Laplacian(dims, axes=(-2, -1), weights=(1, 1), sampling=(1, 1), edge=False, kind='centered', dtype='float64')¶
Apply second-order centered Laplacian operator to a multi-dimensional array.
At least 2 dimensions are required, use
pylops.SecondDerivativefor 1d arrays.
- dims :
Number of samples for each dimension.
- axes :
New in version 2.0.0.
Axes along which the Laplacian is applied.
- weights :
Weight to apply to each direction (real laplacian operator if
- sampling :
Sampling steps for each direction
- edge :
Use reduced order derivative at edges (
True) or ignore them (
False) for centered derivative
- kind :
Derivative kind (
- dtype :
Type of elements in input array.
- l2op :
Laplacian linear operator
samplingdo 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:\[y[i, j] = (x[i+1, j] + x[i-1, j] + x[i, j-1] +x[i, j+1] - 4x[i, j]) / (\Delta x \Delta y)\]
- dims :