fips.CovarianceMatrix

class fips.CovarianceMatrix(data, name=None, index=None, columns=None, dtype=None, copy=None, sparse=False)

Represents a symmetric Covariance Matrix.

Covariance matrices are used to represent error covariances in the inversion framework. They can be constructed from variances and correlation matrices.

Attributes

blocks	Accessor for retrieving MatrixBlock instances from the Matrix.
columns	Return the columns of the underlying DataFrame.
index	Return the index of the underlying data.
is_sparse	True if the internal DataFrame uses pandas sparse storage.
shape	Return the shape of the underlying data.
values	Return values as numpy array or sparse matrix.
variances	Returns the variances (diagonal elements) of the covariance matrix.
data	The underlying data, which must be numeric and non-NaN.
name	Name of the structure, used for error messages and block naming.

Methods

copy([deep])	Create a copy of the structure.
force_symmetry([keep])	Force the matrix to be perfectly symmetric by copying one triangle to the other.
from_file(path)	Load object from a pickle file.
reindex(index[, columns, verify_overlap, ...])	Return a new instance with data reindexed to the specified index and columns.
round_index(decimals[, axis, inplace])	Round float indices on the specified axis to given decimals.
scale(factor)	Scale the matrix by a scalar factor.
to_dense()	Return a copy with dense internal storage.
to_file(path)	Save object to a pickle file.
to_frame()	Get the underlying DataFrame data.
to_numpy()	Get the underlying data as a NumPy array.
to_sparse([threshold])	Return a copy with sparse internal storage.
to_xarray()	Convert to xarray (not implemented for base Matrix class).
xs(key[, axis, level, drop_level])	Cross-select data based on index/column values.