{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bootes 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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "708e28f (Tue May 8 18:05:21 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2018-06-06 14:09:48.308961\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
   },
   "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
   },
   "outputs": [],
   "source": [
    "FIELD = 'Bootes'\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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth maps produced using: master_catalogue_bootes_20180520.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": "markdown",
   "metadata": {},
   "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": 5,
   "metadata": {
    "collapsed": 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": 6,
   "metadata": {
    "collapsed": 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": 7,
   "metadata": {
    "collapsed": 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": {},
   "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": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "depths = Table()\n",
    "depths['hp_idx_O_13'] = list(field_moc.flattened(13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140002449179536-496819\" 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>173015040</td></tr>\n",
       "<tr><td>1</td><td>173015041</td></tr>\n",
       "<tr><td>2</td><td>173015042</td></tr>\n",
       "<tr><td>3</td><td>173015043</td></tr>\n",
       "<tr><td>4</td><td>173015044</td></tr>\n",
       "<tr><td>5</td><td>173015045</td></tr>\n",
       "<tr><td>6</td><td>173015046</td></tr>\n",
       "<tr><td>7</td><td>173015047</td></tr>\n",
       "<tr><td>8</td><td>173015048</td></tr>\n",
       "<tr><td>9</td><td>173015049</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(\"$('#table140002449179536-496819').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",
       "    $('#table140002449179536-496819').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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depths[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": 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": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140002450053048-732921\" 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>173015040</td><td>2703360</td></tr>\n",
       "<tr><td>1</td><td>173015041</td><td>2703360</td></tr>\n",
       "<tr><td>2</td><td>173015042</td><td>2703360</td></tr>\n",
       "<tr><td>3</td><td>173015043</td><td>2703360</td></tr>\n",
       "<tr><td>4</td><td>173015044</td><td>2703360</td></tr>\n",
       "<tr><td>5</td><td>173015045</td><td>2703360</td></tr>\n",
       "<tr><td>6</td><td>173015046</td><td>2703360</td></tr>\n",
       "<tr><td>7</td><td>173015047</td><td>2703360</td></tr>\n",
       "<tr><td>8</td><td>173015048</td><td>2703360</td></tr>\n",
       "<tr><td>9</td><td>173015049</td><td>2703360</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(\"$('#table140002450053048-732921').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",
       "    $('#table140002450053048-732921').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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depths[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table masked=True length=10</i>\n",
       "<table id=\"table140000766621848-203962\" 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_lbc_u_mean</th><th>f_ap_lbc_u_p90</th><th>ferr_lbc_u_mean</th><th>f_lbc_u_p90</th><th>ferr_ap_suprime_z_mean</th><th>f_ap_suprime_z_p90</th><th>ferr_suprime_z_mean</th><th>f_suprime_z_p90</th><th>ferr_ap_lbc_y_mean</th><th>f_ap_lbc_y_p90</th><th>ferr_lbc_y_mean</th><th>f_lbc_y_p90</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_90prime_g_mean</th><th>f_ap_90prime_g_p90</th><th>ferr_90prime_g_mean</th><th>f_90prime_g_p90</th><th>ferr_ap_90prime_r_mean</th><th>f_ap_90prime_r_p90</th><th>ferr_90prime_r_mean</th><th>f_90prime_r_p90</th><th>ferr_ap_mosaic_z_mean</th><th>f_ap_mosaic_z_p90</th><th>ferr_mosaic_z_mean</th><th>f_mosaic_z_p90</th><th>ferr_ap_newfirm_j_mean</th><th>f_ap_newfirm_j_p90</th><th>ferr_newfirm_j_mean</th><th>f_newfirm_j_p90</th><th>ferr_ap_newfirm_h_mean</th><th>f_ap_newfirm_h_p90</th><th>ferr_newfirm_h_mean</th><th>f_newfirm_h_p90</th><th>ferr_ap_newfirm_k_mean</th><th>f_ap_newfirm_k_p90</th><th>ferr_newfirm_k_mean</th><th>f_newfirm_k_p90</th><th>ferr_ap_mosaic_r_mean</th><th>f_ap_mosaic_r_p90</th><th>ferr_mosaic_r_mean</th><th>f_mosaic_r_p90</th><th>ferr_ap_mosaic_i_mean</th><th>f_ap_mosaic_i_p90</th><th>ferr_mosaic_i_mean</th><th>f_mosaic_i_p90</th><th>ferr_ap_mosaic_b_mean</th><th>f_ap_mosaic_b_p90</th><th>ferr_mosaic_b_mean</th><th>f_mosaic_b_p90</th><th>ferr_ap_tifkam_ks_mean</th><th>f_ap_tifkam_ks_p90</th><th>ferr_tifkam_ks_mean</th><th>f_tifkam_ks_p90</th><th>ferr_ap_ukidss_j_mean</th><th>f_ap_ukidss_j_p90</th><th>ferr_ukidss_j_mean</th><th>f_ukidss_j_p90</th><th>ferr_ap_90prime_z_mean</th><th>f_ap_90prime_z_p90</th><th>ferr_90prime_z_mean</th><th>f_90prime_z_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_ap_irac_i3_mean</th><th>f_ap_irac_i3_p90</th><th>ferr_irac_i3_mean</th><th>f_irac_i3_p90</th><th>ferr_ap_irac_i4_mean</th><th>f_ap_irac_i4_p90</th><th>ferr_irac_i4_mean</th><th>f_irac_i4_p90</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th></tr></thead>\n",
       "<tr><td>0</td><td>149674994</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>1</td><td>149675002</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>2</td><td>149675001</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>3</td><td>149675000</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>4</td><td>149674999</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>5</td><td>149674998</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>6</td><td>149674997</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>7</td><td>149674996</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>8</td><td>149674995</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</td></tr>\n",
       "<tr><td>9</td><td>149675006</td><td>2338671</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>0.8392324083173004</td><td>31.046577616192874</td><td>0.8657326770316196</td><td>31.27755293348835</td><td>1.1736306608327893</td><td>85.59320689991542</td><td>1.1421705937590465</td><td>79.70249626319753</td><td>0.9734246929698488</td><td>130.0876987305507</td><td>1.0073965881313725</td><td>124.61356497061342</td><td>1.7379776055106324</td><td>137.75760760948938</td><td>1.783169591999779</td><td>139.75768666421965</td><td>4.687395213906081</td><td>159.90040448321565</td><td>5.899168607331465</td><td>139.58929184793152</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><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>5.8274107</td><td>262.86640472412125</td><td>11.780505</td><td>384.5305541992187</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0048719502606798</td><td>40.8602729001019</td><td>1.3695022571516613</td><td>46.72994387225</td><td>1.4119870764985085</td><td>34.25974040555865</td><td>1.8975087224173106</td><td>38.052712284989354</td><td>6.921893583123945</td><td>66.70683194865015</td><td>7.956744744911518</td><td>65.4251895533033</td><td>7.839228161621938</td><td>118.934219784739</td><td>8.263738796064896</td><td>140.4056065654375</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(\"$('#table140000766621848-203962').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",
       "    $('#table140000766621848-203962').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, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "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": {},
   "source": [
    "## III - Save the depth map table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "depths.write(\"{}/depths_{}_{}.fits\".format(OUT_DIR, FIELD.lower(), SUFFIX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'90prime_g',\n",
       " '90prime_r',\n",
       " '90prime_z',\n",
       " 'gpc1_g',\n",
       " 'gpc1_i',\n",
       " 'gpc1_r',\n",
       " 'gpc1_y',\n",
       " 'gpc1_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'irac_i3',\n",
       " 'irac_i4',\n",
       " 'lbc_u',\n",
       " 'lbc_y',\n",
       " 'mosaic_b',\n",
       " 'mosaic_i',\n",
       " 'mosaic_r',\n",
       " 'mosaic_z',\n",
       " 'newfirm_h',\n",
       " 'newfirm_j',\n",
       " 'newfirm_k',\n",
       " 'suprime_z',\n",
       " 'tifkam_ks',\n",
       " 'ukidss_j'}"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Passbands on Bootes')"
      ]
     },
     "execution_count": 15,
     "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": {},
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lbc_u: mean flux error: 0.06858491941877783, 3sigma in AB mag (Aperture): 25.61662528141226\n",
      "suprime_z: mean flux error: 0.18357881437827364, 3sigma in AB mag (Aperture): 24.54764046147205\n",
      "lbc_y: mean flux error: 0.5952490853023321, 3sigma in AB mag (Aperture): 23.27045002141869\n",
      "gpc1_g: mean flux error: 78.27960273146415, 3sigma in AB mag (Aperture): 17.97307533082698\n",
      "gpc1_r: mean flux error: 5.609668627654016, 3sigma in AB mag (Aperture): 20.834853844388356\n",
      "gpc1_i: mean flux error: 3.2944611808114352, 3sigma in AB mag (Aperture): 21.412735876828357\n",
      "gpc1_z: mean flux error: 8.156313321484136, 3sigma in AB mag (Aperture): 20.42846211151639\n",
      "gpc1_y: mean flux error: 133.39289646848695, 3sigma in AB mag (Aperture): 17.39436510609135\n",
      "90prime_g: mean flux error: 0.14481088519096375, 3sigma in AB mag (Aperture): 24.805193842513994\n",
      "90prime_r: mean flux error: 0.22160203754901886, 3sigma in AB mag (Aperture): 24.34326249006994\n",
      "mosaic_z: mean flux error: 0.815993070602417, 3sigma in AB mag (Aperture): 22.927980686328176\n",
      "newfirm_j: mean flux error: 1.2217798842030239, 3sigma in AB mag (Aperture): 22.48971443699157\n",
      "newfirm_h: mean flux error: 1.4701830284224555, 3sigma in AB mag (Aperture): 22.288768350674268\n",
      "newfirm_k: mean flux error: 2.8174512755388683, 3sigma in AB mag (Aperture): 21.582555827756487\n",
      "mosaic_r: mean flux error: 0.09790217416792393, 3sigma in AB mag (Aperture): 25.23021602187871\n",
      "mosaic_i: mean flux error: 0.21016109009426603, 3sigma in AB mag (Aperture): 24.40081608208593\n",
      "mosaic_b: mean flux error: 0.03277110743675289, 3sigma in AB mag (Aperture): 26.418469068751286\n",
      "tifkam_ks: mean flux error: 13.31141314114591, 3sigma in AB mag (Aperture): 19.896636456515587\n",
      "ukidss_j: mean flux error: 6.271023273468018, 3sigma in AB mag (Aperture): 20.713850831793486\n",
      "90prime_z: mean flux error: 0.7970352172851562, 3sigma in AB mag (Aperture): 22.95350308500948\n",
      "irac_i1: mean flux error: 0.8156020827530097, 3sigma in AB mag (Aperture): 22.92850104785409\n",
      "irac_i2: mean flux error: 1.1191111641340572, 3sigma in AB mag (Aperture): 22.585013792607974\n",
      "irac_i3: mean flux error: 5.830178142347162, 3sigma in AB mag (Aperture): 20.792992300893197\n",
      "irac_i4: mean flux error: 8.119325677152942, 3sigma in AB mag (Aperture): 20.433396958463725\n",
      "lbc_u: mean flux error: 0.1211577576656113, 3sigma in AB mag (Total): 24.998818795690077\n",
      "suprime_z: mean flux error: 0.3241080704421438, 3sigma in AB mag (Total): 23.930472249945744\n",
      "lbc_y: mean flux error: 1.0487786250277948, 3sigma in AB mag (Total): 22.655487294468394\n",
      "gpc1_g: mean flux error: 50.792368180524875, 3sigma in AB mag (Total): 18.442700707790245\n",
      "gpc1_r: mean flux error: 7.43601034342422, 3sigma in AB mag (Total): 20.52884690020074\n",
      "gpc1_i: mean flux error: 3.017807440593753, 3sigma in AB mag (Total): 21.507968050741376\n",
      "gpc1_z: mean flux error: 8.235763927747607, 3sigma in AB mag (Total): 20.41793713973761\n",
      "gpc1_y: mean flux error: 70.1982475064021, 3sigma in AB mag (Total): 18.091381187854942\n",
      "90prime_g: mean flux error: inf, 3sigma in AB mag (Total): -inf\n",
      "90prime_r: mean flux error: inf, 3sigma in AB mag (Total): -inf\n",
      "mosaic_z: mean flux error: inf, 3sigma in AB mag (Total): -inf\n",
      "newfirm_j: mean flux error: 1.7624809402151855, 3sigma in AB mag (Total): 22.091885790324703\n",
      "newfirm_h: mean flux error: 2.406704747863169, 3sigma in AB mag (Total): 21.753639826377842\n",
      "newfirm_k: mean flux error: 4.713215366279398, 3sigma in AB mag (Total): 21.023903650896095\n",
      "mosaic_r: mean flux error: 0.1818732809988264, 3sigma in AB mag (Total): 24.557774609317498\n",
      "mosaic_i: mean flux error: 0.41097359690109714, 3sigma in AB mag (Total): 23.672662059657206\n",
      "mosaic_b: mean flux error: 0.05928284329727458, 3sigma in AB mag (Total): 25.774874300928523\n",
      "tifkam_ks: mean flux error: 607.9493647605982, 3sigma in AB mag (Total): 15.747528340680269\n",
      "ukidss_j: mean flux error: 12.440366744995117, 3sigma in AB mag (Total): 19.97011390407834\n",
      "90prime_z: mean flux error: 1.3416924476623535, 3sigma in AB mag (Total): 22.38806442531496\n",
      "irac_i1: mean flux error: 1.3593818531660908, 3sigma in AB mag (Total): 22.37384319298696\n",
      "irac_i2: mean flux error: 1.8301237652477456, 3sigma in AB mag (Total): 22.050995711625283\n",
      "irac_i3: mean flux error: 9.667920628531377, 3sigma in AB mag (Total): 20.24386417247691\n",
      "irac_i4: mean flux error: 13.412966232566015, 3sigma in AB mag (Total): 19.88838478509266\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": 17,
   "metadata": {
    "collapsed": 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": 18,
   "metadata": {
    "collapsed": 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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap_lbc_u (3340.0, 3820.0, 480.0)\n",
      "ap_suprime_z (8540.0, 9280.0, 740.0)\n",
      "ap_lbc_y (9635.0, 11260.0, 1625.0)\n",
      "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_90prime_g (4180.0, 5470.0, 1290.0)\n",
      "ap_90prime_r (5680.0, 7150.0, 1470.0)\n",
      "ap_mosaic_z (8552.0, 10018.0, 1466.0)\n",
      "ap_newfirm_j (11600.0, 13387.0, 1787.0)\n",
      "ap_newfirm_h (14795.0, 17860.0, 3065.0)\n",
      "ap_newfirm_k (19845.0, 23060.0, 3215.0)\n",
      "ap_mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "ap_mosaic_i (7270.0, 9180.0, 1910.0)\n",
      "ap_mosaic_b (3474.0, 4750.0, 1276.0)\n",
      "ap_tifkam_ks (19500.0, 23000.0, 3500.0)\n",
      "ap_ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "ap_90prime_z (8290.0, 12000.0, 3710.0)\n",
      "ap_irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "ap_irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "ap_irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "ap_irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "lbc_u (3340.0, 3820.0, 480.0)\n",
      "suprime_z (8540.0, 9280.0, 740.0)\n",
      "lbc_y (9635.0, 11260.0, 1625.0)\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",
      "90prime_g (4180.0, 5470.0, 1290.0)\n",
      "90prime_r (5680.0, 7150.0, 1470.0)\n",
      "mosaic_z (8552.0, 10018.0, 1466.0)\n",
      "newfirm_j (11600.0, 13387.0, 1787.0)\n",
      "newfirm_h (14795.0, 17860.0, 3065.0)\n",
      "newfirm_k (19845.0, 23060.0, 3215.0)\n",
      "mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "mosaic_i (7270.0, 9180.0, 1910.0)\n",
      "mosaic_b (3474.0, 4750.0, 1276.0)\n",
      "tifkam_ks (19500.0, 23000.0, 3500.0)\n",
      "ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "90prime_z (8290.0, 12000.0, 3710.0)\n",
      "irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "irac_i4 (64415.2, 92596.8, 28181.598)\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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths on Bootes')"
      ]
     },
     "execution_count": 20,
     "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": {},
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths (5 $\\\\sigma$) vs coverage on Bootes')"
      ]
     },
     "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",
    "    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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}