pylops_distributed.VStack

class pylops_distributed.VStack(ops, chunks=None, compute=(False, False), todask=(False, False), usedelayed=False, dtype=None)

Vertical stacking.

Stack a set of N linear operators vertically.

Parameters:

ops : list

Linear operators to be stacked. Operators must be of pylops_distributed.LinearOperator type for usedelayed=False and pylops.LinearOperator for usedelayed=True

chunks : tuple, optional

Chunks for model and data (an array with a single chunk is created if chunks is not provided)

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

usedelayed : bool, optional

Use dask.delayed to parallelize over the N operators. Note that when this is enabled the input model and data should be passed as numpy.ndarray

dtype : str, optional

Type of elements in input array.

Notes

Refer to pylops.basicoperators.VStack 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__(ops[, chunks, compute, todask, …])	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.