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   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# SGP 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 18:19:16.286033\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 = 'SGP'\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_sgp_20180221.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."
   ]
  },
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   "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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       "<thead><tr><th>idx</th><th>hp_idx_O_13</th><th>hp_idx_O_10</th><th>ferr_ap_omegacam_z_mean</th><th>f_ap_omegacam_z_p90</th><th>ferr_omegacam_z_mean</th><th>f_omegacam_z_p90</th><th>ferr_omegacam_u_mean</th><th>f_omegacam_u_p90</th><th>ferr_ap_omegacam_u_mean</th><th>f_ap_omegacam_u_p90</th><th>ferr_omegacam_g_mean</th><th>f_omegacam_g_p90</th><th>ferr_ap_omegacam_g_mean</th><th>f_ap_omegacam_g_p90</th><th>ferr_omegacam_r_mean</th><th>f_omegacam_r_p90</th><th>ferr_ap_omegacam_r_mean</th><th>f_ap_omegacam_r_p90</th><th>ferr_omegacam_i_mean</th><th>f_omegacam_i_p90</th><th>ferr_ap_omegacam_i_mean</th><th>f_ap_omegacam_i_p90</th><th>ferr_ap_gpc1_g_mean</th><th>f_ap_gpc1_g_p90</th><th>ferr_gpc1_g_mean</th><th>f_gpc1_g_p90</th><th>ferr_ap_gpc1_r_mean</th><th>f_ap_gpc1_r_p90</th><th>ferr_gpc1_r_mean</th><th>f_gpc1_r_p90</th><th>ferr_ap_gpc1_i_mean</th><th>f_ap_gpc1_i_p90</th><th>ferr_gpc1_i_mean</th><th>f_gpc1_i_p90</th><th>ferr_ap_gpc1_z_mean</th><th>f_ap_gpc1_z_p90</th><th>ferr_gpc1_z_mean</th><th>f_gpc1_z_p90</th><th>ferr_ap_gpc1_y_mean</th><th>f_ap_gpc1_y_p90</th><th>ferr_gpc1_y_mean</th><th>f_gpc1_y_p90</th><th>ferr_vista_z_mean</th><th>f_vista_z_p90</th><th>ferr_ap_vista_z_mean</th><th>f_ap_vista_z_p90</th><th>ferr_vista_y_mean</th><th>f_vista_y_p90</th><th>ferr_ap_vista_y_mean</th><th>f_ap_vista_y_p90</th><th>ferr_vista_j_mean</th><th>f_vista_j_p90</th><th>ferr_ap_vista_j_mean</th><th>f_ap_vista_j_p90</th><th>ferr_vista_h_mean</th><th>f_vista_h_p90</th><th>ferr_ap_vista_h_mean</th><th>f_ap_vista_h_p90</th><th>ferr_vista_ks_mean</th><th>f_vista_ks_p90</th><th>ferr_ap_vista_ks_mean</th><th>f_ap_vista_ks_p90</th><th>ferr_decam_g_mean</th><th>f_decam_g_p90</th><th>ferr_ap_decam_g_mean</th><th>f_ap_decam_g_p90</th><th>ferr_decam_r_mean</th><th>f_decam_r_p90</th><th>ferr_ap_decam_r_mean</th><th>f_ap_decam_r_p90</th><th>ferr_decam_i_mean</th><th>f_decam_i_p90</th><th>ferr_ap_decam_i_mean</th><th>f_ap_decam_i_p90</th><th>ferr_decam_z_mean</th><th>f_decam_z_p90</th><th>ferr_ap_decam_z_mean</th><th>f_ap_decam_z_p90</th><th>ferr_decam_y_mean</th><th>f_decam_y_p90</th><th>ferr_ap_decam_y_mean</th><th>f_ap_decam_y_p90</th></tr></thead>\n",
       "<tr><td>0</td><td>268695549</td><td>4198367</td><td>5.318588086478927</td><td>422.821205643106</td><td>8.29950297456952</td><td>435.2215817282329</td><td>1.8093893049167225</td><td>53.303582663926626</td><td>2.1500957965984377</td><td>80.18419989049022</td><td>0.9932729831357368</td><td>1529.9019028269845</td><td>0.8555822624640189</td><td>1563.1185026803985</td><td>1.5075798399094416</td><td>2482.4364445090073</td><td>1.1350740828041666</td><td>2454.7377458840224</td><td>1.860068979348084</td><td>127.22625455644128</td><td>1.220907753137785</td><td>123.53868279337105</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.4572543723919358</td><td>608.3882441894372</td><td>0.38499290216799875</td><td>601.7480218616809</td><td>0.5220544615033763</td><td>113.27777514292514</td><td>0.28953896997532536</td><td>53.60255258084825</td><td>1.0606663136426304</td><td>229.02329726519264</td><td>0.4954255789118883</td><td>70.7849860473087</td><td>2.2464659738502246</td><td>438.19180147940966</td><td>0.8913570505619027</td><td>209.65337458012345</td><td>4.633706208985936</td><td>3775.010295667754</td><td>2.3532332860124106</td><td>4335.911789895676</td></tr>\n",
       "<tr><td>1</td><td>268695548</td><td>4198367</td><td>5.318588086478927</td><td>422.821205643106</td><td>8.29950297456952</td><td>435.2215817282329</td><td>1.8093893049167225</td><td>53.303582663926626</td><td>2.1500957965984377</td><td>80.18419989049022</td><td>0.9932729831357368</td><td>1529.9019028269845</td><td>0.8555822624640189</td><td>1563.1185026803985</td><td>1.5075798399094416</td><td>2482.4364445090073</td><td>1.1350740828041666</td><td>2454.7377458840224</td><td>1.860068979348084</td><td>127.22625455644128</td><td>1.220907753137785</td><td>123.53868279337105</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.4572543723919358</td><td>608.3882441894372</td><td>0.38499290216799875</td><td>601.7480218616809</td><td>0.5220544615033763</td><td>113.27777514292514</td><td>0.28953896997532536</td><td>53.60255258084825</td><td>1.0606663136426304</td><td>229.02329726519264</td><td>0.4954255789118883</td><td>70.7849860473087</td><td>2.2464659738502246</td><td>438.19180147940966</td><td>0.8913570505619027</td><td>209.65337458012345</td><td>4.633706208985936</td><td>3775.010295667754</td><td>2.3532332860124106</td><td>4335.911789895676</td></tr>\n",
       "<tr><td>2</td><td>268695543</td><td>4198367</td><td>5.318588086478927</td><td>422.821205643106</td><td>8.29950297456952</td><td>435.2215817282329</td><td>1.8093893049167225</td><td>53.303582663926626</td><td>2.1500957965984377</td><td>80.18419989049022</td><td>0.9932729831357368</td><td>1529.9019028269845</td><td>0.8555822624640189</td><td>1563.1185026803985</td><td>1.5075798399094416</td><td>2482.4364445090073</td><td>1.1350740828041666</td><td>2454.7377458840224</td><td>1.860068979348084</td><td>127.22625455644128</td><td>1.220907753137785</td><td>123.53868279337105</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.4572543723919358</td><td>608.3882441894372</td><td>0.38499290216799875</td><td>601.7480218616809</td><td>0.5220544615033763</td><td>113.27777514292514</td><td>0.28953896997532536</td><td>53.60255258084825</td><td>1.0606663136426304</td><td>229.02329726519264</td><td>0.4954255789118883</td><td>70.7849860473087</td><td>2.2464659738502246</td><td>438.19180147940966</td><td>0.8913570505619027</td><td>209.65337458012345</td><td>4.633706208985936</td><td>3775.010295667754</td><td>2.3532332860124106</td><td>4335.911789895676</td></tr>\n",
       "<tr><td>3</td><td>268695551</td><td>4198367</td><td>5.318588086478927</td><td>422.821205643106</td><td>8.29950297456952</td><td>435.2215817282329</td><td>1.8093893049167225</td><td>53.303582663926626</td><td>2.1500957965984377</td><td>80.18419989049022</td><td>0.9932729831357368</td><td>1529.9019028269845</td><td>0.8555822624640189</td><td>1563.1185026803985</td><td>1.5075798399094416</td><td>2482.4364445090073</td><td>1.1350740828041666</td><td>2454.7377458840224</td><td>1.860068979348084</td><td>127.22625455644128</td><td>1.220907753137785</td><td>123.53868279337105</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.4572543723919358</td><td>608.3882441894372</td><td>0.38499290216799875</td><td>601.7480218616809</td><td>0.5220544615033763</td><td>113.27777514292514</td><td>0.28953896997532536</td><td>53.60255258084825</td><td>1.0606663136426304</td><td>229.02329726519264</td><td>0.4954255789118883</td><td>70.7849860473087</td><td>2.2464659738502246</td><td>438.19180147940966</td><td>0.8913570505619027</td><td>209.65337458012345</td><td>4.633706208985936</td><td>3775.010295667754</td><td>2.3532332860124106</td><td>4335.911789895676</td></tr>\n",
       "<tr><td>4</td><td>268695550</td><td>4198367</td><td>5.318588086478927</td><td>422.821205643106</td><td>8.29950297456952</td><td>435.2215817282329</td><td>1.8093893049167225</td><td>53.303582663926626</td><td>2.1500957965984377</td><td>80.18419989049022</td><td>0.9932729831357368</td><td>1529.9019028269845</td><td>0.8555822624640189</td><td>1563.1185026803985</td><td>1.5075798399094416</td><td>2482.4364445090073</td><td>1.1350740828041666</td><td>2454.7377458840224</td><td>1.860068979348084</td><td>127.22625455644128</td><td>1.220907753137785</td><td>123.53868279337105</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.4572543723919358</td><td>608.3882441894372</td><td>0.38499290216799875</td><td>601.7480218616809</td><td>0.5220544615033763</td><td>113.27777514292514</td><td>0.28953896997532536</td><td>53.60255258084825</td><td>1.0606663136426304</td><td>229.02329726519264</td><td>0.4954255789118883</td><td>70.7849860473087</td><td>2.2464659738502246</td><td>438.19180147940966</td><td>0.8913570505619027</td><td>209.65337458012345</td><td>4.633706208985936</td><td>3775.010295667754</td><td>2.3532332860124106</td><td>4335.911789895676</td></tr>\n",
       "<tr><td>5</td><td>268696511</td><td>4198382</td><td>4.925707147199608</td><td>987.1570060605761</td><td>10.375760735798988</td><td>1135.0559944605193</td><td>2.1597603280167577</td><td>219.54257070060987</td><td>1.8425879461132257</td><td>214.3657893375337</td><td>0.7302657612764134</td><td>390.09605112474486</td><td>0.540685438387132</td><td>352.4999587095507</td><td>1.1805207406264309</td><td>341.73569130656864</td><td>0.6868760603214307</td><td>292.8242404817245</td><td>2.264313529846699</td><td>633.5036018229687</td><td>1.3265283663388876</td><td>551.9303532832823</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>268696494</td><td>4198382</td><td>4.925707147199608</td><td>987.1570060605761</td><td>10.375760735798988</td><td>1135.0559944605193</td><td>2.1597603280167577</td><td>219.54257070060987</td><td>1.8425879461132257</td><td>214.3657893375337</td><td>0.7302657612764134</td><td>390.09605112474486</td><td>0.540685438387132</td><td>352.4999587095507</td><td>1.1805207406264309</td><td>341.73569130656864</td><td>0.6868760603214307</td><td>292.8242404817245</td><td>2.264313529846699</td><td>633.5036018229687</td><td>1.3265283663388876</td><td>551.9303532832823</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>268696506</td><td>4198382</td><td>4.925707147199608</td><td>987.1570060605761</td><td>10.375760735798988</td><td>1135.0559944605193</td><td>2.1597603280167577</td><td>219.54257070060987</td><td>1.8425879461132257</td><td>214.3657893375337</td><td>0.7302657612764134</td><td>390.09605112474486</td><td>0.540685438387132</td><td>352.4999587095507</td><td>1.1805207406264309</td><td>341.73569130656864</td><td>0.6868760603214307</td><td>292.8242404817245</td><td>2.264313529846699</td><td>633.5036018229687</td><td>1.3265283663388876</td><td>551.9303532832823</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>268696507</td><td>4198382</td><td>4.925707147199608</td><td>987.1570060605761</td><td>10.375760735798988</td><td>1135.0559944605193</td><td>2.1597603280167577</td><td>219.54257070060987</td><td>1.8425879461132257</td><td>214.3657893375337</td><td>0.7302657612764134</td><td>390.09605112474486</td><td>0.540685438387132</td><td>352.4999587095507</td><td>1.1805207406264309</td><td>341.73569130656864</td><td>0.6868760603214307</td><td>292.8242404817245</td><td>2.264313529846699</td><td>633.5036018229687</td><td>1.3265283663388876</td><td>551.9303532832823</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>268696510</td><td>4198382</td><td>4.925707147199608</td><td>987.1570060605761</td><td>10.375760735798988</td><td>1135.0559944605193</td><td>2.1597603280167577</td><td>219.54257070060987</td><td>1.8425879461132257</td><td>214.3657893375337</td><td>0.7302657612764134</td><td>390.09605112474486</td><td>0.540685438387132</td><td>352.4999587095507</td><td>1.1805207406264309</td><td>341.73569130656864</td><td>0.6868760603214307</td><td>292.8242404817245</td><td>2.264313529846699</td><td>633.5036018229687</td><td>1.3265283663388876</td><td>551.9303532832823</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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(\"$('#table140307858249880-830337').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",
       "    $('#table140307858249880-830337').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], 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",
       " 'gpc1_g',\n",
       " 'gpc1_i',\n",
       " 'gpc1_r',\n",
       " 'gpc1_y',\n",
       " 'gpc1_z',\n",
       " 'omegacam_g',\n",
       " 'omegacam_i',\n",
       " 'omegacam_r',\n",
       " 'omegacam_u',\n",
       " 'omegacam_z',\n",
       " 'vista_h',\n",
       " 'vista_j',\n",
       " 'vista_ks',\n",
       " 'vista_y',\n",
       " 'vista_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 SGP')"
      ]
     },
     "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": [
      "omegacam_z: mean flux error: 4.371576376225572, 3sigma in AB mag (Aperture): 21.105601687223874\n",
      "omegacam_u: mean flux error: 1.8050078185529712, 3sigma in AB mag (Aperture): 22.065999144622886\n",
      "omegacam_g: mean flux error: 0.4252830111144532, 3sigma in AB mag (Aperture): 23.63550177772199\n",
      "omegacam_r: mean flux error: 0.6163829980340969, 3sigma in AB mag (Aperture): 23.232570236015654\n",
      "omegacam_i: mean flux error: 1.1522080353669744, 3sigma in AB mag (Aperture): 22.55336961414813\n",
      "gpc1_g: mean flux error: 3042.0990698882947, 3sigma in AB mag (Aperture): 13.999263479929745\n",
      "gpc1_r: mean flux error: 286.0777558985285, 3sigma in AB mag (Aperture): 16.56598663730086\n",
      "gpc1_i: mean flux error: 1573.068357923074, 3sigma in AB mag (Aperture): 14.715327874786716\n",
      "gpc1_z: mean flux error: 1068.0520435262904, 3sigma in AB mag (Aperture): 15.135715824948896\n",
      "gpc1_y: mean flux error: 26.663614747217615, 3sigma in AB mag (Aperture): 19.14239929886022\n",
      "vista_z: mean flux error: 0.6972098081078645, 3sigma in AB mag (Aperture): 23.09878814323489\n",
      "vista_y: mean flux error: 19.454614005169027, 3sigma in AB mag (Aperture): 19.484640316997307\n",
      "vista_j: mean flux error: 1.5820209520587147, 3sigma in AB mag (Aperture): 22.209166285866807\n",
      "vista_h: mean flux error: 2.5079183807712555, 3sigma in AB mag (Aperture): 21.708913367091874\n",
      "vista_ks: mean flux error: 2.661991148736425, 3sigma in AB mag (Aperture): 21.644180345479406\n",
      "decam_g: mean flux error: 0.124224331925782, 3sigma in AB mag (Aperture): 24.971680188693476\n",
      "decam_r: mean flux error: 0.150145578830792, 3sigma in AB mag (Aperture): 24.76591549185435\n",
      "decam_i: mean flux error: 0.24752542081217208, 3sigma in AB mag (Aperture): 24.223147344405426\n",
      "decam_z: mean flux error: 0.4605319044892942, 3sigma in AB mag (Aperture): 23.549047557196126\n",
      "decam_y: mean flux error: 1.2816168530215408, 3sigma in AB mag (Aperture): 22.43780133902886\n",
      "omegacam_z: mean flux error: 7.828921615169762, 3sigma in AB mag (Total): 20.47294200110671\n",
      "omegacam_u: mean flux error: 2.526222121199103, 3sigma in AB mag (Total): 21.701018028759044\n",
      "omegacam_g: mean flux error: 0.6949648691854694, 3sigma in AB mag (Total): 23.10228973483533\n",
      "omegacam_r: mean flux error: 1.1147778583662524, 3sigma in AB mag (Total): 22.58922602769413\n",
      "omegacam_i: mean flux error: 2.119050717925087, 3sigma in AB mag (Total): 21.89184348481296\n",
      "gpc1_g: mean flux error: 8175.586739622989, 3sigma in AB mag (Total): 12.925899536739585\n",
      "gpc1_r: mean flux error: 673.6994630490384, 3sigma in AB mag (Total): 15.636031360170684\n",
      "gpc1_i: mean flux error: 5038.75144097764, 3sigma in AB mag (Total): 13.451389524800156\n",
      "gpc1_z: mean flux error: 411.2514377631323, 3sigma in AB mag (Total): 16.171928289991165\n",
      "gpc1_y: mean flux error: 721.3267183344645, 3sigma in AB mag (Total): 15.561866815720556\n",
      "vista_z: mean flux error: 1.7752544174226499, 3sigma in AB mag (Total): 22.084045358223214\n",
      "vista_y: mean flux error: 21.986426734319004, 3sigma in AB mag (Total): 19.351810230870406\n",
      "vista_j: mean flux error: 4.140983388477638, 3sigma in AB mag (Total): 21.164438142358428\n",
      "vista_h: mean flux error: 6.8396231146263835, 3sigma in AB mag (Total): 20.619616434831705\n",
      "vista_ks: mean flux error: 7.419563568490817, 3sigma in AB mag (Total): 20.53125096299876\n",
      "decam_g: mean flux error: 0.19240155714180482, 3sigma in AB mag (Total): 24.496675406844524\n",
      "decam_r: mean flux error: 0.2401067926721679, 3sigma in AB mag (Total): 24.256185746915925\n",
      "decam_i: mean flux error: 0.4537345119898178, 3sigma in AB mag (Total): 23.565192328543084\n",
      "decam_z: mean flux error: 0.9118401918483625, 3sigma in AB mag (Total): 22.807400035690883\n",
      "decam_y: mean flux error: 2.6129670262505997, 3sigma in AB mag (Total): 21.664362039999794\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_omegacam_z (8433.9, 9274.6, 840.6992)\n",
      "ap_omegacam_u (3296.7, 3807.9, 511.19995)\n",
      "ap_omegacam_g (4077.9, 5369.7, 1291.8003)\n",
      "ap_omegacam_r (5640.7, 6962.8, 1322.0996)\n",
      "ap_omegacam_i (6841.5, 8373.8, 1532.2998)\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_vista_z (8300.0, 9260.0, 960.0)\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_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",
      "omegacam_z (8433.9, 9274.6, 840.6992)\n",
      "omegacam_u (3296.7, 3807.9, 511.19995)\n",
      "omegacam_g (4077.9, 5369.7, 1291.8003)\n",
      "omegacam_r (5640.7, 6962.8, 1322.0996)\n",
      "omegacam_i (6841.5, 8373.8, 1532.2998)\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",
      "vista_z (8300.0, 9260.0, 960.0)\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",
      "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"
     ]
    }
   ],
   "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 SGP')"
      ]
     },
     "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 SGP')"
      ]
     },
     "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
}