fips.ForwardOperator

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

Forward operator matrix mapping state vectors to observation space.

A ForwardOperator wraps a pandas DataFrame and provides methods to convolve state vectors through the operator to produce modeled observations.

The foward operator, or Jacobian matrix, is a key component of inverse problems. It defines how changes in the state vector affect the observations. The rows correspond to observations and the columns to state variables.

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.
obs_index	Return the observation space index (rows).
shape	Return the shape of the underlying data.
state_index	Return the state space index (columns).
values	Return values as numpy array or sparse 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

convolve(state[, round_index, verify_overlap])	Convolve a state vector through the forward operator.
copy([deep])	Create a copy of the structure.
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.