Reproducing paper results about a wavelet transformation using python
With a fake imitation of your data, here is a first try in Matlab, which I may improve later on. It uses the stationary wavelet transform swt.m:

nSample = 256;
timeOnset = 25;

time = linspace(1,nSample,nSample)';
%ramp = max(time,timeOnset)/nSample;
ramp = abs(time-timeOnset)/nSample;

nLevel1 = 4;
nLevel2 = 5;
waveletName = 'sym8';
data = 0.15*((1+rand(nSample,1)/1).^0.5).*sin(2*pi*time/nSample*42)+0.65*(ramp+1);
dataSWT1 = swt(data,nLevel1,waveletName);
dataSWT2 = swt(data,nLevel2,waveletName);
subplot(3,1,1);
plot(time,data);axis tight
subplot(3,1,2);
imagesc(dataSWT1);axis tight
xlabel([num2str(nLevel1),' levels'])
subplot(3,1,3);
imagesc(dataSWT2);axis tight
xlabel([num2str(nLevel2),' levels'])