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 as matrix in grey colors.
  @details This type of visualization is convenient for observing allocated clusters.
  
  @param[in] cnn_output_dynamic (cnn_dynamic): Output dynamic of the chaotic neural network.
  
  @see show_output_dynamic
  @see show_observation_matrix
docstr_tok: ["!","@","brief","Shows","output","dynamic","as","matrix","in","grey","colors",".","@","details","This","type","of","visualization","is","convenient","for","observing","allocated","clusters",".","@","param","[","in","]","cnn_output_dynamic","(","cnn_dynamic",")",":","Output","dynamic","of","the","chaotic","neural","network",".","@","see","show_output_dynamic","@","see","show_observation_matrix"]
code: def show_dynamic_matrix(cnn_output_dynamic):
      """!
      @brief Shows output dynamic as matrix in grey colors.
      @details This type of visualization is convenient for observing allocated clusters.
  
      @param[in] cnn_output_dynamic (cnn_dynamic): Output dynamic of the chaotic neural network.
  
      @see show_output_dynamic
      @see show_observation_matrix
  
      """
  
      network_dynamic = numpy.array(cnn_output_dynamic.output)
  
      figure, axes = plt.subplots(1)
  
      axes.imshow(
          network_dynamic.T,
          cmap=plt.get_cmap("gray"),
          aspect="auto",
          interpolation="None",
          vmin=0.0,
          vmax=1.0,
      )
      plt.show()
      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 2 for calling the cnn_visualizer obj's underlying member method code with example usage: obj.show_dynamic_matrix(cnn_output_dynamic) without return types