Abstract base class documenting the required structure of classes
designed to remove systematic noise from light curves.
The uncorrected light curve. Must be passed into (or computed by) the
Corrected light curve. Must be updated upon each call to the correct() method.
Boolean array with the same length as original_lc.
True indicates that a cadence should be used to fit the noise model.
By setting certain cadences to False, users can exclude those cadences
from informing the noise model, which will help prevent the overfitting
of those signals (e.g. exoplanet transits).
By default, the cadence mask is True across all cadences.
Accepts all the data required to execute the correction. The constructor must set the original_lc attribute.
correct() -> LightCurve
Executes the correction, optionally accepting meaningful parameters that can be used to modify the way the correction is applied. This method must set or update the corrected_lc attribute on each run.
diagnose() -> matplotlib.axes.Axes
Creates plots to elucidate the user’s most recent call to correct().
The constructor shall:
* accept all data required to run the correction (e.g. light curves,
target pixel files, engineering data).
* instantiate the original_lc property.
Measures the degree of over-fitting in the correction.
Measures the degree of under-fitting the correction.
Returns a LightCurve from which systematic noise has been removed.
Returns plots which elucidate the most recent call to correct().