Operators

Operators perfom mathematical operations on blocks and vectors, such as addition, multiplication, etc. The ForwardOperator class represents the forward operator in an inverse problem, and provides methods for applying the operator and its adjoint to vectors.

ForwardOperator

Constructor

ForwardOperator(data[, name, index, ...])

Forward operator matrix mapping state vectors to observation space.

Attributes and underlying data

ForwardOperator.name	Name of the structure, used for error messages and block naming.
ForwardOperator.data	The underlying data, which must be numeric and non-NaN.
ForwardOperator.index	Return the index of the underlying data.
ForwardOperator.obs_index	Return the observation space index (rows).
ForwardOperator.columns	Return the columns of the underlying DataFrame.
ForwardOperator.state_index	Return the state space index (columns).
ForwardOperator.shape	Return the shape of the underlying data.
ForwardOperator.values	Return values as numpy array or sparse matrix.
ForwardOperator.blocks	Accessor for retrieving MatrixBlock instances from the Matrix.

Conversion

ForwardOperator.copy([deep])	Create a copy of the structure.
ForwardOperator.to_frame()	Get the underlying DataFrame data.
ForwardOperator.to_numpy()	Get the underlying data as a NumPy array.

Reindexing / selection / label manipulation

ForwardOperator.xs(key[, axis, level, ...])	Cross-select data based on index/column values.
ForwardOperator.reindex(index[, columns, ...])	Return a new instance with data reindexed to the specified index and columns.
ForwardOperator.round_index(decimals[, ...])	Round float indices on the specified axis to given decimals.

Serialization

ForwardOperator.from_file(path)	Load object from a pickle file.
ForwardOperator.to_file(path)	Save object to a pickle file.

Sparse support

ForwardOperator.is_sparse	True if the internal DataFrame uses pandas sparse storage.
ForwardOperator.to_sparse([threshold])	Return a copy with sparse internal storage.
ForwardOperator.to_dense()	Return a copy with dense internal storage.

Computations

ForwardOperator.convolve(state[, ...])

Convolve a state vector through the forward operator.

Convolution

The convolve method applies the forward operator to a vector, and can be used to compute the predicted observations given a model state.

convolve(state, forward_operator[, ...])

Convolve a state vector with a forward operator matrix.