Source code for bruges.filters.convolve

Convolution in n-dimensions.

:copyright: 2022 Agile Geoscience
:license: Apache 2.0
import numpy as np
from bruges.util import apply_along_axis

[docs]def convolve(arr, v, axis=-1, verbose=False): """ Convolve n-dimensional arr with a 1D wavelet or 2D wavelet bank. Args: arr (ndarray): The trace, or 2D section, or volume. v (ndarray): The wavelet, must be 1D function or a 2D wavelet 'bank'. If a wavelet bank, time should be on the last axis. axis (int): The time axis of the arr data. In other words, the axis corresponding to a single 'trace'. If you index into this axis, you will get a single 'trace'. verbose (bool): If True, print out the shapes of the inputs and output. Returns: ndarray: Discrete, linear convolution of `arr` and `v`. """ if v.shape[-1] > arr.shape[axis]: raise TypeError("v (e.g. wavelet) must be shorter in time than arr (e.g. reflectivity).") arr_ = np.moveaxis(np.asanyarray(arr), axis, -1) # Compute the target shape of the final synthetic. outshape = v.shape[:-1] + arr_.shape # Force v and arr to both be 2D. bank = np.atleast_2d(v) arr_2d = arr_.reshape((-1, arr_.shape[-1])) # Compute synthetic. syn = np.array([apply_along_axis(np.convolve, arr_2d, w, mode='same') for w in bank]) pos = axis + v.ndim - 1 out = np.moveaxis(syn.reshape(outshape), -1, pos) # Show the shapes of the data we're handling. if verbose: print(arr.shape, ' * ', v.shape, ' -> ', out.shape) return out