Time Series Pattern Recognition (Python)

My question is similar to this one , but with much more noise. Basically, I want to extract duplicate "saw" patterns from a time series. They look like this:

I don't have any tagged data, but all templates have one thing in common. The sharp rise was accompanied by a slow decline, which is repeated for some time. (Also recognized in the step function in purple below the figure).

How can I extract these patterns from a large batch of different time series? Preferably in Python. I would rather not spend days creating the model, so the simpler the better :)