pylops.signalprocessing.Patch3D

class pylops.signalprocessing.Patch3D(Op, dims, dimsd, nwin, nover, nop, tapertype='hanning', savetaper=True, scalings=None, name='P')

3D Patch transform operator.

Apply a transform operator Op repeatedly to patches of the model vector in forward mode and patches of the data vector in adjoint mode. More specifically, in forward mode the model vector is divided into patches, each patch is transformed, and patches are then recombined together. Both model and data are internally reshaped and interpreted as 3-dimensional arrays: each patch contains a portion of the array in every axis.

This operator can be used to perform local, overlapping transforms (e.g., pylops.signalprocessing.FFTND or pylops.signalprocessing.Radon3D) on 3-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 patch3d_design to obtain the corresponding dims and number of windows.

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 patch3d_design.

Parameters

Oppylops.LinearOperator

Transform operator

dimstuple

Shape of 3-dimensional model. Note that dims[0], dims[1] and dims[2] should be multiple of the model size of the transform in their respective dimensions

dimsdtuple

Shape of 3-dimensional data

nwintuple

Number of samples of window

novertuple

Number of samples of overlapping part of window

noptuple

Size of model in the transformed domain

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.

scalingstuple or list, optional

Set of scalings to apply to each patch. If None, no scale will be applied

namestr, optional

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).

See also

Sliding1D

1D Sliding transform operator.

Sliding2D

2D Sliding transform operator.

Sliding3D

3D Sliding transform operator.

Patch2D

2D Patching transform operator.

Attributes

taps: :obj:`numpy.ndarray`

Set of tapers to be applied to each patch (if tapertype is not None)

simOpbool

Operator Op is applied to all patches simultaneously (True) or to each patch individually (False)

dimstuple

Shape of the array after the adjoint, but before flattening.

For example, x_reshaped = (Op.H * y.ravel()).reshape(Op.dims).

dimsdtuple

Shape of the array after the forward, but before flattening.

For example, y_reshaped = (Op * x.ravel()).reshape(Op.dimsd).

shapetuple

Operator shape.

Methods

__init__(Op, dims, dimsd, nwin, nover, nop)
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.Patch3D

Patching

Patching