Source code for pylops.basicoperators.gradient

__all__ = ["Gradient"]

from typing import Union

from pylops import LinearOperator
from pylops.basicoperators import FirstDerivative, VStack
from pylops.utils._internal import _value_or_sized_to_tuple
from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray

[docs]class Gradient(LinearOperator): r"""Gradient. Apply gradient operator to a multi-dimensional array. .. note:: At least 2 dimensions are required, use :py:func:`pylops.FirstDerivative` for 1d arrays. Parameters ---------- dims : :obj:`tuple` Number of samples for each dimension. sampling : :obj:`tuple`, optional Sampling steps for each direction. edge : :obj:`bool`, optional Use reduced order derivative at edges (``True``) or ignore them (``False``). kind : :obj:`str`, optional Derivative kind (``forward``, ``centered``, or ``backward``). dtype : :obj:`str`, optional Type of elements in input array. Notes ----- The Gradient operator applies a first-order derivative to each dimension of a multi-dimensional array in forward mode. For simplicity, given a three dimensional array, the Gradient in forward mode using a centered stencil can be expressed as: .. math:: \mathbf{g}_{i, j, k} = (f_{i+1, j, k} - f_{i-1, j, k}) / d_1 \mathbf{i_1} + (f_{i, j+1, k} - f_{i, j-1, k}) / d_2 \mathbf{i_2} + (f_{i, j, k+1} - f_{i, j, k-1}) / d_3 \mathbf{i_3} which is discretized as follows: .. math:: \mathbf{g} = \begin{bmatrix} \mathbf{df_1} \\ \mathbf{df_2} \\ \mathbf{df_3} \end{bmatrix} In adjoint mode, the adjoints of the first derivatives along different axes are instead summed together. """ def __init__(self, dims: Union[int, InputDimsLike], sampling: int = 1, edge: bool = False, kind: str = "centered", dtype: DTypeLike = "float64", name: str = 'G'): dims = _value_or_sized_to_tuple(dims) ndims = len(dims) sampling = _value_or_sized_to_tuple(sampling, repeat=ndims) self.sampling = sampling self.edge = edge self.kind = kind Op = VStack([FirstDerivative( dims=dims, axis=iax, sampling=sampling[iax], edge=edge, kind=kind, dtype=dtype, ) for iax in range(ndims) ]) super().__init__(Op=Op, dims=dims, dimsd=(ndims, *dims), name=name) def _matvec(self, x: NDArray) -> NDArray: return super()._matvec(x) def _rmatvec(self, x: NDArray) -> NDArray: return super()._rmatvec(x)