如果您有一组特定的颜色要用于颜色图,您可以根据这些颜色进行构建。例如:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list('name', ['green', 'yellow', 'red'])
# Generate some data similar to yours
y, x = np.mgrid[-200:1900, -300:2000]
z = np.cos(np.hypot(x, y) / 100) + 1
fig, ax = plt.subplots()
cax = ax.contourf(x, y, z, cmap=cmap)
cbar = fig.colorbar(cax)
cbar.set_label('Z-Values')
plt.show()
但是,如果您确实只想要某个特别复杂的颜色图的上半部分,则可以通过在您感兴趣的范围内评估颜色图来复制其中的一部分。例如,如果您想要“上”半部分,你会从 0.5 到 1 评估它:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
# Evaluate an existing colormap from 0.5 (midpoint) to 1 (upper end)
cmap = plt.get_cmap('gist_earth')
colors = cmap(np.linspace(0.5, 1, cmap.N // 2))
# Create a new colormap from those colors
cmap2 = LinearSegmentedColormap.from_list('Upper Half', colors)
y, x = np.mgrid[-200:1900, -300:2000]
z = np.cos(np.hypot(x, y) / 100) + 1
fig, axes = plt.subplots(ncols=2)
for ax, cmap in zip(axes.flat, [cmap, cmap2]):
cax = ax.imshow(z, cmap=cmap, origin='lower',
extent=[x.min(), x.max(), y.min(), y.max()])
cbar = fig.colorbar(cax, ax=ax, orientation='horizontal')
cbar.set_label(cmap.name)
plt.show()