pylops_distributed.Laplacian

pylops_distributed.Laplacian(dims, dirs=(0, 1), weights=(1, 1), sampling=(1, 1), compute=(False, False), chunks=(None, None), todask=(False, False), dtype='float64')

Laplacian.

Apply second-order centered Laplacian operator to a multi-dimensional array (at least 2 dimensions are required)

Parameters:

dims : tuple

Number of samples for each dimension.

dirs : tuple, optional

Directions along which laplacian is applied.

weights : tuple, optional

Weight to apply to each direction (real laplacian operator if weights=[1,1])

sampling : tuple, optional

Sampling steps for each direction

compute : tuple, optional

Compute the outcome of forward and adjoint or simply define the graph and return a dask.array.array

chunks : tuple, optional

Chunk size for model and data. If provided it will rechunk the model before applying the forward pass and the data before applying the adjoint pass

todask : tuple, optional

Apply dask.array.from_array to model and data before applying forward and adjoint respectively

dtype : str, optional

Type of elements in input array.

Returns:

l2op : pylops.LinearOperator

Laplacian linear operator

Notes

Refer to pylops.basicoperators.Laplacian for implementation details.

Examples using pylops_distributed.Laplacian

Derivatives