TessPLDCorrector¶

class
lightkurve.correctors.
TessPLDCorrector
(tpf, aperture_mask=None)¶ Bases:
lightkurve.correctors.regressioncorrector.RegressionCorrector
Correct TESS light curves by detrending against local pixel time series.
Special case of
RegressionCorrector
where theDesignMatrix
is composed of backgroundcorrected pixel time series.The design matrix also contains columns representing a spline in time design to capture the intrinsic, longterm variability of the target.
 Parameters
 tpf
TargetPixelFile
The target pixel from which a light curve and background model will be extracted.
 tpf
Examples
>>> corrector = TessPLDCorrector(tpf) >>> lc = corrector.correct()
Attributes Summary
Shorthand for self.design_matrix_collection.
Methods Summary
correct
(self[, pixel_components, …])Returns a systematicscorrected light curve.
diagnose
(self)Returns diagnostic plots to assess the most recent call to
correct()
.diagnose_priors
(self)Returns a diagnostic plot visualizing how the bestfit coefficients compare against the priors.
Attributes Documentation

X
¶ Shorthand for self.design_matrix_collection.
Methods Documentation

correct
(self, pixel_components=3, spline_n_knots=100, spline_degree=3, background_mask=None, restore_trend=True, **kwargs)¶ Returns a systematicscorrected light curve.
 Parameters
 pixel_componentsint
Number of principal components derived from the background pixel time series to utilize.
 background_maskarraylike or None
A boolean array flagging the background pixels such that
True
means that the pixel will be used to generate the background systematics model. IfNone
, all pixels which are fainter than 1sigma above the median flux will be used. restore_trendbool
Whether to restore the long term spline trend to the light curve.

diagnose
(self)¶ Returns diagnostic plots to assess the most recent call to
correct()
.If
correct()
has not yet been called, aValueError
will be raised. Returns
Axes
The matplotlib axes object.

diagnose_priors
(self)¶ Returns a diagnostic plot visualizing how the bestfit coefficients compare against the priors.
The method will show the results obtained during the most recent call to
correct()
. Ifcorrect()
has not yet been called, aValueError
will be raised. Returns
Axes
The matplotlib axes object.