lightkurve.correctors.DesignMatrix

class lightkurve.correctors.DesignMatrix(df, columns=None, name='unnamed_matrix', prior_mu=None, prior_sigma=None)

A matrix of column vectors for use in linear regression.

The purpose of this class is to provide a convenient method to interact with a set of one or more regressors which are known to correlate with trends or systematic noise signals which we want to remove from a light curve. Specifically, this class is designed to provide the design matrix for use by Lightkurve’s RegressionCorrector class.

Parameters

dfdict, array, or pandas.DataFrame object

Columns to include in the design matrix. If this object is not a DataFrame then it will be passed to the DataFrame constructor.

columnsiterable of str (optional)

Column names, if not already provided via df.

namestr

Name of the matrix.

prior_muarray

Prior means of the coefficients associated with each column in a linear regression problem.

prior_sigmaarray

Prior standard deviations of the coefficients associated with each column in a linear regression problem.

Examples

>>> from lightkurve.correctors.designmatrix import DesignMatrix, create_spline_matrix
>>> DesignMatrix(np.arange(100), name='slope')
slope DesignMatrix (100, 1)
>>> create_spline_matrix(np.arange(100), n_knots=5, name='spline')
spline DesignMatrix (100, 5)

__init__(df, columns=None, name='unnamed_matrix', prior_mu=None, prior_sigma=None)

Methods

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

Attributes

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

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