{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of XMM-13hr 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-13hr_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=\"table4520252248\" 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>58</td><td>203.58110083369883</td><td>37.4917880022157</td><td>59.979225</td><td>64.07353</td><td>55.937508</td><td>34.057064</td><td>38.77542</td><td>29.276796</td><td>9.733876</td><td>14.55617</td><td>4.6925993</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.9984363</td><td>0.9991087</td><td>0.9988863</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>166</td><td>203.5390792646518</td><td>37.50009161916207</td><td>24.715199</td><td>28.604898</td><td>20.923279</td><td>18.706024</td><td>23.183842</td><td>14.201668</td><td>3.7835758</td><td>8.086675</td><td>1.0598795</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.9991086</td><td>0.9983736</td><td>1.0001389</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>224</td><td>203.59511987537516</td><td>37.467351431706035</td><td>27.783052</td><td>33.636036</td><td>21.7973</td><td>34.859993</td><td>40.188812</td><td>28.695976</td><td>11.623971</td><td>17.223146</td><td>6.0133142</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.9984018</td><td>0.99886376</td><td>0.9995204</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>260</td><td>203.54675311134645</td><td>37.52231917885197</td><td>21.527163</td><td>24.563274</td><td>18.304916</td><td>16.28842</td><td>19.602747</td><td>12.814043</td><td>8.647319</td><td>12.503792</td><td>4.926727</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.99859375</td><td>0.9996638</td><td>0.9987574</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>315</td><td>203.56990420106914</td><td>37.48567088938067</td><td>20.332388</td><td>24.591063</td><td>16.161854</td><td>24.372955</td><td>29.327208</td><td>19.525688</td><td>19.948673</td><td>26.447765</td><td>13.775435</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.9984056</td><td>0.99919885</td><td>0.9989425</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>625</td><td>203.52680978374784</td><td>37.50980198273154</td><td>22.982061</td><td>26.458235</td><td>19.342941</td><td>23.523016</td><td>27.507263</td><td>19.433971</td><td>14.8508005</td><td>19.8244</td><td>10.11936</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.9984414</td><td>0.9984598</td><td>0.9986163</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>746</td><td>203.54011922710566</td><td>37.50759213169401</td><td>15.366308</td><td>19.454998</td><td>11.236373</td><td>18.772438</td><td>22.666807</td><td>15.01966</td><td>13.86425</td><td>18.20086</td><td>9.334939</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.9989052</td><td>0.9985437</td><td>1.000671</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>801</td><td>203.5341449487698</td><td>37.52230830791492</td><td>11.420288</td><td>14.67701</td><td>8.147232</td><td>8.545731</td><td>12.051027</td><td>4.862952</td><td>1.9820776</td><td>4.331666</td><td>0.54030484</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.9990154</td><td>1.0003593</td><td>0.9994764</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>882</td><td>203.57795665375247</td><td>37.484286747628445</td><td>21.82482</td><td>26.285109</td><td>17.385979</td><td>26.494005</td><td>31.933525</td><td>20.817125</td><td>31.467216</td><td>39.141296</td><td>23.553793</td><td>-0.066084296</td><td>-0.10193072</td><td>-0.070930876</td><td>0.006679933</td><td>0.009148511</td><td>0.013392322</td><td>0.9993349</td><td>0.99888355</td><td>0.99834627</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>13</td><td>203.2131734119072</td><td>37.731850905078915</td><td>111.005104</td><td>114.27185</td><td>107.59687</td><td>58.506542</td><td>63.042953</td><td>53.755768</td><td>13.019707</td><td>18.365221</td><td>8.137134</td><td>-0.041215327</td><td>-0.065389305</td><td>-0.057257924</td><td>0.0035761814</td><td>0.0048266333</td><td>0.0070608254</td><td>0.998119</td><td>1.0016934</td><td>0.99950653</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",
       "</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",
       "58                          203.58110083369883 ...          0.0          0.0\n",
       "166                          203.5390792646518 ...          0.0          0.0\n",
       "224                         203.59511987537516 ...          0.0          0.0\n",
       "260                         203.54675311134645 ...          0.0          0.0\n",
       "315                         203.56990420106914 ...          0.0          0.0\n",
       "625                         203.52680978374784 ...          0.0          0.0\n",
       "746                         203.54011922710566 ...          0.0          0.0\n",
       "801                          203.5341449487698 ...          0.0          0.0\n",
       "882                         203.57795665375247 ...          0.0          0.0\n",
       "13                           203.2131734119072 ...          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": [
      "84 174 508 1218\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 =  1218\n",
      "# galaxies =  1218\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/XMM-13hr_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(['XMM-13hr']*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-13hr_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
}
