{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to save a lightcurve in FITS format?\n",
    "\n",
    "Once you have detrended or altered a lightcurve in some way, you may want to save it as a FITS file. This allows you to easily share the file with your collaborators or submit your lightcurves as a [MAST High Level Science Product](https://archive.stsci.edu/hlsp/hlsp_guidelines.html) (HLSP). Lightkurve provides a `to_fits()` method which will easily convert your `LightCurve` object into a fits file.\n",
    "\n",
    "Below is a brief demostration showing how `to_fits()` works.\n",
    "\n",
    "Note: if you are considering contributing a HLSP you may want to read the [guidelines](https://archive.stsci.edu/hlsp/hlsp_guidelines_timeseries.html) for contributing fits files. These include which fits headers are required/suggested for your HLSP to be accepted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: editing and writing a lightcurve\n",
    "\n",
    "First we'll obtain a random Kepler lightcurve from MAST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightkurve import KeplerLightCurveFile\n",
    "lcf = KeplerLightCurveFile.from_archive(757076, quarter=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll make some edits to the lightcurve. Below we use the PDCSAP flux from MAST, remove NaN values and clip out any outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc = lcf.PDCSAP_FLUX.remove_nans().remove_outliers()\n",
    "lc.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the `to_fits` method to save the lightcurve to a file called *output.fits*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdu = lc.to_fits(path='output.fits', overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the file and check that it behaved as we expect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<astropy.io.fits.hdu.image.PrimaryHDU object at 0x7fdf49539eb8>, <astropy.io.fits.hdu.table.BinTableHDU object at 0x7fdf49448438>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.io import fits\n",
    "hdu = fits.open('output.fits')\n",
    "hdu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`hdu` is a set of astropy.io.fits objects, which is what we would expect. Lets take a look at the header of the first extension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standards                     \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    0 / number of array dimensions                     \n",
       "EXTEND  =                    T / file contains extensions                       \n",
       "NEXTEND =                    2 / number of standard extensions                  \n",
       "EXTNAME = 'PRIMARY '           / name of extension                              \n",
       "EXTVER  =                    1 / extension version number (not format version)  \n",
       "ORIGIN  = 'Unofficial data product' / institution responsible for file          \n",
       "DATE    = '2018-06-12'         / file creation date.                            \n",
       "CREATOR = 'lightkurve'         / pipeline job and program used t                \n",
       "TELESCOP= 'KEPLER  '           / telescope                                      \n",
       "INSTRUME= 'Kepler Photometer'  / detector type                                  \n",
       "OBJECT  = '757076  '           / string version of target id                    \n",
       "KEPLERID=               757076 / unique Kepler target identifier                \n",
       "CHANNEL =                   56 / CCD channel                                    \n",
       "RADESYS = 'ICRS    '           / reference frame of celestial coordinates       \n",
       "RA_OBJ  =            291.03872 / [deg] right ascension                          \n",
       "DEC_OBJ =             36.59813 / [deg] declination                              \n",
       "EQUINOX =                 2000 / equinox of celestial coordinate system         \n",
       "PROCVER = '1.0b7   '                                                            \n",
       "QUARTER =                    3                                                  \n",
       "MISSION = 'Kepler  '                                                            \n",
       "DATE-OBS= '2009-09-18T17:53:34.165'                                             \n",
       "CHECKSUM= 'QaoiTRnhQXnhQXnh'   / HDU checksum updated 2018-06-12T21:10:10       \n",
       "DATASUM = '0       '           / data unit checksum updated 2018-06-12T21:10:10 "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu[0].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like it has all the correct information about the target. What about the second extension?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / binary table extension                         \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                   20 / length of dimension 1                          \n",
       "NAXIS2  =                 4133 / length of dimension 2                          \n",
       "PCOUNT  =                    0 / number of group parameters                     \n",
       "GCOUNT  =                    1 / number of groups                               \n",
       "TFIELDS =                    4 / number of table fields                         \n",
       "TTYPE1  = 'TIME    '                                                            \n",
       "TFORM1  = 'D       '                                                            \n",
       "TUNIT1  = 'bkjd    '                                                            \n",
       "TTYPE2  = 'FLUX    '                                                            \n",
       "TFORM2  = 'E       '                                                            \n",
       "TUNIT2  = 'counts  '                                                            \n",
       "TTYPE3  = 'FLUX_ERR'                                                            \n",
       "TFORM3  = 'E       '                                                            \n",
       "TUNIT3  = 'counts  '                                                            \n",
       "TTYPE4  = 'CADENCENO'                                                           \n",
       "TFORM4  = 'J       '                                                            \n",
       "EXTNAME = 'LIGHTCURVE'                                                          \n",
       "CHECKSUM= '5iaGAfX98faGAfW9'   / HDU checksum updated 2018-06-12T21:10:10       \n",
       "DATASUM = '2444846209'         / data unit checksum updated 2018-06-12T21:10:10 "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu[1].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This extension has 4 columns, `TIME`, `FLUX`, `FLUX_ERR` and `CADENCENO`. This is great! What about if we wanted to add new keywords to our fits file? HLSP products require some extra keywords. Let's add some keywords to explain who made the data, and what our HLSP is. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdu = lc.to_fits(path='output.fits',\n",
    "                 overwrite=True,\n",
    "                 HLSPLEAD='Kepler/K2 GO office',\n",
    "                 HLSPNAME='TUTORIAL',\n",
    "                 CITATION='HEDGES2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standards                     \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    0 / number of array dimensions                     \n",
       "EXTEND  =                    T / file contains extensions                       \n",
       "NEXTEND =                    2 / number of standard extensions                  \n",
       "EXTNAME = 'PRIMARY '           / name of extension                              \n",
       "EXTVER  =                    1 / extension version number (not format version)  \n",
       "ORIGIN  = 'Unofficial data product' / institution responsible for file          \n",
       "DATE    = '2018-06-12'         / file creation date.                            \n",
       "CREATOR = 'lightkurve'         / pipeline job and program used t                \n",
       "TELESCOP= 'KEPLER  '           / telescope                                      \n",
       "INSTRUME= 'Kepler Photometer'  / detector type                                  \n",
       "OBJECT  = '757076  '           / string version of target id                    \n",
       "KEPLERID=               757076 / unique Kepler target identifier                \n",
       "CHANNEL =                   56 / CCD channel                                    \n",
       "RADESYS = 'ICRS    '           / reference frame of celestial coordinates       \n",
       "RA_OBJ  =            291.03872 / [deg] right ascension                          \n",
       "DEC_OBJ =             36.59813 / [deg] declination                              \n",
       "EQUINOX =                 2000 / equinox of celestial coordinate system         \n",
       "PROCVER = '1.0b7   '                                                            \n",
       "QUARTER =                    3                                                  \n",
       "HLSPLEAD= 'Kepler/K2 GO office'                                                 \n",
       "HLSPNAME= 'TUTORIAL'                                                            \n",
       "CITATION= 'HEDGES2018'                                                          \n",
       "MISSION = 'Kepler  '                                                            \n",
       "DATE-OBS= '2009-09-18T17:53:34.165'                                             \n",
       "CHECKSUM= 'mEIAoDI5mDIAmDI5'   / HDU checksum updated 2018-06-12T21:10:11       \n",
       "DATASUM = '0       '           / data unit checksum updated 2018-06-12T21:10:11 "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu[0].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our new keywords are included in the primary header! What about if we want to add more data columns to our fits file? We can simply add this in the same way. Let's add the data quality to our fits file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdu = lc.to_fits(path='output.fits',\n",
    "                 overwrite=True,\n",
    "                 HLSPLEAD='Kepler/K2 GO office',\n",
    "                 HLSPNAME='TUTORIAL',\n",
    "                 CITATION='HEDGES2018',\n",
    "                 QUALITY=lc.quality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / binary table extension                         \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                   28 / length of dimension 1                          \n",
       "NAXIS2  =                 4133 / length of dimension 2                          \n",
       "PCOUNT  =                    0 / number of group parameters                     \n",
       "GCOUNT  =                    1 / number of groups                               \n",
       "TFIELDS =                    5 / number of table fields                         \n",
       "TTYPE1  = 'TIME    '                                                            \n",
       "TFORM1  = 'D       '                                                            \n",
       "TUNIT1  = 'bkjd    '                                                            \n",
       "TTYPE2  = 'FLUX    '                                                            \n",
       "TFORM2  = 'E       '                                                            \n",
       "TUNIT2  = 'counts  '                                                            \n",
       "TTYPE3  = 'FLUX_ERR'                                                            \n",
       "TFORM3  = 'E       '                                                            \n",
       "TUNIT3  = 'counts  '                                                            \n",
       "TTYPE4  = 'CADENCENO'                                                           \n",
       "TFORM4  = 'J       '                                                            \n",
       "TTYPE5  = 'QUALITY '                                                            \n",
       "TFORM5  = 'K       '                                                            \n",
       "EXTNAME = 'LIGHTCURVE'                                                          \n",
       "CHECKSUM= 'WNWDXLVBWLVBWLVB'   / HDU checksum updated 2018-06-12T21:10:11       \n",
       "DATASUM = '2548479009'         / data unit checksum updated 2018-06-12T21:10:11 "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu[1].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the quality from our lightcurve appears in the second extension. Once all your lightcurves are saved as fits files and you have a README file, you can consider submitting your data produces to MAST."
   ]
  }
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