py__prodrinva_l_pdaf()

Provide \(\mathbf{R}^{-1}_l \times \mathbf{A}_l\).

Here, one should do \(\mathbf{R}^{-1}_l \times \mathbf{A}_l\). The matrix \(\mathbf{A}_l\) depends on the filter algorithm.

One can also perform observation localisation. This can be helped by the function pyPDAF.PDAF.local_weight() to get the observation weight.

Parameters

domain_p: int

Current local domain index.

step: int

Current time step

dim_obs_l: int

Dimension of observation vector in local analysis domain

rank: int

Rank of the local analysis domain The size of it dpends on the filter algorithms.

obs_l: np.ndarray[np.float, dim=1]

Observation vector in local analysis domain. shape: (dim_obs_l, )

a_l: np.ndarray[np.float, dim=2]

Matrix A in local analysis domain. shape: (dim_obs_l, rank)

c_l: np.ndarray[np.float, dim=2]

\(\mathbf{R}^{-1}_l \times \mathbf{A}_l\) in local analysis domain. shape: (dim_obs_l, rank)

Returns

c_l: np.ndarray[np.float, dim=2]

\(\mathbf{R}^{-1}_l \times \mathbf{A}_l\) in local analysis domain. shape: (dim_obs_l, rank)