class_docstr: !
  @brief Container of output dynamic of the chaotic neural network where states of each neuron during simulation are stored.
  
  @see cnn_network
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docstr: !
  @brief Allocates observation transposed matrix of neurons that is limited by specified periods of simulation.
  @details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.
  
  @return (list) Transposed observation matrix that is limited by specified periods of simulation.
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code: def __allocate_neuron_patterns(self, start_iteration, stop_iteration):
      """!
      @brief Allocates observation transposed matrix of neurons that is limited by specified periods of simulation.
      @details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.
  
      @return (list) Transposed observation matrix that is limited by specified periods of simulation.
  
      """
  
      pattern_matrix = []
      for index_neuron in range(len(self.output[0])):
          pattern_neuron = []
          for iteration in range(start_iteration, stop_iteration):
              pattern_neuron.append(
                  heaviside(self.output[iteration][index_neuron])
              )
  
          pattern_matrix.append(pattern_neuron)
  
      return pattern_matrix
  
imports: import math
  import numpy
  import random
  import matplotlib.pyplot
  from matplotlib import rcParams
  from matplotlib.font_manager import FontProperties
  from mpl_toolkits.mplot3d import Axes3D
  from enum import IntEnum
  from scipy.spatial import Delaunay
  from pyclustering.utils import euclidean_distance_square, average_neighbor_distance, heaviside, draw_dynamics
label_desc: Use image node_id 4 for calling the cnn_dynamic obj's underlying member method code with example usage: obj.__allocate_neuron_patterns(start_iteration, stop_iteration) and returns: pattern_matrix