pylops.Laplacian(dims, axes=(-2, -1), weights=(1, 1), sampling=(1, 1), edge=False, kind='centered', dtype='float64')[source]


Apply second-order centered Laplacian operator to a multi-dimensional array.


At least 2 dimensions are required, use pylops.SecondDerivative for 1d arrays.

dims : tuple

Number of samples for each dimension.

axes : int, optional

New in version 2.0.0.

Axes along which the 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

edge : bool, optional

Use reduced order derivative at edges (True) or ignore them (False) for centered derivative

kind : str, optional

Derivative kind (forward, centered, or backward)

dtype : str, optional

Type of elements in input array.

l2op : pylops.LinearOperator

Laplacian linear operator


If axes. weights, and sampling do not have the same size


The Laplacian operator applies a second derivative along two directions of a multi-dimensional array.

For simplicity, given a two dimensional array, the Laplacian is:

\[y[i, j] = (x[i+1, j] + x[i-1, j] + x[i, j-1] +x[i, j+1] - 4x[i, j]) / (\Delta x \Delta y)\]