pylops.signalprocessing.ConvolveND¶
-
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.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__
(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. - N :