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.