{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Bootes 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",
      "2019-02-01 14:45:37.837299\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 = 'Bootes'\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_bootes_20190201.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",
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       "<thead><tr><th>idx</th><th>hp_idx_O_13</th><th>hp_idx_O_10</th><th>ferr_ap_lbc_u_mean</th><th>f_ap_lbc_u_p90</th><th>ferr_lbc_u_mean</th><th>f_lbc_u_p90</th><th>ferr_ap_suprime_z_mean</th><th>f_ap_suprime_z_p90</th><th>ferr_suprime_z_mean</th><th>f_suprime_z_p90</th><th>ferr_ap_lbc_y_mean</th><th>f_ap_lbc_y_p90</th><th>ferr_lbc_y_mean</th><th>f_lbc_y_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_ap_90prime_g_mean</th><th>f_ap_90prime_g_p90</th><th>ferr_90prime_g_mean</th><th>f_90prime_g_p90</th><th>ferr_ap_90prime_r_mean</th><th>f_ap_90prime_r_p90</th><th>ferr_90prime_r_mean</th><th>f_90prime_r_p90</th><th>ferr_ap_mosaic_z_mean</th><th>f_ap_mosaic_z_p90</th><th>ferr_mosaic_z_mean</th><th>f_mosaic_z_p90</th><th>ferr_ap_newfirm_j_mean</th><th>f_ap_newfirm_j_p90</th><th>ferr_newfirm_j_mean</th><th>f_newfirm_j_p90</th><th>ferr_ap_newfirm_h_mean</th><th>f_ap_newfirm_h_p90</th><th>ferr_newfirm_h_mean</th><th>f_newfirm_h_p90</th><th>ferr_ap_newfirm_k_mean</th><th>f_ap_newfirm_k_p90</th><th>ferr_newfirm_k_mean</th><th>f_newfirm_k_p90</th><th>ferr_ap_mosaic_r_mean</th><th>f_ap_mosaic_r_p90</th><th>ferr_mosaic_r_mean</th><th>f_mosaic_r_p90</th><th>ferr_ap_mosaic_i_mean</th><th>f_ap_mosaic_i_p90</th><th>ferr_mosaic_i_mean</th><th>f_mosaic_i_p90</th><th>ferr_ap_mosaic_b_mean</th><th>f_ap_mosaic_b_p90</th><th>ferr_mosaic_b_mean</th><th>f_mosaic_b_p90</th><th>ferr_ap_tifkam_ks_mean</th><th>f_ap_tifkam_ks_p90</th><th>ferr_tifkam_ks_mean</th><th>f_tifkam_ks_p90</th><th>ferr_ap_ukidss_j_mean</th><th>f_ap_ukidss_j_p90</th><th>ferr_ukidss_j_mean</th><th>f_ukidss_j_p90</th><th>ferr_ap_90prime_z_mean</th><th>f_ap_90prime_z_p90</th><th>ferr_90prime_z_mean</th><th>f_90prime_z_p90</th><th>ferr_ap_irac_i1_mean</th><th>f_ap_irac_i1_p90</th><th>ferr_irac_i1_mean</th><th>f_irac_i1_p90</th><th>ferr_ap_irac_i2_mean</th><th>f_ap_irac_i2_p90</th><th>ferr_irac_i2_mean</th><th>f_irac_i2_p90</th><th>ferr_ap_irac_i3_mean</th><th>f_ap_irac_i3_p90</th><th>ferr_irac_i3_mean</th><th>f_irac_i3_p90</th><th>ferr_ap_irac_i4_mean</th><th>f_ap_irac_i4_p90</th><th>ferr_irac_i4_mean</th><th>f_irac_i4_p90</th></tr></thead>\n",
       "<thead><tr><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></th><th></th><th></th><th></th><th></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></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></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>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>149674994</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>1</td><td>149675002</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>2</td><td>149675001</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>3</td><td>149675000</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>4</td><td>149674999</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>5</td><td>149674998</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>6</td><td>149674997</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>7</td><td>149674996</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>8</td><td>149674995</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>9</td><td>149675006</td><td>2338671</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</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(\"$('#table140165051470736-443811').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",
       "    $('#table140165051470736-443811').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98], 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",
       " '90prime_z',\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",
       " 'lbc_u',\n",
       " 'lbc_y',\n",
       " 'mosaic_b',\n",
       " 'mosaic_i',\n",
       " 'mosaic_r',\n",
       " 'mosaic_z',\n",
       " 'newfirm_h',\n",
       " 'newfirm_j',\n",
       " 'newfirm_k',\n",
       " 'suprime_z',\n",
       " 'tifkam_ks',\n",
       " 'ukidss_j'}"
      ]
     },
     "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, 1.0, 'Passbands on Bootes')"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "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": [
      "lbc_u: mean flux error: 0.06582602533523098, 3sigma in AB mag (Aperture): 25.661202781717414\n",
      "suprime_z: mean flux error: 0.17858133507394464, 3sigma in AB mag (Aperture): 24.577606699635744\n",
      "lbc_y: mean flux error: 0.5812862172771195, 3sigma in AB mag (Aperture): 23.296221799107023\n",
      "gpc1_g: mean flux error: 73.12682863067843, 3sigma in AB mag (Aperture): 18.047005014901465\n",
      "gpc1_r: mean flux error: 5.722261997953992, 3sigma in AB mag (Aperture): 20.813277517162824\n",
      "gpc1_i: mean flux error: 2.730008317477041, 3sigma in AB mag (Aperture): 21.616786937697036\n",
      "gpc1_z: mean flux error: 6.300170123056998, 3sigma in AB mag (Aperture): 20.708816171111586\n",
      "gpc1_y: mean flux error: 151.4673972587472, 3sigma in AB mag (Aperture): 17.25639895625958\n",
      "90prime_g: mean flux error: 0.14448076486587524, 3sigma in AB mag (Aperture): 24.807671782989523\n",
      "90prime_r: mean flux error: 0.2213410586118698, 3sigma in AB mag (Aperture): 24.3445419063922\n",
      "mosaic_z: mean flux error: 0.8128145337104797, 3sigma in AB mag (Aperture): 22.93221821193074\n",
      "newfirm_j: mean flux error: 1.4842238896105333, 3sigma in AB mag (Aperture): 22.27844831925244\n",
      "newfirm_h: mean flux error: 1.665939854796813, 3sigma in AB mag (Aperture): 22.15304856800497\n",
      "newfirm_k: mean flux error: 3.1820827810432313, 3sigma in AB mag (Aperture): 21.45041817941638\n",
      "mosaic_r: mean flux error: 0.09258104917993197, 3sigma in AB mag (Aperture): 25.290891617847713\n",
      "mosaic_i: mean flux error: 0.18261795389985885, 3sigma in AB mag (Aperture): 24.553338181898262\n",
      "mosaic_b: mean flux error: 0.030485112538625268, 3sigma in AB mag (Aperture): 26.496977356702523\n",
      "tifkam_ks: mean flux error: 18.872000889513924, 3sigma in AB mag (Aperture): 19.517651992331842\n",
      "ukidss_j: mean flux error: 6.357367038726807, 3sigma in AB mag (Aperture): 20.69900364849442\n",
      "90prime_z: mean flux error: 0.9037018418312073, 3sigma in AB mag (Aperture): 22.817133944662167\n",
      "irac_i1: mean flux error: 0.8619690718343747, 3sigma in AB mag (Aperture): 22.86846765505623\n",
      "irac_i2: mean flux error: 1.181511486058485, 3sigma in AB mag (Aperture): 22.526101993249036\n",
      "irac_i3: mean flux error: 5.786340766596909, 3sigma in AB mag (Aperture): 20.801186847346266\n",
      "irac_i4: mean flux error: 7.732774418318823, 3sigma in AB mag (Aperture): 20.486358510556805\n",
      "lbc_u: mean flux error: 0.11871575529794391, 3sigma in AB mag (Total): 25.020925963352433\n",
      "suprime_z: mean flux error: 0.3143173649362726, 3sigma in AB mag (Total): 23.963775925867857\n",
      "lbc_y: mean flux error: 1.0797613382098883, 3sigma in AB mag (Total): 22.62387743039219\n",
      "gpc1_g: mean flux error: 58.53693177853172, 3sigma in AB mag (Total): 18.288621975480076\n",
      "gpc1_r: mean flux error: 8.02817246978346, 3sigma in AB mag (Total): 20.445655128465823\n",
      "gpc1_i: mean flux error: 2.8802272151765846, 3sigma in AB mag (Total): 21.55862998893729\n",
      "gpc1_z: mean flux error: 6.612337216168607, 3sigma in AB mag (Total): 20.65630937913334\n",
      "gpc1_y: mean flux error: 31.560301656762157, 3sigma in AB mag (Total): 18.959343999224224\n",
      "90prime_g: mean flux error: 59.86345291137695, 3sigma in AB mag (Total): 18.26429245509825\n",
      "90prime_r: mean flux error: 0.9675227403640747, 3sigma in AB mag (Total): 22.743043909855707\n",
      "mosaic_z: mean flux error: 1.4107751846313477, 3sigma in AB mag (Total): 22.33355233351569\n",
      "newfirm_j: mean flux error: 1.8566344462989504, 3sigma in AB mag (Total): 22.035380853963325\n",
      "newfirm_h: mean flux error: 2.6152105047903254, 3sigma in AB mag (Total): 21.663430233070805\n",
      "newfirm_k: mean flux error: 5.2047704505963805, 3sigma in AB mag (Total): 20.91619291246422\n",
      "mosaic_r: mean flux error: 0.18776629472599635, 3sigma in AB mag (Total): 24.523152772612058\n",
      "mosaic_i: mean flux error: 0.4061876979572217, 3sigma in AB mag (Total): 23.68537994828491\n",
      "mosaic_b: mean flux error: 0.05892621321458029, 3sigma in AB mag (Total): 25.781425531074454\n",
      "tifkam_ks: mean flux error: 30.835213062541975, 3sigma in AB mag (Total): 18.984579479141097\n",
      "ukidss_j: mean flux error: 13.800500869750977, 3sigma in AB mag (Total): 19.857459741215585\n",
      "90prime_z: mean flux error: 1.42275869846344, 3sigma in AB mag (Total): 22.324368739504628\n",
      "irac_i1: mean flux error: 1.514460478111922, 3sigma in AB mag (Total): 22.256552002409087\n",
      "irac_i2: mean flux error: 2.0685033609017034, 3sigma in AB mag (Total): 21.918056286017737\n",
      "irac_i3: mean flux error: 11.279346328376583, 3sigma in AB mag (Total): 20.076487033897443\n",
      "irac_i4: mean flux error: 15.576459790975681, 3sigma in AB mag (Total): 19.726024967335583\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_lbc_u (3340.0, 3820.0, 480.0)\n",
      "ap_suprime_z (8540.0, 9280.0, 740.0)\n",
      "ap_lbc_y (9635.0, 11260.0, 1625.0)\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_newfirm_j (11600.0, 13387.0, 1787.0)\n",
      "ap_newfirm_h (14795.0, 17860.0, 3065.0)\n",
      "ap_newfirm_k (19845.0, 23060.0, 3215.0)\n",
      "ap_mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "ap_mosaic_i (7270.0, 9180.0, 1910.0)\n",
      "ap_mosaic_b (3474.0, 4750.0, 1276.0)\n",
      "ap_tifkam_ks (19500.0, 23000.0, 3500.0)\n",
      "ap_ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "ap_90prime_z (8290.0, 12000.0, 3710.0)\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",
      "lbc_u (3340.0, 3820.0, 480.0)\n",
      "suprime_z (8540.0, 9280.0, 740.0)\n",
      "lbc_y (9635.0, 11260.0, 1625.0)\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",
      "newfirm_j (11600.0, 13387.0, 1787.0)\n",
      "newfirm_h (14795.0, 17860.0, 3065.0)\n",
      "newfirm_k (19845.0, 23060.0, 3215.0)\n",
      "mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "mosaic_i (7270.0, 9180.0, 1910.0)\n",
      "mosaic_b (3474.0, 4750.0, 1276.0)\n",
      "tifkam_ks (19500.0, 23000.0, 3500.0)\n",
      "ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "90prime_z (8290.0, 12000.0, 3710.0)\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, 1.0, 'Depths on Bootes')"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "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, 1.0, 'Depths (5 $\\\\sigma$) vs coverage on Bootes')"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}