# Source code for pylops.signalprocessing.Sliding2D

```
import logging
import numpy as np
from pylops.basicoperators import BlockDiag, Diagonal, HStack, Restriction
from pylops.utils.tapers import taper2d
logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)
def _slidingsteps(ntr, nwin, nover):
"""Identify sliding window initial and end points given overall
trace length, window length and overlap
Parameters
----------
ntr : :obj:`int`
Number of samples in trace
nwin : :obj:`int`
Number of samples of window
nover : :obj:`int`
Number of samples of overlapping part of window
Returns
-------
starts : :obj:`np.ndarray`
Start indices
ends : :obj:`np.ndarray`
End indices
"""
if nwin > ntr:
raise ValueError("nwin=%d is bigger than ntr=%d..." % (nwin, ntr))
step = nwin - nover
starts = np.arange(0, ntr - nwin + 1, step, dtype=int)
ends = starts + nwin
return starts, ends
[docs]def Sliding2D(Op, dims, dimsd, nwin, nover, tapertype="hanning", design=False):
"""2D Sliding transform operator.
Apply a transform operator ``Op`` repeatedly to slices of the model
vector in forward mode and slices of the data vector in adjoint mode.
More specifically, in forward mode the model vector is divided into
slices, each slice is transformed, and slices are then recombined in a
sliding window fashion. Both model and data are internally reshaped and
interpreted as 2-dimensional arrays: each slice contains a portion
of the array in the first dimension (and the entire second dimension).
This operator can be used to perform local, overlapping transforms (e.g.,
:obj:`pylops.signalprocessing.FFT2D`
or :obj:`pylops.signalprocessing.Radon2D`) on 2-dimensional arrays.
.. note:: The shape of the model has to be consistent with
the number of windows for this operator not to return an error. As the
number of windows depends directly on the choice of ``nwin`` and
``nover``, it is recommended to use ``design=True`` if unsure about the
choice ``dims`` and use the number of windows printed on screen to
define such input parameter.
.. warning:: Depending on the choice of `nwin` and `nover` as well as the
size of the data, sliding windows may not cover the entire first dimension.
The start and end indices of each window can be displayed using
``design=True`` while defining the best sliding window approach.
Parameters
----------
Op : :obj:`pylops.LinearOperator`
Transform operator
dims : :obj:`tuple`
Shape of 2-dimensional model. Note that ``dims[0]`` should be multiple
of the model size of the transform in the first dimension
dimsd : :obj:`tuple`
Shape of 2-dimensional data
nwin : :obj:`int`
Number of samples of window
nover : :obj:`int`
Number of samples of overlapping part of window
tapertype : :obj:`str`, optional
Type of taper (``hanning``, ``cosine``, ``cosinesquare`` or ``None``)
design : :obj:`bool`, optional
Print number of sliding window (``True``) or not (``False``)
Returns
-------
Sop : :obj:`pylops.LinearOperator`
Sliding operator
Raises
------
ValueError
Identified number of windows is not consistent with provided model
shape (``dims``).
"""
# model windows
mwin_ins, mwin_ends = _slidingsteps(dims[0], Op.shape[1] // dims[1], 0)
# data windows
dwin_ins, dwin_ends = _slidingsteps(dimsd[0], nwin, nover)
nwins = len(dwin_ins)
# create tapers
if tapertype is not None:
tap = taper2d(dimsd[1], nwin, nover, tapertype=tapertype)
tapin = tap.copy()
tapin[:nover] = 1
tapend = tap.copy()
tapend[-nover:] = 1
taps = {}
taps[0] = tapin
for i in range(1, nwins - 1):
taps[i] = tap
taps[nwins - 1] = tapend
# check that identified number of windows agrees with mode size
if design:
logging.warning("%d windows required...", nwins)
logging.warning("model wins - start:%s, end:%s", str(mwin_ins), str(mwin_ends))
logging.warning("data wins - start:%s, end:%s", str(dwin_ins), str(dwin_ends))
if nwins * Op.shape[1] // dims[1] != dims[0]:
raise ValueError(
"Model shape (dims=%s) is not consistent with chosen "
"number of windows. Choose dims[0]=%d for the "
"operator to work with estimated number of windows, "
"or create the operator with design=True to find "
"out the optimal number of windows for the current "
"model size..." % (str(dims), nwins * Op.shape[1] // dims[1])
)
# transform to apply
if tapertype is None:
OOp = BlockDiag([Op for _ in range(nwins)])
else:
OOp = BlockDiag([Diagonal(taps[itap].ravel()) * Op for itap in range(nwins)])
combining = HStack(
[
Restriction(
np.prod(dimsd), range(win_in, win_end), dims=dimsd, dtype=Op.dtype
).H
for win_in, win_end in zip(dwin_ins, dwin_ends)
]
)
Sop = combining * OOp
return Sop
```