{
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   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# xFLS Selection Functions\n",
    "## Depth maps and selection functions\n",
    "\n",
    "The simplest selection function available is the field MOC which specifies the area for which there is Herschel data. Each pristine catalogue also has a MOC defining the area for which that data is available.\n",
    "\n",
    "The next stage is to provide mean flux standard deviations which act as a proxy for the catalogue's 5$\\sigma$ depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "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",
      "2018-06-26 12:30:40.721169\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 = 'xFLS'\n",
    "#FILTERS_DIR = \"/Users/rs548/GitHub/herschelhelp_python/database_builder/filters/\"\n",
    "FILTERS_DIR = \"/opt/herschelhelp_python/database_builder/filters/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth maps produced using: master_catalogue_xfls_20180501.fits\n"
     ]
    }
   ],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = find_last_ml_suffix()\n",
    "#SUFFIX = \"20171016\"\n",
    "\n",
    "master_catalogue_filename = \"master_catalogue_{}_{}.fits\".format(FIELD.lower(), SUFFIX)\n",
    "master_catalogue = Table.read(\"{}/{}\".format(OUT_DIR, master_catalogue_filename))\n",
    "\n",
    "print(\"Depth maps produced using: {}\".format(master_catalogue_filename))\n",
    "\n",
    "ORDER = 10\n",
    "#TODO write code to decide on appropriate order\n",
    "\n",
    "field_moc = MOC(filename=\"../../dmu2/dmu2_field_coverages/{}_MOC.fits\".format(FIELD))"
   ]
  },
  {
   "cell_type": "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>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th></tr></thead>\n",
       "<tr><td>0</td><td>166821439</td><td>2606584</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.47471377</td><td>4.559648513793945</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>1</td><td>166821502</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>2</td><td>166821497</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>3</td><td>166821499</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>4</td><td>166821486</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>5</td><td>166821481</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>6</td><td>166821482</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>7</td><td>166821483</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>8</td><td>166821485</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>9</td><td>166821500</td><td>2606585</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.9686626437735065</td><td>43.48705376619706</td><td>0.8946720362826709</td><td>41.24389282642428</td><td>0.8440225425193676</td><td>79.85310132875829</td><td>0.956305950523077</td><td>76.93158682341442</td><td>1.1426961530978546</td><td>114.52991396223746</td><td>1.1239302773668471</td><td>105.90578397160583</td><td>1.9870192626752792</td><td>164.54313010623198</td><td>2.0166688048014185</td><td>135.21672006089233</td><td>4.277088265337447</td><td>235.3091379979428</td><td>4.233276687353526</td><td>156.9916492526917</td><td>0.14785433</td><td>1.4804130792617798</td><td>0.19503225</td><td>1.3280870556831361</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1272155</td><td>7.528442859649658</td><td>0.93746966</td><td>8.141989707946777</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.640798</td><td>308.8177246093751</td><td>11.167317</td><td>447.2033920288086</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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(\"$('#table140119104151112-814940').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",
       "    $('#table140119104151112-814940').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 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",
       " 'gpc1_g',\n",
       " 'gpc1_i',\n",
       " 'gpc1_r',\n",
       " 'gpc1_y',\n",
       " 'gpc1_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'irac_i3',\n",
       " 'irac_i4',\n",
       " 'mosaic_r',\n",
       " 'mosaic_z',\n",
       " 'ukidss_j',\n",
       " 'wfc_g',\n",
       " 'wfc_i',\n",
       " 'wfc_r',\n",
       " 'wfc_u',\n",
       " 'wfc_z'}"
      ]
     },
     "execution_count": 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 xFLS')"
      ]
     },
     "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: 5.245199203491211, 3sigma in AB mag (Aperture): 20.907791896731915\n",
      "wfc_g: mean flux error: 1.367920160293579, 3sigma in AB mag (Aperture): 22.36704498771538\n",
      "wfc_r: mean flux error: 1.6690608263015747, 3sigma in AB mag (Aperture): 22.151016452830213\n",
      "wfc_i: mean flux error: 2.8412821292877197, 3sigma in AB mag (Aperture): 21.573410963767337\n",
      "wfc_z: mean flux error: 17.49384880065918, 3sigma in AB mag (Aperture): 19.59998344170527\n",
      "gpc1_g: mean flux error: 10.969284619174118, 3sigma in AB mag (Aperture): 20.106751100115453\n",
      "gpc1_r: mean flux error: 113.48114322899423, 3sigma in AB mag (Aperture): 17.569887607438737\n",
      "gpc1_i: mean flux error: 10.36371456751, 3sigma in AB mag (Aperture): 20.16840825483397\n",
      "gpc1_z: mean flux error: 8.708069435970058, 3sigma in AB mag (Aperture): 20.35739215485446\n",
      "gpc1_y: mean flux error: 25.584175907446657, 3sigma in AB mag (Aperture): 19.187268282044293\n",
      "90prime_g: mean flux error: 0.16103927791118622, 3sigma in AB mag (Aperture): 24.68986732685334\n",
      "90prime_r: mean flux error: 0.4038659632205963, 3sigma in AB mag (Aperture): 23.69160372946103\n",
      "mosaic_z: mean flux error: 1.1628270149230957, 3sigma in AB mag (Aperture): 22.54340908122081\n",
      "mosaic_r: mean flux error: 0.09353521466255188, 3sigma in AB mag (Aperture): 25.279758994993635\n",
      "ukidss_j: mean flux error: 6.2385783195495605, 3sigma in AB mag (Aperture): 20.71948278415531\n",
      "irac_i1: mean flux error: 7.092233751004453, 3sigma in AB mag (Aperture): 20.58023926080447\n",
      "irac_i2: mean flux error: 6.748408987297683, 3sigma in AB mag (Aperture): 20.63419337537608\n",
      "irac_i3: mean flux error: 23.781119508669594, 3sigma in AB mag (Aperture): 19.266616124689968\n",
      "irac_i4: mean flux error: 22.690369544006813, 3sigma in AB mag (Aperture): 19.317592940630853\n",
      "wfc_u: mean flux error: 6.850904941558838, 3sigma in AB mag (Total): 20.617827009226083\n",
      "wfc_g: mean flux error: 4.8921308517456055, 3sigma in AB mag (Total): 20.983451701289063\n",
      "wfc_r: mean flux error: 4.882117394470595, 3sigma in AB mag (Total): 20.9856763177768\n",
      "wfc_i: mean flux error: 8.059729372333768, 3sigma in AB mag (Total): 20.441395714666804\n",
      "wfc_z: mean flux error: 33.52980422973633, 3sigma in AB mag (Total): 18.89361931889166\n",
      "gpc1_g: mean flux error: 23.169739956374997, 3sigma in AB mag (Total): 19.29489396432637\n",
      "gpc1_r: mean flux error: 79.12891111615335, 3sigma in AB mag (Total): 17.961358889470326\n",
      "gpc1_i: mean flux error: 7.160043982142611, 3sigma in AB mag (Total): 20.56990763803764\n",
      "gpc1_z: mean flux error: 10.298759100839051, 3sigma in AB mag (Total): 20.175234614089355\n",
      "gpc1_y: mean flux error: 27.943140951051515, 3sigma in AB mag (Total): 19.091508809628657\n",
      "90prime_g: mean flux error: 1.1264158487319946, 3sigma in AB mag (Total): 22.57794998223107\n",
      "90prime_r: mean flux error: 149.1316375732422, 3sigma in AB mag (Total): 17.273272396319918\n",
      "mosaic_z: mean flux error: 10.01156997680664, 3sigma in AB mag (Total): 20.205941395077012\n",
      "mosaic_r: mean flux error: 0.1743190735578537, 3sigma in AB mag (Total): 24.603834590381005\n",
      "ukidss_j: mean flux error: 11.570333480834961, 3sigma in AB mag (Total): 20.048832172217466\n",
      "irac_i1: mean flux error: 10.003937096882462, 3sigma in AB mag (Total): 20.2067694824646\n",
      "irac_i2: mean flux error: 10.394780834738768, 3sigma in AB mag (Total): 20.165158520630733\n",
      "irac_i3: mean flux error: 32.07657225894224, 3sigma in AB mag (Total): 18.94172698104736\n",
      "irac_i4: mean flux error: 31.869457805393083, 3sigma in AB mag (Total): 18.948760175973128\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_90prime_g (4180.0, 5470.0, 1290.0)\n",
      "ap_90prime_r (5680.0, 7150.0, 1470.0)\n",
      "ap_mosaic_z (8552.0, 10018.0, 1466.0)\n",
      "ap_mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "ap_ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "ap_irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "ap_irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "ap_irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "ap_irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "wfc_u (3355.0, 3925.0, 570.0)\n",
      "wfc_g (4260.0, 5485.0, 1225.0)\n",
      "wfc_r (5575.0, 6910.0, 1335.0)\n",
      "wfc_i (6970.0, 8485.0, 1515.0)\n",
      "wfc_z (8500.0, 9000.0, 500.0)\n",
      "gpc1_g (4260.0, 5500.0, 1240.0)\n",
      "gpc1_r (5500.0, 6900.0, 1400.0)\n",
      "gpc1_i (6910.0, 8190.0, 1280.0)\n",
      "gpc1_z (8190.0, 9210.0, 1020.0)\n",
      "gpc1_y (9200.0, 9820.0, 620.0)\n",
      "90prime_g (4180.0, 5470.0, 1290.0)\n",
      "90prime_r (5680.0, 7150.0, 1470.0)\n",
      "mosaic_z (8552.0, 10018.0, 1466.0)\n",
      "mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "irac_i4 (64415.2, 92596.8, 28181.598)\n"
     ]
    }
   ],
   "source": [
    "wav_range = []\n",
    "for dat in data:\n",
    "    print(dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission'])))\n",
    "    wav_range += [[dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths on xFLS')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dat in data:\n",
    "    wav_deets = FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))\n",
    "    depth = average_depths['5s'][average_depths['band'] == dat[0]]\n",
    "    #print(depth)\n",
    "    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 xFLS')"
      ]
     },
     "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
}