{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Access ESASky products with astroquery.esasky\n",
    "\n",
    "##### Authors: Ivan Valtchanov, Belén López Martí, H. Norman\n",
    "\n",
    "#### Last update: Feb 7, 2019\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 astroquery, the latest version should come with esasky. Alternatively, you can grab the latest astroquery with esasky from [here](https://github.com/imbasimba/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": [],
   "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',\n",
       " 'Herschel',\n",
       " 'ISO']"
      ]
     },
     "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": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: W35: None:4:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:28: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:53: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:83: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 7 tables:\n",
      "\t'0:XMM' with 10 column(s) and 6 row(s) \n",
      "\t'1:CHANDRA' with 10 column(s) and 16 row(s) \n",
      "\t'2:XMM-OM-OPTICAL' with 11 column(s) and 4 row(s) \n",
      "\t'3:XMM-OM-UV' with 10 column(s) and 5 row(s) \n",
      "\t'4:HST' with 12 column(s) and 156 row(s) \n",
      "\t'5:HERSCHEL' with 12 column(s) and 9 row(s) \n",
      "\t'6:ISO' with 7 column(s) and 4 row(s) \n"
     ]
    }
   ],
   "source": [
    "maps = ESASky.query_object_maps('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": [
      "/Users/belenlopezmarti/anaconda3/lib/python3.6/site-packages/astroquery/utils/commons.py:159: UserWarning: Coordinate string is being interpreted as an ICRS coordinate.\n",
      "  warnings.warn(\"Coordinate string is being interpreted as an \"\n",
      "WARNING: W35: None:4:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:28: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:53: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:83: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 7 tables:\n",
      "\t'0:XMM' with 10 column(s) and 6 row(s) \n",
      "\t'1:CHANDRA' with 10 column(s) and 16 row(s) \n",
      "\t'2:XMM-OM-OPTICAL' with 11 column(s) and 4 row(s) \n",
      "\t'3:XMM-OM-UV' with 10 column(s) and 5 row(s) \n",
      "\t'4:HST' with 12 column(s) and 156 row(s) \n",
      "\t'5:HERSCHEL' with 12 column(s) and 9 row(s) \n",
      "\t'6:ISO' with 7 column(s) and 4 row(s) \n"
     ]
    }
   ],
   "source": [
    "maps = ESASky.query_object_maps('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 masked=True length=5>\n",
       "     name       dtype  format\n",
       "-------------- ------- ------\n",
       "  postcard_url  object       \n",
       "   product_url  object       \n",
       "observation_id  object       \n",
       "    instrument  object       \n",
       "        filter  object       \n",
       "        ra_deg float64 {!r:>}\n",
       "       dec_deg float64 {!r:>}\n",
       "     start_utc  object       \n",
       "      duration   int64       \n",
       "         stc_s  object       "
      ]
     },
     "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",
      "-------------- ---------- ------ --------\n",
      "    0212480801         OM      B    49214\n",
      "    0212480801         OM      U    49214\n",
      "    0212480801         OM      V    49214\n",
      "    0303420101         OM      U    54114\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": [
      "Starting download of XMM-OM-OPTICAL data. (4 files)\n",
      "Downloading Observation ID: 0212480801 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action-aio?level=PPS&extension=FTZ&obsno=0212480801&name=RSIMAGB [Done]\n",
      "Downloading Observation ID: 0212480801 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action-aio?level=PPS&extension=FTZ&obsno=0212480801&name=RSIMAGU [Done]\n",
      "Downloading Observation ID: 0212480801 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action-aio?level=PPS&extension=FTZ&obsno=0212480801&name=RSIMAGV [Done]\n",
      "Downloading Observation ID: 0303420101 from http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action-aio?level=PPS&extension=FTZ&obsno=0303420101&name=HSIMAGU [Done]\n",
      "Downloading of XMM-OM-OPTICAL data complete.\n",
      "INFO: Maps available at /Users/dbaines/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(maps,'XMM-OM-OPTICAL', 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 0x11ff7c6a0>, <astropy.io.fits.hdu.image.ImageHDU object at 0x11ff8c860>, <astropy.io.fits.hdu.image.ImageHDU object at 0x12086f828>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x11ff9c198>, <astropy.io.fits.hdu.image.ImageHDU object at 0x12086fe48>, <astropy.io.fits.hdu.image.ImageHDU object at 0x120879470>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x120864a20>, <astropy.io.fits.hdu.image.ImageHDU object at 0x120879a58>, <astropy.io.fits.hdu.image.ImageHDU object at 0x120879fd0>],\n",
       "  [<astropy.io.fits.hdu.image.PrimaryHDU object at 0x11ffa3710>, <astropy.io.fits.hdu.image.ImageHDU object at 0x12087c668>, <astropy.io.fits.hdu.image.ImageHDU object at 0x12087cc50>]]}"
      ]
     },
     "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  =                 2951 / length of data axis 1                          \n",
       "NAXIS2  =                 2925 / length of data axis 2                          \n",
       "EXTEND  =                    T / FITS dataset may contain extensions            \n",
       "BUNIT   = 'Primary Array'      / Units of the image                             \n",
       "XPROC0  = 'ommosaic imagesets=''product/P0303420101OMS006FSIMAGU000.FIT produc&'\n",
       "CONTINUE  't/P0303420101OMS007FSIMAGU000.FIT product/P0303420101OMS008FSIMAGU0&'\n",
       "CONTINUE  '00.FIT'' mosaicedset=product/P0303420101OMX000HSIMAGU000.FIT correl&'\n",
       "CONTINUE  'set='''' nsigma=2 mincorr=0 minfraction=0.5 maxdx=5 binaxis=0 numin&'\n",
       "CONTINUE  'tervals=2 di=10 minnumpixels=100 # (ommosaic-2.5.17) [xmmsas_201212&'\n",
       "CONTINUE  '19_1645-12.0.1]'                                                     \n",
       "XPROC1  = 'addattribute set=product/P0303420101OMX000HSIMAGU000.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=''124006 1&'\n",
       "CONTINUE  '180 2012-12-23T12:09:02 1 00000004_04_cat9.0_20121220.153800 xmmsas&'\n",
       "CONTINUE  '_20121219_1645-12.0.1 16.911 Leicester/SSC 003 \"OM FULL-FRAME HIRES&'\n",
       "CONTINUE  ' SKY IMAGE MOSAIC\"'' integervalue='''' booleanvalue='''' # (addattr&'\n",
       "CONTINUE  'ibute-2.2.6) [xmmsas_20121219_1645-12.0.1]'                          \n",
       "XPROC2  = 'addattribute set=product/P0303420101OMX000HSIMAGU000.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=''124006 1&'\n",
       "CONTINUE  '180 2012-12-23T12:09:02 1 00000004_04_cat9.0_20121220.153800 xmmsas&'\n",
       "CONTINUE  '_20121219_1645-12.0.1 16.911 Leicester/SSC 003 \"OM FULL-FRAME HIRES&'\n",
       "CONTINUE  ' SKY IMAGE MOSAIC\"'' integervalue='''' booleanvalue='''' # (addattr&'\n",
       "CONTINUE  'ibute-2.2.6) [xmmsas_20121219_1645-12.0.1]'                          \n",
       "XDAL0   = 'product/P0303420101OMX000HSIMAGU000.FIT 2012-12-24T05:10:52.000 Mod&'\n",
       "CONTINUE  'ify addattribute (addattribute-2.2.6) [xmmsas_20121219_1645-12.0.1]&'\n",
       "CONTINUE  ' High SAS_MEMORY_MODEL=high SAS_ROWS= SAS_ZERO_ROWS= SAS_COLUMN_WIS&'\n",
       "CONTINUE  'E=      '                                                            \n",
       "XDAL1   = 'product/P0303420101OMX000HSIMAGU000.FIT 2012-12-24T05:10:47.000 Mod&'\n",
       "CONTINUE  'ify addattribute (addattribute-2.2.6) [xmmsas_20121219_1645-12.0.1]&'\n",
       "CONTINUE  ' High SAS_MEMORY_MODEL=high SAS_ROWS= SAS_ZERO_ROWS= SAS_COLUMN_WIS&'\n",
       "CONTINUE  'E=      '                                                            \n",
       "XDAL2   = 'product/P0303420101OMX000HSIMAGU000.FIT 2012-12-23T17:08:26.000 Cre&'\n",
       "CONTINUE  'ate ommosaic (ommosaic-2.5.17) [xmmsas_20121219_1645-12.0.1] High S&'\n",
       "CONTINUE  'AS_MEMORY_MODEL=high SAS_ROWS= SAS_ZERO_ROWS= SAS_COLUMN_WISE='      \n",
       "CREATOR = 'ommosaic (ommosaic-2.5.17) [xmmsas_20121219_1645-12.0.1]' / name of c\n",
       "DATE    = '2012-12-23T17:08:41.000' / 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= '2006-05-20T06:54:35.000'                                             \n",
       "DATE-END= '2006-05-20T14:50:37.000'                                             \n",
       "BINAX1  =                    0                                                  \n",
       "BINAX2  =                    0                                                  \n",
       "BINBPE  =                    F                                                  \n",
       "EXPOSURE=                    1                                                  \n",
       "DEADFRAC= 0.00000000000000E+00                                                  \n",
       "FRAMTIME= 0.00000000000000E+00                                                  \n",
       "WINDOWX0=                    0                                                  \n",
       "WINDOWDX=                 2951                                                  \n",
       "WINDOWY0=                    0                                                  \n",
       "WINDOWDY=                 2925                                                  \n",
       "EQUINOX =                 2000 / Equinox of celestial coordinate system         \n",
       "CDELT1  = -1.32364715682343E-04 / Plate-scale in x direction                    \n",
       "CDELT2  = 1.32364715682343E-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  = 2.02767586731234E+02 / RA at reference  x pixel                       \n",
       "CRVAL2  = 4.69831085495064E+01 / DEC at reference  y pixel                      \n",
       "PA      = 0.00000000000000E+00 / Mean RA pointing direction                     \n",
       "RA_PNT  = 2.02767586731234E+02 / Mean RA pointing direction                     \n",
       "DEC_PNT = 4.69831085495064E+01 / Mean Dec pointingDirection                     \n",
       "POSCOROK=                    T / Images were astrometry corrected               \n",
       "RAOFFSET= 8.83855919043223E-01 / Mean RA offset applied to images               \n",
       "DEOFFSET= 1.71797700723012E+00 / Mean DEC offset applied to images              \n",
       "NSTACKED=                    3 / Number of images stacked                       \n",
       "SEQ_ID  = '124006  '           / Pipeline sequence                              \n",
       "REVOLUT = '1180    '           / Satellite Revolution Number                    \n",
       "PROCDATE= '2012-12-23T12:09:02' / Processing date                               \n",
       "PROCREV = '1       '           / Processing revision                            \n",
       "PPSVERS = '00000004_04_cat9.0_20121220.153800' / PPS configuration              \n",
       "SASVERS = 'xmmsas_20121219_1645-12.0.1' / SAS version used in pipeline processin\n",
       "ODSVER  = '16.911  '           / ODS version                                    \n",
       "ORIGIN  = 'Leicester/SSC'      / Origin of FITS file                            \n",
       "ODFVER  = '003     '           / ODF_VERSION                                    \n",
       "CONTENT = 'OM FULL-FRAME HIRES SKY IMAGE MOSAIC' / Contents of file             \n",
       "HISTORY Created by ommosaic (ommosaic-2.5.17) [xmmsas_20121219_1645-12.0.1] at 2\n",
       "HISTORY 012-12-23T17:08:26                                                      \n",
       "HISTORY Modified by addattribute (addattribute-2.2.6) [xmmsas_20121219_1645-12.0\n",
       "HISTORY .1] at 2012-12-24T05:10:49                                              \n",
       "HISTORY Modified by addattribute (addattribute-2.2.6) [xmmsas_20121219_1645-12.0\n",
       "HISTORY .1] at 2012-12-24T05:10:52                                              "
      ]
     },
     "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 0x1278d8860>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dbaines/anaconda/envs/my_pymc_env/lib/python3.6/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": [
       "['AllWise',\n",
       " '2MASS',\n",
       " 'INTEGRAL',\n",
       " 'CHANDRA',\n",
       " 'XMM-EPIC-STACK',\n",
       " 'XMM-EPIC',\n",
       " 'XMM-OM',\n",
       " 'XMM-SLEW',\n",
       " 'Tycho-2',\n",
       " 'Gaia DR2',\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-PGCC2',\n",
       " 'Planck-PCCS2E-HFI',\n",
       " 'Planck-PCCS2-HFI',\n",
       " 'Planck-PCCS2-LFI',\n",
       " 'Planck-PSZ']"
      ]
     },
     "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: W35: None:4:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:28: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:53: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:83: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W06: None:10:0: W06: Invalid UCD 'pmra': Unknown word 'pmra' [astropy.io.votable.tree]\n",
      "WARNING: W06: None:11:0: W06: Invalid UCD 'pmdec': Unknown word 'pmdec' [astropy.io.votable.tree]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 8 tables:\n",
      "\t'0:ALLWISE' with 13 column(s) and 1 row(s) \n",
      "\t'1:2MASS' with 9 column(s) and 3 row(s) \n",
      "\t'2:XMM-EPIC-STACK' with 14 column(s) and 1 row(s) \n",
      "\t'3:XMM-EPIC' with 6 column(s) and 2 row(s) \n",
      "\t'4:XMM-OM' with 12 column(s) and 3 row(s) \n",
      "\t'5:HSC' with 8 column(s) and 34 row(s) \n",
      "\t'6:HERSCHEL-HPPSC-070' with 15 column(s) and 1 row(s) \n",
      "\t'7:HERSCHEL-HPPSC-100' with 15 column(s) and 1 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_object_catalogs('M51')\n",
    "print (cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 8 tables:\n",
      "\t'0:ALLWISE' with 13 column(s) and 1 row(s) \n",
      "\t'1:2MASS' with 9 column(s) and 2 row(s) \n",
      "\t'2:XMM-EPIC-STACK' with 14 column(s) and 1 row(s) \n",
      "\t'3:XMM-EPIC' with 6 column(s) and 2 row(s) \n",
      "\t'4:XMM-OM' with 12 column(s) and 3 row(s) \n",
      "\t'5:HSC' with 8 column(s) and 35 row(s) \n",
      "\t'6:HERSCHEL-HPPSC-070' with 15 column(s) and 1 row(s) \n",
      "\t'7:HERSCHEL-HPPSC-100' with 15 column(s) and 1 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_object_catalogs('13 29 52.7 +47 11 43')\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": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 2 tables:\n",
      "\t'0:XMM-EPIC' with 6 column(s) and 2 row(s) \n",
      "\t'1:HSC' with 8 column(s) and 34 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_object_catalogs('M51',[\"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 masked=True length=34>\n",
      "    name      dtype  format\n",
      "------------ ------- ------\n",
      "    match_id   int64       \n",
      "        name  object       \n",
      "  num_images   int32       \n",
      "          ra float64 {!r:>}\n",
      "         dec float64 {!r:>}\n",
      "      fluxes  object       \n",
      "fluxesvalues  object       \n",
      " sigmavalues  object       \n"
     ]
    }
   ],
   "source": [
    "hsc_table = cats[\"HSC\"]\n",
    "hsc_table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "match_id   name   ...              sigmavalues              \n",
      "-------- -------- ... --------------------------------------\n",
      " 8984035  NGC5194 ... 0.0000, 0.0000, 0.0000, 0.0092, 0.0000\n",
      " 9146957 SN2005CS ... 0.0000, 0.0000, 0.0000, 0.0000, 0.3905\n",
      " 8992625 SN2005CS ...         0.0000, 0.0000, 0.0000, 0.0000\n",
      " 9004677    M51-3 ...                         0.1920, 0.0000\n",
      " 8986000    M51-3 ...                         0.3470, 0.0000\n",
      " 8992912    M51-3 ...                         0.2579, 0.0000\n",
      " 9147680    M51-3 ...                                 0.0094\n",
      " 8980663    M51-3 ...                         0.0000, 0.0000\n",
      " 8981956    M51-3 ...                                 0.1599\n",
      " 8985840  NGC5194 ...                         0.0000, 0.0000\n",
      "     ...      ... ...                                    ...\n",
      " 8969716    M51-3 ...                                 0.0000\n",
      " 9005534    M51-1 ...                                 0.0000\n",
      " 9146006    M51-3 ...                                 0.0000\n",
      " 8996950  SN1994I ...                                 0.0000\n",
      " 8984122    M51-3 ...                                 0.0000\n",
      " 8987755  SN1994I ...                                 0.0000\n",
      " 8987989    M51-3 ...                                 0.0000\n",
      " 8989041    M51-3 ...                                 0.0000\n",
      " 8990875  SN1994I ...                                 0.0000\n",
      " 8991590    M51-3 ...                                 0.0000\n",
      " 8992270  SN1994I ...                                 0.0000\n",
      "Length = 34 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": [
      "  name           ra                dec        \n",
      "-------- ------------------ ------------------\n",
      " NGC5194  202.4696214722941  47.19528395791828\n",
      "SN2005CS 202.47117517567187  47.19468973805837\n",
      "SN2005CS 202.46967663414804    47.195137221641\n",
      "   M51-3 202.46871789371008   47.1959885181474\n",
      "   M51-3 202.47070891462116  47.19427421105356\n",
      "   M51-3 202.46809779496715  47.19600783872095\n",
      "   M51-3   202.470340592702  47.19614826226936\n",
      "   M51-3 202.46897225496647 47.194237136942014\n",
      "   M51-3 202.47081446307865  47.19420906449286\n",
      " NGC5194 202.46983100759005  47.19420861917729\n",
      "     ...                ...                ...\n",
      "   M51-3 202.46771650529647  47.19475631194131\n",
      "   M51-1 202.47146559032703 47.195541069192046\n",
      "   M51-3 202.46842740940812 47.194226813381455\n",
      " SN1994I 202.46894157707743  47.19545431189097\n",
      "   M51-3 202.46811100328185   47.1954417127405\n",
      " SN1994I 202.46991550718963  47.19531492645547\n",
      "   M51-3 202.46770040291568   47.1952897119087\n",
      "   M51-3  202.4688063032161  47.19585541414903\n",
      " SN1994I 202.47003400101758  47.19556551920438\n",
      "   M51-3 202.46918930028673  47.19649135937144\n",
      " SN1994I 202.46856810633034  47.19618125262504\n",
      "Length = 34 rows\n"
     ]
    }
   ],
   "source": [
    "print (hsc_table['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: W35: None:4:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:28: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:53: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:5:83: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
      "WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 8 tables:\n",
      "\t'0:INTEGRAL' with 8 column(s) and 1 row(s) \n",
      "\t'1:XMM' with 10 column(s) and 6 row(s) \n",
      "\t'2:CHANDRA' with 10 column(s) and 16 row(s) \n",
      "\t'3:XMM-OM-OPTICAL' with 11 column(s) and 4 row(s) \n",
      "\t'4:XMM-OM-UV' with 10 column(s) and 5 row(s) \n",
      "\t'5:HST' with 12 column(s) and 650 row(s) \n",
      "\t'6:HERSCHEL' with 12 column(s) and 25 row(s) \n",
      "\t'7:ISO' with 7 column(s) and 10 row(s) \n"
     ]
    }
   ],
   "source": [
    "maps = ESASky.query_region_maps('13 29 52.7 +47 11 43', '25 arcmin')\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 8 tables:\n",
      "\t'0:INTEGRAL' with 8 column(s) and 1 row(s) \n",
      "\t'1:XMM' with 10 column(s) and 6 row(s) \n",
      "\t'2:CHANDRA' with 10 column(s) and 16 row(s) \n",
      "\t'3:XMM-OM-OPTICAL' with 11 column(s) and 4 row(s) \n",
      "\t'4:XMM-OM-UV' with 10 column(s) and 5 row(s) \n",
      "\t'5:HST' with 12 column(s) and 650 row(s) \n",
      "\t'6:HERSCHEL' with 12 column(s) and 25 row(s) \n",
      "\t'7:ISO' with 7 column(s) and 10 row(s) \n"
     ]
    }
   ],
   "source": [
    "maps = ESASky.query_region_maps('M51', '25 arcmin')\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": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 2 tables:\n",
      "\t'0:XMM-EPIC' with 6 column(s) and 565 row(s) \n",
      "\t'1:HSC' with 8 column(s) and 10000 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_region_catalogs('M51',\"25 arcmin\",[\"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": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 2 tables:\n",
      "\t'0:XMM-EPIC' with 6 column(s) and 565 row(s) \n",
      "\t'1:HSC' with 8 column(s) and 100000 row(s) \n"
     ]
    }
   ],
   "source": [
    "cats = ESASky.query_region_catalogs('M51',\"25 arcmin\",[\"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": [
      "                                                                      postcard_url                                                                       ...\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------- ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454471&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454470&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454915&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=455884&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=455963&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454682&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454688&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454734&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454238&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454916&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "                                                                                                                                                     ... ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454029&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454055&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=453612&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=455078&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454075&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=454846&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=455129&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=453505&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=453993&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=455038&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "http://nxsa.esac.esa.int/nxsa-sl/servlet/data-action?RETRIEVAL_TYPE=POSTCARD&EPIC_SOURCE_CAT_OID=453942&EPIC_SOURCE_CAT_PRODUCT_TYPE=IMAGE&PROTOCOL=HTTP ...\n",
      "Length = 565 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(0,300)\n",
    "flux = plt.hist(xmm_epic_table['ep_8_flux'], NBINS)"
   ]
  }
 ],
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