class pylops.Transpose(dims, axes, dtype='float64')[source]

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.

dims : tuple, optional

Number of samples for each dimension

axes : tuple, optional

Direction along which transposition is applied

dtype : str, optional

Type of elements in input array


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


The Transpose operator reshapes the input model into a multi-dimensional array of size dims and transposes (or swaps) its axes as defined in axes.

Similarly, in adjoint mode the data is reshaped into a multi-dimensional array whose size is a permuted version of dims defined by axes. The array is then rearragned into the original model dimensions dims.

shape : tuple

Operator shape

explicit : bool

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


__init__(dims, axes[, 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.Transpose