Source code for lightkurve.io.pathos

"""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)