Deblend one EGS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deblending using the DM stack\n",
    "\n",
    "Jim Bosch:\n",
    "\n",
    "> all of this code is intended to be very much usable as a Python library, and in fact for simple simulated data it may be easier (and certainly more flexible) than using scripts like processFile. Here's a snippet that may help get you started, assuming you have an image with a constant PSF that you'd like to use instead of doing PSF modeling using our code (which is where a lot of the complexity in processfile comes from). One big caveat: I haven't tested this, so there are probably some trivial bugs.\n",
    "\n",
    "https://community.lsst.org/t/how-to-run-the-dm-stack-on-simulated-fits-images/892/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jlong/research/lsst_stack/DarwinX86/miniconda2/3.19.0.lsst4/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import lsst.afw.table\n",
    "import lsst.afw.image\n",
    "import lsst.afw.math\n",
    "import lsst.meas.algorithms\n",
    "import lsst.meas.base\n",
    "import lsst.meas.deblender\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# jlong addition\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams['image.origin'] = 'lower'\n",
    "matplotlib.rcParams['image.cmap'] = 'viridis'\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "from astropy.wcs import WCS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "High-level algorithms are in classes called \"Tasks\".  Each of these has a Config\n",
    "class to control how it runs; to use it, construct an instance of Task.ConfigClass(),\n",
    "set attributes on it, and pass that as the config kwarg to the Task constructor.\n",
    "It's usually a good idea to construct all Tasks before using any of them, mostly\n",
    "to define the full schema of any catalogs we'll produce up front."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "schema = lsst.afw.table.SourceTable.makeMinimalSchema()\n",
    "detect = lsst.meas.algorithms.SourceDetectionTask(schema=schema)\n",
    "deblend = lsst.meas.deblender.SourceDeblendTask(schema=schema)\n",
    "measure = lsst.meas.base.SingleFrameMeasurementTask(schema=schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load one of our multiextension FITS cutouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def display_blend(blend_hdul, ext_seeing_limited='F606W_SL', ext_original='F606W'):\n",
    "    plt.figure()\n",
    "    imgwcs = WCS(blend_hdul[ext_seeing_limited].header)\n",
    "    coords = imgwcs.all_world2pix(zip(\n",
    "        blend_hdul['CATALOG'].data['RA'],\n",
    "        egs_blend['CATALOG'].data['DEC']\n",
    "    ), 1)\n",
    "\n",
    "    ymax, xmax = blend_hdul[ext_seeing_limited].data.shape\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.imshow(blend_hdul[ext_seeing_limited].data, cmap='viridis')\n",
    "    plt.scatter(coords[:,0], coords[:,1], marker='x', color='red')\n",
    "    plt.xlim(0, xmax)\n",
    "    plt.ylim(0, ymax)\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.imshow(blend_hdul[ext_original].data, cmap='viridis')\n",
    "    plt.scatter(coords[:,0], coords[:,1], marker='x', color='red')\n",
    "    plt.xlim(0, xmax)\n",
    "    plt.ylim(0, ymax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ruDFMPgEUf+yxDaZVH51Fuxapu8k2D4gk/Ku7hp2sWtrcHF0ZmqdrtpHoKaIc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116005750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# jlong: load the FITS for a blend\n",
    "egs_blend = fits.open('./EGS_214.88685460+52.8863737_0.70arcsec.fits')\n",
    "display_blend(egs_blend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "image_array = egs_blend['F606W_SL'].data.copy()\n",
    "image_array_orig = image_array.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fake a variance array using a 10px box of sky from the corner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0034898773"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.nanmean(image_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015912259"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance_ish = np.sqrt(numpy.nanmean(image_array[0:10,0:10]))\n",
    "variance_ish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "variance_array = variance_ish * numpy.ones_like(image_array)  # TODO do RMS image cutouts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert into LSST Stack types\n",
    "\n",
    "Given NumPy float32 arrays containing the image to be measured and its variance, make an LSST\n",
    "\"MaskedImage\" to hold them.  This also holds a mask, which we'll set to None to make a blank mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "image = lsst.afw.image.ImageF(image_array)\n",
    "variance = lsst.afw.image.ImageF(variance_array)\n",
    "masked_image = lsst.afw.image.MaskedImageF(image, None, variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jlong/research/lsst_stack/DarwinX86/miniconda2/3.19.0.lsst4/lib/python2.7/site-packages/numpy/ma/core.py:2293: RuntimeWarning: invalid value encountered in isfinite\n",
      "  condition = ~(np.isfinite(a))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x116d4be90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jlong/research/lsst_stack/DarwinX86/miniconda2/3.19.0.lsst4/lib/python2.7/site-packages/matplotlib/colors.py:949: RuntimeWarning: overflow encountered in subtract\n",
      "  resdat -= vmin\n",
      "/Users/jlong/research/lsst_stack/DarwinX86/miniconda2/3.19.0.lsst4/lib/python2.7/site-packages/matplotlib/colors.py:949: RuntimeWarning: invalid value encountered in subtract\n",
      "  resdat -= vmin\n"
     ]
    },
    {
     "data": {
      "image/png": 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GjKud46Kd4y4zw13mGJfsMfapwR61qCkUXlQ8NimuJOEvYzFnixkLaR+FCQ7s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a16c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,3,1)\n",
    "plt.imshow(image_array)\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(image_array_orig)\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(masked_image.getImage().getArray())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the PSF array from disk and convert to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "psf_array = fits.getdata('./gauss_0.70_0.060.fits')\n",
    "psf_array = psf_array.astype(numpy.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a PSF object from a NumPy array of the PSF image.  This should have odd dimensions, and be\n",
    "centered on the center pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "psf_image = lsst.afw.image.ImageD(psf_array)\n",
    "psf_kernel = lsst.afw.math.FixedKernel(psf_image)\n",
    "psf = lsst.meas.algorithms.KernelPsf(psf_kernel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an Exposure object that combines the MaskedImage with the PSF.  Exposures can hold\n",
    "quite a bit more (WCS, photometric zeropoint (`Calib`), but I think this is all we need for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exposure = lsst.afw.image.ExposureF(masked_image)\n",
    "exposure.setPsf(psf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = lsst.afw.table.SourceTable.make(schema)  # this is really just a factory for records, not a table\n",
    "detect_result = detect.run(table, exposure)\n",
    "catalog = detect_result.sources   # this is the actual catalog, but most of it's still empty\n",
    "deblend.run(exposure, catalog, exposure.getPsf())\n",
    "measure.run(catalog, exposure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This outputs something like:\n",
    "\n",
    "    sourceDetection: Detected 0 positive sources to 5 sigma.\n",
    "    sourceDetection: Resubtracting the background after object detection\n",
    "    sourceDeblend: Deblending 0 sources\n",
    "    sourceDeblend: Deblended: of 0 sources, 0 were deblended, creating 0 children, total 0 sources\n",
    "    measurement: Measuring 0 sources (0 parents, 0 children)\n",
    "\n",
    "in the console. However, I noticed something weird while playing with the variance values. The image I pass in as `image_array` is getting modified in-place by the measurement tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x117193c10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OPNiLFnPdYLLX2QoGV0PFwHmxSnLNT3HEnWtOG8M5Y6hUe9JeEGuxq4vtYlrC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\n4MlxL6b4CAcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106949310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,3,1)\n",
    "plt.imshow(image_array)\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(image_array_orig)\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(masked_image.getImage().getArray())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Catalog should now be filled.  To inspect the results, it'll be easier to first make it contiguous, and\n",
    "then get an Astropy table view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "catalog = catalog.copy(deep=True)\n",
    "catalog.writeFits('./detections.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: hdu= was not specified but multiple tables are present, reading in first available table (hdu=1) [astropy.io.fits.connect]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;Table length=0&gt;\n",
       "<table id=\"table4687702288\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>flags [69]</th><th>id</th><th>coord_ra</th><th>coord_dec</th><th>parent</th><th>deblend_nChild</th><th>deblend_psfCenter_x</th><th>deblend_psfCenter_y</th><th>deblend_psfFlux</th><th>base_GaussianCentroid_x</th><th>base_GaussianCentroid_y</th><th>base_NaiveCentroid_x</th><th>base_NaiveCentroid_y</th><th>base_SdssCentroid_x</th><th>base_SdssCentroid_y</th><th>base_SdssCentroid_xSigma</th><th>base_SdssCentroid_ySigma</th><th>base_SdssShape_xx</th><th>base_SdssShape_yy</th><th>base_SdssShape_xy</th><th>base_SdssShape_xxSigma</th><th>base_SdssShape_yySigma</th><th>base_SdssShape_xySigma</th><th>base_SdssShape_x</th><th>base_SdssShape_y</th><th>base_SdssShape_flux</th><th>base_SdssShape_fluxSigma</th><th>base_SdssShape_flux_xx_Cov</th><th>base_SdssShape_flux_yy_Cov</th><th>base_SdssShape_flux_xy_Cov</th><th>base_CircularApertureFlux_3_0_flux</th><th>base_CircularApertureFlux_3_0_fluxSigma</th><th>base_CircularApertureFlux_4_5_flux</th><th>base_CircularApertureFlux_4_5_fluxSigma</th><th>base_CircularApertureFlux_6_0_flux</th><th>base_CircularApertureFlux_6_0_fluxSigma</th><th>base_CircularApertureFlux_9_0_flux</th><th>base_CircularApertureFlux_9_0_fluxSigma</th><th>base_CircularApertureFlux_12_0_flux</th><th>base_CircularApertureFlux_12_0_fluxSigma</th><th>base_CircularApertureFlux_17_0_flux</th><th>base_CircularApertureFlux_17_0_fluxSigma</th><th>base_CircularApertureFlux_25_0_flux</th><th>base_CircularApertureFlux_25_0_fluxSigma</th><th>base_CircularApertureFlux_35_0_flux</th><th>base_CircularApertureFlux_35_0_fluxSigma</th><th>base_CircularApertureFlux_50_0_flux</th><th>base_CircularApertureFlux_50_0_fluxSigma</th><th>base_CircularApertureFlux_70_0_flux</th><th>base_CircularApertureFlux_70_0_fluxSigma</th><th>base_GaussianFlux_flux</th><th>base_GaussianFlux_fluxSigma</th><th>base_PsfFlux_flux</th><th>base_PsfFlux_fluxSigma</th><th>base_Variance_value</th><th>base_ClassificationExtendedness_value</th><th>base_PsfFlux_apCorr</th><th>base_PsfFlux_apCorrSigma</th><th>base_GaussianFlux_apCorr</th><th>base_GaussianFlux_apCorrSigma</th><th>footprint</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th>pix</th><th>pix</th><th></th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix2</th><th>pix2</th><th>pix2</th><th>pix2</th><th>pix2</th><th>pix2</th><th>pix</th><th>pix</th><th>ct</th><th>ct</th><th>ct pix2</th><th>ct pix2</th><th>ct pix2</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th>ct</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>bool</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int32</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>float32</th><th>float32</th><th>float64</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</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>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>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>int32</th></tr></thead>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=0>\n",
       "flags [69]   id  coord_ra ... base_GaussianFlux_apCorrSigma footprint\n",
       "                          ...                                        \n",
       "   bool    int64 float64  ...            float64              int32  \n",
       "---------- ----- -------- ... ----------------------------- ---------"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.table import Table\n",
    "Table.read('./detections.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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detections.fits

SIMPLE  =                    T / file does conform to FITS standard             BITPIX  =                   16 / number of bits per data pixel                  NAXIS   =                    0 / number of data axes                            EXTEND  =                    T / FITS dataset may contain extensions            COMMENT   FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / 8-bit bytes                                    NAXIS   =                    2 / 2-dimensional binary table                     NAXIS1  =                  449 / width of table in bytes                        NAXIS2  =                    0 / number of rows in table                        PCOUNT  =                    0 / size of special data area                      GCOUNT  =                    1 / one data group (required keyword)              TFIELDS =                   61 / number of fields in each row                   TTYPE1  = 'flags   '           / bits for all Flag fields; see also TFLAGn      TFORM1  = '69X     '           / format of field                                FLAGCOL =                    1 / Column number for the bitflags.                TTYPE2  = 'id      '           / unique ID                                      TFORM2  = '1K      '           / format of field                                TDOC2   = 'unique ID'                                                           TCCLS2  = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE3  = 'coord_ra'           / position in ra/dec                             TFORM3  = '1D      '           / format of field                                TDOC3   = 'position in ra/dec'                                                  TCCLS3  = 'Angle   '           / Field template used by lsst.afw.table          TTYPE4  = 'coord_dec'          / position in ra/dec                             TFORM4  = '1D      '           / format of field                                TDOC4   = 'position in ra/dec'                                                  TCCLS4  = 'Angle   '           / Field template used by lsst.afw.table          TTYPE5  = 'parent  '           / unique ID of parent source                     TFORM5  = '1K      '           / format of field                                TDOC5   = 'unique ID of parent source'                                          TCCLS5  = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG1  = 'flags_negative'                                                      TFDOC1  = 'set if source was detected as significantly negative'                TTYPE6  = 'deblend_nChild'     / Number of children this object has (defaults toTFORM6  = '1J      '           / format of field                                TDOC6   = 'Number of children this object has (defaults to 0)'                  TCCLS6  = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG2  = 'deblend_deblendedAsPsf'                                              TFDOC2  = 'Deblender thought this source looked like a PSF'                     TTYPE7  = 'deblend_psfCenter_x' / If deblended-as-psf, the PSF centroid         TFORM7  = '1D      '           / format of field                                TDOC7   = 'If deblended-as-psf, the PSF centroid'                               TUNIT7  = 'pixel   '                                                            TCCLS7  = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE8  = 'deblend_psfCenter_y' / If deblended-as-psf, the PSF centroid         TFORM8  = '1D      '           / format of field                                TDOC8   = 'If deblended-as-psf, the PSF centroid'                               TUNIT8  = 'pixel   '                                                            TCCLS8  = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE9  = 'deblend_psfFlux'    / If deblended-as-psf, the PSF flux              TFORM9  = '1D      '           / format of field                                TDOC9   = 'If deblended-as-psf, the PSF flux'                                   TCCLS9  = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG3  = 'deblend_tooManyPeaks'                                                TFDOC3  = 'Source had too many peaks; only the brightest were included'         TFLAG4  = 'deblend_parentTooBig'                                                TFDOC4  = 'Parent footprint covered too many pixels'                            TFLAG5  = 'deblend_masked'                                                      TFDOC5  = 'Parent footprint was predominantly masked'                           TFLAG6  = 'deblend_skipped'                                                     TFDOC6  = 'Deblender skipped this source'                                       TFLAG7  = 'deblend_rampedTemplate'                                              TFDOC7  = 'This source was near an image edge and the deblender used "ramp" ed&'CONTINUE  'ge-handling.'                                                        TFLAG8  = 'deblend_patchedTemplate'                                             TFDOC8  = 'This source was near an image edge and the deblender used "patched"&'CONTINUE  ' edge-handling.'                                                     TFLAG9  = 'deblend_hasStrayFlux'                                                TFDOC9  = 'This source was assigned some stray flux'                            TTYPE10 = 'base_GaussianCentroid_x' / centroid from Gaussian Centroid algorithm TFORM10 = '1D      '           / format of field                                TDOC10  = 'centroid from Gaussian Centroid algorithm'                           TUNIT10 = 'pixel   '                                                            TCCLS10 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE11 = 'base_GaussianCentroid_y' / centroid from Gaussian Centroid algorithm TFORM11 = '1D      '           / format of field                                TDOC11  = 'centroid from Gaussian Centroid algorithm'                           TUNIT11 = 'pixel   '                                                            TCCLS11 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG10 = 'base_GaussianCentroid_flag'                                          TFDOC10 = 'general failure flag, set if anything went wrong'                    TFLAG11 = 'base_GaussianCentroid_flag_noPeak'                                   TFDOC11 = 'Fitted Centroid has a negative peak'                                 TTYPE12 = 'base_NaiveCentroid_x' / centroid from Naive Centroid algorithm       TFORM12 = '1D      '           / format of field                                TDOC12  = 'centroid from Naive Centroid algorithm'                              TUNIT12 = 'pixel   '                                                            TCCLS12 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE13 = 'base_NaiveCentroid_y' / centroid from Naive Centroid algorithm       TFORM13 = '1D      '           / format of field                                TDOC13  = 'centroid from Naive Centroid algorithm'                              TUNIT13 = 'pixel   '                                                            TCCLS13 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG12 = 'base_NaiveCentroid_flag'                                             TFDOC12 = 'general failure flag, set if anything went wrong'                    TFLAG13 = 'base_NaiveCentroid_flag_noCounts'                                    TFDOC13 = 'Object to be centroided has no counts'                               TFLAG14 = 'base_NaiveCentroid_flag_edge'                                        TFDOC14 = 'Object too close to edge'                                            TTYPE14 = 'base_SdssCentroid_x' / centroid from Sdss Centroid algorithm         TFORM14 = '1D      '           / format of field                                TDOC14  = 'centroid from Sdss Centroid algorithm'                               TUNIT14 = 'pixel   '                                                            TCCLS14 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE15 = 'base_SdssCentroid_y' / centroid from Sdss Centroid algorithm         TFORM15 = '1D      '           / format of field                                TDOC15  = 'centroid from Sdss Centroid algorithm'                               TUNIT15 = 'pixel   '                                                            TCCLS15 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE16 = 'base_SdssCentroid_xSigma' / 1-sigma uncertainty on x position        TFORM16 = '1E      '           / format of field                                TDOC16  = '1-sigma uncertainty on x position'                                   TUNIT16 = 'pixel   '                                                            TCCLS16 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE17 = 'base_SdssCentroid_ySigma' / 1-sigma uncertainty on y position        TFORM17 = '1E      '           / format of field                                TDOC17  = '1-sigma uncertainty on y position'                                   TUNIT17 = 'pixel   '                                                            TCCLS17 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG15 = 'base_SdssCentroid_flag'                                              TFDOC15 = 'general failure flag, set if anything went wrong'                    TFLAG16 = 'base_SdssCentroid_flag_edge'                                         TFDOC16 = 'Object too close to edge'                                            TFLAG17 = 'base_SdssCentroid_flag_noSecondDerivative'                           TFDOC17 = 'Vanishing second derivative'                                         TFLAG18 = 'base_SdssCentroid_flag_almostNoSecondDerivative'                     TFDOC18 = 'Almost vanishing second derivative'                                  TFLAG19 = 'base_SdssCentroid_flag_notAtMaximum'                                 TFDOC19 = 'Object is not at a maximum'                                          TTYPE18 = 'base_SdssShape_xx'  / elliptical Gaussian adaptive moments           TFORM18 = '1D      '           / format of field                                TDOC18  = 'elliptical Gaussian adaptive moments'                                TUNIT18 = 'pixel^2 '                                                            TCCLS18 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE19 = 'base_SdssShape_yy'  / elliptical Gaussian adaptive moments           TFORM19 = '1D      '           / format of field                                TDOC19  = 'elliptical Gaussian adaptive moments'                                TUNIT19 = 'pixel^2 '                                                            TCCLS19 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE20 = 'base_SdssShape_xy'  / elliptical Gaussian adaptive moments           TFORM20 = '1D      '           / format of field                                TDOC20  = 'elliptical Gaussian adaptive moments'                                TUNIT20 = 'pixel^2 '                                                            TCCLS20 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE21 = 'base_SdssShape_xxSigma' / 1-sigma uncertainty on xx moment           TFORM21 = '1E      '           / format of field                                TDOC21  = '1-sigma uncertainty on xx moment'                                    TUNIT21 = 'pixel^2 '                                                            TCCLS21 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE22 = 'base_SdssShape_yySigma' / 1-sigma uncertainty on yy moment           TFORM22 = '1E      '           / format of field                                TDOC22  = '1-sigma uncertainty on yy moment'                                    TUNIT22 = 'pixel^2 '                                                            TCCLS22 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE23 = 'base_SdssShape_xySigma' / 1-sigma uncertainty on xy moment           TFORM23 = '1E      '           / format of field                                TDOC23  = '1-sigma uncertainty on xy moment'                                    TUNIT23 = 'pixel^2 '                                                            TCCLS23 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE24 = 'base_SdssShape_x'   / elliptical Gaussian adaptive moments           TFORM24 = '1D      '           / format of field                                TDOC24  = 'elliptical Gaussian adaptive moments'                                TUNIT24 = 'pixel   '                                                            TCCLS24 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE25 = 'base_SdssShape_y'   / elliptical Gaussian adaptive moments           TFORM25 = '1D      '           / format of field                                TDOC25  = 'elliptical Gaussian adaptive moments'                                TUNIT25 = 'pixel   '                                                            TCCLS25 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE26 = 'base_SdssShape_flux' / elliptical Gaussian adaptive moments          TFORM26 = '1D      '           / format of field                                TDOC26  = 'elliptical Gaussian adaptive moments'                                TUNIT26 = 'count   '                                                            TCCLS26 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE27 = 'base_SdssShape_fluxSigma' / 1-sigma flux uncertainty                 TFORM27 = '1D      '           / format of field                                TDOC27  = '1-sigma flux uncertainty'                                            TUNIT27 = 'count   '                                                            TCCLS27 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE28 = 'base_SdssShape_flux_xx_Cov' / uncertainty covariance between base_SdsTFORM28 = '1E      '           / format of field                                TDOC28  = 'uncertainty covariance between base_SdssShape_flux and base_SdssSha&'CONTINUE  'pe_xx   '                                                            TUNIT28 = 'count*pixel^2'                                                       TCCLS28 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE29 = 'base_SdssShape_flux_yy_Cov' / uncertainty covariance between base_SdsTFORM29 = '1E      '           / format of field                                TDOC29  = 'uncertainty covariance between base_SdssShape_flux and base_SdssSha&'CONTINUE  'pe_yy   '                                                            TUNIT29 = 'count*pixel^2'                                                       TCCLS29 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE30 = 'base_SdssShape_flux_xy_Cov' / uncertainty covariance between base_SdsTFORM30 = '1E      '           / format of field                                TDOC30  = 'uncertainty covariance between base_SdssShape_flux and base_SdssSha&'CONTINUE  'pe_xy   '                                                            TUNIT30 = 'count*pixel^2'                                                       TCCLS30 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG20 = 'base_SdssShape_flag'                                                 TFDOC20 = 'general failure flag, set if anything went wrong'                    TFLAG21 = 'base_SdssShape_flag_unweightedBad'                                   TFDOC21 = 'Both weighted and unweighted moments were invalid'                   TFLAG22 = 'base_SdssShape_flag_unweighted'                                      TFDOC22 = 'Weighted moments converged to an invalid value; using unweighted mo&'CONTINUE  'ments   '                                                            TFLAG23 = 'base_SdssShape_flag_shift'                                           TFDOC23 = 'centroid shifted by more than the maximum allowed amount'            TFLAG24 = 'base_SdssShape_flag_maxIter'                                         TFDOC24 = 'Too many iterations in adaptive moments'                             TTYPE31 = 'base_CircularApertureFlux_3_0_flux' / flux within 3.000000-pixel aperTFORM31 = '1D      '           / format of field                                TDOC31  = 'flux within 3.000000-pixel aperture'                                 TUNIT31 = 'count   '                                                            TCCLS31 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE32 = 'base_CircularApertureFlux_3_0_fluxSigma' / 1-sigma flux uncertainty  TFORM32 = '1D      '           / format of field                                TDOC32  = '1-sigma flux uncertainty'                                            TUNIT32 = 'count   '                                                            TCCLS32 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG25 = 'base_CircularApertureFlux_3_0_flag'                                  TFDOC25 = 'general failure flag'                                                TFLAG26 = 'base_CircularApertureFlux_3_0_flag_apertureTruncated'                TFDOC26 = 'aperture did not fit within measurement image'                       TFLAG27 = 'base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated'              TFDOC27 = 'full sinc coefficient image did not fit within measurement image'    TTYPE33 = 'base_CircularApertureFlux_4_5_flux' / flux within 4.500000-pixel aperTFORM33 = '1D      '           / format of field                                TDOC33  = 'flux within 4.500000-pixel aperture'                                 TUNIT33 = 'count   '                                                            TCCLS33 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE34 = 'base_CircularApertureFlux_4_5_fluxSigma' / 1-sigma flux uncertainty  TFORM34 = '1D      '           / format of field                                TDOC34  = '1-sigma flux uncertainty'                                            TUNIT34 = 'count   '                                                            TCCLS34 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG28 = 'base_CircularApertureFlux_4_5_flag'                                  TFDOC28 = 'general failure flag'                                                TFLAG29 = 'base_CircularApertureFlux_4_5_flag_apertureTruncated'                TFDOC29 = 'aperture did not fit within measurement image'                       TFLAG30 = 'base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated'              TFDOC30 = 'full sinc coefficient image did not fit within measurement image'    TTYPE35 = 'base_CircularApertureFlux_6_0_flux' / flux within 6.000000-pixel aperTFORM35 = '1D      '           / format of field                                TDOC35  = 'flux within 6.000000-pixel aperture'                                 TUNIT35 = 'count   '                                                            TCCLS35 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE36 = 'base_CircularApertureFlux_6_0_fluxSigma' / 1-sigma flux uncertainty  TFORM36 = '1D      '           / format of field                                TDOC36  = '1-sigma flux uncertainty'                                            TUNIT36 = 'count   '                                                            TCCLS36 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG31 = 'base_CircularApertureFlux_6_0_flag'                                  TFDOC31 = 'general failure flag'                                                TFLAG32 = 'base_CircularApertureFlux_6_0_flag_apertureTruncated'                TFDOC32 = 'aperture did not fit within measurement image'                       TFLAG33 = 'base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated'              TFDOC33 = 'full sinc coefficient image did not fit within measurement image'    TTYPE37 = 'base_CircularApertureFlux_9_0_flux' / flux within 9.000000-pixel aperTFORM37 = '1D      '           / format of field                                TDOC37  = 'flux within 9.000000-pixel aperture'                                 TUNIT37 = 'count   '                                                            TCCLS37 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE38 = 'base_CircularApertureFlux_9_0_fluxSigma' / 1-sigma flux uncertainty  TFORM38 = '1D      '           / format of field                                TDOC38  = '1-sigma flux uncertainty'                                            TUNIT38 = 'count   '                                                            TCCLS38 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG34 = 'base_CircularApertureFlux_9_0_flag'                                  TFDOC34 = 'general failure flag'                                                TFLAG35 = 'base_CircularApertureFlux_9_0_flag_apertureTruncated'                TFDOC35 = 'aperture did not fit within measurement image'                       TFLAG36 = 'base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated'              TFDOC36 = 'full sinc coefficient image did not fit within measurement image'    TTYPE39 = 'base_CircularApertureFlux_12_0_flux' / flux within 12.000000-pixel apTFORM39 = '1D      '           / format of field                                TDOC39  = 'flux within 12.000000-pixel aperture'                                TUNIT39 = 'count   '                                                            TCCLS39 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE40 = 'base_CircularApertureFlux_12_0_fluxSigma' / 1-sigma flux uncertainty TFORM40 = '1D      '           / format of field                                TDOC40  = '1-sigma flux uncertainty'                                            TUNIT40 = 'count   '                                                            TCCLS40 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG37 = 'base_CircularApertureFlux_12_0_flag'                                 TFDOC37 = 'general failure flag'                                                TFLAG38 = 'base_CircularApertureFlux_12_0_flag_apertureTruncated'               TFDOC38 = 'aperture did not fit within measurement image'                       TTYPE41 = 'base_CircularApertureFlux_17_0_flux' / flux within 17.000000-pixel apTFORM41 = '1D      '           / format of field                                TDOC41  = 'flux within 17.000000-pixel aperture'                                TUNIT41 = 'count   '                                                            TCCLS41 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE42 = 'base_CircularApertureFlux_17_0_fluxSigma' / 1-sigma flux uncertainty TFORM42 = '1D      '           / format of field                                TDOC42  = '1-sigma flux uncertainty'                                            TUNIT42 = 'count   '                                                            TCCLS42 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG39 = 'base_CircularApertureFlux_17_0_flag'                                 TFDOC39 = 'general failure flag'                                                TFLAG40 = 'base_CircularApertureFlux_17_0_flag_apertureTruncated'               TFDOC40 = 'aperture did not fit within measurement image'                       TTYPE43 = 'base_CircularApertureFlux_25_0_flux' / flux within 25.000000-pixel apTFORM43 = '1D      '           / format of field                                TDOC43  = 'flux within 25.000000-pixel aperture'                                TUNIT43 = 'count   '                                                            TCCLS43 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE44 = 'base_CircularApertureFlux_25_0_fluxSigma' / 1-sigma flux uncertainty TFORM44 = '1D      '           / format of field                                TDOC44  = '1-sigma flux uncertainty'                                            TUNIT44 = 'count   '                                                            TCCLS44 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG41 = 'base_CircularApertureFlux_25_0_flag'                                 TFDOC41 = 'general failure flag'                                                TFLAG42 = 'base_CircularApertureFlux_25_0_flag_apertureTruncated'               TFDOC42 = 'aperture did not fit within measurement image'                       TTYPE45 = 'base_CircularApertureFlux_35_0_flux' / flux within 35.000000-pixel apTFORM45 = '1D      '           / format of field                                TDOC45  = 'flux within 35.000000-pixel aperture'                                TUNIT45 = 'count   '                                                            TCCLS45 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE46 = 'base_CircularApertureFlux_35_0_fluxSigma' / 1-sigma flux uncertainty TFORM46 = '1D      '           / format of field                                TDOC46  = '1-sigma flux uncertainty'                                            TUNIT46 = 'count   '                                                            TCCLS46 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG43 = 'base_CircularApertureFlux_35_0_flag'                                 TFDOC43 = 'general failure flag'                                                TFLAG44 = 'base_CircularApertureFlux_35_0_flag_apertureTruncated'               TFDOC44 = 'aperture did not fit within measurement image'                       TTYPE47 = 'base_CircularApertureFlux_50_0_flux' / flux within 50.000000-pixel apTFORM47 = '1D      '           / format of field                                TDOC47  = 'flux within 50.000000-pixel aperture'                                TUNIT47 = 'count   '                                                            TCCLS47 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE48 = 'base_CircularApertureFlux_50_0_fluxSigma' / 1-sigma flux uncertainty TFORM48 = '1D      '           / format of field                                TDOC48  = '1-sigma flux uncertainty'                                            TUNIT48 = 'count   '                                                            TCCLS48 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG45 = 'base_CircularApertureFlux_50_0_flag'                                 TFDOC45 = 'general failure flag'                                                TFLAG46 = 'base_CircularApertureFlux_50_0_flag_apertureTruncated'               TFDOC46 = 'aperture did not fit within measurement image'                       TTYPE49 = 'base_CircularApertureFlux_70_0_flux' / flux within 70.000000-pixel apTFORM49 = '1D      '           / format of field                                TDOC49  = 'flux within 70.000000-pixel aperture'                                TUNIT49 = 'count   '                                                            TCCLS49 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE50 = 'base_CircularApertureFlux_70_0_fluxSigma' / 1-sigma flux uncertainty TFORM50 = '1D      '           / format of field                                TDOC50  = '1-sigma flux uncertainty'                                            TUNIT50 = 'count   '                                                            TCCLS50 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG47 = 'base_CircularApertureFlux_70_0_flag'                                 TFDOC47 = 'general failure flag'                                                TFLAG48 = 'base_CircularApertureFlux_70_0_flag_apertureTruncated'               TFDOC48 = 'aperture did not fit within measurement image'                       TTYPE51 = 'base_GaussianFlux_flux' / flux from Gaussian Flux algorithm          TFORM51 = '1D      '           / format of field                                TDOC51  = 'flux from Gaussian Flux algorithm'                                   TUNIT51 = 'count   '                                                            TCCLS51 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE52 = 'base_GaussianFlux_fluxSigma' / 1-sigma flux uncertainty              TFORM52 = '1D      '           / format of field                                TDOC52  = '1-sigma flux uncertainty'                                            TUNIT52 = 'count   '                                                            TCCLS52 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG49 = 'base_GaussianFlux_flag'                                              TFDOC49 = 'general failure flag, set if anything went wrong'                    TFLAG50 = 'base_PixelFlags_flag'                                                TFDOC50 = 'general failure flag, set if anything went wring'                    TFLAG51 = 'base_PixelFlags_flag_offimage'                                       TFDOC51 = 'Source center is off image'                                          TFLAG52 = 'base_PixelFlags_flag_edge'                                           TFDOC52 = 'Source is outside usable exposure region (masked EDGE or NO_DATA)'   TFLAG53 = 'base_PixelFlags_flag_interpolated'                                   TFDOC53 = 'Interpolated pixel in the Source footprint'                          TFLAG54 = 'base_PixelFlags_flag_saturated'                                      TFDOC54 = 'Saturated pixel in the Source footprint'                             TFLAG55 = 'base_PixelFlags_flag_cr'                                             TFDOC55 = 'Cosmic ray in the Source footprint'                                  TFLAG56 = 'base_PixelFlags_flag_bad'                                            TFDOC56 = 'Bad pixel in the Source footprint'                                   TFLAG57 = 'base_PixelFlags_flag_suspect'                                        TFDOC57 = 'Source''s footprint includes suspect pixels'                         TFLAG58 = 'base_PixelFlags_flag_interpolatedCenter'                             TFDOC58 = 'Interpolated pixel in the Source center'                             TFLAG59 = 'base_PixelFlags_flag_saturatedCenter'                                TFDOC59 = 'Saturated pixel in the Source center'                                TFLAG60 = 'base_PixelFlags_flag_crCenter'                                       TFDOC60 = 'Cosmic ray in the Source center'                                     TFLAG61 = 'base_PixelFlags_flag_suspectCenter'                                  TFDOC61 = 'Source''s center is close to suspect pixels'                         TTYPE53 = 'base_PsfFlux_flux'  / flux derived from linear least-squares fit of PTFORM53 = '1D      '           / format of field                                TDOC53  = 'flux derived from linear least-squares fit of PSF model'             TUNIT53 = 'count   '                                                            TCCLS53 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE54 = 'base_PsfFlux_fluxSigma' / 1-sigma flux uncertainty                   TFORM54 = '1D      '           / format of field                                TDOC54  = '1-sigma flux uncertainty'                                            TUNIT54 = 'count   '                                                            TCCLS54 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG62 = 'base_PsfFlux_flag'                                                   TFDOC62 = 'general failure flag'                                                TFLAG63 = 'base_PsfFlux_flag_noGoodPixels'                                      TFDOC63 = 'not enough non-rejected pixels in data to attempt the fit'           TFLAG64 = 'base_PsfFlux_flag_edge'                                              TFDOC64 = 'object was too close to the edge of the image to use the full PSF m&'CONTINUE  'odel    '                                                            TTYPE55 = 'base_Variance_value' / Variance at object position                   TFORM55 = '1D      '           / format of field                                TDOC55  = 'Variance at object position'                                         TCCLS55 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG65 = 'base_Variance_flag'                                                  TFDOC65 = 'Set to True for any fatal failure'                                   TFLAG66 = 'base_Variance_flag_emptyFootprint'                                   TFDOC66 = 'Set to True when the footprint has no usable pixels'                 TTYPE56 = 'base_ClassificationExtendedness_value' / Set to 1 for extended sourceTFORM56 = '1D      '           / format of field                                TDOC56  = 'Set to 1 for extended sources, 0 for point sources.'                 TCCLS56 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG67 = 'base_ClassificationExtendedness_flag'                                TFDOC67 = 'Set to 1 for any fatal failure.'                                     TTYPE57 = 'base_PsfFlux_apCorr' / aperture correction applied to base_PsfFlux   TFORM57 = '1D      '           / format of field                                TDOC57  = 'aperture correction applied to base_PsfFlux'                         TCCLS57 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE58 = 'base_PsfFlux_apCorrSigma' / aperture correction applied to base_PsfFlTFORM58 = '1D      '           / format of field                                TDOC58  = 'aperture correction applied to base_PsfFlux'                         TCCLS58 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG68 = 'base_PsfFlux_flag_apCorr'                                            TFDOC68 = 'set if unable to aperture correct base_PsfFlux'                      TTYPE59 = 'base_GaussianFlux_apCorr' / aperture correction applied to base_GaussTFORM59 = '1D      '           / format of field                                TDOC59  = 'aperture correction applied to base_GaussianFlux'                    TCCLS59 = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE60 = 'base_GaussianFlux_apCorrSigma' / aperture correction applied to base_TFORM60 = '1D      '           / format of field                                TDOC60  = 'aperture correction applied to base_GaussianFlux'                    TCCLS60 = 'Scalar  '           / Field template used by lsst.afw.table          TFLAG69 = 'base_GaussianFlux_flag_apCorr'                                       TFDOC69 = 'set if unable to aperture correct base_GaussianFlux'                 TTYPE61 = 'footprint'          / archive ID for Footprint                       TFORM61 = '1J      '           / format of field                                TDOC61  = 'archive ID for Footprint'                                            TCCLS61 = 'Scalar  '           / Field template used by lsst.afw.table          ALIAS   = 'base_CircularApertureFlux_flag_badCentroid:base_SdssCentroid_flag'   ALIAS   = 'base_GaussianCentroid_flag_badInitialCentroid:base_SdssCentroid_flag'ALIAS   = 'base_GaussianFlux_flag_badCentroid:base_SdssCentroid_flag'           ALIAS   = 'base_GaussianFlux_flag_badShape:base_SdssShape_flag'                 ALIAS   = 'base_NaiveCentroid_flag_badInitialCentroid:base_SdssCentroid_flag'   ALIAS   = 'base_PsfFlux_flag_badCentroid:base_SdssCentroid_flag'                ALIAS   = 'base_SdssShape_flag_badCentroid:base_SdssCentroid_flag'              ALIAS   = 'base_Variance_flag_badCentroid:base_SdssCentroid_flag'               ALIAS   = 'slot_ApFlux:base_CircularApertureFlux_3_0'                           ALIAS   = 'slot_CalibFlux:base_CircularApertureFlux_12_0'                       ALIAS   = 'slot_Centroid:base_SdssCentroid'                                     ALIAS   = 'slot_InstFlux:base_GaussianFlux'                                     ALIAS   = 'slot_ModelFlux:base_GaussianFlux'                                    ALIAS   = 'slot_PsfFlux:base_PsfFlux'                                           ALIAS   = 'slot_Shape:base_SdssShape'                                           AR_HDU  =                    3 / HDU containing the archive index for non-recordHIERARCH AFW_TABLE_VERSION = 1                                                  AFW_TYPE= 'SOURCE  '           / Tells lsst::afw to load this as a Source table.END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / 8-bit bytes                                    NAXIS   =                    2 / 2-dimensional binary table                     NAXIS1  =                  148 / width of table in bytes                        NAXIS2  =                    0 / number of rows in table                        PCOUNT  =                    0 / size of special data area                      GCOUNT  =                    1 / one data group (required keyword)              TFIELDS =                    7 / number of fields in each row                   TTYPE1  = 'id      '           / Archive ID of the persistable object that owns TFORM1  = '1J      '           / format of field                                TDOC1   = 'Archive ID of the persistable object that owns the records pointed &'CONTINUE  'at by this entry'                                                    TCCLS1  = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE2  = 'cat.archive'        / index of the catalog this entry points to, fromTFORM2  = '1J      '           / format of field                                TDOC2   = 'index of the catalog this entry points to, from the perspective of &'CONTINUE  'the archive'                                                         TCCLS2  = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE3  = 'cat.persistable'    / index of the catalog this entry points to, fromTFORM3  = '1J      '           / format of field                                TDOC3   = 'index of the catalog this entry points to, from the perspective of &'CONTINUE  'the Persistable'                                                     TCCLS3  = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE4  = 'row0    '           / first row used by the persistable object in thiTFORM4  = '1J      '           / format of field                                TDOC4   = 'first row used by the persistable object in this catalog'            TCCLS4  = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE5  = 'nrows   '           / number of rows used by the persistable object iTFORM5  = '1J      '           / format of field                                TDOC5   = 'number of rows used by the persistable object in this catalog'       TCCLS5  = 'Scalar  '           / Field template used by lsst.afw.table          TTYPE6  = 'name    '           / unique name for the persistable object's class TFORM6  = '64A     '           / format of field                                TDOC6   = 'unique name for the persistable object''s class'                     TCCLS6  = 'String  '           / Field template used by lsst.afw.table          TTYPE7  = 'module  '           / Python module that should be imported to registTFORM7  = '64A     '           / format of field                                TDOC7   = 'Python module that should be imported to register the object''s fac&'CONTINUE  'tory    '                                                            TCCLS7  = 'String  '           / Field template used by lsst.afw.table          EXTTYPE = 'ARCHIVE_INDEX'                                                       AR_CATN =                    0 / # of this catalog relative to the start of thisAR_NCAT =                    1 / # of catalogs in this archive, including the inHIERARCH AFW_TABLE_VERSION = 1                                                  END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             

EGS_214.88685460+52.8863737_0.70arcsec.fits

EGS_214.88685460+52.8863737_0.70arcsec.png

gauss_0.70_0.060.fits