compute_deta_jets_trs

t3toolbox.backend.sampling_derivatives.compute_deta_jets_trs(var_tt_cores, left_tt_cores, right_tt_cores, mu_jets, nu_jets, sigma_jets, tau_jets, trs)

def compute_deta_jets_trs(
        var_tt_cores:   typ.Sequence[NDArray],  # dG. len=d, elm_shape=K+C+(rLi,nUi,rR(i+1))
        left_tt_cores:  typ.Sequence[NDArray],  # P.  len=d, elm_shape=C+(rLi,nUi,rL(i+1))
        right_tt_cores: typ.Sequence[NDArray],  # Q.  len=d, elm_shape=C+(rRi,nUi,rR(i+1))
        mu_jets:        typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+C+(rLi,)
        nu_jets:        typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+C+(rR(i+1),)
        sigma_jets:     typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+K+C+(rR(i+1),)
        tau_jets:       typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+K+C+(rL(i+1),)
        trs:            NDArray,                # binomial tensor, shape=(order+1,order+1,order+1)
) -> typ.Tuple[NDArray, ...]:                   # deta_jets. len=d, elm_shape=(order+1,)+W+K+C+(nUi,)

Variation-downward edge-variable jets deta (the jet-ified Algorithm-7 deta combine).

deta_i = sigma_{i-1} Q_i nu_i + mu_{i-1} dG_i nu_i + mu_{i-1} P_i tau_i – three combines ('trs,rWCa,Caib,sWCb->tWCi'), mode i free.

Parameters:

• var_tt_cores (t3toolbox.backend.common.typ.Sequence[NDArray])

• left_tt_cores (t3toolbox.backend.common.typ.Sequence[NDArray])

• right_tt_cores (t3toolbox.backend.common.typ.Sequence[NDArray])

• mu_jets (t3toolbox.backend.common.typ.Sequence[NDArray])

• nu_jets (t3toolbox.backend.common.typ.Sequence[NDArray])

• sigma_jets (t3toolbox.backend.common.typ.Sequence[NDArray])

• tau_jets (t3toolbox.backend.common.typ.Sequence[NDArray])

• trs (NDArray)

Return type:

t3toolbox.backend.common.typ.Tuple[NDArray, Ellipsis]