dmu1/3_Tile_tracts_patches.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Tile tract and patch lists\n",
    "\n",
    "In this notebook we will generate the list of patches that are present on a given VISTA tile. We will do so such that the tile is completely covered. That is we aim to provide every patch that contains any region of the tile."
   ]
  },
  {
   "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",
    "import json\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "VIDEO_IN = './data/video_images_overview_20200820.csv'\n",
    "VHS_IN = './data/vhs_images_overview_20201102.csv'\n",
    "VIKING_IN = './data/viking_images_overview_20201218.csv'\n",
    "HSC_IN = './data/hsc_images_overview_20210315.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ims = Table.read(VIDEO_IN)\n",
    "vhs_ims = Table.read(VHS_IN)\n",
    "viking_ims = Table.read(VIKING_IN)\n",
    "hsc_ims = Table.read(HSC_IN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887732, 137269, 229841, 189936)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vhs_ims),len(video_ims),len(viking_ims),len(hsc_ims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "video_ims['type'] = [fileToType(f) for f in video_ims['file']]\n",
    "vhs_ims['type'] = [fileToType(f) for f in vhs_ims['file']]\n",
    "viking_ims['type'] = [fileToType(f) for f in viking_ims['file']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ims = video_ims[video_ims['type']=='tile']\n",
    "vhs_ims = vhs_ims[vhs_ims['type']=='tile']\n",
    "viking_ims = viking_ims[viking_ims['type']=='tile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33971, 2205, 6852)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vhs_ims),len(video_ims),len(viking_ims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TractInfo(id=8524, ctrCoord=[0.8087076683756161, 0.5811684769959703, -0.09074749834931431])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We are using a rings skymap\n",
    "from lsst.geom import SpherePoint \n",
    "from lsst.geom import degrees\n",
    "from lsst.skymap.ringsSkyMap import RingsSkyMap, RingsSkyMapConfig\n",
    "\n",
    "#Following taken from dmu1/2_Survey_comparisons.ipynb test choice\n",
    "coord = SpherePoint(35.429025*degrees,-4.90853*degrees)\n",
    "config = RingsSkyMapConfig()\n",
    "#These config options are chose to be the same as HSC:\n",
    "#https://github.com/lsst/obs_subaru/blob/master/config/hsc/makeSkyMap.py\n",
    "#and copied for obs_vista\n",
    "#https://github.com/lsst-uk/obs_vista/blob/master/config/makeSkyMap.py\n",
    "config.numRings = 120\n",
    "config.projection = \"TAN\"\n",
    "config.tractOverlap = 1.0/60 # Overlap between tracts (degrees)\n",
    "config.pixelScale = 0.168\n",
    "sm = RingsSkyMap(config)\n",
    "sm.findTract(coord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Test on known tile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Row index=0</i>\n",
       "<table id=\"table140367238938256\">\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>type</th></tr></thead>\n",
       "<thead><tr><th>str98</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>str5</th></tr></thead>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VIDEO/20121122/v20121122_00088_st_tl.fit</td><td>35.43265</td><td>-4.72313</td><td>36.1259211592099</td><td>36.13048720302681</td><td>34.91284867786413</td><td>34.914758535953574</td><td>-4.074792762244068</td><td>-5.561089113115699</td><td>-4.077214438128979</td><td>-5.563516534082509</td><td>Ks</td><td>216357120</td><td>95f818d1b99dcee749b276666480cdc6</td><td>tile</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               type\n",
       "                                            str98                                             float64  float64      float64           float64           float64           float64            float64            float64            float64            float64        str2    int64                str32               str5\n",
       "--------------------------------------------------------------------------------------------- -------- -------- ---------------- ----------------- ----------------- ------------------ ------------------ ------------------ ------------------ ------------------ ------ --------- -------------------------------- ----\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VIDEO/20121122/v20121122_00088_st_tl.fit 35.43265 -4.72313 36.1259211592099 36.13048720302681 34.91284867786413 34.914758535953574 -4.074792762244068 -5.561089113115699 -4.077214438128979 -5.563516534082509     Ks 216357120 95f818d1b99dcee749b276666480cdc6 tile"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tile from dmu4/dmu4_Example\n",
    "EX_TILE = \"20121122/v20121122_00088_st_tl.fit\"\n",
    "ex_row = video_ims[\n",
    "    video_ims['file'] == \"/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VIDEO/\"+EX_TILE\n",
    "][0]\n",
    "ex_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.1259211592099"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex_row['ra_0_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "patches = sm.findTractPatchList(\n",
    "        [\n",
    "            SpherePoint(ex_row['ra_0_0']*degrees,ex_row['dec_0_0']*degrees),\n",
    "            SpherePoint(ex_row['ra_0_y']*degrees,ex_row['dec_0_y']*degrees),\n",
    "            SpherePoint(ex_row['ra_x_0']*degrees,ex_row['dec_x_0']*degrees),\n",
    "            SpherePoint(ex_row['ra_x_y']*degrees,ex_row['dec_x_y']*degrees)\n",
    "        ]\n",
    "    )\n",
    "t = patches[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"8766\":[[2,0],[2,1],[2,2],[3,0],[3,1],[3,2],[4,0],[4,1],[4,2],[5,0],[5,1],[5,2],[6,0],[6,1],[6,2],[7,0],[7,1],[7,2],[8,0],[8,1],[8,2]],\"8524\":[[2,2],[2,3],[2,4],[2,5],[2,6],[2,7],[2,8],[3,2],[3,3],[3,4],[3,5],[3,6],[3,7],[3,8],[4,2],[4,3],[4,4],[4,5],[4,6],[4,7],[4,8],[5,2],[5,3],[5,4],[5,5],[5,6],[5,7],[5,8],[6,2],[6,3],[6,4],[6,5],[6,6],[6,7],[6,8],[7,2],[7,3],[7,4],[7,5],[7,6],[7,7],[7,8],[8,2],[8,3],[8,4],[8,5],[8,6],[8,7],[8,8]],\"8765\":[[0,0],[0,1],[0,2]],\"8523\":[[0,2],[0,3],[0,4],[0,5],[0,6],[0,7],[0,8]]}'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tp_dict ={}\n",
    "for tract in patches:\n",
    "    #print(tract[0].getId())\n",
    "    #tp_dict[tract[0].getId()] = [[t.getIndex()[0],t.getIndex()[1]] for t in tract[1]]\n",
    "    tp_dict[int(tract[0].getId())] = [t.getIndex() for t in tract[1]]\n",
    "\n",
    "j = json.dumps(tp_dict, separators=(',', ':')) \n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"8766\":[[2,0],[2,1],[2,2],[3,0],[3,1],[3,2],[4,0],[4,1],[4,2],[5,0],[5,1],[5,2],[6,0],[6,1],[6,2],[7,0],[7,1],[7,2],[8,0],[8,1],[8,2]],\"8524\":[[2,2],[2,3],[2,4],[2,5],[2,6],[2,7],[2,8],[3,2],[3,3],[3,4],[3,5],[3,6],[3,7],[3,8],[4,2],[4,3],[4,4],[4,5],[4,6],[4,7],[4,8],[5,2],[5,3],[5,4],[5,5],[5,6],[5,7],[5,8],[6,2],[6,3],[6,4],[6,5],[6,6],[6,7],[6,8],[7,2],[7,3],[7,4],[7,5],[7,6],[7,7],[7,8],[8,2],[8,3],[8,4],[8,5],[8,6],[8,7],[8,8]],\"8765\":[[0,0],[0,1],[0,2]],\"8523\":[[0,2],[0,3],[0,4],[0,5],[0,6],[0,7],[0,8]]}'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def corners_to_patch_list(ex_row):\n",
    "    \"\"\"Take a ra dec limited region and return a list of all patches contained witing it\n",
    "    \n",
    "    Inputs\n",
    "    =======\n",
    "    ex_row    astropy.table.row\n",
    "              Row of image overview table containing corner columns\n",
    "    \n",
    "    Returns\n",
    "    =======\n",
    "    j        str\n",
    "             String which can be loaded by json to create a dictionary\n",
    "             the tracts indices are strings as required by json\n",
    "    \"\"\"\n",
    "    try:\n",
    "        patches = sm.findTractPatchList(\n",
    "            [\n",
    "                SpherePoint(ex_row['ra_0_0']*degrees,ex_row['dec_0_0']*degrees),\n",
    "                SpherePoint(ex_row['ra_0_y']*degrees,ex_row['dec_0_y']*degrees),\n",
    "                SpherePoint(ex_row['ra_x_0']*degrees,ex_row['dec_x_0']*degrees),\n",
    "                SpherePoint(ex_row['ra_x_y']*degrees,ex_row['dec_x_y']*degrees)\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "        tp_dict ={}\n",
    "        for tract in patches:\n",
    "            tp_dict[int(tract[0].getId())] = [t.getIndex() for t in tract[1]]\n",
    "        j = json.dumps(tp_dict, separators=(',', ':')) \n",
    "    except:\n",
    "        #print(ex_row['file']+\" failed\")\n",
    "        j=''\n",
    "    return j\n",
    "corners_to_patch_list(video_ims[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Row index=0</i>\n",
       "<table id=\"table140367232318480\">\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>type</th></tr></thead>\n",
       "<thead><tr><th>str98</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>str5</th></tr></thead>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VIDEO/20140924/v20140924_00444_st_tl.fit</td><td>35.429542</td><td>-4.72215</td><td>36.12993723683939</td><td>36.13433423332165</td><td>34.912894878395214</td><td>34.91462725348546</td><td>-4.072782101046067</td><td>-5.559458172880838</td><td>-4.07547203760631</td><td>-5.562153562881393</td><td>Y</td><td>148561920</td><td>ccef567359a9d3f1cef197b5b5dad958</td><td>tile</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               type\n",
       "                                            str98                                              float64  float64       float64           float64           float64            float64           float64            float64            float64           float64        str2    int64                str32               str5\n",
       "--------------------------------------------------------------------------------------------- --------- -------- ----------------- ----------------- ------------------ ----------------- ------------------ ------------------ ----------------- ------------------ ------ --------- -------------------------------- ----\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VIDEO/20140924/v20140924_00444_st_tl.fit 35.429542 -4.72215 36.12993723683939 36.13433423332165 34.912894878395214 34.91462725348546 -4.072782101046067 -5.559458172880838 -4.07547203760631 -5.562153562881393      Y 148561920 ccef567359a9d3f1cef197b5b5dad958 tile"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_ims[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: figs: File exists\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x560 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "!mkdir figs\n",
    "fig, ax = plt.subplots(figsize=(4, 4), dpi=140)\n",
    "        \n",
    "test = json.loads(corners_to_patch_list(video_ims[0]))\n",
    "for tract in test:\n",
    "    t = sm.generateTract(int(tract))\n",
    "    for patch in test[tract]:\n",
    "        \n",
    "        p = patch\n",
    "        \n",
    "    \n",
    "    \n",
    "        vertices = t.getPatchInfo([p[0], p[1]]).getInnerSkyPolygon(t.getWcs()).getVertices()\n",
    "        ra = [np.arctan(vertices[n][1]/vertices[n][0])* 180/np.pi for n in np.mod(np.arange(5),4)]\n",
    "        #print(tract, ra)\n",
    "        dec = [(vertices[n][2])* 180/np.pi for n in np.mod(np.arange(5),4)]\n",
    "        ax.fill(ra, dec, c = 'r', alpha=0.3, linewidth=0.5)\n",
    "\n",
    "t =video_ims[0]        \n",
    "ra = [t['ra_0_0'], t['ra_x_0'] , t['ra_x_y'] , t['ra_0_y'] , t['ra_0_0'] ]\n",
    "dec = [t['dec_0_0'], t['dec_x_0'] , t['dec_x_y'] , t['dec_0_y'] , t['dec_0_0'] ]  \n",
    "ax.plot(ra, dec, c = 'b', alpha=0.3, linewidth=1.0)\n",
    "    \n",
    "ax.axis('scaled')\n",
    "#ax.set_xlim([39, 33])\n",
    "ax.set_xlabel('Right Ascension [deg]')\n",
    "ax.set_ylabel('Declination [deg]')\n",
    "fig.savefig('./figs/test_im.pdf', overwrite=True)\n",
    "fig.savefig('./figs/test_im.png', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ims['tract_patch_json'] = [corners_to_patch_list(row) for row in video_ims]\n",
    "vhs_ims['tract_patch_json'] = [corners_to_patch_list(row) for row in vhs_ims]\n",
    "viking_ims['tract_patch_json'] = [corners_to_patch_list(row) for row in viking_ims]\n",
    "#corners_to_patch_list(video_ims[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Row index=0</i>\n",
       "<table id=\"table140367232318480\">\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>type</th><th>tract_patch_json</th></tr></thead>\n",
       "<thead><tr><th>str98</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>str5</th><th>str631</th></tr></thead>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VIDEO/20140924/v20140924_00444_st_tl.fit</td><td>35.429542</td><td>-4.72215</td><td>36.12993723683939</td><td>36.13433423332165</td><td>34.912894878395214</td><td>34.91462725348546</td><td>-4.072782101046067</td><td>-5.559458172880838</td><td>-4.07547203760631</td><td>-5.562153562881393</td><td>Y</td><td>148561920</td><td>ccef567359a9d3f1cef197b5b5dad958</td><td>tile</td><td>{&quot;8766&quot;:[[2,0],[2,1],[2,2],[3,0],[3,1],[3,2],[4,0],[4,1],[4,2],[5,0],[5,1],[5,2],[6,0],[6,1],[6,2],[7,0],[7,1],[7,2],[8,0],[8,1],[8,2]],&quot;8524&quot;:[[2,2],[2,3],[2,4],[2,5],[2,6],[2,7],[2,8],[3,2],[3,3],[3,4],[3,5],[3,6],[3,7],[3,8],[4,2],[4,3],[4,4],[4,5],[4,6],[4,7],[4,8],[5,2],[5,3],[5,4],[5,5],[5,6],[5,7],[5,8],[6,2],[6,3],[6,4],[6,5],[6,6],[6,7],[6,8],[7,2],[7,3],[7,4],[7,5],[7,6],[7,7],[7,8],[8,2],[8,3],[8,4],[8,5],[8,6],[8,7],[8,8]],&quot;8765&quot;:[[0,0],[0,1],[0,2]],&quot;8523&quot;:[[0,2],[0,3],[0,4],[0,5],[0,6],[0,7],[0,8]]}</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               type                                                                                                                                                                                                                                                            tract_patch_json                                                                                                                                                                                                                                                          \n",
       "                                            str98                                              float64  float64       float64           float64           float64            float64           float64            float64            float64           float64        str2    int64                str32               str5                                                                                                                                                                                                                                                                 str631                                                                                                                                                                                                                                                               \n",
       "--------------------------------------------------------------------------------------------- --------- -------- ----------------- ----------------- ------------------ ----------------- ------------------ ------------------ ----------------- ------------------ ------ --------- -------------------------------- ---- -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
       "/home/ir-shir1/rds/rds-iris-ip005/data/private/VISTA/VIDEO/20140924/v20140924_00444_st_tl.fit 35.429542 -4.72215 36.12993723683939 36.13433423332165 34.912894878395214 34.91462725348546 -4.072782101046067 -5.559458172880838 -4.07547203760631 -5.562153562881393      Y 148561920 ccef567359a9d3f1cef197b5b5dad958 tile {\"8766\":[[2,0],[2,1],[2,2],[3,0],[3,1],[3,2],[4,0],[4,1],[4,2],[5,0],[5,1],[5,2],[6,0],[6,1],[6,2],[7,0],[7,1],[7,2],[8,0],[8,1],[8,2]],\"8524\":[[2,2],[2,3],[2,4],[2,5],[2,6],[2,7],[2,8],[3,2],[3,3],[3,4],[3,5],[3,6],[3,7],[3,8],[4,2],[4,3],[4,4],[4,5],[4,6],[4,7],[4,8],[5,2],[5,3],[5,4],[5,5],[5,6],[5,7],[5,8],[6,2],[6,3],[6,4],[6,5],[6,6],[6,7],[6,8],[7,2],[7,3],[7,4],[7,5],[7,6],[7,7],[7,8],[8,2],[8,3],[8,4],[8,5],[8,6],[8,7],[8,8]],\"8765\":[[0,0],[0,1],[0,2]],\"8523\":[[0,2],[0,3],[0,4],[0,5],[0,6],[0,7],[0,8]]}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_ims[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"3053\":[[0,0],[0,1],[0,2],[1,0],[1,1],[1,2],[2,0],[2,1],[2,2]],\"3054\":[[2,0],[2,1],[2,2],[3,0],[3,1],[3,2],[4,0],[4,1],[4,2],[5,0],[5,1],[5,2],[6,0],[6,1],[6,2],[7,0],[7,1],[7,2],[8,0],[8,1],[8,2]],\"2876\":[[0,3],[0,4],[0,5],[0,6],[0,7],[0,8],[1,3],[1,4],[1,5],[1,6],[1,7],[1,8],[2,3],[2,4],[2,5],[2,6],[2,7],[2,8],[3,3],[3,4],[3,5],[3,6],[3,7],[3,8]],\"2877\":[[3,3],[3,4],[3,5],[3,6],[3,7],[3,8],[4,3],[4,4],[4,5],[4,6],[4,7],[4,8],[5,3],[5,4],[5,5],[5,6],[5,7],[5,8],[6,3],[6,4],[6,5],[6,6],[6,7],[6,8],[7,3],[7,4],[7,5],[7,6],[7,7],[7,8],[8,3],[8,4],[8,5],[8,6],[8,7],[8,8]]}'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_ims[6]['tract_patch_json']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#video_ims.write(VIDEO_IN.replace('images','tiles_tracts_patches'),overwrite=True)\n",
    "vhs_ims.write(VHS_IN.replace('images','tiles_tracts_patches'),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "viking_ims.write(VIKING_IN.replace('images','tiles_tracts_patches'),overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ims = Table.read(VIDEO_IN.replace('images','tiles_tracts_patches'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ims.add_column(Column(\n",
    "    data= [t.split('/')[-2] for t in video_ims['file']],\n",
    "    name='date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tract = 8524\n",
    "patch = [3,5]\n",
    "\n",
    "#has_tract_patch = (\n",
    "#    [str(tract) in j for j in video_ims['tract_patch_json']]\n",
    "#    and [patch in j for j in video_ims['tract_patch_json']]\n",
    "#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def has_patch(tract, patch, j):\n",
    "    \"\"\"Take a json string and return true if a given patch is in it\n",
    "    \"\"\"\n",
    "    j = json.loads(j)\n",
    "   \n",
    "    try:\n",
    "        has_patch = patch in j[str(tract)]\n",
    "    except KeyError:\n",
    "        has_patch = False\n",
    "    return has_patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "j = json.loads(video_ims[6]['tract_patch_json'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = [has_patch(tract, patch, j) for j in video_ims['tract_patch_json']]\n",
    "mask &= video_ims['filter'] =='Y'\n",
    "np.sum(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(video_ims[mask]['date']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'makeCoaddTempExp.py data --rerun coadd --selectId filter=VISTA-Y dateObs=2009-11-07  --selectId filter=VISTA-Y dateObs=2009-11-08  --selectId filter=VISTA-Y dateObs=2009-11-10  --selectId filter=VISTA-Y dateObs=2009-11-11  --selectId filter=VISTA-Y dateObs=2009-11-12 --id filter=VISTA-Y tract=8524 patch=3,5 --clobber-config'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"makeCoaddTempExp.py data --rerun coadd\"\n",
    "for date in np.unique(video_ims[mask]['date'])[:5]:\n",
    "    d = date[0:4]+'-'+date[4:6]+'-'+date[6:9]#+'^'\n",
    "    s+=\" --selectId filter=VISTA-Y dateObs={} \".format(d)\n",
    "s + \"--id filter=VISTA-Y tract=8524 patch=3,5 --clobber-config\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_patch(8766, [2,0], video_ims['tract_patch_json'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['file',\n",
       " 'ra',\n",
       " 'dec',\n",
       " 'ra_0_0',\n",
       " 'ra_0_y',\n",
       " 'ra_x_0',\n",
       " 'ra_x_y',\n",
       " 'dec_0_0',\n",
       " 'dec_0_y',\n",
       " 'dec_x_0',\n",
       " 'dec_x_y',\n",
       " 'filter',\n",
       " 'size',\n",
       " 'hash',\n",
       " 'type',\n",
       " 'tract_patch_json',\n",
       " 'date']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_ims.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 5]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "has_8524_33 = np.array([has_patch(8524, [3,3], j) for j in video_ims['tract_patch_json']])\n",
    "has_8524_55 = np.array([has_patch(8524, [5,5], j) for j in video_ims['tract_patch_json']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "234"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(has_8524_33 & has_8524_55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;filter&apos; dtype=&apos;str2&apos; length=4&gt;\n",
       "<table>\n",
       "<tr><td>H</td></tr>\n",
       "<tr><td>J</td></tr>\n",
       "<tr><td>Ks</td></tr>\n",
       "<tr><td>Y</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Column name='filter' dtype='str2' length=4>\n",
       " H\n",
       " J\n",
       "Ks\n",
       " Y"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(video_ims[has_8524_55  ]['filter'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Make total tract_patch_json dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_total_patch_dict = {}\n",
    "vhs_total_patch_dict = {}\n",
    "viking_total_patch_dict = {}\n",
    "\n",
    "for tile in video_ims[video_ims['type']=='tile']:\n",
    "    tile_dict = json.loads(tile['tract_patch_json'])\n",
    "    for tract in tile_dict:\n",
    "        patches = set([str(p) for p in tile_dict[tract]])\n",
    "        try:\n",
    "            video_total_patch_dict[tract] = set(video_total_patch_dict[tract]).union(patches)\n",
    "            \n",
    "        except KeyError:\n",
    "            video_total_patch_dict[tract] = patches\n",
    "            \n",
    "for tile in vhs_ims[vhs_ims['type']=='tile']:\n",
    "    tile_dict = json.loads(tile['tract_patch_json'])\n",
    "    for tract in tile_dict:\n",
    "        patches = set([str(p) for p in tile_dict[tract]])\n",
    "        try:\n",
    "            vhs_total_patch_dict[tract] = set(vhs_total_patch_dict[tract]).union(patches)\n",
    "            \n",
    "        except KeyError:\n",
    "            vhs_total_patch_dict[tract] = patches\n",
    "            \n",
    "for tile in viking_ims[viking_ims['type']=='tile']:\n",
    "    tile_dict = json.loads(tile['tract_patch_json'])\n",
    "    for tract in tile_dict:\n",
    "        patches = set([str(p) for p in tile_dict[tract]])\n",
    "        try:\n",
    "            viking_total_patch_dict[tract] = set(viking_total_patch_dict[tract]).union(patches)\n",
    "            \n",
    "        except KeyError:\n",
    "            viking_total_patch_dict[tract] = patches\n",
    "            \n",
    "for tract in video_total_patch_dict:\n",
    "    video_total_patch_dict[tract] = [[int(p[1]),int(p[4])] for p in video_total_patch_dict[tract]]\n",
    "    \n",
    "for tract in vhs_total_patch_dict:\n",
    "    vhs_total_patch_dict[tract] = [[int(p[1]),int(p[4])] for p in vhs_total_patch_dict[tract]]\n",
    "    \n",
    "for tract in viking_total_patch_dict:\n",
    "    viking_total_patch_dict[tract] = [[int(p[1]),int(p[4])] for p in viking_total_patch_dict[tract]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 670137 patches in VHS.\n",
      "There are 600 patches in VIDEO.\n",
      "There are 49209 patches in VIKING.\n"
     ]
    }
   ],
   "source": [
    "n = 0\n",
    "for tract in vhs_total_patch_dict:\n",
    "    n += len(vhs_total_patch_dict[tract])\n",
    "print(\"There are {} patches in VHS.\".format(n))\n",
    "\n",
    "n = 0\n",
    "for tract in video_total_patch_dict:\n",
    "    n += len(video_total_patch_dict[tract])\n",
    "print(\"There are {} patches in VIDEO.\".format(n))\n",
    "\n",
    "n = 0\n",
    "for tract in viking_total_patch_dict:\n",
    "    n += len(viking_total_patch_dict[tract])\n",
    "print(\"There are {} patches in VIKING.\".format(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./json/video_total_patch_dict.json', 'w') as outfile:\n",
    "    json.dump(video_total_patch_dict, outfile, separators=(',', ':'))\n",
    "with open('./json/vhs_total_patch_dict.json', 'w') as outfile:\n",
    "    json.dump(vhs_total_patch_dict, outfile, separators=(',', ':'))\n",
    "with open('./json/viking_total_patch_dict.json', 'w') as outfile:\n",
    "    json.dump(viking_total_patch_dict, outfile, separators=(',', ':'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "hsc_ims['tract'] = [f.split('/')[16] for f in hsc_ims['file']]\n",
    "hsc_ims['patch'] = [f.split('/')[17] for f in hsc_ims['file']]\n",
    "hsc_ims['depth'] = [f.split('/')[13] for f in hsc_ims['file']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Row index=0</i>\n",
       "<table id=\"table140367232210384\">\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><th>tract</th><th>patch</th><th>depth</th></tr></thead>\n",
       "<thead><tr><th>str270</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><th>str5</th><th>str3</th><th>str9</th></tr></thead>\n",
       "<tr><td>/home/ir-shir1/rds/rds-iris-ip005/ras81/lsst-ir-fusion/dmu0/dmu0_HSC/data/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/9221/1,8/calexp-HSC-Z-9221-1,8.fits</td><td>353.2201634808244</td><td>353.2200778949918</td><td>353.0241201674153</td><td>353.0240600712148</td><td>-1.5826616267455118</td><td>-1.3913733852871641</td><td>-1.5827145450883715</td><td>-1.3914199009363517</td><td>21202560</td><td>3def3355f75860244ddbd62008c906c7</td><td>9221</td><td>1,8</td><td>pdr2_dud</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Row index=0>\n",
       "                                                                                           file                                                                                                 ra_0_0            ra_0_y            ra_x_0            ra_x_y            dec_0_0             dec_0_y             dec_x_0             dec_x_y         size                 hash               tract patch  depth  \n",
       "                                                                                          str270                                                                                               float64           float64           float64           float64            float64             float64             float64             float64        int64                str32                str5  str3   str9  \n",
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ----------------- ----------------- ----------------- ----------------- ------------------- ------------------- ------------------- ------------------- -------- -------------------------------- ----- ----- --------\n",
       "/home/ir-shir1/rds/rds-iris-ip005/ras81/lsst-ir-fusion/dmu0/dmu0_HSC/data/hsc-release.mtk.nao.ac.jp/archive/filetree/pdr2_dud/deepCoadd-results/HSC-Z/9221/1,8/calexp-HSC-Z-9221-1,8.fits 353.2201634808244 353.2200778949918 353.0241201674153 353.0240600712148 -1.5826616267455118 -1.3913733852871641 -1.5827145450883715 -1.3914199009363517 21202560 3def3355f75860244ddbd62008c906c7  9221   1,8 pdr2_dud"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hsc_ims[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "hsc_total_patch_dict = {}\n",
    "\n",
    "for file in hsc_ims:\n",
    "    tract = file['tract']\n",
    "    patch = set([\"[{}]\".format(file['patch'])])\n",
    "    try:\n",
    "        hsc_total_patch_dict[tract] = set(hsc_total_patch_dict[tract]).union(patch)\n",
    "            \n",
    "    except KeyError:\n",
    "        hsc_total_patch_dict[tract] = patch\n",
    "        \n",
    "for tract in hsc_total_patch_dict:\n",
    "    hsc_total_patch_dict[tract] = [[int(p[1]),int(p[3])] for p in hsc_total_patch_dict[tract]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#json.dumps(hsc_total_patch_dict, separators=(',', ':'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./json/hsc_total_patch_dict.json', 'w') as outfile:\n",
    "    json.dump(hsc_total_patch_dict, outfile, separators=(',', ':'))"
   ]
  }
 ],
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