pylops.signalprocessing.NonStationaryConvolve3D

class pylops.signalprocessing.NonStationaryConvolve3D(dims, hs, ihx, ihy, ihz, engine='numpy', num_threads_per_blocks=(2, 16, 16), dtype='float64', name='C')

3D non-stationary convolution operator.

Apply non-stationary three-dimensional convolution. A varying compact filter is provided on a coarser grid and on-the-fly interpolation is applied in forward and adjoint modes. Both input and output have size \(n_x \times n_y \times n_z\).

Parameters

dimslist or int

Number of samples for each dimension (which we refer to as \(n_x \times n_y \times n_z\)).

hsnumpy.ndarray

Bank of 3d compact filters of size \(n_{\text{filts},x} \times n_{\text{filts},y} \times n_{\text{filts},z} \times n_{h,x} \times n_{h,y} \times n_{h,z}\). Filters must have odd number of samples and are assumed to be centered in the middle of the filter support.

ihxtuple

Indices of the x locations of the filters hs in the model (and data). Note that the filters must be regularly sampled, i.e. \(dh_x=\text{diff}(ihx)=\text{const.}\)

ihytuple

Indices of the y locations of the filters hs in the model (and data). Note that the filters must be regularly sampled, i.e. \(dh_y=\text{diff}(ihy)=\text{const.}\)

ihztuple

Indices of the z locations of the filters hs in the model (and data). Note that the filters must be regularly sampled, i.e. \(dh_z=\text{diff}(ihz)=\text{const.}\)

enginestr, optional

Engine used for spread computation (numpy, numba, or cuda)

num_threads_per_blockstuple, optional

Number of threads in each block (only when engine=cuda)

dtypestr, optional

Type of elements in input array.

namestr, optional

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

Raises

ValueError

If filters hs have even size

ValueError

If ihx, ihy or ihz is not regularly sampled

NotImplementedError

If engine is neither numpy, fftw, nor scipy.

Notes

See pylops.signalprocessing.NonStationaryConvolve2D.

Attributes

shapetuple

Operator shape

explicitbool

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

Methods

__init__(dims, hs, ihx, ihy, ihz[, engine, ...])
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()