Function penaltyMatrix2D

cadquery/occ_impl/nurbs.py:1335–1419

Create sparse tensor product 2D derivative matrices.

(
    u: Array,
    v: Array,
    uorder: int,
    vorder: int,
    dorder: int,
    uknots: Array,
    vknots: Array,
    uperiodic: bool = False,
    vperiodic: bool = False,
)

@njit
def penaltyMatrix2D(
    u: Array,
    v: Array,
    uorder: int,
    vorder: int,
    dorder: int,
    uknots: Array,
    vknots: Array,
    uperiodic: bool = False,
    vperiodic: bool = False,
) -> list[COO]:
    """
    Create sparse tensor product 2D derivative matrices.
    """

    # extend the knots and pre-process
    u_, uknots_ext, minspanu, maxspanu, deltaspanu = _preprocess(
        u, uorder, uknots, uperiodic
    )
    v_, vknots_ext, minspanv, maxspanv, deltaspanv = _preprocess(
        v, vorder, vknots, vperiodic
    )

    # number of parameter values
    ni = len(u)

    # chunk size
    nu = uorder + 1
    nv = vorder + 1
    nj = nu * nv

    # number of basis
    nu_total = maxspanu
    nv_total = maxspanv

    # temp chunk storage
    utemp = np.zeros((dorder + 1, nu))
    vtemp = np.zeros((dorder + 1, nv))

    # initialize the empty matrices
    rv = []
    for i in range(dorder + 1):
        rv.append(
            COO(
                i=np.empty(ni * nj, dtype=np.int64),
                j=np.empty(ni * nj, dtype=np.int64),
                v=np.empty(ni * nj),
                shape=(
                    ni,
                    (len(uknots) - 1 - (not uperiodic) * uorder)
                    * (len(vknots) - 1 - (not vperiodic) * vorder),
                ),
            )
        )

    # loop over parameter values
    for i in range(ni):
        ui, vi = u_[i], v_[i]