import numpy as np
from pylops import LinearOperator
from pylops.utils.backend import get_array_module
[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.
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, dtype="complex128"):
self.shape = (np.prod(np.array(dims)), np.prod(np.array(dims)))
self.dtype = np.dtype(dtype)
self.explicit = False
self.clinear = False
def _matvec(self, x):
ncp = get_array_module(x)
return ncp.conj(x)
def _rmatvec(self, x):
ncp = get_array_module(x)
return ncp.conj(x)