{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of COSMOS 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_COSMOS_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=\"table4562654040\" 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>5</td><td>149.65897065886392</td><td>1.0580231204711505</td><td>259.1664</td><td>259.1888</td><td>259.11948</td><td>151.12997</td><td>151.18835</td><td>151.01009</td><td>73.61496</td><td>74.00352</td><td>72.74322</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>nan</td><td>nan</td><td>1.0009263</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>1.0</td><td>0.032</td></tr>\n",
       "<tr><td>25</td><td>149.81227516775007</td><td>1.1380104503254431</td><td>143.4434</td><td>143.7561</td><td>142.81905</td><td>62.70024</td><td>64.04658</td><td>61.34463</td><td>22.0542</td><td>23.578611</td><td>20.43236</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>0.9989214</td><td>0.9997732</td><td>1.0009726</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>1.0</td><td>0.701</td><td>0.0</td></tr>\n",
       "<tr><td>26</td><td>149.7653292922889</td><td>1.146612979378461</td><td>123.01255</td><td>124.32139</td><td>121.75696</td><td>44.73086</td><td>45.82607</td><td>43.534462</td><td>10.970429</td><td>12.379255</td><td>9.4349575</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>0.9992888</td><td>0.9985949</td><td>0.99889654</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.75</td><td>0.23</td><td>0.039</td></tr>\n",
       "<tr><td>562</td><td>149.70979216845038</td><td>1.0838525968572383</td><td>43.113503</td><td>44.684002</td><td>41.490818</td><td>38.97727</td><td>40.860394</td><td>37.11537</td><td>26.840424</td><td>29.030079</td><td>24.74074</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>0.999405</td><td>1.0010028</td><td>1.0005698</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.017</td><td>0.191</td><td>0.0</td></tr>\n",
       "<tr><td>922</td><td>149.70423777318376</td><td>1.0810752233503307</td><td>24.369719</td><td>26.025417</td><td>22.732168</td><td>11.93158</td><td>13.564464</td><td>10.290195</td><td>3.5616693</td><td>5.8070617</td><td>1.6288884</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>0.9991052</td><td>0.9997088</td><td>1.0000036</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.001</td><td>0.038</td><td>0.0</td></tr>\n",
       "<tr><td>938</td><td>149.68867105858163</td><td>1.12327810237206</td><td>35.8519</td><td>37.29199</td><td>34.4345</td><td>17.91227</td><td>19.248915</td><td>16.519487</td><td>8.592545</td><td>10.169408</td><td>7.0114126</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>1.0012283</td><td>1.0004742</td><td>0.9990382</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>1248</td><td>149.83311303861578</td><td>1.1138549923706929</td><td>25.049124</td><td>26.534006</td><td>23.572895</td><td>20.264694</td><td>21.723028</td><td>18.851646</td><td>11.588402</td><td>13.526619</td><td>9.704917</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>0.9988113</td><td>0.9990323</td><td>0.9990334</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>1260</td><td>149.7842249109162</td><td>1.1160711908975174</td><td>34.76683</td><td>36.24847</td><td>33.267197</td><td>26.436922</td><td>28.06083</td><td>24.8438</td><td>16.18805</td><td>18.032375</td><td>14.315778</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>0.99890786</td><td>0.999263</td><td>0.998835</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.004</td><td>0.002</td><td>0.04</td></tr>\n",
       "<tr><td>1492</td><td>149.80699421019696</td><td>1.1532820936327328</td><td>32.127407</td><td>33.31908</td><td>31.001556</td><td>15.776461</td><td>16.862877</td><td>14.631828</td><td>5.037994</td><td>6.5792923</td><td>3.5136344</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>0.9990633</td><td>0.9983134</td><td>1.0019722</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.077</td><td>0.16</td><td>0.0</td></tr>\n",
       "<tr><td>2160</td><td>149.73950170757203</td><td>1.1257841982200738</td><td>21.586315</td><td>23.04759</td><td>20.115265</td><td>10.38266</td><td>11.698925</td><td>8.98211</td><td>1.1254252</td><td>2.352386</td><td>0.29265693</td><td>-0.122337475</td><td>-0.17927988</td><td>-0.12913874</td><td>0.003905733</td><td>0.005946609</td><td>0.0075273667</td><td>0.9989128</td><td>0.99899</td><td>0.9998969</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.005</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",
       "5                           149.65897065886392 ...          1.0        0.032\n",
       "25                          149.81227516775007 ...        0.701          0.0\n",
       "26                           149.7653292922889 ...         0.23        0.039\n",
       "562                         149.70979216845038 ...        0.191          0.0\n",
       "922                         149.70423777318376 ...        0.038          0.0\n",
       "938                         149.68867105858163 ...          0.0          0.0\n",
       "1248                        149.83311303861578 ...          0.0          0.0\n",
       "1260                         149.7842249109162 ...        0.002         0.04\n",
       "1492                        149.80699421019696 ...         0.16          0.0\n",
       "2160                        149.73950170757203 ...          0.0        0.005"
      ]
     },
     "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": [
      "10523 11094 10805 12603\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 =  12603\n",
      "# galaxies =  12603\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/COSMOS_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(['COSMOS']*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_COSMOS_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
}
