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   "cell_type": "markdown",
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   "source": [
    "# AKARI-SEP 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 17:34:48.784227\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-SEP'\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-sep_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": 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."
   ]
  },
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   "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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       "<tr><td>2</td><td>555969274</td><td>8687019</td><td>1.79617</td><td>39.0306854248</td><td>3.99955</td><td>69.8166992188</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.34353</td><td>74.8369064331</td><td>11.3973</td><td>92.5178489685</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.088539758709</td><td>1.59851065142</td><td>0.127978368509</td><td>1.99557468684</td><td>0.10133595357</td><td>4.36875736275</td><td>0.150870605007</td><td>4.97561635035</td><td>0.169381721259</td><td>8.28568691237</td><td>0.26324369252</td><td>8.81878340366</td><td>0.33612726372</td><td>10.9447312646</td><td>0.531454867505</td><td>16.2943515591</td><td>1.18289773347</td><td>12.4146150636</td><td>1.87851042072</td><td>17.6926068865</td></tr>\n",
       "<tr><td>3</td><td>555969273</td><td>8687019</td><td>1.79617</td><td>39.0306854248</td><td>3.99955</td><td>69.8166992188</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.34353</td><td>74.8369064331</td><td>11.3973</td><td>92.5178489685</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.088539758709</td><td>1.59851065142</td><td>0.127978368509</td><td>1.99557468684</td><td>0.10133595357</td><td>4.36875736275</td><td>0.150870605007</td><td>4.97561635035</td><td>0.169381721259</td><td>8.28568691237</td><td>0.26324369252</td><td>8.81878340366</td><td>0.33612726372</td><td>10.9447312646</td><td>0.531454867505</td><td>16.2943515591</td><td>1.18289773347</td><td>12.4146150636</td><td>1.87851042072</td><td>17.6926068865</td></tr>\n",
       "<tr><td>4</td><td>555969262</td><td>8687019</td><td>1.79617</td><td>39.0306854248</td><td>3.99955</td><td>69.8166992188</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.34353</td><td>74.8369064331</td><td>11.3973</td><td>92.5178489685</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.088539758709</td><td>1.59851065142</td><td>0.127978368509</td><td>1.99557468684</td><td>0.10133595357</td><td>4.36875736275</td><td>0.150870605007</td><td>4.97561635035</td><td>0.169381721259</td><td>8.28568691237</td><td>0.26324369252</td><td>8.81878340366</td><td>0.33612726372</td><td>10.9447312646</td><td>0.531454867505</td><td>16.2943515591</td><td>1.18289773347</td><td>12.4146150636</td><td>1.87851042072</td><td>17.6926068865</td></tr>\n",
       "<tr><td>5</td><td>555969263</td><td>8687019</td><td>1.79617</td><td>39.0306854248</td><td>3.99955</td><td>69.8166992188</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.34353</td><td>74.8369064331</td><td>11.3973</td><td>92.5178489685</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.088539758709</td><td>1.59851065142</td><td>0.127978368509</td><td>1.99557468684</td><td>0.10133595357</td><td>4.36875736275</td><td>0.150870605007</td><td>4.97561635035</td><td>0.169381721259</td><td>8.28568691237</td><td>0.26324369252</td><td>8.81878340366</td><td>0.33612726372</td><td>10.9447312646</td><td>0.531454867505</td><td>16.2943515591</td><td>1.18289773347</td><td>12.4146150636</td><td>1.87851042072</td><td>17.6926068865</td></tr>\n",
       "<tr><td>6</td><td>555969276</td><td>8687019</td><td>1.79617</td><td>39.0306854248</td><td>3.99955</td><td>69.8166992188</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.34353</td><td>74.8369064331</td><td>11.3973</td><td>92.5178489685</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.088539758709</td><td>1.59851065142</td><td>0.127978368509</td><td>1.99557468684</td><td>0.10133595357</td><td>4.36875736275</td><td>0.150870605007</td><td>4.97561635035</td><td>0.169381721259</td><td>8.28568691237</td><td>0.26324369252</td><td>8.81878340366</td><td>0.33612726372</td><td>10.9447312646</td><td>0.531454867505</td><td>16.2943515591</td><td>1.18289773347</td><td>12.4146150636</td><td>1.87851042072</td><td>17.6926068865</td></tr>\n",
       "<tr><td>7</td><td>555969278</td><td>8687019</td><td>1.79617</td><td>39.0306854248</td><td>3.99955</td><td>69.8166992188</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.34353</td><td>74.8369064331</td><td>11.3973</td><td>92.5178489685</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.088539758709</td><td>1.59851065142</td><td>0.127978368509</td><td>1.99557468684</td><td>0.10133595357</td><td>4.36875736275</td><td>0.150870605007</td><td>4.97561635035</td><td>0.169381721259</td><td>8.28568691237</td><td>0.26324369252</td><td>8.81878340366</td><td>0.33612726372</td><td>10.9447312646</td><td>0.531454867505</td><td>16.2943515591</td><td>1.18289773347</td><td>12.4146150636</td><td>1.87851042072</td><td>17.6926068865</td></tr>\n",
       "<tr><td>8</td><td>555969279</td><td>8687019</td><td>1.79617</td><td>39.0306854248</td><td>3.99955</td><td>69.8166992188</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.34353</td><td>74.8369064331</td><td>11.3973</td><td>92.5178489685</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.088539758709</td><td>1.59851065142</td><td>0.127978368509</td><td>1.99557468684</td><td>0.10133595357</td><td>4.36875736275</td><td>0.150870605007</td><td>4.97561635035</td><td>0.169381721259</td><td>8.28568691237</td><td>0.26324369252</td><td>8.81878340366</td><td>0.33612726372</td><td>10.9447312646</td><td>0.531454867505</td><td>16.2943515591</td><td>1.18289773347</td><td>12.4146150636</td><td>1.87851042072</td><td>17.6926068865</td></tr>\n",
       "<tr><td>9</td><td>555969272</td><td>8687019</td><td>1.79617</td><td>39.0306854248</td><td>3.99955</td><td>69.8166992188</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.34353</td><td>74.8369064331</td><td>11.3973</td><td>92.5178489685</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.088539758709</td><td>1.59851065142</td><td>0.127978368509</td><td>1.99557468684</td><td>0.10133595357</td><td>4.36875736275</td><td>0.150870605007</td><td>4.97561635035</td><td>0.169381721259</td><td>8.28568691237</td><td>0.26324369252</td><td>8.81878340366</td><td>0.33612726372</td><td>10.9447312646</td><td>0.531454867505</td><td>16.2943515591</td><td>1.18289773347</td><td>12.4146150636</td><td>1.87851042072</td><td>17.6926068865</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(\"$('#table140682452623768-909511').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",
       "    $('#table140682452623768-909511').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], 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": [
       "{'decam_g',\n",
       " 'decam_i',\n",
       " 'decam_r',\n",
       " 'decam_y',\n",
       " 'decam_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'vista_h',\n",
       " 'vista_j',\n",
       " 'vista_ks'}"
      ]
     },
     "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 0x7ff30ec4e3c8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j41HgTAiR0u7du0c8//zzse7b4uLi2nNzc8+cOHHi5JWMTYEzIWTQMGzdBm61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\nnEVEREREfFBwFhERERHx4f8BFHhSgMHdoboAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff32f260a20>"
      ]
     },
     "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": [
      "vista_j: mean flux error: 3.7359588146209717, 3sigma in AB mag (Aperture): 21.276191663444784\n",
      "vista_h: mean flux error: 5.911341667175293, 3sigma in AB mag (Aperture): 20.77798170900025\n",
      "vista_ks: mean flux error: 6.426962375640869, 3sigma in AB mag (Aperture): 20.687182469461625\n",
      "irac_i1: mean flux error: 1.483581781387329, 3sigma in AB mag (Aperture): 22.278918134485288\n",
      "irac_i2: mean flux error: 1.1812387704849243, 3sigma in AB mag (Aperture): 22.52635263097286\n",
      "decam_g: mean flux error: 0.10550510166182817, 3sigma in AB mag (Aperture): 25.14901321245751\n",
      "decam_r: mean flux error: 0.1277219069632386, 3sigma in AB mag (Aperture): 24.941533377730785\n",
      "decam_i: mean flux error: 0.20926986973033335, 3sigma in AB mag (Aperture): 24.40543010329315\n",
      "decam_z: mean flux error: 0.3932180062636109, 3sigma in AB mag (Aperture): 23.720613371061823\n",
      "decam_y: mean flux error: 1.3569837256612631, 3sigma in AB mag (Aperture): 22.375760265234057\n",
      "vista_j: mean flux error: 7.652223110198975, 3sigma in AB mag (Total): 20.497727803345207\n",
      "vista_h: mean flux error: 12.754694938659668, 3sigma in AB mag (Total): 19.94302167370011\n",
      "vista_ks: mean flux error: 14.317732810974121, 3sigma in AB mag (Total): 19.81751122917145\n",
      "irac_i1: mean flux error: 2.617389440536499, 3sigma in AB mag (Total): 21.662525998239595\n",
      "irac_i2: mean flux error: 1.8575319051742554, 3sigma in AB mag (Total): 22.034856158233545\n",
      "decam_g: mean flux error: 0.15475637329383568, 3sigma in AB mag (Total): 24.733075504441395\n",
      "decam_r: mean flux error: 0.19579649272025326, 3sigma in AB mag (Total): 24.47768459302491\n",
      "decam_i: mean flux error: 0.33905077381893145, 3sigma in AB mag (Total): 23.88153501342898\n",
      "decam_z: mean flux error: 0.6548334382117346, 3sigma in AB mag (Total): 23.166869743238443\n",
      "decam_y: mean flux error: 2.252593304947897, 3sigma in AB mag (Total): 21.825489890727077\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_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_irac_i1 (31754.0, 39164.801, 7410.8008)\n",
      "ap_irac_i2 (39980.102, 50052.301, 10072.199)\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",
      "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",
      "irac_i1 (31754.0, 39164.801, 7410.8008)\n",
      "irac_i2 (39980.102, 50052.301, 10072.199)\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": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ff30ec0fc88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tdrsiIiLC5XQ6xe7duwObr48fP962bt26EKDx4cqqqqoOS9VXr14VDzzwQNTs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\nIiIdMXATEREREeno/wNHPHXBjWE9hgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff30eb86518>"
      ]
     },
     "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 0x7ff30e6cf3c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zzz7zdWn97u9ZWRH2jz++qpfG/vHHyr9nZUX4qiYAWLp06di9e/d6rcdnypQp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\n7KTa4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff30e6c5cc0>"
      ]
     },
     "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",
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