pylops.Laplacian¶

class pylops.Laplacian(dims, axes=(-2, -1), weights=(1.0, 1.0), sampling=(1.0, 1.0), edge=False, kind='centered', dtype='float64', name='L')[source]¶

Laplacian.

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

Note

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

Parameters:

dimstuple: Number of samples for each dimension.
axestuple, optional: Added in version 2.0.0.

Axes along which the Laplacian is applied.
weightstuple, optional: Weight to apply to each direction (real laplacian operator if weights=(1, 1))
samplingtuple, optional: Sampling steps for each direction
edgebool, optional: Use reduced order derivative at edges (True) or ignore them (False) for centered derivative
kindstr, optional: Derivative kind (forward, centered, or backward)
dtypestr, optional: Type of elements in input array.
namestr, optional: Added in version 2.0.0.

Name of operator (to be used by pylops.utils.describe.describe)

Attributes:

dimstuple

Shape of the array after the adjoint, but before flattening.

For example, x_reshaped = (Op.H * y.ravel()).reshape(Op.dims).

dimsdtuple

Shape of the array after the forward, but before flattening.

For example, y_reshaped = (Op * x.ravel()).reshape(Op.dimsd).

shapetuple

Operator shape.

Raises:

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

Notes

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:

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

Methods

`__init__`(dims[, axes, weights, sampling, ...])
`adjoint`()
`apply_columns`(cols)	Apply subset of columns of operator
`cond`([uselobpcg])	Condition number of linear operator.
`conj`()	Complex conjugate operator
`div`(y[, niter, densesolver])	Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
`dot`(x)	Matrix-matrix or matrix-vector multiplication.
`eigs`([neigs, symmetric, niter, uselobpcg])	Most significant eigenvalues of linear operator.
`matmat`(X)	Matrix-matrix multiplication.
`matvec`(x)	Matrix-vector multiplication.
`reset_count`()	Reset counters
`rmatmat`(X)	Matrix-matrix multiplication.
`rmatvec`(x)	Adjoint matrix-vector multiplication.
`todense`([backend])	Return dense matrix.
`toimag`([forw, adj])	Imag operator
`toreal`([forw, adj])	Real operator
`tosparse`()	Return sparse matrix.
`trace`([neval, method, backend])	Trace of linear operator.
`transpose`()