class_docstr: !
  @brief Visualizer of output dynamic of chaotic neural network (CNN).
class_docstr_tok: ["!","@","brief","Visualizer","of","output","dynamic","of","chaotic","neural","network","(","CNN",")","."]
docstr: !
  @brief Shows output dynamic (output of each neuron) during simulation.
  
  @param[in] cnn_output_dynamic (cnn_dynamic): Output dynamic of the chaotic neural network.
  
  @see show_dynamic_matrix
  @see show_observation_matrix
docstr_tok: ["!","@","brief","Shows","output","dynamic","(","output","of","each","neuron",")","during","simulation",".","@","param","[","in","]","cnn_output_dynamic","(","cnn_dynamic",")",":","Output","dynamic","of","the","chaotic","neural","network",".","@","see","show_dynamic_matrix","@","see","show_observation_matrix"]
code: def show_output_dynamic(cnn_output_dynamic):
      """!
      @brief Shows output dynamic (output of each neuron) during simulation.
  
      @param[in] cnn_output_dynamic (cnn_dynamic): Output dynamic of the chaotic neural network.
  
      @see show_dynamic_matrix
      @see show_observation_matrix
  
      """
  
      figure, _ = draw_dynamics(
          cnn_output_dynamic.time,
          cnn_output_dynamic.output,
          x_title="t",
          y_title="x",
      )
      plt.close(figure)
  
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 1 for calling the cnn_visualizer obj's underlying member method code with example usage: obj.show_output_dynamic(cnn_output_dynamic) without return types