{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of HDF-N 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_HDF-N_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=\"table4376590432\" 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>75</td><td>189.47207051330057</td><td>61.83578062744778</td><td>34.711334</td><td>36.23167</td><td>33.190384</td><td>19.386705</td><td>21.187244</td><td>17.51052</td><td>3.3328526</td><td>5.1653214</td><td>1.470307</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.99905246</td><td>0.9989022</td><td>1.0012667</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr>\n",
       "<tr><td>112</td><td>189.5238629258716</td><td>61.836233003789474</td><td>25.403498</td><td>26.987457</td><td>23.738743</td><td>21.146824</td><td>23.24672</td><td>19.17715</td><td>9.826296</td><td>11.910365</td><td>7.7862062</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.9989988</td><td>0.99837524</td><td>0.99857944</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr>\n",
       "<tr><td>162</td><td>189.48615133968767</td><td>61.83075473094658</td><td>43.63474</td><td>45.232418</td><td>41.948498</td><td>22.383675</td><td>24.397493</td><td>20.364653</td><td>8.66891</td><td>11.0693035</td><td>6.2495065</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.9990405</td><td>0.9983873</td><td>0.9987331</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>0.999</td><td>0.998</td></tr>\n",
       "<tr><td>239</td><td>189.56380822099334</td><td>61.882527454707855</td><td>27.448551</td><td>28.943016</td><td>25.994795</td><td>12.093296</td><td>13.931255</td><td>10.147441</td><td>0.49339843</td><td>1.2023259</td><td>0.13614236</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.999261</td><td>0.9984511</td><td>0.9984796</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr>\n",
       "<tr><td>270</td><td>189.48726585833506</td><td>61.82353070859996</td><td>9.781449</td><td>11.297208</td><td>8.303107</td><td>0.79481995</td><td>1.827133</td><td>0.21942252</td><td>0.44249716</td><td>1.1876771</td><td>0.11953026</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.99903667</td><td>0.99965024</td><td>0.9985619</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr>\n",
       "<tr><td>311</td><td>189.50077124630025</td><td>61.88572402559173</td><td>26.289492</td><td>27.816673</td><td>24.73249</td><td>22.274868</td><td>24.099386</td><td>20.348732</td><td>17.361462</td><td>19.541756</td><td>15.092925</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.99901617</td><td>0.9986254</td><td>0.9992568</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr>\n",
       "<tr><td>335</td><td>189.5137444621643</td><td>61.88625288078024</td><td>38.04361</td><td>39.532288</td><td>36.50855</td><td>28.59526</td><td>30.520775</td><td>26.655268</td><td>12.135117</td><td>14.264226</td><td>9.908193</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>1.0000267</td><td>0.9999896</td><td>0.9993363</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr>\n",
       "<tr><td>348</td><td>189.4806409579133</td><td>61.87520780875087</td><td>24.721268</td><td>26.571983</td><td>22.983389</td><td>23.526894</td><td>26.054724</td><td>21.080145</td><td>10.578096</td><td>13.497189</td><td>7.3615203</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.99937123</td><td>1.0007389</td><td>0.9985527</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.999</td><td>1.0</td><td>1.0</td></tr>\n",
       "<tr><td>356</td><td>189.49149797544604</td><td>61.836577514074236</td><td>14.518369</td><td>16.236134</td><td>12.866267</td><td>11.295403</td><td>13.394885</td><td>9.354682</td><td>8.656886</td><td>10.960348</td><td>6.47291</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.9982637</td><td>0.99849766</td><td>0.9993845</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.998</td><td>1.0</td><td>0.971</td></tr>\n",
       "<tr><td>868</td><td>189.46331500943853</td><td>61.8449624106938</td><td>7.858822</td><td>9.55664</td><td>6.318145</td><td>0.74476194</td><td>1.6726309</td><td>0.22208466</td><td>0.24079666</td><td>0.5806592</td><td>0.07355579</td><td>-0.97889066</td><td>-1.285639</td><td>-1.6167406</td><td>3.091677</td><td>3.7658827</td><td>3.5072732</td><td>0.99962616</td><td>0.9991022</td><td>0.99939114</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>1.0</td><td>1.0</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",
       "75                          189.47207051330057 ...          1.0          1.0\n",
       "112                          189.5238629258716 ...          1.0          1.0\n",
       "162                         189.48615133968767 ...        0.999        0.998\n",
       "239                         189.56380822099334 ...          1.0          1.0\n",
       "270                         189.48726585833506 ...          1.0          1.0\n",
       "311                         189.50077124630025 ...          1.0          1.0\n",
       "335                          189.5137444621643 ...          1.0          1.0\n",
       "348                          189.4806409579133 ...          1.0          1.0\n",
       "356                         189.49149797544604 ...          1.0        0.971\n",
       "868                         189.46331500943853 ...          1.0          1.0"
      ]
     },
     "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": [
      "1483 1483 1483 1483\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 =  1483\n",
      "# galaxies =  1483\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/HDF-N_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=(100,100))\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(['HDF-N']*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_HDF-N_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
}
