pylops_distributed.SecondDerivative

class pylops_distributed.SecondDerivative(N, dims=None, dir=0, sampling=1.0, compute=(False, False), chunks=(None, None), todask=(False, False), dtype='float64')

Second derivative.

Apply second-order centered second derivative.

Parameters:

N : int

Number of samples in model.

dims : tuple, optional

Number of samples for each dimension (None if only one dimension is available)

dir : int, optional

Direction along which smoothing is applied.

sampling : float, optional

Sampling step dx.

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.

Notes

Refer to pylops.basicoperators.SecondDerivative for implementation details.

Attributes:

shape : tuple

Operator shape

explicit : bool

Operator contains a matrix that can be solved explicitly (True) or not (False)

Methods

__init__(N[, dims, dir, sampling, compute, …])	Initialize this LinearOperator.
adjoint()	Hermitian adjoint.
apply_columns(cols)	Apply subset of columns of operator
cond([uselobpcg])	Condition number of linear operator.
conj()	Complex conjugate operator
div(y[, niter])	Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(x)	Matrix-vector multiplication.
eigs([neigs, symmetric, niter, uselobpcg])	Most significant eigenvalues of linear operator.
matmat(X)	Matrix-matrix multiplication.
matvec(x)	Matrix-vector multiplication.
rmatmat(X)	Adjoint matrix-matrix multiplication.
rmatvec(x)	Adjoint Matrix-vector multiplication.
todense()	Return dense matrix.
tosparse()	Return sparse matrix.
transpose()	Transpose this linear operator.

Examples using pylops_distributed.SecondDerivative

Derivatives