Source code for pylops.basicoperators.smoothing2d

__all__ = ["Smoothing2D"]

from typing import Union

import numpy as np

from pylops.signalprocessing import Convolve2D
from pylops.utils.typing import DTypeLike, InputDimsLike

[docs]class Smoothing2D(Convolve2D): r"""2D Smoothing. Apply smoothing to model (and data) along two ``axes`` of a multi-dimensional array. Parameters ---------- nsmooth : :obj:`tuple` or :obj:`list` Length of smoothing operator in 1st and 2nd dimensions (must be odd) dims : :obj:`tuple` Number of samples for each dimension axes : :obj:`int`, optional .. versionadded:: 2.0.0 Axes along which model (and data) are smoothed. dtype : :obj:`str`, 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``) See Also -------- pylops.signalprocessing.Convolve2D : 2D convolution Notes ----- The 2D Smoothing operator is a special type of convolutional operator that convolves the input model (or data) with a constant 2d filter of size :math:`n_{\text{smooth}, 1} \times n_{\text{smooth}, 2}`: Its application to a two dimensional input signal is: .. math:: y[i,j] = 1/(n_{\text{smooth}, 1}\cdot n_{\text{smooth}, 2}) \sum_{l=-(n_{\text{smooth},1}-1)/2}^{(n_{\text{smooth},1}-1)/2} \sum_{m=-(n_{\text{smooth},2}-1)/2}^{(n_{\text{smooth},2}-1)/2} x[l,m] Note that since the filter is symmetrical, the *Smoothing2D* operator is self-adjoint. """ def __init__(self, nsmooth: InputDimsLike, dims: Union[int, InputDimsLike], axes: InputDimsLike = (-2, -1), dtype: DTypeLike = "float64", name: str = 'S'): nsmooth = list(nsmooth) if nsmooth[0] % 2 == 0: nsmooth[0] += 1 if nsmooth[1] % 2 == 0: nsmooth[1] += 1 h = np.ones((nsmooth[0], nsmooth[1])) / float(nsmooth[0] * nsmooth[1]) offset = [(nsmooth[0] - 1) // 2, (nsmooth[1] - 1) // 2] super().__init__(dims=dims, h=h, offset=offset, axes=axes, dtype=dtype, name=name)