Source code for pylops.basicoperators.symmetrize
__all__ = ["Symmetrize"]
from typing import Union
import numpy as np
from pylops import LinearOperator
from pylops.utils._internal import _value_or_sized_to_tuple
from pylops.utils.backend import get_array_module
from pylops.utils.decorators import reshaped
from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray
[docs]class Symmetrize(LinearOperator):
r"""Symmetrize along an axis.
Symmetrize a multi-dimensional array along ``axis``.
Parameters
----------
dims : :obj:`list` or :obj:`int`
Number of samples for each dimension
(``None`` if only one dimension is available)
axis : :obj:`int`, optional
.. versionadded:: 2.0.0
Axis along which model is symmetrized.
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``)
Notes
-----
The Symmetrize operator constructs a symmetric array given an input model
in forward mode, by pre-pending the input model in reversed order.
For simplicity, given a one dimensional array, the forward operation can
be expressed as:
.. math::
y[i] = \begin{cases}
x[i-N+1],& i\geq N\\
x[N-1-i],& \text{otherwise}
\end{cases}
for :math:`i=0,1,2,\ldots,2N-2`, where :math:`N` is the dimension of the input
model.
In adjoint mode, the Symmetrize operator assigns the sums of the elements
in position :math:`N-1-i` and :math:`N-1+i` to position :math:`i` as follows:
.. math::
\begin{multline}
x[i] = y[N-1-i]+y[N-1+i] \quad \forall i=0,2,\ldots,N-1
\end{multline}
apart from the central sample where :math:`x[0] = y[N-1]`.
"""
def __init__(
self,
dims: Union[int, InputDimsLike],
axis: int = -1,
dtype: DTypeLike = "float64",
name: str = "S",
) -> None:
dims = _value_or_sized_to_tuple(dims)
self.axis = axis
self.nsym = dims[self.axis]
dimsd = list(dims)
dimsd[self.axis] = 2 * dims[self.axis] - 1
super().__init__(dtype=np.dtype(dtype), dims=dims, dimsd=dimsd, name=name)
@reshaped(swapaxis=True)
def _matvec(self, x: NDArray) -> NDArray:
ncp = get_array_module(x)
y = ncp.zeros(self.dimsd, dtype=self.dtype)
y = y.swapaxes(self.axis, -1)
y[..., self.nsym - 1 :] = x
y[..., : self.nsym - 1] = x[..., -1:0:-1]
return y
@reshaped(swapaxis=True)
def _rmatvec(self, x: NDArray) -> NDArray:
y = x[..., self.nsym - 1 :].copy()
y[..., 1:] += x[..., self.nsym - 2 :: -1]
return y