pylops.signalprocessing.Sliding1D

class pylops.signalprocessing.Sliding1D(Op, dim, dimd, nwin, nover, tapertype='hanning', savetaper=True, name='S')

1D Sliding transform operator.

Apply a transform operator Op repeatedly to slices of the model vector in forward mode and slices of the data vector in adjoint mode. More specifically, in forward mode the model vector is divided into slices, each slice is transformed, and slices are then recombined in a sliding window fashion.

This operator can be used to perform local, overlapping transforms (e.g., pylops.signalprocessing.FFT) on 1-dimensional arrays.

Note

The shape of the model has to be consistent with the number of windows for this operator not to return an error. As the number of windows depends directly on the choice of nwin and nover, it is recommended to first run sliding1d_design to obtain the corresponding dims and number of windows.

Note

Two kind of operators Op can be provided: the first applies a single transformation to each window separately; the second applies the transformation to all of the windows at the same time. This is directly inferred during initialization when the following condition holds Op.shape[1] == dim[0].

Warning

Depending on the choice of nwin and nover as well as the size of the data, sliding windows may not cover the entire data. The start and end indices of each window will be displayed and returned with running sliding1d_design.

Parameters

Oppylops.LinearOperator

Transform operator

dimtuple

Shape of 1-dimensional model.

dimdtuple

Shape of 1-dimensional data

nwinint

Number of samples of window

noverint

Number of samples of overlapping part of window

tapertypestr, optional

Type of taper (hanning, cosine, cosinesquare or None)

savetaperbool, optional

New in version 2.3.0.

Save all tapers and apply them in one go (True) or save unique tapers and apply them one by one (False). The first option is more computationally efficient, whilst the second is more memory efficient.

namestr, optional

New in version 2.0.0.

Name of operator (to be used by pylops.utils.describe.describe)

Raises

ValueError

Identified number of windows is not consistent with provided model shape (dims).

Methods

__init__(Op, dim, dimd, nwin, nover[, ...])
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.
reset_count()	Reset counters
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()

Examples using pylops.signalprocessing.Sliding1D

1D, 2D and 3D Sliding

1D, 2D and 3D Sliding