from pylops import Diagonal as pDiagonal
from pylops_distributed import LinearOperator
[docs]class Diagonal(LinearOperator):
r"""Diagonal operator.
Applies element-wise multiplication of the input vector with the vector
``diag`` in forward and with its complex conjugate in adjoint mode.
This operator can also broadcast; in this case the input vector is
reshaped into its dimensions ``dims`` and the element-wise multiplication
with ``diag`` is perfomed on the direction ``dir``. Note that the
vector ``diag`` will need to have size equal to ``dims[dir]``.
Parameters
----------
diag : :obj:`dask.array.ndarray`
Vector to be used for element-wise multiplication.
dims : :obj:`list`, optional
Number of samples for each dimension
(``None`` if only one dimension is available)
dir : :obj:`int`, optional
Direction along which multiplication is applied.
compute : :obj:`tuple`, optional
Compute the outcome of forward and adjoint or simply define the graph
and return a :obj:`dask.array.array`
todask : :obj:`tuple`, optional
Apply :func:`dask.array.from_array` to model and data before applying
forward and adjoint respectively
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
-----
Refer to :class:`pylops.basicoperators.Diagonal` for implementation
details.
"""
def __init__(self, diag, dims=None, dir=0,
compute=(False, False), todask=(False, False),
dtype='float64'):
Op = pDiagonal(diag, dims=dims, dir=dir, dtype=dtype)
super().__init__(Op.shape, Op.dtype, Op, explicit=False,
compute=compute, todask=todask)