docs/source/tutorials/2.08-making-fits-files.ipynb

{
 "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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<astropy.io.fits.hdu.image.PrimaryHDU object at 0x7f2f1b73cf28>, <astropy.io.fits.hdu.table.BinTableHDU object at 0x7f2f1b714cf8>]"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "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    = '2018-10-23'         / 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 = '1.0b20  '                                                            \n",
       "QUARTER =                    3                                                  \n",
       "MISSION = 'Kepler  '                                                            \n",
       "DATE-OBS= '2009-09-18T17:53:34.165'                                             \n",
       "CHECKSUM= 'QTohRTnhQTnhQTnh'   / HDU checksum updated 2018-10-23T14:23:32       \n",
       "DATASUM = '0       '           / data unit checksum updated 2018-10-23T14:23:32 "
      ]
     },
     "execution_count": 6,
     "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": 7,
   "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= 'JEK1MDH1JDH1JDH1'   / HDU checksum updated 2018-10-23T14:23:32       \n",
       "DATASUM = '2548479009'         / data unit checksum updated 2018-10-23T14:23:32 "
      ]
     },
     "execution_count": 7,
     "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": 8,
   "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": 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    = '2018-10-23'         / 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 = '1.0b20  '                                                            \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= 'mDIAm9I3mCI9m9I9'   / HDU checksum updated 2018-10-23T14:23:38       \n",
       "DATASUM = '0       '           / data unit checksum updated 2018-10-23T14:23:38 "
      ]
     },
     "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": [
    "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": 11,
   "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= 'eoG5glG4elG4elG4'   / HDU checksum updated 2018-10-23T14:23:40       \n",
       "DATASUM = '2652111809'         / data unit checksum updated 2018-10-23T14:23:40 "
      ]
     },
     "execution_count": 11,
     "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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