Find the best fit correction for the light curve.
Array of centroid column positions. If None, will use the
centroid_col attribute of the input light curve by default.
Array of centroid row positions. If None, will use the
centroid_row attribute of the input light curve by default.
Number of windows to split the data into to perform the correction.
Number of “knots” to place on the arclength spline. More bins will
increase the number of knots, making the spline smoother in arclength.
Time scale of the b-spline fit to the light curve in time, in units
of input light curve time.
Optionally the user can break the light curve into sections. Set
break index to either an index at which to break, or list of indicies.
The degree of polynomials in the splines in time and arclength. Higher
values will create smoother splines. Default 3.
Mask, where True indicates a cadence that should be used.
Standard deviation at which to remove outliers from fitting
Number of iterations to fit and remove outliers
Whether to restore the long term spline trend to the light curve
Whether to propagate the uncertainties from the regression. Default is False.
Setting to True will increase run time, but will sample from multivariate normal
distribution of weights.
Additional design matrix to remove, e.g. containing background vectors.
Deprecated as of Lightkurve v1.4. Use degree instead.
Corrected light curve, with noise removed.