class pylops.signalprocessing.ConvolveND(N, h, dims, offset=(0, 0, 0), dirs=None, method='fft', dtype='float64')[source]

ND convolution operator.

Apply n-dimensional convolution with a compact filter to model (and data) along a set of directions dirs of a n-dimensional array.

N : int

Number of samples in model

h : numpy.ndarray

nd compact filter to be convolved to input signal

dims : list

Number of samples for each dimension

offset : tuple, optional

Indices of the center of the compact filter

dirs : tuple, optional

Directions along which convolution is applied (set to None for filter of same dimension as input vector)

method : str, optional

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

dtype : str, optional

Type of elements in input array.


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__(self, N, h, dims[, offset, dirs, …]) Initialize this LinearOperator.
adjoint(self) Hermitian adjoint.
apply_columns(self, cols) Apply subset of columns of operator
cond(self, \*\*kwargs_eig) Condition number of linear operator.
conj(self) Complex conjugate operator
div(self, y[, niter]) Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(self, x) Matrix-matrix or matrix-vector multiplication.
eigs(self[, neigs, symmetric, niter]) Most significant eigenvalues of linear operator.
matmat(self, X) Matrix-matrix multiplication.
matvec(self, x) Matrix-vector multiplication.
rmatmat(self, X) Adjoint matrix-matrix multiplication.
rmatvec(self, x) Adjoint matrix-vector multiplication.
todense(self) Return dense matrix.
transpose(self) Transpose this linear operator.

Examples using pylops.signalprocessing.ConvolveND