pylops_distributed.Transpose

class pylops_distributed.Transpose(dims, axes, compute=(False, False), todask=(False, False), dtype='float64')

Transpose operator.

Transpose axes of a multi-dimensional array. This operator works with flattened input model (or data), which are however multi-dimensional in nature and will be reshaped and treated as such in both forward and adjoint modes.

Parameters:

dims : tuple, optional

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

axes : tuple, optional

Direction along which transposition is applied

compute : tuple, optional

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

Raises:

ValueError

If axes contains repeated dimensions (or a dimension is missing)

Notes

Refer to pylops.basicoperators.Transpose 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__(dims, axes[, compute, todask, dtype])	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.