pylops_distributed.Restriction

class pylops_distributed.Restriction(M, iava, dims=None, dir=0, inplace=True, compute=(False, False), todask=(False, False), dtype='float64')

Restriction (or sampling) operator.

Extract subset of values from input vector at locations iava in forward mode and place those values at locations iava in an otherwise zero vector in adjoint mode.

Parameters:

M : int

Number of samples in model.

iava : list or numpy.ndarray

Integer indices of available samples for data selection.

dims : list

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

dir : int, optional

Direction along which restriction is applied.

inplace : bool, optional

Work inplace (True) or make a new copy (False). By default, data is a reference to the model (in forward) and model is a reference to the data (in adjoint).

compute : tuple, optional

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

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.Restriction 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__(M, iava[, dims, dir, inplace, …])	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.Restriction

Restriction