Any experiences for plotting a stationary wavelet transform?
I don't do Python, I'm an old person sticking to his old Matlab (codes and) habits. However, up to extension/wavelet/border issues,  SWT is a discrete equivalent to CWT. And in most versions, the number of samples in approximations or details is the same (which is not the case for the DWT). Hence, you can concatenate 1D rows of details (or approximations) into 2D images, akin to traditional scalograms. The following images and code show the process. I have generated a random piecewise polynomial signal with increasing degrees. Then rows of details, and an image of the rows concatenated. The motivation is to address the impact of vanishing moments on piece-wise polynomials, with border effects.
Here are two different realizations.





The Matlab code, that may be reproduced in Python:

dataLength = 512;
data = zeros(dataLength,1);
nChunk = 4;
lChunk = dataLength/nChunk;
for iChunk = 0:nChunk-1
    idxChunk = iChunk*lChunk+(1:lChunk)';
    polyChunk = rand(iChunk+1,1)-0.5;
    dataChunk = polyval(polyChunk,linspace(-0.5,0.5,lChunk));
    data(idxChunk) = dataChunk;
end

nLevel = 4;
[swa,swd] = swt(data,nLevel,'db2');

figure(1);clf
subplot(1,3,1)
plot(data,'x');axis tight
for iPlot = 1:nLevel
    subplot(nLevel,3,3*(iPlot-1)+2)
    plot(swd(iPlot,:));;axis tight
end
subplot(1,3,3)
imagesc(swd)


Other related questions/answers:


STFT and DWT (Wavelets)
Does SWT/ISWT require intermediate approximation coefficients to represent/reconstruct the original signal?
Extract approximation and detail coefficients
Undecimated DWT vs. CWT. In what cases is one preferable over another?
Additive white Gaussian noise and undecimated DWT
Reproducing paper results about a wavelet transformation using python