{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of Lockman-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_Lockman-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=\"table4578583776\" 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>45747</td><td>159.68151857635937</td><td>55.47504050268262</td><td>4.8409176</td><td>7.5914063</td><td>2.377019</td><td>7.6337776</td><td>10.513602</td><td>4.8249273</td><td>6.0518785</td><td>9.217531</td><td>2.996319</td><td>-0.004344711</td><td>-0.004251874</td><td>-0.009083424</td><td>0.0027976898</td><td>0.0039826636</td><td>0.005473484</td><td>0.9986701</td><td>0.9997284</td><td>1.0000503</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>47396</td><td>159.64899495296208</td><td>55.46824490825277</td><td>18.278954</td><td>21.442526</td><td>15.358194</td><td>26.076792</td><td>29.37539</td><td>22.793898</td><td>23.798712</td><td>27.506744</td><td>19.644882</td><td>-0.004344711</td><td>-0.004251874</td><td>-0.009083424</td><td>0.0027976898</td><td>0.0039826636</td><td>0.005473484</td><td>0.99911183</td><td>0.99854434</td><td>0.99965316</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>47540</td><td>159.65845030852466</td><td>55.46322240241412</td><td>36.482574</td><td>39.765846</td><td>33.18428</td><td>17.67385</td><td>20.673325</td><td>14.720339</td><td>4.5665603</td><td>8.127863</td><td>1.7675993</td><td>-0.004344711</td><td>-0.004251874</td><td>-0.009083424</td><td>0.0027976898</td><td>0.0039826636</td><td>0.005473484</td><td>0.99836725</td><td>0.9992167</td><td>0.9990493</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>195</td><td>160.0332240390528</td><td>55.579111480139275</td><td>120.195816</td><td>122.09578</td><td>117.8921</td><td>54.310596</td><td>56.64293</td><td>51.88438</td><td>22.482191</td><td>25.477785</td><td>19.6548</td><td>-0.100410834</td><td>-0.12629873</td><td>-0.098574206</td><td>0.003083905</td><td>0.0046224142</td><td>0.0062719053</td><td>1.000249</td><td>1.0023887</td><td>0.9998011</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.002</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>802</td><td>160.499590308208</td><td>55.657966862615325</td><td>62.15414</td><td>64.62259</td><td>59.537712</td><td>26.770145</td><td>29.04185</td><td>24.372713</td><td>5.111903</td><td>8.077736</td><td>2.2024164</td><td>-0.100410834</td><td>-0.12629873</td><td>-0.098574206</td><td>0.003083905</td><td>0.0046224142</td><td>0.0062719053</td><td>0.99918175</td><td>0.99904764</td><td>1.0003885</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>1027</td><td>160.2952476540941</td><td>55.78331312599789</td><td>70.6605</td><td>72.12855</td><td>69.009605</td><td>52.807888</td><td>53.2464</td><td>51.955193</td><td>32.80842</td><td>35.221222</td><td>30.213394</td><td>-0.100410834</td><td>-0.12629873</td><td>-0.098574206</td><td>0.003083905</td><td>0.0046224142</td><td>0.0062719053</td><td>0.9994053</td><td>0.998668</td><td>1.001359</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.996</td><td>0.181</td><td>0.0</td></tr>\n",
       "<tr><td>1157</td><td>160.0514504061468</td><td>55.65583021081948</td><td>51.25966</td><td>53.570736</td><td>48.919952</td><td>21.859148</td><td>24.04979</td><td>19.763071</td><td>1.962483</td><td>4.097533</td><td>0.6162174</td><td>-0.100410834</td><td>-0.12629873</td><td>-0.098574206</td><td>0.003083905</td><td>0.0046224142</td><td>0.0062719053</td><td>0.99853754</td><td>1.0017707</td><td>1.0000118</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>1501</td><td>160.13199683147965</td><td>55.677384218088775</td><td>45.98639</td><td>48.04334</td><td>43.772774</td><td>20.686108</td><td>22.740294</td><td>18.655178</td><td>7.9040394</td><td>10.849503</td><td>4.9271173</td><td>-0.100410834</td><td>-0.12629873</td><td>-0.098574206</td><td>0.003083905</td><td>0.0046224142</td><td>0.0062719053</td><td>1.0003701</td><td>0.9990755</td><td>1.0014738</td><td>2000.0</td><td>1602.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2014</td><td>160.42019667992284</td><td>55.757583003033346</td><td>56.996487</td><td>58.70289</td><td>55.052704</td><td>42.405376</td><td>44.316456</td><td>40.420383</td><td>17.39218</td><td>19.750242</td><td>15.001451</td><td>-0.100410834</td><td>-0.12629873</td><td>-0.098574206</td><td>0.003083905</td><td>0.0046224142</td><td>0.0062719053</td><td>1.0008599</td><td>0.999397</td><td>0.99925613</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>2146</td><td>159.7729867253161</td><td>55.47755665857878</td><td>53.331093</td><td>55.78666</td><td>50.771214</td><td>47.03356</td><td>49.688732</td><td>44.392822</td><td>25.791101</td><td>29.060278</td><td>22.35493</td><td>-0.100410834</td><td>-0.12629873</td><td>-0.098574206</td><td>0.003083905</td><td>0.0046224142</td><td>0.0062719053</td><td>0.99889505</td><td>0.99863374</td><td>0.998752</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.001</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",
       "45747                       159.68151857635937 ...          0.0          0.0\n",
       "47396                       159.64899495296208 ...          0.0          0.0\n",
       "47540                       159.65845030852466 ...          0.0          0.0\n",
       "195                          160.0332240390528 ...          0.0          0.0\n",
       "802                           160.499590308208 ...          0.0          0.0\n",
       "1027                         160.2952476540941 ...        0.181          0.0\n",
       "1157                         160.0514504061468 ...          0.0          0.0\n",
       "1501                        160.13199683147965 ...          0.0          0.0\n",
       "2014                        160.42019667992284 ...          0.0          0.0\n",
       "2146                         159.7729867253161 ...          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": [
      "28332 37232 26771 54106\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 =  54106\n",
      "# galaxies =  54106\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/Lockman-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=(400,400))\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(['Lockman-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_Lockman-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
}