pyinthesky_imagecomb_2d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User Interface Concept for 2d Image Registration & Combination \n",
    "\n",
    "Created March 2018\n",
    "\n",
    "This is an example user workflow using a hypothetical set of NIRCam images - 2 dithers from one filter, and 1 in some other filter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Preliminaries\n",
    "\n",
    "This illustrates the simplest possible workflow for *getting* the images.  The imagined `jwsttools` does not exist at the time of writing, so an alternate loading path is also demonstrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#NEEDS IMPLEMENTING\n",
    "\n",
    "import jwsttools\n",
    "\n",
    "program_exposures = jwsttools.download_program(pid=1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Assuming here that the only exposures in this program are the three outlined above.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filter1 = []\n",
    "filter2 = []\n",
    "\n",
    "for exp in program_exposures:\n",
    "    if exp.filter == 'filter1':\n",
    "        filter1.append(exp)\n",
    "    elif exp.filter == 'filter2':\n",
    "        filter2.append(exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternate non-jwsttools path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. User goes to web site, searches for their program ID\n",
    "2. User downloads the specific files and saves them as \"filter1_1.fits\", \"filter1_2.fits\", \"filter2_1.fits\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy import nddata\n",
    "\n",
    "img1_1 = nddata.CCDData.read('filter1_1.fits')\n",
    "img1_2 = nddata.CCDData.read('filter1_2.fits')\n",
    "img2_1 = nddata.CCDData.read('filter2_1.fits')\n",
    "\n",
    "filter1 = [img1_1, img1_2]\n",
    "filter2 = [img2_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Registration "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There should be \"quick\" tools for a user to check registration, assuming the WCS in the images is right.  Something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#NEEDS IMPLEMENTING\n",
    "\n",
    "from someplace import show_image_sets #exactly what package this belongs in is a question..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function will use the `.wcs` in the `img1_1` objects (which should be CCDData or maybe the jwst image object which is an NDData subclass).  It should assume the \"new WCS API\" which is currently being implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Q2jbTZLzVLyKSe3v78EyVKRzf/Zymwjj6Q4W8rdZPoHMTgzWVJ/oc4iRx7MnaBKmpf+Wa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e8da20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.coordinates import SkyCoord\n",
    "coord = SkyCoord(<something>*u.deg, <something>*u.deg)  # this is some star the user sees is there\n",
    "\n",
    "show_image_sets(coord, [img1_1, img1_2, img2_1], size=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At a minimum the user should see the above.  Bonus: an interactive set of ginga windows that zoom and move around in lock-step.\n",
    "\n",
    "This inspection reveals the registration is off for the filter2 image.  Looks like the gwcs will need to be adjusted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import photutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "srces = photutils.detect...(img1_1, ...choices...)  # this is an astropy.table !\n",
    "srces_coords = ing1_1.wcs.pixel_to_world(srces['x'], srces['y'])\n",
    "\n",
    "srces2 = photutils.detect...(img2_1, ...choices...)\n",
    "#srces, srces2 can come from anywhere\n",
    "\n",
    "# the would probably be a triangle-matching algorithm a la daomaster\n",
    "idx1, idx2 = photutils.match_stars(srces, srces2) #NEEDS IMPLEMENTING (code may already be in pipeline)\n",
    "\n",
    "# the below could be some random user thingie that they adjust for their own personal purpose\n",
    "# below uses gwcs, not WCS APE API\n",
    "newwcs = tweak_wcs(srces_coords[idx1], srces2[idx2], img2_1)   #NEEDS IMPLEMENTING (already in pipeline)\n",
    "\n",
    "img2_1.wcs = newwcs  # (will this work in the JWST pipeline? that is, does *setting* wcs work?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should also have the above be available as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img2_1 = align_image(img1_1, img2_1)  # photutils  #NEEDS IMPLEMENTING?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The assumption is that the WCS are now correct based on the above.  So now we \"just\" need to align the images to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import reproject"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets assume the user really just wants to align them to the \"master\" frame of image1_1.  Then just:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# NEEDS IMPLEMENTING\n",
    "# this function should drizzle uncertainties in a ~ reasonable way assuming uncorrelated\n",
    "\n",
    "img1_2_matched = reproject.reproject_drizzle(img1_2, img1_1.header, ... any drizzle parameters here...)\n",
    "img2_1_matched = reproject.reproject_drizzle(img2_1, img1_1.header, ... any drizzle parameters here...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes combination conceptually-trivial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newimg = img1_1\n",
    "for toadd in [img1_2_matched, img2_1_matched]:\n",
    "    newimg = img1_1.add(toadd, propagate_uncertainties=True)  #requires that drizzle propogate uncertainties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T80LSEHJKSFu+Z7trUz9ntYvipE0ObO3xekFx0c6XS47/SFT9k0NHbd/W2ZgLNm3Swp9f\ni/pfP2ETxWqzXj9wfenY+Xaq2mBx3l4TTlwcuuINp4WSEFnfkVmvMShpXGzqPPqqj3fh/soM7Tdh\n4pyRdqFkliSUCAgADaqEyObXddLN3DMAIKfjKGkqjaftMwEA5V32HrP+Ia+ecG30nj12Rdteo2zJ\nG35lM/4eVkmlA4qIjAD4CwC/rKq+NiTwNIDdqjonIo8C+CsAB3kjVX0cwOMAMDawde2IniAI+oI1\npzwi8okeEdX/qb+2XQ0rg8kXVfUvU9uo6oyqznV/fgJATUR8aGgQBHcka76hqOqnAXx6tW1ERAD8\nKYCXVPX3VtluK4Bzqqoi8ghWBrSEMUcQBHciNzXl6Q4ITwJYB6AUkV8G8FYAbwfwswCeF5Fnupv/\nmqo+ISIfBwBV/SyAfw7g34pIG8AigJ9W1VWnNGWjwMKBazkXQ0+/7jfabvUOWbKaQnvI6xI10mKU\njIGKaR+H0t5k8ziUZIraK7YIFACU9+wzy82RNQxuEu+M0rSxEC3KTSqueCOdDpka6zrb9+aE1w9q\nlyl2hfKMcNbqTACgW+0Lph47aZbb77rXtSnr9iSlY3WK7LI3PmLDoeW7ba5S/VyiDelI0iAzq/lE\nrBEbgLOh9rTVjGTIx3UoGZzzccsDiWJuQ2SitWz1EL3PPkcAkB86bpbbb7NG5KyJAUBnvdd8quSm\nBhRVPQvAXw3gmwC8IokfDiRXf/4jAH90M8cMguDOIULvgyCojBhQgiCojBhQgiCojL5PDpR2iYGp\na8E4kkiey6fJ3Tyzcs7QMe+U39piRcr6Eeveri0fLJYN2sQxNspp3+2Dg3MSd3MS+Tq0z9ohL+wK\nBbtx8Bgu+5AfecUmii188B1mefBEIpMxJxmMEtaaD3hhsPE6fUk3ZhP7ikTyHKYoeIzc9HUk4QpP\nAmv9lL2nnISZQunZEa4KmDgO3+PmvfYeF7OJSnx7qGICfe9Q1hIO/dMUhEfHTQUigp7z4oIVg/WE\nf5aa++73+6mQeEMJgqAyYkAJgqAyYkAJgqAy+l9DWWpCXrpmKN18+B63Tf2wNVDSTdYIKVW5nnWW\nzjZresPGRwDQ3GDn6YOvnTfLSfMnjtt7+bhZrO2mbIZxby40/TYbPDZ0xuoykghWaj1gK+0NvWqD\n0tqb/XHYKJm1m1SkEVfBK2m/xTmvX82/2xqCD722drC0cgAZB60lgtRcYiIF96USFzPSWZb22IDH\n+kUb/MfG5YAPjssn7bUvd/nEU6XzyWbt+XCiJgBgmzWqAmtEe3zI2NAhX0WzSuINJQiCyogBJQiC\nyogBJQiCyuh7DUUH6tC39iQ9pbyKaa6fXbRxGa3t3nVh6Ig1KeLEv3KbnTsDPhmwXGe1Cy5yBQC1\n12mOvcMmtTkjnSmvJwyftMcRMn7OjpxybYphq+eUl6lI2e6EUTLrPZQcmErA4xiLnApJtbcliqyd\nsSY/Th9IGXWXq5tUL+3xxynmbQJk7bjV2poH/XNRO2OvU0HmVflZG99Tkl4H+BiS2XdbPWvkBau9\nAQAogVAowRAdH5vT2UBFx1jPSRQHa+1+c91C4g0lCILKiAElCILKiAElCILKiAElCILK6HtRFiIm\nCI1FMwDQIRtwJos20WrwG748kGywIl5Zt+Ja47CvNpivtyJfNkcO4inzOXKbb22zgV+1STqfjk8C\nY5Evp6AnF+AEoLnRirJ1Ev1yCmIDkBTxTD/q/nFxSWvkvF6c8w72LGa7fVzwwjS7numYFSQHXrdi\nKQAotWknAsrccRKVDs0+11HwYsdfs+KM7Uuj2GiW5+9duwIjNpDYm0hk5GqDvdUhAKBc54Xc4vBp\nt65K4g0lCILKiAElCILKiAElCILK6HsNRZaayHuqppUHdrlt2KQImZ1f1xo+sYqTA4t5G8CUNFi6\nQEFNW+3cuLnRz1n5yPmMnQs3d1ktp56ogMcVC9tUeS/7viu+iHzkLWZZB0hDmfHGQNk0GfSMWq0j\nVXWO9ZDWZupb02tC+TOvmWWZsJrCwjupIh6AgUnbt8XdVs+qX/YaQ0GVDospu7x4IJGkR3pcljI2\n6qG12Sdm1jnYb9E+S4PLfp/ZgtXFOuOk1QwkKiMeP2PbXLJ6XP6WA77DG33AZpXEG0oQBJURA0oQ\nBJURA0oQBJXR9xoKagWwfWLVTQqqeNcmLYM1B8DPr7MZuw/d7JPnWuN2v7WzNsaifsRWzUvR/HvW\nIKp+2WoZLUpSTB2H+976ibe5NvVX7fwaVCmxs9PqPwAg50mHUHu+nIwGANm07UuN9IO5u7yR0+CD\nB+0+6PwGj/g4lM4mq5kMP2/Prxz3feOYmM4m25dU7ArHbnRIu2Ddop5IZCxZ06NrIoten1PSztjg\nS9nsCsDyA3vNcuMF+/y1Nnh9p3bOG5pXSbyhBEFQGTGgBEFQGTGgBEFQGTGgBEFQGf0vypYK6Qn6\nySRhvU6JU/kCiWJtn/AlyySMtShZbnRtQau11SZw1VJOY8Tg89ZdrXm3rTLHDv4AUJIT/tIOK0A2\nLnrH96W32gp3jdO278WUF+fKbdbNi5MSlzb7wL32HnKGq5EbfSJ5rjlGSXsj9rh5IvCrThUKOQBN\nG94tT5bsPc5IDNVJ7wDf3nmv7csiVQJoWPf9kqstwgv8LMou7/bucvmCPU4+T7+aiWdr4GUrELf3\n2C8vUkF5ws95xcQbShAElREDShAElREDShAEldH/Gkomdr7cTiRrXbRJUTJg57ntcT/3FzIyKgfs\nHDw/mjCi2WATq2qXyL29kbicJN90yOSn/godZ8j3lZ3xh16zleh00FdGzLkvZBy0cHeqeh3to2nb\nLI/74KrZXfQ3ifYhCb+ibNnuZ+S0vRfrXvUV/Vo7bKBhtmy1gGzO60hsOIRjNvBL79nn2tRPUrAb\nBwRSVcDlRHLg4AlvKtVLbcYnMuYnp8xyOUHnO+2viZLhVzFFx13yCaCaMGqqknhDCYKgMmJACYKg\nMmJACYKgMvpeQ9E8Q3vsmq5QXPTV63S3rcYnZ23MQn0+UXVto51/am7HVt3hjZ87QzZ+orhgE7gk\nESPD83g2Qe7sWLuSm7x83CyX+63JVEo/KMnoiPtRLPp4hNaIfRxao1Y/mHrE921wt523i9iYiyLz\nIkrzu1YfWNxor/3QehtjAgAZ9Te/YONonF4CoMNJoZSUWFzyugT4HlIMSTZndYlGIj5k7qDV2ka/\nd8Isl+t9smvO2hkdN6WTLW2z8Uj1y5TIOOuvCS55k/cqiTeUIAgqIwaUIAgqIwaUIAgqIwaUIAgq\no+9FWWl3UOupPsdu9QBc9TbntlZ6YbCkYLFimhzbElXyaifOm+X2Dut6xiItAHTW+8CnXmb2W+Gw\nWPbJdIMDJCa+QgFaE17YbRyzfWW3slTFu/olG/R07hHbt3X7Lrk2/2LfD8zye4eto/3zS75Kwe9P\n/wO74mUrwjZHvZjI1QOuPGyTKsee8y5vLGzmC5QcWPOBekKBkzJrhdvmfvsFQO25o24fI9MUNDhI\nlS1TFRrZ1Y3uT2fEi7Kg3wX9wct2H7u2uyatu3f6/VRIvKEEQVAZlQ4oIvIhEXlFRA6LyK8mPhcR\n+YPu58+JyDurPH4QBLeXygYUEckBfBrAhwG8FcDPiMhbabMPAzjY/fcYgM9UdfwgCG4/VWoojwA4\nrKpHAUBE/hzARwAc6tnmIwC+oKoK4Dsisl5EtqnqGb+7FbTI0e5xPC/OJ1y7KdHPmSclyGn+rBSg\n1Br3wVX1OTuPzSnIiYPjAK+rtCasKdOGb9igJyQqBy7dZefktUEbBKVNf76t7ZRctkSBYQkTI9Zz\nSpq2Ly97beN9I6+Y5ffTZTtQ81UN/3jw/bZvbdto+i5/DbZ/3QZkDVIAWjtRLSCft5qQNMks6ZJP\n4mvtsxpJNmY1sPopqyOlAuqYksygJGH4xYZRzS32XjQO2+RBAJCOfZbyPVYf0Sv+d6UYWV3Te6NU\nOeXZAaBXLTzVXXez2wRBcIfSl6KsiDwmIk+KyJOtViI8OgiCvqTKAWUSQO93hDu76252G6jq46r6\nkKo+VKv5Il1BEPQnVWoo3wdwUET2YWWQ+GkA/5K2+TKAT3b1lXcDmF5NP7lKr1myXvTV3pS/wycj\npM4WX3FeyVw4p6TDYi6VbJaoTtd73JKjJYCSjI44IW35bjtnr0/6eX0xZ7UAXbQxM6I+rkHU9jVb\nIL1nwJ/f6FHbt5k9NoGyuejbPL241yyXajWhr8y8z7XptO3fMX4INz3nkx3b43buX6NqkfN7bV8B\noEFxGsW01S7apJcAQHHUPo6tgxTLsZl0i8S15yqA2rB6XT5pDbIAoLPdxjQ1jto4opQJN5tul6OU\nYHjBxw1li950qUoqG1BUtS0inwTwNwByAJ9X1RdF5OPdzz8L4AkAjwI4DGABwM9XdfwgCG4/lUbK\nquoTWBk0etd9tudnBfCJKo8ZBEH/0JeibBAEdyYxoARBUBn9nxzYKVFcuSbAde7d47bJOOmL3L4l\nVUGtSWLaFRuAVm72Ih+7hrFzvkz64KOMEvc46K5OFf04SQzwDu+te23CXe2sF3JLqvqXkTgsr3tX\n//a7Dpjlwpr6o3HEB/v9YfYBs/wTB46Y5W++ZveZ2s/QWXvO9dOru8YDcAGAo9993W2iVHFxcY8V\n5xsXqMIfAKXqiS1KVGwP2+MOXfB/k2Xe7re5zQYmFnXv2Laww16TwRES+BPVA/i+C/06L7zfVkEE\ngNpMVA4MguAOIQaUIAgqIwaUIAgqo+81FChMtcDmBj+PH5y1gVCtHVTh77wP3y8HyY1+k20j33rW\ntcnWkUnRNuuM39nv05Lao3Yu3HjdBhuVZJyTX/Ku/pinQK/1nGyWqKZIgXtcGTHb6/u6tNFuM3bc\nzrcX5r0h0TJVFHjq0P1meZR0GAAYmrKCQGPa9r+1ja4zgHyBKgXOkE5xwBouAUAxY6/b4A9IZ9lk\nqwACQDlAGslxm5TIAWatbT5okrW2wRdtMHhnp6/aOPadU3YFJT8u3uN1l2LIPjvZjL3YjfM+QDC/\n/OamssQbShAElREDShAElREDShAEldH3GooWmTF6bpxPTMppvlk/aRMIk8bWw3b+KbN2v9mDbDYH\nyEWqVkeGSmzok4QMebIFatPycQJK2k1tyuosrW1eCyiu2PkzJymmGD1q97s0YfWRkdOJGAalanUk\n5wyf9teEEzPZkDkFXyeh65Qyi+YKiyUnAybMor1pltWNZNIm7YGTBwFkp8kgfLvVTLLjZ10bFPY4\nLlkw8dxn06SHUFXKrOm1tXLEV9GsknhDCYKgMmJACYKgMmJACYKgMmJACYKgMvpelAWsY1s+64N1\nuMqfFjbwqzPqg+FqJ6xrVrnRCp+doYRD1hU7/sqC7Ys2vGObS+zbQC7qpylwquUd7LVgR36buNh4\nzYt8OkaObSR8Zgv+OnbGrN2mktgt6jPURs5QwiQ5unPfAaBxkY5NbVIOdFz9UQdJVB+3TmoAsLDH\nnk99mpz/FxMi85StQKj7SHTdQYl+5321SJ2wFQfyKfslwfL9vppi4zgdlwT/7Lg3NiwpsBJ0rVn8\nBnz1g6qJN5QgCCojBpQgCCojBpQgCCqj7zUUaXdQTPUElKV0CpqjlhNcNc/rEuU4aQwXyKzmRMJ8\nZ6s13wFVG2TNAQAyCoYrOlb/YN1C1q1d2a2YpuCrdb7USMmBe+SQ3hn3bTIK6hp50Qa6sW4BAO31\nNlCKA8NSVfLYRKoconvaTmgodK1lxgZ1pa7B0KS9h2Wd3OePeF1CRux+ljeSHjdg7/Ho960GBgBN\nSk7N6F44Uy0Ara22TXODvSZDl7xGJKQrlYX9dU4GMyae0SqJN5QgCCojBpQgCCojBpQgCCqj7zUU\n6IpR9Q+5kvjen+aSQlXyUjEXi/faRLGBc9b4aOldd7k2A8/aqnite6xJUe2cnxt3Ntu5MVeR47gA\nYTMlAOVGq/eUNdICpnw1xXK91WLyo7bvnXfd49rw/Lps2OP2moVfpT1EyYFs5L2cSijkmBhK/FtK\nJBSSKTVrYLLo27S3WD0ka5Kek0gaLdfb/Q4+ddxusNkmYuqwT7Zz8R90HD4XACiocmVrnT1Oe8Jr\nKMVlmzASSs3fAAAUVUlEQVTYIdOw1OtCftRXLaySeEMJgqAyYkAJgqAyYkAJgqAyYkAJgqAy+l6U\n1XqO5s5rgWr5og9Sy161giPGbPBYKuFu4CRVp9tgRa+B0178FXLVKp61VfKwkxzBAOSXrFDbnrAi\nLbusF9PeZSs/ZpP/8o12H0v3eMf3gdfOmWXdu9Ms1y54d30lsbd2zgZtdbZ4YXDwFXuc5QM2Ya1x\nOSGiU3CinrLnJ5utWxkAKDndcTBcnhBlXQAZJdyh46+1c5NbT88SXaPWhBVxAaCYtX0pTlvBPxVc\n1tppz3nwGAntCSGXxe3aJXJ1S1RD4GqKVRNvKEEQVEYMKEEQVEYMKEEQVEbfayjSbKN+/JqLuCZc\nu3W3NcHpjNpkrCJl2EPrhOb65ZZEVbkdNjmwM2y1i9pp0mUALJGmUCM3+vwCOZcv2aA8AMAoGR+R\nkU5txrdp7bZ9LUgzkcRxyg00vy58pUCG78fCFqqU+Jqfxzf3kos/6QO6nApso4BACoYrxxJJlWsl\nwu312pMcsu75ssE+BzJr71d7x263j/qp1U2zZNj3tXbWtlneYzWV2gXvet/eaJ8LrpSoAz6ZU5YT\nVSYrJN5QgiCojBhQgiCojBhQgiCojL7XUJBlZp7e3DrqNqld5ip5dr69cLePaxg6auesi/fbOI36\nFa8xSNMmutXPUqxA3RtbDxwj8+F5OxfWtt1nyabI8Elf2TefMcvFbtt3ABDSXZZ22RgSNq0GEslz\nhdU6pJWoRPfaMbO8boQMiSg5EgDqpDWVw7ZNc8LrV41Ttk17k43/yKd9UqU0Kf6INKHmFh9DUt9l\nr397zGpEbNI0OOnjeZz2xPdijzUAA4CBI7baYNaie5EwnO7VFgGg3ET3eDpRZfOy1/mqJN5QgiCo\njBhQgiCojBhQgiCojBhQgiCojL4XZbXI0OoRz7KlRMITBTBxglf9kg+UYuok7GaJJERcpIAlSihE\n6YXO1hYrItdPktjWscvOdQtA7Sw58u/bYzdY8E5qLARysiO7xgMAWCDeZMXRMlVN8f67zTJfgWSb\nZXttsxPWfb6x4EVLdjmrnbH3ohzx1SFbExRAR8F99UnvWM8BcpyYif02GC7lLJ+T6x4H/zUSrvcd\nqgZZO20F/84GLyCz0397xAYVpn65O5sjOTAIgjuEGFCCIKiMGFCCIKiMm9ZQROTzAH4KwJSq3t9d\n9zsA/jGAJoAjAH5eVd0EVUSOA5gF0AHQVtWH1jzeUgu1Q6euLbNJDnylwAFy9tZEkltJ81oOAkrN\nWbO6TbhzTusJw576KTLXoQp4veZRgNdLAKC13Wo1tWdtAlvr/n2uTXvY3tr6ZRuoVyQ0FN3og9DM\n54lrv3Irr1HW7DadRiLBcNzqFDJiqwdw9UHAJzOWVGExVbWRdRY9Z58LmaBKkPDBi+VOm9w5u9ce\nd+SkD6jDNAW7UeBee9wnB2ZUHaAkN/0OV1eEr/RYzNHzmKjamKUqOVbIrbyh/A8AH6J1XwNwv6q+\nHcCrAP7zKu0/oKoP3shgEgTBncVNDyiq+g0Al2jd36rq1SH2OwB8LHgQBD/2vBkayi8A+Mp1PlMA\nXxeRp0TksTfh2EEQ3EYqjUMRkV8H0Abwxets8j5VnRSRLQC+JiIvd994eD+PAXgMAAbyEWMOnTI+\nyqhynpKxcGrOms/b+WZrh9UPhJOzAAjNWTuUOCYd34aNgTjBrn7azvNb27yOweY6zQf3m+XGMZsk\nBgD1nBLhtpM59iZvON2hmBEX/5IwqmLtonjJGobnexImRpxkSIZK7VSsRGbvIWs1zqAZcMbOnbfZ\napCsWwBr36/x/2OTIWffTTFBAIYWrO4CMsRKGT/lF2xsig5Z3UUSMU46aHUVjkORRALom01lbygi\n8nNYEWs/qpqySANUdbL7/xSALwF45DrbPa6qD6nqQ/XMO7QFQdCfVDKgiMiHAPxHAP9EVRN/KgAR\nGRaR0as/A/gggBeqOH4QBP3BTQ8oIvJnAL4N4B4ROSUiHwPwRwBGsTKNeUZEPtvddruIPNFtOgHg\nmyLyLIDvAfjfqvrVSs4iCIK+4KY1FFX9mcTqP73OtqcBPNr9+SiAB276eI0amgeuzcPZiBcAmvtt\ngS02WBp4hfIxAOgo5WzM2/yShR2JWIFxO0cdfslqF6k4DVfUinJ1XJvEXJmLgbHRdTlmczoAIJu1\n+T3FrNV/2ut97kv9ldNmuXnQmg3xPgBATtprW95l2+SXEzlDNCPm/ucLidwr0h1q03a/re1eW6tN\n2higIlv776dc9IXJbOfsszX6zBm3CZ9Ph4u5JYqsLR60ukv9or3Hxfk1+gUApKHkifulA29u+l5E\nygZBUBkxoARBUBkxoARBUBkxoARBUBl9b7CEUpGnzI56cYIdiVGJpD1tkPEP7WPktYT5DgmqJQUf\npZzJmxutuOuq/JFQmBIk2cRHuBLdkr8+5SglP16wQWr1Gf/tPidZ5pRsJkdPgdG9VoTlin7tTb5K\nQXGaEiYpYS2b831jgyVO+CwuJcRfCjTkiovJ67beJoWyy31+yQqqqSA8/uKguGjbpKoc1qftdcvp\nfqH0QZMdCvLkvi7u9te+Pr3G79IbJN5QgiCojBhQgiCojBhQgiCojL7XUKTVRj55zRins3Oz24bn\nrPK6DdDCuE+4kzkb+JW1rc6iwz6HqEXBYAUFwxVT3ny4QfqAXLEBSq099nyaY95Ip/HVJ83y/Ece\nNssjL/jkwM4o6Tts5F33t7453jDLxTyZ/iSMnDjJshziBDU/9y83Wt2hM2KPm816PYR1h/aoPU7t\nYkIT2mITIGWZtLRLiSp622yVyYISM13yZjptzdDeaHWZrOmTEp0pekb3a8QHLzIDr50zy8sHtrht\nsoRuVCXxhhIEQWXEgBIEQWXEgBIEQWX0vYaCokDZY56cn/J6AQbsHLzca02P5YpPxirHvQm1IWHm\nmy/auS9rJjpo+wEArXEyYRoj4xw6zsB5X7Sr9YF3muWRF63ZcioWoj1oYxKyJYrBWPZz6YGjdJ1I\nd5G2j+fpbLLHFtIC2LAIADK6H5zImEp2zClpT5YoziYRu8KwIXiRbXTbsKESx/PUjtpkSG0lNAky\nv+YYGUn0tbXbamkFX+tEYmO24JP/zHHaXt9JmV1XSbyhBEFQGTGgBEFQGTGgBEFQGTGgBEFQGf0v\nypYKaV4TvtjRHgDa623QU3HYBrbpFivGAUCLAqMGjlG1wZq/NBlts/jIQbPcOOcDsmqXSWRtkmhJ\nVeU0kWDYOEWJiiTQcZVAABg4TQ5fHGCWSDbTISsqu4CzphdleT/SssK1vmZd4gGgc/8B2oddzE56\nh71yqxUtuZLgwr0Trk3WJMH7Feuu1t7hRdmSKhmUdXutWdJkUX2ljb0f7S1WZK5PeaGaAwBl3j43\nC2+zXzQAQOOcFXdzuva1cz7QsrnDVzuoknhDCYKgMmJACYKgMmJACYKgMvpeQ9EiQ3vTtSC02uuJ\nRLjtNriq2GDniQu7feDX8HOTdh/brM6SnZxybWSDNbTJKdmMzZQAIKMKhKyR1M/YeW5rW8KwhzQT\nrpqX0l1YM2nSNaqf9S7qTjOhvrsENvi5/vJeG9RVxy7ftxnbRhbIxX+HT2qT01a/AuliQy9693mQ\nCZOOcZJewniLqhBklPznDLOmfRWGDrnPN16yzxoHYgJAftY+13wNWC8BgOUtVu9p0OtBftZW1ASA\nxtFERYEKiTeUIAgqIwaUIAgqIwaUIAgqo+81FCkVeY+xc2errxCXkZaxvNNqKHnTxwq0d9m5Ppsr\n64JP0uvssxUK+bjtMR9fUL9k4yUyqnjXIVNkF7cCQBZt3zI2aE5VlaO5fzFn9Y/5A/46jrxodaOl\nvTZOo95KJAdutYZD9Smb+KenvLbRfoeN3ynmfBVDRkh3aI/aZV3vDbGKy6Q7UAxQa8IniPI9ZWPr\nBmleLr4H3lQcQ7ZvbLANAM237TXL9VNW/8iWvPZRJy2NNa7lu+3zCgCN4xfduiqJN5QgCCojBpQg\nCCojBpQgCCojBpQgCCqj70VZlKVJBCtHfHJgvtRedTmFC1zbaMXFzn3e4d0lgpHwOfjtV12b5Yes\nADlAoqySsLY87sXFoUNWdNVRG1y1vNsLrOwul89Zcbh+xd96vWxd4LNddr/s+g94EZaD44pNPjGz\noIqE7I4n56myIIByM1U1ZME1kexYrrdJefzXM5/3gXrs4i8XbWJmSdX6JOWktkzPHyXtSTPlPG+/\nSGhttcv1Yz7Qsjhn+6ZUybI+6V39y3U++LJK4g0lCILKiAElCILKiAElCILK6HsNRWs52hPX5pPF\nlUTgF7mIa8MmZ+lAze94/erV61R8wl05bPfjgqB2b3dtigU7X27usHPw5nrb16Hj3hSns4lMcTjB\n8Hteu8FdO81ia4OdO0ui4p3usiZF9eM2Ia+zwSculsP2urEGkQr8Yu0pI51C1/vjrLWPlC6RTS+s\nuk1niw9sm99lr9PYDygwccbus7nLmzTVLtHzSNUQZNkHqXGbNhs9jSeu/ZB9HosLVmvrJKoHZHP+\n96dK4g0lCILKiAElCILKiAElCILK6HsNBaWa7/XZABgAMjaU5op3Ha8XsKkPp2u1Nyaq183Q3JcS\nx/A6GekAkHv3mmUdsG0GJ21cSrbgDXs4xsIZaJOBMwAX29E4QnEMWUIjOkcmP7m9KjLuY4BK2k05\nYPvWmvAJagOvnTPLnZ22//kUmXLD62A6aLWnhf0+3qU+be9XQWZIRSIOZZC0mc64fQ7yC1bjKp5O\n6Fd7qHIlVwHMfXIgG49zLJV0EgbhsNekud3GUtVeOuFaLD68P7Gf6og3lCAIKiMGlCAIKiMGlCAI\nKiMGlCAIKqPvRVkp1bqpTXnHKSVRMqNANywkEsc22kAhrd2ACxolz8k6EikTiXCtut0vO3GVYzaQ\nqr0pkfx4ySbglesGV/0cADq033LMBmDVTntH9PbDb7F9naRtLvhkMxkmNzISjAeO+ioFfO1LErez\nYZ8gKfNWUG1vtvsYuJAQs5kpm3RYHvCBiCW53hcUvNjZbIMM3RcCgAvma20jsfS4T/TTghz556gS\nwADXLAQyCpqUkr58SIj1Q6/6+1El8YYSBEFl3PSAIiK7ROTvROSQiLwoIr/UXf+bIjIpIs90/z16\nnfYfEpFXROSwiPzqGz2BIAj6h1uZ8rQB/IqqPi0iowCeEpGvdT/7fVX9b9drKCI5gE8D+IcATgH4\nvoh8WVUP3UI/giDoM256QFHVMwDOdH+eFZGXAPjS8GkeAXBYVY8CgIj8OYCPALjugHLwvh346pO/\ndbPdDILgNvCGNBQR2QvgHQC+213170TkORH5vIh4G7GVgedkz/Ip3PhgFARBn3PL3/KIyAiAvwDw\ny6o6IyKfAfBfAGj3/98F8Au3uO/HADzWXZwTkVcAbAJw4fqt7ljivO4sflzP654qdnJLA4qI1LAy\nmHxRVf8SAFT1XM/nfwLgrxNNJwFTPXtnd51BVR8H8Dgd80lVfehW+tvPxHndWfw4n1cV+7mVb3kE\nwJ8CeElVf69n/baezf4ZgBcSzb8P4KCI7BOROoCfBvDlm+1DEAT9ya28ofwEgJ8F8LyIPNNd92sA\nfkZEHsTKlOc4gH8DACKyHcDnVPVRVW2LyCcB/A1WEnw/r6ovvsFzCIKgT7iVb3m+CcDnvgNPXGf7\n0wAe7Vl+4nrbrsHja29yRxLndWcR57UKoglv0SAIglshQu+DIKiMvhhQROSj3fiV50XkWyLyQM9n\nx7vrn7meEi0r/EE3nP85EXnnj67310dE7hWRb4vIsoh8qmd9Mn0h0f4nRWS6J53hN350vb8x1kql\n6Nd7w3Rjp6ZE5IWedb8jIi93+/0lEVl/nbZrPqO3g9uSJqOqt/0fgPcCGO/+/GEA3+357DiATWu0\nfxTAV7Ci7bynt/1tPq8tAB4G8FsAPtWzfhuAd3Z/HgXwKoC3Jtr/JIC/vt3nscr55QCOALgLQB3A\ns3we/XpvEufyfgDvBPBCz7oPAii6P/82gN++Tts1n9HbdE7J5wzAb/Y+j7d6b1P/+uINRVW/papX\nc+W/g5X4lJvhIwC+oCt8B8B6+hr7tqCqU6r6fQAtWn9GVZ/u/jwL4GbSF/qJH6ZSqGoTwNVUil76\n8t4wqvoNAJdo3d+q6lVz11t5Lm8rb/A5u5F76+iLAYX4GFb+ol1FAXxdRJ7qRtCmuGND+hPpC8x7\nu6/cXxGR+35kHbsxbuS637H3hvgF2Oeylxt5Rm8rP6o0mb4aUETkA1gZUP5Tz+r3qeqDWJkKfUJE\n3n9bOvcmwOkLiU2eBrBbVd8O4A8B/NWPsn/BCiLy61jJsv/idTbp62c08Zx9BitTmQexkuj7u1Ud\n67YNKCLyiR5RaLuIvB3A5wB8RFV/aMumqpPd/6cAfAkrr2LMDYX0/yjg81plO5e+wKjqjKrOdX9+\nAkBNRDa9KR2/NW7kuvfNvbkVROTnAPwUgI9qV1xgbvAZvS1cL01GVTuqWgL4E1T5O3W7haPuPdoN\n4DCA99L6YQCjPT9/C8CHEu3/Eazw973bfU7Uv9+EFWUFwBcA/Pc12m3FtVihRwCcuLrcD/+wEhh5\nFMA+XBPu7ruT7g31dS+sKPshrFhrbF6lzQ09o7fpfJLPGYBtPT//BwB/fiv3NnnM233S3c5/DsBl\nAM90/z3ZXX9X90SeBfAigF/vafNxAB/vuXCfxooq/TyAh273OXX7tRUrc88ZAFe6P68D8D6szLuf\n6znnRxPn9cnueT+LFVHwvbfrXFY5x0ex8u3Bkav35064N4nz+DOsvP63uvfpY1j5I3ey5x59trvt\ndgBPrPWM3u5/13vOAPzP7r14Diu5dNv4vK53b9f6F5GyQRBURl+JskEQ3NnEgBIEQWXEgBIEQWXE\ngBIEQWXEgBIEQWXEgBIEQWXEgBIEQWXEgBIEQWX8f/CqvJzepLKXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11608ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutout = nddatautils.cutout_2d(newimg, coord, size=50)  #there is an nddata function that does this, but I forget the name\n",
    "plt.imshow(cutout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TOADD: Utility/documentation:\n",
    "* The difference images of the above from each input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important   functionality note demoed here\n",
    "\n",
    "* Background subtraction/measurement (this one is implied in the above - debatably this should be done as a separate step using the photutils background subtraction tools)\n",
    "* Image model building/stats on the stack (NEEDS IMPLEMENTING)\n",
    "* Outlier detection (this is not - NEEDS IMPLEMENTING)\n",
    "* Support for mosaiced images (NEEDS IMPLEMENTING, although initial work already underway in reproject)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Miscellaneous stuff (not part of the interface document)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below here is code to \"fake\" the plots above, etc.  Not a part of the actual document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from astropy.table import Table\n",
    "from photutils import datasets\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = Table()\n",
    "t['amplitude'] = [10.]\n",
    "t['x_mean'] = [25.]\n",
    "t['y_mean'] = [25.]\n",
    "t['x_stddev'] = [2.]\n",
    "t['y_stddev'] = [2.]\n",
    "t['theta'] = [0.]\n",
    "\n",
    "gaussimg = datasets.make_gaussian_sources_image((50, 50), t)\n",
    "img1 = datasets.make_noise_image((50, 50), mean=0, stddev=1) + gaussimg\n",
    "img2 = datasets.make_noise_image((50, 50), mean=0, stddev=2) + gaussimg\n",
    "\n",
    "t['x_mean'] = 19.5\n",
    "t['y_mean'] = 21.7\n",
    "gaussimg3 = datasets.make_gaussian_sources_image((50, 50), t)\n",
    "img3 = datasets.make_noise_image((50, 50), mean=0, stddev=1.2) + gaussimg3\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 6))\n",
    "ax1.imshow(img1)\n",
    "ax2.imshow(img2)\n",
    "ax3.imshow(img3)\n",
    "\n",
    "for ax in [ax1, ax2, ax3]:\n",
    "    tks = np.linspace(0, 50, 5)\n",
    "    ax.set_xticks(tks)\n",
    "    ax.set_xticklabels(tks - 25)\n",
    "    ax.set_yticks(tks)\n",
    "    ax.set_yticklabels(tks - 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = Table()\n",
    "t['amplitude'] = [10.]\n",
    "t['x_mean'] = [25.]\n",
    "t['y_mean'] = [25.]\n",
    "t['x_stddev'] = [2.]\n",
    "t['y_stddev'] = [2.]\n",
    "t['theta'] = [0.]\n",
    "\n",
    "gaussimg = datasets.make_gaussian_sources_image((50, 50), t)\n",
    "img1 = datasets.make_noise_image((50, 50), mean=0, stddev=.5) + gaussimg\n",
    "\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(4, 4))\n",
    "ax1.imshow(img1)\n",
    "\n",
    "for ax in [ax1]:\n",
    "    tks = np.linspace(0, 50, 5)\n",
    "    ax.set_xticks(tks)\n",
    "    ax.set_xticklabels(tks - 25)\n",
    "    ax.set_yticks(tks)\n",
    "    ax.set_yticklabels(tks - 25)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (anaconda root)",
   "language": "python",
   "name": "anaconda-root"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}