Python Matplotlib.colors.TwoSlopeNorm用法及代码示例

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()