import torch
from pylops_gpu import LinearOperator
from pylops_gpu.signalprocessing import Convolve1D
from pylops_gpu.utils.torch2numpy import torchtype_from_numpytype
"""
def FirstDerivative(N, dims=None, dir=0, sampling=1., device='cpu',
togpu=(False, False), tocpu=(False, False),
dtype=torch.float32):
h = torch.torch.tensor([0.5, 0, -0.5], dtype=dtype).to(device) / sampling
dop = Convolve1D(N, h, offset=1, dims=dims, dir=dir, device=device,
togpu=togpu, tocpu=tocpu, dtype=dtype)
return dop
"""
[docs]class FirstDerivative(LinearOperator):
r"""First derivative.
Apply second-order centered first derivative.
Parameters
----------
N : :obj:`int`
Number of samples in model.
dims : :obj:`tuple`, optional
Number of samples for each dimension
(``None`` if only one dimension is available)
dir : :obj:`int`, optional
Direction along which smoothing is applied.
sampling : :obj:`float`, optional
Sampling step ``dx``.
device : :obj:`str`, optional
Device to be used
togpu : :obj:`tuple`, optional
Move model and data from cpu to gpu prior to applying ``matvec`` and
``rmatvec``, respectively (only when ``device='gpu'``)
tocpu : :obj:`tuple`, optional
Move data and model from gpu to cpu after applying ``matvec`` and
``rmatvec``, respectively (only when ``device='gpu'``)
dtype : :obj:`torch.dtype` or :obj:`np.dtype`, optional
Type of elements in input array.
Attributes
----------
shape : :obj:`tuple`
Operator shape
explicit : :obj:`bool`
Operator contains a matrix that can be solved explicitly (``True``) or
not (``False``)
Notes
-----
Refer to :class:`pylops.basicoperators.FirstDerivative` for implementation
details.
Note that since the Torch implementation is based on a convolution
with a compact filter :math:`[0.5, 0., -0.5]`, edges are treated
differently compared to the PyLops equivalent operator.
"""
def __init__(self, N, dims=None, dir=0, sampling=1., device='cpu',
togpu=(False, False), tocpu=(False, False),
dtype=torch.float32):
# convert dtype to torch.dtype
dtype = torchtype_from_numpytype(dtype)
h = torch.torch.tensor([0.5, 0, -0.5],
dtype=dtype).to(device) / sampling
self.device = device
self.togpu = togpu
self.tocpu = tocpu
self.shape = (N, N)
self.dtype = dtype
self.explicit = False
self.Op = Convolve1D(N, h, offset=1, dims=dims, dir=dir,
zero_edges=True, device=device,
togpu=togpu, tocpu=tocpu, dtype=self.dtype)