{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xFLS 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 13:20:15.559072\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 = 'xFLS'\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_xfls_20180501.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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       "\n",
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       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
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       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140122638340896-95411').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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   "outputs": [
    {
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       "var astropy_sort_num = function(a, b) {\n",
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   "source": [
    "depths[:10].show_in_notebook()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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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></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></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>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>166821439</td><td>2606584</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.1478536</td><td>0.1037476658821106</td><td>0.17208806</td><td>0.1592467576265335</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>0.84574395</td><td>3.1571638584136963</td><td>0.47471377</td><td>4.559648513793945</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821502</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821497</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821499</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821486</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821481</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821482</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821483</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821485</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>166821500</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0605034119893992</td><td>29.71199396172053</td><td>0.8904404287281943</td><td>30.71192752984883</td><td>0.8888188316218366</td><td>63.553853349202825</td><td>1.1355681730274212</td><td>56.870918216491845</td><td>1.1173107671078941</td><td>102.877261703329</td><td>1.124513276081867</td><td>101.35989675459163</td><td>2.0485063845681837</td><td>119.74733147523509</td><td>2.0156262732821038</td><td>112.48581774230766</td><td>4.35498549936339</td><td>143.52031056261558</td><td>5.0346046516889755</td><td>135.1574994058447</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>inf</td><td>0.0</td><td>1.1272194</td><td>6.9913588762283325</td><td>0.85426897</td><td>7.624085760116577</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.589353</td><td>209.3516387939453</td><td>10.647171</td><td>200.32347106933594</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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(\"$('#table140122637584928-179986').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",
       "    $('#table140122637584928-179986').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], 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",
       " '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",
       " 'mosaic_r',\n",
       " 'mosaic_z',\n",
       " 'ukidss_j',\n",
       " 'wfc_g',\n",
       " 'wfc_i',\n",
       " 'wfc_r',\n",
       " 'wfc_u',\n",
       " 'wfc_z'}"
      ]
     },
     "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 xFLS')"
      ]
     },
     "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": [
      "wfc_u: mean flux error: 5.076475143432617, 3sigma in AB mag (Aperture): 20.943291203110682\n",
      "wfc_g: mean flux error: 1.23035728931427, 3sigma in AB mag (Aperture): 22.48211874670529\n",
      "wfc_r: mean flux error: 1.516801118850708, 3sigma in AB mag (Aperture): 22.254875262334885\n",
      "wfc_i: mean flux error: 2.441751480102539, 3sigma in AB mag (Aperture): 21.737943214089093\n",
      "wfc_z: mean flux error: 16.176753997802734, 3sigma in AB mag (Aperture): 19.684968410286665\n",
      "gpc1_g: mean flux error: 75.78848761741543, 3sigma in AB mag (Aperture): 18.00818876152001\n",
      "gpc1_r: mean flux error: 228.4292055023892, 3sigma in AB mag (Aperture): 16.810317790080028\n",
      "gpc1_i: mean flux error: 17.959658020702886, 3sigma in AB mag (Aperture): 19.571451706250095\n",
      "gpc1_z: mean flux error: 13.87429992907835, 3sigma in AB mag (Aperture): 19.851669166526428\n",
      "gpc1_y: mean flux error: 381.5910077390603, 3sigma in AB mag (Aperture): 16.253201533388783\n",
      "90prime_g: mean flux error: 0.1612749546766281, 3sigma in AB mag (Aperture): 24.688279541911847\n",
      "90prime_r: mean flux error: 0.40438205003738403, 3sigma in AB mag (Aperture): 23.690217189169566\n",
      "mosaic_z: mean flux error: 1.1628565788269043, 3sigma in AB mag (Aperture): 22.543381477637233\n",
      "mosaic_r: mean flux error: 0.09364917874336243, 3sigma in AB mag (Aperture): 25.278436930246507\n",
      "ukidss_j: mean flux error: 6.114259243011475, 3sigma in AB mag (Aperture): 20.74133724131527\n",
      "irac_i1: mean flux error: 5.836461942838672, 3sigma in AB mag (Aperture): 20.791822718167218\n",
      "irac_i2: mean flux error: 5.440669724080579, 3sigma in AB mag (Aperture): 20.86806595606469\n",
      "irac_i3: mean flux error: 19.904765159686086, 3sigma in AB mag (Aperture): 19.459804218053172\n",
      "irac_i4: mean flux error: 19.47866972636668, 3sigma in AB mag (Aperture): 19.48329862843388\n",
      "wfc_u: mean flux error: 6.4852681159973145, 3sigma in AB mag (Total): 20.67737702437453\n",
      "wfc_g: mean flux error: 4.083687782287598, 3sigma in AB mag (Total): 21.179565536282745\n",
      "wfc_r: mean flux error: 4.159158640675417, 3sigma in AB mag (Total): 21.159683148814132\n",
      "wfc_i: mean flux error: 6.607170367913834, 3sigma in AB mag (Total): 20.65715809977815\n",
      "wfc_z: mean flux error: 30.17353630065918, 3sigma in AB mag (Total): 19.008131333382998\n",
      "gpc1_g: mean flux error: 84.62049680255278, 3sigma in AB mag (Total): 17.888507936392934\n",
      "gpc1_r: mean flux error: 128.69759763001056, 3sigma in AB mag (Total): 17.433270762951686\n",
      "gpc1_i: mean flux error: 13.21199952632183, 3sigma in AB mag (Total): 19.90477548957835\n",
      "gpc1_z: mean flux error: 12.957271263727522, 3sigma in AB mag (Total): 19.925912985884928\n",
      "gpc1_y: mean flux error: 342.9716913139146, 3sigma in AB mag (Total): 16.3690511754374\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",
      "mosaic_r: mean flux error: 0.17217998206615448, 3sigma in AB mag (Total): 24.617240217583863\n",
      "ukidss_j: mean flux error: 10.784326553344727, 3sigma in AB mag (Total): 20.125214288236343\n",
      "irac_i1: mean flux error: 8.666832274512162, 3sigma in AB mag (Total): 20.362545883374985\n",
      "irac_i2: mean flux error: 8.406512976134104, 3sigma in AB mag (Total): 20.39565714399904\n",
      "irac_i3: mean flux error: 24.813720996072533, 3sigma in AB mag (Total): 19.22046712635251\n",
      "irac_i4: mean flux error: 23.015498675270777, 3sigma in AB mag (Total): 19.30214589029726\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_wfc_u (3355.0, 3925.0, 570.0)\n",
      "ap_wfc_g (4260.0, 5485.0, 1225.0)\n",
      "ap_wfc_r (5575.0, 6910.0, 1335.0)\n",
      "ap_wfc_i (6970.0, 8485.0, 1515.0)\n",
      "ap_wfc_z (8500.0, 9000.0, 500.0)\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_mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "ap_ukidss_j (11695.0, 13280.0, 1585.0)\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",
      "wfc_u (3355.0, 3925.0, 570.0)\n",
      "wfc_g (4260.0, 5485.0, 1225.0)\n",
      "wfc_r (5575.0, 6910.0, 1335.0)\n",
      "wfc_i (6970.0, 8485.0, 1515.0)\n",
      "wfc_z (8500.0, 9000.0, 500.0)\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",
      "mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "ukidss_j (11695.0, 13280.0, 1585.0)\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 xFLS')"
      ]
     },
     "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 xFLS')"
      ]
     },
     "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
}