{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of ELAIS-N1 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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat=Table.read('./data/dmu22_XID+SPIRE_ELAIS-N1_BLIND.fits')\n",
    "cat['RA'].unit=u.deg\n",
    "cat['Dec'].unit=u.deg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table112234241888\" 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>1738</td><td>240.323502369</td><td>53.8664488769</td><td>46.8985</td><td>51.0866</td><td>42.4965</td><td>12.8994</td><td>17.0401</td><td>8.71578</td><td>5.94041</td><td>10.7813</td><td>2.34564</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>0.999219</td><td>0.999439</td><td>1.00074</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>6532</td><td>240.338570856</td><td>53.9192688002</td><td>25.5266</td><td>28.2226</td><td>23.0541</td><td>5.21056</td><td>7.90706</td><td>2.49597</td><td>5.89167</td><td>9.41766</td><td>2.91544</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>0.998157</td><td>1.00253</td><td>0.9997</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>9683</td><td>240.356862126</td><td>53.8449426118</td><td>20.163</td><td>24.3476</td><td>16.0921</td><td>28.5381</td><td>33.4124</td><td>24.0506</td><td>1.53166</td><td>2.59421</td><td>0.603846</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>0.998768</td><td>0.998783</td><td>0.998934</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.004</td></tr>\n",
       "<tr><td>13211</td><td>240.290282679</td><td>53.9296144181</td><td>20.0389</td><td>22.8874</td><td>17.2335</td><td>1.8171</td><td>4.04798</td><td>0.478106</td><td>1.95313</td><td>4.39303</td><td>0.540583</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>0.999948</td><td>0.999067</td><td>0.999734</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>14584</td><td>240.316947032</td><td>53.8957498639</td><td>2.84408</td><td>5.79504</td><td>0.893829</td><td>0.582096</td><td>1.50438</td><td>0.162976</td><td>0.590756</td><td>1.52598</td><td>0.154182</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>0.999288</td><td>0.998752</td><td>0.999</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.006</td></tr>\n",
       "<tr><td>15889</td><td>240.373606377</td><td>53.8405757631</td><td>6.10968</td><td>9.96827</td><td>2.55668</td><td>2.40612</td><td>5.23493</td><td>0.648898</td><td>0.741465</td><td>1.63638</td><td>0.232297</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>0.999656</td><td>0.999318</td><td>0.999897</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>16604</td><td>240.297638115</td><td>53.9400519864</td><td>25.6065</td><td>28.3717</td><td>23.0378</td><td>18.8214</td><td>21.5068</td><td>16.0733</td><td>12.5192</td><td>15.5671</td><td>9.56722</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>0.999479</td><td>0.998762</td><td>0.998926</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>16633</td><td>240.387685886</td><td>53.8489269554</td><td>24.2334</td><td>27.6052</td><td>20.6351</td><td>13.8875</td><td>17.5224</td><td>10.3702</td><td>5.49907</td><td>9.28407</td><td>2.10961</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>1.00083</td><td>1.00112</td><td>0.999313</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>17652</td><td>240.334170797</td><td>53.8991757019</td><td>8.65886</td><td>11.4416</td><td>5.71122</td><td>3.22884</td><td>5.96436</td><td>1.16218</td><td>0.651599</td><td>1.60491</td><td>0.172512</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>1.00005</td><td>0.998502</td><td>0.998611</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.016</td></tr>\n",
       "<tr><td>21117</td><td>240.310179819</td><td>53.9058808283</td><td>10.2594</td><td>13.394</td><td>7.23452</td><td>0.812068</td><td>2.1808</td><td>0.196642</td><td>0.663182</td><td>1.71021</td><td>0.182146</td><td>-0.0085769</td><td>-0.012961</td><td>-0.00895119</td><td>0.00171384</td><td>0.00252763</td><td>0.00360931</td><td>0.998818</td><td>0.998719</td><td>0.999392</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.001</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",
       "1738                        240.323502369 ...          0.0          0.0\n",
       "6532                        240.338570856 ...          0.0          0.0\n",
       "9683                        240.356862126 ...          0.0        0.004\n",
       "13211                       240.290282679 ...          0.0          0.0\n",
       "14584                       240.316947032 ...          0.0        0.006\n",
       "15889                       240.373606377 ...          0.0          0.0\n",
       "16604                       240.297638115 ...          0.0          0.0\n",
       "16633                       240.387685886 ...          0.0          0.0\n",
       "17652                       240.334170797 ...          0.0        0.016\n",
       "21117                       240.310179819 ...          0.0        0.001"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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": 11,
   "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7485 11324 15153 34501\n"
     ]
    }
   ],
   "source": [
    "print(ind_250.sum(),ind_350.sum(),ind_500.sum(),len(cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/ELAIS-N1_SPIRE_all.fits')\n",
    "del cat2['RA']\n",
    "del cat2['Dec']\n",
    "cat_all = hstack([cat,cat2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add field name\n",
    "cat_all.add_column(Column(['ELAIS-N1']*len(cat_all),name='field'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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-N1_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": []
  }
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