.. function:: py__likelihood_hyb_l_pdaf

    Compute the likelihood of the observation for a given ensemble member
    according to the observations used for the local analysis with hybrid weight.

    The function is used in the localized nonlinear filter LKNETF. The likelihood
    depends on the assumed observation error distribution.
    For a Gaussian observation error, the likelihood is
    :math:`\exp(-0.5(\mathbf{y}-\mathbf{H}\mathbf{x})^\mathrm{T}R^{-1}(\mathbf{y}-\mathbf{H}\mathbf{x}))`.
    The vector :math:`\mathbf{y}-\mathbf{H}\mathbf{x} = \mathrm{resid}` is
    provided as an input argument.

    The hybrid weight `gamma` is used weight between LNETF and LETKF. which is applied
    to :math:`R^{-1}(\mathbf{y}-\mathbf{H}\mathbf{x})`.

    This function is also the place to perform observation localisation.
    To initialize a vector of weights, the routine :func:`pyPDAF.PDAF.local_weight`
    can be called.

    Parameters
    ----------
    domain_p: int
        Current local analysis domain index
    step: int
        Current time step
    dim_obs_l: int
        Dimension of the local observation vector.
    obs_l: np.ndarray[np.float, dim=1]
        Observation vector. Shape: (dim_obs_l)
    gamma: float
        Hybrid weight provided by PDAF
    resid_l: np.ndarray[np.float, dim=1]
        Residual vector between observations and state. Shape: (dim_obs_l)
    likely_l: float
        Likelihood of the local observation

    Returns
    -------
    likely_l: float
        Likelihood of the local observation