{
 "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",
      "2018-06-25 17:48:45.857494\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_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": "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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    "depths.add_column(Column(data=hp.pixelfunc.ang2pix(2**ORDER,\n",
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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(\"$('#table139755078094352-185145').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",
       "    $('#table139755078094352-185145').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,'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": {},
     "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: 1.147387138189515e-06, 3sigma in AB mag (Aperture): 37.55792191999155\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,'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": {},
     "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,'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": {},
     "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}