{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of ELAIS-N2 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_ELAIS-N2_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=\"table4548985408\" 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>11</td><td>248.1300087910033</td><td>39.28381521299216</td><td>135.1172</td><td>138.37357</td><td>132.04105</td><td>241.43953</td><td>247.0385</td><td>235.96411</td><td>14.776271</td><td>16.201551</td><td>13.324307</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>0.9983577</td><td>0.9984549</td><td>0.9990002</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>0.504</td><td>1.0</td></tr>\n",
       "<tr><td>1177</td><td>248.25304998851303</td><td>39.38506074310171</td><td>45.13168</td><td>48.055695</td><td>41.99426</td><td>31.747225</td><td>34.681698</td><td>28.925915</td><td>20.46945</td><td>23.802206</td><td>17.398764</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>1.001136</td><td>1.0016457</td><td>0.9991617</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>1240</td><td>248.14556244175256</td><td>39.3444551402551</td><td>39.752747</td><td>43.417805</td><td>36.136375</td><td>24.193138</td><td>27.651049</td><td>20.686766</td><td>2.6165555</td><td>5.5804467</td><td>0.76012623</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>0.9989017</td><td>0.9982267</td><td>0.9982176</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>1428</td><td>248.18724366047473</td><td>39.31790314186429</td><td>38.220043</td><td>42.306248</td><td>33.794426</td><td>18.966982</td><td>23.775515</td><td>13.934008</td><td>18.363619</td><td>23.925161</td><td>12.839725</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>0.9985851</td><td>0.99838936</td><td>0.99888426</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>1882</td><td>248.09562960136293</td><td>39.32456833363475</td><td>36.744347</td><td>40.080452</td><td>33.232845</td><td>19.4309</td><td>22.839464</td><td>15.89841</td><td>7.3751225</td><td>11.266989</td><td>3.6602077</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>1.0008835</td><td>0.99922174</td><td>1.0007613</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>3278</td><td>248.32503255145218</td><td>39.29098365863322</td><td>27.09598</td><td>32.027275</td><td>21.819494</td><td>5.776911</td><td>8.001454</td><td>3.681089</td><td>1.7110959</td><td>3.3006494</td><td>0.5321178</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>0.9983905</td><td>1.00102</td><td>1.0020155</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.945</td><td>0.0</td></tr>\n",
       "<tr><td>3404</td><td>248.21881107876348</td><td>39.343426426010986</td><td>38.65989</td><td>42.778275</td><td>34.42715</td><td>25.771143</td><td>30.0455</td><td>21.102774</td><td>0.5093786</td><td>1.2109596</td><td>0.14446291</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>1.0003226</td><td>0.99964046</td><td>1.0003933</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>3442</td><td>248.23167223339607</td><td>39.39793039979966</td><td>25.090284</td><td>28.167233</td><td>21.906925</td><td>17.909903</td><td>21.15415</td><td>14.619595</td><td>21.020464</td><td>24.996624</td><td>16.620249</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>0.9988038</td><td>0.9986902</td><td>0.9989067</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>3860</td><td>248.33543078136202</td><td>39.291338316436686</td><td>24.810179</td><td>29.798965</td><td>19.862997</td><td>8.262665</td><td>10.347732</td><td>6.173093</td><td>33.527313</td><td>38.256157</td><td>28.788406</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>0.99941206</td><td>0.99866503</td><td>1.0003128</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.855</td><td>0.0</td></tr>\n",
       "<tr><td>3920</td><td>248.1335512620238</td><td>39.33213529914737</td><td>20.635244</td><td>24.43498</td><td>16.76355</td><td>21.83517</td><td>25.240269</td><td>18.54453</td><td>1.32843</td><td>3.1729927</td><td>0.34527078</td><td>-0.042273175</td><td>-0.07063735</td><td>-0.063878626</td><td>0.0041801627</td><td>0.005600419</td><td>0.008272672</td><td>0.99979544</td><td>0.9986509</td><td>0.9988719</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",
       "11                           248.1300087910033 ...        0.504          1.0\n",
       "1177                        248.25304998851303 ...          0.0          0.0\n",
       "1240                        248.14556244175256 ...          0.0          0.0\n",
       "1428                        248.18724366047473 ...          0.0          0.0\n",
       "1882                        248.09562960136293 ...          0.0          0.0\n",
       "3278                        248.32503255145218 ...        0.945          0.0\n",
       "3404                        248.21881107876348 ...          0.0          0.0\n",
       "3442                        248.23167223339607 ...          0.0          0.0\n",
       "3860                        248.33543078136202 ...        0.855          0.0\n",
       "3920                         248.1335512620238 ...          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": [
      "1356 3108 7077 19483\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 =  19483\n",
      "# galaxies =  19483\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/ELAIS-N2_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": 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(['ELAIS-N2']*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_ELAIS-N2_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
}
