{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of xFLS 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_xFLS_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=\"table4360258560\" 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>3004</td><td>261.2806200081048</td><td>58.019473610745486</td><td>31.729315</td><td>35.317196</td><td>28.115198</td><td>29.002312</td><td>32.83311</td><td>25.355696</td><td>7.0030475</td><td>10.288991</td><td>3.8693156</td><td>-0.009975622</td><td>-0.01225606</td><td>-0.015219242</td><td>0.0036281063</td><td>0.0051612323</td><td>0.007401004</td><td>0.998783</td><td>0.9985503</td><td>0.99912477</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>16242</td><td>261.2753283983713</td><td>58.01040121811083</td><td>2.2341926</td><td>5.0016294</td><td>0.5509797</td><td>5.4576225</td><td>8.498919</td><td>2.8126125</td><td>1.292673</td><td>2.7173104</td><td>0.40918913</td><td>-0.009975622</td><td>-0.01225606</td><td>-0.015219242</td><td>0.0036281063</td><td>0.0051612323</td><td>0.007401004</td><td>0.99909717</td><td>0.9982408</td><td>0.9993336</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.045</td><td>0.0</td></tr>\n",
       "<tr><td>17727</td><td>261.328084413602</td><td>58.0389712095576</td><td>15.345567</td><td>26.296324</td><td>4.5530095</td><td>17.654814</td><td>30.44019</td><td>5.3331566</td><td>5.285627</td><td>9.814364</td><td>1.8496672</td><td>-0.009975622</td><td>-0.01225606</td><td>-0.015219242</td><td>0.0036281063</td><td>0.0051612323</td><td>0.007401004</td><td>0.99949354</td><td>1.0014123</td><td>0.9998091</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>17787</td><td>261.328084413602</td><td>58.0389712095576</td><td>15.882659</td><td>26.939163</td><td>5.043784</td><td>18.561924</td><td>30.300022</td><td>5.861497</td><td>5.2218995</td><td>9.624654</td><td>1.855984</td><td>-0.009975622</td><td>-0.01225606</td><td>-0.015219242</td><td>0.0036281063</td><td>0.0051612323</td><td>0.007401004</td><td>0.99929535</td><td>1.0009229</td><td>0.9987323</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>6293</td><td>259.5297621057166</td><td>58.13465654979708</td><td>21.495966</td><td>24.91133</td><td>18.012562</td><td>30.623861</td><td>35.724792</td><td>25.687035</td><td>3.6143913</td><td>6.5318866</td><td>1.154674</td><td>-0.011038939</td><td>-0.019646086</td><td>-0.029727593</td><td>0.004182898</td><td>0.006214998</td><td>0.008492789</td><td>0.9982138</td><td>0.9992393</td><td>1.0011904</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>8701</td><td>259.5309665024096</td><td>58.107164581716944</td><td>4.5793467</td><td>7.1386657</td><td>2.084802</td><td>2.751092</td><td>4.1836214</td><td>1.4224968</td><td>1.3878763</td><td>2.5157845</td><td>0.4874208</td><td>-0.011038939</td><td>-0.019646086</td><td>-0.029727593</td><td>0.004182898</td><td>0.006214998</td><td>0.008492789</td><td>1.0005414</td><td>1.0015237</td><td>1.0014697</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.002</td><td>0.0</td></tr>\n",
       "<tr><td>9181</td><td>259.5218828325151</td><td>58.1355186560356</td><td>2.417852</td><td>5.223044</td><td>0.754518</td><td>10.869545</td><td>15.608155</td><td>6.602502</td><td>2.1417274</td><td>4.706348</td><td>0.6214839</td><td>-0.011038939</td><td>-0.019646086</td><td>-0.029727593</td><td>0.004182898</td><td>0.006214998</td><td>0.008492789</td><td>0.99921906</td><td>1.0005517</td><td>0.9995944</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>10195</td><td>259.5215158921443</td><td>58.14745918131408</td><td>22.802334</td><td>26.429323</td><td>19.083038</td><td>27.415482</td><td>30.958073</td><td>23.84377</td><td>7.5799932</td><td>11.543367</td><td>3.7788484</td><td>-0.011038939</td><td>-0.019646086</td><td>-0.029727593</td><td>0.004182898</td><td>0.006214998</td><td>0.008492789</td><td>0.998551</td><td>0.99882543</td><td>0.99934906</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>13112</td><td>259.46322969061447</td><td>58.113230617312745</td><td>9.489584</td><td>13.429863</td><td>5.6227684</td><td>0.78835607</td><td>1.6364456</td><td>0.23873201</td><td>2.995526</td><td>5.325055</td><td>1.1270082</td><td>-0.011038939</td><td>-0.019646086</td><td>-0.029727593</td><td>0.004182898</td><td>0.006214998</td><td>0.008492789</td><td>0.9992586</td><td>0.99893975</td><td>0.99905</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>17663</td><td>259.45188439726115</td><td>58.11724618582004</td><td>5.0712624</td><td>9.6741705</td><td>1.6606892</td><td>4.599141</td><td>7.285561</td><td>2.173514</td><td>0.8283676</td><td>1.7838962</td><td>0.25101602</td><td>-0.011038939</td><td>-0.019646086</td><td>-0.029727593</td><td>0.004182898</td><td>0.006214998</td><td>0.008492789</td><td>1.0005012</td><td>1.0020282</td><td>0.99845433</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",
       "</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",
       "3004                         261.2806200081048 ...          0.0          0.0\n",
       "16242                        261.2753283983713 ...        0.045          0.0\n",
       "17727                         261.328084413602 ...          0.0          0.0\n",
       "17787                         261.328084413602 ...          0.0          0.0\n",
       "6293                         259.5297621057166 ...          0.0          0.0\n",
       "8701                         259.5309665024096 ...        0.002          0.0\n",
       "9181                         259.5218828325151 ...          0.0          0.0\n",
       "10195                        259.5215158921443 ...          0.0          0.0\n",
       "13112                       259.46322969061447 ...          0.0          0.0\n",
       "17663                       259.45188439726115 ...          0.0          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": [
      "2218 4036 7989 19757\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 =  19757\n",
      "# galaxies =  19757\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/xFLS_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(['xFLS']*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_xFLS_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
}
