class pylops.Identity(N, M=None, dtype='float64', inplace=True)[source]

Identity operator.

Simply move model to data in forward model and viceversa in adjoint mode if \(M = N\). If \(M > N\) removes last \(M - N\) elements from model in forward and pads with \(0\) in adjoint. If \(N > M\) removes last \(N - M\) elements from data in adjoint and pads with \(0\) in forward.

N : int

Number of samples in data (and model, if M is not provided).

M : int, optional

Number of samples in model.

dtype : str, optional

Type of elements in input array.

inplace : 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).


For \(M = N\), an Identity operator simply moves the model \(\mathbf{x}\) to the data \(\mathbf{y}\) in forward mode and viceversa in adjoint mode:

\[y_i = x_i \quad \forall i=1,2,\ldots,N\]

or in matrix form:

\[\mathbf{y} = \mathbf{I} \mathbf{x} = \mathbf{x}\]


\[\mathbf{x} = \mathbf{I} \mathbf{y} = \mathbf{y}\]

For \(M > N\), the Identity operator takes the first \(M\) elements of the model \(\mathbf{x}\) into the data \(\mathbf{y}\) in forward mode

\[y_i = x_i \quad \forall i=1,2,\ldots,N\]

and all the elements of the data \(\mathbf{y}\) into the first \(M\) elements of model in adjoint mode (other elements are O):

\[ \begin{align}\begin{aligned}x_i = y_i \quad \forall i=1,2,\ldots,M\\x_i = 0 \quad \forall i=M+1,\ldots,N\end{aligned}\end{align} \]
shape : tuple

Operator shape

explicit : bool

Operator contains a matrix that can be solved explicitly (True) or not (False)


__init__(N[, M, dtype, inplace]) Initialize this LinearOperator.
adjoint() Hermitian adjoint.
apply_columns(cols) Apply subset of columns of operator
cond([uselobpcg]) Condition number of linear operator.
conj() Complex conjugate operator
div(y[, niter, densesolver]) Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(x) Matrix-matrix or matrix-vector multiplication.
eigs([neigs, symmetric, niter, uselobpcg]) Most significant eigenvalues of linear operator.
matmat(X) Matrix-matrix multiplication.
matvec(x) Matrix-vector multiplication.
rmatmat(X) Matrix-matrix multiplication.
rmatvec(x) Adjoint matrix-vector multiplication.
todense([backend]) Return dense matrix.
toimag([forw, adj]) Imag operator
toreal([forw, adj]) Real operator
tosparse() Return sparse matrix.
trace([neval, method, backend]) Trace of linear operator.
transpose() Transpose this linear operator.