYYYVm0uYUlkxMvEavlmAuTxVepXwmuhF2awPMjnZHJEaV/LXRqPRaDQajcY9oxmuHSNiXSLt\nX22RzpiokUbFVahcPSxPlIlZYuxkdGYlINBy8kHWeKSDZ4YY+1MFslTeKsfvaO1hm6j2iVgpL2c1\nCFO5hFhQevUFsSpQnrFo1eDf6L1qEZOKvynLFsFYvMxVxhhFFTTP8lOuadUHWfnR8sY+6NsbLf87\nd+5sfvPB8MjqMBccC0XAevDuQDWHIHOF9cDmEsZS+b6P7kvl+mTI7mPjkfXfIdMoV8R4ICPjweYS\nlFPNB8pzUa0LnGsrUK45n79aT7DNVBkV64byMEYe72dpM5ZxfLI1U8mHY095OCI0w9VoNBqNRqNx\nzmiGa8fwbBALsEZNusJssZiVTONncViKIUArWlme+FvG+nj50AJHdmbcoywmBhbf5L9XrcDI+jnL\n82Z5/IWKd2DsAqs71j8wMJXJ4vONAu4rcWLRNca++LyWgjFh2Geq7c3aVsWl+PwGor63t7e3YT8w\nfgZjt8Z9zMpmciqmO5IvwtHREY2/Qrm87Es3tixhaaJrin1EsL7P4roYFOsZMS7VdBXTj8jYWiVf\nJgN+4tg7i8egwj5FaTGWlcnLYtcU68/yUawcYxlVXqpMA61w7RgVZWWA0bzKJYPfqxOduh/zwPuX\nKisK7DydaBJnSuL4ZMGd7Pu9gAVU+7SjXS9q8lbtwvLC9veByKvViroXVZvhb8xlVlk8WX0zZS26\n5vNSdYGT79K2vZe+wJS6yFCJ3BrYt1F58UrYarXa6ufMnc/GCvbBysYaxDgbDGXBMer7G8qOkC4o\n5QAAIABJREFU7eTzi95E4MuDY1m5gSPlJqp3fJbVI7umQiHwfjUPotLPgq1ZGbO5r6qEVZQQpvgx\nt+AAc/szRYWRBZHsykDEfCs7SzGPiiHCZMfNJJW+qNAK1w5x7do1++QnP2lmJ41248YNOzg42LFU\njUaj0Wg0qnj88cft8ccft+PjY/v85z8f3tcK1w5x/fp1++AHPxgGMQ8wzVkFHZvdPxbHw1t0zNWB\nqFK6A8x6Zq6lvb09eg6Kyst/Z9buSDtCVtaKtYd54DXlAonOPcJ7kM3C7f+K/UEXdoUNxfvQrayY\nhOw6c0dGv7E0qoyuup6lZ7bd5mdxLQ4ol2rEIDFWxQcTszHFwI6TwXZkx5agG9EzcEx2LD9zXfsj\nR5C1wPPffLlXq1WZ1fH9IWJtPdvF2BxWZ8ztloG5htnGgAq7uwRsHvAbHFRIi/Iu+HJVWEE251Q9\nMciasnkV7/OMGQOTHeVlsiOLdnBwYDdu3KDvD0V00Hyj0Wg0Go3GOaMZrh3DW6QsFoBp8Ki1V+Mx\nPCo+fX8/iyvzTJMK7Eewgw6ZdR8xM5FfnbFGaLFmTErGklTKhnJWnj0+Pqbb6f13FeOxt7dnly9f\nNrO3Ym9YTFzEqlVYKhbDldXTkv6Fn1n+7NmlbRfl4Y8AiPL1VvbSfKM+qALPB1gcFG5XH1h6RAnm\nxWIAo00s45O1n39jwdHREWUDxjVkf/w7OaPNFNW4JX8tisGsHDS7NFaKsSXR2ygYoxkdI+RRjWdT\nc1flsF62Xq3XazonL/VEVO9j7C7mH23K8feNT/YuUF/vjHGuyN4K146hJo/Kabls0EQ7ODBt9onp\nRvd7yp9RsNUFGM/dwWtsgmcTTWXywQGoXGvMxYMKDatHdtq9moAVcBFD2dWg9q69S5cuyZdSq8Un\nApukq4pZ5Ro7+0m5MJgCOU3TlmsNvyulZciAiAKgKztqo4VlicuRuWiZCyUKKGcB2B7sWTaXYL8c\n9+EYraSLMo3P9Xq9eRk39k8vO3tTQ5R3JYjay3rWawpqbsb0sC6UnANVt2XkVmbzmpdJtR1TRiMF\nlSlcqk9n5fHpsnLhWsqMysrYjwx5VXcsQD9CuxQbjUaj0Wg0zhnNcO0YXiNm1ivCu+CmqX6+lU83\no8OZlVI5jwQtjcyy96xO5FL06aBV6KnvyKXo02BysvzHdcxLueIiGaLfo7zYfcx6Zu/gw0/FeGDb\nKUse68dbeZEbybuEIybVn+SdBT97Zi+Sl8mpoCxhZvHjhhF2rwrKxn7kWWN24jpLPzvtmgU0R2Ua\nsgxg2zEXXNRXonnHlxHdTmzOy8rPXEcKVVfV0jRUumw8MhaPMej3Im/mblOuNcWeYr2zMa0YOMW4\n4ffo/YYKWbsw1jaCYrHxWeaZYiyeRzNcjUaj0Wg0GueMZrh2DG/dsMBv5U/PmBZmASrLBe/x1mYW\nUMgsFryHbRf335klxuSrlpXJw5gztPoUM4P5M5k8G4AsyLgWWe+qDdi9KqaFMQUsLXX4IrNooxgq\nxh4yMPYnq2f/vy931N6M7WPyqHcPspiQDF4eNvYwXd8HMRgeWRAfw7Ver7fe31cNTkYZsrL4OQnf\nb8jqltUVe89gNtf5a2ouw3pkTCpjYBH+vupxLSwNNS6YbNin1bMMERvO0lObXXy9V9uEzes+7SGP\nbwOUCY8cUXWWQbG2iunO2H22TgyZFVPp0QrXBYZyFeLkqyjSqsLF3AZ+AcbdNKiEqUWJdXI2CJQy\nELmsKmVUQdQ4kDJ3UpUuryzyEVgbRL+xCRTvq1L9Z3FFLS3bUnePchFUlbvIra2UO3xWBdIPMGUk\narOorlBRYIo4jncv+3q93rjWM4WrsvBHv7N5wLtzWXrMKGMhCZnRWOm/meuT1WMkfwTmnkOouUTN\nX5nh6fP394200XCozHWZ7CxUQ601uCb59HAOZUpLtiFCQRnaVUKCGcj4nK/P1WpFx0C6K13+2mg0\nGo1Go9G4ZzTDtWNUWAJmlTP2B++PaHjmnmPnYSFYvtmZNF7myIJk7hfGLnjrSLnbMH+0XCqUP7Pi\nWHAyppOhwghFQaqM2RrlYagEW2M62J5L2zaDcq0NRK6G8dvSgPfMoleWd6UMS1BhF9BNP4BtoTYG\n4BlVijmKgrKXuu8Y2H3sDRD+WuSmY78rVxgLO8C+XWFOon7JGBkvp5cDv7OzmiL4cR31fcWe4f9q\nPDA35NK5EVk1fx/rs4wVjc54U301Y6hVXVRDFpj7cFzD8+f8WXTHx8dy/jVrhqvRaDQajUbj3NEM\n1wWD8jmz+IPM8vcWFmO/zuI3V3FG2TPqWbQQvH8f71OMS2QdK2sG4dkCFug7TdtHWjD5qgwJWo8Y\nMxOVjQUJo5ysjlVsS8biVZkOFr+DYJsp/CaFatwJO3wR5a4wXCzeBI8s8GniZ1QnrBwVYPuxQzFZ\ne7MYMianZyaieDFfp9UDJFEOlraqT8YIebkwDRWDhFAHtHqZB6LNNVEemBZjqVSMH5NfxZyiTJiv\nnzeYzFkf9P0jWocYG83yYuwgu8ZYNI+I6a8w6CztLA6NsZeKgRzrFMY4R2iFa8eIOk22iEQBlOrZ\n6LfIreIX4Gma6MBUC1A2gfp8WdBv5A6snF2TTThKMfLuGgRbHCLXq3IrsKBfVrcK1R1VrIzZGwMq\nCiNz77KF38tqdvolyWwXK6sTv6BHZ/dU6g7lxMBzVReZK1u1wQALKEclx5eRuUaY4ozpZKjIiddY\n3Vbc4JlMQ4lYrVayH2GdMCVDKR6YnhqP6MqN5tWsjEopiHBW43ppf/eoBMHjWxxUGbNd5T4vZnAz\n5S4yIljberC2QsVL9XdWnqWu6a200zsajUaj0Wg0GveEZrh2jMr5PplrRJ2DwtiNs7IWmFfElPn8\n8TflakCmRTFHit4eiI4EYHlXWbnoeX8/OzbDn12GbBKzFBldr/IfiBgLZh0yZpGlo46eyFxr0f1m\np4NPI6uRycmsTeV6jWRRLBG7xhiuiBkeYOVi5fHpZm45xrT4e/C+yN3my4juqvEbMpCKYcEyMFe7\nYiOwj41nFRuMDCSiMpey+6I6UwwX+y1joKLyZPPQUuYqmps9w6NkYvexdNElv7T+0UvB6lYx/dW6\njtaxKF3/LH5GsqMnpI+FaDQajUaj0dgxmuHaMaK4EGbxMIapGofB8kGLUcWnMGZC+dyjYyZYGX2s\nDF5TcRqM1WDBmFl8WYUlwrQZUHbPZiFbkTFcDBWmTgH7jAoyj55V8SsMqr1VvAvej6yn70fRwZWV\nccFkZzF2zFJlTBRD9WBNFkSN5VYxXFGaHtncwBizCvOq8maxpQwstoYFJ7PNJD4dj2r/ZumpGCGG\nLPZoqTcB+yJjdSqsV8QSqfHP8vfA59jvytPC0sYjIDIvjtqghOlVxmgVqi7wd5yjsmMhWuHaIa5d\nu2bPPPOMmZ001o0bN+zGjRub/9WgYh2UTXZsslKTfeS+8+n7HTz4LOalBkC0OKsdM3iNueC8nFiW\nqiwe2SLKFFe1+49R1OzcH1/2qGzMJZQpmh5somP5Y37seaaYocvBty1bWFQbz/O8VQeRgcHcXazN\n/K5U1n+rijhzU0SGhZdJ3c92T0ULui93FBjM0mO7GZco+1nfGWCbY46Pj+XrUjIlo9IvMG389HUW\nvV7Jp6EMEZbGEkWA5aHuw//Zteh+dh9rn+j+ikuRna+FO5TZusMU8SyvJUpyNEextsJ+MYCbCa5e\nvWpXr161eZ7t05/+dJh3K1w7xPXr1+3DH/6wmd0fjbzRaDQajcZfLG7dumW3bt3qYyEuOqIAzopb\nIkqrunWVHV3AgNtkPYuEFr2idhnVGjEY4ztj0SqsX3RuF2MelCXI8lSMXuZCUM9GFH20SSHrH6O+\nlcvOp1dxw6KbT8nDnsX+g/J5lytjUhl7h3lVmB68j/V9Nvay4Fyfb2ZhV1w3EcusmLOBaOxn5Rjp\nebnY8SfISlbrjOWt5g3FolZZr4htxHKMa748+/v7p85AG9d8GoyBZGOPeRV8uarXIxlYefAe5WJW\n3oksf4WMkWJeCjbXVfuUuu7bmLmr1ftMvdyszjpovtFoNBqNRmPHaIbrAgC15aWnvkesScSMsPgQ\ntPKZpYFp+UPhMH22pXtYiREroKwu/4n3YzmUTAjF+ih5mPVetfBY7AIDs5iiNvCye9bI/6as6qy/\nVViVeZ5L2/kxPbR8xzN4BMF4zlvg1ViUCBUGkrEBEaNYYQHwvgrDZbbNPCA7qFhwhmiMsW3/9yvt\n6LeM/cmCjsezo7+x9NRBrVhGlVfGLCqG2fdxM+1NULG3LH+fpz+eIHsG8436I+vb0dzI+mXlSAm8\nrtYdFm+YIVuXBvzh1lHbDqiNT2xu9GiF64KgQvnjtcylxAIUx6d3Y+FAHZ2cuXCYiwsnBuyAVdca\nA1OgGC3srylX1+Hhodz9yHbBoEuTTT5ssKpJiilrzP3JzqiqvEaI/WbGF5aKYp+1I95XXaB9GbEN\n2MStJmQ2FpRCFrkBKu4x1k7VXXPZfb4/sgUm6kdqRzL+zxRI/2z1HCUGZpRhXmxhV3WbuQqVrMqg\nipRkny/bGc1enaXuZ9cQqFT7cY71yOYNVBq9Sx7BxgjK7I01ZiSwMjBjeCDaCOOvZaEJrG7VGsfq\nh/UVRjTgZ8UYZQZ/Zfy0S7HRaDQajUbjnNEM1wVAFiiYsQdKS/dWAlpE+DmexRdGK5kQzIJQVpdC\ntS7QalbW4507d8zs9AuJkeHzzBHmxYJkByLriN2bnSc2Pn2AeCaLp7SjrdfsmI1qu1RcRsz9Ebk3\nGHsZuYSR4Rufq9VKuqyYK3VpgC8ra8TkVvJlQd7IYnrWL3O5s/7G2GU2H/g08NnI9an6tB97+A5C\nvFf1N6w7xroxmVR6uF3f5xExUaove/YrYsgYW8RYGja/+bGPfZ8xPDife48FMmZqvt7b26NnBPoy\nMnZ5rBORF8ani/2SjZsKU4vpsWusTyvPAHtZPB5NwqCY6Sg/RDNcjUaj0Wg0GueMZrh2DBbvgJ8Z\n0NpjFs7AYByYRcQsKOZLZ0wFAtOrBL8iquXF+yOrHS0sLLeKScB0K/FNERsQyerzZc94K3d/f3+L\nnYrSzGT1/6vnVUwCY50Y+8MsvehZX9/sNxbPl8VYVOKBEFncUOUaYwaYVczan/UfxgYga8D6R8b8\nDTA2lG2o8UD227fFNE2U/ajUX1T/7NR99t2nE7FgXnYWr1r1Kqjf2Dwcsai+n6NMOB69dwLnfxb7\nmJVBzWHMq8BYeF9u/M7i3rLNTQMZC8/iAlksXoXtO0vdDbC5KUIrXDuGaqRsQRmfrONFQfPs/ml6\nKwD80qVLUlYFNklnC8HSPFh+HqhcsV0lql4jV5RSRKsLO3s58ABODEqRYcgWauVOQkSKj7/G5GR5\neHcBKghMaVLpqg0UDKhUVl2KGbxyUw2ax4WK3V917zIXss83c3f5tPD7EkOJLcZmJ64m5pJZqnAx\n9xBDVN6RnsoXXbhZX/LPq3zx/8rcwEIHIqW5kscSo4TJ4r+jEujlW61WW8oVc6F7+bxsVSUMZfd9\nPjojMCpj5D72crK8zop2KTYajUaj0WicM5rh2jEiF1ZmRWVHMEQaOWOc9vb2NswWY1eYK1G5LRjD\nFlkuyspVeTD2iZ11w9ymzBIciKxpxtYoK5JRzuy3AXQfMibMszWq/L6MS6FYgYzhqrKCVcYj2zru\n81dlyBCNDZ8e/lbp56r8GLCtrGe8j+WvAqzxO7YJY4HZs0x2Xz/MDbyUYYwYXX/mFkIFozPZfTki\nVGVWcxRzvTI2jbEqGHag2meeZ7rxp7rZQ5XLs27Ytug29unhOyQZi8U2jKgxpWREZIw8e471Vd8W\nVda30mea4Wo0Go1Go9E4Z7ytGK5pmvbM7F+a2dfneX4arv+smf23ZvbIPM/fnKbpmpn9xDzPP3n3\n9//ezD5uZq/dvf4Hd69/zMz+sZ0opp+d5/nng3zx+Z+c5/lf373+f5nZf2hmn5vn+T9izzLrxKxu\nYTG/Pj6PlrQZtyrwfWks3obJx/JibJJCZGkpxkj51fEoAn+oK2PsWMwIq0t2fARaTtEz476zsC0e\nvozYjpkF5tkPJg+zxpn1yqxnZjFiftWgUtbG1bg7xSSosZTFsbA8WLurQ0sRzNr2YxRlz1hLduK6\nKrdivSJmevzOmA5fLlZPbPww2Rirg0cXZOPIl4cdUYHlUPLcyxxWZdpYXvi72tQ0gDFUWO/RGI/y\nZfmzeEt/dEk0phXDxeZixWJmqOY/EG1IGM9VZMGY2+yAW8TbjeH6GTN7Hi9M0/QBM/ubZvY1d+98\n9/cnzOzfnuf5qpn9bTP7xbvX98zs02b2H5vZv2NmPzZN0+M+w2maPu6e/4zLY/w1Go1Go9H4LsXb\nhuG6q1g9YWb/jZn9XfjpH5nZ3zOz5+DaHTP71t3vT5vZ/2hmNs/z/zFN08PTNL3PzP6amd2a5/lr\nd9P/vJk9Y2Y3XNbPsOfneX7ZzP5fMzsys28KuVOmRWnQkSVf2XHm02Gy4ScD2xEYWRzeIojYEMa0\n+HpBhku9lkJt+WbWZBURA8iORagwhSx+iLEfbGfa/YKvl2hnnbIoI7Zr/MZiCn160eGUUfqsLRQT\nG11jzBGyOuxZxqxFRyn4+wbUwY2qX7JyszEVlZvNNYoZYSzW0ldOqXxYnji/qJhANvdFMT2K4R7I\nYsOU/GquRWYE0/LliDwX4z61EzSSzddjtJtw/MbYbX+obNSPPAvMxjmCvQf2LPDlUX072gXtgcxr\nFueYrSNvG4XL3lKsHh4Xpml6xsz+aJ7nP3SD61+Y2b+4++/3mdkfQTpfv3uNXf8Bkq+/74/vXnt5\nnud//+61H4mEzlwlGdQCF8ErBWyRiCamAVRyKhPrarWiygg7Fdo/i9/ZosTkrEyS0eCqusL8YMVn\ns0nDyxzR+1HAfTaBIdTxBUy5q/YjhrOk4RV2TIv1S1+3zKWI6eKnah9UItTGgAEWyB6lXflNuTAi\nhdxfG3J5OauLmB9n6GJhgfFMTrXo4ELMlAx2RmCmdI5ns0Vz/Mb6KHNlLjHCovmS1aeah9hxHVE5\nPKpGBkuPpaX6O7YPc60pmTJlNjOM/LPMJY4y+XZhaw4z7lkaURjB+P+7wqU4TdOTdqLg/IGZTXev\nPWhmP2dm/xXeWknu/kvYaDQajUbjuxlvF4brB83s6ekkHutBM3u3nbj5Pmxm/+d0op5+wMz+1TRN\nPzDP85/Cs39sZv8W/P+Bu9cum9kHyXWP6PkU165ds0984hOnrg0N+ubNm3bz5k0zyw/o8xq80rJn\n2EbM2I0sGNK77/CwROWiiKyPAeZGYqwBY/QU0+XlQETBpdFGBgRzJ2F6jLWpulgUW6FYPJRJuTv9\nM0o2ddxA1bWYsXJVpi6SbQnDVQEyi4rhYtYwy4exPpnrSLleFYODTM8AWugoexYCYHaa7WP9krmO\nWHmWutVx/FTajTFM1ZAFn7fKI5Idr6n+HD0XsbwoW8aOVaHcbIqdi/qRTzeqS9anVTpnwVnZeVZu\nxnqN9nnsscfs6tWrVN7PfvazYT5vC4VrnudPmdmnzMymkx2IPzvP83+C90wnOwb/vXme/9w9/pyZ\n/Rdm9s+mafoPzOyVeZ5fnqbp/zOz75+m6UNm9g0z+1Ez+zGSPX2+Ivf169ftox/96KlrVZdUo9Fo\nNBqNv3jcunXLbtw4Cedesla/LRSuImYj7sJ5nn9nmqYnpml60e4e63D3+tE0TT9tZl+xt46FeMHM\nbJqmv31yy/zL0fNVeMsZGQWvVWcBewNnZQz8s8oSxHcVevmyDqgCgtHyVpad8ulX48H8d5U2u0dZ\nuSowNZJ9ycBlsSgYQ6BYkKUbBCIopsvLOj5Z/FUkc8YssvuQUa0EukZtodjL7HmfL5NFMUKRvD5/\nxuBU2wK/Z8y4Ym7UkQ2YbjUuUm2AYfMgKw/msdRwZUyT759RHfvfM+YS++pSdkyNb5RfycJkxuus\nX7Lxq9jYbHywOaSytrF88XijChvMxnk0xn26+BqhJQz6207hmuf5upldJ9c/Ip756eD6l83sMXL9\nlyrPV+BdF9iwY/IZLi7mgsvO6WEdnw2aKvz5VvgybJZmRpUzdyA76yWS3Yzvhqu4MJbQ2H5Q4YBX\n9YeTj3IvRtQ8U6oG2G4spXCxvoX9iO18YmCbHzxw8lNBskt3c2bAuvMLNAt+RQWf5YufSuFBsLHJ\nZMH/o3IwVHccZ31auU6UsYV1Vtm4whQBf++S8lSDs7H81TpjGxdUX8iUqkjRxu/RXFKpn0jR9fMF\nU5ZUOtF85BEpV0oBx2eXGn9qjmBG6BJjsHI/ynEWL9TbImi+0Wg0Go1G4yLjbcdw/WWDonTZKdKZ\ntcHSwmvMCmHWe8SIecsWGS51/g2TGV0tS632zKVYZUuWUtpKNrZtumoBMgu0sr3ezLY2QeB3ZCNU\nv1itVtLFpILnM/eqcnkyWr/Sj33+voyRS7EKZimzzSbs2A7PAvtnMF3sMyP99XqduoLGs+za0qNJ\nMjCXkS8j64M4V7DyV47BYKEV+DvmVwmYZowmXsN0WZmYfOMe1n9VO2bsjhrXGViYR4XJzX5jngvG\nplWZJjX/Yl5KloxV9UCGnjG6GWuJaSxFM1yNRqPRaDQa54xmuHaMob1XtOooLqXqrx5g9y9lYaox\nZMzSYBZglKfPF69FW9OjdCqIWAFl5arYFpSPXVNxKWZvnShdsfAiS1ixp8iuVKxSDIBG654xkCzm\nSbXL/WAW8bfKgZlm27FoyJBi/agDQJUsh4eHW3GYyOxWYioZy+zzHZ9Zn2JpRr/N8/YxE6xuMd7U\nB9IjE1ht/6XsHDuyIZtTsf96maO4s3GPr2P2TsNoDLI4Ps+Ms/6ZzWlZzGAlblQxXjhOVDzx8fHx\nVn9nx3Gw+R+RsY3VvhQx56y/Rawxm98qXhePVrguCBhlyTqyoqYrbgjEEpfN+GRUNVuIfHqZIqNc\nN4jqYsKQucUiOZXSaHY60Ja5Jhht7uWP6ocpdeN5FbSOz7N69IpcdD5RJE90Hfuikm/pZKnyYvdV\n2xHTwX7s3Xz7+/ubOhvpsJcj46tomEtEjU28Z7jqVD1FC2xlbDDjaYmSqtL133F8ZGNQjUdluKDh\nWZ0jWH9QZ44hWBv7+Y/VF5vDMX1UXLMA8QjR2uGByr5KT81/qGCzdsxclexF0cpQW+r6Yy7kAdZn\nWFrMTY/XlqBdiv8/e28f6+u23XWNudba+5xTQKgmvWBvS3t77zmnoQH+sdE0ZhMBLdyjl1pS29ha\nQAUDBCxGWyoxRNFwCYpo9dJQvPTSam0MoVVoqaWyTSRo5cW0vWe/XHsttPQNCiJJu/d6efxj77nO\nd43fZ4w5nrXP6jrtGd9k5fes52W+z/mM8R1jjqfRaDQajUbjhtEM1y2DTGVmcRwuf46++bUyFVQ1\n1czxksyCmYmStNdZfr2ftBliCK6j/fl2WjkYr9KrsJLE9BCLqMeZFqf3eM2SyhrR9lm7Z2zfyuRL\nZiRv+vX1qJi2iHHwZhiq36rsZnbAXB0dHR18vJoYrpOTkwMNWb8Zqm2sDvRRmdQB3fdB5PBMYzpD\nNpfJdEIxtAir+FrZmKINONVvtRLLV8Xe+1fsJM2pDDrfyESbuRNk5dP06N2xd+3MTI/VNYLGhf6f\nxQiktq2anzPmit6dWk5tQ2IMm+FqNBqNRqPReAeiGa5bRqTJRgzTBG3Z92n6Y7M6o6AaETEpqnGQ\nz0Lmi0GaFjkiq9ZFzqTEqlSQaYJm9e3Y5DeUMZCZZqksXsYU+jr4+qxCc8xre7VxbWvvdxFppd4R\nebXBI9OWaVySQ+4E+flE/ak+WfN3Hk+WQdmsO3fuHNyndSSfsKje0UYCYpporaiOeUrXt2PkX5Qx\nlZUwH5T/qkzZ/Vm9FCtmmsZAlV0m31N/brVJpOrLRG2b1StiuPx9VO8Mq74gfzpqi4jhj9K7Dmta\nLb+3PkTzYvYLvQv3oAWuW0ZkDoyo9L2D1oPMc1WTWVQOMgFm6a0WqZVg4s9lea0mKplIqY7+pbMy\nrU3oS5EEI82LFn1flqqJdrXLKsuLoPmvXppmV2NJkeCcOatW+pXy3FMP6lsVssikOAUtErh0fHhF\nJRNQTk9P03mjC3wk2OovmUno/qgdfZvS/M42bND9q00nZPai9Ca0jtrWZHbKFCWqY1RGfY7OkfDm\nyzzr6sebjplsnYvyoHeIFxoIutZSmVfCGtVt1os2EJBJMavbah2sClzZ+yhTkCdU4NL7V+9PQpsU\nG41Go9FoNG4YzXDdMjLTm0ekEZDGFqUThZaosmTEEGT3KYi+9gwBaTq0hVw12ioqZs6VaSBz7ozy\n9BG1tdxqkqK2jcwa5PAf9TuxaVn/VVkiLRO1qWcDlU3KPnq8MskQG1DVMol9IfOHZ7ju3LlzaV6c\nDBcxYcpETSd4rcfp6WlYJtKYqV1XZiff3xEzls1ldeCnfszYH2J5JzJmhp6NGLZKfXx+Pt95H8Wg\n0zGVhaOgchOTnTG50TpWib9FGx0i5j4b+zSnPSJ2e4LYrEqInwjZmKqyeBkoLAZtwiCn+euaFJvh\najQajUaj0bhhNMN1y7gJKV2P92iiHmRzryDyg1IWYJ7LWCrSNijI6ISmX2FuovpX/A8IdF8UXI9Y\ntKicVNbIwZn6u+KLomVY+anROdJAPbOn4Q60LJGmTwxXVPaMQcjqo+eJJVJNXR3ozczu3r2bMlye\nvY3KkPlV0fcYJ9TfhNpwxTZqOv6+ifPz84N2VNZr5qH+SJkfUrbOKSM0f8/Ozg7WjW3bDpzM1aeM\n5lTmr0UMNrWdzg+a51TvCotHAXQ1P7qWpatlofVXw01U2MPM+hL5ZlWc8Vd+WFk4mcin0d9H658f\nT9GztJmEWOuKZWmiBa5bxL179+yDH/ygmT3rtIcPH9qjR4/M7GrnUYdmpkWFT4N2O0VyzwRSAAAg\nAElEQVQvAU8LqyCzEhSzl2eWr05CXQTmQM8ch2mB1/wzE9eEOnxmZjdCZcL5fCkPfZlkeVfqverb\nKjJzBQlctOjTx4qrZo2VmS0rs75EK46uJASqmVFNi/O6vsT8jqbI3EV1M7u6wE/zpTrXZy9neolF\n9/v2oTGYCbJ6XBXOI0XQn1u5Vvhy0vXqehm5DEQmRc2T1sGVqbCyDvmyZqD54MugAleWRmXDhd6/\nWl9ImM9MddUyRGNlVa7omoLMhrTxaOLo6Mhef/11e/31183M7Ju+6ZvCtFvgukXcv3/fXnvttSsv\nsb0vwkaj0Wg0GreHBw8e2IMHD5rh+rkCL7mvKNNMU1TTjRfk9mxnzdiSqtOgltNrGCuNdp7TCNTa\nPhWNZaXhZSaZan0zx1gy+a4Yj4wZ8Kyjvz+Li0T1UqzMFPPX90W0rZ0ci4khjSJqE8ugmyVWLAmZ\nSfyzxM7pdc3fmw9PTk4uy67mw4zh8mWja6Ttb9t2xZwxr3nTGrGsEWu8YmJmusT2UT0qyEIN6Njy\n5VcQS5WxvP4czRF/Tuetfy5iSmkOVBiX1bkqtOzZuqYg1vm6Zcrqqn2r8yNzQs/6NmrbLMxFZc2N\nzJzZpq4978V2mm80Go1Go9G4YaQM1xjjvWb2pWb2z5rZP2lmP21m329mf97MvmPbtut5ejcu4TVS\nCoGg/5OGTk7eE+QI7SX9lW2etAoq7yq9TPOO0pz/E2MXaV5VDdfXLUJUTkqbmAzPSpFGXUWmsVLf\nrhy2JyKWx7d7xDZ6B1fqb//MRBQMlZyTKTDhHhYv8//K2FPtM/q+on5ncV6fjNReJoPG+/n5OZbd\n12Pl95exAr6+/jqN2Wz+0NynMUN+PISModjDFNMcqaRJ66D3g/P5V9vnusxWVMdKyBwdK9m6lbUN\nMU1ab2Jr57zQtYRY6Orapc9lY5XSonEZWSr0vqjPVuUMBa4xxkfN7NPN7H8ysw+b2U+Y2ctm9qqZ\nfaGZ/ftjjK/Ztu1/TXNoLDHG4UdodUHSSZ29MKp5ValQv1jo/foi8C93qo+/7uuoyCbBi5gzst2C\n+j/F3cmc9fXXmy21vPP47t27yxfuRMWcov9TXLNKXCZ6iUcvxRcZg9mL119bxVtSQY0W/et+oilS\nSkjg0qjzZvHHq2mOROUhZ2Iy/UQvB38vtRmNC2qnlTBL7U7l1Lr5MtK5lelmZRaK7qHzqzFRrX/F\nHBoh25GXlTUyga2EpPkbCWb0PDnAR/1AJuwpaGUCl+6A1Xyz/HReZgqnr1vVNHx8fHwQN/C6mxwy\nhus/3bbt++H895vZnx1j3DWzz1zm0Gg0Go1Go/EuRyhwBcKWXn9qZp9420v0LkTkQLrX4VLTWLEE\nlbJk1yNGZOYZae5mebR20thUEyPNk+L0+DwjDZSQaaqkyc+y3blz5zKSuFLb8xlvfvLpeWaCGIxs\nLNC3/ZRmpzpmbUYMF2mFxJoSgxKNrYgxI5NMpD1PULtr+5BzdIXh0nbMTIpRPCjql3m/z//i4uJy\nHPno9r4tsu3qtJZMaDmJbcvMljNNBZlj6bp+k47MphROxrdZxAL59FYMUWQGnPdFbDWtUWOwCT3r\nH70nYwira71+u7PCANI5ahNiauevWjgydnPlND+hG0H2mrAVWs7oPUprlOardchMtBkbfJB2ksgv\nHmP84THGgzHGT40x/t4Y483n535Jmmqj0Wg0Go1G4xKZSfFbzex7zOzXbNv2Y2ZmY4xfamZf+fza\nP3/zxfv5D2IQ9FcR+ZZ4RL5RGSp+ORH75NMg6Pb/vb4OEUujTFFUTrLXrzYmZGUjbYns+qT13L17\n9zIN338aUTxjJjKGL/IVIkZRwyxMeA2U2ozKHmmJxNxUkDG7e1i/CsNFfh96rOe85q2MojJcvl0i\n5lF/za628WS4Mt+5aKzO8+TDVt2skbHQFxcXBz4tOo583SiUCPWjhrPRtpvHujGB2oB811b+XD4N\n9SXyz2g0fZ/u06dPD9iXKMyFx8XFxW5WRe+ba4fmu3dTzl7maII2sfiymD1b33zaxOwpsxiFvpn5\nkkXAHxPjquMy8wkjppTWUP1/1X6ZwPVZ27Z9WE88F7w+PMb4bWmqjTLmwpMtpjp5MrqVBAky/9DA\nXw2UmZa+jDP4ibSibitmPEX0OQz/7Cp9yqMqfGYv5Qk1J9ELPZvUUXwrn4beQ7R5ZjYkU6HW0d8X\nCZrUjtkOWVoQK8qEr4cvCwm6meCh53VueaHp5OTkwKSofauxxLx58fz8PDQ96jjWvnjppZcun52o\n7A5dOZ5PZGYlf0x9FpkUtZx7N52QOYmEVYozSC/C6IXusVIAK3OPnt2r9JpdNQtSWbJy6lhQwXEP\nsjU1Mjln5creSaSUrdbLzFldx0+GrF9ovYwIDN/GlXdoJkL/0Bjj3xtjvEcK854xxleb2d9eptxo\nNBqNRqPRMLOc4fpXzOxrzOy+CF0/ZmbfbmZfctMFe7dgSs8kVZODL5kiojQ1vcyJUqlvzTNjwlam\nNTI7EQtBjvRVpsOfr2gYEVYafwba0j0RaaoUbsFHKNfrBGKQiKXy9SETz+qcomJSzJgufz07R8gY\nLv2fxkfGVugvsVn+3MnJyYH5QxmZCZ03flOF3qvtT2PBn9M5RfWZiMw+2i4+DQK1j2c+iVWK0qVx\nRCbX6EsEVAct06o+mWtBZjrSeUYM8d51iELHVNndiGF7O9dGTSN7J1Bb0JwnBiubZzqXlcml8BFa\n1vlbsYTomPX9SHlFbP0K2S7Fv29mX/38r9FoNBqNRqNxTawizb9uZh+yZwFQzcx+xMy+fdu2N2+6\nYO8WTA0h09DJL2Uicrr1DtBZaADS9FYaVsZQqK8ZMWAZ+6HaOGl+Pq3VuSqqTqOKCjtH5/RZ/aUt\n0hmIFfWaJfm7kKZK6VZRce6dZankV/XN2zsGSHuNmC5iX6pO837sa76eESOtXNnOp0+fHpybTtLq\nHJ2xNZpPxobqsxl7GTEYEysfGf31ZSSGa8V0V1BNI/NnozWK5l6GaK7srWPFj5HK7uHnZObjRu+a\niOGi8mpYjZmeH2f67qAxQOXWDUeUb+ZvSKB6kGVHN1FUkUWa/2oz+zIz+xYz+z+en36vmf13Y4xv\n2bbtD5dzaaTQxU8HZcVMMsY4cGTPFgs9zhxOySk9muR+0kRxYCoOrGqu8M95VIW/CCvTVPZC0uPM\npEiTMVqw6b4snQpW5pyVyZBMzVl6+n/FnE3jXPP05aSxtUfwyhbbTOBajfPMzKhmQS+MaTlVoJov\nkZdffvmy/hTjjZzhMwGWxpTOy2rb+rZYmbVp/NA5EvR8W1UVgj0Cmp/LWg9amzNlR0Fr2XUUheh+\nveYFGq0PpRO9d6LykEuJKooVQduMd5fTnCKzu5aFxgqNvcj0R+89fZ9qXpkiq2vUamxmDNe/bma/\nYtu2Uz05xvjPzOwHzKwFrkaj0Wg0Go0CMoHrwp59sPqH3Plf9vxa421EplnujaliFjuBbttbMX6I\nXSGn/IwtijQ20qZW26+j/FasEz1H2k+FJSJmJnKIrTiUk/ZOH4CO0vCmqAnV3LL2IcaBNFtyblXa\n3OcdlVn/z8Yrad6eTSNzX7T1O5sjxKatxlFlnEVmRo0XNc9NZJtTpmZ9enp6GbNtzlU9N82MZ2dn\n6YaFyFw6r2Xf/ZygNFS7p288+j6IXAMyxlvPXdfEHbHLE6uNBN4tw7OZUdknaJyu5sVqrfNtpixn\nxjRV3ROysUMba8ihfMVoZmOW1suoXB5qYo++PUq/ehyNt6yf9Z5VO2cC179tZn9pjPHY3goD8Zlm\n9n4z+91pqo1Go9FoNBqNS2S7FL9zjPGqmX2+XXWa/95t2w7Fx8Zu3Lt3zz70oQ9d/v/gwQN7+PDh\nwX0V7d0j8r+KnAinRvDKK6+Eaei5Fdvmy0XfSdN0VIMglqii5er9vs1W7RRpVvNZcgz1LBHVscoA\n6jmqN2m2Fb+PitY14dssYiT9ddUsFcR0ZN+LJJ8i38YRY5cxixlorMy89zw7f8mHi9IlpmWyWXfv\n3r304dKAqeRw78tO443CR+h9urGm4oekYyAKDxHl79NRECuhY0a/VFF1hM5COuizZBGgMBwRovFf\nmdP+mVmm1XV/jpD5SlZBG3vm79nZGfpIEZvlGV/1w1Lsmbv+/mwzA/lmke9aNIajMm7bZq+99pq9\n/vrrdnFxYR/72MfCMqe7FLdtuxhjfNLMnj4/9SMtbL19uH//vr3vfe+7ci4z5a3oaH3OP0tCgQ7K\naa7IFvOVKWkOaHLQ9/lF9aHFmUx6GYUftZN3Yo5MHVk8Kv2ll1gm3GSmjui+7MVPTqBk2oue88cT\n9DUBuo8W4kzgIadbReZEvRckGFJ/0y64aPz4uUQCl5oUszT0M09TuJofqFbhSnc/+heW5q9jm8pJ\n5fDC3+olP6HmyEhI8s9TGpnip5h5RZ+/mr9Vp20PGudkPtP/KdbaxGqeV03yqzk386qsEdQHq/cJ\n1du3CeW/yitTNgkXF299Fq5qXlYhi+qhaSsidwUS0ifGGPbo0SN7+PBhumPSLN+l+KvN7E+Y2S82\nsx82s2Fm7x1j/AMz+53btv31NOVGo9FoNBqNhpnlDNefNrPfsW3b/64nxxj/tJl91Mx+1Q2W612H\nzDSwcponjcprvsos+HPk6KsMV8YqaYiDiYzG9fkSI5JpdhndS+wgQesfmV71mLYFq/M2PUOaJZkj\nSetbtfe8x/c7mexWeZCjLZkrsjoqG6BlWjlh+zwyponMBrRNn7b1E2OWsVR6Tp8jrd3PzZWp1+Pk\n5OTg+4r03cYo5pcvu2rjq74jZsJD14GpvWftGaVHZicPnY/E2uo1NWn5ulH+fnzoMa1XxMbqupXN\n0QrL7ctL8bCorbKQPooK6xStv/S8P5f1bQSqD62XnnldzScdg/QuijYoabqaV2QdMsvfQ2PwJiNF\nbJQ2+wVe2Hpe6L9qZr8gTbXRaDQajUajcYmM4fqOMcafN7OP2Vu7FD/DzP41M/vOmy7YuwWZ30Tm\nDzNBTAJJ7uTHoloFafkE0japTJH/gy/TXnYq0rI0j6oP0MqvIEPkG0VaccbiEeOg9/u+zUJRKOvl\n8/Hw/UMaNY0jYpPUwVjvz7RkYgCj3wialu97baeV1lkdbxmuE7rF7Cq7TNHqlekifzHy64rGpv8/\nq1u2SaG6CSMbW8SornzN9FnvZ5jNCw/PDkXzNmqf6FrWnjQuJ6K5kvm96Xin+4jNIStKBZV6rdLL\n1m29ruul1nFvCJ4JYqeIyVY2i4IT+zGq692euZ/tUvw9Y4zfYIef9vmvtm37C8uUGyXMAaYRbPfi\nuvGgsmuzbJSmv5Yt5itHVi8gRE7zGTKhbu8LkKCmOnIwXpluKqa1bTt0GNax4POgRej4+Bh36VCe\nmckhM+NFMX7IrJ21D5kNaSwQ9uxE0ryiGF6R6YjuuQlkH8BWgcrfp6ZHvS8TalbIHL51DERtFc33\nbIdfJGhNZP1Dm1gUNM/ILEdKRCZw0Y7aDNl6FG3AmSBhLcuPBC5arzJhqSqQ67MroTETjFSQ8a4s\ndF+0lkSxz8wOI9yfnJwcpKdmVhLW6D1Na1mE1S7F7zCz70hTaDQajUaj0WikyHYpHpvZv2HPvp/4\nHdu2/RW59ge2bftDPwvl+3mPqd14SVs1koxyV2TO8ytKN3N6pWevwwJkGwPoY74E1TC8Vkpmr6yu\nVXMJsT/UP2SaoL5QZEyUsjVV7ZA2SWR1myAH2sgB3KdDYSFUa8/GoLKcND6yMVU1Ieu8yNgx7TNi\nVTJTVNXMRiCWimJ50TlvUlzNnxUjk22imOd0q32FOdO2I1C4joxpj9gnYnU89NnpcE9zpFpmak9d\njyoWi1WfTazGeTamo/AnWV7ZWrZ6h/i2p7Ukmt8Vi41aDnSd8es+paUmfH+dxqK254rJX76jk2tf\nb2b3zOzvmdl/OZ59Q3HiX05TbTQajUaj0WhcIjMpfv62bb/SzGyM8XVm9l+PMf6smX2Zmb24Y0zD\nzN6S8smnhhzd6XkP2h6rWgBJ9aR9+HMRQxPl5Z/xjIduOc9Ypyqbpto5OeP7+6L6EDvmAz1GPiZV\nbZDgNcrM/6HqG6ZlpnRpO3TWdpFm7dkmZSAr9SGsGK4sf9V2iT3UseLDHYwxLs9p+AHPiJyfnx+0\nbfRFhQoy9pLmigZIzZzrNRo4gUIlKNNDfnHZnJ+g9qexR2vEym9p5S/kz1WZlL2MZRaFnphxXX9f\nxM+06jumc28Pw1VlxjWdjMneGy5F06HQILo2ZP5kVO8ZYFj7giwXGnA3S1fzXlp5kmt3peJn27b9\ndjP7m2b2PWb2C9NUG41Go9FoNBqXyBiu/3OM8YXbtl2GgNi27T8cY/wdM/vIzRft3YVs18tqt4bX\nBiM7tD6n1zIWyj/jy7Rth5/5UN8SAtm8V3mQ35D3j8iYDG0nssOvNFv63E3m7zJBvhN6H/UjaYWV\nHXwrLTJjACppmz3T9oiB9GNv5btG+VMaFYZLGc1M8yc/LNrerceT1Xr69OnlvfObh6enp1eOzZ6x\nTlODroJYN4+ILaEdjjN/76/m88p88Wi3XjZWrxMqwq95kd9Whc2iMabrg+ZLO46ztvf1p3lJbLje\np597yta8FXOV7SKl9PRzUdFaQiCri7/u8818aKN1KcsjGg/+euQbOq9FfaXvKWKuMmaerkdssCIL\nC/HlwflvMLNvSFNtlBHRyvoSoQGVDcCK02F0zjvh+usVQcbXw6dN5o+qScKnFaGyIJHjPZkCyVRI\nZiy9To7NZA7UNCjadNQ+kRmYQIJp1vfU7vSdzGz7Nr0wqwL2dRzQ9wruWYRyNWE8ffrsM7InJyeX\nx1O4evr06eU3EVXgmi/XquDlP5J8fn6ehvfQunqT4tHR0WX+s47a//StNxLCsnALmaBwfHycfl2C\nXoQZopduViYv5Pj6eFOUfn+yAsqLHLYjhcnnpW1RdZ+I0vLn9AsilTpma261DNEcpHccKajZ/CeF\nN3uHRMLSTKvS3pkir7i4OIxH6JHtUswc45+Y2f+9bduDNPVGo9FoNBqNRmpS/BcXz33uGOOvbNv2\ne97mMr2rMINVkpS+R+syYw2QJPHMGXrFgGVYmbPU2XeWw7NeewInEn08kWlJmdk20lA840COnFo3\n0nJ9/mb7o6CTk3v1+czh0+ywzSJmbw/joflWsVejXyEzE6lJnLT7yWCdnZ3ZkydPzOwt1uvOnTto\nUvTfRszYWHW8V7M1mQMJnkFR1lbrlbGxiswMma1NGdupoLQozZVJKGMaVlH3M9P1qs18megcscZa\nLxpnFValOo90Xb3OfNGyzvTMrlpdqqjWkdbEqkmTxtTKJWY+l+VB1gydq76NlZmOkJkUf2v24Bjj\nyMy+L0290Wg0Go1Go5GaFL/czP7bbdsike2zzezfupFSvYtwcnJyJZCg+gB5jSnSAElziJ6NtL5M\ni1INgvKqaGDKcOmv/26V2WHoBco305D3MioRtF6+f7QdV+wk+Wb5PHx+/t6Kr1ukyVV8F6Jgn/SN\nxIxxWIX32KNxR76LEbvin80YO03Pszo6VqfP09nZ2QGbdXp6esXHy4yDlt69exf7YKbr/eNoPaD+\nIUSbNHy+dC4ai35erdgpOhexRat01DdKy0bsC30DLysX+Vdpu/vNQIrsM1xUF1pDV/2515eR2O9q\nH2Rr57wWfRaL6kFzj571eWj7rFjyrP2qc6BSdg0dQyw0bRaIkJkU/wkz+xtjjL9mZn/NzH7SzF42\ns/fbs4Cof9fMviZNvZHi3r179sYbb1x21MOHD+3hw4eX1zOBS7F3YmaLni445OxNzrwZva/pkvnD\nn1u9lGkxzRYLeomuzLY04bxAocdZ3KroBehNqJkJg85VBZpqe6qJVAUAX/aLi4srJuGZB7WPd8Be\nmSM0D19HSp+enaD2PDs7QxP2dG6fiyo5nqvpjwQu3Q3mTYo09mZ9Tk9PD0yK6nxL80zrOLHaCVo1\nWVWEgdV4IvcAGr+0a8zfHwktNLZImCbFgspEu9X8s9lmgMg85segriXax759dO5FgkmEaE3bIzCT\n0h6ZB2n992so7QYmIbm6hun8pnlGih/9ZkoZzdGJ09PTKwLo66+/bq+99pqZmX3sYx+zCJlJ8Y+P\nZwFP/zkz+wIz+5Vm9tNm9qaZfcW2bX8rTLVRwv379+1zP/dzcYt2o9FoNBqNdz4ePHhgb7755vK+\n1cerz83sf37+17gBkOaX3ROZJvxxJb+q6S27b6UlaL5Udq+ZR3kR2xdp9artqgacMYYr6jnLn8yi\nGRMX1a+6mcFDtS8yX9KzlXYnc5LG8yEHYyq7pkdaNoXI8PdkdaiaHvR4tpmGDqBnlEmZx5PhIjPj\niuHyTMr5+fnls8p0+fwj081ENk6U3aD6ZGbLqM88sthtxCqt4tP5+xU03qJwLj5dWpu0LNpOGStI\n5Vutk9E9kTnLP1NhprLzKzYuKlN1naQNF3ROXUauy6SarWOgReXU8mbhV7yjvL9OGyKWZU6vNhqN\nRqPRaDReGCnD1bh5TCk70+yqGg75H2RsQaZdrJBpaiutWDUM8hGqaPCkTWS+G5EPl39W2TGqg97n\nmS1y8F1pPNmzvr5aJmKfyO+EGLbIz4Y0wAmKnr1iVShdatuoHzUI4nXYv6wviGnJwgkQI6R+XeRc\nr2wj+aqYPWPGvENupHkTM1PZTKHpUXBVypcCpGq+UduTv82qHyei8B1+zCgDqPdpIFNKJ0qP2GUt\nM40F+rpGZV4QaN5eZ03OoKyg1tVH3ac5T1j5dVU3rExQGIesHYmB02+L0n0VlldB/oHz986dO8uQ\nPoQWuG4Zc9H2g3G160UnSCZAXGfyR6j6memiq79k5qs6ou4pZyRcVQSuiBb29Vk56VbjEmV1yihq\neiFQzC/aVbgy52gdvNlUd+FpvWkRJWTXybG8utFhtSjPe3RxnvUhEyC9ZL05UncYTgFFnWl1DPg2\n04U+E7hW8bgiYVWf0XFJ5kNFxaSo/T3rlW340H6kslIdaTzpx8MzE1A2FleCq94XQed+ts5EsaCq\na0LFlFuFromVtWnb3tqhTC4Tmq4vr+aRjd9I0N37zvJ5epBiMf+nXdj+Pbfq21X+V+5Z3TDG+E/G\nGL9E/v/UMcYfWqbcaDQajUaj0TCzGsP1G7Zt+9r5z7Ztf3+M8RvN7A/cXLHePZjUvt/iujLLKTyD\nocekQUystOfqdn4PYn+U4VJtu+LcWHHypDJoHaJ7IjOG/hK7smL7yGS21+Trj/V//WZd1ibEUun9\nWWRt+uAtORjr8apdKkzUXmazOj/G4G8P0jkPDdWgv/pxa7NnbTZNitpOkXO+prH6lmLVXEygsUzt\nTnPU36fhXCquAzMdjwpjrnM0a4uoPoSMSSbLgf/VuRKVWX99WSqhCCJGqLr+0foSmbUpPzIN+7JE\nzxKidwJZDiZovVq9E3xZIlcA/aWy+jRoft2U0/zxGOMlSfQVM3spub/RaDQajUajIagwXN9sZn9p\njPHR5///VjP7xpsr0rsLkdN8FVlohLcL2ZZ/tcNXQgNEaZP2qFo2fe+uou2R3wnhRZxUV9pu5mdT\nOa8g5/XM8ZOc+0kjjFgvXzYqI8WR28NKRu13nTFM/U0a9ayjOtoSG6tO5J6JOj09PQgC++TJk4P6\nksOyjmPvB0Y+V3tQ0bxX2njmw5VhxZIRu5KlG4WqWJUhArHH5N8089Zz5FuoeWZsI1kTVixd1u6r\nvqP+pqC/876I7fFpZGWhPtXNBf7ZyL/Utzc5rVM70Tn10yJfrgpDTCzrddfwpcC1bduHxxj/l5n9\nuuen/qNt2/7i6rlGDZHTO0EnkjdBmtVfUJWFtlomouP35Js56b6dwWD3UvEeFdNnZAr0zqe0MKx2\nOFL+fmMFmTCielfadpV/Fr8pwh7BVsuYja3oJeAX0ePj44PFnO6LFm51jJ/pkTkyE7hoDNDHq/ea\nx7L60BxdOQeTcEHjIWszUgRWChOl55+9DrINM6s8opew2VWFkdo9EzQzE2l0nxfYVZDKELkW+PLt\n3ewzxkjXEp17ZFKsOJ9r22p7ZyZx2olIO2+p/ll9yDS8RzGs7lJ808zOtm377jHGp4wxftG2bf9f\nOZdGo9FoNBqNdzGWAtcY4980s99uZv+4mX2OmX26mf0JM/u1N1u0dweiDxtXpWbVelZO8POejK3Z\nqwGSZrdKt8J0KWircKYZrba8e20pqg+ZGch8SnWLPvRaydffT89EjBi1NzmS+nMrkwKxTcTIRP03\n0/AMT5XhrTrjE6uj95BJj5ge/y3Do6OjK9Hkza6aGafTfDTuZh7zm4uZSaRqRovMY5mZJBurupYo\nG1IJzUFjW9ejjLmK0vRpZ2M6ejYLO0BlrphBV2sZsSWrNVKZ1Ag6LrO+jcpHprporJAJkrByh9G2\ny9Z9OncdBtT3qZ6rbKSquveQtYnWZI/KW/132bNvKf7D5xk9NrNPKzzXaDQajUaj0bCaSfHJtm1P\nRfI9MbPrG9MbVxAxXJk256/7+0hjmyApXLWAlUZCIO0oC0Ko56hu5KyasU7ZNmsqJyFjZsgvR8My\naNtpgNBZNtIQvV8XOXCS/8z8JU04GjPzvPowZD4jKyZBnbvnb8WPY9u2NJRFxmTQOXXIjeaR3qfj\nUrVd79tB401DIVBU+YkokG/F6TZjuOjZiMXzxzou1ZfM56GsBm3OoPbJ/LSoTFkAVC3Tyo8mYz/0\n/+zbosTGTaiTN/UdrWsVP0NtY2IACcQGZ4wZQdshY+p03JMvlUcU7mOywCt20o9BnT+0DpLPXNaP\nWfl1PZjQ/LOxGr23Vv1REbjujzG+1sxeGWP8ejP7nWb2PxaeaxQwBaBsItEAnaA4I7RrbCIyEXhU\nJzKVmcobCR7+PjN2mvSg+lXjq6wWgcoHoGkR0LbNzLZ6TO1DpsSVuSBDRrNTWsexVH4AACAASURB\nVCthn4S1aIdkhMzhNMuTzFPUP5S2trE60lbMTjOf+Yz+anoau4za0QuLWk6av5lJsQptHxLWvHBJ\nz3tE5YmUnWwskBM35bVnTfKoKrI0jjPhV8tGwrw3v1O70yeg9BlaIzKBk9oxqmtkuqZy6rE30+k5\nrcPqHTOfIWVQ868IcC+y2381xirmSE0nQsWk+DVm9pNm9n1m9jvM7C9YBz1tNBqNRqPRKKMSFuJi\njPHnzOzPbdv2kz8LZXpXwWvnVS1Fj32cEdWQvfaqVHlGKeszE0qZZpoGOS+Sk/lKwyTGJ9M2fZwV\nxfHxcboNPjNBkgmBzE6RiSfTRklDJlQcl6O+8O1BDsGU14uA4ulkzB2VpWI2qpSXzFRapsycTiYG\nDQ9Bc9QzFzqX56/G7/JpUOygi4uLg3PEJlU1/D3tmK0DlTlFY4GeWa0HGdOvbUxlyMxP0dyP2pYY\n3cjtwNdx3mtmV1gbYsc8o6rp0Nqk5czGvN7nxxTdm70nonaiuUzzzM8LCt1wcnKCTH/GDEd1NuN3\nIZnVs2eJLY+eufJ8dGE8wx8cY/xdM3toZg/HGD85xvgP0hQbjUaj0Wg0GleQMVxfZc92J/5T27Z9\n0sxsjPE+M/vIGOOrtm37Yz8bBfz5jHv37tkbb7xhZs8k40ePHtnjx4/TZ8jm7n2XMombmB69r8q4\nrMpYYbg0v8j50ox9alQrzPxdlKmo+IZldYr+X6WTafDkm6TXvGaurB9pjD5d8gdahbSofKNRy+KP\n5/+Z7w+xSd4HpMpARsj8/FSLJWbEs5fKME0tPNoGTr45ka/M0dHRgc+gMlz6S3OK0l2xtfOXmCt/\nvz+e//t2JG1fr5FvY+YLmLFZ0bnqGkbrBs09f0xsm/5SyBNCxqAQo6l5+nak0A4rhkuvZf6uxCJm\nbKeW04OsBMRwRW1MrLG3gFB9dLMLlSljhGkO0FpxfHxsr776qr322mtmZvYN3/ANYZqZwPUVZvbr\nt237u/PEtm0/OMb4cjP7LjNrgesFcf/+/ctOIpOeRzRA1QHYw08QXdQyWnjl5J7R9ithTRfrqlBX\nWZx1saKyVxy7Vy/5VXmzl3z2Uox2uOiLWdNflTMTuEhY88fzf2rHbEegnluN5Vk+L9z4/+d9GWhe\nUMytCmiHlgrsXvCK0s6cwVW4nOnM+09PT/GD1iSERUqHIuof+lKBN8tTP5Kgl42jlcBFaWSIhEoP\nMn3SfKC89SWfzV+dn2TOqgi/83ktJ7l+6G7czI1B06H6kIDi89qz/u+5FmGWSQUm7TO/yzaao34s\naZtVhNA95df15fHjx0uyxCx3mr+jwtbE9syP606pRI1Go9FoNBqNlOF6es1rjR2YWliFlq1iRZHT\nOZL0M8dmcu5cgRxDK5SuajhZuquwB3QuC0+wYuJIMydG0WvIykxoWUjzjajsqI9JsyWGh9gF39/U\n5krRU3k1PTIhUH0idpdCJ5C5hBxY9zCVGcOiY5DYAF8W1bxnjK6IQZ73z3pquAkfeoI+nk0MF80p\n/T+LLVR1I6A+I1ZSz1Xi5+n1yFl+/p+ZhjV936cRK5mtYVWG2IPmHq311Wj+xOrofSumm85F66+2\nJ70TXmRjjaZLVgpfJh3nylZl5shsDSNUN95UGeQImcD1q8YY/xDODzN7uZxDo9FoNBqNxrscocC1\nbVtNNGy8EKaNuepnQtI0+SF5u3Xm0xQFn8yYjIp/TnRMdVUtLXPAJm2OtM1My1dkflMTkc+Kbz9i\nCNQnYT57dnZ20D+r+mTl1XISMrbTa47Rc5mTbsQeUn4VRnPmFYXyqPgHVjeHqJbt/US0LOr7k33v\n7vT09IDNIbaAGC6tg/fLPDs7u8x3MmdadmUKMh8lYqImjo+PDyLnZ47VK2SsFzGqyv6stt/T/PZ9\nS+UmHyFljnQsRGvyihnTevm+UGd0Wk+z/tHrq/eE9zdUeHZwlT69J66zoYrmQOW9d3FxcfANUs2f\n/A0raw5ZCWiDls5fbTtKd8WUVSLNN24QkbNr1PH+3NHRWx/VvXv37uXzkSlKB493KJ3pzXOe5lZB\nTs1kFcdUWhA1Py035Zs5t1eEsNUOLBIqq+aP1aLry3Tnzp2DclUXUHKWJaF6ZVqb52csIB0XmXkj\nMgFWBKnI5BEtuqs+I2FoQp1lV8pBxSSgZiyFf5bG73xef/XFnr0U1Yw45znNM/+reWlZtEzUPrTz\nywsjZNbNXtCR+dsjGluZa0N1LFbdDUjx8G2yUjAUpGT6tSkSkn3+lF9UnwmtT6ZcUfq0hpLiuxIS\nJ/yYiYQ2+gwVzTN6t01UTJ6RQknXM3Mt3R/hxSMcNhqNRqPRaDRSNMN1y/CaDUndqkEQWzDp1iwk\nAYHy1DRIU53IWB2iilfa9gQ5ZUcaHmnjE97BucpkqIM81Tuj0qP+qZpfPTJmL2oTz1JFZjViEQnE\ngvh7tb8rbazInF8ptAOlv9rUEJW7ci5jVygvNYkTC0HMEI0zHwJCTYrEcJEjPYHyoutkeiMzbMX8\nHrHbE9lciVitqjO0B7FoalIkVoPM2lF5Z3oRlCWK0t+DaCzSukH5RpYQn150jvIiREy2z1e/RZpZ\nEHzd/HHVqb9iOaB3BzG/01qQ5lcqVaPRaDQajUbj2miG65YxtcUsgi+xNGSnp6CC5F+1F5nzsbIq\npLlEfjj+f9UwKuyLOi16Ruri4uLAKb3KbkTa+IpZmr8ZY6VlojKTT8I8poCHPv+VHxilm23Xj9qn\n4s9Q9efQ/LzGeHZ2dqA1Zv45EWgcrbRmGr8+bxrvpPFn80LHqt7jnebp+4p6H/koZeyyHpMmn/lG\nRT6alIcHteeK+fCs28pfikBjf+Lk5CTcOKTPUv6aboWRoXV9jMMNOORDS2WrbpTR8mle2VxfpenL\nsmLJiN2lddU/v1rX9Dn/bOaHS+FKIibQWw4Ue9jWFrhuGdGiEwkUZCbJJpKHTniabJkpyufh65At\n+npdBY9MEMpMqVoP78gf7QTy6Wt6mek1WmD9uUyIUEQvnczUURW0snOZkE6LLy10KlSu2sVfW8GP\nI30RUVpVQWsvIlOLF3hUGNS2mkJSNi5m+x8fHx8oB3qsAhcJOX78RnNvIhM6Vy+2iglMd/eRML9K\nj9a3ipKo9db5Q2ucH/vRCzMSRiKzZDYHVmY5vyZHu4EpPR/nT+9djQGfXnWtW6EiJFXXZoX2SUWB\nX+WfjVGtP+1gvs5a1ybFRqPRaDQajRtGM1zvEHgNQ80FGVQyj47N1jQyOehn+WcMjtZH78+c7zNq\nm1i8LO/z8/NL9iGrA7E1xHRFcYSy9CbIXGzGbA6Z+TLzKrEBmZavaWXO8NoWZB7LNLuVZplpgGSa\nqGjUxG5UTYb+eP5PDJdPm6KWKxNGLINnpKoM17ZxWAp/PzFcythNRKzFyiwUIfoqgRmvJRcXF8jM\nZBtWVnWksZeFmNHf7Ht7NM98+sRIRaybxyo9rQMxviuTbHRNvz1Iz1XMfZmJOIKuPZmrC7F4NG9W\nzFZlEwmN34wdXDFxYX7lO98FGGMcjTH++hjj25///6ljjO8aYzwcY/zFMcYvDp77wjHGgzHGozHG\nV8v5j44x7o0x/pcxxmf+bNWj0Wg0Go3GOwvNcF3F7zWzj5vZP/b8/68xs+/etu2PPBekfv/zc5cY\nYxyZ2deZ2a81s79jZt87xvi2bdseyG2pCKxaVyYtRxpuRRMhDY/s0RSskDSNClOheUTa115mImNu\n/D1mV1mIrKykBWUOrCsGgNpAr2UbEYjhyvJawadB6UbpZdvLfbmj9IjRrCLz4cpYqj0MF+Xlx4X6\nJmk/zfGlkbArWrvOLc/0kOa/Cr2RsT/VsaJlz1gTqof+zmPvW6n10U0AlL6uM74vtF1XDDaVj/wi\nKRyGR8Z0KyNFiJi6+euZ/mwt13PKUlF6WVloHdK+o7XOl3NVR6pDNlaiMUhjxftD+nyoHKsyUR1X\nIXgyNtSjBa7nGGO818x+o5n9x2b2+56f/pCZ3Xt+/I1m9pfNCVxm9vlm9njbth96ns63PH/ugZn9\nAzN7YmY/ZWY4k6d5p/oiIlOUP5fR1tHnUrLdatWJrOl54YF25yhol4iCBvKenYj6IqJFtbr46f1Z\nObOFSV+eWk4yIdCzPv+95p8VZnoaEZ/KSXllXyDI+kfv0wW0YhrxxxGie8gMPEEKiD6X7TDMHGxX\nL3gSuHxa2heZgET1pvt0XNJL2W8aUJBAk5lSVVBYCU2ktGWCSfRy9/freCPzZlbHLF3FSrD35aUN\nRVk76o5RHUu+D+g9oWuify5SrKpCRjZvNT1//SaVtyqy8U3lqJyfaIHrLfwxM/t3zUzNhu/Ztu3H\nzcy2bfuxMcanwXOfbmZ/W/7/YXsmhNm2bV/1/NxvfvuL22g0Go1G4+cKWuAyszHGB83sx7dt+5tj\njF+T3Hp92iDAZLcyp70qha73+PQydiHa0lxxJNX7ic1Sx8aMLdnL1pAmrWlMU49u5/UMCpk6NL2V\nmTfT3qj8ZPah9tPnIgajyjauykRae4bqZgrNgz4oTf2dOdyv2Cxqz2jcROkqyIRM4zcDsUS+PyO2\nlVgLn++KTcrKFJlPyXyXhaPIWFhN3zs9R0ygryOxMLpe0vihOaLp+TLT5pDM3SI6R/DzltqYmJ7z\n83Oc+8Q2ZibprEzat7RpitouM+uuQgpNZJunVmtp9T02sW1bqVyV96qWb48ZUdEC1zN8gZn9S2OM\n32hmr5jZLxpj/Bkz+7Exxnu2bfvxMcYvNbOfgGd/xMzUIf69z88tce/ePftNv+k34WL+8OFDe/jw\n4XXq0mg0Go1G44bwgQ98wF599VUzOxS4vv7rvz58rgUuM9u27WvN7GvNzMYY98zs39m27SvGGH/E\nzH6LmX3YzL7SzL4NHv9eM3v/GOOXm9mPmtmXmtmXVfK9f/++ve9977Pj4+OU3dgTEmEickhdsTUT\n6tegbFXml7NiHohVqTij+3Si/Iidy/y19Djzf6gwJRFUQ6f0tN7k27HaKLEH5NuiyDYf0H3kR6jX\n/Lm9yBi+7Jn5Sz5hHlRHcsKNnKIr/myR70tWdvKXIja4wmat5hT59GS+YNk1mp/KpOg1X/bq2lRl\nxikcRlSPCQroTOWojOlqfegclUPHRTaeIvab+jsqK/XjipXMQkRoehmzl71DKqB3jH9e1/fKvCQ8\nevTIHj16dFC+1brcAleOP2xm3zrG+G1m9kNm9iVmZmOMX2Zmf3Lbtje2bTsfY/xuM/suexZm409t\n2/ZmNYMZT4Qo1WwwrExBZBrw6ft7ffoUH8jnT+lomfSlQ0KaFyqjhTMznfi2W72I9Hla9EnwqWxI\nIPOLHmufZAI29Xf18xG+PpRXlG7FrBst5vQCrAoovt7ZS6/6EiNhbSVwZkLoHgHX51fd0aW/Gn/L\nl0/LlNWNzlWFMP3NBC7/nAo5Ohequx89dBzRrsKq8Kl5VeuzR2Dyz0bP0IYQMt9FJj3/7GpsUX2o\nX7L1hcZCFVlbVOtDoPVFXWkqChqNmWjtqyjNFbTA5bBt230zu//8+KfM7NfBPT9qZm/I/99pZq/9\nbJWx0Wg0Go3Gzy20wHXLIHOiWSyZU5RkDzXneGdVRYXR0OuqdWXp0bOkuaxi1yj2Ur4TpJFk26Jp\nw4EiK29k7qx8pzFi27KNARUNeNbJjJkR1bIniHKndBW+nBlL5dPzzCeZzon90nMr5sYjY7i03RUZ\n40tml8xZV+uaxRjSc9FY8OlmDv/ZeFvVJ2vbFetF5czYWC3vPKffrvTrX8QQUxkyFm+1MYj+j6D1\no7mn12aas47UZ3tA87bCtOv9xBrvXYfJ6kHzNusfxYpx3rsORM/rMW0QiMbqKt+ONN9oNBqNRqNx\nw2iG65ZRcb4k9oG0H5XIpw8I+XDRs6Q5kJ8RaWceKv1n2vFKi1NtpWKT13R9Gi8C0noifyDaXp6x\nJSvGjLZrUz6+fJmWr342mXZGmj9pe1HfUluRlh358anvnOZJzIjmG9U7CjBcYTAibTzr76x9Mv+m\nld9d5v+0mmfEOGgQVpovxOz59DJH6D2sSBY2hFivbP0gNili4jwrmfke6X0r5ozKntWH8qP06Djr\nH4J+cSJbf6trr6aloWD014wZrlXoBr9eatmzqPxVPy26vrKOVHxOPVrgumVMOjkz0dE5mlAUO2f+\n6idu/OQ+Ojqy09NTM8ujfGemEQXtcKR8Vw6KWq+V86Mvbzapsujh1U9q0PXoWY0FNu+rOMGrqWGm\nvXJu9ePo/Pz8Mt9prjg7OztYrM7Pzw92dtI4IiFMF84qpa8m70jgofbUOtLY0nKshPhK+SZIGCGB\nkGKN+XLpL8VRopdvFCtPy1KpFz2zusenrZ85IkG8EveI5rMK3yR8Zu2j+ZE5tKp40f0klGRr4WqM\nVU2z9BF0au9MoCDlhaLZrxS5iYqZnMZq9IUTam/aHU95zPUsU+xoHfZpRecyxU+R9YVHmxQbjUaj\n0Wg0bhjNcN0yIu1+Ja1ncVBW3ykjM40yHVFZiPqOmAQPYkH0WWLqNI8KVmYAgtfwyBlSGSmitLXc\npA17zXwMjpSdacbESmq+HqoJk9mHHLAz5+3MBEZ1VFAfE13vzRDR/aQpexZWzYfEaJK52tdd0yNo\nO+r8yZhcYm99uxNDQKztio2l+uj92XyhOa/5ZRtBslAFEbPl71P2NAspkeURmUg9IguDXvfp72ES\no3KuWNsJXZtoPvpyksmVykwMV7Ve2Xqp+c+5rOs/5U8uL9laEn1c27etzpvq+4FcAqL3tK/Pynze\nDFej0Wg0Go3GDaMZrlvGlOhp+3uV6VGNbnXvTNss12ZUU1eNg/y6Mrs/5bGyeVf8gVbnsnSzcmqY\nDt2OrpravI+0HvK78L4G5KxKvmsZlKHIHHjJB6fqLKsaNY0tvY98yyrjgnxLfD4KLdO8rj5FGVbj\nQ6+Rr4xHpIH7Z3Qu+/aO0ictP9P4V+eye6qsxouwOhnLkDmqR5sQiN3wY7U6p/S+zB+oks7qWcor\nSp/YHx8QVxkhWsO0LBSlPtp8sIfpytYX/aW28McrFnW1blUYrkqYE4Wmoew7vZ/bh6vRaDQajUbj\nltEM1y0jYgfMDrWNiBny21MzzWXbtvBalK6C/JH2BsOj9Glrbaa9r7TITKOm8mr+fiszMVxk19e+\nJN8KrVclHMhK8/bQdJVp82zJyclJeTt4pqFHZdC89JjCN0Q+Qr4s1fy1Xtnusqwdq74yBGJfonGR\npVFFZVxE/jt07MtArPbbjRWrku2E1Gc9g0OMhx6TnyMxR1nIiFWb0DpEYUAy0NpIDPZk0pVhoh2J\ntLuY3hM+z6i+1H/ZOf0/Y6So7JFfp7+m9+zxqTQ77I8xDj9zp/fsef+1wPUOgR9kZofbY824w70D\nNt2fLRb0QtJ75/XIIbgyuXzePr/VxKR8KxS1gpzCfV7qID8XsPm9Sy0bCVxRvhXQy2EV42fl0K11\n0vrQgkwbLch8V1181dSg5fD9rebAyqJPIUIuLi6uRCGPQB8o1/yysCpkOqc+IAHX10XTjUyG2Vwh\nxWolFGXpRYKwL5PmnzmZV/P3eUbIQiFkc1DDTGheFad+Gr8EGqPZC53CgGxbzWme0iOFRRVEH5JG\n89BxntWHrlfN23Q9W0PI9Bit9dGcispHea6UDZ9udaxGaJNio9FoNBqNxg2jGa53CIjyJ2fRicx0\noufIJFKhQFU7XJn4vFZMGsSK+SGKvMqWEAtTCYehmpMyPl47jBzkyUxEWpE/Rw7oGpiWtkiT1ucR\naY60+SEz32UM14rRzPqMoH1AWiyFMMnGI4Wv8PXT+2ZZ/bMrM8X8JTaSAt1SO/rntGx0nc6RuSsr\ns/7vmRYy55i9NV51jPr8MwaUGIqoPhVE47xiBqa+XbH+xGxSmTJrApk5tbwZY0guJ7TWZ2tYlI4v\nS8Z+RXVTC8i8J9sIoXlnVgq6bzVW/H2rQNGVddUsd3fIxspBOunVRqPRaDQajcYLoxmuW8S9e/fs\njTfeMLNnUvInPvEJe/z48eX1zL5NTIJqp6ptmLFWXtX6JkjzjpzdibHLtJTMaTLyc4m0HfLzUR+l\nzPFd2zNz5CcN7/j4+NKXaH4qieqt7MtkDbR8+utZNGKVMmYv0tjmfer7NMeMMh+Zj1KGiOGq+PFl\nvmuRPwX5N/k0IkaOmBGfr7I0GXMzxjiYe1Rv0opXjFjFtzFiuHw/np+fHwSa9WWe17K5njFcGdMT\nPePLS2xs5IOTzdEVw55tIolYZj1HFgGCMkf6CS2qwzzW8TTvvXPnzkH5ss0n1I+ZFYDWS3omWiMr\nm1P2MFwZW63nfH4rhpjS8M9GG4B0HXj11Vft1VdftW3b7KMf/WiYfgtct4j79+/b533e511ZTFdm\nMVp057n58qQFKXP807xWDr9kEtm7405R+SgpmbEiswLlG9XBTxpfn6zNVKjVc74d6YV+cfHWx8Xn\n7+npKb6U/QJLXwLIXlzR/bOOKvD5F4D2GbUxLUTanzTOMnNk9H9UR5or2mcUud63mSoxVO/MrBIJ\nv35hjwQzTUtBL+/ISdmXM4p3RGYsnyYpb/QS82lruv7Z7Dl6tqJQUrtHDvLZuuHT8OUjU5kvp9bB\nK0faF6t1fSL6yLO/rvONxtJep/ZIgPbHmUCjeWdrkj5bUXaonCt4YVXLntVnpUxG4+fx48f28OHD\nZbnapNhoNBqNRqNxw2iG6x2ASNvLtA3V3Ogr6J7O1+3/HqSdEbuxV7vwxxljp6wWbQKg9LK0yCTj\n2aeIEapoh8oSaVv7viDHc2K4lMLX8k1my2t9VM4oLy3zTJ/iypBmmZmxyAxMmzkyEyFhxYIRu+DZ\nCC2LMhS+jfX+LJq+zgcywWl9KGJ+xQyraWZzL2OfIqYpMwVlJnZNJ8vjOiAmLhtvmdmP2KfMYTpK\nhzasVExRFGZEoeZ6mptUx0oIHrKOaNoZK6fHlY0LUV9nFo6MeY0YrqxsGfutxxkzT+XO7lM2eLVu\nreZDM1yNRqPRaDQaN4xmuG4ZkTPgypZNz1Rt1BRugnxFKo6KkXaTaU5a3orD50qTj/xj9Jg0Riqn\nBnXMHD/JmZeCeOqWb3VIJc3O+4pk2qkydnoPOTH77f+R83M2zjJ/uqOjowPfEnKaJyaXmFTVJv2Y\n1rGasafEEhGjEDHK5POk9YhA+VZBcy9jdagsNC6iPIgt8ekpI5Olof9nzHTmy6T363iqMlsEyoOY\nNfrOYOQ7tmICq/2+8oHM/M1oQ5HWx495bW9fV0LELmd1oP7O2EFiuKo+wVRWemdGzPTMy/sQU9mp\n/JqulmPV9y1wvUOwh9ql5/RZfdllL2UCmSozCnxF8a7OkyCVCUYrE5gvX0Zpm3H0+Szqs8JPVi2T\nOqUTXU6bFGa+tPmhsghF5fUvbxIoonR8P0cv7JVgMFFZ5HXcZbGDaEEkQYrKRi+iTEilspPC4PPJ\nzkUgIfA6prtVObyAENU1m7dkSq6UNRoHmTsBITM/k6J2fn4eClJR+Ujop3LsFUw0XV9v2pQzz+uv\n5pt9mYTGeaZk7RlvWTtm45eEwJXCoPAuA1SXi4u3vkKRKeZZf/sy++M9bdUmxUaj0Wg0Go0bRjNc\n7wAQPakgSVw1B6/ZZBqCajWZCVIZIdW+/LOR02YVVfOQv0YsBGkaulmgqoFm9HFGPZMzNTmyb9t2\n8O2/s7OzNO5YhWlSR3rNKzP7+LooyCzo2yBK7zoasi+Lprs3YrSmQw7EE9o//jk9JnMFhZTQNluZ\nV2cdtSxUBrOrfUtzLzN3rfqC1gs1g5M525vA9kLToLJG7IO/Ruey+lKMQjIfUmgDKm9matfj2cYn\nJydobvPtqXWONjjMX98+upYQk51ZBgj0ftjzTHSNrC1k4aA1bN6rvwQNm+HTINeKqOxkhr7O+64Z\nrkaj0Wg0Go0bRjNctwzy7zG7asuObPLz+cwHoaIp++P5HGkG2VZlKjv51FA5VywEPTdDKlQ0ZdXE\nK0ygXo8YLs9kRN9D1DLP+7Ogk5F2q/9TObSOma9BVUuNmISsr7JzOqZ0XFTY3azMpG1mbajXSfOv\njgtii5W5IYar6vdB7IovF43piA3w6SozsmqLSjgXfS7zm9Lnr8u8av76P62hxObTpqGJvRsXMrZR\nQc7ZK0vE6huKHuoDSr6pe3xeCZTnatNUNi5X74SV/2R2Ttubwuf4dImBpHQzC0eF8WqB65axGnR6\nTC8WcrCldFbOfhWhZUUtr5wNK4N2D6LJTsJIlGdloVnRx5qeX+hWQokKS9UXfoQxDmOy+fLNX6Lj\nM9O0mi39OcV8JoqATeYp32aREuLrqOWlF3BlY0f0cszmGe0iJXNwZY7QnFoJkNoX3iF4j8knWy9I\n2dD8vek6U2JIWNRnqsInPTuhiufqhU1C/2xHGvOZoFFt75VbhsfJycnSdBpdi9qiMqZIaSMFS+dA\nVUDy12j9XQmGmVJK77NobfDQjUr03qPYej49ysujTYqNRqPRaDQaN4xmuG4ZntZe0ZIrJsbfF2mc\nmqcyHisWyz9LVDGVI9KUPZYagmjWETtF5hKioCPsNf9kICfQiAWpmGHJOVvTrZQ56rPog+c+vczU\nof97TZG+E6f9QtvWPZQtIQdjZS+qLETFPKXjTcuSObITrjsuI20/629ql4zV0utaTppfvm9Xa4nP\nqzoXI3Z5ntMxu4fdU9B8jJgbs3UoFQI56Gd9Ryz9CrSWZKxTlraOrYiR9+nqJqYsrxVLV2HidAxm\nGxdW88KnS88Scxa58jTD1Wg0Go1Go3HLaIbrlnFdfx2CMgNe65hQhoC0hcyhksqrGq2m4aOrU1BQ\nX4YIK18tsrl7rPywlC15O/vEzA4YGTOuN5WfGBFNc5VupOFl/bzaZp0xGKptZg7+9M3Dle/W/M00\ny9V42puHd8L191UDAEehJ6qMmB6TPxAxZnvT1XGRtZOm7X3IVuFXVmWhBFJxLAAAIABJREFUaxnD\nlTF3mV+qP6ZwKtE8qLJpEXNG6+/qebN4Xmb+WpmvYJYXpUtjVcNs6BiIfPxW+VMYEmXk6R2SRd2v\nMr/ZnNH+nnldXFxc2SRVRQtct4xKJOnrLGBR9F0ycfln5y+Zlny5zQ6diOnTNbrbqWruyspZFVSy\nHXUrCnr1cr7ui4WepXxpBx85nNLClEGfpZdCZu6KXqi0M48i5pNwTjtfqczzd6/ARYpIJLD69GiH\npV6j/Obc08V5z8ubBClflvnr2zOKY5TNZUovM/1VhcRMkIrag+YrnfP9SGXStsiEq2jDik+vagal\n5/WYYmRVhIVM8fXwfaB5VIV9Kg+Z8ai/q5sksvm6Ehbn+rJ3A8PKFFgx9Y7BX2JZxaVrk2Kj0Wg0\nGo3GDaMZrltGxMBE2kc1TpXHyszgtbc9VGzGZk3alWhhor4pRhVpH1RGCpFB7IbSwxkzklH+K3Zs\nZYb1x6Q907fUNP3MmTnT+tQhmKh50lSJSdB29AzXyclJynCpmdWbXFehJypmgIjtJCYjM33SJg19\njliQeY6ciCmWFjEZK7Ouz4v6TPspMmnqMc1HxYoZ8GUiRO0QnVtdJ7aRrq9MWvRcNjc9onpn7OVE\nNZSIGX/7ldgsaueM7df0Zp7ERiuT6p/JLAIR81phuHy5zJ7NAR/bLAppkdXXjylyfSF2UPPV+7M1\n26wFrlvFvXv37Iu+6IvM7FnnPXjwwB48eHDLpWo0Go1Go1HFBz7wAfvABz5g27bZRz7ykfC+Frhu\nEffv37fP+IzPuOJTM7Fy3iMQw+IlePI7qYYT0OuZQ6OyXj7UgN4XlT1jlnxdtI5ajoqWFNWvwvZp\nO5KfzUo7rmjPEYOR3e/rqNuXMwZHzxHTNEHjR8fv/FUNlECBJtUH0KdLGnXWJqqhU4gU72eVpePL\noteI4akwDrS5QH2+MmS+ipo2lTNjA7XsCppnEUujjsQZy7JiQTImgfKnsR+NVao/+bxG/mzEmmiZ\nIkY+KvuKbaW0qE31vorVI/NbojFmdhgcedu2K1YMf7/mtZqvFVDfktM8hc+hueoRnVux6o8fP7aH\nDx8u1+kWuG4ZFMdHzyto0d+27eAFqWYaorTJYZomXMVcsJoo2cdYyQQWxU/yVLYKiX63CL1YfX0j\nnJ+fpx/Fvo6J4rrxoNSZl9q9Ym6jZ0l4ifLwdVjFnlJHVm9SJFOhjkcS8LKXyUpYJUHLmwuiMUEv\nyqpJxr94zs/PU2G/qmD4skUvsWzHXvYir+7OpfvmHFR3ApqH+sL27bQyp+mx7wsSuKJ0SOAhF4SV\neUhBa6Pu1tP7aEz79W31ma5Mocw2aJjx3KsqdKTcat9HeWoee9dBUhi07LQOrdLz5fWCpL8/ek/7\n41U7ttN8o9FoNBqNxg2jGa5bhpec6SOnEyTpq9aRmUdW5qQJ1WC8KUEdfDPNOkrXb+OlsqgDNrUB\n1dGbM7Jt+ArSmFdmFf0/c/ylOmbakWq5kXYblX3FoBGTUHF0HWMcjEdluIip1GezsVJBNMbI0bXC\nJPjy6W+Ub6aNk2ZLzFoWaoBY6wh+nu8xOa/mZgVUR39NGWLaRKOgtcxfW20g0Pz3jjN1e6BYUpq2\nQsuUhUdQVjQqs09/tb4Sw1Wtd2ZFycZ51u8RE5iFnaG0qZxkds/WOl1zVq4CM63INK6INldVTZJX\n0l+WqtFoNBqNRqPxQmiG6x0AshFHtm/SzCmgXRZYkOzRVbbG56n50jZavd9rKdG5igZG7JTa5iuO\n9KSlvIgfywoZq0L9acZfCqiksaqjZ18iTbPiO6HP6+/p6emVMpOvTOaTtdIgqQ9Wfl1ZHa+DStvS\nfZk/kt5Pc3/l8J/5ymhekVN4hMy/SetMDNcErRGRv6l/lpggYg+r9aCwHVovX66MVSZGd+U8Tv5X\n0WYnX86sbVfQcvp3R5UtI4vAPHfnzp0SC75i7/U9UF07q0Fl5zX/ronW9cw6sWf9aIHrlhGZm3Ri\nZk6B0UCOnCEjZ7+9L53ILBaVPTLTUb29GSsa5D5trbPfHUnR71eLS3VCVc2rq8ViokL1r8yH+r9f\nTCOhgPqq4piq6ekLa/ajbuqYaZOjLZWT6ufP0+7dzNFY6+OP/X0+H71/ZfbJ0iNFQPuEyuxfYtUX\nVjY+9fpKwFWhxPf3amx5qLC4+qxWJqDQernqezKLkYJGit8sb7YTtGJG9MdRvfTcqm1XylNWHp9e\ntOZmc+nOnTtmdrV9VqY9n15krq3u3vSgtaEKHe9ZPaJ1BctzrZI0Go1Go9FoNMpohuuWsW0bRpuu\nUruqEWSaIjlZZmanSCP07JNqgqrhEQuiZZ7306aBjFL2aeh9es900H/y5MnBfVqm6zgMU1p6zefh\nae6VFqT1zsaB16gjE59nlXR8zHNRxHPqC6ojmZi86UvzJYdqiqLt66rsrV7L2jRjPnza+r8/zrDq\np2x+E+PimQcyN+qxsl57y0xjJSunMsg+JEvEUk3sNcOs0tP6k3mXwtL4Z80OY88p0z5BrhCrslfG\nJZ3Tb5ZWTLmzzFG6M83ovmxtykI7UOgLDQlDYTs0rcwSsjLfESvpLRvkQrNipLQPzJ69SzL3mj1o\nhqvRaDQajUbjhtEM1y0j00r8OdXiVNIm+7Y/p9pN5mSeOWNGWnYVXmtXDVI1mArr5Y8VY4xLzXtq\nWqr9TERbmrP0V/Um/4NMs8w04Cx/7Ue9RlokOUdTZG0/tiKfQfL7mA7yylbNtletk/xMfL5ZEMJI\nwyT2csVszf8rPk8rViPr02xeEuNM/lXEGut1CiiaMZ+UX8a6+ft9IFNKn5AxXFG9Mx8zbcdsbPn7\nFRQomsY+bezRfDIne80/87XSjSbekXvld0dMpZbFf8mA+phYPK2HZwx1Tmj9/TsjGr8EzyxGc9TP\n1yiUx3U2OPn8K+9nLV+EFrjeIcjMfOSMqedol6KHf5np/bqAqvmp8nKIkAlw2YuDJhctcOTASW2i\n6fuynJ2dXUtwjMpJL4fIJEP12WN+IEdfuk+hY8B/DkMXSX+/PyYBITMX6CKdLVL+BZOZMjwyoUXv\nyYSh7Jy+gFZ18P0StaMvO71Qs7lycXFxYNKjPFYvgZV5k/KtjPNqnCdaI2gXHLXJalxk961cFnxb\nqDtFZob2eUb5TpAJmQTIyMTmy6ljInIByMoyn/PXjo+PURnK+idTNrL8o/Si9ZTSmucih/uV8p7t\nxtbjqgJt1ibFRqPRaDQajRtHM1zvAFQpT9U6VppVxo75fJUtqVKnlD85P5PpqmoGyKh5fdbnpWms\ntGdiiXw5fb6V++h+0h4zRjNLL2KVSIvzDsHqBKr3VUx4lIeON4ptpPDsx8rBujoW6HgvqEzKBFIZ\nqM9oC3nFbFkpm4LGgJa/qsH757R8xDBpWIiMUYw2yuhz/pjM39kY0HMUU8qPNx2/2k9UPgqJMn8r\n60XE7mRr5zQpamgFYki1j7M2pX6hemTzS0Fj2zuqr+YjMdfqZkLO8JllpWLZ0etV02Z1rdljsmyG\nq9FoNBqNRuOG0QzXzyHQNmdieggVDWHm4bHSLMmfw7NdUdl9GWibcbRFOqqbakSZL0jUHisfs+ia\nsknUPhlU8676hVD+FT8F1d7pvqh8lTy03YmtmPdmTt6aVqYhZyyMv+7zzxiKiM3K2mjVflGbVecv\nQR2RiUF+ESZDQexl5KNJbRwxgjRHfR0ipimrt+blGf4oECalRxtgfLq+Xqv8T05OUh+uCVoHtQzK\nWvv0jo6O0LfPb1aiemTQvsjWTWK81deMvhes9akwZcR86rmKs360Vvi8IlZyxd4RWuB6ByBbNKJ7\nswESpT3/zxZ4omAz51cSbs7Pz6/EfJqgT5LM6/N+claNJkZkBl3t5iGsBFFqx4pwQ/WJnsmuZ0KY\n3kuLr5ZZf6Myr8wR9ELNxlkm8IwxrgjlZmyG9mn76/TyzsxTqwW2KqBcR4C5DiIhI9tlSyaplaBJ\nc48+7Bz1maa3is5dUXZI+NVdy7qm+PbRMUBxmRTZmkPmdxpHvp0iAXJVZ18frTcJn36trSh489lo\n/JJAswfZDsjsfUeO+at1QLFXadUNVL7cVYV3ohLVvgWuW8S9e/fsQx/60GUnPnjwwB49enTLpWo0\nGo1Go1HFBz7wAXvttdfMzOzrvu7rwvta4LpF3L9/3z77sz/7Cu2ZsQZKy3oNT89lbA2xP/qs/u+1\nqZUEnzEj5+fn9tJLLx2Uk+rjNSH6oCw9m5WJcHx8fKAVahtnDJdqytpnnlVQGp4cXauo3K9lpPz1\nOkW/zxzZ9b4Kq5MxkP5Z3wf0nDdHKCInbho/NEd8/lHohsyMROamCvNMfRaZQSpmrCgkSsWUmrXJ\nTNvsGRvgmS36hibNKYrbRGwEteeM60bO7r6svi00/5WD/0wrGnOUBo1BPaawPFpXX0d9RjduRI78\nmhfN+RdZz2ldX71riPGe0K9b+PSq6xCxlyvTH42PKJ6cppu9ByYeP35sP/iDP7hcp9tpvtFoNBqN\nRuOG0QzXLaMSvE01jCl1q1PkXvbD50XBGomFiRwGiSXyWsrJyckykJzZVYarGuZhQjXCjF2YWqQ6\ndhMyLS769mBWxyzoJeWblUXvoY0Jq8jjPt8oknrGPmSI/KVIM5/HWagBZauICSNtnLRW2sJO7VNh\nfSI2K2OY/f8UrkRRmTMKvY9YJepPnVO+XMrc6Dib64/3W6KxRYh8ZWgMeqZHWSDvS6Zl0eOVM7O/\nX8dPZbzrWlINNaPjM2Ok9FfX7JlXtpGKNguQFcWXNWJ8qhs8iE3yZc8sLFla2X2UTvQ1BPVFpLKQ\nJWaV/wotcN0y/AJFx7Qg0sTM6FSi6LUMlWj1RBVnLwk9HuPwY7BR+WYsGspHnesjM8nREX/02U94\nqmM0sajNfP6ZeUPTJoGwamKMXpQzz8yJWhdf/yLS/iFBPMNK2NA0/Itv9SIkIScTyMh8kAlNkQmO\n+iczrfh7V/XIduYRIvMlmauz5zUdck7OxpT+kqAzkZmSM5DQr+NSf0nQJbNgNtf1vsr4ya6RwOXz\nmOUg4bvi0hGZBX3fHx0d4W5pEiSiPtJyUr2z9qQ1yuxQ2YnmDo3BaP7oMa2/qjD4ctD7VN8rpLSS\nsLrakKFok2Kj0Wg0Go3GDaMZrltGZLYiet8fz+cjc4Ueq4RedaD11zPNzaeRmSMJWlffJkrv6zmv\n+VKZqA0nVoyUajhEx/t0qvlG9PUeWj3S+irsyhiHH9SmPLRukclmplFlM7w2usdc4O+vat7ZmKY8\norGSXdf7onT12Wy861ggVpS07FVZKqZzvY8YLjIzVjauKLJ5SKy1sl76mzHxmfl7FXaGUGFX1NRN\nZcvmD5WdwkJE99PGAL8mRu20YkT1OLMa6DFZGKj+tKlBj6vMPZVbf/2c0/yrViHKh+btyiLQDFej\n0Wg0Go3GDaMZrluGl5JJMqf7q4wRPb9iAaK0SOvZqzmZ5U685KOj2mMFxCgok6CO/OT/MJ+n6MeE\n1fblCdIGq6xWxoBG92TlnXXT9qmyFXt9c/S5is9Tti08YkAperVPR1mDVUBYX7aojzMWONPeNU+a\nA76clFe0RhCqDFcWvDNbG4gh0N+qz4+vN21EId+bFftCWDFN5Ns3f/0aRX1BLAilR/WvzrHV+kvz\nYYLmkqah88bXh5BFiye/1ajNfNtWxzjVgyxFxC5nrGjEZvk8KyxvC1zvAGSLlb+PHA7pBXRTZaJJ\nQy+xiVWUa49oMaMFIULmTK3Pbtuh07G/Puuw98W2MmGSiTISvhWZQB4tvmSK8uXV9FYRwr1glpkx\nV/VZCUje9ElmNDK/aLoU78fvfIvKR+Vc3U9CWiQgkMCl5cvaMRqTe5UDQiYM6DE5dmf9vRc0L8zY\n7JSV0z+nWK0v1O40z30a0eadPS/oFVZjMFt/aV3LBLQI3uQ7xjj4tFCkHFD/Vc2HUVo+3wr5sBJQ\n9V3g79vTZm1SbDQajUaj0bhhNMN1y5havNeK9zBcdN9K88mejXAdjTrTNqP7M1NZlSXzx8QkUJwv\nfzzvpzoSHU7OxBWNlpiBTDv2z5pxuIdVepQu3ZfFRiPtNDIXTNBWbp+XMpCaRsYIVal+0vyvY5by\niMwuGcNFbeFZxGjuZXXM2EM9Xpn4fZynjDVRUxT1BT2neVbG/LYdfrcwyqNisq8yceriQGEx/Bik\nsUDljVjBbF2tzuXVxqCJisk3Yjiz8BUT9JFtWiMiR/a3AxkjT6GM9LnMIjFBsRkP8tlf7Eaj0Wg0\nGo3GHjTDdcvIpG6yuZNz8ES21b+qLayYHJ8eBSEkzS5y5CTtlYI5Zn5ImU9LxnBFbFrFwTgqZ0Wj\npQB51W3jeo9nHtR5XNP1bbbSgDP2KSqLav/ZfZ4lobbIfAIjtrNSVvIH2svARs/uTYNAbEnGwuj9\n+r09nZv+XvINy8JHaH1m356cnByMFZqPWbpaTmWFvT9QleX1ZaD/rws/ts/Pzy99lLSuvo1XfpnV\n8UPzQfNa+bHNaxmT60HhOPRYf8nnM9v4ssr/Npit6juTWDz1B13VrRmuRqPRaDQajRtGM1y3jDHG\nld1YE5FvS+YnEH2JPvp/5YsxUWWaJiJfmMwXgPwZrhscc3UuY+wihsb3R+QDNNPTz1iQBrqXRcrK\nQ1pXVu9Ig/RtsdJOtd70TT3/jLYP7Qr1dVN2TuFDWviy+DSJ0XwRdupFNPCMAVVQX/k6RuwgMRN+\n7kU+NZ41VdA3SLOgw3qu4pcZscHZrlnCXh+qCJ6VohAqOp72MmzV6xlTGO2EzdZVrU80H3Qc0fqi\nZdvDnK1ATKHWM2OksjBKZpaGjtG0yI/Rz5/j4+MrrLIve4QWuG4ZcxDPTtYPs3pEDpCVrd5EC2cO\ngNV0FauXXcW5nhbn1TN74b/ppecIuqjQIkh9cefOnV3lrDpsZ2a2SIigtElwp4WLFp+s7NfpE5+v\nvhi8OScy+fr+q0bqpnJESkQkGCpWQio5dlfKVJ3vNM/oRaBrDr2UqbyZgpaNnUjgikzJK0RrlVea\nImE1e0ETqnOzmsbeeq7KmZmds3rrXMo2Y63OZWOmUm6fXkUwIgGSoGt35b0XrW8+jePj44PQMpV+\nbZNio9FoNBqNxg2jGa5bxpSyV6YvD2IBMg2HKOBMq4miOdM5YhKIevbXojpWNFA1r9LzVM6Mjl8h\nY7gI2ifEjFxXmyfzmGKl7VXKvDIRZMzbqs8q/aJMxbw2nZQ1jZWJOmvjveYPbbuqiStjeCjtLDAk\n9Tdp6NX57dOZ5axo/MRuaJ57mfHM9LnaOEFsDW0+oDWH7iNQmfwatWeDRxUUYDgz2WegNVFNoxPK\n1lTmz4vW0bet5ludv2rmW1lZzHJrz6otabOAprF6vgWuW8S9e/fsgx/8oJk96+iHDx/agwcPbrlU\njUaj0Wg0qnjf+95nn/M5n2MXFxf2R//oHw3va4HrFnH//n17/fXXUVpXXwzyASHHR38PIXKGpPSr\n240pD48V47B6PsuvsoU+wt5wAsR0ZGVSx+YX0QZXbI5P/zptQWNrItPiVprdyv+LfIlm/j7ApDpR\nr/wDfZtFvkRUpozFi8a3P9b5k/VHhRFazR8aY9Ut+XuZUkXFty/zYfNpVf3ufDrqw0djxV/T4zHG\nLlYu8mWj8UEMddZWej/53b0IyEcyGpd7fAuzQKGZn2zUdi/ic5mtk+TbSNd8upEfo5b5k5/8pH3y\nk59sp/l3OqbA5Dt35dBHi3kFtKhV6WkyCfm06dhj5fTrJ0E1RlW2kFCeFBmYdhWS07w6TZIgNRcX\npblXQlAkeBC0nFn/ZXGu9iDr22p9omvRS7tq0iQTrfbPCtl41E0s2cuTFBWKRl4RVql/MjOe5qFz\nVEFjqyJokWvByg0gEzg1XT/Pz8/PDzYNqdBE+WfrgY79yJl//tJutWhcZiZQf99KqfXntB/9+FHh\nUU1bWd/uNZVlJkWaZ5EJ229cUPeAbN0kIUzbgJCZxKN6VNLQcbmak1H+Hu0032g0Go1Go3HDaIbr\nluG/qj5RpVbfDoYrolMzR9CMiSOtq8q0kFM03Rs59Veg2qFvg6Ojo8s+UQbJO99SO6qGQ2YsMtES\n01NBdau43ptFhI7G0WQcMsduZXX0XNY/FbOKtodq3ll8oHkfsQHRmMk2C6y08Ak1Y03t/vT0NEyP\n5hwxJ1pvP46IYYtiEdHGhKy9qX2UuVttuFFEIVfms3O+UX1W5uoJak/6CoaWj85VY7uRO0FmpcjY\nmojpqsTq03lG6xDNPe3vGb6G1kHqYz/PzMxeeumlg/tmunMOaHpk/iVkDCmdi9bPyrqahZrRvMhE\nSoxdhGa4Go1Go9FoNG4YzXDdMrx9mJgRlZx9RG/SjlYSPdncSVvw217J4ZQQadnkC1HZjh1tNd+7\n/VzznelHDpWa/+ocXcv6ImrvPUxlhOumQc9R/0SsSoVlpPahdl+NS3+sTEbk5+LzVS2bWC9igakv\nqS28D1fETvm8ojaI2sLnq/f7PDJtPGOpdB0iZoJ8n1bH8//JbCkrSeMoY3q0f6pzKmMytY5+3dOx\nlfnzZWN61e601qsfFFkTMqd1HRez/DPdO3fuHKx/uubT5pS7d+9eOXf37t3Lc8osznyfPHlyee3p\n06dmdpX59e8z2gRG5whVKwFtBqL0K35je/NvgeuWEcUgomtmhwtsNBAjs2FmplPQh29X5gMqC0V9\nnrhz586BYBI56WaUcraoTkTmUb8wRen5a2qmiUwxHmTaIuwxLfqyZaYlWuwzQZZeYlG7V4TflbOx\nH6vqWF1NT6/5PMgMrHmQOWmFzERJi/k00Wb1J7NpVG8qrxcQVi+rlWBEJrCK4kHzIstfhVUSsiJF\nbqZReSmTKTUqX2T6IzPeCnq/F+TonNlh/60UQFIOMiWH5sO8dnJycjBW79y5c2kqnL8vv/zygdvB\nGG85zU9h7Ozs7ECA07mngq7/MLiul1QvRaaUVMzf9JyOdxUQs6/CRGiTYqPRaDQajcYNoxmuWwZp\nkBMUBZc06olMAyLtmRgKcpBUZGwXaQREs5OGo2lkLFFW5pWZhtIgBq5iutFNAJov9WUlPEFUZmqf\neZ7GQqbl7zXBav/QGNS2IHMOjT1fL4pro/dk37jMoPn7PD2o7BnDpM8R01ypd9bXFK7kOqZiYjsz\ncwoxXVRHCrdQDcFB5jzNk8qcsfRZOaO1Mat3tjmEsFqjsvr6PFdpR/dlm0gU2drk2/jo6OiSwZm/\nd+/etVdeeeXy2CxmuCZLNc2IT548OWDsdMNYxsjTuhptiCBE7wl/PNMiKxKtgxOZC4RHM1yNRqPR\naDQaN4xmuN6hiPxJKuxP5vehvkeZNkchE4jxWGHlM5b5jKwYlHlOg4z6dIl9Ug0302RV2yMtKdve\nraiwANG1iOnI/G4quK4PyqrNViAWyzMOxEZk/jvR/PDaaNWnKBqz/hny+6N5S2wb1Stj1Yi9pDpG\nfomEysYXnXuq8WdM9wSxmBnjrXloHch52+M684H6tjIfIh+ubI3K2KUqOxg54/t2oaDM27ahT1jF\nh0vZrMlwvfzyy5e/05+LQiZMp3ktk16rWExW/qgZ6J21YriI0Z2o+kVGaIHrHQAyJ0UOw3sXk2wy\nrxbcObhWQknFHKkLgy66/pzGMarExtFzfidWVFd6UVJ6mRP1HoGT2rFiPozSm+XNhNUM9HLa44Sa\nXVuZuMk0EDnEaloUQyx7Uevz2p++v6MNDJn5SvOsCG6ZiTL64K6vF/Wtzj2Kp1Y1q9M5nXt+HmZ9\nTPnrPV548se+f8h0vqqHojJvM8FjD7L+I+FbNzdUhC/aQECKcVRH2qAVCSO6+1EFLxW0zMxeeeWV\nyzhcVG+at3OdPjs7O6i3zinaSazpV3aW032ZCZnmGSk2erxHAW2TYqPRaDQajcYNoxkuMxtjvNfM\nPmZm7zGzCzP7k9u2/RdjjE81s//ezH65mf0/ZvYl27b9v/D8F5rZf27PBNg/tW3bh5+f/6iZ/Wkz\n+4Nm9pXbtv0tePZa7ELVYZDSrDw7Rs1hWbV32uasNDc967VX/eaWbsGtaOvXMaV4LSVj6fScto3G\ntyFHeq+BrlgIbc/IpBY5aFacfivOnZqWIkqXzHfXHb9kHssYwUjr9PT/+fn5gfasDM6KYaQ+I9bH\nly/app8hM2v4e3wZabzRfRWGlBhnMzbjzzRo3PgQA4qVqbdiks/MRL58kZk+Si/Dqux+fdNQK1of\nP1aprsfHxymLvypHxnARPMN1fHx8aT6cpsU7d+4cRJpX6wzNvZ/5mZ+5TJ/eC5npnuaeL69PI3Oh\noflFLKvHdc3RzXA9w5mZ/b5t236Fmf0zZva7xhivm9nXmNl3b9v2mpl9j5n9fv/gGOPIzL7OzP4F\nM/sVZvZlz59VXM/g22g0Go1G4+cFmuEys23bfszMfuz58T8aY7xpZu81sw+Z2b3nt32jmf1leyaE\nKT7fzB5v2/ZDZmZjjG95/twDM/sHZvbEzH7KzJAuUjbIrBaIbc+5zMcj0+wi/zEvzZM9fI9PhC+L\namLEsJEPCDmrZnkpMoZCn6M0V35F89mqc71/tqrFZmWPnJN9hOdVvqQBarqk5VVYrCzfqB8rbOe2\nbQfjJ2JwMn8Tvc9D2Vjya8r8cogBzaD+WgrS0CcjQfOcGAe95vtR6+jTUBDzomnROpAxTas+zuY8\nMS36bOZ3RiA/sL0+mFQ+Xcuy9TLzJyTfVBpv1N8UVd+nr8dD/FG1nH7eEIunQULnOfVfpHwJVfab\nkLHEK8f8ah4rtMDlMMb4LDP71Wb2V83sPdu2/bjZM6FsjPFp8Minm9nflv9/2J4JYbZt21c9P/eb\nk/yuDFDaBbEy7dEEi8xs0QK/d/CuJka2cGT0LTkiq2ktmwSZOXC1+OoLg+La+EXl5OQEd9f59tZ8\nyUS6avdIAF+9JLJP3KiZaC8oXzKZRUIYCQGZKbgqhFH62Yu1KrC923iiAAAgAElEQVSSuZEcyrWN\naexXTLiKitkrElpprnhT6tnZGc49MokT6CXry03zcSWUVITp6OVI5v6Kma9qUtSxXVGO1KxN6a7G\n8kpQj0A7X/UZfdf4dUDbkDZLzNhZmsc8N82N5+fnB+menZ0dKCckzK/qtuorGiP0LojSW62rL4o2\nKQrGGL/QzP4HM/u927b9Izs0BbZpsNFoNBqNxm40w/UcY4wTeyZs/Zlt277t+ekfH2O8Z9u2Hx9j\n/FIz+wl49EfM7DPl//c+P7fEvXv37Iu+6ItQov74xz9ub7755pVzVfMDabYrx9OqmaaiWWWmmdWz\nPr8KSNv0Wn4UvsLT+3ouc3qmkBa+HhPEJhFzU+mrqulDy+vZBTUDkFmh6gis/2dml4xVysxDFPYg\nYh58XhGrVh1TmSP9yhy7hy2mskfwDE7U7v6YmD1iuMY4DB2gcykqf1SfjBGieq2g86PSZjqnKJ0q\ns+WvrfpV15Rs7kdmO3+O/s9YGk1HWaVs3vq1Z4y3osXPOpyenl6emx+g1pAWyuT7+87Ozi7P6bcS\naQwSMmac2mRC52OGbI2icfTqq6/a669fddWe+XzkIx8J82mB6y38N2b28W3b/ric+3Yz+y1m9mEz\n+0oz+zZ47nvN7P1jjF9uZj9qZl9qZl9WyfD+/fv2WZ/1WeGLqNFoNBqNxjsLjx49sk984hNmts8d\npwUuMxtjfIGZ/atm9n1jjL9hz0yHX2vPBK1vHWP8NjP7ITP7kuf3/zJ7FjrijW3bzscYv9vMvsve\nCgvxJuUT5B3aoEk78prASoLP/KGygbJiI/Q3c6qtaoPqX+TLRX5dpMmrHw1FWvZl0TAOWnbv8xT5\n5fj8KbDmCplPR8b6RelnTByFm1DfNf9sxA549od80qhMNC5onFE5szqukLVZ5Iw+QXNQWQNiKivl\ny9opKqtn1vbMM0rXO7prP64Yxegc1Z/WqIiV8esbrS/q80qgcUmgehOuq/wqu1xlrT0ztUK0NlI7\nrtLx5fObl54+fXoZ0kG/n+jZsYuLiyvM1nx2fldRmTPv1xWtOSuWXOu6QvauqTD6e/LyaIHLzLZt\n+9/MLLKD/Tq4/0fN7A35/zvN7LXr5L3XREgLYsWsQNeznTDRIK+AnNzJXEH10vpkCyK97Ejg0vsr\nJpRIeMj6SRcav/hQ2SPH/AyVvtA0NH1qJzL/Zi+DzCwX1VFNDPMctU+UJ12n+mgdss0AJCyOcbjz\namWKopfhSgGqvPgp3lB1FxWd1zyz3ZT6f2VnJZ1btadfD9SxOjOxUb2qwkh1/l7n5enHETlpk7Co\nz6xMpNQufqxUTYXUFivlyJsAj46ODgSui4uLy0//TCFr27bL49nHP/MzP3P5mR9NlxzzCRRD7O0E\nzf1MiagqVh7tNN9oNBqNRqNxw2iG65ZxfHx8Raqe0j+ZF4jByOLaRMikdQVpTpRXFu5BNcGMviVt\nuBoBnMpEZSPNkvIi9qmicW/bW7GfsrJHrBNhRbVT2nqNWA4aM6TZ+bpl+ZNW6DV0ovAJ3rTo60ch\nCXy6GrLB36v12rMxwbNn2mYrVi4rR2XbOqVPTLKyKtnmFbqmDtAZMrZEN2QQ6zXbMGIiM/MhjSMq\nVzbGqi4YY7z1pQ0/p4hVyuqwwvn5+RXGaObpTXrHx8fICEWmcg9ql8h0rGuZMld+nj958uQy0rzG\nf5vlm88+ffr0yvGsF33FgODHNM29lWsDwbtWVK1DUfora1AzXI1Go9FoNBo3jGa4bhnTn6fC5owx\nrnzXap7LNOQJYg1W0nrFFyWL3uufyxguKmsG0tKqzpOqjXvNKfIXy8q5Yjn2+o+QJl1pE9JAyY+E\nHG0pPERUhwpDSvdTmAfNI/N3of9pU0PWnuRsHfkbVthdvY98rbL5o/ddx09r3u/n0vn5eTrP6EsN\n/loFxGzNX39tsvj+eVq3/PiN/CKjcvhjYquzMbJam6LnImTjd+Lk5OTgPI1VWg8iPzHKr7Ke0xo2\nGSnayHVycnJ5fY4fnefKaHofLgquqmVSFjj7FmcV5AOYhQrKxhH5yVWYzRa4bhmTxvcCke6g05fU\nHNQ6ALNFYmXa8tD8abHIFiuKT6TX/CdHKA0qO5WZzF57F0tKL6vf3rQz0Isly4+ESTJd0f20cPuF\nc7VYaL700vaLZNX8vBLkKiCTlc4pEvjoXCUfBfXTNJuY8Qeb/RhdCXeEaH7N9GieVYWpSpuvTC3Z\nWKBndN2guUxCLQnJlXhhWV9cB9m81L7NBL7I/JcpJxRNX5XHLH6d75+oXiTcTWFp4uTk5FKQmpHm\nfTr+WTUjZm2v5axurIjSqCISsmj8ZMpWhDYpNhqNRqPRaNwwmuG6ZUwK3msiqnmrGZEYLi9pjzEO\n2CZlUohlIKbFQ8upGhR9uHbiOs7JE9nHoTPNZUWzU/6kuWS0/Wrb/Eq7n4iYrehZOrdqC8ozC4eh\nfZaNFULmmK/HFO+nYpaMUGVV/P1RrLvsebqmDNMctxm7rPMyY84mIjbA9+PJycnB+NV5ROcqptwI\nmYlyZSLNxjuZ0skURGUm1qk6lvayXVWTHYXgoedoA1K1fGTu0jHo21TfBbT+Z2uEjqOZxmR31axM\nrJx+jzFbz7RsVHbfJivrRFYf6h89l809CgsUoRmuRqPRaDQajRtGM1zvMCiD430xaMu1Qv05iKWZ\nz00NI/PFURDTRL5exPpkYStUw9D7KyzOSkslDTnDXn8O0g7Ncg11pTHRtYpGq7976033kWa50rwr\nIJ8WZZgyn8IV40JOzzQHMk3+Om2WMUzE9mVa9ipP6ttMu6d5SQwBMSOKjJGp+lT6sqsvkF7L6kPf\n/1RkrNjeMDqrebdnPdLrEdtbXX8yhpjKsloPsvnsx4qmMdksdfif/aObJPTXO9ITI0T+UrSukRWn\nitWaV1lzaDNHFIpG0QLXOwBk2iIzgApc9ALUiRG9RFYmoQnabaSThkALSOZseHx8fFC+68QVy8yV\nFdPDKq+ozrTo+vrq5ofMHBqZD6O8aVGjmEVUj+qirvXJnqH4WhcXF6mjdmWRXI2jLD1tu5Vpq7rL\ny5/Lotp7+LbXslEd/Tmtz+pFQy/lylwixYHOZXmTOZZeRPSyrZrLad2gF6Cad6tznUBr6AQpJ5kb\nhT6fvdhXSuNqjtJ4I+XW1zET1khIPj8/v2xjVeQpFltUNqqXP0cmUnrGP5+5bKhwpb/zmMzuFK8r\nyo/QJsVGo9FoNBqNG0YzXLeMiA5WSVuvkVbkozeTSZHMBmRqycwLqmkQw7bSkOm+TMsl7T5zVs8c\npilmUbVsJycnB2xJFM/Ma2KqJVU164z9oXxVO8vMORlobFFbRGEUsjYl7A3FsDL/EINS2a5dHVt6\nrOl5Fm81Ryg+ka/bdCXQPCO2k+boarv/hC/fth1+uJ3yorVBmZSMzVqxNhVmnNifiH2rmOSrWDH4\ntNZ60HxUFpzMbZouzVF/X7Qm030Re0fpRqEoonpqetQ/q7VJ61phk1f9GbGlmpdakTJ29OzsDNNr\nhqvRaDQajUbjltEM1y3Dh1Qgfy3SslVL8Jq82tqrUbTJkT5jhFT6Jyau4kdCPgekkWkwSZ+PL7NP\nl+qdaeoUjoMQMVxRGaO6Rf+brX2LiN3wjsV7tL7Md6GKFdO1x8GVGA9lFzRNr91rFGuKoq/pUj9n\nLNHEnTt3ygzTLMscy7SVXH1hKLK1932h8atp69xfjc2Zv2fsiBn2ZdBfYhZXzsTEAq/YRs94EFO5\n8geidBUUAmf+0pz3DCgxV3of1ccHB1VoCIbVO4EYs+j+F8G2vRVEWQOfVkJKVNcWtdhQ+tR/qw1c\n8zliuLKxp+8cb8Wgd6FHC1y3iHv37tkHP/jBy0589OiRPX782MyuDgYCLWbZQpwJBUTRR07Fnmal\nFzblS9cj8+EELTo6yLPF2SOjvX0aVbNlZpqlZ1cg4S+6h14c0bN7nLs1DxUGVv3sBeaVw/BeRONW\nf834syFZHaKXDpkKfR6E1Y6zTDkigXyvGVo3K1RNN/rS8UJD9Fxkxrq4uDhw6j87Ozuoj65ves6b\nNEnxUzNs1saVdSGDV+j0fLZRh9ZOMttqen48kIKo/ZO5lmTCmD/OkI09Tbeycz4SriKTZvRMVVij\n913Wj7T5IUtXBbNt2+zVV1+197///XZxcWHf/M3fHD7fAtct4v79+/b+978/1JgajUaj0Wi8s/Ho\n0SP7gR/4gVQZM2uB69ZBJhOzq9K4StJewo/MZ14D2hsPK2I3fJnIUXulqV9HqPTp0LcZV6a9zGF3\nxShkmn/ElPn0yJSpJmRizCidCBErmTnVEqiO1f6rfrOvGtcr05q1vTLTFrULxd3xZYvyJ2fa7Fll\nMDLGgczqVI7suprWMkR9S2NvxXr7OngnazL3KUOgv/77kxHjcV3FdMXYKXxfrZTibF0hFxAdg5kJ\nThnIbFODlpvYcv/MysxLa5R/JjJzalneTmRlmed9/tE7NipfxnBl77PIHHmlfOnVRqPRaDQajcYL\noxmuW8aUwMk2v1f6zhwFSapXbYHSJ78c0tiIMSNNKNMUlbkiZFph5Gvh88rYBdXQyf+twhJp+1QZ\noZVPVsS8EStB4QeI/fHH/hnNw/dVpMWSllv1FcnYrqztovqacWgUTa/KTq2Q9Yuei0JuHB8fo+9N\nhYm7uDj8LiqFE9C2yOofIfOlyRgcgn4L1m+/j9iSSllXzEI2v+k+yrf6HD2zGk+Zz1E2BhQ0j7J1\nPys3Ofmrv5Zeyxgkwt6NOFHalI53ZKd1NVubdb3ca5WpMFwtcN0yphOlN1npy1vv9YioTboe3b8a\nTFm+kfMolT2i6FflOzo6Othto0IQCatZPTLTyHUEXU2LhL6s/fSXTFWrGFL+/0wgJDOjwgu7Ubwh\nKsd1X+hkOl8tyNVNAHvaTs/R2IoE1wltF++QP+e4mR2YwfWapkV5ZeZqbTsa0xX3AFKeCCpUVdeo\nuYNNBa7ZFtkOMRWQSAjTsvvx83abIxXZ3CNkawr1MZkPo13TJBhWzHvaj5U2iYSXLC/tpz1C/nzG\njAVIVTYmIsVgr6Ds+4XcZqJx3ibFRqPRaDQajVtGM1zvAJAjpWp25DRPmh1p6KTZVrSzldZFW8kz\n6Z+ofNU+qlon0eE+llm2ZXuWxSOL1RRhFQZj5lsxKaqJRftMt4nrNWJcVvGOsvwjTa/SFnup91W+\nmr8fv1EYhYwFofszh1tiYyNTWcY66Tk/b7RsFcbOb0PP6kPfWiSm0pc1Mol7UFsQs6pjW4/NnsVs\n8mywOmrrekhsWhbWI3KR8OXT+7NQEqv1JCtHFl+QTMk6TtTs7J8l5nVC+1bv921L6++8n2KjVVgz\nX05t1yx2WnUNoTG9mj972XdvltRntF4rppvQDFej0Wg0Go3GDaMZrlvGdGjNmJHMt0S158yJOPPt\nUQ1Bn/NOmxqJWrdv7y17Vh8F1ZGcOTVfD3ImJg1P06wwGaqBUpm1TFmfzXZcabSV+tC3zrTsmX8B\nOW+rzwg9m/nFVH3hMt8XZez0l8I4eC08qmum7VbZQXqGWGhfRrM8+CL59KzmR+aDkoX3UMZMmQ8K\nYpn5RmVsAY3tl1566fJcxiRM0Bgjx2lti5WTvx8rNKbo2euco/yJtSUmuTIuqtB8tc38uPIbGbRM\nkW9hxb/p9PT04Lxu5qBo7ZkfKoV9WbFtND6o3b1vYdTXPt1sLbtMO73a+FkBOTOTA2m0KPiFSM13\n/oVFJkhNY0JNXJr+dH4lx/y9Ttk0QaKdhvRCjQY3TYZosaBzvj1pEaQXZZRHBkpPz832yISBLN6U\n7vzS/pzXVaia/XP37t2D+mg5dIz6smUmyMgsp9d9ulkgwZWZs2q29OmRIB7NUX+dlAh6yWdCBpkb\no40wmblHTVe0RlQEVxLqaHekpuGViaOjt+Jr6aYBP38yQUmPI0GTsNfsk5n+dGxXTJX0AiYzms7H\n1S48Mh9O7HWl0DVnQpVsP86pPtGan82BCf300Upo8kplJIhn86ECbRMqO316iYT5CG1SbDQajUaj\n0bhhNMN1y8icT7Oo3cqGkHYUaYCqBUzQ1nw9pzQzaRBV5ijTNrOykybmy6AgTUwZj8wEq9dV6yLW\nK3I4rWBFV0d56HPZ9n9ls+bxZCfHGFfMPZq+z8s77Z+fnx+wTqvwEaS9EiNEyMzE2fyIGEg/Zogp\njUxmKy183pOZTSuMC7EWNG/NmGHyjOdqS35mZlT2pcKmUTgDZVmVLYrWvuhYfyvsT4RqP/p2zBj8\nldkpswIoM6J5VdwTdFxoeTPWkODXVy1TJQ6bP6Zxmb13dGxV6k1lpzz2Ml3Re22OW/34/CoEBKEZ\nrkaj0Wg0Go0bRjNctwyvEZMWSdK/slqZMylprF6bIi3g5OQEn43qoL/ROdJoZ5rqS0QshD9esSJU\nZqoP+SRch7HKQAwKOVtn2mjG1kxNUX11Jmijw0svvXR5bvprRT5C3mfh7OwMGQfv7xG1LY2BSjvT\nWM3GJWnKkX9GlQXIWLwJ8i3Zk4dZ7KtJzCwxr74sEfPp2ezIB4ZCX0SMnaZLY0HXryh4qp6L2JoK\nw+TrMe9bMVszL+/DtWL1K1C2sVrviWiTSsYI+XQVEZNL+WX3Vfy6lBEia0bm+0mg956eqzDJdI3a\n7uzs7HId1DFbYfEOyp1ebTQajUaj0Wi8MJrhumVEWl4UdM1L0KSF6D0rPxd/n7ImVW0j82UiP6ls\nR07kq0VbpCt+Kfq/Z4Q030zDifLKGMIsPdpFSv5aKzYy8u/S4+Pj40vfrbkl/+WXX7489/LLL1/e\nR5ry9Fl48uTJ5X2zzFPrI22PxkDk75FppZU+jtp/MnraxjS+aGxRCASfrz6bab4+7Sgtmo/khxXt\njpz3Z/5CyigTE0XscrYTMbtGbHQ2f8mXKQp1Uh0XFTaf6kE+XJpnFmxT28aH1tEyrFj6LK+VDygF\nGa344On/FfZu9Y4gRp4YrlU69Kw/p+t69fNflbVH20IZu+zdGqEFrlsGfb/OY2XSm9BBo2YmD79Y\n0iDe62zon/ULZnXQRpR/dZHw92YmEnLGJJqdBCS9dzXx6KVEi7in1fcIvfM5vwidnJxcClfTfPgp\nn/Ipl8LXPPfSSy8d1PHi4sKePn16pUynp6cHkczp5TTGuLxP76e2oHpE58gsuGoL+pAt9fNqkaZF\nf6/ZJROQVuM+M5HSCzoTLmmjzHXmPJkb6SVGbUvCEK1Ne8yxVCbNK0ImcPg1IhJEaP6SwBXlqc/q\nGroX28bflcza4EXauCJARriuUj/GOGhbmitZWBmz3JSamX51I8getEmx0Wg0Go1G44bRDNct4t69\ne/bFX/zFl/9//OMft4cPHx7ct2KdMs2Cvt3mtRna4kpMU8QIkebttWwyDVxnGy9pUxUHVmISiPEg\nulufVY1Jgzhm5fcMCjFiZA6kNLJzyhAQwzVZrVdeecVeeeUVM3uL4XrllVcOWJht2+ynf/qnD8rp\nHenVWV/ZLHI0ndd1k0QGMnFVWNgVE6bXou9VrtKLxhsxIhFLEmnJ2XUdq74MEVs976N2VzbEp0fM\nVDbeiTkkExcxbOT0TKzFigX//9v70mhNs6q8Z9e9t6q6oWm6UcGmBcSuEcXYGDTBZRlHpAmIRhxi\nBE2WwcTERBeKmhCSmCXOxmSZpYgMriXggHQTkEl7VcISZJRBqK7WRpQ2gEozdVf1Herkx/eer5+7\nv+fsc76qvnVvW/tZ6677fu975und+9n7nLe1ASSqj2ceuS3UN04r1AHDXC/1ZQG/HrTq4sdAa0OG\nGo+qLTzUUTjRxoRWvT16R36MQrW3OmqE6xptxFDwVh41Bxhqc1cpBYcPH8aRI0cAAM973vPa8YdK\nldgRnDx5Etdcc822e+dDUyYSiUQikdgdnD59WpIlHilw7TIip0G1bbtCac+cZqT9txxe+V5LU43Q\n8zdRTo7MxPAzVV5f5hFH5F55la+D8oGJfI+UY2jk36QYoZ5PhN9w0HJqrXH5YFPPcO3fv3/uLF/v\n1f+Mzc1NefhjZcWqf1fL2dn7erH2qBxSfR1YY42OLmj5+UQ+V9z+vkx8z6fl66MQsVKRz6ByXles\nQm/uq3J45pNZbfW5lFEsy1CrsJEPTmuOLsuYjLLfnL86FLjCz9vWuFT1iRzaI6g1x1+r/FrgcXYh\nPlxqPkSH73rmsJU/tyP7xdV7/D1fH4fzjViv0fGqmLwRNtgjBa49gNZAUOaUCnaKj863UnmNDDLl\nNDoKNQFap9n7F8HKysqQk6oyU/RMcVE9IjMNL/BqEVCLmzJH9pypvQO0+qi4EriUwKdMivw9uypg\n1WcrKyvz52w2rcJV3a24trY2D1fjqlOxGarM0eLE7cmn49d6qW98qu90RsIICxl+12Xr5TPyUuIX\ntepblYZyZPdjSgmrvfSi3cDAdpOwT5fLEZmsVHmjdaMnVKr8eyY1j1Hhoafs+nHGeaoyRdaJSBHh\nuNHuQ59fhVqHIvO3Wi9UHaN27O3E9WOvJZAqs6167/mPaisTrhqf0TrQ6ndfNxWu9V7pjb20XyUS\niUQikUjsMJLh2mVETNRo+Crp87lIIxpopHX3HIIjjJo6WBNTW/dHNfkKrqMy7flwKq5iuHzaPo9R\n2l5pr+r8JkXzR6a4nsamNMvIvMvl9Ayl6jOO02NP/fOtra3msRqrq6sLJlJ2WlX5K81TmTwik+8y\npnMPtRFElZPT8m03OgfUmFmmvJ415fTVOYAqTTUHVB+fz1ENUf7nY/5cJv/V1dXQpKjarEIxqcxw\nRd8ijfqzVWe/EaXFXKl1wo8zLke97pnK/PxqMU3RWq/A88LP/3379i2MKcU2qvkY5TXKXiq2dQTJ\ncCUSiUQikUjsMJLh2mVUm3Hk36O09sgXw6fP8VpaxYg/kr/fKhtfqzIp35JRJ0dVZs/Y9Xy5lNbF\nWk2kbUaMkPIX4PS4vEqjjJyDI4YrQmvs+H5RTJPyIRt11vXXvj48Bnz/scO/12z5HqfP33r0ZVZz\nhRFtYec+G/Fl4ucqrkfPF1DFUwfJRj4rvaMnuG9V26p6+bK2vowRIVoH1FiN2N1Wmf0zZjAYEcvY\nYtL9vYjpYZ9BX/YWqz5Sr1KKZIh92Vt+li0rB39RQr1zfBn4vxq/rXHessTUMtS49br6dLbGtPId\nU+uqz4PLpDZIqfx8esqX1CMFrl1GPU1cncniz23ilwkPIn8uEj8fGeSMSBjrvWz5WeTkrRYB9cKI\nzAZq0Y8En5bTqK9jq33UjlElcPn0lCDVMqNFQpU6O81DCcm8wNdxsrGxMb+uOw3rQubLWZ3leYxd\nyK42VVb/0ubFtTr3V0Fg//79sr99fbjMSghTcyXaBTdqNuD+jhDNM4YSLJQZuoJfTj4NvlZKBCsg\nrfi+rH53sRICWw7Yvt1VHXn8KmfzyJzuy9AK1zK79wRVroNytlZnD3J9eoJ1S6Gs6fTicbgeoo0J\no2NfraGRk7taa5Uiy+bd0fP7uH38J764/7zAxcpRJJgrjJgv06SYSCQSiUQiscNIhmuXoU6Cr7/V\ntm1v7uKzkpQEr9gXZWas6JkSVDm9JsZmAC6Tcu6M8lVl4PAtFo+vR8xuHF5tVa73W/dY6xk14Xr0\nTLMjZi8Vfmtra+F7iHy0Q8Xm5uYCW3HmzJl53Pr/7rvvnrNeveMEKiKzBjMJXos9cODAAsN14MAB\naQaoLBYflxKZASIWkdmKUWZYsTOjGwl8Gi1TsjK39ZhmYDujqjT5yDm8xyap9UutBx495oXZIW8u\nVmwS97WaDz1WXz0fYYd47I6uOdHGCeW83UsjMkmPYoRxbZnGVf5qvVTp+jmizmZcXV2dH09T0zt3\n7tyclefx4dn8gwcPzp/XdetC0DKXqucKyXAlEolEIpFI7DCS4dplVEZFaUXR9lRmtRTD1dK2lFNg\ni2lSzr/Kp8RrslwmZkG8Lwb7m0SOti0GpeXDxWVRzpBRXq3tviNaY8snzIO14Z5WqpgOX/Zab2YF\nanp33333Qh6f/vSn59fMOvpDRs+cOTPXChXDxX5dI23bOhrAa8O1HGtra3PNlk/L9wwpM1w1rfX1\n9YU6sn8VzyOfP7Njis2pUCfSKyzLGtdycTjlB9XKWzkxRwwx34vmN8MzuSp8lAbPR25r78vam6M9\nROx3dFRFz4k98ts83zwBvZkkqlcrP48WI642Lqg4Ufoj/meqnIrZ27dv33zOK6abWXr/Ltzc3Fzw\ne2Z/VV9Ofk9VtDbVeMtSC733RApcu4zW+R5qMCphaHQh5vB+UChKvbeAqjJH5o9e+VTd+Ld3HG3V\noyLamaLyUeYxtZD1zKvRvYiCVju1uPwjLzYWPFjY8PXY3NzcJpgAswWqLnRMy9f07r777vm9Goed\n0r35WzkHt9rRtwELQP5UexbCuA29AMDX7DTrlRP1ZQM2WSmH8p4ZTykCvR1eHL5lPoxe5AqRo7pK\nozfnlfLiBSRuY56zvQ811/8s9NZ7SkhWdVWm0fM1s0XCIvdnyyXE59kbO9F62evv3toW5TsqSERQ\ngtmIIsLuBDzPa5tefvnlAIDLLrtsLnBddtll87i+b8+ePTuvx9mzZwEAd95554Iyxi4R0Q5/hno/\nnE+bpUkxkUgkEolEYoeRDNcuw28ljRwKR7U0tT11VDNaFizlK9aNteeeduvLFLFjihlR6bNmqdLz\nH6EdbZNSFr/lyIi0bDYX9PKLNGOfLre7P82awQ7z/J3FqhUyO+a/L7i+vr5wT33kuqfhjo4FrwEz\n61VRSllwuGdHcXUmT+9oEuWY78usmKvWXI4Y5BGTTMtcrsyCHi2mS43ByHStwqk5EI3VUWdvxRxx\nn3g3BgXus954i1gixRZFpkdl7lf9GLFoXK+e47lKw5evx5xF6IUbYbMYisHmb7tWFut+97sfgJnj\n+5VXXgkA28yNnvHc2NiYs+9nzpyZ5+ed5XncKwuIH1s9VuMu3YYAACAASURBVIvT6x0JkwxXIpFI\nJBKJxA4jGa5dRktDVb4g9b4PP3KgnE/f5x1J7so3qxduxIeqpYFGbIAKo5gexfDxluIRRGwB11Fp\nlj1mKtIaFYs3ynJ6n4x9+/bNtT7uE89Q8AYGZrjYn6v+935QHE4ddKvKN+IjqNqp1Xa+7IrNUvd4\n/qixuizD1dJwfb0Vi6faZtRnUSGat8pPrOd7yYjmtS9fj3FXbRwxD3y9rB+N8v3psUVR2/XWzZE+\na82F1phpxY2Yyl46FRHzu7Ky0vV1iqB8NCvDXpmryy+/fM5s1f9XXnkl7n//+wO4x4eLD2quWF9f\nx5133rktvVIKPv3pT8/rVMPV337DUWuuKJyPhSgFrl3EiRMncMMNN8wp8tOnT+PUqVMA4gkPjDuG\nLmOGBMYXsB6Vryh0XyZlFlRpR8LYaP5miw7lwBhdz+lxGaMXNKer7qkFW/Vzy6zMZVKLq6LD+ePm\nyrE52oXHwpVfdFsvb18WNmMpp/XoxcbwDu3KvMsvLF7o/b3ei783t5R506fn47TyUi879QIeFbTY\n5OvT480UPcEsEpJVXX05ezvuFDhPvzZxekqJUAIr95Nac0YE0orW2ueFxdaO51H4fuyZq3w8f290\n7QS2C1fRDsbWyfl+UwE/r+mtra3NBSc2I1bh6uqrrwYA3P/+98cDHvCA+fOart9gsb6+vu2LFLUc\n/kssNQy7XXBfj2xU8m1y6NAhHDp0CKUUPP/5z19or4oUuHYRJ0+exPHjx8NdTIlEIpFIJPYubr31\nVtxyyy3d93gKXHsU7NBX0aM2z/f0YTNbYAiUxqg0R3VGWE+bVSxNFK7FAkUnZHsGg+njCqXlK5OD\nYmvUKd+sjfty8LViElQ7KnMTh/Hjg38z++bNh1wnptK9Rsvna0VsiW8DVR71W8XxZfPbsdkBtmrH\n6+vrC6ZPlRe3J9+LzJYRS6WYG8VwcR9E81LNKRVOOY/3TLTR/I/6UcVjdjdiXaK2azGX6rliAFV5\n1dl7Ko2oLZjdaJnPeO7xuuHHoEpX1WOUfVJp+HJ5jLKiPrwq5+jRNS0zsE+Xj6I5ePAggBmb9cAH\nPhAA5qzWVVddhSuuuALAPQyX+vbr2bNnFzbPbG5u4q677gJwz1pXHfT5u6uRabhVt/NBOs0nEolE\nIpFI7DCS4dplVM1MMR4jvgOsHS1r61fhWVuJ6FGlZXMaXhNQtn5mehT7EjmmqsMHK5ih4DyXddyN\nNDWlAXNb9JyKe4yjL4v3FVL+UL589ZliCr2zKN9jVkt9z9PXTTE9rbZTPlS+r3ibd2W06pEVKysr\n257Xsim/Jc/OtRifFvvEaB3L4NPjMT3CKjErqpgRNZd53kbHr9S4+/fvl35VESK/nVZZKnoslk8v\nCsc+fpG/o2rrFtuoWGP2b2yVSdVB+WWqMnA/qUM0o7gRi+nr4X9HPp9sRVHrpYdaG1s+fv54Gp63\nzAryFyQA4Iorrpj7cNWjIK666qo5s1V9vRQqS8Z5bGxs4FOf+hSARYbrzjvvXDg6JjpOpwU1tlpI\ngWuXwWYtIBZ4ehM0WnR4YilhiJ2i6381+fwA5efKpBUtDCy0tEwXPt2RurZMLdFp8vw7WsB6DpSq\nvaOzkljIUTvt/AelOZ5/2ZnZXAiJNiG0do35fuRxoZxKlUOsb4cW1CKlTDK8o6iWXW1+8P2tzKHq\n3qjvZMvPcmT89OZtJMD1yhcpFupD6z3FIppXvT5t1ac1H5UpyufROudr1MTj663WLXWO3LI+tars\nPFciBazV/mocKaVVrQ2jwn6rvdVmIlWm1nhX8zEyj1ch6MCBAwvncF1xxRXz5xH27ds3P52+roNn\nzpyZC3BVaatCnnIfaSFSInyYsIzdEIlEIpFIJBKJC0IyXLsM/q4dENPmSiNRGg47iEfmEsWsVQ1n\nY2NjHpdZLa+5qBOrW1pcZAZQTt4RS8YmSs+SKfNka9u4T7fFPvXMTFG9VTjVFiMsRMuMVf9H4SNH\n2xYLoUy+yyLS6lvmbGA2N/gbjsBsLPJ1q7yqv/nbkLxVXNVNscA7hWhO97RmZSby85E/lq4Yntbv\nCmV2ikwnnnltsVS+TXtMmwobsXmK6VEsPJvOR5j5XpmWfcZljtqATYAM5YKhvpSg4qnxA7Tnu19z\n+Gw71XbMmPl3Eo8h/pKEP5vLu1NEUB+7r9e1Tfi7rP6MQrVetzZIqfW3h2S4EolEIpFIJHYYyXDt\nMtiPST3zv0cdVyPfqAq1zbpq/hsbGwt+JGtra6FzpWJDInajpXVFbAJrcd4na9T/hDWsUSZIlT1i\nIVjLVk793leCWUnFRlQoBpQ1SzU+fD8r5qzFEo30xTLaXusgV1VH9kljlio6DkT5p3BcvwmA/Xcq\nlmFa7k0o/yY1xiIWr8VyRsxnRWu8RXEjrV6Nyx7LPNLeo6wtM3uR75jaYNHycasYZTWivlDrENeh\nNzei/EfZy4oW09XDaDu16qjWJh93mTmoGE/vd1zDrK6uLqy1raNe/LvQX3N5I6TAtcvwC2vLfFYx\nssAxooWVBSO/q4SvKwXbcv4dMQFyvqos6l7PvFrLF5n2IlOLvx5BzzSjzKt+opdS5jS5WmjUS1b1\nrXL0jV4EamegAqc72j4ji00PI87WZrbQ75EQyvfUi7W1MzBSFFq/fX4RojGjBBS+joTKljlpVNCK\n1iH1OxK0+V50NlWdC2pcjgqBHDZShM6dO7fwMWM2NbecxRlKeFP58e9ISVDPe0KyGgOclorj81bj\ndMTtgq95jWDlscbnvo0USFaA/Fl66+vr23YgjoDH24hCxXWIhM9IQR1BmhQTiUQikUgkdhjJcO0y\nSinbNJKKlmaitJRIU6lQ59qwNjvqFK00arXV3peTNUdlAo1OYVf14XCKGfFQefXCReY01WctJ9Qo\nn55504PLFH0dgMtUEaVvph1yW+XuldOHreUbgdqazu3qT8Rn87LKSx11MnoUwwjjw1DjV+WhjhLh\n/KPxrliIqG1HGU3lktBKr3VPuQTwPe6zyLwcmfVbY1AdWROtV8x8+vCqDBGL1yqzbyvlAM79EzH+\npSx+V1Ix9z1Gs1W2Ze6p8ilmWpWJz7yq9ahHNtx9991zBrJ+dPrgwYPzYyF6ZanpVGf4jY2N+cab\nmm79r95Trb7w81FtzBpBMlyJRCKRSCQSO4xkuHYZq6urXX8g/j3iU8MsgA+/tbW1sJ2eNSeljSv/\nA9YYoy3VyhlR1bdXb3+ttGalCSq/pUijZs1bwafroZgB1RfKr8CXK/LfGWVBWj4uo9vQ/Rjwz5eF\nYiNb7C6Pd8X+sKaqWFbfFnwEROsYAX9vWfC4jNiCKH01z3t9y3M6Guc+Tf4P6MOBVR2iNGtZeNz7\n8dM6NFf5O6pwKrzqx8ivNDoOhBnnyEe1B7URpnVkTysu//YsnmJ8e4ymml/+Wa9ManNB1C5qjG1u\nbm47oLT+ryfD13QPHjw4j18PRT148OD8OW/0qqxY/f/JT35ynl79piIfNTOyDvO7q+dX3EMKXLsM\nPp8E0I6zFep8LUb0Mub0/WSKqFMuUyQwePBOoRpXmVpU2mqyK8fi1oYDbtPoTB5Gb/fJCEWvnrfa\nacTRn68jgWJZ00BvQVbCQORMq9CrD9drREBQJiE+U6tlQue4/OFtfkmp+aDKNFK+lkk8qqN/YbHw\n0NswEwnJUR8rk3xr3YjaQmFEqGT05qiaK6pMSvHyZVLl7CkT0ct2VOmI+n3fPv0VA3VPmelHBB7G\nqJuByjtqM55TqixeMV9fX58LP7Ven/zkJ+dp86atGq46z1922WULm7/Onj07D/eJT3wCAPCxj31s\nLsxVgauaHTc2NoZcaVrvLiWAd9+L3dwSiUQikUgkEheEZLh2Gcr5Gmg7MY9Q7ZFmyexPjwVpPeP8\nVbjWtmSVTovS5jxaZhLeclzzBbbT9j0ToM9rpMxR3MihOWIIW+VrMVzqo9Ot8qq+UP2nwnmmpcWA\n9BjX+luxBSNjhR3f/WnxreMwFHPk69VyUo6YIw43uhGghV7bVXAd+TiFiM3pMXGKNVXoHWmg6uTD\n99YkH5d/K6fsyLlc5cvmzdH50KqXcrYeTUOVV7V7y33E3+OPQitH+t66FpWttxHDw5uSff5s2ge2\nO7TX9BTDtbq6OneCr0cUHTx4cL7+szN8DXfHHXcAmJkWvUmxhlHf0GzNxZH5OIJkuBKJRCKRSCR2\nGMlw7SJOnDiBJz7xiXPp/vTp0zh9+vRCuGU1y8gpffT4gV6eShOInI5ZY4s0QeUc3KqHz0s5hY86\n0o4yLgyVji97z4mZteboGAXPbCnfg5bvUYTWBocK7/DfYmRUupH2qLRDtZnD11MxXDxmRn1llD/S\nshssfH1bdfTxe22hmEBuT4VRfynla6by5XSXcRJn1Dq3jkLwZVLguVzRsgrcm2gxTMC4nxygN8fw\nAaE1L87X59/Lyz8/Hz/LEaijPJaBd+4HsMBwbW1tzccLM9iVlarPDhw4sFBf9gmrbNbHP/7xBWf5\n6sO1tbUlLQfKz9Svu/54kSNHjuDo0aMAgBe84AXNNkiBaxdx8uRJHD9+XD47n0VOvRQiQSVCL5w6\nq4jLoRYB9VJUE7dFsbfijgpckemG/0cvVBaQejT0SNn5v3LW9y9FtYtMLdKj5xhx2SJTizJXqHJy\n+Ej45A0P3myoTDdsjvAvKt55G7UxX0fClb/24LKrOqoXizJJtNJVwuqFmi5H1hO1EUMJxCo9JchV\ncDspwV2NZTV+68uW52ivXqNmsWhccj1qntH4UIJ2tHOV46i4aicmzwufds/ErtpuZDMWr1HLKuRK\noVV9s7a2NjcBKmf4alI0s/mHqmu49fX1+XUVsu666y4paNX/XpBSpnZec1jg4jY+deoUTp8+nU7z\niUQikUgkEruNZLj2ABRD0DJNKA1EOZqqe8ugtUU7Mt1w3AhRXFW/1onAPj9lEuvR8b69e87oPQfw\nyBH2Qsw+/vtiLc1VOdX6dBXUmFGsoGIwOG21dV6ZkEdMMi2Gz7dBq12jeTE6Llpp1TJ5bZiPqFD1\n9ukxa9AzH0bpRWitLyP3FEu3tbXVNJGqcdkyj/k23tzclOc8qXvRWO6toZ7NUe4J6ssYEVuv8udr\nZnH9vVZ/+01BzNxHLJE3d9X/yupQ6+m/L8lmYMUaKxaXx3G0IaNer6ysLDD2W1tbC/3N4SrW1tYW\nnObNbNsJ88CM9VLMVlROda5ktLEkcqVZCBs+TSQSiUQikUhcMJLh2mUo7RYYd07uOVL2wnGeHI7Z\nEtYElRPoiLbXYt28pjbqu8bamWIrlNN+L736PzoML2JylB8Wg/1DKpS/FP8f2bqvtNgKxVL1xhY7\nrfp2VG2jfI4Uo9BjZvxhiorhUmOVryPmqsf8Rn5IHFb1D592PVqWCv6Wn8+/51c26pvlwysWhO/x\nBgbPskbML0MdjqnK7tlbBq85ai3xfcLXygFalU/NET5uwTMuo/507GTO6StfTX/d2tzkHe59fkDb\nGdxvSjl37tyCg7ra4KAOpvXHbXBeiiVicF7K/7cyUrWcq6ur2w5BBWb+WGqTER85Uf/7svBvz3op\nBp39tXgsqG939pAC1x5Aj6Luobeg1zzq/WgRUguYeh69CNTLNkqzBSX8RfnyAuknem9nk1oYeqcQ\nj9RHCQg8WXvmuUiQUXmoF6bP31979IRfLyTzgqTCRYKwQqvMrXR7ZvYR8yUQv0hb5in+TElNw79k\n+SXm53lrc0FUHyXARmY0Vd5W3RRUmVvPRtNtCemRIMH19mm3hHPVtkogVg7lo+OrVd9WGCW4t9Lw\n91Sb+Xq3zGL+npktCLsslCj3CF927gu1HkRlV4IMfw2iYnNzc8H02XJ58fNRCZ+qndTu74gsWF1d\nXRgzI0iTYiKRSCQSicQOIxmuXUbVECKtPdLMlfQdQWlESlNmR3Xl/Ho+UNqRhzoDik0T6lRqpU35\nsrPGVsFtETm7K6j28Wm37vXaUGmAKg3fj2xCYXjTjupvTkcxDpG2p6AcSXsaf/RMMaXLsmqteldE\nDr6tDQTeTMFxKrOljikZZae4PtHp+H4s+DKNMKSch2rHyGldjQ9lQufwEdvWYw16Tts1LTV+FUaO\nRRjFiMVhJK7vsxYLP7Lposcyj7A0nKZqd4Zv71IWj/Lg+c1mRmW5YMYKaL8HlPlQMXA+3Wh+cB5q\nPeqtK4xkuBKJRCKRSCR2GMlw7QH0fASUBjHql6LyiqR5ZcMfPfBPlW/0mcpDHWnA2kxLm1Dsk/Lr\n8s9baPms+Hqok8xbZez5t7Sg2i5iuJi1iMBsWm/s+bK02jZiSxRTN+Iz08orYvai/KMxxM+VA3Yr\nrnKmvZATuisi3yPFSipfplEfTUYUV40Z5fPl2Q01p4DFdmoxm8qfLKo3pzHCSCt2rMd+XwizpcBj\nr+bv24cZLh+PoY6U6PmTRWVSfp7R2tgaHxGbxfX3Pqmto0kixllZBBSrr8qu5p5Cr79T4NrDiF48\nrUlVMWIa6y08ipZe1iwWvShbjo9ResoJVIVRO94U1ETyi48SpOr91r1e+3tzS0+Y9S8grg+bV1Sf\nRSaEqJw9QUq9KNlpf6RvVT1UXurDuD3z+4jAx4s010G90KNyMpSwP9K3PbNttOhHZmiuT6utWlAv\n5QtB5HDfOs8oqo8vqw/Pggkwfi4dO1RHiNbclhlPrSUqjhL0opPmI3PsysrKggKt+qIlCPt7StDi\nOihl1KO3TlcHed50wnM1qoeaD8q06IX01rjya15LgOyt+2lSTCQSiUQikdhhJMO1R6Ao7YjKjjR5\nlsgjyV3hfCjwEbaEnymTS8+056lippRH2DxFx3NdlRktotkVzdwzDUcm3BZ8HspxWZWz0vLMcHH9\n1TcZfZ4tdks5Q0dshdKGIyZVPRsxA/lyjpiLVdqtOkZ9Ncr8qrwjEzqXadRkpcbKqIlH5b9s3aJw\nikX0z3wa3kFezVsVl+cIm5e9qY7ZkgpmjlS66t5o+6jzsCq4fZSTt9o4oeaeb5eVlZUFEztbCfy8\nZJeSyJTsr309uH08S8SsW5Re61gKtW5Ea2Jkco42x/B62WLJ67M9z3CZ2Q+a2Tkzu5ruPdrM/tDM\n3mtm7zKz/dP9J06/f0Wk84tm9in6/aQp7DvN7C1m9jh69gG6fpqZnTazW8zsO+n+I8zszdOzl5iZ\nFE6D+Deb2cM5r0QikUgkEpcmLgrDZWYnADy9lPJd7v61AL4GwAfp3gqAXwfwT0sp7zWzqwDUDz19\nB4AvAvAcMzteSnnfFOcxAB4IgEXTN5RSbpqefwGA3wRwbHpWpvtXA3g2gOsBGIC3m9mNpZRPAPhJ\nAD9bSvktM/tfAP45gF925b8qiF84rxZaEvno8QQcVmlxFZFm2fMf4nAjvlks6UdMnNLElKbastGP\nMA4Mz1qwVlpPMFZb6ZXTr9LYuN17rJ/SjlQf+HR6viBqHEW+eEqzVO0TMSmsAS570G3kf8FMbXTE\nQc8XRYE15ajdFRQTxYyiKkvLV8XM5CG1ytE3OlKCWTLvr6TmbUsbj/ygOExrvWi1pWdJFGOn4rIP\njiqvOs1etUXE6igGheeNnwNc/2h+qDK12KCIRarl4ANAfXz+z+B3gv82I88p5Q87yopGLDmv7758\ny/gcj2wMWOadOZKPWnMVA7kMLibDpVbfnwfwTHfvawG8q5TyXgAopdxR7mldA7AfwOWYhDAz2wfg\np306pZS76Of9AXDr/DXl9bpSyidKKR8H8DoAj5+efSWA35muXwTgKaL8XxfE/xiALcorkUgkEonE\nJYqL6cO1TVw2sycB+MtSynucJH14ev4aAJ8B4GWllJ+enj0PwBsB/H4p5dbp3vcBeEUp5SPmEjKz\nbwDwEwA+E8AN9X4p5Uumy4cC+EuKcjuAh5rZgwDcUUqpQtqHAFwj6iTjT3n8k+nel/hIrowAxvxD\nVLyeJrJ///6FtDxr0voeli/buXPn5kxQa0eRj6vA2o/352r5sYz6s/nyKIaAw3nNkpkr1nA846Dq\nx30RsTstXwOlLUfjwrODrTxVXorJ8HkqdlLFUWmM3uP7aqu5KosPx+OXNXoV13+Ljn02VPv02tZr\nwSo9Dleh+npkLWC0mJbIR4jHp+rH3rj19VIYOdpB1ZvZElXmCK06turA13xPHag5kj+zwWpOczjl\nI6TmgP9MDcNbNXx5fdvzuubzVr/Vdw5XVlYW+o/r2FurfHpqfWn1o2/b1qG5EROvfvtnLbY8srbw\n756/6I4KXGb2ZswYqSsAXGVm75gePQfAj2JmTlRlehyALwZwFsDvm9nbSik3l1LeMN2v6X82gG8G\ncELlX0p5BYBXmNmXAfjxRn7N4i8RdkegBneFGphqgVNCRr3HE9lPeOU0OTKY/f1IQOHyKVq85/C/\nzEJcyAQZ0fej6AmVvYnpocoSLQx8T72wei+LEdNbS3AdMTFFJqlWWSIhlKFMQt70qI7yaJkGogVZ\nmRUUlFmQ66FMrjWvaIOLMuOMmnMUlMNwZEZs5e/z5a37ak5H86s3tiPzvGrXkeMC/LW65/u0p0SO\nCqQcp4ZXJmQlaFUoQUoJXDz2o3J5hbeVhjfRqm8u8nhXH8NW7dhTMvx61QqvBE1fR7XxSOXV68fR\n+cjYUYGrlPKlU0FOAHhaKeW7p9+fD+ARAN41sVLXAniHmT0WMzbp/5RS7pjCvhozH6mbRRZfBODz\nAPzplM7lZna6lHLYleONZvZIM7u6lPIxenQ7gK+g39cCuLmU8rdmdqWZ7SszluvaKayHjN9rl4oT\nJ07ghhtu2DbhKk6dOoVTp06NJpVIJBKJROIi4MiRIzh69CiARYH8hS98YTPerhwLUWb+WQ+pv222\nk+/6UsodZvZaAM80s4MANjFjr36ukc6rQaY+M/tUFbbM7PNKKX82XV8PYL8TtgDgtQD+m5ldiZk/\n29cAeNb07GbM2LOXAXgagBtFEaL4XZw8eRLHjh2T38Oayq3qvO2/0kCBRS0zoptZI+G8vbTec3SN\n6Ho28bS2cPv6j2qlHj1No1dOlV5Fi6lola/FXPW06x4UC6Iobe7vUfTYr8hRWdVRtUHUz70DJ6Pt\n3XxAasSasik72nzAcyRiuUZNHT1TEIdp5RGxWqPtzm2mjiWIwOH9WNi3b9/C8QP+ugXVF4qh8GVQ\nv2t4xWZHZar32Hzm1y2V173hsM3lZNaW81esU7TGc/lU3/p1L2LOgEWTPBCviTwW1dgfOVzWpxPF\ni6wyy66DEW655Ra8//3v33ZvJP29cg5XwWTCK6V83Mx+DsDbMHN0f1Up5feWSKfim2x2TMM6gDMA\nnroQeCbg/dcprwLgP5eZ8zswE5xeOj1/J4DnA4DNdkT+y1LK93TiJxKJRCKRSAAAbFnflcS9BzMr\nz3jGM5pbTJUGqpioKllXezl/VoU1tpqm15w2Nze3Xdf/Pu7KysrcaZ63GCv7uvdJYO2spre2tjZP\nT/nZ8De1vAMpl9nnxWM62kreOpZCxeF8K9RmgYoRNoKf8/Z3pZWNMovKZ08dSdBjZFr3lK8Ma8Mq\nD4baft5yDm6Vw7ft5ubmtrFfy3TgwAEA2+eF2pzh81Nbw/m5Gl+17Hwgr/JDquAx6FkDHtvKrypi\nkFrt4+dKj71U4479O1ubXdbW1uTBxqo+vuzsI6SYuArl86TYBV4HFdOi6q38Fn3+PIYVi8plGmE9\nSinbxk+FLyezWbwm+7IwovxVW/A8j9hO5RvLvpW+Pfft2yePpfBzrreW1HAbGxtyLPl1QK3xquz1\nM0K8DjP8fFDzrD5/8YtfjFKKbPi9wnBdsqiTUg1uRWVHFDkPSj/IoomsFjVerHgQq9OKlcClFkme\naDU9T0e3JrASrqJFvKK3+4XL58P1lJHWYuXLqaj5EVPqiEmZr5UwpBZObrte+/j8lGmCXyxRfVof\ngG6ZYVUdVbjWdxtVXv4ZKwxKiFVxlBDE43cZ00UrbFRf9V1ABRZe1JzyeXM78pj144v7u5ZBCbU8\njtRcVeN2hACIlFOfr4Jy5FZlbuXBSllkXmw5jPu0W0qRrw/PZS+8+LL7PHpKnn/WSkOVXZXZ16el\nIPpxqdYSf11/ewd+7hde15Sg5cvBGFl/o3gRLuY5XIlEIpFIJBKXJJLh2iNQlKU/A0htYY+YMb6O\ntAbWdDg9T1u3tFelJUWsgdJ8VZmVWaFS7y0zrM/T14XLwdeRVtNiIUa2n7fSU8yjz0+ZJEbLzqaH\nqE6KyeAx4xmrVlrKjBaxBFFZah8r865y4OX70dcWlAlFacWKkVHjnOOo39EY8GFa96O4W1tbC0wH\nz2XuTz+n1AaYUYd3ZseqSwCbcNS6pRguf3TAKKPZMvv7cMxuKPMut7G610q3lddIn6l01JENXCY1\nryNGtTX3PNS6xfPcz3nOKzKXK9aY06vY2tpacCdYXV2VcX39fX4e0Vji+TEyjkbTT4YrkUgkEolE\nYg8gGa49gJ5WrDQ7JXVH/kdKS/A+VcB2hoAdYeu9ER+HFhvgy6I0jFLucaJmfxPvvzLq+MpQWrZi\nPDxYA1WMENdL9V+vr1rhI8271b6t40B8XtEWd+Xs3HKWj7AMW8jPlO9LD1Hf85zx2qhy3GW2YvSI\nih47s2wayu/OY3V1deFwzt4aoZysFYPdOwJDOUXXNFqMQuveskwor0OqfxRbzuUc7R/FHLXKyexP\n5JOmmDDe1KDqoPJTPk+9sqowLZ+sVhsp1sk/UxaOFpvm+6/ltO7Lp8ZZZE3hexEjtYz/5ei4ZaTA\ntcegXqhqIquBoRzjI5NUhJZ5wUOZX0ahJhefSca71kacbqMFr7XzLNpAENHXyozGZz+p/usJoRFd\nr8xeqm4+T1WPXnhlalFhRxZfhjrbqBW29UyZNWraPpxHy1To8+htnODxoRbvSDgeMVWpfld1ZbNg\n9LJrmQ/9fFG74FT78HP1O3qxKXNfqx08lLma20kJp5ScEgAAEMlJREFUeuqFP2r+bglcUV/36sMC\nvgrL7dMTagC9+zsyk3EaLJxHbd+ro+pvNc9VHZd5H3F6LUHTo0cSjKCVj093pDxpUkwkEolEIpHY\nYSTDtcfQY0GUE2i0FbZFGfs8Iw1NmWRGyttCZK5hmjlitUppn/zdopHVN/hU+dV2+xEzmqK5e+WK\n2KboDJmeYyxrXcosqPrxQin2XjwzC010Ko4yqSqGYsT8w+lweqreymFYpT1iYu8xxAqetVDptdJV\njvyKDY6O92il1Sonj2M/z5hZVOxydLzFKLvQgjJtjbChLRN/VI6ordQRN611LSpX9EWQUdaYcb6s\nT1R2xXr58tXfI3Ok5yi/DMPkoeqv5kUUXo2VFpLhSiQSiUQikdhhJMN1H4NiuCpYi/UaumJomGny\nGok6TZnB/gKREyLnNeJkqLThVh1H6qbqOOJ4z+n2jlbo1V/5Ifmwy/hd+DisvXvtmX1B1DEbfK/n\nQN+qj7qn/ENa2nvUbpH2qnzdeozUiEZvZgvO6KP9c2+whD1WZzS982Fql2U8FGOpGOpe+1WM1k21\nReTvd28g6ls13lrsaeTjVsHtpSwXqgzLImKfWqyjYgxHNiMxVHhe33pHHfm4HCZiGUfW697aNrox\nrYUUuPYARgSRimih4XvRbrV67al/vsf3VboRfdwqu5qE0T1VL7UQHDlyBKdPn+6aN/yLgMNFZzr1\ndjap9mFE53UtC2XCUG3IJ/j7duQXoOpvZY5U9anpHT58GLfddtu2ewqtcb6s86mvj6qjCgf0Nwz4\ne9GizC/U3m7GSDmIytFL89ChQ7j11lu7eaox4OvImz6UgM/la5n9eZ5V8HiLNv60hKdl3Rii9bCF\n0fldf9f0Dh06hFtuuaUZb8Tsz+B2Umen9crm4/byPd91SO3o9c9a+baEU0B/7UCt53Vn59GjR+cf\nkVYCNr+fRs3QI8qjCjcyf9OkmLjP48iRI7tdhEsahw8f3u0iXLLItt9dHD16dLeLcEnjvtb+yXDt\nMrx5qUd3RjSmuo60IsVs9dgAf/q8KlNL01DpKe1DnQWkmDXWjsy0QzZrRMqM5o++4OMwog8cK4ZA\nQZkjuaytTQw9tOqrHFPV9y9HPhLcYiO8s3Ntfx8uqg+bhlXdonuRs7cfE3yv1WeKYWsdx+HziD4m\nz2gxW722q2i1Yf2L2owZLq5jtNb0nvmjW3qM98j3C/21z3/UEtA7Pke1d7SGjZRTrVUqXdXfzKDz\nnPIWiFZd/H3ljK7WX2UOVG2i7imGnMOo+R21hVrzFbPlvw/KaxmPabWBQD3zbcJsllr/e+/V3tqd\nDFcikUgkEonEDiMFrl3EiRMn8IQnPAE33HADbrjhBlx33XU4d+7c/L//Y42Woe5V7aGaHDiMSrd+\n12pzc3P+t76+jvX1dWxsbGBjYwPXXXfd/LqG53Kpewx2iKxmQK+hraysYG1tDWtra1hZWZn/Xl1d\nxerq6jwNr3XV4yRUPSq7xRpdzaumV/+OHj06v/baOqfB+XE7cl29qZPrWutW81Jl8Th8+PC2tubx\nMNK3XO7Dhw9vC7exsbEwzvyYY6219gFrfHyv5yRd+7O2w8rKClZXV3H8+PH5MzU+PNPl/2r4ffv2\n4fDhw6FfkG8z1d8VR44cmadd/yoLUvNr/fm+5THGcX36/Kfg2R8152q9fT/yH8+LQ4cONceAml/1\nt18Xtra2cOjQIWxubsq6cZtEdax1O3TokBz7PBbUmFLjo/ZlhVpjW2Pfj71aF/5T/cNzmte1us6t\nrq7iUY961Pxea2z31oiab32HcH+pdYDHSNQH9T3SG4/MOHnUuMeOHVsYD7368LiqdfAn9LfGdjRG\nud/rmqjKXVH7p461+nf8+HE85SlPwVOe8pRmGwIpcO0qTp48iVe+8pW46aabcOONN+LUqVPbXoZ+\nUEQmOQByofY+HkrwUAu3WoSOHTsmn6sXlr/nF9tjx44tLFStRbK1cKqXhheGFKKXIy8Gvk1KKdte\nNPVatUUVnKMXIaP14uUyHDlyZFvf9QRx37dctiNHjiyMBZWe+mOBwwsoSujmcK0+ry+dY8eObet3\nlRe3WctEx2Pfp8H15sVXtWONywKXys+PV/7zz1kQj8aiN42NCGQjQpoStCq8T0xrbqn+rn1erx/1\nqEfN5y73cRWKovHu0Rr7XO+oHaM1UbVPbx6o/lfjTAmatQ3qy7v+sbLB7TPatxXnzp3DoUOHpJDC\nY16Ns5bw74VUVQ6leDJY4PLCalQv9Y7hr5BE7aPGb+sdV8e+Ev6UMM9j+rbbbsOrXvUqvOpVrwr7\nJgWuRCKRSCQSiZ1GT5vNv537A1DU34kTJ+T98/nLtP5upLWXy5Zp/d1Iay+XLdPKcXFfSqv1zrcW\n/ZdIJBKJRCKRuHeQJsVEIpFIJBKJHUYKXIlEIpFIJBI7jBS4EnsCZvZ4MztlZqfN7Iene19oZm8y\ns3ea2VvM7ItH4073rzKz15nZLWb2WjO78mLV576EoP3+jZm938zeY2bPXTJutv0gWm04PftBMztn\nZlcvEzfbfxyuDX9ouvdT09j/YzP7HTN7wEDcS6b9zez5ZvYRM3s33bvZzK4XYa82sz8ws0+Z2S+6\nZ2tm9stTO73PzOJzFRbTvt7M3j21/y/Q/f1m9lIzu3V6hzxsJ+Ivjd12HM+//MNM8P9TAA8HsAbg\nnQCOAXgtgK+dwnw9gJsH4v4xgKPTs58E8EPT9Q8DeO5u13Wv/bXaD8BXAHgdgNUp3Gdk21+89p+e\nXQvgNQA+AODqbP+L1/4AvhrAvinMcwH8RLb/trp/GYC/B+DddO9mANeLsJcD+IcAvgfAL7pnzwHw\nX+j3wjjvlOOPAPz96frVAL5uuv5eAL80XX8LgJfuRPxl/5LhSuwFPBbAraWUD5ZSNgC8FMCTAZwD\nUDXDBwK4fYm4mP6/aLp+EYBvAAAzO25mf2Rm75g02M/bkVrdN+Db7yWYtdP3YvaS2ASAUsrfDMTN\ntl8eURv+PIBnnmfcbP8xyDYspbyhlFIP+nozZsLvUNzp2d/p9i+lvBHAHeLRU6f6nTKzx01h7yql\n/CGAu0X47wbwE5TuxwDAzF5gZr80sUt/amYnJlbtfWb2a1OYhwC4opTy1in6izG1M7a3/28D+Cqf\n8fnEN7OHmNnJqf/eXes4ihS4EnsBDwXwl/T7dgDXAPj3AH7GzP4CwE8B+JGBuB+a7gHAg0spHwGA\nUsqHAXzWdP8ZAH6hlHI9gC+e4lyqUG3/UACHAHy5mb15MhUoc262/YVDtqGZPQnAh0op71k27nSd\n7T+GqA0rvhvA7y0Z91Jt/5VSypdgtnY/JwpIZtYfN7O3m9nLzOwzKcgDSyn/AMAPALgJwM+WUo4D\neLSZPRqztub24/af900pZQvAx4VZ/nzifzuA10z994WYsZrDSIErsVdhmLEs319KeRhmE/jXLjDN\negbKmwD8mJk9E8AjSilK87rUsQbgqlLKlwL4IQC/eYHpZduP434AfhTAs+lefLx4H9n+5wEz+zEA\nG6WU37jApC6V9n/59P/tmJlaI6xixhy+sZTyGMyYxJ+h56+c/r8HwIdLKe+bfv8JgEcsWa4LnT81\n/lsBfJeZPRvAo0spdy6TSApcib2A2wGwU+K1073vLKW8AgBKKb+NGYU/GhcAPmxmDwbm9PFHp7Re\nAuAfAzgL4NVm9hX3Wk3ue1Dt9yHMtLuXA8BEuZ8zswcNxM22Xw6qDW/D7IXyLjP7wHTv7Wb2WQNx\ns/2XQ7MNzezpAJ6AGauxVFxcuu1fBcgtzASqJkopfwvgzlLK7063fgsAO93XtM5huzny3JT27QA+\nh+5z+8+fmdkKgAdUcyVh6fillP8L4Mun5y80s++I6uiRAldiL+CtAK4zs4eb2X4A3wrgRgB/ZWYn\nAMDMvgrA6cG4N03PbgLw9On6aVOaMLPPLaV8oJTyP6Z7j96Zat0n0Gq/VwD4SgAws8MA1qYFciQu\nkG0/CtWGLy+lPKSU8shSyudiJgB/USnlowNxs/2Xg2xDM3s8Zv5zTwpYqEu9/Q0xc6Se+XuvNLN/\nNF1/NYD3QWMhrclU+wkze6yZGYDvxNTOmLX/06brbwbwB/dG/Gm34kdLKc8H8KvYLiD2cW943udf\n/l3oH4DHA7gFwK0AnjXdexyAt2G2a/FNmL10AOCzAfzvKO50/2oAb5ievQ4znwBgtmvovVO6r673\nL9W/RtuvAfh1zOj8twE4kW1/8drfPb8N0+6tbP+L0/7T9QcBvGP6qzvWsv1n9fgNAH+FGfP0F5j5\nuf0Bpl2KAB4E4DYK/wEAfwPgk1P4upvzYQBOYuYL9XoA1073fw3AN07XD8f23ZD87DHTGnUrgP9O\nYQ5g5gZxK2amykfQs3fQ9VLxMRPK3jONiZMAHr5Mu+WnfRKJRCKRSCR2GGlSTCQSiUQikdhhpMCV\nSCQSiUQiscNIgSuRSCQSiURih5ECVyKRSCQSicQOIwWuRCKRSCQSiR1GClyJRCKRSCQSO4wUuBKJ\nxCUNM9uaPkb7HjO70cwe4J7/OzM7Y2ZXBGk8xMxeOV2foOtvN7N3TX9vnL4BV+M8fvrI72kz+2G6\nf5WZvc7MbjGz19I352BmP2Jmt5rZ+83sa+n+6zlcIpHYe0iBK5FIXOq4s5RyfSnlCwDcAeBfu+ff\nCuAtAL4xSOMHAPwK/a4HHN4G4MtLKV8I4MdrGDPbB+B/Avg6AI8C8G1mdnSK8ywAbyilHMHsMMkf\nmeIcB/BUAMcAfD2AX5pOyAaAF4tyJxKJPYQUuBKJROIevAnAQ+sPM3skZh+T/g9of1MPAL4JwGv8\nzVLKm0spn5h+vpnSfiyAW0spHyylbAB4KYAnT8+eDOBF0/WLAHzDdP0kAC8tpWyWUv4cs1Ow6/dF\nXwng2wbrmEgkdgEpcCUSiUsdBsw/UvtVuOd7eMCM3XoJgDcCOGxmn7kQ2ewRAD42CU4R/gWA35uu\nH4rZB8IrPoR7hLEHl1I+Asy/9/ZZjTi31zillI8D2G9mV3XKkEgkdgkpcCUSiUsdl5nZOwD8P8yE\nm9fTs28D8LIy+wbayzH7kK3HZwP46yiD6QO934XZt/SWxej31/4awDXnkX4ikbgISIErkUhc6rir\nlHI9Zh/SNQDfBwBm9vkADgF4vZndBuBboM12ZwAcbCU+Ocr/CoAnlVLumG7fPuVXce10DwA+bGYP\nnuI+BMBHKc7nNOJgKsOZsKaJRGLXkAJXIpG41GEAUEo5C+D7AfzAZF78dgD/qZTyyOnvWgDXmNnn\nuPinATxCJmz2MAC/A+CflVL+jB69FcB1ZvZwM9uPmemymjJvAvD06fppAG6k+99qZvvN7HMBXIeZ\nM3/FgwH8+TIVTyQSFw8pcCUSiUsdc5NdKeWPAbwbMybrqQB+14X9XcyEI1CcuwD82eRgDwCrAO6e\nrv8jgKsx21H4TjN7yxRnCzMm7XUA/gQzZ/j3T3F+EsDXmNktmPmUPXeK8z4AvwngfQBeDeBfTaZO\nmNljALy5lHLuAtohkUjsIGyar4lEIpE4T5jZkwE8ppTybDP7twCuKaU86yLm/wsAbiyl3Hyx8kwk\nEsthdbcLkEgkEvd1lFJuNLMHmdmvYnau1lMvchHek8JWIrG3kQxXIpFIJBKJxA4jfbgSiUQikUgk\ndhgpcCUSiUQikUjsMFLgSiQSiUQikdhhpMCVSCQSiUQiscNIgSuRSCQSiURih5ECVyKRSCQSicQO\n4/8DqyZciZjlCnUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d0b610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import aplpy\n",
    "gc = aplpy.FITSFigure(hdu_list)\n",
    "gc.show_grayscale(stretch='log', vmin=6e-3, vmax=1.4e-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A related example with multiple VO interfaces\n",
    "A very similar example uses the SkyView SIA protocol to retrieve an image from a different survey, SDSS, which is not held on the HEASARC SkyView server but at MAST.  In this case, the user is unaware of where the data come from, which is the point.  But under the hood, SkyView is using SIA to get the data from MAST.  \n",
    "\n",
    "(Again, it will take a few seconds for the image to appear.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "params['survey']='sdssg'\n",
    "r = requests.get('https://skyview.gsfc.nasa.gov/cgi-bin/vo/sia.pl', params=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://skyview.gsfc.nasa.gov/cgi-bin/images?position=19.0186%2C46.7304&survey=sdssg&pixels=300%2C300&sampler=LI&size=0.03%2C0.03&projection=Tan&coordinates=J2000.0&requestID=skv1512519973841&nofits=1&quicklook=jpeg&return=jpeg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=Table.read(io.BytesIO(r.content))\n",
    "display(Image(url=table['URL'][1].decode(\"utf-8\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"tap1\"></a>\n",
    "# 4a. TAP:  Using the Table Access Protocol to find additional information on our sources\n",
    "In this example, we already have a list of 30 sources from above.  <i> Continuing with our hypothetical science example of preparing an observing proposal for these objects, we would now like to find their redshifts.</i>\n",
    "\n",
    "First, find out what catalogs HEASARC holds that might have redshifts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=18&gt;\n",
       "<table id=\"table4726626000\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>value</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>object</th></tr></thead>\n",
       "<tr><td>abellzcat</td><td>Abell Clusters Measured Redshifts Catalog</td></tr>\n",
       "<tr><td>atlasspecz</td><td>AT Large Area Survey (ATLAS) Spectroscopic Classes &amp; Redshifts Catalog</td></tr>\n",
       "<tr><td>ccosmphotz</td><td>Chandra COSMOS (C-COSMOS) Survey Photometric Redshift Catalog</td></tr>\n",
       "<tr><td>cfa2s</td><td>CfA Redshift Survey: South Galactic Cap Data</td></tr>\n",
       "<tr><td>iraspscz</td><td>IRAS Point Source Catalog Redshift (PSCz) Catalog</td></tr>\n",
       "<tr><td>iraszsurv</td><td>IRAS 1.2-Jy Redshift Survey</td></tr>\n",
       "<tr><td>lcrscat</td><td>Las Campanas Redshift Survey Catalog</td></tr>\n",
       "<tr><td>lowzvlqvla</td><td>VLA 6-GHz Observations of Low-Redshift SDSS QSOs</td></tr>\n",
       "<tr><td>planckhzsc</td><td>Planck High-Redshift Source Candidates Catalog</td></tr>\n",
       "<tr><td>sixdfgs</td><td>6dFGS Galaxy Survey Final Redshift Release Catalog</td></tr>\n",
       "<tr><td>twodfqsoz</td><td>2dF QSO Redshift (2QZ) Survey</td></tr>\n",
       "<tr><td>twomassrsc</td><td>2MASS Redshift Survey (2MRS) Catalog</td></tr>\n",
       "<tr><td>xdeep2</td><td>DEEP2 Galaxy Redshift Survey Fields Chandra Point Source Catalog</td></tr>\n",
       "<tr><td>xmmcfrscat</td><td>XMM-Newton/Canada-France Redshift Survey Fields X-Ray Sources</td></tr>\n",
       "<tr><td>xmmcfrsoid</td><td>XMM-Newton/Canada-France Redshift Survey Fields Optical Identifications</td></tr>\n",
       "<tr><td>xmmcphotz</td><td>XMM-Newton COSMOS (XMM-COSMOS) Survey Photometric Redshift Catalog</td></tr>\n",
       "<tr><td>xmmlssclas</td><td>XMM-Newton Large-Scale Structure Optical Counterparts and Redshifts</td></tr>\n",
       "<tr><td>zcat</td><td>CfA Redshift Catalog (June 1995 Version)</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=18>\n",
       "   name    ...\n",
       "  object   ...\n",
       "---------- ...\n",
       " abellzcat ...\n",
       "atlasspecz ...\n",
       "ccosmphotz ...\n",
       "     cfa2s ...\n",
       "  iraspscz ...\n",
       " iraszsurv ...\n",
       "   lcrscat ...\n",
       "lowzvlqvla ...\n",
       "planckhzsc ...\n",
       "   sixdfgs ...\n",
       " twodfqsoz ...\n",
       "twomassrsc ...\n",
       "    xdeep2 ...\n",
       "xmmcfrscat ...\n",
       "xmmcfrsoid ...\n",
       " xmmcphotz ...\n",
       "xmmlssclas ...\n",
       "      zcat ..."
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tap_params[\"query\"]=\"\"\"\n",
    "    select a.name,b.value\n",
    "      from metainfo a, metainfo b\n",
    "      where\n",
    "         a.type='table' and b.type='table' and\n",
    "         a.relation='defaultSearchRadius' and\n",
    "         b.relation='description' and\n",
    "         a.name=b.name and\n",
    "         b.value LIKE '%Redshift%' \n",
    "      order by name    \n",
    "      \"\"\"\n",
    "r = requests.get('https://heasarc.gsfc.nasa.gov/xamin/vo/tap/sync', params=tap_params)\n",
    "table=Table.read(io.BytesIO(r.content))\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "Wrap this, e.g., <br>\n",
    "astroquery.heasarc.surveys_like(\"Redshift\")<br>\n",
    "<br>\n",
    "Annoying case sensitive right now.  Can this be fixed?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that we can query these objects in the CfA Redshift Catalog, 'zcat'.  Since these are galaxies found in close pairs or groups, we expect multiple matches at each location. Let's first start with one object and search within 0.1 degree: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=3&gt;\n",
       "<table id=\"table4731740240\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>ra</th><th>dec</th><th>radial_velocity</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>int32</th></tr></thead>\n",
       "<tr><td>19.018550090000002</td><td>46.730388720000001</td><td>5352</td></tr>\n",
       "<tr><td>19.068407270000002</td><td>46.740033570000001</td><td>5081</td></tr>\n",
       "<tr><td>19.08601651</td><td>46.74722843</td><td>5188</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=3>\n",
       "        ra                dec         radial_velocity\n",
       "     float64            float64            int32     \n",
       "------------------ ------------------ ---------------\n",
       "19.018550090000002 46.730388720000001            5352\n",
       "19.068407270000002 46.740033570000001            5081\n",
       "       19.08601651        46.74722843            5188"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##  Pick a radius in degrees to search around our input coordinates:\n",
    "radius=0.1\n",
    "tap_params = {\n",
    "    \"request\":\"doQuery\",\n",
    "    \"lang\":\"ADQL\",\n",
    "    \"query\":\n",
    "    \"\"\"SELECT ra, dec, Radial_Velocity FROM zcat as cat where \n",
    "    contains(point('ICRS',cat.ra,cat.dec),circle('ICRS',{0},{1},{2}))=1\n",
    "    \"\"\".format(aptable[0]['RA'],aptable[0]['DEC'],radius)\n",
    "    }\n",
    "r = requests.get('https://heasarc.gsfc.nasa.gov/xamin/vo/tap/sync', params=tap_params)\n",
    "#r.content\n",
    "table=Table.read(io.BytesIO(r.content))\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"tap2\"></a>\n",
    "# 4b. TAP:  Using the TAP to cross-correlate our objects with a catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to search all of the sources in one go, we need to upload our table and do a 'cross-correlation' of our own table with the 'zcat' table. Above, we had created a VOTableFile object called votablefile. Now we need to hand it to the requests method as upload data and use it in our TAP request.\n",
    "\n",
    "First, create a \"file-like\" object that you can hand over:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Alternatively, write it to a file:\n",
    "#apvot.writeto(votablefile, \"new_votable.xml\")\n",
    "#files={'uplt':open('new_votable.xml', 'rb')}\n",
    "\n",
    "## In memory only, use an IO stream. \n",
    "vot_obj=io.BytesIO()\n",
    "apvot.writeto(votablefile,vot_obj)\n",
    "vot_obj.seek(0) # Reset seek to beginning of buffer?\n",
    "## 'uplt' is what we'll call it (for 'upload table') \n",
    "##   in the requests parameters below, or what you will:\n",
    "files={'uplt':vot_obj}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "Can this be simplified?  Why do we really need the VOTableFile level with only one table?  Because we read-write to disk?  Can that be avoided?</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we don't want to find all the associated galaxies, but just the match within 0.1 degree. (This takes a good 20 seconds. If you need to rerun this cell, rerun the one above that defines the upload.  The IO object needs to be reset.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<thead><tr><th>float64</th><th>float64</th><th>int32</th></tr></thead>\n",
       "<tr><td>19.068407270000002</td><td>46.740033570000001</td><td>5081</td></tr>\n",
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      "text/plain": [
       "<Table masked=True length=39>\n",
       "        ra                dec         radial_velocity\n",
       "     float64            float64            int32     \n",
       "------------------ ------------------ ---------------\n",
       "19.068407270000002 46.740033570000001            5081\n",
       "19.086435210000001 46.747227780000003            5188\n",
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       "               ...                ...             ...\n",
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       "317.10346952999998 18.202987799999999            4826\n",
       "317.11477339999999 18.191914189999999            5054\n",
       "329.24927524999998 6.6551501899999996            7857\n",
       "      333.82169725 37.299221410000001            5207\n",
       "      333.84683658 37.282608979999999            5984\n",
       "333.91491775999998 37.300255790000001            5684"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## These parameters are defined in the TAP standard.  \n",
    "##  The string 'uplt' is what we set above as the name\n",
    "##  of the parameter containing this data, and\n",
    "##  the string 'mysources' is how we refer to it in \n",
    "##  the ADQL query string:\n",
    "cc_params={\n",
    "    'lang': 'ADQL', \n",
    "    'request': 'doQuery',\n",
    "    'upload':'mysources,param:uplt'\n",
    "    }\n",
    "cc_params[\"query\"]=\"\"\"\n",
    "    SELECT cat.ra, cat.dec, Radial_Velocity \n",
    "    FROM zcat cat, tap_upload.mysources mt \n",
    "    WHERE\n",
    "    contains(point('ICRS',cat.ra,cat.dec),circle('ICRS',mt.ra,mt.dec,0.1))=1\n",
    "    and Radial_Velocity > 0\n",
    "    ORDER by cat.ra\"\"\"\n",
    "r = requests.post('https://heasarc.gsfc.nasa.gov/xamin/vo/tap/sync',data=cc_params,stream=True,files=files)\n",
    "mytable=Table.read(io.BytesIO(r.content))\n",
    "mytable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"combo\"></a>\n",
    "\n",
    "# 4c. Another example with TAP table cross-correlation: Combining data from different catalogs\n",
    "Now we'd like to take the redshift information (above, as a radial velocity) and determine a search radius to use for each galaxy based on its distance, so that we are are searching a given physical distance. Therefore <i>for our science proposal, we need to check that the redshifts (or radial velocities) of nearby (within 50 kpc physical distances) are similar, suggesting these are truly \"close\" or potentially interacting galaxies.</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<thead><tr><th></th><th></th><th></th><th></th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th></tr></thead>\n",
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       "<Table masked=True length=39>\n",
       "        ra                dec         ...     redshift         angDdeg     \n",
       "                                      ...                        deg       \n",
       "     float64            float64       ...     float64          float64     \n",
       "------------------ ------------------ ... --------------- -----------------\n",
       "19.068407270000002 46.740033570000001 ... 0.0169366666667 0.000681042539147\n",
       "19.086435210000001 46.747227780000003 ... 0.0172933333333 0.000667286052277\n",
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       "      146.70334308 22.018272169999999 ...         0.02482 0.000469199968083\n",
       "               ...                ... ...             ...               ...\n",
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       "241.52278516999999        20.78278156 ... 0.0159933333333 0.000720384238888\n",
       "317.10346952999998 18.202987799999999 ... 0.0160866666667 0.000716286082127\n",
       "317.11477339999999 18.191914189999999 ... 0.0168466666667 0.000684605847076\n",
       "329.24927524999998 6.6551501899999996 ...         0.02619 0.000445394170434\n",
       "      333.82169725 37.299221410000001 ... 0.0173566666667 0.000664902427331\n",
       "      333.84683658 37.282608979999999 ... 0.0199466666667 0.000580392639034\n",
       "333.91491775999998 37.300255790000001 ... 0.0189466666667 0.000610283342388"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The radial_velocity is in km/s, and this is just c*z, so\n",
    "c=3.0e5 # km/s\n",
    "redshifts=mytable['radial_velocity'].filled(0.)/c  # Filling masked values with zero\n",
    "mytable['redshift']=redshifts\n",
    "from astropy import units\n",
    "physdist=0.05*units.Mpc # 50 kpc physical distance\n",
    "\n",
    "## This needs scipy.  \n",
    "from astropy.cosmology import Planck15\n",
    "angDdist=Planck15.angular_diameter_distance(mytable['redshift'])\n",
    "## angDdist is returned from the astropy.cosmology module as a Quantity object, \n",
    "##  i.e. a value and a unit.  Arctan is smart enough not to operate on quantities\n",
    "##  that aren't unitless.  So angDdist.value to just get the value.\n",
    "angDrad=numpy.arctan(physdist/angDdist)\n",
    "angDdeg=angDrad*units.deg/units.rad\n",
    "mytable['angDdeg']=angDdeg\n",
    "mytable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "The above needs scipy.  That's just to use the cosmology package for the Planck15 specific computation of angular diameter distance.  Could simplify.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<TableColumns names=('ra','dec','radial_velocity','redshift','angDdeg')>\n"
     ]
    }
   ],
   "source": [
    "## In memory only, use an IO stream. \n",
    "vot_obj=io.BytesIO()\n",
    "print(mytable.columns)\n",
    "apvot.writeto(apvot.from_table(mytable),vot_obj)\n",
    "## (Reset the \"file-like\" object to the beginning.)\n",
    "vot_obj.seek(0)\n",
    "## 'uplt' is what we'll call it (for 'upload table') \n",
    "##   in the requests parameters below, or what you will:\n",
    "files={'uplt':vot_obj}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "<Table masked=True length=49>\n",
       "        ra                dec         radial_velocity\n",
       "     float64            float64            int32     \n",
       "------------------ ------------------ ---------------\n",
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       "135.99423805999999        21.90099584            3157\n",
       "136.00074370999999        21.96791867            3093\n",
       "141.09150181000001 40.683784680000002            8267\n",
       "               ...                ...             ...\n",
       "241.52278516999999        20.78278156            4602\n",
       "241.52278516999999        20.78278156            4798\n",
       "241.52278516999999        20.78278156            4798\n",
       "241.52278516999999        20.78278156            4798\n",
       "317.10346952999998 18.202987799999999            4826\n",
       "317.11477339999999 18.191914189999999            5054\n",
       "329.24927524999998 6.6551501899999996            7857\n",
       "      333.82169725 37.299221410000001            5207\n",
       "      333.84683658 37.282608979999999            5984\n",
       "333.91491775999998 37.300255790000001            5684"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## These are defined in the TAP standard:\n",
    "cc_params={\n",
    "    'lang': 'ADQL', \n",
    "    'request': 'doQuery',\n",
    "    'upload':'mytable,param:uplt'\n",
    "    }\n",
    "## This is your ADQL query, where \"mytable\" here has to \n",
    "##  match what you specified in the upload parameter above\n",
    "cc_params[\"query\"]=\"\"\"\n",
    "    SELECT cat.ra, cat.dec, cat.Radial_Velocity \n",
    "    FROM zcat cat, tap_upload.mytable mt \n",
    "    WHERE\n",
    "    contains(point('ICRS',cat.ra,cat.dec),circle('ICRS',mt.ra,mt.dec,mt.angDdeg))=1\n",
    "    and cat.Radial_Velocity > 0\n",
    "    ORDER by cat.ra\"\"\"\n",
    "## The name you give here (tab1) matches what's in the cc_params\n",
    "r = requests.post('https://heasarc.gsfc.nasa.gov/xamin/vo/tap/sync',data=cc_params,stream=True,files=files)\n",
    "mytable=Table.read(io.BytesIO(r.content))\n",
    "mytable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ssa\"></a>\n",
    "# 6. SSA:  Using the Simple Spectral Access protocol to retrieve spectra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Returning to our science example, suppose that we have analyzed the available Chandra image for the three matches above and discovered that one of these galaxies appears to have a potential ultraluminous X-ray source (ULX)! Now, we want to charatcterize the spectrum for this source. Therefore we are asking for a large exposure time and need to justify this by showing the spectrum of a prototypical ULX. </i>\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "Ugh-- feel like I am stretching the science case a little... God help us if someone wants to judge this as a TAC might! \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=3&gt;\n",
       "<table id=\"table4607959440\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>obsid</th><th>status</th><th>name</th><th>ra</th><th>dec</th><th>time</th><th>detector</th><th>grating</th><th>exposure</th><th>type</th><th>pi</th><th>public_date</th><th>SSA_datalength</th><th>SSA_datamodel</th><th>SSA_title</th><th>SSA_instrument</th><th>SSA_publisher</th><th>SSA_tmid</th><th>SSA_stop_time</th><th>SSA_start_time</th><th>SSA_duration</th><th>SSA_bandpass</th><th>SSA_wavelength_max</th><th>SSA_wavelength_min</th><th>SSA_bandwidth</th><th>SSA_ra</th><th>SSA_dec</th><th>SSA_coord_obs</th><th>SSA_fov</th><th>SSA_reference</th><th>SSA_format</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th>deg</th><th>deg</th><th>mjd</th><th></th><th></th><th>s</th><th></th><th></th><th>mjd</th><th></th><th></th><th></th><th></th><th></th><th>mjd</th><th>mjd</th><th>mjd</th><th>s</th><th>meter</th><th>meter</th><th>meter</th><th>meter</th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th><th>object</th><th>object</th><th>int32</th><th>object</th><th>object</th><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>object</th><th>object</th></tr></thead>\n",
       "<tr><td>17860</td><td>archived</td><td>M82 X-1</td><td>148.95917</td><td>69.67972</td><td>57639.729200000002</td><td>ACIS-S</td><td>HETG</td><td>51060</td><td>TOO</td><td>Walton</td><td>58010</td><td>12.0</td><td>Spectrum-1.0</td><td>acisf17860N002_pha2</td><td>ACIS-S</td><td>HEASARC</td><td>57640.040393247451</td><td>57640.350814633915</td><td>57639.729200000002</td><td>51060.0</td><td>3.16159e-09</td><td>6.1991999999999999e-09</td><td>1.2397999999999999e-10</td><td>6.0752199999999996e-09</td><td>148.95917</td><td>69.679720000000003</td><td>--</td><td>0.81103500403976259</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/science/ao17/cat4/17860/primary/acisf17860N002_pha2.fits.gz</td><td>application/fits</td></tr>\n",
       "<tr><td>19791</td><td>archived</td><td>M82 X-1</td><td>148.95917</td><td>69.67972</td><td>57641.570200000002</td><td>ACIS-S</td><td>HETG</td><td>15310</td><td>TOO</td><td>Walton</td><td>58010</td><td>12.0</td><td>Spectrum-1.0</td><td>acisf19791N002_pha2</td><td>ACIS-S</td><td>HEASARC</td><td>57641.668022753758</td><td>57641.765065583451</td><td>57641.570200000002</td><td>15310.0</td><td>3.16159e-09</td><td>6.1991999999999999e-09</td><td>1.2397999999999999e-10</td><td>6.0752199999999996e-09</td><td>148.95917</td><td>69.679720000000003</td><td>--</td><td>0.81103500403976259</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/science/ao17/cat4/19791/primary/acisf19791N002_pha2.fits.gz</td><td>application/fits</td></tr>\n",
       "<tr><td>19792</td><td>archived</td><td>M82 X-1</td><td>148.95917</td><td>69.67972</td><td>57642.353000000003</td><td>ACIS-S</td><td>HETG</td><td>32440</td><td>TOO</td><td>Walton</td><td>58010</td><td>12.0</td><td>Spectrum-1.0</td><td>acisf19792N002_pha2</td><td>ACIS-S</td><td>HEASARC</td><td>57642.558369954859</td><td>57642.762945849187</td><td>57642.353000000003</td><td>32440.0</td><td>3.16159e-09</td><td>6.1991999999999999e-09</td><td>1.2397999999999999e-10</td><td>6.0752199999999996e-09</td><td>148.95917</td><td>69.679720000000003</td><td>--</td><td>0.81103500403976259</td><td>https://heasarc.gsfc.nasa.gov/FTP/chandra/data/science/ao17/cat4/19792/primary/acisf19792N002_pha2.fits.gz</td><td>application/fits</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=3>\n",
       "obsid   status  ...    SSA_format   \n",
       "                ...                 \n",
       "object  object  ...      object     \n",
       "------ -------- ... ----------------\n",
       " 17860 archived ... application/fits\n",
       " 19791 archived ... application/fits\n",
       " 19792 archived ... application/fits"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import astropy.coordinates as coord\n",
    "m82=coord.SkyCoord.from_name(\"m82\")\n",
    "pos='{},{}'.format(m82.ra.deg,m82.dec.deg)\n",
    "params = {'table': 'chanmaster',\"POS\":pos,\"SIZE\":\".01\", \"REQUEST\":\"queryData\"}\n",
    "r = requests.get('https://heasarc.nasa.gov/xamin/vo/ssa', params=params)\n",
    "spec_table = Table.read(io.BytesIO(r.content))\n",
    "spec_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=12&gt;\n",
       "<table id=\"table4701141712\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SPEC_NUM</th><th>TG_M</th><th>TG_PART</th><th>TG_SRCID</th><th>X</th><th>Y</th><th>CHANNEL [8192]</th><th>COUNTS [8192]</th><th>STAT_ERR [8192]</th><th>BACKGROUND_UP [8192]</th><th>BACKGROUND_DOWN [8192]</th><th>BIN_LO [8192]</th><th>BIN_HI [8192]</th></tr></thead>\n",
       "<thead><tr><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>float32</th><th>int16</th><th>int16</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>1</td><td>-3</td><td>1</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>7.15916666667 .. 0.333333333333</td><td>7.16 .. 0.334166666667</td></tr>\n",
       "<tr><td>2</td><td>-2</td><td>1</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>10.73875 .. 0.5</td><td>10.74 .. 0.50125</td></tr>\n",
       "<tr><td>3</td><td>-1</td><td>1</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>21.4775 .. 1.0</td><td>21.48 .. 1.0025</td></tr>\n",
       "<tr><td>4</td><td>1</td><td>1</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>21.4775 .. 1.0</td><td>21.48 .. 1.0025</td></tr>\n",
       "<tr><td>5</td><td>2</td><td>1</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>10.73875 .. 0.5</td><td>10.74 .. 0.50125</td></tr>\n",
       "<tr><td>6</td><td>3</td><td>1</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>7.15916666667 .. 0.333333333333</td><td>7.16 .. 0.334166666667</td></tr>\n",
       "<tr><td>7</td><td>-3</td><td>2</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>13.985 .. 0.333333333333</td><td>13.9866666667 .. 0.335</td></tr>\n",
       "<tr><td>8</td><td>-2</td><td>2</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>20.9775 .. 0.5</td><td>20.98 .. 0.5025</td></tr>\n",
       "<tr><td>9</td><td>-1</td><td>2</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>41.955 .. 1.0</td><td>41.96 .. 1.005</td></tr>\n",
       "<tr><td>10</td><td>1</td><td>2</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>41.955 .. 1.0</td><td>41.96 .. 1.005</td></tr>\n",
       "<tr><td>11</td><td>2</td><td>2</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>20.9775 .. 0.5</td><td>20.98 .. 0.5025</td></tr>\n",
       "<tr><td>12</td><td>3</td><td>2</td><td>1</td><td>4103.92</td><td>4072.69</td><td>1 .. 8192</td><td>0 .. 0</td><td>1.86603 .. 1.86603</td><td>0 .. 0</td><td>0 .. 0</td><td>13.985 .. 0.333333333333</td><td>13.9866666667 .. 0.335</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=12>\n",
       "SPEC_NUM  TG_M ...          BIN_LO [8192]              BIN_HI [8192]     \n",
       " int16   int16 ...             float64                    float64        \n",
       "-------- ----- ... ------------------------------- ----------------------\n",
       "       1    -3 ... 7.15916666667 .. 0.333333333333 7.16 .. 0.334166666667\n",
       "       2    -2 ...                 10.73875 .. 0.5       10.74 .. 0.50125\n",
       "       3    -1 ...                  21.4775 .. 1.0        21.48 .. 1.0025\n",
       "       4     1 ...                  21.4775 .. 1.0        21.48 .. 1.0025\n",
       "       5     2 ...                 10.73875 .. 0.5       10.74 .. 0.50125\n",
       "       6     3 ... 7.15916666667 .. 0.333333333333 7.16 .. 0.334166666667\n",
       "       7    -3 ...        13.985 .. 0.333333333333 13.9866666667 .. 0.335\n",
       "       8    -2 ...                  20.9775 .. 0.5        20.98 .. 0.5025\n",
       "       9    -1 ...                   41.955 .. 1.0         41.96 .. 1.005\n",
       "      10     1 ...                   41.955 .. 1.0         41.96 .. 1.005\n",
       "      11     2 ...                  20.9775 .. 0.5        20.98 .. 0.5025\n",
       "      12     3 ...        13.985 .. 0.333333333333 13.9866666667 .. 0.335"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get the first spectrum (which has a long-ish exposure.)\n",
    "import astropy.io.fits as fits\n",
    "hdu_list=fits.open(spec_table[0]['SSA_reference'].decode()) # Byte format, so just decode() to string\n",
    "#hdu_list.info()\n",
    "table = Table(hdu_list[1].data)\n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+X1WHrk0XAQAYJ7kgAMB0W+oZhEckefr8hsFt3P9m0P6oJC+sqocveJ+7NgEA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8AAAg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\nUJIkSZIkSSrsb7ZO19fw1Da6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba6b4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(table)):\n",
    "    matplotlib.rcParams['figure.figsize'] = (18, 16)\n",
    "    ax=plt.subplot(6,2,i+1)\n",
    "    pha = plt.plot( table['CHANNEL'][i],table['COUNTS'][i])\n",
    "    ax.set_yscale('log')\n",
    "    if table['TG_PART'][i] == 1:\n",
    "        instr='HEG'\n",
    "    if table['TG_PART'][i] == 2:\n",
    "        instr='MEG'\n",
    "    if table['TG_PART'][i] == 3:\n",
    "        instr='LEG'\n",
    "    ax.set_title(str(table['TG_M'][i])+' '+instr)\n",
    "    plt.tight_layout()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Note: The 12 plots here show all the different HEG and MEG components (+/- 3, +/- 2, and +/- 1 for each grating) for the grating data, as the titles state. In practice, you might want to download the fits file and use a different analysis and plotting tool to create an energy spectrum plot using SHERPA or XSPEC, etc. \n",
    "\n",
    "You can download the file directly using this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute 'request'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-153-3e346c15114c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murlretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SSA_reference'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"my_M82_spectrum.fits\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'request'"
     ]
    }
   ],
   "source": [
    "import urllib\n",
    "urllib.request.urlretrieve(spec_table[0]['SSA_reference'].decode(), \"my_M82_spectrum.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}