pylops.signalprocessing.ConvolveND¶
-
class
pylops.signalprocessing.
ConvolveND
(N, h, dims, offset=None, 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.Parameters: - 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
orfft
).- dtype :
str
, optional Type of elements in input array.
Notes
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.Attributes: Methods
__init__
(N, h, dims[, offset, dirs, 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])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. 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. - N :