heasarc_queries_v9.ipynb

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    "# 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, e.g., 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"
   ]
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    "## 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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    "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"
   ]
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   "source": [
    "<a id=\"votable\"></a>\n",
    "# 1. VOTable:  working with VO Table objects\n"
   ]
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   "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"
   ]
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    "\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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    "params = {'name': 'rosmaster'}\n",
    "r = requests.get('http://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/getvotable.pl', params=params)\n",
    "#r.text"
   ]
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   "source": [
    "table=Table.read(io.BytesIO(r.content))"
   ]
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      "<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,
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"votable2\"></a>\n",
    "## 1.2 Creating a VO Table from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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,
    "deletable": true,
    "editable": true
   },
   "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,
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"scs1\"></a>\n",
    "The SCS is quite straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Couldn't create u'cds_parsetab'. [Errno 13] Permission denied: u'/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/units/format/cds_parsetab.py'\n",
      "WARNING: Couldn't write lextab module u'cds_lextab'. [Errno 13] Permission denied: u'/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/units/format/cds_lextab.py'\n"
     ]
    },
    {
     "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"sia\"></a>\n",
    "# 3. SIA:  Using the Simple Image Access protocol.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=88&gt;\n",
       "<table id=\"table4440039056\" 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": 9,
     "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": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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=skv1512747914072&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=skv1512747914072&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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Resulting image\n",
    "You can display it in python by\n",
    "(give it ~10 seconds to appear)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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=skv1512747914072&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": {
    "deletable": true,
    "editable": true
   },
   "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": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "## Download the FITS image?\n",
    "r=requests.get(table['URL'][4].decode(\"utf-8\"), stream=True)\n",
    "with open('image.fits','wb') as f:\n",
    "    f.write(r.content)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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=skv1512747914072&return=FITS\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10a8ef0d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cnIloWdSj2gm/dRncmY4OfObV530mWs9dt85s3KEkEEwBy2STSXZtg8qXgrRa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\nruh/eZFm0ixm34AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a650450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  This often doesn't work. No idea why.\n",
    "#hdu_list=apfits.open(table['URL'][4].decode(\"utf-8\"))\n",
    "print 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "Why does it so frequently time out when handing the URL to apfits.open()?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Now with aplpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kpND5QVXdSJrurDuU3GLehTh+s0lEFTxSgjNUqz9J7kqeysjI4lG9PF55jeLB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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c6f1810>"
      ]
     },
     "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": {
    "deletable": true,
    "editable": true
   },
   "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": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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=skv1512749884870&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": {
    "deletable": true,
    "editable": true
   },
   "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, e.g., to calculate exposure times based significantly detecting 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": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=18&gt;\n",
       "<table id=\"table4463262352\" 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": 31,
     "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": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=3&gt;\n",
       "<table id=\"table4507069392\" 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": 32,
     "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"tap2\"></a>\n",
    "# 4b. TAP:  Using the TAP to cross-correlate our objects with a catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=39&gt;\n",
       "<table id=\"table4421804304\" 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",
       "<tr><td>125.90444401000001</td><td>21.338192790000001</td><td>5219</td></tr>\n",
       "<tr><td>125.90444401000001</td><td>21.338192790000001</td><td>5364</td></tr>\n",
       "<tr><td>135.99423805999999</td><td>21.90099584</td><td>3157</td></tr>\n",
       "<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",
       "<tr><td>146.70334308</td><td>22.018272169999999</td><td>7446</td></tr>\n",
       "<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>241.52278516999999</td><td>20.78278156</td><td>4602</td></tr>\n",
       "<tr><td>241.52278516999999</td><td>20.78278156</td><td>4798</td></tr>\n",
       "<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": 34,
     "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": {
    "deletable": true,
    "editable": true
   },
   "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 close (within 50 kpc physical distances) are similar, suggesting these are truly close in 3-dimensions and therefore potentially interacting galaxies.</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=39&gt;\n",
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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>"
      ],
      "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": 35,
     "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": {
    "deletable": true,
    "editable": true
   },
   "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": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "&lt;Table masked=True length=49&gt;\n",
       "<table id=\"table4440038864\" 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",
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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": 37,
     "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"ssa\"></a>\n",
    "# 6. SSA:  Using the Simple Spectral Access protocol to retrieve spectra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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 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",
    "<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": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Couldn't write lextab module u'angle_lextab'. [Errno 13] Permission denied: u'/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/coordinates/angle_lextab.py'\n",
      "WARNING: Couldn't create u'angle_parsetab'. [Errno 13] Permission denied: u'/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/coordinates/angle_parsetab.py'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=3&gt;\n",
       "<table id=\"table4500673808\" 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": 38,
     "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": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://heasarc.gsfc.nasa.gov/FTP/chandra/data/science/ao17/cat4/17860/primary/acisf17860N002_pha2.fits.gz [Done]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;Table length=12&gt;\n",
       "<table id=\"table4430190992\" 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": 39,
     "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": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmS3tbTlxhLkiRJkiRJ6kEmCCVJkiRJkqQCM0EoSZIkSZIkFZgJQkmSJEmSJKnATBBK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\nSZIkSZKkHLNAKEmSJEmSJOXY/wfPaJZr78ElTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca09a10>"
      ]
     },
     "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,
    "deletable": true,
    "editable": 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 this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "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_spectru.fits','wb') as f:\n",
    "    f.write(r.content)\n",
    "\n",
    "## Python 3\n",
    "#import urllib.request\n",
    "#urllib.request.urlretrieve(spec_table[0]['SSA_reference'].decode(), \"my_M82_spectrum.fits\")\n",
    "# Python 2\n",
    "#import urllib2\n",
    "#urllib.urlretrieve(spec_table[0]['SSA_reference'].decode(), \"my_M82_spectrum.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}