psf_size_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lsst.daf.persistence import Butler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "butler = Butler('/home/shared/twinkles/output_data_v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = butler.subset('calexp', filter='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 230},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 231},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 232},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 233},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 234},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 235},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 236},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 237},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 238},\n",
       " {'filter': 'r', 'raft': '2,2', 'sensor': '1,1', 'visit': 239}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataid = subset.cache[6]\n",
    "my_calexp = butler.get('calexp', **dataid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lsst.afw.image.exposure.exposure.ExposureF at 0x7f55f7ab7d18>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_calexp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "psf = my_calexp.getPsf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape = psf.computeShape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lsst.afw.geom.ellipses._quadrupole.Quadrupole"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape.getDeterminantRadius() # Units are pixels, documentation\n",
    "type(shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method computeShape in module lsst.afw.detection._psf:\n",
      "\n",
      "computeShape(...) method of lsst.meas.extensions.psfex.psfexPsf.PsfexPsf instance\n",
      "    computeShape(self: lsst.afw.detection._psf.Psf, position: lsst.afw.geom.coordinates.coordinates.Point2D=Point2D(nan, nan), color: lsst.afw.image.color.Color=<lsst.afw.image.color.Color object at 0x7f56197bab90>) -> lsst.afw.geom.ellipses._quadrupole.Quadrupole\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(psf.computeShape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lsst.afw.geom import Point2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Point2D(50, 100)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point = Point2D(50., 100.)\n",
    "point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Extent2I(4000, 4072)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_calexp.getDimensions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10000\n",
    "x_array = np.random.uniform(0, my_calexp.getDimensions()[0], n)\n",
    "y_array = np.random.uniform(0, my_calexp.getDimensions()[1], n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapes = np.empty(n)\n",
    "for ii, (x, y) in enumerate(zip(x_array, y_array)):\n",
    "    point = Point2D(x, y)\n",
    "    shape = psf.computeShape(point)\n",
    "    shapes[ii] = shape.getDeterminantRadius()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2703., 2307., 1656., 1330.,  865.,  553.,  332.,  140.,   73.,\n",
       "          41.]),\n",
       " array([2.21125016, 2.21527225, 2.21929434, 2.22331643, 2.22733852,\n",
       "        2.23136061, 2.2353827 , 2.23940479, 2.24342688, 2.24744897,\n",
       "        2.25147106]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD8CAYAAACRkhiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAEIZJREFUeJzt3X+MZWV9x/H3R6CUCsRF1s122XYwWf8AoiibLVFjsMayBROwf5g1jfAHYTVQo9WmWWyi9I9NV1t/hKSQrJUArbrZRA0bgTZASKwxgAOuLLt0y1aWsJOFXasNNk1owW//mEO8DDM7987cmTPwvF/JzX3mOec553ufvTOfe849926qCklSm97QdwGSpP4YApLUMENAkhpmCEhSwwwBSWqYISBJDTMEJKlhhoAkNcwQkKSGndx3AfM5++yza2Jiou8yJOk15ZFHHvl5Va2eb70VHwITExNMTk72XYYkvaYkeXqY9TwdJEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYISBJDVvxnxhejIltd/Wy38M7Lu9lv5I0Ko8EJKlhhoAkNcwQkKSGGQKS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhs0bAknWJ3kgyYEk+5N8quu/MclUkr3d7bKBMTckOZTkYJJLB/ovSrKvW3ZTkizNw5IkDWOYL5B7EfhsVT2a5AzgkST3dsu+WlV/N7hykvOALcD5wO8C9yV5W1W9BNwCXAs8BNwNbAbuGc9DkSSNat4jgao6WlWPdu1fAU8A604w5ApgV1W9UFVPAYeATUnWAmdW1YNVVcAdwJWLfgSSpAUb6T2BJBPAO5l+JQ/wySSPJbk1yaqubx3wzMCwI13fuq49s3+2/WxNMplk8vjx46OUKEkawdAhkOR04DvAp6vqeaZP7bwVuBA4Cnx5XEVV1c6q2lhVG1evXj2uzUqSZhgqBJKcwnQAfLOqvgtQVc9V1UtV9Wvg68CmbvUpYP3A8HO6vqmuPbNfktSTYa4OCvAN4Imq+spA/9qB1T4MPN619wBbkpya5FxgA/BwVR0Fnk9ycbfNq4A7x/Q4JEkLMMzVQe8BPgbsS7K36/sc8NEkFwIFHAY+DlBV+5PsBg4wfWXR9d2VQQDXAbcBpzF9VZBXBklSj+YNgar6ITDb9fx3n2DMdmD7LP2TwAWjFChJWjp+YliSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkho2zNdGaEQT2+7qbd+Hd1ze274lvfZ4JCBJDTMEJKlhhoAkNcwQkKSGGQKS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1bN4QSLI+yQNJDiTZn+RTXf9ZSe5N8mR3v2pgzA1JDiU5mOTSgf6Lkuzrlt2UJEvzsCRJwxjmSOBF4LNVdR5wMXB9kvOAbcD9VbUBuL/7mW7ZFuB8YDNwc5KTum3dAlwLbOhum8f4WCRJI5o3BKrqaFU92rV/BTwBrAOuAG7vVrsduLJrXwHsqqoXquop4BCwKcla4MyqerCqCrhjYIwkqQcjvSeQZAJ4J/AQsKaqjnaLngXWdO11wDMDw450feu69sx+SVJPhg6BJKcD3wE+XVXPDy7rXtnXuIpKsjXJZJLJ48ePj2uzkqQZhgqBJKcwHQDfrKrvdt3Pdad46O6Pdf1TwPqB4ed0fVNde2b/q1TVzqraWFUbV69ePexjkSSNaJirgwJ8A3iiqr4ysGgPcHXXvhq4c6B/S5JTk5zL9BvAD3enjp5PcnG3zasGxkiSenDyEOu8B/gYsC/J3q7vc8AOYHeSa4CngY8AVNX+JLuBA0xfWXR9Vb3UjbsOuA04Dbinu0mSejJvCFTVD4G5ruf/wBxjtgPbZ+mfBC4YpUBJ0tLxE8OS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYISBJDTu57wI0XhPb7uplv4d3XN7LfiUtjkcCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1bN4QSHJrkmNJHh/ouzHJVJK93e2ygWU3JDmU5GCSSwf6L0qyr1t2U5KM/+FIkkYxzJHAbcDmWfq/WlUXdre7AZKcB2wBzu/G3JzkpG79W4BrgQ3dbbZtSpKW0bwhUFU/AH4x5PauAHZV1QtV9RRwCNiUZC1wZlU9WFUF3AFcudCiJUnjsZj3BD6Z5LHudNGqrm8d8MzAOke6vnVde2a/JKlHCw2BW4C3AhcCR4Evj60iIMnWJJNJJo8fPz7OTUuSBiwoBKrquap6qap+DXwd2NQtmgLWD6x6Ttc31bVn9s+1/Z1VtbGqNq5evXohJUqShrCgEOjO8b/sw8DLVw7tAbYkOTXJuUy/AfxwVR0Fnk9ycXdV0FXAnYuoW5I0BvN+lXSSbwOXAGcnOQJ8AbgkyYVAAYeBjwNU1f4ku4EDwIvA9VX1Urep65i+0ug04J7uJknq0bwhUFUfnaX7GydYfzuwfZb+SeCCkaqTJC0pPzEsSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJati8HxaThjGx7a7e9n14x+W97Vt6rfNIQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYISBJDTMEJKlh84ZAkluTHEvy+EDfWUnuTfJkd79qYNkNSQ4lOZjk0oH+i5Ls65bdlCTjfziSpFEMcyRwG7B5Rt824P6q2gDc3/1MkvOALcD53Zibk5zUjbkFuBbY0N1mblOStMzmDYGq+gHwixndVwC3d+3bgSsH+ndV1QtV9RRwCNiUZC1wZlU9WFUF3DEwRpLUk4W+J7Cmqo527WeBNV17HfDMwHpHur51XXtm/6ySbE0ymWTy+PHjCyxRkjSfRb8x3L2yrzHUMrjNnVW1sao2rl69epybliQNWGgIPNed4qG7P9b1TwHrB9Y7p+ub6toz+yVJPVpoCOwBru7aVwN3DvRvSXJqknOZfgP44e7U0fNJLu6uCrpqYIwkqScnz7dCkm8DlwBnJzkCfAHYAexOcg3wNPARgKran2Q3cAB4Ebi+ql7qNnUd01canQbc090kST2aNwSq6qNzLPrAHOtvB7bP0j8JXDBSdZKkJeUnhiWpYYaAJDXMEJCkhhkCktQwQ0CSGjbv1UHSSjex7a5e9nt4x+W97FcaJ48EJKlhhoAkNcwQkKSGGQKS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSw07uuwDptWpi21297fvwjst727deXxZ1JJDkcJJ9SfYmmez6zkpyb5Inu/tVA+vfkORQkoNJLl1s8ZKkxRnH6aD3V9WFVbWx+3kbcH9VbQDu734myXnAFuB8YDNwc5KTxrB/SdICLcV7AlcAt3ft24ErB/p3VdULVfUUcAjYtAT7lyQNabEhUMB9SR5JsrXrW1NVR7v2s8Carr0OeGZg7JGu71WSbE0ymWTy+PHjiyxRkjSXxb4x/N6qmkryFuDeJP82uLCqKkmNutGq2gnsBNi4cePI4yVJw1nUkUBVTXX3x4DvMX1657kkawG6+2Pd6lPA+oHh53R9kqSeLDgEkrwxyRkvt4E/Ah4H9gBXd6tdDdzZtfcAW5KcmuRcYAPw8EL3L0lavMWcDloDfC/Jy9v5VlX9c5IfA7uTXAM8DXwEoKr2J9kNHABeBK6vqpcWVb0kaVEWHAJV9TPgHbP0/yfwgTnGbAe2L3SfkqTx8msjJKlhhoAkNcwQkKSGGQKS1DBDQJIaZghIUsMMAUlqmP+pjPQa1Nd/aON/ZvP645GAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYXyUtaWh+hfXrj0cCktQwQ0CSGmYISFLDDAFJaphvDEta8fp6Qxpe/29KeyQgSQ1b9hBIsjnJwSSHkmxb7v1Lkn5jWU8HJTkJ+Hvgg8AR4MdJ9lTVgeWsQ5KG9Xr/bMRyHwlsAg5V1c+q6n+BXcAVy1yDJKmz3CGwDnhm4OcjXZ8kqQcr8uqgJFuBrd2P/53k4IxVzgZ+vrxVDcW6RmNdo1mJda3EmuB1UFe+uOh9/f4wKy13CEwB6wd+Pqfre4Wq2gnsnGsjSSarauP4y1sc6xqNdY1mJda1EmsC6xrFcp8O+jGwIcm5SX4L2ALsWeYaJEmdZT0SqKoXk/wZ8C/AScCtVbV/OWuQJP3Gsr8nUFV3A3cvcjNznirqmXWNxrpGsxLrWok1gXUNLVXVdw2SpJ74tRGS1LDeQyDJ+iQPJDmQZH+ST82yzp8meSzJviQ/SvKOgWW3JjmW5PEZY85Kcm+SJ7v7VSukrhuTTCXZ290uW666TjR2MfO1hDX1OVe/neThJD/txv71OOZqievqbb4Glp+U5CdJvj/Q1+vv4gnq6nW+khzu+vcmmRzoX9R8jayqer0Ba4F3de0zgH8HzpuxzruBVV37j4GHBpa9D3gX8PiMMV8CtnXtbcAXV0hdNwJ/0cd8nWjsYuZrCWvqc64CnN61TwEeAi7u+7k1T129zdfA8s8A3wK+P9DX6+/iCerqdb6Aw8DZs2x3UfM18uNYyo0vcGLvBD54guWrgKkZfRO8+o/tQWDtwD/WwRVS16KeeOOoa7ax45yvMda0IuYK+B3gUeAPVspza466ep0vpj/3cz/whzP+2PY6Xyeoq+/5OszsITDW+Zrv1vvpoEFJJoB3Mv3qZi7XAPcMsbk1VXW0az8LrFkhdQF8sjtEvHUxh3qLqWuWsWOZrzHXBD3OVXcKYS9wDLi3qsY6V0tQF/T73Poa8JfAr2es1/fv4lx1Qb/zVcB9SR7J9LckvGxs8zWUpUyYEVP0dOAR4E9OsM77gSeAN8/on+DVr7j/a8bPv1whda1h+jMSbwC2M/1ZieWu61VjxzFfS1BT73PVLXsT8ABwwUp5bs1RV2/zBXwIuLlrX8IrX3H3Nl/z1NXr8wtY192/Bfgp8L5xztfQ9S/lxkeYxFOY/gDZZ06wztuB/wDeNsuyCZbgdNBS1DXK8qWoa66xi52vpaip77masc7n6U4drITn1mx19TlfwN8w/YWQh5l+9fo/wD/1PV8nqmuFPb9uHOfza6THsJQbH3ISA9wBfO0E6/wecAh49xzLX/UPCPwtr3xz5UsrpK61A+0/B3YtV10nGruY+VrCmvqcq9XAm7r2acC/Ah/q+7k1T129zdeMdS7hla+4e/9dnKOuPp9fbwTOGGj/CNg8jvka9bZkGx5hIt/L9Lmxx4C93e0y4BPAJ7p1/gH45cDyyYHx3waOAv/HdOJf0/W/mek3g54E7gPOWiF1/SOwr9vunsEn4lLXNdfYxc7XEtbU51y9HfhJN/Zx4PMD2+3tuTVPXb3N14ztXMIr/9j2+rt4grr6fH69lelTQD8F9gN/Na75GvXmJ4YlqWEr6uogSdLyMgQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWrY/wP7EE/meHyhjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56012459b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f55f7a515c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55f8e563c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_array, y_array, c=shapes, s=10)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Schema(\n",
       "    (Field['L'](name=\"id\", doc=\"unique ID\"), Key<L>(offset=0, nElements=1)),\n",
       "    (Field['Angle'](name=\"coord_ra\", doc=\"position in ra/dec\"), Key<Angle>(offset=8, nElements=1)),\n",
       "    (Field['Angle'](name=\"coord_dec\", doc=\"position in ra/dec\"), Key<Angle>(offset=16, nElements=1)),\n",
       "    (Field['L'](name=\"parent\", doc=\"unique ID of parent source\"), Key<L>(offset=24, nElements=1)),\n",
       "    (Field['Flag'](name=\"calib_detected\", doc=\"Source was detected as an icSource\"), Key['Flag'](offset=32, bit=0)),\n",
       "    (Field['Flag'](name=\"calib_psfCandidate\", doc=\"Flag set if the source was a candidate for PSF determination, as determined by the star selector.\"), Key['Flag'](offset=32, bit=1)),\n",
       "    (Field['Flag'](name=\"calib_psfUsed\", doc=\"Flag set if the source was actually used for PSF determination, as determined by the\"), Key['Flag'](offset=32, bit=2)),\n",
       "    (Field['Flag'](name=\"calib_psf_reserved\", doc=\"set if source was reserved from PSF determination\"), Key['Flag'](offset=32, bit=3)),\n",
       "    (Field['I'](name=\"deblend_nChild\", doc=\"Number of children this object has (defaults to 0)\"), Key<I>(offset=40, nElements=1)),\n",
       "    (Field['Flag'](name=\"deblend_deblendedAsPsf\", doc=\"Deblender thought this source looked like a PSF\"), Key['Flag'](offset=32, bit=4)),\n",
       "    (Field['D'](name=\"deblend_psfCenter_x\", doc=\"If deblended-as-psf, the PSF centroid\", units=\"pixel\"), Key<D>(offset=48, nElements=1)),\n",
       "    (Field['D'](name=\"deblend_psfCenter_y\", doc=\"If deblended-as-psf, the PSF centroid\", units=\"pixel\"), Key<D>(offset=56, nElements=1)),\n",
       "    (Field['D'](name=\"deblend_psfFlux\", doc=\"If deblended-as-psf, the PSF flux\"), Key<D>(offset=64, nElements=1)),\n",
       "    (Field['Flag'](name=\"deblend_tooManyPeaks\", doc=\"Source had too many peaks; only the brightest were included\"), Key['Flag'](offset=32, bit=5)),\n",
       "    (Field['Flag'](name=\"deblend_parentTooBig\", doc=\"Parent footprint covered too many pixels\"), Key['Flag'](offset=32, bit=6)),\n",
       "    (Field['Flag'](name=\"deblend_masked\", doc=\"Parent footprint was predominantly masked\"), Key['Flag'](offset=32, bit=7)),\n",
       "    (Field['Flag'](name=\"deblend_skipped\", doc=\"Deblender skipped this source\"), Key['Flag'](offset=32, bit=8)),\n",
       "    (Field['Flag'](name=\"deblend_rampedTemplate\", doc=\"This source was near an image edge and the deblender used \"ramp\" edge-handling.\"), Key['Flag'](offset=32, bit=9)),\n",
       "    (Field['Flag'](name=\"deblend_patchedTemplate\", doc=\"This source was near an image edge and the deblender used \"patched\" edge-handling.\"), Key['Flag'](offset=32, bit=10)),\n",
       "    (Field['Flag'](name=\"deblend_hasStrayFlux\", doc=\"This source was assigned some stray flux\"), Key['Flag'](offset=32, bit=11)),\n",
       "    (Field['D'](name=\"base_GaussianCentroid_x\", doc=\"centroid from Gaussian Centroid algorithm\", units=\"pixel\"), Key<D>(offset=72, nElements=1)),\n",
       "    (Field['D'](name=\"base_GaussianCentroid_y\", doc=\"centroid from Gaussian Centroid algorithm\", units=\"pixel\"), Key<D>(offset=80, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_GaussianCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=12)),\n",
       "    (Field['Flag'](name=\"base_GaussianCentroid_flag_noPeak\", doc=\"Fitted Centroid has a negative peak\"), Key['Flag'](offset=32, bit=13)),\n",
       "    (Field['Flag'](name=\"base_GaussianCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=32, bit=14)),\n",
       "    (Field['D'](name=\"base_NaiveCentroid_x\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key<D>(offset=88, nElements=1)),\n",
       "    (Field['D'](name=\"base_NaiveCentroid_y\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key<D>(offset=96, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_NaiveCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=15)),\n",
       "    (Field['Flag'](name=\"base_NaiveCentroid_flag_noCounts\", doc=\"Object to be centroided has no counts\"), Key['Flag'](offset=32, bit=16)),\n",
       "    (Field['Flag'](name=\"base_NaiveCentroid_flag_edge\", doc=\"Object too close to edge\"), Key['Flag'](offset=32, bit=17)),\n",
       "    (Field['Flag'](name=\"base_NaiveCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=32, bit=18)),\n",
       "    (Field['D'](name=\"base_SdssCentroid_x\", doc=\"centroid from Sdss Centroid algorithm\", units=\"pixel\"), Key<D>(offset=104, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssCentroid_y\", doc=\"centroid from Sdss Centroid algorithm\", units=\"pixel\"), Key<D>(offset=112, nElements=1)),\n",
       "    (Field['F'](name=\"base_SdssCentroid_xSigma\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key<F>(offset=120, nElements=1)),\n",
       "    (Field['F'](name=\"base_SdssCentroid_ySigma\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key<F>(offset=124, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_SdssCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=19)),\n",
       "    (Field['Flag'](name=\"base_SdssCentroid_flag_edge\", doc=\"Object too close to edge\"), Key['Flag'](offset=32, bit=20)),\n",
       "    (Field['Flag'](name=\"base_SdssCentroid_flag_noSecondDerivative\", doc=\"Vanishing second derivative\"), Key['Flag'](offset=32, bit=21)),\n",
       "    (Field['Flag'](name=\"base_SdssCentroid_flag_almostNoSecondDerivative\", doc=\"Almost vanishing second derivative\"), Key['Flag'](offset=32, bit=22)),\n",
       "    (Field['Flag'](name=\"base_SdssCentroid_flag_notAtMaximum\", doc=\"Object is not at a maximum\"), Key['Flag'](offset=32, bit=23)),\n",
       "    (Field['Flag'](name=\"base_SdssCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=32, bit=24)),\n",
       "    (Field['D'](name=\"base_Blendedness_old\", doc=\"blendedness from dot products: (child.dot(parent)/child.dot(child) - 1)\"), Key<D>(offset=128, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_flux\", doc=\"measure of how flux is affected by neighbors: (1 - flux.child/flux.parent)\"), Key<D>(offset=136, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_flux_child\", doc=\"flux of the child, measured with a Gaussian weight matched to the child\", units=\"count\"), Key<D>(offset=144, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_flux_parent\", doc=\"flux of the parent, measured with a Gaussian weight matched to the child\", units=\"count\"), Key<D>(offset=152, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_flux\", doc=\"measure of how flux is affected by neighbors: (1 - flux.child/flux.parent)\"), Key<D>(offset=160, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_flux_child\", doc=\"flux of the child, measured with a Gaussian weight matched to the child\", units=\"count\"), Key<D>(offset=168, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_flux_parent\", doc=\"flux of the parent, measured with a Gaussian weight matched to the child\", units=\"count\"), Key<D>(offset=176, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_child_xx\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=184, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_child_yy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=192, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_child_xy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=200, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_parent_xx\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=208, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_parent_yy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=216, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_raw_parent_xy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=224, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_child_xx\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=232, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_child_yy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=240, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_child_xy\", doc=\"shape of the child, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=248, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_parent_xx\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=256, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_parent_yy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=264, nElements=1)),\n",
       "    (Field['D'](name=\"base_Blendedness_abs_parent_xy\", doc=\"shape of the parent, measured with a Gaussian weight matched to the child\", units=\"pixel^2\"), Key<D>(offset=272, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_Blendedness_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=25)),\n",
       "    (Field['Flag'](name=\"base_Blendedness_flag_noCentroid\", doc=\"Object has no centroid\"), Key['Flag'](offset=32, bit=26)),\n",
       "    (Field['Flag'](name=\"base_Blendedness_flag_noShape\", doc=\"Object has no shape\"), Key['Flag'](offset=32, bit=27)),\n",
       "    (Field['D'](name=\"base_SdssShape_xx\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key<D>(offset=280, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_yy\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key<D>(offset=288, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_xy\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key<D>(offset=296, nElements=1)),\n",
       "    (Field['F'](name=\"base_SdssShape_xxSigma\", doc=\"1-sigma uncertainty on xx moment\", units=\"pixel^2\"), Key<F>(offset=304, nElements=1)),\n",
       "    (Field['F'](name=\"base_SdssShape_yySigma\", doc=\"1-sigma uncertainty on yy moment\", units=\"pixel^2\"), Key<F>(offset=308, nElements=1)),\n",
       "    (Field['F'](name=\"base_SdssShape_xySigma\", doc=\"1-sigma uncertainty on xy moment\", units=\"pixel^2\"), Key<F>(offset=312, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_x\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel\"), Key<D>(offset=320, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_y\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel\"), Key<D>(offset=328, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_flux\", doc=\"elliptical Gaussian adaptive moments\", units=\"count\"), Key<D>(offset=336, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=344, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_psf_xx\", doc=\"adaptive moments of the PSF model at the object position\", units=\"pixel^2\"), Key<D>(offset=352, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_psf_yy\", doc=\"adaptive moments of the PSF model at the object position\", units=\"pixel^2\"), Key<D>(offset=360, nElements=1)),\n",
       "    (Field['D'](name=\"base_SdssShape_psf_xy\", doc=\"adaptive moments of the PSF model at the object position\", units=\"pixel^2\"), Key<D>(offset=368, nElements=1)),\n",
       "    (Field['F'](name=\"base_SdssShape_flux_xx_Cov\", doc=\"uncertainty covariance between base_SdssShape_flux and base_SdssShape_xx\", units=\"count*pixel^2\"), Key<F>(offset=376, nElements=1)),\n",
       "    (Field['F'](name=\"base_SdssShape_flux_yy_Cov\", doc=\"uncertainty covariance between base_SdssShape_flux and base_SdssShape_yy\", units=\"count*pixel^2\"), Key<F>(offset=380, nElements=1)),\n",
       "    (Field['F'](name=\"base_SdssShape_flux_xy_Cov\", doc=\"uncertainty covariance between base_SdssShape_flux and base_SdssShape_xy\", units=\"count*pixel^2\"), Key<F>(offset=384, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_SdssShape_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=28)),\n",
       "    (Field['Flag'](name=\"base_SdssShape_flag_unweightedBad\", doc=\"Both weighted and unweighted moments were invalid\"), Key['Flag'](offset=32, bit=29)),\n",
       "    (Field['Flag'](name=\"base_SdssShape_flag_unweighted\", doc=\"Weighted moments converged to an invalid value; using unweighted moments\"), Key['Flag'](offset=32, bit=30)),\n",
       "    (Field['Flag'](name=\"base_SdssShape_flag_shift\", doc=\"centroid shifted by more than the maximum allowed amount\"), Key['Flag'](offset=32, bit=31)),\n",
       "    (Field['Flag'](name=\"base_SdssShape_flag_maxIter\", doc=\"Too many iterations in adaptive moments\"), Key['Flag'](offset=32, bit=32)),\n",
       "    (Field['Flag'](name=\"base_SdssShape_flag_psf\", doc=\"Failure in measuring PSF model shape\"), Key['Flag'](offset=32, bit=33)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_x\", doc=\"HSM Centroid\", units=\"pixel\"), Key<D>(offset=392, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_y\", doc=\"HSM Centroid\", units=\"pixel\"), Key<D>(offset=400, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_xx\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=408, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_yy\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=416, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_xy\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=424, nElements=1)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=32, bit=34)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_no_pixels\", doc=\"no pixels to measure\"), Key['Flag'](offset=32, bit=35)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_not_contained\", doc=\"center not contained in footprint bounding box\"), Key['Flag'](offset=32, bit=36)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_parent_source\", doc=\"parent source, ignored\"), Key['Flag'](offset=32, bit=37)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmShapeRegauss_e1\", doc=\"PSF-corrected shear using Hirata & Seljak (2003) ''regaussianization\"), Key<D>(offset=432, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmShapeRegauss_e2\", doc=\"PSF-corrected shear using Hirata & Seljak (2003) ''regaussianization\"), Key<D>(offset=440, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmShapeRegauss_sigma\", doc=\"PSF-corrected shear using Hirata & Seljak (2003) ''regaussianization\"), Key<D>(offset=448, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmShapeRegauss_resolution\", doc=\"resolution factor (0=unresolved, 1=resolved)\"), Key<D>(offset=456, nElements=1)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmShapeRegauss_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=32, bit=38)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmShapeRegauss_flag_no_pixels\", doc=\"no pixels to measure\"), Key['Flag'](offset=32, bit=39)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmShapeRegauss_flag_not_contained\", doc=\"center not contained in footprint bounding box\"), Key['Flag'](offset=32, bit=40)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmShapeRegauss_flag_parent_source\", doc=\"parent source, ignored\"), Key['Flag'](offset=32, bit=41)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmShapeRegauss_flag_galsim\", doc=\"GalSim failure\"), Key['Flag'](offset=32, bit=42)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_x\", doc=\"HSM Centroid\", units=\"pixel\"), Key<D>(offset=464, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_y\", doc=\"HSM Centroid\", units=\"pixel\"), Key<D>(offset=472, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_xx\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=480, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_yy\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=488, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_xy\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=496, nElements=1)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=32, bit=43)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_no_pixels\", doc=\"no pixels to measure\"), Key['Flag'](offset=32, bit=44)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_not_contained\", doc=\"center not contained in footprint bounding box\"), Key['Flag'](offset=32, bit=45)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_parent_source\", doc=\"parent source, ignored\"), Key['Flag'](offset=32, bit=46)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_x\", doc=\"HSM Centroid\", units=\"pixel\"), Key<D>(offset=504, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_y\", doc=\"HSM Centroid\", units=\"pixel\"), Key<D>(offset=512, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_xx\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=520, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_yy\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=528, nElements=1)),\n",
       "    (Field['D'](name=\"ext_shapeHSM_HsmSourceMomentsRound_xy\", doc=\"HSM moments\", units=\"pixel^2\"), Key<D>(offset=536, nElements=1)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMomentsRound_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=32, bit=47)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMomentsRound_flag_no_pixels\", doc=\"no pixels to measure\"), Key['Flag'](offset=32, bit=48)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMomentsRound_flag_not_contained\", doc=\"center not contained in footprint bounding box\"), Key['Flag'](offset=32, bit=49)),\n",
       "    (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMomentsRound_flag_parent_source\", doc=\"parent source, ignored\"), Key['Flag'](offset=32, bit=50)),\n",
       "    (Field['F'](name=\"ext_shapeHSM_HsmSourceMomentsRound_Flux\", doc=\"HSM flux\"), Key<F>(offset=544, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_3_0_flux\", doc=\"flux within 3.000000-pixel aperture\", units=\"count\"), Key<D>(offset=552, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_3_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=560, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=51)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=32, bit=52)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=32, bit=53)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_4_5_flux\", doc=\"flux within 4.500000-pixel aperture\", units=\"count\"), Key<D>(offset=568, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_4_5_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=576, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=54)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=32, bit=55)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=32, bit=56)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_6_0_flux\", doc=\"flux within 6.000000-pixel aperture\", units=\"count\"), Key<D>(offset=584, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_6_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=592, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_6_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=57)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_6_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=32, bit=58)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=32, bit=59)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_9_0_flux\", doc=\"flux within 9.000000-pixel aperture\", units=\"count\"), Key<D>(offset=600, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_9_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=608, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_9_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=60)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_9_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=32, bit=61)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=32, bit=62)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_12_0_flux\", doc=\"flux within 12.000000-pixel aperture\", units=\"count\"), Key<D>(offset=616, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_12_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=624, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_12_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=32, bit=63)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_12_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=0)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_12_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=632, bit=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_17_0_flux\", doc=\"flux within 17.000000-pixel aperture\", units=\"count\"), Key<D>(offset=640, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_17_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=648, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_17_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=2)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_17_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=3)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_25_0_flux\", doc=\"flux within 25.000000-pixel aperture\", units=\"count\"), Key<D>(offset=656, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_25_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=664, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_25_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=4)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_25_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=5)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_35_0_flux\", doc=\"flux within 35.000000-pixel aperture\", units=\"count\"), Key<D>(offset=672, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_35_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=680, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_35_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=6)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_35_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=7)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_50_0_flux\", doc=\"flux within 50.000000-pixel aperture\", units=\"count\"), Key<D>(offset=688, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_50_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=696, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_50_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=8)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_50_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=9)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_70_0_flux\", doc=\"flux within 70.000000-pixel aperture\", units=\"count\"), Key<D>(offset=704, nElements=1)),\n",
       "    (Field['D'](name=\"base_CircularApertureFlux_70_0_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=712, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_70_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=10)),\n",
       "    (Field['Flag'](name=\"base_CircularApertureFlux_70_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=632, bit=11)),\n",
       "    (Field['D'](name=\"base_GaussianFlux_flux\", doc=\"flux from Gaussian Flux algorithm\", units=\"count\"), Key<D>(offset=720, nElements=1)),\n",
       "    (Field['D'](name=\"base_GaussianFlux_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=728, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_GaussianFlux_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=12)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag\", doc=\"general failure flag, set if anything went wring\"), Key['Flag'](offset=632, bit=13)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_offimage\", doc=\"Source center is off image\"), Key['Flag'](offset=632, bit=14)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_edge\", doc=\"Source is outside usable exposure region (masked EDGE or NO_DATA)\"), Key['Flag'](offset=632, bit=15)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_interpolated\", doc=\"Interpolated pixel in the Source footprint\"), Key['Flag'](offset=632, bit=16)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_saturated\", doc=\"Saturated pixel in the Source footprint\"), Key['Flag'](offset=632, bit=17)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_cr\", doc=\"Cosmic ray in the Source footprint\"), Key['Flag'](offset=632, bit=18)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_bad\", doc=\"Bad pixel in the Source footprint\"), Key['Flag'](offset=632, bit=19)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_suspect\", doc=\"Source''s footprint includes suspect pixels\"), Key['Flag'](offset=632, bit=20)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_interpolatedCenter\", doc=\"Interpolated pixel in the Source center\"), Key['Flag'](offset=632, bit=21)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_saturatedCenter\", doc=\"Saturated pixel in the Source center\"), Key['Flag'](offset=632, bit=22)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_crCenter\", doc=\"Cosmic ray in the Source center\"), Key['Flag'](offset=632, bit=23)),\n",
       "    (Field['Flag'](name=\"base_PixelFlags_flag_suspectCenter\", doc=\"Source''s center is close to suspect pixels\"), Key['Flag'](offset=632, bit=24)),\n",
       "    (Field['D'](name=\"base_PsfFlux_flux\", doc=\"flux derived from linear least-squares fit of PSF model\", units=\"count\"), Key<D>(offset=736, nElements=1)),\n",
       "    (Field['D'](name=\"base_PsfFlux_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=744, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_PsfFlux_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=632, bit=25)),\n",
       "    (Field['Flag'](name=\"base_PsfFlux_flag_noGoodPixels\", doc=\"not enough non-rejected pixels in data to attempt the fit\"), Key['Flag'](offset=632, bit=26)),\n",
       "    (Field['Flag'](name=\"base_PsfFlux_flag_edge\", doc=\"object was too close to the edge of the image to use the full PSF model\"), Key['Flag'](offset=632, bit=27)),\n",
       "    (Field['Flag'](name=\"base_Variance_flag\", doc=\"Set for any fatal failure\"), Key['Flag'](offset=632, bit=28)),\n",
       "    (Field['D'](name=\"base_Variance_value\", doc=\"Variance at object position\"), Key<D>(offset=752, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_Variance_flag_emptyFootprint\", doc=\"Set to True when the footprint has no usable pixels\"), Key['Flag'](offset=632, bit=29)),\n",
       "    (Field['D'](name=\"ext_photometryKron_KronFlux_flux\", doc=\"flux from Kron Flux algorithm\", units=\"count\"), Key<D>(offset=760, nElements=1)),\n",
       "    (Field['D'](name=\"ext_photometryKron_KronFlux_fluxSigma\", doc=\"1-sigma flux uncertainty\", units=\"count\"), Key<D>(offset=768, nElements=1)),\n",
       "    (Field['F'](name=\"ext_photometryKron_KronFlux_radius\", doc=\"Kron radius (sqrt(a*b))\"), Key<F>(offset=776, nElements=1)),\n",
       "    (Field['F'](name=\"ext_photometryKron_KronFlux_radius_for_radius\", doc=\"radius used to estimate <radius> (sqrt(a*b))\"), Key<F>(offset=780, nElements=1)),\n",
       "    (Field['F'](name=\"ext_photometryKron_KronFlux_psf_radius\", doc=\"Radius of PSF\"), Key<F>(offset=784, nElements=1)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=632, bit=30)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_edge\", doc=\"bad measurement due to image edge\"), Key['Flag'](offset=632, bit=31)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_bad_shape_no_psf\", doc=\"bad shape and no PSF\"), Key['Flag'](offset=632, bit=32)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_no_minimum_radius\", doc=\"minimum radius could not enforced: no minimum value or PSF\"), Key['Flag'](offset=632, bit=33)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_no_fallback_radius\", doc=\"no minimum radius and no PSF provided\"), Key['Flag'](offset=632, bit=34)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_bad_radius\", doc=\"bad Kron radius\"), Key['Flag'](offset=632, bit=35)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_used_minimum_radius\", doc=\"used the minimum radius for the Kron aperture\"), Key['Flag'](offset=632, bit=36)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_used_psf_radius\", doc=\"used the PSF Kron radius for the Kron aperture\"), Key['Flag'](offset=632, bit=37)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_small_radius\", doc=\"measured Kron radius was smaller than that of the PSF\"), Key['Flag'](offset=632, bit=38)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_bad_shape\", doc=\"shape for measuring Kron radius is bad; used PSF shape\"), Key['Flag'](offset=632, bit=39)),\n",
       "    (Field['D'](name=\"base_GaussianFlux_apCorr\", doc=\"aperture correction applied to base_GaussianFlux\"), Key<D>(offset=792, nElements=1)),\n",
       "    (Field['D'](name=\"base_GaussianFlux_apCorrSigma\", doc=\"aperture correction applied to base_GaussianFlux\"), Key<D>(offset=800, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_GaussianFlux_flag_apCorr\", doc=\"set if unable to aperture correct base_GaussianFlux\"), Key['Flag'](offset=632, bit=40)),\n",
       "    (Field['D'](name=\"ext_photometryKron_KronFlux_apCorr\", doc=\"aperture correction applied to ext_photometryKron_KronFlux\"), Key<D>(offset=808, nElements=1)),\n",
       "    (Field['D'](name=\"ext_photometryKron_KronFlux_apCorrSigma\", doc=\"aperture correction applied to ext_photometryKron_KronFlux\"), Key<D>(offset=816, nElements=1)),\n",
       "    (Field['Flag'](name=\"ext_photometryKron_KronFlux_flag_apCorr\", doc=\"set if unable to aperture correct ext_photometryKron_KronFlux\"), Key['Flag'](offset=632, bit=41)),\n",
       "    (Field['D'](name=\"base_PsfFlux_apCorr\", doc=\"aperture correction applied to base_PsfFlux\"), Key<D>(offset=824, nElements=1)),\n",
       "    (Field['D'](name=\"base_PsfFlux_apCorrSigma\", doc=\"aperture correction applied to base_PsfFlux\"), Key<D>(offset=832, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_PsfFlux_flag_apCorr\", doc=\"set if unable to aperture correct base_PsfFlux\"), Key['Flag'](offset=632, bit=42)),\n",
       "    (Field['D'](name=\"base_ClassificationExtendedness_value\", doc=\"Set to 1 for extended sources, 0 for point sources.\"), Key<D>(offset=840, nElements=1)),\n",
       "    (Field['Flag'](name=\"base_ClassificationExtendedness_flag\", doc=\"Set to 1 for any fatal failure.\"), Key['Flag'](offset=632, bit=43)),\n",
       "    (Field['I'](name=\"base_FootprintArea_value\", doc=\"Number of pixels in the source''s detection footprint.\", units=\"pixel\"), Key<I>(offset=848, nElements=1)),\n",
       "    (Field['Flag'](name=\"calib_astrometryUsed\", doc=\"set if source was used in astrometric calibration\"), Key['Flag'](offset=632, bit=44)),\n",
       "    (Field['Flag'](name=\"calib_photometry_used\", doc=\"set if source was used in photometric calibration\"), Key['Flag'](offset=632, bit=45)),\n",
       "    (Field['Flag'](name=\"calib_photometry_reserved\", doc=\"set if source was reserved from photometric calibration\"), Key['Flag'](offset=632, bit=46)),\n",
       "    'base_CircularApertureFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'base_GaussianCentroid_flag_badInitialCentroid'->'base_SdssCentroid_flag'\n",
       "    'base_GaussianFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'base_GaussianFlux_flag_badShape'->'ext_shapeHSM_HsmSourceMoments_flag'\n",
       "    'base_NaiveCentroid_flag_badInitialCentroid'->'base_SdssCentroid_flag'\n",
       "    'base_PsfFlux_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'base_SdssShape_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'base_Variance_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'ext_photometryKron_KronFlux_flag_badInitialCentroid'->'base_SdssCentroid_flag'\n",
       "    'ext_shapeHSM_HsmPsfMoments_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'ext_shapeHSM_HsmShapeRegauss_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'ext_shapeHSM_HsmSourceMoments_flag_badCentroid'->'base_SdssCentroid_flag'\n",
       "    'slot_ApFlux'->'base_CircularApertureFlux_12_0'\n",
       "    'slot_CalibFlux'->'base_CircularApertureFlux_12_0'\n",
       "    'slot_Centroid'->'base_SdssCentroid'\n",
       "    'slot_InstFlux'->'base_GaussianFlux'\n",
       "    'slot_ModelFlux'->'base_GaussianFlux'\n",
       "    'slot_PsfFlux'->'base_PsfFlux'\n",
       "    'slot_PsfShape'->'ext_shapeHSM_HsmPsfMoments'\n",
       "    'slot_Shape'->'ext_shapeHSM_HsmSourceMoments'\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_src = butler.get('src', **dataid)\n",
    "my_src.schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(my_src.get('calib_psfCandidate')) # Number of stars used for PSF fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "star_idx = np.where(my_src.get('base_ClassificationExtendedness_value') == 0)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "star_shape = []\n",
    "psf_shape = []\n",
    "for x, y in zip(my_src.getX()[star_idx], my_src.getY()[star_idx]):\n",
    "    point = Point2D(x, y)\n",
    "    psf_shape.append(psf.computeShape(point).getTraceRadius())\n",
    "    #psf_shape.append(psf.computeShape(point).getDeterminantRadius())\n",
    "#for xx, yy, xy in zip(my_src.get('base_SdssShape_psf_xx')[star_idx], \n",
    "#                      my_src.get('base_SdssShape_psf_yy')[star_idx],\n",
    "#                      my_src.get('base_SdssShape_psf_xy')[star_idx]):\n",
    "for xx, yy, xy in zip(my_src.get('base_SdssShape_xx')[star_idx], \n",
    "                      my_src.get('base_SdssShape_yy')[star_idx],\n",
    "                      my_src.get('base_SdssShape_xy')[star_idx]):    \n",
    "    star_shape.append(np.sqrt((xx + yy) / 2.)) # For trace radius\n",
    "    #star_shape.append((xx * yy - xy * xy)**(1. / 4.)) # For determinant radius\n",
    "star_shape = np.array(star_shape)\n",
    "psf_shape = np.array(psf_shape)\n",
    "cut = np.isfinite(star_shape) & np.isfinite(psf_shape)\n",
    "star_shape = star_shape[cut]\n",
    "psf_shape = psf_shape[cut]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.9749031140501225 5.317208786354462 -0.0032552543928822683\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55e3651b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(psf_shape, np.array(psf_shape) / np.array(star_shape), alpha=0.5)\n",
    "#plt.xlim(2., 5.)\n",
    "#plt.ylim(.5, 1.)\n",
    "print(xx, yy, xy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.hist(psf_shape)\n",
    "#plt.hist(star_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9958168441901718"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(psf_shape / star_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8932545671067953"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.2 * 2. * np.median(star_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "LSST_Stack (Python 3)",
   "language": "python",
   "name": "lsst_stack"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}