ESASky_astroquery_use_cases.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Access ESASky products with astroquery.esasky\n",
    "\n",
    "##### Authors: Ivan Valtchanov, Belén López Martí, H. Norman, Nuria Álvarez Crespo\n",
    "\n",
    "#### Last update: Jul 07, 2021\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrates some example usages of the ESASky implementation in astroquery.\n",
    "\n",
    "First you need to install astroquery and esasky.\n",
    "\n",
    "Astroquery can be installed with `pip install --pre astroquery`, the latest version should come with esasky. Alternatively, you can grab the latest astroquery with esasky from [here](https://github.com/astropy/astroquery/).\n",
    "\n",
    "The documentation for astroquery.esasky is available [here](https://astroquery.readthedocs.io/en/latest/esasky/esasky.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Use Case 1: Retrieve imaging data for a single object\n",
    "\n",
    "In this use case, imaging data are retrieved for a single object, indicated by its name (resolved by Simbad) or coordinates.\n",
    "\n",
    "We start by importing the ESASky astroquery module and other necessary packages:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v20200428.1) - Connection:\n",
      "\tHost: sky.esa.int\n",
      "\tUse HTTPS: True\n",
      "\tPort: 443\n",
      "\tSSL Port: 443\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "import os\n",
    "from astroquery.esasky import ESASky"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's check the available maps: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['INTEGRAL',\n",
       " 'XMM',\n",
       " 'Chandra',\n",
       " 'SUZAKU',\n",
       " 'XMM-OM-OPTICAL',\n",
       " 'XMM-OM-UV',\n",
       " 'HST-UV',\n",
       " 'HST-OPTICAL',\n",
       " 'HST-IR',\n",
       " 'ISO-IR',\n",
       " 'Herschel',\n",
       " 'AKARI',\n",
       " 'Spitzer',\n",
       " 'ALMA']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ESASky.list_maps()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do a search around the position of our object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 12 tables:\n",
      "\t'0:INTEGRAL' with 18 column(s) and 1 row(s) \n",
      "\t'1:XMM' with 15 column(s) and 11 row(s) \n",
      "\t'2:CHANDRA' with 53 column(s) and 17 row(s) \n",
      "\t'3:XMM-OM-OPTICAL' with 17 column(s) and 12 row(s) \n",
      "\t'4:XMM-OM-UV' with 17 column(s) and 19 row(s) \n",
      "\t'5:HST-UV' with 15 column(s) and 19 row(s) \n",
      "\t'6:HST-OPTICAL' with 15 column(s) and 260 row(s) \n",
      "\t'7:HST-IR' with 15 column(s) and 41 row(s) \n",
      "\t'8:ISO-IR' with 18 column(s) and 7 row(s) \n",
      "\t'9:HERSCHEL' with 15 column(s) and 9 row(s) \n",
      "\t'10:AKARI' with 11 column(s) and 3 row(s) \n",
      "\t'11:SPITZER' with 14 column(s) and 4 row(s) \n"
     ]
    }
   ],
   "source": [
    "maps = ESASky.query_object_maps(position='M51')\n",
    "print (maps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is a TableList with the keys corresponding to the mission (or in some cases, mission and instrument) names for which there are images available covering our target position.\n",
    "\n",
    "We can also do a search by coordinates, either by using an [astroquery.coordinates](http://docs.astropy.org/en/stable/coordinates/) object or by typing the coordinates. In this latter case, we have to define the units we are using (d = degrees; h = hours; m = minutes; s = seconds)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: InputWarning: Coordinate string is being interpreted as an ICRS coordinate. [astroquery.utils.commons]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 12 tables:\n",
      "\t'0:INTEGRAL' with 18 column(s) and 1 row(s) \n",
      "\t'1:XMM' with 15 column(s) and 11 row(s) \n",
      "\t'2:CHANDRA' with 53 column(s) and 17 row(s) \n",
      "\t'3:XMM-OM-OPTICAL' with 17 column(s) and 12 row(s) \n",
      "\t'4:XMM-OM-UV' with 17 column(s) and 19 row(s) \n",
      "\t'5:HST-UV' with 15 column(s) and 19 row(s) \n",
      "\t'6:HST-OPTICAL' with 15 column(s) and 260 row(s) \n",
      "\t'7:HST-IR' with 15 column(s) and 41 row(s) \n",
      "\t'8:ISO-IR' with 18 column(s) and 7 row(s) \n",
      "\t'9:HERSCHEL' with 15 column(s) and 9 row(s) \n",
      "\t'10:AKARI' with 11 column(s) and 3 row(s) \n",
      "\t'11:SPITZER' with 14 column(s) and 4 row(s) \n"
     ]
    }
   ],
   "source": [
    "maps = ESASky.query_object_maps(position='13h29m52.7s +47d11m43s')\n",
    "print (maps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method has a tolerance of 5 arcsec to allow for positional errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the content of the 'XMM-OM-OPTICAL' table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Table length=19>\n",
       "        name          dtype  unit                description               \n",
       "-------------------- ------- ---- -----------------------------------------\n",
       "             dec_deg float64                                               \n",
       "            duration   int64    s        Total exposure time of observation\n",
       "             end_utc  object                                               \n",
       "              filter  object                                         Filter\n",
       "                 fov  object                                               \n",
       "          instrument  object                                     Instrument\n",
       "      observation_id  object      The XMM-Newton observation identification\n",
       "     observation_oid   int32                Observation internal identifier\n",
       "      position_angle float64                                               \n",
       "        postcard_url  object       The URL to download the postcard preview\n",
       "         product_url  object                The URL to download the product\n",
       "proprietary_end_date  object                                               \n",
       "              ra_deg float64                                               \n",
       "           start_utc  object                                               \n",
       "               stc_s  object                                               \n",
       "              target  object                                               \n",
       "       xmm_om_uv_oid   int64                                               "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maps['XMM-OM-UV'].info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, it contains a table with URLs for the maps, and some additional info, like map ra, dec, bandpass ('filter'), or the exposure time ('duration').\n",
    "\n",
    "Let's now see what bands are available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observation_id instrument filter duration\n",
      "                                    s    \n",
      "-------------- ---------- ------ --------\n",
      "    0830191501         OM      U    63000\n",
      "    0830191601         OM      B    63000\n",
      "    0830191601         OM      U    63000\n",
      "    0212480801         OM      U    49214\n",
      "    0212480801         OM      B    49214\n",
      "    0212480801         OM      V    49214\n",
      "    0303420101         OM      U    54114\n",
      "    0852030101         OM      B    77000\n",
      "    0824450901         OM      B    78000\n",
      "    0830191401         OM      U    98000\n",
      "    0830191401         OM      B    98000\n",
      "    0830191501         OM      B    63000\n"
     ]
    }
   ],
   "source": [
    "maps['XMM-OM-OPTICAL']['observation_id', 'instrument', 'filter', 'duration'].pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is stored in memory. To get the actual images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Starting download of XMM-OM-OPTICAL data. (12 files) [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0830191501 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_U&PROTOCOL=HTTP&OBSERVATION_ID=0830191501 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0830191601 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_B&PROTOCOL=HTTP&OBSERVATION_ID=0830191601 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0830191601 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_U&PROTOCOL=HTTP&OBSERVATION_ID=0830191601 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0212480801 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_U&PROTOCOL=HTTP&OBSERVATION_ID=0212480801 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0212480801 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_B&PROTOCOL=HTTP&OBSERVATION_ID=0212480801 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0212480801 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_V&PROTOCOL=HTTP&OBSERVATION_ID=0212480801 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0303420101 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_U&PROTOCOL=HTTP&OBSERVATION_ID=0303420101 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0852030101 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_B&PROTOCOL=HTTP&OBSERVATION_ID=0852030101 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0824450901 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_B&PROTOCOL=HTTP&OBSERVATION_ID=0824450901 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0830191401 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_U&PROTOCOL=HTTP&OBSERVATION_ID=0830191401 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0830191401 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_B&PROTOCOL=HTTP&OBSERVATION_ID=0830191401 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 0830191501 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=PRODUCT&OBS_IMAGE_TYPE=OBS_OM_B&PROTOCOL=HTTP&OBSERVATION_ID=0830191501 [astroquery.esasky.core]\n",
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading of XMM-OM-OPTICAL data complete. [astroquery.esasky.core]\n",
      "INFO: Maps available at C:\\Users\\Henrik\\Downloads. [astroquery.esasky.core]\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# set the download dir for ESASky products\n",
    "#\n",
    "download_dir = os.path.expanduser('~') + '/Downloads/' # change this to your desired directory\n",
    "\n",
    "maps_data = ESASky.get_maps(query_table_list=maps, missions='XMM-OM-OPTICAL', download_dir=download_dir)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XMM-OM-OPTICAL': [[<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13DFB130>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13C0D050>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E4C2B0>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13E7AF30>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E4C310>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E4C350>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x05EEA250>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13DB1090>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13DB1C50>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x1398B470>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13DB1A50>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E58210>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13DBDFB0>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E58330>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13DC3930>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13DD74F0>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13DC39D0>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E02270>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13E54750>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E02870>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E02C70>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13DE0350>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E0D110>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E02810>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13F7AF90>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E02630>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E58670>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13DD8750>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E58090>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13DB1AF0>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13DBD810>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13DB1A70>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13DB1390>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x13DEE190>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E4CF30>, <astropy.io.fits.hdu.image.ImageHDU object at 0x13E4C1F0>]]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maps_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output maps_data is a product containing all the FITS files in memory, which are also downloaded to a local folder. If no folder is specified, the current working directory is used by default.\n",
    "\n",
    "It is possible to download all the available images at once by typing 'all' as the mission name in the ESASky.get_maps() method.\n",
    "    \n",
    "We are ready to work with these maps. For example, let's inspect the header of one of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / file does conform to FITS standard             \n",
       "BITPIX  =                  -32 / number of bits per data pixel                  \n",
       "NAXIS   =                    2 / number of data axes                            \n",
       "NAXIS1  =                 1339 / length of data axis 1                          \n",
       "NAXIS2  =                 1373 / length of data axis 2                          \n",
       "EXTEND  =                    T / FITS dataset may contain extensions            \n",
       "XPROC0  = 'ommosaic imagesets=''product/P0212480801OMS007SIMAGE2000.FIT produc&'\n",
       "CONTINUE  't/P0212480801OMS405SIMAGE2000.FIT product/P0212480801OMS406SIMAGE20&'\n",
       "CONTINUE  '00.FIT product/P0212480801OMS407SIMAGE2000.FIT product/P0212480801O&'\n",
       "CONTINUE  'MS408SIMAGE2000.FIT'' mosaicedset=product/P0212480801OMX000USIMAGU0&'\n",
       "CONTINUE  '00.FIT correlset='''' nsigma=2 mincorr=0 minfraction=0.5 maxdx=5 bi&'\n",
       "CONTINUE  'naxis=0 numintervals=2 di=10 minnumpixels=100 # (ommosaic-2.10) [xm&'\n",
       "CONTINUE  'msas_20190401_1820-18.0.0]'                                          \n",
       "XPROC1  = 'omdetect set=product/P0212480801OMX000USIMAGU000.FIT outset=product&'\n",
       "CONTINUE  '/P0212480801OMX000USISWSU000.FIT wdxset='''' nsigma=2 minsignifican&'\n",
       "CONTINUE  'ce=3 maxrawcountrate=50 levelimage='''' backgroundimage='''' region&'\n",
       "CONTINUE  'file=product/P0212480801OMX000USISWSU000.ASC detectextended=yes psf&'\n",
       "CONTINUE  'photometryenabled=no neighboursforpsfphotometry=1 psfphotometryset&' \n",
       "CONTINUE  '='''' backgroundmethod=1 # (omdetect-5.41) [xmmsas_20190401_1820-18&'\n",
       "CONTINUE  '.0.0]   '                                                            \n",
       "XPROC2  = 'addattribute set=product/P0212480801OMX000USIMAGU000.FIT attributen&'\n",
       "CONTINUE  'ame=''SEQ_ID REVOLUT PROCDATE PROCREV PPSVERS SASVERS ODSVER ORIGIN&'\n",
       "CONTINUE  ' ODFVER CONTENT'' attributetype=''string string string string strin&'\n",
       "CONTINUE  'g string string string string string'' attributecomment=''\"Pipeline&'\n",
       "CONTINUE  ' sequence\" \"Satellite Revolution Number\" \"Processing date\" \"Process&'\n",
       "CONTINUE  'ing revision\" \"PPS configuration\" \"SAS version used in pipeline pro&'\n",
       "CONTINUE  'cessing\" \"ODS version\" \"Origin of FITS file\" ODF_VERSION \"Contents &'\n",
       "CONTINUE  'of file\"'' attributeunit='''' realvalue='''' stringvalue=''133003 1&'\n",
       "CONTINUE  '018 2019-04-04T22:41:15 1 17.56_20190403_1200 xmmsas_20190401_1820-&'\n",
       "CONTINUE  '18.0.0 15.0.0 XMM-Newton/SOC 003 \"OM USER WINDOWS SKY IMAGE MOSAIC&' \n",
       "CONTINUE  '\"'' integervalue='''' booleanvalue='''' # (addattribute-2.3) [xmmsa&'\n",
       "CONTINUE  's_20190401_1820-18.0.0]'                                             \n",
       "XPROC3  = 'addattribute set=product/P0212480801OMX000USIMAGU000.FIT attributen&'\n",
       "CONTINUE  'ame=''SEQ_ID REVOLUT PROCDATE PROCREV PPSVERS SASVERS ODSVER ORIGIN&'\n",
       "CONTINUE  ' ODFVER CONTENT'' attributetype=''string string string string strin&'\n",
       "CONTINUE  'g string string string string string'' attributecomment=''\"Pipeline&'\n",
       "CONTINUE  ' sequence\" \"Satellite Revolution Number\" \"Processing date\" \"Process&'\n",
       "CONTINUE  'ing revision\" \"PPS configuration\" \"SAS version used in pipeline pro&'\n",
       "CONTINUE  'cessing\" \"ODS version\" \"Origin of FITS file\" ODF_VERSION \"Contents &'\n",
       "CONTINUE  'of file\"'' attributeunit='''' realvalue='''' stringvalue=''133003 1&'\n",
       "CONTINUE  '018 2019-04-04T22:41:15 1 17.56_20190403_1200 xmmsas_20190401_1820-&'\n",
       "CONTINUE  '18.0.0 15.0.0 XMM-Newton/SOC 003 \"OM USER WINDOWS SKY IMAGE MOSAIC&' \n",
       "CONTINUE  '\"'' integervalue='''' booleanvalue='''' # (addattribute-2.3) [xmmsa&'\n",
       "CONTINUE  's_20190401_1820-18.0.0]'                                             \n",
       "XDAL0   = 'product/P0212480801OMX000USIMAGU000.FIT 2019-04-05T08:03:38.000 Mod&'\n",
       "CONTINUE  'ify addattribute (addattribute-2.3) [xmmsas_20190401_1820-18.0.0] H&'\n",
       "CONTINUE  'igh SAS_MEMORY_MODEL=high SAS_ROWS= SAS_ZERO_ROWS= SAS_COLUMN_WISE= '\n",
       "XDAL1   = 'product/P0212480801OMX000USIMAGU000.FIT 2019-04-05T08:03:37.000 Mod&'\n",
       "CONTINUE  'ify addattribute (addattribute-2.3) [xmmsas_20190401_1820-18.0.0] H&'\n",
       "CONTINUE  'igh SAS_MEMORY_MODEL=high SAS_ROWS= SAS_ZERO_ROWS= SAS_COLUMN_WISE=' \n",
       "XDAL2   = 'product/P0212480801OMX000USIMAGU000.FIT 2019-04-05T01:26:19.000 Mod&'\n",
       "CONTINUE  'ify omdetect (omdetect-5.41) [xmmsas_20190401_1820-18.0.0] High SAS&'\n",
       "CONTINUE  '_MEMORY_MODEL=high SAS_ROWS= SAS_ZERO_ROWS= SAS_COLUMN_WISE='        \n",
       "XDAL3   = 'product/P0212480801OMX000USIMAGU000.FIT 2019-04-05T01:26:12.000 Cre&'\n",
       "CONTINUE  'ate ommosaic (ommosaic-2.10) [xmmsas_20190401_1820-18.0.0] High SAS&'\n",
       "CONTINUE  '_MEMORY_MODEL=high SAS_ROWS= SAS_ZERO_ROWS= SAS_COLUMN_WISE='        \n",
       "CREATOR = 'ommosaic (ommosaic-2.10) [xmmsas_20190401_1820-18.0.0]' / name of cod\n",
       "DATE    = '2019-04-05T01:26:18.000' / creation date                             \n",
       "LONGSTRN= 'OGIP 1.0'                                                            \n",
       "INSTRUME= 'OM      '                                                            \n",
       "DATAMODE= 'IMAGING '                                                            \n",
       "FILTER  = 'U       '                                                            \n",
       "OBS_ID  = '        '                                                            \n",
       "OBJECT  = 'UNKNOWN '                                                            \n",
       "DATE-OBS= '2005-07-01T08:54:01.000'                                             \n",
       "DATE-END= '2005-07-01T11:25:27.000'                                             \n",
       "BINAX1  =                    1                                                  \n",
       "BINAX2  =                    1                                                  \n",
       "BINBPE  =                    F                                                  \n",
       "EXPOSURE=                    1                                                  \n",
       "DEADFRAC= 1.75921055754711E-02                                                  \n",
       "FRAMTIME= 9.89080012358556E+00                                                  \n",
       "WINDOWX0=                    0                                                  \n",
       "WINDOWDX=                 1339                                                  \n",
       "WINDOWY0=                    0                                                  \n",
       "WINDOWDY=                 1373                                                  \n",
       "EQUINOX =                 2000 / Equinox of celestial coordinate system         \n",
       "CDELT1  = -2.64729431364685E-04 / Plate-scale in x direction                    \n",
       "CDELT2  = 2.64729431364685E-04 / Plate-scale in y direction                     \n",
       "CTYPE1  = 'RA---TAN'                                                            \n",
       "CTYPE2  = 'DEC--TAN'                                                            \n",
       "CRPIX1  = 5.00000000000000E-01 / Reference x pixel                              \n",
       "CRPIX2  = 5.00000000000000E-01 / Reference y pixel                              \n",
       "CRVAL1  = 2.02745683772947E+02 / RA at reference  x pixel                       \n",
       "CRVAL2  = 4.69996920100087E+01 / DEC at reference  y pixel                      \n",
       "PA      = 0.00000000000000E+00 / Mean RA pointing direction                     \n",
       "RA_PNT  = 2.02745683772947E+02 / Mean RA pointing direction                     \n",
       "DEC_PNT = 4.69996920100087E+01 / Mean Dec pointingDirection                     \n",
       "POSCOROK=                    T / Images were astrometry corrected               \n",
       "RAOFFSET= 2.70897626876831E+00 / Mean RA offset applied to images               \n",
       "DEOFFSET= -8.65564709901808E-01 / Mean DEC offset applied to images             \n",
       "NSTACKED=                    5 / Number of images stacked                       \n",
       "SEQ_ID  = '133003  '           / Pipeline sequence                              \n",
       "REVOLUT = '1018    '           / Satellite Revolution Number                    \n",
       "PROCDATE= '2019-04-04T22:41:15' / Processing date                               \n",
       "PROCREV = '1       '           / Processing revision                            \n",
       "PPSVERS = '17.56_20190403_1200' / PPS configuration                             \n",
       "SASVERS = 'xmmsas_20190401_1820-18.0.0' / SAS version used in pipeline processin\n",
       "ODSVER  = '15.0.0  '           / ODS version                                    \n",
       "ORIGIN  = 'XMM-Newton/SOC'     / Origin of FITS file                            \n",
       "ODFVER  = '003     '           / ODF_VERSION                                    \n",
       "CONTENT = 'OM USER WINDOWS SKY IMAGE MOSAIC' / Contents of file                 \n",
       "HISTORY Created by ommosaic (ommosaic-2.10) [xmmsas_20190401_1820-18.0.0] at 201\n",
       "HISTORY 9-04-05T01:26:12                                                        \n",
       "HISTORY Modified by omdetect (omdetect-5.41) [xmmsas_20190401_1820-18.0.0] at 20\n",
       "HISTORY 19-04-05T01:26:19                                                       \n",
       "HISTORY Modified by addattribute (addattribute-2.3) [xmmsas_20190401_1820-18.0.0\n",
       "HISTORY ] at 2019-04-05T08:03:38                                                \n",
       "HISTORY Modified by addattribute (addattribute-2.3) [xmmsas_20190401_1820-18.0.0\n",
       "HISTORY ] at 2019-04-05T08:03:38                                                "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu = maps_data['XMM-OM-OPTICAL'][3]\n",
    "hdu[0].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now display it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x14007ad0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\henrik\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\matplotlib\\image.py:412: RuntimeWarning: invalid value encountered in subtract\n",
      "  A_scaled -= a_min\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = hdu[0].data\n",
    "\n",
    "from astropy import visualization \n",
    "from astropy.visualization import (MinMaxInterval, SqrtStretch, ImageNormalize, ManualInterval)\n",
    "\n",
    "# Create an ImageNormalize object\n",
    "#norm = ImageNormalize(image, interval=MinMaxInterval(), stretch=SqrtStretch())\n",
    "norm = ImageNormalize(image, interval = ManualInterval(-0.05,0.1))\n",
    "\n",
    "# Display the image\n",
    "fig = plt.figure(figsize=(8,8),dpi=100)\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "im = ax.imshow(image, cmap='gray', origin='lower', norm=norm)\n",
    "fig.colorbar(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Use Case 2: Retrieve catalogue data for a single object\n",
    "\n",
    "We will now inspect and retrieve the catalogue data available for a given object, using either a name (resolved by Simbad) or coordinates.\n",
    "\n",
    "As we did in Use Case 1 with the images, let's first get a list of the available catalogues:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LAMOST',\n",
       " 'AllWise',\n",
       " 'AKARI-IRC-SC',\n",
       " 'TwoMASS',\n",
       " 'INTEGRAL',\n",
       " 'CHANDRA-SC2',\n",
       " 'XMM-EPIC-STACK',\n",
       " 'XMM-EPIC',\n",
       " 'XMM-OM',\n",
       " 'XMM-SLEW',\n",
       " 'Tycho-2',\n",
       " 'Gaia-eDR3',\n",
       " 'Hipparcos-2',\n",
       " 'HSC',\n",
       " 'Herschel-HPPSC-070',\n",
       " 'Herschel-HPPSC-100',\n",
       " 'Herschel-HPPSC-160',\n",
       " 'Herschel-SPSC-250',\n",
       " 'Herschel-SPSC-350',\n",
       " 'Herschel-SPSC-500',\n",
       " 'Planck-PGCC',\n",
       " 'Planck-PCCS2E-HFI',\n",
       " 'Planck-PCCS2-HFI',\n",
       " 'Planck-PCCS2-LFI',\n",
       " 'Planck-PSZ2']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ESASky.list_catalogs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look for an object in these catalogues by name or coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n",
      "WARNING: UnitsWarning: 'erg/cm(2)/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n",
      "WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 9 tables:\n",
      "\t'0:ALLWISE' with 25 column(s) and 1 row(s) \n",
      "\t'1:TWOMASS' with 14 column(s) and 3 row(s) \n",
      "\t'2:CHANDRA-SC2' with 41 column(s) and 9 row(s) \n",
      "\t'3:XMM-EPIC-STACK' with 345 column(s) and 1 row(s) \n",
      "\t'4:XMM-EPIC' with 220 column(s) and 11 row(s) \n",
      "\t'5:XMM-OM' with 122 column(s) and 5 row(s) \n",
      "\t'6:HSC' with 27 column(s) and 230 row(s) \n",
      "\t'7:HERSCHEL-HPPSC-070' with 21 column(s) and 1 row(s) \n",
      "\t'8:HERSCHEL-HPPSC-100' with 21 column(s) and 1 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_object_catalogs(position='M51')\n",
    "print (cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: InputWarning: Coordinate string is being interpreted as an ICRS coordinate. [astroquery.utils.commons]\n",
      "WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n",
      "WARNING: UnitsWarning: 'erg/cm(2)/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n",
      "WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 9 tables:\n",
      "\t'0:ALLWISE' with 25 column(s) and 1 row(s) \n",
      "\t'1:TWOMASS' with 14 column(s) and 2 row(s) \n",
      "\t'2:CHANDRA-SC2' with 41 column(s) and 9 row(s) \n",
      "\t'3:XMM-EPIC-STACK' with 345 column(s) and 1 row(s) \n",
      "\t'4:XMM-EPIC' with 220 column(s) and 11 row(s) \n",
      "\t'5:XMM-OM' with 122 column(s) and 5 row(s) \n",
      "\t'6:HSC' with 27 column(s) and 231 row(s) \n",
      "\t'7:HERSCHEL-HPPSC-070' with 21 column(s) and 1 row(s) \n",
      "\t'8:HERSCHEL-HPPSC-100' with 21 column(s) and 1 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_object_catalogs(position='13h29m52.7s +47d11m43s')\n",
    "print (cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in the previous use case, the query results are stored in a TableList with the keys corresponding to the catalog name. \n",
    "\n",
    "It is also possible to specify the catalogues to search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 2 tables:\n",
      "\t'0:XMM-EPIC' with 220 column(s) and 11 row(s) \n",
      "\t'1:HSC' with 27 column(s) and 230 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_object_catalogs(position='M51', catalogs=['XMM-EPIC','XMM-SLEW', 'HSC'])\n",
    "print (cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we only get two tables, because there are no available sources in the XMM-SLEW catalogue.\n",
    "\n",
    "Let's now visualise the XMM-EPIC results table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Table length=230>\r\n",
      "         name          dtype        unit                                                                      description                                                                \r\n",
      "--------------------- ------- --------------- -------------------------------------------------------------------------------------------------------------------------------------------\r\n",
      "             abs_corr  object                                                              Y/N indicator of whether the astrometric correction included alignment with a standard catalog\r\n",
      "                   ci float64                 Normalized (by detector and filter) mean value of Concentration Index, defined as difference between magnitude in small and large apertures\r\n",
      "             ci_sigma float64                                                                                                   Std dev in measurements of normalized Concentration Index\r\n",
      "                  dec float64             deg                                                                                                           Declination for the match (J2000)\r\n",
      "               dsigma float64 milliarcseconds                                                                                              Std dev of source positions and match position\r\n",
      "           extinction float64           ABMag                                                                                                          E(B-V) from Schlegel et al. (1998)\r\n",
      "               filter  object                 Filter for the match, one filter per row. First part of the filter name corresponds to the instrument, where w3 = WPC3; w2 = WFPC2; a = ACS\r\n",
      "                 flux float64             mag                                                                                               Median magnitude for the corresponding filter\r\n",
      "           flux_sigma float64             mag                                                                                       Median absolute deviation around the median magnitude\r\n",
      "                htmid   int64                                                                                                              Hierarchical triangular index (HTM) identifier\r\n",
      "          kron_radius float64      arcseconds                                                                                         Mean value of the Kron Radius from Source Extractor\r\n",
      "    kron_radius_sigma float64      arcseconds                                                                                                      Std dev in measurements of Kron Radius\r\n",
      "             match_id   int64                                                                                                                                    The HSC match ID number.\r\n",
      "          num_filters   int32                                                                                                                         Number of filters used in the match\r\n",
      "           num_images   int32                                                                                                                          Number of images used in the match\r\n",
      "num_images_per_filter   int32                                                                                                                                 Number of images per filter\r\n",
      "           num_visits   int32                                                                                                                          Number of visits used in the match\r\n",
      "                   ra float64             deg                                                                                                       Right ascension for the match (J2000)\r\n",
      "        spectrum_flag  object                                                                                                                             Indicates spectrum is available\r\n",
      "            start_mjd float64             MJD                                                           Observation Start Time (Mean Julian Date - MJD) for earliest observation in match\r\n",
      "           start_time  object            time                                                                                    Observation Start Time for earliest observation in match\r\n",
      "             stop_mjd float64             MJD                                                                                       Observation Stop Time for latest observation in match\r\n",
      "            stop_time  object            time                                                                                       Observation Stop Time for latest observation in match\r\n",
      "          target_name  object                                                                                                                     Target name for one of the observations\r\n",
      "                    x float64                                                                                                             x coordinate of source on unit celestial sphere\r\n",
      "                    y float64                                                                                                             y coordinate of source on unit celestial sphere\r\n",
      "                    z float64                                                                                                             z coordinate of source on unit celestial sphere\r\n"
     ]
    }
   ],
   "source": [
    "hsc_table = cats['HSC']\n",
    "hsc_table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abs_corr         ci         ...          y                   z         \n",
      "                            ...                                        \n",
      "-------- ------------------ ... -------------------- ------------------\n",
      "       Y 3.2462957073308605 ... -0.25972112925441787 0.7336669758079536\n",
      "       Y 3.2462957073308605 ... -0.25972112925441787 0.7336669758079536\n",
      "       Y 3.2462957073308605 ... -0.25972112925441787 0.7336669758079536\n",
      "       Y 3.2462957073308605 ... -0.25972112925441787 0.7336669758079536\n",
      "       Y 3.2462957073308605 ... -0.25972112925441787 0.7336669758079536\n",
      "       Y 3.2462957073308605 ... -0.25972112925441787 0.7336669758079536\n",
      "       Y 3.1274725137085753 ...  -0.2597201914230254 0.7336636936033848\n",
      "       Y 3.1274725137085753 ...  -0.2597201914230254 0.7336636936033848\n",
      "       Y 3.0291378498077393 ...   -0.259721191276137 0.7336619947383093\n",
      "       Y  2.477840943769975 ...  -0.2597191720847075    0.7336613582694\n",
      "     ...                ... ...                  ...                ...\n",
      "       Y  2.346555602090835 ...  -0.2596805843969818 0.7336826358073371\n",
      "       Y  2.346555602090835 ...  -0.2596805843969818 0.7336826358073371\n",
      "       Y  2.346555602090835 ...  -0.2596805843969818 0.7336826358073371\n",
      "       Y  2.346555602090835 ...  -0.2596805843969818 0.7336826358073371\n",
      "       Y  2.346555602090835 ...  -0.2596805843969818 0.7336826358073371\n",
      "       Y  2.346555602090835 ...  -0.2596805843969818 0.7336826358073371\n",
      "       Y 1.4051175818723791 ...  -0.2596798726735644 0.7336833004766261\n",
      "       Y 1.4051175818723791 ...  -0.2596798726735644 0.7336833004766261\n",
      "       Y  3.090344831861299 ...  -0.2596806836920403  0.733680358161309\n",
      "       Y 3.6193103625856597 ... -0.25967897333661133 0.7336779095802272\n",
      "Length = 230 rows\n"
     ]
    }
   ],
   "source": [
    "print (hsc_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also choose the columns to display:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   target_name             ra                dec        \n",
      "                          deg                deg        \n",
      "------------------ ------------------ ------------------\n",
      "          SN2005CS 202.47118208518816 47.194697000649406\n",
      "          SN2005CS 202.47118208518816 47.194697000649406\n",
      "          SN2005CS 202.47118208518816 47.194697000649406\n",
      "          SN2005CS 202.47118208518816 47.194697000649406\n",
      "          SN2005CS 202.47118208518816 47.194697000649406\n",
      "          SN2005CS 202.47118208518816 47.194697000649406\n",
      "             M51-2   202.470972916654  47.19442024801208\n",
      "             M51-2   202.470972916654  47.19442024801208\n",
      "          M-51-V05  202.4710001790092  47.19427700167262\n",
      "             M51-3  202.4707919684451  47.19422333550497\n",
      "               ...                ...                ...\n",
      "           SN1994I   202.468072131606   47.1960174580918\n",
      "           SN1994I   202.468072131606   47.1960174580918\n",
      "           SN1994I   202.468072131606   47.1960174580918\n",
      "           SN1994I   202.468072131606   47.1960174580918\n",
      "           SN1994I   202.468072131606   47.1960174580918\n",
      "           SN1994I   202.468072131606   47.1960174580918\n",
      "SN-1994I-COMPANION 202.46803221528108  47.19607350403027\n",
      "SN-1994I-COMPANION 202.46803221528108  47.19607350403027\n",
      "          M-51-V05 202.46799543213092  47.19582540402095\n",
      "          M-51-V05 202.46774717260243  47.19561893725088\n",
      "Length = 230 rows\n"
     ]
    }
   ],
   "source": [
    "print (hsc_table['target_name', 'ra', 'dec'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot these sources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('RA (deg)')\n",
    "plt.ylabel('DEC (deg)')\n",
    "plt.xlim(reversed(plt.xlim(202.467,202.472)))\n",
    "plt.ylim(47.193,47.197)\n",
    "plt.scatter(hsc_table['ra'], hsc_table['dec'])\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Use Case 3: Retrieve data in a sky region\n",
    "\n",
    "It is also possible retrieve imaging and catalogue data in a circular sky region. The procedure is very similar to the case of a single object discussed above, but using the query_region_maps() and query_region_catalogs() methods instead of query_object_maps() and query_object_catalogs(). \n",
    "\n",
    "Let's first see the case where imaging data are retrieved. To query the region, we have to enter the central coordinates and the radius:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: InputWarning: Coordinate string is being interpreted as an ICRS coordinate. [astroquery.utils.commons]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 12 tables:\n",
      "\t'0:INTEGRAL' with 18 column(s) and 1 row(s) \n",
      "\t'1:XMM' with 15 column(s) and 11 row(s) \n",
      "\t'2:CHANDRA' with 53 column(s) and 17 row(s) \n",
      "\t'3:XMM-OM-OPTICAL' with 17 column(s) and 12 row(s) \n",
      "\t'4:XMM-OM-UV' with 17 column(s) and 19 row(s) \n",
      "\t'5:HST-UV' with 15 column(s) and 139 row(s) \n",
      "\t'6:HST-OPTICAL' with 15 column(s) and 767 row(s) \n",
      "\t'7:HST-IR' with 15 column(s) and 234 row(s) \n",
      "\t'8:ISO-IR' with 18 column(s) and 15 row(s) \n",
      "\t'9:HERSCHEL' with 15 column(s) and 25 row(s) \n",
      "\t'10:AKARI' with 11 column(s) and 4 row(s) \n",
      "\t'11:SPITZER' with 14 column(s) and 6 row(s) \n"
     ]
    }
   ],
   "source": [
    "maps = ESASky.query_region_maps(position='13h29m52.7s +47d11m43s', radius='25arcmin')\n",
    "print (maps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference with the query_object_maps() module is that now we have to explicitly indicate the search radius; if not, an error message is returned.\n",
    "\n",
    "To search in a region around a given object, we can also enter the object's name instead of its coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 12 tables:\n",
      "\t'0:INTEGRAL' with 18 column(s) and 1 row(s) \n",
      "\t'1:XMM' with 15 column(s) and 11 row(s) \n",
      "\t'2:CHANDRA' with 53 column(s) and 17 row(s) \n",
      "\t'3:XMM-OM-OPTICAL' with 17 column(s) and 12 row(s) \n",
      "\t'4:XMM-OM-UV' with 17 column(s) and 19 row(s) \n",
      "\t'5:HST-UV' with 15 column(s) and 139 row(s) \n",
      "\t'6:HST-OPTICAL' with 15 column(s) and 767 row(s) \n",
      "\t'7:HST-IR' with 15 column(s) and 234 row(s) \n",
      "\t'8:ISO-IR' with 18 column(s) and 15 row(s) \n",
      "\t'9:HERSCHEL' with 15 column(s) and 25 row(s) \n",
      "\t'10:AKARI' with 11 column(s) and 4 row(s) \n",
      "\t'11:SPITZER' with 14 column(s) and 6 row(s) \n"
     ]
    }
   ],
   "source": [
    "maps = ESASky.query_region_maps(position='M51', radius='25arcmin')\n",
    "print (maps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we compare the result of this query with that of Use Case 1, we will see that now we get more data, because we are using a larger search radius (recall that the search by object uses a radius of only 5 arcsec). In particular, we now have INTEGRAL data, and the number of observations from the other missions has increased.\n",
    "\n",
    "The query for catalogues works similarly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 2 tables:\n",
      "\t'0:XMM-EPIC' with 220 column(s) and 1418 row(s) \n",
      "\t'1:HSC' with 27 column(s) and 10000 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_region_catalogs(position='M51', radius='25arcmin', catalogs=['XMM-EPIC', 'HSC'])\n",
    "print (cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also note here how the number of rows in each table is larger than before, because of the larger search radius. \n",
    "\n",
    "By default, the query returns a maximum of 10,000 rows per table. Note that this limit has been reached for the HSC. If we want to make sure that all the rows are retrieved, we can set the row_limit parameter to -1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 2 tables:\n",
      "\t'0:XMM-EPIC' with 220 column(s) and 1418 row(s) \n",
      "\t'1:HSC' with 27 column(s) and 2000 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_region_catalogs(position='M51', radius='25arcmin', catalogs=['XMM-EPIC', 'HSC'], row_limit=-1)\n",
    "print (cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the number of rows in the HSC table is 100,000, which is the absolute maximum that can be queried.\n",
    "\n",
    "Let's now see how the XMM-EPIC table looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     bii       confused        date_end       ... src_num sum_flag tseries\n",
      "     deg                                      ...                         \n",
      "-------------- -------- --------------------- ... ------- -------- -------\n",
      "68.39369195462        F 2018-06-13T19:09:03.0 ...     136        0    True\n",
      "68.39512377171        F 2018-06-15T18:54:21.0 ...     128        0    True\n",
      "68.43946914708        F 2018-05-26T23:40:18.0 ...     302        0   False\n",
      "68.43970079476        F 2018-05-26T23:40:18.0 ...      77        0    True\n",
      "68.43988627182        F 2018-06-13T19:09:03.0 ...      74        0    True\n",
      "68.43971006525        F 2018-05-14T18:58:47.0 ...     107        0    True\n",
      "68.43974621096        F 2018-06-15T18:54:21.0 ...      91        0    True\n",
      "68.44024068989        F 2011-06-07T08:38:48.0 ...      42        0   False\n",
      "68.50160505737        F 2018-06-13T19:09:03.0 ...     131        1   False\n",
      "68.51018593309        F 2005-07-01T20:18:14.0 ...     141        0   False\n",
      "           ...      ...                   ... ...     ...      ...     ...\n",
      "68.69731779334        F 2005-07-01T20:18:14.0 ...      75        0    True\n",
      "68.69784784469        F 2003-01-15T19:01:31.0 ...      69        0   False\n",
      "68.69809385995        F 2006-05-24T21:25:34.0 ...      46        0    True\n",
      "68.74009537627        F 2006-05-24T21:25:34.0 ...     137        0   False\n",
      " 68.7407965355        F 2003-01-15T19:01:31.0 ...      75        0   False\n",
      "68.74081864068        F 2006-05-20T21:32:55.0 ...      91        0    True\n",
      "68.74084447844        F 2005-07-01T20:18:14.0 ...      61        0    True\n",
      "68.70202051935        F 2003-01-15T19:01:31.0 ...     126        0   False\n",
      "68.77260986516        F 2003-01-15T19:01:31.0 ...      54        0    True\n",
      "68.79512709194        F 2003-01-15T19:01:31.0 ...     106        0   False\n",
      "Length = 1418 rows\n"
     ]
    }
   ],
   "source": [
    "xmm_epic_table = cats['XMM-EPIC']\n",
    "print (xmm_epic_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready to work with the data. For example, let's plot a histogram of the EPIC fluxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NBINS = 100\n",
    "plt.xlim(0, 3e-13)\n",
    "plt.ylim(0,800)\n",
    "flux = plt.hist(xmm_epic_table['ep_8_flux'], NBINS)"
   ]
  }
 ],
 "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": "3.6.5"
  },
  "metadata": {
   "interpreter": {
    "hash": "b7f181dfe8712eab604f66e16f1e368b7509f564beb06590a5ef1754c0400de4"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}