{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# AKARI-NEP Selection Functions\n",
    "## Depth maps and selection functions\n",
    "\n",
    "The simplest selection function available is the field MOC which specifies the area for which there is Herschel data. Each pristine catalogue also has a MOC defining the area for which that data is available.\n",
    "\n",
    "The next stage is to provide mean flux standard deviations which act as a proxy for the catalogue's 5$\\sigma$ depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2018-06-24 15:18:44.737290\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n",
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table, join\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "import healpy as hp\n",
    "#import pandas as pd #Astropy has group_by function so apandas isn't required.\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "#We ignore warnings - this is a little dangerous but a huge number of warnings are generated by empty cells later\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from herschelhelp_internal.utils import inMoc, coords_to_hpidx, flux_to_mag\n",
    "from herschelhelp_internal.masterlist import find_last_ml_suffix, nb_ccplots\n",
    "\n",
    "from astropy.io.votable import parse_single_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "FIELD = 'AKARI-NEP'\n",
    "#FILTERS_DIR = \"/Users/rs548/GitHub/herschelhelp_python/database_builder/filters/\"\n",
    "FILTERS_DIR = \"/opt/herschelhelp_python/database_builder/filters/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth maps produced using: master_catalogue_akari-nep_20180215.fits\n"
     ]
    }
   ],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = find_last_ml_suffix()\n",
    "#SUFFIX = \"20171016\"\n",
    "\n",
    "master_catalogue_filename = \"master_catalogue_{}_{}.fits\".format(FIELD.lower(), SUFFIX)\n",
    "master_catalogue = Table.read(\"{}/{}\".format(OUT_DIR, master_catalogue_filename))\n",
    "\n",
    "print(\"Depth maps produced using: {}\".format(master_catalogue_filename))\n",
    "\n",
    "ORDER = 10\n",
    "#TODO write code to decide on appropriate order\n",
    "\n",
    "field_moc = MOC(filename=\"../../dmu2/dmu2_field_coverages/{}_MOC.fits\".format(FIELD))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove sources whose signal to noise ratio is less than five as these will have been selected using forced \n",
    "# photometry and so the errors will not refelct the RMS of the map \n",
    "for n,col in enumerate(master_catalogue.colnames):\n",
    "    if col.startswith(\"f_\"):\n",
    "        err_col = \"ferr{}\".format(col[1:])\n",
    "        errs = master_catalogue[err_col]\n",
    "        fluxes = master_catalogue[col]\n",
    "        mask = fluxes/errs < 5.0\n",
    "        master_catalogue[col][mask] = np.nan\n",
    "        master_catalogue[err_col][mask] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## I - Group masterlist objects by healpix cell and calculate depths\n",
    "We add a column to the masterlist catalogue for the target order healpix cell <i>per object</i>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Add a column to the catalogue with the order=ORDER hp_idx\n",
    "master_catalogue.add_column(Column(data=coords_to_hpidx(master_catalogue['ra'],\n",
    "                                                       master_catalogue['dec'],\n",
    "                                                       ORDER), \n",
    "                                   name=\"hp_idx_O_{}\".format(str(ORDER))\n",
    "                                  )\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Convert catalogue to pandas and group by the order=ORDER pixel\n",
    "\n",
    "group = master_catalogue.group_by([\"hp_idx_O_{}\".format(str(ORDER))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Downgrade the groups from order=ORDER to order=13 and then fill out the appropriate cells\n",
    "#hp.pixelfunc.ud_grade([2599293, 2599294], nside_out=hp.order2nside(13))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## II Create a table of all Order=13 healpix cells in the field and populate it\n",
    "We create a table with every order=13 healpix cell in the field MOC. We then calculate the healpix cell at lower order that the order=13 cell is in. We then fill in the depth at every order=13 cell as calculated for the lower order cell that that the order=13 cell is inside."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "depths = Table()\n",
    "depths['hp_idx_O_13'] = list(field_moc.flattened(13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table139795555414480-645769\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>hp_idx_O_13</th></tr></thead>\n",
       "<tr><td>0</td><td>190280724</td></tr>\n",
       "<tr><td>1</td><td>167640605</td></tr>\n",
       "<tr><td>2</td><td>190280725</td></tr>\n",
       "<tr><td>3</td><td>190280726</td></tr>\n",
       "<tr><td>4</td><td>167640564</td></tr>\n",
       "<tr><td>5</td><td>190280727</td></tr>\n",
       "<tr><td>6</td><td>167640606</td></tr>\n",
       "<tr><td>7</td><td>190280732</td></tr>\n",
       "<tr><td>8</td><td>190280733</td></tr>\n",
       "<tr><td>9</td><td>190280734</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139795555414480-645769').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
       "});\n",
       "\n",
       "    $('#table139795555414480-645769').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depths[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "depths.add_column(Column(data=hp.pixelfunc.ang2pix(2**ORDER,\n",
    "                     hp.pixelfunc.pix2ang(2**13, depths['hp_idx_O_13'], nest=True)[0],\n",
    "                     hp.pixelfunc.pix2ang(2**13, depths['hp_idx_O_13'], nest=True)[1],\n",
    "                     nest = True),\n",
    "                     name=\"hp_idx_O_{}\".format(str(ORDER))\n",
    "                        )\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table139795555389848-857919\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>hp_idx_O_13</th><th>hp_idx_O_10</th></tr></thead>\n",
       "<tr><td>0</td><td>190280724</td><td>2973136</td></tr>\n",
       "<tr><td>1</td><td>167640605</td><td>2619384</td></tr>\n",
       "<tr><td>2</td><td>190280725</td><td>2973136</td></tr>\n",
       "<tr><td>3</td><td>190280726</td><td>2973136</td></tr>\n",
       "<tr><td>4</td><td>167640564</td><td>2619383</td></tr>\n",
       "<tr><td>5</td><td>190280727</td><td>2973136</td></tr>\n",
       "<tr><td>6</td><td>167640606</td><td>2619384</td></tr>\n",
       "<tr><td>7</td><td>190280732</td><td>2973136</td></tr>\n",
       "<tr><td>8</td><td>190280733</td><td>2973136</td></tr>\n",
       "<tr><td>9</td><td>190280734</td><td>2973136</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139795555389848-857919').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
       "});\n",
       "\n",
       "    $('#table139795555389848-857919').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depths[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table139797229886712-876030\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>hp_idx_O_13</th><th>hp_idx_O_10</th><th>ferr_ap_gpc1_g_mean</th><th>f_ap_gpc1_g_p90</th><th>ferr_gpc1_g_mean</th><th>f_gpc1_g_p90</th><th>ferr_ap_gpc1_r_mean</th><th>f_ap_gpc1_r_p90</th><th>ferr_gpc1_r_mean</th><th>f_gpc1_r_p90</th><th>ferr_ap_gpc1_i_mean</th><th>f_ap_gpc1_i_p90</th><th>ferr_gpc1_i_mean</th><th>f_gpc1_i_p90</th><th>ferr_ap_gpc1_z_mean</th><th>f_ap_gpc1_z_p90</th><th>ferr_gpc1_z_mean</th><th>f_gpc1_z_p90</th><th>ferr_ap_gpc1_y_mean</th><th>f_ap_gpc1_y_p90</th><th>ferr_gpc1_y_mean</th><th>f_gpc1_y_p90</th><th>ferr_ap_irac_i1_mean</th><th>f_ap_irac_i1_p90</th><th>ferr_irac_i1_mean</th><th>f_irac_i1_p90</th><th>ferr_ap_irac_i2_mean</th><th>f_ap_irac_i2_p90</th><th>ferr_irac_i2_mean</th><th>f_irac_i2_p90</th><th>ferr_megacam_u_mean</th><th>f_megacam_u_p90</th><th>ferr_megacam_g_mean</th><th>f_megacam_g_p90</th><th>ferr_megacam_r_mean</th><th>f_megacam_r_p90</th><th>ferr_megacam_i_mean</th><th>f_megacam_i_p90</th><th>ferr_megacam_z_mean</th><th>f_megacam_z_p90</th><th>ferr_wircam_j_mean</th><th>f_wircam_j_p90</th><th>ferr_wircam_ks_mean</th><th>f_wircam_ks_p90</th><th>ferr_wircam_y_mean</th><th>f_wircam_y_p90</th></tr></thead>\n",
       "<tr><td>0</td><td>167639484</td><td>2619366</td><td>7.346057284648794</td><td>2679.130378442577</td><td>23.11294016696532</td><td>3921.5895364974053</td><td>170.00000481747838</td><td>4625.116550817112</td><td>197.8952908041597</td><td>6850.630952367077</td><td>328.2075088911762</td><td>7972.906097390461</td><td>292.1664082988967</td><td>7179.767287143879</td><td>2.0559352373508157</td><td>196.89616005697417</td><td>3.428294530228441</td><td>209.19527793446213</td><td>1674.6003781995441</td><td>28034.94725995064</td><td>131.31104490536828</td><td>10791.643610931656</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>1</td><td>167639487</td><td>2619366</td><td>7.346057284648794</td><td>2679.130378442577</td><td>23.11294016696532</td><td>3921.5895364974053</td><td>170.00000481747838</td><td>4625.116550817112</td><td>197.8952908041597</td><td>6850.630952367077</td><td>328.2075088911762</td><td>7972.906097390461</td><td>292.1664082988967</td><td>7179.767287143879</td><td>2.0559352373508157</td><td>196.89616005697417</td><td>3.428294530228441</td><td>209.19527793446213</td><td>1674.6003781995441</td><td>28034.94725995064</td><td>131.31104490536828</td><td>10791.643610931656</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>2</td><td>167639486</td><td>2619366</td><td>7.346057284648794</td><td>2679.130378442577</td><td>23.11294016696532</td><td>3921.5895364974053</td><td>170.00000481747838</td><td>4625.116550817112</td><td>197.8952908041597</td><td>6850.630952367077</td><td>328.2075088911762</td><td>7972.906097390461</td><td>292.1664082988967</td><td>7179.767287143879</td><td>2.0559352373508157</td><td>196.89616005697417</td><td>3.428294530228441</td><td>209.19527793446213</td><td>1674.6003781995441</td><td>28034.94725995064</td><td>131.31104490536828</td><td>10791.643610931656</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>3</td><td>167639485</td><td>2619366</td><td>7.346057284648794</td><td>2679.130378442577</td><td>23.11294016696532</td><td>3921.5895364974053</td><td>170.00000481747838</td><td>4625.116550817112</td><td>197.8952908041597</td><td>6850.630952367077</td><td>328.2075088911762</td><td>7972.906097390461</td><td>292.1664082988967</td><td>7179.767287143879</td><td>2.0559352373508157</td><td>196.89616005697417</td><td>3.428294530228441</td><td>209.19527793446213</td><td>1674.6003781995441</td><td>28034.94725995064</td><td>131.31104490536828</td><td>10791.643610931656</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>4</td><td>167639482</td><td>2619366</td><td>7.346057284648794</td><td>2679.130378442577</td><td>23.11294016696532</td><td>3921.5895364974053</td><td>170.00000481747838</td><td>4625.116550817112</td><td>197.8952908041597</td><td>6850.630952367077</td><td>328.2075088911762</td><td>7972.906097390461</td><td>292.1664082988967</td><td>7179.767287143879</td><td>2.0559352373508157</td><td>196.89616005697417</td><td>3.428294530228441</td><td>209.19527793446213</td><td>1674.6003781995441</td><td>28034.94725995064</td><td>131.31104490536828</td><td>10791.643610931656</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>5</td><td>167639481</td><td>2619366</td><td>7.346057284648794</td><td>2679.130378442577</td><td>23.11294016696532</td><td>3921.5895364974053</td><td>170.00000481747838</td><td>4625.116550817112</td><td>197.8952908041597</td><td>6850.630952367077</td><td>328.2075088911762</td><td>7972.906097390461</td><td>292.1664082988967</td><td>7179.767287143879</td><td>2.0559352373508157</td><td>196.89616005697417</td><td>3.428294530228441</td><td>209.19527793446213</td><td>1674.6003781995441</td><td>28034.94725995064</td><td>131.31104490536828</td><td>10791.643610931656</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>6</td><td>167639480</td><td>2619366</td><td>7.346057284648794</td><td>2679.130378442577</td><td>23.11294016696532</td><td>3921.5895364974053</td><td>170.00000481747838</td><td>4625.116550817112</td><td>197.8952908041597</td><td>6850.630952367077</td><td>328.2075088911762</td><td>7972.906097390461</td><td>292.1664082988967</td><td>7179.767287143879</td><td>2.0559352373508157</td><td>196.89616005697417</td><td>3.428294530228441</td><td>209.19527793446213</td><td>1674.6003781995441</td><td>28034.94725995064</td><td>131.31104490536828</td><td>10791.643610931656</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>7</td><td>167639483</td><td>2619366</td><td>7.346057284648794</td><td>2679.130378442577</td><td>23.11294016696532</td><td>3921.5895364974053</td><td>170.00000481747838</td><td>4625.116550817112</td><td>197.8952908041597</td><td>6850.630952367077</td><td>328.2075088911762</td><td>7972.906097390461</td><td>292.1664082988967</td><td>7179.767287143879</td><td>2.0559352373508157</td><td>196.89616005697417</td><td>3.428294530228441</td><td>209.19527793446213</td><td>1674.6003781995441</td><td>28034.94725995064</td><td>131.31104490536828</td><td>10791.643610931656</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>8</td><td>167639545</td><td>2619367</td><td>1.342477536775247</td><td>1028.2002545401745</td><td>1.6694622176892937</td><td>190.02045514791604</td><td>1.6050348984643852</td><td>234.0431190101886</td><td>1.198555425191257</td><td>170.35396610538805</td><td>3.881882230197803</td><td>306.96592445416536</td><td>3.262388995482855</td><td>269.33890891161263</td><td>4.32279245255675</td><td>431.3477990903934</td><td>3.4622616712466376</td><td>389.45149660120035</td><td>7.094694185642594</td><td>494.11113099154306</td><td>7.053290983212952</td><td>451.10818964651907</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>9</td><td>167639547</td><td>2619367</td><td>1.342477536775247</td><td>1028.2002545401745</td><td>1.6694622176892937</td><td>190.02045514791604</td><td>1.6050348984643852</td><td>234.0431190101886</td><td>1.198555425191257</td><td>170.35396610538805</td><td>3.881882230197803</td><td>306.96592445416536</td><td>3.262388995482855</td><td>269.33890891161263</td><td>4.32279245255675</td><td>431.3477990903934</td><td>3.4622616712466376</td><td>389.45149660120035</td><td>7.094694185642594</td><td>494.11113099154306</td><td>7.053290983212952</td><td>451.10818964651907</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139797229886712-876030').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
       "});\n",
       "\n",
       "    $('#table139797229886712-876030').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if col.startswith(\"f_\"):\n",
    "        errcol = \"ferr{}\".format(col[1:])\n",
    "        depths = join(depths, \n",
    "                      group[\"hp_idx_O_{}\".format(str(ORDER)), errcol].groups.aggregate(np.nanmean),\n",
    "                     join_type='left')\n",
    "        depths[errcol].name = errcol + \"_mean\"\n",
    "        depths = join(depths, \n",
    "                      group[\"hp_idx_O_{}\".format(str(ORDER)), col].groups.aggregate(lambda x: np.nanpercentile(x, 90.)),\n",
    "                     join_type='left')\n",
    "        depths[col].name = col + \"_p90\"\n",
    "\n",
    "depths[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## III - Save the depth map table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "depths.write(\"{}/depths_{}_{}.fits\".format(OUT_DIR, FIELD.lower(), SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IV - Overview plots\n",
    "\n",
    "### IV.a - Filters\n",
    "First we simply plot all the filters available on this field to give an overview of coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gpc1_g',\n",
       " 'gpc1_i',\n",
       " 'gpc1_r',\n",
       " 'gpc1_y',\n",
       " 'gpc1_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'megacam_g',\n",
       " 'megacam_i',\n",
       " 'megacam_r',\n",
       " 'megacam_u',\n",
       " 'megacam_z',\n",
       " 'wircam_j',\n",
       " 'wircam_ks',\n",
       " 'wircam_y'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_bands = [column[2:] for column in master_catalogue.colnames \n",
    "             if (column.startswith('f_') & ~column.startswith('f_ap_'))]\n",
    "ap_bands = [column[5:] for column in master_catalogue.colnames \n",
    "            if column.startswith('f_ap_') ]\n",
    "bands = set(tot_bands) | set(ap_bands)\n",
    "bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Passbands on AKARI-NEP')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for b in bands:\n",
    "    plt.plot(Table(data = parse_single_table(FILTERS_DIR + b + '.xml').array.data)['Wavelength']\n",
    "            ,Table(data = parse_single_table(FILTERS_DIR + b + '.xml').array.data)['Transmission']\n",
    "            , label=b)\n",
    "plt.xlabel('Wavelength ($\\AA$)')\n",
    "plt.ylabel('Transmission')\n",
    "plt.xscale('log')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Passbands on {}'.format(FIELD))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### IV.a - Depth overview\n",
    "Then we plot the mean depths available across the area a given band is available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gpc1_g: mean flux error: 6.44914900044236, 3sigma in AB mag (Aperture): 20.683440835835633\n",
      "gpc1_r: mean flux error: 8.558501233065563, 3sigma in AB mag (Aperture): 20.376202569264784\n",
      "gpc1_i: mean flux error: 5.953007383970741, 3sigma in AB mag (Aperture): 20.7703558100989\n",
      "gpc1_z: mean flux error: 12.075842961189762, 3sigma in AB mag (Aperture): 20.00240322155394\n",
      "gpc1_y: mean flux error: 41.63594831625647, 3sigma in AB mag (Aperture): 18.65852571129107\n",
      "irac_i1: mean flux error: 1.7354671955108643, 3sigma in AB mag (Aperture): 22.108655841004598\n",
      "irac_i2: mean flux error: 1.4195250272750854, 3sigma in AB mag (Aperture): 22.326839228545886\n",
      "gpc1_g: mean flux error: 8.63034778102784, 3sigma in AB mag (Total): 20.367126120628335\n",
      "gpc1_r: mean flux error: 9.143008063255937, 3sigma in AB mag (Total): 20.30447410626551\n",
      "gpc1_i: mean flux error: 5.786378609486695, 3sigma in AB mag (Total): 20.801179746613208\n",
      "gpc1_z: mean flux error: 13.653246871809756, 3sigma in AB mag (Total): 19.869107005669214\n",
      "gpc1_y: mean flux error: 39.19437944777665, 3sigma in AB mag (Total): 18.724137381225454\n",
      "irac_i1: mean flux error: 2.5667898654937744, 3sigma in AB mag (Total): 21.68372107347782\n",
      "irac_i2: mean flux error: 2.093653440475464, 3sigma in AB mag (Total): 21.90493487536579\n",
      "megacam_u: mean flux error: 0.025823067873716354, 3sigma in AB mag (Total): 26.677177271330073\n",
      "megacam_g: mean flux error: 0.02217855490744114, 3sigma in AB mag (Total): 26.842363749895434\n",
      "megacam_r: mean flux error: 0.04556003212928772, 3sigma in AB mag (Total): 26.060736809013328\n",
      "megacam_i: mean flux error: 0.08159869909286499, 3sigma in AB mag (Total): 25.427988775792095\n",
      "megacam_z: mean flux error: 0.22471025586128235, 3sigma in AB mag (Total): 24.328139627610092\n",
      "wircam_j: mean flux error: 1.4524400234222412, 3sigma in AB mag (Total): 22.301951343650863\n",
      "wircam_ks: mean flux error: 1.6621395349502563, 3sigma in AB mag (Total): 22.15552816428694\n",
      "wircam_y: mean flux error: 0.8295084834098816, 3sigma in AB mag (Total): 22.910144783384105\n"
     ]
    }
   ],
   "source": [
    "average_depths = []\n",
    "for b in ap_bands:\n",
    "    \n",
    "    mean_err = np.nanmean(depths['ferr_ap_{}_mean'.format(b)])\n",
    "    print(\"{}: mean flux error: {}, 3sigma in AB mag (Aperture): {}\".format(b, mean_err, flux_to_mag(3.0*mean_err*1.e-6)[0]))\n",
    "    average_depths += [('ap_' + b, flux_to_mag(1.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(3.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(5.0*mean_err*1.e-6)[0])]\n",
    "    \n",
    "for b in tot_bands:\n",
    "    \n",
    "    mean_err = np.nanmean(depths['ferr_{}_mean'.format(b)])\n",
    "    print(\"{}: mean flux error: {}, 3sigma in AB mag (Total): {}\".format(b, mean_err, flux_to_mag(3.0*mean_err*1.e-6)[0]))\n",
    "    average_depths += [(b, flux_to_mag(1.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(3.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(5.0*mean_err*1.e-6)[0])]\n",
    "    \n",
    "average_depths = np.array(average_depths,  dtype=[('band', \"<U16\"), ('1s', float), ('3s', float), ('5s', float)])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def FWHM(X,Y):\n",
    "    \n",
    "    half_max = max(Y) / 2.\n",
    "    #find when function crosses line half_max (when sign of diff flips)\n",
    "    #take the 'derivative' of signum(half_max - Y[])\n",
    "    d = half_max - Y\n",
    "    #plot(X,d) #if you are interested\n",
    "    #find the left and right most indexes\n",
    "    low_end = X[np.where(d < 0)[0][0]]\n",
    "    high_end = X[np.where(d < 0)[0][-1]]\n",
    "    return low_end, high_end, (high_end - low_end)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for b in ap_bands:\n",
    "    data += [['ap_' + b, Table(data = parse_single_table(FILTERS_DIR + b +'.xml').array.data)]]\n",
    "    \n",
    "for b in tot_bands:\n",
    "    data += [[b, Table(data = parse_single_table(FILTERS_DIR + b +'.xml').array.data)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap_gpc1_g (4260.0, 5500.0, 1240.0)\n",
      "ap_gpc1_r (5500.0, 6900.0, 1400.0)\n",
      "ap_gpc1_i (6910.0, 8190.0, 1280.0)\n",
      "ap_gpc1_z (8190.0, 9210.0, 1020.0)\n",
      "ap_gpc1_y (9200.0, 9820.0, 620.0)\n",
      "ap_irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "ap_irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "gpc1_g (4260.0, 5500.0, 1240.0)\n",
      "gpc1_r (5500.0, 6900.0, 1400.0)\n",
      "gpc1_i (6910.0, 8190.0, 1280.0)\n",
      "gpc1_z (8190.0, 9210.0, 1020.0)\n",
      "gpc1_y (9200.0, 9820.0, 620.0)\n",
      "irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "megacam_u (3500.0, 4100.0, 600.0)\n",
      "megacam_g (4180.0, 5580.0, 1400.0)\n",
      "megacam_r (5680.0, 6880.0, 1200.0)\n",
      "megacam_i (6831.7305, 8388.556, 1556.8252)\n",
      "megacam_z (8280.0, 9160.0, 880.0)\n",
      "wircam_j (11748.0, 13334.0, 1586.0)\n",
      "wircam_ks (19870.0, 23135.0, 3265.0)\n",
      "wircam_y (9702.0, 10806.0, 1104.0)\n"
     ]
    }
   ],
   "source": [
    "wav_range = []\n",
    "for dat in data:\n",
    "    print(dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission'])))\n",
    "    wav_range += [[dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths on AKARI-NEP')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dat in data:\n",
    "    wav_deets = FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))\n",
    "    depth = average_depths['5s'][average_depths['band'] == dat[0]]\n",
    "    #print(depth)\n",
    "    plt.plot([wav_deets[0],wav_deets[1]], [depth,depth], label=dat[0])\n",
    "    \n",
    "plt.xlabel('Wavelength ($\\AA$)')\n",
    "plt.ylabel('Depth')\n",
    "plt.xscale('log')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Depths on {}'.format(FIELD))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### IV.c - Depth vs coverage comparison\n",
    "\n",
    "How best to do this? Colour/intensity plot over area? Percentage coverage vs mean depth?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths (5 $\\\\sigma$) vs coverage on AKARI-NEP')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAGBCAYAAACZ0uOCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XlclOX6P/DPBa7IyCKICCKLMoAI4oL7lraomZblMbW0NI91tLTs5Mnft3O+33M86Tla5rFFK7VFM5fSMs3KXI5aGqYgi5gsKgoIijCKG3D//pgZGxBkmRlmRj7v12tezjzrNQ/jXPPc9/PclyilQERERI7FydYBEBERUe0xgRMRETkgJnAiIiIHxARORETkgJjAiYiIHBATOBERkQNiAiciInJATOBEREQOiAn8LiUimSIy1ELbel1EZlliW5Vs+5CIdLLGtomI7mZM4PXEkFCviohORC6JyAERmS4iFvkbWDJhV9iuN4AnASw3mbZbRK6JyGXDI9WMXSwC8H/mxknWZfibF4hI0wrTy33uRGScYbmBtVj/quFzlCMiq0XEtartVxJXpojkikgLk2lTRWR3FfswPpZVMT9XRFaZxkBkr5jA69dIpZQGQHsACwC8AuBD24ZUrckAtimlrlaYPkMp5Wp4aM3Y/lcABouIrxnbsGsi0sjWMZhDRAIB9AegADx0h+UmAXgbwAil1J5arD9SKeUKoAuAGAB/qWWIjQC8UM0yI00+r65KqRlVxNAVQA8A/6+WMRDVOyZwG1BKFSqlvgLwBwCTRCQSAESkrYhsEpE8EckQkeeN6xjOEv4iIsmGM5lVItLMMO8TAAEAvjacRfzZsFoXEUkQkUIR+dy4vGGdV0TkrKFFIFVEhlQR7jAAe6qYVy0RaSwi8w3x3xQRZXjEG47FNQCHAdxXybpzRWRjhWlvicjSWr4HiEg7EfnCcGwvGM/ARCTccHZ4SUSSROShmuzb8Lq6v9crIpIA4IqINDJsM80Qb7KIPGyyfFcROWKYt8Hw9/pHTfZVyXut9D2ZxDWnqs9FFZ4E8DOA1QAmVbHPaQAWA7hfKXWgtusDgFIqB8AO6BN5bfwbwBwRca/lepXFcBbAdgCR5m6LyOqUUnzUwwNAJoChlUw/DeBZ6H9MHQbwGoAmAIIBpEP/hWhcPxFAOwCeAPYD+EdV2ze8PgSgrWH5FADTDfO0AM4AaGt4HQggpIq48wD0qDBtt2F6viGOQXd43wuh//JuB6AFgB8AfAEg2GSZpQDeqGTd9gCKAbQ0vHYGkA2gVy3fgzOAeABvGmJoBqAfgMYATgJ41XDM7wGgM2y7yn0bXtfk73XU8L6bG6Y9Zvh7OEH/4+0KAF/D+qegP4tsDOARADeMf9/q9lXhvVb5nqr7XNzhb3gSwHMAugG4CcCnwudsE4BcANF1XH+o4bk/gGMA3qru/03F+YbPlPF4TQWwu7bbMDxvByAJwN9t/Z3BBx/VPWweQEN5VPUlAn1ymwegJ4DTFeb9BcAqk/Wnm8wbDiCtqu0bXk80ef0vAO8ZnncAcN7wxde4mrhvAgirMK0nAA2AptCfUelQSfI0LHMVQEeTac+afrkaps0HsLKK/e8D8KTh+b3G91zL99Ab+h8cjSpM7w8gB4CTybTPAPztTvs2OQbV/b2eriauowBGARgA4CwAqfC+/1GTfdXyPVX5uagixn6Gz4CX4fVxALMrfM6KAGwx3Wct179s+AwpADsBuFf3/6bifOjPmAsBeKPyBH4ZwCWTxzNVzD8F4B0YfnTxwYc9P9iEbnt+AC5Cf8bX1tDseUlELkF/FuVjsuwZk+enoD+LupMck+fFAFwBQCl1EsAsAH8DcF5E1olIVdsqgD4R36KUOqiU0imlriulPoL+LHx4JesOAJCulPrNZJpHhbhg2P6lKva/FsDjhufjDa9r+x7aATillCqpML0tgDNKqTKTaaeg/5tUuW+D2v69ICJPishRk+UjAXgZ4jirlFJVrFuTfdX0PQFVfC6qMAnAd0qpfMPrtbi9GXw6gFAAH4iI1GH90Up/bcggAGHQH5PbiMgEk4vQtpvOU0olAtgKYG4V72O0Usrd5PF+FfPbK6WeU7df80Fkd5jAbUhEekD/xboP+i/sjApfMhqllGlibGfyPADAOZPXtSrsrpRaq5TqB31yUNA3dVcmAfov5ztuDkDFL25AfzZUYHxh+HJ/GPovWlPh0DdxV2YDgEEi4m9Y91YSrcV7OAMgoJKLyc4BaCfl7wQIgP5s+I77Rs3+Xrf+JiLSHsD7AGYAaKWUcoe+S0Sgb5r3q5D8TP/WNdlXTd9TjYlIcwBjAQwU/RXiOQBmA4gWkWiTRc8DGAL92f87dVgfAKD0F76thv7OhNsopdao3y9CG1bJIn8F8AzK/1ghumsxgduAiLQUkQcBrAPwqVLqGPT9kkWGC5+ai4iziEQakrzRn0TEX0Q8oT8D+9xkXi70faM12b9WRO4R/S0916Bv5i6tYvFtAAaarOsuIveLSDPDhVkToD/T3lHJuokAuopIF8OX+evQJ7XPTbbXFPq+0e8r27lSKg/6PvdV0CexlDq8h0PQJ8kFItLCEHtfAAeh74f+s+gvthsEYCT0f5cq922yzer+XqZaGN57niH+p/D7hVI/GWKfYTimowDE1nFfd3xPtTTaEFcE9BeWdYH+x9Z/ob8w7Ral1Dno+9sfEJE3a7u+iSUA7hWR2l7IZmyV+RxAlRf4Ed1NmMDr19ciooP+jGoegDcAPAUASqlS6L9ouwDIgP4CsQ8AuJmsvxbAd9BfwJQO4B8m814H8P8MTaxzqomjKfS3seVD35zaGvofBJX5GMBwQwIG9BdJ/QO/X8Q2E/rmx9vuBVdKxUHfv73NEG8bAMOVUjdNFnsI+v7KcxXXN7EW+n5O0zPgGr8Hk2PbAfqLBrMA/EEpdcOw/2GG7bwDfZ/38Wr2XdO/l+nyydBfpf0T9D+2OkPf9QBDHI8AmAJ9V8JE6Fsprtd2XzV8TzU1Cfp+9tNKqRzjA8AyABMqtmgopc5An8QfFZHXa7u+YRt50H/m/qcO8QL6MQVaVDLdeIeG8fFlHbdPZDekfLcb2SsRyQQwVSn1gw32/U8A55VSS6yw7YMAphj6MMnAcFzeU0qtsnUsRGSfHHqACaofSqmqzs4tse2e1tq2IxH9yGWp0J81TwAQBeBbmwZFRHaNCZzIPmgBrIf+ivA0AI8qpbJtGxIR2TM2oRMRETkgXsRGRETkgJjAiYiIHJBD9IF7eXmpwMBAW4dBRORQDh8+nK+U8jZj/daNGjX6APoxC3jCV7/KACSWlJRM7dat2/nKFnCIBB4YGIi4uDhbh0FE5FBE5JQ56zdq1OiDNm3ahHt7exc4OTnxgql6VFZWJnl5eRE5OTkfoIoyvvxFRUREVYn09vYuYvKuf05OTsrb27sQdyhtywRORERVcWLyth3Dsa8yTzOBExEROSAmcCIiMtuiHak+P6Tklis9/ENKrmbRjtTKyt46pO3bt7tGRESEN2rUqNuqVas8bB0PEzgREZmtS4B78YvrjwYbk/gPKbmaF9cfDe4S4F5s69gsJTg4+MaqVasyR44cecHWsQBM4OUc2rIRpxMTyk07nZiAQ1s22igiIiLHMDTcR/fG2C7pL64/Gvy/Xye1fXH90eA3xnZJHxruozN720OHhnTq1Cm8Q4cOnRYtWuQFAC4uLjHPPPOMf0RERHjv3r1Dz507V+VdVXv27HEJDQ2N6NKlS9gf//hH/44dO3YCgKVLl7YaMmRISP/+/TsGBgZGvvTSS77GdZYtW9YqNDQ0QqvVRowePToIALRa7Y2ePXtedXKqPnWWlpZi4sSJAR06dOg0ePDgDgMHDuxg6bN2JnATbUJCsXXJgltJ/HRiArYuWYA2IaE2joyIyP4NDffRjenqn7dqf6bvmK7+eZZI3gCwZs2azKSkpJSjR48mL1++3CcnJ8f56tWrTl27di1OTk5O6du3r27u3Lltq1p/6tSpQW+//fapo0ePHnd2di53UV5CQkKLDRs2pCcmJiZ99dVXnnv37nWJi4trtmjRIt89e/acSE1NTV6+fPnp2sb88ccfe5w5c6ZJampq0kcffZR55MgR17q89ztxiPvA60tAZBQenDUXW5csQPR9wxH/3TY8OGsuAiKjbB0aEZHd+yElV7Pp1yzvp/oGZm/6Ncu7bwcvnSWS+MKFC32++eYbdwDIyclpnJSU1MzJyQlTp069CABPP/30hUceeaRDZevm5+c7X7lyxenee++9AgCTJk26+P3337sb5/fr16+oTZs2pQAwYsSIgt27d7s6Oztj5MiRBb6+viUA4OPjU1rbmP/73/+6PvLIIwXOzs4ICAgo6dWrl0V+zJhiAq8gIDIK0fcNx8+b1qHXmHFM3kRENWDs8zY2m/ft4KWzRDP61q1bNXv27NHExcUd12g0ZbGxsdqrV6/e1nosIpWuX13BrorriQiUUhARs26fq49CYQ26Cf3XHaeQlVpQbtrh7ftx+Jut6DVmHOK/23ZbnzgREd3u6OlLLqbJ2tgnfvT0JRdztnvp0iVnNze3Uo1GU3bkyJFm8fHxLQCgrKwMxj7l1atXt4qNja30R4K3t3dpixYtynbu3NkCAD755BNP0/n79u1rmZub63z58mXZtm2b+8CBAy8/8MADRV999ZVnTk6OMwDk5uY61zbu/v37X968ebNHaWkpzpw50+jgwYOa6teqnQZ9Bt46sCV2vJ+I+5+JhL/WA4e378eej5dg4JOz0G1YX7SLiMLWJQvYjE5EVI0592tzK04bGu5jdhP6mDFjClesWOEdGhoaERISci06OvoKADRv3rwsKSmpeadOndpoNJrSL774Ir2qbSxfvjxz+vTp7V1cXMr69u2r02g0t5rEu3fvfvkPf/hDUGZmZrMxY8ZcGDBgQDEAvPTSS9n9+/cPc3JyUpGRkcWbNm3K3LNnj8vYsWM7FBUVOe/cudN9/vz5bU+ePJlU2T4nTZpU8MMPP2hCQ0M7BQUFXYuOjr7i7u5e66b4O7FaPXARaQfgYwBtoB+UfYVS6i3DvJkAZgAoAfCNUurPd9pW9+7dlbXGQs9KLcCO9xMROcAPcVu/QK+He6PbsL635p9OTEBO2gnEjnrUKvsnIrIWETmslOpe1/Xj4+Mzo6Oj8y0Zk6W4uLjEFBcXH6nJsoWFhU5ubm5lAPDqq6+2yc7Obrxq1aozS5cubRUXF9fi448/rvVFarXZb05OjnOPHj3C9+/ffzwgIKCkNtuIj4/3io6ODqxsnjXPwEsAvKSU+lVENAAOi8j3AHwAjAIQpZS6LiKtrRhDtfy1HvrkvS0T3R98BN2GBZebHxAZxbNvIiIHtn79erfFixf7lpaWip+f3/W1a9dm1sd+77333o5FRUXON2/elJdffjm7tsm7OlZL4EqpbADZhuc6EUkB4AfgGQALlFLXDfMqLZNWX7JSC5C49yy6Dw9E4t6z8NN6wF9r8wF2iIjoDio7+37iiScCfvnll3K3az377LO5L7zwwoVnnnmmoOLyzz///AUAZg3KcujQoeZPPvlkkOm0Jk2alCUkJBw/dOhQqjnbrk699IGLSCCAGAAHAfwbQH8RmQ/gGoA5Sqlf6iOOiozN58Y+cD+tR7nXRETkOD755BOrNIXfSWxs7NXjx48n1/d+gXq4Cl1EXAFsAjBLKVUE/Y8GDwC9ALwMYL1Ucv2/iEwTkTgRicvLy7NKbOczi8ola3+tB+5/JhLnM4ussj8iIiJLseoZuIg0hj55r1FKfWGYnAXgC6W/eu6QiJQB8AJQLksrpVYAWAHoL2KzRnxd729/2zR/NqETEZEDsNoZuOGs+kMAKUqpN0xmbQZwj2GZUABNANjlVY5ERET2yppn4H0BPAHgmIgcNUx7FcBKACtFJBHADQCTVH0MWUNERHQXsdoZuFJqn1JKlFJRSqkuhsc2pdQNpdREpVSkUqqrUupHa8VARET1ZOfffZC6vfxoY6nbNdj5d9YDt5IGPZQqERFZiH/3Ynw5PfhWEk/drsGX04Ph373B1wMvKbHo7d+3MIETEZH5tMN0ePi9dHw5PRjb57bFl9OD8fB76dAOa5D1wLdu3arp2bNn6MiRI4O0Wm0nc49BZRr0WOhERGRB2mE6RD+eh4Pv+qLns9mWSN6Avh64j49P6eXLlyUmJiZi4sSJBcZ64O+//37WnDlzfOfOndu2qiFRp06dGvTOO+9k3nvvvVeee+45P9N5CQkJLY4dO5bk6upaFhMTEzFq1KhCFxeXskWLFvn+9NNPx319fUvqUszEuO0jR44khYWF3ajL+tXhGTgREVlG6nYN4j/zRs9nsxH/mfdtfeJ1tHDhQh+tVhvRrVu38KrqgR86dMi1snUrqwduOt9YD9zV1VUZ64Hv2LGjpbn1wAEgKirqirWSN8AEXo5uzxlcS7tUbtq1tEvQ7Tljo4iIiByEsc/74ffSMWzBuVvN6WYmcdN64Kmpqcnh4eFXHaEeOAC4uLiUmbuNO2ECN9HYX4OLa1NuJfFraZdwcW0KGvtbvIwrEdHdJSvOpVyft7FPPCuuQdYDrw/sAzfRLMQdnuPDcXFtClr09MWVg9nwHB+OZiHutg6NiMi+Dfmf2+qBQztMZ24/uKPWA68PVqsHbknWrAdemcLvMqH78Qw097SD232B9bZfIiJLYj1wPVvVA7cEW9UDd0jX0i7hysFsaO5physHs9E0xJ1n4EREDsxW9cCtjQnchLHP29hs3jTEvdxrIiKyD45QD9yc7dYEE7iJm1m6csna2Cd+M0vHBE5EZOcaWj1wJnATmoHtbpvWjE3oRERkh3gbGRERkQNiAiciInJATOBEREQOiAmciIjMtvTXpT67z+wuN2zl7jO7NUt/Xdog64FnZmY2fuCBB4KtGQ8TOBERmS3KO6p43r55wcYkvvvMbs28ffOCo7yjGmQ98MDAwJvffvttlaPDWQITOBERmW1Qu0G6+f3mp8/bNy94waEFbeftmxc8v9/89EHtBjXIeuCpqalNjPuxFiZwIiKyiEHtBulGhozMW5OyxndkyMg8SyRvQF8PPCkpKeXo0aPJy5cv98nJyXE21gNPTk5O6du3r27u3Lltq1p/6tSpQW+//fapo0ePHnd2di43fnhCQkKLDRs2pCcmJiZ99dVXnnv37nWJi4trtmjRIt89e/acSE1NTV6+fLldDrXKBG5i3759yMjIKDctIyMD+/bts1FERESOY/eZ3Zqv0772nhA+IfvrtK+9K/aJ15Wj1gO3NiZwE35+ftiwYcOtJJ6RkYENGzbAz8/PxpEREdk3Y5/3/H7z0+fGzj1nbE43N4k7cj1wa2MCNxEUFITHHnsMGzZswI8//ogNGzbgscceQ1BQUPUrExE1YAl5CS6mfd7GPvGEvATWA7cSDqVaQVBQELp37469e/diwIABTN5ERDXwfNfnb6sHPqjdIJ25/eCsB141JvAKMjIyEBcXhwEDBiAuLg5BQUFM4kRENtK8eXO1d+/e3yqb99Zbb50DcK66bXTr1u3qiRMnkgF9PXDjjwAA8PLyKqmsHvjMmTMvzJw5s9ztYgMHDizOzc1NqEncWq32xm+//WbV5M4EbsLY521sNg8KCmIzOhGRg2M98Abg7Nmz5ZK1sU/87NmzTOBERHaE9cCZwMvp16/fbdPYhE5E5BgaWj1wXoVORETkgJjAiYiIHBATOBERkQNiAiciInJATOBERGS280uW+Oh27So3bKpu1y7N+SVLGmQ98PrABE5ERGZrHh1dfO6VucHGJK7btUtz7pW5wc2joxtkPfD6wARORERm0wwerGu7cEH6uVfmBuf8859tz70yN7jtwgXpmsGDG2Q98FmzZrUNCwuLCAsLi2jdunXUo48+GmjucaiICZyIiCxCM3iwzm30qLyCjz/xdRs9Ks8SyRtwzHrgS5YsOXf8+PHk/fv3p7q7u5e88MIL5+vy3u+ECZyIiCxCt2uXpnDzFm+PJ5/ILty8xbtin3hdOWo98LKyMjz66KNBf/rTn3L79+9v8a4EjsRGRERmM/Z5G5vNW/TurbNEM7ppPXCNRlMWGxurdZR64C+99FJbX1/fGy+88IJV+sx5Bk5ERGa7Gh/vYpqsjX3iV+PjG2Q98M8++8xt9+7dLVeuXHmmtuvWFM/AiYjIbK1nzbqtHrhm8GCduf3gjloPfMmSJT7nz59v3KVLl3AAeOCBBy4tWbKk2tKntSHVNS/UecMi7QB8DKANgDIAK5RSb5nMnwPg3wC8lVL5d9pW9+7dVVxcnFXiJCK6W4nIYaVU97quHx8fnxkdHX3H72dbcXFxiamsIlllCgsLndzc3MoAfT3w7OzsxqtWrTqzdOnSVnFxcS0qqwduL+Lj472io6MDK5tnzTPwEgAvKaV+FRENgMMi8r1SKtmQ3O8FYLcHjYiI7g6sB15LSqlsANmG5zoRSQHgByAZwJsA/gxgi7X2T0REdy/WA6+nPnARCQQQA+CgiDwE4KxSKr6qqwaJiIhqq6HVA7d6AhcRVwCbAMyCvll9HoD7arDeNADTACAgIMCaIRIRETkcq95GJiKNoU/ea5RSXwAIARAEIF5EMgH4A/hVRNpUXFcptUIp1V0p1d3b29uaYRIRETkcqyVw0bePfwggRSn1BgAopY4ppVorpQKVUoEAsgB0VUrlWCuO2jh1ajkuFvxUbtrFgp9w6tRyG0VERERUOWuegfcF8ASAe0TkqOEx3Ir7M5umZRQSE5+/lcQvFvyExMTnoWkZZePIiIiIyrNaAldK7VNKiVIqSinVxfDYVmGZwOruAa9Pnh69ERm5FImJzyMt/U0kJj6PyMil8PTobevQiIjs2s9b0nwyEvLLjX2ekZCv+XlLGuuBWwmHUq3A06M3/PzGIzNzGfz8xjN5ExHVgE+QW/HO1cnBxiSekZCv2bk6OdgnyI31wK2ECbyCiwU/4ezZtQgMnIGzZ9fe1idORES3C4ry0g2ZHJG+c3Vy8H/Xn2i7c3Vy8JDJEelBUV4Nsh746NGjgz799FN34+uHHnooaM2aNW5mHIbbMIGbMPZ5R0YuRUjw7FvN6UziRETVC4ry0ml7tclL+DHLV9urTZ4lkjfgmPXAn3nmmbzVq1e3AoALFy44Hz582HXs2LGFtX/3VWMCN6ErSijX523sE9cVJdg4MiIi+5eRkK9J/TnHO+oe/+zUn3O8K/aJ15Uj1gMfMWLE5VOnTjU7e/Zsow8//NBzxIgRBY0bN679m78DViMz0b79H2+b5unRm/3gRETVMPZ5G5vN/cM8dZZoRnfkeuBjx4698MEHH3hu2rTJc+XKlZnmbq8inoETEZHZcjMKXUyTtbFPPDejsEHWAweA6dOn5y9fvtwHALp3736tLtu4E56BExGR2XqNCrmtHnhQlJfO3H5wR60HDgDt2rUrCQkJuTZy5MhL5hyDqjCBExGR3WrevLnau3fvb5XNe+utt84BOFfdNrp163b1xIkTyYC+HrjxRwAAeHl5lVRWD3zmzJkXZs6cWe52sYEDBxbn5ubW+KIonU7nlJmZ2XTKlCkXq1+69tiETkREd7X169e7hYWFRXTs2LHTgQMHXOfPn59t7X1u3rxZExoa2umZZ54536pVq1pfBFcTPAMnIiKH4wj1wEePHn3MnG1XhwmciIjuCg2tHjib0ImIiBwQEzgREZEDYgInIiJyQEzgREREDogJnIiIzLZv3cc+aYcPlRv7PO3wIc2+dR/bvB74rFmz2m7evNki47JXJiYmJsz4vH///h01Gk2XwYMHd7DW/oyYwImIyGy+HcOKt7+9ONiYxNMOH9Jsf3txsG/HMJvXA1+yZMm50aNH3zYiXElJiUW2f+TIkePG53PmzMlZvnx5hkU2XA0mcCIiMltIt1jdsD+9lL797cXBu1avaLv97cXBw/70UnpIt8rHKK8Nc+uBjxkzJtA4brqfn1/nOXPm+Hbr1k27cuVKj8WLF3tFRkaGa7XaiPvvvz9Ep9M5AcCZM2ca3XvvvSFarTZCq9VGfP/99y2q2r6Li0uM8fmoUaN0LVu2LDP3PdcEEzgREVlESLdYXacBQ/J+3f6Vb6cBQ/IskbwB8+uBV9SsWbOyw4cPp06bNq1gwoQJBYmJiSmpqanJWq326tKlS70AYPr06QH9+/fXpaamJiclJSV37drV4sVIzMWBXIiIyCLSDh/SJO3d6d112EPZSXt3egd07qKzRBJfuHChzzfffOMOAFXVA3/kkUdq3Of85JNP3hqV7fDhw81fe+01P51O53zlyhXngQMHFgLAgQMHNBs3bswAgEaNGsFaw6Gag2fgRERkNmOf97A/vZQ+ePK0c8bm9IoXttWWaT3w1NTU5PDw8Ku1qQdeGY1Gc6uJe9q0aUHLli07feLEieRXXnnl3PXr1x0mLzpMoEREZL+yfzvuYtrnbewTz/7tuE3rgVenuLjYKSAg4Ob169dl3bp1t2qF9+3bV/fvf//bG9Bf7Hbx4kW7y5dsQiciIrP1G/fkbfXAQ7rFmt2Ebol64Hcyd+7cc7GxseF+fn43wsPDiy9fvuwMAO++++7pyZMntw8NDfVycnLCsmXLTg0dOvRKddvr1q2bNj09vdnVq1edfXx8ot55553MMWPGFNUltuqIUsoa27Wo7t27q7i4OFuHQUTkUETksFKqe13Xj4+Pz4yOjs63ZEyW4uLiElNZRbK7TXx8vFd0dHRgZfPsrkmAiIiIqscmdCIicji1rQdu7v5ycnKcBw0apK04fffu3alt2rSxyRXqNUrgItIUwBgAgabrKKX+zzphERER1Y4164G3adOm1FZ1v6tS0zPwLQAKARwGcN164RAREVFN1DSB+yulHrBqJERERFRjNb2I7YCIdLZqJERERFRjdzwDF5FjAJRhuadEJB36JnQBoJRSUdYPkYiIiCqq7gz8QQAjAQwD0AHAfYbXxulEREQo3JHpczXlQrlhU6+mXNAU7shsMPXADxw40LxLly5hHTp06BQaGhrx/vvve1hrn0A1CVwpdUopdQrAP4zPTadZMzCz7FsCZOwtPy1jr346ERFZXJM1YYSPAAAgAElEQVQATfHF9SeCjUn8asoFzcX1J4KbBGgaTD1wV1fXsk8++STj5MmTSd99991vr776arv8/Hxni+ykEjXtA+9k+kJEnAF0s3w4FuLXFdgw+fcknrFX/9qvqy2jIiK6azUPb6XzHBuafnH9ieBLX6e1vbj+RLDn2ND05uGtGkw98KioqOudO3e+DgCBgYE3PT09S7Kzs6023sodE7iI/EVEdACiRKRIRHSG1+ehv7XMPgUNAB5brU/aP87X//vYav10IiKyiubhrXQturbOu7z/nG+Lrq3zLJG8AcesB75r1y6XmzdvSkREhNVuvb7jLwOl1OsAXheR15VSf7FWEFYRNADoPgXY+y9gwJ+ZvImIrOxqygXNlV/Pe7v2bZt95dfz3k07uOsskcQdrR74qVOnGj/11FPBH374YYazs9Va0GvchP6qiDwiIm+IyGIRGW21iCwlYy8Q96E+ecd9eHufOBERWYyxz9tzbGi6+8iQc8bm9IoXttWWo9UDv3jxotOwYcM6vPbaa2eHDBlSbfUyc9Q00LcBTAdwDEAigOki8rbVojKXsc/7sdXAPfN+b05nEicisoobp3Uupn3exj7xG6d1DaYe+LVr12TEiBEdxo0bd+Hpp58uqG55c9W0c30ggEhlqD0qIh9Bn8zt09lfy/d5G/vEz/7KpnQiIitwuz/wtnrgzcNbmd2E7kj1wFeuXOnxyy+/uBYUFDRau3atl2FaRp8+fa7WJbbq1KgeuIh8AWC24fYxiEh7AAuUUo9bI6iKWA+ciKj2WA/c8VmiHngrACkisltEdgNIBuAtIl+JyFeVrSAi7URkl4ikiEiSiLxgmP5vETkuIgki8qWIuNf+LRERETVsNW1Cf60O2y4B8JJS6lcR0QA4LCLfA/gewF+UUiUishDAXwC8UoftExFRA8V64DVM4EqpPYZm845KqR9EpDmARkqpKvs2lFLZALINz3UikgLATyn1ncliPwN4tO7hExER6TW0euA1akIXkWcAbASw3DDJH8Dmmu5ERAIBxAA4WGHW0wC2V7HONBGJE5G4vLy8mu6KiIioQahpH/ifAPQFUAQASqnfALSuyYoi4gpgE4BZSqkik+nzoG9mX1PZekqpFUqp7kqp7t7e3jUMk4iIqGGoaR/4daXUDeON8iLSCPoyo3ckIo2hT95rlFJfmEyfBH1FsyGqJpfBExERUTk1PQPfIyKvAmguIvcC2ADg6zutIPps/yGAFKXUGybTH4D+orWHlFJWqVKz7FQu9hWU757fV6DDslO33aZIRPVAt+cMrqVdKjftWtol6PacsVFE1NDMnDnTr02bNlHGwiN3g5om8LkA8qAfvOWPALYB+H/VrNMXwBMA7hGRo4bHcADLAGgAfG+Y9l7dQq9al5YumJaUeSuJ7yvQYVpSJrq0NGtAICKqo8b+Glxcm3IriV9Lu4SLa1PQ2N9qJZqpnu3cudMnNTW13B80NTVVs3PnTpvXAweA0aNHXzp48GCKreOwpJpehV4mIpsBbFZK1eiKMqXUPgCVDU67rRbx1Uk/Dw1WdArEtKRMTGrrhY/O5WNFp0D08+CXBZEtNAtxh+f4cFxcm4IWPX1x5WA2PMeHo1kIh4G4W/j7+xd/+eWXwQ8//HC6VqvVpaamaoyvzd32yy+/7Ltx40ZPX1/fG61atSqJiYkp/vbbb90jIyOLjxw50uLy5cvOK1asyBg8eHBxYWGh05QpUwISEhJcAODVV189N3ny5Eu1GZc8KSmp6fjx44NKS0tl6NChhStWrPCxx0FjqisnKiLyNxHJB3AcQKqI5IlIXe4Lr1f9PDSY1NYLb57KxaS2XkzeRDbWLMQdLXr6QvfjGbTo6cvkfZfRarW6hx9+OP3LL78M3r59e1vTZG7Odvfu3evy9ddfexw7diz5m2++SUtISLhVl7u4uNjpyJEjx5cuXXpq2rRpQQAwd+5c35YtW5aeOHEi+cSJE8kjRoyo9f5nzJjR7rnnnjufmJiY0rZt25vmxG9N1TWhz4K+KbyHUqqVUsoTQE8AfUVkttWjq6P39qRhxdEz+OhcPma399GfgR89g/f2pNk6NKIG61raJVw5mA3NPe1w5WD2bX3i5Pi0Wq0uOjo67+DBg77R0dF55iZvANi9e7frsGHDLrm6uioPD4+ye++999YHZ/z48RcBYNiwYZcvX77slJ+f77x3796Ws2fPPm9cxtvbu9aDrBw5csT16aefvggAU6dONXsQGGupLoE/CeBxpVSGcYJSKh3ARMM8u+Tk1gT//CIRs1zd8UqwL2a5uuOfXyTCya2JrUMjanAObdmI0z/8iotrU+A5Phxu9wWipHdT5K6KZxK/y6Smpmri4+O9e/bsmR0fH+9dsU+8Lu50o1LFEqIiAqVUrUqLOrLqEnhjpdRtA9kb+sEbWyck893waIJXH4nEe18fxxvfpeK9r4/j1UciccODCZyovrUJCcXxLT+gpHdTNAtxx+nEBGxdtwgySIObWWafoJGdMO3zHjZs2Dljc7q5SXzQoEGXd+zY4VZcXCyFhYVOP/zww62+l88++8wDAHbs2OGq0WhKW7VqVTpo0KCiN95449Y4JXl5ec613WeXLl0ur1692gMAVq5c6Vnd8rZSXQK/Ucd5NjWjvQ+mdWmHiT0DsPTHk5jYMwDTurTDjPZ2cTEkUYMSEBmFsD8+gK3rFmH/+k+xdckCPDhrLgKGdoVmYDtbh0cWkpWV5WLa523sE8/KyjLr9p+BAwcWP/DAA4URERGdhg8fHhIVFXXFzc2tFAA8PDxKY2JiwmbMmNF++fLlmQDw+uuvZ1+6dMm5Y8eOnbRabcS2bds0ADB9+nR/Hx+fqGvXrjn5+PhEvfjii22r2ud//vOfM//5z398OnfuHJ6dnd3Y1dXVJmOdV+eO5URFpBRAZVfuCYBmSql6OQuvSznRA2n5mLH2CCb2DMCnB09j2fgY9AnxslKERFSd/es/xc+b1qHXmHHoO3aircNpEO6WcqKFhYVObm5uZTqdzql3797a995779SLL77YbtGiRWcGDBhg8fFEdDqdU4sWLcqcnJywYsUKj88//9xz586dNrmI6k7lRO94G5lSqtZND/bAmLyNSbtXSKtyr4mofp1OTED8d9vQa8w4xH+3De0iohAQGWXrsMhBTJw4sf1vv/3W/Pr16zJu3LgL/fr1s8ogYEb79+93eeGFFwKUUmjZsmXp6tWrM625v7qq6VCqDiUhq7Bcsu4T4oVl42OQkFXIBE5Uz04nJiBhzmwMmzwFQWPHo11EFLYuWYBhQx9Ey8vFaDV1qq1DJDv39ddfZ1ScdujQoVRzt/vKK6+02bJlS7k+7lGjRl1cuHBhTmpqql1VHqvMXZnApw8MuW1anxAvJm8iG8hJO4FOk6fg5lvLcCUwBAG9emLY0AdR/O/FaP32O7YOjxqwhQsX5ixcuDDH1nHU1V2ZwInIfsSOehQAcCUwBGdnz4bH4+Nw87N1CHz7HbTo1dPG0RE5rpqOhU5EZJYWvXrC4/FxyH/nXXg8Po7Jm8hMTOBEVC+u/HwQBZ+tg9dzz6Lgs3W48vNBW4dE5NDYhE5EVrUycSWiTztD8/fl8HvzTbTo1ROnO7gh7flnEbL0XZ6JE9URz8CJyKoiW0Vi+7b/QPc/f0SLXj1xKPsQZhd9iJt/ewHXEo/ZOjxqIBpyPXAiojqJ9Y3FffPeweyiD7HsyDLM2TMHiwYuQrfhk3gL2V0kLW2xT17+znLDpubl79SkpS22iyEw61oP/OZNuy1GxiZ0IrK+WN9YjNWOxfKE5fhj1B8R6xtr65DIwlq6dSlOTp4THBGxKN3ba4guL3+nxvja3G3Xdz3wMWPGBHp4eJQcO3bMJSoqqvj999/PMvc9WAMTOBFZ3aHsQ1ifuh5/jPoj1qeuR2ybWCbxu4y31xBdRMSi9OTkOcG+bR7Jy875wtuYzM3Zrmk98Js3b0qXLl0iYmJiioHf64Fv377dddq0aUG//fZbkmk9cKBuxUwAIC0trdn+/ftPNGpkv2nSfiMjorvCoexDt5rNY31jEdsmttxrunt4ew3R+bZ5JO9M1mrfdv6Ts81N3kD5euAAVE3qga9bt+7WWX9d6oEDwCOPPFJgz8kbYB84EVlZ4oXEcsk61jcWiwYuQuKFRBtHRpaWl79Tk53zhXc7/8nZ2TlfeFfsE68LW9UDd3V1LTN7I1bGBE5EVvV05NO3nWnH+sbi6cinbRQRWYNpn3do6P+cMzanm5vEbVEP3FEwgRMRkdmKCo+6mPZ5G/vEiwqPOlw9cEdxx3rg9qIu9cCJiBo61gN3fHWuB05ERGRr9V0P3FEwgRMRkV2zRT1wc7ddH5jAiYioQXL0euC8iI2IiMgBMYETERE5ICZwIiIiB8QETkRE5ICYwImI6K5Xm3rg//rXv7yXLVvWqj7iMgcTOBERme319Gyf7/ILyw2b+l1+oeb19GyHqwf+5z//OW/GjBkXrB2TuZjAiYjIbN1auhTPTDkdbEzi3+UXamamnA7u1tLF7EFXXn75Zd+goKBOffr06Thy5Mig1157zSc2Nlb79NNPt4uJiQnr2LFjp127drkA+lHbHn300cDQ0NCI0NDQiNWrV7sDwJAhQ660b9/+Zk329+KLL7Z97bXX7OKHx53wPnAiIjLbfV5uuv+EB6TPTDkdPLaNR976nALv/4QHpN/n5eaQ9cAdARM4ERFZxH1ebrqxbTzy3s/K933G3yvb3OQN2K4euCNgEzoRkZ2ZfOgbfHDyl3LTPjj5CyYf+sZGEdXMd/mFmvU5Bd7P+Htlr88p8K7YJ14XtqoH7giYwImI7Ew/z9b466nrt5L4Byd/wV9PXUc/z9bVrGk7xj7v/4QHpP+9o/85Y3O6uUmc9cCrxgRORGRnpnbogf9t3xR/PXUdjx74Cn89dR3/274ppnboYevQqnS4qNjFtM/b2Cd+uKiY9cCthPXAiYjszHt70hDl74Y3cg9g3/UA9Gt6Gi/69EFCViGmDwyp8XZYD9zxsR44EZEDifJ3w5TvvkVhx/bo53IaB4o9kPTdt/jwvgdsHZpNsB545ayWwEWkHYCPAbQBUAZghVLqLRHxBPA5gEAAmQDGKqUKrBUHEZGjSVYZKOzYHm6/nUKfkB5ISvsFhR3bI1lloA+8bB1evWM98MpZ8wy8BMBLSqlfRUQD4LCIfA9gMoCdSqkFIjIXwFwAr1gxDiIih7Lv4nn8X2BrFJX1wNIfT+L5e3qgZWAR9l08j6m2Du4uwnrgVVBKZSulfjU81wFIAeAHYBSAjwyLfQRgtLViICJyRKtjRyBCgvDpwdN4/p4O+PTgaURIEFbHjrB1aGRH6uUqdBEJBBAD4CAAH6VUNqBP8gDs974IIiIbOJCWjxlrj2DZ+Bi8eJ8Wy8bHYMbaIziQZvPryciOWD2Bi4grgE0AZimlimqx3jQRiRORuLy8POsFSERkZxKyCrFsfAz6hOj7u/uEeGHZ+BgkZBXaODKyJ1ZN4CLSGPrkvUYp9YVhcq6I+Brm+wI4X9m6SqkVSqnuSqnu3t7e1gyTiMiuRJ7ZAN/c/eWm+ebuR+SZDTaKiOyR1RK46Mey+xBAilLqDZNZXwGYZHg+CcAWa8VAROSI/ILDseG7n5BxQP/1mHFgCzZ89xP8gsNtHFnVFu1I9fkhJbfcqGs/pORqFu1ItYuqXrWpB+4orHkG3hfAEwDuEZGjhsdwAAsA3CsivwG41/CaiIgMnPxy8OAQL2z47if8uPL/sOG7n/DgEC84+dnvBdNdAtyLX1x/NNiYxH9IydW8uP5ocJcAd7u4Z7s29cAdhdVuI1NK7QNQ1YjyQ6y1XyIiR6dpGYVTeB7dQrtj74lm6B9aiHzsRJuWS20dWpWGhvvo3hjbJf3F9UeDx3T1z9v0a5b3G2O7pA8N9zG7ItnLL7/su3HjRk9fX98brVq1KomJiSn+9ttv3SMjI4uPHDnS4vLly84rVqzIGDx4cHFhYaHTlClTAhISElwA4NVXXz03efLkS0OGDLlS0/2FhYVFGJ9nZmY227Rp04kRI0ZcNvd9WBpHYiMisjOeHr3hhT/gtOeH6B8Vhpsux+GLKfD06G3r0O5oaLiPbkxX/7xV+zN9n+obmG2J5G2LeuDHjx9PBoC1a9e6LV68uM3QoUNrnPzrE4uZEBHZmYwDW7B1Zz68mw8E3BPg3Xwgtu7Mv9Unbq9+SMnVbPo1y/upvoHZm37N8q7YJ14XpvXAPTw8ympSD3z27Nm3Lo6uaz3wY8eONZ03b57/pk2b0ps2bWqXRUOYwImI7MzZ9BQ8OMQLV5wOIzBwBq44HcaDQ7xwNt1+u3CNfd5vjO2S/teRnc4Zm9PNTeK2qAdeVFTkNHbs2JB33333VGBg4E2zNmZFTOBERHYmYsRA5ONzREYuRUjwbERGLkU+PkfEiIG2Dq1KR09fcjHt8zb2iR89fcmscqK2qAc+bty4wAkTJuQ/8MADdtfvbYoJnIjIzuiKEhAZufRWn7enR29ERi6FrijBxpFVbc792tyKfd5Dw310c+7X5pqz3fquB37ixIkm3377rcenn37qFRYWFhEWFhaxd+9es36EWAvrgRMR2ZkLH3yAZpGd0aJXz1vTrvx8ENcSj6HV1JqXM2E9cMd3p3rgPAMnIrIzzSI74+zs2bjy80EA+uR9dvZsNIvsbOPIbGPixIntw8LCIqKiosJHjhxZwHrgeryNjIjIzrTo1RN+b76Js7Nnw+PxcSj4bB383nyz3Bl5Q8J64JVjAicisjPv7UlDlH8IOj4+DvnvvAuv555FvHcIEvakYfrAEFuHd9dgPXAiIrKoKH83/OmjQ9i1/Sd4Pfcsdm3/CX/66BCi/N1sHRrZEZ6BExHZmei8NPzll0/weo8nkBUWgk/yO+Avv3yC6OGtAEOJUSImcCIiO3Mt8RhG/HUWsorcsfTHk3j+ng4YMXwWriUea7D94HQ7NqETEdmZVlOnIt47BJ8ePI3n7+mATw+eRrx3SK1uIaO7HxM4EZGdOZCWjxlrj2DZ+Bi8eJ8Wy8bHYMbaIziQZvNbsqu28+8+SN1eftjU1O0a7Pw764FbCRM4EZGdScgqxLLxMehj6O/uE+KFZeNjkJBVaOPI7sC/ezG+nB58K4mnbtfgy+nB8O9uF/dssx44ERFZXWW3ivUJ8bqV0O2SdpgOD7+Xji+nByP68TzEf+aNh99Lh3aYQ9UDLygocIqMjOyUnp6e2LRpU3Xx4kWnzp0733pt7nuxJCZwIiKyDO0wHaIfz8PBd33R89lsSyTv+q4H7uHhUda7d2/d+vXr3Z544olLK1eu9Bw+fHiBvSVvgE3oRERkKanbNYj/zBs9n81G/Gfet/WJ14Et6oFPmzYtb/Xq1a0A4NNPP/WaNm2aXV58wARORETmM/Z5P/xeOoYtOHerOd3MJG6LeuD33XfflaysrKbffPONa2lpqfTo0eOaWRu0EiZwIiIyX1acS7k+b2OfeFacw9UDB4Bx48ZdeOqpp4InTpxol2ffABM4ERFZwpD/yb2tz1s7TIch/+NQ9cCNpkyZcqGoqKjRlClTLpoTvzWxHjgR0V2K9cDrbtWqVR5btmxx37x5822V0OrTneqB8yp0IiKyaxMnTmz/22+/Nb9+/bqMGzfugrXrgU+aNKndrl273LZu3fqbNfdjLiZwIiKya/VdD/yjjz46A+CMudu3NiZwIiJqkFgPnIiIiOodEzgREZEDYgInIiJyQEzgREREDogJnIiIzLb016U+u8/sLjds6u4zuzVLf11qVj3wmJiYMPMiq9qaNWvcXn311TYAsH37dteIiIjwRo0adVu1apWHtfZpSUzgRERktijvqOJ5++YFG5P47jO7NfP2zQuO8o4y657tI0eOHK84raSkxJxN3jJhwoTCf/7znzkAEBwcfGPVqlWZI0eOvGCRjdcDJnAiIjLboHaDdPP7zU+ft29e8IJDC9rO2zcveH6/+emD2g0yq6Soi4tLDABs3bpV07Nnz9CRI0cGabXaTgAwdOjQkE6dOoV36NCh06JFi24VS9+4cWPLiIiIcK1WG9G7d+/Qqra9dOnSVk8++WQAAGi12hs9e/a86uTkOGmR94ETEZFFDGo3SDcyZGTempQ1vhPCJ2Sbm7wrSkhIaHHkyJGksLCwGwCwZs2aTB8fn9LLly9LTExMxMSJEwvKyspkxowZgbt37z4eFhZ2Izc3t07FTBwBEzgREVnE7jO7NV+nfe09IXxC9tdpX3v38u2ls2QSj4qKumJM3gCwcOFCn2+++cYdAHJychonJSU1y83NbRQbG6szLufj41PreuCOggmciIjMZuzzNjab9/LtpbNUM7qRi4tLmfH51q1bNXv27NHExcUd12g0ZbGxsdqrV686WaIeuKNwnMZ+IiKyWwl5CS6mydrYJ56Ql2BWPfCqXLp0ydnNza1Uo9GUHTlypFl8fHwLABg8ePCVgwcPao4fP94EANiETkRE9WffEsCvKxA04PdpGXuBs78C/WbZLq47eL7r87fV/R7UbpBFm9BNjRkzpnDFihXeoaGhESEhIdeio6OvAEDbtm1Lli5dmvnwww93KCsrQ6tWrW4eOHCg2qpie/bscRk7dmyHoqIi5507d7rPnz+/7cmTJ5OsEbulsB44EZGdObF+A0LS/gzncR/pk3jGXpSum4S0kH8hdOxjNd7O3VIPvCG7Uz1wNqETEdkZl+ih2HFpDkrXTQJ+nI/SdZOw49IcuEQPtXVoZEeslsBFZKWInBeRRJNpXUTkZxE5KiJxIhJrrf0TETkqf60Hop4ejwTdfcDefyFBdx+inh4Pf61DDBBmd956661WYWFhEaaPJ554IsDWcZnLmn3gqwEsA/CxybR/AfhfpdR2ERlueD3IijEQETkk/ybH4OPyLX659Bi6uH+Lxk0mABhQ7Xp0uxdeeOHCCy+84DAjrNWU1c7AlVJ7AVysOBlAS8NzNwDnrLV/IiKHZejz/l73Z5QNeBXf6/6sb07P2GvryMiO1PdV6LMA7BCRRdD/eOhTz/snIrJ7l47ux4FLc241m2dpx2PHSqDP0f1wD+JZOOnVdwJ/FsBspdQmERkL4EMAlV6VISLTAEwDgIAAh++qICKqsXSX8Yh6uuWtPm9/rQfw9HikZxahq41jI/tR31ehTwLwheH5BgBVXsSmlFqhlOqulOru7e1dL8EREdmDrve3v+2CNX+tB7re395GEZE9qu8Efg7AQMPzewBUe3M9ERHZv/NLlvjodu0qVw9ct2uX5vySJQ5RD/xvf/ubT0hISKfQ0NCI3r17h544caKJtfZrKda8jewzAD8B0IpIlohMAfAMgMUiEg/gnzA0kRMRkWNrHh1dfO6VucHGJK7btUtz7pW5wc2jox2iHni3bt2Kjx49mnLixInk0aNHF8yePdvfIjuxImtehf64UspXKdVYKeWvlPpQKbVPKdVNKRWtlOqplDpsrf0TEVH90QwerGu7cEH6uVfmBuf8859tz70yN7jtwgXpmsGDHaIe+MiRI3UajaYMAPr163c5Ozvb7s/AORY6ERFZhGbwYJ3b6FF5BR9/4uvx5BPZ5ibviuqrHvjy5cu9hw4dWmjJ2K2BCZyIiCxCt2uXpnDzFm+PJ5/ILty8xbtF7946Sybx+qgH/s4773jGx8e7LF++PNVScVuLQyTww4cP54vIKVvHYWFeAFgkQI/HQo/H4Xc8Fr8z51jU22Xrxj5vY7N5i969dZZqRjeydj3wzZs3axYtWuT73//+N7V58+Z2X+nLIRK4Uuquu49MROLMqRJ0N+Gx0ONx+B2Pxe8c5VhcjY93MU3Wxj7xq/HxLpZuSgfuXA/8pZdean/8+PEmxib0mpyF79+/v/nMmTPbb9u27Tc/Pz/LXCVnZQ6RwImIyL61njXrtnrgmsGDLdqEbsrS9cBffvnldsXFxc6PPfZYiGE7N3788ceT1ojdUhyiHvjdyFF+VdcHHgs9Hoff8Vj8zpbHgvXAbY/1wO3TClsHYEd4LPR4HH7HY/E7HguqFJvQbUQpxf+UBjwWejwOv+Ox+B2PhfneeuutVu+++265EeF69Ohx+ZNPPjltq5gsgU3oRERUKTah2x6b0ImIiO4yTOBWJiIPiEiqiJwUkbmVzH9RRJJFJEFEdorIXVtuqLpjYbLcoyKiROSuvIipJsdBRMYaPhdJIrK2vmOsLzX4/xEgIrtE5Ijh/8hwW8RpbSKyUkTOi0hiFfNFRJYajlOCiLCqKDGBW5OIOAN4G8AwABEAHheRiAqLHQHQXSkVBWAjgH/Vb5T1o4bHAiKiAfA8gIP1G2H9qMlxEJGOAP4CoK9SqhOAWfUeaD2o4Wfi/wFYr5SKATAOwDv1G2W9WQ3ggTvMHwago+ExDcC79RAT2TkmcOuKBXBSKZWulLoBYB2AUaYLKKV2KaWM1Xp+BmD3FXDqqNpjYfB36H/EXKvP4OpRTY7DMwDeVkoVAIBS6nw9x1hfanIsFICWhudu0JckvusopfYCuHiHRUYB+Fjp/QzAXUR86yc6sldM4NblB+CMyessw7SqTAGw3aoR2U61x0JEYgC0U0ptrc/A6llNPhOhAEJFZL+I/Cwidzozc2Q1ORZ/AzBRRLIAbAMws35Cszu1/S6pdz9vSfPJSMgvVw88IyFf8/OWNLPqgVPVmMCtq7IBeSu97F9EJgLoDuDfVo3Idu54LETECcCbAF6qt4hsoyafiUbQN5UOAvA4gA9ExN3KcdlCTY7F4wBWK6X8AQwH8Inhs9LQ1Pi7xFZ8gtyKd65ODjYm8YyEfM3O1cnBPkFuZtUDp6o1xP8I9SkLQDuT1/6opAlQRIYCmAfgIaXU9XqKrb5Vdyw0ACIB7BaRTAC9AHx1F17IVpPPRBaALUqpm0qpDACp0Cf0u01NjsUUAOsBQCn1E4V1lOMAABw/SURBVIBm0Bf3aGhq9F1iS0FRXrohkyPSd65ODv7v+hNtd65ODh4yOSI9KMrLrKFUU1NTmwQFBXX6wx/+0L5jx46dHnrooaDNmzdrunbtGta+ffvIXbt2uRQVFTk99thjgZGRkeHh4eERn376qTsA6HQ6p+HDhweHhoZGjBgxIjgqKips7969LgAwYcKEgMjIyPAOHTp0mj17dlvj/vbs2eMSExMTptVqIzp37hxeUFDglJqa2qRbt27aiIiI8IiIiPDvv/++BaAvqNKjRw/t8OHDgwMDAyOfe+45v3fffdezc+fO4aGhoRFJSUlNq3pfY8aMCVy1apWH8bWx7nltcCAX6/oFQEcRCQJwFvqLcMabLmBoNl4O4IG7uK8TqOZYKKUKYfLFLCK7AcxRSsXVc5zWVu1nAsBmGM48RcQL+ib19HqNsn7U5FicBjAE+mMRDn0Cz6vXKO3DVwBmiMg6AD0BFCqlsm0c022Corx02l5t8hJ+zPKNusc/29zkbXTmzJlmn3/+eXq3bt1ORUVFha9Zs6ZVXFzc8bVr17rPnz/fNyws7NrgwYOLNmzYkJmfn+/cvXv38Iceeqho0aJF3u7u7qUnTpxI/uWXX5r17t27k3Gbb7zxxlkfH5/SkpIS9OnTR3vw4MHm0dHR1yZMmBCyZs2atIEDBxZfvHjRydXVtaxRo0Yl//3vf0+4uLioY8eONX388ceDExMTUwDg+PHjzTdu3JjeunXrkvbt23du2rRp/rFjx1L+/ve/t168eHHrlStXnqn6nZmHCdyKlFIlIjIDwA4AzgBWKqWSROT/AMQppb6CvsncFcAGQwm800qph2wWtJXU8Fjc9Wp4HHYAuE9EkgGUAnhZKXXBdlFbRw2PxUsA3heR2dA3GU9Wd+HoUyLyGfRdJl6G/v6/AmgMAEqp96Dv/x8O4CSAYgBP2SbSO8tIyNek/pzjHXWPf3bqzzne/mGeOkskcT8/v+uxsbFXASA0NPTqPffcU+Tk5ISuXbsW/+Mf/2ibk5PTZMeOHe5Lly5tAwDXr1+XkydPNjlw4IDrCy+8cB4AevTocS00NPRWc/5HH33kuXr1aq+SkhLJy8trHB8f30xE0Lp165sDBw4sBgBPT88yACgqKpIpU6a0T05Obu7k5IRTp07dOrPu3Lnzlfbt298EgICAgOvDhg0rBIDo6Oire/bsKXdNgKUxgVuZUmob9P/5TKe9ZvJ8aL0HZSPVHYsK0wfVR0y2UIPPhALwouFxV6vBsUgG0Le+46pvSqnHq5mvAPypnsKpE2Oft7HZ3D/MU2epZvQmTZrc+tHm5OSEZs2aKQBwdnZGaWmpODs7q40bN56Mjo4u1wVZ1W+948ePN1m2bJnP4cOHU7y9vUvHjBkTeO3aNWMt8dtWmj9/vk/r1q1vbtq0KaOsrAzNmzfvZpzXtGnTSmNzcnJCaWlplYXJGzVqpEpL9VVOy8rKcPPmzVoXMWcfOBERmS03o9DFNFkb+8RzMwpdrL3vwYMHFy1evNinrKwMgL62NwD06dPn8rp16zwA4PDhw81OnDjRHAAKCgqcmzdvXubp6Vl65syZRrt373YDgOjo6Gu5ublN9uzZ42JYzunmzZsoLCx09vX1vens7Ix33nmnlTHxmqN9+/Y3Dh8+7AIAa9ascS8pKal1AucZOBERma3XqJDb6oEHRXlZpAm9OgsWLDg3bdq0gLCwsAillPj7+1/ftWvXyZdffjlv7NixgaGhoRGRkZHFWq32qoeHR2nnzp2vR0ZGFnfs2LFTQEDA9W7dul0GgGbNmqk1a9akPf/88wHXrl1zatasWdnevXtPzJo16/yYMWNCNm/e7NGvXz9d8+bNy8yNeebMmXkPPvhgh86dO4cPGDCgqC7bZDETIiKqlKMXMykpKcGNGzfExcVFJSUlNb3vvvtC09LSEo3N3I7gTsVMeAZORER3JZ1O59S/f3/tzZs3RSmFN99885QjJe/qMIETEdFdycPDo8x4u5etvPLKK222bNniaTpt1KhRFxcuXJhj7rbZhE4Ngoi0AbAEQA8A1wFkApillDphy7iI7JmjN6HfDVgPnBo00d9g/yWA3UqpEKVUBIBXAVh8jGZDhS0iIqtjAqeGYDCAm4YBMQAA/7+9uw+Lssz3AP67B2VgZFSQ4XVEHGXeAAdBcTUKQWLT3WyBI2luZepmmam5qeec7cVWLetIbqS51V5Fu2lqGnjaPWTJIhR11DAJeZlRWFAScBCC0VFgZp7zBzMexMG3Z1Cf8fu5Lq7s4Zl7npl/vjz3PXN/OY47SkTfMMb+izF2jDFWzhh7mIiIMbazd+80YyyHMZbBGPOwn3/Y3sm8yP77qfbO6u1EVG4/lscYK7X3eT/Za6wFjDEDY+wAY+x9xthm+3EZY2yPfezDjDG3/+4zAPCDNXC4G0QRUamT4+lEFENEOurZxvUwY6yYemotHyai/2GMeVLPVp5PU8++3O0cx01kjImJqIQx9qV9rHgiirLvXU5ENJ/juFbGmLd93D1EJCaiF4kolohMRPRPIiqzn/8WEW3iOO4bxlgY9exOpnHdWwAA7gYBDnezBCL6hOM4KxE1M8aKqGeNPJ+Isu0h/QARFXMcd4ExlkpE4xhj/2Z//DDqKRnpIqJDvcKbiGgpYyzN/u+R9vOCiKiI47hWIiLG2KfUs885EVEKEWnt2+kSEQ1ljEk5jhvw79ACgDBhCh3uBhVEFOfkuNOdjziOu0hEB4jol9RzJ76j1/nPchwXY/8ZzXGc4w78/KVBGZtKPYE8meM4HRH9QD0lHFfbaUlkP98xdijCG4Tkmx1/DawpPXTZ3t81pYek3+z4K/rABwgCHO4G/yQiMWPsd44DjLGJRNRGRA/b17ZlRHQfER2yn7KDegoj7qWe6Wyy//dpxthg+xhKxtgQJ883jIjaOI4zM8bU1FONSvaxExljvoyxQUSU0esxXxLRkl7XF8PrFQPcYsERanP+liyFI8RrSg9J87dkKYIj1OgDHyAIcHB79iKINCK6nzFWwxirIKI1RLSdiH6knnXofxLRKo7jHN/N/JJ6An0/x3Fd9mN/IaJKIjrCGDtGPTWwzpahviCiQYyxH4loLRH9r/06fiKiV4noIBHtt4/Vbn/MUiKaYP9wXCURPeWilw9wS4yJizdNf+b3tflbshSFOe+F5G/JUkx/5ve1Y+Li0QfuREVFhVin06mjoqI0y5cvD0EfOEA/OI47TUSZTn610v7T9/xuIhrR55iNer5+9p99Tj9g/3Gc10lE0/u5lO0cx71nvwPPpZ4/FIjjuBbqma4HEKwxcfGmyPumGY/k/3dw7PSZjXzD28Ed+8CXLFkycvHixWcWLVrU+sYbb8hu5n3BHTjArbWGMXaUiI4R0b+IKO82Xw+Ay9SUHpJWFBfIYqfPbKwoLpD1XRO/WY4+cA8Pjyv6wBsaGsQHDhwYumnTpmC1Wq1NSEhQ9e4DnzNnTiuR8z5w+x219vjx415lZWVeP/74o1ffPvDBgwdTV1cXe+SRR8KVSqV21qxZY2pqarwc4zj6wL29vbm+feAnT5707O81/fDDDz7z589vJSJauHDh2Zt5X3AHDnALcRz3/O2+BoCB4Fjzdkybh0XHmFw1je6OfeCugDtwAADgrfF4taR3WDvWxBuPV6MP3ImYmJhzOTk5vkREH3zwgd+1zncGd+AAAMBbwuzHrugDHxMXb3LVOvjVCLEP/O233z41d+7c0dnZ2UGpqak/+/j43PBfBSgzAQAAp4ReZnIn94GbTCbRkCFDbCKRiN577z3fnTt3+hUUFNT0PQ994AAAcNe5k/vAS0pKJMuWLQvjOI6GDh1qzcnJqbvRMRDgAADglu70PnC9Xl/JZ2xMoQMAgFNCn0J3B+gDBwAAcDMIcAAAAAFCgAMAAAgQAhwAAECAEOAAAMBb+766wAtVZy/b+/xC1Vlp+7469IFfQ3d39009DgEOAAC8eYZJza27DApHiF+oOitt3WVQeIZJefWBu2udaEZGRvjChQvlkyZNUi5evFh+M+8NvgcOAAC8eWtGmPwylbWtuwyKIbEBxvNHzsj8MpW13poRvLdSdcc6USKimpoar5KSEsOgQTcXxQhwAABwCW/NCNOQ2ADjuZLTwT73hDS6IryJ/r9OlIiuqBNdt25dSFNTk+e+ffuGZ2dnBxER9a4TXbZs2Rki53WiOTk5/haLhRmNxsFlZWVejDHqWydKRNTR0cEWLFgwqrKy0lskElF9ff2lO2tHnSgRUd860aKioqvWqaanp7fdbHgTIcABAMBFLlSdlZ4/ckbmc09I4/kjZ2TiscNNrghxd60T9fHx4VWKgjVwAADgzbHm7ZeprB3+4JjTjun0vh9sGwhCrBN1BdyBAwAAb10nTZLea96ONfGukyaJq6bS+yPEOlFXwF7oAADglND3Qr+T60SvF+pEAQDgrnMn14m6AgIcAADc0p1eJ8p3bEyhAwCAU0KfQncHqBMFAABwMwhwAAAAAUKAAwAACBACHAAAQIAQ4AAAAAKEAAcAAN4KCgoC9Xr9Zdum6vV6aUFBAfrAnXjjjTdkmzdvHsFnDAQ4AADwJpfLzbm5uQpHiOv1emlubq5CLpfz6gN3V6tWrTIuWbLkLJ8xEOAAAMCbSqUypaWl1ebm5iry8/NDcnNzFWlpabUqlYrXPuh6vd5z9OjRkQ8//PCoiIiIyJkzZ47Oy8uTxsbGqkeNGhVVWFgo6ejoEM2aNSs8KipKo9FotB9//PFwop6d2GbMmKFQKpXaX/3qV4px48api4uLJUREc+fODYuKitKMHTs28rnnngtxPF9RUZFk/PjxapVKpY2Ojta0tbWJ9Hq9Z1xcnEqr1Wq0Wq3mq6++GkJE9Pe//106ceJE1YwZMxTh4eFRixcvDt26datfdHS0RqlUaisqKsTOXxXRihUrQl566SVesxPYiQ0AAFxCpVKZdDqd8eDBg8GTJk1q5BveDqdOnfLauXNnbVxcXP24ceM027ZtG/H9999Xb9++ffj69euD1Wr1xaSkpI5PP/20rqWlxWPChAmamTNndmzcuFE2fPhwq8FgqDx8+LDX5MmTIx1jvvnmmz8FBgZaLRYLTZkyRXXw4EFvnU53ce7cuWO2bdtWk5iYaG5tbRX5+PjYBg0aZPn6668NEomEKy8vF8+ZM0fh2OGturrae/fu3bUBAQGWUaNGRYvF4pby8vKqtWvXBmRlZQV88MEHp1zxHjiDAAcAAJfQ6/XSsrIy2aRJkxrLyspkCoXC5IoQDw0N7YyPj79ARKRUKi8kJyd3iEQiio2NNa9bty6kqanJc9++fcOzs7ODiIg6OzvZiRMnPL/99lufZcuWnSEimjhx4kWlUnlpOv+jjz7yy8nJ8bdYLMxoNA4uKyvzYoxRQEBAd2JiopmIyM/Pz0ZE1NHRwRYsWDCqsrLSWyQSUX19/aU76+jo6POjRo3qJiIKCwvrnD59ejsRkU6nu1BUVDSgVaoIcAAA4M2x5u2YNlcoFCZXTaN7enpe2vNbJBKRo5DEw8ODrFYr8/Dw4Hbv3n1Cp9N19n5cf1uFV1dXe27evDmwtLS0SiaTWTMyMsIvXrwo4jiOGGNXPGj9+vWBAQEB3Xv27PmXzWYjb2/vOMfvxGKx02sTiURktVoZn9d9LVgDBwAA3hoaGiS9w9qxJt7Q0CAZ6OdOSkrqyMrKCrTZemq6S0pKvImIpkyZcm7Hjh2+RESlpaVeBoPBm4iora3Nw9vb2+bn52c9derUoAMHDgwjItLpdBebm5s9i4qKJPbzRN3d3dTe3u4RHBzc7eHhQe+8884Iq9U60C/puuAOHAAAeJs2bVpz32MqlcolU+jXsmHDhtNPPvlkmFqt1nIcx+RyeWdhYeGJlStXGjMzM8OVSqU2KirKrFKpLvj6+lqjo6M7o6KizBEREZFhYWGdcXFx54iIvLy8uG3bttUsXbo07OLFiyIvLy9bcXGxYfny5WcyMjLG5OXl+SYkJJi8vb1tA/2argfayAAAwCmht5FZLBbq6upiEomEq6ioEKempipramqOCakT/GptZLgDBwAAt2QymUT33nuvqru7m3EcR5s2baoXUnhfCwIcAADckq+vr83xda/bZfXq1UF79+71633soYcean399deb+I6NKXQAAHBK6FPo7uBqU+j4FDoAAIAAIcABAAAECAEOAAAgQAhwAAAAAUKAAwAAbzU1WYHGloLL9v42thRIa2qy0Ac+QBDgAADA29BhMebKyucVjhA3thRIKyufVwwdFsOrD9xd60TVarXW8ePl5RX7j3/8w+dG3xt8DxwAAHiT+U8zabUbaysrn1cEB6UbG5s+k2m1G2tl/tN4b6XqjnWi1dXVlURE27dvH5aVlRWUkpJy/kbfFwQ4AAC4hMx/mik4KN14qiEneKR8XqMrwpvIfetEy8vLxX/4wx/khYWFht6tZtcLAQ4AAC5hbCmQNjZ9Jhspn9fY2PSZzNdviskVIe6OdaIdHR2izMzMMVu3bq0PDw/vvoG34xKsgQMAAG+ONW+tdmOtUvniacd0et8Ptg0EIdaJzp49O3zu3LktDzzwwLmbHQMBDgAAvHW0H5X0XvN2rIl3tB8d8D7wDRs2nLZYLEytVmsjIiIiX3jhhVAiopUrVxrPnj07SKlUatevXx/kqBOdPHnyBUed6KOPPhrurE5UpVJpp06dqjSbzaLly5ef+eSTT0bodDq1wWDw4lsnajAYPL/44gvfjz/+2N/xQTbHh+tuBPZCBwAAp4S+FzrqRAEAAAQIdaIAAAAChDpRAAC4Kwl9Ct0doE4UAADAzSDAAQAABAgBDgAAIEAIcAAAAAFCgAMAgGAlJiaObWlp8bid17B8+fKQvLy8Ad9xri98jQwAAHh7rbYxMG6oxJzqP+zS3udftrRLSzvMkv9QBDcP1PMWFRWd6HvMZrMRx3Hk4XFrcv1Pf/rT6VvyRH3gDhwAAHiLGyoxP1t1UvFlS7uUqCe8n606qYgbKuHVB/7CCy8Erlu3LoCIaMGCBSN/8YtfKImI9u7dK33ooYdGh4aGRjc2Ng7S6/WeCoUi8re//W1YZGSktqamxnP37t1DtVqtRqVSaSdPnqwkIiosLJSMHz9erdFotOPHj1eXlZWJiYiys7NHpKSkjElOTh4bGhoa/eqrr8rWrFkTqNFotDqdTt3c3NzvXwMZGRnhH374oS+f13kzEOAAAMBbqv8w09uasNpnq04qXjzeEPJs1UnF25qw2t535DcjKSnpXElJiQ8R0dGjRyXnz5/36OzsZMXFxT4JCQmXjV1XV+f1xBNPnK2qqqr08fGxLVmyJPyzzz6r0ev1lXl5eTVEPYUlhw4dqq6qqqp8+eWXf1q1apXc8XiDweC9Z8+e2sOHD1e99tproRKJxFZVVVU5YcKE8+++++4IPq9jICDAAQDAJVL9h5kyg3yN7ze0BGcG+Rr5hjcRUUJCgrm8vHxIW1ubSCwWcxMmTDj39ddfS7777jtpcnLyZU1ewcHBXdOmTTtPRHTgwIEh8fHxJrVa3UVEFBgYaCUiam1t9ZgxY8aYiIiIyFWrVo00GAxejsdPmTLF5OvrawsJCbH4+PhYZ82a9TMRUXR0tLmurk5MdxgEOAAAuMSXLe3SXU1tst/J/Rt3NbXJHNPpfIjFYk4ul3du2bLFPz4+/tx99913bv/+/dL6+nrx+PHjL/Y+VyKRXGoJs3d7XzHe6tWrQxMTE03Hjx+v+Pzzz090dXVdysH+esdFIhFZLJZ+u71vFwQ4AADw5ljzflsTVrs2Qn7aMZ3uihCfMmXKuS1btgROnTrVlJKSYvroo49kWq3WLBL1H2FJSUnnDx48KK2urvYkInKsYXd0dHjI5fIuIqJ3333Xn++13U4IcAAA4K20wyzpvebtWBMv7TDz7gNPTEw0GY3GwcnJyedHjhxpEYvF3D333HPuao8JCQmxZGdn16WlpY1VqVTatLQ0BRHR6tWrm9asWSOPjY1VW61Wvpd2CWPslheLoMwEAACcQpnJ9UlOTh773HPPNT/44IO81/z7QpkJAADAAJg1a1b4hQsXRKmpqVedERgI2MgFAADgOjz66KNhhw8f9ul97Omnn25etmzZ2dtxPQhwAACA6/C3v/3t5O2+ht4whQ4AACBACHAAAAABQoADAAAIEAIcAABAgBDgAADA28Z9+sD9Vc2X7bq2v6pZunGfPnAgn/dO6AOPj49XFRcX896w5kYhwAEAgLeYsOHmFbuOKhwhvr+qWbpi11FFTNhwXnWi11JUVHTC39//si3VbDYbuXKXtTsVAhwAAHhL0QSa3syMqV2x66jilc8rQlbsOqp4MzOmNkUTyGt3MiH0gTtYrVZKT08PX7p0aYjFYqGMjIzwiIiISKVSqX3llVcC+LwPziDAAQDAJVI0gaaMWLnxw5K64IxYuZFveBMJpw+8u7ub/eY3vxkdERFxMTs7+/R3330naWxsHHz8+PEKg8FQ+cwzz7h8sxcEOAAAuMT+qmbpniMNsifuCW/cc6RB1ndN/GYIpQ988eLFo7Ra7YXXX3+9iYhIrVZ3njp1Svz444+P3L1791BfX1+Xz+kjwAEAgDfHmvebmTG1Lz8Yedoxnc43xIXSB27/w2Ko2WxmREQymcx67NixyqSkJNM777wTMHv27PCbfQ/6gwAHAADejp78WdJ7zduxJn705M+8P50thD7wRYsWtaSmprb/+te/HtPd3U2NjY2DrFYrzZs37+d169b9VF5e7vJPqSPAAQCAt+d/qWruu+adogk0Pf9LVTPfsYXQB05EtGbNmmadTmdOT08fXVdXNzghIUGlVqu18+fPH/3HP/6xwaVPRugDBwCAfqAP/PZDHzgAAICbQZ0oAADAdUAfOAAAgAChDxwAAAB4Q4ADAAAIEAIcAABAgBDgAAAAAoQABwAA/grWBpI+//JtU/X5UipY6/Z94LcLAhwAAPiTTzBT7lOKSyGuz5dS7lMKkk9AH/gAQYADAAB/qukmSvtzLeU+paD8fw+h3KcUlPbnWlJNd+s+8IqKCrFWq9U4/r+8vFwcGRmpcXauqyHAAQDANVTTTaSbY6SDW4NJN8fIN7yJ7vw+8MjIyE6pVGr99ttvvYl6ClIeeeSRW7KxCwIcAABcQ58vpbJPZDTp6UYq+0R2xZr4TRBCH/i8efNa3n//fX+LxUJ79+71XbBgAQIcAAAEwrHmnfbnWpq+4fSl6XSeIS6EPvDHH3+8rbCwcNiOHTuGR0dHm4OCgm7JAjwCHAAA+Gv4XnLZmrdjTbzhe7fvA5dIJFxiYmL7ihUrwubNm3fL2tsQ4AAAwN+0F5uvWPNWTTfRtBfvij7wxx57rJWIKD09vcNlg14D+sABAMAp9IFfv5deeimwvb3d46233jrtynGv1geONjIAAAAe7r///jH19fXioqIiw618XgQ4AADAdeivD/yrr76quR3XgwAHAAC4DugDBwAAobDZbLZ+vz4FA8v+3tv6+z0CHAAA+nPMaDQOQ4jfejabjRmNxmFEdKy/czCFDgAATlksloVNTU1/aWpqiiLc8N1qNiI6ZrFYFvZ3Ar5GBgAAIED4iwoAAECAEOAAAAAChAAHAAAQIAQ4AACAACHAAQAABOj/AM0c0pDnpxYdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dat in data:\n",
    "    wav_deets = FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))\n",
    "    depth = average_depths['5s'][average_depths['band'] == dat[0]]\n",
    "    #print(depth)\n",
    "    coverage = np.sum(~np.isnan(depths['ferr_{}_mean'.format(dat[0])]))/len(depths)\n",
    "    plt.plot(coverage, depth, 'x', label=dat[0])\n",
    "    \n",
    "plt.xlabel('Coverage')\n",
    "plt.ylabel('Depth')\n",
    "#plt.xscale('log')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Depths (5 $\\sigma$) vs coverage on {}'.format(FIELD))"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}