t3frame_to_ut3frame =================== .. py:function:: t3toolbox.backend.ufv_conversions.t3frame_to_ut3frame(frame_data, N = None, nU = None, nD = None, rL = None, rR = None) .. code-block:: python def t3frame_to_ut3frame( frame_data: typ.Tuple[ typ.Tuple[NDArray, ...], # up_tucker_cores, len=d, elm_shape=C+(nUi, Ni) typ.Tuple[NDArray, ...], # down_tt_cores, len=d, elm_shape=C+(rLi, nDi, rR(i+1)) typ.Tuple[NDArray, ...], # left_tt_cores, len=d, elm_shape=C+(rLi, nUi, rL(i+1)) typ.Tuple[NDArray, ...], # right_tt_cores, len=d, elm_shape=C+(rRi, nUi, rR(i+1)) ], N: typ.Optional[int] = None, # padded mode dim (default max(shape)) nU: typ.Optional[int] = None, # padded up rank (default max(up_ranks)) nD: typ.Optional[int] = None, # padded down rank (default max(down_ranks)) rL: typ.Optional[int] = None, # padded left rank (default max(left_ranks)) rR: typ.Optional[int] = None, # padded right rank (default max(right_ranks)) ) -> typ.Tuple[ NDArray, # up_tucker_supercore, (d,)+C+(nU, N) NDArray, # down_tt_supercore, (d,)+C+(rL, nD, rR) NDArray, # left_tt_supercore, (d,)+C+(rL, nU, rL) NDArray, # right_tt_supercore, (d,)+C+(rR, nU, rR) typ.Tuple[int, ...], # shape = (N0,...,N(d-1)) typ.Tuple[NDArray, NDArray, NDArray, NDArray], # (up, down, frame_left, frame_right) masks ]: Pack a ragged ``T3Frame`` core-tuple into uniform frame ``.data`` (supercores + shape + masks). Inverse of :py:func:`ut3frame_to_t3frame`. A *single* ragged frame has ranks shared across its ``C`` stack, so the masks come out **uniform across the stack** (varying-rank uniform batches arise only by ``stack``-ing a heterogeneous tree). Pads each family to common dims (default: max over modes; pass ``N``/``nU``/``nD``/``rL``/``rR`` to force larger) and records the real extents as prefix masks (``ufv_make_frame_masks``; the real content lands in the upper-left, so the prefix masks are correct).