{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Herschel Stripe 82 IRAC merging\n",
    "\n",
    "Both SHELA and SpIES provide IRAC fluxes which have marginally overlapping coverage. We chose which to use here since in order to run in low memory mode we must have one catalogue per band before merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "33f5ec7 (Wed Dec 6 16:56:17 2017 +0000)\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "\n",
    "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot\n",
    "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n",
    "\n",
    "try:\n",
    "    os.makedirs(OUT_DIR)\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Reading the prepared pristine catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shela = Table.read(\"{}/SHELA.fits\".format(TMP_DIR))\n",
    "spies= Table.read(\"{}/SpIES.fits\".format(TMP_DIR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones: HSC, VHS, VICS82, UKIDSS-LAS, PanSTARRS, SHELA, SpIES.\n",
    "\n",
    "At every step, we look at the distribution of the distances separating the sources from one catalogue to the other (within a maximum radius) to determine the best cross-matching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SHELA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = shela\n",
    "master_catalogue['shela_ra'].name = 'ra'\n",
    "master_catalogue['shela_dec'].name = 'dec'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add SpIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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psJeFlpOekWlJhdXDKmtZfaVa6yrowA4AwBnMecmZcy5hZu+X9JCkoKQvOOf2mdk9mdfv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\n9IoYY4cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f157c4c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(spies['spies_ra'], spies['spies_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, spies, \"spies_ra\", \"spies_dec\", radius=1.5*u.arcsec)\n",
    "del spies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning\n",
    "\n",
    "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n",
    "        master_catalogue[col].fill_value = np.nan\n",
    "    elif \"flag\" in col:\n",
    "        master_catalogue[col].fill_value = 0\n",
    "    elif \"id\" in col:\n",
    "        master_catalogue[col].fill_value = -1\n",
    "        \n",
    "master_catalogue = master_catalogue.filled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=10&gt;\n",
       "<table id=\"table140253140630664-338052\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>shela_intid</th><th>ra</th><th>dec</th><th>f_shela_irac1</th><th>ferr_shela_irac1</th><th>f_ap_shela_irac1</th><th>ferr_ap_shela_irac1</th><th>f_shela_irac2</th><th>ferr_shela_irac2</th><th>f_ap_shela_irac2</th><th>ferr_ap_shela_irac2</th><th>m_shela_irac1</th><th>merr_shela_irac1</th><th>flag_shela_irac1</th><th>m_ap_shela_irac1</th><th>merr_ap_shela_irac1</th><th>m_shela_irac2</th><th>merr_shela_irac2</th><th>flag_shela_irac2</th><th>m_ap_shela_irac2</th><th>merr_ap_shela_irac2</th><th>shela_flag_cleaned</th><th>shela_flag_gaia</th><th>flag_merged</th><th>spies_intid</th><th>f_ap_spies_irac1</th><th>ferr_ap_spies_irac1</th><th>f_spies_irac1</th><th>ferr_spies_irac1</th><th>spies_stellarity_irac1</th><th>f_ap_spies_irac2</th><th>ferr_ap_spies_irac2</th><th>f_spies_irac2</th><th>ferr_spies_irac2</th><th>spies_stellarity_irac2</th><th>m_ap_spies_irac1</th><th>merr_ap_spies_irac1</th><th>m_spies_irac1</th><th>merr_spies_irac1</th><th>flag_spies_irac1</th><th>m_ap_spies_irac2</th><th>merr_ap_spies_irac2</th><th>m_spies_irac2</th><th>merr_spies_irac2</th><th>flag_spies_irac2</th><th>spies_flag_cleaned</th><th>spies_flag_gaia</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>1379391</td><td>22.9160109027</td><td>0.238312267292</td><td>2.62986</td><td>2.07571</td><td>-0.0224474</td><td>0.459005</td><td>36.9146</td><td>3.31507</td><td>42.6651</td><td>1.13833</td><td>22.8502</td><td>0.856956</td><td>False</td><td>nan</td><td>-22.2011</td><td>19.982</td><td>0.097503</td><td>False</td><td>19.8248</td><td>0.028968</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>1433853</td><td>23.6639931364</td><td>0.297376305167</td><td>-1.66275</td><td>nan</td><td>-0.999434</td><td>0.478796</td><td>41.9828</td><td>0.879404</td><td>57.0925</td><td>1.2495</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-0.520141</td><td>19.8423</td><td>0.0227427</td><td>False</td><td>19.5086</td><td>0.023762</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>1501461</td><td>17.1780743659</td><td>0.371209289855</td><td>0.809025</td><td>1.80919</td><td>-0.367886</td><td>0.480663</td><td>11.0821</td><td>2.47177</td><td>8.7921</td><td>0.76204</td><td>24.1301</td><td>2.42799</td><td>False</td><td>nan</td><td>-1.41857</td><td>21.2885</td><td>0.242165</td><td>False</td><td>21.5398</td><td>0.0941042</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>2041910</td><td>18.0883776053</td><td>0.955992888107</td><td>1.32359</td><td>0.404158</td><td>1.53133</td><td>0.48068</td><td>2.93804</td><td>0.634261</td><td>4.19599</td><td>0.76519</td><td>23.5956</td><td>0.331529</td><td>False</td><td>23.4373</td><td>0.340809</td><td>22.7299</td><td>0.234388</td><td>False</td><td>22.3429</td><td>0.197997</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>2051282</td><td>14.3497873938</td><td>0.961695722436</td><td>0.465728</td><td>0.691827</td><td>1.6928</td><td>0.480812</td><td>2.2341</td><td>0.981212</td><td>1.80439</td><td>0.652208</td><td>24.7297</td><td>1.61283</td><td>False</td><td>23.3285</td><td>0.308385</td><td>23.0272</td><td>0.476854</td><td>False</td><td>23.2592</td><td>0.392445</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>1497273</td><td>18.7890799452</td><td>0.368352033577</td><td>1.83974</td><td>0.899652</td><td>2.05973</td><td>0.482332</td><td>2.03328</td><td>1.36676</td><td>2.42053</td><td>0.710066</td><td>23.2381</td><td>0.530935</td><td>False</td><td>23.1155</td><td>0.25425</td><td>23.1295</td><td>0.729826</td><td>False</td><td>22.9402</td><td>0.318502</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>545026</td><td>22.4245506266</td><td>-0.642594144188</td><td>1.70425</td><td>0.915279</td><td>1.75536</td><td>0.485114</td><td>2.86204</td><td>1.23268</td><td>2.4144</td><td>0.64323</td><td>23.3212</td><td>0.583102</td><td>False</td><td>23.2891</td><td>0.300056</td><td>22.7583</td><td>0.467627</td><td>False</td><td>22.943</td><td>0.289256</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>477159</td><td>22.4517375046</td><td>-0.714030299391</td><td>1.61952</td><td>0.745305</td><td>1.45646</td><td>0.485506</td><td>4.26386</td><td>0.985851</td><td>5.67917</td><td>0.677957</td><td>23.3765</td><td>0.499657</td><td>False</td><td>23.4918</td><td>0.361926</td><td>22.3255</td><td>0.251034</td><td>False</td><td>22.0143</td><td>0.129611</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>543888</td><td>24.8499402825</td><td>-0.642484862009</td><td>-0.368214</td><td>nan</td><td>-0.790527</td><td>0.486557</td><td>6.09079</td><td>0.334958</td><td>8.4227</td><td>0.788449</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-0.668253</td><td>21.9383</td><td>0.0597091</td><td>False</td><td>21.5864</td><td>0.101636</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>869586</td><td>24.6311943916</td><td>-0.300427956383</td><td>0.784429</td><td>0.672938</td><td>1.70511</td><td>0.487496</td><td>4.06112</td><td>1.11325</td><td>4.08163</td><td>0.789322</td><td>24.1636</td><td>0.93142</td><td>False</td><td>23.3206</td><td>0.310415</td><td>22.3784</td><td>0.297625</td><td>False</td><td>22.3729</td><td>0.209964</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140253140630664-338052').dataTable()\");\n",
       "    $('#table140253140630664-338052').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Merging flags and stellarity\n",
    "\n",
    "Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process.  We merge these flags in a single one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_cleaned_columns = [column for column in master_catalogue.colnames\n",
    "                        if 'flag_cleaned' in column]\n",
    "\n",
    "flag_column = np.zeros(len(master_catalogue), dtype=bool)\n",
    "for column in flag_cleaned_columns:\n",
    "    flag_column |= master_catalogue[column]\n",
    "    \n",
    "master_catalogue.add_column(Column(data=flag_column, name=\"irac_flag_cleaned\"))\n",
    "master_catalogue.remove_columns(flag_cleaned_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely).  We merge these flags taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
    "                     if 'flag_gaia' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "    name=\"irac_flag_gaia\"\n",
    "))\n",
    "master_catalogue.remove_columns(flag_gaia_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:343: RuntimeWarning: All-NaN slice encountered\n",
      "  warnings.warn(\"All-NaN slice encountered\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "stellarity_columns = [column for column in master_catalogue.colnames\n",
    "                      if 'stellarity' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.nanmax([master_catalogue[column] for column in stellarity_columns], axis=0),\n",
    "    name=\"irac_stellarity\"\n",
    "))\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(data=(np.char.array(master_catalogue['shela_intid'].astype(str)) \n",
    "                                    +  np.char.array(master_catalogue['spies_intid'].astype(str) )), \n",
    "                              name=\"irac_intid\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['shela_intid', 'spies_intid', 'irac_intid']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#master_catalogue[id_names].write(\n",
    "#    \"{}/master_list_irac_cross_ident_herschel-stripe-82{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "\n",
    "#Lets leave the shela and spies ids in so that the final cross ident table is complete\n",
    "#master_catalogue.remove_columns(['shela_intid', 'spies_intid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI - Choosing between multiple values for the same filter\n",
    "\n",
    "Both SHELA and SpIES provide IRAC1 and IRAC2 fluxes. SpIES seems to go deeper and neither apear to suffer from the bright drop off that affects both SERVS and SWIRE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'e/count' did not parse as fits unit: At col 0, Unit 'e' not supported by the FITS standard.  [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'image' did not parse as fits unit: At col 0, Unit 'image' not supported by the FITS standard.  [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "seip = Table.read(\"../../dmu0/dmu0_SEIP/data/SEIP_Herschel-Stripe-82.fits\")\n",
    "seip_coords = SkyCoord(seip['ra'], seip['dec'])\n",
    "idx, d2d, _ = seip_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "mask = d2d <= 2 * u.arcsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9cN26MseUDByp0+Bh08wDRCQctbrOmYiIhFhdrTJKCkh/c2P96YfS4qju9h1w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\nDyFD1I+ZHM5LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f157e06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# servs -> shela and swire -> spies\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_shela_irac1'], label=\"SHELA\", s=2.)\n",
    "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_spies_irac1'], label=\"SpIES\", s=2.)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"SEIP flux [μJy]\")\n",
    "ax.set_ylabel(\"SHELA/SpIES flux [μJy]\")\n",
    "ax.set_title(\"IRAC 1\")\n",
    "ax.legend()\n",
    "ax.axvline(2000, color=\"black\", linestyle=\"--\", linewidth=1.)\n",
    "ax.plot(seip['i1_f_ap1'][mask], seip['i1_f_ap1'][mask], linewidth=.1, color=\"black\", alpha=.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BfCtEP3zhfWKprmnh8aW3F6XuSpIueRjSp4Y7SpGoZxqbI2CM+UngU+BsoDtO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f2e6b8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_shela_irac2'], label=\"SHELA\", s=2.)\n",
    "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_spies_irac2'], label=\"SpIES\", s=2.)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"SEIP flux [μJy]\")\n",
    "ax.set_ylabel(\"SHELA/SpIES flux [μJy]\")\n",
    "ax.set_title(\"IRAC 2\")\n",
    "ax.legend()\n",
    "ax.axvline(2000, color=\"black\", linestyle=\"--\", linewidth=1.)\n",
    "\n",
    "ax.plot(seip['i1_f_ap2'][mask], seip['i1_f_ap2'][mask], linewidth=.1, color=\"black\", alpha=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When both SHELA and SpIES fluxes are provided, we use the SpIES flux.\n",
    "\n",
    "We create a table indicating for each source the origin on the IRAC1 and IRAC2 fluxes that will be saved separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin = Table()\n",
    "irac_origin.add_column(master_catalogue['irac_intid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2240390 sources with SHELA flux\n",
      "3365594 sources with SpIES flux\n",
      "64802 sources with SHELA and SpIES flux\n",
      "2240390 sources for which we use SHELA\n",
      "3300792 sources for which we use SpIES\n"
     ]
    }
   ],
   "source": [
    "# IRAC1 aperture flux and magnitudes\n",
    "has_shela = ~np.isnan(master_catalogue['f_ap_shela_irac1'])\n",
    "has_spies = ~np.isnan(master_catalogue['f_ap_spies_irac1'])\n",
    "has_both = has_shela & has_spies\n",
    "\n",
    "print(\"{} sources with SHELA flux\".format(np.sum(has_shela)))\n",
    "print(\"{} sources with SpIES flux\".format(np.sum(has_spies)))\n",
    "print(\"{} sources with SHELA and SpIES flux\".format(np.sum(has_both)))\n",
    "\n",
    "use_shela = has_shela\n",
    "use_spies = (has_spies & ~has_shela)\n",
    "\n",
    "print(\"{} sources for which we use SHELA\".format(np.sum(use_shela)))\n",
    "print(\"{} sources for which we use SpIES\".format(np.sum(use_spies)))\n",
    "\n",
    "f_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_shela] = master_catalogue['f_ap_shela_irac1'][use_shela]\n",
    "f_ap_irac[use_spies] = master_catalogue['f_ap_spies_irac1'][use_spies]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_shela] = master_catalogue['ferr_ap_shela_irac1'][use_shela]\n",
    "ferr_ap_irac[use_spies] = master_catalogue['ferr_ap_spies_irac1'][use_spies]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_shela] = master_catalogue['m_ap_shela_irac1'][use_shela]\n",
    "m_ap_irac[use_spies] = master_catalogue['m_ap_spies_irac1'][use_spies]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_shela] = master_catalogue['merr_ap_shela_irac1'][use_shela]\n",
    "merr_ap_irac[use_spies] = master_catalogue['merr_ap_spies_irac1'][use_spies]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_ap_irac_i1\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_ap_shela_irac1', 'f_ap_spies_irac1', \n",
    "                                 'ferr_ap_shela_irac1', 'ferr_ap_spies_irac1', \n",
    "                                 'm_ap_shela_irac1', 'm_ap_spies_irac1',\n",
    "                                 'merr_ap_shela_irac1', 'merr_ap_spies_irac1'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_shela] = \"SHELA\"\n",
    "origin[use_spies] = \"SpIES\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC1_ap\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2240390 sources with SHELA total flux\n",
      "3365594 sources with SpIES total flux\n",
      "64802 sources with SHELA and SpIES total flux\n",
      "2240390 sources for which we use SHELA\n",
      "3300792 sources for which we use SpIES\n"
     ]
    }
   ],
   "source": [
    "# IRAC1 total flux and magnitudes\n",
    "has_shela = ~np.isnan(master_catalogue['f_shela_irac1'])\n",
    "has_spies = ~np.isnan(master_catalogue['f_spies_irac1'])\n",
    "has_both = has_shela & has_spies\n",
    "\n",
    "print(\"{} sources with SHELA total flux\".format(np.sum(has_shela)))\n",
    "print(\"{} sources with SpIES total flux\".format(np.sum(has_spies)))\n",
    "print(\"{} sources with SHELA and SpIES total flux\".format(np.sum(has_both)))\n",
    "\n",
    "use_shela = has_shela\n",
    "use_spies = (has_spies & ~has_shela)\n",
    "\n",
    "print(\"{} sources for which we use SHELA\".format(np.sum(use_shela)))\n",
    "print(\"{} sources for which we use SpIES\".format(np.sum(use_spies)))\n",
    "\n",
    "f_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_shela] = master_catalogue['f_shela_irac1'][use_shela]\n",
    "f_ap_irac[use_spies] = master_catalogue['f_spies_irac1'][use_spies]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_shela] = master_catalogue['ferr_shela_irac1'][use_shela]\n",
    "ferr_ap_irac[use_spies] = master_catalogue['ferr_spies_irac1'][use_spies]\n",
    "\n",
    "flag_irac = np.full(len(master_catalogue), False, dtype=bool)\n",
    "flag_irac[use_shela] = master_catalogue['flag_shela_irac1'][use_shela]\n",
    "flag_irac[use_spies] = master_catalogue['flag_spies_irac1'][use_spies]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_shela] = master_catalogue['m_shela_irac1'][use_shela]\n",
    "m_ap_irac[use_spies] = master_catalogue['m_spies_irac1'][use_spies]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_shela] = master_catalogue['merr_shela_irac1'][use_shela]\n",
    "merr_ap_irac[use_spies] = master_catalogue['merr_spies_irac1'][use_spies]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=flag_irac, name=\"flag_irac_i1\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_shela_irac1', 'f_spies_irac1', \n",
    "                                 'ferr_shela_irac1', 'ferr_spies_irac1', \n",
    "                                 'm_shela_irac1', 'm_spies_irac1',\n",
    "                                 'merr_shela_irac1', 'merr_spies_irac1',\n",
    "                                'flag_shela_irac1', 'flag_spies_irac1'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_shela] = \"SHELA\"\n",
    "origin[use_spies] = \"SpIES\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC1_total\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2244367 sources with SHELA flux\n",
      "3365594 sources with SpIES flux\n",
      "66244 sources with SHELA and SpIES flux\n",
      "2244367 sources for which we use SHELA\n",
      "3299350 sources for which we use SpIES\n"
     ]
    }
   ],
   "source": [
    "# IRAC2 aperture flux and magnitudes\n",
    "has_shela = ~np.isnan(master_catalogue['f_ap_shela_irac2'])\n",
    "has_spies = ~np.isnan(master_catalogue['f_ap_spies_irac2'])\n",
    "has_both = has_shela & has_spies\n",
    "\n",
    "print(\"{} sources with SHELA flux\".format(np.sum(has_shela)))\n",
    "print(\"{} sources with SpIES flux\".format(np.sum(has_spies)))\n",
    "print(\"{} sources with SHELA and SpIES flux\".format(np.sum(has_both)))\n",
    "\n",
    "use_shela = has_shela\n",
    "use_spies = (has_spies & ~has_shela)\n",
    "\n",
    "print(\"{} sources for which we use SHELA\".format(np.sum(use_shela)))\n",
    "print(\"{} sources for which we use SpIES\".format(np.sum(use_spies)))\n",
    "\n",
    "f_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_shela] = master_catalogue['f_ap_shela_irac2'][use_shela]\n",
    "f_ap_irac[use_spies] = master_catalogue['f_ap_spies_irac2'][use_spies]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_shela] = master_catalogue['ferr_ap_shela_irac2'][use_shela]\n",
    "ferr_ap_irac[use_spies] = master_catalogue['ferr_ap_spies_irac2'][use_spies]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_shela] = master_catalogue['m_ap_shela_irac2'][use_shela]\n",
    "m_ap_irac[use_spies] = master_catalogue['m_ap_spies_irac2'][use_spies]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_shela] = master_catalogue['merr_ap_shela_irac2'][use_shela]\n",
    "merr_ap_irac[use_spies] = master_catalogue['merr_ap_spies_irac2'][use_spies]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_ap_irac_i2\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_ap_shela_irac2', 'f_ap_spies_irac2', \n",
    "                                 'ferr_ap_shela_irac2', 'ferr_ap_spies_irac2', \n",
    "                                 'm_ap_shela_irac2', 'm_ap_spies_irac2',\n",
    "                                 'merr_ap_shela_irac2', 'merr_ap_spies_irac2'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_shela] = \"SHELA\"\n",
    "origin[use_spies] = \"SpIES\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC2_ap\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2244367 sources with SHELA total flux\n",
      "3365594 sources with SpIES total flux\n",
      "66244 sources with SHELA and SpIES total flux\n",
      "2244367 sources for which we use SHELA\n",
      "3299350 sources for which we use SpIES\n"
     ]
    }
   ],
   "source": [
    "# IRAC2 total flux and magnitudes\n",
    "has_shela = ~np.isnan(master_catalogue['f_shela_irac2'])\n",
    "has_spies = ~np.isnan(master_catalogue['f_spies_irac2'])\n",
    "has_both = has_shela & has_spies\n",
    "\n",
    "print(\"{} sources with SHELA total flux\".format(np.sum(has_shela)))\n",
    "print(\"{} sources with SpIES total flux\".format(np.sum(has_spies)))\n",
    "print(\"{} sources with SHELA and SpIES total flux\".format(np.sum(has_both)))\n",
    "\n",
    "use_shela = has_shela\n",
    "use_spies = (has_spies & ~has_shela)\n",
    "\n",
    "print(\"{} sources for which we use SHELA\".format(np.sum(use_shela)))\n",
    "print(\"{} sources for which we use SpIES\".format(np.sum(use_spies)))\n",
    "\n",
    "f_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_shela] = master_catalogue['f_shela_irac2'][use_shela]\n",
    "f_ap_irac[use_spies] = master_catalogue['f_spies_irac2'][use_spies]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_shela] = master_catalogue['ferr_shela_irac2'][use_shela]\n",
    "ferr_ap_irac[use_spies] = master_catalogue['ferr_spies_irac2'][use_spies]\n",
    "\n",
    "flag_irac = np.full(len(master_catalogue), False, dtype=bool)\n",
    "flag_irac[use_shela] = master_catalogue['flag_shela_irac2'][use_shela]\n",
    "flag_irac[use_spies] = master_catalogue['flag_spies_irac2'][use_spies]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_shela] = master_catalogue['m_shela_irac2'][use_shela]\n",
    "m_ap_irac[use_spies] = master_catalogue['m_spies_irac2'][use_spies]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_shela] = master_catalogue['merr_shela_irac2'][use_shela]\n",
    "merr_ap_irac[use_spies] = master_catalogue['merr_spies_irac2'][use_spies]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=flag_irac, name=\"flag_irac_i2\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_shela_irac2', 'f_spies_irac2', \n",
    "                                 'ferr_shela_irac2', 'ferr_spies_irac2', \n",
    "                                 'm_shela_irac2', 'm_spies_irac2',\n",
    "                                 'merr_shela_irac2', 'merr_spies_irac2',\n",
    "                                'flag_shela_irac2', 'flag_spies_irac2'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_shela] = \"SHELA\"\n",
    "origin[use_spies] = \"SpIES\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC2_total\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin.write(\"{}/herschel-stripe-82_irac_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['shela_intid',\n",
       " 'ra',\n",
       " 'dec',\n",
       " 'flag_merged',\n",
       " 'spies_intid',\n",
       " 'irac_flag_cleaned',\n",
       " 'irac_flag_gaia',\n",
       " 'irac_stellarity',\n",
       " 'irac_intid',\n",
       " 'f_ap_irac_i1',\n",
       " 'ferr_ap_irac_i1',\n",
       " 'm_ap_irac_i1',\n",
       " 'merr_ap_irac_i1',\n",
       " 'f_irac_i1',\n",
       " 'ferr_irac_i1',\n",
       " 'm_irac_i1',\n",
       " 'merr_irac_i1',\n",
       " 'flag_irac_i1',\n",
       " 'f_ap_irac_i2',\n",
       " 'ferr_ap_irac_i2',\n",
       " 'm_ap_irac_i2',\n",
       " 'merr_ap_irac_i2',\n",
       " 'f_irac_i2',\n",
       " 'ferr_irac_i2',\n",
       " 'm_irac_i2',\n",
       " 'merr_irac_i2',\n",
       " 'flag_irac_i2']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[\"ra\"].name = \"irac_ra\"\n",
    "master_catalogue[\"dec\"].name = \"irac_dec\"\n",
    "master_catalogue[\"flag_merged\"].name = \"irac_flag_merged\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = [\"irac_intid\", \"shela_intid\", \"spies_intid\",\n",
    "           'irac_ra', 'irac_dec', 'irac_flag_merged',  \n",
    "           'irac_flag_cleaned',  'irac_flag_gaia', 'irac_stellarity']\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                \"flag_{}\".format(band)]    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: set()\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=10&gt;\n",
       "<table id=\"table140252933159288-437892\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>shela_intid</th><th>irac_ra</th><th>irac_dec</th><th>irac_flag_merged</th><th>spies_intid</th><th>irac_flag_cleaned</th><th>irac_flag_gaia</th><th>irac_stellarity</th><th>irac_intid</th><th>f_ap_irac_i1</th><th>ferr_ap_irac_i1</th><th>m_ap_irac_i1</th><th>merr_ap_irac_i1</th><th>f_irac_i1</th><th>ferr_irac_i1</th><th>m_irac_i1</th><th>merr_irac_i1</th><th>flag_irac_i1</th><th>f_ap_irac_i2</th><th>ferr_ap_irac_i2</th><th>m_ap_irac_i2</th><th>merr_ap_irac_i2</th><th>f_irac_i2</th><th>ferr_irac_i2</th><th>m_irac_i2</th><th>merr_irac_i2</th><th>flag_irac_i2</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>1379391</td><td>22.9160109027</td><td>0.238312267292</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>1379391-1</td><td>-0.0224474351853</td><td>0.459004551172</td><td>nan</td><td>-22.2011032104</td><td>2.62986135483</td><td>2.07571077347</td><td>22.8501663208</td><td>0.856955587864</td><td>False</td><td>42.6650848389</td><td>1.13832569122</td><td>19.8248214722</td><td>0.0289679840207</td><td>36.9146118164</td><td>3.31506538391</td><td>19.9820098877</td><td>0.09750302881</td><td>False</td></tr>\n",
       "<tr><td>1</td><td>1433853</td><td>23.6639931364</td><td>0.297376305167</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>1433853-1</td><td>-0.999433636665</td><td>0.478795945644</td><td>nan</td><td>-0.520140647888</td><td>-1.66275298595</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>57.0925140381</td><td>1.24950385094</td><td>19.5085525513</td><td>0.0237619858235</td><td>41.9827575684</td><td>0.87940376997</td><td>19.8423233032</td><td>0.0227426812053</td><td>False</td></tr>\n",
       "<tr><td>2</td><td>1501461</td><td>17.1780743659</td><td>0.371209289855</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>1501461-1</td><td>-0.367885500193</td><td>0.480662703514</td><td>nan</td><td>-1.41857421398</td><td>0.809025406837</td><td>1.80919122696</td><td>24.1300964355</td><td>2.42798852921</td><td>False</td><td>8.7921037674</td><td>0.762039601803</td><td>21.5397720337</td><td>0.0941042080522</td><td>11.0820875168</td><td>2.47177433968</td><td>21.2884521484</td><td>0.242165103555</td><td>False</td></tr>\n",
       "<tr><td>3</td><td>2041910</td><td>18.0883776053</td><td>0.955992888107</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>2041910-1</td><td>1.53133165836</td><td>0.480679512024</td><td>23.4373245239</td><td>0.340808659792</td><td>1.32359325886</td><td>0.404158234596</td><td>23.5956192017</td><td>0.331528753042</td><td>False</td><td>4.19599294662</td><td>0.765189886093</td><td>22.3429107666</td><td>0.1979970783</td><td>2.93803620338</td><td>0.634261488914</td><td>22.7298583984</td><td>0.234388068318</td><td>False</td></tr>\n",
       "<tr><td>4</td><td>2051282</td><td>14.3497873938</td><td>0.961695722436</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>2051282-1</td><td>1.69280123711</td><td>0.480811774731</td><td>23.3284912109</td><td>0.308385133743</td><td>0.465728461742</td><td>0.691826999187</td><td>24.7296676636</td><td>1.61283159256</td><td>False</td><td>1.80439436436</td><td>0.65220785141</td><td>23.2591705322</td><td>0.392445057631</td><td>2.23409605026</td><td>0.981211781502</td><td>23.0272445679</td><td>0.476853758097</td><td>False</td></tr>\n",
       "<tr><td>5</td><td>1497273</td><td>18.7890799452</td><td>0.368352033577</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>1497273-1</td><td>2.05972695351</td><td>0.482331544161</td><td>23.1154785156</td><td>0.254249602556</td><td>1.83974432945</td><td>0.899651825428</td><td>23.2381057739</td><td>0.530934929848</td><td>False</td><td>2.42052817345</td><td>0.71006578207</td><td>22.9402236938</td><td>0.31850245595</td><td>2.03327870369</td><td>1.36675858498</td><td>23.1295089722</td><td>0.729825854301</td><td>False</td></tr>\n",
       "<tr><td>6</td><td>545026</td><td>22.4245506266</td><td>-0.642594144188</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>545026-1</td><td>1.7553601265</td><td>0.485114395618</td><td>23.2890853882</td><td>0.30005595088</td><td>1.70425009727</td><td>0.915278851986</td><td>23.3211669922</td><td>0.583101809025</td><td>False</td><td>2.41439676285</td><td>0.643229901791</td><td>22.9429779053</td><td>0.289255678654</td><td>2.86203837395</td><td>1.23268008232</td><td>22.7583084106</td><td>0.46762663126</td><td>False</td></tr>\n",
       "<tr><td>7</td><td>477159</td><td>22.4517375046</td><td>-0.714030299391</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>477159-1</td><td>1.45646131039</td><td>0.485505580902</td><td>23.4917526245</td><td>0.36192587018</td><td>1.61951887608</td><td>0.745305120945</td><td>23.3765411377</td><td>0.499657481909</td><td>False</td><td>5.67917251587</td><td>0.677956938744</td><td>22.014289856</td><td>0.129610851407</td><td>4.26385736465</td><td>0.985850870609</td><td>22.3254928589</td><td>0.251034170389</td><td>False</td></tr>\n",
       "<tr><td>8</td><td>543888</td><td>24.8499402825</td><td>-0.642484862009</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>543888-1</td><td>-0.790526807308</td><td>0.48655667901</td><td>nan</td><td>-0.668253362179</td><td>-0.368213504553</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>8.42269706726</td><td>0.788448810577</td><td>21.5863723755</td><td>0.101635783911</td><td>6.09079408646</td><td>0.334957569838</td><td>21.9383163452</td><td>0.0597090534866</td><td>False</td></tr>\n",
       "<tr><td>9</td><td>869586</td><td>24.6311943916</td><td>-0.300427956383</td><td>False</td><td>-1</td><td>False</td><td>0</td><td>nan</td><td>869586-1</td><td>1.70511245728</td><td>0.487496495247</td><td>23.3206176758</td><td>0.310415029526</td><td>0.784428715706</td><td>0.672937572002</td><td>24.1636123657</td><td>0.931420087814</td><td>False</td><td>4.08163404465</td><td>0.789322197437</td><td>22.3729171753</td><td>0.209963873029</td><td>4.06112241745</td><td>1.11324501038</td><td>22.3783874512</td><td>0.297624707222</td><td>False</td></tr>\n",
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       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
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       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140252933159288-437892').dataTable()\");\n",
       "    $('#table140252933159288-437892').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
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       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/iraq_merged_catalogue_herschel-stripe-82.fits\".format(TMP_DIR), overwrite=True)"
   ]
  }
 ],
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