I want to script a PPI analysis with nipype, but Level1Design does not offer all the features needed for this kind of analysis.

Does someone have a pimped version of Level1Design?

At the moment I can only define one kernel for all conditions. For PPI I need a list of kernels (level1design.inputs.bases), one kernel / condition.

I am thining of an extension of this bases. So I end up with
level1design.inputs.bases=[{‘dgamma’:{‘derivs’: False}, ‘none’:{‘derivs’: False}, ‘ppi’:{0:‘min’, ‘1’:‘mean’}]

hrf kernel for my task, none for the seed, and the interaction of the filtered 0 (task) and 1 (seed)

Has someone successfully run a PPI analysis with nilearn? It seems like nilearn has a lot of useful functionality for functional connectivity analysis but I have been unable to find an example with a PPI analysis.

as far as I know, PPI analyses are not possible using nilearn.
Not PPI, but maybe something you might find interesting: task based networks example and nibetaseries.