Matplotlib.colors.TwoSlopeNorm class in Python

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import matplotlib.colors as colors

file = cbook.get_sample_data('topobathy.npz', 
                             asfileobj = False)

with np.load(file) as example:
    topo = example['topo']
    longi = example['longitude']
    latit = example['latitude']

figure, axes = plt.subplots(constrained_layout = True)

# creating a colormap that has land
# and ocean clearly delineated and 
# of the same length (256 + 256)
undersea = plt.cm.terrain(np.linspace(0, 0.17, 256))
land = plt.cm.terrain(np.linspace(0.25, 1, 256))
every_colors = np.vstack((undersea, land))
terrain_map = colors.LinearSegmentedColormap.from_list('terrain_map',
    every_colors)

# the center is offset so that 
# the land has more dynamic range 
# while making the norm
diversity_norm = colors.TwoSlopeNorm(vmin =-500,
                                     vcenter = 0, 
                                     vmax = 4000)

pcm = axes.pcolormesh(longi, latit, topo,
                      rasterized = True,
                      norm = diversity_norm,
                      
                      cmap = terrain_map, )
axes.set_xlabel('Longitude $[^o E]$')
axes.set_ylabel('Latitude $[^o N]$')
axes.set_aspect(1 / np.cos(np.deg2rad(49)))

figure.colorbar(pcm, shrink = 0.6, extend ='both',
                label ='Elevation [m]')
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors

data = np.random.normal(.4, 2, (10, 10))

two_slope_norm = mcolors.TwoSlopeNorm(vmin = data.min(), 
                                      vmax = data.max(), 
                                      vcenter = 0)

plt.imshow(data, cmap = plt.cm.RdBu, 
           norm = two_slope_norm)

plt.colorbar()
plt.show()