{
 "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 search_lightcurvefile\n",
    "lcf = search_lightcurvefile(757076, quarter=3).download()"
   ]
  },
  {
   "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": {
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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 755
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc = lcf.PDCSAP_FLUX.remove_nans().remove_outliers()\n",
    "lc.scatter();"
   ]
  },
  {
   "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 0x1c29b42c18>, <astropy.io.fits.hdu.table.BinTableHDU object at 0x1c29aff908>]"
      ]
     },
     "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    = '2019-01-25'         / file creation date.                            \n",
       "CREATOR = 'lightkurve.LightCurve.to_fits()' / 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 = '1.0b26.dev'                                                          \n",
       "MISSION = 'Kepler  '                                                            \n",
       "DATE-OBS= '2009-09-18T17:53:34.165'                                             \n",
       "QUARTER =                    3                                                  \n",
       "CHECKSUM= 'KXc1KXc0KXc0KXc0'   / HDU checksum updated 2019-01-25T15:35:55       \n",
       "DATASUM = '0       '           / data unit checksum updated 2019-01-25T15:35:55 "
      ]
     },
     "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  =                   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  = 'SAP_QUALITY'                                                         \n",
       "TFORM5  = 'K       '                                                            \n",
       "EXTNAME = 'LIGHTCURVE'                                                          \n",
       "CHECKSUM= 'IEGqJBGoIBGoIBGo'   / HDU checksum updated 2019-01-25T15:35:55       \n",
       "DATASUM = '2548479009'         / data unit checksum updated 2019-01-25T15:35:55 "
      ]
     },
     "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": [
    "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": [],
   "source": [
    "hdu = fits.open('output.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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    = '2019-01-25'         / file creation date.                            \n",
       "CREATOR = 'lightkurve.LightCurve.to_fits()' / 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 = '1.0b26.dev'                                                          \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",
       "QUARTER =                    3                                                  \n",
       "CHECKSUM= 'fFCDiC9CfCACfC9C'   / HDU checksum updated 2019-01-25T15:35:55       \n",
       "DATASUM = '0       '           / data unit checksum updated 2019-01-25T15:35:55 "
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdu = fits.open('output.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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  =                   36 / 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 =                    6 / 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",
       "TTYPE6  = 'SAP_QUALITY'                                                         \n",
       "TFORM6  = 'K       '                                                            \n",
       "EXTNAME = 'LIGHTCURVE'                                                          \n",
       "CHECKSUM= 'dmH3ejE2djE2djE2'   / HDU checksum updated 2019-01-25T15:35:55       \n",
       "DATASUM = '2652111809'         / data unit checksum updated 2019-01-25T15:35:55 "
      ]
     },
     "execution_count": 12,
     "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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