Source code for pylops.basicoperators.transpose

__all__ = ["Transpose"]

import numpy as np
from numpy.core.multiarray import normalize_axis_index

from pylops import LinearOperator
from pylops.utils._internal import _value_or_sized_to_tuple
from pylops.utils.decorators import reshaped
from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray

[docs]class Transpose(LinearOperator): r"""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 : :obj:`tuple`, optional Number of samples for each dimension axes : :obj:`tuple`, optional Direction along which transposition is applied dtype : :obj:`str`, optional Type of elements in input array name : :obj:`str`, optional .. versionadded:: 2.0.0 Name of operator (to be used by :func:`pylops.utils.describe.describe`) Attributes ---------- shape : :obj:`tuple` Operator shape explicit : :obj:`bool` Operator contains a matrix that can be solved explicitly (``True``) or not (``False``) Raises ------ ValueError If ``axes`` contains repeated dimensions (or a dimension is missing) Notes ----- 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``. """ def __init__( self, dims: InputDimsLike, axes: InputDimsLike, dtype: DTypeLike = "float64", name: str = "T", ) -> None: dims = _value_or_sized_to_tuple(dims) ndims = len(dims) self.axes = [normalize_axis_index(ax, ndims) for ax in axes] # find out if all axes are present only once in axes if len(np.unique(self.axes)) != ndims: raise ValueError("axes must contain each direction once") # find out how axes should be transposed in adjoint mode axesd = np.empty(ndims, dtype=int) axesd[self.axes] = np.arange(ndims, dtype=int) dimsd = np.empty(ndims, dtype=int) dimsd[axesd] = dims self.axesd = list(axesd) super().__init__(dtype=np.dtype(dtype), dims=dims, dimsd=dimsd, name=name) @reshaped def _matvec(self, x: NDArray) -> NDArray: return x.transpose(self.axes) @reshaped def _rmatvec(self, x: NDArray) -> NDArray: return x.transpose(self.axesd)