Append to FITS BinTable HDU.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.5.2 (default, Jul 14 2016, 10:29:47) \\n[GCC 4.2.1 Compatible Apple LLVM 7.3.0 (clang-703.0.29)]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import astropy\n",
    "astropy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# example recarray\n",
    "stars_rec = rec.array([(1,'Sirius', -1.45, 'A1V'),\n",
    "                    (2,'Canopus', -0.73, 'F0Ib'),\n",
    "                    (3,'Rigil Kent', -0.1, 'G2V')],\n",
    "                    formats='int16,a20,float32,a10',\n",
    "                    names='order,name,mag,Sp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.recarray"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(stars_rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stars_bintable = fits.BinTableHDU(stars_rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FITS_rec([(1, 'Sirius', -1.45, 'A1V'), (2, 'Canopus', -0.73000002, 'F0Ib'),\n",
       "       (3, 'Rigil Kent', -0.1, 'G2V')], \n",
       "      dtype=(numpy.record, [('order', '<i2'), ('name', 'S20'), ('mag', '<f4'), ('Sp', 'S10')]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_bintable.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.io.fits.fitsrec.FITS_rec"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(stars_bintable.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: (No file associated with this HDUList)\n",
      "No.    Name         Type      Cards   Dimensions   Format\n",
      "0    PRIMARY     PrimaryHDU       4   ()              \n",
      "1                ImageHDU         7   (16, 16)     float64   \n",
      "2                BinTableHDU     16   3R x 4C      ['I', '20A', 'E', '10A']   \n"
     ]
    }
   ],
   "source": [
    "# Build up multi-extension FITS\n",
    "extensions = [\n",
    "    fits.PrimaryHDU(),\n",
    "    fits.ImageHDU(np.random.randn(16, 16)),\n",
    "    stars_bintable,\n",
    "]\n",
    "hdul = fits.HDUList(extensions)\n",
    "hdul.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hdul.writeto('./multi_extension_img_and_table.fits', clobber=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try appending a new record to the `FITS_rec` array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stars_fits_rec = stars_bintable.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 'Sirius', -1.45, 'A1V')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_fits_rec[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_row = np.copy(stars_fits_rec[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', 'Sirius', '-1.45', 'A1V'], \n",
       "      dtype='<U6')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "invalid type promotion",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-d79cc0b62aea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstars_fits_rec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_row\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/jlong/homebrew/Cellar/python3/3.5.2/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mappend\u001b[0;34m(arr, values, axis)\u001b[0m\n\u001b[1;32m   4584\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4585\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: invalid type promotion"
     ]
    }
   ],
   "source": [
    "np.append(stars_fits_rec, new_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, maybe the problem is that it's not really a FITS_rec?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(new_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.io.fits.fitsrec.FITS_record"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(stars_fits_rec[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try appending an existing row..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "invalid type promotion",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-5852a3c4b04f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstars_fits_rec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstars_fits_rec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/jlong/homebrew/Cellar/python3/3.5.2/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mappend\u001b[0;34m(arr, values, axis)\u001b[0m\n\u001b[1;32m   4584\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4585\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: invalid type promotion"
     ]
    }
   ],
   "source": [
    "np.append(stars_fits_rec, stars_fits_rec[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For comparison, the same thing with NumPy `recarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stars_rec_copy = stars_rec.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_row = np.copy(stars_rec_copy[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1, b'Sirius', -1.4500000476837158, b'A1V'),\n",
       "       (2, b'Canopus', -0.7300000190734863, b'F0Ib'),\n",
       "       (3, b'Rigil Kent', -0.10000000149011612, b'G2V'),\n",
       "       (1, b'Sirius', -1.4500000476837158, b'A1V')], \n",
       "      dtype=(numpy.record, [('order', '<i2'), ('name', 'S20'), ('mag', '<f4'), ('Sp', 'S10')]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.append(stars_rec_copy, new_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instead, there's a workaround with `astropy.table.Table`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=3&gt;\n",
       "<table id=\"table4410908288\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>order</th><th>name</th><th>mag</th><th>Sp</th></tr></thead>\n",
       "<thead><tr><th>int16</th><th>str20</th><th>float32</th><th>str10</th></tr></thead>\n",
       "<tr><td>1</td><td>Sirius</td><td>-1.45</td><td>A1V</td></tr>\n",
       "<tr><td>2</td><td>Canopus</td><td>-0.73</td><td>F0Ib</td></tr>\n",
       "<tr><td>3</td><td>Rigil Kent</td><td>-0.1</td><td>G2V</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "order    name      mag     Sp \n",
       "int16   str20    float32 str10\n",
       "----- ---------- ------- -----\n",
       "    1     Sirius   -1.45   A1V\n",
       "    2    Canopus   -0.73  F0Ib\n",
       "    3 Rigil Kent    -0.1   G2V"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_tbl = Table.read('./multi_extension_img_and_table.fits', hdu=2)\n",
    "stars_tbl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note this uses a different keyword argument from `getdata`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FITS_rec([(1, 'Sirius', -1.45, 'A1V'), (2, 'Canopus', -0.73000002, 'F0Ib'),\n",
       "       (3, 'Rigil Kent', -0.1, 'G2V')], \n",
       "      dtype=(numpy.record, [('order', '>i2'), ('name', 'S20'), ('mag', '>f4'), ('Sp', 'S10')]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fits.getdata('./multi_extension_img_and_table.fits', ext=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Appending rows to an Astropy table is much easier!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stars_tbl.add_row([4, 'Deneb', 1.25, 'A2Ia'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=4&gt;\n",
       "<table id=\"table4410908288\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>order</th><th>name</th><th>mag</th><th>Sp</th></tr></thead>\n",
       "<thead><tr><th>int16</th><th>str20</th><th>float32</th><th>str10</th></tr></thead>\n",
       "<tr><td>1</td><td>Sirius</td><td>-1.45</td><td>A1V</td></tr>\n",
       "<tr><td>2</td><td>Canopus</td><td>-0.73</td><td>F0Ib</td></tr>\n",
       "<tr><td>3</td><td>Rigil Kent</td><td>-0.1</td><td>G2V</td></tr>\n",
       "<tr><td>4</td><td>Deneb</td><td>1.25</td><td>A2Ia</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=4>\n",
       "order    name      mag     Sp \n",
       "int16   str20    float32 str10\n",
       "----- ---------- ------- -----\n",
       "    1     Sirius   -1.45   A1V\n",
       "    2    Canopus   -0.73  F0Ib\n",
       "    3 Rigil Kent    -0.1   G2V\n",
       "    4      Deneb    1.25  A2Ia"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_tbl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, writing is not as easy, especially when updating a Mulit-Extension FITS file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_bintable_hdu = fits.table_to_hdu(stars_tbl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: ./multi_extension_img_and_table.fits\n",
      "No.    Name         Type      Cards   Dimensions   Format\n",
      "0    PRIMARY     PrimaryHDU       4   ()              \n",
      "1                ImageHDU         7   (16, 16)     float64   \n",
      "2                BinTableHDU     16   3R x 4C      [I, 20A, E, 10A]   \n"
     ]
    }
   ],
   "source": [
    "hdul = fits.open('./multi_extension_img_and_table.fits')\n",
    "hdul.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdul[2] = new_bintable_hdu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: ./multi_extension_img_and_table.fits\n",
      "No.    Name         Type      Cards   Dimensions   Format\n",
      "0    PRIMARY     PrimaryHDU       4   ()              \n",
      "1                ImageHDU         7   (16, 16)     float64   \n",
      "2                BinTableHDU     16   4R x 4C      ['I', '20A', 'E', '10A']   \n"
     ]
    }
   ],
   "source": [
    "hdul.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hdul.writeto('./multi_extension_img_and_table-updated.fits', clobber=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=4&gt;\n",
       "<table id=\"table4410905712\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>order</th><th>name</th><th>mag</th><th>Sp</th></tr></thead>\n",
       "<thead><tr><th>int16</th><th>str20</th><th>float32</th><th>str10</th></tr></thead>\n",
       "<tr><td>1</td><td>Sirius</td><td>-1.45</td><td>A1V</td></tr>\n",
       "<tr><td>2</td><td>Canopus</td><td>-0.73</td><td>F0Ib</td></tr>\n",
       "<tr><td>3</td><td>Rigil Kent</td><td>-0.1</td><td>G2V</td></tr>\n",
       "<tr><td>4</td><td>Deneb</td><td>1.25</td><td>A2Ia</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=4>\n",
       "order    name      mag     Sp \n",
       "int16   str20    float32 str10\n",
       "----- ---------- ------- -----\n",
       "    1     Sirius   -1.45   A1V\n",
       "    2    Canopus   -0.73  F0Ib\n",
       "    3 Rigil Kent    -0.1   G2V\n",
       "    4      Deneb    1.25  A2Ia"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table.read('./multi_extension_img_and_table-updated.fits', hdu=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternatively, casting to `numpy.recarray` and back works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_rec_arr = np.asarray(fits.getdata('./multi_extension_img_and_table.fits', ext=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1, b'Sirius', -1.4500000476837158, b'A1V'),\n",
       "       (2, b'Canopus', -0.7300000190734863, b'F0Ib'),\n",
       "       (3, b'Rigil Kent', -0.10000000149011612, b'G2V'),\n",
       "       (4, b'Deneb', 1.25, b'A2Ia')], \n",
       "      dtype=(numpy.record, [('order', '>i2'), ('name', 'S20'), ('mag', '>f4'), ('Sp', 'S10')]))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_rec_arr = np.append(temp_rec_arr, np.array([(4, 'Deneb', 1.25, 'A2Ia')], dtype=temp_rec_arr.dtype))\n",
    "new_rec_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: ./multi_extension_img_and_table.fits\n",
      "No.    Name         Type      Cards   Dimensions   Format\n",
      "0    PRIMARY     PrimaryHDU       4   ()              \n",
      "1                ImageHDU         7   (16, 16)     float64   \n",
      "2                BinTableHDU     16   4R x 4C      ['I', '20A', 'E', '10A']   \n"
     ]
    }
   ],
   "source": [
    "# reusing the open FITS file in hdul from above\n",
    "hdul[2] = fits.BinTableHDU(new_rec_arr)\n",
    "hdul.info()  # ensure it's still four rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdul.writeto('./multi_extension_img_and_table-updated-2.fits', clobber=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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