{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# NGP Selection Functions\n",
    "## Depth maps and selection functions\n",
    "\n",
    "The simplest selection function available is the field MOC which specifies the area for which there is Herschel data. Each pristine catalogue also has a MOC defining the area for which that data is available.\n",
    "\n",
    "The next stage is to provide mean flux standard deviations which act as a proxy for the catalogue's 5$\\sigma$ depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2019-02-05 12:01:17.720961\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n",
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table, join\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "import healpy as hp\n",
    "#import pandas as pd #Astropy has group_by function so apandas isn't required.\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "#We ignore warnings - this is a little dangerous but a huge number of warnings are generated by empty cells later\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from herschelhelp_internal.utils import inMoc, coords_to_hpidx, flux_to_mag\n",
    "from herschelhelp_internal.masterlist import find_last_ml_suffix, nb_ccplots\n",
    "\n",
    "from astropy.io.votable import parse_single_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "FIELD = 'NGP'\n",
    "#FILTERS_DIR = \"/Users/rs548/GitHub/herschelhelp_python/database_builder/filters/\"\n",
    "FILTERS_DIR = \"/opt/herschelhelp_python/database_builder/filters/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth maps produced using: master_catalogue_ngp_20190204.fits\n"
     ]
    }
   ],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = find_last_ml_suffix()\n",
    "#SUFFIX = \"20171016\"\n",
    "\n",
    "master_catalogue_filename = \"master_catalogue_{}_{}.fits\".format(FIELD.lower(), SUFFIX)\n",
    "master_catalogue = Table.read(\"{}/{}\".format(OUT_DIR, master_catalogue_filename))\n",
    "\n",
    "print(\"Depth maps produced using: {}\".format(master_catalogue_filename))\n",
    "\n",
    "ORDER = 10\n",
    "#TODO write code to decide on appropriate order\n",
    "\n",
    "field_moc = MOC(filename=\"../../dmu2/dmu2_field_coverages/{}_MOC.fits\".format(FIELD))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove sources whose signal to noise ratio is less than five as these will have been selected using forced \n",
    "# photometry and so the errors will not refelct the RMS of the map \n",
    "for n,col in enumerate(master_catalogue.colnames):\n",
    "    if col.startswith(\"f_\"):\n",
    "        err_col = \"ferr{}\".format(col[1:])\n",
    "        errs = master_catalogue[err_col]\n",
    "        fluxes = master_catalogue[col]\n",
    "        mask = fluxes/errs < 5.0\n",
    "        master_catalogue[col][mask] = np.nan\n",
    "        master_catalogue[err_col][mask] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## I - Group masterlist objects by healpix cell and calculate depths\n",
    "We add a column to the masterlist catalogue for the target order healpix cell <i>per object</i>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Add a column to the catalogue with the order=ORDER hp_idx\n",
    "master_catalogue.add_column(Column(data=coords_to_hpidx(master_catalogue['ra'],\n",
    "                                                       master_catalogue['dec'],\n",
    "                                                       ORDER), \n",
    "                                   name=\"hp_idx_O_{}\".format(str(ORDER))\n",
    "                                  )\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Convert catalogue to pandas and group by the order=ORDER pixel\n",
    "\n",
    "group = master_catalogue.group_by([\"hp_idx_O_{}\".format(str(ORDER))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Downgrade the groups from order=ORDER to order=13 and then fill out the appropriate cells\n",
    "#hp.pixelfunc.ud_grade([2599293, 2599294], nside_out=hp.order2nside(13))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## II Create a table of all Order=13 healpix cells in the field and populate it\n",
    "We create a table with every order=13 healpix cell in the field MOC. We then calculate the healpix cell at lower order that the order=13 cell is in. We then fill in the depth at every order=13 cell as calculated for the lower order cell that that the order=13 cell is inside."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "depths = Table()\n",
    "depths['hp_idx_O_13'] = list(field_moc.flattened(13))"
   ]
  },
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   "execution_count": 10,
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       "<tr><td>0</td><td>145686492</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686511</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686495</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686508</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686509</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686510</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686519</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686512</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686516</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>145686513</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9669804821869009</td><td>18.795520689537184</td><td>0.7509066860888011</td><td>17.562178780584194</td><td>1.516106814960501</td><td>18.439960710445437</td><td>1.62843644771509</td><td>18.213767155988364</td><td>0.776012533256879</td><td>40.05907616044797</td><td>0.8154295631758885</td><td>36.310280355324295</td><td>1.4103034232788316</td><td>67.41895470943709</td><td>1.533133210572915</td><td>58.55041393440158</td><td>4.825557181722259</td><td>100.09773030981476</td><td>4.8391129446464625</td><td>98.4498912562413</td><td>2.980242</td><td>103.15769958496094</td><td>4.94755</td><td>122.69877243041992</td><td>3.765761</td><td>121.59164428710938</td><td>5.394019</td><td>137.1686004638672</td><td>5.562918</td><td>157.20433120727537</td><td>9.994245</td><td>256.6878440856934</td><td>6.0813127</td><td>91.83061828613282</td><td>12.217868</td><td>272.9746978759766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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(\"$('#table140266434326368-551766').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",
       "    $('#table140266434326368-551766').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], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if col.startswith(\"f_\"):\n",
    "        errcol = \"ferr{}\".format(col[1:])\n",
    "        depths = join(depths, \n",
    "                      group[\"hp_idx_O_{}\".format(str(ORDER)), errcol].groups.aggregate(np.nanmean),\n",
    "                     join_type='left')\n",
    "        depths[errcol].name = errcol + \"_mean\"\n",
    "        depths = join(depths, \n",
    "                      group[\"hp_idx_O_{}\".format(str(ORDER)), col].groups.aggregate(lambda x: np.nanpercentile(x, 90.)),\n",
    "                     join_type='left')\n",
    "        depths[col].name = col + \"_p90\"\n",
    "\n",
    "depths[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## III - Save the depth map table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "depths.write(\"{}/depths_{}_{}.fits\".format(OUT_DIR, FIELD.lower(), SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IV - Overview plots\n",
    "\n",
    "### IV.a - Filters\n",
    "First we simply plot all the filters available on this field to give an overview of coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'90prime_g',\n",
       " '90prime_r',\n",
       " 'decam_z',\n",
       " 'gpc1_g',\n",
       " 'gpc1_i',\n",
       " 'gpc1_r',\n",
       " 'gpc1_y',\n",
       " 'gpc1_z',\n",
       " 'mosaic_z',\n",
       " 'ukidss_h',\n",
       " 'ukidss_j',\n",
       " 'ukidss_k',\n",
       " 'ukidss_y'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_bands = [column[2:] for column in master_catalogue.colnames \n",
    "             if (column.startswith('f_') & ~column.startswith('f_ap_'))]\n",
    "ap_bands = [column[5:] for column in master_catalogue.colnames \n",
    "            if column.startswith('f_ap_') ]\n",
    "bands = set(tot_bands) | set(ap_bands)\n",
    "bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Passbands on NGP')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for b in bands:\n",
    "    plt.plot(Table(data = parse_single_table(FILTERS_DIR + b + '.xml').array.data)['Wavelength']\n",
    "            ,Table(data = parse_single_table(FILTERS_DIR + b + '.xml').array.data)['Transmission']\n",
    "            , label=b)\n",
    "plt.xlabel('Wavelength ($\\AA$)')\n",
    "plt.ylabel('Transmission')\n",
    "plt.xscale('log')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Passbands on {}'.format(FIELD))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### IV.a - Depth overview\n",
    "Then we plot the mean depths available across the area a given band is available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decam_z: mean flux error: 1.1473863124847412, 3sigma in AB mag (Aperture): 22.557922701330178\n",
      "gpc1_g: mean flux error: 2398.5983275884178, 3sigma in AB mag (Aperture): 14.257303046863377\n",
      "gpc1_r: mean flux error: 207.90936803887814, 3sigma in AB mag (Aperture): 16.912511717362186\n",
      "gpc1_i: mean flux error: 8.386333830675945, 3sigma in AB mag (Aperture): 20.39826649780337\n",
      "gpc1_z: mean flux error: 19.808317193732414, 3sigma in AB mag (Aperture): 19.46507790864512\n",
      "gpc1_y: mean flux error: 47272.16926305221, 3sigma in AB mag (Aperture): 11.020683033170386\n",
      "ukidss_y: mean flux error: 3.4326672554016113, 3sigma in AB mag (Aperture): 21.368117595057903\n",
      "ukidss_j: mean flux error: 4.331186294555664, 3sigma in AB mag (Aperture): 21.11567970279247\n",
      "ukidss_h: mean flux error: 5.383535861968994, 3sigma in AB mag (Aperture): 20.87952783716451\n",
      "ukidss_k: mean flux error: 5.877554416656494, 3sigma in AB mag (Aperture): 20.784205216499025\n",
      "90prime_g: mean flux error: 0.2104857712984085, 3sigma in AB mag (Aperture): 24.399140005354106\n",
      "90prime_r: mean flux error: 0.29841721057891846, 3sigma in AB mag (Aperture): 24.020137196863523\n",
      "mosaic_z: mean flux error: 0.8932815790176392, 3sigma in AB mag (Aperture): 22.829725917721383\n",
      "decam_z: mean flux error: 0.9422832727432251, 3sigma in AB mag (Total): 22.771743159043858\n",
      "gpc1_g: mean flux error: 4138.912200278543, 3sigma in AB mag (Total): 13.664981328933045\n",
      "gpc1_r: mean flux error: 851.7467205267048, 3sigma in AB mag (Total): 15.381420687994058\n",
      "gpc1_i: mean flux error: 50.367572530764036, 3sigma in AB mag (Total): 18.451819311939936\n",
      "gpc1_z: mean flux error: 53.60297128030063, 3sigma in AB mag (Total): 18.3842247035737\n",
      "gpc1_y: mean flux error: 88633.77420087233, 3sigma in AB mag (Total): 10.338198756167223\n",
      "ukidss_y: mean flux error: 6.43052339553833, 3sigma in AB mag (Total): 20.686581056651043\n",
      "ukidss_j: mean flux error: 6.505740165710449, 3sigma in AB mag (Total): 20.673955078444457\n",
      "ukidss_h: mean flux error: 11.055870056152344, 3sigma in AB mag (Total): 20.098214549121288\n",
      "ukidss_k: mean flux error: 12.048725128173828, 3sigma in AB mag (Total): 20.004844121253505\n",
      "90prime_g: mean flux error: 5.834963321685791, 3sigma in AB mag (Total): 20.792101537114355\n",
      "90prime_r: mean flux error: 1.131285548210144, 3sigma in AB mag (Total): 22.573266265211466\n",
      "mosaic_z: mean flux error: 7.9294657707214355, 3sigma in AB mag (Total): 20.459087041389573\n"
     ]
    }
   ],
   "source": [
    "average_depths = []\n",
    "for b in ap_bands:\n",
    "    \n",
    "    mean_err = np.nanmean(depths['ferr_ap_{}_mean'.format(b)])\n",
    "    print(\"{}: mean flux error: {}, 3sigma in AB mag (Aperture): {}\".format(b, mean_err, flux_to_mag(3.0*mean_err*1.e-6)[0]))\n",
    "    average_depths += [('ap_' + b, flux_to_mag(1.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(3.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(5.0*mean_err*1.e-6)[0])]\n",
    "    \n",
    "for b in tot_bands:\n",
    "    \n",
    "    mean_err = np.nanmean(depths['ferr_{}_mean'.format(b)])\n",
    "    print(\"{}: mean flux error: {}, 3sigma in AB mag (Total): {}\".format(b, mean_err, flux_to_mag(3.0*mean_err*1.e-6)[0]))\n",
    "    average_depths += [(b, flux_to_mag(1.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(3.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(5.0*mean_err*1.e-6)[0])]\n",
    "    \n",
    "average_depths = np.array(average_depths,  dtype=[('band', \"<U16\"), ('1s', float), ('3s', float), ('5s', float)])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def FWHM(X,Y):\n",
    "    \n",
    "    half_max = max(Y) / 2.\n",
    "    #find when function crosses line half_max (when sign of diff flips)\n",
    "    #take the 'derivative' of signum(half_max - Y[])\n",
    "    d = half_max - Y\n",
    "    #plot(X,d) #if you are interested\n",
    "    #find the left and right most indexes\n",
    "    low_end = X[np.where(d < 0)[0][0]]\n",
    "    high_end = X[np.where(d < 0)[0][-1]]\n",
    "    return low_end, high_end, (high_end - low_end)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for b in ap_bands:\n",
    "    data += [['ap_' + b, Table(data = parse_single_table(FILTERS_DIR + b +'.xml').array.data)]]\n",
    "    \n",
    "for b in tot_bands:\n",
    "    data += [[b, Table(data = parse_single_table(FILTERS_DIR + b +'.xml').array.data)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap_decam_z (8490.0, 9960.0, 1470.0)\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_ukidss_y (9790.0, 10820.0, 1030.0)\n",
      "ap_ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "ap_ukidss_h (14925.0, 17840.0, 2915.0)\n",
      "ap_ukidss_k (20290.0, 23820.0, 3530.0)\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",
      "decam_z (8490.0, 9960.0, 1470.0)\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",
      "ukidss_y (9790.0, 10820.0, 1030.0)\n",
      "ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "ukidss_h (14925.0, 17840.0, 2915.0)\n",
      "ukidss_k (20290.0, 23820.0, 3530.0)\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"
     ]
    }
   ],
   "source": [
    "wav_range = []\n",
    "for dat in data:\n",
    "    print(dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission'])))\n",
    "    wav_range += [[dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Depths on NGP')"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dat in data:\n",
    "    wav_deets = FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))\n",
    "    depth = average_depths['5s'][average_depths['band'] == dat[0]]\n",
    "    #print(depth)\n",
    "    plt.plot([wav_deets[0],wav_deets[1]], [depth,depth], label=dat[0])\n",
    "    \n",
    "plt.xlabel('Wavelength ($\\AA$)')\n",
    "plt.ylabel('Depth')\n",
    "plt.xscale('log')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Depths on {}'.format(FIELD))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### IV.c - Depth vs coverage comparison\n",
    "\n",
    "How best to do this? Colour/intensity plot over area? Percentage coverage vs mean depth?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Depths (5 $\\\\sigma$) vs coverage on NGP')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}