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()