class_docstr: !
  @brief Visualizer of output dynamic of pulse-coupled neural network (PCNN).
class_docstr_tok: ["!","@","brief","Visualizer","of","output","dynamic","of","pulse-coupled","neural","network","(","PCNN",")","."]
docstr: !
  @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.
docstr_tok: ["!","@","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","."]
code: 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()
  
imports: import random
  import numpy
  import matplotlib.pyplot
  import matplotlib.animation
  from PIL import Image
  from pyclustering.nnet import *
  from pyclustering.core.wrapper import ccore_library
  import pyclustering.core.pcnn_wrapper
  from pyclustering.utils import draw_dynamics
label_desc: Use image node_id 1 for calling the pcnn_visualizer obj's underlying member method code with example usage: obj.show_time_signal(pcnn_output_dynamic) without return types