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
dirsof 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
Nonefor filter of same dimension as input vector)- method :
str, optional Method used to calculate the convolution (
directorfft).- 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.Convolve2Doperator.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 :
