python设置色条

1、最简单的“三步走”：

from matplotlib import cm
import matplotlib.colors
import csv
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib as mpl
from matplotlib import colors


cmap = mpl.colormaps.get_cmap('jet')
norm1 = mpl.colors.Normalize(vmin=n0, vmax=n1)
im1 = mpl.cm.ScalarMappable(norm=norm1, cmap=cmap)
fig, ax = plt.subplots()
cbar = plt.colorbar(im1, ax=ax)
plt.show()

2、更改默认色条中某部分的颜色：

# 额外设置前几个颜色
viridis = cm.get_cmap('jet', 256)
newcolors = viridis(np.linspace(0, 1, 256))

white = np.array([255/255, 255/255, 255/255, 1])
newcolors[:9, :] = white    # 设置前9个颜色为白色
blue1 = np.array([176/255, 226/255, 255/255, 1])
newcolors[8:10, :] = blue1   # 设置为蓝色

cmap = ListedColormap(newcolors)
norm1 = mpl.colors.Normalize(vmin=0, vmax=10)
im1 = mpl.cm.ScalarMappable(norm=norm1, cmap=cmap)

fig, ax = plt.subplots()
cbar = plt.colorbar(im1, ax=ax)
plt.show()

3、自定义色条：

colors_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#FF00FF']
# 创建自定义颜色映射
cmap = colors.LinearSegmentedColormap.from_list('custom_cmap', colors_list)
norm1 = mpl.colors.Normalize(vmin=0, vmax=20)
im1 = mpl.cm.ScalarMappable(norm=norm1, cmap=cmap)

fig, ax = plt.subplots()
cbar = plt.colorbar(im1, ax=ax)
plt.show()