{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ELAIS-S1 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 15:02:53.802288\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 = 'ELAIS-S1'\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_elais-s1_20180416.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": [
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       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
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       ".paginate_button { margin-right: 5px; }\n",
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       "<script>\n",
       "\n",
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       "    var b_num = parseFloat(b);\n",
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       "\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(\"$('#table139660999196400-93908').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
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     "execution_count": 9,
     "metadata": {},
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   ],
   "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",
    "                 )"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
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    "depths[:10].show_in_notebook()"
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  {
   "cell_type": "code",
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   "metadata": {},
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       "<thead><tr><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></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></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>579661755</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10253683943416918</td><td>1.4265529385386937</td><td>0.1460923498077918</td><td>1.7070464050850118</td><td>0.14869203352370855</td><td>3.0258897439856653</td><td>0.2509830730496574</td><td>3.5084690905699087</td><td>0.21142687973133892</td><td>3.914251987075183</td><td>0.38763642313471236</td><td>5.110825495537506</td><td>0.39847476807310633</td><td>5.537832756051641</td><td>0.7360941498592666</td><td>7.022521970779043</td><td>0.9973134591903171</td><td>4.447060326671522</td><td>1.5564902081199687</td><td>5.676530085106684</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>579661759</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10253683943416918</td><td>1.4265529385386937</td><td>0.1460923498077918</td><td>1.7070464050850118</td><td>0.14869203352370855</td><td>3.0258897439856653</td><td>0.2509830730496574</td><td>3.5084690905699087</td><td>0.21142687973133892</td><td>3.914251987075183</td><td>0.38763642313471236</td><td>5.110825495537506</td><td>0.39847476807310633</td><td>5.537832756051641</td><td>0.7360941498592666</td><td>7.022521970779043</td><td>0.9973134591903171</td><td>4.447060326671522</td><td>1.5564902081199687</td><td>5.676530085106684</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>579661757</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10253683943416918</td><td>1.4265529385386937</td><td>0.1460923498077918</td><td>1.7070464050850118</td><td>0.14869203352370855</td><td>3.0258897439856653</td><td>0.2509830730496574</td><td>3.5084690905699087</td><td>0.21142687973133892</td><td>3.914251987075183</td><td>0.38763642313471236</td><td>5.110825495537506</td><td>0.39847476807310633</td><td>5.537832756051641</td><td>0.7360941498592666</td><td>7.022521970779043</td><td>0.9973134591903171</td><td>4.447060326671522</td><td>1.5564902081199687</td><td>5.676530085106684</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>579661758</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10253683943416918</td><td>1.4265529385386937</td><td>0.1460923498077918</td><td>1.7070464050850118</td><td>0.14869203352370855</td><td>3.0258897439856653</td><td>0.2509830730496574</td><td>3.5084690905699087</td><td>0.21142687973133892</td><td>3.914251987075183</td><td>0.38763642313471236</td><td>5.110825495537506</td><td>0.39847476807310633</td><td>5.537832756051641</td><td>0.7360941498592666</td><td>7.022521970779043</td><td>0.9973134591903171</td><td>4.447060326671522</td><td>1.5564902081199687</td><td>5.676530085106684</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>579661806</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.10859126356645225</td><td>3.875006286770472</td><td>0.14107561960120038</td><td>4.916960101650682</td><td>0.15667704403476393</td><td>10.891986970814688</td><td>0.24632590218390513</td><td>13.226128363941061</td><td>0.202028980030533</td><td>18.93836655764752</td><td>0.34149243436641685</td><td>24.065521326050522</td><td>0.3874088076472132</td><td>24.817926021695747</td><td>0.6741922195621901</td><td>36.407716048320516</td><td>1.000018923854838</td><td>27.306215942671347</td><td>1.4856517397648452</td><td>39.905540971609426</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>2.041012658227848</td><td>173.3840000000001</td><td>4.285569620253165</td><td>146.39600000000004</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.0540328919887543</td><td>549.8460754394532</td><td>6.582599020004272</td><td>537.4501342773439</td><td>3.8200550134791884</td><td>529.6742309570319</td><td>9.352321846540583</td><td>544.59169921875</td><td>4.313794877794054</td><td>381.8941528320315</td><td>10.533315987057156</td><td>468.2950744628906</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>5</td><td>579661783</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.10859126356645225</td><td>3.875006286770472</td><td>0.14107561960120038</td><td>4.916960101650682</td><td>0.15667704403476393</td><td>10.891986970814688</td><td>0.24632590218390513</td><td>13.226128363941061</td><td>0.202028980030533</td><td>18.93836655764752</td><td>0.34149243436641685</td><td>24.065521326050522</td><td>0.3874088076472132</td><td>24.817926021695747</td><td>0.6741922195621901</td><td>36.407716048320516</td><td>1.000018923854838</td><td>27.306215942671347</td><td>1.4856517397648452</td><td>39.905540971609426</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>2.041012658227848</td><td>173.3840000000001</td><td>4.285569620253165</td><td>146.39600000000004</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.0540328919887543</td><td>549.8460754394532</td><td>6.582599020004272</td><td>537.4501342773439</td><td>3.8200550134791884</td><td>529.6742309570319</td><td>9.352321846540583</td><td>544.59169921875</td><td>4.313794877794054</td><td>381.8941528320315</td><td>10.533315987057156</td><td>468.2950744628906</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>6</td><td>579661814</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.10859126356645225</td><td>3.875006286770472</td><td>0.14107561960120038</td><td>4.916960101650682</td><td>0.15667704403476393</td><td>10.891986970814688</td><td>0.24632590218390513</td><td>13.226128363941061</td><td>0.202028980030533</td><td>18.93836655764752</td><td>0.34149243436641685</td><td>24.065521326050522</td><td>0.3874088076472132</td><td>24.817926021695747</td><td>0.6741922195621901</td><td>36.407716048320516</td><td>1.000018923854838</td><td>27.306215942671347</td><td>1.4856517397648452</td><td>39.905540971609426</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>2.041012658227848</td><td>173.3840000000001</td><td>4.285569620253165</td><td>146.39600000000004</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.0540328919887543</td><td>549.8460754394532</td><td>6.582599020004272</td><td>537.4501342773439</td><td>3.8200550134791884</td><td>529.6742309570319</td><td>9.352321846540583</td><td>544.59169921875</td><td>4.313794877794054</td><td>381.8941528320315</td><td>10.533315987057156</td><td>468.2950744628906</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>7</td><td>579661813</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.10859126356645225</td><td>3.875006286770472</td><td>0.14107561960120038</td><td>4.916960101650682</td><td>0.15667704403476393</td><td>10.891986970814688</td><td>0.24632590218390513</td><td>13.226128363941061</td><td>0.202028980030533</td><td>18.93836655764752</td><td>0.34149243436641685</td><td>24.065521326050522</td><td>0.3874088076472132</td><td>24.817926021695747</td><td>0.6741922195621901</td><td>36.407716048320516</td><td>1.000018923854838</td><td>27.306215942671347</td><td>1.4856517397648452</td><td>39.905540971609426</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>2.041012658227848</td><td>173.3840000000001</td><td>4.285569620253165</td><td>146.39600000000004</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.0540328919887543</td><td>549.8460754394532</td><td>6.582599020004272</td><td>537.4501342773439</td><td>3.8200550134791884</td><td>529.6742309570319</td><td>9.352321846540583</td><td>544.59169921875</td><td>4.313794877794054</td><td>381.8941528320315</td><td>10.533315987057156</td><td>468.2950744628906</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>8</td><td>579661804</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.10859126356645225</td><td>3.875006286770472</td><td>0.14107561960120038</td><td>4.916960101650682</td><td>0.15667704403476393</td><td>10.891986970814688</td><td>0.24632590218390513</td><td>13.226128363941061</td><td>0.202028980030533</td><td>18.93836655764752</td><td>0.34149243436641685</td><td>24.065521326050522</td><td>0.3874088076472132</td><td>24.817926021695747</td><td>0.6741922195621901</td><td>36.407716048320516</td><td>1.000018923854838</td><td>27.306215942671347</td><td>1.4856517397648452</td><td>39.905540971609426</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>2.041012658227848</td><td>173.3840000000001</td><td>4.285569620253165</td><td>146.39600000000004</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.0540328919887543</td><td>549.8460754394532</td><td>6.582599020004272</td><td>537.4501342773439</td><td>3.8200550134791884</td><td>529.6742309570319</td><td>9.352321846540583</td><td>544.59169921875</td><td>4.313794877794054</td><td>381.8941528320315</td><td>10.533315987057156</td><td>468.2950744628906</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>9</td><td>579661781</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.10859126356645225</td><td>3.875006286770472</td><td>0.14107561960120038</td><td>4.916960101650682</td><td>0.15667704403476393</td><td>10.891986970814688</td><td>0.24632590218390513</td><td>13.226128363941061</td><td>0.202028980030533</td><td>18.93836655764752</td><td>0.34149243436641685</td><td>24.065521326050522</td><td>0.3874088076472132</td><td>24.817926021695747</td><td>0.6741922195621901</td><td>36.407716048320516</td><td>1.000018923854838</td><td>27.306215942671347</td><td>1.4856517397648452</td><td>39.905540971609426</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>2.041012658227848</td><td>173.3840000000001</td><td>4.285569620253165</td><td>146.39600000000004</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.0540328919887543</td><td>549.8460754394532</td><td>6.582599020004272</td><td>537.4501342773439</td><td>3.8200550134791884</td><td>529.6742309570319</td><td>9.352321846540583</td><td>544.59169921875</td><td>4.313794877794054</td><td>381.8941528320315</td><td>10.533315987057156</td><td>468.2950744628906</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(\"$('#table139659562487880-224403').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",
       "    $('#table139659562487880-224403').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], 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": [
       "{'decam_g',\n",
       " 'decam_i',\n",
       " 'decam_r',\n",
       " 'decam_y',\n",
       " 'decam_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'irac_i3',\n",
       " 'irac_i4',\n",
       " 'vista_h',\n",
       " 'vista_j',\n",
       " 'vista_ks',\n",
       " 'vista_y',\n",
       " 'vista_z',\n",
       " 'wfi_b',\n",
       " 'wfi_b123',\n",
       " 'wfi_r',\n",
       " 'wfi_v'}"
      ]
     },
     "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 ELAIS-S1')"
      ]
     },
     "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": [
      "wfi_b: mean flux error: 0.054401807487010956, 3sigma in AB mag (Aperture): 25.868163540031013\n",
      "wfi_b123: mean flux error: 0.05324237421154976, 3sigma in AB mag (Aperture): 25.891553329403074\n",
      "wfi_v: mean flux error: 0.10746744275093079, 3sigma in AB mag (Aperture): 25.129004576412946\n",
      "wfi_r: mean flux error: 0.09391207247972488, 3sigma in AB mag (Aperture): 25.275393301217242\n",
      "irac_i3: mean flux error: 5.8527259007131, 3sigma in AB mag (Aperture): 20.788801399744038\n",
      "irac_i4: mean flux error: 5.78632855538187, 3sigma in AB mag (Aperture): 20.801189138634122\n",
      "decam_g: mean flux error: 0.12168966494844545, 3sigma in AB mag (Aperture): 24.994062624824856\n",
      "decam_r: mean flux error: 0.15984290092380252, 3sigma in AB mag (Aperture): 24.697963481232442\n",
      "decam_i: mean flux error: 0.24659686578722279, 3sigma in AB mag (Aperture): 24.227227982024026\n",
      "decam_z: mean flux error: 0.4820690610782045, 3sigma in AB mag (Aperture): 23.49942371419764\n",
      "decam_y: mean flux error: 1.1746954519327797, 3sigma in AB mag (Aperture): 22.53238364495531\n",
      "irac_i1: mean flux error: 0.8373222812628816, 3sigma in AB mag (Aperture): 22.899965243203603\n",
      "irac_i2: mean flux error: 1.070826479498174, 3sigma in AB mag (Aperture): 22.632899108394376\n",
      "vista_y: mean flux error: 0.14592709001697396, 3sigma in AB mag (Aperture): 24.796857057854076\n",
      "vista_j: mean flux error: 2.2535465742758944, 3sigma in AB mag (Aperture): 21.825030517931076\n",
      "vista_h: mean flux error: 3.167867061696901, 3sigma in AB mag (Aperture): 21.4552794924467\n",
      "vista_ks: mean flux error: 4.095491801562442, 3sigma in AB mag (Aperture): 21.176431710975557\n",
      "vista_z: mean flux error: 0.1127531528709974, 3sigma in AB mag (Aperture): 25.076875126397205\n",
      "wfi_b: mean flux error: 0.07563285529613495, 3sigma in AB mag (Total): 25.51042062265787\n",
      "wfi_b123: mean flux error: 0.07156137377023697, 3sigma in AB mag (Total): 25.570500190285223\n",
      "wfi_v: mean flux error: 0.15549196302890778, 3sigma in AB mag (Total): 24.72792699719563\n",
      "wfi_r: mean flux error: 0.1313200145959854, 3sigma in AB mag (Total): 24.911369557379935\n",
      "irac_i3: mean flux error: 5.839967250197483, 3sigma in AB mag (Total): 20.791170834074983\n",
      "irac_i4: mean flux error: 6.198815773303567, 3sigma in AB mag (Total): 20.726425039553156\n",
      "decam_g: mean flux error: 0.15755736996434655, 3sigma in AB mag (Total): 24.713600056422187\n",
      "decam_r: mean flux error: 0.24832557664463079, 3sigma in AB mag (Total): 24.21964323161631\n",
      "decam_i: mean flux error: 0.41516537499062856, 3sigma in AB mag (Total): 23.661644048291272\n",
      "decam_z: mean flux error: 0.825016533179737, 3sigma in AB mag (Total): 22.916040233653966\n",
      "decam_y: mean flux error: 1.7063437262444947, 3sigma in AB mag (Total): 22.127030563157653\n",
      "irac_i1: mean flux error: 1.0062078712067173, 3sigma in AB mag (Total): 22.70047758736468\n",
      "irac_i2: mean flux error: 1.1883765497745837, 3sigma in AB mag (Total): 22.519811679984663\n",
      "vista_y: mean flux error: 0.30092307832512366, 3sigma in AB mag (Total): 24.01105812362041\n",
      "vista_j: mean flux error: 4.070542734073568, 3sigma in AB mag (Total): 21.183066066981176\n",
      "vista_h: mean flux error: 6.7313166026445606, 3sigma in AB mag (Total): 20.636946818784956\n",
      "vista_ks: mean flux error: 8.963785936436242, 3sigma in AB mag (Total): 20.32596817162632\n",
      "vista_z: mean flux error: 0.23512196721122497, 3sigma in AB mag (Total): 24.278963846397254\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_wfi_b (408.5, 505.5, 97.0)\n",
      "ap_wfi_b123 (3875.0, 5105.0, 1230.0)\n",
      "ap_wfi_v (495.0, 583.5, 88.5)\n",
      "ap_wfi_r (570.5, 729.0, 158.5)\n",
      "ap_irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "ap_irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "ap_decam_g (4180.0, 5470.0, 1290.0)\n",
      "ap_decam_r (5680.0, 7150.0, 1470.0)\n",
      "ap_decam_i (7090.0, 8560.0, 1470.0)\n",
      "ap_decam_z (8490.0, 9960.0, 1470.0)\n",
      "ap_decam_y (9510.0, 10170.0, 660.0)\n",
      "ap_irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "ap_irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "ap_vista_y (9740.0, 10660.0, 920.0)\n",
      "ap_vista_j (11670.0, 13380.0, 1710.0)\n",
      "ap_vista_h (15000.0, 17900.0, 2900.0)\n",
      "ap_vista_ks (19930.0, 23010.0, 3080.0)\n",
      "ap_vista_z (8300.0, 9260.0, 960.0)\n",
      "wfi_b (408.5, 505.5, 97.0)\n",
      "wfi_b123 (3875.0, 5105.0, 1230.0)\n",
      "wfi_v (495.0, 583.5, 88.5)\n",
      "wfi_r (570.5, 729.0, 158.5)\n",
      "irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "decam_g (4180.0, 5470.0, 1290.0)\n",
      "decam_r (5680.0, 7150.0, 1470.0)\n",
      "decam_i (7090.0, 8560.0, 1470.0)\n",
      "decam_z (8490.0, 9960.0, 1470.0)\n",
      "decam_y (9510.0, 10170.0, 660.0)\n",
      "irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "vista_y (9740.0, 10660.0, 920.0)\n",
      "vista_j (11670.0, 13380.0, 1710.0)\n",
      "vista_h (15000.0, 17900.0, 2900.0)\n",
      "vista_ks (19930.0, 23010.0, 3080.0)\n",
      "vista_z (8300.0, 9260.0, 960.0)\n"
     ]
    }
   ],
   "source": [
    "wav_range = []\n",
    "for dat in data:\n",
    "    print(dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission'])))\n",
    "    wav_range += [[dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths on ELAIS-S1')"
      ]
     },
     "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 ELAIS-S1')"
      ]
     },
     "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
}