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 Constructor of PCNN dynamic.
  
  @param[in] dynamic (list): Dynamic of oscillators on each step of simulation. If ccore pointer is specified than it can be ignored.
  @param[in] ccore (ctypes.pointer): Pointer to CCORE pcnn_dynamic instance in memory.
docstr_tok: ["!","@","brief","Constructor","of","PCNN","dynamic",".","@","param","[","in","]","dynamic","(","list",")",":","Dynamic","of","oscillators","on","each","step","of","simulation",".","If","ccore","pointer","is","specified","than","it","can","be","ignored",".","@","param","[","in","]","ccore","(","ctypes.pointer",")",":","Pointer","to","CCORE","pcnn_dynamic","instance","in","memory","."]
code: def __init__(self, dynamic, ccore=None):
      """!
      @brief Constructor of PCNN dynamic.
  
      @param[in] dynamic (list): Dynamic of oscillators on each step of simulation. If ccore pointer is specified than it can be ignored.
      @param[in] ccore (ctypes.pointer): Pointer to CCORE pcnn_dynamic instance in memory.
  
      """
      self.__OUTPUT_TRUE = 1  # fire value for oscillators.
      self.__OUTPUT_FALSE = 0  # rest value for oscillators.
  
      self.__dynamic = dynamic
      self.__ccore_pcnn_dynamic_pointer = ccore
  
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 3 to create a new pcnn_dynamic object  with example: obj = pcnn_dynamic(dynamic, ccore)