class_docstr: !
  @brief Represents output dynamic of PCNN (pulse-coupled neural network).
class_docstr_tok: ["!","@","brief","Represents","output","dynamic","of","PCNN","(","pulse-coupled","neural","network",")","."]
docstr: !
  @brief Analyses output dynamic and calculates time signal (signal vector information) of network output.
     
  @return (list) Time signal of network output.
docstr_tok: ["!","@","brief","Analyses","output","dynamic","and","calculates","time","signal","(","signal","vector","information",")","of","network","output",".","@","return","(","list",")","Time","signal","of","network","output","."]
code: def allocate_time_signal(self):
      """!
      @brief Analyses output dynamic and calculates time signal (signal vector information) of network output.
  
      @return (list) Time signal of network output.
  
      """
  
      if self.__ccore_pcnn_dynamic_pointer is not None:
          return wrapper.pcnn_dynamic_allocate_time_signal(
              self.__ccore_pcnn_dynamic_pointer
          )
  
      signal_vector_information = []
      for t in range(0, len(self.__dynamic)):
          signal_vector_information.append(sum(self.__dynamic[t]))
  
      return signal_vector_information
  
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 8 for calling the pcnn_dynamic obj's underlying member method code with example usage: obj.allocate_time_signal() and returns: signal_vector_information, wrapper