compute_dxi_tilde_jets

t3toolbox.backend.sampling_derivatives.compute_dxi_tilde_jets(down_tt_cores, mu_jets, nu_jets, sigma_tildes, tau_tildes, trs)

def compute_dxi_tilde_jets(
        down_tt_cores:  typ.Sequence[NDArray],  # O.  len=d, elm_shape=C+(rLi,nOi,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_tildes:   typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+K+C+(rR(i+1),)
        tau_tildes:     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, ...]:                   # dxi_tildes. len=d, elm_shape=(order+1,)+W+K+C+(nOi,)

Adjoint-var-down edge-variable jets (jet-ified probing.compute_dxi_tilde): two adjoint-hooked combines giving delta-xi-tilde on the mode (output at the order-<=1 leg u).

Parameters:

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

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

• trs (NDArray)

Return type:

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