Cone Search Example using AKARI/FIS All-Sky Survey Bright Source Catalogue

Conesearch_AKARI.ipynb

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   "metadata": {},
   "source": [
    "*I wanted to search this SCS resource in the registry:\n",
    "http://vao.stsci.edu/directory/getRecord.aspx?id=ivo://irsa.ipac/AKARI/Catalog/FISBSC*\n",
    "\n",
    "*How would I do that?*\n",
    "\n",
    "(Solution below adapted from http://astroquery.readthedocs.io/en/latest/vo_conesearch/vo_conesearch.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solution 1: By Querying STScI VAO Registry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This solution also uses the \"classic\" interface inherited from its days in `astropy.vo` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "from astroquery.vo_conesearch.vos_catalog import VOSDatabase\n",
    "from astroquery.vo_conesearch.validator import conf as validator_conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://vao.stsci.edu/directory/NVORegInt.asmx/VOTCapabilityPredOpt?predicate=1%3D1&capability=conesearch&VOTStyleOption=2 [Done]\n"
     ]
    }
   ],
   "source": [
    "# Obtain STScI VAO cone search registry (takes several seconds).\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "    registry_db = VOSDatabase.from_registry(\n",
    "        validator_conf.conesearch_master_list, encoding='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AKARI/FIS All-Sky Survey Bright Source Catalog, Version 1 1',\n",
       " 'AKARI/FIS All-Sky Survey Bright Source Catalogue 1']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find catalogs that might be AKARI/FIS All-Sky Survey Bright Source.\n",
    "registry_db.list_catalogs(pattern='AKARI/FIS All-Sky Survey Bright Source')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"capabilityClass\": \"ConeSearch\",\n",
      "    \"capabilityStandardID\": \"ivo://ivoa.net/std/ConeSearch\",\n",
      "    \"capabilityValidationLevel\": \"2\",\n",
      "    \"contentLevel\": \"#Research#\",\n",
      "    \"description\": \"The AKARI/FIS Bright Source Catalogue Version 1.0 provides the positions and fluxes of 427,071 point sources in the four far-infrared wavelengths centred at 65, 90, 140, and 160 microns. The sensitivity in the 90 micron band is about 0.55 Jy. The Far-Infrared Surveyor (FIS) instrument scanned 98 percent of the entire sky more than twice during the 16 months of the cryogenic mission phase. The AKARI/FIS Bright Source Catalogue is the primary data product from the AKARI survey. The catalogue is designed to have a uniform detection limit (corresponding to per scan sensitivity) over the entire sky (except for high background regions where a different data acquisition mode was used). Redundant observations are used to increase the reliability of the detection.\",\n",
      "    \"duplicatesIgnored\": 0,\n",
      "    \"identifier\": \"ivo://irsa.ipac/AKARI/Catalog/FISBSC#1\",\n",
      "    \"interfaceClass\": \"ParamHTTP\",\n",
      "    \"interfaceRole\": \"std?\",\n",
      "    \"interfaceVersion\": \"\",\n",
      "    \"maxRadius\": 180.0,\n",
      "    \"maxRecords\": 2147483647,\n",
      "    \"publisher\": \"NASA/IPAC Infrared Science Archive\",\n",
      "    \"publisherID\": \"ivo://irsa.ipac/IRSA\",\n",
      "    \"referenceURL\": \"https://irsa.ipac.caltech.edu/data/AKARI/gator_docs/akari_fis_colDescriptions.html\",\n",
      "    \"regionOfRegard\": NaN,\n",
      "    \"resourceID\": \"ivo://irsa.ipac/AKARI/Catalog/FISBSC\",\n",
      "    \"shortName\": \"AKARI/FIS [1]\",\n",
      "    \"subject\": \"#all sky survey#\",\n",
      "    \"tags\": \"Catalog\",\n",
      "    \"title\": \"AKARI/FIS All-Sky Survey Bright Source Catalogue\",\n",
      "    \"type\": \"#Catalog#\",\n",
      "    \"updated\": \"2017-07-25T00:13:41Z\",\n",
      "    \"url\": \"https://irsa.ipac.caltech.edu/SCS?table=akari_fis&\",\n",
      "    \"version\": \"\",\n",
      "    \"waveband\": \"#Infrared#Millimeter#\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Select the one you want and inspect its metadata.\n",
    "akari_cat = registry_db.get_catalog('AKARI/FIS All-Sky Survey Bright Source Catalogue 1')\n",
    "print(akari_cat.dumps())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astroquery.vo_conesearch import conesearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define your search object.\n",
    "# See http://astropy.readthedocs.io/en/stable/coordinates/index.html\n",
    "c = SkyCoord(0, 0, frame='icrs', unit='deg')\n",
    "\n",
    "# Define your search radius.\n",
    "# See http://astropy.readthedocs.io/en/stable/units/index.html\n",
    "sr = 0.1 * u.degree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, due to a bug with this particular service, we need to sanitize the service URL a little below. See https://github.com/astropy/astroquery/issues/992 for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://irsa.ipac.caltech.edu/SCS?table=akari_fis\n"
     ]
    }
   ],
   "source": [
    "url = akari_cat['url'][:-1]  # Remove [:-1] for astroquery>=0.3.7\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying https://irsa.ipac.caltech.edu/SCS?table=akari_fis\n"
     ]
    }
   ],
   "source": [
    "result = conesearch.conesearch(c, sr, catalog_db=url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.io.votable.tree.Table'>\n",
      " objid     objname              ra         ...  spt_ind   cntr \n",
      "                               deg         ...                 \n",
      "------- -------------- ------------------- ... --------- ------\n",
      "3119859 2359487-000418          359.952740 ... 132000000 119859\n"
     ]
    }
   ],
   "source": [
    "# This is a VO Table object.\n",
    "print(result.__class__)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.table.table.Table'>\n",
      " objid     objname              ra         ...  spt_ind   cntr \n",
      "                               deg         ...                 \n",
      "------- -------------- ------------------- ... --------- ------\n",
      "3119859 2359487-000418          359.952740 ... 132000000 119859\n"
     ]
    }
   ],
   "source": [
    "# Convert it to a more user-friendly Astropy Table object.\n",
    "# See http://astropy.readthedocs.io/en/latest/table/index.html\n",
    "tab = result.to_table()\n",
    "print(tab.__class__)\n",
    "print(tab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solution 2: If You Already Know the Service URL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This solution also uses the \"new-style\" interface as recommended in AstroQuery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astroquery.vo_conesearch import ConeSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = SkyCoord(0, 0, frame='icrs', unit='deg')\n",
    "sr = 0.1 * u.degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ConeSearch.URL = 'https://irsa.ipac.caltech.edu/SCS?table=akari_fis'\n",
    "result = ConeSearch.query_region(c, sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " objid     objname              ra         ...  spt_ind   cntr \n",
      "                               deg         ...                 \n",
      "------- -------------- ------------------- ... --------- ------\n",
      "3119859 2359487-000418          359.952740 ... 132000000 119859\n"
     ]
    }
   ],
   "source": [
    "tab = result.to_table()\n",
    "print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "       objid            objname         ra      dec  poserrmj  poserrmi  \\\n",
      "0  3119859.0  b'2359487-000418'  359.95274 -0.07168       6.0       6.0   \n",
      "\n",
      "   posserrpa  flux65  flux90  flux140    ...     mconf65  mconf90  mconf140  \\\n",
      "0        0.0  0.1588  0.6644    1.092    ...         0.0      1.0       0.0   \n",
      "\n",
      "   mconf160  ndens         x         y         z      spt_ind      cntr  \n",
      "0       0.0    0.0  0.999999 -0.000825 -0.001251  132000000.0  119859.0  \n",
      "\n",
      "[1 rows x 41 columns]\n"
     ]
    }
   ],
   "source": [
    "# And if you prefer pandas DataFrame\n",
    "pantab = tab.to_pandas()\n",
    "print(pantab.__class__)\n",
    "print(pantab)"
   ]
  }
 ],
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