class pylops.signalprocessing.ConvolveND(dims, h, offset=None, axes=(-2, -1), method='fft', dtype='float64', name='C')[source]

ND convolution operator.

Apply n-dimensional convolution with a compact filter to model (and data) along the axes of a n-dimensional array.

dims : list or int

Number of samples for each dimension

h : numpy.ndarray

nd compact filter to be convolved to input signal

offset : tuple, optional

Indices of the center of the compact filter

axes : int, optional

New in version 2.0.0.

Axes along which convolution is applied

method : str, optional

Method used to calculate the convolution (direct or fft).

dtype : str, optional

Type of elements in input array.

name : str, optional

New in version 2.0.0.

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


The ConvolveND operator applies n-dimensional convolution between the input signal \(d(x_1, x_2, ..., x_N)\) and a compact filter kernel \(h(x_1, x_2, ..., x_N)\) in forward model. This is a straighforward extension to multiple dimensions of pylops.signalprocessing.Convolve2D operator.

shape : tuple

Operator shape

explicit : bool

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


__init__(dims, h[, offset, axes, method, …]) 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.
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() Transpose this linear operator.

Examples using pylops.signalprocessing.ConvolveND