{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mc741/anaconda3/lib/python3.6/site-packages/mpl_toolkits/axes_grid/__init__.py:12: MatplotlibDeprecationWarning: \n",
      "The mpl_toolkits.axes_grid module was deprecated in Matplotlib 2.1 and will be removed two minor releases later. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist, which provide the same functionality instead.\n",
      "  obj_type='module')\n"
     ]
    }
   ],
   "source": [
    "import pylab\n",
    "import pymoc\n",
    "import xidplus\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from astropy.io import fits\n",
    "from astropy import wcs\n",
    "from astropy.table import Table\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat1=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat40039570.fits')\n",
    "cat2=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat40039610.fits')\n",
    "cat3=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat40049400.fits')\n",
    "cat4=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60080410.fits')\n",
    "cat5=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60092780.fits')\n",
    "cat6=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_0.fits')\n",
    "cat7=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_1.fits')\n",
    "cat8=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_2.fits')\n",
    "cat9=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_3.fits')\n",
    "cat10=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_4.fits')\n",
    "cat11=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_5.fits')\n",
    "cat12=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_6.fits')\n",
    "cat13=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_8.fits')\n",
    "cat14=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_9.fits')\n",
    "cat15=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_10.fits')\n",
    "cat16=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_11.fits')\n",
    "cat17=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_12.fits')\n",
    "cat18=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_13.fits')\n",
    "cat19=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat60096211_7.fits')\n",
    "cat20=Table.read('data/output/dmu26_XID+MIPS_AKARI-NEP_cat40019880.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalogs=[cat2,cat3,cat4,cat5,cat6,cat7,cat8,cat9,cat10,cat11,cat12,cat13,cat14,cat15,cat16,cat17,cat18, cat19,cat20]\n",
    "for c in catalogs:\n",
    "    for i,source in enumerate(c['help_id']):\n",
    "        if source in cat1['help_id']:\n",
    "            if c[i]['FErr_MIPS_24_u'] < cat1[i]['FErr_MIPS_24_u']:\n",
    "            #if c[i]['Rhat_MIPS_24'] < cat1[i]['Rhat_MIPS_24']:\n",
    "                cat1[i]== c[i]\n",
    "            else:\n",
    "                continue\n",
    "        else:\n",
    "            cat1.add_row(c[i])\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NEP_cat=cat1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at Symmetry of PDFs to determine depth level of catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7453837\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ak9KvNwD/faReT7jznWDKZ/x1EwWy7L8hz+s8/zxfmNH6nPVtrh4QWm97FJPsA9UpV5Rk8Z1cilhoxsSFDabONeKeTEZPk0XK+tGOejMh6byCMvREtrNRAtRifmRjZl8LLK/2JHd/zN1vS78/B9xFcg9V3quB93jiJmCPmR0aufcTyswIA+tJnw6Mvrb88WWy9G13L7lNtlf+gn0+F+2y4JB/H1nQbMZJ++6pgF1Vox0755oxJ5YjltvOmZWYlZaz0nIePdvm6GLEV0+1ePB0izhNG3egGTmnliIa7XxANiqhJaWKCp1rd7L7nIDuneLFIGVpwkXP01EGn8hWNMoa1JuB95nZo+nPh4DvXcuLmNlVwHOAmwsPHQYezv18JG17bC3nn1SWlgoqtgWMPpnVLQe0liSBkQ/tf15Jvb1sb6h2IRBMVY37TrX6+nt6JWK51RtSzzVjWnGScp4/dqUdUwmCnvcXmOElWYuxQ63s/Vn556PAJLK1DQ1QZhaQXBOeAlxP8m/+bndvDXte4RyzwJ8AP+3uZ4sPlzyl79ptZm8gmQLkiiu2X6aKiEhe/pq3/5LixFO5fEZf3lbO7hs6xefuMfBr7t5y9y+5+x1rDE5VkuD0h+7+gZJDjgCX536+DHi0eJC7v8Pdb3D3Gw4cODDqy2+69awOsZGKffO0tHlxejKKnf0zIfX8qMid44ttji21iXLrTrE7Xz3Z4MRSu2ddLYqTYrL5Y5MitDHtOO5ZdzKSe6iKa1GxQ7uk6oTIVpW/5s3t2bfZ3dk0o0zx/ZWZfTfwgbVsWmjJnMvvAXe5+38dcNhHgJ8ysz8GXgCccfctP71XrD4O3eoQZZUPVjNKMMsfcz7beXTOk9tqw92JSSuJmxHgBJYkLaxESYXzXTVjuhqy3HIePN3k/lOtNFgkgWffdEg9SNauFoFTK01mawHX7atRqwREDlEEzShmumJUArpTiZ4EtkoAlcA6n1fbwTzpS/6Ntj2pdh5qXk9kWxglQP0ssAuIzGyZzrXL51d53ouBHwTuMLPb07ZfAK4gOcHbSW76/Q7gXpL7ql6/5newxRSDR3GBv9vufW2D5CtJrIfsPLEnwanYv5V2nFuPyhI/Yv7xRJNCsh4rrRirdgfqsSc1+SrFxTmSxImyN1EJIChE9ywppD+xI3lUN+uKbH2jbLcxdz4ndve/ZZVf/tMR2RvP5/xb1ajVIdZ6gR3L9XhA58r6246zenq97aVZi5T/xRj0HtZSjUJljkS2j1G22zAz+wEz+3/Tny83s+ePv2siInKh3nvzQwMTKCbdKPdB/TbwIuD7058XgN8aW492GO9MVhXaByz3DW5fz15lJx39UCsZPcHa+pWk1CvRQUQSowSoF7j7G4EVAHc/RZJ6LmtUvJE2n81W3C9qte8pZLKte1+9ZCou7cNU1XraY3emK8bFu8K+9hPLbZZacc/eUA48dKbVt99TWYCDZG2qWEmCYt+yc8QKciLbxShJEq20QGxyr7/ZAWDETbp3pmH7N3n236xKRP6xNYyakgQBx7B1XX/yfL/NqHT2afJOSndoxmwNGpGz3HJOr0ScWIq4aKbCbC3ggdMtllvO0cU2y23nobMRB2ZCrt1bpRoa1dA404hZPNHgivkqs/WQaghhyQ23yW9QRjOCMICKJW1Bemx+/FkNkpt8RWR7GGUE9ZvAB4GDZvYfgL8F3jLWXm0Dw/ZZyu6PupDf87PRzTiux8V+ZRf9/AjHzJiqBNx3ssnxpajznHolYKpiPHK2xXKu9MSxpYgwNGqVbtWIdpy018I0jTxfTYIkEObb4jgJUGHuWCNpq4em4CSyzYySxfeHZnYr8C1p02vc/a7xdktERHa6Uab4IKlenk3zTa9yrIwguyH2QhRv/u1sz07/vVbF9sHHdmvoZaMUd89ty9577CWzFU4uRzSi7vrYQjMmDKAddY+NYucLjy1z1d46B2e7f+1ih6OLbS7eVSEMuq/XiJP7n/LTfu6w0nbqlf6pvCj2zrSfiPTbiqWQVg1QZvaLwPeQlCwy4A/M7H3u/svj7tx20FPhgd6AkAWp84lT2bmyxIviY2XBb+ix3l8vPb/2FQRG4I4btNIqEpHD/pmQi2ZCTi5F3HuqyZePNVhsxtQrAbXQWWzGnGnEnF6OeMzgnuNNrthT5UWXzzBfD5NEiqWYk8tNDs2GzNdDWukKZzsNOlMVyD7JOIZW06mHSeHZIN0qPssiDHSTrsi2McoI6nXAc9x9BcDMfgW4DVCAGkF+xNKt+JCviOB0V1PoHDwsaK3lxtVRrRYkO6/p3eCUNCUZfbWKccsjy32VJxaaMWdWop7g+PDpFt96bdAdMaWPnW04U5XeAJPVMgyst25EO04CV/FjSEaVClIi28EoSRIPAFO5n+vAfWPpzTaWJU30V5Ioqauw2sV1Ey++WeZcUTPyTsDJi2LvSx+PSZIiivr2gcpeM/ffbj8GH6vgJLI9jDKCagB3mtnHSX5BfSnwt2b2mwDu/qYx9k9ERHaoUQLUB9OvzKfG0xXZ6uIB92uV2egxzoVUeBeRzTFKmvm7N6IjO9GgBIlOJl1nnaf/8c2axnKHamg0cvc4uTt7pgIum6/y8JlWZx0qdmemGnBqOUqSGUgCU+xw26PLPPvQdCdLz4DFZsyeekgYeE8G4UoLpqrJOfP3UEWebK9RXLMK0g/O0gSKfN+T9nF8MiJbUzG7b5Ky+kZZg5J1llUqKsuac/fOBoeeO77/HF7+wHkwkptfi8tCgXXbs5dre3Lhr1eS7ezdncVWzGMLEV9zaJoXXzlDLUwy8O470eQfTzRZbDmNKO6csxbA5x9b4QN3nuHkcpKLPlM1dtcDFltxsl28O3HsrLSdxXbMyeUo2Y4j7W8lgEaUfCWfW7ff2U3QSRZit9rhsM9TRCaPAtQmKbtGZhlrqx03ymOj6k17t05A6qnWYEYMtHJB08yoVgIeOtvmxHLc6fe+6Qr7p0O++PgyJ5a7N0I1o6TiQ75ixNlGzOeOLDNfhZlqt8JEK06C3krUzWZ0YLEVExpUwu45IodWXDbKzAJV7wOKTSJbhwLUDjaoVNKFTh8Oev7AzLs1vJ6m50R2jqEByswuM7N/aWYfNrPPmdmNZvbbZvYKM1NwW2clCecDdaYJ13m+qnvefFt3aq33WGd3PaC4OW4zcvZNhz1tcRzz+KlzLDWaPe1R7Nx1vEm7ZOhYfGtGcpNw8T07EJVUOxeRrW1gkoSZ/QFwGPgz4D8BR0nuh3oy8DLg35jZz7v7jRvR0e0mu6Z77ufsPiAH0iWbkqBQrIB+ftUTOq/vgyta5CtMhGkVcSeZgsuO71SSWI54/FybO55Y4Wwj5tp9dS6bd+46tsITp5d44sQp3J2HgYN7Z7niwB5m6yGLrZibH17i848t881X7+KKPbVkLSyrVJ7e4FyvGLXQaMbQjJ1a6FSDZErSPUmaSNamvFAGqXuDrwZfIqsbtrnhRidQDMvi+zV3/1JJ+5eAD5hZDZicdI8tpO/62Wnvlp3IEhPKauoVeZryN2qQKpvaG5RN2NfvNNsgvxZlwFRofPqBRaI4eSwwY7pqWHOJx4+f7ElBP3p6gadeuo9amOTbtR3aLeeeE02u3FPrex8ztaBvK44ohulCJYmka911s+J77rwHEdkSBk7TDQhO+ceb7n7v+ndpZ8nX5eu2WV/b8JNsXPUEs/JgthJ5J5U87+xKq+/+qNidaiXoO3a2FpQOc8qKwA56uwFrXwMTkck0SrHYFwO/BFyZHm+Au/s14+3azqELp4hIv1EqSfwe8DPArUC0yrGyg5VVklBet4icr1Ey8c64+8fc/ai7n8i+xt6zHWytyWjZGtT5vsZaX69SUtlidz3g4K4KlaC3/ZqL56gEQU8hWY9jjjx+nCiKOv0ODR4+06QZeV+ga7T7M/SitJJEsb2dZvMpo09k6xuWxffc9NtPmtl/AT5AUjgWAHe/bcx923Gya6qTlekZkKTQ/YPASCtAlFcZL32dwjcD6+WV9AGSG2UrQCNyotiJPLkR92VPnuOh000+9dVFGpHz6NkWx5shT7/mUh47fpqjp85Bu0HzxCPcdmSF+/fs4fk3fC0zMzMcnK1weL7Kl55ocMlshcPzlU4CRjt2FlvOVKWbLOHAQtOpBjBTTZM10hjY9nSjw6zkEaZpVJF1kGX4bVQ239AsvsLPN+S+d+Cb1787O1sxFOTr0XXb0j/TDL8gNzLJ6tyd7+uVKfYhn4AwVTGOLrZ7jr9iT41r9jX50F3nWEnr9YVBwGUX7+PofV+isbTYOfb06dP8451f4Ade8fXsqtc6fXpsoc3uqYD5etizn9ZKK2ZXrXfQ34qh2cnoy2X5eRa8FZlEtqqBAcrdvwnAzK5x9/vzj5mZEiQ2WdneUuN9vdFfLDArDZTmcenxZXtDJa858kuW9k+hSWRrG2UN6v0lbe9b747IYGUX2qzWXLFtXMrWddydemh9/auFxpW7q/3Pr0xB0Fthou3GPY+c6lt3akVOK+p/Q+24vx+xe38lioHvo79ShohMpmFrUE8Bng7sNrPvyj00T+8Ou7JOyu7fTZeX0tz+bqXu7CCPPK1AMXgNauB5S9qLyoJS3lzNcIyFprPcjlloxFyxu8ahp1d59FyLj9x9jodPLvDgI0/ArouozewlWjhJtHiaysw8T7TqfPiW+7nxrkd47QuexJX7Z9kzFbDUclbabXbVjN1TYWeqrhElpZTqlbTwLNCKoBXFyXpULaBi1knWyLYmKb3J2Td2FCoiazNsDep64JXAHuBVufZzwE+Ms1M7VX69pbfyQXcdqK8cEUlGWxB0S/p0zjfkvMX2MqNkwmWJDNMV55GzUWdUVw2Ny3dXuWJqhU8++AhR9oAFhLP7CKbnCHBijFYUc/TsMn9/9xGe+9KnEQZB570tNp2ZarYflHXam1GSIJGf2mvFEMdQqfa2D6zAkf5HQUpkMg1bg/ow8GEze5G7//0G9mnHy2rjFS+c+Sy+3gc6/yk9V/H74rmz17sQJbNxBGbcf2yxG5xSWTmiuNDnS/ZMU5wwdJJgV3x/A5atqIWjr5cNquYuIsPl6/WNM6Nv4BqUmb3WzPa5+9+b2QEze7eZ3WFm/8vMLhtbjwQY74VTF2UR2QqGJUn8B3c/mX7/NuB24OXAx4A/GHfHZD2UD4s2MkFgo3MRlPsgsn0MC1D5dKvr3P3X3f2Iu78LODDebknRsMyzsn2hsp/z7flzrFpJYg1RzD1ZD6oWar3G7rzwmouohkFvKnnUJm6uQNS9hyow457HzrDSjvumBJeacX/FiMiThJFCe7ZdfPf9D0kAWdvbFJENNixAfcrM/r2ZTaffvwbAzL4JOLMhvRMgdxG1ZDv24gxdvtp3/uKcbSHfc8Gme8HupFz3vJaXJmMM7punGwYal+2usn9XmL628/hCm+r0DP/spU/nukvmwWN8ZYGFL/wlZz/7xyzc+Sm8tQJAbXqGM+Fe/uMnH+UfHj7XCVKhwblmxImliHa6WWHsTiuG0ysxS63uewst2QDxbCOmHWfBa/A70RqUyGQblsX3U8C/Ae5Jf/4ZM1sE/hT4wXF3TLryl9ispE9ZZYdBOgl0hb2T1kMr7t0bavdUyLHFiDueWGElLS08O1Xlu55/LW95y1s4/djDkN6w2z55hMZXbuLgS76HcGoXkJRP+uCdpzm8u8Y1+6Y6768VO8eX2uybDnve80o72bhwttb9LGKHhWbMfD0Y+PkMSrIQkbUZtMHheiRPDMvia5Fss/FLZrYbqKhI7OTYqP2fVlMa6CwJXH3NjYVOcOqeICas9P81jOK1vcc1HTvykSKymUapJIG7n8kHp/QmXpkwG13Bu3hfVSY0mK8HhWOdXRdfTljrvcc7bjVYfPiuvr4vtmKWSqJcVFJJwt371q2A0jWqQe9D1SVEJs8o+0GV+Su03fuGyYLAsOvnahfiYiApqy4BvVNfw6oHZWtXYXp85Mm6UxTDodkKB3dVePRcm88/vsLJpTZ3PLHCk77ldVz9DU3u+dQHefzuW4nOHmXpvs9x5vMfY9dlT+HQq36a2QOHueaiGe4+3uIrJ9o842CdpxyoUwmMamCsRBCklSTCwKj9pRrPAAAgAElEQVQGSfXys82YegjTlYAgMKqh0XYHN6qBdypR9FXQ6FuD09qUyKQYVuroNwc9RFJdQjbIhVZ86NsuvXDe/BU6n2yR3Rjshfa45PjA486ak5kRGhyer/DVUwF/dvdS8pwgpFKf5inf9F0cu+WjNE48DHGExxFLD32Jcx//bb7uZ/9bZ8ovcrjzaIP9MxUu313trjGRrFXtrVlPtfJGBNUQZsNse41s/QqmwqTSho0YgEVk8w0bQb0e+Dlye0DlvG483ZFhBhWSGPqcAUOBskoSvW2WBCfznuoONqDshJf0LjDjnuONvioTFoQsPPFgz3niOGb/lU/u2T4EoB3DxbOVvvdRCcqnF+sVWz0gr0KjJ5HJMCxAfQ74krt/tviAmf3S2Hokm2LwRXlcV+vycKvYILI9rMfmhsMC1D8BVsoecPerz/sVZQL0F5bdnD6Ut252z0RkMgzM4nP3k+6+lG/LbQMvm6C84EO3YsLqJ8hXWDi/BZhBzyu7gdjdedqBel+FCXdn7tC1BJXunlFBEHDs/i/Tbrd6UtFDg8fOtvr2i2rH5Z9Ho91bSQK6NyevWj1jhMdEZOOMlGae87tj6YUMVZYCna/4kO0RNXBbiUJ1iO7x/SnbeZYPOrljy6tZGLM1oxZ2X7MZOU8/OMXPvng/V+6pYh7TbjW5745bqD7j25i+/iVYpZpswTE9z5kW/Pnb/wOPfuVOPIoIDC6dq7DQjHnodIt2uphlJGWVlppOs93tl5FsC39mJUrWvdL2am6/LKWTi2wdaw1Qmn3ZBGUFezqByXvbugkB/ckCPc/37nHDmCUBqfj6WfDqfKXllqbSnQLPNWOW2klQPLCrwhtfsI9Hv3QTt33iw5x49MHk2MuextzzvovpK55B9dKnEFSnWFk8x41/8nvMNZ7gmQenuGQuyd5baMbcd7JBNYB6SCd7rxklqe0VSzcwNKMdw+nliEpg1AJ6Mv2c8s+T9H0kZaOGfiQiskHWGqD+v7H0QtbVRlWZMOt+FduLKdxmRuPcKaJ2u6c9qNYJp+f7zl2Nm9TC3hM7vXUHi+cvGnRs6XtBgUlk0owUoMzssJl9HXDSzL7BzL5hzP2SCzDq+lJZFfT1EpZc7A/snacSFipMxBFxu9V37LnlBiuNZl97KyqvJFFcoxp0rIhsrPfe/NDAen2rWbWShJn9J+B7gS8D6a2YOHDjeb2irFlZJYlsk9msYnl2XDbNl9xoO3xIkK3JWHoXU9nxyTpV+fOLVSeyNbCZijEzW+FcM+ZsI04KvS62+dkffBVnF5Z5xwc/wZfue5jWqcdYeeDzeLtJZWoXTO/GKjWmLrmGD/7dnXz0c//Id3z9DXzN9VdTDZNCtI8vRtRD46LpMN1pN7kRt9WIqYfWuQ/KgBPLEWEAe6ZCauHg38U6n69rFCUySUYZQb0GuN7dv8PdX5V+fee4OyZd2UWzd80nuRAH1pu0UMyWI3fsIFlgKR+ZlKw/0Q1O2XnzdzVlrzdbC6gG8PDpFkstJwwC9s7v4qdf93IOnPtHVr56K95ORklxc5m4tcL0dc+nsvsSHFhptvizGz/HAw8fYe90SJi+aCNyji62Ae8Jko3IabQ9CbhZCaYYTixFpbX68p9X2VSliGyuUQLU/UB11aNkrPIX0L6KD4x2gR0WpAaNoAb3xXKvbaWvHZhxcinqC3CBwf333IXnNiyM45jK7osJwgpxLsw2W22efvWhvr5Vy+YQgVpJJYlwwGfTTe4Y/F5FZPMMq8X3VpJfipeA283sE+TKHrn7m8bfPSnachfTQf0dUDJpPU4tItvDsDWoW9I/bwU+UnhMK8+bZNusk6xD8oKqTohsHe+9+aE1lz0aVkni3e7+bmBP9n2ube9qJzaz3zezo2b2pQGPf6OZnTGz29OvX1xTz3eg7JpevLav10W6fw1qwHElxw4631wt6N+91gIuvuQQ1Vq9pzleOEUctdNbahNhGPLg4ydpRb17Q7UHrCmVZe7Fnc+t8P7Qjbsik2yUNagfLmn7kRGe9y7gZasc8xl3/5r069+PcM4dqbOhHt2ha/7CGgQ28hbmgZUfmyRDWO/mfQOGau7dzQCzLzPrqTDu7sTAgdkqTz1QZ6piuDvt2LnnRIuX/+yv86xv/W7Cai15V60Vzt3xcY596D/SOv4gxBEWBMwdvIIPPWD85b0LNNoxeJJxWA2N0ysxzSju9CF251wz5kwj7kmKqFeM5VaclEcqKYNU/DxFZDIMW4N6HfD9wNVmlp/imwNW3frd3W80s6sutINSXkUCekdOZkZAd6+m0ptZyWLO4GP7K0Ykj5WNmGLvvRnWzKiEySimlauVN1cPefYlU/z+bad4+EybRuQElSrPfuk/YXZuN3/zWz9P49RjAESNJY796a/x5J/6PeafdAPV6VkA7niiwaNn2/zUC/dRD5NECAfONZ3pCtTC7rirGTknliOu2F2lFnaTJlpxkjJfTLDIpgq3xdSpyDYybA3qs8BjwH7g13Lt54AvrtPrv8jMvgA8CvxLd7+z7CAzewPwBoArrtBGvoNk9/+UPkZ/9t84r8dlge7EUkSjsDnU9Ow81lzse36tVu0Ep0wjcsKSlPmyVHhIAlHxWA2SZCvIX/P2X3J4k3uzeYatQT3o7p8iuQ/qHHAWuNvdb3P39qDnrcFtwJXu/mzgrcCHhvTlHe5+g7vfcODAgXV46Z1ltaKwF3TuNRw7XekPig4EM7v7jo1WFonb/ZUkmsXdD4eI4vL3rak8mXT5a97cnn2b3Z1NMzBAmdnXmNlNwKeA/wz8F+DTZnbTemy74e5n3X0h/f6jQNXM9l/oebej7ObcYlteti5U1Nleg/4L9qDzlrUXbwjOiwvVJioB7KpaWkU86UM7ct7wvH288QX7ODSbDNzPLLd4eOoqrvnXH+TS1/wrrDad9CGscPfv/Ry3/+K3cfquvwNgpmo842Cdf3hkmTuPrnQCVTWgU0E9rxoYjy9EHF2MeqqgmyXlUPKfRb6ShIhMjmFJEu8C3uzuT3X3b02/ngL8NPAHF/rCZnaJpfMvZvb8tC+rrm3tROWVJLrtZVNcg0ZNWXJDdt5iVfLBr2edG3J7Z82SI3K7W3SOrYVGNfDO3k2BGQdmQn7suXtYXFrmi0dO04gDLKyy/8X/lGv+r3cQTM0m+0TFEc0zR/nKO97E/D9+lBddPsN8PYlExxcjbnt0mXoIU4UbcwODemidqhPNyHl8oY27Ewbd9bIYiDzruW7YFZlEw9agdrn7zcVGd7/JzHatdmIz+yPgG4H9ZnYE+HekFSnc/e0kO/b+MzNrA8vA97kqew6UXTwv9D6osqdm98z2rlGVv56ZjVTnLzu2FfW3mTm3HlnoCaoe1lh66A7wmDjuppRHzWWe9ZJv7QQcSILL/FTQ2V4jr1KyRlUJoFKyHhXmEjxEZPIMC1AfM7M/B94DPJy2XQ78EPAXq53Y3V+3yuNvA942Yj8lNa7r6aDzlreP3olh5y37dcQCy2+om77aGt604o3ItjEwQLn7m8zs5cCrgcMk//SPAL+VrhmJFPTXdtCYWETO19DtNtz9Y8DHNqgvsoUl036923y4O6FBcbcnA3ZPhSw2456svOreS3DvDXBBGHLikQc4NL0rvak30WiVR77s3qy87CWKU5Ne1pY7rWb/RNbPWsscwfAsvmflvq+a2b81s4+Y2VvMbOY8+yhjUJqNN+Dqauuw7tLz7FwyRpaskU/QqFWCXHp50t6M4a2vuYZvvm53cq9SHBE3lmjHMPe811KZ3UsQViEIqe4+yPt+979x8yf+jFazAZ5ssVENja+caLLUjDubFWafQ7Y3VaZicGKxTaOd9SuLWP0VMeg+quoSIptstSy+zK8A15HcsDsNvH2MfZI1ymfj9bbntsRgbVugr/Z6Wcp5f/ZgN0Bkae/V0JitGcst53QjZrntzNRC3vDCS/jRp1dZ/OrneeKmD9M89TjVvYeYf8kPMHXls5i+6jlUDz8Nt5Cb/vrPeN9v/0f2ThlX7K4yVw9pxXDfqRYLzZhKYFTDoDt6A2pBkp5eqwQ4xtlGzHIz2cwjpJAFmZV6yt5HyXsTkY01bIovfyX7FuB57t4ysxuBL4y3W3I+BiYepFUj1nvKysoiVKqskkQz8r57tQ7uCoiOP0g+M8IsoH7gClpL53qOXTx7ml2VJF09rxX1t0F5JYkktbz/s1BldJHJMyxA7Taz15KMsuru3gJwdzcz/XK5xZRdgLv3LW10b3oFQf9A3j0uTWeP4rjkWC89dlA6vG+bPUtEtrdhU3yfBr4TeCVwk5kdhOQGW+D4BvRNzkPZelSmM5XlufUiLmydJSi5wXVQ1YnLd1c5NFuhkv6tW2nHVOYO8Mv/+l/w0q9/IUEQ4O40HrmLM7d/nMYDnydaTkZRVpshvO4l/MqHbuEvbrufRnqDVWhJwdj7TjVZbPYGr4UWLLVya1SWvN+zTafRjnsqSST97f0gFMJENtewNPPXD2h/nGTKTybQahvVlj2UBanzmQa09AXLtqHPXi1fYWKqApfNV7nt0WUePtPGCalW4VXf+g084+pDvOVNP0Tj3EmII6KVRaIHv8j8Da9i/nmvJQgruMPn7n2cu46c4Gde8VymppK/wlEMj5xtsXc6YP9MpdOHdgwLTWfvVDbll/RlqZ1s/TGXVqdIbkDurqsVq2qIyMYblsX3kmFPNLN5M3vG+ndJLlRZwsSqz+H8L8Y9yRi5k+Tbio8/dKbdEyyr1Sr33PIZmmdPELdziekeM/fsl2NhFU/fVSuKObR3F/Vq0PN6DszVwtKKEWXrUbU0qaLbt1wCiEofiWy6YWtQ321m/5mkasStwDFgiiSb75uAK4GfG3sPZcsozxBc21V+LacoHSgOOVbxRmRrGTbF9zNmtpekZt73AIdIaubdBfyOu//txnRRRER2omE76r4IuMnd3wm8c+O6JOvhfPIeisltY8vyc6cSJOtG+X7uuehigrAC7e52Y4EZ7YVT1GvTeND967rUaJeO2FqRUw/7K0aUvYVoQPafiEyGYVl8PwzcamZ/bGY/kmbvyYTLsvTO55KbJVh0vugmUJxvVYV8P7J08BjjG66a4eJdYVqWyIli56qvewWv/fnfYNeei6hUa0ngMOPo+/89i1/+NN5udrb+WAym+cjd5zizEhHFyepUNUiyE9uFfa/c4fRKTCvq3QMqxpKt6QuVJERkMthq/yjN7CnAy4FvB3YDnyRZl/o7d4+GPXccbrjhBr/llls2+mW3jCywlMknQmTHFZMjsr8OfTfaZn+e52DDPUluiAr9u/tYg5uPLHFsKercxNtqrPDOf/EqTjzyQM85pi9/Ok/60V9l70UHqFSrnX5919PmePJFNebqvQkS05WgZw8ogNmaJZsphr0JFvWw/GZfkTEa6S/cNU99lv/yu/5s3H0ZiyH190Z678NGUAC4+93u/uvu/jLgm4G/JVmT6tsrSjbfKMEJerPVRj7HBVy/zegLTgC7agGnV+KeChPV+hR7L7m87xzR6cfYt29fJzhlfT3XjJmfqvRN1RWDEyQjrEohOInIZBpazTyTJktcSpIk8RfabkNERMZt2H1Qu83sF8zsDuAm4HeA/w08aGbvM7Nv2qhOyuiKlSQ66z7p1yjrLIPGFue7RJNVrqiFRiW3F4a7s7se8APP3s2TL+pupfHYmRXqr/h/uPrHf5Pq3kPdXgUVbvqVH+DIZ95PHCWJFLO1gIWm86mvLnJ6pTvjXA36K6sDNNrOEwttVtq9VSdacbI9vNahRCbHsCm+95PspPv17n69u7/E3W9w98tJqpu/2sx+bEN6KSPLZq4M+i623Urjgy/C+croPV8XsPaUvZqZERrUQyOKkqrmldCYrgZ809W7+LZrZ7jp/pP8w4NnoD7HrquezXVvfg97n/cqqnsvwao1osYSD/3Ne7n9bW/k6ukVnnFxHUim+f7uoSXuO9lkpmrUK0Fx68ROlYjY4eRSxKmldqed9LFmDFGxoq2IbIph90G9dMhjt5LcvCsTKNs4sPSxNZwDelPPzydIlVU1d3cahZqv1dC4/ZEFnjjX7G4waAFBdYrlR78CFhCngaPdXOHgpZdxYPcugtyILHa4bL5SmuzQlwwC1CvlVd6LGx6KyNqcz+aEZUZdgzpMUjmic7y737guPZCJtrG5BDZ0C4+eI42eLTq67eUdLm218uOVQCEyGVYNUGb2n4DvBb4MZJP8DihAiYjI2IwygnoNcL27N8bdGZksnZtaN2hEsasWEJrRLgyhgvoM3lwijrpJEM2VZbD+JdSVVkw8FfRN85VVk4jj8j2jVF1CZDKseh8UcD9QXfUomShmdt5rKZ3KCnSTKs630kJp7VczdtdDKj03CDvf8qTd/OQLDzJTDZI9ozwmbjXY+80/Tv3S67Ew/WsYhBw9u8Jf/tXHWV5aIo4jAqASwINnWpxcipI9oLIMxtjT3Xx7y2E02jEr6b5Q+QoTIjIZhtXieyvJL55LwO1m9gmgM4py9zeNv3tyIcyMAO+UKwoGrLnkZYEpH4tiP78tPJI+ZBmFvUtLYWDM1UOWWjGLzZhWDGB8+/V7+bqr5vnnf3wHR06dorl4Fip15l70fdQev5elu26kftnTCGd289CRR/gff/he/ulrXsl1V17KRTNJgsSRc22OL0c8aV+t53WbbadeMWoBTFUs3YY+qXAxPxVSCZLf2DR6EpkMw6b4snpCtwIfKTymPNwtwizJVFtLdBk0ULqQC3fZRopmyX1RhVuSmKuHHAiXuH/xbE977cBVELd7ThRFEedOHuXA067sOXYl3ZCwONVndINTxkn2jAoVmETO23pl7uUNSzN/N4CZvdndfyP/mJm9ed17IiIikjPKGtQPl7T9yDr3Q8Ykuzk3LlQjL2tPKk6Un6d442/++cVzD1JW5SIMjP0zIbWw+0gzcl7/zc/gZ17xHOana+mxMdGZJ4hXFojbrU5fgvoMXzgzzbv+/iFOLjY755iqGKeWY5aacU+/I4dzjZh27o0GwFIzZqkZdY51TyqsR7GqS4hslmFrUK8Dvh+42szyU3xzwIlxd0wuXDHYFNeW8u3xgMhUtvZUVjHd0/8MmyXLpvkMkoQF0qm10Ng3HbDcjHnobJvltnNgfoZ9s1N87TUX818/cCN//9mbOtN7FjUxj5i+8plU91/JuThg4cQyb/vUA7z86Qf4tqdcRCUwYmCp7SxHzr6pgGoaBLMgVQ9hvp5s+eEk5Y7ajZjpatCTYBKnH9woa3gisn6GrUF9FngM2A/8Wq79HPDFcXZKLtx6/dKf7b/Uc+4LOd+AKheBGQutmJV297EwCJiuBdx5+63E7e7oyN0J5y6itv9KCMJO4I3decrFM1SC7hqTk4yQqqH1vY9aGPQFnSQdvT8pXWFJZOMNW4N6kKQw7GuAwyT/dh919yc2qnOy01hp8IujkooRBATmFB8p7vOUKbsPqiz4MqBNRDbesCm+rwHeTrJJ4SNp82Vmdhr45+5+2wb0T0REdqhhU3zvAn7S3Xs2JjSzFwJ/ADx7jP2SbSKbzRtlUNK5Z6rQXq/VCJZWOutWyYnbRCVrXkvNiDj2niKyUD5Fl23HUVZJIumPRlIim2lYFt+uYnACcPebgF3j65Ksh9FurF29QkRWfCFfhGHgflElx3vJeYpVLrLn7ZsOuXg27G5/4cm9TG/60e/jyssupVardp7RPvkoi3f/Hd5uEhB3dgf+n58/zpePLtPMTQtGDqeW476MvKV0zStpy1WYiLwTvIa9NxEZr2EB6mNm9udm9r1m9nXp1/ea2Z8Df7FRHZTzVwxSRlJNIshVdxj+/OSq379lRvccRV7yvRfas3NnQco9rUJsxsHZKtcfqNOKYk4uRzxwqsn8nn28+Sd+kO/6jm+FqEW0dJb20llWHrmHk5/5I6LF01SqVebm5mlS4T2fP8nv33Kc2L2T2t6MnWNLEY0oaasEEATGUts504iBpD+VdJv4Vkxn249hn4WIjM+wJIk3mdnLgVeTJEkYcAT4LW35vnUMGkmttq1FefJA/8+D0taH9Sf/Gkb/vVe10FhuOSeXo55jn/7kawlay0TpbroA3m7gZx5n+tKre85x/8lmMlor/ArWjJzZWu/7y6YKw8K0YNavUT4LEVl/Q6uZu/vHgI9tUF9EREQ6Bk7xmdmzct9XzezfmtlHzOwtZjazMd2TcYndaXsyeigdBfVUjUgqKrQjp1hNYi261dG753Xv/0voDk+7uM4zLq4TWvfY5bjCq//PH+Oyq6/tHmzG0vEjHP/ip4may53m+amQT9y3wFdPNnqqQyw0I46cbdGMejt/eiVioVBJohU5i83eqhNJtQ1VmBDZCMPWoN6V+/5XgOtIbtidJkk/ly0ou/C20hwCJ1n/KSskEaelfmLvriO1Y9I2P+/1mKxyRXcKDULAsrJJwO6pkGv21fn2J80xVwt44HSL062Ag4cv5xXf84O88nt/iKm5PYS79hItn2Ppia9y5Mb30Xj8Xp58YJprL5rm1ErMLY+u8Ff3LnB2JWK57TTasNRyHjjV5Phit2RSO4aFZrpO1XaaURK8I4fFQhmkzufj5Tcdi8j6GDbFl59l/xbgee7eMrMbgS+Mt1syLpHTd3MrpP+zy+5mHYPiRT2rtp5vNrOkDFJg3HWs0VNholKtElRrxGENoigtRRQDMU86fIC5WthZJGrHsNx2gvRXsXziRnHNCbpBp/Rm3zW2i8iFGRagdpvZa0lGWXV3bwG4u5uZfm3cjtZ0jV3/C/KgxI12yfAuimIqlZAot8suQK1W68tgCC0JPMV4FFh/+aOkH2tLilBwEhmPYQHq08B3pt/fZGYH3f0JM7sEOD7+romIyE42LM389QPaHyeZ8pMdrDittR7VFwYt5yRTcb0PhmFIVFKjr9VqZXcDd9piLx/vxelNymWVJPrbhvX7/D+LQZU21lKBQ2S7GmU/qA4ze8e4OiIbI7Ty/+lruRBmM275jLfkzywzr+zW3N7ndH/u/hmQDyTdKhevfeoce6cCqrmOX3rZ5Tzzuc8jrFQIg7RArAV84Ytf4PS5c52pPwOWW84dT6zQKmQhnliKWGjGPSWU3J3Tjf6qE604+cr6lFWTyGLksM9iUCJF5713ju+vUpH/fER2mjUFKOCGsfRCNoyZUQ2NapBWlgBqQTJKKa7RlFWMyKpRZLKLdZZ9Fw+5oA67UHt68jDN6Ms2QmxFzsHZKq9/7l5eeNk0sXuSZRcHPOtrX8hrvu+HmbvoYoLpeaYueyqNyiyfveV2vvDle7qbGho8crbNJ+9f4ORS1ImdsRuPnYt45Ey7k0bfjKDRTrL5ltJUxyBN1mjFsNzur5DRjrvJFXHJZzHsPefPRe77ni8FJ9mh1hqgjo6lF7LhAjNqofXsk1SskZfJAlVW7y4vuxjnnc/9UcXXy1Lc8/19+sFporj3wj87N8/Vz3w+tYPXYJVap/3xY8cJcMKg2+dWDA+fbaebJnbfyHLbWW45hVujWInSShyF/aKiuL/PcW401fPeBnwWZc2Dx52a6pOdaU0Byt1fNq6OiIiI5K0aoMzsyWb2TjP7KzP7m+xrIzonG698aq475VZcH4kpTE+lN7e2494pvdi9U4A1v66S3Qzbe17vjNp6OwLfft0sB2bCnmN3793HdVdfRRB0/zpX6tN85fgyp5fbPadYbkbcfbzRU+28HTv3n2xydKF74667027HnFhs08oNrdydlbbTaMcl788L61np2lVUXpmj2DZszarvcy/5/yGy3QytxZd6H0nliHeSFp2W7SdbP+ltK5m+oxuYetrSb/LtzRiC9AxZe3ajcOiQDx0RdObunGRKz3ECS9ahllrJ7rnX7qtx1d4adx9b4eP3LnK6ETM3N8/srlkuPXQJd33lPlrhNNVdezi9EnG2scTuqZDr9s8wWwtoRHB0oc2xxTbX7qsxXw9YbCWve6YR8cRCm+v21alVjBhot52Vcy1m6wFztbDzeURRUnh2qtq7h1UrTvYFDg2i3DRiFEHFvOfYOP0wy6ZVPavEXpJN2Lde5dnuwP3nEdko7735Ib7/BVes6zlHCVBtd//v6/qqMnGGLej3HVvSVhbMBh5Lb3DKt+dlF+fFVtxTWbxiMF0JONeKu88JAqpBwOzFlydlidLgEDusNGN21ZJMv3wwPbMS9VSTiB0WW06lO0Dr9CuKktFRcT0qdicoBhHSrTpK1uvKg1F5pfiyoKMBk+wkw4rF7jOzfcCfmtk/N7NDWVvaLrIhyqaxWg6VAUMGL0QGC6z0HGWljoC+gJOcpPzYcQ5aVKFCdrphI6hboac627/KPebANePqlIiIyLBKElcDmNmUu6/kHzOzqdVObGa/D7wSOOruzyh53IDfAL4DWAJ+xN1vW1v3ZfJlQ5f1HQ0EQLRKMkG+B2WDkXhAhsFair+WzOQNby+Zzht4bhWhlR1ulDWozwLPHaGt6F3A24D3DHj85cCT0q8XAP89/VM20PlkgQ3ajDcgW3NKLs/5bDRjlSvzkJt4Z6oBS7n1Jnfnyj1Vnnlwii8+sdK5L8mA6/ZPc9+JZRrtmHac3PjbjJxjC20OzFY603dGso3GfN07U31Z7x5faHNwtpLu+JtYacVMVYykUHr3fbQjqIZJz8y679kxAjx9y+l6GMlNyKMEqeyYtQW0tT9H5HysdzLEIAMDVFoU9jAwbWbPofvvdx5YdcNCd7/RzK4acsirgfd48i/6JjPbY2aH3P2xUTsvFyZfaid/cc0kqd7Wk+GXVZLIp5gbSSDILo4t768akYYssltks2MdH5janiQhQCU05oKA5VZMM93LKsb49ifN8ZxD0/zJl8+w3HL2TodUQ+Pw7hoPnlrhnqPLzE5VmJ+qcq7lLJ9pcXiuynTV2DMVUq8kGXyVwNlVDZiuGnP1kLEMFgAAAA/5SURBVCitMLFvOmC6GiQ3+wLnmjGVAObrIUG6HYhZklIfWJKxmKV/Q5J1GIZJoMoiRpR9XtlnTHkwyRI6Oh9e5//H4OzK3m9Etr5hI6hvB34EuIxko8Lsn9FZ4BfW4bUPAw/nfj6StilAbZBBWXNFZtZJF++2dS+yfe1enqVXvBhn22uUZv8V2s2MeiXgbLP3ToeLZyu88PIZ7jza6Dn2qn3TLLas5xztGFbayeirZxQUw0UzIdWwmzPkJBsYztZ7kyaSskZOLez9rLqBiZ5zxLETFJIxsqA+aJSz2nYfwz634rEiW9mwNah3A+82s+929z8Zw2sPmrrvP9DsDcAbAK64YmOGliIimyV/zdt/yeFN7s3mWbWSRD44rXMFiSPA5bmfLwMeHdCHd7j7De5+w4EDB9axCzvboF+0L+QX8Gy6ruwvVrHwLAweBWS1/4pt+6YCKoWTXzZf4ZkH6z3nnqoYL7p8motmesd5h+Yq7JkKeo6tBDBVCSgMiqiF1tdfA6pBeXtZ2vrgUVJ5O5RPj5ZNgw46hapLbH35a97cnp17V8+wNagvFpuAJ2ft7v6sC3ztjwA/ZWZ/TJIccUbrTxsvu8gVs84GJUIMk62NmCUBKsm0S6uB506e3cjaye+z7kW1M/Vl3XO24ySIhGZMV4y5esCZlZiFZsxU1Zir1bhyT41nXzLFJ+5bpBrCodkKZnD9gTr3n2zy5WMNXnjZNHP1kNBgrg4nlyN2VQMOz1c7wTCroH7RrpCpSu+Ou1MVY+9U2Akulh6frwQfhJZM98VOJXvTuXOEhc9i8GfZTbzIPrOyihH5/0+a2ZPtZtga1AMk602/DCyT/P3/DPCqUU5sZn8EfCOw38yOAP8OqAK4+9uBj5KkmN9Lkmb++vN5A3L+LBcErNCWtXePtVx7eXp3PmcvOzwr79Pz/Lj32M7rpjX48scaTj3s7YMBu2pGnBunVSxJXnjmJXXOrkSdYyuWlEe6YnfvulNocMXuKtPVoGeNKTS4eDbsu1l3umLsme5vz/aoyp87wAnC/u3kK5ZVhB/+Wa6mL0svl0QhMshGZd6tp2FrUN9pZq8F3gH8qrt/xMxa7v7gKCd299et8rgDb1xTb2UsBk5DceFJYZYLVj2vV3Zi6z+WXFAapXdR3H/vUDHoZZKRT397aVvJtF73/MXXG5RssrbqEMPOM+xnke1i6BqUu3+Q5H6lbzSzjwC1YceLiIisl1Vv1HX3ReBnzezZwIvG3yWRDbKW4eGmJB4MqkchsjMMHUGZ2TeY2fXpj3PArJm9Yvzdku0iW9zvXc8q/4uXy43oay+qBlAruRHr8t1VqkE3A9BI1pV213un6LIswUrQnxxS9vrtOC6/Pwkoi15l54nSPbLOZ90powQ92UmGZfH9N+D5QMXM/hL4FuBjwM+Y2Te6+78a9FzZHrItH/IJEEayHlPcP6qs6kSmLMusGibHttKEiUoaLCDJjGuniRuVoHverKRRYEkf9lUqNNoxZxrJA9O1gN1TIRfvqvLg6SYPn2mxZyrg0rkqYWAstWK+eqpJM3Ku2Vtj73QS4c6sRJxYipmpGofmqlRDI4qdxWZEK4aLZgJ2p5kasSf7XBlJgEz61t3nKkj7bGadTRqhmyBR/FSCdN0t/7l1j+u90biYwSey3Q2b4nsp8AxgGngEOOzuS2b2K8Dn6a1uLttYFqh625JSP8VglNSvKy9flD2e/74W9l94Q+uvUmFmffs0AdQrAXvMyG16SxgY1+yrs2eq9wkz1YBnXDzVV9lhz3SF/TO9fQsDY+90hdlabzZeaDBdGP6ZJQGo+P4CM2pB+QdRzGzMPs+y40SKtmJG3vkYNsXnaaZdtudc9i8t+0VRRERkbIaNoP7czD4DTAG/C/xvM7sJ+D+AGzeic7I1Wfqf4ihqXKOBbEAUFV5vVy1gpRX3tFdDIwyMZuHgesWI4t5zZMVgiwZmyQ9p09qRyNoNuw/q/zazFyXf+k1mdi3wWpJg9f6N6qBMtuIFuGe6zpLEAHqqJ+Trmq+PrDpD6NCKu1NutVrAbC1gsRmz1IqZrQVU045MVZI1qcBgrtYtfdSMoNF2pqtGvdLtYXF9yHLtxSnQnjW33PFxoRKE9nsSGW5YkoS5+99nP7v7fcCvlhyjXw53MCsZIvSu5Xhf26DKFeffh+QkAU4tzCotdG/w3VUNOsEmaw8N5mtBX2WHWuhMp+UhitUh+t+HdwNWTwmO/r4NPFZEBho2xfdJM/sT4MPu/lDWaGY14CXADwOfJNmYUHa4YRvllVV2GMemetmeVqWVJMpqUZRUrkjayo/t3y9r8OsV39+gY0VGsVOSIoqGBaiXAT8K/JGZXQ2cJsnoC4C/An7d3W8ffxdlqxglOA07dp16Ma4TDwxc5ceO9nwRGWzYGtQK8NvAb5tZFdgPLLv76Y3qnIiI7FyrljoCcPcW2ulWtrCyzLtBi6exe2nBWBHZWCMFKJGtIrDi1h/JDbNhQE9lh/y+TPmbjYtJEyKyeRSgZNvJl2jKx5p8ZYdidYjiBoEisvkUoGTbWlvShgKTTKZ9u3buLkcqWSQiIhNJAUpERCaSApSIiEwkBSgREZlIClAiIjKRFKBERGQiKUCJiMhEUoASEZGJpAAlIiITSQFKREQmkgKUiIhMJAUoERGZSApQIiIykRSgRERkIilAiYjIRFKAEhGRiaQAJSIiE0kBSkREJpIClIiITCQFKBERmUgKUCIiMpEUoEREZCIpQImIyERSgBIRkYmkACUiIhNJAUpERCaSApSIiEwkBSgREZlIClAiIjKRFKBERGQiKUCJiMhEUoASEZGJpAAlIiITSQFKREQmkgKUiIhMJAUoERGZSGMNUGb2MjO7x8zuNbOfL3n8R8zsmJndnn79+Dj7IyIiW0dlXCc2sxD4LeClwBHgc2b2EXf/cuHQ/+XuPzWufoiIyNY0zhHU84F73f1+d28Cfwy8eoyvJyIi28g4A9Rh4OHcz0fStqLvNrMvmtn7zezyshOZ2RvM7BYzu+XYsWPj6KuIyMTQNS8xzgBlJW1e+PlPgavc/VnAXwPv/v/bu/9YreoCjuPvTyDTUOYfkmNeF7Zc/dEPrt65GUtdWdFCqq0WDdtottrMojlzkZutWn+Uq/VHW5OAoiJMUTbWSG0l2Y9Z/FAiRJtTkBs5VNaKYhj06Y9z2J4xeJ7nEg/ne57n89rueM655zzPh7t77+c533Pu95zsiWyvsD1he2L27NlnOGZERFnyO68yyIKaBDqPiMaA/Z0b2H7Z9pF68XvAlQPMExERLTLIgtoCXC7pMkkzgMXAxs4NJM3pWFwE7B5gnoiIaJGBXcVn+6ikW4CHgGnAatu7JH0F2Gp7I/BZSYuAo8BBYOmg8kRERLsMrKAAbG8CNp2w7s6Ox8uB5YPMEBER7ZSZJCIiokgpqIiIKFIKKiIiipSCioiIIqWgIiKiSCmoiIgoUgoqIiKKlIKKiIgipaAiIqJIKaiIiChSCioiIoqUgoqIiCKloCIiokgpqIiIKFIKKiIiipSCioiIIqWgIiKiSCmoiIgoUgoqIiKKJNtNZ5gSSS8Ce8/gU14EvHQGn+9sSOazo22Z25YXRjvzS7YX9NpI0oP9bDeMWldQZ5qkrbYnms4xFcl8drQtc9vyQjJHdxnii4iIIqWgIiKiSCkoWNF0gNOQzGdH2zK3LS8kc3Qx8uegIiKiTDmCioiIIqWgIiKiSCNdUJIWSHpa0jOSvtB0nl4krZZ0QNKfm87SD0mXSnpE0m5JuyQtazpTL5LOlfRHSTvqzF9uOlO/JE2T9LiknzWdpR+S9kjaKekJSVubztOLpAslrZf0VP09fXXTmYbdyJ6DkjQN+AvwLmAS2AJ81PaTjQbrQtI1wCHgh7bf1HSeXiTNAebY3i7pAmAb8IHCv8YCZto+JOkc4LfAMtuPNRytJ0m3AhPALNsLm87Ti6Q9wITtVvyhrqQ1wG9sr5Q0A3i17b83nWuYjfIR1FXAM7aftf0KcA/w/oYzdWX7UeBg0zn6ZftvtrfXj/8J7AYuaTZVd64cqhfPqT+KfxcnaQx4H7Cy6SzDSNIs4BpgFYDtV1JOgzfKBXUJsK9jeZLCf3m2maS5wDjwh2aT9FYPlT0BHAB+Ybv4zMC3gduB/zYdZAoMPCxpm6RPNh2mh9cBLwLfr4dRV0qa2XSoYTfKBaWTrCv+nXIbSTofuB/4nO1/NJ2nF9vHbM8DxoCrJBU9nCppIXDA9rams0zRfNtXAO8FPl0PYZdqOnAF8F3b48C/gOLPW7fdKBfUJHBpx/IYsL+hLEOrPo9zP7DW9gNN55mKeghnM1D6RJ3zgUX1OZ17gHdI+nGzkXqzvb/+9wCwgWrYvVSTwGTH0fR6qsKKARrlgtoCXC7psvqE52JgY8OZhkp9wcEqYLftbzWdpx+SZku6sH58HnA98FSzqbqzvdz2mO25VN/Hv7J9Y8OxupI0s75whnqo7N1AsVen2n4B2CfpDfWqdwLFXuwzLKY3HaApto9KugV4CJgGrLa9q+FYXUlaB1wHXCRpEviS7VXNpupqPvAxYGd9Tgfgi7Y3NZiplznAmvoqz1cB99puxWXbLXMxsKF6D8N04Ce2H2w2Uk+fAdbWb2ifBT7ecJ6hN7KXmUdERNlGeYgvIiIKloKKiIgipaAiIqJIKaiIiChSCioiIoqUgoqIiCKloKJVJB2rb89w/GPuKba7TpIl3dSxbrxed1u9/ANJH6ofb65vvbJD0u+O/0GmpIX13Gs7JD0p6VNdst1ab/MnSb+U9NoTPj9L0l8lfef//0pEDL8UVLTNYdvzOj72dNl2J/CRjuXFwI4u2y+x/VZgDXBXPU3TCuCGev041dRHp/I41e0j3kI1Fc43Tvj8V4Ffd9k/IjqkoGKYPQ+cK+nietqlBcDP+9jvUeD1wAVUsxy8DGD7iO2nT7WT7Uds/7tefIxqfkcAJF1JNXvCw6fzH4kYRSmoaJvzOob3NvSx/Xrgw8DbgO3AkT72uQHYafsg1fyMeyWtk7REUr8/MzdRl2G9zzeBz/e5b0QwwnPxRWsdrm+F0a97gZ8CbwTWURXVqayVdBjYQzXvGrY/IenNVJPG3kZ1B+al3V5Q0o1Ud7a9tl51M7DJ9r567rmI6EMKKoaa7Rck/YeqWJbRvaCW2N56kufYSTXh7Y+A5+hSUJKuB+4ArrV9/GjtauDtkm4GzgdmSDpkO/cTiugiBRWj4E7gNbaPTeUIpr7R4oTtzfWqecDeLtuPA3cDC+p7HAFge0nHNkvr50w5RfSQgoqhZ/v3p7mrgNsl3Q0cprqL6tIu299FdYR0X12Ez9tedJqvHTHycruNiIgoUq7ii4iIImWIL1pN0nuAr5+w+jnbHxzga95Bdel6p/tsf21QrxkxijLEFxERRcoQX0REFCkFFRERRUpBRUREkVJQERFRpP8BEZSGLHx+HZcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "skew=(NEP_cat['FErr_MIPS_24_u']-NEP_cat['F_MIPS_24'])/(NEP_cat['F_MIPS_24']-NEP_cat['FErr_MIPS_24_l'])\n",
    "skew.name='(84th-50th)/(50th-16th) percentile'\n",
    "use = skew < 5\n",
    "g=sns.jointplot(x=np.log10(NEP_cat['F_MIPS_24'][use]),y=skew[use], kind='hex')\n",
    "print(np.max(skew[use]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both seem to have flux pdfs that become Gaussian at ~30$\\mathrm{\\mu Jy}$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add flag to catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.table import Column\n",
    "NEP_cat.add_column(Column(np.zeros(len(NEP_cat), dtype=bool),name='flag_mips_24'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ind_NEP=(NEP_cat['Pval_res_24']>0.5) | (NEP_cat['F_MIPS_24'] < 30.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NEP_cat['flag_mips_24'][ind_NEP]=True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'degrees' did not parse as fits unit: At col 0, Unit 'degrees' not supported by the FITS standard.  [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'muJy' did not parse as fits unit: At col 0, Unit 'muJy' not supported by the FITS standard. Did you mean MJy, mJy or uJy? [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "NEP_cat.write('./data/output/dmu26_XID+MIPS_AKARI-NEP_cat_20190227.fits', format='fits',overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Map\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "MIPS_pval=fits.open('./data/output/Pval/dmu26_XID+MIPS_AKARI-NEP_Bayes_Pval1.fits')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(MIPS_pval[1].data[np.isfinite(MIPS_pval[1].data)],bins=np.arange(-6,6,0.05));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (herschelhelp_internal)",
   "language": "python",
   "name": "helpint"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}