{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of HATLAS-NGP 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_HATLAS-NGP_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=\"table4423431952\" 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>5620</td><td>198.67810403277073</td><td>22.782825747021217</td><td>75.33906</td><td>81.00346</td><td>68.97522</td><td>39.556675</td><td>45.78977</td><td>33.04933</td><td>28.148378</td><td>35.49167</td><td>21.063042</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>1.0007315</td><td>0.99976194</td><td>0.9981818</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>10126</td><td>198.78645688873345</td><td>22.795614824151485</td><td>54.42634</td><td>60.748096</td><td>48.047337</td><td>43.680668</td><td>50.739826</td><td>36.880558</td><td>21.175581</td><td>30.390871</td><td>11.401822</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>0.9995334</td><td>1.001019</td><td>0.999801</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>11030</td><td>198.60591398412348</td><td>22.839873783401888</td><td>61.662197</td><td>66.01873</td><td>57.179432</td><td>27.499857</td><td>32.024944</td><td>23.064465</td><td>23.492508</td><td>29.267403</td><td>18.256636</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>0.9991033</td><td>1.0002187</td><td>0.9987274</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>13921</td><td>198.44773550238446</td><td>22.78641246761983</td><td>55.012283</td><td>60.59494</td><td>49.40419</td><td>25.63158</td><td>30.68366</td><td>20.804756</td><td>4.423079</td><td>9.333742</td><td>1.2865666</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>1.0000682</td><td>0.9984229</td><td>0.99952877</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>15669</td><td>198.40193447011904</td><td>22.76217348263982</td><td>50.336792</td><td>56.91064</td><td>43.986225</td><td>35.300045</td><td>41.145355</td><td>29.23841</td><td>6.912807</td><td>13.845295</td><td>2.2600143</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>0.998864</td><td>0.9989364</td><td>0.99818075</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>16184</td><td>198.71723374736786</td><td>22.788190710682134</td><td>41.7909</td><td>47.97651</td><td>35.42771</td><td>17.260077</td><td>23.729965</td><td>11.180018</td><td>18.289879</td><td>25.09809</td><td>11.579571</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>0.9989734</td><td>0.9991646</td><td>0.99965584</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>16797</td><td>198.7883606221909</td><td>22.810555132868856</td><td>64.81837</td><td>70.72275</td><td>59.292595</td><td>41.29376</td><td>47.051853</td><td>35.82295</td><td>28.714626</td><td>36.389214</td><td>20.952528</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>0.9992116</td><td>0.9987821</td><td>0.99917877</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>20777</td><td>198.56766655970537</td><td>22.77024775790825</td><td>66.62577</td><td>73.42457</td><td>59.633186</td><td>40.797215</td><td>46.68347</td><td>34.75578</td><td>26.350426</td><td>32.89486</td><td>19.980335</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>1.0018829</td><td>0.99859524</td><td>0.9986872</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>22820</td><td>198.70220481683936</td><td>22.840583800064806</td><td>46.197536</td><td>50.97291</td><td>41.116272</td><td>30.46033</td><td>35.161453</td><td>25.57153</td><td>21.57527</td><td>27.576866</td><td>15.720423</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>0.99944633</td><td>0.99897194</td><td>0.99971336</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>24339</td><td>198.68948078757478</td><td>22.773917975366206</td><td>31.032272</td><td>44.4949</td><td>15.007576</td><td>33.452995</td><td>48.430088</td><td>16.294952</td><td>14.099572</td><td>27.125715</td><td>3.9723184</td><td>-0.46322954</td><td>-0.853918</td><td>-0.8942393</td><td>0.026352758</td><td>0.05728266</td><td>0.08157045</td><td>0.99997103</td><td>0.99958706</td><td>0.9990499</td><td>1798.0</td><td>2000.0</td><td>1735.0</td><td>0.005</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",
       "5620                        198.67810403277073 ...          0.0          0.0\n",
       "10126                       198.78645688873345 ...          0.0          0.0\n",
       "11030                       198.60591398412348 ...          0.0          0.0\n",
       "13921                       198.44773550238446 ...          0.0          0.0\n",
       "15669                       198.40193447011904 ...          0.0          0.0\n",
       "16184                       198.71723374736786 ...          0.0          0.0\n",
       "16797                        198.7883606221909 ...          0.0          0.0\n",
       "20777                       198.56766655970537 ...          0.0          0.0\n",
       "22820                       198.70220481683936 ...          0.0          0.0\n",
       "24339                       198.68948078757478 ...          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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qZr+fvF5uZgd1tlsiItLrdhmgzOxDwPuZ3Jo9C/zfTnZKRESknRHUbwNnAyMA7v40sKCTnRIREWknQOXd3QEHMLPBznZJRESkvQD1TTP7N2Cxmf0RcB1wWWe7JSIivS6zqxPc/Z/N7JXATuAQ4IPufm3HeyYiIj1tlwEKIAlICkoiIjJtmgYoMxsiWXeqPwS4uy/sWK9ERKTnNQ1Q7q5MPRERmTGtRlAL3X2nmS1NO+7uz3euWyIi0utarUF9FXgNcAfxVJ9VHXNgTQf7JSIiPa7VFN9rkl9V1khERKZdO6WOftxOm4iIyJ7Uag2qH5gHLDOzJUxO8S0E9p+GvomISA9rtQb1duDPiIPRHUwGqJ3AZzvcLxER6XGt1qA+BXzKzN7l7p+Zxj6JiIi0VeroM2b2P4DV1ee7+5dbvS+ZIrwR6Eved6W7f6junD7gy8AJwFbgje7+2NR+BBERmYt2GaDM7P8ABwMbgVLS7MSBpZUJ4BXuPmxmWeBmM7vG3W+tOucPgG3uvtbMzgM+Drxxqj+EiIjMPe3U4lsHHJ5sudG25Pzh5Nts8lV/jdcCH05eXwlcYmY21c8SEZG5p53tNu4B9t2di5tZaGYbgeeAa939trpTVgBPArh7EdgB7JVynQvNbIOZbdi8efPudEVEZNbQPS/WToBaBtxnZj8ys6vKX+1c3N1L7n4ssBJYb2ZH1p1iaW9Luc6l7r7O3dctX768nY8WEZm1dM+LtTPF9+EX+iHuvt3MbgDOIB6RlW0CDgA2mVkGWASoxp+IiOx6BOXuPwUeA7LJ69uBO3f1PjNbbmaLk9cDwG8CD9SddhXw1uT1ucD1Wn8SERFor9TRHxEnMPxb0rQC+E4b194P+ImZ3U0c1K519++b2UfM7OzknC8Ae5nZQ8B7gIun+gOIiMjc1M4U3zuA9cBtAO7+oJntvas3ufvdwHEp7R+sej0OvKHt3oqISM9oJ0liwt3z5W+StSJNw4mISEe1E6B+amZ/BQyY2SuBbwHf62y3RESk17UToC4GNgO/JC4gezXwgU52SkREpJ01qAHgi+5+GcQP3yZto53smIiI9LZ2RlA/Jg5IZQPAdZ3pjoiISKydANXv7uWaeiSv53WuSyIiIu0FqBEzO778jZmdAIx1rksiIiLtrUG9G/iWmT2dfL8f2hJDREQ6rGWAMrMAyAGHAocQF3d9wN0L09A3ERHpYS0DlLtHZvYJd38JtUVeRUREOqqdNaj/Z2avN7O0rTFEREQ6op01qPcAg0DJzMaIp/nc3Rd2tGciItLTdhmg3H3BdHRERESkWjvbbZiZvdnM/r/k+wPMbH3nuyYiIr2snTWozwEvAX43+X4Y+GzHeiQiIkJ7a1AnufvxZnYXgLtvM7Nch/slIiI9rp0RVCEpEOsQb+UORB3tlYiI9Lx2AtSngf8E9jGzjwI3A3/f0V6JiEjPayeL7ytmdgdwetL0One/v7PdEhGRXtfOGhTE1cvL03wDuzhXRETkBWsnzfyDwBXAUmAZ8CUz0466IiLSUe2MoN4EHOfu4wBm9jHgTuDvOtkxERHpbe0kSTwG9Fd93wc83JHeiIiIJNoZQU0A95rZtcRrUK8EbjazTwO4+0Ud7J+IiPSodgLUfyZfZTd0pisiIiKT2kkzv2I6OiIiIlKtnTUoERGRaacAJSIiXUkBSkREulLLNSgzWwmcB/wGsD8wBtwD/AC4xt1VNFZERDqiaYAysy8BK4DvAx8HniN+HurFwBnAX5vZxe5+43R0VEREekurEdQn3P2elPZ7gG8ne0Kt6ky3RESk1zUNUE2CU/XxPPDQHu+RiIgIbTwHZWanAB8GDkzON8DdfU1nuyYiIr2snUoSXwD+HLgDKLV7YTM7APgysC/xDryXuvun6s45Dfgu8GjS9G13/0i7nyEiInNXOwFqh7tfsxvXLgLvdfc7zWwBcIeZXevu99Wdd5O7v2Y3ri8iInNYqyy+45OXPzGzfwK+TVw4FgB3v7PVhd39GeCZ5PWQmd1PnBVYH6BEREQatMziq/t+XdVrB17R7oeY2WrgOOC2lMMvMbNfAE8D/8vd7015/4XAhQCrVilxUETmNt3zYuburU8wW+Puj+yqrcX75wM/BT7q7t+uO7YQiNx92MzOAj7l7i9qdb1169b5hg0b2vloEZFuZu2cNEfveW397O2UOroype1bbfXALAv8B/CV+uAE4O473X04eX01kDWzZe1cW0RE5rZWa1CHAkcAi8zsnKpDC6ndYbfZ+404A/B+d//fTc7ZF3jW3d3M1hMHzK1T6L+IiMxRrdagDgFeAywGfquqfQj4ozaufQrwe8AvzWxj0vZXJNUn3P1fgXOBPzGzInGdv/N8V3OOIiLSE1pVkvgu8F0ze4m73zLVC7v7zexintHdLwEumeq1RURk7mu6BmVmv21mS939FjNbbmZXmNkvzewbSZVzERGRjmmVJPFRd38+eX0JsBE4E7gG+FKnOyYiIr2tVYAKq16vdfd/cfdN7n45sLyz3RIRkV7XKkDdYGYfMbOB5PXrAMzs5cCOaemdiIj0rFYB6p3ERV7/G3gD8R5Q5Qy+35uGvomISA9rlcVXIN5m48NmtgjIuLueURIRkWnRTiUJ3H1HdXBKHuIVERHpmLYCVIr/t0d7ISIiUqdVqaNPNztEXF1CRESkY1qVOvp94L1U7QFV5U2d6Y6IiEisVYC6HbjH3X9ef8DMPtyxHomIiNA6QJ0LjKcdcPeDOtMdERGRWNMkCXd/3t1Hq9uqtoEXERHpqKlm8f17R3ohIiJSZ6oBqq1tekVERF6oqQaov+lIL0REROq0SpKoMLMVwIHA82b2UgB3v7GTHet17mAp49Xm7fFGxFZ3sLw/cdp7RKT7PT+Sn+kuzJhdBigz+zjwRuA+oJQ0O6AA1QHu8W9u+bURB5fm7V5zLMAxs5q2+E0KUiIyu7QzgnodcIi7pz2wK3tQ5I1tzuQoqL49SnlDlLwhDkZWc351YBMR6XbtrEE9AmQ73ZFelxaE9jTFJRGZTVrV4vsM8T+8R4GNZvZjqsoeuftFne+e7L70cKTRk4jMFq2m+DYkv94BXFV3bBr+vS8iIr2s1YaFVwCY2bvd/VPVx8zs3Z3umHSC02xk1Sw7UERkprSzBvXWlLYL9nA/elpDxt0uz/f4q/xerz5W/vJK6nn5PVFq++T7p2MdTESkXa3WoN4E/C5wkJlVT/EtALT1+x6wOwGhHFyqE/is6lo1qejVBxKRl8dQnvy3btik56ZEpEu0WoP6OfAMsAz4RFX7EHB3JzvVS6Yao5qloqdeu0mwqaScp0Sh5pOAIiLTq9Ua1OPA42b2OmAF8b3raXd/dro6JyIivavVFN+xwL8Ci4CnkuaVZrYd+FN3v3Ma+iciIj2q1RTf5cDb3f226kYzOxn4EnBMB/vVM4z2pvnKJY0a2uveb0xO0UXulJI1p2wwOaXnntSscidj1e1euWZA7fledX3QGpWIdF6rADVYH5wA3P1WMxvsYJ96RuUmX5fgUK+cgdfQnvIeJw5M1ec7kI+SOn0BVK8yFR3MnbAu4LQqmVR+oSAlIp3UKkBdY2Y/AL4MPJm0HQC8BfhhpzvWS8zSkx/Kmh1r9pam5xtYkyyIqVZPFxHptFZJEheZ2ZnAa4mTJAzYBHzW3a+epv71jHan+l74BynaiMjs0LKaubtfA1wzTX0RERGpaFpJwsyOrnqdNbMPmNlVZvb3ZjZvVxc2swPM7Cdmdr+Z3ZtWHslinzazh8zsbjM7fvd/FBERmUtalTq6vOr1x4C1xA/sDhCnn+9KEXivux8GnAy8w8wOrzvnTOBFydeFwOfb63bv8CblJjzJkKg/XMnES2n3yJuUQIov1rwEUos+iEjHffW2J2a6CzOi1RRf9WLF6cCJ7l4wsxuBX+zqwu7+DHElCtx9yMzuJ17Luq/qtNcCX/b4DnirmS02s/2S9/YUs8mSRU5tSnlQtaNuuT2iNqOu/F0xmkySCABjsjRSFKf4kQmMMKhtLwIZg0wwmVBevclh+fqV1POaz9bSlojsea0C1CIz+23i+1yfuxcA3N3NbEr/djaz1cBxQH3a+gomMwQhTsJYQRLYelF5e/e0dpLnl9JSy4tR4/ui8iir7vxiFKeh15c6KjpEEcmzUbXXd48DZT2VRhKRTmkVoH4KnJ28vtXM9nH3Z81sX2BLux9gZvOB/wD+zN131h9OeUvD7dnMLiSeAmTVqlXtfvSs1Syjr1nwghbtTT5DgUWke1Xf85btu2KGezNzWqWZ/36T9l8TT/ntkplliYPTV9z92ymnbCJ+tqpsJfB0ymdeClwKsG7dOq18iMicVn3PW3PY0T17z2uVxXdqqzea2UIzO7LFcQO+ANzv7v+7yWlXAW9JsvlOBnb04vpTmsDSRzhZa/xDc/eaEkdlkTvFKP6qTnSI3BkvOGOFKEmQmLxOoeSMFZ1SVNtejJx8qbG9nGRRnXzhPrm2FaUkcoiItKPVFN/rzewfiatG3AFsBvqJs/leDhwIvLfF+08Bfg/4pZltTNr+ClgF4O7/ClwNnAU8BIwCqaO2XmNVNZCMur2fDELir0IU19ortydviWvwRU5Udc2igyVrTyUmzx0tONnACc1qpgPHik4miBMqqtvjz3SydQtS5T5ayuRhOYdDiRQiMhWtpvj+3MyWAOcCbwD2A8aA+4F/c/ebW104Od7ylpRk771jqp3uFWaWmuJdvtGnlTQyg2LJU9eeiikJE0BcUNYcq/vjKjkE7g3JFOUde1P3k2oRiFQ2SUSmotV2Gy8BbnX3y4DLpq9LUs1aZEbsqZmzeHqwMXLs6Vii4CQiU9HqQd23AneY2dfN7IIke09ERGRatJri+2MAMzuUuOLD5Wa2CPgJ8brUz9y91Oz9Mtso8VxEukurERQA7v6Au/+Lu58BvAK4mXhNqmGvKNnzWj33FDZ5T/3eTru6XinyyrpStSjy5CHduvZKVYvadpVGEpE9qWU187IkWWJ/4iSJH2q7jc6qvpE3VI3wyfZMaATuFKLaczJhELeXvFIFInIqGX/VpZEih0LJmSg6/RkjG06eUoxgIu/0hUYurC2BNFGKA2F2l6WRJo/V/0BakxKRVlo9B7XIzP7KzH4J3Ar8G/BN4HEz+5aZvXy6OtmLkipFQJwoUV0DrzpoBWbkUv4UAzNyoVGK4kBTqgt65eeaCskBJ04tHytEFKM46JXfMlFyxgpeGTmVlRzGS7VbwpevNZlhmJJyjoKTyFT1YsHYViOoK4l30/0Nd99efcDMTgB+z8zWuPsXOtlBmdQso8/KhfqatKfNrFVXK68WOUSNzS0zBrV6JSKd0CpJ4pUtjt1B/PCuiIhIR7S7BrWCuHJE5Xx3v7FTnZLmBWPDIL18UC6Ip9xqpvKceJrPIV91IH7INp7fjWrOdyYiyEfOQDYgKE8rujNRcvJFGOwLyQST7ZHDWAR9IYRV1SWc+MHgMKmGYVXXKi+BBdZYUV1EpGyXAcrMPg68kXgfp3JauQMKUB1SXbao/ItVjhmG4walqLo9vuFngHxx8g8qDCBwyATGeCGqBLAg2YAqSOrslTxeqyp/4NBERF9oZANqkjC2j5XozxgD2aAmuI0VndCcvrq9OkpJ0kSYEm4jh4D0ihQiIu2MoF4HHOLuE53ujNQqLzlVB6H41/hAeSPDcqZc+b9u1VkLVtkMMbDaEVblWvhkcKqST7IA66tMTJScXNgYWEqevh5Vye5rWgJJQUpEGu3yOSjgESDb6Y5IOrPJr9p2qxxv0OzZqSaf4U1SHKavBJKCk0g7ei2Tr1Utvs8Q39NGgY1m9mOgMopy94s63z0REelVrab4NiS/3kG8b1M11QQQEZGOapVmfgWAmb3b3T9VfczM3t3pjsnuCwOIUtaUQjOKKf+2aDbPG29E2DgFF5Uf4m1YO3Lc4zWt6nb3OKmDlGulX0dEpL01qLemtF2wh/shuyFIMvfq5UKjP9O4epTLBMzLBjXvcQeSqhNBpS3eJXe85OyYiMgXo5rdcgsOW8cixoqTO+mWU85HChETpdp29zgTsOiTu/BWf35U1d6sjp+I9J5Wa1BvAn4XOMjMqqf4FgBbO90x2bUmSAOmAAAgAElEQVTyqCPAJ3e0TdoyBmE23jG3+pYfBsa8bMBIPqJYVc4oMCOXMcaLUby1e9WbRopONnL6MwFRVdgbKcTlkhbkarM4CiWnWHIGsrUb0UcOeSe1NFOk56JEpE6rNaifA88Ay4BPVLUPAXd3slMyNfGzUentgXlqanlg6QuJ9VvMl5UfAq6PH7ubWi4isiut1qAeJy4M+zpgBfE952l3f3a6OiciIr2r1RTfscC/AouAp5LmlWa2HfhTd79zGvonbWpWGikTGOaND+LmQiMM4inAMk8e/u3PGOPFyalBT0ZPY4WI/qoSSBDvJbVtLGJBX0g2rJ3OGyk4/RkqpZHK18qXIBN4bWmkSpkmJwyqSyNVVUBHIzKRXtJqiu9y4O3uXrMxoZmdDHwJOKaD/ZLdUFchCYBMABmMXEgcdJK1njATnz2QMYbyERNFT86PA1dfaIwW4hp85SnCkkMhKYGUC+PSSOXpwPFiicGcMT8XEPnk/k/DeScb1K5HOZCPIIg82X+qNuoUIwiDxonDcg0/BSmR3tAqi2+wPjgBuPutwGDnuiS7I63aRNxulTWqbBBvMmg22R4ERl/dFrxGfCwbNpZGgrjU0UTRG9aqRvO1+0iVFaK4bmD9kYjJUVO9qEl7eW1LROa+ViOoa8zsB8R7Qj2ZtB0AvAX4Yac7JrunyZZRmFm8M1RKEEtLigBwTwrT1l+r8p+Uz2nSr6DJm4z09mZX0zSfSO9olSRxkZmdCbyWOEnCgE3AZ7Xlu2iLQpGZ8dXbnuB3T1o1092YFi2rmbv7NcA109QXmVW0j66IdFbTNSgzO7rqddbMPmBmV5nZ35vZvOnpnuxJzf6wwyYHgiA9M7DJ8lBlfSitGkQptd2bng/p7VqDEukdrZIkLq96/TFgLfEDuwPE6efSpWrrN0zKhAHZlNpIA9mAZYMZMnV/G/pCY6+BgFxQlSHoTqHkDOfjXydLIMXfPz1UZDiflEaqlEByhibSSyNNlOJqFLsqgVQdEScDoYKVyFzWaoqv+k52OnCiuxfM7EbgF53tlrwQ1Tvy1t+/wyCuIlHeAr6c0ZcNYdlghh1jJcaLXtVuLBkwhiYihpLySGXxLrrxdu/Fqs/aPh4xko/YezBT8/kTxXJppNpIWPL4Wmn1A6MkrdzqZhSrn40SkbmpVYBaZGa/TTzK6nP3AoC7u5np362zQKuMvjBl+s7M6MsG5OtKoZcDVbMSSPmosRp5IYqroTdUL/e4PUhJxfNWy1pK3RPpOa0C1E+Bs5PXt5rZPu7+rJntC2zpfNdERKSXtUoz//0m7b8mnvKTWaxcLLZ+VBQaDGSNsYI3tC/sC9gxHtWMvJy4jFKmqjwRxKOkzaNFFvdnyFU9COwer18NZIKa0kjucRV0p640EpOJF2nVzlVdQmTuamc/qAozu7RTHZHOMBqTJow44GQsri5RfSwbxuWK9poXVgKLAX0ZY3F/wAGLMgxm48d3y4/wOlCInGIUEUURE8WI0YKzfdx5fHuBzSNFSlGcRDFegrEiPD8esWOiVNkU0YnXscaLzlghituTazuTVSeU2SfSO6YUoIB1HemFdEx1CaRKsKoqdVQOVpPnW7JGZQzmLBm11LYvGQhSl4pKEYwWqUmkcOKkieFCRLEuiIwXk/qAdathJY+TOKK6qOMoSIn0kqkGqOc60gvpuEqQsvp2a0iWKGtWAilKr5hU0fi0U+20XbW4vfFYkATEepYyzScic9OUApS7n9HuuWb2RTN7zszuaXL8NDPbYWYbk68PTqUvMnXdeF/vwi6JSJdoWeoIwMxeDLwPOLD6fHd/xS7eejlwCXGx2WZucvfX7LqbIu1Iz1PXzr4is9MuAxTwLeLKEZcBpXYv7O43mtnq3euWTKfmFcjTj4RB82nBZkoRWND4XFQpapKdlyRPNEzneZw5mHZ+WnfL61IKUjKX9ErB2HYCVNHdP9+hz3+Jmf0CeBr4X+5+b9pJZnYhcCHAqlVz/w9luoXJrruFul13s6GxuD9kaKJUsy9UNjAOWJTluZEiE8nOu+VMvPpNE92dUuQ8tHWCfeZnWDwQEjCZ1PDcSMSCvoAFfUElgcMdxkuQdehL/oaWA1KUvC9ssodUUNM+GUiNyWClQCXdrvqet2zfFTPcm5nTqljsUjNbCnzPzP7UzPYrtyXtL9SdwIHufgzwGeA7zU5090vdfZ27r1u+fPke+GipF5iRCyb/Qhjx61xoLB0IGUh24A2MeJPDTMDKhVmWzcsQeZxdVylLZPH7C6WIkULESCHelffpoSKPPJ+nmKSc55PnnnZORPx6qEgpqc9XjpOFyBnJR5WafGXllPS0UVzkk8Gy9nmt9PNFulH1PW/B4j1xu52dWo2g7qD2H8XvqzrmwJoX8sHuvrPq9dVm9jkzW+buqlIxQ8yM0Brv/GZGfyagENU+pGtmzMsF+HDatWC00BgSxovxbrxhXVZfyeOAlKvb3Td+mNgJg8Z/S8UjpsYRkR7eFZkbWlWSOAjAzPrdfbz6mJn1v9APTkomPZvU9ltPfK/Z+kKvKyIic0M7aeY/b7Othpl9DbgFOMTMNpnZH5jZH5vZHyennAvck6xBfRo4z5ttDCTTKm3kYcTbb6S1Lx0IqH/MyT2eHqx/h7vz/HipUk29un14Iq5CUX+dQimeEqwXJVt5aD8pkbmp6QgqGeGsAAbM7Dgmp/oWArvcsNDd37SL45cQp6FLFzGDEHCLM+8g/oPPhnHSxDw3hvIRhShu78/Avgsy7D0fnt5ZZHtVrb6BrNGfsWRaLyIirjKxeaTE5pESe80LWT4vxJPKEWNFZ9tYnDSx17wQM6MYxZXRx4oR2QAGc3EwLD9EHBFvxZEJmlVIT8n4S/6jKUCR7tZqDepVwAXASuKNCsv/O+8E/qqz3ZKZUrmZe7zXU3nEUW4PDRbkjJ35yfw4I06eWLEwZCQfka8MggwzGMjGZY3Kg6PyO58fLYE7C/rCyuc7MDQRB7klAyHVSfCFCIbzEYPZoCa4OPG1sylp7PGPoiAlMhu1WoO6ArjCzF7v7v8xjX2SLhDf0D31+SGPk7bT3tWQqt6qPQL667fxTa7cn0kvgZQJLH0KUoFGZM7Z5RpUdXAys+s72x2RmOKNiLRag7q7vgl4cbnd3Y/uZMdERKS3tVqDeox4venvgDHiAHUT8Fud75Z0s93ZlT1g8gHcaqXIGzY7hHhNKW1r+PL+UWlbyad1bFcljlQCSaR7NZ3ic/ezgf8ALgWOcffHgIK7P+7uj09T/2QGmVlD+jjEpZEW5BpTywMzDl2WZV628X1LBkL66tLOI3ce3pZn21ipZu+nyJ0nd+TZPFKsSiOPfx3NRwzno6RWX7JhoscBbaJEpd19Mp28XImiPu18shzT1H9vRGbaV297Yqa70HEt16Dc/T+BM4HTzOwqIDctvZKukRakzIxsGLCoL0ySGeK/SKEZA9mQQ/bKsWpRtuY9YWAs6g9Z1B9QiuJdc4cm4nT1J3YWeXBrnnwpYqwYsWMizgT89XCR/94yQb7klKL4eajIYazgbBktxd9HcRml8maGo0WSjRG9prxRpQRSSpDSs1Ei3WmXxWLdfQR4j5kdA7yk812SbmNmlQKv9e39YUChFDW0Lx0IeWx7oeFauTBgrFCg/rnbsaKzbbxELqz9N1O+5OwcL7GoP6xpd4/LJg1kG7P6ihEEQXoJJM3micweLUdQZvZSMzsk+XYBMN/MXt35bomISK9rlcX3SWA9kDGzHwGnA9cAf25mp7n7+5q9V3pLJph8CLfa0oGA7eNRw9bxC3IBQ/moZhTl7gznI+bnrKFg7HA+IhMYg7naf08VSo5ZxEC2dnQVuVOMaEi+iAvPxlty6MFdke7XaorvlcCRwADwFLDC3UfN7GPAXdRWN5ceUL/XE8Q39HnZIFkbioOOAWEABy/NUYrg0W15to1HccmkAFYszBJ5vMa0bbxEKXLGi85wvgSUWD4YsnwwLoG0cyLiueESZgX2nR+yZkmOwOIpwXK5pYFsxLJ5GcKkPJMDBSA0pz8Tbw/iVX2PHDIKUiJdr9UUnyfFW8v/Nq6sN+/ifTIHlfd5amy3eJuOwJiXNbKhEQbl5Ir4+4OX5thrICAb1J6/34IM87NW2S+qnLCwZbTEo9sKbBmNKJQmg8qvh0vc/ew4O5NagCTnjxacZ4YKFJL9pcpKHh+LUjIgik5quxImZDaZ65l8rUZQPzCzm4B+4N+Bb5rZrcDLgBuno3PSfZIKSOmFjpKD9SOTMDBG8o3tgRnPjzfODUZJJXSo/ZzIYX6utj5fWf204ORntPhZmrVrBCXSFVrV4nu/mb0kfum3mtnBwG8TB6srp6uDMkc0K9/X4nQR6W2tkiTM3W8pf+/uDwP/nHKOJkRERGSPa7WW9BMze5eZrapuNLOcmb3CzK4A3trZ7slc0ewvWmDpo6WU/Qnj9ih9TSlqMu3Y9N9PLTY5TKs6UX6L/jkmMn1aBagzgBLwNTN72szuM7NHgQeBNwH/4u6XT0MfpctYk6ASP7jbuIsuwIuX5VJLIB29Tx9L6nbkdXe2jRYZzZdqAkXkzgNbJnhwywTFqLrUkbN5pMgT2wuU6trHSx6XUqprj4CJUu2OvOVfy88dV5dS8qoAqCAlMj1arUGNA58DPmdmWWAZMObu26erc9K90pIlDMgkWXyFkpNP0sDNYF4u5NDlAVtHSzyxvUBg8S69ZiHH7TfAltEid/96glLkle3gt4yWyIURSwZCipHz6+EixQieGynx0PMFTjlwHgMZY9tYiUIE24jYPFrixXtlmZ8LGS1ESdkjGC0UWdIfMC8XVFLRIa5GkQni9PfqIFSM4tFdQDkzsC5pw6t+H0SkI3ZZ6gjA3QvAMx3ui8wyrUZSuYxRKnpD+7LBDFtHiw1TeMvmZciFE+wo1B7Il5xNOwsNDwIP5yPu+fU4By6pLQ9ZiJwndhTZfyE1ldAd2JmPyITWUCG9GKU/vBtVglB6FFJwkm5QnWr+uyetanHm7KPnmUREpCspQEnHNPvLNZBJX6da2BeQTXmeKX3z97hyxY7xUkP7RDHi+dHG9lLkqee7x9XVGyqdu8cP/6a0V6911X9GWnvk6ckd3qK9VaKG1sGkF7Q1xSeyO3JhUnaoNFmOJABWL4lLIG3aWWAoH5dA6s8YL1k1SCmCO58e49FteQBCgyA0cuHkulbkznPDRe55roQBhy/vY/3KeWQCeHpngUe3xVXUD1qS45QD59GfMXaMRzy1s4ADi/tDDt+7j3nZgLFCxNbREpFDNiyx7/wM/ZmAYuSMFOI6goHB/FxALgyI3BmvKuk0kA3IBPHPOVH0ytRlLvBKsC1Ek1mJGYvXvCBuK7cHTLZXr4XhniSQND64rLJMMtcpQEnHmMUjpVwYF2+d3L3WCEI4cFGWraNFhgqT6z9BCCesGGDpQMDGX0/UjJxyGWNiosQDWyZwn1wjun/LBI9sy3Pw0hxRVTB4dFueTTvyrFs5D5g8f9tYiZ8/McrapTkCm6zTly/BkzuKLBkICKvSCqOkJmAu8JqAEJdZigjLmSBV8lG8HhbUpS0WHYqlxgoXUfKeTErAiRwsJRqpdqDMdZrik44rB5/6G2kQGONRYxJCJjCeGS4Cjc82bR0rUYxqn5MqRpANjEKptr3k0JeNR0PVFdWdeGRWrv1XzaEmODUebbeVKZfDMJpP3WlGT3qRRlDSldJXqXaTdiqUHrGr4rGzLctPIygREelKClAyo5oNbJqVQGqW0QfpZY3cPXV6rFUJyWbHmr0jzqprlnE3lQy9tEnH1n1tOu2oND+ZAxSgZFqETf6m7T2YIRc0Bp0TVw6w7/ywktlWtmZJlv0XhFRno7s7W0YKPDOUJ6pabCqVIh7fMsJP7nuKfHGybFKhFPHscIGv3fkso/kSxaS2UaHk7Bwv8X82bmf7eIl80l6K4jT0q381xOaRYqU9cidfivjpYyM8tXOyvZye/stfj/HYtjyF0mQppWLkPLEjzjSsLtdUipwdExG/Hi7WpKqXdwceykcNZZniz6+tHVh+HXnzQCgyW2gNSqZFYIYF8Y2zUqGBOL185cIMQxMRW8YiAoOBrBFYhrMOWcgT2/Pc8OgI7jA/eU5q6bwMz4+WuG3TKONFZ8dYgUIp4vnRAk/tyHPIsj76MwF3PfQUm7bElbl+/qtnecPJB3Pw3gv50f2bufWxHTjwowe28vvr9+PUNUu49qEhvv+rIQoRfP9Xw7zxyIWcfehC7npmjKseGGK86Fz78Ai/ceA8zj5kAQ9vy/Od+4fYORFxzYPDHLNvP+ccvpDt4yV+9NAw28bigHXg4ixnvmg+kcPPnxxlS/KM1tLNIS89cB792YBHt02wI9kb65nhIi9ammMwF7BjPGIsqcgxko9Y3B/Sn4kzBMvVNQqRkwuNbOA1Gy5GXpuc3qwihki3stn2L6x169b5hg0bZrob8gKUovS/c9vGShRTDt359CibdhYb2jc+Pcodm0aoq4LExNgIo9u3Uopqj1gQ0jd/EV43XjMzFi9enHoDX7Mk21AaCWAwa6nnL5sXkk0ZLi7oCxjMNbYv7g/YZzDTkOHYnzH2Hsw0fHZg8QPNaZ/dH6YHocAUnLpUW38oaw472v/u8u/vkQ/soiSJtn52jaBERHrETG8RP9UAqTUo6RrNHj/KBtawFgWQDY152cYDZpDNNP7by92JivnU9vyOzamfPfzck6nrOEPPbaJULDSev20LhYnxhvbR0RHGxsYa2scLJXaON44OS5Ezkq8fG8Z9naivnEvr8kutkzgamkW6hkZQMu3Kgah6ps8MlgyERA47JqLKlht9ofE/DhykWHL+66nJEkhLB0LecORiXn/4Iq7+1Q5+8sgwkcP8vpB9ly3DWMazW57nsWeeI4qcqDBONLyNQlQkt2AJ2SUrsDBDftvTbP/ZN8hve4ZFqw5nv9PfQm7hMia2Ps3j3/xbbn9wA0tWvZiT/+ijLD7gxYzvfJ47L/8wj/7se8xfth8vf/cn2f/IkylMjHHXdy7jvuu/Td/AIGe9/S855MTTiKKIu277GbfedD1BEHDWWWexfv16zIxfPrWDH9//HJE7rz5yOa8+Ym+yYcDmkSL3PDtBIXIOW9bHSQfMIxca48WILaPxliOL+wNWLsyRDa2SLDFWhEzgzM/GlTDc4woV5YoT2cDjyhl126SoGoV0q46tQZnZF4HXAM+5+5Epxw34FHAWMApc4O537uq6WoOaGypZZ0xORpfXSdydiZJX6t2V24slZ+tokSd25MkEk9tmxFl5Rb593864vFL5busR4/kCd91xOxNjI5XhQhAYpQgKT93H2ON346V4JBSEIVi8TrXj3psgKhCVSpgZQTbHsjVH8tx9t0GpQLEQB8pM3wD7HHoiO3dsJypMUMhPAJDr62f5wUdSWnogE+Nj5PPxZ/Tlcizae38WHv2bDFUH4oyxcCDDbx1zABNVFTFCiytrvHzNIMkWXBWBwerF2dQ1r8GskUlpDyhnVDYpyqtANZ2mfQ1qplVN8c34GtTlwCXAl5scPxN4UfJ1EvD55FfpAZNBpHEfpnINv/opv0xoFCMnV3fjzYYB2yciMmFQMyrDAkaHhyhOjNbMZUWRE40NMfrYRogmq5tHpRLuRbZtvLbm+u5OKT/B03f9pGFOrDgxxuYnH4YwW9Oenxjn+ZECQWZnTftEPs/OYCGF0SLV/49OFJ2BXI7hfG39vpLD/IxRiryhBJMRB680zco1VReerb+WgpN0m46tQbn7jcDzLU55LfBlj90KLDaz/TrVH+lOTbPLmjW3uIk2W8MKgvS/5lPNbGtWfqnphoa79X/XFB8S3p2PEJklZjJJYgXwZNX3m5I2ERGZY3YnxX0mA1TaPztT/0FoZhea2QYz27B5c3q2lfS2ZmMhM6upLlF1AI8as+FaSS+aBFGT60SlKV7fIe0naVbrdk/XwFVGX/eovucNbW81ETW3zWSA2gQcUPX9SuDptBPd/VJ3X+fu65YvXz4tnZOZlU0pfwSwfDDL/FxQM51nwBF793H48j5yYe01DthnL045fDW5TFiZHsxmQgYXLuHol55JJpsjDOM3hZksmb4B9j3tfMK+eYTZHABBmMEyfSw8/jWEg4sJc/3x55phmRz9Kw8ns2AvwmzfZJ8yOcJ5C8jOX0IQTi71hpksueIoA9nah3AzoTEyUWh4uDgwGC/GKefVCU3lrTmG8407AUO8ppVW6qhcAqmeYlN3qb7nLVi8dKa7M2NmMs38KuCdZvZ14uSIHe7+zAz2R7pIYEZfstFhubpENoC+MOCw5X1sGy/x6PN5HNhnMGRRf8hhy/t5cOsEX964nR3jESeuGODoffsJjjuJs086hEu+dwuPP7edI9YexPFHvJhMJsNLTj+LH33133jsgV+y9JD17P/S88gMzGfVq/6Qx//zn3n2v65mYPWxLP6f7yCzaG+WnnkRO274Itt+/k1y+6xh6avfQ275ajwqMXrv9Qxt+B6ZBUtZ+j//lP4Vh8YZic88yOiDtxKEGQ58yVksXvkizIyRsXG2btuBu3Pcqr1Ys/eCyY0biYPGigUZ9luYJcIYyjuD2TgBYiBrLOgLcYzhvNOfiZ8LCyx+bszMyJfijL1MUng3bFJRQgkS0q06mWb+NeA0YBnwLPAhIAvg7v+apJlfApxBnGb+++6+y/xxpZn3nnJppPqb61ihRL7kDeWAnh8tsmlHkTCsbd8xVuS2J4cI6jLuCiXnhge3EOTmNXz2k488iPUPNvZp53N4pr+hT9lsFnLzsCCsaV8yv48Fg4M1oymAveYFrFqYbUgJX9hnvGhpH9m6J5RzoXHgokxqCvmygSA1IWRexjAFp27UU2nmdWtQM5tm7u5v2sVxB97Rqc+XuaNZllxglpq5FwYWP8Cacp1cNktKIQYyffNIW6oK+gdTp7+CvsHUtacg14/XBSeIpwnrg1P5Z0gLNmbWIlV8itmECk4yS6mShMxJVv9UayIbkBqg+jPGaKHxDQPZgNFC4xsGsiEjE43tudAoQEOwywRGaLVb0kO8xpTWDp3fBLhcYUJBam7ookKwe4wClHS98v2z/h7enzH6MsZ4wSkkESE02HdBlv0WZHliR4Fnh4uVcw9dOcBpBw1y26ZRfvbEKMUI5ucCTj94kPeeuozrHx7m3+/czkg+Yn4u4ILjFvEbq1dy66Pb+dh1j7F5OE9fJuCC9fvzhuP25aHnhvnoD+7jsa0jhGacc/xK/uhla9kykufjP36c+58dBeDlaxfzrt84gAi4YuN2fvlsXG3isOV9XHDcYgayAbc+OcqDW+PqFMvmhbzioEEW9AU8N1Jiy2gJJ642cfCSOEkkX4KJJKoFBov6AjJB/HtUHRxzQTk7L32aVEFKupm225BZo/6v6mQxirgsUpRUWyjfhEtRXKNuaKLEkoGwUjapGDkTRef+zeO8eFk/YRBPmxVL8fk/fmSYUw6M699lgriKQ6EU8cP7t3Ly6sUM5kJymYDInUIx4qYHN3PYfotYOpijLxtP700UI+7aNMRegxlWLuqnPylqmy9GPLGjQMGdg5bkKlUxilG8WeJwPmLlwmwloaG8YWG+5CybF1am68o7BRswmAtqSkJ5UiKqnNGYOr2nKb9usEfXoGbZCGrGSx2J7FFmk0Gq+kZqZgQ4FtTu0RQGRr9BNghr2jOBEWThiH36qf7/JBMaYQCvXDu/5nPDwAiDkDMPr33EITCjLxvy8kP3abjh92UCTly1EKO2lFMuE7BmaS4OLnV9WjIQstdAbV8DM/oysKi/dg+ocjmotH2pzOKRU/M1qfTgJNJtFKBkVmleGclalEdq+hjvHurTFOvetQgcaUemK3ho9CTdRvtBiYhIV1KAEhGRrqQpPpnz0jLOjbjCQrEh8SKuYDFRokF/BiaKjdfqCyAf1bYbcRWHKNm1tvpY+fnhUl17ea+m+hJ+7vFaVFo6U+ST12unPb5e4xYnyubrfrMsCWKPUICSOaH6uafaTf0MCyByr9z4jTghIpfsszRW9MoNfSBrBJahFDk7J0oUorh9YV9INozP3zZWYqzohBbvbNuXZPTtGI8YKThG3N6fie/2QxMRw8n27Qv6Aubl4omL0ULEjvEIJ052mJ+LEyEmihE7JyLc4/T4+X0BgcWfPV6Ms/fCAPozQbJDbm2NvkwwtYd5lb0n3UoBSuaMyk02JdMvDlSNzwKFgTGYTUYcQW37koEMxchratiFgbFsMMNEMappDyzOwpufc8xqA8TC/pCBXNDQPpgL6csYUVT72X2ZgGWhpfZpXpYkQE3OzpfTzrPWvGpE/c9daUPBSbqXApTMOU0z/VrcuJtNhzXbsXaqO9m2KluUtrFhsz41zfSzcr291I9RcJJZSUkSIiLSlRSgRHZDkDJaKa//pNR+JWOT215UywZGX9hY9DZscn6z9vL2HOWkjEZNtpKfXYVkpMdoik9kN1hS1qJc3aIcrMrtmXIGH1TKELnH61lO/BVUXSubnF+KPA5WFj98HCb7+MaZfJPnJ8tpROXrVEWs+oy8hkoTlc/dc78f0lm9mMEHClAiu23yxl+bfFH5FU8CRW077jW188rHzL1hrSpec/KG0kjll0GT1PBWW2xUv1+kmylAibxArUoppQePpvWamszEpV8nvtau+7cn3iMyE7QGJSIiXUkBSkREupIClEiHTHUmbU/OvMWJFY0pfeWEC5HZQGtQIh1SLr+UVgcQGtsDMwKLyy5V74pbnTdR3V5+0La+1p/VfUa5ip+1WMsS6UYKUCIdZMkzS6kbLaa2xxUkLGrM3AOw6rp7VvVrdeBKS8xQcJJZSAFKZBpMNQuveVmmqV0HVNJIZi+tQYmISFdSgBLpUntq0BOXQHIlR8isowAl0oXKlcmrEx7idpvSdhrJEcqpEgpSMpsoQJx2ejQAAAg8SURBVIl0MbP04hLV225UB62a16QHLQUpmS2UJCHS5ZpXQErfGyo+1HyCUAkTMltoBCUiIl1JAUpERLqSApRID1GihMwmWoMS6XK7KpkEjaWO0trLx7QGJbOFApTILFBfMqncVjHVdpk1lg7mZroLM0YBSmQWmXrJpM71RaTTtAYlIiJdqaMByszOMLP/NrOHzOzilOMXmNlmM9uYfP1hJ/sjIiKzR8em+MwsBD4LvBLYBNxuZle5+311p37D3d/ZqX6IiMjs1MkR1HrgIXd/xN3zwNeB13bw80REZA7pZIBaATxZ9f2mpK3e683sbjO70swOSLuQmV1oZhvMbMPmzZs70VcRka6he16skwEqLX+o/rGM7wGr3f1o4DrgirQLuful7r7O3dctX758D3dTRKS76J4X62SA2gRUj4hWAk9Xn+DuW919Ivn2MuCEDvZHRERmkU4GqNuBF5nZQWaWA84Drqo+wcz2q/r2bOD+DvZHRERmkY5l8bl70czeCfwICIEvuvu9ZvYRYIO7XwVcZGZnA0XgeeCCTvVHRERml45WknD3q4Gr69o+WPX6L4G/7GQfRERkdlIlCRER6Urms6z2vpltBh6f4tuWAVs60J1OUF/3vNnST1BfO6Ub+7rF3c/Y1Ulm9sN2zpuLZl2A2h1mtsHd1810P9qhvu55s6WfoL52ymzqq0zSFJ+IiHQlBSgREelKvRKgLp3pDkyB+rrnzZZ+gvraKbOpr5LoiTUoERGZfXplBCUiIrOMApSIiHSlORmgzGypmV1rZg8mvy5pcl6pajffq9LO6WAfd7XbcJ+ZfSM5fpuZrZ7O/lX1Y1bsimxmXzSz58zsnibHzcw+nfwcd5vZ8dPdx6q+7Kqvp5nZjqrf0w+mnTcdzOwAM/uJmd1vZvea2btTzpnx39s2+9k1v6/SJnefc1/APwIXJ68vBj7e5LzhGepfCDwMrAFywC+Aw+vO+VPgX5PX5xHvPNyN/bwAuKQL/sxfChwP3NPk+FnANcTbwJwM3NbFfT0N+P5M/54mfdkPOD55vQD4VcrfgRn/vW2zn13z+6qv9r7m5AiKeOfe8t5SVwCvm8G+pGlnt+Hqn+FK4HQzS9tjq5Nmza7I7n4jccHhZl4LfNljtwKL66rpT5s2+to13P0Zd78zeT1EvONA/cajM/5722Y/ZZaZqwFqH3d/BuK/uMDeTc7rT3at/P/bu5fQuKo4juPfn7Q+qiLSoHVTxZiF2kKFUi0hCzUoDSIIVbrRIpUSV7qz6kIXXdSN2+qiLpRa0NoX+EDbUozgW6IJrYVWq2ilBRctwaBW/y7uGbkd53ETmpmT8feBYe7ce+byzyHJmXtm5vw+kdTJQaxK2vC/bSLiHHAGWNyR6hrUkMw6FTkDVX+WXKyW9LWkdyXd2u1iANI0823Ap3WHsurbFnVChv1qzc3pauZzSdJ+YEmDQ8/O4DRLI+KkpBuBg5ImIuL4hamwpSppw1XazLWqqcg7IuJ3SaMUV313zXllM5dDf1b1FXB9RExJGgH2AAPdLEjSFcBbwJMRcbb+cIOndKVv29SZXb9aa/P2CioihiNiWYPbXuBUbYoh3Z9uco6T6f474BDFq65OaJs2XG4jaQFwFZ2fFuqlVOQqfZ6FiDgbEVNp+x1goaS+btUjaSHFP/3tEbGrQZMs+rZdnbn1q7U3bweoNvYB69P2emBvfQNJV0u6JG33AYPA4Q7V1zZtmPN/hrXAwYjo9KvSXkpF3gc8kj5xdgdwpjYNnBtJS2rvN0paRfF3+muXahGwDTgSES82adb1vq1SZ079atXM2ym+NrYAb0jaAPwIPAggaSUwGhGPATcDL0v6m+IXdUtEdGSAimppw9uA1yQdo7hyWteJ2mZRZxapyJJ2UHxKq0/ST8BzwEKAiHiJIjhzBDgG/AY82o06oVKta4HHJZ0DpoF1XXhxUjMIPAxMSBpP+54BlkJWfVulzpz61SrwUkdmZpalXp3iMzOzec4DlJmZZckDlJmZZckDlJmZZckDlJmZZckDlJmZZckDlGVL58ehjKtJ5IikRZK2S5qQNCnpo7TkTfkck5LelLQo7Z9K9zdImk5tDkt6Na1I0CieYVzScIt6T6QaxiV9UdrfMP4lfbE1iwgQsxx5gLKcTUfEitLtRJN2TwCnImJ5RCwDNgB/1p1jGfAHMNrg+ccjYgWwnGKZnodKx8bqatjfpuY7U7uVpX2bgAMRMQAcSI8B1lCsBTcAbAS2tjm32f+KByjrBdcBP9ceRMTR0vqAZWPATc1OEhF/AZ9x4Vfibhb/0vWYCrOceYCynF1Wmlrb3aLdK8BTkj6WtFnSf1aoTgvurgEmmp1E0qXA7cB7pd1DdVN8/S3qCOB9SV9K2lja3yz+JauYCrPc9OpafNYbptPUW0sRMZ4iU+4BhoHPJa2OiCOkQS41HaNY47Bef2ozAOyMiG9Kx8Yi4r6K9Q6m+JZrgA8kfZvCCZvJJqbCLEceoKwnpBiFXcCutADwCMXK6lUGueMRsSJNrx2SdH9aCHemNdTiW06nK75VwIek+JeI+KUu/iWLmAqzXHmKz+Y9SYOlT8ZdDNwC/DDT86Tpt03A07Oo4XJJV9a2Ka7mJtPhZvEvXY+pMMuZr6CsF/QDW1PWz0XA2xTBdbOxB3he0lB6PFSaIgTYHBE7GzzvWmB3ihtaALweEbX3shrGv5BHTIVZthy3YWZmWfIUn5mZZclTfDZvSLoXeKFu9/cR8UAHa1hM8WXbendHhOPDzS4gT/GZmVmWPMVnZmZZ8gBlZmZZ8gBlZmZZ8gBlZmZZ+gfCiqQwSjC1ygAAAABJRU5ErkJggg==\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": [
      "10024 27644 80407 344635\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 =  344635\n",
      "# galaxies =  344635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: MergeConflictWarning: Cannot merge meta key 'EXTNAME' types <class 'str'> and <class 'str'>, choosing EXTNAME='HATLAS-NGP_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='2018-06-09T13:08:29' [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/HATLAS-NGP_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": 17,
   "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=(5000,5000))\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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add field name\n",
    "cat_all.add_column(Column(['HATLAS-NGP']*len(cat_all),name='field'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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_HATLAS-NGP_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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