text: import matplotlib
  import matplotlib.pyplot as plt
  from matplotlib.colors import ListedColormap
  import numpy as np
  
  # Let's design a dummy land use field
  A = np.reshape([7,2,13,7,2,2], (2,3))
  vals = np.unique(A)
  
  # Let's also design our color mapping: 1s should be plotted in blue, 2s in red, etc...
  col_dict={1:"blue",
            2:"red",
            13:"orange",
            7:"green"}
  
  # We create a colormar from our list of colors
  cm = ListedColormap([col_dict[x] for x in col_dict.keys()])
  print(cm(0))
  # Let's also define the description of each category : 1 (blue) is Sea; 2 (red) is burnt, etc... Order should be respected here ! Or using another dict maybe could help.
  labels = np.array(["Sea","City","Sand","Forest"])
  len_lab = len(labels)
  
  # prepare normalizer
  ## Prepare bins for the normalizer
  norm_bins = np.sort([*col_dict.keys()]) #+ 0.5
  # norm_bins = np.insert(norm_bins, 0, np.min(norm_bins) - 1.0)
  print(norm_bins)
  ## Make normalizer and formatter
  norm = matplotlib.colors.BoundaryNorm(norm_bins, len_lab, clip=True)
  # fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: labels[norm(x)])
  
  # Plot our figure
  # fig,ax = plt.subplots()
  # im = ax.imshow(A, cmap=cm, norm=norm)
  plt.imshow(A, cmap=cm, norm=norm)
  plt.show()
  # diff = norm_bins[1:] - norm_bins[:-1]
  # tickz = norm_bins[:-1] + diff / 2
  # cb = fig.colorbar(im, format=fmt, ticks=tickz)
  # fig.savefig("example_landuse.png")
  # plt.show()