Source code for pylops.basicoperators.Smoothing2D

import numpy as np

from pylops.signalprocessing import Convolve2D

[docs]def Smoothing2D(nsmooth, dims, nodir=None, dtype="float64"): r"""2D Smoothing. Apply smoothing to model (and data) along two directions of a multi-dimensional array depending on the choice of ``nodir``. Parameters ---------- nsmooth : :obj:`tuple` or :obj:`list` Lenght of smoothing operator in 1st and 2nd dimensions (must be odd) dims : :obj:`tuple` Number of samples for each dimension nodir : :obj:`int`, optional Direction along which smoothing is **not** applied (set to ``None`` for 2d arrays) 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. """ if isinstance(nsmooth, tuple): 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]) return Convolve2D(, h=h, offset=[(nsmooth[0] - 1) / 2, (nsmooth[1] - 1) / 2], dims=dims, nodir=nodir, dtype=dtype, )