{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of CDFS-SWIRE 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_CDFS-SWIRE_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=\"table4547586256\" 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>18</td><td>53.437731335599054</td><td>-30.167655437573263</td><td>255.09866</td><td>255.19112</td><td>254.92038</td><td>110.93803</td><td>113.05074</td><td>108.94333</td><td>32.53375</td><td>35.195442</td><td>30.066885</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>nan</td><td>0.999673</td><td>1.0000894</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>437</td><td>53.3774850665501</td><td>-30.212191377395392</td><td>57.557198</td><td>59.76958</td><td>55.441654</td><td>12.91694</td><td>15.036146</td><td>10.738269</td><td>1.1627761</td><td>2.6546347</td><td>0.31012693</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.99953556</td><td>0.99900496</td><td>0.9989088</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.024</td><td>0.018</td><td>0.066</td></tr>\n",
       "<tr><td>911</td><td>53.58617098415919</td><td>-30.187159789362585</td><td>52.63154</td><td>54.31575</td><td>50.799606</td><td>24.747866</td><td>26.346804</td><td>23.167223</td><td>12.69491</td><td>14.643439</td><td>10.771673</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.99950325</td><td>0.99882865</td><td>0.9986981</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.002</td><td>0.309</td><td>0.015</td></tr>\n",
       "<tr><td>950</td><td>53.38096745763396</td><td>-30.193594939172897</td><td>57.45491</td><td>59.329433</td><td>55.51467</td><td>41.192356</td><td>42.937397</td><td>39.418385</td><td>28.611572</td><td>30.723436</td><td>26.360697</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.9995385</td><td>0.99878097</td><td>0.99858695</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.003</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1402</td><td>53.30224445804902</td><td>-30.17737400923022</td><td>48.902775</td><td>51.12255</td><td>46.740208</td><td>37.582428</td><td>39.776993</td><td>35.377907</td><td>38.867966</td><td>41.399834</td><td>36.175217</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.9984182</td><td>0.999618</td><td>0.9985983</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.552</td><td>0.671</td><td>0.001</td></tr>\n",
       "<tr><td>1419</td><td>53.521692591044356</td><td>-30.126899511658692</td><td>37.497044</td><td>39.431816</td><td>35.47938</td><td>19.019632</td><td>20.999352</td><td>16.92874</td><td>15.094801</td><td>17.516722</td><td>12.547998</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.9995087</td><td>0.99909127</td><td>0.9990826</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>1686</td><td>53.65209409877481</td><td>-30.263467971467858</td><td>25.975597</td><td>28.158813</td><td>23.678898</td><td>14.176922</td><td>16.489271</td><td>12.01407</td><td>0.59103614</td><td>1.5090369</td><td>0.1561964</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.9999533</td><td>0.9983857</td><td>1.0000907</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>2122</td><td>53.42533280681198</td><td>-30.206807797453102</td><td>43.14925</td><td>45.20906</td><td>41.103855</td><td>10.700847</td><td>12.734738</td><td>8.695177</td><td>1.0359224</td><td>2.3431666</td><td>0.24461809</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.99863315</td><td>0.9988661</td><td>0.99900424</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>2358</td><td>53.386309946682225</td><td>-30.151687338709003</td><td>32.897717</td><td>34.574303</td><td>31.243422</td><td>21.52104</td><td>23.192188</td><td>19.837456</td><td>7.0889482</td><td>8.994638</td><td>5.1040273</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.99890614</td><td>0.99880886</td><td>0.999069</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.401</td><td>0.0</td></tr>\n",
       "<tr><td>2391</td><td>53.42445097665958</td><td>-30.131341921058365</td><td>35.092503</td><td>37.290653</td><td>32.8702</td><td>6.967719</td><td>9.138034</td><td>4.643543</td><td>1.314955</td><td>2.9628437</td><td>0.34391034</td><td>-0.105121404</td><td>-0.16556713</td><td>-0.10856155</td><td>0.00295228</td><td>0.0043948945</td><td>0.00637461</td><td>0.9985914</td><td>0.99917257</td><td>0.9986059</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",
       "18                          53.437731335599054 ...          0.0          0.0\n",
       "437                           53.3774850665501 ...        0.018        0.066\n",
       "911                          53.58617098415919 ...        0.309        0.015\n",
       "950                          53.38096745763396 ...          0.0          0.0\n",
       "1402                         53.30224445804902 ...        0.671        0.001\n",
       "1419                        53.521692591044356 ...          0.0          0.0\n",
       "1686                         53.65209409877481 ...          0.0          0.0\n",
       "2122                         53.42533280681198 ...        0.001          0.0\n",
       "2358                        53.386309946682225 ...        0.401          0.0\n",
       "2391                         53.42445097665958 ...          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": [
      "18981 28643 19715 40880\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# galaxies =  40880\n",
      "# galaxies =  40880\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/CDFS-SWIRE_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": 14,
   "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": 15,
   "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": 16,
   "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=(300,300))\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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add field name\n",
    "cat_all.add_column(Column(['CDFS-SWIRE']*len(cat_all),name='field'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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_CDFS-SWIRE_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
}