Python中的 Matplotlib.colors.DivergingNorm 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 数组构建的多平台数据可视化库，旨在与更广泛的 SciPy 堆栈配合使用。

matplotlib.colors.DivergingNorm

matplotlib.colors.DivergingNorm类属于matplotlib.colors模块。 matplotlib.colors 模块用于将颜色或数字参数转换为 RGBA 或 RGB。该模块用于将数字映射到颜色或在一维颜色数组（也称为颜色图）中进行颜色规范转换。
matplotlib.colors.DivergingNorm 类在映射概念中心周围变化率不均匀或不相等的数据时非常有用。例如数据范围在 -2 到之间，以 0 为中心或中点。

Syntax: matplotlib.colors.DivergingNorm(vcenter, vmin, vmax)
Parameters: 
 

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

 

示例 1：

Python3

import numpy
from matplotlib import pyplot as plt
from matplotlib import colors
 
 
# dummy data to plot
x = numpy.linspace(0, 2*numpy.pi, 30)
y = numpy.linspace(0, 2*numpy.pi, 20)
[A, B] = numpy.meshgrid(x, y)
Q = numpy.sin(A)*numpy.cos(B)
 
fig = plt.figure()
plt.ion()
 
#  yellow to green to red
# colormap
plt.set_cmap('brg')
 
ax = fig.add_subplot(1, 2, 1)
plt.pcolor(A, B, Q)
plt.colorbar()
 
ax = fig.add_subplot(1, 2, 2)
 
# defining the scale, with white
# at zero
vmin = -0.2
vmax = 0.8
norms = colors.DivergingNorm(vmin=vmin,
                             vcenter=0,
                             vmax=vmax)
 
plt.pcolor(A, B, Q,
           vmin=vmin,
           vmax=vmax,
           norm=norms) 
 
plt.colorbar()

输出：

示例 2：

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.DivergingNorm(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：