{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "708e28f (Tue May 8 18:05:21 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2018-06-07 11:59:08.053871\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 = '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": {},
   "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": "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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       "<script>\n",
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       "\n",
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       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140584629609248-716947').dataTable()\");\n",
       "    \n",
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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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       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
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       ".paginate_button { margin-right: 5px; }\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></tr></thead>\n",
       "<tr><td>0</td><td>145686492</td><td>2276351</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>1.1301835477559954</td><td>9.970993955235604</td><td>1.3103154995763573</td><td>11.083637933154272</td><td>1.6498037261044969</td><td>16.25355244460686</td><td>1.6155911030878978</td><td>17.123460600939033</td><td>0.8435574852762031</td><td>37.33996186232079</td><td>0.8917986423528267</td><td>33.742887452577875</td><td>1.4916435908976988</td><td>60.09467301264435</td><td>1.6338681326190645</td><td>53.91082353981114</td><td>5.623712621178792</td><td>83.97696141152863</td><td>7.06735189476108</td><td>67.86793805904185</td><td>2.9729369</td><td>100.42395172119141</td><td>5.5040326</td><td>93.6902557373047</td><td>3.765761</td><td>121.59164428710938</td><td>5.3853407</td><td>118.1675796508789</td><td>5.5526786</td><td>146.97162170410158</td><td>10.234796</td><td>153.6384399414063</td><td>6.0764227</td><td>90.45872955322267</td><td>13.00041</td><td>91.04026336669924</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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(\"$('#table140582982802848-289781').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",
       "    $('#table140582982802848-289781').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": 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",
       " '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": 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 NGP')"
      ]
     },
     "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": [
      "decam_z: mean flux error: 1.1463661166999373e-06, 3sigma in AB mag (Aperture): 37.55888851050684\n",
      "gpc1_g: mean flux error: 4807.692928162375, 3sigma in AB mag (Aperture): 13.502355060485058\n",
      "gpc1_r: mean flux error: 856.2731977410074, 3sigma in AB mag (Aperture): 15.375665987308935\n",
      "gpc1_i: mean flux error: 400.42818884877096, 3sigma in AB mag (Aperture): 16.200885256176683\n",
      "gpc1_z: mean flux error: 664.2480392517627, 3sigma in AB mag (Aperture): 15.65137116040058\n",
      "gpc1_y: mean flux error: 72191.37734621752, 3sigma in AB mag (Aperture): 10.560983543604614\n",
      "ukidss_y: mean flux error: 3.391916036605835, 3sigma in AB mag (Aperture): 21.381084130189983\n",
      "ukidss_j: mean flux error: 4.302145481109619, 3sigma in AB mag (Aperture): 21.12298413225701\n",
      "ukidss_h: mean flux error: 5.362170696258545, 3sigma in AB mag (Aperture): 20.88384527583154\n",
      "ukidss_k: mean flux error: 5.858597755432129, 3sigma in AB mag (Aperture): 20.787712661019206\n",
      "90prime_g: mean flux error: 0.21095767617225647, 3sigma in AB mag (Aperture): 24.39670853121944\n",
      "90prime_r: mean flux error: 0.2985023856163025, 3sigma in AB mag (Aperture): 24.019827347352653\n",
      "mosaic_z: mean flux error: 0.8973140716552734, 3sigma in AB mag (Aperture): 22.82483566720375\n",
      "decam_z: mean flux error: 0.9615241289138794, 3sigma in AB mag (Total): 22.749796395475805\n",
      "gpc1_g: mean flux error: 15224.37419036471, 3sigma in AB mag (Total): 12.250848239044842\n",
      "gpc1_r: mean flux error: 2651.880278334602, 3sigma in AB mag (Total): 14.148312079359478\n",
      "gpc1_i: mean flux error: 585.2270004453892, 3sigma in AB mag (Total): 15.788885976121257\n",
      "gpc1_z: mean flux error: 419.4584464526905, 3sigma in AB mag (Total): 16.150474503150512\n",
      "gpc1_y: mean flux error: 116445.53948719491, 3sigma in AB mag (Total): 10.041889719972126\n",
      "ukidss_y: mean flux error: 6.215388774871826, 3sigma in AB mag (Total): 20.723526115430523\n",
      "ukidss_j: mean flux error: 6.273008346557617, 3sigma in AB mag (Total): 20.71350719972913\n",
      "ukidss_h: mean flux error: 10.831151962280273, 3sigma in AB mag (Total): 20.120510240507308\n",
      "ukidss_k: mean flux error: 11.837139129638672, 3sigma in AB mag (Total): 20.024079982728075\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"
     ]
    }
   ],
   "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_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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths on NGP')"
      ]
     },
     "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 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",
    "    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
}