{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Puts ALL WISE Astrometry reference catalogues into GAIA reference frame\n",
    "\n",
    "<img src=https://avatars1.githubusercontent.com/u/7880370?s=200&v=4>\n",
    "\n",
    "The WISE catalogues were produced by ../dmu16_allwise/make_wise_samples_for_stacking.csh\n",
    "\n",
    "In the catalogue, we keep:\n",
    "\n",
    "- The position;\n",
    "- The chi^2\n",
    "\n",
    "This astrometric correction is adapted from master list code  (dmu1_ml_XMM-LSS/1.8_SERVS.ipynb) written by Yannick Rohlly and Raphael Shirley.\n",
    "\n",
    "<hr>\n",
    "This code adapted by Seb Oliver 19th June 2018\n",
    "The only difference between each of the WISE_2_GAIA_*.ipynb files is the field name set in the next box\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "field='GAMA-12' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "0aab440 (Thu Mar 22 09:41:13 2018 +0000)\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sjo/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/seaborn/apionly.py:6: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "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",
    "from collections import OrderedDict\n",
    "import os\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table\n",
    "import numpy as np\n",
    "\n",
    "from herschelhelp_internal.flagging import  gaia_flag_column\n",
    "from herschelhelp_internal.masterlist import nb_astcor_diag_plot, remove_duplicates\n",
    "from herschelhelp_internal.utils import astrometric_correction, flux_to_mag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUT_DIR =  os.environ.get('TMP_DIR', \"../dmu16_allwise/data/\")\n",
    "try:\n",
    "    os.makedirs(OUT_DIR)\n",
    "except FileExistsError:\n",
    "    pass\n",
    "\n",
    "RA_COL = \"servs_ra\"\n",
    "DEC_COL = \"servs_dec\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## I - Reading in WISE astrometric catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table4547010800-876288\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>ra</th><th>dec</th><th>w4mag</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mag</th></tr></thead>\n",
       "<tr><td>0</td><td>172.8547705</td><td>-0.563878</td><td>8.398</td></tr>\n",
       "<tr><td>1</td><td>172.8552322</td><td>-0.5569676</td><td>8.859</td></tr>\n",
       "<tr><td>2</td><td>172.861555</td><td>-0.5630416</td><td>8.501</td></tr>\n",
       "<tr><td>3</td><td>172.8707569</td><td>-0.5609742</td><td>8.485</td></tr>\n",
       "<tr><td>4</td><td>172.8517613</td><td>-0.5431076</td><td>8.736</td></tr>\n",
       "<tr><td>5</td><td>172.8655226</td><td>-0.5491376</td><td>8.869</td></tr>\n",
       "<tr><td>6</td><td>172.870662</td><td>-0.5495074</td><td>8.854</td></tr>\n",
       "<tr><td>7</td><td>172.8682607</td><td>-0.5475752</td><td>8.765</td></tr>\n",
       "<tr><td>8</td><td>172.8663434</td><td>-0.5353826</td><td>8.853</td></tr>\n",
       "<tr><td>9</td><td>172.8701766</td><td>-0.5316966</td><td>8.967</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(\"$('#table4547010800-876288').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",
       "    $('#table4547010800-876288').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], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wise = Table.read(f\"../dmu16_allwise/data/Allwise_PSF_stack_{field}.fits\")\n",
    "wise_coords=SkyCoord(wise['ra'], wise['dec'])\n",
    "\n",
    "epoch = 2009\n",
    "\n",
    "wise[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Astrometry correction\n",
    "\n",
    "We match the astrometry to the Gaia one. We limit the Gaia catalogue to sources with a g band flux between the 30th and the 70th percentile. Some quick tests show that this give the lower dispersion in the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../dmu0/dmu0_GAIA/data/GAIA_GAMA-12.fits\n"
     ]
    }
   ],
   "source": [
    "#gaia = Table.read(\"./dmu17_XMM-LSS/data/GAIA_XMM-LSS.fits\")\n",
    "print(f\"../../dmu0/dmu0_GAIA/data/GAIA_{field}.fits\")\n",
    "gaia = Table.read(f\"../../dmu0/dmu0_GAIA/data/GAIA_{field}.fits\")\n",
    "gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sjo/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_astcor_diag_plot(wise_coords.ra, wise_coords.dec, \n",
    "                    gaia_coords.ra, gaia_coords.dec, near_ra0=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RA correction: -0.029878104624003754 arcsec\n",
      "Dec correction: -0.051933119813751816 arcsec\n"
     ]
    }
   ],
   "source": [
    "delta_ra, delta_dec =  astrometric_correction(\n",
    "    wise_coords,\n",
    "    gaia_coords, near_ra0=True\n",
    ")\n",
    "\n",
    "print(\"RA correction: {}\".format(delta_ra))\n",
    "print(\"Dec correction: {}\".format(delta_dec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ra    \n",
      "    deg    \n",
      "-----------\n",
      "172.8547705\n",
      "172.8552322\n",
      " 172.861555\n",
      "172.8707569\n",
      "172.8517613\n",
      "172.8655226\n",
      " 172.870662\n",
      "172.8682607\n",
      "172.8663434\n",
      "172.8701766\n",
      "        ...\n",
      "185.6658963\n",
      "185.6765457\n",
      "185.6955746\n",
      "185.6964508\n",
      " 185.700368\n",
      "185.6956611\n",
      "185.7051693\n",
      "185.7053139\n",
      "185.7163897\n",
      "185.7215271\n",
      "Length = 835613 rows\n",
      "-8.29947350666771e-06 deg\n"
     ]
    }
   ],
   "source": [
    "print( wise[\"ra\"])\n",
    "print(delta_ra.to(u.deg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wise[\"ra\"] +=  delta_ra.to(u.deg)\n",
    "wise[\"ra\"] = wise[\"ra\"]+ delta_ra.to(u.deg)\n",
    "wise[\"dec\"] = wise[\"dec\"]+ delta_dec.to(u.deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sjo/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_astcor_diag_plot(wise[\"ra\"], wise[\"dec\"], \n",
    "                    gaia_coords.ra, gaia_coords.dec, near_ra0=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V - Saving to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "wise.write(f\"../dmu16_allwise/data/Allwise_PSF_stack_GAIA_{field}.fits\", overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}