matplotlib.pyplot.viridis()函数

Matplotlib是Python中的一个库，它是NumPy库的数值-数学扩展。Pyplot是一个基于状态的Matplotlib模块接口，该模块提供了一个类似matlab的接口。

matplotlib.pyplot.viridis()函数:

使用matplotlib库pyplot模块中的viridis()函数将colormap设置为“viridis”。

语法:

matplotlib.pyplot.viridis()

参数:此方法不接受任何参数。

返回:此方法不返回任何值。

下面的例子演示了matplotlib.pyplot.viridis()函数在matplotlib.pyplot中的作用:

示例1

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
     
ang = 32
rad = 10
radm = 0.35
radii = np.linspace(radm, 0.95, rad)
     
angles = np.linspace(0, 1.4 * np.pi, ang)
angles = np.repeat(angles[..., np.newaxis], rad, axis = 1)
angles[:, 1::2] += np.pi / ang
     
x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
z = (np.sin(4 * radii) * np.cos(4 * angles)).flatten()
     
triang = tri.Triangulation(x, y)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis = 1),
                         y[triang.triangles].mean(axis = 1))
                         < radm)
     
tpc = plt.tripcolor(triang, z, shading ='flat')
plt.colorbar(tpc)
plt.viridis()
plt.title('matplotlib.pyplot.viridis() function Example', 
                                      fontweight ="bold")
plt.show()

输出:

示例2

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm
        
dx, dy = 0.015, 0.05
x = np.arange(-3.0, 3.0, dx)
y = np.arange(-3.0, 3.0, dy)
X, Y = np.meshgrid(x, y)
     
extent = np.min(x), np.max(x), np.min(y), np.max(y)
      
    
Z1 = np.add.outer(range(6), range(6)) % 2
plt.imshow(Z1, cmap ="binary_r", interpolation ='nearest',
                               extent = extent, alpha = 1)
     
def geeks(x, y):
    return (1 - x / 2 + x**5 + y**6) * np.exp(-(x**2 + y**2))
     
Z2 = geeks(X, Y)
     
plt.imshow(Z2, alpha = 0.7, interpolation ='bilinear',
                                      extent = extent)
plt.viridis()
plt.title('matplotlib.pyplot.viridis() function Example', 
                                     fontweight ="bold")
  
plt.show()

输出: