{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of EGS 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_EGS_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=\"table4518025592\" 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>43</td><td>214.07912964383556</td><td>51.59612354015088</td><td>88.791435</td><td>90.03198</td><td>87.36463</td><td>33.162735</td><td>34.482357</td><td>31.75847</td><td>5.8392153</td><td>7.768917</td><td>3.9485471</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>1.0002216</td><td>0.99900615</td><td>1.0001154</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.901</td><td>0.871</td><td>0.016</td></tr>\n",
       "<tr><td>106</td><td>213.92774456738178</td><td>51.262257435683416</td><td>78.92177</td><td>81.29145</td><td>76.62691</td><td>54.544025</td><td>57.039055</td><td>51.974224</td><td>25.143751</td><td>27.861378</td><td>22.203253</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>1.002044</td><td>0.9984268</td><td>0.99847406</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.029</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>121</td><td>214.07582878612325</td><td>51.60470669138264</td><td>60.130295</td><td>61.45031</td><td>58.81982</td><td>25.246359</td><td>26.497229</td><td>23.915623</td><td>6.865479</td><td>8.565803</td><td>5.1637454</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>1.0005301</td><td>0.9982263</td><td>0.9986275</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.009</td><td>0.621</td><td>0.095</td></tr>\n",
       "<tr><td>136</td><td>213.98293159998425</td><td>51.37656001607475</td><td>69.62544</td><td>71.458664</td><td>67.75094</td><td>65.04498</td><td>66.849945</td><td>63.106308</td><td>44.72334</td><td>46.985374</td><td>42.4425</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>1.0004747</td><td>0.9995111</td><td>0.9993734</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.004</td><td>0.0</td></tr>\n",
       "<tr><td>185</td><td>214.10278255942006</td><td>51.5751963654372</td><td>62.150112</td><td>63.533077</td><td>60.7292</td><td>48.28504</td><td>49.720467</td><td>46.89035</td><td>22.591106</td><td>24.26268</td><td>20.980946</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>0.99893034</td><td>0.9986249</td><td>0.99858385</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.208</td><td>0.111</td><td>0.0</td></tr>\n",
       "<tr><td>371</td><td>213.96618724944787</td><td>51.30950938066573</td><td>40.064686</td><td>42.421135</td><td>37.834393</td><td>14.610609</td><td>17.087997</td><td>12.142665</td><td>12.919537</td><td>17.150444</td><td>8.629437</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>0.9992768</td><td>1.0002468</td><td>1.0008574</td><td>2000.0</td><td>772.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>504</td><td>214.10348766155306</td><td>51.46941061509884</td><td>46.74123</td><td>48.50933</td><td>44.93424</td><td>43.431194</td><td>45.24353</td><td>41.722458</td><td>25.184244</td><td>27.325142</td><td>23.021866</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>1.0002522</td><td>0.99824554</td><td>0.9998521</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.005</td><td>0.004</td><td>0.0</td></tr>\n",
       "<tr><td>574</td><td>213.95255529272626</td><td>51.480419842633154</td><td>39.859318</td><td>41.422863</td><td>38.303402</td><td>15.737937</td><td>17.192427</td><td>14.21961</td><td>1.9042679</td><td>3.4760995</td><td>0.6607906</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>0.99961257</td><td>0.9989497</td><td>1.0002283</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.015</td><td>0.029</td><td>0.001</td></tr>\n",
       "<tr><td>577</td><td>214.05262797620483</td><td>51.36213194836945</td><td>32.219704</td><td>34.38508</td><td>29.990696</td><td>8.086773</td><td>10.473448</td><td>5.748993</td><td>0.93976206</td><td>2.193487</td><td>0.2535693</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>0.99905264</td><td>1.0012585</td><td>0.9995355</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.071</td><td>0.0</td></tr>\n",
       "<tr><td>613</td><td>213.87346300010915</td><td>51.34116347839875</td><td>30.256723</td><td>32.012985</td><td>28.499193</td><td>9.777271</td><td>11.496249</td><td>8.03679</td><td>1.1323739</td><td>2.3625124</td><td>0.34631503</td><td>-0.16521987</td><td>-0.2229195</td><td>-0.17339727</td><td>0.0037063432</td><td>0.0053316527</td><td>0.007125567</td><td>0.9998335</td><td>0.99816555</td><td>1.0001352</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.004</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",
       "43                          214.07912964383556 ...        0.871        0.016\n",
       "106                         213.92774456738178 ...          0.0          0.0\n",
       "121                         214.07582878612325 ...        0.621        0.095\n",
       "136                         213.98293159998425 ...        0.004          0.0\n",
       "185                         214.10278255942006 ...        0.111          0.0\n",
       "371                         213.96618724944787 ...          0.0          0.0\n",
       "504                         214.10348766155306 ...        0.004          0.0\n",
       "574                         213.95255529272626 ...        0.029        0.001\n",
       "577                         214.05262797620483 ...        0.071          0.0\n",
       "613                         213.87346300010915 ...        0.004          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": [
      "3929 6146 5553 9551\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 =  9551\n",
      "# galaxies =  9551\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/EGS_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(['EGS']*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_EGS_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
}
