{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of SSDF 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_SSDF_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=\"table4541647168\" 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>29272</td><td>-358.690604142773</td><td>-60.7789257799088</td><td>1.9311442</td><td>4.276367</td><td>0.55752003</td><td>3.4529607</td><td>8.130131</td><td>0.8875977</td><td>1.1784822</td><td>2.9302742</td><td>0.29293534</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>0.99919355</td><td>0.99900794</td><td>0.99884003</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.005</td><td>0.0</td><td>0.006</td></tr>\n",
       "<tr><td>29392</td><td>-358.71098613970406</td><td>-60.78313384514085</td><td>6.8959517</td><td>10.648925</td><td>3.7296543</td><td>1.5518979</td><td>3.3732264</td><td>0.46102715</td><td>0.45343405</td><td>0.97065026</td><td>0.13672246</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>1.0046676</td><td>0.9999775</td><td>1.0008192</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>45878</td><td>-358.6768248818768</td><td>-60.76937592767701</td><td>1.6654271</td><td>3.242882</td><td>0.5385522</td><td>0.569171</td><td>1.1434054</td><td>0.16872375</td><td>0.5450551</td><td>1.2133816</td><td>0.16232073</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>1.0006626</td><td>1.0011783</td><td>0.99897754</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>49735</td><td>-358.6783899236406</td><td>-60.755659866426676</td><td>13.734138</td><td>23.978998</td><td>4.7529683</td><td>2.061744</td><td>4.2274585</td><td>0.6503994</td><td>0.4178695</td><td>0.8882754</td><td>0.1247902</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>1.0000433</td><td>0.99911</td><td>0.9985493</td><td>1586.0</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>52847</td><td>-358.65926317805116</td><td>-60.76121427378042</td><td>0.8605088</td><td>1.6592047</td><td>0.26506725</td><td>0.4937332</td><td>0.96917343</td><td>0.14699751</td><td>0.5852099</td><td>1.3318017</td><td>0.14091687</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>0.9990124</td><td>0.99921167</td><td>0.99882424</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>76608</td><td>-358.6539913742425</td><td>-60.74562830500216</td><td>15.7693</td><td>25.506556</td><td>6.9482317</td><td>0.50266427</td><td>1.0234169</td><td>0.15288344</td><td>1.2838043</td><td>2.8656046</td><td>0.37894502</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>0.99945277</td><td>0.99908125</td><td>0.9993646</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>85468</td><td>-358.675863611176</td><td>-60.756586669273844</td><td>14.811441</td><td>24.661407</td><td>5.449873</td><td>1.0021937</td><td>2.3219097</td><td>0.26102442</td><td>0.32142544</td><td>0.7345886</td><td>0.08693438</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>1.0009478</td><td>1.000874</td><td>0.9987951</td><td>1686.0</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>124962</td><td>-358.6326903281298</td><td>-60.75489612016789</td><td>0.83059317</td><td>1.6953614</td><td>0.257114</td><td>0.5194643</td><td>0.99510443</td><td>0.16867247</td><td>0.49320814</td><td>0.90919644</td><td>0.16459176</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>1.0011467</td><td>1.0008078</td><td>0.9989334</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>147246</td><td>-358.72298014161186</td><td>-60.78895848969828</td><td>1.3983359</td><td>2.8019547</td><td>0.420495</td><td>0.80825204</td><td>1.653118</td><td>0.22377178</td><td>1.3481537</td><td>2.8470695</td><td>0.43727788</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>1.0003278</td><td>0.9997558</td><td>0.99839056</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.572</td><td>0.0</td><td>0.001</td></tr>\n",
       "<tr><td>164509</td><td>-358.70059339241817</td><td>-60.766498294475284</td><td>16.47891</td><td>22.750696</td><td>10.188234</td><td>22.875937</td><td>28.431227</td><td>17.650877</td><td>1.1221809</td><td>2.4255242</td><td>0.2952997</td><td>-0.026589109</td><td>-0.13064381</td><td>-0.2500538</td><td>0.018365432</td><td>0.027923811</td><td>0.038403932</td><td>1.0003802</td><td>0.99878544</td><td>0.9994449</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.823</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",
       "29272                         -358.690604142773 ...          0.0        0.006\n",
       "29392                       -358.71098613970406 ...          0.0          0.0\n",
       "45878                        -358.6768248818768 ...          0.0          0.0\n",
       "49735                        -358.6783899236406 ...          0.0          0.0\n",
       "52847                       -358.65926317805116 ...          0.0          0.0\n",
       "76608                        -358.6539913742425 ...          0.0          0.0\n",
       "85468                         -358.675863611176 ...          0.0          0.0\n",
       "124962                       -358.6326903281298 ...          0.0          0.0\n",
       "147246                      -358.72298014161186 ...          0.0        0.001\n",
       "164509                      -358.70059339241817 ...        0.823          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": [
      "7113 18240 48984 196895\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 =  196895\n",
      "# galaxies =  196895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: MergeConflictWarning: Cannot merge meta key 'EXTNAME' types <class 'str'> and <class 'str'>, choosing EXTNAME='SSDF_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='2018-06-09T12:38:45' [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/SSDF_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=(1000,1000))\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(['SSDF']*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_SSDF_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
}