Source code for pylops.basicoperators.conj
__all__ = ["Conj"]
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.typing import DTypeLike, InputDimsLike, NDArray
[docs]class Conj(LinearOperator):
r"""Complex conjugate operator.
Return the complex conjugate of the input. It is self-adjoint.
Parameters
----------
dims : :obj:`int` or :obj:`tuple`
Number of samples for each dimension
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
-----
In forward mode:
.. math::
y_{i} = \Re\{x_{i}\} - i\Im\{x_{i}\} \quad \forall i=0,\ldots,N-1
In adjoint mode:
.. math::
x_{i} = \Re\{y_{i}\} - i\Im\{y_{i}\} \quad \forall i=0,\ldots,N-1
"""
def __init__(
self,
dims: Union[int, InputDimsLike],
dtype: DTypeLike = "complex128",
name: str = "C",
) -> None:
dims = _value_or_sized_to_tuple(dims)
super().__init__(
dtype=np.dtype(dtype), dims=dims, dimsd=dims, clinear=False, name=name
)
def _matvec(self, x: NDArray) -> NDArray:
return x.conj()
def _rmatvec(self, x: NDArray) -> NDArray:
return x.conj()