{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HDF-N 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",
      "2018-06-07 11:57:43.532090\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 = 'HDF-N'\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_hdf-n_20180427.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=\"table139834877140832-394903\" 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>184352768</td></tr>\n",
       "<tr><td>1</td><td>184352769</td></tr>\n",
       "<tr><td>2</td><td>184352770</td></tr>\n",
       "<tr><td>3</td><td>184352771</td></tr>\n",
       "<tr><td>4</td><td>184352772</td></tr>\n",
       "<tr><td>5</td><td>184352773</td></tr>\n",
       "<tr><td>6</td><td>184352774</td></tr>\n",
       "<tr><td>7</td><td>184352775</td></tr>\n",
       "<tr><td>8</td><td>184352776</td></tr>\n",
       "<tr><td>9</td><td>184352777</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(\"$('#table139834877140832-394903').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",
       "    $('#table139834877140832-394903').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=\"table139833219872136-169720\" 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>184352768</td><td>2880512</td></tr>\n",
       "<tr><td>1</td><td>184352769</td><td>2880512</td></tr>\n",
       "<tr><td>2</td><td>184352770</td><td>2880512</td></tr>\n",
       "<tr><td>3</td><td>184352771</td><td>2880512</td></tr>\n",
       "<tr><td>4</td><td>184352772</td><td>2880512</td></tr>\n",
       "<tr><td>5</td><td>184352773</td><td>2880512</td></tr>\n",
       "<tr><td>6</td><td>184352774</td><td>2880512</td></tr>\n",
       "<tr><td>7</td><td>184352775</td><td>2880512</td></tr>\n",
       "<tr><td>8</td><td>184352776</td><td>2880512</td></tr>\n",
       "<tr><td>9</td><td>184352777</td><td>2880512</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(\"$('#table139833219872136-169720').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",
       "    $('#table139833219872136-169720').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=\"table139833220117504-468131\" 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_acs_f435w_mean</th><th>f_acs_f435w_p90</th><th>ferr_acs_f606w_mean</th><th>f_acs_f606w_p90</th><th>ferr_acs_f775w_mean</th><th>f_acs_f775w_p90</th><th>ferr_acs_f814w_mean</th><th>f_acs_f814w_p90</th><th>ferr_acs_f850lp_mean</th><th>f_acs_f850lp_p90</th><th>ferr_wfc3_f105w_mean</th><th>f_wfc3_f105w_p90</th><th>ferr_wfc3_f125w_mean</th><th>f_wfc3_f125w_p90</th><th>ferr_wfc3_f140w_mean</th><th>f_wfc3_f140w_p90</th><th>ferr_wfc3_f160w_mean</th><th>f_wfc3_f160w_p90</th><th>ferr_moircs_k_mean</th><th>f_moircs_k_p90</th><th>ferr_ap_mosaic_u_mean</th><th>f_ap_mosaic_u_p90</th><th>ferr_mosaic_u_mean</th><th>f_mosaic_u_p90</th><th>ferr_ap_suprime_b_mean</th><th>f_ap_suprime_b_p90</th><th>ferr_suprime_b_mean</th><th>f_suprime_b_p90</th><th>ferr_ap_suprime_v_mean</th><th>f_ap_suprime_v_p90</th><th>ferr_suprime_v_mean</th><th>f_suprime_v_p90</th><th>ferr_ap_suprime_r_mean</th><th>f_ap_suprime_r_p90</th><th>ferr_suprime_r_mean</th><th>f_suprime_r_p90</th><th>ferr_ap_suprime_ip_mean</th><th>f_ap_suprime_ip_p90</th><th>ferr_suprime_ip_mean</th><th>f_suprime_ip_p90</th><th>ferr_ap_suprime_zp_mean</th><th>f_ap_suprime_zp_p90</th><th>ferr_suprime_zp_mean</th><th>f_suprime_zp_p90</th><th>ferr_ap_quirc_hk_mean</th><th>f_ap_quirc_hk_p90</th><th>ferr_quirc_hk_mean</th><th>f_quirc_hk_p90</th><th>ferr_wircam_ks_mean</th><th>f_wircam_ks_p90</th><th>ferr_irac_i1_mean</th><th>f_irac_i1_p90</th><th>ferr_irac_i2_mean</th><th>f_irac_i2_p90</th><th>ferr_irac_i3_mean</th><th>f_irac_i3_p90</th><th>ferr_irac_i4_mean</th><th>f_irac_i4_p90</th></tr></thead>\n",
       "<tr><td>0</td><td>183959151</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959162</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959163</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959161</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959164</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959167</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959160</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959165</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959166</td><td>2874361</td><td>1.363497282674552</td><td>38.468634120802186</td><td>0.9426538147901358</td><td>32.19875177227638</td><td>1.0027750347684428</td><td>56.860910339650104</td><td>0.8231540041994462</td><td>51.45495288851104</td><td>2.0255022315124083</td><td>95.76497885446148</td><td>2.802394650138773</td><td>112.12098489003019</td><td>2.5900180096689485</td><td>198.04258256816408</td><td>2.3234836982805764</td><td>179.28497905558473</td><td>3.8971351365822837</td><td>258.88431214855905</td><td>4.803028108481139</td><td>242.96330472757515</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>183959225</td><td>2874362</td><td>1.43261960371305</td><td>85.53385697204412</td><td>1.5873599271375647</td><td>102.48758908097513</td><td>1.373677702940366</td><td>174.36903217485144</td><td>1.6138222516422884</td><td>222.80330242802876</td><td>1.9421217039324508</td><td>252.5169612102601</td><td>1.8158588779852065</td><td>342.3128661821219</td><td>3.7378654948534598</td><td>289.7953063322273</td><td>2.8396180902727153</td><td>384.0092665646449</td><td>5.781585147451137</td><td>300.4929742086996</td><td>8.944130774023883</td><td>475.57933031058644</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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(\"$('#table139833220117504-468131').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",
       "    $('#table139833220117504-468131').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], 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": [
       "{'acs_f435w',\n",
       " 'acs_f606w',\n",
       " 'acs_f775w',\n",
       " 'acs_f814w',\n",
       " 'acs_f850lp',\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",
       " 'moircs_k',\n",
       " 'mosaic_u',\n",
       " 'quirc_hk',\n",
       " 'suprime_b',\n",
       " 'suprime_ip',\n",
       " 'suprime_r',\n",
       " 'suprime_v',\n",
       " 'suprime_zp',\n",
       " 'wfc3_f105w',\n",
       " 'wfc3_f125w',\n",
       " 'wfc3_f140w',\n",
       " 'wfc3_f160w',\n",
       " 'wircam_ks'}"
      ]
     },
     "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 HDF-N')"
      ]
     },
     "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": [
      "gpc1_g: mean flux error: 49.31889105795856, 3sigma in AB mag (Aperture): 18.474663606044878\n",
      "gpc1_r: mean flux error: 3.9650023892121498, 3sigma in AB mag (Aperture): 21.21158822982887\n",
      "gpc1_i: mean flux error: 28.839910761999484, 3sigma in AB mag (Aperture): 19.05721208262043\n",
      "gpc1_z: mean flux error: 21.881597184502564, 3sigma in AB mag (Aperture): 19.356999315950922\n",
      "gpc1_y: mean flux error: 42.15458136573825, 3sigma in AB mag (Aperture): 18.64508491142542\n",
      "mosaic_u: mean flux error: 0.037849102429692624, 3sigma in AB mag (Aperture): 26.262057900932525\n",
      "suprime_b: mean flux error: 0.036868076515944356, 3sigma in AB mag (Aperture): 26.290570662818745\n",
      "suprime_v: mean flux error: 0.043454611888431194, 3sigma in AB mag (Aperture): 26.11210717467946\n",
      "suprime_r: mean flux error: 0.034602777768592184, 3sigma in AB mag (Aperture): 26.35941945428616\n",
      "suprime_ip: mean flux error: 0.09358766967248164, 3sigma in AB mag (Aperture): 25.279150279441858\n",
      "suprime_zp: mean flux error: 0.12159221232312142, 3sigma in AB mag (Aperture): 24.994932462318793\n",
      "quirc_hk: mean flux error: 2.6074063198573043, 3sigma in AB mag (Aperture): 21.666675078603895\n",
      "gpc1_g: mean flux error: 21.940918363220646, 3sigma in AB mag (Total): 19.354059859373486\n",
      "gpc1_r: mean flux error: 3.539784110499412, 3sigma in AB mag (Total): 21.334754924575584\n",
      "gpc1_i: mean flux error: 5.930459506566338, 3sigma in AB mag (Total): 20.774476001023437\n",
      "gpc1_z: mean flux error: 9.88114732694127, 3sigma in AB mag (Total): 20.220178426622915\n",
      "gpc1_y: mean flux error: 40.950411302280756, 3sigma in AB mag (Total): 18.67655119286733\n",
      "acs_f435w: mean flux error: 24518589460.49422, 3sigma in AB mag (Total): -3.266541841469696\n",
      "acs_f606w: mean flux error: -3485900157.9875946, 3sigma in AB mag (Total): nan\n",
      "acs_f775w: mean flux error: -8150367983.31671, 3sigma in AB mag (Total): nan\n",
      "acs_f814w: mean flux error: -6035782824.048011, 3sigma in AB mag (Total): nan\n",
      "acs_f850lp: mean flux error: -19538527428.49734, 3sigma in AB mag (Total): nan\n",
      "wfc3_f105w: mean flux error: 17951690756.750423, 3sigma in AB mag (Total): -2.9280665325595123\n",
      "wfc3_f125w: mean flux error: 3149191470.7425175, 3sigma in AB mag (Total): -1.0383008031245353\n",
      "wfc3_f140w: mean flux error: 18078406457.130688, 3sigma in AB mag (Total): -2.93570350284228\n",
      "wfc3_f160w: mean flux error: 0.06776823408194037, 3sigma in AB mag (Total): 25.62963144106113\n",
      "moircs_k: mean flux error: 832.740080918873, 3sigma in AB mag (Total): 15.405923192329148\n",
      "mosaic_u: mean flux error: 0.037338022674080984, 3sigma in AB mag (Total): 26.27681857545057\n",
      "suprime_b: mean flux error: 0.015313337191511709, 3sigma in AB mag (Total): 27.244522249302413\n",
      "suprime_v: mean flux error: inf, 3sigma in AB mag (Total): -inf\n",
      "suprime_r: mean flux error: 0.025553774485105058, 3sigma in AB mag (Total): 26.68855921875754\n",
      "suprime_ip: mean flux error: 0.05139216547444444, 3sigma in AB mag (Total): 25.929954569144165\n",
      "suprime_zp: mean flux error: 0.07008859890513011, 3sigma in AB mag (Total): 25.593078417260124\n",
      "quirc_hk: mean flux error: inf, 3sigma in AB mag (Total): -inf\n",
      "wircam_ks: mean flux error: 0.7207638901599277, 3sigma in AB mag (Total): 23.062714311645685\n",
      "irac_i1: mean flux error: 0.11422102499281485, 3sigma in AB mag (Total): 25.062831730441992\n",
      "irac_i2: mean flux error: 0.1062667161755575, 3sigma in AB mag (Total): 25.141203712353082\n",
      "irac_i3: mean flux error: -94.31939782470842, 3sigma in AB mag (Total): nan\n",
      "irac_i4: mean flux error: -6.697126659591726, 3sigma in AB mag (Total): nan\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_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_mosaic_u (3276.0, 3890.0, 614.0)\n",
      "ap_suprime_b (3827.0, 4906.0, 1079.0)\n",
      "ap_suprime_v (4941.6, 5925.8, 984.1997)\n",
      "ap_suprime_r (5440.0, 6960.0, 1520.0)\n",
      "ap_suprime_ip (6895.0, 8437.5, 1542.5)\n",
      "ap_suprime_zp (8073.5, 8416.0, 342.5)\n",
      "ap_quirc_hk (8073.5, 8416.0, 342.5)\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",
      "acs_f435w (3919.51, 4798.78, 879.2698)\n",
      "acs_f606w (4835.4, 7088.47, 2253.0703)\n",
      "acs_f775w (7004.51, 8521.38, 1516.8701)\n",
      "acs_f814w (7069.67, 9138.11, 2068.4404)\n",
      "acs_f850lp (8308.93, 9584.25, 1275.3203)\n",
      "wfc3_f105w (9072.924, 11989.37, 2916.4463)\n",
      "wfc3_f125w (10993.5, 13997.47, 3003.9697)\n",
      "wfc3_f140w (12005.91, 15946.44, 3940.5303)\n",
      "wfc3_f160w (13996.34, 16869.92, 2873.58)\n",
      "moircs_k (20072.0, 24010.0, 3938.0)\n",
      "mosaic_u (3276.0, 3890.0, 614.0)\n",
      "suprime_b (3827.0, 4906.0, 1079.0)\n",
      "suprime_v (4941.6, 5925.8, 984.1997)\n",
      "suprime_r (5440.0, 6960.0, 1520.0)\n",
      "suprime_ip (6895.0, 8437.5, 1542.5)\n",
      "suprime_zp (8073.5, 8416.0, 342.5)\n",
      "quirc_hk (8073.5, 8416.0, 342.5)\n",
      "wircam_ks (19870.0, 23135.0, 3265.0)\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 HDF-N')"
      ]
     },
     "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 HDF-N')"
      ]
     },
     "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
}