Listing 2.

Example of a derived HRLAnalysis^™ base object in Python. In this example the derived constructor prototype matches the base class definition. Notice on line 6 that the base class constructor needs to be called by the derived class. The programmatic loop outlined in line 12 is repeated for each of the spike trains and it is assumed that cell_spike_train() on line 14 is provided by that loop. The data structures are incrementally constructed with calls to append(). These are C++ data structures, so they map to the C++ vector push_back() function. The function call on line 20 sorts the spikeActivity structure by spike time. Note that the spikeAcitivity structure can be created in C++ by the HrlNeuralAnalysis object using the buildSpikeActFromCellAct function. This should provide a nominal performance increase over the method presented here.