{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of GAMA-15 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_GAMA-15_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=\"table4591653104\" 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>11369</td><td>215.786882269</td><td>-1.85700483338</td><td>40.7796</td><td>45.267</td><td>36.4862</td><td>18.5809</td><td>22.7936</td><td>14.7728</td><td>6.91201</td><td>11.6472</td><td>2.93799</td><td>-0.00521353</td><td>-0.0073088</td><td>-0.00549904</td><td>0.00170607</td><td>0.0024041</td><td>0.00340054</td><td>1.0</td><td>0.998391</td><td>1.00066</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>933</td><td>215.083694988</td><td>-1.98640608268</td><td>92.8056</td><td>94.0883</td><td>90.2895</td><td>53.6436</td><td>57.08</td><td>49.723</td><td>23.6917</td><td>28.6811</td><td>18.3563</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>0.999429</td><td>1.00086</td><td>0.999419</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>2451</td><td>215.378151138</td><td>-2.00802188288</td><td>69.0956</td><td>72.3874</td><td>65.4161</td><td>56.6288</td><td>60.3859</td><td>52.6174</td><td>28.5699</td><td>33.2069</td><td>23.9816</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>0.999793</td><td>0.99936</td><td>0.999799</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>3363</td><td>215.262710346</td><td>-2.09120853808</td><td>65.8632</td><td>70.2713</td><td>61.3451</td><td>47.744</td><td>51.9021</td><td>43.7428</td><td>24.7111</td><td>29.6462</td><td>19.7473</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>0.999231</td><td>1.00048</td><td>0.99849</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>3369</td><td>215.314349816</td><td>-1.99481607322</td><td>54.6203</td><td>58.9302</td><td>50.1897</td><td>26.2653</td><td>30.4857</td><td>22.1539</td><td>4.71526</td><td>8.66761</td><td>1.61304</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>0.999159</td><td>0.998536</td><td>0.99845</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>3376</td><td>215.225922741</td><td>-1.96664420907</td><td>62.8947</td><td>67.1365</td><td>58.5005</td><td>15.3957</td><td>19.6849</td><td>11.1401</td><td>11.6182</td><td>16.6958</td><td>6.28812</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>1.00013</td><td>0.998833</td><td>0.998752</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>4762</td><td>215.14007811</td><td>-2.03012672193</td><td>61.0752</td><td>62.8368</td><td>58.445</td><td>55.4213</td><td>59.1139</td><td>51.3223</td><td>37.9302</td><td>42.5041</td><td>33.1661</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>0.999716</td><td>0.9992</td><td>0.999442</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",
       "<tr><td>5204</td><td>215.232344654</td><td>-2.10180690157</td><td>50.59</td><td>55.0235</td><td>45.821</td><td>27.6658</td><td>31.7817</td><td>23.8302</td><td>5.95923</td><td>10.6724</td><td>2.07065</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>0.998302</td><td>1.00006</td><td>0.999608</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>7342</td><td>215.340369905</td><td>-2.00951568751</td><td>45.3826</td><td>49.8266</td><td>40.9956</td><td>13.3627</td><td>17.12</td><td>9.31206</td><td>2.52604</td><td>5.81531</td><td>0.695829</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>0.998608</td><td>0.998843</td><td>0.999101</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>8373</td><td>215.143974156</td><td>-1.81464706029</td><td>51.8925</td><td>54.4134</td><td>48.2186</td><td>37.576</td><td>41.5608</td><td>33.4788</td><td>19.0456</td><td>23.9243</td><td>13.8975</td><td>-0.004432</td><td>-0.00913993</td><td>-0.00936414</td><td>0.00194111</td><td>0.00267443</td><td>0.00400367</td><td>0.998649</td><td>0.998486</td><td>0.99891</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",
       "11369                       215.786882269 ...          0.0          0.0\n",
       "933                         215.083694988 ...          0.0          0.0\n",
       "2451                        215.378151138 ...          0.0          0.0\n",
       "3363                        215.262710346 ...          0.0          0.0\n",
       "3369                        215.314349816 ...          0.0          0.0\n",
       "3376                        215.225922741 ...          0.0          0.0\n",
       "4762                         215.14007811 ...          0.0          0.0\n",
       "5204                        215.232344654 ...          0.0          0.0\n",
       "7342                        215.340369905 ...          0.0          0.0\n",
       "8373                        215.143974156 ...          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/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/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/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": [
      "6011 12636 32227 116436\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": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: MergeConflictWarning: Cannot merge meta key 'EXTNAME' types <class 'str'> and <class 'str'>, choosing EXTNAME='GAMA-15_SPIRE500_cat_MF0.fits' [astropy.utils.metadata]\n",
      "WARNING: MergeConflictWarning: Cannot merge meta key 'DATE-HDU' types <class 'str'> and <class 'str'>, choosing DATE-HDU='2018-06-09T12:14:01' [astropy.utils.metadata]\n",
      "WARNING: MergeConflictWarning: Cannot merge meta key 'STILVERS' types <class 'str'> and <class 'str'>, choosing STILVERS='3.1-' [astropy.utils.metadata]\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/GAMA-15_SPIRE_all.fits')\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": 18,
   "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=(600,600))\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(['GAMA-15']*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_GAMA-15_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
}