You can read off how the waveforms list is constructed, so you can easily read out how to assign variables to the unpacked pickle list. Also the actual pickled file, is on an a google drive link.
The pickled data is a list of the neo analogue signals for each v_m in a list of models. Element 0, of the list is time from model.results['time'], but I think you can also just extract the time from the neo alanogue signals somehow.
@rgerkin,
The code for pickling the waveforms, and realigning the plots is here:
https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/optimization/net_graph.py#L435-L496
You can read off how the waveforms list is constructed, so you can easily read out how to assign variables to the unpacked pickle list. Also the actual pickled file, is on an a google drive link.
The pickled data is a list of the neo analogue signals for each v_m in a list of models. Element 0, of the list is time from model.results['time'], but I think you can also just extract the time from the neo alanogue signals somehow.