bartorch.fwt

bartorch.fwt(input, axes, *, wavelet='dau2')

Forward multi-level discrete wavelet transform along axes.

Parameters:

• input (torch.Tensor) – Every transformed axis needs at least 16 samples, the coarsest scale.

• axes (int or tuple of int) – Axes to transform, as indices into input.shape.

• wavelet ({"haar", "dau2", "cdf44", "cdf97"}) – "haar", "dau2" and "cdf44" run bart wavelet with -H, -D and -C: along one axis, PyWavelets’ wavedec with "haar", "db2" and "bior4.4" in mode="symmetric", concatenated coarse first. Each level keeps (n + taps - 1) // 2 coefficients per band, so there are more coefficients than samples unless the filter has two taps and every level’s length is even; levels continue while that half-length is at least 16. "cdf97" runs bart cdf97: CDF 9/7 by lifting, in place, coarse band first, levels continuing while every transformed axis is longer than 32; one level of an even length is PyWavelets’ "bior4.4" in mode="periodization" with the detail band negated.

Returns:

For "cdf97", the shape of input. Otherwise the coefficients of all transformed axes are flattened into the last of axes, and the other transformed axes have size one.

Return type:

torch.Tensor