heasarc_queries_Overview.ipynb

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   "cell_type": "markdown",
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    "# Summary 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. This notebook presents the broad summary of available protocol. We offer more details in separate notebook on each of the topics:  [SCS](heasarc_Cone_Search.ipynb), [SIA](heasarc_Image_Access.ipynb), [SSA](heasarc_Spectral_Access.ipynb), [TAP](heasarc_TAP_queries.ipynb), and [VOTables](heasarc_VO_Tables.ipynb).  \n",
    "\n",
    "<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. </i> \n",
    "\n",
    "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"
   ]
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  {
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   "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",
    "\n",
    "See the notebook dedicated to [Using VOTables](heasarc_VO_Tables.ipynb) for more details."
   ]
  },
  {
   "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:"
   ]
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   "cell_type": "code",
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   "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": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "table=Table.read(io.BytesIO(r.content))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.table.table.Table'>\n"
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       "RP900513N00 114.7000  85.7000 127.6963  27.9300 ...   128  1196  1196  3900\n",
       "RP900161N00 111.7200  85.7500 127.6760  27.7031 ...    12   704   706  3900\n",
       "RH600498A01 111.8000  85.7600 127.6640  27.7078 ...   140  1573  1575  6900\n",
       "RH600498N00 111.8000  85.7600 127.6640  27.7078 ...   336  1406  1410  6900\n",
       "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": 5,
     "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": 6,
   "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": 7,
   "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.  See also the dedicated notebook on [using SCS](heasarc_Cone_Search.ipynb).  \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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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    ... 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    ... type    pi   public_date Search_Offset\n",
      "                           deg    ...                  mjd                  \n",
      "----- -------- -------- --------- ... ---- ------- ----------- -------------\n",
      "19370 observed NGC 5297 206.59875 ...   GO McHardy       58417        1.5692\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",
    "    if len(table) > 0:\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. See also the dedicated notebook on [using SIA](heasarc_Image_Access.ipynb).  \n",
    "\n",
    "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": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=88&gt;\n",
       "<table id=\"table4611600848\" 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": 8,
     "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": [
    "### 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": 16,
   "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'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=skv1512590621763&return=FITS'\n",
      "b'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=skv1512590621763&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": 18,
   "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=skv1512590621763&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": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('image.fits', <http.client.HTTPMessage at 0x112c08ac8>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Download the FITS image?\n",
    "import urllib.request\n",
    "urllib.request.urlretrieve(table['URL'][4].decode(\"utf-8\"), \"image.fits\")\n"
   ]
  },
  {
   "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": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x114187e48>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SRII2jhFvNjIQMzlRY8dlhSppoE7OP6q4cYnKtcU2caSmqlcJ6sMPfKmxK/3z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\nspKVXGBZgfRKVrKSlVxg+f8Bz9rcme+psVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112c089b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hdu_list=apfits.open(table['URL'][4].decode(\"utf-8\"))\n",
    "hdu_list=apfits.open('image.fits')\n",
    "plt.imshow(hdu_list[0].data, cmap='gray', origin='lower',vmax=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternatively, we can use Aplpy to make an image with coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E/Q6hzH2K+zjrF1TWLq6wVoyMMcYYY4wxB48VI7MBU1SYKoACtKtB2wwlzS9T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NjJEkGizdcw9L6BHHRu8C1o5rLmaZ60J7Vvp97XrcdNNNO+psf99yyy0AtrvZZc/mqnPp22Mu\nK9n9a/e59b25z/V/Z/tYEG67Hq1M78qjBOEyF8os+F5xd2UwV7a56Wxj3SzxiOoqqswBmcuY4v6i\npIkedc9rdaqJMpjb1IhLFUuQ0r/XRtILMxdcdWwo8+Go61icr5mLqVJPT+YaFed51eV0ZGmKdRl1\n11Puo5ooQ00qEY+Lz2G/LybB6V2aY1KJ3hU6jvnMRfykk07aVhbY/j7abZSEFXOwYmSMMcYYY4zZ\neKwYmW3s27dPTm+oWMiy5ARMMWKKRlShsn1Tn/s6VWtrLMPOIQvCzayt0SKXWXczFIs/szzFfmcW\nK5ZqNYMForJtDCXZQwZTHEas/Mza3tOubWZNbu01BSfry6c//ekd+6LC2FvrWrmmGPVWOKYYxcQM\nLElIhrI4ZmbhbH3vz6FZJtu2XjFiizK2c2jtZApcdn4skDw+h9kzylSLBgu+ZyhKimqtZ8qYkpac\n9UHpC0uwwOZatV9RVWDHq3XHOS+ztmeJh0bmw1EUpV9drFa5z1mdyvy7G/1rsNT/St9ZPxmKFwBL\n5T5ST8ZoAopsH3vfx7mvV4WiYt/Pw9GjIVPSs+RZbV7MkgpNXdf4d6wzki2zcawWU7ZiZIwxxhhj\njNl4rBiZbcR03Y1MaYhWCabSzLViji6ExvrA6mQwJUWxuo/GdygW0UzVU1Qztsip0ndmIWdWu9G4\nLmWfaqlmi+o2FLWMjcXMQpZZnGP5vm9NCWnPX2/la+1l6ambUpSltY7l+/vHFmVUnqesTJwTslSw\nTE2K7fd1sTHR6maqbt+XeA6ZZZSlnB6Jz+nriO0xi6caS8PiiBQrP0NJdc3qHn3Gs/5GVT7bl43h\nqOqOxnxkamK8fyyVc08cg6PvsFGUuMpsDLI4rtjPrK7R52KkndFYH9bGiKJ2LFQJ9dlWyMZinGv7\nue+UU04BAJx88skAtitGbRtbUPbmm28GsP26tG033ngjgDz2rqlJ2TuIKUUZLHZ2N+6XFSNjjDHG\nGGPMxuMfRsYYY4wxxpiNx650ZgeZKx2Tz9d1AVCDpAXHcQAAIABJREFUP5U+7SbM5S+6rWQBk0rf\nVfeziOKmw9rPXPFYe026nrOKdCRLSjGF6nISXTlG+6a4gmT3KnNf6t0IpsjSdMcU3s09oW8vSxPc\n2m4udNm+tm00+ULGyLjO7pGy4nrfp5gCPAv6zdxJWPIF1s+RRB7MlUpxX1NdaZVrzlIeM1dopQ3F\npa7vg+JWrZ6nMl8ozy1LIc3uceaCme1jcwKDjcWpsmp5xTVZTdoQ92X3PbvGiltfdq1GUlWz+rPz\nG3UVVBg5bnTOzYjuyv0c2tzj2julucgBwKmnngrgaGrte9zjHlv7mktzqyub35pLXO+y3ZICNbIU\n3dl4Y8tBxOdo1G1OcbmfwoqRMcYYY4wxZuOxYmS2MZV8IQsMHEl4oFoqRxQiZhnNGE3JrBw3msxA\nsQ6qVuSp8lndSuKKuLjbKKrFOV47ZjlWU8ePqJZsDKtjcap9YGfwfm9Zi+Ol70tU5TJrdBZ0GhMs\nZAkIsuNYmmd2fiPJTNj1VK91VHCYKsT2ZXMQs1iuqxgp1kn2jGb9ZQHa6y58ncHmvuwzU4XmKpTs\nHkXYNZsqFz/H68/mdqbE7EoQOPHKGFFN1CQYI+9P9d010odRzwtFdVTHrjK+2LwR36OjCQUUsvkt\nLpEAHFV+WqKFphL1fzel6LTTTtva15Slvq5Ie5+1RAt9e+16tuUogJ0eDdmyCdn7SRnDGbvhSWTF\nyBhjjDHGGLPxWDEyOxhd0GzUX3fUl3oEJQ0uY9QvnPlWx7L938zvfe4CtnP72/Y1xSGre9Tfm1m2\nY4pdRcVS9zFrLRunLD1pdi7M+s18ots1zvoZ7012fkz5YalPRy34bOHFmHp4VE3IYO3F88msvJli\nFPeNqkKj8xSLk8rKr+pL1iemUGf3c0RJzfoeY9Sy9rJ01qNKE5svmAoZt2WK0brxHJmKwRYNzuZY\nRZ2NMXhZH5QYnqyd0fiM2G5fXnk/zSWbZ9S4EFbX1L6571G2MDRrjy2N0MjSp2dlW3vZ8gdN+Wnq\nUK8K3ete99q27fTTT9/a18q3dvv227k25acfp00xyuaLuHB5FoPL1EslTk4dI+r99g8js8XBgwdx\nySWX4O53v/vWtsOHD+OKK644jr0yxhhjjDFG5/zzz8f555+/9fnKK6/EG9/4xpXH+YeR2eLQoUP4\n2Mc+hgc96EFb247F4mbGGGOMMcYcK6644gpcfvnlW5+vvvpq6Tj/MDJDZNJ6YzQ4TkmfqkqfMTi9\nh0myLAUpC6Yecc/IXGKU40evNVtVvZG5FTGXhbhqfN8uCwyO/csSAoxK43GbmnKc3UfF3Yq5Z7Jt\n2TVjbmHRTS5z02jbsuuZuUIq7iTKuTDYfcxWOW/7spSuzR2kT1jRymfJJSKZq0m2byT4nrXD5gTm\nSpeNDWXuY2ORHZfNDYqLyqg7YDye7WP1ZON7NJkF26e4dbK5Orv/SuICFrSffVaWXsjaz56/SDaW\n4vzbnztbhoDNsXG+H3Vfy64rGwuKW/tc1z8l+Yri7phd86xPivt3e7abOxtwNBV3c5c744wztvY1\n17l73vOeALDNQ6i50mXJF1qf2zIS2XILbYx85jOf2drXxmBLId7P7bHuDDY/7ZYLZ8TJF4wxxhhj\njDEbjxUjs41aa2rNaKj7snobMQ3q3ODKLPCV9VOxfrMF+7L6M4sFs5BFS6waKK8ssBiD4ntGLNXZ\ncdlnxcKtqgmxzuyeKdah0TKx71ngM0tAkMGsw8yyGq9HZo2MiRb6v9k1U1KjZ+eQlVGUwrgYYP93\nZnGM5fvzY4kA4nUZPT/FgqwumBrby5TpEbUlU2eZ1Z7No9lcy5SROG56a3S8ZqPz92hyn9hf5mmg\nHJ+1px4XE46oSV7YfWfPmqJox4QuU32IKIuqqopzHEvZ8ztap6JoNpR3V79NeUaPxeLt7DtBI1ss\nPFtKI6p7vcrT1JmmBvXJF1qa7qYY9am8+4Vgp2hqVE/0Arjhhhu29kXFqE/zHRfoVhZHV2H3YRVW\njIwxxhhjjDEbjxUjs41aqxSn02/rj83qm0KxomXWb6bSKBZ15oPPrJEsBmPUUh1jdzIyP/uMqTpY\nOtysv1kqaNbGiNI0Gk+kqB+svcwynsVZxBS5LLYpUyhbXb0ywtTLqeOzc8mUpixlbrynarrXeLwK\nUwxiPFBPXOCvP46limcqK3vu2TUbQY0jUdTVxuhcqShUo+fHrNjt70x9VhYdVS34DUW5YbDYHSX+\nkM2VPSxmJ7YzGv/AFiKOfQN2xtypirYCuwbZPMPGZ6yTbVOe/6x/bN5W70Nsh91/tnxF7GO2TT1P\n9n0h1tH3qakz7f9eCWoKUVOTWhmVdl3b4rHA0fffTTfdBGC7QtU8BJqilcX4jj4rikIcy45gxcgY\nY4wxxhiz8fiHkTHGGGOMMWbjsSud2cbtt99OXel6WFAl28dcjWKQuJrWNNbVn0OTeVudveQc28v6\nqfQl65fiksZWsGeJLjI3Kya7szTRygrTWbuK+9poULtyXUeDr1kAM0sgwcYbc5th7gFKmlkWGJx9\nZu49c5l7H+JY6l3qbrnllm3b+ucwblNdeOJ96Ntrf7f/M/dadn7xnI4nynw66jLCxnDcliWlUZ5f\ntU8sEYSS3CFzz1Tc3lja86nPq8ie/+h61feT3b/Yv8x9LUvhryQnUFCPG6k/c6Vi9SiuotlcmSUj\nUly3mEtdllxiiux5aufO0pn3RNe9zG05ez6aa3g7rk/M0BKpZImGRugTsrT6m1te317sQ7+vuVcr\nbqRsfI+GDazCipExxhhjjDFm47FiZHagWqBHFKOekQVFGSwourccN8Uoszw160VmqWQB0/EaqUGn\nowknVpXtyytJJrLjR6zRoypII7sGiqU7s6yydORZcGwcb8wq1cOSi4yobExJY8HiWdujiQQUVUCx\nqI0GzGaKYVRu+2dUWZyYBSdndbax0P7P0nzHc1q17XjDEgiw50lR27K5T0mwMKoKx36rdSrjVD0/\nRRWfqxQ11EVI49hXk0TE5Auj17OhXpeIOiaissHeIcqc0pebG3w/cm+z67OuAjf6nUC5dqrHxrrn\n0GCKaOaV08r3SlX8LqAumM1URCUp2CqsGBljjDHGGGM2HitGZhu1bl/gVVkYUl3kLjInBXXfJxYL\nlaULbtt6H1dmlYjbRn24VYvh1D71ukZrueL7z3xyj4X1nKk0qlWLKU2tfLNGsYVhmY87G5Ns0WDV\n6qoocKztda19c6xnIzBFjKlXceyysc/2scVf1cVRVfV3VdmsfwrK88BUs+w4RW3L0rwrilEGS5+s\nxF6yOUhVu7MxEWnnnFmxR+d0Nvbj8ep4yxZKbsQYOiWFdA9Tpljfsm2K4hvL9uVHxxkb16xPyjhj\nz+poHG7cls19cfHWvu42LuO7L6O/d1Elz+bFFt+TLdg6SnzGlNi9flv7vx8b7H0Rj2dz3xysGBlj\njDHGGGM2Hv8wMsYYY4wxxmw8dqUz26i1zk6nybaprkZxX+bekaWpbNvmpi7OJPLoiqOu9B1dGjLX\nxOyzItMr7jJsFfgM1qeR49SEAMztYiQgnPVpNIFERjwf5rqpSvlKH5iLKmM0/bly/NxU1WyF+Oze\ntvKZK1287lmfYqKFvn72rLJrPeL6lcHmPNaX7D5EdxI1da3icqTcY3UuiXWyBDtqe2yf4l6Z9YUl\nrBhJosDKsXcDu8fsHZQlX4jHr+q74nrJjm9k58dSQGdzn+JSnr3vWXp3xT2PMeJKq+6LsGQ26rm0\nOmISDgC4+eabARxdIqF3xfz0pz8N4KgLXUuxPXoOrQ3gqFtea6e1229r/zM3d+Y63xPHaTanrONe\nZ8XIGGOMMcYYs/FYMTLb2LdvH02H3KOkU1TILMfZ8dEKnVlGWVrLLMCPKVsjC+hlaaUzS1dsVw3+\nVKzC0QrKFoNjFivVesqswpHs+jCYVZmNERb0yRICZJ9ZgoVYRk3pGq//XHU2C97NWDdpRmM0FbBi\n/cyep8yqyJTbeP2zRVyz65Nd/7hvXbK5RLE4K+2zuZYpRkzFUC3VSr+yZyUuJ8BQrfytrnYO/f3P\n5vtG3MaeVTZfqIp6vLfK4shZO+rCxGyxaNbOCOr5jSSMYc8jW5i9vy5zPQRYgoRYZvRclKQ0DPY8\nNcWoV4Vuuummbf/fcMMNO86hKUZ93SeffPK2fX0/YztNeer/vv7663e095nPfAbAzgW+AZ7sI16r\nbEwpCapUb6UeK0bGGGOMMcaYjceKkdnGCSecQBUj9utbTfMdj2dKTNYHZt0bqSera25cR6ZCKTEm\nasxWI7NmTSlGffvRtz1TcEZiB1Zti/0cVS5UNSgymqo8bmNKhaqIKvtGrwtTEZTYNMaoGqzEsilj\nOLvWWXpZNs/EMv1x0crO1LlRBVU5P8VSqdbNrNmj6f2ztkf6wtqI93bu3D43LmSu2jc6HypeACxe\ngi1kqc4tI0pxphhl++YqzMrYZeN0qm89TGUbVWAypu7D6DXJxg+7V3F+Yv3vlbGYdruP62l1NQWn\nbz8uW9If15Siphz117y1HeOXAOBTn/oUAOATn/gEgKNKFXBUMWrH9crWSEy4qtwyRVT9fmfFyBhj\njDHGGLPx+IeRMcYYY4wxZuOxK53ZRilFTtHJZEnFnayhuj9EdzDV5WDE7Ul1K2Arw69qQ93GAvqz\n84urZGfBqsyVTmE0dfGoK07cxuRw1d1uJJg9c+/K5P6RNLZsLLLxxo7LAnsVVyXFtTWrk7nijD5r\nWWrt6ELHEixkdY4kvMhQk2Ao92G3UN2DmKthHNd9OmUlHfmoyyBLlMFgKfwVRtysWftZndk2luZb\nGWesn1kCCXZ+I8kN+nJKQpDdcLNTXOiUc8j2tfvGltJgLq3H8pzUdrLEIQ3m0tzKZymyW3vMla7N\nBc0VDzjqXtdc6vr5Iqbd7o+77rrrABxNwpAlX2jls+QLiju4GsbBEjOoWDEyxhhjjDHGbDxWjMw2\nSilSMHY8Zm5bgJ7Oet00nArsPNXUvtH6pVrdotVEteROLdCaqUKKYsQCppV02CrrpsFmlvxM2VQW\nMM0WFmUBosxyPHo9lBTwsW9Ze6PBxywJipqoohFTP2cWzuxzVIzU+97I5hKWIlex/M9VIZRrF9ti\nZfpt2TPD9s1NWKLAnlHWz6zddVNHN/rrqSw2Oto+Ux/XVb12kxEFZjTRRYO9C3qFYmSenzuPZsft\nZjKaEbL3zW6NiSwteSNTjFr5/lmIqnyv/LRybdHXTLXOkj00VeiTn/zkts99uZZ8IUsgkXlnxLHB\n3s2ZYrjOnOIfRmaLgwcP4pJLLsFpp522te2KK67AFVdccRx7ZYwxxhhjjM5FF12Eiy66aOvzVVdd\nhTe96U0rj/MPI7PFoUOH8LGPfQznnnvuZJndsuwB3BKrKCMqI8epMSrMqryuFWzUFzvWOVcxUvx7\nMwuwYj1n6TRH4iem6mT3I/rUZ3Uxq9SopXN0wdtYp2LJzVQBZimda91n9y9TW+Liu9nCsK3OzHKY\n7WPxfCMq0uhxjMwqzBTfLGV4LBPrZu2yeLDdjnWKjLwDMhWSPY9ZrA+Ls2BzSJzXmKrAnhn2PPeW\n+Fh+9F2p3Dc2XrPlMtg7djQ2iamdSrxSVg+rM55DpsBkYz5ej9H25iqbbHyv67XC1Dk2blrMUB/z\nw9Jut/Kt7gMHDuw4rilG/RzdFKK4mGvfTuaBEbexeMXsfZEpRv0z/ba3vQ2XX3751r73v//9UHCM\nkTHGGGOMMWbj8Q8jY4wxxhhjzMZjVzqzA+aqoAQUT21rsEB5tm8uSnpv5m7B6lRQ62TXjLkqxG1K\ngoXRgFslmJ5dExbYz9wXR1dCV2Djm6US7WFuWg3FLShDCfpX3eVY+/HasoQHav9Ye/F+M1c69d4q\nc8loMgwlQUIjC/pt//djibkmRrL+xrrVBAuj7lKr6un/Zu6Zyj7mTpolF2F9ycoqrqnZs9LuWzYH\nxeeeuYoy2DkoboH9tuxznHtYP/s6R95P2XGZSxRze4v0ronMNVxxI8tSxitu7m1fu4YtPfVUP+Px\n2XibcqXN+snmlOy4LN13fI6ypDQsTXhzqcvSdbfz6pM2xBTezX2ur58lSsjOM5ZnbnYZc78fAFaM\njDHGGGOMMcaKkdnOkSNHaGBvZpFTUC2WcxWjkSDOzOrGghsVq2tmIVOs7Sw1tkq0cCmKw1Tf1bb6\nv1k66+w4plQolsCsHqaIxXaYipFZszIUBUBZQFO99s1yF63ZPew+ZNZaJXB9VGmIlmo12UNDeWYU\ntSUrNzomWP3ZcVEp6i3Nc8+h0atPU/0dTSQxN4lCPJ6lys32xZTu/d9sfLO+NDKVlsGU4oz4vmDz\nPntXZsq7ovhmi3Zn5ePCp5liMDdJB0s8Esv0KAku2PObpQBn/WMKRdvX1xnHbJxz+20ZUdnKvoNk\nc7SSZCAjLtTKkkT1Ck47z+z8mlLUyjOFq+9nXPxVTZQQ1STmRZC9u7Lvb+2eZn1n96/HipExxhhj\njDFm47FiZHagWiPnMmItZb7KahzTunEdzNLcWFeJYX3q6x/pZ6aosBS5I2mKMxTlqGf0+jBVkFmj\nWT0sdkOtoz9+VR/mKqIji9apsSnsuKm4NRVVDYr7FCVt3binHsVqzizdPW38xwUUgZ3W/cxyGeda\npu6NXh+mALI62T5FoVRh5z5VZp12mXqpxMKwRTyV66jeWyVOKsbiZMeNjpdYz2j57P2rPkdT+9Rr\npigwmbdDjOPNnuNYZ6bENVi829x3UHYNsuNav9o5MBW6V82a4qN4DGQLdM+NI2KLqbN4sAaLTetR\n3w9WjIwxxhhjjDEbj38YGWOMMcYYYzYeu9KZbUy5+jBJXpEnmQyqwtwRmAtelMiZxL2bZNeMtROl\n315WjkHFWZCx4mo4mko01sVcB1TY/Zhqf9W+kfuXjQ3m5sHcH7L0tBF2r6b61bfR72OuOHPdybL7\nPjfF/VSZvpwyzjKUpCLKde1R0uGyIHM2JrK6YqB0llJ/rrtbdn2ydqaOz64Bc21Rns1Rd+BsWQJ2\nzVkyG+WcFXfAUTfSkdTMfTssoQODJWGIbazDaMKQOXWOujQy1y12vJqYiG2fImsv1qEk9gF2vnuy\nuSh71mJiBvas9ckXsqQUU31hCRaYq2B/7q1tZU7J+rIbbnMZVoyMMcYYY4wxG48VI7OD0UBNZv2Y\na8Vm/VKsC1mAX2Y9XVcxGlXLIqwvbIFQNbi1wVQaZTFVZplh7cU+TvVhqi+jSTR2q8xU/xqKYjR6\nzWLdzHKsqnWxD+qCeOweMSVtJNhbVR8VNU7pr3rfWYD2yByU9ZMtynksVWsWvK8EsLOEALvxPCl1\nsrk62xcVv+z84vE9LO1ydpySQIIpRqPnzPrOVKgRpf9YqESMvp9RDcqelUwxyuqKsEVj2VgcUd7m\neiaw5DKjx2WqUJzXspTlbR9rN/uOFdvNyrNkGOydxxKPqGPRyReMMcYYY4wxRsSKkZmNYhVWrDY9\nu2XNytplvsZzlSPmS898jdeNs8osMtFqk1msmArFGLU4M+UnllGUIxUWFzKq7jHf/ahCsrHFFKPR\nmK1R5Sf2PWtvJF17D7surF+tvazsaCyjErfCFJHsejKLf2M0Nf2UT/yopTMby4pVX7k+2ZzSyK6P\neo+mGFUa10VNAc5UvXhcVr/yXmLXjsWWjqqeo+NUiYVR4nNY/C+riylbTN1hi39mZAru1Nw6qjRm\n+1icXEPxpMgYfbe3lNzZkgFxXmTvJxYPxvaxVN7qd7r4bh5VkVdhxcgYY4wxxhiz8fiHkTHGGGOM\nMWbjsSudoTAJWEmnqbi/jLpgKexmYOioW1cs10vcURJnLhys7t5FIgZKrut+xtx0WICnGqTK3HtG\nEn+oyTcUsr7EMZidX9aHuNo4S8mcBfaz/ikudCxlcea+pLgKsXurBj6PuktMkbnUjCa4iH3IXEbm\nuvqum9ClkT2HiruMmtBB2ZfdM2VOUFxc1PIKynHZPVbclrIyWUpudl1YnXF+6e9x3NfPFywVO3s3\nK0H+o0HtkSygn7mWK25W2Twa2+j7zJJntP4p1zqb+9j9Z+N76nMPS4KTuUlnboFKe0poAZvb1feh\nkshFWRqhv49KcohsTlFdUa0YGWOMMcYYYzYeK0ZmG1OWBkUdUmFB30qw+KiFksGsC0pg/WhygbiN\nWcFY0OCocpcdF2FpUdv/+/fv39qnJHvIEhjEc2dB2KPJCVjaTzY2FLWFBRSrwabxeDVAlyUZGAm+\nzyzOU+2vQrEmZuegLBrK6p6rBrJUsD2K+sSUm6gYZmWUgOn++FaOWWSz66MsGpupUTGVbzZOmcqa\noSQeiGWn+q7sUzwTmGqSoSwe2sjubdZGTEaSvQ/ZwplZnSyoXUnkMfoeVZJnZNeczXkx1TS7nuw9\n2hO/c/QJCLI5Mvvc91MdP7G86hkR35VsMe6ekTlyNNX5bnwHHKmbKfjZe3s3kuZ8VipGpZQDpZR3\nlVKOlFIenOz/N6WUvyyl3FxK+btSyo+E/T++3P5npZR/OtHGw0opt5VS3hG2f28p5cpSyqeW/95a\nSnlCKPPBUsoju8/3LKW8Yln+ulLKr5VSTg3HPHjZn5tKKX9TSvl/hOtw31LKq0opN5ZSrimlvLCU\nsq/b/5RSyp8s//6TUsp3rKrTGGOMMcaYTeROpxgtv8i/tNb6MlLshQD+HsCXJsf/HICvBPBDAN4D\n4F7Lf23/VwB4IoAnA3gogF8A8PhQx+kAfgvA6wGcHZr4OwDPAfD+5eenAvjvpZQvq7X+xUR/f3tZ\nz2MBHADwmwB+GcC3L9u7O4DXAngdgO9ZntdLSynX1Vp/Latw+QPo1QA+DOBiAPcB8HIAtwL4sa7o\nkBk4WjvWVSEUa21mYTlw4MBkO5mVgC0wFq1m2b6mhIxaZBTLWmYByvyCldSs68Z1ZXUrFhl2H5na\nkvkVs2s2YlnLjmP+z8waqozzTPlT+sd8sdW4rJGYCMXHfTf2ZWXYQobsHOJxmWUvU5hZna08819n\n92HkHmftsPbiMT1snGZz2AhMSWUK1W6oniNjaVS9HF2Mlb2D4nmpKasZTN0Zef7Y9cwUlXUVoKyf\nilrCVEE2X/TPfb8oaYQprmxcM4+NqXqybU1pyo7PVFam0o28e7L22DthdIkSNv/GMqNKrrJN9RRg\n86ASX7WKO90Po1WUUp4I4F8A+EYAXxX2fSGA7wXwRbXW9sPlb0IV98Tix8R7AJwI4ClJM78M4BUA\njgD42n5HrfVVoeyPlVK+D4sfJzt+GC379HgA59Va37ncdgmAV5VS/k2t9RosfiDtB/CdtdbbAfxF\nKeXLsfhxl/4wWtb5IACPqbV+HMC7Syn/DsB/KqU8d1mPMcYYY4wxRuCzypWulHI2gF/B4ofETUmR\nrwbw1wC+ppTygaVL26+WUu7ZlXktgJMAfAYLxeX/DW08DcDnA/gJoT/7SikHAZwC4NJuV/8T9WIA\n17UfRUtevyxzUVfmz8KPmdcC+IJSyj0mmr8YwLuXP4r6Y+4B4IuT8uM/m40xxhhjjNkQPtsUo5cC\neEmt9Z2llH+S7P98APcD8E1Y/Hi6G4AXAfg9LNzrsPzx8VWllDMBfLL/MVJKeSCAnwLw8FrrkSmp\nsJTyJVj8EDoJwA0Avr7W+r62v9b6+V3xcwB8tD++1npHKeUTy32tzAdCM9d2+z6VdOOcrkx2zJW1\n1t/CwiUQtdZ/np7MGihuT4qriOq+Fl1UMqk9k11jas3MbSJjxE1OdRVrZO42c1cPHymT1R2vGXPz\nmCNLT7Wt9FcNDJ3rjqK4dWb74hhmLkoMFkydoaTmzdpl7g9KMDxDdRVkdcbg8swtM0uRG89ZDdBV\n7jtzMWWwRAnxurBkGJkLELvH7N4yt5mMkWdTvf+xbqW/WR19wHyDzafKnDX63CtLOLB7y5LKqK6J\nkWwfc+tiKG6SKsw1MW5j7nM9zCWOudLF8so5Ze7uSt39/W/72ByWzQnZWI91Zoy42mbvoLmu5Ypb\n36gb6ei4U1w+V3HcfxiVRWKEH+02nQzg4lLKLyw/VwBfBOAbANwdwAvaoUl1+7CI4fm/a61/vaz/\nOwG8vZTywFrrX7WCQWlpMTuvAPDj7diJNgDgfQAeAuAMLFz6XlZKeWT/40iggKs4re05s9GsGezg\nwYO45JJLcOqpR/NCZA/I5Zdfjssuu2xOE8YYY4wxxuwaF110ES666CJa5qqrrsKb3vSmlXUd9x9G\nAH4RwO90n38bwCsB/H637SMAHoOF+9gt4RfgFaWUV9Ran7Ysd3v3wwY4GvfzeQD+CtPcHcD5AL6s\n+1G2D0AppdwK4HG11jcCW6pTU3jeUUq5EMAPAPi+pN5rAJzVbyilnIBFrNM1XZmY5KEdE1Whvt4L\nwrZWx9QxlEOHDuGjH/0oHvCAB2xtU1IOZyi/2jNLQLT8MSt9FtycBQ0yRWskbatiAe6PYxbnzOK0\nW5bDDHYNIqPnydprMOvpaB+yz8r5rWvxzLa1OtV7PBcWCB5R0wQzxZdda2WcqmlRY51MhYhpjQGe\nxjYGaI+m/s+syiNjdzSAeSSgXLUIK1bXbJyy4+JYzO4VW8xTITs+uy5xwU5VSWMwC3esS02VrCzM\nrMxh6pzLkmcoatexTMncE/vHFlpm6oyqGE3VzfrGlBG2wG8/XuP1VN+H8Tns64nPw+icG9vo/1bq\nYte8ZzfefwqXXXYZLrvsMpro5AMfiI5ZOcf9h1Gt9ZMAPtk+l1JuAvDRWuu2MyiLhAX/ttt0Hyxi\nar4ZwOXLbW8BcLdSyv1rrR9cbvsCLBSUmIQhcj2ALwnbnonFD7JvBPAhcuw+LBI5ZFwK4IxSypd3\ncUaPxUIRurwr8/xSygm11jYiHwfgL2utmRtdO+ZHSylndurX47Bwu3sv6asxxhhjjDEmcNx/GKnU\nWv++/1xKuRGLHxcfqLV+eLn59QDeAeA3Sik23I2sAAAeJklEQVTPBnACgBcDeF09mqVuqv6K8IOi\nlPJRADfXLg13KeU/AHgNFmm77w7gXwJ4FBY/SrJ631dKeS2AXy2L7HUHAPw8gP9SFxnpgIVK9u+X\n/X4BFum6vx8LFaq1+3UA/mOt9QuXm1637O/LSynPAfC5AJ4H4MW11tvYua4is8ywhQgzy5qy0B/b\nl1lPmZVnxI+8rzNakzMriGI9zawuzALILHmN3mrDrLQsRXI8PkOJt5hreczaZfdxRDXL2M04C0Y8\njvlWs/E6ak1jYynzbVcUUeUaKIu5xrZX1c3uLfOzV/vCxjWzqCuqzKgv/brqoXI8e9aUdtn9yJY4\nGIlH6PugxBj1sDk6KkVsThlVYkbUtqwcG4uj8aMNlh56N+YZ5R07EqO0ijh3ZYoIu+9MRVRQ55KI\n8jwyhSsj8z5QFEq2+K/SLusLuwZqnbvl7TLSRr9t9DsEcOfMSjdy1baVXf64eTKAjwP4UwD/A8Cf\nA/jWXevdwl3tZVjEGb0ewHlYuNn9MTnm27ryfwTgz7BYr6j1+3os0m/fD8AVAP4/AM+ttf56V8c9\nAJzbHXMEiyx8dwB467JPvwngx9c5OWOMMcYYYzaRO51iVMXsabXWv8FCEYrbrwHwf+1SX34CIW13\nrfW7ZtTzSSwXcyVl3o2F8jS1/7ewzDDXbfs7LH4cGWOMMcYYY9bgTvfDyBxfSilpsGFGlN0z94AR\nt7n+uMwtKLab1cVcMVrdvWtOlORZAHLmTsIk3Jiis28nS9sZ22GudCzlqSIXs3SxqnsIc3tTUFJx\nZ+epuIepSS1GEohk+5S08kqK7b6cslo5C3hXE0Ewl7EYhJu1N+piGvdltH29i8zIPVKex6ltI33P\n9sW5Sx1nU4y6I7F97T6yxBwZ7bhs7svGBgtAj9cnux8s0L7Vlc3frE5lrp7rnt0z4lI86s7L0jaz\nc2d9yeYbJSGDkkI6az8mysj6lNWVzaOtD4qLMKs764MyXpS62TXI6myw5CvsuGyJEjbvs7qz7y7x\nfrB3F0tHrj5XiuumUtddxZXOGGOMMcYYY/YUK0aGEi0O/S/uaBllAaWK8pRtUy0d0SrBglRZ8oXR\nlLWK5ZlZMdm5Z2pJ+79fCE8JgmZWl2ihUi3lauDpFEwhmdseUxqzekYCwTPLEwvCZymEs76wtL3x\necjGvtIOO3fFyqyeQxufqjLZYNZSpupmn2N7LBVwdtxo8C5bxDMePzegmJVT6sySWmTKoaKWZ9Zh\nNu+PpNTOnvv9+/dvq6evn91HpgC069GrIGxMRbL3oZKMIuunkpY6uy6jCuWIF8dc1ZOpJYo6lLXH\nFu9dN7lMD/MwiHMt85Jhcy1T9bNrztT9Nob7Z7vNv21cszp72Pe0kTGlfocYeVZGx/To/N1jxcgY\nY4wxxhiz8VgxMjsYtXRmKo1ixVZiTJhVo98XFYLMt71ta5bHft+o1XzEX5YpRln9mQUottfvm7K6\nZJZVBrOsji7YGC16LA16D7O6xTIZykLEoxZZ5R5n1swRhWuq71n9sSxTKkasvOsqgH05FocwCktH\n3MjmG/bMMOU2K6+gxCbMhdUdrwtrK4vPmTt/KwuuMnWH0Z+DkrZ59JrPVYUiTBVg44d5CjBlZJTR\n+ZN5QijjjClNTcUYXQA3a4/F0ERG4/uUGKHR977SP+Ylwb4vZDHJjZG4Y2Dn8zsan6XEbKrP01zW\nnWsBK0bGGGOMMcYY4x9GxhhjjDHGGGNXOiOhuD+xgMIMJv0qwfAqI8HwWXujcjRDCQRuZfoEC8rx\n0SVGdYOIMr3q6hClcTVwM7o/ZC6RSqCuej9Ye0oK0tGgUSXxyGiijOiqMPdaq+01MpcKJYmC0o4a\nEKwkB8n2sSQKsR3FrTfbl6XbZ8+mMo+y9rKyceyya5i5WWZ9YuONje9YPnMLy8bUiMseS/fMAq1Z\nwoOpcqvInjXmmqiMDeYOzNpb1T/luLmB7o0shTs7Pr5j1XmKveva+FDc87L64/OkjoeRa8fc8tlc\ny67dut+P+rqU90ZGdtxufG8a6Yvijr8KK0bGGGOMMcaYjceKkZEYDTaMlpx+H1sYlikFzEo+Yq1R\nF6LdLUtHFjSaEYMos4Dw7POoYjN1fGbtU6xRbNFIJRFBVtfoeGuw5BbZcbF/zHo213LIEiWwcXes\ng1SVupRAcrUulmJXUfWUNnpYIDm7tsp9H7XEx3aVcxhVRJmSMmoBZgk9RpRpdh+VJAxZXSxlMbOo\nMyVcXfhWuX6j80UjGxusfyPzw9xEAOo9itd/9N019Tlrg/U3tt2XGWW33v9ZXapiFI9jY1hVYBuj\nqbnXUWCmYNeYzSXK8Vl5dTxbMTLGGGOMMcZsPFaMzDGBKUYN5out+LZnSkxmdYmWJmY16C1zLEaB\noZRnSkFm4WTnPqV6KKkzgZ3XTkkFDuy0YqqLOTZYbANDPa/GiK85s35n94Ole8785tmYivtG0ndP\nnYOyL1NulXiZqc9T7SkWx2y+YAvfZu0o/WLHR9VybqzCsVT8Rv3t57a3m2rwuhZ4ploylX30/jXm\nXjN2zffKEs9QxiV7xkaU0f5vRaXtifPS3DlvFEWlURTGTMFh7wvWXgbz5mCxflP1TPV9qryqvsTx\nMndezPbNXfJlFVaMjDHGGGOMMRuPfxgZY4wxxhhjNh670pkdjLqAZSjuBMyth7lpZAHhsV+Z2wVz\nWciSPowERWYwSVzZxwJmmZR/0UUX4fLLL09duBrZtcvcUeJxWbtZ8PfcFcLj8RmZ61djNwNmlfS7\nGTHlOEux29cTx2w23lgAeiNzP73gggtwxRVXUNfUjNFEAnPdguKcwNJKs+N6RlOix32Ky0d27mqi\nini84pq2my54559/Pq644gqpvJrmO27rrwVLdJL1rxHnJZY4pic+Y2p7SrKPdZP17GZSirlzbHYf\nLrjgAlx22WVSXaOu5RksEdO6LnTsPaH0czffJdGFbq7LN/sOko2pzKWdXZf2HLWlQvrxwJLZxD6x\nOUGFPReKG2H2WU4+I/fSGPNZwQUXXHC8u2DuZHhMmMj5559/vLtg7mRceOGFx7sL5k7ExRdffLy7\ncFywYmS2UWvdlRTCSgCcasWaKpP1pVk8Ri3IzbLSqx6sHcVqwtpj1nYWMMmUrd6SW0qRgpWBo9Yh\nlmqVWZ6yaxdh/VUtSSyhQ1QRldTuKplCGVFTATOLupLsQ7F6932JbavWM0UpOhYJILIyUaFg+1RL\nZdzHLKrsmvfHxfGhXGum4LHzZIwo/rVW6V5l/WRzmNIHtZ9xwetRVf9ud5v+mqN4Jkz1a+Qcptpd\n1V7cN6pesfvH2mXvHtYue0azd1ec0zNPhrnXhaWxZu9opu7EdtR9cSmOjGwMz73m7DsE8x7J1Nl4\nPbJ5laVIV6517He/j32vGPm+OIIVI2OMMcYYY8zGY8XIbKO3EMTt/f8ZqhVsJF3jaMpLxSKjqkks\nXXMsw6xu2XFZeaa8xHZYSu12D9l9bNbXfhtLS5pZoJglkFnm2Fga8bdn1zArl7WrqHostoHVk92r\nOCYyn/p2b7JrragYzDqcHTcSEwMctcArltx+nI1Yqkct3Gxf9twrFk5WJ1OolBgRRf3qGYk7GVUx\n5noDsLqYdTiO5WxbX3cbn/H//ris3f379+/oe4PFsiqKMptLmNoyN8W5oogw7wP1vV1KwQknnCB5\nRGR1ZFZ+JeW7Msf2/VSeB2UsZs8vU35HGL3HircDm4syxWik3f7v/vrE9xFTZ9VrHY/L5oRIdu7s\nGc+O8wKvZtfZK//jvYyHeOhDH7pnbV100UV71tZesVfndN555+1JO8DejfO74ngA9u767WWMzF7N\nSXfFWLC9HOd7OZ/fFd+He8Vejom74rN7V3x33JnOyT+MzBYHDx7E85//fDzzmc/c+tc/7HfFCeYr\nvuIr9qytu2Jgq78Ez+euOB6Au+Y8cVc8p71iL8f5Xs7nnifms5fndFe8T3emHxG7xbE4pwsvvHDb\n99mnP/3p0nF2pTNbHDp0CNdeey0e8IAHbG3rJdLoZsfk9iifK25TU9uyvkztyz5H6beXU9s5Kak9\nmVsfC7TuA0wVt4JM7mXXM3P5mnKlU1KCZi4LWd9OOOGEHQH9sU+Z6x5rb8qdaCopiEKsM3PhyYK4\n47Vi7oCjLlyZewFrTwnCjeeUlcvaU9xYslTsU8/vvn37diTm6OvMnpms71P72HHZPVJSm2fP9ogL\nX79fcSnO5qL4LI26DrHryvo86jbF9qmpkePx8XlibtLZc9Gfe3T/zZK2jCSzYLA5lrkdK++5UbJn\nO3P5jOXjdVHndPbeZ+9DYHEd9u/fv8N9LXOhnHuv+nktusepyx3Esqvu39S1Y31nrmasntjPLMkP\ng4UksPc1ux/Z85u5+se/lWQfPdGde+qav+Md78Db3/72rW1XX331ZJ3b2pZKGWOMMcYYY8xdGCtG\npnEaADzoQQ/Cwx72sLTAve99bzz2sY895h35nM/5HDziEY845u0AwDnnnIMnP/nJe9LWmWeeiUc/\n+tHHvJ29uk/A4pz24l7d61732rP4gTPPPBOPecxjjnk79773vfG4xz3umLcDLK7f1HN9LNraqzHx\n8Ic//Ji3A+zdnLRX1661tRdjYi/no7POOgtPeMIT9qStM888c8/eh4985COPeTutrUc96lF70s5e\nndNezed7eU579UydffbZeNKTnnTM2wH25pz27duHP/mTPwGW33cnaS4q/rfZ/wC8GEBl/w4ePEj3\n79a/vWrnrtqWz+mzo6274jn5+n12tONz+uxpy+f02dGWz+mzpy0AL2bfh8vyS7HZcEop/xTADwK4\nCsCnj3N3jDHGGGOM2S1OA/BgAC+qtb5/qpB/GBljjDHGGGM2HidfMMYYY4wxxmw8/mFkjDHGGGOM\n2Xj8w8gYY4wxxhiz8fiHkTHGGGOMMceBUsoHSynff7z7YRb4h5Exx5lSyjOXE+NNpZS3lVIu6Pad\nXUp5eSnlI6WUT5dS3l5K+YZ16lzuP7GU8gullI+XUm4opbyylHLWsTg/M45w/x5aSnnDckx8qpTy\nxlLKiWvW6TFxJ2XVvevKvaaUcqSU8jVCnT9ZSvlwKeUzpZT/tcxM2u+/ZynlFcvxdV0p5ddKKafu\n1jmZ9ZgaE8v79nOllPeVUm4spfxNKeVnSymnC3V6TBwDSimPKKX8YSnlH9TnM6njR0spb1ne00+Q\nck8tpVy5HBfXlFJ+fr3eb9W762NDeeeUUu5bSnnV8ryvKaW8sJRyTH+7+IeRMceRUsq3APj/Afw4\ngC8HcCWA15ZSzlwWeTmABwL4agBfAuD3AfxuKeUha9QJAC8C8CQA3wjgkQDuA+C/7t6Zmbmsun+l\nlIcCeA2A/wng/OW/FwM4MrfOJR4Td0LEe4dSyrMB3IHFOh2r6nwOgGcB+B4AFwK4cVnnga7YbwP4\nQgCPxWJcPBLAL697PmZ9VoyJ+wD4XAA/hMU74ykAngDg11bU6TFx7DgVwLsAPBPC8znBfgC/C+AX\npwqUUn4IwPMA/BSALwLwlQBeO7O9vt5jNTboO2f5A+jVAO4G4GIsxvJTAfzkuudEOd4Li/qf/23y\nPwBvA/Cz3ecC4O8B/PDy8w0A/mU45uMAnr5GnacDuAXA13dlvgCLL9YXLj+fAeAVAD4K4DMA/hLA\nU4739dqEf8L9uxTAc3e5To+JO+m/Vfduue0hAP4GwFnLe/Y1K+r8MIBnd59PB3ATgG9efv7CZT1f\n3pV5PIDbAZyz/Px5AP4QwCewWPvu3QCecLyv1yb8U8ZEKP9Ny/u7z2PiuN+7Hc8ngA8C+BEAvw7g\n+uWz/K8mjn8KgE8k28/A4gfLo0nbTwFwHRY/Rt63LP+7AE5e7vvg8t79LJbL+ezW2Ej6orxzngjg\nNgBndmW+Z3kOd1t+fjCAP15et08BOAzgn61zj6wYGXOcKKXsB3AegDe0bXXxpL8ewEOXm94C4FuW\nMnUppRwEcCKAN65R5/lYWGD6Mn8J4G+7Ms8H8CAsJrcHAfg+LH6QmWPIqvtXSrk3gIsAfHzpVnHN\n0o3uYXPrXG7ymLgToty7UsopWFhrn1lr/ahQ5/0BnBPqvB7AZTh6ry8GcF2t9Z3doa/Hwtp90fLz\nSwAcAPBwLJSJ58CLgx9zxOc5cgaA62utqarsMXGn4Iew+FL/ZVhcx18spZw7cPy/wOIH8n1LKe8t\npfxdKeV3Sin/Zyh3CoBLAHwzFnP5YwD8ARaq4hMBfDsWPz6+CdjVsRE5D6vfORcDeHettX/PvBbA\nPQB88fLzKwD83bK+fwbgP2HxY2o2d1vnYGPMWpwJ4AQA14bt12JhOQGAbwHwOwD+EQvry41YWFg+\nsEadZwO4dTm5xTLnLP++L4B3dhPd3yonZNZm1f37/OXnHwfwr7FwoXkKgDeUUr641vrXM+oEPCbu\nrCj37mcAvLnW+kdinedg8YUlq/Ocrsy2H1m11juWsQ39eHhlrfW9y88fEts366GMiS2W7nU/Bu7W\n5DFx/HlVrfWXln+/YOka+2gAV4vHfz4W4+JHAHw/FgrKfwDwv0opX1prvX1Z7m4AvrfW+iEAKKW8\nEosfQ2fVWm8C8L5Syp9g8YPp97B7YyNyDla/c86ZaLftuxILlfKFtda/Wm7P3oFDWDEy5s5HwVE/\n5OdjYR3551hYRH4awO+VUr544lilTqXMLwL41lLKO0spL1jGtZjjR7s3bc7+pVrry2qtV9ZafwgL\nt7anz6xTLeMxceehAKillCdj8QXm2btV50CZnwPw70opby6lPLeU8qW70Acznx33r5RydwCvAvAe\nAD+xG3WuKOMxMZ93h8/XYOEaq7IPix89l9RaX19rvRzAt2IRo/yYrtxn2o+iJdcC+NDyR1G/bVXb\no2NDRT2mlflpAL++TAjxnFLK57ODFPzDyJjjx8exCJY+O2w/C8C1ywf8mQCeVmt9Y6313bXW5wG4\nYrl9uM7l39cAOJBkKdoqU2v9n1hYYn4Gi0DeN5RSXjh4fmacVffvI8vPfxH2/wUW92tOnYDHxJ2V\nVffun2NhKf5UKeW2UkpzIfn9UsofT9R5DRZfPlaNh5gd6gQA98TR8fDrAO4P4GVYuE0dLqVMzUtm\n91CeZ5RSTsPC7eiTAL6h1noHqdNj4vgT3b96Q5jCjnfD0gXt49j+bsjaYW3vythIWPnOWZaJ7bbP\nbcz9BBaJJv4Ii/nwz0spXzvRpoR/GBlznKi13gbg7VhkcQEAlFLK8vNbsfAFBnZaT+7AxLMr1Inl\n/ttDmXOxmDwv7er6x6Uq8R0AfhDAdw+fpBli1f1bWvo+jJ0uM+diEbA7XOdyk8fEnZAV9+5SAP8R\ni+Djh3T/AOAHADxtos4PYvGFo6/zdCxiAdp4uBTAGaWUL+8OfSwWX5Au6+r6h1rrr9RavwkLy+2/\nmnuuRkN5npdK0euwCJD/mlrrrSvq9Jj47Octy/+33g2llHth4Xr5obmV7ubYCLB3Tl/vl4YMnI/D\nIslCc9dErfX9tdafrbU+Hot4qXTuk1knc4P/+Z//rfcPiwDImwB8BxYB7b+MRTzRmVjI4ldjkWjh\nAiwsw/96OZk8vqvjDQCeIdR5767MS7DIQPNoLFz03gLgTd3+nwDwNQAegEWQ4x9i8cX8uF+zu/q/\nVfcPiy+912GR4vQBWKRnvRHA/T0m7nr/lHsXymdZr94H4Gu7zz+8rOPJAL4UwH8D8FcADnRlXo2F\nOn0BgIdh4a758m7/z2DxJeV+WAQ9Xwrgt4/39dqEf2xMADgNi6x178JCvTm7+7evq8NjYu/u16lY\nGC2+bPl8/uDy832X+z8I4PvDMe8E8O+7z/ddHvPvsfhh0Awhp3Zl/gDAVVgkL/gSAP9j+fmE5f4d\nGe2wiFd9R9j2UgC/v8tj4z5YqFnnd9tWvXP2YRFH9BosDECPx0Ipet5y/0kAfh7Ao7D4QfWwZb9+\naq37dbwHjP/536b/A/AMLCw6Ny1fJP3E8QAsAiA/gkXq7ncC+LZw/Af6CXRVncv9Jy4nlI8v6/09\nLIIv2/5/i4Vf+qcBfAyL9ZP+yfG+VpvyT7h/P4yFQnQDgDcDeKjHxF3336p7F8regZ0/jO4A8B1h\n23OxUB8/g4XL1T8N+88A8J+x+BJ2HYBfBXBKt//nsDDcfAYLi/JLAdzzeF+rTfk3NSaWXxLvCP+O\nLP//PI+J43KvHtXdg/7fbyz3fwA7fxi9A9t/GL00Of4OAI/sypy2vCf/uJyjfw/A/9Htn/XDaJfG\nxj9J+kvfOcsy98XCTe7TWPwoegGWP/CxWNvpt7vn4O+wWBvpQF/H6L+yrNwYY4wxxhhjNhbHGBlj\njDHGGGM2Hv8wMsYYY4wxxmw8/mFkjDHGGGOM2Xj8w8gYY4wxxhiz8fiHkTHGGGOMMWbj8Q8jY4wx\nxhhjzMbjH0bGGGOMMcaYjcc/jIwxxhhjjDEbj38YGWOMMcYYYzYe/zAyxhhjdolSyvNLKb90vPuh\nUEo5VEp59vHuhzHG3FnwDyNjjDEbQSnlpaWUI6WUO0opt5ZSPlBKeUEp5cSJ8r9SSrm9lPINYv1n\nA7gEwPNDm7/fff6RUsrlpZTrSynXllL+oJRybqjnxFLKL5RSPl5KuaGU8spSylmhzH1LKa8qpdxY\nSrmmlPLCUsq+UObRpZS3l1JuLqVcXUp5Sujy8wD8WCnl7sr5GWPMXR3/MDLGGLNJvAbAOQDuD+AH\nAXwPgOfGQqWUkwF8M4AXAvguse7vAvCWWuvfkzKPAPDzAC4C8JUA9gN43bK9xosAPAnANwJ4JID7\nAPivXd/2AXg1gLsBuBjAUwA8FcBPdmXuB+CPALwBwEMA/CyAXyul/ItWptb65wD+GsC3i+dnjDF3\naUqt9Xj3wRhjjDnmlFJeCuAetdZv6La9EsD9aq3nh7JPAfDdAJ4A4CMAvqDW+g8r6n83gF+otf5S\nt21Hm+GYMwF8FMAja61vLqWcDuBjAA7WWv9gWeYLAPwFgItrrZeXUp4I4A8BfG6t9ePLMt8D4D8B\nuHet9fZSygsAPLHW+uCurf+y7MtXddv+HYCvrLU+ip2bMcZsAlaMjDHGbCSllC8B8BUAbk12Px3A\ny2utN2ChMj11RV33BPBFAK4Y7MYZACqATyw/n4eFEvSGVqDW+pcA/hbAQ5ebLgbw7vajaMlrAdwD\nwBd3ZV4f2nptV0fjcgAXllL2D/bbGGPucviHkTHGmE3iycu4nZsAXAXg3li4y21RSnkgFq5uv7Pc\n9J8BPG1FvZ+3/P8jakdKKQULt7k311rfu9x8DoBba63Xh+LXLve1Mtcm+yGUOT3EVH0YwIHuOGOM\n2Vj8w8gYY8wm8ccAHgzgQgC/CeA3aq3/LZR5OoDX1lqvW35+DYAzSimPJvW2GKGbB/ryEixUpm8V\nyhYslKVVsDIlKXPTcvspQt3GGHOXxj+MjDHGbBI31lo/WGt9N4DvBPDQUsqWGrRMbPAdAJ5USrmt\nlHIbgBsB3HNZform1nZPpROllBcD+CoAj661frjbdQ2AA8tYo56zcFQBugbA2WH/2d2+qTJnAbi+\n1tq7Dt4Lix9KH1P6bYwxd2X8w8gYY8xGUhfZh34KwH8opZy03PwkAKcB+DIssrm1f98G4BtKKfeY\nqO6vAdyAhQJEWf4o+loAj6m1/m3Y/XYAtwN4bFf+XCxc9d663HQpgC9dJm5oPA7Ap7BI0tDKPBbb\nedxye8+XAPj7WusnYIwxG45/GBljjNlkfg/AHQCesfz8nQBeVWt9T631ve0fgN/F4ofHt2WVLH9k\nvR7Aw1ljpZSXAPiXy3puLKWcvfx30rKe6wH8OoCfXq5DdB6Al2KRBvzwsprXAXgvgJeXUh5cSnk8\nFmsSvbjWetuyzC8BeMBynaYvKKU8A8A3Afjp0KVHLOszxpiNxz+MjDHGbCy11jsAvBjAD5dSPg/A\nEwG8MilXAfwBuDvdrwE4GLbtw0IBanwvgNMBvBGLxAft3zd3ZZ6NxRpEr+zKfWPXlyMAvhqLH3Rv\nBfAyLOKlfrwr8yEs1K+vBPCuZZ3fWWvdylS3TMLwdQB+hZyTMcZsDF7HyBhjjNklSilvA/Aztdbf\nWX5+DYC/qrV+//Ht2U5KKd8L4OtqrU843n0xxpg7A1aMjDHGmN3ju/93u3ZMgwAUBFFwL0EAYugR\ngBYsoAUFGMAZzUcBoSH5JDujYNuXuySHmTnOzCXJOclz86ZPXkmuu0cA/AsXIwD4sZl5JDklua+1\nbrv3APCdMAIAAOp5pQMAAOoJIwAAoJ4wAgAA6gkjAACgnjACAADqCSMAAKCeMAIAAOoJIwAAoN4b\nPMStACg1YikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141c2208>"
      ]
     },
     "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": 28,
   "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": 29,
   "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=skv1512590945539&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": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"tap1\"></a>\n",
    "# 4a. TAP:  Using the Table Access Protocol to find additional information on our sources\n",
    "TAP is described in more detail in the dedicated [TAP notebook](heasarc_TAP_queries.ipynb).\n",
    "\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, e.g., to calculate exposure times based on being able to significantly detect a 10<sup>39</sup> erg/s source.</i>\n",
    "\n",
    "First, find out what catalogs HEASARC holds that might have redshifts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=18&gt;\n",
       "<table id=\"table4611983680\" 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": 30,
     "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": [
    "We see that we can query these objects in the CfA Redshift Catalog, 'zcat'.  <i> Since these are galaxies found in close pairs or groups, we expect multiple matches at each location. </i> Let's first start with one object and search within 0.1 degree: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=3&gt;\n",
       "<table id=\"table4632234584\" 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>19.009757430000001</td><td>46.730402349999999</td><td>-1</td></tr>\n",
       "<tr><td>19.068407270000002</td><td>46.740033570000001</td><td>5081</td></tr>\n",
       "<tr><td>19.086435210000001</td><td>46.747227780000003</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",
       "19.009757430000001 46.730402349999999              -1\n",
       "19.068407270000002 46.740033570000001            5081\n",
       "19.086435210000001 46.747227780000003            5188"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "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": [
    "(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": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=39&gt;\n",
       "<table id=\"table4637347400\" 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>19.068407270000002</td><td>46.740033570000001</td><td>5081</td></tr>\n",
       "<tr><td>19.086435210000001</td><td>46.747227780000003</td><td>5188</td></tr>\n",
       "<tr><td>20.334799289999999</td><td>40.487715479999999</td><td>5859</td></tr>\n",
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       "<tr><td>136.00074370999999</td><td>21.96791867</td><td>3093</td></tr>\n",
       "<tr><td>141.09150181000001</td><td>40.683784680000002</td><td>8267</td></tr>\n",
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       "<tr><td>146.70334308</td><td>22.018272169999999</td><td>7597</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>237.58156191</td><td>20.762982279999999</td><td>10926</td></tr>\n",
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       "<tr><td>317.10346952999998</td><td>18.202987799999999</td><td>4826</td></tr>\n",
       "<tr><td>317.11477339999999</td><td>18.191914189999999</td><td>5054</td></tr>\n",
       "<tr><td>329.24927524999998</td><td>6.6551501899999996</td><td>7857</td></tr>\n",
       "<tr><td>333.82169725</td><td>37.299221410000001</td><td>5207</td></tr>\n",
       "<tr><td>333.84683658</td><td>37.282608979999999</td><td>5984</td></tr>\n",
       "<tr><td>333.91491775999998</td><td>37.300255790000001</td><td>5684</td></tr>\n",
       "</table>"
      ],
      "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",
       "20.334799289999999 40.487715479999999            5859\n",
       "125.90444401000001 21.338192790000001            5219\n",
       "125.90444401000001 21.338192790000001            5364\n",
       "135.99423805999999        21.90099584            3157\n",
       "136.00074370999999        21.96791867            3093\n",
       "141.09150181000001 40.683784680000002            8267\n",
       "      146.70334308 22.018272169999999            7446\n",
       "      146.70334308 22.018272169999999            7597\n",
       "               ...                ...             ...\n",
       "      237.58156191 20.762982279999999           10926\n",
       "241.52278516999999        20.78278156            4584\n",
       "241.52278516999999        20.78278156            4602\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": 33,
     "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 within a given physical distance. <i>E.g., for our science proposal, we need to check that the redshifts (or radial velocities) of the galaxies that are close on the sky (within 50 kpc physical distances) have similar distances, suggesting these are truly close in 3-dimensions and therefore potentially interacting galaxies.</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=39&gt;\n",
       "<table id=\"table4637347400\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>ra</th><th>dec</th><th>radial_velocity</th><th>redshift</th><th>angDdeg</th></tr></thead>\n",
       "<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",
       "<tr><td>19.068407270000002</td><td>46.740033570000001</td><td>5081</td><td>0.0169366666667</td><td>0.000681042538686</td></tr>\n",
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       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
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       "</table>"
      ],
      "text/plain": [
       "<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 0.000681042538686\n",
       "19.086435210000001 46.747227780000003 ... 0.0172933333333 0.000667286051816\n",
       "20.334799289999999 40.487715479999999 ...         0.01953 0.000592475054061\n",
       "125.90444401000001 21.338192790000001 ... 0.0173966666667 0.000663405922543\n",
       "125.90444401000001 21.338192790000001 ...         0.01788  0.00064585248346\n",
       "135.99423805999999        21.90099584 ... 0.0105233333333  0.00108755212566\n",
       "136.00074370999999        21.96791867 ...         0.01031   0.0011097660328\n",
       "141.09150181000001 40.683784680000002 ... 0.0275566666667 0.000424005225996\n",
       "      146.70334308 22.018272169999999 ...         0.02482 0.000469199967618\n",
       "               ...                ... ...             ...               ...\n",
       "      237.58156191 20.762982279999999 ...         0.03642 0.000324263272089\n",
       "241.52278516999999        20.78278156 ...         0.01528 0.000753359548568\n",
       "241.52278516999999        20.78278156 ...         0.01534 0.000750467785564\n",
       "241.52278516999999        20.78278156 ... 0.0159933333333 0.000720384238428\n",
       "317.10346952999998 18.202987799999999 ... 0.0160866666667 0.000716286081667\n",
       "317.11477339999999 18.191914189999999 ... 0.0168466666667 0.000684605846616\n",
       "329.24927524999998 6.6551501899999996 ...         0.02619 0.000445394169968\n",
       "      333.82169725 37.299221410000001 ... 0.0173566666667  0.00066490242687\n",
       "      333.84683658 37.282608979999999 ... 0.0199466666667 0.000580392638572\n",
       "333.91491775999998 37.300255790000001 ... 0.0189466666667 0.000610283341926"
      ]
     },
     "execution_count": 34,
     "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": "code",
   "execution_count": 35,
   "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": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<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",
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       "</table>"
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      "text/plain": [
       "<Table masked=True length=49>\n",
       "        ra                dec         radial_velocity\n",
       "     float64            float64            int32     \n",
       "------------------ ------------------ ---------------\n",
       "19.068407270000002 46.740033570000001            5081\n",
       "19.086435210000001 46.747227780000003            5188\n",
       "20.334799289999999 40.487715479999999            5859\n",
       "125.90444401000001 21.338192790000001            5219\n",
       "125.90444401000001 21.338192790000001            5219\n",
       "125.90444401000001 21.338192790000001            5364\n",
       "125.90444401000001 21.338192790000001            5364\n",
       "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": 36,
     "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": [
    "See also the [notebook](heasarc_Spectral_Access.ipynb) specific to the Simple Spectral Access __[Protocol](http://www.ivoa.net/documents/SSA/)__.\n",
    "\n",
    "<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 will ask for a large exposure time and need to justify this by showing the spectrum of a prototypical ULX, M82 X-1. </i>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=3&gt;\n",
       "<table id=\"table4519440056\" 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": 10,
     "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=12&gt;\n",
       "<table id=\"table4519866264\" 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": 11,
     "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Adq+POVtXdD12o87hxr29XY8n7VL0AwB6wzP9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\nT/KgoWHXl1L+V5LPpk4kX1dV1eUrLPbGSXL++eePsqnswvb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HmWck2/RyT5Sppr9NUk7+kuPyrJobXWY0sp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\nxzM++98IAIAN4CMfaT5/5SvJq16VnHfetsvf+tbm89atye/9X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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d69c550>"
      ]
     },
     "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 titled. 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 requests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Because urllib is hard to make Python 2/3 compatible, just use requests:\n",
    "r=requests.get(spec_table[0]['SSA_reference'].decode(), stream=True)\n",
    "with open('my_M82_spectrum.fits','wb') as f:\n",
    "    f.write(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}