Correcting systematics

Telescope data is always affected by noise contributed by the instrument. The lightkurve.correctors sub-package provides classes which offer different strategies to remove such noise. At the core of the package lies the generic RegressionCorrector class. It uses linear regression to correlate a light curve against a DesignMatrix of column vectors which are known to correlate with additive noise components.

The CBVCorrector, PLDCorrector, and SFFCorrector classes extend RegressionCorrector by providing the user with pre-configured DesignMatrix objects which are known to be effective at removing different types of noise.

Cotrending Basis Vectors (CBV)

load_tess_cbvs([cbv_dir, sector, camera, ...])	Loads TESS cotrending basis vectors, either from a directory of CBV files already saved locally if cbv_dir is passed, or else will retrieve the relevant files programmatically from MAST.
load_kepler_cbvs([cbv_dir, mission, ...])	Loads Kepler or K2 cotrending basis vectors, either from a local directory cbv_dir or searches the public data archive at MAST <https://archive.stsci.edu>.
CBVCorrector(lc[, interpolate_cbvs, ...])	Class for removing systematics using Cotrending Basis Vectors (CBVs) from Kepler/K2/TESS.
CBVCorrector.correct([cbv_type, ...])	Optimizes the correction by adjusting the L2-Norm (Ridge Regression) regularization penalty term, alpha, based on the introduced noise (over-fitting) and residual correlation (under-fitting) goodness metrics.
CBVCorrector.diagnose()	Returns diagnostic plots to assess the most recent correction.

Pixel Level Decorrelation (PLD)

PLDCorrector(tpf[, aperture_mask])	Implements the Pixel Level Decorrelation (PLD) systematics removal method.
PLDCorrector.correct([pld_order, ...])	Returns a systematics-corrected light curve.
PLDCorrector.diagnose()	Returns diagnostic plots to assess the most recent call to correct().
PLDCorrector.diagnose_masks()	Show different aperture masks used by PLD in the most recent call to correct().

Self Flat Fielding (SFF)

SFFCorrector(lc)	Special case of RegressionCorrector where the DesignMatrix includes the target's centroid positions.
SFFCorrector.correct([centroid_col, ...])	Find the best fit correction for the light curve.
SFFCorrector.diagnose()	Returns a diagnostic plot which visualizes what happened during the most recent call to correct().
SFFCorrector.diagnose_arclength()	Returns a diagnostic plot which visualizes arclength vs flux from most recent call to correct().

Regression Corrector

RegressionCorrector(lc)	Remove noise using linear regression against a DesignMatrix.
RegressionCorrector.correct(...[, ...])	Find the best fit correction for the light curve.
RegressionCorrector.diagnose()	Returns diagnostic plots to assess the most recent call to correct().

Creating a design matrix

DesignMatrix(df[, columns, name, prior_mu, ...])	A matrix of column vectors for use in linear regression.
DesignMatrixCollection(matrices)	Object which stores multiple design matrices.
SparseDesignMatrix(X[, columns, name, ...])	A matrix of column vectors for use in linear regression.
SparseDesignMatrixCollection(matrices)	A set of design matrices.

A DesignMatrix has the following attributes:

DesignMatrix.X	Design matrix "X" to be used in RegressionCorrector objects
DesignMatrix.rank	Matrix rank computed using numpy.linalg.matrix_rank.
DesignMatrix.shape	Tuple specifying the shape of the matrix as (n_rows, n_columns).
DesignMatrix.values	2D numpy array containing the matrix values.

A DesignMatrix supports the following operations:

DesignMatrix.append_constant([prior_mu, ...])	Returns a new DesignMatrix with a column of ones appended.
DesignMatrix.collect(matrix)	Join two designmatrices, return a design matrix collection
DesignMatrix.copy()	Returns a deepcopy of DesignMatrix
DesignMatrix.pca([nterms, n_iter])	Returns a new DesignMatrix with a smaller number of regressors.
DesignMatrix.plot([ax])	Visualize the design matrix values as an image.
DesignMatrix.plot_priors([ax])	Visualize the coefficient priors.
DesignMatrix.split(row_indices[, inplace])	Returns a new DesignMatrix with regressors split into multiple columns.
DesignMatrix.standardize([inplace])	Returns a new DesignMatrix in which the columns have been median-subtracted and sigma-divided.
DesignMatrix.to_sparse()	Convert this dense matrix object to a SparseDesignMatrix.
DesignMatrix.validate([rank])	Emits LightkurveWarning if matrix has low rank or priors have incorrect shape.

corrector.Corrector(original_lc)	Abstract base class documenting the required structure of classes designed to remove systematic noise from light curves.
corrector.Corrector.correct([cadence_mask, ...])	Returns a LightCurve from which systematic noise has been removed.
corrector.Corrector.diagnose()	Returns plots which elucidate the most recent call to correct().

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