pylops.Flip

class pylops.Flip(N, dims=None, dir=0, dtype='float64')

Flip along an axis.

Flip a multi-dimensional array along a specified direction dir.

Parameters:

N : int

Number of samples in model.

dims : list, optional

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

dir : int, optional

Direction along which flipping is applied.

dtype : str, optional

Type of elements in input array.

Notes

The Flip operator flips the input model (and data) along any chosen direction. For simplicity, given a one dimensional array, in forward mode this is equivalent to:

\[y[i] = x[N-1-i] \quad \forall i=0,1,2,\ldots,N-1\]

where \(N\) is the lenght of the input model. As this operator is self-adjoint, \(x\) and \(y\) in the equation above are simply swapped in adjoint mode.

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, 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, 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.
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()	Transpose this linear operator.

Examples using pylops.Flip

Flip along an axis