"""Reader for A PSF-Based Approach to TESS High Quality Data Of Stellar Clusters (PATHOS) light curve files.
Website: https://archive.stsci.edu/hlsp/pathos
A product description file wasn't obvious on the MAST website
"""
from ..lightcurve import TessLightCurve
from ..utils import TessQualityFlags
from .generic import read_generic_lightcurve
[docs]def read_pathos_lightcurve(
filename, flux_column="PSF_FLUX_COR", quality_bitmask="default"
):
"""Returns a TESS PATHOS `~lightkurve.lightcurve.LightCurve`.
More information: https://archive.stsci.edu/hlsp/pathos
Parameters
----------
filename : str
Local path or remote url of PATHOS light curve FITS file.
flux_column : 'psf_flux_cor' or 'ap#_flux_cor' (# = 1, 2, 3, or 4)
or 'psf_flux_raw' or 'ap#_flux_raw' (# = 1, 2, 3, or 4)
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.lower(),
time_format="btjd",
quality_column="DQUALITY",
)
# 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["dquality"], bitmask=quality_bitmask
)
lc = lc[quality_mask]
lc.meta["AUTHOR"] = "PATHOS"
lc.meta["TARGETID"] = lc.meta.get("TICID")
lc.meta["QUALITY_BITMASK"] = quality_bitmask
lc.meta["QUALITY_MASK"] = quality_mask
# QLP light curves are normalized by default
lc.meta["NORMALIZED"] = True
return TessLightCurve(data=lc)