dmu1/2_Survey_comparisons.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing survey coverages\n",
    "\n",
    "In notebook 1 we created tables with the file image corners and sizes. In this notebook we will look at their location on teh sky. The original notebook took the first image. Perhaps we will want to rerun it and get all the ccd corners. Although the tiles should tell use the coverage of individual ccds. WE need to figure out how to get the pointings from tiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table, Column, vstack # Perhaps we should use LSST tables\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ims = Table.read('./data/video_images_overview.csv')\n",
    "vhs_ims = Table.read('./data/vhs_images_overview.csv')\n",
    "hsc_ims = Table.read('./data/hsc_images_overview.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "vista_ims = vstack([video_ims,vhs_ims])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "569008"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vista_ims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "568965"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vhs_ims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(video_ims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53298"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hsc_ims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vista_ims.add_column(Column(\n",
    "    data= [t.split('/')[-2] for t in vista_ims['file']],\n",
    "    name='date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fileToSurvey(filename):\n",
    "    survey = ''\n",
    "    surveys = ['VIDEO', 'VHS', 'VIKING', 'VEILS']\n",
    "    for f in surveys:\n",
    "        if f in filename:\n",
    "            survey = f\n",
    "    return survey\n",
    "    \n",
    "vista_ims['survey'] = [fileToSurvey(t) for t in vista_ims['file']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fileToType(filename):\n",
    "    filetype = ''\n",
    "    types = {\n",
    "        'tile':'_tl.fit',\n",
    "        'stack':'_st.fit',\n",
    "    }\n",
    "    for k,v in types.items():\n",
    "        #print(k,v)\n",
    "        if filename.endswith(v):\n",
    "            filetype = k\n",
    "       \n",
    "    return filetype\n",
    "vista_ims['type'] = [fileToType(f) for f in vista_ims['file']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table4724693328-324603\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>file</th><th>ra</th><th>dec</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>filter</th><th>size</th><th>hash</th><th>date</th><th>survey</th><th>type</th></tr></thead>\n",
       "<tr><td>0</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00102_st.fit</td><td>35.616442</td><td>-4.72313</td><td>36.12365296903962</td><td>36.12552008187017</td><td>35.92586958047189</td><td>35.92719310164043</td><td>-4.077977314187725</td><td>-4.276795598599529</td><td>-4.077171797589524</td><td>-4.276339668965059</td><td>Ks</td><td>84542400</td><td>3319fb54e5a06ed024ee2c779d3eea16</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>1</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00235_st.fit</td><td>35.611492</td><td>-4.90492</td><td>36.12610999045986</td><td>36.12799560019295</td><td>35.92567612832217</td><td>35.927013085064964</td><td>-4.260849258430862</td><td>-4.458864267061002</td><td>-4.26000521655501</td><td>-4.458377949976223</td><td>Ks</td><td>86400000</td><td>3311861dca0dc2cc4c591c74ade46697</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>2</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00088_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20121122</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>3</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00214_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20121122</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>4</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00207_st.fit</td><td>35.436996</td><td>-4.90735</td><td>35.94415038681624</td><td>35.946083891256066</td><td>35.74415693249861</td><td>35.74553771326806</td><td>-4.25982111275322</td><td>-4.458153923454694</td><td>-4.259167034486192</td><td>-4.457851984006502</td><td>Ks</td><td>87131520</td><td>30c4cf93b9f0f0cf0d3261792887984c</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>5</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00186_st.fit</td><td>35.620817</td><td>-4.72418</td><td>36.1273099599587</td><td>36.129253995984364</td><td>35.927356864252125</td><td>35.928750217721166</td><td>-4.076418668899508</td><td>-4.2747963003828655</td><td>-4.075634526012312</td><td>-4.274362759715051</td><td>Ks</td><td>86996160</td><td>f2d7ce96af8ba0e7dacad9502b130968</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>6</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00151_st.fit</td><td>35.619563</td><td>-4.91269</td><td>36.12897457417563</td><td>36.1309440114325</td><td>35.926964033757244</td><td>35.92836295863879</td><td>-4.25902765584969</td><td>-4.4604201350789845</td><td>-4.258262828599911</td><td>-4.460015583328658</td><td>Ks</td><td>87891840</td><td>b31848fb6f8fda487334ad79edf9f671</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>7</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00116_st.fit</td><td>35.524558</td><td>-4.90631</td><td>36.03226173409147</td><td>36.03414857812651</td><td>35.834395481720996</td><td>35.83573620433609</td><td>-4.260776925843634</td><td>-4.459607645003357</td><td>-4.259978566975024</td><td>-4.459159214724387</td><td>Ks</td><td>84648960</td><td>a48b9f8674556a541dc64ae56646d847</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>8</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00179_st.fit</td><td>35.528933</td><td>-4.72417</td><td>36.036102623233226</td><td>36.038019421876925</td><td>35.83608127168605</td><td>35.83744677984761</td><td>-4.075840155220711</td><td>-4.27419221211796</td><td>-4.075110862928901</td><td>-4.273814512007562</td><td>Ks</td><td>86526720</td><td>ab1338e0fca179e1783af7e2d9a78187</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>9</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00130_st.fit</td><td>35.435729</td><td>-4.72953</td><td>35.94541034612505</td><td>35.94739404313425</td><td>35.74341133771648</td><td>35.7448269875313</td><td>-4.0761093665967385</td><td>-4.277639042080953</td><td>-4.075351693245579</td><td>-4.277242337224645</td><td>Ks</td><td>88286400</td><td>2def2dc5fa82d81c006112aa76b725ab</td><td>20121122</td><td>VIDEO</td><td>stack</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(\"$('#table4724693328-324603').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",
       "    $('#table4724693328-324603').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, 13], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_ims[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131440"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(vista_ims['type'] == 'stack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20121122, 20171027)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Date range of observations\n",
    "(np.min(vista_ims['date'][vista_ims['survey'] == 'VIDEO'].astype(int)), \n",
    "np.max(vista_ims['date'][vista_ims['survey'] == 'VIDEO'].astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=15</i>\n",
       "<table id=\"table4725586256-237571\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>file</th><th>ra</th><th>dec</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>filter</th><th>size</th><th>hash</th><th>date</th><th>survey</th><th>type</th></tr></thead>\n",
       "<tr><td>0</td><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00271.fit</td><td>181.435454</td><td>-53.14541</td><td>182.2699684256211</td><td>182.27508947891405</td><td>181.95473142042866</td><td>181.95770976393467</td><td>-52.500334487275154</td><td>-52.691661599656214</td><td>-52.50051135075514</td><td>-52.69217903289173</td><td>J</td><td>73728000</td><td>730416b224f9b934da710a6770337d3a</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>1</td><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00253_st_conf.fit</td><td>163.755087</td><td>-32.91194</td><td>164.3520240174799</td><td>164.35570543667168</td><td>164.1184643820727</td><td>164.1210603126868</td><td>-32.2660963747985</td><td>-32.46300877975218</td><td>-32.26637724257059</td><td>-32.46364426169791</td><td>J</td><td>31046400</td><td>beea31eb8137a152bdc73c79261d87c8</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>2</td><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00249_st_conf.fit</td><td>163.645883</td><td>-32.912</td><td>164.24397687889805</td><td>164.24742058383342</td><td>164.01043222454663</td><td>164.012789752506</td><td>-32.26664552513899</td><td>-32.46365979897796</td><td>-32.26668703984101</td><td>-32.464055134522155</td><td>J</td><td>30977280</td><td>b473697810ace78260ba50cf16a0ec2a</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>3</td><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00303.fit</td><td>172.778062</td><td>-32.72868</td><td>173.37524373208828</td><td>173.37826754342257</td><td>173.14875042099698</td><td>173.15075057467376</td><td>-32.08353724384342</td><td>-32.274872525972924</td><td>-32.08338256135142</td><td>-32.275051305217474</td><td>J</td><td>72855360</td><td>acc79f38544c86b0f0c6e311dca5a92e</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>4</td><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>181.740187</td><td>-53.14543</td><td>182.5684104871822</td><td>182.57571865223184</td><td>182.24400437178605</td><td>182.24905062242806</td><td>-52.497518927023314</td><td>-52.69450922592677</td><td>-52.49890513826607</td><td>-52.69626411860069</td><td>Ks</td><td>29664000</td><td>c61c3ae84e31c269ea1139040a1d81f5</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>5</td><td>/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00303.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>6</td><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>181.740187</td><td>-53.14543</td><td>182.5684104871822</td><td>182.57571865223184</td><td>182.24400437178605</td><td>182.24905062242806</td><td>-52.497518927023314</td><td>-52.69450922592677</td><td>-52.49890513826607</td><td>-52.69626411860069</td><td>Ks</td><td>29664000</td><td>c61c3ae84e31c269ea1139040a1d81f5</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>7</td><td>r-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>8</td><td>ir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>9</td><td>r-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>10</td><td>hir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>11</td><td>ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>12</td><td>/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>13</td><td>me/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00303.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>14</td><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>181.740187</td><td>-53.14543</td><td>182.5684104871822</td><td>182.57571865223184</td><td>182.24400437178605</td><td>182.24905062242806</td><td>-52.497518927023314</td><td>-52.69450922592677</td><td>-52.49890513826607</td><td>-52.69626411860069</td><td>Ks</td><td>29664000</td><td>c61c3ae84e31c269ea1139040a1d81f5</td><td>20140217</td><td>VHS</td><td></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(\"$('#table4725586256-237571').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",
       "    $('#table4725586256-237571').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, 13], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_ims[-15:].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43681"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.isnan(vista_ims['ra']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table5134043600\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>file</th><th>ra</th><th>dec</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>filter</th><th>size</th><th>hash</th><th>date</th><th>survey</th><th>type</th></tr></thead>\n",
       "<thead><tr><th>str101</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str2</th><th>int64</th><th>str32</th><th>str8</th><th>str5</th><th>str5</th></tr></thead>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00088_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20121122</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00214_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20121122</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00130_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20121122</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00172_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20121122</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00133_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20171027</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00181_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20171027</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140924/v20140924_00570_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140924</td><td>VHS</td><td>tile</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140924/v20140924_00582_st_tl_conf.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140924</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140924/v20140924_00606_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140924</td><td>VHS</td><td>tile</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140924/v20140924_00594_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20140924</td><td>VHS</td><td>tile</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=10>\n",
       "                                                 file                                                 ...\n",
       "                                                str101                                                ...\n",
       "----------------------------------------------------------------------------------------------------- ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00088_st_tl.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00214_st_tl.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00130_st_tl.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00172_st_tl.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00133_st_tl.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00181_st_tl.fit ...\n",
       "          /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140924/v20140924_00570_st_tl.fit ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140924/v20140924_00582_st_tl_conf.fit ...\n",
       "          /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140924/v20140924_00606_st_tl.fit ...\n",
       "          /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140924/v20140924_00594_st_tl.fit ..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_ims[np.isnan(vista_ims['ra'])][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_mwd(RA,Dec,org=0,title='Object positions', projection='mollweide'):\n",
    "    ''' RA, Dec are arrays of the same length.\n",
    "    RA takes values in [0,360), Dec in [-90,90],\n",
    "    which represent angles in degrees.\n",
    "    org is the origin of the plot, 0 or a multiple of 30 degrees in [0,360).\n",
    "    title is the title of the figure.\n",
    "    projection is the kind of projection: 'mollweide', 'aitoff', 'hammer', 'lambert'\n",
    "    '''\n",
    "    x = np.remainder(RA+360-org,360) # shift RA values\n",
    "    ind = x>180\n",
    "    x[ind] -=360    # scale conversion to [-180, 180]\n",
    "    x=-x    # reverse the scale: East to the left\n",
    "    tick_labels = np.array([150, 120, 90, 60, 30, 0, 330, 300, 270, 240, 210])\n",
    "    tick_labels = np.remainder(tick_labels+360+org,360)\n",
    "    fig = plt.figure(figsize=(10, 5))\n",
    "    ax = fig.add_subplot(111, projection=projection)\n",
    "    ax.scatter(np.radians(x),np.radians(Dec))  # convert degrees to radians\n",
    "    ax.set_xticklabels(tick_labels)     # we add the scale on the x axis\n",
    "    ax.set_title(title)\n",
    "    ax.title.set_fontsize(15)\n",
    "    ax.set_xlabel(\"RA\")\n",
    "    ax.xaxis.label.set_fontsize(12)\n",
    "    ax.set_ylabel(\"Dec\")\n",
    "    ax.yaxis.label.set_fontsize(12)\n",
    "    ax.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rs548/GitHub/lsst_stack/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-1a1d771/lib/python3.7/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in greater\n",
      "  result = getattr(super(), op)(other)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mwd(vista_ims['ra_0_0'], vista_ims['dec_0_0'], title='VISTA positions', projection='aitoff')\n",
    "plt.savefig('./data/vista_coverage.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These appear to not show the VHS full coverage. Is this because we are looking at DR1\n",
    "\n",
    "![](./figs/VISTA-Status-20190620.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43681"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.isnan(vista_ims['dec_0_0']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=525327</i>\n",
       "<table id=\"table4507453584\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>file</th><th>ra</th><th>dec</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>filter</th><th>size</th><th>hash</th><th>date</th><th>survey</th><th>type</th></tr></thead>\n",
       "<thead><tr><th>str101</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str2</th><th>int64</th><th>str32</th><th>str8</th><th>str5</th><th>str5</th></tr></thead>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00102_st.fit</td><td>35.616442</td><td>-4.72313</td><td>36.12365296903962</td><td>36.12552008187017</td><td>35.92586958047189</td><td>35.92719310164043</td><td>-4.077977314187725</td><td>-4.276795598599529</td><td>-4.077171797589524</td><td>-4.276339668965059</td><td>Ks</td><td>84542400</td><td>3319fb54e5a06ed024ee2c779d3eea16</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00235_st.fit</td><td>35.611492</td><td>-4.90492</td><td>36.12610999045986</td><td>36.12799560019295</td><td>35.92567612832217</td><td>35.927013085064964</td><td>-4.260849258430862</td><td>-4.458864267061002</td><td>-4.26000521655501</td><td>-4.458377949976223</td><td>Ks</td><td>86400000</td><td>3311861dca0dc2cc4c591c74ade46697</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00207_st.fit</td><td>35.436996</td><td>-4.90735</td><td>35.94415038681624</td><td>35.946083891256066</td><td>35.74415693249861</td><td>35.74553771326806</td><td>-4.25982111275322</td><td>-4.458153923454694</td><td>-4.259167034486192</td><td>-4.457851984006502</td><td>Ks</td><td>87131520</td><td>30c4cf93b9f0f0cf0d3261792887984c</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00186_st.fit</td><td>35.620817</td><td>-4.72418</td><td>36.1273099599587</td><td>36.129253995984364</td><td>35.927356864252125</td><td>35.928750217721166</td><td>-4.076418668899508</td><td>-4.2747963003828655</td><td>-4.075634526012312</td><td>-4.274362759715051</td><td>Ks</td><td>86996160</td><td>f2d7ce96af8ba0e7dacad9502b130968</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00151_st.fit</td><td>35.619563</td><td>-4.91269</td><td>36.12897457417563</td><td>36.1309440114325</td><td>35.926964033757244</td><td>35.92836295863879</td><td>-4.25902765584969</td><td>-4.4604201350789845</td><td>-4.258262828599911</td><td>-4.460015583328658</td><td>Ks</td><td>87891840</td><td>b31848fb6f8fda487334ad79edf9f671</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00116_st.fit</td><td>35.524558</td><td>-4.90631</td><td>36.03226173409147</td><td>36.03414857812651</td><td>35.834395481720996</td><td>35.83573620433609</td><td>-4.260776925843634</td><td>-4.459607645003357</td><td>-4.259978566975024</td><td>-4.459159214724387</td><td>Ks</td><td>84648960</td><td>a48b9f8674556a541dc64ae56646d847</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00179_st.fit</td><td>35.528933</td><td>-4.72417</td><td>36.036102623233226</td><td>36.038019421876925</td><td>35.83608127168605</td><td>35.83744677984761</td><td>-4.075840155220711</td><td>-4.27419221211796</td><td>-4.075110862928901</td><td>-4.273814512007562</td><td>Ks</td><td>86526720</td><td>ab1338e0fca179e1783af7e2d9a78187</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00130_st.fit</td><td>35.435729</td><td>-4.72953</td><td>35.94541034612505</td><td>35.94739404313425</td><td>35.74341133771648</td><td>35.7448269875313</td><td>-4.0761093665967385</td><td>-4.277639042080953</td><td>-4.075351693245579</td><td>-4.277242337224645</td><td>Ks</td><td>88286400</td><td>2def2dc5fa82d81c006112aa76b725ab</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00165_st.fit</td><td>35.435704</td><td>-4.9127</td><td>35.94561871123936</td><td>35.94765256521272</td><td>35.74363364276948</td><td>35.74509765698566</td><td>-4.259284945697468</td><td>-4.460698118492988</td><td>-4.258674764519787</td><td>-4.4604498842183045</td><td>Ks</td><td>87626880</td><td>f59516da74921bc5cedf5feb4a38e12f</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00221_st.fit</td><td>35.519567</td><td>-4.72175</td><td>36.03481214889815</td><td>36.03675975960754</td><td>35.83439696723595</td><td>35.83579819829734</td><td>-4.077283639558862</td><td>-4.275402304486855</td><td>-4.076507139976842</td><td>-4.274984699778887</td><td>Ks</td><td>86950080</td><td>9ced0b22e057b78dfb6a14ac1bd63693</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00265_st_conf.fit</td><td>181.741133</td><td>-53.3288</td><td>182.57320799977896</td><td>182.58059923862058</td><td>182.24774338752331</td><td>182.2528585013564</td><td>-52.680724236341504</td><td>-52.877446480508134</td><td>-52.682167085359104</td><td>-52.87925795648641</td><td>Ks</td><td>29698560</td><td>023e7c39c6f21ee32130d039b2135061</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00251_st_conf.fit</td><td>163.646008</td><td>-32.7288</td><td>164.2426248708985</td><td>164.2459915329822</td><td>164.00950032417114</td><td>164.01178584550732</td><td>-32.08339061646039</td><td>-32.2803946600907</td><td>-32.08342215527195</td><td>-32.280779748674064</td><td>J</td><td>30902400</td><td>f7df2f179142629ea05ccb68cfcf82a5</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00259_st_conf.fit</td><td>181.435454</td><td>-53.14541</td><td>182.2686170886104</td><td>182.2745842020512</td><td>181.9439676353853</td><td>181.94766800531144</td><td>-52.49939795173296</td><td>-52.696424522292375</td><td>-52.50000084914484</td><td>-52.69739108208449</td><td>Ks</td><td>29854080</td><td>90a1a1f6276bd25ff41eab6b2d8ab01e</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00271.fit</td><td>181.435454</td><td>-53.14541</td><td>182.2699684256211</td><td>182.27508947891405</td><td>181.95473142042866</td><td>181.95770976393467</td><td>-52.500334487275154</td><td>-52.691661599656214</td><td>-52.50051135075514</td><td>-52.69217903289173</td><td>J</td><td>73728000</td><td>730416b224f9b934da710a6770337d3a</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00253_st_conf.fit</td><td>163.755087</td><td>-32.91194</td><td>164.3520240174799</td><td>164.35570543667168</td><td>164.1184643820727</td><td>164.1210603126868</td><td>-32.2660963747985</td><td>-32.46300877975218</td><td>-32.26637724257059</td><td>-32.46364426169791</td><td>J</td><td>31046400</td><td>beea31eb8137a152bdc73c79261d87c8</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00249_st_conf.fit</td><td>163.645883</td><td>-32.912</td><td>164.24397687889805</td><td>164.24742058383342</td><td>164.01043222454663</td><td>164.012789752506</td><td>-32.26664552513899</td><td>-32.46365979897796</td><td>-32.26668703984101</td><td>-32.464055134522155</td><td>J</td><td>30977280</td><td>b473697810ace78260ba50cf16a0ec2a</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00303.fit</td><td>172.778062</td><td>-32.72868</td><td>173.37524373208828</td><td>173.37826754342257</td><td>173.14875042099698</td><td>173.15075057467376</td><td>-32.08353724384342</td><td>-32.274872525972924</td><td>-32.08338256135142</td><td>-32.275051305217474</td><td>J</td><td>72855360</td><td>acc79f38544c86b0f0c6e311dca5a92e</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>181.740187</td><td>-53.14543</td><td>182.5684104871822</td><td>182.57571865223184</td><td>182.24400437178605</td><td>182.24905062242806</td><td>-52.497518927023314</td><td>-52.69450922592677</td><td>-52.49890513826607</td><td>-52.69626411860069</td><td>Ks</td><td>29664000</td><td>c61c3ae84e31c269ea1139040a1d81f5</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>181.740187</td><td>-53.14543</td><td>182.5684104871822</td><td>182.57571865223184</td><td>182.24400437178605</td><td>182.24905062242806</td><td>-52.497518927023314</td><td>-52.69450922592677</td><td>-52.49890513826607</td><td>-52.69626411860069</td><td>Ks</td><td>29664000</td><td>c61c3ae84e31c269ea1139040a1d81f5</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit</td><td>181.740187</td><td>-53.14543</td><td>182.5684104871822</td><td>182.57571865223184</td><td>182.24400437178605</td><td>182.24905062242806</td><td>-52.497518927023314</td><td>-52.69450922592677</td><td>-52.49890513826607</td><td>-52.69626411860069</td><td>Ks</td><td>29664000</td><td>c61c3ae84e31c269ea1139040a1d81f5</td><td>20140217</td><td>VHS</td><td></td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=525327>\n",
       "                                               file                                                ...\n",
       "                                              str101                                               ...\n",
       "-------------------------------------------------------------------------------------------------- ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00102_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00235_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00207_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00186_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00151_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00116_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00179_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00130_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00165_st.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00221_st.fit ...\n",
       "                                                                                               ... ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00265_st_conf.fit ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00251_st_conf.fit ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00259_st_conf.fit ...\n",
       "             /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00271.fit ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00253_st_conf.fit ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00249_st_conf.fit ...\n",
       "             /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00303.fit ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit ...\n",
       "     /home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VHS/20140217/v20140217_00267_st_conf.fit ..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_ims[~np.isnan(vista_ims['dec_0_0'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I was expecting the VHS images to be all sky. Perhaps I am having an issue opening those files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mwd(hsc_ims['ra_0_0'], hsc_ims['dec_0_0'], title='HSC positions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get fields\n",
    "\n",
    "We want to look at the XMM-LSS field as a test. Lets get all the files in XMM-LSS and choose a set as our practice images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select test region\n",
    "\n",
    "We want an area in XMM-LSS with VIDEO and HSC DUD data. Let us choose a HSC tract (composed of nine patches) and get a list of file names to move to our local repository for processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A point in SXDS is 35.0, -4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra_test, dec_test = 35.0,  -4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "#First lets get the HSC tract containing that point\n",
    "#For now lets just assume the images are lined up with the axes\n",
    "in_im = (\n",
    "    (ra_test < hsc_ims['ra_0_0']) &\n",
    "    (dec_test > hsc_ims['dec_0_0']) &\n",
    "    (ra_test > hsc_ims['ra_x_y']) &\n",
    "    (dec_test < hsc_ims['dec_x_y']) )\n",
    "\n",
    "print(np.sum(in_im))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=40</i>\n",
       "<table id=\"table4761391888\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>file</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>size</th><th>hash</th></tr></thead>\n",
       "<thead><tr><th>str164</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>str32</th></tr></thead>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/8765/0,3/calexp-HSC-Z-8765-0,3.fits</td><td>35.056859995378</td><td>35.05667278346978</td><td>34.865093653377386</td><td>34.86494907158138</td><td>-4.003241696593322</td><td>-3.8072651168892024</td><td>-4.00341561935237</td><td>-3.8074305032734497</td><td>141505920</td><td>ddcd8c638f16208db848385c00d1b817</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/8765/0,2/calexp-HSC-Z-8765-0,2.fits</td><td>35.05703836986104</td><td>35.05685107863718</td><td>34.86523141012769</td><td>34.8650867670717</td><td>-4.1898799341053765</td><td>-3.9939095932239588</td><td>-4.190061985963134</td><td>-3.994083109503531</td><td>163016640</td><td>0ce608368f49e49a5d04ed98d1e4dda3</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/8766/8,2/calexp-HSC-Z-8766-8,2.fits</td><td>35.05212396177302</td><td>35.052268604829</td><td>34.86031700203964</td><td>34.86050429326351</td><td>-4.190061985963127</td><td>-3.9940831095035314</td><td>-4.189879934105375</td><td>-3.9939095932239557</td><td>163609920</td><td>414f501c865410b188c2e98c08f142ab</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/8766/8,3/calexp-HSC-Z-8766-8,3.fits</td><td>35.052261718523305</td><td>35.052406300319326</td><td>34.86049537652268</td><td>34.86068258843092</td><td>-4.003415619352376</td><td>-3.8074305032734497</td><td>-4.003241696593322</td><td>-3.8072651168892</td><td>141289920</td><td>163b09282be3e840300537ceff30a341</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-G/8765/0,3/calexp-HSC-G-8765-0,3.fits</td><td>35.056859995378</td><td>35.05667278346978</td><td>34.865093653377386</td><td>34.86494907158138</td><td>-4.003241696593322</td><td>-3.8072651168892024</td><td>-4.00341561935237</td><td>-3.8074305032734497</td><td>134055360</td><td>c328249a04c41efa1147f125d9cefa27</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-G/8765/0,2/calexp-HSC-G-8765-0,2.fits</td><td>35.05703836986104</td><td>35.05685107863718</td><td>34.86523141012769</td><td>34.8650867670717</td><td>-4.1898799341053765</td><td>-3.9939095932239588</td><td>-4.190061985963134</td><td>-3.994083109503531</td><td>143435520</td><td>d8e55ee7636a3372638af4acf74cc112</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-G/8766/8,2/calexp-HSC-G-8766-8,2.fits</td><td>35.05212396177302</td><td>35.052268604829</td><td>34.86031700203964</td><td>34.86050429326351</td><td>-4.190061985963127</td><td>-3.9940831095035314</td><td>-4.189879934105375</td><td>-3.9939095932239557</td><td>143732160</td><td>2dabd20ddbd2fab92c29c2a47bca6c44</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-G/8766/8,3/calexp-HSC-G-8766-8,3.fits</td><td>35.052261718523305</td><td>35.052406300319326</td><td>34.86049537652268</td><td>34.86068258843092</td><td>-4.003415619352376</td><td>-3.8074305032734497</td><td>-4.003241696593322</td><td>-3.8072651168892</td><td>134046720</td><td>597ad322a54f519f72a0ba36e9b773d7</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-I/8765/0,3/calexp-HSC-I-8765-0,3.fits</td><td>35.056859995378</td><td>35.05667278346978</td><td>34.865093653377386</td><td>34.86494907158138</td><td>-4.003241696593322</td><td>-3.8072651168892024</td><td>-4.00341561935237</td><td>-3.8074305032734497</td><td>125611200</td><td>fa2c3cca1aa531bd34a90abfa9753e92</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-I/8765/0,2/calexp-HSC-I-8765-0,2.fits</td><td>35.05703836986104</td><td>35.05685107863718</td><td>34.86523141012769</td><td>34.8650867670717</td><td>-4.1898799341053765</td><td>-3.9939095932239588</td><td>-4.190061985963134</td><td>-3.994083109503531</td><td>141868800</td><td>d94ba1303ca1529a3eb36f2ed8faa504</td></tr>\n",
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       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-I/8766/8,2/calexp-HSC-I-8766-8,2.fits</td><td>35.05212396177302</td><td>35.052268604829</td><td>34.86031700203964</td><td>34.86050429326351</td><td>-4.190061985963127</td><td>-3.9940831095035314</td><td>-4.189879934105375</td><td>-3.9939095932239557</td><td>133341120</td><td>c7d169b4612b02ce2ff03b531bc8dace</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-I/8766/8,3/calexp-HSC-I-8766-8,3.fits</td><td>35.052261718523305</td><td>35.052406300319326</td><td>34.86049537652268</td><td>34.86068258843092</td><td>-4.003415619352376</td><td>-3.8074305032734497</td><td>-4.003241696593322</td><td>-3.8072651168892</td><td>132670080</td><td>f69dc3bcc2726603baf8c0e29edacafd</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-Y/8765/0,3/calexp-HSC-Y-8765-0,3.fits</td><td>35.056859995378</td><td>35.05667278346978</td><td>34.865093653377386</td><td>34.86494907158138</td><td>-4.003241696593322</td><td>-3.8072651168892024</td><td>-4.00341561935237</td><td>-3.8074305032734497</td><td>129018240</td><td>00f6e41b6a63fd4888e8c5c2c29b0dd4</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-Y/8765/0,2/calexp-HSC-Y-8765-0,2.fits</td><td>35.05703836986104</td><td>35.05685107863718</td><td>34.86523141012769</td><td>34.8650867670717</td><td>-4.1898799341053765</td><td>-3.9939095932239588</td><td>-4.190061985963134</td><td>-3.994083109503531</td><td>128416320</td><td>4cfe8d2a6135240887572498f291c7ee</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-Y/8766/8,2/calexp-HSC-Y-8766-8,2.fits</td><td>35.05212396177302</td><td>35.052268604829</td><td>34.86031700203964</td><td>34.86050429326351</td><td>-4.190061985963127</td><td>-3.9940831095035314</td><td>-4.189879934105375</td><td>-3.9939095932239557</td><td>128376000</td><td>614856656b91bd47e6bd34134010c98f</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-Y/8766/8,3/calexp-HSC-Y-8766-8,3.fits</td><td>35.052261718523305</td><td>35.052406300319326</td><td>34.86049537652268</td><td>34.86068258843092</td><td>-4.003415619352376</td><td>-3.8074305032734497</td><td>-4.003241696593322</td><td>-3.8072651168892</td><td>128943360</td><td>7b0905d38b25ad054375a4fc00591d17</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-R/8765/0,3/calexp-HSC-R-8765-0,3.fits</td><td>35.056859995378</td><td>35.05667278346978</td><td>34.865093653377386</td><td>34.86494907158138</td><td>-4.003241696593322</td><td>-3.8072651168892024</td><td>-4.00341561935237</td><td>-3.8074305032734497</td><td>124231680</td><td>69163d81b969ed69e720a2ce19a8b5be</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-R/8765/0,2/calexp-HSC-R-8765-0,2.fits</td><td>35.05703836986104</td><td>35.05685107863718</td><td>34.86523141012769</td><td>34.8650867670717</td><td>-4.1898799341053765</td><td>-3.9939095932239588</td><td>-4.190061985963134</td><td>-3.994083109503531</td><td>124856640</td><td>dd156f31dc0ebfd5d240e628dadaf896</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-R/8766/8,2/calexp-HSC-R-8766-8,2.fits</td><td>35.05212396177302</td><td>35.052268604829</td><td>34.86031700203964</td><td>34.86050429326351</td><td>-4.190061985963127</td><td>-3.9940831095035314</td><td>-4.189879934105375</td><td>-3.9939095932239557</td><td>124894080</td><td>d0b3bc7cd7e43850c6cd94e145d6dfd2</td></tr>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-R/8766/8,3/calexp-HSC-R-8766-8,3.fits</td><td>35.052261718523305</td><td>35.052406300319326</td><td>34.86049537652268</td><td>34.86068258843092</td><td>-4.003415619352376</td><td>-3.8074305032734497</td><td>-4.003241696593322</td><td>-3.8072651168892</td><td>124306560</td><td>bbc095cb066f00aec3810d629f485d4a</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=40>\n",
       "                                                                               file                                                                                ...\n",
       "                                                                              str164                                                                               ...\n",
       "------------------------------------------------------------------------------------------------------------------------------------------------------------------ ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/8765/0,3/calexp-HSC-Z-8765-0,3.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/8765/0,2/calexp-HSC-Z-8765-0,2.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/8766/8,2/calexp-HSC-Z-8766-8,2.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/8766/8,3/calexp-HSC-Z-8766-8,3.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-G/8765/0,3/calexp-HSC-G-8765-0,3.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-G/8765/0,2/calexp-HSC-G-8765-0,2.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-G/8766/8,2/calexp-HSC-G-8766-8,2.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-G/8766/8,3/calexp-HSC-G-8766-8,3.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-I/8765/0,3/calexp-HSC-I-8765-0,3.fits ...\n",
       " /home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-I/8765/0,2/calexp-HSC-I-8765-0,2.fits ...\n",
       "                                                                                                                                                               ... ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-I/8766/8,2/calexp-HSC-I-8766-8,2.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-I/8766/8,3/calexp-HSC-I-8766-8,3.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-Y/8765/0,3/calexp-HSC-Y-8765-0,3.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-Y/8765/0,2/calexp-HSC-Y-8765-0,2.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-Y/8766/8,2/calexp-HSC-Y-8766-8,2.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-Y/8766/8,3/calexp-HSC-Y-8766-8,3.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-R/8765/0,3/calexp-HSC-R-8765-0,3.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-R/8765/0,2/calexp-HSC-R-8765-0,2.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-R/8766/8,2/calexp-HSC-R-8766-8,2.fits ...\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/public/HSC/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_wide/deepCoadd-results/HSC-R/8766/8,3/calexp-HSC-R-8766-8,3.fits ..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hsc_ims[in_im]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This point apears to be in the overlap region between tract 8523 and 8524. Lets choose 8523 arbitraily. Each tract contains 9*9 = 81 patches. Lets try to process one patch to begin. We therefor want all the VISTA images that overlap with one patch. Lets arbitrality take the first patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hsc_ims[in_im][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rs548/GitHub/lsst_stack/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-1a1d771/lib/python3.7/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in less\n",
      "  result = getattr(super(), op)(other)\n"
     ]
    }
   ],
   "source": [
    "in_vista_im = (\n",
    "    (ra_test < vista_ims['ra_0_0']) &\n",
    "    (dec_test > vista_ims['dec_0_0']) &\n",
    "    (ra_test > vista_ims['ra_x_y']) &\n",
    "    (dec_test < vista_ims['dec_x_y']) )\n",
    "\n",
    "print(np.sum(in_vista_im))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def float_to_int(floats):\n",
    "    floats = np.array(floats.copy())\n",
    "    floats[np.isnan(floats)] = -99\n",
    "    ints = np.array([int(f) for f in floats])\n",
    "    return ints\n",
    "close = (float_to_int( vista_ims['ra']) == int(ra_test)) & (float_to_int( vista_ims['dec']) == int(dec_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum((float_to_int( vista_ims['dec']) == int(dec_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-99, -63, -62, -61, -60, -59, -58, -57, -56, -54, -53, -51, -50,\n",
       "       -48, -47, -46, -45, -44, -43, -42, -41, -40, -38, -37, -35, -34,\n",
       "       -32, -31, -30, -29, -28, -27, -26, -25, -24, -23, -21, -19, -18,\n",
       "       -16, -15, -13, -12, -11, -10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,\n",
       "        -2,   0])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(float_to_int( vista_ims['dec']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Row index=0</i>\n",
       "<table id=\"table5051548624\">\n",
       "<thead><tr><th>file</th><th>ra</th><th>dec</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>filter</th><th>size</th><th>hash</th><th>date</th><th>survey</th><th>type</th></tr></thead>\n",
       "<thead><tr><th>str101</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str2</th><th>int64</th><th>str32</th><th>str8</th><th>str5</th><th>str5</th></tr></thead>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00102_st.fit</td><td>35.616442</td><td>-4.72313</td><td>36.12365296903962</td><td>36.12552008187017</td><td>35.92586958047189</td><td>35.92719310164043</td><td>-4.077977314187725</td><td>-4.276795598599529</td><td>-4.077171797589524</td><td>-4.276339668965059</td><td>Ks</td><td>84542400</td><td>3319fb54e5a06ed024ee2c779d3eea16</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Row index=0>\n",
       "                                               file                                                    ra      dec          ra_0_0            ra_0_y            ra_x_0            ra_x_y           dec_0_0            dec_0_y            dec_x_0            dec_x_y       filter   size                 hash                 date   survey  type\n",
       "                                              str101                                                float64  float64       float64           float64           float64           float64           float64            float64            float64            float64        str2   int64                str32                 str8    str5   str5\n",
       "-------------------------------------------------------------------------------------------------- --------- -------- ----------------- ----------------- ----------------- ----------------- ------------------ ------------------ ------------------ ------------------ ------ -------- -------------------------------- -------- ------ -----\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00102_st.fit 35.616442 -4.72313 36.12365296903962 36.12552008187017 35.92586958047189 35.92719310164043 -4.077977314187725 -4.276795598599529 -4.077171797589524 -4.276339668965059     Ks 84542400 3319fb54e5a06ed024ee2c779d3eea16 20121122  VIDEO stack"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_ims[close][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=0</i>\n",
       "<table id=\"table5051551312\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>file</th><th>ra</th><th>dec</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>filter</th><th>size</th><th>hash</th><th>date</th><th>survey</th><th>type</th></tr></thead>\n",
       "<thead><tr><th>str101</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str2</th><th>int64</th><th>str32</th><th>str8</th><th>str5</th><th>str5</th></tr></thead>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=0>\n",
       " file     ra     dec    ra_0_0  ra_0_y  ra_x_0 ...  size  hash date survey type\n",
       "str101 float64 float64 float64 float64 float64 ... int64 str32 str8  str5  str5\n",
       "------ ------- ------- ------- ------- ------- ... ----- ----- ---- ------ ----"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_ims[np.isclose(vista_ims['ra'], 35.428) & (vista_ims['filter'] == 'J')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;MaskedColumn name=&apos;filter&apos; dtype=&apos;str2&apos; length=1&gt;\n",
       "<table>\n",
       "<tr><td>Ks</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<MaskedColumn name='filter' dtype='str2' length=1>\n",
       "Ks"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(vista_ims[np.isclose(vista_ims['ra'], 35.428)]['filter'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = np.isclose(vista_ims['ra'], 35.428, atol=0.1)\n",
    "test_set &= np.isclose(vista_ims['dec'], -4.90777, atol=0.1)\n",
    "test_set &= (vista_ims['survey']== 'VIDEO')\n",
    "#test_set &= (\n",
    "#    (np.array(vista_ims['filter']) == 'Y') | \n",
    "#    (np.array(vista_ims['filter']) == 'Ks') \n",
    "#)\n",
    "\n",
    "#test_set &="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;date&apos; dtype=&apos;str8&apos; length=2&gt;\n",
       "<table>\n",
       "<tr><td>20121122</td></tr>\n",
       "<tr><td>20171027</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Column name='date' dtype='str8' length=2>\n",
       "20121122\n",
       "20171027"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(vista_ims[test_set]['date'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "850599360"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(vista_ims[test_set &(vista_ims['type']=='stack')]['size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=11</i>\n",
       "<table id=\"table5051551056-921810\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>file</th><th>ra</th><th>dec</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>filter</th><th>size</th><th>hash</th><th>date</th><th>survey</th><th>type</th></tr></thead>\n",
       "<tr><td>0</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00207_st.fit</td><td>35.436996</td><td>-4.90735</td><td>35.94415038681624</td><td>35.946083891256066</td><td>35.74415693249861</td><td>35.74553771326806</td><td>-4.25982111275322</td><td>-4.458153923454694</td><td>-4.259167034486192</td><td>-4.457851984006502</td><td>Ks</td><td>87131520</td><td>30c4cf93b9f0f0cf0d3261792887984c</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>1</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00116_st.fit</td><td>35.524558</td><td>-4.90631</td><td>36.03226173409147</td><td>36.03414857812651</td><td>35.834395481720996</td><td>35.83573620433609</td><td>-4.260776925843634</td><td>-4.459607645003357</td><td>-4.259978566975024</td><td>-4.459159214724387</td><td>Ks</td><td>84648960</td><td>a48b9f8674556a541dc64ae56646d847</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>2</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00165_st.fit</td><td>35.435704</td><td>-4.9127</td><td>35.94561871123936</td><td>35.94765256521272</td><td>35.74363364276948</td><td>35.74509765698566</td><td>-4.259284945697468</td><td>-4.460698118492988</td><td>-4.258674764519787</td><td>-4.4604498842183045</td><td>Ks</td><td>87626880</td><td>f59516da74921bc5cedf5feb4a38e12f</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>3</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00242_st.fit</td><td>35.519567</td><td>-4.90494</td><td>36.03462911783436</td><td>36.03659155520072</td><td>35.83423558450388</td><td>35.83565018719635</td><td>-4.260864499424222</td><td>-4.458846009642759</td><td>-4.260043878490914</td><td>-4.458383394814115</td><td>Ks</td><td>86132160</td><td>30dc6c0720fecc343e7037583e2657cf</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>4</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00158_st.fit</td><td>35.527642</td><td>-4.91272</td><td>36.0374114351255</td><td>36.03936021861576</td><td>35.83545453771271</td><td>35.83683273533792</td><td>-4.258752116030969</td><td>-4.460185522258053</td><td>-4.257936566923111</td><td>-4.4597300997089615</td><td>Ks</td><td>88053120</td><td>89a8864ed1c242b8fecc1562b892ea6c</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>5</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00249_st.fit</td><td>35.427633</td><td>-4.90493</td><td>35.94287537092362</td><td>35.94475163389872</td><td>35.742420446203745</td><td>35.743748425144865</td><td>-4.261165633336233</td><td>-4.4591626315246415</td><td>-4.260401596591839</td><td>-4.458757586918079</td><td>Ks</td><td>86411520</td><td>27e65f1059bd076dbea3b79e8d53e565</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>6</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20121122/v20121122_00123_st.fit</td><td>35.432625</td><td>-4.9063</td><td>35.94044296819832</td><td>35.94237419669329</td><td>35.74260946975893</td><td>35.74399541881637</td><td>-4.261384583389258</td><td>-4.460170054766773</td><td>-4.260724450373826</td><td>-4.459861003202436</td><td>Ks</td><td>84424320</td><td>80488596aedcf2fd07926f139f909716</td><td>20121122</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>7</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00173_st.fit</td><td>35.435225</td><td>-4.90779</td><td>35.943588982159184</td><td>35.945534640039945</td><td>35.74298527341724</td><td>35.744372243765596</td><td>-4.259059664741104</td><td>-4.458820336557813</td><td>-4.258365216461935</td><td>-4.458481834393038</td><td>Y</td><td>61666560</td><td>0ae44410a6d7f6fc317b233b9b5b714a</td><td>20171027</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>8</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00213_st.fit</td><td>35.525212</td><td>-4.90638</td><td>36.03748204073455</td><td>36.03938267195677</td><td>35.83466901967988</td><td>35.836005340341934</td><td>-4.258024543865595</td><td>-4.4579656850480625</td><td>-4.257153959023732</td><td>-4.457456529627805</td><td>Y</td><td>60860160</td><td>d56e745db72b84d12afc79ee76f99d5e</td><td>20171027</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>9</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00165_st.fit</td><td>35.527158</td><td>-4.90781</td><td>36.03531006747089</td><td>36.03720753962785</td><td>35.83474228118</td><td>35.83608039124861</td><td>-4.258438926310406</td><td>-4.458239480930942</td><td>-4.257575477014601</td><td>-4.457730373880786</td><td>Y</td><td>61963200</td><td>6ab2e6c4c3da68d0b8875d65ede796fb</td><td>20171027</td><td>VIDEO</td><td>stack</td></tr>\n",
       "<tr><td>10</td><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00221_st.fit</td><td>35.433279</td><td>-4.90636</td><td>35.94569928577793</td><td>35.947652543592426</td><td>35.74289029365579</td><td>35.744279823177294</td><td>-4.258478819153203</td><td>-4.458417847619478</td><td>-4.257762118759249</td><td>-4.458063956312508</td><td>Y</td><td>61680960</td><td>6c7afd3db9f4e26b66f99b51eeda7791</td><td>20171027</td><td>VIDEO</td><td>stack</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(\"$('#table5051551056-921810').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",
       "    $('#table5051551056-921810').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, 13], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_ims[test_set &(vista_ims['type']=='stack')].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(test_set &(vista_ims['type']=='stack')&(vista_ims['filter']=='J'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;date&apos; dtype=&apos;str8&apos; length=0&gt;\n",
       "<table>\n",
       "</table>"
      ],
      "text/plain": [
       "<Column name='date' dtype='str8' length=0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(vista_ims[test_set &(vista_ims['type']=='stack')&(vista_ims['filter']=='J')]['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to choose a minimal test data set. That is I want a set of six stacks on the same position in the four VIDEO bands.\n",
    "\n",
    " - 20171027, Y images at 35.4, -4.9\n",
    " - 20121021, J\n",
    " - 20120924, H \n",
    " - 20121122, Ks \n",
    " \n",
    " look like two VIDEO tiles over the same area in Y and Ks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(vista_ims[test_set & (vista_ims['date']=='20121021')]['size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=2</i>\n",
       "<table id=\"table4859523408\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>file</th><th>ra</th><th>dec</th><th>ra_0_0</th><th>ra_0_y</th><th>ra_x_0</th><th>ra_x_y</th><th>dec_0_0</th><th>dec_0_y</th><th>dec_x_0</th><th>dec_x_y</th><th>filter</th><th>size</th><th>hash</th><th>date</th><th>survey</th><th>type</th></tr></thead>\n",
       "<thead><tr><th>str101</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str2</th><th>int64</th><th>str32</th><th>str8</th><th>str5</th><th>str5</th></tr></thead>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00133_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20171027</td><td>VIDEO</td><td>tile</td></tr>\n",
       "<tr><td>/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00181_st_tl.fit</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>--</td><td>0</td><td>--</td><td>20171027</td><td>VIDEO</td><td>tile</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "                                                 file                                                 ...\n",
       "                                                str101                                                ...\n",
       "----------------------------------------------------------------------------------------------------- ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00133_st_tl.fit ...\n",
       "/Users/rs548/GitHub/lsst-ir-fusion/dmu0/dmu0_VISTA/dmu0_VIDEO/data/20171027/v20171027_00181_st_tl.fit ..."
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_ims[ (vista_ims['date']=='20171027') &(vista_ims['type']=='tile')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum((vista_ims['type']=='tile') & ~np.isnan(vista_ims['ra']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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