{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HST Python Tutorial: Hubble Catalog of Variables light curve period analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Author: Deborah Baines, Juan-Carlos Segovia, Raúl Gutierrez*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This use case is partially based on the paper by [Brown et. al. 2004, AJ 127, 2738](https://ui.adsabs.harvard.edu/abs/2004AJ....127.2738B/abstract) 'RR Lyrae Stars in the Andromeda Halo from Deep Imaging with the Advanced Camera for Surveys'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Workflow:\n",
    "1. Query the Hubble Catalog of Variables (HCV) for an RR Lyrae in the Halo of M31 (matchid = 106259089).\n",
    "2. Plot the light curves as a function of observation date.\n",
    "3. Compute the Lomb-Scargle periodogram of the light curve in one band.\n",
    "4. Phase and plot the light curve.\n",
    "\n",
    "Additional:\n",
    "5. Perform a cone search around the RR Lyrae star and retrieve the HCV sources. \n",
    "6. Download an associated HLA image.\n",
    "7. Plot these sources on an associated HLA image and plot the RR Lyrae star in a different colour. \n",
    "8. For the RR Lyrae star, perform a cutout around the star and show the images for the brightest and faintest magnitudes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the relevant modules\n",
    "from astroquery.utils.tap.core import TapPlus\n",
    "import numpy as np\n",
    "from astropy.stats import LombScargle\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "from astropy.io import fits\n",
    "from astropy import visualization\n",
    "\n",
    "# suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Query the Hubble Catalog of Variables (HCV) for an RR Lyrae in the Halo of M31 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the RR Lyrae of interest is matchid = 106259089 (or V10 in Brown+ 2004)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v1.0.1) - Connection:\n",
      "\tHost: hst.esac.esa.int\n",
      "\tUse HTTPS: False\n",
      "\tPort: 80\n",
      "\tSSL Port: 443\n",
      "Retrieving tables...\n",
      "403 Forbidden\n"
     ]
    },
    {
     "ename": "HTTPError",
     "evalue": "Forbidden",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-e67b34b1186d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Call the HST TAP and print the available tables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mhst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTapPlus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"http://hst.esac.esa.int/tap-server/tap\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monly_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m       \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_qualified_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ehst/lib/python3.6/site-packages/astroquery-0.3.9.dev5117-py3.6.egg/astroquery/utils/tap/core.py\u001b[0m in \u001b[0;36mload_tables\u001b[0;34m(self, only_names, include_shared_tables, verbose)\u001b[0m\n\u001b[1;32m    657\u001b[0m         return self._Tap__load_tables(only_names=only_names,\n\u001b[1;32m    658\u001b[0m                                       \u001b[0minclude_shared_tables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude_shared_tables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 659\u001b[0;31m                                       verbose=verbose)\n\u001b[0m\u001b[1;32m    660\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mload_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/ehst/lib/python3.6/site-packages/astroquery-0.3.9.dev5117-py3.6.egg/astroquery/utils/tap/core.py\u001b[0m in \u001b[0;36m__load_tables\u001b[0;34m(self, only_names, include_shared_tables, verbose)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Parsing tables...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mHTTPError\u001b[0m: Forbidden"
     ]
    }
   ],
   "source": [
    "# Call the HST TAP and print the available tables\n",
    "hst = TapPlus(url=\"http://hst.esac.esa.int/tap-server/tap\")\n",
    "tables = hst.load_tables(only_names=True)\n",
    "for table in (tables):\n",
    "      print(table.get_qualified_name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v1.0.1) - Connection:\n",
      "\tHost: hst.esac.esa.int\n",
      "\tUse HTTPS: False\n",
      "\tPort: 80\n",
      "\tSSL Port: 443\n",
      "Retrieving table 'hcv.hcv'\n",
      "Parsing table 'hcv.hcv'...\n",
      "Done.\n",
      "chi2\n",
      "ci_d\n",
      "ci_v\n",
      "d_d\n",
      "dec\n",
      "d_v\n",
      "expert_class\n",
      "filter\n",
      "groupid\n",
      "lightcurve_cm\n",
      "lightcurve_d\n",
      "lightcurve_e\n",
      "lightcurve_i\n",
      "lightcurve_m\n",
      "lightcurve_r\n",
      "mad\n",
      "matchid\n",
      "pipeline_class\n",
      "ra\n"
     ]
    }
   ],
   "source": [
    "# Inspect the columns of the HCV table\n",
    "hst = TapPlus(url=\"http://hst.esac.esa.int/tap-server/tap\")\n",
    "hcv_table = hst.load_table('hcv.hcv')\n",
    "for column in (hcv_table.columns):\n",
    "    print(column.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query the HCV via an Astronomical Data Query Language (ADQL) query. See [here](https://gea.esac.esa.int/archive-help/adql/examples/index.html) for more information and examples of ADQL queries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query finished.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<i>Table masked=True length=33</i>\n",
       "<table id=\"table34879554616\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>chi2</th><th>ci_d</th><th>ci_v</th><th>d_d</th><th>dec</th><th>d_v</th><th>expert_class</th><th>filter</th><th>groupid</th><th>lightcurve_cm</th><th>lightcurve_d</th><th>lightcurve_e</th><th>lightcurve_i</th><th>lightcurve_m</th><th>lightcurve_r</th><th>mad</th><th>matchid</th><th>pipeline_class</th><th>ra</th></tr></thead>\n",
       "<thead><tr><th></th><th>days</th><th></th><th>days</th><th>deg</th><th>milliarcsec</th><th></th><th></th><th></th><th>mag</th><th>days</th><th>mag</th><th></th><th>mag</th><th></th><th></th><th></th><th></th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>object</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>bool</th><th>float64</th><th>int32</th><th>int32</th><th>float64</th></tr></thead>\n",
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       "<tr><td>17.9332353549977981</td><td>52636.535665495554</td><td>0.9396296143531799</td><td>52636.535665495554</td><td>40.684165954589844</td><td>1.7726587057113647</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>25.34867465169749</td><td>52636.535665495554</td><td>0.051899999</td><td>hst_9453_50_acs_wfc_f814w</td><td>25.3444</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>17.9332353549977981</td><td>52639.632320828736</td><td>0.9510185122489929</td><td>52639.632320828736</td><td>40.684165954589844</td><td>1.160384178161621</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>24.907127633888535</td><td>52639.632320828736</td><td>0.034200002</td><td>hst_9453_42_acs_wfc_f814w</td><td>24.9067</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>17.9332353549977981</td><td>52644.20626751962</td><td>1.0006481409072876</td><td>52644.20626751962</td><td>40.684165954589844</td><td>1.3576633930206299</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>25.386741185391116</td><td>52644.20626751962</td><td>0.051899999</td><td>hst_9453_54_acs_wfc_f814w</td><td>25.3859</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>17.9332353549977981</td><td>52644.339669304434</td><td>1.0690741539001465</td><td>52644.339669304434</td><td>40.684165954589844</td><td>1.6359196901321411</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>25.06148505670546</td><td>52644.339669304434</td><td>0.041299999</td><td>hst_9453_52_acs_wfc_f814w</td><td>25.0674</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>17.9332353549977981</td><td>52646.140631039394</td><td>1.0164815187454224</td><td>52646.140631039394</td><td>40.684165954589844</td><td>1.0026894807815552</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>25.350860450582495</td><td>52646.140631039394</td><td>0.051100001</td><td>hst_9453_56_acs_wfc_f814w</td><td>25.354799</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>17.9332353549977981</td><td>52646.27403380303</td><td>0.9950925707817078</td><td>52646.27403380303</td><td>40.684165954589844</td><td>1.4151310920715332</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>25.377541952522026</td><td>52646.27403380303</td><td>0.052299999</td><td>hst_9453_57_acs_wfc_f814w</td><td>25.374701</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>17.9332353549977981</td><td>52646.407442505704</td><td>0.9368519186973572</td><td>52646.407442505704</td><td>40.684165954589844</td><td>3.7254488468170166</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>25.003620345310782</td><td>52646.407442505704</td><td>0.037700001</td><td>hst_9453_58_acs_wfc_f814w</td><td>24.999001</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>17.9332353549977981</td><td>52648.34181157872</td><td>0.9812037348747253</td><td>52648.34181157872</td><td>40.684165954589844</td><td>1.5127025842666626</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>25.434809714509754</td><td>52648.34181157872</td><td>0.054200001</td><td>hst_9453_60_acs_wfc_f814w</td><td>25.4345</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>17.9332353549977981</td><td>52650.40960101038</td><td>1.0606480836868286</td><td>52650.40960101038</td><td>40.684165954589844</td><td>3.3990793228149414</td><td>1</td><td>ACS_F814W</td><td>1043756</td><td>25.49112826041771</td><td>52650.40960101038</td><td>0.057</td><td>hst_9453_59_acs_wfc_f814w</td><td>25.488199</td><td>False</td><td>0.16130864386038724</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=33>\n",
       "        chi2               ci_d        ... pipeline_class         ra        \n",
       "                           days        ...                       deg        \n",
       "       object            float64       ...     int32           float64      \n",
       "------------------- ------------------ ... -------------- ------------------\n",
       "17.9332353549977981  52611.26638278016 ...              2 11.502689361572266\n",
       "17.9332353549977981  52611.57040482061 ...              2 11.502689361572266\n",
       "17.9332353549977981  52612.09501705086 ...              2 11.502689361572266\n",
       "17.9332353549977981 52614.096183951246 ...              2 11.502689361572266\n",
       "17.9332353549977981   52614.7064349039 ...              2 11.502689361572266\n",
       "17.9332353549977981  52615.44050916098 ...              2 11.502689361572266\n",
       "17.9332353549977981  52616.09770231927 ...              2 11.502689361572266\n",
       "17.9332353549977981  52616.44162601838 ...              2 11.502689361572266\n",
       "17.9332353549977981  52618.30910287611 ...              2 11.502689361572266\n",
       "                ...                ... ...            ...                ...\n",
       "17.9332353549977981 52634.534542709356 ...              2 11.502689361572266\n",
       "17.9332353549977981 52636.535665495554 ...              2 11.502689361572266\n",
       "17.9332353549977981 52639.632320828736 ...              2 11.502689361572266\n",
       "17.9332353549977981  52644.20626751962 ...              2 11.502689361572266\n",
       "17.9332353549977981 52644.339669304434 ...              2 11.502689361572266\n",
       "17.9332353549977981 52646.140631039394 ...              2 11.502689361572266\n",
       "17.9332353549977981  52646.27403380303 ...              2 11.502689361572266\n",
       "17.9332353549977981 52646.407442505704 ...              2 11.502689361572266\n",
       "17.9332353549977981  52648.34181157872 ...              2 11.502689361572266\n",
       "17.9332353549977981  52650.40960101038 ...              2 11.502689361572266"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query the HCV for the RR Lyrae variable star with matchid = 106259089 and for the filter ACS_F814W:\n",
    "job1 = hst.launch_job_async(\"SELECT * FROM hcv.hcv WHERE matchid = 106259089 AND filter LIKE '%ACS_F814W%'\")\n",
    "hcv_f814w = job1.get_results()\n",
    "job1.get_data()\n",
    "\n",
    "# Brings back a table of 33 rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query finished.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<i>Table masked=True length=29</i>\n",
       "<table id=\"table34882614552\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>chi2</th><th>ci_d</th><th>ci_v</th><th>d_d</th><th>dec</th><th>d_v</th><th>expert_class</th><th>filter</th><th>groupid</th><th>lightcurve_cm</th><th>lightcurve_d</th><th>lightcurve_e</th><th>lightcurve_i</th><th>lightcurve_m</th><th>lightcurve_r</th><th>mad</th><th>matchid</th><th>pipeline_class</th><th>ra</th></tr></thead>\n",
       "<thead><tr><th></th><th>days</th><th></th><th>days</th><th>deg</th><th>milliarcsec</th><th></th><th></th><th></th><th>mag</th><th>days</th><th>mag</th><th></th><th>mag</th><th></th><th></th><th></th><th></th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>object</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>bool</th><th>float64</th><th>int32</th><th>int32</th><th>float64</th></tr></thead>\n",
       "<tr><td>60.0598271277244109</td><td>52610.161817079876</td><td>1.0532407760620117</td><td>52610.161817079876</td><td>40.684165954589844</td><td>1.876853346824646</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.194610885020445</td><td>52610.161817079876</td><td>0.028000001</td><td>hst_9453_01_acs_wfc_f606w</td><td>25.195101</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52611.43696170254</td><td>1.087592601776123</td><td>52611.43696170254</td><td>40.684165954589844</td><td>1.9586243629455566</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>24.949582840792328</td><td>52611.43696170254</td><td>0.023600001</td><td>hst_9453_03_acs_wfc_f606w</td><td>24.9569</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52611.703842436196</td><td>0.9923148155212402</td><td>52611.703842436196</td><td>40.684165954589844</td><td>0.8318588733673096</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.386888549877014</td><td>52611.703842436196</td><td>0.032900002</td><td>hst_9453_05_acs_wfc_f606w</td><td>25.395599</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52612.33606466558</td><td>0.9299073815345764</td><td>52612.33606466558</td><td>40.684165954589844</td><td>3.672166585922241</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.358675223412227</td><td>52612.33606466558</td><td>0.031500001</td><td>hst_9453_07_acs_wfc_f606w</td><td>25.356199</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52614.30502305669</td><td>0.9236111044883728</td><td>52614.30502305669</td><td>40.684165954589844</td><td>2.7837789058685303</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.122607184615944</td><td>52614.30502305669</td><td>0.0263</td><td>hst_9453_09_acs_wfc_f606w</td><td>25.119301</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52615.09689807985</td><td>1.0269443988800049</td><td>52615.09689807985</td><td>40.684165954589844</td><td>2.8672263622283936</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.336226176420528</td><td>52615.09689807985</td><td>0.031399999</td><td>hst_9453_11_acs_wfc_f606w</td><td>25.337601</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52615.707459393656</td><td>0.9747222661972046</td><td>52615.707459393656</td><td>40.684165954589844</td><td>3.2435302734375</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.23670991332121</td><td>52615.707459393656</td><td>0.028200001</td><td>hst_9453_13_acs_wfc_f606w</td><td>25.232201</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52616.306695474545</td><td>0.9244444370269775</td><td>52616.306695474545</td><td>40.684165954589844</td><td>2.2560760974884033</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.023412784196488</td><td>52616.306695474545</td><td>0.023800001</td><td>hst_9453_15_acs_wfc_f606w</td><td>25.0245</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52616.57511546649</td><td>1.010555624961853</td><td>52616.57511546649</td><td>40.684165954589844</td><td>1.6625279188156128</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.453671853616125</td><td>52616.57511546649</td><td>0.034400001</td><td>hst_9453_17_acs_wfc_f606w</td><td>25.449301</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52632.099195476854</td><td>1.0450925827026367</td><td>52632.099195476854</td><td>40.684165954589844</td><td>1.1953842639923096</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.03668623089168</td><td>52632.099195476854</td><td>0.0242</td><td>hst_9453_39_acs_wfc_f606w</td><td>25.038799</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52632.39987250231</td><td>0.9700926542282104</td><td>52632.39987250231</td><td>40.684165954589844</td><td>0.8609312772750854</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.439492511152125</td><td>52632.39987250231</td><td>0.033799998</td><td>hst_9453_43_acs_wfc_f606w</td><td>25.4391</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52632.53329266794</td><td>0.9382407069206238</td><td>52632.53329266794</td><td>40.684165954589844</td><td>3.5272231101989746</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.61165283094627</td><td>52632.53329266794</td><td>0.038899999</td><td>hst_9453_45_acs_wfc_f606w</td><td>25.609699</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52633.53394658561</td><td>0.9938889145851135</td><td>52633.53394658561</td><td>40.684165954589844</td><td>2.1130869388580322</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.12785408906986</td><td>52633.53394658561</td><td>0.0261</td><td>hst_9453_47_acs_wfc_f606w</td><td>25.129101</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52636.40226843394</td><td>0.9246296286582947</td><td>52636.40226843394</td><td>40.684165954589844</td><td>2.82668137550354</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.25209888468125</td><td>52636.40226843394</td><td>0.0287</td><td>hst_9453_49_acs_wfc_f606w</td><td>25.2523</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52636.7406423497</td><td>1.1949999332427979</td><td>52636.7406423497</td><td>40.684165954589844</td><td>4.103062629699707</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.445038014728738</td><td>52636.7406423497</td><td>0.037300002</td><td>hst_9453_51_acs_wfc_f606w</td><td>25.437799</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52640.40434069326</td><td>0.9737963080406189</td><td>52640.40434069326</td><td>40.684165954589844</td><td>2.4760165214538574</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.11754497263483</td><td>52640.40434069326</td><td>0.0263</td><td>hst_9453_41_acs_wfc_f606w</td><td>25.1166</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52644.473090491956</td><td>1.0048147439956665</td><td>52644.473090491956</td><td>40.684165954589844</td><td>0.4666833281517029</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.04464239639793</td><td>52644.473090491956</td><td>0.024499999</td><td>hst_9453_53_acs_wfc_f606w</td><td>25.040501</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52650.24182896991</td><td>1.008796215057373</td><td>52650.24182896991</td><td>40.684165954589844</td><td>3.2974252700805664</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.49795708409161</td><td>52650.24182896991</td><td>0.036200002</td><td>hst_9453_55_acs_wfc_f606w</td><td>25.496099</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>55204.29098817939</td><td>0.9488888382911682</td><td>55204.29098817939</td><td>40.684165954589844</td><td>4.4939727783203125</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.00825036271675</td><td>55204.29098817939</td><td>0.0337</td><td>hst_11684_04_acs_wfc_f606w</td><td>25.008699</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=29>\n",
       "        chi2               ci_d        ... pipeline_class         ra        \n",
       "                           days        ...                       deg        \n",
       "       object            float64       ...     int32           float64      \n",
       "------------------- ------------------ ... -------------- ------------------\n",
       "60.0598271277244109 52610.161817079876 ...              2 11.502689361572266\n",
       "60.0598271277244109  52611.43696170254 ...              2 11.502689361572266\n",
       "60.0598271277244109 52611.703842436196 ...              2 11.502689361572266\n",
       "60.0598271277244109  52612.33606466558 ...              2 11.502689361572266\n",
       "60.0598271277244109  52614.30502305669 ...              2 11.502689361572266\n",
       "60.0598271277244109  52615.09689807985 ...              2 11.502689361572266\n",
       "60.0598271277244109 52615.707459393656 ...              2 11.502689361572266\n",
       "60.0598271277244109 52616.306695474545 ...              2 11.502689361572266\n",
       "60.0598271277244109  52616.57511546649 ...              2 11.502689361572266\n",
       "                ...                ... ...            ...                ...\n",
       "60.0598271277244109 52632.099195476854 ...              2 11.502689361572266\n",
       "60.0598271277244109  52632.39987250231 ...              2 11.502689361572266\n",
       "60.0598271277244109  52632.53329266794 ...              2 11.502689361572266\n",
       "60.0598271277244109  52633.53394658561 ...              2 11.502689361572266\n",
       "60.0598271277244109  52636.40226843394 ...              2 11.502689361572266\n",
       "60.0598271277244109   52636.7406423497 ...              2 11.502689361572266\n",
       "60.0598271277244109  52640.40434069326 ...              2 11.502689361572266\n",
       "60.0598271277244109 52644.473090491956 ...              2 11.502689361572266\n",
       "60.0598271277244109  52650.24182896991 ...              2 11.502689361572266\n",
       "60.0598271277244109  55204.29098817939 ...              2 11.502689361572266"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query the HCV for the RR Lyrae variable star with matchid = 106259089 and for the filter ACS_F606W:\n",
    "job2 = hst.launch_job_async(\"SELECT * FROM hcv.hcv WHERE matchid = 106259089 AND filter LIKE '%ACS_F606W%'\")\n",
    "hcv_f606w = job2.get_results()\n",
    "job2.get_data()\n",
    "\n",
    "# Brings back a table of 29 rows. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above steps can also be performed using the ESA_Hubble astroquery module. Currently this module can be downloaded from the dev version of astroquery, details [here](https://astroquery.readthedocs.io/en/latest/). \n",
    "\n",
    "The ESA_Hubble module should be available in astropy version 4.0 onwards. More details can be found [here](https://astroquery.readthedocs.io/en/latest/esa_hubble/esa_hubble.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v1.0.1) - Connection:\n",
      "\tHost: hst.esac.esa.int\n",
      "\tUse HTTPS: False\n",
      "\tPort: 80\n",
      "\tSSL Port: 443\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<i>Table masked=True length=29</i>\n",
       "<table id=\"table34879779336\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>chi2</th><th>ci_d</th><th>ci_v</th><th>d_d</th><th>dec</th><th>d_v</th><th>expert_class</th><th>filter</th><th>groupid</th><th>lightcurve_cm</th><th>lightcurve_d</th><th>lightcurve_e</th><th>lightcurve_i</th><th>lightcurve_m</th><th>lightcurve_r</th><th>mad</th><th>matchid</th><th>pipeline_class</th><th>ra</th></tr></thead>\n",
       "<thead><tr><th></th><th>days</th><th></th><th>days</th><th>deg</th><th>milliarcsec</th><th></th><th></th><th></th><th>mag</th><th>days</th><th>mag</th><th></th><th>mag</th><th></th><th></th><th></th><th></th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>object</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>bool</th><th>float64</th><th>int32</th><th>int32</th><th>float64</th></tr></thead>\n",
       "<tr><td>60.0598271277244109</td><td>52610.161817079876</td><td>1.0532407760620117</td><td>52610.161817079876</td><td>40.684165954589844</td><td>1.876853346824646</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.194610885020445</td><td>52610.161817079876</td><td>0.028000001</td><td>hst_9453_01_acs_wfc_f606w</td><td>25.195101</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52611.43696170254</td><td>1.087592601776123</td><td>52611.43696170254</td><td>40.684165954589844</td><td>1.9586243629455566</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>24.949582840792328</td><td>52611.43696170254</td><td>0.023600001</td><td>hst_9453_03_acs_wfc_f606w</td><td>24.9569</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52611.703842436196</td><td>0.9923148155212402</td><td>52611.703842436196</td><td>40.684165954589844</td><td>0.8318588733673096</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.386888549877014</td><td>52611.703842436196</td><td>0.032900002</td><td>hst_9453_05_acs_wfc_f606w</td><td>25.395599</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52612.33606466558</td><td>0.9299073815345764</td><td>52612.33606466558</td><td>40.684165954589844</td><td>3.672166585922241</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.358675223412227</td><td>52612.33606466558</td><td>0.031500001</td><td>hst_9453_07_acs_wfc_f606w</td><td>25.356199</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52614.30502305669</td><td>0.9236111044883728</td><td>52614.30502305669</td><td>40.684165954589844</td><td>2.7837789058685303</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.122607184615944</td><td>52614.30502305669</td><td>0.0263</td><td>hst_9453_09_acs_wfc_f606w</td><td>25.119301</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52615.09689807985</td><td>1.0269443988800049</td><td>52615.09689807985</td><td>40.684165954589844</td><td>2.8672263622283936</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.336226176420528</td><td>52615.09689807985</td><td>0.031399999</td><td>hst_9453_11_acs_wfc_f606w</td><td>25.337601</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52615.707459393656</td><td>0.9747222661972046</td><td>52615.707459393656</td><td>40.684165954589844</td><td>3.2435302734375</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.23670991332121</td><td>52615.707459393656</td><td>0.028200001</td><td>hst_9453_13_acs_wfc_f606w</td><td>25.232201</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52616.306695474545</td><td>0.9244444370269775</td><td>52616.306695474545</td><td>40.684165954589844</td><td>2.2560760974884033</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.023412784196488</td><td>52616.306695474545</td><td>0.023800001</td><td>hst_9453_15_acs_wfc_f606w</td><td>25.0245</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52616.57511546649</td><td>1.010555624961853</td><td>52616.57511546649</td><td>40.684165954589844</td><td>1.6625279188156128</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.453671853616125</td><td>52616.57511546649</td><td>0.034400001</td><td>hst_9453_17_acs_wfc_f606w</td><td>25.449301</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52632.099195476854</td><td>1.0450925827026367</td><td>52632.099195476854</td><td>40.684165954589844</td><td>1.1953842639923096</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.03668623089168</td><td>52632.099195476854</td><td>0.0242</td><td>hst_9453_39_acs_wfc_f606w</td><td>25.038799</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52632.39987250231</td><td>0.9700926542282104</td><td>52632.39987250231</td><td>40.684165954589844</td><td>0.8609312772750854</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.439492511152125</td><td>52632.39987250231</td><td>0.033799998</td><td>hst_9453_43_acs_wfc_f606w</td><td>25.4391</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52632.53329266794</td><td>0.9382407069206238</td><td>52632.53329266794</td><td>40.684165954589844</td><td>3.5272231101989746</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.61165283094627</td><td>52632.53329266794</td><td>0.038899999</td><td>hst_9453_45_acs_wfc_f606w</td><td>25.609699</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52633.53394658561</td><td>0.9938889145851135</td><td>52633.53394658561</td><td>40.684165954589844</td><td>2.1130869388580322</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.12785408906986</td><td>52633.53394658561</td><td>0.0261</td><td>hst_9453_47_acs_wfc_f606w</td><td>25.129101</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52636.40226843394</td><td>0.9246296286582947</td><td>52636.40226843394</td><td>40.684165954589844</td><td>2.82668137550354</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.25209888468125</td><td>52636.40226843394</td><td>0.0287</td><td>hst_9453_49_acs_wfc_f606w</td><td>25.2523</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52636.7406423497</td><td>1.1949999332427979</td><td>52636.7406423497</td><td>40.684165954589844</td><td>4.103062629699707</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.445038014728738</td><td>52636.7406423497</td><td>0.037300002</td><td>hst_9453_51_acs_wfc_f606w</td><td>25.437799</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52640.40434069326</td><td>0.9737963080406189</td><td>52640.40434069326</td><td>40.684165954589844</td><td>2.4760165214538574</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.11754497263483</td><td>52640.40434069326</td><td>0.0263</td><td>hst_9453_41_acs_wfc_f606w</td><td>25.1166</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52644.473090491956</td><td>1.0048147439956665</td><td>52644.473090491956</td><td>40.684165954589844</td><td>0.4666833281517029</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.04464239639793</td><td>52644.473090491956</td><td>0.024499999</td><td>hst_9453_53_acs_wfc_f606w</td><td>25.040501</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>52650.24182896991</td><td>1.008796215057373</td><td>52650.24182896991</td><td>40.684165954589844</td><td>3.2974252700805664</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.49795708409161</td><td>52650.24182896991</td><td>0.036200002</td><td>hst_9453_55_acs_wfc_f606w</td><td>25.496099</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "<tr><td>60.0598271277244109</td><td>55204.29098817939</td><td>0.9488888382911682</td><td>55204.29098817939</td><td>40.684165954589844</td><td>4.4939727783203125</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.00825036271675</td><td>55204.29098817939</td><td>0.0337</td><td>hst_11684_04_acs_wfc_f606w</td><td>25.008699</td><td>False</td><td>0.16173090767108178</td><td>106259089</td><td>2</td><td>11.502689361572266</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=29>\n",
       "        chi2               ci_d        ... pipeline_class         ra        \n",
       "                           days        ...                       deg        \n",
       "       object            float64       ...     int32           float64      \n",
       "------------------- ------------------ ... -------------- ------------------\n",
       "60.0598271277244109 52610.161817079876 ...              2 11.502689361572266\n",
       "60.0598271277244109  52611.43696170254 ...              2 11.502689361572266\n",
       "60.0598271277244109 52611.703842436196 ...              2 11.502689361572266\n",
       "60.0598271277244109  52612.33606466558 ...              2 11.502689361572266\n",
       "60.0598271277244109  52614.30502305669 ...              2 11.502689361572266\n",
       "60.0598271277244109  52615.09689807985 ...              2 11.502689361572266\n",
       "60.0598271277244109 52615.707459393656 ...              2 11.502689361572266\n",
       "60.0598271277244109 52616.306695474545 ...              2 11.502689361572266\n",
       "60.0598271277244109  52616.57511546649 ...              2 11.502689361572266\n",
       "                ...                ... ...            ...                ...\n",
       "60.0598271277244109 52632.099195476854 ...              2 11.502689361572266\n",
       "60.0598271277244109  52632.39987250231 ...              2 11.502689361572266\n",
       "60.0598271277244109  52632.53329266794 ...              2 11.502689361572266\n",
       "60.0598271277244109  52633.53394658561 ...              2 11.502689361572266\n",
       "60.0598271277244109  52636.40226843394 ...              2 11.502689361572266\n",
       "60.0598271277244109   52636.7406423497 ...              2 11.502689361572266\n",
       "60.0598271277244109  52640.40434069326 ...              2 11.502689361572266\n",
       "60.0598271277244109 52644.473090491956 ...              2 11.502689361572266\n",
       "60.0598271277244109  52650.24182896991 ...              2 11.502689361572266\n",
       "60.0598271277244109  55204.29098817939 ...              2 11.502689361572266"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform exactly the same query as above, but this time using the astroquery.esa_hubble module\n",
    "\n",
    "# Import ESAHubble from astroquery.esa_hubble\n",
    "from astroquery.esa_hubble import ESAHubble\n",
    "\n",
    "job = ESAHubble.query_hst_tap(\"SELECT * FROM hcv.hcv WHERE matchid = 106259089 AND filter LIKE '%ACS_F606W%'\")\n",
    "hcv_f606w = job.get_results()\n",
    "job.get_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Plot the light curves as a function of observation date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSSpjgCBJksoYIEhqaM5IKNWHOQiaVTGxbtOmetZCrcwZCaX6sQVBUsNyRkKpfgwQpBoaHoYtW2DzZti4MdtuVUPbhxjaPnRc+zgjoVQ/djFIJ6j4ZTfbELbhYejrg8lJWLYMxsaybYAeW8cr0r20m7GJsWllzkgozQ9bEKQaGRyEXA46O6GtDbq6su1BW8cr5oyEUv0YIEg1MjKSBQSlcrmsXJVxRkKpfuxikGqkuzvrViiVz2flqpwzEkr1YYCgGRWT60ZHs+GOvb0Lu9+8Hl8svb1HchA6OrJgIZ+H/v55r4rqxIBGC5ldDCpTTK4bH5+eXNfKGfgnoqcHBgayHITdu7MchIGBhR1oSWodtiCoTDG5bt++bLur60i5X27Hp6cH9mat4044JWlBsQVBZUyukyQZIKhMd3fWV17K5DpJai0GCCrT25sFBOPjMDV1JLmu16HnktQyDBBUxuQ6SZJJipqRyXWS1NoMEKQaM8DKlm3esnULoxOjDG0fondDr7MhSg3OLgbpBBS/8DbfspmNQxsZ3ukkEbMZ3jlM3619jB8YZ9niZYxNjNF3a5/nTGpwtiBIx6n4hTd5aHLaFx7gr+IZDG4bJNeeY9+hbGKNro6uw+WVni9nJJTmny0I0nEqfuF1tnfS1tZGV0cXufYcg9tcpnEmI3tGyC2ePrFGbnGOkT1OrLGQDG0fOrzEuVqDAYJ0nPzCOz7dS7vJ758+sUZ+f57upU6sITUyAwTpOPmFd3x6N/SSP5Bn/MA4U1NTjE2MkT+Qp3eDE2tIjcwAQbPatMkM/Jn4hXd8etb2MHD5AJ3tnezev5uuji4GLh8wX0NqcCYpSsep+MXWf3s/oxOjrMytpP+yfr/w5tCztoe9k9nEGiYcSguDAYJ0AvzCk9Ts7GKQKmAGt6RWY4AgSZLKGCBIkqQyBgiSJKmMAYIkSSpTswAhIp4REbdHxAMRsTMi3lYof09EjETE9sLliln2/4WI+E5EfD8i3lWrekqSpHK1HOZ4EHhnSuneiFgK3BMRtxTue39K6drZdoyIRcD/Av4dsAvYFhGfTSn9aw3rK0mSCmrWgpBSeiSldG/h9h7gAaDSuWhfAHw/pfRgSukA8PfAa2tTU0nSXFzevDXNSw5CRKwBLgS+WSjqjYgdEXF9RCyfYZdu4Ecl27uYJbiIiLdGxN0Rcfdjjz1WxVqr0QwNZRdJ86e4vPn4gfFpy5sbJDS/mgcIEXE68I/A21NKeeDDwNnAOuAR4M9m2m2GsjTT8VNK16WULkopXXTWWWdVqdaSJHB581ZW06mWI+JUsuDgEymlGwBSSj8tuf+vgM/NsOsu4Bkl26uAH9ewqpJqzCmpF6aRPSOs6FzBvif3HS5zefPWULMAISIC+CjwQErpfSXlK1JKjxQ2Xw/cP8Pu24CfjYhnAiPAG4H/WKu6SifCLzy1gu6l3YxNjE0rc3nz1lDLLoZLgV8BXn7UkMaBiPhWROwAXga8AyAiVkbEjQAppYNAL/BFsuTGT6WUdtawrpKkGbi8eeuqWQtCSmkrM+cS3DjL438MXFGyfeNsj5UkzQ+XN29dLvesllZcoXGu7oLiEK/RiVGGtg/Ru6HXf45VUhyVsmlTPWuhY3F589ZkgCDNoTjEa/LQ5LQhXoBBgqSm5loMWhCGh2HLFti8GTZuzLbng0O8JLUqAwQ1vOFh6OuD8XFYtgzGxrLt+QgSRvaMkFucm1bmEC8tREPbh3jLf9vqZGOqmAGCGt7gIORy0NkJbW3Q1ZVtD87Dj/jupd3k9+enlTnES1IrMEBQwxsZyQKCUrlcVl5rDvGS1KoMENTwurshP/1HPPl8Vl5rPWt7GLh8gM72Tnbv301XRxcDlw+YoCip6TmKQQ2vtzfLOZichI6OLAchn4f+/vl5fod41UYx8XR0NBvu2NsLPcZdUsOwBUENr6cHBgayHITdu7MchIEBv0wWsnomnkqqjC0IWhB6emBv9iPeSXWaQDHxdF9h/Z+uriPlBn5SY7AFQdK8q2fiqaTKGCBoTkPbhw5PR6zaaqVzXc/E01a17atdfO6Tz5j3yca0cBkgSJp3vb1ZQDA+DlNTRxJPex09WhPDw/CpvzifAxOnnHDOx6Z1m0zSbTEGCJLmnYmn82twEJacdoDFHYfmfbIxLVwmKapluUpjfZl4On9GRqBjxQH2PHmkzJwPHYsBghaMan6JuEqjWkl3N/wg3z6tzJwPHYtdDGpJrtKoVtLbC/uebGf/xCJzPlQxAwS1JFdpVCvp6YE3/Pb9tHccNOdDFbOLQS2pe2k3YxNj08pcpVHNanjnMLe19TPxi52sWPKz9L6px640HZMtCGpJx7tKo0O8tFAV823GD4yzhDMZnxql79Y+hnc6EYLmZoCgluQqjWoV0/Jtoo3OtuXm26gidjGoZblKY/05vLH2RvaMsKJzBfue3He4zHwbVcIWBElqYt1Lu8nvnz6vtfk2qoQBgiQ1sWn5NmmK8anROfNtpCK7GCSpiRXzavpv7+fA07Zz9lnn0bvBfBsdmwGC1ACc9lm1ZL6NToRdDJpV8Utr8y2b2Ti00WFRNVI6DK102mfPt6R6MkDQjPzSmj9O+yypERkgaEazfWn1397P0PahelevqTjts6RGZICgGc32pTU6MVqnGjUvh6FJakQGCJrRbF9ayzuW16lGzet4p32WpPlwzAAhIp4WER+NiC8Utp8TEf+p9lVTPc32pfWKNa+od9WajtM+S2pElQxzHAL+Gnh3Yfu7wD8AH61RndQASsdOj06MsjK3kv7L+g8PlVJ1OQxNUqOpJEA4M6X0qYj4rwAppYMRcajG9VIDmOlLywRFSWoNleQgjEfEGUACiIiLgd01rZUkNbGhoewiNbJKWhCuBj4LnB0RXwfOAuwcVVOwOV+SZnbMACGldG9EvBQ4FwjgOymlyZrXTE2p+KvJZX4lqbHNGiBExJWz3PVzEUFK6YYa1UmSJNXZXC0Iv1S4firwIuBLhe2XAV8GDBAkSWpSswYIKaVfB4iIzwHPSSk9UtheAfyv+amepOLIkVbPl7B76uS0+vtHx6+SUQxrisFBwU+Bn6tRfSRJUgOoZBTDlyPii8DfkQ11fCNwe01rJUmS6qqSUQy9hYTFlxSKrkspfaa21ZIkSfVUSQtCccSCSYktbnjnMFu2bmF0YpSh7UP0buh1vQDpOA0Pw5YtMDqa5VX09kKPHyM1oGMGCBGxh8IsikA7cCownlLKzb6Xms3wzmH6bu1j8tAkyxYvY2xijL5b+wAMEqQKDQ9DXx9MTsKyZTA2lm2DQYIazzGTFFNKS1NKucJlCfDvgcHaV02NZHDbILn2HJ3tnbS1tdHV0UWuPcfgtsrfCsVfTps3w8aN2bbUSgYHIZeDzk5oa4Ourmx70P+oakAVdTGUSin9U0S8qxaVUeMpDo1679fey4rOFex7ct/h+3KLc4zsGanoOP5yOjaHoTW/kRFYsQL2HfkYkctl5VKjqaSLoXRGxTbgIo50OahFdC/tZmxibFpZfn+e7qXdFe1f/OVU/MfY1XWk3ABBx9Is/fbd3VlwXCqfz8qlRlPJPAi/VHJ5JbAHeG0tK6XG07uhl/yBPOMHxpmammJsYoz8gTy9G3or2n9kJAsQSvnLSZUotj6Nj09vfVqIXVS9vVlAMD4OU1PZa8nns3Kp0VTSxfCRlNLXSwsi4lLg0dpUSY2omIjYf3s/oxOjrMytpP+y/ooTFP3lpBPVTK1Pxfr292etIStXZrcX2utQa6gkQPgQsL6CMjW5nrU97J3cCxx/f3lv75EchI6OI7+c+vtrUFE1lWbrt+/pgb3Zx8hpo9XQ5lrN8RKyRZrOioirS+7KAYtqXTE1F3856UTZ+iTVx1wtCO3A6YXHLC0pzwP+W9dx85eTZjPXglS2Pkn1Mddqjl8BvhIRQymlH85jnSTpMFufpPqYq4vhAymltwODEVE2rDGl9Jqa1kySCmx9kubfXF0Mf1O4vnY+KiKpnOtfSKqXuboY7ilcf2X+qiOpyPUvJNVTJTMpXgq8B/iZwuMDSCmlZ9W2alJrK65/se9QNr6vq6PrcLkBwsJmN4kWgkrmQfgo8A7gHuBQbasjqWhkz8hJrX8hSSejkgBhd0rpCzWviVqCv5wqd7LrX0jSyahkLYbbI+J/RsQlEbG+eKl5zaQWd7LrX0jSyaikBeGFheuLSsoS8PLqV0dS0cmuf9FsbH2S5tcxA4SU0svmoyJSJeaaca8Zncz6F5J0MioZxXD1DMW7gXtSSturXyVJklRvlXQxXFS4/J/C9quBbcBvRsSnU0oDtaqcGo+/YiWpNVQSIJwBrE8p7QWIiD8EhoHLyIY+GiBIktRkKhnFsBo4ULI9CfxMSmkC2F+TWkmSpLqqpAXhk8CdEfHPhe1fAv4uIjqBf61ZzVQzQ0PZtVnhkqTZVDKK4Y8j4gvApWTTLP9mSunuwt2/XMvKSWp+LkglNaZKWhBIKd0dEQ8DSwAiYnVK6eGa1kxS03NBKqlxHTMHISJeExHfA/4N+Erh2qmXJZ204oJUne2dtLW10dXRRa49x+C2wXpXTWp5lSQp/jFwMfDdlNIzgcuBr9e0VpJawsieEXKLc9PKXJBKagyVBAiTKaUngLaIaEsp3Q6sq3G9JLWA7qXd5Pfnp5W5IJXUGCoJEMYi4nTgq8AnIuKDwMHaVktSK3BBKqlxVRIgvBaYAN4B3AT8gGyooySdlJ61PQxcPkBneye79++mq6OLgcsHTFCUGkAlwxzHSzY/VsO6SGpBLkglNaZZA4SI2EO2rPPhosJ2ACmllJtxRzW04WHYsgVGR7MJk3p7occfaw3NL01J9TBXC8JtwNOBG4C/d96DhW94GPr6YHISli2DsbFsGwwSJEnTzZqDkFJ6HfBK4DHgryLiKxHx2xHxlEoOHBHPiIjbI+KBiNgZEW8rlL8nIkYiYnvhckWl++rkDA5CLgedndDWBl1d2fagQ85rZmj7EEPbh+pdDUk6bnPmIKSUdgN/HREfA/4D8CGy2RTfV8GxDwLvTCndGxFLgXsi4pbCfe9PKV17vPumlFz74SSMjMCKFbBv35GyXC4rXwickleS5s+cAUJEvAh4E/ASYCvw+pTS1yo5cErpEeCRwu09EfEAUNHg5jn2NUA4Cd3dWbdCqXw+K290TskrSfNr1i6GiHgI+AtgBHgrcD0wHhHrI2L98TxJRKwBLgS+WSjqjYgdEXF9RCw/zn2Pvv+tEXF3RNz92GOPHU+1Wk5vbxYQjI/D1FQWLOTzWXmjc0peSZpfc7UgPEQ2auGVwM+TjV4oSsDLK3mCwiRL/wi8PaWUj4gPk03fnArXfwZcVcm+Mz0mpXQdcB3ARRddlGZ6jDLFRMT+/mwUw8qV2e2FkKA4smeEFZ0r2Pfkkf4Rp+SVpNqZNUBIKW082YNHxKlkX/CfSCndUDjuT0vu/yvgc5Xuq5PX0wN7syHnbNpU16ocl+6l3YxNTO8fcUpeSaqdSmZSPCEREcBHgQdSSu8rKV9R8rDXA/dXuq8WrpPN5ndKXkmaXzULEIBLgV8BXn7UkMaBiPhWROwAXkY2hTMRsTIibjzGvmoRQ0PZpcgpeSVpfh1zquUTlVLayvS8haIbZygjpfRj4Ipj7KsW5pS8kjR/5ppqec6RCimle6tfHUmS1AjmakH4s8L1EuAi4D6yX/UXkA05fHFtqyZJkuplrlEMLwOIiL8H3ppS+lZh+3zgmvmpnsTh5Ea7FZqXf1up8VSSpPjsYnAAkFK6H1hXuypJkqR6qyRJ8YGI+Ajwt2STG70ZeKCmtVJNLaT5DyRJ9VFJgPDrwG8BxRUVvwp8uGY1kqR5UhxKa9AslTtmgJBS2hcRfwncmFL6zjzUSZIk1dkxcxAi4jXAduCmwva6iPhsrSum1jU8DFu2wObNsHEjbPtqV72rJEktp5IkxT8EXgCMAaSUtgNralgntbDhYejry1acXLYsW3HyU39xvkGCJM2zSgKEgyml3TWviQQMDkIuB52d0NYGXV2w5LQD3HbDz9S7asdteOcwW7ZuYfMtm9k4tJHhncP1rpIkVaySAOH+iPiPwKKI+NmI+BBwR43rpRY1MpIFCKU6Tj8jeidyAAAe30lEQVTA6E9Pr0+FTtDwzmH6bu1j/MA4yxYvY2xijL5b+wwSJC0YlQQIvwusBfYDnwR2c2REg1RV3d2Qz08vm9jbzvKn7a1PhU7Q4LZBcu05Ots7aWtro6uji1x7jsFtg/WumiRVpJIA4dUppXenlDYULv3Aa2pdMbWm3t4sQBgfh6mpLAdh35PtvOLKH9a7asdlZM8IucXTm0Jyi3OM7BmpU40k6fhUEiD81wrLpJPW0wMDA1kOwu7dWQ7CG377fjZcNlbvqh2X7qXd5PdPbwrJ78/TvbS7TjWSpOMz12qOryJbfrk7Iv685K4ccLDWFVPr6umBvYUehU2bYGj7keBgoczZ37uhl75b+5g8NEnHKR2MTYyRP5Cn/7L+aY9znYn6KQ6nHR3NJkzq7c3ee5Iyc02U9GPgbrLuhHtKyvcA76hlpdRcitn8oxOjDG0fondDLz1rm/s/cfH19d/ez+jEKCtzK+m/rL/pX/dCURxOOzl5ZDhtX192n0GClJlrNcf7gPsi4jPAeErpEEBELAIWz1P9tMAVs/knD01Oy+YHmv7LsmdtD3sns6YQWwhq73haY4rDaffty7a7uo6UGyBImUpyEG4GOkq2O4Bba1MdNRuz+dWIZhpOm8tl5ZIylQQIS1JKh8eYFW6fVrsqqZmYza9GNNNw2nw+K5eUqSRAGI+I9cWNiHg+MFG7KqmZnGw2v7MRqhZmGk6bz2flkjKVBAhvBz4dEV+LiK8B/wD4MVJFejf0kj+QZ/zAOFNTU4ez+Xs3HPsttO2Jm52NUDUx03DagQHzD6RSlSz3vC0ing2cCwTw7ZTSZM1rpqZwMtn8t41eT647x75DWSZZV0eWSTa4bbDpExxVe0cPp5U03TEDhIg4Dbga+JmU0m8U1mM4N6X0udpXT83gRLP5Rw/t4tzFOfY9ue9wmfkLkjQ/Kuli+GvgAHBJYXsX8Cc1q5FE9ovuvHNPcTZCSaqTSgKEs1NKA8AkQEppgqyrQaqpk8lfOBFDQ9lFklRZgHAgIjqABBARZ5Ot7CjVVM/aHgYuH6CzvZPd+3fT1dHFwOUD5h9I0jw4Zg4C8IfATcAzIuITwKXAplpWSipyNkJJqo85A4SICODbwJXAxWRdC29LKT0+D3WTJEl1MmeAkFJKEfFPKaXnA5+fpzpJkqQ6q6SL4c6I2JBS2lbz2kjSPHL+A2l2lQQILwP+c0T8EBgn62ZIKaULalozSZJUN5UECK+qeS0kSVJDOVaSYhvw+ZTS+fNUH6llFBeiGp0YZWj7EL0beh3CKalhHCtJcSoi7ouI1Smlh+erUlKzmG1o5vDOYfpu7WPy0OS0hagAgwRJDaGSiZJWADsj4raI+GzxUuuKSfNpeBi2bIHNm2Hjxmy7lga3DZJrz9HZ3klbWxtdHV3k2nMMbhus7RM3KZcFl6qvkhyEP6p5LaQ6Gh6Gvj6YnIRly2BsLNuG2i3/O7JnhBWdK1yIqgpsjZFq45gtCCmlr5BNlrS0cHmgUCY1hcFByOWgsxPa2qCrK9serOGP+e6l3S5EVSUn2xoztH2Ioe1Dta2ktAAdM0CIiDcAdwH/H/AG4JsRYViupjEykgUEpXK5rLxW5nshqmY2smeE3OLpf0BbY6STV0kXw7uBDSmlRwEi4izgVsBOPjWF7u6sW6FUPp+V10qx6bv/9n5GJ0ZZmVtJ/2X9NomfgO6l3YxNTP8D2hpTPcXWFddCaT2VBAhtxeCg4AkqS26UDmvkfy69vUdyEDo6smAhn4f+/to+rwtRVUfvht7DOQgdp3Qcbo3pv6zGf0CpyVUSINwUEV8E/q6w/R+AL9SuSmpFQ0OFG+uyG/P5hVlMROzvh9FRWLkyu12rBEVVl60xUm0cM0BIKf2XiLgSeDHZNMvXpZQ+U/OaSfOopwf2Zj/mnZ9/AbI1Rqq+WQOEiDgHeFpK6esppRuAGwrll0XE2SmlH8xXJSVJ0vyaqwXhA8Dvz1D+ZOG+X6pJjaSj+ItQkubfXMmGa1JKO44uTCndDaypWY0kSVLdzRUgLJnjvo5qV0SSJDWOuQKEbRHxG0cXRsR/Au6pXZWkxuAMe43Bv4NUH3PlILwd+ExE/DJHAoKLgHbg9bWumCRJqp9ZA4SU0k+BF0XEy4DzC8WfTyl9aV5qJkmS6qaSeRBuB26fh7pIkhrQ1oe3Ao4oajVOmazjNjRUMvOhJKkpVTLVslRTw8OwZUs2zfHy857HK678IZvWzX89nEFRko6wBUF1NTycLZQ0Pg7LlsF4fjGf+ovzGXatUEmqKwME1dXgIORy0NkJbW3QmTvAktMOMDhY75pJzcOhojoRBgiqq5GRLEAo1XH6AUZG6lMfSVLGAEF11d0N+fz0som97XR316c+kqSMAYLqqrc3CxDGx2FqCsbz7ex7sp3e3nrXTK1geOcwW7ZuYfMtm9k4tJHhnSa/SEWOYlBd9fRk1/39hVEMK/fzmk3fpafnnPpWbJ44rrx+hncO03drH5OHJlm2eBljE2P03doHQM/anjrXrjEUA6hH9jzC1oe3cvqpp3tuWogBguqupwf27i1srLuvrnXRwnW8wdbgtkFy7Tn2HdoHQFdH1+FyvwSnB1BLTlnC+IFxA6gWYxeDypjxrFYwsmeE3OLpGbK5xTlG9pghC0cCqM72Ttqijc72TnLtOQa3OcSoVRgg6LgUJzXavBk2bqSq8xVse+Jm+4M1b7qXdpPfPz1DNr8/T/dSM2TBAEp2MagCxWmVTz89m9RocjKb1GhsLNuGI7kEJ2rbEzfzqcf/gCVnjdsfrHnRu6H3cBN6xykdjE2MkT+Qp/+y/npXrSF0L+1mbGJsWpkBVGuxBUEVO3pSo66ubPtEJzUq7cq4bfR6lsSyrDmzrY2uji6bM1VTPWt7GLh8gM72Tnbv301XRxcDlw8YkBb0buglfyDP+IFxptIU4wfGyR/I07vBIUatwhYEVWxkBFasgH37jpTlclRlUqPRQ7tYtmjFtDKbM1VrPWt72DuZZcg6omS6YqDUf3s/+T15lncs509e9icGUC3EAEEV6+7OuhVK5fNUZVKj5YtWMT41SmfpsW3OlOqqGEBtfXgrL179YoODFmMXgyp29KRGY2PZdjUmNXrF8qvYl3ZnzZlTU4f7g+erOfPokRtOoNMY/DtI9WMLgip29KRGK1dmt082QRFgwxk/D8Bt7b/H6MQoK3Mr6b+svy6/WJxApzH4d5DqywBBx6V0UqNNm6p33E2bYBM/z9D2d2XbdewPdgKdxuDfQaovuxikozj+uzH4d5DqywBBOooT6DQG/w6NYduubXzuu58zD6QFGSBIR5k2/rskYXLtE79/eNIo1d5sfwfH4c+f4Z3DfOqBT3Hg4IFpeSAGCa3BAEE6ymwT6BQTKTU/nMio/ga3DbJk0RIWn7LYCcxakEmK0gxmmkBnaFsdK9SinMiovkb2jNBxSgd7Duw5XGYeSOswQNCcioszjY5mazJUY84DSfOnOJfE6MQoQ9uH6N3QW3ErTPfSbn7wf38wrex480CK84sY4C08djFoVsPD2WJM4+PTF2fa5i9paUEoziUxfmD8hHIIejf0su/QPvYf3G8eSAsyQNCsZluc6bbb6l0zSZUoziVxooug9azt4Q3nvYH2U9rNA2lBdjFoVrMtzvTII/Wrk6TKjewZYUXnCvY9eeRDfLw5BBtWbWD/1H5evPrFdhO0GFsQNKvu7mythVL5PJx3XnVnUZRUG84loZNRswAhIp4REbdHxAMRsTMi3lYof09EjETE9sLlijmOsSgi/iUiPleremp2tVycSVLtOZeETkYtuxgOAu9MKd0bEUuBeyLilsJ9708pXVvBMd4GPADkjvVAVUdpxvN5Zw3x+rf9IZ//8MuqvjiTpNor5gr0395f90XQtPDULEBIKT0CPFK4vSciHgAqbteKiFXAq4H3AlfXpJKaZqbV8z7Df+JVv/GXbDjj5+1WkBYg55LQiZqXHISIWANcCHyzUNQbETsi4vqIWD7Lbh8A+oCpYxz7rRFxd0Tc/dhjj1Wryi1ptozn20avr/pzFVsqFsr87sX5IDZvho0bs21JamY1H8UQEacD/wi8PaWUj4gPA38MpML1nwFXHbXPLwKPppTuiYiNcx0/pXQdcB3ARRddlKr/ClrHbBnPjxzaVdXnmamlou/Wvqo+RzUV54OYnJw+HwTY3dIM/FU9N89P66ppC0JEnEoWHHwipXQDQErppymlQymlKeCvgBfMsOulwGsi4iHg74GXR8Tf1rKumj3jefmiVVV9npMdmz3fZpsPYrAxqytJVVHLUQwBfBR4IKX0vpLyFSUPez1w/9H7ppT+a0ppVUppDfBG4EsppTfXqq7KzJbx/IrlVx175+MwsmeE3OLpeaeNPL/7yEgWEJTK5bJyVc/QEK6WKTWQWnYxXAr8CvCtiNheKPt94E0RsY6si+Eh4D8DRMRK4CMppVmHPaq2Zst43rutuqsYdi/tZmxibFpZcWx2PZozjzVXfXd31q1QKp/PytU8isGJybhSppajGLYCMcNdN87y+B8DZcFBSunLwJerWTfNbj5WMezd0Hs4B6HjlI7DLRX9l/VX94kqUEk+RG/vkRyEjo4j80H0z391JWneOJOi5l3P2h4GLh+gs72z7vO7V5IP0dMDAwNZDsLu3VkOwsCACYqSmptrMaguGmVsdqVz1ff0wN6sujZBS2oJtiCopTlXvSTNzBYEHVOj/WKuZjLZXPkQxRYO1Zfj8KX6MEBQS3OuekmamQGCWl6j5ENIUiMxB0GSVBMLbc0VTWcLgiRpTifSsjbXHCN24S0MtiBIkqpuoa25onIGCJLqzuW0m0+11lwZ2j7E0PahKtZMlbKLQVJduZx2c5przRUtDLYgqOUNDcHWz55T72q0rEZYTrvZWzA2rds07yN0ZlsdtndD77zWQyfOFgSpQo02YVSzGBmBFStg35HZrud1OW1bMGrDOUYWPgMESXVV7+W0iy0YxQClq+tIuQHCyXGOkYXNLgZJddXbmwUE4+MwNXVkOe3eeWqJHhnJAoRS89mCITUqAwQtKM3eV9yK6r2cdnd3FpCUms8WDKlR2cWgBcO+4oXhRBbTqudy2r29R95XHR1HWjD6++e3HlKjsQVBC0YjZLur+dS7BUNqVLYgqEyjJhPVO9tdzaueLRhSo7IFQQtGNfqKh4aONIFLkmZngKC6Od7JW+qd7S5JrcQuBi0YxT7h/n4YHYWVK7Pb9hVLzedwS9+6etaitRkgaEGxr1iS5ocBglpacV6FR/LnsPVLnZzea4uEJIEBglpY6bwKS04/wHh+sfMqSFKBSYpqWaXzKixvW83Zuec4r4IkFdiCoJblvAqNxZwSqbHYgqCW5Rz8kjQ7AwS1LOdVkKTZGSCoZTkHf/W52qbUPMxBUEtzXoXqWeirbfr3l6azBUEtw1+3teVqm6q2bU/czJatW9h8y2Y2Dm1keKcf2vlkC4Jawly/blUdjgpRNW174mY+9fgfsOSscZYtXsbYxBh9t2Yf2p61C6BJqgkYIGjBOZGm4OKv2+KXV1fXkXKblqujuzsLvEo5KkQnunz8baPXsySW0dmevam6OrIP7eC2QQOEeWIXg1rCyEgWIJTy1211OSpE1TI8DD+48TX85O/+iIf/7r+w+/trAcgtzjGyxw/tfDFAUEtwzoPac1SIqqHYHdg2vpLIjXDoyRyPf/GtPHr/c8jvz9O91A/tfLGLQS2ht/dIDkJHx5Fft/39R0Yx6OQ5KkQn63B34P5n8diiEaJjD20kfvi1l/C0Z9xE/2X99a5iy7AFQS3BX7fSwlDsDsyl1Zw18RIW0c6hjseZGl3NwOUD5h/MI1sQ1DL8dSs1vtJk11xaTe7U9Yzn2zl75XPoWVvfurUaWxAkSQ3j6GTX8Xw7+55sN9m1DgwQJEkN4+juwM7cft7w2/fbHVgHdjGoqQ0NZdd2KUgLR2l3IOvuq2tdWpkBglqewYMklbOLQZIklTFAkGpgaOhI94YkLUQGCJIkqYwBgiRJKmOSoqSqMulTag62IEiSpDK2IKil+OtWkipjC4IkSSpjgCBJksoYIEiSpDLmIEiSGk4xX2hoe12r0dJsQZAkSWUMENS0hodhyxbYvBk2bsy2JUmVsYtBTWl4GPr6YHISli2DsbFsG3BdeUmqgC0IakqDg5DLQWcntLVBV1e2PThY75qVc2EnSY3IAEFNaWQkCwhK5XJZea3ZtSGpGdjFoKbU3Z11K5TK57PyWrJrQ6quTes21bsKLcsWBDWl3t4sIBgfh6mp7Is6n8/Ka2khdW1I0lwMENSUenpgYCD7ot69O/uiHhio/a/4enZtSFI12cWgptXTA3v3Zrfna5GmenVtSFK12YIgVVG9ujYkqdoMEKQqqlfXhiRVm10MUpXVo2tDkqrNFgRJklTGAEGSJJUxQJAkSWUMECQ1DNelkBqHAYIkSSpjgCDVkQs7SWpUDnOU6sSFnSQ1MlsQpDpxYSdJjcwAQaoTF3aS1MjsYlBTa+SZDF3YSVIjswVBqhMXdpLUyGxBkGqgkpaLYiJifz+MjsLKldltExQlNYKaBQgR8Qzg48DTgSngupTSByPiPcBvAI8VHvr7KaUbZ9i/C/gIcD6QgKtSSt+oVX2lenBhJ0mNqpYtCAeBd6aU7o2IpcA9EXFL4b73p5SuPcb+HwRuSin1REQ7cFoN6ypJkkrULEBIKT0CPFK4vSciHgAqSr+KiBxwGbCpsP8B4EBtaipJko42L0mKEbEGuBD4ZqGoNyJ2RMT1EbF8hl2eRdYF8dcR8S8R8ZGI6Jzl2G+NiLsj4u7HHntspodIkqTjVPMAISJOB/4ReHtKKQ98GDgbWEfWwvBnM+x2CrAe+HBK6UJgHHjXTMdPKV2XUroopXTRWWedVYuXIElSy6lpgBARp5IFB59IKd0AkFL6aUrpUEppCvgr4AUz7LoL2JVSKrY4DJMFDJKalOtSSI2lZgFCRATwUeCBlNL7SspXlDzs9cD9R++bUvoJ8KOIOLdQ9ArgX2tVV0n1VVyXYnx8+roUBglS/dSyBeFS4FeAl0fE9sLlCmAgIr4VETuAlwHvAIiIlRFROtzxd4FPFB63DvjvNayrpDpyXQqp8dRyFMNWIGa4q2zOg8LjfwxcUbK9HbioNrWT1EhGRmDFCti370iZ61JI9eVUy5Lqrrs7m2a6lOtSSPVlgCCp7lyXQmo8BgiS6q6nBwYGshyE3buzHISBAdelkOrJxZqkOnMNhozrUkiNxRYESZJUxgBBkiSVMUCQJEllDBAkSVIZAwRJklTGAEGSJJUxQJAkSWUMECRJUhkDBEmSVMYAQZIklTFAkCRJZQwQJElSGRdrktQwXKRJahy2IEiSpDIGCJIkqYwBgiRJKmOAIEmSyhggSJKkMgYIkiSpjAGCJEkqY4AgSZLKGCBIkqQyBgiSJKmMAYIkSSpjgCBJksoYIEiSpDIGCJIkqYwBgiRJKmOAIEmSyhggSJKkMgYIkiSpjAGCJEkqY4AgSZLKGCBIkqQykVKqdx2qJiL2AN+pdz0WgDOBx+tdiQXA81Q5z1VlPE+V81xV5tyU0tJaHPiUWhy0jr6TUrqo3pVodBFxt+fp2DxPlfNcVcbzVDnPVWUi4u5aHdsuBkmSVMYAQZIklWm2AOG6eldggfA8VcbzVDnPVWU8T5XzXFWmZuepqZIUJUlSdTRbC4IkSaoCAwRJklSmIQOEiHgoIr4VEduLQzgi4n9GxLcjYkdEfCYiukoef0FEfCMidhb2W1Iof35h+/sR8ecREYXyp0TELRHxvcL18vq80pNTxfP03oj4UUTsPer4iyPiHwrn75sRsWY+X1+1VOM8RcRpEfH5wj47I2JLyeOb4jxBVd9TN0XEfYXyv4yIRYVyP3sl56nk/s9GxP0l201xnqCq76kvR8R3CsfZHhFPLZQ3xeeviuepPSKui4jvFvb994Xy4z9PKaWGuwAPAWceVfbzwCmF2/8D+B+F26cAO4DnFbbPABYVbt8FXAIE8AXgVYXyAeBdhdvvKh5roV2qeJ4uBlYAe4861m8Df1m4/UbgH+r9mut1noDTgJcVytqBr5W8n5riPFX5PZUrXAfwj8AbC9t+9krOU2H7SuCTwP0lZU1xnqr8nvoycNEMx2+Kz18Vz9MfAX9SuN1WPOaJnKeGbEGYSUrp5pTSwcLmncCqwu2fB3aklO4rPO6JlNKhiFhB9k/qGyk7Ix8HXlfY57XAxwq3P1ZSvuAd73kq3L4zpfTIDIcrPU/DwCsislaYhe54z1NK6cmU0u2FsgPAvSX7NO15ghN+T+ULjzmFLKAqZkP72Ss5TxFxOnA18CdHHa5pzxOc2LmaQ9N+/k7wPF0F/GmhfCqlVJyN8rjPU6MGCAm4OSLuiYi3znD/VWQtAgA/B6SI+GJE3BsRfYXybmBXyT67CmUATyt+IRaun1r1VzA/qnGe5tIN/Aig8CbdTRapLjRVPU+FZr5fAm4rFDXLeYIqnquI+CLwKLCH7B8S+Nk7+jz9MfBnwJNH7d8s5wmq+/n760IT/P9f8uXWLJ+/kz5PJV0Qf1wo/3REPK1QdtznqVGnWr40pfTjQh/TLRHx7ZTSVwEi4t3AQeAThceeArwY2ED2IbstIu4B8jMct9nGdJ70eUop3TbTgQtmii4X4jms2nmKiFOAvwP+PKX0YGGfZjlPUMVzlVJ6ZaFf9BPAy4Fb5vm11FI1/kc9AZyTUnrHQu03r1C13lO/nFIaiYilZN1Wv0LWMtwsn79qvKfuI2tl+HpK6eqIuBq4luxcHfd5asgWhJTSjwvXjwKfAV4AEBG/Bvwi2Rul+MJ2AV9JKT2eUnoSuBFYXyhfVXLYVcCPC7d/WuiCoHD9aG1fUW1U6TzNZRfwjMIxTwGWAf+32q+j1qp8nq4DvpdS+kBJWVOcJ6j+eyqltA/4LFnzJvjZKz1PlwDPj4iHgK3Az0XElwv7NMV5guq9p1JKI4XrPWQ5Gy8o2WfBf/6qdJ6eIAsYPlN43Kc58pk87vPUcAFCRHQWIkQiopOsr+X+iPgFYDPwmsIJKfoicEFkWeanAC8F/rXQLLcnIi4uNEX9KvDPhX0+C/xa4favlZQvGNU6T8d4mtLz1AN8qeQNuiBU8zxFxJ+QfajeftTTLPjzBNU7VxFxesmX2ynAFcC3C/v42TvyP+rDKaWVKaU1ZL8Gv5tS2ljYZ8GfJ6jqe+qUiDizcJxTyb4wi6M+Fvznr4rvqQT8H2Bj4XGv4Mj/+eM/T6kBsjdLL8CzyJpJ7gN2Au8ulH+frP9ke+HylyX7vLnw2PuBgZLyiwplPwAGOTJz5Blk/cffK1w/pd6vu87naYAsupwqXL+nUL6ELAL9PtmIkGfV+3XX6zyRtUAl4IGSfd7SLOepyufqacA2sizrncCHOJKJ7Wev5LNXcv8apo9iWPDnqcrvqU7gnpL31Ac5krW/4D9/1XxPAT8DfLVwrm4DVp/oeXKqZUmSVKbhuhgkSVL9GSBIkqQyBgiSJKmMAYIkSSpjgCBJksoYIEhNLCJSRPxNyfYpEfFYRHyusL0pIgYLt98TESOFqWy/FxE3RMRz5jj2ByLishnKNxaPX6XXcFZE3FSt40mqjAGC1NzGgfMjoqOw/e+AkTke//6U0rqU0s8C/wB8KSLOOvpBEfEU4OJUmAq2llJKjwGPRMSltX4uSUcYIEjN7wvAqwu330S2lsQxpZT+AbgZ+I8z3N0DHP5VHxG/ENna81vJli8ulr8gIu6IiH8pXJ9bKP9aRKwredzXI1vf/qWFFozthX2WFh7yT8AvV/6SJZ0sAwSp+f098MbCwkkXAN88jn3vBZ49Q/mlZDPbUTjuX5GtcPkS4Oklj/s2cFlK6ULgD4D/Xij/CLCpsP/PAYtTSjuAa4DfSSmtKxxrovD4uwvbkuaJAYLU5ApfvGvIWg9uPM7dZ1svfgXwWOH2s4F/Syl9L2VTs/5tyeOWAZ+OiPuB9wNrC+WfBn6xMK/+VcBQofzrwPsi4veArpQtSwvZYkUrj7Pukk6CAYLUGj5LtuxrRd0LJS4kW3/iaBNkc7sXzTZn+x8Dt6eUzidrYVgCkLKFZ24hW+XxDWSr85FS2gK8BegA7oyIYuvFEo60JkiaBwYIUmu4HvhvKaVvVbpDRPx7slXlZgoqHgDOKdz+NvDMiDi7sP2mksct40hS5KajjvER4M+BbSml/1t4zrNTSt9KKf0Psm6FYoDwcxxZvU/SPDBAkFpASmlXSumDM9x1CrC/ZPsdxWGOZKvFvbwwiuBon6ewpGxKaR/wVuDzhSTFH5Y8bgD404j4OrDoqDrdA+SBvy4pfntE3B8R95G1GHyhUP6ywnNKmieu5ii1sIh4P/C9lNJfnMC+W4FfTCmNneBzrwS+DDw7pTR1jMd+FXhtSmn0RJ5L0vGzBUFqURHxBbJRDZ84wUO8E1h9gs/9q2SjKd5dQXBwFvA+gwNpftmCIEmSytiCIEmSyhggSJKkMgYIkiSpjAGCJEkqY4AgSZLK/D/RmgTvdieMSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the light curves (note, ignoring the one F606W value at MJD = 55204.29)\n",
    "x = hcv_f814w['lightcurve_d']\n",
    "y = hcv_f814w['lightcurve_cm']\n",
    "yerr = hcv_f814w['lightcurve_e']\n",
    "x2 = hcv_f606w['lightcurve_d']\n",
    "y2 = hcv_f606w['lightcurve_cm']\n",
    "yerr2 = hcv_f606w['lightcurve_e']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter(x, y, color='g', alpha=0.5)\n",
    "plt.errorbar(x, y, yerr, fmt='o', color='g', alpha=0.5)\n",
    "plt.scatter(x2, y2, color='b', alpha=0.5)\n",
    "plt.errorbar(x2, y2, yerr2, fmt='o', color='b', alpha=0.5)\n",
    "plt.xlim(52600.0, 52660.0)\n",
    "plt.ylim(25.7, 24.7)  # flip the y axis\n",
    "plt.xlabel('MJD (days)')\n",
    "plt.ylabel('Corrected Magnitude')\n",
    "plt.legend([\"F814W\", \"F606W\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Compute the Lomb-Scargle periodogram of the light curve in one band"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a periodgram using the [Astropy 'LombScargle'](https://docs.astropy.org/en/stable/api/astropy.timeseries.LombScargle.html) function and obtain the period based on the best frequency. More information can be found [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.lombscargle.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best found period: 0.6874079029387833 days\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For the ACS_F814W filter:\n",
    "hcv_f814w['lightcurve_d'].unit = None\n",
    "hcv_f814w['lightcurve_cm'].unit = None\n",
    "hcv_f814w['lightcurve_e'].unit = None\n",
    "\n",
    "#generate periodogram\n",
    "frequency, power = LombScargle(hcv_f814w['lightcurve_d'], \\\n",
    "                               hcv_f814w['lightcurve_cm'], \\\n",
    "                               hcv_f814w['lightcurve_e']).autopower(minimum_frequency=1.0,\\\n",
    "                                                                        maximum_frequency=5.0)\n",
    "\n",
    "best_frequency = frequency[np.argmax(power)]\n",
    "best_power = power[np.argmax(power)]\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter([best_frequency], [best_power], facecolors='none', edgecolors='r', s=80)\n",
    "plt.plot(frequency, power)\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('Relative Power')\n",
    "\n",
    "#print(best_frequency)\n",
    "period=1/best_frequency\n",
    "print(\"Best found period: \" + str(period) + \" days\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This result compares well with Brown+ 2004, who found a period of 0.687 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Phase and plot the light curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phase = (hcv_f814w['lightcurve_d'] / period) % 1\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.axes().invert_yaxis()\n",
    "plt.scatter(phase, hcv_f814w['lightcurve_cm'], color='b', alpha=0.5)\n",
    "plt.errorbar(phase, hcv_f814w['lightcurve_cm'], hcv_f814w['lightcurve_e'], fmt='o', color='b', alpha=0.5)\n",
    "plt.scatter(phase+1, hcv_f814w['lightcurve_cm'], color='b', alpha=0.5)\n",
    "plt.errorbar(phase+1, hcv_f814w['lightcurve_cm'], hcv_f814w['lightcurve_e'], fmt='o', color='b', alpha=0.5)\n",
    "plt.xlim(0.0, 2.0)\n",
    "#plt.ylim(25.7, 24.7)  # flip the y axis\n",
    "plt.xlabel('Phase')\n",
    "plt.ylabel('Corrected Magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the results by increasing and decreasing the period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0873cd53fdac48f9ae0b10bd0fefda81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.6874079029387833, description='x', max=0.6942819819681711, min=0.680…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "def fn(x):\n",
    "    print(\"period=\" + str(x))\n",
    "    phase = (hcv_f814w['lightcurve_d'] / x) % 1\n",
    "    #print(phase)\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.axes().invert_yaxis()\n",
    "    plt.scatter(phase, hcv_f814w['lightcurve_cm'], color='b', alpha=0.3)\n",
    "    plt.errorbar(phase, hcv_f814w['lightcurve_cm'], hcv_f814w['lightcurve_e'], fmt='o', color='b', alpha=0.3)\n",
    "    plt.xlabel('Phase')\n",
    "    plt.ylabel('Corrected Magnitude')\n",
    "    plt.show()\n",
    "#interact(fn, x=period)\n",
    "#interact(fn, x=period)\n",
    "max = (period + period*0.01)\n",
    "min = (period - period*0.01)\n",
    "interact(fn, x=widgets.FloatSlider(min=min,max=max,step=0.00005,value=period));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Steps\n",
    "\n",
    "### Step 5. Perform a cone search around the RR Lyrae star and retrieve the HCV sources. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v1.0.1) - Connection:\n",
      "\tHost: hst.esac.esa.int\n",
      "\tUse HTTPS: False\n",
      "\tPort: 80\n",
      "\tSSL Port: 443\n",
      "Query finished.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<i>Table masked=True length=3335</i>\n",
       "<table id=\"table34898320520\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>chi2</th><th>ci_d</th><th>ci_v</th><th>d_d</th><th>dec</th><th>d_v</th><th>expert_class</th><th>filter</th><th>groupid</th><th>lightcurve_cm</th><th>lightcurve_d</th><th>lightcurve_e</th><th>lightcurve_i</th><th>lightcurve_m</th><th>lightcurve_r</th><th>mad</th><th>matchid</th><th>pipeline_class</th><th>ra</th></tr></thead>\n",
       "<thead><tr><th></th><th>days</th><th></th><th>days</th><th>deg</th><th>milliarcsec</th><th></th><th></th><th></th><th>mag</th><th>days</th><th>mag</th><th></th><th>mag</th><th></th><th></th><th></th><th></th><th>deg</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>object</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>bool</th><th>float64</th><th>int32</th><th>int32</th><th>float64</th></tr></thead>\n",
       "<tr><td>3.09628240816075495</td><td>52611.26638278016</td><td>1.059999942779541</td><td>52611.26638278016</td><td>40.70929718017578</td><td>4.754214763641357</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>24.553740054403757</td><td>52611.26638278016</td><td>0.0266</td><td>hst_9453_02_acs_wfc_f814w</td><td>24.5581</td><td>False</td><td>0.03035902804316848</td><td>98239091</td><td>1</td><td>11.494104385375977</td></tr>\n",
       "<tr><td>3.09628240816075495</td><td>52614.7064349039</td><td>1.1368519067764282</td><td>52614.7064349039</td><td>40.70929718017578</td><td>3.1014959812164307</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>24.454888789590164</td><td>52614.7064349039</td><td>0.0243</td><td>hst_9453_10_acs_wfc_f814w</td><td>24.451599</td><td>False</td><td>0.03035902804316848</td><td>98239091</td><td>1</td><td>11.494104385375977</td></tr>\n",
       "<tr><td>3.09628240816075495</td><td>52616.09770231927</td><td>1.1049073934555054</td><td>52616.09770231927</td><td>40.70929718017578</td><td>7.896210670471191</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>24.47698945440076</td><td>52616.09770231927</td><td>0.0272</td><td>hst_9453_14_acs_wfc_f814w</td><td>24.477301</td><td>False</td><td>0.03035902804316848</td><td>98239091</td><td>1</td><td>11.494104385375977</td></tr>\n",
       "<tr><td>3.09628240816075495</td><td>52626.490075099515</td><td>1.1442593336105347</td><td>52626.490075099515</td><td>40.70929718017578</td><td>4.49559211730957</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>24.50593209383585</td><td>52626.490075099515</td><td>0.0363</td><td>hst_9453_61_acs_wfc_f814w</td><td>24.4995</td><td>False</td><td>0.03035902804316848</td><td>98239091</td><td>1</td><td>11.494104385375977</td></tr>\n",
       "<tr><td>3.09628240816075495</td><td>52631.39916067012</td><td>1.074814796447754</td><td>52631.39916067012</td><td>40.70929718017578</td><td>7.790589809417725</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>24.507348482443927</td><td>52631.39916067012</td><td>0.026000001</td><td>hst_9453_38_acs_wfc_f814w</td><td>24.5056</td><td>False</td><td>0.03035902804316848</td><td>98239091</td><td>1</td><td>11.494104385375977</td></tr>\n",
       "<tr><td>3.09628240816075495</td><td>52644.20626751962</td><td>0.9599074125289917</td><td>52644.20626751962</td><td>40.70929718017578</td><td>7.809937477111816</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>24.59013931092299</td><td>52644.20626751962</td><td>0.0265</td><td>hst_9453_54_acs_wfc_f814w</td><td>24.590799</td><td>False</td><td>0.03035902804316848</td><td>98239091</td><td>1</td><td>11.494104385375977</td></tr>\n",
       "<tr><td>3.09628240816075495</td><td>52646.27403380303</td><td>1.060092568397522</td><td>52646.27403380303</td><td>40.70929718017578</td><td>8.634530067443848</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>24.526681052691977</td><td>52646.27403380303</td><td>0.026000001</td><td>hst_9453_57_acs_wfc_f814w</td><td>24.5263</td><td>False</td><td>0.03035902804316848</td><td>98239091</td><td>1</td><td>11.494104385375977</td></tr>\n",
       "<tr><td>5.55475441578078222</td><td>52611.26638278016</td><td>0.9527778029441833</td><td>52611.26638278016</td><td>40.7080078125</td><td>0.7770352363586426</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>23.584190315361923</td><td>52611.26638278016</td><td>0.012</td><td>hst_9453_02_acs_wfc_f814w</td><td>23.589199</td><td>False</td><td>0.021911071428846185</td><td>85054530</td><td>1</td><td>11.492891311645508</td></tr>\n",
       "<tr><td>5.55475441578078222</td><td>52611.57040482061</td><td>0.9789814352989197</td><td>52611.57040482061</td><td>40.7080078125</td><td>0.48111963272094727</td><td>2</td><td>ACS_F814W</td><td>1043756</td><td>23.577598627816684</td><td>52611.57040482061</td><td>0.012</td><td>hst_9453_04_acs_wfc_f814w</td><td>23.582899</td><td>False</td><td>0.021911071428846185</td><td>85054530</td><td>1</td><td>11.492891311645508</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52632.099195476854</td><td>1.0135184526443481</td><td>52632.099195476854</td><td>40.72599792480469</td><td>3.0463757514953613</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.912892871453717</td><td>52632.099195476854</td><td>0.0528</td><td>hst_9453_39_acs_wfc_f606w</td><td>25.9156</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52632.39987250231</td><td>0.9375925660133362</td><td>52632.39987250231</td><td>40.72599792480469</td><td>4.641005992889404</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.906379075083034</td><td>52632.39987250231</td><td>0.052000001</td><td>hst_9453_43_acs_wfc_f606w</td><td>25.907301</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52632.53329266794</td><td>1.0228703022003174</td><td>52632.53329266794</td><td>40.72599792480469</td><td>2.021033763885498</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.55147467856299</td><td>52632.53329266794</td><td>0.039000001</td><td>hst_9453_45_acs_wfc_f606w</td><td>25.5527</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52633.53394658561</td><td>0.9825000166893005</td><td>52633.53394658561</td><td>40.72599792480469</td><td>5.543918609619141</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.906709789001926</td><td>52633.53394658561</td><td>0.053399999</td><td>hst_9453_47_acs_wfc_f606w</td><td>25.908199</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52636.40226843394</td><td>0.9477777481079102</td><td>52636.40226843394</td><td>40.72599792480469</td><td>3.5023529529571533</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.896994696105338</td><td>52636.40226843394</td><td>0.0524</td><td>hst_9453_49_acs_wfc_f606w</td><td>25.895599</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52636.7406423497</td><td>0.9320370554924011</td><td>52636.7406423497</td><td>40.72599792480469</td><td>1.2833119630813599</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.714898499205404</td><td>52636.7406423497</td><td>0.044</td><td>hst_9453_51_acs_wfc_f606w</td><td>25.7113</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52640.40434069326</td><td>1.1238888502120972</td><td>52640.40434069326</td><td>40.72599792480469</td><td>1.8247007131576538</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.905899646319572</td><td>52640.40434069326</td><td>0.0526</td><td>hst_9453_41_acs_wfc_f606w</td><td>25.9074</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52644.473090491956</td><td>1.0012037754058838</td><td>52644.473090491956</td><td>40.72599792480469</td><td>1.8107696771621704</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.790836267475434</td><td>52644.473090491956</td><td>0.047400001</td><td>hst_9453_53_acs_wfc_f606w</td><td>25.7946</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>52650.24182896991</td><td>1.0362036228179932</td><td>52650.24182896991</td><td>40.72599792480469</td><td>4.1564555168151855</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.630031976721146</td><td>52650.24182896991</td><td>0.041999999</td><td>hst_9453_55_acs_wfc_f606w</td><td>25.628099</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "<tr><td>10.5620890982210387</td><td>55204.29098817939</td><td>0.9985185265541077</td><td>55204.29098817939</td><td>40.72599792480469</td><td>5.1229472160339355</td><td>1</td><td>ACS_F606W</td><td>1043756</td><td>25.920661378733925</td><td>55204.29098817939</td><td>0.078000002</td><td>hst_11684_04_acs_wfc_f606w</td><td>25.920601</td><td>False</td><td>0.09967318683652593</td><td>58227924</td><td>1</td><td>11.4900484085083</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=3335>\n",
       "        chi2               ci_d        ... pipeline_class         ra        \n",
       "                           days        ...                       deg        \n",
       "       object            float64       ...     int32           float64      \n",
       "------------------- ------------------ ... -------------- ------------------\n",
       "3.09628240816075495  52611.26638278016 ...              1 11.494104385375977\n",
       "3.09628240816075495   52614.7064349039 ...              1 11.494104385375977\n",
       "3.09628240816075495  52616.09770231927 ...              1 11.494104385375977\n",
       "3.09628240816075495 52626.490075099515 ...              1 11.494104385375977\n",
       "3.09628240816075495  52631.39916067012 ...              1 11.494104385375977\n",
       "3.09628240816075495  52644.20626751962 ...              1 11.494104385375977\n",
       "3.09628240816075495  52646.27403380303 ...              1 11.494104385375977\n",
       "5.55475441578078222  52611.26638278016 ...              1 11.492891311645508\n",
       "5.55475441578078222  52611.57040482061 ...              1 11.492891311645508\n",
       "                ...                ... ...            ...                ...\n",
       "10.5620890982210387 52632.099195476854 ...              1   11.4900484085083\n",
       "10.5620890982210387  52632.39987250231 ...              1   11.4900484085083\n",
       "10.5620890982210387  52632.53329266794 ...              1   11.4900484085083\n",
       "10.5620890982210387  52633.53394658561 ...              1   11.4900484085083\n",
       "10.5620890982210387  52636.40226843394 ...              1   11.4900484085083\n",
       "10.5620890982210387   52636.7406423497 ...              1   11.4900484085083\n",
       "10.5620890982210387  52640.40434069326 ...              1   11.4900484085083\n",
       "10.5620890982210387 52644.473090491956 ...              1   11.4900484085083\n",
       "10.5620890982210387  52650.24182896991 ...              1   11.4900484085083\n",
       "10.5620890982210387  55204.29098817939 ...              1   11.4900484085083"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hst = TapPlus(url=\"http://hst.esac.esa.int/tap-server/tap\")\n",
    "\n",
    "# Performing a cone search of radius 3 arcminutes:\n",
    "job3 = hst.launch_job_async(\"SELECT * FROM hcv.hcv \\\n",
    "WHERE 1=CONTAINS( \\\n",
    "  POINT('ICRS', ra, dec), \\\n",
    "  CIRCLE('ICRS', 11.50269, +40.68417, 0.05))\")\n",
    "hcv = job3.get_results()\n",
    "job3.get_data()\n",
    "\n",
    "# Returns 3335 rows. Note the number of rows is larger than the number of sources since the filter information\n",
    "# and magnitudes (light curves) are all given in the one (flattened) table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Download an associated HLA image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These steps require the ESA_Hubble astroquery module. Currently this module can be downloaded from the dev version of astroquery, details [here](https://astroquery.readthedocs.io/en/latest/). \n",
    "\n",
    "The ESA_Hubble module should be available in astropy version 4.0 onwards. More details can be found [here](https://astroquery.readthedocs.io/en/latest/esa_hubble/esa_hubble.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       lightcurve_i      \n",
      "-------------------------\n",
      "hst_9453_02_acs_wfc_f814w\n",
      "hst_9453_04_acs_wfc_f814w\n",
      "hst_9453_06_acs_wfc_f814w\n",
      "hst_9453_08_acs_wfc_f814w\n",
      "hst_9453_10_acs_wfc_f814w\n",
      "hst_9453_12_acs_wfc_f814w\n",
      "hst_9453_14_acs_wfc_f814w\n",
      "hst_9453_16_acs_wfc_f814w\n",
      "hst_9453_20_acs_wfc_f814w\n",
      "hst_9453_22_acs_wfc_f814w\n",
      "                      ...\n",
      "hst_9453_46_acs_wfc_f814w\n",
      "hst_9453_48_acs_wfc_f814w\n",
      "hst_9453_50_acs_wfc_f814w\n",
      "hst_9453_42_acs_wfc_f814w\n",
      "hst_9453_54_acs_wfc_f814w\n",
      "hst_9453_52_acs_wfc_f814w\n",
      "hst_9453_56_acs_wfc_f814w\n",
      "hst_9453_57_acs_wfc_f814w\n",
      "hst_9453_58_acs_wfc_f814w\n",
      "hst_9453_60_acs_wfc_f814w\n",
      "hst_9453_59_acs_wfc_f814w\n",
      "Length = 33 rows\n"
     ]
    }
   ],
   "source": [
    "# The associated HLA images are found in the HCV column lightcurve_i:\n",
    "print (hcv_f814w['lightcurve_i'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://archives.esac.esa.int/ehst-sl-server/servlet/data-action?ARTIFACT_ID=hst_9453_02_acs_wfc_f814w_drz.fits\n",
      "File hst_9453_02_acs_wfc_f814w_drz.fits downloaded to current directory\n"
     ]
    }
   ],
   "source": [
    "# Download a single image from the ESA Hubble Science Archive using the astroquery.esa_hubble module.\n",
    "\n",
    "# A single observation has the prefix 'drz' (drizzle). Therefore the files to download are: \n",
    "# hst_9453_**_acs_wfc_f814w_drz.fits, where ** is a number\n",
    "\n",
    "from astroquery.esa_hubble import ESAHubble\n",
    "ESAHubble.get_artifact(\"hst_9453_02_acs_wfc_f814w_drz.fits\")\n",
    "\n",
    "image_file = \"hst_9453_02_acs_wfc_f814w_drz.fits\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: hst_9453_02_acs_wfc_f814w_drz.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     861   ()      \n",
      "  1  SCI           1 ImageHDU        86   (5185, 5166)   float32   \n",
      "  2  WHT           1 ImageHDU        46   (5185, 5166)   float32   \n",
      "  3  CTX           1 ImageHDU        39   (5185, 5166)   int32   \n",
      "  4  HDRTAB        1 BinTableHDU    631   8R x 311C   [9A, 3A, J, D, D, D, D, D, D, D, D, D, D, D, D, D, J, 3A, 9A, 7A, 18A, 4A, D, D, D, D, 3A, D, D, D, D, D, D, D, D, D, D, D, D, J, 8A, 23A, D, D, D, D, J, J, J, 8A, J, 23A, 9A, 19A, J, 4A, J, J, J, J, J, J, 23A, D, D, D, D, J, J, 3A, 3A, 4A, 4A, J, D, D, D, 3A, 1A, J, D, D, D, 13A, 3A, 4A, 4A, 12A, 12A, 23A, 8A, 23A, 10A, 10A, D, D, 3A, 3A, 23A, 4A, 8A, 7A, 23A, D, J, D, 6A, 9A, 8A, D, D, J, 4A, 18A, 3A, J, 7A, 5A, 3A, D, 13A, 8A, 4A, 3A, J, J, J, J, J, J, J, D, D, D, D, D, D, 3A, 1A, D, 23A, D, D, D, 3A, 23A, J, 1A, 3A, 1A, D, 3A, 6A, J, D, D, D, D, D, D, D, D, D, D, 23A, D, D, D, D, 3A, D, D, D, 1A, J, J, J, J, J, J, 23A, J, 5A, 7A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, 13A, D, 24A, 23A, D, 1A, 1A, D, J, D, D, 1A, 1A, D, 4A, J, D, J, 8A, D, J, D, J, J, 23A, 23A, D, 8A, D, 29A, D, 3A, D, J, D, D, 3A, 6A, 5A, 2A, D, 3A, J, 1A, 1A, 1A, 1A, D, D, D, D, D, D, 4A, D, 4A, D, 4A, J, 4A, 3A, 1A, J, J, J, 1A, D, D, D, D, J, 3A, J, J, 6A, J, D, D, 11A, 1A, 1A, 3A, 8A, 1A, D, J, D, J, J, 5A, 4A, J, D, D, D, D, D, D, D, D, D, D, D, D, D, D, 8A, 8A, 3A, 3A]   \n",
      "  5  EXP           1 ImageHDU         8   (5185, 5166)   float32   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / Fits standard                                  \n",
       "BITPIX  =                   16 / Bits per pixel                                 \n",
       "NAXIS   =                    0 / Number of axes                                 \n",
       "EXTEND  =                    T / File may contain extensions                    \n",
       "ORIGIN  = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator        \n",
       "DATE    = '2017-08-23         ' / Date FITS file was generated                  \n",
       "IRAF-TLM= '2017-08-23T16:02:22' / Time of last modification                     \n",
       "COMMENT   FITS (Flexible Image Transport System) format is defined in 'Astronomy\n",
       "COMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H \n",
       "                                                                                \n",
       "TELESCOP= 'HST'                / telescope used to acquire data                 \n",
       "INSTRUME= 'ACS   '             / identifier for instrument used to acquire data \n",
       "                                                                                \n",
       "              / DATA DESCRIPTION KEYWORDS                                       \n",
       "                                                                                \n",
       "IMAGETYP= 'EXT               ' / type of exposure identifier                    \n",
       "PRIMESI = 'ACS   '             / instrument designated as prime                 \n",
       "                                                                                \n",
       "              / TARGET INFORMATION                                              \n",
       "                                                                                \n",
       "TARGNAME= 'NGC224-HALO                   ' / proposer's target name             \n",
       "                                                                                \n",
       "              / PROPOSAL INFORMATION                                            \n",
       "                                                                                \n",
       "PROPOSID=                 9453 / PEP proposal identifier                        \n",
       "                                                                                \n",
       "              / EXPOSURE INFORMATION                                            \n",
       "                                                                                \n",
       "SUNANGLE=           128.445786 / angle between sun and V1 axis                  \n",
       "MOONANGL=           137.613449 / angle between moon and V1 axis                 \n",
       "SUN_ALT =            24.114336 / altitude of the sun above Earth's limb         \n",
       "FGSLOCK = 'FINE              ' / commanded FGS lock (FINE,COARSE,GYROS,UNKNOWN) \n",
       "GYROMODE= '3'                  / number of gyros scheduled, T=3+OBAD            \n",
       "                                                                                \n",
       "DATE-OBS= '2002-12-03'         / UT date of start of observation (yyyy-mm-dd)   \n",
       "EXPSTART=   5.261119857568E+04 / exposure start time (Modified Julian Date)     \n",
       "EXPEND  =       52611.33419003 / exposure end time (Modified Julian Date)       \n",
       "EXPTIME =               5060.0 / exposure duration (seconds)--calculated        \n",
       "TEXPTIME=               5060.0                                                  \n",
       "EXPFLAG = 'NORMAL       '      / Exposure interruption indicator                \n",
       "                                                                                \n",
       "DARKTIME=          1233.247202 / fiducial pixel dark time (secs)                \n",
       "                                                                                \n",
       "              / POINTING INFORMATION                                            \n",
       "                                                                                \n",
       "                                                                                \n",
       "              / TARGET OFFSETS (POSTARGS)                                       \n",
       "                                                                                \n",
       "POSTARG1=            -0.227366 / POSTARG in axis 1 direction                    \n",
       "POSTARG2=             2.969150 / POSTARG in axis 2 direction                    \n",
       "                                                                                \n",
       "              / DIAGNOSTIC KEYWORDS                                             \n",
       "                                                                                \n",
       "CSYS_VER= 'hstdp-2016.2'       / Calibration software system version id         \n",
       "                                                                                \n",
       "              / SCIENCE INSTRUMENT CONFIGURATION                                \n",
       "                                                                                \n",
       "OBSTYPE = 'IMAGING       '     / observation type - imaging or spectroscopic    \n",
       "OBSMODE = 'ACCUM     '         / operating mode                                 \n",
       "CTEIMAGE= 'NONE'               / type of Charge Transfer Image, if applicable   \n",
       "SCLAMP  = 'NONE          '     / lamp status, NONE or name of lamp which is on  \n",
       "NRPTEXP =                    1 / number of repeat exposures in set: default 1   \n",
       "SUBARRAY=                    F / data from a subarray (T) or full frame (F)     \n",
       "DETECTOR= 'WFC'                / detector in use: WFC, HRC, or SBC              \n",
       "FILTER1 = 'CLEAR1L           ' / element selected from filter wheel 1           \n",
       "FILTER2 = 'F814W             ' / element selected from filter wheel 2           \n",
       "FW1OFFST=                    0 / computed filter wheel offset                   \n",
       "FW1ERROR=                    F / filter wheel position error flag               \n",
       "FW2OFFST=                    0 / computed filter wheel offset                   \n",
       "FW2ERROR=                    F / filter wheel position error flag               \n",
       "FWSOFFST=                    0 / computed filter wheel offset                   \n",
       "FWSERROR=                    F / filter wheel position error flag               \n",
       "LRFWAVE =             0.000000 / proposed linear ramp filter wavelength         \n",
       "APERTURE= 'WFC             '   / aperture name                                  \n",
       "PROPAPER= 'WFC             '   / proposed aperture name                         \n",
       "DIRIMAGE= 'NONE     '          / direct image for grism or prism exposure       \n",
       "CTEDIR  = 'NONE    '           / CTE measurement direction: serial or parallel  \n",
       "CRSPLIT =                    1 / number of cosmic ray split exposures           \n",
       "                                                                                \n",
       "              / CALIBRATION SWITCHES: PERFORM, OMIT, COMPLETE                   \n",
       "                                                                                \n",
       "WRTERR  =                    T / write out error array extension                \n",
       "DQICORR = 'COMPLETE'           / data quality initialization                    \n",
       "ATODCORR= 'OMIT    '           / correct for A to D conversion errors           \n",
       "BLEVCORR= 'COMPLETE'           / subtract bias level computed from overscan img \n",
       "BIASCORR= 'COMPLETE'           / Subtract bias image                            \n",
       "FLSHCORR= 'OMIT    '           / post flash correction                          \n",
       "CRCORR  = 'OMIT    '           / combine observations to reject cosmic rays     \n",
       "EXPSCORR= 'COMPLETE'           / process individual observations after cr-reject\n",
       "SHADCORR= 'OMIT    '           / apply shutter shading correction               \n",
       "DARKCORR= 'COMPLETE'           / Subtract dark image                            \n",
       "FLATCORR= 'COMPLETE'           / flat field data                                \n",
       "PHOTCORR= 'COMPLETE'           / populate photometric header keywords           \n",
       "DRIZCORR= 'COMPLETE'           / drizzle processing                             \n",
       "                                                                                \n",
       "              / CALIBRATION REFERENCE FILES                                     \n",
       "                                                                                \n",
       "BPIXTAB = 'jref$n9p09145j_bpx.fits' / bad pixel table                           \n",
       "CCDTAB  = 'jref$o151506dj_ccd.fits' / CCD calibration parameters                \n",
       "ATODTAB = 'N/A     '           / analog to digital correction file              \n",
       "OSCNTAB = 'jref$lch1459bj_osc.fits' / CCD overscan table                        \n",
       "BIASFILE= 'jref$mc91206ej_bia.fits' / bias image file name                      \n",
       "FLSHFILE= 'N/A     '           / post flash correction file name                \n",
       "CRREJTAB= 'N/A     '           / cosmic ray rejection parameters                \n",
       "SHADFILE= 'N/A     '           / shutter shading correction file                \n",
       "DARKFILE= 'jref$mc91206cj_drk.fits' / dark image file name                      \n",
       "DFLTFILE= 'N/A                    ' / delta flat field file name                \n",
       "LFLTFILE= 'N/A                    ' / low order flat                            \n",
       "PHOTTAB = 'N/A                    ' / Photometric throughput table              \n",
       "GRAPHTAB= 'N/A                    ' / the HST graph table                       \n",
       "COMPTAB = 'N/A                    ' / the HST components table                  \n",
       "IDCTAB  = 'jref$0461802ej_idc.fits' / image distortion correction table         \n",
       "DGEOFILE= 'jref$qbu16429j_dxy.fits' / Distortion correction image               \n",
       "MDRIZTAB= 'jref$1371334kj_mdz.fits' / MultiDrizzle parameter table              \n",
       "CFLTFILE= 'N/A     '           / Coronagraphic spot image                       \n",
       "SPOTTAB = 'N/A     '           / Coronagraphic spot offset table                \n",
       "IMPHTTAB= 'jref$08b18470j_imp.fits' / Image Photometry Table                    \n",
       "                                                                                \n",
       "              / COSMIC RAY REJECTION ALGORITHM PARAMETERS                       \n",
       "                                                                                \n",
       "MEANEXP =             0.000000 / reference exposure time for parameters         \n",
       "SCALENSE=             0.000000 / multiplicative scale factor applied to noise   \n",
       "SKYSUB  = '    '               / sky value subtracted (MODE or NONE)            \n",
       "SKYSUM  =                  0.0 / sky level from the sum of all constituent image\n",
       "CRRADIUS=             0.000000 / rejection propagation radius (pixels)          \n",
       "CRTHRESH=             0.000000 / rejection propagation threshold                \n",
       "BADINPDQ=                    0 / data quality flag bits to reject               \n",
       "REJ_RATE=                  0.0 / rate at which pixels are affected by cosmic ray\n",
       "CRMASK  =                    F / flag CR-rejected pixels in input files (T/F)   \n",
       "                                                                                \n",
       "              / OTFR KEYWORDS                                                   \n",
       "                                                                                \n",
       "                                                                                \n",
       "              / PATTERN KEYWORDS                                                \n",
       "                                                                                \n",
       "PATTERN1= 'NONE                    ' / primary pattern type                     \n",
       "P1_SHAPE= '                  ' / primary pattern shape                          \n",
       "P1_PURPS= '          '         / primary pattern purpose                        \n",
       "P1_NPTS =                    0 / number of points in primary pattern            \n",
       "P1_PSPAC=             0.000000 / point spacing for primary pattern (arc-sec)    \n",
       "P1_LSPAC=             0.000000 / line spacing for primary pattern (arc-sec)     \n",
       "P1_ANGLE=             0.000000 / angle between sides of parallelogram patt (deg)\n",
       "P1_FRAME= '         '          / coordinate frame of primary pattern            \n",
       "P1_ORINT=             0.000000 / orientation of pattern to coordinate frame (deg\n",
       "P1_CENTR= '   '                / center pattern relative to pointing (yes/no)   \n",
       "PATTSTEP=                    0 / position number of this point in the pattern   \n",
       "                                                                                \n",
       "              / POST FLASH  PARAMETERS                                          \n",
       "                                                                                \n",
       "FLASHDUR=                  0.0 / Exposure time in seconds: 0.1 to 409.5         \n",
       "FLASHCUR= 'OFF '               / Post flash current: OFF, LOW, MED, HIGH        \n",
       "FLASHSTA= 'NOT PERFORMED   '   / Status: SUCCESSFUL, ABORTED, NOT PERFORMED     \n",
       "SHUTRPOS= 'MULTIPLE'           / Shutter position: A or B                       \n",
       "                                                                                \n",
       "              / ENGINEERING PARAMETERS                                          \n",
       "                                                                                \n",
       "CCDGAIN =                    1 / commanded gain of CCD                          \n",
       "CCDOFSTA=                    3 / commanded CCD bias offset for amplifier A      \n",
       "CCDOFSTB=                    3 / commanded CCD bias offset for amplifier B      \n",
       "CCDOFSTC=                    3 / commanded CCD bias offset for amplifier C      \n",
       "CCDOFSTD=                    3 / commanded CCD bias offset for amplifier D      \n",
       "                                                                                \n",
       "              / CALIBRATED ENGINEERING PARAMETERS                               \n",
       "                                                                                \n",
       "ATODGNA =        9.9989998E-01 / calibrated gain for amplifier A                \n",
       "ATODGNB =        9.7210002E-01 / calibrated gain for amplifier B                \n",
       "ATODGNC =        1.0107000E+00 / calibrated gain for amplifier C                \n",
       "ATODGND =        1.0180000E+00 / calibrated gain for amplifier D                \n",
       "READNSEA=        4.9699998E+00 / calibrated read noise for amplifier A          \n",
       "READNSEB=        4.8499999E+00 / calibrated read noise for amplifier B          \n",
       "READNSEC=        5.2399998E+00 / calibrated read noise for amplifier C          \n",
       "READNSED=        4.8499999E+00 / calibrated read noise for amplifier D          \n",
       "                                                                                \n",
       "              / ASSOCIATION KEYWORDS                                            \n",
       "                                                                                \n",
       "CRDS_CTX= 'hst_0515.pmap'                                                       \n",
       "CRDS_VER= '7.0.1, opus_2016.1-universal, af27872'                               \n",
       "UPWCSVER= '1.2.3.dev'          / Version of STWCS used to updated the WCS       \n",
       "PYWCSVER= '1.2.1   '           / Version of PYWCS used to updated the WCS       \n",
       "HISTORY CCD parameters table:                                                   \n",
       "HISTORY   reference table jref$o151506dj_ccd.fits                               \n",
       "HISTORY     inflight                                                            \n",
       "HISTORY     June 2002                                                           \n",
       "HISTORY DQICORR complete ...                                                    \n",
       "HISTORY   values checked for saturation                                         \n",
       "HISTORY   DQ array initialized ...                                              \n",
       "HISTORY   reference table jref$n9p09145j_bpx.fits                               \n",
       "HISTORY BIASCORR complete ...                                                   \n",
       "HISTORY   reference image jref$mc91206ej_bia.fits                               \n",
       "HISTORY     INFLIGHT 09/11/2002 04/12/2002                                      \n",
       "HISTORY     Superbias created by Doug Van Orsow from proposal 9647              \n",
       "HISTORY BLEVCORR complete; bias level from overscan was subtracted.             \n",
       "HISTORY BLEVCORR does not include correction for drift along lines.             \n",
       "HISTORY   Overscan region table:                                                \n",
       "HISTORY   reference table jref$lch1459bj_osc.fits                               \n",
       "HISTORY Uncertainty array initialized.                                          \n",
       "HISTORY CCD parameters table:                                                   \n",
       "HISTORY   reference table jref$o151506dj_ccd.fits                               \n",
       "HISTORY     inflight                                                            \n",
       "HISTORY     June 2002                                                           \n",
       "PCTETRSH=  -1.000000000000E+01 / PCTE over subtraction threshold                \n",
       "HISTORY PCTECORR complete ...                                                   \n",
       "HISTORY   reference table jref$xa81724cj_cte.fits                               \n",
       "DISTNAME= 'j8f802lcq_0461802ej-02c1450rj-02c1450oj'                             \n",
       "SIPNAME = 'j8f802lcq_0461802ej'                                                 \n",
       "RULESVER=                  1.1 / Version ID for header kw rules file            \n",
       "BLENDVER= '1.2.0   '           / Version of blendheader software used           \n",
       "RULEFILE= '/home/pipemgr/anaconda2/lib/python2.7/site-packages/fitsblender/acs&'\n",
       "CONTINUE  '_header.rules&'                                                      \n",
       "CONTINUE  '' / Name of header kw rules file                                     \n",
       "NEXTEND =                    4                                                  \n",
       "FILENAME= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits'                            \n",
       "PROD_VER= 'DrizzlePac 2.1.6'                                                    \n",
       "ROOTNAME= 'hst_9453_02_acs_wfc_f814w_run_1'                                     \n",
       "ASN_MTYP= 'PROD-DTH'                                                            \n",
       "NDRIZIM =                    8 / Drizzle, No. images drizzled onto output       \n",
       "D001OUDA= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits' / Drizzle, output data imag\n",
       "D001VER = 'Callable C-based DRIZZLE Version 0.8 (20th M' / Drizzle, task version\n",
       "D001SCAL=                 0.05 / Drizzle, pixel size (arcsec) of output image   \n",
       "D001COEF= 'SIP     '           / Drizzle, source of coefficients                \n",
       "D001OUWE= 'hst_9453_02_acs_wfc_f814w_run_1_drz_wht.fits' / Drizzle, output weigh\n",
       "D001OUCO= 'hst_9453_02_acs_wfc_f814w_run_1_drz_ctx.fits' / Drizzle, output conte\n",
       "D001WTSC=                    1 / Drizzle, weighting factor for input image      \n",
       "D001MASK= 'j8f802lcq_sci1_final_mask.fits' / Drizzle, input weighting image     \n",
       "D001FVAL= 'INDEF   '           / Drizzle, fill value for zero weight output pix \n",
       "D001WKEY= '' / Input image WCS Version used                                     \n",
       "D001OUUN= 'cps     '           / Drizzle, units of output image - counts or cps \n",
       "D001KERN= 'square  '           / Drizzle, form of weight distribution kernel    \n",
       "D001GEOM= 'wcs     '           / Drizzle, source of geometric information       \n",
       "D001ISCL=                 0.05 / Drizzle, default IDCTAB pixel size(arcsec)     \n",
       "D001PIXF=                  1.0 / Drizzle, linear size of drop                   \n",
       "D001DATA= 'j8f802lcq_flc.fits[sci,1]' / Drizzle, input data image               \n",
       "D001DEXP=               1230.0 / Drizzle, input image exposure time (s)         \n",
       "D002OUDA= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits' / Drizzle, output data imag\n",
       "D002VER = 'Callable C-based DRIZZLE Version 0.8 (20th M' / Drizzle, task version\n",
       "D002SCAL=                 0.05 / Drizzle, pixel size (arcsec) of output image   \n",
       "D002COEF= 'SIP     '           / Drizzle, source of coefficients                \n",
       "D002OUWE= 'hst_9453_02_acs_wfc_f814w_run_1_drz_wht.fits' / Drizzle, output weigh\n",
       "D002OUCO= 'hst_9453_02_acs_wfc_f814w_run_1_drz_ctx.fits' / Drizzle, output conte\n",
       "D002WTSC=                    1 / Drizzle, weighting factor for input image      \n",
       "D002MASK= 'j8f802lcq_sci2_final_mask.fits' / Drizzle, input weighting image     \n",
       "D002FVAL= 'INDEF   '           / Drizzle, fill value for zero weight output pix \n",
       "D002WKEY= '' / Input image WCS Version used                                     \n",
       "D002OUUN= 'cps     '           / Drizzle, units of output image - counts or cps \n",
       "D002KERN= 'square  '           / Drizzle, form of weight distribution kernel    \n",
       "D002GEOM= 'wcs     '           / Drizzle, source of geometric information       \n",
       "D002ISCL=                 0.05 / Drizzle, default IDCTAB pixel size(arcsec)     \n",
       "D002PIXF=                  1.0 / Drizzle, linear size of drop                   \n",
       "D002DATA= 'j8f802lcq_flc.fits[sci,2]' / Drizzle, input data image               \n",
       "D002DEXP=               1230.0 / Drizzle, input image exposure time (s)         \n",
       "D003OUDA= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits' / Drizzle, output data imag\n",
       "D003VER = 'Callable C-based DRIZZLE Version 0.8 (20th M' / Drizzle, task version\n",
       "D003SCAL=                 0.05 / Drizzle, pixel size (arcsec) of output image   \n",
       "D003COEF= 'SIP     '           / Drizzle, source of coefficients                \n",
       "D003OUWE= 'hst_9453_02_acs_wfc_f814w_run_1_drz_wht.fits' / Drizzle, output weigh\n",
       "D003OUCO= 'hst_9453_02_acs_wfc_f814w_run_1_drz_ctx.fits' / Drizzle, output conte\n",
       "D003WTSC=                    1 / Drizzle, weighting factor for input image      \n",
       "D003MASK= 'j8f802loq_sci1_final_mask.fits' / Drizzle, input weighting image     \n",
       "D003FVAL= 'INDEF   '           / Drizzle, fill value for zero weight output pix \n",
       "D003WKEY= '' / Input image WCS Version used                                     \n",
       "D003OUUN= 'cps     '           / Drizzle, units of output image - counts or cps \n",
       "D003KERN= 'square  '           / Drizzle, form of weight distribution kernel    \n",
       "D003GEOM= 'wcs     '           / Drizzle, source of geometric information       \n",
       "D003ISCL=                 0.05 / Drizzle, default IDCTAB pixel size(arcsec)     \n",
       "D003PIXF=                  1.0 / Drizzle, linear size of drop                   \n",
       "D003DATA= 'j8f802loq_flc.fits[sci,1]' / Drizzle, input data image               \n",
       "D003DEXP=               1230.0 / Drizzle, input image exposure time (s)         \n",
       "D004OUDA= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits' / Drizzle, output data imag\n",
       "D004VER = 'Callable C-based DRIZZLE Version 0.8 (20th M' / Drizzle, task version\n",
       "D004SCAL=                 0.05 / Drizzle, pixel size (arcsec) of output image   \n",
       "D004COEF= 'SIP     '           / Drizzle, source of coefficients                \n",
       "D004OUWE= 'hst_9453_02_acs_wfc_f814w_run_1_drz_wht.fits' / Drizzle, output weigh\n",
       "D004OUCO= 'hst_9453_02_acs_wfc_f814w_run_1_drz_ctx.fits' / Drizzle, output conte\n",
       "D004WTSC=                    1 / Drizzle, weighting factor for input image      \n",
       "D004MASK= 'j8f802loq_sci2_final_mask.fits' / Drizzle, input weighting image     \n",
       "D004FVAL= 'INDEF   '           / Drizzle, fill value for zero weight output pix \n",
       "D004WKEY= '' / Input image WCS Version used                                     \n",
       "D004OUUN= 'cps     '           / Drizzle, units of output image - counts or cps \n",
       "D004KERN= 'square  '           / Drizzle, form of weight distribution kernel    \n",
       "D004GEOM= 'wcs     '           / Drizzle, source of geometric information       \n",
       "D004ISCL=                 0.05 / Drizzle, default IDCTAB pixel size(arcsec)     \n",
       "D004PIXF=                  1.0 / Drizzle, linear size of drop                   \n",
       "D004DATA= 'j8f802loq_flc.fits[sci,2]' / Drizzle, input data image               \n",
       "D004DEXP=               1230.0 / Drizzle, input image exposure time (s)         \n",
       "D005OUDA= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits' / Drizzle, output data imag\n",
       "D005VER = 'Callable C-based DRIZZLE Version 0.8 (20th M' / Drizzle, task version\n",
       "D005SCAL=                 0.05 / Drizzle, pixel size (arcsec) of output image   \n",
       "D005COEF= 'SIP     '           / Drizzle, source of coefficients                \n",
       "D005OUWE= 'hst_9453_02_acs_wfc_f814w_run_1_drz_wht.fits' / Drizzle, output weigh\n",
       "D005OUCO= 'hst_9453_02_acs_wfc_f814w_run_1_drz_ctx.fits' / Drizzle, output conte\n",
       "D005WTSC=                    1 / Drizzle, weighting factor for input image      \n",
       "D005MASK= 'j8f802m1q_sci1_final_mask.fits' / Drizzle, input weighting image     \n",
       "D005FVAL= 'INDEF   '           / Drizzle, fill value for zero weight output pix \n",
       "D005WKEY= '' / Input image WCS Version used                                     \n",
       "D005OUUN= 'cps     '           / Drizzle, units of output image - counts or cps \n",
       "D005KERN= 'square  '           / Drizzle, form of weight distribution kernel    \n",
       "D005GEOM= 'wcs     '           / Drizzle, source of geometric information       \n",
       "D005ISCL=                 0.05 / Drizzle, default IDCTAB pixel size(arcsec)     \n",
       "D005PIXF=                  1.0 / Drizzle, linear size of drop                   \n",
       "D005DATA= 'j8f802m1q_flc.fits[sci,1]' / Drizzle, input data image               \n",
       "D005DEXP=               1300.0 / Drizzle, input image exposure time (s)         \n",
       "D006OUDA= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits' / Drizzle, output data imag\n",
       "D006VER = 'Callable C-based DRIZZLE Version 0.8 (20th M' / Drizzle, task version\n",
       "D006SCAL=                 0.05 / Drizzle, pixel size (arcsec) of output image   \n",
       "D006COEF= 'SIP     '           / Drizzle, source of coefficients                \n",
       "D006OUWE= 'hst_9453_02_acs_wfc_f814w_run_1_drz_wht.fits' / Drizzle, output weigh\n",
       "D006OUCO= 'hst_9453_02_acs_wfc_f814w_run_1_drz_ctx.fits' / Drizzle, output conte\n",
       "D006WTSC=                    1 / Drizzle, weighting factor for input image      \n",
       "D006MASK= 'j8f802m1q_sci2_final_mask.fits' / Drizzle, input weighting image     \n",
       "D006FVAL= 'INDEF   '           / Drizzle, fill value for zero weight output pix \n",
       "D006WKEY= '' / Input image WCS Version used                                     \n",
       "D006OUUN= 'cps     '           / Drizzle, units of output image - counts or cps \n",
       "D006KERN= 'square  '           / Drizzle, form of weight distribution kernel    \n",
       "D006GEOM= 'wcs     '           / Drizzle, source of geometric information       \n",
       "D006ISCL=                 0.05 / Drizzle, default IDCTAB pixel size(arcsec)     \n",
       "D006PIXF=                  1.0 / Drizzle, linear size of drop                   \n",
       "D006DATA= 'j8f802m1q_flc.fits[sci,2]' / Drizzle, input data image               \n",
       "D006DEXP=               1300.0 / Drizzle, input image exposure time (s)         \n",
       "D007OUDA= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits' / Drizzle, output data imag\n",
       "D007VER = 'Callable C-based DRIZZLE Version 0.8 (20th M' / Drizzle, task version\n",
       "D007SCAL=                 0.05 / Drizzle, pixel size (arcsec) of output image   \n",
       "D007COEF= 'SIP     '           / Drizzle, source of coefficients                \n",
       "D007OUWE= 'hst_9453_02_acs_wfc_f814w_run_1_drz_wht.fits' / Drizzle, output weigh\n",
       "D007OUCO= 'hst_9453_02_acs_wfc_f814w_run_1_drz_ctx.fits' / Drizzle, output conte\n",
       "D007WTSC=                    1 / Drizzle, weighting factor for input image      \n",
       "D007MASK= 'j8f802mfq_sci1_final_mask.fits' / Drizzle, input weighting image     \n",
       "D007FVAL= 'INDEF   '           / Drizzle, fill value for zero weight output pix \n",
       "D007WKEY= '' / Input image WCS Version used                                     \n",
       "D007OUUN= 'cps     '           / Drizzle, units of output image - counts or cps \n",
       "D007KERN= 'square  '           / Drizzle, form of weight distribution kernel    \n",
       "D007GEOM= 'wcs     '           / Drizzle, source of geometric information       \n",
       "D007ISCL=                 0.05 / Drizzle, default IDCTAB pixel size(arcsec)     \n",
       "D007PIXF=                  1.0 / Drizzle, linear size of drop                   \n",
       "D007DATA= 'j8f802mfq_flc.fits[sci,1]' / Drizzle, input data image               \n",
       "D007DEXP=               1300.0 / Drizzle, input image exposure time (s)         \n",
       "D008OUDA= 'hst_9453_02_acs_wfc_f814w_run_1_drz.fits' / Drizzle, output data imag\n",
       "D008VER = 'Callable C-based DRIZZLE Version 0.8 (20th M' / Drizzle, task version\n",
       "D008SCAL=                 0.05 / Drizzle, pixel size (arcsec) of output image   \n",
       "D008COEF= 'SIP     '           / Drizzle, source of coefficients                \n",
       "D008OUWE= 'hst_9453_02_acs_wfc_f814w_run_1_drz_wht.fits' / Drizzle, output weigh\n",
       "D008OUCO= 'hst_9453_02_acs_wfc_f814w_run_1_drz_ctx.fits' / Drizzle, output conte\n",
       "D008WTSC=                    1 / Drizzle, weighting factor for input image      \n",
       "D008MASK= 'j8f802mfq_sci2_final_mask.fits' / Drizzle, input weighting image     \n",
       "D008FVAL= 'INDEF   '           / Drizzle, fill value for zero weight output pix \n",
       "D008WKEY= '' / Input image WCS Version used                                     \n",
       "D008OUUN= 'cps     '           / Drizzle, units of output image - counts or cps \n",
       "D008KERN= 'square  '           / Drizzle, form of weight distribution kernel    \n",
       "D008GEOM= 'wcs     '           / Drizzle, source of geometric information       \n",
       "D008ISCL=                 0.05 / Drizzle, default IDCTAB pixel size(arcsec)     \n",
       "D008PIXF=                  1.0 / Drizzle, linear size of drop                   \n",
       "D008DATA= 'j8f802mfq_flc.fits[sci,2]' / Drizzle, input data image               \n",
       "D008DEXP=               1300.0 / Drizzle, input image exposure time (s)         \n",
       "PA_V3   =           255.070404 / position angle of V3-axis of HST (deg)         \n",
       "HISTORY CCD parameters table:                                                   \n",
       "HISTORY   reference table jref$o151506dj_ccd.fits                               \n",
       "HISTORY     inflight                                                            \n",
       "HISTORY     June 2002                                                           \n",
       "HISTORY DARKCORR complete ...                                                   \n",
       "HISTORY   reference image jref$z552106tj_dkc.fits                               \n",
       "HISTORY     INFLIGHT 24/09/2002 07/10/2002                                      \n",
       "HISTORY     CTE corrected dark for data taken after Oct 07 2002---------------- \n",
       "HISTORY FLATCORR complete ...                                                   \n",
       "HISTORY   reference image jref$n6u12592j_pfl.fits                               \n",
       "HISTORY     INFLIGHT 18/04/2002 - 09/05/2002                                    \n",
       "HISTORY     Ground LP-flat corrected by L-flat from program 9018                \n",
       "HISTORY PHOTCORR complete ...                                                   \n",
       "HISTORY   reference table jref$08b18470j_imp.fits                               \n",
       "HISTORY EXPSCORR complete ...                                                   \n",
       "HISTORY ============================================================            \n",
       "HISTORY Header Generation rules:                                                \n",
       "HISTORY     Rules used to combine headers of input files                        \n",
       "HISTORY     Start of rules...                                                   \n",
       "HISTORY ------------------------------------------------------------            \n",
       "HISTORY !VERSION = 1.1                                                          \n",
       "HISTORY !INSTRUMENT = ACS                                                       \n",
       "HISTORY ROOTNAME                                                                \n",
       "HISTORY EXTNAME                                                                 \n",
       "HISTORY EXTVER                                                                  \n",
       "HISTORY A_0_2                                                                   \n",
       "HISTORY A_0_3                                                                   \n",
       "HISTORY A_0_4                                                                   \n",
       "HISTORY A_1_1                                                                   \n",
       "HISTORY A_1_2                                                                   \n",
       "HISTORY A_1_3                                                                   \n",
       "HISTORY A_2_0                                                                   \n",
       "HISTORY A_2_1                                                                   \n",
       "HISTORY A_2_2                                                                   \n",
       "HISTORY A_3_0                                                                   \n",
       "HISTORY A_3_1                                                                   \n",
       "HISTORY A_4_0                                                                   \n",
       "HISTORY ACQNAME                                                                 \n",
       "HISTORY A_ORDER                                                                 \n",
       "HISTORY APERTURE                                                                \n",
       "HISTORY ASN_ID                                                                  \n",
       "HISTORY ASN_MTYP                                                                \n",
       "HISTORY ASN_TAB                                                                 \n",
       "HISTORY ATODCORR                                                                \n",
       "HISTORY ATODGNA                                                                 \n",
       "HISTORY ATODGNB                                                                 \n",
       "HISTORY ATODGNC                                                                 \n",
       "HISTORY ATODGND                                                                 \n",
       "HISTORY ATODTAB                                                                 \n",
       "HISTORY B_0_2                                                                   \n",
       "HISTORY B_0_3                                                                   \n",
       "HISTORY B_0_4                                                                   \n",
       "HISTORY B_1_1                                                                   \n",
       "HISTORY B_1_2                                                                   \n",
       "HISTORY B_1_3                                                                   \n",
       "HISTORY B_2_0                                                                   \n",
       "HISTORY B_2_1                                                                   \n",
       "HISTORY B_2_2                                                                   \n",
       "HISTORY B_3_0                                                                   \n",
       "HISTORY B_3_1                                                                   \n",
       "HISTORY B_4_0                                                                   \n",
       "HISTORY BADINPDQ                                                                \n",
       "HISTORY BIASCORR                                                                \n",
       "HISTORY BIASFILE                                                                \n",
       "HISTORY BIASLEVA                                                                \n",
       "HISTORY BIASLEVB                                                                \n",
       "HISTORY BIASLEVC                                                                \n",
       "HISTORY BIASLEVD                                                                \n",
       "HISTORY BINAXIS1                                                                \n",
       "HISTORY BINAXIS2                                                                \n",
       "HISTORY BITPIX                                                                  \n",
       "HISTORY BLEVCORR                                                                \n",
       "HISTORY B_ORDER                                                                 \n",
       "HISTORY BPIXTAB                                                                 \n",
       "HISTORY BUNIT                                                                   \n",
       "HISTORY CAL_VER                                                                 \n",
       "HISTORY CBLKSIZ                                                                 \n",
       "HISTORY CCDAMP                                                                  \n",
       "HISTORY CCDCHIP                                                                 \n",
       "HISTORY CCDGAIN                                                                 \n",
       "HISTORY CCDOFSTA                                                                \n",
       "HISTORY CCDOFSTB                                                                \n",
       "HISTORY CCDOFSTC                                                                \n",
       "HISTORY CCDOFSTD                                                                \n",
       "HISTORY CCDTAB                                                                  \n",
       "HISTORY CD1_1                                                                   \n",
       "HISTORY CD1_2                                                                   \n",
       "HISTORY CD2_1                                                                   \n",
       "HISTORY CD2_2                                                                   \n",
       "HISTORY CENTERA1                                                                \n",
       "HISTORY CENTERA2                                                                \n",
       "HISTORY CFLTFILE                                                                \n",
       "HISTORY COMPTAB                                                                 \n",
       "HISTORY COMPTYP                                                                 \n",
       "HISTORY CRCORR                                                                  \n",
       "HISTORY CRMASK                                                                  \n",
       "HISTORY CRPIX1                                                                  \n",
       "HISTORY CRPIX2                                                                  \n",
       "HISTORY CRRADIUS                                                                \n",
       "HISTORY CRREJTAB                                                                \n",
       "HISTORY CRSIGMAS                                                                \n",
       "HISTORY CRSPLIT                                                                 \n",
       "HISTORY CRTHRESH                                                                \n",
       "HISTORY CRVAL1                                                                  \n",
       "HISTORY CRVAL2                                                                  \n",
       "HISTORY CTE_NAME                                                                \n",
       "HISTORY CTE_VER                                                                 \n",
       "HISTORY CTEDIR                                                                  \n",
       "HISTORY CTEIMAGE                                                                \n",
       "HISTORY CTYPE1                                                                  \n",
       "HISTORY CTYPE2                                                                  \n",
       "HISTORY D2IMFILE                                                                \n",
       "HISTORY DARKCORR                                                                \n",
       "HISTORY DARKFILE                                                                \n",
       "HISTORY DATE                                                                    \n",
       "HISTORY DATE-OBS                                                                \n",
       "HISTORY DEC_APER                                                                \n",
       "HISTORY DEC_TARG                                                                \n",
       "HISTORY DETECTOR                                                                \n",
       "HISTORY DFLTFILE                                                                \n",
       "HISTORY DGEOFILE                                                                \n",
       "HISTORY DIRIMAGE                                                                \n",
       "HISTORY DQICORR                                                                 \n",
       "HISTORY DRIZCORR                                                                \n",
       "HISTORY DRKCFILE                                                                \n",
       "HISTORY EQUINOX                                                                 \n",
       "HISTORY ERRCNT                                                                  \n",
       "HISTORY EXPEND                                                                  \n",
       "HISTORY EXPFLAG                                                                 \n",
       "HISTORY EXPNAME                                                                 \n",
       "HISTORY EXPSCORR                                                                \n",
       "HISTORY EXPSTART                                                                \n",
       "HISTORY EXPTIME                                                                 \n",
       "HISTORY EXTEND                                                                  \n",
       "HISTORY FGSLOCK                                                                 \n",
       "HISTORY FILENAME                                                                \n",
       "HISTORY FILETYPE                                                                \n",
       "HISTORY FILLCNT                                                                 \n",
       "HISTORY FILTER1                                                                 \n",
       "HISTORY FILTER2                                                                 \n",
       "HISTORY FLASHCUR                                                                \n",
       "HISTORY FLASHDUR                                                                \n",
       "HISTORY FLASHSTA                                                                \n",
       "HISTORY FLATCORR                                                                \n",
       "HISTORY FLSHCORR                                                                \n",
       "HISTORY FLSHFILE                                                                \n",
       "HISTORY FW1ERROR                                                                \n",
       "HISTORY FW1OFFST                                                                \n",
       "HISTORY FW2ERROR                                                                \n",
       "HISTORY FW2OFFST                                                                \n",
       "HISTORY FWSERROR                                                                \n",
       "HISTORY FWSOFFST                                                                \n",
       "HISTORY GCOUNT                                                                  \n",
       "HISTORY GLINCORR                                                                \n",
       "HISTORY GLOBLIM                                                                 \n",
       "HISTORY GLOBRATE                                                                \n",
       "HISTORY GOODMAX                                                                 \n",
       "HISTORY GOODMEAN                                                                \n",
       "HISTORY GOODMIN                                                                 \n",
       "HISTORY GRAPHTAB                                                                \n",
       "HISTORY GYROMODE                                                                \n",
       "HISTORY IDCSCALE                                                                \n",
       "HISTORY IDCTAB                                                                  \n",
       "HISTORY IDCTHETA                                                                \n",
       "HISTORY IDCV2REF                                                                \n",
       "HISTORY IDCV3REF                                                                \n",
       "HISTORY IMAGETYP                                                                \n",
       "HISTORY IMPHTTAB                                                                \n",
       "HISTORY INHERIT                                                                 \n",
       "HISTORY INITGUES                                                                \n",
       "HISTORY INSTRUME                                                                \n",
       "HISTORY JWROTYPE                                                                \n",
       "HISTORY LFLGCORR                                                                \n",
       "HISTORY LFLTFILE                                                                \n",
       "HISTORY LINENUM                                                                 \n",
       "HISTORY LOSTPIX                                                                 \n",
       "HISTORY LRC_FAIL                                                                \n",
       "HISTORY LRC_XSTS                                                                \n",
       "HISTORY LRFWAVE                                                                 \n",
       "HISTORY LTM1_1                                                                  \n",
       "HISTORY LTM2_2                                                                  \n",
       "HISTORY LTV1                                                                    \n",
       "HISTORY LTV2                                                                    \n",
       "HISTORY MDECODT1                                                                \n",
       "HISTORY MDECODT2                                                                \n",
       "HISTORY MDRIZSKY                                                                \n",
       "HISTORY MDRIZTAB                                                                \n",
       "HISTORY MEANBLEV                                                                \n",
       "HISTORY MEANDARK                                                                \n",
       "HISTORY MEANEXP                                                                 \n",
       "HISTORY MEANFLSH                                                                \n",
       "HISTORY MLINTAB                                                                 \n",
       "HISTORY MOFFSET1                                                                \n",
       "HISTORY MOFFSET2                                                                \n",
       "HISTORY MOONANGL                                                                \n",
       "HISTORY MTFLAG                                                                  \n",
       "HISTORY NAXIS                                                                   \n",
       "HISTORY NAXIS1                                                                  \n",
       "HISTORY NAXIS2                                                                  \n",
       "HISTORY NCOMBINE                                                                \n",
       "HISTORY NEXTEND                                                                 \n",
       "HISTORY NGOODPIX                                                                \n",
       "HISTORY NPOLFILE                                                                \n",
       "HISTORY NRPTEXP                                                                 \n",
       "HISTORY OBSMODE                                                                 \n",
       "HISTORY OBSTYPE                                                                 \n",
       "HISTORY OCD1_1                                                                  \n",
       "HISTORY OCD1_2                                                                  \n",
       "HISTORY OCD2_1                                                                  \n",
       "HISTORY OCD2_2                                                                  \n",
       "HISTORY OCRPIX1                                                                 \n",
       "HISTORY OCRPIX2                                                                 \n",
       "HISTORY OCRVAL1                                                                 \n",
       "HISTORY OCRVAL2                                                                 \n",
       "HISTORY OCTYPE1                                                                 \n",
       "HISTORY OCTYPE2                                                                 \n",
       "HISTORY OCX10                                                                   \n",
       "HISTORY OCX11                                                                   \n",
       "HISTORY OCY10                                                                   \n",
       "HISTORY OCY11                                                                   \n",
       "HISTORY ONAXIS1                                                                 \n",
       "HISTORY ONAXIS2                                                                 \n",
       "HISTORY OORIENTA                                                                \n",
       "HISTORY OPUS_VER                                                                \n",
       "HISTORY ORIENTAT                                                                \n",
       "HISTORY ORIGIN                                                                  \n",
       "HISTORY OSCNTAB                                                                 \n",
       "HISTORY P1_ANGLE                                                                \n",
       "HISTORY P1_CENTR                                                                \n",
       "HISTORY P1_FRAME                                                                \n",
       "HISTORY P1_LSPAC                                                                \n",
       "HISTORY P1_NPTS                                                                 \n",
       "HISTORY P1_ORINT                                                                \n",
       "HISTORY P1_PSPAC                                                                \n",
       "HISTORY P1_PURPS                                                                \n",
       "HISTORY P1_SHAPE                                                                \n",
       "HISTORY PA_APER                                                                 \n",
       "HISTORY PATTERN1                                                                \n",
       "HISTORY PATTSTEP                                                                \n",
       "HISTORY PA_V3                                                                   \n",
       "HISTORY PCOUNT                                                                  \n",
       "HISTORY PCTECORR                                                                \n",
       "HISTORY PCTEFRAC                                                                \n",
       "HISTORY PCTENSMD                                                                \n",
       "HISTORY PCTERNCL                                                                \n",
       "HISTORY PCTESHFT                                                                \n",
       "HISTORY PCTESMIT                                                                \n",
       "HISTORY PCTETAB                                                                 \n",
       "HISTORY PFLTFILE                                                                \n",
       "HISTORY PHOTBW                                                                  \n",
       "HISTORY PHOTCORR                                                                \n",
       "HISTORY PHOTFLAM                                                                \n",
       "HISTORY PHOTMODE                                                                \n",
       "HISTORY PHOTPLAM                                                                \n",
       "HISTORY PHOTTAB                                                                 \n",
       "HISTORY PHOTZPT                                                                 \n",
       "HISTORY PODPSFF                                                                 \n",
       "HISTORY POSTARG1                                                                \n",
       "HISTORY POSTARG2                                                                \n",
       "HISTORY PRIMESI                                                                 \n",
       "HISTORY PR_INV_F                                                                \n",
       "HISTORY PR_INV_L                                                                \n",
       "HISTORY PR_INV_M                                                                \n",
       "HISTORY PROCTIME                                                                \n",
       "HISTORY PROPAPER                                                                \n",
       "HISTORY PROPOSID                                                                \n",
       "HISTORY QUALCOM1                                                                \n",
       "HISTORY QUALCOM2                                                                \n",
       "HISTORY QUALCOM3                                                                \n",
       "HISTORY QUALITY                                                                 \n",
       "HISTORY RA_APER                                                                 \n",
       "HISTORY RA_TARG                                                                 \n",
       "HISTORY READNSEA                                                                \n",
       "HISTORY READNSEB                                                                \n",
       "HISTORY READNSEC                                                                \n",
       "HISTORY READNSED                                                                \n",
       "HISTORY REFFRAME                                                                \n",
       "HISTORY REJ_RATE                                                                \n",
       "HISTORY RPTCORR                                                                 \n",
       "HISTORY SCALENSE                                                                \n",
       "HISTORY SCLAMP                                                                  \n",
       "HISTORY SDQFLAGS                                                                \n",
       "HISTORY SHADCORR                                                                \n",
       "HISTORY SHADFILE                                                                \n",
       "HISTORY SHUTRPOS                                                                \n",
       "HISTORY SIMPLE                                                                  \n",
       "HISTORY SIZAXIS1                                                                \n",
       "HISTORY SIZAXIS2                                                                \n",
       "HISTORY SKYSUB                                                                  \n",
       "HISTORY SKYSUM                                                                  \n",
       "HISTORY SNRMAX                                                                  \n",
       "HISTORY SNRMEAN                                                                 \n",
       "HISTORY SNRMIN                                                                  \n",
       "HISTORY SOFTERRS                                                                \n",
       "HISTORY SPOTTAB                                                                 \n",
       "HISTORY STATFLAG                                                                \n",
       "HISTORY STDCFFF                                                                 \n",
       "HISTORY STDCFFP                                                                 \n",
       "HISTORY SUBARRAY                                                                \n",
       "HISTORY SUN_ALT                                                                 \n",
       "HISTORY SUNANGLE                                                                \n",
       "HISTORY TARGNAME                                                                \n",
       "HISTORY TDDALPHA                                                                \n",
       "HISTORY TDDBETA                                                                 \n",
       "HISTORY TELESCOP                                                                \n",
       "HISTORY TIME-OBS                                                                \n",
       "HISTORY T_SGSTAR                                                                \n",
       "HISTORY VAFACTOR                                                                \n",
       "HISTORY WCSAXES                                                                 \n",
       "HISTORY WCSCDATE                                                                \n",
       "HISTORY WFCMPRSD                                                                \n",
       "HISTORY WRTERR                                                                  \n",
       "HISTORY XTENSION                                                                \n",
       "HISTORY WCSNAMEO                                                                \n",
       "HISTORY WCSAXESO                                                                \n",
       "HISTORY LONPOLEO                                                                \n",
       "HISTORY LATPOLEO                                                                \n",
       "HISTORY RESTFRQO                                                                \n",
       "HISTORY RESTWAVO                                                                \n",
       "HISTORY CD1_1O                                                                  \n",
       "HISTORY CD1_2O                                                                  \n",
       "HISTORY CD2_1O                                                                  \n",
       "HISTORY CD2_2O                                                                  \n",
       "HISTORY CDELT1O                                                                 \n",
       "HISTORY CDELT2O                                                                 \n",
       "HISTORY CRPIX1O                                                                 \n",
       "HISTORY CRPIX2O                                                                 \n",
       "HISTORY CRVAL1O                                                                 \n",
       "HISTORY CRVAL2O                                                                 \n",
       "HISTORY CTYPE1O                                                                 \n",
       "HISTORY CTYPE2O                                                                 \n",
       "HISTORY CUNIT1O                                                                 \n",
       "HISTORY CUNIT2O                                                                 \n",
       "HISTORY APERTURE  APERTURE  multi                                               \n",
       "HISTORY DETECTOR  DETECTOR  first                                               \n",
       "HISTORY EXPEND    EXPEND    max                                                 \n",
       "HISTORY EXPSTART  EXPSTART  min                                                 \n",
       "HISTORY EXPTIME   TEXPTIME  sum                                                 \n",
       "HISTORY EXPTIME   EXPTIME   sum                                                 \n",
       "HISTORY FILTER1   FILTER1   multi                                               \n",
       "HISTORY FILTER2   FILTER2   multi                                               \n",
       "HISTORY GOODMAX   GOODMAX   max                                                 \n",
       "HISTORY GOODMEAN  GOODMEAN  mean                                                \n",
       "HISTORY GOODMIN   GOODMIN   min                                                 \n",
       "HISTORY INHERIT   INHERIT   first # maintain IRAF compatibility                 \n",
       "HISTORY INSTRUME  INSTRUME  first                                               \n",
       "HISTORY LRFWAVE   LRFWAVE   first                                               \n",
       "HISTORY NCOMBINE  NCOMBINE  sum                                                 \n",
       "HISTORY MDRIZSKY  MDRIZSKY  mean                                                \n",
       "HISTORY PHOTBW    PHOTBW    mean                                                \n",
       "HISTORY PHOTFLAM  PHOTFLAM  mean                                                \n",
       "HISTORY PHOTMODE  PHOTMODE  first                                               \n",
       "HISTORY PHOTPLAM  PHOTPLAM  mean                                                \n",
       "HISTORY PHOTZPT   PHOTZPT   mean                                                \n",
       "HISTORY PROPOSID  PROPOSID  first                                               \n",
       "HISTORY SNRMAX    SNRMAX    max                                                 \n",
       "HISTORY SNRMEAN   SNRMEAN   mean                                                \n",
       "HISTORY SNRMIN    SNRMIN    min                                                 \n",
       "HISTORY TARGNAME  TARGNAME  first                                               \n",
       "HISTORY TELESCOP  TELESCOP  first                                               \n",
       "HISTORY ATODCORR  ATODCORR  multi                                               \n",
       "HISTORY ATODGNA   ATODGNA   first                                               \n",
       "HISTORY ATODGNB   ATODGNB   first                                               \n",
       "HISTORY ATODGNC   ATODGNC   first                                               \n",
       "HISTORY ATODGND   ATODGND   first                                               \n",
       "HISTORY ATODTAB   ATODTAB   multi                                               \n",
       "HISTORY BADINPDQ  BADINPDQ  sum                                                 \n",
       "HISTORY BIASCORR  BIASCORR  multi                                               \n",
       "HISTORY BIASFILE  BIASFILE  multi                                               \n",
       "HISTORY BLEVCORR  BLEVCORR  multi                                               \n",
       "HISTORY BPIXTAB   BPIXTAB   multi                                               \n",
       "HISTORY CCDCHIP   CCDCHIP   first                                               \n",
       "HISTORY CCDGAIN   CCDGAIN   first                                               \n",
       "HISTORY CCDOFSTA  CCDOFSTA  first                                               \n",
       "HISTORY CCDOFSTB  CCDOFSTB  first                                               \n",
       "HISTORY CCDOFSTC  CCDOFSTC  first                                               \n",
       "HISTORY CCDOFSTD  CCDOFSTD  first                                               \n",
       "HISTORY CCDTAB      CCDTAB    multi                                             \n",
       "HISTORY CFLTFILE  CFLTFILE  multi                                               \n",
       "HISTORY COMPTAB   COMPTAB   multi                                               \n",
       "HISTORY CRCORR      CRCORR    multi                                             \n",
       "HISTORY CRMASK      CRMASK    first                                             \n",
       "HISTORY CRRADIUS  CRRADIUS  first                                               \n",
       "HISTORY CRREJTAB  CRREJTAB  multi                                               \n",
       "HISTORY CRSPLIT   CRSPLIT   first                                               \n",
       "HISTORY CRTHRESH  CRTHRESH  first                                               \n",
       "HISTORY CTEDIR      CTEDIR    multi                                             \n",
       "HISTORY CTEIMAGE  CTEIMAGE  first                                               \n",
       "HISTORY DARKCORR  DARKCORR  multi                                               \n",
       "HISTORY DARKFILE  DARKFILE  multi                                               \n",
       "HISTORY DATE-OBS  DATE-OBS  first                                               \n",
       "HISTORY DEC_APER  DEC_APER  first                                               \n",
       "HISTORY DFLTFILE  DFLTFILE  multi                                               \n",
       "HISTORY DGEOFILE  DGEOFILE  multi                                               \n",
       "HISTORY DIRIMAGE  DIRIMAGE  multi                                               \n",
       "HISTORY DQICORR   DQICORR   multi                                               \n",
       "HISTORY DRIZCORR  DRIZCORR  multi                                               \n",
       "HISTORY EXPFLAG   EXPFLAG   multi                                               \n",
       "HISTORY EXPSCORR  EXPSCORR  multi                                               \n",
       "HISTORY FGSLOCK   FGSLOCK   multi                                               \n",
       "HISTORY FLASHCUR  FLASHCUR  multi                                               \n",
       "HISTORY FLASHDUR  FLASHDUR  first                                               \n",
       "HISTORY FLASHSTA  FLASHSTA  first                                               \n",
       "HISTORY FLATCORR  FLATCORR  multi                                               \n",
       "HISTORY FLSHCORR  FLSHCORR  multi                                               \n",
       "HISTORY FLSHFILE  FLSHFILE  multi                                               \n",
       "HISTORY FW1ERROR  FW1ERROR  multi                                               \n",
       "HISTORY FW1OFFST  FW1OFFST  first                                               \n",
       "HISTORY FW2ERROR  FW2ERROR  multi                                               \n",
       "HISTORY FW2OFFST  FW2OFFST  first                                               \n",
       "HISTORY FWSERROR  FWSERROR  multi                                               \n",
       "HISTORY FWSOFFST  FWSOFFST  first                                               \n",
       "HISTORY GRAPHTAB  GRAPHTAB  multi                                               \n",
       "HISTORY GYROMODE  GYROMODE  multi                                               \n",
       "HISTORY IDCTAB      IDCTAB    multi                                             \n",
       "HISTORY IMAGETYP  IMAGETYP  first                                               \n",
       "HISTORY IMPHTTAB  IMPHTTAB  multi                                               \n",
       "HISTORY LFLGCORR  LFLGCORR  multi                                               \n",
       "HISTORY LFLTFILE  LFLTFILE  multi                                               \n",
       "HISTORY LTM1_1    LTM1_1    float_one                                           \n",
       "HISTORY LTM2_2    LTM2_2    float_one                                           \n",
       "HISTORY MDRIZTAB  MDRIZTAB  multi                                               \n",
       "HISTORY MEANEXP   MEANEXP   first                                               \n",
       "HISTORY MOONANGL  MOONANGL  first                                               \n",
       "HISTORY NRPTEXP   NRPTEXP   first                                               \n",
       "HISTORY OBSMODE   OBSMODE   multi                                               \n",
       "HISTORY OBSTYPE   OBSTYPE   first                                               \n",
       "HISTORY OSCNTAB   OSCNTAB   multi                                               \n",
       "HISTORY P1_ANGLE  P1_ANGLE  first                                               \n",
       "HISTORY P1_CENTR  P1_CENTR  multi                                               \n",
       "HISTORY P1_FRAME  P1_FRAME  multi                                               \n",
       "HISTORY P1_LSPAC  P1_LSPAC  first                                               \n",
       "HISTORY P1_NPTS   P1_NPTS   first                                               \n",
       "HISTORY P1_ORINT  P1_ORINT  first                                               \n",
       "HISTORY P1_PSPAC  P1_PSPAC  first                                               \n",
       "HISTORY P1_PURPS  P1_PURPS  multi                                               \n",
       "HISTORY P1_SHAPE  P1_SHAPE  multi                                               \n",
       "HISTORY P2_ANGLE  P2_ANGLE  first                                               \n",
       "HISTORY P2_CENTR  P2_CENTR  multi                                               \n",
       "HISTORY P2_FRAME  P2_FRAME  multi                                               \n",
       "HISTORY P2_LSPAC  P2_LSPAC  first                                               \n",
       "HISTORY P2_NPTS   P2_NPTS   first                                               \n",
       "HISTORY P2_ORINT  P2_ORINT  first                                               \n",
       "HISTORY P2_PSPAC  P2_PSPAC  first                                               \n",
       "HISTORY P2_PURPS  P2_PURPS  multi                                               \n",
       "HISTORY P2_SHAPE  P2_SHAPE  multi                                               \n",
       "HISTORY PATTERN1  PATTERN1  multi                                               \n",
       "HISTORY PATTERN2  PATTERN2  multi                                               \n",
       "HISTORY PATTSTEP  PATTSTEP  first                                               \n",
       "HISTORY PHOTCORR  PHOTCORR  multi                                               \n",
       "HISTORY PHOTTAB   PHOTTAB   multi                                               \n",
       "HISTORY POSTARG1  POSTARG1  first                                               \n",
       "HISTORY POSTARG2  POSTARG2  first                                               \n",
       "HISTORY PRIMESI   PRIMESI   multi                                               \n",
       "HISTORY PROPAPER  PROPAPER  multi                                               \n",
       "HISTORY RA_APER   RA_APER   first                                               \n",
       "HISTORY READNSEA  READNSEA  first                                               \n",
       "HISTORY READNSEB  READNSEB  first                                               \n",
       "HISTORY READNSEC  READNSEC  first                                               \n",
       "HISTORY READNSED  READNSED  first                                               \n",
       "HISTORY REJ_RATE  REJ_RATE  first                                               \n",
       "HISTORY SCALENSE  SCALENSE  first                                               \n",
       "HISTORY SCLAMP      SCLAMP    multi                                             \n",
       "HISTORY SHADCORR  SHADCORR  multi                                               \n",
       "HISTORY SHADFILE  SHADFILE  multi                                               \n",
       "HISTORY SHUTRPOS  SHUTRPOS  multi                                               \n",
       "HISTORY SKYSUB      SKYSUB    multi                                             \n",
       "HISTORY SKYSUM      SKYSUM    sum                                               \n",
       "HISTORY SPOTTAB   SPOTTAB   multi                                               \n",
       "HISTORY SUBARRAY  SUBARRAY  first                                               \n",
       "HISTORY SUNANGLE  SUNANGLE  first                                               \n",
       "HISTORY SUN_ALT   SUN_ALT   first                                               \n",
       "HISTORY WRTERR      WRTERR    multi                                             \n",
       "HISTORY ------------------------------------------------------------            \n",
       "HISTORY     End of rules...                                                     \n",
       "HISTORY ============================================================            \n",
       "HISTORY AstroDrizzle processing performed using:                                \n",
       "HISTORY     AstroDrizzle Version 2.1.6                                          \n",
       "HISTORY     Numpy Version 1.11.3                                                \n",
       "HISTORY     PyFITS Version 1.3                                                  \n",
       "HISTORY All refereed publications based on data obtained from the HLA must      \n",
       "HISTORY carry the following footnote:                                           \n",
       "HISTORY                                                                         \n",
       "HISTORY  \"Based on observations made with the NASA/ESA Hubble Space Telescope,  \n",
       "HISTORY   and obtained from the Hubble Legacy Archive, which is a collaboration \n",
       "HISTORY   between the Space Telescope Science Institute (STScI/NASA), the Space \n",
       "HISTORY   Telescope European Coordinating Facility (ST-ECF/ESA) and the         \n",
       "HISTORY   Canadian Astronomy Data Centre (CADC/NRC/CSA).\"                       \n",
       "HISTORY                                                                         \n",
       "HISTORY One copy of each paper resulting from data obtained from the HLA        \n",
       "HISTORY should be sent to the STScI.                                            \n",
       "HISTORY                                                                         \n",
       "HISTORY In addition, publications of research supported by an STScI grant       \n",
       "HISTORY must carry the following acknowledgment:                                \n",
       "HISTORY                                                                         \n",
       "HISTORY  \"Support for Program number                                            \n",
       "HISTORY   ____________ was provided by NASA through a grant from the Space      \n",
       "HISTORY   Telescope Science Institute, which is operated by the Association of  \n",
       "HISTORY   Universities for Reasearch in Astronomy, Incorporated, under NASA     \n",
       "HISTORY   contract NAS5-26555.\"                                                 \n",
       "                                                                                \n",
       "                                                                                \n",
       "                                                                                \n",
       "                                                                                \n",
       "                                                                                \n",
       "                                                                                \n",
       "                                                                                \n",
       "                                                                                \n",
       "                                                                                \n",
       "                                                                                "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the headers\n",
    "hdu_list = fits.open(image_file)\n",
    "hdu_list.info()\n",
    "hdu_list[0].header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(5166, 5185)\n"
     ]
    }
   ],
   "source": [
    "# Inspect the array\n",
    "image_data = fits.getdata(image_file)\n",
    "print(type(image_data))\n",
    "print(image_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Plot the sources on top of the HLA image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.wcs import WCS\n",
    "\n",
    "wcs = WCS(hdu_list[1].header)\n",
    "pix = wcs.wcs.cdelt[1]*3600.0 # pixel size in arcsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WCS Keywords\n",
      "\n",
      "Number of WCS axes: 2\n",
      "CTYPE : 'RA---TAN'  'DEC--TAN'  \n",
      "CRVAL : 11.52852151701503  40.70915097493346  \n",
      "CRPIX : 2592.999999999834  2583.499999999835  \n",
      "CD1_1 CD1_2  : -1.3888888888888e-05  0.0  \n",
      "CD2_1 CD2_2  : 0.0  1.38888888888896e-05  \n",
      "NAXIS : 5185  5166\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x8202dd7f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.visualization import (MinMaxInterval, SqrtStretch, ImageNormalize, ManualInterval)\n",
    "\n",
    "# Create an ImageNormalize object\n",
    "norm = ImageNormalize(image_data, interval = ManualInterval(-0.1,0.5))\n",
    "print(wcs)\n",
    "\n",
    "# Display the image\n",
    "fig = plt.figure(figsize=(10,10),dpi=100)\n",
    "ax = fig.add_subplot(111,projection=wcs)\n",
    "im = ax.imshow(image_data, cmap='gray', origin='lower', norm=norm)\n",
    "p1 = ax.scatter(hcv['ra'],hcv['dec'],transform=ax.get_transform('world'), \\\n",
    "                s=30, edgecolor='salmon', facecolor='none', label='HCV')\n",
    "p2 = ax.scatter(hcv_f814w['ra'],hcv_f814w['dec'],transform=ax.get_transform('world'), \\\n",
    "                s=30, edgecolor='green', facecolor='none', label='RR Lyrae')\n",
    "fig.colorbar(im)\n",
    "ax.set_xlabel(\"RA\")\n",
    "ax.set_ylabel(\"Dec\")\n",
    "ax.legend([\"HCV Sources\", \"RR Lyrae star\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Plot cutouts around the RR Lyrae star for the brightest and faintest magnitudes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In this example we are calling the [HLA cutout service from the STScI](\"https://hla.stsci.edu/fitscutcgi_interface.html\") to plot the cutouts (within a notebook markdown cell). The RR Lyrae star is in the centre of the image.**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The brightest corrected magnitude for F814W is : lightcurve_cm = 24.820026461811118; and corresponds to image lightcurve_i = hst_9453_61_acs_wfc_f814w :\n",
    "\n",
    "<img src=\"https://hla.stsci.edu/cgi-bin/fitscut.cgi?red=hst_9453_61_acs_wfc_f814w&RA=11.50269&Dec=40.68417&size=29&output_size=256\n",
    "\"> \n",
    "\n",
    "And the faintest corrected magnitude for F814W is : lightcurve_cm = 25.49112826041771; and corresponds to image lightcurve_i = hst_9453_59_acs_wfc_f814w\n",
    "<img src=\"https://hla.stsci.edu/cgi-bin/fitscut.cgi?red=hst_9453_59_acs_wfc_f814w&RA=11.50269&Dec=40.68417&size=29&output_size=256\n",
    "\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The brightest corrected magnitude for F606W is : lightcurve_cm = 24.827273229936768; and corresponds to image  lightcurve_i = hst_9453_27_acs_wfc_f606w :\n",
    "\n",
    "<img src=\"https://hla.stsci.edu/cgi-bin/fitscut.cgi?red=hst_9453_27_acs_wfc_f606w&RA=11.50269&Dec=40.68417&size=29&output_size=256\n",
    "\">\n",
    "\n",
    "The faintest corrected magnitude for F606W is : lightcurve_cm = 25.61165283094627; and corresponds to image lightcurve_i = hst_9453_45_acs_wfc_f606w :\n",
    "\n",
    "<img src=\"https://hla.stsci.edu/cgi-bin/fitscut.cgi?red=hst_9453_45_acs_wfc_f606w&RA=11.50269&Dec=40.68417&size=29&output_size=256\n",
    "\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}