pylops_gpu.SecondDerivative¶
-
class
pylops_gpu.
SecondDerivative
(N, dims=None, dir=0, sampling=1.0, device='cpu', togpu=(False, False), tocpu=(False, False), dtype=torch.float32)[source]¶ Second derivative.
Apply second-order centered second derivative.
Parameters: - N :
int
Number of samples in model.
- dims :
tuple
, optional Number of samples for each dimension (
None
if only one dimension is available)- dir :
int
, optional Direction along which smoothing is applied.
- sampling :
float
, optional Sampling step
dx
.- device :
str
, optional Device to be used
- togpu :
tuple
, optional Move model and data from cpu to gpu prior to applying
matvec
andrmatvec
, respectively (only whendevice='gpu'
)- tocpu :
tuple
, optional Move data and model from gpu to cpu after applying
matvec
andrmatvec
, respectively (only whendevice='gpu'
)- dtype :
torch.dtype
ornp.dtype
, optional Type of elements in input array.
Notes
Refer to
pylops.basicoperators.SecondDerivative
for implementation details.Note that since the Torch implementation is based on a convolution with a compact filter \([1., -2., 1.]\), edges are treated differently compared to the PyLops equivalent operator.
Attributes: Methods
__init__
(N[, dims, dir, sampling, device, …])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, tol])Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\). dot
(x)Matrix-vector multiplication. eigs
([neigs, symmetric, niter, uselobpcg])Most significant eigenvalues of linear operator. matmat
(X[, kfirst])Matrix-matrix multiplication. matvec
(x)Matrix-vector multiplication. rmatmat
(X[, kfirst])Adjoint 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. transpose
()Transpose this linear operator. - N :