def show_time_signal(pcnn_output_dynamic):
        """!
        @brief Shows time signal (signal vector information) using network dynamic during simulation.
        
        @param[in] pcnn_output_dynamic (pcnn_dynamic): Output dynamic of the pulse-coupled neural network.
        
        """
        
        time_signal = pcnn_output_dynamic.allocate_time_signal()
        time_axis = range(len(time_signal))
        
        plt.subplot(1, 1, 1)
        plt.plot(time_axis, time_signal, '-')
        plt.ylabel("G (time signal)")
        plt.xlabel("t (iteration)")
        plt.grid(True)
        
        plt.show()
def show_time_signal(pcnn_output_dynamic):
        """!
        @brief Shows time signal (signal vector information) using network dynamic during simulation.
        
        @param[in] pcnn_output_dynamic (pcnn_dynamic): Output dynamic of the pulse-coupled neural network.
        
        """
        
        time_signal = pcnn_output_dynamic.allocate_time_signal()
        time_axis = range(len(time_signal))
        
        plt.subplot(1, 1, 1)
        plt.plot(time_axis, time_signal, '-')
        plt.ylabel("G (time signal)")
        plt.xlabel("t (iteration)")
        plt.grid(True)
        
        plt.show()
def show_time_signal ( pcnn_output_dynamic ) : time_signal = pcnn_output_dynamic . allocate_time_signal ( ) time_axis = range ( len ( time_signal ) ) plt . subplot ( 1 , 1 , 1 ) plt . plot ( time_axis , time_signal , '-' ) plt . ylabel ( "G (time signal)" ) plt . xlabel ( "t (iteration)" ) plt . grid ( True ) plt . show ( )
!
        @brief Shows time signal (signal vector information) using network dynamic during simulation.
        
        @param[in] pcnn_output_dynamic (pcnn_dynamic): Output dynamic of the pulse-coupled neural network.
!