{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of SPIRE-NEP 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_SPIRE-NEP_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=\"table4385374672\" 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>2</td><td>265.130578575</td><td>68.8963841003</td><td>83.6616</td><td>84.4247</td><td>82.2962</td><td>38.222</td><td>39.7325</td><td>36.1447</td><td>14.5488</td><td>16.9491</td><td>12.1281</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.998811</td><td>1.0017</td><td>0.999426</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.028</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>265.220810646</td><td>68.8210389958</td><td>2.12994</td><td>4.50126</td><td>0.654917</td><td>6.4692</td><td>13.5645</td><td>1.91667</td><td>6.1775</td><td>13.75</td><td>1.71562</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.999131</td><td>0.999137</td><td>0.998241</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>7</td><td>264.692836192</td><td>68.8548615379</td><td>9.66038</td><td>16.838</td><td>3.33856</td><td>7.44328</td><td>14.057</td><td>2.24507</td><td>7.00689</td><td>14.9722</td><td>1.94987</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.999767</td><td>0.999337</td><td>0.998947</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>9</td><td>264.990677511</td><td>68.846817537</td><td>53.5928</td><td>55.4711</td><td>50.8564</td><td>42.334</td><td>45.0892</td><td>39.2733</td><td>27.1066</td><td>32.0719</td><td>21.8479</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.999731</td><td>0.99934</td><td>0.999472</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>10</td><td>265.062466028</td><td>68.983556726</td><td>43.7225</td><td>45.205</td><td>42.3002</td><td>31.0255</td><td>32.9741</td><td>29.2113</td><td>15.8279</td><td>18.0674</td><td>13.4266</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.998833</td><td>0.999148</td><td>0.99861</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>11</td><td>264.966089615</td><td>68.8491044468</td><td>55.2075</td><td>56.4675</td><td>53.0041</td><td>41.6726</td><td>44.8444</td><td>37.5899</td><td>16.2961</td><td>21.5967</td><td>10.9443</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.998669</td><td>1.00004</td><td>1.00004</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>22</td><td>264.950507066</td><td>68.879977483</td><td>34.4304</td><td>36.5695</td><td>32.2169</td><td>21.191</td><td>23.9461</td><td>18.4683</td><td>4.37924</td><td>7.40216</td><td>1.8624</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.999497</td><td>0.9987</td><td>1.0001</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>25</td><td>265.222542933</td><td>68.9013139736</td><td>32.1009</td><td>33.8447</td><td>30.3096</td><td>17.0104</td><td>19.3978</td><td>14.629</td><td>4.07678</td><td>6.66724</td><td>1.83731</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.999326</td><td>0.998875</td><td>0.999033</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>26</td><td>265.229092621</td><td>69.0354601936</td><td>38.3512</td><td>39.8679</td><td>36.9208</td><td>29.7149</td><td>31.8769</td><td>27.6223</td><td>16.0092</td><td>18.875</td><td>13.2505</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.99867</td><td>0.999075</td><td>0.998298</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>32</td><td>265.300490257</td><td>68.8793120358</td><td>29.2289</td><td>31.7867</td><td>26.5429</td><td>18.3143</td><td>21.3693</td><td>15.2516</td><td>4.81096</td><td>8.07449</td><td>2.12285</td><td>-0.113443</td><td>-0.178841</td><td>-0.176173</td><td>0.00673625</td><td>0.010885</td><td>0.0155962</td><td>0.999411</td><td>0.999485</td><td>0.998794</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",
       "2                           265.130578575 ...          0.0          0.0\n",
       "4                           265.220810646 ...          0.0          0.0\n",
       "7                           264.692836192 ...          0.0          0.0\n",
       "9                           264.990677511 ...          0.0          0.0\n",
       "10                          265.062466028 ...          0.0          0.0\n",
       "11                          264.966089615 ...          0.0          0.0\n",
       "22                          264.950507066 ...          0.0          0.0\n",
       "25                          265.222542933 ...          0.0          0.0\n",
       "26                          265.229092621 ...          0.0          0.0\n",
       "32                          265.300490257 ...          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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zMjJCEAb8p//0Ic497zz+5E//G1ddeQWnnHwK551/PvmxsfiOjgr0n/vc57j33ntxXZdzzz2X3/zN3ySdTvOzn/2MTZs2ISL85V/+JatXr+b5559P6BmY2Rvf+EbuvvtuNm7cSFtbG1/72tcmr9u8efMRabZvfvOb3H333Uf8/ec//3m+973v4XkePT09k5VrAwMDNQ96yuUyl112GVEUHTNzmatyucx1112H7/uEYcjrXvc6br75ZiAOmo888gif+MQnuPXWW9mxYwef/OQn+eQnPwnExRErV67ktttu48Ybb5wsmvjUpz7FmWeeeUQ7ruvymc98ht/4jd9AVbnkkku4+eab8TyPL33pS7z97W/HcRyWL1/OV7/61QU9JnMkadWF85ls2bJFH3nkkaa0VQ6UchLTmBm8MFTmQL55EfG8lRm6s81bjzp6Z9znnnsu0SPOyizT3p07d/LWt1zPLx9/siHtLeQrBEvFD37wA1588UU+9KEPHXPdhg0beOSRR+jr61uEni2uGd7rdb1ZTjvnQv3UbcfuIn4i7Kh73M2gjDGt681vfvNid8EsIRagzAlrw4YNDZs9mdlNnH3BmHodd0USZmk53lLMxsyVvcfnzwKUWTTZbJbBwUH7AJsla2I/qGw2u9hdOS5Zis8smvXr19Pf3z9ZvttoQZNP8rpY3yszrW1iR10zdxagzKJJpVKJ7jK6e8RP7L6PJsC6Lu+Er+IzppEsxWeMMaYlWYAyxhjTkixAGWOMaUkWoIwxxrSkxAKUiJwkIveKyHMi8oyI/ME0txER+byI7BCRJ0Xk4qT6Y4wx5viSZBVfAPyhqj4mIp3AoyLyr6r67JTb/CZwRvXfZcD/rP5vjDHmBJfYDEpV96nqY9Wfx4DngHVH3ezfAbdrbCuwTESmP+e/McaYE0pT1qBEZANwEfDwUVetA3ZP+b2fY4OYMcaYE1DiAUpEOoBvAx9W1dGjr57mT475+r+I/J6IPCIijyR11gFjjGkVU8e8seGhxe7Ookk0QIlIijg43amq35nmJv3ASVN+Xw/sPfpGqvr3qrpFVbesWLEimc5OS5l1x7sGanZJZaja1PPgqTb3xJl2TgdzvJo65nUu61ns7iyaJKv4BPgK8Jyq/tUMN/se8J5qNd/lwIiq7pvhtkC8E2zUhIE1UqUcKOPlKPH2VJVIlfa0Q6pJUUpVef5QmeFSWHNL9Ea1FUbK9sEK436UeHuRKkGoFKptNSso5isR2uSgb8xSlmQV35XAbwNPicjE/tH/GTgZQFW/CNwNvBHYARSA9812p2Gk7M8H9OZcUi5H7NDaCJEqYQR7xnzGK/FAk6pE9LZ5eI42/FxrkSp+qOwe8SkFSi7lkIqUoq+JzN3iYAiFICJS2NpfZHWHx/krs7hO45/PMFJGSiEvDFXwI9g57HNyd4oz+9K4Dd7+feKxHRoPeHnYZ2Iz5O6MQ0fGIT6Xa+PnVRlXyHiCIoxVlKwnpJy4cTs3nzHzl1iAUtUHmWU00PhQ84Nzve8wgoPjIbmU0JN1kQYMdKpxQBgshBwaD48IDn4E+/MBHWmH7qyD0Lj29o8FDBWP3Obdc4SOdLzlfKX2ruVzbrMYKP5RM5j9+YCBQp5z+jKs6UzhNOD5jFQJqrOmkdKRD2LXiM+BfMB5qzL05lzcBpwFPIyUSqhsG6wwftSTNlKOGPcjenIuabdxQcMVyKWcY/pfCpSKQM4THBp/UGPMieK4Ppt50Vf2+gHLcg5tqfkHjol0Xv9oQCWced6Sr0QUqgNdxpv/bCNSZbwSsWfUJ5ghAIkI2ZSQipRSoNTo1qxU46BUDGa+kyCCpw6W2TXis2l1jqzHvALHxCxm31hA/6jPTNm8cqg8trdEX5vLBasyeI4sqL2Xh33254MZbxdUD2raUsLynLvgg4xcSkg5MuN9RArjvpJ2IePGl1mgMmZujusABXEJw+FixHhF6cm5uI7WHTgmB9N8cMxR/kwihYFCSNYTenLx7K3e9iJVogj6R33ydU6NXEdoS4EfKqVw9ttP12bBj+oOcCPliAdeHmfDshRn9GbmNJsKI6XoR2wfrNQMhlMNFELu21ngjJ40Jy+b2+wtjJTRcsQLQ5WaBxZTFXyl6AcszznkUs6cDzJSTjxrqrePlTB+7bKeJJIiNieuf3x4V123e/dlJyfck+Qc9wFqQiXUOaXhIlVGSxH788G8ZielQNk3FtBVXd+o1d5EOm+oEHIgH8x5bUlESHtCyo1nU34dsU1VKYfxv7lS4KVhn335gPNXZquBv/ZzGSm8dLjCofG5R9FI4VeDFfpHfTatztKePjZtdsTtIyVQ2DFYZrjOA4upFBgqRqQrSm+bW9damFNN581nU0IFioHiOZD1aEiK2JgTwZIJUBPylYiiH7F8hjTcxNpI/2hA0V9YGYLyyvpGb87Fm6ZoI4riINE/4s8rWEwlInFqqZr2Uz22CF41TgcW/GjBRRalQHlkb5EV7S4XrsziTpOGCyNlqBjy0uHKjOnKeo37yk93F1nX6XHOynj2NvX5nJjx7s8H7B6ZOX1Yr0oYH2R0ph26ahzUTBRBLDSoBBHkK0rGFdKuFVEYM5slF6AgLkU/Og0nxIP5ofGQgcI8cmU1BBEcOGp9A5hcixmeT26uBs8R2lPxAFuecteRKsUgWnCgONqh8ZCf7BznjN40J3encST+TlMlUrYPVBhrZCUHsGcs4OB4wLkrM6xs93AdIYyUUhCxbbCy4AOLo41NWVtMTzmo8QRy6bmnAWdTDuNZsBVRGFPbkgxQEybScB1pB8eJK+bqSY/NV8FXSkF8RB5p3PYCJ00zEomP6lNuvA4Tr1El9/2bUOH5gQr9owHnr8wwWorYOzb3dGW9/Aie2F9medbnnBUZDo4HHJxH+rBeocKh6kHNqg6PrOeQcpMLHJNFFA5kPJtJGTOdJR2gIJ41DRZDykEy3ys6WqRwYDygXGeRwEI5Up1dJBicpspXIp4+UG5IaXg9DpcinjpQatoAXgqUzomyuybwI8g0rTVjji+2YaExxpiWZAHKGGNMS7IAZYwxpiVZgDLGGNOSLEAZY4xpSRagjDHGtCQLUMYYY1qSBShjjDEtyQKUMcaYlmQByhhjTEuyAGWMMaYlWYAyxhjTkixAGWOMaUkWoIwxxrQkC1DGGGNa0pIPUAJ0ph1WtLt4TXi0ngOn96Q5py9NOsEN7yYkvVHh0cbKIT/bXeC5QyXChe65XoesJ3Rk3KY8l6rKgbzPN54a5lcDJVSTfXyqykgpZMdQhbFycpsxTm2v5EfkKxFBE147YxZqSW9YmHKEXOqVgW1Vh0e+HDFajhLZvLAn57Kqw53cXv6irMvuEZ+9Y0HD24pUGS2H5CvN2YgxiJSdhysMFEIihYIf76h7waosK9sb/zZyheoW7IIjgiuQcYWCHyWyS3HBj9gxWKYYKKHCQ7sKPHuwzDWndrAs1/gNDMtBxGAxJIri98rukYD2dMjazlQiO/n6oVKasmlnwVc8R8l5Yrv5mpa1JAOUI5BLObhy5FbaAnRmHNrTDkPFkFKDdr3NecK6Lo+UEw+mE20hcFJ3ilUdHtsHK+QrjdlvvhhEDBVDVEk8OKkqh8YDXjrso8Q7BkO8RXoYKo/vK9KTczl/VZZsg6aoHWmH7qwz+VxC/DqKQHvawQ+VYoNeuzBSdo/67M8HTJ1UBBEMFEK+/ewIF6zKcvHaHF4DdhGOVDlcDCn6Rx5YKJCvKNsHK6xsd+ltcxsSOCJVir5OG9SDCMYqSs6LZ/4WqEyrWXIBKuMJGVeqAeLYD5xUj8Z721zKQTxYzPeI3BFY3eHRnXUQpv+Au46QFThvZYbBQjzQz7e9MFIOl8IjjoSTVPQjdgxWas5aQo0H8vt3jnNGb5oNy9LzHujSrtCbc3FrDJYiQtoTUm488PoLSFUdLobsGCoTKUx3N0r8+J4+UGLbYJmrN3Swvjs1r7ZUlfFKxHCp9uxdgYPjIYdLIeu7UuRS8wv6qkolVOrJHBYDjQ/qvPj9akyrWDIBynWgLTVzoDiaI0LWg9WdHiOlaM6zm66Mw9pODxGOONKfzkRQ7Gv36GnzeHEoTpXVS1XJVyJGEkpNHi1SZfewz758UNcsbWIg3z5YYfeIz6bVObqz9afFBFiec+LXr87gJiLkUpDROO03lzhVCZUXhsqMlOv7u0Ah8JUfvTDGus4Ur9nQTtscAocfKkPFED+s78BCgUoILx326c46rO7w5hQ4wkgp+HM7iIkUxn0l7Wp8gGezKdMCjvsAJUAuJXjO3D9UIvFMqzvr0JF2GKwOIrWkXVjXmSLrCc4cjzYdERyJiyjWdEZsH6zMmmashMpgMSCMkk/nAQyXQnYMVggjndOgD3GQGveVrf0F1nWmOKsvM+t6Si4l9OTcug8sppoI/B1ph3KglGd57VSVffmAXSP+vNKjQQS7R3y+/tQwl67Lce7KbM2Dk4kiiPmuEyowUooYLVdY2+nRlakdwFXjdSZ/AZnkShgH1FyKhqQ0jVmI4zpApV0h61XXfBZwxOeIII6yst2lMEMaRoC+Npe+9vkNplO5jtCRdti0Osu+MZ/dI8Ex7UXVwW18jkfC81UJlZeGyhwuzW02Mp1IYe+Yz768z/krs6zu8I55vlwHenMuKVdmnYHORkTIpiDtVYsophmg85WI7YNlyuHcA+9UERBF8PM9RZ47VOaa0zroazv2Y1QKIgYLC18nVEAV9owGDKWEdV2pYyoaVZUgomHrcsorRRRZb+GvjzHzdVwGKEfidJ4jjVvYnZhNtafjVNNQMZz8wLelhPVdKVxn9nTeXNpzBdZ2pVjRHhdRjJYjVOMCgMPFMB6cGtLazOLS6oCdwz5oPAA3Qljt/FMHSrw84nLhquxkWqwr49CZcRo88MWz0/aUQxDpZBFCECm7hiscrFYfNkoQweFSxHefG+WsvgyXrm8j7QphFKfzyg1eJ5wIGjsGK5MHSo5IzSKIhQqiuHAj4yppS/uZRXDcBSinmtKRiTK5BpuoFutpcwnC+IPZ0fDB9BWOCBlPOGdFhv15n0f2lCjXuVaxUAU/YttAmXKwsFlFLaHCcDHkgZfH2bw6y+Y1uWOqKxtJREi5gufE1Xm/Gpi5CKIRQoVfDZR5YajC1ae244ok+topcVHKcClkTWeqKe+Tcgh+pLSlGneAZkw9jrsAFQeQ5D8kjgjt6TiF2Iz2XEfYnw+a+qXbnYcrFPzk25tIU53Rm2nauoaI8PyhciIzi6OFClL9Um8zXr2JNiLVps1qHEnicNCY2pb8mSQWovkHi81tsNnnEmj2mnvTz5XQ5AbtXBBmqbMAZYwxpiXVFaBE5CoReV/15xUicmqy3TLGGHOimzVAicifALcA/3f1ohTw/ybZKWOMMaaeGdRbgeuBcQBV3Qt0JtkpY4wxpp4AVdF43wEFEJH2eu5YRL4qIgdF5OkZru8Wke+LyBMi8sxECtEYY4yB+gLUN0Xk74BlInIzcA/wpTr+7jbgDTWu/yDwrKpuAq4GPisi6Tru1xhjzAlg1u9BqepnRORaYBQ4C/hjVf3XOv7ufhHZUOsmQKfEX+ToAIaAxm+cZIwx5rhU1xd1qwFp1qA0R7cC3wMm1rRuUNVpz7QjIr8H/B7ASSed3OBuGGNMa5k65vWtXreg+/rHh3dNe/m7L2v9sXTGFJ+IjInI6DT/xkRktAFtXwc8DqwFNgO3ikjXdDdU1b9X1S2quqV3xYoGNG2MMa1r6pjXuaxnsbuzaGacQalq0pV67wP+vFqAsUNEXgLOBn6ecLvGGGOOAzMGKBHpUtVREZk2fKvq0ALb3gX8BvCAiKwiXt96cYH3aYwxZomotQb1j8CbgUeJCxqmnklNgdNq3bGI3EVcndcnIv3AnxB/yRdV/SLwSeA2EXmqet+3qOrA/B6GMcaYpaZWiu/N1f/ndVojVb1xluv3Aq+fz30bY4xZ+uo51dGP67nMGGOMaaRaa1BZoI04RbecV1J8XcSVd8YYY0xiaq1BvR/4MHEwepRXAtQo8IWE+9USktqFdSZpN94zqVnteo40tb1ioHRmmtMWxI/Pb9LuxJFObMzYnE0EI534QB69PJwMtc2nzCKYMcWnqv83odb/AAAgAElEQVSjuv70f6nqaap6avXfJlW9tYl9PEIzd/Z0m9RQpEoYKcsyLivbvaY8PlVlbadHb85NvL2UA8uzDu0pIdWEHchUlUoQcUZPmo60k/imZ6qKHyo/2pHncCkkaELEdwTy5ZAwittPmms7x5lFUM+pjv5GRF4NbJh6e1W9PcF+zUiAjrRQDpTKtOedWDhXIJcSnKYcCSujpYh9+YBIoa/NozvjsnfMJ1+JGn70r6pUQiiH8ZH3qs4Uy3Iue0Z9SoE2dDblAI4DV57cxsVrc5PPZypSSr7S6JdPNZ4tHS6GFPx4JnPKsjRj5ZA9oz4RjZ8tqioFX6tbyyv/+sI4pyxLccmaHK5Dw99DDvF703WECMhXItKukK1+Mhs9e3MEcl7cnjHNNmuAEpE7gNOJz/oQVi9WYFECVLVPZFNCKlKKDRxUBch6QqoJU6dIlSBU+kcDisGRDyDlvjKw9o/6KI0ZWMMZnq+M53Dq8jTDpZB9Y0G1fwtry3PgpK4Ur9/YQWfGPeo6oT0NlVAphzPcwRxFqhT9iMPFY4N6Z8blzD6Hg/mAwWLYkKCvqvgRlIJj7+3lYZ99YwEXr8myriuF16DBPeNC2pVjglAljGdwuZSD5zQuxZh14/diM1KWxkynnnPxbQHO1WbkEebIdYT2FPihUlrgQJd2IOMl/2GcOMo/mA8ZLNbudGfG5aw+hwP5gKEFDKyqSimIB9SZiAjLcx6dGZcDYz7DpfnN3jwnHkSv29jJ6T0zn5xeRMh4QspVipMzkLlTVcIIBoshlRp34oiwespssRzO/8AmipTCLAdGlVDZ2l+kN1fhipNyZD1n3rOQemb0ChT8CM+BXMpBmP9synPiWZMFJrPY6glQTwOrgX0J92VeRIT0xEAXKMEc80bNTGFEqhQqEXvHgprBYipHhDWdKZbnXPpHfSpzGFhV4+ejFNRfKOA5wrruNMtzEf2jFYKovtmUEK9TbFqV5cpT2knXOQt1RGhPx8UMc+knvJIeHZtDrjfrOZy2PM3hUsj+fIBWixvqUU+gP9pgMeR/b8tzdl+ac1dmcaX+wDGfGX0QwVg5IusJaRfipupvL5eShs34jFmoegJUH/CsiPwcKE9cqKrXJ9areRAR2lJCEMVH5PUMOjOlTBot0jio7B0N5jSYTpX1HE6fw8Aap7zmPzNpSzuc0ZthYDzg4Hg805vprlIOLMu6vPHMTla013WC/GPvwxU8h7oCQKRKJVCGiuG8Hp+I0JPz6Mq47BvzGS3Xni1OzNKKcwygk38PPDdQ4eURn8vWtdHT5s4aBBY6oy8FSiVU2lIOjsye9mvWZ8GYuahnNPnTpDvRSJ4jdKTjIoDKDBk0z4mPTJMugpi6aH9wPFzwuk49A2tcBNGYtR0RYUVHiu6cx97RCgX/yNmbK3ERxDUb2rlgVXbBg5uIkEsJ6RnWyrQa6IeK4bRrP3PlOcJJ3WnGK/FsMdRjZ4sLDfRTFXzl3p3jrOv0eNW6HClHcI4KVI2c0UcaF1GkXCE3QxFFMwuCjJmreqr47hORU4AzVPUeEWkD3Nn+bjGJSDXFceTg0qwUxkRgqlSLIMoNGEynOnpgjdNw8eBdqHP2OBfpyaKNiD3Vog1H4LTlaV53egdtDa4dn1hbfCXQKlodbEfmuTZWS/vEbLEQcGg8nPw+UyOLOKbaMxZwYNsYm1Zl2bA8Pfl+TGoW44dxQU783o8/H80sCDJmvuqp4ruZeOOsHuJqvnXAF4nPRN7SHBHaUkwO4M1KYYxVIsbKEcOlhOrgqyYG1ucPlee1/jYXIkJX1qU9HX+f6bSeNCd3z1wE0Yj2Jooo9o2FjFeiOa39zJUjwsr2FN0Zl6cPlqkk/AXfIIJH95XYnw+4ekM72YRnMXERhZLzoD0dH8BZOs+0unpSfB8ELgUeBlDV7SKyMtFeNZCIkHKheV/vjfP/SQenCY7E6aAkg9NUriNcsjZ3TOl4UhwRxspRQ1Js9Ui7Uv2OWHOMluMUXLNSbJFCxtaazHGintxMWVUrE7+IiEf9hU/GGGPMvNQToO4Tkf8M5ETkWuCfgO8n2y1jjDEnunoC1MeBQ8BTxCeQvRv4L0l2yhhjjKlnDSoHfFVVvwQgIm71skKSHTPGGHNiq2cG9WPigDQhB9yTTHeMMcaYWD0BKquq+Ylfqj+3JdclY4wxpr4ANS4iF0/8IiKXAMXkumSMMcbUtwb1B8A/icje6u9rgBuS65IxxhgzS4ASEQdIA2cDZxF/2/V5VfWb0DdjjDEnsJoBSlUjEfmsql5BvO2GMcYY0xT1rEH9SETeLnZuFGOMMU1UzxrUR4F2IBSRInGaT1W1K9GeGWOMOaHVs91GZzM6Yowxxkw1a4pPYv+HiPzX6u8nicilyXfNGGPMiayeNai/Ba4A3l39PQ98IbEemZYXqaK6dE9o3+zF1mChWy3PgS7x184sLfUEqMtU9YNACUBVDxOXnpsZ9LW5nNmbpiOd/M69lSBiZbvLGctT5LzmDK1b+0ts7S+SrzRnE6qz+tKc3O3hNXbj3mkJcPn6HBuXp0h6s1k/VJ49WOI/33OA7/9qFD/hfajCSDlcivjVoM9gIbBAZVpePR95v3qCWAUQkRVAk7bHOz45Em+lfVJ3KrGBNYyU8UpEMVAcEdKew4ZlKdZ2eiS8oz1KvNHeT3cX2DZQJkx4BiAidKQdzuhN05NLOEpJvJnfinaPS9bm6M01fmNGVWVw3Gf7YJnRckSkcP/OcT7xk4P8aqCcSHslP2LcV0KNX78D+ZAdQz7FJLcpNmaB6vm0fx74Z2CViPx34EHg/0m0V0uEMzGw9jRuYJ0YbPKVY3eZdURYlnE4sydNVybZgVyJd2d9ecTn/pcLDBSCRNsTiXedXdXhsbEnRTbh2aKI4DnCGT1pLliZaVh7RT9ix2CFg+Mhkb6y82cljIP+lx89zFceHWK0HDakvSBS8hXl6MmuApVQeemwz95RP/GDDGPmo54qvjtF5FHgN6oXvUVVn0u2W0uHiCACqzo8enJK/2hAKZjfYBCESsGPam5nLCK4Aus6PXpzyp5R/5jBaa6EmbdQDhXCUPnlvhJ9bS7nrsiQSTAX54iQ8eDU5SmGiyEHqgN9Yu05QmfGZdOqLHvHfPpHg3ltJx1GyoG8z+Fi7devEirPHCzz/E8Ocf3ZnVx5ctu8toOPVCkFSjDLa6/AcClipFxhbadHV8ax7eBNy6h3JGkD3Ortc7Pc1kzDESHtCqcuT7Gmw51TGi5SpVAJGZ8lOB3dXlvK4fSeNCva3AUt/NfTZqhwcDzk/pcL7BquJLy+Ec+mlufitb6kZ4sAriOs60px8ZrsnNpTVUZKIdsGyoyU6nv9Qo0D1feeH+UvHxhgz2j9ZxZTVcpBRL4ye3Ca/Bvi2fCe0YCdwz6VhNfCjKlXPWXmfwz8A9AD9AFfExHbUXceJtJUy3IuZ9QxsE4MNmPliPkuFTgirGhzOaMnTVsq4aIN4sH1V4MVHtpdaFiaaiYiUg0cHhuWpUglHKfi2ZvDuX0ZzupNz7q2WAkidh6usGfUj2eacxz3KyHsywf89U8H+NbTI5RniThhpIz7ynyfdgUKvlZTkAGRFVGYRVbPmSRuBC5S1RKAiPw58BjwqSQ7tpQ5IjjVNFxPNQ13dAAKozid14j0lYiQcuGU7hT5csTefDDnwXIuQoV8RdnaX2R9l8eZvRm8BCs34tkibOxNMzAeMlAI55WGq7s9R+jJuSzL5nhpOF5PmipSZaAQcCgfX77QvvgRbO0v8Ni+Iu+6YBkXrs4ecb1W03mNqndQYGA8ZLgYsq4rRXu6CeWTxkyjnnfeTmDqJyIDvJBIb04wjiO0pYSNvWn6qmk4VaXoh+QrjQlOR7QnQmc2nk0tyyY/6EQK/aMB9+0c50C+OUUUfe0uG3tTic8WJ2Zvpy1Ps2lVZrK98UrE9oEyg+NxkGzUS+hHMO4rdzwxzBceHmSoGJeJ+2FcBNHoYjyttvnysE//iN/U72oZM6GeUaoMPCMit4nI14jPap4Xkc+LyOdn+iMR+aqIHBSRGc+CLiJXi8jjIvKMiNw39+4f/yYG1hXtLqs7XcbKEZUEM2NCvJ6ypsNjdXtzgpQfwZMHSuw8nPTa1MRan8Mpy1KJfw9tor32tMsFKzMcLvi8PFzBj+aezqtXJVR2DFX4ywcOsS8fUAw00dmiAiPliBeHKlbpZ5qunhTfP1f/TfhJnfd9G3ArcPt0V4rIMuKzVLxBVXeJyMo673dJckQ4nHBq6uj2xirNG3BCjSsZm1Uh5ogw3sTHF0SwL59sReGESGFZziOd9DeJp8imnKafYcOYesrM/2E+d6yq94vIhho3eTfwHVXdVb39wfm0s6TYCGDmoOk1DPb+NE22mKufZwLLReQnIvKoiLxnphuKyO+JyCMi8sihQ4ea2EVjjGm+qWPe2PDQYndn0SxmgPKAS4A3AdcB/1VEzpzuhqr696q6RVW3rFixopl9NMaYpps65nUu61ns7iyaetagktIPDKjqODAuIvcDm4Bti9gnY4wxLaJmgBKR9cC7gNcAa4EicRXf/wZ+qKoLKW79LnCriHjEZ0e/DPjrBdyfMcaYJWTGAFUtKV8H/AD4C+Ag8fehzgTeAPyRiHxcVe+f4e/vAq4G+kSkH/gTIAWgql9U1edE5F+AJ4nPjv5lVZ2xJN0YY8yJpdYM6rMzBIynge+ISBo4eaY/VtUbZ2tcVT8NfHrWXhpjjDnhzBigZpvNqGoF2NHwHhljjDHUUSQhIlcCfwqcUr199Yw8elqyXTPGGHMiq6eK7yvAR4BHgWRPT22MMcZU1ROgRlT1h4n3xBhjjJmiVhXfxdUf7xWRTwPfIT5xLACq+ljCfTPGGHMCq1nFd9TvW6b8rMCvN747xhhjTKxWFd81ACJymqq+OPU6EbECCWOMMYmq51x835rmsn9qdEeMMcaYqWqtQZ0NnAd0i8jbplzVxZE77JpGsf3gjDFmUq01qLOANwPLgN+acvkYcHOSnToRqSrtaUHGmxencp5DMWjOJnsAw6WQtBtvlZ60MFJSLpQCxWnCJomuQNYTyoEmtpvuVCU/atrmjwCVIH5QqtrUds2JrdYa1HeB74rIFar6syb26YSjqkQKHRmPU5c79I/6+GFyW3kLkPGEzWuyDBVDnj5YJoySH1ifOFBmbWfIuX0ZHIdEAodq/DiePVjikb0lVnd4rO9KJRoUI1XyvnLVKe1sHyjz8oiPajIHGp4DniO86cxOerJCKYx3802SAI7A/rGA5TmXjJfMa2fM0Wql+N4K3KeqPxORFcBngIuBZ4E/VNX+JvVxydLqlqilQPGrg0xb2uGM3jQDhYCD+cZuAS+ACPTkXHKpePlxZbvHa09x2T5UYdeIn/hsau9YwKHxgHNWZFjV7jU0cASRMlwKufelcYZL8RO6ZyzgUCHkjJ40HWmnoe2pKuVQGSqGhAquI5y9Msu67hRP7i8xXokaGvRTDmxaneVt53bTno5fvzYnftxFP5kDmrQTH8yICKHCQCEk6wk9ORcRC1QmWbVSfP9dVc+t/nwrsBX4I+B1wNeAaxPu25KmGgelcnDswCIirGhP0Z312DNSodCAwUeA9rTQnXWPGVRcRzi7L8O6To8nD5Qp+I0dWI/mR/DkgTLLsj6bVmUXnPaLVAki+NnuAtsGK8dcXwmVZw6VWZ512diTxnNYcJoqjJShUkgpOPaJ6sy4vPrkNvpHfJ47FH91cCHPZ9oVOtMOv715GacuTx9zvecIHWkoh0qlQed6cQRy3vSvSylQ9o0FdGcd2tNO9cDHApVpvFoByp3y80ZVvaH6820i8uEE+7SkqcbBpujPnlJLu8KpPRlGSyF7RuPZzVzHOSFOC/W0eaTd2oNIZ8bl1Sfl6B/1eX6ggmq8D0pShksR979c4NRlKU7vSePI3Ae6IFJ2DVd4cFeR8ixP6OFSyKP7imzoTrGy3cOZR1BUVfKViJFyVPO1EBFOWpZmZYfHc4fKHMwHcw5SjsRrW6/f2MGvn9peM4iLCFlPSLta13urlqwLKVdqvhZK/PqNVyJ62jw8pzlrfebEUitA/UREPgH8WfXnt6jq/xKRa4CR5nRv6ZhI583nKLcr69KedjiQDzhcrD/tJ0B31qEj7dQ98IsIJ3WnWdWR4pmDJQ4V6iuiEOa35qLAi8M++/IBF6zK0J1x65pNhZFSDCLufanA/nxQd3uRxu3tHw84szdD1pO6BlZVJYiUwWI4mY6tR8Zz2Lwmx2Ah4Mn9JfxICev4+7QLp3Snefembnpy9W987YjQlorXpUrTzM5r8Rzqfj4m+BEcyAe0px2WZW02ZRqr1jv/94lTer+q/v4RERkHvg/8dtIdW0omFu4Xsk7gOsLarhTLcy79Iz6VGkUUQjzQLM/VN9hPJ+0KF1UH1qcOlvHD2kflC80IFgPl53tKrGp3OX9lFneGIoqJ5/KJ/SUe31+a95pZwVce319iVbvHhmWpmrO3SOO1rXF//o+yt83j1za08+JQhRcPV2Ysokg58ezlXRcs48JVmXkN9iJCyo0DztT1zRlvD+RSgreANOt4JaLoRyzPumRTtjZlGqNWFZ9PvM3Gn4pIN+Cp6mCzOrYUTMyaioE2rNIql3LY2JtmqBhyIB8cMUBPVFv1tLlkvXq+gz273jaPXzvF5YWhCi8NJ1edNuHAeMjAznHO7kuztvPIwBGEykAx4CcvFRirNOYJPTAeMFQMOL0nzbKj1udUlVIQrzU1onjEdYQz+jKs7Urx1IEio6Uj1/pSDly2vo3fOruzIa+fiJBLCelIKQY67WNIu5CZJZ1Xr0hhsBiSqQi9be68UrbGTFVX7kBVj0jpicjZqvp8Ml06/k0EpkrIrOsi8yEi9LZ5dGdc9oz55MvxYN2RcejO1J/Oq5cjwhm9GdZ1pXjyQImRUu31l4UKFZ45VGH3aMCFqzJkPYcwUh54ucBLw37D2/MjeH6gQncmrqD0HEGrg20Sr1972uGy9W3szwc8c6AExAcCv71pGeu7Uw1vz3WE9lRcLFKuppddiWdNScx0yqGydyygO+PQmYkDrQUqMx/1J7eP9CNqbPd+ovNDqETTH7E2kucKpyxLM1jwURVSsxRBLFRbyuGydTnuf7lAcZrqtUYbLUc8uKuIEFeNzWXtZz5GyhGP7i2xsTc9bXVeI4kIazpTnNSVYlWHx3krM4mmxUSEjCek3Ph96TZhdjNSjohU6cq6WHgy81Hre1Cfn+kq4rNLmBkoNO3sDABZz0n8y5oTRCbWKpr3APtHg6Y9nwqJB6epUq5wzopkg9NUjghNOJHHpGZ+DszSU2sG9T7gD5myB9QUNybTHWOMMSZWK0D9AnhaVX969BUi8qeJ9cgYY4yhdoB6B1Ca7gpVPTWZ7hhjjDGxGWtZVXVIVQtTL5uyDbwxxhiTqLl+2eLLifTCGGOMOcpcA5RVixpjjGmKuQao/5ZIL4wxxpij1PVFXRFZB5wCDInIrwGo6v1JdswYY8yJbdYAJSJ/AdxAvFHhxHm4FbAAZYwxJjH1zKDeApylqtN9YdcYY4xJRD1rUC8CjT+DpTHGGFNDrXPx/Q1xKq8APC4iP2bKaY9U9UPJd88YY8yJqlaK75Hq/48C3zvqOjsFpDHGmETV2rDwHwBE5A9U9X9MvU5E/iDpjhljjDmx1bMG9TvTXPbeBvcjUZEqYRPP+z+xYWGzCM39BnXWExLeeuoIqWbuDwGUg6hpr2GkysB40LT2/FAZm9i1sEmiJn8ezNJRaw3qRuDdwKkiMjXF1wkcF1u/q+oRu4imHCXrNWZ765naK4dKg3Yjr1suFR9nlIL48SYlqm6BvrEnTajwwlCFwWLyg92KdjfeTryQzA63Rzs4HpJyoKfNI51gJC4FEXtHQ1487LOy3eW1G9rpyriJtKWq8Q6+h8pECivaXM5dkSHTgK3lZ5J2BdcRCj6knCjRz55ZmmqtQf0U2Af0AZ+dcvkY8ORsdywiXwXeDBxU1fNr3O5VwFbgBlX9Vj2drkcYKcXgyF1t/Qj8ipLzwHMau6NoEClFXxdlcW7icWS9eFAo+FFDN4o7OtCLCJ7Axp40a/yI7YOVRAOHSDxjW9HuUgqUoWKY+EZ4fgQH8wHtaaE76zZ0Q8EwUg4XQ0rBK++XA/mQbz0zyqbVWTavzuI2cNY4Xol46mCJsXLExMt0cDxkoFDgrL40J3WlGvpZcCTefdmZsmuvH0FQUbIJfPbM0lXrbOYvq+pPiL8HNQaMAs+r6mOqGtRx37cBb6h1AxFxgb8A/r96OzwbVaXoR4z7M2+5XgyUgq8NST1EqhT8iMIiBaepRATXgY60Q9ZrzAAQRsq4/0pwmsp1hM60w+bVWdZ3eomnGUWEXMphTadHe6qxrU13bwoU/Hi7+YK/8LSfapxe2zcWHBGcJtoKFZ48UOKbz4ywb8xfUFsQvze3D5Z5aHeBkdIrwWlqe78aqPDT3cWGpf2yntCRdnCnCUJKYz97ZumrleLbDHwR6Ab2VC9eLyLDwH9U1cdq3bGq3i8iG2Zp/z8B3wZeVW+Ha/FDPeaDP5NQIV9R0q6Sceeeejh6VtE6BBFIu/F24kU/mtd28FpN5/mz/O3E7GZ9d4pVHR7bhyqMlpPNcToiLM+5dGaUwUI4ax9nMnXj+pneMxMHOUOFkIwXt+vNY3ZTCZWhQkAY1S6BDaL4ffkvO/Kc3J3iypPbyM4jDTdYCHjqQBk/mvlADeLPwVgl4mf9RU7qSnFGb3pej89z4lSzMPvsaKGfPXPiqJXiuw14v6o+PPVCEbkc+BqwaSENV8/v91bg15klQInI7wG/B3DyyScfc32kcXptPlmmShgHtlyKuj+Y06UPW42IIMSpljCCQhBR70HrXAL9BEeEjCec05fhcCnkxcOVeQXGeokIKVdY1SGMVyKGS1GiM1gFyoGyfyygK+PQmXHqGlgjVUZLIfnK3J7PIIKXh312j4xw+focZ/Vl6mqvEirPHixxsDC3NGiksHvUZ++YzwWrsqxsr+s0nYhAmzf9jGn2vs79s3eimDrm9a1et8i9WTy1Ds3ajw5OAKq6FWhvQNufA25R1VnnIKr696q6RVW3rFixYurllIOIfGV+wWnyfohTOfHazcx3pKqUZkkftpqJtF9n2pl1wT9SZbwSUZxjcJrKdYTenMsla3KsbE9mwX8qEaEj47Km06s7rTlxq7k+Rq3+G6tE7M8HlGeJwAU/Yt9YwPg807+hxms3P+sv8s/PjTFUoyBFVdk9UuG+neMcHJ/fGl1Ube+J/SV+sadIcZapacaNU7zzCU6T/eaVz16zq19b2dQxr3NZz2J3Z9HUOkz6oYj8b+B2YHf1spOA9wD/0oC2twBfr76x+4A3ikigqv+rnj9OoihhIr2SdZXUUamH+cwqWsXUIopMtYjiiPWIBqcrJ9J+py5Ls6YjYttghWKQ7DPnOkJfm0sljNN+Rx+w1JPOq1ek8b9D4yFtqYjurHtEUUNQLYIoN+j9EkQwWAz5X8+Ncu6KDFvW5Y6YcYyVQ548UD7mdZ2vUGGoGPLArgIbe9JsWJY6okjErRZBiDSu2CGIYGyGz545cdX6ou6HROQ3gX8HrCP+jPcDX1DVuxfasKqeOvGziNwG/KDe4FSY57pKvUohVKK42k+EeacPW41IvD7VnnYmA64fKSVfSeLpdB2hLe1w4aosB/IBu0b9ySP7qQGjUaSaZlzdKYyWIsYSrvd/5eg/YFnWoS0l5Cs6uQbX6McXKjw3UGbHUIXXbmhnTafH9sEKu6c8r42igFa/StA/6nPhqizLsy5ZTxINIFM/e42sZDTHp5qJZlX9IfDD+dyxiNwFXA30iUg/8CdUTzqrql+cz31C/CFNMjhNiBTG/SUQlaYRr99AMYirDxNtizjFuLrTI+9HDBTiaVqSrToidGcdymFEpTorTKq9ifs9XIoYLsUHNEk+tiCKZ2g/fjHP6s4UrpBoujnUOAg/d6jMtad3HFE6npRI4+/0taWsHP1EV6uK70JVfbL6cwq4BbgUeBr4lKoWat2xqt5YbydU9b313tY0hohQrufLAg3iiDCWcHXfVCIyGZyaZWLW0bT2VAmbdA6RzoyD0ryA4Sb3/WFzHKn1Nrhtys9/Dmwk/sJujrj83BhjjElMrRTf1EOl3wBepaq+iNwPPJFst4wxxpzoagWobhF5K/EsK6OqPoCqqogszcUZY4wxLaNWgLoPuL7681YRWaWqB0RkNTCQfNeMMcacyGqVmb9vhsv3E6f8jDHGmMTMWCQhIlfV+kMR6RKRGc9SbowxxixErRTf20XkL4nPGvEocAjIElfzXQOcAvxh4j00xhhzQqqV4vuIiCwH3gG8E1gDFIHngL9T1Qeb00VjjDEnolpf1L0C2KqqXwK+1LwuGWOMMbW/qPs7wKMi8nUReW+1es8YY4xpilopvv8AICJnA78J3CYi3cC9xOtSD9WzVYYxxhgzH7Oe8UpVn1fVv1bVNxBvLvgg8ZrUMXtFGWOMMY1S17aZ1WKJtcRFEv/SiO02jDHGmFpqFUl0Ax8EbgTSvFJmvkpEtgJ/q6r3NqWXxhhjTji1ZlDfIt5N9zWqOjz1ChG5BPhtETlNVb+SZAeNMcacmGoVSVxb47pHib+8a4wxxiSi3jWodcRnjpi8varen1SnavZlMRpdotrTQqWoie4AO0FVWdHmsmcsaEp7AizLOoyWo0R3nJ3Kc+LdYJvRXhjF7bhN+EAIMFQMCSLFkXjzyaQFEaRdQNV21T2BzRqgROQvgBuAZ4GJsnIFFiVAOQIZF8oJF7h7DmQ9QYByoFQS3gw2VW1Pibe7Tnpb+7QLnR0evW2wd9Sn4CcXqFSVYqB0Z10ynrBnNKASaSIDuSPxgLq206Mrk2GoGPLUgRKVUAkTas8VWNeVoi0ljFUi9o7GQSBi27kAACAASURBVDiJxydAyoXlOQ/XSX7gFqA9JazpTPHCYIW+NpcVHR5CsrvrRgr5ipJxhbQbP5EWqE489cyg3gKcparlpDtTr4znkHKVot/4QUeAXErwpnz4sykhFcWDbKMHHUcg58nkYCNAW0oIovjxNXqMcyV+fBNHwWkXTlmWmhxYI6VhbarqMY8j6zmctjzF4VLIgfEQbWB7AizLOKzs8HCrj68n5/KaU9p48XCFFw/7DWtv4t3R1+bS1+ZODp5dGZf2XoeD+YDDpaihjw3iWWF72kl8sD4y0LtA/LwdKoQMlyPWd3nkPAcn4SBZDhU/ij8jDjabOtHUE6BeBFJAywQoiNMMbak4FVAKGjOQp13IuDLth8B1hPYUVEJt2Owt40J6hvY8R+hIxx/QSgPaE+IZmucceyQqIpMD64F8wPACB1bV+PUo+BHhNDNBEaEnFw98+8YCxioLa88RSDnC+i6PrHfsV/scETb2ZFjbmeKpAyVGy9H/396dx0lS1ocf/3zr6GPO3Zm9Z4FlYVFYblZOCSgqoBFiTKJ4AIoBFY9fSPxp4k3M72fU/IwmEUSjoFEIaFQU8MQEElxkuW92l2Vh72N27umr6vv7o2qgd3amu2emq2dm9/t+vfo1PVXV/Xy7pub51vPU0/VM6cRm5KRiSZtPaow+NtcRFrf5zMmGbO4rUZxia3HkpGlOxm1Yq2l0oi9XDJQNe4q0pR2WtPmJd/uFCoNFjXsZ4hgtUR0QKg0z/yeik6Yh4CER+Q1lSUpVP5R8eJWJCL4bdcflStGZ1mSMblVUKi/tCb6rU+qGG+k+rKW8jBd1cUyltTjSfVjtn9p1okp3blyxFoKJJ35VJV9S8jUE6znCQe0+g4WQTf1FwhAmskslfixscpmbdat+vibf4eSuLNsHAx7fkSNUJrRPy1sVrenqrZis73BYh0/3cMCOgQBlYq03BxCBjiZ3zMRbb9US/Wh9+ZCBXXkWtXjMybqJd/sVQygVND7RstbURL3tlIOnO4QJq9SCWhP/vB+4ddS6GTXlu4iQ9YXUJLrhsuO0KiqJWm8T74Ybq/uw9vKi1uJwqfYPN7r7sFblFev2uGKtRjVKoMPFiQ9KaE45rOhIsWsoYNdQbeUJ0JpyWNzqTWh/igiLWjzmNTXz9K48m/tLNcW7V6tiguV1NkWtxW0DJQYK1ffPyLu3pp2aEuFURYml9kRfLlTY0l+iezjgoHYf3022NaVE/wPRSSWJJ0UzvSoNM78BQEQ+rKpfKV8nIh9OOrDJmEg3XK2tikom0g1XqfuwFhNtLVbqPqy1vJGKdWt/VLGOV6+qRom6OIV+LEeEBc0e7WmHzf0l8sHYJxoO4DjQ1erTkpp8q8JzhJULMixtC3hke45caewW6kiroqvNI+tPvjzfjVqLA4WQzX3FcUf7CdHfrSPr4tVxiJ4w9lnlZBP9aLmSsnZ3gY6sy6IWD5FkE0dggygOCLX8x10yxrJL6xxH3UTdcA4tqahltM96olFJWb8+Z6ZRN1xU3lj/345E5WW8+pWX9R2a/bHL8xxoSUX7oB7l+a5w8Byfg+f4UUuzbJ2qUiiF9OXDKSWncmnP4dA5PktavOjaRtk6IeruWtGRmlJyKteecXnlwU2s6EzhykufT4jKXtDscliHP6XkVK4l5bCiM0VH1tlrXzpEx0pH1mV+c32TE+ybnByiY+Xgdp+D2v0pJady3cMBz+zOxy3F5Dta8oEyGHd/awPKM41V6RrURcDbgENFpLyLrxXYnXRgUzVWN9xUWxXVymse1Q2XcaMKPonyRrcWJ9t9WKuRinXnYIldQyFhqNEgiCSGUovQnnFpSTlsGyjRkw/JekJXq0c6gWsxIsKyOSkWtXg8viPPzqGAlrhV4SfwRSNHhIUtPnMy0bW+4ZJGgyCybuLfMRppSXVkHeY3e4mUVwphY0+RlpTD0nY/SvwJD6IYskEU+6VK16DuAbYC84B/KFveDzySZFD1NNINB8kftOXdcI0qL+01rotjpGLd2j/MUDH5s1XXEbrafBaGmnglB9EQ+BMXZ3iut5hYoi+X9hwOmePRl9eGjM4DyHqwtM3Hd5MfdDFQCNneX2Jxm9eQL9gXQ3ACSHuWnPYXla5BbQQ2isgfAV1EJ15bVHV7o4Krl0afTe3v5TVaI5LFdJUnIjQgV4wqb/8+Xsz+o1IX3/HAtUA7sDlevFREeoD3q+oDDYjPGGPMAapSF9/1wBWqutfEhCJyKvBt4LgE4zLGGHOAq9S50Dw6OQGo6mqgObmQjDHGmMotqDtE5DaiOaFeiJcdBFwM/DzpwIwxxhzYKg2S+JCInA9cSDRIQoBNwL/YlO/GGGOSVvFmsap6B3BHg2IxxhhjXjTuNSgRObbsuS8inxCRW0Xk/4hIU2PCM8YYc6CqNEji+rLnnwcOJ/rCbpZo+LkxxhiTmEpdfOXf5jsHeIWqFkXkLuDhZMMyxhhzoKvUgmoXkTeJyJuBtKoWATS6I2PV+9yIyLdEZIeIPDbO+reLyCPx4x4Rse9VGWOMeVGlBPVfwAXAHwKrRWQhgIgsAnbV8N7XA+dVWL8BOEtVjwX+FriuloCNMcYcGCoNM3/XOMu3EXX5VaSqd4nIsgrr7yn7dTWwtNp7GmOMOXBM6DaVIpJUK+cyKgxnF5HLRWSNiKzZuXNnQiEYY8zMUF7n9fd0T3c402ai91FeVe8ARORVRAnqo+Nto6rXqeoqVV01f/78eodgjDEzSnmd1zqnY7rDmTYTTVA76ll4/F2rbwIXquqMnwTRRDqzLk1+Y6ZsEKLJEhOYN3BMw8WQ5/YUKJQaMztrKVTypbBhs8H25QOe2pUnqNMMyNUUA6U3FzTs8wWqFJOYRXMMqspgISRfChtS3oGo4p0kRlPVSoMeJkREDgb+A3inqj5Tr/c1yetq91nS5rNjsMTGniJJ1XVZT5ibdXEE2jMOffmQ/nwylYGq8uSuPPduGiZUWLu7wMvnpVja7icy35aq0p8P6Ys/T76kZH0Hz0lmfq8gVDb2FNgxGOAIPLwtz9nLmljc6te9rHIDxZDBYsjuoYClbT4ZP9nJr0phlPQLQTRLcVIzFBcCZfdQiSA+HDNeyNysa3Nt1VnVBCUiRwAfAQ4p315VX13ldTcCZwPzRGQT8GnAj197LfApoBP4WvwPWVLVunchmvpzREBgYYvHvCaP9d0FuoeDur2/K9DR5JJyX6pgBGhPO7SkHHYPBRTqeJa8e6jEfz43SF8+pPxk+KldBV7oK3HMwjStabdu5eVKId1DAap7f19juBjiOpD1nbpVrKpK93DA+u4CqtH06KFGs93+fO0Ah8zxOf3gJjJecolDgVxJWd9doCPrsrA1manmywUKAwUl7SopV+qW9ENVenIBg4W9j7/hkpLrLzEn49Cccvb7SUQbpZYW1C1Ed474BlBzLaSqF1VZ/x7gPbW+n5l5HBEcF1Z0pujPh6zvLpCfYuJoTTm0ZRyiE9G9/8lFBE9gfrPLcDGqKKbSeisGyn2bh6MurzHeJ1Doy4f87oVhDm73ObwzNaXZdoMwinm4qGN+kVCBIIT+fEjGFVLe1CrWXCn6m/TnwzH3U0nhuZ4iz/f2ctpBWY7oTCdasSrQPRzQkwvoavdpq2PSH08+IG5NTW2mZFVluKTsGR7/mFOgJxcyUAjpbPLwG9UvvR+rJUGVVPWaxCMxs5brCG0Zh+MXZ9jUW2RLf6n6N7lHSblCZ5OLK9W7uBwRmnzI+h57hgOGihPPUht7Cty1cYhSqGMmp3Khwgu9Rbb0Fzl6QYYFLRPqGX/xWkVPLmqeVSpuZF0+UArhSLffxCq6UJUtfUU29ZX2aaWNFmj0uOeFYZ7YWeDsZc3MzSaXODQu84WeIs2pEl1tqcQrcgWGiornKNlJJP1SqHTHrfZqR5oCxRC2DZRoSQntGTfx1uL+rNKU7yNDR34qIu8HfgTkR9ar6oE79tHsY6Tbb2m7z8JWj7W7CvQXql8vcgTmZNwJXy8QEQSYm3VpTSu7hwJquVY9UAi5+7lBtg2Watp+RKAQBPDwthwdWZeVC9I1XU8pBkr3UIliWMPtV8oooAqDhZCUC2mvtm6/vnzAut0FioFOqHVZCmH3UMCPnuxj5YI0Jy3JTqnFUY0SdcE9syvPghaXeU1e4t1ipRD6C0rGVfwauv3KrxNOpqE+WFCGiiU6si7ZhK+97a8qnQreT3QcjfwVP1K2ToHlSQVlZi/XEVxHOGpBmt1DJZ7rKY6bCJr8aBCEMPmBAY4IKVdY2CIMVKhMQlUe3Z7jga25F6/DTEagsGso4O6NQxzemeKQOf6YiSNUpS8XMFCoftZdTTGAQhCS8WTc6ynFQHmup8Duocl3e460bp7YmWft7gJnLWvmoPZkB1EosGMwYM9wyNJ2n6YGVOS5gKh16jHuoIZ8KXxxX0727zdykrF7KCDthXTYIIoJq3QniUMBRCSjqrnydSKSSTowM7u5jjCv2aOjyePZ7gK7hl66fOk50VD1Ws5ia+WI0JqOLlB3DwfkyoaJ7xgs8dsNgwwVwwm1msYzUpGv211gU2+RYxZlmJN5qVtsuBi+OGikHkM5Xuz2KynFUMl6zosVnaqyc7DEhj1FlMkn3nIjI+F+/ewAS1p9XnlwE82pBAdRaHSdaEN3gfaMw+JWP/GKPFQYLCopR0mXdftVu044GSODRLb2l2jPRAN9bBBFbWrpTL8HOLGGZcbsxRHBETisI8Xi1pC1uwukXaElXb9RauVEBFegs8klX1K29he554Vh1ncXql5nmowgruR+v2mYJa0eh3ekGCiE5Er1q9zKjQyiGCiEpN2okl3XXWS4GCby+UohbOotcvPjvbxiSZajFqQTvZ6iQG8upC+fZ0mbT3s6+Yq8EEKxoGQ8JV9SenKT686rxcjnGxlEkbJBFFVVuga1iGiq96yInMBLXX1tgE1YaGrmOkJLyqGr1SMfaOKVjiNCxoMfP9VPfz6ZyrtcqLCtv0RrKmrZNOJrogMFZe3uAlCfVtp4QiAMYWNvgSPnp0cPrKy7kW6x/nxAe7ox120U2DMcTnkEaq1llULYMVBiacLdp/uDSi2oc4FLiW7i+g+8dGj2AX+TbFhmfyMiBJrMl1DHK2+gkHxyGhECSLLJolwpVByhYZ8v5ToN+2wwtSHhkxE26E4XZmIqXYO6AbhBRN6sqj9sYEzGGGNM9XvxlScnEbkz2XCMMcaYSKVrUI+MXgQcMbI8nmjQGGOMSUSla1DPEV1v+hwwTJSg7gbemHxYxhhjDnTjdvGp6gXAD4mmYj9OVZ8Diqq6UVU3Nig+Y4wxB6iK16BU9UfA+cDZInIrkGpIVMYYYw54Vb+oq6qDwFUichxwWvIhGWOMMVUSlIj8AbBdVZ8GWoEWEXmDqt7WkOiMMcbUxffvfX66Q+Btpxw8oe0rjeL7R+BkwBORXwDnAHcAfyEiZ6vqR8Z7rTHGGDNVlVpQrwWOBrLAZqBLVYdE5PPAg+x9d3NjjDGmrioNklBVVeK7uPDSXVzCKq8zxhhjpqxSC+o2EbkbyADfBG4WkdXAWcBdjQjOGGPMgavSvfg+KiKnRU91tYgcBryJKFn9oFEBGmOMOTBVGiQhqvq7kd9VdT3wpTG2sdsAG2OMqbtK15J+KyIfFJG9xgWKSEpEXi0iNwCXJBueMcaYA1Wla1DnAe8GbhSRQ4EeohF9DvBL4Muq+lDyIRozOzR6ftRoDqMGltrI8jQalWVzzh7YKt2LL6eqX1PVM4BDiL4HdYKqHqKqf27JyUzU3KxLo+ahE+DPVraRdgUv8TKVYhDy5LZ+SkFI0tMWBsUC4WAP2x68k1IhFyeO5IRBkXWbd/Lg40+Rz+cTLQugUAp5avsQT20fYrgYJF5eECrFEPKlsCETFwqQSf6g3C9UvdURgKoWga0Jx2L2c1nfYbEn9OQCBgvJVAQC+K7Q2eSytL2Vk5c28d2He3h0e45CAnVdGCo9gznWbeuhGIQ8/EIPr375AjqbU3hunb+NoUqxkGPN7Tfxs699lsLwIB3Lj+bsv/hnmud34aaydS4vJCiVeOa+O3l69Z38NAw4buWR/NWVf05rczOpVH2nLA9DpRCE3HT/Nv5r/R4UeM0Rc/mb1xxC1nfw67w/Q1WCENZ3F+gejg6OuVmHRS0eQv1nfxbAEehocsl49k2dWshsG+OwatUqXbNmzXSHYaaoECi7h0oEYf3aG0L0z5/1ZJ/K5YkdOb5x/x6GimFdEpWqUiyFPLO1m96hwj7rl89r5swj5uG7Dk4dKrpifpg9W1/ge5+5gi3rHttrnTgOLz/vEk58x1/jp9LguFMuLygW6N2xiftuv5Gh3u691vm+x0VveiNvesO5+J6H40y9si2UQh7d2s/1926hP7/3H6g55XLVWUs59+WdpMf4206UqhIq7BgssbGnSDjqAPQcWNLq0Zyqz98OomOzNe3QlnbK46/pzZcfeax+7vqf1SWO6VZ2q6OaPrslKDNtVJWBQkhvLpxSkhKgKSXMybgVK5RioNz6dD+/WNtPaQqJMQxDtuwZ4Pld/RV713xXOP2wTg6b34LnCpO5oqJBiWIhz23XXM3vfnw9Gobjbpudu4BXvv+LLDz6dLz0JFtTYYlCocCDv7yFLc+MnrN0b0sWLeAjV17OsoO6SKfTkyquGIQM5AO+fs8mnto+WHHbIxc28XevX86CFp+MP7kkHIRKPlCe2ZVnqFj5CGhOCV2tPp4z+dZUeYve27d/2xJUFZagzLQLQqV7OCBf0gklDQFcBzqzLqkJdJls6y/yjfv3sLmvRD6ovcQwVIbyRZ7e0k1uAtdG5rekefWRC2hJu7gTaG0Uc8Osu/8ubvn7q+jv3lHz65YcfxZnfvDLpFvacfxaE4cSFEtseuoBHvntTygVar/WdPbpp3DlZe8knUrheTVdNSBUpRQoP39qF7c+upPS6GbMOFyBt5ywgPed0UXKdXBrvKg50mp6rqfI9oFSTa+B6Bhb0OzS0eROuNuvUou+bJOqLEHNIpag9l/DxZDu4QDV6q0bAdozDi0pZ1Jnt6rK6heG+O7DvRRDpTR+wwSIKtP123rY1T884bJG4j2mq42TlnXgOwIVYg4KeQb7erjpc1eyds1/Tao8N5XhxIv+kpeddyleKg0yfmIMS0WG+vZw323fo2f7pkmV19LcxBUXX8QrT3kF6XTlaeMKpZAXenJcd88mtvfv2z1ai/ktPp963TJO6Gqp2poKQqU3F7C+u0Cx4t95fClXWNrmkfakardfrS16LEFVZQnKzCihKn25gIHC2K0pAdKe0JF1az57rmSwEHLjoz3ctzlHYYzWVBgqu/uHWL+9l6DGs/xKmlIuZ79sPovaMvsOoghDisU8/33LN/jlt74woVbMeNq7Duesv/gn2rsOxx3d7achpVKJJ+++nXUP/nddRgO+7PDlfPQDl9Mxdw6p1N6JKghD8iXlht9v4d6NvVMuC+CMQ9v59LnLaEnt24oeaaWt7S7Qm5tkZhqlPeOwuMUjOsfY+/ibRIveElQVlqDMjFSMB1GMXCuKulegI+uS9es/Amp9d4Gvr+mmLxeSDxRVJV8MeHpLNwO5Yt3LO6gjy9lHLIjOyB2HYm6IHRuf4XuffR87n19X9/IOO/tPOPU9f4ufzoLrERQL7N60nvt/cTO5gb66luU4Dn/8hnN525svIOX7iAjFQPn98718f81WhibbjBlH2nO48owl/PGxC0h5ggChwtb+Ei/0Fus+6N8VWNzq0Zp+aRCFAG1ph9b0hFr0lqCqsARlZixVZbAQ0psPafKF9updJlMShMov1vXz/Yd72Nzdz+buyhftp8p1hFd0ZVnWJtz2tc/ywC9uSbS8dMscTnvfF1hw1Kk89Osfsn3DU4mWN7+zg7/44JXMX7iYf129mWd3T657tFbLOzP8vwtX0JbxWN9dYLiUbN3W5AuHzvXJeA5zs2MOgqjGElQVlqCMKaOqvOzz9zWsvGL3Zp7/6tsJC8lW3iPc9gW0HfUHhDL1oei1yBx0NHOOPadh5f3hyvm87sj5DSkL4KQlGeY31zYwZAyWoKqwb4sZY4yZkSxBGWOMmZEsQRljjJmREktQIvItEdkhIo+Ns15E5Ksisk5EHhGRE5OKxRhjzOyTZAvqeqIpO8ZzPrAiflwOXJNgLMYYY2aZxBKUqt4FdFfY5ELgOxpZDcwRkcVJxWOMMWZ2mc5rUF3AC2W/b4qXGWOMMdOaoMYaBz/ml7JE5HIRWSMia3bu3JlwWMYYM73K67z+nkodUfu36UxQm4CDyn5fCmwZa0NVvU5VV6nqqvnzG/clPGOMmQ7ldV7rnI7pDmfaTGeCuhW4OB7NdyrQq6o2a68xxhigxinfJ0NEbgTOBuaJyCbg04APoKrXArcDrwfWAUPAu5KKxRhjzOyTWIJS1YuqrFfgyqTKN8YYM7vZnSSMMcbMSJagjDHGzEiWoIwxxsxIlqCMGeWaN6/gxK6WhpT12qO7uOWm73PUUUclXpaI8OY/vYhPXfV+Fs5Lfuiy67q89oyTeOOxixOZBXm0tOdwzJIWUo2ZegpXIF9SenMlZtu8erNFYoMkjJmNRIRzjpjLGYe28etn9nD1r56nZ7hU93KWtKX4/BuWc1xXM2l3Feefdy7XXnstH//4xxkerv/khSuOXMkXrrmBg5YtJ5XOcOYpJ/Htm3/CjT++gyCs7xTsAIcffjiXXfZuWlvbcF2PY7ra+NljO3hoU32nlx9x4tJW3n1qF1nfwXUcUq6SKyml+n80AOZkHBa1eISq9OdhsFiiM+uS9uycv55sRl1jxlEMQvIl5epfPsePH9tdl/f0HOGyUxbx/jOWkHId3LJpwnO5HH19fVx66aXccccddSkv29TEh//mav7kHZeRTqcR56UKNF8osKu7h89++Voef2Z9Xcprbm7m7W+7iGOOPY5UKrXXumIQsnOgwC0PbGXXQKEu5XU2+7zn1C4Om9dEalRyUFUCheGijn2LmklIu8LSdo+UKziy981wBMj6wtysu8+6cdiMulVYgjKmiuFiwPpdOf7q1vU8252b9Puc0NXCly44jPnNPpkKXV5DQ0PcfffdvPvd72bLljFvrlKTs157Pp/7yjdobm0llUqPu10+X+DO393HV775b/QPDk26vNNOO423vOUtpFMpHHfsfjZVpRQq927Yw2+e3k0pnFz94wqcd+Q8LjhmAb4jOM749Z2qkg+UQjCpooCoNl3Y4jI36yJELe1K287NOjT5TsXtsARVlSUoY2oQhkohUP7t/m185e7N5Eu1/9+0Z1w+8dpDOPdlHRUTU7kgCMjlcnzyk5/kq1/9KkFQe+26cHEXn/vKdRz/itPINjXV9JpSqUQuX+BLX7+BX929uuayABYtWsR7LruMhYsW7dNqGre8IGS4GPLDh7ayfufEkuJh87K894yDmJPx8CfQpRaqMlRUJpoTW1IOXW0ejlBrywgBfFfoyLr47rivsQRVhSUoYyYgXwrpy5X43z97lv/ZUP16ygUrO/n06w4h4zv47sSvTwwNDbFp0ybe/va3U+24d12Xi6/4EO//yCdJp9PjtmIqyeXzrN+4iav/8ets2rq94ra+73PhhRdw1lln4/seIhP/fMUgZMOuIX708DYG8pWTcFPK4Z2rFnPSQe37dOfVSlUphpCr4QTDc6Cr1aMp5dScmEYToCXt0J4eszU1pQRVVtnPRpagjEnKcDFg9cY+PnH7c+wcLO6z/tCODF9843JWzM+S9ac2rExVGR4e5sYbb+Sqq66ir2/fxHj0Cav4wjU3sGDREjLZ7JTKC8OQYrHETT/9Bd/+9x9TLO07SGTlypVceumlNDVl8Tx/SuWpKsVA+dWTO7n3uZ4xrxedtqydd75iCWlv7+t2UylzuMIgio6sy8KW6t15tXIFOppcMnsnVktQ1TayBGXM5JRCpVAK+dJvX+D7D+4gVEi5wofO7OLiVYuiC+l1qExHFAoFBgcHueKKK7jlllsAaG1r56N/+0XOu/BPSWcydalMy8vr7R/k6q98nQcefRKA9vZ2Lr74nRxxxMtq7s6rVTEI6RkucssDW9namwdgUWuKK85YSld7ZtKtpvGoRt19Q2WDKLKe0NXm4Y8xCGKqBMh40SCKOMlagqq2kSUoY6ZmuBiwpbfADWu28YFXdtGe8RIdbjw4OMiDDz7IN75zE+/7yCfJNDXh+/VNFuVy+Ty/f+gx/ufRtbzu3PPxfR/HSejzqVIMlUc295F24JwjOvFdqWvi3bfIaODG3KxHe2by3Xm1EqA949CaHv/iVLkDOUHZ96CMmaKs77K8M8Nnz12WaEU6orm5mZNOPo25K06CSVz3mahMOs1xxx7L3GVHTuo604SI4LvCmcvnMifj1qU7r3qRwvxml5ZU1VF3daFAby6kNd2gbxTPYvatMmPqQCTZs/wxCkyuFTNmedEXYBvFdaQhyWmEI/W51mTqyxKUMcaYGckSlDHGmBnJEpQxxpgZyRKUMcaYGckSlDHGmBnJEpQxxpgZyRKUMcaYGckSlDHGmBnJEpQxxpgZyRKUMcaYGckSlDHGmBnJEpQxxpgZyRKUMcaYGckSlDHGmBnJEpQxxpgZyRKUMbOQI9DIybAbODXTtAgVoHE7dHbNYz59LEEZMwulPYeuNi+aaC/hsgSY3+SyoiOF24DyXIH5zS6LWtyGJEYBfFfwG5iFM95+nvHrxKZ8N2aWas9E05RvGyjRmwvrflYugAh0NrlkPIf5wJI2n8e25+geDgjqXKAr0JxyOGVpEx3ZaDr0xa0ha3cXGCqGcSunfgTI+kJXm0/ai87VS6EynEBZIzwHsr6DY7P31sQSlDGzmOtEFezcbMjmviLFoD7dRwK0pB3a085eU6FnPIdVXU3sHCzxyPYcQahTTlRC1IV47MIMh3em9qq8yzO25gAADG9JREFUs77DMQvT7BwssWFPkVCn/vlGEm9Xm0/bqM/nOUJLyiFfUvJ1zMACNPkOnmuJaSIsQRmzH2jyHQ7vSLF7KGDHYDDpSnyku6sj6+JXqEznN3ucvayZtbvzbOwtTrrF4QosbPFYtSRL1h/7ioOIsKDFp6PJY0N3gd3DwaTLE6A947C41ccdp0tPRMj4QspThgshpSnmqbQrpD3ZKxGa2liCMmY/ISLMa/Zoz7hs7i8yVNAJJSoB5mYdmnynpsrUdYSXz8/Q1ebzyPYcg4Ww5taUK1EiPLkry+JWv6bXeI6wYl6ahfmAtbsKFEOtOVE5Er1+abtP0ziJcN/XCM1pl2IQdftNNE+5ErUAx0uEpjpLUMbsZ3xXWDYnRX8+YHNfqWq32Mi1mLlZd1LXRlrTLqcf1MSmviJP7coThhBW2N4VWNGZYuWCDN4kKu+2tMsJSzJs6Suyqa+E1vD5FjS7dDZ5k2rF+K7gOQ65UkghqO01WU/wXWs1TZUlKGP2U61plyPmOWwfKLFneN8WgACOA51Z98VBApMlIhzUnmJhi8cTO/JsHyzt07pxBdrSDqcubaIt406pPEeEpe0p5jV7rN9doL+w78AGIRp00dXmV+yurIWIkPVdUq5WHLDhx4MgLDHVR6LDzEXkPBF5WkTWicjHxlh/sIj8VkQeFJFHROT1ScZjzIHGEWFxq8/yDp+0Ky8OEReiZLG4xZtyciqXch2OX5zlFV1ZMp7gykj3Gpy0JMNrD2uZcnIql/EcVi7MsKIzhefEAyCIkuHBc3yWzU1NOTmVc+NBFKOHiTvxCMSmlGvJqY4Sa0GJiAv8C/BaYBNwn4jcqqpPlG32CeBmVb1GRI4CbgeWJRWTMQeqjOdwWIfPrqGAgUJIe8adVPdarTqyHmcta2bjngKFQDl2UaauiXC0ziaPORmXDd0FAqKBF0kN5RaJBj34rpIrhjhigyCSkmQX38nAOlV9FkBEbgIuBMoTlAJt8fN2YEuC8RhzQBOJrjMlmSjKOSIcMS9NpkGVtxsPgihUugBWR44ITan6tQbNvpI8UruAF8p+3xQvK/cZ4B0isomo9fTBsd5IRC4XkTUismbnzp1JxGqMMTNGeZ3X39M93eFMmyQT1FinTKMvLV4EXK+qS4HXA98VkX1iUtXrVHWVqq6aP39+AqEaY8zMUV7ntc7pmO5wpk2SCWoTcFDZ70vZtwvvMuBmAFX9HZAB5iUYkzHGmFkiyQR1H7BCRA4VkRTwVuDWUds8D5wDICJHEiUo68MzxhiTXIJS1RLwAeAXwJNEo/UeF5GrReSCeLO/BP5cRB4GbgQuVW3kJALGGGNmqkS/qKuqtxMNfihf9qmy508AZyQZgzHGmNnJ5oMyxhgzI8ls61ETkZ3AxjFWzQN2NTicibIYp26mxwcWY73s7zHuUtXzqm0kIj+vZbv90axLUOMRkTWqumq646jEYpy6mR4fWIz1YjEa6+IzxhgzI1mCMsYYMyPtTwnquukOoAYW49TN9PjAYqwXi/EAt99cgzLGGLN/2Z9aUMYYY/YjlqCMMcbMSLMqQYlIh4j8SkTWxj/njrHN8SLyOxF5PJ6l9y1l664XkQ0i8lD8OL6OsVWbPTgtIv8er79XRJaVrfvrePnTInJuvWKaYHxXicgT8T77jYgcUrYuKNtno++n2MgYLxWRnWWxvKds3SXxcbFWRC6Zxhi/XBbfMyLSU7Yu8f0oIt8SkR0i8tg460VEvhrH/4iInFi2rlH7sFqMb49je0RE7hGR48rWPScij8b7cM00xni2iPSW/T0/Vbau4jFiJkBVZ80D+ALwsfj5x4C/H2ObI4AV8fMlwFZgTvz79cCfJBCXC6wHlgMp4GHgqFHbvB+4Nn7+VuDf4+dHxdungUPj93GnIb5XAU3x8/eNxBf/PtCAv20tMV4K/PMYr+0Ano1/zo2fz52OGEdt/0HgWw3ej38AnAg8Ns761wN3EE2HcypwbyP3YY0xnj5SNnD+SIzx788B82bAfjwb+NlUjxF7VH7MqhYU0Yy8N8TPbwD+aPQGqvqMqq6Nn28BdgBJTyL14uzBqloARmYPLlce+w+Ac0RE4uU3qWpeVTcA6+L3a2h8qvpbVR2Kf11NND1KI9WyD8dzLvArVe1W1T3Ar4Akvnk/0RgvIroJcsOo6l1ApRnuLgS+o5HVwBwRWUzj9mHVGFX1njgGmJ5jsZb9OJ6pHMdmlNmWoBaq6laA+OeCShuLyMlEZzHryxb/Xdx18GURSdcprlpmD35xG43u9N4LdNb42kbEV+4yorPsERmJZvdcLSL7nBTUSa0xvjn++/1AREbmG2vEPpxQOXEX6aHAnWWLG7EfqxnvMzRqH07U6GNRgV+KyP0icvk0xTTiNBF5WETuEJGV8bKZuh9npUTvZj4ZIvJrYNEYqz4+wfdZDHwXuERVw3jxXwPbiJLWdcBHgasnH+1LxY2xbPT4/fG2qeW1U1VzGSLyDmAVcFbZ4oNVdYuILAfuFJFHVXX9WK9POMafAjeqal5E3kvUIn11ja+th4mU81bgB6oalC1rxH6sZjqPwwkRkVcRJahXli0+I96HC4BfichTcWun0R4ADlHVARF5PfBjYAUzcD/OZjOuBaWqr1HVo8d4/ATYHieekQS0Y6z3EJE24DbgE3E3xsh7b427NvLAt6lfV1otswe/uI2IeEA7URdCLa9tRHyIyGuITgQuiPcR8GJXKar6LPCfwAl1jq+mGFV1d1lc3wBOqvW1jYqxzFsZ1b3XoP1YzXifoVH7sCYicizwTeBCVd09srxsH+4AfkT9u8Nroqp9qjoQP78d8EVkHjNsP856030RbCIP4IvsPUjiC2NskwJ+A/yvMdYtjn8K8I/A5+sUl0d0UflQXrowunLUNley9yCJm+PnK9l7kMSz1H+QRC3xnUDUFbpi1PK5QDp+Pg9YSwIXfWuMcXHZ8zcBq+PnHcCGONa58fOO6Ygx3u5lRBfzpdH7MX7/ZYx/cf8N7D1I4veN3Ic1xngw0bXY00ctbwZay57fA5w3TTEuGvn7EiXJ5+N9WtMxYo8a/wbTHcAED5hOouSzNv7ZES9fBXwzfv4OoAg8VPY4Pl53J/Ao8Bjwb0BLHWN7PfBMXMl/PF52NVFrBKLp7G+J//F+Dywve+3H49c9DZyf0L6rFt+vge1l++zWePnp8T57OP55WYJ/32ox/l/g8TiW3wIvL3vtu+N9uw5413TFGP/+GUad/DRqPxK12rbG/wObiLrI3gu8N14vwL/E8T8KrJqGfVgtxm8Ce8qOxTXx8uXx/ns4Pg4+Po0xfqDsWFxNWTId6xixx+QedqsjY4wxM9KMuwZljDHGgCUoY4wxM5QlKGOMMTOSJShjjDEzkiUoY4wxM5IlKGOMMTOSJSgzY42anuKh8ilKRm3XJCLfi6dheExE/ltEWka9x2MicouINMXLB+Kfy0RkON7mCRH5joj48brRUyo8FN9tY7x4x5wKQsaZJqbS1BfGGEtQZmYbVtXjyx7PjbPdh4HtqnqMqh5N9KXK4qj3OBooEH3ZcrT1qno8cAzRrWn+rGzd3aNi+HWVmF8Vb7eqbNnHgN+o6gqiL5iPzBF0PtH921YAlwPXVHlvYw4olqDM/mAxsHnkF1V9WsvuJVjmbuDw8d5Eoxu7/p763316vGlixpv6whiDJSgzs2XLutZ+VGG7bwEflWgm5c+JyIrRG8Q36D2f6PY+YxKRDHAK8POyxWeO6uI7rEIc400FMd40MTY1gzEVzLjpNowpMxx3vVWkqg/FU1i8DngNcJ+InKaqTxInuXjTu4F/HeMtDou3WUE0RcYjZevuVtU/rDHeiU4FYVMzGFOBJSizX9Bo6oP/AP5DREKiG3Y+SW1Jbr2qHh93r/2niFygqrdOIoYXp4KIW3wnA3cRTxOjqltHTRNjUzMYU4F18ZlZT0TOKBsZlwKOAjZO9H3i7rePEU1sOdEYmkWkdeQ5UWvusXj1rcAl8fNLgJ+ULb84Hs13KtA70hVojLEWlNk/HAZcIyJCdNJ1G/DDSb7Xj4HPiMiZ8e9nlnURAnxOVX8wxusWAj+KQsADvq+qI9eyPg/cLCKXEc0b9Kfx8tuJWnrrgCHgXZOM2Zj9kk23YYwxZkayLj5jjDEzknXxmVlDRM4F/n7U4g2q+qYGxjAyq/No56jq7kbFYcyBwLr4jDHGzEjWxWeMMWZGsgRljDFmRrIEZYwxZkayBGWMMWZG+v+EPUF0i39dzQAAAABJRU5ErkJggg==\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": [
      "62 94 194 374\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": [],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/SPIRE-NEP_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": 15,
   "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=(100,100))\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(['SPIRE-NEP']*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_SPIRE-NEP_BLIND_Matched_MF.fits', format='fits',overwrite=True)"
   ]
  },
  {
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    "*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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