Python中的 Matplotlib.colors.TwoSlopeNorm 类

Matplotlib是Python中用于数组二维图的惊人可视化库。 Matplotlib 是一个基于 NumPy 数组的多平台数据可视化库，旨在与更广泛的 SciPy 堆栈一起使用。

Matplotlib.colors.TwoSlopeNorm

matplotlib.colors.TwoSlopeNorm 类用于使用设置的中心对数据进行归一化。在绘制概念中心周围变化率不等的数据时，它会派上用场。例如，-3 到 6 之间的范围以 0 为中心。

Syntax: class matplotlib.colors.TwoSlopeNorm(vcenter, vmin=None, vmax=None)
Parameters:
 

1. vcenter: It holds a float value that defines 0.5 in normalization.
    
2. vmin: This is an optional parameter that defines the data value 0.0 in normalization. It defaults to minimum value of the dataset.
    
3. vmax: This is an optional parameter that defines the data value 1.0 in normalization. It defaults to maximum value of the dataset. 
    

 

上课方法：

• autoscale_none(self, A)：此方法用于通过获取 vmax 和 vmin 在 vcenter 处进行剪辑。
  

示例 1：

Python3

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()

输出：

示例 2：

Python3

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()

输出：