Deblend Experiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4dd3693a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current environment is configured for:\n",
      "  CIAO        : CIAO 4.15 Monday, November 21, 2022\n",
      "  Tools       : Package release 1  Monday, November 21, 2022\n",
      "  Sherpa      : Package release 1  Monday, November 21, 2022\n",
      "  Obsvis      : Not Installed\n",
      "  Core        : Package release 1  Monday, November 21, 2022\n",
      "  Contrib     : Package release 1  Tuesday, January 24, 2023\n",
      "  bindir      : /export/ciao_from_source/ciao-4.15/bin\n",
      "  Python path : /export/ciao_from_source/ciao-4.15/ots/bin\n",
      "  CALDB       : 4.10.2\n",
      "\n",
      "CIAO Installation: Linux\n",
      "System information:\n",
      "Linux lenin.cfa.harvard.edu 4.18.0-425.13.1.el8_7.x86_64 #1 SMP Thu Feb 2 13:01:45 EST 2023 x86_64 x86_64 x86_64 GNU/Linux\n"
     ]
    }
   ],
   "source": [
    "ciaover -v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48feb66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "download_chandra_obsid 635 --quiet --exclude evt2,vvref,adat,eph1,osol,full_img,cntr_img,oif,aqual,readme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6747c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "chandra_repro 635 out=./ clob+ verb=0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e6c9544",
   "metadata": {},
   "source": [
    "## Look at the data\n",
    "\n",
    "\n",
    "Okay, this is not a great example but this observation has a couple of very close sources that we can experiment with.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d0ceba79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds9 acisf00635_repro_evt2.fits -log \\\n",
    "  -pan to  16:27:18.0805 -24:28:54.728 fk5 \\\n",
    "  -zoom to 8 \\\n",
    "  -regions command \"fk5; point 246.8233546631 -24.4824038252\" \\\n",
    "  -regions command \"fk5; point 246.8256837046 -24.4813770818\" \\\n",
    "  -saveimage png events.png -exit\n",
    "\n",
    "display < events.png"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815d9e93",
   "metadata": {},
   "source": [
    "## Regions and PSFs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23934e28",
   "metadata": {},
   "source": [
    "We need two things.\n",
    "\n",
    "1. Regions for each source and\n",
    "2. \"The\" PSF for each source.\n",
    "\n",
    "Just for simplicity I'm going to use the `psf_contour` script to do this since it simulates the PSF and creates regions.\n",
    "\n",
    "If you already have regions, then you can use the `simulate_psf` script to create the PSF.  Or run `ChaRT` and `marx` to simulate the PSF.  It's important that the PSF be normalized so that the sum of the pixels is ~ 1.0. That is that the PSF is representing the probability of event being detected at (x,y) given a source at the input location.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c765e6",
   "metadata": {},
   "source": [
    "These source really don't overlap that much but for illustrative purposes I'll make the region bigger by increating the `fraction` parameter.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "32d32e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "psf_contour acisf00635_repro_evt2.fits faint pos=\"246.8233546631,-24.4824038252\" \\\n",
    "  method=ecf_ellipse clob+ frac=0.95 verb=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "48025e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "psf_contour acisf00635_repro_evt2.fits bright pos=\"246.8256837046,-24.4813770818\" \\\n",
    "  method=ecf_ellipse clob+ frac=0.95 verb=0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea5a189",
   "metadata": {},
   "source": [
    "and now we can display the regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "68f113df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds9 acisf00635_repro_evt2.fits -log \\\n",
    "  -pan to  16:27:18.0805 -24:28:54.728 fk5 \\\n",
    "  -zoom to 8 \\\n",
    "  -region bright_i0001_src.reg \\\n",
    "  -region faint_i0001_src.reg \\\n",
    "  -saveimage png events.png -exit\n",
    "\n",
    "display < events.png"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa33d9b3",
   "metadata": {},
   "source": [
    "Well, the regions really don't overlap.  \n",
    "\n",
    "But that's okay, this same approach is applicable when the regions do overlap.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68693743",
   "metadata": {},
   "source": [
    "## Overlapping regions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc54f74",
   "metadata": {},
   "source": [
    "Next thing I want to do is create regions that have the other region excluded from it.\n",
    "\n",
    "This is easy to do with the CXC region module.\n",
    "\n",
    "I also will go ahead and create the region that is the intersection, ie overlap between the two regions, and the region that is the union of the two regions.  I'll use the later to restrict the analysis to just the bounding box around these sources.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "78335d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "python << EOM\n",
    "from region import CXCRegion\n",
    "\n",
    "faint = CXCRegion(\"faint_i0001_src.reg\")\n",
    "bright = CXCRegion(\"bright_i0001_src.reg\")\n",
    "\n",
    "faint_no_bright = faint - bright\n",
    "faint_no_bright.write(\"faint_no_bright.reg\", clobber=True, fits=True)\n",
    "\n",
    "bright_no_faint = bright - faint\n",
    "bright_no_faint.write(\"bright_no_faint.reg\", clobber=True, fits=True)\n",
    "\n",
    "overlap = faint * bright   # Intersection \n",
    "overlap.write(\"overlap.reg\", clobber=True, fits=True)\n",
    "\n",
    "bounds = faint + bright    # Union\n",
    "bounds.write(\"bounds.reg\", clobber=True, fits=True)\n",
    "EOM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "486c2ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds9 \\\n",
    "  acisf00635_repro_evt2.fits -log \\\n",
    "  -pan to  16:27:18.0805 -24:28:54.728 fk5 \\\n",
    "  -zoom to 8 \\\n",
    "  -region faint_no_bright.reg \\\n",
    "  acisf00635_repro_evt2.fits -log \\\n",
    "  -pan to  16:27:18.0805 -24:28:54.728 fk5 \\\n",
    "  -zoom to 8 \\\n",
    "  -region bright_no_faint.reg \\\n",
    "  -saveimage png excluded_regions.png -exit\n",
    "\n",
    "display < excluded_regions.png\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c090da",
   "metadata": {},
   "source": [
    "## Turning PSF into expected counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0be4007",
   "metadata": {},
   "source": [
    "Next I want to turn the PSFs from a probabiliy into an expected value, ie the surface brightness (counts/pixel) in each pixel.\n",
    "\n",
    "Basically just need to get the counts in each of the regions with the overlaps cutout and the PSF fraction of the same.  The just multiple the PSF image by the counts divided by the psf fraction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6641e9d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1326 177\n",
      "0.95056893444 0.94013478787\n"
     ]
    }
   ],
   "source": [
    "python << EOM\n",
    "from ciao_contrib.runtool import dmstat\n",
    "\n",
    "events = \"acisf00635_repro_evt2.fits[energy=500:7000]\"\n",
    "psf_faint = \"faint_i0001.psf\"\n",
    "psf_bright = \"bright_i0001.psf\"\n",
    "\n",
    "\n",
    "dmstat(f'{events}[sky=region(faint_no_bright.reg)][bin sky=1]', centroid=False)\n",
    "counts_faint = dmstat.out_sum\n",
    "\n",
    "dmstat(f'{events}[sky=region(bright_no_faint.reg)][bin sky=1]', centroid=False)\n",
    "counts_bright = dmstat.out_sum\n",
    "\n",
    "print(counts_bright, counts_faint)\n",
    "\n",
    "dmstat(f'{psf_faint}[sky=region(faint_no_bright.reg)][bin sky=1]', centroid=False)\n",
    "psffrac_faint = dmstat.out_sum\n",
    "\n",
    "dmstat(f'{psf_bright}[sky=region(bright_no_faint.reg)][bin sky=1]', centroid=False)\n",
    "psffrac_bright = dmstat.out_sum\n",
    "\n",
    "print(psffrac_bright, psffrac_faint)\n",
    "\n",
    "\n",
    "from ciao_contrib.runtool import dmimgcalc\n",
    "\n",
    "dmimgcalc(psf_faint, outfile=\"faint.img\", \n",
    "          operation=f\"imgout=img1*({counts_faint}/{psffrac_faint})\", clobber=True)\n",
    "\n",
    "dmimgcalc(psf_bright, outfile=\"bright.img\", \n",
    "          operation=f\"imgout=img1*({counts_bright}/{psffrac_bright})\", clobber=True)\n",
    "\n",
    "\n",
    "\n",
    "EOM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3402e6bb",
   "metadata": {},
   "source": [
    "## Estimate a background"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf8c026c",
   "metadata": {},
   "source": [
    "It wasn't mentioned but I want to leave open the possiblity that there are background events in the source regions.\n",
    "\n",
    "For this I'm just going to use a flat/constant estimate of the background surface brightness.  I'll get the \n",
    "background count rate using a circular regions just offset from these sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6617022a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.018867924528301886\n"
     ]
    }
   ],
   "source": [
    "dmstat acisf00635_repro_evt2.fits\"[energy=500:7000][sky=circle(4113,4834,25)][bin sky=1]\" cen- sig- verb=0\n",
    "\n",
    "bkg_cts=$(pget dmstat out_sum)\n",
    "bkg_area=$(pget dmstat out_good)\n",
    "bkg_rate=$(python -c \"print(1.0*$bkg_cts/$bkg_area)\")\n",
    "echo $bkg_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54ccd2a",
   "metadata": {},
   "source": [
    "### Now let's filter the event file to just the events around the sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "67fd872a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmcopy acisf00635_repro_evt2.fits\"[sky=bounds(region(bounds.reg))][energy=500:7000]\" bounds.evt clob+\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4b5922",
   "metadata": {},
   "source": [
    "### Create background image\n",
    "\n",
    "And now we create a flat/constant background with all the pixel values equal to the background rate we just computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "99f483e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# an estimate of background counts per pixel\n",
    "dmimgcalc bounds.evt\"[bin sky=1]\" none bkg.img op=imgout=\"(img1-img1)+${bkg_rate}\" clob+  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4dcf674",
   "metadata": {},
   "source": [
    "## Magic happens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6896a56b",
   "metadata": {},
   "source": [
    "This is where things get fun.  \n",
    "\n",
    "What I want to do now is for each event look up the expected counts in the faint image, the bright image, and the background image.\n",
    "\n",
    "Turns out that this is easy to do with `dmimgpick`.  The only thing \"special\" here is that to make things easier I'm going to rename the FITS extensions on-the-fly using the DM virtual file syntax.\n",
    "\n",
    "The way dmimgpick works is that it creates a new column in the output file with the same name as the input image. I'm going to just keep adding columns for each of the 3 files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "56c5cbc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmimgpick bounds.evt faint.img\"[1][faint]\" evts_to_faint method=close clob+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a4384b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmimgpick evts_to_faint bright.img\"[1][bright]\" evts_to_bright method=close clob+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3ffd5787",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmimgpick evts_to_bright bkg.img\"[1][bkg]\" evts_weights method=close clob+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cb98d67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "\u001b[A\u001b[33m\n",
      "⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼\n",
      "\u001b[A\u001b[1m\n",
      "Columns for Table Block EVENTS\n",
      "\u001b[A\u001b[0m\u001b[33m\n",
      "⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼\n",
      "\u001b[0m\u001b[A\n",
      " \n",
      "\u001b[1m\u001b[4mColNo  Name                 Unit        Type             Range            Null\u001b[0m\n",
      "\u001b[2m   1   time                 s            Real8          72038003.8537459970:72141281.8325330019 -          S/C TT corresponding to mid-exposure\u001b[0m\n",
      "   2   expno                             Int4           0:2147483647         -          Exposure number of CCD frame containing event\n",
      "\u001b[2m   3   ccd_id                            Int2           0:9                  -          CCD reporting event\u001b[0m\n",
      "   4   node_id                           Int2           0:3                  -          CCD serial readout amplifier node\n",
      "\u001b[2m   5   chip(chipx,chipy)    pixel        Int2           1:1024               -          Chip coords\u001b[0m\n",
      "   6   tdet(tdetx,tdety)    pixel        Int2           1:8192               9999       ACIS tiled detector coordinates\n",
      "\u001b[2m   7   det(detx,dety)       pixel        Real4          0.50:8192.50         -          ACIS detector coordinates\u001b[0m\n",
      "   8   sky(x,y)             pixel        Real4          0.50:8192.50         -          sky coordinates\n",
      "\u001b[2m   9   phas[3,3]            adu          Int2(3x3)      -4096:4095           -          array of pixel pulse heights\u001b[0m\n",
      "  10   pha                  adu          Int4           0:36855              0          total pulse height of event\n",
      "\u001b[2m  11   pha_ro               adu          Int4           0:36855              0          total read-out pulse height of event\u001b[0m\n",
      "  12   energy               eV           Real4          0:1000000.0          -          nominal energy of event (eV)\n",
      "\u001b[2m  13   pi                   chan         Int4           1:1024               0          pulse invariant energy of event\u001b[0m\n",
      "  14   fltgrade                          Int2           0:255                -          event grade, flight system\n",
      "\u001b[2m  15   grade                             Int2           0:7                  -          binned event grade\u001b[0m\n",
      "  16   status[4]                         Bit(4)                                         event status bits\n",
      "\u001b[2m  17   faint_img                         Real8          -Inf:+Inf            -          \u001b[0m\n",
      "  18   bright_img                        Real8          -Inf:+Inf            -          \n",
      "\u001b[2m  19   bkg_img                           Real8          -Inf:+Inf            -          \u001b[0m\n",
      " \n",
      "\u001b[A\u001b[33m\n",
      "⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼\n",
      "\u001b[A\u001b[1m\n",
      "World Coord Transforms for Columns in Table Block EVENTS\n",
      "\u001b[A\u001b[0m\u001b[33m\n",
      "⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼\n",
      "\u001b[0m\u001b[A\n",
      " \n",
      "\u001b[1m\u001b[4mColNo    Name\u001b[0m\n",
      "\u001b[2m\u001b[A\n",
      "5:    CPC(CPCX) = (0)[mm] +(+0.0240)* (chip(chipx)-(+0.50))\n",
      "         (CPCY)   (0)      (+0.0240)  (    (chipy) (+0.50))\n",
      "\u001b[0m\u001b[A\n",
      "7:    MSC(PHI  ) = (0)[deg] +TAN-P[(+0.000136667)* (det(detx)-(+4096.50))]\n",
      "         (THETA)   (0)             (+0.000136667)  (   (dety) (+4096.50)) \n",
      "\u001b[2m\u001b[A\n",
      "8:    EQPOS(RA ) = (+246.8259)[deg] +TAN[(-0.000136667)* (sky(x)-(+4096.50))]\n",
      "           (DEC)   (-24.5739 )           (+0.000136667)  (   (y) (+4096.50)) \n",
      "\u001b[0m\u001b[A\n"
     ]
    }
   ],
   "source": [
    "dmlist evts_weights cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f9416a",
   "metadata": {},
   "source": [
    "## Make assignments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66800b7",
   "metadata": {},
   "source": [
    "We can now assign events based on the \n",
    "\n",
    "We assume all the events are attributed to either the faint source, the bright source, or the background.\n",
    "\n",
    "For each event we know the probability that it belongs to each of these categories.  The sum of these probabilities must equal 1.0.\n",
    "\n",
    "We generate a uniform random variable and use that to select which ID to assign each event to.\n",
    "\n",
    "I'm using `dmtcalc`.  The variable that start with \".\" are not written to the output file, eg `.sum` and `.randval`.  The only variable written to the output is `src_id`.  `#rand(1234)` is a uniform random number with random seed = 1234.\n",
    "\n",
    "Hopefull the conditional logic is clear\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f302a061",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat << EOM > expr.lis\n",
    ".sum=(bkg_img+faint_img+bright_img)\n",
    ".prob_faint=(faint_img/.sum)\n",
    ".prob_bright=(bright_img/.sum)\n",
    ".randval=#rand(1234)\n",
    "src_id=\"background\"\n",
    "if(.randval<=.prob_faint)then(src_id=\"faint\")\n",
    "if((.randval>.prob_faint)&&(.randval<=(.prob_faint+.prob_bright)))then(src_id=\"bright\")\n",
    "EOM\n",
    "\n",
    "dmtcalc evts_weights evts_ids exp=@expr.lis clob+\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5089b74c",
   "metadata": {},
   "source": [
    "## Split the event files\n",
    "\n",
    "Now we just splt the event list into different files based on the `src_id` value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ef217366",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmcopy evts_ids\"[src_id=faint]\" faint.evts clob+\n",
    "dmcopy evts_ids\"[src_id=bright]\" bright.evts clob+\n",
    "dmcopy evts_ids\"[src_id=background]\" bkg.evts clob+\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9601551d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds9 bkg.evts -log \\\n",
    "  -pan to  16:27:18.0805 -24:28:54.728 fk5 \\\n",
    "  -zoom to 5 \\\n",
    "  faint.evts -log \\\n",
    "  -pan to  16:27:18.0805 -24:28:54.728 fk5 \\\n",
    "  -zoom to 5 \\\n",
    "  -region faint_i0001_src.reg \\\n",
    "  bright.evts -log \\\n",
    "  -pan to  16:27:18.0805 -24:28:54.728 fk5 \\\n",
    "  -zoom to 5 \\\n",
    "  -region bright_i0001_src.reg \\\n",
    "  -tile grid\\\n",
    "  -match colorbar -match scale -match scalelimits \\\n",
    "  -saveimage png separated.png -exit\n",
    "\n",
    "display < separated.png\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38f964b",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edfbfa01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27b83336",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2edfd0f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36535e0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Bash",
   "language": "bash",
   "name": "bash"
  },
  "language_info": {
   "codemirror_mode": "shell",
   "file_extension": ".sh",
   "mimetype": "text/x-sh",
   "name": "bash"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}