"""Reader for official TESS light curve FITS files produced by the Ames SPOC pipeline."""
from ..lightcurve import TessLightCurve
from ..utils import TessQualityFlags
from .generic import read_generic_lightcurve
[docs]def read_tess_lightcurve(
filename, flux_column="pdcsap_flux", quality_bitmask="default"
):
"""Returns a TESS `~lightkurve.lightcurve.LightCurve`.
Parameters
----------
filename : str
Local path or remote url of a TESS light curve FITS file.
flux_column : 'pdcsap_flux' or 'sap_flux'
Which column in the FITS file contains the preferred flux data?
quality_bitmask : str or int
Bitmask (integer) which identifies the quality flag bitmask that should
be used to mask out bad cadences. If a string is passed, it has the
following meaning:
* "none": no cadences will be ignored.
* "default": cadences with severe quality issues will be ignored.
* "hard": more conservative choice of flags to ignore.
This is known to remove good data.
* "hardest": removes all data that has been flagged.
This mask is not recommended.
See the `~lightkurve.utils.TessQualityFlags` class for details on the bitmasks.
"""
lc = read_generic_lightcurve(filename, flux_column=flux_column, time_format="btjd")
# Filter out poor-quality data
# NOTE: Unfortunately Astropy Table masking does not yet work for columns
# that are Quantity objects, so for now we remove poor-quality data instead
# of masking. Details: https://github.com/astropy/astropy/issues/10119
quality_mask = TessQualityFlags.create_quality_mask(
quality_array=lc["quality"], bitmask=quality_bitmask
)
lc = lc[quality_mask]
lc.meta["AUTHOR"] = "SPOC"
lc.meta["TARGETID"] = lc.meta.get("TICID")
lc.meta["QUALITY_BITMASK"] = quality_bitmask
lc.meta["QUALITY_MASK"] = quality_mask
return TessLightCurve(data=lc)