lightkurve.correctors.SparseDesignMatrix#
- class lightkurve.correctors.SparseDesignMatrix(X, columns=None, name='unnamed_matrix', prior_mu=None, prior_sigma=None)[source]#
A matrix of column vectors for use in linear regression.
This class is similar to the
DesignMatrix
class, but uses thescipy.sparse
library to improve speed in the case of sparse matrices.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
- X
scipy.sparse
matrix The values to build the design matrix with
- columnsiterable of str (optional)
Column names
- 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.
- X
Methods
__init__
(X[, columns, name, prior_mu, ...])append_constant
([prior_mu, prior_sigma, inplace])Returns a new
SparseDesignMatrix
with a column of ones appended.collect
(matrix)Join two designmatrices, return a design matrix collection
copy
()Returns a deepcopy of DesignMatrix
pca
([nterms])Returns a new
SparseDesignMatrix
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
SparseDesignMatrix
with regressors split into multiple columns.standardize
([inplace])Returns a new
SparseDesignMatrix
in which the columns have been mean-subtracted and sigma-divided.to_dense
()Convert a SparseDesignMatrix object to a dense DesignMatrix
to_sparse
()Convert this dense matrix object to a
SparseDesignMatrix
.validate
([rank])Checks if the matrix has the right shapes.
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.