{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAMA-09 master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on GAMA-09."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "708e28f (Tue May 8 18:05:21 2018 +0100)\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": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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",
    "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\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "\n",
    "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot, specz_merge\n",
    "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n",
    "\n",
    "try:\n",
    "    os.makedirs(OUT_DIR)\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Reading the prepared pristine catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cfhtlens = Table.read(\"{}/CFHTLENS.fits\".format(TMP_DIR))\n",
    "cfhtls = Table.read(\"{}/CFHTLS.fits\".format(TMP_DIR))\n",
    "decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR))\n",
    "hsc = Table.read(\"{}/HSC-SSP.fits\".format(TMP_DIR))\n",
    "kids = Table.read(\"{}/KIDS.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "las = Table.read(\"{}/UKIDSS-LAS.fits\".format(TMP_DIR))\n",
    "vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR))\n",
    "viking = Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones: CFHTLenS, CFHTLS, DECaLS, HSC, KIDS, PanSTARRS, UKIDSS-LAS, VISTA-VHS, and VISTA-VIKING.\n",
    "\n",
    "At every step, we look at the distribution of the distances to the nearest source in the merged catalogue to determine the best crossmatching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFHTLenS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = cfhtlens\n",
    "master_catalogue['cfhtlens_ra'].name = 'ra'\n",
    "master_catalogue['cfhtlens_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add CFHTLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(cfhtls['cfhtls_ra'], cfhtls['cfhtls_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, cfhtls, \"cfhtls_ra\", \"cfhtls_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add DECaLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(decals['decals_ra'], decals['decals_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, decals, \"decals_ra\", \"decals_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add HSC-PSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(hsc['hsc_ra'], hsc['hsc_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, hsc, \"hsc_ra\", \"hsc_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add KIDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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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Iv27iEpHqE+7gH55dqQeywa8ev4hUo9AHf9Mse/yLm+s5ohq/iFShUAd/Xyo7qmc22pP1HO0fJjN2ulsaREQqU6iDv394lOb6md21m7O4uZ4xR7N0ikjVCW3wj6THSI2O0dIw81E9kO3xAyr3iEjVCW3w96ayE6y1JuYW/LrAKyLVJrzBH8ysOdse/+JmBb+IVKfQBn9uSuXWRpV6RCRcQhv8vcHMnC2NsxvV0xSP0BiLqMcvIlUntME/1x6/mbE4GVePX0SqTmiDf641fsjOy6+J2kSk2oQ2+HM9/pZZ9vghe4FXpR4RqTahDf7e1CjxaB0NM3zsYj5N1CYi1Si8wT80Ouv6fk57sp7ugRFGM2NFapWISOnNbkhLDegdStMyi5k5v/7AC+PLzx0ZAOBLP3uO67dM+ihhEZGKE9oef08Revy5ufz7h9PFaJKIyLwIbfD3pkbndGEXoDkYEdQXTP8gIlINQhv8xejxLwiOPz6o4BeR6lFQ8JvZVjPbZWa7zeyDk2z/EzPrMrNHg9d78ra908yeDV7vLGbj56J3aHROY/gBmhuiROuMY5qaWUSqyLRXN80sAnwWeB3QCew0sx3u/tSEXb/p7jdMOHYh8FFgE+DAQ8Gxx4rS+llyd3pT6Tn3+OvMWJCIcWxQwS8i1aOQHv9mYLe773H3EeAbwFUFfv7vAne7e3cQ9ncDW2fX1OIZGMmQGfNZz9OTry0R55hKPSJSRQoJ/hXAvrz3ncG6if7IzB4zs2+b2aoZHjuv5jpPT762priewiUiVaWQ4LdJ1k180Oz3gbXufj7wY+ArMzg2u6PZNjPrMLOOrq6uApo1e8WYpyenLRFnaDSjkT0iUjUKCf5OYFXe+5XA/vwd3P2ou+cmrfm/wMWFHpv3GTe7+yZ339Te3l5I22etqD3+4AlenceG5vxZIiLzoZDg3wmsN7N1ZhYHrgF25O9gZsvz3r4JeDpYvgu40szazKwNuDJYV1a9RZigLWdhUxyAfd2Dc/4sEZH5MO3VTXdPm9kNZAM7Amx39yfN7Eagw913AO83szcBaaAb+JPg2G4z+zjZXx4AN7p7dwm+x4wUs8e/IBEEv3r8IlIlChrW4u53AHdMWPeRvOUPAR86zbHbge1zaGPRjT99qwg1/qZ4hHikjs5j6vGLSHUI5Z27vUOjmEFyFpO0TWRmtDXF2NetHr+IVIdQBn/P0CjN9VHq6iYbdDRzbYm4evwiUjVCGfy9qbnP05OvLRFnX/cg7pOOVBURqSjhDP4izNOTr60pzsBIRpO1iUhVCGnwz32ennwLg7H8+1TuEZEqEMrg7xkaLco8PTnjQzp1gVdEqkAog7/YNf7xm7jU4xeRKhDK4O8pco2/IRahtTGmkT0iUhVCF/yjmTEGRzJF7fEDrF6YYO8RBb+IVL7QBX8x5+nJd86yJE8f6NWQThGpeOEL/mC6hmL3+M87o4WjAyMc7huefmcRkTIKXfD3jPf4izeqB2DDGa0APLW/t6ifKyJSbKEL/t4izsyZ79zlSQCeOqDgF5HKFrrg7yni07fyJRtirFmU4Mn9PUX9XBGRYgtd8B/tz9bgc2Pvi2nD8haVekSk4oUu+A/2DhOP1JUs+PceHaR/OF30zxYRKZbwBX/PEEta6jErzpTM+c5b0QLAM6rzi0gFC1/w96ZY3tpQks/esDw7sudJlXtEpIKFLvgP9Q6ztKU0wb+0pZ6FTXHV+UWkohUU/Ga21cx2mdluM/vgJNv/ysyeMrPHzOwnZrYmb1vGzB4NXjuK2fiZcncO9AyxrETBb2bZC7wq9YhIBZs2+M0sAnwWeD2wAbjWzDZM2O0RYJO7nw98G/hk3rYhd98YvN5UpHbPSu9QmtToGMtKVOqB7B28uw71MZoZK9k5RETmopAe/2Zgt7vvcfcR4BvAVfk7uPtP3T03Q9n9wMriNrM4DvRm58svZfBfsGoBI+kxHus8XrJziIjMRSHBvwLYl/e+M1h3Ou8G7sx732BmHWZ2v5n9wSzaWDQHe1IAJSv1AFx+5mLqDO7d1VWyc4iIzEUhE9ZMNu5x0ikozew6YBPwqrzVq919v5m9BLjHzB53999Mcuw2YBvA6tWrC2jWzB3qDYK/hD3+1kSMi1a3ce+uLv76yrNLdh4RKY2hkQyHelMc6k1xdGCEo/3D9A9nSI1miEWMtYubOLO9mXOWJTEzvv7AC5N+ztsuKU2OFUMhwd8JrMp7vxLYP3EnM3st8PfAq9x9fIpKd98f/NxjZvcCFwKnBL+73wzcDLBp06aSzG18IOjxL0kWP/jz/+W3NcW5+6lDdPUN056sL/q5RGTmMmPOkf5hDvakxoP9UO8wB3tPvD/YkxqfwXc66xY38cbzl7O8tbHELS++QoJ/J7DezNYBLwLXAG/L38HMLgS+CGx198N569uAQXcfNrPFwOWcfOF3Xh3qTbG4OU48WtpRrGctTXL3U4e479dd/NHFFXm5Q6RmuDu9qfR4cE8M9cO9qeDn8CmlijrLzrOVbIjS0hBjwxkttDTEaGnMrkvWx2iqj9AQixCpM9IZ5+jAMHu6BrjnmcPcdM9uXnPuUl59zpKyfPfZmjb43T1tZjcAdwERYLu7P2lmNwId7r4D+GegGfjP4I7YF4IRPOcCXzSzMbLXEz7h7k+V6LtM62BPqqRlnpzlrQ0010e5V8EvMiejmTEO953oped+HuhJnRTqqdFTR9E1Bo9ETTZEWdmWYMPyVloaswGfDfcoTfVR6mZwF388aixvbWR5ayMXrl7Ajl/t58dPH2JJsp6XrWgt5lcvqYImpXf3O4A7Jqz7SN7ya09z3C+Al8+lgcV0oCfFyrbS/1lWZ8ZZS5v52bNdZMacSF3xp4cQqXYDw2kOBmF+YDzQhzjYM5wN+d4UR/qHmfhQu2id0dIYo6UhSktjjJVtiRPv83rrsUhp/7JPxKO85aKVdA+McPvDnSxvbWBRc3WUdov7NJIKd6g3xcVr2ublXOuXJnn4heM8uu/4vJ1TpBK4Oz1Do+O98vFg70lxoDfFwZ4hDvSk6Juklp7rpbc0RlmzMMHLV7TSGvTOWxpjtDbEaIxHSjLX1mxEI3Vcu3k1N92zm68/+ALXv+pMoiX+hVMMoQn+1GiGY4OjJZunZ6L1S5qJ1Bl3PXlQwS81Y2zMOTIwPB7mB08K9yEO9Q5zoGfolNKLAc1Bj7y1McZ5Z7RmA74hSmtjLCjJxEp+/a0U2hJx3nzRCm594AUeeeE4r1i3sNxNmlZogj83lLNU8/RMlIhHuXLDUr7VsY8PvPYsGuOReTmvyGyNpMc43DcxzE8s5+rr6bGTay91xnhvvKUxxqY1C7PvG2O0BuWYZEOspkueG5a3sGJBI//zbBcXrWmr+O8amuAfv3lrnnr8AH9y2VrufOIg33v0Ra7ZXLljeqX2DY1kODheQ8+vqZ8I+snq6bGIBT3zGEuS9bx0SfP4+1xJZqYXSGuRmXHF2e38xwMv8PiLx9m4qrL/yg9P8PeW/q7diTavW8i5y1u45Rd7ufoVqyqmLim1ZXAkzf7jQ7x4/ET9fGKvPffI0XwNsbrxMst4PT14nwv2hlid/rst0DnLW1iSrOfeXV2cv3JBuZszpfAEfxl6/GbGn162lr+9/THu39PNpWcumrdzS23I1dT3H0/x4rGhIOCzr9zy8cFTQ725PjpeQz9nWTLonefKL9nlaqynV7I6M7acvYRvdezj6QqfoTc8wd+boikeIVnkh6xP500bz+Af73ya7T9/TsEvp0iNZjjQkzoR6Hnhvv/4EJ3Hhk6pqcejdSxojNGWiHPW0iQLGmMsSMRpbYyxoDFGsjFKtE6hXg4vX9HK3U8d5Be/OVrupkwpPMHfk2LpPPb2cxpiEd5x6Vo+9ZNn+fnuI1z+0sXz3gYpD3fn+ODoyT30Y0Ps78mWZV48NsSR/uGTjjEgGYx0WZCIc+mZifFgX5CIsaAxrvJLBYvUGZvXLeKuJw/y7KE+1i9NlrtJkwpN8L/QPciKBeWZU+P6LWfyvUdf5O+++zh3/eXv0BDTCJ9aMJoZ42Bebz1XZ88t7z8+xOBI5qRjGmJ1NNfHWJCIsXZRgo2rWlnQGIR6Ik6LeutV7+I1bfz46UPc+sALfOxN55W7OZMKRfD3pUZ5+kAvN7x6/byeN3/ittecu5Qv/7/n+LOvPcRX3rV5XtshszM0khnvrb94bIgXjw8GP7MlmEO9KSZUYWiKR8Z75xeuWjBegmlLxGlNxGiqoJuPpDSa66O87IwWbn+4k7/dejaJeOXFbOW1qAQefuE4Yw6b15bvxooz25u5eHUbP3u2i4693WwqY1skmNhrKE1nXpiP/wyWjw6MnHRMtM5INkRZkIizrKWBc5a10JaI0ZqI0daYDfZSTxMg1eGSdYv4VWcP3//Vfq5+ReUN5Q5F8D/43FEidcaFq8s7xOr1L1/G3qMDvOuWnXzrvZdyzrKWsran1vUMjdJ5bJDOY9ke+r7u3HI27PuGT54yIDtmPU5bIsZL2pu5eE1svK6+IJEdCRP28epSmDWLEpy9NMnX7n+et26qvKHcoQj+nc8d42UrWmmqL+/XTcSjvOvydXz1/r2848sP8u33XsbqRYmytqma9aVGJwT60HjQ7zs2eMpcMPFoHQuDMsx5K1ppC+rquZ8qw0ixmBnvvGwtf/fdx/nlb45yWYUN6qj54E+NZnh033HeedmacjcFyD6k5avvuoS3fvGXvPnzP+fT11xYcf9RVIr+4XQ2yLtPDvRcyE+8KSkWMdoScdoScc47o2V8uS0I90qa3Etq35svWsG//fjXfO7e31Tc/+M1H/yPdfYwkhlj87rKGUN/9rIk337vpVx/68Nc9+UHeP9r1nP9ljOpj4ZrtM/AcDq4UDrIvrxwz/Xcjw2ePtjPWZbMLjdlQ70tESehYJcK0hCL8J7fWsc/3vkMj3Uer6i7eWs++B98LnsjxaYKmiEzN9rn7Zes5nuP7ufff/wstz/cyd/87jm88eXLqavwCZ4K1ZsapbN7aDzcXwxCPfd+YrBH64Jgb4qxfmlyvCyTC3iVYqTavO2S1dz00918/t7f8PnrLi53c8bVfvDvPcZZS5tpa4qXuymnqI9GeOumVVy4agG/3HOU99/2CP/yo11c/YpVvOWilSyZx3mFZmokPUZX/zCHg8fcneipnwj2iTX2WMTGa+rrlyZpa4yxoOlEKaa5Pqpgl5qSbIjxjkvX8Ll7f8OvD/VxVoXc0FXTwZ/OjPHQ3m7+8KIV5W7KlNYvTfLhN27gvx8/wK33P88nf7iLT/5wFxesbGXL2UvYvG4h569snZfpJlKjGbr6hjncl31G6aHeFIf7hk+8gvfdE4Y6AtRH62jLXTw9QxdPRQDedfk6bn3gBf7qW49y+/WXVURJt6aD/zuPvMjASIZLX1JZF1Ym882d+wC4auMKLjtzMU/s7+GZA718+p5ncQczWNWWYM2i4LWwidWLEixqipNsiNHcECXZEKU5HsWBzJgz5k56zBlJj9EzNMrxwRF6hkbpGRrlSP8Ih/tSdPUOcygI+cN9w5PO4pj/QOpkQ4yXLmkefzh17mdbQlMJiExmUXM9n/yj89n2tYf4lx/9mr97w7nlblJhwW9mW4FPkX3Y+pfc/RMTttcDXwUuBo4CV7v73mDbh4B3Axng/e5+V9FaP4XnjgzwsR1P8sqXLGTry5bNxymLpj1ZzxVnL+GKs5cwNJJh37FB9h0bpKtvmOeODPBYZ8+kAT1TkTqjJQjzZEOUc5cnx8M8P+gT8YjGr4vMwZXnLeO6V67m5vv2cOmZi7ji7CVlbc+0wW9mEeCzwOuATmCnme1w96fydns3cMzdX2pm1wD/BFxtZhuAa4DzgDOAH5vZWe5+8gQmRTaSHuP9tz1CLFLHv129seKfhjOVxniEs5YmT6kNDo1k6B4YYXAkTSo9xvBohtRohuH0GFh2itg6suOJoxGjMRYhEY/QGI+SiEVI1EdojKn0IjJfPvx7G+jYe4z3fKWDG654KTe8+qVlu9O7kB7/ZmC3u+8BMLNvAFcB+cF/FfCxYPnbwE2WTZSrgG+4+zDwnJntDj7vl8Vp/skO96b4wWMH+M4jnTzxYi9fuO6TQ74CAAAHXElEQVRilreWZ2K2UmuMR1gRr83vJlKLGmIRvrntUv7h+0/yqZ88y51PHOD1L1vOJS9ZyOqFiexf2fXReRnVV0jwrwD25b3vBC453T7unjazHmBRsP7+CceW5Err4Eia3/7kTxlOj3Hu8hY+8eaXV12JR0RqW2sixr9evZErz1vGZ3+6m8/c8yyf+smJ7Qub4jz8f15X8nYUEvyT/frxAvcp5NjsB5htA7YFb/vNbFcBbZvU88APgWsLP2QxcGS256sCtfz9avm7QW1/v1r+brx9Fsc8D9hHZn3KgqcnKCT4O4FVee9XAvtPs0+nmUWBVqC7wGMBcPebgZsLa3ZxmVmHu28qx7nnQy1/v1r+blDb36+Wv1ulK+TKwk5gvZmtM7M42Yu1OybsswN4Z7D8FuAed/dg/TVmVm9m64D1wIPFabqIiMzGtD3+oGZ/A3AX2eGc2939STO7Eehw9x3Al4GvBRdvu8n+ciDY71tkLwSngfeVekSPiIhMzbId83Azs21Bqakm1fL3q+XvBrX9/Wr5u1U6Bb+ISMjoOXEiIiET6uA3s61mtsvMdpvZB8vdnmIzs+1mdtjMnih3W4rNzFaZ2U/N7Gkze9LM/qLcbSoWM2swswfN7FfBd/uHcrepFMwsYmaPmNkPyt2WsAlt8OdNRfF6YANwbTDFRC25Bdha7kaUSBr4a3c/F3gl8L4a+vc3DLza3S8ANgJbzeyVZW5TKfwF8HS5GxFGoQ1+8qaicPcRIDcVRc1w9/vIjrKqOe5+wN0fDpb7yAZIZc+/XSDP6g/exoJXTV2MM7OVwO8BXyp3W8IozME/2VQUNREcYWNma4ELgQfK25LiCcogjwKHgbvdvWa+W+Dfgb8FxsrdkDAKc/AXPJ2EVC4zawZuB/7S3XvL3Z5icfeMu28ke7f7ZjN7WbnbVCxm9kbgsLs/VO62hFWYg7/g6SSkMplZjGzo3+ru3yl3e0rB3Y8D91Jb12ouB95kZnvJllhfbWb/Ud4mhUuYg7+QqSikQgXTfn8ZeNrd/7Xc7SkmM2s3swXBciPwWuCZ8raqeNz9Q+6+0t3Xkv3/7h53v67MzQqV0Aa/u6eB3FQUTwPfcvcny9uq4jKz28g+++BsM+s0s3eXu01FdDnwx2R7i48GrzeUu1FFshz4qZk9RraDcre7a8ijFI3u3BURCZnQ9vhFRMJKwS8iEjIKfhGRkFHwi4iEjIJfRCRkFPwiIiGj4JeyM7NMMA7/yWAq4r8ys7pg2yYz+/QUx641s7fNX2tPOfdQMKdORTCzq4NpxjXuX05LwS+VYMjdN7r7ecDrgDcAHwVw9w53f/8Ux64FyhL8gd8Ec+oULJgSvCTc/ZvAe0r1+VIbFPxSUdz9MLANuMGytuR6r2b2qry7dB8xsyTwCeC3g3UfCHrhPzOzh4PXZcGxW8zsXjP7tpk9Y2a3BtM+YGavMLNfBH9tPGhmyWB2zH82s51m9piZ/Vkh7Tez/zKzh4K/Xrblre83sxvN7AHg0tOc87xg+dHgnOuDY6/LW//F3C+O4EFCDwef8ZMi/muQWufueulV1hfQP8m6Y8BSYAvwg2Dd94HLg+VmIJq/PVifABqC5fVAR7C8BeghOxlfHdmpLH4LiAN7gFcE+7UEn7sN+HCwrh7oANZNaONa4IkJ6xYGPxuBJ4BFwXsH3hosn+6cnwHenrdPI3Bu8L1jwfrPAe8A2slOK74u/7x53/UHk/2z1ksvdyc6w98TIvNlsmmzfw78q5ndCnzH3TuDTnu+GHCTmW0EMsBZedsedPdOgKAuv5bsL4MD7r4TwIOpnc3sSuB8M3tLcGwr2V8kz03T7veb2R8Gy6uCY44Gbbk9WH/2ac75S+Dvg4eUfMfdnzWz1wAXAzuD79pIdo7+VwL3uftzwWfU5AN3pDQU/FJxzOwlZIPyMNkeLwDu/gkz+2+y1wDuN7PXTnL4B4BDwAVke/apvG3DecsZsv/9G5M/h8GAP3f3u2bQ7i1kZ9K81N0HzexeoCHYnHL3TN5nn3JOd/96UAr6PeAuM3tPsO9X3P1DE871ptO0W2RaqvFLRTGzduALwE3u7hO2nenuj7v7P5EtvZwD9AHJvN1ayfamx8jO3jndhdRngDPM7BXBOZJmFiU7a+v1wZz/mNlZZtY0zWe1AseC0D+HbK+84HMGv/D2uPunyU4Rfj7wE+AtZrYk2Hehma0hW6p6lZmty62fpm0i49Tjl0rQGJReYmQfov41YLI59v/SzK4g21t/CriT7KP70mb2K7IPl/8ccLuZ/S/gp8DAVCd29xEzuxr4TDD3/RDZXvuXyJaCHg4uAncBfzDN9/gh8N5gOuVdwP0zPOfVwHVmNgocBG50924z+zDwo2CI6yjwPne/P7h4/J1g/WGyI6JEpqVpmUVmybLP+v2Bu1fUYxGDktP/dvc3lrstUplU6hGZvQzQWmk3cJH9q+dYudsilUs9fhGRkFGPX0QkZBT8IiIho+AXEQkZBb+ISMgo+EVEQub/A0LJ8U1V3Uf2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(kids['kids_ra'], kids['kids_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, kids, \"kids_ra\", \"kids_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ps1['ps1_ra'], ps1['ps1_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add UKIDSS LAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(las['las_ra'], las['las_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, las, \"las_ra\", \"las_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(vhs['vhs_ra'], vhs['vhs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, vhs, \"vhs_ra\", \"vhs_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VIKING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(viking['viking_ra'], viking['viking_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, viking, \"viking_ra\", \"viking_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning\n",
    "\n",
    "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n",
    "        master_catalogue[col].fill_value = np.nan\n",
    "    elif \"flag\" in col:\n",
    "        master_catalogue[col].fill_value = 0\n",
    "    elif \"id\" in col:\n",
    "        master_catalogue[col].fill_value = -1\n",
    "        \n",
    "master_catalogue = master_catalogue.filled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140440544746352-64432\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>cfhtlens_id</th><th>ra</th><th>dec</th><th>cfhtlens_stellarity</th><th>m_cfhtlens_u</th><th>merr_cfhtlens_u</th><th>m_cfhtlens_g</th><th>merr_cfhtlens_g</th><th>m_cfhtlens_r</th><th>merr_cfhtlens_r</th><th>m_cfhtlens_i</th><th>merr_cfhtlens_i</th><th>m_cfhtlens_z</th><th>merr_cfhtlens_z</th><th>f_cfhtlens_u</th><th>ferr_cfhtlens_u</th><th>flag_cfhtlens_u</th><th>f_cfhtlens_g</th><th>ferr_cfhtlens_g</th><th>flag_cfhtlens_g</th><th>f_cfhtlens_r</th><th>ferr_cfhtlens_r</th><th>flag_cfhtlens_r</th><th>f_cfhtlens_i</th><th>ferr_cfhtlens_i</th><th>flag_cfhtlens_i</th><th>f_cfhtlens_z</th><th>ferr_cfhtlens_z</th><th>flag_cfhtlens_z</th><th>cfhtlens_flag_cleaned</th><th>cfhtlens_flag_gaia</th><th>flag_merged</th><th>cfhtls_id</th><th>cfhtls_stellarity</th><th>m_cfhtls_u</th><th>merr_cfhtls_u</th><th>m_cfhtls_g</th><th>merr_cfhtls_g</th><th>m_cfhtls_r</th><th>merr_cfhtls_r</th><th>m_cfhtls_i</th><th>merr_cfhtls_i</th><th>m_cfhtls_z</th><th>merr_cfhtls_z</th><th>m_ap_cfhtls_u</th><th>merr_ap_cfhtls_u</th><th>m_ap_cfhtls_g</th><th>merr_ap_cfhtls_g</th><th>m_ap_cfhtls_r</th><th>merr_ap_cfhtls_r</th><th>m_ap_cfhtls_i</th><th>merr_ap_cfhtls_i</th><th>m_ap_cfhtls_z</th><th>merr_ap_cfhtls_z</th><th>f_cfhtls_u</th><th>ferr_cfhtls_u</th><th>flag_cfhtls_u</th><th>f_cfhtls_g</th><th>ferr_cfhtls_g</th><th>flag_cfhtls_g</th><th>f_cfhtls_r</th><th>ferr_cfhtls_r</th><th>flag_cfhtls_r</th><th>f_cfhtls_i</th><th>ferr_cfhtls_i</th><th>flag_cfhtls_i</th><th>f_cfhtls_z</th><th>ferr_cfhtls_z</th><th>flag_cfhtls_z</th><th>f_ap_cfhtls_u</th><th>ferr_ap_cfhtls_u</th><th>f_ap_cfhtls_g</th><th>ferr_ap_cfhtls_g</th><th>f_ap_cfhtls_r</th><th>ferr_ap_cfhtls_r</th><th>f_ap_cfhtls_i</th><th>ferr_ap_cfhtls_i</th><th>f_ap_cfhtls_z</th><th>ferr_ap_cfhtls_z</th><th>cfhtls_flag_cleaned</th><th>cfhtls_flag_gaia</th><th>decals_id</th><th>f_decam_g</th><th>f_decam_r</th><th>f_decam_z</th><th>ferr_decam_g</th><th>ferr_decam_r</th><th>ferr_decam_z</th><th>f_ap_decam_g</th><th>f_ap_decam_r</th><th>f_ap_decam_z</th><th>ferr_ap_decam_g</th><th>ferr_ap_decam_r</th><th>ferr_ap_decam_z</th><th>m_decam_g</th><th>merr_decam_g</th><th>flag_decam_g</th><th>m_decam_r</th><th>merr_decam_r</th><th>flag_decam_r</th><th>m_decam_z</th><th>merr_decam_z</th><th>flag_decam_z</th><th>m_ap_decam_g</th><th>merr_ap_decam_g</th><th>m_ap_decam_r</th><th>merr_ap_decam_r</th><th>m_ap_decam_z</th><th>merr_ap_decam_z</th><th>decals_stellarity</th><th>decals_flag_cleaned</th><th>decals_flag_gaia</th><th>hsc_id</th><th>m_ap_suprime_g</th><th>merr_ap_suprime_g</th><th>m_suprime_g</th><th>merr_suprime_g</th><th>m_ap_suprime_r</th><th>merr_ap_suprime_r</th><th>m_suprime_r</th><th>merr_suprime_r</th><th>m_ap_suprime_i</th><th>merr_ap_suprime_i</th><th>m_suprime_i</th><th>merr_suprime_i</th><th>m_ap_suprime_z</th><th>merr_ap_suprime_z</th><th>m_suprime_z</th><th>merr_suprime_z</th><th>m_ap_suprime_y</th><th>merr_ap_suprime_y</th><th>m_suprime_y</th><th>merr_suprime_y</th><th>hsc_stellarity</th><th>f_ap_suprime_g</th><th>ferr_ap_suprime_g</th><th>f_suprime_g</th><th>ferr_suprime_g</th><th>flag_suprime_g</th><th>f_ap_suprime_r</th><th>ferr_ap_suprime_r</th><th>f_suprime_r</th><th>ferr_suprime_r</th><th>flag_suprime_r</th><th>f_ap_suprime_i</th><th>ferr_ap_suprime_i</th><th>f_suprime_i</th><th>ferr_suprime_i</th><th>flag_suprime_i</th><th>f_ap_suprime_z</th><th>ferr_ap_suprime_z</th><th>f_suprime_z</th><th>ferr_suprime_z</th><th>flag_suprime_z</th><th>f_ap_suprime_y</th><th>ferr_ap_suprime_y</th><th>f_suprime_y</th><th>ferr_suprime_y</th><th>flag_suprime_y</th><th>hsc_flag_cleaned</th><th>hsc_flag_gaia</th><th>kids_id</th><th>kids_stellarity</th><th>m_kids_u</th><th>merr_kids_u</th><th>m_kids_g</th><th>merr_kids_g</th><th>m_kids_r</th><th>merr_kids_r</th><th>m_kids_i</th><th>merr_kids_i</th><th>f_ap_kids_u</th><th>ferr_ap_kids_u</th><th>f_ap_kids_g</th><th>ferr_ap_kids_g</th><th>f_ap_kids_r</th><th>ferr_ap_kids_r</th><th>f_ap_kids_i</th><th>ferr_ap_kids_i</th><th>f_kids_u</th><th>ferr_kids_u</th><th>flag_kids_u</th><th>f_kids_g</th><th>ferr_kids_g</th><th>flag_kids_g</th><th>f_kids_r</th><th>ferr_kids_r</th><th>flag_kids_r</th><th>f_kids_i</th><th>ferr_kids_i</th><th>flag_kids_i</th><th>m_ap_kids_u</th><th>merr_ap_kids_u</th><th>m_ap_kids_g</th><th>merr_ap_kids_g</th><th>m_ap_kids_r</th><th>merr_ap_kids_r</th><th>m_ap_kids_i</th><th>merr_ap_kids_i</th><th>kids_flag_cleaned</th><th>kids_flag_gaia</th><th>ps1_id</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>ps1_flag_cleaned</th><th>ps1_flag_gaia</th><th>las_id</th><th>m_ukidss_y</th><th>merr_ukidss_y</th><th>m_ap_ukidss_y</th><th>merr_ap_ukidss_y</th><th>m_ukidss_j</th><th>merr_ukidss_j</th><th>m_ap_ukidss_j</th><th>merr_ap_ukidss_j</th><th>m_ap_ukidss_h</th><th>merr_ap_ukidss_h</th><th>m_ukidss_h</th><th>merr_ukidss_h</th><th>m_ap_ukidss_k</th><th>merr_ap_ukidss_k</th><th>m_ukidss_k</th><th>merr_ukidss_k</th><th>las_stellarity</th><th>f_ukidss_y</th><th>ferr_ukidss_y</th><th>flag_ukidss_y</th><th>f_ap_ukidss_y</th><th>ferr_ap_ukidss_y</th><th>f_ukidss_j</th><th>ferr_ukidss_j</th><th>flag_ukidss_j</th><th>f_ap_ukidss_j</th><th>ferr_ap_ukidss_j</th><th>f_ap_ukidss_h</th><th>ferr_ap_ukidss_h</th><th>f_ukidss_h</th><th>ferr_ukidss_h</th><th>flag_ukidss_h</th><th>f_ap_ukidss_k</th><th>ferr_ap_ukidss_k</th><th>f_ukidss_k</th><th>ferr_ukidss_k</th><th>flag_ukidss_k</th><th>las_flag_cleaned</th><th>las_flag_gaia</th><th>vhs_id</th><th>vhs_stellarity</th><th>m_vhs_y</th><th>merr_vhs_y</th><th>m_ap_vhs_y</th><th>merr_ap_vhs_y</th><th>m_vhs_j</th><th>merr_vhs_j</th><th>m_ap_vhs_j</th><th>merr_ap_vhs_j</th><th>m_vhs_h</th><th>merr_vhs_h</th><th>m_ap_vhs_h</th><th>merr_ap_vhs_h</th><th>m_vhs_k</th><th>merr_vhs_k</th><th>m_ap_vhs_k</th><th>merr_ap_vhs_k</th><th>f_vhs_y</th><th>ferr_vhs_y</th><th>flag_vhs_y</th><th>f_ap_vhs_y</th><th>ferr_ap_vhs_y</th><th>f_vhs_j</th><th>ferr_vhs_j</th><th>flag_vhs_j</th><th>f_ap_vhs_j</th><th>ferr_ap_vhs_j</th><th>f_vhs_h</th><th>ferr_vhs_h</th><th>flag_vhs_h</th><th>f_ap_vhs_h</th><th>ferr_ap_vhs_h</th><th>f_vhs_k</th><th>ferr_vhs_k</th><th>flag_vhs_k</th><th>f_ap_vhs_k</th><th>ferr_ap_vhs_k</th><th>vhs_flag_cleaned</th><th>vhs_flag_gaia</th><th>viking_id</th><th>viking_stellarity</th><th>m_viking_z</th><th>merr_viking_z</th><th>m_ap_viking_z</th><th>merr_ap_viking_z</th><th>m_viking_y</th><th>merr_viking_y</th><th>m_ap_viking_y</th><th>merr_ap_viking_y</th><th>m_viking_j</th><th>merr_viking_j</th><th>m_ap_viking_j</th><th>merr_ap_viking_j</th><th>m_viking_h</th><th>merr_viking_h</th><th>m_ap_viking_h</th><th>merr_ap_viking_h</th><th>m_viking_k</th><th>merr_viking_k</th><th>m_ap_viking_k</th><th>merr_ap_viking_k</th><th>f_viking_z</th><th>ferr_viking_z</th><th>flag_viking_z</th><th>f_ap_viking_z</th><th>ferr_ap_viking_z</th><th>f_viking_y</th><th>ferr_viking_y</th><th>flag_viking_y</th><th>f_ap_viking_y</th><th>ferr_ap_viking_y</th><th>f_viking_j</th><th>ferr_viking_j</th><th>flag_viking_j</th><th>f_ap_viking_j</th><th>ferr_ap_viking_j</th><th>f_viking_h</th><th>ferr_viking_h</th><th>flag_viking_h</th><th>f_ap_viking_h</th><th>ferr_ap_viking_h</th><th>f_viking_k</th><th>ferr_viking_k</th><th>flag_viking_k</th><th>f_ap_viking_k</th><th>ferr_ap_viking_k</th><th>viking_flag_cleaned</th><th>viking_flag_gaia</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</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>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</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></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></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>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</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></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></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></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></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></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></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></tr></thead>\n",
       "<tr><td>0</td><td>W2p3p3_84208</td><td>136.57007116730503</td><td>-1.4692217754255301</td><td>0.524383</td><td>19.7944</td><td>0.0015</td><td>18.3121</td><td>0.0005</td><td>17.766</td><td>0.0006</td><td>nan</td><td>nan</td><td>17.4234</td><td>0.0006</td><td>43.87731</td><td>0.060618747</td><td>False</td><td>171.85419</td><td>0.07914178</td><td>False</td><td>284.1839</td><td>0.15704581</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>389.6185</td><td>0.21531115</td><td>False</td><td>False</td><td>2</td><td>True</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>W2p3p3_150700</td><td>136.66092926730502</td><td>-1.0801202594255301</td><td>0.98291</td><td>20.2256</td><td>0.0017</td><td>19.5382</td><td>0.0008</td><td>19.3059</td><td>0.0011</td><td>19.1443</td><td>0.0008</td><td>19.0802</td><td>0.0012</td><td>29.495785</td><td>0.046183255</td><td>False</td><td>55.554535</td><td>0.04093409</td><td>False</td><td>68.808136</td><td>0.069712095</td><td>False</td><td>79.85091</td><td>0.058836322</td><td>False</td><td>84.70711</td><td>0.09362176</td><td>False</td><td>False</td><td>3</td><td>False</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>W2p1p3_180414</td><td>134.28387446730503</td><td>-0.98080379492553</td><td>0.000380777</td><td>19.5153</td><td>0.0022</td><td>18.8893</td><td>0.0011</td><td>18.7173</td><td>0.0015</td><td>18.5433</td><td>0.0013</td><td>18.7677</td><td>0.0048</td><td>56.73872</td><td>0.11496825</td><td>False</td><td>100.9904</td><td>0.102317154</td><td>False</td><td>118.325836</td><td>0.16347319</td><td>False</td><td>138.89279</td><td>0.16630249</td><td>False</td><td>112.95875</td><td>0.4993865</td><td>False</td><td>False</td><td>0</td><td>False</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>W2m1p3_85804</td><td>132.38221556730502</td><td>-1.4950963584255301</td><td>0.0285282</td><td>21.5398</td><td>0.0053</td><td>20.5847</td><td>0.0015</td><td>19.5565</td><td>0.0011</td><td>19.2541</td><td>0.0015</td><td>18.9681</td><td>0.002</td><td>8.791832</td><td>0.04291716</td><td>False</td><td>21.18946</td><td>0.029274322</td><td>False</td><td>54.626083</td><td>0.055343725</td><td>False</td><td>72.17043</td><td>0.099707134</td><td>False</td><td>93.92046</td><td>0.17300789</td><td>False</td><td>False</td><td>0</td><td>True</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>W2m1p2_205391</td><td>132.21779586730503</td><td>-1.98702480042553</td><td>0.0262686</td><td>22.4017</td><td>0.0053</td><td>21.6145</td><td>0.0022</td><td>21.2304</td><td>0.0028</td><td>21.0491</td><td>0.0022</td><td>20.9906</td><td>0.0053</td><td>3.974847</td><td>0.019403137</td><td>False</td><td>8.207291</td><td>0.016630229</td><td>False</td><td>11.690676</td><td>0.030149028</td><td>False</td><td>13.815271</td><td>0.027993536</td><td>False</td><td>14.580073</td><td>0.071172334</td><td>False</td><td>False</td><td>0</td><td>False</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>W2m0p3_124333</td><td>133.47038076730502</td><td>-1.35636653742553</td><td>0.0468833</td><td>22.0428</td><td>0.0054</td><td>21.675</td><td>0.003</td><td>21.1386</td><td>0.0025</td><td>20.7969</td><td>0.0024</td><td>20.8859</td><td>0.0071</td><td>5.531965</td><td>0.027513688</td><td>False</td><td>7.7624774</td><td>0.021448517</td><td>False</td><td>12.722132</td><td>0.029293792</td><td>False</td><td>17.427698</td><td>0.03852361</td><td>False</td><td>16.056091</td><td>0.10499627</td><td>False</td><td>False</td><td>2</td><td>False</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>W2p2p2_133751</td><td>135.70847486730503</td><td>-2.05797713742553</td><td>0.138056</td><td>22.9689</td><td>0.0063</td><td>22.0566</td><td>0.0026</td><td>21.3203</td><td>0.0021</td><td>20.6965</td><td>0.0019</td><td>20.2757</td><td>0.003</td><td>2.3574333</td><td>0.01367904</td><td>False</td><td>5.4620986</td><td>0.013080025</td><td>False</td><td>10.761687</td><td>0.020814946</td><td>False</td><td>19.11612</td><td>0.033452533</td><td>False</td><td>28.165657</td><td>0.077824585</td><td>False</td><td>False</td><td>0</td><td>False</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>W2m1p3_157146</td><td>132.78117686730502</td><td>-1.10072547842553</td><td>0.0253082</td><td>22.3368</td><td>0.0066</td><td>21.4297</td><td>0.0022</td><td>20.8712</td><td>0.0019</td><td>20.442</td><td>0.0021</td><td>20.1614</td><td>0.0039</td><td>4.219684</td><td>0.025650717</td><td>False</td><td>9.730151</td><td>0.01971596</td><td>False</td><td>16.274954</td><td>0.028480593</td><td>False</td><td>24.165743</td><td>0.046740692</td><td>False</td><td>31.292482</td><td>0.11240362</td><td>False</td><td>False</td><td>0</td><td>True</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>W2p3p3_89016</td><td>136.018186067305</td><td>-1.44005898442553</td><td>0.00032555</td><td>22.8928</td><td>0.0082</td><td>21.9879</td><td>0.0039</td><td>21.5323</td><td>0.0042</td><td>21.2609</td><td>0.004</td><td>21.139</td><td>0.0071</td><td>2.5286005</td><td>0.0190972</td><td>False</td><td>5.8188896</td><td>0.020901643</td><td>False</td><td>8.852779</td><td>0.034245584</td><td>False</td><td>11.366839</td><td>0.041876987</td><td>False</td><td>12.71744</td><td>0.083163686</td><td>False</td><td>False</td><td>0</td><td>False</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>W2p2p3_167403</td><td>135.67937746730502</td><td>-1.0091006144255301</td><td>0.0277074</td><td>22.5287</td><td>0.0083</td><td>21.9027</td><td>0.0029</td><td>21.5464</td><td>0.004</td><td>20.9836</td><td>0.004</td><td>20.8401</td><td>0.0069</td><td>3.5360637</td><td>0.027031729</td><td>False</td><td>6.293898</td><td>0.016810993</td><td>False</td><td>8.738555</td><td>0.03219403</td><td>False</td><td>14.674377</td><td>0.054062407</td><td>False</td><td>16.747883</td><td>0.10643505</td><td>False</td><td>False</td><td>0</td><td>False</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</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(\"$('#table140440544746352-64432').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",
       "    $('#table140440544746352-64432').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, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 22, 24, 25, 27, 28, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 61, 62, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 141, 142, 143, 145, 146, 147, 148, 150, 151, 152, 153, 155, 156, 157, 158, 161, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 183, 184, 186, 187, 189, 190, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 233, 234, 235, 236, 238, 239, 240, 241, 243, 244, 245, 246, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 283, 284, 285, 286, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 311, 312, 313, 314, 316, 317, 318, 319, 321, 322, 323, 324, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 355, 356, 357, 358, 360, 361, 362, 363, 365, 366, 367, 368, 370, 371, 372, 373, 375, 376, 378], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Merging flags and stellarity\n",
    "\n",
    "Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process.  We merge these flags in a single one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_cleaned_columns = [column for column in master_catalogue.colnames\n",
    "                        if 'flag_cleaned' in column]\n",
    "\n",
    "flag_column = np.zeros(len(master_catalogue), dtype=bool)\n",
    "for column in flag_cleaned_columns:\n",
    "    flag_column |= master_catalogue[column]\n",
    "    \n",
    "master_catalogue.add_column(Column(data=flag_column, name=\"flag_cleaned\"))\n",
    "master_catalogue.remove_columns(flag_cleaned_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely).  We merge these flags taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
    "                     if 'flag_gaia' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "    name=\"flag_gaia\"\n",
    "))\n",
    "master_catalogue.remove_columns(flag_gaia_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cfhtlens_stellarity, cfhtls_stellarity, decals_stellarity, hsc_stellarity, kids_stellarity, las_stellarity, vhs_stellarity, viking_stellarity\n"
     ]
    }
   ],
   "source": [
    "stellarity_columns = [column for column in master_catalogue.colnames\n",
    "                      if 'stellarity' in column]\n",
    "\n",
    "print(\", \".join(stellarity_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "# origin.  Some sources may not have an associated stellarity.\n",
    "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n",
    "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "max_stellarity = np.max(stellarity_array, axis=0)\n",
    "max_stellarity.fill_value = np.nan\n",
    "\n",
    "no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    ebv(master_catalogue['ra'], master_catalogue['dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V - Adding HELP unique identifiers and field columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "                                   name=\"help_id\"))\n",
    "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"GAMA-09\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI - Cross-matching with the spec-z catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz = Table.read(\"../../dmu23/dmu23_GAMA-09/data/HELP-SPECZ_GAMA-09_20170202-2.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(specz['ra'], specz['dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = specz_merge(master_catalogue, specz, radius=.8 * u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII - Choosing between multiple values for the same filter\n",
    "\n",
    "Both CFHTLenS and CFHTLS, and VISTA-VIKING and VISTA-VHS have measurements from the same camera and filters. We wish to choose the superior measurement where both are present."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VII.a CFHTLenS and CFHTLS\n",
    "CFHTLS is optimised for deep photometry so we take that for "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_origin = Table()\n",
    "megacam_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Megacam band u:\n",
      "898336 sources with CFHTLS flux\n",
      "403760 sources with CFHTLenS flux\n",
      "386292 sources with CFHTLS and CFHTLenS flux\n",
      "898336 sources for which we use CFHTLS\n",
      "17468 sources for which we use CFHTLenS\n",
      "For Megacam band g:\n",
      "968859 sources with CFHTLS flux\n",
      "506111 sources with CFHTLenS flux\n",
      "486700 sources with CFHTLS and CFHTLenS flux\n",
      "968859 sources for which we use CFHTLS\n",
      "19411 sources for which we use CFHTLenS\n",
      "For Megacam band r:\n",
      "984214 sources with CFHTLS flux\n",
      "509786 sources with CFHTLenS flux\n",
      "489579 sources with CFHTLS and CFHTLenS flux\n",
      "984214 sources for which we use CFHTLS\n",
      "20207 sources for which we use CFHTLenS\n",
      "For Megacam band i:\n",
      "959749 sources with CFHTLS flux\n",
      "529203 sources with CFHTLenS flux\n",
      "498878 sources with CFHTLS and CFHTLenS flux\n",
      "959749 sources for which we use CFHTLS\n",
      "30325 sources for which we use CFHTLenS\n",
      "For Megacam band z:\n",
      "846472 sources with CFHTLS flux\n",
      "421512 sources with CFHTLenS flux\n",
      "396727 sources with CFHTLS and CFHTLenS flux\n",
      "846472 sources for which we use CFHTLS\n",
      "24785 sources for which we use CFHTLenS\n"
     ]
    }
   ],
   "source": [
    "megacam_bands = ['u','g','r','i','z'] # Lowercase naming convention (k is Ks)\n",
    "for band in megacam_bands:\n",
    "    print('For Megacam band ' + band + ':')\n",
    "    # Megacam total flux \n",
    "    has_cfhtls = ~np.isnan(master_catalogue['f_cfhtls_' + band])\n",
    "    has_cfhtlens = ~np.isnan(master_catalogue['f_cfhtlens_' + band])\n",
    "    has_both = has_cfhtls & has_cfhtlens\n",
    "\n",
    "    print(\"{} sources with CFHTLS flux\".format(np.sum(has_cfhtls)))\n",
    "    print(\"{} sources with CFHTLenS flux\".format(np.sum(has_cfhtlens)))\n",
    "    print(\"{} sources with CFHTLS and CFHTLenS flux\".format(np.sum(has_both)))\n",
    "\n",
    "\n",
    "    use_cfhtls = has_cfhtls \n",
    "    use_cfhtlens = has_cfhtlens & ~has_both\n",
    "\n",
    "    print(\"{} sources for which we use CFHTLS\".format(np.sum(use_cfhtls)))\n",
    "    print(\"{} sources for which we use CFHTLenS\".format(np.sum(use_cfhtlens)))\n",
    "\n",
    "    f_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    f_megacam[use_cfhtls] = master_catalogue['f_cfhtls_' + band][use_cfhtls]\n",
    "    f_megacam[use_cfhtlens] = master_catalogue['f_cfhtlens_' + band][use_cfhtlens]\n",
    "\n",
    "    ferr_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_megacam[use_cfhtls] = master_catalogue['ferr_cfhtls_' + band][use_cfhtls]\n",
    "    ferr_megacam[use_cfhtlens] = master_catalogue['ferr_cfhtlens_' + band][use_cfhtlens]\n",
    "    \n",
    "    m_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    m_megacam[use_cfhtls] = master_catalogue['m_cfhtls_' + band][use_cfhtls]\n",
    "    m_megacam[use_cfhtlens] = master_catalogue['m_cfhtlens_' + band][use_cfhtlens]\n",
    "\n",
    "    merr_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_megacam[use_cfhtls] = master_catalogue['merr_cfhtls_' + band][use_cfhtls]\n",
    "    merr_megacam[use_cfhtlens] = master_catalogue['merr_cfhtlens_' + band][use_cfhtlens]\n",
    "\n",
    "    flag_megacam = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_megacam[use_cfhtls] = master_catalogue['flag_cfhtls_' + band][use_cfhtls]\n",
    "    flag_megacam[use_cfhtlens] = master_catalogue['flag_cfhtlens_' + band][use_cfhtlens]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_megacam, name=\"f_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_megacam, name=\"ferr_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_megacam, name=\"m_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_megacam, name=\"merr_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_megacam, name=\"flag_megacam_\" + band))\n",
    "\n",
    "    master_catalogue.remove_columns(['f_cfhtls_' + band, \n",
    "                                     'f_cfhtlens_' + band, \n",
    "                                     'ferr_cfhtls_' + band,\n",
    "                                     'ferr_cfhtlens_' + band, \n",
    "                                     'm_cfhtls_' + band, \n",
    "                                     'm_cfhtlens_' + band, \n",
    "                                     'merr_cfhtls_' + band,\n",
    "                                     'merr_cfhtlens_' + band,\n",
    "                                     'flag_cfhtls_' + band, \n",
    "                                     'flag_cfhtlens_' + band])\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_cfhtls] = \"CFHTLS\"\n",
    "    origin[use_cfhtlens] = \"CFHTLenS\"\n",
    "    \n",
    "    megacam_origin.add_column(Column(data=origin, name= 'f_megacam_' + band ))\n",
    "    \n",
    "    \n",
    "    \n",
    "    # There are no Megacam aperture flux in CFHTLenS\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Aperture flux only in CFHTLS\n",
    "for band in megacam_bands:\n",
    "    master_catalogue['f_ap_cfhtls_' + band].name = 'f_ap_megacam_' + band\n",
    "    master_catalogue['ferr_ap_cfhtls_' + band].name = 'ferr_ap_megacam_' + band\n",
    "    master_catalogue['m_ap_cfhtls_' + band].name = 'm_ap_megacam_' + band\n",
    "    master_catalogue['merr_ap_cfhtls_' + band].name = 'merr_ap_megacam_' + band\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_origin.write(\"{}/gama-09_megacam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VII.b VISTA-VIKING and VISTA-VHS\n",
    "VIKING is deeper than VHS so we take the VIKING photometry if available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vista_origin = Table()\n",
    "vista_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For VISTA band y:\n",
      "1386617 sources with VIKING flux\n",
      "157730 sources with VHS flux\n",
      "65293 sources with VIKING and VHS flux\n",
      "1386617 sources for which we use VIKING\n",
      "92437 sources for which we use VHS\n",
      "1386533 sources with VIKING aperture flux\n",
      "157725 sources with VHS aperture flux\n",
      "65293 sources with VIKING and VHS aperture flux\n",
      "1386533 sources for which we use VIKING aperture fluxes\n",
      "92432 sources for which we use VHS aperture fluxes\n",
      "For VISTA band j:\n",
      "1614888 sources with VIKING flux\n",
      "431419 sources with VHS flux\n",
      "278769 sources with VIKING and VHS flux\n",
      "1614888 sources for which we use VIKING\n",
      "152650 sources for which we use VHS\n",
      "1614759 sources with VIKING aperture flux\n",
      "431399 sources with VHS aperture flux\n",
      "278747 sources with VIKING and VHS aperture flux\n",
      "1614759 sources for which we use VIKING aperture fluxes\n",
      "152652 sources for which we use VHS aperture fluxes\n",
      "For VISTA band h:\n",
      "1616502 sources with VIKING flux\n",
      "122508 sources with VHS flux\n",
      "77750 sources with VIKING and VHS flux\n",
      "1616502 sources for which we use VIKING\n",
      "44758 sources for which we use VHS\n",
      "1610310 sources with VIKING aperture flux\n",
      "122497 sources with VHS aperture flux\n",
      "77727 sources with VIKING and VHS aperture flux\n",
      "1610310 sources for which we use VIKING aperture fluxes\n",
      "44770 sources for which we use VHS aperture fluxes\n",
      "For VISTA band k:\n",
      "1364692 sources with VIKING flux\n",
      "307880 sources with VHS flux\n",
      "204046 sources with VIKING and VHS flux\n",
      "1364692 sources for which we use VIKING\n",
      "103834 sources for which we use VHS\n",
      "1364254 sources with VIKING aperture flux\n",
      "307839 sources with VHS aperture flux\n",
      "203983 sources with VIKING and VHS aperture flux\n",
      "1364254 sources for which we use VIKING aperture fluxes\n",
      "103856 sources for which we use VHS aperture fluxes\n"
     ]
    }
   ],
   "source": [
    "vista_bands = ['y','j','h','k'] # Lowercase naming convention (k is Ks)\n",
    "for band in vista_bands:\n",
    "    print('For VISTA band ' + band + ':')\n",
    "    # VISTA total flux \n",
    "    has_viking = ~np.isnan(master_catalogue['f_viking_' + band])\n",
    "    has_vhs = ~np.isnan(master_catalogue['f_vhs_' + band])\n",
    "    has_both = has_viking & has_vhs\n",
    "\n",
    "    print(\"{} sources with VIKING flux\".format(np.sum(has_viking)))\n",
    "    print(\"{} sources with VHS flux\".format(np.sum(has_vhs)))\n",
    "    print(\"{} sources with VIKING and VHS flux\".format(np.sum(has_both)))\n",
    "\n",
    "\n",
    "    use_viking = has_viking \n",
    "    use_vhs = has_vhs & ~has_both\n",
    "\n",
    "    print(\"{} sources for which we use VIKING\".format(np.sum(use_viking)))\n",
    "    print(\"{} sources for which we use VHS\".format(np.sum(use_vhs)))\n",
    "\n",
    "    f_vista = np.full(len(master_catalogue), np.nan)\n",
    "    f_vista[use_viking] = master_catalogue['f_viking_' + band][use_viking]\n",
    "    f_vista[use_vhs] = master_catalogue['f_vhs_' + band][use_vhs]\n",
    "\n",
    "    ferr_vista = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_vista[use_viking] = master_catalogue['ferr_viking_' + band][use_viking]\n",
    "    ferr_vista[use_vhs] = master_catalogue['ferr_vhs_' + band][use_vhs]\n",
    "    \n",
    "    m_vista = np.full(len(master_catalogue), np.nan)\n",
    "    m_vista[use_viking] = master_catalogue['m_viking_' + band][use_viking]\n",
    "    m_vista[use_vhs] = master_catalogue['m_vhs_' + band][use_vhs]\n",
    "\n",
    "    merr_vista = np.full(len(master_catalogue), np.nan)\n",
    "    merr_vista[use_viking] = master_catalogue['merr_viking_' + band][use_viking]\n",
    "    merr_vista[use_vhs] = master_catalogue['merr_vhs_' + band][use_vhs]\n",
    "\n",
    "    flag_vista = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_vista[use_viking] = master_catalogue['flag_viking_' + band][use_viking]\n",
    "    flag_vista[use_vhs] = master_catalogue['flag_vhs_' + band][use_vhs]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_vista, name=\"f_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_vista, name=\"ferr_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_vista, name=\"m_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_vista, name=\"merr_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_vista, name=\"flag_vista_\" + band))\n",
    "\n",
    "    master_catalogue.remove_columns(['f_viking_' + band, \n",
    "                                     'f_vhs_' + band, \n",
    "                                     'ferr_viking_' + band,\n",
    "                                     'ferr_vhs_' + band, \n",
    "                                     'm_viking_' + band, \n",
    "                                     'm_vhs_' + band, \n",
    "                                     'merr_viking_' + band,\n",
    "                                     'merr_vhs_' + band,\n",
    "                                     'flag_viking_' + band, \n",
    "                                     'flag_vhs_' + band])\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_viking] = \"VIKING\"\n",
    "    origin[use_vhs] = \"VHS\"\n",
    "    \n",
    "    vista_origin.add_column(Column(data=origin, name= 'f_vista_' + band ))\n",
    "    \n",
    "    \n",
    "    \n",
    "    # VISTA Aperture flux\n",
    "    has_ap_viking = ~np.isnan(master_catalogue['f_ap_viking_' + band])\n",
    "    has_ap_vhs = ~np.isnan(master_catalogue['f_ap_vhs_' + band])\n",
    "    has_ap_both = has_ap_viking & has_ap_vhs\n",
    "\n",
    "    print(\"{} sources with VIKING aperture flux\".format(np.sum(has_ap_viking)))\n",
    "    print(\"{} sources with VHS aperture flux\".format(np.sum(has_ap_vhs)))\n",
    "    print(\"{} sources with VIKING and VHS aperture flux\".format(np.sum(has_ap_both)))\n",
    "\n",
    "\n",
    "    use_ap_viking = has_ap_viking \n",
    "    use_ap_vhs = has_ap_vhs & ~has_ap_both\n",
    "\n",
    "    print(\"{} sources for which we use VIKING aperture fluxes\".format(np.sum(use_ap_viking)))\n",
    "    print(\"{} sources for which we use VHS aperture fluxes\".format(np.sum(use_ap_vhs)))\n",
    "\n",
    "    f_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    f_ap_vista[use_ap_viking] = master_catalogue['f_ap_viking_' + band][use_ap_viking]\n",
    "    f_ap_vista[use_ap_vhs] = master_catalogue['f_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "    ferr_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ap_vista[use_ap_viking] = master_catalogue['ferr_ap_viking_' + band][use_ap_viking]\n",
    "    ferr_ap_vista[use_ap_vhs] = master_catalogue['ferr_ap_vhs_' + band][use_ap_vhs]\n",
    "    \n",
    "    m_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    m_ap_vista[use_ap_viking] = master_catalogue['m_ap_viking_' + band][use_ap_viking]\n",
    "    m_ap_vista[use_ap_vhs] = master_catalogue['m_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "    merr_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ap_vista[use_ap_viking] = master_catalogue['merr_ap_viking_' + band][use_ap_viking]\n",
    "    merr_ap_vista[use_ap_vhs] = master_catalogue['merr_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_vista, name=\"f_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_vista, name=\"ferr_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_vista, name=\"m_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_vista, name=\"merr_ap_vista_\" + band))\n",
    "\n",
    "\n",
    "    master_catalogue.remove_columns(['f_ap_viking_' + band, \n",
    "                                     'f_ap_vhs_' + band, \n",
    "                                     'ferr_ap_viking_' + band,\n",
    "                                     'ferr_ap_vhs_' + band,\n",
    "                                     'm_ap_viking_' + band, \n",
    "                                     'm_ap_vhs_' + band, \n",
    "                                     'merr_ap_viking_' + band,\n",
    "                                     'merr_ap_vhs_' + band])\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_viking] = \"VIKING\"\n",
    "    origin_ap[use_ap_vhs] = \"VHS\"\n",
    "    \n",
    "    vista_origin.add_column(Column(data=origin_ap, name= 'f_ap_vista_' + band ))\n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "      \n",
    "#Z band only in viking\n",
    "               \n",
    "master_catalogue['f_ap_viking_z'].name = 'f_ap_vista_z'\n",
    "master_catalogue['ferr_ap_viking_z'].name = 'ferr_ap_vista_z'\n",
    "master_catalogue['f_viking_z'].name = 'f_vista_z'\n",
    "master_catalogue['ferr_viking_z'].name = 'ferr_vista_z'\n",
    "master_catalogue['m_ap_viking_z'].name = 'm_ap_vista_z'\n",
    "master_catalogue['merr_ap_viking_z'].name = 'merr_ap_vista_z'\n",
    "master_catalogue['m_viking_z'].name = 'm_vista_z'\n",
    "master_catalogue['merr_viking_z'].name = 'merr_vista_z'\n",
    "master_catalogue['flag_viking_z'].name = 'flag_vista_z'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vista_origin.write(\"{}/gama-09_vista_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cfhtlens_moc = MOC(filename=\"../../dmu0/dmu0_CFHTLenS/data/CFHTLenS_GAMA-09_MOC.fits\")\n",
    "cfhtls_moc = MOC(filename=\"../../dmu0/dmu0_CFHTLS/data/CFHTLS-WIDE_GAMA-09_MOC.fits\")\n",
    "decals_moc = MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_GAMA-09_MOC.fits\")\n",
    "hsc_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_wide_GAMA-09_MOC.fits\")\n",
    "kids_moc = MOC(filename=\"../../dmu0/dmu0_KIDS/data/KIDS-DR3_GAMA-09_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_GAMA-09_MOC.fits\")\n",
    "las_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-LAS/data/UKIDSS-LAS_GAMA-09_MOC.fits\")\n",
    "vhs_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VHS/data/VHS_GAMA-09_MOC.fits\")\n",
    "viking_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VIKING/data/VIKING_GAMA-09_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    cfhtlens_moc + cfhtls_moc + decals_moc + hsc_moc + ps1_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    las_moc + vhs_moc + viking_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = np.zeros(len(master_catalogue), dtype=bool)\n",
    "\n",
    "#was_observed_mir = inMoc(\n",
    "#    master_catalogue['ra'], master_catalogue['dec'],   \n",
    "#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * was_observed_optical + 2 * was_observed_nir + 4 * was_observed_mir,\n",
    "        name=\"flag_optnir_obs\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.b Wavelength domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n",
    "\n",
    "- 1 for detection in optical;\n",
    "- 2 for detection in near-infrared;\n",
    "- 4 for detection in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n",
    "\n",
    "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SpARCS is a catalogue of sources detected in r (with fluxes measured at \n",
    "# this prior position in the other bands).  Thus, we are only using the r\n",
    "# CFHT band.\n",
    "# Check to use catalogue flags from HSC and PanSTARRS.\n",
    "nb_optical_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_kids_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_kids_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_kids_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_kids_i']) \n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_k']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_k'])\n",
    ")\n",
    "\n",
    "nb_mir_flux = np.zeros(len(master_catalogue), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "has_optical_flux = nb_optical_flux >= 2\n",
    "has_nir_flux = nb_nir_flux >= 2\n",
    "has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "        name=\"flag_optnir_det\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them.\n",
    "\n",
    "For convenience, we also cross-match the master list with the SDSS catalogue and add the objID associated with each source, if any. **TODO: should we correct the astrometry with respect to Gaia positions?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "910 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_GAMA-09.fits\")['objID', 'ra', 'dec']\n",
    "sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)\n",
    "idx_ml, d2d, _ = sdss_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "idx_sdss = np.arange(len(sdss))\n",
    "\n",
    "# Limit the cross-match to 1 arcsec\n",
    "mask = d2d <= 1. * u.arcsec\n",
    "idx_ml = idx_ml[mask]\n",
    "idx_sdss = idx_sdss[mask]\n",
    "d2d = d2d[mask]\n",
    "nb_orig_matches = len(idx_ml)\n",
    "\n",
    "# In case of multiple associations of one master list object to an SDSS object, we keep only the\n",
    "# association to the nearest one.\n",
    "sort_idx = np.argsort(d2d)\n",
    "idx_ml = idx_ml[sort_idx]\n",
    "idx_sdss = idx_sdss[sort_idx]\n",
    "_, unique_idx = np.unique(idx_ml, return_index=True)\n",
    "idx_ml = idx_ml[unique_idx]\n",
    "idx_sdss = idx_sdss[unique_idx]\n",
    "print(\"{} master list rows had multiple associations.\".format(nb_orig_matches - len(idx_ml)))\n",
    "\n",
    "# Adding the ObjID to the master list\n",
    "master_catalogue.add_column(Column(data=np.full(len(master_catalogue), -1, dtype='>i8'), name=\"sdss_id\"))\n",
    "master_catalogue['sdss_id'][idx_ml] = sdss['objID'][idx_sdss]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cfhtlens_id', 'cfhtls_id', 'decals_id', 'hsc_id', 'kids_id', 'ps1_id', 'las_id', 'vhs_id', 'viking_id', 'help_id', 'specz_id', 'sdss_id']\n"
     ]
    }
   ],
   "source": [
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_gama-09{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "id_names.remove('help_id')\n",
    "master_catalogue.remove_columns(id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X - Adding HEALPix index\n",
    "\n",
    "We are adding a column with a HEALPix index at order 13 associated with each source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(\n",
    "    data=coords_to_hpidx(master_catalogue['ra'], master_catalogue['dec'], order=13),\n",
    "    name=\"hp_idx\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - Renaming some columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We use vista_ks as filter ID for VISTA Ks\n",
    "for column in master_catalogue.colnames:\n",
    "    if \"vista_k\" in column:\n",
    "        master_catalogue[column].name = column.replace(\"vista_k\", \"vista_ks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We use omegacam_XXX for the KIDS bands\n",
    "for column in master_catalogue.colnames:\n",
    "    if \"_kids_\" in column:\n",
    "        master_catalogue[column].name = column.replace(\"_kids_\", \"_omegacam_\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = [\"help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                \"flag_{}\".format(band)]    \n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\",\"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\", \n",
    "            \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: set()\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_gama-09{}.fits\".format(OUT_DIR, SUFFIX))"
   ]
  }
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