{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Processing of AKARI-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_AKARI-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=\"table4516095424\" 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>6116</td><td>269.9352764161237</td><td>65.30344107513443</td><td>19.365185</td><td>24.262014</td><td>14.551109</td><td>10.213417</td><td>15.59878</td><td>5.1017795</td><td>4.498611</td><td>8.77975</td><td>1.3436549</td><td>-3.4561088</td><td>-3.4228199</td><td>-2.6352546</td><td>7.3613796</td><td>6.662783</td><td>4.8949933</td><td>0.9992947</td><td>0.9992975</td><td>0.99874425</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.004</td><td>0.093</td><td>0.001</td></tr>\n",
       "<tr><td>1450</td><td>268.5384225583178</td><td>65.21978166389113</td><td>25.239248</td><td>29.775616</td><td>20.606003</td><td>18.664059</td><td>22.543772</td><td>14.677635</td><td>7.381647</td><td>12.276728</td><td>3.163921</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.9985717</td><td>0.9986359</td><td>0.99874973</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>1460</td><td>268.61297686057435</td><td>65.12898192924604</td><td>33.923973</td><td>38.17171</td><td>29.653618</td><td>18.833506</td><td>23.097734</td><td>14.177865</td><td>20.46039</td><td>25.440794</td><td>15.100911</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.99838173</td><td>0.99917674</td><td>0.99919295</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>1590</td><td>268.4951607121733</td><td>65.23888681865698</td><td>27.699142</td><td>31.356236</td><td>23.884893</td><td>30.224438</td><td>34.41281</td><td>25.816626</td><td>14.8894005</td><td>19.532925</td><td>10.481851</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.99913436</td><td>0.99886173</td><td>0.9984704</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.005</td><td>0.0</td></tr>\n",
       "<tr><td>1631</td><td>268.5560216533588</td><td>65.21102792418007</td><td>23.328575</td><td>28.438553</td><td>18.325937</td><td>23.276283</td><td>27.31639</td><td>19.076084</td><td>15.7844305</td><td>20.65358</td><td>10.683285</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.99924904</td><td>0.9989662</td><td>1.0009508</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.002</td><td>0.0</td></tr>\n",
       "<tr><td>1986</td><td>268.5653045736632</td><td>65.25801825108488</td><td>21.88394</td><td>25.907604</td><td>17.768368</td><td>19.996824</td><td>24.14037</td><td>16.170895</td><td>4.3121157</td><td>8.288043</td><td>1.380259</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.9988979</td><td>0.99858385</td><td>0.99951136</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>2024</td><td>268.6374922227674</td><td>65.2615823173701</td><td>18.231367</td><td>21.715109</td><td>14.760711</td><td>17.576052</td><td>21.810854</td><td>13.239466</td><td>4.2947783</td><td>8.077456</td><td>1.3922281</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.9988087</td><td>0.9989191</td><td>0.9992715</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.01</td><td>0.001</td></tr>\n",
       "<tr><td>2037</td><td>268.5612243894294</td><td>65.27603588752082</td><td>27.501774</td><td>32.087074</td><td>22.934856</td><td>29.058744</td><td>32.912148</td><td>25.129822</td><td>5.633396</td><td>9.993704</td><td>1.8741359</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.99899477</td><td>0.99873984</td><td>0.99830097</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>2360</td><td>268.63537135078457</td><td>65.29849557952751</td><td>22.68537</td><td>26.983055</td><td>18.04876</td><td>30.277493</td><td>34.75042</td><td>25.879398</td><td>13.26035</td><td>17.985916</td><td>8.62507</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.9996385</td><td>0.9988642</td><td>0.998948</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>3111</td><td>268.64524985693214</td><td>65.16501775888689</td><td>9.657656</td><td>13.732609</td><td>5.48737</td><td>1.0299151</td><td>2.625244</td><td>0.27589554</td><td>1.4923036</td><td>3.8808331</td><td>0.35977823</td><td>-0.14303307</td><td>-0.13455679</td><td>-0.07149754</td><td>0.57633674</td><td>0.0050773527</td><td>0.0061848643</td><td>0.9990747</td><td>0.9986552</td><td>0.9991522</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.113</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",
       "6116                         269.9352764161237 ...        0.093        0.001\n",
       "1450                         268.5384225583178 ...          0.0          0.0\n",
       "1460                        268.61297686057435 ...          0.0          0.0\n",
       "1590                         268.4951607121733 ...        0.005          0.0\n",
       "1631                         268.5560216533588 ...        0.002          0.0\n",
       "1986                         268.5653045736632 ...          0.0          0.0\n",
       "2024                         268.6374922227674 ...         0.01        0.001\n",
       "2037                         268.5612243894294 ...        0.001          0.0\n",
       "2360                        268.63537135078457 ...          0.0          0.0\n",
       "3111                        268.64524985693214 ...        0.113          0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at Symmetry of PDFs to determine depth level of catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/Steven/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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1adv/BNYAuPvngGuAFwEPAGPAm9Nt283sw8At6fs+5O7b+9hXEREZIH0LXu5+A53zJrJ9HHhbh22XAZf1oWvp8fNHZJ3aRURk7pj3tQ3z1CpweH4ZqdZ2ERGZW+ZleahO9lY2qi1eqZyUiMictLCCV49tjdsUt0RE5p4FedtQREQGm4KXiIgMnAUVvDqVjerWLiIic8/CCl5dykY1fY8Cl4jIXLagEjagIXOwJZMw+1MLVoqIzH0LLnhlOgUnBS0RkblvQd02FBGR+UHBS0REBo6Cl4iIDBwFLxERGTgKXiIiMnAUvEREZOAoeImIyMBR8BIRkYGj4CUiIgNHwUtERAaOgpeIiAycnoKXmZ1rZm9Ov15lZkf3t1siIiKd7TV4mdlfAe8H/ixtKgL/bz87JSIi0k0vI6+XAxcCowDuvglY2s9OiYiIdNNL8Cq7u5OugGVmi/vbJRERke56CV7/18z+BTjAzP4b8EPgC/3tloiISGd7XYzS3T9mZs8DdgHHA3/p7tf1vWciIiId9LSSchqsFLBERGRO6Bi8zGw36TxX6ybA3X1Z33o1gNzrPywDzGazNyIi81vH4OXuyijsUWPgguRrdwUxEZF+6TbyWubuu8xsRd52d9/ev24NjtbAJSIi/ddtzuurwIuB20iuz41jCAeO6WO/REREOup22/DF6Z8qBSUiInNKL+WhftRLW84+l5nZZjO7s8P295nZ+vR1p5lF2S1KM9tgZr9Jt93aywcREZGFo9uc1zCwCFhpZgdSv224DDi8h2NfDnwa+HLeRnf/KPDR9FwvAd7dMo92vrtv7eE8IiKywHSb8/pj4F0kgeo26sFrF/CZvR3Y3a83s7U99uNi4Gs97junmAFK2hARmVHd5rz+CfgnM/sTd//nfnXAzBYBFwBvbzw98AMzc+Bf3P3z/Tr/dDDLHn6rZ7YoRV5EpH96KQ/1z2b2n4C1jfu7e+7twH3wEuDnLbcMz3H3TWZ2MHCdmd3r7tfnvdnMLgUuBVizZs00dWnfZEFMRKRf5tI1bzb1krDxf4CPAecCZ6SvddPYh9fQcsswXXYFd98MXAWc2enN7v55d1/n7utWrVo1jd0SEZl7dM1L9FLbcB3wtHRZlGllZsuB84DXNbQtBgJ3351+/XzgQ9N9bhERGVy9BK87gUOBx6ZyYDP7GvAckmzFjcBfkazCjLt/Lt3t5cAP3H204a2HAFdZMmlUAL7q7t+byrlFRGR+6yV4rQTuNrNfApNZo7tf2O1N7n7x3g7s7peTpNQ3tj0InNpDv0REZIHqJXj9db87ISIiMhW9ZBv+1MyOAo5z9x+mqe1h/7smIiKSr5dsw/8GXAn8S9p0BPCdfnZqvpj+FBcREYEeghfwNuAcksoauPv9wMH97NR8kAWu1gDmXn+JiMi+6WXOa9Ldy2n2H2ZWQNWQcjUGpMZVldsCWEu7qnGIiExNLyOvn5rZ/wRGzOx5wDeB/6+/3Rpsbasq7+VrERGZml6C1weALcBvSIr1XgN8sJ+dGmSdApIClYjI9OnltuEIcJm7fwHAzMK0bayfHRMREemkl5HXj0iCVWYE+GF/urPwOEreEBGZql6C17C778m+Sb9e1L8uDTZj6pXlHYiVgSgi0rNegteomZ2efWNmzwTG+9elwWVWf+0rBTARkb3rZc7rncA3zWxT+v1hwKv716X5IV1geervU9q8iMhedQ1eZhYAJeAE4HiSa/K97l6Zgb6JiIjk6hq83D02s4+7+7NIlkYRERGZdb3Mef3AzF5pphtaM0HlpERE9q6XOa/3AIuByMzGySobuS/ra88GnKWTXlONOdmvCHmlphoPpl8lRGQh62VJlKUz0ZH5qNcAlsWh1oDU+j5n6mn4IiLzUS9LopiZvc7M/iL9/kgzO7P/XZsfzLoHHCM/vb5bwNOoS0QWul7mvD4LPAt4bfr9HuAzfeuRiIjIXvQy53WWu59uZr8CcPcnzazU534tCN4wsdWYD6PkDBGR7noJXpW0GK8DmNkqIO5rr+ahLDQltQzr0SmrbWju6e1A63jLsHYM161DEVnYerlt+CngKuAQM/s74Abgf/W1V/NMU9moDsOqWn3DDqGrMalDgUtEFrpesg2/Yma3Ac9Nm17m7vf0t1vzl9nebgu2R6YsqUNERBK93DaEpIp8dutwZC/7ioiI9FUvqfJ/CVwBrABWAl8yM62kLCIis6aXkdfFwDPcfQLAzD4C3A78bT87ttBkDyC7O3mVuJSkISJS10vCxgZguOH7IeB3fenNAmBmBK0PJDvgUPX2bMT6+2akeyIiA6GXkdckcJeZXUdybX0ecIOZfQrA3d/Rx/7NS2ZGkOYVxnHy3EEWriJPRmAFS1oMU+ASEWnRS/C6Kn1lftKfriwsZoYBlZzU+OwWogr5i4jk6yVV/oqZ6IiIiEivek2Vl/3QOIXVOJjy9BahqkGJiEyNglcfZUGrKTh5/UFlB8I0mEVev11YCLL352ceiogsdApefZIFp7Z2WkdiSXAK072zTEQFLRGRzrqmypvZajP7UzP7rpndYmbXm9lnzewPzWxv773MzDab2Z0dtj/HzHaa2fr09ZcN2y4ws9+a2QNm9oF9+2iDJUuhNzMFLhGRveg48jKzLwFHAP8G/D2wmeR5r6cCFwB/bmYfcPfrOxzicuDTwJe7nP9n7v7ilvOGJOuFPQ/YCNxiZle7+909faKBpqAlItKLbrcNP+7ueaOmO4Fvp2t6ren0Zne/3szW7kOfzgQecPcHAczs68BLgQUQvEREpBcdb/11CFyN28vu/sB+nv9ZZvZrM7vWzE5K244AHmnYZ2PalsvMLjWzW83s1i1btuxnd2ab51bXcM9vF5GFZ35d8/ZdL4V5zzGz68zsPjN70Mx+b2YPTsO5bweOcvdTgX8GvpOdMmffjldud/+8u69z93WrVq2ahm5ND7PebwJmwakaN5eHytprWYsKYCIL3ly95s20XrINvwi8G7gNiKbrxO6+q+Hra9JEkJUkI60jG3ZdDWyarvPOJEsf4moMOdnaXFk2ors3ZSBGcbI9pLk9+zNIj6akDhFZyHoJXjvd/drpPrGZHQo84e5uZmeSjAK3ATuA48zsaOBR4DXAa6f7/DOl0wgsa6/E7dvc0xqHHeKTApeILHTdsg1PT7/8sZl9FPg2SZFeANz99m4HNrOvAc8BVprZRuCvgGL63s8BFwFvNbMqMA68xpP7YlUzezvwfZIFMC9z97v27eOJiMh81DXbsOX7dQ1fO/AH3Q7s7hfvZfunSVLp87ZdA1zT7f0iIrJwdQxe7n4+gJkdk6WtZ8zsmH53bKHISkFFcX1urNNNwexuocpGichC18tilFfmtH1zujuyUGUVNcIgKQ2VJXS0xqb6NlXgEBHpNud1AnASsNzMXtGwaRnNKyvLNLA0BTEvLmVlo0REJNFtzut44MXAAcBLGtp3A/+tn50SERHpptuc13eB75rZs9z9xhnsk4iISFcd57zM7OVmtsLdbzSzVWZ2hZn9xsy+YWarZ7KTkl9dw715eZXGfVVmSkTms24JG3/n7tvTrz8NrAdeCFwLfKnfHVtIOgWhTNxSHqp1/+avndiT9zQGqtp7URATkcHXLXiFDV8f6+7/6O4b3f1yYOEW1Jpmjastd0vKiD0LWg5Z6ajGlzuxO5UYqp68KnE9UNUDWnswFBEZNN2C10/M7ENmNpJ+/TIAMzsf2DkjvVsAsuCT6ZYK76QBKOdJsEqcBquW/Stx8gxZq1hxS0QGWLfg9XYgBn4LvIpkDa8s0/D1M9A3ERGRXN2yDSvAXwN/bWbLgYK7b5upjs0n2d256XhUKznW7FTY8C7FgkVEZlIvFTZw952NgSt9gFl6UFv6hPqcUyOjuRxU50zB7JZhczJGtn9AMknZeKzAoBhAGLSXnAqmEITaz937e0VE+qGn4JXjB9Pai3mo8YLf1E7zfFM2kjE6J1B4znFih6jhQFlJqSyIFQMoWH0OzSyr1FGv2NHL6K2XzyAiMtO6lYf6VKdNJFU3ZB+1jnpqMaRDQOgUJ9K1LptGVY3BsDE4Wb2qb8+3HLuNsHT3UERmU7fyUG8G3kvDGl4Nui53IjMnd6HLbvtr0kpE5oFuwesW4E53/0XrBjP76771SEREZC+6Ba+LgIm8De5+dH+6szBMZ9aeMgBFZCHqmLDh7tvdfayxzcxO73+X5r9OU0mdMgDzmp3kITxoLxWVJVnsbwWNbkFR+RoiMpummm34r33pxTxUy+prbadLkDLL3RYEVjtWa1moiCSIZfUOa+1pZY39rWM41c8gIjITphq8dMmaIstWQKaeqt59fyMM2rMEzYwgbc9NXe/UPg1DpMZA3MtnEBHpt25zXnn+pi+9mOf25WI/F7MC52CXRGSB6il4mdkRwFHAdjN7NoC7X9/Pjsn0yG4ltgaebiWrlAQiInPdXoOXmf098GrgbpIpFkiuhwpefdT63HI2b5Xd541a35Aju50YORRir1XWaKzY4Z490EzHdhGRuaaXkdfLgOPdPe9hZemTLJi0lo0ySwJM2JCcAS31EWmf/6o6mEMhaK3JkWUmtvchrQGsACYic04vCRsPAsV+d0TaZfUKW+NK44iotbDv3qiorojMB91qG/4zyXVzDFhvZj+ioVSUu7+j/90TERFp1+224a3pn7cBV7ds0+/vIiIya7otRnkFgJm9093/qXGbmb2z3x0TERHppJc5rzfmtL1pmvshHXRb4wvykzM6Hit7aeJLRAZctzmvi4HXAkebWeNtw6XAtvx3yXTJAkxrhYws7uRV1MgSN7LfSFr3Merp8EG6pdvD0EqVF5G5qtuc1y+Ax4CVwMcb2ncDd/SzU9K5rFOn57uyrMPGYBNSL9LbGoNi716fULULRWQu6zbn9RDwkJm9DDiC5Bq4yd2fmKnOydTkjZL2JQYpbonIXNfttuFpwOeA5cCjafNqM9sB/Hd3v30G+iciItKm223Dy4E/dvebGxvN7GzgS8Cp3Q5sZpcBLwY2u/vTc7ZfArw//XYP8FZ3/3W6bQPJ7ckIqLr7ul4+zHwSWH7li1KQzGVV43qbu1NJq3EUAgjSIVhjuadGWeJG7BDi6e1Gq82zOUk1DsspJwWaCxOR2dct23Bxa+ACcPebgMU9HPty4IIu238PnOfupwAfBj7fsv18dz9tIQYuSIJGbQmS9M+sNmFAEsQCnCj22vyYA5UYKpETu/e0dErkyas1AzELbp2Oo4RFEZlN3UZe15rZvwNfBh5J244E3gB8b28HdvfrzWxtl+2/aPj2JmD13o650NQzAZszA7M/4y4RZCqV4bNgpArzIjIouiVsvMPMXgi8lCRhw4CNwGfc/Zpp7sd/Ba5tPD3wAzNz4F/cvXVUVmNmlwKXAqxZs2aauzU3zMW1vURkdiyEa14vulaVd/draQ4q087MzicJXuc2NJ/j7pvM7GDgOjO7t9P6YWlg+zzAunXrdDNLROY1XfMSHee8zOyUhq+LZvZBM7vazP6XmS2ajpOn5/hX4KXuXnvw2d03pX9uBq4CzpyO88l06l75o+ejLNh/eiKyP7olbFze8PVHgGNJHlYeIUmh3y9mtgb4NvB6d7+voX2xmS3NvgaeD9y5v+ebj8IOdxNrCRwNkcHdm157417PVmxL5kiTOFqPVStZ5b0FpanuLyLNto+WZ7sLs6bbbcPGS+NzgTPcvWJm1wO/3tuBzexrwHOAlWa2Efgr0nXB3P1zwF8CBwGfTed0spT4Q4Cr0rYC8FV332uCyEJUCAPCwClHnmYMJu0OlOMkuIXpCClyKEfJ12GQpNRnGY2tQTALWtkxDSgG9egSp7n2lh6rnmJfP1BWigraEz4a+9nL/iIirboFr+Vm9nKS0dmQu1cA3N3TRIqu3P3ivWz/I+CPctofZC/PkEmdmTFUMCYqMdWWbZFDNfa257SiGHBnuGAELXWgYneqLftnwbAATb/SOMnzZlkKPy3v6fQ8WKdnz1oOLyLSUbfg9VPgwvTrm8zsEHd/wswOBbb2v2syFWbkRoVuVec7jXA67Z9XI1FEZDZ0S5V/c4f2x0luI4qIiMyKbtmG53balm5fZmZtZZ9k5mUPK4ctf5tqKW18AAAgAElEQVTdkiDMkjmwuCWpoxJ5vUJHNpflThw75djT25D192TVP1rHa+7JvtWoNakjqQoSxe2JIxrViUivut02fKWZ/QNJNY3bgC3AMEnW4fnAUcB7+95D6SgJEEnJJ8wI3LEgmdOKGuKCWXMyRJiWnHKSABZYkmpRjuohKIqzwORt5aRiTxI4CqHV6iJmO8XuTcu51ObF8LZbmLGDuefOmYmIdNPttuG7zexA4CLgVcBhwDhwD0nVixtmpovSSS1wZdLswfY8vrTILvmBojXYZbL6hm3ZgkAxbC5Vle0Tx+Tq0JwW/1WGoYhMTbclUZ4F3OTuXwC+MHNdkv3WIXljum/MTddoSaMuEZmqbg8pvxG4zcy+bmZvSrMMRUREZl2324b/D4CZnQC8ELjczJYDPyaZB/u5u3dalV5kv7l77qhsrrXvC1XrF9k/3UZeALj7ve7+j+5+AfAHwA0kc2Bta33JzAo6XPzym7PMwtYsvy5lo3L3T7MSc7IFG7MT89YHy11fzPPKTDVnOu6tvfXc09Xeeu5On20qGkthqSSWyL7rWlU+kyZuHE6SsPG9PiyJIvsgDJIMw0rcvECkmVEInKjWnmQAVmNnMoJSaLVyTw5MRkkmYGAQ0jxllpWHagw95cgZrzql0FhaCmpBdKKa9MWAUpi9uT2FJPvGSZfKjqAY1EtZxQ7VdJ/GElfZNmjuU5Y1GXtS9qrY8CtZY+ZjQGMwqvepY7s1lr6q/3wb23sdiTUGqryfhUZhIlPTLWFjOfA24GKgRD1V/hAzuwn4rLv/eEZ6KR2ZGaWwHoAa2wshlKOYStycvl6OnEqUXOgbswyzlZMLLRmJDsSx11ZdbjzOtvGIJcUgqYPYsH8SJNtHgd7walSJk0DWkHUP1OsrBtBWmipbRbqxT9U4CdJDYcsb0s+Xl8vSrT1PloEZTDHi5B1OVUtkf3315od57VkLb12vbiOvK0lWUf7P7r6jcYOZPRN4vZkd4+5f7GcHpTfdLoB5F2EnPz2+m077V+IOc0Qd+tXptDH597GzYJd3rE596rgydIdz6w6eyGDplrDxvC7bbiN5cFlERGTG9TrndQRJRY3a/p1WNpaZ515P3mgciXg6gVOw+hxSJrRkzqzaMCxzT0ZpEVAMnTCbr0oreWQV5FsTRZL25gegY3fGyk4hSKreN7ZPVJJ9i2G9PZuXi0nKXDW27ynHRDEsHw4oBPX2XZMR41VnaSmsPTTt7kxUnZ1VZ9lwwHChPpabqMbsnoxZUgoYbuhTNXZGKzGlwJraY3fKUfJ5i019Sn7OhteWlmn8DJBfab/j43ciMmV7DV5m9vfAq4G7Sa5rkPwbVPCaZa3JBdnaXJU4CTZReiUNAwjSC64DxaBeFaMYWC2Zo3k+C0JzQkuWQ8kuynEa4EKrn7O5PW6aG4uiZL2xkYIRA5Wovn81dkqF5tuBThIMjZhqDLsn49pn3DwasaRkFANj+3hU+zzbxiNGCsZI0Rid9Fqg3jYWM1RwlpWM3eWYyfTcOyZiCkESDCejJEBB0p+JyFlSDJqSRpKlZaAUJrdHG+f2knXTvO2WZlb6ylqCmDW8V3NdIvuul5HXy4Dj3X2y352RqWn+Ld7qczxp8dvWbSHJxbfpYmpgHufOHVVjqHQ4d1gr6dR8CS7n1IFyYLzquQkOUeSEgbXNT01WndFKe6d2Teb3dbzq6WKbzYkmE1VnsupNx3eSBJGdk3Fbn2JPjhW2DC+dJJgVaJ9My4J53ufOmpvO7/ntItK7XoLXgyQrICt4DYhOt6aSwJW3/77e0Or9yttxzw51DTvVQpxOnfrUKaBYt43TcHyRffXVmx9ua5vvGYjdUuX/meSKNgasN7Mf0RDA3P0d/e+eiIhIu24jr1vTP28Drm7ZpnlnmXMGqeRSx1T+AfoMIrOpW6r8FQBm9k53/6fGbWb2zn53TPZdGNiUHuKyDr+LON5UtaPW7k7sRoC3tePJ+9r2J1sfrKU9hjhN/GjM2rPseC3njjxJRGl9SLgaJ9mR5s3zVdm+5jS1x2kGZWD57WFLe5ZJGLT+PLx5nbLWh7tzn3Pz+p+tc2F57SLSrpc5rzcC/9TS9qacNplhSZmi9mFwMTAKpYCxSnNyQ7Z4ZGPZKEgSEeqzXvWANV5JkiBKoTHc8H/KZJSkrw+FxuJSUHvvnsmYnZMRQwXjgOEw6R8wVk4qcZRC45DFhWTFZ4fRSszDOysUAuMpK4q1tPbxSsy9WycpR87qZUUWF4M0+SLmF4+MsWMi5rRDh1m9vIiRBK5bHh3n8d1VTj54mKeuGiKwJJHiricmeHhnhWMOLHHiwUO1n9nGXRU27qpy8OKQ41cO1Vah3jYa8ejuKkuHAo5bUWr6mY1VnCBwlpSCWskqTzMRAYZCmgJ6Y7p8Xnko69De+I2CmEi+bnNeFwOvBY42s8bbhkuBbf3umPTG0sjRmtlmlgSWycipRk4Q1LMMCyFUIm9Kj7d09BPFaeZetV5uKiknlQSxalxvn4yc8nhEYDBejWsX8Ymq88SeKkOFgMmqM5meZKLqPLSzwkhoTETO7jQ1sRI7d24us3woIMbZMlpfrOD3OyrgScbgA9vLtXPf8ug4928rs3JRyO+2l2ufY/0TE/zuyTLHrCjyyM5qmoEIv3uyzMZdFU5YNcTWsajWpydGI7aOjfGUFSX2TMaMp/nxOydibts0wUmHDFFIHycg/fnsnIhZVLSmdoCJKAlgBct/zqv1l4xuY2Ol0ot0123k9QvgMWAl8PGG9t3AHf3slExNFnja241CkGUTtr+n053FxsCVcZIglreq8u6c/Hin+RmtRk9ORrVA12jbeNT00HRmx0TMY7srbcd6cjziyfGorX1POeaBbeW24DEZORt3VdpXknZ4Yk+1LW3eSUaBi0thW5+yW46tnPzAJTLT8jIQezEoWYrd5rweAh4ys5cBR5D8u9zk7k/MVOdERETydFzPy8xOS6vH/wT4B+CjwE/N7CYzO32G+if7IZvTan2ANkmUSKrH51lUsrZRRSVKyjFVWoZr1Tgp99Q6YqpEzubRKrsno6b1r8qR85vHJ3hw+2S6vlh6nMj55cYxfrlxjGrDOaLYueXhnfziwSeZqNRvJ7o7G7aN8fMHtzM6WW0690ObHufq//gFW7Y31ZNm62iFf7t7Oxt3ND+yOFmNuXvzBFtGq019jWLn/q2TPLKj3Nb+6K4yj+4qN30Gd2eiErOnHLW1T1ZjxirN7d3E6ZpprftnJby0FpgsdN1uG14O/LG7Ny06aWZnA18CTu1jv2Q/ZdlxRvIbSpaAWHWvBYfGslFZvAiDJItvKDTGq85oOWa0HNdKKJWjmOECLCoaYxVnIo0bURWq5hRDZ8dEzNax5JbhnjLsmoxYuSjk0V1V1j8+QeRJnx7aWeHUQ4bZORnzo9/trs1P3btlkmevXYzHVf7vrzaza6JK7M7vto6ybs0BrFpa4qbf72DHeJU4dh5+coKnH7aEI5eG/OyXt/PIY09QrUbct+FRTj3hGJ71jJO544kJfrd9giiGB7ZN8pSDhjj/2OXsnojZPJrcetw2FrF0KOD4lUO4J7cfAbaORTy8s8JJBw9hZsktzDQjcPNoxDEHllhSCqjUamjBRDU5VmhJoke2qRLFDBeMUmi5txbdvWl5mUqcJIGELWvCJAk3WZbmfv/vIjJwugWvxa2BC8DdbzKzxX3sk+yn2L3pN/PaxS32plFNVjYqcCdqaQMYCp1NE3HbkioT1ST5ovWaGTk8tjOi3LC2mJOs7fXzh8dr9QghqaBRrTjfv393Uzsko7nv3rWdx57c01LmyrnloR21Obzaltj5zSPb+clvbyUgrr2nGkXc8dsH2RgcSrFUJIrrx//d1gkOXFRi2XChdpzIk2SMR3dVWDpUn+eK0kD2262TLBuqL43pDnHkbNlTobS82JYmv3syrhUSbpRlcLZy97YCytnPKnAwy0/jUGq9LETdgte1ZvbvJGt6PZK2HQm8Afhevzsm06/jnaaO1aGs6+2pvE2N2YiNJiPPTRCpxPntk9Uo995YNXYKobUF1EqlkmZLtt6+jCEstCWIVB2GC0HbcRwYyitUCBSC/CfiCh1GUZ3se5zJf6cClyxE3RI23mFmLwReSpKwYcBG4DPufs0M9U9ERGbQvmYpdtKv7MWuDym7+7XAtX05swh7f9Zpuo41G8epH23mh0bu+atbi8wX3bINT2n4umhmHzSzq83sf5nZopnpnuyLTpesvOeSuu0P3vEZsk7y5nig0624ZL0wz7k9WADiOKY1lHh1kqhaxbz5PqDheBwRtNSjt7hKVB7HoubFXcwj9kxWiarti76MVWKqUfuDaJPVODdbcDLydJ6xNTMwm3tsbo8btjfvn9/esEd+a+utT/emP0Xmo47BiyTbMPMR4FiSh5VHgM/1sU+yn8zaU90hWbl4SSloS503M0pBPbi5Jxe+cgQrFoUMdcqpbxC7M1qOmKjGxA253FHsPDle5ZGdFfbU0uadKI7ZPVZm/QOb+P2jT1CpVHGP8ahKeWw3D/3yWrbffi3R6A6Iq1hUIRrfzWPf+SgP/vMbGdvwa7wyAXFEPDnG5u9/jie+9SEmHroDjyqA41GFsftu5N5/uIitN3+HuDIJ6Tn2bPwtV132T9x+40+pVsq4J4keW/eU+cb6LfzogR1Nwcrd2TYe8/ieajKvl9VdJCmjtXFnlUpMLYjF7oxXnV3lpPJIUyBxGE1Xh/Z0f09T4yernqbCJz8nSP6R5q2dlh6qlnnoDQE0+ytoPL7IfGKd/qc2s1+5+zPSr9cDZ7h7xZJ7Eb9291Ny39h8jMuAFwOb3f3pOduNpEbii0iWXnmTu9+ebnsj8MF017/NCgV3s27dOr/11lv3ttuCUf/Nv14yKmufjJILZbKtflGsRDGjlWRb4/8ZlWrMkxP5q2ztnozYU45rJZcyT+yp8sSeKlvGoqb2PaNjbNk5ysNbdjW1Lw8miHdv4bH71uNx/Vzh8BKojLPtZ1/Dy+O19kVPWcfiY57B1p98mWj0yVp78aA1jBx3JuP330g8Wn/Wa2jlGg596fsYfeQeyru21o+zZCnPetmb2ToB28fqI7FSaFz8jINZPBQ2JXYYcOTyAosKQbqQZv3nt2pxyKKiUWn5UY0UjOFC0FZ9IysAXImaf94GDBes9uhCJq+WZZ23jcKgXoFFtxEHRs9/UceceIr/7eX/1s++7Ld9mPPq6fN3m/NabmYvJ/nFb8jdKwDu7pbk7PbicuDTJBmLeV4IHJe+zgL+N3CWma0A/gpYR/Jv9TYzu9rdn+xwHMnRafHJrGxUucO2yWp7WadChww8gCcnorasPYCHdpTZXW7f8MjWXWzeMdrWvvnRhyhvuretfeyhOyg/cDNeKTe1j95/M2O/vSG9vVhX2fYw8Wh7+c3JrQ+z/Tc/xYLmGw5je3Zz76YnCYaXNLWXI2fXZJWRlvJQTlIeqhC237gYq8QUgiC3BFVe2ajIk9WkWzlQCNr3N+v8gHK3wZUCl8w33YLXT4EL069vMrND3P0JMzsU2NrlfTXufr2Zre2yy0uBL3sy/LvJzA4ws8OA5wDXuft2ADO7DrgA+Fov5xURkbmhl+zFfclI7JYq/+YO7Y8Dz53ymfIdQf0ZMkhS8Y/o0i7TxIBSSK1yRiYwWD4csGuy+eHkwGDlopCdE1HTLTEDDltSYPt4VKvIDsnczwHDIU5cq1QByS3LoDjEyEjM+Ph4U7uZES5ZQbRne1M7UQVbvAJ2PtE2vPCgAHHziMzjmGhilKA0jAWNoyZj8tF7KK5cQziytN4cFhjbuY1hjMLw4oa94dFdVbCQQ5Y0/1MZLccEVmX5UNh827Xq7CFmyVDz6KsaOXsmIxYPhU23AaPYiWKn2PKsWFbhPwza10aL4vxRXMfH9UTmoV7W86oxs8+7+6XTeP78FLTO7Xl9uhS4FGDNmsGohjwXBJZkAJbCpFpGNYZiAKEZiwrG8qGA7eMxu8tJOaMsW/CAkYAnx2O2jyXrdi0qJgWoDl5SYPtYxCM7y4xVnLGKc9CiAgeOJGt/PbKzwo6JmC1jVYrDi1kxNEK1UmHbtu2Ux/cQ796ChUWKBx5OYekqytseIRp9kmj7RrxapnDAoYRLV1Le+jDx+C7CQkgcJxd8LxSIy2XiqELgMV4tgxlReYJgeBFWHCYsDuEWUt3yeypbH2Lo4LUUD30qpYNWU1y1lsqenVRGdzG0ZDnDBx3O4pFhViwe5vE9MZtHx1kxEvL0Q4dZVAwIA9hTccaqEdvHYw5fWqitRVaOoVJ2xioRB4wElEKrrZ82GcFopcry4ZChMCn9lN0xrFadYuAUAqMYGgUDx6nGRmBeC0y1ClRplkYWxLJ8jmy/LMbXk3CUOj9fNF7zVh66cH+n75ZtmGfdNJ9/I0nVjsxqYFOX9jbu/nl3X+fu61atWjXN3Zu/kvkwIzBjOLQkOcCa2w8cNg4YDhgKm9tXjASsTBMTGtsPWhSCJTUPs980AjOWDYVEMTwxWqUap6niFlAoDTFScKo7HiOuVpIEEwwKJaw0wuSm+4gmRvE4nYMLCxQOPLRpziq542wEpRJUJjGPGtodKhNYWMLCYr09jqhueYjSyjUMHXw0FoS1xTnLoztZQpmDlgwTpBU1Ik+Wa9m4s5IGgyQIxJ6kyW8day4+nL1n10TclojhwK6JZJTaOtVViZMkjUJtVFU/T+Tkzisme3ktIScLZIHVR2fZS+aHxmve0gNWzHZ3Zs1Ug9fmaT7/1cAbLHE2sNPdHwO+DzzfzA40swOB56dt0g8dstGy7/PaY89v77SG147J/KSO6vhobqZBPL47CR6ttwmjajrqilvaI8yMKGq+DxpHEQTt+0dRlcIBh+LW/E8gjp0lS5e2fbbYYcVImBsEhjqUhwo6PPPWKY5k4WpKpabybh92+HsTmU+mdNvQ3S+Yyv5m9jWS5IuVZraRJIOwmB7rc8A1JGnyD5Ckyr853bbdzD4M3JIe6kNZ8oYMpo6X0S7XV+u4scvVfwqTPvP50q7AJfPdXoOXmT0VeB9wVOP+7v4He3uvu1+8l+0OvK3DtsuAy/Z2DhlwXYON0g9EJF8vI69vklTU+AIQ7WVfGUQdYkS3390Dy5+DGS4kD9y2zueMFAKKQdT28G6hNEQYBkQt5ZjC4cXEOT0oDI0wEcdtDzyFpSGiMQiCkDiu/28aFpO5riAsEEf1RSuDICQa301paFFTyScDJicnGB4ZoXXRl9FynJv4kFXcaG33uMMSJm0t+y6pPqVkDJk9/Sq8uze9zHlV3f1/u/sv3f227NX3nklfuddfdCgnZWYcMBRQbPm/JABWLQrSTMPaEXF3Tjt0mCOXF2vHy7IaLzppKc89ZnGa0Zg8gDtcMJ57+nH8l7NOZahUpBAGhEFAGBZYe+bzOOmV72Ro2QoKxSGCMCQolDjk2Rdz/H//FxYdcjRhaQSzAAuLLDn1Ag57y6cZPvIkguJw0v9CiUXHns3KF72T4dVPqyVtBMUhho86mcLBRxOMLCELMIUwYOnSJaxcvoiR9EMbSV8Xl4wVi5rT3ANLPstIMWgrGWXAsuGQRcWgLXwlC1G2/7yHClNb3mRf5shE5otu5aGyNJZ3kCRqXAXU1k+fi3NQKg/Vu1oR2KY2r5UfSi6K9W3lalI2qrVcUTlytoxW20ZhY5WYuzdPsqhoHNiQ6DBajrnqnl2EBk9ZUSJMo1y5UuW6W+9l10SVg488hrCQ3BSIoyoP3XgNe7Y9zsHPejnFxcuTvsYxm2+8iu13/pRlZ7+KwgGH1j7D2P03see2f2PZGS+jdMgx9b5u2cD4725hyWkXMHJUfSFwr5ZZygSHHnIwhx5ycK2vUZwsGnnIkgJPWVGqfW735Bm5oWLAipHmgDZSMEYKxopFhdpnc3cqkRPjLCoEtUSO7O+gEMCSUtBU1LjT30MtBT5Q4JrHBqo8VB9GXvtdHuo2mp+5el/DNgeOaXuHDJTWX1uSC2H7CsmQFPUt5pQ2LIX5CzQuKgasPbDYtgjk4lLAfzpypK1OYqlY4KQTj2fDjuYq70FY4PCz/5CJcvNqyxYEHLjuRYRrTiNqyCQ0MxYdexbDhz21va+r1rL4pPPbs/MKJU566tGUis3/HMLAOOXQYZYNNZeHMktGVUtb2gGGCsljBE0PHJsxlNYpbD1OaLBsKGgKgNm2vL8Hs/wHlEUWmm4VNo4GMLNhd59o3GZmw/3umIiISCe9zHn9osc2mSc6/U7fOveVSW55tbcvGwpy1/E6fGmRgxe3/950zIFFTj54qK39KStKPGvNorb5oJWLi6x7yqra7bnaeUdKnHniUQy1jKRKxQLHHrKc4WLziKkQGIcsLbYt/WKQVI7P+YFkD3W3Ci253dh6Oz6bH2tl1lwRoxdT2b9pblNkHuk48koL8B4BjJjZM6hf05YBWoxyHsj+Qr3h+yxAGM3ZhGZJKalSmFSVqMb1/VeMhPhIUvdw50ScJDgUA7CAVYth50TE5tGIUmisXl6kmFau2LSrwk2PjBEExplHjLBiOCR2Z+OuKpf/egcTFefVT1/GSYcM4+687GnL+ezN23hkZ4X/vHYxTz94BHD+y9PX8M2b7ueBx3dy7gmHc+4JR2AG/+WZJ/Bvv7iTOx58lKesOZxnnnQ8YRhwypqDuGvjdu5/fCfHrFrC859+GEOFEDPYvKfKxl0VDhgOOf2wEYaLydNm1dgZrzilgrFmeRLojKTyxXglKdO0YiRZ+6wcg8XOUCEptzVUsFrgd5J5wthhKC275Q6RQeDtc1y5HNwcd+u6f+vyKd7r8WXem60MwenULWHjjcCbSEpC3UL9WrcLuMLdvz0THZwKJWzsG28KUvWv49hz57OS9cCS4rutRWPHKklJpNZ2SCpONCYZxO5Uq8kijI3zOLEnwdFJRywNiQ+j5Zg7N08mJZAaRlyTlYjxSkxg1rRUSblSZcOOMtUYwrA+4orjmANHQhaXwqb93Z1FRWNJKWwb0S0utmccQjKiWlRqn7cqBGlVDmj7eRRCy024yAZ/eT+/1nao/6NsDUh5jzE0vkcBbE7re8LGHA9e+5ewkS7+eIWZvdLdvzVt3ZI5J3tkqvWC1mntKDPDaX+2yCwpQpvXnpdkEJhh5m1BIjCjGOYnlFQ9SRJpfY6sWAiIvP3/+WIhxC0kbMmtCIKApUOFthJOZsbinMAFSRJKa4CC5LZjp/a8ABVYfntjH9q/71Sveh/S6xW4ZB7Y65xXY+Ays//ob3dktizIC9pAfeaB6qxI33Wb87qjtQl4atbu7qf0s2MiIiKddHvOawPJ/NbfAuMkwetnwEv63y0ZVJ1uNXZ+A1Oql1Sw/N07jUusYVvr++IYwpzbk1HsTQ8M19q9Qxmo2n9b2jt8rr193KmuvZV3y3c69xeZi7rNeV1oZi8HPg98zN2vNrOKuz80c92T2WRmBHju5H+2mGJTRiJJgsJoOW5aVTkwOGAooOIwVmk+2EghmSdr3N+AJaVknmznZHPSyMFLCiwdDrnj8YlaDcXAYOXiAgcvLnDX5kkmKnGtfclQyAXHLeHWTeM8OR4liRuWPCy99oAiu8sxu9JlXLJ09iWlgMCSxSOz/kCy6nQ5ckphw7IjZGtwJfNxTT8/oBI5WWZ+Y0DKVknO2rNAV/Ek0WMq9Qrzduv0O0HnmTOZT+Z4Qsa06FqY192vMrMfAB82sz8CSjPTLZkrOgWwMEhWFI5ipxw5hSBNUDDjgJGARVHMromYoTCpRmFmjACLis6T40lUSGr8JdOuQ0VnrJyU4l0+Uk+YGCk5OyeSoLN8KKQQGsuBVccUuG/rJI/vqXLcQSWWDycR4ry1IRt2lNnwZIXVy0ocMJK0v+DYAg/vrLD+sXHWHlDiyOVFzIwVi2BPOWLT7ipLSwEHLapXxyjFznglSf1fVAwwMypxkja/dMgohclCm1lfi55kTkL6fFj6SEA5SgJSaI5Rr2sYexZIvKmuSSVOAmkx3T/5e0i2Naa/d0u+yCp09Lq/yKDZa1V5dx8F3mNmpwLP6n+XZK5JAlj+b/JhYIzk3GIrhQGrFgdtF8tCYCwuBW1lowIzlg+HhEHzCCUw44DhQtu5w8B46sohVi8vtvX1qANKLCkVcttbSz0BLCmFHH1ge+5SIbBaUGzkJKOt5cPt5xgpWO4tx9iTf2ytPw/P/pOT6p4XbDqVjepkqvuLDIqu2YZm9mwzOz79dimwxMz+sP/dEhER6axbtuEngTOBgpl9H3gucC3wbjN7jru/r9N7RboJLH89sMbkilad5nCKAW1rhBnJEiaj5fZ3LC4aY5X2h69LYbIOWWv7UGhU4/b1yVqL7Daeu1O7RkAi06fbbcPnAU8HRoBHgSPcfczMPgL8iuYq87IA5GXtNV6Q89rzMtuGQmMoTOaCJtOoMBRarT6iQy1YZIEOIIba7UYDSgGUgoDIkyVYIodiYIwUA5aRJEVs3lNlvOrpmlwBEOAOW8ciRstxLXkjO8eeNNkkq45RTAsS7pmM2V1O5r8OWVJoWqcr+9zZWmWtP49imATHTFsCTMvPJ7TOdSRFJNEteLm7u5llv9dm/+RieivoK/OIdYhSndrbtjW1pwkRoVMM65l21pCV0FgjN2sP3Cla8j8gXm8PcZaUgmSeKK3kYUAQwqFLQ3ZOeHMpK4NVi0NGCsmoqnGObUkpYOlQlkxSb186FLB8OGAkDVpNS56k63vllW7KaiPWt6WpGbUsiub3lAIteSL7ZiFkGDbqFrz+3cx+BgwD/wr8XzO7CTgPuH4mOidz097KSfV63U1SxPPLTCWaMxlq6421JDjUghvtJw86lLIKzNJg137uYl4gsiS7MK8MVGuSSVM7rduSYJZTySrtlwKXSC+6Pef1fjN7VvKl32RmTwFeThLIrpypDsrc1C1Fe5bRjwwAABW1SURBVIpH2sdt03LyKZ1VIUVk7uiWsGHufmP2vbv/DvhYzj5aKUhERGZUt7mrH5vZn5hZ041UMyuZ2R+Y2RXAG/vbPZGZ162sU6ff1XLbp/HXOi0oKdKs25zXBcBbgK+Z2dHADpLMwwD4AfCP7r6+/12U+axTCvxU35Oloset7WYcOBSwqxw3pbsbsHJRyM7JuClF3khLVkHbg9RJZqRRCJrn0GKv/xbYMr1Vl87TtS4Q2foZWo+VBaxsmk91CUUS3ea8JoDPAp81syKwEhh39x0z1TmZ//ZWxqj1Ym9m6UW9XrLKGo/lSQDLLvYBUCgYB4UBYxVntJKkwWfZhEMFY7ziPDkRMRTCkqH6ml2VKNm/sU+VOFkoc7iQJH2EDQkW2TmNZJXkxrXCskoanQJXwerJGt7wpqaVkFu+UBCThWyv5aEA3L0CPNbnvsgC1qmMUbYOY97ClEFOKDCD9oJOpItMptGtpX1RySjkvKkYGsWo/QHl5PYhFML2HodBe5p94/va+kXyTFev+9f73WWjLDgLLU0e9LyWiIgMIAUvmXey5IbWBIduyRadEiLCLoVtOx+v0zk6j6VatyW3Rbu/p9VU9xcZZD3dNhSZbXmlqRqLt2fzX7E3J22EaYJDFHttfa5GsSdLumTvD6jPbxUDY2g4xEnKRk2kC3aVQiMMkvOYe+1B5CzQRQ5x5LX6h7E3lIRyr81tGTRVEske2I69+VZliNfm4fLKbrk7UVz/2QTmethZ5j0FL5nzGpM3mpL5skSJNIBUc+bGqu5EEW3zVgDlKG7LKIzTxSCHwmzdrSTILC0FDIVONY1CjUkakCzO2dreeuzGcwyZ15JPMu7pas0t+0cOhqfBuvk9Udy+1lrsSQeCYGorMosMEgUvGRhNQaylPJTjHZ+Dygtc0Dm4FIP2hIvsHJ3KQGX79CIgq8HYvq3TTb9OI6m8Va4BrEMSiMh8oTkvGThz8ZqsQCEysxS8RERk4PQ1eJnZBWb2WzN7wMw+kLP9H81sffq6z8x2NGyLGrZd3c9+yuCb7nFPXtZet3NMJctvpvIBlXko81nf5rzMLAQ+Q7Ko5UbgFjO72t3vzvZx93c37P8nwDMaDjHu7qf1q38yvyTZfu0PFJsZxcDbVluGJCmjnLN68mTVGS6ky6+kT0nXyjS1zLe5J4tqAhQDryV5tMaNpveQzMOFLduyCiF5IScm/+HrQpA/d6cyUjLf9XPkdSbwgLs/6O5l4OvAS7vsfzHwtT72R+a5QmDJYo4NbZa2D4fNqxwDhIExXLDaqsVGsk8QQDl2KrHX0tDHKkkAdJIkiey5sChNg5+oJqWkkm1JQGx6NZSyCgxijEpWyqoxLT6tEJJ1NSBZoDLsEInMkhWos8cGks+bfDbNw8l81s/gdQTwSMP3G9O2NmZ2FHA08B8NzcNmdquZ3WRmL+t0EjO7NN3v1i1btkxHv2WA1S7m1J/ZytpLOeWcksUng/SC3zxaiR1GK854NT99vXWUF3syauuUARgYBC1BJW9fsySIlYKkRNXegpCZEQZGMTQKPewvg63xmrd7x/bZ7s6s6WfwyvsX1Okm/GuAK9298THSNe6+Dngt8Ml0Mcz2A7p/3t3Xufu6VatW7V+PRUTmuMZr3tIDVizIuobQ3+C1ETiy4fvVwKYO+76GlluG7r4p/fNB4Cc0z4eJTKv5MFbRml+ykPQzeN0CHGdmR5tZiSRAtWUNmtnxwIHAjQ1tB5rZUPr1SuAc4O7W94p0kt0GzDjJ/FIpTKu4t+y7uBSwuBjU5sU8ncvKljhplFWCLwbNJarqS6207x/UjluvP9haHqpRMIVomvW1dX5NZD7rW7ahu1fN7O3A90nmoC9z97vM7EPAre6eBbKLga97c17vicC/mFlMEmA/0pilKNJNbc7Hk7W7Kg11/5Kagk4xTBd+tIZ2g5FiwGQ1rtVBtIZECPes0kX9XCHJ/FQhsNwqGIE1r09Wf19Sr7At0FnzPN3e5M2ZZQEs77wi80Vfy0O5+zXANS1tf9ny/V/nvO8XwMn97JvMf2ZWy/xr2ZAuVNle7snMctPqoT1wpYdKEjFyAlc2aus0EssLLFmR3/2lwCXznSpsyII1nVl5/397ZxcjyXWW4eetntnNmljYji2x2AYbYxAOhMVZLUZRUCAhMRHyKoqFfANepAjxYyUgLnC4IGC44V/iR0SxspKDLOzEMdHGyo9sIGAkcLwxG3vtxbBOlGQTK9l4iZOA42WmPy7qVPeZmqqe7vH0dFf3+0itrTp16tTXpe16p+p89X5tY1lAjJkOFi9jjDGdw+JlzA4wSWHKnRzfmGXF4mUWluqCP+l/8n2rRWPqfNsjwEGmX70acss+lT1Uc7XnjRmJ1XK9fRBTy3eokjaseWZRcT0vs3AMLvxkGYKVFRMpYYJSWCJrJ23bU4jVvQXfWgu+tV4WulztiSIlgOSVjstxSi/DYJgEkidxNN019VFZ+LK0ThzWKiPzJaxspuq+ilWqfZWt2OKHGLUFz7+ZRcLiZRaSegq5kmfg5kKWZXslRnma+r5VIfr0GSZklAbAItb7g/VNx0k2UGRjRSY4OevR/K5XtAlSVIUs8/HLuNtsqao+xiwSFi+zVLRdxNuu7Upp9U3tkxzDfoPG7Cye8zJmBLsxZdR2jLb5qvb+m+fEjFlUfOdlFpJCm62SqsdrTRZK1VO+/mB+KVjrtwtFvX/VtlK03Kmlf9tqdUU0z8Mpa9+wTwRCqX/pZF/F0vTytet7mUXD4mUWjtweqhKDYcFHbWjPbaMigkKw1g/Or4++66qOUaRevRZ7qOEcWlpoKVIZwHrD9qq9iNzvUINt/f7mEiz9pM4rRdnXomUWEYuXWViUKcbGBIfq4l/evdTbm6orjzpGmz3UsE/z8iS0jd+WpFGZElu4zKLiOS+z8LQnSyy2PZSTRMwiY/EyxhjTOSxexuwA7fZQzv4zZhpYvIzJiAj29CbbZzDvlAnVwNaJnRGwthFa7aH6Fk6z2DhhwywtRZZxmF/oVwrRU5lxuBZV2zDbb72/0ZGjyDIGKwEbZDKmopC9orSZmnQSTKTKz5kFVb5tpVdmSa5naf29AgrPd5kFx+Jllprq3a/1TSnsYu8KFP3Ntk4rmZ9UXSOabJ2CUvB6xWQpIqvaaAU1eE+t5lUoiZXeUICdqGGWAYuXMSOYZRZh3cNwq2NbtMwy4TkvY4wxncN3XmapaXNvr+iRnCw27NO+R/Vy8HrNWmqlGFpTjcv5fungsTKY84oN8eYlV5rajVlkLF5maWmyh6pTaUAvSj/BtRb1qeajyv6iV8QgYaOXW1MxmYD1KUVsRQ01wZpMGlO7kgWWRcwsKhYvs7TUL/sDcWmqVqzybqoJVZ+aBVWTgIwy6B0Z64TGutYss+hYvIwZk1GP/RrvcFpqge0Wvusyi4wTNowxxnQOi5cxU2QRPC7s1GHmEYuXMWNStDyFy501WrePWB+HSffpx0sXnbwyswXMzBue8zJLS24PVTFMvNhc5LFXiB5lscrKkaNI4yA1Cswkl/wq8SNq+/VU2j01Z0Nubq+yHrc75zUQLDI3j0GL59LmiUu+bc+sQ5gZFi+z1KiWhTG8LoueYL3BHmq1J5RSD+uZhC0JiVuKWCVcZP8W1O2hNgpV3l4eZGfS45sy8Ku2trtPY3Ybi5dZekZd69vtoVramfwRXy5cG9oahGiUMElyirxZGjznZYwxpnP4zsuYFvqp1AhstlzKbaByCprspDInDzbe6RWMvlvLneIjynm4PsO5tiqmfpTzcAUM7KTy76AU8ziPFKsY63N+VHFO+MK0MdPA4mVMjXp9LBhaLhUF5K8erxTltn5snLPqpX3W62OT5o7IbaMYHCxoLpvS78eGsfpp/KIqeJm1V56IuSgGsNaHQjG292GhpvkvDdrqQmzMbmLxMqZGXbgq6v6Fg/aI5kSGNh9ESuHKL/yD5Za7mroIVmw3QWQUubBFSgKZrBKZ2S3O/c/5WYcwM6Y65yXpRklPSzot6faG7UcknZV0In3elm27VdJ/pc+t04zTmJxWCyjabKBG7DAhu1MnbPyDqDGdxJjZM7U7L0k94K+AnwLOAI9KOhYRT9W63hsRt9X2vQR4F3CQ8lryqbTvf08rXmOMMd1hmndeh4DTEfGZiDgP3AMcHnPfNwEPRsS5JFgPAjdOKU5jDCNNQtr2mNFxjZmueF0OfCFbP5Pa6rxV0uOS7pN05YT7IukXJR2XdPzs2bM7EbdZctpexG2zXGp7qNb249rOtXrSB3ft2YuV7VO9bbi89eA7ZxuVH9ciNh75Ne8bXzs363BmxjTFa5wp7A8DV0XEq4CHgLsm2LdsjHhPRByMiIOXXXbZtoM1pqJXaFD5uGJUqrmkRsGTYE+x8UcmYG8vjVXrX6hKf9/cvqcnVmu/1l4af7U2VtVe/w5VTFGZPeWiRZaZmLVXSSpVr0jZjVCJ+UYPxHGpjlEdt+nYppn8mnfhRZfMOpyZMU3xOgNcma1fAXwp7xARz0XEi2n1TuDV4+5rzDSRRK8oRWalgJWetnS36BXDJPrS7aL8rPY0EJm9vdKnUBJFoYFgVenrpRBqIGJ5WnuhcpyVgTgN+6+mOPP26jtU4/eK4XcINgsHDevldxltStz0PthWNB1n8C6c80PGZpm9DacpXo8C10q6WtIe4BbgWN5B0v5s9SbgVFr+OPBGSRdLuhh4Y2ozZteohGGi7LyW/pLoNWzLRW6cYw9Esql9gvG3gzXFzBNTyzaMiDVJt1GKTg84GhFPSroDOB4Rx4C3S7oJWAPOAUfSvuck/R6lAALcERHL+3DXGGPMBrRIdXoOHjwYx48fn3UYZonJ55Byho8TX+r4MXDzyI17R1lQbRXTuLFW14qmx4Tl3NjQGDg/ZtN49VI0Wx17yRj72y/oNW+s72+HDWOmQG7NtBPX4Uq0ButUFk2bDaXa/AfbYpo01q1sozbFni1UMY1jiWXMKCxexuwgGwQjGtq2SVtSRARIzZf/iOrubIyYxoi10TYqttipJaYNx9rB82SWB4uXMVNi9y7GzQdqdLJqiWnSWJXutCa9ZZokJmNG4XpexhhjOofFyxhjTOeweBnTeTZPiLUlT8yaeYzJdBOLlzEdoNDm6SUN2hteima6c0lqiGfc/YzZCZywYUwHKF0yGHgLbqotFnnf3Yoppdm3HLut3ZidwOJlTIeQmu6zZisOO5XBaMwkLJTDhqSzwOdmHceYXAp8ddZBvAS6HH+XYwfHP0t2I/avRsRY9QslfWzcvovGQolXl5B0PCIOzjqO7dLl+LscOzj+WdLl2BcNJ2wYY4zpHBYvY4wxncPiNTveM+sAXiJdjr/LsYPjnyVdjn2h8JyXMcaYzuE7L2OMMZ3D4mWMMaZzWLymjKQbJT0t6bSk2xu2H5F0VtKJ9HnbLOJsQtJRSV+RdLJluyT9efpuj0u6frdjbGOM2F8n6fnsvP/2bsc4CklXSvpHSackPSnpHQ195vL8jxn73J5/SS+T9ElJn07x/25Dn72S7k3n/hFJV+1+pEtORPgzpQ/QA54BvgfYA3wauK7W5wjwl7OOtSX+HweuB062bH8z8FFKl6AbgEdmHfMEsb8OeGDWcY6Ifz9wfVq+EPjPhv87c3n+x4x9bs9/Op8vT8urwCPADbU+vwK8Oy3fAtw767iX7eM7r+lyCDgdEZ+JiPPAPcDhGcc0NhHxz8C5EV0OA++Lkn8DLpK0f3eiG80Ysc81EfFsRDyWlr8BnAIur3Wby/M/ZuxzSzqf30yrq+lTz2w7DNyVlu8DXi/ZEGs3sXhNl8uBL2TrZ2j+Eb81Pfa5T9KVuxPajjDu95tXfiw9GvqopFfOOpg20iOpH6G8A8iZ+/M/InaY4/MvqSfpBPAV4MGIaD33EbEGPA+8YnejXG4sXtOl6S+x+l9wHwauiohXAQ8x/GuuC4zz/eaVx4DvjogfBv4C+NCM42lE0suBDwK/FhFfr29u2GVuzv8Wsc/1+Y+I9Yg4AFwBHJL0g7Uuc33ulwGL13Q5A+R3UlcAX8o7RMRzEfFiWr0TePUuxbYTbPn95pWI+Hr1aCgiPgKsSrp0xmFtQNIq5cX/7oi4v6HL3J7/rWLvwvkHiIivAZ8A6ua3g3MvaQX4djr8mLqLWLymy6PAtZKulrSHcmL3WN6hNkdxE+X8QFc4Bvx8ynq7AXg+Ip6ddVDjIOk7qjkKSYcofwvPzTaqISm29wKnIuJPW7rN5fkfJ/Z5Pv+SLpN0UVreB7wB+I9at2PArWn5ZuAfIsJ3XruI63lNkYhYk3Qb8HHKzMOjEfGkpDuA4xFxDHi7pJuANcq/3I7MLOAakv6WMivsUklngHdRTl4TEe8GPkKZ8XYa+F/gF2YT6WbGiP1m4JclrQEvALfM2cXnNcDPAU+kuReA3wK+C+b+/I8T+zyf//3AXZJ6lKL6/oh4oPa7fS/wN5JOU/5ub5lduMuJ7aGMMcZ0Dj82NMYY0zksXsYYYzqHxcsYY0znsHgZY4zpHBYvY4wxncPiZYwxpnNYvEznkLSeldI40VaOQtIFku6W9ISkk5L+JVkW5WOclPQBSRek9m+mf6+S9ELq85Sk9yXXiKZyHickvaElhtbyIJJ+R9IXszHenG17Zyq38bSkN+3UuTNmUfBLyqaLvJB857biHcCXI+KHACR9P/B/9TEk3Q38ElB3g3gmIg6kl1UfBH4WuDttezgifmaMGNaA34iIxyRdCHxK0oMR8VTa/mcR8cf5DpKuo3zp9ZXAdwIPSfq+iFgf43jGLAW+8zKLzH7gi9VKRDyd+UjmPAx8b9sgSTQ+yTYc27dZHuQwcE9EvBgRn6V00Dg06bGNWWQsXqaL7Msetf3diH5Hgd+U9K+Sfl/StfUOyVT1p4En2gaR9DLgR4GPZc2vrT02vGaroFvKg9yWyuEclXRxapv7UifGzBqLl+kiL0TEgfR5S1uniDhBWcX6j4BLgEcl/UDavC/57h0HPk/pVVfnmtTnOeDzEfF4tu3hLIYDEfHMqIBbyoP8NXANcAB4FviTqnvT1xk1vjHLhue8zEKTym7cD9wvqU9pZHuK8ebNqjmv/cAnJN2UTFknoq08SER8OetzJ/BAWp3bUifGzAu+8zILi6TXVI/iUkma64DPTTpOKjNyO/DObcTQWh6kVg7nLcDJtHwMuEXSXklXA9dSzrkZYxIWL7PIXAP8k6QngH+nfET4wW2O9SHgAkmvTev1Oa+bW/aryoP8ZENK/B+mNP7HgZ8Afh0gIp4E3g88RTnP9qvONDRmIy6JYowxpnP4zssYY0zncMKG6TzJgeIPas2fHZWJOIUYXgH8fcOm10fEXJS3N2aR8GNDY4wxncOPDY0xxnQOi5cxxpjOYfEyxhjTOSxexhhjOsf/A3xqj7ooemNjAAAAAElFTkSuQmCC\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": [
      "483 1420 2689 9848\n"
     ]
    }
   ],
   "source": [
    "print(ind_250.sum(),ind_350.sum(),ind_500.sum(),len(cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat['flag_spire_250'][ind_250]=True\n",
    "cat['flag_spire_350'][ind_350]=True\n",
    "cat['flag_spire_500'][ind_500]=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set XID+ cahtalogue back to orignal order of objects, as used in MF detection files\n",
    "use = cat['HELP_ID'].astype(int) -1\n",
    "use = np.argsort(use)\n",
    "cat = cat[use]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# galaxies =  9848\n",
      "# galaxies =  9848\n"
     ]
    }
   ],
   "source": [
    "# Reads MF table, removes duplicate RA and DEC\n",
    "cat2=Table.read('./data/AKARI-NEP_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])\n"
   ]
  },
  {
   "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=(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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add field name\n",
    "cat_all.add_column(Column(['AKARI-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_AKARI-NEP_BLIND_Matched_MF.fits', format='fits',overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*This is a default HELP jupyter notebook *\n",
    "\n",
    " ![HELP LOGO](https://avatars1.githubusercontent.com/u/7880370?s=75&v=4)\n",
    "\n",
    "**Authors**: S. Duivenvoorden\n",
    "\n",
    " \n",
    "For a full description of the database and how it is organised in to `dmu_products` please the top level [readme](../readme.md).\n",
    " \n",
    "The Herschel Extragalactic Legacy Project, ([HELP](http://herschel.sussex.ac.uk/)), is a [European Commission Research Executive Agency](https://ec.europa.eu/info/departments/research-executive-agency_en)\n",
    "funded project under the SP1-Cooperation, Collaborative project, Small or medium-scale focused research project, FP7-SPACE-2013-1 scheme, Grant Agreement\n",
    "Number 607254.\n",
    "\n",
    "[Acknowledgements](http://herschel.sussex.ac.uk/acknowledgements)"
   ]
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