基本用法
使用内置的颜色映射非常简单,只需将所需颜色映射的名称(如 colormaps 参考中所示 )传递给期望它的绘图函数(例如 pcolormesh
或 contourf
),通常采用 cmap
关键字参数的形式:
import matplotlib.pyplot as plt
import numpy as np
plt.figure()
plt.pcolormesh(np.random.rand(20,20),cmap='hot')
plt.show()
色彩图对于在二维图上可视化三维数据特别有用,但是一个好的色图也可以使得正确的三维图更清晰:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import LinearLocator
# generate example data
import numpy as np
x,y = np.meshgrid(np.linspace(-1,1,15),np.linspace(-1,1,15))
z = np.cos(x*np.pi)*np.sin(y*np.pi)
# actual plotting example
fig = plt.figure()
ax1 = fig.add_subplot(121, projection='3d')
ax1.plot_surface(x,y,z,rstride=1,cstride=1,cmap='viridis')
ax2 = fig.add_subplot(122)
cf = ax2.contourf(x,y,z,51,vmin=-1,vmax=1,cmap='viridis')
cbar = fig.colorbar(cf)
cbar.locator = LinearLocator(numticks=11)
cbar.update_ticks()
for ax in {ax1, ax2}:
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
ax.set_xlim([-1,1])
ax.set_ylim([-1,1])
ax.set_aspect('equal')
ax1.set_zlim([-1,1])
ax1.set_zlabel(r'$\cos(\pi x) \sin(\p i y)$')
plt.show()