{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of XMM-LSS 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_XMM-LSS_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=\"table4579678248\" 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>2218</td><td>36.7480808334078</td><td>-7.052145434569921</td><td>39.204155</td><td>43.153137</td><td>35.098793</td><td>23.942287</td><td>27.845127</td><td>20.009989</td><td>12.111338</td><td>16.306581</td><td>8.182208</td><td>-0.0016953372</td><td>-0.0025208844</td><td>-0.0026725244</td><td>0.0018706206</td><td>0.0024673948</td><td>0.0036161733</td><td>1.0021555</td><td>1.0001214</td><td>1.0054194</td><td>2000.0</td><td>794.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>12845</td><td>36.760928488491736</td><td>-7.049884548176648</td><td>33.782276</td><td>38.087944</td><td>29.350658</td><td>23.20576</td><td>27.11195</td><td>19.530603</td><td>15.020255</td><td>20.081295</td><td>10.053983</td><td>-0.0016953372</td><td>-0.0025208844</td><td>-0.0026725244</td><td>0.0018706206</td><td>0.0024673948</td><td>0.0036161733</td><td>0.9986852</td><td>0.99922943</td><td>0.99884653</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>44809</td><td>36.74242230737745</td><td>-7.041402534705651</td><td>9.197481</td><td>12.892316</td><td>5.3042684</td><td>15.714023</td><td>19.304167</td><td>12.107429</td><td>15.907669</td><td>19.946238</td><td>11.891371</td><td>-0.0016953372</td><td>-0.0025208844</td><td>-0.0026725244</td><td>0.0018706206</td><td>0.0024673948</td><td>0.0036161733</td><td>0.99936455</td><td>0.9993786</td><td>0.99908274</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>15103</td><td>35.177014919002914</td><td>-7.201778565897052</td><td>5.5803676</td><td>9.228039</td><td>2.2146797</td><td>1.7606028</td><td>4.4341307</td><td>0.5069792</td><td>2.0385528</td><td>5.0005684</td><td>0.51146895</td><td>-0.008181288</td><td>-0.013476827</td><td>-0.011018422</td><td>0.0029314223</td><td>0.0042432942</td><td>0.005684148</td><td>0.9983619</td><td>1.0006562</td><td>0.99877685</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>45480</td><td>35.166091679175175</td><td>-7.195019089663649</td><td>1.3216298</td><td>3.0564082</td><td>0.35105765</td><td>4.9257464</td><td>8.883197</td><td>1.9859923</td><td>1.7027897</td><td>4.1779885</td><td>0.4485338</td><td>-0.008181288</td><td>-0.013476827</td><td>-0.011018422</td><td>0.0029314223</td><td>0.0042432942</td><td>0.005684148</td><td>0.99830836</td><td>0.9990976</td><td>0.99974734</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>1.0</td><td>0.0</td></tr>\n",
       "<tr><td>48800</td><td>35.16143026710993</td><td>-7.188089827760059</td><td>1.2116406</td><td>2.9128726</td><td>0.30252212</td><td>5.747626</td><td>9.649314</td><td>2.3913972</td><td>1.1924134</td><td>2.960211</td><td>0.29562888</td><td>-0.008181288</td><td>-0.013476827</td><td>-0.011018422</td><td>0.0029314223</td><td>0.0042432942</td><td>0.005684148</td><td>1.0009773</td><td>0.99965197</td><td>0.9994526</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>255</td><td>35.52237585839879</td><td>-7.124849315074219</td><td>122.9378</td><td>123.779465</td><td>121.57131</td><td>92.115555</td><td>93.422585</td><td>89.80973</td><td>43.369507</td><td>48.237144</td><td>38.65148</td><td>-0.0500456</td><td>-0.07262875</td><td>-0.06625985</td><td>0.0029851412</td><td>0.004205069</td><td>0.005805382</td><td>1.00091</td><td>0.99898773</td><td>1.0001098</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.465</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>352</td><td>35.48902444414912</td><td>-6.967128135078509</td><td>91.12236</td><td>93.48963</td><td>88.115845</td><td>50.001392</td><td>53.475056</td><td>46.76923</td><td>16.030807</td><td>19.784487</td><td>12.653168</td><td>-0.0500456</td><td>-0.07262875</td><td>-0.06625985</td><td>0.0029851412</td><td>0.004205069</td><td>0.005805382</td><td>1.0006281</td><td>1.0015072</td><td>1.0010539</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>423</td><td>35.21393350538158</td><td>-7.210090014287288</td><td>89.4564</td><td>93.58212</td><td>85.34673</td><td>85.76751</td><td>89.758705</td><td>81.44578</td><td>48.753273</td><td>54.018124</td><td>43.34694</td><td>-0.0500456</td><td>-0.07262875</td><td>-0.06625985</td><td>0.0029851412</td><td>0.004205069</td><td>0.005805382</td><td>1.0010396</td><td>1.0033684</td><td>0.9987398</td><td>2000.0</td><td>2000.0</td><td>1043.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>446</td><td>35.560044153285936</td><td>-6.97927185425532</td><td>78.67002</td><td>82.06801</td><td>75.208755</td><td>62.876938</td><td>65.96965</td><td>59.934147</td><td>31.492777</td><td>35.334282</td><td>27.98502</td><td>-0.0500456</td><td>-0.07262875</td><td>-0.06625985</td><td>0.0029851412</td><td>0.004205069</td><td>0.005805382</td><td>0.99844414</td><td>1.0046273</td><td>0.99859154</td><td>2000.0</td><td>2000.0</td><td>1448.0</td><td>0.0</td><td>0.0</td><td>0.016</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",
       "2218                          36.7480808334078 ...          0.0          0.0\n",
       "12845                       36.760928488491736 ...          0.0          0.0\n",
       "44809                        36.74242230737745 ...          0.0          0.0\n",
       "15103                       35.177014919002914 ...          0.0          0.0\n",
       "45480                       35.166091679175175 ...          1.0          0.0\n",
       "48800                        35.16143026710993 ...          0.0          0.0\n",
       "255                          35.52237585839879 ...          0.0          0.0\n",
       "352                          35.48902444414912 ...          0.0          0.0\n",
       "423                          35.21393350538158 ...          0.0          0.0\n",
       "446                         35.560044153285936 ...          0.0        0.016"
      ]
     },
     "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": [
      "15079 19976 24942 50362\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 =  50362\n",
      "# galaxies =  50362\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/XMM-LSS_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=(500,500))\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(['XMM-LSS']*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_XMM-LSS_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)"
   ]
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