pylops_gpu.FirstDerivative

class pylops_gpu.FirstDerivative(N, dims=None, dir=0, sampling=1.0, device='cpu', togpu=(False, False), tocpu=(False, False), dtype=torch.float32)[source]

First derivative.

Apply second-order centered first 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 and rmatvec, respectively (only when device='gpu')

tocpu : tuple, optional

Move data and model from gpu to cpu after applying matvec and rmatvec, respectively (only when device='gpu')

dtype : torch.dtype or np.dtype, optional

Type of elements in input array.

Notes

Refer to pylops.basicoperators.FirstDerivative for implementation details.

Note that since the Torch implementation is based on a convolution with a compact filter \([0.5, 0., -0.5]\), edges are treated differently compared to the PyLops equivalent operator.

Attributes:
shape : tuple

Operator shape

explicit : bool

Operator contains a matrix that can be solved explicitly (True) or not (False)

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.

Examples using pylops_gpu.FirstDerivative