{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter transmission profiles for COMBO-17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "\n",
    "import numpy as np\n",
    "from astropy.table import Table as astropy_table\n",
    "from astropy.io.votable.tree import VOTableFile, Resource, Table, Field, Param\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two sources for transmission profiles for COMBO-17:\n",
    "\n",
    "- The project provides total **efficiency** curves for the filter used on this page \n",
    "  http://www.mpia.de/COMBO/combo_filters.html\n",
    "\n",
    "- The Spanish Virtual Observatory provides the LaSilla/WFI **transmission** curves\n",
    "  taken from the ESO web page \n",
    "  (http://www.eso.org/sci/facilities/lasilla/instruments/wfi/inst.html).\n",
    "  \n",
    "The profiles given by SVO/ESO does not seem to take into account the CDD response while the files provided by the project do. For instance, compare the I band provided by ESO (the response table contains only the blue line):\n",
    "\n",
    "![I band](http://www.ls.eso.org/sci/facilities/lasilla/instruments/wfi/inst/filters/845.gif)\n",
    "\n",
    "to the one from the COMBO-17 page:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgtdKaWChBa6UkoFCS10pZQKEv8ft9B9M/DfnL8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01c83ddf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i_band = astropy_table.read(\"data/combo_filters/epsi_I.fits\")\n",
    "plt.plot(i_band['lamb_I__D'], i_band['epsi_I__D']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then use the files from the COMBO-17 page and convert them to VO-table to be ingested in the HELP filter database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Filter and band name were choosen to reflect the names from the ESO/WFI web page.\n",
    "\n",
    "filter_name = {\n",
    "    '420m': \"wfi_416nm\",\n",
    "    '464m': \"wfi_461nm\",\n",
    "    '485m': \"wfi_485nm\",\n",
    "    '518m': \"wfi_518nm\",\n",
    "    '571m': \"wfi_571nm\",\n",
    "    '604m': \"wfi_604nm\",\n",
    "    '646m': \"wfi_646nm\",\n",
    "    '696m': \"wfi_696nm\",\n",
    "    '753m': \"wfi_753nm\",\n",
    "    '815m': \"wfi_815nm\",\n",
    "    '855m': \"wfi_856nm\",\n",
    "    '915m': \"wfi_914nm\",\n",
    "    'B': \"wfi_b\",\n",
    "    'I': \"wfi_i\",\n",
    "    'R': \"wfi_r\",\n",
    "    'U': \"wfi_u\",\n",
    "    'V': \"wfi_v\"\n",
    "}\n",
    "\n",
    "filter_band_name = {\n",
    "    '420m': \"WFI 416/20\",\n",
    "    '464m': \"WFI 461/13\",\n",
    "    '485m': \"WFI 485/31\",\n",
    "    '518m': \"WFI 518/16\",\n",
    "    '571m': \"WFI 571/25\",\n",
    "    '604m': \"WFI 604/21\",\n",
    "    '646m': \"WFI 646/27\",\n",
    "    '696m': \"WFI 696/20\",\n",
    "    '753m': \"WFI 753/18\",\n",
    "    '815m': \"WFI 815/20\",\n",
    "    '855m': \"WFI 856/14\",\n",
    "    '915m': \"WFI 914/27\",\n",
    "    'B': \"WFI B/99\",\n",
    "    'I': \"WFI Ic/Iwp\",\n",
    "    'R': \"WFI Rc/162\",\n",
    "    'U': \"WFI U/38\",\n",
    "    'V': \"WFI V/89\"  \n",
    "}\n",
    "\n",
    "description = {\n",
    "    band: \"Efficiency of filter {} of the WFI instrument on La Silla telescope. It includes two telescope \"\n",
    "          \"mirrors, the CCD detector and an average La Silla atmosphere.\".format(filter_band_name[band][4:])\n",
    "    for band in filter_band_name\n",
    "}\n",
    "\n",
    "facility = \"La Silla\"\n",
    "instrument = \"WFI\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for f in glob(\"data/combo_filters/*fits\"):\n",
    "    name = f[24:][:-5]\n",
    "    orig_table = astropy_table.read(f)\n",
    "    wave, trans = orig_table.columns[0], orig_table.columns[1]\n",
    "    \n",
    "    votable = VOTableFile()\n",
    "    resource = Resource()\n",
    "    votable.resources.append(resource)\n",
    "    table = Table(votable)\n",
    "    resource.tables.append(table)\n",
    "    \n",
    "    table.fields.extend([\n",
    "            Field(votable, name=\"Wavelength\", datatype=\"float\", unit=\"nm\"),\n",
    "            Field(votable, name=\"Transmission\", datatype=\"float\", unit=\"\"),\n",
    "        ])\n",
    "    table.create_arrays(len(wave))\n",
    "\n",
    "    table.array[\"Wavelength\"] = wave\n",
    "    table.array[\"Transmission\"] = trans\n",
    "    \n",
    "    table.params.extend([\n",
    "            Param(votable, name=\"Description\", datatype='char', arraysize='*',\n",
    "                 value=description[name]),\n",
    "            Param(votable, name=\"Band\", datatype='char', arraysize='*',\n",
    "                 value=filter_band_name[name]),\n",
    "            Param(votable, name=\"Facility\", datatype='char', arraysize='*',\n",
    "                 value=\"La Silla\"),\n",
    "            Param(votable, name=\"Instrument\", datatype='char', arraysize='*',\n",
    "                 value=\"WFI\"),\n",
    "            Param(votable, name=\"AdditionalProcessing\", datatype='char', arraysize='*'),\n",
    "        ])\n",
    "        \n",
    "    table.get_field_by_id(\"AdditionalProcessing\").description = \\\n",
    "        \"Efficiency profile taken from the COMBO-17 project - http://www.mpia.de/COMBO/combo_filters.html\"\n",
    "    \n",
    "    votable.to_xml(\"data/help_filters/{}.xml\".format(filter_name[name]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}