run_network.py 文件源码

def parallel_cone(pipe,cells,time,cone_input,cone_layer,Vis_dark,Vis_resting_potential):
    # Initialize array of cone_response copying cone_input
    cone_response = cone_input

    for cell in cells:
        if multiprocessing.current_process().name=="root":
            progress = 100*(cell-cells[0])/len(cells)
            stdout.write("\r progress: %d %%"% progress)
            stdout.flush()

        # Time-driven simulation
        for t in np.arange(0,time):
            # Update dynamics of the model
            cone_layer[cell].feedInput(cone_input[cell,t])
            cone_layer[cell].update()
            # Record response
            cone_response[cell,t] = (cone_layer[cell].LF_taum.last_values[0] -\
            cone_layer[cell].LF_tauh.last_values[0] - Vis_dark - Vis_resting_potential)

    pipe.send(cone_response[cells,:])
    pipe.close()

#! ================
#! Class runNetwork
#! ================