import numpy as np
import dask.array as da
from pylops_distributed import LinearOperator
[docs]class Identity(LinearOperator):
r"""Identity operator.
Simply move model to data in forward model and viceversa in adjoint mode if
:math:`M = N`. If :math:`M > N` removes last :math:`M - N` elements from
model in forward and pads with :math:`0` in adjoint. If :math:`N > M`
removes last :math:`N - M` elements from data in adjoint and pads with
:math:`0` in forward.
Parameters
----------
N : :obj:`int`
Number of samples in data (and model, if ``M`` is not provided).
M : :obj:`int`, optional
Number of samples in model.
inplace : :obj:`bool`, optional
Work inplace (``True``) or make a new copy (``False``). By default,
data is a reference to the model (in forward) and model is a reference
to the data (in adjoint).
compute : :obj:`tuple`, optional
Compute the outcome of forward and adjoint or simply define the graph
and return a :obj:`dask.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``)
Raises
------
ValueError
If ``M`` is different from ``N``
Notes
-----
Refer to :class:`pylops.basicoperators.Identity` for implementation
details.
"""
def __init__(self, N, M=None, inplace=True, compute=(False, False),
todask=(False, False), dtype='float64'):
M = N if M is None else M
self.inplace = inplace
self.shape = (N, M)
self.dtype = np.dtype(dtype)
self.compute = compute
self.todask = todask
self.Op = None
self.explicit = False
def _matvec(self, x):
if not self.inplace:
x = x.copy()
if self.shape[0] == self.shape[1]:
y = x
elif self.shape[0] < self.shape[1]:
y = x[:self.shape[0]]
else:
y = da.pad(x, (0, self.shape[0] - self.shape[1]), mode='constant')
return y
def _rmatvec(self, x):
if not self.inplace:
x = x.copy()
if self.shape[0] == self.shape[1]:
y = x
elif self.shape[0] < self.shape[1]:
y = da.pad(x, (0, self.shape[1] - self.shape[0]), mode='constant')
else:
y = x[:self.shape[1]]
return y