{
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   "cell_type": "markdown",
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   "source": [
    "# AKARI-NEP 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",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n",
      "This notebook was executed on: \n",
      "2018-02-27 17:30:59.886785\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 = 'AKARI-NEP'\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_akari-nep_20180215.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": null,
   "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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
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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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       "<tr><td>2</td><td>167639486</td><td>2619366</td><td>6.07996386774</td><td>2310.0013186</td><td>18.7020527759</td><td>3382.58621008</td><td>136.164235334</td><td>3987.11259183</td><td>197.895290804</td><td>6850.63095237</td><td>328.207508891</td><td>7972.90609739</td><td>292.166408299</td><td>7179.76728714</td><td>2.05593523735</td><td>196.896160057</td><td>3.42829453023</td><td>209.195277934</td><td>1340.91922352</td><td>24064.3947274</td><td>106.865876141</td><td>9280.06192774</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>167639484</td><td>2619366</td><td>6.07996386774</td><td>2310.0013186</td><td>18.7020527759</td><td>3382.58621008</td><td>136.164235334</td><td>3987.11259183</td><td>197.895290804</td><td>6850.63095237</td><td>328.207508891</td><td>7972.90609739</td><td>292.166408299</td><td>7179.76728714</td><td>2.05593523735</td><td>196.896160057</td><td>3.42829453023</td><td>209.195277934</td><td>1340.91922352</td><td>24064.3947274</td><td>106.865876141</td><td>9280.06192774</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>167639482</td><td>2619366</td><td>6.07996386774</td><td>2310.0013186</td><td>18.7020527759</td><td>3382.58621008</td><td>136.164235334</td><td>3987.11259183</td><td>197.895290804</td><td>6850.63095237</td><td>328.207508891</td><td>7972.90609739</td><td>292.166408299</td><td>7179.76728714</td><td>2.05593523735</td><td>196.896160057</td><td>3.42829453023</td><td>209.195277934</td><td>1340.91922352</td><td>24064.3947274</td><td>106.865876141</td><td>9280.06192774</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>5</td><td>167639481</td><td>2619366</td><td>6.07996386774</td><td>2310.0013186</td><td>18.7020527759</td><td>3382.58621008</td><td>136.164235334</td><td>3987.11259183</td><td>197.895290804</td><td>6850.63095237</td><td>328.207508891</td><td>7972.90609739</td><td>292.166408299</td><td>7179.76728714</td><td>2.05593523735</td><td>196.896160057</td><td>3.42829453023</td><td>209.195277934</td><td>1340.91922352</td><td>24064.3947274</td><td>106.865876141</td><td>9280.06192774</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>167639480</td><td>2619366</td><td>6.07996386774</td><td>2310.0013186</td><td>18.7020527759</td><td>3382.58621008</td><td>136.164235334</td><td>3987.11259183</td><td>197.895290804</td><td>6850.63095237</td><td>328.207508891</td><td>7972.90609739</td><td>292.166408299</td><td>7179.76728714</td><td>2.05593523735</td><td>196.896160057</td><td>3.42829453023</td><td>209.195277934</td><td>1340.91922352</td><td>24064.3947274</td><td>106.865876141</td><td>9280.06192774</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>167639483</td><td>2619366</td><td>6.07996386774</td><td>2310.0013186</td><td>18.7020527759</td><td>3382.58621008</td><td>136.164235334</td><td>3987.11259183</td><td>197.895290804</td><td>6850.63095237</td><td>328.207508891</td><td>7972.90609739</td><td>292.166408299</td><td>7179.76728714</td><td>2.05593523735</td><td>196.896160057</td><td>3.42829453023</td><td>209.195277934</td><td>1340.91922352</td><td>24064.3947274</td><td>106.865876141</td><td>9280.06192774</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>167639551</td><td>2619367</td><td>1.07194313625</td><td>77.6303976546</td><td>1.40551183767</td><td>72.1968489819</td><td>1.46664167429</td><td>128.210359622</td><td>1.14450474493</td><td>120.243284083</td><td>3.60041252317</td><td>268.21645616</td><td>3.13399191371</td><td>251.27246932</td><td>3.87799407956</td><td>401.123386811</td><td>3.40483422613</td><td>376.918130985</td><td>7.07685065978</td><td>485.771815871</td><td>8.69308821518</td><td>427.219682411</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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>167639550</td><td>2619367</td><td>1.07194313625</td><td>77.6303976546</td><td>1.40551183767</td><td>72.1968489819</td><td>1.46664167429</td><td>128.210359622</td><td>1.14450474493</td><td>120.243284083</td><td>3.60041252317</td><td>268.21645616</td><td>3.13399191371</td><td>251.27246932</td><td>3.87799407956</td><td>401.123386811</td><td>3.40483422613</td><td>376.918130985</td><td>7.07685065978</td><td>485.771815871</td><td>8.69308821518</td><td>427.219682411</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><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(\"$('#table140277486217088-721338').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",
       "    $('#table140277486217088-721338').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], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "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": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gpc1_g',\n",
       " 'gpc1_i',\n",
       " 'gpc1_r',\n",
       " 'gpc1_y',\n",
       " 'gpc1_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'megacam_g',\n",
       " 'megacam_i',\n",
       " 'megacam_r',\n",
       " 'megacam_u',\n",
       " 'megacam_z',\n",
       " 'wircam_j',\n",
       " 'wircam_ks',\n",
       " 'wircam_y'}"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f94d0b51198>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9LSwsTLS3/esrK1asiPnss8/CnbfNmTOnbuXKlRW9Hbsva7wJIYRcpxZ/tRg/3vcjJvxj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bNnZ9psNtTW1socDgfuvvvu1hdeeKH66NGjgrueSIH3BY610sfAW6EAUSi6ZrxtNv8y3jUHAaUO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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f951ee71ba8>"
      ]
     },
     "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": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gpc1_g: mean flux error: 8.806699357169498, 3sigma in AB mag (Aperture): 20.34516393653916\n",
      "gpc1_r: mean flux error: 12.067129104246586, 3sigma in AB mag (Aperture): 20.00318696519175\n",
      "gpc1_i: mean flux error: 8.251892546976062, 3sigma in AB mag (Aperture): 20.41581295290225\n",
      "gpc1_z: mean flux error: 16.296125057512807, 3sigma in AB mag (Aperture): 19.676985991179073\n",
      "gpc1_y: mean flux error: 97.42729605212693, 3sigma in AB mag (Aperture): 17.735495239381372\n",
      "irac_i1: mean flux error: 1.7294846773147583, 3sigma in AB mag (Aperture): 22.112405066560406\n",
      "irac_i2: mean flux error: 1.4147037267684937, 3sigma in AB mag (Aperture): 22.330533119207182\n",
      "gpc1_g: mean flux error: 7.616552343703014, 3sigma in AB mag (Total): 20.50280078555148\n",
      "gpc1_r: mean flux error: 11.285212165987328, 3sigma in AB mag (Total): 20.075922542249153\n",
      "gpc1_i: mean flux error: 6.779790275782894, 3sigma in AB mag (Total): 20.62915621388678\n",
      "gpc1_z: mean flux error: 15.101349154690517, 3sigma in AB mag (Total): 19.759657490950865\n",
      "gpc1_y: mean flux error: 81.92646632009088, 3sigma in AB mag (Total): 17.9236363053817\n",
      "irac_i1: mean flux error: 2.5585498809814453, 3sigma in AB mag (Total): 21.687212142426283\n",
      "irac_i2: mean flux error: 2.087070941925049, 3sigma in AB mag (Total): 21.908353834496303\n",
      "megacam_u: mean flux error: 0.025724060833454132, 3sigma in AB mag (Total): 26.681348043321556\n",
      "megacam_g: mean flux error: 0.02217845991253853, 3sigma in AB mag (Total): 26.842368400316808\n",
      "megacam_r: mean flux error: 0.04555840045213699, 3sigma in AB mag (Total): 26.060775694031584\n",
      "megacam_i: mean flux error: 0.08159784972667694, 3sigma in AB mag (Total): 25.428000077349985\n",
      "megacam_z: mean flux error: 0.2246941775083542, 3sigma in AB mag (Total): 24.32821731642934\n",
      "wircam_j: mean flux error: 1.4508192539215088, 3sigma in AB mag (Total): 22.30316358695483\n",
      "wircam_ks: mean flux error: 1.6619582176208496, 3sigma in AB mag (Total): 22.15564661013537\n",
      "wircam_y: mean flux error: 0.8292735815048218, 3sigma in AB mag (Total): 22.9104522879201\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": 17,
   "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": 18,
   "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": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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_irac_i1 (31754.0, 39164.801, 7410.8008)\n",
      "ap_irac_i2 (39980.102, 50052.301, 10072.199)\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",
      "irac_i1 (31754.0, 39164.801, 7410.8008)\n",
      "irac_i2 (39980.102, 50052.301, 10072.199)\n",
      "megacam_u (3500.0, 4100.0, 600.0)\n",
      "megacam_g (4180.0, 5580.0, 1400.0)\n",
      "megacam_r (5680.0, 6880.0, 1200.0)\n",
      "megacam_i (6831.7305, 8388.5557, 1556.8252)\n",
      "megacam_z (8280.0, 9160.0, 880.0)\n",
      "wircam_j (11748.0, 13334.0, 1586.0)\n",
      "wircam_ks (19870.0, 23135.0, 3265.0)\n",
      "wircam_y (9702.0, 10806.0, 1104.0)\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": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f94d0780c88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WEMIJYAiAK63HSil3A9gNANHR0d9LwomIiIioHZ1UnpWSlJSkP3bsmA8AWK3WARaLxV2l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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94ceeab128>"
      ]
     },
     "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": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f94ccc32cf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GDBlSr1C0HXGff/75wMjISF1kZKTu3nvvjX3yySeD7fkZtIRBm4iIiKgHUY0c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\nQGgDAECA0AYAgAChDQAAAQdAUwIsxz5HCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94cedd3198>"
      ]
     },
     "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
}