{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of GAMA-09 Blind source catalogue\n",
    "\n",
    "![HELP LOGO](https://avatars1.githubusercontent.com/u/7880370?s=100&v=4>)\n",
    "\n",
    "\n",
    "The final processing stage requires:\n",
    "1. Quick validation of blind catalogues and Bayesian Pvalue maps\n",
    "2. Skewness level\n",
    "3. Adding flag to catalogue\n",
    "4. Merging MF catalogue with XID+ flux densities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from astropy.table import Table,hstack\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.table import Column\n",
    "\n",
    "import herschelhelp_internal\n",
    "from herschelhelp_internal.utils import gen_help_id\n",
    "import numpy.core.defchararray as np_f\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat=Table.read('./data/dmu22_XID+SPIRE_GAMA-09_BLIND.fits')\n",
    "cat['RA'].unit=u.deg\n",
    "cat['Dec'].unit=u.deg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table4413052464\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>HELP_ID</th><th>RA</th><th>Dec</th><th>F_SPIRE_250</th><th>FErr_SPIRE_250_u</th><th>FErr_SPIRE_250_l</th><th>F_SPIRE_350</th><th>FErr_SPIRE_350_u</th><th>FErr_SPIRE_350_l</th><th>F_SPIRE_500</th><th>FErr_SPIRE_500_u</th><th>FErr_SPIRE_500_l</th><th>Bkg_SPIRE_250</th><th>Bkg_SPIRE_350</th><th>Bkg_SPIRE_500</th><th>Sig_conf_SPIRE_250</th><th>Sig_conf_SPIRE_350</th><th>Sig_conf_SPIRE_500</th><th>Rhat_SPIRE_250</th><th>Rhat_SPIRE_350</th><th>Rhat_SPIRE_500</th><th>n_eff_SPIRE_250</th><th>n_eff_SPIRE_500</th><th>n_eff_SPIRE_350</th><th>Pval_res_250</th><th>Pval_res_350</th><th>Pval_res_500</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy/Beam</th><th>mJy/Beam</th><th>mJy/Beam</th><th>mJy/Beam</th><th>mJy/Beam</th><th>mJy/Beam</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>bytes27</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>915</td><td>138.59789358631673</td><td>-1.8817322422782055</td><td>87.736984</td><td>91.61259</td><td>83.525696</td><td>26.7445</td><td>30.949732</td><td>22.5869</td><td>4.5046678</td><td>8.449425</td><td>1.769085</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>1.0004654</td><td>0.9990438</td><td>0.99911004</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>8531</td><td>138.53965795674353</td><td>-1.836896394671342</td><td>45.055576</td><td>49.093254</td><td>40.96506</td><td>33.58103</td><td>37.522087</td><td>29.53859</td><td>29.55369</td><td>34.21334</td><td>24.9732</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.99910873</td><td>0.99843144</td><td>0.9991135</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>28704</td><td>138.5216346884573</td><td>-1.8105827298441761</td><td>32.091778</td><td>35.90825</td><td>28.0898</td><td>34.357796</td><td>38.24887</td><td>30.20314</td><td>36.58795</td><td>41.057125</td><td>32.138214</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.9988994</td><td>0.99876726</td><td>1.0003121</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>56589</td><td>138.5793073481131</td><td>-1.8947804442587832</td><td>15.922404</td><td>20.393217</td><td>11.446998</td><td>7.7597785</td><td>12.365875</td><td>3.7113056</td><td>11.967472</td><td>16.716703</td><td>7.3789215</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.9995268</td><td>0.9996675</td><td>0.99930596</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>67018</td><td>138.57210475227632</td><td>-1.8593115115472016</td><td>11.518164</td><td>15.918615</td><td>7.4835763</td><td>5.7076316</td><td>9.669136</td><td>2.473075</td><td>4.3498135</td><td>8.283557</td><td>1.5340434</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.9984197</td><td>1.0008957</td><td>0.9996445</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>87665</td><td>138.55962469359352</td><td>-1.8535049957125482</td><td>8.549577</td><td>12.58059</td><td>4.6063695</td><td>1.245686</td><td>2.8870606</td><td>0.3370921</td><td>1.1627764</td><td>2.8808472</td><td>0.28897628</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.99883854</td><td>0.9998204</td><td>1.000934</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>99598</td><td>138.52014401500958</td><td>-1.8329381456708922</td><td>8.896649</td><td>13.223549</td><td>4.8192596</td><td>13.2629795</td><td>16.85571</td><td>9.682945</td><td>5.9289327</td><td>8.433864</td><td>3.5111754</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.998592</td><td>0.99850625</td><td>0.9994391</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>103055</td><td>138.59113518108853</td><td>-1.894944485441341</td><td>3.2282677</td><td>6.6833067</td><td>1.0032742</td><td>8.940746</td><td>12.936686</td><td>4.817684</td><td>2.0889976</td><td>4.982457</td><td>0.6145482</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.9987955</td><td>1.0005333</td><td>0.9996692</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.001</td><td>0.0</td></tr>\n",
       "<tr><td>104302</td><td>138.5641525706873</td><td>-1.8391721987271754</td><td>4.547817</td><td>8.243489</td><td>1.720989</td><td>10.807755</td><td>14.717266</td><td>6.881477</td><td>6.7977033</td><td>11.379776</td><td>2.993065</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.9990001</td><td>0.99860185</td><td>0.9998943</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>110302</td><td>138.56931888290987</td><td>-1.8842530460192783</td><td>3.3592908</td><td>6.616763</td><td>1.0017303</td><td>5.221814</td><td>9.379476</td><td>1.9606109</td><td>1.6619023</td><td>3.7962878</td><td>0.45734128</td><td>-0.002682326</td><td>-0.0034668269</td><td>-0.0022368066</td><td>0.0016793854</td><td>0.0023008003</td><td>0.0033450942</td><td>0.9992636</td><td>0.99873</td><td>0.9999246</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.001</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=10>\n",
       "          HELP_ID                   RA         ... Pval_res_350 Pval_res_500\n",
       "                                   deg         ...                          \n",
       "          bytes27                float64       ...   float32      float32   \n",
       "--------------------------- ------------------ ... ------------ ------------\n",
       "915                         138.59789358631673 ...          0.0          0.0\n",
       "8531                        138.53965795674353 ...          0.0          0.0\n",
       "28704                        138.5216346884573 ...          0.0          0.0\n",
       "56589                        138.5793073481131 ...          0.0          0.0\n",
       "67018                       138.57210475227632 ...          0.0          0.0\n",
       "87665                       138.55962469359352 ...          0.0          0.0\n",
       "99598                       138.52014401500958 ...          0.0          0.0\n",
       "103055                      138.59113518108853 ...        0.001          0.0\n",
       "104302                       138.5641525706873 ...          0.0          0.0\n",
       "110302                      138.56931888290987 ...        0.001          0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at Symmetry of PDFs to determine depth level of catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skew=(cat['FErr_SPIRE_250_u']-cat['F_SPIRE_250'])/(cat['F_SPIRE_250']-cat['FErr_SPIRE_250_l'])\n",
    "skew.name='(84th-50th)/(50th-16th) percentile'\n",
    "g=sns.jointplot(x=np.log10(cat['F_SPIRE_250']),y=skew, kind='hex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 250 $\\mathrm{\\mu m}$ depth is ~ 6mJy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skew=(cat['FErr_SPIRE_350_u']-cat['F_SPIRE_350'])/(cat['F_SPIRE_350']-cat['FErr_SPIRE_350_l'])\n",
    "skew.name='(84th-50th)/(50th-16th) percentile'\n",
    "g=sns.jointplot(x=np.log10(cat['F_SPIRE_350']),y=skew, kind='hex')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 350 $\\mathrm{\\mu m}$ depth is ~ 6mJy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skew=(cat['FErr_SPIRE_500_u']-cat['F_SPIRE_500'])/(cat['F_SPIRE_500']-cat['FErr_SPIRE_500_l'])\n",
    "skew.name='(84th-50th)/(50th-16th) percentile'\n",
    "g=sns.jointplot(x=np.log10(cat['F_SPIRE_500']),y=skew, kind='hex')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 500 $\\mathrm{\\mu m}$ depth is ~ 6mJy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add flag to catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat.add_column(Column(np.zeros(len(cat), dtype=bool),name='flag_spire_250'))\n",
    "cat.add_column(Column(np.zeros(len(cat), dtype=bool),name='flag_spire_350'))\n",
    "cat.add_column(Column(np.zeros(len(cat), dtype=bool),name='flag_spire_500'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_250=(cat['Pval_res_250']>0.5) | (cat['F_SPIRE_250'] < 6)\n",
    "ind_350=(cat['Pval_res_350']>0.5) | (cat['F_SPIRE_350'] < 6)\n",
    "ind_500=(cat['Pval_res_500']>0.5) | (cat['F_SPIRE_500'] < 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5929 14048 32105 112461\n"
     ]
    }
   ],
   "source": [
    "print(ind_250.sum(),ind_350.sum(),ind_500.sum(),len(cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat['flag_spire_250'][ind_250]=True\n",
    "cat['flag_spire_350'][ind_350]=True\n",
    "cat['flag_spire_500'][ind_500]=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set XID+ cahtalogue back to orignal order of objects, as used in MF detection files\n",
    "use = cat['HELP_ID'].astype(int) -1\n",
    "use = np.argsort(use)\n",
    "cat = cat[use]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# galaxies =  112461\n",
      "# galaxies =  112461\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: MergeConflictWarning: Cannot merge meta key 'EXTNAME' types <class 'str'> and <class 'str'>, choosing EXTNAME='GAMA-09_SPIRE500_cat_MF0.fits' [astropy.utils.metadata]\n",
      "WARNING: MergeConflictWarning: Cannot merge meta key 'DATE-HDU' types <class 'str'> and <class 'str'>, choosing DATE-HDU='2018-06-09T12:05:32' [astropy.utils.metadata]\n",
      "WARNING: MergeConflictWarning: Cannot merge meta key 'STILVERS' types <class 'str'> and <class 'str'>, choosing STILVERS='3.1-' [astropy.utils.metadata]\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/GAMA-09_SPIRE_all.fits')\n",
    "print('# galaxies = ',np.size(cat2['RA']))\n",
    "print('# galaxies = ',np.size(cat['RA']))\n",
    "del cat2['RA']\n",
    "del cat2['Dec']\n",
    "cat_all = hstack([cat,cat2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Created HELP_ID, and changes HELP to HELP_BLIND to avoid confusion with HELP-Masterlist objects\n",
    "ID = gen_help_id(cat_all['RA'], cat_all['Dec'])\n",
    "ID_new = [IDs.replace('HELP','HELP_BLIND') for IDs in ID]\n",
    "ID_new = Column(ID_new,name=\"HELP_ID\")\n",
    "cat_all['HELP_ID'] = ID_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all flux denisties are in mJy in the final BLIND catalogues\n",
    "cat_all['F_BLIND_MF_SPIRE_250'].unit = 'mJy'\n",
    "cat_all['F_BLIND_MF_SPIRE_250'] = 1000*cat_all['F_BLIND_MF_SPIRE_250']\n",
    "cat_all['FErr_BLIND_MF_SPIRE_250'].unit = 'mJy'\n",
    "cat_all['FErr_BLIND_MF_SPIRE_250'] = 1000*cat_all['FErr_BLIND_MF_SPIRE_250']\n",
    "\n",
    "cat_all['F_BLIND_MF_SPIRE_350'].unit = 'mJy'\n",
    "cat_all['F_BLIND_MF_SPIRE_350'] = 1000*cat_all['F_BLIND_MF_SPIRE_350']\n",
    "cat_all['FErr_BLIND_MF_SPIRE_350'].unit = 'mJy'\n",
    "cat_all['FErr_BLIND_MF_SPIRE_350'] = 1000*cat_all['FErr_BLIND_MF_SPIRE_350']\n",
    "\n",
    "cat_all['F_BLIND_MF_SPIRE_500'].unit = 'mJy'\n",
    "cat_all['F_BLIND_MF_SPIRE_500'] = 1000*cat_all['F_BLIND_MF_SPIRE_500']\n",
    "cat_all['FErr_BLIND_MF_SPIRE_500'].unit = 'mJy'\n",
    "cat_all['FErr_BLIND_MF_SPIRE_500'] = 1000*cat_all['FErr_BLIND_MF_SPIRE_500']\n",
    "\n",
    "cat_all['F_BLIND_pix_SPIRE'].unit = 'mJy'\n",
    "cat_all['F_BLIND_pix_SPIRE'] = 1000*cat_all['F_BLIND_pix_SPIRE']\n",
    "cat_all['FErr_BLIND_pix_SPIRE'].unit = 'mJy'\n",
    "cat_all['FErr_BLIND_pix_SPIRE'] = 1000*cat_all['FErr_BLIND_pix_SPIRE']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XID+ flux density vs. MF flux densities\n",
    "plt.hexbin(cat_all['F_SPIRE_250'],cat_all['F_BLIND_MF_SPIRE_250'], cmap=plt.cm.Blues,gridsize=(600,600))\n",
    "plt.plot([0,100],[0,100], color = 'red')\n",
    "plt.xlim(0,100)\n",
    "plt.ylim(0,100)\n",
    "plt.xlabel('F_SPIRE_250')\n",
    "plt.ylabel('F_BLIND_MF_SPIRE_250')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add field name\n",
    "cat_all.add_column(Column(['GAMA-09']*len(cat_all),name='field'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'mJy/Beam' did not parse as fits unit: At col 4, Unit 'Beam' not supported by the FITS standard. Did you mean beam? [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "cat_all.write('./data/dmu22_XID+SPIRE_GAMA-09_BLIND_Matched_MF.fits', format='fits',overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*This is a default HELP jupyter notebook *\n",
    "\n",
    " ![HELP LOGO](https://avatars1.githubusercontent.com/u/7880370?s=75&v=4)\n",
    "\n",
    "**Authors**: S. Duivenvoorden\n",
    "\n",
    " \n",
    "For a full description of the database and how it is organised in to `dmu_products` please the top level [readme](../readme.md).\n",
    " \n",
    "The Herschel Extragalactic Legacy Project, ([HELP](http://herschel.sussex.ac.uk/)), is a [European Commission Research Executive Agency](https://ec.europa.eu/info/departments/research-executive-agency_en)\n",
    "funded project under the SP1-Cooperation, Collaborative project, Small or medium-scale focused research project, FP7-SPACE-2013-1 scheme, Grant Agreement\n",
    "Number 607254.\n",
    "\n",
    "[Acknowledgements](http://herschel.sussex.ac.uk/acknowledgements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": 1
}