{
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   "cell_type": "markdown",
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    "editable": true
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   "source": [
    "# ELAIS-S1 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": {
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    "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 12:22:39.200682\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 = 'ELAIS-S1'\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_elais-s1_20180416.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[:10].show_in_notebook()"
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    "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",
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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>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><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></tr></thead>\n",
       "<tr><td>0</td><td>579661755</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10586841352369333</td><td>2.036264116795251</td><td>0.17562586939400462</td><td>3.0839124404755855</td><td>0.14919463410890824</td><td>3.100395399126548</td><td>0.3073946109077909</td><td>7.492644699872381</td><td>0.2095996150881277</td><td>4.555816184652934</td><td>0.435095117601232</td><td>8.29587060688078</td><td>0.4131408864967625</td><td>7.424160496057169</td><td>0.8057193578763671</td><td>13.58614990060725</td><td>0.9621373913737008</td><td>9.481623489032547</td><td>2.2142936711552696</td><td>17.14441298186984</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>579661759</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10586841352369333</td><td>2.036264116795251</td><td>0.17562586939400462</td><td>3.0839124404755855</td><td>0.14919463410890824</td><td>3.100395399126548</td><td>0.3073946109077909</td><td>7.492644699872381</td><td>0.2095996150881277</td><td>4.555816184652934</td><td>0.435095117601232</td><td>8.29587060688078</td><td>0.4131408864967625</td><td>7.424160496057169</td><td>0.8057193578763671</td><td>13.58614990060725</td><td>0.9621373913737008</td><td>9.481623489032547</td><td>2.2142936711552696</td><td>17.14441298186984</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>579661757</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10586841352369333</td><td>2.036264116795251</td><td>0.17562586939400462</td><td>3.0839124404755855</td><td>0.14919463410890824</td><td>3.100395399126548</td><td>0.3073946109077909</td><td>7.492644699872381</td><td>0.2095996150881277</td><td>4.555816184652934</td><td>0.435095117601232</td><td>8.29587060688078</td><td>0.4131408864967625</td><td>7.424160496057169</td><td>0.8057193578763671</td><td>13.58614990060725</td><td>0.9621373913737008</td><td>9.481623489032547</td><td>2.2142936711552696</td><td>17.14441298186984</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>579661758</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10586841352369333</td><td>2.036264116795251</td><td>0.17562586939400462</td><td>3.0839124404755855</td><td>0.14919463410890824</td><td>3.100395399126548</td><td>0.3073946109077909</td><td>7.492644699872381</td><td>0.2095996150881277</td><td>4.555816184652934</td><td>0.435095117601232</td><td>8.29587060688078</td><td>0.4131408864967625</td><td>7.424160496057169</td><td>0.8057193578763671</td><td>13.58614990060725</td><td>0.9621373913737008</td><td>9.481623489032547</td><td>2.2142936711552696</td><td>17.14441298186984</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>579661806</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.1117633689557505</td><td>6.8119181402314855</td><td>0.15158880988431847</td><td>11.334188148333375</td><td>0.158809126626115</td><td>13.404624122238094</td><td>0.27602555848544585</td><td>23.325181612351173</td><td>0.2042677236910098</td><td>21.096973994337382</td><td>0.3796106854158977</td><td>36.44002799936304</td><td>0.39062223259970513</td><td>39.68385521832558</td><td>0.7777992774745999</td><td>68.17488396212829</td><td>1.0307218276996757</td><td>268.5665308166574</td><td>1.8166164900835104</td><td>398.801688598933</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>1.9040259740259742</td><td>180.61800000000022</td><td>1.9772727272727273</td><td>149.86200000000008</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.094108415974511</td><td>582.1039123535156</td><td>7.327802605099148</td><td>658.3484008789067</td><td>3.896970685790567</td><td>631.9167541503906</td><td>10.801349773406983</td><td>765.8167846679689</td><td>4.387109133932325</td><td>514.135986328125</td><td>12.606568295022715</td><td>654.0425781250001</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>5</td><td>579661783</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.1117633689557505</td><td>6.8119181402314855</td><td>0.15158880988431847</td><td>11.334188148333375</td><td>0.158809126626115</td><td>13.404624122238094</td><td>0.27602555848544585</td><td>23.325181612351173</td><td>0.2042677236910098</td><td>21.096973994337382</td><td>0.3796106854158977</td><td>36.44002799936304</td><td>0.39062223259970513</td><td>39.68385521832558</td><td>0.7777992774745999</td><td>68.17488396212829</td><td>1.0307218276996757</td><td>268.5665308166574</td><td>1.8166164900835104</td><td>398.801688598933</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>1.9040259740259742</td><td>180.61800000000022</td><td>1.9772727272727273</td><td>149.86200000000008</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.094108415974511</td><td>582.1039123535156</td><td>7.327802605099148</td><td>658.3484008789067</td><td>3.896970685790567</td><td>631.9167541503906</td><td>10.801349773406983</td><td>765.8167846679689</td><td>4.387109133932325</td><td>514.135986328125</td><td>12.606568295022715</td><td>654.0425781250001</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>6</td><td>579661814</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.1117633689557505</td><td>6.8119181402314855</td><td>0.15158880988431847</td><td>11.334188148333375</td><td>0.158809126626115</td><td>13.404624122238094</td><td>0.27602555848544585</td><td>23.325181612351173</td><td>0.2042677236910098</td><td>21.096973994337382</td><td>0.3796106854158977</td><td>36.44002799936304</td><td>0.39062223259970513</td><td>39.68385521832558</td><td>0.7777992774745999</td><td>68.17488396212829</td><td>1.0307218276996757</td><td>268.5665308166574</td><td>1.8166164900835104</td><td>398.801688598933</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>1.9040259740259742</td><td>180.61800000000022</td><td>1.9772727272727273</td><td>149.86200000000008</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.094108415974511</td><td>582.1039123535156</td><td>7.327802605099148</td><td>658.3484008789067</td><td>3.896970685790567</td><td>631.9167541503906</td><td>10.801349773406983</td><td>765.8167846679689</td><td>4.387109133932325</td><td>514.135986328125</td><td>12.606568295022715</td><td>654.0425781250001</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>7</td><td>579661813</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.1117633689557505</td><td>6.8119181402314855</td><td>0.15158880988431847</td><td>11.334188148333375</td><td>0.158809126626115</td><td>13.404624122238094</td><td>0.27602555848544585</td><td>23.325181612351173</td><td>0.2042677236910098</td><td>21.096973994337382</td><td>0.3796106854158977</td><td>36.44002799936304</td><td>0.39062223259970513</td><td>39.68385521832558</td><td>0.7777992774745999</td><td>68.17488396212829</td><td>1.0307218276996757</td><td>268.5665308166574</td><td>1.8166164900835104</td><td>398.801688598933</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>1.9040259740259742</td><td>180.61800000000022</td><td>1.9772727272727273</td><td>149.86200000000008</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.094108415974511</td><td>582.1039123535156</td><td>7.327802605099148</td><td>658.3484008789067</td><td>3.896970685790567</td><td>631.9167541503906</td><td>10.801349773406983</td><td>765.8167846679689</td><td>4.387109133932325</td><td>514.135986328125</td><td>12.606568295022715</td><td>654.0425781250001</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>8</td><td>579661804</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.1117633689557505</td><td>6.8119181402314855</td><td>0.15158880988431847</td><td>11.334188148333375</td><td>0.158809126626115</td><td>13.404624122238094</td><td>0.27602555848544585</td><td>23.325181612351173</td><td>0.2042677236910098</td><td>21.096973994337382</td><td>0.3796106854158977</td><td>36.44002799936304</td><td>0.39062223259970513</td><td>39.68385521832558</td><td>0.7777992774745999</td><td>68.17488396212829</td><td>1.0307218276996757</td><td>268.5665308166574</td><td>1.8166164900835104</td><td>398.801688598933</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>1.9040259740259742</td><td>180.61800000000022</td><td>1.9772727272727273</td><td>149.86200000000008</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.094108415974511</td><td>582.1039123535156</td><td>7.327802605099148</td><td>658.3484008789067</td><td>3.896970685790567</td><td>631.9167541503906</td><td>10.801349773406983</td><td>765.8167846679689</td><td>4.387109133932325</td><td>514.135986328125</td><td>12.606568295022715</td><td>654.0425781250001</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>9</td><td>579661781</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.351818181818182</td><td>2007.48</td><td>9.131818181818183</td><td>1205.59</td><td>7.557272727272727</td><td>316.98</td><td>6.734545454545454</td><td>404.88</td><td>0.1117633689557505</td><td>6.8119181402314855</td><td>0.15158880988431847</td><td>11.334188148333375</td><td>0.158809126626115</td><td>13.404624122238094</td><td>0.27602555848544585</td><td>23.325181612351173</td><td>0.2042677236910098</td><td>21.096973994337382</td><td>0.3796106854158977</td><td>36.44002799936304</td><td>0.39062223259970513</td><td>39.68385521832558</td><td>0.7777992774745999</td><td>68.17488396212829</td><td>1.0307218276996757</td><td>268.5665308166574</td><td>1.8166164900835104</td><td>398.801688598933</td><td>1.7189565217391305</td><td>323.8600000000001</td><td>1.5052173913043478</td><td>245.3780000000002</td><td>1.9040259740259742</td><td>180.61800000000022</td><td>1.9772727272727273</td><td>149.86200000000008</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.094108415974511</td><td>582.1039123535156</td><td>7.327802605099148</td><td>658.3484008789067</td><td>3.896970685790567</td><td>631.9167541503906</td><td>10.801349773406983</td><td>765.8167846679689</td><td>4.387109133932325</td><td>514.135986328125</td><td>12.606568295022715</td><td>654.0425781250001</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(\"$('#table140598306312320-344330').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",
       "    $('#table140598306312320-344330').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], 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": [
       "{'decam_g',\n",
       " 'decam_i',\n",
       " 'decam_r',\n",
       " 'decam_y',\n",
       " 'decam_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'irac_i3',\n",
       " 'irac_i4',\n",
       " 'vista_h',\n",
       " 'vista_j',\n",
       " 'vista_ks',\n",
       " 'vista_y',\n",
       " 'vista_z',\n",
       " 'wfi_b',\n",
       " 'wfi_b123',\n",
       " 'wfi_r',\n",
       " 'wfi_v'}"
      ]
     },
     "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 ELAIS-S1')"
      ]
     },
     "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": [
      "wfi_b: mean flux error: 0.054431844502687454, 3sigma in AB mag (Aperture): 25.867564235024993\n",
      "wfi_b123: mean flux error: 0.05326399952173233, 3sigma in AB mag (Aperture): 25.89111242841839\n",
      "wfi_v: mean flux error: 0.107560895383358, 3sigma in AB mag (Aperture): 25.128060841147864\n",
      "wfi_r: mean flux error: 0.09393284469842911, 3sigma in AB mag (Aperture): 25.27515317600831\n",
      "irac_i3: mean flux error: 5.835037230082685, 3sigma in AB mag (Aperture): 20.792087784754706\n",
      "irac_i4: mean flux error: 5.767967869343094, 3sigma in AB mag (Aperture): 20.804639782035217\n",
      "decam_g: mean flux error: 0.12409217093119666, 3sigma in AB mag (Aperture): 24.972835907210573\n",
      "decam_r: mean flux error: 0.1606534612428858, 3sigma in AB mag (Aperture): 24.692471646286343\n",
      "decam_i: mean flux error: 0.24771897329838952, 3sigma in AB mag (Aperture): 24.222298684814298\n",
      "decam_z: mean flux error: 0.48429203397132253, 3sigma in AB mag (Aperture): 23.494428549508463\n",
      "decam_y: mean flux error: 1.2032902616833396, 3sigma in AB mag (Aperture): 22.506270858355045\n",
      "irac_i1: mean flux error: 0.720426178615064, 3sigma in AB mag (Aperture): 23.063223148981827\n",
      "irac_i2: mean flux error: 0.9236782654664204, 3sigma in AB mag (Aperture): 22.793395051667382\n",
      "vista_y: mean flux error: 0.14550178476193182, 3sigma in AB mag (Aperture): 24.800026061897675\n",
      "vista_j: mean flux error: 2.333885304387101, 3sigma in AB mag (Aperture): 21.786998089642687\n",
      "vista_h: mean flux error: 3.2412973330384074, 3sigma in AB mag (Aperture): 21.430399683510664\n",
      "vista_ks: mean flux error: 4.157712518793808, 3sigma in AB mag (Aperture): 21.160060720347722\n",
      "vista_z: mean flux error: 0.11121916679827804, 3sigma in AB mag (Aperture): 25.091747770143492\n",
      "wfi_b: mean flux error: 0.07615247368812561, 3sigma in AB mag (Total): 25.502986825089174\n",
      "wfi_b123: mean flux error: 0.07194452732801437, 3sigma in AB mag (Total): 25.56470245409468\n",
      "wfi_v: mean flux error: 0.15643367171287537, 3sigma in AB mag (Total): 24.72137126592505\n",
      "wfi_r: mean flux error: 0.13188579678535461, 3sigma in AB mag (Total): 24.906701794537362\n",
      "irac_i3: mean flux error: 5.814713966138103, 3sigma in AB mag (Total): 20.79587597309908\n",
      "irac_i4: mean flux error: 6.187252024048002, 3sigma in AB mag (Total): 20.728452347183413\n",
      "decam_g: mean flux error: 0.17293168328544636, 3sigma in AB mag (Total): 24.61251044109948\n",
      "decam_r: mean flux error: 0.2678574498755784, 3sigma in AB mag (Total): 24.137437538604665\n",
      "decam_i: mean flux error: 0.4593448690155973, 3sigma in AB mag (Total): 23.551849689269936\n",
      "decam_z: mean flux error: 0.9517604251369653, 3sigma in AB mag (Total): 22.760877756774924\n",
      "decam_y: mean flux error: 2.062134010955861, 3sigma in AB mag (Total): 21.921404650299245\n",
      "irac_i1: mean flux error: 0.8774210574046896, 3sigma in AB mag (Total): 22.849176730913975\n",
      "irac_i2: mean flux error: 1.105609095260832, 3sigma in AB mag (Total): 22.59819285631172\n",
      "vista_y: mean flux error: 0.30577073858104614, 3sigma in AB mag (Total): 23.993707057508367\n",
      "vista_j: mean flux error: 4.964516844951413, 3sigma in AB mag (Total): 20.967504391636815\n",
      "vista_h: mean flux error: 7.973414665953241, 3sigma in AB mag (Total): 20.453085986627165\n",
      "vista_ks: mean flux error: 10.637262667214141, 3sigma in AB mag (Total): 20.14012215455127\n",
      "vista_z: mean flux error: 0.23762176437678417, 3sigma in AB mag (Total): 24.26748132255576\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_wfi_b (408.5, 505.5, 97.0)\n",
      "ap_wfi_b123 (3875.0, 5105.0, 1230.0)\n",
      "ap_wfi_v (495.0, 583.5, 88.5)\n",
      "ap_wfi_r (570.5, 729.0, 158.5)\n",
      "ap_irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "ap_irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "ap_decam_g (4180.0, 5470.0, 1290.0)\n",
      "ap_decam_r (5680.0, 7150.0, 1470.0)\n",
      "ap_decam_i (7090.0, 8560.0, 1470.0)\n",
      "ap_decam_z (8490.0, 9960.0, 1470.0)\n",
      "ap_decam_y (9510.0, 10170.0, 660.0)\n",
      "ap_irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "ap_irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "ap_vista_y (9740.0, 10660.0, 920.0)\n",
      "ap_vista_j (11670.0, 13380.0, 1710.0)\n",
      "ap_vista_h (15000.0, 17900.0, 2900.0)\n",
      "ap_vista_ks (19930.0, 23010.0, 3080.0)\n",
      "ap_vista_z (8300.0, 9260.0, 960.0)\n",
      "wfi_b (408.5, 505.5, 97.0)\n",
      "wfi_b123 (3875.0, 5105.0, 1230.0)\n",
      "wfi_v (495.0, 583.5, 88.5)\n",
      "wfi_r (570.5, 729.0, 158.5)\n",
      "irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "decam_g (4180.0, 5470.0, 1290.0)\n",
      "decam_r (5680.0, 7150.0, 1470.0)\n",
      "decam_i (7090.0, 8560.0, 1470.0)\n",
      "decam_z (8490.0, 9960.0, 1470.0)\n",
      "decam_y (9510.0, 10170.0, 660.0)\n",
      "irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "vista_y (9740.0, 10660.0, 920.0)\n",
      "vista_j (11670.0, 13380.0, 1710.0)\n",
      "vista_h (15000.0, 17900.0, 2900.0)\n",
      "vista_ks (19930.0, 23010.0, 3080.0)\n",
      "vista_z (8300.0, 9260.0, 960.0)\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 ELAIS-S1')"
      ]
     },
     "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 ELAIS-S1')"
      ]
     },
     "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
}