{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of Bootes 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_Bootes_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=\"table4437055472\" 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>1384</td><td>216.83469146343285</td><td>32.30486548106547</td><td>38.60862</td><td>43.313313</td><td>33.499905</td><td>18.610111</td><td>22.988752</td><td>14.341537</td><td>17.731499</td><td>22.894682</td><td>12.644978</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>0.99854577</td><td>0.9985964</td><td>0.99920756</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>2389</td><td>216.96028637891257</td><td>32.38879784513622</td><td>48.81379</td><td>51.13212</td><td>46.15546</td><td>35.26526</td><td>38.23742</td><td>32.13048</td><td>6.8006606</td><td>10.631729</td><td>3.5758748</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>1.0006881</td><td>1.0004221</td><td>1.0006945</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.002</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>3160</td><td>216.98470387434244</td><td>32.30938985890007</td><td>1.5930656</td><td>2.6220481</td><td>0.6664394</td><td>16.38991</td><td>19.506617</td><td>12.969491</td><td>0.5948687</td><td>1.124767</td><td>0.18894313</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>0.9993304</td><td>0.998746</td><td>1.0005128</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>3299</td><td>216.75216474261202</td><td>32.27096361903366</td><td>37.318966</td><td>42.539772</td><td>32.16349</td><td>11.504098</td><td>13.9512415</td><td>9.051861</td><td>0.8958458</td><td>1.6993258</td><td>0.3042283</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>0.99852496</td><td>0.9992796</td><td>0.9996253</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>3854</td><td>216.96557266626246</td><td>32.36304153931766</td><td>24.997746</td><td>28.161108</td><td>21.625547</td><td>18.39659</td><td>22.1143</td><td>14.703232</td><td>1.3419731</td><td>3.5029461</td><td>0.36323905</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>0.9998452</td><td>0.999163</td><td>1.0002427</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.013</td></tr>\n",
       "<tr><td>4671</td><td>216.9040949489291</td><td>32.29696747230436</td><td>21.853088</td><td>27.710247</td><td>16.116243</td><td>2.2474527</td><td>4.3459916</td><td>0.6593055</td><td>0.8223688</td><td>1.7909786</td><td>0.24378788</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>0.9994894</td><td>1.0003002</td><td>0.9991738</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>5000</td><td>216.85161070855477</td><td>32.33694860618468</td><td>29.050058</td><td>32.035824</td><td>26.19089</td><td>8.802275</td><td>12.013272</td><td>5.638985</td><td>14.135133</td><td>17.78596</td><td>10.146422</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>0.99872637</td><td>0.9987364</td><td>0.9989994</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.001</td><td>0.0</td></tr>\n",
       "<tr><td>5969</td><td>216.8079749962344</td><td>32.33344049868627</td><td>27.640207</td><td>31.049795</td><td>24.38271</td><td>13.986638</td><td>17.193256</td><td>10.980188</td><td>1.8294557</td><td>4.1499515</td><td>0.51276547</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>0.9986691</td><td>0.9984233</td><td>0.99914503</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>7688</td><td>216.93919255540303</td><td>32.41606883170201</td><td>16.20498</td><td>19.778511</td><td>12.7240305</td><td>16.55145</td><td>19.847446</td><td>13.306448</td><td>3.59761</td><td>6.619345</td><td>1.1532733</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>0.99823886</td><td>1.0011429</td><td>1.0000999</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>7761</td><td>216.95459433242868</td><td>32.34906417184562</td><td>12.052772</td><td>15.559241</td><td>8.783887</td><td>7.8021183</td><td>11.241981</td><td>4.554034</td><td>1.871071</td><td>4.521032</td><td>0.46754155</td><td>-0.054186035</td><td>-0.09015137</td><td>-0.07167324</td><td>0.005305533</td><td>0.0073923627</td><td>0.010451002</td><td>1.0004827</td><td>0.9996035</td><td>0.9992119</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.007</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",
       "1384                        216.83469146343285 ...          0.0          0.0\n",
       "2389                        216.96028637891257 ...          0.0          0.0\n",
       "3160                        216.98470387434244 ...          0.0          0.0\n",
       "3299                        216.75216474261202 ...          0.0          0.0\n",
       "3854                        216.96557266626246 ...          0.0        0.013\n",
       "4671                         216.9040949489291 ...          0.0          0.0\n",
       "5000                        216.85161070855477 ...        0.001          0.0\n",
       "5969                         216.8079749962344 ...          0.0          0.0\n",
       "7688                        216.93919255540303 ...          0.0          0.0\n",
       "7761                        216.95459433242868 ...          0.0        0.007"
      ]
     },
     "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": [
      "9569 12285 14719 30566\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 =  30566\n",
      "# galaxies =  30566\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/Bootes_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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+9QSpSy4j9fBj6KabVnQZm1RqgygOc1dV5wOo6jMi8mXgHhFZG/M4GMaQJJe/kev6B51h8aVWAfL22yQP3Bf3ySfI7vJV0ldeha6/vhmCIUyUlUdLob8jNCQ7EdS6+mw/6WUYRh9TLnEOOv0anu+T9Xw8X/F8zRuO4JzQmPiK74d+kHSa+AXnUr/tJJw3Xid19TWk7/k3VGA4CpP2HLGIqlohysrjQIreb6raIiJfB37QL1oZhtF/FBsQhaxf+SaCvPgidQfui/vSi2S/8z3Sf7gc1lgjmqwU/jRjUYv0uvJQ1ZdVdVaJ4xlVvSH3WESe7mvlomBfUgxjJan0M9TRQeJ3J9Hwhe2Q+fNI3Xwb6Ztvi2w48sNafZCaJnIP8whUnvFjGEZN4T71ZODbeOdtMr/4FelzL0DGRStkaAwtIuV5RGRAnOfWhtYwclQaVFso1wstLSSPPIz6Xb6EdHTQ9q9/0/Gnv8DYsVWMlxvWPry1TF+uPAzDWKV07wmeeyiF7Vm7+RW6Z4mLlJ/L3RkPUHfwAcjcD8kceAip08+EpiYAfAVBulw9ylay5gfN6YvtYNUYfWk87E9vGKuAfBhtT+d0Ob+rMSmVXR4k4nW9Pp98QvK4o4n//Xq8TTal48H/4G2/Q/78nscrbUTKRVJp0UXMlzn4iZJhvmnB/WTRc1MLHv68D/UyDKMM1W5M+Uq+vEhPiAixf95Bw9abE7vpBtLHn0j7zBfwd/hC5Gzv/ArIssSHLFF8HjcW3C+OqPpj7o6qvtYnGhmGMWDI/Pkkf7gXdT/6PrrmWnQ8/Szp08+EOouHMboSxXhImfulHhuG0Q90FiasbM3RmdhXojBi0V5T7LprqJ/0Wdx/30vqzHNof2Im3paTKta165aVOcWHKlF8HlrmfqnHhmH0AVH8GqXluv6Mcr68/z7Jg/cn9tAMvB12pOOqP6MbfzrymMX+jWoT/2xXq7aIYjzWFpHLCN4NufuEjyf2m2aGMYyp9ltZRQsTzyN+1ZUkfncSiNBx6RVk9z0AnOgR/E4uW72Kmd8irGqbKMbj2IL7zxU9V/x4lWPfVgyjcuStN6k74De4/32a7Fe/TuqKq9B1163yYlV+CO2zW9NE6SR4XbnnRGS9vlXHMIx+JZMhftH5JM4+A5qa6PjLdWR/8jP7FmZUTNROgp8Xkb1EZLXw8ZYiciPwRLUDi8hoEfmHiLwlIm+GY4wVkRki8m74c0xv17EkVcMI6eWz4LzwPPXbb0vytFPIfntP2l58nexPf77ShiPwz1TxQbTPbk0TJc/jAuCvwPeAe0XkVGAGMBPYeCXGvhS4X1U3BbYC3gROAB5S1Y2Bh8LHhjHsiFQysChyqmxh3PZ2EiefQP2OU5HFi2i/9Q5Sf78ZXX31ldMxlyAuudyT4F/kTBRb7NQ0UXwe3wS2VtWOcCUwD9hSVd+tdlARGQl8EfgVgKqmgbSI7EHQKwTgOuBR4PhqxzGMWiWXUJebX7Vgie1XEFHlPP4YdQfuizPrXTK/+jWpcy+E0aOr1Knrz3J0L5EikeSM2iLKtlW7qnYAqOpS4O2VMRwhGwKLgGtE5EURuVpEGoHVC7oWzgdWKyUsIvuJyHMi8tyixYtWUhXDGPwUZml3y9EoxfLlJA4/mIZdd4Jslvb7ZpC66uqqDIdIEICVa9hUud5mOIYiUVYeG4nI3QWP1y98rKrfrnLcbYBDVXWmiFxKBVtUqjodmA4wefIU2zk1ahJVrapkR29y7v33kTzkQOSjuaQPPZz0aWdCYyOKFpUwNIzqiWI89ih6fFEfjDsXmKuqM8PH/yAwHgtEZE1VnS8iawIL+2AswxgUlEr8yx3rWpW2qOigar6XeOeWVQm5JUtIHnsU8Zv+jveZzWh/9An87XLl53Jjd/+uVc6glGtZ2xvdSyaawRqKRAnV/U9fD6qqH4vIhyKyiaq+DewMvBHefgmcG/68q6/HNoxViWqp6brEeUUyvirq9+52DqrRKrHbb6PuqMOQpUtJ/fZk0iecCMlkL9K5a2iYJV6qtHrxxN/99yldjt0MxlCnV+MhIq/Sw3tYVbescuxDgRtEJAHMBvYm8MHcKiL7AB8A3+/1KvYeNQYx1e6p+n6082TePOoOP5j4PXfjbTOFtnv/D92y8o+kE7nqbfE6pbqtN6P2ibJttXt/DKyqLwFTSjy1cyXXsbetMSxRJX7dX6k74VhIpeg4+3zShx4Osdgq/kzYJ3C4EmXbao6I7Al8CnhVVR/of7UMwyiH/G829QftT+zRh8nu+CXap01HN/rUQKtlDDOiJAn+ETgSGAecISKn9LtWFWAZ5kZ/06WEeeRqtdrpIM/f7124WK7LoJ5H4rI/0DRlK9znn6X98j/Sdv+DfWI4NDdeFa2m7DM4PImybfVFYCtV9USkAXgcOKN/1TKMAaKXTnvd2qXS3YUctHnVbhnfQdZ4+av7Cr6v3drECuC+8Rr1B+5L7NlnyOz2DToum4auvXbnOSUio6LM6Z1yCtKlWW1+7K6alL6GMfyIYjzSquoBqGqbmHfMGKJU8w06aO8aFuXoRb7YKHSG4PYglE6TvOg86s47Gx05khV//Rv+j36M40g+QqocpbLTO7PEo32MOwNtLUvc6EoU47GpiLwS3heCpMFXwvu6EtFWhjEk6HHy7wGvl4gq9/lnaThwP2Kvv0r6+z+i7YJL0AkTiDmV9QMvPLfq6ulmNIwiohiPz/S7FoZRo2iVG/49yrW1UX/maSQv/wO6xpqsuO1OMt/4VoFsmJtRzYxuOXtGHxEp2qrwsYiMI/CDfKCqz/eXYoYxGMlN+n7E5L9iucKtq8Bf0pXYY4/ScMgBuO/NIvXrfWk781wYNapbq1e/YJ+s0N8RZRurxyy/sofM6hhdiRJtdY+IbB7eXxN4Dfg18DcROaKf9TOMVUaugF/hLYfn+2R9H08VrwLD4fuK5wfOc7/YGV841vJmGg47iBG77QKqrLhvBh1XTMMZPQonX5iw9HZVzm+Sc7jnjUrRrSxhgFWX86VrMUYzHEYxUarqbqCqr4X39wZmqOq3gO0IjMiAYnuxRn+yshVhoxiZ2H33MHLyliSuuZqOw4+k5ZkX8Xb6clWTt5Ob9CvU04yFUSlRjEem4P7OwH0AqtoCRCyiYBhGMbJoEQ2/+hlNe+2JjhnDikeeoOOcC6ChYaBVM4xeieIw/1BEDiWohLsNcD+AiNQD8X7UzTCGJqrEb7uF+mOOQJqbaT/5VFLHHA+JxEBrZhiRibLy2Af4LEHXvx+q6rLw+FTgmn7SKzKW3Wr0H9ol47taucIMc5k7l8a99qTxVz/DX38DWp56ltSJp3QzHLlquZUSuDz6IQLMMIqIEm21EDigxPFHgEdyj0XkclU9tG/VM4xVR/HkWfjQCR0f+WirMnK5vhvFCYH4Pslr/0LDyScgmQxt515A+qBDIdb5ESz0NOQc471N53nndpFcFApPtdxfo1KibFtFZYc+vJZhrHKifPHOTbJuXkZJ+91LkRTivDeLpkMOIP74f8h8cSdWXHEV/oYb4TqSX/qXm7xLlRxxqDxTvFN/MxRG3xBl28owjDL0OBFns9RdejGjt9sa9+UXWXHFVSy/9//wN9wokKV8+G3Z8egaGVWZrmY4jL6jL1cehjHgFK8eosyV+a0oP0h26K1mVKFcUNuqc9ycmPvaqzQdvB+x558j/c1v0XrJ5fhrTewqXzB2pZN6YaKhYQwEfWk87H1srFKibDOVPicsZOgTJPx1qVbYeUfI5U10vZ6nnYl/3a7ckaLhwnOpv/BcdPQYWq69gfT3vg8i3bLEocB3EipRbhury8NCfYrGL/4QdvFr5CXso2qsPFHa0MZUNRvhWpf2gT6GEYlqA4Oynh+5kKECXph97fm9y8WenUnTwfsTe/N1On70E1rPuQiZMB6nAv9Ezpg4UCAHkSf8Ls7zCuQMo0Ki+Dyeyd0RkcvLnaSq1/aFQpViW7hGJVQbjNqjsWptpfGEYxi1847I8maab7uLFX++Dh0/PixBUrl/wukiV6lPxLLEjf4nyrZV4TvQIqoMo4D4fx6h6dADcP83m/bf7E/b6UHfjRw2fRtDlSgrj0GdOTSolTOGLLJsGU2H7M+o3b8KjsOyfz9E6yVXdDEcYO9PY+hSSTOowkZQMFiaQdmnc/jQS4vY0jJdK+AK4BccKeySVzxYbqsq19Xb18CJnrj3bpqOPBRn4QLajjiGthN/B/X1pYf3QZ2CfnwRlyK+r7iFCR0R0bBProXkGv2NNYMyBi3VOsULe2f4aD7ju+S5Bf/nf4RZ4sGt81xZtJARxx1J3R23kdl8C5pvvgNvm8ldO/XRcwJfoR7588L7jnRvLRs1w7zceIbRX1TcDMowBjvZCJFRpUh7fmkjo0rdrTcy4vijkdYVrDj5NFqPOBbicVBwpdPBXQkuBL3IozRwKj5uCX/GABMlVLcFuq38c+twVdWRJQUNYwjgzP2QkUceQvL//k162+1YfsWf8DbdrMs5UZMKi6kmCiuQM8NhDDxRtq0eAtYA7gBuVtUP+lclY7hTuO1USG8TpobbTF5Rl5ne5lkNt7Vy34oA8H3q//pnmk47EfE8lp97Ee37HQSu203eV1BfK8rnyJ0SJrVH0rNIayyWyxhIomxb7Skio4DvAn8WkTrgFgJD8kl/K2gMbYor0kY/V0Ek8Ev0Upiw8Lqqikhn69auYwqOgPvu2zQdegCJp54gvdPONF/6R/z1Nyg4q0AiX203TCgMHgWrEadzVeIICFIyUzwnli9xkr9215/556SLmTOMASFSeRJVbQauEZHrgB8ClwN1wMX9qJsxDKg6U9wPHNqV4vs9TLvZLPVX/IHGs09H6+pY/sc/k/rpL4PSIpqrexXt236u/EncFVwnZzSiyzo5wwNlliS26jAGlkjGQ0S2B34M7Ag8AXxHVR/vT8WiYlu/Rl/gvvoyIw7el/hLL5L61p6suOgy/DXWzD9frY/BqdavsRJjGsaqIIrD/H1gGXAzsB+QDY9vA6CqL/SjfobRv6RSNJx/Ng2XnI+OGUvz9TeT3uO79q3EMHohysrjfYKV9NeAr9J1vazAV/perehY58xhSrU5IPn/hdjMpxlxyP7E3n6Tjh//jBVnX4iOG9d3OhaNV520GTFjcBLFYb5TlAuJyK6qOmOlNTKGMF2T9bToZ44SqXVd5HzN9cIokikWLMguVw38JFnPhxUrGH32aTT86Ur8tddh2e3/IrPr1yPpWspZXgoh8FtUYwCE7v1BDGOw0Zf9PM4DzHgY3ciH3ob/9bZo6AxaCpst+XTL9i4pk08UDyb+rOeT9RXP6zQidY8+xLijDyb+wRyW73MAy07+Pc7Ikbiej+tI1+v1pFv4e3VWsQ2SBV3HCZzdvWSXF1IcmWUYtYA1gzL6Ha2mJhWQ8Xo2GOVoT3tkva6CzrKljPndCYy46Xoyn/o08//1IKmpQZFo31eyvpJwBdetrDOzAnWxaHLFdqGarHTDGCz0pfEw74MxKGm49y7GHncE7pJFLDv8WJqPORGtqxtotQyjphnQHuYi4gLPAR+p6u4isgFBVNdY4AXg56qaHkgdhzu9BSSU++Kc6++t3RLxepfz/OCWWz3kynH0tK2Tk8t6fl7OXfAxY397FI3/upPU5lux8MY7SG+1dTdZR8IaU45E8L904jqSD8UNtuYKCiT2sqDofN6c4kZt0pfG4/0qZA4H3gRy9bHOAy5R1ZtF5CpgH2Ba36hnRKHS6LV82fKCkiJRLlEo5/ma7wteanzt4h3P+RnoNDLFcqo03nojY08+Fqe9jaUn/Z7mg48IChkSGApH6OLjKGuUCu7HnCDhz3WkV8d5cfVckWJnO5jRMGqZqEmC44CfAJuGh94EblLVJblzVPW7lQwsImsD3wTOAo6S4BP4lXAcgOuA0zDjURMURz9FJZ0NnNqVjpXKeN1qWAG4H85h/NGHUv/IDDq2ncqSS68is/Em+edj+YzvyibuuphTlZxjjnBjiNKrl09EPgO8BkwG3gHeBbYFXhWRTXuS7YU/AMcBuSlgHLBMVbPh47nAxDI67Sciz4nIc4sXL1oJFYwhg+8z4uppTPzCZJIzn2LJORfz8T2wvS2oAAAgAElEQVQPdTEcsBIT+Uo4t81wGEORKCuPM4DDVfXWwoMi8j2CVcP3Kh1URHYHFqrq8yKyU+5wiVNLfiVV1enAdIApU6aYo36YE5v1DuMPP4C6Z56m/cu7sviiy/HWWW+g1TKMIU0U47GFqu5VfFBVbxeRs6scdwfg2yLyDYICiyMJViKjRSQWrj7WBub1diHLMO9LKnEXF8lV8XfQKv94mnOsZDKMuvIPjL7wLPz6BhZd/mdaf/jTHr3VSq6ybmWrgcDxr1X5KqoZzzAGO1GC2lurfK4sqvpbVV1bVdcHfgQ8rKo/BR4Bcobql8Bd1VzfiIqGvSw03wtDKbx1/itKj+sq11Ol2kKpApmM59OR8Uh7PlnPx/P9sF2sdjMqxXKpjA8vvcgaX9uRMWf9jtavfoOPHn+eFb0YDoCsp4GfxfPx/dLjFeJIUBlX6ExU1C56lpaVUNZMhjFUibLyWE1EjipxXIAJfazP8cDNInIm8CLwlz6+vkFRxndUmUAwcpZ4IVk/mKiznpZtEds1ogpEgkGC5k6dEVXS0cHYi89mzJWX4I0dx7y/3EjrN/cMhPxAzhXpZkOKv/l7PnjhbxFkeCsJN8gOd0XCiKzeI7BUO42EZYkbw4koxuPPwIgyz129sgqo6qPAo+H92cDnVvaaRu9Us2FUbURVJuuTypYIjeppLA0isQojqupmPsXqRx9EYtY7NP/o5yw+7Vz80WO6yalQduIvORZB741YXIjHKsswh848EcMYTkQpjHj6qlDEMMohK1oYf9bvGH3Nn8issx4f3Xw3bTvtMtBqGcawJko/j9/18LSq6hl9qM+wIOq3977a/Yjc6rVovC6Jfz3IFW/TqGroW/BIZ33SBZnijuQaJJWW8xS8XEFDHxoffZC1jjuU2Ly5LN3nQBb/9jRoKr0Qzl0fyHcZ7C2ZL+YIMVfyHf+iZnznEhU729B2l7PdK2MoE2XbqpRTvJEg+3scQSiv0QvVbPcUZylXJls6W7tnoU65ivwhYXZ4ezpLOhv4J0p12c71+e70NQTOeF8DQ1HoC3GWfsJap53A6H/cSOpTn+b9O2fQvu3UQM7zgwxx18kbiygZ4qqK6wjJmJP3b+SI4qdwwl7knTLBb1GOlfn7GcZgJ8q21UW5+yIygqCkyN4ENaguKie3yrAPZZ9SPn6oZzoyHm3pTgdFpOgrSlfOHXHPnax50lG4y5ay6LBjWXz48V0KGSqBjyEoZFvZG6A+7pCIORU7tXMlSapqKWvvUWMIErU8yVjgKOCnBGVDtlHVpf2pWFSGw+dyuEw+sQUfs8bJRzHyvrtp32ISc264i9TmW/YgsWozvi2KyjA6ieLzuAD4LkFG9xaquqLftTK6MtQLr6oy6ta/s8bpv0VSHSw46QyW7HcoxAa06LNhGD0Q5dN5NJACTgZOKvj2FXTLVB1ZTnBVMBwyzCuzHdqrg7snuSCHo9DZ3PPIudLr1USqqirxD95n9WMPpenxR2jdbnvmX3Al6Y02jiBbXea2FyYGVpVhLopUYcVVh8/q0Rg+RPF5VB74bnRDpHpDV37i0ZIJe72P070/t2oQoVQoKgRO7RKjIgT+ilTWI50NMr+1UC70mOecyoURX56vpNMZxlwznTXPPx0chzlnXMTSn/0aN+bi5CKlin7x4ggnzw/0y5U8D87pntYdc4JoqnjMIVZBZdzceE6+BHu1211ViRnGoCbKttXYnp5X1U/6Tp2hTfEkUrandcTJproVRtgTnN6jqoqd57lkv97KqBe13yCb9ciGGeaeKnWz3maDEw6l6YVnWPalXZlz5sWkJ64DCplM4HR3BJIxl1gvEVX5sbTzSH0icIrHXAe3gmq4Il0jqnqTW9m/n2HUMlG2rZ6n/M6JAhv2qUbDiIGaZBSq6g2e8Xza0l7FcllfSWV9JJNhzemXstbl5+M3NDL74j+xZI8flHwhcnW23Cr2w+KuQ13crVjOEaloPDMSxnAmyrbVBuWeE5GS/TaMyijc0qlkL75w5VDJPFZYDLCS8Tw/yMmopPRHTq7ulRfZ6IRDaHjrdT755neYc+r5ZMf3Xhqt2oq01cuZUTCMKKxsOMvTwLp9ochwoUu2dw/P5+avrhnM5etLFR/Kyecc2r6fa/dafF6Rz6BgvIynpDI+7eksHZmuBQ1dJ8y1KMoWz8l1ZDzaMh7plhVseOUFrH/dNNJjx/PK5dfR/NXdg45+RRN80BrWybd7hXCFpEHSYaE7o1DODX0aiZgQc52wAm7nCyVl5Ap9KJ1HFdXu1sMMimF0ZWWNh32kIqJFzuhezw9/+tXsLwGeKpls6Qq2xeMU+gzSWY/mtgzpbM8Jf54frCgy4bNZ36cllQ3kwnfF6GefZvKpR9I4ZzYffu9nvH30qWRHjkIyHmSC6zbGXcY0JPLGosdM8YKVVsKBpvoYMUfye6plZQvkHJSY43QxBlFWKDmDbUbEMAJW1ngMeKCsfZhL4/vdM7ejkK6iAi5AR6ZTzm1pYZNLzmDdW66lbe11eebqf/DJ1C/mzy1UKx46tislGXeIh3KVvAUcR6qugGvvNcPoJEq01eWUNhICjO5zjYyaZvxjD/LZ3x9L3YJ5vP+L/Xn3kBPwGhoHWi3DMPqYKCuP56p8zhhGxD5ZwhZn/paJ9/yDlo024b9/v5fmraYMtFqGYfQTUaKtrlsVilRLLWSYV1qltlCusvMLy7hCYTJgT/v6+Qq8oWNZC5wLPfsDFN9Xxtx7J5v97lhiy5fx7gFHM3u/w9FEXQ9ygXPc11xhRI1cdDBXrTfj5SLGuncN7An1w0xxi8QyjJUiyrbVeOBgYCnwV+ACYEfgPeBoVZ3VrxrWED32vAh35ku6zas2Lp1SvhL2Ag+iqrpliocTphScLxL4OFo6MqxIZWlNe3h+Z7RX0OsiKF/uSmeuuQBp3yf74Vw2Ov1Y1nj4fj7ZbCuenXYTzZ/ejJgIrucRd5y8UchN+nFHqIs5JGIuyZiDI5IPCggiv6RL5FdOLuYE0VTxmEvc7doitvh17+IMh3xLWaeChMFizGgYRleibFvdSLA9tTHwDHANcCmBAbka2Km/lBuKFNdGqiwGqytZT0mHBqMnOiOqgvNa2jMsbUvTmvbKjq5Axlcyvkd7JkgMzPpKyvdJZbKsf+dNbHvJGTiZNC8deQrv/uQ3aFjIMKtKVoNzAcbVJRhdFyfh9lwKPVCx05I2Jh0akrGKSooAuGHIb6mGU1ExY2EYPRPFeKyuqidK8Cmco6oXhMffEpGD+1G3YYGELZGqpTfDUYqsrz0ajnJkfB/3/dl88YxjWf2ZJ1k4+fM897sLWLFu2TxSIPjmn4xVnvEd5G9UHoklYhFVhtHfRDEeHgTlc0VkcdFzlcd0DlFK5XFEqcCay/bObwlFnL2CVq9+Rf4CCPIxlqcydHgeMSfYjopCJp1hjWuuYtK089FYjOdOPo/Z3/kJXdrxlcARcEXwfK1o28gRiLud21aVYPO/YfQ/UYzHhiJyN8FnMnef8HHPXzmHMIUZ32XPKXpWwuqyCqhfer2hBVnRebnQL5D1NShK6JVK/ssZka7Z4h1Zj+aODEvb0yxrz5DK+oGfIVdKnaAWVMJxiIcZ4wAdWZ/mVJal7Rlib73JNy/6Leu8/TJvf24n7jrkdNpWW5Om5hRNyRiNCZeEG2wtxUSoi7k0xFzqYm7+ehmv06/h5CrVSmdyX8wRkvGgJlVdIvCFFBdUy+1oFccFBD6N4jpY5cqxlcdWHYYRnSjGY4+C+xcWPVf8eMhTaaZ4oVwlW0y5CTLrK60dmUhjFmaLL+/I8Oai5XjhyqSwLIlX6GgHUp5P2gsWkStSHh82p/BViWfSbH/rdHa6+SpSDU3cctxFvPKlbwazrK8s7fBYngq2v9ZoTDJ5rVE40nO7Vg118UKFmupirDEqmZ/nCx3hxVcIyrwrKt2LGFp7WMNYtUQJ1f1PlAuJyO2q+r2VV6kyhvoE4JdZofRGe9bD0+hZ5rnTOrKBA36td17hu5ecxBpz3uGlnXbn3v1Pom1U9+r8OaPUlIyFda4q+4Mk405l/okC42TOcMMYOPqyz6eVZh9kVDNHxjra+frVF7L9P6+lZcwE/nbqNN7a7it9rpthGLVNXxqPAUnXq4EcwQGj0kCs1Z57it1OP5bRH83hmd1+yP2/PpZU44j+Uc4wjJqmL43HwFBxFnbp45L/r7RQTkzz/xULlxIr8C34XTO3O8uLdxf2c3IKnucHSXRlyooXkg0GIatKSypLe8bHcYIEO6BsHw5neTPbXHo2G995A0vXWpdpZ1zD+5OmBj6FHvpixJzA8R5zhIznk8p6+ZDcnraUci15XYFs1ifj+Xkdc8/3tm7SwheyQixT3DBWnr40HoPu41hJeY+uRqGrYK/Xyc/1XTO+tSefg9KtR7ivQf+MXIXaUg52N+x1EQujlXyFtozH3OYO3l/WzrzlHbSkvCIZaEq4jEy6jKqLkYgJaODfGDnjfna95HeMXLqIu772c27eY3/SyTpY2IrrCPVxl8akS2Myls/sro85jKtPMLYhzpi6OHWhwVCCa0JniG4uuxsg5gr1cYe6hEsy7uYNhmpBNJbkWsHmHkv+ek5R75DIf58QMxiG0XdEKU+yrqp+EOFax/eBPoOCautlqdJjb+9u54e3jnTQQyOKaK78SBp4b0kbz3y0jNZMz+k2ng/NHR7NHR4fNqdp7cjgf7yAn99wATvOfIA5EzfinAPPY9aGm3cba0Uqy4pUFkix9Voj2X69McTd3hP+fA1XUL4yrinO+BHJSB0IVYNoMA9IxJwg16MXuXK94c1YGEb/EWXl8U9gG+g5okpV/68vFRtuVGOwFEgXtwaMMNCUx+/lB9ecQ317KzfvsT93fuNXZGPxXkWDfIwqM76rjoyyEFzDGIxEMR6FH8UhHVGVT/zTrilmvU1GudwPz9euCWwR5NJZpTWVJetrl6S5nsh4Ph8sbeeZOUuZs6SVxro4jXWxXpsqNS2cz3euPJVJLz7GG+tuxqU/P4kF63+6KLmuO64jrNaUYM2RSdJhZroTIVTWEahPuDQlXXIpf1FNXW57rtpe5IZh9C9RjIeWuT/oKZxzSvb9LjIW3Z4vkpX84+CAF5YyL+vWKHrC94NChqmMR0fGJ+Npl/IbWnAnny0OtKWzzP6knXcWruDNBS0saEkRd4SM7+P5EGtJ4flKPOYwoj5OU12cproYoLS0ZWhubmeHh27nwPumEfM9Lv76gdy43XeQWAxv3nLiMYf6pEsyGaMuEQsS90YkmTiqjrVH1dGUjOUd4x7ghQUPhdCvEd4SrkNDMjAWTXUx4jEH1cCIFJcZUe1qpN2ww1/Ol5PD7IZhDE6iGI+tRGQ5wWe8PrxP+FhVdWS/adeHFE9C1bRpzVV9rcSvkSOd9Vm4PJWPNCq8Zvmx4J2FK5j2xP9IxISOjJ8/v9CZntMnnfVZ0pKiuTVNRzpLa2uGDZbN4+Q7L2TK+y/zzAZbc8a3j+KjsWsFgqFcJuuTyfq47Vk2nziS728zEVXFLahm65aIh1CCyC5RZfyIJOuPawgCAQqd2oWGoEA2ly3uFBU/tFWGYdQGUTLMKy+Hugqpdq6RairuVScCBMaq2HBEoTWdJe4K7b04xQvJ+opkMvzyqVvZ/6FryLhxfv/to/jnNt/o8QXzfKUhEbwlKukrrgR9OgSQCrPFo2x/GYYx+BiQPA8RWQe4HliDoLzSdFW9VETGArcA6wPvAz9Q1aUDoWMts8G89zjmprPZ7MO3eHST7Tln98NYNHLCQKtlGMYQYqCSBLMEXQhfEJERwPMiMgP4FfCQqp4rIicAJ9BLCPCqb0Nb3YCKVqdrBeG/8Wyan864np/OuJ6W+hEc//1TmPHZL1W0PBuIRYA5xQ2j9hgQ46Gq84H54f0WEXkTmEhQwXen8LTrgEfps/yRrpO3ancnbhldu8h4SomMbyjOkfRDZ7pIkMexvD1DOkygi7lSNls8k/XxfB8R4b0FLbw4axGtze14IiTr40Ep885B82z2v1c55uZz2WDB+9y31S78buovWOQ2Ur+inWR9EifsrFcsl3CdfHLjxDH1NNa5NHdkGFkXy5dTLzWxO3mXhtCUjJGMOfg+REgB6XYNwzBqjwEvTyIi6wNbAzMJuhbmjMp8EVmtjMx+wH4A66y7btlrF0/83a9TGEEV/gwfKIEByBmLbuKFIbka9AjXMKu8Pe3R0pGlLR1EVZX8HQiMSMwJGiWlsz5vz2vmmVmLeX3uMj5Y1FrSsLmuQ319nMamBHV1ceoz7fzqX9P5ycw7md8whu9ufwQPrLklLM4CzXm5WNylviHJiJH1jB7TSF0yxrrjGlh/tUbWGdvAmMZ4FyPRmvU69XSCPh3JWJAVPrIuzqj6OCPr4iRj3VvLlqsDnOu5ketNbqsNw6hdBtR4iEgTcDtwhKour6CL3nRgOsDkyVN6XDxUWrpCCFYXuTIbkfQJx2lNZfnok/ZIUVxKUJIj4ynPvbeYaQ+8RVva61XO83xWrEixYkWKz81+joufmM76rYuYvuGX+d3me9ESry8pl814tDS30dLcxqcn1POzXTbCjeAUV8Je5iirjaxjkwkjes/4LlqFuY4Qc81QGMZQYsCMh4jECQzHDap6R3h4gYisGa461gQWDpR+g5mRqVZOfvpafvLmg8xqWp2vfvF4npywyUCrZRjGMGKgoq0E+AvwpqpeXPDU3cAvgXPDn3dVeu3etqp6kvM1CFfNeEHyHXS26C7bGU+VtrTHso4Mi1Z0sKIjS8J1SLguiTATuxSe7zPnw4W89tYcHnr6dT7+cAmxUWOJjRxDfMQoxC39p9l19kzOeewqxncs5+J1tueMNaeSio2E9hZINiBOaaeDI8K6a4zg0+uMYaet147cgEmAEckYo+sTTBxZj68gql1a3ZaVlbCgoYOVsjWMIcZArTx2AH4OvCoiL4XHTiQwGreKyD7AB8D3o1xMtfJIJg39GZ7nk/U1byyK8fPH8+nftKSzNLdnWNKWpiWVyTvec9tVmaxHe9bDJ8jATjoOMRHenTWX19+aw3MvvcO7s+fjug6+r6TSGQCyrS04C+bieR6xunrio8bgNI1BXJdxS+dx9ou38t1Fb/BK/Xj22vSHPF83HjIZZNkCxFmE7/lIIgH1I3HqR7DJpuvymQ0msNXGE1hrfGOQhe46vW5XjWlIMKYuzvimJI2JILs8l0WeeyUKs+EhV/UWHMfBcYoTAs1oGMZQY6CirZ6gfAn3nSu/XuU6+Aptqd59DMUs78jy8vxlwbg9XT/86anS5nm88cb/uPgPt6Hqk835UzJdZVQVL5sFINveRra9DZF5/PCTt7lw7uM0+WlOXXMqF6++DVlxu8ipF/wufjqFpBex3WdXZ+8fTSEed/OTd++lD2HiqHo2Hj8iKCmSL4fe++QfZIqXj84yDGNoMeDRVivLqp6nlGCiLNVroyeynk887tLWlo0ss066hcs+fJSvt3zAfxvX4IB1d+btuu59xMvpWGgAopKvqbUKq+AahlF71LzxGIqIKr9Z8jpnzXsKB+XYdb7IH8dtgS+Vl0M3DMPoD2reeFTVB0O1i2O9IjlfgyTBCuUESKUyuL2sWj7VsYxpHz7MF1rn89CItTl4na8wJzkq8FOUc8wU4bpOWK5WIiVC5qiy3JdhGMOQ2jceUc4p7CWuQT+MrK/5XuHlnLu+Bl37HBGWpzLMbW5nfkuKT9pSxByHxoRLXczp1ofD93zSmSyxmMvHHy/hlVff44033sd1HdLpbBcDkrvvqs8Ri17mpPkz6XBiHLDh17hu7amQaATHxct04KZb8TpaQRXXcfBCP0ciHsN1HbJZjw3WW53JW36Kz266HiPiMcRxyGjw++bKn+dejdy2lqoyIhlnfFOCsQ2JIJGP8qXqCwmc5NJrTxDDMIYWNW88oFRP684+HVnfJ+sFZdTL1YjSrsKkMkHo7bzlHXy8IsXi1hTdG/b5NKcC/0XMEepiDn57O8sWL+W112bz9jtzmTPnY7LZ7k75wpWH5ytbppcxbc6DbNO6gLtW24IjtvopH9eP7ipU14jHuLBGSgYv1cbqDcpaY+rZdtKn2GyTddlwvTVKRlLVkwtFVjK+4jiQjDtMaEoypiHBiGSsx1Dk3GuZs8GuK7hl+okbhjE8GBLGo5j8CkCVFdVEVKWzPPjeIjIRW7xmfWVF2uN/sxdw+/X30NGeiiSX9LOcsOA5jl74Ip8kmvjxlP3451rb9CwkArEExBJ8ZtI6HL7XFBLx3gtKiQQTvuvAhBEJ1h3XECmKKvda5nI23MBiRPr9DMMYugxJ41ELTG2dz7QPHmHT1FL+NmFzTpz8KxYnmgZaLcMwjEgMOePh+0rG98O6UdHrU7VnPJa2p1nSlmZRa4qx9TGyvpLK+qTDGlTF6xDVoM3r/MWtzF3Qwpz5y6n77FTqOlrp+GQx2ZZlZFcsB+3Uo9FLc/r8mRy4+BXmN4zhuB+fwuwv7cYOySTLm9uYN28pixctZ+nS1pKO+fVWH8mkjSaww+YT2fbTq9PUkCDr+XRkfTKeX3a1VBd3GFEXCwoa1sfKJtmUImgjG7aHtUWHYRgMEePRkfECJ3iJCb4cvipzm9tZ1NrBsvYMXhgRlZt7RYKkt7jr5Pf9fYUVHVmee2cx789fzvzFrYGz2xFSYfVccV1oHElD4wjwfXwE0h34K5axZ8f7nP7UP1hj6QIe2PkH3LrXIaSaRlIX6jR23AjGjm3C84KS7K2tHTR/0sIWq4/gcxuvxmfWHRdsPznSZasqEXNJhP3CobPESmNdjJH1sXx3QIFIZUlyjnTLEjcMoxxDwnhEqUZbzIp0lrcWLY9UATffx1tg2fIOnnh5HtmCb/jZEt/2FQEn6Pw9SnzOfuduvvPqDOatuT6/P/ka3tlk63KD4cYCwzBiZAPbbjSBb222epc+32UE866ImCuMaoh3K7MehaDMiLWGNQyjZ2reeOTCZKvJT3BE8uG6lRBzHbJeNIO12+z/cvaTf2Zc+3Lu2n1v/vmdA8gkkhWPuSoxs2EYRm/UvPEYrFltE9qWctYTV7P7//7La+M24OAfnEHiyzvgOIM/SzyXD2IYhlGOmjcefoREtkI07BLohqsOR6Ilw+WoT7pkPZ9kPKiI281Brcr333mU056+lvpsinM+91OunfId3MY6thYh7ki+Q2FPCJCMObRn/M6kvYg6ilBx7a0C9c1yGIbRKzVvPBBoSrr5XhxBefXCnh6dvcSzvtKR9vLtYTca2UTK82n3PFozWTqyXq6qR94XomHvCl8DJ/uYkXUc/5NJfLykjQ8WruC9j5YzN2wZu87yhZz56DR2+vAlnlljU87Z/UgaJ23Bz9cezcZrjqSpPs4n7RkWrkgxb3mKxW2Z/Dzta1AuPeP5jGtIsPGERj41vpH1xtTTmHCD383r/vsR6qsKcVeoi7vUxR2ScTffIzyKGck50609rGEYUZBqajwNJraePEX/8+QzXY7lDEZHxmNxS5qOrE8q4/XqHNcwA7s5lWZxRyryikQ9jw1vvJad/noRivDkPsfQ8uv9qavruQi6qrI8lWV5yiMRc9hgbAPrjKkn0YtzPNe4SlWD7PZEEG3VW9Jf4d9aCiKqzFgYxvCjPi7Pq+qUauVrf+VRAglLZziO0NyeiRRRlZNLuEJjPBYYjwgyo99/jy+dewJrvvIcH039Eu+cfhGZievmw297G29UXZy1R9ez7piG6N39RHAF4q5DQ8KNPPnns8XJdUg0o2EYRnUMSeORI7flVI1TPek6tGfLJxk62Qxb3TCdKX+9jExdPQ+fciFzd9+LNZsaKh7PEZvGDcOoLYaU8fD8oKhhR8YPEweVpmQs/1ymnM+AoLptzBFirjCizmVCUxBO25rJ8kl7mpZ0ltZ0FgXGvf06Xz77OMa/+wZzd92d9087n3Hrrs3EMD8DhVTWozXt0ZH1SBUZobgrjKiLM6ouxsj6OImY0+n78MP2uCVa64oERRjjrhBzHSopZCvhf2akDMPoC2rfeCgsbknRkfHxfM07j3PktmpirhBz6ZIt7ocrEyk6F4KEQICRyTgjEjF8BSfVwYSLz2Gtqy8nO248H119Iyu+sQerFVeXFahPxKiLu/lFiOf7NCZj1CdcXJGg21+JqrSuKziqxEOtVAPdXTdixneBn0aKjIX5NgzD6Ctq3niIBL3IcxNmb/7/fF9utCJfwchnn2bt4w6mbvYsmn/0cxafdi7+6DH0VM+2sMRHMhFjRF350ueldARwncB4RJ74C40hZjAMw+gfat94QLfVRl/irGhhzfNOY/x1fya1znr874Z/kvnKrjUxKdeAioZh1Cg1bzyU/jMcIx6dwdq/PYL4vLks+vWBfHzsKdDUxOAuLmIYhtH/1LzxyFW1zfraZz243aVLWOv3JzL29pvo+NQmzLrj/2ifvF3gp9DKvtHndPIL2sBWwsoYRq1QV8MwjKjUvPEQgfEjkkGCn6ekw+imUn0thFzF2OBRrn93fnJXnxH33sXEU44mtmwpCw47lkWHHUesoZ4RMYd4zAnbrwZynl9w084xcs7wRMwhGXeIuw6uQ5fxCtu6dtPRCfR0HMkn/uXbwfb2euRfF7MahmH0HzVvPHKICImYkIg5NBFMtqmsT0tHtkfHcc6pHV/8MWuceBQj/n03HVtuzcLb/oVstTXjy8TDikg+gotwPMJQ2kTMKZvwl3eiS6dcTj/XKe8YL0zwy49X4nnDMIxVwZAxHsUEk7uTD4stiyqjbvkbq53+WyTVwcKTzqD5wMNpakxWNCGLCI4DybhTsVwuz6RSOcMwjIFiyBoPVQ3auPbgCIl/8D5rHHsIjY8/QtvULzD/givIbLQxyViQgFepu8EyxQ3DGC4MGeOR80FkfSXr+/l6VjFXOv0FoZ/Bz3qMueYqJnwyiiYAAAlxSURBVJxzGuq6LDr/Ujr23pcRyRgxR7o5tnMJhep3NSgxJ0jei7lSMttbw/+KjVBum6pQxhYShmHUErVvPBRWpHoufpj3FwjE336TCUceRP1zM0nt+nXaL/8j7trr0Eh3v0IOV8BV0LDYbSLmdJnsy/opAKSzzEhh1VspdHwYhmHUGDVvPFxHooWzZjKMvvwixl58Ln7TCBZN+yvuz36GOE60KTxfvryznHk0JJKhMQzDqCVq3nhEmfkTL7/AaocfSPKNV2nZcy+WnHUhrL46I20iNwzDqIqaNx49NWyS9nbGXHAWo//4B7wJqzH/ulto2+1bAD3WpDIMwzB6puaNh69Kwg0yzAv9HnVPP8GEIw8iMXsWy3++N0t+dxb+qNH54KugtaziVrj6sLWKYRjGEDAeEJQ/hzA8d1kzjaeeRNM108mstwHzbr+Pjh13Iu4Kibgb9MIoyqnIZ3373VcxjgR+lVxmerEc9LDyKbxvW2SGYQwhhoTxyJH4v/tpOvwgnPnzaD/kCFpPOpX6kU009ZKAl8/6zu1laVAvy3F67iVeLuvbDIVhGEOdnmfHAUBEvi4ib4vILBE5IZLM4sU0/eYXjNrr2+iIkTQ/+Bit51yANDVVnLkd6lCVAahWzjAMo9YYVCsPEXGBK4FdgbnAsyJyt6q+UVZm6VLGbLslsmwpbb89hbajj4dkZ9H0rKdhUcLoKwKb/w3DMHpmUBkP4HPALFWdDSAiNwN7AGWNh/v+//C2mcyKex7A++wW+eO5HDwFsnlPetB21inI7i70ZXT1UfThb2UYhjHEGGzGYyLwYcHjucB2xSeJyH7AfuHDVOKF519j6jarQL1Bz3hg8UArMUiw16ITey06sdeik01WRniwGY9S3/e7BTOp6nRgOoCIPKeqU/pbsVrAXotO7LXoxF6LTuy16EREnlsZ+cHmMJ8LrFPweG1g3gDpYhiGYZRhsBmPZ4GNRWQDEUkAPwLuHmCdDMMwjCIG1baVqmZF5BDgAYKsi7+q6uu9iE3vf81qBnstOrHXohN7LTqx16KTlXotpLidqWEYhmH0xmDbtjIMwzBqADMehmEYRsXUrPGopozJUEFE1hGRR0TkTRF5XUQOD4+PFZEZIvJu+HPMQOu6qhARV0ReFJF7wscbiMjM8LW4JQzAGPKIyGgR+YeIvBW+Pz4/XN8XInJk+Pl4TURuEpG64fK+EJG/ishCEXmt4FjJ94EEXBbOpa+ISKSkuZo0HgVlTHYDNgN+LCKbDaxWq5QscLSqfgaYChwc/v4nAA+p6sbAQ+Hj4cLhwJsFj88DLglfi6XAPgOi1arnUuB+Vd0U2IrgNRl27wsRmQgcBkxR1c0JAnB+xPB5X1wLfL3oWLn3wW7AxuFtP2BalAFq0nhQUMZEVdNArozJsEBV56vqC+H9FoIJYiLBa3BdeNp1wJ4Do+GqRUTWBr4JXB0+FuArwD/CU4bFayEiI4EvAn8BUNW0qi5jmL4vCKJJ60UkBjQA8xkm7wtVfQz4pOhwuffBHsD1GvBfYLSIrNnbGLVqPEqVMZk4QLoMKCKyPrA1MBNYXVXnQ2BggNUGTrNVyh+A4wA/fDwOWKaq2fDxcHl/bAgsAq4Jt/CuFpFGhuH7QlU/Ai4EPiAwGs3A8wzP90WOcu+DqubTWjUekcqYDHVEpAm4HThCVZcPtD4DgYjsDixU1ecLD5c4dTi8P2LANsA0Vd0aaGUYbFGVItzP3wPYAFgLaCTYnilmOLwveqOqz0utGo9hX8ZEROIEhuMGVb0jPLwgt9wMfy4cKP1WITsA3xaR9wm2L79CsBIZHW5XwPB5f8wF5qrqzPDxPwiMyXB8X+wC/E9VF6lqBrgD2J7h+b7IUe59UNV8WqvGY1iXMQn39P8CvKmqFxc8dTfwy/D+L4G7VrVuqxpV/a2qrq2q6xO8Dx5W1Z8CjwB7hacNl9fiY+BDEclVS92ZoJ3BsHtfEGxXTRWRhvDzknstht37ooBy74O7gV+EUVdTgebc9lZP1GyGuYh8g+AbZq6MyVkDrNIqQ0S+ADwOvErnPv+JBH6PW4F1CT4831fVYqfZkEVEdgKOUdXdRWRDgpXIWOBF4GeqmhpI/VYFIjKJIHAgAcwG9ib4kjjs3hcicjrwQ4LoxBeB3xDs5Q/594WI3ATsRFCCfgFwKvBPSrwPQuN6BUF0Vhuwt6r2WnG3Zo2HYRiGMXDU6raVYRiGMYCY8TAMwzAqxoyHYRiGUTFmPAzDMIyKMeNhGIZhVIwZD8MwDKNizHgYQx4R8UTkpYLb+mXOaxCRG0Tk1bCM9xNhCZjCa7wmIreJSEN4fEX4c30RaQ/PeUNErg+rACAiO4lIc5EOu5TRoWS5/fC500Tko4JrfKPgud+GJbXfFpGv9dVrZxjlGFQ9zA2jn2hX1UkRzjscWKCqWwCEmdqZ4muIyA3AAcDFRfLvqeqksGXADOAHwA3hc4+r6u4RdMiV239BREYAz4vIjP9v7/5dowjCMI5/30pja5VSTgW1uc4ipFArLZSARRr/Am2s1N7GX1haiKmtVMTCQkFJIcQUIWpAUIKChUU6QSz0sZhZbjl218zKXXPPBw5ud+d255obdmbveSVt5eN3Jd2ufyDH8S8Dx0g5Ti8i4rCk37u4nlkvvvMwG5kHvlUbkj62/Pt4FTjYdpL8o71Gj8TWjrj9LueAh5J+SdoGPpHKFphNjAcPmwVztamexx3tVoArEfEmIq5HxKHxBjlU7zQpGqZRROwFjgPPa7sXx6atBv/q9FjcfuVSrva2EqOKgC5RYFPnwcNmwU9Jw/xaamskaYNUE+MWKfvobUQcyYfnImIDWCflAj1oOMUgt9kBvkrarB1brfVhKOlzV4db4vbvAQNgSKpRcadq3vR1us5v9r+85mFWI+kHKb77UUT8Ac6Qpo52s25SrXnMA68i4qyk4rTnlrh9JH2vtbkPPMubM1+iwKbPdx5mWUQsVFNBOer/KPCl9Dw5zvoqcK1HH9ri9qsaDJUl4H1+/xRYjog9EXGAVIt6rfTaZiU8eJiNDIDXEfGOFNe9TroD6OMJsC8iFvP2+JrH+ZbPLQAXgJMNj+TezI8RbwIngMsAkj6Qora3SOssF/2klU2aI9nNzKyY7zzMzKyYF8xt5uR/YN8Y273d9STWBPqwH3jZcOiUpJ1p9cOsL09bmZlZMU9bmZlZMQ8eZmZWzIOHmZkV8+BhZmbF/gLrkf12cDa6WQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XID+ flux density vs. MF flux densities\n",
    "plt.hexbin(cat_all['F_SPIRE_250'],cat_all['F_BLIND_MF_SPIRE_250'], cmap=plt.cm.Blues,gridsize=(400,400))\n",
    "plt.plot([0,100],[0,100], color = 'red')\n",
    "plt.xlim(0,100)\n",
    "plt.ylim(0,100)\n",
    "plt.xlabel('F_SPIRE_250')\n",
    "plt.ylabel('F_BLIND_MF_SPIRE_250')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add field name\n",
    "cat_all.add_column(Column(['Bootes']*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_Bootes_BLIND_Matched_MF.fits', format='fits',overwrite=True)"
   ]
  },
  {
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   "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)"
   ]
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