{
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   "cell_type": "markdown",
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    "deletable": true,
    "editable": true
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   "source": [
    "# Lockman-SWIRE 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 17:39:23.297401\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 = 'Lockman-SWIRE'\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_lockman-swire_20180219.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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       "<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></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>99014124</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>1</td><td>99014110</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>2</td><td>99014107</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>3</td><td>99014122</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>4</td><td>99014137</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>5</td><td>99014111</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>6</td><td>99014116</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>7</td><td>99014143</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>8</td><td>99014136</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>9</td><td>99014140</td><td>1547095</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.024032957421443544</td><td>3.716121292114259</td><td>nan</td><td>nan</td><td>0.029294107102703117</td><td>5.991833686828613</td><td>nan</td><td>nan</td><td>0.20979423448443413</td><td>26.321258735656723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.143670727224911</td><td>165.8157501220703</td><td>13.52750695546468</td><td>218.27861022949216</td><td>0.064625464</td><td>7.064011192321773</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(\"$('#table140345098855368-329415').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",
       "    $('#table140345098855368-329415').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86], 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": [
       "{'gpc1_g',\n",
       " 'gpc1_i',\n",
       " 'gpc1_r',\n",
       " 'gpc1_y',\n",
       " 'gpc1_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'irac_i3',\n",
       " 'irac_i4',\n",
       " 'megacam_g',\n",
       " 'megacam_i',\n",
       " 'megacam_r',\n",
       " 'megacam_u',\n",
       " 'megacam_y',\n",
       " 'megacam_z',\n",
       " 'ukidss_j',\n",
       " 'ukidss_k',\n",
       " 'wfc_g',\n",
       " 'wfc_i',\n",
       " 'wfc_r',\n",
       " 'wfc_u',\n",
       " 'wfc_z'}"
      ]
     },
     "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 Lockman-SWIRE')"
      ]
     },
     "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": [
      "wfc_u: mean flux error: 5.2085442543029785, 3sigma in AB mag (Aperture): 20.91540596697964\n",
      "wfc_g: mean flux error: 1.1326767206192017, 3sigma in AB mag (Aperture): 22.571931926329164\n",
      "wfc_r: mean flux error: 1.8826671838760376, 3sigma in AB mag (Aperture): 22.02026298161538\n",
      "wfc_i: mean flux error: 2.6147279739379883, 3sigma in AB mag (Aperture): 21.663630580061472\n",
      "wfc_z: mean flux error: 9.994864463806152, 3sigma in AB mag (Aperture): 20.207754590182113\n",
      "gpc1_g: mean flux error: 8504.632577621123, 3sigma in AB mag (Aperture): 12.883057973960923\n",
      "gpc1_r: mean flux error: 849.5694843940146, 3sigma in AB mag (Aperture): 15.38419960161307\n",
      "gpc1_i: mean flux error: 37.62675430195133, 3sigma in AB mag (Aperture): 18.768454969377437\n",
      "gpc1_z: mean flux error: 5.484589703453642, 3sigma in AB mag (Aperture): 20.859336503201114\n",
      "gpc1_y: mean flux error: 248.09838345120278, 3sigma in AB mag (Aperture): 16.720637026890635\n",
      "megacam_u: mean flux error: 0.01268487423658371, 3sigma in AB mag (Aperture): 27.44898144871886\n",
      "megacam_g: mean flux error: 0.009590083733201027, 3sigma in AB mag (Aperture): 27.752640865423196\n",
      "megacam_r: mean flux error: 0.013690946623682976, 3sigma in AB mag (Aperture): 27.36611316996065\n",
      "megacam_z: mean flux error: 0.03710334748029709, 3sigma in AB mag (Aperture): 26.283664129147162\n",
      "ukidss_k: mean flux error: 0.6471256017684937, 3sigma in AB mag (Aperture): 23.17972540858876\n",
      "irac_i3: mean flux error: 5.525122670941097, 3sigma in AB mag (Aperture): 20.851342051099202\n",
      "irac_i4: mean flux error: 5.5077456804926905, 3sigma in AB mag (Aperture): 20.854762166822432\n",
      "irac_i1: mean flux error: 0.7197846988171833, 3sigma in AB mag (Aperture): 23.064190337743064\n",
      "irac_i2: mean flux error: 0.9457514896110237, 3sigma in AB mag (Aperture): 22.767754278192832\n",
      "ukidss_j: mean flux error: 4.300438184520177, 3sigma in AB mag (Aperture): 21.123415089703236\n",
      "wfc_u: mean flux error: 10.910295486450195, 3sigma in AB mag (Total): 20.11260558104781\n",
      "wfc_g: mean flux error: 5.087491512298584, 3sigma in AB mag (Total): 20.940937619000927\n",
      "wfc_r: mean flux error: 6.696905136108398, 3sigma in AB mag (Total): 20.642511495566403\n",
      "wfc_i: mean flux error: 8.58229923248291, 3sigma in AB mag (Total): 20.373187731512935\n",
      "wfc_z: mean flux error: 20.045745849609375, 3sigma in AB mag (Total): 19.452141313587383\n",
      "gpc1_g: mean flux error: 12981.944764851556, 3sigma in AB mag (Total): 12.423847470757678\n",
      "gpc1_r: mean flux error: 357.71884390962697, 3sigma in AB mag (Total): 16.32334231692115\n",
      "gpc1_i: mean flux error: 18.316620269336813, 3sigma in AB mag (Total): 19.550083508346425\n",
      "gpc1_z: mean flux error: 6.319279754375486, 3sigma in AB mag (Total): 20.705527908209056\n",
      "gpc1_y: mean flux error: 159.63515737059438, 3sigma in AB mag (Total): 17.199375501388182\n",
      "megacam_u: mean flux error: 0.013985814526677132, 3sigma in AB mag (Total): 27.342977451869935\n",
      "megacam_g: mean flux error: 0.021257538860002225, 3sigma in AB mag (Total): 26.888414409039406\n",
      "megacam_r: mean flux error: 0.02648057002438533, 3sigma in AB mag (Total): 26.64987853932356\n",
      "megacam_z: mean flux error: 0.09421186504480134, 3sigma in AB mag (Total): 25.27193285996092\n",
      "ukidss_k: mean flux error: 0.9033187627792358, 3sigma in AB mag (Total): 22.817594285624715\n",
      "irac_i3: mean flux error: 5.938429955976886, 3sigma in AB mag (Total): 20.773017767408355\n",
      "irac_i4: mean flux error: 5.9370388856742595, 3sigma in AB mag (Total): 20.773272129643296\n",
      "irac_i1: mean flux error: 0.8740727864932741, 3sigma in AB mag (Total): 22.85332786555022\n",
      "irac_i2: mean flux error: 1.1245602870951248, 3sigma in AB mag (Total): 22.579740006455303\n",
      "ukidss_j: mean flux error: 9.354144077236235, 3sigma in AB mag (Total): 20.279686726085224\n",
      "megacam_i: mean flux error: 0.06339684873819351, 3sigma in AB mag (Total): 25.7020276857531\n",
      "megacam_y: mean flux error: 0.06556633859872818, 3sigma in AB mag (Total): 25.66549453278116\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_wfc_u (3355.0, 3925.0, 570.0)\n",
      "ap_wfc_g (4260.0, 5485.0, 1225.0)\n",
      "ap_wfc_r (5575.0, 6910.0, 1335.0)\n",
      "ap_wfc_i (6970.0, 8485.0, 1515.0)\n",
      "ap_wfc_z (8500.0, 9000.0, 500.0)\n",
      "ap_gpc1_g (4260.0, 5500.0, 1240.0)\n",
      "ap_gpc1_r (5500.0, 6900.0, 1400.0)\n",
      "ap_gpc1_i (6910.0, 8190.0, 1280.0)\n",
      "ap_gpc1_z (8190.0, 9210.0, 1020.0)\n",
      "ap_gpc1_y (9200.0, 9820.0, 620.0)\n",
      "ap_megacam_u (3500.0, 4100.0, 600.0)\n",
      "ap_megacam_g (4180.0, 5580.0, 1400.0)\n",
      "ap_megacam_r (5680.0, 6880.0, 1200.0)\n",
      "ap_megacam_z (8280.0, 9160.0, 880.0)\n",
      "ap_ukidss_k (20290.0, 23820.0, 3530.0)\n",
      "ap_irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "ap_irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "ap_irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "ap_irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "ap_ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "wfc_u (3355.0, 3925.0, 570.0)\n",
      "wfc_g (4260.0, 5485.0, 1225.0)\n",
      "wfc_r (5575.0, 6910.0, 1335.0)\n",
      "wfc_i (6970.0, 8485.0, 1515.0)\n",
      "wfc_z (8500.0, 9000.0, 500.0)\n",
      "gpc1_g (4260.0, 5500.0, 1240.0)\n",
      "gpc1_r (5500.0, 6900.0, 1400.0)\n",
      "gpc1_i (6910.0, 8190.0, 1280.0)\n",
      "gpc1_z (8190.0, 9210.0, 1020.0)\n",
      "gpc1_y (9200.0, 9820.0, 620.0)\n",
      "megacam_u (3500.0, 4100.0, 600.0)\n",
      "megacam_g (4180.0, 5580.0, 1400.0)\n",
      "megacam_r (5680.0, 6880.0, 1200.0)\n",
      "megacam_z (8280.0, 9160.0, 880.0)\n",
      "ukidss_k (20290.0, 23820.0, 3530.0)\n",
      "irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "megacam_i (6831.7305, 8388.556, 1556.8252)\n",
      "megacam_y (7040.0, 8360.0, 1320.0)\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 Lockman-SWIRE')"
      ]
     },
     "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 Lockman-SWIRE')"
      ]
     },
     "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
}