{
 "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()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.to_fits.html?highlight=to_fits#lightkurve.LightCurve.to_fits) method which will easily convert your [LightCurve](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.html?highlight=lightcurve#lightkurve.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": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:16.375717Z",
     "iopub.status.busy": "2023-11-03T14:13:16.375437Z",
     "iopub.status.idle": "2023-11-03T14:13:18.455652Z",
     "shell.execute_reply": "2023-11-03T14:13:18.455139Z"
    }
   },
   "outputs": [],
   "source": [
    "from lightkurve import search_lightcurve\n",
    "lc = search_lightcurve('KIC 757076', author=\"Kepler\", 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 using the [.remove_nans()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.remove_nans.html?highlight=remove_nans) function, and clip out any outliers using the [.remove_outliers()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.remove_outliers.html?highlight=remove_outliers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.458005Z",
     "iopub.status.busy": "2023-11-03T14:13:18.457848Z",
     "iopub.status.idle": "2023-11-03T14:13:18.690677Z",
     "shell.execute_reply": "2023-11-03T14:13:18.690295Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc = lc.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": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.692952Z",
     "iopub.status.busy": "2023-11-03T14:13:18.692833Z",
     "iopub.status.idle": "2023-11-03T14:13:18.718537Z",
     "shell.execute_reply": "2023-11-03T14:13:18.718218Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: TimeDeltaMissingUnitWarning: Numerical value without unit or explicit format passed to TimeDelta, assuming days [astropy.time.core]\n"
     ]
    }
   ],
   "source": [
    "lc.to_fits(path='demo-lightcurve.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": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.720207Z",
     "iopub.status.busy": "2023-11-03T14:13:18.720098Z",
     "iopub.status.idle": "2023-11-03T14:13:18.723093Z",
     "shell.execute_reply": "2023-11-03T14:13:18.722751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.io.fits.hdu.hdulist.HDUList"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.io import fits\n",
    "hdu = fits.open('demo-lightcurve.fits')\n",
    "type(hdu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.724558Z",
     "iopub.status.busy": "2023-11-03T14:13:18.724428Z",
     "iopub.status.idle": "2023-11-03T14:13:18.727304Z",
     "shell.execute_reply": "2023-11-03T14:13:18.727022Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: demo-lightcurve.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU      26   ()      \n",
      "  1  LIGHTCURVE    1 BinTableHDU     28   4133R x 7C   [D, E, E, J, K, D, D]   \n"
     ]
    }
   ],
   "source": [
    "hdu.info()"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.728912Z",
     "iopub.status.busy": "2023-11-03T14:13:18.728789Z",
     "iopub.status.idle": "2023-11-03T14:13:18.731472Z",
     "shell.execute_reply": "2023-11-03T14:13:18.731208Z"
    }
   },
   "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    = '2023-11-03'         / 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 = '2.4.2   '                                                            \n",
       "MISSION = 'Kepler  '                                                            \n",
       "DATE-OBS= '8730-10-27T17:53:34.165'                                             \n",
       "QUARTER =                    3                                                  \n",
       "CAMPAIGN=                                                                       \n",
       "CHECKSUM= 'DaZDGUY9DZYCDZY9'   / HDU checksum updated 2023-11-03T10:13:18       \n",
       "DATASUM = '0       '           / data unit checksum updated 2023-11-03T10:13:18 "
      ]
     },
     "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": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.733140Z",
     "iopub.status.busy": "2023-11-03T14:13:18.733024Z",
     "iopub.status.idle": "2023-11-03T14:13:18.735416Z",
     "shell.execute_reply": "2023-11-03T14:13:18.735063Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / binary table extension                         \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                   44 / 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 =                    7 / number of table fields                         \n",
       "TTYPE1  = 'TIME    '                                                            \n",
       "TFORM1  = 'D       '                                                            \n",
       "TUNIT1  = 'bkjd    '                                                            \n",
       "TTYPE2  = 'FLUX    '                                                            \n",
       "TFORM2  = 'E       '                                                            \n",
       "TUNIT2  = 'e-/s    '                                                            \n",
       "TTYPE3  = 'FLUX_ERR'                                                            \n",
       "TFORM3  = 'E       '                                                            \n",
       "TUNIT3  = 'e-/s    '                                                            \n",
       "TTYPE4  = 'CADENCENO'                                                           \n",
       "TFORM4  = 'J       '                                                            \n",
       "TTYPE5  = 'SAP_QUALITY'                                                         \n",
       "TFORM5  = 'K       '                                                            \n",
       "TTYPE6  = 'MOM_CENTR1'                                                          \n",
       "TFORM6  = 'D       '                                                            \n",
       "TTYPE7  = 'MOM_CENTR2'                                                          \n",
       "TFORM7  = 'D       '                                                            \n",
       "EXTNAME = 'LIGHTCURVE'                                                          \n",
       "CHECKSUM= 'BFhBCEe9BEeABEe7'   / HDU checksum updated 2023-11-03T10:13:18       \n",
       "DATASUM = '390244549'          / data unit checksum updated 2023-11-03T10:13:18 "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu[1].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This extension has 7 columns, `TIME`, `FLUX`, `FLUX_ERR`, `SAP_QUALITY`, `CADENCENO`, `MOM_CENTR1`, and `MOM_CENTR2`.  What 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": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.737010Z",
     "iopub.status.busy": "2023-11-03T14:13:18.736893Z",
     "iopub.status.idle": "2023-11-03T14:13:18.747842Z",
     "shell.execute_reply": "2023-11-03T14:13:18.747432Z"
    }
   },
   "outputs": [],
   "source": [
    "lc.to_fits(path='demo-lightcurve.fits',\n",
    "           overwrite=True,\n",
    "           HLSPLEAD='Kepler/K2 GO office',\n",
    "           HLSPNAME='TUTORIAL',\n",
    "           CITATION='HEDGES2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.749557Z",
     "iopub.status.busy": "2023-11-03T14:13:18.749432Z",
     "iopub.status.idle": "2023-11-03T14:13:18.751978Z",
     "shell.execute_reply": "2023-11-03T14:13:18.751711Z"
    }
   },
   "outputs": [],
   "source": [
    "hdu = fits.open('demo-lightcurve.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.753554Z",
     "iopub.status.busy": "2023-11-03T14:13:18.753435Z",
     "iopub.status.idle": "2023-11-03T14:13:18.755999Z",
     "shell.execute_reply": "2023-11-03T14:13:18.755678Z"
    }
   },
   "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    = '2023-11-03'         / 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 = '2.4.2   '                                                            \n",
       "HLSPLEAD= 'Kepler/K2 GO office'                                                 \n",
       "HLSPNAME= 'TUTORIAL'                                                            \n",
       "CITATION= 'HEDGES2018'                                                          \n",
       "MISSION = 'Kepler  '                                                            \n",
       "DATE-OBS= '8730-10-27T17:53:34.165'                                             \n",
       "QUARTER =                    3                                                  \n",
       "CAMPAIGN=                                                                       \n",
       "CHECKSUM= 'ZG6RhF3QZF3QfF3Q'   / HDU checksum updated 2023-11-03T10:13:18       \n",
       "DATASUM = '0       '           / data unit checksum updated 2023-11-03T10:13:18 "
      ]
     },
     "execution_count": 10,
     "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 data columns in the same way. Let's add the data quality to our fits file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.757767Z",
     "iopub.status.busy": "2023-11-03T14:13:18.757507Z",
     "iopub.status.idle": "2023-11-03T14:13:18.772496Z",
     "shell.execute_reply": "2023-11-03T14:13:18.772174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TimeDelta object: scale='None' format='jd' value=[-0.61680215 -0.61654885 -0.61577695 ...  0.61676757  0.61705094\n",
       "  0.61726379]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_vector = lc.fold(period=1.23456789).phase\n",
    "demo_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.774069Z",
     "iopub.status.busy": "2023-11-03T14:13:18.773957Z",
     "iopub.status.idle": "2023-11-03T14:13:18.784627Z",
     "shell.execute_reply": "2023-11-03T14:13:18.784253Z"
    }
   },
   "outputs": [],
   "source": [
    "lc.to_fits(path='demo-lightcurve.fits',\n",
    "           overwrite=True,\n",
    "           HLSPLEAD='Kepler/K2 GO office',\n",
    "           HLSPNAME='TUTORIAL',\n",
    "           CITATION='HEDGES2018',\n",
    "           DEMO_COLUMN=demo_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.786147Z",
     "iopub.status.busy": "2023-11-03T14:13:18.786038Z",
     "iopub.status.idle": "2023-11-03T14:13:18.788346Z",
     "shell.execute_reply": "2023-11-03T14:13:18.788113Z"
    }
   },
   "outputs": [],
   "source": [
    "hdu = fits.open('demo-lightcurve.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T14:13:18.789784Z",
     "iopub.status.busy": "2023-11-03T14:13:18.789682Z",
     "iopub.status.idle": "2023-11-03T14:13:18.798290Z",
     "shell.execute_reply": "2023-11-03T14:13:18.798025Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColDefs(\n",
       "    name = 'TIME'; format = 'D'; unit = 'bkjd'\n",
       "    name = 'FLUX'; format = 'E'; unit = 'e-/s'\n",
       "    name = 'FLUX_ERR'; format = 'E'; unit = 'e-/s'\n",
       "    name = 'CADENCENO'; format = 'J'\n",
       "    name = 'SAP_QUALITY'; format = 'K'\n",
       "    name = 'MOM_CENTR1'; format = 'D'\n",
       "    name = 'MOM_CENTR2'; format = 'D'\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu[1].data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vector is populated as a data column in the HDU extension.  Once all your lightcurves are saved as fits files and you have a README file, you can consider submitting your data products to MAST."
   ]
  }
 ],
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